Proceedings of the 12th European Conference on Knowledge ...

<strong>Proceedings</strong>
<strong>of</strong> <strong>the</strong>
<strong>12th</strong> <strong>European</strong> <strong>Conference</strong>
on Knowledge Management
University <strong>of</strong> Passau
Germany
1-2 September 2011
Volume Two
Edited by
Dr. Franz Lehner
University <strong>of</strong> Passau, Germany
&
Dr. Klaus Bredl
University <strong>of</strong> Augsburg, Germany

Copyright The Authors, 2011. All Rights Reserved.
No reproduction, copy or transmission may be made without written permission from <strong>the</strong> individual authors.
Papers have been double-blind peer reviewed before final submission to <strong>the</strong> conference. Initially, paper
abstracts were read and selected by <strong>the</strong> conference panel for submission as possible papers for <strong>the</strong>
conference.
Many thanks to <strong>the</strong> reviewers who helped ensure <strong>the</strong> quality <strong>of</strong> <strong>the</strong> full papers.
These <strong>Conference</strong> Proceeding have been submitted to <strong>the</strong> Thomson ISI for indexing.
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ISBN: 978-1-908272-106-5 Book
Published by Academic Publishing Limited
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Contents
Title Author(s)
Picture <strong>of</strong>
Preface VIII
Biographies <strong>of</strong> <strong>Conference</strong> Chairs, Programme
Chair, Keynote Speaker and Mini-track Chairs
Biographies <strong>of</strong> contributing authors XI
Developing an Innovative Knowledge Management
Implementation
Knowledge Dynamics and Organisational Learning
Cycles
Identifying and Ranking <strong>the</strong> Critical Success Factors
in <strong>the</strong> Implementation <strong>of</strong> Knowledge Management
Using <strong>the</strong> DELPHI Method: A Case Study <strong>of</strong> <strong>the</strong>
Municipality <strong>of</strong> <strong>the</strong> 22nd District <strong>of</strong> Tehran
Serious Games in <strong>the</strong> Context <strong>of</strong> Organizational
Knowledge Management Practices
Sharing and Transferring Knowledge – how to
Increase Efficiency <strong>of</strong> S<strong>of</strong>t Techniques for KS
The role <strong>of</strong> Critical Success Factors in Acquiring
Competitive Advantages in Two Industrial Factories,
in Tehran
Contextual Adaptive Visualization Environments: a
Knowledge Creation, Transfer and Sharing Platform
Knowledge Production and Transfer: Advantages
and Costs
A Framework for <strong>the</strong> Assessment <strong>of</strong> KM Readiness
<strong>of</strong> an Organization While Transferring into a Learning
Organization
Experiential Knowledge Creation Processes in <strong>the</strong>
Higher Education Teaching-Learning Process
Meta-Analysis <strong>of</strong> Publications on Web 2.0: Impact,
Productivity, Prevalent Topics and Research
Agendas
Communities <strong>of</strong> Practice: Comparing Experiences <strong>of</strong>
Private Companies and Public Organisations
Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas
and Mike Sharp
Page
No.
Eckhard Ammann 10
Manouchehr Ansari et al 20
Albena Antonova and Anandasivakumar
Ekambaram
Albena Antonova and Aniko Csepregi 37
Seyed Esmaeil Asgharpour and Gholamreza
Taleghani
Xiaoyan Bai, David White and David
Sundaram
Simona-Clara Bârsan, Mihaela-Georgia Sima
and Dan Săvescu
Leila Beig et al 74
Didiosky Benítez et al 88
Pavel Bogolyubov 97
Ettore Bolisani, Francesca Gambarotto and
Enrico Scarso
KM Issues in KIBS Companies Ettore Bolisani, Marco Paiola and Enrico
Scarso
Strategies for Increasing Knowledge Retention in
Universities Through Intergenerational Knowledge
Transfer
Immersive Knowledge Communication in 3D Virtual
Environments
Assessing <strong>the</strong> Knowledge Economy’s Performance in
Romania
The Relationship between <strong>the</strong> <strong>European</strong> Social Fund
and Knowledge Management in Romanian
Organizations
Constantin Bratianu, Adriana Agapie and
Ivona Orzea
IX
1
28
45
56
65
107
115
124
Klaus Bredl et al 131
Camelia Burja and Vasile Burja 139
Adriana Schiopoiu Burlea 148
i

Title Author(s)
Picture <strong>of</strong>
Organizational Strategy and Research Productivity: a
Comparison <strong>of</strong> two Academic Institutions
Why Should I Share my new ideas? Cultural Barriers
to Innovation Spreading
The Roles <strong>of</strong> Tacit Knowledge and Knowledge
Management Systems for Writing Academic Papers:
A Research Case
Knowledge Sources, Innovation and Organizational
Learning in Small Firms
National Knowledge Management Strategy for <strong>the</strong>
TRNC: Recommendations for Small Island
Economies
Implementing a work-life balance culture in SMEs
though relational learning
The Influence <strong>of</strong> Repatriation Support and Social
Climate Perceptions on Repatriate Knowledge
Sharing
Social Capital, Knowledge Sharing and Intellectual
Capital in <strong>the</strong> Web 2 Enabled World
An Exploratory Study <strong>of</strong> Knowledge Strategy in a
Knowledge-Intensive Firm using a Strategy-as-
Practice approach
The Application <strong>of</strong> <strong>the</strong> SECI Model in Cross-Cultural
Contexts
Network Management as a way to Manage
Intellectual Capital
Page
No.
Simon Cadez and Vlado Dimovski 159
Francesco Calza, Rossella Canestrino and
Chiara Cannavale
166
José Manuel Cardenas and Mauro Spinola 175
Vincenzo Cavaliere and Daria Sarti 181
Behiye Çavuşoğlu and Mustafa Sagsan 190
Juan-Gabriel Cegarra-Navarro et al 198
Huei-Fang Chen and Yi-Wen Lin 206
Marguerite Cronk 215
Françoise de Viron et al 222
Nasser Easa and Robin Fincham 232
Eva Eckenh<strong>of</strong>er 240
The Essence <strong>of</strong> Knowledge Management Emmanuel Innocents Edoun and Valdenisa
Norris
Managing Uncertainty in Projects – A Means to
Knowledge Transfer, Learning and Organization
Development
Anandasivakumar Ekambaram and Agnar
Johansen
A KM Strategy for Improving <strong>the</strong> BPR Process Jamal El-Den and Xin Zhou 267
Perceptions <strong>of</strong> Knowledge Management
Prerequisites and Challenges <strong>of</strong> Organisational
Learning
Examining <strong>the</strong> Strategic Objectives <strong>of</strong> Implementing
KM in <strong>the</strong> National Health Service (NHS) {Medway
NHS and Medway Community Healthcare (MCH)}
A proposed Framework for Discovering Key
Knowledge Areas in Supply Chain and Determining
<strong>the</strong> Relationship With Major Logistic Processes: A
Case Study
Knowledge Workers: A Typology Framework as a
Theoretical Basis for Knowledge Worker
Identification Instrument
Middle Managers’ Maturity <strong>of</strong> Knowledge Sharing:
Investigation <strong>of</strong> Middle Managers Working at
Medium- and Large-sized Enterprises
250
259
Tiit Elenurm 275
Isaac Enakimio, and Abdallah Al-Shawabkeh 283
Mohamad Ali Feyz et al 290
Jiří Franek and Eva Grublova 298
Zoltán Gaál et al 306
ii

Title Author(s)
Picture <strong>of</strong>
Knowledge Management in <strong>the</strong> Quebec Mining
Industry: A Framework <strong>of</strong> Practice to Ensure
Evidence-Based Knowledge Translation
Adoption <strong>of</strong> Knowledge Management Systems in
SMEs; Realities and Challenges from Ethiopia
The Chain Value Process and Knowledge Transfer in
a Bioengineering Case
Managing Knowledge-Intensive Centers for
Research, Experimental Development and
Application <strong>of</strong> Scientific Discoveries - <strong>the</strong> Biggest
Challenge <strong>of</strong> Knowledge Management <strong>of</strong> <strong>the</strong> 21st
Century
Page
No.
Charles Gagné et al 315
Tendayi Gondo and Edmore Kori 322
Manel González-Piñero et al 332
Nebojsa Graca and Ana Lucija Gojakovic 341
Cyclic Process Model Transformation Norbert Gronau et al 349
The Not-Invented-Here Syndrome in Academia –
How to Measure and Manage it
Individual Level Influencers on Tacit Knowledge
Sharing Processes
Knowledge Identification, Capture and
Dissemination: ECOPETROLS.A. Real Example <strong>of</strong>
Implementation
Understanding Personal Knowledge Development in
Online Learning Environments: An Instrument for
Measuring Externalisation, Combination and
Internalisation
Chaordic Knowledge Management – Shifting
Paradigms for Corporate Knowledge Networks
Intellectual Capital in Universities: Faculty and
Student Perceptions
Developing a Knowledge Strategy Using Tacit
Knowledge Measurement: Implications for <strong>the</strong>
Balanced Scorecard Innovation and Learning
Perspective
Understanding <strong>the</strong> Fit between KAs and <strong>the</strong> Firm in
Five S<strong>of</strong>tware SMEs
Key Knowledge Sharing Points: Exploring a new
Concept for Studying Crossroads in Global
Innovation Projects
Using Web 2.0 Technologies to Support Technology
Surveillance in a University Context
Knowledge Creation in Multinational Corporations:
The Role <strong>of</strong> Organizational Structure
David Grosse Kathoefer, and Jens Leker 360
Claire Gubbins et al 372
Oscar Guerra and Janeth Rojas 381
Markus Haag and Yanqing Duan 390
Frank Habermann, Jörg Fehlinger and Karen
Schmidt
398
Meliha Handzic and Kursad Ozlen 408
Harold Harlow 416
Ciara Heavin and and Frederic Adam 424
Tore Hoel and Jan Pawlowski 436
Marta Infante Abreu, Florian Mat<strong>the</strong>s and
Alexander Steinh<strong>of</strong>f
444
Pamela Chidiogo Izunwanne 453
A Pattern-Based Ontology Engineering Platform Thomas Janke 461
Towards a Detailed View on <strong>the</strong> Influence <strong>of</strong>
Organizational Culture on Knowledge Sharing
The Challenge <strong>of</strong> Scientific Cooperation in Large
Complex Research Clusters – Experiences from <strong>the</strong>
Cluster <strong>of</strong> Excellence “Integrative Production
Technology for High-Wage Countries”
Vincent de Jong and Remko Helms 470
Claudia Jooß et al 481
iii

Title Author(s)
Picture <strong>of</strong>
Expert Knowledge: Does it Help or Hinder Idea
Generation and Creativity for Innovation?
Integration <strong>of</strong> Knowledge into <strong>the</strong> Value Stream
Mapping Method – Benefits, Challenges, Solution
Statement
Knowledge Management Practices (KMP) and its
Impact on Organizational Performance in
Pharmaceuticals Firms
Knowledge Management Across <strong>the</strong> Globe – An
International Survey <strong>of</strong> KM Awareness, Spending,
Practices and Performance
Page
No.
Selvi Kannan 488
Eva-Maria Kern et al 496
Radwan Kharabsheh, Ihab Magableh and
Sukina Sawadha
506
Aino Kianto, Tatiana Andreeva and Xing Shi 514
Facilitating Knowledge Sharing in Virtual Networks Andrea Kő, Péter Fehér and Krisztián Varga 514
Practices to Promote Organizational Knowledge: a
Case Study in a Mining Company
Knowledge Translation and Transfer Research
Across Québec’s Occupational Health and Safety
Research Network
Knowledge Sharing Practices, Managerial Tacit
Knowledge, and Individual Performance: Their
Interrelationships and <strong>the</strong> Moderating Role <strong>of</strong>
Employee Personality
Es<strong>the</strong>r Lage et al 535
Monique Lortie and Lise Desmarais 543
Halimah Abdul Manaf, Steven Armstrong and
Alan Lawton
Knowledge sharing in Romanian companies Anca Mândruleanu 563
The Relational Capital as a key Factor for a
Company’s Success: General Insights
Modelling Knowledge Sharing Into a Medical Facility
Using Human and Virtual Agents (Knowbots)
From Knowledge Acquisition to Knowledge Elicitation
- A Roadmap
Simone Manfredi, Domenico Celenza and
Rosa Lombardi
551
569
Virginia Maracine et al 578
Peter Marshall and Damian Gordon 590
iv

Volume Two
The Influence <strong>of</strong> <strong>the</strong> Organizational Learning Phases in
<strong>the</strong> Total Process: A Special Analysis <strong>of</strong> Organizational
Structure
Buzz Network Strategies and Their Impact on Knowledge
Management Process
Enabling Knowledge Workers to Collaboratively Add
Structure to Enterprise Wikis
Inocencia Mª Martínez-León and Isabel
Olmedo-Cifuentes
599
Maurizio Massaro and Roland Bardy 609
Florian Mat<strong>the</strong>s and Christian Neubert 617
Germany – Towards a Knowledge-Based Economy Kai Mertins, Sven Wuscher and Markus
Will
Knowledge Management Implementation in <strong>the</strong> UK - Does
Size Matter?
An IT-based KMS for Large-Scale Sense-Making: An
Application <strong>of</strong> a KMSD Methodology
Using a FuzzyQFD Approach for Successful
Implementation <strong>of</strong> Knowledge Management
Knowledge Mapping Based on EFQM Excellence Model:
A Practical Tool to Make Visible Organizational
Knowledge
The University Institution’s Improvement <strong>of</strong> Quality from a
Knowledge Management’s Point <strong>of</strong> View
Facilitating Trust Among Entrepreneurs’ To Access Tacit
Knowledge: The SLNIW Story
A Structural Model for Organizational Learning in
Universities Based on Managers' Emotional Intelligence
Validation a Scale for Measuring <strong>the</strong> Intellectual Capital in
Universities
The Knowledge Clinic: Concepts, Methods and tools to
Support Productive Knowledge Management in
Companies
A Tenth Anniversary Assessment <strong>of</strong> Davenport and
Prusak (2000) Working Knowledge: Practitioner
Approaches to Knowledge in Organisations
A Different View to Knowledge and Personal Knowledge
Management Systems
Research Notes on <strong>the</strong> Practical Deployment <strong>of</strong> Semantic
Knowledge Bases
Getting Ready for Knowledge Management: A UK Local
Government Case Study
Knowledge Transfer, Knowledge Sharing and Knowledge
Barriers – Three Blurry Terms in KM
The Global Knowledge Management Framework:
Understanding Knowledge Management in Globally
Distributed Settings
Understanding Inter-firm Networks and Types <strong>of</strong>
Innovation in SMEs: A Social Network Perspective
Sandra M<strong>of</strong>fett, Rodney McAdam, and
Paul Humphreys
Syed Moneeb Ali, Mark Woodman, and
Aboubakr A Moteleb Zade
626
637
647
Mohammad Mirkazemi Mood et al 657
Mahmoud Moradi, Mohammad Rahim
Ramazanian and Sayyed Maisam
Momeni
Oliver Moravcik, Dagmar Caganova and
Jana Stefankova
668
676
Martina Mullally et al 687
Fattah Nazem 695
Fattah Nazem 703
Gaby Neumann and Eduardo Tomé 710
Gary R Oliver 718
Kaspars Osis and Janis Grundspenkis 727
Taha Osman, Dhavalkumar Thakker
and Matt Nathan
Paul Parboteeah, Thomas Jackson and
Ge<strong>of</strong>f Smith
737
746
Dan Paulin and Kaj Suneson 752
Jan Pawlowski and Markus Bick 761
Meysam Poorkavoos, Yanqing Duan
and John Edwards
Institutional Matrix <strong>of</strong> Knowledge Generation Evgeny Popov et al 780
Developing Institutions <strong>of</strong> Knowledge Economy Evgeny Popov et al 789
v
772

Knowledge Management Assessment <strong>of</strong> Khorasan Razavi
Gas Company; Viewpoint <strong>of</strong> Employees
An Intelligent Model to Assess Organizational Maturity for
<strong>the</strong> Implementation <strong>of</strong> Knowledge Management
Risk Analysis for Knowledge Management Projects Using
PMBOK Guidelines and FMEA Techniques: A Case Study
Farnaz Rahimi and Mohamad Ebrahim
Maroosi
801
Kamaladdin Rahmani Youshanloui et al 806
Lila Rajabion and Jafar Zanganeh 814
The Business Group in a Systemic Perspective Anna Maria Costanza Rinaldi 825
Country Strategic Risk and Knowledge Management: A
Proposed Framework for Improvement
Information Intensive Systems: Enabler or Inhibitor <strong>of</strong>
Sustained Knowledge Capability
Eduardo Rodriguez, John Edwards and
Angel Facundo
836
Max Erik Rohde and David Sundaram 849
A Ranking Method for Identification <strong>of</strong> Crucial Knowledge Inès Saad 858
Knowledge Sharing and Innovation: <strong>the</strong> case <strong>of</strong> Spanish
and Colombian high-tech firms
Josune Sáenz, Nekane Aramburu and
Carlos Blanco
Social Media Mindset and Knowledge Management Risto Säntti 872
The Importance <strong>of</strong> Knowledge in Regional Development Dan Săvescu, Mihaela-Georgia Sima
and Simona-Clara Bârsan
Analysis <strong>of</strong> Knowledge Work Execution at Computer
Workplaces
Applying Web Analytics Tools in <strong>the</strong> Context <strong>of</strong> Enterprise
Social S<strong>of</strong>tware
Benedikt Schmidt, Todor Stoitsev and
Max Mühlhäuser
Alexander Schneider and Alexander
Steinh<strong>of</strong>f
Intangible Assets: From Evaluation to Valuation Camilo Augusto Sequeira and Eloi
Fernández y Fernández
Intellectual Capital Evaluation: Relationship between
Knowledge Management Implementation and Company’s
Performance
The Major Challenges <strong>of</strong> Electronic Communities <strong>of</strong>
Practice in an Iranian Leading Virtual University: A
Qualitative Approach
Existing Dimensions <strong>of</strong> Absorptive Capacity and <strong>the</strong> Way
Foreward: The Mediating Functionality <strong>of</strong> Mental Models
Manifested by Entrepreneurs
863
879
889
899
907
Elena Shakina and Anna Bykova 917
Mehdi Shami Zanjani, Hamid Rahimian
and Farnoosh Alami
Evangelia Siachou, S<strong>of</strong>ia Daskouand
Peter Yannopoulos
Relevance <strong>of</strong> Intellectual Capital for <strong>the</strong> Public Sector Christina Suciu et al 944
Managing Intellectual Capital in SMEs in <strong>the</strong> Framework
<strong>of</strong> <strong>the</strong> Knowledge Based Society
Universities as Knowledge Creation and Sharing
Institutions – Research Perspectives from Romania
Using Naturalistic Decision Making to Understand
Knowledge Barriers in Launching Telecommunication for
Public Safety
Infrastructure <strong>of</strong> Innovative Universities Based on ICT and
KM for Building Smart Economy
930
935
Marta-Christina Suciu et al 952
Marta-Christina Suciu et al 960
Kaj Suneson and Ilona Heldal 967
Natalia Tikhomirova et al 976
Measurement <strong>of</strong> Customer Knowledge Value Kamila Tislerova 982
Multi Perspective Framework to Improve <strong>the</strong> Knowledge
Flow
A S<strong>of</strong>t Systems Approach to Solving Knowledge
Management Problems in Organisational Change
Environments
vi
Choon-Bae Yoo, Igor Hawryszkiewycz
and Kyeong-Soon Kang
988
John Young 997

The Symbolic Innovation <strong>of</strong> <strong>the</strong> Bio-informatics Discipline:
A Political Networks Approach to IT development
Alexander K. K<strong>of</strong>inas and Abdallah Al-
Shawakbeh
PhD Research Papers 1021
Measuring Utility <strong>of</strong> Geospatial maps for Information
Seeking: Findings <strong>of</strong> a Structured Literature Review and
Preliminary Think-Aloud-Study
Knowledge Boundaries <strong>of</strong> <strong>the</strong> Firm in Russian Heavy
Engineering Companies
Multipr<strong>of</strong>essional Communities <strong>of</strong> Practice in a Largescale
Healthcare Collaboration: Formation, Identity
Building and Knowledge Sharing
Evaluation <strong>of</strong> Knowledge Quality <strong>of</strong> Lessons Learned by
Asking Questions
KM as a solution for <strong>the</strong> shortage <strong>of</strong> competent employees
in SMEs at <strong>the</strong> developing country (Case study: Vietnam)
Knowledge Base Development <strong>of</strong> Companies in R&D
Consortia; An Organisational Process and Social
Interaction Perspective on Learning
The Mutual Transmission <strong>of</strong> Knowledge and
Competencies between Generations: an enabler <strong>of</strong>
Dynamic Capabilities
Organizational Learning through University-Industry
Research Cooperation
1011
Nadine Amende 1023
Evgeny Blagov 1033
Roman Kislov 1041
Tony K. M. Lo and Patrick S.W. Fong 1049
Thi Hai Hang Nguyen et al 1058
Omid Omidvar 1066
Sakura Shimada 1073
Simon Woll 1081
Non Academic Papers 1089
Institutional Leadership: An Inconvenient Truth Chris Blodgett 1091
Can Knowledge Management Survive Without Information
Technologies?
Enterprise 2.0: Knowledge Management for Decision
Support
Capturing Lessons That Should be Learned: An After
Event Review for Whole-<strong>of</strong>-Government Security Planning
and Operation
Stefanie Dannemann 1103
Mohamed Farid 1107
Susan McIntyre and Kate Kaminska 1119
Work in Progress papers with Posters 1127
Knowledge Ontology in Labor Outsourcing Environments Óscar Arias Londoño 1129
Proposal for A 2.0 Knowledge Management Model for
“Medellin, Cluster City (Medellin, City <strong>of</strong> Knowledge)
Similarity and Accuracy <strong>of</strong> Shared Mental Models and its
Impact on Process Stability in Steel Production: First
Results <strong>of</strong> a Knowledge Audit Methodology
What’s your Strategy for Measuring IT & Non-IT for
Knowledge Management in an Organisation?
Scope <strong>of</strong> Knowledge Management for Improving
Performance in Call Centre Service Delivery
A Model for Risk Analysis in <strong>the</strong> Process <strong>of</strong> Building a
Knowledge Cluster in Colombia
vii
Lillyana María Giraldo Marín et al 1134
Nina Groß et al 1138
Ravinder Singh Kahlon and Man-Chie
Tse
Pushkal Pandey, Sandra M<strong>of</strong>fett and
Rodney McAdam
1141
1147
José Vásquez Paniagua 1151

Preface
These proceedings represent <strong>the</strong> work <strong>of</strong> presenters at <strong>the</strong> <strong>12th</strong> <strong>European</strong> <strong>Conference</strong> on Knowledge
Management (ECKM 2011). We are delighted to be hosting <strong>the</strong> 12 th annual ECKM at <strong>the</strong> University <strong>of</strong>
Passau, Germany.
We are pleased to welcome three keynote speakers to <strong>the</strong> conference. On <strong>the</strong> first morning we will hear
Pr<strong>of</strong>. Dr. Ronald Maier, from Leopold-Franzens-University <strong>of</strong> Innsbruck, Austria talk about “Re-focusing
knowledge management: concepts <strong>of</strong> knowledge maturing”. In <strong>the</strong> afternoon we welcome Ms Latha
Alaguvelu, from Infosys Limited, Bangalore, India who will address <strong>the</strong> topic “Emerging approaches to
organizational learning”. Finally on <strong>the</strong> second morning we will hear from Pr<strong>of</strong>. Dr. Klaus Tochtermann, from
ZBW – Leibniz Information Centre for Economics, Germany, talk about “10 years <strong>of</strong> Knowledge Management
– will ano<strong>the</strong>r 10 years follow?”.
A primary aim <strong>of</strong> this conference is for academics concerned with current research findings and for those
from <strong>the</strong> wider community involved in Knowledge Management, to present <strong>the</strong>ir findings and ideas to peers
from Knowledge Management and associated fields. We also hope that <strong>the</strong> conference provides a platform
for practitioners and academics across <strong>the</strong> field <strong>of</strong> Knowledge Management to meet those who hold ideas in
a face to face interaction, forge long-lasting networks and linkages with colleagues from similar areas <strong>of</strong>
interests. We hope that <strong>the</strong> conference will help attendees advance in <strong>the</strong>ir understanding <strong>of</strong> how firms and
countries generate and exploit knowledge to achieve a competitive advantage, and drive <strong>the</strong>ir innovations
forward. The range <strong>of</strong> issues and mix <strong>of</strong> approaches followed will ensure an interesting two days.
304 initial abstracts were received for this conference. However, <strong>the</strong> academic rigueur <strong>of</strong> ECKM meant that,
after <strong>the</strong> double blind, peer review process <strong>the</strong>re are 137 papers published in <strong>the</strong>se <strong>Conference</strong>
<strong>Proceedings</strong>. These papers reflect <strong>the</strong> growth in <strong>the</strong> field <strong>of</strong> Knowledge Management, and <strong>the</strong>y represent
truly global research from some 40 different countries, including Australia, Austria, Belgium, Boznia and
Herzegovina, Brazil, Bulgaria, Canada, Columbia, Cuba, Czech Republic, Egypt, Estonia, Finland, France,
Germany, Greece, Hong Kong, Hungary, Iran, Ireland, Italy, Jordan, Latvia, New Zealand, Norway, Portugal,
Romania, Russia, Serbia, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Taiwan, The
Ne<strong>the</strong>rlands, Turkey, United Kingdom, USA.
We hope that you have an enjoyable conference.
Dr. Franz Lehner and Dr. Klaus Bredl
viii

Biographies <strong>of</strong> <strong>Conference</strong> Chairs, Programme Chairs and
Keynote Speakers
<strong>Conference</strong> Chairs
Pr<strong>of</strong>. Dr. Franz Lehner has been assistant pr<strong>of</strong>essor at <strong>the</strong> Institute for
Organizational Research at <strong>the</strong> University <strong>of</strong> Linz, Austria, since 1986. Before this
he ga<strong>the</strong>red experience in <strong>the</strong> field <strong>of</strong> EDP as head <strong>of</strong> <strong>the</strong> educational centre at a
s<strong>of</strong>tware house and as an independent consultant. From 1992 to 1994 he was
pr<strong>of</strong>essor for business administration and information management (MIS) at <strong>the</strong>
Koblenz School <strong>of</strong> Corporate Management (WHU, Germany) where he was
elected as dean <strong>of</strong> <strong>the</strong> faculty in 1994. After that a short period followed as
president <strong>of</strong> <strong>the</strong> new founded Danube-University at Krems which is a centre for
postgraduate studies in Austria. In 2004 he accepted a call to <strong>the</strong> University <strong>of</strong>
Passau where he holds now <strong>the</strong> Chair for Information Systems
(Wirtschaftsinformatik) since
April 2004. He has published 25 Books (including some as co-author) and
more than 100 articles in national and international journals to various topics in <strong>the</strong> field <strong>of</strong> information
science and information management. His research is focusing on E-Learning as well as Information and
Knowledge Management.
Programme Chair
Pr<strong>of</strong>. Dr. Klaus Bredl was assistant pr<strong>of</strong>essor at <strong>the</strong> Institute for Information
Systems at <strong>the</strong> University <strong>of</strong> Regensburg, Germany from 2001 until 2005. Before
this he had ga<strong>the</strong>red experience in <strong>the</strong> training centre at Toshiba Europe. In
2006, he was first pr<strong>of</strong>essor for media at a private College. After that he received
a call to <strong>the</strong> University <strong>of</strong> Applied Sciences Neubrandenburg where his teaching
and research was in <strong>the</strong> field <strong>of</strong> Social Informatics. In 2009, he accepted a call to
<strong>the</strong> Institute <strong>of</strong> Media and Educational Technology (Chairman <strong>of</strong> <strong>the</strong> Board) at
<strong>the</strong> University <strong>of</strong> Augsburg. His team is focused on <strong>the</strong> research <strong>of</strong> Digital Social
Media in <strong>the</strong> field <strong>of</strong> E-Learning and Knowledge Management.
Keynote Speakers
Dr. Ronald Maier holds a PhD in Management Information Systems (MIS) from
WHU Otto Beisheim School <strong>of</strong> Management in Vallendar, Germany, and a
habilitation degree from University <strong>of</strong> Regensburg, Germany. After positions as
Visiting Pr<strong>of</strong>essor at University <strong>of</strong> A<strong>the</strong>ns, Georgia (USA) and as Chair <strong>of</strong> MIS at
Martin-Lu<strong>the</strong>r-University <strong>of</strong> Halle-Wittenberg, Germany, he has been appointed
Pr<strong>of</strong>essor <strong>of</strong> Information Systems at University <strong>of</strong> Innsbruck, Austria, since 2007.
He has published numerous articles in journals and conference proceedings and
books on knowledge management systems and enterprise knowledge
infrastructures. His research interests include flexible and adaptive business
processes, knowledge management and technology enhanced learning.
Pr<strong>of</strong>. Dr Klaus Tochtermann is a pr<strong>of</strong>essor for Computer Media at <strong>the</strong>
Christian-Albrechts University <strong>of</strong> Kiel (Germany). From 1997 to 2000 he was
deputy head and later head <strong>of</strong> <strong>the</strong> department for Environmental Information
Systems at <strong>the</strong>, FAW Ulm (Germany). The FAW was an industry research
institute with a strong focus on knowledge management and knowledge
technologies. From October 2000 to Jun 2010, Pr<strong>of</strong>. Tochtermann was <strong>the</strong>
director <strong>of</strong> Austria’s first industry-based research institute on knowledge
management Know-Center. The work <strong>of</strong> <strong>the</strong> Know-Center includes projects on
knowledge management, knowledge relationship discovery, semantic
technologies, workplace-integrated learning, Web2.0. Since 2004 Klaus
Tochtermann has also been head <strong>of</strong> <strong>the</strong> institute for knowledge management
at Graz University <strong>of</strong> Technology. Klaus’s doctoral <strong>the</strong>sis in <strong>the</strong> field <strong>of</strong> formal models for hypermedia and
Internet-based services resulted is a scholarship from <strong>the</strong> Max-Kade Foundation that enabled him to pursue
post doctoral research at Texas A&M in <strong>the</strong> USA. In addition to his pr<strong>of</strong>essorial duties, Klaus is <strong>the</strong> Director
<strong>of</strong> <strong>the</strong> ZBW – Leibniz Information Centre for Economics. With about 270 employees maintaining more than
4,2 Million documents related to economics, ZBW is <strong>the</strong> world’s largest library for economics.
ix

Mini Track Chairs
Dr.-Ing. Peter Heisig is Senior Research Fellow at <strong>the</strong> Leeds University
Business School and visiting researcher at <strong>the</strong> Engineering Design Centre at
Cambridge University. Since 1988 he works in <strong>the</strong> area <strong>of</strong> Knowledge
Management and has been responsible for several KM projects in industry (e.g.
Siemens, Volkswagen), public administration (e.g. Police, Ministry) and
research organizations (e.g. Fraunh<strong>of</strong>er). Peter served as KM-Expert to <strong>the</strong> EU
and CEN („<strong>European</strong> Guide to Good Practice in Knowledge Management“, CWA
14924). He is a member <strong>of</strong> <strong>the</strong> VDI KM Working Group and <strong>of</strong> German
Knowledge Management Society (GfWM), <strong>the</strong> Design Society and INCOSE
Dr. Mag Claudia Thurner-Scheuerer studied Business Administration with
Erasmus period abroad and Doctoral Studies in Social and Economic Sciences at
Karl-Franzens-University Graz. Since 2003 in <strong>the</strong> interdisciplinary field <strong>of</strong>
Knowledge Management. 2008 completion <strong>of</strong> her dissertation. Since 2007 she is
<strong>the</strong> community manager <strong>of</strong> Plattform Wissensmanagement, <strong>the</strong> largest KMcommunity
in <strong>the</strong> German-speaking area and is working for Know-Center, Austria's
competence center for knowledge management.
Dr. Mag Alexander Stocker studied business Administration at <strong>the</strong> University <strong>of</strong> Graz
and at Graz, Technical University. He has been Dealing with computer-based
information systems for more than ten years. Currently he is senior researcher at <strong>the</strong>
Institute DIGITAL, JOANNEUM RESEARCH.
Dr. Dipl.-Kfm Alexander Richter studied Business Administration at <strong>the</strong>
Universities <strong>of</strong> Augsburg and Rennes. As a research assistant at <strong>the</strong> Cooperation
Systems Center Munich (Pr<strong>of</strong>. Michael Koch) at <strong>the</strong> Bundeswehr University
Munich he observes with great interest <strong>the</strong> adoption <strong>of</strong> Social S<strong>of</strong>tware in various
German and international companies.
Dr Paulette DeGard serves as a Vice President <strong>of</strong> West Coast Operations for
ProCarta, a process/knowledge management capture tool. Her responsibilities include
creating products that capture business processes for government, financial
institutions, medical practices and real estate. Paulette has also worked with <strong>the</strong> US
Navy and The Boeing Company as well as numerous Fortune 500 companies around
North America conducting knowledge management, process management and
s<strong>of</strong>tware deployments. Additionally, Paulette started a management training school in
nor<strong>the</strong>rn Poland, worked on <strong>the</strong> world's largest re-engineering project at Boeing, and
created a knowledge transfer method to keep 40,000 end-users informed about s<strong>of</strong>tware changes.
Selvi Kannan is a lecturer, researcher and advisory consultant. She has over
twenty years prior experience at senior management level in both <strong>the</strong> private and
public sector internationally, managing change in a variety <strong>of</strong> specialized areas
including; Knowledge Management & Learning in Organizations, Workplace
Diversity and Organizational Culture and Mentoring and Coaching Business
Executives.
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Jan Pawlowski works as Pr<strong>of</strong>essor in Digital Media - Global Information Systems at
<strong>the</strong> University <strong>of</strong> Jyväskylä, Finland. Born in 1971, originally from Essen, Germany.
Masters' Degree and Doctorate in Business Information Systems (University <strong>of</strong>
Duisburg-Essen). Since 10.2007 working as Adjunct Pr<strong>of</strong>essor within <strong>the</strong> Faculty <strong>of</strong>
Information Technology. Since 12.2009 Pr<strong>of</strong>essor <strong>of</strong> Digital Media with <strong>the</strong>
specialization "Global Information Systems". This includes <strong>the</strong> research coordination
<strong>of</strong> several national and <strong>European</strong> projects. Main research interests and activities are
in <strong>the</strong> field <strong>of</strong> Global Information Systems, E-Learning, Modeling Learning-related
Processes, Procedural Models, Learning Technology Standardization, Quality
Management and Quality Assurance for Education, and Mobile / Ambient Learning. Actively involved in
research organizations (AACE, GI, IEEE) and in standardization organizations (DIN, CEN, ISO/ IEC JTC1
SC36). Acting chair <strong>of</strong> <strong>the</strong> CEN/ISSS Workshop Learning Technologies.
Eduardo Rodriguez is experienced in knowledge management, risk management
and analytics in <strong>the</strong> insurance and banking industry. He has been Knowledge
Management Advisor at EDC Export Development Canada in Ottawa, Regional
Director <strong>of</strong> PRMIA (Pr<strong>of</strong>essional Risk Managers International Association)in Ottawa
and currently, he is Quantitative Analyst for EDC, Director <strong>of</strong> IQAnalytics, Director <strong>of</strong>
Strategic Intelligence UNAD Colombia. Ma<strong>the</strong>matician, MBA, MSc. Ma<strong>the</strong>matics,
certification <strong>of</strong> <strong>the</strong> Advanced Management Program McGill University. PhD Candidate
at Aston Business School in <strong>the</strong> UK research in <strong>the</strong> field <strong>of</strong> Knowledge Management
applied to Enterprise Risk Management.
Biographies <strong>of</strong> contributing authors (in alphabetical order)
Luis Joyanes Aguilar is a Physics, Informatics Engineer Ph.D., and, Sociology Ph.D. Full-time researcher
and pr<strong>of</strong>essor (Catedrático) <strong>of</strong> Lenguajes y Sistemas Informáticos, Universidad Pontificia de Salamanca, in
Madrid. He is also a Director <strong>of</strong> GISSIC (Investigation group in S<strong>of</strong>tware Engineering), doctoral <strong>the</strong>sis tutor.
Topics to learning, teaching and investigation: Knowledge management, management, business architect,
information management, information and communication technologies.
Manouchehr Ansari is a Associate pr<strong>of</strong>essor at <strong>the</strong> Faculty <strong>of</strong> Management, University <strong>of</strong> Tehran. He has
Bachelor’s Degree in Production Management from University <strong>of</strong> Caen, MSC in Innovation Engineering from
University <strong>of</strong> Rouen and PhD in Industrial Management from University <strong>of</strong> Rouen. His main Research
interests are Technology and Innovation, and knowledge management.
Albena Antonova is a Lecturer at S<strong>of</strong>ia University, Faculty <strong>of</strong> Economics and Business Administration. She
Her research interests include knowledge management, serious games, knowledge management systems,
innovation processes, technology enterpreneurship, knowledge sharing, knowledge transfer and o<strong>the</strong>rs.
Nadine Amende studied Management Information Systems at Martin-Lu<strong>the</strong>r-University Halle-Wittenberg,
Germany. Currently, she works as a PhD researcher at <strong>the</strong> Chair <strong>of</strong> Management Information Systems II <strong>of</strong>
<strong>the</strong> University <strong>of</strong> Passau, Germany. Her fields <strong>of</strong> research and interest are information visualisation
especially geovisualisation, IS success measurement and knowledge management.
Eckhard Ammann Dr is a pr<strong>of</strong>essor for computer science at <strong>the</strong> Reutlingen University, Germany, since
1992. Before that, he spent 8 years with <strong>the</strong> IBM company doing research and development in parallel
systems and system structures. His research interests include knowledge management,intellectual capital,
business process modeling, distributed systems,and virtual organisations.
Óscar Arias Londoño is a Business Administrator, at <strong>the</strong> Universidad Nacional de Colombia. Specialist
Teaching <strong>of</strong> Social Sciences. MSc Administration, Universidad Eafit-HEC Montreal. He is also a Student <strong>of</strong>
PhD in Administration, Universidad Eafit-HEC Montreal and a full-time researcher pr<strong>of</strong>essor MBA,
Universidad de Medellin. Oscar is a member <strong>of</strong> <strong>the</strong> Group <strong>of</strong> Research CyGO.
Seyed Esmaeil Asgharpour (PhD). Associate Pr<strong>of</strong>essor at Islamic Azad University Shahre Rey Branch
Tehran Iran He has a PhD degree in Leadership and Human Behavior from California International
University, San Diego, California, United States, 1980. Also a M.A Degree in Human Geography, University
<strong>of</strong> Tehran, 1977. B.A Degree in Economics& Human Geography, Tabriz University,
xi

Roland Bardy M.B.A., served in a major German multinational in various assignments (administration,
accounting and controlling). He took up teaching after retirement at Emory University, Atlanta, in 1999, and,
as <strong>of</strong> 2010, he has several o<strong>the</strong>r lecturing contracts in <strong>the</strong> U.S. and in Europe. Publications on accounting
and controlling, leadership and ethics.
Simona-Clara Bârsan has been <strong>the</strong> director <strong>of</strong> <strong>the</strong> Technology Transfer Centre CENTI from Cluj-Napoca,
Romania, since 2004. She managed two projects concerning national technology transfer in <strong>the</strong>
environmental field. Co-author in several papers regarding company’s management, innovation and
technology transfer and 2 books concerning <strong>the</strong>ory and practice in technology audit.
Leila Beig has a Masters degree, from Alzahra University in Information Technology focusing on knowledge
management and organizational memory in dynamic virtual organizations. Studied business management
during undergraduate level in Shahid Bahonar University <strong>of</strong> Kerman. Faculty member <strong>of</strong> Iran Telecom
Research Center.
Didiosky Benítez Erice is a PhD student at Vrije Universiteit Brussel, Belgium. He holds a master in New
Technologies for Education and is <strong>the</strong> author <strong>of</strong> <strong>the</strong> first Cuban eLearning platform (SEPAD). His current
research interests are in <strong>the</strong> field <strong>of</strong> <strong>the</strong> KM in <strong>the</strong> higher education teaching-learning process.
Evgeny Blagov has obtained a specialist degree in international management at <strong>the</strong> Graduate School <strong>of</strong>
Management <strong>of</strong> Saint Petersburg State University in 2007, a Master <strong>of</strong> Research in Management Sciences
degree at ESADE Business School in 2010 and finished a PhD program at <strong>the</strong> Graduate School <strong>of</strong>
Management <strong>of</strong> Saint Petersburg State University in 2010.
Chris Blodgett. Lieutenant-Colonel Royal Military College Canada graduate and Air Force Officer in
Canadian Forces. Began as a Navy Tactical Maritime Helicopter Navigator. Possessing a MA in
Management & Policy and certifications in KM, IM, strategy, change and HR, he was <strong>the</strong> DND/CF KM
Director prior to his present KMO position at NATO School Germany.
Pavel Bogolyubov is a Management and Business Development Fellow, at Lancaster University
Management School, UK. He Gained first degree in Physics at Herzen University,St. Petersburg, Russia,
and an MBA from Bradford School <strong>of</strong> Management, UK. Prior to returning to academia spent ten years
working in various Continuous Improvement roles in FMCG multinationals across Europe. Research interests
are centred around “s<strong>of</strong>ter” aspects <strong>of</strong> Web 2.0 and its role in KM.
Ettore Bolisani Laurea, Electronic Engineering; PhD in Innovation Studies from Padua University. Was
‘Marie Curie’ Research Fellow at PREST,University <strong>of</strong> Manchester; Researcher at Universities <strong>of</strong> Trieste and
Padua. Associate Pr<strong>of</strong>essor , Faculty <strong>of</strong> Engineering , University <strong>of</strong> Padua. Researches in ICT/knowledge
management. Research projects funded by EU, Italian institutions, and private organisations.
Constantin Bratianu is a pr<strong>of</strong>essor <strong>of</strong> Strategic Management and Knowledge Management at <strong>the</strong> Academy
<strong>of</strong> Economic Studies, Bucharest, Romania. He is <strong>the</strong> Head <strong>of</strong> UNESCO Department for Business
Administration, and Director <strong>of</strong> <strong>the</strong> Research Center for Intellectual Capital. His main academic interests are:
knowledge dynamics, knowledge management, intellectual capital, strategic management and university
management.
Klaus Bredl Pr<strong>of</strong>. Dr Assistant pr<strong>of</strong>essor, Institute for Information Systems, University <strong>of</strong> Regensburg,
Germany 200-2005. Pr<strong>of</strong>essor for media at a private College (2006), <strong>the</strong>n University <strong>of</strong> Applied Sciences
Neubrandenburg where teaching and research was in field <strong>of</strong> Social Informatics. 2009, went to Institute <strong>of</strong>
Media and Educational Technology, University <strong>of</strong> Augsburg, focusing on research <strong>of</strong> Digital Social Media in
E-Learning and Knowledge Management.
Camelia Burjav is an associate pr<strong>of</strong>essor at <strong>the</strong> University <strong>of</strong> Alba Iulia, Romania. She holds a PhD in
Economics at <strong>the</strong> Academy <strong>of</strong> Economic Studies Bucharest. She has participated in research projects
related to <strong>the</strong> business environment and in various International <strong>Conference</strong>s on Economics. Her interest
fields include: economic analysis, investments, sustainable development, knowledge management.
Adriana Schiopoiu Burlea is Pr<strong>of</strong>essor PhD at University <strong>of</strong> Craiova and visiting pr<strong>of</strong>essor at <strong>the</strong>
universities in France and Poland. She is Romanian ambassador for AGRH. She has written more than 20
books and 130 articles in management field. She is a member <strong>of</strong> <strong>the</strong> Editorial Board <strong>of</strong> <strong>the</strong> many prestigious
journals and conferences.
Simon Cadez is an assistant pr<strong>of</strong>essor <strong>of</strong> management accounting at <strong>the</strong> Faculty <strong>of</strong> Economics, University
<strong>of</strong> Ljubljana. His main research interests are strategic management accounting and knowledge management
xii

in academia. He holds <strong>the</strong> position <strong>of</strong> <strong>the</strong> president <strong>of</strong> <strong>the</strong> national examining committee for high school
education.
Chiara Cannavale is assistant pr<strong>of</strong>essor <strong>of</strong> business management and international management at <strong>the</strong>
Università degli Sudi di Napoli Par<strong>the</strong>nope (Italy). Her research focuses on firms’ internationalization in
emerging countries and she is particularly interested in cross-cultural management issues
José Manuel Cárdenas Medina: PhD student from <strong>the</strong> Polytechnic School (Department <strong>of</strong> Industrial
Engineering), University <strong>of</strong> São Paulo. Interested in <strong>the</strong> field <strong>of</strong> Knowledge Management on <strong>the</strong> technology
management and innovation. Carrying out research on <strong>the</strong> role <strong>of</strong> information technology in management
skills for <strong>the</strong> strategic knowledge.
Vincenzo Cavaliere is an Associate Pr<strong>of</strong>essor <strong>of</strong> Organizational Behaviour and Human Resource
Management at <strong>the</strong> University <strong>of</strong> Florence, Department <strong>of</strong> Business Administration His research interests are
in knowledge <strong>the</strong>ory, organizational learning, entrepreneurship and small enterprises.
Juan-Gabriel Cegarra-Navarro is a Doctor in Business Administration, and Master in marketing and
communications. Currently, he is an associate pr<strong>of</strong>essor <strong>of</strong> <strong>the</strong> Facultad de Ciencias de la Empresa,
Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 50, 30203 Cartagena (Spain). His research is
focused in Knowledge Management.
Domenico Celenza is a researcher <strong>of</strong> Economics and Business Management and Phd <strong>of</strong> Economics and
Business Management, Faculty <strong>of</strong> Economics, University <strong>of</strong> Cassino.
Huei-Fang Chen is a pr<strong>of</strong>essor <strong>of</strong> International Business Department at Soochow University, Taipei, Taiwan.
She earned her Ph. D. degree from National Taiwan University. Her major is Organizational Behavior and
Human Resource Management.
Marguerite (Reet) Cronk PhD has been <strong>the</strong> director <strong>of</strong> <strong>the</strong> Management Information Systems program at
Harding University, USA since 2000. Dr Cronk has been associated with <strong>the</strong> <strong>European</strong> and International
Academic <strong>Conference</strong>s since 1997.Research interests include: Technology in Education, Web 2
technologies, Knowledge Management, Information system design, and IT evaluation.
Anikó Csepregi is a Lecturer at <strong>the</strong> Department <strong>of</strong> Management, University <strong>of</strong> Pannonia Veszprém,
Hungary. Lecturer at University <strong>of</strong> Pannonia, Hungary. Between 2006 and 2009 she was a Ph.D.Student.
Since September 2009 she is a Lecturer. Her Main fields <strong>of</strong> interest include knowledge management,
knowledge sharing and competence management.
Stefanie Dannemann has several years experience in communication and knowledge/information
management - as coordinator and project manager <strong>of</strong> various IT, KM and communication projects for UN and
EU organizations. My ultimate goal is to provide <strong>the</strong> right tools and techniques for <strong>the</strong> right audience
contributing to knowledge sharing culture <strong>of</strong> an organization.
S<strong>of</strong>ia Daskou is Assistant Pr<strong>of</strong>essor in Marketing at Hellenic American University, visiting faculty to <strong>the</strong>
University <strong>of</strong> Strathclyde and President <strong>of</strong> <strong>the</strong> International Advisory Council for <strong>the</strong> Marketing Pr<strong>of</strong>ession.
She has published at <strong>the</strong> Global Business and Economics Review, <strong>the</strong> Journal <strong>of</strong> Relationship Marketing, <strong>the</strong>
Journal <strong>of</strong> Financial Services Marketing, <strong>the</strong> International Journal <strong>of</strong> Arts and Sciences.
Françoise de Viron is Pr<strong>of</strong>essor at Louvain School <strong>of</strong> Management <strong>of</strong> ‘Université catholique de Louvain’.
Since 2005, she is in charge <strong>of</strong> <strong>the</strong> Knowledge Management Course at <strong>the</strong> Master level. Previously, she has
been during more than 10 years Manager <strong>of</strong> Knowledge Management projects by an engineering company.
Lise Desmarais Ph.D., tenure pr<strong>of</strong>essor at University <strong>of</strong> Sherbrooke’s Business Faculty in <strong>the</strong> management
and human resources’ department. In charge <strong>of</strong> <strong>the</strong> health and safety certificate. Director <strong>of</strong> <strong>the</strong>. Researcher
in <strong>the</strong> Chaire d’Étude en Organisation du Travail (CEOT) and <strong>the</strong> Dynamic Knowledge Transfer Laboratory.
Eva Maria Eckenh<strong>of</strong>er, Austrian citizen, graduated in Media Management from University <strong>of</strong> Applied
Sciences in St.Pölten (Austria) in 2008 and works at <strong>the</strong> moment as a PhD Student on Tomas Bata
University in Zlín (Czech Republic) on <strong>the</strong> Faculty <strong>of</strong> Management and Economics in <strong>the</strong> fields network
management, organizational networks, corporate culture and cluster.
Emmanuel Edoun has a DBA in Business Administration and Phd on Decentralisation and Local Economic
Development, University <strong>of</strong> <strong>the</strong> Witwatersrand in Johannesburg. He has 12 years <strong>of</strong> experience in
xiii

public/private sectors. He has engaged with local government practioners at international level through
workshops/conferences.
Anandasivakumar Ekambaram works as a research scientist at SINTEF – Technology and Society,
Productivity and Project Management, Trondheim, Norway. He obtained his doctoral degree, which focuses
on project management and knowledge transfer in organizations, from <strong>the</strong> Norwegian University <strong>of</strong> Science
and Technology (NTNU). Besides his research work, he is involved in teaching activities at NTNU
Jamal El-Den is a Senior Lecturer, at <strong>the</strong> Charles Darwin University (CDU), Darwin, Australia, School <strong>of</strong>
Engineering and IT. Taught at variety <strong>of</strong> institutions in Australia, France, Lebanon, and Syria. Research
interests are Knowledge Management, Group Support Systems, Tacit Knowledge Transfer, Data and
Knowledge Base, Systems Development, and Information Security. Over 23 conference and journal papers
in <strong>the</strong>se areas <strong>of</strong> research. Member <strong>of</strong> The Australian Computing Society, The Institute <strong>of</strong> Engineers
Australia, and Order <strong>of</strong> Engineers Lebanon.
Tiit Elenurm is head <strong>of</strong> <strong>the</strong> entrepreneurship department at <strong>the</strong> Estonian Business School. PhD. in 1980 for
<strong>the</strong> dissertation “Management <strong>of</strong> <strong>the</strong> Process <strong>of</strong> Implementation <strong>of</strong> New Organizational Structures”. Author <strong>of</strong>
more than 110 research publications. Research interests include knowledge management, change
management and international transfer <strong>of</strong> management knowledge.
Isaac Enakimio is an Entrepreneur, project management practitioner, tutor at Greenwich University, bible
teacher, charity organisation Founder in UK and works with Kent and Medway Health Informatics Service
(KMHIS) <strong>of</strong> <strong>the</strong> National Health Service (NHS) as a Senior IT Support Analyst. Currently holds BSC. Hons,
University <strong>of</strong> Greenwich UK in Information Technology Management for Business (ITMB). Manages
Practitioners Prince II project qualification involved in NHS KM research
Mohamed Farid is an Information Technology expert and one <strong>of</strong> <strong>the</strong> core members <strong>of</strong> <strong>the</strong> Information and
Decision Support Center (IDSC) in Egypt, with over 12 years' experience building various information
systems. Farid holds a Master degree in Business Information Technology from Middlesex University, UK
and has obtained his dissertation in Knowledge Management.
Mohamd Ali Feyz After high school, he chose Industrial Engineering at Sharif University <strong>of</strong> Technology.
Graduated with bachelor’s degree, accepted at University <strong>of</strong> Tehran to do a masters degree in IT
management. Writing <strong>the</strong>sis titled "A proposed framework for discovering key knowledge areas in supply
chain and determining <strong>the</strong> relationship with major logistic processes: a case study".
Eloi Fernández y Fernández-PhD in Mechanical Engineering from Catholic University, Rio de Janeiro
(PUC-Rio) . Mechanical Engineer currently Pr<strong>of</strong>essor <strong>of</strong> Mechanical Engineering at PUC.Rio and General
Director <strong>of</strong> ONIP (National Organization <strong>of</strong> <strong>the</strong> Petroleum Industry - Brazil). Was Director <strong>of</strong> ANP (National
Petroleum Agency – Brazil), State Secretary for Science and Technology, and Management Superintendent
at FAPERJ.
Jiri Franek is currently studying PhD. programme at VSB-Technical University <strong>of</strong> Ostrava. In 2009 he
graduated as a Master <strong>of</strong> Business Economics on Faculty <strong>of</strong> Economics. In winter terms 2006 and 2007 he
has been studying in Finland and Liechtenstein respectively. His research activities are focused mostly on
knowledge work and workers, business competitiveness and potential.
Charles Gagné is a Knowledge Transfer Advisor at IRSST’s Knowledge Transfer and Partner Relations
Department. The KTPR mandate consists <strong>of</strong> ensuring <strong>the</strong> use <strong>of</strong> research results and <strong>the</strong>ir diffusion to
partners and stakeholders involved in <strong>the</strong> prevention <strong>of</strong> occupational accidents.
Lillyana María Giraldo Marìn Informatic PhD Candidate, Universidad Pontificia de Salamanca,
España.System Engineer, Universidad San Buenaventura (Colombia). Master <strong>of</strong> Education, Universidad de
Manizales (Colombia), Information management specialist, Universidad de Medellín (Colombia). Full-time
researcher and pr<strong>of</strong>esor System Engineer, Universidad de Medellín.
Tendayi Gondo is a Lecturer in <strong>the</strong> Department <strong>of</strong> Urban and Regional Planning at <strong>the</strong> University <strong>of</strong> Venda.
He holds two masters degrees, one in Business Administration and ano<strong>the</strong>r in Urban and Regional Planning.
He is currently a co-Editor <strong>of</strong> Journal for Entrepreneurship and Public Policy (JEPP) <strong>of</strong> <strong>the</strong> Emerald
Publishing Group.
Manel González-Piñero has a MBA and Master’s degree in Management Techniques (UB). Bachelor’s
Degree in Business Administration and English Studies (UB). Currently, he is <strong>the</strong> Innovation Manager at <strong>the</strong>
xiv

Biomedical Engineering Research Centre <strong>of</strong> <strong>the</strong> Technical University <strong>of</strong> Catalonia (UPC) and Associate
Pr<strong>of</strong>essor <strong>of</strong> Political Economy at <strong>the</strong> Business School (UB). He has long experience in Innovation
Management and Entrepreneurship.
Nebojša Graca is an Independent scientific researcher. Research areas: - The nature and phenomenlogy <strong>of</strong>
consciousness; Tacit Knowledge Management; Knowledge-intensive companies. Author <strong>of</strong> <strong>the</strong> scientific
system for appliance and development <strong>of</strong> consciousness “Consciousness and health”.Patentee <strong>of</strong> <strong>the</strong>
imperative <strong>of</strong> knowledge and intellectual capital portfolio “Consciousness and health”
Norbert Gronau studied engineering and business administration at Berlin University <strong>of</strong> Technology.
Received Ph.D. (1994) and finished habilitation <strong>the</strong>sis in industrial information systems. He is Chair <strong>of</strong>
Business Information Systems and Electronic Government at <strong>the</strong> University <strong>of</strong> Potsdam, Germany. Main
research activities concentrate on <strong>the</strong> areas <strong>of</strong> Knowledge Management and Business Resource
Management.
Nina Gross studied Educational Sciences with emphasis on knowledge management at <strong>the</strong> University <strong>of</strong>
Duisburg-Essen. Since 2010 she works as a research assistant at <strong>the</strong> Department <strong>of</strong> Computer and
Cognitive Sciences (Organisation and Economic Psychology, Pr<strong>of</strong>. Dr. Annette Kluge) in Duisburg,
Germany.
David Grosse Kathoefer graduated from <strong>the</strong> University <strong>of</strong> Muenster with a Master <strong>of</strong> Science in Business
Management. Currently, he works as a research assistant focusing on knowledge transfer in academia. He is
member <strong>of</strong> <strong>the</strong> Chinese-German Transregional Collaborative Research Centre TRR 61 and Editor <strong>of</strong> <strong>the</strong>
Journal <strong>of</strong> Business Chemistry.
Markus Haag has a PhD at <strong>the</strong> University <strong>of</strong> Bedfordshire, UK, investigating <strong>the</strong> impact <strong>of</strong> personal values
on personal knowledge development in e-learning environments. Holds BA in Information Management from
Stuttgart School <strong>of</strong> Media, Germany, and MA in Intercultural Communication from <strong>the</strong> University <strong>of</strong>
Bedfordshire. Was a researcher at KIeM Institute for Intercultural Management, Values and Communication
at Konstanz University <strong>of</strong> Applied Sciences, Germany.
Frank Habermann is a co-founder <strong>of</strong> Becota – The Berlin Consulting & Talent Association and Pr<strong>of</strong>essor <strong>of</strong>
Business at Berlin School <strong>of</strong> Economics and Law. He spent almost ten years in top management positions
and was a senior researcher at <strong>the</strong> German Institute for Artificial Intelligence as well as Michael Smurfit
Business School in Dublin, Ireland.
C. Hakan kağnicioğlu Faculty <strong>of</strong> Economics and Business Administration, Master Science: Management.
Science: Production Management and Marketing. Interests: Production Management, Design Management,
Supply Chain Management, Quality Improvement Tools, Quantitative Methods in Decision Making,
Management Information Systems. Associate Pr<strong>of</strong>essor: Management, Production
Management, 04/03/2009.
Meliha Handzic is Pr<strong>of</strong>essor <strong>of</strong> Information Systems at <strong>the</strong> International Burch University, Sarajevo. Her
PhD is from <strong>the</strong> University <strong>of</strong> New South Wales, Sydney. Meliha’s main research interests include knowledge
management and decision support systems. She has published extensively on <strong>the</strong>se topics in leading
journals. Currently, she is regional editor for Knowledge Management Research & Practice.
Harold Harlow is a Associate pr<strong>of</strong>essor, strategic management, Wingate University,NC teaching strategy
and corporate innovation. Taught management and strategy at American University, Cairo. Research and
publications include case studies in technology, tacit knowledge and innovation papers on entrepreneurship
in emerging countries.
Ciara Heavin is a College Lecturer in Business Information Systems at University College Cork, Ireland. She
also holds a BSc and MSc in Information Systems from UCC. Her main research interests include <strong>the</strong>
development <strong>of</strong> <strong>the</strong> ICT industry, primarily focusing on Ireland’s s<strong>of</strong>tware industry and knowledge
management in s<strong>of</strong>tware SMEs.
Remko Helms is an assistant pr<strong>of</strong>essor at <strong>the</strong> Department <strong>of</strong> Information and Computing Science at Utrecht
University where he teaches Knowledge Management and Strategic Management <strong>of</strong> ICT. His researches
focus on knowledge management with a focus on knowledge sharing networks and social media. Dr Helms
regularly reviews for various IS conferencea and journals.
xv

Tore Hoel works as a researcher at <strong>the</strong> Centre for Educational Research and Development, Oslo University
College, Norway. The last decade Hoel has been active in learning technology standards development,
acting as vice-chair <strong>of</strong> CEN Workshop on Learning Technologies since 2007. His research interest is
standards governance, and he is currently doing PhD research within this field.
Marta Beatriz Infante Abreu has graduated with honors from <strong>the</strong> Industrial Engineering specialty Business
Organization, at <strong>the</strong> Politechnic University <strong>of</strong> Habana (Cujae), Cuba. She finished with a Business Informatic
Master in Science degree. Since her graduation she works as a research assistant in <strong>the</strong> Industrial
Engineering Faculty at Cujae.
Pamela Chidiogo Izunwanne is a Research Fellow in International Management at <strong>the</strong> University <strong>of</strong> Agder.
She holds a double master degree in international business/management. Her work experience in project
and database management instilled a passion for research within <strong>the</strong> field <strong>of</strong> knowledge management. Her
main research interest is centered on knowledge creation in multinational corporations.
Thomas Janke holds a master's degree in computer science from <strong>the</strong> Technische Universität Dresden. His
research work focuses on ontology engineering as well as on <strong>the</strong> application <strong>of</strong> semantic technologies in real
world business applications. He also has a strong background in <strong>the</strong> field <strong>of</strong> model driven development
(MDD) and mobile application development.
Agnar Johansen works as a senior scientist at SINTEF – Technology and Society, Productivity and Project
Management, Trondheim, Norway. He has wide experience as a consultant, researcher and lecturer in <strong>the</strong>
field <strong>of</strong> project management. He has led several development projects, start-up processes and uncertainty
analyses within <strong>the</strong> field <strong>of</strong> project management – in public and private sectors.
Claudia Jooss IMA - Institute <strong>of</strong> Information Management in Mechanical Engineering & ZLW - Center for
Learning and Knowledge Management & IfU Institute for Management Cybernetics, RWTH Aachen
University, Germany
Luis Joyanes Aguilar Physics, Informatics Engineer Ph.D., and, Sociology PhD. Full-time researcher and
pr<strong>of</strong>essor (Catedrático) <strong>of</strong> Lenguajes y Sistemas Informáticos, Universidad Pontificia de Salamanca, Madrid.
He is also Director <strong>of</strong> GISSIC (Investigation group in S<strong>of</strong>tware Engineering), doctoral <strong>the</strong>sis tutor.
Sabina Jeschke IMA - Institute <strong>of</strong> Information Management in Mechanical Engineering & ZLW - Center for
Learning and Knowledge Management & IfU Institute for Management Cybernetics, RWTH Aachen
University, Germany
Ravinder Singh Kahlon BSc and MSc degrees from Middlesex University, London, UK. During that time
published and presented research findings on S<strong>of</strong>tware Quality, Digital Libraries and Knowledge
Management (KM). Research interests lie in KM Strategies and Systems, especially in success factors
implementing KM within organisations.
Eva-Maria Kern is a pr<strong>of</strong>essor for knowledge management and business process design at <strong>the</strong> Universität
der Bundeswehr München since 2007. Her research interests include value-oriented knowledge
management and business process design, with a special focus on emergency services.
Radwan Alyan Kharabsheh is <strong>the</strong> Head <strong>of</strong> Department <strong>of</strong> Business Administration at <strong>the</strong> Hashemite
University in Jordan. He is a member <strong>of</strong> <strong>the</strong> Australia-New Zealand Marketing Academy and <strong>the</strong> Australia-
New Zealand International Business Academy. His research interests include organizational learning,
knowledge management, online forums and international joint ventures.
Aino Kianto (née Pöyhönen) is a Pr<strong>of</strong>essor <strong>of</strong> Knowledge Management in <strong>the</strong> School <strong>of</strong> Business at
Lappeenranta University <strong>of</strong> Technology, Finland. She has authored and co-authored several academic
articles, papers, books and book chapters related to knowledge management, intellectual capital and
innovation.
Roman Kislov is currently a second-year PhD student at Manchester Business School. He has a medical
background with five years <strong>of</strong> experience as a doctor in Kumtor Operating Company, Bishkek, Kyrgyzstan.
Prior to starting his doctoral studies, Dr Kislov got an MSc in Healthcare Management (Distinction) at
Manchester Business School.
Andrea Kő, PhD: Associate Pr<strong>of</strong>essor with University Doctoral degree in Computer Science (1992) and PhD
degree in Management and Business Administration (2005) from Corvinus University <strong>of</strong> Budapest. MSc in
xvi

Ma<strong>the</strong>matics and Physics, Eötvös Lóránd University <strong>of</strong> Budapest(1988). Participated in international/national
research projects.
Edmore Kori is a Lecturer in <strong>the</strong> Department <strong>of</strong> Geography and Geo-information Sciences at University <strong>of</strong>
Venda. He holds and Honours in geography and Environmental Studies. He is currently studying towards an
MSc Qualification in Geography and Geo-Information Sciences.
Es<strong>the</strong>r Lage is graduated in Economics and has a Master degree in Business Administration from <strong>the</strong>
Federal University <strong>of</strong> Minas Gerais/Brazil. Currently she is a PHD student in Information Science at Lisbon
University Institute."
Yi-Wen Lin is a teacher at Jinou Girls High School, Taipei, Taiwan. She earned her Master's degree from
Soochow University.
Tony Kam-ming Lo is a PhD student in <strong>the</strong> Department <strong>of</strong> Building and Real Estate <strong>of</strong> <strong>the</strong> Hong Kong
Polytechnic University, focusing on lessons learned in various industries. His research interests are lessons
learning, organizational learning, knowledge quality, storytelling and terminology.
Rosa Lombardi is PhD student in Business Administration - Department <strong>of</strong> Business Environment and
Management - Faculty <strong>of</strong> Economics - University <strong>of</strong> Cassino.
Elena Lopez Cano: Degree in Laws and in Politic Science (UB). 1998-2001: Technician in Promotion in
Linguistic and Computers Research Group (UB).2001-2008: Project Manager <strong>of</strong> research projects in <strong>the</strong>
Technology Transfer Office (UPC). 2008-now: Tech Transfer Legal Consultant in <strong>the</strong> IPR Office (UPC).
2008-now: collaborates as a tutor <strong>of</strong> Law Degree (UOC).
Monique Lortie Ph.D.,is tenure pr<strong>of</strong>essor at Université du Québec à Montréal in ergonomics; her initial
background is in industrial engineering. She is in charge <strong>of</strong> a graduate program in Ergonomics and <strong>of</strong> <strong>the</strong>
knowledge transfert strategic arm for <strong>the</strong> Réseau de Recherché en Santé et Sécurité au Travail (RRSSTQ)
Valentina Maksimova has a PhD in Economics; She is a Pr<strong>of</strong>essor and <strong>the</strong> Head <strong>of</strong> Economics and
Investment Department, Moscow State University <strong>of</strong> Economics, Statistics and Informatics. Her research
interests are Economics, Intellectual Capital, Knowledge Economy, Investments in Human Capital. Leads
courses in Microeconomics, Knowledge Management, and Investments.
Halimah Abdul Manaf is a PhD student in “Centre <strong>of</strong> Management and Learning ” at Hull University
Business School. Her received master and bachelor degree in Public Management from <strong>the</strong> University <strong>of</strong>
Utara Malaysia in Malaysia. She is doing PhD research regarding sharing tacit knowledge and personality
traits to enhanced individual performance.
Simone Manfredi is a researcher <strong>of</strong> Economics and Business Management and Phd <strong>of</strong> Economics and
Business Management, Faculty <strong>of</strong> Economics, University <strong>of</strong> Cassino.
Virginia Maracine Pr<strong>of</strong>essor, Operational Research, Risk Management, and Business Logistics, Vice-Dean
within Faculty <strong>of</strong> Economic Cybernetics, Statistics and Informatics. University <strong>of</strong> Bucharest, Romania.
Scientific activity consists <strong>of</strong> many articles, papers, books and has 16 national research grants.
Peter Marshall is a Student at <strong>the</strong> Dublin Institute <strong>of</strong> Technology in Ireland. The work presented in this paper
was conducted as part <strong>of</strong> his Masters <strong>of</strong> Science Degree in Computing for Knowledge Management and
inspired by his research in <strong>the</strong> field <strong>of</strong> Knowledge Elicitation.
Jenny Martínez Crespo.Business PhD Candidate, Universidad Eafit – HEC Montreal, Canadá. Business
Administrator, Universidad de Nariño (Colombia). Master <strong>of</strong> Bussiness Administration qand MSc in
Organizations, Universidad del Valle (Colombia). Full-time researcher and pr<strong>of</strong>esor Business Administration,
Universidad de Medellin.
Inocencia María Martínez-León PhD from Technical University <strong>of</strong> Cartagena, Spain. She teaches in
Organization Studies, She is Head <strong>of</strong> <strong>the</strong> Business Management Department at Technical University <strong>of</strong>
Cartagena, Spain. Her Research interests are organizational learning, organizational structure, corporate
reputation, intangibles management and gender studies.
Lillyana María Giraldo Marìn.Informatic Ph.D Candidate, Universidad Pontificia de Salamanca,
España.System Engineer, Universidad San Buenaventura (Colombia). Master <strong>of</strong> Education, Universidad de
xvii

Manizales (Colombia), Information management specialist, Universidad de Medellín (Colombia). Full-time
researcher and pr<strong>of</strong>esor System Engineer, Universidad de Medellín. Topics to learning, teaching and
investigation: knowledge management, management, business architec, information management,
information and communication technologies.
Maurizio Massaro, PhD is an aggregate pr<strong>of</strong>essor at Udine University since 2008, having worked as
teacher <strong>the</strong>re since 2001. He is a Visiting scholar at <strong>the</strong> Florida Gulf Coast University, Florida, USA, 2010.
His Academic interests primarily in <strong>the</strong> field <strong>of</strong> measurement <strong>of</strong> business performance, intangible assets and
entrepreneurship.
Susan McIntyre Knowledge Manager for Defence R&D Canada – Centre for Security Science. Master’s
degree in Library Science and worked in scientific information services, communications, management and
policy before becoming a practitioner in knowledge management in 2000. Areas <strong>of</strong> interest are in metaorganizational
learning, lessons learned processes for public security S&T and building communities from
disparate sectors.
Kai Mertins is a Division head “Corporate Management” at <strong>the</strong> Fraunh<strong>of</strong>er Institute for Production Systems
and Design Technology (IPK), Berlin. He is a Pr<strong>of</strong>essor and Production Management, at <strong>the</strong> Technical
University <strong>of</strong> Berlin, Germany. He is experienced in design, planning, simulation and control <strong>of</strong> flexible
manufacturing systems, manufacturing control systems, business reengineering and enterprise modelling
and knowledge management systems.
Sandra M<strong>of</strong>fett is a Lecturer <strong>of</strong> Computer Science with <strong>the</strong> University <strong>of</strong> Ulster’s School <strong>of</strong> Computing and
Intelligent Systems, Magee Campus. She is a core member <strong>of</strong> <strong>the</strong> Business and Management Research
Institute. Her expertise on Knowledge Management contributes to her being one <strong>of</strong> <strong>the</strong> UK leading authors
in this field.
Mohammad Mirkazemi Mood is a graduate student in industrial management at university <strong>of</strong> Tehran. The
subject <strong>of</strong> his dissertation is knowledge transfer and innovation strategies in supply chain management. His
key research interests are supply chain management, knowledge management, operat3rdresearch /
management science, system thinking and system dynamics approach
Oliver Moravcik Technische Hochschule Ilmenau/Germany, Dipl.-Ing. in Automation, <strong>the</strong>n moved to
Technische Hochschule Ilmenau/Germany, Dr.-Ing. in Computer Science, 1990 Slovak University <strong>of</strong>
Technology, assoc. Pr<strong>of</strong>essor/ Applied Informatics and Automation, visiting pr<strong>of</strong>esor in Koe<strong>the</strong>n and
Darmstadt/Germany, 1998 Pr<strong>of</strong>essor/Applied Informatics and Automation at Slovak University <strong>of</strong> Technology
Bratislava, 2006 Dean <strong>of</strong> Faculty <strong>of</strong> Materials Science and Technology at Slovak University <strong>of</strong> Technology
Bratislava
Martina Mullalli holds a Masters in Business Studies from Waterford Institute <strong>of</strong> Technology. She is Project
Coordinator on <strong>the</strong> Sustainable Learning Networks in Ireland and Wales (SLNIW) research project. Her main
research interests and activities are related to Learning Networks, Knowledge Management and Tacit
Knowledge Transfer.
Matt Nathan is a programmer working for <strong>the</strong> Press Association UK in Nottingham. Over his career he has
worked on many aspects <strong>of</strong> data persistence and information retrieval; most recently on increasing business
value through <strong>the</strong> use <strong>of</strong> <strong>the</strong> semantic web and NLP technologies.
Fattah Nazem .Associate Pr<strong>of</strong>essor with two books, eighty-four articles, fifteen research. Co-chair & as a
scientific committee member 43; Chief Executive <strong>of</strong> Quarterly Journal <strong>of</strong> Educational Science; Guiding 229
MA Thesis;Cooperating with 101Organization for 380 different Seminars and Courses. Vice–President <strong>of</strong>
research department 4.5 years.Head <strong>of</strong> <strong>the</strong> department educational Sciences 18 months
Christian Neubert is research assistant at <strong>the</strong> chair for S<strong>of</strong>tware Engineering <strong>of</strong> Business Information
Systems at <strong>the</strong> Technische Universität München since September 2008. Christian Neubert holds a diploma
degree in Informatics (Minor: business economics) from Universität Paderborn since 2006. From 2006 to
2008 he worked as s<strong>of</strong>tware engineer in <strong>the</strong> area <strong>of</strong> logistics.
Gaby Neumann is a Pr<strong>of</strong>essor in Engineering Logistics at <strong>the</strong> Technical University <strong>of</strong> Applied Sciences in
Wildau, Germany. Her activities and research interests are amongst o<strong>the</strong>rs linked to problem solving and
knowledge management in logistics. She has widely published and has been or is being involved in a couple
<strong>of</strong> respective research projects.
xviii

Thi Hai Hang Nguyen is a lecturer at <strong>the</strong> Vietnam Aviation Academy, Ministry <strong>of</strong> Transportation <strong>of</strong> Vietnam.
She has 15 year experience working in Vietnam Aviation sector. She has been taking <strong>the</strong> PhD program at
<strong>the</strong> Tomas Bata University in Zlin, Czech Republic and selected Knowledge Management as <strong>the</strong> topic <strong>of</strong>
PhD dissertation.
Gary R Oliver researches knowledge sharing behaviour and is currently Senior Lecturer at <strong>the</strong> University <strong>of</strong>
Sydney where he obtained a PhD in economics, an M. Ed. and a Graduate Certificate in Higher Education.
Gary has over 20 years commercial and government experience in strategy, information and knowledge,
project management, and procurement including CIO and GM roles.
Isabel Olmedo-Cifuentes is an assistant pr<strong>of</strong>essor in <strong>the</strong> Business Management Department at Technical
University <strong>of</strong> Cartagena, Spain. Her current research interests are in corporate reputation and <strong>the</strong> influence
<strong>of</strong> <strong>the</strong> different stakeholders in its configuration and evaluation. Her papers are publishing in prestigious
Spanish journals.
Omid Omidvar following his BSc. and MSc. in electronics and IT management, entered <strong>the</strong> University <strong>of</strong>
Manchester/MIoIR PhD programme (2009). Aim <strong>of</strong> PhD is to understand how absorptive capacity and social
capital contribute to learning process in R&D partnerships. Also contributed to some MIoIR research projects
and has worked as a seminar leader during his PhD
Kaspars Osis is doctoral student at Riga Technical University, Riga, Latvia. He got MSc.comp.sc. in 2001
and graduate certificate in Business Computing in 2004 both from Central Michigan University, Mt. Pleasant,
USA. He is a lecturer at Vidzeme University <strong>of</strong> Applied Sciences, Valmiera, Latvia. His research interests are
knowledge management, agent and mobile technologies.
Taha Osman Senior Lecturer at <strong>the</strong> College <strong>of</strong> Science and Technology, Nottingham Trent University. He
gained his PhD in Fault-Tolerance <strong>of</strong> Distributed Computing Systems from <strong>the</strong> same University. Dr Osman
leads <strong>the</strong> Semantic Web research network at <strong>the</strong> department <strong>of</strong> computing and informatics and his research
interests include Multi-Agent Systems, Semantic Web, Knowledge Engineering and Intelligent Information
Retrieval.
Pushkal Kumar Pandey come’s from India and is currently pursuing PhD student at <strong>the</strong> University <strong>of</strong> Ulster.
After completing his Masters in Business Studies, he has worked as a customer support agent for a privately
owned contact centre in nor<strong>the</strong>rn Ireland Belfast. He is originally from India.
Paul Parboteeah is a Research Associate in <strong>the</strong> Department <strong>of</strong> Information Science at Loughborough
University. Current research focuses on introducing knowledge management initiatives in local government
and using autopoiesis to give KM a <strong>the</strong>oretical foundation. Committee member for a number <strong>of</strong> KM
conferences and has recently attracted AHRC funding to host a research conference.
Jan Pawlowski is a Pr<strong>of</strong>essor in Digital Media - Global Information Systems at <strong>the</strong> University <strong>of</strong> Jyväskylä,
Finland. Doctorate in Business Information Systems (University <strong>of</strong> Duisburg-Essen). Now a Pr<strong>of</strong>essor within
<strong>the</strong> Faculty <strong>of</strong> Information Technology. Main research interests: Cultural aspects in Information Systems,
Knowledge Management in Global Settings, Process Management, Tools for Global Collaboration,
Internationalization Competences.
Dan Paulin is a Lecturer and program director for M.Sc programs in Technology Management at Chalmers
University <strong>of</strong> Technology Go<strong>the</strong>nburg, Sweden. Dan manages executive education programs conducted in
Europe and Asia. His research interest includes KM issues in international settings and he has been working
with Swedish multinationals such as AB Volvo, SonyEricsson, IKEA, SKF and AtlasCopco.
Meysam Poorkavoos is a PhD student at <strong>the</strong> University <strong>of</strong> Bedfordshire, UK, doing research on <strong>the</strong> impact
<strong>of</strong> inter-firm knowledge transfer networks on different types <strong>of</strong> innovation in SMEs. He holds a B.Sc. in
Computer Science from University <strong>of</strong> Tehran, Iran, and a M.Sc. in Information Systems from <strong>the</strong> Linnaeus
University, Sweden.
Evgeny Popov investigates <strong>the</strong> problems <strong>of</strong> institutional economics modeling and transaction cost
estimation for <strong>the</strong> firm and region levels. He is author <strong>of</strong> papers “Transaction Function” in <strong>the</strong> International
Advances in Economic Research (2008) and “Minieconomics as a Separate Part <strong>of</strong> Microeconomics” in <strong>the</strong>
Atlantic Economic Journal (2005).
xix

Arttu Puhakka (M.Soc.Sci, Cooperation Trainer) works as a coordinator in Aducate - Centre for Training and
Development in <strong>the</strong> University <strong>of</strong> Eastern Finland. In his carrier he has focused on knowledge management,
leadership coaching and training, solution-focused working environments and well-being at work.
Farnaz Rahimi is studying IT management in Alzahra University(MS degree) and work in Kish Island oil
Company. I am interested in <strong>the</strong> field <strong>of</strong> "knowledge management" and "implementing new IT technologies in
organization"
Hossein Rahmany Youshanlouei is a graduate student in EMBA at university <strong>of</strong> Tehran. He has a
Bachelor’s degree in industrial engineering and he is also a member <strong>of</strong> Young Researchers Club <strong>of</strong> Islamic
Azad University <strong>of</strong> Salmas. His researches focus on project management, knowledge management, and
information technologies.
Kamal addin Rahmani Youshanloui is a Assistant Pr<strong>of</strong>essor at Islamic Azad university <strong>of</strong> Tabriz. He has a
Bachelor’s degree in Industrial management and Master’s in Operations and Production Management and
PhD in Operations and Production Management. His main research interests are Operations and Production
Management and knowledge management.
Lila Rajabion hold a Doctoral degree in Management <strong>of</strong> Information Technology from Lawrence
TechnologicalUniversity in Michigan, U.S.A.. Sheis currently teaching IT courses in Penn State University .
Lila has taught at various universities all around <strong>the</strong> world , also have more than eight years <strong>of</strong> industrial
experience.
Mohammad Rahim Ramazanian is assistant Pr<strong>of</strong>essor <strong>of</strong> Production and Operations Management at
University <strong>of</strong> Guilan, Iran. He holds a PhD in Production and Operations Management from University <strong>of</strong>
Tehran. His research interests include Productivity System, Suggestions System, and Knowledge
Management
Anja Richert IMA - Institute <strong>of</strong> Information Management in Mechanical Engineering & ZLW - Center for
Learning and Knowledge Management & IfU Institute for Management Cybernetics, RWTH Aachen
University, Germany
Anna Rinaldi PhD in Law &Economics, 2008 –MA, MiDIC (International Master in Development, Innovation
and Change) 2004: University <strong>of</strong> Bologna. Assistant pr<strong>of</strong>essor <strong>of</strong> economics -temporary pr<strong>of</strong>essor <strong>of</strong>
microeconomics and international economics: University <strong>of</strong> Bari.Fields <strong>of</strong> Interest industrial organization;
<strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm
Josune Sáenz has a PhD in Economics and Business Administration and member <strong>of</strong> <strong>the</strong> Finance and
Accounting department <strong>of</strong> Deusto Business School (San Sebastián, Spain). She is also head <strong>of</strong> <strong>the</strong>
Innovation chair sponsored by BBVA at DBS. She specializes in Management Accounting and Strategic
Management Control. Her research focus is currently on Innovation, Intellectual Capital, and Knowledge
Management.
Mustafa Sağsan is an assistant pr<strong>of</strong>essor <strong>of</strong> Management and Organization Science. He is head <strong>of</strong> <strong>the</strong>
Department <strong>of</strong> Knowledge Management at <strong>the</strong> Near East University in <strong>the</strong> Turkish Republic <strong>of</strong> Nor<strong>the</strong>rn
Cyprus. His teaching and research interest include knowledge management, MIS, organizational <strong>the</strong>ory,
behavior and e-government. He is conducting seminars on knowledge management and related topics since
2001.
Risto Säntti works as a researcher in <strong>the</strong> University <strong>of</strong> Vaasa, Finland. Previously he has held HR
management positions in multinational companies. Risto’s research interests focus on corporate knowledge
management. Currently <strong>of</strong> special interest has been <strong>the</strong> issue <strong>of</strong> social media and its influence on mindsets
within corporations and in <strong>the</strong> society in general.
Nima Sarabi is a graduate student in industrial management at university <strong>of</strong> Tehran. He has Bachelor’s
degree in industrial engineering from university <strong>of</strong> Tabriz. His main research interests are knowledge
management, project management and operation research.
Daria Sarti is an Assistant Pr<strong>of</strong>essor <strong>of</strong> Organizational Behaviour at <strong>the</strong> University <strong>of</strong> Florence, Department
<strong>of</strong> Business Administration. Her research interests are in Knowledge enterprises and reward system in social
firms, entrepreneurship and small enterprises.
xx

Dan Savescu graduate TCM Faculty, practice in a Ball-bearing factory, assistant, lecturer and pr<strong>of</strong>essor till
now. PhD. in ball-bearings, author <strong>of</strong> 170 scientific articles, 16 monographs. ART, ARoTMM local president,
ROAMET, ARoTT, RENITT- member, „Products and Technologies for Sustainable Energy” Incubator –
Director.
Benedikt Schmidt is Research Associate at SAP Research and Ph.D. student in <strong>the</strong> telecooperation group
at University <strong>of</strong> Technology Darmstadt. He received his Diploma in Computer Science 2009 and his Diploma
in Media studies in 2008 from <strong>the</strong> University <strong>of</strong> Paderborn, Germany. Benedikt works on proactive
knowledge worker support.
Alexander Schneider is studying Information Systems at <strong>the</strong> Technische Universität München. In 2009 he
achieved his Bachelor’s degree and is now a Master’s candidate. His research is mostly concerned with web
based collaboration systems and enterprise architecture management.
Camilo Augusto Sequeira-has a Master’s degree in Electronic Engineering from Catholic University, Rio de
Janeiro, and has taught in both undergraduate and graduate programs. He has an MBA from Salford
University, England. Camilo has been top executive for multinational companies. He is currently a consultant
and a researcher for <strong>the</strong> Institute <strong>of</strong> Energy <strong>of</strong> PUC-Rio.
Elena Shakina has defended <strong>the</strong> <strong>the</strong>sis and got a degree “Candidate <strong>of</strong> Science” in 2008. She has been
working for economic department <strong>of</strong> Higher School <strong>of</strong> Economics in Perm since 2003. Her research line is
connected with strategic financial management, as well as intellectual capital evaluation She has12
publications, most <strong>of</strong> <strong>the</strong>m are academic papers.
Mehdi Shami Zanjani is Associate Pr<strong>of</strong>essor <strong>of</strong> IT Management at <strong>the</strong> Faculty <strong>of</strong> Management, University <strong>of</strong>
Tehran. His current research interests are in knowledge management and project management. He has a
bachelor’s degree in industrial management, a master’s degree in information technology management, and
a PhD in systems management from <strong>the</strong> University <strong>of</strong> Tehran.
Sakura Shimada is a PhD student at Paris Dauphine University, Organizational and Strategic management
field <strong>of</strong> research. Her <strong>the</strong>sis is about linking <strong>the</strong> intergenerational transmission <strong>of</strong> knowledge and
competencies with <strong>the</strong> notion <strong>of</strong> dynamic capability. She uses <strong>the</strong> strategic-as-practice approach in a France
and Japan comparison. She belongs to <strong>the</strong> Chair Management & Diversity <strong>of</strong> her University.
Evangelia Siachou has a PhD in Knowledge Management from A<strong>the</strong>ns University <strong>of</strong> Economics and
Business and an MSc in Industrial Relations and Personnel Management from <strong>the</strong> London School <strong>of</strong>
Economics (LSE). Assistant Pr<strong>of</strong>essor in Management at Hellenic American University. Her current research
interests include Knowledge Transfer and Acquisition, Business Model Innovation and Strategic Human
Resource Management.
Päivi Sihvo (RN, M.Sc) works as a Project Manager and as a Teacher in North Karelia University <strong>of</strong> Applied
Sciences. In her carrier she has focused on knowledge management and Management <strong>of</strong> Health Care.
Mauro Spinola PhD. and Associated Pr<strong>of</strong>essor, His main research and educacation focus is IT process
management. He developed relevant IT research and development projects related to traffic control system
development and systems engineering process improvement. Recent researches are focused on
implementation effectiveness <strong>of</strong> IT models and knowledge management modeling and measuring.
Christina Suciu has a Phd in Economics. Graduate <strong>of</strong> Cybernetics Faculty, Academy <strong>of</strong> Economic Studies
Bucharest (ASE), 1981. Research fellow, National Institute for Economic Research, Romanian
Academy.Since 1993 teaching & research at ASE. Now full pr<strong>of</strong>essor & PhD supervisor in Economics, ASE.
Topic <strong>of</strong> interest: Knowledge-based society, intellectual capital, KM, creative economy, investing in people
and skills.
David Sundaram is Associate Pr<strong>of</strong>essor in Information Systems and Operations Management at <strong>the</strong>
University <strong>of</strong> Auckland. He is an engineer by background, a teacher, researcher, and consultant by
pr<strong>of</strong>ession, and a lifelong student. He is passionate about <strong>the</strong> modelling, design, and implementation <strong>of</strong>
flexible and evolvable information, visualization, decision, knowledge, and social systems.
Kaj Sunesson is a lecturer in industrial economics and organization at Chalmers University <strong>of</strong> technology in
Göteborg, Sweden. His background is from cognitive psychology, organization and business economics.
Earlier areas <strong>of</strong> activities have been within management. At present <strong>the</strong> main interest is in how new
technology affects different types <strong>of</strong> decision making processes.
xxi

Lajos Szabó is a Associate Pr<strong>of</strong>essor at <strong>the</strong> Department <strong>of</strong> Management, University <strong>of</strong> Pannonia Veszprém,
Hungary. Also a Associate Pr<strong>of</strong>essor and Head <strong>of</strong> Department <strong>of</strong> Management at University <strong>of</strong> Pannonia,
Vice Dean for Strategy and Development <strong>of</strong> <strong>the</strong> Faculty <strong>of</strong> Economics. Founder member <strong>of</strong> <strong>the</strong> Hungarian
Project Management Association, published numerous articles and presented work at national and
international conferences. Main fields <strong>of</strong> interest include intercultural, project and knowledge management.
Yury Telnov is a Doctor in Economics, Pr<strong>of</strong>essor, Vice-rector, Moscow State University <strong>of</strong> Economics,
Statistics and Informatics. Head <strong>of</strong> Department <strong>of</strong> Applied Informatics in Economics, and a member <strong>of</strong> <strong>the</strong>
Council <strong>of</strong> Russian Association <strong>of</strong> Artificial Intelligence. He teaches Knowledge Management, Business
Process Reengineering and Artificial Intelligence.
Dhavalkumar Thakker is a Research fellow in Knowledge Engineering at <strong>the</strong> Leeds University. Gained a
PhD. in Semantic Web Services Integration from Nottingham Trent University. Areas <strong>of</strong> interest include:
domain modelling, content search, knowledge management and text mining. He specialises in semantic web
driven technologies such as knowledge representation using ontologies, knowledge base systems and <strong>the</strong>
Linked Data Cloud.
Natalia Tikhomirova is a Doctor in Economics, Pr<strong>of</strong>essor, Rector <strong>of</strong> Moscow State University <strong>of</strong> Economics,
Statistics and Informatics. Member <strong>of</strong> <strong>European</strong> Organisation for Quality(Russia), Head <strong>of</strong> working group on
e-learning, distance education and new educational technologies at Committee <strong>of</strong> <strong>the</strong> State Duma. Her
Current research is in <strong>the</strong> field <strong>of</strong> knowledge and quality management, e-learning.
Vladimir Tikhomirov is a Doctor <strong>of</strong> Economics, Pr<strong>of</strong>essor, President <strong>of</strong> <strong>the</strong> Moscow State University <strong>of</strong>
Economics, Statistics and Informatics (MESI), Russia. Chairman <strong>of</strong> Council on questions <strong>of</strong> e-education,
Committee on education and science <strong>of</strong> State Duma, president Russian Association <strong>of</strong> Universities
specializing in economics, president Euro-Asian Association <strong>of</strong> Distance Education, President International
Academy <strong>of</strong> Open Education
Kamila Tislerova works in <strong>the</strong> Department <strong>of</strong> Business Admnistration, University <strong>of</strong> JE Purkyne in Usti nad
Labem, Czech Republic. For almost 15 years working in managerial positions in marketing field, now a
lecturer <strong>of</strong> management and marketing specialized on Customer Relationship Management. Also delivering
lectures abroad (UWS Scotland, BCBUU China).
Man-Chie Tse completed her master’s degree in Business Information Systems Management at Middlesex
University. Her research interests follow on from her BA (Honours) Management <strong>of</strong> Business Information
specialised Business Information Technology. Her focus surrounds engineering methods for analysis <strong>of</strong>
intangible modelling properties in personal KM, strategic organisational design, knowledge management
development and s<strong>of</strong>tware engineering methodologies.
José Alfredo Vásquez Paniagua is a PhD Candidate, at <strong>the</strong> Universidad Eafit – Hec Montreal, Canadá.
Civil Engineer, and MSc Studies in Water Resources Planning, Universidad Nacional de Colombia. MSc in
Economics, Universidad de Antioquia, Colombia. Master in Environmental Management, España. MSc
Studies in Administration, Univesidad Eafit, Colombia. Full-time researcher and pr<strong>of</strong>essor MBA at <strong>the</strong>
Universidad de Medellin. Consultant on risk analysis.
Katja Väyrynen (M.Soc.Sci) works as a Vocational Teacher, Training Planner and Entrepreneur, Wellness
Sector. She has focused on knowledge management, vocational adult education, adult employment training
and competence tests in Social services, health and sport.
Florian Welter M.A. is a Economic Geographer and Economist. He is research assistant at IMA - Institute <strong>of</strong>
Information Management in Mechanical Engineering & ZLW - Center for Learning and Knowledge
Management & IfU Institute for Management Cybernetics at RWTH Aachen University, Germany. His
research focuses on Knowledge Management and Performance Measurement in interdisciplinary clusters.
Peter Yannopoulos is a PhD and MBA from <strong>the</strong> University <strong>of</strong> Toronto and MA from York University,
Toronto. Associate Pr<strong>of</strong>essor <strong>of</strong> Marketing at Brock University and has published in <strong>the</strong> <strong>European</strong> Journal <strong>of</strong>
Marketing, International Marketing Review, Journal <strong>of</strong> Business Research and his textbook Marketing
Strategy is used in various universities in Canada and around <strong>the</strong> world.
Choon-bae (Paul) Yoo was manager <strong>of</strong> quality systems at <strong>the</strong> Hosing NSW in Australia, and involved in
projects; design business processes and create business technology plans aligned for all business units.
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Teaches Business Computing and Statistics at Sydney Institute <strong>of</strong> Technology. Paul is Currently, a Ph.D
candidate in School <strong>of</strong> Systems, Management & Leadership at <strong>the</strong> University <strong>of</strong> Technology Sydney.
Simon Woll studied educational science and economics at <strong>the</strong> University <strong>of</strong> Regensburg (Germany) and
Fribourg (Switzerland). After his graduation in 2008, he started to work at <strong>the</strong> Chair for Organisational Theory
and Human Resource Management at <strong>the</strong> Catholic University Eichstaett-Ingolstadt. His research interest is
about organizational learning in inter-organizational project working.
Sven Wuscher is a senior researcher Division Corporate Management at Fraunh<strong>of</strong>er IPK, Berlin since 2005.
Completed studies in business economics focusing on controlling and organizational development. Changed
to Fraunh<strong>of</strong>er Competence Center Knowledge Management where involved in several customer projects on
intellectual capital management.
John Young has degrees in Mechanical Engineering, System Engineering and a PhD in S<strong>of</strong>t System
Methodology from RMIT University. He had an extensive career with Caterpillar in dealer administration,
followed by principal <strong>of</strong> a management consulting company. More recently he has been lecturing on
organisations, human resources, quality management and systems engineering.
José Alfredo Vásquez Paniagua is a PhD Candidate, at <strong>the</strong> Universidad Eafit – Hec Montreal, Canadá.
Civil Engineer, and MSc Studies in Water Resources Planning, Universidad Nacional de Colombia. MSc in
Economics, Universidad de Antioquia, Colombia. Master in Environmental Management, España. MSc
Studies in Administration, Univesidad Eafit, Colombia. He is a Full-time researcher and Pr<strong>of</strong>essor
Universidad de Medellin.
xxiii

The Influence <strong>of</strong> <strong>the</strong> Organizational Learning Phases in <strong>the</strong>
Total Process: A Special Analysis <strong>of</strong> Organizational
Structure
Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Technical University <strong>of</strong> Cartagena, Cartagena, Spain
ino.martinez@upct.es
isabel.olmedo@upct.es
Abstract: Organizational learning (OL) is a process that transforms information into knowledge within an
organization, by a set <strong>of</strong> sequential phases (information acquisition, information distribution, shared interpretation,
and organizational memory). The previous OL phases are considered as precursors <strong>of</strong> <strong>the</strong> next OL activity. The
organizational structure also plays a crucial role in determining learning processes. This study aims to analyze
<strong>the</strong> importance <strong>of</strong> prior OL phases on OL, and examine empirically whe<strong>the</strong>r <strong>the</strong> organizational structure (job
specialization –vertical and horizontal-, formalization, centralization and indoctrination) affects directly to <strong>the</strong> OL
process. Carrying out regression analysis, this study has two different implications. First, all OL phases have a
positive and significant effect on OL activity. And second, organizational structure directly affects <strong>the</strong> OL, where
high vertical job specialization and low centralization are significantly associated with greater capacity for
information distribution, low horizontal job specialization and formalization with shared interpretation, and low
formalization with organizational memory.
Keywords: Organizational learning, organizational learning phases, organizational structure, organizational
design parameters
1. Introduction
The past forty years have seen increasingly rapid advances in <strong>the</strong> field <strong>of</strong> organizational learning
(OL). Information and knowledge are <strong>the</strong> key elements for firms’ pr<strong>of</strong>itability and survival (Grant,
1996). Knowledge is also considered as one <strong>of</strong> <strong>the</strong> main sources <strong>of</strong> <strong>the</strong> competitive advantage
(Nonaka, 1991; Zollo and Winter, 2002). OL has been recognized as <strong>the</strong> process that transforms
information to knowledge.
Organizational learning process (OLP) is an increasingly important area in OL research. Different OL
perspectives have studied <strong>the</strong> OLP and its organizational learning phases (OLPh). Consequently, this
paper tries to explain <strong>the</strong> OL concept, its process and <strong>the</strong> transforming process from prior OLPh to <strong>the</strong>
next organizational learning activity (OLA). Most studies in OLP have only been carried out in OLA,
but <strong>the</strong>y have not considered <strong>the</strong> influence <strong>of</strong> organizational structure on OLP.
Therefore, <strong>the</strong> objectives <strong>of</strong> this research are to determine whe<strong>the</strong>r previous OLPh influence on OLA
and organizational structure impact on <strong>the</strong> transforming process. Hence, this paper tries to improve
our understanding about <strong>the</strong> OLPh and factors (organizational structure) affect to OLP, enabling <strong>the</strong>
organization to learn.
This paper is organised in <strong>the</strong> following way. First, we present an analysis <strong>of</strong> <strong>the</strong> concept <strong>of</strong> OLP and
its different phases (OLPh). Next, <strong>the</strong> relationships between OLPh are considered. The fourth section
examines <strong>the</strong> influence <strong>of</strong> organizational structure to <strong>the</strong> OLP, and <strong>the</strong> research model to be tested is
presented. After that, we present <strong>the</strong> empirical results <strong>of</strong> <strong>the</strong> influence <strong>of</strong> previous OLPh on OLA, and
(<strong>the</strong> influence) <strong>of</strong> organizational structure on <strong>the</strong> transforming process. Finally, a discussion and
conclusion section about <strong>the</strong> main results and implications derived from this research is included.
2. Organizational learning process
There is a large volume <strong>of</strong> published studies describing <strong>the</strong> concept <strong>of</strong> OL. An overview <strong>of</strong> previous
research allows us to identify some varying perspectives on OL as: business, an outcome, and a
process. From a business perspective, Senge (1990) defined OL as “a continuous testing <strong>of</strong>
experience and its transformation into knowledge available to whole organization and relevant to <strong>the</strong>ir
mission” (Senge, 1990). On <strong>the</strong> o<strong>the</strong>r hand, researchers <strong>of</strong>ten agree that people act and learn within
<strong>the</strong> organizational framework, being <strong>the</strong> primary learning entity in firms, since OL is a result <strong>of</strong>
individual learning (Hedberg, 1981; Dogson, 1993; Nicolini and Meznar, 1995).
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
There is increasing concern that OL is a process. Argyris and Schön (1978) suggested that OL is a
process <strong>of</strong> detecting and correcting errors, while Fiol and Lyles (1985) also pointed out that OL is “<strong>the</strong>
process <strong>of</strong> improving actions through better knowledge and understanding”. Huber (1991) defines
learning as a process which changes <strong>the</strong> scope <strong>of</strong> potential behaviour. Additionally, Duncan and
Weiss (1979) assert that “organizational learning is a process leading to an outcome: knowledge or
skill”.
Hence, OL is a process concerned with transforming information into knowledge and knowledge into
action (e.g., Argyris and Schön, 1978; Fiol and Lyles, 1985; Huber, 1991; Crossan et al., 1999), which
implies acquiring information, knowledge, understanding, know-how, techniques, and practices that
lead to changes in its routines (Argyris and Schön, 1996). Consequently, organizational learning is “a
process aimed at helping organizations to develop and to use knowledge to change and improve
<strong>the</strong>mselves continuously” (Cummings and Worley, 1997) and, thus, to <strong>the</strong>ir members’ capabilities.
These definitions emphasize OL is a process, not an end state, which main outcome is <strong>the</strong>
knowledge. OL is a key variable in <strong>the</strong> enhancement <strong>of</strong> organizational performance (Fiol and Lyles,
1985; Stata, 1989; Dodgson, 1993; Garvin, 1993; Nevis et al., 1995; Jiménez and Cegarra, 2007). OL
is consequently a source <strong>of</strong> competitive advantage (Spender and Grant, 1996; Matusik and Hill, 1998;
Nonaka et al., 2000).
3. Organizational learning phases
Previously, OL has been defined as a process. Different perspectives have studied <strong>the</strong> OLP, such as
cognitive and behavioural (Kim, 1993; Crossan et al., 1999) or social cognition (Huber, 1991).
The cognitive perspective focuses primarily on providing an efficient process to OL, independent <strong>of</strong>
<strong>the</strong> context. However, <strong>the</strong> competitive and global context should be considered in <strong>the</strong> OLP. Both
reasons are important, and are jointly considered in <strong>the</strong> social cognition view: Huber’s (1991) model.
Huber suggests that OL is a process, whereby organisations aim to incorporate and disseminate
valuable experience and knowledge across its communities <strong>of</strong> practice over time, updating and
replacing “organizational memory” (Huber, 1991). He constitutes a more grounded approach for
understanding <strong>the</strong> learning process via examines aspects <strong>of</strong> human information-processing; what it
influences, and what it is influenced by, within <strong>the</strong> complex social interactions <strong>of</strong> groups (social
cognitive process). In this sense, Huber (1991) asserts that organizations learn about “<strong>the</strong>
environment”, “<strong>the</strong> competition”, or “production processes”. Therefore, Huber defines “learning” as a
process that enables an entity to increase its range <strong>of</strong> potential behaviour through its processing <strong>of</strong>
information.
Most studies <strong>of</strong> OL see it as a combination <strong>of</strong> four-stage processes: information acquisition,
information distribution, information interpretation and organizational memory. O<strong>the</strong>rs consider
knowledge acquisition (Huber, 1991). Organizations acquire information to transform into knowledge,
using a learning process. Information is patterned data and knowledge is capability to act. For that
reason, <strong>the</strong> knowledge acquisition proposed by Huber (1991) is replaced by information acquisition.
Therefore, <strong>the</strong> four different phases identified in this research are:
Information acquisition refers to <strong>the</strong> processes by which information is obtained from various
sources (internal and external), and <strong>the</strong> generated information flow from supplier to user.
External information acquisition means collecting information from sources outside <strong>the</strong> company
(external experts, consultants, customers, competitors, economic assessments, financial
statements, social reports, etc.). If <strong>the</strong> provider <strong>of</strong> quality information is an employee, organization
even explicitly rewarding him/her. External acquisition can also take place on ei<strong>the</strong>r an upcoming
purchase decision (pre-purchase search) or on a regular basis regardless <strong>of</strong> sporadic purchase
needs (on-going search). The most important benefits come from gaining access to knowledge
and experiences developed in o<strong>the</strong>r firms.
Internal information acquisition involves treating employees as an important information source,
encouraging workers to take part in formal and informal networks <strong>of</strong> people or grasping
knowledge from o<strong>the</strong>r organizational units within <strong>the</strong> same company.
Information distribution is a social process by which information from different sources is spread
among <strong>the</strong> organization’s members (individual and functional units, through formal and informal
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
channels). Subsequently, this process also consists <strong>of</strong> <strong>the</strong> transmission <strong>of</strong> <strong>the</strong> acquired
information at an individual level principally through individual conversations and interrelations
between employees <strong>of</strong> <strong>the</strong> organization (Brown and Duguid, 1991), using formal communication
(memos, report, bulletin boards and face-to-face meetings) and/or informal communication
(c<strong>of</strong>fee-breaks and hall-way meetings). If <strong>the</strong> organization identifies where <strong>the</strong> information is and
its characteristics and formats, retrieval efforts are more likely to succeed. Then, individuals, units
and organization can learn by combining <strong>the</strong> information that is disseminated and shared by
different units (Slater and Narver, 1995), creating new information or understanding (Huber, 1991;
Sinkula, 1994).
Shared interpretation is <strong>the</strong> process by which distributed information is given various commonly
understood interpretations, creating knowledge and reducing <strong>the</strong> ambiguity related to information.
Therefore, this stage implies (a) <strong>the</strong> consensus among organizational members with regard to <strong>the</strong>
meaning <strong>of</strong> information (Slater and Narver, 1995), and (b) some cognitive and behavioural
changes in employees. As a result, information is given meaning and events are translated into
shared understanding. Shared interpretation is pr<strong>of</strong>oundly connected to <strong>the</strong> conditions under
which information is learned and stored.
Organizational memory is <strong>the</strong> means by which knowledge from <strong>the</strong> past (organization’s history)
can be used in present and future decisions (Walsh and Ungson, 1991). It is not just a facility for
capturing, accumulating, preserving, organizing, indexing, and retrieving only <strong>the</strong> explicit and
implicit knowledge but also for sharing and reusing knowledge. Consequently, organizational
memory also extends and amplifies this asset, reducing <strong>the</strong> great loss <strong>of</strong> knowledge by means <strong>of</strong>
<strong>the</strong> explotation <strong>of</strong> good experience and avoiding <strong>the</strong> repetition <strong>of</strong> failure (Chen, 2005). As a result,
<strong>the</strong> company stores detailed information for guiding operations by formal data management
function and employees retrieve archived information when making decisions. To store complete
information, an organization can use traditional (expert report, <strong>of</strong>ficial memorandums, manuals or
guidelines) and electronic storage, such as databases (Martínez-León et al., 2008), data
warehouses and scanned documents. As consequence <strong>of</strong> that knowledge is made explicit and
well managed, <strong>the</strong> organizational intellect enhances, becoming a basis for communication and
learning.
3.1 The influence <strong>of</strong> organizational learning phases in <strong>the</strong> next phase
There is an unambiguous relationship between each OLPh and <strong>the</strong> next one (OLA). For this reason,
<strong>the</strong> aim <strong>of</strong> this section is improve our knowledge about <strong>the</strong>se close relationships.
Information acquisition is a critical component in OL (Huber 1991) and a critical intermediate outcome
<strong>of</strong> <strong>the</strong> OLP (Slater and Narver, 1995), but <strong>the</strong> type <strong>of</strong> information (external and internal) ga<strong>the</strong>red is
equally important. Generating <strong>the</strong> right information at <strong>the</strong> time it is needed and presented in a format
that organization members can understand is necessary for information distribution and knowledge
creation that can lead to organizational learning success. On <strong>the</strong> basis <strong>of</strong> <strong>the</strong> above arguments, we
propose that:
Hypo<strong>the</strong>sis 1. External and Internal information acquisition will have a positive impact on
information distribution.
Information distribution is necessary for OL to occur since information that is acquired (say, by an
individual) but not distributed through <strong>the</strong> organization can, at best, lead to individual-level learning
(Huber, 1991). Consequently, this activity leads to more broadly based OL, being a determinant <strong>of</strong>
both <strong>the</strong> occurrence and breadth <strong>of</strong> shared interpretation. Thus, combining information from different
members and subunits leads not only to new information, but also to new understanding (Huber,
1991). This fact justifies that information distribution is also a precursor to information interpretation.
From this discussion, it can be stated:
Hypo<strong>the</strong>sis 2. Information distribution will have a positive impact on shared interpretation.
Some previous studies highlight <strong>the</strong> shared interpretation is considered critical for OL (Sinkula, 1994).
Information on how to do things and how to behave may be stored in formal and informal means.
Therefore, organizational memory is necessary to keep information and knowledge. If varied
interpretations have been developed, more learning has occurred and, hence, <strong>the</strong> organizational
memory is more necessary. To summarize that, we propose:
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Hypo<strong>the</strong>sis 3. Shared interpretation will have a positive impact on organizational
memory.
4. The influence <strong>of</strong> organizational structure on organizational learning phases
Since Fiol and Lyles (1985) identified culture, strategy, structure and <strong>the</strong> external environment as
important contextual influences on <strong>the</strong> learning, some studies have considered <strong>the</strong> organizational
structure as one <strong>of</strong> <strong>the</strong> most important OL factor. However, a lack <strong>of</strong> empirical investigations into OL
on various aspects <strong>of</strong> organizational structure is still reported. Only a few studies have tackled some
aspects <strong>of</strong> this issue in recent years (Martínez and Olmedo, 2010; Martínez and Martínez, 2011).
Organizational structure is defined as “<strong>the</strong> sum total <strong>of</strong> <strong>the</strong> ways in which its labor is divided into
distinct tasks and <strong>the</strong>n its coordination is achieved among <strong>the</strong>se tasks” (Mintzberg, 1993).
Organizational structure reflects <strong>the</strong> formal scheme <strong>of</strong> relationships, communications, decision
processes, procedures and systems (Zerilli, 1978), which allow organization to develop its functions
and achieve its objectives. It is also likely to affect <strong>the</strong> social interaction among organizational
members and <strong>the</strong> way in which information and knowledge is distributed within an organization.
Organizational structure can affect <strong>the</strong> subsequent OL, by <strong>the</strong> way OLP is realized, how <strong>the</strong>
information and knowledge is organized and used, and <strong>the</strong> extent to which new knowledge is
embedded in <strong>the</strong> daily work processes.
Analyzing <strong>the</strong> organizational structure means considering <strong>the</strong> different design variables and <strong>the</strong>ir
influence on OL. Mintzberg (1979) describes in detail all design variables proposed by o<strong>the</strong>r authors:
specialization, formalization, centralization, indoctrination and training. In this study, training is not
considered because <strong>the</strong>re are different educational levels and training requirements for similar jobs.
Justified <strong>the</strong>ir influence on <strong>the</strong> OLP, we proposed <strong>the</strong> hypo<strong>the</strong>ses for each design variable.
Specialization is a design parameter <strong>of</strong> <strong>the</strong> organizational structure. Job specialization consists <strong>of</strong> two
dimensions: horizontal and vertical (Mintzberg, 1979). Horizontal job specialization concerns <strong>the</strong>
number <strong>of</strong> tasks and <strong>the</strong> breadth <strong>of</strong> each in a given position (quantity and diversity <strong>of</strong> tasks). Vertical
job specialization refers to <strong>the</strong> depth-control over work, separating <strong>the</strong> performance <strong>of</strong> <strong>the</strong> work from
<strong>the</strong> administration <strong>of</strong> it.
Horizontal job specialization is <strong>the</strong> chief parameter for determining <strong>the</strong> division <strong>of</strong> labor. High
horizontal job specialization entails performing a limited range <strong>of</strong> narrow and unvarying tasks. It
implies <strong>the</strong> standardization <strong>of</strong> work from repetition, which reduces <strong>the</strong> employee’s organizational
vision, and his/her range <strong>of</strong> perspectives and points <strong>of</strong> view on <strong>the</strong> same information, dipping <strong>the</strong>
possibility <strong>of</strong> acquiring extra information. However, this parameter is determinant <strong>of</strong> OL because it
implies <strong>the</strong> possession <strong>of</strong> a great quantity <strong>of</strong> information on only a few variables (Ouksel and
Vyhmeister, 2000), as specialized individuals in specific knowledge areas require.
Low horizontal job specialization, on <strong>the</strong> contrary, shows that various different tasks are included in a
job description, constantly moving from one activity to ano<strong>the</strong>r. This creates a wide range <strong>of</strong>
perspectives on <strong>the</strong> same information, increases <strong>the</strong> employee’s knowledge, expertise and global
vision, which in turn facilitates OL (Martínez and Martínez, 2011). For those reasons, low horizontal
specialization directly affects individuals and <strong>the</strong>ir positive learning capacity.
Vertical job specialization refers to a job which can vary in terms <strong>of</strong> its depth, or <strong>the</strong> extent <strong>of</strong> control
<strong>the</strong> worker has over <strong>the</strong> work. High vertical job specialization implies <strong>the</strong> employee has little control
over changes in work, or <strong>the</strong> goals and standards associated with it, so <strong>the</strong>re is a clear separation
between <strong>the</strong> administration <strong>of</strong> <strong>the</strong> work and its performance. In contrast, low vertical job specialization
involves <strong>the</strong> employees not only carrying out <strong>the</strong> tasks but also participating in <strong>the</strong> design and control
<strong>of</strong> <strong>the</strong>ir own work. Consequently, it provides <strong>the</strong>m with <strong>the</strong> necessary freedom to design and create
new knowledge, improving <strong>the</strong>ir knowledge base and expertise, which in turn facilitates OL (Martínez
and Martínez, 2011). It can be hypo<strong>the</strong>sized:
Hypo<strong>the</strong>sis 4: Specialization will have a positive impact on organizational learning. Two
sub-hypo<strong>the</strong>ses are derived from <strong>the</strong> main one:
H4a: Low horizontal specialization will have a positive impact on organizational learning.
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
H4b: Low vertical specialization will have a positive impact on organizational learning.
Formalization refers to <strong>the</strong> standardization <strong>of</strong> work processes by imposing operating instructions, job
descriptions, rules and regulations (Robbins and Decenzo, 2001), which promote efficient procedures.
Organizations formalize behaviour to reduce its variability and ultimately to predict and control itself.
High formalization implies organizations make extensive use <strong>of</strong> written procedures, explicit rules and
actions in an organization or subunit; that is, not only are <strong>the</strong>re more procedures covering possible
contingencies, but it eliminates <strong>the</strong> discussion about how work should be done, reduces decision
latitude and dimishes <strong>the</strong> alternatives to developing “creative solutions”. Consequently, this generates
a worse alignment between (formalized) procedures and OL, leading, in turn, to worse adjustment <strong>of</strong>
OLP.
On <strong>the</strong> contrary, low formalization refers to job behaviours that are relatively unstructured and
members have greater freedom in dealing with <strong>the</strong> demands <strong>of</strong> <strong>the</strong>ir relevant tasks (Sivadas and
Dwyer, 2000). Hence, members would consider and discuss alternatives (Robbins and Decenzo,
2001), allowing openness and variation, which facilitates <strong>the</strong> development <strong>of</strong> new ideas and activities.
Consequently, low formalization has greater willingness in knowledge creation and sharing (Chen and
Huang, 2007). These arguments suggest that:
Hypo<strong>the</strong>sis 5: Low formalization will have a positive impact on organizational learning.
Centralization describes <strong>the</strong> delegation <strong>of</strong> formal power down <strong>the</strong> hierarchy <strong>of</strong> authority. High
centralization inhibits interactions among organizational members, creating a non-participatory
environment that reduces communication and motivation. It also reduces <strong>the</strong> opportunity for individual
growth and advancement, and prevents imaginative solutions to problems (Deal and Kennedy, 1982).
These effects impede learning in <strong>the</strong> workplace.
Instead, <strong>the</strong> decentralized structure encourages: a) <strong>the</strong> participation <strong>of</strong> employees, who take part in
decision making process; b) <strong>the</strong> free flow <strong>of</strong> lateral and vertical internal communication (Burns and
Stalker, 1961; Bennett and Gabriel, 1999); c) <strong>the</strong> interpersonal exchange and social interaction (Chen
and Huang, 2007); d) <strong>the</strong> assimilation and association <strong>of</strong> new patterns (Fiol and Lyles, 1985); e)
higher levels <strong>of</strong> creativity; f) <strong>the</strong> opportunities for employees to learn from <strong>the</strong>ir colleagues; g) <strong>the</strong>
posibility <strong>of</strong> change <strong>the</strong>ir behaviour, beliefs and actions (Fiol and Lyles, 1985); and h) <strong>the</strong> adoption <strong>of</strong>
innovation.
Consequently, decentralization enhance <strong>the</strong> employee’s satisfaction and motivation (Dewar and
Werbel, 1979). For experts or knowledge workers could facilitate <strong>the</strong>ir own pr<strong>of</strong>essional development.
Therefore, a decentralized organizational structure is conducive to OL, and to organizational
effectiveness (Burns and Stalker, 1961; Floyd and Wooldridge, 1992; Rapert and Wren, 1998).
The previous arguments can be summarized in:
Hypo<strong>the</strong>sis 6: Low centralization will have a positive impact on organizational learning.
Indoctrination is conceptualized as programmes and techniques by which norms, rules and
regulations are standardized so that <strong>the</strong> workers can be trusted to make decisions and take actions in
keeping with <strong>the</strong> ideology <strong>of</strong> <strong>the</strong> organization. Indoctrination (socialization) programs are particularly
important when OLP are producing, because it facilitates <strong>the</strong> transfer <strong>of</strong> information and knowledge
from <strong>the</strong> individual to <strong>the</strong> group and organization, favoring OLP. In this sense, Von Krogh (1998)
maintains that “for <strong>the</strong> effective creation <strong>of</strong> knowledge it is necessary to pay special attention to <strong>the</strong>
way in which people are related to <strong>the</strong> company”. This is reflected in:
Hypo<strong>the</strong>sis 7. High indoctrination will have a positive impact on organizational learning.
5. Research methodology
5.1 Sample and data collection
The survey data analysed in this article come from Spanish private firms and are from January 2010.
The activities <strong>of</strong> <strong>the</strong> firms sampled from industry and service-oriented dynamic and competitive
environments - covered a wide range to give a broad analysis scope. This initial sample included 91
industry sector companies (47.2%) and 102 service sector companies (52.8%). The data was
collected via a personal survey.
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5.2 Measures
Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Research constructs were operationalized based on pretest and related studies. The design
parameters <strong>of</strong> organizational structure were built following Mintzberg’s framework. The measures <strong>of</strong>
OLPh come from <strong>the</strong> study <strong>of</strong> Pérez et al. (2004).
For <strong>the</strong> questionnaires, a multiple-items method was used and each item was based on a seven point
Likert scale from ‘very low’ to ‘very high’.
6. Data analysis and results
A stepwise regression analysis was used to predict <strong>the</strong> influence <strong>of</strong> prior OLPh in <strong>the</strong> next OLA, and
to test <strong>the</strong> effects <strong>of</strong> organizational structure (independent variables) on <strong>the</strong> considered OLA
(dependent variables). A correlation matrix generated from <strong>the</strong> variables ruled out multicollinearity in
<strong>the</strong> multiple regression equations used. A p-value 0.1 was considered statistically significant. All <strong>of</strong> <strong>the</strong>
analyses were performed using <strong>the</strong> SPSS 17.0.
Two separate equations have been used for two different models. Previous OLPh (independent
variable) and considered OLA were entered in <strong>the</strong> first model, which allows testing hypo<strong>the</strong>sis 1 to 3.
Design parameters (organizational structure) and studied OLA were introduced in <strong>the</strong> second model,
testing hypo<strong>the</strong>sis 4 to 7.
Table 1 shows <strong>the</strong> two models. Model 1 includes only previous OLPh with influence on <strong>the</strong>
Information Distribution, and model 2 introduces design parameters. The second model represented
an improvement <strong>of</strong> fit relative to <strong>the</strong> first model. The results <strong>of</strong> <strong>the</strong> analysis do not generally support
all affected hypo<strong>the</strong>ses (1, 4-7). Information acquisition has a significant influence on information
distribution. Consequently, Hypo<strong>the</strong>sis 1 is confirmed. The empirical research shows that high vertical
specialization and centralization have a significant impact on Information distribution. Therefore,
hypo<strong>the</strong>sis 4b should be reformulated and hypo<strong>the</strong>sis 6 remains confirmed.
Table 1: Results <strong>of</strong> Regression Analyses <strong>of</strong> Previous OLPH on information distribution
O<strong>the</strong>r design parameters are not significant, but <strong>the</strong>ir scores are very close to <strong>the</strong> established
hypo<strong>the</strong>sis: horizontal specialization and formalization. However, high indoctrination had not got a
positive impact on information distribution, when it should be a facilitator.
Table 2 and 3 consider <strong>the</strong> influence <strong>of</strong> previous OLPh and organizational structure on Shared
interpretation and Organizational memory. The second model represented an improvement <strong>of</strong> fit
relative to <strong>the</strong> first model for both constructs. In general, prior OLPh have a positive impact on OLA. In
consequence, Hypo<strong>the</strong>sis 1, 2 and 3 are confirmed.
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Table 2: Results <strong>of</strong> Regression analysis <strong>of</strong> previous OLPH on shared interpretation
Table 2 is quite relevant in several ways. First, all previous OLPh have a positive impact on Shared
interpretation. Hence, <strong>the</strong> development <strong>of</strong> this OLA requires Information acquisition and distribution,
supporting hypo<strong>the</strong>sis 2. Second, low horizontal specialization and low formalization have a positive
and significant impact on Shared interpretation, confirming hypo<strong>the</strong>sis 4a and 5. Finally,
decentralization and indoctrination have a positive effect but not significant on Shared interpretation.
Table 3 presents <strong>the</strong> results obtained from <strong>the</strong> regression analysis <strong>of</strong> previous OLPh and design
parameters on Organizational memory. Preceding OLPh have a positive impact on Organizational
memory, confirming hypo<strong>the</strong>sis 3. Surprisingly, high formalization has a positive and significant effect
on this OLA, not supporting hypo<strong>the</strong>sis 5. Low horizontal and vertical job specialization have not a
positive and significant influence on Organizational memory, rejecting hypo<strong>the</strong>sis 4 and requiring
reformulation. Finally, high decentralization and indoctrination have a positive score but do not
determine this OLA. The hypo<strong>the</strong>sis 6 and 7 are, consequently, declined.
Table 3: Results <strong>of</strong> Regression Analysis <strong>of</strong> Previous OLPh on Organisational Memory
Comparing <strong>the</strong> three tables, it can be seen that Horizontal job specialization only has a significant and
positive effect on interpretation activity. In consequence, <strong>the</strong> employee’s wide range <strong>of</strong> perspective
increases <strong>the</strong>ir capacity to interpret information and transform it into knowledge. However, it is not on
<strong>the</strong> side <strong>of</strong> o<strong>the</strong>r OLA, confirming partially Hypo<strong>the</strong>sis 4a. Low vertical job specialization shows a
significant negative effect on all OLA, except on Organizational memory. Spanish workers, underwent
relatively high revision and control procedures in <strong>the</strong>ir work, develop less OLP as a result <strong>the</strong>y are
focused on <strong>the</strong>ir job, and have not got enough information, knowledge, experience, organizational
vision nei<strong>the</strong>r capacity to participate in OLP. Therefore, <strong>the</strong>se employees should have more time for
<strong>the</strong> joint development <strong>of</strong> control, management and learning. Consequently, Hypo<strong>the</strong>sis 4b is not
accepted and should be reconsidered.
Low formalization has a significant effect on some OLA: Shared interpretation (positive) and
Organizational memory (negative). Consequently, Hypo<strong>the</strong>sis 5 is partially accepted. Flexibility does
not improve significantly <strong>the</strong> Information distribution jointly with <strong>the</strong> Information acquisition.
Never<strong>the</strong>less, low formalization facilitates <strong>the</strong> Shared interpretation, due to encourage social
interactions among organizational members and stimulate <strong>the</strong> discussion and creativity’s capacity,
increasing generally <strong>the</strong> employees’ capacity to interpret information and transform it into knowledge.
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Surprisingly, high formalization have a positive impact on Organizational memory, because <strong>the</strong>
establishment <strong>of</strong> standard practices to capture, codify, organize, store and retrieve <strong>the</strong> information
and knowledge favour its development. This finding corroborates <strong>the</strong> ideas <strong>of</strong> Alavi and Leidner
(2001), who suggested that high level formalization generates greater knowledge creation.
Low centralization shows significant positive effect on previous OLPh to Information distribution,
confirming partially Hypo<strong>the</strong>sis 6. Decentralization requires employee’s participation in decisionmaking
in multiple-locations and moments, which demand and generate knowledge. Consequently, all
OLPh are highly demanded but not achieved. Hence, it has not a significant impact on all prior OLPh
to Shared interpretation and Organizational memory. Despite this result, decentralization influences
positively from <strong>the</strong> internal information acquisition to <strong>the</strong> shared interpretation. In a similar way, it has
a joint effect on information acquisition and distribution as previous OLPh to organizational memory.
Finally, high Indoctrination does not show significant effects in Model 2 (rejecting Hypo<strong>the</strong>sis 7).
Indoctrination implies enjoying <strong>the</strong> same culture, <strong>the</strong> mutual trust, team spirit and friendship between
organizational members. These activities reduce <strong>the</strong> information distribution, because <strong>the</strong>
organization and its members provide wide information that diminishes this OLA; but <strong>the</strong>ir interactions
facilitate shared interpretation and organizational memory.
7. Discussion and conclusion
The most striking result to emerge from <strong>the</strong> data is that all OLPh have a positive and significant
influence on OLA. These results are consistent with <strong>the</strong> idea that when <strong>the</strong> previous OLPh is reduced,
<strong>the</strong> development <strong>of</strong> <strong>the</strong> following OLA is minor compared with <strong>the</strong> adequate development <strong>of</strong> prior
OLPh. This aspect has an important influence on <strong>the</strong> OLP, and on <strong>the</strong> organizational capacity in
knowledge creation. Subsequently, this consequence reflects on organizational performance. In
reviewing <strong>the</strong> literature, no empirical studies were found in <strong>the</strong> association between OLPh and OLA.
The present study was designed to demonstrate <strong>the</strong> close significant relationship between both
concepts.
Ano<strong>the</strong>r important finding is that organizational structure directly affects OL. Prior studies had noted
<strong>the</strong> importance <strong>of</strong> organizational structure on OL (Martínez and Olmedo, 2010; Martínez and
Martínez, 2011). However, no empirical studies were found in <strong>the</strong> literature on <strong>the</strong> question <strong>of</strong>
analyzing <strong>the</strong> influence <strong>of</strong> design parameters on OLPh and OLA. Therefore, this study set out with <strong>the</strong>
aim <strong>of</strong> assessing <strong>the</strong> importance <strong>of</strong> design parameters in <strong>the</strong> development <strong>of</strong> OLP. The results <strong>of</strong> this
study show that information distribution process is induced by high vertical specialization and low
centralization (decentralization), toge<strong>the</strong>r with <strong>the</strong> external and internal information acquisition. High
vertical specialization limits <strong>the</strong> worker’s control and his/her vision, but can actively participate in <strong>the</strong>
information acquisition, favouring information distribution. Decentralization creates a participatory
environment that also facilitates both activities.
Shared interpretation is always positively affected by prior OLPh, and low horizontal specialization
and formalization. Low horizontal specialization implies that different tasks are included in a job
description, which expands <strong>the</strong> vision <strong>of</strong> <strong>the</strong> worker and <strong>the</strong> information acquisition and distribution,
facilitating shared interpretation. Low formalization provides relative freedom in <strong>the</strong> job, expanding
previous OLPh and <strong>the</strong> employee’s understanding (interpretation). On <strong>the</strong> contrary, high formalization
and previous OLPh have a direct effect on <strong>the</strong> organizational memory process. The existence <strong>of</strong>
procedures, rules and regulations (consequence <strong>of</strong> high formalization) and significant development <strong>of</strong>
previous OLPh facilitate <strong>the</strong> development <strong>of</strong> organizational memory.
It is also interesting to note that several design parameters influence jointly with some OLPh on OLA,
but it is not a general effect on all <strong>the</strong> previous OLPh which are included in <strong>the</strong> activity in question.
Considering information distribution, low formalization affects jointly with <strong>the</strong> previous design
parameter and external information acquisition in this OLA due to <strong>the</strong> job flexibility, which allows
distributing <strong>the</strong> external information to be acquired in a better way.
Besides, shared interpretation is affected by o<strong>the</strong>r significant relationships. Low centralization and
high vertical specialization (plus o<strong>the</strong>r design parameters), and internal information acquisition (OLPh)
have a combined effect on this OLA. The participation <strong>of</strong> worker in <strong>the</strong> decision making process
facilitates shared interpretation, acquiring internal information. In addition, high vertical specialization
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Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
justifies that <strong>the</strong> worker needs to acquire internal and external information, to understand better what
criteria are used by o<strong>the</strong>rs to manage his/her job successfully and interpret his/her contribution.
In <strong>the</strong> current study, low decentralization develops a combined influence with information acquisition
and distribution on organizational memory. A possible explanation for this might be that <strong>the</strong> decisionmaking
process requires information and knowledge, which should be captured, organized, indexed
and retrieved for future uses. Contrary to expectations, shared interpretation and high horizontal
specialization jointly influence on organizational memory. The reason is that <strong>the</strong> standardization <strong>of</strong> <strong>the</strong>
job requires knowing exactly <strong>the</strong> different procedures and knowledge, which facilitate <strong>the</strong>
development <strong>of</strong> organizational memory.
Ano<strong>the</strong>r unexpected fact is that this study did not find any significant influence <strong>of</strong> indoctrination on
OLP. Finally, <strong>the</strong>se findings have important implications for developing OLP. The present results are
significant in two ways: (a) organizational structure influences on <strong>the</strong> development <strong>of</strong> each OLA, and
b) <strong>the</strong> design parameters have different behaviour when <strong>the</strong>y influence jointly with OLPh on OLA. The
results do not determine what type <strong>of</strong> organizational structure (mechanistic or organic structure)
provides appropriate conditions for <strong>the</strong> development <strong>of</strong> OL. However, more research on this topic
needs to be undertaken before <strong>the</strong> association between OLPh, organizational structure (independent
variables) and OLA. Fur<strong>the</strong>r research should be also done to confirm <strong>the</strong> type <strong>of</strong> organizational
structure that facilitates OL.
References
Alavi, M. and Leidner, D.E. (2001) “Review: Knowledge management and knowledge management systems:
Conceptual foundations and research issues”, MIS Quarterly, Vol 25, No. 1, pp 107-133.
Argyris, C. and Schön, D.A. (1978) Organizational learning: A <strong>the</strong>ory <strong>of</strong> action perspective, Addison-Wesley,
Boston, MA.
Argyris, C. and Schön, D.A. (1996) Organizational learning II: <strong>the</strong>ory, method and practice. Addison Wesley,
Boston, MA.
Bennett, R. and Gabriel, H. (1999) "Organisational factors and knowledge management within large marketing
departments: an empirical study", Journal <strong>of</strong> Knowledge Management, Vol 3, No. 3, pp 212-225.
Brown, J.S. and Duguid, P. (1991) “Organizational learning and Communities-<strong>of</strong>-practice: Toward a unified view
<strong>of</strong> working, learning, and innovation”, Organization Science, Vol 2, No. 1, pp 40-57.
Burns, T. and Stalker, G.M. (1961) “Mechanistic and organic systems”, in Shafritz, J.M. and Ott, J.S. (1992)
Classics <strong>of</strong> Organization Theory, Wadsworth Publishing, Belmont, pp 214-225.
Chen, G. (2005) “An organisational learning model based on western and Chinese management thoughts and
practices”, Management Decision, Vol 43, No. 4, pp 479-500.
Chen, C.J. and Huang, J.W. (2007) “How organizational climate and structure affect knowledge management -
The social interaction perspective”, International Journal <strong>of</strong> Information Management, Vol 27, No. 2, pp 104-
Crossan, M., Lane, H. and White, R. (1999) “An organizational learning framework: From intuition to institution”,
Academy <strong>of</strong> Management Review, Vol 24 No. 3, pp 522-538.
Cummings, T.G. and Worley, C.G. (1997) Organization development and change, ITP, USA.
Deal T.E. y Kennedy, A.A. (1982) Corporate Cultures: The Rites and Rituals <strong>of</strong> Corporate Life. Penguin Books.
Harmondsworth.
Dewar, R. and Werbel, J. (1979) “Universalistic and contingency perdictions <strong>of</strong> employee satisfaction and
conflict”, Administration Science Quarterly, Vol 24, pp 426-448.
Dodgson, M. (1993), “Organizational learning: A review <strong>of</strong> some literatures”, Organization Studies, Vol 14, No. 3,
Duncan, R.B. and Weiss, A. (1979) Organizational learning: Implications for organizational design. In B. Staw
(Ed.), Research in organizational behavior (pp 75-123), JAI Press Greenwich, CT.
Fiol, C.M. and Lyles, M.A. (1985) “Organizational learning”, Academy <strong>of</strong> Management Review, Vol 10, No. 4, pp
Floyd, S.W, and Wooldridge, B. (1992) “Middle management involvement in strategy and its association with
strategic type”, Strategic management Journal, Vol 13, pp 153-167.
Garvin, D. (1993) “Building a learning organization”, Harvard Business Review, Vol 71, No. 4, pp 78-91.
Grant, R.M. (1996) “Prospering in dynamically-competitive environments: Organizational capability as knowledge
integration”, Organization Science, Vol 7, No. 4, pp 375-387.
Hedberg, B. (1981) How organizations learn and unlearn? In P. C. Nystrom & W. H. Starbuck (Eds.), Handbook
<strong>of</strong> organizational design (pp 8-27). Oxford University Press, London.
Huber, G.P. (1991) “Organizational learning: The contributing processes and <strong>the</strong> literatures”, Organization
Science, Vol 2, No. 1, pp 88-115.
Jiménez, D. and Cegarra, J.G. (2007) “The performance effect <strong>of</strong> organizational learning and market orientation”,
Industrial Marketing Management, Vol 36 No. 6, pp 694-708.
Kim, D.H. (1993) “The Individual and Organizational Learning”, Sloan Management Review, Vol 35 No. 1, pp 37-
Martínez-León, I.M., Ruiz-Mercader, J. and Martínez-León, J.A. (2008) “The Effect <strong>of</strong> Organizational Learning
Tools in Business Results”, International Journal <strong>of</strong> Knowledge and Learning, Vol 4, No. 6, pp 539-552.
607

Inocencia Mª Martínez-León and Isabel Olmedo-Cifuentes
Martínez León, I.M. and Martínez, J.A. (2011) “The Influence <strong>of</strong> Organizational Structure on Organizational
Learning”, International Journal <strong>of</strong> Manpower, Vol. 32, No 5-6, in press.
Martínez, I.M. and Olmedo, I. (2010) “The Influence <strong>of</strong> Organizational Structure and Information Technologies on
Organizational Learning”. 11th <strong>European</strong> <strong>Conference</strong> on Knowledge Management, Portugal.
Matusick, S.F. and Hill, C.W. (1998) “The utilization <strong>of</strong> contingent work, knowledge creation, and competitive
advantage”, Academy <strong>of</strong> Management Review, Vol 23, pp 680-697.
Mintzberg, H. (1979) The Structuring <strong>of</strong> Organizations: A Syn<strong>the</strong>sis <strong>of</strong> <strong>the</strong> Research, Prentice-Hall, Englewood
Cliffs, New Jersey.
Mintzberg, H. (1993) La estructuración de las organizaciones, Ariel Economía, Barcelona.
Nevis, E.G., DiBella, A.J. and Gould, J.M. (1995) “Understanding Organizations as Learning Systems”, Sloan
Management Review, Vol 36, No. 2, pp 73-85.
Nicolini, D. and Meznar, M.B. (1995) “The social construction <strong>of</strong> organizational learning: Conceptual and practical
issues in <strong>the</strong> field”, Human Relations, Vol 48 No. 7, pp 727-746.
Nonaka, I. (1991) “The knowledge-creating company”, Harvard Business Review, Vol 69, No. 6, pp 96-104.
Nonaka, l., Toyama, R. and Nagata, A. (2000) “A firm as a knowledge-creating entity: A new perspective on <strong>the</strong>
<strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm”, Industrial and Corporate Change, Vol 9 No. 1, pp 1-20.
Ouksel, A. and Vyhmeister, R. (2000) “Performance <strong>of</strong> organizational design models and <strong>the</strong>ir impact on
organization learning”, Computational & Ma<strong>the</strong>matical Organization Theory, Vol 6 No. 4, pp 395-410.
Pérez, S., Montes, J.M. and Vazquez, C.J. (2004) “Managing knowledge: The link between culture and
organizational learning”, Journal <strong>of</strong> Knowledge Management, Vol 8, No. 6, pp 93-104.
Rapert, M.I. and Wren, B.M. (1998) “Reconsidering organizational structure: A dual perspective <strong>of</strong> frameworks
and processes”, Journal <strong>of</strong> Management Studies, Vol 10, No. 3, pp 287-302.
Robbins, S.P. and Decenzo, D.A. (2001) Fundamentals <strong>of</strong> Management: Essential Concepts and Applications,
Prentice-Hall, New York.
Senge, P. (1990) The fifth discipline: The Art and Practice <strong>of</strong> Learning Organization, Doubleday New York.
Spanish version (1992): La quinta disciplina: el arte y práctica de la organización abierta al aprendizaje,
Granica, Barcelona.
Sinkula, J.M. (1994) “Market information processing and organizational learning”, Journal <strong>of</strong> Marketing, Vol 58,
January, pp 35-45.
Sivadas, E. and Dwyer, F.R. (2000) “An examination <strong>of</strong> organizational factors influencing new product success in
internal and alliance based processes”, Journal <strong>of</strong> Marketing, Vol 64, No. 1, pp 31-50.
Slater, S.F. and Narver, J.C. (1995) “Market orientation and <strong>the</strong> learning organization”, Journal <strong>of</strong> Marketing,
Spender, J.C. and Grant, R. (1996) “Knowledge and <strong>the</strong> firm: Overview”, Strategic Management Journal, Vol 17,
Stata, R. (1989) “Organizational learning: <strong>the</strong> key to management innovation”, Sloan Management Review, Von
Krogh, G. (1998) “Care in knowledge creation”, California Management Review, Vol. 40; pp 133–153.
Walsh, J.P. and Ungson, G.R. (1991) “Organizational memory”, Academy <strong>of</strong> Management Review, Vol 16, No. 1,
Zerilli, A. (1978) Fundamentos de Organización y Dirección General, Editorial Deusto S.A., Bilbao.
Zollo, M. and Winter, S.G. (2002) “Deliberate learning and <strong>the</strong> evolution <strong>of</strong> dynamic capabilities”, Organization
Science, Vol 13, No. 3, pp 339-351.
608

Buzz Network Strategies and Their Impact on Knowledge
Management Process
Maurizio Massaro 1 and Roland Bardy 2
1 DISES, Udine University, Udine, Italy
2 DLI, Florida Gulf Coast University, FL, USA
maurizio.massaro@uniud.it
rbardy@t-online.de
Abstract: The evolution in technology and social behavior, which is happening nowadays, is changing traditional
ways <strong>of</strong> managing knowledge. An example <strong>of</strong> this evolution is represented by buzz network strategies (BNS).
Originally developed in <strong>the</strong> field <strong>of</strong> marketing, <strong>the</strong>se strategies refer to <strong>the</strong> interaction <strong>of</strong> consumers and users <strong>of</strong>
a product or service in order to amplify <strong>the</strong> marketing message and create a word-<strong>of</strong>-mouth effect. Nowadays <strong>the</strong>
original marketing usage is changing and companies are creating new solutions for managing <strong>the</strong> knowledge
that this word-<strong>of</strong>-mouth is generating with a substantial impact on traditional knowledge management (KM)
practice. We have conducted an empirical analysis in <strong>the</strong> field <strong>of</strong> BNS using a case-study, <strong>the</strong> ”kids’ space”. This
project consists <strong>of</strong> an alliance <strong>of</strong> several Italian companies with <strong>the</strong> aim <strong>of</strong> creating a web 2.0 solution where
consumers, architects, designers, inventors and o<strong>the</strong>r experts can talk about products and services for children.
A qualitative approach was used throughout several semi- structured interviews with 10 managers involved in
<strong>the</strong> project. Focusing on <strong>the</strong>se lines <strong>of</strong> research, <strong>the</strong> aim <strong>of</strong> this exploratory paper is to:
(i) identify a <strong>the</strong>oretical framework <strong>of</strong> knowledge management process that could be used in empirical analysis;
(ii) recognize how buzz strategies are affecting <strong>the</strong> traditional knowledge management process in participating
companies.
The <strong>the</strong>oretical implications <strong>of</strong> this paper are to create better assimilation <strong>of</strong> network buzz strategies and to
recognize how <strong>the</strong>se strategies could affect traditional Knowledge Management Process.
The managerial implications <strong>of</strong> this paper are to assist managers in developing BNS.
Keywords: Knowledge management process, buzz network strategy, web 2.0, knowledge management, change
1. Introduction and research questions
Scholars advise that to acquire a sustainable competitive advantage, companies must treat
knowledge as a strategic asset (Teece, 2000). Knowledge management (KM) thus becomes a
strategic task. The ability to manage knowledge in order to increase competitive advantage is
perceived as <strong>the</strong> key to solving problems and exploiting opportunities to innovate companies’
business models (Drew, 1999). Therefore, it is imperative to pay greater attention to <strong>the</strong> development
change information <strong>of</strong> competitive skills and capabilities (Calcantone, Cavusgil and Zhao 2002;
Loermans 2002), and literature seems to recognize <strong>the</strong> key role <strong>of</strong> a networking approach in this
process (Faems, Janssens and Van Looy, 2010). Despite <strong>the</strong> great importance <strong>of</strong> <strong>the</strong> topic, we do
not find it treated uniformly in <strong>the</strong> literature and <strong>the</strong>re are several diverse definitions <strong>of</strong> network,
knowledge and knowledge management. In this study we refer to a network as a very close
relationship between buyers, suppliers or partners in general which involves knowledge and
personnel transfer with a specific aim (Powell 1990; Ring and Van de Ven 1992; Cuganesan, 2006).
The meaning <strong>of</strong> knowledge that we use is derived from <strong>the</strong> contribution <strong>of</strong> Davenport and Prusak
(2000) who present knowledge as being automatically derived from information coming from real-time
data. The authors contend that ,for <strong>the</strong> transformation <strong>of</strong> information into knowledge, human
intervention is required. Consequently, humans apply <strong>the</strong>ir skills, ability, experience, know-how,
values and culture via some transformation (comparison, communication, connections and
consequences) thus changing information into knowledge. ”Knowledge Management <strong>the</strong>refore draws
on existing resources that an organization may already have in place - good information systems
management, organizational change management and human resources management practices”
(Davenport and Prusak, 2000, p.163).
Generating and managing knowledge within networks requires intensive and fine-grained interaction
between collaborating members <strong>of</strong> <strong>the</strong> network but it seems to be a forced road for developing
sustainable competitive advantage as <strong>the</strong> increasing number <strong>of</strong> papers in <strong>the</strong> field seem to testify
(Faems, Van Looy and Debackere, 2005). In this field, <strong>the</strong> technology evolution in general and <strong>the</strong>
web 2.0 development in particular have been helping <strong>the</strong> network oriented KM practice (Levy, 2009).
Indeed, <strong>the</strong> evolution <strong>of</strong> social s<strong>of</strong>tware, defined as ”<strong>the</strong> use <strong>of</strong> computing tools to support, extend or
derive added value from social activity” (Avram, 2006, p.1), is helping companies to generate and
share knowledge. The evolution <strong>of</strong> <strong>the</strong>se tools is followed by <strong>the</strong> evolution <strong>of</strong> social networks into
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people’s lives. This combined effect is creating new knowledge opportunities but also new challenges
for companies and customers. According to a statistic recently published in <strong>the</strong> German ”Frankfurter
Allgemeine Zeitung (FAZ), this even includes companies which do not market <strong>the</strong>ir goods to endusers
e.g. <strong>the</strong> chemical manufacturer BASF is reported to have regular connections with about 10 000
social media users (FAZ, March 08, 2011).
An example <strong>of</strong> this new evolution is represented by buzz network strategies (BNS). Originally
developed in marketing science, <strong>the</strong>se strategies were applied to <strong>the</strong> interaction <strong>of</strong> consumers and
users <strong>of</strong> a product or service in order to amplify a marketing message and create a word-<strong>of</strong>-mouth
effect (Kirby and Marsden, 2006). Tools originally used were: viral marketing campaigns, blogs and
o<strong>the</strong>r web 2.0 applications (for a review <strong>of</strong> web 2.0 applications see Levy, 2009). Nowadays <strong>the</strong>
original marketing use is changing and it is pushing network BNS into a more complex role. Creating a
partially uncontrolled communication effect, <strong>the</strong>se strategies present <strong>the</strong> following characteristics:
• <strong>the</strong> role <strong>of</strong> users is active and not passive. This places <strong>the</strong> users in a different position relative to
traditional systems where managers and experts were <strong>the</strong> leaders <strong>of</strong> <strong>the</strong> communication process
(Levy, 2009);
• <strong>the</strong> definition <strong>of</strong> users involves different pr<strong>of</strong>iles: experts, consumers, companies etc, each <strong>of</strong>
<strong>the</strong>se having specific interests and aims. This approach creates a substantial difference
compared to o<strong>the</strong>r communities such as communities <strong>of</strong> practices, ”which bind toge<strong>the</strong>r a group
<strong>of</strong> people who share a concern, a set <strong>of</strong> problems, an expertise or a passion about a topic”
(Bourhis, Dubé and Jacob, 2005; Restler and Woolis 2007);
• <strong>the</strong> information flow is a blend <strong>of</strong> situations. We can find conditions where i) companies talk to
consumers, experts and o<strong>the</strong>r companies ii) consumers talk to o<strong>the</strong>r consumers, experts and
companies iii) experts talk to companies, consumers and o<strong>the</strong>r companies, creating a mix <strong>of</strong>
messages;
• <strong>the</strong> part represented by companies is generally made up <strong>of</strong> companies <strong>of</strong> diverse businesses
that are working toge<strong>the</strong>r in a network in order to share specific interests that usually involve <strong>the</strong>
generation <strong>of</strong> specific cross-field-products for targeted markets (e.g. products for children “<strong>the</strong>
kids’ space” like furniture, linens, toys, cleaning appliances, …);
• types <strong>of</strong> data and information that could be: advertisements, free consultancies given by experts,
conversations among consumers, complains, tips for product development, e-learning platforms,
contests… (Kirby and Marsden, 2006);
• <strong>the</strong> web is a platform which means that several applications could access, share and use <strong>the</strong>
information within <strong>the</strong> web platform: Facebook, App for mobile devices, websites and specific
programs could interact using <strong>the</strong> same platform (Levy, 2009) creating a pervasive usage during
<strong>the</strong> daily routine.
These new opportunities have great impact on traditional knowledge management practice,
introducing a different knowledge perspective also in more traditional companies that have developed
a BNS. Focusing on <strong>the</strong>se lines <strong>of</strong> research, <strong>the</strong> aims <strong>of</strong> this paper are:
• to identify a <strong>the</strong>oretical framework <strong>of</strong> knowledge management process that could be used in
empirical analysis;
• to recognize how buzz network strategies are affecting <strong>the</strong> traditional knowledge management
process in participating companies.
2. Knowledge management process
The emerging knowledge economy is creating a revolution that is forcing companies to look for ways
to reinvent <strong>the</strong>mselves and <strong>the</strong>y have to systematically manage this creative process (Tapp, 1997).
The ability to learn, to generate new ideas, to propose new untried solutions to problems and in
general to be creative, seems to be related to <strong>the</strong> ability <strong>of</strong> <strong>the</strong> company to create a significant
network. Several studies have shown that companies which create significant relational networks
obtain better performances (Zaheer and Bell, 2005; McEvily and Marcus, 2005). In this perspective,
<strong>the</strong> idea <strong>of</strong> acquiring, sharing and generating knowledge is viewed as a primary level in organizational
evolution that helps companies to choose and adopt new practice in order to increase <strong>the</strong>ir
competitive advantage (Bijaya and Uday Bhaskar, 2011). While a narrow definition <strong>of</strong> Knowledge
Management would be ”a set <strong>of</strong> techniques and practice that facilitate <strong>the</strong> flow <strong>of</strong> knowledge into and
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within <strong>the</strong> firm” (Birkinshaw, 2001), we think that we have to focus on <strong>the</strong> process perspective (Bijaya
and Uday Bhaskar, 2011). The literature review shows multiple approaches on KM processes
distinguishing several phases (a short review is given by Bijaya and Uday Bhaskar (2011). In this
paper we use and refer to <strong>the</strong> knowledge chain model developed by Holsapple (2004, 2005) who
describes five major classes <strong>of</strong> knowledge manipulation activities that take place in a variety <strong>of</strong>
patterns within KM episodes, and four secondary KM classes. Nold (2011) pointed out that this goes
beyond <strong>the</strong> SECI model (Socialization, Externalization, Combination, Internalization, developed by
Nonaka and Takeuchi) and <strong>the</strong> Knowledge Chains ”are more robust than many o<strong>the</strong>r models because
<strong>the</strong>y attend to identify enabling factors that drive KM influence and integrate <strong>the</strong> process”. Thus, this
paper focuses on <strong>the</strong> five major classes developed by Holsapple, which, in <strong>the</strong> perspective <strong>of</strong> <strong>the</strong>
author, are as follows:
knowledge acquisition, which refers to activities <strong>of</strong> acquiring knowledge form external sources and
making it suitable for subsequence use. The acquisition process involves <strong>the</strong> phases <strong>of</strong>
identification <strong>of</strong> knowledge in <strong>the</strong> organization’s external environment; capturing and organizing
<strong>the</strong> identified knowledge;
knowledge selection, which refers to activities <strong>of</strong> selecting required knowledge from internal
sources and making it suitable for subsequence use. The selection process refers to <strong>the</strong> activity
<strong>of</strong> identifying needed knowledge within an organization’s existing knowledge resources and
providing it in an appropriate representation to an activity that needs it. The process involved <strong>the</strong>
following activities: identifying appropriate knowledge; capturing and organizing <strong>the</strong> identified
knowledge;
knowledge generation, which refers to activities <strong>of</strong> discovering new knowledge from existing
resources. Sub activities involved are: monitoring <strong>the</strong> organization and environmental sources <strong>of</strong>
knowledge, selecting and/or acquiring it; evaluated selected knowledge in terms <strong>of</strong> its usability for
<strong>the</strong> generation task;
knowledge assimilation, which refers to activities <strong>of</strong> altering <strong>the</strong> state <strong>of</strong> an organization’s
knowledge resources by distributing and storing acquired selected or generated knowledge. A
processor performing knowledge assimilation receives knowledge flows from <strong>the</strong>se activities and
procedures knowledge flows that impact <strong>the</strong> organization’s state <strong>of</strong> knowledge. Sub activities are:
screening, filtering knowledge to be assimilated, targeting knowledge resources that are to be
impacted by assimilation, structuring and representing resources <strong>of</strong> knowledge and delivering <strong>the</strong>
targeted knowledge;
knowledge emission, which refers to activities <strong>of</strong> embedding knowledge into organizational
outputs to be released into <strong>the</strong> environment. Some activities involved are: targeting <strong>the</strong> output,
producing, applying embodying, controlling and leveraging knowledge that it is supposed to
transfer.
Within <strong>the</strong>se lines <strong>of</strong> research, a wide variety <strong>of</strong> KM systems have been created for a variety <strong>of</strong>
purposes such as knowledge repositories, corporate directories and general knowledge networks
which support <strong>the</strong> knowledge process (Nold, 2011). This paper tries to demonstrate how BNSs affect
<strong>the</strong> Knowledge Management Process using <strong>the</strong> Holsapple’s model.
3. Methodology
In order to analyze <strong>the</strong> impact <strong>of</strong> <strong>the</strong> BNS on KM process we have decided to use a qualitative case
study (in <strong>the</strong> denomination <strong>of</strong> Yin 1994). Empirical analyses were conducted using <strong>the</strong> case <strong>of</strong> ”kids’
space” which is an alliance <strong>of</strong> several Italian companies with <strong>the</strong> aim <strong>of</strong> creating a web 2.0 buzz
network where consumers, architects and designers, inventors and o<strong>the</strong>r experts can talk about
products and services for kids such as : bedding and décor, toys, strollers, cribs, feeding, etc..
Within <strong>the</strong> case <strong>of</strong> study, we adopted a multi-method approach that involved collection <strong>of</strong> information
through interviews and o<strong>the</strong>r sources. We analyzed public company reports in order to acquire
general data about <strong>the</strong> firms (turnover, number <strong>of</strong> employees, etc.)and about <strong>the</strong>ir market positioning,
business fields and so on. A qualitative approach was also used through several semi-structured
interviews with 10 managers involved in <strong>the</strong> project. A preliminary interview was developed in order
to acquire information about <strong>the</strong> buzz network strategy and to widen <strong>the</strong> information retrieved from
public sources. Four CEOs <strong>of</strong> <strong>the</strong> major companies involved in <strong>the</strong> project were interviewed in person.
A second round <strong>of</strong> interviews was developed within a selected company in order to acquire specific
information about <strong>the</strong> knowledge management process within <strong>the</strong> selected company before and after
<strong>the</strong> adoption <strong>of</strong> <strong>the</strong> buzz network project. Six line managers were interviewed in this company
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The main topic <strong>of</strong> <strong>the</strong> interviews was how <strong>the</strong> BNS could affect <strong>the</strong> KM process. Thus, based on <strong>the</strong>
aims <strong>of</strong> <strong>the</strong> project, we asked:
• What kind <strong>of</strong> knowledge is produced through <strong>the</strong> BNS? (What)
• Who is <strong>the</strong> Knowledge source in <strong>the</strong> BNS and <strong>the</strong> knowledge destination? (Who)
• How does <strong>the</strong> BNS affect your process <strong>of</strong> Knowledge management? (How)
• What is <strong>the</strong> main effect <strong>of</strong> <strong>the</strong> introduction <strong>of</strong> BNS in your KM process? (Effects)
4. The Kid’s space
The ”kid’s space” (in Italian: ”Star in cameretta”, reference www.starincameretta.it) is a result <strong>of</strong> a BNS
developed by some Italian companies regarding <strong>the</strong> world <strong>of</strong> a child’s room. The URL will be online
from mid April, 2011.
The project aims to build several Web 2.0 solutions in <strong>the</strong> field <strong>of</strong> a child’s room like a web portal
(including: web sites, forums, blogs, video contests), mobile App (iphone, android, …), social network
applications (Facebook, Youtube, …) . The topics are organized in five major groups: (i) Safety (ii)
Ecology (iii) Design (iv) Sleep (v) Play.
Using specific sections <strong>of</strong> each Media solution (Web, Mobile App, etc.) users could acquire specific
information (product features,) <strong>of</strong>fered by <strong>the</strong> companies‘ experts and <strong>the</strong>y could leave <strong>the</strong>ir
comments. Specific sections were designed in order to allow users to share information using blogs,
forums etc and to ask for tips from experts. Specific parts will be realized for creating idea contests.
As shown in table 1, Kid’s space is achieved through a partnership between several leading
enterprises operating in <strong>the</strong> kid’s room business. The project manager is Heritage Group, a leading
Italian company in <strong>the</strong> field <strong>of</strong> new media strategy that has catalyzed o<strong>the</strong>r companies as partners in
<strong>the</strong> project. Enterprises selected are recognized as business leaders in <strong>the</strong>ir specific field. Table 1
gives a short overview <strong>of</strong> <strong>the</strong> companies <strong>of</strong> <strong>the</strong> network.
Table 1: Partners <strong>of</strong> <strong>the</strong> Buzz Network Strategy
Name Field
Heritage Group Advertising, strategy, new medias
Moretti Compact spa Kids room furniture
Rex Electrolux spa (Italy branch) Household appliances
Artsana spa Baby care (under <strong>the</strong> brands <strong>of</strong> Chicco and Prenatal)
Emmecia Textile
Calligaris spa Furniture
A.P.T. Val di Fassa Holidays
We were informed by <strong>the</strong> Heritage Group CEO that <strong>the</strong> communication approach, being <strong>the</strong> first step
<strong>of</strong> <strong>the</strong> project development, will be set in motion in April 2011. O<strong>the</strong>r steps will be taken in <strong>the</strong> middle
<strong>of</strong> 2012 since <strong>the</strong>y require high investments and are only possible when <strong>the</strong> project has acquired
enough users and high visibility. The following sections give a short description <strong>of</strong> <strong>the</strong> project’s
objectives.
3.1 Communication aims
Kid’s space is a multi-platform solution based on web 2.0 technology. This means that several kinds
<strong>of</strong> media will be used: web sites, social network sites (facebook, …), apps, forums, web-tv and o<strong>the</strong>r
platforms. Using a multi-platform approach and involving <strong>the</strong> leaders in <strong>the</strong> business field, <strong>the</strong>se
companies want to create high impact by acquiring numerous users and giving high visibility to <strong>the</strong>
initiative in order to create a new communication channel. The communication process involves:
experts (eg. architects and o<strong>the</strong>r pr<strong>of</strong>essionals) that <strong>of</strong>fer <strong>the</strong>ir help to consumers; several
features will be activated like e.g. ”Ask for your personal projects”, etc; best projects will be
published and selected into specific forum categories;
consumers using <strong>the</strong> forum may create an information sharing process versus consumers,
experts and companies;
companies that use specific tools may create an information sharing process versus experts and
companies.
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3.2 Product/service development aims
Maurizio Massaro and Roland Bardy
The companies that <strong>of</strong>fer solutions in <strong>the</strong> field <strong>of</strong> <strong>the</strong> kid’s space want to create a new approach to
product development. They will do this by involving consumers, architects, experts and companies
from <strong>the</strong> field in a common platform:,
Experts (eg. architects and o<strong>the</strong>r pr<strong>of</strong>essionals) could create new solutions and new goals;
consumers using forums and o<strong>the</strong>r specific sessions <strong>of</strong> <strong>the</strong> project could talk about needs and
make requests;
companies could verify consensus, acquiring tips and opinions from <strong>the</strong> shared information <strong>the</strong>y
share. They could also use <strong>the</strong> web in order to create focus groups for testing new product trends
or for monitoring product reputation.
3.3 Market development aims
Employing different types <strong>of</strong> active users, <strong>the</strong> companies will be able to create a base for acquiring,
selecting and sharing technical information regarding <strong>the</strong> constitution <strong>of</strong> new markets, <strong>the</strong> techniques
for selling products directly or by indicating <strong>the</strong> nearest dealer, <strong>the</strong> approach to recognizing new
markets opportunities (e.g. show rooms, trade fairs, etc.) and to develop a better understanding <strong>of</strong> <strong>the</strong>
market (customer segmentation, etc.).
4. Results
In this chapter we present <strong>the</strong> results <strong>of</strong> <strong>the</strong> case study on <strong>the</strong> impact <strong>of</strong> BNS on KM process. We
have used <strong>the</strong> Holsapple (2004, 2005) framework in order to analyze how <strong>the</strong> objectives <strong>of</strong> <strong>the</strong> project
impact <strong>the</strong> KM process in terms <strong>of</strong>:
What kind <strong>of</strong> knowledge is produced through <strong>the</strong> BNS;
Who is <strong>the</strong> origin and who is <strong>the</strong> destination <strong>of</strong> <strong>the</strong> knowledge;
How <strong>the</strong> BNS is affecting <strong>the</strong> KM process;
What effect is produced on <strong>the</strong> organization structure;
The main results obtained for each <strong>of</strong> <strong>the</strong> objectives are:
Communication aims. The managers we interviewed have stated that <strong>the</strong> BNS creates a new
multi-media channel <strong>of</strong> communication with consumers, experts, o<strong>the</strong>r companies and dealers.
They find that this allows <strong>the</strong>m to basically communicate information on new product features and
tips for better use <strong>of</strong> new products. The tools that <strong>the</strong>y envisage are forums, blogs and
newsletters as <strong>the</strong>se generate a cost reduction effect. The power <strong>of</strong> <strong>the</strong> BNS in terms <strong>of</strong>
communication aims is <strong>the</strong> network effect. One <strong>of</strong> <strong>the</strong> managers said: ”. The key for acquiring
visibility in <strong>the</strong> web is to make it evident that several companies recognized as leaders <strong>of</strong> <strong>the</strong>ir
business field are working toge<strong>the</strong>r ”. Ano<strong>the</strong>r one said: ”Company websites are working well but
we are not able to catch <strong>the</strong> attention <strong>of</strong> <strong>the</strong> people on our own”. Ano<strong>the</strong>r one said: ”We are multiproduct
companies that usually <strong>of</strong>fer products for different needs. The power <strong>of</strong> <strong>the</strong> idea is to
catch <strong>the</strong> needs <strong>of</strong> customers with a very targeted <strong>of</strong>fer”. Table 2 gives a better description <strong>of</strong> <strong>the</strong>
topics that were discussed.
Product/service development aims. The managers we interviewed have recognized that <strong>the</strong> BNS
creates new opportunities for acquiring information on new trends, for elaborating a database
which helps to fix technical and o<strong>the</strong>r problems and for facilitating knowledge generation through
discussion groups. Tools are: FAQ, forums, free expert services; …. Main effects are cost
reduction, new knowledge source acquisition which would o<strong>the</strong>rwise not be available and higher
effectiveness in general. The power <strong>of</strong> BNS is <strong>the</strong> buzz effect. One <strong>of</strong> <strong>the</strong> managers we
interviewed said: ”All <strong>the</strong>se people interested in one topic is <strong>the</strong> real power <strong>of</strong> <strong>the</strong> strategy”.
Ano<strong>the</strong>r said: ”We have to use <strong>the</strong> power <strong>of</strong> <strong>the</strong> word <strong>of</strong> mouth for supporting our innovation
activities”. Table 3 gives a better description <strong>of</strong> <strong>the</strong> topics that were discussed.
Market development aims. The managers we interviewed agree that <strong>the</strong> BNS creates new market
opportunities by giving information on new markets and new trends that allow a better target
segmentation. Using geographical marketing tools, <strong>the</strong> companies can create databases for
targeting customer needs by gender, age, culture, geographical origin. Main effects are cost
reduction <strong>of</strong> <strong>the</strong>se activities. One <strong>of</strong> <strong>the</strong> managers said: ”The power <strong>of</strong> social networks is
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incredible as users give us precious information for free and we can use this to get a better
understanding <strong>of</strong> <strong>the</strong> market”. Table 4 gives a description <strong>of</strong> <strong>the</strong> topics that were discussed.
Analyzing <strong>the</strong> data <strong>of</strong> tables 2,3 and 4 we apprehend <strong>the</strong> significant impact that BNS creates on <strong>the</strong>:
Knowledge acquisition process. New knowledge (o<strong>the</strong>rwise not available) is acquired. Cost is
reduced for some activities for acquiring information that would be available in o<strong>the</strong>r ways only
through expensive procedures and effectiveness generally improves;
Knowledge selection process. BNSs can help companies, customers and experts to discover
knowledge that is not available o<strong>the</strong>rwise by using tools like FAQ, search engine tools and by
accessing forums and o<strong>the</strong>r sections <strong>of</strong> <strong>the</strong> Web;
Knowledge generation process. BNSs can help companies with <strong>the</strong> generation <strong>of</strong> new ideas
through discussion groups, using, e.g. beta tester tools;
Knowledge assimilation process. BNSs can help companies in <strong>the</strong> assimilation process by
improving <strong>the</strong> ways to store and distribute knowledge;
Knowledge emission process. BNSs can help companies in <strong>the</strong> emission process by creating new
channels <strong>of</strong> communication.
5. Conclusions
This paper deals with a project <strong>of</strong> Buzz Network Strategy (BNS) that was carried out in several
leading Italian companies collectively known by <strong>the</strong> name <strong>of</strong> ”Kid’s Space”. BNSs are defined as
strategies that involve <strong>the</strong> development <strong>of</strong> web 2.0 solutions in a network perspective in order to
create a word-<strong>of</strong>-mouth effect which companies could use for several aims. The case study shows
that BNSs can serve to obtain: communication effects; product/service development effects; market
development effects.
The study employed a <strong>the</strong>oretical framework for analyzing <strong>the</strong> Knowledge Management (KM) process
using <strong>the</strong> Holsapple’s model. Through a qualitative approach, based on semi-structured interviews,
<strong>the</strong> paper tries to analyze how BNSs are affecting <strong>the</strong> KM process <strong>of</strong> <strong>the</strong> companies included in <strong>the</strong>
sample. The main questions focus on: what kind <strong>of</strong> knowledge; who is <strong>the</strong> originator and <strong>the</strong> user <strong>of</strong>
<strong>the</strong> knowledge; how is knowledge acquired, selected, generated, assimilated and emitted. The overall
purpose <strong>of</strong> <strong>the</strong> research is to identify <strong>the</strong> main effects produced by <strong>the</strong> BNS on <strong>the</strong> whole KM process.
Our results show an important effect <strong>of</strong> BNS on <strong>the</strong> KM process. The main effects are acquisition <strong>of</strong>
knowledge o<strong>the</strong>rwise not available and higher effectiveness <strong>of</strong> <strong>the</strong> selection and generation process.
Ano<strong>the</strong>r important impact is on <strong>the</strong> assimilation and emission process. A general cost reduction can
be expected for <strong>the</strong>se activities and it is seen that a more formalized approach will develop on <strong>the</strong> use
<strong>of</strong> knowledge that is embedded in databases and o<strong>the</strong>r data structures.
Fur<strong>the</strong>r research might be pursued into <strong>the</strong> issue <strong>of</strong> continuous learning: What can <strong>the</strong> members <strong>of</strong>
<strong>the</strong> project (or any o<strong>the</strong>r buzz-strategist) glean from <strong>the</strong> experience gained in <strong>the</strong> network? Learning
organizations and knowledge management are to be seen as interlinked and <strong>the</strong>refore <strong>the</strong> next step
<strong>of</strong> our research would deal with <strong>the</strong> intellectual capital and <strong>the</strong> intangible assets which <strong>the</strong> member
companies build through <strong>the</strong> project. Also, based on our model, more quantitative and longitudinal
studies could be conducted so that more dynamic learning-based knowledge management practices
may be explored.
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Table 2: BNS and KM process. The impact considering communication aims
Know-ledge
Process
KM
Acquisition
KM
Selection
KM
Generation
KM
Assimilation
KM
Emission
What Who (From / For) How Effects
(whole process)
• Product
• Consumers, experts • Information posted in Higher visibility <strong>of</strong>fered by
reputational
and Companies
forum that indicates <strong>the</strong> power <strong>of</strong> <strong>the</strong> network.
capital
likes and dislikes and This generates:
• Product quality
expectations <strong>of</strong> • Cost reduction <strong>of</strong> <strong>the</strong>
ratings perceived
product features
process (vs. catalogs
vs expected
that are expensive and
difficult to update)
--- • --- ---
• Effectiveness
communication
<strong>of</strong>
--- • --- --process
(e.g. videos
and movies instead <strong>of</strong>
pictures, ….)
• Barrier reduction in
--- • --- ---
customer service
• New product
information
• Tips for a better
use <strong>of</strong> products
• Knowledge on
product
functionality and
features (e.g.
functionality <strong>of</strong>
appliances)
-
• Companies connecting
to dealers and
consumers
• Search engine tools for
different search keys
instead <strong>of</strong> corporate
catalogs
• Online ”Tips and
Tricks”, blogs and
o<strong>the</strong>r tools (e.g.
newsletters, …)
instead <strong>of</strong> flyers and
brochures
• e-learning tools
substituting frontal
teaching lessons, user
guide books.
Table 3: BNS and KM process. The impact considering Product/service development aims
Knowledge
Process
KM
Acquisition
KM
Selection
KM
Generation
KM
Assimilation
KM
Emission
What Who (From / For) How Effects
(whole process)
• New needs and • Consumers, experts / • Experts, like, e.g., Power <strong>of</strong> word <strong>of</strong> mouth
trends
companies
architects, create free that generates:
specific solutions for • New knowledge
consumers, building a acquired that is not
database <strong>of</strong> new available o<strong>the</strong>rwise
trends
• Cost reduction <strong>of</strong> <strong>the</strong>
• Information posted in process <strong>of</strong> knowledge
forum that indicates acquiring (e.g. different
likes and dislikes, new architects could
expectations on enroll <strong>the</strong> website
products features
searching visibility)
• Free targeted test
• Product feature, • Consumers, experts / • Frequently asked • Higher effectiveness
problem
consumer experts
questions
• Formalization <strong>of</strong> <strong>the</strong>
solutions,
• Companies / • Help forum
knowledge process in
common doubts consumer experts • Online contact form specific report and
and questions • Consumer experts / • Online experts
statistics that will be
companies
created from: experts
suggestions, forum,
• New prototypes • Companies / • Companies could comments on blogs
and ideas testing Consumers, experts create beta testers and
discussion groups,
specific idea contests
• New knowledge
acquired,
selected or
generated
• Companies /
Consumers, experts
• --- • --- • ---
615
• Storing <strong>of</strong> structured
information

Maurizio Massaro and Roland Bardy
Table 4: BNS and KM process. The impact considering Market development aims
Knowledge
Process
KM
Acquisition
KM
Selection
KM
Generation
KM
Assimilation
KM
Emission
References
What Who (From / For) How Effects
(whole process)
• New markets data • Consumers, experts / • Tools for geo- • Better understanding
from users wishes, companies
marketing
<strong>of</strong> <strong>the</strong> market which t
needs for creating
produces cost
better
reduction in <strong>the</strong>
understanding <strong>of</strong>
process <strong>of</strong> geo-
<strong>the</strong> market
•
marketing etc.
• --- • --- • ---
• --- • --- • ---
• --- • --- • ---
• --- • --- • ---
Avram, G (2006), ”At <strong>the</strong> Crossroads <strong>of</strong> Knowledge Management and Social S<strong>of</strong>tware”, The Electronic Journal <strong>of</strong>
Knowledge Management, Vol. 4, No. 1, pp 1-10.
Bijaya M. and Uday Bhaskar A., (2011) "Knowledge management process in two learning organisations", Journal
<strong>of</strong> Knowledge Management, Vol. 15, No. 2, pp.344-359
Birkinshaw, J. (2001), ”Making sense <strong>of</strong> knowledge management” Ivey Business Journal, Vol. 65, No.4, pp.32-36.
Bourhis A., Dubé L. and Jacob R. (2005), ”The Success <strong>of</strong> Virtual Communities <strong>of</strong> Practice: The Leadership
Factor”, The Electronic Journal <strong>of</strong> Knowledge Management, Vol. 3, No. 1, pp. 23-34
Calcantone, R.J., Cavusgil, V., and Zhao, Y. (2002), “Learning orientation, firm innovation capability, and firm
performance”, Industrial Marketing Management, Vol. 31, No. 6, pp.515–524.
Cuganesan, S. (2006), ”The role <strong>of</strong> functional specialists in shaping controls within supply networks”, Accounting,
Auditing & Accountability Journal, Vol. 19, No. 4, pp.465-492.
Davenport, T. and Prusak, L. (2000), The Pragmatics <strong>of</strong> Knowledge Management. Working Knowledge : How
Organisations Manage What They Know, Harvard Business School Press, Boston: MA.
Drew, S. (1999) ”Building Knowledge Management into Strategy: Making Sense <strong>of</strong> a New Perspective”, Long
Range Planning, Vol. 32, No. 1, pp. 130-136.
Faems, D., Janssens, M. and Van Looy, B. (2010), ”Managing <strong>the</strong> Cooperation–Competition Dilemma in R&D
Alliances”, Creativity and Innovation Management, Vol.19, No. 1, pp.3–22.
Faems, D., Van Looy, B. and Debackere, K. (2005) ”Inter-organizational Collaboration and Innovation: Toward a
Portfolio Approach”, Journal <strong>of</strong> Product Innovation Management, Vol. 22, No. 3, pp.238–50.
Frankfurter Allgemeine Zeitung (FAZ), March 08., 2011 ”Jeden Tag verbinden sich 10 000 Seiten mit Facebook”.
Holsapple, C.W. and Jones, K. (2004) ”Exploring Primary Activities <strong>of</strong> <strong>the</strong> Knowledge Chain”, Knowledge and
Process Management, Vol. 11, No. 3, pp 155-174.
Holsapple, C.W. and Jones, K. (2005) ”Exploring Secondary Activities <strong>of</strong> <strong>the</strong> Knowledge Chain”, Knowledge and
Process Management, Vol. 12, No. 1, pp 3-31.
Kirby J. and Marsden P. (2006), The viral, buzz and word <strong>of</strong> mouth revolution, Elsevier, Boston, MA.
Levy, M. (2009), ”Web 2.0 implications on knowledge management”, Journal <strong>of</strong> Knowledge Management, Vol.
13, No.1, pp.120-134.
Loermans, J. (2002), “Synergizing <strong>the</strong> learning organization and knowledge management”, Journal <strong>of</strong> Knowledge
Management, Vol.6, No.3, pp.285-294.
Mcevily, B. and Marcus, A. (2005), ”Embedded Ties and The Acquisition <strong>of</strong> Competitive Capabilities”, Strategic
Management Journal, Vol. 26, No. 11, pp.1033-1055.
Nold, H.A. (2011), ”Making Knowledge management work: tactical to practical”, Knowledge Management
Research, Vol.9, pp. 84-94, DOI 10.1057/kmrp.2010.27.
Powell, W.W. (1990), ”Nei<strong>the</strong>r market nor hierarchy: network forms <strong>of</strong> organization”, Research in Organisational
Behaviour, Vol. 12, pp.295-336.
Restler, S. G. and Woolis D. D. (2007) ”Actors and Factors: Virtual Communities for Social Innovation” The
Electronic Journal <strong>of</strong> Knowledge Management, Vol. 5, No. 1, pp 89-96.
Ring, P.S. and Van de Ven, A.H. (1992), ”Structuring cooperative relationships between organizations”, Strategic
Management Journal, Vol. 13, No. 7, pp.483-498.
Tapp, L. (1997), ”Emerging triumphant”, Business Quarterly, Vol. 63, No.3, pp. 1-27.
Teece, D.J. (2000) ”Strategies for Managing Knowledge Assets: <strong>the</strong> Role <strong>of</strong> Firm Structure and Industrial
Context”, Long Range Planning, Vol. 33, No. 1, pp. 35-54.
Yin, R.K. (1994) Case study research: Design and methods, 2nd edition, Sage, Thousand Oaks: CA.
Zaheer, A. and Bell, G.G. (2005), ”Benefiting From Network Position”, Strategic Management Journal, Vol. 26,
No. 9, pp. 809-825.
616

Enabling Knowledge Workers to Collaboratively Add
Structure to Enterprise Wikis
Florian Mat<strong>the</strong>s and Christian Neubert
Technische Universität München, Munich, Germany
mat<strong>the</strong>s@in.tum.de
neubert@in.tum.de
Abstract: Varied fields <strong>of</strong> application, fast access to <strong>of</strong>ten needed information, easy collaboration capabilities and
low maintenance costs make wikis very attractive for enterprises. For <strong>the</strong>se reasons in many companies wikis
have already been firmly established as tools for collaboration and knowledge exchange. Since most <strong>of</strong> <strong>the</strong>
content in wikis is completely unstructured (plain hypertext, links, etc.) it is difficult for programs to process <strong>the</strong>
information on <strong>the</strong> particular wiki pages. Therefore individual pages can only be found by means <strong>of</strong> a full-text
search engine, but searching for particular pages with specific attributes and attribute values is not possible. In
this paper we present Hybrid Wikis, a lightweight approach for structuring content and management <strong>of</strong>
information structures in enterprise wikis. Hybrid Wikis are realized based on <strong>the</strong> commercial Enterprise 2.0
s<strong>of</strong>tware Tricia and supported by our experiences made with classical wikis, semantic wikis and integrated
Enterprise 2.0 platforms used for knowledge and information management in enterprises. Inspired by <strong>the</strong>se web
technologies Hybrid Wikis extend <strong>the</strong> wiki provided by Tricia with a few mechanisms for classification, linking,
consistency checking, and visualization <strong>of</strong> wiki pages, which can be combined flexibly. We explain how <strong>the</strong>se
mechanisms facilitate <strong>the</strong> structuring <strong>of</strong> content in enterprise wikis and how both can benefit from it, knowledge
workers and enterprises. Hybrid Wikis create incentives for users to apply structure by giving suggestions <strong>of</strong>
frequently used structured elements, provide lightweight web-interfaces which enable users to manage <strong>the</strong>
structured elements directly as part <strong>of</strong> <strong>the</strong> page content, and help to avoid information redundancies by <strong>of</strong>fering
structured searches as well as autocompletion mechanisms for structured elements. Fur<strong>the</strong>rmore we show how
Hybrid Wikis enable knowledge workers to manage and integrate structured and unstructured information uniform
across <strong>the</strong> enterprise, which is one <strong>of</strong> <strong>the</strong> key challenges knowledge management systems are faced with.
Keywords: Enterprise 2.0, Social S<strong>of</strong>tware, Semantic Web, Wikis, Structuring <strong>of</strong> Content, Knowledge
Management Systems
1. Motivation and overview
1.1 Learning from <strong>the</strong> Wikipedia Encyclopedia
The idea <strong>of</strong> <strong>the</strong> Wikipedia (Leuf 2001) is quite simple: Collecting and providing knowledge as an
encyclopedia based on an open Internet platform freely available for all users in <strong>the</strong> web. Each
particular (wiki) page describes an article about a narrowly defined topic. A main characteristic <strong>of</strong> <strong>the</strong>
Wikipedia is that users directly can edit <strong>the</strong> pages and immediately contribute to <strong>the</strong> content. In
contrast to classical content management systems, in wikis changes made by <strong>the</strong> users are
immediately visible for o<strong>the</strong>r users without having <strong>the</strong> changes be revised. Thus, wikis make it
possible for information consumers, e.g., readers <strong>of</strong> a topic, to contribute <strong>the</strong>ir knowledge very quickly
(wikiwiki = quick, Hawaiian). Therefore <strong>the</strong> boundary between information consumer (reader) and
producer (editor) becomes increasingly blurred. In (Chang 2006) this phenomena is called Prosumer.
Fur<strong>the</strong>rmore wikis are easy to use, since <strong>the</strong> editors normally do not need system instructions for
applying <strong>the</strong>ir contributions. Even if only a small number <strong>of</strong> readers contribute to <strong>the</strong> community, i.e.,
write contributions or make corrections, a huge amount <strong>of</strong> knowledge and topics has been collected in
<strong>the</strong> last years.
Since different editors contribute to <strong>the</strong> same topic <strong>the</strong> content in wikis is conjointly created and
revised by all participants (Surowiecki 2004). With <strong>the</strong> increasing number <strong>of</strong> topics and complexity in
<strong>the</strong> Wikipedia project structures (categories, folder, formatting conventions, etc. …), processes
(discussion, consolidation, etc. …) and roles (user groups with different access rights) emerged over
time in order to ensure <strong>the</strong> quality <strong>of</strong> <strong>the</strong> encyclopedia. In <strong>the</strong> Wikipedia authors are motivated to
share <strong>the</strong>ir competences and concepts with o<strong>the</strong>r users in order to get feedback by means <strong>of</strong> page
view counts and backlinks to <strong>the</strong>ir contributions. Besides <strong>the</strong> authors, moderators and users
reorganizing <strong>the</strong> contents play ano<strong>the</strong>r very important role in <strong>the</strong> project, since <strong>the</strong>y are responsible
for <strong>the</strong> linguistical and structural consistency between different topics as well as for <strong>the</strong> categorization
and linking <strong>of</strong> <strong>the</strong> content. Those users are ra<strong>the</strong>r different to <strong>the</strong> page authors.
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The description <strong>of</strong> each topic is primary effected in a textual form and can be complemented by
images or embedded media objects (e.g., videos). Fur<strong>the</strong>rmore all topics can be set in relation to
each o<strong>the</strong>r by means <strong>of</strong> hyperlinks. Thus over time a very complex net <strong>of</strong> information emerges, which
is primary used by readers to navigate and search in this information space. Moreover links also
enable search engines or visualization tools to automatically explore and process <strong>the</strong> wiki contents.
Besides <strong>the</strong> unstructured mainly textual described topics <strong>the</strong> pages in <strong>the</strong> Wikipedia project are
complemented by semi-structured elements. For instance, typical attributes <strong>of</strong> a topic are shown as
key-value-pairs in a tabular view on <strong>the</strong> wiki page, e.g., for <strong>the</strong> topic Passau: Population = 50.627.
The Wikipedia provides <strong>the</strong> concept <strong>of</strong> templates (Haake 2005) in order to enable authors <strong>of</strong> wiki
pages to conserve <strong>of</strong>ten used structures and descriptions form <strong>the</strong> same class <strong>of</strong> elements (e.g., <strong>the</strong>
structure <strong>of</strong> a page describing a town) and to reuse this structure in a similar context (e.g., describing
ano<strong>the</strong>r town). One <strong>the</strong> one hand templates define <strong>the</strong> layout <strong>of</strong> a wiki page and on <strong>the</strong> o<strong>the</strong>r hand <strong>the</strong>
structure <strong>of</strong> <strong>the</strong> wiki page content. For example, a template representing a town topic could specify,
that <strong>the</strong> template always includes two sections describing <strong>the</strong> history and <strong>the</strong> climate as well as an
attribute representing <strong>the</strong> population, which is shown in a tabular view at <strong>the</strong> right margin. Wikipedia
templates are created independent <strong>of</strong> <strong>the</strong> particular wiki pages, i.e., authors manually derive <strong>the</strong>m
from patterns observed on <strong>the</strong> different pages. Similarly to <strong>the</strong> wiki page contents <strong>the</strong> templates are
conjointly developed by <strong>the</strong> template authors. Fur<strong>the</strong>rmore, templates are also freely available for all
authors. One <strong>the</strong> one hand templates facilitate <strong>the</strong> reuse <strong>of</strong> existing structures, one <strong>the</strong> o<strong>the</strong>r hand
<strong>the</strong>y also represent a jointly produced consensus <strong>of</strong> <strong>the</strong> authors for a particular concept, e.g., <strong>the</strong>
description <strong>of</strong> a town should always contains <strong>the</strong> attributes Population and Headquarters. Therefore,
by using templates an implicit conceptualization takes place, i.e., a typing <strong>of</strong> <strong>the</strong> particular wiki pages.
1.2 Enterprise wikis and social s<strong>of</strong>tware
Motivated by <strong>the</strong> success in <strong>the</strong> Internet wikis are firmly established as tools for communication and
collaboration tasks in enterprises today (Enterprise 2.0 Fallstudien - Aus Erfahrung lernen Webseite
2011). Since wikis are universally applicable in different enterprise contexts (e.g., <strong>the</strong> documentation
<strong>of</strong> complex business processes), <strong>the</strong>y are very attractive for enterprises. Besides wikis fur<strong>the</strong>r means
<strong>of</strong> communication frequently used in <strong>the</strong> Internet are applied in enterprise contexts. For instance,
blogs and microblogs are used to interchange enterprise-relevant news or expert knowledge is
exchanged across <strong>the</strong> departmental borders via social enterprise networks. The application <strong>of</strong> <strong>the</strong>ses
web techniques and technologies in companies is called Enterprise 2.0 (McAfee 2005). By means <strong>of</strong>
social interaction as well as incremental, decentralized extension and cross-linking emergent
structures for information, functions and processes arise, which are <strong>of</strong>ten superior to classical
standard solutions for information and knowledge management in enterprises.
A great number <strong>of</strong> s<strong>of</strong>tware vendors combine different individual Web 2.0 (O'Reilly 2008) solutions to
integrated Enterprise 2.0 platforms which are especially optimized for <strong>the</strong>ir application in <strong>the</strong>
enterprise (T. Büchner 2009). Besides <strong>the</strong> advantages <strong>of</strong> Web 2.0 applications (easy to use, support
<strong>of</strong> multimedia objects, management <strong>of</strong> revisions, discussions, scalability, open programming
interfaces) <strong>the</strong>se platforms provide extended search capabilities, tagging, access control and webinterfaces,
which are similar to desktop applications. Since users and groups in enterprises are more
coherent and <strong>the</strong> number <strong>of</strong> collaborating users in companies is much smaller than in <strong>the</strong> Internet,
fur<strong>the</strong>r additional (wiki-) services are supported, e.g., alerting (e-mail notification in case <strong>of</strong> changes in
<strong>the</strong> wiki page content), awareness (individual filters concerning all changes in <strong>the</strong> platform).A
prominent example for an integrated Enterprise 2.0 platform is <strong>the</strong> Micros<strong>of</strong>t Share Point 2010
(Micros<strong>of</strong>t SharePoint 2010 Webseite 2010).
2. Problem statement - semantic wikis: from unstructured to structured
content
Since contents in (enterprise) wikis mainly consist <strong>of</strong> unstructured elements (hypertext, links), for
programs is very difficult to automatically process <strong>the</strong> information on <strong>the</strong> wiki pages. Thus, it is only
possible to find individual topics by means <strong>of</strong> <strong>the</strong>ir textual descriptions, but a selective search for a
topic with a specific attribute value is not possible, e.g., it is not possible to define a search for towns
with a population greater than 500.000.
Semantic web technologies are targeting this problem by providing authors mechanisms to enrich <strong>the</strong>
page contents with additional information (metadata). These metadata are not visible for human users
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Florian Mat<strong>the</strong>s and Christian Neubert
and can be processed and understand and by programs (Semantic MediaWiki Webseite 2006).
Wikipedia templates are ra<strong>the</strong>r targeting <strong>the</strong> author needs (e.g., reuse <strong>of</strong> structures) and types as well
as information models emerge implicitly by applying <strong>the</strong>m to particular wiki pages. In contrast to
templates in semantic wikis <strong>the</strong> typing <strong>of</strong> wiki pages as well as <strong>the</strong> definition <strong>of</strong> attributes and relations
explicitly takes place. The definition <strong>of</strong> types, attributes and relations in semantic wikis is <strong>of</strong>ten based
on a formal language which is processable by o<strong>the</strong>r programs. In order to annotate wiki pages with
metadata <strong>the</strong> authors are ei<strong>the</strong>r supported by a tool or <strong>the</strong>y have to append <strong>the</strong> metadata manually by
means <strong>of</strong> a specific wiki language (Wiki markup 2011).
Thus, in semantic wikis <strong>the</strong> pages and its metadata correspond to an explicitly formulized information
model <strong>of</strong> <strong>the</strong> authorship and <strong>the</strong> schema designers 1 . In order to enable programs to define and
access <strong>the</strong>se metadata uniformly via <strong>the</strong> Internet (e.g., semantic search engines) a great number <strong>of</strong>
semantic wikis implement <strong>the</strong> standardized Resource Description Framework (Resource Description
Framework (RDF) 2011). By using a standardized language for <strong>the</strong> definition <strong>of</strong> resources by means
<strong>of</strong> RDF it is possible to define very specific queries to find relevant elements in <strong>the</strong> universe <strong>of</strong> <strong>the</strong>
available RDF sources in <strong>the</strong> Internet.
Semantic technologies and semantic wikis already exist for many years. However, <strong>the</strong>y are still less
common in <strong>the</strong> Internet as well as in enterprises. We assume that this is for <strong>the</strong> following reasons:
Authors have additional expenditure when annotating <strong>the</strong> contents with metadata
The tools and wiki languages provided for <strong>the</strong> management <strong>of</strong> <strong>the</strong> annotations are unsuitable for
<strong>the</strong> authors
Authors do not immediately benefit from <strong>the</strong> annotations (metadata)
The modeling elements provided by <strong>the</strong> RDF are difficult to understand for authors
The schema <strong>the</strong> metadata are based on is <strong>of</strong>ten created by an (ontology) engineer in a separate
process independent <strong>of</strong> <strong>the</strong> wiki page authors and contents. Therefore <strong>the</strong> underlying domain is
not understandable and transparent for <strong>the</strong> authorship
In <strong>the</strong> following we introduce a new approach, called Hybrid Wikis, facilitating <strong>the</strong> structuring <strong>of</strong>
content in an enterprise wiki. We introduce a few concepts and mechanisms and subsequently
explain how <strong>the</strong>y contribute to an improvement <strong>of</strong> <strong>the</strong> aforementioned reasons.
3. Hybrid wikis - enable knowledge workers to collaboratively add structure
to content
The concept <strong>of</strong> Hybrid Wikis is developed since 2009 and based on <strong>the</strong> experiences made with
classical wikis, semantic wikis and integrated Enterprise 2.0 platforms. Hybrid Wikis are realized
based on <strong>the</strong> commercial Java-based Enterprise 2.0 s<strong>of</strong>tware Tricia (Tricia - Hybrid Wikis for
Collaboration and Information Management Webseite 2010) providing a generic framework for <strong>the</strong>
development <strong>of</strong> Web 2.0 applications (Büchner 2010). Thereby <strong>the</strong> Tricia Enterprise 2.0 s<strong>of</strong>tware is
extended by a few mechanisms for classification, linking, consistency checking, and visualization <strong>of</strong>
wiki pages, which can be combined flexibly.
Similar to traditional enterprise wikis in Hybrid Wikis authors can easily and quickly modify
unstructured content, but at <strong>the</strong> same time <strong>the</strong>y are also empowered to structure <strong>the</strong> content <strong>of</strong> wiki
pages in <strong>the</strong> same manner (easy and quick). Hybrid Wiki provide a simple way for structuring contents
without <strong>the</strong> authors have to learn a specific (wiki) syntax for annotating metadata or to use a
specialized tool for <strong>the</strong> definition <strong>of</strong> a formal description language for resources (e.g., RDF).
Therefore, Hybrid Wikis lower <strong>the</strong> technical hurdle for authors in order to provide structured data.
Hybrid Wikis work as described in <strong>the</strong> following subsections.
3.1 Attributes and values
Besides <strong>the</strong> unstructured content for each wiki page a table consisting <strong>of</strong> simple key-value-pairs
(attribute-value-pairs) can be created (c.f., Figure 1). In <strong>the</strong> contrast to template-based wiki systems
(c.f., Section 1.1 Learning from <strong>the</strong> Wikipedia Encyclopedia) in which <strong>the</strong> attribute definitions are
statically bound to <strong>the</strong> corresponding template, in our approach <strong>the</strong> attributes (with corresponding
1 The schema designers provide <strong>the</strong> underlying ontology
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Florian Mat<strong>the</strong>s and Christian Neubert
values) can arbitrarily be assigned to <strong>the</strong> wiki pages, i.e., <strong>the</strong> attributes in Hybrid Wikis are
independent <strong>of</strong> <strong>the</strong> underlying templates. An attribute value can ei<strong>the</strong>r be a link or a plain string.
Figure 1: Wiki page with unstructured and structured (type tags, attributes, values) content.
3.2 Type Tags
Typing <strong>of</strong> wiki pages takes not place implicitly by <strong>the</strong> usage <strong>of</strong> templates, in Hybrid Wikis <strong>the</strong> type <strong>of</strong> a
page is determined by so called type tags. A type tag is a keyword (Golder 2005) with a special
semantic (<strong>the</strong> type <strong>of</strong> <strong>the</strong> page), which can be explicitly attached to <strong>the</strong> wiki page by a user. For each
wiki page an arbitrary set <strong>of</strong> type tags can be assigned, i.e., it is possible that a wiki page belongs to
more than one type. Besides <strong>the</strong> type tags, conventional tags (e.g., todo, interesting) can also be
assigned to <strong>the</strong> pages. In Figure 1 <strong>the</strong> conventional tags rss and feed as well as <strong>the</strong> type tags issue
and feature request are used. When an user clicks on a type tag, <strong>the</strong> table as illustrated in Figure 2 is
shown. This table lists all instances <strong>of</strong> <strong>the</strong> corresponding type ordered by <strong>the</strong> frequency <strong>of</strong> non-empty
attributes (i.e., non-empty attribute values). As shown in Figure 1 <strong>the</strong> (type) tags as well as attributes
and values are directly available as part <strong>of</strong> <strong>the</strong> page content. Therefore in our approach typing is
supported by editing <strong>the</strong> page content and not by manipulating <strong>the</strong> underlying markup.
3.3 Suggestions and Autocompletion
Based on a statistical analysis <strong>of</strong> frequently used combinations <strong>of</strong> tags, type tags and attributes in
Hybrid Wikis type and attribute suggestions are generated. In Figure 1 <strong>the</strong> attribute Installation is
suggested to be filled out by <strong>the</strong> author. Thereby one <strong>the</strong> one hand structuring <strong>of</strong> content is facilitated
on <strong>the</strong> o<strong>the</strong>r hand <strong>the</strong> authors are urged to reuse frequently used and well-established structures. In
our approach templates are not created in a separate process derived from <strong>the</strong> author’s observations
(pattern), but <strong>the</strong>y ra<strong>the</strong>r emerge from <strong>the</strong> management and structuring <strong>of</strong> <strong>the</strong> contents <strong>of</strong> particular
wiki pages, <strong>the</strong>ir tags and by means <strong>of</strong> an intelligent suggestion mechanism. Fur<strong>the</strong>rmore templates
can be derived by means <strong>of</strong> an analysis <strong>of</strong> <strong>the</strong> data set (wiki pages) in <strong>the</strong> wiki system. By providing
type and attribute suggestions <strong>the</strong> authors are urged but not forced to contribute structure. In our
approach <strong>the</strong> information model (domain model) emerges by <strong>the</strong> management <strong>of</strong> contents, i.e., <strong>the</strong>
structures and properties <strong>of</strong> <strong>the</strong> model arise spontaneously due to interplay <strong>of</strong> <strong>the</strong> particular wiki
pages. The advantage <strong>of</strong> this approach is that <strong>the</strong> underlying information model (with its concepts,
attributes, and relations) emerges from <strong>the</strong> collaboration in <strong>the</strong> wiki and <strong>the</strong>refore mainly represents
<strong>the</strong> consensus <strong>of</strong> <strong>the</strong> entire authorship.
Fur<strong>the</strong>rmore <strong>the</strong> editing process <strong>of</strong> type tags, attributes and values is assisted by autocompletion
results (c.f., Figure 3). Thereby <strong>the</strong> reuse <strong>of</strong> frequently used structure is facilitated and <strong>the</strong> terminology
<strong>of</strong> <strong>the</strong> wiki community converges toward a certain set <strong>of</strong> terms and elements, <strong>the</strong> domain language.
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Florian Mat<strong>the</strong>s and Christian Neubert
Figure 2: Tabular overview <strong>of</strong> all wiki pages with type tag feature request.
Figure 3: Autocompletion with highlighting for values <strong>of</strong> <strong>the</strong> attribute Installation.
3.4 Constraints
In addition to <strong>the</strong> possibility to directly and dynamically derive <strong>the</strong> information model from <strong>the</strong> wiki
pages (bottom-up modeling) in Hybrid Wikis <strong>the</strong> state <strong>of</strong> a model can be defined in a more specific
way. It is possible to bind attributes to a conceptual definition, which is called type tag definition. By
doing so, integrity constraints can be specified for attributes, e.g., <strong>the</strong> type and <strong>the</strong> range <strong>of</strong> <strong>the</strong> values
<strong>of</strong> an attribute. For instance, in Figure 3 <strong>the</strong> range <strong>of</strong> attribute values is specified as {open, rejected
…}. Fur<strong>the</strong>rmore concepts and attributes explicitly can be specified, i.e., without <strong>the</strong> existence and
analysis <strong>of</strong> a certain set <strong>of</strong> wiki pages.
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Type tag definitions and attribute definitions enable users to create information models (concepts,
attributes, and relations) in a more conventional way, top-down.
Type tag definitions are not separated from <strong>the</strong> data, <strong>the</strong>y immediately have an impact on <strong>the</strong>
particular wiki pages. For example, if an attribute definition status for type tag definition bug is
specified, on all pages having <strong>the</strong> type tag bug a corresponding attribute suggestion is shown. Thus,
<strong>the</strong> decisions made by a schema designer are immediately visible for <strong>the</strong> authorship.
Fur<strong>the</strong>rmore <strong>the</strong> constraints influence <strong>the</strong> autocompletion results for attribute values. For instance, if
<strong>the</strong> range <strong>of</strong> an attribute is specified as {open, rejected …}, <strong>the</strong>se values are preferred in algorithm
which is calculating <strong>the</strong> autocompletion results. Since users are never forced to enter a specific value
constraints in Hybrid Wikis are called s<strong>of</strong>t constraints.
Beside currently assigned attribute definitions <strong>the</strong> dialog for <strong>the</strong> management <strong>of</strong> a type tag definition
also shows attributes which are used in combination with <strong>the</strong> corresponding type tag (c.f., Figure 2).
Additionally for all attributes it is shown how frequently <strong>the</strong>y are used. In this way even <strong>the</strong> schema
designer receives attribute suggestions and she could also identify candidates being adopted in
schema (type tag definition). Fur<strong>the</strong>rmore she is able to reflect <strong>the</strong> quality <strong>of</strong> her decisions with regard
to <strong>the</strong> schema by checking <strong>the</strong> usage <strong>of</strong> currently bound attributes.
By providing attribute suggestions for both, particular wiki pages and <strong>the</strong> schema management dialog,
model designer (ontology engineers) and domain experts (wiki page authors) certainly enter into
dialogue. Hybrid Wikis support building <strong>of</strong> consensus in both directions, from <strong>the</strong> data to an adapted
type definition and from a given type definition to a revision <strong>of</strong> incompatible or missing values.
Figure 4: Definition for <strong>the</strong> attribute status with enumeration values {open, rejected, …} and
multiplicity Exactly one value.
3.5 Conceptual Modeling with Hybrid Wikis
Summarizing, Hybrid Wikis provide two methods supporting conceptual modeling:
bottom-up, i.e., by <strong>the</strong> management and analysis <strong>of</strong> <strong>the</strong> wiki pages with its (type) tags and
attributes
top-down, i.e., by means <strong>of</strong> <strong>the</strong> definition <strong>of</strong> types, attributes, and consistency rules for attributes
and relations
Hybrid Wikis provide <strong>the</strong> following modeling concepts:
a (hybrid) wiki conforms to a model, defines a namespace and represents a container for <strong>the</strong>
particular model elements (wiki pages)
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Florian Mat<strong>the</strong>s and Christian Neubert
a wiki page represents a particular model element with URL (Uniform Resource Locator) and
content (hypertext, file attachments, attributes)
a hyperlink conforms to a model reference
an attribute on a wiki page consists <strong>of</strong> a label (<strong>the</strong> name <strong>of</strong> <strong>the</strong> attribute) and an ordered list <strong>of</strong>
attribute values
an attribute value is ei<strong>the</strong>r a link or a string value
a tag classifies a wiki page, type tags (0..n) specify <strong>the</strong> type <strong>of</strong> a page, i.e., <strong>the</strong> type <strong>of</strong> <strong>the</strong> model
element
an attribute definition refers to a type tag and consists <strong>of</strong> a label and a definition <strong>of</strong> consistency
rules for <strong>the</strong> number <strong>of</strong> admissible attribute values (e.g., at least one), value ranges (e.g.,
numbers, boolean, date, …) and valid hyperlinks (e.g., only links to wiki pages <strong>of</strong> a specific type)
it is possible to search for incompatibilities between attribute values and attribute definitions.
Fur<strong>the</strong>rmore <strong>the</strong>se incompatibilities are marked as invalid when displaying <strong>the</strong> wiki pages.
4. Communication and collaboration with hybrid wikis
In addition to <strong>the</strong> modeling capabilities as introduced in Section “3.5 Conceptual Modeling with Hybrid
Wikis“ Hybrid Wikis benefit from <strong>the</strong> services provided by <strong>the</strong> integrated Enterprise 2.0 platform Tricia.
In <strong>the</strong> following we explain <strong>the</strong> interplay between <strong>the</strong>se built-in services and <strong>the</strong> structuring services
provided by <strong>the</strong> Hybrid Wiki extension.
In Tricia it is possible to specify extended views on <strong>the</strong> underlying data model by means <strong>of</strong> integrated
search capabilities. Thereby search results can be sorted, filtered and saved. Saved queries can be
embedded in <strong>the</strong> wiki page content in order to show current search hits when displaying <strong>the</strong> page. In
Hybrid Wikis users are able to define custom queries with regards to structured content, e.g., a
specific attribute value.
In Tricia all changes to content objects (e.g., wiki pages) are recorded. Thereby it is traced which user
has changed which content object at which time and for what reason. In Hybrid Wikis changes to
structured elements are also traced. Thus, <strong>the</strong> corresponding editor (author) can directly be contacted
in case <strong>of</strong> uncertainty regarding a particular change <strong>of</strong> <strong>the</strong> structure.
Fur<strong>the</strong>rmore in Tricia users can register to receive a notification (e.g., via email) in case <strong>of</strong> changes to
a certain element (e.g., wiki page). Now, in Hybrid Wikis it is possible that users can register for
changes on structured elements (e.g., changes to a specific attribute).
Tricia provides a flexible access control concept. Users can define read and write access for all
content objects (e.g., wiki pages). In Hybrid Wiki this access control mechanisms also apply for
attributes.
4.1 Closing <strong>the</strong> gap between structured data and unstructured content within
enterprise knowledge management
Beside integrated Enterprise 2.0 platforms in companies a wide variety <strong>of</strong> o<strong>the</strong>r application is used
also containing business critical data and information. These data and information are <strong>of</strong>ten scattered
across <strong>the</strong> individual application repositories respectively data storages and are available in a
structured or unstructured form (c.f., Figure 5). Examples <strong>of</strong> applications providing highly structured
data are Enterprise Resource Planning (ERP), Business Intelligence (BI), and Product Lifecycle
Management (PLM) systems. Unstructured information is given by applications providing objects like
emails, images, and textual documents, e.g., MS Outlook, MS Word.
Since structured data as well as unstructured content may contain business critical information both
kinds must be accessible to knowledge management solutions. In particular it is one <strong>of</strong> <strong>the</strong> most
important technological challenges <strong>of</strong> knowledge management systems to create connections
between and contextualization on <strong>the</strong>se information and data in order to provide a holistic view on
business critical knowledge.
Hybrid Wikis contribute to close <strong>the</strong> gap between structured and unstructured information within
knowledge management solutions by providing a single user interface to create, access, search, and
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Florian Mat<strong>the</strong>s and Christian Neubert
share business critical knowledge for <strong>the</strong>se both information types. Fur<strong>the</strong>rmore linking within
unstructured content as well as adding tags and comments allows a smooth transformation <strong>of</strong> raw
unstructured data into rich understandable knowledge.
With Hybrid Wikis it is possible to describe all types <strong>of</strong> structured data by using attributes and type
tags respectively attribute definitions and type tag definitions, relations by means <strong>of</strong> hyperlinks
respectively (link) constraints.
Unstructured content can also be described and connected with attributes and types provided by
highly structured data.
The integrated search capabilities in Hybrid Wikis enable users to create custom and uniform views
on structured and unstructured information according <strong>the</strong>ir needs and expectations by providing fulltext
search mechanisms as well as features to search for tags, attributes, and o<strong>the</strong>r relevant facets,
which can be combined flexibly. Fur<strong>the</strong>rmore users can easily generate hierarchical menus, tables,
lists, and diagrams for information access supporting <strong>the</strong> requirements and <strong>the</strong> context <strong>of</strong> <strong>the</strong>ir daily
business.
Since today’s business environments are characterized by dynamic and rapid changes, it is <strong>of</strong>ten
required to adapt <strong>the</strong> models underlying <strong>the</strong> knowledge management solutions, e.g., by providing
additional attributes or concepts. The modeling concepts <strong>of</strong> Hybrid Wikis support this requirement by
providing flexible modeling capabilities (type tags, attributes, type tag definitions, attribute definitions)
and <strong>the</strong>reby allow knowledge workers to adapt <strong>the</strong> models <strong>of</strong> <strong>the</strong>se solutions <strong>the</strong>mselves according to
<strong>the</strong> changing business demands without any need for expensive IT projects.
Figure 5: Closing <strong>the</strong> gap between structured data and unstructured content
5. Conclusion
In this paper, we first gave an overview <strong>of</strong> <strong>the</strong> ideas given by <strong>the</strong> Wikipedia project and Enterprise 2.0
applications. Than we illustrated <strong>the</strong> idea <strong>of</strong> Hybrid Wikis and <strong>the</strong> particular elements facilitating <strong>the</strong>
structuring <strong>of</strong> content, namely type tags, attributes, type tag definitions, attribute definitions and (s<strong>of</strong>t)
constraints. We illustrated <strong>the</strong> application <strong>of</strong> Hybrid Wikis by providing screenshots taken from a
s<strong>of</strong>tware engineering company in which Hybrid Wikis are used as an issue tracker. Fur<strong>the</strong>rmore we
sketched how conceptual modeling is enabled by Hybrid Wikis from two perspectives, top-down and
bottom-up, and described how <strong>the</strong>se structuring capabilities are integrated in <strong>the</strong> built-in services
provided by <strong>the</strong> commercial Enterprise 2.0 platform Tricia.
Finally we explained how Hybrid Wikis provide a holistic view on information within <strong>the</strong> enterprise
scattered across different application repositories by integrating information from both kind <strong>of</strong> data
sources structured and unstructured. Thereby Hybrid Wikis contribute to bridge <strong>the</strong> gap between
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structured and unstructured information which is one <strong>of</strong> <strong>the</strong> key challenges in knowledge
management endeavors.
References
Büchner, Thomas and Mat<strong>the</strong>s, Florian and Neubert, Christian. "Data Model Driven Implementation <strong>of</strong> Web
Cooperation Systems with Tricia." Objects and Databases, Lecture Notes in Computer Science, 2010: 70-
84.
Chang, S. "Are <strong>the</strong>y willing to contribute? Prosumer characteristics among <strong>the</strong> Australian youth." Digital Natives
in Australia and Korea. <strong>Conference</strong> at <strong>the</strong> University <strong>of</strong> Melbourne, 2006.
Enterprise 2.0 Fallstudien - Aus Erfahrung lernen Webseite. 2011. http://www.e20cases.org/fallstudien (accessed
5 th <strong>of</strong> January 2011).
Golder, S. and Huberman, B. A. "The Structure <strong>of</strong> Collaborative Tagging Systems." Information Dynamics Lab:
HP Labs. Palo Alto, USA: http://arxiv.org/abs/cs.DL/0508082, 2005.
Haake, Anja and Lukosch, Stephan and Schümmer, Till. "Wiki-templates: adding structure support to wikis on
demand." WikiSym '05: <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 2005 international symposium on Wikis, 2005: 41--51.
Leuf, B Cunningham and Leuf Cunningham W and. The Wiki Way. Quick Collaboration on <strong>the</strong> Web. Reading,
MA, Addison-Wesley, 2001.
McAfee, A. P. "Enterprise 2.0: The Dawn <strong>of</strong> Emergent Collaboration." In MIT Sloan Management Review, 47(3),
pp. 21-28. 2005.
Micros<strong>of</strong>t SharePoint 2010 Webseite. 2010. http://sharepoint.micros<strong>of</strong>t.com (accessed 4 th <strong>of</strong> January 2011).
O'Reilly, Tim. What Is Web 2.0: Design Patterns and Business Models for <strong>the</strong> Next Generation <strong>of</strong> S<strong>of</strong>tware.
O'Reilly Media, 2008.
Resource Description Framework (RDF). 2011. http://www.w3.org/RDF/ (accessed 5 th <strong>of</strong> May 2011).
Semantic MediaWiki Website. 2006. http://semantic-mediawiki.org/wiki/Semantic_MediaWiki (accessed 4 th <strong>of</strong>
January 2011).
Surowiecki, J. "The Wisdom <strong>of</strong> Crowds." In Random House. New York, USA, 2004.
T. Büchner, F. Mat<strong>the</strong>s, and C. Neubert. "A concept and service based analysis <strong>of</strong> commercial and open."
International <strong>Conference</strong> on Knowledge Management and Information Sharing. Madeira, Portugal, 2009.
37–45.
Tricia - Hybrid Wikis for Collaboration and Information Management Website. 2010. http://www.infoasset.de
(accessed 3 rd <strong>of</strong> January 2011).
Wiki markup. 2011. http://en.wikipedia.org/wiki/Help:Wiki_markup (accessed 5 th <strong>of</strong> June 2011).
625

Germany – Towards a Knowledge-Based Economy
Kai Mertins, Sven Wuscher and Markus Will
Fraunh<strong>of</strong>er IPK, Berlin, Germany
Kai.mertins@ipk.fraunh<strong>of</strong>er.de
sven.wuscher@ipk.fraunh<strong>of</strong>er.de
markus.will@ipk.fraunh<strong>of</strong>er.de
Abstract: Due to changes in <strong>the</strong> value creation processes <strong>the</strong> management <strong>of</strong> Intellectual Capital (IC) is <strong>of</strong>
increasing importance for <strong>the</strong> industrialised nations. Previous research has indicated that material resources are
less important than intangible assets. We confirm that this is true and that it applies in particular to small and
medium-sized enterprises (SME). SMEs are <strong>the</strong> backbone <strong>of</strong> <strong>the</strong> German economy and <strong>the</strong>refore <strong>the</strong> target
group for IC management. Unlike large scale enterprises, SMEs are incapable <strong>of</strong> facing <strong>the</strong> increasing cost
pressure by volume growth or relocation due to <strong>the</strong>ir specific characteristics. Against this background, <strong>the</strong>
purpose <strong>of</strong> this paper is to assess and describe Germany as a knowledge site. The paper shows <strong>the</strong> stage <strong>of</strong>
development <strong>of</strong> German enterprises on <strong>the</strong> way to a knowledge-based economy from a practitioner’s
perspective. The results <strong>of</strong> <strong>the</strong> analysis are used to derive recommendations for <strong>the</strong> management <strong>of</strong> Intellectual
Capital. For this purpose, <strong>the</strong> research group “Arbeitskreis Wissensbilanz” has undertaken an empirical study.
The data for <strong>the</strong> survey was collected in <strong>the</strong> time period <strong>of</strong> July 2009 and August 2010 under <strong>the</strong> supervision <strong>of</strong>
<strong>the</strong> Fraunh<strong>of</strong>er Institute for Production Systems and Design Technology (IPK) within <strong>the</strong> initiative “Wissensbilanz-
Made in Germany”. The initiative is funded by The Federal Ministry <strong>of</strong> Economics and Technology <strong>of</strong> germany
(BMWi). In <strong>the</strong> course <strong>of</strong> <strong>the</strong> empirical study more than 2300 managers and decision makers <strong>of</strong> German
enterprises <strong>of</strong> various sizes in various branches were questioned. They were asked to which degree different
factors <strong>of</strong> <strong>the</strong> Intellectual Capital influence <strong>the</strong>ir business success and how well <strong>the</strong>se different factors are
developed in <strong>the</strong>ir enterprise from <strong>the</strong>ir point <strong>of</strong> view. The most important factors <strong>of</strong> <strong>the</strong> IC <strong>of</strong> German enterprises
were identified for different sub-groups <strong>of</strong> <strong>the</strong> sample, e.g. enterprises <strong>of</strong> different size, from different sectors etc.
The sub-groups were compared and significant differences were analysed.
Keywords: Intellectual capital, intellectual capital statement, ic management, intangible assets, knowledge
society, sme
1. Introduction
Due to changes in <strong>the</strong> value creation processes <strong>the</strong> management <strong>of</strong> Intellectual Capital is <strong>of</strong>
increasing importance for <strong>the</strong> industrialised nations. The transition from <strong>the</strong> industrialised economy to
<strong>the</strong> knowledge-based economy manifests itself in two general trends: On <strong>the</strong> one hand, <strong>the</strong>
automation <strong>of</strong> classic industrial processes and/or <strong>the</strong>ir relocation to low-income countries. On <strong>the</strong>
o<strong>the</strong>r hand, changes <strong>of</strong> services and products with <strong>the</strong> aim to outweigh <strong>the</strong> advantages <strong>of</strong> German
locations with expensive though highly qualified personnel. This development particularly applies to
small and medium-sized enterprises which, unlike large-scale enterprises, are incapable <strong>of</strong> facing
increasing cost pressure by volume growth and <strong>the</strong>reby economies <strong>of</strong> scale or relocation to more
favourable locations.
Obvious potential for improvement like automation and rationalisation have been exhausted by most
enterprises. Additional improvement can in most cases only be achieved through <strong>the</strong> management <strong>of</strong>
intangible success factors such as employees, team work, knowledge structures as well as
relationships to customers, suppliers and cooperation partners. As a result, <strong>the</strong> demand for
instruments to manage intangible assets is strongly increasing (Horváth 2004). In this context <strong>the</strong> IC
depicts all intangible factors that are essential for business operations and <strong>the</strong>ir network.
The purpose <strong>of</strong> this paper is to asses and describe Germany as a knowledge site and <strong>the</strong>
development <strong>of</strong> German enterprises on <strong>the</strong> way to <strong>the</strong> knowledge-based economy from a
practitioner’s perspective. The results presented here contribute to <strong>the</strong> scientific debate by delivering
answers to <strong>the</strong> following questions:
Where do German enterprises stand on <strong>the</strong> way to <strong>the</strong> knowledge based economy?
Which intangible resources are crucial to knowledge-based corporate management?
How do small and large enterprises, enterprises operated by owners and operated by managers,
service and production enterprises differ in terms <strong>of</strong> <strong>the</strong>ir strategies to streng<strong>the</strong>n knowledgebased
competitive advantages?
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Kai Mertins, Sven Wuscher and Markus Will
In order to answer <strong>the</strong>se questions <strong>the</strong> research group “Arbeitskreis Wissensbilanz” has undertaken
an empirical study in <strong>the</strong> period from July 2009 to August 2010 under <strong>the</strong> supervision <strong>of</strong> <strong>the</strong>
Fraunh<strong>of</strong>er Institute for Production Systems and Design Technology. As part <strong>of</strong> <strong>the</strong> initiative
“Wissensbilanz- Made in Germany” <strong>the</strong> study was funded by <strong>the</strong> Federal Ministry <strong>of</strong> Economics and
Technology (BMWi). In <strong>the</strong> course <strong>of</strong> <strong>the</strong> study more than 900 managers and decision makers <strong>of</strong>
German enterprises <strong>of</strong> various sizes in various branches were questioned in an online survey. The
participants were asked to which degree different factors <strong>of</strong> <strong>the</strong> Intellectual Capital (IC) influence <strong>the</strong>ir
business success and how well <strong>the</strong>se different factors are developed in <strong>the</strong>ir enterprise from <strong>the</strong>ir
point <strong>of</strong> view.
2. Summary
2.1 Where do German enterprises stand on <strong>the</strong> way to <strong>the</strong> knowledge based
economy?
Already today, <strong>the</strong> intangible resources have a significant higher impact on <strong>the</strong> business success than
material resources like machinery, plants and buildings. Within <strong>the</strong> single types <strong>of</strong> <strong>the</strong> IC <strong>the</strong> Human
Capital (HC) is <strong>the</strong> most important type for <strong>the</strong> enterprises surveyed. This circumstance is even more
apparent for <strong>the</strong> service industry than for <strong>the</strong> producing industry. The group comparison shows that in
particular <strong>the</strong> HC-factors leadership abilities and social competence are not only more important to
service enterprises, but are even better developed. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> traditional factors, i.e.
material and financial factors, are more important for <strong>the</strong> business success <strong>of</strong> producing enterprises
and are also better developed here in comparison to <strong>the</strong> service enterprises. This result demonstrates
<strong>the</strong> increasing importance <strong>of</strong> <strong>the</strong> service sectors as a key pillar <strong>of</strong> <strong>the</strong> knowledge society in Germany.
Fur<strong>the</strong>rmore results <strong>of</strong> <strong>the</strong> study show that <strong>the</strong> establishment <strong>of</strong> new enterprises takes place to a large
extent in <strong>the</strong> service sector. In conclusion, <strong>the</strong> importance <strong>of</strong> <strong>the</strong> IC will increase in <strong>the</strong> case <strong>of</strong> very
small and young service enterprises in future.
2.2 Which intangible resources are central to knowledge based corporate
management?
The most important success factors <strong>of</strong> knowledge-based corporate management with one exception
stem all from <strong>the</strong> HC: pr<strong>of</strong>essional competence, social competence and leadership ability. Only <strong>the</strong>
customer relationships as <strong>the</strong> most important factor <strong>of</strong> <strong>the</strong> Relational Capital (RC) shows a similar
importance.Also <strong>the</strong> current characteristics <strong>of</strong> <strong>the</strong> success factors, i.e. how well a factor is rated,
comply with this order.
How do small and large enterprises, enterprises operated by owners and managers,
service and production enterprises differ in terms <strong>of</strong> <strong>the</strong>ir strategies to streng<strong>the</strong>n
knowledge-based competitive advantages?
The group comparison provides fur<strong>the</strong>r interesting differences: While service enterprises count on cooperation
partners for external relationships and focus on public relationships producing enterprises
are more reliant on supplier relationships and manage <strong>the</strong>se relationships better than <strong>the</strong> service
enterprises surveyed. While small enterprises strongly depend on single employees – <strong>the</strong>ir HC– <strong>the</strong>
importance <strong>of</strong> <strong>the</strong> internal structures <strong>of</strong> an enterprise and external relationships become more
important with corporate growth.
Young enterprises established after 2000 are, in comparison to older enterprises, better positioned
with regard to HC and Structural Capital (SC). In contrast, <strong>the</strong> importance <strong>of</strong> material resources is
significantly higher for enterprises established before 1970 than in enterprises established from 1971-
1999 and younger enterprises. Owner-operated enterprises rate <strong>the</strong> factors <strong>of</strong> <strong>the</strong> HC and <strong>the</strong> most
factors <strong>of</strong> <strong>the</strong> SC significantly better than enterprises lead by managers. With regard to <strong>the</strong> increasing
importance <strong>of</strong> <strong>the</strong> HC this could provide an advantage for <strong>the</strong> owner-operated enterprises for <strong>the</strong>
future.
3. Conclusion
German enterprises follow <strong>the</strong> way to <strong>the</strong> knowledge-based economy and have <strong>the</strong>reby
acknowledged that <strong>the</strong> importance <strong>of</strong> <strong>the</strong> IC is higher than <strong>the</strong> importance <strong>of</strong> material resources.
Especially <strong>the</strong> HC is <strong>the</strong> most important resource for German enterprises. Particular focus is put on
motivated and competent employees as well as leaders by all enterprises no matter <strong>of</strong> <strong>the</strong>ir size, age
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Kai Mertins, Sven Wuscher and Markus Will
or sector. Fur<strong>the</strong>rmore it is valid that <strong>the</strong> single employee becomes more important as <strong>the</strong> size <strong>of</strong> a
company decreases. Thus particularly very small enterprises depend on <strong>the</strong>ir employees to keep
pace with competition.
When comparing <strong>the</strong> importance <strong>of</strong> qualified employees in German enterprises with <strong>the</strong> demographic
developments and <strong>the</strong> on-going shortage <strong>of</strong> qualified employees in Germany, it can be assumed that
that <strong>the</strong> competition for talents will continue to increase. In this context especially SMEs have to
develop strategies facing this challenge.
In this competition especially owner-operated enterprises seem to have advantages in comparison to
enterprises lead by managers. Besides employees, good customer relationships are also seen as a
very important factor on <strong>the</strong> way to <strong>the</strong> knowledge-based economy. This shows that German
enterprises are trying to dissolve <strong>the</strong> image <strong>of</strong> <strong>the</strong> service desert Germany. By comparison <strong>the</strong> survey
results imply that <strong>the</strong>re is less focus on innovative processes, products and services.
The high importance <strong>of</strong> <strong>the</strong> IC for German enterprises manifests itself in <strong>the</strong> high rate <strong>of</strong> dissemination
<strong>of</strong> <strong>the</strong> management instrument “Wissensbilanz – Made in Germany”. 9.9% <strong>of</strong> <strong>the</strong> enterprises have
implemented one ICS and 3.5% have implemented more than one ICS.
4. Survey design
The survey design is based on <strong>the</strong> management method “Wissensbilanz – Made in Germany”. A
“Wissensbilanz” (Intellectual Capital Statement) records and assesses all success factors <strong>of</strong> <strong>the</strong> IC <strong>of</strong>
an enterprise and analyses <strong>the</strong>ir impact on business success (BMWi 2009). With <strong>the</strong> help <strong>of</strong> <strong>the</strong> ICS
toolbox more than 300 ICSs have been created by German enterprises until <strong>the</strong> end <strong>of</strong> 2009. Besides
a large number <strong>of</strong> small enterprises, concerns and larger SMEs trust in <strong>the</strong> instrument, too.
Meanwhile, <strong>the</strong> differentiation between three different kinds <strong>of</strong> IC has become a standard:
The Human Capital (HC) comprises all characteristics and capabilities which employees contribute to
an enterprise. The Structural Capital (SC) comprises all structures which are applied by <strong>the</strong>
employees to conduct business. The Relational Capital (RC) consists <strong>of</strong> all relations to external
groups and persons used for conducting business.
On <strong>the</strong> basis <strong>of</strong> 42 generated ICSs <strong>the</strong> transferability <strong>of</strong> <strong>the</strong> single factors <strong>of</strong> <strong>the</strong> IC was analysed. The
result is a set <strong>of</strong> 15 factors which capture 80% <strong>of</strong> <strong>the</strong> enterprise-specific IC (Mertins 2007). This
empirical tested set <strong>of</strong> factors provides <strong>the</strong> basis for <strong>the</strong> survey presented here.
4.1 Data collection
The results <strong>of</strong> this survey are based on <strong>the</strong> “Wissensbilanz-Schnelltest” (IC Quick Check). The
“Wissensbilanz-Schnelltest” provides enterprises with a quick overview <strong>of</strong> <strong>the</strong> status quo <strong>of</strong> <strong>the</strong>ir IC as
well as first recommendations for action to manage <strong>the</strong>ir s<strong>of</strong>t factors. In <strong>the</strong> time period <strong>of</strong> July 2009
and December 2010 2300 data sets were captured through <strong>the</strong> websites www.wissensbilanzschnelltest.de,
www.wiwo.de and www.impulse.de. The participating enterprises were asked how
important certain factors are with regard to <strong>the</strong>ir business success (impact) on a scale from 0-10.
Fur<strong>the</strong>rmore <strong>the</strong>y were asked to estimate how well <strong>the</strong>se factors are developed in <strong>the</strong>ir enterprise at
present (rating). After <strong>the</strong> revision <strong>of</strong> all data sets 947 questionnaires could be used for evaluation.
4.2 Statistical evaluation
Methodological basis <strong>of</strong> <strong>the</strong> statistical evaluation is <strong>the</strong> analysis <strong>of</strong> variance (ANOVA). It was tested
whe<strong>the</strong>r <strong>the</strong> arithmetic means <strong>of</strong> <strong>the</strong> sub-groups are equal. In case <strong>of</strong> statistical significance <strong>of</strong> <strong>the</strong>
result (alpha level 5%) it is assumed that <strong>the</strong> arithmetic averages are different. All groups and subgroups
were tested on <strong>the</strong> assumptions <strong>of</strong> homogeneity <strong>of</strong> variance and normal distribution.
Results <strong>of</strong> <strong>the</strong> f-test which where significant at <strong>the</strong> alpha level <strong>of</strong> 5% were matched to <strong>the</strong>
corresponding sub-groups by using conservative post-hoc tests (Scheffè-test and Tamhane-test). If
<strong>the</strong> assumption <strong>of</strong> normality was violated (i.e. rejection <strong>of</strong> Shapiro-Wilk-test) it was controlled for <strong>the</strong>
third and fourth moment <strong>of</strong> <strong>the</strong> distribution (Cou and Bentler 1995). In case <strong>the</strong> assumption <strong>of</strong>
normality was rejected again <strong>the</strong> data sets were removed. Non-parametric tests were not used.
Homogeneity <strong>of</strong> variance was tested by Levené test. If homogeneity was rejected Tamhane's test
(post-hoc-test) was applied.
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4.3 Sample: Structure and Strategic Alignment <strong>of</strong> <strong>the</strong> Participating Enterprises
The surveys sample comprises 947 enterprises. 828 <strong>of</strong> <strong>the</strong>se enterprises answered <strong>the</strong> questions
regarding <strong>the</strong> sector <strong>the</strong>y are active in. In this context 287 enterprises (34.7%) stated that <strong>the</strong>y belong
to <strong>the</strong> production sector and 541 (65.3%) stated to be part <strong>of</strong> <strong>the</strong> service sector (see figure 1).
Figure 1: Sample according to sectors
902 enterprises answered <strong>the</strong> question regarding <strong>the</strong> number <strong>of</strong> employees (see figure 2) and 893 <strong>the</strong>
question regarding <strong>the</strong> year in which <strong>the</strong> enterprise was established (see figure 3).
Figure 2: Sample according to <strong>the</strong> number <strong>of</strong> employees
Figure 3: Sample according to <strong>the</strong> year <strong>of</strong> establishment
Of <strong>the</strong> 947 enterprises surveyed 795 enterprises stated <strong>the</strong>ir annual turnover (see figure 4). The
majority <strong>of</strong> <strong>the</strong> companies participating in <strong>the</strong> survey (38.9%) quoted an annual turnover <strong>of</strong> up to 2
Mio. €. About one fifth <strong>of</strong> <strong>the</strong> enterprises stated that <strong>the</strong>y have an annual turnover <strong>of</strong> more than 50
Mio. € (20.4%).
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Kai Mertins, Sven Wuscher and Markus Will
Figure 4: Sample according to annual turnover
856 enterprises answered <strong>the</strong> question whe<strong>the</strong>r <strong>the</strong>y are operated by <strong>the</strong> owners <strong>of</strong> <strong>the</strong> enterprise or
a management board. 68% <strong>of</strong> <strong>the</strong>se enterprises said that <strong>the</strong>y are operated by <strong>the</strong>ir owners while <strong>the</strong>
o<strong>the</strong>r 32% stated to be operated by a management board (see figure 5).
Figure 5: Sample according to type <strong>of</strong> operation
The question regarding <strong>the</strong> enterprises’ prime markets was answered by 900 enterprises. The
distribution is relatively homogenous, whereby slightly more enterprises (38.1%) sell <strong>the</strong>ir products
and services nationwide. In contrast, only 26.2% <strong>of</strong> <strong>the</strong> enterprises are operating on international
markets (see figure 6).
Figure 6: Sample according to prime markets
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Kai Mertins, Sven Wuscher and Markus Will
74.7% <strong>of</strong> <strong>the</strong> 894 companies that answered <strong>the</strong> question concerning corporate objectives stated that
<strong>the</strong>y aim at future growth while 24.4% stated that <strong>the</strong>y aim at maintaining <strong>the</strong>ir current position (see
figure 7).
Figure 7: Sample according to corporate objectives
Around one third <strong>of</strong> <strong>the</strong> enterprises which answered <strong>the</strong> question regarding <strong>the</strong>ir competitive strategy
stated that <strong>the</strong>y pursue a niche strategy (see figure 8). The minority (2%) said that <strong>the</strong>y are
concentrating on cost leadership.
Figure 8: Sample according to competitive strategy
947 enterprises answered <strong>the</strong> question regarding <strong>the</strong>ir competitive differentiation. Customer
orientation is <strong>the</strong> most important factor to differentiate <strong>the</strong>m from competition (66.8%). In this context
factors like price (20.1%), additional product value (16.9%) and process innovations (15.5%) are
considered less important by <strong>the</strong> enterprises in order to stand out from <strong>the</strong> competition (see figure 9).
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Kai Mertins, Sven Wuscher and Markus Will
Figure 9: Sample according to competitive differentiation
5. Results
5.1 German enterprises on <strong>the</strong> way to <strong>the</strong> knowledge-based economy
5.1.1 The intellectual capital has a greater impact on <strong>the</strong> business success than material resources.
As part <strong>of</strong> <strong>the</strong> study, <strong>the</strong> most important enterprise resources were assessed with regard to <strong>the</strong> factors
individual impact on <strong>the</strong> enterprises’ business success. From <strong>the</strong> assessment <strong>of</strong> <strong>the</strong> impact, it was
possible to derive <strong>the</strong> importance <strong>of</strong> <strong>the</strong> specific factors for <strong>the</strong> surveyed enterprises.
Figure 10: Impact and rating <strong>of</strong> <strong>the</strong> different types <strong>of</strong> capital with regard to business success
All in all <strong>the</strong> different types <strong>of</strong> IC are already <strong>of</strong> higher importance for business success than <strong>the</strong>
material resources are today (see figure 10). In addition <strong>the</strong> IC is also rated better by <strong>the</strong> surveyed
enterprises. The impact <strong>of</strong> <strong>the</strong> HC on business success is by far <strong>the</strong> greatest (8.2) and has <strong>the</strong> best
rating (6.7) as well.
The factors <strong>of</strong> <strong>the</strong> SC (7.2) are seen as <strong>the</strong> second most important type <strong>of</strong> capital by <strong>the</strong> surveyed
participants and were rated with 6.1 in <strong>the</strong> mean. When analysing <strong>the</strong> difference between rating and
impact it becomes obvious that <strong>the</strong> rating <strong>of</strong> <strong>the</strong> factors is in most cases lower than <strong>the</strong>ir impact on
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Kai Mertins, Sven Wuscher and Markus Will
business success. The biggest difference is to be found in <strong>the</strong> HC (-1.5) followed by <strong>the</strong> SC (-1.1) and
<strong>the</strong> RC (-0.4). The factors <strong>of</strong> <strong>the</strong> material resources only show slight differences. In conclusion <strong>the</strong>
highest demand for action <strong>of</strong> knowledge-based corporate management constitutes itself in <strong>the</strong> HC.
5.2 Success Factors <strong>of</strong> Knowledge-based Corporate Management
5.2.1 The factors <strong>of</strong> <strong>the</strong> HC as well as <strong>the</strong> customer relationships are <strong>the</strong> most important success
factors for German enterprises.
The IC as a whole and especially <strong>the</strong> HC has obviously a higher impact on <strong>the</strong> business success <strong>of</strong>
<strong>the</strong> surveyed enterprises than <strong>the</strong> material resources. In figure 11 is shown which factors <strong>of</strong> <strong>the</strong> IC are
most important and <strong>the</strong>reby critical for corporate success. In addition <strong>the</strong> figure shows how well <strong>the</strong>
enterprises are presently doing with respect to <strong>the</strong> factors critical for business success (rating).
Figure 11: Impact and rating <strong>of</strong> <strong>the</strong> success factors <strong>of</strong> knowledge-based corporate management
Pr<strong>of</strong>essional competence (8.5), customer relationships (8.3), motivation <strong>of</strong> employees (8.3), social
competence (8.1) and leadership ability are <strong>the</strong> factors with <strong>the</strong> highest impact on business success,
followed by <strong>the</strong> factors <strong>of</strong> <strong>the</strong> SC such as internal co-operation and knowledge transfer (7.7), IT and
explicit knowledge (7.6), corporate culture (7.4) and management instruments (7.0).
The first material factor is financial resources (6.8) which rank in <strong>the</strong> midfield. Material factors such as
machinery, plants and buildings (4.5) and material and raw material (4.4) have <strong>the</strong> relatively lowest
impact on business success.
Surprisingly process innovation (6.9) as well as product innovation (6.5) show a relatively low
importance as well. Hence innovative products or new products and processes do not seem to be in
<strong>the</strong> focus <strong>of</strong> German enterprises and are seen as less critical for business success.
Ano<strong>the</strong>r interesting fact is that <strong>the</strong> impact <strong>of</strong> <strong>the</strong> investor relationships ranks last (6.5) as shown by
previous surveys on <strong>the</strong> importance <strong>of</strong> <strong>the</strong> IC (Mertins, Will, Wuscher 2007). A reason for this
circumstance could be found in <strong>the</strong> fact that enterprises are currently holding back investments and
thus <strong>the</strong> importance <strong>of</strong> investor relationships seems little. On <strong>the</strong> o<strong>the</strong>r side <strong>the</strong> low impact could also
be based on enterprises’ expectations with regard to <strong>the</strong> support to solve financial problems.
Fur<strong>the</strong>rmore <strong>the</strong> surveyed enterprises assign a relatively low impact to <strong>the</strong> factors <strong>of</strong> <strong>the</strong> RC except
customer relationships. This allows <strong>the</strong> conclusion that <strong>the</strong> participating enterprises rate <strong>the</strong>ir internal
resources as more important than public relationships, supplier relationships or investor relationships.
In German enterprises exists a considerable backlog demand in <strong>the</strong> areas <strong>of</strong> motivation <strong>of</strong> employees
and internal co-operation and knowledge transfer. The factors regarding innovation fall short <strong>of</strong>
expectations, too.
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If <strong>the</strong> rating <strong>of</strong> a factor is smaller than its impact on business success this leads to <strong>the</strong> conclusion that
<strong>the</strong>re is an imbalance between importance and status quo. Consequently considerable pressure to
act exists. This is most clearly for <strong>the</strong> factor motivation <strong>of</strong> employees which is seen as very influential,
but falls short in comparison to <strong>the</strong> rating <strong>of</strong> o<strong>the</strong>r factors (6.6). This also applies to internal cooperation
and knowledge transfer. Thus it appears that both factors demonstrate <strong>the</strong> pressure to act.
But also <strong>the</strong> factors concerned with innovation have a relatively bad rating (product innovation: 5.7;
process innovation: 5.7). If innovations are essential for <strong>the</strong> sustainability, as <strong>of</strong>ten proclaimed in <strong>the</strong>
media and politics, <strong>the</strong> previous results from business practice must give everyone pause for thought.
Because <strong>the</strong> question is, how can enterprises face <strong>the</strong> challenges <strong>of</strong> global competition in future?
A similar picture also applies to <strong>the</strong> financial resources (6.0). For <strong>the</strong> last quarter <strong>of</strong> 2009 and <strong>the</strong> first
half <strong>of</strong> <strong>the</strong> year 2010 <strong>the</strong> ifo Institute for Economic Research reported about a credit crunch for <strong>the</strong>
commercial industry (ifo Dec. 2009; ifo May 2010). Considering <strong>the</strong>se results which can be partly
explained by <strong>the</strong> difficult conditions <strong>of</strong> <strong>the</strong> financial and economic crisis it is not surprising that <strong>the</strong>
participating enterprises did not rate <strong>the</strong> investor relationships very good ei<strong>the</strong>r (5.7).
5.2.2 Success Factors <strong>of</strong> <strong>the</strong> Service Sector and <strong>the</strong> Production Sector
If one compares <strong>the</strong> ratings <strong>of</strong> <strong>the</strong> enterprises from <strong>the</strong> service sector with those <strong>of</strong> <strong>the</strong> production
sector, differences could be determined. As part <strong>of</strong> <strong>the</strong> following sector comparison only significant
differences are considered.
Figure 12: Impact and rating <strong>of</strong> <strong>the</strong> different types <strong>of</strong> capital according to sectors
5.3 The service sector is gaining ground in knowledge-based corporate management
Significant differences in <strong>the</strong> area <strong>of</strong> <strong>the</strong> HC show that <strong>the</strong> service sector is one step ahead <strong>of</strong> <strong>the</strong>
production sector with regard to structural and employee-related corporate development. The
participating service enterprises rate <strong>the</strong> impact and <strong>the</strong>reby <strong>the</strong> importance <strong>of</strong> <strong>the</strong> HC (8.3)
significantly higher than <strong>the</strong> participating production enterprises (7.9). On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> material
resources are more important to <strong>the</strong> production enterprises (6.7) than to <strong>the</strong> service enterprises (4.7).
These results ratify <strong>the</strong> dependency <strong>of</strong> production enterprises on machinery, plants and raw materials
and <strong>the</strong> capital needed to finance <strong>the</strong>se, while <strong>the</strong> service enterprises strongly depend on <strong>the</strong> abilities
<strong>of</strong> <strong>the</strong>ir employees. Consequently <strong>the</strong> investor relationships are considerably more important to
production enterprises than to service enterprises (+0.7).
Although material resources are <strong>of</strong> great importance for <strong>the</strong> business success <strong>of</strong> production
enterprises, <strong>the</strong>y are seen as less important than <strong>the</strong> HC. Hence <strong>the</strong> employees are <strong>the</strong> most
important type <strong>of</strong> capital in <strong>the</strong> production sector. Significant differences are to be found for <strong>the</strong> impact
and <strong>the</strong> rating <strong>of</strong> <strong>the</strong> factors social competence and leadership ability. For service enterprises <strong>the</strong>se
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factors are <strong>of</strong> considerably higher importance than for production enterprises. In <strong>the</strong> case <strong>of</strong> <strong>the</strong> factor
<strong>the</strong> participating service enterprises rate <strong>the</strong> impact <strong>of</strong> employee motivation on <strong>the</strong> business success
significantly higher (8.4) than production enterprises (8.1).
All in all, <strong>the</strong> impact <strong>of</strong> all <strong>the</strong> factors <strong>of</strong> <strong>the</strong> HC, except <strong>the</strong> pr<strong>of</strong>essional competence, is higher for <strong>the</strong>
service enterprises than for <strong>the</strong> production enterprises. The picture is <strong>the</strong> same when taking a look at
<strong>the</strong> rating <strong>of</strong> <strong>the</strong>se factors. It is in almost all cases slightly higher. Only <strong>the</strong> rating <strong>of</strong> <strong>the</strong> pr<strong>of</strong>essional
competence is almost <strong>the</strong> same for enterprises from both sectors.
With regard to <strong>the</strong> SC service enterprises assess <strong>the</strong> impact <strong>of</strong> <strong>the</strong> corporate culture as well as IT and
explicit knowledge on <strong>the</strong> business success significantly better than <strong>the</strong> production enterprises.
Especially <strong>the</strong> corporate culture is rated higher by <strong>the</strong> service enterprises. Production enterprises on
<strong>the</strong> o<strong>the</strong>r hand rate <strong>the</strong> impact <strong>of</strong> process innovations on business success significantly higher.
Altoge<strong>the</strong>r, service enterprises estimate <strong>the</strong> impact and <strong>the</strong> rating <strong>of</strong> almost all factors <strong>of</strong> <strong>the</strong> SC
higher than <strong>the</strong> production enterprises. Exceptions are <strong>the</strong> factors product innovation and process
innovation which are <strong>of</strong> higher importance for <strong>the</strong> production enterprises.
5.4 Service enterprises focus on different relationships than production enterprises
Regarding <strong>the</strong> RC customer relationships shows <strong>the</strong> greatest impact on <strong>the</strong> business success (8.4) <strong>of</strong>
enterprises from both sectors. The factor is also rated best by <strong>the</strong>m (6.4), although o<strong>the</strong>r factors <strong>of</strong> <strong>the</strong>
RC show significant differences for both sectors. While <strong>the</strong> relationship to co-operation partners (+0.6)
and public relationships (+0.7) have a significantly higher impact on business success <strong>of</strong> service
enterprises, supplier relationships (+1.6) as well as investor relationships (+0.7) have a considerably
higher impact on <strong>the</strong> business success <strong>of</strong> production enterprises.
5.4.1 Success Factors <strong>of</strong> Small and Big and Young and Old Enterprises
Besides <strong>the</strong> differences between <strong>the</strong> sectors <strong>the</strong>re are fur<strong>the</strong>r differences to be found between small
and big enterprises as well as between young and old enterprises.
Figure 13: Impact and rating <strong>of</strong> <strong>the</strong> different types <strong>of</strong> capital on business success according to
number <strong>of</strong> employees
5.5 Employees come first
In enterprises with up to 10 employees all factors <strong>of</strong> <strong>the</strong> HC are in average rated better than in larger
enterprises. Major differences are evident between enterprises with up to 10 employees and
enterprises with more than 1000 employees for <strong>the</strong> factors social competence (+1.6), employee
motivation (+1.6) and leadership ability (+1.5). It may be assumed that <strong>the</strong> results for leadership ability
are a consequence <strong>of</strong> <strong>the</strong> difference in <strong>the</strong> effort, since <strong>the</strong> requirements are normally much higher in
larger enterprises. Never<strong>the</strong>less, compared to o<strong>the</strong>r factors <strong>the</strong> difference between impact and rating
<strong>of</strong> leadership ability for larger enterprises is relatively high (-2.2), showing pressure to act.
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Kai Mertins, Sven Wuscher and Markus Will
Young enterprises are leading with regard to communication and IT and are positioned considerably
better for <strong>the</strong> knowledge contest today.
Young enterprises founded after <strong>the</strong> year 2000 rate <strong>the</strong>ir SC all in all significantly better than older
enterprises which have been founded before 1970 (+0.6). As a consequence <strong>the</strong> factors <strong>of</strong> <strong>the</strong> SC
internal co-operation and knowledge transfer (+1.0), management instruments (+0.7) and corporate
culture (+1.1) are rated significantly better than in <strong>the</strong> older participating enterprises.
There are significant differences between factors <strong>of</strong> <strong>the</strong> HC and between factors <strong>of</strong> <strong>the</strong> RC.
Enterprises founded after <strong>the</strong> year 2000 rate public relationships (-0.4) and investor relationships (-
0.7) significantly worse than enterprises founded before 1970. With regard to <strong>the</strong> HC <strong>the</strong> factors
employee motivation (+1.0), leadership ability (+1.0) and social competence (+1.0) are rated
considerably better by young enterprises than by older enterprises.
Altoge<strong>the</strong>r, young enterprises rate <strong>the</strong> intangible factors slightly better than old companies and also
give <strong>the</strong>m a higher priority. Conversely, material resources play a more important role for <strong>the</strong> business
success <strong>of</strong> old enterprises than for <strong>the</strong> business success <strong>of</strong> middle-aged enterprises founded between
1971 and 1999 and young enterprises founded after 2000.
5.5.1 Success Factors <strong>of</strong> Enterprises Operated by Owners and Enterprises Operated by Managers
Owner-operated enterprises are partly better positioned than management-operated enterprises with
regard to HC and SC. Owner-operated enterprises rate all factors <strong>of</strong> <strong>the</strong> HC significantly higher than
management-operated enterprises. Consequently, <strong>the</strong> surveyed owner-operated enterprises state
that <strong>the</strong>y have a considerably higher employee motivation (+0.7), pr<strong>of</strong>essional competence (+0.4),
social competence (+0.8) and leadership ability (+1.0). With regard to <strong>the</strong> factors process innovations
and product innovations no significant differences can be found.
Since <strong>the</strong> SC by definition comprises all those structures that are applied by all employees to conduct
business <strong>the</strong> owner-operated enterprises rate nearly all factors <strong>of</strong> <strong>the</strong> SC higher than managementoperated
enterprises. In consequence, owner-operated enterprises rate <strong>the</strong> factors internal cooperation
and knowledge transfer (+1.0), management instruments (+0.7), IT and explicit knowledge
(+0.6) and corporate culture (+0.8) significantly better.
The importance <strong>of</strong> <strong>the</strong> public relationships as part <strong>of</strong> <strong>the</strong> RC is much higher in management-operated
enterprises than in owner-operated enterprises (+0.6). The rating <strong>of</strong> <strong>the</strong>se factors is slightly better for
<strong>the</strong> management-operated enterprises than for <strong>the</strong> owner-operated enterprises (+0.4).
The comparison <strong>of</strong> owner-operated and management-operated enterprises leads to <strong>the</strong> conclusion
that owner-operated enterprises have a better access to <strong>the</strong>ir employees and <strong>the</strong>reby promote and
maintain more sustainable corporate structures. With regard to <strong>the</strong> knowledge contest <strong>the</strong> analysis
shows that owner-operated are equally or even better adjusted for <strong>the</strong> knowledge contest than
management-operated enterprises. This is particularly apparent in <strong>the</strong> area <strong>of</strong> <strong>the</strong> SC.
References
BMWi: Bundesministerium für Wirtschaft und Technologie (2009) Wissensbilanz – Made in Germany, Leitfaden
2.0, Berlin.
Chou, C.-P., and Bentler, P. M. (1995) “Estimates and tests in structural equation modelling”, in: R. H. Hoyle
(Ed.), Structural equation modeling - concepts, issues and applications, pp. 37-55.
ifo Institut für Wirtschaftsforschung an der Universität München (2009): Kredithürde – Ergebnisse des ifo
Konjunkturtests im Dezember 2009.
ifo Institut für Wirtschaftsforschung an der Universität München (2010): Die Kredithürde - Ergebnisse des ifo
Konjunkturtests im Mai 2010. ifo Institut für Wirtschaftsforschung an der Universität München.
Mertins, K.; Will, M.; Wuscher, S. (2007): „Erfolgsfaktoren des intellektuellen Kapitals in mittelständischen
Unternehmen“, in: Bentele, M.; Hochreiter, R.; Riempp; G. Schütt, P.; Weber, M. (2007): Mit Wissen – mehr
Erfolg!, KnowTech 2007, pp.197-205.
636

Knowledge Management Implementation in <strong>the</strong> UK - Does
Size Matter?
Sandra M<strong>of</strong>fett 1 , Rodney McAdam 2 and Paul Humphreys 3
1
School <strong>of</strong> Computing and Intelligent Systems, University <strong>of</strong> Ulster at Magee,
Londonderry, Nor<strong>the</strong>rn Ireland
2
Head <strong>of</strong> Department <strong>of</strong> Marketing, Entrepreneurship and Strategy, University
<strong>of</strong> Ulster at Jordanstown, Newtownabbey, Nor<strong>the</strong>rn Ireland
3
Research Institute Director, Department <strong>of</strong> Business Organisation and
Management, University <strong>of</strong> Ulster at Jordanstown, Newtownabbey, Nor<strong>the</strong>rn
Ireland
sm.m<strong>of</strong>fett@ulster.ac.uk
pk.humphreys@ulster.ac.uk
r.mcadam@ulster.ac.uk
Abstract: Knowledge Management (KM) continues to develop as an emerging discourse within business
management. The area is eclectic in nature and covers systematic management <strong>of</strong> knowledge, <strong>of</strong> all kinds,
within all levels and types <strong>of</strong> organisations. However, <strong>the</strong> majority <strong>of</strong> studies, in common with o<strong>the</strong>r emergent
business philosophies, are focused on larger organisations where, for example, readily available, somewhat
unlimited resources is an underlying assumption. In contrast KM investigation within Small Medium Size
Enterprises (SMEs) tends to focus on specific cases with no key trends identified for KM adoption across <strong>the</strong>
sector. Considering <strong>the</strong> downturn in <strong>the</strong> current economic climate empirical research to identify key factors
common to all organisations, irrelevant <strong>of</strong> size is necessary, supported by both empirical findings and case
experiences. This paper presents <strong>the</strong> findings from a large-scale empirical investigation conducted in 2009 with a
number <strong>of</strong> UK companies. The purpose <strong>of</strong> <strong>the</strong> research was to investigate how KM implementation can influence
organisational sustainability, development and maturity in both small and large companies. This research builds
on a ten-year project investigating KM implementation within UK companies employing both quantitative and
qualitative approaches. The research considers how KM affects internal organisational processes, to streng<strong>the</strong>n
<strong>the</strong> link between operations and strategy, resulting in better decision maker, faster products to market, better
service quality and enhanced customer loyalty. Core aspects <strong>of</strong> technological application, information
management, employee emancipation and process improvement are considered providing a framework for KM
adoption and uptake. This paper compares KM implementation and development between large corporations and
SMEs to ascertain if organisation size matters. The paper commences with a brief literature review outlining key
elements which effect KM implementation. Based on <strong>the</strong> MeCTIP model and utilising <strong>the</strong> ‘Benchmarking KM’
online survey tool, empirical analysis <strong>of</strong> KM implementation and development with a large sample <strong>of</strong> UK
companies is undertaken. Initial statistical analysis finds that KM can contribute to organisational sustainability,
development and maturity by a number <strong>of</strong> internal organisational factors, such as technology, information and
people. As organisations are receptive to influences beyond <strong>the</strong>ir boundaries, changes in <strong>the</strong> macro-environment
tend to affect both organisational climate and internal technical climate, which has a knock-on effect for KM
adoption and implementation. This paper compares results for SMEs and larger firms.
Keywords: Knowledge Management, empirical investigation, organisational size
1. Introduction
For KM to reach <strong>the</strong> same level <strong>of</strong> acceptance as o<strong>the</strong>r business improvement concepts, such as
Total Quality Management (TQM), more comprehensive studies relating to its implementation in
organisations <strong>of</strong> different sizes and types (Beijerse, 2000) is needed. This point is emphasised by
Claycomb et al., (2001) who having completed a study on KM in larger organisations state, ‘it would
be interesting to study knowledge in smaller firms’. Many questions arise in relation to KM and
organisational size. Can current KM models, frameworks and programmes be applied across all
organisations uniformly? If not, what changes are necessary? Are such changes largely
implementation modifications or are <strong>the</strong>re more fundamental issues to be resolved? Considering large
investment capital expended by many firms in KM systems (Curley, 1998) and <strong>the</strong> growing number <strong>of</strong>
companies that see KM as potentially helping <strong>the</strong>m survive and compete, <strong>the</strong>re is a need for more
definitive and comprehensive studies in this field.
The aim <strong>of</strong> this paper is to explore <strong>the</strong> effects <strong>of</strong> organisational size on KM via empirical study. A
survey instrument, known as ‘Benchmarking KM’ (created based on <strong>the</strong> MeTCIP model by M<strong>of</strong>fett et
al., 2002) is employed with a sample <strong>of</strong> UK organisations across a bandwidth <strong>of</strong> sizes. Statistical
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
analysis is undertaken in <strong>the</strong> form <strong>of</strong> descriptives and factor analysis. Key contributors to KM
implementation in both SME and larger organisations are presented and discussed.
2. The MeCTIP knowledge management model
Exploratory literary research found a number <strong>of</strong> aspects which contribute to KM implementation.
M<strong>of</strong>fett et al., (2002; 2003) have grouped <strong>the</strong>se into five key categories that can ei<strong>the</strong>r support or
hinder KM implementation, <strong>the</strong>se five form <strong>the</strong> basis <strong>of</strong> <strong>the</strong> MeCTIP model (shown in figure 1).
MeCTIP is an acronym <strong>of</strong> <strong>the</strong> components <strong>of</strong> <strong>the</strong> model, namely,
Me Macro Environment
C Culture
T Technology
I Information
P People
Figure 1: The MeCTIP model
3. Research methodology
Employing <strong>the</strong> MeCTIP model for guidance an on-line survey based measurement instrument, known
as <strong>the</strong> ‘Benchmarking KM’ tool was created, with input from senior managers (n=10) based in
Nor<strong>the</strong>rn Ireland organisations. The ‘Benchmarking KM’ tool is available at
http://www.business.ulster.ac.uk/questionnaires/m<strong>of</strong>fett/
To administer an empirical study throughout UK organisations, a database <strong>of</strong> UK companies was
created, containing 4709 records with complete company contact details, including email addresses.
2000 organisations were selected at random to participate in <strong>the</strong> study. Following a combination <strong>of</strong> email
(three iterations) and postal (two iterations) contact to identified managers, <strong>the</strong> total number <strong>of</strong>
surveys completed, and suitable for statistical analysis, is 588. This represents a very acceptable
return rate <strong>of</strong> 29.4%.
4. Participant categorisation
Although a full statistical investigation was performed on all data obtained via <strong>the</strong> survey, only data
that relates KM and organisational size is utilised for this paper. The majority <strong>of</strong> respondents were
from large corporations (n=200, 34%) followed by small-medium sized organisations (n=169, 29%).
Respondents belonged to three main industrial sectors, namely Service (n=127, 22%), Engineering
(n=99, 17%) and Public Sector (n=99, 17%). The group that contributed least were from Construction
(n=23, 4%), this could be attributed to current downturn in <strong>the</strong> housing market.
Fur<strong>the</strong>r insight into respondents was gained by assessing market stage. A large percentage <strong>of</strong> <strong>the</strong>
respondents (n=218, 37%) operated within mature markets. Only ten organisations (2%) were in
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
introductory stages, while 120 organisations (20%) were growing. From <strong>the</strong>se figures one can
ascertain that <strong>the</strong> majority <strong>of</strong> organisations who responded to <strong>the</strong> survey are looking to KM as a
business improvement initiative and a way to sustain or increase market share. Surprisingly a large
number <strong>of</strong> respondents (n=183, 31%) failed to outline <strong>the</strong> stage <strong>of</strong> development <strong>the</strong>ir organisation is
currently in. Perhaps <strong>the</strong> participants deemed this question unnecessary as KM can be applied to all
organisations irrelevant <strong>of</strong> market position.
5. Analysis findings
Prior to investigation <strong>the</strong> dataset was re-coded according to organization size* with 54% within <strong>the</strong> 0-
250 employee bracket (SME) and 46% with over 251 employees (LARGE). Data collected was
subjected to statistical analysis using SPSS v17. This section presents <strong>the</strong> results <strong>of</strong> construct validity
and subsequent factor analysis for five categories (organisation climate, technical climate,
technological tools, information, people). Presentation <strong>of</strong> <strong>the</strong> results is followed by a comparative
discussion <strong>of</strong> <strong>the</strong> findings (section 6).
* SME defined by <strong>the</strong> UK Government's Small Business Service to mean any business with 0-250
employees
5.1 Organisational Climate (OC)
Originally 35 constructs were tested for reliability and appropriateness, however three failed to meet
<strong>the</strong> acceptable level <strong>of</strong> 0.30 and were removed from <strong>the</strong> data scale. From <strong>the</strong> resulting 32 variables
20 were above <strong>the</strong> 0.60 level. Therefore a total <strong>of</strong> 32 items-<strong>of</strong>-scale met reliability criteria and are
found to be internally consistent, with an alpha <strong>of</strong> .957. To verify <strong>the</strong> appropriateness <strong>of</strong> <strong>the</strong> remaining
organisational climate variables, both <strong>the</strong> Kaiser-Meyer-Olkin Measure <strong>of</strong> Sampling Adequacy and
Barlett’s Test <strong>of</strong> Sphericity were utilised. Variables for OC display a .933 KMO measure; a
meritorious value, showing that <strong>the</strong>se variables are acceptable for FA. Barlett’s test <strong>of</strong> Sphericity is
5594.162. This acceptable value indicates that <strong>the</strong> data is suitable for FA. Using Principal
Component Analysis (PCA), factors were extracted using <strong>the</strong> eigenvalue technique where only factors
with an eigenvalue over 1.0 are considered and all o<strong>the</strong>r factors are ignored.
Within <strong>the</strong> SME category total variance derived from relevant factors is 67.613. Factor loadings
(presented in table 1) confirm that significance criteria for all considered variables has been met;
37.5% (n=12) <strong>of</strong> <strong>the</strong> factor loadings are rated highly significant (above 0.7). Within <strong>the</strong> LARGE
category total variance <strong>of</strong> 60.862 was obtained extracted factors using <strong>the</strong> same variables as for
SMEs. Factor loadings confirm that <strong>the</strong> significance criteria for all considered variables has been met;
25% (n=8) <strong>of</strong> <strong>the</strong> factor loadings are rated highly significant (above 0.7). Factor descriptions capture
<strong>the</strong> underlying nature <strong>of</strong> <strong>the</strong> factor.
Making technology available and ready for use by employees is crucial for all organisations. In
LARGE organisations technology is used for efficiency and connectivity, linking internal people and
systems and externally to stakeholders such as customers and suppliers. In <strong>the</strong> SMEs column it is
interesting to note that one <strong>of</strong> <strong>the</strong> highest variables places emphasis on employees to contribute to
<strong>the</strong> content <strong>of</strong> technological systems, giving <strong>the</strong>m ownership <strong>of</strong> system content and its management.
5.2 Technology
To support TC findings, FA was conducted on technology data for both SMEs and LARGE
organisations, using <strong>the</strong> same processes as outlined in <strong>the</strong> previous sections. A total <strong>of</strong> 13 variables
with an alpha <strong>of</strong> .781 were included in data analysis, two additional scales were removed as <strong>the</strong>y did
not meet <strong>the</strong> acceptable threshold. Six items were above <strong>the</strong> 0.60 level (43%). Tests <strong>of</strong>
appropriateness <strong>of</strong> <strong>the</strong> remaining datascales indicated a .867 KMO measure; a meritorious value,
showing that <strong>the</strong>se variables are acceptable for FA. Barlett’s test <strong>of</strong> Sphericity is 5090.963. This
acceptable value indicates that <strong>the</strong> technology dataset is suitable for FA.
Within SMEs three factors are worthy <strong>of</strong> fur<strong>the</strong>r investigation, total variance from <strong>the</strong>se three is high at
87.581 (table 3). A similar story exists within LARGE organisations, where <strong>the</strong> three extracted factors
have a total variance <strong>of</strong> 86.209. The loading values selected are all above <strong>the</strong> highly significant rating
<strong>of</strong> >0.7.
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
Table 1: Factor Loadings – OC and SME/LARGE organisations
Variables SME LARGE
Factor 1 – NEW APPROACHES
Orgn developed capacity for listening to members .552 .654
New approaches to capturing customer knowledge adopted/supported .586 .586
New approaches to capturing employee knowledge adopted/supported .728 .619
New approaches to sharing knowledge about customers adopted/supported .778 .705
Orgn ensures all employees are inducted promptly .537 .561
Recruitment/promotion procedures explained/supported .654 .497
Mgmt team always clear about what it wants .599 .608
Factor 2 – TEAMS
Problems/errors openly discussed .690 .423
Team members always clear about team goals .680 .731
Teams set up to include all necessary experience to deliver solutions .750 .611
Teams readily collaborate with o<strong>the</strong>r teams .781 .560
Team acknowledges when it lacks experience to reach solution .798 .576
Each team is open to knowledge/guidance from o<strong>the</strong>rs .806 .694
Teams look to see how <strong>the</strong>y can contribute to wider orgn .731 .616
Factor 3 – COLLABORATION
Orgn uses cross-functional teams .589 .789
Orgn uses multi-level teams .582 .713
Teams are clearly valued by orgn
Solutions to problems are openly discussed
Divisions, departments, units <strong>of</strong>ten work on joint projects
Factor 4 – EXPERIMENTATION
New approaches to sharing knowledge about employee experiences
adopted/supported
Creative boosting techniques are encouraged
Physical environment organised to facilitate info flow
Informal meetings held regularly
Risk-taking and experimentation are encouraged
Failure is viewed as opportunity for learning
Orgn allows employees to choose work location
Factor 5 – EMPLOYEE WELFARE
New employees need only few days to fit in
Orgn takes responsibility for welfare <strong>of</strong> employees/families
Senior mgmt lead by example
Orgn encourages external contact with o<strong>the</strong>r orgns
.564
.734
.632
.726
.537
.662
.722
.564
.512
-.733
.759
.630
.646
.626
.540
.449
.606
.544
.579
.412
.740
.704
.460
.718
.633
.723
.352
.491
Factor 6 – APPARENCY
Orgn is apparent to newcomers/outsiders .575 -.366
Interpreting <strong>the</strong> factor loadings was easy. Factor 1 in each case refers to key knowledge roles which
exist for KM implementation, such as Chief Knowledge Officer, Chief Information Officer, Chief
Learning Officer, Community <strong>of</strong> Practice Co-ordinator, Knowledge Broker and Web Master. Factor 2
shows <strong>the</strong> location in which training normally occurs, ei<strong>the</strong>r internally, in-house or externally in that
order. Factor three refers to technological training in commonly used tools such as <strong>the</strong> Internet, Office
Automation Systems, Intranet and Groupware/Workflow Systems as presented in graphical format in
figure 2.
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
Table 2: Factor Loadings – TC and SME/LARGE organisations
Variables
Factor 1 – EFFICIENCY
SME LARGE
Technology designed to help employees work more efficiently .712 .821
IT and applications developed with clear vision <strong>of</strong> business needs .665 .713
IT and applications designed for specific organisational problems .677 .789
Technology designed to aid better decision making .673 .756
Technology is a means <strong>of</strong> enhancing collaboration .629 .629
Technology used to minimise geographical/time barriers .615 .789
Senior mgmt leads by example in using tech .726 .414
SW applications designed to share info only with those who need it .595 .461
ICT permits orgn members to connect directly with one ano<strong>the</strong>r
.711 .666
Factor 2 – EXTERNAL LINKS
Priority is given to tech that serves as information bridges .615 .510
ICT permits orgn members to connect directly with customers .491 .814
ICT permits orgn members to connect directly with suppliers
.618 .820
Factor 3 – TECHNOLOGY
Orgn regularly upgrades/replaces HW/SW .513 .473
All employees trained to use technologies .786 .863
Technology available to all employees .787 .820
Factor 4 – CONTENT
Users <strong>of</strong> tech systems decide on <strong>the</strong>ir content .644 .728
SW applications designed to recognise/retain info .470 .752
Factor 5 – FLEXIBILITY
SW applications designed to share info across whole orgn
Tech systems are flexible and evolve
All employees must contribute to content <strong>of</strong> tech systems
Table 3: Factor Loadings – Technology and SME/LARGE organisations
.768
.735
.797
.493
.732
.547
Variables SME LARGE
Factor 1 – KNOWLEDGE ROLES
Chief Knowledge Officer
Chief Information Officer
Chief Learning Officer
Community <strong>of</strong> Practice Co-ordinator
Knowledge Author
Knowledge Broker
Web Master
.946
.967
.964
.988
.961
.927
.987
.928
.918
.951
.917
.933
.944
.920
Factor 2 – TRAINING LOCATION
Internal .776 .933
In-house
.784 .961
External
.907 .961
Factor 3 – TECHNOLOGY TRAINING
Training provided technology 1 .918 .856
Training provided technology 2 .909 .925
Training provided technology 3 .767 .847
While <strong>the</strong> graph below shows that training is awarded to four key technologies (three <strong>of</strong> which are
web-based technologies) a number <strong>of</strong> o<strong>the</strong>r tools are applied to knowledge work. FA has been
undertaken with technological tools data variables, <strong>the</strong> results <strong>of</strong> which are presented below.
To ensure data reliability item to total correlation and Cronbach Alpha statistical tests were employed.
Only one item failed to meet <strong>the</strong> acceptable level <strong>of</strong> 0.30 and was removed from <strong>the</strong> data scale
leaving a total <strong>of</strong> 16 variables, with an alpha <strong>of</strong> .8801. While all scale items are acceptable for fur<strong>the</strong>r
analysis only one item is above <strong>the</strong> 0.60 level (6%). To verify <strong>the</strong> appropriateness <strong>of</strong> <strong>the</strong> remaining
variables relating to technology tools, both <strong>the</strong> Kaiser-Meyer-Olkin Measure <strong>of</strong> Sampling Adequacy
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
and Barlett’s Test <strong>of</strong> Sphericity were utilised. Variables for organisational climate display a .886 KMO
measure; this is considered a meritorious value, showing that <strong>the</strong>se variables are acceptable for factor
analysis. Barlett’s test <strong>of</strong> Sphericity is 3347.737. This is a relatively low value, indicating that <strong>the</strong>
tools dataset may be unsuitable for factor analysis, however as <strong>the</strong> purpose <strong>of</strong> this section is purely to
classify technological tools utilised for KM <strong>the</strong> analysis will proceed.
Figure 2: Technologies with training provided
Using PCA, factors were extracted using <strong>the</strong> eigenvalue technique, three factors have eigenvalues
over 1.0 with total variance <strong>of</strong> 84.018. This high figure represents almost 85% <strong>of</strong> <strong>the</strong> total factor.
Table 4 presents <strong>the</strong> results <strong>of</strong> this selection process. Of <strong>the</strong> 16 variables, six (37.5%) are above <strong>the</strong>
0.7 threshold. Factor descriptions have been included to capture <strong>the</strong> underlying nature <strong>of</strong> <strong>the</strong> factor.
Table 4: Factor Loadings – Technology Tools
Variables SME LARGE
Factor 1 – DATA MANAGEMENT TOOLS
Knowledge-based Systems .692 .572
Electronic Publishing Systems .678 .460
Data Warehousing .674 .770
Data Mining Tools .651 .831
Intelligent Support Systems .636 .715
Document Management Systems .562 .649
Meeting Support Systems .449 .456
Factor 2 – SUPPORT TOOLS
Extranet .619 .820
Knowledge Directories .478 .744
Help-desks .550 .597
Factor 3 – COMMERCE TOOLS
Agents/Filters/Navigation Tools .439 .820
Electronic Commerce .645 .720
Factor 4 – IN-HOUSE TOOLS
Information Retrieval Engines .560 .725
Intranet .701 .722
Groupware/Workflow Systems .471 .536
Factor 5 – GENERAL USE TOOLS
Office Automation Systems .695 .757
Internet .682 .724
In LARGE organisations, <strong>the</strong> focus <strong>of</strong> technological tools is to capture information from external
sources to cherry pick relevant information so it can be applied internally. To this end emphasis is
awarded to external scanning tools (extranet, data mining tools and agents/filters/navigation tools
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
being <strong>the</strong> three most popular). In contrast SMEs use technological tools for everyday operations, with
<strong>of</strong>fice automation systems, <strong>the</strong> Internet and knowledge-based systems being <strong>the</strong> three most popular.
Generally, technological tools for KM can be classified into three main categories, namely, intelligent
tools, support tools and web-based tools. Figure 3 presents an overview <strong>of</strong> technology tool usage in
diagrammatic format. Not surprisingly <strong>the</strong> four most popular tools are those which organisations have
provided most training in as outlined above.
Figure 3: Technology tools for KM
6. Information
Information dataset was <strong>the</strong> next subjected to FA. To ensure data reliability item-to-total correlation
and Cronbach Alpha statistical tests were employed. Again, four items failed to meet <strong>the</strong> acceptable
level <strong>of</strong> 0.30 and were removed from <strong>the</strong> data scale leaving a total <strong>of</strong> 18 variables, with an alpha <strong>of</strong>
.938. 10 items were above <strong>the</strong> 0.60 level (55%). Variables were deemed appropriate with a .930
KMO measure. Within <strong>the</strong> SME category total variance derived is 59.616, almost 60% <strong>of</strong> <strong>the</strong> total
factor, loading values are presented in table 4. Within <strong>the</strong> LARGE category total variance derived
from variables with eigenvalues over 1.0 is 62.326. Table 4 presents <strong>the</strong> selection <strong>of</strong> factor loadings
for <strong>the</strong>se variables. In both cases significance criteria for all variables has been met with 44% (n=8)
rated highly significant (>0.7).
for <strong>the</strong> job. In SMEs information is utilised across functions for efficiency, this also eliminates data
duplication on internal systems. Not surprisingly information hoarding is discouraged, within <strong>the</strong> SME
environment <strong>the</strong>re tends to be clearer vision <strong>of</strong> <strong>the</strong> organisation as a whole, so operational
departments (if <strong>the</strong>y exist) and individual employees are discouraged from withholding information
that is needed across <strong>the</strong> organisation. In LARGE organisations capturing external information is key.
Using external knowledge sources, and information from remote locations, are two <strong>of</strong> <strong>the</strong> highest
rating variables, showing that larger organisation appreciate <strong>the</strong> value <strong>of</strong> bringing expert information
into <strong>the</strong> organisation for improved performance.
7. People
People was <strong>the</strong> final category subjected to FA. Data reliability was checked via item-to-total
correlation and Cronbach Alpha statistical tests. Two items failed to meet acceptable level <strong>of</strong> 0.30
and were removed from <strong>the</strong> data scale leaving a total <strong>of</strong> 21 variables, with an alpha <strong>of</strong> .932. Variables
for People display a .929 KMO measure, outlining <strong>the</strong>se variables as acceptable for FA.
Using PCA, factors were extracted for <strong>the</strong> SME category employing <strong>the</strong> eigenvalue technique.
Factors with eigenvalues over 1.0 had total variance <strong>of</strong> 64.419, almost 65% <strong>of</strong> <strong>the</strong> total factor. Factor
loadings selected are shown in table 5. Within <strong>the</strong> LARGE category, total variance for variables with
eigenvalues over 1.0 is 64.419. In both cases loading values confirm that significance criteria for all
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
variables has been met; one third (33%, n=7) are rated highly significant (>0.7). Factor descriptions
have been included to capture <strong>the</strong> underlying nature <strong>of</strong> <strong>the</strong> factor.
Table 4: Factor Loadings – Information and SME/LARGE organisations
Variables SME LARGE
Factor 1 - INFORMATION FLOW
Information flows easily
Structure <strong>of</strong> organisation aids information flow
Information hoarding is discouraged
Employees are encouraged to own information
Employees determine action steps from information
Information capture crosses functional boundaries
Customer information is shared across organisation
.770
.752
.855
.697
.853
.856
.628
.596
.602
.675
.727
.820
.721
.468
FACTOR 2 – INFORMATION CAPTURE
Employees ga<strong>the</strong>r customer information for product development .519 .492
Information is rapidly captured and used .538 .460
Information reaches decision makers in timely fashion .733 .682
Information from remote sites is quickly incorporated into organisational systems
New information is quickly analysed
Employees able to locate relevant information from remote locations
.465
.618
.527
.818
.525
.660
Factor 3 – EMPLOYEE USE
Each employee is aware <strong>of</strong> database contents .817 .740
Organisation systems tailored to information needs <strong>of</strong> employees .592 .721
Orgn maps information repositories .830 .811
Each employee is able to access relevant information easily
.579 .704
Factor 4 – EXTERNAL INFORMATION
Employees encouraged to use external information sources
.691 .831
Both SMEs and LARGE organisations encourage employees to use information to determine actions
Table 5: Factor Loadings – People and SME/LARGE organisations
Variables SME LARGE
Factor 1 – EMPLOYEE EMANCIPATION
Employees given time, resources, support to pursue new ideas
Employees share common way <strong>of</strong> talking/thinking about issues
Employees have freedom to adopt own approaches to workload
Employees able to use skills/capabilities to full
Employees in position to freely express disagreement with senior mgmt
Employees are able to change <strong>the</strong>ir minds without losing face
Employees have confidence that orgn is progressing
Employees can influence orgn event/future plans
Employees feel that are surrounded by people who co-operate
Every team member gets something <strong>the</strong>y personally value from membership
Employees know <strong>the</strong>y will receive recognition for achievement
Employees acknowledge access to latest tech
Factor 2 – TEAMWORK
Employees participate in cross-functional teams
Employees within same unit frequently volunteer to help each o<strong>the</strong>r
Employees believe best results are gained through teamwork
Employees know what teams <strong>the</strong>y are in
Employees know who o<strong>the</strong>r team members are
Individuals move easily from team to team
.526
.545
.612
.581
.496
.530
.741
.508
.718
.541
.496
.731
.662
.646
.771
.869
.869
.714
.602
.472
.544
.636
.726
.413
.655
.779
.680
.631
.757
.631
.659
.618
.807
.899
.901
.494
FACTOR 3 – SHARING
Employees routinely share ideas .597 .639
Employees routinely share tech tools .570 .784
Employees from various units frequently volunteer to help each o<strong>the</strong>r .645 .443
The highest rating variables confirm that teamworking is crucial for KM within all organizations. This
finding is consistent with <strong>the</strong> literature as <strong>the</strong> connection between teamwork and KM has been widely
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Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
discussed (Davenport and Prusak, 1998; M<strong>of</strong>fett et al., 2009). A simple google search (accessed 5 th
June 2011) linking <strong>the</strong> words ‘teamwork’ and ‘knowledge management’ returned 4,880,000 results,
this figure confirms <strong>the</strong> growing interest in employee collaboration for knowledge sharing and work
operations.
8. Discussion
The results <strong>of</strong> FA in this paper support a number <strong>of</strong> key issues for KM as identified in <strong>the</strong> literature.
The first point to note is <strong>the</strong> close relationship between Organisation Culture and People. A number
<strong>of</strong> factors within <strong>the</strong>se two categories are similar highlighting <strong>the</strong> fact that people tend to work for an
organisation that projects a culture which conforms to <strong>the</strong>ir mindset (Davenport and Prusak, 1998). In
addition, <strong>the</strong> findings show that human intervention is an all-pervasive, essential element <strong>of</strong> KM,
irrelevant <strong>of</strong> organisation size; this is supportive <strong>of</strong> <strong>the</strong> work <strong>of</strong> Corso et al., (2003) and Beijerse
(2000). Teamworking and employee emancipation have been identified as necessary elements for
KM success, where active collaborative practice is evident, employee value is also strong. Finally,
quality <strong>of</strong> life is important to employees who work in SMEs while those within larger organizations
relish confidence in <strong>the</strong> company and <strong>the</strong> roles <strong>the</strong>y play. This finding is supportive <strong>of</strong> Demerest,
(1997) who stated that many companies successful at KM implementation <strong>of</strong>fer both personal and
career rewards to employees.
This paper has not surprisingly identified a strong link between Technical Climate and Technology.
SMEs are undertaking a technical presence for knowledge discovery, thus hinting that gaps exist in
current data/information provision (M<strong>of</strong>fett et al., 2003, 2009). Across all organisations training in <strong>the</strong>
realms <strong>of</strong> technological systems and tools is at <strong>the</strong> forefront <strong>of</strong> business improvement. This may be
supportive <strong>of</strong> <strong>the</strong> fact that more web-based platforms for technological systems are being adopted,
utilising both internal and external resources. System standardisation will lead to better KM within and
between organizations, facilitating information flow.
Current practice on information flow, adoption and utilisation is an issue for SMEs to consider. While
SMEs are focused on information application, results from <strong>the</strong> technology section indicate that this
may be a fruitless exercise if <strong>the</strong> correct information is not available within <strong>the</strong> organisation. Offsey,
(1997) has classified this syndrome as ‘inf<strong>of</strong>amine’. Perceived lack <strong>of</strong> information causes SMEs to
duplicate and re-invent information that may be readily available elsewhere. This point is supported
by <strong>the</strong> fact that smaller organisations view technology as an enabler for knowledge capture while
larger organisations use systems to enhance employee confidence and job capability.
All organisations are concerned with information flow and utilization. They also express concern with
information overload, or ‘infoglut’ (Offsey, 1997). One contributor to this may be <strong>the</strong> adoption <strong>of</strong> webbased
platforms; <strong>the</strong> Internet has been <strong>the</strong> cause <strong>of</strong> extensive information exchange. Bair, (1997)
outlines how KM can reduce this problem by stating, ‘what we see are organisations that are overfed
with information coming in through e-mail, intranets and <strong>the</strong> Internet, yet under-nourished in terms <strong>of</strong>
information <strong>the</strong>y really require. KM aims to capture <strong>the</strong> knowledge that employees really need in a
central repository and filter out <strong>the</strong> surplus’.
9. Conclusion
The aim <strong>of</strong> this paper was to ascertain effects <strong>of</strong> KM adoption in SME and larger companies,
presenting <strong>the</strong> results <strong>of</strong> empirical research undertaken in 2009 with 588 UK companies. Literature
research identified a number <strong>of</strong> factors to be considered when incorporating KM into organisations.
Five overarching <strong>the</strong>mes were utilised, two <strong>of</strong> which relate to <strong>the</strong> infrastructure <strong>of</strong> <strong>the</strong> organisation in
terms <strong>of</strong> culture and technical infrastructure (Organisation Climate, Technical Climate) while three
relate to process-orientated activity for information management, technology application and human
expertise. Data collected was analysed, following datascale testing for accuracy and validity, with
computational techniques such as descriptives and Factor Analysis.
The main conclusion from this paper is that SMEs do not suffer from innate limitations when trying to
apply KM. Based on <strong>the</strong> results <strong>of</strong> FA it can be stated that KM elements, as represented by <strong>the</strong>
MeCTIP model, are applicable to all organizations irrelevant <strong>of</strong> size and it is <strong>the</strong> approaches to
implementation that vary. Similar findings have been recorded by Beijerse, (2000), Corso et al.,
(2003) and Wong and Aspinwall (2005). Thus, KM <strong>the</strong>ory and practice, as represented by <strong>the</strong> MeCTIP
model, presents an opportunity for SMEs to improve and develop using existing resources.
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References
Sandra M<strong>of</strong>fett, Rodney McAdam and Paul Humphreys
Bair, J. (1997), Knowledge Management: <strong>the</strong> era <strong>of</strong> shared ideas, Forbes, Vol. 160, No. 6, pp. 28
Beijerse, R. P. (2000), Knowledge management in small and medium-sized companies: knowledge management
for entrepreneurs, Journal <strong>of</strong> Knowledge Management, Vol. 4, No. 2, pp. 162-177
Claycomb, C., Droge, C. and Germain, R. (2001), Applied process knowledge and market performance: The
moderating effect <strong>of</strong> environmental uncertainty, Journal <strong>of</strong> Knowledge Management, Vol. 5, No. 3, pp. 264-
278
Corso, M., Martini, A., Paolucci, E. and Pellegrini, L. (2003), Knowledge management configurations in Italian
small-to-medium enterprises, Integrated Manufacturing Systems, Vol. 14, No. 1, pp. 46-56
Curley, K. (1998), The Role <strong>of</strong> Technology, Knowledge Management: A Real Business Guide, Caspian
Publishing Ltd., London
Davenport, T. and Prusak, L. (1998), Working Knowledge - How Organisations Manage What They Know,
Harvard Business School Press, Boston
Demerest, M. (1997), Understand Knowledge Management, Journal <strong>of</strong> Long Range Planning, Vol. 30, No. 3, pp.
374-384
M<strong>of</strong>fett, S. McAdam, R. and Parkinson, S. (2002), The inter-relationship <strong>of</strong> cultural and technological factors in
Knowledge Management: An empirical analysis, 7 th International <strong>Conference</strong> on ISO 9000 & TQM,
Melbourne, Australia, 2-4 April
M<strong>of</strong>fett, S. McAdam, R. and Parkinson, S. (2003), An empirical analysis <strong>of</strong> knowledge management application,
Journal <strong>of</strong> Knowledge Management, Vol. 7, No. 3, MCB University Press Ltd, ISSN 1367-3270, pp. 6 – 26
M<strong>of</strong>fett, S. and McAdam, R. (2009), Knowledge Management: a factor analysis <strong>of</strong> sector effects, Journal <strong>of</strong>
Knowledge Management, Vol. 13, No. 3, pp. 44-59
Offsey, S, (1997), Knowledge Management: Linking People to Knowledge for Bottom Line Results, Journal <strong>of</strong>
Knowledge Management, Vol. 1, Issue 2, pp. 113-122
Yu-Chung Hung, Shi-Ming Huang, Quo-Pin Lin, Mei-Ling -Tsai, (2005), Critical factors in adopting a knowledge
management system for <strong>the</strong> pharmaceutical industry, Industrial Management & Data Systems, Vol. 105 Iss:
2, pp.164 - 183
Wong, K. Y. and Aspinwall, E. (2005), An empirical study <strong>of</strong> <strong>the</strong> important factors for knowledgemanagement
adoption in <strong>the</strong> SME sector, Journal <strong>of</strong> Knowledge Management, Vol. 9, No. 3,
2005, pp. 64-82
646

An IT-based KMS for Large-scale Sense-Making: An
Application <strong>of</strong> a KMSD Methodology
Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
University e-Centre, Middlesex University, School <strong>of</strong> Engineering &
Information Sciences, The Burroughs, Hendon, London, UK
syedmoneebali@gmail.com
m.woodman@mdx.ac.uk
a.zade@mdx.ac.uk
Abstract: Understanding <strong>the</strong> behaviour <strong>of</strong> stakeholders for better decision-making and for gaining competitive
advantage is <strong>of</strong> interest to large organisations, especially as <strong>the</strong> stakeholders have a tendency to behave
differently in <strong>the</strong> same or different situations. This creates complexity and unpredictability, making individual and
collective behaviour difficult to understand and inhibiting management’s ability to discover and exploit
organisational knowledge. Hence, practices such as sense-making and narrative-based investigations can be
utilised for insights into complex situations. Key to all such approaches is some kind <strong>of</strong> capture <strong>of</strong> anecdotes or
narrative fragments from individual stakeholders, with <strong>the</strong> intention that <strong>the</strong>se allow patterns <strong>of</strong> behaviour to be
uncovered. A Cynefin-based sense-making project for a large university was initiated to explore behaviours.
However, problems emerged with <strong>the</strong> volume <strong>of</strong> narrative data collection from <strong>the</strong> main stakeholder group –
students. This paper is concerned with a second knowledge management project to investigate <strong>the</strong> first’s
problems and to propose a solution to <strong>the</strong>m. We focus on <strong>the</strong> emergent requirements for an IT-based knowledge
management system and <strong>the</strong> application <strong>of</strong> a proven methodology for developing knowledge management
system. That used frame analysis for its sense-making <strong>of</strong> <strong>the</strong> first project’s problems. This is described and
samples <strong>of</strong> <strong>the</strong> frame analysis data are provided. It was found that students are disinclined to engage in <strong>the</strong>
capture <strong>of</strong> <strong>the</strong>ir experiences for no perceived net benefit. Accordingly, a knowledge sharing system was
envisioned and designed to facilitate better data collection for <strong>the</strong> original large-scale sense-making. Hence <strong>the</strong>
paper reports <strong>the</strong> use <strong>of</strong> <strong>the</strong> knowledge management system development methodology and <strong>the</strong> design and
implementation <strong>of</strong> <strong>the</strong> knowledge management system. That implementation is based on current ‘Web 2.0’
technologies; <strong>the</strong> architecture for <strong>the</strong>se is described and compared with <strong>the</strong> original project’s approach to
collecting data for large-scale sense-making.
Keywords: knowledge management, knowledge management systems, development methodology, Web 2.0,
XML
1. Introduction
This paper is concerned about developing an IT-based knowledge management system (KMS) that
can make sense <strong>of</strong> data at a large scale in a large, complex organization. The organisation under
consideration is a university that is based in <strong>the</strong> UK and has overseas campuses in <strong>the</strong> Middle East
and Asia, and partners in many o<strong>the</strong>r countries; that also puts it in <strong>the</strong> category <strong>of</strong> a networked
organisation. As <strong>the</strong> university has been growing, it has become ever more critical for <strong>the</strong>
management to recognise <strong>the</strong> behaviour <strong>of</strong> its stakeholders (e.g. students, alumni, academic staff,
administrative staff, management and governors, not to mention <strong>the</strong> regional community and local
and national authorities) for better understanding and decision-making. However, <strong>the</strong> myriad,
conventional surveying techniques used to ga<strong>the</strong>r data from students such as <strong>the</strong> UK’s National
Student Survey (NSS), which is only for final-year undergraduates, do not always provide <strong>the</strong>
university with <strong>the</strong> level <strong>of</strong> understanding it seeks for effective decision-making (cf. Machell &
Saunders 2007). Much <strong>of</strong> data is collected for quality assurance purposes (i.e. as metrics) or to aid
<strong>the</strong> framing <strong>of</strong> internal and external marketing messages. Fur<strong>the</strong>rmore, <strong>the</strong> data from such surveys
does not involve all <strong>the</strong> stakeholders <strong>of</strong> <strong>the</strong> institution and is <strong>the</strong>refore not able to facilitate
understanding <strong>the</strong> behaviour <strong>of</strong> <strong>the</strong>m all. For example, alumni who graduated in recent years are not
routinely consulted about <strong>the</strong>ir experiences or to seek <strong>the</strong>ir knowledge. In addition, <strong>the</strong> mix <strong>of</strong> cultures
and organisational structures in different regions increases <strong>the</strong> difficulty <strong>of</strong> understanding <strong>the</strong>
behaviour <strong>of</strong> all <strong>the</strong> stakeholders.
To address <strong>the</strong> need <strong>of</strong> <strong>the</strong> university to better understand its stakeholders, a group <strong>of</strong> academics
proposed a sense-making approach in order to improve <strong>the</strong> understanding <strong>of</strong> <strong>the</strong> stakeholders and
decision-making based on large-scale sense-making with <strong>the</strong> help <strong>of</strong> story narratives. The sensemaking
approach took Snowden’s Cynefin model (Kurtz & Snowden, 2003) as its philosophical basis.
A project was initiated and <strong>the</strong> university endorsed what we term here a “Pilot Solution” (PS) to
address <strong>the</strong> above stated problem situation with <strong>the</strong> help <strong>of</strong> Cognitive Edge’s SenseMaker suite (cf.
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
http://www.sensemaker-suite.com). The PS was designed to help <strong>the</strong> academic team make sense
<strong>of</strong> <strong>the</strong> stakeholders’ experiences through collecting story fragments from <strong>the</strong>m in a way that allowed
story-tellers, ra<strong>the</strong>r than academic researchers, to index/signify <strong>the</strong>ir own story narratives according to
<strong>the</strong>ir own experiences. This signification allows both statistical investigation <strong>of</strong> behaviours for patterns
(cf. Kurtz & Snowden, 2003) and provides <strong>the</strong> university team as sense-makers with <strong>the</strong> story
provider’s own interpretation <strong>of</strong> <strong>the</strong>ir story data. Despite publicising <strong>the</strong> PS through e-marketing and
face-to-face marketing and facilitation, <strong>the</strong> number <strong>of</strong> stakeholders’ contributions was unexpectedly
low – currently around 200, where ten times as many might have been expected.
Consequently, a second project, <strong>the</strong> subject <strong>of</strong> this paper, was created to explore <strong>the</strong> problem <strong>of</strong> nonparticipation
and to explore whe<strong>the</strong>r o<strong>the</strong>r approaches would facilitate large-scale sense-making. This
second project was framed in terms <strong>of</strong> knowledge management and KM systems (KMS), and a fivephase
development methodology proven in business situations was selected (Moteleb et al., 2009);
hence <strong>the</strong> paper also reports on its novel use, including <strong>the</strong> use <strong>of</strong> frame analysis (Grbich, 2007) in
early phases <strong>of</strong> <strong>the</strong> methodology. Ultimately, to facilitate large-scale sense-making with collected
narratives, <strong>the</strong> anticipated KMS was to be IT-based.
The paper proceeds in Section 2 by outlining <strong>the</strong> KMS development methodology and by briefly
reviewing frame analysis used in its first three phases. Space does not allow for a separate literature
review here; critiques <strong>of</strong> work relevant to <strong>the</strong> methodology can be found in (Moteleb & Woodman,
2009) and (Moteleb et al., 2009), with appropriate references for frame analysis and o<strong>the</strong>r concepts
used being given in situ. Section 3 describes <strong>the</strong> first three phases <strong>of</strong> <strong>the</strong> KMSD applied, resulting in a
KMS design. The options for <strong>the</strong> IT design for <strong>the</strong> KMS are explored in Section 4. An evaluation <strong>of</strong> <strong>the</strong>
work to date is given in Section 5, with a concluding discussion being provided in <strong>the</strong> subsequent,
final section.
2. KMSD Methodology: principles and practice
2.1 KMSD approach
In terms <strong>of</strong> <strong>the</strong> selected KMSD methodology (Moteleb et al., 2009), a KMS is an output that
encapsulates a KM-based solution to an organisation’s problematic situation. Here that was <strong>the</strong>
inadequate participation <strong>of</strong> stakeholders in narrative-based sense-making. This section briefly
describes <strong>the</strong> approach taken for <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> solution using <strong>the</strong> KMSD methodology in
<strong>the</strong> five overlapping, interacting phases as follows. Note that Phase I is about making sense <strong>of</strong> <strong>the</strong>
problematic situation (not <strong>of</strong> <strong>the</strong> university stakeholders’ experiences):
Phase I – Sensemaking <strong>of</strong> <strong>the</strong> Problematic Situation: finding out how <strong>the</strong> organisation works and
what are <strong>the</strong> problems and challenges <strong>of</strong> concern.
Phase II – Envisioning an Improved Situation: investigates <strong>the</strong> stakeholders’ visions for improved
situations in <strong>the</strong> future that would address <strong>the</strong> problematic situation that <strong>the</strong>y aspire to change.
Phase III – Designing <strong>the</strong> KMS: based on <strong>the</strong> vision for an improved situation in <strong>the</strong> future, this is
about selecting what knowledge agents, knowledge flows and knowledge interfaces (Moteleb &
Woodman, 2009) would make up a KMS.
Phase IV – Exploring IT Options for <strong>the</strong> KMS: explores what IT tools and technologies might be
used to support <strong>the</strong> designed KMS.
Phase V – Managing <strong>the</strong> Evolutionary Potential <strong>of</strong> <strong>the</strong> KMS: sets an ongoing process for
monitoring <strong>the</strong> KMS to identify signals and trends (weak signal detection) that could influence <strong>the</strong>
KMS.
This KMSD methodology provides <strong>the</strong> flexibility that <strong>the</strong> second project required for developing a KMS
to address non-participation in <strong>the</strong> original large-scale sense-making project.
2.2 Frame analysis for sense-making, envisioning and design
The project had severe time constraints, so it was necessary to compress <strong>the</strong> timescale for <strong>the</strong> KMSD
methodology. Frame analysis was used for <strong>the</strong> first three phases: <strong>the</strong> approach is used to arrange
information that emerges from conversations and interactions (Grbich, 2007). It is based on <strong>the</strong>
seminal work <strong>of</strong> G<strong>of</strong>fman (1974) and Bateson (1972) that identified how, during social interactions,
individuals classify <strong>the</strong> conversations, narratives, rituals, behaviours and situational encounters with
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
which <strong>the</strong>y are presented, <strong>the</strong>reby setting parameters for understanding information. G<strong>of</strong>fman (cf.
Schick, 2008, p247) gives <strong>the</strong> basis thus:
It has been argued that a strip <strong>of</strong> activity will be perceived by its participants in terms <strong>of</strong>
<strong>the</strong> rules or premises <strong>of</strong> a primary framework, whe<strong>the</strong>r social or natural. … <strong>the</strong>se
frameworks are not merely a matter <strong>of</strong> mind but correspond in some sense to <strong>the</strong> way in
which an aspect <strong>of</strong> <strong>the</strong> activity itself is organized – especially activity directly involving
social agents. Organizational premises are involved, and <strong>the</strong>se are something cognition
somehow arrives at, not something cognition creates or generates. Given <strong>the</strong>ir
understanding <strong>of</strong> what it is that is going on, individuals fit <strong>the</strong>ir actions to this
understanding and ordinarily find that <strong>the</strong> ongoing world supports this fitting.
The social agents in <strong>the</strong> university scenario under consideration were all those stakeholders involved
in <strong>the</strong> activity <strong>of</strong> <strong>the</strong> PS. Two techniques for ga<strong>the</strong>ring conversation data for frame analysis from those
agents were used: interviews and focus groups. Ethnography (Atkinson & Hammersley, 1994) was
used as <strong>the</strong> research methodology for developing <strong>the</strong> questions used in <strong>the</strong> interviews and focus
groups. In an ethnographic approach <strong>the</strong> researcher adopts <strong>the</strong> simultaneous dual role <strong>of</strong> active
participant in a community’s activities and observer <strong>of</strong> those activities (Spradley, 1980): prior to <strong>the</strong>
identification <strong>of</strong> a problem, <strong>the</strong> first author was a participant-observer in <strong>the</strong> PS development team
and in <strong>the</strong> student body who used <strong>the</strong> PS.
Interviews were used to capture responses <strong>of</strong> <strong>the</strong> development team <strong>of</strong> <strong>the</strong> PS that comprised
academics and students. A series <strong>of</strong> questions was divided into three sets put to team members:
expectations (<strong>of</strong> <strong>the</strong> research), reality (<strong>of</strong> <strong>the</strong> PS) and marketing (<strong>of</strong> <strong>the</strong> availability <strong>of</strong> <strong>the</strong> PS). The
results were organised into frames structured into two categories for academic and student team
members.
Focus group meetings were used for three categories <strong>of</strong> non-team students – undergraduates,
postgraduates and alumni. Questions to stimulate discussion were again broken into expectations,
reality and marketing sets. Three different focus group meetings were held, questioning a total <strong>of</strong>
thirty students from different departments and levels and from different nationalities. The results were
organised into frames structured according to three categories <strong>of</strong> student – undergraduates,
postgraduates and alumni. Frames were constructed on <strong>the</strong> basis <strong>of</strong> <strong>the</strong> majority views <strong>of</strong> <strong>the</strong>
participating stakeholders (Grbich, 2007) and were used to depict <strong>the</strong> experiences <strong>of</strong> different
university stakeholders (academic, student, PS team members and non-team members) to see how
have <strong>the</strong>y perceived <strong>the</strong> PS and how has it turned out to be in reality.
2.3 Summarising <strong>the</strong> five phases
The KMSD methodology was fully executed but for <strong>the</strong> sake <strong>of</strong> brevity different levels <strong>of</strong> detail are
provided. Section 3 described how Phases I–III were executed in <strong>the</strong> <strong>the</strong>n current situation for
achieving large-scale narrative data collection in <strong>the</strong> university. Section 4 deals with Phase IV,
concentrating on <strong>the</strong> IT aspects <strong>of</strong> <strong>the</strong> KMS. Phase V is largely omitted from <strong>the</strong> paper, but some
aspects <strong>of</strong> it concerning <strong>the</strong> ongoing monitoring arrangements are discussed as necessary. Each
section is supported by excerpts from <strong>the</strong> frame analysis, while more detail is given in <strong>the</strong> appendices.
There, all questions prefixed with ‘S’ are <strong>the</strong> one asked to students (undergraduates, postgraduates
and alumni) in <strong>the</strong> focus groups and those prefixed with ‘T’ were asked to <strong>the</strong> development team
(academic staff and students) in structured interviews.
3. KMSD Phases I-III: Sense-making to Design
3.1 Phase I: Sense-making <strong>of</strong> <strong>the</strong> Problematic Situation
In order to develop an understanding <strong>of</strong> <strong>the</strong> problematic situation <strong>of</strong> non-participation in <strong>the</strong> largescale
sense-making project, interviews and focus group meetings were conducted as described
above (for sample questions and summary responses see Appendix/Phase I). The interviews were
used with <strong>the</strong> PS development team and focus group meetings with <strong>the</strong> students. The technique <strong>of</strong>
frame analysis (Grbich, 2007) was employed to make sense <strong>of</strong> <strong>the</strong> current situation including <strong>the</strong> PS.
In essence <strong>the</strong> dominant views <strong>of</strong> stakeholders were used to bound and so interpret <strong>the</strong> multiple
views that emerged in <strong>the</strong> interviews and focus groups.
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
The analysis showed that <strong>the</strong> stakeholders are not satisfied with <strong>the</strong> PS as <strong>the</strong> purpose behind it was
not clear to <strong>the</strong>m. Data collection in <strong>the</strong> PS was perceived by <strong>the</strong> stakeholders to be long and tedious.
These conclusions were enough to explain <strong>the</strong> low story count. However, <strong>the</strong> analysis also showed
that a narrative-based application (i.e. <strong>the</strong> configuration <strong>of</strong> <strong>the</strong> SenseMaker Data Collector) is an
appropriate method for large-scale data collection.
3.2 Phase II: Envisioning an Improved Situation
The envisioning phase also used <strong>the</strong> interviews and focus groups (see sample questions and
summary responses under Appendix/Phase II). These showed that stakeholders possess
experiences and knowledge that if shared with <strong>the</strong> university would improve mutual understanding,
and so allow <strong>the</strong> university to better address <strong>the</strong> needs <strong>of</strong> stakeholders, enhancing trust and loyalty.
Hence Phase II resulted in a vision for creating a (communication) network among <strong>the</strong> stakeholders
and <strong>the</strong> organisation to address. This envisioned improved situation was related to <strong>the</strong> three KMSD
<strong>the</strong>mes proposed in (Moteleb & Woodman, 2009) as follows:
Locating knowledge: <strong>the</strong> stakeholders can use <strong>the</strong> network to find about experiences and
knowledge <strong>of</strong> o<strong>the</strong>rs. For example, a new student would be able to explore experiences and
knowledge <strong>of</strong> more senior students.
Communicating knowledge: experiences and knowledge can be communicated through <strong>the</strong>
network, ei<strong>the</strong>r individually or as groups. For example, class representatives could ga<strong>the</strong>r
narratives (positive or negative) about students’ experiences and communicate <strong>the</strong>m through <strong>the</strong>
network. A simple incentive (addressing concerns that emerged in Phase I) could be to simply to
allow stakeholders to read and comment on each o<strong>the</strong>r’s contributions.
Interacting with knowledge: each stakeholder has <strong>the</strong> freedom to determine <strong>the</strong> degree <strong>of</strong>
separation or closeness (Moteleb & Woodman, 2009) with respect to connecting with experiences
and knowledge <strong>of</strong> o<strong>the</strong>rs.
3.3 Phase III: Designing <strong>the</strong> KMS
In order to design <strong>the</strong> network envisioned in Phase II for facilitating <strong>the</strong> sharing <strong>of</strong> experiences and
knowledge among stakeholders (knowledge agents), knowledge flows among <strong>the</strong>m and knowledge
interfaces (cf. Moteleb & Woodman, 2009) are identified as follows:
Knowledge Agents: In <strong>the</strong> context <strong>of</strong> <strong>the</strong> university, <strong>the</strong> knowledge agents are mainly <strong>the</strong>
students, academic staff, non-academic staff and management.
Knowledge Flows: represents tacit and explicit knowledge being shared among <strong>the</strong>
stakeholders.
Knowledge Interfaces: represents <strong>the</strong> means and media for interaction among <strong>the</strong> stakeholders,
e.g. face-to-face interactions and through <strong>the</strong> Internet.
Figure 2 presents <strong>the</strong> connections among knowledge agents, <strong>the</strong> type <strong>of</strong> knowledge flows among
<strong>the</strong>m and <strong>the</strong> interfaces employed. Here, university management and <strong>the</strong> PS development team
come under <strong>the</strong> university agent. The students exchange experiences and knowledge among each
o<strong>the</strong>r, whereas <strong>the</strong>y provide updates and feedback to <strong>the</strong> university. On <strong>the</strong> o<strong>the</strong>r hand, academic
staff provides advice and study guidance information to <strong>the</strong> students and present <strong>the</strong>ir review and
feedback to <strong>the</strong> university. The university <strong>the</strong>n processes feedback updates and reviews to provide
best value to <strong>the</strong> stakeholders.
Example knowledge flows are:
Student’s module (degree), non-academic issues from students
Information/ updates regarding issues from non-academic staff
Course discussion, assignments and reviews about study methods from students
Study guide, lectures and academic support from academic staff
Review <strong>of</strong> students, updates on tasks from academic staff
Updates and feedbacks on performance and university’s policies from <strong>the</strong> university
Review <strong>of</strong> problems faced and task accomplished
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
Updates on funds, feedback on performance and policies from <strong>the</strong> university
Review <strong>of</strong> students and resource related issues, timetabling and scheduling from academic staff
Updates regarding students, resources and administration from non-academic staff
Review and feedback from <strong>the</strong> students regarding <strong>the</strong>ir experiences in <strong>the</strong> university
Feedback on reviews <strong>of</strong> students from <strong>the</strong> university
Figure2: Knowledge agents, flows and interfaces
Interview and focus group questions, responses and frame analyses (for brevity omitted here) were
used to refine <strong>the</strong> KMS design. Because <strong>of</strong> <strong>the</strong> needs <strong>of</strong> <strong>the</strong> original project to better understand
narratives at a large scale, <strong>the</strong> notion <strong>of</strong> sense-making anecdotes (SMA) was associated with <strong>the</strong>
design. In effect <strong>the</strong> SMA KMS is <strong>the</strong> platform that unites <strong>the</strong> stakeholders to share <strong>the</strong>ir experiences
and knowledge and build a community with <strong>the</strong> help <strong>of</strong> narratives that would be beneficial to all
concerned. This term is used hereafter.
4. KMSD Phase IV: Exploring IT Options to Support <strong>the</strong> KMS
From <strong>the</strong> earlier phases it was clear that <strong>the</strong> university, being a large, complex and international
organisation, needs an IT basis for <strong>the</strong> KMS designed as a result <strong>of</strong> Phases I–III. The largest
stakeholder groups <strong>of</strong> <strong>the</strong> university, students and academics, are generally social-technology savvy;
hence, exploring Web 2.0 technologies, e.g. blogs, word clouds, tagging (used for students), dynamic
charts, interactive feeds with data mashups and reports (used for <strong>the</strong> PS development team).
The use <strong>of</strong> <strong>the</strong>se technologies is envisaged to enable <strong>the</strong> stakeholders to create, update and share
<strong>the</strong>ir experiences and knowledge in a social environment. The idea <strong>of</strong> <strong>the</strong> platform conforms to Konno
& Nonaka’s (1998) “Ba”, a place where stakeholders can work toge<strong>the</strong>r and share <strong>the</strong>ir experiences
and knowledge to promote <strong>the</strong> concept <strong>of</strong> knowledge creation and provide platform for advancing
individual or collective knowledge. Such a platform would provide <strong>the</strong> university with valuable
knowledge in <strong>the</strong> form <strong>of</strong> SMA.
Figure 3 illustrates a pro<strong>of</strong> <strong>of</strong> concept for <strong>the</strong> IT-based KMS. The figure depicts <strong>the</strong> centrality <strong>of</strong> <strong>the</strong>
SMA in relation to knowledge agents. The circles represent <strong>the</strong> knowledge agents while <strong>the</strong> clouds
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
denote <strong>the</strong> replication <strong>of</strong> all <strong>the</strong> knowledge agents at different campuses. SMA works as a portal in
which different tools and technologies are co-located for registered stakeholders. SMA comprises two
main sections. One is related to capturing narratives with <strong>the</strong> help <strong>of</strong> a blogging system, where
stakeholders can share <strong>the</strong>ir experiences and knowledge in <strong>the</strong> form <strong>of</strong> narratives and o<strong>the</strong>rs would
be able to read <strong>the</strong>m. The o<strong>the</strong>r part provides management tools to <strong>the</strong> PS development team so that
<strong>the</strong>y can make sense <strong>of</strong> large-scale data using XML. The figure shows how <strong>the</strong> stakeholders
(knowledge agents) are connected to <strong>the</strong> SMA. This connectivity is expected to result in a more
efficient approach (than <strong>the</strong> PS) to share experiences and knowledge in a friendly environment. For
example if a similar kind <strong>of</strong> problem is being faced by o<strong>the</strong>r stakeholders, <strong>the</strong> PS team will be able to
detect it with <strong>the</strong> help <strong>of</strong> blogs, where stakeholders can read stories by o<strong>the</strong>rs, tag and comment on
<strong>the</strong>se stories. This contrasts with <strong>the</strong> orthodox approach currently used by <strong>the</strong> university, and enables
<strong>the</strong> university to respond faster to <strong>the</strong> needs <strong>of</strong> its stakeholders.
Figure3: Sense-making Anecdotes (KM System)
The primary output <strong>of</strong> this pro<strong>of</strong> <strong>of</strong> concept is to convert <strong>the</strong> XML file from PS into a relational table to
allow data to be read and interpreted on any platform, which was not available before to <strong>the</strong> PS
development team. For <strong>the</strong> pro<strong>of</strong> <strong>of</strong> concept, Oracle 11g was used, although <strong>the</strong> relational tables
could be made on virtually any database. All <strong>the</strong> tools used were integrated using IBM Mashups,
which also supports <strong>the</strong> Oracle database. The mashup allows <strong>the</strong> story telling blogs and <strong>the</strong>
management tools to appear on <strong>the</strong> same mashup (with different tabs).
Figure 4 shows <strong>the</strong> technical view <strong>of</strong> Sense-making Anecdotes (SMA). The tools and <strong>the</strong> technologies
are applied in <strong>the</strong> KMS as follows:
Blogs: are used by <strong>the</strong> students, academic staff and non-academic staff to input <strong>the</strong>ir
experiences and knowledge in <strong>the</strong> form <strong>of</strong> story narratives and to give feedback on <strong>the</strong> story
narratives published by o<strong>the</strong>rs in <strong>the</strong> form <strong>of</strong> Blog comments. For example a student can improve
his/her learning abilities by following <strong>the</strong> Blogs <strong>of</strong> o<strong>the</strong>r more experienced colleagues in <strong>the</strong> same
course for better results and this student can also comment on <strong>the</strong>se Blogs, for instance to
provide feedback on how <strong>the</strong>se experiences and knowledge were <strong>of</strong> use (or not) to <strong>the</strong>m.
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
Dynamic Data Mashups: are used by <strong>the</strong> PS development team to check narratives with
different perspectives. The PS development team can see, for example, how many stories have
come from a certain campus and <strong>the</strong>n drill down into a specific narrative and see what <strong>the</strong>
students have written, and hence make sense <strong>of</strong> <strong>the</strong>ir experiences. All information in <strong>the</strong> data
mashup is being pulled from <strong>the</strong> Oracle database.
Figure 4: Technical view <strong>of</strong> proposed IT system
Dynamic Graphs: A dynamic chart is populated from <strong>the</strong> database giving a visual representation
<strong>of</strong> <strong>the</strong> number <strong>of</strong> stories that are coming from different campuses. This enables <strong>the</strong> management
and PS development team to determine which campuses are active and which are not. Therefore,
in order to get more responses <strong>the</strong>y can target campuses which are not contributing to <strong>the</strong>
network and/ or read <strong>the</strong> stories that are coming from <strong>the</strong>re to check if <strong>the</strong>re is some sort <strong>of</strong>
problem that is preventing sharing story narratives at that campus.
Mashups: The above-mentioned tools are integrated on a common platform. This enables <strong>the</strong>
stakeholders to access one webpage combining <strong>the</strong> two aspects <strong>of</strong> <strong>the</strong> system. So <strong>the</strong>
stakeholders are able to post <strong>the</strong>ir Blogs, comment on <strong>the</strong>m, tag <strong>the</strong>m and even rate <strong>the</strong>m with
<strong>the</strong> help <strong>of</strong> SMA. On <strong>the</strong> o<strong>the</strong>r hand <strong>the</strong> PS development team is able to use <strong>the</strong> set <strong>of</strong>
management tools provided for large-scale data collection.
As mentioned earlier <strong>the</strong>re is also a need for signal detectors in order to identify trends. For that
purpose <strong>the</strong> following technologies were used.
Tagging: allows two or more stakeholders in <strong>the</strong> same domain to share <strong>the</strong>ir experiences and
knowledge. For example a student with knowledge in a certain domain can tag topics related to
that domain and/or fellow students working in <strong>the</strong> same domain. So by following <strong>the</strong> pattern <strong>of</strong>
tagging SMA is enabling <strong>the</strong> indicators <strong>of</strong> <strong>the</strong> same domain to form a community <strong>of</strong> practice.
Word Cloud: enables <strong>the</strong> stakeholders to be aware <strong>of</strong> prominent keywords acknowledged or that
appear frequently during discussions. For example, if <strong>the</strong> students are not happy with <strong>the</strong> quality
<strong>of</strong> ‘food’ that is being provided at <strong>the</strong> university <strong>the</strong>y tend to use <strong>the</strong> term ‘food’ more and more in
<strong>the</strong>ir story narratives. The database is used to extract <strong>the</strong> count <strong>of</strong> such keywords and show it in a
grid view on <strong>the</strong> mashup.
Rating: helps prioritising <strong>the</strong> data and enables <strong>the</strong> stakeholders to be acquainted with <strong>the</strong>
importance <strong>of</strong> experiences and knowledge being shared. For example, if a stakeholder reads a
good write-up he/she can rate it so as to add to its credibility.
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5. Evaluation
Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
Evaluation <strong>of</strong> <strong>the</strong> SMA KMS is ongoing, in accordance with Phase V <strong>of</strong> <strong>the</strong> KMSD outlined in Section
2.1. The project adopted Kaplan & Maxwell’s (2005) five reasons for using qualitative research to
evaluate IT systems. These (user perception, organisational and social context, causal processes,
formative evaluation and utilization <strong>of</strong> ongoing evaluation) are inherent in <strong>the</strong> KMSD and so are not
used separately here; instead several comparisons between <strong>the</strong> original PS and <strong>the</strong> SMA KMS are
given.
A significant problem found with <strong>the</strong> PS was that users could not relate to <strong>the</strong> original PS. They
should be able to understand <strong>the</strong> SMA, which is capable <strong>of</strong> displaying <strong>the</strong> behaviour <strong>of</strong> stakeholders
with <strong>the</strong> help <strong>of</strong> narratives that are being populated in <strong>the</strong> IBM mashup.
Ano<strong>the</strong>r finding from <strong>the</strong> frame analysis was that stakeholders did not experience a sense <strong>of</strong>
community, which would motivate <strong>the</strong>m to share experiences, nor did <strong>the</strong>y feel a sense <strong>of</strong> immediacy
that would indicate <strong>the</strong>ir stories mattered. Through <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> tools and technologies
<strong>the</strong> logical network through SMA is formed (Figure 3): it acts as a core linking stakeholders situated at
various geographical locations to form sub-networks. For example, students from one campus can get
acquainted with <strong>the</strong> help <strong>of</strong> <strong>the</strong> pr<strong>of</strong>ile that can be maintained with <strong>the</strong> blog. This is envisaged to give
stakeholders a sense <strong>of</strong> belonging and to allow for fast feedback on what is being posted.
From an operational/management perspective <strong>the</strong> PS did not support Cynefin’s probe-sense-respond
pattern (Kurtz & Snowden 2003), which would ideally be informed by continuous monitoring. In <strong>the</strong>
prototyped KMS, activity is monitored by <strong>the</strong> PS team with <strong>the</strong> help <strong>of</strong> signal-detectors embedded in
<strong>the</strong> SMA. The team members can thus observe <strong>the</strong> network and store valuable knowledge in <strong>the</strong>ir
repositories; this potential should enable <strong>the</strong> management stakeholders make better sense <strong>of</strong>
stakeholders’ experiences and react effectively. For example, students are expected to be more
engaged with blogs, being similar to <strong>the</strong>ir favourite, Facebook. They will be able to make <strong>the</strong>ir own
pr<strong>of</strong>ile and read each o<strong>the</strong>r’s story narratives. Students can also locate experiences and knowledge <strong>of</strong>
o<strong>the</strong>rs with <strong>the</strong> help <strong>of</strong> word clouds and tagging.
Sharing was a real problem for student stakeholders: with <strong>the</strong> PS <strong>the</strong>re is no centralised platform
sharing <strong>the</strong>ir experiences and knowledge with each o<strong>the</strong>r. The PS only provided individuals <strong>the</strong>
means to record narratives but does not give <strong>the</strong>m <strong>the</strong> opportunity to read <strong>the</strong> stories <strong>of</strong> o<strong>the</strong>rs or
comment on <strong>the</strong>m. The SMA uses blogs (amalgamated in an IBM mashup) and a centralised
database to store story narratives and related data (e.g. comments), yet allows o<strong>the</strong>r users to read
<strong>the</strong>se story narratives and comment on <strong>the</strong>m.
Similarly, stakeholders are not able to locate what o<strong>the</strong>r stakeholders are discussing in <strong>the</strong> PS as an
indication <strong>of</strong> what is happening in <strong>the</strong> university. This problem was solved by <strong>the</strong> provision <strong>of</strong> <strong>the</strong> word
cloud that shows how many times a certain keyword appears in all <strong>of</strong> <strong>the</strong> story narratives.
Finally, management information is missing from <strong>the</strong> PS. This was considered crucial by stakeholders
committed to large-scale data collection for sense-making. The mashup in <strong>the</strong> SMA configured
widgets, dynamic graphs and dynamic data mashups that enable <strong>the</strong> sense-making project team to
analyse <strong>the</strong> acquisition <strong>of</strong> narratives in a way that was not possible using <strong>the</strong> PS, for example, by
showing graphically <strong>the</strong> story count at each campus to plan interventions where contributions were
lacking. In addition, individual narratives can be viewed as <strong>the</strong>y are contributed with <strong>the</strong> SMA
management tool, which was not possible using <strong>the</strong> PS.
A capacity limitation is evident from <strong>the</strong> incompatibility <strong>of</strong> <strong>the</strong> SMA repository and <strong>the</strong> proprietary
sense-making database <strong>of</strong> <strong>the</strong> SenseMaker-based PS tool. This would lead to <strong>the</strong> SMA KMA
replacing or being unconnected from <strong>the</strong> use <strong>of</strong> <strong>the</strong> PS, which is not desirable. Functional limitations
are not currently apparent as <strong>the</strong> KMSD approach used produces a KMS tailored to <strong>the</strong> problematic
situation(s) considered. The evolutionary phase <strong>of</strong> <strong>the</strong> KMSD will oblige <strong>the</strong> SMA owners to
continuously monitor <strong>the</strong> environment for change. However, <strong>the</strong>rein lies a potential limitation. The
(Moteleb et al., 2009) KMSD approach demands full engagement <strong>of</strong> <strong>the</strong> problem owners in <strong>the</strong>
development <strong>of</strong> <strong>the</strong> KMS. This was not possible for time and resource reasons. It remains to be seen
whe<strong>the</strong>r <strong>the</strong> owners will adopt <strong>the</strong> SMA KMS.
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
6. Conclusion and future work
The aim <strong>of</strong> this project was to develop an IT-based KMS that would better facilitate <strong>the</strong> sharing <strong>of</strong>
experiences and knowledge <strong>of</strong> stakeholders and to make sense <strong>of</strong> <strong>the</strong>m in <strong>the</strong> context <strong>of</strong> a large
complex organisation. Whereas a Cynefin-based (Kurtz & Snowden, 2003) data collector tool worked
in principle, <strong>the</strong> low number <strong>of</strong> stories contributed by students pointed at a need to develop a KMS
that would help deal with poor participation. This development <strong>of</strong> <strong>the</strong> KMS followed <strong>the</strong> five-phase
KMSD methodology <strong>of</strong> Moteleb et al. (2009).
Phases I–III <strong>of</strong> <strong>the</strong> KMSD methodology were accelerated by using frame analysis (Grbich, 2007) to
identify <strong>the</strong> main problems, to envision solutions and to design a KMS. Questions for stakeholders
that generated data for frame analysis were developed from ethnographic observations made before
<strong>the</strong> PS problem had been identified and before <strong>the</strong> project reported here was initiated. The analysis
showed that low participation was largely due to a lack <strong>of</strong> understanding among <strong>the</strong> stakeholders
especially students <strong>of</strong> <strong>the</strong> significance <strong>of</strong> collecting <strong>the</strong> story narratives. Envisioning a better situation
exposed <strong>the</strong> stakeholder need to share in sets <strong>of</strong> overlapping communities.
The KMSD methodology guided <strong>the</strong> design <strong>of</strong> an IT-based KMS around <strong>the</strong> idea <strong>of</strong> sense-making
anecdotes (SMA). Technically, <strong>the</strong> design built on a flexible repository for <strong>the</strong> narrative fragments that
provided opportunities to ga<strong>the</strong>r data from different sources. The tools and practices used in <strong>the</strong>
development <strong>of</strong> pro<strong>of</strong> <strong>of</strong> concept included many <strong>of</strong> state-<strong>of</strong>-<strong>the</strong>-art technologies such as mashups and
blogs.
The SMA KMS overcomes <strong>the</strong> deficiencies <strong>of</strong> <strong>the</strong> PS, for example providing an interactive platform for
<strong>the</strong> stakeholders where knowledge sharing becomes easier, which was not available before, but <strong>the</strong>
incompatibility <strong>of</strong> its repository and <strong>the</strong> PS’s proprietary database limits this aspiration.
Future work is in two areas: firstly, Phase V monitoring suggested that observing <strong>the</strong> building <strong>of</strong> faceto-face
knowledge sharing networks would fur<strong>the</strong>r enhance <strong>the</strong> configuration <strong>of</strong> <strong>the</strong> PS, and secondly
that <strong>the</strong> redesigning to allow all forms <strong>of</strong> narrative data collection, including that from <strong>the</strong> PS, should
be undertaken. Both <strong>the</strong>se extensions to <strong>the</strong> work are underway.
References
Atkinson, P., & Hammersley, M. (1994). Ethnography and participation observation. In N. K. Denzin & Y. S.
Lincoln (Eds.), Handbook <strong>of</strong> qualitative research (pp. 248–261). Thousand Oaks, CA: Sage.
Bateson, G. (1972). Steps to an Ecology <strong>of</strong> Mind. Ballantine, New York.
Glaser, B. G. & Strauss, A. L. (1967). The Discovery <strong>of</strong> Grounded Theory. Aldine, Chicago, IL.
G<strong>of</strong>fman, E. (1974). Frame Analysis: An Essay on <strong>the</strong> Organisation <strong>of</strong> Experience. Harper and Row, New York.
Grbich,C. (2007). Qualitative data analysis: an introduction. Sage, London.
Kaplan, B. & Maxwell, J.A. (2005). Evaluating <strong>the</strong> organisational impact <strong>of</strong> healthcare information systems.
Springer: Business Media.
Konno, N. & Nonaka, I. (1998). The Concept <strong>of</strong> “Ba”: Building a Foundation for Knowledge Creation. California
Management Review, Vol. 40, No. 3.
Kurtz, C.F. & Snowden, D. (2003). The new dynamics <strong>of</strong> strategy: Sense-making in a complex and complicated
world. IBM Systems Journal, Vol. 42, No. 3.
Machell, J., & Saunders, M. 2007. An exploratory evaluation <strong>of</strong> <strong>the</strong> use <strong>of</strong> <strong>the</strong> National Student Survey (NSS)
results dissemination website. The Higher Education Academy.
Moteleb, A. & Woodman, M. (2009). Uncovering a KMSD Approach from Practice, Electronic Journal <strong>of</strong>
Knowledge Management, 2009.
Moteleb A., Woodman M. & Critten P. (2009). Towards a Practical Guide for Developing Knowledge
Management Systems in Small Organizations. <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 10th <strong>European</strong> <strong>Conference</strong> on
Knowledge Management (ECKM2009), September 2009, Vicenza, Italy.
Schick, L. (2008). Breaking frame in a role-play simulation: A language socialization perspective. Sage
Publications
Spradley, J. P. (1980). Participant Observation, Holt, Rinehart and Winston, New York.
Appendix – Sample Questions/Responses for Stakeholders
Phase I Questions
S2. Do you know what PS is about?
Undergraduates: No, <strong>the</strong>y have no clue
Postgraduates: They have a vague idea, almost close to no clue
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Syed Moneeb Ali, Mark Woodman and Aboubakr A Moteleb Zade
Alumni: <strong>the</strong>y know nothing about it
S4. Have you tried it (PS) so far?
Undergraduates: Yes, but could not complete it. Most <strong>of</strong> <strong>the</strong>m have used it. Some tried to open it but didn’t work
Postgraduates: No, <strong>the</strong>y don’t see <strong>the</strong> point <strong>of</strong> using it. Tried to open it but did not work.
Alumni: No, <strong>the</strong>y never got to know about it
S8. What was it like to use PS?
Undergraduates: it was too long and boring.
Postgraduates: it was okay
Alumni: it is too long but ok o<strong>the</strong>rwise
T3. What ways <strong>of</strong> qualitative analysis can be used in a large complex organisation?
Academic staff: Narratives is a good option as o<strong>the</strong>r methods like interviews would take more time <strong>of</strong> both <strong>the</strong>
interviewer and interviewee and <strong>the</strong>n needs to be transcribed.
Students: Narratives can be used for that purpose.
T4. What is your opinion about sense-making in large organisation? Is it applicable?
Academic staff: <strong>the</strong>re are so many variations <strong>of</strong> <strong>the</strong> term that it’s hard to say if it can be used or not but when talking in
context to sense-making as defined by Snowden <strong>the</strong>n it is justified and can be applied to large organisations.
Students: <strong>the</strong>y only know sense-making with respect to Snowden and believe it can be applied
T7. How important is <strong>the</strong> involvement <strong>of</strong> all stakeholders in a large organisation?
Academic staff: All stakeholders must be involved with <strong>the</strong> help <strong>of</strong> representatives who can speak on <strong>the</strong>ir behalf.
Students: All stakeholders should be involved.
T8. What was it like to use Pilot Solution (PS)?
Academic staff: It was okay, for some it was good
Students: It was okay (<strong>the</strong>y are being diplomatic)
Phase II Questions
S9. How could this experience <strong>of</strong> PS been better for you?
Undergraduates: If it was shorter and funkier
Postgraduates: <strong>the</strong> triangles and bars were not really useful
Alumni: Too many dropdown menus and length is too long
S13. Do you want o<strong>the</strong>rs to see your stories on PS?
Undergraduates: Yes <strong>the</strong>y want to see
Postgraduates: Yes <strong>the</strong>y want to see
Alumni: Yes <strong>the</strong>y want to see
S14. Do you prefer being anonymous on PS?
Undergraduates: No, <strong>the</strong>y do not want to be anonymous
Postgraduates: They don’t care about it
Alumni: The choice should be given to <strong>the</strong> user to be anonymous or not
S15. Do you want to know what topics are people posting about on PS?
Undergraduates: Yes <strong>the</strong>y want to see what o<strong>the</strong>rs are writing about
Postgraduates: Yes <strong>the</strong>y want to see what o<strong>the</strong>rs are writing about
Alumni: Yes <strong>the</strong>y want to see what o<strong>the</strong>rs are writing about
S18. What would make you use PS more?
Undergraduates: If it is more fun, if people would have <strong>the</strong>ir own pr<strong>of</strong>iles on it
Postgraduates: If <strong>the</strong>y can understand what will happen with <strong>the</strong>se stories and if something will be done with <strong>the</strong>ir
complaints and suggestions
Alumni: if some sort <strong>of</strong> incentive is given like a raffle or something
T1. What methods do you prefer to use for (quantitative or qualitative) research / analysis and why?
Academic staff: They like to use qualitative analysis like narratives and discourse analysis as it gives more liberty and
better power to analyse things in a better way.
Students: They have only worked on qualitative analysis like narratives and action research as it gives better methods
to understand behaviours.
T2. How strongly do you believe in <strong>the</strong> use <strong>of</strong> narratives (story telling) for problem solving?
Academic staff: They strongly believe that narratives are <strong>the</strong> best choice as it provides context.
Students: They don’t have a justification but strongly believe in it
T9. How could <strong>the</strong> PS experience been better for you and <strong>the</strong> rest <strong>of</strong> <strong>the</strong> stakeholders?
Academic staff: The length is too long, needs to be quicker and flexible and <strong>the</strong> interface can be improved.
Students: Make it short and a bit more colourful
T10. Do you think such an application (PS) can be used in a large organisation like <strong>the</strong> university?
Academic staff: They think it can be used depending on <strong>the</strong> degree <strong>of</strong> participants who contribute.
Students: They think it can be used if marketed properly to <strong>the</strong> students
T12. What are your views on anonymity regarding PS? Do you support it and why?
Academic staff: The choice should be given to <strong>the</strong> user
Students: The choice should be given to <strong>the</strong> user
T13. Would you like to share your story and see what o<strong>the</strong>rs are writing about on PS?
Academic staff: They have no problem in sharing <strong>the</strong>ir stories and see o<strong>the</strong>rs as well
Students: They have no problem in sharing <strong>the</strong>ir stories and see o<strong>the</strong>rs as well
656

Using a FuzzyQFD Approach for Successful
Implementation <strong>of</strong> Knowledge Management
Mohammad Mirkazemi Mood 1 , Farhad Daneshgar 2 , Mona Mirkazemi Mood 3 ,
Nima sarabi 1 and Hossein Rahmany Youshanlouei 1
1
University <strong>of</strong> Tehran, Tehran, Iran
2
University <strong>of</strong> Technology Sydney, Sydney, Australia
3
University <strong>of</strong> Windsor, Windsor, Canada
mohamad_mirkazemi@yahoo.com
f.daneshgar@unsw.edu.au
mona.mkm@gmail.com
nimasarabi@yahoo.com
rahmani82@gmail.com
Abstract: There are a lot <strong>of</strong> differences between Knowledge Management Strategies regarding <strong>the</strong>ir
infrastructures, tools and methods. The cost and time, dedicated to <strong>the</strong> implementation <strong>of</strong> <strong>the</strong>se strategies make
<strong>the</strong> evaluation and selection <strong>of</strong> a single Knowledge Management strategy a critical affair for all those
organizations that hope to administer <strong>the</strong>m successfully. The number <strong>of</strong> studies which, in <strong>the</strong> literature <strong>of</strong>
Knowledge Management, have tried to apply an applied approach to <strong>the</strong> process <strong>of</strong> choosing a knowledge
management strategy ge knowledge management strategy c approach to <strong>the</strong> process <strong>of</strong> choosing a knowled و
organizations tha , management strategy nagement is limited. Therefore, <strong>the</strong> present paper seeks to propose an
applicable solution, based on <strong>the</strong> Fuzzy Set Theory and <strong>the</strong> concept <strong>of</strong> <strong>the</strong> House <strong>of</strong> Quality, to make <strong>the</strong>
selection and evaluation <strong>of</strong> a suitable strategy possible and also localized for each organization's needs. The
suitable strategy, in <strong>the</strong> proposed approach, is chosen via a multi-level process and based on <strong>the</strong> relationship
between Knowledge Management performance criteria, Knowledge Creation Processes and Knowledge
Management Strategies. In addition, <strong>the</strong> knowledge management strategies are ranked based on <strong>the</strong>
organization's needs and expectations out <strong>of</strong> <strong>the</strong> implementation <strong>of</strong> knowledge management. For this purpose,
<strong>the</strong> Houses <strong>of</strong> Quality which have been successfully adopted in <strong>the</strong> new product development processes are
used. Also, Fuzzy Set Theory has been used to eliminate <strong>the</strong> ambiguities <strong>of</strong> linguistic variables which are used in
<strong>the</strong> judgments and <strong>the</strong>ir conversion to fuzzy numbers. Finally, a case study is conducted to show and prove <strong>the</strong>
applicability <strong>of</strong> <strong>the</strong> proposed approach.
Keywords: KM strategies, knowledge creation processes, KM performance, QFD, fuzzy logic.
1. Introduction
Nowadays, knowledge has become a Competitive Advantage for all organizations. In order to turn this
knowledge to <strong>the</strong>ir advantage, organizations need to manage it. Knowledge Management (KM) is a
process that helps organizations to identify, select, organize, disseminate, and transfer important
skills and information (Gupta et al. 2000). Organizations should pursue an appropriate strategy to
manage <strong>the</strong>ir knowledge so that <strong>the</strong>y can organize all <strong>the</strong>ir resources and abilities in <strong>the</strong>ir hope to
attain <strong>the</strong> ultimate goals <strong>of</strong> Knowledge Management Strategies. Due to <strong>the</strong> fact that any KM strategy
is broad and includes different dimensions <strong>of</strong> humans, organizational structure and technology
(Davenport and prusak 2000), any organization may choose a localized special strategy to attain its
desired goals. Because <strong>of</strong> <strong>the</strong> fact that KM strategies and tools impose remarkably large amounts <strong>of</strong>
money on organizations, and also <strong>the</strong> fact that <strong>the</strong> process is time-consuming, it is obligatory that
organizations be cautious in choosing <strong>the</strong> suitable approaches, tools and techniques <strong>of</strong> KM and select
<strong>the</strong> most appropriate one. Very few studies, however, have been conducted in <strong>the</strong> literature <strong>of</strong> KM
that aspire to apply an applied approach to <strong>the</strong> process <strong>of</strong> choosing a suitable KM strategy based on
<strong>the</strong> organization's needs and expectations out <strong>of</strong> <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> KM. Therefore, <strong>the</strong>
present paper seeks to propose a FuzzyQFD approach that is based on <strong>the</strong> concept <strong>of</strong> "House <strong>of</strong>
Quality" as well as Fuzzy Set Theory. The approach helps <strong>the</strong> organizations choose a suitable KM
strategy based on <strong>the</strong>ir desired outcome <strong>of</strong> implementing <strong>the</strong> knowledge management.
First, <strong>the</strong> paper conducts a Literature Review regarding previous researches on Knowledge Creation
Processes, KM strategies, KM Performance Criteria, and <strong>the</strong>n <strong>the</strong> paper introduces <strong>the</strong> proposed
approach. A case study is also conducted in <strong>the</strong> end that shows how <strong>the</strong> approach has been
implemented in a real organization.
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
2. Literature review
2.1 Knowledge creation
The organization's interests in intra-organizational knowledge have remarkably increased in recent
years. A reason for this fact is that knowledge depreciates much more quickly in today's competitive
market and <strong>the</strong> time to utilize organizational knowledge is noticeably limited (Szulanski, 2000) so
much so that businesses that can't constantly create knowledge, and as a result <strong>the</strong>ir previous
knowledge turns obsolete, are doomed to fail (Choi and Lee 2002). Knowledge creation signifies <strong>the</strong>
expansion and extension <strong>of</strong> <strong>the</strong> new organizational knowledge.
Knowledge creation and Knowledge acquisition can lead to <strong>the</strong> creation <strong>of</strong> new knowledge in <strong>the</strong>
organization which, in turn, provides <strong>the</strong> organization with an accumulated knowledge that can be
used as a source for o<strong>the</strong>r KM strategies and activities (Nielsen 2006). It is for this reason that <strong>the</strong>
organization's ability to create knowledge is its most important source <strong>of</strong> competitive advantage (Choi
and Lee 2002). Knowledge creation is one <strong>of</strong> <strong>the</strong> most important phases <strong>of</strong> KM that is in close relation
to Creativity.
Knowledge creation is considered as a continuous process in which people and groups, inside or
among organizations, share <strong>the</strong>ir tacit and explicit knowledge (Choi and Lee 2002). Nonaka and
Takeuchi (2005) proposed <strong>the</strong> SEDI model, in <strong>the</strong> field <strong>of</strong> knowledge creation, which explains <strong>the</strong>
knowledge creation process in <strong>the</strong> interchange <strong>of</strong> tacit and explicit knowledge. These processes have
been illustrated in figure 1.
Socialization: is a process that creates such new tacit knowledge as shared mental models, technical
skills and shared experiences.
Externalization: is a process during which tacit knowledge turns into objective and tangible concepts.
Combination: is a process in which <strong>the</strong> new explicit knowledge is made out <strong>of</strong> different sources <strong>of</strong>
explicit knowledge.
Internalization: is a process during which explicit knowledge is changed into tacit knowledge. The
explicit knowledge can <strong>the</strong>n be internalized as <strong>the</strong> persons' tacit knowledge.
Figure 1: Knowledge creation processes (adapted from Nonaka & Takeuchi 1995)
2.2 Knowledge management strategies
Just as knowledge is divided into Tacit and Explicit Knowledge, in a general classification, KM
strategies, too, are divided into two categories:
The first category includes those strategies that pursue <strong>the</strong> management <strong>of</strong> explicit knowledge. They
put an emphasis on <strong>the</strong> organization's ability to create, store, share and use codified knowledge.
Information Technologies are <strong>of</strong> paramount importance in this category. The second category
includes a KM strategy that focuses on <strong>the</strong> human aspects. This strategy focuses on <strong>the</strong> interpersonal
interactions and <strong>the</strong>ir face to face relationships in social networks which, in turn, lead to <strong>the</strong>
acquisition and sharing <strong>of</strong> knowledge in an informal way (Choi and Lee 2002).
Choi and Lee (2003) divided <strong>the</strong> required strategies <strong>of</strong> KM implementation into four categories,
namely: System oriented, Human oriented, Dynamic, Passive.
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
Figure 2 shows <strong>the</strong> status <strong>of</strong> <strong>the</strong>se strategies regarding <strong>the</strong> kind <strong>of</strong> knowledge and level <strong>of</strong> tacit and
explicit knowledge.
Figure 2: KM Strategies (Choi and Lee 2003)
Organizations that are passive in implementing KM have little tendency to apply <strong>the</strong> strategies and
<strong>the</strong>refore, no attention is paid to <strong>the</strong> systematic implementation <strong>of</strong> KM in <strong>the</strong>ir work processes. Those
organizations that capitalize on <strong>the</strong> human oriented strategy are more likely to favor tacit knowledge,
its creation and sharing via human relationships in unauthorized social networks. There are o<strong>the</strong>r
organizations, however, that choose <strong>the</strong> system oriented strategy; consequently, <strong>the</strong>y pursue <strong>the</strong>
processes <strong>of</strong> explicit knowledge acquisition and storage. It should be noted that information
technologies are <strong>of</strong> critical significance in this strategy. The third group <strong>of</strong> organizations includes
those which, partaking <strong>of</strong> a dynamic strategy, tend to use both kinds <strong>of</strong> tacit and explicit knowledge.
According to <strong>the</strong> characteristics <strong>of</strong> <strong>the</strong> desired knowledge, those organizations can use <strong>the</strong> tools <strong>of</strong>
both human and system oriented strategies (Choi and Lee 2002, 2003).
Few studies, however, have tried to propose an applied approach in choosing a suitable strategy <strong>of</strong>
KM for organizations. Wu and Lee (2007) proposed a four level approach for <strong>the</strong> selection <strong>of</strong> a
suitable KM strategy. They considered <strong>the</strong> problem <strong>of</strong> KM strategy selection as a multi-criteria
decision making issue and <strong>the</strong>refore used <strong>the</strong> analytic network process to choose a suitable method.
In this study, human and system oriented, as well as dynamic strategies were chosen as model option
for KM strategies. Wu (2008), too, presented a similar model like <strong>the</strong> previous study. The difference,
however, lies in <strong>the</strong> approach in that he proposed a combination <strong>of</strong> ANP and DEMATEL methods for
<strong>the</strong> selection <strong>of</strong> a suitable KM strategy.
2.3 The relationship between KM strategies, knowledge creation and performance
Choi and Lee (2002) investigated, in a research, <strong>the</strong> relationship <strong>of</strong> KM Strategies and Knowledge
Creation. They concluded that, <strong>the</strong> most suitable strategy, regarding <strong>the</strong> process <strong>of</strong> socialization, is
that <strong>of</strong> <strong>the</strong> human oriented one, whereas, regarding <strong>the</strong> process <strong>of</strong> combination, this strategy is <strong>the</strong>
least relevant and <strong>the</strong>refore not suitable. They also concluded that <strong>the</strong> best strategy for "Combination"
is <strong>the</strong> system oriented one which has <strong>the</strong> least relevancy to <strong>the</strong> socialization process. Balanced
strategy, too, is suitable for both processes <strong>of</strong> Internalization and Externalization. Choi (2002) also
concluded in his research that <strong>the</strong> knowledge creation process in <strong>the</strong> organization, achieved through
using creativity in <strong>the</strong> production <strong>of</strong> <strong>the</strong> products and services, leads to innovation and is <strong>the</strong>refore
important in <strong>the</strong> performance <strong>of</strong> <strong>the</strong> organization. Alongside this study, a number <strong>of</strong> o<strong>the</strong>r studies
include Koh (2000) who confirmed <strong>the</strong> positive relationship between knowledge creation and
creativity, as well as Shani et al. (2000) who confirmed <strong>the</strong> positive relationship between creativity and
organizational performance. Lee and Lee (2007) tried, in a study, to investigate <strong>the</strong> structural
relationships among capabilities, processes and performance <strong>of</strong> KM. They concluded that <strong>the</strong>re
existed a positive relationship among capabilities, KM processes and <strong>the</strong> performance efficiency <strong>of</strong>
KM. In <strong>the</strong> aforementioned study, one <strong>of</strong> <strong>the</strong> investigated processes was that <strong>of</strong> <strong>the</strong> Knowledge
Creation. The writers <strong>of</strong> this study divided <strong>the</strong> performance criteria into two groups <strong>of</strong> financial and
customer oriented criteria.
One important point to remember is that organizations pursue different goals out <strong>of</strong> <strong>the</strong>
implementation <strong>of</strong> KM and, most certainly, managers, too, expect different outcomes from <strong>the</strong>
process. Plessis (2005) outlined <strong>the</strong> goals as: Increase in organizational competitive advantages,
supporting <strong>the</strong> strategy <strong>of</strong> <strong>the</strong> business, performance improvements, turning <strong>the</strong> organization into an
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
intelligent institute, nurturing a creative and innovative circumstance and culture and cooperation
opportunities. Wu (2008), too, outlined <strong>the</strong>se goals as: Performance improvements, efficiency,
competitiveness, and developing such processes as knowledge acquisition, sharing and use.
However, Chong et al. (2006), having investigated <strong>the</strong> literature <strong>of</strong> KM, recognized five groups <strong>of</strong>
criteria that can be considered as <strong>the</strong> expected consequences <strong>of</strong> <strong>the</strong> organizations as <strong>the</strong>y implement
KM. Chong et al. named <strong>the</strong>se criteria "KM Performance Criteria." Table 1 explains each one <strong>of</strong> <strong>the</strong>
five groups.
Table 1: KM Performance Criteria (adapted from Chong et al. 2006)
Criteria
Systematic knowledge activities
Employee development
Customer satisfaction
Good external relationship
Organizational success
3. The Proposed approach
Definition
Includes <strong>the</strong> processes <strong>of</strong> creating, ga<strong>the</strong>ring, organizing,
distributing, utilizing, transferring and storing <strong>the</strong> necessary
knowledge <strong>of</strong> <strong>the</strong> organization.
Creating a circumstance in which people, with different
expertise and from different fields, can share <strong>the</strong>ir
knowledge and, as a result, create new knowledge for <strong>the</strong>
organization.
Improving <strong>the</strong> satisfaction <strong>of</strong> <strong>the</strong> customers through
interacting with <strong>the</strong>m; recognizing <strong>the</strong>ir needs and
expectations and so valuing <strong>the</strong>m more than before.
Maintaining an appropriate relationship with partners,
shareholders, and suppliers via sharing and transferring
knowledge, getting feedback, and <strong>the</strong>refore improving <strong>the</strong>
possibilities <strong>of</strong> innovation and creativity as well as new
knowledge creation.
The success <strong>of</strong> a knowledge oriented organization,
regarding <strong>the</strong>ir financial criteria and <strong>the</strong> intangible
resources and assets such as Intellectual resources.
The unique aspect <strong>of</strong> <strong>the</strong> concept <strong>of</strong> "House <strong>of</strong> Quality" in QFD method is its connecting <strong>the</strong> two sets
<strong>of</strong> variables known as HOWs and WHATs. The House <strong>of</strong> Quality, in <strong>the</strong> proposed approach, is
combined with fuzzy set <strong>the</strong>ory. The reason for this is that in <strong>the</strong> traditional QFD, most <strong>of</strong> <strong>the</strong> input
variables were considered as precise and were treated as numerical data (Kahraman et al. 2006)
whereas, in our proposed approach, needs and judgments are converted into linguistic data which is
inherently ambiguous. The point, however, is that since, instead <strong>of</strong> one, three groups <strong>of</strong> variables are
evaluated, two connected Houses <strong>of</strong> Quality are to be used. Using <strong>the</strong>se two units for <strong>the</strong> process <strong>of</strong>
decision making and choosing one <strong>of</strong> <strong>the</strong> items was first used by Bottani (2009). FuzzyQFD method
has been used for such numerous purposes as Product Development, Agility, Strategic Management
and supplier selection (Bevilacqua et al. 2006, Bottani and Rizzi 2006). It, however, has never been
used for KM.
Also, Triangular Fuzzy Numbers, TFN, are used to convert <strong>the</strong> linguistic judgments into numerical
data. TFNs are in fact <strong>the</strong> most common type <strong>of</strong> <strong>the</strong> Fuzzy Numbers. They are shown with three
points (Amin and Razmi 2011):
A = (a1, a2, a3)
And its membership function is:
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
Also, if A and B, as fuzzy numbers, are defined as:
A = (a1, a2, a3)
B = (b1, b2, b3)
So:
A+B = (a1 + b1, a2 + b2, a3 + b3)
A × B = (a1 * b1, a2 * b2, a3 * b3)
The proposed approach includes three main parts, namely:
Part 1: Choosing <strong>the</strong> intended performance that <strong>the</strong> organization expects out <strong>of</strong> <strong>the</strong> implementation <strong>of</strong>
KM.
Part 2: Identifying and assessing knowledge creation processes that improve <strong>the</strong> intended outcome
and performance.
Part 3: Identifying a suitable strategy to implement <strong>the</strong> intended knowledge creation processes.
Figure 3 illustrates <strong>the</strong> two Houses <strong>of</strong> Quality as well as <strong>the</strong> WHATs and HOWs variables in <strong>the</strong>
proposed approach.
The proposed approach has some levels and steps which have been thoroughly explained in <strong>the</strong> case
study.
Figure 3: The Houses <strong>of</strong> Quality
3.1 The advantages <strong>of</strong> proposed approach
In implementing KM, as well as o<strong>the</strong>r fields <strong>of</strong> management, <strong>the</strong>y face different issues and choices
that force <strong>the</strong>m to make decisions. These decision making issues can range from choosing specific
s<strong>of</strong>tware to determining <strong>the</strong> most important <strong>of</strong> <strong>the</strong> enablers and even to choosing <strong>the</strong> most suitable
strategy among <strong>the</strong> options. Therefore, this paper seeks to propose a suitable decision making
strategy by adopting <strong>the</strong> FuzzyQFD method in KM. The main advantage <strong>of</strong> this method is that it
considers Customer Demands along with experts' opinions in order to select <strong>the</strong> most appropriate
option. What we mean by customer demands in this paper is <strong>the</strong> expectations and goals <strong>of</strong> <strong>the</strong>
organization when it has implemented KM. The second advantage <strong>of</strong> this approach is that it takes all
<strong>the</strong> qualitative criteria and experts' judgments into consideration. The issue <strong>of</strong> strategy selection has
always included qualitative and mental criteria and unlike <strong>the</strong> decision making issues that are carried
661

Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
out in <strong>the</strong> lower levels <strong>of</strong> <strong>the</strong> organizations, cannot be solved by using purely quantitative methods. In
fact, in all <strong>the</strong> steps <strong>of</strong> <strong>the</strong> processes <strong>of</strong> selection, <strong>the</strong> judgments <strong>of</strong> experts as well as <strong>the</strong> managers'
expectations are used to determine <strong>the</strong> relative importance <strong>of</strong> WHATs and <strong>the</strong> internal relationship <strong>of</strong>
WHATs and HOWs variables. The final advantage <strong>of</strong> <strong>the</strong> proposed approach is its evaluation <strong>of</strong> <strong>the</strong>
relationship between KM strategies and KM performance via knowledge creation processes, which is
<strong>the</strong> distinguishing element <strong>of</strong> <strong>the</strong> present paper with such similar studies in <strong>the</strong> field <strong>of</strong> strategy
selection as Wu and Lee (2007) and Wu (2008). In <strong>the</strong> aforementioned studies, Multiple Criteria
Decision Making methods have been proposed in which <strong>the</strong> strategies are selected based on <strong>the</strong>
specified criteria. However, <strong>the</strong> proposed approach focuses on <strong>the</strong> Knowledge creation as <strong>the</strong> most
important process <strong>of</strong> KM and investigates its relation to KM strategies and also its effects on <strong>the</strong>
outcome <strong>of</strong> KM. Also, <strong>the</strong>re are similarities between <strong>the</strong>se studies as well. These similarities include
evaluating <strong>the</strong> internal relationships <strong>of</strong> <strong>the</strong> intended variables <strong>of</strong> <strong>the</strong> research as well as paying
attention to <strong>the</strong> operative goals <strong>of</strong> KM to be able to choose a suitable strategy.
4. Case Study
A case study has been conducted in one <strong>of</strong> Iran's companies to show how applied <strong>the</strong> approach is
and clarify <strong>the</strong> ways through which <strong>the</strong> suitable strategy is chosen by using <strong>the</strong> Fuzzy QFD method.
The aforementioned company is intending to pursue <strong>the</strong> specification <strong>of</strong> its KM strategy so that it
could invest and provide <strong>the</strong> required infrastructure to implement KM. As a result, a group, consisting
<strong>of</strong> <strong>the</strong> executives <strong>of</strong> <strong>the</strong> company, authors <strong>of</strong> <strong>the</strong> paper and experts, was formed. All <strong>the</strong> steps will be
explained thoroughly in <strong>the</strong> following lines:
4.1 Part 1
Step 1: Having reviewed <strong>the</strong> literature and previous studies, we need to assess <strong>the</strong> most important
performance criteria which are supposed to improve after <strong>the</strong> implementation. The KM performance
criteria, proposed by Chong et al. (2006) and explained in Table 1, have been used for this case
study.
Step 2: Having assessed <strong>the</strong> criteria, we asked <strong>the</strong> managers to determine <strong>the</strong> relative importance <strong>of</strong>
each criterion based on <strong>the</strong> company's needs, using <strong>the</strong> linguistic variables <strong>of</strong> Table 2. The calculated
fuzzy numbers is Wi or <strong>the</strong> relative importance <strong>of</strong> each KM performance criteria from <strong>the</strong> viewpoint <strong>of</strong>
<strong>the</strong> managers. The determined or specified importance, evaluated by managers, is illustrated in front
<strong>of</strong> each criterion in figure 4.
Table 2: The linguistics scale (Bottani 2009)
Importance judgment
Very High (VH)
High (H)
Low (L)
Very Low (VL)
Fuzzy number
(0.7; 1; 1)
(0.5; 0.7; 1)
(0; 0.3; 0.5)
(0; 0; 0.3)
4.2 Part 2
Step 1: After reviewing <strong>the</strong> literature, we decided that we could use <strong>the</strong> SEDI model, explained in
figure 1, for this study. The reason for this selection is <strong>the</strong> acceptable and valid status <strong>of</strong> this model in
most <strong>of</strong> KM researches and studies.
Step 2: The corresponding relationships between each knowledge creation process (HOWs) and KM
performance criteria (WHATs) are established. This is done by <strong>the</strong> experts, partaking <strong>of</strong> one <strong>of</strong> <strong>the</strong>
proposed phrases in Table 3. The scale <strong>of</strong> <strong>the</strong> relationship determined by <strong>the</strong> experts in terms <strong>of</strong>
fuzzy numbers is illustrated in Figure 4.
Table 3: Relationships, graphic symbols and corresponding fuzzy numbers (Bottani 2009)
Degree <strong>of</strong>
Graphic symbol Fuzzy number
relationship
Strong (S)
● (0.7; 1; 1)
Medium (M)
○ (0.3; 0.5; 0.7)
Weak (W) ▲ (0; 0; 0.3)
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
Step 3: According to Bottani (2009) we can conclude <strong>the</strong> relative importance <strong>of</strong> each knowledge
creation process by Formula 1. The calculated results for <strong>the</strong> relative importance <strong>of</strong> each process are
shown in Figure 4.
RIj= i ⊗ RIij j = 1, …. , m
Wi : The weighted importance <strong>of</strong> <strong>the</strong> i-th KM Performance criterion.
Rij : The fuzzy number expressing <strong>the</strong> relationship between <strong>the</strong> j-th knowledge creation process and
<strong>the</strong> i-th KM Performance criterion.
Step 4: This step shows <strong>the</strong> correlation between knowledge creation processes (HOWs). This
correlation is rephrased by experts, in terms <strong>of</strong> one <strong>of</strong> <strong>the</strong> phrases in Table 4. The correlation
determined by experts has been illustrated in <strong>the</strong> ro<strong>of</strong> part <strong>of</strong> <strong>the</strong> House <strong>of</strong> Quality in Figure 4.
Table 4: Correlations, graphic symbols and corresponding fuzzy numbers (Bottani 2009)
Degree <strong>of</strong>
correlation
(1)
Graphic symbol Fuzzy number
Strong positive(SP) ● (0.3; 0.5; 0.7)
Positive(P) ○ (0; 0.3; 0.5)
Negative(N) □ (-0.5; -0.3; 0)
Strong
Negative(SN)
■ (-0.7; -0.5; -0.3)
Step 5: Using Formula 2, <strong>the</strong> score <strong>of</strong> each knowledge creation process is calculated.
Scorej = RI j
⊕ T jj́
⊗
RI j́
j = 1, ….. , m
Tjj́ : The correlation between <strong>the</strong> j-th and <strong>the</strong> j́-th knowledge creation process.
RIj́ : The relative importance <strong>of</strong> j́-th knowledge creation process
Step 6: Having found <strong>the</strong> obtained scores <strong>of</strong> each process in <strong>the</strong> form <strong>of</strong> TFN, we should normalize
<strong>the</strong>se numbers. The way to normalize is to divide <strong>the</strong> scores by <strong>the</strong> highest score (Bevilacqua et al.
2006). For instance, in <strong>the</strong> given example <strong>of</strong> this study, 8.51 is <strong>the</strong> highest number.
Each <strong>of</strong> <strong>the</strong> knowledge creation processes is now considered as a WHAT variable in <strong>the</strong> House <strong>of</strong>
Quality. Besides, <strong>the</strong>ir normalized weight is considered as <strong>the</strong> relative importance (Wj) in <strong>the</strong> second
House <strong>of</strong> Quality.
4.3 Part 3
Step 1: Having reviewed <strong>the</strong> literature, we used a study conducted by Chong and Lee (2003) and
referred to in figure 2. These strategies, considered as <strong>the</strong> choices for <strong>the</strong> company, are now known
as HOW variables in <strong>the</strong> second House <strong>of</strong> Quality.
Step 2: Quite similar to what we did in <strong>the</strong> second step <strong>of</strong> part 2, <strong>the</strong> corresponding relationships
between knowledge creation processes and KM strategies are established. This time, Knowledge
creation processes are considered as WHAT variables. This can be done, like <strong>the</strong> second step <strong>of</strong> part
2, by <strong>the</strong> experts who use <strong>the</strong> language variables <strong>of</strong> Table 3. The relationship between knowledge
creation processes and KM strategies is illustrated in Figure 5. The relationships determined by
experts are almost identical with <strong>the</strong> results <strong>of</strong> Choi (2002) study.
Step 3: Similar to what we did in <strong>the</strong> third step <strong>of</strong> part 2, <strong>the</strong> relative importance <strong>of</strong> knowledge creation
processes is calculated with Formula 3 (Yager 1981, Bottani 2009).
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RI = w
k j
Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
⊗
RI jk
k = 1, …. , n
Wj : The weighted importance <strong>of</strong> <strong>the</strong> j-th Knowledge creation process
Rjk : The fuzzy number expressing <strong>the</strong> relationship between <strong>the</strong> j-th knowledge creation process and
<strong>the</strong> k-th KM strategy.
Step 4: The correlation rate <strong>of</strong> KM strategies are specified. The correlation is determined by one <strong>of</strong>
<strong>the</strong> experts who use one <strong>of</strong> <strong>the</strong> phrases <strong>of</strong> Table 4. The determined correlations are illustrated in <strong>the</strong>
top ro<strong>of</strong> part <strong>of</strong> <strong>the</strong> House <strong>of</strong> Quality in Figure 5.
Step 5: The score <strong>of</strong> each KM strategy is calculated by using Formula 4 (Bottani 2009).
Score k = RI k
⊕ T ⊗ RI
kḱ ḱ
k= 1, …. , n
Tkḱ= The correlation between <strong>the</strong> k- th and <strong>the</strong> ḱ -th knowledge creation process.
Step 6: The obtained score <strong>of</strong> each strategy in <strong>the</strong> previous step is in <strong>the</strong> form <strong>of</strong> V(l, m, u) which is a
TFN. In order to rank <strong>the</strong> strategies, <strong>the</strong> obtained scores need to be converted to crisp values. To do
so, Formula 5, proposed by Yager (1981), is utilized. The crisp value <strong>of</strong> each strategy is calculated
and <strong>the</strong>n <strong>the</strong> strategies are ranked. The strategy that has obtained <strong>the</strong> highest crisp value is <strong>the</strong> most
suitable KM strategy that can help <strong>the</strong> organization to achieve <strong>the</strong> goals it pursues out <strong>of</strong> <strong>the</strong>
implementation <strong>of</strong> KM.
Crisp Value =
5. Conclusion
The successful implementation <strong>of</strong> KM in organizations requires, in <strong>the</strong> first place, <strong>the</strong> selection <strong>of</strong> a
suitable strategy for which, <strong>the</strong> present paper has sought to propose a simple and applied solution
and approach. The attained results <strong>of</strong> <strong>the</strong> case study, conducted on a given company in Iran, prove
<strong>the</strong> claims made by <strong>the</strong> paper. We can deduce from <strong>the</strong> Crisp Values in figure 5 that <strong>the</strong> dynamic
strategy is <strong>the</strong> most suitable for <strong>the</strong> company. Then we have <strong>the</strong> system oriented and human oriented
strategies respectively. These results are predicted and confirmed by <strong>the</strong> experts <strong>of</strong> <strong>the</strong> company.
Also, <strong>the</strong> lowest Crisp values belonged, as expected, to <strong>the</strong> passive strategy. The reason for this is
because those organizations that choose <strong>the</strong> passive strategy do not, in fact, care for <strong>the</strong> KM at all.
The proposed approach, because it presents a tangible step by step guide, can be an effective way to
choose a suitable KM strategy. One <strong>of</strong> <strong>the</strong> pivotal aspects <strong>of</strong> this approach is <strong>the</strong> credit it gives to
mental and qualitative criteria that are very important in <strong>the</strong> process <strong>of</strong> choosing <strong>the</strong> strategy. Experts'
judgments as well as managers' opinions are also taken into consideration. Alongside this, in most <strong>of</strong>
<strong>the</strong> decision making methods, <strong>the</strong> independent decision variable is also taken into consideration. One
<strong>of</strong> <strong>the</strong> o<strong>the</strong>r advantages <strong>of</strong> <strong>the</strong> proposed approach is its ability to evaluate correlation and <strong>the</strong>
relationship between decision variables; for instance, considering <strong>the</strong> effects <strong>of</strong> correlation between
knowledge creation processes and KM strategies. This approach isn't limited only to KM strategy
selection but can also be used in o<strong>the</strong>r issues <strong>of</strong> KM decision making, though <strong>of</strong> course with some
differences. For instance, choosing and ranking <strong>the</strong> most important enablers <strong>of</strong> KM to achieve KM
goals as well as taking all knowledge creation processes into consideration, instead <strong>of</strong> focusing on
one single process, to choose a suitable strategy are among <strong>the</strong> issues that can be solved with this
method. Finally, it should be noted, that <strong>the</strong>re are some limitations in <strong>the</strong> proposed approach like: So
much reliance on <strong>the</strong> Experts' opinions, and ignoring objective and quantitative criteria.
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References
Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
Amin, S. H. Razmi, J. and Zhang, G. (2011) “Supplier selection and order allocation based on fuzzy SWOT
analysis and fuzzy linear programming”, Expert Systems with Applications, Vol. 38, pp 334-342.
Bevilacqua, M. Ciarapica, F. E. and Giacchetta, G. (2006) “A fuzzy-QFD approach to supplier selection”. Journal
<strong>of</strong> Purchasing & Supply Management, Vol. 12, pp 14-27.
Bottani, E. (2009) “A fuzzy QFD approach to achieve agility”. Journal Production Economics, Vol. 119, pp 380–
391.
Bottani, E. and Rizzi, A. (2006) “Strategic management <strong>of</strong> logistics service: a fuzzy QFD approach”. International
Journal <strong>of</strong> Production Economics, Vol. 103, No. 2, pp 585-599.
Choi, B. (2002) “Knowledge Management Enablers, Processes, and Organizational Performance: An Integration
and Empirical Examination”. PhD diss., Korea Advanced Institute <strong>of</strong> Science and Technology.
Choi, B. and Lee, H. (2003) “An empirical investigation <strong>of</strong> KM styles and <strong>the</strong>ir effect on corporate performance”,
Information & Management, Vol. 40, pp 403-417.
Choi, B. and Lee, H. (2002) “Knowledge management strategy and its link to knowledge creation process”,
Expert Systems with Applications, Vol. 23, pp 173-187.
Chong, S.C., Wong, K.Y. and Binshan, L. (2006) “Criteria for measuring knowledge management performance
outcomes in organizations”, Industrial Management & Data Systems, Vol. 106 No. 7, pp. 917-36.
Davenport, T. H. and Prusak, L. (2000) Working Knowledge: How Organizations Manage What <strong>the</strong>y
Know. Harvard Business School Press.
Gupta, B. and Iyer, L. S., & Aronson, J. E. (2000) “Knowledge management: Practices and challenges”, Industrial
Management and Data Systems, Vol. 100, No. 1, pp 17–21.
Jia, G. Z., and Bai, M. (2010) “An approach for manufacturing strategy development based on fuzzy-QFD”,
Computers & Industrial Engineering.
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Mohammad Mirkazemi Mood, Farhad Daneshga and Mona Mirkazemi Mood
Kahraman, C., Ertay, T., and Buyukozkan, G. (2006) “A fuzzy optimization model for QFD planning process using
analytic network approach”, <strong>European</strong> Journal <strong>of</strong> Operational Research, Vol. 171, pp 390-411.
Koh, A. T. (2000) “Linking learning, knowledge creation, and business creativity: a preliminary assessment <strong>of</strong> <strong>the</strong>
East Asian quest for creativity”, Technological Forecasting and Social Change, Vol. 64, pp 85-100.
Lee, Y. and Lee, k. (2007) “Capabilities, Processes, and Performance <strong>of</strong> Knowledge Management: A Structural
Approach”, Human Factors and Ergonomics in Manufacturing, Vol. 17 No. 1, pp 21–41.
Nielsen, A. P. (2006) “Understanding dynamic capabilities through knowledge management”, Journal <strong>of</strong>
Knowledge Management, Vol. 10, No. 4 2006, pp 59-71.
Nonaka, I. and Takeuchi, H. (1995) <strong>the</strong> knowledge creating company. New York: Oxford University Press.
Plessis, M. (2005) “Drivers <strong>of</strong> knowledge management in <strong>the</strong> corporate environment”, International Journal <strong>of</strong>
Information Management, Vol. 25, No. 3, pp 193–202.
Szulanski, G. (2000) “The process <strong>of</strong> knowledge transfer: a diachronic analysis <strong>of</strong> stickiness”, Organizational
Behavior and Human Decision Processes, Vol. 82, No. 1, pp 9-27.
Wu, W. and Lee. Y. (2007) “Selecting knowledge management strategies by using <strong>the</strong> analytic network process”,
Expert Systems with Applications, Vol. 32, pp 841–847.
Wu, W. (2008) “Choosing knowledge management strategies by using a combined ANP and DEMATEL
approach”, Expert Systems with Applications. Vol.35, pp 828–835.
Yager, R. (1981) “A procedure for ordering fuzzy subsets <strong>of</strong> <strong>the</strong> unit interval”. Information Science, Vol. 24, pp
143–161.
667

Knowledge Mapping Based on EFQM Excellence Model: A
Practical Tool to Make Visible Organizational Knowledge
Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam
Momeni
Management Department, University <strong>of</strong> Guilan, Rasht, Iran
m.moradi@guilan.ac.ir
m_ramazanian391@yahoo.com
maisam_mmeni@yahoo.com
Abstract: As organizations strive to improve <strong>the</strong>ir business performance and capacity for innovation, <strong>the</strong>ir
attention is increasingly focused on how <strong>the</strong>y manage knowledge. This is a reason why Utilizing organizational
knowledge is a strategic weapon to acquire a competitive advantage. In knowledge management (KM)
processes, Representing and capturing Knowledge is an important constituent. One tool to represent and make
knowledge visible throughout <strong>the</strong> organizations is knowledge mapping. Knowledge mapping plays an important
role in <strong>the</strong> construction, learning, and dissemination <strong>of</strong> knowledge. KM is most effective when it is approached
holistically. This is achieved through a series <strong>of</strong> integrated initiatives aligning human resource issues, ICT
infrastructures and informal learning interventions that enable <strong>the</strong> organization to improve <strong>the</strong> quality <strong>of</strong> <strong>the</strong>
knowledge it holds, enhance access to and <strong>the</strong> retrieval <strong>of</strong> <strong>the</strong> knowledge. <strong>European</strong> Foundation for Quality
Management excellence (EFQM) model could be considered as an interface to integrate KM. EFQM excellence
model is an appropriate assessment tool for organization to identify which sectors have strengths and which has
weaknesses. To do so, model provides some criteria and sub criteria in two main sections: enablers and results.
In order to achieve bullet points in EFQM model, organization require a set <strong>of</strong> skills and knowledge in <strong>the</strong>
organization. Exploitation <strong>of</strong> proper knowledge maps supports <strong>the</strong> organization to classify <strong>the</strong>se knowledge and
skills. This paper proposes a practical framework to capture and represent organizational knowledge in a holistic
approach based on EFQM enablers. In order to achieve this aim, paper focus on enablers components.
Subsequently by exploring <strong>the</strong>se enablers in a systematic view, knowledge maps were developed for enablers.
Proposed framework is subject to implement in a real case in shipyard industries to provide practical evidences.
Following <strong>the</strong> illustration <strong>of</strong> knowledge maps role in KM process, paper proceeds by analyzing different
knowledge maps. After a brief review <strong>of</strong> EFQM model and <strong>the</strong> role on knowledge maps in this model, <strong>the</strong>
appropriate map will be selected to map organizational knowledge based on bullet points in EFQM model. Finally
lessons learned from industrial case study will discuss.
Keywords: knowledge representation, knowledge mapping, knowledge about knowledge, EFQM Excellence
Model, knowledge capturing, shipyard industry
1. Introduction
Knowledge management is an emerging field that has commanded attention and support from <strong>the</strong>
industrial community. Many organizations currently engage in KM in order to leverage knowledge both
within <strong>the</strong>ir organization and externally to <strong>the</strong>ir shareholders and customers (Rubenstein-Montano et
al. 2001). As organizations strive to improve <strong>the</strong>ir business performance and capacity for innovation,
<strong>the</strong>ir attention is increasingly focused on how <strong>the</strong>y manage knowledge. This is a reason why Utilizing
organizational knowledge is a strategic weapon to acquire a competitive advantage. In knowledge
management (KM) processes, Representing and capturing Knowledge is an important constituent.
One tool to represent and make knowledge visible throughout <strong>the</strong> organizations is knowledge
mapping. Knowledge mapping plays an important role in <strong>the</strong> construction, learning, and dissemination
<strong>of</strong> knowledge.
Capturing and representing knowledge buried in people and organization are <strong>the</strong> fundamental
building blocks <strong>of</strong> knowledge management implementation (Kim et al. 2003). Adopting knowledge
map permit <strong>the</strong> organization to find and locate right knowledge at right time for right person.
EFQM excellence model is an appropriate assessment tool for organization to identify which sectors
have strengths and which has weaknesses. To do so, model provides some criteria and sub criteria in
two main sections: enablers and results. In order to achieve bullet points in EFQM model,
organization require a set <strong>of</strong> skills and knowledge in <strong>the</strong> organization. Exploitation <strong>of</strong> proper
knowledge maps supports <strong>the</strong> organization to classify <strong>the</strong>se knowledge and skills.
In <strong>the</strong> next section <strong>the</strong> role <strong>of</strong> knowledge maps on KM especially in KM process will illustrate. Paper
proceeds by analyzing different knowledge maps in third section. After a brief review <strong>of</strong> EFQM model
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
and <strong>the</strong> role on knowledge maps in this model, <strong>the</strong> appropriate one will be selected to map
organizational knowledge based on bullet points in EFQM model. In order to verify <strong>the</strong> presented
model, a case study is used in <strong>the</strong> Shipyard Industry. Finally, we discuss lessons learned from
industrial case study.
2. Knowledge Map and KM Processes
A knowledge map is generally a visual representation <strong>of</strong> 'knowledge about knowledge' ra<strong>the</strong>r than <strong>of</strong>
knowledge itself (Haun 2002; Eppler 2001; Wexler 2001). It basically helps to detect <strong>the</strong> sources <strong>of</strong>
knowledge and structure <strong>of</strong> knowledge by representing <strong>the</strong> elements and structural links <strong>of</strong> application
domains. According to Grey (1999), a knowledge map is a navigation aid to explicit and tacit
knowledge, illustrating how knowledge flows throughout an organization. The knowledge map
portrays <strong>the</strong> sources, flows, constraints and terminations <strong>of</strong> knowledge within an organization.
Knowledge mapping helps to understand <strong>the</strong> relationships between knowledge stores and dynamics.
Effective KM requires an organization to identify, generate, acquire, diffuse, and capture <strong>the</strong> benefits
<strong>of</strong> knowledge that provide a strategic advantage to that organization. Nonaka and Takeuchi (1995)
defined <strong>the</strong> most cited four processes <strong>of</strong> organizational knowledge creation. They defined processes
as: Socialization, Externalization, Combination, and Internalization. Socialization is <strong>the</strong> process <strong>of</strong>
transferring tacit knowledge between individuals. Externalization is <strong>the</strong> process in which <strong>the</strong> person
turns <strong>the</strong>ir tacit knowledge into explicit knowledge through documentation, verbalization, etc.
Combination is <strong>the</strong> process where new explicit knowledge is created through <strong>the</strong> combination <strong>of</strong> o<strong>the</strong>r
explicit knowledge. Internalization is <strong>the</strong> process in which an individual internalizes explicit knowledge
to create tacit knowledge. Davenport and Prusak (1998), Alavi and Leidner (2001) identify four
practical knowledge processes: knowledge generation (Creation and acquisition), knowledge
codification (storing), knowledge transfer (sharing), and knowledge application (use).
knowledge codification is <strong>the</strong> process by which explicit knowledge is detached from its source and put
in a state in which it can easily be transferred via different media, such as s<strong>of</strong>tware or books.
According to definition <strong>of</strong> knowledge map it mainly deals with explicit knowledge as ‘knowledge about
knowledge’. Hence it can be consider as a convenient tool for knowledge combination and knowledge
codification processes. As a result knowledge map help to integrating and storing <strong>of</strong> organizational
knowledge.
3. Types <strong>of</strong> Knowledge Maps
Even though a knowledge map has a clear usage in KM process, however, it is not easily
recognizable by <strong>the</strong> map users what kind <strong>of</strong> knowledge map to choose, since many types <strong>of</strong>
knowledge maps can be and are applied for making visible organizational tacit knowledge. A visual
description <strong>of</strong> knowledge flows in a given situation can, for example, be descriptive, with <strong>the</strong> purpose
<strong>of</strong> assessing <strong>the</strong> knowledge domain or prescriptive with <strong>the</strong> intention <strong>of</strong> instruction map users on how
to apply knowledge (Jetter et al 2006). This section briefly presents five commonly known types <strong>of</strong>
knowledge maps and will compare <strong>the</strong>m by <strong>the</strong>ir characteristics in table 1.
Hierarchical knowledge map, so-called concept maps (Novak and Gowin 1984) provide one model for
<strong>the</strong> hierarchical organization <strong>of</strong> knowledge: top-level concepts are abstract with few characteristics.
Concepts on <strong>the</strong> level below have detailed individual traits <strong>of</strong> <strong>the</strong> super concept. The propositions
between concepts can represent any type <strong>of</strong> relations as ”is part <strong>of</strong>”, ”influences”, ”can determine”,
etc. A similar approach is radial knowledge map or mind map (Buzan and Buzan 1996) which consists
<strong>of</strong> concepts that are linked through propositions. However, it has radially organized. Networked
knowledge map is also called causal map which is defined as a technique ”for linking strategic
thinking and acting, helping make sense <strong>of</strong> complex problems, and communicating to oneself and
o<strong>the</strong>rs what might be done about <strong>the</strong>m” (Buzan and Buzan 1996). This approach is normally served
for systematizing knowledge about cause and effects. Knowledge source map (Eppler 2001) is a kind
<strong>of</strong> organizational charts that does not describe functions, responsibility and hierarchy, but expertise. It
helps to detect experts in a specific knowledge domain. The last one, knowledge flow map (Eppler
2001) represents <strong>the</strong> order in which knowledge resources are and should be used ra<strong>the</strong>r than a map
<strong>of</strong> knowledge.
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
Table 1: Attributes <strong>of</strong> knowledge maps
4. The EFQM Excellence Model
The EFQM Excellence Model (EFQM 1999) is a generic model for quality management, which is used
in all types <strong>of</strong> organizations, regardless <strong>of</strong> sector, size, structure or maturity. The model has nine
dimensions, which are called criteria. The nine criteria are: leadership, people, policy and strategy,
partnership and resources, processes, people result, customer result, society result and key
performance result. The nine criteria are classified into 'enabler' and 'result' criteria. 'Results' are
caused by 'enablers' and 'enablers' are affected and improved using feedback from <strong>the</strong> 'results'
achieved (CIBIT 2005). basically, it means that an organization with well-developed enablers will have
excellent result. An illustration <strong>of</strong> model is given in Figure 1.
Each criterion <strong>of</strong> <strong>the</strong> EFQM model includes a number <strong>of</strong> sub criteria; <strong>the</strong> total number <strong>of</strong> sub criteria is
32. The enablers are broken down into 24 sub criteria, which are used to assess <strong>the</strong> approach, <strong>the</strong>
deployment and <strong>the</strong> evaluation. The result dimensions are broken down into 8 sub criteria, which
require objective measures, data and facts, allowing comparison <strong>of</strong> performance with o<strong>the</strong>r
organizations. The sub criteria are illustrated with 173 examples, which are called areas to address
and measurements. The areas to address and <strong>the</strong> measurements make <strong>the</strong> sub criteria and <strong>the</strong>
dimensions understandable and clear; an overview is given in Table 2.
Figure 1: The EFQM Excellence model (Adopted from EFQM 1999)
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
Table 2: Overview <strong>of</strong> criteria in EFQM model
Criteria Number <strong>of</strong> sub criteria Areas to address and measurements
1. Leadership 4 24
2. Policy and strategy 5 28
3. People 5 30
4. Partnership and resources 5 33
5. Processes 5 29
6. Customer results 2 8
7. People result 2 5
8. Society result 2 8
9. Key performance result 2 8
Total 32 173
5. Integration <strong>of</strong> EFQM and Knowledge Map
As mentioned above, EFQM excellence model provides a comprehensive view for <strong>the</strong> organizations
to <strong>the</strong>ir performance and processes. It looks at all areas <strong>of</strong> <strong>the</strong> organization by <strong>of</strong>fering a holistic
approach. The model <strong>the</strong>refore provides a basis based on which we can develop organizational
knowledge maps. Knowledge maps developed in this foundation will provide a holistic and integrated
view to <strong>the</strong> organizational knowledge in various functional areas. Since <strong>the</strong>se maps ga<strong>the</strong>r and
present in a unified approach organizational knowledge <strong>the</strong>n developing knowledge maps based on
EFQM model will be beneficial. Some benefits are:
Based on holistic approach <strong>of</strong> EFQM model toward organizational performance and processes,
knowledge need to effectively perform <strong>the</strong>se processes could be made explicit by knowledge
maps.
Based upon <strong>the</strong> knowledge maps provided in this manner, organizations could develop and
define learning strategies to obtain <strong>the</strong> right knowledge and competence to do efficiently <strong>the</strong> main
processes.
As a strategy to capture knowledge, organizations try to employ employees. Because based on
focal processes identified by EFQM criteria, knowledge maps drawn on <strong>the</strong>se processes provide
efficient roadmap to capture <strong>the</strong> people who have coherent knowledge to perform processes.
Knowledge need to perform principal activity <strong>of</strong> <strong>the</strong> enterprises could provide start point to
develop organizational knowledge strategy.
6. Case study
This study was carried out in an Iranian shipyard industry which constitutes one <strong>of</strong> <strong>the</strong> four main
industries in Iran. This industry adopted to participate in national wide EFQM excellence model.
At <strong>the</strong> start point <strong>of</strong> <strong>the</strong> research for criteria and sub criteria in EFQM we developed 15 communities <strong>of</strong>
practices (CoPs). Members are experts and people who work more than 5 years in main activities <strong>of</strong>
industry and are referenced experts <strong>of</strong> <strong>the</strong>ir domains. Table 3 depicts <strong>the</strong>se CoPs and
correspondence criteria in EFQM. Each CoP is <strong>the</strong> responsible <strong>of</strong> one or more than one sub criteria.
6.1 Knowledge item extraction
In this step to knowledge extraction we had two type <strong>of</strong> knowledge. We regard to <strong>the</strong> knowledge
needed to perform tasks which constitute bullet point for sub criteria as explicit knowledge. To
determine this knowledge, we developed interview with <strong>the</strong> members <strong>of</strong> correspondence CoP and
experts in each field with more than 10 years experience. This part on knowledge extraction is related
to tacit knowledge <strong>of</strong> <strong>the</strong> participants. Table 4 illustrated an example <strong>of</strong> <strong>the</strong> tasks and criteria
correspond to a CoP.
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
Table 3: CoPs for EFQM model criteria and sub criteria
672

Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
Table 4: A knowledge extraction form
COP's name: Knowledge
3b. How people’s knowledge and competencies are identified, developed and sustained.
Tasks
1. people’s knowledge and competencies are identified, classified and matched
to <strong>the</strong> needs <strong>of</strong> <strong>the</strong> organization
2. training and development plans are developed and used to help ensure that
staff match present and future capability needs <strong>of</strong> <strong>the</strong> organization
3. individuals are developed, mentored and trained to help <strong>the</strong>m realize <strong>the</strong>ir
full potential
4.individual, team and organizational learning opportunities are developed,
captured and shared
5. team skills are developed
6. individual and team objectives are reviewed, updated and aligned with
organization objectives and targets
7. .staff are helped to improve <strong>the</strong>ir own performance through appraisal and
o<strong>the</strong>r related activities
6.2 knowledge pr<strong>of</strong>iling
Required knowledge
We produce a knowledge pr<strong>of</strong>ile <strong>of</strong> extracted knowledge. knowledge management has two aspects:
connecting people with information and connecting people with people. Knowledge pr<strong>of</strong>iling can
support <strong>the</strong>se two aspects by providing informational attributes, such as keywords, description,
importance, and people-finder attributes such as an expert or author. Table 5 is a type <strong>of</strong> knowledge
pr<strong>of</strong>ile. Each knowledge item will have a unique code.
Table 5: knowledge pr<strong>of</strong>ile
Title Knowledge ID
Organization Author
Last modified Creating date
Expired date Keyword
Rating Importance
Access right Type
Location Format
Expert
Description
Prerequisite
6.3 Knowledge linking
The knowledge link is identified after completing <strong>the</strong> knowledge pr<strong>of</strong>ile. The knowledge link is first
indicated when producing knowledge pr<strong>of</strong>ile, and is later confirmed. We identify new links and
examine and confirm existing links. Knowledge link is represented as an arrow in a knowledge map.
The knowledge map shows a navigation path <strong>of</strong> knowledge. The knowledge map is a type <strong>of</strong> directed
graph and consists <strong>of</strong> nodes and links, each node denoting knowledge item and link denoting preand
post-relationship between knowledge. Knowledge linking will be useful to define learning courses
in <strong>the</strong> organization.
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
6.4 Personnel knowledge C.V
After defining knowledge links, based on interview with knowledge expert in organization, we
developed a questionnaire. This questionnaire was spread to <strong>the</strong> knowledge worker to complete it.
Then, verified and completed questionnaire show <strong>the</strong> knowledge assets exist in organization and <strong>the</strong>
knowledge need to acquire by enterprise. Also this questionnaire allows reorganizing <strong>the</strong> employees
around activities that <strong>the</strong>ir knowledge C.V is more related. Employee’s Knowledge C.V plays an
important role to know knowledge gap in industry.
6.5 Knowledge validation
User validation is performed on knowledge map. A structured walkthrough is conducted with domain
experts, business managers, and knowledge map producer. The following are review checkpoints:
Are all knowledge extracted? (Unidentified knowledge should be found)
Is any knowledge redundant over organizations?
Are pr<strong>of</strong>iles and links fully described for all knowledge?
Are <strong>the</strong> knowledge pr<strong>of</strong>ile and knowledge map consistent?
Figure 2 is a validated knowledge map about ‘knowledge’ CoP in shipyard industry.
Figure 2: Validated knowledge map for knowledge CoP
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Mahmoud Moradi, Mohammad Rahim Ramazanian and Sayyed Maisam Momeni
7. Discussion and conclusion
Knowledge maps provide a visual orientation for managers or specialists who wish to locate, evaluate
or develop knowledge in an organizational context. They concentrate information about knowledge
sources, assets, structures, applications, or development needs in an accessible way. The main idea
in this paper was to use processes and criteria on EFQM excellence model as a platform to develop
practical and organizational knowledge maps. Knowledge maps developed in this foundation will
provide a holistic and integrated view to <strong>the</strong> organizational knowledge in various functional areas.
Since <strong>the</strong>se maps ga<strong>the</strong>r and present in a unified approach organizational knowledge <strong>the</strong>n developing
knowledge maps based on EFQM model will be beneficial.
The extracted knowledge from knowledge mapping based on EFQM excellence model was very
useful for shipyard industry. This enterprise uses extracted knowledge in various subject, for example,
it develop and define learning strategies to obtain <strong>the</strong> right knowledge and competence to do
efficiently its main processes by using this maps.
This paper has highlighted a start point for <strong>the</strong> KM practitioners to build <strong>the</strong>ir improvement project in
regard to EFQM excellence model and based on knowledge maps developed from this model. Paper
provided a start point to this approach and more case studies in various enterprises can be reviewed
to validate, refine or add to <strong>the</strong> overall completeness <strong>of</strong> <strong>the</strong> model.
References
Alavi, M. Dorothy E. Leidner. (2001) " review: knowledge management and knowledge management systems:
conceptual foundations and research issues"; journal <strong>of</strong> MIS quarterly. Vol. 25. No.1, pp.107-136.
Buzan, T. and Buzan, B. (1996) The Mind Map Book, Plume. in Jetter, A. Kraaijenbrink, J. Schroder, H.
Wijnhoven, F. (2006), Knowledge Integration: The Practice <strong>of</strong> Knowledge Management in Small and
Medium Enterprises, Physica-Verlag Heidelberg, Germany, eds.
CIBIT Consultants (2005), The EFQM Framework for Knowledge Management, Educators BV. & PACE
Performance Ltd.
Davenport, T. H., and Prusak, L. (1998) Working Knowledge, Harvard Business School Press, Boston.
Eppler, M. J. (2001) "Making Knowledge Visible Through Intranet Knowledge Maps: Concepts, Elements, Cases"
Proc. <strong>of</strong> <strong>the</strong> 34th Hawaii International <strong>Conference</strong> on System Sciences
<strong>European</strong> Foundation for Quality Management (1999), The EFQM Excellence Model, Brussels: EFQM
Representative Office.
Grey, D. (1999), ‘‘Knowledge mapping: a practical overview’’, SWS Journal, available at:
http://www.smithweaversmith.com/knowledg2.htm, March.
Haun, M. (2002) Handbuch Wissensmanagement: Grundlagen und Umsetzung, Systeme und Praxisbeispiele.
Springer, Berlin. in Jetter, A. Kraaijenbrink, J. Schroder, H. and Wijnhoven, F. (2006), Knowledge
Integration: The Practice <strong>of</strong> Knowledge Management in Small and Medium Enterprises, Physica-Verlag
Heidelberg, Germany, eds
Jetter, A. Kraaijenbrink, J. Schroder, H. Wijnhoven, F. (2006), Knowledge Integration: The Practice <strong>of</strong> Knowledge
Management in Small and Medium Enterprises, Physica-Verlag Heidelberg, Germany.
Kim, S. Suh, E. and Hwang, H. (2003) "Building <strong>the</strong> Knowledge Map: An Industrial Case Study", Journal <strong>of</strong>
Knowledge Management, Vol. 7, No. 2, pp. 34-45
Nonaka, I., and Takeuchi, H. (1995),"The Knowledge-Creating Company: How Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation", Oxford University Press, New York.
Novak, J.D. and Gowin, D.B. (1984) Learning How to Learn, Cambridge University Press. in Jetter, A.
Kraaijenbrink, J. Schroder, H. Wijnhoven, F. (2006), Knowledge Integration: The Practice <strong>of</strong> Knowledge
Management in Small and Medium Enterprises, Physica-Verlag Heidelberg, Germany, eds.
Rubenstein-Montano, B. Liebowitz, J. Buchwalter, J. McCaw, D. Newman, B. and Rebeck, K. (2001) "A Systems
Thinking Framework for Knowledge Management", Journal <strong>of</strong> Decision Support Systems; Vol.31; pp5–16.
Wexler, M.N. (2001) "The Who, What and Why <strong>of</strong> Knowledge Mapping", Journal <strong>of</strong> Knowledge Management, vol.
5, 249-263.
675

The University Institution’s Improvement <strong>of</strong> Quality from a
Knowledge Management’s Point <strong>of</strong> View
Oliver Moravcik, Dagmar Caganova and Jana Stefankova
Slovak University <strong>of</strong> Technology, Faculty <strong>of</strong> Materials Science and Technology
in Trnava, Slovakia
oliver.moravcik@stuba.sk
dagmar.caganova@stuba.sk
jana.stefankova@stuba.sk
Abstract: Four years ago, <strong>the</strong> Faculty <strong>of</strong> Materials Science and Technology <strong>of</strong> <strong>the</strong> Slovak University <strong>of</strong>
Technology in Bratislava started <strong>the</strong> journey <strong>of</strong> applying up-to-date tools and control processes within its own
management in order to rank amongst excellent university institutions. In <strong>the</strong> decision-making process <strong>of</strong> how to
proceed and primarily how to bring <strong>the</strong> institution inline with <strong>the</strong> excellent faculties <strong>of</strong> this type, not only in
Slovakia but also within <strong>the</strong> <strong>European</strong> Union which Slovakia was associated with in 2004, <strong>the</strong> following <strong>the</strong>mes
have been implemented and are being considered, e.g. dynamic development <strong>of</strong> an institution in <strong>the</strong> category <strong>of</strong>
<strong>the</strong> universities optimal adaptation <strong>of</strong> <strong>the</strong> faculty’s structure relating to <strong>the</strong> system <strong>of</strong> <strong>the</strong> university’s social
financing, diversity (Stefankova, Caganova, Moravcik, 2010), multiculturalism (Caganova, Sujanova,
Lenhardtova, 2010), up-to-date management models <strong>of</strong> public institutions (Cambal, Caganova, 2010) and quality
control models, etc. The paper does not only deal with evaluating <strong>the</strong> tools used for <strong>the</strong> general improvement <strong>of</strong>
<strong>the</strong> institution from a managerial perspective <strong>of</strong> <strong>the</strong> faculty but also from that <strong>of</strong> employees and governing bodies
(university, ministry, rating and ranking agencies) with respect to <strong>the</strong> results <strong>of</strong> <strong>the</strong> last four years, especially in
<strong>the</strong> area <strong>of</strong> grant fruitfulness, overall development and also <strong>the</strong> construction <strong>of</strong> <strong>the</strong> faculty by <strong>the</strong> deployment <strong>of</strong>
<strong>the</strong> Common Assessment Framework (CAF) model. By <strong>the</strong> faculty’s preference <strong>of</strong> <strong>the</strong> CAF model and its
experience from two successful competitions in <strong>the</strong> framework <strong>of</strong> <strong>the</strong> Slovak university institutions is highlighted.
Ano<strong>the</strong>r significant part <strong>of</strong> <strong>the</strong> paper includes <strong>the</strong> analysis <strong>of</strong> customer satisfaction and <strong>the</strong> institution’s quality
research from a viewpoint <strong>of</strong> employees and students that have carried out several years <strong>of</strong> research on
document development trends on <strong>the</strong> demarcated way to <strong>the</strong> institution’s excellence. The statistics, analyses,
and final comments for decisive quality and economic indices are included. To summarize it, <strong>the</strong> authors’
intention is to share <strong>the</strong> knowledge and experience <strong>of</strong> <strong>the</strong> best practice in how to improve <strong>the</strong> institutional
processes with a focus on <strong>the</strong> institutional results.
Keywords: knowledge management, process management, quality, questionnaire survey, trend
1. Introduction - History
The Faculty <strong>of</strong> Materials Science and Technology (MTF) <strong>of</strong> <strong>the</strong> Slovak University <strong>of</strong> Technology (STU)
based in Trnava has been applying <strong>the</strong> principles <strong>of</strong> modern management for <strong>the</strong> past five years.
Besides significant structural changes in <strong>the</strong> organization <strong>of</strong> <strong>the</strong> faculty we integrated into <strong>the</strong>
management a modernization process with innovative elements which according to our conviction will
considerably help improve <strong>the</strong> main indicators <strong>of</strong> <strong>the</strong> institution not only from <strong>the</strong> university’s point <strong>of</strong>
view but especially in regard to international comparison. Inter alia we staked for example on
increasing <strong>the</strong> awareness <strong>of</strong> students and faculty employees via <strong>the</strong> use <strong>of</strong> modern media, on <strong>the</strong>
transparency <strong>of</strong> economic-administrative processes, support <strong>of</strong> diversity (not only gender diversity),
and <strong>the</strong> monitoring <strong>of</strong> quality. At present we are mapping <strong>the</strong> progress according to individual
indicators from <strong>the</strong> quantitative and if possible from a qualitative viewpoint.
2. Tools Applied for an Increase in Performance <strong>of</strong> <strong>the</strong> Institution
The tools to increase performance <strong>of</strong> a statutory academic institution can be divided into several
groups, <strong>the</strong> efficiency <strong>of</strong> which does not always need to come up to expectations. It is necessary to
realize that at <strong>the</strong> faculty we work with students as an educational objective and also as our
customers, we prepare <strong>the</strong>m for a practical career on one hand and on <strong>the</strong> o<strong>the</strong>r hand we are in
contact with teachers and researchers who analogous to students use to a large extent academic
freedom which includes important decision-making situations which do not appear in <strong>the</strong> private
business activities. Accordingly, all reform efforts in <strong>the</strong> academic environment demand previous
approvals from several self-governing panels. In regard to traditional university conservativeness, <strong>the</strong>
persuasive processes are very demanding. Students who, due to <strong>the</strong>ir age, are more disposed for
reform changes than <strong>the</strong>ir teachers, this can be an accelerator <strong>of</strong> changes. When formulating new
targets we accredited a large significance to experience <strong>of</strong> our partners in previous projects from
abroad, especially from Germany, Belgium, Ireland, France and Denmark.
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What <strong>the</strong> faculty staked on five years ago:
definite redistribution <strong>of</strong> priorities in financing budgeted items (Increased motivating support <strong>of</strong>
research, preferring motilities <strong>of</strong> teachers, researchers and students, motivating applications for
international and national research and education grants preparation for finance drawing from
structural funds <strong>of</strong> <strong>the</strong> <strong>European</strong> Union, only to indicate <strong>the</strong> most important ones)
based on analysis <strong>of</strong> <strong>the</strong> financing rules <strong>of</strong> universities, search for financial reserves
acquisition <strong>of</strong> complementary financial resources
job reduction
organizational structure change <strong>of</strong> <strong>the</strong> institution and delegation <strong>of</strong> powers from <strong>the</strong> centre on
newly arisen bodies
transparency <strong>of</strong> all economic-administrative processes
application <strong>of</strong> <strong>the</strong> CAF-system to ensure quality
responsible preparation <strong>of</strong> complex accreditation and continuous evaluation
qualified choice <strong>of</strong> managers via public competition
curricula reform <strong>of</strong> individual study programs in accordance with common principles valid for all
study programs at <strong>the</strong> faculty
visualization <strong>of</strong> <strong>the</strong> faculty in media at home and in abroad
a focus on decisive foreign and inland partners to cooperate in research
consequent enforcement <strong>of</strong> feedback from students and employees by a periodical
inquiries and a black box as <strong>the</strong> main channel for situation surveys at <strong>the</strong> faculty.
It is not possible herein to report on individual steps and <strong>the</strong>ir efficiency in details due to space
allocated for this paper. However it is necessary to say that all our measures applied at once as a unit
had a mutually multiplicative character. This effect would not have manifested so intensively if
individual actions were implemented step by step.
3. Development trends
As examples to document progress and changes at <strong>the</strong> faculty we have chosen several significant
areas where we can track development trends according to individual indicators. The main sources <strong>of</strong>
financing for public universities are state budget grants, which are assigned on <strong>the</strong> basis <strong>of</strong> <strong>the</strong>
number <strong>of</strong> students, economic difficulty <strong>of</strong> carried out programmes, <strong>the</strong> results in <strong>the</strong> area <strong>of</strong> science
and technology, <strong>the</strong> number <strong>of</strong> graduates, evaluation <strong>of</strong> creative activity <strong>of</strong> <strong>the</strong> university, project
solutions, etc. (Act No. 131/2002 Coll. on Higher Education: § 89). Most <strong>of</strong> <strong>the</strong> tables and figures are
ei<strong>the</strong>r from <strong>the</strong> area <strong>of</strong> economic development or from research on satisfaction <strong>of</strong> students and
employees within our institution and <strong>the</strong>y convincingly confirm <strong>the</strong> correctness <strong>of</strong> our decision to
reform <strong>the</strong> institution and <strong>the</strong> selected reform process. The especially marked economic progress <strong>of</strong>
<strong>the</strong> faculty is support by <strong>the</strong> results (expressed in EUR) described in Figures 1 – 5:
10 000 000,00
9 000 000,00
8 000 000,00
7 000 000,00
6 000 000,00
5 000 000,00
4 000 000,00
3 000 000,00
2 000 000,00
1 000 000,00
0,00
2007 2008 2009 2010
Figure 1: MTF STU Grant from <strong>the</strong> State Budget after Modification at <strong>the</strong> MTF STU
The budget resources increase trend is a reflection <strong>of</strong> <strong>the</strong> reform steps and it is also <strong>the</strong> result <strong>of</strong> <strong>the</strong>
fact that <strong>the</strong> main faculty processes adapted to <strong>the</strong> rules <strong>of</strong> <strong>the</strong> grant calculation set by <strong>the</strong>
government <strong>of</strong> <strong>the</strong> Slovak Republic for universities and faculties in <strong>the</strong> conditions <strong>of</strong> <strong>the</strong> present
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decrease in <strong>the</strong> number <strong>of</strong> students at <strong>the</strong> faculty – increasing share <strong>of</strong> <strong>the</strong> science funding versus
<strong>the</strong> number <strong>of</strong> <strong>the</strong> students in <strong>the</strong> pedagogic process.
5 000 000,00
4 500 000,00
4 000 000,00
3 500 000,00
3 000 000,00
2 500 000,00
2 000 000,00
1 500 000,00
1 000 000,00
500 000,00
0,00
Figure 2: Salaries at MTF STU without taxes
2007 2008 2009 2010
Increase in <strong>the</strong> salaries financial volume is a reflection <strong>of</strong> <strong>the</strong> increased activity in <strong>the</strong> field <strong>of</strong>
publications and grant fruitfulness. At <strong>the</strong> same time, it is necessary to have a regard for <strong>the</strong> generally
diminishing number <strong>of</strong> staff.
1 400 000,00
1 200 000,00
1 000 000,00
800 000,00
600 000,00
400 000,00
200 000,00
Figure 3: Goods and Services at <strong>the</strong> MTF STU
0,00
2007 2008 2009 2010
It is possible to make a connection with <strong>the</strong> previous Figure 2 by analogy, because both financial
circles follow very similar rules for calculating which is set by <strong>the</strong> Ministry <strong>of</strong> Education.
1 000 000,00
800 000,00
600 000,00
400 000,00
200 000,00
0,00
2007 2008 2009 2010
Figure 4: Scholarship <strong>of</strong> PhD students at <strong>the</strong> MTF STU
The faculty took a gamble on <strong>the</strong> preference <strong>of</strong> <strong>the</strong> second and third degree <strong>of</strong> study under <strong>the</strong>
conditions <strong>of</strong> <strong>the</strong> present decrease in <strong>the</strong> number <strong>of</strong> first-grade students due to different reasons: <strong>the</strong>
demandingness coefficient, formulized financially, is <strong>the</strong> lowest on <strong>the</strong> first degree. As a research
faculty, <strong>the</strong> main focus is moved to <strong>the</strong> second and PhD degree. The result <strong>of</strong> this activity is definitely
due to <strong>the</strong> consequent, relevant increase in funds within state scholarships on <strong>the</strong> third degree.
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600 000,00
500 000,00
400 000,00
300 000,00
200 000,00
100 000,00
Figure 5: Program 07712 - Science & Technology
0,00
2007 2008 2009 2010
The volume <strong>of</strong> funds for <strong>the</strong> fields <strong>of</strong> science and technology shows a long-term slight increase. It is
primarily characterised by <strong>the</strong> volume <strong>of</strong> funds from <strong>the</strong> grant agencies, except for projects concerning
structural funds, on which <strong>the</strong> faculty was focused in preference within <strong>the</strong> given period <strong>of</strong> time.
The following Figures 6, 7, 8, 9 do not prove to be negligible job cuts for particular groups <strong>of</strong>
employees. On <strong>the</strong> o<strong>the</strong>r side, <strong>the</strong> number <strong>of</strong> research workers increased (Figure 9 this is a positive
trend towards <strong>the</strong> high aims <strong>of</strong> <strong>the</strong> faculty in <strong>the</strong> area <strong>of</strong> science and research.
490
480
470
460
450
440
430
420
Figure 6: Employees <strong>of</strong> MTF STU
300
250
200
150
100
50
Figure 7: Teachers <strong>of</strong> <strong>the</strong> MTF STU
175
170
165
160
155
150
0
2007 2008 2009 2010
2007 2008 2009 2010
2007 2008 2009 2010
Figure 8: Administrative & technical employees <strong>of</strong> <strong>the</strong> MTF STU
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persons
persons

Figure 9: Researchers at <strong>the</strong> MTF STU
Oliver Moravcik, Dagmar Caganova and Jana Stefankova
30
25
20
15
10
5
0
Researchers at MTFSTU
2007 2008 2009 2010
A total decrease in <strong>the</strong> number <strong>of</strong> staff is <strong>the</strong> result <strong>of</strong> <strong>the</strong> working positions’ systemization at <strong>the</strong>
faculty concerning all units – science and pedagogic, also administrative and economic units for <strong>the</strong>
purpose <strong>of</strong> streng<strong>the</strong>ning <strong>the</strong> research base <strong>of</strong> <strong>the</strong> faculty. During <strong>the</strong> past period, <strong>the</strong> retirement and
moving <strong>of</strong> <strong>the</strong> staff to new fields <strong>of</strong> activities have occurred. There have not been any collective
dismissals.
In
<strong>the</strong> tracking
period, <strong>the</strong> faculty registered a significant increase in <strong>the</strong> amount <strong>of</strong> PhD students. Just
increasing <strong>the</strong> trend in <strong>the</strong> amount <strong>of</strong> PhD students at <strong>the</strong> faculty is a high plus parameter, this proves
to be positive faculty progress in <strong>the</strong> area <strong>of</strong> science and research, as well as in <strong>the</strong> process <strong>of</strong>
obtaining projects (explanation: <strong>the</strong> number <strong>of</strong> allocated scholarships <strong>of</strong> internal PhD students
depends on <strong>the</strong> faculty’s results in <strong>the</strong> area <strong>of</strong> research). The mentioned trend is confirmed in Figure
10 and deserves attention.
It
is important to mention that <strong>the</strong> Slovak Republic funds every PhD position by <strong>the</strong> ministerial
scholarship, during which, it allocates <strong>the</strong>se places to individual universities/faculties according to
previous results <strong>of</strong> <strong>the</strong>se institutions in <strong>the</strong> field <strong>of</strong> science. However, <strong>the</strong> Slovak institutions do not
have <strong>the</strong>ir own available resources to create PhD study positions, so Figure 10 ra<strong>the</strong>r documents <strong>the</strong>
progress in science at <strong>the</strong> faculty.
350
300
250
200
150
100
Figure 10: PhD students at <strong>the</strong> MTF STU
50
0
1 2 3 4
persons
internal
external
Total teaching volume reduction (from 71 757 teaching hours in <strong>the</strong> academic year <strong>of</strong> 2006/07 to
47 864 hours in <strong>the</strong> academic year <strong>of</strong> 2009/10) due to curriculum unification and a partial decrease <strong>of</strong>
student numbers at <strong>the</strong> faculty, mainly due to demographic development in Slovakia and <strong>the</strong> criterion
<strong>of</strong> <strong>the</strong> Accreditation Commission <strong>of</strong> <strong>the</strong> Slovak Republic permitting a maximum <strong>of</strong> 20 students
(without <strong>the</strong> third degree – that means PhD students) per qualified lecturer (with a PhD qualification
and higher) are significant. In <strong>the</strong> monitored period <strong>the</strong> number <strong>of</strong> faculty students decreased from
5600 to 4200.
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4. Survey analysis
Oliver Moravcik, Dagmar Caganova and Jana Stefankova
Presented figures and tables issued from regular surveys among <strong>the</strong> faculty students and employees.
From our point <strong>of</strong> view, <strong>the</strong> only weak point <strong>of</strong> <strong>the</strong>se surveys consists in a relatively low participation <strong>of</strong>
students in inquiries that do not exceed 10 per cent, inspite <strong>of</strong> <strong>the</strong> fact that <strong>the</strong>y are repeatedly
encouraged to fulfil <strong>the</strong>m. This 10 % participation can be compared with <strong>the</strong> employees’
questionnaires as a very low number.
Information becomes knowledge only when we are able to work with it, when we succeed in using it
and it has a meaning for us within <strong>the</strong> context <strong>of</strong> acquired knowledge and attainments. (Porvaznik,
and coll., 2010:434)
Consequently, <strong>the</strong> faculty management starts to work with information received from <strong>the</strong>
questionnaires. Firstly, <strong>the</strong> numerical parameters and consequently <strong>the</strong>ir results have been published
showing <strong>the</strong> direct answers and reactions <strong>of</strong> <strong>the</strong> faculty’s management to so called 'free questions'
(verbal attitude <strong>of</strong> individual questions). After this process, on <strong>the</strong> basis <strong>of</strong> vice-deans’ competencies,
<strong>the</strong> questions, remarks and recommendations are <strong>the</strong>n discussed in certain faculty boards (faculty
management, dean's commissions, dean's collegium). This is followed by analyses <strong>of</strong> <strong>the</strong> trends
which are identified toge<strong>the</strong>r with defined problem areas and a determination on prioritising <strong>the</strong>ir
solutions. The aim is to find a way <strong>of</strong> improvement and to improve <strong>the</strong> identified weak areas. The
information obtained from <strong>the</strong> questionnaire surveys became input knowledge leading towards <strong>the</strong>
process solutions to improve <strong>the</strong> institution itself.
The employees survey had a participation <strong>of</strong> 65% faculty employees, which is regarded as clear
evidence <strong>of</strong> interest in <strong>the</strong> operation <strong>of</strong> <strong>the</strong> faculty by our staff. (Moravcik, Stefankova, Caganova,
2010) Information obtained from <strong>the</strong> questionnaire survey <strong>of</strong> employees has become a source <strong>of</strong>
identification and subsequent stabilization <strong>of</strong> areas and processes that <strong>the</strong> faculty employees are
happy with, and identification <strong>of</strong> <strong>the</strong> areas and processes that need improvement.
Questionnaires are voluntary and are continuously available to respondents on <strong>the</strong> faculty website as
interactive forms. Their evaluation is also fully automated. Despite “limited” evidence <strong>the</strong> value <strong>of</strong> <strong>the</strong>
students’ questionnaires in consequence <strong>of</strong> <strong>the</strong> low active participation <strong>of</strong> students, we can find out
that <strong>the</strong> feed back again confirms <strong>the</strong> correctness <strong>of</strong> <strong>the</strong> reform steps taken at <strong>the</strong> faculty, as shown in
several cases in a notably convincing way (for example <strong>the</strong> Figures 11, 12, 13).
Students questionnaire survey (data in <strong>the</strong> figures are recorded as a percentage)
50
45
40
35
30
25
20
15
10
5
0
Excellent Very Good Good Sufficient Satisfactory
Figure 11: Satisfaction rate with computerisation (IT) at <strong>the</strong> faculty
Students positively rate <strong>the</strong> level <strong>of</strong> computerization at <strong>the</strong> faculty. The current level has been secured
using our own resources and <strong>the</strong> structural funds <strong>of</strong> <strong>the</strong> <strong>European</strong> Union. Nowadays, <strong>the</strong> faculty
certainly belongs to <strong>the</strong> best equipped faculties in this field within Slovakia.
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60
50
40
30
20
10
0
Excellent Very Good Good Sufficient Satisfactory
Figure 12: Satisfaction rate with <strong>the</strong> faculty choice
Moreover, more pleasant evidence for <strong>the</strong> faculty’s management is <strong>the</strong> fact that <strong>the</strong> students are
highly satisfied with <strong>the</strong>ir choice <strong>of</strong> <strong>the</strong> faculty. However, a problem in <strong>the</strong> future may be <strong>the</strong>
permanent sustainability <strong>of</strong> this trend. Excellent results <strong>of</strong> this technically focused faculty are not just a
matter <strong>of</strong> <strong>the</strong> faculty’s own point <strong>of</strong> view.
60
50
40
30
20
10
0
Excellent Very Good Good Sufficient Satisfactory
Figure 13: Satisfaction rate with informedness at <strong>the</strong> faculty
For completeness it is necessary to mention that questionnaires also <strong>of</strong>fer an opportunity <strong>of</strong> verbal
attitude to individual questions, if appropriate, to present one’s own commentary or critique. The
faculty management and/or <strong>the</strong> dean deal with <strong>the</strong> verbal opinions <strong>of</strong> questionnaires and publish a
competent standpoint on <strong>the</strong> faculty web page.
The following selected questionnaire survey results <strong>of</strong> <strong>the</strong> students’ satisfaction is processed in <strong>the</strong>
figures:
60
50
40
30
20
10
0
Excellent Very Good Good Sufficient Satisfactory
Figure 14: Satisfaction rate with <strong>the</strong> number and quality <strong>of</strong> individually solved projects
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40
35
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25
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15
10
5
0
Excellent Very Good Good Sufficient Satisfactory
Figure 15: Satisfaction rate with <strong>the</strong> study process and organization
There is an opinion that <strong>the</strong> stated facts document <strong>the</strong> progress at <strong>the</strong> faculty that has been
appreciated by <strong>the</strong> students and it is obvious that it is possible to successfully go fur<strong>the</strong>r in this field.
A great advantage may be <strong>the</strong> fact that <strong>the</strong> students also operate in two o<strong>the</strong>r universities, so <strong>the</strong>y
can compare <strong>the</strong> conditions <strong>of</strong> <strong>the</strong> study and environment to o<strong>the</strong>r academic subjects. This fact places
<strong>the</strong>se results in a different light.
Selected employees’ questionnaire survey (data in <strong>the</strong> figures are recorded as a percentage)
results in <strong>the</strong> monitored period:
60
50
40
30
20
10
0
Yes More yes No change More no No
Figure 16: Question: Do you think that <strong>the</strong> faculty image has considerably improved recently?
60,00
50,00
40,00
30,00
20,00
10,00
0,00
Yes More yes No change More no No
Figure 17: Question: Have you enough information about courses <strong>of</strong> events at <strong>the</strong> faculty?
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50,00
45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
0,00
Yes More yes No change More no No
Figure 18: Question: Do you consider <strong>the</strong> atmosphere at <strong>the</strong> faculty to be open and friendly?
70,00
60,00
50,00
40,00
30,00
20,00
10,00
0,00
Yes More yes No change More no No
Figure 19: Question: Are you satisfied with <strong>the</strong> communication level, manager-subordinate?
60
50
40
30
20
10
0
Web
Meetings
Direct communication wi..
University newsletter
Figure 20: Question: What is <strong>the</strong> most frequent resource for information, acquisition about
happenings at <strong>the</strong> faculty?
45
40
35
30
25
20
15
10
5
0
O<strong>the</strong>rs
Yes More yes I don´t know More no No
Figure 21: Question: Are you satisfied with <strong>the</strong> rate <strong>of</strong> <strong>the</strong> works ideas implemented at <strong>the</strong> faculty?
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Results concerning <strong>the</strong> staff’s research <strong>of</strong> <strong>the</strong> satisfaction mainly show positive moods and trends and
<strong>the</strong>y are very similar to <strong>the</strong> students’ questionnaires. Fur<strong>the</strong>rmore, <strong>the</strong>ir value concerning <strong>the</strong> number
<strong>of</strong> respondents is incomparably higher in comparison with <strong>the</strong> students’ questionnaires. In <strong>the</strong> staff’s
researches, where <strong>the</strong> respondents more <strong>of</strong>ten prefer <strong>the</strong> verbal completion <strong>of</strong> <strong>the</strong> questionnaire, <strong>the</strong>
management <strong>of</strong> <strong>the</strong> faculty responded to <strong>the</strong> suggestions immediately and made instant corrections in
several cases. It is considered to be significant that in <strong>the</strong> verbal part <strong>of</strong> both researches (staff and
students), <strong>the</strong>re is an absence <strong>of</strong> scepticism concerning <strong>the</strong> remedy for <strong>the</strong> common wealth or <strong>the</strong>
unwillingness to express one’s opinion <strong>of</strong> <strong>the</strong> problems defined not just by <strong>the</strong> management <strong>of</strong> <strong>the</strong>
faculty but also by <strong>the</strong> staff and students <strong>the</strong>mselves. For completeness’ sake, it is important to say
that besides <strong>the</strong> traditional publishing <strong>of</strong> <strong>the</strong> questionnaire results on <strong>the</strong> website <strong>of</strong> <strong>the</strong> faculty, within
<strong>the</strong> past two years, <strong>the</strong> determining results have been presented at <strong>the</strong> pedagogic conference. This
conference concerned all <strong>the</strong> teachers <strong>of</strong> <strong>the</strong> faculty and <strong>the</strong>re was a possibility to discuss <strong>the</strong>
problems.
5. Conclusions
Decisive information <strong>of</strong> <strong>the</strong> passed five years shows <strong>the</strong> fact that identification <strong>of</strong> our students and
employees with “<strong>the</strong>ir” faculty has increased considerably.
Using <strong>the</strong> approved methods <strong>of</strong> feedback, <strong>the</strong> management <strong>of</strong> <strong>the</strong> faculty permanently monitors <strong>the</strong>
reactions <strong>of</strong> <strong>the</strong> staff and <strong>the</strong> students to <strong>the</strong> set goals, <strong>the</strong>ir willingness to participate in <strong>the</strong> process <strong>of</strong>
reforms and corrections <strong>of</strong> <strong>the</strong> control processes at <strong>the</strong> faculty. Therefore, <strong>the</strong> stated corporation
identity presents <strong>the</strong> determining identifier <strong>of</strong> <strong>the</strong> students and staff participation in <strong>the</strong> changes. We
want to understand <strong>the</strong> corporation identity as a complex, positive symptom that is a guaranty <strong>of</strong>
successful development <strong>of</strong> our institution in <strong>the</strong> future and an acceptance <strong>of</strong> new practices applied in
its development.
The management <strong>of</strong> <strong>the</strong> faculty identified <strong>the</strong> following aims to improve its quality and <strong>the</strong> following
development:
to play an important role in a selected segment <strong>of</strong> science, at least in Central Europe, but at best
in <strong>the</strong> <strong>European</strong> research area
to be placed in prominent positions <strong>of</strong> rating and ranking positions <strong>of</strong> national and international
grant agencies
to improve <strong>the</strong> faculty in all main and supporting processes
to implement <strong>the</strong> faculty to <strong>the</strong> highest category <strong>of</strong> university establishments in <strong>the</strong> process <strong>of</strong>
complex accreditation
to obtain important projects like FP7, FP8, EUROATOM, Competence centres
international dimension – to increase <strong>the</strong> students as well as employees mobility (Kachanakova,
Stachova, 2010)
to join <strong>the</strong> top 500 <strong>European</strong> universities
to support <strong>the</strong> faculty internationalization by foreign students and pr<strong>of</strong>essors in <strong>the</strong> range
essentially larger than at present (Beblavy, Kiss,2010:9)
to work actively with <strong>the</strong> faculty graduates including <strong>the</strong> feedback from <strong>the</strong>m (ALUMNI)
(Pisut,2010:5)
It is obvious that reaching <strong>the</strong>se challenging goals is a difficult task in <strong>the</strong> time <strong>of</strong> 5-10 years. This task
requires employment <strong>of</strong> <strong>the</strong> adequate tools and means to reach it. These tools are: enhancing <strong>the</strong>
quality <strong>of</strong> <strong>the</strong> institution by means <strong>of</strong> improving <strong>the</strong> applied model CAF, respecting <strong>the</strong> rules and
principles <strong>of</strong> diversity besides <strong>the</strong> approved tools <strong>of</strong> <strong>the</strong> modern institutions management: utilizing
knowledge/holistic management, application <strong>of</strong> modern trends concerning <strong>the</strong> motivation <strong>of</strong> <strong>the</strong> staff
and students and creating a creative and modern environment at <strong>the</strong> faculty. For this purpose, <strong>the</strong>re
are considerable funds available coming from <strong>the</strong> structural funds <strong>of</strong> <strong>the</strong> <strong>European</strong> Union. The
Government <strong>of</strong> <strong>the</strong> Slovak Republic defined <strong>the</strong> priority axes for <strong>the</strong>se funds in <strong>the</strong> first decade <strong>of</strong> this
century.
To summarise, obtaining, sustaining and improving <strong>the</strong> quality is a permanent and continuous
process. One <strong>of</strong> this paper’s aims is to highlight <strong>the</strong> continual and targeted work with questionnaire
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Oliver Moravcik, Dagmar Caganova and Jana Stefankova
survey results that contribute to <strong>the</strong> efficient utilization <strong>of</strong> knowledge and experience for <strong>the</strong> best
practice <strong>of</strong> how to improve <strong>the</strong> institutional processes with a focus on <strong>the</strong> institutional results.
References
Beblavy, M. and Kiss, S (2010) 12 riešení pre kvalitnejšie vysoké školy [Online], Available:
http://noveidey.skcomponent(content/article/ [08 Mar 2010]
Caganova, D., Sujanova, J., Lenhardtova, Z. (2010) The multicultural environment influence on innovation and
knowledge management in <strong>the</strong> Slovak Republic.
In: Annals <strong>of</strong> DAAAM and <strong>Proceedings</strong> <strong>of</strong> DAAAM Symposium. - ISSN 1726-9679. - Vol. 21, No 1. Annals <strong>of</strong>
DAAAM for 2010 & <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 21st International DAAAM Symposium "Intelligent Manufacturing &
Automation: Focus on Interdisciplinary Solutions" 20-23rd October 2010, Zadar, Croatia. - Vienna : DAAAM
International Vienna. ISBN 978-3-901509-73-5, s. 0113-0114
Cambal, M. and Caganova, D.(2010) Corporate Culture Influence on Effective Initialization and Application <strong>of</strong>
Knowledge Management in Enterprises.
In: <strong>Proceedings</strong> <strong>of</strong> 11th <strong>European</strong> <strong>Conference</strong> on Knowledge Management (ECKM 2010) : Universidade
Lusíada de Vila Nova de Famalicao, Portugal, 2-3 September 2010. - Famalicao : Academic publishing
International, ISBN 978-1-906638-71-9. – s. 176-181
Kachanakova, A. and Stachova, K. (2010) The level <strong>of</strong> education and development <strong>of</strong> employees in organisations
operating in Slovakia, In: TOIOTARITY. Knowledge using is service management. Warszawa: Publisher
Institute Wydawniczy PTM,. ISBN 978-83-61949-24-4 p. 177-186
Law number 131/2002 Collection <strong>of</strong> Laws: Zákon o vysokých školách a o zmene a doplnení niektorých zákonov,
Národná rada SR
Moravcik, O., Stefankova, J., Caganova, D. (2010) The Faculty <strong>of</strong> Materials Science and Technology in Trnava
on <strong>the</strong> Way to Excellency., In: <strong>Proceedings</strong> <strong>of</strong> 11th <strong>European</strong> <strong>Conference</strong> on Knowledge Management
(ECKM 2010) : Universidade Lusíada de Vila Nova de Famalicao, Portugal, 2-3 September 2010. -
Famalicao : Academic Publishing International, 2010. - ISBN 978-1-906638-71-9. - S. 702-710, September
2010
Pisut, J. (2010) Čo je viac a čo menej dôležité pri diskusii o kvalite VŠ [Online], Available: http://noveidey.sk
Porvaznik, J. and coll., (2010) Holistic Management, Pillars <strong>of</strong> Competence in Management, Bratislava IRIS,
2010, ISBN 978-80-89256-11-2
Stefankova, J., Caganova, D., Moravcik, O. (2010) Promoting Gender Diversity Research in Slovakia within 7th
Framework Programme. – article published in proceedings 21st EAEEIE (<strong>European</strong> Association for
Education in Electrical and Information Engineering) Annual <strong>Conference</strong>, June 28-30 2010 Palanga,
Lithuania, ISBN 978-9955-25-826-1.In: Electronics and electrical engineering. - ISSN 1392-1215. - No.
6(102) , pp. 59-62
686

Facilitating Trust Among Entrepreneurs’ To Access Tacit
Knowledge: The SLNIW Story
Martina Mullally 1 , Valerie Brett 1 , Bill O’Gorman 1 , James Carr 2 and Nerys Fuller-
Love 2
1
Centre for Enterprise Development and Regional Economy (CEDRE), School
<strong>of</strong> Business, Waterford Institute <strong>of</strong> Technology, Waterford, Ireland
2
School <strong>of</strong> Management and Business, Aberystwyth University, Wales
mmmullally@wit.ie
vbrett@wit.ie
wogorman@wit.ie
jac27@aber.ac.uk
nfl@aber.ac.uk
Abstract: The Sustainable Learning Networks in Ireland and Wales (SLNIW) project aims to increase <strong>the</strong>
competitiveness, creativity and innovative capacities and capabilities <strong>of</strong> micro-enterprises and SMEs in South
East Ireland and West Wales by increasing accessibility to local entrepreneurial knowledge via <strong>the</strong> establishment
<strong>of</strong> self-learning networks. To achieve this end <strong>the</strong> project created six networks, three in Ireland and three in
Wales where explicit and tacit knowledge flows between <strong>the</strong> participants. The networks consisted <strong>of</strong> two all male
groups, two all female groups and two mixed gender groups. The SLNIW project is part funded by <strong>the</strong> <strong>European</strong>
Regional Development Fund (ERDF) through <strong>the</strong> INTERREG 4A Ireland Wales Programme 2007-2013 and is
being undertaken by <strong>the</strong> Centre for Enterprise Development and Regional Economy (CEDRE) at <strong>the</strong> School <strong>of</strong>
Business, Waterford Institute <strong>of</strong> Technology, in partnership with <strong>the</strong> School <strong>of</strong> Management and Business,
Aberystwyth University in Wales. SLNIW is a longitudinal study spanning two years <strong>of</strong> engagement with <strong>the</strong>
network participants. The data ga<strong>the</strong>ring methods used include observations and questionnaires. This paper
outlines <strong>the</strong> role <strong>of</strong> SLNIW in facilitating trust among entrepreneurs and <strong>the</strong> monitoring <strong>of</strong> initial trustful
communication in <strong>the</strong> learning networks in supporting <strong>the</strong> development <strong>of</strong> tacit knowledge flows. The findings <strong>of</strong>
this study suggest that when establishing learning networks trust is a key factor in increasing accessibility to tacit
knowledge. Therefore, reinforcing <strong>the</strong> importance <strong>of</strong> establishing trust, <strong>the</strong> consequences <strong>of</strong> breaking trust and
<strong>the</strong> necessity <strong>of</strong> trust is very important to increasing accessibility to entrepreneurial tacit knowledge. This paper
contributes to <strong>the</strong> existing body <strong>of</strong> research on tacit knowledge and highlights <strong>the</strong> importance <strong>of</strong> trust in <strong>the</strong>
knowledge transfer process and how a self-learning network can enable greater accessibility to tacit knowledge.
This research has implications for academics, practitioners, entrepreneurs and policy makers who are concerned
with <strong>the</strong> establishment <strong>of</strong> networks to propagate knowledge and technology transfer.
Keywords: Tacit knowledge, entrepreneurs, trust, self-learning networks, business networks and longitudinal
study
1. Introduction
The SLNIW project aimed to facilitate trust among entrepreneurs to access tacit knowledge via <strong>the</strong>
establishment <strong>of</strong> business learning networks in South East Ireland and West Wales. The SLNIW
project has established six self-learning networks; three in South East Ireland and three in West
Wales each consisting <strong>of</strong> a male, a female, and a mixed gender network in each region. The
establishment <strong>of</strong> networks creates a platform for knowledge sharing in which entrepreneurial minded
owner/managers <strong>of</strong> micro-enterprises and SMEs can come toge<strong>the</strong>r in <strong>the</strong>ir respective groups or
through <strong>the</strong> groups combined to learn and generate new knowledge. The basis for <strong>the</strong> foundation <strong>of</strong>
<strong>the</strong> project was centred on <strong>the</strong> economic value that can be derived from social communication,
networks and <strong>the</strong> networking <strong>of</strong> entrepreneurs. The external relationships developed through
networking helps to foster trust as high trust levels are central in terms <strong>of</strong> extracting a deeper value
from <strong>the</strong> exchange relationship (Hoang and Antoncic, 2003).
Entrepreneurs hold valuable deposits <strong>of</strong> tacit knowledge. Increasing accessibility to this knowledge
resource can help entrepreneurs overcome <strong>the</strong> challenges <strong>the</strong>y face to implementing growth and
development. The benefit <strong>of</strong> networking for entrepreneurs is <strong>the</strong> access to information,
entrepreneurial advice and a constant flow <strong>of</strong> up to date industrial and commercial knowledge.
Empirical research shows that <strong>the</strong>re is an association between networking and growth (Jarillo, 1989;
Chell and Baines, 2000) and that an entrepreneur’s personal networks and external relations are a
critical resource in overcoming barriers associated with growth and development.
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A self-learning network is an alternative approach to networking as <strong>the</strong> participants are responsible for
<strong>the</strong>ir own learning. The objective <strong>of</strong> <strong>the</strong>se self-directed learning networks is to bring business owners
in contact with likeminded individuals who are willing to work toge<strong>the</strong>r, share ideas, <strong>of</strong>fer knowledge
and skills in order to grow <strong>the</strong>ir respective businesses. This paper reviews knowledge sharing
literature and discusses networks and in particular <strong>the</strong> concept <strong>of</strong> <strong>the</strong> SLNIW self-learning network.
Therefore, <strong>the</strong> purpose <strong>of</strong> this paper is to review how self-learning networks can facilitate trust leading
to greater accessibility to entrepreneurial tacit knowledge.
2. A review <strong>of</strong> knowledge sharing literature
There are varying definitions to describe knowledge but according to Davenport and Prusak (2000)
knowledge can be defined as a:
fluid mix <strong>of</strong> framed experience, values, contextual information and expert insight that
provides a framework for evaluating and incorporating new experiences and information.
It originates and is applied in <strong>the</strong> minds <strong>of</strong> knowers. In organisations it <strong>of</strong>ten becomes
embedded not only in documents and repositories but also in organisational routines,
processes, practices and norms (p. 5).
There are two main types <strong>of</strong> knowledge, explicit and tacit. Explicit knowledge is codified and easily
transferable (Clarke and Rollo, 2001; Tamer Cavusgil, Calatone and Zhao, 2003) and it can be easily
written down and learned (Nonaka, 1994). Explicit knowledge can be stored in manuals, paperwork
and information systems, whereas tacit knowledge is mainly stored within <strong>the</strong> individual (Haldin-
Herrgard, 2000). According to Choo (2000), explicit knowledge can be captured in products, code,
databases, prototypes, copyrights, trademarks and patents (Meso and Smith, 2000). In contrast to
explicit knowledge humans are <strong>the</strong> main repositories <strong>of</strong> tacit knowledge and converting tacit to explicit
knowledge is difficult because knowledge is “sticky” and does not easily travel (Polanyi, 1962). Tacit
knowledge is difficult to express, understand, interpret and transfer from one party to ano<strong>the</strong>r and <strong>the</strong>
conversion can be time consuming (Tamer Cavusgil et al., 2003). Tamer Cavusgil et al. (2003)
maintain that mutual trust, frequent and close interactions and an open relationship between <strong>the</strong>
sender and <strong>the</strong> recipient will lead to tacit knowledge transfer.
Zack (1999) claimed that tacit knowledge is best transferred through conversation, storytelling and
sharing experiences and that tacit knowledge transfer is best achieved through sharing experiences
that are based on trust (Scott, 2000). Establishing a mode to facilitate initial trustful communication
will encourage <strong>the</strong> development <strong>of</strong> tacit knowledge flows. Foos, Schum and Ro<strong>the</strong>nberg (2006)
maintained that <strong>the</strong> key to tacit knowledge transfer is <strong>the</strong> willingness <strong>of</strong> individuals to share <strong>the</strong>ir
knowledge. In support <strong>of</strong> this, Hughes, O’Regan and Sims (2009) argued that effective knowledge
networks can foster knowledge sharing but can be solely reliant upon <strong>the</strong> nature <strong>of</strong> <strong>the</strong> community <strong>of</strong>
practice and <strong>the</strong>ir willingness to share <strong>the</strong>ir knowledge. Most importantly, <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong>
knowledge network and <strong>the</strong> extent <strong>of</strong> proximity between parties can have huge repercussions for <strong>the</strong>
effectiveness <strong>of</strong> <strong>the</strong> network and <strong>the</strong> organisation’s performance (Hughes et al., 2009). Therefore, it
is imperative when establishing networks that relationships are based on mutual trust and
understanding while also maintaining close proximity. Knowledge sharing networks are gaining
immense popularity within <strong>the</strong> context <strong>of</strong> Knowledge Management (Chen, Duan and Edwards, 2006;
Nirmala and Vemuri, 2009) <strong>the</strong>refore, <strong>the</strong> following section discusses networks with a specific focus
on self-learning networks.
3. Networks and self-learning networks
Networking can be loosely described as a level <strong>of</strong> collaboration among business individuals. This
collaboration is primarily based on interpersonal relationships derived from association, proximity, and
membership with trade and business organisations. A business network can be defined as:
intentionally formed groups <strong>of</strong> small to medium sized pr<strong>of</strong>it-orientated companies in
which firms are geographically proximate, potentially share inputs and outputs, and
undertake direct interactions with each o<strong>the</strong>r for specific business outcomes (Kingsley
and Malecki, 2004, p.72).
An entrepreneur’s network can also be built over a period <strong>of</strong> time extending and deepening
relationships and acquaintances. While <strong>the</strong> network is <strong>the</strong> access to a range <strong>of</strong> acquaintances and
contacts <strong>the</strong> act <strong>of</strong> networking is <strong>the</strong> collaboration, information exchange and resource sharing that
can provide mutual benefits (Huxham, 1996). Through a shared platform <strong>of</strong> face to face group
interaction, entrepreneurs can share <strong>the</strong>ir knowledge with each o<strong>the</strong>r to resolve business problems
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and to develop and streng<strong>the</strong>n <strong>the</strong>ir businesses. The provision <strong>of</strong> a knowledge sharing space and <strong>the</strong>
development <strong>of</strong> trust via <strong>the</strong> repeated face to face interaction establishes <strong>the</strong> foundation <strong>of</strong> increased
accessibility <strong>of</strong> tacit knowledge (Granovetter, 1973 and 1974).
SLNIW is an alternative approach to business networking, which is derived from <strong>the</strong> benefit <strong>of</strong> firms
and entrepreneurs engaging in self-initiated learning activity. The self-learning network process is
tw<strong>of</strong>old; first <strong>the</strong> individual entrepreneur has <strong>the</strong>ir own level <strong>of</strong> tacit knowledge to bring to <strong>the</strong> group
and second, <strong>the</strong> entrepreneur is exposed to <strong>the</strong> tacit knowledge held by <strong>the</strong> o<strong>the</strong>r network members.
In effect, each network acts as a board <strong>of</strong> directors for each participant’s individual business at no
financial cost. The following section discusses <strong>the</strong> methods and data collection techniques used in
this study.
4. Methodology
This study incorporated a mixed methodology approach through implying quantitative and qualitative
techniques. Mixed methods research is growing in popularity (Bryman, 1988) predominantly to do
with reliability <strong>of</strong> findings, had led to <strong>the</strong> consideration <strong>of</strong> quantitative research in tandem with
qualitative methods (Schrøder, 1999). Quantitative data was collected through questionnaires that
were distributed to <strong>the</strong> participants and qualitative data was ga<strong>the</strong>red through <strong>the</strong> researchers
observing each network at <strong>the</strong>ir monthly meeting to gain an in-depth understanding into how <strong>the</strong>
participants were engaging with <strong>the</strong> SLNIW process and knowledge sharing across <strong>the</strong> networks.
The SLNIW project is a longitudinal study in which each <strong>of</strong> <strong>the</strong> six self-learning networks are observed
over a period <strong>of</strong> 16 months from March 2010 to June 2011.
These observations enabled <strong>the</strong> researchers to assess <strong>the</strong> networks’ activities, communication
patterns, performance, behaviour, attendance at <strong>the</strong> network meetings and <strong>the</strong> level <strong>of</strong> knowledge
being exchanged. Based on <strong>the</strong>se observations, a report was collated for each network meeting.
The data and evidence collected to date will address <strong>the</strong> objective <strong>of</strong> <strong>the</strong> project to increase
accessibility to tacit knowledge by addressing; (1) <strong>the</strong> level <strong>of</strong> attendance at network meetings, (2)
data collected from a questionnaire based on trust within <strong>the</strong> networks and (3) data derived from <strong>the</strong>
network monthly meeting evaluations.
The network sessions facilitate knowledge sharing and to understand <strong>the</strong> extent <strong>of</strong> knowledge sharing
a questionnaire was administered to <strong>the</strong> participants, which focused on trust within <strong>the</strong> networks,
because trust is important for <strong>the</strong> transfer <strong>of</strong> tacit knowledge. Of course, trust needs time to build,
which requires participants to attend monthly network meetings in order to develop trust. The final
data source comes from <strong>the</strong> monthly evaluations <strong>of</strong> each network session. Data from <strong>the</strong> monthly
evaluations demonstrated <strong>the</strong> level <strong>of</strong> communication between network participants since <strong>the</strong>
previous network session and provided evidence <strong>of</strong> <strong>the</strong> primary reason for that communication.
5. Findings
5.1 Network attendance
Entrepreneurial tacit knowledge cannot be received or transmitted without network members
attending <strong>the</strong> network meetings. Therefore, accessibility to tacit knowledge is increased through
network attendance. Over <strong>the</strong> course <strong>of</strong> this longitudinal study, <strong>the</strong> network sessions were divided
into facilitated and self-facilitated sessions. The objective <strong>of</strong> facilitated sessions was to provide <strong>the</strong>
networks with training in preparation for <strong>the</strong> self-facilitation stage. Facilitated sessions (conducted
from March 2010 to July 2010) incorporated a series <strong>of</strong> workshops. The workshops and attendance
levels are depicted in Table 1.
Table 1: Network Attendance - Facilitated Sessions
Month Session Number Workshop Attendance
March 1 Introduction and Training Needs Analysis 78%
April 2 Team Building 61%
May 3 Communication and Conflict Management 75%
June 4 Change Management 71%
July 5 Sustainability <strong>of</strong> Networks 65%
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In <strong>the</strong> self-facilitated stage, <strong>the</strong> networks had complete autonomy, governed <strong>the</strong>mselves, set <strong>the</strong>ir own
agenda and decided what learning was <strong>of</strong> value for <strong>the</strong> network participants and <strong>the</strong> group. Table 2
displays <strong>the</strong> attendance at <strong>the</strong> self-facilitated sessions from (August 2010 to January 2011). The
figures in Table 1 and 2 indicate that attendance at <strong>the</strong>se sessions is quite high but did drop <strong>of</strong>f in
December due to <strong>the</strong> bad wea<strong>the</strong>r.
Table 2: Network Attendance - Self-Facilitated Sessions
Month Session Number Attendance
August 6 60%
September 7 72%
October 8 77%
November 9 61%
December 10 59%
January 11 66%
During <strong>the</strong> facilitated stage, <strong>the</strong> participants were only beginning to get to know one ano<strong>the</strong>r.
Dialogue and interaction during this stage was limited as <strong>the</strong> workshops were facilitated sessions and
<strong>the</strong>refore time for group interaction was limited. However, <strong>the</strong> network did enable closer proximity
between <strong>the</strong> participants due to <strong>the</strong> time spent in <strong>the</strong> network sessions, which facilitated <strong>the</strong>
development <strong>of</strong> trust. The researchers noted that in <strong>the</strong> transition from <strong>the</strong> facilitated to <strong>the</strong> selffacilitated
stage, <strong>the</strong> dialogue and interaction between network participants greatly increased as <strong>the</strong>
sessions progressed. Strong network attendance, high trust levels and a willingness to engage in <strong>the</strong>
networking process will deliver increased accessibility to tacit knowledge for <strong>the</strong> individual network
participant.
5.2 Tacit Knowledge Accessibility – The Role <strong>of</strong> Trust
As discussed trust is an important facet for <strong>the</strong> transfer <strong>of</strong> tacit knowledge and <strong>the</strong> following
discussion draws on data collected via a questionnaire conducted 8 months after <strong>the</strong> networks were
established in November 2010. In <strong>the</strong> first instance, <strong>the</strong> network participants were asked how long it
had taken to develop trust with o<strong>the</strong>r network members. The majority <strong>of</strong> <strong>the</strong> responses cite between 3
to 6 months, however some network members are still unsure <strong>of</strong> how much <strong>the</strong>y trust <strong>the</strong> o<strong>the</strong>r
network participants. The supporting statements for this question highlights that <strong>the</strong> building <strong>of</strong> trust is
still ongoing as comments from network participants include:
“Only starting to get to know <strong>the</strong> personalities within <strong>the</strong> group now, so would have a
certain amount <strong>of</strong> trust, which I would hope will increase with continued participation”
“Still in <strong>the</strong> process <strong>of</strong> developing trust with o<strong>the</strong>r team members”
“I think we are still working on it, we are still getting to know each o<strong>the</strong>r. Much depends
upon what you mean by trust”
Trusting network members infers that network participants will feel comfortable in discussing business
issues and potential sensitive information. Network participants were asked if <strong>the</strong>y would share
sensitive information with o<strong>the</strong>r network members <strong>of</strong> which 12 out <strong>of</strong> 23 agreed <strong>the</strong>y would and 8 out
<strong>of</strong> 23 said <strong>the</strong>y would not. Supporting comments highlight that <strong>the</strong> sharing <strong>of</strong> sensitive information is
acceptable “to a certain level” and or “up to a certain point but hopefully in <strong>the</strong> future I would share
more”. The supporting statements indicate that for <strong>the</strong> network participants <strong>the</strong> building <strong>of</strong> trust is still
an ongoing process and that when trust builds knowledge will flow more freely.
The participants were asked if <strong>the</strong>y trusted <strong>the</strong> o<strong>the</strong>r network members. Fourteen <strong>of</strong> 23 network
participants indicated that <strong>the</strong>y did trust <strong>the</strong> o<strong>the</strong>r network members and 9 <strong>of</strong> 23 stated that <strong>the</strong>y did
not know if <strong>the</strong>y trusted <strong>the</strong> o<strong>the</strong>r network participants. Supporting statements for <strong>the</strong> question
indicate that trust is a complicated issue that “varies from person to person” and <strong>the</strong> concept is more
about trusting <strong>the</strong> individual than <strong>the</strong> network. I trust “some <strong>of</strong> <strong>the</strong> members; don’t distrust everyone
but time and more interactions are required to establish trust”. Supporting statements highlight that
“trust is a very strong word! Some <strong>of</strong> my friends I may not trust fully”. While <strong>the</strong> networks may have
moved on to <strong>the</strong> self-facilitated stage <strong>of</strong> <strong>the</strong> project for some participants <strong>the</strong>y are still in <strong>the</strong> early
stages <strong>of</strong> <strong>the</strong> trust building process “too early - I trust <strong>the</strong> members that I have had <strong>the</strong> opportunity to
speak with one on one”.
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The network participants were asked if networks enable trust and co-operation between competitors.
Of <strong>the</strong> network participants 8 out <strong>of</strong> 23 agreed that it did while 10 out <strong>of</strong> 23 stated that as yet <strong>the</strong>y did
not know if networks enable trust and co-operation between competitors. The statement also
depends upon whe<strong>the</strong>r network participants consider that <strong>the</strong>y have competitors within <strong>the</strong>ir own
prescribed network “not for me I would imagine, but I have no competitors in <strong>the</strong> network”. O<strong>the</strong>r
network members did express that having competitors within <strong>the</strong> group would have an impact and
<strong>the</strong>refore “would be less likely to express problems with a competitor in <strong>the</strong> room”. A question was
raised by a network participant in terms <strong>of</strong> “should competitors trust each o<strong>the</strong>r and should <strong>the</strong>y cooperate?”
Fur<strong>the</strong>rmore, supportive statements suggest that “co-operation yes, on trust <strong>the</strong> jury is out but in my
experience to date you cannot have trust between competitors”. As discussed trust is important in
networking; Figure 1 shows that <strong>the</strong> network participants strongly agree that trust is important for <strong>the</strong>
success <strong>of</strong> <strong>the</strong> network. Fur<strong>the</strong>rmore, once trust is established <strong>the</strong> majority <strong>of</strong> participants consider
that if trust has been broken it cannot be re-established. The majority <strong>of</strong> <strong>the</strong> respondents agreed that
a breach <strong>of</strong> trust will reduce many <strong>of</strong> <strong>the</strong> participant’s willingness to share information with o<strong>the</strong>r
members and many <strong>of</strong> <strong>the</strong> network participants do not know.
Over <strong>the</strong> course <strong>of</strong> this longitudinal study <strong>the</strong> networks meet once a month for a three hour period.
Figure 1 indicates that <strong>the</strong> development <strong>of</strong> trust is still ongoing within <strong>the</strong> networks. However, 19 out
<strong>of</strong> 23 <strong>of</strong> <strong>the</strong> network participant’s surveyed agreed that attending network sessions helps to build trust
with network members. Fourteen strongly agreed and 9 agreed that successful networking depends
on trust while 13 <strong>of</strong> 23 confer that one breach <strong>of</strong> trust would reduce <strong>the</strong> willingness <strong>of</strong> network
members to share information and could result in <strong>the</strong>m leaving <strong>the</strong> network.
Figure 1: Trust in <strong>the</strong> SLNIW Self-Learning Networks
5.3 Monthly network meeting evaluations
After every network session, <strong>the</strong> network participants are required to complete a monthly
questionnaire. The questionnaire requests information on how many network members have been in
contact with each o<strong>the</strong>r since <strong>the</strong> previous network meeting, how many network members were
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contacted, <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> communication and <strong>the</strong> manner in which <strong>the</strong> communication was
conducted. The data collected concentrated on <strong>the</strong> self-facilitated sessions (i.e. Session 6 to Session
11) as this represents <strong>the</strong> participants and networks operating autonomously.
From <strong>the</strong> first self-facilitated session in August 2010, 69% <strong>of</strong> <strong>the</strong> network participants had been in
contact with o<strong>the</strong>r network members since <strong>the</strong> previous network meeting. Over <strong>the</strong> course <strong>of</strong> <strong>the</strong>
following 6 network sessions <strong>the</strong> highest percentage <strong>of</strong> contact registered at 81% (i.e. session 8 and
session 10). Over <strong>the</strong> course <strong>of</strong> <strong>the</strong> 6 self-facilitated sessions, many <strong>of</strong> <strong>the</strong> network participants
contacted more than one network member between <strong>the</strong> monthly meetings. For example, in session 6
two people contacted 6 network members since <strong>the</strong> previous network meeting, in session 7 eight
contacted three network members since <strong>the</strong> previous network meeting. Over <strong>the</strong> course <strong>of</strong> <strong>the</strong> selffacilitated
sessions <strong>the</strong> level <strong>of</strong> communication overall has been high (ranging from 69% to 81% <strong>of</strong> a
max <strong>of</strong> 30 participants) and as sessions progressed <strong>the</strong> level <strong>of</strong> contact each month was in excess <strong>of</strong>
75%.
Creating a trusting environment to allow greater accessibility to entrepreneurial tacit knowledge is an
objective <strong>of</strong> <strong>the</strong> SLNIW project and to date <strong>the</strong> data suggests that <strong>the</strong> network participants are
engaging with <strong>the</strong> process as <strong>the</strong>y are communicating and sharing ideas. This is fur<strong>the</strong>r supported by
evidence indicating <strong>the</strong> purpose for such communication (i.e. social, knowledge seeking,
collaboration, business, information sharing and problem solving). From session 6 to session 11, <strong>the</strong>
highest rated purpose <strong>of</strong> communication was for business issues ranging from 39% to 56%. The
second highest purpose for communication was information sharing ranging from 26% to 33% over
<strong>the</strong> six months and social as a purpose for communication ranged from 17% to 31%.
A self-learning network is an alternative approach to networking, as <strong>the</strong> regular monthly meetings
facilitates closer proximity between <strong>the</strong> participants allowing for repeated face to face communication
and allowing participants to develop a trusting relationship with each o<strong>the</strong>r. Trust is a key issue for
<strong>the</strong> success <strong>of</strong> <strong>the</strong> networks. A reflection from a network participant on <strong>the</strong> difference between <strong>the</strong>
SLNIW networks and o<strong>the</strong>r approaches to entrepreneurial networking stated that “she was
comfortable with divulging information and discussing business problems with <strong>the</strong> group as she felt
<strong>the</strong> network was confidential”. Fur<strong>the</strong>rmore, she stated <strong>the</strong> willingness <strong>of</strong> a network participant to
engage came from <strong>the</strong> trust built within <strong>the</strong> group, <strong>the</strong>ir autonomy over <strong>the</strong>ir learning, <strong>the</strong> support <strong>the</strong>y
provided for each o<strong>the</strong>r and <strong>the</strong> confidence an individual participant derived from being able to help
o<strong>the</strong>rs.
6. Discussion
Individual entrepreneurs hold deposits <strong>of</strong> tacit knowledge, which is a valuable regional resource and
<strong>the</strong> intervention <strong>of</strong> SLNIW to increase accessibility to this resource, will aid <strong>the</strong> economic
enhancement <strong>of</strong> both regions. The focus <strong>of</strong> this paper was to highlight and discuss how SLNIW
facilitated an environment for entrepreneurs to begin to access tacit knowledge based on <strong>the</strong>
establishment <strong>of</strong> initial trustful communication via <strong>the</strong> networks. Attendance at network meetings is
important as <strong>the</strong> network participants cannot engage in <strong>the</strong> sharing <strong>of</strong> tacit knowledge if <strong>the</strong>y are not
present at <strong>the</strong> monthly meetings. Tables 1 and 2 display that attendance overall has been high and
ranged from 59% to 78%. This ranged from 61% to 78% attending <strong>the</strong> facilitated sessions and
between 59% and 77% attending <strong>the</strong> self-facilitated sessions. These levels <strong>of</strong> attendance indicate
that each <strong>of</strong> <strong>the</strong> groups are engaging with <strong>the</strong> process and even though <strong>the</strong>re may be a drop <strong>of</strong>f in
attendance in some weeks; this does not have a significant bearing on <strong>the</strong> transfer <strong>of</strong> tacit knowledge
or does not lead to a dilution <strong>of</strong> trust between network members. Therefore, attendance and
communication events are distributed among all participants.
Evidence suggests that trust is necessary for <strong>the</strong> transfer <strong>of</strong> tacit knowledge (Scott, 2000; Tamer
Cavusgil, Calatone and Zhao, 2003). The observations have identified that <strong>the</strong> transition from <strong>the</strong>
facilitated to self-facilitated stage saw an increase in communication with <strong>the</strong> participants being more
open and honest with one ano<strong>the</strong>r as <strong>the</strong> sessions progressed, which contributed to an increase <strong>of</strong>
trust.
The development <strong>of</strong> trust between <strong>the</strong> network participants was investigated via a questionnaire in
order to understand how trustful <strong>the</strong>y felt with each o<strong>the</strong>r. The network participants agreed that trust
is important for <strong>the</strong> success <strong>of</strong> networking however, while <strong>the</strong>y trusted some members <strong>the</strong>y
considered <strong>the</strong> development <strong>of</strong> trust takes time and <strong>the</strong>re is an expectation from <strong>the</strong> network
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Martina Mullally et al
participants that information exchange will grow with increased trust. Increased trust has developed
from attendance at network meetings and hence <strong>the</strong> value <strong>of</strong> participant attendance.
The monthly evaluations collected after each network session indicated that 69% <strong>of</strong> <strong>the</strong> network
participants had been in contact with o<strong>the</strong>r network members and that between <strong>the</strong> monthly meetings
communication between networks members has ranged from 69% to 81% with many network
members contacting more than one network participant. What is valuable to know is that business
issues were <strong>the</strong> primary purpose for that communication, which shows that <strong>the</strong> entrepreneurs’
accessibility to tacit knowledge has increased via <strong>the</strong>ir involvement with <strong>the</strong> SLNIW networks. The
value <strong>of</strong> increasing accessibility to tacit knowledge will in turn increase <strong>the</strong> propensity for <strong>the</strong> transfer
<strong>of</strong> tacit knowledge. The personal benefits and skills that <strong>the</strong> participants have gained from being part
<strong>of</strong> <strong>the</strong> SLNIW network can only have a positive economic affect on <strong>the</strong> economies <strong>of</strong> South East
Ireland and West Wales. Subsequently, SLNIW extends <strong>the</strong> findings <strong>of</strong> Chen et al. (2006) as it
provides an alternative solution to business learning networks and it can act as a mechanism to
effectively manage sources <strong>of</strong> embedded knowledge as it enables participants to develop a trust
relationship to share business problems and experiences with each o<strong>the</strong>r.
However, <strong>the</strong> quantitative data ga<strong>the</strong>red did not <strong>of</strong>fer much insight into how trust was established or
broken or about <strong>the</strong> process and content <strong>of</strong> tacit knowledge exchange between <strong>the</strong> network
participants. Therefore, <strong>the</strong> researchers propose to undertake a more in-depth analysis by ga<strong>the</strong>ring
fur<strong>the</strong>r qualitative data via focus groups and interviews with a sample <strong>of</strong> <strong>the</strong> network participants,
which will take place directly after <strong>the</strong> self-facilitated stage <strong>of</strong> <strong>the</strong> project and at a time when <strong>the</strong>
networks have been operating without <strong>the</strong> support <strong>of</strong> <strong>the</strong> SLNIW project team. The objective <strong>of</strong>
carrying out this analysis is to provide a dynamic account <strong>of</strong> any changes in trust relationships and
tacit knowledge exchange as a result <strong>of</strong> <strong>the</strong> change in process or over a period <strong>of</strong> time. This study
could provide <strong>the</strong> foundation for a fur<strong>the</strong>r and much needed study about Knowledge Management
(KM) for entrepreneurs and SME networks. In this knowledge driven global economy, knowledge
itself can be seen as a commodity that <strong>of</strong>fers <strong>the</strong> only ‘true sustainable competitive edge’. If
knowledge is effectively managed i.e. collected, structured and disseminated, it will bring significant
benefits to organisations and <strong>the</strong> communities in which <strong>the</strong>y work (Lee and Lan, 2011).
7. Conclusions
Essentially, small business learning networks contain rich sources <strong>of</strong> tacit knowledge, which can be
found in entrepreneurs’ skills, behaviour and embedded knowledge. Given <strong>the</strong> difficulties in
transferring tacit knowledge, direct face-to-face social interaction, hands on learning through
communication, observation and experience are found to be effective in <strong>the</strong> diffusion <strong>of</strong> tacit
knowledge (Haldin-Herrgard, 2000). Subsequently, <strong>the</strong> vast cohort <strong>of</strong> knowledge transfer does not
happen through formal structures, ra<strong>the</strong>r it is more associated with people engaging on an informal
basis and developing interpersonal relationships based on this interaction.
Working toge<strong>the</strong>r in <strong>the</strong> current economic climate is crucial for small business owners and
entrepreneurs seeking to be innovative and creative. Through learning from o<strong>the</strong>rs this project has
enabled network members to direct <strong>the</strong>ir own learning to achieve business growth, expansion and
long term sustainability.
The research has implications for academics, practitioners, entrepreneurs and policy makers who are
concerned with <strong>the</strong> establishment <strong>of</strong> networks to propagate knowledge and technology transfer as it
illustrates that <strong>the</strong> establishment <strong>of</strong> trust between participants in a business learning network is vital
for <strong>the</strong> transfer <strong>of</strong> tacit knowledge. Thus, if universities and o<strong>the</strong>r intermediaries engaged in
developing such networks can find, <strong>the</strong> best ways to facilitate a learning environment in which
participants feel <strong>the</strong>y can place <strong>the</strong>ir trust tacit knowledge flows will increase and this is positively
correlated with both sustainable business growth and regional economic development (Mohammed
and Stankosky, 2009).
References
Bryman, A. (1988) Doing Research in Organisations, London: Routledge.
Chell, E., and Baines, S. (2000) “Networking, Entrepreneurship and Micro-business Behaviour”, Entrepreneurship
& Regional Development, Vol.12, pp 195-215.
Chen, S., Duan, Y. & Edwards, J.S. (2006) In Encyclopaedia <strong>of</strong> Communities <strong>of</strong> Practice in Information and
Knowledge. IN E., C & S., C. (Edns) Inter-organisational Knowledge Transfer Process Model. Ideal Group
Publisher.
693

Martina Mullally et al
Choo, C. W (2000) “Working with knowledge: how information pr<strong>of</strong>essionals manage what <strong>the</strong>y know”, Library
Management, Vol. 21, No. 8, pp.395-403.
Clarke, T. and Rollo, C. (2001) “Corporate initiatives in knowledge management”, Education and Training, Vol.
43, No. 5, pp. 206-14.
Davenport, T. and Prusak, L. (2000) Working Knowledge: How Organisations Manage What They Know, Harvard
Business School Press, Boston, MA.
Foos, T., Schum, G. and Ro<strong>the</strong>nberg, S. (2006) “Tacit knowledge transfer and <strong>the</strong> knowledge disconnect”,
Journal <strong>of</strong> Knowledge Management, Vol. 10, No. 1, pp. 6-18.
Granovetter, M. (1973) “The Strength <strong>of</strong> Weak ties”, American Journal <strong>of</strong> Sociology, Vol. 78, pp 1360-1380.
Granovetter, M. (1974) Getting a job: A study <strong>of</strong> Contacts and Careers. Boston, MA: Harvard University Press.
Haldin-Herrgard, T. (2000) “Difficulties in diffusion <strong>of</strong> tacit knowledge in organizations”, Journal <strong>of</strong> Intellectual
Capital, Vol. 1, No. 4, pp. 357-365.
Hoang, H., and Antoncic, B. (2003) “Network based research in entrepreneurship. A critical review”,
Journal <strong>of</strong> Business Venturing, Vol. 18, pp. 165-187.
Hughes, T., O’Regan, N. and Sims, M. A. (2009) ‘The effectiveness <strong>of</strong> knowledge networks’, Education and
Training, Vol. 51, No. 8-9, pp.665-681.
Huxham, C. (1996) Creating collaborate advantage. Sage Publications: London.
Jarillo, C.J. (1989) “Entrepreneurship and growth: <strong>the</strong> strategic use <strong>of</strong> external sources”, Journal <strong>of</strong> Business
Venturing, Vol. 4, pp 133-147.
Kingsley, G. and Malecki, J.E. (2004) “Networking for competitiveness”, Small Business Economics, Vol. 23, No.
1, pp. 71-84.
Lee, M.R. & Lan, Y. (2011) “Toward a unified knowledge management model for SMEs”, Expert Systems with
Applications, Vol. 38, No. 1, pp. 729-735.
Meso, P. and Smith, R. (2000) “A resource-based view <strong>of</strong> organisational knowledge management systems”,
Journal <strong>of</strong> Knowledge Management, Vol. 4, No. 3, pp. 224-34.
Mohamed, M., Stankosky M. and Mohamed M. (2009), ‘An empirical assessment <strong>of</strong> knowledge management
criticality for sustainable development’, Journal <strong>of</strong> Knowledge Management, Vol. 13, No. 5, pp. 271-286.
Nirmala, M. and Vemuri, M. (2009) “Leveraging informal networks in knowledge management”, Journal <strong>of</strong>
Knowledge Management, Vol. 13, No. 3, pp. 146-156.
Nonaka, I. (1994) ‘A Dynamic Theory <strong>of</strong> Organizational Knowledge Creation’, Organization Science, Vol. 5, No.
1, pp. 14-37.
Polanyi, M. (1962) Personal Knowledge, London: Routledge.
Schrøder, K. C. (1999) ‘The Best <strong>of</strong> Both Worlds? Media Audience Research between Rival Paradigms’ in P.
Alasuutari (edn.) Rethinking <strong>the</strong> Media Audience, London: Sage.
Scott, J. (2000) ‘Facilitating interorganisational learning with information technology’, Journal <strong>of</strong> Management
Information Systems, Vol. 17, No. 2, pp. 81-113.
Tamer Cavusgil, S., Calantone, R. J. and Zhao, Y. (2003) ‘Tacit knowledge transfer and firm innovation
capability’, Journal <strong>of</strong> Business & Industrial Marketing, Vol. 18, No. 1, pp. 6-21.
Zack, M.H. (1999) ‘Developing a Knowledge Strategy’, California Management Review, Vol. 41, No. 3, pp. 125-
145.
694

A Structural Model for Organizational Learning in
Universities Based on Managers' Emotional Intelligence
Fattah Nazem
Department <strong>of</strong> Education, Roudehen Branch, Islamic Azad University,
Roudehen, Iran
nazem@riau.ac.ir
Abstract: The purpose <strong>of</strong> <strong>the</strong> present study was to provide a structural model <strong>of</strong> organizational learning in
universities based on managers’ emotional intelligence. The population <strong>of</strong> <strong>the</strong> research included all employees <strong>of</strong>
Islamic Azad University (IAU). 554 managers and three employees under <strong>the</strong>ir supervision (totally 1662
employees) were selected using stratified and cluster random sampling method. The research instruments were
two questionnaires which were administered in 96 IAU branches and education centers: Watkins and Marsick’s
(1997) organizational learning Questionnaire (α = 0.90) and <strong>the</strong> Cyberia - Shrink’s Questionnaire <strong>of</strong> emotional
intelligence (α = 0.77). The results <strong>of</strong> path analysis using LISREL s<strong>of</strong>tware indicated that dimensions <strong>of</strong>
managers’ emotional intelligence had a direct effect on organizational learning with <strong>the</strong> indices <strong>of</strong> 0.17 for <strong>the</strong>
variable. The model also showed that <strong>the</strong> factor <strong>of</strong> self-awareness in managers’ emotional intelligence had <strong>the</strong>
highest direct effect on <strong>the</strong> organizational learning (in organizational level). It was also concluded that <strong>the</strong>
proposed model showed full fit.
Keywords: structural model, organizational learning, emotional intelligence, universities
1. Introduction and purpose <strong>of</strong> <strong>the</strong> study
Higher education system, at <strong>the</strong> present time, is <strong>the</strong> most important system in any society. The degree
<strong>of</strong> contribution <strong>of</strong> higher education in economic development has been widely identified both in <strong>the</strong>
economies which rely on advanced technology and those <strong>of</strong> newly industrialized or developing
countries. Higher education is also considered as <strong>the</strong> most important organization which focuses on
“thoughts” and has a pr<strong>of</strong>ound effect on culture, political and religious affairs. Regarding <strong>the</strong> key role
<strong>of</strong> higher education, Green (1997) explains that higher education provides <strong>the</strong> technical knowledge
and skill that industry requires in future and <strong>the</strong> fact that governments depend on this knowledge to
have an effective and strategic programming. In addition, universities are included in those social
systems which have been recognized as <strong>the</strong> society’s engine for raising awareness and <strong>the</strong> basic
center <strong>of</strong> thought and reflection.
The advent <strong>of</strong> information technology has imposed a new condition over <strong>the</strong> world <strong>of</strong> trade and
business. Unlike industrial societies which are competing against more financial assets, post-industrial
societies have based <strong>the</strong>ir main objective over accessing more updated knowledge, in a way that
knowledge is, for <strong>the</strong>m, <strong>the</strong> master key <strong>of</strong> world competition and organizational learning is regarded
as one <strong>of</strong> <strong>the</strong> competing benefit <strong>of</strong> <strong>the</strong> organizations. Organizational learning means <strong>the</strong> process <strong>of</strong>
improving actions through better knowledge and understanding. (Fiol & Lyles, 1985, p.811). King
(2002) asserted that organizational learning is important to <strong>the</strong> success <strong>of</strong> quality-focused
organizations since only through learning can organizations capture and retain <strong>the</strong> knowledge
necessary to continually refine and improve business processes responsible for product and service
quality. Consequently, organizational learning is an essential component <strong>of</strong> a comprehensive <strong>the</strong>ory
<strong>of</strong> quality management. Within this complicated world with such dramatic every day changes,
organizations , especially higher education institutions, can merit comparison with o<strong>the</strong>rs only if, with a
unified and integrated identity, <strong>the</strong>y are able to learn much quicker than <strong>the</strong>ir counterparts. Crossam
et al. (1999) believe that organizational learning is multi-level consisting <strong>of</strong> individual, team/group, and
organizational. Moilanen (2001) and Wany (2005) also speculate that Watkins and Marsick’s
Dimensions <strong>of</strong> Organizational Learning Questionnaire (DLOQ) is <strong>the</strong> most comprehensive one. It
includes 62 items with 7 dimensions. Watkins and Marsick’s (1993, 1996) identify seven core
practices at <strong>the</strong> individual, group, and organizational levels as follows:
Individual level
Creating continuous learning opportunities
Promoting inquiry and dialogue
Team/group level
Encouraging collaboration and team learning
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Fattah Nazem
Organizational level
Creating systems to capture and share learning
Empowering people toward a collective vision
Connecting <strong>the</strong> organization to its environment
Providing strategic leadership for learning
In a climate <strong>of</strong> accelerating change, organizations can not flourish without nurturing <strong>the</strong> seeds <strong>of</strong>
learning. Established as a wellspring <strong>of</strong> value-producing knowledge, organizational learning is bulding
block to innovative, quality, and pr<strong>of</strong>itable products and services (Argyris & Schon, 1999; and
Schwandt & Marquardt, 2000).
The results <strong>of</strong> Howard (2003) also indicated a statistically significant relationship between <strong>the</strong>
presence <strong>of</strong> an organizational learning culture, positive value expression, and organizational
commitment. The <strong>the</strong>ory <strong>of</strong> organizational learning and practices <strong>of</strong> a learning organization appears to
be expressing values and promoting behaviors that create a positive working environment.
Studies show that organizational learning has meaningful positive relationship with <strong>the</strong> factors such as
<strong>the</strong> job satisfaction and organizational commitment (Howard, 2003; Wang, 2005; Krishna, 2008; and
Hsu, 2009), organizational change and innovation (Lin, 2006; DellaNeve, 2007; and Wang, 2008),
organizational culture (Garmon, 2004),organizational success (Truran, 2001), and performance (Galy,
2003; Hudspeth, 2004; Moore, 2004; Xie, 2005; Nordtvedt, 2005; Forrest, 2006;Photis, 2007; and
Chen, 2007).
One <strong>of</strong> <strong>the</strong> obstacles in institutionalizing organizational learning is believed to be <strong>the</strong> lack <strong>of</strong> effective
leadership (Joeong, 2004; Beard, 2006). Organizations ought to take into account <strong>the</strong> way leaders
educate <strong>the</strong> staffs regarding <strong>the</strong> role <strong>of</strong> organizational learning. Leaders should create an atmosphere
in which organizational learning is institutionalized in <strong>the</strong> organization. This can, finally, lead
knowledge and information systems, which are <strong>of</strong> determining factors in any organization, into
organizational learning under <strong>the</strong> leaders’ support. The result <strong>of</strong> <strong>the</strong> study carried out by King (2002)
indicated that <strong>the</strong>re was a relationship between leaders’ behaviors and organizational learning.
Devereaux (2005) also found out that <strong>the</strong> increase <strong>of</strong> open relationship, cooperation, individual
support, focus on pr<strong>of</strong>essional learning and growth and development are among <strong>the</strong> leaders’
behaviors that can streng<strong>the</strong>n organizational learning through dialogue and negotiation. Leuci (2005)
also underlined that organizational learning was a necessity for organizations’ success. What
organizational learning really is and how it can be used are among <strong>the</strong> major activities <strong>of</strong> managers in
<strong>the</strong> future.
The most important issue in each system, especially in higher education system, is <strong>the</strong> selection and
appointment <strong>of</strong> <strong>the</strong> managers and leaders in a way that <strong>the</strong>y can effectively administer <strong>the</strong> higher
education institutes. The managers are <strong>the</strong> most valuable resources <strong>of</strong> <strong>the</strong> organizations. The
qualified and knowledgeable managers are able to achieve <strong>the</strong> organization's goal using <strong>the</strong>ir own
abilities, specialized knowledge and vocational experiences while <strong>the</strong>y apply less resource and
increase <strong>the</strong> efficiency <strong>of</strong> <strong>the</strong> organization. One <strong>of</strong> <strong>the</strong> qualifications that <strong>the</strong> university managers
should possess is high emotional intelligence.
Mayer and Salovey (1997) consider emotional intelligence as a kind <strong>of</strong> social intelligence which
consists <strong>of</strong> <strong>the</strong> ability to control one’s and o<strong>the</strong>rs’ emotions as well as <strong>the</strong> difference between <strong>the</strong>se
two. Bar-On (1997) defined emotional intelligence as “an array <strong>of</strong> noncognitive capabilities,
competencies, and skills that influence one’s ability to succeed in coping with environmental demands
and pressures” (p.14). In Working with Emotional Intelligence, Goleman (1998) described <strong>the</strong>
construct as “<strong>the</strong> capacity for recognizing our own feelings and those <strong>of</strong> o<strong>the</strong>rs, for motivating
ourselves, and for managing emotions well in ourselves and in our relationships” (p.317). He
described <strong>the</strong> construct as each person’s potential for learning practical skills centered around <strong>the</strong>
following five elements:
Self-awareness: knowing what we are feeling in <strong>the</strong> moment, and using those preferences to
guide our decision making; having a realistic assessment <strong>of</strong> our own abilities and a well-grounded
sense <strong>of</strong> self confidence.
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Fattah Nazem
Self-regulation: handling our emotions so that <strong>the</strong>y facilitate ra<strong>the</strong>r than interfere with <strong>the</strong> task at
hand; being conscientious and delaying gratification to pursue goals; recovering well from
emotional distress.
Motivation: using our deepest preferences to move and guide us toward our goals, to help us take
initiative and strive to improve, and to persevere in <strong>the</strong> face <strong>of</strong> setbacks and frustrations.
Empathy: sensing what people are feeling, being able to take <strong>the</strong>ir perspective, and cultivating
rapport and attunement with a broad diversity <strong>of</strong> people.
Social skills: handling emotions in relationships well and accurately reading social situations and
networks, interacting smoothly; using <strong>the</strong>se skills to persuade and lead, negotiate and settle
disputes, for cooperation and teamwork (p.318).
In his study, Smith (2006) found that <strong>the</strong>re is a relationship between <strong>the</strong> emotional intelligence and
leadership <strong>of</strong> change. The results <strong>of</strong> <strong>the</strong> study done by Cook (2006) showed that emotional
intelligence has a positive effect on <strong>the</strong> principles <strong>of</strong> performance leadership. In o<strong>the</strong>r words, he
found that <strong>the</strong> effective leadership requires a series <strong>of</strong> skills including emotional intelligence. In
addition, Jones (2007) found that emotional intelligence can be considered as a predictive factor <strong>of</strong>
productivity and <strong>the</strong> fact that <strong>the</strong> managers who possess this skill perform more competitively.
Regarding <strong>the</strong> relationship between emotional intelligence and <strong>the</strong> effective leadership in
organizations, various studies have been done. Some <strong>of</strong> <strong>the</strong>m are as follows: Downey et al. (2006),
Vartanian (2006), Danehy (2006), Kluemper (2006), Durham (2006), Higley (2007), Rego et al.
(2007), Heiken (2007), and Chadnick (2007).Hayashi (2005) found that <strong>the</strong> higher <strong>the</strong> emotional
intelligence, <strong>the</strong> better and more effective <strong>the</strong> managers’ performance will be. Emotional intelligence
is a necessary factor for <strong>the</strong> managers and leaders. Eicher (2003), in his research entitled, “<strong>the</strong>
relationship between <strong>the</strong> emotional intelligence and social skills necessary for staff” concluded that
emotional intelligence is very suitable in developing <strong>the</strong> staff’s executive programs. Fur<strong>the</strong>rmore,
regarding <strong>the</strong> effect <strong>of</strong> emotional intelligence and job progress, Watkin (2000) found that <strong>the</strong>
application <strong>of</strong> emotional intelligence leads to <strong>the</strong> development and improvement <strong>of</strong> <strong>the</strong> process <strong>of</strong>
pr<strong>of</strong>essional decision making and <strong>the</strong> fact that it has a direct effect on <strong>the</strong> level <strong>of</strong> product sale. The
final remark <strong>of</strong> his study is that people with high level <strong>of</strong> emotional intelligence have higher efficiency
in jobs which need technical expertise. The result <strong>of</strong> a study in India by Singh(2007) also depicted
emotional intelligence as being positively and significantly related with three phases <strong>of</strong> organizational
learning (innovation, implementation, and stabilization) and five mechanisms <strong>of</strong> organizational
learning (experimentation, mutuality, planning, uses <strong>of</strong> temporary systems, and competency
mechanisms). The findings have implications for how to manage employees in a way that creates and
maintains organisational learning. Gottleib ( 2006) found that to promote learning, humanistic
leadership that embodies cultural and relational competencies may be essential. Data from <strong>the</strong> survey
revealed statistically significant results that emotional intelligence in managers positively correlates to
organizational learning in teams.
Senge (1990) identifies <strong>the</strong> five disciplines associated with <strong>the</strong> organisational learning to be personal
mastery, mental models, systems thinking, team learning, and building a shared vision for <strong>the</strong>
organisation. I contend that each <strong>of</strong> <strong>the</strong>se disciplines can be linked to specific emotional intelligence
abilities <strong>of</strong> (a) emotional awareness, (b) emotional facilitation, (c) emotional knowledge, and (d)
emotional regulation (Mayer & Salovey, 1997). Jordan (2006) found that by linking emotional
intelligence to <strong>the</strong> learning organisation principles and o<strong>the</strong>r interpersonal skills needed by
management and employees in <strong>the</strong> workplace during organisational change, we may gain a deeper
insight into how to make <strong>the</strong>se change programs successful. The need for management and
leadership is vital and sensible in all fields <strong>of</strong> social activities. The related background information
shows that if universities have managers who enjoy a high degree <strong>of</strong> emotional intelligence, <strong>the</strong>
universities will be able to achieve <strong>the</strong>ir goals while <strong>the</strong>y spend fewer sources. They can also
increase <strong>the</strong> level <strong>of</strong> organizational learning <strong>of</strong> <strong>the</strong> higher education system.
2. Research questions
What is <strong>the</strong> structural model <strong>of</strong> <strong>the</strong> organizational learning based on managers’ emotional
intelligence in higher education Institutions?
Which variables have <strong>the</strong> highest effectiveness on organizational learning?
How predictive is emotional intelligence on promoting organizational learning?
How much is <strong>the</strong> goodness <strong>of</strong> fit in this study?
697

3. Method <strong>of</strong> <strong>the</strong> study
Fattah Nazem
The research methods which were used in this study are: library research to access <strong>the</strong> <strong>the</strong>oretical
framework and <strong>the</strong> related literature; Survey method to collect, classify, describe, and analyze <strong>the</strong>
data. The population under investigation in this study consists <strong>of</strong> <strong>of</strong>ficial staff working in 420 branches
and educational centers in 14 zones <strong>of</strong> Islamic Azad University in Iran. In order to estimate <strong>the</strong> least
2 2
z σ
volume <strong>of</strong> sample, n = 2 formula was used. Regarding <strong>the</strong> minimum sample required for
d
<strong>the</strong> managers’ group which was estimated as 554 and staff group as 1662 (three staffs for each
manager), emotional intelligence questionnaire was administered to <strong>the</strong> managers and organizational
learning questionnaire was administered to <strong>the</strong> staffs in 96 branches and educational centers. In order
to select <strong>the</strong> research sample, two methods <strong>of</strong> stratified and cluster random sampling were used.
The research instruments were as follows: Watkins and Marsick’s (1997) organizational learning
Questionnaire which consisted <strong>of</strong> 43 items with three underlying constructs <strong>of</strong> individual level , group
level, and organizational level and Cronbach Alpha <strong>of</strong> 0.90; and <strong>the</strong> Questionnaire <strong>of</strong> emotional
intelligence (Cyberia-Shrink, 1998) with 33 items and consisted <strong>of</strong> self-awareness , self-regulation ,
motivation , empathy , and social skills (α = 0.77). The results <strong>of</strong> <strong>the</strong> study were calculated through
path analysis using LISREL s<strong>of</strong>tware.
4. Findings <strong>of</strong> <strong>the</strong> study
The data collected from <strong>the</strong> administration <strong>of</strong> <strong>the</strong> instruments were analyzed. These data included <strong>the</strong>
different indexes <strong>of</strong> central tendency, variability and <strong>the</strong> distribution <strong>of</strong> both groups, <strong>the</strong> managers’
scores obtained from <strong>the</strong> questionnaire <strong>of</strong> emotional intelligence and its 5 components and <strong>the</strong> staffs’
scores obtained from organizational learning questionnaire and <strong>the</strong>ir related components. The
distribution <strong>of</strong> <strong>the</strong> managers’ and <strong>the</strong> staffs’ scores in <strong>the</strong> given variables had tendency toward
normality.
Self
awareness
Self‐
regulation
Motivation
Empathy
Social skills
0.78
0.58
0.43
0.27
0.41
Emotional
Intelligence
0.17 Organizational
Learning
4.96
3.17
6.86
Individual level
Group level
Organizational
level
Figure 1: Path analysis model for components <strong>of</strong> managers’ emotional intelligence and organizational
learning
As shown in Figure 1, Chi-square index is 353.92 which is significant at 0.000. In o<strong>the</strong>r words, <strong>the</strong>
model <strong>of</strong> <strong>the</strong> relationship between emotional intelligence components which are self-awareness
measured by 8 items, self-regulation measured by 7 items, motivation measured by 7 items, empathy
measured by 6 items and social skills measured by 5 items as independent variable with components
<strong>of</strong> organizational learning which are individual level measured by 14 items, group level measured by 6
items, organizational level measured by 14 items as dependent variable.
698

Fattah Nazem
The Lambda rate <strong>of</strong> external latent variable <strong>of</strong> emotional intelligence components was 0.78 for selfawareness,
0.58 for self-regulation, 0.27 for motivation, 0.43 for empathy and 0.41 for social skills
whose accumulation form <strong>the</strong> emotional intelligence variable with <strong>the</strong> effectiveness rate <strong>of</strong> 0.17. It
means that 17% <strong>of</strong> <strong>the</strong> variation in <strong>the</strong> dependant variable <strong>of</strong> organizational learning is explained by a
collection <strong>of</strong> <strong>the</strong>se indices. The variables <strong>of</strong> self-awareness indicate <strong>the</strong> highest amount <strong>of</strong> internal
consistency in <strong>the</strong> external latent variable.
The Lambda rate <strong>of</strong> internal latent variable <strong>of</strong> organizational learning components was 4.96 for
individual level, 3.17 for group level, 6.86 for organizational level whose accumulation form <strong>the</strong>
organizational learning variable. The variable <strong>of</strong> organizational level indicates <strong>the</strong> highest amount <strong>of</strong>
internal consistency in <strong>the</strong> internal latent variable.
Since <strong>the</strong> model’s goodness <strong>of</strong> fit index is 0.92, it can be stated that it has an acceptable fit. The
calculated index indicates <strong>the</strong> direct effect <strong>of</strong> emotional intelligence components on organizational
learning. Moreover, <strong>the</strong> model shows that <strong>the</strong> highest direct effect is related to self-awareness, <strong>the</strong>
component <strong>of</strong> emotional intelligence, on organizational learning in organizational level.
The following table presents <strong>the</strong> indices related to <strong>the</strong> model’s fit:
Table 1: Model’s fit indices
Index
Rate Interpretation
Chi-Square
535.92 Full fit (α=0.001)
Lewis-Tucker (Non-normed fit index) 0.92 High fit (more than 0.90)
Bentler-Bonett’s (Normed fit index) 0.92 High fit (more than 0.90)
Hoelter
0.81 High fit (more than 0.70)
Root Mean Square Error (RMSE) 0.037 High fit (equal to or less than 0.05)
GFI
0.92 High fit (more than 0.90)
The six goodness <strong>of</strong> fit indices presented model’s fit and empirical data. Therefore, desirability
adaptation is provided for <strong>the</strong> designed model and empirical data and can approve it as an
appropriate model for <strong>the</strong> organizational learning.
5. Discussion and conclusions
The results <strong>of</strong> path analysis indicated that since model’s goodness <strong>of</strong> fit index is 0.92, it can be stated
that it has an acceptable fit. The calculated index shows <strong>the</strong> direct effect <strong>of</strong> emotional intelligence
components (0.17) on organizational learning. The results <strong>of</strong> this study are in line with <strong>the</strong> research by
Singh (2007), Jordan (2006), and Gottleib (2006).
Singh (2007) found that emotional intelligence is positively and significantly related with <strong>the</strong> three
phases <strong>of</strong> organizational learning and five mechanisms <strong>of</strong> organizational learning. Jordan (2006)
found that by linking emotional intelligence to <strong>the</strong> learning organisation principles and o<strong>the</strong>r
interpersonal skills, we may gain a deeper insight into how to make <strong>the</strong>se change programs
successful. Data from Gottleib’s( 2006) survey revealed statistically significant results that emotional
intelligence in managers positively correlates to organizational learning in teams. Senge (1990)
identifies <strong>the</strong> five disciplines associated with <strong>the</strong> organisational learning to be personal mastery,
mental models, systems thinking, team learning, and building a shared vision for <strong>the</strong> organisation. I
contend that each <strong>of</strong> <strong>the</strong>se disciplines can be linked to specific emotional intelligence abilities <strong>of</strong> (a)
emotional awareness, (b) emotional facilitation, (c) emotional knowledge, and (d) emotional regulation
(Mayer & Salovey 1997).
Close attention to organizational learning is an absolute necessity within all organizations particularly
higher education institutions. Many authors agreed that “… successful organizations that forge ahead
in a rapidly changing business environment will do so through creating and sharing new knowledge”
(Argyris & Schon, 1996; Senge, 1990; Petrides, 2002).The result <strong>of</strong> <strong>the</strong> study conducted by Beard
(2006) showed that <strong>the</strong> indices <strong>of</strong> organizational learning included identities, thoughts, common ideas,
group working and group learning, sharing information and systematic thought, having leader, staff’s
skills, and competition. Miller (1996) also found out that <strong>the</strong> successful results <strong>of</strong> organizational
learning are: successful financial and business performance, self-learning at individual and
team/group levels, and group learning. Moreover, Duffy (2002) indicated that <strong>the</strong> key to significance
and high quality in fostering values, nurturing personal qualifications, and caring social values in
699

Fattah Nazem
streng<strong>the</strong>ning team/group leadership lies in following organizational learning. The results <strong>of</strong> <strong>the</strong>
studies conducted by Nordtvedt (2005) and Lin (2006) also clarified that using organizational learning
and effective teaching in organizations would enhance <strong>the</strong> income, market share, pr<strong>of</strong>itability, and
company’s performance and played a leading role in <strong>the</strong> increase <strong>of</strong> innovation rate.
In <strong>the</strong> age <strong>of</strong> increasing changes and complexities, <strong>the</strong> concept <strong>of</strong> organization nowadays varies from
<strong>the</strong> time when <strong>the</strong>re were simpler relations among organizations. In our age, learning organizations
are considered as <strong>the</strong> most successful ones since <strong>the</strong>y are able to comply <strong>the</strong>mselves with <strong>the</strong>
characteristics <strong>of</strong> <strong>the</strong>ir age. Unlike <strong>the</strong> premature image, learning organization is not <strong>the</strong> one that
holds instructional courses but it is a much deeper concept. Never-ending instruction is a need for all
<strong>the</strong> staff through which <strong>the</strong>y not only focus on learning, how to learn, and distributing knowledge, but
also create new information and knowledge which should <strong>the</strong>n emerge in <strong>the</strong>ir performance and
behaviors. This would lead to institutionalizing teaching and organizational learning in all levels <strong>of</strong> <strong>the</strong>
organization that can pave <strong>the</strong> way to have a continuous reform in <strong>the</strong> structures and processes
which finally end up in efficiency and effectiveness <strong>of</strong> <strong>the</strong> organization.
Bohrer (2007) believes that <strong>the</strong> organizations should search for <strong>the</strong> leaders who create an
environment which positively affect <strong>the</strong> job satisfaction and <strong>the</strong> staff’s performance. The managers
who enjoy high emotional intelligence can create this desired environment. Chadnick (2007) also
found that emotional intelligence is considered as a criterion for <strong>the</strong> success <strong>of</strong> organizations.
Bradberry and Griaves (2005) believe that emotional intelligence is so necessary for success that
60% <strong>of</strong> performance in any kind <strong>of</strong> job depends on it. The emotional intelligence, by itself, is <strong>the</strong>
greatest factor for predicting <strong>the</strong> person’s performance in <strong>the</strong> work place and <strong>the</strong> strongest power for
leadership and success.
The results <strong>of</strong> <strong>the</strong> studies conducted in <strong>the</strong> field reiterate that success in performing organizational
learning models requires managers who have emotional intelligence. The need for management and
leadership is vital and sensible in all fields <strong>of</strong> social activities. Without effective leadership and
guidance, material and human resources are doomed to decrease and destruction, while <strong>the</strong>
managers <strong>of</strong> higher education as people who are in charge <strong>of</strong> organizations and university branches
are considered as <strong>the</strong> main and determining factor in preparing and supplying <strong>the</strong> human resources
required by o<strong>the</strong>r organizations which provide services or products in <strong>the</strong> society. The appointment <strong>of</strong>
managers with emotional intelligence in universities can help manage <strong>the</strong> organizational learning
more effectively. It is also suggested that <strong>the</strong> research projects as this are to be endorsed in all
branches <strong>of</strong> Islamic Azad University (IAU) to upgrade <strong>the</strong> performing organizational learning. Having
<strong>the</strong> effective role <strong>of</strong> higher education in <strong>the</strong> economic, social, political, and cultural development, it is
suggested that this study can also be carried out in o<strong>the</strong>r universities all around <strong>the</strong> world so as to
practically take giant steps in <strong>the</strong> performing organizational learning.
Acknowledgements
This paper is extracted from a research project sponsored by <strong>the</strong> research department <strong>of</strong> <strong>the</strong> Islamic
Azad University, Roudehen Branch to whom I owe a debt <strong>of</strong> gratitude.
References
Argyris, C. & Schon, D.A. (1996) Organizational learning 2: Theory, method, and practice. Reading, MA:
Addison-Wesley
Bar – On, R. (1997) Bar-on emotional quotient inventory: EQ-i technical manual, Toronto: Multi-Health Systems,
Inc.
Beard, R. (2006) Accreditation processes and organizational learning capabilities in institutions higher education.
[Ph.D. Dissertation]. Capella University.
Bohrer, V. C. (2007). A study <strong>of</strong> <strong>the</strong> relationship between leader emotional intelligence ability and demographic,
Performance, job satisfaction measures, and MBTI type in <strong>the</strong> United States Intelligence Community.
[D.Mgt Dissertation]. Webster University.
Bradberry, T., & Griaves, J. (2005) The emotional intelligence quick book: Everything you need to know to put
your EQ to work . : Simon & Schuster.
Chadnick, E. (2007) Emotional intelligence at work: Don't leave home without it. Canadian HR Reporter. Toronto,
20(9), 2-14.
Chen, L., X. (2007) External organizational learning and firm performance. [Ph.D. Dissertation]. Hong Kong,
University <strong>of</strong> Hong Kong.
Cook, C. R. (2006) Effects <strong>of</strong> emotional intelligence on principals' leadership performance. [ Ed.D. Dissertation ].
Montana State University.
700

Fattah Nazem
Crossan, M., Lane, H.W., and White, R. E. (1999) An organizational learning framework: From intuition to
institution, Academy <strong>of</strong> Management Review,24 ,3.
Danehy, L. S. (2006) The relationship between emotional intelligence and leadership in NCAA Division III college
coaches. [ Ed.D. Dissertation ]. Wilmington College (Delaware).
DellaNeve, J. R. (2007) The relationship between organizational learning and organizational change: Analyzing
adult learning and organizational learning factors. [Ed.D. Dissertation]. United States, Pepperdine
University.
Devereaux, L. (2005) Merging role- negotiation and leadership practices that influence organizational learning.
[Ed.D. Dissertation]. Canada ,University <strong>of</strong> Toronto.
Downey, L. A., Papageorgiou, V. & Stough, C. (2006) Examining <strong>the</strong> relationship between leadership, emotional
intelligence and intuition in senior female managers. Leadership & Organization Development Journal.
Bradford: 2006. 27(4), 1-25.
Duffy, A., (2002) Collective executive leadership: An exploration <strong>of</strong> this new leadership phenomenon and its
relationship to organizational learning, performance and results. [Ed.D. Dissertation], Pepperdine University.
Durham, T. G. (2006) Emotional intelligence and supervision. Addiction Pr<strong>of</strong>essional, 4(6), 1,46.
Eicher, D. (2003). Essential executive skills, for <strong>the</strong> future: Emotional intelligence. Futurics 27(3/4) St. Paul,
Publication title.
Fiol, M. C., & Lyles, A. M. (1985) Organizational learning. Academy <strong>of</strong> Management Review, 10, 803-813.
Forrest, C.M. (2006) An empirical examination <strong>of</strong> <strong>the</strong> relationship between organizational culture and
organizational performance, [Ph.D. Dissertation], United States, The University <strong>of</strong> New Mexico.
Galy, E.(2003) The mediating role <strong>of</strong> organizational learning between absorptive capacity and performance in
companies employing enterprise resource planning s<strong>of</strong>tware. [Ph.D. Dissertation]. United States, University
<strong>of</strong> Texas.
Garmon, M. S. (2004) The relationship between organizational learning , culture, image, identity and
identification: An empirical study. [Ph.D. Dissertation] . Touro University International.
Goleman, D. (1998) Working with emotional intelligence, New Yourk: Bantam Books.
Gottleib, M. F.( 2006) Humanistic leadership: Emotional intelligence and team learning. [D.M. Dissertation].
University <strong>of</strong> Phoenix.
Green, A. (1997) Education and state formation in Europe and Asia, In K. Kennedy (Ed), Citizenship educational
and <strong>the</strong> modern state, London: The Falmer Press.
Hayashi. A. (2005) Emotional intelligence and outdoor leadership: The Journal <strong>of</strong> experiential education.
www.Encyclopedia.com.
Heiken, S. E. (2007) The perceived relationship and leadership effectiveness in school leaders: A comparison <strong>of</strong>
self ratings with those <strong>of</strong> superiors and reports. [ Ed.D. Dissertation]. Wilmington (Delawere).
Higley, W. J., (2007) The relationship between <strong>the</strong> lead pastor’s emotional intelligence and pastoral leadership
team effectiveness. [Ed. D. Dissertation]. The Sou<strong>the</strong>rn Baptist Theological Seminary.
Howard, B. D. (2003) Organizational learning capacity in <strong>the</strong> context <strong>of</strong> <strong>the</strong> symlog most effective value Pr<strong>of</strong>ile. A
[Ph.D. Dissertation]. Alliant International University.
Hsu, H. Y. (2009) Organizational learning culture’s influence on job satisfaction, organizational commitment, and
turnover intention among R&D Pr<strong>of</strong>essionals in Taiwan during an economic downturn. [Ph.D. Dissertation].
United States, University <strong>of</strong> Minnesota.
Hudspeth. L. J. (2004) A study <strong>of</strong> organizational learning culture, strategic responsiveness and mass
customization capabilities <strong>of</strong> United States manufacturing enterprises. [Ph.D. Dissertation] .United States,
The University <strong>of</strong> Toledo.
Joeong, J. (2004) Analysis <strong>of</strong> <strong>the</strong> factors and <strong>the</strong> roles <strong>of</strong> HRD in organizational learning styles as identified by
key informants at selected corporations in <strong>the</strong> Republic <strong>of</strong> Korea. [Ph.D. Dissertation]. Texas A & M
University.
Jones, S. M. (2007) Emotional intelligence within organizations: A study <strong>of</strong> emotional intelligence and
performance ranking within a biomedical company. [ Ed.D. Dissertation]. Pepperdine University.
Jordan P. J.(2006) Dealing with organisational change: Can emotional intelligence enhance organisational
learning?, International Journal <strong>of</strong> Organisational Behaviour, Volume 8 (1), 456-471.
King, S.W. (2002). Effective leadership for quality achievement and organizational learning. [Ph.D. Dissertation].
Portland State University.
Kluemper, D. H. (2006). An examination <strong>of</strong> ability-based emotional intelligence in <strong>the</strong> structured employment
interview. [Ph.D. Dissertation ]. Oklahoma State University.
Krishna, V. (2008) Exploring organizational Commitment from an organizational Perspective: organizational
learning as a determinant <strong>of</strong> affective commitment in India s<strong>of</strong>tware frims. [Ed.D. Dissertation]. United
States, The George Washington University.
Leuci, M. S. (2005) The role <strong>of</strong> middle leaders in fostering organizational learning in a State cooperative
extension service. [Ed.D. Dissertation]. University <strong>of</strong> Missouri- Columbia.
Lin, Y., Y. (2006) An examination <strong>of</strong> <strong>the</strong> relationship between organizational learning culture, structure,
organizational innovativeness and effectiveness: Evidence from Taiwanese organizations. [Ph.D.
Dissertation]. United States, University <strong>of</strong> Minnesota.
Mayer, J, D. & Salovey, P. (1997) What is Emotional intelligence? In P. Salovey & D. J. Sluyter (Eds), emotional
development and emotional intelligence. New York: Basic Books.
701

Fattah Nazem
Miller, R. (1996). Learning Organization, Academy <strong>of</strong> Human Resource Development <strong>Conference</strong>(AHRD).
http://searchERIC.org/EDd4 03479 .html.
Moilanen, R. (2001) Diagnostic tools for learning organizations. The Learning Organization, 8(1), 6-20.
Moore, M. L. (2004) The correlation <strong>of</strong> preceptorships to organizational learning and performance. [Ed.D.
Dissertation]. United States, The George Washington University.
Nordtvedt, L., P. (2005). Organizational learning from international business affiliations: Effects on <strong>the</strong> effective
and efficient transfer <strong>of</strong> knowledge on firm performance.[Ph.D. Dissertation]. United States, The University
<strong>of</strong> Memphis.
Petrides, L. A. (2002) Organizational learning and <strong>the</strong> case for knowledge-based systems. New Directions for
Institutional Research, 113(Spring), 69-84.
Photis, M. P. (2007) The impact <strong>of</strong> organizational on relationship orientation, logistics service effectiveness and
performance, Industrial Marketing Management, New York, 36(1), 68.
Rego, A., Sousa, F., Cunha, M. P. C., Correia, A., & Sour-Amaral, I. (2007) Leader self- reported emotional
intelligence and perceived employee creativity: An exploratory study. [Ph.D. Dissertation]. Creativity and
Innovation Management. University <strong>of</strong> Oxford.
Schwandt, D., & Marquardt, M. (2000) Organizational learning: From world-class <strong>the</strong>ories to global best
practices. Boca Raton, FL: CRC Press.
Senge, P. M. (1990). The fifth discipline: The art and practice <strong>of</strong> <strong>the</strong> learning organization. New York: Currency
Doubleday.
Singh, S. K. (2007) Role <strong>of</strong> emotional intelligence in organisational learning: An empirical study. Singapore
Management Review, 29(2), 55-74.
Smith, Renee M., (2006) An examination <strong>of</strong> <strong>the</strong> relationship between emotional intelligence and leader
effectiveness. [D.B.A. Dissertation] .Nova Sou<strong>the</strong>astern University.
Truran, W. R. (2001) How organizational learning influences organizational success. [Ph.D. Dissertation].
Stevens, Institute <strong>of</strong> Technology.
Vartanian, L. (2006) The relationship between emotional intelligence and situational leadership II. [Ph.D.
Dissertation ]. Alliant International University, San Diego.
Wang, X. (2005) Relationships among organizational learning culture, job satisfaction and organizational
commitment in Chinese state – owned and privately owned enterprises. [Ph.D. Dissertation] .United States,
University <strong>of</strong> Minnesota.
Wang, Y, L. (2008) Organizational learning, entrepreneurial opportunity recognition, and innovation Performance
in high technology firms in Taiwan. [Ph.D. Dissertation]. United States, University <strong>of</strong> Illinois.
Watkin, C. (2000) Developing emotional intelligence, International Journal <strong>of</strong> selection and Assessment, 8(2), 89-
92.
Watkins, K. E., & Marsick, V. J. (1993) Sculpting <strong>the</strong> learning organization: Lessons in <strong>the</strong> art and science <strong>of</strong>
systemic change. San Francisco: Jossey-Bass.
Watkins, K. E., & Marsick, V. J. (1996) In action: Creating <strong>the</strong> learning organization.
Alexandria: American Society for Training and Development.
Xie, D. (2005) Exploring organizational learning culture, job satisfaction, motivation to learn, organizational
commitment, and internal service quality in a sport organization. [Ph.D. Dissertation]. United States. The
Ohio State University.
702

Validation a Scale for Measuring <strong>the</strong> Intellectual Capital in
Universities
Fattah Nazem
Department <strong>of</strong> Education, Roudehen Branch, Islamic Azad University,
Roudehen, Iran
nazem@riau.ac.ir
Abstract: The purpose <strong>of</strong> <strong>the</strong> present study is to validate a scale for measuring <strong>the</strong> intellectual capital (IC) <strong>of</strong>
universities. The population <strong>of</strong> <strong>the</strong> study included all <strong>the</strong> staffs who were employed in all branches <strong>of</strong> Islamic
Azad University in Iran (i.e., 420 branches and educational centers). The research sample consisted <strong>of</strong> 996 staffs
randomly selected from 86 branches and educational centers using stratified and cluster random sampling
methods. The research instrument was <strong>the</strong> Bontis’s (1997) intellectual capital questionnaire which consisted <strong>of</strong>
52 items with three underlying constructs <strong>of</strong> human capital, customer capital, and structural capital and Cronbach
Alpha <strong>of</strong> 0.95;The results <strong>of</strong> factor analysis and principal components analysis, using a varimax rotation, showed
that building blocks <strong>of</strong> intellectual capital includes customer capital (Items 2, 4, 6, 7, 9, 14, 17, 18, 22, 25, 26, 27,
32, 34, 40, 42, 44, 46, 50, 51, and 52), structural capital (Items 1, 15, 16, 17, 19, 20, 21, 29, 30, 31, 33, 43, 47,
and 48), and human capital (Items 5, 12, 28, 35, 37, 38, 39, 41, 45 and 49). The three underlying factors in
intellectual capital show that <strong>the</strong> scale almost generally covers <strong>the</strong>se factors and <strong>the</strong> results <strong>of</strong> its administration,
its validity, and <strong>the</strong> level <strong>of</strong> IC in universities determined by <strong>the</strong> scale are generally acceptable. Structural capital
showed <strong>the</strong> highest contribution to <strong>the</strong> construction <strong>of</strong> <strong>the</strong> intellectual capital.
Keywords: Measuring, Intellectual Capital, Factor Analysis, Universities
1. Introduction and purpose <strong>of</strong> <strong>the</strong> study
Higher education system is one <strong>of</strong> <strong>the</strong> most important and complicated products <strong>of</strong> human
achievements. In recent decades, <strong>the</strong> changes have revolutionized <strong>the</strong> social systems and
organizations; hence, scientific centers, in general, and universities, in particular, are more addressed
to satisfy new expectations.
Regarding <strong>the</strong> key role <strong>of</strong> higher education, Green (1997) explains that higher education provides <strong>the</strong>
technical knowledge and skill that industry requires it in future and <strong>the</strong> fact that governments depend
on this knowledge to have an effective and strategic programming. Because <strong>of</strong> pr<strong>of</strong>ound changes
which have affected our today's world, <strong>the</strong> universities, even more than ever, have been in <strong>the</strong> focus
<strong>of</strong> prolonged international and social discussions which devote to <strong>the</strong> goals and ideals <strong>of</strong> universities
as well as <strong>the</strong>ir roles in guidance and leadership (Feigenbaum, 1994).
Intellectual Capital is a commodity necessary to guarantee organizational survival and universities’
competitive advantage in <strong>the</strong> complex and competitive world. The growing importance <strong>of</strong> intellectual
capital has its foundations in several studies carried out since <strong>the</strong> 70s, which focused on so-called
invisible assets. The concept <strong>of</strong> “intellectual capital” was advanced by Adam Mueller, Friedrich List
(Kendrick, 1961, p. 105) and referred to technical knowledge, know-how, forms <strong>of</strong> organization and
tangible capital goods. It is <strong>the</strong> result <strong>of</strong> investment in <strong>the</strong> discovery and spread <strong>of</strong> productive
knowledge. Among <strong>the</strong> studies about <strong>the</strong> intellectual capital, <strong>the</strong> works <strong>of</strong> Itarni and Roehl (1991) can
be highlight although <strong>the</strong> concept has gained prominence only after <strong>the</strong> works <strong>of</strong> Sveiby (1997) in
Sweden.
New economy imposes new requirements to <strong>the</strong> enterprises. The strategic role <strong>of</strong> intellectual capital
in value creation is widely discussed both on <strong>the</strong>oretical (Bontis, 2001) and empirical (Kremp and
Mairesse, 2004; Chen et al., 2005) levels. However, measuring <strong>the</strong> intangible resources presents a
problem today. There are several methods both financial and nonfinancial ones that allow us to
manage <strong>the</strong>m, to provide benchmarking, and to analyze its value added function (Sveiby, 2007).
Several studies suggest pro<strong>of</strong>s regarding <strong>the</strong> point that <strong>the</strong> intellectual capital has positive impact on
market value, productivity, return on assets and sales growth (Pulic, 2000; S<strong>of</strong>ian et al., 2002; Chen
et al., 2005; Firer and Williams, 2003; Kremp and Mairesse, 2004; Diez et al., 2010; Laing et al.,
2010).
Intellectual capital (IC) provides <strong>the</strong> concept <strong>of</strong> knowledge management that helps managers to
identify and to classify <strong>the</strong> knowledge components <strong>of</strong> an organization. Intellectual capital concepts
have been an explosion <strong>of</strong> interesting studies since FORTUNE magazine published in 1991 (Stewart,
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Fattah Nazem
1991). Intellectual capital — such as knowledge, skill, relationships — is, more than ever, <strong>the</strong> vital
strategic and competitive resources. Academics believe that <strong>the</strong> benefits, which all economic
participants –employees, managers, investors, governments - gain by accepting intellectual capital as
a resource and by measuring its efficiency (Pulic, 2004). In knowledge-based economy, companies
do not produce just products or services but create added value to survive in <strong>the</strong> new economic
reality. Academics believe that intellectual capital is <strong>the</strong> lever for maintaining competitive advantages
and sustainable performance. Accordingly, identifying, valuing, managing intellectual capital is
becoming increasingly important for companies (Bontis, 1996; Edvinsson and Sullivan, 1996; Prusak,
1998; Ross and Ross, 1997; and Stewart, 1991, 1995). In Fortune, Steward (1991) defines
intellectual capital as “knowledge that transfers raw materials and makes <strong>the</strong>m more valuable”.
The concepts <strong>of</strong> intellectual capital seem to have classified as a consensus <strong>of</strong> dividing an intellectual
capital into three different groups (Edvinsson and Sullivan, 1996; Ross and Ross, 1997; and Steward,
1995). Human capital simply comprises <strong>the</strong> competence, skills, experience, and intellectual agilities <strong>of</strong>
<strong>the</strong> individual employees (Bounfour, 2003; Brooking, 1996; Edvinnson and Malone, 1997;and Ross et
al., 1997), structural capital includes processes, systems, structures, brands, intellectual property, and
o<strong>the</strong>r intangibles that are owned by <strong>the</strong> firm but do not appear on its balance sheet (Bounfour, 2003;
Brooking, 1996; Edvinsson and Malone, 1997; and Ross et aL, 1997;). The remaining type <strong>of</strong>
intellectual capital is customer (social) capital resides ei<strong>the</strong>r at <strong>the</strong> individual or <strong>the</strong> organizational
level. Customer (social) capital is an intermediary form <strong>of</strong> intellectual capital consisting <strong>of</strong> knowledge
in groups and networks <strong>of</strong> knowledge resources embedded within and derived from a network <strong>of</strong>
relationships (Edvinsson and Malone, 1997; and Ross et al., 1997).
Studies show that intellectual capital has meaningful relationship with <strong>the</strong> factors such as <strong>the</strong>
performance (Cheng, 2010; Sharabati et al., 2010; Patricia, 2007; and Shu-Lien, 2007)), financial
performance and economic success(Maditinos et al., 2011; Zeghal and Maaloul, 2010; and Carlucci,
2010), organizational justice (Yaghoubi et al., 2010), value creation (Diez et al., 2010;and Schiuma et
al., 2008), and entrepreneurial orientation (Hayton, 2002) . Intellectual capital also is as an
instruments to face <strong>the</strong> new challenge(Ramirez,2010), and an indicator <strong>of</strong> corporate value(Khan,
2011) , and even its also is a factor <strong>of</strong> <strong>the</strong> success <strong>of</strong> new ventures(Hormiga et al.,2011).
Herremans et al. (2011) found that if managers structure <strong>the</strong>ir organizational control system
appropriately for developing intellectual capital capabilities, <strong>the</strong>se systems can lead to reduce internal
uncertainty regarding human, structural, and relational capital.
Intellectual Capital is among <strong>the</strong> variables whose efficiency have been investigated and proved by
various researchers. Therefore, in order to increase intellectual capital in universities, it should first be
measured through a valid instrument. Consequently, necessary managerial actions should be taken.
Taking intellectual capital variable as a guarantee to survive and a competitive advantage for
universities and <strong>the</strong> lack <strong>of</strong> a valid measurement tool for intellectual capital and its components in
universities were <strong>the</strong> main motives <strong>of</strong> <strong>the</strong> present research to design a valid instrument that identifies
<strong>the</strong> constructs which form <strong>the</strong> intellectual capital, measure <strong>the</strong> variable <strong>of</strong> intellectual capital in each <strong>of</strong>
<strong>the</strong> underlying dimensions, and find a way to streng<strong>the</strong>n <strong>the</strong> intellectual capital in universities.
2. Research questions
What are <strong>the</strong> indexes which construct <strong>the</strong> intellectual capital in universities?
What items are included in each index?
Which <strong>of</strong> <strong>the</strong>se indexes has more contribution in forming intellectual capital?
3. Method <strong>of</strong> <strong>the</strong> study
The population <strong>of</strong> <strong>the</strong> study includes all <strong>the</strong> staffs who work in 420 branches and educational centers
in 14 zones <strong>of</strong> Islamic Azad University.
2 2
z σ
In order to estimate <strong>the</strong> least volume <strong>of</strong> sample, n = formula was used. The basis in <strong>the</strong>
2
d
formula was <strong>the</strong> variance <strong>of</strong> <strong>the</strong> intellectual capital calculated in o<strong>the</strong>r research studies. Regarding <strong>the</strong>
minimum sample required for <strong>the</strong> staff’s group which was estimated as 996 people, <strong>the</strong> same number
<strong>of</strong> questionnaires <strong>of</strong> intellectual capital was administered to <strong>the</strong> staff in 86 branches and educational
704

Fattah Nazem
centers. In order to select <strong>the</strong> research sample, two methods <strong>of</strong> stratified and cluster random sampling
were used.
To assess intellectual capital, Bontis’s (1997) Intellectual Capital Questionnaire was applied. This
questionnaire consists <strong>of</strong> 52 items which are answered by choosing <strong>the</strong> alternatives <strong>of</strong> “strongly
agree= 5”, “agree = 4”, “neutral = 3”, “disagree = 2”, and “strongly disagree = 1”. The questionnaire
includes 3 subscales <strong>of</strong> customer capital (Items 1, 6, 10, 11, 17, 18, 23, 24, 26, 29, 30, 36, 37, 44, 45,
50, and 51), structural capital (Items 3, 4, 8, 13, 14, 16, 20, 21, 27, 28, 40, 41, 47 and 48), and human
capital (Items 2, 5, 7, 9, 12, 15, 19, 22, 25, 31, 32, 33, 35, 38, 39, 42, 43, 46, 49, and 52). The
obtained Cronbach’s Alpha was 0.95.
The researcher has used factor analysis and principal components analysis, using a varimax rotation
in order to identify <strong>the</strong> underlying constructs <strong>of</strong> intellectual capital.
4. Findings <strong>of</strong> <strong>the</strong> study
The preliminary analysis <strong>of</strong> different indexes <strong>of</strong> central tendency, variability, and <strong>the</strong> distribution <strong>of</strong> <strong>the</strong>
staff’s scores obtained from <strong>the</strong> questionnaire <strong>of</strong> intellectual capital and its 3 components show that
<strong>the</strong> distribution <strong>of</strong> <strong>the</strong> staff’s scores in <strong>the</strong> given variables have tendency toward normality.
To answer <strong>the</strong> research questions, factor analysis procedure was applied. The first step in factor
analysis process which is also its first assumption is checking missing data. In this step, subjects
number 492,493,494,495,496,769,927,933,945,953, and 958 including eleven persons altoge<strong>the</strong>r
were eliminated from statistical analysis so that <strong>the</strong> factor analysis assumption under <strong>the</strong> heading <strong>of</strong> at
least missing (0.02) could be observed in each subject. Hence, in this research no item has been
eliminated except eleven subjects. And <strong>the</strong> given situation shows that <strong>the</strong>re is no need to omit some
<strong>of</strong> <strong>the</strong> items and it is possible to follow <strong>the</strong> process <strong>of</strong> Factor Analysis while having all <strong>the</strong> items.
The second factor analysis assumption denotes enough sample size. In this research, Kaiser- Meyer-
Olkin (KMO) equals 0.96 consequently, <strong>the</strong> sample size is sufficient. The third factor analysis
assumption is normality <strong>of</strong> multi-variation distribution known as sphericity. As <strong>the</strong> Approximate Chi
Square equalled 25109.798 with <strong>the</strong> 1326 degrees <strong>of</strong> freedom, it can be stated that <strong>the</strong> amount <strong>of</strong> <strong>the</strong>
Approximate Chi Square is statistically significant and <strong>the</strong> given statistics is significant at least at <strong>the</strong>
0.999 level <strong>of</strong> confidence (α = 0.001).
According to component matrix <strong>of</strong> items we can determine both <strong>the</strong> specific factor <strong>of</strong> each item and its
position in <strong>the</strong> related factor based on loading factor. After studying table <strong>of</strong> component matrix
precisely, <strong>the</strong> researcher used Rotation Method so that loading factor <strong>of</strong> each item can be determined
stressing at recognition <strong>of</strong> each item in one <strong>of</strong> <strong>the</strong> 3 factors. Reiterating that in this research, <strong>the</strong>
researcher has followed Exploratory Factor Analysis and has used Principal Component Methods
from Extraction <strong>of</strong> Factors, Varimax Method was applied (table 1).
According to varimax, <strong>the</strong> researcher was able to determine both <strong>the</strong> factor to which <strong>the</strong> item belongs
after rotation and <strong>the</strong> position <strong>of</strong> each item in related factor with reference to loading factor. This table
shows in which factor each item has been located after <strong>the</strong> rotation. For instance, Items 5, 12, 28, 35,
37, 38, 39, 41, 45 and 49 have been located in <strong>the</strong> third factor (human capital).
To fulfill <strong>the</strong> purposes <strong>of</strong> <strong>the</strong> study to determine <strong>the</strong> indexes <strong>of</strong> intellectual capital and its components,
<strong>the</strong> underlying items, and <strong>the</strong> index with <strong>the</strong> highest contribution, eventually, 3 factors have been
extracted from rotation <strong>of</strong> factor analysis; in fact, intellectual capital consists <strong>of</strong> 3 factors respectively
as follows: human capital, customer capital, and structural capital. The table also indicates that
structural capital has <strong>the</strong> highest level <strong>of</strong> contribution to <strong>the</strong> formation <strong>of</strong> intellectual capital in
universities.
705

Fattah Nazem
Table 1: Rotated Component Matrix
Component
Human
Capital
Structural
Capital
Customer
Capital
X
.518
X01
.616
X02
X03
.531
X04
.562
X05
.620
X06
.507
X07
X08
.530
X09
X10
X11
.608
X12
X13
.600
X14
.676
X15
.721
X16
.526
.563
X17
.604
X18
.599
X19
.711
X20
.641
X21
.690
X22
X23
X24
.658
X25
.589
X26
.666
X27
.666
X28
.578
X29
.632
X30
.743
X31
.687
X32
.679
X33
.633
X34
.502
X35
X36
.713
X37
.652
X38
.629
X39
.595
X40
.537
X41
.573
X42
.641
X43
.583
X44
.611
X45
.604
X46
.531
X47
.688
X48
.512
X49
.517
X50
.641
X51
.751
X52
706

Fattah Nazem
Hence, emphasizing at <strong>the</strong> three-fold factors <strong>of</strong> intellectual capital, items related to each factor have
been summarized in table 2 respectively.
Table 2: Results <strong>of</strong> Factor Analysis <strong>of</strong> Intellectual Capital Construct
Factors
First Factor
Second Factor
Third Factor
Index
customer capital
structural capital
human capital
Items
2,4,6,7,9,14,17,18,22,25,26,27,32,34,40,42,44,46,50,51,and 52
1,15,16,17,19,20,21,29,30,31,33, 43,47,and 48
5,12,28,35,37,38,39,41,45 and 49
5. Discussion and conclusions
The higher education system has caused a great deal <strong>of</strong> changes in social, economic and political
fields. In addition, universities are social systems which have been known as <strong>the</strong> centers <strong>of</strong>
knowledge and information as well as <strong>the</strong> thinking bases for leading societies. Intellectual Capital is a
commodity necessary to guarantee organizational survival and universities’ competitive advantage in
<strong>the</strong> complex and competitive world. Intellectual Capital is among <strong>the</strong> variables whose efficiency have
been investigated and proved by various researchers. Therefore, in order to increase intellectual
capital in universities, it should first be measured through a valid instrument. Consequently, necessary
managerial actions should be taken. The purpose <strong>of</strong> <strong>the</strong> present study is to validate a scale for
measuring <strong>the</strong> intellectual capital <strong>of</strong> universities. The lack <strong>of</strong> a valid measurement tool for intellectual
capital and its components in universities were also one <strong>of</strong> <strong>the</strong> reasons <strong>of</strong> <strong>the</strong> present research.
In this study, in order to assess <strong>the</strong> intellectual capital, Bontis’s (1997) intellectual capital
questionnaire was applied which contains 3 scales <strong>of</strong> human capital, customer capital, and structural
capital. The 3 factors which were used to assess <strong>the</strong> intellectual capital in this study agree with <strong>the</strong>
<strong>the</strong>ories and studies carried out in and out <strong>of</strong> Iran. Some <strong>of</strong> <strong>the</strong> similar studies done in <strong>the</strong> same field
are as follows: Herremans et al. (2011) ; Martin-de-Castro, et al. (2011) ; Yaghoubi, et al. (2010) ;
Kamukama, et al. (2010) ; Diez, et al. (2010) ; Shih, et al. (2010) ; Halim (2010) ; Jaradaat (2010) ; Li,
et al. (2008) ;, Ordonez de Pablos (2004) ; Vandal (2003) ; Edvinson and Malone (1997) ; Bontis
(1997) ; and Saint-Onge (1996). In this study, unlike <strong>the</strong> o<strong>the</strong>rs, structural capital has <strong>the</strong> highest level
<strong>of</strong> contribution to <strong>the</strong> formation <strong>of</strong> intellectual capital in universities.
Regarding <strong>the</strong> research background and <strong>the</strong> related <strong>the</strong>ories, <strong>the</strong> three factors involved in intellectual
capital show that <strong>the</strong> intellectual capital scale almost generally covers <strong>the</strong> underlying factors. Hence,
it can be concluded that <strong>the</strong> results obtained from <strong>the</strong> administration <strong>of</strong> <strong>the</strong> tool and <strong>the</strong> level <strong>of</strong>
intellectual capital in universities determined by <strong>the</strong> application <strong>of</strong> <strong>the</strong> tool as well as its validity are
generally acceptable. The increasing need <strong>of</strong> universities for determining <strong>the</strong> level <strong>of</strong> intellectual
capital from <strong>the</strong> one side and <strong>the</strong> lack <strong>of</strong> valid instrument <strong>of</strong> <strong>the</strong> intellectual capital from <strong>the</strong> o<strong>the</strong>r side
were <strong>the</strong> main causes <strong>of</strong> doing <strong>the</strong> present study. In addition, <strong>the</strong> research was done to identify <strong>the</strong>
precise and complete dimensions, aspects and factors which make intellectual capital through
measuring <strong>the</strong> validity <strong>of</strong> a scale which was designed and administered to <strong>the</strong> staff <strong>of</strong> higher
education institutions. In this way, it is possible to locate <strong>the</strong> <strong>the</strong>oretical position <strong>of</strong> intellectual capital
and identify <strong>the</strong> importance <strong>of</strong> <strong>the</strong> variables which have been introduced by different <strong>the</strong>ories as <strong>the</strong>
factors which form intellectual capital. The ultimate purpose <strong>of</strong> <strong>the</strong> study, <strong>the</strong>n, is to design and
administer a valid tool which can determine <strong>the</strong> extent <strong>of</strong> intellectual capital in higher education
institutions.
The function <strong>of</strong> higher education system in every society is to develop and present science to <strong>the</strong>
society. It is considered a fundamental system which affects <strong>the</strong> o<strong>the</strong>r systems such as production,
technical, economic, social and administrative systems. Therefore, it can be stated that <strong>the</strong>
dynamism <strong>of</strong> <strong>the</strong> systems <strong>of</strong> every society, to a great extent, depends on <strong>the</strong> dynamism <strong>of</strong> <strong>the</strong> higher
education system. At <strong>the</strong> present time, <strong>the</strong> degree <strong>of</strong> contribution <strong>of</strong> higher education in economic
development has been widely identified both in <strong>the</strong> economies which rely on advanced technology
and those <strong>of</strong> newly industrialized or developing countries. Higher education system, as <strong>the</strong> most
important system in any society, is also comprised <strong>of</strong> organizations whose main foci are on “thoughts”
and has a pr<strong>of</strong>ound effect on cultural, political and religious affairs. Intellectual capital is among <strong>the</strong>
variables whose efficiency have been investigated and proved by various researchers (Kaplan and
Norton, 1992; Saint-Onge, 1996; Brooking, 1996; Sveiby, 1997; Edvinson and Malone, 1997; and
Bontis, 1997).
707

Fattah Nazem
Intellectual capital is a firm's source <strong>of</strong> competitive advantage, to become knowledge driven,
companies must learn how to recognize changes in intellectual capital in <strong>the</strong> worth <strong>of</strong> <strong>the</strong>ir business
and ultimately in <strong>the</strong>ir balance sheets. A firm's intellectual capital – employees' knowledge, brain
power, know- how, and process, as well as <strong>the</strong>ir ability to continuously improve those process-is a
source <strong>of</strong> competetive advantage (Vargas-Hernández, and Noruzi, 2010). Successful organizations
hire intelligent staff and this is <strong>the</strong> usual form <strong>of</strong> developing intellectual capital in <strong>the</strong> organization
(Sveiby, 1997; Ross et al 1997; Stewart 1997; Edvinsson and Sullivan 1996; Edvinsson and Malone,
1997). Intellectual capital can foster organizational competitiveness (Vargas-Hernández, and Noruzi,
2010).
Close attention to intellectual capital is an absolute necessity within all organizations particularly
higher education institutions. Intellectual capital is a commodity necessary to guarantee organizational
survival and universities’ competitive advantage in <strong>the</strong> complex and competitive world.The results <strong>of</strong>
this study are also in line with those <strong>of</strong> o<strong>the</strong>r research projects <strong>the</strong> breadth <strong>of</strong> which are mentioned in
<strong>the</strong> previous sections. Fur<strong>the</strong>rmore, universities, as one <strong>of</strong> <strong>the</strong> social systems, have been recognized
as <strong>the</strong> engine <strong>of</strong> disseminating knowledge and awareness as well as <strong>the</strong> center <strong>of</strong> directing <strong>the</strong>
societies. They are <strong>the</strong> basic centers <strong>of</strong> thought and reflection. The questionnaire administered here
also enjoys some psychoanalytic features, specifically construct validity. These are some <strong>of</strong> <strong>the</strong>
reasons which lead <strong>the</strong> researcher to recommend that <strong>the</strong> same study be carried out not only in
Islamic Azad University but in all o<strong>the</strong>r universities and its findings, in turn, be taken into consideration
in those universities. In order to increase intellectual capital in universities, it should first be measured
through a valid instrument. Consequently, necessary managerial actions should be taken. Also since
structural capital shows <strong>the</strong> highest contribution in forming <strong>the</strong> intellectual capital in universities,
efforts should be spent to streng<strong>the</strong>n <strong>the</strong> index. Leaders are also urged to take practical steps
towards materializing <strong>the</strong> principles <strong>of</strong> Intellectual capital since <strong>the</strong>y guarantee <strong>the</strong> survival <strong>of</strong> <strong>the</strong>
organization and are <strong>the</strong> only competitive benefits in <strong>the</strong> third millennium.
Acknowledgements
This paper is extracted from a research project sponsored by <strong>the</strong> research department <strong>of</strong> <strong>the</strong> Islamic
Azad University, Roudehen Branch to whom I owe a debt <strong>of</strong> gratitude.
References
Bontis, N. (1996) There's a price on your head: Managing intellectual capital strategically, Business Quarterly,
Bontis, N. (2001) Assessing knowledge assets: a review <strong>of</strong> <strong>the</strong> models used to measure intellectual capital,
International Journal <strong>of</strong> Management Reviews, 3(1) , 41-60.
Bounfour, A. (2003) The IC-Dval approach, Journal <strong>of</strong> Intellectual Capital, 4(3), 396-412.
Brooking, A. (1996) Intellectual Capital: Core assets for <strong>the</strong> third millennium enterprise, Thompson Business
Press, London.
Carlucci, D., Lerro, A. (2010) Foreword: investigating <strong>the</strong> role <strong>of</strong> intellectual capital in today's business
landscape, Measuring Business Excellence. 14( 4) , 3-10.
Chen, M-C., Cheng, S-J., Hwang, Y. (2005) “An empirical investigation <strong>of</strong> <strong>the</strong> relationship between intellectual
capital and firms’ market value and financial performance”, Journal <strong>of</strong> Intellectual Capital, 6(2), 159-176.
Cheng, M-Y., Lin, J-Y., Hsiao, T-Y., and Lin, T. W. (2010) Invested resource, competitive intellectual capital,
and corporate performance, Journal <strong>of</strong> Intellectual Capital 11( 4) , 433-450.
Diez, J. M., Ochoa, M. L., Prieto, M. B., and Santidrian, A. (2010) Intellectual capital and value creation in
Spanish firms, Journal <strong>of</strong> Intellectual Capital, 11(3), 348- 367.
Diez, J.M., Ochoa, M.L., Prieto, M. B., Santidrian, A. (2010) Intellectual capital and value creation in Spanish
firms, Journal <strong>of</strong> Intellectual Capital, 11 (3),348-367.
Edvinsson, L. and Sullivan, P. (1996) Developing a model for managing intellectual capital, <strong>European</strong>
Management Journal, 14(4),356-364.
Edvinsson, L. and Malone, M.S. (1997) Intellectual capital. Realizing your company's true value by finding its
hidden brainpower, Harper Collins, New York, NY.
Feigenbaum, A. V. (1994) Quality education and American’s competitiveness. Quality progress, 27(9).
Firer, S. and Williams, M. (2003) “Intellectual capital and traditional measures <strong>of</strong> corporate performance”, Journal
<strong>of</strong> Intellectual Capital,4 (3),348-360.
Green, A. (1997) Education and state formation in Europe and Asia, In K. Kennedy (Ed), Citizenship educational
and <strong>the</strong> modern state, London: The Falmer Press.
Halim, S. (2010) Statistical analysis on <strong>the</strong> intellectual capital statement, Journal <strong>of</strong> Intellectual Capital, 11(1),
Hayton, J. C., (2002) The effect <strong>of</strong> intellectual capital on entrepreneurial orientation in high technology new
ventures. [Ph. D. Dissertation]. Atlanta, Georgia, Georgia State University.
Herremans, I. M., Isaac, R. G., Kline, T. J. B., and Nazari, J. A. (2011) Intellectual capital and uncertainty <strong>of</strong>
knowledge: control by design <strong>of</strong> <strong>the</strong> management system. Journal <strong>of</strong> Business Ethics. 98( 4), 627-640.
708

Fattah Nazem
Hormiga, E. , Batista-Canino, R. M., and Sánchez-Medina, A. (2011) The role <strong>of</strong> intellectual capital in <strong>the</strong>
success <strong>of</strong> new ventures, International Entrepreneurship and Management Journal, NewYork: 7(1) , 71-92.
Itami ,H.and Roehl ,T.W. (1991) Mobilizing invisible assets. London: Harvard University Press.
Jaradaat, N. MS, and AL- Saleh, A.R . (2010) Measuring intellectual capital in corporations, Interdisciplinary
Journal <strong>of</strong> Contemporary Research In Business, 2( 4) , 42-52.
Kamukama, N., Ahiauzu, A., and Ntayi, J., M. (2010) Intellectual capital and performance: Testing interaction
effects, Journal <strong>of</strong> Intellectual Capital 11( 4) , 554-574.
Kaplan, R.S. and Norton, D.P. (2004) La disponibilidad estratégica de los activos intangibles, Harvard Deusto
Business Review, March, pp. 38-51.
Kendrick, J.W. (1961) Some Theoretical Aspects <strong>of</strong> Capital Measurement, The American Economic Review,
Khan, S. (2011) The interactive effects <strong>of</strong> intellectual capital components on <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> balance
sheet as an indicator <strong>of</strong> corporate value, The Journal <strong>of</strong> American Academy <strong>of</strong> Business, Cambridge, 16(2),
Kremp, E., Mairesse J., (2004) Knowledge management, innovation and productivity: a firm level
exploration based on manufacturing CIS3 DATA, National Bureau <strong>of</strong> Economic Research
Laing G., Dunn J., Hughes-Lucas S. ( 2010) Applying <strong>the</strong> VAIC model to Australian hotels, Journal <strong>of</strong> Intellectual
Capital, 11(3) , 269-283.
Li, J., Pike, R., and Haniffa, R. (2008) Intellectual capital disclosure and corporate governance structure in UK
firms, Accounting and Business Research, 38(2), 137-159.
Maditinos, D., Chatzoudes, D., Tsairidis, C., and Theriou, G. (2011) The impact <strong>of</strong> intellectual capital on firms'
market value and financial performance. Journal <strong>of</strong> Intellectual Capital. 12( 1) , 132-151.
Martin- de- Castro, G., Delgado- Verde, M., Lopez- Saez, P., and Navas- Lopez, J. E. (2011). Towards an
intellectual capita- based view <strong>of</strong> <strong>the</strong> firm: Origins and nature, Journal <strong>of</strong> Business Ethics, 98: 649-662.
Odonez de Pablos, P. (2004) Measuring and reporting structural capital: Lessons from leading firms, Journal <strong>of</strong>
Intellectual Capital. 5,629-647.
Patricia, M. W. (2007) Board performance in a post Sarbanes-Oxley environment: An examination <strong>of</strong> <strong>the</strong>
relationships among board processes, board intellectual capital and board changes. [Ph. D. Dissetrtation].
Capella University.
Prusak, L. (1998). Working Knowledge: How organizations manage what <strong>the</strong>y know, Harvard Business School
Press, Cambridge, MA.
Pulic, A. (2000) MVA and VAIC analysis <strong>of</strong> randomly selected companies from FTSE 250, [online] www.vaicon.net.
Pulic, A (2004) Intellectual capital- does it create or destroy value? Measuring Business Excellence, 8(1),
Ramirez, Y. (2010) Intellectual capital models in Spanish public sector, Journal <strong>of</strong> Intellectual Capital, 11(2),
Ross, G And Ross, I. (1997) Measuring your company's intellectual performance, Long Range Planning, 30(3),
Ross, J. G., Ross, E. L., and Dragonetti, N. C. (1997) Intellectual capital: navigating in <strong>the</strong> new business
landscape, New York: MacMillan.
Saint-Onge, H. (1996) Tacit knowledge: <strong>the</strong> key to <strong>the</strong> strategic alignment <strong>of</strong> intellectual capital, Strategy and
Leadership, 24(2), 10-14.
Schiuma, G., Lerro, A., and Carlucci, D. (2008) The knoware tree and <strong>the</strong> regional intellectual capital index,
Journal <strong>of</strong> Intellectual Capital, 9(2), 283-300.
Sharabati, A. A. A., Jawad, S. N., and Bontis, N. (2010) Intellectual capital and business performance in <strong>the</strong>
pharmaceutical sector <strong>of</strong> Jordan, Management Decision, 48(1), 105-131.
Shih, K. H., Chang, C. J., and Lin, B. (2010) Assessing knowledge creation and intellectual capital in banking
industry, Journal <strong>of</strong> Intellectual Capital, 11 (1), 74-89.
Shu-Lien, C. (2007) Valuing intellectual capital and firms' performance: modifying value added
intellectual coefficient (VAIC(TM)) in Taiwan IT industry. [D. B. A. Dissertation]. United States ,
California: Golden Gate University.
S<strong>of</strong>ian, S., Tayles, M., Pike R., (2002) The implications <strong>of</strong> intellectual capital on performance measurement and
corporate performance, [online], www.fppsm.utm.my/.../57-<strong>the</strong>-implications-<strong>of</strong>-intellectual-capital-onperformance-measurement-and-corporate-performance.html.
Sveiby, K.-E., (2007) Methods for Measuring Intangible Assets, [online]http://www.sveiby.com/articles/
IntanbibleMethods.htm
Stewart, T.A. (1991) Brainpower: How Intellectual capital is becoming america's most valuable asset, Fortune, 3,
June, pp. 44-60.
Stewart, T.A. (1995) Trying to grasp <strong>the</strong> intangible, Fortune, 2 October, pp. 157-161.
Stewart, T.A. (1991) Intellectual capital, Nicholas Brealey, London.
Sveiby, K.E. (1997) The new organizational wealth: Managing and measuring knowledge-based assets, Berrett-
Koehler, New York, NY.
Vandal. B., A. (2003) An intellectual capital framework for community college and business partnerships. [Ph. D.
Dissertation]. University <strong>of</strong> Minnesota.
Vargas-Hernández , J. G. and Noruzi M., R .(2010) How intellectual capital and learning organization can
foster organizational competitiveness? International Journal <strong>of</strong> Business and Management, 5( 4),183-193.
Yaghoubi, N. M., Salarzehi, H., Moloudi, J., and Yaghoubi, E. (2010) Review <strong>of</strong> relationship between
intellectual capital and organizational justice ,Asian Social Science. 6( 10) , 120-126 .
Zeghal, D. and Maaloul, A. (2010) Analysing value added as an indicator <strong>of</strong> intellectual capital and its
consequences on company performance, Journal <strong>of</strong> Intellectual Capital, 11(1), 39-60.
709

The Knowledge Clinic: Concepts, Methods and tools to
Support Productive Knowledge Management in Companies
Gaby Neumann 1 and Eduardo Tomé 2
1
Technical University <strong>of</strong> Applied Sciences Wildau – Engineering Logistics –
Germany
2
Universidade Lusíada de Famalicão – Department <strong>of</strong> Economics – Portugal
gaby.neumann@th-wildau.de
eduardo.tome@clix.pt
Abstract: In order to better manage knowledge for making it become productive and unlocking its developmental
potential a comprehensive set <strong>of</strong> concepts, methods and tools is required that helps companies in purposefully
intervening in <strong>the</strong>ir core processes. Challenges mainly consist in <strong>the</strong> wide and ever growing variety <strong>of</strong> those
supportive means, missing guidance in selecting appropriate methods and tools, <strong>the</strong> need for adapting <strong>the</strong>oretical
concepts to a certain company environment while implementation, requirements for predicting efforts and impact
from knowledge-related interventions. Therefore, <strong>the</strong> paper introduces and elaborates <strong>the</strong> concept <strong>of</strong> a
“knowledge clinic” that is meant to support companies in <strong>the</strong>ir knowledge investments in <strong>the</strong> same way as any
process <strong>of</strong> medical treatment with <strong>the</strong> human body is organized: diagnosis, healing, cross-checking. In particular
<strong>the</strong> paper motivates <strong>the</strong> approach, discusses its <strong>the</strong>oretical background, explains its structure and way <strong>of</strong>
working, and derives conclusions on implications on change processes at company and economy levels. With
this <strong>the</strong> paper wants to contribute to step forward on <strong>the</strong> road to support organizations in dealing with and
pr<strong>of</strong>iting from knowledge.
Keywords: knowledge-based development, knowledge clinic, knowledge management maturity diagnosis,
change processes.
1. Introduction and motivation
Knowledge has been on <strong>the</strong> agenda since <strong>the</strong> mid 1990s, but still hasn’t unfolded its full potential in
<strong>the</strong> organizational practice. Even worse, in many companies and industrial sectors, like e.g. logistics,
knowledge management (KM) did become a buzzword indicating a big (technological) wave around
<strong>the</strong> turn <strong>of</strong> <strong>the</strong> millennium (obviously) without any particular benefit to (most <strong>of</strong>) <strong>the</strong> companies. This
has been caused by <strong>the</strong> variety <strong>of</strong> <strong>the</strong>oretical concepts related to knowledge and its management
without clear suggestions and guidelines on how to deal with it, implement it in a certain company
setting and link it to <strong>the</strong> company’s day-by-day challenges. Fur<strong>the</strong>rmore, managing knowledge and
benefiting from this was said to be a technological issue only or at least mainly with a large number <strong>of</strong>
KM projects and initiatives being launched that focused on <strong>the</strong> introduction <strong>of</strong> certain information
technology such as knowledge data bases, intranet, information storage and retrieval functionality etc.
– and failed because <strong>of</strong> being too single-dimensional, forgetting about <strong>the</strong> people as key stakeholders
and shareholders in any knowledge-creating and knowledge-dependent process, and also because <strong>of</strong>
being too short-term focused with regard to measurable impacts and especially financial returns.
According to “The 2002 Knowledge Management Hype Cycle” (Caldwell and Harris 2002) this
development <strong>of</strong> KM obviously follows <strong>the</strong> typical evolutionary path <strong>of</strong> any technology-driven
implementation process from innovation trigger up to <strong>the</strong> peak <strong>of</strong> inflated expectations and pretty
much down again to <strong>the</strong> trough <strong>of</strong> disillusionment (see Figure 1). From <strong>the</strong>re a slow recovery process
starts via <strong>the</strong> slope <strong>of</strong> enlightenment finally leading to <strong>the</strong> plateau <strong>of</strong> productivity. Based upon this,
Caldwell et al. (2003) predicted <strong>the</strong> following fur<strong>the</strong>r evolution:
By 2005, knowledge workers will move fluidly among teams, assignments and communities <strong>of</strong>
practice within 70 percent <strong>of</strong> knowledge-based businesses (0.8 probability).
Through 2006, more than two-thirds <strong>of</strong> enterprises that successfully implement real-time
enterprise capabilities will also have successful knowledge management programs (0.7
probability).
By 2006, more than two-thirds <strong>of</strong> enterprises with proven revenue-enhancing quality management
programs, such as Six Sigma, will have incorporated information quality management measures
into those programs (0.8 probability).
According to this and based upon an online survey with 540 participating companies in <strong>the</strong> Germanspeaking
area (see Fraunh<strong>of</strong>er 2005), wide-scale KM implementation in companies across all
industrial sectors at a realistic, productive level could have been expected to be achieved in <strong>the</strong>
middle <strong>of</strong> <strong>the</strong> previous decade (see Figure 1). But looking into a certain industrial sector, as for
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Gaby Neumann and Eduardo Tomé
example <strong>the</strong> logistics one, even nowadays <strong>the</strong>re is still a slack implementation <strong>of</strong> KM into practice
(Neumann and Tomé 2006) indicating a serious delay in <strong>the</strong> evolution at least – obviously because <strong>of</strong>
<strong>the</strong> bad reputation <strong>of</strong> <strong>the</strong> term KM and all ideas related to this (and <strong>the</strong> deeper than normal valley KM
evolution has to go through).
visibility
technology
trigger
peak <strong>of</strong>
inflated
expectations
trough <strong>of</strong>
disillusionment
slope <strong>of</strong>
enlightenment
plateau <strong>of</strong>
productivity
1990 1995 2000 2005 2010
maturity
Figure 1: The Knowledge Management Hype Cycle [adapted from Fraunh<strong>of</strong>er 2005]
Since <strong>the</strong>n, science in all KM-related areas has been working to overcome this, amongst o<strong>the</strong>rs by
changing focus from <strong>the</strong> IT and accounting perspective towards an integrated, application-driven and
process-oriented view. Never<strong>the</strong>less, <strong>the</strong>re is still a wide-range uncertainty about how to best
introduce and run KM in practice, <strong>the</strong> methods and tools that might be applied and <strong>the</strong> way this should
happen, how to gain maximum achievements, or what to measure and how to evaluate <strong>the</strong>ir outcome
and impact. Precisely this kind <strong>of</strong> uncertainty keeps company managers away from practically dealing
with what <strong>the</strong>oretically everybody agrees with: knowledge has become a strategic resource directly
enabling a company’s fur<strong>the</strong>r performance at <strong>the</strong> market if used and handled carefully or eventually
causing a company to vanish from <strong>the</strong> market if ignored or treated inexpediently.
After ten years in <strong>the</strong> KM scene, <strong>the</strong> authors <strong>of</strong> this paper think it is necessary and it is time to put
forward <strong>the</strong> new concept <strong>of</strong> a knowledge clinic (KC). The KC aims at dealing with knowledge-based
and knowledge-related problems in a way corresponding to a medical perspective. Therefore,
knowledge can be described, <strong>the</strong> problems detected, <strong>the</strong> solutions selected, and if possible
implemented. The main reason for <strong>the</strong> existence <strong>of</strong> <strong>the</strong> knowledge clinic is that nowadays, <strong>the</strong> main
problem organizations face, is not to know that knowledge is important, but ra<strong>the</strong>r how to manage
knowledge. In consequence we may state that companies have doubts about what in fact is <strong>the</strong>ir
particular need for knowledge or knowledge-based interventions, and how to solve that problem. In
order to introduce <strong>the</strong> concept, Section 2 enumerates <strong>the</strong> <strong>the</strong>oretical background underlining <strong>the</strong>
existence <strong>of</strong> <strong>the</strong> KC before in Section 3 <strong>the</strong> idea <strong>of</strong> KC is explained in detail. Finally, Section 4
presents conclusions, limitations, implications and suggestions for fur<strong>the</strong>r research including details
on <strong>the</strong> possible implementation <strong>of</strong> <strong>the</strong> KC.
2. Theoretical Background: What <strong>the</strong>ories and concepts may base a KC?
2.1 The underlying concepts
In this paper it is assumed that knowledge is understood information and that information is classified
data. It is also assumed that knowledge and information are possessed by people, organizations and
regions.
Knowledge management (KM) is defined as having two different meanings:
The activities performed by all those that have to manage <strong>the</strong>ir knowledge;
The scientific body <strong>of</strong> work made by scientists trying to make sense <strong>of</strong> <strong>the</strong> mentioned activities.
As a management activity, KM is essentially made by “knowledge” persons, companies,
organizations, regions or countries. Those social and economic entities possess more intellectual
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capital (IC) than <strong>the</strong> non-knowledge entities. Intellectual capital is also a complex notion to define,
encompassing o<strong>the</strong>r already known and studied phenomena like education, training, experience,
competences, skills, research and development (R&D), organizational routines, and relations with
clients. As a cycle, knowledge is created, developed, stored, transferred, shared (Nonaka and
Takeuchi, 1995), learnt, unlearnt (Cegarra Navarro and Moya 2005), recycled, renewed, and lost
(Kianto, 2007).
2.2 Theories on knowledge and organizations
Probably one <strong>of</strong> <strong>the</strong> reasons KM lost a bit <strong>of</strong> its enthusiastic aura <strong>of</strong> <strong>the</strong> nineties was because with
every new study <strong>the</strong> complexity grew and <strong>the</strong> overall perspective was somehow lost. In this context
Edwards (2010) provides a good guide for <strong>the</strong> actual situation in <strong>the</strong> KM analysis.
The KC is based in <strong>the</strong> analysis <strong>of</strong> companies and organizations. The most important works
regarding <strong>the</strong> scientific analysis <strong>of</strong> organizations are well known (Taylor, 1911; Mayo, 1933; Emery
and Trist, 1972; Womack, Jones and Roos, 1991; Nonaka and Takeuchi, 1995). To be credible, <strong>the</strong>
KC will <strong>of</strong> course have to base its actions in such scientific generic landmarks. However in <strong>the</strong> case
<strong>of</strong> <strong>the</strong> KC, a particular attention has to be given to <strong>the</strong> phenomenon <strong>of</strong> change in organizations:
a) <strong>the</strong> definition <strong>of</strong> <strong>the</strong> problem is only done in a dynamic way, because we consider that <strong>the</strong>
company’s situation is different from <strong>the</strong> one it should be in;
b) <strong>the</strong> definition <strong>of</strong> <strong>the</strong> path to heal <strong>the</strong> problem is also dynamic;
c) <strong>the</strong> definition <strong>of</strong> <strong>the</strong> intervention aims to materialize <strong>the</strong> change;
d) <strong>the</strong> evaluation <strong>of</strong> all <strong>the</strong> procedure is made by comparing <strong>the</strong> final result <strong>of</strong> <strong>the</strong> intervention
with <strong>the</strong> expected situation.
2.2.1 Main analysis on change in organizations
In <strong>the</strong> scientific analysis <strong>of</strong> organizations change has always been a major topic. We may consider
that all <strong>the</strong> high landmarks we just mentioned in fact wanted to transform <strong>the</strong> organization, and
<strong>the</strong>refore to change it:
a) Taylor (1911) wanted to find <strong>the</strong> one best way to organize labor and in consequence <strong>of</strong>
changing from lazy to efficient companies.
b) Mayo (1933) and Emery and Trist (1972) pointed to <strong>the</strong> importance <strong>of</strong> considering <strong>the</strong> human
and social aspects <strong>of</strong> organizations when related to technology.
c) Womack, Jones and Roos (1991) effectively wanted to change organizations by eliminating
waste.
d) Nonaka and Takeuchi (1995) considered knowledge as <strong>the</strong> main driver <strong>of</strong> change in
organizations.
However, change in itself has also been <strong>the</strong> subject <strong>of</strong> <strong>the</strong>oretical analysis:
a) The first seminal model (Lewin,1951) dealt with <strong>the</strong> problematic <strong>of</strong> change from <strong>the</strong> point <strong>of</strong>
view <strong>of</strong> <strong>the</strong> individual; to Lewin change occurred in three steps: first challenge <strong>the</strong> basis <strong>of</strong> a
behavior, secondly propose a new one and third give basis for <strong>the</strong> new behavior to be
established; for change to be successful, all <strong>the</strong> three phases <strong>of</strong> <strong>the</strong> model had to go right.
b) Bullock and Batten’s (1985) model described four stages for planned change: exploration,
planning, action, and integration; integration is <strong>the</strong> main goal <strong>of</strong> change, meaning that <strong>the</strong> new
behaviors have been aligned with <strong>the</strong> organization; in social policy science (Simmering, 2011)
<strong>the</strong> model usually used has much links with this one: diagnosis, priorities, strategies,
programming, implementation, control and evaluation.
c) For Beckhard and Harris's (1987) change happens, if <strong>the</strong> level <strong>of</strong> disaffection, plus <strong>the</strong>
desirability <strong>of</strong> change and <strong>the</strong> practicality <strong>of</strong> <strong>the</strong> change surpassed <strong>the</strong> cost <strong>of</strong> changing;
<strong>the</strong>refore, change happens when all three factors are high and is compromised if at least one
<strong>of</strong> <strong>the</strong> factors is low.
d) For Carnall (1990) skills were <strong>the</strong> key to good change namely three kinds <strong>of</strong> skills: to know
how to manage transitions effectively, to deal with organizational cultures; and to manage
organizational politics.
e) Kotter (1996) identified <strong>the</strong> following eight steps organizational change should follow in order
to be a success: 1) establish a sense <strong>of</strong> urgency; 2) form a powerful guiding coalition; 3)
create a vision and strategy; 4) communicate <strong>the</strong> vision; 5) empower o<strong>the</strong>rs to act on <strong>the</strong>
vision; 6) generate short-term wins; 7) consolidate improvements and produce still more
change; 8) institutionalize <strong>the</strong> new approach and <strong>the</strong>refore make it a part <strong>of</strong> <strong>the</strong> organizational
culture).
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f) Bridges (1991), and Bridges and Mitchel (2002) referred to <strong>the</strong> important distinction between
transition and change in <strong>the</strong> sense that transition is requires abandoning old practices and
adopting new behaviors or ways <strong>of</strong> thinking, and planned change related to working in new
physical locations or new organizational structures.
g) Finally, one o<strong>the</strong>r important analysis related to congruency between internal and external
organizational resources (Nadler and Tushman 1987).
One author (Stacey 1993 and 2001) highlighted <strong>the</strong> fact that change is a natural consequence <strong>of</strong>
communication and conflict. In that context, managers are very influential; “change managers” may be
involved included in a change transition team, which is led by a change agent; a change agent is
someone whose organizational function is to lead <strong>the</strong> management <strong>of</strong> change. Change managers
may overcome resistance to change by implementing a specific set <strong>of</strong> techniques like <strong>the</strong> following: a)
timely planning; b) implement two-way communication and allow time and space for discussion and
negotiation; c) foster participation; d) emphasize <strong>the</strong> financial benefits; e) introduce change slowly
overtime, and be aware that too much changes may be stressful and not understood; f) gain <strong>the</strong>
political support <strong>of</strong> <strong>the</strong> key employees; g) make changes that have been successful in o<strong>the</strong>r
companies or o<strong>the</strong>r areas <strong>of</strong> <strong>the</strong> company; h) reduce uncertainty about <strong>the</strong> change; i) involve workers
by asking about <strong>the</strong>ir feelings and ideas about change; j) build strong working relationships.
2.2.2 Main analysis on knowledge related to change
Several authors produced recently seminal work on <strong>the</strong> perspective <strong>of</strong> change in organizations related
to knowledge. The most important analysis was made by Stam (2007) who stated that knowledge
productivity could only be measured by innovation, and that innovation could be incremental or
radical. That analysis was in <strong>the</strong> same direction <strong>of</strong> that <strong>of</strong> Kianto (2007) who described <strong>the</strong> process <strong>of</strong>
knowledge renewal as a dynamic situation that ultimately would lead to changes in <strong>the</strong> organization.
Cegarra and Moya (2005) stressed that organizational learning implied an unlearning process; that
circumstance meant organizational change was more difficulty than it was previously thought. More
recently Edwards (2010) made clear that after <strong>the</strong> focusing on technology (KM1) and on people
(KM2), knowledge management should focus on processes (KM3); that shift would also result in a
closer consideration <strong>of</strong> change as problem for knowledge management. Finally, Pook (2011)
advocates that intellectual capital management (ICM) unlocks individual knowledge and experience
for <strong>the</strong> organization, contributes to make <strong>the</strong> organization more attractive as an employer ICM
supports strategic thinking as an important part <strong>of</strong> leadership and valuable employees as relevant
knowledge holders.
3. The concept <strong>of</strong> a knowledge clinic
3.1 The reason
In order to better manage knowledge for making it become productive and unlocking its
developmental potential a comprehensive set <strong>of</strong> concepts, methods, tools and supporting services is
required that helps companies in purposefully intervening in <strong>the</strong>ir core processes. As those
interventions can ei<strong>the</strong>r be caused by a certain problem or unsatisfying situation or initiated from a
kind <strong>of</strong> regular routine check-up, this conceptual, methodological and eventually even technological
environment also describes <strong>the</strong> fundamental concept <strong>of</strong> a knowledge clinic. This knowledge clinic
would enable companies to identify knowledge-related problems and what <strong>the</strong>y are caused by, to plan
and manage intra-organizational development in order to adjust from <strong>the</strong>ir actual maturity level to <strong>the</strong>
desired one and also to run sensitivity analysis on <strong>the</strong>ir knowledge investments before spending
efforts or money in <strong>the</strong> same way as any process <strong>of</strong> medical treatment with <strong>the</strong> human body is (or at
least should be) organised: diagnosis – healing – evaluating/cross-checking. And, as in human
medicine too, <strong>the</strong>re must be a phase to re-think and reflect for deriving any lessons learnt. This phase
avails oneself with methods like forensic pathology or autopsy.
3.2 Knowledge clinic and change
In general, any change process in a company – as long as it aims to initiate a certain development
instead <strong>of</strong> just happening accidentally – can be seen as a particular problem solving process. Such
process is usually initiated by recognizing a certain situation or behavior as not satisfying (anymore)
and eventually having a clear vision <strong>of</strong> how it should be ideally or even knowing what needs to be
done to improve situation or change behavior. In companies those processes typically aim at
improving certain performance parameters like throughput, service level, time <strong>of</strong> delivery etc.,
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improving <strong>the</strong> cost-benefit ratio <strong>of</strong> <strong>the</strong> company as a whole or <strong>of</strong> a particular product, service, process,
or department, and maintaining or extending <strong>the</strong> company’s position at <strong>the</strong> market. The success,
effectiveness and efficiency <strong>of</strong> any company development depends on how well <strong>the</strong> company knows
its situation, how clearly and correctly it specifies problems and potential for improvements including
<strong>the</strong>ir sources and drivers, and how purposefully and efficiently it selects and applies methods for
solving problems and changing situation or behavior. Here, knowledge and experience play a major
role as problem-solving in a company context needs to be always knowledge-based and nowadays
even more and more knowledge-focused. Due to <strong>the</strong> complexity <strong>of</strong> today’s problems and processes in
a company and because <strong>of</strong> <strong>the</strong> multi-facet appearance <strong>of</strong>, view on and benefit from applying <strong>the</strong>
concept <strong>of</strong> knowledge, companies face <strong>the</strong> challenge to really manage, operate and understand
knowledge as a strategic resource.
Consequently, <strong>the</strong> knowledge clinic provides an environment for well-structured collection <strong>of</strong> and
targeted access to numerous and scattered <strong>the</strong>oretical and practical knowledge management
resources, such as concepts and approaches, methods and procedures, tools and frameworks,
covering all three dimensions <strong>of</strong> knowledge management, i.e. individual, technological and
organisational aspects. With this, <strong>the</strong> knowledge clinic is not meant to be ano<strong>the</strong>r knowledge
management method or tool, but to support a purposeful selection <strong>of</strong> <strong>the</strong> right knowledge
management resource for a certain company setting as characterized before and developmental
objective as clearly identified.
3.3 Knowledge clinic departments
The key objective <strong>of</strong> introducing a knowledge clinic mainly consists in supporting companies and
organisations in solving knowledge-related problems, i.e. – to some extent – improve <strong>the</strong>ir knowledge
management maturity. For this, <strong>the</strong> company or organization needs to specify <strong>the</strong> problem or question
it deals with, characterize pre-conditions, settings and environment or currently level <strong>of</strong> KM maturity
<strong>the</strong> problem is situated in, and define or explain <strong>the</strong> change that is intended or to be achieved.
Therefore, <strong>the</strong> knowledge clinic needs to host <strong>the</strong> following departments:
The reception department enables registration and specification <strong>of</strong> <strong>the</strong> “patient”, i.e. defining <strong>the</strong>
particular company setting (e.g. <strong>the</strong> organizational technical and cultural environment KM measures
have to fit in, any KM activities or systems already in place, <strong>the</strong> current state-<strong>of</strong>-implementation <strong>of</strong> KM,
<strong>the</strong> planned investment volume, methodological preferences), but also <strong>the</strong> economy or society as <strong>the</strong>
framework a company acts in. In addition to this, reception also aims to specify <strong>the</strong> “disease”, i.e. <strong>the</strong>
problem, in order to decide about <strong>the</strong> right department to go on. As <strong>the</strong>re is no focus on details but
just a rough orientation that is required here, this stage <strong>of</strong> determining <strong>the</strong> company situation and
problems can be defined as macro detection mainly basing on self-assessment methodology. Here,
<strong>the</strong> company manager (or any o<strong>the</strong>r representative dealing with a particular problem) can directly
specify <strong>the</strong> concrete problem or might more or less clearly describe <strong>the</strong> symptoms observed or just
mention impressions or a fuzzy feeling on a certain situation. If <strong>the</strong> problem cannot be mentioned
clearly or is even not known so far, assistance for diagnosis is needed.
The diagnostics department supports companies in defining <strong>the</strong> path from symptoms to problems. In
particular this requires determining (knowledge-related) problems a company faces from
investigating/questioning on symptoms, seeing/observing (in terms <strong>of</strong> sensing or screening)
situations, lab testing, assuming. This procedure leads to micro detection <strong>of</strong> symptoms first and
problem identification <strong>the</strong>n. Clarification might be achieved by simply naming <strong>the</strong> problem (if known or
being aware <strong>of</strong>), matching symptoms with types or classes <strong>of</strong> problems, developing and testing
hypo<strong>the</strong>sis, associating patterns, or – if major difficulties in problem specification appear - consulting
with specialists. In <strong>the</strong> end problems should be known and specified with regard to type and area,
characteristics (qualitative and quantitative) and constraints.
For healing <strong>the</strong> company by introducing and applying knowledge-based interventions a variety <strong>of</strong>
specialised departments might be needed:
• Medication (e.g. in form <strong>of</strong> guidelines and guidance for fur<strong>the</strong>r intervention) represents <strong>the</strong>
non-invasive way <strong>of</strong> solving problems or achieving change. Here, already existent methods or
tools are to be applied more effectively and efficiently in order to achieve <strong>the</strong> intended or a
stronger impact.
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• Anaes<strong>the</strong>sia might be required in order to put certain parts or particular processes <strong>of</strong> a
company to sleep while being changed in such way to avoid serious damages to <strong>the</strong>m and
ensure <strong>the</strong>ir successful “re-animation” afterwards.
• Surgery in an organizational context is to be understood as fundamental redesign <strong>of</strong> <strong>the</strong>
range <strong>of</strong> products/services <strong>the</strong> company provides, its organizational structure, currently active
technical systems or operational and management processes in place in order to achieve
intended changes.
In any case, healing aims to define (and follow) <strong>the</strong> path from problems to solutions or – in o<strong>the</strong>r
words – intervening in <strong>the</strong> company through knowledge-based activities. Particularly, a sequence <strong>of</strong>
different action is required ranging from choosing <strong>the</strong> mode <strong>of</strong> action to solve <strong>the</strong> problem (i.e.
specifying <strong>the</strong> type <strong>of</strong> treatment, proposing medication/intervention, scheduling treatment/intervention)
or selecting solutions from catalogue, and applying model building and simulation to test potential
solutions, intervening in <strong>the</strong> company processes and supervising those interventions.
A recovery department is required as (medical) interventions usually cannot happen once at a certain
point in time, but imply necessary follow-ups. Also, <strong>the</strong> implementation <strong>of</strong> a new KM strategy is not a
“once in a moment” intervention and surely requires intense and continuous follow-up. In some way
<strong>the</strong> KC is a fundamental organization because KM is a “chronic disease” <strong>of</strong> organizations, a constant
problem and not something that once in a time can be healed permanently for all times.
Finally, <strong>the</strong> forensic department allows learning from failure in order to understand what problems
were caused by <strong>the</strong>m, why certain interventions did not succeed or which <strong>of</strong> alternative interventions
worked best. From this, lessons are learned even though <strong>the</strong> intended changes were not achieved.
3.4 The tasks performed by <strong>the</strong> knowledge clinic
The existence <strong>of</strong> a knowledge clinic (KC) would help solving <strong>the</strong> problem we mentioned. By a KC we
mean a scientific mechanism in <strong>the</strong> form <strong>of</strong> both a (self-) consulting support and a problem-solving
infrastructure that would perform <strong>the</strong> following tasks:
• Identify <strong>the</strong> organization setting and performance problem;
• Describe <strong>the</strong> current state <strong>of</strong> use <strong>of</strong> knowledge in <strong>the</strong> organization;
• Describe <strong>the</strong> desired state <strong>of</strong> knowledge use in <strong>the</strong> organization, in line with <strong>the</strong> organizational
objectives;
• Describe <strong>the</strong> required changes in <strong>the</strong> use <strong>of</strong> knowledge and <strong>the</strong> required investment in
knowledge in order to achieve <strong>the</strong> organizational objectives;
• Evaluate <strong>the</strong> success <strong>of</strong> <strong>the</strong> investment in knowledge once it is done.
Forensics
Diagnostics
exam
room
lab
Healing
treatment
room
critical care
unit
entrance
surgery
anaes<strong>the</strong>sia
waitingarea
reception
Figure 2: Knowledge clinic floor plan and possible paths
To achieve this, <strong>the</strong> client (or patient) entering <strong>the</strong> knowledge clinic might follow different paths from
<strong>the</strong> reception to or through specific departments (see Figure 2). If <strong>the</strong> client just knows about
symptoms with no or just a little clue on potential problems, that eventually might cause those
symptoms, a visit to <strong>the</strong> diagnostics department is a must. Without a sound diagnosis and clear
identification <strong>of</strong> <strong>the</strong> real problem(s) <strong>the</strong>re is a big risk for taking measures that affect symptoms only
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Gaby Neumann and Eduardo Tomé
instead <strong>of</strong> solving <strong>the</strong> problem(s). Organizations that are already known to <strong>the</strong> KC and already know
<strong>the</strong> problem(s) to be solved might directly proceed to <strong>the</strong> healing department(s) for receiving input and
support in <strong>the</strong> form <strong>of</strong> physical treatment sessions (i.e. application <strong>of</strong> certain methods, adjustment <strong>of</strong>
tools in place to current needs, launching new KM initiatives), pills (i.e. new knowledge from <strong>the</strong>
outside or implementation <strong>of</strong> new concepts or tools), surgery (i.e. plastic or emergency or normal cutdown
or rebuilding <strong>of</strong> <strong>the</strong> organization, its structure or infrastructure) or even just some training (i.e.
qualify organizational resources). Depending on <strong>the</strong> progress <strong>the</strong> company or a part <strong>of</strong> it might require
special attention as processes run in <strong>the</strong>re or changes applied to this are critical to <strong>the</strong> entire
organization and its survival. For this particular methods and resources are available. In case <strong>of</strong> any
treatment not being successful <strong>the</strong> forensics department helps in fur<strong>the</strong>r specifying unsolved
problems or reassigning symptoms to problems in order to learn from experience, increase <strong>the</strong>
company’s and <strong>the</strong> KC’s knowledge base, and identify needs for fur<strong>the</strong>r research and development in
terms <strong>of</strong> appropriate KM concepts, methods and tools.
4. Conclusions and fur<strong>the</strong>r work
The paper presents a first approach <strong>of</strong> a knowledge clinic. The idea and general structure is
elaborated. Next steps need to be related to: (i) collecting available concepts, methods and tools
related top knowledge, KM <strong>the</strong>ory, implementation, operation and evaluation, knowledge-based
change processes; (ii) designing <strong>the</strong> infrastructure and environment for bringing <strong>the</strong> knowledge clinic
to life. Only <strong>the</strong>n <strong>the</strong> KC might be implemented through <strong>the</strong> use <strong>of</strong> an interactive portal. Its ultimate
aim would be to analyze company/organization needs for support in dealing with <strong>the</strong>ir knowledge and
designing <strong>the</strong>ir individual knowledge-based change processes.
To fur<strong>the</strong>r specify methods, tools and activities available in <strong>the</strong> knowledge clinic <strong>the</strong> question on what
are knowledge-related problems a company or organization (or even economy or society) has to deal
with needs to be answered. Those problems can be related directly to <strong>the</strong> company’s performance
and how it might be improved, <strong>the</strong>y can be <strong>of</strong> indirect nature aiming to better deal with knowledge,
benefit more from <strong>the</strong> organizational knowledge base and reach ano<strong>the</strong>r level <strong>of</strong> knowledge
management maturity, or <strong>the</strong>y can even address <strong>the</strong> company’s fur<strong>the</strong>r existence at <strong>the</strong> market by
identifying <strong>the</strong> need for new products or services to be <strong>of</strong>fered to survive and also what kind <strong>the</strong>y
should be <strong>of</strong>.
In order to fur<strong>the</strong>r specify this and go into detail with <strong>the</strong> functioning and resources (in terms <strong>of</strong>
methods and tools) <strong>of</strong> <strong>the</strong> KC departments, a set <strong>of</strong> questions needs to be answered in relation to <strong>the</strong>
four phases <strong>of</strong> knowledge-based change processes in companies. Most relevant ones <strong>of</strong> <strong>the</strong>m are:
• What are subject, procedure and objective/outcome <strong>of</strong> diagnosis in this context – what is
needed for diagnosis – which approaches/methods/tools for diagnosis do exist already – who
needs to be involved in those diagnoses steps?
• What is meant by healing <strong>the</strong> company in this context – how and where knowledge-based
healing can be achieved in a certain company situation – which knowledge management
approaches/methods/tools for healing <strong>the</strong> company do exist and when <strong>the</strong>y should/could be
used?
• What is <strong>the</strong> purpose <strong>of</strong> evaluating/cross-checking knowledge-based interventions into a
company – how frequently this should be done – what are methods/tools/approaches for
evaluating <strong>the</strong> success <strong>of</strong> knowledge-based interventions – which conclusions could be drawn
from evaluation results?
• What did go wrong with knowledge-based interventions in a company – what was <strong>the</strong> reason
for any failing <strong>of</strong> knowledge management or knowledge-based interventions – how failing can
be measured – which lessons should be learnt from <strong>the</strong> company’s disease (e.g. where did it
come from, how could it be avoided?), its treatment (e.g. was <strong>the</strong> chosen one really <strong>the</strong> best,
most effective and efficient way to overcome problems) and its situation after intervention?
The paper provides only a sketchy version <strong>of</strong> <strong>the</strong> KC. Anyway, <strong>the</strong> awareness needed about <strong>the</strong>
importance <strong>of</strong> knowledge in companies is not in question. Future work should be done in order to
implement <strong>the</strong> KC which in fact is a very complex task. At <strong>the</strong> current stage <strong>of</strong> our analysis we think
about it as a multidisciplinary and long running project containing <strong>the</strong> following next steps:
1) Collecting available concepts, methods and tools related to knowledge, KM <strong>the</strong>ory,
implementation, operation and evaluation, knowledge-based change processes. We intend
that in <strong>the</strong> last ten years hundreds <strong>of</strong> useful KM methods and tools have been produced and
myriads <strong>of</strong> useful insights on how to correctly manage KM in companies and organizations
716

Gaby Neumann and Eduardo Tomé
have been given. However, <strong>the</strong>re is no place where all those insights have been
systematized, and that effort should be done.
2) Designing, implementing and testing a sophisticated infrastructure and environment for
bringing <strong>the</strong> knowledge clinic to life. With that portal companies would be in contact with <strong>the</strong>
several departments <strong>of</strong> <strong>the</strong> KC, and would receive feedback according to <strong>the</strong>ir questions.
3) “Opening” <strong>the</strong> KC to <strong>the</strong> public.
In general, it is not questionable anymore that <strong>the</strong> most performing countries and organizations <strong>of</strong> <strong>the</strong>
world are those that know how to pr<strong>of</strong>it better from knowledge. However, <strong>the</strong> more organizations need
knowledge, <strong>the</strong> harder <strong>the</strong>y feel how to make sense <strong>of</strong> that most valuable economic resource.
Knowledge is such a difficult topic that <strong>the</strong> field has been invaded by consultants, who endlessly
create new models and sell new solutions to <strong>the</strong> same old problems. And again, those problems are
basically two, economically speaking (but meaning it in a wider sense): what is <strong>the</strong> investment
needed, and how much will <strong>the</strong> organization benefit from it? A KC as proposed in this paper
addresses <strong>the</strong>se problems and provides support according to a particular company’s needs.
References
Beckhard, R. F.; Harris, R. T. (1987). Organizational Transitions: Managing Complex Change. Reading, MA:
Addison-Wesley.
Bridges, W. (1991). Managing Transitions. Reading, MA: Perseus.
Bridges, W.; Mitchell, S. (2002). Leading Transition: A New Model for Change. In: F. Hesselbein and
R. Johnston (eds.), On Leading Change. New York: Jossey-Bass.
Bullock, R. J.; Batten, D. (1985). It's Just a Phase We're Going Through. Group and Organizational
Studies 10 (1985): 383–412.
Caldwell, F.; Harris, K. (2002). The 2002 Knowledge Management Hype Cycle. Gartner Research: 3 January
2002.
Caldwell, F.; Harris, K.; Logan, D. (2003). Using Knowledge Management to Drive Business: These
knowledge management Key Issues are critical to ensuring return on investment in knowledge
processes and maximizing <strong>the</strong> productivity <strong>of</strong> intellectual capital. Research Note K-19-2410,
Gartner Research: 7 February 2003.
Carnall, C. A. (1990). Managing Change in Organizations. London: Prentice Hall.
Cegarra Navarro, J. G.; Rodrigo Moya, B. (2005). Business performance management and unlearning process.
Knowledge Process and Management, 12(2005)3: 161-170.
Edwards, J. (2010). A process view <strong>of</strong> knowledge management: it ain’t what you do, it’s <strong>the</strong> way that you do it.
ECKM – <strong>European</strong> <strong>Conference</strong> on Knowledge Management 2010, keynote speech.
Emery, F.; Trist, E. (1972). Toward a Social Ecology, Plenum, London.
Fraunh<strong>of</strong>er-Wissensmanagement Community (Hrsg.; 2005). Wissen und Information 2005. Stuttgart: Fraunh<strong>of</strong>er
IRB. (Knowledge and Information 2005; in German).
Kianto, A. (2008). Assessing organizational renewal capability International. Journal <strong>of</strong> Innovation and Regional
Development 1(2008)2:115-129.
Kotter, J. P. (1996). Leading Change. Boston: Harvard Business School Press, 1996.
Lewin, K. (1951). Field Theory in Social Science. New York: Harper and Row, 1951.
Mayo, E. (1933). The Human Problems <strong>of</strong> an Industrialized Civilization. Routledge.
Nadler, D.; Tushman, M. L.; Nadler, M. B. (1997). Competing by Design: The Power <strong>of</strong> Organizational
Architecture. New York: Oxford University Press.
Neumann, G.; Tomé, E.: Knowledge Management and Logistics: Where we are and where we might go to. In:
LogForum 2(2006)3, no. 3.
Nonaka, I.; Takeuchi, H. (1995). The Knowledge-Creating Company. New York: Oxford University Press, Inc.
Pook, K. (2011). Getting people involved: The benefit <strong>of</strong> Intellectual Capital Management for addressing HR
challenges. Journal <strong>of</strong> <strong>European</strong> Industrial Training, forthcoming.
Simmering, M. (2011). Proactive and Reactive Change – Encyclopedia <strong>of</strong> Business, 2 nd edition. Online:
http://www.referenceforbusiness.com/management/Pr-Sa/Reactive-vs-Proactive-Change.htm (last
accessed on May 14, 2011).
Stacey, R. D. (1993). Strategic Management and Organisational Dynamics: The Challenge <strong>of</strong> Complexity.
London: Pitman Publishing.
Stacey, R. D. (2001). Complex Responsive Processes in Organizations: Learning and Knowledge Creation.
London: Routledge.
Stam, C. (2007). Knowledge Productivity. PhD Dissertation, In Holland University, The Ne<strong>the</strong>rlands.
Taylor, F. (2003). The Principles <strong>of</strong> Scientific Management. (1911), Routledge.
Womack, J.; Jones, D.; Roos, D. (1991). The Machine that Changed <strong>the</strong> World – <strong>the</strong> History <strong>of</strong> Lean Production.
Harper.
717

A Tenth Anniversary Assessment <strong>of</strong> Davenport and Prusak
(2000) Working Knowledge: Practitioner Approaches to
Knowledge in Organisations
Gary R Oliver
The University <strong>of</strong> Sydney, Sydney, Australia
gary.oliver@sydney.edu.au
Abstract: Davenport and Prusak’s “Working Knowledge” is aimed at practitioners. The tenth anniversary <strong>of</strong> <strong>the</strong>
paperback edition <strong>of</strong> Davenport and Prusak’s “Working Knowledge” (first published in 1998 and <strong>the</strong>n issued in
paperback with a new preface in 2000) is evaluated using two approaches. First, a practitioner view <strong>of</strong>
Knowledge Management (KM) is examined by considering <strong>the</strong>ir definition <strong>of</strong> knowledge and <strong>the</strong>ir heuristics on
knowledge processes. Their advice is drawn from <strong>the</strong> thirty-one businesses in which <strong>the</strong>y conducted research as
well as some o<strong>the</strong>r companies which are used for illustration. These are listed in a table which identifies <strong>the</strong> KM
lessons which Davenport and Prusak suggest should influence o<strong>the</strong>r organisations seeking to improve <strong>the</strong>ir use
<strong>of</strong> knowledge (presumably <strong>the</strong> readers <strong>of</strong> Working Knowledge). Their advice is to understand <strong>the</strong> rationale for
knowledge sharing. Discussion focuses on <strong>the</strong> value <strong>of</strong> <strong>the</strong> heuristics and <strong>the</strong> definition <strong>of</strong> knowledge and its
status in organisations from a practitioner point <strong>of</strong> view. At a time when <strong>the</strong> cost pressures facing contemporary
businesses reduce funds available for technological codification <strong>of</strong> knowledge and knowledge supporting
activities Working Knowledge reminds us that knowledge involves individuals making judgements and providing
insights. However, <strong>the</strong> continuing lack <strong>of</strong> a clear definition <strong>of</strong> knowledge makes for difficulties in gaining
acceptance for knowledge and knowledge practices.
Keywords: Davenport and Prusak; Knowledge; Knowledge hoarding; Knowledge management; Knowledge
sharing; Practitioner; Working knowledge
1. Introduction to Working Knowledge
It has been 10 years since <strong>the</strong> release <strong>of</strong> <strong>the</strong> paperback edition <strong>of</strong> Davenport and Prusak’s Working
Knowledge. At that time it set out <strong>the</strong> place <strong>of</strong> knowledge in relation to both <strong>the</strong> increasing numbers <strong>of</strong>
employees and managers with responsibilities that depend upon using knowledge and <strong>the</strong> increasing
role <strong>of</strong> technology in business. The popularity <strong>of</strong> <strong>the</strong> first edition (1998) resulted in a paperback (2000)
with a new preface reflecting on its status and <strong>the</strong> challenges facing businesses who had committed
to Knowledge Management (KM). (References in this study are to <strong>the</strong> paperback edition). Since <strong>the</strong>n
Working Knowledge has been translated in 12 languages and has sold over 150,000 copies.
2. Method
In this 10 year evaluation <strong>of</strong> WK two approaches are followed. First, <strong>the</strong> account given by Davenport
and Prusak (hereafter D&P) is assessed in terms <strong>of</strong> its value to practitioners. This assessment is
based on what D&P regard as <strong>the</strong> central issues <strong>of</strong> KM as evidenced by <strong>the</strong> structure and content <strong>of</strong>
WK. Second, <strong>the</strong> account <strong>of</strong> knowledge sharing and hoarding proposed in WK is subject to a critical
evaluation.
Working Knowledge used a sample <strong>of</strong> companies drawn from <strong>the</strong> thirty-one businesses in which D&P
conducted research as well as some o<strong>the</strong>r companies which are used for illustration. Table 1 lists all
<strong>the</strong> companies discussed by D&P and identifies <strong>the</strong> key knowledge example or insight which <strong>the</strong>y
suggest should influence o<strong>the</strong>r companies (presumably <strong>the</strong> readers <strong>of</strong> WK). The key example or
insight is selected on <strong>the</strong> basis <strong>of</strong> its discussion in WK. It should be noted that some companies
wished to remain anonymous and, from <strong>the</strong>ir discussion, it appears that all <strong>the</strong> organisations had a
significant budget for KM initiatives.
Table 1: Knowledge example or insight provided in Working Knowledge (1998/2000)
Company discussed in
Working Knowledge
(Note 1)
Current status <strong>of</strong>
company (Note 2)
Key knowledge example or insight from Working
Knowledge
(Note 3)
1 American Express Operating Example <strong>of</strong> a stable expert system
2 *Andersen Consulting
Evolved
Accenture
into
Establish competency groups;
3 Apple Operating Put knowledge gained into products
4 AT&T Operating Knowledge transfer inhibited by a conqueror culture
5 *Boeing Operating Manage configuration knowledge
718

Company discussed in
Working Knowledge
(Note 1)
Current status <strong>of</strong>
company (Note 2)
Gary R Oliver
Key knowledge example or insight from Working
Knowledge
(Note 3)
6 Booz Allen Hamilton
Operating as two
companies
Knowledge transfer through mentoring
7 *British Petroleum Operating Make knowledge accessible; Create a sense <strong>of</strong> crisis
8
*Buckman
Laboratories
Operating Position <strong>of</strong> Director <strong>of</strong> Knowledge Transfer
9 *Chaparral Steel
Acquired by
Gerdau Ameristeel
Flat organisation structure; Learning culture
10 *Chemical bank
Evolved into J P
Morgan Chase
Phone directory on what employees did
11 Chevron Operating Knowledge management best practice
12 *Chrysler Operating Codify process knowledge
13 Citicorp Operating Knowledge management best practice
14 *Coca-Cola Operating Trade secret <strong>of</strong> <strong>the</strong> formula
15 Coopers & Lybrand
Merged with Price
Waterhouse
Vice President role for learning
16 *CSIRO Operating Knowledge/research relationship with industry
17
*Dai-Ichi
Pharmaceuticals
Merged
Sankyo
with
Discussion opportunities
Acquired by
18 Digital Equipment Compaq and <strong>the</strong>n
HP
CEO narrow vision <strong>of</strong> <strong>the</strong> business and strategy
19 *Dow Chemical Operating
Chief Knowledge
management
Officer <strong>of</strong> intellectual capital
20 EDS
Acquired by
Hewlett-Packard
Chief Knowledge Officer role
21 EL Products
Acquired
Grimes
by
Takeover does not necessarily transfer knowledge
22 *Ernst & Young Operating Establish competency groups
23 *Ford Operating Bringing external knowledge into <strong>the</strong> organisation
24 General Electric Operating Learning centre established for knowledge
Operating after
25 *General Motors chapter
bankruptcy
11 Linking knowledge and decisions
26
*Hewlett-Packard
(HP)
Operating Use <strong>of</strong> electronic communication
27 *Hoechst-Celanese
Evolved into
Celanese listed in
Germany
Informal discussion leads to formal projects; create a
sense <strong>of</strong> crisis
Consolidate and make knowledge accessible (relevance
28 *H<strong>of</strong>fman-LaRoche Operating
is more important than
Knowledge/research relationship with
inhibits knowledge sharing
completeness);
industry; Ego
29
*Hughes Space and
Communications
Acquired
Boeing
by
Consolidate and make knowledge accessible
Use <strong>of</strong> technology for knowledge growth; Establish
30 *IBM Operating
competency groups; Knowledge transfer inhibited by a
conqueror culture
31 *IDEO Operating Culture <strong>of</strong> questioning what is not known
32
International
Harvester
Acquired by J I
Case
Had ‘forgotten’ how to build a truck factory completed 20
years earlier
33
Javelin Development
Acquired by Oracle
Corp
Knowledge market inefficiencies
34 Matsushita Operating Understand what has to be done well
35 McDonalds
Universities
Operating Establish competency groups
36
*McDonnell Douglas
Corp.
Merged
Boeing
with Difficulty <strong>of</strong> change when successful; create an artificial
sense <strong>of</strong> crisis
37
*McKinsey
Company
&
Operating Make knowledge accessible
38 Merck Operating Dedicate distinct resources to generate knowledge
39 *Micros<strong>of</strong>t Operating Use <strong>of</strong> technology for knowledge growth
719

Company discussed in
Working Knowledge
(Note 1)
Current status <strong>of</strong>
company (Note 2)
40 *Mobil Oil Operating
Gary R Oliver
Key knowledge example or insight from Working
Knowledge
(Note 3)
Expertise under politics influence codification and
coordination
41 *Monsanto Operating Common definitions for codification and consolidation
42
*National
Semiconductor
43 NationsBank
Operating Knowledge sharing with one and many s<strong>of</strong>tware tools
Evolved into Bank
America
Project to consolidate knowledge
44 NEC Operating
Faster model changes; Mobilising knowledge produces
better results than robots
45 Nissan Operating
Combining different ideas, skills and values creates
innovation
46 *NYNEX
Evolved
Verizon
into
Knowledge brokering
47 *Owens-Corning Operating Librarians create navigation tools
48 Polaroid Ceased Founder dominates <strong>the</strong> firm’s knowledge market
49 Raychem Operating Database <strong>of</strong> employees willing to share information
50
*Sandia National
Laboratories
Evolved into Martin
Marietta now
Lockheed Martin
Knowledge conservation programme
51 Sears Operating
Ignore Wal-Mart retailing changes until forced by
declining sales
Operating as an
52 *Sematech
association
member
companies
<strong>of</strong>
Structures to support knowledge transfer
53 *Senco Products Operating Understand failures <strong>of</strong> knowledge in decisions
54 *Sequent Computer Acquired by IBM CKO role in capturing and leveraging knowledge
55 Sharp Operating
Research knowledge routinely transferred to business
units
56 Silicon Graphics Operating Created internal knowledge repository
57 *Skandia Operating Measure <strong>the</strong> economic value <strong>of</strong> knowledge
58 Standard Life Operating Uses sources <strong>of</strong> competitor knowledge
59 Sun Microsystems Acquired by Oracle Created internal knowledge repository
60 *Teltech Operating External expert systems
61 *Texas Instruments Operating Re-use <strong>of</strong> knowledge
62 *3M Operating Encouraged private research
63 *Time Life
Evolved into Time-
Warner
Quality and depth <strong>of</strong> information
64 *US Army Operating After action reviews
65 Verifone
Acquired by
Hewlett-Packard
Circulate stories <strong>of</strong> excellence in action
66 Wal-Mart Operating Orchestrating core competencies
Acquired by
67 Wang Laboratories
Getronics <strong>of</strong> The
Ne<strong>the</strong>rlands after
bankruptcy
Failed to adapt
68 World Bank Operating
Dispense knowledge as well as money to improve
developing countries
69 Xerox PARC Operating
Failure to capitalise on innovative use <strong>of</strong> knowledge;
Establish competency groups
70 *Young & Rubicam Operating
Managing knowledge is <strong>the</strong> responsibility <strong>of</strong> all knowledge
workers
71
Zildjian
Company
Cymbal
Operating Trade secret formula for metal composition
Explanatory Notes to Table 1
1. An * indicates <strong>the</strong> company is one <strong>of</strong> <strong>the</strong> 31 companies identified by D&P in <strong>the</strong>ir acknowledgements (p.
xxiv). However, <strong>the</strong> original research program comprised “about twenty-five client companies” (p. xxi).
2. Status is categorised as one <strong>of</strong> operating, ceased, evolved, acquired or merged.
3. Specific examples or insights are found in more than one company.
In total, <strong>the</strong> same 71 companies feature in both <strong>the</strong> 1998 and 2000 editions <strong>of</strong> WK. Since <strong>the</strong> reissue
<strong>of</strong> WK retained <strong>the</strong> same text as <strong>the</strong> original it will be argued that D&P saw no need to amend or
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Gary R Oliver
expand <strong>the</strong>ir views and thus <strong>the</strong>ir views can be taken as fair account <strong>of</strong> <strong>the</strong>ir (<strong>the</strong>n) current position
and direction. Indeed <strong>the</strong> preface claims knowledge management has “widespread legitimacy” (p. vii)
and that management needs to “change <strong>the</strong> core <strong>of</strong> <strong>the</strong> business” (p. ix) using <strong>the</strong> guidelines already
provided by D&P. For <strong>the</strong> purposes <strong>of</strong> this evaluation, <strong>the</strong> broad practitioner literature will be
referenced since Working Knowledge was entirely aimed at practitioners. In <strong>the</strong> main, practitioner
literature described how organisations discover and manage what <strong>the</strong> employees know (e.g., Dixon,
2000; Leonard-Barton, 1995) although <strong>the</strong>re were some early attempts to value knowledge (e.g.,
Edvinson and Malone, 1997). Table 1 contains two striking features. It shows that <strong>the</strong> companies vary
widely in <strong>the</strong> definition <strong>of</strong> knowledge from trade secrets (e.g., Zildjian Cymbal) to expertise (e.g.,
American Express) in varying forms such as stories (e.g., Verifone). It shows a range <strong>of</strong> processes
and commitment. These ranged from a project basis (e.g., Hoechst-Celanese) to broad operational
application (e.g., Sharp) with some harnessing <strong>of</strong> technology (e.g., Raychem). Table 1 also shows <strong>the</strong>
fate <strong>of</strong> <strong>the</strong> companies mentioned with one ceasing business, 11 acquired, three mergers, six evolving
into new organisations, and 49 continuing to operate. While no conclusion can be drawn from <strong>the</strong>se
numbers <strong>the</strong>re appears to be no exception from <strong>the</strong> business cycle for knowledge-aware companies.
3. Assessment <strong>of</strong> <strong>the</strong> value <strong>of</strong> Working Knowledge to practitioners
WK provided practitioners (defined as both managers and knowledge workers) with a handbook
delineating a range <strong>of</strong> actions that establish <strong>the</strong> value <strong>of</strong> knowledge to organisations in three ways.
First <strong>the</strong>y suggested that knowledge is different to information and data. Knowledge is seen as a
distinct property <strong>of</strong> individuals (‘knowing’). Second <strong>the</strong>y suggest nine knowledge heuristics to guide
KM endeavours. Both are used to assess <strong>the</strong> claims made in WK.
D&P define knowledge using three descriptions. First <strong>the</strong>y claim that knowledge is a refinement <strong>of</strong>
data and information and possessing an experiential factor (p. 7). Data is regarded as <strong>the</strong> elements <strong>of</strong>
a transaction which can be captured and extracted (p. 2). Information is <strong>the</strong> use <strong>of</strong> data for a purpose
which allows meaning to be understood (p. 3). This implies that who determines meaning is always
<strong>the</strong> recipient not <strong>the</strong> provider. Accordingly, <strong>the</strong> method suggested by D&P (facilitating contacts and
allowing individuals opportunities to communicate) overlooks <strong>the</strong> meat <strong>of</strong> <strong>the</strong> problem, namely
whe<strong>the</strong>r sharing or hoarding <strong>of</strong> knowledge will take place. It also fails to make clear how activities
labelled as KM went beyond exchanging data or information.
The second aspect <strong>of</strong> knowledge is that it can be difficult to isolate. In adopting this view D&P support
<strong>the</strong> argument proposed by Polyani (1966/1983) that individuals possess knowledge which is not
entirely apprehended by <strong>the</strong>m because explicit knowledge and tacit knowledge is intermingled. This
presents a problem which D&P do not entirely satisfactorily resolve although <strong>the</strong>y consistently
distinguish between <strong>the</strong>m. Supporting Polanyi’s view requires <strong>the</strong>m to locate knowledge in <strong>the</strong>
individual as part <strong>of</strong> cognition. D&P use <strong>the</strong> conversion model provided by Nonaka and Takeuchi
(1995) to give certainty that <strong>the</strong> tacit knowledge can be made explicit. D&P cite <strong>the</strong> example from
Nonaka and Takeuchi (1995) <strong>of</strong> <strong>the</strong> bread-making machine at Panasonic which required <strong>the</strong>
combination <strong>of</strong> expert knowledge (computer control, induction heating, and rotating motors) from
three different business units (p. 60-61). In also following Nonaka and Takeuchi (1995) D&P, depart
from Polanyi (1966/1983) who claims that tacit knowledge is only made explicit both infrequently and
in small part. (Both <strong>the</strong> first and second criterion resemble <strong>the</strong> knowledge as resource conception
proposed by Grant (1996) and Choo (1998) and <strong>the</strong>reby entails making it available encouraging <strong>the</strong>
use <strong>of</strong> technology which may reduce it to data.)
The third criterion is its active use outside <strong>the</strong> individual to <strong>the</strong> benefit <strong>of</strong> <strong>the</strong> organisation in terms <strong>of</strong>
<strong>the</strong> judgement, expertise, innovation or creativity (p. xix) that occurs within and between individuals (p.
6). D&P recognise that <strong>the</strong> receiver may understand a completely different message to that which was
intended by <strong>the</strong> sender (p. 3) but suggest that <strong>the</strong> adoption <strong>of</strong> suitable methods will overcome this
problem. The methods <strong>the</strong>y instance are essentially putting searchers for knowledge in touch with
people who have made claims to possess <strong>the</strong> category or class <strong>of</strong> knowledge sought. This is a
practical approach and is far superior to <strong>the</strong> wholesale codification <strong>of</strong> knowledge approach that
prevailed at <strong>the</strong> time Working Knowledge was written and which continues to be policy in some
knowledge-intensive companies but is carried out haphazardly by employees (for example, customer
service staff who make a summary record favourable to <strong>the</strong>mselves since <strong>the</strong> note is not shared with
<strong>the</strong> customer). In essence, to overcome miscommunication D&P rely on <strong>the</strong> solution proposed by
Shannon (1948) that <strong>of</strong> redundancy. D&P replace Shannon’s focus on content with <strong>the</strong> “impact on
judgement and behaviour <strong>of</strong> <strong>the</strong> recipient” (p. 3).
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The difficulty with using manifold criteria for knowledge is <strong>the</strong> problem that arises when only one or
two are present. Separating tacit from explicit knowledge is not as simple as Nonaka and Takeuchi
(1995) depict and this is conceded by proposing that knowledge is in some way refined data or
information. This utility view encounters difficulties in to determining which individual knowledge
contributes to favourable business outcomes. Their answer that it should be evaluated in terms <strong>of</strong> <strong>the</strong>
decisions or actions associated with it (p. 6) is not entirely satisfactory for two reasons. The first is that
<strong>the</strong> providers <strong>of</strong> knowledge may curb <strong>the</strong>ir tendency when <strong>the</strong>y are not recognised. It overlooks <strong>the</strong>
fact that some employees are adept at manufacturing opportunities to promote <strong>the</strong>ir knowledge or
pass-<strong>of</strong>f knowledge gained from o<strong>the</strong>r employees. Second, a just-in-time view <strong>of</strong> knowledge is implied
where knowledge does not require a gestation period in which to develop nor circumstances in which
it can be uniquely applied. In <strong>the</strong> meantime organisational resources may seem to be consumed
unproductively (for example, <strong>the</strong> quadruple flying systems which gave <strong>the</strong> Boeing 747 an enviable
safety record). D&P acknowledge that many managers do not make any distinction between
knowledge and information (p. 6) and instead suggest <strong>the</strong> focus should be on its components
“experience, truth, judgement, and rules <strong>of</strong> thumb” (p. 7).
4. Heuristics for knowledge
Working Knowledge sought to provide mainly heuristics from anecdotal evidence for successful KM
from apparently successful approaches adopted by leading companies in KM. D&P <strong>of</strong>fer nine
heuristics which roughly correspond to <strong>the</strong> chapter structure in WK. Table 2 below summarises each
heuristic by chapter and <strong>the</strong> key elements <strong>of</strong>fered by D&P to support <strong>the</strong>ir heuristic.
Table 2: Summary <strong>of</strong> <strong>the</strong> heuristics by chapter with <strong>the</strong> key elements <strong>of</strong> each heuristic
Chapter
# Title
What do we talk
about when we
1
talk about
knowledge?
2
3
4
5
6
The promise
and challenge <strong>of</strong>
knowledge
markets
Knowledge
generation
Knowledge
codification and
coordination
Knowledge
transfer
Knowledge roles
and skills
Heuristic Key elements
Make clear where
knowledge not data
or information utilised
Recognise where
knowledge markets
exist in <strong>the</strong>
organisation
Foster knowledge
generation
Selectively codify <strong>the</strong>
bounded,
unambiguous and
rule-based knowledge
and coordinate
unstructured
knowledge
Enable knowledge
transfer which result
in absorption and use
so it thrives beyond
where it was
developed
Identify knowledge
roles and skills
• Knowledge as insights, judgements, and
understanding.
• Knowledge as refinement <strong>of</strong> data and information
possessing an experiential factor.
• Roles include ‘seeker’, ‘buyer’ and ‘broker’.
• Marketplaces include face-to-face and technology
mediated.
• Signals include position title, status, reputation, informal
networks and communities <strong>of</strong> practice.
• Value is determined by both buyer and seller using
repute, reciprocity, altruism, trust (based on social
exchange <strong>the</strong>ory).
• Pathologies include monopolies, artificial scarcity, and
trade barriers.
• Acquisition, rental and dedicated resources, and
networks are avenues.
• Copying, fusion, and adaptation are methods.
• Types <strong>of</strong> knowledge (articulatable, teachable,
observable, documentable, rich, complex).
• Mapping and modelling knowledge.
• Capturing tacit knowledge.
• Embedding knowledge in systems.
• Knowledge transfer is accomplished through a variety
<strong>of</strong> strategies including:
• Formal strategies include partnership,
mentoring, apprenticeship.
• Informal strategies include gossip.
• Random strategies include e.g., water cooler
meetings.
• Structured activities include e.g., knowledge
fairs, open forums, technology infrastructure.
• Knowledge specialists.
• Workers with knowledge.
• Managers <strong>of</strong> knowledge projects.
• Chief Knowledge Officers and equivalents.
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Chapter
# Title
7
8
9
Technologies for
knowledge
management
Knowledge
management
projects in
practice
The pragmatics
<strong>of</strong> knowledge
management
Gary R Oliver
Heuristic Key elements
Carefully utilise
technology
Better manage
knowledge projects
Leverage existing
approaches and
avoiding common
pitfalls
• Expert systems and artificial intelligence.
• Case-based reasoning.
• Broad knowledge repositories using a <strong>the</strong>saurus to
index and search
• Knowledge domains and communities.
• Real-time (short and long-term) knowledge systems.
• Use many types <strong>of</strong> repositories (external knowledge,
structured knowledge and informal knowledge).
• Undertake many types <strong>of</strong> projects (expert, internal
records, new knowledge, lessons learnt, high level
processes, evaluation, and compensation systems to
change behaviour).
• Attempt projects with multiple characteristics <strong>of</strong> types
listed above.
• Measure success in several dimensions (e.g.,
resources, knowledge value, spread beyond
participants, comfort using knowledge, financial return).
• Senior management support with clarity <strong>of</strong> vision.
• Knowledge oriented culture.
• Infrastructure with multiple channels.
• Start with high value knowledge on multiple fronts
leveraging existing approaches which involve decisionmaking.
• Avoid leading with technology, quality, best practices,
or organisational learning.
• Get help when encountering difficulties.
It is clear from this table that WK <strong>of</strong>fers a corporate view <strong>of</strong> KM. The most notable characteristic <strong>of</strong> <strong>the</strong>
examples used is <strong>the</strong> attempt to provide insights. D&P explain <strong>the</strong>se are “best practices, new ideas,
creative synergies, and breakthrough processes that information cannot supply, regardless <strong>of</strong> how
well it is managed” (p. xxi) and will come from knowledge that already exists with <strong>the</strong> organisation but
“was not accessible or available when required” (p. xxi). The heuristics use <strong>the</strong> goal <strong>of</strong> utility to <strong>the</strong>
organisation to guide successful knowledge transfer and are essentially advice to secure knowledge
from individuals. Having in <strong>the</strong>ir preface pointed out <strong>the</strong> challenge <strong>of</strong> KM lies beyond “awareness” and
“broad acceptance” (p. vii), D&P portray KM as a chess game where <strong>the</strong> company is pursuing
strategies to get employees to unburden <strong>the</strong>ir knowledge and <strong>the</strong> employees are coming to <strong>the</strong>
realisation that <strong>the</strong> less personal knowledge <strong>the</strong>y possess <strong>the</strong> less reason <strong>the</strong> company has to retain
<strong>the</strong>m. Many <strong>of</strong> <strong>the</strong> heuristics are <strong>the</strong>refore directed to employees as individuals. This is appropriate
where a single employee possesses all <strong>the</strong> relevant knowledge but is atypical in organisations where
knowledge is possessed by more than one employee (e.g., through having formerly performed those
duties) or where <strong>the</strong> knowledge is made available to employees (in a F. W. Taylor style job design).
There is <strong>of</strong>ten a fundamental divide between individual needs and group needs and uses <strong>of</strong>
knowledge. It is not simply a matter <strong>of</strong> extending <strong>the</strong> heuristics to a work group employing tactics such
as creating communities <strong>of</strong> practice. A catalyst triggers sharing <strong>of</strong> knowledge and this occurs in a
social situation in which trust is only one factor. New readers <strong>of</strong> WK will want to consider what
additional heuristics would be appropriate to recognise <strong>the</strong> social context <strong>of</strong> knowledge and how <strong>the</strong>
advice to managers may need to change.
5. Discussion
WK opens by suggesting that when organisations downsized <strong>the</strong>y found that some <strong>of</strong> <strong>the</strong> retrenched
managers performed coordination functions <strong>of</strong> knowledge syn<strong>the</strong>sis (p. xix) which were not part <strong>of</strong>
<strong>the</strong>ir <strong>of</strong>ficial job descriptions. In o<strong>the</strong>r words, KM improves performance, productivity and innovation
through by adding value to information in ways which organisations find difficult to understand. D&P
explain this with social exchange <strong>the</strong>ory (p. 26). This accounts for employees focusing on <strong>the</strong>
immediate consequences including loss <strong>of</strong> power and <strong>the</strong> prospect <strong>of</strong> satisfying future needs.
However, D&P overlook a number <strong>of</strong> promising alternatives expounded around <strong>the</strong> same time which
may resolve some <strong>of</strong> <strong>the</strong> difficulties knowledge initiatives may face. The most notable is <strong>the</strong>
information space (Boisot, 1995). It is a three dimensional dynamic view <strong>of</strong> knowledge to represent its
flows. The information space three dimensions consider <strong>the</strong> abstraction <strong>of</strong> information, <strong>the</strong> extent to
which it is codified, and its diffusion. Somewhat disappointingly, Boisot (2000) later follows an
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Gary R Oliver
accounting precedent considering knowledge as an asset. D&P suggest that knowledge sharing
ultimately benefits <strong>the</strong> employees through <strong>the</strong> continuity <strong>of</strong> <strong>the</strong> organisation and this may be more
effective than conformance to corporate values or policies for sharing knowledge.
WK takes a practical view <strong>of</strong> knowledge providing heuristics for <strong>the</strong> market for knowledge in
organisations but <strong>the</strong>se depend upon what constitutes knowledge. WK opens by differentiating
between data, information and knowledge but treating <strong>the</strong>m as a continuum (p. 7). This suggests an
oscillation between absolute and relative definitions. The absolute view relies on separate categories
for each <strong>of</strong> data (discrete facts and opinions), information (data endowed with relevance and purpose)
and knowledge (experience, values, contextual information, and expert insight). The relative view
depends upon <strong>the</strong> value <strong>of</strong> <strong>the</strong> message to <strong>the</strong> recipient (p. 3). This dual approach has two major
difficulties. The first is to <strong>the</strong> many knowledge dichotomies that exist. They include <strong>the</strong> distinction
between tacit and explicit knowledge (Polanyi, 1966/1983), <strong>the</strong> difference between know how, <strong>of</strong>ten
called procedural knowledge, and know what, <strong>of</strong>ten called descriptive knowledge (Ryle, 1949), <strong>the</strong>
difference between codified knowledge available to <strong>the</strong> organisation and <strong>the</strong> knowing possessed by
individuals (Hansen et al., 1999) and a priori knowledge, obtained without needing to observe <strong>the</strong>
world, and a posteriori knowledge empirically obtained (Kant, 1996). These are simple dichotomies
and although <strong>the</strong>y suggest difficulties with characterising knowledge <strong>the</strong>y are unsophisticated
compared to <strong>the</strong> conceptions <strong>of</strong> knowledge available from classical Greece. The second difficulty is
<strong>the</strong> intrusion <strong>of</strong> <strong>the</strong> recipient <strong>of</strong> <strong>the</strong> message in applying <strong>the</strong> definition: that for some people a specific
message would be data, for o<strong>the</strong>rs it would be information and for yet o<strong>the</strong>rs it would be knowledge.
So that <strong>the</strong> same message can be regarded in any <strong>of</strong> three ways depending upon <strong>the</strong> recipient. An
alternative is to concede that knowledge is multi-faceted.
If knowledge is considered as an omnibus term that is, providing many things at once, it becomes
possible to accept competing views <strong>of</strong> knowledge. The view <strong>of</strong> knowledge provided by Aristotle in
Nicomachean Ethics (2011) enumerates five types <strong>of</strong> hexis (durable dispositions) which a person has
to disclose truth: (1) Episteme, concerned codified rules with universal validity; (2) Techne, or any
capability which can be explained; (3) Phronesis, or making social judgements against a criteria; (4)
Metis is savvy understanding; and (5) Nous or <strong>the</strong> capacity developed with experience to evaluate
sources <strong>of</strong> knowledge and assumptions. These five aspects <strong>of</strong> knowledge suggest that <strong>the</strong>re are
several forms <strong>of</strong> knowledge and that it is pointless to use a single definition for knowledge. The
Grecian view also works against a relative view <strong>of</strong> knowledge as it implies that phronesis may be
information. The advantage <strong>of</strong> regarding knowledge as an ombibus term is that is buffers attempts to
reduce it to a knowing individual (e.g., Blackler, 1995) while allowing to be mediated and contested.
Despite moving from knowledge held by <strong>the</strong> individual to knowledge in <strong>the</strong> organisation D&P <strong>of</strong>ten
return to it. The discussion <strong>of</strong> culture and <strong>the</strong> power <strong>of</strong> <strong>the</strong> leader as CEO in setting organisational
values or CKO in setting knowledge sharing values <strong>the</strong>refore reduces knowledge sharing to <strong>the</strong>
formal processes. O<strong>the</strong>r parts <strong>of</strong> WK emphasise <strong>the</strong> informal and unfettered aspects <strong>of</strong> knowledge
sharing which can occur irrespective <strong>of</strong> <strong>the</strong> politics <strong>of</strong> <strong>the</strong> organisation. At <strong>the</strong> level <strong>of</strong> <strong>the</strong> organisation
it is possible to escape <strong>the</strong> minutiae <strong>of</strong> what and whe<strong>the</strong>r individuals have learnt and be concerned
with broadly what knowledge is <strong>of</strong> value to <strong>the</strong> organisation (p. 174) in terms <strong>of</strong> its improving
processes, satisfying customers and understanding competitor activity. The advice from D&P that
knowledge requires multifaceted programs which take longer and have less obvious results than a
single focus initiative (p. 165) but result in changes to valuing knowledge sharing and behaviour.
Knowledge does not move without friction or motivating force (p. 97), people will not share knowledge
without concern for what <strong>the</strong>y may gain or loose by doing so (p. 26). While D&P equate knowledge to
a valuable possession <strong>the</strong>y <strong>of</strong>ten describe solely an individual return ra<strong>the</strong>r than as <strong>the</strong> activities and
initiatives which increase knowledge.
6. Conclusion
Despite this critique, WK remains relevant ten years after publication. It remains unique for
emphasising <strong>the</strong> local nature <strong>of</strong> knowledge at a time when <strong>the</strong> effects <strong>of</strong> <strong>the</strong> internet were becoming
apparent. Its attention to local culture and context as <strong>the</strong> places for discovering and developing
knowledge in individuals reminds practitioners that for much <strong>of</strong> <strong>the</strong> time <strong>the</strong> operation <strong>of</strong> knowledge
markets is local. While D&P endorse initially <strong>of</strong>fering rewards <strong>the</strong>y predict that <strong>the</strong>y are not viable in
<strong>the</strong> long term. This suggests that creating communication events or forums is likely to have limited
success since <strong>the</strong>y can be insufficient to build trust despite face-to-face interaction. For practitioners
seeking clear-cut knowledge support activities to undertake, D&P show that knowledge is only shared
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Gary R Oliver
when <strong>the</strong> parties <strong>the</strong>mselves have what <strong>the</strong>y consider to be an appropriate price but also an
appropriate context. WK does not deal with <strong>the</strong> increasing workload pressures on employees, use <strong>of</strong>
job performance indicators and reduced free time which all limits <strong>the</strong> freedom <strong>of</strong> employees and
managers to be selective with <strong>the</strong>ir knowledge.
WK provides practitioners with a proven social <strong>the</strong>ory, that <strong>of</strong> social exchange, to understand
knowledge sharing. Although <strong>the</strong>y consider trust it is only one <strong>of</strong> four influences. Although WC only
touches on organisation-level knowledge sharing it does <strong>of</strong>fer clear warnings about <strong>the</strong> dangers <strong>of</strong>
over-reliance on technology (p. 123, 128, 142, 201) and that knowledge is not a thing (p. 53). For
organisations seeking to understand <strong>the</strong>ir informal lines <strong>of</strong> reporting and communication, WK will be <strong>of</strong>
considerable assistance by reminding managers and employees that knowledge sharing markets may
also have a group as well as an individual dimension. WK ought to have gone fur<strong>the</strong>r by clarifying
within <strong>the</strong> organisation what role is played by formally established committees or communities and
informal groups. Their observation that interaction (or face-time) is essential for people to know what
kinds <strong>of</strong> use can be made <strong>of</strong> <strong>the</strong> knowledge that <strong>the</strong>y possess is somewhat muted by not extending
<strong>the</strong>ir discussion <strong>of</strong> politics (later addressed by Cross and Prusak, 2003) and considering <strong>the</strong> strategic
use <strong>of</strong> knowledge by individuals at times when it brings <strong>the</strong>m <strong>the</strong> best advantage.
One disappointment is that WK reinforces <strong>the</strong> priority that practitioners mostly have an expectation for
immediate results with knowledge. While this is understandable as a justification for <strong>the</strong> use <strong>of</strong> scarce
organisational resources it overlooks <strong>the</strong> evolving nature <strong>of</strong> knowledge. D&P appear to see this issue
as one <strong>of</strong> ei<strong>the</strong>r research for patents or <strong>the</strong> use <strong>of</strong> groups or communities to incubate knowledge until
it is wanted. Practitioners may be inclined to err on <strong>the</strong> side <strong>of</strong> funding groups for defined outcomes
overlooking that every local environment in <strong>the</strong> organisation contains people with unique knowledge
capable <strong>of</strong> improving its performance at different times depending upon circumstances. The hijacking
<strong>of</strong> KM by s<strong>of</strong>tware vendors and pr<strong>of</strong>essionals <strong>of</strong>fering consulting products has erroneously suggested
that knowledge is a commodity. This has left practitioners with an unresolved <strong>the</strong> difficulty in
distinguishing between data, information and knowledge. The disbanding <strong>of</strong> KM functions and
allocation to support functions opens up a new audience for Working Knowledge. If D&P had rejected
<strong>the</strong> hierarchy view <strong>of</strong> data-information-knowledge (Ack<strong>of</strong>f, 1989; Cleveland, 1982) and elaborated
<strong>the</strong>ir view that knowledge is insights, judgements, and understanding (p. 28) practitioners would have
a simple yardstick to reduce confusion and accept a more fluid view <strong>of</strong> knowledge.
Acknowledgement
Larry Prusak kindly commented on a draft <strong>of</strong> this paper during his visit to The University <strong>of</strong> Sydney
School <strong>of</strong> Business in July 2011 but is not responsible for any misinterpretations which remain. This
paper is dedicated to <strong>the</strong> memory <strong>of</strong> my mo<strong>the</strong>r who encouraged my knowledge endeavours and who
died unexpectedly during its revision. An extended version is available from <strong>the</strong> author.
References
Ack<strong>of</strong>f, R. L. (1989). ‘From data to wisdom.’ Journal <strong>of</strong> Applied Systems Analysis 16 (1): 3-9.
Aristotle (2011). Nicomachean ethics. Translated by R. C. Bartlett and S. D. Collins. Chicago: University Of
Chicago Press.
Blackler, F. (1995). Knowledge, work and organisation. Organisation Studies, 16 (6): 1021-1045.
Boisot, M. H. (1995). Information space: A framework for learning in organisations, institutions and
culture. London. Routledge.
Boisot, M. H. (2000). Knowledge assets: Securing competitive advantage in <strong>the</strong> knowledge economy.
London. Oxford University Press.
Choo, C. W. (1998). The knowing organization: how organizations use information to construct meaning, create
knowledge, and make decisions. New York: Oxford University Press.
Cleveland, H. (1982). ‘Information as resource.’ The Futurist, December 1982, 34-39.
Cross, R and L. Prusak (2003). The political economy <strong>of</strong> knowledge markets in organisations. In Easterby-Smith,
M. and M. Lyles (Eds.): The Blackwell handbook <strong>of</strong> organisational learning and knowledge management.
Oxford: Blackwell: 454-472.
Davenport, T. H. and L. Prusak (1998/2000). Working knowledge: How organisations manage what <strong>the</strong>y
know. First published 1998. New preface edition. Boston, Massachusetts. Harvard Business School Press.
Dixon, N. (2000). Common knowledge: How companies thrive by sharing what <strong>the</strong>y know. Boston,
Massachusetts. Harvard Business School Press.
Easterby-Smith, M. and M. Lyles (2003). Introduction: Waterhseds <strong>of</strong> organisational learning and knowledge
management. In Easterby-Smith, M. and M. Lyles (Eds.): The Blackwell handbook <strong>of</strong> organisational learning
and knowledge management. Oxford: Blackwell: 1-15.
725

Gary R Oliver
Grant, R. M. (1996). Toward a knowledge-based <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm. Strategic Management Journal, 17 Special
Issue: 109-122.
Hansen, M. T., N. Nohria and T. Tierney (1999). What’s your strategy for managing knowledge? Harvard
Business Review, 77 (2): 106-116.
Kant, I. (1996). Critique <strong>of</strong> Pure Reason. Translated by. W. S. Pluhar. (Originally published 1787.) Indianapolis,
Indiana. Hackett.
Leonard-Barton, D. (1995). Wellsprings <strong>of</strong> knowledge: Building and sustaining <strong>the</strong> sources <strong>of</strong> innovation. Boston,
Massachusetts. Harvard Business School Press.
Nonaka, I. and N. Konno (1998). ‘The concept <strong>of</strong> “Ba”’: Building foundation for knowledge creation.’ California
Management Review, 40 (3): 40-54.
Nonaka, I. and H. Takeuchi (1995). The knowledge creating company: How Japanese companies create <strong>the</strong>
dynamics <strong>of</strong> innovation. Oxford, Oxfordshire: Oxford University Press.
Polanyi, M. (1966/1983). The tacit dimension. First published 1966. London. Routledge and Kegan Paul.
Ryle, G. (1949). The concept <strong>of</strong> mind. Hutchinson University Library
Shannon, C. E. (1948). A ma<strong>the</strong>matical <strong>the</strong>ory <strong>of</strong> communication. .
726

A Different View to Knowledge and Personal Knowledge
Management System
Kaspars Osis 1,2 and Janis Grundspenkis 2
1
Faculty <strong>of</strong> Engineering, Vidzeme University <strong>of</strong> Applied Sciences, Valmiera,
Latvia
2
Institute <strong>of</strong> Applied Computer Systems, Riga Technical University, Riga,
Latvia
Kaspars.Osis@va.lv
Janis.Grundspenkis@cs.rtu.lv
Abstract: Many times it is stated that nowadays we live in <strong>the</strong> information age. Knowledge has been recognized
as <strong>the</strong> most important asset both for organizations and for individual knowledge workers. Thus knowledge and
knowledge management is an actively researched area. So far a lot <strong>of</strong> attention has been geared towards
knowledge, which we know and which we know that we do not know. However <strong>the</strong>re still remains such unknown
knowledge about which we do not know that we know, and such knowledge about which we do not know that we
do not know. This paper proposes a new notion <strong>of</strong> knowledge substance, which encompasses <strong>the</strong> whole
expanse <strong>of</strong> knowledge around people and around <strong>the</strong> whole environment <strong>the</strong>y live in. Also it introduces a
knowledge utility quotient, which in particular cases displays <strong>the</strong> ratio <strong>of</strong> available knowledge and all <strong>the</strong> required
knowledge to perform a particular task. Knowledge workers represent increasingly high number <strong>of</strong> different
pr<strong>of</strong>essions. Along with that individual knowledge and personal knowledge management receive more attention
among pr<strong>of</strong>essionals and among researchers as well. This paper proposes a personal trinity model approach for
developing a personal knowledge management system. It is proposed that such system includes both social and
partially technological aspects <strong>of</strong> personal knowledge management. In addition also a psychological aspect <strong>of</strong>
personal knowledge management is partially encompassed in this approach. Several knowledge acquisition
principles are suggested by taking into account a knowledge substance notion and personal trinity model
approach. Paper views personal trinity model approach from individual’s necessities aspect and gives a set <strong>of</strong>
characteristics a knowledge worker should strive for. The next step <strong>of</strong> this research is seen to combine personal
trinity model approach with personal knowledge management system strictly technical approach. Possible future
opportunities for personal knowledge management are explored and potential benefiting parties are identified.
Keywords: knowledge, knowledge substance, personal trinity model, personal knowledge management,
personal knowledge management system
1. Introduction
During several previous decades <strong>the</strong>re is a historic shift from <strong>the</strong> industrial age to <strong>the</strong> information age.
Creation and consumption <strong>of</strong> information becomes prevalent instead <strong>of</strong> creation and consumption <strong>of</strong>
material goods. Work is becoming increasingly complex (Wiig 2004). Workspace equipment and tools
are turning to be more sophisticated to make sense out <strong>of</strong> vast amounts <strong>of</strong> information. That requires
additional knowledge and skills to handle it. In turn that leads to recognize that knowledge has
become a very important asset. Knowledge more increasingly has been viewed as an active area <strong>of</strong>
research.
Much <strong>of</strong> attention is geared towards researching different knowledge related areas. Knowledge
management (KM) is one <strong>of</strong> such areas. Based on (Tiwana 2002) KM has three basic processes:
knowledge acquisition, sharing, and utilization. Knowledge is divided in two broad categories: tacit
and explicit. There are also some o<strong>the</strong>r ways <strong>of</strong> classifying knowledge. Majority <strong>of</strong> research is dealing
with knowledge that we do know and with knowledge we know that we do not know. Per (Frappaolo
2004) <strong>the</strong>re still remains knowledge which we do not know that we know and knowledge which we do
not know that we do not know. We propose a new concept <strong>of</strong> knowledge substance to encompass all
knowledge elements (KE) as a basis for fur<strong>the</strong>r research. In addition this paper introduces a
knowledge utility quotient, which in particular cases displays <strong>the</strong> ratio <strong>of</strong> available knowledge and all
<strong>the</strong> required knowledge to perform a particular task.
Notions <strong>of</strong> “knowledge work” and “knowledge worker” (KW) have reappeared out <strong>of</strong> <strong>the</strong> shift from
information to knowledge. Increased importance and usage <strong>of</strong> knowledge in business and in everyday
life creates a necessity for skilled and knowledgeable individuals. That implies a need for an effective
personal knowledge management system (PKMS). Such system should pave <strong>the</strong> road for a new
knowledge-based society, economy, and allow ambient participation in a social and economic life. Per
Jefferson currently available PKMS are just a bit more than just productivity tools (Jefferson 2006). He
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suggests that <strong>the</strong>re is a necessity for systems with ability to adjust to individual. This paper reports on
one stage <strong>of</strong> broader research targeted at perspectives <strong>of</strong> developing well-rounded PKMS. Proposed
PKMS is encompassing three personal knowledge management (PKM) aspects: social, psychological
and technical. Technical aspect is based on agent and mobile technologies (Osis and Grundspenkis
2009, Osis and Grundspenkis 2010, and Osis and Grundspenkis 2011). Social and partially
psychological aspect <strong>of</strong> PKM is addressed within this paper. We propose <strong>the</strong> personal trinity model
approach for developing a well-rounded PKMS. Next we propose <strong>the</strong> personal knowledge cone-spiral
(PKCS) which makes it possible to avoid KW “burn-out” by detecting PKCS pattern in knowledge
acquisition process. We also propose five knowledge acquisition principles to support PKM. These
five principles are based on knowledge substance and its elements characteristics.
The paper is organized as follows. First knowledge is overviewed. The notion <strong>of</strong> knowledge substance
is introduced. That is followed by introduction to PKMS from perspective <strong>of</strong> KW. Next is proposed <strong>the</strong>
personal trinity model approach for developing a conceptual PKMS and <strong>the</strong> PKCS along with five
knowledge acquisition principles to support PKM. Finally it is followed by concise conclusions and
accordant references.
2. Knowledge
From individual’s perspective knowledge is ones memories and skills, and its physical location is
mainly in brain (Apshvalka 2004). She also states that knowledge is ra<strong>the</strong>r intangible and it can not be
fully sensed. Thus an important role is played by person’s characteristics such as motivation, will,
psychological traits and human intelligence. Per (Davenport and Prusak 2000) knowledge is defined
even in a more detailed manner. Thus knowledge has several components such as experience,
intuition, beliefs, core values, assumptions and intelligence. About intelligence speak in cases when
knowledge in instant is used in <strong>the</strong> right place and in <strong>the</strong> right time to create better results or
processing. To rephrase Wiig, <strong>the</strong> goal and main idea <strong>of</strong> information is its ability to describe, but <strong>the</strong>
goal and main idea <strong>of</strong> knowledge is geared towards action (Wiig 2004).
Knowledge has been classified in several ways among which most popular is to distinguish between
two categories: tacit knowledge and explicit knowledge (Dalkir 2005, and Tiwana 2002). These two
categories <strong>of</strong> knowledge are used in knowledge conversion module also known as Socialization-
Externalization-Combination-Internalization (SECI) model (Nonaka and Takeuchi 1995). Tacit
knowledge belongs to a person and it is difficult to clearly express this type <strong>of</strong> knowledge in words,
written or visual form. The location <strong>of</strong> tacit knowledge is in knowing individual’s heads. Explicit
knowledge can be codified and can be shared with o<strong>the</strong>rs in such clear cut ways as expressed in
writing, schemas, audio or video records. Wiig (Wiig 2004) divides knowledge into:
actionable knowledge – used to receive information and to identify, analyze and evaluate it; to
perform planning, decision making, monitoring, acting, and adapting;
passive knowledge – contains knowledge stored in documents, books, system repositories and in
o<strong>the</strong>r forms.
Ano<strong>the</strong>r categorization <strong>of</strong> knowledge has been given by focusing on requirements engineering
(Kirikova and Grundspenkis 2000). They propose to distinguish natural knowledge inherently
represented in human brains, and artificial knowledge represented by some natural or artificial
knowledge carrier. As natural knowledge <strong>the</strong>y consider a non-tangible knowledge system, which is
located in human beings. They state also that artificial knowledge depending on knowledge
possessor’s type can be tangible or non-tangible.
Tiwana classifies knowledge in four dimensions with accordant subcategories: type (technological,
business, environmental), focus (operational, strategic), complexity (explicit, tacit) and perish ability
(low, high) (Tiwana 2002).
There is also a notion <strong>of</strong> knowledge object (Machlup 1962), which by itself is not knowledge, but a
depiction <strong>of</strong> knowledge within an individual, for example, a painting or a transcript <strong>of</strong> knowledge
sharing speech. Rehm states that knowledge object is a precise way to describe <strong>the</strong> subject matter
content or knowledge that is ga<strong>the</strong>red (Rehm 2006). In addition <strong>the</strong>re is used a notion <strong>of</strong> KE what
YouKui describes as <strong>the</strong> basic unit in knowledge (YouKui 2005). Zou and Liu state that this concept is
useful and it is applicable for ga<strong>the</strong>ring, storing and arranging knowledge (Zou and Liu 2009).
Similarly some talk about knowledge components (Davenport and Prusak 2000, and Tiwana 2002).
Rehm states that knowledge object is a framework for identifying necessary knowledge components
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(Rehm 2006). Within this paper we consider KE as an elementary particle <strong>of</strong> whole available
knowledge which is not decomposable. Also we consider a knowledge object as a set <strong>of</strong> KE-s.
Similarly as material objects knowledge has several characteristics which are ra<strong>the</strong>r unusual if to
compare with ones describing material objects (Dalkir 2005):
after usage <strong>of</strong> knowledge it is still not consumed;
after transfer <strong>of</strong> knowledge it is still not lost;
<strong>the</strong>re is plenty <strong>of</strong> knowledge, but <strong>the</strong> ability to use it is scarce;
in organizations much <strong>of</strong> <strong>the</strong>ir valuable knowledge walks out <strong>the</strong> door at <strong>the</strong> end <strong>of</strong> <strong>the</strong> day.
So far a lot <strong>of</strong> attention has been focused towards knowledge, which we know and which we know
that we do not know. Though <strong>the</strong>re still remains such unknown knowledge about which we do not
know that we know, and such knowledge about which we do not know that we do not know (see
Figure 1). Thus <strong>the</strong>re exists knowledge that is outside and exists independently <strong>of</strong> human
consciousness. Also Polanyi asserts that humans know more than <strong>the</strong>y can tell (Polanyi 1996). Thus
individuals already have a lot <strong>of</strong> such knowledge in <strong>the</strong>ir heads which <strong>the</strong>y do not realize that <strong>the</strong>y
have it. As follows, <strong>the</strong> potential <strong>of</strong> human knowledge is considerably larger than <strong>the</strong> amount <strong>of</strong> used
knowledge.
Figure 1: The square <strong>of</strong> known-unknown (adapted from Abraham 2007 and Frappaolo 2004)
Previously noted knowledge characteristics and <strong>the</strong> wealth <strong>of</strong> knowledge based on <strong>the</strong> square <strong>of</strong>
known-unknown (Figure 1) incite to define a concept <strong>of</strong> knowledge substance. This concept
encompasses a breadth <strong>of</strong> all knowledge all around humans and around <strong>the</strong> environment individuals
live in as a community by communicating and interacting with each o<strong>the</strong>r. The knowledge substance
is integrity <strong>of</strong> all knowledge, and it is not fur<strong>the</strong>r dividable in knowledge sub-substances but only in
KE-s. Thus <strong>the</strong> breath <strong>of</strong> all known and unknown KE-s is forming <strong>the</strong> knowledge substance. It can be
described with formula (1), where MZ is knowledge substance, ZE is KE and n is <strong>the</strong> number <strong>of</strong> all KEs.
In addition knowledge an individual is striving to acquire in <strong>the</strong> context <strong>of</strong> knowledge substance can
not be viewed only as a dichotomous item (or items) as it is in <strong>the</strong> case <strong>of</strong> knowledge domain stated in
knowledge space <strong>the</strong>ory (DeRose 2005 and Cognitive Science Section 2007). It means that <strong>the</strong>re
exist such KE-s which can not be fully cognizable or can not be identifiable at all. However <strong>the</strong>y are
part <strong>of</strong> knowledge substance. Based on previously noted knowledge characteristic (i.e. after usage <strong>of</strong>
knowledge it is still not consumed) one can derive also <strong>the</strong> opposite – <strong>the</strong> amount <strong>of</strong> knowledge in our
environment we live in has not increased, that is, <strong>the</strong> amount <strong>of</strong> knowledge within knowledge
substance is constant. Only amount <strong>of</strong> knowledge acquired by humans is changing which is relatively
<strong>the</strong> same as <strong>the</strong> concept <strong>of</strong> World brain nailed by Wells in <strong>the</strong> beginning <strong>of</strong> 20 th century (Wells 1938).
By discovering new knowledge or coherence <strong>the</strong> amount <strong>of</strong> people-acquired-knowledge accordingly is
increasing. However we can not say that a coherence described by just-acquired knowledge did not
exist before it was discovered because this coherence existed and described people environment
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also before it was discovered. This once again underlines that <strong>the</strong> amount <strong>of</strong> knowledge within
knowledge substance is constant. For an individual it is important to find <strong>the</strong> right approach for
learning to spot, acquire and use unknown elements <strong>of</strong> knowledge substance (i.e. those KE-s which
individual knows that he or she does not know; those which deep inside individual are known, but
he/she does not realize that yet; and also those which individual does not know that he/she does not
know). More details about <strong>the</strong> way to acquire knowledge substance elements are given in <strong>the</strong> fourth
chapter <strong>of</strong> this paper.
3. Personal knowledge management from knowledge worker’s perspective
KM usually is viewed in business context in organization and is defined as such which enables
creation, sharing and consumption <strong>of</strong> knowledge in order to achieve business goals (Quintas 1999).
The main goal <strong>of</strong> KM in this context is to support opportunistic application <strong>of</strong> fragmented knowledge
through integration (Tiwana 2002).
PKM in turn is ra<strong>the</strong>r overlooked area within KM even though lately it attracts more attention.
“Definitions <strong>of</strong> PKM revolve around a set <strong>of</strong> core issues: managing and supporting personal
knowledge and information so that it is accessible, meaningful and valuable to <strong>the</strong> individual;
maintaining networks, contacts and communities; making life easier and more enjoyable; and
exploiting personal capital” (Higgison 2004). Eric Tsui defines PKM as “a collection <strong>of</strong> processes that
an individual needs to carry out in order to ga<strong>the</strong>r, classify, store, search and retrieve knowledge in
his/her daily activities”. He adds that “activities are not confined to business/work-related tasks but
also include personal interests, hobbies, home, family and leisure activities” (Tsui 2002). Barth is
much shorter as he defines PKM as taking responsibility for what you know, who you know, and what
<strong>the</strong>y know (Barth 2000). Within core <strong>of</strong> this laconic definition rests <strong>the</strong> cultural and collaborative
aspect <strong>of</strong> PKM, meaning that PKM is not geared just on an individual, but it is more focusing on
collaboration and culture between KWs. Thus PKM is promoting a creation <strong>of</strong> communities <strong>of</strong> practice
(CoPs) which function as a fertile ground for knowledge sharing and subsequently for knowledge
creation. There are already a number <strong>of</strong> different types <strong>of</strong> CoPs established online starting from
pr<strong>of</strong>essional, business, everyday practical questions related ones and ending with social network type
<strong>of</strong> CoPs. For example, LinkedIn is both a representative <strong>of</strong> a pr<strong>of</strong>essional CoP and a social network
CoP.
There are distinguished two characterizing concepts in <strong>the</strong> context <strong>of</strong> knowledge transfer or sharing
(Davenport and Prusak 2000):
knowledge velocity – it is <strong>the</strong> speed <strong>of</strong> transfer with which knowledge moves through an
organization or through an informal group <strong>of</strong> individual, having common interests, and how quickly
and broadly particular knowledge is available;
knowledge viscosity – it refers to <strong>the</strong> richness or density <strong>of</strong> <strong>the</strong> knowledge transferred.
In o<strong>the</strong>r words knowledge viscosity means how much <strong>of</strong> communicated knowledge is actually
absorbed and fur<strong>the</strong>r applied by a receiving individual. We propose to define a notion <strong>of</strong> knowledge
utility quotient (KUQ) in <strong>the</strong> context <strong>of</strong> knowledge usage. KUQ describes <strong>the</strong> proportion between
amount <strong>of</strong> useful knowledge ZL possessed for performing a certain task in particular setting and all <strong>the</strong>
necessary knowledge ZV required to accomplish a given task:
Such KUQ measurement can be very useful when performing a knowledge audit also in <strong>the</strong> case <strong>of</strong>
PKM. However it is important to point out that KUQ is applicable only in specific environments such as
m-learning task where all KE-s required can be identified ahead <strong>of</strong> time. There still have to be done
fur<strong>the</strong>r work to improve this formula so that it can be applicable for broader set <strong>of</strong> tasks and situations.
4. Personal knowledge management system’s conception
An appearance <strong>of</strong> new ideas and innovations in <strong>the</strong> KW level happens in all environments and in all
moments over individual’s time frame. For KW it is important to have a PKMS support. It is a complex
system and it contains social, psychological and technological aspects (Apshvalka and Grundspenkis
2005). PKMS performance is closely tied with KW-s perception, emotions, beliefs, needs, motivation,
surrounding society, environment and goals. PKMS from technological aspect only will not make a
successful system’s integration and usage for backing KW task supporting process. It is important that
individual himself/herself is willing to use PKMS and is willing to stick with PKM guidelines. Here
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stands out <strong>the</strong> social and psychological aspects <strong>of</strong> PKMS. It is important to realize individual’s
everyday needs, motivation and task accomplishing satisfaction.
A well-known hierarchy <strong>of</strong> needs was published more than a half century ago (Maslow 1954). Maslow
proposed that individual’s needs are hierarchically ordered in five levels: biological and physiological,
safety, belongingness and love, esteem, and self-actualization. Hierarchy <strong>of</strong> needs usually is depicted
in <strong>the</strong> form <strong>of</strong> pyramid where at its basic level are positioned biological and physiological needs
(Chapman 1995). In 1970-s this hierarchy was extended with cognitive and aes<strong>the</strong>tic needs levels,
but in 1990-s <strong>the</strong>re was added transcendence needs level (see Figure 2).
Figure 2: Eight-level hierarchy <strong>of</strong> needs (adapted from Maslow 1954 and Chapman 1995)
Needs hierarchy <strong>the</strong>ory has been also criticized being subjective and <strong>the</strong> order <strong>of</strong> needs levels being
not appropriate (Boeree 2006). However from <strong>the</strong> perspective <strong>of</strong> PKMS <strong>the</strong> Maslow hierarchy order is
not important. More essential is to realize that such individual needs exist and <strong>the</strong>y clearly have an
impact on KW. Thus <strong>the</strong>se needs have to be taken into account when developing a PKMS.
Maslow realized <strong>the</strong> existence <strong>of</strong> attitude problems regarding workers such that <strong>the</strong>y have certain
human basic needs and that <strong>the</strong>y have <strong>the</strong> rights for self-actualization (Chapman 1995). There are
also some o<strong>the</strong>r problems KW-s have to deal with (Etzel and Thomas 1996). KW-s have to identify
problems within <strong>the</strong>mselves and should be willing to tackle <strong>the</strong>m. Thus <strong>the</strong>y can improve PKMS
performance from all <strong>of</strong> its three aspects.
Motivation and satisfaction about <strong>the</strong> done task are also important traits <strong>of</strong> KW. In regard <strong>of</strong>
satisfaction a valuable research is done by Herzberg developing <strong>the</strong>ory <strong>of</strong> two factors (Herzberg et. al.
1959) which can be directly referred also to KW-s. Regarding motivation a considerable research
effort is done by McClelland who developed a <strong>the</strong>ory <strong>of</strong> motivation and research and individuals
needs for achievements (McClelland 1967). As follows it is considered that individual’s needs are
developing during his/her life. Haslam states that individual’s motivation is dependent on his/her selfcategorization
(Haslam 2004).
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Individuals need PKMS in order to deal in daily work with vast amount <strong>of</strong> information. Currently
available PKMS are just a bit more than productivity tools as <strong>the</strong>re is a necessity for systems
adjustable for KW needs, individual style and working methods (Jefferson 2006).
Within this paper we propose a PKMS development approach taking into account several individuals
influencing factors. This is named personal trinity model (PTM) approach. PTM has three views:
whole-human imitation point <strong>of</strong> view;
individual needs point <strong>of</strong> view;
PKMS development point <strong>of</strong> view.
Whole-human simulation point <strong>of</strong> view <strong>of</strong> PTM is described first. A personal knowledge is not a set <strong>of</strong>
notifications, settings, expressions or actions that are stored for later use. “As organic beings we are
embedded in situations and culturally formed, and we developmentally acquire and extend <strong>the</strong>
learned activities that come to constitute our being in <strong>the</strong> world” (Day 2007). Thus individuals act on
behalf <strong>of</strong> <strong>the</strong>ir experience according to <strong>the</strong>ir surrounding customs and habits. Individual is developing
as an oak which grows out <strong>of</strong> acorn with its encoding (i.e. that it has roots, trunk, branches and
leaves). While growing it is influenced from surrounding environment – sun and amount <strong>of</strong> warmth,
precipitation and seasons. Similarly it is with individuals with <strong>the</strong>ir own initial “encoding” and fur<strong>the</strong>r
development influenced by surrounding environment. Thus <strong>the</strong> PTM can be considered as a symbolic
simulation <strong>of</strong> human entirety in direct and indirect meaning. There are three basic parts: head, hart
and body. Head part relates to human cognitive and exploring aspect. Hart part relates to human
social, emotional and cultural aspect, while body part relates to human physiologic aspect.
Figure 3: Individuals – hunches – ideas
From whole-human simulation point <strong>of</strong> view we will look more closely at <strong>the</strong> head part. Steven
Johnson talks about innovation and new ideas. He states that at <strong>the</strong> beginning <strong>the</strong>re is a hunch
(Johnson 2010) in <strong>the</strong> mind <strong>of</strong> an individual (see Figure 3). There it incubates. A new idea has not
born yet. After a while ano<strong>the</strong>r hunch appears in <strong>the</strong> mind <strong>of</strong> this individual. In <strong>the</strong> process <strong>of</strong> thoughts
exchange two or more hunches create a bases or directly foster <strong>the</strong> creation <strong>of</strong> a new idea (Johnson
2010). An exchange <strong>of</strong> thoughts can take place within individual himself/herself or between several
individuals in <strong>the</strong> process <strong>of</strong> knowledge sharing. A hunch can be located in <strong>the</strong> mind <strong>of</strong> one individual
or <strong>the</strong> same hunch can come up in <strong>the</strong> minds <strong>of</strong> several individuals (see Figure 3).
Based on <strong>the</strong> knowledge substance defined in <strong>the</strong> second chapter we propose five knowledge
acquisition principles to support PKM and KE acquisition.
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1 st principle: a KW has to posses an intuition to come to verity about those KE-s, which in foreseeable
time manner can not be find out or known (example: NP complete problems (Fortnow 2009)) or which
can not be known at all thus leaving <strong>the</strong>m in semi-find-out state (i.e. even <strong>the</strong>y are find out in a hunch
level though <strong>the</strong>y stay as tacit knowledge, which fur<strong>the</strong>r can not be explicated).
2 nd principle: an individual does not need to strive to find out and to pierce each and every
surrounding KE till <strong>the</strong> last detail. They are too many. Instead one has to strive to common
understanding about particular knowledge area to a certain level and to get additional knowledge how
to acquire more detailed knowledge about this area in case it is needed later.
Regarding KE acquisition nowadays a KW is facing an increasing intensity tempo <strong>of</strong> acquiring new
knowledge in particular time frame. Earlier individual acquired all <strong>the</strong> main skills and knowledge for
his/her life during childhood or young adulthood. Once becoming an adult individual usually also
possessed a craftsmanship to be relied upon for entire life. Nowadays <strong>the</strong>re are much more dynamics
<strong>of</strong> economics. Similarly as before much <strong>of</strong> knowledge is acquired in childhood or young adulthood.
However it may turn out to be not good enough in nowadays world. Thus individual might have to be
forced or stimulated to change occupation or to retrain pressed by economics, society or by o<strong>the</strong>r
means. That means to learn substantially new knowledge and skills (i.e. vertical growth) as depicted
in Figure 3 based on proposed personal knowledge cone-spiral.
Figure 3: Personal knowledge cone-spiral
For nowadays individual such additional knowledge and skills acquisition stages or spiral twines are
more than if to compare with people centuries before. In order to be competitive in job market KW has
to acquire a qualitatively new knowledge even in shorter periods <strong>of</strong> time if to compare that with
previous knowledge acquisition cycles or PKCS twines. That is similar to <strong>the</strong> coherence described by
Moor’s law regarding computer development (Intel 2011). This pattern <strong>of</strong> even shorter spiral twines
bears a potential side effect. Such pattern can indicated <strong>the</strong> potential <strong>of</strong> KW burn-out. Thus PKCS not
only describes nowadays <strong>the</strong> common pattern <strong>of</strong> even shorter periods <strong>of</strong> knowledge acquisition, but
also may serve as warning signal built in PKMS for KW to adjust his/her learning intensity in order to
avoid potential burn-out symptoms. Thus this serves as an example that by using PTM in
development process and in execution <strong>of</strong> PKMS a KW has a support to voluntary leverage his/her life
areas.
Regarding PTM heads part <strong>the</strong>re are proposed three more principles related with knowledge sharing
and acquisition.
3 rd principle: in order to successfully acquire new KE-s an individual has to work out a desire to
acquire <strong>the</strong>m and to strive for this knowledge.
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4 th principle: <strong>the</strong>re is a better chance to acquire new KE-s by collaborating (e.g. within communities <strong>of</strong>
practice) ra<strong>the</strong>r than to try it alone. It is easier to maintain ones knowledge level by being within a
group and by collaborating while by being alone knowledge gradually fades unless much more effort
is applied to prevent that.
5 th principle: sharing with tacit knowledge is best accomplished by using analogies and similarities.
Thus it is possible to transfer not only <strong>the</strong> direct meaning <strong>of</strong> knowledge but also <strong>the</strong> spirit <strong>of</strong>
knowledge.
Individual needs point <strong>of</strong> view <strong>of</strong> PTM is described next. The PTM is projected on eight-level Maslow
needs hierarchy described above as depicted in Figure 4. Accordingly individual needs are grouped
into three parts, which correspond to <strong>the</strong> PTM three views.
Figure 4: Personal trinity model from individual needs point <strong>of</strong> view
Usually it is important for a KW to reach self-actualization level needs. In this regard Maslow states
that it is important to strive for specific set <strong>of</strong> fifteen values in order to be happy and to reach selfactualization
(Maslow 1954). However Boeree does not agree with this proposition. He argues that
individual being in such critical situations as war or economic crisis his/her desire to strive for this set
<strong>of</strong> primary values will not exist due to more primary needs as food, warmth and home (Boeree 2006).
By taking into account <strong>the</strong>se arguments we propose that a KW-s should strive for o<strong>the</strong>r super-set <strong>of</strong>
individual improving values encompassing love for o<strong>the</strong>rs and for <strong>the</strong>mselves:
meekness and not being ho<strong>the</strong>aded;
politeness and not being rude;
wishing well and not being envious;
simplicity and not being flatulent;
low-keyed and not being indecent;
unselfish and not being selfish;
not being angry;
forgiveness and not to remember/remind mischief;
joy about truth and not joy about falsehood.
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By taking into account human weaknesses, lust, etc. it is clear that this super-set is a running target
which will never be reached. However it is important for an individual to start to strive for this super-set
<strong>of</strong> values and constantly to try to achieve <strong>the</strong>m. Then that will be <strong>the</strong> real gain for KW from <strong>the</strong>
individual perspective and even more valuable from collaboration with o<strong>the</strong>rs perspective. Especially
that applies to <strong>the</strong> 5 th principle stated above.
From organizations perspective one can also observe an increasing shift toward <strong>the</strong> adoption <strong>of</strong> PKM
approaches within corporations (Rumizen 2001). Caldwell forecasted <strong>the</strong> growth <strong>of</strong> PKM importance
for knowledge workers in organizations (Caldwell 2002). However not many organizations have
followed <strong>the</strong> initiative <strong>of</strong> enabling individuals to support <strong>the</strong>ir PKM needs (Jefferson 2006). There have
been reports that in many cases companies are not fully capable <strong>of</strong> extracting personal knowledge
due to poor PKM practices (TFPL, 2002). In addition organizations have suggested an urgent
necessity for an alignment <strong>of</strong> personal skills (i.e. social perspective <strong>of</strong> PKM) and available
tools/systems (i.e. technological perspective <strong>of</strong> PKM) (TFPL, 2002). We see our proposed PKMS
conception and particularly PTM from individual needs point <strong>of</strong> view as <strong>the</strong> first step towards
alignment <strong>of</strong> social/psychological perspective <strong>of</strong> PKM and technological perspective <strong>of</strong> PKM in order
to support organizational needs, which are heavily influenced by companies culture environment,
knowledge workers skills development and career management.
5. Conclusion
Currently available PKMS are ra<strong>the</strong>r productivity tools than systems as such (Jefferson 2006). There
is a considerable number <strong>of</strong> KM supportive tools available (Barth 2005, Tsui 2002). However <strong>the</strong>y are
not integrated with each o<strong>the</strong>r, and frequently are overlapping. In addition <strong>the</strong>y do not provide support
to KW from <strong>the</strong> social, psychological and technical point <strong>of</strong> views in <strong>the</strong> same time. Also such tools
and systems usually can not be adjusted to individual KW and <strong>the</strong>refore are needed such systems,
which have <strong>the</strong> capability to adapt to individual styles and work methods (Jefferson 2006). Thus to our
knowledge we see that our approach to include all three aspects <strong>of</strong> PKM in development <strong>of</strong> PKMS is
first such an attempt.
So far we have worked on technological aspect <strong>of</strong> PKMS based on agent and mobile technologies.
Within this paper we described <strong>the</strong> social and partially psychological aspects <strong>of</strong> PKMS. The main
contribution <strong>of</strong> this paper is an attempt to incorporate social and psychological aspects <strong>of</strong> PKM into
<strong>the</strong> PKMS, and to stimulate <strong>the</strong> debate in this regard. There are at least two directions for fur<strong>the</strong>r
research. Firstly future work is related with enhancement <strong>of</strong> PKMS development point <strong>of</strong> view <strong>of</strong> PTM.
Thus <strong>the</strong> next step <strong>of</strong> our work is related with connecting all three aspects <strong>of</strong> PKM into a well-rounded
PKMS. Secondly, in general, <strong>the</strong>re is a need for in depth research into direction <strong>of</strong> finding as much as
possible clear-cut connection points between each <strong>of</strong> <strong>the</strong>se three aspects <strong>of</strong> PKM, which fur<strong>the</strong>r on
could serve as guidance for developing and using PKMS.
Acknowledgements
The present activity is funded by <strong>the</strong> project "Support <strong>of</strong> FP7 ICT STREP project “Simulation highway”
proposal development” supported by <strong>European</strong> Regional Development Fund Nr.
2010/0191/2DP/2.1.1.2.0/10/APIA/VIAA/001.
References
Abraham, C. (2007) "The Road to Known Knowns", [online], http://idvux.wordpress.com/2007/10/05/<strong>the</strong>-road-toknown-knowns
(accessed: 11.19.2010)
Apshvalka, D. (2004) “Personal Knowledge Management,” in: Proc. <strong>of</strong> <strong>the</strong> 11th ECITE <strong>European</strong> Conf. on
Information Technology Evaluation, Amsterdam, Ne<strong>the</strong>rlands, pp.17–22
Apshvalka, D., Grundspenkis, J. (2005) “Personal and organizational knowledge management as a driving force
for business process effectiveness,” in: Proc. International <strong>Conference</strong> on Information Technologies for
Business, Vilnius University, Kaunas, pp.9–14
Barth, S. (2000) The Power <strong>of</strong> One, Knowledge Management Magazine, [online],
http://www.quantum3.co.za/KMM Article Dec2000.htm (accessed: 05.30.2009)
Barth, S. (2005) “Self-organization: taking a personal approach to KM,” in: Rao, M. ed., Knowledge management
tools and techniques practitioners and experts evaluate KM solutions, Butterworth-Heinemann, pp.347-361
Boeree, C. G. (2006) “Personality <strong>the</strong>ories - Abraham Maslow”, [online],
http://webspace.ship.edu/cgboer/maslow.html (accessed: 05.19.2010)
Caldwell, F. (2002) “Personal knowledge networks emerge with grassroots KM”, Gartner Research Strategic
Planning Research Note, 25 November
735

Kaspars Osis and Janis Grundspenkis
Chapman, A. (1995) Maslow's hierarchy <strong>of</strong> needs, [online], http://www.businessballs.com/maslow.htm
(accesseds: 04.19.2010)
Cognitive Science Section (2007) “Knowledge Space Theory - Knowledge Domain”, University <strong>of</strong> Graz, [online],
http://css.uni-graz.at/projects/CbKSTCourse/release/ (accessed: 04.19.2010)
Dalkir, K. (2005) Knowledge Management in Theory and Practice, Butterworth-Heinemann, Oxford
Davenport, T.H., Prusak, L. (2000) Working Knowledge: How Organizations Manage What They Know, Harvard
Business School Press, Boston, Massachusetts
Day, R., E. (2007) “Knowing and Indexical Psychology,” in: McInerney, C., R., Day, R., E. eds., Rethinking
Knowledge Management – From knowledge objects to knowledge processes, pp.331-348
DeRose, K. (2005) “What Is Epistemology? A Brief Introduction to <strong>the</strong> Topic”, Yale University, Department <strong>of</strong>
Philosophy, [online], http://pan<strong>the</strong>on.yale.edu/~kd47/What-Is-Epistemology.htm (accessed: 04.19.2010)
Etzel, B., Thomas, P. (1996) Personal Information Management - Tools and Techniques for Achieving
Pr<strong>of</strong>essional Effectiveness, London, Macmillan Business
Fortnow, L. (2009) “The Status <strong>of</strong> <strong>the</strong> P versus NP Problem,” Communications <strong>of</strong> <strong>the</strong> ACM, Vol. 52, No. 9, pp.78-
86
Frappaolo, C. (2004) What is Your Knowledge IQ? Intelligent Portals, [online]
www.intelligentkm.com/feature/08/feat1.jhtml (accessed: 10.21.2008)
Haslam, S., A. (2004) Psychology in Organizations - The Social Identity Approach, 2nd Ed., London, Sage
Herzberg, F., Mausner, B., Snyderman, B. (1959) The motivation to work, New York, John Wiley&Sons
Higgison, S. (2004) “Your say: Personal Knowledge Management”, Inside Knowledge, 7(7), [online],
http://www.ikmagazine.com (accessed: 05.30.2009)
Intel (2011) “Moore's Law,” [online], http://www.intel.com/technology/mooreslaw (accessed: 02.21.2011)
Jefferson, T., L. (2006) Taking it personally: personal knowledge management. The journal <strong>of</strong> information and
knowledge management systems, Vol. 36 No. 1, pp.35-37
Johnson, S. (2010) Where Good Ideas Come From: The Natural History <strong>of</strong> Innovation. Riverhead.
Kirikova, M., Grundspenkis, J. (2000) „Using Knowledge Distribution in Requirements Engineering,” in: Leondes
C. T. ed. Knowledge Based Systems. Techniques and Applications, Vol. 1, Academic Press, San Diego,
USA, pp.149-184
Machlup, F. (1962) Production and Distribution <strong>of</strong> Knowledge in <strong>the</strong> United States, Princeton University Press,
Princeton
Maslow, A., H. (1954) Motivation and Personality, New York, Harper & Bros
McClelland, D., C. (1967) The Achieving society, New York, Free Press
Nonaka, I., Takeuchi, K. (1995) The Knowledge Creating Company: how Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation, Oxford University Press, New York
Osis, K., Grundspenkis, J. (2009) „Perspectives on Usage <strong>of</strong> Agents in Personal Knowledge Management”, in:
Proc. <strong>of</strong> International <strong>Conference</strong> on Knowledge Management (KMIS), Madeira, Portugal, pp.332-337
Osis, K., Grundspenkis, J. (2010) „Agent and Mobile Technologies and Their Usage in Development <strong>of</strong> Learning
Environment Supportive System”, in: Proc. <strong>of</strong> <strong>the</strong> 3rd WSEAS International <strong>Conference</strong> on Visualization,
Imaging and Simulation (VIS), University <strong>of</strong> Algarve, Faro, Portugal, pp.58-63
Osis, K., Grundspenkis, J. (2011) „Modular Personal Knowledge Management System and Mobile Technology
Cross-Platform Solution Towards Learning Environment Support”, in: Proc. <strong>of</strong> Annual International
<strong>Conference</strong> on "Virtual and Augmented Reality in Education” (VARE), Vidzeme University <strong>of</strong> Applied
Sciences, Valmiera, Latvia, pp.114-124
Polanyi, M. (1996) The tacit dimension, Gloucester, MA: Peter Smith
Quintas, P. (1999) Quoted in: Open Eye - Head Back to <strong>the</strong> Business Cafe, The Independent, London, [online],
http://www.independent.co.uk/news/education/education-news/open-eye-head-back-to-<strong>the</strong>-business-cafe-
1068608.html (accessed 05.30.2009)
Rehm, A., J. (2006) UML for Developing Knowledge Management Systems, Auerbach Publications, Boca Raton,
Florida
Rumizen, M., C. (2001) The Complete Idiot’s Guide to Knowledge Management, Alpha Books, New York
TFPL (2002) “New directions for knowledge strategies”, in: Executive report from TFPL’s 5th International CKO
Summit, Dublin, October
Tiwana, A. (2002) The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms,
Prentice hall, Upper Saddle River
Tsui, E. (2002) Technologies for Personal and Peer-to-Peer (P2P) Knowledge Management, in: CSC Leading
Edge Forum, Technical Report, Australia
Wells, H. G. (1938) World brain, Garden City, NY: Doubleday, Doran & Co
Wiig, K. M. (2004) People-Focused Knowledge Management: how Effective Decision Making Leads to Corporate
Success, Butterworth-Heinemann, Oxford
YouKui, W. (2005) Knowledge Element Mining, XiDian University Press, Xi An
Zou, J., Liu, Q. (2009) “A Knowledge Element Model for Knowledge Abstract and Fusion System”, in: Proc. <strong>of</strong>
International <strong>Conference</strong> on New Trends in Information and Service Science, Beijing, China, pp.23-26
736

Research Notes on <strong>the</strong> Practical Deployment <strong>of</strong> Semantic
Knowledge Bases
Taha Osman 1 , Dhavalkumar Thakker 2 and Matt Nathan 3
1
School <strong>of</strong> Science and Technology, Nottingham Trent University, CIB
Building, Nottinbgham, UK
2
School <strong>of</strong> Computing, University <strong>of</strong> Leeds, Leeds
3
Press Association, 16 Castle Boulevard, Pavilion House, Nottingham, UK
taha.osman@ntu.ac.uk
d.thakker@leeds.ac.uk
matt.nathan@pressassociation.com
Abstract: Utilising semantic web technologies in knowledge management systems provides an opportunity for
news/media providers to enrich <strong>the</strong>ir content with information from public datasets such as Linked Data Cloud
and develop intelligent retrieval engines to search/browse <strong>the</strong> content. The semantic web technologies provide
applications with machine-understandable metadata representing relevant knowledge domains, which can be
reasoned by autonomous s<strong>of</strong>tware agents to align <strong>the</strong> discrepancies in knowledge presentation by various
contributing information sources and deliver intelligent query methods against <strong>the</strong> information and <strong>the</strong> underlying
metadata. However, delivering a semantic-based knowledge management system requires <strong>the</strong> development and
integration <strong>of</strong> processes that utilise a number <strong>of</strong> constantly evolving technologies ranging from using natural
language processing for information extraction to ontology management and intelligent inferencing. The expertise
required to develop such complex workflow cannot be provided <strong>of</strong>f-<strong>the</strong>-shelf and is beyond <strong>the</strong> reach <strong>of</strong> most
commercial organisations. The proposed paper reflects on <strong>the</strong> experience <strong>of</strong> developing an intelligent browsing
engine for a commercial media application to propose a methodology for deploying semantic technologies in <strong>the</strong>
construction <strong>of</strong> knowledge management systems. The developed semantic knowledge management system
bootstraps <strong>the</strong> applications’ knowledgebase by leveraging <strong>the</strong> rich amount <strong>of</strong> structured knowledge that is
publicly available in <strong>the</strong> Linked Data Cloud using ontology mapping techniques. The knowledge management
system also incorporates an information extraction system that aids <strong>the</strong> labour-intensive semantic tagging
process by text-mining <strong>the</strong> manually annotated free-text image captions. The paper reports on an interesting and
novel mutual-benefit workflow between <strong>the</strong> information extraction system and <strong>the</strong> knowledgebase. While <strong>the</strong>
knowledgebase plays a crucial role in resolving disambiguation in <strong>the</strong> extracted information, <strong>the</strong> information
extraction system, in addition to known entity recognition, was developed with <strong>the</strong> capacity <strong>of</strong> learning new facts
with a confidence rating mechanism that ei<strong>the</strong>r recommends <strong>the</strong> direct injection <strong>of</strong> new knowledge back into <strong>the</strong>
knowledgebase, or its logging for manual verification.
Keywords: Knowledge management, semantic web, information retrieval, text mining
1. Introduction
Recent years have witnessed a phenomenal increase in <strong>the</strong> amount <strong>of</strong> information available to news
and media organizations both through <strong>the</strong> ease <strong>of</strong> digitising and storing <strong>the</strong>ir produced content and
through readily access to public information sources on <strong>the</strong> Internet. Harnessing this colossal
resource requires knowledge management systems (KMS) (Akscyn, 1998) that can extract, utilize,
and share <strong>the</strong> knowledge embedded in <strong>the</strong>se information sources.
Semantic web technologies have proven to be one <strong>of</strong> <strong>the</strong> promising platforms for developing
knowledge management systems (Warren, 2006). Based on <strong>the</strong> interpretation <strong>of</strong> machineunderstandable
metadata representing knowledge domains, semantic technologies can
autonomously reconcile <strong>the</strong> inconsistencies in knowledge presentation by different information
sources and deliver intelligent query methods against <strong>the</strong> information and <strong>the</strong> underlying metadata
(Davis, 2009). However, delivering <strong>the</strong>se services requires tightly coupled integration <strong>of</strong> a number <strong>of</strong>
constantly evolving technologies ranging from using natural language processing for information
extraction to ontology management and intelligent inferencing. Such expertise cannot be provided <strong>of</strong>f<strong>the</strong>-shelf
and is beyond <strong>the</strong> reach <strong>of</strong> most commercial organisations. This paper contributes towards
<strong>the</strong> efforts <strong>of</strong> sketching a roadmap for utilising Semantic Web technologies in knowledge management
systems.
A distinctive advantage <strong>of</strong> this effort is that it is motivated by <strong>the</strong> practical utilisation <strong>of</strong> semantic
knowledge management technologies in a commercial caption-based image retrieval application
(Thakker, 2009), thus giving us a unique access to <strong>the</strong> application’s essential and secondary
requirements, which we carefully analyse to inform <strong>the</strong> design <strong>of</strong> <strong>the</strong> semantic knowledgebase. Based
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
on <strong>the</strong> analysis <strong>of</strong> <strong>the</strong>se requirements we can, for instance, make a critical decision that proactive
intelligent browsing or faceted search (Suominen, 2007) is a more appropriate method for semantic
retrieval as opposed to explicit query-based search, and we can also strike a balance between
automated bootstrapping <strong>of</strong> our knowledgebase with information from public semantic datasets and
<strong>the</strong> manual verification <strong>of</strong> machine-learned facts for enriching <strong>the</strong> knowledgebase.
The rest <strong>of</strong> <strong>the</strong> paper is organised as follows: section 2 introduces <strong>the</strong> topic <strong>of</strong> semantically driven
knowledge management, followed by explanation <strong>of</strong> <strong>the</strong> case study that motivates this research in
section 3. Section 4 discusses <strong>the</strong> requirement analysis for <strong>the</strong> investigated commercial semantic
retrieval application, while section 5 explores our proposed roadmap to integrating information
extraction, ontology engineering, and knowledge management technologies to address <strong>the</strong>se
requirements. Finally section 6 concludes <strong>the</strong> paper and presents future challenges.
2. Overview <strong>of</strong> semantic knowledge management
The main distinction <strong>of</strong> semantic-based over traditional knowledgebase systems is <strong>the</strong> requirement for
a meta-data layer that provides for machine-level understanding <strong>of</strong> <strong>the</strong> knowledge representation.
This layer, known in <strong>the</strong> Semantic web terminology as Ontology, comprises taxonomy <strong>of</strong> entity types,
<strong>the</strong>ir attributes, and relationships between <strong>the</strong>m, described using an open standard (formalism).
Hence <strong>the</strong> ontology represents a schema that is used to semantically annotate domain entities and
<strong>the</strong>ir interrelations. These annotations represent <strong>the</strong> formal body <strong>of</strong> knowledge about <strong>the</strong> entities
traditionally known as a knowledgebase (KB). The machine-understandable semantic knowledgebase
can be autonomously reasoned by s<strong>of</strong>tware agents in order to map storage and retrieval queries to
<strong>the</strong> annotated information.
The standardisation <strong>of</strong> Semantic web formalism provides for linking to and using knowledge held at
o<strong>the</strong>r information repositories to bootstrap and/or enrich <strong>the</strong> knowledgebase. A prime example <strong>of</strong> <strong>the</strong>
latter is <strong>the</strong> direct links at <strong>the</strong> ontology (schema) and data (information instances) levels between
semantic datasets in <strong>the</strong> Linked Open Data Cloud project (Hausenblas, 2009). These advantages
enthused <strong>the</strong> utilisation <strong>of</strong> semantic technologies for knowledgebase management in a variety <strong>of</strong>
fields. For instance, The SEKT project at British Telecom investigates <strong>the</strong> utilisation <strong>of</strong> semantic
knowlegebases and semantic search for improving <strong>the</strong> end-user experience while browsing scientific
and business articles (Warren, 2005). A similar effort was reported in (Zhou, 2004), where semanticsbased
computer-aided manufacturing knowledge management systems was deployed to support
knowledge-intensive manufacturing processes. Semantic manufacturing knowledge management
provides a new solution for meeting <strong>the</strong> requirements in such environments such as knowledge
accuracy, lower knowledge cost, knowledge timeliness and knowledge unification.
Reviewing semantic-based knowledge management cannot be concluded without drawing attention to
<strong>the</strong> problem <strong>of</strong> semantic annotation. Semantic annotation is a labour-intensive process, <strong>the</strong> cost <strong>of</strong>
which can be especially prohibitive for organisations that regularly add large amounts <strong>of</strong> data to <strong>the</strong>ir
repositories. Hence, information extraction (IE) technologies are <strong>of</strong>ten utilised to automate <strong>the</strong> process
<strong>of</strong> annotating unstructured textual data (documents, image captions, web pages, etc.) with semantic
descriptions (Osman, 2007).
3. Case study: utilising semantic knowledge management in information
retrieval applications
This study is motivated by <strong>the</strong> authors’ involvement in a project aiming to utilise semantic web
technologies to improve <strong>the</strong> image browsing experience for customers <strong>of</strong> PA Images, <strong>the</strong> photography
arm <strong>of</strong> <strong>the</strong> Press Association (PA) - <strong>the</strong> UK’s leading multimedia news and information provider and
supplier <strong>of</strong> business-to-business media services.
The Press Association’s current digital archive holds about 7.5 million images, which is growing by an
average <strong>of</strong> 35.000 new images per week. The images are supplied by <strong>the</strong> company’s own
photographers and subcontracted photo agencies around <strong>the</strong> world. However, currently PA Images’
customers can only interact with <strong>the</strong> images repository via an on-line free-text search <strong>of</strong> manually
annotated image captions which does not deliver <strong>the</strong> level <strong>of</strong> recall and accuracy that does justice to
PA’s rich repository <strong>of</strong> images. Hence, PA Images is investing in <strong>the</strong> utilization <strong>of</strong> semantic web
technologies in a bid to improve <strong>the</strong> image retrieval experience for <strong>the</strong>ir customers. The core
component <strong>of</strong> any semantic-based intelligent retrieval mechanism is <strong>the</strong> semantic knowledgebase,
which is <strong>the</strong> focal point <strong>of</strong> this investigation. The objective <strong>of</strong> <strong>the</strong> documented research in this paper is
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
to deliver a research and development roadmap for constructing semantic knowledgebases for this
class <strong>of</strong> applications.
Below we explore <strong>the</strong> elements <strong>of</strong> business’s (PA Images’) operational dynamics that are relevant to
<strong>the</strong> construction <strong>of</strong> <strong>the</strong> knowledgebase, and subsequently devise a set <strong>of</strong> specifications for building
<strong>the</strong> semantic knowledge management system. The operational dynamics can be summarised as
follows:
A caption-based images search engine represents <strong>the</strong> main outlet <strong>of</strong> <strong>the</strong> business’s products.
Hence, a considerable resource goes into <strong>the</strong> manual annotation <strong>of</strong> images to create free-text
description <strong>of</strong> <strong>the</strong> images. The descriptive metadata is mainly in terms <strong>of</strong> headline and caption,
where headline is generally a short description <strong>of</strong> <strong>the</strong> main event in <strong>the</strong> image and caption is more
elaborate description <strong>of</strong> <strong>the</strong> image with more event information.
A significant sector <strong>of</strong> <strong>the</strong> customer base relies on fast relay <strong>of</strong> images to enable <strong>the</strong>m to report
events soon after <strong>the</strong>y occur. The editorial team has generally few seconds <strong>of</strong> time for annotation.
The image repository is <strong>of</strong> considerable size, currently standing at 7.5 million, and is steadily
increasing by an average <strong>of</strong> 7000 images per day.
PA Images’ customer base are fee-paying clients that are prepared to go <strong>the</strong> extra mile in terms
<strong>of</strong> navigating a more complex user interface to get value for <strong>the</strong>ir investment.
A study commissioned by PA Images analysed <strong>the</strong> user logs over June 2006 to May 2007 (Yang,
2007). The results revealed that though <strong>the</strong>re is an advanced search facility to build complex
retrieval queries, most searches only use simple terms where <strong>the</strong> number <strong>of</strong> results returned
could be very high. An example is searching for “Messi [football player] scores” or “Avatar [movie]
premier”.
By analysing <strong>the</strong> above business’s attributes and restrictions we arrived at <strong>the</strong> following requirements
specification for <strong>the</strong> development <strong>of</strong> <strong>the</strong> semantic system for knowledge management:
The manual annotation performed on <strong>the</strong> images uses controlled vocabulary and is restricted to a
specific domain (e.g. sports or entertainment), thus providing for building fairly accurate
information extraction rules to recognise both entities and events and subsequently semantically
annotate <strong>the</strong>m.
The requirement for fast response time rules out on-line use <strong>of</strong> time-consuming retrieval
technologies such as image processing or face recognition.
As with most media companies, <strong>the</strong> information domain changes rapidly and hence requires
constant update <strong>of</strong> <strong>the</strong> knowledgebase. A stark example can be <strong>the</strong> number <strong>of</strong> new movies
released every week, with <strong>the</strong> accompanying semantic relations to new/existing actors, premiers
in various cities, and later nominations, etc. Hence, <strong>the</strong> task <strong>of</strong> knowledgebase maintenance has
to be managed very carefully and resourced with <strong>the</strong> appropriate human labour and machine
intelligence in order to strike a good balance between cost-effectiveness and business return.
Information extraction system can play a vital role <strong>of</strong> feeding learned knowledge to <strong>the</strong>
knowledgebase system.
For scenarios where simple queries can result in response (recall) overload, an alternative
proactive retrieval strategy is intelligent browsing, i.e. suggesting relevant images to <strong>the</strong> client that
have some semantic relationship to <strong>the</strong> simple query terms. PA Images’ search engine currently
does not facilitate image browsing. The implementation <strong>of</strong> such technology for a media company
such as PA is very challenging taking into account <strong>the</strong> complex information taxonomy and rich
evolving datasets as discussed above.
4. Roadmap to deployment <strong>of</strong> semantic web technologies for building KMS in
commercial applications
Based on <strong>the</strong> above specifications, <strong>the</strong> research team embarked on a project that aims to utilise
semantic web technologies to improve <strong>the</strong> image browsing experience for PA Images’ customers. The
semantic annotation <strong>of</strong> <strong>the</strong> images can significantly improve <strong>the</strong> computer’s understanding <strong>of</strong> <strong>the</strong>
image objects and <strong>the</strong>ir interactions by providing a machine-understandable conceptualisation <strong>of</strong> <strong>the</strong>
various domains that <strong>the</strong> image represents, thus enabling retrieval engines to make more intelligent
decisions about <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> image to a particular user query. Achieving this level <strong>of</strong>
intelligence involves <strong>the</strong> integration <strong>of</strong> a number <strong>of</strong> technologies ranging from text mining to semantic
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
inferencing. In this contribution, we focus on <strong>the</strong> technologies that are directly related to knowledge
management, namely information extraction (IE), ontology engineering, and <strong>the</strong> knowledgebase.
Next we document our experience in exploiting and integrating <strong>the</strong> above technologies to build an
intelligent browsing engine for PA Images, in view to contributing to <strong>the</strong> methodology <strong>of</strong> building
semantic-driven knowledge management frameworks for this class <strong>of</strong> applications.
4.1 Information extraction
Image
captions
GATE-based IE System
Gazetteer (known entities)
JAPE Grammar (context rules)
Disambiguation/Summarisation
Annotated Image
Captions
What to store
What to extract
Confirmation
Entities <strong>of</strong> Interest Learned Facts
PA Knowledgebase
PA Images
Ontology
Schema
Data
PA Images view
Linked
Data Cloud
Figure 1: Semantic Annotation System
It is fundamental to semantic retrieval systems that <strong>the</strong>re is a mechanism to annotate images with
descriptive metadata (entities) representing key concepts and relations <strong>of</strong> <strong>the</strong> investigated application
domain. In our framework as illustrated in Figure 1 below, <strong>the</strong> metadata generation process is
handled by <strong>the</strong> GATE based text mining system (Cunningham, 2002) that takes advantage <strong>of</strong> <strong>the</strong> rich,
domain-specific PA Images’ knowledgebase. PA Images’ knowledgebase consists <strong>of</strong> ontologies
(schema) and data operating on <strong>the</strong> schema.
4.1.1 Entity recognition and disambiguation
The company receives images from a number <strong>of</strong> contracted photographers and photo agencies with
minimal metadata, such as a short description <strong>of</strong> <strong>the</strong> main event in <strong>the</strong> image (headline) and a more
elaborate description <strong>of</strong> <strong>the</strong> image with detailed event information (caption). The text mining system
operates on headline and caption to extract entities <strong>of</strong> interest.
The IE system utilises GATE for text mining and contains three components: gazetteers, JAPE
grammar and a disambiguation module. The gazetteers are <strong>the</strong> list <strong>of</strong> known entities that <strong>the</strong> system
utilises during <strong>the</strong> initialization process. The ontology influences <strong>the</strong> decision on what information
needs to be stored in <strong>the</strong>se gazetteers. The JAPE grammar rules allow detecting additional entities
and at <strong>the</strong> same time confirming <strong>the</strong> entities detected by <strong>the</strong> gazetteers.
The disambiguation module deals with any disambiguation generated by previous components. Here,
<strong>the</strong> disambiguation refers to <strong>the</strong> cases where <strong>the</strong> same piece <strong>of</strong> text is ei<strong>the</strong>r given two class labels
(e.g. “Liverpool” as City and Club) or where two or more entity identifier is assigned to <strong>the</strong> same piece
<strong>of</strong> text (e.g. “Premier League” as “id:Premier_Legue_Darts” and “id: Premier_Legue_Football”). We
made a design decision whereby <strong>the</strong> IE system contains limited knowledge relating to <strong>the</strong> entities as
<strong>the</strong> gazetteers contain only alternate labels and classification <strong>of</strong> <strong>the</strong> entities. Moreover, <strong>the</strong>
knowledgebase also contains relationships between <strong>the</strong>se entities. Therefore, <strong>the</strong> knowledgebase
plays a crucial role in resolving disambiguation as it has more embedded intelligence compared to <strong>the</strong>
IE system.
The information extracted using <strong>the</strong> IE system generally has a confidence rating to indicate <strong>the</strong>
confidence <strong>of</strong> <strong>the</strong> system in <strong>the</strong> process. For example, higher level <strong>of</strong> confidence is assigned to
extracted entities where <strong>the</strong> entities are already known to <strong>the</strong> gazetteers and <strong>the</strong>re is sufficient context
in <strong>the</strong> text to confirm <strong>the</strong> validity <strong>of</strong> <strong>the</strong> extraction. The summarization module filters <strong>the</strong> results for
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
various systems depending on <strong>the</strong> computed level <strong>of</strong> confidence. If <strong>the</strong> extracted information has low
confidence rates, <strong>the</strong>n it is passed for manual auditing if such provision exists, o<strong>the</strong>rwise it is
suppressed.
4.1.2 Enriching <strong>the</strong> knowledge based by learned facts from <strong>the</strong> IE system
The IE system also features a feedback loop where learned facts are passed to <strong>the</strong> knowledgebase.
There are two possible scenarios here. First, <strong>the</strong> learned fact consists <strong>of</strong> reference to an already
known (confirmed) entity. For example, in a scenario where an award ceremony such as <strong>the</strong>
“Academy Awards” is a known entity to <strong>the</strong> knowledgebase and <strong>the</strong> IE system using this detects
previously unknown occurrence <strong>of</strong> <strong>the</strong> award, hence a new fact such as “21st Academy Awards”, <strong>the</strong>n
<strong>the</strong> learned facts will be “21st Academy Awards” and ““21 st Academy Awards is <strong>the</strong> 21st occurrence <strong>of</strong>
<strong>the</strong> Academy Awards ceremony”.
We also cater for <strong>the</strong> o<strong>the</strong>r possibility where a new fact (e.g. 21 st Bafta Awards) is found that consists
<strong>of</strong> a reference to an unknown (unconfirmed) entity (Bafta Awards). In this case, <strong>the</strong> new fact relies on
<strong>the</strong> quality <strong>of</strong> both <strong>the</strong> referenced unconfirmed entity and <strong>the</strong> new fact extraction process. Both <strong>of</strong>
which are tied closely to <strong>the</strong> source (headline and caption) in which <strong>the</strong>y were discovered.
For <strong>the</strong> integrity <strong>of</strong> <strong>the</strong> knowledge base, it is important that <strong>the</strong> confirmations or corrections on <strong>the</strong>
referenced entity should also affect <strong>the</strong> new fact. We accomplish this by using a proxy object that acts
as a mediator between <strong>the</strong> new fact and <strong>the</strong> referenced entity and also maintains <strong>the</strong> link back to <strong>the</strong>
source document. Use <strong>of</strong> such pattern allows us to propagate corrections and confirmations on an
entity through all links to that entity in an efficient manner. Our workflow suggests an additional level
<strong>of</strong> verification for updating <strong>the</strong> knowledgebase whereby a human librarian makes <strong>the</strong> decision <strong>of</strong>
committing learned facts to <strong>the</strong> knowledgebase.
4.2 Ontology engineering
Ontology design is a challenging task that involves analysing <strong>the</strong> application domain to identify key
entities and <strong>the</strong>ir interactions and building a consistent semantic conceptualisation <strong>of</strong> <strong>the</strong> domain that
is <strong>the</strong>n encoded into a formal ontology language.
4.2.1 Knowledge Elicitation
One <strong>of</strong> <strong>the</strong> challenges facing ontology engineers is how to engage non-technical domain experts in
informing <strong>the</strong> ontology design without overwhelming <strong>the</strong>m with <strong>the</strong> underlying technical complexities.
Our approach to domain’s knowledge elicitation is inspired by <strong>the</strong> contributions <strong>of</strong> Li, Yang, and
Ramani (Li, 2009) and can be summarised as follows:
Define scope <strong>of</strong> <strong>the</strong> ontology by identifying <strong>the</strong> main domain areas <strong>the</strong> ontology should cover.
Here competency questions (Gruninger, 1995) can be composed for <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong>
ontology coverage.
Identify <strong>the</strong> basic/main terms used in <strong>the</strong>se areas. If specific terms cannot be agreed, <strong>the</strong>n use
broad terminology and resolve it in consultation with <strong>the</strong> domain experts at later stage. After <strong>the</strong>
introductory meeting(s), as <strong>the</strong> knowledge engineers gain understanding, <strong>the</strong>y can use concept
maps (Cañas, 2004) to communicate with <strong>the</strong> domain experts. Concept maps are visual tools for
engaging domain experts in communicating back ongoing work that hides complexity <strong>of</strong>
semantics.
Identify taxonomy <strong>of</strong> terms by identifying relationship between terms. Expand <strong>the</strong> terminology with
<strong>the</strong>se ontological aspects: relationship between terms (classification, properties), features <strong>of</strong>
terms (properties), example <strong>of</strong> terms (instances).
4.2.2 Ontology Implementation
The developed ontology for PA Images defines entities in <strong>the</strong> news, entertainment and sports
domains, which primarily consist <strong>of</strong> people, places, organisations and events. The ontologies also
contain hierarchical classification and interrelationships between <strong>the</strong>se entities such as footballers,
sport teams, politicians, stadiums, tournaments, actors and award events.
741

Taha Osman, Dhavalkumar Thakker and Matt Nathan
PA Images’ ontology in its current form has a total 147 classes, 60 object properties and 30 object
properties. The OWL species <strong>of</strong> <strong>the</strong> ontology is OWL-DL and <strong>the</strong> DL expressivity is ALCHOIN (D). In
this section, we highlight some <strong>of</strong> <strong>the</strong> design decision we made for designing this ontology.
Apart from <strong>the</strong> standard classification hierarchy and object and data type properties, we utilise what
we see as <strong>the</strong> “smart” properties <strong>of</strong> OWL. One example <strong>of</strong> <strong>the</strong>se properties is <strong>the</strong> inverse property
“owl:inverseOf”, which implicitly allows defining relationship in both directions. For example, an
application based on PA Images’ ontology has a relationship category where fa<strong>the</strong>r-son, parent-child,
husband-wife bi-directional relationship are heavily utilised. The o<strong>the</strong>r property which we find very
useful is <strong>the</strong> value constraint in OWL-DL “owl:hasValue”, which links a restriction class to a value. For
example, “Actor is a person who has value for <strong>the</strong> property pr<strong>of</strong>ession equal to acting”. This is very
useful property as it allows for <strong>the</strong> automatic classification <strong>of</strong> individuals into categories depending on
<strong>the</strong> necessary and sufficient values <strong>of</strong> some <strong>of</strong> <strong>the</strong>ir properties. This is a desirable functionality as
instead <strong>of</strong> relying on <strong>the</strong> annotator to remember category <strong>of</strong> an entity while entering data it could be
automatically inferred based on <strong>the</strong> properties <strong>of</strong> entities. These properties make reasoning
challenging and require a level <strong>of</strong> language expressivity in <strong>the</strong> domains <strong>of</strong> OWL-LITE and OWL-DL.
For example, when inverse properties are used in some <strong>of</strong> <strong>the</strong> reasoning engines, it prohibits <strong>the</strong> use
<strong>of</strong> highly efficient optimization techniques (Weithöner, 2006).
During ontology design, with respect to <strong>the</strong> knowledgebase, it is also important to provide labels for all
<strong>the</strong> elements (classes, properties and instances) <strong>of</strong> ontology. This is useful in terms <strong>of</strong> browsing <strong>the</strong>
knowledgebase as every entity in <strong>the</strong> ontology can be displayed in natural language form. We restrict
ontology elements to one preferred label for <strong>the</strong> convenience <strong>of</strong> programming and interfacing.
In our experience it is also important to make an early decision whe<strong>the</strong>r <strong>the</strong> main semantic retrieval
method relies on explicit search technology or <strong>the</strong> proactive intelligent browsing technology. This
decision can have implications on <strong>the</strong> ontology design as browsing users are generally less intentdriven
than <strong>the</strong> search system users and <strong>the</strong> focus <strong>of</strong> <strong>the</strong> ontology will be different in each case. For
example, it is important for browsing systems’ ontology to expose all <strong>the</strong> probable explicit relations
between entities allowing users more flexibility in making a selection. In contrast, search based
retrieval is intent-driven where <strong>the</strong> searcher generally provides contextual information to register <strong>the</strong>
intent hence search-based system ontology can have implicit relations encoded with higher-order <strong>of</strong>
inferencing to deal with variety <strong>of</strong> user-centric contextual information.
4.3 Building <strong>the</strong> knowledgebase
PA Images’ dataset is <strong>the</strong> data annotated using <strong>the</strong> PA Images’ ontology. The rich and evolving set <strong>of</strong>
entities (people, places, locations, organisations etc) that is necessary for images annotation and
retrieval <strong>of</strong> PA Images’ domains (sport, news, and entertainment) can easily run into millions
(currently <strong>the</strong> number is approximately 5 million). The manual generation <strong>of</strong> such colossal amount <strong>of</strong>
data as part <strong>of</strong> a knowledgebase is a daunting task. In order to alleviate <strong>the</strong> burden <strong>of</strong> <strong>the</strong> manual
compilation <strong>of</strong> such colossal knowledgebase, we leveraged <strong>the</strong> rich amount <strong>of</strong> structured knowledge
publicly available in <strong>the</strong> Linked Data Cloud. The practical adoption <strong>of</strong> <strong>the</strong> semantic web received
boost from <strong>the</strong> emergence <strong>of</strong> Linked Data Cloud concept. Linked Data Cloud refers to <strong>the</strong> data
published on <strong>the</strong> Web in such a way that it is machine-readable, its meaning is explicitly defined, it is
linked to o<strong>the</strong>r external data sets, and can in turn be linked to from external data sets (Bizer, 2009b).
Linked Data Cloud can be considered as medium for domain experts to come toge<strong>the</strong>r and share <strong>the</strong>
knowledge about <strong>the</strong> domains <strong>the</strong>y are expert in by utilising open semantic web standards. The linked
data cloud is a distributed architecture where <strong>the</strong> suppliers <strong>of</strong> data benefit from each o<strong>the</strong>r’s
contributions. Among <strong>the</strong>se datasets is DBpedia (Bizer, 2009a), which is a community effort to extract
structured information from Wikipedia and to make this information accessible on <strong>the</strong> Web. The
resulting DBpedia knowledgebase currently describes over 2.6 million entities.
Organisations such as <strong>the</strong> BBC (Kobilarov, 2009) and New York Times (ZDNet, 2009) utilise Linked
data cloud for enriching <strong>the</strong>ir metadata by integrating with <strong>the</strong> cloud datasets. The BBC Music beta
and New York Times websites utilise DBpedia in its original form. As <strong>the</strong>se websites are mainly
information providing sites compared to our browsing application, <strong>the</strong> main difference in requirements
is that our application requires optimal data quality especially in terms <strong>of</strong> classification and
interrelationships. In terms <strong>of</strong> data quality, we have found following limitation <strong>of</strong> <strong>the</strong> DBpedia
knowledgebase:
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
DBpedia is less formally structured and is governed by number <strong>of</strong> ontologies where retrieving a
particular class <strong>of</strong> an entity requires joining a number <strong>of</strong> ontologies. For example, retrieving a
comprehensive list <strong>of</strong> footballers requires interrogating <strong>the</strong> Yago, DBpedia, and SKOS ontologies.
The data quality is inferior to <strong>the</strong> standard required by commercial retrieval applications, as <strong>the</strong>re
are considerable inconsistencies within DBpedia. For example, some <strong>of</strong> <strong>the</strong> object properties do
not link to o<strong>the</strong>r entities and instead link to temporal templates. Ano<strong>the</strong>r example is <strong>the</strong> incorrect
classification <strong>of</strong> entities. For example, some <strong>of</strong> <strong>the</strong> bands are incorrectly classified as people.
In addition to <strong>the</strong> above shortcomings, <strong>the</strong> design <strong>of</strong> our ontology has to distinctively convey <strong>the</strong>
business’s conceptualisation (view) <strong>of</strong> each domain. As suggested by <strong>the</strong> DBpedia authors in
(Kobilarov 2009), an approach to combine <strong>the</strong> advantages <strong>of</strong> both worlds is to interlink DBpedia with
handcrafted ontologies, which enables applications to use <strong>the</strong> formal knowledge from <strong>the</strong>se
ontologies toge<strong>the</strong>r with <strong>the</strong> instance data from DBpedia.
This introduced <strong>the</strong> challenging problem <strong>of</strong> ontology mapping as PA Images’ ontology has different
set <strong>of</strong> concepts compared to <strong>the</strong> DBpedia ontology, and in some situations <strong>the</strong>re is no one-to-one
mapping between concepts in <strong>the</strong> two ontologies; especially that PA Images’ ontology contains less
number <strong>of</strong> classes than DBpedia. The majority <strong>of</strong> ontology mapping research efforts focus on
automation <strong>of</strong> ontology mapping (Ding, 2005), however we opted for manual mapping owing to <strong>the</strong>
following reasons:
The accuracy required needs to be close to 100%.
As mentioned earlier, <strong>the</strong> coverage <strong>of</strong> data under DBpedia is richer when using multiple
ontologies, which requires mapping to several ontologies and doing so that all <strong>the</strong> possible
mappings are covered.
Current automatic ontology mapping techniques cannot fulfil <strong>the</strong> aforementioned criteria. We have
successfully used SPARQL CONSTRUCT queries to achieve ontology mapping between PA Images’
and DBpedia ontologies and to extract <strong>the</strong> entities from DBpedia KB and generate a clean,
contextualised PA Images’ KB. SPARQL CONSTRUCT is a query form that can be used to specify
partial matching graph from existing dataset to construct ano<strong>the</strong>r graph possibly for ano<strong>the</strong>r dataset.
Table 1 highlights one example <strong>of</strong> mapping using <strong>the</strong> CONSTRUCT query form. The target for <strong>the</strong>
mapping is to construct (hence extract) <strong>the</strong> graph <strong>of</strong> cities from <strong>the</strong> DBpedia dataset. In this case,
<strong>the</strong>re is a direct mapping between <strong>the</strong> PA Images’ ontology and <strong>the</strong> DBpedia with <strong>the</strong> property and
classes <strong>of</strong> <strong>the</strong> ontology different in syntax. In <strong>the</strong> event where <strong>the</strong> direct mapping is not available a
workaround can be applied. For example, according to <strong>the</strong> PA Images’ ontology definition <strong>of</strong> a city,
each city belongs to a country and <strong>the</strong> inverse relationship also holds (e.g. countries have number <strong>of</strong>
cities); however DBpedia ontology has no such properties readily available. Instead, <strong>the</strong> City definition
has subdivisions, such as counties, states, or country. Hence, <strong>the</strong> query in Table 2, while extracting
cities, only maps those subdivisions <strong>of</strong> cities that are countries using a join operation.
Table 1: CONSTRUCT based ontology mapping (direct)
PREFIX dbpedia-ont:
PREFIX db:
PREFIX pa:
PREFIX foaf: http://xmlns.com/foaf/0.1/
prefix rdf:
CONSTRUCT
{
?newLoc rdf:type pa:City .
?newLoc pa:locationName ?name .
?newLoc pa:latitutedegrees ?lat
}a
WHERE
{
?newLoc rdf:type dbpedia-ont:City .
?newLoc foaf:name ?name .
?newLoc dbpedia-ont:latitutedegrees ?lat
}
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Taha Osman, Dhavalkumar Thakker and Matt Nathan
Table 2: CONSTRUCT based ontology mapping (indirect)
PREFIX dbpedia-ont:
PREFIX db:
PREFIX pa:
PREFIX foaf: http://xmlns.com/foaf/0.1/
prefix rdf:
CONSTRUCT
{
?newLoc rdf:type pa:City.
?newLoc pa:cityOfCountry ?country .
?newLoc pa:locationName ?name .
?country pa:hasCity ?newLoc
}
WHERE
{
?newLoc rdf:type dbpedia-ont:City .
?newLoc db-prop:subdivisionName ?country .
?country a dbpedia-ont:Country .
?newLoc foaf:name ?name
}
5. conclusions and future challenges
This paper proposes a methodology for deploying semantic knowledgebases to aid information
retrieval in commercial media/news applications with stringent query-response time.
Based on utilisation in a commercial project, our study gives unique insights into <strong>the</strong> requirements that
need to be taken into consideration when integrating <strong>the</strong> technologies necessary for building a
semantic knowledgebase that can deliver <strong>the</strong> appropriate quality <strong>of</strong> service. By adopting our roadmap
for <strong>the</strong> methodical analysis <strong>of</strong> relevant application requirements against <strong>the</strong> effectiveness <strong>of</strong> semantic
retrieval tools, R&D teams <strong>of</strong> media/news applications should be able to compose elaborate
development plan comprising <strong>the</strong> use <strong>of</strong> in-time content-based image retrieval, <strong>the</strong> exploitation <strong>of</strong> textmining
technology to aid <strong>the</strong> labour-intensive semantic-annotation process, automatic enrichment <strong>of</strong>
<strong>the</strong> application knowledgebase from public datasets, and optimising <strong>the</strong> Ontology design and
selection <strong>of</strong> its reasoning expressiveness’ for <strong>the</strong> intelligent browsing retrieval method.
As intelligent browsing <strong>of</strong>fers <strong>the</strong> navigation <strong>of</strong> a potentially large and intertwined semantic space, a
forthcoming challenge would be <strong>the</strong> dynamic computation <strong>of</strong> <strong>the</strong> necessary constraints for retrieval
and display <strong>of</strong> relevant information to <strong>the</strong> user query from response-time and visualisation points <strong>of</strong>
view. Equally important is <strong>the</strong> investigation <strong>of</strong> cost-effective update <strong>of</strong> <strong>the</strong> information injected into <strong>the</strong>
knowledgebase form dynamically evolving datasets such DBpedia, while maintaining <strong>the</strong> quality <strong>of</strong> <strong>the</strong>
data held by <strong>the</strong> knowledgebase from an end-user perspective.
Acknowledgements
We would like to thank <strong>the</strong> East midlands Development Agency and <strong>the</strong> Technology Strategy Board in
<strong>the</strong> UK for partially funding this work through a Knowledge Transfer Partnership.
References
Akscyn, R., McCracken, D., and Yoder, E., 1988, KMS: A distributed hypermedia system for managing
knowledge in organizations, Communications <strong>of</strong> <strong>the</strong> ACM 31 (7), pp 820-835.
Bizer, B., et al, 2009a, DBpedia – A Crystallization Point for <strong>the</strong> Web <strong>of</strong> Data, Journal <strong>of</strong> Web Semantics:
Science, Services and Agents on <strong>the</strong> World Wide Web, Issue 7, Pages 154–165, 2009.
Bizer, C., Heath, T., and Berners-Lee, T., 2009b, Linked Data - The Story So Far. International Journal on
Semantic Web and Information Systems, Vol. 5(3), pp 1-22.
Cunningham, H., 2002, GATE, a General Architecture for Text Engineering, The <strong>of</strong>ficial Journal <strong>of</strong> Computers
and <strong>the</strong> Humanities, Springer, Volume 36, Number 2 / May-2002, pp 223-254.
Cañas, J. et al , 2004, CmapTools: A Knowledge Modeling and Sharing Environment, In: Concept Maps: Theory,
Methodology, Technology, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> First International <strong>Conference</strong> on Concept Mapping,
Pamplona, Spain, pp 125-133.
Davies, J., Grobelnik, M., Mladenic, D., 2009, Semantic Knowledge Management: Integrating Ontology
Management, Knowledge Discovery, and Human Language Technologies, Springer, Jan 2009. ISBN-10:
3540888446
744

Taha Osman, Dhavalkumar Thakker and Matt Nathan
Ding, Z., Peng, Y., Pan, R., Yu, Y., 2005, A Bayesian Methodology towards Automatic Ontology Mapping, In
proceedings <strong>of</strong> <strong>the</strong> AAAI-05 C&O Workshop on Contexts and Ontologies: Theory, Practice and
Applications. University <strong>of</strong> Maryland Baltimore County.
Hausenblas, M., 2009, Exploiting Linked Data to Build Web Applications. IEEE Internet Computing, vol. 13, no.
4, pp 68-73.
Gruninger, M., and Fox, M.S., 1995, Methodology for <strong>the</strong> Design and Evaluation <strong>of</strong> Ontologies, Workshop on
Basic Ontological Issues in Knowledge Sharing, IJCAI-95, Montreal.
Kobilarov, G, et al, 2009, Media Meets Semantic Web - How <strong>the</strong> BBC Uses DBpedia and Linked Data to Make
Connections, <strong>European</strong> Semantic Web <strong>Conference</strong>, Semantic Web in Use Track, Crete, June 2009.
Li, Z., Yang, M., and Ramani, K., 2009, A Methodology for Engineering Ontology Acquisition and Validation, Artif.
Intell. Eng. Des. Anal. Manuf. 23, 1 (Feb. 2009), pp 37-51.
Osman, T., Thakker, D., Schaefer, G., and Lakin, P., 2007, An Integrative Semantic Framework for Image
Annotation and Retrieval, The 2007 IEEE/WIC/ACM International <strong>Conference</strong> on Web Intelligence, Nov
2007 in Silicon Valley, USA, pp 366-373
Suominen, O., Viljanen, K., HyvÄnen, E., 2007, User-Centric Faceted Search for Semantic Portals, Lecture
Notes in Computer Science, Book - The Semantic Web: Research and Application, Volume 4519/2007, pp.
356-370.
Thakker, D., Osman, T., Gohil, S., Lakin, P., 2009, A Pragmatic Approach to Semantic Repositories
Benchmarking, to appear in <strong>the</strong> proceedings <strong>of</strong> <strong>the</strong> 7th Extended Semantic Web <strong>Conference</strong> – ESWC 2010.
Warren, P., 2006, Knowledge Management and <strong>the</strong> Semantic Web: From Scenario to Technology. IEEE
Intelligent Systems, Vol.2l, pp 53-59.
Warren, P., lsmeyer, D. (2005) "Applying semantic technology to a digital library: a case study", Library
Management, Vol. 26 Iss: 4/5, pp.196 - 205
Weithöner, T., Liebig, T., Lu<strong>the</strong>r, M., and Böhm, S., 2006, What's Wrong with OWL Benchmarks?,<strong>Proceedings</strong> <strong>of</strong>
<strong>the</strong> Second International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2006),
A<strong>the</strong>ns, GA, USA November 2006.
Yang, D., 2007, What do people search for in PA Photos? MSc Dissertation, University <strong>of</strong> Sheffield.
Zhou, J. & Dieng-Kuntz, J (2004). A Semantic Knowledge Management System for Knowledge-Intensive
Manufacturing. In <strong>Proceedings</strong> <strong>of</strong> IADIS International e-Commerce 2004 <strong>Conference</strong>, Lisbon, Portugal,
14th-16th December 2004.
ZDNet, 2009, http://www.zdnet.com/blog/semantic-web/new-york-times-embraces-linked-data/309.
745

Getting Ready for Knowledge Management: A UK Local
Government Case Study
Paul Parboteeah 1 , Thomas Jackson 1 and Ge<strong>of</strong>f Smith 2
1
Department <strong>of</strong> Information Science, Loughborough University,
Loughborough, UK
2
School <strong>of</strong> Computing, Engineering and Information Sciences, Northumbria
University, Newcastle, UK
p.parboteeah@lboro.ac.uk
t.w.jackson@lboro.ac.uk
ge<strong>of</strong>f.smith@northumbria.ac.uk
Abstract: Knowledge management in local government organisations presents two challenges not found in forpr<strong>of</strong>it
organisations: a focus on efficiency savings and a wide ranging service provision, unlike anything found in
<strong>the</strong> private sector. For instance, a pharmaceutical organisation might be focused purely on drug development,
whereas a local government organisation has services ranging from <strong>the</strong> provision <strong>of</strong> leisure facilities, to social
care responsibilities and education requirements. Coupled with <strong>the</strong> fact that local government tend to be less
agile than o<strong>the</strong>r organisations this presents a substantial challenge to knowledge management initiatives. This
paper presents an initial scoping <strong>of</strong> a case study <strong>of</strong> a local government organisation in <strong>the</strong> UK and <strong>the</strong>ir efforts to
prepare to become a knowing council. From <strong>the</strong> initial assessment <strong>of</strong> <strong>the</strong> council, this paper presents an initial
assessment <strong>of</strong> <strong>the</strong> current culture, <strong>the</strong> current information management strategies and makes recommendations
for conducting a full case study analysis, necessary before a wide scale adoption <strong>of</strong> KM.
Using observation and document analysis, this research has found that <strong>the</strong> council has a clear role based culture
with evidence <strong>of</strong> ‘empire building’. This is reinforced by <strong>the</strong> current technology architecture, although projects are
underway to share information more consistently across <strong>the</strong> whole council. The current information management
strategies are clearly based on an extended records management philosophy, a fact reflected by <strong>the</strong> heavy use
<strong>of</strong> <strong>the</strong> electronic records and document management system. The recommendations proposed by this research
is a novel approach to bring <strong>the</strong> council into <strong>the</strong> knowledge era and focuses on <strong>the</strong> creation <strong>of</strong> a knowledge
sharing culture, enabled by <strong>the</strong> employees. The end goal <strong>of</strong> this research, whilst outside <strong>the</strong> scope <strong>of</strong> this
research is <strong>the</strong> creation <strong>of</strong> a knowing council that effectively shares knowledge to support innovation and
efficiency savings.
Keywords: case study, knowing council, information management, local government, knowledge management
1. Introduction
Knowledge management (KM) has always existed in <strong>the</strong> public sector, whe<strong>the</strong>r intentionally or
unintentionally, and is for <strong>the</strong> most part inseparable from <strong>the</strong> strategy, planning, consultation and
implementation <strong>of</strong> public services (Riege and Lindsay, 2006). Fur<strong>the</strong>rmore, <strong>the</strong> responsibility <strong>of</strong>
delivering public services emphasise <strong>the</strong> value KM can bring to both central and local government
(Wiig, 2002). KM in <strong>the</strong> public sector tends to focus on “doing more with less” (McAdam and Reid,
2000), but with <strong>the</strong> pressure and attention from <strong>the</strong> general public (Riege and Lindsay, 2006). The
generally very structured culture <strong>of</strong> local government organisations also presents a problem in that it
is difficult to translate KM visions and mission statements into actionable plans with clear results
(Girard and McIntyre, 2010). The aim <strong>of</strong> this paper is to present and analyse a case study <strong>of</strong> one local
government organisation’s journey to get ready for KM initiatives and strategies. This paper seeks to
make a contribution to <strong>the</strong> literature on public sector knowledge management. In order to identify <strong>the</strong>
<strong>the</strong>oretical context within which this research sits, this paper first presents a review <strong>of</strong> <strong>the</strong> literature on
KM in <strong>the</strong> public sector, with a focus on introducing KM strategies. This is followed by an overview <strong>of</strong>
<strong>the</strong> research methodology before presenting and evaluating <strong>the</strong> findings obtained. This paper
concludes by presenting a framework <strong>of</strong> findings that tie toge<strong>the</strong>r <strong>the</strong> challenges KM presents to local
government with <strong>the</strong> unique characteristics <strong>of</strong> <strong>the</strong> public sector and potential ways <strong>of</strong> successfully
taking forward knowledge management initiatives.
2. Knowledge management in <strong>the</strong> public sector
With increasing numbers <strong>of</strong> public sector organisations adopting knowledge management practices
(Riege and Lindsay, 2006), and with a mature KM domain in <strong>the</strong> private sector <strong>the</strong>re is little reason for
public sector organisations to remain with just information management strategies. The public sector
would appear to have recognised that KM is a powerful enabler in <strong>the</strong> current drive for increased
746

Paul Parboteeah, Thomas Jackson and Ge<strong>of</strong>f Smith
efficiency in all areas (McAdam and Reid, 2000). However, with an increasing stock <strong>of</strong> research, case
studies and best practice lessons on knowledge management, late adopters <strong>of</strong> KM are in an excellent
position to try and avoid <strong>the</strong> pitfalls more commonly associated with KM. This review goes onto
critically review <strong>the</strong> key areas <strong>of</strong> KM, with regard to local government priorities.
Government organisations are typically large and cumbersome, slow to adapt to changes in
technology and it has become difficult for <strong>the</strong>m to change at <strong>the</strong> speed citizens <strong>of</strong>ten demand
(Sinclair, 2006). This is coupled with problems such as contending with silos <strong>of</strong> information (Riege and
Lindsay, 2006), <strong>the</strong> knowledge is power paradigm (Syed-Ikhsan and Rowland, 2004) and an overreliance
on <strong>the</strong> clichés <strong>of</strong> knowledge management like ‘we don’t know what we know’ as opposed to
actual business needs (Skok and Kalmanovitch, 2005). The bureaucratic, role based nature (Handy,
1985) <strong>of</strong> local government organisations has even lead to <strong>the</strong> introduction <strong>of</strong> stealth knowledge
management (Sinclair, 2006) as a means <strong>of</strong> introducing knowledge management quietly under <strong>the</strong>
guise <strong>of</strong> just solving business problems. Indeed, most KM initiatives in local government tends to be
driven by departments focussed on change management, improving service quality or better resource
utilisation (Peel and Rowley, 2010). The diffuse nature <strong>of</strong> local government also makes effecting
culture change difficult, with numerous outposts, mobile workers and <strong>the</strong> unique position <strong>of</strong> having
elected councillors almost acting as resistors to change.
However, despite numerous culture barriers to change, <strong>the</strong>re exists one significant driver for a KM
supporting culture: <strong>the</strong> need to multi-agency information sharing (Peel and Rowley, 2010). Employees
tend associate information or knowledge with power (Riege, 2005) and a requirement, which may be
legislative (e.g. Children Act, 2004), to share information both inside and outside <strong>the</strong> organisation
represents a major culture change. KM however, should also be linked to employee advancement
(McAdam and Reid, 2000); else <strong>the</strong> personal commitment from employees will just not be present.
2.1 The Embodiment <strong>of</strong> Knowledge in Local Government
The comparison <strong>of</strong> public and private sector knowledge management by McAdam and Reid (2000)
found that in local government staff were considered <strong>the</strong> main knowledge depository, as opposed to
an amalgamation <strong>of</strong> IT systems in <strong>the</strong> private sector. This finding is perhaps confirmation <strong>of</strong> <strong>the</strong>
knowledge is power paradigm and, recognition that knowledge is socially constructed, with <strong>the</strong> main
knowledge capture technique being informal discussions (Nonaka and Takeuchi, 1995). This finding
also correlates with Gorry (2008) who found that people based approaches to knowledge
management were preferred in <strong>the</strong> public sector and that social workers in particular were keen on
creating knowledge sharing communities. This correlates with findings from Skok and Kalmanovitch
(2005) who found that intranets could only effectively manage cognitivistic knowledge, or knowledge
that was considered explicit, objective an easily stored, and that it was autopoietic knowledge which
was entirely tacit, could only exist in <strong>the</strong> minds <strong>of</strong> people and could only be shared through informal
discussions.
2.2 Local Government Regulatory Environment
Operating in <strong>the</strong> public sector, KM is constrained by a complex legal framework (Peel and Rowley,
2010), and more so than <strong>the</strong> private sector. Whilst <strong>the</strong> Data Protection Act (1998) applies to all
organisations, regulations such as <strong>the</strong> Freedom <strong>of</strong> Information Act (2000), Environmental Information
Regulations (2004) and <strong>the</strong> Regulation <strong>of</strong> Investigatory Powers Act (2000) apply to just public sector
organisations. Local government are also subject to new legislation which requires <strong>the</strong>m to publish a
wide range <strong>of</strong> data sets which would never be revealed in private sector organisations. The wide
range <strong>of</strong> services provided by councils in <strong>the</strong> UK also impacts KM because authorities are required to
keep a wide range <strong>of</strong> information for varying lengths <strong>of</strong> time, and without any additional processing or
data mining. The implications being that public sector organisations are unable to learn effectively
about its citizens, and how to best serve <strong>the</strong>m, because <strong>the</strong>y are constrained by various regulations,
whereas most private sector organisations only need to comply with <strong>the</strong> Data Protection Act (1998).
3. Methodology
With <strong>the</strong> aim <strong>of</strong> presenting a scoping study to assist a more detailed case study and analysis to assist
local government organisations prepare for KM initiatives, this research follows an interpretivist, as
opposed to a positivist, research philosophy. This is because <strong>the</strong> act <strong>of</strong> observation, interpretation and
analysis is a subjective activity that cannot be objectified. This research is exploratory in nature and
uses a combination <strong>of</strong> document analysis, observation and informal interviews to understand <strong>the</strong> KM
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Paul Parboteeah, Thomas Jackson and Ge<strong>of</strong>f Smith
landscape at a UK local government organisation. The documents selected for analysis were <strong>the</strong>
information management strategy, various policies such as email use, classification schemes,
disposal guidelines, metadata standards and records management policies. The information
management programme board meetings were also used as well as documents from projects such as
<strong>the</strong> Micros<strong>of</strong>t® SharePoint® implementation and <strong>the</strong> customer data integration project. Various
practices were observed at <strong>the</strong> council, ranging from <strong>the</strong> use <strong>of</strong> <strong>the</strong> electronic data and records
management system (EDRMS) and <strong>the</strong> associated index card and file plan, <strong>the</strong> use <strong>of</strong> network hard
drives for individual teams and <strong>the</strong> resulting silos <strong>of</strong> information, <strong>the</strong> use <strong>of</strong> workflows to facilitate <strong>the</strong>
efficient transfer <strong>of</strong> information through <strong>the</strong> council and, finally, <strong>the</strong> use <strong>of</strong> information audits to ensure
data quality across <strong>the</strong> council. Informal interviews were conducted with members <strong>of</strong> <strong>the</strong> information
management team. They were very much respondent led, supporting <strong>the</strong> notion that this was just a
‘scoping study’ designed to articulate a framework to guide a more detailed assessment <strong>of</strong> <strong>the</strong> KM
landscape.
3.1 Justification for Choice <strong>of</strong> Organisation
The UK local government organisation chosen for this research employs approximately 15,000
people, achieved unitary status in 1997 and is responsible for providing services to approximately
300,000 residents. The local government organisation chosen had already identified a number <strong>of</strong>
information management problems and was making <strong>the</strong> very initial steps towards implementing KM
strategies. A number <strong>of</strong> internally commissioned reports identified that <strong>the</strong> council had 54 terabytes <strong>of</strong>
unstructured data, unconnected silos <strong>of</strong> information and an over-reliance on paper. The council
suffers from poor data quality with numerous missing or incomplete data, but with high levels <strong>of</strong>
duplication. Massive duplication and with <strong>the</strong> over reliance on paper has resulted in issues with <strong>of</strong>fice
and storage space. There have been several information breaches, resulting in <strong>the</strong> council signing an
undertaking with <strong>the</strong> Information Commissioner’s Office to serve as a commitment to take steps to
preventing data law breaches. A report by an outside organisation labelled <strong>the</strong> council as ‘data rich
but information poor’ (Audata, 2005). The council is also exposing itself to significant risk as long
serving members <strong>of</strong> staff leave <strong>the</strong> organisation, with little effort to capture <strong>the</strong> knowledge/wisdom
<strong>the</strong>y hold.
The new age <strong>of</strong> austerity in local government is a major driver for change, as <strong>the</strong> council looks to cut
costs through efficiency savings. For instance, <strong>the</strong> accommodation strategy that aims to reduce <strong>the</strong>
amount <strong>of</strong> <strong>of</strong>fice space <strong>the</strong> council uses, as well as encouraging hot-desking and mobile working. The
accommodation strategy in particular will increase <strong>the</strong> amount <strong>of</strong> electronic information <strong>the</strong> council will
need to hold as <strong>the</strong> focus will fall on removing paper and filing cabinets. The increasing pressure to
work with external partners (such as <strong>the</strong> police, local health authority and charities) will also force new
ways <strong>of</strong> working because it becomes impractical for increasing amounts <strong>of</strong> paper records to be
shared, and if personal records are being shared, a significant security risk exists. With issues like
<strong>the</strong>se only starting to be addressed and with <strong>the</strong> adoption <strong>of</strong> technologies such as Micros<strong>of</strong>t®
Sharepoint®, an EDRMS and customer data integration, <strong>the</strong> council is ideally placed for a concerted
effort to adopt KM. Ideally placed because <strong>the</strong> adoption <strong>of</strong> technologies identified provides impetus to
address <strong>the</strong> current information management problems, which in turn lays <strong>the</strong> groundwork for future
KM initiatives.
4. Findings and Analysis
The first finding, which perhaps comes as no surprise, is <strong>the</strong> lack <strong>of</strong> a KM strategy or even vision. The
current information management strategy only talks in brief, vague terms about information sharing
and learning from past experience, with <strong>the</strong> implication. The lack <strong>of</strong> awareness, or adoption, <strong>of</strong> KM in
<strong>the</strong> council is indicative <strong>of</strong> <strong>the</strong> late take up <strong>of</strong> knowledge management by <strong>the</strong> public sector (Riege and
Lindsay, 2006). The reference to <strong>the</strong> ‘s<strong>of</strong>ter’ aspects <strong>of</strong> KM: organisational learning and information
sharing, does however support <strong>the</strong> favourable view that councils should, and need to focus on <strong>the</strong>
human side <strong>of</strong> KM (McAdam and Reid, 2000). The lack <strong>of</strong> any substantive KM in <strong>the</strong> council could
also be attributed to <strong>the</strong> problem highlighted by Girard and McIntyre (2010) that organisations find it
difficult to effectively implement KM visions, due to <strong>the</strong>ir abstract high-level nature. A member <strong>of</strong> <strong>the</strong>
IM team also commented on <strong>the</strong> lack <strong>of</strong> focus on KM, or information management, by <strong>the</strong> council;
explaining that <strong>the</strong> bulk <strong>of</strong> <strong>the</strong>ir work was in fact largely records management. Figure 1, taken from <strong>the</strong>
latest information management strategy, confirms this, with <strong>the</strong> first half <strong>of</strong> <strong>the</strong> graph showing
records/data management activities. The figure is significant because it highlights just three IM
initiatives: collaboration tools, digital mailroom and <strong>the</strong> one council, one contact initiative. In light <strong>of</strong>
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Paul Parboteeah, Thomas Jackson and Ge<strong>of</strong>f Smith
this, it might even be argued that <strong>the</strong> council is still only starting to realise <strong>the</strong> full potential <strong>of</strong>
information management. One criticism, which does not help develop <strong>the</strong> credibility <strong>of</strong> KM, is that <strong>the</strong>
cost, direct benefits and potential benefits lines are not based on any cost-benefit analysis, but just for
illustrative purposes.
The council is also in <strong>the</strong> process <strong>of</strong> obtaining Micros<strong>of</strong>t SharePoint, and this is instigating a number
<strong>of</strong> projects that are undoubtedly KM initiatives. The introduction <strong>of</strong> blogs, wikis, enterprise search and
a frequently asked questions facility support <strong>the</strong> people based KM strategy. The introduction <strong>of</strong>
SharePoint also brings with it <strong>the</strong> potential to change working patterns in <strong>the</strong> council. On a
fundamental level, SharePoint will enable easier collaboration, both internally and externally, <strong>the</strong>
ability to version control documents and <strong>the</strong> ability <strong>of</strong> create records. However, looking more widely,
<strong>the</strong> introduction <strong>of</strong> SharePoint should trigger new ways <strong>of</strong> working in <strong>the</strong> council, with <strong>the</strong> aim to
enabling more <strong>of</strong> <strong>the</strong> workforce to become knowledge workers. Several members <strong>of</strong> <strong>the</strong> IM team
commented that <strong>the</strong> SharePoint implementation will force a big culture change on staff, with more <strong>of</strong> a
focus on delivering work, irrespective <strong>of</strong> a person’s physical location. Off site working, using a central
repository for documents and encouraging internal information sharing are all challenges created by
<strong>the</strong> current culture at <strong>the</strong> council. The role based culture which is so prevalent (Handy, 1985) will be
challenged as new technological tools and ways <strong>of</strong> working become live in <strong>the</strong> near future.
One area in which <strong>the</strong> information management team is excelling is <strong>the</strong> introduction <strong>of</strong> a content
model, which is designed to control and govern all organisational content, with <strong>the</strong> ultimate aim <strong>of</strong>
mitigating <strong>the</strong> risk <strong>of</strong> information explosion. Content models aid <strong>the</strong> understanding <strong>of</strong> organisation
content (Päivarinta and Munkvold, 2005) and are best described as a “wrapper”. The content model
developed by <strong>the</strong> IM team has 5 parts: user pr<strong>of</strong>iles, metadata, security, policies and taxonomy. The
user pr<strong>of</strong>ile aspect details which roles are permitted to access what content, <strong>the</strong> metadata (based on
<strong>the</strong> central government standard) details <strong>the</strong> metadata elements that must associated with each piece
<strong>of</strong> content, <strong>the</strong> security aspect details <strong>the</strong> protection that will be applied to each bit <strong>of</strong> content (based
on <strong>the</strong> National Protective Marking Scheme, policies refers to <strong>the</strong> retention and disposal schedule for
content and taxonomy refers to <strong>the</strong> filing scheme against which all content will be saved. Content
models are essential for <strong>the</strong> successful operation <strong>of</strong> an enterprise content management system
(Päivarinta and Munkvold, 2005) because <strong>the</strong>y provide a common foundation/understanding for al
content. In light <strong>of</strong> adopting KM initiatives, <strong>the</strong> content model would benefit from being updated to take
account <strong>of</strong> Web 2.0 technologies, such as social tagging and rating.
One finding is particularly concerning: <strong>the</strong> lack <strong>of</strong> general information management skills across all
council staff. This is a concern because <strong>the</strong> lack <strong>of</strong> training, and adoption <strong>of</strong>, IM could indicate a
similar fate for KM initiatives. This again links back to being a cultural issue because most <strong>of</strong>ten in <strong>the</strong>
council, things are done <strong>the</strong> way <strong>the</strong>y have always been, irrespective <strong>of</strong> new technology. There have
been several severe data losses at <strong>the</strong> council, owing to poor IM skills <strong>of</strong> both staff and managers.
One recent case involved <strong>the</strong> loss <strong>of</strong> an unencrypted memory stick containing a selection <strong>of</strong> health
and social care records. The memory stick was being used to back up <strong>the</strong> data. This was a serious
security breach which cost a substantial sum to rectify, but more importantly, <strong>the</strong> council had already
signed an ICO undertaking, so <strong>the</strong> latest incident is likely to result in a financial penalty.
The commitment to KM by senior management and <strong>the</strong> executive board could also be improved. The
council does not have a Chief Knowledge Officer, or even Chief Information Officer, representation at
<strong>the</strong> board level is through <strong>the</strong> Chief Operating Officer. This also couples with comments that <strong>the</strong>
information management team is located in <strong>the</strong> wrong part <strong>of</strong> <strong>the</strong> council’s structure. The information
management team currently reports into <strong>the</strong> Head <strong>of</strong> ICT for Enterprise Services. When considering
that <strong>the</strong> council has 4 Heads <strong>of</strong> ICT, one for each major strategic area, who all report into <strong>the</strong> Head <strong>of</strong>
IT and Support, it becomes obvious that <strong>the</strong> team is not best placed to instigate a lot <strong>of</strong> KM initiatives
it might need to consider. According to <strong>the</strong> current Head <strong>of</strong> Information Management, <strong>the</strong> team would
be best placed as part <strong>of</strong> <strong>the</strong> change management agenda. This, however, is impacted by <strong>the</strong> size <strong>of</strong>
<strong>the</strong> current information management team, which has 6 employees. This could be extended to 10 or
12 if information governance and information security is considered part <strong>of</strong> IM, even though those
additions do not report into <strong>the</strong> Head <strong>of</strong> Information Management. This situation <strong>of</strong> course supports
<strong>the</strong> need for a stealth approach to knowledge management (Sinclair, 2006) but this <strong>the</strong>n ignores <strong>the</strong>
extra burden on <strong>the</strong> information management team to deliver projects and results whilst hiding <strong>the</strong>
approach in business processes.
749

Figure 1 Information Management Strategy
Paul Parboteeah, Thomas Jackson and Ge<strong>of</strong>f Smith
5. Conclusions and recommendations
This initial scoping study <strong>of</strong> one UK council’s journey <strong>of</strong> getting ready for KM has produced a number
<strong>of</strong> areas <strong>of</strong> concern that enable a more detailed analysis to be performed with focus. The 4 areas <strong>the</strong>
detailed case study should centre around are: executive support/representation, cultural change
issues, appropriate use <strong>of</strong> technology and information management skills training. The direct focus
should not be on KM, despite being a precursor to KM, because <strong>the</strong> focus should be on adapting KM
approaches to <strong>the</strong> existing culture, as opposed to trying to change <strong>the</strong> organisational culture
(McDermott and O’Dell, 2001). This should also be <strong>the</strong> aim <strong>of</strong> <strong>the</strong> KM initiative at <strong>the</strong> council.
However, <strong>the</strong>re will be some degree <strong>of</strong> cultural change occurring, as a result <strong>of</strong> moving to new,
smaller <strong>of</strong>fices, encouraging mobile working, adopting new collaboration tools and substantial cuts to
funding.
This paper presented an initial scoping study to assess <strong>the</strong> current information management
landscape at <strong>the</strong> UK local government council and to outline <strong>the</strong> key areas that need to be examined
and analysed in detail. This scoping study presented an overview <strong>of</strong> <strong>the</strong> current research in KM in
local government and found that <strong>the</strong>oretically, public sector organisations are very well suited to KM,
but that <strong>the</strong>y are generally behind <strong>the</strong> curve when it comes to adoption. The current research also
showed that in <strong>the</strong> public sector <strong>the</strong> focus should very much be on s<strong>of</strong>t KM, or people based KM, with
technology in a purely supportive role. Coincidently, this also supports <strong>the</strong> notion that in <strong>the</strong> public
sector, tacit knowledge is more favoured and valuable. This paper moved onto present an introduction
to <strong>the</strong> council under study, highlighting <strong>the</strong> important features that <strong>the</strong> council had an extraordinary
amount <strong>of</strong> data, unconnected silos <strong>of</strong> information, an over-reliance on paper and several information
breaches from poor information management training. After this scoping study, <strong>the</strong> next stage is a
detailed analysis <strong>of</strong> <strong>the</strong> KM environment at <strong>the</strong> council, which should yield a KM framework unique to
<strong>the</strong> council. The main impetus <strong>of</strong> <strong>the</strong> research would <strong>the</strong>n be <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> framework.
The results obtained from <strong>the</strong> initial analysis showed <strong>the</strong> council suffers from several information
management problems such as a lack <strong>of</strong> executive support, a lack <strong>of</strong> KM awareness and a focus on
records management. The council is also introducing Micros<strong>of</strong>t SharePoint, but only just considering
how <strong>the</strong> tool will support collaborative working. With <strong>the</strong> numerous information losses, and attention
from <strong>the</strong> ICO, <strong>the</strong> council also has a severe need for comprehensive information management skills
training. This paper concluded with <strong>the</strong> identification <strong>of</strong> 4 main areas that need to be explored and
analysed in depth before KM initiatives can be rolled out.
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References
Paul Parboteeah, Thomas Jackson and Ge<strong>of</strong>f Smith
Audata Ltd (2005) Classification Report for [local government organisation]
Brown, M.M. and Brudney, J.L. (2003) “Learning Organizations in <strong>the</strong> Public Sector? A Study <strong>of</strong> Police Agencies
Employing Information and Technology to Advance Knowledge”. Public Administration Review, Vol. 63, No.
1, pp. 30-43.
Data Protection Act (1998) HMSO, London, available at:
http://www.legislation.gov.uk/ukpga/1998/29/contents (accessed 23 March 2011).
Environmental Information Regulations Act (2004) HMSO, London, available at:
http://www.legislation.gov.uk/uksi/2004/3391/contents/made (accessed 23 March 2011)
Girard, J.P. and McIntyre, S. (2010) “Knowledge Management Modeling in Public Sector Organizations: A Case
Study”. International Journal <strong>of</strong> Public Sector Management, Vol. 23, No. 1, pp. 71-77.
McAdam, R. and Reid, R. (2000) “A Comparison <strong>of</strong> Public and Private Sector Perceptions and Use <strong>of</strong> Knowledge
Management”. Journal <strong>of</strong> <strong>European</strong> Industrial Training, Vol. 24, No. 6, pp. 317-329.
McDermot, R. and O’Dell, C. (2000) “Overcoming Cultural Barriers to Sharing Knowledge”. Journal <strong>of</strong> Knowledge
Management, Vol. 5, No. 1, pp. 76-85.
Päivarinta, T. and Munkvold, B.E. (2005) “Enterprise Content Management: An Integrated Perspective on
Information Management”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 38 th <strong>Conference</strong> Hawaii International <strong>Conference</strong> on Systems
Science, Hawaii, pp. 1-10.
Peel, M. and Rowley, J. (2010) “Information Sharing Practice in Multi-Agency Working”. Aslib <strong>Proceedings</strong>: New
Information Perspectives, Vol. 62, No. 1, pp. 11-28.
Regulation <strong>of</strong> Investigatory Powers Act (2000) HMSO, London, available at:
http://www.legislation.gov.uk/ukpga/2000/23/contents (accessed 23 March 2011)
Riege, A. (2005) “Three-Dozen Knowledge-Sharing Barriers Managers Must Consider”. Journal <strong>of</strong> Knowledge
Management, Vol. 9, No. 3, pp. 18-35.
Riege, A. and Lindsay, N. (2006) “Knowledge Management in <strong>the</strong> Public Sector: Stakeholder Partnerships in <strong>the</strong>
Public Policy Development”. Journal <strong>of</strong> Knowledge Management, Vol. 10, No. 3, pp. 24-39.
Sinclair, N. (2006) “Stealth KM: How to Make KM Successful in Any Organization”. VINE: The Journal <strong>of</strong>
Information and Knowledge Management Systems, Vol. 36, No. 1, pp. 97-107.
Skok, W. and Kalmanovitch, C. (2005) “Evaluating <strong>the</strong> Role and Effectiveness <strong>of</strong> an Intranet in Facilitating
Knowledge Management: A Case Study at Surrey Council Council”. Information & Management, Vol. 42,
No. 5, pp. 731-744.
Syed-Ikhsan, S.O.S. and Rowland, F. (2004) “Knowledge Management in a Public Organization: A Study on <strong>the</strong>
Relationship Between Organizational Elements and <strong>the</strong> Performance <strong>of</strong> Knowledge Transfer”. Journal <strong>of</strong>
Knowledge Management, Vol. 8, No. 2, pp. 95-111.
751

Knowledge Transfer, Knowledge Sharing and Knowledge
Barriers – Three Blurry Terms in KM
Dan Paulin and Kaj Suneson
Department <strong>of</strong> Technology Management and Economics, Chalmers University
<strong>of</strong> Technology, Go<strong>the</strong>nburg, Sweden
dan.paulin@chalmers.se
suneson@chalmers.se
Abstract: In <strong>the</strong> knowledge management world <strong>the</strong>re are many different terms flying around. Some are more
important and frequently used than o<strong>the</strong>rs. In this paper, we present and discuss <strong>the</strong> development and views <strong>of</strong>
three terms: knowledge transfer, knowledge sharing and knowledge barriers. Knowledge transfer and knowledge
sharing are sometimes used synonymously or have overlapping content. Several authors have pointed out this
confusion while o<strong>the</strong>r authors have attempted to clarify <strong>the</strong> differences and define <strong>the</strong> terms. Knowledge barriers
in <strong>the</strong>mselves seem to have a more obvious content although <strong>the</strong> borders between knowledge barriers and
connecting terms, such as ‘barriers to knowledge sharing’, seem to blur discussions and views. Our aim is to
make a contribution to finding appropriate demarcations between <strong>the</strong>se concepts. After reviewing Knowledge
Management literature, we can state that <strong>the</strong> three terms, knowledge transfer, knowledge sharing and knowledge
barriers, are somewhat blurred. For knowledge transfer and knowledge sharing, <strong>the</strong> blurriness is linked mainly to
<strong>the</strong> fact that <strong>the</strong> analytical level each term is related to has come and gone and come back again. For knowledge
barriers, <strong>the</strong> blurriness comes from <strong>the</strong> development <strong>of</strong> <strong>the</strong> term. The mere existence <strong>of</strong> <strong>the</strong> many different
categorizations <strong>of</strong> knowledge barriers implies that <strong>the</strong> concept itself is blurry. The concept seems clear cut and
focuses on knowledge although it is also broad and later sources have included much more than knowledge. This
paper concludes by highlighting <strong>the</strong> effects on <strong>the</strong> terms when two different knowledge perspectives, knowledge
as an object (or <strong>the</strong> K-O view) and knowledge as a subjective contextual construction (or <strong>the</strong> K-SCC view) are
applied. The clarifications are supported by examples from companies in different industries (such as Cargotec
and IKEA) and <strong>the</strong> public sector (police, fire brigade, ambulance and o<strong>the</strong>r emergency services).
Keywords: Knowledge barriers, knowledge management, knowledge sharing, knowledge transfer
1. Introduction
During <strong>the</strong> last ten years numerous publications dealing with knowledge management-related issues
have been published in journals ranging from Conservation Biology, Post-Communist Economies,
Childhood and <strong>European</strong> History Quarterly to more business-oriented journals such as Research
Policy, Journal <strong>of</strong> Knowledge Management, Harvard Business Review and KM World.
It can be argued that in aiming for efficient Knowledge Management (KM), <strong>the</strong> search for “correct”
choices <strong>of</strong> methods and steps is crucial. These choices require a well-defined taxonomy with clear
concepts and terms. The content and meaning must be clear cut and <strong>the</strong>re should be no ambiguity
about <strong>the</strong> aim when fundamental concepts are used. Although this is undoubtedly a desirable
objective, it is hardly <strong>the</strong> current state <strong>of</strong> affairs regarding commonly used terminology in KM. In many
cases, <strong>the</strong> authors use central terms interchangeably and without making a distinction between <strong>the</strong>m.
When we have reviewed KM literature, <strong>the</strong>re are some terms that seem more fundamental than
o<strong>the</strong>rs. For example, in <strong>the</strong> view <strong>of</strong> <strong>the</strong> knowledge-based firm creation, coordination, transfer, and
integration <strong>of</strong> knowledge creates competitive advantages for firms (Ghosal and Moran 1996 (in
Sambamurthy and Subramani (2005))). When King (in Schwartz (ed.) 2006) in addition to <strong>the</strong>
statement above, proposes that knowledge transfer (KT) is a fundamental process <strong>of</strong> civilization and
that it is central to learning which in turn is critical to development, <strong>the</strong>re is clear support for exploring
<strong>the</strong> term knowledge transfer. KT is sometimes used interchangeably with knowledge sharing (Jonsson
2008), so in order to explore knowledge transfer, knowledge sharing (KS) should not be ignored.
Riege (2005; 2007) argues that <strong>the</strong> barriers affecting KS and KT have received little attention at <strong>the</strong>
same time that <strong>the</strong>y have a negative effect on KM and its possibilities to deliver a positive return on
investment.
For those reasons, we present and discuss <strong>the</strong> development and views <strong>of</strong> <strong>the</strong>se three terms:
Knowledge transfer (KT), knowledge sharing (KS) and knowledge barriers (KBs). As will be shown in
this paper <strong>the</strong> terms KT and KS are sometimes used synonymously or have overlapping content. KBs
in <strong>the</strong>mselves seem to have a more obvious content although <strong>the</strong> border between KBs and
connecting terms, such as “barriers to knowledge sharing”, makes discussions and views blurry.
752

Dan Paulin and Kaj Suneson
Our aim is to make a contribution to finding appropriate demarcations between <strong>the</strong> concepts.
The paper is structured in <strong>the</strong> following way. Firstly, we show examples from <strong>the</strong> literature <strong>of</strong> this
blurriness. Secondly, we show <strong>the</strong> development <strong>of</strong> research in this area by using <strong>the</strong> findings <strong>of</strong> a
literature review. Thirdly, key similarities and differences between uses <strong>of</strong> <strong>the</strong> terms are presented.
Here, we introduce different views <strong>of</strong> knowledge using real-life examples since we argue that this is
fundamental to <strong>the</strong> interpretation <strong>of</strong> <strong>the</strong>se terms. Finally, we discuss <strong>the</strong> effects <strong>of</strong> <strong>the</strong> different views.
2. Problem definition
The starting point for our argument is to present examples <strong>of</strong> articles that state this blurriness, authors
that use <strong>the</strong> terms without any clear distinction, books that use different (and overlapping) definitions
in different parts <strong>of</strong> <strong>the</strong> text and authors who have different interpretations <strong>of</strong> <strong>the</strong>se three terms.
2.1 KT and KS
In an article published in 2008, Anna Jonson points out this blurriness by stating: “Within <strong>the</strong> frame <strong>of</strong>
reference both ‘knowledge sharing’ and ‘knowledge transfer’ are used and discussed interchangeably.
As it is not clear if <strong>the</strong>re is a difference, both terms will be used.” (Jonsson (2008), p. 39). Ano<strong>the</strong>r
example is “… many authors and researchers have failed to provide a clear-cut definition for
knowledge transfer and, at times, it has been discussed toge<strong>the</strong>r with <strong>the</strong> term “knowledge sharing””
(Liyanage, et al. (2009), p. 122).
There are authors that use both terms when discussing <strong>the</strong> same concept. For example, one author
identifies over three dozen knowledge-sharing barriers in one article (Riege (2005)). In a more recent
article, <strong>the</strong> same author uses <strong>the</strong> term knowledge transfer when suggesting actions to overcome <strong>the</strong>
same and similar barriers (Riege (2007)). He even refers to his own research in <strong>the</strong> following way:
“Indeed, organizations wishing to make <strong>the</strong>ir knowledge management strategy a success need to pay
attention to a potentially more than three dozen human, organizational and technological obstacles to
transferring knowledge (Riege, 2005)” (Riege (2007) p. 50).
A third clear example <strong>of</strong> <strong>the</strong> blurriness is taken from The Encyclopedia <strong>of</strong> Knowledge Management
(Schwartz (2006)) in which several definitions <strong>of</strong> knowledge sharing, knowledge transfer and
knowledge sharing barriers are presented. All <strong>of</strong> <strong>the</strong> following quotations are taken from this
encyclopedia.
Knowledge sharing is defined, for example, as:
“The exchange <strong>of</strong> knowledge between and among individuals, and within and among teams,
organizational units, and organizations. This exchange may be focused or unfocused, but it
usually does not have a clear a priori objective.” (c. 0922978417).
“An exchange <strong>of</strong> knowledge between two individuals: one who communicates knowledge and one
who assimilates it. In knowledge sharing, <strong>the</strong> focus is on human capital and <strong>the</strong> interaction <strong>of</strong>
individuals. Strictly speaking, knowledge can never be shared. Because it exists in a context; <strong>the</strong>
receiver interprets it in <strong>the</strong> light <strong>of</strong> his or her own background.” (c. 0353891369).
The differences between <strong>the</strong> definitions <strong>of</strong> knowledge transfer are perhaps even clearer.
“Includes a variety <strong>of</strong> interactions between individuals and groups; within, between, and across
groups; and from groups to <strong>the</strong> organization.” (c. 0900650841).
“The focused, unidirectional communication <strong>of</strong> knowledge between individuals, groups, or
organizations such that <strong>the</strong> recipient <strong>of</strong> knowledge (a) has a cognitive understanding, (b) has <strong>the</strong>
ability to apply <strong>the</strong> knowledge, or (c) applies <strong>the</strong> knowledge.” (c. 0676846059).
Contradictions and discrepancies between <strong>the</strong> definitions can be found on several levels:
Sharing taking place between individuals only versus between individuals, teams, units or
organizations
Focused or unfocused versus clearly focused
A transaction versus saying that knowledge can never be shared
Unidirectional versus multidirectional
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Dan Paulin and Kaj Suneson
Attewell (1992)) refers to knowledge barriers as ‘lack <strong>of</strong> knowledge’ about a new technology and how
it should be used in organizations as a reason for why <strong>the</strong> technology does not spread. Szulanski
(2003) uses <strong>the</strong> concept “knowledge barriers” to describe a set <strong>of</strong> factors that explains why
knowledge might not transfer. This makes it easy to believe that <strong>the</strong>re is a tight and immediate
connection between a company’s efforts to reach knowledge transfer and <strong>the</strong> concept <strong>of</strong> “knowledge
barriers”. He exemplifies <strong>the</strong>se barriers with <strong>the</strong> recipients’ level <strong>of</strong> knowledge prior to <strong>the</strong> transfer,
how well <strong>the</strong> transferred practice is understood in <strong>the</strong> organization, and <strong>the</strong> ability to unlearn.
Szulanski (1996) refers to three constructs as knowledge barriers, namely <strong>the</strong> absorptive capacity <strong>of</strong>
<strong>the</strong> recipient, casual ambiguity and an arduous relationship between source and <strong>the</strong> recipient.
One interpretation <strong>of</strong> <strong>the</strong> descriptions <strong>of</strong> barriers is, in Szulanski's view, like a filter that makes
transferring more or less sluggish while for Attewell it is a barrier that cannot be passed through but
must be climbed over. This leads to major differences in how to deal with <strong>the</strong>se barriers and what
phenomena are interpreted as knowledge barriers.
These contradictions, discrepancies and differing views create blurriness which will have an effect on
<strong>the</strong> conclusions and recommendations provided by authors using <strong>the</strong>se definitions in <strong>the</strong> same way
that a perfectly engineered building might crumble to dust if its foundation is not solid.
3. Development <strong>of</strong> <strong>the</strong> terms knowledge transfer and knowledge sharing
In <strong>the</strong> first part <strong>of</strong> this section, we try to show <strong>the</strong> emergence, reemergence and development <strong>of</strong> KT
and KS. In Figure X, an attempt to visualize <strong>the</strong> different authors’ use <strong>of</strong> <strong>the</strong> terms with regards to <strong>the</strong>ir
level on an individual-industry scale and <strong>the</strong> publication year. This is followed by <strong>the</strong> development <strong>of</strong>
KBs.
3.1 The emergence and reemergence <strong>of</strong> KT and KS
Knowledge, its definition, source and method in which it is acquired has been discussed (at least)
since <strong>the</strong> time <strong>of</strong> <strong>the</strong> philosophical debates by Aristotle and Plato. We would, <strong>the</strong>refore, propose that
<strong>the</strong> initial emergence <strong>of</strong> <strong>the</strong> terms comes from <strong>the</strong>se discussions and that <strong>the</strong> suggestions on how to
deal with efficient and effective knowledge transfer and sharing has been ongoing to a varying degree
<strong>of</strong> intensity since <strong>the</strong>n.
The reemergence <strong>of</strong> <strong>the</strong> terms can be traced to two different streams <strong>of</strong> research.
The first can be found in product innovation and technology transfer literature in which <strong>the</strong> relationship
and communication between units have been studied (e.g. Allen (1977) and Clark and Fujimoto
(1991)).
The second stream is based on <strong>the</strong> writings <strong>of</strong> Michael Polanyi and <strong>the</strong> terms tacit and explicit
knowledge. In an influential Harvard Business Review article, Ikujiro Nonaka touches on <strong>the</strong> issues <strong>of</strong>
KT and KS, even though he does not mention <strong>the</strong>m explicitly. He writes “Explicit knowledge is formal
and systematic. For this reason, it can be easily communicated and shared…” (Nonaka (1991) p. 98).
Later in <strong>the</strong> same article, he says “This helps create a “common cognitive ground” among employees
and thus facilitates <strong>the</strong> transfer <strong>of</strong> tacit knowledge.” (Nonaka (1991), p. 102).
These two streams have, to some extent, merged after Nonaka’s original article. Since that article and
later articles and books by him (such as Nonaka and Takeuchi (1995)), in which <strong>the</strong>y say that KS is a
critical stage in KT) have had a strong impact on <strong>the</strong> research community, we regard this as <strong>the</strong>
starting point for <strong>the</strong> reemergence <strong>of</strong> KT and KS as we know <strong>the</strong>m today.
Since <strong>the</strong>n, <strong>the</strong> terms have developed gradually and extensively. Initially, <strong>the</strong> terms were used
interchangeably (e.g. Badaracco (1991); Hansen (1999)) but lately <strong>the</strong>re has been an ongoing
separation between <strong>the</strong>m, which we will demonstrate in <strong>the</strong> following sections.
3.2 The development <strong>of</strong> KT
During <strong>the</strong> first years after its reemergence, KT was usually treated in line with <strong>the</strong> notion <strong>of</strong> <strong>the</strong>
knowledge-based <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm (Kogut and Zander (1992); Grant (1996)). One <strong>of</strong> <strong>the</strong> most
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commonly cited authors here is Szulanski, who in numerous books and articles has developed <strong>the</strong>
notion <strong>of</strong> KT, especially regarding intra-firm knowledge. His early work clearly states that knowledge is
regarded as a firm’s stock (Szulanski (1996)).
During <strong>the</strong> late ‘90s and early ‘00s, <strong>the</strong> focus within this area remains on <strong>the</strong> strategic level with
authors who address <strong>the</strong> question <strong>of</strong> <strong>the</strong> role <strong>of</strong> weak ties in sharing (!) knowledge across
organization subunits (Hansen (1999)), o<strong>the</strong>rs who focus on intracorporate knowledge flows within
multinational corporations (Gupta and Govindarajan (2000)) and o<strong>the</strong>rs who study business unit
innovation and performance (Tsai (2001)). One noticeable exception is when <strong>the</strong> psychological and
sociological aspects <strong>of</strong> this issue merge into <strong>the</strong> research stream when <strong>the</strong> effects from extrinsic and
intrinsic motivation in individuals on KT within a firm are studied (Osterloh and Frey (2000)).
During this time period, <strong>the</strong>re is a switch from conceptual and <strong>the</strong>oretically-oriented research towards
more empirically centered research. Paulin (2002 and 2006) studies KT processes in <strong>the</strong> automotive
industry with a particular focus on <strong>the</strong> production process verification process. Schlegelmilch and
Chini (2003) present a literature review in which <strong>the</strong> literature referred to (mainly from 1997 to 2002)
predominates primarily in <strong>the</strong> direction <strong>of</strong> empirical studies.
More recently published reviews on knowledge transfer still align to <strong>the</strong> higher level <strong>of</strong> analysis. Both
<strong>the</strong> review by Easterby-Smith, et al. (2008)) and van Wijk, et al. (2008)) have a clear focus on intra-
and/or inter-organizational knowledge transfer.
However, Easterby-Smith, et al. (2008)) identified a number <strong>of</strong> questions <strong>of</strong> both <strong>the</strong>oretical and
practical significance to <strong>the</strong> current research frontier within <strong>the</strong> area <strong>of</strong> inter-organizational knowledge
transfer and in <strong>the</strong>ir question “How does <strong>the</strong> process <strong>of</strong> knowledge transfer unfold at different levels <strong>of</strong>
analysis?” <strong>the</strong>y also open up for analysis on <strong>the</strong> individual level. This diversion from <strong>the</strong> main track is
continued by Liyanage, et al. (2009)), when <strong>the</strong>y state that “knowledge transfer is <strong>the</strong> conveyance <strong>of</strong>
knowledge from one place, person or ownership to ano<strong>the</strong>r.” (Liyanage, et al. (2009) p 122).
3.3 The development <strong>of</strong> KS
In <strong>the</strong> early work presented after Nonaka’s HBR article, KT and KS is used interchangeably with
predominance towards KT. One author that adopts <strong>the</strong> term KS is Appleyard (1996)). Here, she
includes both comparisons on <strong>the</strong> industry level <strong>of</strong> interaction (by comparing KS in <strong>the</strong> semiconductor
industry with KS in <strong>the</strong> steel industry) and on a national level (Japan is compared to <strong>the</strong> US) using
individual respondents. O<strong>the</strong>r researchers in <strong>the</strong> same stream are Dyer and Nobeoka (2000)). Their
findings include <strong>the</strong> statement that Toyota’s relative productivity advantages are explained in part by
<strong>the</strong>ir ability to create and sustain network-level KS processes.
O<strong>the</strong>r perspectives that are strong in <strong>the</strong> KS stream <strong>of</strong> research are <strong>the</strong> psychological and <strong>the</strong>
sociological. Cabrera and Cabrera (2002)), for example, include <strong>the</strong> psychological notion <strong>of</strong> social
dilemmas when analyzing <strong>the</strong> inclination <strong>of</strong> individuals to share knowledge with o<strong>the</strong>r individuals
regardless <strong>of</strong> <strong>the</strong> fact that <strong>the</strong> company that <strong>the</strong>y work for has invested in specific technology to
enable such knowledge sharing.
Fernie, et al. (2003)) has a strong focus on personal knowledge. They argue that knowledge is highly
individualistic and that it is embedded in specific social contexts. This article is a good example <strong>of</strong> <strong>the</strong>
direction within knowledge sharing that is focused on <strong>the</strong> individual level – context-specific subjective
knowledge. Ano<strong>the</strong>r example <strong>of</strong> this stream is when KS between individuals in organizations is
examined (Ipe (2003)). Here, four major factors that influence KS are identified: 1) The nature <strong>of</strong>
knowledge, 2) The motivation to share, 3) The opportunities to share and 4) The culture and <strong>the</strong> work
environment.
In a recently published article, an in-depth review <strong>of</strong> articles on individual-level knowledge sharing is
presented (Wang and Noe (2010)). They state that <strong>the</strong>ir article is <strong>the</strong> first to systematically review
individual knowledge sharing and that previous reviews have focused on technological issues <strong>of</strong>
knowledge sharing or knowledge transfer across units or organizations, or within inter-organizational
networks.
Areas <strong>of</strong> previous studies are:
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Organizational context (including organizational culture and climate, management support,
rewards and incentives and organizational structure)
Interpersonal and team characteristics (including team characteristics and process, diversity,
social networks)
Cultural characteristics
Individual characteristics
Motivational factors (including beliefs <strong>of</strong> knowledge ownership, perceived benefits and costs,
interpersonal trust and justice and individual attitudes)
To summarize sections 3.2 and 3.3, a visualization <strong>of</strong> <strong>the</strong> different authors’ use <strong>of</strong> <strong>the</strong> terms based on
<strong>the</strong> organizational level is shown in Figure 1 below.
Figure 1: Different authors’ use <strong>of</strong> <strong>the</strong> terms with regards to <strong>the</strong>ir level on an individual-industry scale
and <strong>the</strong> publication year
3.4 The emergence and development <strong>of</strong> KBs
During <strong>the</strong> '90s, <strong>the</strong> spread <strong>of</strong> computers and <strong>the</strong> computerization <strong>of</strong> industry were seen as important.
In this setting, Paul Attewell studied factors that inhibited <strong>the</strong> spread <strong>of</strong> computer technology in
companies. He found that when <strong>the</strong> companies lacked knowledge <strong>of</strong> how to use <strong>the</strong> technology, <strong>the</strong>
possibilities inherent in <strong>the</strong> technology and <strong>the</strong> efforts to maintain <strong>the</strong> technology in <strong>the</strong> company
became barriers to <strong>the</strong> use <strong>of</strong> <strong>the</strong> technology (Attewell (1992)).
Although Attewell’s work is important in stating <strong>the</strong> content and highlighting <strong>the</strong> term, it has been
known and used before in many different settings. For example, a quick search on Google scholar
shows that it had been used to discuss <strong>the</strong> construction <strong>of</strong> a <strong>the</strong>ory for socialist economy (Zielinski
(1962)), Caldwell (1967) used it in a discussion on how knowledge set up a barrier to its own
development and in ano<strong>the</strong>r setting Ramaswami and Yang (Ramaswami and Yang (1990)) claimed
that knowledge barriers affected <strong>the</strong> potential <strong>of</strong> companies to export.
A similarity here is that knowledge barriers are regarded as a lack <strong>of</strong> knowledge, which leaves a
person beyond all hope <strong>of</strong> grasping <strong>the</strong> content <strong>of</strong> <strong>the</strong> subject that is being discussed. The lack <strong>of</strong> a
frame <strong>of</strong> reference from memories and experiences makes <strong>the</strong> topic impossible to understand or to
connect to previous knowledge.
In 1996, Szulanski presented <strong>the</strong> concept <strong>of</strong> stickiness. The main purpose <strong>of</strong> his article was to explain
why knowledge and skills might be difficult to transfer between persons, entities and organizations.
Factors affecting such transfer were divided into motivational factors and knowledge barriers. Within
knowledge barriers, three factors were identified: 1) Lack <strong>of</strong> absorptive capacity (in which lack <strong>of</strong>
knowledge is a part). 2) Causal ambiguity – uncertainty regarding how aspects <strong>of</strong> <strong>the</strong> knowledge
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Dan Paulin and Kaj Suneson
interact and respond to factors in <strong>the</strong> environment as well as uncertainty if necessary factors are
present in a given situation. 3) An arduous relationship between <strong>the</strong> source and <strong>the</strong> recipient. How
easy or frictionless is <strong>the</strong> communication and intimacy between sender and receiver?
Later, knowledge barriers were divided into three different categories to explain problems in <strong>the</strong>
adoption <strong>of</strong> complex technologies (Venkatesh, et al. (1999)): 1) “Backbone and connectivity” –
Knowledge barriers to understanding <strong>the</strong> technology and regulations <strong>of</strong> how different technologies are
permitted to be used, ei<strong>the</strong>r separate or combined. 2) The need to understand customers’ equipment
and <strong>the</strong> need for interoperability. Lack <strong>of</strong> such knowledge can be seen as a knowledge barrier on <strong>the</strong>
supply side. 3) The understanding <strong>of</strong> how customers’ applications and services interact with
technology and regulations.
Tanriverdi and Iacono (1999) suggested that <strong>the</strong> technical knowledge barriers presented by Attewell
(1992) should be expanded with three additional barriers in order to understand what inhibits <strong>the</strong>
spread <strong>of</strong> telemedicine. These barriers are: 1) Economic – viewing <strong>the</strong> economic model in terms <strong>of</strong>
<strong>the</strong> benefits gained by <strong>the</strong> organization. 2) Organizational – understanding how use <strong>of</strong> <strong>the</strong> technology
fits into organizational processes. 3) Behavioral – <strong>the</strong> potential for <strong>the</strong> members in <strong>the</strong> organization to
see how <strong>the</strong> technology functions in, and impacts on, <strong>the</strong>ir daily work.
Building on Tanriverdi's and Iacono's work, Suneson and Heldal (2010) suggested that in situations
when complex (information and communication) technology will be used jointly by two or more
organizations, an understanding <strong>of</strong> <strong>the</strong> o<strong>the</strong>r organizations and <strong>the</strong>ir view <strong>of</strong> <strong>the</strong> technology might be
needed for efficient use. Lack <strong>of</strong> understanding might act as an interorganizational KB that impedes
co-operation.
4. Discussion
In <strong>the</strong> research streams presented, similarities and differences in use <strong>of</strong> <strong>the</strong> terms can be found.
One common dividing line between KT and KS is related to <strong>the</strong> levels <strong>of</strong> analysis, in that KS is used
more frequently by authors focusing on <strong>the</strong> individual level, while KT is used more frequently when
groups, departments, organizations or even businesses are in focus (Argote and Ingram (2000)). This
view can still be regarded as valid since <strong>the</strong>re is support for this in a more recent review (Choo and
Alvarenga Neto (2010)).
However, we propose that <strong>the</strong> main difference is derived from <strong>the</strong> basic view <strong>of</strong> knowledge. In a
recent article, Sveiby (2007) focuses on two dominating views <strong>of</strong> knowledge and <strong>the</strong>ir influence on
research. The two views are:
Knowledge as an object (K-O).
Sveiby (2007) exemplifies <strong>the</strong> stream <strong>of</strong> research based on this view with numerous references and
points out relevant variations on this <strong>the</strong>me; knowledge contained in stock, derived from its form or
content, or as objects implicitly defined by <strong>the</strong> choice <strong>of</strong> variables <strong>of</strong> statistical analysis.
One example <strong>of</strong> <strong>the</strong> K-O view is taken from <strong>the</strong> Finnish cargo handling company Cargotec and <strong>the</strong>ir
transfer <strong>of</strong> <strong>the</strong> manufacturing solution <strong>of</strong> reach stackers (heavy forklifts) from <strong>the</strong>ir main and original
manufacturing facility in Lidhult, Sweden to <strong>the</strong>ir Shanghai plant in 2005-2006. The strategy was to
replicate <strong>the</strong> manufacturing set-up without (initially) adapting to local conditions. The products were
designed to be dismantled, transported to China and re-assembled in Shanghai. On <strong>the</strong> individual
level, <strong>the</strong> operators from Lidhult acted as teachers and informants for <strong>the</strong> Chinese operators who
visited Lidhult to learn how to assemble <strong>the</strong> reach stackers.
Knowledge as something that is constructed in a social context and which cannot be separated
from <strong>the</strong> context or <strong>the</strong> individual (or knowledge as a subjective contextual construction, K-SCC).
Sveiby states that this view is based on Polanyi's idea <strong>of</strong> personal knowledge (Polanyi (1958)).
Among authors that subscribe to this view, we would like to highlight Nonaka (Nonaka 1994; Nonaka
and Takeuchi 1995) as well as Sveiby himself (Sveiby (1997)).
An example <strong>of</strong> <strong>the</strong> K-SCC view can be taken from <strong>the</strong> Swedish home furnishing giant IKEA and a
concept called “Development on <strong>the</strong> Factory Floor”. Here, <strong>the</strong> product and <strong>the</strong> manufacturing process
are developed jointly by <strong>the</strong> R&D engineer and <strong>the</strong> manufacturing representatives at <strong>the</strong> local plant.
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The R&D engineer shares his/her thoughts and ideas about <strong>the</strong> prospective product with <strong>the</strong>
manufacturing engineer, who in turn shares relevant knowledge about <strong>the</strong> possibilities and limitations
<strong>of</strong> <strong>the</strong> manufacturing systems.
These different views <strong>of</strong> knowledge influence <strong>the</strong> choice <strong>of</strong> using KT or KS. In <strong>the</strong> literature that has
been reviewed in previous sections, <strong>the</strong>re is a bias towards using KT if <strong>the</strong> author's view <strong>of</strong> knowledge
tends towards K-O and a similar (if not as clear) bias towards KS if <strong>the</strong> K-SCC view is adopted.
As regards KBs, we can say that <strong>the</strong> original definition <strong>of</strong> KBs as a lack <strong>of</strong> knowledge (if you adopt <strong>the</strong>
K-O view), or a lack <strong>of</strong> possibility to make sense <strong>of</strong> something (in line with <strong>the</strong> K-SCC view) becomes
blurred and diluted by later contributions.
For example, Szulanski's term ‘lack <strong>of</strong> absorptive capacity’ is partly a lack <strong>of</strong> knowledge (in
accordance with Attewell (1992) but it also allows o<strong>the</strong>r influencing factors, such as intelligence or
logic skills, to be included. Ano<strong>the</strong>r <strong>of</strong> Szulanski's terms is ‘arduous relationship’. This factor is hardly
connected to knowledge. Instead, it is clearly connected to interpersonal relations. Ano<strong>the</strong>r problem
with <strong>the</strong> diversion from <strong>the</strong> original definition can be exemplified by using Riege. He refers to different
experience levels – which might be interpreted as different content in personal knowledge – as a KB
(Riege (2005)). Later, he modifies this to “resistance to sharing knowledge because <strong>of</strong> differences in
experience levels” (Riege (2007) p. 55) and suggests that this might be overcome by, for example,
better integration. Nei<strong>the</strong>r <strong>of</strong> <strong>the</strong>se suggestions will help to overcome differences in knowledge.
The solutions to overcome <strong>the</strong>se types <strong>of</strong> barriers within an organization will be radically different
depending on <strong>the</strong> prevailing view <strong>of</strong> knowledge.
The following example is taken from a study conducted to explore <strong>the</strong> launch <strong>of</strong> an information and
communication system for public safety organizations based on mobile phone technology. Here, it
became quite obvious how many different types <strong>of</strong> KB interact in <strong>the</strong> same situation. In this study,
user organizations and launching organizations were questioned regarding <strong>the</strong>ir impressions <strong>of</strong> <strong>the</strong>
launch. The user organizations stated that it was problematic and that confidence in <strong>the</strong> system
decreased when observing deficiencies in reception due to insufficient mobile network coverage.
However, <strong>the</strong> specialists in <strong>the</strong> launching organization stated that <strong>the</strong>y could not understand why that
was a cause for concern – it was easily solved by installing a new radio base station. In this example,
<strong>the</strong> traditional technical KB is present and it can easily be identified both from an objectivistic (K-O)
view and a subjectivist (K-SCC) view. However from a K-SCC view, two o<strong>the</strong>r KBs can be identified:
<strong>the</strong> lack <strong>of</strong> understanding from <strong>the</strong> launching organization can be interpreted as a KB related to how
<strong>the</strong> user organizations are constituted. For <strong>the</strong> user organizations, KBs are not limited to <strong>the</strong> technical
problem (<strong>the</strong> traditional view <strong>of</strong> KBs) but also to how <strong>the</strong> launch <strong>of</strong> <strong>the</strong> process to acquire a new base
station is started.
5. Conclusions
After having reviewed literature in KM, we conclude that <strong>the</strong> three terms knowledge transfer,
knowledge sharing and knowledge barriers are blurry. For KT and KS, <strong>the</strong> blurriness is related mainly
to which analytical level each term is related to has come and gone and <strong>the</strong>n come back again. For
KBs, <strong>the</strong> blurriness comes from <strong>the</strong> development <strong>of</strong> <strong>the</strong> term. The mere existence <strong>of</strong> <strong>the</strong> many
different KB categorizations implies that <strong>the</strong> concept itself is blurry. The concept seems clear cut and
focuses on knowledge, but is in turn broad and later sources have included much more than
knowledge.
We can also say that all three terms are affected by <strong>the</strong> basic understanding <strong>of</strong> knowledge. Regarding
use <strong>of</strong> <strong>the</strong> terms, <strong>the</strong>re are clear indications that authors who use <strong>the</strong> term KT have a tendency
towards <strong>the</strong> K-O perspective and that authors who use <strong>the</strong> term KS are drawn more towards <strong>the</strong> K-
SCC perspective. This tendency is not applicable to KBs and interpretations <strong>of</strong> what constitutes a KB
differ depending on <strong>the</strong> view. To find useful content in any definition, it is necessary adapt it to <strong>the</strong>
specific situation.
What effects would <strong>the</strong>se two perspectives have on our blurry terms?
One effect would definitely be how to manage <strong>the</strong> processes <strong>of</strong> KT and KS and KBs related to those
processes. If you have a K-O perspective and want to create good conditions for knowledge flow, you
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amplify <strong>the</strong> enablers, suppress disabling conditions and overcome obstacles, including <strong>the</strong> barriers. In
a K-SCC perspective, you focus more on <strong>the</strong> development <strong>of</strong> “ba” (“ba” is a Japanese word that
roughly translates as “space” or “environment” and it was introduced into <strong>the</strong> KM sphere by Nonaka
and Konno (1998)), to better fit individuals who need to develop personal knowledge with <strong>the</strong> help <strong>of</strong>
those who have already developed it.
6. A final thought
O<strong>the</strong>r key terms in KM are also likely to be affected by different perspectives. Even though it is not
this paper's original focus, we cannot refrain from making <strong>the</strong> following comment: The concept "ba"
(Nonaka and Konno (1998)) is also affected by <strong>the</strong> knowledge perspective. From a K-O perspective,
"ba" has to do with designing <strong>the</strong> physical (or virtual) space to optimize KT or KS. However, from a K-
SCC perspective "ba" has more to do with <strong>the</strong> "spacetime" (cf. Einstein (1905)) since <strong>the</strong> context
changes over time and affects knowledge. In o<strong>the</strong>r words, a particular line <strong>of</strong> reasoning and logic built
on knowledge seems straight if you have a similar background and experience (or frame <strong>of</strong> reference)
to <strong>the</strong> individual who harbors <strong>the</strong> knowledge but appears more bent if you have a background and
experience that differs significantly!
References
Allen, T. J. (1977) Managing <strong>the</strong> Flow <strong>of</strong> Technology, MIT Press, Cambridge, MA.
Appleyard, M. M. (1996) "How does knowledge flow? Interfirm patterns in <strong>the</strong> semiconductor industry", Strategic
Management Journal, Vol 17, No. pp 137-154.
Argote, L. and Ingram, P. (2000) "Knowledge transfer: A basis for competitive advantage in firms", Organizational
Behavior and Human Decision Processes, Vol 82, No. 1, pp 150-169.
Attewell, P. (1992) "Technology Diffusion and Organizational Learning - <strong>the</strong> Case <strong>of</strong> Business Computing",
Organization Science, Vol 3, No. 1, pp 1-19.
Badaracco, J. L., Jr. (1991) "Alliances Speed Knowledge Transfer", Planning Review, Vol 19, No. 2, pp 10-16.
Cabrera, A. and Cabrera, E. F. (2002) "Knowledge-sharing dilemmas", Organization Studies, Vol 23, No. 5, pp
687-710.
Caldwell, L. K. (1967) "Managing <strong>the</strong> Scientific Super-Culture: The Task <strong>of</strong> Educational Preparation", Public
Administration Review, Vol 27, No. 2, pp 128.
Choo, C. W. and Alvarenga Neto, R. (2010) "Beyond <strong>the</strong> ba: managing enabling contexts in knowledge
organizations", Journal <strong>of</strong> Knowledge Management, Vol 14, No. 4, pp 592-610.
Clark, K. B. and Fujimoto, T. (1991) Product development performance: strategy, organization, and management
in <strong>the</strong> world auto industry, Harvard Business School Press, Boston, Mass.
Dyer, J. H. and Nobeoka, K. (2000) "Creating and managing a high-performance knowledge-sharing network:
The Toyota case", Strategic Management Journal, Vol 21, No. 3, pp 345-367.
Easterby-Smith, M., Lyles, M. A. and Tsang, E. W. K. (2008) "Inter-organizational knowledge transfer: Current
<strong>the</strong>mes and future prospects", Journal <strong>of</strong> Management Studies, Vol 45, No. 4, pp 677-690.
Einstein, A. (1905) "Zur Elektrodynamik bewegter Körper", Annalen der Physik, Vol 322, No. 10, pp 891-921.
Fernie, S., Green, S. D., Weller, S. J. and Newcombe, R. (2003) "Knowledge sharing: context, confusion and
controversy", International Journal <strong>of</strong> Project Management, Vol 21, No. 3, pp 177-187.
Grant, R. M. (1996) "Toward a knowledge-based <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm", Strategic Management Journal, Vol 17, No.
pp 109-122.
Gupta, A. K. and Govindarajan, V. (2000) "Knowledge flows within multinational corporations", Strategic
Management Journal, Vol 21, No. 4, pp 473-496.
Hansen, M. T. (1999) "The search-transfer problem: The role <strong>of</strong> weak ties in sharing knowledge across
organization subunits", Administrative Science Quarterly, Vol 44, No. 1, pp 82-111.
Ipe, M. (2003) "Knowledge Sharing in Organizations: A Conceptual Framework", Human Resource Development
Review, Vol 2, No. 4, pp 337-359.
Jonsson, A. (2008) "A transnational perspective on knowledge sharing: lessons learned from IKEA’s entry into
Russia, China and Japan", The International Review <strong>of</strong> Retail, Distribution and Consumer Research, Vol 18,
No. 1, pp 17-44.
Kogut, B. and Zander, U. (1992) "Knowledge <strong>of</strong> <strong>the</strong> Firm, Combinative Capabilities, and <strong>the</strong> Replication <strong>of</strong>
Technology", Organization Science, Vol 3, No. 3, pp 383-397.
Liyanage, C., Elhag, T., Ballal, T. and Li, Q. P. (2009) "Knowledge communication and translation - a knowledge
transfer model", Journal <strong>of</strong> Knowledge Management, Vol 13, No. 3, pp 118-131.
Nonaka, I. (1991) "The Knowledge-Creating Company", Harvard Business Review, Vol 69, No. 6, pp 96-104.
Nonaka, I. and Konno, N. (1998) "The concept <strong>of</strong> "ba": Building a foundation for knowledge creation", California
Management Review, Vol 40, No. 3, pp 40-54.
Nonaka, I. and Takeuchi, H. (1995) The knowledge-creating company : how Japanese companies create <strong>the</strong>
dynamics <strong>of</strong> innovation, Oxford University Press, New York.
Osterloh, M. and Frey, B. S. (2000) "Motivation, knowledge transfer, and organizational forms", Organization
Science, Vol 11, No. 5, pp 538-550.
759

Dan Paulin and Kaj Suneson
Paulin, D. (2002) Virtual verification: Impact <strong>of</strong> a new work method on <strong>the</strong> final verification process, Licentiate
<strong>the</strong>sis, Chalmers University <strong>of</strong> Technology, Göteborg.
Paulin, D. (2006) "The Effects on Knowledge Creation and Transfer in Production Process Verification due to
Virtual Prototypes", [online], Academic <strong>Conference</strong>s Ltd, www.ejkm.com
Polanyi, M. (1958) Personal knowledge; towards a post-critical philosophy, University <strong>of</strong> Chicago Press, Chicago,
Ramaswami, S. N. and Yang, Y. (1990) "Perceived Barriers to Exporting and Export Assistance Requirements",
International Perspectives on Trade Promotion and Assistance, Vol No. pp 187-206
Riege, A. (2005) "Three-dozen knowledge-sharing barriers managers must consider", Journal <strong>of</strong> Knowledge
Management, Vol 9, No. 3, pp 18-35.
Riege, A. (2007) "Actions to overcome knowledge transfer barriers in MNCs", Journal <strong>of</strong> Knowledge
Management, Vol 11, No. 1, pp 48-67.
Sambamurthy, V. and Subramani, M. (2005) "SPECIAL ISSUE ON INFORMATION TECHNOLOGIES AND
KNOWLEDGE MANAGEMENT", MIS Quarterly, Vol 29, No. 1, pp 1-7.
Schlegelmilch, B. B. and Chini, T. C. (2003) "Knowledge transfer between marketing functions in multinational
companies: a conceptual model", International Business Review, Vol 12, No. 2, pp 215-232.
Schwartz, D. G. (2006) Encyclopedia <strong>of</strong> Knowledge Management, [online], IGI Global.
http://library.books24x7.com/toc.asp?bookid=14700.
Suneson, K. and Heldal, I. (2010) "Knowledge Barriers When Launching new Telecommunications for Public
Safety", In proceedings from The 7th International <strong>Conference</strong> on Intellectual Capital, Knowledge
Management & Organisational Learning, The Hong Kong Polytechnic University, Hong Kong, China, 11-12
November 2010, pp 429-438.
Sveiby, K. E. (1997) The new organizational wealth : managing & measuring knowledge-based assets, Berrett-
Koehler Publishers, San Francisco, CA.
Sveiby, K. E. (2007) "Disabling <strong>the</strong> context for knowledge work: <strong>the</strong> role <strong>of</strong> managers' behaviours", Management
Decision, Vol 45, No. 10, pp 1636-1655.
Szulanski, G. (1996) "Exploring Internal Stickiness: Impediments to <strong>the</strong> Transfer <strong>of</strong> Best Practice Within <strong>the</strong>
Firm", Strategic Management Journal, Vol 17, No. Winter Special Issue, pp 27-43.
Szulanski, G. (2003) Sticky knowledge: barriers to knowing in <strong>the</strong> firm, Sage Publications, London.
Tanriverdi, H. and Iacono, C. S. (1999) "Diffusion <strong>of</strong> telemedicine: A knowledge barrier perspective",
Telemedicine Journal, Vol 5, No. 3, pp 223-244.
Tsai, W. P. (2001) "Knowledge transfer in intraorganizational networks: Effects <strong>of</strong> network position and
absorptive capacity on business unit innovation and performance", Academy <strong>of</strong> Management Journal, Vol
44, No. 5, pp 996-1004.
van Wijk, R., Jansen, J. J. P. and Lyles, M. A. (2008) "Inter- and Intra-Organizational Knowledge Transfer: A
Meta-Analytic Review and Assessment <strong>of</strong> its Antecedents and Consequences", Journal <strong>of</strong> Management
Studies, Vol 45, No. 4, pp 830-853.
Wang, S. and Noe, R. A. (2010) "Knowledge sharing: A review and directions for future research", Human
Resource Management Review, Vol 20, No. 2, pp 115-131.
Venkatesh, M., Nosovitch, J. and Khan, S. (1999) "Knowledge barriers to technology adoption: The case <strong>of</strong>
second-generation community networking", [online], http://www.scn.org/<strong>the</strong>_network/Proj/ws99/venkateshet-al-pp.html.
Zielinski, J. G. (1962) "An attempt to construct a realistic <strong>the</strong>ory <strong>of</strong> socialist economy", Economics <strong>of</strong> planning,
Vol 2, No. 1, pp 87-104.
760

The Global Knowledge Management Framework:
Understanding Knowledge Management in Globally
Distributed Settings
Jan Pawlowski 1 and Markus Bick 2
1
Global Information Systems, University <strong>of</strong> Jyväskylä, Finland
2
Business Information Systems, ESCP Europe Wirtschaftshochschule Berlin,
Germany
jan.pawlowski@jyu.fi
markus.bick@escpeurope.de
Abstract: Our paper introduces <strong>the</strong> Global Knowledge Management Framework which describes components
and influence factors <strong>of</strong> knowledge management in globally distributed settings. The framework identifies <strong>the</strong> key
aspects when designing knowledge management processes and systems and can be used for two main
purposes. On <strong>the</strong> one hand, it guides development processes by providing a solution space and success factors
for decision makers as well as implementers. On <strong>the</strong> o<strong>the</strong>r hand, it is a reference for researchers to compare
research in <strong>the</strong> field by providing a common set <strong>of</strong> aspects influencing <strong>the</strong> success <strong>of</strong> knowledge management
solutions. We illustrate and validate <strong>the</strong> model in a case <strong>of</strong> global knowledge management, in particular for
knowledge management in educational organizations.
Keywords: Global Knowledge Management, Knowledge Management Framework, Knowledge Management
Processes, Culture
1. Introduction
In this paper, we describe <strong>the</strong> Global Knowledge Management Framework (GKMF) which is a model
to structure and compare influence factors on knowledge management (KM) in global settings. It
serves as a guideline for researchers and practitioners to design, compare and validate knowledge
management systems based on a thorough analysis <strong>of</strong> current research <strong>of</strong> influence factors for
successful KM around <strong>the</strong> globe.
Knowledge management becomes more and more important in global settings (Holden, 2002). The
influence <strong>of</strong> aspects like geographical dispersion, communication across time zones as well as
cultural influence factors has become a focus issue in research for <strong>the</strong> past decade. A variety <strong>of</strong> topics
has come up in <strong>the</strong> field to understand global knowledge management, focusing on foundational
issues, KM implementation and adoption processes as well as specific issues in <strong>the</strong>se processes,
such as supporting single tasks or using certain interventions (Alavi & Leidner, 2001). However,
recent studies show that still a lot <strong>of</strong> KM projects fail (Coakes et al, 2010) and not all influence factors
are clearly understood. This is in particular <strong>the</strong> case for global settings in which <strong>the</strong> context plays a
major role such as cultural aspects (Holden, 2002), political aspects, legal aspects, or infrastructural
aspects (Richter & Pawlowski, 2007).
It seems necessary to map current research to corresponding context information to make project or
research results comparable and validate transferability across different contexts. In particular, <strong>the</strong>
context <strong>of</strong> research projects as well as implementation and adoption processes should be captured in
a clear way. By making KM project results comparable and mapping results and context, we might
achieve a better understanding <strong>of</strong> what works in which organizational or cultural context.
Frameworks define <strong>the</strong> relevant objects and <strong>the</strong>ir coherences as well as <strong>the</strong> scaffolding for aspects
that have to be considered during <strong>the</strong> design and implementation process. By that, frameworks are a
proper solution to map <strong>the</strong> different contextual aspects, influence factors as well as results. We derive
such a framework for global knowledge management research and practice. After starting with <strong>the</strong>
conceptual foundations and methodological considerations, we <strong>the</strong>n present our Global Knowledge
Management Framework (GKMF) and its application.
2. Related work
Frameworks describe concepts, aspects, processes and <strong>the</strong>ir relations <strong>of</strong> a certain domain or problem
to create a better understanding or to support specific purposes. Often, <strong>the</strong> concepts <strong>of</strong> reference
models or architectures are used in a similar way. In many domains such as s<strong>of</strong>tware development,
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Jan Pawlowski and Markus Bick
frameworks are used to understand <strong>the</strong> relation between components (such as program modules)
and to structure and guide through a problem domain.
In <strong>the</strong> following, we describe two example frameworks for knowledge management, both from a
generic perspective as well as specific frameworks for a global context. This is followed by a
discussion which aspects need to be taken into account when knowledge management is done in
global organizations.
2.1 Frameworks and Models for Knowledge Management
In <strong>the</strong> domain <strong>of</strong> knowledge management, frameworks and corresponding approaches (architectures,
models, reference models) are widely used to describe components, design aspects or technical
architectures (cf. Hahn & Subramani, 2000, CEN, 2004).
In many cases, KM frameworks are created to achieve a common understanding <strong>of</strong> <strong>the</strong> domain (CEN,
2004, Maier, 2007), to structure approaches and practices (Grover & Davenport, 2001) and to identify
research gaps or compare research works (Alavi & Leidner, 2001, Grover & Davenport, 2001).
Surprisingly, most <strong>of</strong> those frameworks do not cover global aspects – typical aspects which need to
be taken into account on top <strong>of</strong> intra-organizational domestic KM projects are for example:
interorganizational processes and collaboration, communication processes, work in distributed teams,
as well as additional barriers, new type <strong>of</strong> tools or instruments (cf. Holden, 2002, Desouza & Evaristo,
2003, Prikladnicki, Audy, Evaristo, 2003). Thus, as a first step, it is necessary to analyze which
current model can be used as a basis building a framework for global purposes. Within this paper we
exemplary analyze just two frameworks, illustrating <strong>the</strong> idea <strong>of</strong> using frameworks.
One <strong>of</strong> <strong>the</strong> main frameworks currently used in practice is <strong>the</strong> framework by CEN (2004) created in <strong>the</strong>
<strong>European</strong> standardization community which aims at creating a common terminology and frame <strong>of</strong>
reference for organizations involved in knowledge management (Figure 1).
Figure 1: Knowledge Management Framework (CEN, 2004)
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Jan Pawlowski and Markus Bick
Figure 2: Knowledge Management Architecture (Maier, 2007)
The CEN framework shows a clear process orientation, aiming at describing core business processes
as well as knowledge-related processes. It extends those processes by enablers: knowledge
capabilities on an organizational (e.g., vision, strategy) and individual level (such as skills,
competences, methods, tools). Due to its relevance to practice – despite <strong>of</strong> its lack <strong>of</strong> research-base
and global aspects – it is a good candidate to be used as a basis for a global framework.
As second – research-oriented – framework, we considered <strong>the</strong> framework by Maier (2007). This
framework is organized on different levels (strategic, design, organizational) and by knowledge types
which are connected by generic knowledge activities. The framework identifies key aspects <strong>of</strong>
knowledge management as well as potential tools and methods around those (e.g. ontologies,
technical architectures, roles). It is based on clear, research-based classifications and categorizations
and identifies influence factors and solutions for different purposes. Thus, <strong>the</strong> framework is usable for
structuring both research and practice approaches.
Both <strong>of</strong> <strong>the</strong> above mentioned frameworks do no cover – and do not intend to – global aspects. Thus, it
is necessary to identify how global knowledge management is different from domestic or intraorganizational
knowledge management. For this purpose, we have analyzed models and practices
regarding knowledge management related aspects and specifics.
Knowledge management in a broad sense is a critical aspect <strong>of</strong> globally distributed work (Holden,
2002) which needs to take processes between organizations and strategies for global knowledge
exchange into account. This leads to a variety <strong>of</strong> additional influence factors, barriers and challenges
in global settings (Holden, 2002, Desouza & Evaristo, 2003, Prikladnicki, Audy, Evaristo, 2003) –
examples for this are culture-specific factors, communication factors, additional individual and
organizational competences as well as fur<strong>the</strong>r requirements towards (internationally usable) tools.
This brief analysis has shown that suitable, extensible frameworks exist but <strong>the</strong>y need to be extended
regarding global processes.
3. Global knowledge management framework
In <strong>the</strong> following, we describe <strong>the</strong> Global Knowledge Management Framework (GKMF) which provides
a reference for structuring research and practice projects as well as guiding adopters through <strong>the</strong>
implementation and deployment process.
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3.1 Methodology
Jan Pawlowski and Markus Bick
Knowledge management is highly dependent on <strong>the</strong> context and cannot be validated separated from
practical implementations. It is <strong>the</strong> main aim <strong>of</strong> knowledge management research to construct
solutions which achieve practical impact and benefits. Therefore, our framework is built on a design
science research approach (Hevner et al., 2004) – based on a thorough literature analysis <strong>of</strong>
frameworks and global influence factors, we have identified gaps and extension needs to create <strong>the</strong>
new artifact. The framework will be validated in a case study (Yin, 2003). By this, we aim at
progressing from a conceptual framework towards <strong>the</strong>ory building for knowledge management
settings.
3.2 Global Knowledge Management Framework Construction
As shown above, we base our development on a combination <strong>of</strong> frameworks and an analysis <strong>of</strong>
influence factors, barriers and challenges in global settings.
We have identified commonalities <strong>of</strong> <strong>the</strong> frameworks (strategies, processes, knowledge resources,
tools) and harmonized <strong>the</strong> different terminologies. We <strong>the</strong>n derived extensions and mapped those to
<strong>the</strong> initial components <strong>of</strong> <strong>the</strong> framework. The components were revised during <strong>the</strong> case study
validation.
We have also derived a description format to clarify relations between components - this is important
for understanding <strong>the</strong> mechanisms and impact (e.g. which intervention has positive impact on which
metrics for which process?). This distinguishes out framework clearly from o<strong>the</strong>r frameworks which
just specify components.
Context
Strategies
create
enable
Knowledge
Stakeholders
Problems Resources
Measured
by
influences Results
change
guide
use
Society Organization Individual
Intervention A Intervention B Intervention N
Processes
influences
influences
embedded in
runs
External Processes
Business Processes
Knowledge
Processes
Improved by
Validation, Feedback, Improvement
measures
Performance Knowledge …
Culture
perform
influences
Measured
by
Figure 3: Global Knowledge Management Framework
In <strong>the</strong> following, we describe <strong>the</strong> GKMF components (Figure 3) in detail – a particular focus is not
solely identifying those components, providing potential instantiations as well as exemplary relations
(only main relations are illustrated in <strong>the</strong> figure). As an example, potential instruments are listed as
options / design alternatives. By this, <strong>the</strong> framework also creates a solution space for knowledge
management design.
764
Support
Human-based
instruments
Technologies and tools
Instruments
enable
Infrastructures

3.3 Processes
Jan Pawlowski and Markus Bick
The core <strong>of</strong> <strong>the</strong> framework is described by processes on three levels. Business processes denote <strong>the</strong>
core processes <strong>of</strong> an organization such as teaching for educational organizations or s<strong>of</strong>tware
development and deployment for s<strong>of</strong>tware developing organizations. It is not possible to provide a
process scheme independent <strong>of</strong> <strong>the</strong> domain – thus, we propose a general scheme such as ebXML
(UN/CEFACT & OASIS, 2001) and extend those by domain specific processes (Fettke and Loos,
2003b). The business processes are supported by embedded knowledge processes which enable
knowledge management within and outside <strong>the</strong> organization, e.g., knowledge identification,
knowledge sharing or knowledge distribution (CEN, 2004). In <strong>the</strong> global context, those processes are
highly related to external processes with stakeholders who are distributed across <strong>the</strong> globe (such as
negotiations, cooperation agreement, coordination <strong>of</strong> distributed development). These processes are
accompanied by intervention processes which describe newly integrated processes to improve
knowledge management as well as validation processes measuring <strong>the</strong> success <strong>of</strong> <strong>the</strong> interventions
(Maier and Remus, 2003). The following table summarizes <strong>the</strong> main aspects <strong>of</strong> <strong>the</strong> process
components: For each process class, we have also identified possible solutions (e.g., business
processes can be specified using ebXML as a template).
Table 1: Process categories
Sample Values /
Process Description
Source / References
Business
Process
Knowledge
Processes
External
Processes
Core business processes <strong>of</strong>
an organization
Knowledge related activities
<strong>of</strong> <strong>the</strong> organization
Processes with external
stakeholders (cooperation
partners, customers,
outsourcing partners)
Attributes
Category
Procurement
Human resources
knowledge
identification
knowledge acquisition
knowledge sharing
Cooperation
establishment
Awareness building
Negotiation
UN / CEFACT &
OASIS (2001), Fettke
and Loos (2003b)
CEN (2004)
Maier (2007)
Pirkkalainen et al.
(2010)
The above process specifications show sample processes which can be addressed and which might
be modeled – based on those process specifications, it is necessary to identify which processes are
necessary and how those processes are embedded (including embedding interventions). The process
design has a main impact on acceptance, performance as well as speed and quality <strong>of</strong> knowledge
creation.
3.4 Stakeholders and context
The categories stakeholders and context are discussed in one paragraph as <strong>the</strong>re are several
overlaps. The organizational background is – in some research works – considered as context, in
o<strong>the</strong>r works <strong>the</strong> organizational aspects are designed and changed (e.g. organizational culture).
Barriers are somehow overlapping. In our context, some barriers are caused by <strong>the</strong> cultural
background but are observed when studying individuals. Thus, it is useful to combine those
categories. The category stakeholders describes characteristics <strong>of</strong> participating stakeholders. This
can be related to individuals (e.g. preferences, interests), organizations or societies. The stakeholder
category is an important factor and in most cases taken as a constraint as research investigations are
done for certain target groups or types <strong>of</strong> organizations. Whereas <strong>the</strong>se characteristics are mostly part
<strong>of</strong> <strong>the</strong> context, o<strong>the</strong>r aspects are subject to major research, in particular <strong>the</strong> barriers (individual as well
as organizational).
The sub-category context describes <strong>the</strong> context or environment in which knowledge management
takes place. In most cases, it relates to organizations (organizational culture, strategies, cf. Desouza
& Evaristo, 2003) or society (culture, technological infrastructures, policies, see Pawlowski & Richter,
2007). A focus in this category is <strong>the</strong> analysis <strong>of</strong> cultural aspects influencing communication,
collaboration and coordination <strong>of</strong> knowledge processes (DeLong & Fahey, 2002).
The following table summarizes our findings regarding <strong>the</strong> categories <strong>of</strong> contents and stakeholders.
The table below shows samples for <strong>the</strong> context and stakeholder view. The components have a strong
impact on o<strong>the</strong>r framework components – as an example, it is clearly necessary to include dedicated
765

Jan Pawlowski and Markus Bick
awareness building and training processes into <strong>the</strong> knowledge processes to facilitate cultural
understanding. Cultural factors also influence how and which knowledge is shared.
Table 2: Stakeholder and context categories
Source /
Category Description Sample Values / Attributes
Individual:
Personal
Characteristics
Individual:
Barriers
Context:
Organizational
Characteristics
Context:
Organizational
Barriers
Context: Cultural
Characteristics
Context: Cultural
Barriers
Context:
Infrastructure
Characteristics
Context: Success
factors
4. Knowledge
Description <strong>of</strong>
individuals’
characteristics
Potential barriers
towards knowledge
management
utilization
Description <strong>of</strong>
organization
characteristics
Potential
organizational
barriers towards
knowledge
management
utilization
Description <strong>of</strong>
cultural
characteristics
Potential cultural
barriers towards
knowledge
management
utilization
Description <strong>of</strong>
infrastructure
Success factors for
KM in organizations
Demographic data (name, age,
gender, …)
Media Preferences
Qualifications
lack <strong>of</strong> time
fear about job security;
lack <strong>of</strong> awareness
Name
Size
Type (private, government, NGO, …)
lack <strong>of</strong> leadership and managerial
direction / strategies
shortage <strong>of</strong> formal and informal spaces
to share, reflect and generate (new)
knowledge;
internal competitiveness within
business units,
Power Distance, Uncertainty
avoidance, individualism, …
Value <strong>of</strong> errors
Roles <strong>of</strong> knowledge experts
Inability <strong>of</strong> communication
Inability <strong>of</strong> collaboration
Fear / insecurity
National ICT policies
Strategies
Communication networks
Integrated Technical Infrastructure
Knowledge Strategy (regarding users,
sources, processes, storage strategy,
…)
Clear knowledge structure
References
Maier & Thalmann
(2010), Pawlowski
et al. (2010),
Stewart (1998)
Fahey & Prusak
(1998), Lugger &
Kraus (2007),
Maier (2007)
Desouza and
Evaristo (2003)
McDermott and
O’Dell (2001),
CEN (2004), Bick
(2004),
Maier (2007)
Richter and
Pawlowski (2007)
Desouza &
Evaoristo (2003),
Holden (2002),
McDermott and
O’Dell (2001),
DeLong & Fahey
(2000)
Gibbs et al. (2002)
Richter and
Pawlowski (2007)
Bick (2004), Maier
(2007), Fahey and
Prusak (1998),
Lehner and Haas
(2010)
This category describes knowledge aspects and elements which are shared or required in <strong>the</strong>
organization. This category can contain for example problems to which knowledge is applied as well
as resources representing codification <strong>of</strong> knowledge.
Table 3: Knowledge categories
Source /
Category Description Sample Values / Attributes
Knowledge
element
Knowledge type
Problem
Description <strong>of</strong>
knowledge areas <strong>of</strong>
an organization
What kind <strong>of</strong>
knowledge
Problems to which
knowledge is applied
Subject area
Type (procedural, factual, …)
Representation / codification
Knowing that / knowing how
Tacit / implicit / explicit
Knowledge as object / knowledge
as process
Problem description
Context
Related knowledge
766
References
Thalmann (2011),
Pirkkalainen et al.
(2010)
Nonaka and
Tackeuchi (1995)
Kalz et al. (2010)

Jan Pawlowski and Markus Bick
This category obviously describes <strong>the</strong> core <strong>of</strong> knowledge management systems – it shows only one
way <strong>of</strong> knowledge aspects with <strong>the</strong> assumption that knowledge is intended for problem solutions.
However, it covers both, human- as well as technology-oriented knowledge aspects (e.g. represented
in a codified form or attached to actors). The main influences on this category is <strong>the</strong> question which
and how knowledge is made explicit: In highly contextualized cultures, less knowledge is made
explicit. Also common knowledge is perceived differently. Thus, a strong relation to cultural aspects is
given. This category is strongly influenced by cultural aspects. Secondly, interventions need to be
tailored to <strong>the</strong> types <strong>of</strong> knowledge shared / problems addressed. As an example, highly complex
knowledge innovations can only be achieved using certain interventions such as focused think-tanks,
open spaces or using creativity tools.
4.1 Instruments and interventions
Instruments describe methods and actions to realize <strong>the</strong> knowledge processes. The main categories
(Maier, 2002) are human-oriented instruments (e.g., job rotation, knowledge fairs) and technological
instrument (e.g. knowledge bases, communication tools). These interventions need to be embedded
in <strong>the</strong> above described process areas (Maier and Remus, 2003). The following table shows a (small)
subset <strong>of</strong> potential interventions and instruments.
Table 4: Instrument categories
Source /
Category Description Sample Values / Attributes
Human-based
instruments
Technologybased
instruments
Description <strong>of</strong> <strong>the</strong>
instrument
Problems to which
knowledge is applied
• Mentoring
• Open Space
• Document Management
• Email
• Social S<strong>of</strong>tware
References
Maier (2007),
CEN (2004), Bick
(2004),
Maier (2007),
CEN (2004), Bick
(2004),
The list <strong>of</strong> instrument is <strong>of</strong> course a small set <strong>of</strong> options as this is <strong>the</strong> main research field <strong>of</strong>
constructive research in information systems, human resource management (HRM) and related
areas. However, a focus is <strong>the</strong> usage and validation <strong>of</strong> instruments to address certain barriers.
Interventions are a main research topic in <strong>the</strong> design-oriented knowledge management community.
5. Results
Results describe <strong>the</strong> key outcome <strong>of</strong> <strong>the</strong> knowledge processes using some form <strong>of</strong> metrics (Bose,
2004, Grossman, 2006). This can incorporate newly generated / learned knowledge as well as
measurements <strong>of</strong> <strong>the</strong> knowledge and its impact (Shin, 2004). Measuring knowledge management
success can be in principal done on a general level (e.g. using <strong>the</strong> Information System Success
Model, Jennex and Olfman, 2006, Lindsey, 2002) or for specific components such as organizational
capabilities (Gold et al., 2006), performance (Lee et al, 2005) or knowledge / competence
development.
Table 5: Result categories
Category Description Sample Values / Attributes
Knowledge
Intellectual
capital
Global aspects
Measurement <strong>of</strong>
knowledge and core
processes
General knowledgerelated
metrics <strong>of</strong> an
organization
Measuring
international aspects
Acceptance <strong>of</strong> knowledge
management systems (KMS)
Usability / usefulness <strong>of</strong> KMS
Knowledge assets (number,
usefulness, complexity, …)
Human capital / knowledge
development (no. <strong>of</strong> employees,
employee turnover, pr<strong>of</strong>its / employee,
motivation, satisfaction, …)
Customer benefits (rating, sales /
customer, satisfaction, length <strong>of</strong>
customer relationship, response time,
…)
Improvement <strong>of</strong> global competences
Awareness and sensitivity
Team understanding, team-related
aspects
767
Source /
References
Kankanhalli &
Tan (2004), Lee
et al (2005)
Maier (2007)
Bose (2004),
Maier (2007),
Stewart (1998)
Dawes et al.
(2011), Desouza
& Evaristo
(2003)

Jan Pawlowski and Markus Bick
This category is related to different combinations <strong>of</strong> <strong>the</strong> o<strong>the</strong>r components. In case that success
models are used, this is modeled in structural equations, in o<strong>the</strong>r cases <strong>the</strong> relations are implicitly
described in publications.
For global settings, <strong>the</strong>se metrics are applied in a similar way than traditional KM assessment (cf.
Cummings, 2004) in which mainly <strong>the</strong> influence factors are analyzed. However, <strong>the</strong>re are very few
dedicated publications on measuring <strong>the</strong> specific global effects <strong>of</strong> knowledge management – most
publications address specific aspects such as <strong>the</strong> communication and team work in global settings.
Finding appropriate, explanatory, comprehensive metrics for knowledge management in global
settings beyond performance and effectiveness is still a challenge.
This section has shown <strong>the</strong> variety <strong>of</strong> potential components, attributes and instantiations <strong>of</strong> our
GKMF. It serves as an initial set or solution space. Describing <strong>the</strong> above mentioned categories and
elements enables us to compare both research works as well as implementations. Thus, <strong>the</strong>
framework serves as a basis for comparing current and future research with a focus on <strong>the</strong> global
context.
6. Application scenarios: Results and discussion
In <strong>the</strong> following, we describe how <strong>the</strong> framework is applied in a short scenario: How to guide a design
process based on <strong>the</strong> identified influence factors.
Validating a framework or reference model mainly takes into account how <strong>the</strong>ir <strong>the</strong>oretical foundation
as well as <strong>the</strong>ir practical relevance and applicability in practice is (cf. Fettke & Loos, 2003a). We will
not discuss <strong>the</strong> <strong>the</strong>oretical foundation as this is an inherent part <strong>of</strong> <strong>the</strong> model. We focused on <strong>the</strong>
aspects <strong>of</strong> usefulness, adaptability, understanding, comparative value and contribution to <strong>the</strong>orybuilding.
Based on <strong>the</strong>se criteria, we discuss how KM implementations can be guided by using <strong>the</strong>
framework.
6.1 Guiding <strong>the</strong> KM design process
Our framework has been applied in a typical scenario: Designing KM support for an international
project. The project OpenScout is a <strong>European</strong> project aiming at creating new opportunities for <strong>the</strong> use
<strong>of</strong> digital resources for education in international groups (Niemann et al., 2010, Kalz et al., 2010). In
<strong>the</strong> project, it is a key aspect to create and facilitate globally distributing groups to work toge<strong>the</strong>r on
re-using, re-purposing and enhancing / improving educational resources. This is a typical process for
knowledge management in culturally diverse groups with a task which is not clearly defined in
advance. Knowledge sharing is a crucial process for such a project as <strong>the</strong> adaptation <strong>of</strong> educational
resources is highly context- and culture-dependent. For <strong>the</strong> adaptation and implementation <strong>the</strong>
following steps were chosen:
Creating access to multi-lingual open educational resources through a portal solution
Creating a social network to for communication and collaboration purposes
Creating multi-national groups to share and improve educational resources
Facilitating <strong>the</strong> internationalization process
Facilitating knowledge exchange and feedback on <strong>the</strong> adapted resources
These processes need a clear planning <strong>of</strong> knowledge management activities as those are crucial for
success in inter-organizational, internationally distributed processes. Thus, <strong>the</strong> following steps were
taken:
Identifying barriers <strong>of</strong> stakeholders (e.g. teachers, technology providers): In an initial study,
stakeholders across different project for international education were asked about barriers
towards using and providing resources. The framework was used to identify potential cultural
barriers towards knowledge sharing. The questionnaire was based on <strong>the</strong> GKMF and thus
provided a guideline for <strong>the</strong> requirement analysis.
Providing knowledge sharing processes: Based on <strong>the</strong> knowledge process component, a set
<strong>of</strong> processes and activities for knowledge sharing as well as cultural preparation was modeled
and implemented, taking guidance on process embedding into account (in particular for teachers,
additional activities need to be embedded into <strong>the</strong>ir everyday routine). Thus, <strong>the</strong> knowledge
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Jan Pawlowski and Markus Bick
processes served as guidance to take different phases into account and to connect <strong>the</strong>m to basic
work processes. Additionally, <strong>the</strong> international collaboration processes were designed.
Providing a supporting infrastructure: Based on <strong>the</strong> barriers, support tools were planned which
are made available in a tool library (Niemann et al., 2010). The barriers from <strong>the</strong> GKMF were
extended by specific barriers in <strong>the</strong> educational domain. Based on those, tools as well as multiple
tools were selected to 1) overcome barriers, 2) support <strong>the</strong> combination <strong>of</strong> business and
knowledge processes, and 3) to address culture specific issues.
Assessing <strong>the</strong> success <strong>of</strong> collaboration: The success <strong>of</strong> <strong>the</strong> scenario is essential for both, <strong>the</strong>
project itself as it is funded by <strong>the</strong> <strong>European</strong> Commission and thus needs to show clear success
indicators as well as <strong>the</strong> internal validation to create continuous improvements. The indicators are
based on <strong>the</strong> knowledge-focused indicators <strong>of</strong> <strong>the</strong> GKMF.
Providing recommendations for improvement: Based on <strong>the</strong> assessment and <strong>the</strong> framework
relations itself, improvement recommendations within <strong>the</strong> project are generated. However, <strong>the</strong>re is
no guidance for this process in <strong>the</strong> GKMF, just <strong>the</strong> inherent idea <strong>of</strong> <strong>the</strong> model to understand <strong>the</strong>
relations <strong>of</strong> <strong>the</strong> KM components.
GKMF was not used in a normative way but as a guiding instrument to support stakeholders such as
designers, researchers and project members. The project made us aware <strong>of</strong> mainly all knowledge
related aspects <strong>of</strong> <strong>the</strong> model. Some extensions needed to be done due to <strong>the</strong> domain-specific
characteristics. For all users, <strong>the</strong> model was seen as helpful to avoid time-consuming comparison <strong>of</strong>
fur<strong>the</strong>r scenarios as those are already incorporated in <strong>the</strong> framework. In most cases, <strong>the</strong> guidance
was appreciated as a useful guideline. As <strong>the</strong> model is formulated in natural language, understanding
<strong>of</strong> <strong>the</strong> components was ra<strong>the</strong>r simple and needed no fur<strong>the</strong>r facilitation. Also <strong>the</strong> assessment metrics
were considered as very useful to provide evidence <strong>of</strong> success to <strong>the</strong> funding agency.
As a summary, <strong>the</strong> model served its intended purposes as a guideline which provides a solution
space but not <strong>the</strong> solution itself. In particular, <strong>the</strong> availability <strong>of</strong> barriers, success factors and inherent
recommendations (process embedding, analysis references) were regarded as most useful. It can be
said that <strong>the</strong> model was appreciated as a “toolbox” but not as a restricting procedure. The
observations show also that <strong>the</strong> adaptation need for <strong>the</strong> domain was small which could also be due to
<strong>the</strong> reason that educational processes were given as an example in <strong>the</strong> description <strong>of</strong> process
alternatives.
The model has proven successful based on <strong>the</strong> observations and results <strong>of</strong> <strong>the</strong> case study. The case
was successfully supported by utilizing components and component-relations <strong>of</strong> <strong>the</strong> GKMF. However,
<strong>the</strong> case study is limited, methodologically as well as regarding <strong>the</strong> application domain. Fur<strong>the</strong>r
studies for o<strong>the</strong>r domains, contexts, and stakeholders need to be performed to understand <strong>the</strong>
generalizability <strong>of</strong> <strong>the</strong> GKMF framework.
7. Conclusion and future research
In our paper, we have created a solution space for global knowledge management by providing a
framework identifying and harmonizing KM research efforts in <strong>the</strong> global context. We were able to
observe <strong>the</strong> usefulness <strong>of</strong> <strong>the</strong> model in a case study which also led us to fur<strong>the</strong>r research needs.
As a next step, we intend to utilize <strong>the</strong> model for fur<strong>the</strong>r research plans and apply and assess <strong>the</strong>
model in different contexts. We believe that <strong>the</strong> model can contribute to provide proven guidance,
create comparative research models and to provide an evaluation opportunity for actors in <strong>the</strong> field.
References
Alavi, M., and Leidner, D.E. (2001) Review: Knowledge Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues, MISQ, Vol. 25, No 1, pp 107-136.
Bick, M. (2004) Knowledge Management Support System, Doctoral Thesis, University <strong>of</strong> Duisburg-Essen.
Bose, R. (2004) Knowledge management metrics, Industrial Management & Data Systems, Vol. 104, No.6, pp
457-468.
Coakes, E., Amar, A.D., and Luisa Granados,M.L. (2010) Knowledge management, strategy, and technology: a
global snapshot, Journal <strong>of</strong> Enterprise Information Management, Vol. 23, No. 3, pp 282 – 304.
CEN (2004) CEN CWA 14924 <strong>European</strong> Guide to good Practice in Knowledge Management - Part 1 to 5,
Brussels.
Cummings, J.N. (2004) Work Groups, Structural Diversity, and Knowledge Sharing in a Global Organization,
Management Science, Vol. 50, No. 3, pp 352-364
769

Jan Pawlowski and Markus Bick
Dawes, S.S., Gharawi, M., Burke, B. (2011) Knowledge and Information Sharing in Transnational Knowledge
Networks: A Contextual Perspective, Hawaii International <strong>Conference</strong> on System Sciences, 2011.
De Long, D. W. and Fahey, L. (2000) Diagnosing cultural barriers to knowledge management. Academy <strong>of</strong>
Management Executive, Vol. 14, No. 4, pp 113-128.
Desouza K, and Evaristo R. (2003) Global knowledge management strategies. <strong>European</strong> Management Journal,
Vol. 21, No. 1, pp 62–67.
Fahey, L. and Prusak, L. (1998) The Eleven Deadliest Sins <strong>of</strong> Knowledge Management, California Management
Review, Vol. 40, No. 3, pp 265-276.
Fettke, P., and Loos, P. (2003a) Multiperspective Evaluation <strong>of</strong> Reference Models – Towards a Framework,
Lecture Notes in Computer Science: Conceptual Modeling for Novel Application Domains, pp 80-91.
Springer.
Fettke, P. and Loos, P. (2003b) Classification <strong>of</strong> reference models—a methodology and its application,
Information Systems and e-Business Management, Vol. 1, No. 1, pp 35–53.
Gibbs, J., Kraemer, K.L., and Dedrick, J. (2002) Environment and Policy Factors Shaping E-commerce Diffusion:
A Cross-Country Comparison. UC Irvine: Center for Research on Information Technology and
Organizations.
Gold, A.H.; Malhotra, A.; and Segars, A.H. (2006) Knowledge management: An organizational capabilities
perspective. Journal <strong>of</strong> Management Information Systems, Vol. 18, No. 1, pp 185-214.
Grossman, M. (2006) An Overview <strong>of</strong> Knowledge Management Assessment Approaches. Journal <strong>of</strong> American
Academy <strong>of</strong> Business Cambridge, Vol. 8, No. 2, p.242-247.
Grover, V., and Davenport, T.H. (2001) General perspectives on knowledge management: Fostering a research
agenda. Journal <strong>of</strong> Management Information Systems, Vol. 18, No. 1, pp 5-21.
Hahn J, and Subramani MR. (2000) A framework <strong>of</strong> knowledge management systems: issues and challenges for
<strong>the</strong>ory and practice. In: <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> twenty-first international conference on information systems,
Brisbane, Australia, pp. 302–312.
Holden, N. (2002) Cross-Cultural Management – A Knowledge Management Perspective. Harlow: Prentice Hall.
Jennex, M.E. and Olfman, L. (2006) A Model <strong>of</strong> Knowledge Management Success, International Journal <strong>of</strong>
Knowledge Management, Vol. 2, No. 3, 2006, pp 51-68.
Lee, K.C., Lee, S., Kang, I.W. (2005) KMPI: Measuring Knowledge Management Performance, Information &
Management, Vol. 42, No. 3, pp 469-482.
Lehner, F and Haas, N. (2010) Knowledge Management Success Factors – Proposal <strong>of</strong> an Empirical Research”
Electronic Journal <strong>of</strong> Knowledge Management Vol. 8, No. 1, pp79 - 90
Li, W (2009) Virtual knowledge sharing in a cross-cultural context, Journal <strong>of</strong> Knowledge Management, Vol. 14, No.
1.
Lugger, K. and Kraus, H. (2001) Mastering Human barriers in Knowledge Management, Journal <strong>of</strong> Universal
Computer Science Vol. 7, No. 6, pp 488-497.
Kalkan, V.J. (2008) An overall view <strong>of</strong> knowledge management challenges for global business, Business Process
Management Journal, Vol. 14, No. 3, pp 390-400.
Kalz, M., Specht, M., Nadolski, R., Bastiaens, Y., Leirs, N., and Pawlowski, J.M. (2010) OpenScout: Competence
based management education with community-improved open educational resources, 17th EDINEB
<strong>Conference</strong>: Crossing borders in Education and Work-based Learning, London, UK, June 2010.
Kankanhalli, A., and Tan, B.C.Y. (2004) Knowledge Management Metrics: A Review and Directions for Future
Research, International Journal <strong>of</strong> Knowledge Management, Vol. 1, No. 2,pp 20-32.
Lehner, F and Haas, N. (2010) Knowledge Management Success Factors – Proposal <strong>of</strong> an Empirical Research”
Electronic Journal <strong>of</strong> Knowledge Management Vol. 8, No. 1, pp79 – 90.
Lindsey, K. (2002) Measuring Knowledge Management Effectiveness: A Task-Contingent Organizational
Capabilities Perspective, AMCIS 2002, pp 2085-2090.
Maier, R. (2007) Knowledge Management Systems: Information and Communication Technologies for
Knowledge Management, 3rd ed. Springer, Berlin et al.
Maier, R., and Remus, U. (2003) Implementing process-oriented knowledge management strategies, Journal <strong>of</strong>
Knowledge Management, Vol. 7, No. 4, pp 62–74.
Maier, R. and Thalmann, S. (2010) Using personas for designing knowledge and learning services: results <strong>of</strong> an
ethnographically informed study, International Journal <strong>of</strong> Technology Enhanced Learning, Vol. 2, No. 1/2,
pp58–74.
McDermott, R. and O’Dell, C. (2001) Overcoming culture barriers to sharing knowledge’’, Journal <strong>of</strong> Knowledge
Management, Vol. 5, No. 1, pp 76-85.
Niemann, K., Schwertel, U., Kalz, M., et al (2010) Skill-based Scouting <strong>of</strong> Open Management Content.
<strong>Proceedings</strong> <strong>of</strong> EC-TEL 2010. Barcelona, Spain.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company, Oxford University Press, New York.
Pawlowski, J.M., Holtkamp, P., and Kalb, H. (2010) Globalization Competences in Information Systems and E-
Learning, Workshop on Competencies for <strong>the</strong> Globalization <strong>of</strong> Information Systems in Knowledge-Intensive
Settings, ICSOB, June 2010.
Pirkkalainen, H., Thalmann, S., Pawlowski, J.M., Bick, M., Holtkamp, P., Ha, K.H. (2010) Internationalization
Processes for Open Educational Resources, Workshop on Competencies for <strong>the</strong> Globalization <strong>of</strong>
Information Systems in Knowledge-Intensive Settings, ICSOB, June 2010.
Prikladnicki R, Audy J, and Evaristo R (2003) Global s<strong>of</strong>tware development in practice lessons learned. S<strong>of</strong>tware
Process: Improvement and Practice, Vol. 8, No. 4, pp 267–281.
770

Jan Pawlowski and Markus Bick
Richter, T., and Pawlowski, J.M. (2007) Adaptation <strong>of</strong> E-Learning Environments: Determining National
Differences through Context Metadata. TRANS - Internet Journal for Cultural Studies, Vol. 17.
Shin, M. (2004) A framework for evaluating economics <strong>of</strong> knowledge management systems, Information &
Management, Vol. 42, pp 179–196.
Stewart, Th. A. (1998), Intellectual Capital: The New Wealth <strong>of</strong> Organizations, Crown Business.
Thalmann, S. (2011) Decision Support Framework for Selecting Techniques to Prepeare Knowledge Elements
for Adaptive Use, Doctoral Dissertation, University <strong>of</strong> Innsbruck.
UN/CEFACT & OASIS (2001) ebXML Catalog <strong>of</strong> Common Business Processes v1.0,
http://www.ebxml.org/specs/bpPROC.pdf
Yin, R. K. (2003) Case Study Research - Design and Methods, 5th ed., Vol. 45. Thousand Oaks, California: Sage
Publications, Inc.
771

Understanding inter-firm networks and types <strong>of</strong> innovation
in SMEs: A social network perspective
Meysam Poorkavoos 1 , Yanqing Duan 1 and John Edwards 2
1 University <strong>of</strong> Bedfordshire, Luton, UK
2 Aston University, Birmingham, UK
Meysam.Poorkavoos@beds.ac.uk
Yanqing.Duan@beds.ac.uk
J.S.Edwards@aston.ac.uk
Abstract: Innovation is one <strong>of</strong> <strong>the</strong> key drivers for gaining competitive advantages in any firms. Understanding
knowledge transfer through inter-firm networks and its effects on types <strong>of</strong> innovation in SMEs is very important in
improving SMEs innovation. This study examines relationships between characteristics <strong>of</strong> inter-firm knowledge
transfer networks and types <strong>of</strong> innovation in SMEs. To achieve this, social network perspective is adopted to
understand inter-firm knowledge transfer networks and its impact on innovation by investigating how and to what
extend ego network characteristics are affecting types <strong>of</strong> innovation. Therefore, managers can develop <strong>the</strong> firms’
network according to <strong>the</strong>ir strategies and requirements. First, a conceptual model and research hypo<strong>the</strong>ses are
proposed to establish <strong>the</strong> possible relationship between network properties and types <strong>of</strong> innovation. Three
aspects <strong>of</strong> ego network are identified and adopted for hypo<strong>the</strong>ses development: 1) structural properties which
address <strong>the</strong> potential for resources and <strong>the</strong> context for <strong>the</strong> flow <strong>of</strong> resources, 2) relational properties which reflect
<strong>the</strong> quality <strong>of</strong> resource flows, and 3) nodal properties which are about quality and variety <strong>of</strong> resources and
capabilities <strong>of</strong> <strong>the</strong> ego partners. A questionnaire has been designed based on <strong>the</strong> hypo<strong>the</strong>ses. Second, semistructured
interviews with managers <strong>of</strong> five SMEs have been carried out, and a <strong>the</strong>matic qualitative analysis <strong>of</strong>
<strong>the</strong>se interviews has been performed. The interviews helped to revise <strong>the</strong> questionnaire and provided preliminary
evidence to support <strong>the</strong> hypo<strong>the</strong>ses. Insights from <strong>the</strong> preliminary investigation also helped to develop research
plan for <strong>the</strong> next stage <strong>of</strong> this research.
Keywords: inter-firm knowledge transfer, inter-firm networks, social networks, radical innovation, incremental
innovation
1. Introduction
SMEs are an important part <strong>of</strong> <strong>the</strong> most economic structures and large number <strong>of</strong> people are
employed by SMEs (Hausman 2005). By being innovative organizations can stay ahead <strong>of</strong> <strong>the</strong>ir
competitors and <strong>the</strong>y have more chance to survive in <strong>the</strong> competitive environment. Radical and
incremental innovations are <strong>the</strong> most established classification <strong>of</strong> innovation. Being radical innovator
is an important factor for firms' long-term survival. This type <strong>of</strong> innovation makes <strong>the</strong> foundation for
o<strong>the</strong>r new product development (McDermott and O'Connor 2002). Firms that are mainly focused on
one product development cannot keep <strong>the</strong>ir leadership after a new technology shift (Anderson and
Tushman 1990), so <strong>the</strong>y need to make changes to <strong>the</strong>ir products, processes or services to be able to
survive in <strong>the</strong> current competitive environment. There are studies on inter-firm networks and its effects
on firms’ innovation (Ahuja 2000, Julien et al. 2004, Moller et al. 2007, Rodan and Galunic 2004,
Rothwell 1991, Stuart 2000). Reviewing literature in this area revealed that most <strong>of</strong> <strong>the</strong>m studied
network effects on innovation performance and <strong>the</strong>re are few articles about <strong>the</strong> effects <strong>of</strong> inter-firm
networks on each type <strong>of</strong> innovation (De Propris 2002, Gilsing et al. 2008). Moreover, most <strong>of</strong> <strong>the</strong>m
have focused on large organizations. Therefore, <strong>the</strong> aim <strong>of</strong> this research is to examine <strong>the</strong>
relationships <strong>of</strong> <strong>the</strong> characteristics <strong>of</strong> inter-firm knowledge transfer networks and types <strong>of</strong> innovation in
SMEs in high-tech sector. The reason for studying high-tech sector is that companies in this sector
rely more on alliances and inter-firm knowledge transfer ra<strong>the</strong>r than companies in medium or low-tech
sectors (Hagedoorn 2002). So far most <strong>of</strong> <strong>the</strong> research in this area considered innovation as a
general concept; <strong>the</strong>refore <strong>the</strong> aim <strong>of</strong> this research is testing <strong>the</strong> effects <strong>of</strong> network structures on
specific types <strong>of</strong> innovations. It will help to better understand network characteristics’ effects on radical
and incremental innovation, so managers can develop <strong>the</strong> firm’s network strategies and <strong>the</strong>
associated requirements.
This paper reports <strong>the</strong> early development <strong>of</strong> our research. The study so far has established a
framework to depict <strong>the</strong> relationships between inter-firm network properties and types <strong>of</strong> innovation. A
number <strong>of</strong> hypo<strong>the</strong>ses are proposed. A pilot study was conducted to test <strong>the</strong> research questionnaire
with SMEs managers and to gain preliminary insights into <strong>the</strong> effects <strong>of</strong> inter-firm network properties
and o<strong>the</strong>r factors on types <strong>of</strong> innovation in SMEs.
772

2. Literature review
Meysam Poorkavoos, Yanqing Duan and John Edwards
2.1 Inter-firm networks and innovation
Previous literature addressed different motivations for establishment <strong>of</strong> inter-firm relationships e.g.
spreading <strong>the</strong> cost, sharing risks, and knowledge sharing. Among <strong>the</strong>m acquisition <strong>of</strong> knowledge and
capabilities from partner firms are one <strong>of</strong> <strong>the</strong> most cited reason for inter-firm collaboration (Mowery et
al. 1996). Knowledge is <strong>of</strong>ten tacit and difficult to price, thus it is rarely possible for a firm to buy it from
market (Mowery et al. 1996). A firm that requires a particular knowledge for a special purpose ,such
as creating a new product, has a better chance <strong>of</strong> success by forming a relation with a partner which
already has <strong>the</strong> expertise in that area. So <strong>the</strong> focal firm is able to fill <strong>the</strong> gap in its knowledgebase by
<strong>the</strong> transferred knowledge from <strong>the</strong> partner.
2.2 Innovation
Innovations are not all similar, <strong>the</strong>y reflect <strong>the</strong> strategic objectives pursued by <strong>the</strong> company (Anderson
and Tushman 1990). Because <strong>of</strong> <strong>the</strong> heterogeneity in innovation, a distinction between its different
types is necessary (Moller et al. 2007). Van de Ven et al. (1999) defined innovation as “<strong>the</strong>
development and implementation <strong>of</strong> new ideas and knowledge into a socially and economically
successful product, process or service". Incremental innovation is defined as minor changes or
extensions to <strong>the</strong> current products, existing services or processes <strong>of</strong> <strong>the</strong> organizations (Dewar and
Dutton 1986) and radical innovation is <strong>the</strong> development <strong>of</strong> new products that requires significantly
new technology or ideas that were not existed in <strong>the</strong> market before or requires fundamental changes
to <strong>the</strong> existing market (McDermott and O'Connor 2002). As it is shown in figure 1, product and
process innovations are subgroup <strong>of</strong> radical and incremental innovations. In <strong>the</strong> o<strong>the</strong>r word
companies can have both radical product/service innovation and incremental product/service
innovation. This is same with process innovation (radical process innovation, incremental process
innovation). Since products/services and processes in SMEs are limited and <strong>the</strong>y do not have
different variety in <strong>the</strong>ir product or services comparing to larger organizations, it is more appropriate
for this research to study innovation in radical and incremental level.
Figure 1: A taxonomy <strong>of</strong> innovation
3. Hypo<strong>the</strong>sis development
Ego network approach has been adopted because it helps to explain how being part <strong>of</strong> a network
affect firms action and outcomes (Provan et al. 2007). Ego network is defined as networks consisting
<strong>of</strong> one focal actor (ego) toge<strong>the</strong>r with all partners in first order neighbourhood (alters) <strong>of</strong> <strong>the</strong> ego and
all <strong>the</strong> links among <strong>the</strong> partners (Everett and Borgatti 2005). Three aspects <strong>of</strong> ego network are
identified: 1) structural properties (Burt 1992), 2) relational properties (Gulati et al. 2002), and 3) nodal
properties (Beckman and Haunschild 2002). Following section presents <strong>the</strong> hypo<strong>the</strong>ses <strong>of</strong> <strong>the</strong> study.
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3.1 Structural properties
3.1.1 Betweenness centrality
Meysam Poorkavoos, Yanqing Duan and John Edwards
The main argument in betweenness centrality is that a firm that lies between two o<strong>the</strong>r firms that are
not connected to each o<strong>the</strong>r directly has control on <strong>the</strong> information and resource flow (Freeman
1979). Network helps to transfer <strong>the</strong> required information and combine <strong>the</strong>m toge<strong>the</strong>r to provide <strong>the</strong>
potential for <strong>the</strong> company for a radical innovation (Gilsing et al. 2008). Burt (2004) argued that
knowledge, values and behaviors are more heterogeneous between groups ra<strong>the</strong>r than within groups.
According to this argument it is possible to say that inter-organizational networks are not transferring
just <strong>the</strong> existing knowledge but <strong>the</strong>y create opportunities to generate new knowledge. Firms’ centrality
helps <strong>the</strong>m to find opportunities to develop non-redundant relations with o<strong>the</strong>r companies (Gilsing et
al. 2008) which will contribute to <strong>the</strong> novelty <strong>of</strong> information that is being transferred to <strong>the</strong> company.
Thus firms with higher network centrality because <strong>of</strong> <strong>the</strong>ir position and novelty <strong>of</strong> <strong>the</strong> knowledge are
more radical innovator. Although network centrality is important but <strong>the</strong> research by Gnyawali and
Madhavan (2001) shows that one consequence <strong>of</strong> having too many non-redundant network ties is
dealing with high speed fellow <strong>of</strong> diverse information. For SMEs with limited resources and time from
a threshold point it is not possible to process and absorb <strong>the</strong> transferred knowledge. Therefore, <strong>the</strong>
following hypo<strong>the</strong>ses are proposed:
Hypo<strong>the</strong>sis 1a: Radical innovation has an inverse U-shaped relation with betweenness
centrality <strong>of</strong> <strong>the</strong> firm
Hypo<strong>the</strong>sis 1b: Incremental innovation has an inverse U-shaped relation with
betweenness centrality <strong>of</strong> <strong>the</strong> firm
3.1.2 Network density
Based on <strong>the</strong> previous research network density will increase <strong>the</strong> absorptive capacity by limiting <strong>the</strong>
novelty creation. (Gilsing et al. 2008). Inter-firm networks transfer and process <strong>the</strong> information (Ahuja
2000). Direct tie in <strong>the</strong> network not only provide access to <strong>the</strong> partners’ knowledge but also can act as
a medium to gain access to <strong>the</strong> partners’ partners knowledge (Gulati and Gargiulo 1999). Previous
researchers (Argote and Ingram 2000) found that firms in a network can act as an information
processing devices. They can process <strong>the</strong> transferred knowledge from <strong>the</strong>ir partners, process <strong>the</strong>
knowledge and transfer it to <strong>the</strong> focal firm. This feature can be helpful when <strong>the</strong>re is a large
technological distance between firms. High density networks may cause redundant information
transfer from partners to focal firm. The effects <strong>of</strong> indirect ties will be diminished since companies
already know that <strong>the</strong> knowledge from indirect ties will be almost same as <strong>the</strong> knowledge from <strong>the</strong>
direct ties. As a result <strong>the</strong> opportunities for novel combination will decrease (Gilsing et al. 2008).
Ano<strong>the</strong>r argument against density is unwanted knowledge spill over to <strong>the</strong> partners (Gilsing et al.
2008). This will cause to limit <strong>the</strong> opportunities <strong>of</strong> novelty and make firms less attractive to look for
such novelty in <strong>the</strong>ir network (Gilsing and Nooteboom 2005). Therefore, <strong>the</strong> following hypo<strong>the</strong>ses are
proposed:
Hypo<strong>the</strong>sis 2a: Radical innovation has an inverse U-shaped relation with network density
Hypo<strong>the</strong>sis 2b: Incremental innovation has an inverse U-shaped relation with network
density
3.1.3 Degree centrality
Freeman (1979) defines network centrality as “<strong>the</strong> degree to which <strong>the</strong> firm has a strategically
important position in <strong>the</strong> network”. Companies with higher degree centrality have more visibility in <strong>the</strong>
network and are more desirable for networking by o<strong>the</strong>r companies (Gulati et al. 2002). As a result, a
firm with higher degree centrality can attract more resource rich partners (Gulati 1999). Ano<strong>the</strong>r
benefit <strong>of</strong> degree centrality is <strong>the</strong> experience that <strong>the</strong>y gain in firm cooperation (Gulati et al. 2002).
Moreover it increases <strong>the</strong> abilities <strong>of</strong> <strong>the</strong> focal firm to extract value from alliances. Power <strong>of</strong>
endorsement is ano<strong>the</strong>r benefit <strong>of</strong> having higher status in <strong>the</strong> network (Stuart 2000). According to
resource dependency <strong>the</strong>ory (Pfeffer and Salancik 1987) <strong>the</strong> power <strong>of</strong> endorsement can provide
opportunities for <strong>the</strong> focal firm that is not available to o<strong>the</strong>rs.
Higher ego network size helps focal firms to receive feedback about <strong>the</strong>ir products or services from
different perspectives. They will have <strong>the</strong> opportunity to look at <strong>the</strong>ir products/services from different
aspects and improve <strong>the</strong>m (incremental innovation). Moreover higher degree centrality provides <strong>the</strong>
managers’ <strong>of</strong> <strong>the</strong> focal firms with more opportunities to interact with managers <strong>of</strong> o<strong>the</strong>r firms and
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Meysam Poorkavoos, Yanqing Duan and John Edwards
access <strong>the</strong>ir ways <strong>of</strong> doing things and different approaches <strong>of</strong> problem solving. Based on <strong>the</strong> above
arguments it is proposed that:
Hypo<strong>the</strong>sis 3a: The greater <strong>the</strong> size <strong>of</strong> <strong>the</strong> ego network, <strong>the</strong> higher <strong>the</strong> incremental
innovation
Hypo<strong>the</strong>sis 3b: The greater <strong>the</strong> size <strong>of</strong> <strong>the</strong> ego network, <strong>the</strong> higher <strong>the</strong> radical innovation
3.2 Relational properties
3.2.1 Strength <strong>of</strong> tie
Configuration <strong>of</strong> ties (e.g. strength vs. weak, cohesive vs. bridging) is important because actors can
extract various forms <strong>of</strong> benefits from each type <strong>of</strong> <strong>the</strong>se ties (Baker 1990). At <strong>the</strong> inter-organizational
level Rowley et al. (2000) defined tie strength between firms as “frequency <strong>of</strong> interaction between
partners and <strong>the</strong>ir level <strong>of</strong> resource commitment to <strong>the</strong> relationship”. Strong ties are sources <strong>of</strong> private
information and critical resources and increase trust and reciprocity between firms (Gulati et al. 2002).
In an exploitation environment that firms try to use <strong>the</strong>ir current information, technologies, skills and
abilities (March 1991), firms belong to a network with strong ties are more likely to perform better
(Gulati et al. 2002). In incremental innovation in order to exploit <strong>the</strong> current information in <strong>the</strong>
company deeper knowledge about firm’s current direction is necessary (Rowley et al. 2000). Uzzi
(1996) argued that this type <strong>of</strong> information is provided by intense linkages. Although frequent
interactions with network partners and commitment <strong>of</strong> resources (Granovetter 1985, Krachardt 1992)
are necessary for building strong ties, but <strong>the</strong>y are very good at providing rich customized information
(Rowley et al. 2000) and confirming <strong>the</strong> opinions <strong>of</strong> <strong>the</strong>ir group members (Julien et al. 2004) which
both <strong>of</strong> <strong>the</strong>m are vital to refine <strong>the</strong>ir current technologies and reduce <strong>the</strong> production costs. According
to this, <strong>the</strong> following hypo<strong>the</strong>ses are proposed.
Hypo<strong>the</strong>sis 4a: The higher <strong>the</strong> number <strong>of</strong> strong ties, <strong>the</strong> higher <strong>the</strong> incremental
innovation
Hypo<strong>the</strong>sis 4b: The higher <strong>the</strong> number <strong>of</strong> strong ties, <strong>the</strong> higher <strong>the</strong> radical innovation
Weak ties are sources <strong>of</strong> new information and opportunities in market, (Granovetter 1985) it reduces
<strong>the</strong> firms’ resource dependence on strong partners (Baker 1990). Configuration <strong>of</strong> ties depends on <strong>the</strong>
environment that firms are situated. In an explorative environment firms are looking for new
knowledge and information, thus <strong>the</strong> strong ties are not helpful in this situation. Weak ties not only can
be a medium for new knowledge from o<strong>the</strong>r companies but also can act as a bridge between o<strong>the</strong>r
social entities and can be a trigger to mix new ideas that lead to change and innovation (Rothwell
1991). According to <strong>the</strong> above discussion <strong>the</strong> following hypo<strong>the</strong>ses are proposed.
Hypo<strong>the</strong>sis 5a: The higher <strong>the</strong> number <strong>of</strong> weak ties, <strong>the</strong> higher <strong>the</strong> radical innovation
Hypo<strong>the</strong>sis 5b: The higher <strong>the</strong> number <strong>of</strong> weak ties, <strong>the</strong> higher <strong>the</strong> incremental
innovation
3.2.2 Diversity <strong>of</strong> ties
Diversity <strong>of</strong> ties refers to different types <strong>of</strong> relationships that connect a focal firm to its partners. Tie
diversity will provide <strong>the</strong> focal firm with complementary resources and opportunities to overcome
resource barriers and uncertainties in <strong>the</strong>ir business. Innovation is a complicated process and firms
require knowledge and resources in different areas to be able to develop or improve new products or
services. Successful innovation requires <strong>the</strong> focal firm to understand <strong>the</strong> market needs, knowledge <strong>of</strong>
manufacturing process, technical knowledge <strong>of</strong> products and access to inputs (Srivastava 2007).
Sammarra and Biggiero (2008) in <strong>the</strong>ir research found that in an inter-firm relationship three different
types <strong>of</strong> knowledge are being transferred (managerial, technological and market knowledge). They
argued that all <strong>the</strong>se different types <strong>of</strong> knowledge are necessary for a successful innovation.
According to this argument firms not only have different types <strong>of</strong> ties with different partners but also
<strong>the</strong>y have diversity <strong>of</strong> ties with a single partner. Fur<strong>the</strong>rmore, Diversity <strong>of</strong> ties helps <strong>the</strong> focal firm to
acquire a better perspective and wholesome development (Srivastava 2007). According to this,
greater diversity <strong>of</strong> ties can help <strong>the</strong> focal firm to develop and enhance its abilities to generate radical
innovations. Therefore it is proposed that:
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Meysam Poorkavoos, Yanqing Duan and John Edwards
Hypo<strong>the</strong>sis 6a: The higher <strong>the</strong> diversity <strong>of</strong> ties <strong>of</strong> <strong>the</strong> focal firm, <strong>the</strong> higher <strong>the</strong>ir radical
innovation
Hypo<strong>the</strong>sis 6b: The higher <strong>the</strong> diversity <strong>of</strong> ties <strong>of</strong> <strong>the</strong> focal firm, <strong>the</strong> higher <strong>the</strong>ir
incremental innovation
3.3 Nodal properties
3.3.1 Diversity <strong>of</strong> partners
Diversity <strong>of</strong> nodes refers to <strong>the</strong> differences in ego network members’ abilities like experiences,
resources and practices. Based on previous studies diversity <strong>of</strong> partners have an impact on focal
firms’ innovation (Kaufmann and Todtling 2001). Romjin and Albu (2002) supported Kaufman and
Todtling’s study in <strong>the</strong>ir research on small high-technology firms in <strong>the</strong> UK. Ego networks with diverse
nodes provide <strong>the</strong> focal firm with access to information and resources in different areas and shed
lights on different approaches and technologies (Pennings and Harianto 1992). It helps to make firms
understandings broader and let <strong>the</strong> focal firm to look at <strong>the</strong> same topic from different perspectives.
This will provides <strong>the</strong> opportunity for <strong>the</strong> focal firm to use various channels to seek different solutions
in <strong>the</strong>ir business (Laursen and Salter 2006).
Access to altars with different type <strong>of</strong> knowledge and resources provides <strong>the</strong> opportunity for <strong>the</strong> focal
firm to overcome its barriers. Focal firms in networks with node diversity have <strong>the</strong> opportunity to
observe various innovation approaches and <strong>the</strong>ir consequences which <strong>the</strong>y can use this information
to improve <strong>the</strong> quality <strong>of</strong> <strong>the</strong>ir innovation efforts (Beckman and Haunschild 2002). According to this
<strong>the</strong> following hypo<strong>the</strong>ses are proposed:
Hypo<strong>the</strong>sis 7a: The higher <strong>the</strong> diversity <strong>of</strong> partners <strong>of</strong> <strong>the</strong> focal firm, <strong>the</strong> higher <strong>the</strong>ir
radical innovation
Hypo<strong>the</strong>sis 7b: The higher <strong>the</strong> diversity <strong>of</strong> partners <strong>of</strong> <strong>the</strong> focal firm, <strong>the</strong> higher <strong>the</strong>ir
incremental innovation
Figure 2 illustrates <strong>the</strong> research model and <strong>the</strong> hypo<strong>the</strong>ses. To address <strong>the</strong> objectives <strong>of</strong> <strong>the</strong> study it
is necessary to collect <strong>the</strong> empirical data and test <strong>the</strong> hypo<strong>the</strong>ses. Only after that it will be possible to
find which properties affect specific type <strong>of</strong> innovation.
Figure 2: Network properties and innovation
4. Research methods
Most <strong>of</strong> <strong>the</strong> previous studies (Ahuja 2000, Gilsing et al. 2008) used archival data and quantitative
analysis to study inter-firm networks. But information on collaborative linkage for SMEs is extremely
hard to obtain over an extended time period; <strong>the</strong>refore survey method is selected for collecting
empirical data. Before conducting <strong>the</strong> survey a pilot study has been conducted to test <strong>the</strong> developed
questionnaire and find preliminary support for <strong>the</strong> hypo<strong>the</strong>ses. Case study interview is <strong>the</strong> adopted
method for pilot study. Five companies have been chosen and contacted for <strong>the</strong> interview. Table 1
shows <strong>the</strong> pr<strong>of</strong>ile <strong>of</strong> <strong>the</strong> interviewed companies. The interviews were 40 minutes in average and
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Meysam Poorkavoos, Yanqing Duan and John Edwards
consisted <strong>of</strong> two parts. The first part was testing <strong>the</strong> questionnaire which interviewees were asked to
answer <strong>the</strong> questions and provide comments if <strong>the</strong>re is any problem in responding to <strong>the</strong>m. The
questions in <strong>the</strong> second part were designed in a way to understand what o<strong>the</strong>r factors contribute to
different types <strong>of</strong> innovation, what managers think about different types <strong>of</strong> innovation, its usefulness in
<strong>the</strong>ir business, <strong>the</strong> barriers for <strong>the</strong>m for being innovative, and questions about some <strong>of</strong> <strong>the</strong> social
network characteristics.
Table 1: Interviewed companies' pr<strong>of</strong>ile
Company Name Interviewee Position
Number <strong>of</strong>
Employees
Products and Services
ACU Managing Director 21-50 S<strong>of</strong>tware and market research
CGCO Production Manager 21-50
SW Managing Director 1-9
EFM Managing Director 21-50
Customized online assessments for
organizations
Design and development <strong>of</strong> bespoke
s<strong>of</strong>tware application for businesses
Financial management outsourcing
business
BBA Managing Director 1-9 Procurement for essential utilities
5. Pilot study analysis
5.1 Understanding factors affecting innovation
Thematic analysis on interview data provides rich information and in-depth understanding on several
important issues associated with <strong>the</strong> current practice and factors related to <strong>the</strong> innovation in SMEs.
Following <strong>the</strong>matic based discussions are based on <strong>the</strong> pilot interview analysis.
Barriers: One objective for pilot study is identifying o<strong>the</strong>r factors that contribute to firms’ innovation
performance. To address this objective interviewees have been asked to list <strong>the</strong> barriers and
resources for <strong>the</strong>ir innovation. All <strong>the</strong> interviewees listed cost and resource as <strong>the</strong> main barriers for
innovation in <strong>the</strong>ir company, for example:
“One is <strong>the</strong> cost <strong>of</strong> <strong>the</strong> innovation, and <strong>the</strong> o<strong>the</strong>r is <strong>the</strong> space that you need for innovation
[…] so you’ve got to create space for people to innovate. They [employees] got a busy
diary and doing lots and lots <strong>of</strong> things it requires space for <strong>the</strong>m to have <strong>the</strong> intellectual
capacity to do things”. (Interviewee 1)
“Financial I would say is <strong>the</strong> main barrier. We struggle to find <strong>the</strong> funds to invest in our
innovative ideas”. (Interviewee 3)
Resources for Innovation: Regarding to resources for innovation almost all <strong>the</strong> interviewees agreed
that partners, customers and managers’ experience are <strong>the</strong> most important resources for new ideas
and innovation in <strong>the</strong>ir company. Interviewee 2 mentioned
“We have a big partner relationship with <strong>the</strong>m. It is a global consultancy firm. So we use
some idea from <strong>the</strong>m…” (Interviewee 2)
“Can be talking to our customers, <strong>the</strong>y might have <strong>the</strong> idea want us to implement, or we
can see some opportunities for something that we can do for <strong>the</strong>m that we might also sell
to somebody else”. (Interviewee 3)
One <strong>of</strong> <strong>the</strong> managers mentioned that <strong>the</strong>ir R&D team is very important in <strong>the</strong>ir innovation and ano<strong>the</strong>r
manager pointed out using forums and online resources are important for him in finding new ideas or
making changes in <strong>the</strong>ir current processes or services. They specially have been asked about <strong>the</strong>
factors that help in radical innovation and incremental innovation. Almost all <strong>of</strong> <strong>the</strong>m mentioned <strong>the</strong>
same factors are important in both radical and incremental innovation. Regarding incremental
innovation, one manager mentioned
“It is kind <strong>of</strong> similar but <strong>the</strong> o<strong>the</strong>r important things with incremental innovation is to have
ways <strong>of</strong> listening to your customers to get feedback so to find out from <strong>the</strong>m what <strong>the</strong>y
would like different”. (Interviewee 4)
Regarding radical innovation, ano<strong>the</strong>r manager mentioned
“I think listening to our customers is ano<strong>the</strong>r thing that let <strong>the</strong> idea to come into <strong>the</strong>
organization and <strong>the</strong>n come up with some radical ideas”. (Interviewee 5)
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Meysam Poorkavoos, Yanqing Duan and John Edwards
Their opinion about o<strong>the</strong>r factors for different types <strong>of</strong> innovation was different so it is not possible to
say which one is really important for which types <strong>of</strong> innovation.
5.2 Preliminary analysis on research hypo<strong>the</strong>ses
Although <strong>the</strong> number <strong>of</strong> pilot interviews may not be appropriate for hypo<strong>the</strong>sis test, <strong>the</strong> comments
made by interviewees provided support for some <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>ses.
Diversity <strong>of</strong> partners: In order to support <strong>the</strong> developed hypo<strong>the</strong>ses and find some preliminary
evidence, some questions have been asked about <strong>the</strong>m (e.g. different types <strong>of</strong> partners, diversity <strong>of</strong>
relations). Regarding to <strong>the</strong> diversity <strong>of</strong> nodes and its effects on radical innovation <strong>the</strong>y asserted
interesting ideas which support <strong>the</strong> hypo<strong>the</strong>sis 4a.
“I had an idea to license out our business. No one else in this type <strong>of</strong> company came up
with that idea. What we now developed as a service has been enhanced because I have
been spoken to <strong>the</strong> legal partner, I spoken to <strong>the</strong> IT partner, I spoken to <strong>the</strong> recruitment
partner. People have been given me ideas how I execute <strong>the</strong>m how I deliver that”.
(Interviewee 4)
This example supports <strong>the</strong> argument that variety in partners will help <strong>the</strong> focal firm to use various
channels to seek for different solutions and make <strong>the</strong> opportunity for radical innovation.
Diversity <strong>of</strong> Ties: They also mentioned that diversity <strong>of</strong> ties is important for being innovative. One
manager pointed out that
“We have limited number <strong>of</strong> partners because we haven’t got <strong>the</strong> capacity to do lot or lots
<strong>of</strong> that. When you work with someone you are effectively learning and <strong>the</strong>y learn too. And
you accept what you receive and you implement <strong>the</strong>m. And sometime <strong>the</strong> views <strong>of</strong>
different people are different [….] so for <strong>the</strong> company <strong>of</strong> our size somebody is helping for
marketing somebody is helping with business planning or technology”. (Interviewee 1)
Strength <strong>of</strong> Ties: Regarding to <strong>the</strong> strength <strong>of</strong> ties one <strong>of</strong> <strong>the</strong> interviewees provided an interesting
example. The example was about introducing a new service in <strong>the</strong>ir sector (radical innovation) by
using cloud computing. They have an IT-partner and according to <strong>the</strong> definition <strong>the</strong>y have a weak
relationship with this partner but this partner gave <strong>the</strong> idea about using cloud computing as a new way
<strong>of</strong> delivering <strong>the</strong>ir service to <strong>the</strong>ir customers.
6. Conclusion
There is no doubt that Inter firm networks contribute to firm’s innovation. However, this research aims
to make fur<strong>the</strong>r contribution by examining <strong>the</strong> relationships between network properties and types <strong>of</strong>
innovation. Our research framework distinguishes three aspects <strong>of</strong> ego network which will enables us
to understand which type <strong>of</strong> innovation benefits more from particular network properties. The results
<strong>of</strong> <strong>the</strong> pilot study provided preliminary support for a number <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>ses and streng<strong>the</strong>n <strong>the</strong>
existing understanding in <strong>the</strong> literature. For example, although prior literature has confirmed <strong>the</strong>
importance <strong>of</strong> using weak ties to increase innovation performance <strong>of</strong> <strong>the</strong> company, this research has
proposed a different approach to distinguish <strong>the</strong> role <strong>of</strong> this type <strong>of</strong> ties on different types <strong>of</strong>
innovation. Ano<strong>the</strong>r finding <strong>of</strong> <strong>the</strong> research so far is <strong>the</strong> importance <strong>of</strong> <strong>the</strong> diversity <strong>of</strong> partners,
because <strong>the</strong> varieties in <strong>the</strong>ir abilities and resources can be more beneficial in radical innovations.
Next step for this research is to conduct a survey and analyze <strong>the</strong> findings in order to test <strong>the</strong>
hypo<strong>the</strong>ses and finalize <strong>the</strong> framework. This validated framework will help managers to have a better
understanding <strong>of</strong> <strong>the</strong> impact <strong>of</strong> <strong>the</strong>ir network activities on different types <strong>of</strong> innovation and be more
focused in developing <strong>the</strong>ir strategies and utilizing <strong>the</strong>ir limited resources.
References
Ahuja, G. (2000) "Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study", Administrative
Science Quarterly, Vol 45, No 3, pp 425-455.
Anderson, P. & Tushman, M. L. (1990) "Technological Discontinuities and Dominant Designs: A Cyclical Model <strong>of</strong>
Technological Change", Administrative Science Quarterly, Vol 35, No 4, pp 604-633.
Argote, L. & Ingram, P. (2000) "Knowledge Transfer: A Basis for Competitive Advantage in Firms", Organizational
Behavior and Human Decision Processes, Vol 82, No 1, pp 150-169.
Baker, W. E. (1990) "Market Networks and Corporate Behavior", American Journal <strong>of</strong> Sociology, Vol 96, No 3
Beckman, C. M. & Haunschild, P. R. (2002) "Network Learning: The Effects <strong>of</strong> Partners' Heterogeneity <strong>of</strong>
Experience on Corporate Acquisitions", Administrative Science Quarterly, Vol 47, No 1, pp 92-124.
Burt, R. S. (1992) Structural holes : <strong>the</strong> social structure <strong>of</strong> competition. ed.,Harvard University Press, Cambridge,
Mass.
Burt, R. S. (2004) "Structural holes and good ideas", American Journal <strong>of</strong> Sociology, Vol 110, No 2, pp 349-399.
778

Meysam Poorkavoos, Yanqing Duan and John Edwards
De Propris, L. (2002) "Types <strong>of</strong> innovation and inter-firm co-operation", Entrepreneurship and Regional
Development, Vol 14, No 4, pp 337-353.
Dewar, D. & Dutton, J. E. (1986) "The Adoption <strong>of</strong> Radical and Incremental Innovations: An Empirical Analysis",
Management Science, Vol 32, No 11, pp 1422-1433.
Everett, M. & Borgatti, S. P. (2005) "Ego network betweenness", Social Networks, Vol 27, No 1, pp 31-38.
Freeman, L. C. (1979) "Centrality in Social Networks Conceptual Clarification", Social Networks, Vol 1, No 3
Gilsing, V. & Nooteboom, B. (2005) "Density and strength <strong>of</strong> ties in innovation networks: an analysis <strong>of</strong>
multimedia and biotechnology", <strong>European</strong> Management Review, Vol 2, No, pp 179-197.
Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G. & Van Den Oord, A. (2008) "Network
embeddedness and <strong>the</strong> exploration <strong>of</strong> novel technologies: Technological distance, betweenness centrality
and density", Research Policy, Vol 37, No 10, pp 1717-1731.
Gnyawali, D. R. & Madhavan, R. (2001) "Cooperative networks and competitive dynamics: A structural
embeddedness perspective", Academy <strong>of</strong> Management Review, Vol 26, No 3, pp 431-445.
Granovetter, M. (1985) "Economic Action and Social Structure: The Problem <strong>of</strong> Embeddedness", American
Journal <strong>of</strong> Sociology, Vol 91, No 3, pp 481-510.
Gulati, R. (1999) "Network location and learning: The influence <strong>of</strong> network resources and firm capabilities on
alliance formation", Strategic Management Journal, Vol 20, No 5, pp 397-420.
Gulati, R., Dialdin, D. A. & Wang, L. (2002) Organizational Networks. IN BAUM, J. A. C. (Ed.) Blackwell
companion to organizations. Malden, MA, Blackwell Publishers.
Gulati, R. & Gargiulo, M. (1999) "Where do interorganizational networks come from?", American Journal <strong>of</strong>
Sociology, Vol 104, No 5, pp 1439-1493.
Hagedoorn, J. (2002) "Inter-firm R&D partnerships: an overview <strong>of</strong> major trends and patterns since 1960",
Research Policy, Vol 31, No 4, pp 477-492.
Hausman, A. (2005) "Innovativeness among small businesses: Theory and propositions for future research",
Industrial Marketing Management, Vol 34, No 8, pp 773-782.
Julien, P.-A., Andriambeloson, E. & Ramangalahy, C. (2004) "Networks, weak signals and technological
innovations among SMEs in <strong>the</strong> land-based transportation equipment sector", Entrepreneurship and
Regional Development, Vol 16, No 4, pp 251-269.
Kaufmann, A. & Todtling, F. (2001) "Science-industry interaction in <strong>the</strong> process <strong>of</strong> innovation: <strong>the</strong> importance <strong>of</strong>
boundary-crossing between systems", Research Policy, Vol 30, No 5, pp 791-804.
Krachardt, D. (1992) The strength <strong>of</strong> strong ties: The importance philos in organizations. IN NOHRIA, N. (Ed.)
Networks and organizations: Structure, form, and action. Boston, Harvard Business School Press.
Laursen, K. & Salter, A. (2006) "Open for innovation: <strong>the</strong> role <strong>of</strong> openness in explaining innovation performance
among U.K. manufacturing firms", Strategic Management Journal, Vol 27, No 2, pp 131-150.
March, J. G. (1991) "Exploration and Exploitation in Organizational Learning", Organization Science, Vol 2, No 1
Mcdermott, C. M. & O'connor, G. C. (2002) "Managing radical innovation: An overview <strong>of</strong> emergent strategy
issues", The Journal <strong>of</strong> Product Innovation Management, Vol 19, No 6, pp 424-438.
Moller, K., Partanen, J., Rajala, R. & Westerlund, M. (2007) Fostering innovations in <strong>the</strong> SME context: a network
perspective. A work-in-progress paper submitted to <strong>the</strong> IMP <strong>Conference</strong>. Manchester UK.
Mowery, D. C., Oxley, J. E. & Silverman, B. S. (1996) "Strategic alliances and interfirm knowledge transfer",
Strategic Management Journal, Vol 17, No, pp 77-91.
Pennings, J. M. & Harianto, F. (1992) "Technological Networking and Innovation Implementation", Organization
Science, Vol 3, No 3, pp 356-382.
Pfeffer, J. & Salancik, G. R. (1987) The external control <strong>of</strong> organizations: a resource dependence perspective.
ed.,Harper & Row, NewYork.
Provan, K. G., Fish, A. & Sydow, J. (2007) "Interorganizational Networks at <strong>the</strong> Network Level: A Review <strong>of</strong> <strong>the</strong>
Empirical Literature on Whole Networks", Journal <strong>of</strong> Management, Vol 33, No 3, pp 479-516.
Rodan, S. & Galunic, C. (2004) "More than network structure: How knowledge heterogeneity influences
managerial performance and innovativeness", Strategic Management Journal, Vol 25, No 6, pp 541-562.
Romijn, H. & Albu, M. (2002) "Innovation, networking and proximity: Lessons from small high technology firms in
<strong>the</strong> UK", Regional Studies, Vol 36, No 1, pp 81-86.
Rothwell, R. (1991) "External networking and innovation in small and medium-sized manufacturing firms in
Europe", Technovation, Vol 11, No 2, pp 93-112.
Rowley, T., Behrens, D. & Krackhardt, D. (2000) "Redundant governance structures: An analysis <strong>of</strong> structural
and relational embeddedness in <strong>the</strong> steel and semiconductor industries", Strategic Management Journal,
Sammarra, A. & Biggiero, L. (2008) "Heterogeneity and specificity <strong>of</strong> inter-firm knowledge flows in innovation
networks", Journal <strong>of</strong> Management Studies, Vol 45, No 4, pp 200-829.
Srivastava, M. K. (2007) "Friends or Neighbors? The Effects <strong>of</strong> Inter-firm Networks and Clusters on
Technological Innovations in <strong>the</strong> U.S. Semiconductor Industry", [Online], Doctor <strong>of</strong> Philosophy, Virginia
Polytechnic Institute and State University, http://scholar.lib.vt.edu/<strong>the</strong>ses/available/etd-10102007-
144411/unrestricted/Dissertation_Manish_Srivastava_ETD.pdf.
Stuart, T. E. (2000) "Interorganizational Alliances and <strong>the</strong> Performance <strong>of</strong> Firms: A Study <strong>of</strong> Growth and
Innovation Rates in a High-Technology Industry", Strategic Management Journal, Vol 21, No 8, pp 791-811.
Uzzi, B. (1996) "The sources and consequences <strong>of</strong> embeddedness for <strong>the</strong> economic performance <strong>of</strong>
organizations: The network effect", American Sociological Review, Vol 61, No 4, pp 674-698.
Van De Ven, A. H., Polley, D. E., Garud, R. & Venkataraman, S. (1999) The innovation journey. ed.,Oxford
University Press, New York.
779

Institutional Matrix <strong>of</strong> Knowledge Generation
Evgeny Popov, Maxim Vlasov and Anna Veretennikova
Institute <strong>of</strong> Economics; Russia
epopov@mail.ru
mvlassov@mail.ru
veretenya@inbox.ru
Abstract: Development <strong>of</strong> a knowledge economy in Russia is hindered by a number <strong>of</strong> factors. One <strong>of</strong> <strong>the</strong>se is
<strong>the</strong> current level <strong>of</strong> its institutional regime. According to <strong>the</strong> yearly research <strong>of</strong> <strong>the</strong> World Bank on <strong>the</strong> knowledge
economy status in 146 countries, <strong>the</strong> level <strong>of</strong> Russia’s institutional development has been estimated to reach
1.76 points out <strong>of</strong> 10, being in this respect ranked <strong>the</strong> 126 th out <strong>of</strong> 146 countries under consideration. At <strong>the</strong> same
time <strong>the</strong> knowledge economy index is 5.55, and <strong>the</strong> knowledge index is 6.82. Thus, while possessing a certain
level <strong>of</strong> intellectual capital, <strong>the</strong> knowledge economy development is restrained by its inadequate institutional
provision. The purpose <strong>of</strong> this study is to systematize institutions for knowledge generation at <strong>the</strong> level <strong>of</strong> a firm.
The research object is economic processes underpinning <strong>the</strong> formation <strong>of</strong> knowledge generation institutions. The
research subject is a system <strong>of</strong> economic relations within internal and external environment <strong>of</strong> a firm in <strong>the</strong>
sphere <strong>of</strong> knowledge generation. By an institutional matrix <strong>the</strong> authors understand <strong>the</strong> entity <strong>of</strong> economic
institutions systematized in a particular way and ensuring <strong>the</strong> firm activity in creation and application <strong>of</strong> new
knowledge. In <strong>the</strong> course <strong>of</strong> research a list <strong>of</strong> institutions for knowledge generation has been drawn, based on <strong>the</strong>
firm’s expenditure analysis according to <strong>the</strong> Tax Code <strong>of</strong> <strong>the</strong> Russian Federation. The institutions have been
specified due to <strong>the</strong> following indicators:
Place <strong>of</strong> emergence (endogenous and exogenous);
Management functions performed (planning, organization, control, and motivation institutions);
Manufacture functions performed (manufacture, distribution, sale, and consumption institutions);
Type <strong>of</strong> knowledge generated (institutions for operational, structural, and functional knowledge generation).
These indicators have a number <strong>of</strong> winning points. Firstly, <strong>the</strong>y take advantage <strong>of</strong> <strong>the</strong> type <strong>of</strong> generated
knowledge. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong>y are plausible at <strong>the</strong> firm’s level. Finally, <strong>the</strong>y are based on <strong>the</strong> target activity
<strong>of</strong> an enterprise. Thus, having singled out consistent expenses <strong>of</strong> <strong>the</strong> Tax Code <strong>of</strong> <strong>the</strong> Russian Federation, an
institutional matrix <strong>of</strong> knowledge generation has been simulated. Scientific relevance <strong>of</strong> <strong>the</strong> model suggested is
<strong>the</strong> advancement <strong>of</strong> K. Polaniy and D.North’s institutional matrix phenomenon and practical application <strong>of</strong> <strong>the</strong>
institutional design in <strong>the</strong> knowledge economy framework. To obtain <strong>the</strong> institutional matrix for a single firm its
actual economic situation should be made allowance for. Institutional matrices designed for different enterprises,
though possessing some common characteristics, are individual and depend on <strong>the</strong> industry peculiarities, target
activity <strong>of</strong> a firm, management system adapted, and o<strong>the</strong>r factors. The matrix developed creates a <strong>the</strong>oretical
platform for graphical conceptual depiction <strong>of</strong> different mechanisms coordinating economic institutions.Practical
significance <strong>of</strong> <strong>the</strong> study is that it <strong>of</strong>fers a tool to judge how in homogenous <strong>the</strong> distribution <strong>of</strong> economic
institutions throughout <strong>the</strong> economic spheres <strong>of</strong> an enterprise is. The lack <strong>of</strong> norms and institutions in a certain
field <strong>of</strong> economic activity reveals <strong>the</strong> shortage <strong>of</strong> resources in this sphere. Ano<strong>the</strong>r practical aspect is a possibility
to detect <strong>the</strong> weak points <strong>of</strong> institutional design in providing an economic activity with norms. Hence, practical
realization <strong>of</strong> an institutional matrix lays a basis for working out recommendations to <strong>the</strong> firm’s administration on
reinforcing certain institutional blocks.
Keywords: institutions, institutional matrix, knowledge economy
1. Introduction
Increasing role <strong>of</strong> knowledge for sustainable economic growth <strong>of</strong> a single country has nowadays
become an apparent tendency. It is proved by <strong>the</strong> political objective <strong>of</strong> <strong>the</strong> EU development, declared
in 2000 - encouragement <strong>of</strong> a knowledge based economy, and by Russia’s commitment to innovation
economy. Knowledge and processes <strong>of</strong> knowledge generation, being <strong>the</strong> impetus <strong>of</strong> economic
growth, are <strong>the</strong> key elements underlying and, <strong>the</strong>reby, defining innovation activity <strong>of</strong> economic units
and have become <strong>the</strong> main factors influencing <strong>the</strong> economic development <strong>of</strong> a country. Establishment
<strong>of</strong> a new economic type and economic activity involves definite institutional changes. At a
macroeconomic level this objective is supposed to be met with <strong>the</strong> development <strong>of</strong> a national
innovation system. However, this challenge has not been drawn enough attention at a firm’s level,
though being <strong>the</strong> direct knowledge source and fundamental to enhancing competitiveness <strong>of</strong> a
country. That is why <strong>the</strong> study <strong>of</strong> knowledge generation processes and <strong>the</strong>ir institutional aspect, as
well as <strong>of</strong> <strong>the</strong> activity <strong>of</strong> economic units has become drastically significant. Providing <strong>the</strong> sphere <strong>of</strong>
interest with necessary norms contributes to <strong>the</strong> systematization <strong>of</strong> economic activity in <strong>the</strong> knowledge
generation field, its monitoring and relevant optimization.
780

Evgeny Popov, Maxim Vlasov and Anna Veretennikova
For <strong>the</strong> time-being <strong>the</strong> followers <strong>of</strong> <strong>the</strong> institutional economic <strong>the</strong>ory have achieved impressive results
ranging from <strong>the</strong> prognosis <strong>of</strong> transaction sector development in national economy up to
understanding <strong>the</strong> nature <strong>of</strong> emergence and evolution <strong>of</strong> economic institutions. Significant contribution
has been made by Russian researchers as well. Never<strong>the</strong>less, both foreign and national economic
literature has little to <strong>of</strong>fer on specification <strong>of</strong> institutional systems, <strong>the</strong>ir multifactor description and
analysis concerning knowledge economy. Challenge stems, first <strong>of</strong> all, from <strong>the</strong> variety and complexity
<strong>of</strong> economic institutions. The aim <strong>of</strong> <strong>the</strong> present research is to develop a multifactor systemic
characteristic – an institutional matrix <strong>of</strong> knowledge generation.
2. Background
The issues <strong>of</strong> institutional design have been addressed by many distinguished scholars <strong>of</strong> <strong>the</strong>
economic <strong>the</strong>ory. The analysis <strong>of</strong> institutional structure <strong>of</strong> social sector was carried out by J.M.
Buchanan (Buchanan 1993). The works <strong>of</strong> M. Dewatipont and G. Roland (Dewatipont and Roland
1995), G.H Jefferson, and T.G. Rawski, (Jefferson and Rawski 1997), T.M. Moe (Moe 1984) were
devoted to <strong>the</strong> possibility <strong>of</strong> institutional design in reforming and reorganizing an enterprise. The
researchers D.C. North and B.R. Weingast (North and Weingast 1989), F. Parisi (Parisi 2001), G.
Stigler (Stigler 1970), B.W. Weingast and W. Marshall (Weingast Marshall 1988) were concerned with
<strong>the</strong> issues <strong>of</strong> application <strong>the</strong> institutional analysis and design to <strong>the</strong> state sector. However, <strong>the</strong>
advantage <strong>of</strong> applying <strong>the</strong> institution analysis techniques to knowledge economy has not been
embraced by contemporary economics researchers yet.
Any institutional analysis should be started with differentiation <strong>of</strong> institutions. What classifying
characteristics <strong>of</strong> institution differentiation have been employed so far?
H. Spenser, when studying social institutions, distinguished <strong>the</strong> regulative ones, which he associated
with politics and religion, <strong>the</strong> distribution ones concerned with economy functioning, and <strong>the</strong> kinship
institutions including marriage, family and so on. (Spencer, 1891).
P. Blau focused on <strong>the</strong> values embodied in a normative structure. Thus, he differentiated <strong>the</strong>
integrative, distributive and organizational institutions. The former type is based on solidarity and
identity <strong>of</strong> participants in <strong>the</strong> course <strong>of</strong> exchange. The second type is expressed through <strong>the</strong> values
responsible for maintenance <strong>of</strong> benefits, investments and rewards. The latter are <strong>the</strong> institutions
formed on <strong>the</strong> laws securing authorities in mobilization and coordination <strong>of</strong> efforts in achieving goals.
Much attention has been drawn to <strong>the</strong> D. North’s classification <strong>of</strong> institutions according to <strong>the</strong> level <strong>of</strong>
formalization, with <strong>the</strong> formal and informal institutions being singled out (North, 1981).
Ano<strong>the</strong>r criterion taken as a corner stone for such a classification is <strong>the</strong> level <strong>of</strong> institution
effectiveness, where <strong>the</strong> effective and ineffective institutions are differentiated. In Russian literature
<strong>the</strong>re are a number <strong>of</strong> works conveying this approach, such as <strong>the</strong> research <strong>of</strong> V.M. Polterovich
(Polterovich, 1999) and O.S. Sukharev (Sukharev, 2002). To describe <strong>the</strong> ineffective institutions <strong>the</strong>
researchers use notions like “institutional traps” and “disfunctionality <strong>of</strong> institutions”.
The use <strong>of</strong> a subject type as a characteristic basic for <strong>the</strong> differentiation highlights <strong>the</strong> types <strong>of</strong>
subjects being embraced by an institution, as well as <strong>the</strong> set <strong>of</strong> subjects a norm is developed for. Due
to this principle, G.B. Kleiner differentiates <strong>the</strong> macro-, micro- and nano-economic institutions (Kleiner,
2004). In <strong>the</strong> given framework <strong>the</strong> specification <strong>of</strong> institution types can be extended to <strong>the</strong> mezzo- and
mini-economic institutions (Popov, 2005).
O. V. Inshakov justifies considering a kind <strong>of</strong> economic activity, setting apart <strong>the</strong> institutions
functioning in construction, mining industry and food sector, as well as industrial, transportation,
agricultural and sale economic spheres, etc. (Inshakov, 2003).
According to <strong>the</strong> range <strong>of</strong> subjects holding <strong>the</strong> same norm, <strong>the</strong> general, specific and single institutions
can be distinguished. There is also a differentiation <strong>of</strong> <strong>the</strong> general and auxiliary institutions.
The listed classifying characteristics are ra<strong>the</strong>r general and can be applied to <strong>the</strong> entire range <strong>of</strong>
institutions acting in socio-economic relations. They do allow <strong>the</strong> consideration <strong>of</strong> specific peculiarities
<strong>of</strong> an environment where <strong>the</strong>y take place, however, without <strong>of</strong>fering an opportunity to analyze <strong>the</strong>
inside and outside environment <strong>of</strong> a single economic unit in depth and greater detail.
781

Evgeny Popov, Maxim Vlasov and Anna Veretennikova
The present research introduces <strong>the</strong> criteria which have made up a basis for a design and analysis <strong>of</strong>
an institutional environment in a firm in <strong>the</strong> knowledge generation framework. The main purpose <strong>of</strong>
<strong>the</strong>se criteria is to assist in forming such a system <strong>of</strong> institutions, which would let detect and analyze
ineffective zones <strong>of</strong> activity arising as a result <strong>of</strong> insufficient institutional provision.
2.1 Structure <strong>of</strong> an institutional matrix
An institutional matrix is a notion basic core <strong>of</strong> which was developed by K. Polanyi in his work “The
livelihood <strong>of</strong> Man”. Though, despite its being almost as old as neoinstitutional approach in economics
itself, it is essential to note that so far <strong>the</strong> notion has been developed very little (Polanyi, 1977).
R.I. Kapelushnikov describes <strong>the</strong> problem <strong>of</strong> institutional matrix as follows: «Institutional matrix has
been and still is immensely imperfect; in some key areas general rules have not thus far been
developed ; in o<strong>the</strong>rs <strong>the</strong>re still co-exist <strong>the</strong> norms being in so an evident contradiction with each
o<strong>the</strong>r that it gives way to <strong>the</strong>ir occasional misuse and misunderstanding; in thirds “good”, at first
glance, laws do not work, because nothing can make follow <strong>the</strong>m» (Kapelushnikov, 2001).
It is worth mentioning that K. Polanyi does not give a definition <strong>of</strong> a constitutional matrix itself, but
uses it in order to highlight <strong>the</strong> social determination <strong>of</strong> any economy and <strong>the</strong> necessity to consider
social relations when analyzing <strong>the</strong> forms <strong>of</strong> economic interactions (Polanyi, 1977). The authors
believe that each economic system is characterized by its own institutional matrix. However, it is
possible to form <strong>the</strong> basis for an institutional matrix, common for most economic systems <strong>of</strong> <strong>the</strong> same
type.
The present study deals with <strong>the</strong> institutional matrix <strong>of</strong> knowledge generation at a firm’s level. By an
institutional matrix <strong>the</strong> authors understand a whole set <strong>of</strong> economic institutions systematized in a
particular way and meant to provide <strong>the</strong> firm’s activity with generation and application <strong>of</strong> new
knowledge. To design a constitutional matrix a set <strong>of</strong> institutions to fill it up should be drawn. An initial
premise being <strong>the</strong> cost estimation <strong>of</strong> economic institutions via transaction expenses, a list <strong>of</strong> firm’s
expenses connected with <strong>the</strong> knowledge generation sphere has been <strong>of</strong>fered. As an information
source about <strong>the</strong> detail spending we used <strong>the</strong> expenses given in <strong>the</strong> chapter 25 (Tax on pr<strong>of</strong>it) <strong>of</strong> <strong>the</strong>
Tax Code <strong>of</strong> <strong>the</strong> Russian Federation.
Table 1 presents <strong>the</strong> list <strong>of</strong> <strong>the</strong> knowledge generation institutions obtained. Having established <strong>the</strong> list
<strong>of</strong> <strong>the</strong> institutions, <strong>the</strong> institutional matrix <strong>of</strong> knowledge generation was designed.The institution matrix
definition requires all <strong>the</strong> institutions to be systematized in a particular way. The first differentiation
criterion is <strong>the</strong> place <strong>of</strong> <strong>the</strong>ir emergence. That is, <strong>the</strong> endogenous institutions <strong>of</strong> knowledge generation
are <strong>the</strong> institutions emerging inside an economic object, and exogenous are ones being outside it, but
effecting <strong>the</strong> knowledge generation <strong>of</strong> a economic unit under consideration. Systematization on <strong>the</strong>
place <strong>of</strong> emergence makes it easier to understand which institutions can be changed and optimized,
and which ones can only be influenced indirectly and try to reduce <strong>the</strong> expenses resulting from <strong>the</strong>ir
use. The former are <strong>the</strong> endogenous institutions, <strong>the</strong> latter are <strong>the</strong> exogenous ones.
Ano<strong>the</strong>r criterion <strong>of</strong> institutional matrix design is <strong>the</strong> type <strong>of</strong> generated knowledge. In previous studies
<strong>the</strong> authors presented <strong>the</strong> differentiation <strong>of</strong> knowledge regarding <strong>the</strong> rate <strong>of</strong> changes introduced in
innovations and setting apart <strong>the</strong> operational, structural and functional knowledge (E.V. Popov,
M.V.Vlasov, 2009). So, this criterion forms three groups <strong>of</strong> knowledge generation institutions: <strong>the</strong>
institutions <strong>of</strong> operational, structural and functional knowledge.
The institutions <strong>of</strong> operational knowledge are settled norms <strong>of</strong> interaction between economic agents,
emerging (acting) as operational response toward outside conditions, resulting in insignificant
changes in technological processes and followed by a change in information resources.
The institutions <strong>of</strong> structural knowledge are settled norms <strong>of</strong> interaction between economic agents,
resulting in changes in <strong>the</strong> structure <strong>of</strong> an economic subject and followed by a change in human
resources. The institutions <strong>of</strong> functional knowledge are settled norms <strong>of</strong> interaction between economic
agents, acting under significant changes in technological process and followed by a change in capital
assets <strong>of</strong> an economic unit.
Of utmost importance is <strong>the</strong> fact that functioning <strong>of</strong> <strong>the</strong> institutions for functional knowledge generation
causes changes not only in material resources, but in information and labour ones. It is possible to
talk ei<strong>the</strong>r about mutual interaction <strong>of</strong> <strong>the</strong> institutions for generation <strong>of</strong> operational, structural and
782

Evgeny Popov, Maxim Vlasov and Anna Veretennikova
functional knowledge, or that in <strong>the</strong> institutions for functional knowledge generation not only material
resources are changed, but all <strong>the</strong> o<strong>the</strong>rs as well.
Hence, <strong>the</strong> institutions for operational knowledge generation encourage, as a rule, <strong>the</strong> change in
information resources, <strong>the</strong> institutions for structural knowledge result it <strong>the</strong> change in labour
resources, and <strong>the</strong> institutions for functional knowledge generation are followed by <strong>the</strong> change in
material resources. The third criterion suggested for <strong>the</strong> institutional matrix design is <strong>the</strong> hierarchy <strong>of</strong>
management functions, according to which <strong>the</strong> institutions <strong>of</strong> planning, organization, stimulation and
control are specified. The forth criterion is <strong>the</strong> hierarchy <strong>of</strong> manufacture functions, allowing to
distinguish <strong>the</strong> institutions for production itself, distribution, sale and consumption <strong>of</strong> knowledge.
The criteria outlined above make up <strong>the</strong> matrix <strong>of</strong> knowledge generation institutions, where <strong>the</strong>y are
given in a particular order (table 2).
Table 1: Institutions for knowledge generation
№ Institutions for knowledge generation № Institutions for knowledge generation
Institution for efficiency and incentive bonus Institution for dealing with notary
1 payments 26
Institution for special work conditions bonus Institution for audit service
2
payments
27
Institution for public utilities and food Institution for organization and structural units
3 provision 28 management
Institution for transportation expenditure on Institution for technical and management staff provision
4 annual leaves 29 to third parties
Institution for compensations on unspent Institution for submission <strong>of</strong> forms and information on
5
leaves
30
state statistical observation
6 Institution for long-service bonus payments 31 Negotiation institution
7 Institution for regional bonus payments 32 Staff training institution
Institution for uninterrupted service in hard
Institution for postal services
8 climatic conditions bonus payments 33
Institution for compensation payments to <strong>the</strong> Institution for acquisition <strong>of</strong> rights to use <strong>the</strong> s<strong>of</strong>tware and
9
dismissed
34
databases on contracts with <strong>the</strong> copyright owner
Institution for salary maintenance during
Marketing institution
10
education leaves
35
Institution for transportation compensations
Advertisement institution
11 in shift works 36
Institution for additions in case <strong>of</strong> employee’s
Institution for <strong>the</strong> use <strong>of</strong> payment systems and digital
training or retraining with a break in work
systems <strong>of</strong> information delivery provided by foreign
12
37
organizations
Institution for civil-law contract payment to Institution for non-capital technology, organization,
non-staff employees
production and management improvements
13
14
Institution for R&D expenditure
Institution for voluntary property insurance
38
39
Institution for <strong>the</strong> intellectual activity rights and intellectual
property rights
Institution for security and reserve contributions
15
40
16 Institution for goods and service certification 41 Institution for collaboration with law courts
17
Institution for relocation allowance payment
42
Institution for dealing with cancelled industrial orders
18 Institution for security activity 43 Institution for holding share holders meetings
Institution for economic security <strong>of</strong> bank and
Institution for discount provision
economic activity, as well as material
19 valuables security 44
Institution for acceptable work conditions Institution for collaboration with trade unions
20 and safety measures 45
Institution for pr<strong>of</strong>essional diseases
Institution for bonus payments
21 treatment 46
Institution for staff’s employment
Institution for bonus payments from <strong>the</strong> special-purpose
22
47 funds or directed receipts
23
Institution for business trips
48
Institution for sale <strong>of</strong> goods at a bargain price
Institution for law and information service Institution for outside R&D funding
24
provision
49
25 Institution for consulting service provision
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Evgeny Popov, Maxim Vlasov and Anna Veretennikova
Table 2: Institutional matrix <strong>of</strong> knowledge generation 1
Endogenous
Exogenous
Knowledg
e type
Management
functions
Manufacturing
fanctions
Planning Organization Stimulation Control
Operation Manufacturing 23, 38 1, 2,3, 6,15, 17, 42
al
Distribution
46,47, 48
Sale 44
Consumption 18,19 10
Srtuctural Manufacturing
1, 2,3, 4, 5, 6, 7
,8, 11, 15, 17,
42
Distribution
Sale
23, 38
46,47, 48
Consumption 18,19 20, 21 28
Functiona Manufacturing 14 23, 40 42
l
Distribution
Sale
Consumption 18,19 1, 2, 3, 6, 15,
17, 46,47, 48
Operation
al
Manufacturing 32, 34 13,31,33, 49
Distribution 30
Sale 35,36,37
Consumption 12,24 25 26, 27
Structural Manufacturing 22 13,31,43,49 45
Distribution 29
Sale
Functiona
l
Consumption 9
Manufacturing 13,31,43,49 45
Distribution
Sale 16
Consumption 39
Table 2 depicts that <strong>the</strong> knowledge generation institutions in <strong>the</strong> matrix given are not distributed
homogeneously. The absence <strong>of</strong> institutions in some areas <strong>of</strong> economic activity can give evidence <strong>of</strong>:
<strong>the</strong> area is not provided with necessary resources;
<strong>the</strong> distribution <strong>of</strong> resources in <strong>the</strong> area <strong>of</strong> interest is not reasonable;
<strong>the</strong> economic unit is not involved in this activity.
The analysis <strong>of</strong> <strong>the</strong> institutional matrix <strong>of</strong> knowledge generation can be performed via <strong>the</strong> following
stages:
to analyze <strong>the</strong> distribution <strong>of</strong> institutions according <strong>the</strong> knowledge type;
to observe how <strong>the</strong> knowledge are distributed between <strong>the</strong> management functions;
to consider <strong>the</strong> distribution between <strong>the</strong> manufacturing functions, if necessary;
to draw <strong>the</strong> conclusion.
Functional knowledge. This type <strong>of</strong> knowledge is determinative when designing an innovation-active
enterprise. The matrix indicates that 22 institutions ensure <strong>the</strong> creation <strong>of</strong> functional knowledge,
whereas 33 and 31 institutions provide <strong>the</strong> creation <strong>of</strong> operative and structural knowledge
respectively. Studying <strong>the</strong> distribution <strong>of</strong> functional knowledge institutions considering <strong>the</strong>
management functions, it can be seen that 1 institution is singled out in <strong>the</strong> area <strong>of</strong> planning <strong>the</strong>
1 The institution order correspond to <strong>the</strong> numbers given in <strong>the</strong> table 1
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Evgeny Popov, Maxim Vlasov and Anna Veretennikova
functional knowledge, 9 institutions - in <strong>the</strong> area <strong>of</strong> organization, 10 ones - in <strong>the</strong> motivation area, and
2 institutions - in <strong>the</strong> area <strong>of</strong> control.
Inadequate institutional security <strong>of</strong> planning <strong>the</strong> generation <strong>of</strong> functional knowledge results in <strong>the</strong> fact,
that this type <strong>of</strong> activity is not a permanent, systematic one in most cases. The same takes place in
<strong>the</strong> area <strong>of</strong> knowledge generation control. Though this activity might entail <strong>the</strong> rise <strong>of</strong> expenditure on
<strong>the</strong> creation <strong>of</strong> functional knowledge, it can prevent significant losses in <strong>the</strong> area. The number <strong>of</strong>
institutions in <strong>the</strong> area <strong>of</strong> stimulating <strong>the</strong> functional knowledge generation shows this area to be most
developed among <strong>the</strong> areas under consideration. However, comparing this value to <strong>the</strong> number <strong>of</strong>
institutions involved in <strong>the</strong> creation <strong>of</strong> o<strong>the</strong>r knowledge types, it may be concluded that <strong>the</strong> level <strong>of</strong>
institutional provision <strong>of</strong> functional knowledge generation is still lower than that <strong>of</strong> operative and
structural knowledge generation.
Figure 1 graphically demonstrates <strong>the</strong> distribution <strong>of</strong> <strong>the</strong> institutions described. Note should be made
that <strong>the</strong> way <strong>the</strong> planning takes place determines <strong>the</strong> organization <strong>of</strong> knowledge creation, its
stimulation and control. The form <strong>of</strong> control organization also underpins <strong>the</strong> processes <strong>of</strong> knowledge
generation. The functioning <strong>of</strong> control processes influences <strong>the</strong> quality <strong>of</strong> knowledge generated, as
well as <strong>the</strong> effectiveness <strong>of</strong> knowledge generation in <strong>the</strong> long run.
Figure 2 provides <strong>the</strong> distribution <strong>of</strong> institutions for functional knowledge generation depending on <strong>the</strong>
manufacturing function <strong>the</strong>y exercise. The sale activity is not shown to be provided with <strong>the</strong>
institutions for knowledge generation. Thus, this knowledge is unlikely to be realized. This fact inhibits
fur<strong>the</strong>r development in <strong>the</strong> area <strong>of</strong> functional knowledge generation. The absence <strong>of</strong> institutions in <strong>the</strong>
area <strong>of</strong> functional knowledge distribution can be a reason for ineffective allocation <strong>of</strong> <strong>the</strong> knowledge
created with a poor performance.
Figure 1: Distribution <strong>of</strong> institutions for functional knowledge generation in terms <strong>of</strong> management
functions
Figure 2: Distribution <strong>of</strong> institutions for functional knowledge generation in terms <strong>of</strong> manufacturing
functions
Structural knowledge. The structural knowledge, embodying <strong>the</strong> changes in <strong>the</strong> structure <strong>of</strong> an
economic unit, is sufficiently provided with <strong>the</strong> institutions for knowledge generation – 31 institutions.
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Evgeny Popov, Maxim Vlasov and Anna Veretennikova
Studying <strong>the</strong> distribution <strong>of</strong> institutions for structural knowledge generation in terms <strong>of</strong> management
functions, it is worth paying attention to <strong>the</strong> insufficient provision <strong>of</strong> such areas as planning and control
(fig.3). A positive aspect here is <strong>the</strong> security <strong>of</strong> <strong>the</strong>se institutions in <strong>the</strong> area <strong>of</strong> stimulation. The
organization <strong>of</strong> structural knowledge generation is effected, on <strong>the</strong> one hand, by <strong>the</strong> institutions for
stimulating <strong>the</strong> structural knowledge generation, acting as a catalyst in <strong>the</strong>ir creation, but, on <strong>the</strong> o<strong>the</strong>r
hand, <strong>the</strong>ir effectiveness might be decreased by <strong>the</strong> lack <strong>of</strong> necessary norms in <strong>the</strong> area <strong>of</strong> planning,
which hinders systematic development <strong>of</strong> structural knowledge. Insufficient number <strong>of</strong> norms in <strong>the</strong>
area <strong>of</strong> control acts as a factor slowing down <strong>the</strong> creation <strong>of</strong> structural knowledge, which, in turn, does
not allow detecting mistakes in <strong>the</strong> process <strong>of</strong> structural knowledge generation.
Studying <strong>the</strong> distribution <strong>of</strong> institutions for structural knowledge generation in terms <strong>of</strong> manufacturing
functions, being <strong>the</strong> case with <strong>the</strong> functional knowledge generation, an almost total absence <strong>of</strong>
institutions in <strong>the</strong> areas <strong>of</strong> distribution and sale can be noticed. This fact explains <strong>the</strong> reason why <strong>the</strong>
structural knowledge is not able to reach <strong>the</strong> end-user partly or entirely (fig.4).
Figure 3: Distribution <strong>of</strong> structural knowledge generation in terms <strong>of</strong> control functions
Figure 4: Distribution <strong>of</strong> structural knowledge generation in terms <strong>of</strong> manufacturing functions
Operational knowledge. Generation <strong>of</strong> <strong>the</strong> operational knowledge is <strong>the</strong> area most provided with <strong>the</strong>
institutions, though quantitatively a little less provided than <strong>the</strong> generation <strong>of</strong> <strong>the</strong> structural knowledge
(33 and 31 institutions respectively).
The distribution <strong>of</strong> <strong>the</strong> institutions in terms <strong>of</strong> management functions is homogeneous enough (fig.3).
However, it makes sense to talk about bigger institualization <strong>of</strong> <strong>the</strong> operational knowledge (4
institutions).
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Evgeny Popov, Maxim Vlasov and Anna Veretennikova
The control <strong>of</strong> knowledge generation implies <strong>the</strong> record <strong>of</strong> <strong>the</strong> knowledge being created, which
contributes to <strong>the</strong> detection <strong>of</strong> areas where <strong>the</strong> knowledge is potentially or actually used.
The distribution <strong>of</strong> <strong>the</strong> institutions for operational knowledge generation is not homogeneous. As in <strong>the</strong>
case with <strong>the</strong> generation <strong>of</strong> functional and structural knowledge <strong>the</strong>re are a small number <strong>of</strong>
institutions in <strong>the</strong> areas <strong>of</strong> sale and distribution. This phenomenon is a weak point <strong>of</strong> <strong>the</strong> institutional
repletion <strong>of</strong> <strong>the</strong> entire matrix, i.e. <strong>of</strong> institutional provision <strong>of</strong> knowledge generation activity.
The analysis given above depicts <strong>the</strong> areas in knowledge generation, which are not institutionally
provided enough:
planning <strong>of</strong> functional knowledge generation;
control <strong>of</strong> functional knowledge generation;
planning <strong>of</strong> structural knowledge generation;
control <strong>of</strong> structural knowledge generation;
distribution <strong>of</strong> functional knowledge;
sale <strong>of</strong> functional knowledge;
distribution <strong>of</strong> structural knowledge;
sale <strong>of</strong> structural knowledge;
distribution <strong>of</strong> operational knowledge;
sale <strong>of</strong> regular knowledge.
3. Conclusion
This work has provided a conceptual framework – <strong>the</strong> institutional matrix <strong>of</strong> knowledge generation –
on <strong>the</strong> basis <strong>of</strong> singling out consistent expenditures <strong>of</strong> <strong>the</strong> Tax Code <strong>of</strong> <strong>the</strong> Russian Federation.
Scientific relevance <strong>of</strong> <strong>the</strong> model suggested is <strong>the</strong> advancement <strong>of</strong> K. Polaniy and D.North’s
institutional matrix phenomenon and practical application <strong>of</strong> <strong>the</strong> institutional design in <strong>the</strong> knowledge
economy framework.
The institutional matrix considered is only a model, which can be completed and developed for <strong>the</strong>
real firms.
Institutional matrices designed for different enterprises, though possessing some common
characteristics, are individual and depend on <strong>the</strong> industry peculiarities, target activity <strong>of</strong> a firm,
management system adapted, and o<strong>the</strong>r factors.
Theoretical significance <strong>of</strong> <strong>the</strong> matrix developed is <strong>the</strong> formation <strong>of</strong> a <strong>the</strong>oretical platform for graphical
conceptual depiction <strong>of</strong> different mechanisms coordinating economic institutions.
Practical significance <strong>of</strong> <strong>the</strong> study is that it <strong>of</strong>fers:
a tool to judge how inhomogenous <strong>the</strong> distribution <strong>of</strong> economic institutions throughout <strong>the</strong>
economic spheres <strong>of</strong> an enterprise is. The lack <strong>of</strong> norms and institutions in a certain field <strong>of</strong>
economic activity reveals <strong>the</strong> shortage <strong>of</strong> resources in this sphere;
a possibility to detect <strong>the</strong> weak points <strong>of</strong> institutional design in providing an economic activity with
norms. Hence, practical realization <strong>of</strong> an institutional matrix lays a basis for working out
recommendations to <strong>the</strong> administration <strong>of</strong> a firm on reinforcing certain institutional blocks;
a possibility to apply it to <strong>the</strong> strategy planning <strong>of</strong> <strong>the</strong> development <strong>of</strong> <strong>the</strong> knowledge generation
processes in economic units.
In our opinion, it is critically important to consider <strong>the</strong> possible prospects arising during institualization
<strong>of</strong> knowledge generation.
The design and practical application <strong>of</strong> <strong>the</strong> institutional matrix can bring about <strong>the</strong> stimulation <strong>of</strong>
competitiveness, reflected in an ongoing need to look for new comparative advantages, setting <strong>the</strong>
firm apart. Ano<strong>the</strong>r consequence <strong>of</strong> knowledge generation institualization, being as crucial, is an
opportunity to scrutinize systemic problems (factors) inhibiting <strong>the</strong> development <strong>of</strong> innovation
economy.
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References
Evgeny Popov, Maxim Vlasov and Anna Veretennikova
Blau P. A Theory <strong>of</strong> Social Integration // The American Journal <strong>of</strong> Sociology", Vol. LXV, No. 6, p. 545, May 1960
Buchanan J.M. How can constitutions be designed so that politicians who seek to serve <strong>the</strong> public interests can
survive? // Constitutional Political Economy, 1993, v. 4, n.1, pp. 19-42
Dewatipont M. and Roland G.The design <strong>of</strong> reform packages under uncertainty // American Economic Review,
1995, v. 85, n. 5, pp. 1207-1223
Jefferson, G.H and Rawski, T.G. Chinese Enterprise Reform as a Market Process. Working Paper # 76, The
Davidson Institute Working Paper Series, June 1997
Kapeluchnikov R.I. Russian labour market: adaptation without restructurization Moscow, 2001. - p.139.
Kleiner G.B. Systematic Paradigm and Theory <strong>of</strong> an Enterprise // Economics Issues, 2002. №10. - p. 47-69.
Kleiner G.B. Evolution <strong>of</strong> Institutional Systems. Moscow, 2004.
Moe T.M. The New Economics <strong>of</strong> Organization // American Journal <strong>of</strong> Political Science, 1984, v. 28, n. 4, pp.
739-777
North D.C. and Weingast B.R. Constitutions and Commitment: The Evolution <strong>of</strong> Institutions Govering Public
Choice in Seventeenth-Century England // The Journal <strong>of</strong> Economic History, 1989, v. 49, n. 4, pp.803-832
Noth D. Strukture and change in economic history. – N.Y.: Norton, 1981
Parisi F. Sources <strong>of</strong> Law and <strong>the</strong> Institutional Design <strong>of</strong> Lawmaking // Journal <strong>of</strong> Public Finance and Public
Choice, 2001,19.2-3, pp. 95-122
Polanyi К. The Livelihood <strong>of</strong> Man. N.Y.: Academic Press Inc., 1977. P. XXXII.
Inshakov O.V. Economic Institutions: on typology and classification// Sociology Study, 2003. №9.- pp.42-51.
Polterovich V.M. Institutional Traps and Economic Reforms // Economics and Ma<strong>the</strong>matical Models. 1999. V. 35.
№. 2.
Popov E. Economics as a Separate Part <strong>of</strong> Microeconomics, 2006.
Popov E.V., Vlasov M.V. Кknowledge differentiation // Montenegrin journal <strong>of</strong> economics. 2009. №9
Spencer H. Essays: Scientific, Political, and Speculative, 3 vol., 1891
Stigler G. The Optimum Enforcement <strong>of</strong> Laws // Journal <strong>of</strong> Political Economy, 1970, v. 78, May-June, pp. 526-
536
Sukharev O.S. Concept <strong>of</strong> Economic Disfunction and Evolution <strong>of</strong> a Firm // Economics Issues, 2002. №10
Weingast B.W. and Marshall W. The Industrial Organization <strong>of</strong> Congress // Journal <strong>of</strong> Political Economy, 1988, v.
96, n.1, pp.132-163
788

Developing Institutions <strong>of</strong> Knowledge Economy
Evgeny Popov, Maxim Vlasov and Marina Zubareva
Institute <strong>of</strong> Economics UB <strong>of</strong> RAS, Ekaterinburg, Russia
epopov@mail.ru
mvlassov@mail.ru
marjushka@mail.ru
Abstract: The aim <strong>of</strong> <strong>the</strong> present research is to show <strong>the</strong> working-out <strong>of</strong> institutional structure <strong>of</strong> economic
development system with respect to <strong>the</strong> concept <strong>of</strong> Knowledge Economy. Evaluation <strong>of</strong> <strong>the</strong> functioning <strong>of</strong>
development institutions, analysis <strong>of</strong> <strong>the</strong> degree <strong>of</strong> its elaboration and establishment <strong>of</strong> mechanisms to control
institutions rendered <strong>the</strong>ir representation possible in <strong>the</strong> form <strong>of</strong> a single institutional framework with clearly
defined hierarchical links. This structure may take <strong>the</strong> form <strong>of</strong> institutional atlas, acting at both country and
regional level. In this paper <strong>the</strong> institutional atlas is a summary classification <strong>of</strong> institutions, which combines
several types <strong>of</strong> systematization <strong>of</strong> <strong>the</strong>se institutions by various criteria: place <strong>of</strong> appearing (endogenous,
exogenous), sphere <strong>of</strong> knowledge (social, technological, economical, political, cultural, and ecological),
management functions (developing institutions <strong>of</strong> planning, organizing, incentive and control) and spheres <strong>of</strong>
activity (developing institutions <strong>of</strong> producing, distributing, using and merchandizing). We attempt to institutional
model <strong>of</strong> economic institutions <strong>of</strong> region development, which is caused by one <strong>of</strong> <strong>the</strong> features <strong>of</strong> Russian
economic space – its heterogeneity, and uneven development. As a result we got <strong>the</strong> institutional atlas <strong>of</strong> region
development <strong>of</strong> entrepreneurship as a special case <strong>of</strong> <strong>the</strong> atlas <strong>of</strong> economic development institutions. The atlas
<strong>of</strong> region development entrepreneurship institutions includes 79 institutions aimed to ensure <strong>of</strong> sustainable
development, economic security and stimulating innovation activity <strong>of</strong> <strong>the</strong> Ural region through entrepreneurship
development as a main driving force <strong>of</strong> economic development. This atlas can be used for analysis <strong>of</strong> <strong>the</strong> degree
<strong>of</strong> elaboration <strong>of</strong> region development entrepreneurship institutions and to develop mechanisms to control <strong>the</strong>se
institutions. Accordingly in this paper <strong>the</strong> hierarchical structure <strong>of</strong> region economic development institutions was
worked out. Such a hierarchy is responsible to develop strategic decisions on investments <strong>of</strong> existing economic
institutions. It’s a good illustration <strong>of</strong> agent’s interactions in <strong>the</strong> development <strong>of</strong> economic systems to addition <strong>the</strong>
atlas by traditional institutions – organizations, such as business incubators, venture and innovation funds, and
o<strong>the</strong>rs. Selected sequence <strong>of</strong> economic institutions would allow defining weak places <strong>of</strong> institutional designing.
And so <strong>the</strong> practical content <strong>of</strong> <strong>the</strong> atlas by specific norms and rules can be <strong>the</strong> basis <strong>of</strong> recommending
authorities to streng<strong>the</strong>ning regulatory support those or o<strong>the</strong>r institutional units.
Keywords: developing institutions, institutional atlas, knowledge economy, region development,
entrepreneurship
1. Introduction
Developing institutions have been one <strong>of</strong> <strong>the</strong> most popular <strong>the</strong>mes <strong>of</strong> <strong>the</strong> world economic policy for
last decades. Many countries <strong>of</strong> <strong>the</strong> world connect <strong>the</strong>ir future prosperity and welfare with emerging
and evolution <strong>of</strong> developing institutions. Most countries (both developed and developing), seek to
create such a system <strong>of</strong> developing institutions, which would allow to“tighten” underdeveloped
segments <strong>of</strong> <strong>the</strong> economy or to fur<strong>the</strong>r streng<strong>the</strong>ning <strong>the</strong> key segments, that is at that case will lead to
acceleration <strong>of</strong> GDP growth (Rodrick 1999). Now it is very obvious at <strong>the</strong> epoch <strong>of</strong> economic and
policy course <strong>of</strong> standing Knowledge Economy and Knowledge Society, acclaimed by <strong>the</strong> world
community, as inalienablestep <strong>of</strong> developing people civilization.
Conscious transition from society developed by material production to knowledge-based economy,
led to organizing <strong>the</strong> concept <strong>of</strong> Knowledge economy, founded by Austrian-American scientist Fritz
Machlup. In 1962 he coined into scientific area <strong>the</strong> term “Knowledge Economy” (Machlup 1962),
which along with definition “Knowledge-based economy” has become widely used at economic <strong>the</strong>ory
and practice since <strong>the</strong> late 1990s. The Knowledge economy is <strong>the</strong> economy, in which <strong>the</strong> generation,
distribution and using <strong>of</strong> knowledge are <strong>the</strong> main factors <strong>of</strong> economic growth.
There are a lot <strong>of</strong> scientific issues which were devoted to solving problems <strong>of</strong> <strong>the</strong> Knowledge
economy. Theoretical and methodological foundations <strong>of</strong> <strong>the</strong> concept <strong>of</strong> <strong>the</strong> Knowledge economy
were laid by famous scientists such as Blacker F. (Blacker 1995), Cabrera B. and Cabrera E.F.
(Cabrera & Cabrera 2002), Drucker P. (Drucker 1999), Foray D. (Foray 2004), Gertler V.S. and Wolfe
D.A. (Gertler & Wolfe 2004), Nonaka I. (Nonaka 1991) and o<strong>the</strong>rs. Knowledge generation problems
and <strong>the</strong>ir effect on <strong>the</strong> development <strong>of</strong> economic agents are regards in <strong>the</strong> works <strong>of</strong> Hayek F. (Hayek
1945, Hayek 1973). Knowledge management matters disclosed at works <strong>of</strong> Kogut B. and Zander U.
(Kogut & Zander 1992), Spender J.-C. (Spender 1996) and o<strong>the</strong>rs.
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Evgeny Popov, Maxim Vlasov and Marina Zubareva
The notion <strong>of</strong> Knowledge Economy reflects <strong>the</strong> recognition that scientific knowledge has directly
determined <strong>the</strong> parameters <strong>of</strong> economic growth in recent years. The application <strong>of</strong> knowledge - as
manifested in areas such as entrepreneurship and innovation, research and development, s<strong>of</strong>tware
and product design, and in people’s education and skills levels - is now recognized to be one <strong>of</strong> <strong>the</strong>
key sources <strong>of</strong> growth in <strong>the</strong> global economy. But, sustainability <strong>of</strong> growth is a challenge in many
countries both due to internal pressures linked to emerging skills shortages as well as to external
competitiveness pressures linked to <strong>the</strong> intensified market rivalries brought on by <strong>the</strong> knowledge and
ICT-spurred globalization process. These trends make it very important for economies to be well
prepared to be able to adapt, create, use, and disseminate knowledge (The World Bank 2008).
However <strong>the</strong> transition to formation and development <strong>of</strong> Knowledge economy was decelerated by
some institutional, social, infrastructural and o<strong>the</strong>r barriers. It possible to overcome <strong>the</strong>se barriers by
creating effective system <strong>of</strong> developing institutions that is <strong>the</strong> system <strong>of</strong> political, economic, social and
o<strong>the</strong>r institutions leading to realize full country’s potential <strong>of</strong> economic growth and to improving <strong>the</strong>
welfare <strong>of</strong> <strong>the</strong> citizens.
Some countries already can boast <strong>of</strong> a successful experience in creating such developing institutions
systems, for example Israeli program Yozma, CORFO in Chili, AVANCI in Mexico, SITRA in Finland,
programs <strong>of</strong> innovation stimulation in South Korea and Brazil, SBIR in <strong>the</strong> USA and ect.
In line with global trends <strong>the</strong> system <strong>of</strong> developing institutions is beginning to emerge in Russia, <strong>the</strong>
aim <strong>of</strong> which are <strong>the</strong> stimulation <strong>of</strong> innovations, entrepreneurship development and infrastructure
development. However <strong>the</strong> formation <strong>of</strong> such system complicated by imperfection <strong>of</strong> <strong>the</strong> institutional
framework, lack <strong>of</strong> development <strong>of</strong> social, politic and economic institutions, lack <strong>of</strong> incentives for
entrepreneurs to create and apply innovations and <strong>the</strong> presence <strong>of</strong> regional disparities.
Fundamentals <strong>of</strong> <strong>the</strong>oretical studies <strong>of</strong> <strong>the</strong> innovation component <strong>of</strong> entrepreneurship were
established by works <strong>of</strong> J. Schumpeter (Schumpeter 1969), P. Drucker, R. Cantillon (Cantillon 1959),
J. Keynes, F.A. Hayek, A. Hosting, that <strong>the</strong> essence <strong>of</strong> entrepreneurship through research, observe it
as a kind <strong>of</strong> creativity, during which constantly arise innovation and change.
In this context <strong>the</strong> aim <strong>of</strong> this research is to point out <strong>the</strong> working-out <strong>of</strong> institutional structure <strong>of</strong>
economic development system with respect to <strong>the</strong> concept <strong>of</strong> Knowledge economy.
2. Essence <strong>of</strong> Developing Institutions
One <strong>of</strong> <strong>the</strong> topics <strong>of</strong> many discussions devoted to <strong>the</strong> Knowledge Society is a question <strong>of</strong> formation
<strong>the</strong> modern and actual system <strong>of</strong> political, social and economic institutions.
For Russia <strong>the</strong>re is a series <strong>of</strong> researches devoted to <strong>the</strong> solving <strong>of</strong> <strong>the</strong>se problems, in which groups
<strong>of</strong> institutions defining potential courses <strong>of</strong> possible structural changes are fit into <strong>the</strong> logical hierarchy.
The researches identify <strong>the</strong> following groups <strong>of</strong> institutions:
Politico-legal institutions connected with civil and political rights <strong>of</strong> citizens, in particular, economic
agents. These institutions ensure <strong>the</strong> protection <strong>of</strong> basic rights: guarantee <strong>of</strong> inviolability <strong>of</strong> person
and property, judicial independence, effectiveness <strong>of</strong> law enforcement, media freedom.
Institutions which provide human capital development. It’s includes, for example, education,
health, pension system, housing and etc.
Economic institutions, namely legislation, which ensure stabile operation and development <strong>of</strong>
national economy, economic growth and structural economy modernization.
Special institutions, <strong>the</strong> purposes <strong>of</strong> which are to solve <strong>the</strong> concrete, specific problems <strong>of</strong>
economic growth. There are so-called “developing institutions”, that are <strong>the</strong> rules <strong>of</strong> <strong>the</strong> game
aimed not at all participants <strong>of</strong> economic or political life, but at some <strong>of</strong> <strong>the</strong>m definitely selected. In
o<strong>the</strong>r words <strong>the</strong>re are <strong>the</strong> institutions <strong>of</strong> discrete effects on <strong>the</strong> economy, <strong>the</strong> action <strong>of</strong> which is
general in nature.
The special institutions – developing institutions - occupy an especial place among o<strong>the</strong>rs. They have
been at <strong>the</strong> centre <strong>of</strong> economic-policy debate in recent years. The developing institutions are
identified as especial organizations, which differ from <strong>the</strong> o<strong>the</strong>rs that contribute to resources
distribution to <strong>the</strong> benefit <strong>of</strong> <strong>the</strong> realization <strong>of</strong> new potential <strong>of</strong> economic growth <strong>of</strong> a department,
region or a country as a whole in <strong>the</strong> most common understanding.
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Evgeny Popov, Maxim Vlasov and Marina Zubareva
In this sense <strong>the</strong> developing institutions is one <strong>of</strong> <strong>the</strong> forms <strong>of</strong> state support. Even so researches
emphasized <strong>the</strong> following features <strong>of</strong> <strong>the</strong>se organizations:
The activity <strong>of</strong> <strong>the</strong> developing institutions is to reallocation <strong>of</strong> resources to development projects.
Development projects aim at generation <strong>of</strong> new potential <strong>of</strong> economic growth (infrastructure,
human capital, new technology) and supporting <strong>of</strong> emerging sectors <strong>of</strong> economy, which require
an initial impulse for its development.
Stable organizational structure and determined rules <strong>of</strong> activity. The activity <strong>of</strong> developing
institutions has a systematical character because <strong>of</strong> clear rules and structure in contradistinction
to state support programs. The developing institutions also have a clear system <strong>of</strong> responsibility
and control.
The purpose <strong>of</strong> maximization <strong>of</strong> benefit is absent. Generally development institutions are nonpr<strong>of</strong>it
organizations because <strong>the</strong>ir activities connect with significant external effects that cannot
capitalize.
International experience allows us to distinguish a lot <strong>of</strong> different forms <strong>of</strong> developing institutions.
Generally <strong>the</strong>y specialize on <strong>the</strong> supporting some economic departments (department developing
institutions), regions (region developing institutions) and operations (special developing institutions).
Ano<strong>the</strong>r classification for developing institutions as organizations includes financial and non-financial
institutions. The first group includes invest and venture funds, banks and credit institutions. The
second group includes special economic zones, science, technology and industrial parks, business
incubators, centers <strong>of</strong> collective uses, centers <strong>of</strong> technology transfer and etc.
Accordingly <strong>the</strong> approach described above for developing institutions as especial organizations is
reflected in <strong>the</strong> creation <strong>of</strong> numerous researches at economical, political and social literature for <strong>the</strong>
last few years. The authors substantiate <strong>the</strong> need <strong>of</strong> such organizations almost all works, point to <strong>the</strong>
importance <strong>of</strong> innovation infrastructure, <strong>the</strong> main agents <strong>of</strong> which will be new created developing
institutions, though some important points such as principles, rules and norms <strong>of</strong> functioning <strong>of</strong> <strong>the</strong>se
organizations, control mechanisms and appearance conditions <strong>of</strong> <strong>the</strong>m are missed.
These omissions are partly explained by <strong>the</strong> fact that approach <strong>of</strong> defining developing institutions
using in <strong>the</strong> most articles is quite narrow. It regards organization aspects <strong>of</strong> specific mechanisms <strong>of</strong>
such institutions only.
In this paper <strong>the</strong> developing institution is well-established rules <strong>of</strong> agent interactions, <strong>the</strong> result <strong>of</strong>
which is to change <strong>the</strong> system state. This definition <strong>of</strong> developing institution as a set <strong>of</strong> norms and
rules corresponds to <strong>the</strong> views <strong>of</strong> <strong>the</strong> majority traditional and modern institutionalizes (Veblen 1919,
Hodgson 2006, Popov 2008). In this case, different organizational structures, which contribute to
resources distribution in favor <strong>of</strong> developing projects, will be a special case <strong>of</strong> developing institutions
(figure 1).
Developing Institution as a norm
Developing Institution
as an organization
Figure 1: Illustration <strong>of</strong> <strong>the</strong> fact that <strong>the</strong> developing institution as an organization is <strong>the</strong> part <strong>of</strong> <strong>the</strong>
developing institutions as a norm
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This approach <strong>of</strong> defining developing institutions as a set <strong>of</strong> norms is wider. It allows us to look deeper
on <strong>the</strong> processes <strong>of</strong> institutional interactions between <strong>of</strong> economic agents within a single industry,
region or a country. This awareness provides <strong>the</strong> opportunity to track <strong>the</strong> basic laws <strong>of</strong> <strong>the</strong> formation
<strong>of</strong> developing institutions <strong>of</strong> Knowledge economy, which promotes <strong>the</strong> development <strong>of</strong> innovative
capacity as <strong>the</strong> country as a whole, as well as a separate region.
3. Institutional Atlas
Evaluation <strong>of</strong> <strong>the</strong> functioning <strong>of</strong> development institutions, analysis <strong>of</strong> <strong>the</strong> degree <strong>of</strong> its elaboration and
establishment <strong>of</strong> mechanisms to control institutions made <strong>the</strong>ir representation possible in <strong>the</strong> form <strong>of</strong> a
single institutional framework with clearly defined hierarchical links. This structure may take <strong>the</strong> form
<strong>of</strong> institutional atlas, acting at both country and regional level.
In this paper <strong>the</strong> institutional atlas is a summary classification <strong>of</strong> institutions, which combines several
types <strong>of</strong> systematization <strong>of</strong> <strong>the</strong>se institutions by various criteria: place <strong>of</strong> appearing, sphere <strong>of</strong>
knowledge, management functions and spheres <strong>of</strong> activity.
On <strong>the</strong> place <strong>of</strong> appearing <strong>the</strong> authors emphasized endogenous institutions, which appeared inside
analyzing object (analyzing economic system), and exogenous institutions, which formed outside.
Institutions distinguish by <strong>the</strong> criterion <strong>of</strong> <strong>the</strong> sphere <strong>of</strong> knowledge:
social institutions responsible for social development (medicine, health, education, social
protection, etc.),
technological institutions that regulate a technological development <strong>of</strong> a country or a region
(science, industry and technology development),
economical institutions <strong>the</strong> aim <strong>of</strong> which is supporting <strong>of</strong> country (region) economic development,
political institutions that regulate a development <strong>of</strong> a country (region) political sphere, namely
management system, political activity <strong>of</strong> citizens, legal protection <strong>of</strong> <strong>the</strong> population and etc.
cultural institutions responsible for culture development <strong>of</strong> a country (region),
ecological institutions <strong>the</strong> aim <strong>of</strong> which is maintaining <strong>of</strong> <strong>the</strong> country (region) ecology equilibrium
on <strong>the</strong> optimum level.
On <strong>the</strong> management functions <strong>the</strong> authors emphasized developing institutions <strong>of</strong> planning, organizing,
incentive and control. On spheres <strong>of</strong> activity we divided institutions on <strong>the</strong> institutions <strong>of</strong> producing,
distributing, using and merchandizing.
The above-mentioned criteria form <strong>the</strong> Atlas <strong>of</strong> Developing Institutions, in which <strong>the</strong>y presented in <strong>the</strong>
certain order (figure 2).
In this work we attempt to institutional model <strong>of</strong> economic institutions <strong>of</strong> region development, which is
caused by one <strong>of</strong> <strong>the</strong> features <strong>of</strong> Russian economic space – its heterogeneity, and uneven
development. As a result we got <strong>the</strong> institutional atlas <strong>of</strong> region development <strong>of</strong> entrepreneurship as a
special case <strong>of</strong> <strong>the</strong> atlas <strong>of</strong> economic development institutions.
It should be noted that in this paper we consider endogenous institutions only. This is due to <strong>the</strong>ir
dominant role in <strong>the</strong> country economic development and <strong>the</strong>ir prevailing majority, compared with
exogenous institutions. For filling <strong>of</strong> <strong>the</strong> Atlas <strong>of</strong> entrepreneurship region development we used
institutions published in different economic researches.
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Figure 2: The model <strong>of</strong> <strong>the</strong> Atlas <strong>of</strong> Developing Institutions
4. Entrepreneurship Region Development Institutions <strong>of</strong> Planning
4.1 Institutions <strong>of</strong> Producing
Strategic planning institution (norms, rules and mechanisms that ensure <strong>the</strong> functioning <strong>of</strong>
strategic planning processes <strong>of</strong> a region);
Productive activities planning institute (norms, rules and mechanisms that ensure <strong>the</strong> tactical and
operational planning <strong>of</strong> region productive activities);
Institution <strong>of</strong> priorities (tactical and operational planning at <strong>the</strong> priority sphere <strong>of</strong> country and
region economic development);
Institution <strong>of</strong> fundamental researches (norms, rules and mechanisms that aimed to generate new
scientific approaches and principals <strong>of</strong> products and technology creating);
Institution <strong>of</strong> applied researches (provides a working-out <strong>of</strong> laboratory technologies, test methods,
production <strong>of</strong> models, industrial designs and non-standard equipment);
Institution <strong>of</strong> competitive selection <strong>of</strong> scientific ideas and technologies (norms, rules and
mechanisms that search new perspective scientific ideas, which analyzes, selects and evaluates
<strong>of</strong> opportunity to implement by entrepreneurship).
4.2 Institutions <strong>of</strong> Distributing
− Institution <strong>of</strong> education (norms, rules and mechanisms that ensure <strong>the</strong> purposeful process <strong>of</strong>
cognitive activity <strong>of</strong> skills and knowledge acquiring or knowledge and skills improving);
− Institution <strong>of</strong> information activity planning (<strong>the</strong> aim is planning <strong>of</strong> region needs (government and
business entities) at information resources, prediction and assessment <strong>of</strong> its quantity and quality, and
planning <strong>of</strong> potential channels <strong>of</strong> receipt, transmission and distribution <strong>of</strong> information);
− Institution <strong>of</strong> employment (planning <strong>of</strong> region availability <strong>of</strong> qualified personnel);
− Institution <strong>of</strong> investment planning (<strong>the</strong> aim is prediction <strong>of</strong> <strong>the</strong> most effective distribution <strong>of</strong>
financial resources to region investment projects);
− Institution <strong>of</strong> foreign economic activity (regulation <strong>of</strong> operations <strong>of</strong> international exchange <strong>of</strong>
goods, information, works, services, results <strong>of</strong> intellectual activity and <strong>the</strong> rights to <strong>the</strong>m).
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4.3 Institutions <strong>of</strong> Merchandising
Institution <strong>of</strong> partner research (norms, rules and mechanisms that ensure <strong>the</strong> process <strong>of</strong> partner
research on <strong>the</strong> most optimum level, that is to say with volumes <strong>of</strong> demand and supply, timing,
purchasing power, productive capacity, market volume, region, industry and o<strong>the</strong>r specificity,
rules <strong>of</strong> trade, cultural features and o<strong>the</strong>rs);
Institution <strong>of</strong> distribution infrastructure (provides <strong>the</strong> most effective processes <strong>of</strong> distribution
(delivery) <strong>of</strong> products taking into account <strong>the</strong> timing and volume <strong>of</strong> delivery, product specificity and
forms <strong>of</strong> payment).
4.4 Institutions <strong>of</strong> Using
Institution <strong>of</strong> merchandising planning (a set <strong>of</strong> norms, rules and mechanisms that planning <strong>the</strong>
movement <strong>of</strong> goods to place <strong>of</strong> its sale and using);
Institution <strong>of</strong> consumer and production orders (<strong>the</strong> aim is <strong>the</strong> most effective planning <strong>of</strong> volumes,
timing, character and o<strong>the</strong>r features <strong>of</strong> orders).
5. Entrepreneurship Region Development Institutions <strong>of</strong> Organizing
5.1 Institutions <strong>of</strong> Producing
Institution <strong>of</strong> hi-tech industries (a set <strong>of</strong> norms, rules and mechanisms that ensure <strong>the</strong> regulating
<strong>of</strong> hi-tech economy sector activity);
Institution <strong>of</strong> integration <strong>of</strong> science and business (norms, rules and mechanisms that ensure an
effective partnership <strong>of</strong> scientific organizations and business entities);
Institution <strong>of</strong> industrial cooperation (norms, rules and mechanisms that ensure a cooperation <strong>of</strong>
industrial structures on different economic questions in order to improve <strong>of</strong> production efficiency);
Institution <strong>of</strong> recruitment (norms, rules and mechanisms aimed to satisfy business needs for
qualified personnel taking into account modern complex techniques <strong>of</strong> personnel management);
Institution <strong>of</strong> innovative projects realizing (a set <strong>of</strong> norms, rules and mechanisms that realizing
innovative projects at all stages <strong>of</strong> innovative process <strong>of</strong> different business structures);
Institution <strong>of</strong> scientific-technical and innovative programs and projects expertise (expert survey
selected scientific-technical and innovative programs and projects in terms <strong>of</strong> its perceptiveness
and opportunity <strong>of</strong> its realization);
Institution <strong>of</strong> business consulting (a set <strong>of</strong> norms, rules and mechanisms aimed to learning
processes occurring in <strong>the</strong> organization to identify key problems in <strong>the</strong> functioning <strong>of</strong> <strong>the</strong> company
and at finding <strong>of</strong> internal resources potential for effective solving this problems);
Institution <strong>of</strong> innovative clusters (a set <strong>of</strong> norms, rules and mechanisms aimed to achieve a high
level <strong>of</strong> competitiveness because <strong>of</strong> informal combine <strong>of</strong> efforts <strong>of</strong> different organizations);
Institution <strong>of</strong> working-out <strong>of</strong> developing programs (ensure <strong>the</strong> working-out and realizing programs
and projects <strong>of</strong> innovative development stimuli and improving innovative potential <strong>of</strong> both
separate business structures and single regions);
Institution <strong>of</strong> innovative organizations (working-out and introduction <strong>of</strong> new or advanced products,
technological processes or o<strong>the</strong>r types <strong>of</strong> innovative activity);
Institution <strong>of</strong> research activity (aimed to generation and using <strong>of</strong> new knowledge for solving
technological, engineering, social, economic, humanitarian and o<strong>the</strong>r problems and for ensuring
<strong>of</strong> science, technology and production functioning as a single system);
Institution <strong>of</strong> experimental development activity (norms, rules and mechanisms that aimed to
generation <strong>of</strong> new models, tested in <strong>the</strong> course <strong>of</strong> research activity and suitable for industrial and
commercial use).
5.2 Institutions <strong>of</strong> Distributing
Institution <strong>of</strong> communication activities (norms, rules and mechanisms that ensure <strong>the</strong> information
influence to internal and external environment <strong>of</strong> <strong>the</strong> organization in order to form <strong>of</strong> demand and
sales promotion products);
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Institution <strong>of</strong> information activity organization (realizes <strong>the</strong> processes <strong>of</strong> generation,
transformation, accumulation and transmission <strong>of</strong> information both into single organization and
into inter-organizational space on <strong>the</strong> region level);
Institution <strong>of</strong> knowledge and technology transfer (provides distribution <strong>of</strong> applied technological
knowledge and experience on <strong>the</strong> subject <strong>of</strong> <strong>the</strong> processes, production methods and innovative
products inside <strong>the</strong> organization, industry or inside <strong>the</strong> inter-industrial space);
Institution <strong>of</strong> innovations diffusion (realizes <strong>the</strong> process <strong>of</strong> innovations distribution inside a single
social system and from one social system to o<strong>the</strong>r);
Institution <strong>of</strong> innovative infrastructure (it’s a complex <strong>of</strong> organizational and economic institutions,
that provide realizing conditions <strong>of</strong> innovative processes by economic agents based on principals
<strong>of</strong> economic efficiency);
Institution <strong>of</strong> financial resources using (norms, rules and mechanisms that ensure <strong>the</strong> most
optimal distribution <strong>of</strong> financial resources inside both an organization and a region);
Institution <strong>of</strong> material and labor resources using (norms, rules and mechanisms that ensure <strong>the</strong>
most optimal distribution <strong>of</strong> material and labor resources inside both an organization and a
region);
Institution <strong>of</strong> information resources using (norms, rules and mechanisms that ensure <strong>the</strong> most
optimal distribution <strong>of</strong> information resources inside both an organization and a region);
Institution <strong>of</strong> financial intermediation (a set <strong>of</strong> norms, rules and mechanisms aimed to regulation
and improvement <strong>the</strong> efficiency <strong>of</strong> financial operations).
5.3 Institutions <strong>of</strong> Merchandising
Institution <strong>of</strong> commercialization <strong>of</strong> science activity (norms, rules and mechanisms that transform
novation into innovation through its market introduction, commercial using and economic
benefits);
Institution <strong>of</strong> transport and logistics systems (a system <strong>of</strong> norms, rules and mechanisms <strong>of</strong>
products and raw materials delivery from one point to ano<strong>the</strong>r with <strong>the</strong> optimal route);
Institution <strong>of</strong> wholesale and retail trade (realize an effective wholesale and retail activity);
Institution <strong>of</strong> cross-border cooperation (ensures <strong>the</strong> transparency <strong>of</strong> inter-regional boundaries and
activates local economic links with <strong>the</strong> aim <strong>of</strong> investment attraction, using <strong>of</strong> advantages <strong>of</strong>
interregional division <strong>of</strong> labor, etc.).
5.4 Institutions <strong>of</strong> Using
Institution <strong>of</strong> public goods (norms, rules and mechanisms that ensure <strong>the</strong> processes <strong>of</strong> effective
public goods using);
Institution <strong>of</strong> club goods (norms, rules and mechanisms that ensure <strong>the</strong> processes <strong>of</strong> effective
club goods using);
Institution <strong>of</strong> private goods (norms, rules and mechanisms that ensure <strong>the</strong> processes <strong>of</strong> effective
private goods using);
Institution <strong>of</strong> technology using (norms, rules and mechanisms that ensure <strong>the</strong> possibility <strong>of</strong>
technology using).
6. Entrepreneurship Region Development Institutions <strong>of</strong> Incentive
6.1 Institutions <strong>of</strong> Producing
Institution <strong>of</strong> joint financing (realize <strong>the</strong> stimulation <strong>of</strong> entrepreneurial activity by lending in <strong>the</strong>
form <strong>of</strong> credit union <strong>of</strong> some creditors from one or different countries, financed a one large
project);
Institutions <strong>of</strong> learning measures (a set <strong>of</strong> norms, rules and mechanisms, stimulated organization
activity by different types <strong>of</strong> learning programs and measures);
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Institution <strong>of</strong> monetary incentives (norms, rules and mechanisms aimed to material stimulating <strong>of</strong>
organizations on <strong>the</strong> base <strong>of</strong> using <strong>of</strong> different types <strong>of</strong>monetary rewards and sanctions with <strong>the</strong>
aim <strong>of</strong> improvement <strong>of</strong> innovation activity);
Institution <strong>of</strong> parity support for government and business (a set <strong>of</strong> norms, rules and mechanisms
<strong>of</strong> medium and long-time interactions between state and business for solving socially important
problems on mutually beneficial terms);
Institution <strong>of</strong> micro financial (aimed to providing financial services for entrepreneurs - newcomers);
Institution <strong>of</strong> contribution (norms, rules and mechanisms that provide a complex support <strong>of</strong>
entrepreneurship region development);
Institution <strong>of</strong> business support (aimed to complex supporting to newcomers from large business
structures);
Institution <strong>of</strong> seeding funding (norms, rules and mechanisms that provide a financial <strong>of</strong> innovative
projects at <strong>the</strong> beginning level <strong>of</strong> development);
Institution <strong>of</strong> government support (norms, rules and mechanisms that provide <strong>the</strong> stimulation <strong>of</strong>
business activity throughgovernment support);
Institution <strong>of</strong> direct support (norms, rules and mechanisms that provide measures <strong>of</strong> realizing
financial, information, property, educational and consulting support <strong>of</strong> entrepreneurship);
Institution <strong>of</strong> inter-industry support (norms, rules and mechanisms that provide a complex support
<strong>of</strong> <strong>the</strong> development <strong>of</strong> business activity <strong>of</strong> one industry by o<strong>the</strong>rs);
6.2 Institutions <strong>of</strong> Distributing
Institution <strong>of</strong> tax and o<strong>the</strong>r benefits (norms, rules and mechanisms that provide <strong>the</strong> stimulation <strong>of</strong>
region business activity through tax and o<strong>the</strong>r benefits);
Institution <strong>of</strong> non-financial incentives (norms, rules and mechanisms aimed to support <strong>of</strong>
entrepreneurship region development at <strong>the</strong> form <strong>of</strong> non-financial incentive);
Institution <strong>of</strong> communication <strong>of</strong> state agencies (provide <strong>the</strong> stimulation <strong>of</strong> entrepreneurship region
development through <strong>the</strong> mechanism <strong>of</strong> business representations and o<strong>the</strong>r form <strong>of</strong> interactions
with government.
6.3 Institutions <strong>of</strong> Merchandising
Institution <strong>of</strong> competitive tendering (a set <strong>of</strong> norms, rules and mechanisms that provide <strong>the</strong>
organizing and stimulation <strong>of</strong> merchandising through <strong>the</strong> preliminary selection <strong>of</strong> partners on a
competitiveness basis);
Institution <strong>of</strong> intercompany cooperation (norms, rules and mechanisms that provide <strong>the</strong>
partnership <strong>of</strong> business structures in order to improving <strong>the</strong> efficiency <strong>of</strong> producing activity).
6.4 Institutions <strong>of</strong> Using
Institution <strong>of</strong> social policy (norms, rules and mechanisms that influence to <strong>the</strong> stimuli <strong>of</strong> business
activity by <strong>the</strong> realization <strong>of</strong> special measures <strong>of</strong> social policy);
Institution <strong>of</strong> lending (provide <strong>the</strong> encouraging consumption through mechanisms <strong>of</strong> consume and
production lending).
7. Entrepreneurship Region Development Institutions <strong>of</strong> Control
7.1 Institutions <strong>of</strong> Producing
Institution <strong>of</strong> licensing and certification (a set <strong>of</strong> norms, rules and mechanisms that realize a
regulation and control <strong>of</strong> entrepreneurial activity through state permission for certain actions);
Institution <strong>of</strong> activity control (a set <strong>of</strong> norms, rules and mechanisms that realize a verification and
monitoring <strong>of</strong> compliance with regulations for <strong>the</strong> implementation <strong>of</strong> productive activities by
business structures);
Institution <strong>of</strong> patenting (norms, rules and mechanisms that provide <strong>the</strong> legal protection <strong>of</strong>
organization intellectual property);
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Institution <strong>of</strong> monitoring (allows to diagnose <strong>of</strong> object management, conduct systematic
observations for any process in order to identify its match <strong>the</strong> desired result or initial
assumptions);
Institution <strong>of</strong> self-regulation (norms, rules and mechanisms that provide <strong>the</strong> market regulation by
business structures without <strong>the</strong> intervention <strong>of</strong> government agencies);
Institution <strong>of</strong> insurance (a set <strong>of</strong> norms, rules and mechanisms which provides <strong>the</strong> insurance
protecting <strong>of</strong> entrepreneurial interests).
7.2 Institutions <strong>of</strong> Distributing
Institution <strong>of</strong> information security (aims to information security <strong>of</strong> organization, providing an
opportunity <strong>of</strong> its form, use and development);
Institution <strong>of</strong> anti-corruption (a system <strong>of</strong> norms, rules and mechanisms, formed in order to
elimination or minimization <strong>of</strong> causes and conditions that generate and facilitate corruption
<strong>of</strong>fenses);
Institution <strong>of</strong> pr<strong>of</strong>essional unions (a set <strong>of</strong> norms, rules and mechanisms which ensure <strong>the</strong>
voluntary protection <strong>of</strong> labor rights and social and economic interests <strong>of</strong> members);
Institution <strong>of</strong> contractual relationship (ensure <strong>the</strong> satisfaction control <strong>of</strong> concluded agreements).
7.3 Institutions <strong>of</strong> Merchandising
Institution <strong>of</strong> behavior patterns (realize <strong>the</strong> compliance control <strong>of</strong> elements <strong>of</strong> business rules for
behavior patterns <strong>of</strong> business environment <strong>of</strong> <strong>the</strong> organization);
Institution <strong>of</strong> legal protection (ensure <strong>the</strong> legal protection <strong>of</strong> sale operations);
Institution <strong>of</strong> currency and export control (control <strong>of</strong> <strong>the</strong> compliance <strong>of</strong> <strong>the</strong> currency legislation for
currency transactions and <strong>the</strong> order <strong>of</strong> region foreign trade activity).
7.4 Institutions <strong>of</strong> Using
Institution <strong>of</strong> cultural features (norms and rules adopted in this cultural environment, regulating
decision-making process <strong>of</strong> buying and selling goods and services);
Institution <strong>of</strong> market functioning (norms, rules and mechanisms that provide <strong>the</strong> market regulation
by market mechanism);
Institution <strong>of</strong> feedback (a set <strong>of</strong> norms, rules and mechanisms that realize <strong>the</strong> information
providing about organization activity results).
8. Directions for fur<strong>the</strong>r researches
Because <strong>the</strong> forming <strong>of</strong> economic institutions atlas is <strong>the</strong> considerable research task, so <strong>the</strong> following
directions <strong>of</strong> solving <strong>of</strong> this problem are most perspective applied to <strong>the</strong> analysis <strong>of</strong> regional economic
systems.
At first, it is extremely important to “point” comparison <strong>of</strong> analyzed economic institutions with <strong>the</strong>
possible criteria <strong>of</strong> <strong>the</strong>ir classification. The number <strong>of</strong> analyzed institutions may be more; classification
criteria may be supplemented by o<strong>the</strong>r methods.
At second, we can assume that <strong>the</strong> finally formed institutional atlas will be a pyramid, at <strong>the</strong> top <strong>of</strong>
which <strong>the</strong>re is a basic developing institution. At <strong>the</strong> base <strong>of</strong> <strong>the</strong> pyramid <strong>the</strong>re are subordinate
institutions that define norms <strong>of</strong> private interactions between economic agents. Thereby <strong>the</strong> problem
<strong>of</strong> detailed hierarchy working-out <strong>of</strong> economic institutions may be indicated (figure 3).
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Figure 3: Institutional Atlas as a pyramidal institutional structure
At third, <strong>the</strong> defining <strong>of</strong> subordination <strong>of</strong> formal and informal institutions is an important task.
Traditionally it is believed that formal institutions are published and accepted in society norms <strong>of</strong>
interactions, while informal institutions are <strong>the</strong> rules <strong>of</strong> interactions formed by practice activity. But this
margin is very blurred because any informal institution may be formalized (published, accepted) very
simple and vice versa any formal institution may be transformed into informal if it expend <strong>the</strong> scope <strong>of</strong>
its functioning (figure 4).
Figure 4: The problem <strong>of</strong> distinguishing formal and informal institutions
At forth, <strong>the</strong> problem <strong>of</strong> relationship norms and organizations illustrated at <strong>the</strong> Figure 1. As far as <strong>the</strong>
organization is a system set <strong>of</strong> formal and informal institutions <strong>of</strong> interaction between employees, so
<strong>the</strong> question <strong>of</strong> <strong>the</strong> transition boundary from simple institution system to system set <strong>of</strong> institutions <strong>of</strong>
<strong>the</strong> organization arises.
At fifth, as to developing institutions <strong>the</strong> institutional atlas should be supplemented by traditional
institutions – organizations such as business incubators, venture and innovation funds and o<strong>the</strong>rs. It’s
a good illustration <strong>of</strong> agent’s interactions between traditional and perspective institutions in <strong>the</strong>
development <strong>of</strong> economic systems. The last <strong>of</strong> this directions can be realized for example by <strong>the</strong>
limitation <strong>of</strong> <strong>the</strong> investigation, namely by selecting <strong>the</strong> more narrow block <strong>of</strong> developing institutions
among region economic developing institutions, for example entrepreneurship region developing
institutions (that was demonstrated in this work).
9. The discussion <strong>of</strong> <strong>the</strong> institutional atlas model
The atlas <strong>of</strong> region development entrepreneurship institutions includes 79 institutions aimed to ensure
<strong>of</strong> sustainable development, economic security and stimulating innovation activity <strong>of</strong> <strong>the</strong> country or
region through entrepreneurship development as a main driving force <strong>of</strong> economic development. This
atlas can be used to analysis <strong>of</strong> <strong>the</strong> degree <strong>of</strong> elaboration and activity <strong>of</strong> <strong>the</strong> system <strong>of</strong> region
development entrepreneurship institutions and to develop mechanisms to control <strong>the</strong>se institutions.
It should be noted that <strong>the</strong> least filled block is <strong>the</strong> block <strong>of</strong> planning institutions that is institutions upon
which all <strong>the</strong> system <strong>of</strong> region development innovation system are based. It’s impossible to
functioning <strong>of</strong> <strong>the</strong>se or o<strong>the</strong>r innovative institutions without working-out adequate strategic plan <strong>of</strong>
development, which should reflect <strong>the</strong> overall vision <strong>of</strong> <strong>the</strong> region future. Such a weak concentration
<strong>of</strong> institutions <strong>of</strong> this block indicates about necessity <strong>of</strong> elaboration <strong>of</strong> norms that ensure <strong>the</strong> planning
<strong>of</strong> institutional region development at <strong>the</strong> power level.
Following <strong>the</strong> direction <strong>of</strong> increasing <strong>the</strong> number <strong>of</strong> institutions is <strong>the</strong> block <strong>of</strong> institutions <strong>of</strong> control.
Control function is a management function which allows identifying problems and correcting
organization activity before <strong>the</strong>se problems become more serious. The lack <strong>of</strong> such institutions impact
798

Evgeny Popov, Maxim Vlasov and Marina Zubareva
on <strong>the</strong> efficiency <strong>of</strong> innovative development system activity negatively. Therefore it’s causes <strong>of</strong> <strong>the</strong>
necessity <strong>of</strong> <strong>the</strong>ir detail elaboration both on <strong>the</strong> regional and federal levels.
As to developing institutions <strong>of</strong> incentive, in this case, <strong>the</strong> situation is quite ambiguous. On <strong>the</strong> one
hand, <strong>the</strong>re are a lot <strong>of</strong> institutions <strong>of</strong> this block at <strong>the</strong> institutional atlas, that indicating that <strong>the</strong>
problem <strong>of</strong> entrepreneurial activity stimuli is a very actually and <strong>the</strong> power structures understand
about <strong>the</strong> necessity <strong>of</strong> such measures. However <strong>the</strong> distribution <strong>of</strong> such institutions <strong>of</strong> <strong>the</strong> spheres <strong>of</strong>
activity is more irregular: <strong>the</strong> majority <strong>of</strong> developing institutions are institutions <strong>of</strong> producing while <strong>the</strong>
institutions <strong>of</strong> using and merchandising are absent. This can be explained by <strong>the</strong> underdevelopment
<strong>of</strong> entrepreneurial incentive system that caused <strong>of</strong> <strong>the</strong> lack <strong>of</strong> <strong>the</strong> apparent urgent need for solving
<strong>the</strong>se problems against <strong>the</strong> background <strong>of</strong> <strong>the</strong> obvious difficulties in <strong>the</strong> industrial sector.
The largest group <strong>of</strong> institutions is <strong>the</strong> block <strong>of</strong> organizational institutions that indicating <strong>of</strong> <strong>the</strong>ir
sufficiently widespread. But <strong>the</strong> latitude <strong>of</strong> distribution does not allow to judge <strong>of</strong> <strong>the</strong> stage <strong>of</strong> <strong>the</strong>ir
development and function efficiency. So <strong>the</strong> institutional atlas analysis should be supplemented by
function efficiency analysis <strong>of</strong> institutions included in it. Thereby <strong>the</strong> institutional atlas can be used
separately as an independent method <strong>of</strong> investigating or can be combined with o<strong>the</strong>rs additional
methods.
10. Conclusion
There is no strict definition <strong>of</strong> developing institutions now. In this paper <strong>the</strong> developing institution is
well-established rules <strong>of</strong> agent interactions, <strong>the</strong> result <strong>of</strong> which is to change <strong>the</strong> system state. In o<strong>the</strong>r
words, it’s a discrete rule <strong>of</strong> game, which has an effect not on <strong>the</strong> all economic space, but on specific
subjects <strong>of</strong> economic activity. Such an understanding <strong>of</strong> <strong>the</strong> developing institution provides <strong>the</strong>
opportunity to track <strong>the</strong> basic laws <strong>of</strong> <strong>the</strong> formation <strong>of</strong> developing institutions <strong>of</strong> Knowledge economy,
which promotes <strong>the</strong> development <strong>of</strong> innovative capacity as <strong>the</strong> country as a whole, as well as a
separate region.
In this paper <strong>the</strong> atlas <strong>of</strong> economic development institutions was worked out. The institutional atlas is
a multivariable system characteristic <strong>of</strong> institutional structure <strong>of</strong> economic system development. The
Atlas <strong>of</strong> entrepreneurship region development that defines <strong>the</strong> weak places <strong>of</strong> institutional designing
was worked out as a private case <strong>of</strong> institutional atlas <strong>of</strong> economic development. The Atlas added by
traditional institutions – organizations such as business incubator, venture and innovation funds and
o<strong>the</strong>rs.
The <strong>the</strong>oretical significance <strong>of</strong> <strong>the</strong> methodology and <strong>the</strong> Atlas consists <strong>of</strong> <strong>the</strong> elaboration <strong>of</strong> economic
institutions hierarchy and formation <strong>of</strong> <strong>the</strong> <strong>the</strong>oretical platform for presentation <strong>of</strong> different coordination
mechanisms <strong>of</strong> economic institutions. The practical significance is possible to determine weak places
<strong>of</strong> institutional designing and that this method can be <strong>the</strong> basis <strong>of</strong> strategic planning <strong>of</strong> economic
system development.
References
Blackler, F. (1995) “Knowledge, knowledge work and organizations: an overview and interpretation”, Organization
Studies, Vol. 16, No 6, pp 1020–1046.
Brunetti, A., Kisunko, G. and Weder, B. (1997), “Institutions in Transition: Reliability <strong>of</strong> Rules and Economic
Performance in Former Socialist Countries”, World Bank Policy Research Working Paper No. 1809, World
Bank, Washington, D.C.
Cabrera, B. and Cabrera, E.F. (2002) “Knowledge-sharing dilemmas”, Organization Studies, Vol. 23, No 5, pp
687-710.
Cantillon R. (1959) “Essay on <strong>the</strong> Nature <strong>of</strong> Trade in General”. London: Frank Cass and Co., Ltd.
“Developing Knowledge Economy Strategies to Improve Competitiveness” (2008). Seoul, Republic <strong>of</strong> Korea.
World Bank Policy Research Working Paper “Knowledge for development” http://web.worldbank.org
Drucker, P.F. (1999) “Knowledge-Worker Productivity: The Biggest Challenge”, California Management review,
Vol. 41, No 2, pp 79-94.
Foray, D. (2004) The Economics <strong>of</strong> Knowledge. MIT Press, Cambridge, MA. pp 91-94.
Gertler, M.S. and Wolfe D.A. (2004) “Local Social Knowledge Management: Community Actors, Institutions and
Multilevel Governance”, Future, Vol. 36, No. 1, pp 45-65.
Hayek, F.A. (1945) “The use <strong>of</strong> knowledge in society”, American Economic Review, Vol. 35, pp 519-532.
Hayek, F.A. (1973) Law, Legislation and Liberty: A New Statement <strong>of</strong> <strong>the</strong> Liberal Principles <strong>of</strong> Justice and
Political Economy. Rules and Order, Routledge and Kegan Paul, London.
Henisz, W.J. (2000), “The Institutional Environment for Economic Growth”, Economics and Politics,
Vol. 12, No. 1, pp. 1-31.
799

Evgeny Popov, Maxim Vlasov and Marina Zubareva
Hodgson, G.M. (2006) “What Are Institutions?” Journal <strong>of</strong> Economic Issues, Vol. 50, No 1, pp 1-26.
Jütting J. (2003) “Institutions and development: a critical review”. Working Paper No. 210 OECD Development
Centre. http://www.oecd.org/dataoecd/19/63/4536968.pdf
Esponda G., Gonzalez M. (2004) “Current Development in Science and Technology in Mexico”
Kogut, B. and Zander, U. “Knowledge <strong>of</strong> <strong>the</strong> firm, combinative capabilities, and <strong>the</strong> replication <strong>of</strong> technology”
Organization Science, Vol. 3, No 3, pp 383-397.
Machlup, F. (1962) The Production <strong>of</strong> Information and Knowledge, Princeton University Press, Princeton, NJ.
Nonaka, I. (1991) “The knowledge-creating company”, Harvard Business Review. Vol.69, No 6, pp 96–104.
Pejovich, S. (1999), “The Effects <strong>of</strong> <strong>the</strong> Interaction <strong>of</strong> Formal and Informal Institutions on Social Stability and
Economic Development”, Journal <strong>of</strong> Markets and Morality, Vol. 2, No. 2, pp. 164-181.
Popov E.V. Vlasov M.V. (2009) Institutes <strong>of</strong> Knowledge Economics (monograph). Moscow: Academia. 288 pp.
Popov E.V., Vlasov M.V. (2008) “Institutional Analysis for Economy <strong>of</strong> Knowledge”. Economy and Business.
International Scientific Publication. Vol. 2. Part 1. pp. 1116-1126.
Rodrick, D. (1999), “Institutions for High Quality Growth: What They Are and How They Affect Growth”,
International Monetary Fund <strong>Conference</strong> on Second-Generation Reforms, Washington, D.C.
Schumpeter J. (1969) “The Theory <strong>of</strong> Economic Development: An Inquiry into Pr<strong>of</strong>its, Capital, Credit, Interest and
<strong>the</strong> Business Cycle”. Oxford University Press. 255 pp.
“SITRA Finnish National Fund for Research and Development” (1999). Annual report. Helsinki, Finland. 40 p.
http://www.sitra.fi
Spender, J.-C. (1996) “Making knowledge <strong>the</strong> basis <strong>of</strong> a dynamic <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm”, Strategic Management
Journal. Vol. 17. pp 45-62.
Veblen, T. (1919) The Place <strong>of</strong> Science in Modern Civilization and O<strong>the</strong>r Essays. Huebsch, NY.
800

Knowledge Management Assessment <strong>of</strong> Khorasan Razavi
Gas Company; Viewpoint <strong>of</strong> Employees
Farnaz Rahimi 1 and Mohamad Ebrahim Maroosi 2
1
School <strong>of</strong> Social Science and Economy, Alzahra University, Tehran, Iran,
Department <strong>of</strong> Contract, Gas company, Mashhad, Iran
2
Tarbiyat Moalem University, Mashhad, Iran
farnaz12200@yahoo.com
maroosi_m@yahoo.com
Abstract: The purpose <strong>of</strong> this study is to better understand knowledge levels from <strong>the</strong> view point <strong>of</strong> employees in
Khorasan Razavi Gas Company. This understanding can help managers to plan more accurate about <strong>the</strong>
knowledge management. Awareness <strong>of</strong> Current knowledge in an organization can support <strong>the</strong> decision making
process. It is only in recent years that knowledge has taken more seriously. This no doubt resulted from a poor
awareness <strong>of</strong> <strong>the</strong> level <strong>of</strong> existing knowledge in organization. In this paper using a survey questionnaire, we
tested 5 factors from <strong>the</strong> view point <strong>of</strong> employees to assess KM level in Khorasan Razavi Gas Company
(knowledge management process, leadership support, organizational culture, and technological infrastructure
and KM measurement). We found that from <strong>the</strong> viewpoint <strong>of</strong> employees KM is not in a in a suitable level. This
can be a warning for KM planers in <strong>the</strong> organization.
Keywords: Knowledge management .knowledge management process, leadership support, organizational
culture, technological infrastructure .KM measurement
1. Literature review
1.1 What is knowledge?
In <strong>the</strong> traditional economy, knowledge was seen as unrelated to <strong>the</strong> economic process (uitBeijerse,
1999).Tangible assets such as land, labour, capital natural resources were seen as <strong>the</strong> main
production factors.
From <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> 1990s, Knowledge has been considered as an important source <strong>of</strong>
competitive advantage and value creation (King and Zei thalm, 2003)
Knowledge is untouchable, it will not loss any because <strong>of</strong> share by many people. On <strong>the</strong> contrary, <strong>the</strong>
value <strong>of</strong> knowledge will be more important with <strong>the</strong> greater <strong>the</strong> scope <strong>of</strong> <strong>the</strong> role.
The distinctions between data, information and knowledge have <strong>of</strong>ten been made in <strong>the</strong> literature
(Alavi &Leidner 2001; Beckman, 1999; Bhatt 2001;Clarke& Rollo,2001) Data are merely raw objective
facts ,while information is considered as structured and organized data. Knowledge can be
conceptualized as meaningful and value added information which has been filtered by human minds.
When <strong>the</strong>y (i.e. data, information and knowledge) are arranged in a single continuum, knowledge has
<strong>the</strong> highest value, <strong>the</strong> greatest relevance to decisions and actions, <strong>the</strong> greatest dependence on
context, and requires <strong>the</strong> maximum amount <strong>of</strong> human involvement (Grover & Davenport 2001).
There are many definitions <strong>of</strong> knowledge (Hildreth &Kimble 2002) knowledge is viewed as <strong>the</strong> set <strong>of</strong>
justified beliefs that enhance an entity's ability for effective action (Alavi and Leidner 2001,Nonaka
1994).The emphasis <strong>of</strong> this definition lies in <strong>the</strong> role <strong>of</strong> knowledge in guiding future action <strong>of</strong> an
individual , a group or an organization .Davenport and Prusak (1998) defined knowledge as a fluid mix
<strong>of</strong> framed experience , values , contextual information and exert insight that provides a framework for
evaluating and new experiences and information.( .Davenport and Prusak1998.p.5)
Organizational knowledge is stored in <strong>the</strong> organizations procedures, norms, rules and forms (March
1991).
Tow interdependent dimensions <strong>of</strong> knowledge are tacit knowledge and explicit knowledge (Alavi and
Leidner 2001; Nonaka 1994).Tacit knowledge is rooted in individual's experiences, beliefs and
involvement in a specific context. It is a product <strong>of</strong> <strong>the</strong> interplay between two elements: <strong>the</strong> cognitive
element and <strong>the</strong> technical element (Nonaka 1994).The cognitive element refers to a persons mental
models that consist <strong>of</strong> mental maps , beliefs , paradigm and viewpoints (Alavi and Leidner 2001).The
801

Farnaz Rahimi and Mohamad Ebrahim Maroosi
technical element refers to concrete know-how , crafts and skills ( Alavi and Leidner 2001). Tacit
knowledge is difficult to articulate and is challenging to transfer (Alavi and Leidner 2001) .explicit
knowledge is confinable this is not to suggest that this form <strong>of</strong> knowledge equals information. As Alavi
and Leinder 1999 point out, knowledge is information made actionable.
2. Knowledge management
Gupta et al (2000) defined Knowledge management as “a process that helps organizations find,
select, organize disseminate and transfer important information and expertise necessary for activities
such as problem solving, dynamic learning, strategic planning and decision making” .Liebowitz (2003)
considered it as dealing with capturing, sharing applying and creating knowledge in an organization to
best leverage this resource internally and externally. Various o<strong>the</strong>r definitions abound in <strong>the</strong> literature
(Bassi,1997 Hibbard, 1997; Horwitch & Armacost, 2002; Malhotra 1998; Rowley, 1999;vander Spek &
Spijkervet,1997;Wiig 1997)
Knowledge management in organizations refers to identifying and leveraging collective knowledge in
an organization to help <strong>the</strong> organization compete (Alavi and Leidner 2001)
A review <strong>of</strong> <strong>the</strong> KM literature shows <strong>the</strong> changing view <strong>of</strong> <strong>the</strong> focus <strong>of</strong> KM, resulting in different
definitions (see, Table1)
Table 1: KM definitions (1994-2006)
2006
2005
2003
2001
1999
1998
1996
1994
Knowledge management addresses policies, strategies, and techniques aimed at supporting
an organization’s competitiveness by optimizing <strong>the</strong> conditions needed for efficiency
improvement, innovation, and collaboration among employees.’’
(C.A.A.Sousa,P.H.J.Hendriks.2006)
Knowledge management is defined as doing what is needed to get <strong>the</strong> most out <strong>of</strong> knowledge
resources.’’ (Sabherwal , Sabherwal.2005)
Knowledge management is defined as <strong>the</strong> organized and systematic process <strong>of</strong> generating and
disseminating information and selecting , distilling ,and deploying explicit and tacit knowledge
to create unique value that can be used to achieve a competitive advantage in <strong>the</strong> marketplace
by an organization.’’ (Hult.2003)
Knowledge management refers to identifying and leveraging <strong>the</strong> collective knowledge in an
organization to help <strong>the</strong>
Organization compete....‘‘Knowledge management is largely regarded as a process involving
various activities ... At a minimum, one considers <strong>the</strong> four basic processes <strong>of</strong> creating, storing/
retrieving ,transferring ,and applying knowledge.’’ (Alavi and Leidner 2001)
Knowledge management is <strong>the</strong> formal management <strong>of</strong> knowledge for facilitating creation
,access ,and reuse <strong>of</strong>
Knowledge, typically using advanced technology.’’ (O’Leary.1999)
[a] term which has now come to be used to describe everything from organizational learning
efforts to database
Management tools.’’ (Ruggles.1994)
[t]he management <strong>of</strong> knowledge goes far beyond <strong>the</strong> storage and manipulation <strong>of</strong> data, or even
<strong>of</strong> information.
It is <strong>the</strong> attempt to recognize what is essentially a human asset buried in <strong>the</strong> minds <strong>of</strong>
individuals, and leverage it
Into an organizational asset that can be accessed and used by a broader set <strong>of</strong> individuals on
whose decisions
The firm depends.’’ (Maier.2004)
In its broadest sense ,knowledge management(KM)is a conceptual framework that
encompasses all activities and
perspectivesrequiredtomaking<strong>the</strong>organizationintelligentactingonasustainedbasis.KMincludesactivitiestogaining
overview<strong>of</strong>,dealingwith,andbenefitingfrom<strong>the</strong>areasthatrequiremanagementattentionbyidentifyin
gsalient alternatives ,suggesting methods for dealing with <strong>the</strong>m and conducting activities to
achieve desired results.’’ (Wiig.1994)
Nonaka and Takeuchi (1995) define organizational knowledge as ‘‘justified true belief’’, which can be
shared within an organization as information <strong>of</strong> which validity has been established through tests <strong>of</strong>
pro<strong>of</strong> (Liebeskind, 1996, p. 94). As organizational knowledge guides decision-making (Tsoukas &
Vladimirou) an organization must be aware <strong>of</strong> knowledge level in <strong>the</strong> organization.
802

3. Research model and hypo<strong>the</strong>ses
Farnaz Rahimi and Mohamad Ebrahim Maroosi
In this paper we assessed knowledge management level in <strong>the</strong> organization by measuring 5 factors:
3.1 Knowledge management process
This factor refers to <strong>the</strong> management <strong>of</strong> procedural knowledge and workflow (Housel, Thomas, Bell, &
Arthur, 2001) that may cover process management <strong>of</strong> knowledge capture and creation: <strong>the</strong> learning
and training activity arrangement in <strong>the</strong> organization’s knowledge retention (Nissen & Espino, 2000).
Knowledge management process emphasis on ga<strong>the</strong>ring, organizing, maintaining and using
knowledge in organizations. It is agreed that <strong>the</strong> only sustainable competitive advantage for a firm
comes from <strong>the</strong> value it can generate from its knowledge (Beveren, 2002).
In this section we ask <strong>the</strong> respondents to state if exiting knowledge management processes in
Khorasan Razavi Gas Company is appropriate.
3.2 Leadership support
Leadership Support and commitment is critical to a KM planning (Martensson, 2000; Manasco, 1996
Truch, 2001; Jarrar, 2002; Sharp, 2003; Davenport et al., 1998)
Leaders are important in acting as models to typify <strong>the</strong> desired behavior for KM. They can influence
o<strong>the</strong>r employees to follow <strong>the</strong>m and increase <strong>the</strong> employee's willing to take part in KM. O<strong>the</strong>r
leadership competencies that would be important include steering <strong>the</strong> change effort, conveying <strong>the</strong>
importance <strong>of</strong> KM to employees, maintaining <strong>the</strong>ir morale, and creating a culture that promotes
knowledge sharing and creation.
(Holsapple and Joshi, 2000).
In this part we ask <strong>the</strong> respondent to specify whe<strong>the</strong>r exiting knowledge management
support by top management.
3.3 Organizational Culture
Verbeke et al. (1998). “A system <strong>of</strong> shared norms and behaviors that are learned by members <strong>of</strong> <strong>the</strong>
organization and shape <strong>the</strong>ir way <strong>of</strong> doing” (p. 313). In general, a culture supportive <strong>of</strong> KM is one that
highly values knowledge. The biggest challenge for most KM efforts actually lies in developing such a
culture. Davenport et al. (1998) suggested that companies should ensure that <strong>the</strong>ir KM initiatives fit
into <strong>the</strong>ir organizational culture, or <strong>the</strong>y should be ready to change it.
We ask our respondent if exiting organizational culture support knowledge management.
3.4 Technological infrastructures
Though KM is not about technology, it plays an important role in KM. (Skyrme and Amidon (1997),
Davenport et al. (1998), APQC1999.Alavi and Leidner (2001)). It should be said that one <strong>of</strong> <strong>the</strong> key
enablers for performing KM is IT .Its capability is being a connector <strong>of</strong> a human to information and <strong>of</strong>
one human to ano<strong>the</strong>r. IT can enable speed up search, access and retrieval <strong>of</strong> information, and can
support communication between organizational members. In essence, it can certainly play a variety <strong>of</strong>
roles to support an organization's KM processes (Alavi and Leidner, 2001; Lee and Hong, 2002). In
this part we ask <strong>the</strong> respondents to specify whe<strong>the</strong>r exiting technological infrastructure is a supportive
factor for KM or not.
3.5 Measurement
Measurement acts like a data collection system that gives useful information about a particular
situation or activity .KM will fail, if it is left unmeasured.According to Arora (2002) and Ahmed et al.
(1999), measuring KM is necessary in order to ensure that its KM objectives are being attained.
Measurement enables organizations to follow <strong>the</strong> progress <strong>of</strong> KM and to determine its benefits and
effectiveness. Essentially, it provides a basis for organizations to evaluate compare, control and
improve upon <strong>the</strong> performance <strong>of</strong> KM (Ahmed et al., 1999). Based on this factor we ask <strong>the</strong>
respondents whe<strong>the</strong>r exiting KM measures properly.
803

4. Research design
Farnaz Rahimi and Mohamad Ebrahim Maroosi
The propositions in last Section formed <strong>the</strong> basis for carrying out <strong>the</strong> investigation. A 5-point Likert
scale was used for each item, anchored by Strongly Agree at one end to Strongly Disagree at <strong>the</strong>
o<strong>the</strong>r. The target population for this study was Khorasan Razavi Gas Company employees (382
employees) Due to resource constraints, we chose a sample, using random sampling and at <strong>the</strong> end,
38 questioner used for our analysis.
4.1 Reliability
Cronbach’s Coefficient Alpha in a small sample size was 0/93 which means <strong>the</strong> questioner has a high
degree <strong>of</strong> reliability.
5. Data analysis
Using SPSS s<strong>of</strong>tware and Student's t-test, we test employee's viewpoint about 5 factors, mentioned
earlier as follow:
Table 2: results <strong>of</strong> analyzed data
KM process
Leadership support
Organizational
culture
technological
infrastructure
KM Measurement
MAIN QUESTION*
sample size
34
34
34
34
34
34
average
1/3835
1/3088
1/6412
1/5588
0/9632
1/3711
Measures
<strong>of</strong> Spread
0/61416
0/72837
0/61255
0/74797
0/74145
0/58660
P-value
0.6165
0/6912
0/3588
0/4412
1/0368
0/6289
Degree
<strong>of</strong>
freedom
33
33
33
33
33
33
statistic
-5/580
-5/533
-3/416
-3/439
-8/153
-6/251
*Main question in this research is: Is exiting knowledge management agreeable from <strong>the</strong> viewpoint <strong>of</strong>
employees?
6. Findings
As mentioned in <strong>the</strong> table, <strong>the</strong> results are as followed:
From <strong>the</strong> viewpoint <strong>of</strong> employees none <strong>of</strong> <strong>the</strong> factors we measured in this research (KM process,
leadership support, organizational culture, and technological infrastructure and KM measurement) is
desirable. This can be a warning for <strong>the</strong> decision makers. For having a beneficial KM program in <strong>the</strong>
organization (Gas Company) decision makers should take <strong>the</strong> mentioned factors into consideration
more.
References
Ahmed ,P.K. ,Lim, K.K. and Zairi ,M.(1999),“Measurement practice for knowledge Management”, Journal <strong>of</strong> Work
place Learning: Employee Counselling Today, Vol. 11.No.8,pp. 304-11
Alavi, M. and Leidner, D.E. (2001)Knowledge management and knowledge management systems: Conceptual
foundations and research issues. MIS Quarterly 25(1), 107–11.
APQC (1999),“Knowledge Management: Executive Summary”, Consortium Benchmarking Study Best-Practice
Report, American Productivity &Quality Center, available at:www.apqc.org(accessed10October2003)
Arora ,R.(2002),“Implementing KM–a balanced scorecard approach”, Journal <strong>of</strong> Knowledge Management, Vol. 6
Bassi ,L.J.(1997).Harnessing <strong>the</strong> power <strong>of</strong> intellectual capital. Training and Development, 51(12),25–30
Beckman, T.J.(1999).The current state <strong>of</strong> knowledge management .InJ Liebowitz (Ed.),Knowledge management
hand book. Boca Raton: CRC Press
Beveren, V.J. (2002) “A Model <strong>of</strong> Knowledge Acquisition That Refocuses Knowledge Management.” Journal <strong>of</strong>
Knowledge Management, Vol6.No 1 . pp.18-22
Bhatt G.D.(2001).Knowledge management in organizations: Examining <strong>the</strong> interaction between technologies,
techniques, and people. Journal <strong>of</strong> Knowledge Management, 5(1),68–75.
Clarke, T., &Rollo, C.(2001).Corporate initiatives in knowledge management. Education C Training, 43(4/5),
Davenport, T.H., & Prusak ,L.(1998).Working knowledge :How organizations manage What <strong>the</strong>y know.
Boston, MA: Harvard Business School Press.
Davenport, T.H., DeLong, D.W .and Beers ,M.C. (1998),“Successful knowledge management Projects”, Sloan
Management Review, Vol. 39 No. 2, pp. 43-57
D.O’Leary ,Using AI in knowledge management :knowledge bases and ontologies, The Knowledge Management
Yearbook 1999–2000,Butterworth-Heinemann,1999
804

Farnaz Rahimi and Mohamad Ebrahim Maroosi
Grover, V., & Davenport, T.H.(2001).General perspectives on knowledge management: Fostering are search
agenda. Journal <strong>of</strong> Management Information Systems, 18(1), 5–21.
Gupta,A., & Govindarajan, V.(2000). Knowledge management’s social dimensions: Lessons from Nucor Steel.
Sloan Management Journal, 42(1), 71–80
Hibbard,J.(1997).Knowingwhatweknow.InformationWeek653,46–54
Hildreth ,P.,& Kimble ,C.(2002).The duality <strong>of</strong> knowledge. Information Research, 8(1).
Holsapple ,C.W .and Joshi ,K.D.(2000),“An investigation <strong>of</strong> factors that influence <strong>the</strong> Management <strong>of</strong> knowledge
in organizations”, Journal <strong>of</strong> Strategic Information Systems Vol.9Nos.2/3,pp.235-61.
Horwitch ,M. ,& Armacost,R.(2002).Helping knowledge management be all it can be. Journal <strong>of</strong> Business
Strategy, 23(3),26–31
Housel, ThomasJ .,&Bell,ArthurH.(2001). Measuring and managing knowledge McGraw-Hill
Hult .G.T. M. An integration <strong>of</strong> thoughts on knowledge management, Decision Sciences 34, 2003, p.189.
jarrar ,Y.F(2002) “Knowledge management: learning for organizational experience Managerial Auditing Journal,
Vol. 17 No. 6, pp. 322-
King ,A. W. and Zeithalm, C.P.(2003)Measuring organizational knowledge: A conceptual and methodological
framework. Strategic Management Journal 24(8),763–772.
Lee, S.M. and Hong, S.(2002),“An enterprise wide knowledge management system infrastructure”, Industrial
Management &Data Systems, Vol. 102 No. 1, pp. 17-25.
Liebeskind ,J.P. (1996). Knowledge, strategy, and <strong>the</strong> <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm. Strategic Management Journal,17
(WinterSpecialIssue),93–107.
Liebowitz ,J.,& Chen,Y.(2001).Developing knowledge sharing pr<strong>of</strong>iciencies :Building a supportive culture for
knowledge-sharing Knowledge Management Review, 3(6),12–15
Liebowi tz,J.(2003).A knowledge management implementation plan at a leading US technical government
organization: A case study. Knowledge and Process Management, 10(4),254–259
Maier. R, Knowledge Management Systems: Information and Communication Technologies for Knowledge
Management 2nded., Springer-Verlag,Berlin,2004.
Malhotra ,Y.(1998).Deciphering <strong>the</strong> knowledge management hype Journal for Quality and Participation, 21
Manasco ,B.(1996),“Leading firms develop knowledge strategies”, Knowledge Inc., Vol. 1 No. 6,
March,J.G.,1991. Exploration and exploitation in organizational learning Organization Science2(1),71–87.
Martensson ,M.(2000),“A critical review <strong>of</strong> knowledge management as a management tool Journal <strong>of</strong> Knowledge
Management, Vol. 4 No. 3, pp. 204-
Nissen ,M.E.,& Espino ,J.(2000).Knowledge process and system design for <strong>the</strong> coast guard Knowledge and
Process Management,7(3).165-175.
Nonaka, I. (1994) “A Dynamic Theory <strong>of</strong> Organizational Knowledge Creation,” Organizational Science (5:1),
Nonaka ,I. ,&Takeuchi ,H.(1995). The knowledge creating company. New York: Oxford University Press.
Rao, N Janardhan (2005). Knowledge Management Cultivating Knowledge Pr<strong>of</strong>essionals Book Review). In <strong>the</strong>
ICFAI Journal <strong>of</strong> Knowledge Management. Hyderabad: ICFAI University Press; Vol. III, No. 4, December
2005, 72-73p
Rowley ,J.(1999).What is knowledge management? Library Management 20(8).416-420.
Ruggles. R, The state <strong>of</strong> <strong>the</strong> notion: knowledge management in practice, California Management Review 40,
1998,p.80
Sabherwal R., Sabherwal. S., Knowledge management using information technology: determinants <strong>of</strong> short-term
impact <strong>of</strong> firm value , Decision Sciences36,2005 ,p.531
Skyrme, D. and Amidon ,D.(1997),“The knowledge agenda”, Journal <strong>of</strong> Knowledge Management, Vol. 1 No. 1,
Sharp, D.(2003),“ Knowledge management today :challenges and opportunities”, Information Systems
Management, Vol. 20 No. 2, pp. 32-7.
Sousa C.A. A Hendriks .,P.H. J, The diving bell and <strong>the</strong> butter- fly <strong>the</strong> need for grounded <strong>the</strong>ory in developing a
knowledge- based view <strong>of</strong> organizations, Organizational Research Methods.p315.2006.9
Truch ,E.(2001),“Managing personal knowledge :<strong>the</strong> key to tomorrow’s employ ability”, Journal Of Change
Management, Vol. 2 No. 2, pp. 102-5
Tsoukas ,H .,&Vladimirou, E.(2001).What is organizational knowledge? Journal <strong>of</strong> Management Studies,
38(7),973–993.
uitBeijerse ,R .P.(1999).Questions in knowledge management :Defining and conceptualizing a phenomenon.
Journal <strong>of</strong> Knowledge Management, 3(2),94–109.
vander Spek,R.,& Spijkervet,A.(1997).Knowledge management: Dealing intelligently with knowledge. InJ
.Liebowitz ,&L .Wilcox (Eds.), Knowledge management and its integrative elements. Boca
Raton:CRCPress.
Verbeke, Willem, Marco Volgering, and Marco Hessels (1998), “Exploring <strong>the</strong> Conceptual Expansion within <strong>the</strong>
Field <strong>of</strong> Organizational Behaviour Organizational Climate and Organizational Culture,” Journal <strong>of</strong>
Management Studies, 35 (3), 303-29
Wiig . K.M,Knowledge Management,SchemaPress,USA,1994.
Wiig,K.M.(1997).Knowledge management :An introduction and perspective. Journal <strong>of</strong> Knowledge Management,
1(1),6–14.
805

An Intelligent Model to Asses Organizational Maturity for
Implementation <strong>of</strong> Knowledge Management
Kamaladdin Rahmani Youshanloui 1 , Farhad Daneshgar 2 , Nima Sarabi 3 ,
Hossein Rahmany Youshanlouei 4 and Mohammad Mirkazemi Mood 3
1
Department <strong>of</strong> Industrial Management, Tabriz Branch, Islamic Azad University,
Tabriz, Iran
2
University <strong>of</strong> Technology Sydney, Sydney, Australia
3
University <strong>of</strong> Tehran, Tehran, Iran
4
Young Researchers Club, Salmas Branch, Islamic Azad University Salmas ,
Iran & University <strong>of</strong> Tehran, Tehran, Iran
kamaladin.rahmani@gmail.com
rahmani82@gmail.com
f.daneshgar@unsw.edu.au
nimasarabi@yahoo.com
mohamad_mirkazemi@yahoo.com
Abstract: Successful knowledge management implementation requires identification <strong>of</strong> <strong>the</strong> key success factors
as well as <strong>the</strong> relationship between <strong>the</strong>se factors. The key success factors <strong>of</strong> KM can be classified by
characteristics, situations and variables. This paper aims to develop intelligent models using fuzzy tools for
determination <strong>of</strong> <strong>the</strong> relationships between different key success factors in knowledge management
implementation. To develop <strong>the</strong> proposed intelligent model, <strong>the</strong> current literature was reviewed in order to identify
existing factors. The Fuzzy Cognitive Maps (FCM) method was <strong>the</strong>n utilized to specify <strong>the</strong> cause and effect
relationships between <strong>the</strong>se factors. Under <strong>the</strong> FCM method, individuals’ experience is integrated with existing
knowledge, and based on that, <strong>the</strong> cause and effect relationships between <strong>the</strong> factors are identified. We argue
that our proposed model can be used for exploring ways for enhanced implementation <strong>of</strong> <strong>the</strong> KM systems.The
methodology developed uses four matrices to represent <strong>the</strong> results that <strong>the</strong> methodology provides in each one <strong>of</strong>
its stages. These are Initial Matrix <strong>of</strong> Factors (IMF), Fuzzified Matrix <strong>of</strong> Factors (FZMF), Strength <strong>of</strong> Relationships
Matrix <strong>of</strong> Factors (SRMF) and Final Matrix <strong>of</strong> Factors (FMF). The results <strong>of</strong> this approach can help to identify <strong>the</strong>
most important factors to more attention and thus investment <strong>of</strong> <strong>the</strong> organization to enhance implementation <strong>of</strong>
<strong>the</strong> KM systems.
Keywords: knowledge management, critical success factors, fuzzy cognitive maps
1. Introduction
Lack <strong>of</strong> proper mechanisms for <strong>the</strong> implementation <strong>of</strong> knowledge management (KM) has made <strong>the</strong>
managers to consider this type <strong>of</strong> investment as only an additional cost. Thus, organizations should
provide <strong>the</strong>ir members with environments in which <strong>the</strong>y can share, transfer and compare <strong>the</strong>ir
knowledge. They should also educate <strong>the</strong>ir members to signify <strong>the</strong>ir interactions with one ano<strong>the</strong>r and
also try to prepare <strong>the</strong> basic infrastructures and identify <strong>the</strong> underlying concepts <strong>of</strong> <strong>the</strong> process <strong>of</strong> KM
implementation. By identifying <strong>the</strong>se factors and determining <strong>the</strong> type and amount <strong>of</strong> <strong>the</strong> influence
that <strong>the</strong>se factors exert on each o<strong>the</strong>r, <strong>the</strong> organization can focus on <strong>the</strong> most important <strong>of</strong> <strong>the</strong>se
factors and increase <strong>the</strong> chances <strong>of</strong> successful implementation <strong>of</strong> KM projects. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong>
amount <strong>of</strong> <strong>the</strong> influence <strong>of</strong> <strong>the</strong>se factors is almost always stated in imprecise and qualitative terms,
knowing that <strong>the</strong> conversion <strong>of</strong> qualitative values to quantitative ones is a complicated process
(Xirogiannis 2007). One <strong>of</strong> <strong>the</strong> ways we can use to convert <strong>the</strong>se qualitative values into quantitative
ones is <strong>the</strong> Fuzzy Logic. For this purpose, this paper will use <strong>the</strong> FCM method. FCM is a directed
graphical chart whose aim is to display <strong>the</strong> cause and effect relationships among factors in a way that
<strong>the</strong> relationship between each pair <strong>of</strong> numerical models is defined in <strong>the</strong> range [1, -1] (Kandasamy
and Florentin 2003).
2. Literature review
2.1 Knowledge Management
Since KM has been investigated and studied through different approaches and many definitions <strong>of</strong> it
have been presented, it's difficult to find a definition about which <strong>the</strong>re will be a global consensus.
Davenport believes that "knowledge management is an effort to discover latent knowledge in <strong>the</strong>
minds <strong>of</strong> people and turn this hidden treasure to organizational assets so that a wide range <strong>of</strong> people,
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Kamaladdin Rahmani Youshanloui et al
who are involved in <strong>the</strong> decision-making <strong>of</strong> <strong>the</strong> organization, have access to this treasured asset and
are able to use it (Davenport and Prusak 1998).
According to Jung et al. (2003), KM is an integrated systematic approach to identify, manage and
share all <strong>the</strong> informational assets <strong>of</strong> organizations which include databases, documents, policies and
procedures. KM is also considered as a goal-oriented and systematic approach for <strong>the</strong> organizations
to guide and control all <strong>of</strong> <strong>the</strong>ir palpable and non-palpable resources. They partake <strong>of</strong> <strong>the</strong>ir existing
knowledge as well as outside sources <strong>of</strong> knowledge to create New Knowledge, Value, creativity and
progress (Yang 2004).
Drucker believes <strong>the</strong> "secret <strong>of</strong> <strong>the</strong> success <strong>of</strong> organizations in <strong>the</strong> 21 st century is <strong>the</strong> proper
implementation <strong>of</strong> KM." So it is necessary for <strong>the</strong> third millennium organizations to implement KM and
planning agencies must take action to implement it. The success <strong>of</strong> organizations depends highly on
how <strong>the</strong>y can effectively collect, store and retrieve different levels <strong>of</strong> knowledge between <strong>the</strong>
personnel (Mumford and Licuanan 2004).
2.2 Key Success Factors
Enabling factors have <strong>the</strong> power to guide KM in <strong>the</strong> organization (Allameh et. al. 2011). In <strong>the</strong>
Literature <strong>of</strong> key success factors, many definitions <strong>of</strong> <strong>the</strong>m are presented. One <strong>of</strong> <strong>the</strong> most important
definitions is Rockart's. Key success factors, according to him, are: a limited number <strong>of</strong> areas <strong>of</strong>
activity that will result in a successful competitive performance (Rockart 1979). Bruno and Leidecker,
in ano<strong>the</strong>r definition, state that <strong>the</strong> key success factors include: characteristics, conditions or variables
that, if managed properly, can have a significant effect on <strong>the</strong> success <strong>of</strong> <strong>the</strong> competitive position <strong>of</strong>
organizations (Bruno and Leidecker 1984). Pinto and Slevin, instead, believe that key success factors
are those that significantly improve <strong>the</strong> chances <strong>of</strong> successful project implementation (Pinto and
Slevin 1987).
Skyrme and Amidon (1997) have identified seven key factors regarding <strong>the</strong> implementation <strong>of</strong> KM that
include: Strong business requirements, architecture and landscape, leadership, culture, continuous
learning, technology infrastructure and organizational knowledge processes. Ano<strong>the</strong>r study to identify
key success factors was conducted by Holsapple and Joshi (2000) introduced three main categories
<strong>of</strong> factors, namely, Managerial, Resource-related, and environmental and believed that <strong>the</strong> key factors
will fall into each <strong>of</strong> <strong>the</strong>se categories. Managerial factors include <strong>the</strong> following four: Coordination,
control, leadership and measurements. Resource-related factors include knowledge, people, financial
and non-financial resources; and finally, environmental factors include competition, markets, time
emergencies, as well as economic and governmental circumstances.
Davenport et al (1998) conducted an exploratory study in thirty-one knowledge management projects
in twenty-four companies. The purpose <strong>of</strong> this extensive study was to determine <strong>the</strong> key factors
associated with <strong>the</strong> implementation <strong>of</strong> KM. Among <strong>the</strong> 18 successful projects, seven key success
factors were identified which included: <strong>the</strong> value <strong>of</strong> <strong>the</strong> industry, common goal and language, flexible
and standard knowledge structure, multiple channels for knowledge transfer, culture, organizational
and technical infrastructure, motivational measures and <strong>the</strong> support <strong>of</strong> <strong>the</strong> senior management.
Chourides et al (2003) identified different key success factors, in <strong>the</strong> realm <strong>of</strong> <strong>the</strong> organizational
duties, for <strong>the</strong> successful implementation <strong>of</strong> KM. These factors included: strategy, human resources
management, information technology, marketing and quality. In a study by Hung et al (2005)
regarding <strong>the</strong> key factors <strong>of</strong> success in applying knowledge management system for <strong>the</strong>
pharmaceutical industry, seven factors have been identified which include: Strategy <strong>of</strong> Modeling and
effective knowledge structure, organizational culture, infrastructure <strong>of</strong> information systems, engaging
and educating people, leadership and strong commitment <strong>of</strong> senior management, learning
environment, and <strong>the</strong> control and assessment <strong>of</strong> resources.
Yeh et al (2006) suggested that enabler agents <strong>of</strong> KM in organizations lead to developing knowledge
as well as stimulating Knowledge Creation within <strong>the</strong> organization. They also result in its sharing, and
protection which causes <strong>the</strong> improvement and effectiveness <strong>of</strong> KM activities. They, according to <strong>the</strong>ir
reviewing <strong>of</strong> <strong>the</strong> literature <strong>of</strong> <strong>the</strong> past and also <strong>the</strong>ir conducted researches, introduced "corporate
culture", "People", "information technology "and "Strategy and Leadership" as empowering agents <strong>of</strong>
KM. The support <strong>of</strong> Senior Manager is required for <strong>the</strong> most important part, i.e. "strategy and
leadership."
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Kamaladdin Rahmani Youshanloui et al
According to o<strong>the</strong>r researches, done by various researchers, regarding <strong>the</strong> influencing factors for
implementing KM, <strong>the</strong> most important influencing factors <strong>of</strong> KM can be summarized as in <strong>the</strong> table 1:
Table 1: Critical Success Factors
Critical Success Factors
Culture
structure
Strategy & goals
Leadership
Information Technology
Measurement
Human Resource
Financial resource
Learning
Benchmarking
2.3 Fuzzy Cognitive Maps
Researchers
Davenport et al(1998), Lee & Choi(2003), Yeh et al(2006), Skyrme &
Amidon(1997), Hung et al(2005), Buckman(1999), M<strong>of</strong>fett et
al(2003), Hasanali (2002), Liebowitz (1999)
Davenport et al(1998), Lee & Choi(2003), Hung et al(2005),
Kuan(2005)
Yeh et al(2006), Chourides et al(2003), Mathi(2004), Khalifa &
Liu(2003), Liebowitz(1999), Zack(1999)
Davenport et al(1998), Choi(2000), Skyrme & Amidon(1997),
Holsapple & Joshi(2000), Hung et al(2005), Dess & Picken(2000),
M<strong>of</strong>fett et al(2003), Hasanali(2002), Liebowitz(1999)
Lee & Choi(2003), Yeh et al(2006), Choi (2000), Skyrme &
Amidon(1997), Chourides et al(2003), Hung et al(2005), Lee &
Hong(2002), Davenport et al(1998), Hasanali(2002)
Holsapple & Joshi(2000), Mathi(2004), Hung et al(2005),
Kuan(2005), Hasanali(2002)
Lee & Choi(2003), Yeh et al(2006), Holsapple & Joshi(2000),
Chourides et al(2003), Kuan(2005), Davenport et al(1998),
Holsapple & Joshi(2000), Davenport & Volpel(2001)
Choi (2000), Skyrme & Amidon (1997), Hung et al(2005),
Greengard(1998), Cohen & Backer(1999), M<strong>of</strong>fett et al(2003)
Choi (2000), Hung et al(2005), Davis(1996), Drew(1997), Day &
Wendler(1998), M<strong>of</strong>fett et al(2003)
Cognitive maps or <strong>the</strong> causal graphical models were first introduced by Robert Ekslord in 1976 in <strong>the</strong>
field <strong>of</strong> political science (Alizadeh et. al. 2008). In 1986, Kosko, for <strong>the</strong> first time, used fuzzy tools to
map <strong>the</strong>se models and introduced <strong>the</strong> models <strong>of</strong> fuzzy cognitive maps. Based on his definition, FCM
is a directed graphical diagram with such concepts as Rules, events and <strong>the</strong> like, along with such
items as nodes and causal relations among <strong>the</strong>m. This graph or diagram intends to illustrate <strong>the</strong>
causal relationship <strong>of</strong> mentioned concepts in <strong>the</strong> nodes (Kosko 1986).
In <strong>the</strong> original concept <strong>of</strong> cognitive map, only two numeric values can be allocated for <strong>the</strong> arcs,
namely 1 (which represents <strong>the</strong> exertion <strong>of</strong> positive influence <strong>of</strong> on <strong>the</strong> node) and -1 (which indicates
<strong>the</strong> reverse effect between two nodes). But in <strong>the</strong> fuzzy cognitive map, <strong>the</strong> value <strong>of</strong> <strong>the</strong> relation
between two nodes can be any number between 1 and -1 (Pedrycz2010).
Fuzzy cognitive maps are an asset for modeling complex systems. This method is widely used in
complex decision-making problems in <strong>the</strong> areas <strong>of</strong> business, formulating strategic issues, identifying
Management problems; knowledge management, communication management in airlines, etc.
(Rodriguez et. al. 2007). Creating an FCM model requires <strong>the</strong> inputs that have been obtained from <strong>the</strong>
experiences and knowledge <strong>of</strong> experts in <strong>the</strong> intended subject. Therefore, in <strong>the</strong> models <strong>of</strong> Fuzzy
Cognitive Maps, <strong>the</strong> accumulated experience <strong>of</strong> people is integrated with <strong>the</strong> existing knowledge in
<strong>the</strong> field for which <strong>the</strong> model is mapped. Based on this, <strong>the</strong> causal relationship <strong>of</strong> <strong>the</strong> formative factors
<strong>of</strong> <strong>the</strong> system is formed (Kandasamy 2003).
The approach <strong>of</strong> FCM used in this paper has four sections. These sections include: initial matrix <strong>of</strong>
success factors, <strong>the</strong> fuzzified matrix <strong>of</strong> success factors, Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success
factors, and <strong>the</strong> final matrix <strong>of</strong> success factor. Initial matrix <strong>of</strong> success is an NXM matrix in which each
cell represents <strong>the</strong> coefficient or <strong>the</strong> scale that a "j" expert has, based on his experience, assigned to
an "i" factor. In <strong>the</strong>se matrices, "n" represents <strong>the</strong> number <strong>of</strong> factors and "m" is <strong>the</strong> number <strong>of</strong> experts.
Fuzzified matrix <strong>of</strong> success factors has been obtained through <strong>the</strong> conversion <strong>of</strong> numerical vectors <strong>of</strong>
<strong>the</strong> previous step to <strong>the</strong> fuzzy sets in which <strong>the</strong> value <strong>of</strong> each <strong>of</strong> its components is between zero and
one. The Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success is an NXN matrix that is obtained from <strong>the</strong>
previous ones. The rows and columns <strong>of</strong> <strong>the</strong> matrix illustrate <strong>the</strong> identified factors. Each component <strong>of</strong>
this matrix represents <strong>the</strong> amount <strong>of</strong> influence that "i" exerts on "j," which can be a value between [-
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Kamaladdin Rahmani Youshanloui et al
1.1]. When <strong>the</strong> Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success was completed, it is possible that some <strong>of</strong>
<strong>the</strong> data within it be misleading. In this case, <strong>the</strong> Expert's opinions needed for data analysis and <strong>the</strong>
conversion <strong>of</strong> <strong>the</strong> Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success to <strong>the</strong> final matrix <strong>of</strong> success factors so
that <strong>the</strong> only components that are in <strong>the</strong> final matrix <strong>of</strong> success are those that can represent <strong>the</strong> cause
and effect relationship between factors.
3. Numerical example
In this section, <strong>the</strong> proposed approach is using to assess <strong>the</strong> relationship between success factors.
For this purpose, a questionnaire about <strong>the</strong> importance <strong>of</strong> identified factors <strong>of</strong> Knowledge
Management implementation was prepared and given to 12 experts <strong>of</strong> <strong>the</strong> field. Respondents were
asked to rate <strong>the</strong> importance <strong>of</strong> each factor with a number between 0 and 100. To continue <strong>the</strong>
following steps have to be taken:
Step one: Initial Matrix <strong>of</strong> Success (IMS): This matrix is an "NXM" matrix where "n" indicates <strong>the</strong>
number <strong>of</strong> identified factors, which are indeed <strong>the</strong> primary variables in decision making, and "m" is a
number indicating Experts participating in this research. Each cell in this Oij matrix represents <strong>the</strong>
scale or coefficient that a "j" expert has assigned to an "i" factor based on his experience. The
obtained results <strong>of</strong> this matrix, in <strong>the</strong> next step, are converted to fuzzy sets with a membership degree
between zero and one. Each cell in a row, i.e. Oi1, Oi2, ………, Oim is a component <strong>of</strong> <strong>the</strong> Vi vector.
Table 2: ID <strong>of</strong> CSFs
CSF culture structure Strategy Leadership IT
ID in tables 1 2 3 4 5
CSF Measurement HR Finance
resource
Learning Benchmarking
ID in tables 6 7 8 9 10
Table 3: The IMS Matrix
ID <strong>of</strong> Individuals
CSF 1 2 3 4 5 6 7 8 9 10 11 12
1 95 82 80 78 89 90 70 80 65 90 87 75
2 79 90 67 76 42 80 80 90 60 95 95 75
3 76 52 56 64 52 90 60 75 68 75 57 45
4 80 74 67 87 68 90 60 80 73 75 79 65
5 75 69 78 90 86 70 50 50 88 65 74 40
6 55 48 61 54 65 80 40 60 58 55 53 50
7 82 75 89 70 95 80 50 50 74 65 80 60
8 40 45 34 47 35 60 60 50 51 75 50 30
9 63 45 50 74 42 50 50 65 78 65 50 75
10 60 54 60 83 45 85 60 75 87 70 59 70
Step two: fuzzified Matrix <strong>of</strong> Success: In order to fuzzify <strong>the</strong> obtained results <strong>of</strong> <strong>the</strong> previous step,
each Vi numerical vector is converted to a fuzzy set. Thus, in <strong>the</strong> obtained image, each cell <strong>of</strong> <strong>the</strong>
matrix <strong>of</strong> this fuzzy set represents <strong>the</strong> membership degree <strong>of</strong> each Oij component in <strong>the</strong>ir
corresponding vector. It should be noted that <strong>the</strong> value <strong>of</strong> each component <strong>of</strong> vectors is placed in <strong>the</strong>
range <strong>of</strong> [0, 1]. To obtain this matrix, <strong>the</strong> following steps have to be taken (Schneider et.. al. 1998;
Rodriguez et al. 2007).
Having found <strong>the</strong> largest number in <strong>the</strong> Vi vector, we assign <strong>the</strong> value Xi = 1 to it. The result <strong>of</strong> this
calculation is:
Max(Oiq) → X(Oiq)=1
Having found <strong>the</strong> smallest number in <strong>the</strong> Vi vector, we assign <strong>the</strong> value Xi = 0 to it. The result <strong>of</strong>
this calculation is:
Min(Oip) → X(Oip)=0
As previously mentioned, each <strong>of</strong> <strong>the</strong> o<strong>the</strong>r components <strong>of</strong> <strong>the</strong> vector Vi is calculated using <strong>the</strong>
following formula, and is placed in <strong>the</strong> interval [0, 1].
Xi(Oij)= (Oij – Min(Oij))/(Max(Oiq) – Min(Oip))
In this formula Xi (Oij) is simply <strong>the</strong> degree <strong>of</strong> membership <strong>of</strong> each Oij component in <strong>the</strong> Vi vector.
Membership degrees obtained from this function may not reflect <strong>the</strong> real world. To overcome this
problem, we can take <strong>the</strong> lower and upper limits <strong>of</strong> <strong>the</strong> results into consideration and interpret <strong>the</strong>
results using <strong>the</strong> two limits.
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Kamaladdin Rahmani Youshanloui et al
Having applied <strong>the</strong> upper and lower limits, <strong>the</strong> matrix <strong>of</strong> this stage is achieved. It should be mentioned
that due to some scattering in <strong>the</strong> collected data, it is necessary, for accurate calculation <strong>of</strong> <strong>the</strong>
collected data, for <strong>the</strong> upper and lower limits to be chosen according to experts' opinion (Schneider et
al, 1998 & Kosko, 1986 & Rodriguez, 2007). Here, according to <strong>the</strong> experts' opinions, <strong>the</strong> upper limit
is equal to 0.8 and <strong>the</strong> lower limit is equal to 0.2.
Table 4: The FZMS Matrix
ID
<strong>of</strong>
CSF
Individuals
1 2 3 4 5 6 7 8 9 10 11 12
1 1 0.57 0.5 0.43 0.8 1 0 0.5 0 1 0.73 0.33
2 0.7 1 0.47 0.64 0 0.72 0.72 1 0.34 1 1 0.62
3 0.69 0 0.24 0.42 0 1 0.33 0.67 0.51 0.67 0.27 0
4 0.67 0.47 0.23 1 0.27 1 0 0.67 0.43 0.5 0.63 0
5 0.7 0.58 0.76 1 1 0.6 0.2 0.2 1 0.5 0.68 0
6 0.38 0.2 0.53 0.35 0.63 1 0 0.5 0.45 0.38 0.33 0.25
7 0.71 0.56 1 0.44 1 0.67 0 0 0.53 0.33 0.67 0.22
8 0.22 0.33 0 0.33 0 0.67 0.67 0.44 0.47 1 0.44 0
9 0.58 0 0.22 1 0 0.22 0.22 0.64 1 0.64 0.22 1
10 0.36 0.21 0.36 1 0 1 0.36 0.71 1 0.6 0.33 0.6
Step three: Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success: The Matrix obtained in this step is an NXN
matrix. Rows and columns <strong>of</strong> <strong>the</strong> matrix are <strong>the</strong> identified factors. Our aim at this stage is to review
<strong>the</strong> two-by-two relationships among <strong>the</strong>m. Each component <strong>of</strong> this matrix, which is called Sij,
represents <strong>the</strong> amount <strong>of</strong> influence that "i" exerts on <strong>the</strong> factor "j". Sij can have an assigned value
between [-1.1]. Therefore, three types <strong>of</strong> relationship exist between each two factors (Schneider et al,
1998 & Kosko, 1986 & Rodriguez, 2007).
Sij>0 which represents a direct (and positive) causal relationship between <strong>the</strong> two "i" and "j"
factors.
Sij

Table 5: The SRMS Matrix
Kamaladdin Rahmani Youshanloui et al
ID <strong>of</strong>
ID <strong>of</strong> CSF
CSF 1 2 3 4 5 6 7 8 9 10
1 0.67 0.68 0.73 0.66 0.76 0.73 0.63 0.46 0.64
2 0.67 0.64 0.64 0.53 0.56 0.54 0.68 0.65 0.64
3 0.68 0.64 0.81 0.6 0.78 0.61 0.79 0.73 0.79
4 0.73 0.64 0.81 0.74 0.77 0.77 0.69 0.67 0.67
5 0.66 0.53 0.6 0.74 0.66 0.82 0.56 0.61 0.65
6 0.76 0.56 0.78 0.77 0.66 0.76 0.71 0.6 0.74
7 0.73 0.54 0.61 0.77 0.82 0.76 0.57 0.47 0.52
8 0.63 0.68 0.79 0.69 0.56 0.71 0.57 0.61 0.68
9 0.46 0.65 0.73 0.67 0.61 0.6 0.47 0.61 0.82
10 0.64 0.64 0.79 0.67 0.65 0.74 0.52 0.68 0.82
Step Four: <strong>the</strong> final matrix <strong>of</strong> success: When <strong>the</strong> matrix <strong>of</strong> <strong>the</strong> previous step is completed, it is
possible that some <strong>of</strong> <strong>the</strong> data within <strong>the</strong> matrix be misleading. This means that all factors <strong>of</strong> <strong>the</strong>
previous matrix, whose relations were previously presumed to be clear, may have no relation with
each o<strong>the</strong>r or may not always be causally related. In this case an expert is needed for <strong>the</strong> data
analysis and <strong>the</strong> conversion <strong>of</strong> <strong>the</strong> SRMS matrix into an FMS matrix. The importance <strong>of</strong> using an
expert is realized even more when we consider it possible that <strong>the</strong> factors used in ma<strong>the</strong>matical logic,
which have an acceptable relationship and proximity, may not logically be associated with one
ano<strong>the</strong>r. These unacceptable relations can easily be identified and removed by experts (Schneider et
al. 1998 & Kosko 1986).
Table 6: The FMS Matrix
ID <strong>of</strong>
ID <strong>of</strong> CSF
CSF 1 2 3 4 5 6 7 8 9 10
1 0.67 0.73 0.73 0.46
2 0.67 0.64 0.64 0.53
3 0.64 0.81
4 0.73 0.64 0.81 0.77
5 0.53 0.66 0.56 0.61
6 0.66 0.74
7 0.73 0.77 0.57 0.47
8 0.56 0.57 0.61
9 0.46 0.61 0.47 0.61 0.82
10 0.74 0.82
Partaking <strong>of</strong> <strong>the</strong> obtained final matrix, we can illustrate <strong>the</strong> influence <strong>of</strong> <strong>the</strong>se factors on each o<strong>the</strong>r
through <strong>the</strong> following graph. The nodes, in this graph, represent <strong>the</strong> factors and <strong>the</strong> arcs are indicative
<strong>of</strong> <strong>the</strong>ir influences on each o<strong>the</strong>r.
0.74
10
Figure 1: The Diagram <strong>of</strong> FCM
6
0.66
0.82
0.82
0.61
5
9
0.56
0.74
0.46
0.47
0.46
811
8
0.53
7
0.73
0.57
1
0.77
0.73
0.6
7 0.64
2
0.64
4
0.81
3
3

4. Conclusion
Kamaladdin Rahmani Youshanloui et al
Implementing KM in <strong>the</strong> absence <strong>of</strong> preparedness and lack <strong>of</strong> attention to critical success factors <strong>of</strong>
implementing KM can lead to failure and can incur heavy costs on <strong>the</strong> organization ra<strong>the</strong>r than being
pr<strong>of</strong>itable. In this study we, first, were looking for a way to identify <strong>the</strong> key factors in implementing KM.
Next, we tried to concretize <strong>the</strong> amount <strong>of</strong> <strong>the</strong>ir impact on one ano<strong>the</strong>r as well as <strong>the</strong>ir impact on <strong>the</strong>
readiness <strong>of</strong> <strong>the</strong> organizations to utilize KM. To achieve this goal we, in <strong>the</strong> first place, reviewed <strong>the</strong>
literature and asked for <strong>the</strong> opinions <strong>of</strong> two experts; as a result, ten key factors in implementing KM
were selected as <strong>the</strong> most important ones. In <strong>the</strong> next step twelve experts in <strong>the</strong> field <strong>of</strong> KM rated <strong>the</strong>
importance <strong>of</strong> each and every one <strong>of</strong> <strong>the</strong>se factors. In this stage, we needed a tool by using which we
could concretize <strong>the</strong>ir impact on one ano<strong>the</strong>r. One <strong>of</strong> <strong>the</strong> tools, used for this purpose, is fuzzy
cognitive maps. Fuzzy cognitive maps are useful in modeling complex systems. Using <strong>the</strong> obtained
data and partaking <strong>of</strong> <strong>the</strong> FCM method, we <strong>the</strong>n evaluated <strong>the</strong> impact <strong>of</strong> <strong>the</strong> selected factors. The
sum <strong>of</strong> <strong>the</strong> impact <strong>of</strong> each factor on <strong>the</strong> o<strong>the</strong>r ones was calculated based on <strong>the</strong> obtained data. The
ultimate results were as <strong>the</strong> followings: Culture, 2.59; Structure, 2.48; Strategy, 1.45; Leadership,
2.59; IT, 2.36; Measurement 1.40; Human Resources, 2.54, Finance, 1.74; Education, 2.97 and
modeling, 1.56. Given <strong>the</strong>se results, it is manifest that education, leadership, organizational culture
and human resources are <strong>the</strong> most important and influential factors in successfully implementing
knowledge management for organizations. The findings <strong>of</strong> this paper are in accordance with those <strong>of</strong>
Yeh et al. who, in <strong>the</strong>ir studies, enumerated Culture, Leadership and Human Resources as <strong>the</strong> most
important factors <strong>of</strong> implementing knowledge management. Skyrme and Amido, among many o<strong>the</strong>r
researches, also put great emphasis on <strong>the</strong> role Education in implementing knowledge management.
Different organizations can make use <strong>of</strong> <strong>the</strong> findings <strong>of</strong> this paper in order to prepare <strong>the</strong> ground for
<strong>the</strong> implementation <strong>of</strong> KM. Finally, it should be noted that it is obligatory that organizations focus on
<strong>the</strong> factors with higher scores when <strong>the</strong>y are preparing <strong>the</strong> ground for <strong>the</strong> successful implementation
<strong>of</strong> KM as it is believed that those factors will help <strong>the</strong>m significantly.
References
Akhavan, P., Jafari, M. and Fathian, Mohammad. (2006). "Critical success factors <strong>of</strong> knowledge management
systems: a multi-case analysis". Department <strong>of</strong> Industrial Engineering. Vol. 18 No. 2. p. 97-113.
Alizadeh, S. G., Jafari M. and M. Hooshmand,. (2008). "Learning FCM by Tabu Search,". International Journal <strong>of</strong>
Computer Science. VOL.2 NO. 2, pp. 142-149.
Allameh, S.M., Zare, S.M., and Davoodi, S. M. R. (2011). “Examining <strong>the</strong> Impact <strong>of</strong> KM Enablers on Knowledge
Management Processes”. Procedia Computer Science. NO 3, pp 1211-1223.
Bender , S . and Fish, A .(2000). "The transfer <strong>of</strong> knowledge and expertise: The continuing need for Global
assignments ". Journal <strong>of</strong> knowledge management , VOL. 4, pp. 125-137.
Bruno, A. and Leidecker, J. (1984). "Identifying and Using Critical Success Factors". In: Long Range Planning,,
Vol. 17, No.1, pp. 23-32.
Buckman, R. (1999). "Collaborative knowledge, Human Resource Planning", Vol. 22 No. 1, pp. 22-38.
Chourides, P., Longbottom, D. and Murphy, W. (2003). "Excellence in knowledge Management: an Empirical
Study to Identify Critical Factors and Performance Measures", Measuring Business Excellence". Vol. 7
No.2, pp. 29-45.
Cohen, S. and Backer, N. (1999). "Making and mining intellectual capital: method or madness?", Training &
Development, Vol. 53 No. 9, pp. 46- 50.
Davenport, T. and Grover, V. (2001). "Knowledge management". Journal <strong>of</strong> Management Information Systems.
Vol.18 No.1, pp. 3-4.
Davenport, T., De Long, D. and Beers, M. (1998). "Successful Knowledge Management Projects", Sloan
Management Review, Vol. 39 No. 2, pp. 43-57.
Davenport, T.H. and Prusak, L. (1998) . “Working Knowledge : How Organizations Manage What They Know”.
Harvard Business School Press, Boston, MA.
Davenport, T.H. and Volpel, S.C. (2001). “The rise <strong>of</strong> knowledge towards attention management”. Journal <strong>of</strong>
Knowledge Management, Vol. 5 No. 3, pp. 212-21.
Davis, T. (1996). "Managing Knowledge-work Support Functions", Journal <strong>of</strong> General Management. Vol. 22 No.
1, pp. 68-86.
Day, J. and Wendler, J. (1998) "Best Practice and Beyond: Knowledge Strategies", <strong>the</strong> McKinsey Quarterly, No.
1, pp. 19-25.
Dess, G. and Picken, J. (2000). "Changing roles: Leadership in <strong>the</strong> 21 st century", Organizational Dynamics, Vol.
28 No. 3, pp. 18-34.
Drew, S. (1997). "From knowledge to action: <strong>the</strong> Impact <strong>of</strong> Benchmarking on Organizational Performance", Long
Range Planning, Vol. 30 No. 3, pp. 427-41.
Drucker,P .(1988). " The coming <strong>of</strong> <strong>the</strong> new organization " , Harvard Business Review. No 66 , pp . 45-53.
Greengard, S. (1998). "Will your culture support KM?" Workforce, Vol. 77 No. 10, pp. 93-114.
812

Kamaladdin Rahmani Youshanloui et al
Hasanali, F. (2002), “Critical success factors for KM”, available at:
www.kmadvantage.com/docs/km_articles/Critical_Success_Factors_<strong>of</strong>_KM.pdf (accessed January 10,
2008).
Holsapple, C.W. and Joshi, K.D. (2000). "An investigation <strong>of</strong> factors that influence <strong>the</strong> management <strong>of</strong> knowledge
in organizations", Journal <strong>of</strong> Strategic Information Systems, No. 9, pp. 235-61.
Holt G.D., Love P.E.D and Li H. (2000). "The learning organization: toward a paradigm for mutually beneficial
strategic construction alliances", International journal <strong>of</strong> project management. No 18, pp. 415-421.
Hung, Y.C., Huang, S.M Lin, Q and Tsai, M.L. (2005). Critical Factors in Adopting a Knowledge Management
System for <strong>the</strong> Pharmaceutical Industry, Industrial Management & Data Systems. Vol.105, No.2, pp. 164-
183.
Jung, D.I., Chow, C. and Wu, A. (2003) “The role <strong>of</strong> transformational leadership in enhancing organizational
innovation: hypo<strong>the</strong>ses and some preliminary findings ”. The Leadership Quarterly. Vol. 14 No. 2003, pp.
525-44.
Kandasamy, V and S. Florentin. (2003). "Fuzzy Cognitive Maps and Neutrosophic cognitive Maps" Phoenix,
Phenix. Available at: www.gallup.unm.edu
Khalifa, M. and Liu, V. (2003). “Determinants <strong>of</strong> Successful Knowledge Management Programs” Electronic
Journal on Knowledge Management. Vol. 1, No.2, pp. 103-112.
Kosko, B. (1986). "Fuzzy Cognitive Maps." International Journal on Man– Machine Studies. NO 24, 101-110.
Kuan, Y.W. (2005). “Critical Success Factors for Implementing Knowledge Management in Small and Medium
Enterprises”. Industrial management & Data Systems. Vol. 105, No. 3, pp. 262-273.
Lee, H., and Choi, B. (2003)." Knowledge management enablers, processes, and organizational performance: An
integrated view and empirical examination". Journal <strong>of</strong> Management Information Systems. VOL, 20 NO, 1,
pp 179-228.
Lee, S.M. and Hong, S. (2002). "An Enterprise-wide Knowledge Management System". Industrial Management &
Data Systems. Vol. 102, No.1, pp. 17-25.
Liebowitz, J. (1999), “Key ingredients to <strong>the</strong> success <strong>of</strong> an organization’s KM strategy”, Knowledge and Process
Management, Vol. 6 No. 1, pp. 37-40.
Luis Rodriguez-Repiso, R. S., Jose L. Salmeron. (2007). "Modelling IT projects success with Fuzzy Cognitive
Maps." Expert Systems with Applications, Vol. 32, No. 2, pp 543-559.
Mathi, K. (2004). “Key Success Factors for Knowledge Management”. Master Thesis, MBA: International
Business Management& Consulting, Germany: University <strong>of</strong> Applied Sciences.
M<strong>of</strong>fett, S., McAdam, R. and Parkinson, S. (2003). "An Empirical Analysis <strong>of</strong> Knowledge Management
Applications", Journal <strong>of</strong> Knowledge Management, Vol. 23 No. 3, pp. 6-26.
Mumford, M.D. and Licuanan, B. (2004). “Leading for innovation: conclusions, issues and directions ”. The
Leadership Quarterly, Vol. 15 No. 1, pp. 163-71.
Pedrycz, Witold. (2010). “The design <strong>of</strong> cognitive maps: A study in synergy <strong>of</strong> granular computing and
evolutionary optimization”. Expert Systems with Applications. NO. 37, pp 7288–7294.
Pinto, J. and Slevin, D. (1987). "Critical Factors in Successful Project Implementation". In IEEE Transactions on
Engineering Management, Vol.34, No.1, pp. 22-27.
Rockart, J. (1979). “Chief Executives Define Their Own Information Needs”. Harvard Business Review, March -
April, pp. 81-92.
Luis Rodriguez-Repiso, R. S., Jose L. Salmeron (2007). "Modelling IT projects success with Fuzzy Cognitive
Maps." Expert Systems with Applications, Vol. 32, No. 2, pp 543-559.
Schneider, M. S., E. Kandel, and A. Chew, G. (1998). "Automatic construction <strong>of</strong> FCMs." Fuzzy Sets and
Systems, Vol. 93, No. 2, pp 161-172.
Skyrme, David J. and Amidon, Debra M. (1997). Creating <strong>the</strong> Knowledge-based Business, London: Business
Intelligence.
Xirogiannis.G (2007). "Intelligent modeling <strong>of</strong> e-business maturity." Expert Systems with Applications, Vol. 32, pp
125-132.
Yang, B., Watkins, K. E. and Marsick, V.J. (2004). “The Construct <strong>of</strong> <strong>the</strong> Learning Organization: Dimensions,
Measurement and Validation”. Human Resource Development Quarterly. VOL 15, NO 1, pp.31-55.
Yeh, Ying-Jung; Lai, Sun-Quae and Ho,Chin-Tsang.(2006). “Knowledge management enablers: a case study”.
Industrial Management & Data Systems, Vol. 106 No. 6,pp. 793-810; Emerald Group Publishing.
Zack, M.H. (1999), “Enveloping a knowledge strategy”, California Management Review, Vol. 41 No. 3, pp. 125-
45.
813

Risk Analysis for Knowledge Management Projects Using
PMBOK Guidelines and FMEA Techniques: A Case Study
Lila Rajabion 1 and Jafar Zanganeh 2
1
School <strong>of</strong> Information Sciences and Technology, Pennsylvania State
University, USA
2
School <strong>of</strong> Business, Isfahan University, Iran
Lzr1@psu.edu
kia_zgn@yahoo.com
Abstract: In many cases Knowledge management (KM) projects in developing countries were unsuccessful
during establishing and deployment; <strong>the</strong>refore before starting KM projects it is necessary to identify, evaluate,
control and manage potential risks. One <strong>of</strong> <strong>the</strong> main accomplishments <strong>of</strong> <strong>the</strong> modern world is <strong>the</strong> transformation
<strong>of</strong> <strong>the</strong> risk, from just accepting it to study <strong>the</strong> area and being able to analyze it. In this study a new model has
been designed and applied for analyzing risks in KM projects in accordance with PMBOK guideline, FMEA
technique and KM concepts. The severity <strong>of</strong> each potential risk (failure), <strong>the</strong> occurrence and detection rank <strong>of</strong>
each potential risk mechanism was determined by group <strong>of</strong> industry experts (Barez Industrial Group) in
brainstorming meeting. The risk priority number (RPN) was calculated for each potential risk by multiplying
relevant severity, occurrence and detection ranks. Finally suitable series <strong>of</strong> actions for higher risks (top RPNs)
and severe risks (risk with high severity) were conducted.
Keywords: Risk management, Knowledge management, PMBOK, Risk analysis model for KM project
1. Introduction
Today, effective risk management is one <strong>of</strong> <strong>the</strong> most important factors in <strong>the</strong> project management
success.
In order to achieve or execute a new complicated technology or service, accurate resources and
plans are required (Souza Santos, 2008). These factors are main challenges that in order to face
<strong>the</strong>m, <strong>the</strong> organizations create new technologies with special projects.
About 26 percent <strong>of</strong> s<strong>of</strong>tware & information technology projects were successful; however o<strong>the</strong>r 84
percent faced failure, that in <strong>the</strong> case <strong>of</strong> knowledge management project this number reaches 10
percent. Therefore, we shall use risk evaluation methods which are expending in different industries
to <strong>the</strong> extent that currently <strong>the</strong>re are various types <strong>of</strong> qualitative and quantitative risk evaluation
methods all around <strong>the</strong> word (Carbone, 2004).
Statistical studies on <strong>the</strong> success rate <strong>of</strong> projects show that successful projects do not follow a special
rule. Among 30000 projects in <strong>the</strong> ground <strong>of</strong> information technology which were studied by Sundish
group, 63 percent were delayed and 49 percent had unexpected expense. In o<strong>the</strong>r words, success
rate, including planning and budgeting in 28 percent <strong>of</strong> <strong>the</strong> related projects was conforming to reality.
Therefore, successful development and execution <strong>of</strong> <strong>the</strong> project management, and especially risk
management mechanism, is <strong>the</strong> basis <strong>of</strong> <strong>the</strong> project success in <strong>the</strong> ground <strong>of</strong> information technology.
According to <strong>the</strong> project management institute, risk is a situation or event that would influence <strong>the</strong>
project goals negatively or positively (PMI, 2008).risk is usually in <strong>the</strong> future; it is an uncertain event or
reality which, if it occurs, would influence at least one <strong>of</strong> <strong>the</strong> project goals. These goals can be area,
time, expense and quality. A risk can cause failure in <strong>the</strong> program, product or service. The objective <strong>of</strong>
this research is to present some strategies for evaluating risks <strong>of</strong> knowledge management projects in
Iran organizations. Risks <strong>of</strong> knowledge management projects shall be recognized and classified
according to <strong>the</strong>ir occurrence probability and <strong>the</strong>ir severity. According to <strong>the</strong>se classifications, risk
management models shall be used in order to reduce <strong>the</strong>se risks or to make <strong>the</strong>m acceptable, so that
<strong>the</strong> project goals, which are effective establishment <strong>of</strong> <strong>the</strong> project management, shall be achieved. In
order to do this, in this research, we use two techniques and one concept, so that we can achieve a
model for recognizing and evaluating management <strong>of</strong> knowledge management projects. Many
production organizations use FMEA to recognize failures and errors in <strong>the</strong>ir designs, products and
processes. In this research we use FMEA and PMBOK techniques and knowledge management
concepts. We are looking for <strong>the</strong> answer to two main questions: whe<strong>the</strong>r a reasonable relationship
can be found between FMEA and PMBOK and knowledge management concepts, and whe<strong>the</strong>r this
model can be carried out in <strong>the</strong> real environment <strong>of</strong> Iran organizations projects.
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2. Literature review
Lila Rajabion and Jafar Zanganeh
In 2003,Carbone tried to recognize and omit <strong>the</strong> project risks using FMEA technique (Carbone, 2004).
He altered FMEA to an improved model named RFMEA. This model could recognize and evaluate
project management risks and present corrective and preventive solutions. After that in 2008, Ms.
Samaneh Shokravi used FMEA to evaluate and analyze <strong>the</strong> road-making project risks (Shokravi,
2008). In <strong>the</strong> following research, a model for risk management was presented using FMEA & PMBOK
tools (Souza Santos, 2008). Also, in <strong>the</strong> case <strong>of</strong> knowledge management, a special model was
designed by <strong>the</strong> experts <strong>of</strong> NASA organization, for risk evaluating (Lengyel, 2008).
2.1 Knowledge
Knowledge, learning and cognition are classical terms that have been re-discovered in <strong>the</strong> context <strong>of</strong>
<strong>the</strong> information technology and knowledge management revolutions. Beckman (1998) compiled a
number <strong>of</strong> useful and relevant definitions <strong>of</strong> knowledge and organizational knowledge:
Knowledge is organized information applicable to problem solving (Woolf, 1990).
Knowledge is information that has been organized and analyzed to make it understandable and
applicable to problem solving or decision-making (Turban, 1992).
2.2 Knowledge management
Knowledge management is defined as: <strong>the</strong> systematic, explicit, and deliberate building, renewal, and
application <strong>of</strong> knowledge to maximize an enterprise‘s knowledge- related effectiveness and returns
from its knowledge assets (Wiig, 1993).
Some o<strong>the</strong>r researches show; linking <strong>the</strong> individual perspective <strong>of</strong> knowledge to <strong>the</strong> organizational
level, organizational knowledge creation <strong>the</strong>ory is concerned with <strong>the</strong> processes which make available
individual knowledge to <strong>the</strong> organizational knowledge system (Nonaka and von Krogh, 2009). A
crucial and difficult step in <strong>the</strong> organizational knowledge process is <strong>the</strong> conversion <strong>of</strong> tacit knowledge
into explicit knowledge.as we explain before; Tacit (implicit) knowledge is unarticulated and rooted in
experience and intuition and tied to <strong>the</strong> senses and explicit knowledge is uttered, can be formulated in
sentences, has a universal character and is accessible through consciousness (Nonaka and von
Krogh, 2009). Knowledge management (KM) involves all practices <strong>of</strong> an organization to create, store,
use and share knowledge (Probst et al. 1998).
3. Knowledge management projects
Today, many organizations have found that in order to be successful in competitive challenging world,
<strong>the</strong>y need an integral and systematic view on knowledge inside <strong>the</strong> organization. So in recent years,
<strong>the</strong>y have focused <strong>the</strong>ir researches on managing business, and maintaining and using <strong>the</strong> knowledge.
It is why that one <strong>of</strong> top managers <strong>of</strong> Hewlett Packett says: if Hewlett Packett knew what it had, its
pr<strong>of</strong>itability would increase up to triplet. Understanding real role <strong>of</strong> knowledge in organizations helps
finding <strong>the</strong> answer <strong>of</strong> this question” why are some organizations successful? People, technology,
products and services will change over <strong>the</strong> time. So what left? Which factor makes a pleased survival
in today challenging world? Studies regarding organizational knowledge show <strong>the</strong> reason <strong>of</strong> stability
and success <strong>of</strong> organizations. Tom Stewart in a paper in Fortune in 1994 recalled companies to give
priority to what <strong>the</strong>y know ( intellectual capital) more <strong>the</strong>n what <strong>the</strong>y have (material capital). Then,
Peter Draker defined knowledge as bases <strong>of</strong> competition in Meta industrial society. Stanford Roemer
defined knowledge as a lasting source that <strong>the</strong> more we use it, <strong>the</strong> more is its volume. Companies
such as De chemical, Scandia and I.B.M Consultants rented knowledge superior managers and
scientific capital managers in order to control and manage intellectual capital <strong>of</strong> <strong>the</strong> companies.
Knowledge management is a ra<strong>the</strong>r new and challenging concept in management literature and has
been paid more attention at <strong>the</strong> beginning <strong>of</strong> 1990’s. According to above definition, four functions <strong>of</strong>
identifying and problem solving, deciding, creating and innovating and making change are <strong>of</strong> main
functions <strong>of</strong> knowledge management that are factors in failing many projects <strong>of</strong> knowledge
management. Simply put, we classified <strong>the</strong>se factors into three classes: motivational, cultural and
conceptual factors. In Iran, many projects <strong>of</strong> knowledge management are due to lack <strong>of</strong>
understanding <strong>of</strong> knowledge management on <strong>the</strong> behalf <strong>of</strong> <strong>the</strong> superior managers and employees.
815

4. Risk management
Lila Rajabion and Jafar Zanganeh
Risk management consists <strong>of</strong> understanding internal and external effects which can be reasons for
<strong>the</strong> project failure. Project risk management includes processes such as leading, risk management
planning, recognizing, analyzing, and planning, supervising and controlling <strong>the</strong> project. Granting risk
management <strong>of</strong> <strong>the</strong> project increases <strong>the</strong> probability and <strong>the</strong> effect <strong>of</strong> positive events and decreases
<strong>the</strong> probability and <strong>the</strong> effect <strong>of</strong> negative events in <strong>the</strong> project. The source <strong>of</strong> project risks stems in
uncertainty present in all projects. Organizations receive <strong>the</strong> risks as <strong>the</strong> effect <strong>of</strong> uncertainty on goals
<strong>of</strong> <strong>the</strong> project and organization. The organizations and stakeholders accept different levels <strong>of</strong> risk that
is called risk tolerance. The risks that are considered threatening can be accepted if <strong>the</strong>y are in <strong>the</strong>
range <strong>of</strong> tolerance or balance with awards resulted from <strong>the</strong> risk. For example, accepting accelerating
schedule is a risk that is followed by getting award resulted from completion. Risk management helps
organizations control <strong>the</strong> risks while doing daily operations <strong>of</strong> <strong>the</strong> project. a set <strong>of</strong> guidelines and
processes consists <strong>the</strong> process <strong>of</strong> risk management. Guidelines which should be considered in risk
management are as follows (PMI2008):
Evaluate <strong>the</strong> risks permanently
Incorporate processes <strong>of</strong> risk management to all roles and operations
Use planning based on risk management
Make an acceptable level <strong>of</strong> formality in organization
5. Project risk categorizations
Project risks can be categorized in a number <strong>of</strong> ways according to <strong>the</strong> level <strong>of</strong> detail or a selected
viewpoint.(Peltonen, T., Kiiras, J., 1998). One <strong>of</strong> <strong>the</strong> most typical risk categorizations is presented in
Table 1. This four-level categorization is presented e.g. by Artto and Kähkönen. This categorisation
tries to fade a project type and be a general categorisation. Risks are divided into pure risks (e.g.
hazards and wea<strong>the</strong>r conditions), financial risks (e.g. cash flow or credit risk), business risks (almost
anything that can happen in a project) and political risks, which refer to <strong>the</strong> certain political
environment and risks that are caused mostly by extreme conditions, such as, among o<strong>the</strong>rs, war.
(Artto, K., Kähkönen, K. , Management <strong>of</strong> Uncertainty (2011)
Table 1: categories <strong>of</strong> Risks
Pure Risks
6. PMBOK and FMEA Introduction
Financial Risks
Business Risks
Political Country Risks
Designing and setting processes in modern technologies usually require paying special attention to
organization sections and this includes factors such as complexity, resource risk (both human and
financial risks) and normalizing <strong>the</strong> program (Souza, Santos, 2008). In order to understand <strong>the</strong>
differences correctly, <strong>the</strong> processes are done as project in modern technologies but many <strong>of</strong> <strong>the</strong>se
projects fail because <strong>of</strong> delaying in program and imposing unexpected expenses. In order to prevent
<strong>the</strong>se scenarios, strategy <strong>of</strong> risk management should be run in organizations (Carbone, 2004). The
risk management is a methodology from project manager institute that is presented as a guide to
knowledge range about project management (PMBOK) and is one <strong>of</strong> <strong>the</strong> most important techniques
for controlling risk in projects. We believe that extended concept <strong>of</strong> FMEA can be a supplement for
PMBOK in order to make an integral model for risk management <strong>of</strong> <strong>the</strong> projects(PMI2008). Adding
FMEA to processes <strong>of</strong> risk management makes risks identification and evaluation easy and simple.
Following concepts describe <strong>the</strong>se techniques
7. FMEA
Technique <strong>of</strong> risk analysis includes interviewing with experienced people, analyzing expected money
value (EMV) and responding matrix and o<strong>the</strong>r methods. Then data obtained from risks are compared
with references and will be modified. One <strong>of</strong> techniques for risk management was <strong>the</strong> probability times
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risk effect that is called risk degree (Carbone, 2004). This guide was used to evaluate all risks. By
evaluating risk probability, potential effects on <strong>the</strong> goals <strong>of</strong> project are investigated such as schedule,
expense, quality or operation including negative effects related to threats and positive effects related
to opportunities. Risks can be given priority by analyzing <strong>the</strong>m quantitatively and based on <strong>the</strong>ir
degrees. Risk probability should be multiplied by its effect and severity which is <strong>the</strong> same as analyzing
failure cases and is used in processes, designing and planning services(Carbone, 2004). FMEA
technique has been used primary at US army, <strong>the</strong>n o<strong>the</strong>r industries identified its usefulness(MIL-
STD1629) and recently it has been used in car manufacturing and part making industries. At <strong>the</strong>
moment, <strong>the</strong> technique is being used in quality management system as one <strong>of</strong> integral tools with o<strong>the</strong>r
techniques. In fact, this method is a tool to prevent errors and is an efficient method for updating,
creating new designs and reviewing <strong>the</strong>m. The method also is being used in risk management <strong>of</strong>
project management and is suitable for risk management with some changes. In recent years, many
researches have been done in respect <strong>of</strong> using FMEA, analyzing and evaluating risk management.
Studies done by Canon led to development <strong>of</strong> RFMEA model. US defense ministry planned a new
model <strong>of</strong> FMEA in order to determine critical points and is called EMECA. Recently, a new method
called analyzing critical effects and disorders has been designed to determine possible disorders in
complex processes <strong>of</strong> management. In this study, considering researches done by o<strong>the</strong>r researchers
in respect <strong>of</strong> FMEA technique, we have used following stages to develop a conceptual model from
FMEA technique to identify risks <strong>of</strong> projects <strong>of</strong> knowledge management.
Determining failure graph
Determining potential risk
Determining effects <strong>of</strong> potential risks
Determining risk severity
Determining risk level and degree
Determining causes/ potential mechanism <strong>of</strong> risk
Determining current controls in project process
1- preventing potential risk/ causes <strong>of</strong> risk mechanism
2- finding potential risk or its causes/ mechanism
Determining risk occurrence
Determining risk discovery
Risk number
Strategy <strong>of</strong> facing risk
When risk number obtained from determined range was more <strong>the</strong>n what it was at <strong>the</strong> beginning,
preventive or modifying actions are issued. The risk will be controlled or omitted regarding <strong>the</strong> defined
strategy. After defining corrective measure, analyses <strong>of</strong> <strong>the</strong> risk that can not be corrected are
documented by redesigning and determining more controls to reduce risks. RPN is <strong>the</strong> product <strong>of</strong>
three standards that have already been discussed: severity number, occurrence number and
probability number
In each project, in order to determine acceptable level <strong>of</strong> RPN, opinions <strong>of</strong> top managers, project
manager and supporters should be defined.
8. PMBOK and project risk management
The project is a temporal attempt that is done in direction <strong>of</strong> creating a product, service or a unique
result. Temporal nature <strong>of</strong> projects suggests a certain opening and closing(PMI2008). The concept <strong>of</strong>
risk management relates to all events or conditions that can have positive or negative effects on
project goals. The risks can be categorized into internal risks, which <strong>the</strong> project team can determine or
control it, and external risks, on which <strong>the</strong>re is no control(Carbone, 2004). Risk is <strong>the</strong> forth area in
guide to project knowledge management. Several tools available for risk management <strong>of</strong> projects
have been defined. These tools list how risks happen in critical points <strong>of</strong> projects. PMBOK content is
used for project managers who manage projects’ risks. Risk management <strong>of</strong> projects includes leading
<strong>the</strong> process, planning risk management, identifying, analyzing, response planning and controlling <strong>the</strong>
projects. The goals <strong>of</strong> risk management <strong>of</strong> <strong>the</strong> project are to increase <strong>the</strong> probability and <strong>the</strong> effect <strong>of</strong>
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positive events and decrease <strong>the</strong> probability and <strong>the</strong> effect <strong>of</strong> negative events in <strong>the</strong>
project(PMI2008).
8.1 Model defined for evaluating risk <strong>of</strong> knowledge management projects
At first, we are going to define <strong>the</strong> proposed model. This model is consisted <strong>of</strong> concepts <strong>of</strong> knowledge
management and techniques <strong>of</strong> FMEA and PMBOK. In <strong>the</strong> following table, <strong>the</strong> items are brought
generally. This simple model, which can be used by project manager and people who work with
projects <strong>of</strong> knowledge management, is applied to identify project risks. After describing this model, we
deal with a case study in a project <strong>of</strong> knowledge management in tire manufacturing industry.
row FMEA PMBOK KM output
1 Forming FMEA team Planning risk
management
2 Brain storming in order to
identify WBS
Planning risk
management
Process <strong>of</strong> primary
planning <strong>of</strong>
knowledge
management project
Process <strong>of</strong> planning
knowledge
management project
3 Listing effects <strong>of</strong> WBS Identifying risks Process <strong>of</strong> planning
knowledge
management project
4 Specifying an intensity degree
for each failure model
5 Specifying an occurrence
degree to each failure model
6 Specifying a discovery degree
to each failure model
7 Specifying score <strong>of</strong> risk taking
priority for each failure
8 Regulated list <strong>of</strong> risks
considering RPN number
9 Definition <strong>of</strong> strategy <strong>of</strong> how
to face risk
Qualitative risk analysis Process <strong>of</strong> planning
knowledge
management project
Qualitative risk analysis Process <strong>of</strong> planning
knowledge
management project
Qualitative risk analysis Process <strong>of</strong> planning
knowledge
management project
Quantitative risk
analysis
Process <strong>of</strong> planning
knowledge
management project
Planning risk responses Process <strong>of</strong> planning
knowledge
management project
Planning risk responses Process <strong>of</strong> planning
knowledge
management project
10 Taking Preventive measures Planning risk responses Process <strong>of</strong> planning
knowledge
management project
11 Actions taken in terms <strong>of</strong>
strategy <strong>of</strong> facing risk
12 Computing RPN after omitting
or reducing risk effects
Planning risk responses Process <strong>of</strong> planning
knowledge
management project
Controlling risk Process <strong>of</strong>
controlling
knowledge
management
Risk break structure
(RBS)
risk evaluation Matrix
corrective measures List
Change demands
Table 2: relationship between FMEA, PMBOK and KM
According to researches done in <strong>the</strong> papers, how to face risk is based on RPN and strategy <strong>of</strong> facing
it (Carbone, 2004). Considering <strong>the</strong> above table, <strong>the</strong>re is an improved method <strong>of</strong> FMEA technique for
risk management <strong>of</strong> projects risk management <strong>of</strong> organizations and is applied in processes and
designs based on PMBOK guide. According to above table, <strong>the</strong> presented model includes following
stages:
Determining main goals <strong>of</strong> knowledge management project
Identifying risks and mechanism <strong>of</strong> <strong>the</strong>ir effects
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Lila Rajabion and Jafar Zanganeh
Determining severity <strong>of</strong> risk occurrence for each group and challenges <strong>of</strong> knowledge management
projects
Specifying risk probability happened for each risk
Specifying rate <strong>of</strong> discovery ability for each group
Computing risk degree resulted from <strong>the</strong> effect times <strong>the</strong> probability
Regular list <strong>of</strong> calculated risks based on risk priority number
Determining corrective and modifying actions based on response strategy
Calculating priority number after taking corrective actions
This model is applied for different phases <strong>of</strong> project management process from beginning process to
control process. Output <strong>of</strong> <strong>the</strong> first stage is after <strong>the</strong> third stage and by using tools such as fish bone
graph and brain storming, we can obtain risk failure structure in this stage. Risk evaluation matrix is
calculated by severity times <strong>the</strong> probability and discovery <strong>of</strong> risk priority number (RPN). According to
<strong>the</strong> model, we calculate risk priority number and <strong>the</strong> risks are set based on RPN and <strong>the</strong> biggest risk
number in <strong>the</strong> table. Considering strategy <strong>of</strong> facing risk in guide to project knowledge range (PMBOK),
we can define a separate strategy for each risk that are: 1- avoidance 2- transmission 3- reduction 4-
accepting negative risks. On <strong>the</strong> o<strong>the</strong>r hand, we suggest three responses <strong>of</strong> fourfold responses
interacting with risks and with positive effects in <strong>the</strong> project. The fourth strategy is acceptance that can
be used for negative risks or threats and also positive risks and opportunities. These strategies are as
following: 1- exploitation 2- contribution 3-making better 4- acceptance
9. Methodology
Case study research methodology has been introduced in this study. Researchers have used <strong>the</strong>
case study research method for many years across a variety <strong>of</strong> disciplines. Social scientists, in
particular, have made wide use <strong>of</strong> this qualitative research method to examine contemporary real-life
situations and provide <strong>the</strong> basis for <strong>the</strong> application <strong>of</strong> ideas and extension <strong>of</strong> methods. Case studies
could also be used to build <strong>the</strong> ground for building a <strong>the</strong>ory. “[..] <strong>the</strong>ory building from case study
research is particularly appropriate because <strong>the</strong>ory building does not rely upon previous literature or
prior empirical evidence.”(Eisenhardt, 1989).
9.1 Case study
9.1.1 Project <strong>of</strong> settling knowledge management in Barez Industrial Group
In order to evaluate above mentioned model, we use Barez Industrial Group as a selected company
to conduct <strong>the</strong> case study. This organization was chosen because it is technological centered and
depended on staff knowledge. If someone leave <strong>the</strong> company and doesn’t transfer his or her
experiences, <strong>the</strong> production will be stopped. So <strong>the</strong> organization tries to settle knowledge
management. This organization is <strong>the</strong> leader <strong>of</strong> tire manufacturing market in Iran and Middle East.
Transactions related to knowledge management in Barez Industrial Group started in 2007 with
participation <strong>of</strong> Parseh Company. In <strong>the</strong> first phase <strong>of</strong> this project, two active areas were determined.
We tested <strong>the</strong> above mentioned model in this organization. Considering main processes <strong>of</strong> knowledge
management including identifying <strong>the</strong> knowledge required, knowledge development and knowledge
sharing, performance <strong>of</strong> processes <strong>of</strong> knowledge management is described.
According to Yin in case study research sources <strong>of</strong> data could be documentation, archivial reports,
interviews, direct observation , participant observation and physical artifacts.(Yin, 1994). In this case
study we have used some <strong>of</strong> <strong>the</strong>se methods to collect <strong>the</strong> sample data. We have used <strong>the</strong> interview
method with managers <strong>of</strong> this company also we have used <strong>the</strong> secondary sources <strong>of</strong> data such as
company information sources such as reports and news archives. We have conducted meetings with
<strong>the</strong> managers and have followed <strong>the</strong> following steps.
formulating strategic issues: totally 55 meetings were held by 29 managers <strong>of</strong> Barez Industrial
Group and managers polled for a conceptual model for strategic descriptions using <strong>the</strong> framework
presented in primary version. Totally 417 topics and opinions were obtained and classified by 8
intra organizational areas and 24 extra organizational areas.
Formulating structure <strong>of</strong> company knowledge
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Describing risks made during project management process: generally, failure factors <strong>of</strong>
knowledge management projects are categorized in four groups and each <strong>of</strong> <strong>the</strong>se groups has
many subgroups.
10. A. Issues related to organizational structure and culture
Issues related to organizational culture are divided into three sub groups: relationship and
communication <strong>of</strong> people with each o<strong>the</strong>r, resistance against changes and inefficiency <strong>of</strong> knowledge
management project. List <strong>of</strong> sub factors <strong>of</strong> every subgroup has been shown. Generally, problems
related to organizational structure and culture stem in human resources and views governing <strong>the</strong>
organization.
A, 1- relationship <strong>of</strong> people with each o<strong>the</strong>r
- Lack <strong>of</strong> enough motivation in personnel and lack <strong>of</strong> suitable awarding and punishing system
- Inappropriate relationship between knowledge management project team and <strong>the</strong> staff
- High hierarchical levels and problems resulted from <strong>the</strong>se levels
- Lack <strong>of</strong> group working among <strong>the</strong> staff
A, 2- resistance against changes (issues related to change management)
- fear <strong>of</strong> failure and not gaining suitable result among staff and lack <strong>of</strong> proper bedding
- Lack <strong>of</strong> enough planning for making changes
- Fear <strong>of</strong> losing occupational security
- Fear <strong>of</strong> changes
- Managers' inappropriate understanding <strong>of</strong> creating changes
A, 3- lack <strong>of</strong> efficiency and effectiveness <strong>of</strong> knowledge management system
- Lack <strong>of</strong> power <strong>of</strong> knowledge management team
- Lack <strong>of</strong> sufficient skill among members <strong>of</strong> knowledge management team
- Lack <strong>of</strong> appropriate education among members <strong>of</strong> knowledge management team
- Problems related to employment for knowledge management team
- Lack <strong>of</strong> familiarity and incapability <strong>of</strong> knowledge management team in identifying correctly <strong>the</strong>
organization and organizational relations
B- Issues related to support, protection and leadership
B-1 management commitments and supports
- Superior manager's unfamiliarity with dimensions <strong>of</strong> knowledge management and requirements for
setting <strong>the</strong>m in organizations
- Superior manager's lack <strong>of</strong> commitment and support from <strong>the</strong> project
- Managerial doubts
- Lack <strong>of</strong> managerial skills and motivations for running knowledge management
- B-2-b issues related to consultants and supporters
- Lack <strong>of</strong> support consultants outside <strong>the</strong> organization
- Incorrect choice <strong>of</strong> supporters and consultants
- delaying in supporting incompletes
C- Technical and applicable issues
C-1 problems related to incorrect use <strong>of</strong> IT tools and managing use <strong>of</strong> IT.
- Misunderstanding <strong>of</strong> role <strong>of</strong> IT in knowledge management
- Non-optimal use <strong>of</strong> IT and its tools
- Negative view toward IT
- Inefficiency <strong>of</strong> applied available s<strong>of</strong>tware in <strong>the</strong> organization
- Not paying attention to knowledge management in s<strong>of</strong>tware architecture
C, 2- Issues related to <strong>the</strong> staff and creating innovation and creativity among <strong>the</strong>m
- Lack <strong>of</strong> creativity in designing knowledge management model
- Lack <strong>of</strong> applied trainings
D- issued related to use <strong>of</strong> organizational methods and planning
D, 1- Issues related to managing and planning <strong>the</strong> project
- Lack <strong>of</strong> proper planning for executing <strong>the</strong> project
- Lack <strong>of</strong> enough time and improper time management
- Lack <strong>of</strong> sufficient ability for controlling budget and expense
- Lack <strong>of</strong> sufficient resources, inefficiency <strong>of</strong> <strong>the</strong> existed resources and not managing <strong>the</strong>m correctly
D, 2- Issues related to applying incorrect approaches and strategies (route management)
- Lack <strong>of</strong> appropriate methods and approaches for executing knowledge management
- Not defining <strong>the</strong> key issues and subjects
- Insufficient familiarity with <strong>the</strong> processes
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The project team is responsible for managing <strong>the</strong> above mentioned risks. This team is consisted <strong>of</strong> 6
people and a consultant. This group classified <strong>the</strong> activities into ten steps and four sections: Risk
break graph, risk evaluation matrix, change control and corrective measures table.
10.1 Studying and analyzing failure factors <strong>of</strong> knowledge management projects in
Barez Industrial Group:
The main objective was to recognize failure factors and to classify <strong>the</strong>m according to priority, so that
corrective measure can be taken. After ga<strong>the</strong>ring information about failure factors <strong>of</strong> knowledge
management project using brain storming technique, 30 managers <strong>of</strong> <strong>the</strong> company were asked to fill
<strong>the</strong> attached questionnaire, and <strong>the</strong>y were separately scored using three indexes <strong>of</strong> severity,
occurrence, and discovery. The tables were designed according to <strong>the</strong> reference book and <strong>the</strong> project
consultants'' and journalists' opinion. After ga<strong>the</strong>ring opinions <strong>of</strong> <strong>the</strong> managers and o<strong>the</strong>r persons who
were involved in <strong>the</strong> knowledge management, a meeting was held in which <strong>the</strong> participants did <strong>the</strong>
scoring and agreed on determining <strong>the</strong> accurate numbers <strong>of</strong> risks using consensus method; using
<strong>the</strong>se numbers, a list <strong>of</strong> risks with high scores was provided, so that corrective measures can be taken
on <strong>the</strong> base <strong>of</strong> this list.
A-Providing risk break structure graph:
The tree graph shows <strong>the</strong> causes <strong>of</strong> knowledge management projects failure in Barez industrial
group. In order to provide this graph, several meetings were held by brain storming method.
B-Providing evaluation matrix:
Creating <strong>the</strong> mentioned graph in section A, now we can compute FMEA criteria including severity,
occurrence probability and discovery, and also we can estimate <strong>the</strong>ir effect on <strong>the</strong> risk and <strong>the</strong>
accomplished controls.
Table 3: a sample <strong>of</strong> evaluation matrix for knowledge management risks in Barez Industrial Group
In RBS (risk break structure) column, main risks, which have been deducted from <strong>the</strong> brain-storm
meeting, are presented. Potential risk column is related to <strong>the</strong> minor risks which are placed at <strong>the</strong>
subgroups <strong>of</strong> <strong>the</strong> main risks.
Risk potential effects column shows <strong>the</strong> risk positive or negative effect on <strong>the</strong> project goals.
Severity column (S) depends on <strong>the</strong> risk severity rate which, according to <strong>the</strong> attached standard table,
can be a number between one and ten. Risk potential causes column shows some reasons which can
cause risk in <strong>the</strong> project. Occurrence column shows <strong>the</strong> probability <strong>of</strong> risk occurrence in <strong>the</strong> project,
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that it can be a number between one and ten. In control column, some controls are shown which shall
be done during <strong>the</strong> project in order to control <strong>the</strong> risk. The project control plan can be concluded
from this section <strong>of</strong> <strong>the</strong> model. Project control plan is a new topic in <strong>the</strong> project that we would speak
about it in <strong>the</strong> next research. In this column <strong>the</strong> effectiveness <strong>of</strong> risk and risk causes recognizing
methods shall be evaluated. Column D refers to discovery. This column shows <strong>the</strong> probability <strong>of</strong>
recognizing risk or risk causes. In this column one stands for certainly recognizable and ten stands for
unrecognizable. In RPN column, risk priority number is product <strong>of</strong> <strong>the</strong> three said criteria: severity
number, occurrence number and discovery number. In every project <strong>the</strong> acceptable level <strong>of</strong> RPN must
be determined. This level shall be defined in <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> project by <strong>the</strong> project supporter and
team members.
C- List <strong>of</strong> recommended measures:
Having <strong>the</strong> priority number, now we can present risk strategies and list <strong>of</strong> recommended measures
and actions. In <strong>the</strong> following table a sample <strong>of</strong> taken measures is shown.
Risk potential
causes
Management's lack
<strong>of</strong> familiarity with
knowledge
management
Personnel's lack <strong>of</strong>
cooperation with <strong>the</strong>
project
Project managers'
lack <strong>of</strong> pr<strong>of</strong>iciency in
planning
Lack <strong>of</strong> training
RPN
576
504
320
210
Risk strategy
Risk decreasing
Risk omitting
Risk decreasing
Risk decreasing
Recommended measure
Seminar <strong>of</strong> familiarity with
knowledge management,
justification <strong>of</strong> usefulness <strong>of</strong>
knowledge management for <strong>the</strong>
organization
Cultural bedding
Project management training
Scientific training <strong>of</strong> knowledge
management concepts
Table 3: a sample <strong>of</strong> recommended measures, considering risk-responding strategies (concluded
from <strong>the</strong> main list)
In <strong>the</strong> risks causes column, risks are arranged according to <strong>the</strong>ir priority number. In risk-responding
column, we decide to react against <strong>the</strong> risk considering <strong>the</strong> opinions <strong>of</strong> project supporters and <strong>the</strong>
organization superior management. These strategies have been concluded from PMBOK, and have
been allocated to any risk by <strong>the</strong> management. Also, in <strong>the</strong> recommended measure column some
measures are defined by <strong>the</strong> knowledge management project team, considering <strong>the</strong> risk priority
number and risk-responding strategy. However, we shall not be misled by RPN, and we shall not pay
extra attention to those risks which do not influence <strong>the</strong> project goals. In order to prevent this mistake,
we should consider <strong>the</strong> following two issues:
Giving priority to <strong>the</strong> risks with severities equal to or larger than 9, and studying and evaluating
<strong>the</strong>se risks in order to prevent <strong>the</strong>m
Considering and paying attention to <strong>the</strong> risks with both high severity and high occurrence
probability while deciding on <strong>the</strong> recommended measures
Choosing and deciding on <strong>the</strong> best recommended measure, which can prevent risk with <strong>the</strong> least
expense and minimize its consequences, are completely depended on how <strong>the</strong> team evaluates and
interprets <strong>the</strong> findings resulted from <strong>the</strong> evaluation matrix.
D- Change demand:
Considering <strong>the</strong> defined recommended measure, some important and critical changes may take place
beyond <strong>the</strong> responsibilities and tasks <strong>of</strong> <strong>the</strong> project team members, including cancellation <strong>of</strong> contract
with <strong>the</strong> consultant, increasing <strong>the</strong> approved budget and increasing <strong>the</strong> project time. In this stage,
after taking <strong>the</strong> measures, we shall compute <strong>the</strong> new risk priority number (RPN), and compare it with
<strong>the</strong> previous RPN. The following graph is Pareto graph <strong>of</strong> <strong>the</strong> project management risks which are
included in <strong>the</strong> graph according <strong>the</strong> risk importance.
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Figure 1.0 Pareto graph <strong>of</strong> risks priority number <strong>of</strong> Barez Industrial Group knowledge management:
11. Limitations <strong>of</strong> <strong>the</strong> research
During this research we faced several limitations and problems including lack <strong>of</strong> accurate and clear
understanding <strong>of</strong> knowledge management on <strong>the</strong> behalf <strong>of</strong> <strong>the</strong> organization superior managers. The
organization superior managers didn’t have an accurate understanding <strong>of</strong> knowledge management
and <strong>the</strong> project management. O<strong>the</strong>r problems were lack <strong>of</strong> tolerance and enough time for completing
<strong>the</strong> designed questionnaires and participating in <strong>the</strong> brain storming meetings. Also one <strong>of</strong> <strong>the</strong>
limitations we faced with during project was competition with o<strong>the</strong>r projects such as EFQM project
which consumed most <strong>of</strong> <strong>the</strong> organization managers' time. This study focuses on Knowledge
management, a new technology in <strong>the</strong> Information Technology field. Research is required whe<strong>the</strong>r <strong>the</strong>
results <strong>of</strong> this research could be generalized to o<strong>the</strong>r companies around <strong>the</strong> globe or a specific region.
12. Conclusion
Today, managers have fully realized <strong>the</strong> importance <strong>of</strong> knowledge and knowledge management in <strong>the</strong>
organizations, and most <strong>of</strong> <strong>the</strong>m are looking for executing <strong>the</strong> knowledge management system in <strong>the</strong>ir
organizations. However, <strong>the</strong>y worry that <strong>the</strong>y can not execute <strong>the</strong> said system in <strong>the</strong>ir organization
and <strong>the</strong>refore knowledge management fails in <strong>the</strong>ir organization. Analyzing, classifying and being
aware <strong>of</strong> <strong>the</strong> most important effective factors in failure <strong>of</strong> knowledge management projects in <strong>the</strong>
organizations would remarkably influence manner <strong>of</strong> facing, designing and carrying out knowledge
management projects. Since, according to <strong>the</strong> majority <strong>of</strong> researchers, knowledge management in
today's world is an issue which all organization are forced to use it in order to survive, <strong>the</strong>refore
research on <strong>the</strong> ground <strong>of</strong> causes <strong>of</strong> failure and risks in knowledge management projects seems
necessary. By being aware <strong>of</strong> <strong>the</strong> main causes <strong>of</strong> failure in knowledge management projects, <strong>the</strong> rate
<strong>of</strong> failure would be decreased to a high degree. Therefore, in this research, first we reviewed risk
process and use <strong>of</strong> risk management techniques such as FMEA and PMBOK, and <strong>the</strong>n, by
considering <strong>the</strong> researchers' opinions in this ground, using <strong>the</strong> concept <strong>of</strong> knowledge management
and considering <strong>the</strong> live document <strong>of</strong> FMEA, we took measures to provide a model for evaluating,
controlling and supervising knowledge management risks. In <strong>the</strong> presented model in this research,
most <strong>of</strong> requirements <strong>of</strong> FMEA, PMBOK and knowledge management have been covered and met. In
this research, while studying knowledge management throughly, failure causes <strong>of</strong> knowledge
management project <strong>of</strong> Barez Industrial Group were analyzed. Studying <strong>the</strong> results showed that lack
<strong>of</strong> support and commitment on <strong>the</strong> behalf <strong>of</strong> <strong>the</strong> superior management, incorrect arrangement and
improper selection <strong>of</strong> knowledge management team members, inappropriate planning and predicting
for executing <strong>the</strong> project, not allocating separate budget to <strong>the</strong> knowledge management project,
organizational culture, lack <strong>of</strong> cooperation on <strong>the</strong> behalf <strong>of</strong> <strong>the</strong> employees , and <strong>the</strong> organizational
resistance against change were <strong>the</strong> main failure causes <strong>of</strong> knowledge management project.
13. Future research
Considering that <strong>the</strong> designed model is a live document for any project and it shall be considered as a
holy book <strong>of</strong> <strong>the</strong> project, in <strong>the</strong> next research we would look for a new model <strong>of</strong> risk control plan, so
that, using <strong>the</strong> findings resulted from <strong>the</strong> presented FMEA-PMBOK model in this research, we may
control risks <strong>of</strong> knowledge management projects second by second. RCP model would be able to
design a set <strong>of</strong> actions and measures for placing <strong>the</strong> project risks in an acceptable level.
823

References
Lila Rajabion and Jafar Zanganeh
Artto K., Davies A., Kujala J., and Prencipe A. (2011). ”The project business: Analytical framework and research
opportunities,” in The Oxford handbook <strong>of</strong> project management, ed. Morris P. W. G., Pinto J. and Söderlund
J., 133-153. New York: Oxford University Press.Beckman, T. (1998). Knowledge management: a technical
review. GWU Working Paper.
Carbone, Thomas, and Tippett, Donald D. (2004) “ Project Risk Management Using <strong>the</strong> Project Risk FMEA”,
Engineering Management journal,16:4 pp28-35
Eisenhardt, K.M. (1989), “Building from case study research” in Academy <strong>of</strong> Management Review, Volume 14,
Number 4.
Flavio, Roberto Souza Santos, Sandro, Cabrol. (2008) “ FMEA And PMBOK Applied to Project Risk
Management”, Journal <strong>of</strong> Information Systems and Technology Management ,5:2,pp347-364
Greenfield, Michael A.(2000) ” Risk Management Tools ”, NASA ,Langley Research Center,U.S.A
Ghodsipour,Hasan.(2002) “AHP; Analytical Hierachy Process”,AmirKabir University Press
Malhotra, Y.(2001), Expert Systems for Knowledge Management: Crossing <strong>the</strong> Chasm Between Information
Processing and Sense Making, Expert Systems With Applications: An International Journal, 20(1), 7-16,
Peltonen, T., Kiiras, J. (1999). Development <strong>of</strong> Project Management (PMI) based delivery systems for
construction market in Findland. Espoo: Helsinki University <strong>of</strong> Technology , Construction Economics and
Management. Publication No.183
Project Management Institute(PMI). (2008) “A guide to <strong>the</strong> Project Mamagement Body Of Knowledge(PMBOK
2008).
Rezaei, Kamran. (2003) “FMEA ;Failure Mode and Effect Analysis”,RWTUV IRAN.
Shafer, Don. (2004) “S<strong>of</strong>tware Risk: Why must We Keep Learning from experience?”, Dynamic Positioning
<strong>Conference</strong>,2004
US MILITARY STANDARD, MIL-STD1629A, (1980) Procedure for Performing Failure Mode, Effect and Criticality
Analysis, Department <strong>of</strong> Defense, USA.
Vincent, David W, Honeck, Bill. (2004). "Risk Management Analysis Techniques For Validation Programs”,
Journal <strong>of</strong> Validation Technology, 10:3, pp235-250
Wiig, K. (1993). Knowledge Management Foundations: Thinking about Thinking – How Organizations Create,
Represent and Use Knowledge.
Yin, R. K. (1984). Case study research: Design and methods. Newbury Park, CA: Sage.
824

The Business Group in a Systemic Perspective
Anna Maria Costanza Rinaldi
DSSM, Faculty <strong>of</strong> Law, University <strong>of</strong> Bari, Italy
amc.rinaldi@gmail.com
Abstract: In Italy business groups are <strong>the</strong> most significant phenomenon <strong>of</strong> <strong>the</strong> industrial dynamics over <strong>the</strong> last
decade (Cainelli and Iacobucci, 2011). Business groups are also very common in many o<strong>the</strong>r countries, as many
studies about Europe and Asia show (Almeida and Wolfenzon, 2006; Cayssialis and Peter, 2007; Dow et al.,
2011; Almeida et al., 2011). In <strong>the</strong> literature business groups have been interpreted using a financial or
organizational approach (Rinaldi, 2010). Although <strong>the</strong>re are many studies on <strong>the</strong> subject, non-financial groups,
and in particular manufacturing ones, still lack an adequate etiological explanation. This article represents a first
attempt to propose a <strong>the</strong>ory <strong>of</strong> <strong>the</strong> group aimed to overcome this gap, interpreting <strong>the</strong> manufacturing group in a
systemic perspective (Potts, 2000; Romagnoli, 1996), which has its core concept in <strong>the</strong> production function. The
aim <strong>of</strong> this work is to provide an explanation <strong>of</strong> why and how <strong>the</strong> group is an organizational form that is more
efficient than <strong>the</strong> U-form. In fact, manufacturing groups are not created because <strong>of</strong> financial reasons, since <strong>the</strong>
property is in <strong>the</strong> hands <strong>of</strong> <strong>the</strong> person that also exercises control. So it is appropriate to investigate which kind <strong>of</strong>
advantages this organization <strong>of</strong>fers. The acquisition <strong>of</strong> knowledge and learning processes are crucial to explain
organizational boundaries and competitiveness. In <strong>the</strong> specific case <strong>of</strong> <strong>the</strong> groups belonging to <strong>the</strong> manufacturing
sector it can be assumed that this organizational architecture has <strong>the</strong> most pr<strong>of</strong>ound motivation in <strong>the</strong> production
logic, understood as competence-based. Given <strong>the</strong> general macroeconomic instability, <strong>the</strong> crucial factor
influencing <strong>the</strong> choice <strong>of</strong> <strong>the</strong> organization <strong>of</strong> <strong>the</strong> firm is <strong>the</strong> generation, acquisition and management <strong>of</strong> tacit and
explicit knowledge. The various cognitive <strong>the</strong>ories <strong>of</strong> <strong>the</strong> firm have focused on capabilities and innovation, but it is
<strong>the</strong> ‘competence <strong>the</strong>ory’ that allows to focus on <strong>the</strong> production function. Considering <strong>the</strong> choice to organize <strong>the</strong>
firm in <strong>the</strong> H-form driven by <strong>the</strong> production means also that <strong>the</strong> organization <strong>of</strong> production affects <strong>the</strong> strategic
and organizational decisions <strong>of</strong> <strong>the</strong> enterprise. In <strong>the</strong> neoclassical perspective <strong>the</strong> verification <strong>of</strong> this hypo<strong>the</strong>sis
would be difficult, given <strong>the</strong> allocative focus <strong>of</strong> this <strong>the</strong>ory, aimed mainly at finding equilibrium price. Instead, one
can verify this proposition in a cognitive perspective: conceiving <strong>the</strong> firm as a knowledge creating system, which
has as its primary goal <strong>the</strong> transformation <strong>of</strong> competence (Foss and Knudsen, 1996), namely <strong>the</strong> ability to create
fur<strong>the</strong>r connections, among which <strong>the</strong>re is <strong>the</strong> transformation <strong>of</strong> inputs into outputs.
Keywords: production, organization, knowledge
1. Introduction
There have been several definitions <strong>of</strong> business group in economy and sociology, and <strong>the</strong> criteria
used to mark <strong>the</strong> boundaries greatly differ based on <strong>the</strong> Country analyzed and <strong>the</strong> researchers
conducted.
All definitions refer to <strong>the</strong> plurality <strong>of</strong> legal entities forming <strong>the</strong> group and to <strong>the</strong> existing ties between
<strong>the</strong>m that must own <strong>the</strong> characteristic <strong>of</strong> stability. In most countries, such membership is never<strong>the</strong>less
informal or not controlled by a single subject. In fact, in some countries <strong>the</strong> group is regarded like an
association between firms. Examples <strong>of</strong> such groups are Japanese keiretsu (Dow et al., 2011) or
Korean chaebol (Almeida et al., 2011).
Two are <strong>the</strong> approaches to groups in <strong>the</strong> literature: financial and organizational (Rinaldi 2010). Both
approaches regard <strong>the</strong> group as a reaction to underdevelopment in institutions and markets,
especially capital markets (Khanna and Palepu, 1997; Khanna, 2000). The financial approach
considers <strong>the</strong> group just a simple financial device and mainly investigates large and pyramidal groups,
which carry into effect <strong>the</strong> subdivision between property and control to maximize <strong>the</strong> invested capital
and control a broader range <strong>of</strong> activities (Brioschi et al., 1990). This literature investigates mainly
phenomena such as equity leverage (Barca et al., 1994) and tunneling (Johnson et al., 2000) and <strong>the</strong>
conflicts that can arise between majority and minority shareholders, and third parties. Groups are
viewed as a device to separate control rights from cash flow rights that are in common with <strong>the</strong> noncontrolling
owners (Morck et al., 2005). This mechanism is obviously more effective for groups listed
on stock exchange. This explanation does not justify <strong>the</strong> presence <strong>of</strong> family owned small groups –
such as those covered by <strong>the</strong> empirical research <strong>of</strong> <strong>the</strong> present paper- where <strong>the</strong>re is not separation
between family ownership and control. The second approach interprets <strong>the</strong> group not only as a simple
financial mechanism but also as an answer to an organizational issue <strong>of</strong> <strong>the</strong> firm. This trend can be
traced back to Chandler's seminal studies (1962, 1977, 1982, 1990) on firms in capitalist countries
and Williamson (1975, 1985) on multidivisional firms, and considers <strong>the</strong> group as an organizational
form that allows transaction costs reductions (Khanna and Rivkin, 2001), ensuring relationships
stability (e.g. Chang and Choi, 1988; Kester, 1992). According to this approach it is only possible to
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state that a group is more efficient than <strong>the</strong> market (Khanna and Palepu, 1997), while it is not
comparable to <strong>the</strong> efficiency <strong>of</strong> an U-firm or a M-firm, as first stated by Chandler (1982).
Chandler compares <strong>the</strong> group with <strong>the</strong> M-form and determines that both organizations were born from
<strong>the</strong> same need in Europe and in <strong>the</strong> United States: to efficiently control diversified activities. The
author believes <strong>the</strong> main difference between <strong>the</strong> two is that <strong>the</strong> group is based on contractual
relationships between units formally independent, while <strong>the</strong> M-form is based on administrative
relationships. Chandler also believes <strong>the</strong> administrative coordination typical <strong>of</strong> <strong>the</strong> multidivisional
enterprise to be more efficient for <strong>the</strong> control <strong>of</strong> diversified activities, for <strong>the</strong> allocation <strong>of</strong> financial
resources and to take advantage <strong>of</strong> single economic resources. Chandler also maintains that <strong>the</strong>
persistence <strong>of</strong> groups in Europe in adopting <strong>the</strong> M-form is to be attributed to <strong>the</strong> persistence <strong>of</strong>
families' control in large enterprises and to <strong>the</strong> relative underdevelopment <strong>of</strong> <strong>European</strong> capital
markets.
Williamson (1985, 1986) confirms Chandler's idea, which states that <strong>the</strong> group is an embryonic form
<strong>of</strong> multidivisional firm. According to Williamson, <strong>the</strong> M-form main characteristic consists on <strong>the</strong>
subdivision between strategic and operating responsibilities, which allows <strong>the</strong> enterprise to more
efficiently manage <strong>the</strong> activities. The group represents an improvement in allocating financial
resources but lacks some characteristics typical <strong>of</strong> <strong>the</strong> M-forms. The worst case <strong>of</strong> subdivision <strong>of</strong> <strong>the</strong>
H-form (Williamson, 1986) is represented by <strong>the</strong> general management essentially used as an <strong>of</strong>fice
for earnings collection and <strong>the</strong> holding company acting as a risk-pooling agency. For <strong>the</strong>se reasons,
<strong>the</strong> group could generate inefficiencies in strategic and operating processes and in <strong>the</strong> internal capital
market. In <strong>the</strong> end, in Williamson's description, <strong>the</strong> group turns out as a segmented firm, where <strong>the</strong>
weakness <strong>of</strong> general management can never generate <strong>the</strong> inherent advantages belonging to <strong>the</strong>
<strong>the</strong>oretical model <strong>of</strong> <strong>the</strong> M-form.
Instead, in this paper one maintains that <strong>the</strong> group can be considered as an efficient organizational
form also with respect to <strong>the</strong> unitary firm, in a competence-based view. Therefore, it is necessary to
expand <strong>the</strong> meaning <strong>of</strong> <strong>the</strong> production function. We can state that <strong>the</strong> group arises as an answer to a
primary need <strong>of</strong> <strong>the</strong> firm to create competences, specializing its units that will handle single functions.
Therefore, it is necessary to take a systemic outlook and propose that <strong>the</strong> production function
performs a decisive role in <strong>the</strong> choice <strong>of</strong> this organizational form. This hypo<strong>the</strong>sis requires fur<strong>the</strong>r
specifications. We want to verify if <strong>the</strong> group allows to fulfill <strong>the</strong> tasks subdivision between different
functions and <strong>the</strong> possible phases <strong>of</strong> <strong>the</strong> production process. The tasks subdivision causes an
increased specialization <strong>of</strong> <strong>the</strong> elements involved in <strong>the</strong> process and allows <strong>the</strong> analytical review <strong>of</strong>
<strong>the</strong> different functions and phases <strong>of</strong> <strong>the</strong> production process and an easier and faster acquisition <strong>of</strong>
<strong>the</strong> affiliated units required, discarding <strong>the</strong> inefficient ones.
This might explain why in Italy <strong>the</strong> group phenomenon concerns mainly small and medium firms,
where <strong>the</strong> property is handled by <strong>the</strong> same person that exercises control and represents <strong>the</strong> typical
form <strong>of</strong> firm during <strong>the</strong> size expansion phase (Rosa and Scott 1999) or ra<strong>the</strong>r is a common way to
grow firm, fostering <strong>the</strong> entrepreneurial team (Iacobucci and Rosa, 2010). In fact, in 2008 in Italy,
apart from agricultural sector, <strong>the</strong>re are more than 76,000 groups, mostly small size: <strong>the</strong> 64% has
from 1 to 19 employees, <strong>the</strong> 66,5% from 20 to 99, <strong>the</strong> 7.7% from 100 to 499, while only <strong>the</strong> 1.7% from
500 to 4,999 and <strong>the</strong> 0.1% more than 5,000 employees (ISTAT 2010).
2. The production function from a systemic perspective: <strong>the</strong> cognitive
efficiency
The nexus among production function, manufacturing process and juridical/organizational
choices has never been investigated. The production function is central for <strong>the</strong> organizational choices
<strong>of</strong> <strong>the</strong> firm but this has been neglected for too long, while it could have opened <strong>the</strong> road to interesting
outcomes.
The economic <strong>the</strong>ory developed at least three approaches to <strong>the</strong> study <strong>of</strong> production: <strong>the</strong> mainstream
production function by Walras and Wicksteed; <strong>the</strong> linear approach based on <strong>the</strong> activities analysis by
Koopmans; and <strong>the</strong> funds-flows model approach <strong>of</strong> Georgescu-Roegen. The first <strong>of</strong> <strong>the</strong>se <strong>the</strong>oretical
analysis starts where <strong>the</strong> second one ends: <strong>the</strong> mainstream production function presumes <strong>the</strong>
problem solution by <strong>the</strong> efficient combination <strong>of</strong> elementary processes where it is possible to divide
<strong>the</strong> general production process <strong>of</strong> a certain commodity (Zamagni, 1985). Georgescu-Roegen's
approach is <strong>the</strong> only one that gets, even if partially, <strong>the</strong> link between production and organization. This
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Anna Maria Costanza Rinaldi
gap in <strong>the</strong> economic <strong>the</strong>ory hinders especially <strong>the</strong> contributions <strong>of</strong> successive authors, because we
missed <strong>the</strong> chance to consider <strong>the</strong> connection, inseparable in real life, between production and
organizational choices and to make efficiency evaluations from this combined perspective.
Mainstream economists have fundamentally ignored <strong>the</strong> aspects related to <strong>the</strong> nature <strong>of</strong> <strong>the</strong> firm and
its boundaries, preferring instead a <strong>the</strong>ory <strong>of</strong> production functions ra<strong>the</strong>r than a <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm
(Marshall, 1987). On <strong>the</strong> opposite, in heterodox <strong>the</strong>ories, <strong>the</strong> firm plays a central role, giving value to
<strong>the</strong> structure itself (among o<strong>the</strong>rs: Coase, 1937; Penrose, 1959; Simon, 1959 and 1965; Cyert and
March, 1963; Alchian and Demsetz, 1972; Williamson, 1975; Nelson and Winter, 1982; Richardson,
1959 and 1972; Foss and Knudsen, 1996). The firm, from a cognitive perspective, can be defined as
a production system that coordinates information flows and creates knowledge (Potts, 2000). If we
define competences as <strong>the</strong> sum <strong>of</strong> knowledge created by <strong>the</strong> mean <strong>of</strong> a specific combination <strong>of</strong>
agents, it becomes <strong>the</strong> central element <strong>of</strong> <strong>the</strong> production system and, as a consequence, <strong>of</strong> <strong>the</strong> firm.
Foss (1996) defines competences as idiosyncratic knowledge which allows whomever has it to carry
out tasks, and particularly to solve problems in certain ways, and especially, more efficiently than
o<strong>the</strong>rs. Competence endowment is what defines <strong>the</strong> boundaries <strong>of</strong> <strong>the</strong> firm and <strong>the</strong> degree <strong>of</strong>
diversification. Potts (2000) states that if competences are <strong>the</strong> most important asset <strong>of</strong> <strong>the</strong> firm to
acquire and use strategically, firms are production stock <strong>of</strong> knowledge capital, <strong>of</strong> abilities to perform
activities with different degrees <strong>of</strong> efficiency. As a consequence, <strong>the</strong> competence-based approach
does not follow <strong>the</strong> logic <strong>of</strong> production function, but it follows <strong>the</strong> firm logic, seen in a systemic
perspective. So, competences are key elements that allow <strong>the</strong> firm to transform inputs in outputs, with
variable degrees <strong>of</strong> efficiency, depending on <strong>the</strong>ir development and combination. In fact, according to
Potts (2000, p. 139):
“Competence […] is <strong>the</strong> working (self-)expression <strong>of</strong> knowledge; it is knowledge that has
acted upon itself to reproduce itself. Knowledge builds competence and competence
builds fur<strong>the</strong>r knowledge”;
and (ibidem, p. 75) :
“It is not difficult to conceive <strong>of</strong> a firm as a system, in <strong>the</strong> sense that it is a set <strong>of</strong>
component parts interacting in specific ways. Within any firm, individual actions are
guided by <strong>the</strong> systematic specification <strong>of</strong> subordinate and superordinate relations, as
pertaining to departments, teams, divisions, and so forth, and by o<strong>the</strong>r forms <strong>of</strong> relation
external to <strong>the</strong> firm, such as interactions with consumers, suppliers and contractors. A
firm as a species <strong>of</strong> organization is obviously a species <strong>of</strong> system.”
For this reason, <strong>the</strong> firm constitutes an organization, and <strong>the</strong>refore a system, and like every system it
is a set formed by two categories <strong>of</strong> parts: elements and connections; and <strong>the</strong> organization-firm is
constituted internally by relationships and externally by contracts. Thus, we should recall some
<strong>the</strong>oretical contributions that, even if sporadic and at different times, considered <strong>the</strong> enterprise in a
systemic perspective. One can start from <strong>the</strong> general definition <strong>of</strong> a system (Rapoport, 1968, vol.15,
pag. 453):
“an entity composed by a set (finite or infinite) <strong>of</strong> interdependent elements, whose
relationships allow to infer o<strong>the</strong>r relationships or <strong>the</strong> system behavior or <strong>the</strong>
story”.Romagnoli (1996) also states:
“[...] Production processes can be analyzed as a production unit, a firm or a relational
system where <strong>the</strong> firm can be an element. In such a way, <strong>the</strong>re are no objective
boundaries and aggregates are differentiated economically and conceptually by <strong>the</strong>ir
function, and lead back to separate spheres <strong>of</strong> <strong>the</strong>oretical research, namely to <strong>the</strong>
production <strong>the</strong>ory in <strong>the</strong> first case, <strong>the</strong> <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm in <strong>the</strong> second case, <strong>the</strong> industrial
organization <strong>the</strong>ory in <strong>the</strong> third case [...]. Each systemic aggregates has its own operative
task to generate <strong>the</strong> creation process <strong>of</strong> a good, performed with an appropriated
organization, coordinated by a distinct subject with specific procedures, following his
objectives, and using his own criteria for economic efficiency.”
For this reason, if obtaining a certain output at <strong>the</strong> lowest cost possible, through <strong>the</strong> reduction <strong>of</strong> idle
times, is <strong>the</strong> objective <strong>of</strong> <strong>the</strong> technical unit <strong>of</strong> production, <strong>the</strong> objective <strong>of</strong> <strong>the</strong> firm is to create <strong>the</strong>
conditions for <strong>the</strong> survival <strong>of</strong> <strong>the</strong> production organization through <strong>the</strong> maximization <strong>of</strong> an efficiency
index, while <strong>the</strong> operating environment objective is exploiting <strong>the</strong> firm system output through
competitive and collaborative relationships. Therefore, <strong>the</strong> firm can be considered as a system where
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Anna Maria Costanza Rinaldi
single actions and decisions are guided internally by order relationships and externally by contractual
relationships.
To summarize, <strong>the</strong> enterprise can be considered like a production system <strong>of</strong> competences, that is, <strong>the</strong>
ability to create fur<strong>the</strong>r connections, where one aspect is to transform inputs into outputs.
The efficiency criteria from a systemic perspective cannot be anything different than <strong>the</strong>
“maximization” <strong>of</strong> <strong>the</strong> production <strong>of</strong> competences.
3. The empirical research.
3.1 The data set.
The empirical research is based on a regional data set from administrative source (InfoCamere, that is
an i.t. consortium <strong>of</strong> <strong>the</strong> Italian chambers <strong>of</strong> commerce) made up <strong>of</strong> 254 holding companies with
registered <strong>of</strong>fice in Puglia in 2004. Manufacturing groups <strong>of</strong> <strong>the</strong> list have been identified on <strong>the</strong> basis
<strong>of</strong> following alternative requisites: <strong>the</strong> existence <strong>of</strong> at least two production firms, one <strong>of</strong> which
operating in <strong>the</strong> manufacturing sector (Ateco 15-37); or <strong>the</strong> largest firm <strong>of</strong> <strong>the</strong> group, in terms <strong>of</strong>
employees, being a manufacturing firm (Cainelli and Iacobucci, 2007). The research has been
conducted, generally on <strong>the</strong> premises <strong>of</strong> <strong>the</strong> holding company, via a structured questionnaire during
direct interviews <strong>of</strong> all <strong>the</strong> 19 groups identified. The sample comprises groups vertically integrated
(47%) or, groups horizontally differentiated (34%). On <strong>the</strong> o<strong>the</strong>r hand, 14% present hybrid
characteristics between <strong>the</strong> two structures, where <strong>the</strong> group enterprises are only partial sub-suppliers
one <strong>of</strong> <strong>the</strong> o<strong>the</strong>r, and only for certain products series, directing <strong>the</strong> majority <strong>of</strong> <strong>the</strong>ir production to <strong>the</strong>
market. Only a small percentage <strong>of</strong> <strong>the</strong> groups analyzed (5%) can be classified as conglomerates,
whose units handle uncorrelated economic activities.
To contextualize <strong>the</strong> research results, it is preliminarily indispensable to restate <strong>the</strong> specificity <strong>of</strong> <strong>the</strong>
sample used. The typical group, <strong>the</strong> object <strong>of</strong> <strong>the</strong> empirical research is manufacturing, small or
medium sized, whose structure is mainly horizontal, vertically integrated or horizontally differentiated,
with family ownership. The sample is uniform and it is plausible to believe that <strong>the</strong>se results can be
verified also outside <strong>the</strong> spatial dimension that characterizes <strong>the</strong>m. Unlike what maintained by
Williamson (1985, 1986), none <strong>of</strong> <strong>the</strong> groups interviewed is lacking administrative control and
coordination.
3.2 The organization <strong>of</strong> production
The aim <strong>of</strong> <strong>the</strong> empirical research is to understand <strong>the</strong> organization <strong>of</strong> <strong>the</strong> group, starting from <strong>the</strong>
nature <strong>of</strong> <strong>the</strong> main production function. I needed to verify, and <strong>the</strong>n describe, from an economic
perspective, <strong>the</strong> relevance, if any, <strong>of</strong> <strong>the</strong> type <strong>of</strong> production process on <strong>the</strong> organization, including
juridical organization, <strong>of</strong> <strong>the</strong> firm.
Suppose <strong>the</strong> n-th manufacturing group comprises <strong>the</strong> holding (h) and two subsidiaries (a and b) and
that <strong>the</strong>y manufacture only one good, x, where <strong>the</strong> production process can divided into two phases. In
this instance we can determine a few hypo<strong>the</strong>sis:
The relevant production function is performed only by firm a, while h and b are responsible for o<strong>the</strong>r
functions, for example, administration, finance and procurement.
The production function is performed by enterprises a and b, where each performs one <strong>of</strong> <strong>the</strong> two
phases, while h is responsible for administration, finance and procurement.
Enterprises a and b both perform <strong>the</strong> entire production process, while h performs administration,
finance and procurement.
In fact it is interesting to note that in vertical integrated groups, who have responded to <strong>the</strong> structured
questionnaire, <strong>the</strong> number <strong>of</strong> enterprises equals <strong>the</strong> number <strong>of</strong> elementary processes in which it is
possible to subdivide <strong>the</strong> production process <strong>of</strong> <strong>the</strong> good.
The empirical evidence shows that if a possibility to break <strong>the</strong> process exists, <strong>the</strong> management will
create an autonomous legal entity, finding it convenient, in <strong>the</strong> terms described above.
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Anna Maria Costanza Rinaldi
Let us look at <strong>the</strong> i-th group example. The main economic activity <strong>of</strong> <strong>the</strong> group enterprise is footwear
manufacturing, which can be divided into three distinct phases: cut and sewing, upper preparation,
and final product assembly (Figure 1). Each phase corresponds to a legally separate enterprise, while
<strong>the</strong> holding performs procurement and logistics, as well as financial and administrative functions. In
figure 1 <strong>the</strong> solid line represents <strong>the</strong> production process latu senso that is knowledge that creates
fur<strong>the</strong>r knowledge, whose direction is shown. The functions <strong>of</strong> <strong>the</strong> holding company (finance,
administration, procurement and logistics) are auxiliary to <strong>the</strong> production process strictu senso, <strong>of</strong>
manufacturing footwear. The direction <strong>of</strong> funds and flows (Georgesu-Roegen, 1970) <strong>of</strong> production
related to <strong>the</strong> aforementioned functions is from <strong>the</strong> holding to <strong>the</strong> subsidiaries. In a restricted sense
funds and flows <strong>of</strong> <strong>the</strong> footwear production move from <strong>the</strong> holding, that buys raw materials to
subsidiary n. 1, that is in charge <strong>of</strong> cut and sewing; from subsidiary n.1 to subsidiary n.2 that works on
upper preparation; from subsidiaries 1 and 2 to subsidiary n. 3, that assembles <strong>the</strong> finished product;
and from subsidiary 3 to <strong>the</strong> holding that is in charge <strong>of</strong> <strong>the</strong> logistic function and deals with <strong>the</strong>
delivery <strong>of</strong> <strong>the</strong> finished product to <strong>the</strong> client, a well-known brand <strong>of</strong> <strong>the</strong> Italian fashion industry. The
flow <strong>of</strong> detailed information about <strong>the</strong> production process is from each subsidiary, individually, to <strong>the</strong>
holding. The decisions flow follows <strong>the</strong> opposite direction from <strong>the</strong> holding to each single subsidiary.
These flows must be seen in <strong>the</strong> dynamic perspective <strong>of</strong> an adaptive process, that decisions taken by
<strong>the</strong> holding company are corrected on <strong>the</strong> basis <strong>of</strong> information received by subsidiaries from time to
time. It is thus that <strong>the</strong> production process strictu senso becomes <strong>the</strong> production process latu senso,
ie knowledge creation, which begets o<strong>the</strong>r knowledge. It is interesting to note that this osmosis <strong>of</strong>
knowledge takes place among legally separate units. Also from a legal standpoint, <strong>the</strong> group
facilitates <strong>the</strong> process <strong>of</strong> knowledge creation and upgrading <strong>of</strong> production process.
The o<strong>the</strong>r examples below confirm <strong>the</strong>se findings. Let us consider <strong>the</strong> j-th group, whose main
economic activity consists in <strong>the</strong> production <strong>of</strong> medical gases. This legal architecture turns out to be a
direct consequence <strong>of</strong> <strong>the</strong> production process structure. In this case, <strong>the</strong> production process can be
subdivided into three phases defined as primary, secondary, and improper secondary following a
radial schema (Figure 2). In <strong>the</strong> first phase, <strong>the</strong> main plant, which corresponds to one <strong>of</strong> <strong>the</strong>
companies <strong>of</strong> <strong>the</strong> group, isolates oxygen from air. This company acts as <strong>the</strong> production fulcrum
serving all companies <strong>of</strong> <strong>the</strong> group responsible for <strong>the</strong> secondary production. In <strong>the</strong> second phase, in
more production sites, corresponding to equal legal entities, <strong>the</strong> oxygen is transformed from a liquid to
a gaseous state. In <strong>the</strong> third phase, <strong>the</strong> tanks are filled with gas and shipped on <strong>the</strong> market. The first
and second phase are linked by <strong>the</strong> transportation process, internal to <strong>the</strong> group, corresponding to
ano<strong>the</strong>r company, while <strong>the</strong> second and third phase are performed in <strong>the</strong> plant itself, and represents
<strong>the</strong> economical activity <strong>of</strong> <strong>the</strong> same company (legal). From this perspective, <strong>the</strong> group is a production
“muscle”, not a financial instrument, as demonstrated from <strong>the</strong> spreading horizontal structures. This is
consistent with Brioschi and Cainelli (2001) and with Iacobucci (2002) on small groups. As one can
observe in figure 2 <strong>the</strong> decisions flow goes from <strong>the</strong> holding company to each subsidiary; while
knowledge flow goes from all <strong>the</strong> subsidiaries to <strong>the</strong> holding company: in this way <strong>the</strong> holding returns
a fur<strong>the</strong>r flow <strong>of</strong> decisions correct and more adequate for production.
So far we have described two group structures, both horizontal and vertically integrated. As previously
stated, <strong>the</strong> subdivision in several companies <strong>of</strong> <strong>the</strong> group is based mainly on <strong>the</strong> production process
phases. When <strong>the</strong> legal and quantitative level <strong>of</strong> complexity <strong>of</strong> <strong>the</strong>se organizational structures
increases, <strong>the</strong> subdivision between subsidiaries is also based on functions.
As an example, let us consider <strong>the</strong> k-th, a vertical structure that manufactures lea<strong>the</strong>r furniture. At this
level, <strong>the</strong> group different subsidiaries will correspond to operative functions not necessarily <strong>of</strong> a
manufacturing nature. Opposite to <strong>the</strong> preceding examples, in <strong>the</strong> k-th group some subsidiaries will
perform <strong>the</strong> elementary processes derived from <strong>the</strong> subdivision <strong>of</strong> <strong>the</strong> group production function, while
o<strong>the</strong>rs will perform auxiliary operative functions, for example, sales and marketing (Figure 3).
Considering also different functions <strong>of</strong> <strong>the</strong> firm beside production, “caesuras” result convenient
whenever possible.
Therefore, <strong>the</strong> choice between vertical or horizontal group structure, and corresponding complexity,
will be determined by <strong>the</strong> dynamics relating to <strong>the</strong> third production subsystem, <strong>the</strong> operating
environment (Romagnoli, 1996).
Up to now we have discussed in detail vertically integrated groups.
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Anna Maria Costanza Rinaldi
With horizontally differentiated groups, is even clearer <strong>the</strong> need to keep each company separate as
<strong>the</strong>y handle differentiated activities.
In group p-th (Figure 4), formed by <strong>the</strong> holding and two subsidiaries, <strong>the</strong> subdivision on <strong>the</strong> legal
entities is made by economic activities and functions. In <strong>the</strong> specific case, it is not convenient to
separate <strong>the</strong> production process <strong>of</strong> <strong>the</strong> subsidiary that produces PVC granules. The o<strong>the</strong>r company
produces and sales an additive, which is a component <strong>of</strong> <strong>the</strong> PVC mix, and for this reason, subsidiary
2 is also <strong>the</strong> supplier <strong>of</strong> subsidiary 1.
Figure 1: Group i - Footwear
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Figure 2: Group J – Medical gases
Anna Maria Costanza Rinaldi
831

Figure 3 : Group k – Lea<strong>the</strong>r forniture
Anna Maria Costanza Rinaldi
832

Figure 4: Group P – Different productions
4. Conclusion
Anna Maria Costanza Rinaldi
The group institutionalizes <strong>the</strong> division between managerial and strategic functions and operative
functions, assigning <strong>the</strong> former to <strong>the</strong> holding and <strong>the</strong> latter to <strong>the</strong> subsidiaries. The need to organize
knowledge inside <strong>the</strong> firm, describes in a new way <strong>the</strong> economic activities organization thus allowing
top management to obtain <strong>the</strong> objectives proper <strong>of</strong> each one <strong>of</strong> <strong>the</strong> three subsystems (Romagnoli
1996). From a systemic perspective it is production own nature to determine <strong>the</strong> group organizational
criteria. The activities subdivision inside <strong>the</strong> group can be made by economic activity, by firm function
or by phase in <strong>the</strong> production process, if this can be sliced in more elementary processes
(Georgescu-Roegen, 1970). The firm (in a legal sense) in a group is outlined as an entity less and
less multifunctional and more and more specialized. The maximum level <strong>of</strong> specialization is realized in
those subsidiaries whose corporate purpose is exclusively <strong>the</strong> execution <strong>of</strong> certain phases <strong>of</strong> <strong>the</strong>
production process, where <strong>the</strong> firm subsystem objective coincides with <strong>the</strong> technical production unit
objective. If <strong>the</strong> manufacturing activity executed by <strong>the</strong> group is unique and splitting <strong>the</strong> production
process is possible, <strong>the</strong> subdivision <strong>of</strong> firm activity is made by process phases and by functions. If not
divisible, <strong>the</strong> subdivision by <strong>the</strong> different legal entities is made only by function. If multiple economic
activities are performed by <strong>the</strong> group, and <strong>the</strong> production process cannot be subdivided, we will have
an intercompany subdivision by functions. In <strong>the</strong> first two cases we will have a vertical integrated
group, in <strong>the</strong> second a differentiated horizontal group. Ei<strong>the</strong>r way, <strong>the</strong> break frequency between
phases and functions depends on <strong>the</strong> nature <strong>of</strong> <strong>the</strong> production process. The advantages deriving from
subdivision by phases and functions is <strong>the</strong> increased simplification and specialization <strong>of</strong> <strong>the</strong> elements
involved in <strong>the</strong> process, as well as <strong>the</strong> analytical control <strong>of</strong> <strong>the</strong> different production process functions
and phases that also allows for an easier and faster acquisition <strong>of</strong> subsidiaries required and discharge
<strong>of</strong> inefficient ones, on <strong>the</strong> basis <strong>of</strong> competences acquired by management. In fact, <strong>the</strong> top
management control function is extremely simplified by <strong>the</strong> group structure.
For such reasons, <strong>the</strong> group, from a cognitive perspective, represents a suitable architecture to ga<strong>the</strong>r
and manage information, because it creates a feedback system from periphery to core, and vice versa
and, as a consequence, ideal to generate competences. Therefore, <strong>the</strong> group can be configured as
an organism easily adaptable to internal needs and external incentive: on <strong>the</strong> basis <strong>of</strong> acquired
knowledge relating to inputs, costs, outputs and performances <strong>of</strong> its own units, it is easier to
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Anna Maria Costanza Rinaldi
determine <strong>the</strong> type <strong>of</strong> organization to create and act on it. Borrowing from <strong>the</strong> information technology
jargon, we can state that a group acts on <strong>the</strong> “divide et impera” principle.
In what appears to be <strong>the</strong> first development phase <strong>of</strong> this organizational form, <strong>the</strong> subdivision by
functions is minimal, while <strong>the</strong> one by phases is highest.
Is <strong>the</strong>refore possible to assume a sequential process <strong>of</strong> creation and evolution <strong>of</strong> groups, that leads
not in <strong>the</strong> direction indicated by Chandler (1982) and Williamson (1985) <strong>of</strong> transformation from group
to multidivisional enterprise but in ano<strong>the</strong>r: from an horizontal structure to a pyramidal structure,
where <strong>the</strong> first represents <strong>the</strong> prodromal stage <strong>of</strong> <strong>the</strong> second. Once <strong>the</strong> desirable levels <strong>of</strong> division <strong>of</strong>
labor are reached, <strong>the</strong> entrepreneur can opt for a wider organization articulation, as well as
“institutionalize” o<strong>the</strong>r functions, previously handled by <strong>the</strong> holding, or outsourced.
Therefore, <strong>the</strong> group institutionalizes such peculiar division <strong>of</strong> labor, by fixing it in separate legal
entities.
We initially assumed that <strong>the</strong> chosen efficiency criteria by <strong>the</strong> group was to rationalize <strong>the</strong> input and
output flow <strong>of</strong> knowledge, achieved by assigning an autonomous legal status to <strong>the</strong> different
enterprise functions and phases <strong>of</strong> <strong>the</strong> production process.
In this way, I want to emphasize that <strong>the</strong> group, as an organizational architecture <strong>of</strong> <strong>the</strong> firm, has a
fundamental advantage: <strong>the</strong> ability to produce competences (Potts, 2000), that is cognitive efficiency
as it has been named in this paper.
From this perspective, <strong>the</strong> small groups, as stated by Rosa and Scott (1999), can be looked at as <strong>the</strong>
physiological form <strong>of</strong> <strong>the</strong> expansion phase <strong>of</strong> <strong>the</strong> enterprise.
References
Alchian, A. and Demsetz, H. (1972) “Production, Information Costs and Economic Organization”, American
Economic Review, Vol 62, No 5, pp 772-795.
Almeida, H. and Wolfenzon, D. (2006) “A <strong>the</strong>ory <strong>of</strong> Pyramidal Ownership and Family Business Groups”, The
Journal <strong>of</strong> Finance, vol 61, No 6, pp 2637-2680.
Almeida, H., Park, S. Y., Subrahmanyam, M.G. and Wolfenzon, D. (2011) “The Structure and Formation <strong>of</strong>
Business Groups: Evidence from Korean Chaebols”, Journal <strong>of</strong> Financial Economics, Vol 99, pp. 447-475.
Barca, F., Bianco, M., Cannari, L., Cesari, C., Gola, C., Manitta, G., Salvo, G. and Signorini, L.F. (1994) Assetti
proprietari e mercato delle imprese, vol. I, Proprietà, modelli di controllo e riallocazione delle imprese
industriali italiane, Il Mulino, Bologna.
Brioschi, F., Buzzacchi, L. and Colombo, M.G. (1990) Gruppi di imprese e mercato finanziario. La struttura di
potere nell’industria italiana, La Nuova Italia Scientifica, Roma.
Brioschi, F. and Cainelli, G. (2001) Diffusione e Caratteristiche dei Gruppi di Piccole e Medie Imprese nelle Aree
Distrettuali, Milano, Giuffrè Editore.
Cainelli, G. and Iacobucci, D. (2007) Agglomeration Technology and Business Groups, Edward Elgar ,
Cheltenam (UK).
Cainelli, G. and Iacobucci, D. (2011) “Business Groups and <strong>the</strong> Boundaries <strong>of</strong> <strong>the</strong> Firm”, Management Decision,
Vol. 46 (forthcoming).
Cayssialis, J. L. K. and Peter, C. (2007), “Dix Années De Dynamique Financière Des Pme En France”, Banque
de France Bulletin, 165.
Chandler, A.D. (1962) Strategy and Structure: Chapters in <strong>the</strong> History <strong>of</strong> <strong>the</strong> Industrial Enterprise, MIT Press,
Cambridge.
Chandler, A. D. (1977) The Visible Hand: The Managerial Revolution in American Business, Harvard University
Press, Cambridge.
Chandler, A. D. (1982) “M-Form: Industrial Groups, American Style”, <strong>European</strong> Economic Review, Vol 19, No 1,
pp 3–23.
Chandler, A. D. (1990) Scale and Scope: The Dynamics <strong>of</strong> Industrial Capitalism, Harvard University Press,
Cambridge.
Chang, S. J. (2003) Financial Crisis and Transformation <strong>of</strong> Korean Business Groups, Cambridge University
Press, Cambridge.
Chang, S. J. and Choi, U. (1988) “Strategy, Structure and Performance <strong>of</strong> Korean Business Groups: A
Transactions Cost Approach”, Journal <strong>of</strong> Industrial Economics, Vol 37, No 2, pp 141–158.
Coase, R. (1937) “The Nature <strong>of</strong> <strong>the</strong> Firm”, Economica, vol 4(16), 386-405.
Cyert R., March J. (1963) A Behavioural Theory <strong>of</strong> <strong>the</strong> Firm, Prentice-Hall, Englewood Cliffs, NJ.
Dow, S., McGuire, J. and Joshikawa T. (2011) “Disaggregating <strong>the</strong> Group Effect: Vertical and Horizontal Keiretsu
in Changing Economic Periods”, Asia Pacific Journal <strong>of</strong> Management, Vol. 28, No 3, pp 299-323
Foss, N. and Knudsen, C. (1996) Toward a Competence Theory <strong>of</strong> <strong>the</strong> Firm, Routledge, London.
834

Anna Maria Costanza Rinaldi
Foss, N. (1996) The Emerging Competence Perspective, in Foss e Knudsen (eds.), Toward a Competence
Theory <strong>of</strong> <strong>the</strong> Firm, Routledge, London.
Georgescu-Roegen N., 1970, “The Economics <strong>of</strong> Production”, Richard T. Ely Lecture, American Economic
Review, Vol 40, pp 1-9.
Iacobucci, D. (2002) “Explaining Business Groups Started by Habitual Entrepreneurs in <strong>the</strong> Italian Manufacturing
Sector”, Entrepreneurship and Regional Development, Vol 14, No 1, pp 31-48.
Iacobucci, D. and Rosa, P. (2010), “The Growth <strong>of</strong> Business Groups by Habitual Entrepreneurs: The Role <strong>of</strong>
Entrepreneurial Teams”, Entrepreneurship Theory & Practice, Vol 34, No 2, pp 351-377.
ISTAT (2010), I Gruppi Di Imprese in Italia. Anno 2008, ISTAT, Roma.
Keister, L. A. (2000) Chinese Business Groups: The Structure and Impact <strong>of</strong> Interfirm Relations During Economic
Development, New York, Oxford University Press.
Johnson, S., La Porta, R., Lopez-de-Silanes, F. and Shleifer, A. (2000) “Tunneling” The American Economic
Review, Vol. 90, No. 2, pp 22-27.
Kester, W. C. (1992) “Industrial Groups as Systems <strong>of</strong> Contractual Governance”, Oxford Review <strong>of</strong> Economic
Policy, Vol 8, No 3, pp 24–44.
Khanna, T. and Palepu, K. (1997) “Why Focused Strategies May Be Wrong for Emerging Markets”, Harvard
Business Review , Vol 75, No 4, pp 41-51.
Khanna, T. (2000) “Business Groups and Social Welfare: Existing Evidence and Unanswered Questions”,
<strong>European</strong> Economic Review, Vol 44, pp 748-761.
Khanna, T. and Rivkin, J. W. (2001) “Estimating <strong>the</strong> Performance Effects <strong>of</strong> Business Groups in Emerging
Markets”, Strategic Management Journal, Vol 22, pp 45-74.
Marshall, A (1987) Principi di Economia, Utet, Torino.
Morck, R. and Poterba, J. M. (2005), “How to Eliminate Pyramidal Business Groups: The Double Taxation <strong>of</strong>
Intercorporate Dividends and O<strong>the</strong>r Incisive Uses <strong>of</strong> Tax Policy”, Tax Policy and <strong>the</strong> Economy, Vol 19,
Cambridge and London: MIT Press for <strong>the</strong> National Bureau <strong>of</strong> Economic Research, pp. 135-179.
Nelson, R. and Winter, S. (1982) An Evolutionary Theory <strong>of</strong> Economic Change, Harvard University Press,
Cambridge, MA.
Penrose, E. (1959) The Theory <strong>of</strong> <strong>the</strong> Growth <strong>of</strong> <strong>the</strong> Firm, Basil Blackwell, Oxford.
Potts, J. (2000) The New Evolutionary Microeconomics, Complexity, Competence and Adaptive Behavior,
Edward Elgar, Chaltenham (UK) - Northampton (USA).
Rapoport, A. (1968) “General System Theory”, International Encyclopedia <strong>of</strong> <strong>the</strong> Social Science, Vol.15, pp 452-
458, Macmillan Company and Free Press, London.
Richardson, G. (1959) “Equilibrium, Expectations and Information”, Economic Journal, (69) 223-37.
Rinaldi, A. (2010), La divisione del Lavoro Infra-Gruppo, Cacucci Editore, Bari.
Romagnoli, A. (1996) “Caratteri Generali dell’Approccio Analitico”, in Teoria dei Processi Produttivi. Uno Studio
sull’Unità Tecnica di Produzione, Giappichelli Editore,Torino.
Rosa, P., and Scott, M. (1999) “The Prevalence <strong>of</strong> Multiple Owners and Directors in <strong>the</strong> Sme Sector: Implications
for Our Understanding <strong>of</strong> Start-up” and Growth, Entrepreneurship and Regional Development, Vol 11, No 1,
pp 21–38.
Simon, H. (1959) “Theories <strong>of</strong> Decision-making in Economics and Behavioral Science”, American Economic
Review, Vol 49, No 1, pp 253-83.
Simon, H. (1965) Administrative Behaviour, The Free Press, New York.
Williamson, O. (1975) Market and Hierarchies: Analysis and Antitrust Implications, The Free Press, New York.
Williamson, O. (1985) The Economic Institutions <strong>of</strong> Capitalism: Firms, Markets, Relational Contracting, The Free
Press, New York.
Williamson, O. (1986) Economic Organization, Brighton, England, Wheatsheaf Publishing - it. ed:
L’Organizzazione Economica (1991) Il Mulino, Bologna.
Zamagni, S. (1985) Economia Politica, La Nuova Italia Scientifica, Roma.
835

Country Strategic Risk and Knowledge Management: A
Proposed Framework for Improvement
Eduardo Rodriguez 1 , John Edwards 2 and Angel Facundo 3
1
IQAnalytics, Canada
2
Aston Business School, UK
3
IQAnalytics, Colombia
eduardo.rodriguez@iqanalytics.com
j.s.edwards@aston.ac.uk
angel.facundo@gmail.com
Abstract: The world is in a period <strong>of</strong> reflection about social and economic models. In particular <strong>the</strong>re is a review
<strong>of</strong> <strong>the</strong> capacities that countries have for improving <strong>the</strong>ir competitiveness. The experiences in a society are part <strong>of</strong>
<strong>the</strong> process <strong>of</strong> learning and knowledge development in that society: especially in <strong>the</strong> development <strong>of</strong>
communities. Risks appear continually in <strong>the</strong> process <strong>of</strong> <strong>the</strong> search for, analysis and implementation <strong>of</strong> solutions
to problems. This paper discusses <strong>the</strong> issues related to <strong>the</strong> improvement <strong>of</strong> productivity and knowledge in a
society, <strong>the</strong> risk that poor or even declining productivity brings to <strong>the</strong> communities and <strong>the</strong> need to develop
people that support <strong>the</strong> decision making process in communities.The approach to improve <strong>the</strong> communities’
development is through <strong>the</strong> design <strong>of</strong> a research programme in knowledge management based on distance
learning. The research programme implementation is designed to provide value added to <strong>the</strong> decisions in
communities in order to use collective intelligence, solve collective problems and to achieve goals that support
local solutions. This program is organized and focused on four intelligence areas, artificial, collective, sentient
and strategic. These areas are productivity related and seek to reduce <strong>the</strong> risk <strong>of</strong> lack <strong>of</strong> competitiveness through
formal and integrated problem analysis. In a country such as Colombia, where different regions face varying
problems to solve and <strong>the</strong>re is a low level <strong>of</strong> infrastructure, <strong>the</strong> factors <strong>of</strong> production such as knowledge, skilled
labour and “s<strong>of</strong>t” infrastructure can be a way to develop <strong>the</strong> society. This entails using <strong>the</strong> local physical
resources adequately for creating value with <strong>the</strong> support <strong>of</strong> people in <strong>the</strong> region to lead <strong>the</strong> analysis and search
for solutions in <strong>the</strong> communities. The paper will describe <strong>the</strong> framework and programme and suggest how it could
be applied in Colombia.
Keywords: Knowledge management, risk management, strategic risk
1. Introduction
Knowledge is a factor <strong>of</strong> development in <strong>the</strong> so-called information and knowledge society that has
acquired a key value for <strong>the</strong> development <strong>of</strong> individuals and society. During <strong>the</strong> last two decades a
rapid transformation <strong>of</strong> global institutional and productive structures has taken place, largely related to
knowledge and its technological applications.
The digital technologies <strong>of</strong> information and communication technology (ICT) <strong>of</strong>fer <strong>the</strong> possibilities <strong>of</strong>
accessing knowledge accumulated at <strong>the</strong> global level. They have permeated all institutional and
social areas, thus promoting changes in society’s functioning. These changes have generated
important impacts on <strong>the</strong> various institutions in society (Government, services, companies,
educational institutions, etc.), as well as in individuals and in communities (Zavadsks et al. 2010,
Christ<strong>of</strong>erson and Clark 2010, Auffret 2010, Jung-Hyup 2010).
Across <strong>the</strong> world, several universities <strong>of</strong>fer postgraduate qualifications (masters and doctorates),
providing centers <strong>of</strong> research in knowledge management (KM). Many magazines and journals also
specialize in <strong>the</strong> dissemination <strong>of</strong> research advances in this area, and <strong>the</strong>re are now even digital
portals promoting <strong>the</strong> KM discipline. The emphasis <strong>of</strong> most <strong>of</strong> <strong>the</strong>m is on KM as applied to business,
in <strong>the</strong> improvement <strong>of</strong> <strong>the</strong> competitiveness <strong>of</strong> enterprises. However, o<strong>the</strong>r strands <strong>of</strong> research are
relevant to our discussion, such as <strong>the</strong> design <strong>of</strong> technological tools and expert systems for multiple
fields <strong>of</strong> activity (economic, educational, social and Government); and <strong>the</strong> design <strong>of</strong> strategies to
create knowledge sharing means, through networks and open systems and collective knowledge.
In Colombia, KM has been recognized by COLCIENCIAS (The Government Science and Technology
body) (Davila and Malaver 2004) as a new area <strong>of</strong> scientific research. KM has begun to be <strong>the</strong> subject
<strong>of</strong> research, courses, publications, seminars, conferences and experience sharing. Some Colombian
universities have courses in KM and some have set up specialized centers (e.g. Center <strong>of</strong> Knowledge
Management and Innovation, Rosario University), research projects and publications on <strong>the</strong> subject.
836

Eduardo Rodriguez, John Edwards and Angel Facundo
However, <strong>the</strong>re is a lack <strong>of</strong> formal KM academic training programs in <strong>the</strong> country, with even less at <strong>the</strong>
research level.
As a result, an academic doctoral research program in KM has been designed. This program is
interdisciplinary and, as such, takes place in four streams <strong>of</strong> research and specialization: artificial
intelligence, collective intelligence; sentient intelligence, strategic intelligence.
The following sections will present: risk concepts, leverage on knowledge to mitigate risk, some
experience <strong>of</strong> KM applications and <strong>the</strong> conceptualization <strong>of</strong> a research program adapted to
Colombia’s socio-economic environment. The bases <strong>of</strong> <strong>the</strong> article come from <strong>the</strong> holistic and
integrated view <strong>of</strong> risk, as it is presented in <strong>the</strong> enterprise risk management field, applied to <strong>the</strong>
country with <strong>the</strong> support <strong>of</strong> <strong>the</strong> concepts that come from KM <strong>the</strong>ory.
2. Risk Concepts: <strong>the</strong> issue <strong>of</strong> country competitiveness
This section presents <strong>the</strong> bases <strong>of</strong> <strong>the</strong> analysis <strong>of</strong> country risk in terms <strong>of</strong> <strong>the</strong> holistic and integral view
<strong>of</strong> risk management (RM) embodied in <strong>the</strong> enterprise risk management concept. Risk is defined in this
paper as “<strong>the</strong> uncertainty about <strong>the</strong> world and uncertainty expressed by probabilities related to <strong>the</strong>
observable quantities (Performance Measures)” (Aven 2003). For managers <strong>the</strong> meaning <strong>of</strong> risk is
more related to <strong>the</strong> concept <strong>of</strong> loss and not a clear identification with <strong>the</strong> positive changes <strong>of</strong>
performance (Tanriverdi and Ruefli 2004). In this research risk is identified more with <strong>the</strong> study <strong>of</strong> <strong>the</strong>
variance <strong>of</strong> expected results conditioned to previous knowledge.
Different types <strong>of</strong> risk appear in a country: financial, natural and human contingencies. One <strong>of</strong> <strong>the</strong>
risks relates to <strong>the</strong> strategy or strategic risk. “Strategic Risk for an individual firm can be defined in
terms <strong>of</strong> <strong>the</strong> probability <strong>of</strong> losing rank position vis a vis <strong>the</strong> o<strong>the</strong>r firms in <strong>the</strong> reference set.” ( Collins
and Ruefli 1992, p.1709) In <strong>the</strong> context <strong>of</strong> a country, strategic risk is understood as <strong>the</strong> variance <strong>of</strong> <strong>the</strong>
competitiveness position that a country has. This risk is observed as a lack <strong>of</strong> competitiveness in a
country, reducing possibilities for its socio-economic development. Why is strategic risk so important?
Slywotzky and Drzik (2005) referring to business organizations pointed out that strategic risk “can
disrupt or even destroy your business.” (p.78) They define strategic risk as: ”<strong>the</strong> array <strong>of</strong> external
events and trends that devastate a company’s growth trajectory and shareholder value.” (p.80) This at
<strong>the</strong> country level is represented by <strong>the</strong> potential loss <strong>of</strong> growth <strong>of</strong> <strong>the</strong> country’s wealth and <strong>the</strong> citizens’
standard <strong>of</strong> living. Strategic risk can take different forms such as: reputation, industry margin
reductions, economic efficiency, environmental issues, energy issues, geographical hazards, threats
and terrorism, citizen-customer dissatisfaction, distribution <strong>of</strong> justice, and so on (Young and Hasler
2010; Gates 2006).
RM is considered important in <strong>the</strong> strategic management process (Meulbroek 2002) because an
integral view <strong>of</strong> risk management (Brown 2001; Froot et al. 1994; Banham 2004) has <strong>the</strong> capacity to
create value in order to develop a competitive advantage (Galloway and Funston 2000). The
competitive advantage <strong>of</strong> <strong>the</strong> industrial organization can be limited because <strong>of</strong> risk <strong>of</strong> potential losses,
caused by expansion, cultural pressures, reduced controls, communication <strong>of</strong> business values,
learning systems and concentration on information (Simmons 1999). From <strong>the</strong> country and region
perspective development can be limited because <strong>of</strong> <strong>the</strong> lack <strong>of</strong> capabilities to support <strong>the</strong> required
country competitive advantages contemplated in <strong>the</strong> Porter’s Diamond Model (Porter 1990) or
Malerba’s Sectoral Innovation Model (Malerba 2004). Porter’s model is led by <strong>the</strong> need to identify
conditions to support <strong>the</strong> level <strong>of</strong> competitiveness in a global economy.
The RM concept has evolved into Enterprise Risk Management (ERM). ERM has been defined
(Dickinson 2001) as “a systematic and integrated approach to <strong>the</strong> management <strong>of</strong> <strong>the</strong> total risks that a
company faces.” (p.360) ERM takes into consideration three elements in its holistic view: people
involved, processes and scope (Baran<strong>of</strong>f 2004).
Even though <strong>the</strong>se definitions are for enterprises, <strong>the</strong> principle <strong>of</strong> observing risk for <strong>the</strong> whole
organization is applicable to a state or a country as <strong>the</strong> ultimate social organizations.
In <strong>the</strong> global competitiveness index, Colombia appears in <strong>the</strong> 68th position worldwide, below Chile,
Costa Rica, Mexico, Brazil and Uruguay amongst Latin American countries (Economic Forum, 2010).
(Table 2-1 shows <strong>the</strong> top 20 countries plus Romania and Colombia)
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Eduardo Rodriguez, John Edwards and Angel Facundo
Country
2010 GCI
2010 GCI 2009 GCI
ranking
score ranking
Change
from
2009 to
GCI
2008–2009
▴
2010
Switzerland 1 5.63 1 0 2
Sweden 2 5.56 4 2 4
Singapore 3 5.48 3 0 5
United States 4 5.43 2 -2 1
Germany 5 5.39 7 2 7
Japan 6 5.37 8 2 9
Finland 7 5.37 6 -1 6
Ne<strong>the</strong>rlands 8 5.33 10 2 8
Denmark 9 5.32 5 -4 3
Canada 10 5.3 9 -1 10
Hong Kong SAR 11 5.27 11 0 11
United Kingdom 12 5.25 13 1 12
Taiwan, China 13 5.21 12 -1 17
Norway 14 5.14 14 0 15
France 15 5.13 16 1 16
Australia 16 5.11 15 -1 18
Qatar 17 5.1 22 5 26
Austria 18 5.09 17 -1 14
Belgium 19 5.07 18 -1 19
Luxembourg 20 5.05 21 1 25
Romania 67 4.16 64 -3 68
Colombia 68 4.14 69 1 74
Table 2-1 2010 GCI Countries position Top 20 plus Romania and Colombia (Source World Economic
Forum)
The improvement <strong>of</strong> competitiveness is a long journey, whose first step lies in <strong>the</strong> development <strong>of</strong> <strong>the</strong>
skills <strong>of</strong> <strong>the</strong> people to carry out <strong>the</strong>ir work and <strong>the</strong> ability to create value and wealth. The following
paragraphs identify what a benchmark should be for a country such as Colombia in order to develop
its productivity and competitiveness. It is perhaps significant that <strong>the</strong> indicators presented in <strong>the</strong> tables
are not available in <strong>the</strong> Colombia government statistics system; however, values for developed
countries provide a benchmark for Colombian improvement.
Investment must be large in terms <strong>of</strong> creating means to improve <strong>the</strong> current levels <strong>of</strong> productivity.
Colombia is at 58 th position in global network readiness (2010-2011) with 61st position in <strong>the</strong>
innovation and sophistication factor and 63rd position in <strong>the</strong> technological readiness index (World
Economic Forum) having been in better positions in 2002-2003 (Table 2-2) and with reducing
investment in innovation and technological development (Table 2-3). However, <strong>the</strong> most recent survey
<strong>of</strong> <strong>the</strong> Colombian Statistics department shows that people are using computers and technology in
considerable percentages (Table 2-4) 47% use computers and 40% <strong>the</strong> internet mainly for information
ga<strong>the</strong>ring (78.8%) and education and learning (62.2%).
Colombia Position in <strong>the</strong> world
2002‐2003 Indexes Position
Technology Index 58
Innovation Subindex 57
Technology Transfer Subindex
Source World Economic Forum
50
Table 2-2 Colombia position in 2002-2003 Technology and Innovation Indices (Source DANE
Colombia)
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Eduardo Rodriguez, John Edwards and Angel Facundo
Investment % GDP Innov.and Tech. development
2003 2004 2005 2006
1.00% 1.04% 0.70% 0.78%
Table 2-3 Colombia Investment in Innovation and Technology (Source DANE Colombia)
Information Technology use 2009
% Homes with computer 22,6
% Homes with Internet 15,0
%people use computer 47,6
%people use internet 40,1
%People use once per day 47,2
% Internetused for information ga<strong>the</strong>ring 78,8
%internet used for education and learning 62,2
Table 2-4 Use <strong>of</strong> technology by Colombian 2009 (Source DANE Colombia)
The agreement by <strong>the</strong> <strong>European</strong> Union to require minimum standards among countries in order to join
what is intended as “[t]he most dynamic knowledge based economy in <strong>the</strong> world…improve industrial
competitiveness, support growth and <strong>the</strong> creation <strong>of</strong> jobs and to help address key societal
challenges.” (Zavadskas et al. 2010, p. 415) that is an example <strong>of</strong> benchmarking that can be applied
to <strong>the</strong> Colombia case.
Table 2-5 shows investment in tangible and intangible assets. Countries such as Japan, Sweden,
United States and Canada exceed 4.5% <strong>of</strong> GDP in R&D. Investment in human and organizational
capital is high in <strong>the</strong> United States, Great Britain, Sweden and Finland.
Country
Machinery and
equipment
S<strong>of</strong>tware
and
databases
R&D and
o<strong>the</strong>r
intellectual
property
products
Brand equity,
firm specific
human capital,
organisational
capital
Slovak Republic 19.76 0.37 1.76 2.39
Italy 16.95 0.63 2.20 2.19
Czech Republic 16.22 0.72 2.78 2.95
Japan (2005) 15.26 2.23 6.07 2.81
Australia 13.22 0.77 2.21 2.89
Spain 11.86 0.79 2.78 1.91
Canada (2005) 11.58 1.03 4.99 3.76
Portugal (2005) 10.70 0.83 3.27 3.48
Austria 9.97 0.90 3.12 2.44
Sweden 9.69 1.98 5.48 4.44
France 8.88 1.41 3.18 3.30
Denmark 8.83 1.91 2.96 2.99
Germany 8.32 0.73 3.58 2.84
Finland 7.52 1.01 3.99 4.05
United States 7.52 1.43 4.73 5.86
United Kingdom 5.92 1.42 2.90 5.36
Table 2-5 Intangible and fixed asset investment (Source OECD 2010)
However, not all investments have similar returns. For example, not all organizations need to have a
department <strong>of</strong> research and development; but companies need to build platforms for innovation based
on organizational learning and KM (Jonash and Sommerlate 1999). Innovation and, <strong>the</strong>refore, <strong>the</strong>
increase in competitiveness, do not necessarily imply having an area specifically dedicated to
research and development.
These findings open a window to look at <strong>the</strong> need for programs <strong>of</strong> knowledge generation, without
requiring areas specializing in research and development, and <strong>the</strong>y can achieve significant advances
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Eduardo Rodriguez, John Edwards and Angel Facundo
in <strong>the</strong> competitiveness <strong>of</strong> organizations. Innovative new products launched into <strong>the</strong> market with and
without <strong>the</strong> existence <strong>of</strong> departments <strong>of</strong> research and development.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> increase in intangible assets is a factor that can contribute to growth in
productivity. Figure 2-1 shows how Sweden has <strong>the</strong> largest investment in intangible capital. Finland,
Denmark and <strong>the</strong> USA all invest more in intangible capital than tangible. The Czech Republic, Italy
and Spain, which are not part <strong>of</strong> <strong>the</strong> top 20 GCI, still emphasize investment in tangible capital
suggesting <strong>the</strong> need for a different balance to improve GCI.
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Share <strong>of</strong> GDP in tangible and intangible capital
Physical capital deepening Intangible capital deepening
Figure 2-1 Tangible and Intangible capital (Source OECD 2010)
Now, innovation, productivity, working capacity and qualification <strong>of</strong> human resources <strong>of</strong> companies
could make countries attractive to stable and consolidated foreign investment to contribute
strategically to organizations and economic development.
Given <strong>the</strong> importance <strong>of</strong> creating value and adding richness in a process <strong>of</strong> improvement, a question
<strong>the</strong>n arises on: what types <strong>of</strong> innovation are possible? Innovation can occur in processes, products
and systems. The important point is to understand that <strong>the</strong>re are various innovation levels and that
<strong>the</strong> costs and benefits are different. Table 2.6 shows that countries adopt different routes: while Japan
concentrates on marketing or organizational innovation, Finland concentrates on product and process
innovation as well.
In short, organizations that are in that learning process, have strategies where <strong>the</strong> generation <strong>of</strong>
knowledge and learning have positive effects on <strong>the</strong> results <strong>of</strong> <strong>the</strong> organization.
Mintzberg and Waters (1985) explained that <strong>the</strong> strategies can be originated by different sources, by
beliefs shared on restrictions in <strong>the</strong> process, <strong>the</strong> consensus about <strong>the</strong> risk or <strong>the</strong> environment.
Mintzberg (1994) pointed out that <strong>the</strong> strategic process has to be an adaptation to <strong>the</strong> strategy
environment. Organizations can, for example, use scenarios instead <strong>of</strong> forecasts or adapt <strong>the</strong>ir
analysis to possible changes in culture, society and political circumstances. A condition for learning in
<strong>the</strong> development <strong>of</strong> strategies in an organization is based on <strong>the</strong> dissemination <strong>of</strong> knowledge, not only
in <strong>the</strong> implementation <strong>of</strong> strategies, but also <strong>the</strong>ir formulation. This goes hand in hand with success in
<strong>the</strong>ir results.
Therefore, such as Alavi and Leidner 2001 support <strong>the</strong> notion that <strong>the</strong> knowledge is a competitive
factor and that <strong>the</strong> SECI model introduces innovation and competitiveness. Von Krogh (1998) pointed
out that KM is a process <strong>of</strong> <strong>the</strong> organization in order to compete. Then developing an academic
program, which forms researchers capable <strong>of</strong> promoting <strong>the</strong> creation <strong>of</strong> solid bases for innovation and
productivity, is not only a necessity, but one that is highly relevant for <strong>the</strong> development <strong>of</strong> <strong>the</strong> country.
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Marketing or
organisational Marketing or Product or
Manufacturing
& Product or organisational process
process
innovation
innovation only innovation only
Germany 54 22 7
South Africa (2002-04) 45 10 21
Luxembourg 38 20 12
Belgium 41 13 14
Estonia 39 12 16
Aus tria 41 15 11
Japan (1999-2001) 22 40 4
United Kingdom 29 17 19
Ireland 32 7 24
Denmark 41 12 8
Australia (2006-07) 36 7 18
Portugal 32 21 7
Iceland (2002-04) 31 7 20
Finland 36 6 16
Ne<strong>the</strong>rlands 23 10 20
Italy 21 16 16
Czech Republic 24 12 12
Spain 21 12 14
Norway 22 15 9
Chile 25 7 7
Korea (2005-07) 16 3 17
Table 2-6 Types <strong>of</strong> innovation (Source OECD 2010)
.
3. Leverage on Knowledge Management to mitigate risk
The first part <strong>of</strong> this section introduces <strong>the</strong> terms used for <strong>the</strong> academic program based on intelligence
approaches. The second part introduces <strong>the</strong> KM concepts that support <strong>the</strong> integration <strong>of</strong> <strong>the</strong> research
lines. The concept <strong>of</strong> a unique intelligence, a unique way <strong>of</strong> seeing <strong>the</strong> intelligence and a single
measurement has changed, and <strong>the</strong> <strong>the</strong>mes <strong>of</strong> intelligence are involved in multiple fields <strong>of</strong> human
activity. Based on leveraging individuals’ intelligence and <strong>the</strong>ir work in organizations and
communities, <strong>the</strong> proposed doctoral program in KM is based on <strong>the</strong> following views:
3.1 Knowledge Management and Artificial Intelligence
The study <strong>of</strong> human intelligence and its applications in improving <strong>the</strong> learning <strong>of</strong> human expertise
through automation <strong>of</strong> intelligent behavior (Luger and Stubblefield 1993), reasoning, recognizing
patterns, acquiring and retaining knowledge for solving problems (Min 2010) falls in <strong>the</strong> field <strong>of</strong>
artificial intelligence developments. Artificial intelligence (AI) focuses on <strong>the</strong> simulation <strong>of</strong> natural
intelligence, taking as a paradigm human intelligence and behavior (Turing, 1950) and introduces
some methodological and for analysing questions about intelligence. Knowledge creation through
data mining techniques, some decision support systems for making decisions and new technologies
for inference provide value to <strong>the</strong> organization
3.2 Knowledge Management and Collective Intelligence
Within <strong>the</strong> social sciences and computer science, surrounding what today is known as collective
intelligence (Levy 1997) and following various trends ranging from <strong>the</strong> analysis <strong>of</strong> social interactions
and networks <strong>of</strong> cyber-culture to cyber-societies, and even tries to create a new anthropology (Levy
1997) or new mechanisms and forms <strong>of</strong> social behavior (Noubel, 2004) or <strong>the</strong> creation <strong>of</strong> a global
brain (Bloom, 1995).
The behavior <strong>of</strong> a group is not <strong>the</strong> sum <strong>of</strong> individual behaviors, but <strong>the</strong> integration <strong>of</strong> individuals into a
whole. A well-integrated human group shows traits <strong>of</strong> intelligence that may be higher than that <strong>of</strong>
groups with ‘smarter’ members who may be acting clumsily. This leads to <strong>the</strong> <strong>the</strong>ory <strong>of</strong> intelligent
organizations, collective intelligence and learning organizations.
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Eduardo Rodriguez, John Edwards and Angel Facundo
Bloom (1995), spoke <strong>of</strong> <strong>the</strong> Group IQ, and Atlee (2003) argued that <strong>the</strong> co-intelligence which is
reflected in cooperation, in contrast to individual bias, can achieve collective intelligence as <strong>the</strong>
construction <strong>of</strong> a society and wise democratic goals, through processes <strong>of</strong> conscious evolution <strong>of</strong>
social systems.
The work in social networks is ano<strong>the</strong>r field <strong>of</strong> great contemporary activity Network <strong>the</strong>ory refers to
systems that are connected and a social network “[c]onsists <strong>of</strong> a finite set or sets <strong>of</strong> actors and <strong>the</strong>
relation or relations defined on <strong>the</strong>m.” (Wasserman 1994).
Each <strong>of</strong> <strong>the</strong>se seeks to make <strong>the</strong> greatest possible knowledge asset value and make <strong>the</strong> actions <strong>of</strong>
<strong>the</strong> various types <strong>of</strong> social organizations smart enough to ensure <strong>the</strong>ir viability and success.
3.3 Knowledge Management and Sentient Intelligence
Sentient intelligence is a concept raised by <strong>the</strong> Spanish philosopher Xavier Zubiri in his trilogy
Intelligence and reality, comprised <strong>of</strong> Sentient Intelligence(1980); Intelligence and logos (1982); and
intelligence and reason (1983). What Zubiri aims to address is <strong>the</strong> effect <strong>of</strong> <strong>the</strong> human environment
and <strong>the</strong> way humans react in order to solve problems through <strong>the</strong> socio-cultural relationships that
exist in <strong>the</strong> social environment and feelings and perception.
Sentient intelligence should not be confused with emotional intelligence, <strong>the</strong> latter concerning <strong>the</strong>
human capacity to feel, understand, monitor and modify emotional states in self and o<strong>the</strong>rs (Goleman
1995), targeting a balance that allows managing emotions in an intelligent way. A proper identification
<strong>of</strong> feelings involves a good understanding, but to know how to express <strong>the</strong>m and relate <strong>the</strong>m to what
makes <strong>the</strong>m be, requires a more rational effort; that is, sentient intelligence.
3.4 Knowledge Management in Business and Strategic Intelligence
The concept <strong>of</strong> ‘intellectual capital’ (Edvinson and Malone, 1997; and Stewart, 1997), which consists
<strong>of</strong> <strong>the</strong> value added by intangible assets, requires analysis and application <strong>of</strong> knowledge to <strong>the</strong>
improvement <strong>of</strong> <strong>the</strong> organization's value and <strong>the</strong> strategic decision process in what has been called
strategic intelligence. This is defined as “a journey <strong>of</strong> planning, implementing, evaluating and
adjusting while paying attention and focusing on <strong>the</strong> right things” (Service, 2006, p.Abstract).
For Nonaka (1991), <strong>the</strong> data and systematic procedures contribute to "explicit knowledge". But <strong>the</strong>re
is also "tacit knowledge" (expertise, mental models, beliefs, prospects or <strong>the</strong> "Know how") with a
process <strong>of</strong> creation, which is highly personal. These skills are difficult to communicate and
systematize.
Nonaka (1991) suggests <strong>the</strong> SECI model that as Alavi and Leidner (2001) pointed out knowledge
creation contributes to <strong>the</strong> organizations productivity. Additionally, strategic intelligence requires a
strategic intelligence system (Montgomery and Weinberg 1981) that provides information and
knowledge for supporting and guiding <strong>the</strong> strategy formulation and implementation <strong>of</strong> organizations.
According to Liebowitz (2006) strategic intelligence is <strong>the</strong> “outer layer <strong>of</strong> <strong>the</strong> onion, with <strong>the</strong> inside
layers being AI, KM, BI [Business Intelligence], CI [Competitive Intelligence].”
4. Knowledge management processes
Having reviewed <strong>the</strong> concept <strong>of</strong> knowledge and <strong>the</strong> four intelligence approaches, <strong>the</strong> following KM
concepts are used in order to provide <strong>the</strong> KM framework. Alavi and Leidner (2001) adopt <strong>the</strong>
definition <strong>of</strong> KM as a process, with four sub processes: Creation, storage/retrieve, transfer and
application <strong>of</strong> knowledge, that identifies and leverages <strong>the</strong> collective knowledge <strong>of</strong> <strong>the</strong> organization in
order to compete (von Krogh, 1998). Equally, Alavi and Leidner (2001) adopt <strong>the</strong> definition <strong>of</strong> KM as a
process that requires more than IT; it requires <strong>the</strong> creation <strong>of</strong> a means to share knowledge,
information processed by individuals and adapted to be communicated.
Alavi and Leidner go on to divide this process into four sub-processes, which are:
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Eduardo Rodriguez, John Edwards and Angel Facundo
Table 3-1 KM Processes
In order to support <strong>the</strong>se processes a Knowledge Management System (KMS) is required. Alavi and
Leidner (2001) identified <strong>the</strong> KMS as <strong>the</strong> “kind <strong>of</strong> information systems applied to managing
organisational knowledge.” However, <strong>the</strong> transition <strong>of</strong> an information system into a knowledge
management system (KMS) requires several components that take into consideration <strong>the</strong> system
design stage. Thus <strong>the</strong> KMS components (Lehaney et al., 2004; Davemport and Prusak, 1998;
Malhotra, 1999; Edwards et al., 2005) can be summarized in Table 3-2:
Table 3-2 Components <strong>of</strong> <strong>the</strong> KMS
Therefore, a KMS, to be designed, should take into consideration <strong>the</strong> communities, environment and
technological means to generate development in society and where doctoral researchers could be <strong>the</strong>
engine and support. The purpose <strong>of</strong> a KMS in <strong>the</strong> communities is to achieve better evidence based
orientation for making decisions, and technology that supports access to community services and
government. The next section presents some examples <strong>of</strong> KMS applications in communities and
society.
5. KMS applied to <strong>the</strong> communities and society
Zavadskas et al. (2010) summarized <strong>the</strong> work that societies are performing in order to develop
capabilities that provide better services to <strong>the</strong> citizens. There are categories such as: intelligent cities,
e-cities, geographic information management and training, e-government strategies, e-procurement,
e-commerce, e-interoperability, transportation management, management <strong>of</strong> resources etc.
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Eduardo Rodriguez, John Edwards and Angel Facundo
Applications <strong>of</strong> KM appear in various organizations in society such as <strong>the</strong> example <strong>of</strong> <strong>the</strong> Finnish
Parliament (Suurla et al.2002) or developing intelligent cities and regions using knowledge networks
(Kratke and Brandt 2009) where a cooperation between scientific institutions and firms is identified.
Equally, developing knowledge-based regions is a potential result <strong>of</strong> KM efforts (Etzkowitz and
Kl<strong>of</strong>tsen 2005). Komninos (2006) stated that “Intelligent communities and cities belong to an
emerging movement targeting <strong>the</strong> creation <strong>of</strong> environments that improve cognitive skills and abilities
to learn and innovate.” (p.Abstract) Similarly, <strong>the</strong> KnowCis methodology (Ergazakis et al. 2006) has
been applied to <strong>the</strong> development <strong>of</strong> knowledge cities, and KM has also been used to support
collective learning for innovation (Hasan and Crawford 2007). Ano<strong>the</strong>r example is collaboration
among organizations in order to perform better service (Gorry 2006).
Porter and Stern (2001) pointed out “The vitality <strong>of</strong> innovation in a location is shaped by national
innovative capacity. National innovative capacity is a country’s potential-as both a political and
economic entity- to produce a stream <strong>of</strong> commercially relevant innovations.” (p.29)This leads to <strong>the</strong>
review <strong>of</strong> <strong>the</strong> value <strong>of</strong> working directly in regions where <strong>the</strong> innovation might not be in products but in
processes and ways to solve problems. Probably <strong>the</strong> purpose is not <strong>the</strong> commercialization <strong>of</strong> <strong>the</strong> idea
but <strong>the</strong> consolidation <strong>of</strong> a better standard <strong>of</strong> living.
The capacity <strong>of</strong> users in regions and <strong>the</strong> access to technology in regions <strong>of</strong> a country might be a
barrier to overcome and priority to analyze, even though Colombia has climbed from 52 nd in 2008 to
31 st in 2010 in <strong>the</strong> United Nations e-government rankings (United Nations, 2010). Additionally CRM
(Customer Relationship Management) is used by government as a means to understand citizen
demands and to provide input to <strong>the</strong> organizations that serve citizens (Ho and Chuang, 2006).
Equally, Liebowitz (2003) and Osborne (2004) identified KM applications in <strong>the</strong> improvement process
<strong>of</strong> government and its organizations. Misra et al. (2003) and Zavadskas et al. (2010) indicate in
specific that electronic solutions for KM in a region supports organizations and e-technologies in
regional development lead organizing cities with electronic services.
6. Research Program in Knowledge Management based on development <strong>of</strong>
intelligences
The previous sections have introduced concepts <strong>of</strong> competitiveness, risk and KM and examples <strong>of</strong>
KM actions in communities through KMSs. What is missing in particular in Colombia is people that
support <strong>the</strong> processes, identify needs and KM solutions. Access to ICT is a barrier to overcome in
some regions but even if <strong>the</strong> ICT is available <strong>the</strong> communities have a lack <strong>of</strong> human capital that
supports KM transformation for <strong>the</strong>m. Additionally, universities in <strong>the</strong> regions are supporting <strong>the</strong>
development but it has not been enough. Hautala et al. (2009) pointed out that <strong>the</strong>re are two needs
that affect negatively <strong>the</strong> innovation coming from universities: ”The quality <strong>of</strong> scientific research is still,
<strong>of</strong> course, highly appreciated, but innovative application <strong>of</strong> new knowledge for creating new business
and making established ones more successful has also become a serious and essential ingredient <strong>of</strong>
a successful university.”(p.19)
Thus this paper presents <strong>the</strong> created research program that is based on <strong>the</strong> development <strong>of</strong> <strong>the</strong>
combination <strong>of</strong> different intelligences for KM. It seeks to use intelligence in relation to <strong>the</strong> tools that
individuals and groups possess, along with thought processes to find solutions to real community
problems: solutions based on knowledge and creativity. The four facets <strong>of</strong> artificial, collective,
sentient and strategic intelligence produce a structure in which <strong>the</strong> elements <strong>of</strong> KM constitute a
response to <strong>the</strong> problems <strong>of</strong> organizations, communities and countries.
The research program has as its objective to train researchers with holistic and interdisciplinary vision,
capable <strong>of</strong> promoting <strong>the</strong> generation <strong>of</strong> knowledge, innovations or technological developments, using
KM (symbiosis <strong>of</strong> intellectual capital and technology) in different lines <strong>of</strong> research focused on <strong>the</strong> four
intelligences, as well as <strong>the</strong> ability to solve problems in <strong>the</strong> context <strong>of</strong> regional organizations in order
to contribute to <strong>the</strong> transformation and scientific, technological, cultural, and socio-economic advance
<strong>of</strong> developing countries.
Doctoral students will have some specific scenarios <strong>of</strong> action and communities will benefit through
development <strong>of</strong> <strong>the</strong>ir doctoral research projects. The doctoral student will be exposed to <strong>the</strong>
production chains in <strong>the</strong> regions, <strong>the</strong> sustainability <strong>of</strong> projects in <strong>the</strong> regions, <strong>the</strong> integration <strong>of</strong> science
and technology in <strong>the</strong> production chains, <strong>the</strong> recognition <strong>of</strong> <strong>the</strong> artistic, cultural and technological
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Eduardo Rodriguez, John Edwards and Angel Facundo
knowledge <strong>of</strong> <strong>the</strong> regional actors, <strong>the</strong> integration <strong>of</strong> analysis <strong>of</strong> <strong>the</strong> possible future actions and <strong>the</strong>
assessment <strong>of</strong> risk to <strong>the</strong> regional organization.
The doctoral program <strong>of</strong>fers four streams <strong>of</strong> research, namely: (See Table 5-1)
Artificial intelligence
Collective intelligence
Sentient Intelligence
Strategic Intelligence
Table 5-1 Main Research streams <strong>of</strong> <strong>the</strong> program
These 4 streams <strong>of</strong> research are organized as part <strong>of</strong> a six semester program with two main cycles:
<strong>the</strong> first cycle <strong>of</strong> <strong>the</strong> program is based on specialized seminars in each <strong>of</strong> <strong>the</strong> 4 streams
complemented by additional courses and <strong>the</strong> research proposal and <strong>the</strong> second cycle is <strong>the</strong> research
project development:
In <strong>the</strong> first cycle, <strong>the</strong> focus is on providing <strong>the</strong> fundamental <strong>the</strong>ory on which students concentrate
in order to deepen and update <strong>the</strong>ir knowledge in <strong>the</strong> conceptualization and work methods <strong>of</strong> KM.
The program will provide seminars to support <strong>the</strong> concentration <strong>of</strong> <strong>the</strong> research, exposing <strong>the</strong>
student to interdisciplinary work with o<strong>the</strong>r streams and problems to solve, This process seeks to
provide to <strong>the</strong> student better
understanding to help select a stream <strong>of</strong> research and improve <strong>the</strong> proposed research project that
<strong>the</strong> doctoral student presented at <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> program. See Table 5-1 for more detail <strong>of</strong>
<strong>the</strong> first cycle.
845

Eduardo Rodriguez, John Edwards and Angel Facundo
Theoric‐conceptual deepening cycle
(<strong>the</strong> 3 first semesters)
General Objective
Courses
• Seminar historical perspective and
Through research and training approaches to knowledge
seminars, students deepen in
• Seminar preparation <strong>of</strong> state <strong>of</strong> art
concepts, models, methods and
and meta‐analysis research
• Workshop specialized in each <strong>of</strong> <strong>the</strong>
techniques <strong>of</strong> research in
lines <strong>of</strong> research. Four distinct
knowledge management and in
• Seminar "Local and/or regional
<strong>the</strong> respective lines <strong>of</strong> research, in
development from <strong>the</strong> perspective <strong>of</strong>
order to identify <strong>the</strong> state <strong>of</strong> art,
• Ontological systems and
collect and analyze information
representation <strong>of</strong> knowledge" seminar
(qualitative and
• Semiology seminar
quantitative)make use <strong>of</strong> digital • Seminar methods, models and
technologies and apply <strong>the</strong>m to statistical techniques in <strong>the</strong> study <strong>of</strong>
<strong>the</strong>ir respective proposals for
research
research development, to ensure
<strong>the</strong> production <strong>of</strong> new
knowledge, innovations or
technological developments in
<strong>the</strong> resolution <strong>of</strong> <strong>the</strong> problems <strong>of</strong>
<strong>the</strong>ir environment.
Table 5-2 First -Cycle <strong>of</strong> <strong>the</strong> research program
In <strong>the</strong> second cycle <strong>the</strong> doctoral students participate some workshops for <strong>the</strong>ir research design
and focus on <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> research project, which will serve as an input for <strong>the</strong>ir
doctoral dissertation. Supervisors will provide asynchronous and synchronous direction and
assistance for <strong>the</strong> implementation <strong>of</strong> <strong>the</strong>se projects. This cycle can be extended in some cases to
a maximum <strong>of</strong> two additional calendar semesters depending on <strong>the</strong> progress in <strong>the</strong> research
process. Table 5-2 shows <strong>the</strong> structure.
Cycle <strong>of</strong> development and support <strong>of</strong> research
Fourth, fifth and sixth semester
General Objective Courses
• Data mining in knowledge
Students have been working on
<strong>the</strong> development <strong>of</strong> its research
project since <strong>the</strong> beginning <strong>of</strong> <strong>the</strong>
program, in <strong>the</strong> last three
academic semesters <strong>the</strong> students
are concentrated on workshops
for refinement <strong>of</strong> <strong>the</strong> design <strong>of</strong>
research projects, <strong>the</strong> regional
problem, <strong>the</strong> solution <strong>of</strong> problems
and in <strong>the</strong> implementation and
support <strong>of</strong> research, as an input
for <strong>the</strong>ir doctoral <strong>the</strong>sis, under <strong>the</strong>
direction <strong>of</strong> <strong>the</strong>ir supervisor (s).
They must also demonstrate <strong>the</strong>ir
adequacy in foreign language, as a
prerequisite for <strong>the</strong> degree.
Table 5-3 Second Cycle <strong>of</strong> <strong>the</strong> Research Program
846
management ‐ seminar
• Workshop by interdisciplinary
groups for review and adjustments
<strong>of</strong> <strong>the</strong> doctoral research project
(product: publication <strong>of</strong> <strong>the</strong>
respective state <strong>of</strong> art in indexed
journal).
• Development <strong>of</strong> <strong>the</strong> project <strong>of</strong>
doctoral research
• Interdisciplinary workshops for
groups, on <strong>the</strong> topics <strong>of</strong> research:
regional issues (products: release <strong>of</strong>
article indexed journal and
presentation <strong>of</strong> international paper)
• Research development and
support <strong>of</strong> <strong>the</strong> Doctoral <strong>the</strong>sis
• Internship

7. Conclusion
Eduardo Rodriguez, John Edwards and Angel Facundo
In this article <strong>the</strong> concepts <strong>of</strong> four different intelligences have been reviewed and related to KM. This
intelligence identification has been <strong>the</strong> support to design an academic research program in order to
contribute to improving <strong>the</strong> competitiveness societies. The principles <strong>of</strong> ERM applied to a state,
country or society through strategic risk has been <strong>the</strong> pillar <strong>of</strong> analysis. In general strategic risk is
related to strategic decisions and <strong>the</strong> effect on <strong>the</strong> performance <strong>of</strong> <strong>the</strong> organization. In a country
context <strong>the</strong> strategic problem is in productivity-competitiveness and this affects <strong>the</strong> possibility <strong>of</strong> a
better standard <strong>of</strong> living for <strong>the</strong> citizens. Several countries have implemented KM programs for <strong>the</strong>
improvement <strong>of</strong> society; similarly <strong>the</strong>re are new initiatives around <strong>the</strong> world to provide better services
to citizens and to connect organizations and individuals. In Colombia <strong>the</strong>re are not <strong>the</strong>se kind <strong>of</strong>
programs and <strong>the</strong>re is a high diversity <strong>of</strong> regions with problems to solve. The design <strong>of</strong> an academic
research program in KM might drive <strong>the</strong> use <strong>of</strong> KM as a mitigator <strong>of</strong> strategic risk <strong>of</strong> <strong>the</strong> country/region
because <strong>the</strong> adequate use <strong>of</strong> KM processes pushes and pulls innovation development and supports
society; particularly, regional development. However, to achieve KM as mitigator, society needs to
have agents <strong>of</strong> change and support. These agents are <strong>the</strong> doctoral researchers that in <strong>the</strong> regions
should be catalysts for <strong>the</strong> modification <strong>of</strong> <strong>the</strong> status quo and to promote development and innovation.
The way to develop <strong>the</strong>se agents was to analyze from <strong>the</strong> viewpoint <strong>of</strong> <strong>the</strong> different kinds <strong>of</strong>
intelligence, understood as capacity to solve problems, <strong>the</strong> creation <strong>of</strong> an academic program to train
researchers through <strong>the</strong> proposed curriculum, possibly convert <strong>the</strong> researchers into regional leaders
after <strong>the</strong> completion <strong>of</strong> <strong>the</strong> program, to provide capabilities to <strong>the</strong> regions in improving <strong>the</strong>ir
competitiveness.
References
Alavi M and Leidner D 2001, “Review: Knowledge Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues”, MIS Quarterly ,Vol. 25, No.1, pp 107-136
Atlee T 2008, “Reflections on <strong>the</strong> evolution <strong>of</strong> choice and collective intelligence” access May 2011
http://www.newmediaexplorer.org/tom_atlee/2008/05/15/reflections_on_<strong>the</strong>_evolution_<strong>of</strong>_choice_and_colle
ctive_intelligence.htm,
Auffret JP 2010, rural and municipal e-Government initiatives in developing countries: best practices and a
framework for success, Journal <strong>of</strong> e-Governance, Vol. 33, pp 139-143
Aven T 2003, Foundation <strong>of</strong> Risk Analysis, John Wiley, Chichester
Banham R 2004, “Enterprising Views <strong>of</strong> Risk Management”, Journal <strong>of</strong> Accountancy, Vol. 197, No. 6, pp 65-72
Baran<strong>of</strong>f E 2004a, “Mapping <strong>the</strong> Evolution <strong>of</strong> Risk Management”, Contingencies, Vol16, No.4, pp 23-27
Blanke J, Paua F and Sala-I-Martin X World Economic Forum, Columbia University and Universitat Pompeu
Fabra The Growth Competitiveness Index: Analyzing Key Underpinnings <strong>of</strong> Sustained Economic Growth
Bloom H 2000, Global Brain: The Evolution <strong>of</strong> Mass Mind from <strong>the</strong> Big Bang to <strong>the</strong> 21st Century, Wiley, New
York
Brown B 2001, “Step by step Enterprise Risk Management”, Risk Management, Vol. 48, No. 9, pp 43-50
Collins J and Ruefli T 1992, Strategic Risk: An Ordinal Approach, Management Science, Vol. 38, No, 12, pp
1707-1731
Christopherson S and Clark J 2010, Limits to “The Learning region”: What University-centered Economic
Development Can (and Cannot) do to Create Knowledge-based Regional Economies, Local Economy, Vol.
25, No.2, pp 120-130
Davenport T and Prusak L 1998, Working Knowledge, Harvard Business School Press, Boston
Davila C and Malaver F 2004 A propósito del reconocimiento de Colciencias a grupos de investigación en
administración, Cuadernos de Administración, Vol. 17 No.27, pp 5-9
Dickinson G 2001, “Enterprise Risk Management: Its Origins and Conceptual Foundation”, The Geneva Papers
on Risk and Insurance, Vol. 26, No. 3, pp 360-366
Economic Forum (2010). Index <strong>of</strong> global competitiveness comparison 2009-2010 with 2008-2009).
Edvinson L and Malone M 1997, Intellectual Capital, Harper Business, Ney York
Edwards J S, Handzic M, Carlsson S and Nissen M 2003, “Knowledge Management Research and Practice:
vision and directions”, Knowledge Management Research and Practice, Vol.1, No.1, pp 49-60
Ergazakis K, Metaxiotis K, Psarras J and Grammatikos K 2006, “ Applying <strong>the</strong> KnowCis methodology to a Greek
municipality: a case study, Knowledge Managament Research and Practice, Vol. 4 No. 4 pp 293-309
Etzkowitz H and Kl<strong>of</strong>sten M 2005 “ The innovating region: Toward a <strong>the</strong>ory <strong>of</strong> Knowledge-Based regional
development” R7D Management, Vol. 35, No. 3 pp 243-255
Froot K and Scharfstein D 1994, “A Framework for Risk Management”, Harvard Business Review, Vol.72, No. 6,
pp 91-102
Galloway D and Funston R 2000, “The challenges <strong>of</strong> enterprise risk management”, Balance Sheet, Vol.8, No. 8,
pp 22-25
Gates S 2006, “ Incorporating Strategic Risk into Enterprise Risk Management: A survey <strong>of</strong> Current Corportate
Practice”, Journal <strong>of</strong> Applied Corporate Finance, Vol. 18, No. 4, pp 81-90
Goleman D 1995, Emotional Intelligence, Bantam Books, New York
847

Eduardo Rodriguez, John Edwards and Angel Facundo
Gorry A 2006, “Sharing Knowledge in <strong>the</strong> Public Sector: Two case studies” Knowledge Management Research
and Practice, Vol. 6, No. 2, pp 105-111
Hasan H and Crawford K 2007, “Knowledge Mobilization in Communities through socio-Technical Systems”
Knowledge Management Research and Practice, Vol. 5, No. 4, pp 237-247
Hautala V, Malinem P, Orava M and Puhakainen J 2009, “Developing <strong>the</strong> Regional Innovation System-Business
Development Laboratory as a promoter <strong>of</strong> science-based New Ventures”, Research in Economics and
Business Central and Eastern Europe, Vol. 27 No.1, pp 18-34
Ho L and Chuang C 2006, “ The application <strong>of</strong> Knowledge Management and Customer Relationship
Management <strong>of</strong> ROC Governmement” The Journal <strong>of</strong> Amrican Academy <strong>of</strong> Business,Vol. 9, No. 2, pp 63-71
Jonash R and Sommerlate T 1999, The Innovation premium, Perseus Books, New York
Jung-Hyup K 2010, E-Government in Korea, Journal <strong>of</strong> e-governance, Vol. 33, Special report, pp 130-138
Komninos N 2006, “ The Architecture <strong>of</strong> Intelligent Cities”, 2nd International <strong>Conference</strong> on Intelligent
Environments A<strong>the</strong>ns, 5-6 July
Kovacic A 2007 “Process-Based Knowledge Management:Towards e-governement in Slovenia”, Management,
Vol. 12, No. 1, pp 45-64
Krake and Brant 2009
Kratkes s and Brandt A 2009 “Knowledge Networks as a Regional Development Resource: A Network analysis <strong>of</strong>
<strong>the</strong> interlinks between scientific institutions in <strong>the</strong> metropolitan region <strong>of</strong> Hanover, <strong>European</strong> Planning
Studies, Vol. 17 No. 1
Lehaney B, Clarke S, Coakes E and Jack G 2004, Beyond Knowledge Management. Hershey, PA.: Idea Group
Publishing
Levy P 1997, Collective Intelligence, Perseus Books, Cambridge
Liebowitz J 2003, “A Knowledge Management Implementation plan at a leading US Technical Government
Organization: A case Study” Knowledge and Process management, Vol. 10, No. 4, pp 254-259
Liebowitz J 2006, Strategic Intelligence, Aurbach Publications, Boca Raton
Luger G and Stubblefield W 1993, Artificial Intelligence: structures and strategies for complex problem solving,
Benjamin/Cummings Publishing Co. Redwood City
Malerba F 2004, Sectoral Systems <strong>of</strong> Innovation, Cambridge University Press, Cambridge
Malhotra Y 1999, “Beyond Hi-tech hidebound Knowledge Management: Strategic Information System for <strong>the</strong> New
World <strong>of</strong> Business”, Working Paper, Brint Research Institute
Meulbroek L 2002, “The promise and Challenge <strong>of</strong> Integrated Risk Management”, Risk Management and
Insurance Review, Vol.5, No. 1, pp 55-66
Min H 2010,” Artificial Intelligence in supply Chain Management: Theory and Application”, Vol. 13, No. 1, pp 13-
39
Misra DC Hariharan R and Khaneja M 2003, “E-Knowledge Management Framework for Government
Organizations” Information Systems Management, Spring pp 38-48
Montomery D and Weinberg C 1981 “ Toward Strategic Intelligence Systems”, The McKinzey Quartelly, Spring,
pp 82-102
Nonaka I 1991, “The Knowledge Creating Company”, Harvard Business Review, Vol.69 No.6 pp 96-104
Noubel JF 2004 Collective Intelligence: The invisible revolution, accessed May 2011,
http://www.<strong>the</strong>transitioner.org/Collective_Intelligence_Invisible_Revolution_JFNoubel.pdf
Osborne M 2004, “ Making <strong>the</strong> most <strong>of</strong> <strong>the</strong> best: Formulating a knowledge management strategy for a
government advisory body” Australian Journal <strong>of</strong> Public Administration, Vol. 63 No. 4 pp 43-62
Porter M 1990 The Competitive Advantage <strong>of</strong> Nations , Free Press, New York
Porter M and Stern S 2001, “Innovation Location Matters” MIT Sloan Management School, Summer, pp 28-36
Russell S and Norvig P 1995 Artificial Intelligence: A modern approach, Prentice Hall
Service R 2006 “ The development <strong>of</strong> Strategic Intelligence: A Managerial Perspective” International Journal <strong>of</strong>
Management, Vol. 23, No. 1, pp 61-77
Simons R 1999, “How Risky is Your Company”, Harvard Business Review, Vol. 77, No. 3, pp 85-94
Slywotzky A and Drzik J 2005, “Countering <strong>the</strong> Biggest Risk <strong>of</strong> All”, Harvard Business Review, Vol. 83, No. 4 pp
78-88
Steward T 1997, “Intellectual Capital” Doubleday, New York
Tanriverdi H and Ruefli T 2004, “The Role <strong>of</strong> Information Technology in Risk/Return Relations <strong>of</strong> firms” Journal
<strong>of</strong> <strong>the</strong> Association for Information Systems, Vol. 5, No. 11-12, pp 421-447
Turing A 1950 Computing Machinery and Intelligence, Mind October Vol. 59 pp 433-460
United Nations e-Government Survey 2010 at http://unpan1.un.org/intradoc/groups/public/documents/undpadm/unpan038848.pdf
Von Krogh G 1998, Care in Knowledge Creation, California Management Review, Vol.40, No. 3, pp 133-153
Wasserman S 1994, Social Network Analysis: method and applications, Cambridge University Press, Cambridge
Young G and Hasler D 2010, “Managing Reputational Risks”, Strategic Finance, Vol. No. ,pp 37-46
Zavadskas E, Kaklauskas A and Banaitis A 2010, Application <strong>of</strong> e-technologies for regional development: The
case <strong>of</strong> Vilnius city, Journal <strong>of</strong> Business Economics and Management, Vol. 11, No. 3, pp 415-427
Zubiri X 1980 Inteligencia Sentience, Inteligencia y realidad, Alianza Editorial, Madrid
848

Information Intensive Systems: Enabler or Inhibitor <strong>of</strong>
Sustained Knowledge Capability
Max Erik Rohde and David Sundaram
The University <strong>of</strong> Auckland Business School, Auckland, New Zealand
m.rohde@auckland.ac.nz
d.sundaram@auckland.ac.nz
Abstract: The ability to leverage <strong>the</strong> potentials <strong>of</strong> knowledge capabilities has become an essential
source <strong>of</strong> competitiveness for many organizations. Information systems have been considered to be a
fundamental catalyst in increasing performance in working with organizational knowledge. However,
<strong>the</strong>re is growing disillusion regarding <strong>the</strong> capabilities <strong>of</strong> information intensive systems to facilitate <strong>the</strong>
work with knowledge. In many studies, information technology has been presented as an inhibitor <strong>of</strong>
emerging and unpredictable knowledge work ra<strong>the</strong>r than its enabler. One sustained challenge for
research lies in exploring novel ways, which enable to overcome <strong>the</strong> pitfalls <strong>of</strong> technology while still
reaping its potential benefits in working with knowledge. The particular objectives <strong>of</strong> our study are to
(1) explore <strong>the</strong> ability <strong>of</strong> information intensive mechanisms to facilitate sustainable knowledge
capability and (2) to design, implement and evaluate information intensive mechanisms to create and
access complex information in alignment with natural ways <strong>of</strong> working with knowledge. In pursuit <strong>of</strong>
<strong>the</strong> first objective <strong>of</strong> this research, an extensive review <strong>of</strong> <strong>the</strong> knowledge management literature as
well as related business and computing disciplines is conducted. The syn<strong>the</strong>sized conclusions drawn
from this review are postulated as guidelines for designing information intensive knowledge support
systems: (1) connect contexts through information, (2) be aware that situational context and structural
parameters <strong>of</strong> <strong>the</strong> same are mutually constitutive, (3) focus on individuals as mediators <strong>of</strong> knowledge
flows, and (4) be aware <strong>of</strong> both <strong>the</strong> benefits and costs <strong>of</strong> information. In fulfilment <strong>of</strong> <strong>the</strong> second
objective, a prototypical information intensive knowledge-support system in alignment with aforementioned
guidelines has been implemented. This prototype: (1) works with generic networks <strong>of</strong> information in order to be
able to adapt to versatile and unpredictable contexts, (2) allows gradual imposition <strong>of</strong> syntactical rules on
complex information in order to reflect structural constraints in different degrees <strong>of</strong> severity, (3) allows individuals
to design personalized gateways into complex organizational information networks and (4) endeavours to
minimize interference in emergent knowledge processes potentially caused by information intensive systems.
The prototype is implemented in Java utilizing <strong>the</strong> frameworks Google Web Toolkit, XStream and <strong>the</strong> Jena
Semantic Web Framework.
Keywords: Knowledge management, knowledge management systems, sustained knowledge capability
1. Introduction
Information technology undoubtedly is at <strong>the</strong> midpoint <strong>of</strong> an unprecedented transformation <strong>of</strong> personal
and workspace environments. Information technology changes <strong>the</strong> way we work, spend our pastime,
<strong>the</strong> way we organize our lives, and some argue, <strong>the</strong> way we think and act. Few predictors doubted
that computers would quickly become man’s equal in thinking and knowing; according to <strong>the</strong> popular
work <strong>of</strong> Kubrick “2001: A Space Odyssey”, for instance, computers should have long been able to
understand, speak and intrigue just like <strong>the</strong>ir human creators.
Maybe such visions spawned and fuelled extensive investments into organizational systems to
manage, store, and apply ‘knowledge’. In <strong>the</strong> light <strong>of</strong> <strong>the</strong> success <strong>of</strong> IT-based systems in supporting
production, logistic and account processes, it was expected that similar systems would soon aid
workers and decision makers in knowledge-intensive domains. However, notwithstanding significant
efforts <strong>of</strong> practitioners and researchers alike, <strong>the</strong> ability <strong>of</strong> information-centric systems to support <strong>the</strong>
work with knowledge has <strong>of</strong>ten met with limited success (McDermott 1999; van Zolingen, Streumer, &
Stooker 2001).
Many researchers draw <strong>the</strong> conclusion that <strong>the</strong> role <strong>of</strong> information systems in <strong>the</strong> work with
knowledge should principally be as <strong>the</strong> facilitator <strong>of</strong> social processes (Thompson & Walsham 2004).
The purpose <strong>of</strong> this research is to investigate if and how information intensive approaches could still
aid in facilitating organizational knowledge capability. For this, a review <strong>of</strong> a number <strong>of</strong> current
perspectives on organizational knowledge capability is conducted (section 2.1). Then, it is reflected
upon how <strong>the</strong>se perspectives are reflected in technological approaches to support <strong>the</strong> work with
knowledge (section 2.2). This discussion is syn<strong>the</strong>sized into a number <strong>of</strong> guidelines to design
849

Max Erik Rohde and David Sundaram
information intensive knowledge support systems (section 3). Lastly, a possible implementation <strong>of</strong>
<strong>the</strong>se guidelines in an exploratory prototype is presented (section 4).
2. Literature review
2.1 Perspectives on sustained knowledge capability in organizations
While <strong>the</strong>re is general agreement that knowledge is an important source <strong>of</strong> sustained economical
advantage for organizations (Spender & Grant 1996), <strong>the</strong> nature <strong>of</strong> this knowledge or <strong>the</strong> related
organizational capability is subject to continued disagreement (Kraaijenbrink, Spender, & Groen 2010;
Spender & Scherer 2007). In order to explore in how far information intensive systems are suitable to
support organizations in establishing and maintaining sustained knowledge-based capabilities, a
number <strong>of</strong> popular yet <strong>of</strong>ten conflicting perspectives on several important properties <strong>of</strong> organizational
knowledge and knowledge capability are portrayed.
(1) Knowledge is sometimes understood as having objective characteristics, sometimes
knowledge is understood as inherently tacit (Objectiveness)
Since Polanyi’s work (1966) and various earlier philosophical discussions (Spender & Scherer 2007),
it is understood that <strong>the</strong>re are important aspects <strong>of</strong> knowledge, which cannot be brought into explicit
representations. Consequently, knowledge should not be treated as an ‘object’ but in consideration <strong>of</strong>
its tacit properties. However, despite aforementioned insights, knowledge is <strong>of</strong>ten given objective
characteristics. For instance, knowledge and associated organizational capabilities are described as
being ‘created’, ‘stored’, ‘transferred’ and ‘shared’ (Alavi & Leidner 2001) in various works <strong>of</strong>
organizational literature.
(2) Some perspectives imply that knowledge is a strategic and slowly changing
capability, o<strong>the</strong>rs emphasize that knowledge is inherently bound to its situational
application (Dynamicity)
Among <strong>the</strong> manifold epistemological positions in <strong>the</strong> context <strong>of</strong> organizational knowledge, Cook and
Brown (1999) have identified two popular and <strong>of</strong>ten exclusively different interpretations: some
organizational scientists believe knowledge to be possessed by organizations (‘epistemology <strong>of</strong>
possession’), while o<strong>the</strong>rs believe that knowledge is closely bound to its application in <strong>the</strong>
organization’s practices (‘epistemology <strong>of</strong> practice’). For knowledge as a ‘possessed’ resource <strong>of</strong>
organization, it is <strong>of</strong>ten argued that capabilities based on this resource are difficult to imitate by
competitors and difficult to attain (Teece, Pisano, & Shuen 1997). Knowledge bound to practices
shares <strong>the</strong> fluid nature <strong>of</strong> <strong>the</strong>se practices; it has, for instance, been argued that each execution <strong>of</strong> a
practice is unique caused by specific environmental conditions during execution (Pentland & Feldman
2005).
(3) Some authors see knowledge as closely bound to individuals or ‘knowers’, o<strong>the</strong>rs
stress <strong>the</strong> collective nature <strong>of</strong> strategic knowledge capabilities (Collectivity)
In <strong>the</strong>ir popular definition <strong>of</strong> organizational knowledge, Davenport and Prusak (1998) pose knowledge
as being inseparable from <strong>the</strong> mind <strong>of</strong> “knowers”; individuals who mindfully apply <strong>the</strong>ir experiences
and skills. Conversely, Kogut and Zander (1992) argue that <strong>the</strong> overall knowledge-related capability <strong>of</strong>
an organization does not depend on single individuals; according to <strong>the</strong>m, if one individual leaves <strong>the</strong>
organization, its overall capability is only marginally affected. Later research emphasizes that <strong>the</strong> key
in utilizing knowledge capabilities lies in <strong>the</strong> coordination <strong>of</strong> individuals (Grant 1996; Orlikowski 2002),
<strong>the</strong>rewith conceptualizing <strong>the</strong> capability as collective but rooted in <strong>the</strong> practices individuals engage in.
(4) While mostly it is agreed upon or implied that knowledge capability is beneficial for
organizations, some studies portray knowledge capabilities as possibly detrimental for
organizations (Value)
It is <strong>of</strong>ten implied that an increase in knowledge or <strong>the</strong> ‘creation’ <strong>of</strong> new knowledge is positively
related to strategic organizational capabilities (Nonaka 1994). However, knowledge related
capabilities have been said to be mostly <strong>of</strong> long-term benefit (Cohen & Levinthal 1990) and expensive
to ‘create’ (Hill & Hoskisson 1987). Moreover, dormant knowledge (not unlike o<strong>the</strong>r organizational
resources) is <strong>of</strong> little use unless applied (Makadok 2001; McDermott 1999). Fur<strong>the</strong>rmore, a knowledge
related capability can turn into a non-capability as a result <strong>of</strong> environmental changes (Orlikowski
2002). Consequently, knowledge not only creates costs for organizations, potentially outweighing <strong>the</strong>
benefits it provides (Prieto & Easterby-Smith 2006), but might also require a resource- and timeintensive
‘unlearning’ process (Autio, Sapienza, & Almeida 2000; Bettis & Prahalad 1995).
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Max Erik Rohde and David Sundaram
2.2 Technological approaches for fostering knowledge-based capability
In <strong>the</strong> previous section, a number <strong>of</strong> conflicting perspectives on <strong>the</strong> objectiveness <strong>of</strong> organizational
knowledge capability, its dynamicity, its collectivity and value are portrayed. These perspectives are
reflected in alternative technological approaches to increase knowledge capabilities. In <strong>the</strong> following,
technological approaches aligning to each <strong>of</strong> <strong>the</strong> perspectives discussed above are discussed.
(1) ‘Objective’ and context focused approaches
Following an ‘objective’ understanding <strong>of</strong> knowledge, information technology is utilized to model,
engineer (Studer, Benjamins, & Fensel 1998) or represent knowledge (Mylopoulos 1981). Such
approaches, for instance, advocate and enable <strong>the</strong> codification <strong>of</strong> knowledge in a knowledge base.
However, recent knowledge management literature is increasingly disillusioned with approaches
being focused on <strong>the</strong> codification <strong>of</strong> knowledge (Davenport 2011). Many contemporary researchers
argue for systems, which appreciate <strong>the</strong> inherently tacit contexts <strong>of</strong> knowledge (Kwan &
Balasubramanian 2003; Thompson & Walsham 2004).
(2) Repository- and event-based approaches
The different perspectives on <strong>the</strong> dynamicity <strong>of</strong> organizational knowledge have resulted in different
technological responses. Knowledge as a slowly changing capability an organization can ‘possess’
requires large knowledge repositories, in which organizations ‘store’ and access <strong>the</strong> knowledge <strong>the</strong>y
have ga<strong>the</strong>red. Yet, such repositories, for instance <strong>of</strong> <strong>the</strong> ‘lessons learned’ from projects, <strong>of</strong>ten exhibit
low adoption and use (von Zedtwitz 2002). As a result, many contemporary systems to support
knowledge-intensive work focus on <strong>the</strong> provision <strong>of</strong> real-time contextual information, for instance in
<strong>the</strong> form <strong>of</strong> dashboards or personal as well as organizational information feeds (socialtext.com,
liferay.com).
(3) Personal knowledge management systems and group-based approaches
As knowledge is both seen as bound to individuals or essentially collective, technological responses
have been developed to support <strong>the</strong> individual knowledge worker as well as for <strong>the</strong> enhancement <strong>of</strong>
group coordination. So called personal knowledge management systems (PKMS) are traditionally
rooted in psychology and human computer interaction (Lansdale 1988). These systems are aligned to
enhance cognitive performances <strong>of</strong> individuals, for instance creativity or memory. Better performances
<strong>of</strong> <strong>the</strong> individual knowledge worker in turn can lead to increased organizational capabilities
(Davenport, Jarvenpaa, & Beers 1996). In alignment with an emphasis on coordination ra<strong>the</strong>r than on
<strong>the</strong> individual’s performance, group based systems (DeSanctis & Gallupe 1987) have been adopted
for enhancing knowledge-related capabilities (Alavi & Leidner 2001). Such systems can attempt to
streamline communication and decision making as well as contribute to <strong>the</strong> emergence and
stabilization <strong>of</strong> a community <strong>of</strong> individuals engaged in similar practices (Brown & Duguid 1991).
Although personal knowledge management systems and group based approaches represent different
and potentially complementary approaches to facilitate knowledge-based capabilities, <strong>the</strong>re are few
studies, which compare and contrast <strong>the</strong>se approaches directly.
(4) Technology-centric and social approaches
Some perspectives assume that more knowledge leads to increased capabilities. Likewise, a number
<strong>of</strong> technological approaches inherently assume that more technology and automation leads to
increased knowledge-related performances. Yet, it has been argued that technological systems may
inherently embed disciplinary mechanisms <strong>of</strong> surveillance and control (Doolin 1998; Sewell &
Wilkinson 1992); potentially lessening <strong>the</strong> performances <strong>of</strong> knowledge workers, who <strong>of</strong>ten perform
better when granted autonomy (Amabile, Schatzel, Moneta, & Kramer 2004). Recent studies suggest
that <strong>the</strong> requirements <strong>of</strong> knowledge-intensive work are served best by systems, which help to foster
social exchange and structures ra<strong>the</strong>r than ‘managing’ knowledge explicitly (Thompson & Walsham
2004).
The <strong>of</strong>ten fundamentally different perspectives on organizational knowledge lead not only to different
technological approaches to facilitate better knowledge-related performances but also result in
different perceptions <strong>of</strong> technology itself. Recent literature <strong>of</strong>ten favors approaches which place
context over objectified information, real-time information over extensive repositories, and social
aspects over technological mechanisms. While <strong>the</strong>se positions are grounded on solid arguments, a
number <strong>of</strong> fundamental problems can be resulting from <strong>the</strong>m:
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Max Erik Rohde and David Sundaram
While knowledge undeniably requires <strong>the</strong> consideration <strong>of</strong> complex and multidimensional
contexts, a negligence <strong>of</strong> any ‘material’ manifestations results in an underutilization <strong>of</strong> <strong>the</strong>
potential capabilities <strong>of</strong> information systems (Orlikowski 2006).
Although static knowledge repositories <strong>of</strong>ten find little adoption and use, <strong>the</strong>ir potential benefits for
organizations are still substantial. The search engine Google, for instance, provides access to <strong>the</strong>
more or less static repository <strong>of</strong> information accessible through <strong>the</strong> World Wide Web; a repository
<strong>of</strong> significant usefulness for many knowledge-intensive activities.
Both <strong>the</strong> individual knowledge worker as well as <strong>the</strong> coordination <strong>of</strong> groups have been identified
as important sources <strong>of</strong> knowledge capability. However, few approaches investigate how <strong>the</strong>
‘personal knowledge management’ <strong>of</strong> individuals can be linked with coordinating mechanisms to
enable collective capabilities.
While technological mechanisms might be difficult to adapt to <strong>the</strong> complexity <strong>of</strong> knowledge work,
using systems primarily as means to establish and manage social networks might not do justice to
<strong>the</strong>ir full potential. For instance, many knowledge workers spent a significant amount <strong>of</strong> <strong>the</strong>ir time
working with technologies such as email, word processors or web browsers.
These problems motivate <strong>the</strong> objective <strong>of</strong> this research: How can information-intensive systems be
designed which (1) appreciate both <strong>the</strong> tacit context and material manifestations <strong>of</strong> organizational
knowledge, (2) account for <strong>the</strong> dynamic and static dimension <strong>of</strong> knowledge, and (3) link <strong>the</strong> work <strong>of</strong>
individuals with work <strong>of</strong> groups.
3. Methodology
In order to approach <strong>the</strong> objective mentioned in <strong>the</strong> previous section, a multi-methodological system
development approach based on Nunamaker, Chen and Purdin (1991) is adopted. This approach
defines <strong>the</strong> interdependent and non-sequential phases <strong>of</strong> observation, <strong>the</strong>ory building, system
development and experimentation. For <strong>the</strong> initial phase <strong>of</strong> observation, case studies and fur<strong>the</strong>r
academic studies have been consulted to understand contemporary perspectives on <strong>the</strong> problem
area (Section 2). Fur<strong>the</strong>rmore, current technological approaches have been observed through fur<strong>the</strong>r
academic sources and various industry blogs (Section 3). Web resources for each possible
technology have been recorded and categorized using <strong>the</strong> tool diigo.com. Theory building is
conducted through a syn<strong>the</strong>sis <strong>of</strong> claims in <strong>the</strong> literature into implementable guidelines (Section 4).
For <strong>the</strong> phase <strong>of</strong> system development, a s<strong>of</strong>tware prototype has been implemented (Section 5). The
process <strong>of</strong> system development has been extensively documented in source code management
systems, handwritten notes and blog posts. For <strong>the</strong> phase <strong>of</strong> experimentation, <strong>the</strong> behavior <strong>of</strong> <strong>the</strong>
prototype has been constantly verified using an automated test framework. At <strong>the</strong> current stage <strong>of</strong>
development, <strong>the</strong> important ‘last mile’ (Winter 2010) <strong>of</strong> deployment <strong>of</strong> <strong>the</strong> prototype to end users has
not been reached; however, <strong>the</strong> prototype is designed using industry-standard practices and
technologies with a clear intention to disseminate later iterations to possible end users.
4. Guidelines for designing information intensive knowledge support systems
Researchers and practitioners attempting to design systems to facilitate sustained knowledge
capability can build on a rich foundation provided in <strong>the</strong> organizational, knowledge management and
information systems literature in some measure discussed in <strong>the</strong> previous sections. However, few
studies explicitly address all four abovementioned problems. Based on our discussions <strong>of</strong>
perspectives on sustained knowledge capability and technological approaches to enhance
knowledge-related performances, a number <strong>of</strong> guidelines for designing information intensive
knowledge support systems is syn<strong>the</strong>sized. Each <strong>of</strong> <strong>the</strong>se guidelines is aligned to address one <strong>of</strong> <strong>the</strong>
problems mentioned in <strong>the</strong> previous section.
Design Guideline 1: Connect versatile contexts through information
Knowledge is both seen in an objectivist fashion as well as uniquely bound to complex contexts.
Systems handling knowledge as an object have lead to unarguably useful applications, such as <strong>the</strong>
Google search engine, which is part <strong>of</strong> <strong>the</strong> essential repertoire <strong>of</strong> many knowledge workers. However,
<strong>the</strong> negligence <strong>of</strong> important contextual information has led to <strong>the</strong> failure <strong>of</strong> many organizational
knowledge management systems. The first guideline proposes that systems should focus on
connecting invariably tacit and versatile contexts through necessarily explicit information. In that,
designers should acknowledge that information systems ultimately cannot process knowledge but
only link contexts separated by space and time through information. Many successful knowledge
management applications are built upon this principle. For instance, emails can be <strong>the</strong> link between
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o<strong>the</strong>rwise distributed knowledge capabilities. A simple email to an IT help desk connects <strong>the</strong>
knowledge <strong>of</strong> <strong>the</strong> problem with <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> expert to solve <strong>the</strong> problem. Fur<strong>the</strong>rmore, many
knowledge workers see <strong>the</strong>ir email archive as an important support <strong>of</strong> <strong>the</strong>ir memory, for instance a
lawyer might access an email sent from a client sent a few month ago, establishing a link between her
own memories with knowledge embedded with <strong>the</strong> situational message <strong>of</strong> <strong>the</strong> client.
Design Guideline 2: Be aware that situational context and structural parameters <strong>of</strong> <strong>the</strong>
same are mutually constitutive
Some perspectives on knowledge portray it as more or less static capability <strong>of</strong> organizations whereas
o<strong>the</strong>rs pose that knowledge is inherently situational. IT based approaches <strong>of</strong>ten fail if <strong>the</strong>ir focus is on
<strong>the</strong> capture and storage <strong>of</strong> organizational knowledge in extensive repositories. Research on
organizational routes suggests that <strong>the</strong> structural dimension <strong>of</strong> routines is enmeshed with <strong>the</strong>ir
situational application. Fur<strong>the</strong>rmore, organizational research suggests that organizations should align
<strong>the</strong> structural foundation and situational application <strong>of</strong> routines carefully in order to take right and
‘knowledgeable’ decisions (Weick & Sutcliffe 2006; Whyte, Ewenstein, Hales, & Tidd 2008).
Accordingly, <strong>the</strong> second guideline suggests that designers <strong>of</strong> knowledge-supporting IT systems
should consider both <strong>the</strong> immediate situational contexts knowledge workers encounter as well as <strong>the</strong>
constraints and support <strong>of</strong> established structures. For example, sending an email message is an
immediate action <strong>of</strong> a knowledge worker originating from a situational context, and should be fast and
effortless. However, <strong>the</strong> worker might be required or wanting to store <strong>the</strong> email message for future
reference. It is desirable that categories available to aid <strong>the</strong> storing <strong>of</strong> <strong>the</strong> message are well-aligned
with <strong>the</strong> problem at hand.
Design Guideline 3: Focus on individuals as mediators <strong>of</strong> knowledge flows
It has both been argued that <strong>the</strong> individual's role for <strong>the</strong> organizational capability is negligible as well
as that knowledge cannot be seen apart from <strong>the</strong> 'knower'. Most organizational knowledge
management systems are aligned to support <strong>the</strong> work <strong>of</strong> groups; less attention is given to support <strong>the</strong>
cognitive processes <strong>of</strong> <strong>the</strong> individual. However, most actions and decisions in <strong>the</strong> organizational
context ultimately depend on <strong>the</strong> insights <strong>of</strong> individuals; especially since individual knowledge workers
<strong>of</strong>ten significantly influence organizational decisions (Scarbrough 1999). For this reason, <strong>the</strong> third
design guideline suggests to acknowledge that individuals and <strong>the</strong>ir cognitive abilities and constraints
are central for facilitating and enacting organizational knowledge capability (Schneckenberg 2009).
For instance, an organization might build up <strong>the</strong> capability to react mindfully to nuclear emergencies
through monitored procedures, redundant systems and constant quality control. However, without <strong>the</strong>
right judgment <strong>of</strong> key personnel in critical situations this capability cannot be fully enacted (Weick,
Sutcliffe, & Obstfeld 1999).
Design Guideline 4: Be aware <strong>of</strong> both <strong>the</strong> benefits and costs <strong>of</strong> knowledge and
technology
Knowledge is rarely seen as something potentially detrimental to an organization. However, some
studies portray organizational capabilities as useful only in relationship to <strong>the</strong> environment in which
<strong>the</strong>y can be enacted (Levinthal & Rerup 2006). Likewise, technological systems designed to support
organizational work might ei<strong>the</strong>r enable better performances or prohibit <strong>the</strong>m by constraining workers
to find <strong>the</strong> best response to a situation (Berente & Yoo forthcoming; Pentland & Feldman 2005).
Designers <strong>of</strong> technological knowledge support systems should <strong>the</strong>refore weigh <strong>the</strong> benefits <strong>of</strong>
additional knowledge capability and technological systems with <strong>the</strong>ir potential costs such as avoidable
restrictions to knowledge workers’ autonomy.
5. Information intensive knowledge support system implementation
In order to illustrate <strong>the</strong> guidelines proposed in <strong>the</strong> previous section as well as to explore <strong>the</strong>ir
feasibility, a pro<strong>of</strong>-<strong>of</strong>-concept prototype <strong>of</strong> an information intensive knowledge support system
adhering to <strong>the</strong> proposed guidelines has been implemented. A number <strong>of</strong> practical design challenges
encountered in design and implementation <strong>of</strong> <strong>the</strong> prototype are listed in <strong>the</strong> following.
Design Challenge 1: Versatility <strong>of</strong> contexts prohibits defining an a priori structure for <strong>the</strong>
persistence <strong>of</strong> information
Guideline 1 suggests connecting versatile context through information. One fundamental challenge in
adhering to this guideline in our implementation has been that <strong>the</strong> nature and kind <strong>of</strong> contexts which
needed to be supported cannot be ultimately specified. In response, generic directed, unlabelled
graphs whose nodes can be any Java or JavaScript object (this includes basic data types such as
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Max Erik Rohde and David Sundaram
Strings, Booleans and Integers as well as complex composite objects) are adopted. Exhibit 1 shows a
simple graph specified in <strong>the</strong> domain specific language <strong>of</strong> <strong>the</strong> prototype:
Network n = Nx.newNetwork();
String primes = "A few prime numbers";
Nx.put().node(primes).inNetwork(n);
Nx.append().node(2).to(primes).inNetwork(n);
Nx.append().node(3).to(primes).inNetwork(n);
Exhibit 1: Definition <strong>of</strong> a simple graph
The created graph can easily be rendered into <strong>the</strong> semantic web format RDF, XML or end user
friendly HTML using <strong>the</strong> Jena Semantic Web Framework and XStream Framework. While many
technologies allow persisting data in graph-like structures, few <strong>of</strong> <strong>the</strong>se allow <strong>the</strong> flexibility <strong>of</strong> working
with generic objects as nodes <strong>of</strong> <strong>the</strong> network.
Design Challenge 2: Without strict data structures, structural parameters <strong>of</strong> knowledge
cannot be established
Guideline 2 suggests that a system should be able to consider both situational requirements as well
as possible structural parameters for knowledge work. The example above, for instance, could reflect
<strong>the</strong> situational requirement to specify a selection <strong>of</strong> prime numbers. The only structural parameter
embedded in <strong>the</strong> system is to adhere to a directed, unlabelled graph. However, if specifying prime
numbers were a recurring task, it would be desirable to specify restricting rules to assure <strong>the</strong> quality <strong>of</strong>
data added to <strong>the</strong> graph. For instance, it could be specified that only numbers or integers are valid
prime numbers. Such a rule can be specified with <strong>the</strong> domain specific language <strong>of</strong> <strong>the</strong> prototype as
follows:
Tree primesRule = Nx.defineTree().withRoot(primes)
.whichLinksTo(Nodes.only(Nx.instanceOf(Integer.class)));
Exhibit 2: Definition <strong>of</strong> simple syntactical rule
The initially created network can now be validated against this rule and any violation can be reported
to <strong>the</strong> user. Such syntactical rules can be gradually imposed on complex information in order to reflect
structural constraints in different degrees <strong>of</strong> severity. Although <strong>the</strong>re are a number <strong>of</strong> approaches to
validate data, most <strong>of</strong> <strong>the</strong>se focus on individual objects or two-dimensional data sets and few support
<strong>the</strong> specification <strong>of</strong> network-based rules.
Design Challenge 3: ‘Local’ personal information must be presented to <strong>the</strong> individual with
reduced complexity while still allowing access to ‘global’ organizational information
Guideline 3 suggests focusing on individuals as mediators <strong>of</strong> complex organizational knowledge
flows. Consequently, information must be presented in a form tailored to <strong>the</strong> individual while
remaining fully compatible with richer and more complex organizational repositories. To meet this
challenge, <strong>the</strong> prototype supports ‘local’ and ‘global’ networks, which can be woven toge<strong>the</strong>r into
customized networks using two-way synchronization. In <strong>the</strong> example below (Exhibit 3), <strong>the</strong> initial
‘prime number network’ is extended by adding a uniquely identified node numberRanges. This node
is <strong>the</strong>n added to a customized local user network as an address reference. This reference can be
resolved into <strong>the</strong> actual node residing in <strong>the</strong> global network by utilizing <strong>the</strong> network synchronization
mechanism <strong>of</strong> <strong>the</strong> prototype.
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Max Erik Rohde and David Sundaram
String url = "http://www.example.com/numberRanges";
Object numberRanges = Nx.define("Number Ranges")
.at(url);
Nx.put().node(numberRanges).inNetwork(n);
Nx.append().node(primes).to(numberRanges).inNetwork(n);
…
// local client
SynchronizedNetwork user1Network =
NxSync.connectTo("http://www.example.com/");
Object user1 = Nx.define("User 1 Data")
.at("http://www.example.com/user1");
Nx.put().node(user1).inNetwork(user1Network);
Nx.append().node(Nx.reference(url))
.to(user1).inNetwork(user1Network);
user1Network.synchronize(url);
Exhibit 3: Connection <strong>of</strong> global and local network
Design Challenge 4: Graphs are complex structures; <strong>the</strong>ir composition potentially
interferes with natural ways <strong>of</strong> conducting knowledge work.
Guideline 4 suggests considering both <strong>the</strong> benefits and potential effects <strong>of</strong> technology and
knowledge. If <strong>the</strong> interaction with <strong>the</strong> s<strong>of</strong>tware is complex, knowledge workers are likely to perceive
<strong>the</strong> s<strong>of</strong>tware as invasive with <strong>the</strong>ir work practices. To mitigate <strong>the</strong> complexities <strong>of</strong> graphs, a number <strong>of</strong>
mechanisms are employed in <strong>the</strong> prototype: (1) although recursive and multidimensional graphs can
be defined, <strong>the</strong> user is primarily exposed to tree structures familiar from navigating file and folder
structures, and (2) <strong>the</strong> networks are composed using a very small set <strong>of</strong> operations. The operations
supported to compose networks are: (1) Add a new text based node, (2) drag and drop a file or image
onto a node, (3) move a node from one parent to ano<strong>the</strong>r, (4) edit <strong>the</strong> content <strong>of</strong> textual nodes and (5)
delete nodes. Exhibit 4 illustrates how <strong>the</strong>se interactions are supported in <strong>the</strong> user interface
component <strong>of</strong> <strong>the</strong> prototype implemented using Google Web Toolkit (code.google.com/webtoolkit).
Exhibit 4: JavaScript based drag and drop interface
While <strong>the</strong>re are many solutions to manage complex content repositories, many <strong>of</strong> <strong>the</strong>se are ei<strong>the</strong>r not
graph-based or difficult to use; Micros<strong>of</strong>t SharePoint would align into <strong>the</strong> first category, while many
semantic web based approaches would align into <strong>the</strong> second. In <strong>the</strong> process <strong>of</strong> designing,
implementing and using <strong>the</strong> system, a great number <strong>of</strong> design challenges have been uncovered.
While four central challenges are documented in this paper, many more such challenges are
documented in more than 100 posts on a project blog (nexnet.wordpress.com). Moreover, <strong>the</strong>
stringent documentation and monitoring <strong>of</strong> <strong>the</strong> design and implementation process allows having
some understanding <strong>of</strong> <strong>the</strong> program complexity necessary to meet essential aspects <strong>of</strong> <strong>the</strong> proposed
guidelines. The current prototype encompasses 28 modules with 22,000 lines <strong>of</strong> code and 434 Java
classes/interfaces (including JUnit tests), <strong>the</strong> spent development time approximates 250 person days.
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Max Erik Rohde and David Sundaram
6. Conclusion, limitations and future research
Knowledge management was inspired in its very roots by <strong>the</strong> novel possibilities <strong>of</strong>fered by information
technology (McDermott 1999). Yet, recent research recommends great caution in focussing on
technological mechanisms to ‘manage’ inherently social knowledge (Davenport 2011; Thompson &
Walsham 2004). A number <strong>of</strong> guidelines for designing information intensive knowledge-support
systems are proposed in this paper, which acknowledge and consider <strong>the</strong> tacit nature <strong>of</strong> knowledge
but none<strong>the</strong>less aspire to leverage <strong>the</strong> potentials an information-centric approach <strong>of</strong>fers.
One <strong>of</strong> <strong>the</strong> key claims in <strong>the</strong>se guidelines is to understand information as a way to connect tacit
contexts o<strong>the</strong>rwise separated by time and space. As one possible implementation <strong>of</strong> a generic
mechanism to author, navigate and query such connections, a prototype based on a directed,
unlabeled graph <strong>of</strong> objects has been implemented. This prototype allows <strong>the</strong> fusion <strong>of</strong> local, personal
and global organizational information networks, <strong>the</strong> representation <strong>of</strong> information in various formats,
as well as <strong>the</strong> manipulation <strong>of</strong> networks using an intuitive drag and drop based user interface.
This research is primarily an illustration and an explorative implementation <strong>of</strong> <strong>the</strong> guidelines derived
from <strong>the</strong> literature. The process <strong>of</strong> design and implementation has been carefully documented using a
project blog, a source code management system and more than 150 illustrative test cases; critical
reflection upon this process against <strong>the</strong> claims and requirements set forth in <strong>the</strong> literature is constantly
undertaken. However, a formal evaluation to establish a claim <strong>of</strong> pre-eminence <strong>of</strong> <strong>the</strong> propsoed
guidelines or prototype over related approaches has not been pursued.
Both <strong>the</strong> proposed guidelines and <strong>the</strong> implemented prototype <strong>of</strong>fer promising avenues for future
research. Alternative implementations for <strong>the</strong> proposed guidelines could be explored and compared
with <strong>the</strong> current iteration <strong>of</strong> <strong>the</strong> prototype. In addition, <strong>the</strong> current iteration <strong>of</strong> <strong>the</strong> prototype can be
adapted to and tested against <strong>the</strong> requirements <strong>of</strong> a specific domain, for instance knowledge
management in <strong>the</strong> legal pr<strong>of</strong>ession. Moreover, networks authored with <strong>the</strong> prototype can be
analysed qualitatively and quantitatively as every user interaction in <strong>the</strong> prototype is recorded and <strong>the</strong>
prototype has been extensively used by a number <strong>of</strong> testers.
References
Alavi, M., & Leidner, D. (2001). Review: Knowledge Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues. MIS Quarterly, 25(1).
Amabile, T., Schatzel, E., Moneta, G., & Kramer, S. (2004). Leader behaviors and <strong>the</strong> work environment for
creativity: Perceived leader support. The Leadership Quarterly, 15(1), 5-32.
Autio, E., Sapienza, H., & Almeida, J. (2000). Effects <strong>of</strong> Age at Entry, Knowledge Intensity, and Imitability on
International Growth. The Academy <strong>of</strong> Management Journal, 43(5).
Berente, N., & Yoo, Y. (forthcoming). Institutional Contradictions and Loose Coupling: Post-Implementation <strong>of</strong>
NASA’s Enterprise Information System. Information Systems Research
Bettis, R., & Prahalad, C. K. (1995). The Dominant Logic: Retrospective and Extension. Strategic Management
Journal, 16(1), 5-14.
Brown, J., & Duguid, P. (1991). Organizational Learning and Communities-<strong>of</strong>-Practice: Toward a Unified View <strong>of</strong>
Working, Learning, and Innovation. Organization Science, 2(1), 40-57.
Cohen, W., & Levinthal, D. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation.
Administrative Science Quarterly, 35(1), 128-152.
Cook, S., & Brown, J. (1999). Bridging Epistemologies: The Generative Dance between Organizational
Knowledge and Organizational Knowing. Organization Science, 10(4), 381-400.
Davenport, T. (2011). Rethinking knowledge work: A strategic approach. McKinsey Quarterly.
Davenport, T., Jarvenpaa, S., & Beers, M. (1996). Improving Knowledge Work Processes. Sloan management
review, 37(4), 53-65.
Davenport, T., & Prusak, L. (1998). Working Knowledge: How Organizations Manage What They Know: Harvard
Business School Press.
DeSanctis, G., & Gallupe, B. (1987). A Foundation for <strong>the</strong> Study <strong>of</strong> Group Decision Support Systems.
Management Science, 33(5), 589-609.
Doolin, B. (1998). Information technology as disciplinary technology: being critical in interpretive research on
information systems. Journal <strong>of</strong> Information Technology (Routledge, Ltd.), 13(4), 301.
Grant, R. (1996). Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge
Integration. Organization Science, 7(4), 375-387.
Hill, C., & Hoskisson, R. (1987). Strategy and Structure in <strong>the</strong> Multiproduct Firm. The Academy <strong>of</strong> Management
Review, 12(2), 331-341.
Kogut, B., & Zander, U. (1992). Knowledge <strong>of</strong> <strong>the</strong> Firm, Combinative Capabilities, and <strong>the</strong> Replication <strong>of</strong>
Technology. Organization Science, 3(3), 383-397.
856

Max Erik Rohde and David Sundaram
Kraaijenbrink, J., Spender, J. C., & Groen, A. (2010). The Resource-Based View: A Review and Assessment <strong>of</strong>
Its Critiques. Journal <strong>of</strong> Management, 36(1), 349-372.
Kwan, M., & Balasubramanian, P. (2003). KnowledgeScope: managing knowledge in context. Decision Support
Systems, 35(4), 467-486.
Lansdale, M. W. (1988). The psychology <strong>of</strong> personal information management. Applied Ergonomics, 19(1), 55-
66.
Levinthal, D., & Rerup, C. (2006). Crossing an Apparent Chasm: Bridging Mindful and Less-Mindful Perspectives
on Organizational Learning. Organization Science, 17(4), 502-513.
Makadok, R. (2001). Toward a Syn<strong>the</strong>sis <strong>of</strong> <strong>the</strong> Resource-Based and Dynamic-Capability Views <strong>of</strong> Rent
Creation. Strategic Management Journal, 22(5), 387-401.
McDermott, R. (1999). Why information technology inspired but cannot deliver knowledge management.
California Management Review, 41(4), 103.
Mylopoulos, J. (1981). An overview <strong>of</strong> Knowledge Representation. SIGART Bull.(74), 5-12.
Nonaka, I. (1994). A Dynamic Theory <strong>of</strong> Organizational Knowledge Creation. Organization Science, 5(1), 14-37.
Nunamaker, J., Chen, M., & Purdin, T. (1991). Systems Development in Information Systems Research. Journal
<strong>of</strong> Management Information Systems, 7(3).
Orlikowski, W. (2002). Knowing in Practice: Enacting a Collective Capability in Distributed Organizing.
Organization Science, 13(3), 249-273.
Orlikowski, W. (2006). Material knowing: <strong>the</strong> scaffolding <strong>of</strong> human knowledgeability. <strong>European</strong> Journal <strong>of</strong>
Information Systems, 15(5), 460-466.
Pentland, B., & Feldman, M. (2005). Organizational routines as a unit <strong>of</strong> analysis. Industrial and Corporate
Change, 14(5), 793-815.
Polanyi, M. (1966). The Tacit Dimension: Doubleday & Co Inc.
Prieto, I., & Easterby-Smith, M. (2006). Dynamic capabilities and <strong>the</strong> role <strong>of</strong> organizational knowledge: an
exploration. <strong>European</strong> Journal <strong>of</strong> Information Systems, 15(5), 500-510.
Scarbrough, H. (1999). Knowledge as Work: Conflicts in <strong>the</strong> Management <strong>of</strong> Knowledge Workers. Technology
Analysis & Strategic Management, 11(1), 5-16.
Schneckenberg, D. (2009). Web 2.0 and <strong>the</strong> empowerment <strong>of</strong> <strong>the</strong> knowledge worker. Journal <strong>of</strong> Knowledge
Management, 13.
Sewell, G., & Wilkinson, B. (1992). `Someone to Watch Over Me': Surveillance, Discipline and <strong>the</strong> Just-in-Time
Labour Process. Sociology, 26(2), 271-289.
Spender, J. C., & Grant, R. (1996). Knowledge and <strong>the</strong> Firm: Overview. Strategic Management Journal, 17, 5-9.
Spender, J. C., & Scherer, A. (2007). The Philosophical Foundations <strong>of</strong> Knowledge Management: Editors'
Introduction. Organization, 14(1), 5-28.
Studer, R., Benjamins, R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data &
Knowledge Engineering, 25(1-2), 161-197.
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic
Management Journal, 18(7), 509-533.
Thompson, M. P. A., & Walsham, G. (2004). Placing Knowledge Management in Context. Journal <strong>of</strong>
Management Studies, 41(5), 725-747.
van Zolingen, S. J., Streumer, J. N., & Stooker, M. (2001). Problems in Knowledge Management: A Case Study
<strong>of</strong> a Knowledge-Intensive Company. International Journal <strong>of</strong> Training and Development, 5(3), 168-184.
von Zedtwitz, M. (2002). Organizational learning through post–project reviews in R&D. R&D Management, 32(3),
255-268.
Weick, K., & Sutcliffe, K. (2006). Mindfulness and <strong>the</strong> Quality <strong>of</strong> Organizational Attention. Organization Science,
17(4), 514-524.
Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (1999). Organizing for high reliability: Processes <strong>of</strong> collective
mindfulness. Research in organizational behavior, 21, 23-81.
Whyte, J., Ewenstein, B., Hales, M., & Tidd, J. (2008). Managing Knowledge Representation in Design. In H.
Scarbrough (Ed.), The evolution <strong>of</strong> business knowledge (pp. 189-214): Oxford University Press.
Winter, R. (2010). Interview with Jay F. Nunamaker, Jr. on “Toward a Broader Vision <strong>of</strong> IS Research”. Business &
Information Systems Engineering, 2(5), 321-329.
857

A Ranking Method for Identification <strong>of</strong> Crucial Knowledge
Inès Saad
Amiens School <strong>of</strong> Management, 80000 Amiens, France
ines.saad@u-picardie.fr
Abstract: The objective <strong>of</strong> this paper is to propose a methodology based on multi-criteria decision aid to rank
crucial knowledge. The methodology is composed <strong>of</strong> three phases. The first phase consists on inferring <strong>the</strong>
preference model <strong>of</strong> <strong>the</strong> decision makers, <strong>the</strong> second phase applies <strong>the</strong> previous model to identify crucial
knowledge, and finally <strong>the</strong> third phase consists on ranking this crucial knowledge into classes <strong>of</strong> equivalence.
Using our method allows to take into account <strong>the</strong> preferences <strong>of</strong> decision makers that can be different or even
contradictory while exploiting and managing <strong>the</strong>ir multiple point <strong>of</strong> views to identify knowledge, without using a
quantitative measuring approach.
Keywords: Knowledge Management, crucial knowledge, ranking problematic, multicriteria decision aid
1. Introduction
Companies should invest in methods and tools in order to preserve knowledge especially those <strong>of</strong>
tacit nature. Researchers in knowledge management (e.g. Davenport et al. (1998); Nonaka and
Takeuchi (1995); O’Learly (1998); Sanchez (1997); Schreiber et al. (2000)) have been focusing on <strong>the</strong>
problems <strong>of</strong> acquisition, preservation and transfer <strong>of</strong> knowledge. However, considering <strong>the</strong> large
amount <strong>of</strong> knowledge to be preserved, <strong>the</strong> firm must first determine <strong>the</strong> specific knowledge that
should be targeted by capitalization. We should indeed focus on only <strong>the</strong> so-called “crucial
knowledge”, i.e. <strong>the</strong> risk <strong>of</strong> <strong>the</strong>ir loss and <strong>the</strong> cost <strong>of</strong> <strong>the</strong>ir (re)creation is considered to be important. In
o<strong>the</strong>r words <strong>the</strong>ir contribution to reach <strong>the</strong> firm objectives is very important and <strong>the</strong>ir use duration is
long. Previous research works (Saad et al. 2005) also revealed <strong>the</strong> interest <strong>of</strong> <strong>the</strong> identification <strong>of</strong>
crucial knowledge. Not enough works exist concerning <strong>the</strong> identification <strong>of</strong> knowledge on which it is
necessary to capitalize (Grundstein 2000; Tseng and Huang 2005), thus, we have proposed a
multicriteria method based on DRSA (Dominance-based Rough Set Approach) (Greco et al. (2001))
to identify crucial knowledge in order to justify a situation where knowledge capitalization is advisable.
The objective <strong>of</strong> our methodology is to elicit <strong>the</strong> preference <strong>of</strong> <strong>the</strong> decision makers (Saad & chakhar
2009; Saad et al., 2009).
However, if <strong>the</strong> amount <strong>of</strong> crucial knowledge to preserve is large and/or <strong>the</strong> resources <strong>of</strong> organization
are limited, it is necessary to rank crucial knowledge and to construct a best class <strong>of</strong> crucial
knowledge to be preserved. The aim <strong>of</strong> this paper is to propose a multicriteria methodology. The first
phase consists on inferring <strong>the</strong> preference model <strong>of</strong> <strong>the</strong> decision makers, <strong>the</strong> second phase applies
<strong>the</strong> previous model to identify crucial knowledge, and finally <strong>the</strong> third phase consists on ranking this
crucial knowledge belonging to a given set <strong>of</strong> crucial knowledge into classes <strong>of</strong> equivalence. In this
paper, we have applied <strong>the</strong> ELECTRE IV method (Roy, 1996) to construct a best class <strong>of</strong> crucial
knowledge.
The rest <strong>of</strong> <strong>the</strong> paper is structured as follows. Section 2 provides an overview on <strong>the</strong> related works.
Section 3 presents <strong>the</strong> methodology used to identify knowledge to be capitalized. In section 4, we
present an application. Section 5 summarizes our contribution.
2. Related works
Not enough works exist concerning <strong>the</strong> identification <strong>of</strong> knowledge on which it is necessary to
capitalize. In this section, we describe four methods: GAMETH framework (Grundstein (2000),
Grundstein et al., (2006)), <strong>the</strong> method proposed by Tseng and Huang (2005), <strong>the</strong> method <strong>of</strong>
identification <strong>of</strong> critical knowledge proposed in Ermine et al. (2006), and <strong>the</strong> method proposed by
(Saad et al. 2005). As for knowledge collection, we think that GAMETH framework proposed by
Grundstein (2000) enables to study <strong>the</strong> area and to clarify <strong>the</strong> needs in knowledge required to deal
with pertinent problems through <strong>the</strong> modelling and analysis <strong>of</strong> sensitive processes in <strong>the</strong> company.
This approach involves all <strong>the</strong> actors participating in <strong>the</strong> area <strong>of</strong> <strong>the</strong> study. Tseng and Huang (2005)
use <strong>the</strong> DELPHI method to collect <strong>the</strong> need in knowledge. The merit <strong>of</strong> this method is <strong>the</strong> fact that
<strong>the</strong>y are faster to apply than <strong>the</strong> one <strong>of</strong> GAMETH ® . Fur<strong>the</strong>r, DELPHI technique may be used
remotely. Finally, <strong>the</strong> method proposed by Ermine et al. (2006) is evenly based on both a series <strong>of</strong>
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Inès Saad
interviews with <strong>the</strong> leaders, and <strong>the</strong> study <strong>of</strong> strategic documents. This last approach assumes
however that <strong>the</strong> leaders are able to identify <strong>the</strong> knowledge to evaluate.
(Saad et al. 2009) have proposed a multicriteria method to justify a situation where knowledge
capitalization is advisable. The methodology is composed <strong>of</strong> three phases. The first phase is relative
to constructive learning. The second phase consists in inferring <strong>the</strong> preference model <strong>of</strong> <strong>the</strong> decision
makers, and finally <strong>the</strong> third phase applies <strong>the</strong> previous model to evaluate o<strong>the</strong>r knowledge used in
<strong>the</strong> same process or o<strong>the</strong>r processes <strong>of</strong> similar projects. Using this methodology allows to take into
account <strong>the</strong> preferences <strong>of</strong> decision makers that can be different or even contradictory while
exploiting and managing <strong>the</strong>ir multiple point <strong>of</strong> views to evaluate knowledge, without using a
quantitative measuring approach. Moreover, because <strong>of</strong> <strong>the</strong> large amount <strong>of</strong> crucial knowledge to
analyze and capitalize, it is necessary to identify a subset <strong>of</strong> crucial knowledge.
3. Methodology
The methodology for crucial knowledge identification and evaluation is composed <strong>of</strong> three phases.
.
Figure 1: Methodology
3.1 Phase 1: Constructing Preference model
The first phase is relative to constructive learning devoted to infer <strong>the</strong> preference model <strong>of</strong> <strong>the</strong>
decision makers. Constructive learning, in contrast to descriptive learning, assumes that <strong>the</strong>
preference model is not pre-existing but is interactively constructed by explicitly involving <strong>the</strong> decision
maker. Practically, it consists in inferring a set <strong>of</strong> decision rules from some holistic information in terms
<strong>of</strong> assignment examples provided by <strong>the</strong> decision makers. This is done through <strong>the</strong> DRSA (Greco et
al. (2001)) method which is devoted to multi-criteria sorting problems. This phase includes also <strong>the</strong>
identification, <strong>of</strong> a set <strong>of</strong> reference crucial knowledge.
The construction <strong>of</strong> <strong>the</strong> reference crucial knowledge is based on <strong>the</strong> identification and <strong>the</strong> analysis <strong>of</strong>
sensitive processes to determine <strong>the</strong> need <strong>of</strong> knowledge necessary to solve problems related to <strong>the</strong>se
sensitive processes.
The first phase includes <strong>the</strong> construction <strong>of</strong> preference model and <strong>the</strong> evaluation <strong>of</strong> knowledge with
<strong>the</strong> respect to a convenient set <strong>of</strong> criteria. Three sub-families <strong>of</strong> criteria were constructed: (i)
knowledge vulnerability family that are devoted to measure <strong>the</strong> risk <strong>of</strong> knowledge lost and <strong>the</strong> cost <strong>of</strong>
<strong>the</strong>ir (re)creation; (ii) knowledge role family that are used to measure <strong>the</strong> contribution <strong>of</strong> <strong>the</strong>
knowledge to reach <strong>the</strong> project objectives; and (iii) knowledge use duration family that aims at
measuring <strong>the</strong> duration <strong>of</strong> using <strong>the</strong> knowledge based on <strong>the</strong> company average and long term
objectives. We have developed an algorithm for maximizing <strong>the</strong> degree <strong>of</strong> <strong>the</strong> minimal contribution <strong>of</strong>
knowledge to each objective (Saad et al. 2005).
The evaluation <strong>of</strong> knowledge with respect to criteria <strong>of</strong> families (i) and (iii) are normally provided by <strong>the</strong>
decision maker. However, in practice <strong>the</strong> decision makers may show some difficulty in directly
evaluating knowledge due to <strong>the</strong> complexity <strong>of</strong> some criteria. To overcome this problem, complex
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Inès Saad
criteria are decomposed into several more simple indicators so that decision makers can easily
evaluate <strong>the</strong>se indictors.
Once all knowledge items are evaluated with respect to all criteria, <strong>the</strong> next step is an iterative
procedure aiming at jointly infers <strong>the</strong> decision rules. Two decision classes have been defined Cl1:
“non crucial knowledge” and Cl2: “crucial knowledge”. This procedure is based on DRSA and it is
composed <strong>of</strong> four steps
- Step 1: Individual assignment
The first step assigns, with <strong>the</strong> help <strong>of</strong> each decision-maker, a set <strong>of</strong> knowledge items called
“Reference crucial knowledge” in <strong>the</strong> decision classes Cl1 and Cl2. The decision table contains, in
addition to <strong>the</strong> columns related to vulnerability, contribution degree and use duration criteria, as many
columns as decision makers (D1, D2...Dn). Once <strong>the</strong> whole decision table is generated, it will be used
as <strong>the</strong> input <strong>of</strong> <strong>the</strong> second step.
- Step 2: Generation <strong>of</strong> individual decision rules
The second step infers decision rules for each assignment sample determined in <strong>the</strong> previous step.
The obtained results are translated to <strong>the</strong> form <strong>of</strong> approximation quality which allows to verify <strong>the</strong>
presence <strong>of</strong> inconsistencies in <strong>the</strong> decision rules:
≥
card( A − ( Bn ( Cl ))
γ F =
U t = 1 ,..., n F t
card(
A )
We have applied <strong>the</strong> DOMLEM algorithm (Greco et al. 2001), proposed in DRSA method to infer rules
permitting to characterize knowledge assigned to classes Cl1 and Cl2. The obtained results are
transformed in <strong>the</strong> form <strong>of</strong> approximation quality, and permitted us to verify <strong>the</strong> presence <strong>of</strong>
inconsistencies in <strong>the</strong> decision rules. These rules are deduced from <strong>the</strong> comparison <strong>of</strong> information
related to <strong>the</strong> assignment examples intuitively provided by each decision maker, and <strong>the</strong> assignment
generated by <strong>the</strong> algorithm.
- Step 3: suppression <strong>of</strong> inconsistencies in individual decision rules
The third step modifies sample assignments or evaluations in collaboration with <strong>the</strong> concerned
decision-maker when inconsistencies are detected in <strong>the</strong> decision rule base. Thus, we ask this
decision maker to carefully reconsider <strong>the</strong> evaluation <strong>of</strong> knowledge.
- Step 4: Generation <strong>of</strong> collective decision rules
In <strong>the</strong> last step we determine decision rules that are collectively accepted. Thus, based on <strong>the</strong>
multicriteria procedure (cf. § 4) we have determined a collective decision rules used to identify <strong>the</strong>
crucial knowledge.
3.2 Phase 2: Classifying “Potential Crucial Knowledge”
In <strong>the</strong> second phase, <strong>the</strong> decision maker uses <strong>the</strong> preference models (collectively decision rules) <strong>of</strong>
<strong>the</strong> different decision makers defined in <strong>the</strong> second phase to assign <strong>the</strong> new knowledge which is
called potential crucial knowledge, to <strong>the</strong> classes Cl1 or Cl2. The results are stored in a performance
table. More specifically, a multi-criteria classification <strong>of</strong> potential crucial knowledge is performed on
<strong>the</strong> basis <strong>of</strong> <strong>the</strong> decision rules that have been collectively identified by <strong>the</strong> decision maker(s) in <strong>the</strong>
second phase. The term <strong>of</strong> potential crucial knowledge refers to <strong>the</strong> knowledge that has been
temporarily identified as crucial by at least one decision maker. The generated potential crucial
knowledge are analyzed and <strong>the</strong>n evaluated against <strong>the</strong> criteria identified in <strong>the</strong> second phase. Then,
<strong>the</strong>y are assigned in one between <strong>the</strong> two decision classes Cl1 or Cl2. One potential crucial
knowledge is considered as effectively crucial if <strong>the</strong>re exists at least one decision rule within <strong>the</strong> rule
base, whose premises are paired with <strong>the</strong> evaluation <strong>of</strong> this knowledge on <strong>the</strong> set <strong>of</strong> criteria. The
general form <strong>of</strong> a decision rule is:
If gj (k) ≥ rgj ; ∀ j ∈{1,…,m} <strong>the</strong>n k ∈ Cl2 where
- g1,… , gm is a family <strong>of</strong> m criteria,
- gj (k) is <strong>the</strong> performance <strong>of</strong> <strong>the</strong> knowledge k on criterion gj ,
- (rg1, … , rgm) ∈ Vg1 x …x Vgm is <strong>the</strong> minimum performance <strong>of</strong> a knowledge k on <strong>the</strong> set <strong>of</strong>
criteria.
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Inès Saad
3.3 Phase 3: Ranking <strong>of</strong> <strong>the</strong> crucial knowledge
If <strong>the</strong> number <strong>of</strong> crucial knowledge identified in <strong>the</strong> phase 2 is very important and/or resources <strong>of</strong>
company are limited, it is necessary to preserve only some crucial knowledge and to construct a best
class <strong>of</strong> crucial knowledge. To construct this class <strong>of</strong> crucial knowledge, we apply a ranking method
(Roy, 1996).
There are many ELECTRE methods to deal with <strong>the</strong> ranking problematic. In this paper we have used
ELECTRE IV (Roy, 1996) which is devoted to <strong>the</strong> ranking problematic. This method assigns a set <strong>of</strong>
crucial knowledge described by different evaluation criteria to a set <strong>of</strong> ordered classes. The ELECTRE
IV method is always able to select <strong>the</strong> best knowledge or a subset <strong>of</strong> knowledge to be analyzed and
preserved by decision makers. In ranking problematic, we are concerned with <strong>the</strong> ranking <strong>of</strong> all <strong>the</strong>
crucial knowledge belonging to a given set <strong>of</strong> crucial knowledge from <strong>the</strong> best to <strong>the</strong> worst, possibly
with ex æquo. This method allows to ranking <strong>the</strong> knowledge without introducing any weighting <strong>of</strong> <strong>the</strong>
criteria.
ELECTRE IV is an example <strong>of</strong> methods that use outranking relations. The outranking relation defined
such that Ki S Kj means <strong>the</strong> evaluation <strong>of</strong> Ki upon all criteria is “at least as good” as evaluation <strong>of</strong> <strong>the</strong>
Kj.
The principal <strong>of</strong> this approach is to establish firstly comparisons <strong>of</strong> knowledge in pairs and <strong>the</strong>n in a
second step aggregating this relations into outranking relation. This outranking relation means that
<strong>the</strong>re is enough evidence to consider that knowledge is at least as good as ano<strong>the</strong>r.
4. Application
The criteria used in our application are: (i) Knowledge vulnerability family including <strong>the</strong> eight criteria
g1,…, g8 (see table1) that are devoted to measure <strong>the</strong> risk <strong>of</strong> knowledge loss and <strong>the</strong> cost <strong>of</strong> its
(re)creation; (ii) Knowledge role family including <strong>the</strong> criteria g9,…,g14 (e.g. <strong>the</strong> criterion g9 : Impact
on product cost and <strong>the</strong> criterion g11: Impact on product developing) that are used to measure <strong>the</strong>
contribution <strong>of</strong> <strong>the</strong> knowledge in <strong>the</strong> project objectives. and (iii) Use duration family including <strong>the</strong>
criterion g15 that is devoted to measure <strong>the</strong> use duration <strong>of</strong> <strong>the</strong> knowledge based on <strong>the</strong> company
average and long term objectives. Once criteria family is constructed, we need to evaluate each
“Reference crucial knowledge” in respect to all criteria (see table 2).
gi
g1
g2
g 3
g 4
g 5
g 6
g 7
g 8
Table1. Knowledge vulnerability family
Criterion
Complexity
Accessibility
Substitutability
Validation type
Transferability
Scarcity
Acquisition cost
Acquisition time
Ki g1 g2 g3 g4 g5 g6 g7 g8 g9 g10 g11 g12 g13 g14 g15
K1
3 3 3 3 3 2 4 4 5 2 5 5 3 5 2
K2 3 3 2 2 3 3 4 4 4 2 4 4 3 4 2
K3 3 3 3 2 3 3 4 4 5 2 5 5 3 5 3
K4 3 3 3 2 3 3 4 4 5 2 5 5 3 5 3
Table 2: Performance table
K1 (crucial knowledge) is outranking K9 (crucial knowledge) (See appendix 1) because <strong>the</strong>re is
enough evidence to consider that knowledge K7 is at least as good as K9. Indeed, For all criteria,
<strong>the</strong>re is no K2PK1 but for <strong>the</strong> criterion g6, <strong>the</strong>re is K2QK1 (Q indicates a weak preference). The
knowledge K1 is good as K2 on <strong>the</strong> criteria: g3, g4, g9, g11, g12 and g14.
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Inès Saad
K3 (Crucial knowledge) and K4 (Crucial knowledge) are ex æquo (see table 3) because <strong>the</strong> two
knowledge are ex æquo on all criteria.
According to <strong>the</strong> preferences <strong>of</strong> decisions makers, knowledge K3 and Knowledge K4 are in equal first
position. The knowledge K1 is ranked in <strong>the</strong> second position. Finally, knowledge K2 is ranked third.
Thus, K3 and K4 are <strong>the</strong> only one chosen to be preserved.
Table 3- Ranking <strong>of</strong> <strong>the</strong> crucial knowledge
5. Conclusion
Ranking Crucial Knowledge
1 st K3, K4
2 nd K1
3 rd K2
This paper deals with <strong>the</strong> analysis <strong>of</strong> company knowledge in advance <strong>of</strong> engaging a capitalization
operation on knowledge. This analysis includes <strong>the</strong> identification <strong>of</strong> resource knowledge to be
capitalized.The method we propose is based on <strong>the</strong> multiple criteria decision approach. This
approach leads to an efficient solution for all decision makers since it helps <strong>the</strong>m to work toge<strong>the</strong>r in
order to identify crucial knowledge. The multicriteria decision reflects well <strong>the</strong> preferences <strong>of</strong> decision
makers who may be different or even conflicting. It also allows <strong>the</strong> management <strong>of</strong> <strong>the</strong>ir multiple
points <strong>of</strong> view to identify crucial knowledge. The multicriteria decision also incorporates a degree <strong>of</strong>
subjectivity in <strong>the</strong> assessment <strong>of</strong> knowledge relative to <strong>the</strong> objectives <strong>of</strong> <strong>the</strong> company, because it
takes into account <strong>the</strong> different evaluations given by decision makers. Our achievement is about
building a satisfactory decision and not an objective decision even if optimal.
References
Davenport TH, De Long DW and Beers MC(1998) Successful knowledge management projects. Sloan
Management Review 39(2), 43-57.
Ermine JL, Boughzala I and Tounkara T (2006) Critical Knowledge Map As A Decision Tool For Knowledge
Transfer Actions. The Electronic Journal <strong>of</strong> Knowledge Management 4(2), 129–140.
Greco S, Matarazzo B and Slowinski R (2001) Rough Sets Theory for Multicriteria Decision Analysis. <strong>European</strong>
Journal <strong>of</strong> Operational Research 12(9), 1-47.
Grundstein M, Rosenthal-Sabroux C and Pachulski A (2006) Reinforcing Decision Aid By Capitalizing On
Company’s Knowledge. <strong>European</strong> Journal <strong>of</strong> Operational Research 14(5), 256-272.
Grundstein M (2000) From capitalizing on Company Knowledge to Knowledge Management. In Knowledge
Management, Classic and Contemporary Works Handbook (Morey D, Maybury M and Thuraisingham B,
Ed), pp 261-287, The MIT Press, Massachusetts.
Lee D & Van den Steen E (2010) Managing Know-How, Management Science, pp.270-285
Nonaka I and Takeuchi H (1995) The knowledge creating company: How Japanese companies create <strong>the</strong>
dynamics <strong>of</strong> innovation. Oxford University Press, New York.
Roy B (1996) Multicriteria Methodology for Decision Aiding, Dordrecht: Kluwer Academic Publishers.
O'Leary DE (1998) Using AI in knowledge management: knowledge bases and ontologies. Ieee Intelligent
Systems & Their Applications 13(3), 34–39.
Saad I and Chakhar S (2009) A decision support for identifying crucial knowledge requiring capitalizing operation.
<strong>European</strong> Journal <strong>of</strong> Operational Research, 195(3), 889-904.
Saad I, Rosenthal-Sabroux C and Grundstein M (2005) Improving <strong>the</strong> Decision Making Process in <strong>the</strong> Design
Project By Capitalizing On Company’s Crucial Knowledge. Group Decision and Negotiation, 14(2), 131-145.
Sanchez R (1997) Managing articulated knowledge in competence-based competition. In Strategic Learning and
Knowledge Management (SANCHEZ R and HEENE A, Eds), pp. 163–187, John Wiley, Chichester.
Schreiber G, Akkermans A, Anjewierden R, De Hoog N, Shadbolt W, Van De Velde and Wielinga, B (2000),
Knowledge Engineering and Management The CommonKADS Methodology, <strong>the</strong> MIT Press,
Massachusetts, Cambridge.
Tseng B and Huang C (2005) Capitalizing on Knowledge: A Novel Approach to Crucial Knowledge
Determination. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems And Humans. 35
(6), 919-931.
Van Heijst G, Van Der Speak R, and Kruizinga E (1996) Organizing Corporate Memories. In <strong>Proceedings</strong> <strong>of</strong> The
Kaw’96 In Gaines B, Musen M (Eds), p17-42, Banff, Canada.
862

Knowledge sharing and innovation: <strong>the</strong> case <strong>of</strong> Spanish
and Colombian high-tech firms
Josune Sáenz 1 , Nekane Aramburu 1 and Carlos E. Blanco 2
1
University <strong>of</strong> Deusto, San Sebastián, Spain
2
KM-INOVA, Bogotá, Colombia
josune.saenz@deusto.es
nekane.aramburu@deusto.es
carlo_co@yahoo.com
Abstract: The aim <strong>of</strong> tis paper is to empirically test <strong>the</strong> degree <strong>of</strong> influence <strong>of</strong> different knowledge sharing
mechanisms (ICT-based, personal interaction-based and embedded in management processes) on innovation
capability (both on ideation and on innovation project management), as well as <strong>the</strong> influence <strong>of</strong> each first-level
innovation capacity on company performance. In order to ga<strong>the</strong>r information about <strong>the</strong> relevant variables involved
in <strong>the</strong> research, a questionnaire has been designed and addressed to <strong>the</strong> CEOs <strong>of</strong> <strong>the</strong> companies making up <strong>the</strong>
target population (Spanish and Colombian medium-high and high technology firms with more than 50 employees
and R&D activities). Structural equation modelling (SEM) based on partial least squares (PLS) has <strong>the</strong>n been
applied to test <strong>the</strong> hypo<strong>the</strong>ses drawn from <strong>the</strong> research. The results obtained show that knowledge sharing is a
key issue in order to enhance innovation capability. With <strong>the</strong> exception <strong>of</strong> ICT-based knowledge sharing
mechanisms (whose influence on <strong>the</strong> generation <strong>of</strong> new ideas is not statistically significant), all types <strong>of</strong>
mechanism considered exert a significant impact both on ideation and on innovation project management
(although <strong>the</strong>ir degree <strong>of</strong> relevance varies), and account for a great deal <strong>of</strong> variance in both constructs.
Differences between countries arise when it comes to <strong>the</strong> influence <strong>of</strong> each first-level innovation capacity on
company performance. Whereas in <strong>the</strong> case <strong>of</strong> Spanish high-tech firms both capacities (ideation and innovation
project management) are equally important, in <strong>the</strong> case <strong>of</strong> Colombian firms, implementation is by far <strong>the</strong> most
relevant first-order capacity. The main contribution <strong>of</strong> this paper is to provide empirical evidence about <strong>the</strong> impact
<strong>of</strong> knowledge sharing on innovation. Moreover, it reveals what <strong>the</strong> most effective knowledge sharing mechanisms
are for this purpose and provides companies with a basic framework in order to shape <strong>the</strong>ir knowledge
management strategies in this domain.
Keywords: knowledge sharing; innovation capability; ideation; innovation project management; company
performance
1. Introduction
Since <strong>the</strong> seminal works by Nonaka in 1991, and Nonaka and Takeuchi in 1995, a close link has been
established between innovation and knowledge creation. In o<strong>the</strong>r words, it is assumed that innovation
involves <strong>the</strong> generation <strong>of</strong> new knowledge. This is <strong>the</strong> point <strong>of</strong> view <strong>of</strong> authors such as Subramaniam
and Youndt (2005) – who assume that innovation consists <strong>of</strong> an ongoing pursuit <strong>of</strong> harnessing new
and unique knowledge; Du Plessis (2007) – who identifies innovation with <strong>the</strong> creation <strong>of</strong> new
knowledge and ideas to facilitate new business outcomes; and Lundvall and Nielsen (2007) – who
state that “innovation represents – by definition – something new and <strong>the</strong>refore adds to existing
knowledge” (p. 214).
Thus, <strong>the</strong> study <strong>of</strong> innovation-permitting conditions involves <strong>the</strong> study <strong>of</strong> those conditions that foster
<strong>the</strong> creation <strong>of</strong> new knowledge. According to Nonaka et al. (2006), knowledge creation involves a
continuous process through which one overcomes <strong>the</strong> individual boundaries and constraints imposed
by information and past learning by acquiring a new context, a new view <strong>of</strong> <strong>the</strong> world and new
knowledge. By interacting and sharing tacit and explicit knowledge with o<strong>the</strong>rs, <strong>the</strong> individual
enhances <strong>the</strong> capacity to define a situation or problem, and apply his or her knowledge so as to act
and specifically solve <strong>the</strong> problem. In <strong>the</strong> case <strong>of</strong> organizational knowledge creation, this means
making available and amplifying <strong>the</strong> knowledge created by individuals as well as crystallizing and
connecting it with <strong>the</strong> organization’s knowledge system (Nonaka and Takeuchi 1995; Nonaka et al.,
2006). Hence, an organizational context which favours knowledge creation and subsequent
innovation is a context which favours <strong>the</strong> exchange <strong>of</strong> ideas and experiences among people (in o<strong>the</strong>r
words, knowledge sharing), and which encourages experimentation and continuous questioning <strong>of</strong>
established patterns.
In order to make knowledge sharing possible, several researchers have focused on <strong>the</strong> study <strong>of</strong>
different mechanisms and initiatives which could act as facilitators. Many <strong>of</strong> <strong>the</strong>se mechanisms take
advantage <strong>of</strong> information and communication technologies (on-line discussion forums, blogs, intranets
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and knowledge repositories) whereas, in o<strong>the</strong>r cases, personal interaction between individuals is <strong>the</strong>
key (communities <strong>of</strong> practice, coaching, mentoring and employee functional rotation, to name but a
few). The mechanisms mentioned above are consciously used to facilitate knowledge sharing.
However, this does not mean that all knowledge is shared through <strong>the</strong>m. Knowledge exchange can
take place naturally, as a part <strong>of</strong> daily management processes.
With this idea in mind, <strong>the</strong> aim <strong>of</strong> this paper is to analyze <strong>the</strong> influence <strong>of</strong> each type <strong>of</strong> knowledge
sharing mechanism (ICT-based, personal interaction-based and embedded in management
processes) on innovation capability.
Actually, innovation lies at <strong>the</strong> core <strong>of</strong> what is known as “dynamic capabilities”. This refers to <strong>the</strong>
particular capacity business enterprises possess to shape, reshape, configure and reconfigure assets
so as to respond to changing technologies and markets and escape <strong>the</strong> zero-pr<strong>of</strong>it condition (i.e. a
situation that occurs when <strong>the</strong>re are no points <strong>of</strong> differentiation amongst firms with respect to
technology, markets, information or skills, and which involves companies only making just enough to
cover <strong>the</strong>ir cost <strong>of</strong> capital) (Teece and Augier, 2009). In particular, innovation allows <strong>the</strong> resource
base <strong>of</strong> an organization to be shaped or reshaped by <strong>the</strong> addition <strong>of</strong> new knowledge embedded in
new products, services, processes, technologies or business models.
According to Teece (2007, 2009), <strong>the</strong> “dynamic capability” concept encompasses three first-level (i.e.
more simple) capacities. The first one is <strong>the</strong> capacity to sense and shape opportunities and threats.
This can be related to <strong>the</strong> ideation stage <strong>of</strong> innovation processes (i.e. <strong>the</strong> generation <strong>of</strong> new ideas).
The second one is <strong>the</strong> capacity to seize opportunities. This refers to <strong>the</strong> selection <strong>of</strong> <strong>the</strong> new ideas to
be addressed and to <strong>the</strong>ir subsequent development and fulfilment (i.e. implementation <strong>of</strong> new ideas or
innovation project management). The last one is <strong>the</strong> capacity to maintain competitiveness through
enhancing, combining, protecting, and where necessary, reconfiguring <strong>the</strong> business enterprise’s
tangible and intangible assets. This refers to <strong>the</strong> company’s capacity to reinvent/transform itself and
not die because <strong>of</strong> unfavourable path dependencies generated by past success. In <strong>the</strong> case <strong>of</strong> this
paper, <strong>the</strong> focus will be on <strong>the</strong> first two first-level capacities – that is, on ideation and on innovation
project management.
Once this distinction has been drawn, research questions could be summarized as follows:
Do innovation first-level capacities (ideation and innovation project management) need to be
reinforced by means <strong>of</strong> different knowledge sharing mechanisms?
Is <strong>the</strong>re any mechanism more effective than o<strong>the</strong>rs in order to reinforce <strong>the</strong> generation <strong>of</strong> new
ideas and subsequent implementation?
What is <strong>the</strong> role <strong>of</strong> each innovation first-level capacity in enhancing company performance? Are
both dimensions (ideation and innovation project management) equally relevant?
Finally, is <strong>the</strong>re any difference between countries depending on <strong>the</strong>ir degree <strong>of</strong> economic and
technological development (Spain, first world country, compared to Colombia, second world
country)?
2. Conceptual framework and research hypo<strong>the</strong>ses
Knowledge sharing has been studied in <strong>the</strong> literature by several authors (Hansen, 1999; Tsai, 2002)
and under different terminologies. Along <strong>the</strong>se lines, some authors refer to knowledge sharing as
knowledge transfer (Mowery et al. 1996; Wijk et al. 2008), or as knowledge flows (Gupta and
Govindarajan, 2000; Schulz, 2001). It implies “<strong>the</strong> process through which organizational actors –
teams, units, or organizations – exchange, receive and are influenced by <strong>the</strong> experience and
knowledge <strong>of</strong> o<strong>the</strong>rs” (Wijk et al., 2008, p.832).
As previously mentioned, <strong>the</strong>re are serveral initiatives that companies could implement in order to
facilitate <strong>the</strong> exchange <strong>of</strong> knowledge. Some <strong>of</strong> <strong>the</strong>m are ICT-based (Dalkir, 2005; Davenport, 2007),
whereas in o<strong>the</strong>r cases, face-to-face interaction is <strong>the</strong> key (i.e. people-focused knowledge
management; Wiig, 2004). However, although specific initiatives for knowledge transfer could be very
helpful in order to enhance knowledge creation and subsequent innovation, knowledge sharing should
not be understood as something to be apart from day-to-day business management. On <strong>the</strong> contrary,
knowledge sharing should pervade everyday work and be embedded in <strong>the</strong> firm’s basic management
processes (strategy formulation, organization and control).
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Josune Sáenz, Nekane Aramburu and Carlos E. Blanco
Never<strong>the</strong>less, early literature on knowledge management shows a clear prevalence <strong>of</strong> ICT-related
initiatives. In a survey reported by Swan et al. in 2001, it was proven that <strong>the</strong> dominant discourse in
this domain was not related to <strong>the</strong> role <strong>of</strong> people in organizations, but to technologies that facilitate <strong>the</strong>
acquisition, codification and exploitation <strong>of</strong> knowledge. As Allee points out (2003), “Companies need
an integrated technology foundation that will allow people to document <strong>the</strong>ir work and processes,
build learning modules, develop specialized portals, create shared files and content, manage <strong>the</strong>ir email,
and locate both internal and exteneral resources” (p. 89).
Knowledge repositories provide <strong>the</strong> vehicle for collecting and methodically integrating core knowledge
components from <strong>the</strong> organization into a single unified system which can be searched in many
different ways. They act as a link between users and core knowledge, operating as a single point <strong>of</strong>
entry to help people find relevant information from many different organizational sources (Debowski,
2006). Thanks to <strong>the</strong>m, employees may discover many colleagues working on similar projects and,
subsequently, collaboration among <strong>the</strong>m may emerge. Actually, knowledge repositories can
encourage new connections in <strong>the</strong> organization and be <strong>the</strong> source <strong>of</strong> new ideas. In <strong>the</strong> same vein,
intranets allow employees to have access to a wide array <strong>of</strong> documents, data bases, news and useful
links that may help <strong>the</strong>m to boost <strong>the</strong>ir creativity or to manage <strong>the</strong>ir projects more efficiently.
Moreover, o<strong>the</strong>r ICT-based mechanisms, such as blogs and on-line discussion forums, help people
share <strong>the</strong>ir thoughts, opinions and viewpoints on a variety <strong>of</strong> topics in a quick and agile way.
Questions and answers can be cross-indexed and archived, which helps to build a knowledge
network among participating members (Dalkir, 2005).
Hence, considering <strong>the</strong> prominent role that ICT-based mechanisms could play in facilitating
knowledge sharing and <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> latter in <strong>the</strong> generation <strong>of</strong> new knowledge and
subsequent innovation (see <strong>the</strong> Introduction section), <strong>the</strong> following hypo<strong>the</strong>ses have been formulated:
H1: ICT-based knowledge sharing mechanisms (i.e. knowledge repositories,
intranets, blogs and on-line discussion forums) exert a positive influence on:
H1a: The generation <strong>of</strong> new ideas.
H1b: Innovation project management.
However, although ICT-based practices could help in <strong>the</strong> exchange and assimilation <strong>of</strong> explicit
knowledge (i.e. internalization and combination processes), <strong>the</strong>y leave aside “social construction”
(Berger and Luckmann, 1966) and <strong>the</strong> organizational learning processes underlying socialization. As
Allee describes (2003), “in all types <strong>of</strong> knowledge work, even where technology is very helpful, people
require conversation, experimentation and experiences shared with o<strong>the</strong>r people who do what <strong>the</strong>y
do” (p. 113). This brings us to personal interaction-based (or people-focused) knowledge sharing
initiatives.
Within this group, communities <strong>of</strong> practice are <strong>of</strong> special relevance (Dalkir, 2005; Saint-Onge and
Wallace, 2003). They refer to a group or network <strong>of</strong> individuals who share a concern, a set <strong>of</strong>
problems or a passion about a topic, and who expand <strong>the</strong>ir knowledge and expertise in this area by
interacting with each o<strong>the</strong>r on an ongoing basis (Wenger et al., 2002). Along <strong>the</strong>se lines, “<strong>the</strong> critical
component <strong>of</strong> a community lies in <strong>the</strong> sharing <strong>of</strong> common work problems between members, a
membership that sees <strong>the</strong> clear benefits <strong>of</strong> sharing knowledge among <strong>the</strong>mselves” (Dalkir, 2005,
p.123).Therefore, communities <strong>of</strong> practice are a vehicle for increasing knowledge creation as well as
for expanding <strong>the</strong> extent and accelerating <strong>the</strong> speed at which knowledge is exchanged across <strong>the</strong>
organization (Saint-Onge and Wallace, 2003, p.12).
Coaching, mentoring and employee functional rotation also facilitate personal interaction-based
knowledge sharing and learning processes. In particular, <strong>the</strong>y are used to allow <strong>the</strong> transmission <strong>of</strong>
knowledge that is grounded on experience (i.e. tacit knowledge). According to action learning <strong>the</strong>ory
(Revans, 1983), individuals learn through experience within a social context. This type <strong>of</strong> experiencegrounded
learning is quite difficult to transmit to o<strong>the</strong>rs because <strong>of</strong> its tacit nature. For this reason, a
social context is needed to allow this transmission. As work-based learning <strong>the</strong>ory maintains (Raelin,
2000), individuals learn in <strong>the</strong>ir work environment, which constitutes <strong>the</strong> immediate social context in
which <strong>the</strong>y gain experience. In this context, individuals not only gain experience, but also share it with
o<strong>the</strong>rs. Hence, socialization processes (i.e. tacit knowledge sharing) take place.
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Thus, given <strong>the</strong> role that personal interaction-based mechanisms could play in knowledge transfer
and <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> latter in <strong>the</strong> generation <strong>of</strong> new knowledge and subsequent innovation, <strong>the</strong>
following hypo<strong>the</strong>ses have been formulated:
H2: Personal interaction-based knowledge sharing mechanisms (i.e. communities
<strong>of</strong> practice, coaching and/or mentoring, and employee functional rotation) exert a positive
influence on:
H2a: The generation <strong>of</strong> new ideas.
H2b: Innovation project management.
Beyond specific initiatives (ICT-based or personal interaction-based) that companies could implement
to foster <strong>the</strong> exchange <strong>of</strong> knowledge among people in <strong>the</strong>ir organizations, knowledge that is shared in
management processes (strategy formulation, organization and control) may have a paramount
relevance when it comes to shaping <strong>the</strong> future <strong>of</strong> innovation. Actually, knowledge shared during <strong>the</strong>
strategy formulation process or during <strong>the</strong> implementation and control phase could lead to decisions
that involve reorienting innovation projects. Therefore, it is vital that all knowledge that is relevent for
<strong>the</strong> assessment <strong>of</strong> such projects (market knowledge, technology knowledge, financial knowledge) is
shared and made available to decision makers.
As suggested in <strong>the</strong> literature, interactive (i.e. dialogue- and discussion-based) management systems
are necessary to focus organizational attention on strategic uncertainties (a core characteristic <strong>of</strong>
innovation) and provide a lever for fine-tuning and altering strategy as changes occur (Simons, 2000).
For instance, using <strong>the</strong> balanced scorecard interactively (i.e. carrying out discussion meetings to
analyze strategic performance) involves challenging underlying assumptions in previously adopted
strategic decisions (e.g. innovation decisions) and plans <strong>of</strong> action. Meetings are conducted so as to
brainstorm and use every possible piece <strong>of</strong> data to collectively make sense <strong>of</strong> changing
circumstances. Ongoing dialogue and debate among participants highlight <strong>the</strong> need for changing
ways <strong>of</strong> doing things and force organizational learning, which loops back to <strong>the</strong> adjustment <strong>of</strong> strategy
(Simons, 2000). In o<strong>the</strong>r words, a new strategy emerges as a result <strong>of</strong> bottom-up plans <strong>of</strong> action and
experimentation.
In this process, middle managers play a substantial role. As Nonaka and Takeuchi (1995) point out,
“They serve as <strong>the</strong> strategic “knot” that binds top management with front-line managers. They work as
a “bridge” between <strong>the</strong> visionary ideals <strong>of</strong> <strong>the</strong> top and <strong>the</strong> <strong>of</strong>ten chaotic realities confronted by frontline
workers” (p. 128). Actually, middle managers provide <strong>the</strong>ir subordinates with a conceptual
framework that helps <strong>the</strong>m make sense <strong>of</strong> <strong>the</strong>ir own experience.
Considering this, <strong>the</strong> following hypo<strong>the</strong>ses have been formulated:
H3: Knowledge sharing in management processes (strategy formulation,
organization and control) has a positive influence on:
H3a: The generation <strong>of</strong> new ideas.
H3b: Innovation project management.
Additionally, it has been argued that innovation is a “dynamic capability”. This dynamic capability
concept is considered to be <strong>the</strong> basis <strong>of</strong> sustainable competitive advantage and value creation in fastmoving
business environments open to global competition (Teece, 2007, 2009). Although previous
studies have already demonstrated <strong>the</strong> relevance <strong>of</strong> innovation as a source <strong>of</strong> higher growth and
performance, <strong>the</strong>y have usually done so by calculating <strong>the</strong> statistical correlation between R&D
investment and some specific measure <strong>of</strong> business growth or pr<strong>of</strong>itability (Aboody and Lev, 2001;
Griliches, 1995; Hall, 1993).
In this case, <strong>the</strong> aim is slightly different: <strong>the</strong> analysis carried out is intended to estimate <strong>the</strong> specific
contribution <strong>of</strong> each innovation first-level capacity to company performance. For this to be checked,
<strong>the</strong> following hypo<strong>the</strong>ses have been formulated:
H4: The effectivenes <strong>of</strong> <strong>the</strong> new idea generation process positively affects
company performance.
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Josune Sáenz, Nekane Aramburu and Carlos E. Blanco
H5: The effectiveness <strong>of</strong> <strong>the</strong> implementation stage positively affects company
performance.
Finally, to bring <strong>the</strong> presentation <strong>of</strong> <strong>the</strong> research model to a close, it would be interesting to see
whe<strong>the</strong>r <strong>the</strong> degree <strong>of</strong> economic and technological development a country may have could give rise
to any difference in <strong>the</strong> relationships previously outlined.
3. Constructs and measures
Once <strong>the</strong> research model has been presented, <strong>the</strong> specific indicators making up each construct will
be specified.
ICT-based knowledge sharing is <strong>the</strong> first exogenous construct <strong>of</strong> <strong>the</strong> research. It is made up <strong>of</strong> four
indicators that measure <strong>the</strong> extent to which <strong>the</strong> company uses different ICT-based mechanisms to
foster <strong>the</strong> exchange <strong>of</strong> knowledge: on-line knowledge repositories, intranets, blogs and on-line
discussion forums.
Personal interaction-based knowledge sharing is <strong>the</strong> second exogenous construct within <strong>the</strong> model. It
encompasses three items that measure <strong>the</strong> extent to which <strong>the</strong> company supports specific knowledge
sharing initiatives based on face-to-face interaction: communities <strong>of</strong> practice and/or meetings by fields
<strong>of</strong> interest, coaching and/or mentoring, and employee functional rotation.
Knowledge sharing in management processes is <strong>the</strong> last exogenous construct <strong>of</strong> <strong>the</strong> research. It is
made up or three indicators: whe<strong>the</strong>r <strong>the</strong> strategy formulation process allows a wide set <strong>of</strong>
contributions and exchange <strong>of</strong> viewpoints to occur among <strong>the</strong> members <strong>of</strong> <strong>the</strong> company; whe<strong>the</strong>r
middle managers contribute effectively to <strong>the</strong> exchange <strong>of</strong> knowledge and information among upper
and lower hierarchical levels; and whe<strong>the</strong>r strategy follow-up meetings based on <strong>the</strong> use <strong>of</strong> <strong>the</strong>
balanced scorecard are an important point for reflection, exchange <strong>of</strong> viewpoints and subsequent
adoption <strong>of</strong> action.
New idea generation is <strong>the</strong> first endogenous construct within <strong>the</strong> model. In this case, <strong>the</strong> following
items have been checked, all <strong>of</strong> <strong>the</strong>m referring to <strong>the</strong> last five years: whe<strong>the</strong>r <strong>the</strong> company has been
able to identify numerous opportunities for incremental improvement; whe<strong>the</strong>r <strong>the</strong> company has been
able to identify numerous opportunities for radical innovation; whe<strong>the</strong>r <strong>the</strong> firm has been able to
identify many alternative or new uses for already-existing technologies; whe<strong>the</strong>r <strong>the</strong> new idea
generation process has been managed in a conscious and effective way; and whe<strong>the</strong>r <strong>the</strong> company
has been able to clearly distinguish which <strong>of</strong> <strong>the</strong> new opportunities identified had a greater potential
for development.
Innovation project management is <strong>the</strong> second endogenous construct <strong>of</strong> <strong>the</strong> research. It encompasses
<strong>the</strong> following items: whe<strong>the</strong>r during <strong>the</strong> last five years <strong>the</strong> method used by <strong>the</strong> company for innovation
project management has made things really easy; whe<strong>the</strong>r <strong>the</strong> company has been able to reuse <strong>the</strong>
knowledge generated through innovation projects carried out in <strong>the</strong> past; whe<strong>the</strong>r different innovation
projects have been suitably coordinated; and whe<strong>the</strong>r role distribution in innovation projects carried
out in cooperation has been <strong>the</strong> best it could be.
Finally, Company performance is <strong>the</strong> last endogenous construct <strong>of</strong> <strong>the</strong> research. It encompasses
three indicators, all <strong>of</strong> <strong>the</strong>m referring to <strong>the</strong> last five years: whe<strong>the</strong>r innovation outcomes have had a
very positive impact on <strong>the</strong> company’s income statement; whe<strong>the</strong>r innovation outcomes have had a
very positive impact on <strong>the</strong> company’s competitive position; and whe<strong>the</strong>r innovation outcomes have
allowed <strong>the</strong> company to grow and improve its market share.
All <strong>the</strong> indicators mentioned above have been measured by means <strong>of</strong> 1 to 7 Likert scales and are
reflective in nature (that is, <strong>the</strong>y reflect <strong>the</strong> existence <strong>of</strong> <strong>the</strong> latent variable that <strong>the</strong>y represent).
4. Research method
The population subject to study is made up <strong>of</strong> Spanish and Colombian medium-high and high
technology firms with more than 50 employees and which carry out R&D activities. 75 firms out <strong>of</strong> 446
answered <strong>the</strong> questionnaire in Spain (average response rate: 17%), whereas 69 did it so in Colombia.
In <strong>the</strong> case <strong>of</strong> <strong>the</strong> latter, <strong>the</strong>re was no data base that could allow us to know <strong>the</strong> entire population
under analysis.
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The sample sizes obtained are large enough to carry out a statistical study based on structural
equation modelling (partial least squares approach) by means <strong>of</strong> PLS-Graph s<strong>of</strong>tware (Chin and Frye,
2003). According to <strong>the</strong> complexity level <strong>of</strong> <strong>the</strong> model to be tested, <strong>the</strong> minimum sample size required
was calculated, and this was made up <strong>of</strong> 30 firms.
A PLS model is analyzed and interpreted in two stages: first, <strong>the</strong> assessment <strong>of</strong> <strong>the</strong> reliability and
validity <strong>of</strong> <strong>the</strong> measurement model and second, <strong>the</strong> assessment <strong>of</strong> <strong>the</strong> structural model. This
sequence ensures that <strong>the</strong> constructs’ measures are valid and reliable before attempting to draw
conclusions regarding relationships among constructs (Barclay et al., 1995).
5. Research findings
Following <strong>the</strong> sequence previously described in <strong>the</strong> method section, <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> results
obtained should begin with <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong> measurement model. This assessment differs
depending on <strong>the</strong> nature <strong>of</strong> <strong>the</strong> construct under scrutiny. In <strong>the</strong> case <strong>of</strong> constructs made up <strong>of</strong>
reflective indicators (as is <strong>the</strong> case in this study), individual item reliability, construct reliability,
convergent validity and discriminant validity should be checked.
In this respect, all <strong>the</strong> tests carried out have shown satisfactory results. Indicator loadings (individual
item reliability) are greater than 0.7, with a few exceptions that indeed are very close to this limit (<strong>the</strong>
lowest loading is 0.64). Thus, all <strong>the</strong> indicators have been retained in <strong>the</strong> model. Moreover, composite
reliability (which measures construct reliability) is higher than 0.8 in all cases; average variance
extracted (which measures convergent validity) is greater than 0.5 in all constructs; and discriminant
validity is excellent too.
Once <strong>the</strong> quality <strong>of</strong> <strong>the</strong> measurement model has been guaranteed, <strong>the</strong> structural model should <strong>the</strong>n
be assessed. This refers to <strong>the</strong> strength <strong>of</strong> <strong>the</strong> research hypo<strong>the</strong>ses and to <strong>the</strong> amount <strong>of</strong> variance
explained (R 2 ) in <strong>the</strong> endogenous constructs, as well as to an analysis <strong>of</strong> <strong>the</strong> predictive power
achieved. In our case, this is going to be complemented with a multi-group analysis in order to assess
whe<strong>the</strong>r country differences apply.
In order to assess <strong>the</strong> research hypo<strong>the</strong>ses, path-coefficient levels should be examined, as well as
<strong>the</strong>ir degree <strong>of</strong> significance, by means <strong>of</strong> bootstrapping techniques. Tables 1, 2 and 3 summarize <strong>the</strong>
results obtained. In <strong>the</strong>se tables, we can also see <strong>the</strong> contribution <strong>of</strong> each exogenous construct to <strong>the</strong>
amount <strong>of</strong> variance explained (which has been obtained by multiplying correlation and path
coefficients), as well as <strong>the</strong> predictive power achieved. The latter has been confirmed by means <strong>of</strong> a
Stone Geiser test, where cross-validated redundancy (Q 2 ) must be higher than 0 in order for <strong>the</strong>
model to be considered to have predictive power for that specific construct.
Table 1:Impact <strong>of</strong> knowledge sharing on new idea generation
ICT-based
knowledge
sharing
Personal
interactionbased
knowledge
sharing
Knowledge
sharing in
management
processes
Total amount
<strong>of</strong> variance
explained
Spanish Path 0.087 0.464*** 0.217*
high-tech Correlation 0.440 0.641 0.540
firms
R 2 3.83% 29.74% 11.72% 45.29% 0.1623
Colombian Path -0.095 0.425** 0.322**
high-tech
firms
Correlation
R
0.198 0.578 0.532
2 -1.88% 24.57% 17.13% 39.81% 0.1516
Notes
***p

Josune Sáenz, Nekane Aramburu and Carlos E. Blanco
Table 2: Impact <strong>of</strong> knowledge sharing on innovation project management
Spanish
high-tech
firms
Colombian
high-tech
firms
ICT-based
knowledge
sharing
Personal
interactionbased
knowledge
sharing
Knowledge
sharing in
management
processes
Path 0.209* 0.336*** 0.204*
Correlation 0.488 0.569 0.511
Total amount
<strong>of</strong> variance
explained
R 2 10.20% 19.12% 10.42% 39.74% 0.1338
Path 0.161 † 0.397** 0.263*
Correlation 0.420 0.613 0.562
R 2 6.76% 24.34% 14.78% 45.88% 0.2189
Notes
***p

Josune Sáenz, Nekane Aramburu and Carlos E. Blanco
balanced and significant (in this sample hypo<strong>the</strong>ses H4 and H5 are clearly supported), in <strong>the</strong> case <strong>of</strong>
Colombian medium-high and high technology firms only innovation project management (i.e.
implementation) exerts a significant and paramount impact on company performance (in this sample
hypo<strong>the</strong>sis H4 is not supported).
6. Discussion and conclusions
The results obtained show that knowledge sharing plays a key role when it comes to enhancing <strong>the</strong>
innovation capability <strong>of</strong> firms, although <strong>the</strong> degree <strong>of</strong> relevance <strong>of</strong> each type <strong>of</strong> knowledge sharing
mechanism varies. In particular, personal-interaction based knowledge sharing initiatives
(communities <strong>of</strong> practice, coaching, mentoring, and employee functional rotation) appear to be <strong>the</strong>
most influential ones, both for <strong>the</strong> generation <strong>of</strong> new ideas and for innovation project management.
Hence, it can be concluded that socialization processes – i.e. <strong>the</strong> exchange <strong>of</strong> tacit knowledge - are
key in order to foster innovation.
On <strong>the</strong> contrary, ICT-based mechanisms (knowledge repositories, intranets, blogs and on-line
discussion forums) play a much more secondary role: <strong>the</strong>ir influence is only significant in <strong>the</strong>
implementation phase. Perhaps this is due to <strong>the</strong> degree <strong>of</strong> complexity and sophistication <strong>of</strong> <strong>the</strong>
knowledge being dealt with in <strong>the</strong> companies studied (medium-high and high technology firms). This
complexity may make it more difficult to articulate it (especially in <strong>the</strong> ideation phase), a necessary
condition for ICT-based knowledge sharing to take place. Conversely, previous studies in medium-low
and low technology firms (where knowlege complexity is supposed to be lower), show that ICT-based
knowledge sharing mechanisms are extremely effective in order to foster <strong>the</strong> generation <strong>of</strong> new ideas
(Sáenz, et al., 2009).
However, <strong>the</strong> lack <strong>of</strong> specific procedures to capture, diffuse and retrieve relevant knowledge via ICT
could be ano<strong>the</strong>r reason behind <strong>the</strong> low contribution <strong>of</strong> this type <strong>of</strong> knowledge sharing mechanism to
<strong>the</strong> generation <strong>of</strong> new ideas. In o<strong>the</strong>r words, <strong>the</strong> technological infrastructure may exist, but <strong>the</strong>re could
be lack <strong>of</strong> guidance for its exploitation as a knowledge source. This may be <strong>the</strong> case especially for
external knowledge. Considering that, “<strong>the</strong> scope and breadth <strong>of</strong> knowledge available from outside
sources is generally much greater than that available from inside sources” (Maznevski and
Athanassiou, 2007:69), <strong>the</strong> lack <strong>of</strong> specific guidelines for external knowledge management (capture
and diffusion among potential users via ICT) may limit <strong>the</strong> influence <strong>of</strong> ICT-based knowledge sharing
mechanisms in <strong>the</strong> identification <strong>of</strong> new ideas.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> results obtained highlight <strong>the</strong> relevance <strong>of</strong> highly interactive (i.e. dialogue- and
discussion-based) management processes as a means for guaranteeing effective ideation and
innovation project management. As previously mentioned, knowledge shared during <strong>the</strong> strategy
formulation process or during <strong>the</strong> implementation and control phase could lead to decisions that
involve reorienting innovation projects. Therefore, managers should guarantee <strong>the</strong> participation <strong>of</strong> key
people in such processes, and <strong>the</strong> availability and diffusion <strong>of</strong> all knowledge that is relevant for
decision making in order to use every piece <strong>of</strong> data to collectively make sense <strong>of</strong> changing
circumstances. Moreover, although in <strong>the</strong> case <strong>of</strong> Spanish medium-high and high technology firms
both ideation and innovation project management exert quite a balanced and significant influence on
company performance, in <strong>the</strong> case <strong>of</strong> Colombian firms only <strong>the</strong> latter is statistically significant.
Perhaps this is due to <strong>the</strong> greater difficulties that Colombian firms may encounter in <strong>the</strong>
implementation <strong>of</strong> <strong>the</strong>ir innovative ideas. As a consequence, this stresses <strong>the</strong> influence <strong>of</strong> this firstlevel
capacity on company performance. Finally, and as regards <strong>the</strong> practical implications <strong>of</strong> this
research, <strong>the</strong> findings obtained might shed some light to managers in order to increase <strong>the</strong> innovation
capacity <strong>of</strong> <strong>the</strong>ir firms, through <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> right measures and initiatives that could help
to achieve this goal.
References
Aboody, D. and Lev, B. (2001) The Productivity <strong>of</strong> Chemical Research & Development, working paper, New York
Stern University, Stern School <strong>of</strong> Business, New York, NY.
Allee, V. (2003) The Future <strong>of</strong> Knowledge: Increasing Prosperity through Value Networks, Elsevier, Burlington,
MA.
Barclay, D., Higgins, C., and Thompson, R. (1995) “The Partial Least Squares (PLS) Approach to Causal
Modeling: Personal Computer Adoption and Use as an Illustration”, Technological Studies, special issue on
Research Methodology, pp. 285-309.
Berger, P. and Luckmann, T. (1966) The Social Construction <strong>of</strong> Reality: A Treatise on <strong>the</strong> Sociology <strong>of</strong>
Knowledge, Doubleday, New York, NY.
870

Josune Sáenz, Nekane Aramburu and Carlos E. Blanco
Chin, W.W. and Frye, T. (2003) PLS-Graph Version 3.00, Build 1017, University <strong>of</strong> Houston, Houston.
Dalkir, K. (2005) Knowledge Management in Theory And Practice, Elsevier Butterworth-Heinemann, Burlington,
MA.
Davenport, T.H. (2007) “Information Technologies for Knowledge Management”, in Ichijo, K. and Nonaka, I.
(Eds.), Knowledge Creation and Management: New Challenges for Managers, pp. 97-117, Oxford
University Press, New York, NY.
Debowski, S. (2006) Knowledge Management, John Wiley & Sons Australia, Milton, Qld.
Du Plessis, M. (2007) “The Role <strong>of</strong> Knowledge Management in Innovation”, Journal <strong>of</strong> Knowledge Management,
Vol. 11, No. 4, pp. 20-29.
Griliches, Z. (1995) “R&D and Productivity: Econometric Results and Measurement Issues”, in Stoneman, P.
(Ed.), Handbook <strong>of</strong> <strong>the</strong> Economics <strong>of</strong> Innovation and Technological Change, pp. 52-89, Blackwell
Publishing, Malden, MA.
Gupta, A.K. and Govindarajan, V. (2000) “Knowledge Flows within Multinational Corporations”, Strategic
Management Journal, Vol. 21, No. 4, pp. 473-496.
Hall, R.E. (1993) “Macro Theory and <strong>the</strong> Recession <strong>of</strong> 1990-1991”, American Economic Review, Vol. 83, No. 3,
pp. 275-279.
Hansen, M.T. (1999) “The Search-Transfer Problem: The Role <strong>of</strong> Weak Ties in Sharing Knowledge across
Organizational Subunits” Administrative Science Quaterly, Vol. 44, No. 1, pp. 82-111.
Lundvall, B.A. and Nielsen, P. (2007) “Knowledge Management and Innovation Performance”, International
Journal <strong>of</strong> Manpower, Vol. 28, No. 3-4, pp. 207-223.
Maznevski M. and Athanassiou N. (2007) “Bringing <strong>the</strong> Outside in: Learning and Knowledge Management
Through External Networks”, in Ichijo, K. and Nonaka, I. (Eds.), Knowledge Creation and Management:
New Challenges for Managers, pp. 69-82, Oxford University Press, New York.
Mowery, D., Oxley, J. and Silverman, B. (1996) “Strategic Alliances and Interfirm Knowledge Transfer”, Strategic
Management Journal, Vol. 17, winter special issue, pp. 77-91.
Nonaka, I. (1991) "The Knowledge-Creating Company", Harvard Business Review, Vol. 69, No. 6, pp.96-104.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company, Oxford University Press, Oxford.
Nonaka, I., Krogh Von, G., and Voelpel, S. (2006) “Organizational Knowledge Creation Theory: Evolutionary
Paths and Future Advances”, Organization Studies, Vol. 27, No. 8, pp. 1179-1208.
Raelin, J. (2000) Work-based learning: The new frontier <strong>of</strong> management development, Addison-Wesley,
Reading, MA.
Revans, R. (1983) ABC <strong>of</strong> Action Learning, Chartwell-Bratt, Bromley, UK.
Sáenz, J., Aramburu, N. and Rivera, O. (2009) “Knowledge Sharing and Innovation Performance: A Comparison
between High-Tech and Low-Tech Companies”, Journal <strong>of</strong> Intellectual Capital, Vol. 10, No. 1, pp. 22-36.
Saint-Onge, H. and Wallace, D. (2003) Leveraging Communities <strong>of</strong> Practice for Strategic Advantage, Elsevier,
Burlington, MA.
Schulz, M. (2001) “The Uncertain Relevance <strong>of</strong> Newness: Organizational Learning and Knowledge Flows”,
Academy <strong>of</strong> Management Journal, Vol. 44, No. 4, pp. 661-681.
Simons, R. (2000) Performance measurement and control systems for implementing strategy, Prentice Hall,
Upper Saddle River, NJ.
Subramaniam, M. and Youndt, M.A. (2005) “The Influence <strong>of</strong> Intellectual Capital on <strong>the</strong> Types <strong>of</strong> Innovative
Capabilities”, Academy <strong>of</strong> Management Journal, Vol. 48, No. 3, pp. 450-463.
Swan, J., Robertson, M. and Newell, S. (2001) “Knowledge Management: The Next Fad to Forget People?”, in
Barnes, S. (Ed.), Knowledge Management Systems: Theory and Practice, pp. 179-194, International
Thompson Business Press, London, UK.
Teece, D.J. (2007) “Explicating Dynamic Capabilities: The Nature and Micr<strong>of</strong>oundations <strong>of</strong> (Sustainable)
Enterprise Performance”, Strategic Management Journal, Vol. 28, No. 13, pp. 1319-1350.
Teece, D.J. (2009) “The Nature and Micr<strong>of</strong>oundations <strong>of</strong> (Sustainable) Enterprise Performance”, in Teece, D.J.
(Ed.), Dynamic Capabilities and Strategic Management – Organizing for Innovation and Growth, pp. 3-64,
Oxford University Press, Oxford, NY.
Teece, D.J. and Augier, M. (2009) “The Foundations <strong>of</strong> Dynamic Capabilities”, in Teece, D.J. (Ed.), Dynamic
Capabilities & Strategic Management – Organizing for Innovation and Growth, pp. 82-112, Oxford University
Press, Oxford, NY.
Tsai, W. (2002) “Social Structure <strong>of</strong> “Coopetition” within a Multiunit Organization: Coordination, Competition, and
Intraorganizational Knowledge Sharing, Organization Science, Vol. 13, No. 2, pp. 179-190.
Wenger, E., McDermott, R. and Snyder, W.M. (2002) Cultivating Communities <strong>of</strong> Practice: A Guide to Managing
Knowledge,Harvard Business School Press, Cambridge, MA.
Wiig, K. (2004) People-Focused Knowledge Management, Elsevier, Burlington, MA.
Wijk van R., Jansen, J.J. and Lyles, M.A. (2008) “Inter- and Intra-Organizational Knowledge Transfer: A Meta-
Analytic Review and Assessment <strong>of</strong> its Antecedents and Consequences”, Journal <strong>of</strong> Management Studies,
Vol. 45, No. 4, pp. 830-853.
871

Social Media Mindset and Knowledge Management
Risto Säntti
University <strong>of</strong> Vaasa, Vaasa, Finland
risto.santti@uwasa.fi
Abstract: The term Web 2.0 is associated with web applications that facilitate interactive information sharing,
interoperability, user-centered design and collaboration on <strong>the</strong> internet. There is an ongoing broad Web 2.0 or
social media focused discussion within society and within organizations. The focus <strong>of</strong> this discussion is mainly on
Web 2.0 technology, its variations, and potential ways to use social media in order to produce business benefits.
Social hardware and social s<strong>of</strong>tware, both with <strong>the</strong>ir constantly renewing contents, dominate <strong>the</strong> discussion
focus. The properties and functionalities <strong>of</strong> collaborative s<strong>of</strong>tware are at <strong>the</strong> core <strong>of</strong> this discussion. Social media
focused discussion is also about what is acceptable, what should be forbidden, how to maintain intellectual
property rights, what kind <strong>of</strong> Web 2.0 activities are worthwhile and which are a waste <strong>of</strong> time and effort. What this
mainstream Web 2.0 discussion seems to miss are <strong>the</strong> considerable and relevant mindset changes taking place.
From this angle <strong>the</strong> core issue concerning how Web 2.0 will influence knowledge management is not about
technologies, rules and regulations. What seems to be relevant is <strong>the</strong> new kind <strong>of</strong> thinking, emerging through
broad use <strong>of</strong> social media, experiences <strong>of</strong> this use and new variations in user behavior. This new thinking,
defined here as <strong>the</strong> social media mindset, is <strong>the</strong> focus <strong>of</strong> this paper. This social media mindset has its roots in
history, it can be partly recognized, and it can be estimated to have huge influence on future organizational
behavior and knowledge management within organizations. The issues <strong>of</strong> interest in this paper are <strong>the</strong> following:
How does <strong>the</strong> use <strong>of</strong> social media (Web 2.0) influence user thinking and what kind <strong>of</strong> new mindsets are
emerging? What can be learned from <strong>the</strong> historical perspective to understand changes in <strong>the</strong> social media
mindset? What kind <strong>of</strong> differences can be recognized between people <strong>of</strong> various ages and generations in <strong>the</strong>ir
social media mindsets? How does this new kind <strong>of</strong> thinking influence organizational knowledge management,
and how should <strong>the</strong> social media mindset change be taken into consideration? The analysis <strong>of</strong> social media
mindset in this article is based on a literature review supported by first hand corporate social media experiments
in one case organization. Social media as a technology-based multidirectional human interaction is a
phenomenon that made an appearance in <strong>the</strong> early history <strong>of</strong> technology. The roots <strong>of</strong> a social media mindset
can be found in <strong>the</strong> late 19th and early 20th century. The social media mindset may be <strong>the</strong> largest and most
influential shift in <strong>the</strong> ways we understand human interaction, knowledge creation, location, sharing, control and
ownership. The social media mindset is about how we understand availability, access, place and location, groups
and membership, information and knowledge, borderlines and identity among o<strong>the</strong>r things. In <strong>the</strong> corporate
context, <strong>the</strong> approach to social media still focuses today more on technical solutions and damage limitation
guidelines. The approach obviously should be more interested in facilitating and supporting a mindset change
that will influence action, with technology taking only a supporting role.
Keywords: Social media, Social media mindset, Web 2.0
1. Introduction
There is a variety <strong>of</strong> definitions <strong>of</strong> social media. The core principles <strong>of</strong> social media solutions have
been listed as being participative, collective, transparent, independent, persistent and emergent
(Bradley 2010). Social media is defined in this paper as technology facilitated mutual communication
and understanding construction where <strong>the</strong> participants can be both content users and content
producers. This new participatory environment where <strong>the</strong> boundaries between producers and
consumers are broken down is described with <strong>the</strong> term produsage (e.g. Snurb 2007).
The influence <strong>of</strong> social media on our vision <strong>of</strong> reality and our mindsets is undoubtedly huge. However,
<strong>the</strong> influence <strong>of</strong> this close, large and constantly changing phenomenon is hard to estimate and to
understand. Some researchers report mainly positive phenomena in relation to internet use. The
internet-adept generation is seen to be more intelligent, quicker acting and more accepting <strong>of</strong> diversity
than any previous generation. The digital user generation is seen as producing revolutionary new
ways <strong>of</strong> communicating, thinking and working. (Tapscott 2009) The counter argument views use <strong>of</strong>
<strong>the</strong> internet and social media as influencing our brains in a fundamental way making us more
superficial, unable to concentrate, unable to comprehend complex systemic phenomena. Access to
knowledge does not necessary produce relevant knowledge and understanding. Unrelated facts do
not combine to produce wisdom, and nor is availability <strong>of</strong> knowledge <strong>the</strong> same thing as wisdom. (Carr
2010) These two opposing understandings are not compatible.
In this paper, Web 2.0 is defined as <strong>the</strong> technological development phase <strong>of</strong> <strong>the</strong> World Wide Web
containing new technical properties that facilitate social interaction in different ways and on a broader
scale. The concept founder definition <strong>of</strong> Web 2.0 (O’Reilly 2005) is used as <strong>the</strong> baseline; <strong>the</strong> key
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Risto Säntti
issues being: platform, harnessing collective intelligence, data ownership, s<strong>of</strong>tware no longer being an
artifact but a service, lightweight constant programming and use <strong>of</strong> multi-deviser s<strong>of</strong>tware. Emerging
new web features are multi-dimensional participation and information sharing, possibilities to operate
same programs from different locations, collaboration and user-centered design. Cloud computing
and s<strong>of</strong>tware as a service (SaaS) concepts describe how s<strong>of</strong>tware exists in <strong>the</strong> web and is accessible
anywhere with no user platform dependency. All this is very much about technology, hardware and
programs. What is <strong>of</strong> interest in this article is how <strong>the</strong> use <strong>of</strong> <strong>the</strong> potential <strong>of</strong> this social s<strong>of</strong>tware and
hardware influences human thinking.
The social media mindset is a new concept and in need <strong>of</strong> a detailed definition. It is a term used in
relation to marketing and social commerce (Chaumond 2010). The social media mindset is presented
in <strong>the</strong> context <strong>of</strong> electronic commerce, <strong>of</strong>ten from <strong>the</strong> angle <strong>of</strong> business-to-customer through social
media. It is seen as a corporate way <strong>of</strong> thinking anew and in a way that makes it possible to market
and sell products and services to customers through <strong>the</strong> web. This thinking is very <strong>of</strong>ten limited to
marketing through one media, namely Facebook. The idea in this article is to take a completely
different approach to <strong>the</strong> social media mindset.
Social media technology and social media mindset development is a complex process within
societies. This process is directed by <strong>of</strong>ten sporadic and haphazard technology development phases.
These influence <strong>the</strong> use <strong>of</strong> <strong>the</strong> new technologies available, which happens to a large extent at
random. The use <strong>of</strong> available technology, <strong>the</strong> selection <strong>of</strong> certain versions <strong>of</strong> it for broader use, and
<strong>the</strong> experiences ga<strong>the</strong>red from <strong>the</strong>se experiences are <strong>the</strong> forming blocks <strong>of</strong> a constantly evolving
social media mindset. Of note are <strong>the</strong> various commercial players and o<strong>the</strong>r parties with a vested
interest. Their success is sometimes influenced by chance, to establish a short or long term
dominating position in <strong>the</strong> field is relevant to <strong>the</strong> process. Certain peculiarities <strong>of</strong> <strong>the</strong> social media
landscape, like dominating positions which can be occupied by one solution only add to <strong>the</strong>
complexity <strong>of</strong> <strong>the</strong> setting.
The concept utilized here defines <strong>the</strong> social media mindset as a prevailing, constantly changing and
new technology influenced understanding <strong>of</strong> how people interact in society, both privately and within
organizations. It is important to notice that <strong>the</strong> social media mindset is not a synonym for <strong>the</strong>
corporate marketing mindset, as it is very <strong>of</strong>ten understood to be. The social media mindset – as
understood here – is a central part <strong>of</strong> common societal and human understanding <strong>of</strong> <strong>the</strong> reality we are
living in, and it influences what kind <strong>of</strong> social realities we construct and how we construct <strong>the</strong>m.
The research questions set in this paper are:
How does <strong>the</strong> use <strong>of</strong> social media (Web 2.0) influence user thinking and what kind <strong>of</strong> new
mindsets are emerging?
What can be learned from <strong>the</strong> historical perspective to understand social media mindset change?
What kind <strong>of</strong> differences can be recognized in <strong>the</strong> social media mindsets <strong>of</strong> people <strong>of</strong> various
ages (or generations)?
How does this new kind <strong>of</strong> thinking influence organizational knowledge management, and how
should <strong>the</strong> social media mindset change be taken into consideration?
Due to <strong>the</strong> novelty <strong>of</strong> <strong>the</strong> social media mindset phenomenon, as well as <strong>the</strong> broad focus <strong>of</strong> research
questions set, this exploratory article adopts a literature-based approach is emphasized. The area <strong>of</strong>
<strong>the</strong> social media mindset as defined here has to date prompted surprisingly little research attention.
The question is <strong>of</strong> considerable importance and relevance from <strong>the</strong> point <strong>of</strong> view <strong>of</strong> social media
influence generally and knowledge management especially. In addition to this interpretative literary
based approach, <strong>the</strong> social media mindset is explored through first hand corporate social media
mindset related experiments conducted in one case organization.
The rest <strong>of</strong> <strong>the</strong> paper is organized as follows. Chapter 2 presents a couple <strong>of</strong> viewpoints on <strong>the</strong> history
<strong>of</strong> social media. The idea here is not to list what has happened in <strong>the</strong> field, as excellent descriptions
can be found elsewhere. The point is more an attempt to illustrate potential alternative development
trends that for one reason or ano<strong>the</strong>r did not materialize. Such development paths could have
produced broad scale social media solutions at an earlier phase as well as different kinds <strong>of</strong> social
media mindsets. Chapter 3 digs deeper into what social media mindset is from different angles.
Features <strong>of</strong> this mindset are described, with focus on emerging new mindsets Chapter 4 combines <strong>the</strong>
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Risto Säntti
social media mindset and knowledge management. Chapter 5 concludes <strong>the</strong> findings in relation to <strong>the</strong>
research questions set. This paper has utilized and developed fur<strong>the</strong>r findings presented in a Finnish
language article (Säntti & Säntti 2011) published by <strong>the</strong> Committee for <strong>the</strong> Future <strong>of</strong> <strong>the</strong> Parliament <strong>of</strong>
Finland.
2. The historical perspective on <strong>the</strong> development <strong>of</strong> <strong>the</strong> social media mindset
Technology and its development facilitate social media, its use, and also form <strong>the</strong> basis for social
media mindset development. Technology facilitates, but does not determine, social media related
thinking. The use <strong>of</strong> technology, experiments and reflection based upon <strong>the</strong>se experiences define <strong>the</strong>
ways <strong>of</strong> using social media that emerge and which <strong>of</strong> those become dominant. Technology has got a
decisive role in social media mindset development. Therefore <strong>the</strong> forces that influence technology
development are relevant in relation to social media thinking. Technology development is <strong>of</strong>ten
considered to be unplanned and impersonal. According to Ellul (1976) technology development is
strongly based on haphazard technology-internal regulations. Lanier (2010) has added <strong>the</strong>
perspective <strong>of</strong> choices made during a certain technology development phase to this view, as those
choices close <strong>of</strong>f <strong>the</strong> selected solution and make alternative ( and maybe potentially better solutions)
unviable. Social media technology, and <strong>the</strong> mindset based on it, can be seen to develop to a
considerable degree in an unplanned and undirected manner. The outcomes <strong>of</strong> such sporadic
development are not necessarily <strong>the</strong> most desirable ones. Alternative development trends exist and,
connected to <strong>the</strong>m, also alternative social media mindsets.
It is possible to recognize some alternative social media development phases from <strong>the</strong> history <strong>of</strong>
technology. Social media – technology supported multi-directional communication and common
interpretation building – has germinated at different phases <strong>of</strong> history. Operator controlled manual
telephone exchanges that were in widely in use during several decades at <strong>the</strong> end <strong>of</strong> 19th and <strong>the</strong>
beginning <strong>of</strong> <strong>the</strong> 20th century contained social media potential, albeit mostly unutilized. Switchboards
were able to connect numerous telephones, including <strong>the</strong> exchange unit, in common discussions.
This social media development opportunity was mostly missed, only to emerge some hundred years
later in <strong>the</strong> form <strong>of</strong> conference calls and speakerphones. In hindsight, <strong>the</strong> social media technology
based on <strong>the</strong> telephone switchboard could have been adopted in <strong>the</strong> 19th century instead <strong>of</strong> <strong>the</strong> 20th.
The reasons why this development opportunity was missed may be due to mindsets excluding such a
possibility. Ano<strong>the</strong>r and an even more developed genuine social media technology existed, and exists
still today, in <strong>the</strong> form <strong>of</strong> amateur radio activities. Amateur radio enthusiasts are involved in social
media. They are in contact with o<strong>the</strong>r activists through <strong>the</strong>ir personal radio stations digitally or
o<strong>the</strong>rwise to form common interest groups. (Säntti & Säntti 2011)
In <strong>the</strong> 40 years since <strong>the</strong> 1970’s, technological development has speeded up social media
development both through continuous improvements in processor capacity and through innovations
including Arpanet, <strong>the</strong> PC, <strong>the</strong> cell phone, <strong>the</strong> internet, e-mail, <strong>the</strong> commercial internet, chat, <strong>the</strong>
intranet and so on. New social media knowledge management tools are being introduced at an
increasing speed (e.g. Hart 2011) The use <strong>of</strong> <strong>the</strong>se tools will have a considerable impact on <strong>the</strong> social
media mindset, but that influence has been studied to only a limited degree.
The evolution <strong>of</strong> <strong>the</strong> book is an important phenomenon influencing <strong>the</strong> social media mindset. Carr
(2010) has described this development trend in detail. His hypo<strong>the</strong>sis is that reading through digital
media influences human learning and knowledge management processes in a fundamental way.
Reading on <strong>the</strong> net is considerably different from <strong>the</strong> processes involved in long text reading. The
tool, that is, <strong>the</strong> computer does not invite <strong>the</strong> reader to stay long on one issue, various kinds <strong>of</strong>
disturbances and temptations influence <strong>the</strong> reading process. Platform owners (e.g. Google,
Facebook) have an interest in disturbing longer term reading processes. Internet reading has
influenced <strong>the</strong> way we approach papers and books. Magazine articles have become shorter and
books written in traditional or classical form are too long and <strong>the</strong> plots too complex for present day
readers. Social media may not be <strong>the</strong> best means <strong>of</strong> supporting <strong>the</strong> elements <strong>of</strong> constructivist learning
orientations (Ruohotie 2000): reflection, internal construction <strong>of</strong> reality, meaning schemes and
knowledge construction.
Lanier (2010) describes how social technology development contains excluding choices. Such
choices fill a position that can be occupied by only one player. This lucky position may be constant or
for a limited period <strong>of</strong> time only. Facebook can be seen to be in such a position. It has become <strong>the</strong>
global networking standard that defines <strong>the</strong> practices <strong>of</strong> social networking. Facebook is broadly
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accepted as <strong>the</strong> medium, and it can disregard criticism owing to <strong>the</strong> power derived from its mass and
volume. Many companies rely for <strong>the</strong>ir social media marketing solely on Facebook. Issues attracting
criticism in this standard-in-practice are constantly changing, but have included <strong>the</strong> rules concerning
privacy, and <strong>the</strong> general clumsiness <strong>of</strong> <strong>the</strong> system. The social media mindset in relation to Facebook
needs to understand who <strong>the</strong> customers <strong>of</strong> <strong>the</strong> company are. According to F-Secure research
director, Mikko Hyppönen, Facebook users understand <strong>the</strong>mselves to be <strong>the</strong> customers, though in
reality <strong>the</strong>y are <strong>the</strong> product. The genuine customers are <strong>the</strong> advertisers (Tuurala 2011). This state <strong>of</strong>
affairs explains certain privacy leaks and <strong>the</strong> limited options to hide personal information. From <strong>the</strong>
perspective <strong>of</strong> only one dominating player position in <strong>the</strong> market Facebook clearly is <strong>the</strong>re. From this
position <strong>the</strong> company is able to influence <strong>the</strong> development <strong>of</strong> <strong>the</strong> social media mindset considerably,
and in <strong>the</strong> meantime prevent alternative solutions that might better fit user needs from entering <strong>the</strong>
field.
3. The social media mindset
The social media mindset is a cultural phenomenon, <strong>the</strong> existence and nature <strong>of</strong> which may go
unnoticed. The reason can be <strong>the</strong> apparent self-evident and ordinary nature <strong>of</strong> it. The mobile phone
has influenced our understanding <strong>of</strong> accessibility in a fundamental way. We no longer expect people
to ever be inaccessible. This reflects one relevant area <strong>of</strong> <strong>the</strong> social media mindset.
In its role as a cultural phenomenon, <strong>the</strong> social media mindset has scarcely been touched upon as a
research topic. Ribiere et al. (2010) have studied how national cultures influence web 2.0 use. The
focus was on H<strong>of</strong>stede’s cultural traits typology and attempted to understand what motivates people
to use web 2.0 technologies. This analysis is a step towards social media mindsets prevailing and
developing through <strong>the</strong> use <strong>of</strong> <strong>the</strong>se tools.
Table 1 below presents some recognizable key features <strong>of</strong> present and anticipated future social
media mindset (modified from Säntti & Säntti 2011). We see reality through <strong>the</strong>se lenses.
Table 1: Features <strong>of</strong> social media mindset
THE FEATURE DESCRIPTION
Incessant accessibility and rules
for contacting
People are constantly accessible via different social media tools. The
social acceptability <strong>of</strong> contact making depends upon <strong>the</strong> time point,
media, situation etc.
Practically everyone is accessible It is possible to make contact with an unlimited number <strong>of</strong> people.
Broad scale <strong>of</strong> friendship forms There exist light and deep forms <strong>of</strong> network relationships, and <strong>the</strong>y
Huge amount <strong>of</strong> potential group
can be short and long term in nature.
It is potentially possible to be a member <strong>of</strong> numerous virtual and <strong>of</strong> a
memberships
limited number <strong>of</strong> physical communities.
Broad amount <strong>of</strong> identities One can have various network identities that differ considerably from
each o<strong>the</strong>r, and <strong>the</strong>y can be close or distant from <strong>the</strong> physical identity.
Presence in virtual and physical Virtual and physical spaces overlap, and it is possible to be present in
spaces
<strong>the</strong> same space in different ways as well as be present in different
spaces simultaneously.
Virtual view reaches everywhere It is possible to see almost anywhere synchronously or
asynchronously.
Multiple overlapping time and Time is multiple and overlapping, same things are being done
activities
simultaneously in different spaces and different things in <strong>the</strong> same
space.
Distant touch It is possible to touch from <strong>the</strong> distance and to be touched by a
program.
Shared body feelings Body feelings can be monitored and shared according to mutual
agreement.
Hive-mind, groupthink Individual and group thinking combine in various forms.
Accessibility and modification <strong>of</strong>
information
Information is broadly accessible and users can modify it.
Limited information control Control over information after release is limited.
Quality <strong>of</strong> information and
Information consists <strong>of</strong> a mixture <strong>of</strong> contrasting viewpoints, and <strong>the</strong>
knowledge
user is responsible for evaluating <strong>the</strong> information quality.
Social media related thinking is considerably different from preceding mindsets. The attitudes in
relation to time, space, knowledge – private and public have changed. The social media mindset is
strongly interwoven with private and pr<strong>of</strong>essional activities. This mindset change is by no means
nearing its end phase, but in continuous evolution.
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Risto Säntti
4. The social media mindset and knowledge management
Combining social media with knowledge management <strong>of</strong>ten focuses on tools, what <strong>the</strong>y are, how <strong>the</strong>y
can be adopted for use, what are <strong>the</strong> pros and cons. Suggestions are made concerning additions to
<strong>the</strong> existing social media toolbox in use. The principles <strong>of</strong> social media use are ano<strong>the</strong>r frequently
discussed topic. (Levy 2007) Tools and principles may be set upon unrealistic premises unless
emerging and existing mindsets are understood.
The empirical experience <strong>of</strong> this article emerges from explorative activities within one case
organization (described in more detail in Säntti 2008; Säntti & Säntti 2011). This company
implemented some intra-organizational social media mindset related interventions. One <strong>of</strong> <strong>the</strong>se was
<strong>the</strong> forming <strong>of</strong> a shadow management group that consisted <strong>of</strong> younger members <strong>of</strong> <strong>the</strong> organization.
They were expected to represent different thinking and working styles compared to those more
mature employees <strong>of</strong> <strong>the</strong> company. Differences in generation-related thinking and in working behavior
were made visible, and <strong>the</strong>se differences focused upon work itself, <strong>the</strong> work place, development, and
<strong>the</strong> balance <strong>of</strong> work and leisure time. Based upon this intervention <strong>of</strong> a social media mindset focusing
on conclusions, a new organizational unit was formed. This new unit was to be a social media task
force charged with giving continuous development impulses and suggestions to <strong>the</strong> company related
to social media, <strong>the</strong> social media mindset and business initiatives based upon <strong>the</strong>m.
This corporate attempt to capture <strong>the</strong> potential <strong>of</strong> social media through a new kind <strong>of</strong> organizing,
mindset recognition and change is fresh and challenging. Behind this decision lies an expressed
corporate management understanding <strong>of</strong> <strong>the</strong> relevance <strong>of</strong> social media for <strong>the</strong> future <strong>of</strong> <strong>the</strong> business.
The core task force is expected to recognize <strong>the</strong> social media mindset features that can be cultivated
and harvested. The core group is expected to find <strong>the</strong> right time points for new interventions, to define
actions to be taken, and to support <strong>the</strong> start <strong>of</strong>f <strong>of</strong> a critical mass <strong>of</strong> employees.
Findings from discussions focused on social media utilization within organizations reveal <strong>the</strong> following
topics to be relevant (Säntti 2008). The idea here was to dig into <strong>the</strong> topic <strong>of</strong> expanding <strong>the</strong>
understanding <strong>of</strong> factors influencing <strong>the</strong> willingness <strong>of</strong> employees to utilize social media tools within
organizations. The focus was on knowledge workers who had excellent technical abilities to utilize
social media, and an interest in <strong>the</strong> challenges preventing <strong>the</strong> adoption <strong>of</strong> those tools – <strong>the</strong> key factors
being mindset related. The availability <strong>of</strong> relevant information and knowledge clearly functions as a
trigger. If <strong>the</strong>re is relevant information available, <strong>the</strong> interest in utilizing social media increases (see
also Awad et al. 2004). Ano<strong>the</strong>r factor <strong>of</strong> relevance is <strong>the</strong> perceived privacy <strong>of</strong> information. This
relates to <strong>the</strong> personal privacy, inter-organizational privacy etc (e.g. Dhillon & Moores 2001). A special
privacy related mindset issue is <strong>the</strong> topic <strong>of</strong> anonymity. This means whe<strong>the</strong>r discussions are
conducted under one’s own name or under an alias. The issue <strong>of</strong> user age is a topic commonly
referred to as a factor influencing social media mindset (e.g. Jones & Fox 2009).
The case organization focus on social media mindset and knowledge management revealed that <strong>the</strong>
mindsets <strong>of</strong> knowledge workers did not differ considerably from those generally identified among <strong>the</strong>
wider public (Säntti 2008). Knowledge workers are no more apt to utilize social media tools in <strong>the</strong>ir
work, and <strong>the</strong>y carry similar doubts and prejudices to any o<strong>the</strong>r potential users. The user age and
generation seems to be a relevant factor in this respect.
The simplified interpretations <strong>of</strong> generations in relation to <strong>the</strong>ir social media practices – and <strong>the</strong>
mindsets behind those practices – prevail. Social media divides people into groups, <strong>the</strong> dividing
factors being <strong>the</strong> familiarity <strong>of</strong> tools, practices, and finally mindsets formed based upon experiences
and interpretations <strong>of</strong> <strong>the</strong>m. Generations X, Y and Z (Stauss & Howe 1991) are <strong>of</strong> relevance from <strong>the</strong>
analysis point <strong>of</strong> view. Generation X is formed <strong>of</strong> people born between 1961 – 1979, generation Y
between 1980 – 1998 and generation Z between 1999 and approximately 2019. There exist
considerable differences in between <strong>the</strong> social media experiences and mindsets <strong>of</strong> <strong>the</strong>se groups <strong>of</strong>
people. A basic assumption is that generations grow to interpret realities according to <strong>the</strong> reality <strong>of</strong>
experiences during childhood and young adulthood. Worldviews are shaped by experience, <strong>the</strong>y are
internally coherent and consistent, and <strong>the</strong>y illuminate experience and guide our action (Olthuis
1988).
The mindset differences <strong>of</strong> generations are noteworthy. The reason why <strong>the</strong> generation approach is
relevant for organizational social media related knowledge management is <strong>the</strong> fairly long period <strong>of</strong>
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Risto Säntti
time when all three generations will be active in working life. The mindset differences <strong>of</strong> <strong>the</strong>se
generations can be considerable, and will influence organizational reality. For example
understandings related to <strong>the</strong> issue <strong>of</strong> privacy vary. The oldest X generation expresses greater
concern about information security, visibility and <strong>the</strong> non-perishable nature <strong>of</strong> it. The Y generation
shares information more broadly and more freely with less concern about its potentially harmful future
influences. The emerging Z generation probably will form its own more multifaceted approach to
social media information.
Table 2: Below attempts to capture <strong>the</strong> key knowledge management related mindset features <strong>of</strong>
generations X, Y and Z.
GENERATION X GENERATION Y GENERATION Z
The adoption <strong>of</strong> a social media
mindset demands rethinking and
reformulation or previous beliefs.
Social media tool use demands
considerable learning activities in
adulthood.
Has experienced different social
media technology development
phases as well as those weakest
functioning solutions.
Difficult to grasp where relevant
knowledge may be situated. May
still be able to understand that
information search can take time
and demand paper form.
Network activities focus on areas
experienced as necessary.
Focuses on a few known tools.
No special demands concerning
organizational social media tool
use at work.
Social media tools are a part <strong>of</strong>
normal human interaction.
Social media tools have been
learned as a part <strong>of</strong> o<strong>the</strong>r childhood
and youth situated learning
processes.
Used to existing broad array <strong>of</strong>
technological solutions. Accepts
technological deficiencies as a
feature <strong>of</strong> reality.
Searches for information from
different sources and through an
array <strong>of</strong> tools. The speed <strong>of</strong>
information availability is a key
factor.
Open to utilize new tools.
Fashionable commercial
approaches direct tool selection
and use.
Expects and demands social media
tools at work.
Social media will integrate as
a normal and unnoticed part
<strong>of</strong> everyday life. There is no
internet or social media, it
exists everywhere.
Social media tool user
competences are not an issue
<strong>of</strong> relevance.
Gets used to functioning userfriendly
unnoticeable
technology.
Information is expected
promptly. Information quality
may be supported by analysis
and technology development.
Tools less in focus.
Commercial fashions probably
influence interests strongly.
Probably does not think about
social media tools, but uses
<strong>the</strong>m everywhere.
One key challenge in <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> social media mindset and knowledge management is <strong>the</strong>
fragmentation <strong>of</strong> information, general information quality and need to acquire information quickly.
Conceptual and systemic thinking are issues that are clearly not supported by present development
trends. Therefore <strong>the</strong>re is considerable risk that merely adequate, sporadic and opinion-based
information bypasses structured, analyzed, model based and tested knowledge.
5. Conclusions
The research questions set in this paper attempt to discover how <strong>the</strong> use <strong>of</strong> social media influences
user thinking and emerging new mindsets, what <strong>the</strong> historical perspective teaches us in social media
mindset change, how generations <strong>of</strong> people differ in <strong>the</strong>ir social media mindsets, and how should all
this be taken into consideration in organizational knowledge management.
The <strong>the</strong>me <strong>of</strong> social media mindset has prompted surprisingly little research attention. There are
reasons for this. The social media mindset is a cultural phenomenon, <strong>the</strong> existence and nature <strong>of</strong>
which may go unnoticed. Technological innovations, tools and gadgets may easily steal <strong>the</strong> interest
focus. Never<strong>the</strong>less <strong>the</strong> question <strong>of</strong> social media mindset is <strong>of</strong> considerable relevance, it being about
how we understand availability, access, place and location, groups and membership, information and
knowledge, borderlines and identity among o<strong>the</strong>r things.
What <strong>the</strong> historical perspective reveals, is that <strong>the</strong> development trends <strong>of</strong> social media technology are
sporadic, unplanned, and at <strong>the</strong> same time strongly influenced by special interests <strong>of</strong> some key
players in <strong>the</strong> field. Technology has got a decisive role in social media mindset development, and
<strong>the</strong>refore <strong>the</strong> forces that influence technology development are relevant in relation to social media
thinking. The outcomes <strong>of</strong> sporadic development are not necessarily <strong>the</strong> most desirable ones. The
recognition and support to alternative technology development trends will help produce alternative
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social media mindsets.
Risto Säntti
Experience <strong>of</strong> social media use divides people into groups. Social media mindsets emerge based
upon experiences <strong>of</strong> social media use, <strong>the</strong> familiarity <strong>of</strong> tools and practices, and interpretations <strong>of</strong>
<strong>the</strong>se experiences. These mindset differences <strong>of</strong> generations are noteworthy, and clearly will
influence organizational reality.
How <strong>the</strong>n should all this be taken into consideration in organizational knowledge management? The
use <strong>of</strong> social media and interpretations based upon that use create <strong>the</strong> social media mindset. This
mindset is one key factor influencing knowledge management within organizations and in everyday
life. There is good reason to avoid over-positive interpretations <strong>of</strong> <strong>the</strong> outcomes <strong>of</strong> this development.
In <strong>the</strong> corporate context, <strong>the</strong> approach to social media still focuses today more on technical solutions
and damage limitation guidelines. The approach obviously should be more interested in facilitating
and supporting a mindset change that will influence action, with technology taking only a supporting
role.
References
Awad, E.M. & Ghaziri, H.M. (2003). Knowledge management. Pretience Hall. New Jersey.
Berger, P. & Luckman, T. (1966). The social construction <strong>of</strong> reality: A treatise in <strong>the</strong> sociology <strong>of</strong> knowledge,
Anchor Books, New York.
Bradley, A.J. (2010). “A new definition <strong>of</strong> social media”, Gartner Blog Network,
http://blogs.gartner.com/anthony_bradley/2010/01/07/a-new-definition-<strong>of</strong>-social-media/ (March 25, 2011).
Carr, N. (2010). The shallows, What <strong>the</strong> internet is doing to our brains, W. W. Norton, New York.
Chaumond, J. (2010). Social commerce: Quand le e-commerce rencontre le Web d'aujourd'hui, Digital
Mammouth Editions.
Dhillon, G.S. & Moores, T.T. (2001). “Internet privacy: interpreting key issues”. Information Resource
Management Journal, Vol 14, No 4.
Ellul, J. (1964).The technological society, Vintage Books, New York.
Hart, J. “The emerging top 100 tools for learning 2011”. Centre for Learning & Performance Technologies.
http://c4lpt.co.uk/recommended/2011.html (March 30,2011)
Jones, S. & Fox, S. (2009) Generations online in 2009. http://pewinternet.org/Reports/2009/Generations-Onlinein-2009.aspx
(March 23, 2011)
Lanier, J. (2010). You are not a gadget: A manifesto. Alfred A. Knopf, New York.
Levy, M. (2009). “Web 2.0 implications on knowledge management”, Journal <strong>of</strong> Knowledge Management, Vol 13,
No 1.
Olthuis, J.H. (1988). “On worldviews”, in Marshall, P.A. & Griffioen, S. Stained glass: workviews and social
science. University Press <strong>of</strong> America.
O’Reilly, T. (2005). “What Is Web 2.0”, http://oreilly.com/web2/archive/what-is-web-20.html (March 28, 2011).
Ribière, V.M., Haddad, M. & Wiele, P.V. (2010). “The impact <strong>of</strong> national culture traits on <strong>the</strong> usage <strong>of</strong> web 2.0
technologies”, VINE: The journal <strong>of</strong> information and knowledge management systems,
Vol 40, No 3/4, pp 334-361.
Säntti, P. (2008). Developing corporate knowledge management through social media, Helsinki University <strong>of</strong>
Technology.
Säntti, R. & Säntti, P. (2011). “Organisaatio ja sosiaalisen median ajattelutapa” (“Organization and <strong>the</strong> social
media mindset”). In Aaltonen-Ogbeide,T. (ed.). Silmät auki sosiaaliseen mediaan (“To social media eyes
wide open”). Publications <strong>of</strong> <strong>the</strong> Committee for <strong>the</strong> Future, Parliament <strong>of</strong> Finland, No. 4, pp 16-47.
Snurb (2007). “Produsage: a working definition”, Produsage.org, http://produsage.org/produsage (March 15,
2011).
Strauss, W. & Howe, N. (1991). Generations: The history <strong>of</strong> America’s future, Bantam Doubleday Dell, New York.
Tapscott, D. (2009). Grown up digital, McGraw-Hill, New York.
Tuurala, M. (2011). “Tietotyön kapinalliset” (“The rebels <strong>of</strong> ICT work”), Tietokone 2/2011.
878

The Importance <strong>of</strong> Knowledge in Regional Development
Dan Săvescu 1 , Mihaela-Georgia Sima 2 and Simona-Clara Bârsan 3
1 Transilvania University <strong>of</strong> Braşov, Faculty <strong>of</strong> Product Design and Environment
2 Bucharest Academy <strong>of</strong> Economic Studies
3 Research Institute for Analytical Instrumentation Cluj-Napoca, Technology
Transfer Centre
dsavescu@unitbv.ro
georgiasima@yahoo.com
simonaclara_barsan@yahoo.com
Abstract: The paper presents <strong>the</strong> relationship between knowledge creation, knowledge management and
transfer processes, <strong>the</strong> intellectual capital <strong>of</strong> an organization and <strong>the</strong> role <strong>of</strong> knowledge in regional development.
First <strong>of</strong> all, one defines <strong>the</strong> main involved elements: knowledge management, knowledge society, knowledge
based economy, intellectual capital. Then, <strong>the</strong> correlation between <strong>the</strong>m is explained. The authors have
considered necessary to analyze <strong>the</strong> main identified entities that play an important role in regional development.
One has started from <strong>the</strong> hypo<strong>the</strong>sis that entities such as business incubators, clusters, knowledge centres or
nods are <strong>the</strong> main “locations” where knowledge is created. Each <strong>of</strong> <strong>the</strong>se entities is taken into detailed analysis,
as a source <strong>of</strong> competitive advantage, emphasizing <strong>the</strong> reason for its existence, <strong>the</strong> facilities <strong>of</strong>fered to <strong>the</strong>
“knowledge creators”, <strong>the</strong> impact it has on <strong>the</strong> community it is build in and <strong>the</strong> way each <strong>of</strong> <strong>the</strong>m provides a
certain effect on local, regional or general development. Since <strong>the</strong> authors have come to <strong>the</strong> conclusion that <strong>the</strong>
proposed entities actually do have a strong impact on regional development, due to <strong>the</strong> fact that <strong>the</strong>y have
studied <strong>the</strong> <strong>the</strong>ory applied at <strong>the</strong> level <strong>of</strong> <strong>the</strong>se entities, <strong>the</strong>y have chosen to present <strong>the</strong> results obtained at <strong>the</strong>
level <strong>of</strong> two <strong>of</strong> <strong>the</strong>m, respectively, <strong>the</strong> Technological and Business Incubator ITA Pro-Energ, built within
Transilvania University <strong>of</strong> Braşov, Romania, and <strong>the</strong> cluster created around <strong>the</strong> Commercial Society
“Electroprecizia” Sacele, Romania. The paper also explains <strong>the</strong> notion <strong>of</strong> competitive region and its main
components. These components need to be reflected into <strong>the</strong> regional politics whose application should lead to
<strong>the</strong> development and efficient valorisation <strong>of</strong> <strong>the</strong> whole intellectual capital <strong>of</strong> <strong>the</strong> region. As an important
conclusion, <strong>the</strong> authors have stated that <strong>the</strong> reason for knowledge and intellectual capital accumulation at
regional level is to influence <strong>the</strong> strategies <strong>of</strong> <strong>the</strong> regional actors and to create <strong>the</strong> necessary competences for an
efficient collaboration towards growing <strong>the</strong> potential <strong>of</strong> <strong>the</strong> whole community.
Keywords: business incubators, clusters, intellectual capital, knowledge management
1. Introduction
The purpose <strong>of</strong> this paper is to debate <strong>the</strong> role <strong>of</strong> knowledge in regional development.
For that reason, before actually weighting <strong>the</strong> importance <strong>of</strong> different factors influential for <strong>the</strong>
phenomenon, certain notions are to be defined, so as to speak a common language.
Knowledge management is a part <strong>of</strong> general management dealing with knowledge actions, such as:
organizing, blocking and filtering, ga<strong>the</strong>ring, depositing, sharing, dissemination and knowledge
objects, such as: information, data, experiences, evaluations, analysis and initiatives. It supposes
capturing knowledge right where is created and applying <strong>the</strong>m into a constructive process.
According to Sveiby (Sveiby, 1997), knowledge and intellectual capital are physical “secret” assets <strong>of</strong>
a company that supports Stewart’s idea (Stewart, 1997) <strong>of</strong> creating some competitive advantages for
a good management <strong>of</strong> this kind <strong>of</strong> assets. There are two ways <strong>of</strong> developing <strong>the</strong> concept <strong>of</strong>
intellectual capital: <strong>the</strong> first one is centred on strategy and <strong>the</strong> o<strong>the</strong>r one on measurement. The
strategic point <strong>of</strong> view begins with <strong>the</strong> “learning organization” and with <strong>the</strong> relation between
knowledge and value successful realization.
The intangible character <strong>of</strong> <strong>the</strong> new type <strong>of</strong> capital generates several problems.
Sveiby (2000) defined <strong>the</strong> intellectual capital as a sum <strong>of</strong> three components: human capital, structural
capital and customer capital. It represents <strong>the</strong> intellectual material that has been formalised, captured
and exploited for producing more valuable assets; it is acknowledged as value in most organisations,
but it is not measured or evaluated in any financial statement <strong>of</strong> <strong>the</strong> company, excepting <strong>the</strong> reached
stock market value;
879

Dan Săvescu, Mihaela-Georgia Sima and Simona-Clara Bârsan
Charles Armstrong (Armstrong, Saint-Onge, 2004) divided <strong>the</strong> intellectual capital in three
components: <strong>the</strong> individual component, <strong>the</strong> internal component and <strong>the</strong> external component (Table 1).
The individual component includes: personal talents and capacities, competences, as well as
personnel’s education and training, experience and expertise. The internal component is defined by
<strong>the</strong> organisational modalities, organisational culture and internal climate, production system, quality
management system and by <strong>the</strong> informational system. The external component includes <strong>the</strong> relations
with customers, suppliers and o<strong>the</strong>r partners on <strong>the</strong> value chain, as well as <strong>the</strong> registered trademarks.
Table 1 The market value <strong>of</strong> a company
Visible capital
(book value)
Corporal assets
minus debts.
External component
-trademarks;
-relations with
customers and
suppliers.
Immaterial assets
(stock market valuation)
Internal component
- organisation;
- personnel;
- legal status;
-procedures and
methods;
-trade policies;
-research&development;
-s<strong>of</strong>tware.
Individual
component
- education;
- experience.
In this paper it is taken into account <strong>the</strong> conceptual evolutions regarding innovation, management <strong>of</strong>
knowledge transfer and <strong>the</strong> key competences to <strong>the</strong> success <strong>of</strong> a strategic approach. The idea <strong>of</strong>
measurement has started with <strong>the</strong> human resources management and with <strong>the</strong> financial
measurement techniques <strong>of</strong> capital, solving problems related to <strong>the</strong> knowledge measurement <strong>of</strong>
employees and company (Bahra, 2001). At <strong>the</strong> level <strong>of</strong> <strong>the</strong> general vision upon an organisation, <strong>the</strong>
two concepts have emerged, for leading to <strong>the</strong> development <strong>of</strong> <strong>the</strong> intellectual capital concept.
The question that rises and <strong>the</strong> main element <strong>of</strong> <strong>the</strong> study that serves as a base for this paper is
whe<strong>the</strong>r “instruments” such as universities, business incubators or clusters, are as efficient as <strong>the</strong>y
seem or is just ano<strong>the</strong>r legend.
2. Indicatives <strong>of</strong> regional development
Knowledge plays a key role in <strong>the</strong> process <strong>of</strong> innovation, especially in SMEs situated in medium
technology sectors, where innovation is based on <strong>the</strong> capability to informally search for a solution to
local problems toge<strong>the</strong>r with o<strong>the</strong>r partners. This process is different from <strong>the</strong> formal research
activities in high technology sectors.
Figure 1 presents <strong>the</strong> way and <strong>the</strong> stages <strong>of</strong> <strong>the</strong> innovative companies, from market demands to
industrial development. For <strong>the</strong> very beginning (I) market represents a stimulus in technology
development, new technologies are necessary to stay on <strong>the</strong> market, to realise products at <strong>the</strong> right
price that is imposed by <strong>the</strong> competition and, at <strong>the</strong> same time, by <strong>the</strong> customer’s demands. After
knowing <strong>the</strong> market demands, <strong>the</strong> second stage (II) is about new innovation projects, new aspects
concerning product design. After <strong>the</strong> project stage, <strong>the</strong> process continues with innovation adoption,
and finally, with <strong>the</strong> industrial development <strong>of</strong> <strong>the</strong> innovative SMEs.
IV
Industrial development
<strong>of</strong> innovative firms
III
Innovation adoption
and financing
Figure 1 - The fields <strong>of</strong> innovation policies
880
I
Market and
technology stimulus
II
Knowledge
development and
design <strong>of</strong> innovation
projects

Dan Săvescu, Mihaela-Georgia Sima and Simona-Clara Bârsan
To consider a region as an organisation opens <strong>the</strong> possibility <strong>of</strong> applying <strong>the</strong> <strong>the</strong>ories linked to
knowledge creation for regional development. Regional development is an important preoccupation at
<strong>European</strong> level, stated as priority among <strong>the</strong> social and economic cohesion objectives.
The development <strong>of</strong> organizational knowledge through partnership relations, <strong>the</strong> impact <strong>of</strong> <strong>the</strong>
products and services upon citizens’ lifestyle as well as <strong>the</strong>ir involvement in knowledge creation lead
us to <strong>the</strong> conclusion that <strong>the</strong> limits <strong>of</strong> <strong>the</strong> knowledge existing in different organizations – companies,
universities, research centres or administrative institutions – overcome <strong>the</strong>ir internal space.
Knowledge is diffused within <strong>the</strong> economic and social environment <strong>of</strong> <strong>the</strong> creating organization and
modifies both work and life styles <strong>of</strong> those who get in touch with <strong>the</strong>m.
The region, as an entity oriented towards cooperation, having its proper development objectives, must
establish a framework for <strong>the</strong> formal or informal relations and dependencies existing among <strong>the</strong>
organizations from <strong>the</strong> region.
Starting from triple helix concept (R- research entities involved in P- innovative products are
depending on G –government policies, and I – industry possibilities – Michael Porter) and upgrading
so as to correspond to <strong>the</strong> demands <strong>of</strong> <strong>the</strong> market, a new concept was developed, namely <strong>the</strong> “Fourleaf
Clover Model” (Figure 2). It involves a good relationship between academia, industry, government
policy and consulting companies (VINNOVA, GTZ, PROVENDIS, also Business Incubators, Transfer
Centres, and Chambers <strong>of</strong> Commerce).
ACADEMIA INDUSTRY
POLICY
CONSULTING COMPANIES
Figure 2 – The Four-leaf clover model
Based on <strong>the</strong>se modes, <strong>the</strong> international or national policies for regional development are <strong>of</strong>ten
oriented to eliminate <strong>the</strong> development differences existing between regions, to support <strong>the</strong> industrial
conversion in <strong>the</strong> areas with structural difficulties, to modernize <strong>the</strong> educational system and to
promote employment (<strong>European</strong> Commission). To conclude, one seeks for enhancing <strong>the</strong> economic,
social and territorial cohesion.
3. Instruments <strong>of</strong> regional development
So far, we have seen what <strong>the</strong> main elements are, how <strong>the</strong>y interact, and now we presume that
throughout two important instruments, development is accomplished. These elements are
Technological business incubators and clusters. They will be analysed one by one.
3.1 Technological and business incubators
As we already know (Savescu, 2009, 2010), <strong>the</strong> Technological and Business Incubator is a place
where, in a limited space, new created companies are concentrated. The incubators' objective is to
increase, for <strong>the</strong>se companies, <strong>the</strong> chances to grow up and survive (start-up or spin-<strong>of</strong>f). This
objective can be accomplished due to <strong>the</strong> supplying <strong>of</strong> modular spaces with common services
(copiers, communication services, and computers) and an enrolment for specific services (production
spaces having modern technology). The accent essentially is stressed on local development and
creation <strong>of</strong> new jobs, <strong>the</strong> technological orientation coming secondly.
The Business Incubator represents a property initiative which assures a small <strong>of</strong>fice and/or
manufacturing units for new or young companies. It usually assures a flexible work space for
accessible prices, common services and direct business consulting, access to specialized assistance
(such as support in R&D and risk capital).
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Business Incubators transform an idea into an opportunity. Once <strong>the</strong> idea is moving, <strong>the</strong> contractor's
way is open and it's time to look for <strong>the</strong> working instruments. These instruments include business
analyses, management, marketing and technological support. They also include business s<strong>of</strong>tware,
communication skills and presentation means. The Incubation and Technological Transfer Program
aim, objectives, possibilities <strong>of</strong>fered to companies have already been presented in o<strong>the</strong>r papers.
To become one <strong>of</strong> <strong>the</strong> selected potentially valuable businesses that is assisted within an incubator, a
company is submitted to a rigorous analysis, with <strong>the</strong> following stages: <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> economic
potential to be incubated; <strong>the</strong> selection <strong>of</strong> <strong>the</strong> incubated, depending on potential, interests, <strong>the</strong> activity
field; incubation with a coherent and steady program <strong>of</strong> support / collaboration activities; making <strong>the</strong>
product in prototype phase; accreditation and technology transfer; consultation and logistic support;
transfer from incubation to independent activity.
3.1.1 Technological and Business Incubator "Products and Technologies for Sustainable Energy"
ITA Pro-Energ
An example <strong>of</strong> well functioning business incubator is <strong>the</strong> Technological and Business Incubator
"Products and Technologies for Sustainable Energy" (ITA Pro-Energ), build within "Transilvania"
University <strong>of</strong> Brasov, certified to work from February 2008. It was created to initiate and develop
innovative companies, based on advanced technologies in <strong>the</strong> field <strong>of</strong> Sustainable Energy, especially
in: energy efficiency in industrial processes, energy performance <strong>of</strong> renewable energy systems and
buildings. ITA Pro-Energ is affiliated to: ARoTT – Romanian Association for Technology Transfer and
Innovation; ReNITT – National Network for Innovation and Technology Transfer and, at <strong>the</strong> same
time, an active participant in an international consortium that belongs to Enterprise Europe Network,
<strong>the</strong> largest <strong>European</strong> network that <strong>of</strong>fers free business consultancy for SMEs, launched in February
2008 by <strong>the</strong> Commission’s Directorate-General for Enterprise and Industry.
Following its goals and <strong>the</strong> very essence <strong>of</strong> a business incubator, ITA Pro-Energ <strong>of</strong>fers its incubated
SMEs all sorts <strong>of</strong> facilities, helping companies to start-up and evolve, preparing <strong>the</strong>m for <strong>the</strong> most
important confrontation, namely that with <strong>the</strong> real, competitive market.
Due to its activities, besides common spaces, ITA Pro-Energ provides:
accomplishment and promoting <strong>of</strong> activities in <strong>the</strong> field <strong>of</strong> sustainable energy;
partnership building and financial incomes through grants;
logistic support for incubated companies and partnerships created in <strong>the</strong> national industry;
prototyping and micro-production in <strong>the</strong> sustainable energy field;
testing and homologation for products and materials in <strong>the</strong> sustainable energy field;
presentation <strong>of</strong> products, systems and results at fairs, exhibitions, symposiums and / or publishing
scientific paper works;
promotion <strong>of</strong> <strong>of</strong>fers and necessities <strong>of</strong> incubated companies, innovations, inventions and
technology transfer that require systems through its own network and also through partnerships,
at national and international level;
<strong>the</strong> support needed by SMEs to remain successful in <strong>the</strong> competitive environment;
human resources improvement, training courses, dedicated s<strong>of</strong>tware; <strong>the</strong> company level mainly
depends on <strong>the</strong> company's accessibility to competitive human resources.
Figure 3 - Modular spaces with common services
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Figure 4 - Teaching and meeting space
How was this helpful? In its first 2 years <strong>of</strong> existence, 11 companies were incubated and helped to
develop into something better.
For example, one <strong>of</strong> <strong>the</strong>m was provided <strong>the</strong> necessary environment so as to create a brand new
material, awarded at Genoa and viable within construction industry. This invention help <strong>the</strong> company
develop from a very fragile start up to a well-known on <strong>the</strong> market SME.
If we were to take into analysis all 11 incubates, in each case, <strong>the</strong>re has been a significant
improvement.
To what extent is this relevant at a regional level? These companies, besides becoming competitive,
created working places, improve <strong>the</strong> R&D level and cooperation between industry, universities and
customers.
Most important, may be <strong>the</strong> fact that after 2 years <strong>of</strong> incubation process, all 11 structures are
functioning on <strong>the</strong>ir own, are consolidated economic structures and new starters replace <strong>the</strong>m,
following <strong>the</strong> same cyclic process.
After all, this is what economy and region need.
3.2 Clusters and business centres
A business cluster is a geographical concentration <strong>of</strong> interconnected businesses and institutions,
activating in a certain field. Clusters lead to <strong>the</strong> growth <strong>of</strong> productivity and national or international
competitiveness <strong>of</strong> companies.
This concept is known as competitive clusters, industrial clusters or Porter’s clusters, as developed by
its creator, Michael Porter (Porter, 1990). Since <strong>the</strong>n, <strong>the</strong> clusters development has become a
focalising element <strong>of</strong> several governmental programmes (Solvell et al, 2003).
A business cluster is a geographical concentration <strong>of</strong> entities that:
have, in <strong>the</strong> area, enough resources and competences for reaching <strong>the</strong> critical point beyond
which <strong>the</strong> <strong>the</strong>ir field development becomes self sustained;
have a key-position in a certain field <strong>of</strong> economic activity;
have a sustainable competitive advantage (Porter, 1985) as compared to o<strong>the</strong>r regions.
Taking into account <strong>the</strong> type <strong>of</strong> knowledge <strong>the</strong>y possess, <strong>the</strong>re are two types <strong>of</strong> business clusters:
technological clusters (techno clusters) – oriented towards high-tech, well adapted to <strong>the</strong>
conditions <strong>of</strong> knowledge based economy. They usually have universities and research centres
(ex. Silicon Valley) as central point <strong>of</strong> <strong>the</strong>ir development;
know-how based clusters (traditional clusters) – based on more traditional activities that maintain
<strong>the</strong> competitive advantages in know-how terms for periods <strong>of</strong> tens or even hundreds <strong>of</strong> years.
Very <strong>of</strong>ten, <strong>the</strong>y are specific for industry.
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Clusters have <strong>the</strong> potential to affect competition on three ways: increase productivity <strong>of</strong> <strong>the</strong> cluster
companies, induce innovations in <strong>the</strong> field and stimulate <strong>the</strong> creation <strong>of</strong> new businesses in <strong>the</strong> field,
within <strong>the</strong> region.
On <strong>the</strong>se three ways, one can obtain a modification <strong>of</strong> competition, namely to raise it and, as a
consequence <strong>of</strong> <strong>the</strong> market natural selectivity, one can reach at an environment characterised both by
fierce competition and increased competitiveness <strong>of</strong> <strong>the</strong> cluster organizations.
The process <strong>of</strong> identification, defining and describing a cluster is not standardized yet.
The consultants and researchers use different analysis and evaluation methods, most <strong>of</strong> <strong>the</strong>m based
on highlighting <strong>the</strong> specific <strong>of</strong> employment in <strong>the</strong> region, resulted from <strong>the</strong>ir grouping in industries or
activity types, defined by NACE code.
Regional development should include <strong>the</strong> creation <strong>of</strong> some competitive clusters in <strong>the</strong> respective
region, following a model that will have to stimulate <strong>the</strong> processes <strong>of</strong> knowledge creation and which
should contain those four spaces appropriate for <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> activities responsible for
accomplishing <strong>the</strong> SECI cycle and its continuous replay (Nonaka et al, 2000):
spaces for achieving common experiences;
spaces for dialogue, to express and conceptualize tacit accumulated knowledge;
spaces for systematization and analysis <strong>of</strong> accumulated knowledge;
spaces for applying and testing new knowledge.
The starting point in identifying <strong>the</strong> four types <strong>of</strong> spaces is industry or, generally speaking, productive
and commercial activities. Here one can feel <strong>the</strong> need for competitiveness. The role within <strong>the</strong> SECI
regional cycle, most frequently assumed by organizations <strong>of</strong> this kind, is that <strong>of</strong> application space and
commercial exploitation <strong>of</strong> <strong>the</strong> available knowledge.
O<strong>the</strong>r types <strong>of</strong> organizations involved in knowledge use are universities and research units. The
traditional role assumed by universities is that <strong>of</strong> scientific innovative research, developing new
products, realised by teaching pr<strong>of</strong>essors, students, master and doctoral students, and having <strong>the</strong>
possibility to transfer research to <strong>the</strong> industry. It can be conclude that universities look like a dialog
space.
The research units, whe<strong>the</strong>r public institutions or private research centres, traditionally assume <strong>the</strong>
role <strong>of</strong> space for systematization and testing new knowledge (Figure 5).
Space for achieving common experiences Space for dialogue
Universities
Space for application
Companies
Space for testing new knowledge
Research units
Space for systematization and analysis
<strong>of</strong> accumulated knowledge
Universities
Research units
Figure 5: Participation <strong>of</strong> different organisations to knowledge production; <strong>the</strong>ir strict traditional roles;
knowledge flow situation.
Industry regional competitiveness is a determinant factor <strong>of</strong> <strong>the</strong> welfare level <strong>of</strong> <strong>the</strong> inhabitants from
<strong>the</strong> region, thus making <strong>the</strong> regional administration institutions be directly involved in issues <strong>of</strong>
developing competitive clusters. Moreover, <strong>the</strong>ir role in regulating <strong>the</strong> economic and social system
allows <strong>the</strong>m to take a wide range <strong>of</strong> measures to boost cooperation between <strong>the</strong> three categories <strong>of</strong>
organizations that contribute to <strong>the</strong> development <strong>of</strong> knowledge in a cluster.
A considerable advantage is conferred by <strong>the</strong> disconnection <strong>of</strong> this type <strong>of</strong> institutions from <strong>the</strong>
competitive environment, which allows <strong>the</strong>m a great deal <strong>of</strong> objectivity in implementing <strong>the</strong> strategy <strong>of</strong>
developing a competitive cluster. In addition to <strong>the</strong> catalyst role <strong>of</strong> <strong>the</strong> cluster development, <strong>the</strong> public
administration institutions should play an active role in fostering dialogue and knowledge transfer as
well, thus becoming an active participant in <strong>the</strong> "space <strong>of</strong> dialogue” (Figure 6).
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Dan Săvescu, Mihaela-Georgia Sima and Simona-Clara Bârsan
Space for achieving common
experiences
Companies (in cooperation with
individuals from all o<strong>the</strong>r
organisations)
Space for application
Companies, research units
Regional administration
Space for testing new knowledge
Companies, research units,
Universities
Regional
administration
Space for dialogue
Companies,
universities, local and regional
administration
Space for systematization and
analysis <strong>of</strong> accumulated
knowledge
Universities, research units,
practitioners from companies
Figure 6: Participation <strong>of</strong> different organisations to knowledge production within a cluster
Besides clusters, under globalisation conditions, one recognises <strong>the</strong> existence <strong>of</strong> some cooperation
forms beyond regional limits, <strong>the</strong> so-called centres or nodes. Through centres and nodes, one creates
interregional cooperation arrangements for, on one hand, completing <strong>the</strong> necessary elements to
achieve a competitive value chain <strong>of</strong> a particular industry and, on <strong>the</strong> o<strong>the</strong>r hand, for creating a critical
mass to trigger an increase based on innovation and knowledge creation. Nodes and centres creation
is derived from <strong>the</strong> idea proposed by Porter, but it is better adapted to <strong>the</strong> current needs <strong>of</strong> <strong>the</strong> global
markets, particularly in terms <strong>of</strong> recognising <strong>the</strong> increased possibilities <strong>of</strong> distance cooperation,
through communication networks and by reducing <strong>the</strong> barriers <strong>of</strong> circulation for goods and people.
The companies realise clearer and clearer that a geographical proximity is not necessary for
achieving a certain production stage. The competitiveness barriers related to big distances and
international transactions have almost disappeared following <strong>the</strong> technology and logistics evolutions,
so as it becomes easier and easier <strong>the</strong> reallocation <strong>of</strong> some operations such as production and
research-development towards countries with good expertise and lower costs. More and more <strong>of</strong>ten,
<strong>the</strong> elements <strong>of</strong> new models development, production and distribution are separately realised, beyond
<strong>the</strong> frontiers <strong>of</strong> historical clusters.
The geographic clusters seem to fall apart, while <strong>the</strong> functioning model remains intact, a large number
<strong>of</strong> organisations <strong>of</strong> different factions having to cooperate, but no longer linked to a geographical
proximity.
In this context, <strong>the</strong> primary concern for regional development is to ensure a sufficient attractiveness
for maintaining, developing and implanting in <strong>the</strong> region some companies that provide good value for
<strong>the</strong> knowledge and local workforce. Companies are attracted by two potential benefits related to this
factor: cheaper labour and / or highly trained work force and <strong>the</strong>refore, with more knowledge. As
regional development essentially involves an increase in population living standards, <strong>the</strong> main strands
<strong>of</strong> regional or local administration are, on one hand, to ensure in <strong>the</strong> region an important flow <strong>of</strong>
qualified manpower in <strong>the</strong> respective interest field and, on <strong>the</strong> o<strong>the</strong>r hand, to create a better
infrastructure to allow intra-and interregional cooperation.
3.2.1 ETREC Cluster
The numerous changes occurred in <strong>the</strong> Brasov economy map as a result <strong>of</strong> <strong>the</strong> vast globalization
process currently underway, have led to <strong>the</strong> rupture <strong>of</strong> most business relationships existing in <strong>the</strong>
past, fact that has been very negatively perceived by all <strong>the</strong> economic actors present on <strong>the</strong> market.
The appearance <strong>of</strong> many SMEs in sectors traditionally occupied by corporations has given <strong>the</strong>
necessary flexibility and adaptability <strong>of</strong> those sectors but, at <strong>the</strong> same time, one has remarked that <strong>the</strong>
investments that SMEs can do to continue modernizing and adapting to <strong>the</strong> new market requirements
are insufficient and moreover, targeted depending on <strong>the</strong> timing requirements.
Under those circumstances, it was necessary to coordinate activities in <strong>the</strong> industrial sector within a
well-chosen strategy that set realistic goals for all competitors according to <strong>the</strong> available resources or
those that can be attracted from <strong>the</strong> market.
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For that purpose, <strong>the</strong> ETREC industrial cluster was set up starting with <strong>the</strong> existing industrial structure
on <strong>the</strong> platform <strong>of</strong> Electroprecizia SA – Săcele, with <strong>the</strong> participation <strong>of</strong> an elite higher education
institution – Transilvania University from Brasov and with <strong>the</strong> unconditioned help <strong>of</strong> <strong>the</strong> local and
regional authorities: <strong>the</strong> City hall <strong>of</strong> Săcele town and <strong>the</strong> Centre Regional Development Agency. To
<strong>the</strong>se three pillars mentioned by <strong>the</strong> classical <strong>the</strong>ory <strong>of</strong> clusters as well, one has added o<strong>the</strong>r partners
that have signed <strong>the</strong> cooperation protocol, which conferred <strong>the</strong> ETREC cluster <strong>the</strong> status <strong>of</strong> regional
cluster.
The aim <strong>of</strong> setting up this cluster was to contribute mainly through economic means to <strong>the</strong> economic,
social and cultural development <strong>of</strong> <strong>the</strong> entire geographic area in which <strong>the</strong> cluster activates.
The strategic objective on medium and long term was to give <strong>the</strong> cluster, through its future activity,
through co-optation <strong>of</strong> new members and <strong>the</strong> strategic partnerships that will be carried out with o<strong>the</strong>r
similar structures from country and abroad, <strong>the</strong> status <strong>of</strong> national cluster and excellence pole in <strong>the</strong>
electrotechnical field.
The structure <strong>of</strong> <strong>the</strong> industries developed by <strong>the</strong> industrial field in which <strong>the</strong> ETREC cluster activates
establishes its strategic development fields on three main directions:
Motors, generators and electric drives;
Electrical and electronic equipments for automotives;
Logistics and support services.
To achieve <strong>the</strong> intended purpose, this protocol:
Nominates <strong>the</strong> organisations that have decided to participate in cluster formation;
Enumerates <strong>the</strong> main objectives <strong>of</strong> <strong>the</strong> cluster for <strong>the</strong> current stage;
Designates <strong>the</strong> initiative committee that prepares <strong>the</strong> necessary documentation for <strong>the</strong> cluster
creation / registration.
The following organisations have been decided, as founding members <strong>of</strong> this cluster:
SC. Electroprecizia SA- Automotive engines and electrical equipments;
Transilvania University from Brasov;
City hall <strong>of</strong> Săcele town;
Centre Regional Development Agency;
Chamber <strong>of</strong> Commerce and Industry Braşov;
National Institute for Road Vehicles Braşov;
County School Inspectorate Braşov;
SC Tramar SA - Sf. Gheorghe- electric motors, automotive electrical equipments;
SC Caditec SRL – Braşov – design, data conversion, finite element analyses, structures;
SC DistrictServ SRL- Braşov- high-tech products in <strong>the</strong> fields <strong>of</strong> motorsport, telecommunications,
and road transport;
SC Eur<strong>of</strong>ordi Company SRL- business and management consultancy, financing, opportunity
studies;
SC. EP Construct MaintenanceSRL- equipments and production tools maintenance;
SC EP Cast Production SRL- cast products;
SC EP Automotive Equipment SRL- automotive equipments;
SC EP Electrical Equipment SRL- electrical equipments;
SC EP Electrical Motors SRL- electrical motors;
SC EP Tools Production SRL- production tools;
Centre for Technologies, Inventions and Business SA - industrial production, regional
representative <strong>of</strong> <strong>the</strong> State Office for Patents and Trademarks OSIM;
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Dan Săvescu, Mihaela-Georgia Sima and Simona-Clara Bârsan
"Victor Jinga" industrial high school – Săcele;
Forum A+ Association – consultancy in accessing <strong>European</strong> funds;
Romanian Association for Technology Transfer and Innovation – AroTT;
SC Real Project SRL- design, business consultancy;
SC BRUMM SRL- consultancy in regional development and innovative management;
SC Metromat SRL- ISO metrology, measuring equipments;
SC KG Clean Energy SA- clean energy equipments;
Cristian Radu – expert.
What were <strong>the</strong> main achievements up to now? First <strong>of</strong> all, a whole dead industry was partially brought
to life. Throughout <strong>the</strong> cooperation with <strong>the</strong> university, several research projects were written and
financing was obtained.
Using that money, <strong>the</strong> new management team, was able to put toge<strong>the</strong>r a viable working plan and
restructured Electroprecizia.
It took time and effort, but things are functioning better now.
The results began to show: people were hired again, unimploiment in Sacele area decreased, wages
were a little raised and machines were bought, but this time toge<strong>the</strong>r with <strong>the</strong> necessary instructions
for those using <strong>the</strong>m.
These are <strong>the</strong> tangible results so far.
Besides that, through cooperation with o<strong>the</strong>r companies and <strong>the</strong> university, new research products
were forecasted to be made. If registered, innovation will become intellectual property and produce
fur<strong>the</strong>r pr<strong>of</strong>it.
4. Conclusions
The application <strong>of</strong> knowledge management models in regional development is possible only with an
adjustment <strong>of</strong> <strong>the</strong> vision from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> new entity. The regional specificity leaves its mark
on <strong>the</strong> modality that can apply <strong>the</strong> concepts developed at <strong>the</strong> level <strong>of</strong> a single organization.
Fortunately, <strong>the</strong> knowledge management models have good adaptability to structures that can be
designed as networks <strong>of</strong> entities that are in collaboration and knowledge transfer relationships.
Nonaka’s model can be successfully used for designing strategies <strong>of</strong> knowledge creation at regional
level. The main adaptation required by <strong>the</strong> proposed concept is to identify <strong>the</strong> entities involved in
creating <strong>the</strong> flow <strong>of</strong> knowledge as organizations or individuals.
The hypo<strong>the</strong>sis that <strong>the</strong> original model starts from is to conduct organizational knowledge creation
processes within a community led by certain social institutions. The social concept interferes with <strong>the</strong>
knowledge creation processes, thus orientating <strong>the</strong> perception upon <strong>the</strong> relations between individuals.
The networks interconnected by regional actors represent such a community form that can govern
itself. They also represent <strong>the</strong> large communities having common practices <strong>of</strong> such kind as to create
knowledge at regional level.
The intellectual capital model applied at organization level can be transposed so as to reflect <strong>the</strong>
development and competitive potential <strong>of</strong> a region.
Therefore, for a certain region, to be competitive means to:
be aware <strong>of</strong> <strong>the</strong> reasons for being in a competition;
be able to develop strategic processes that put toge<strong>the</strong>r various visions and strategies belonging
to <strong>the</strong> actors <strong>of</strong> its framework;
be able to mobilise resource and capacities and to develop o<strong>the</strong>r new ones;
be able to connect to knowledge production networks;
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be able to easily modify <strong>the</strong> structure and nature <strong>of</strong> its institutions.
These five elements defining <strong>the</strong> competitiveness <strong>of</strong> a region must be translated into regional
development policies that should lead to an efficient development and make available <strong>the</strong> intellectual
capital in <strong>the</strong> region, in <strong>the</strong> context <strong>of</strong> a knowledge society and <strong>of</strong> a new economy based on an
intensive use knowledge,
You can not manage what you can not measure. Therefore, accurate and complete evaluation <strong>of</strong> <strong>the</strong>
intellectual capital <strong>of</strong> a region is <strong>the</strong> key for designing development strategies, meaning to increase
<strong>the</strong> regional competitiveness. The reason for knowledge and intellectual capital accumulation at
regional level is to influence <strong>the</strong> strategies <strong>of</strong> <strong>the</strong> regional actors and to create skills for more effective
cooperation in <strong>the</strong> growth potential <strong>of</strong> <strong>the</strong> entire community.
The examples given as case studies <strong>of</strong>fer a broad picture on <strong>the</strong> level <strong>of</strong> knowledge, innovation,
competitiveness enhancement at regional level, both through incubators, science parks, technology
parks, o<strong>the</strong>r technology transfer centres and through clusters as well.
References
Ahonen, G. (2000). „Generative and commercially exploitable intangible assets”, in Grojer, JE şi Stolowy, H.
(Eds), Classification <strong>of</strong> Intangibles, Groupe HEC, Jouy-en-Josas
Armstrong C., Saint-Onge H. (2004), The Conductive Organization Building Beyond Sustainability, Butterworth-
Heinemann
Bahra N. (2001), Competitive Knowledge Management, Palgrave, New York
Boutellier R., Gassmann O., von Zedtwitz M. (2000). Managing global innovation: uncovering <strong>the</strong> secrets <strong>of</strong>
future competitiveness, Springer, Berlin
David P.A., Foray D. (January 2003). Economic Fundamentals <strong>of</strong> <strong>the</strong> Knowledge Society, în Policy Futures In
Education. An e-Journal, 1(1): Special Issue: Education and <strong>the</strong> Knowledge Economy
Drăgănescu, M. (2004). Societatea informaţională şi a cunoaşterii. Vectorii societăţii cunoaşterii,
www.edemocratie.ro
Jones Alan Burton, (1999), Knowledge capitalism. Business, Work and Learning in <strong>the</strong> New Economy, Oxford
University Press
José M. Viedma Marti, (2005) RICBS: Regions Intellectual Capital Benchmarking System – A methodology and a
framework for measuring and managing intellectual capital <strong>of</strong> regions, la „The First World Congress on
Intellectual Capital for Communities”, Paris, 20 iunie 2005
Nonaka, Ikujiro, Hirotaka Takeuchi (1995). The Knowledge-Creating Company: How Japanese Companies
Create <strong>the</strong> Dynamics <strong>of</strong> Innovation? New York: Oxford University Press
Nonaka, I., R. Toyama, and N. Konno (2000). SECI, Ba and leadership: a unified model <strong>of</strong> dynamic knowledge
creation, Long Range Planning, vol. 33
Porter, M.E. (1985). Competitive Advantage, Free Press, New York
Săvescu, D. Business Incubators, a Good Opportunity to <strong>the</strong> Technological Transfer. International Scientific
<strong>Conference</strong> in „Management <strong>of</strong> Technology – Step to Sustainable Production” MOTSP 09, Sibenik, Croaţia,
2009, pag. 410- 415.
Săvescu, D. Some aspects regarding <strong>the</strong> concept „Research and Business”. Journal <strong>of</strong> Industrial Engineering
and Management, JIEM, 2010, Vol. 3 nr.2, pag.337-352.
Săvescu,D., Popescu,R. Technological Transfer Realised by Business Incubators. Revista Metalurgia
International, nr 9/2010, Editura Stiinţifică F.M.R., pag. 61-65.
Solvell, O., Lindqvist, G. & Ketels, C. (2003). The Cluster Initiative, Greenbook
Stewart, T.A. (1997). Intellectual Capital – The New Wealth <strong>of</strong> Organizations, Doubleday, New York
Sveiby K.E. (2000). La nouvelle richesse des entreprises. Savoir tirer pr<strong>of</strong>it des actifs immateriels de sa societe,
Maxima, Paris
Sveiby, K.E. (1997). The New Organizational Wealth. Managing & Measuring Knowledge-Based Assets. San
Francisco: Barrett-Hoehler Publishers, Inc.
888

Analysis <strong>of</strong> Knowledge Work Execution at Computer
Workplaces
Benedikt Schmidt 1 , Todor Stoitsev 1 and Max Mühlhäuser 2
1
SAP Research, Darmstadt Germany
2
TU Darmstadt, Telecooperation Group, Darmstadt, Germany
benedikt.schmidt@sap.com
todor.stoitsev@sap.com
max@tk.informatik.tu-darmstadt.de
Abstract: Work execution at computer workplaces is an important facet <strong>of</strong> knowledge work. For <strong>the</strong> knowledge
worker, <strong>the</strong> interaction with <strong>the</strong> computer is seen in terms <strong>of</strong> goals and subgoals with accompanying tasks ra<strong>the</strong>r
than system commands. Consequently, knowledge workers apply tacit knowledge to pursue goals by a weakly
structured process involving s<strong>of</strong>tware tools to consume, modify or create information. Interruptions, appointments
and deadlines result in goal realization in parallel or in rapid succession. To extract tacit knowledge solid
knowledge about work is essential. The paper analyzes knowledge work execution at computer workplaces. Two
user studies to analyze work processes based on interaction histories have been conducted. The first study
focused on task execution processes. Differences and regularities <strong>of</strong> tasks executed by different people were
examined. The main outcome <strong>of</strong> <strong>the</strong> first study is <strong>the</strong> description <strong>of</strong> core task execution processes that are
mixtures <strong>of</strong> work techniques. The second study focused on text visible at <strong>the</strong> user screen during task execution.
Topics are extracted from <strong>the</strong> logged screen content and textual similarities are measured. The main outcome <strong>of</strong>
<strong>the</strong> second study is <strong>the</strong> identification <strong>of</strong> text similarities for tasks executed by different persons and <strong>the</strong> existence
<strong>of</strong> topics that identify tasks.
Keywords: knowledge work, experience management, interaction histories
1. Introduction
Many knowledge work processes at computer workplaces present <strong>the</strong>mselves as application <strong>of</strong> tacit
knowledge, as <strong>the</strong>y are not driven by defined procedures but by expected results. Tacit knowledge is
a veiled asset, as it only manifests in <strong>the</strong> fleetingness <strong>of</strong> work processes it produces (Walsham,
2005). Consequently, harvesting tacit knowledge from work processes is a complex challenge
(Stenmark, 2001) that requires reasoning about <strong>the</strong> meaning <strong>of</strong> work process fragments. To enable
such reasoning a solid knowledge base about work processes is essential. Research on work
processes in computer workspaces reveals <strong>the</strong>se processes as organized by tasks with goals ra<strong>the</strong>r
than by system commands (Bannon, Cypher, Greenspan, & Monty, 1983). Thereby multiple, <strong>of</strong>ten
interleaving tasks are common for knowledge work (Czerwinski et al. 2004), (Mark, Gonzalez, &
Harris, 2005). The goals are solved by using recurring solution techniques, as (Hädrich, 2008) points
out.
The intention <strong>of</strong> this paper is to evaluate and analyze <strong>the</strong> execution <strong>of</strong> knowledge intensive tasks. The
research is intended to improve <strong>the</strong> understanding <strong>of</strong> knowledge work execution. Based on an
improved understanding models for knowledge work execution can be created that facilitate <strong>the</strong>
design <strong>of</strong> support systems and <strong>the</strong> development <strong>of</strong> task detection methods. Understanding knowledge
work is complicated due to <strong>the</strong> contingence <strong>of</strong> knowledge work processes. Two people working on <strong>the</strong>
same task will not solve it completely similar, due to situational constraints and <strong>the</strong> relevance <strong>of</strong>
personal experience and preferences for knowledge work execution. The scientific issue tackled in
this paper is <strong>the</strong> identification <strong>of</strong> knowledge work execution characteristics that exist independent from
situational constraints and individual preferences.
The paper focuses two aspects: (a) execution processes as combination <strong>of</strong> s<strong>of</strong>tware applications and
(b) information access as text <strong>the</strong> user interacts with. Two laboratory studies on task execution
processes are presented. Both studies followed a similar process: predefined tasks were given to
users and <strong>the</strong> task execution was documented through video recordings and survey sheets.
Additionally, interaction histories were captured. These are temporally ordered lists <strong>of</strong> classified
events that stand for user-system interactions, and comprise complex mixtures <strong>of</strong> information about
resources a user operated on, using different applications. To capture interaction histories, an
extension <strong>of</strong> a monitoring application described in (Lokaiczyk, Faatz, Beckhaus, & Goertz, 2007) has
been used.
This paper is structured as follows. A study on task execution as sequence <strong>of</strong> application usage is
presented and analyzed in section 2, identifying core task execution processes. Section 3 analyzes
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
regularities in textual content that is accessed during work execution based on data from a second
study. Findings <strong>of</strong> both studies are consolidated in <strong>the</strong> description <strong>of</strong> a task model in section 4. The
paper concludes with summary and outlook.
2. Study <strong>of</strong> task execution processes
To analyze task execution processes, we conducted a study during which interaction histories were
generated for tasks, executed by different persons (section 2.1). The study is analyzed with respect to
task execution processes that unfold based on <strong>the</strong> used applications and <strong>the</strong> purpose <strong>of</strong> <strong>the</strong>
application usage (section 2.2). Finally, a summary <strong>of</strong> <strong>the</strong> study is given (section 2.3).
2.1 Methodology
Twenty employees <strong>of</strong> an international s<strong>of</strong>tware company working in <strong>the</strong> field <strong>of</strong> IT research executed
predefined, knowledge-intensive tasks. Six study participants had post-doctoral positions, eight were
PhD students, four were Master Thesis students and two were research workers. Each participant
worked 90 minutes on tasks that were randomly selected from a set <strong>of</strong> seven knowledge-intensive
tasks (see table 1 for <strong>the</strong> task list). The execution resulted in 115 instances <strong>of</strong> tasks. The tasks were created by
an expert group based on discussions with members <strong>of</strong> <strong>the</strong> research organization. Each task required
individual planning <strong>of</strong> <strong>the</strong> execution steps, including <strong>the</strong> selection <strong>of</strong> involved information sources and
applications in <strong>the</strong> given s<strong>of</strong>tware environment. The task execution processes <strong>of</strong> <strong>the</strong> participants were
recorded during each study, using video (computer screen capturing and video recording <strong>of</strong> user face)
and <strong>the</strong> context monitor application to create interaction histories. The interaction histories were used
to compare <strong>the</strong> task execution processes <strong>of</strong> <strong>the</strong> participants with different analysis techniques. To
reason about significant deviations between work processes for <strong>the</strong> same task relevant sections <strong>of</strong>
<strong>the</strong> video were reviewed and discussed by two domain experts. The subjective understanding <strong>of</strong> <strong>the</strong>
study participants was inquired by a survey sheet. Immediately after finishing a task, <strong>the</strong> participants
answered <strong>the</strong> question “What have you done to execute <strong>the</strong> task and why”. A structure was provided
for <strong>the</strong> answer, e.g.: “I HAVE browsed detailed job <strong>of</strong>fers WITH THE GOAL examine applicants.
(italics denote participant input). Participants could mark that subtasks were executed in a loop.
Table 1: Tasks for <strong>the</strong> user study on task execution processes
Task Description
Task 1 Provide information on related work on individual topic
Task 2 Set up meeting to discuss conference paper review
Task 3 Decide on applicant invitation and communicate your decision
Task 4 Plan a trip and inform your colleague with all involved information
Task 5 Create presentation <strong>of</strong> a paper in a foreign language
Task 6 Find Application partners and experts for research project
Task 7 Search for Information on s<strong>of</strong>tware functionality and save for later use
2.2 Task Execution Process as Application <strong>of</strong> Work Techniques and Application
Goals
In <strong>the</strong> following, <strong>the</strong> task execution processes are analyzed with respect to <strong>the</strong> used applications and
application switches. Core execution processes are calculated based on applications and application
switches. The role <strong>of</strong> <strong>the</strong> applications in <strong>the</strong> processes is explained based on an analysis <strong>of</strong> <strong>the</strong>
survey sheets, resulting in <strong>the</strong> identification <strong>of</strong> recurring work techniques and application goals.
2.2.1 Application Usage
The study was performed on a computer with different applications <strong>of</strong>fered by <strong>the</strong> research company
(e.g. <strong>of</strong>fice suite, pdf-viewer, etc). Table 3 shows a list <strong>of</strong> monitored applications and <strong>the</strong> accumulated
time, during which <strong>the</strong> main window <strong>of</strong> an application was in focus on <strong>the</strong> desktop (<strong>the</strong> time is
accumulated for all users. Focused applications, users did not interact with were filtered, e.g. virus
scanner).
Individual application preferences for different situations exist. E.g. to write notes, some users made
use <strong>of</strong> notepad, o<strong>the</strong>rs used word or empty mail objects. To elaborate on individual application
preferences <strong>of</strong> users, we focus on task 5 executed by participant 14 and participant 15. The
participants used different applications to execute <strong>the</strong> task (see Figure 1). Both participants had <strong>the</strong>
requirement <strong>of</strong> capturing text that was output from a translator application in <strong>the</strong> internet explorer. The
requirement was fulfilled by participant 14 by using notepad, whereas participant 15 used Micros<strong>of</strong>t
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
Word. A review <strong>of</strong> <strong>the</strong> complete interaction histories <strong>of</strong> user 14 and 15 shows that <strong>the</strong> preference is
not restricted to <strong>the</strong> single task, but is valid for all tasks.
The following sections need to elaborate on <strong>the</strong> role <strong>of</strong> different applications in <strong>the</strong> task execution
process. Notably, standard applications have a given purpose by <strong>the</strong>ir design (e.g. Mircos<strong>of</strong>t Word is
a text processor). Never<strong>the</strong>less, <strong>the</strong> role and <strong>the</strong> relevance <strong>of</strong> an application in a task execution
process are not completely prescribed by <strong>the</strong> design. The following sections investigate in <strong>the</strong> role
and <strong>the</strong> relevance <strong>of</strong> different applications for different tasks.
Table 3: List <strong>of</strong> used applications with accumulated focus time for all users
Application Name Focus time
Micros<strong>of</strong>t Internet Explorer 25 743
Micros<strong>of</strong>t Outlook 16 801
Windows XP - Explorer 9 238
Micros<strong>of</strong>t Word 7 214
Windows XP - Notepad 4 237
Acrobat Reader 4 216
Micros<strong>of</strong>t Communicator 4 024
Micros<strong>of</strong>t Excel 2 620
Mozilla Firefox 1 729
Micros<strong>of</strong>t PowerPoint 1 444
Windows XP - Calculator 350
Micros<strong>of</strong>t Visual Studio 50
Figure 1: Distribution <strong>of</strong> applications for task 5 executed by user 14 and 15
2.2.2 Application switches
Tasks are executed by a mixture <strong>of</strong> different applications. Switches between applications occur very
frequently during task execution. The data <strong>of</strong> <strong>the</strong> study includes 3229 application switches, resulting in
a mean usage duration <strong>of</strong> 25.8 seconds for an application before a switch occurs.
The amount <strong>of</strong> application switches differs with respect to <strong>the</strong> executed task (see Figure 2, left). Some
tasks, only have a low amount <strong>of</strong> application switches, e.g. task 2 (mean <strong>of</strong> 5.7 switches per task
execution). O<strong>the</strong>r tasks involve a high amount <strong>of</strong> application switches like task 5 (mean <strong>of</strong> 39.5
switches per task execution). Task 2 “Set up meeting” can be executed relatively straight-forward by
an experienced user, using only a few applications and resources. Task 5 “Paper from a foreign
language” involves multiple resources and requires <strong>the</strong> transformation <strong>of</strong> knowledge from one
resource to ano<strong>the</strong>r. For <strong>the</strong> given task an increase <strong>of</strong> information results in more application
switches.
Apart from <strong>the</strong> task, <strong>the</strong> switching behavior also has an individual characteristic. Figure 2 right shows
<strong>the</strong> amount <strong>of</strong> application switches per minute with respect to <strong>the</strong> user. Some users switch extensively
between applications and resource (e.g. user 18), while o<strong>the</strong>r users remain in one application for a
longer time and thus have a small amount <strong>of</strong> switches per minute (e.g. user 2). The video review
implied that <strong>the</strong> switch amount has no obvious reason but seems to be an individual characteristic <strong>of</strong>
work. Despite <strong>of</strong> <strong>the</strong> individuality <strong>of</strong> applications switches it is important to state that switches are
important facet <strong>of</strong> work execution. The following section will investigate which applications trigger
most task switches and which abstract processes emerge based on <strong>the</strong> switches.
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
Figure 2: Mean number <strong>of</strong> application switches per task (left), Situation switches per minute with
regard to <strong>the</strong> user (right)
2.2.3 Core task execution process
Considering task switches, a process graph can emerge with switches as edges between
applications. This idea is applied in <strong>the</strong> following to generate core task execution processes. A core
task execution process is a generalized execution process for a task instance that is valid for all
executions. Sequence mining on <strong>the</strong> applications and application switches was applied (D. Ferreira,
Zacarias, Malheiros, & P. Ferreira, 2007). Sequence mining is a model-based clustering technique
that partitions sequence data according to <strong>the</strong> order <strong>of</strong> occurrence. The number <strong>of</strong> clusters is an input
parameter. Each cluster is a first-order Markov chain that is capable to reproduce a set <strong>of</strong> sequences
associated with <strong>the</strong> respective cluster (Veiga & D. R. Ferreira, 2010).
The sequence mining method generated one Markov chain per task. It is a graph with nodes,
representing applications with application relevance and edges, representing node transitions (cluster
number was set to one, because tests showed that more clusters did not improve <strong>the</strong> insight). Based
on <strong>the</strong> probabilities and relevance, irrelevant applications and transitions have been excluded. The
resulting Markov chain is considered as a core execution process, including <strong>the</strong> relevant elements <strong>of</strong>
work execution and <strong>the</strong> respective transition probabilities.
Figure 3 shows <strong>the</strong> resulting Markov chains. Highly relevant nodes (most time spent using <strong>the</strong>
respective applications) are generally connected with high transition probability. The connections
stand for application switch cycles that occur when information is transferred between two
applications. For example task 2 is executed, using outlook and internet explorer with users starting
generally by using internet explorer. Once outlook use is switched <strong>the</strong>re is a high probability that
internet explorer is used.
2.2.4 The meaning <strong>of</strong> task execution process fragments
The core task execution processes show different relevant applications. To investigate in <strong>the</strong> meaning
<strong>of</strong> <strong>the</strong> applications involved in task execution, <strong>the</strong> descriptions <strong>of</strong> task execution processes filled in <strong>the</strong>
survey sheets are used in <strong>the</strong> following. The sheet data maps user intention with applications (e.g.
“used internet explorer to search for conference details”). Each step was described in <strong>the</strong> structure “I
HAVE + action-verb + object WITH THE GOAL + intention verb + object”. Equivalent to <strong>the</strong> extracted
Markov chains, all users showed a strong awareness <strong>of</strong> a similar core process required to complete a
task. Never<strong>the</strong>less, <strong>the</strong> descriptions varied with respect to <strong>the</strong> detail. Many users focused on few
execution steps and missed o<strong>the</strong>r, fine granular steps, that <strong>the</strong>y considered as unimportant or forgot
(e.g. <strong>the</strong> transfer <strong>of</strong> data from one application to ano<strong>the</strong>r). An additional problem is <strong>the</strong> linear
description: users described <strong>the</strong>ir work step by step and did not hint to loops between different steps
(e.g. reading document, <strong>the</strong>n authoring, <strong>the</strong>n returning to reading, etc.), although <strong>the</strong>y had <strong>the</strong>
possibility to draw loops (only 1 participant did this). The used action-verbs repeat among <strong>the</strong> users
and among <strong>the</strong> different tasks. The most frequently used action-verbs have been counted and listed
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
(see table 4). Notably, users tend to use a similar vocabulary that partly follows <strong>the</strong> desktop metaphor.
The frequency hints to a repetition <strong>of</strong> similar actions for different tasks. Inserting <strong>the</strong> vocabulary in <strong>the</strong>
core execution processes identified in <strong>the</strong> previous section presents tasks as combinations <strong>of</strong> work
actions and higher level actions.
Ano<strong>the</strong>r striking aspect is <strong>the</strong> complexity <strong>of</strong> action-verbs. Some users make use <strong>of</strong> action verbs <strong>of</strong> low
complexity and precisely describe <strong>the</strong> access <strong>of</strong> a file or <strong>the</strong> typing <strong>of</strong> text. Whereas o<strong>the</strong>rs aggregate
whole sub-process through complex action-verbs, e.g. authoring involves file access and typing.
k 1
Task 2
k 3 Task 4
k 5
Task 6
k 7
Figure 3: Markov Chains for task execution processes (<strong>the</strong> darker a node, <strong>the</strong> more time was spend
using <strong>the</strong> node/<strong>the</strong> numbers at <strong>the</strong> edges denote <strong>the</strong> transition probabilities)
To get a better understanding <strong>of</strong> <strong>the</strong> meaning <strong>the</strong> different types <strong>of</strong> actions verbs, we have separated
<strong>the</strong>m. The action-verbs <strong>of</strong> low complexity are termed “desktop operations” and <strong>the</strong> complex
techniques are termed “knowledge actions” (<strong>the</strong> chosen terms follow work on regularities in work
execution, e.g. (Schultze, 2000), (Hädrich, 2008). A mapping <strong>of</strong> <strong>the</strong> user actions to desktop
operations and knowledge actions is visible in table 5. We consider <strong>the</strong>m as hierarchical connected,
i.e. an organizing knowledge action requires desktop operations like focus, cut etc. to be executed.
After a first extraction based on <strong>the</strong> user vocabulary <strong>the</strong> interaction histories have been used to
identify additional elements (five last elements in table 5). The resulting vocabulary <strong>of</strong> desktop
operations and knowledge actions allows <strong>the</strong> description <strong>of</strong> task execution processes. Core execution
processes can be created as combination <strong>of</strong> desktop operations and knowledge actions with
respective transition probabilities.
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
Table 4: Most frequently used terms in <strong>the</strong> questionnaire
Action specified by participant Occurrences
Search 65
Browse 57
Read 41
Open/access 34
Author/Write 31
Create 28
Communicate 22
Send 20
Use 16
Copied 13
Save 11
Organize 10
Examine 9
Calculate 8
Execute 5
Table 5: Mapping user action words to desktop operations and knowledge actions
2.3 Study summary
Action specified by participant Desktop Operations Knowledge Action
Search Browse
Browse Browse
Read Consume
open/access Open
author/write Author
Create Create
Communicate Communicate
Send Communicate
Use Focus
Copied Copy
Save Save
Organize Organize
Examine Consume
Calculate Execute
Execute Execute
Renaming
Deleting
Cutting
Pasting
Printing
The previous sections have shown that knowledge work execution can be captured by core execution
processes based on a vocabulary <strong>of</strong> hierarchically connected desktop operations and knowledge
actions. Knowledge actions are work techniques that require desktop operations as application usage
to execute tasks. The different applications and thus <strong>the</strong> different operations guided by <strong>the</strong> actions are
connected by transitions that occur during task execution in <strong>the</strong> form <strong>of</strong> application switches. As <strong>the</strong>
actions and operations can be applied in different tasks <strong>the</strong>y do not support <strong>the</strong> identification <strong>of</strong> task
instances in work execution. Information about differences <strong>of</strong> tasks is assumed to manifest in <strong>the</strong>
textual content that is accessed during task execution. The following section presents a study that
focuses on textual content.
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
3. Study <strong>of</strong> text involved in task execution processes
Whereas, <strong>the</strong> previous section has identified operations and actions that are mixed to create work
processes, <strong>the</strong> following section examines <strong>the</strong> textual content accessed during work execution and its
significance for task identification. When a user works, different content is on <strong>the</strong> screen, tables, texts
etc. The displayed content changes frequently due to application switches or actions on resources like
opening and closing. Tracking <strong>the</strong> screen content shows a meta-text <strong>of</strong> work execution, comprised <strong>of</strong>
different information types. In <strong>the</strong> following, a second study is presented (section 3.1). The focus <strong>of</strong>
<strong>the</strong> examination is set on <strong>the</strong> textual content that is described (section 3.2) and analyzed with respect
to similarity <strong>of</strong> involved information and <strong>the</strong> possibility to find topics as guiding <strong>the</strong>me for task
execution processes. Finally, a summary <strong>of</strong> <strong>the</strong> study is given (section 3.3).
3.1 Methodology
To make assumptions about <strong>the</strong> relevance <strong>of</strong> information involved in work execution, a dataset was
created through a user study. Eight knowledge workers, four PhD students and four post-docs in
computer science working in <strong>the</strong> IT research <strong>of</strong> a big s<strong>of</strong>tware company executed five different tasks
listed in table 6 (avg. execution time was one hour). The tasks were given in random order to <strong>the</strong>
participants. To correspond to <strong>the</strong> dynamic and highly individual execution <strong>of</strong> knowledge-intensive
tasks in a real work setting, task switches were stimulated by giving a new task to a participant before
a former task was finalized. An example is given in Figure 4 with interruptions for task 2, task 5 and
task 3.
Figure 4: Example for task execution with disruption, as applied in <strong>the</strong> study setting
The resulting dataset contains eight interaction histories. Each interaction history contains an instance
<strong>of</strong> each task. Each interaction history especially contains <strong>the</strong> textual content that is visible in <strong>the</strong>
focused window <strong>of</strong> an application. For <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> dataset, <strong>the</strong> interaction history events were
manually labeled with <strong>the</strong> tasks, <strong>the</strong>y belonged to. To label <strong>the</strong> dataset <strong>the</strong> two study supervisors
reviewed <strong>the</strong> logs and decided on <strong>the</strong> belonging <strong>of</strong> a sensor event based on notes taken during <strong>the</strong>
study and <strong>the</strong>ir knowledge <strong>of</strong> <strong>the</strong> tasks.
Table 6: Tasks for <strong>the</strong> user study on text used in <strong>the</strong> execution process
Task Short Description/Name
Task 1 Plan a trip and inform your colleague
Task 2 Summarize and present <strong>the</strong> content <strong>of</strong> research papers
Task 3 Consolidate a paper using external resources
Task 4 Create an application partner catalogue
Task 5 Decide on applicant invitations and communicate your decision
3.2 Text involved in task execution
Textual content <strong>of</strong> a work process involves a large numbers <strong>of</strong> words (e.g. <strong>the</strong> study data contains on
avg. 60000 nouns and verbs for 1 hour <strong>of</strong> desktop work per user). The textual content itself is a
mixture <strong>of</strong> highly structured textual elements (e.g. hyperlinks), weakly structured textual elements (e.g.
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
sentences in natural language) and unstructured textual elements (e.g. single words in natural
language). The text has been separated in two sets. On <strong>the</strong> one hand, application related text, like
menu labels, help texts etc. On <strong>the</strong> o<strong>the</strong>r hand, text that is part <strong>of</strong> <strong>the</strong> work process, e.g. <strong>the</strong> content <strong>of</strong>
a text file viewed during work executions. In <strong>the</strong> following, we will focus on textual information that is
part <strong>of</strong> <strong>the</strong> work process.
Generally, document-centric views on textual content dominate <strong>the</strong> literature (Steyvers & Griffiths,
2010). In a work process textual information is displayed based on passages <strong>of</strong> resources from
different repositories: <strong>the</strong> private system, shared repositories <strong>of</strong> a group/community/company or <strong>the</strong>
internet. Generally, it is unforeseeable which kind <strong>of</strong> content might occur in a work process. As <strong>the</strong>
textual content <strong>of</strong> a work process is a complex mixture <strong>of</strong> different, unconnected information, <strong>the</strong>
document-centric view is not applicable. One difference is especially important: <strong>the</strong> temporalization <strong>of</strong>
text in <strong>the</strong> work process. A piece <strong>of</strong> text gets associated with <strong>the</strong> time-span, during which it was in <strong>the</strong>
focus <strong>of</strong> an application (e.g. Micros<strong>of</strong>t Word displaying <strong>the</strong> text in a focused window). This duration
can be interpreted as an identifier <strong>of</strong> <strong>the</strong> relevance <strong>of</strong> text (assuming that <strong>the</strong> user was actively
working with <strong>the</strong> textual content).
3.3 Topics and Text similarity
In <strong>the</strong> following, two types <strong>of</strong> regularities <strong>of</strong> textual content that occur within work processes are
examined. The first regularity addresses <strong>the</strong> similarity <strong>of</strong> textual content <strong>of</strong> different users, when <strong>the</strong>y
are executing <strong>the</strong> same task. As <strong>the</strong>re are no restrictions to <strong>the</strong> information that users may use during
task execution, it is an important question, whe<strong>the</strong>r people, working in similar environment and having
comparable backgrounds, use <strong>the</strong> same type <strong>of</strong> information. The second regularity addresses topics
that are hidden in <strong>the</strong> textual content. Topic could be a guiding <strong>the</strong>me which structures a task
execution process.
3.3.1 Similarity <strong>of</strong> textual content
To examine <strong>the</strong> similarity <strong>of</strong> <strong>the</strong> text involved in <strong>the</strong> task execution processes, we modeled <strong>the</strong> textual
content <strong>of</strong> tasks in a vector space, using Latent Semantic Analysis (LSA). LSA realizes a bag-<strong>of</strong>words
approach by using a matrix with columns representing task instances and a word count for all
different words given in <strong>the</strong> row vectors. Time was considered as relevance factor, i.e. text presented
for a longer period <strong>of</strong> time to a user was considered more important than text presented for a short
period <strong>of</strong> time. Text display duration was used to weight <strong>the</strong> word count in <strong>the</strong> rows. Based on <strong>the</strong>
matrix a semantic space <strong>of</strong> lower dimension is created with a vector for each task. Task instances are
compared by calculating <strong>the</strong> cosine similarity <strong>of</strong> <strong>the</strong> respective vectors (with 1 denoting total similarity
and 0 denoting no similarity). The idea is that instances <strong>of</strong> <strong>the</strong> same tasks should have a high
similarity value, compared to instances <strong>of</strong> different tasks.
Each task instance has been compared with o<strong>the</strong>r existing task instances. Resulting similarities are
shown in Figure 5 as 40x40 matrix with an entry for each task comparison. The red boxes in <strong>the</strong> figure
stand for <strong>the</strong> comparisons <strong>of</strong> a task instance with o<strong>the</strong>r instances <strong>of</strong> <strong>the</strong> same task. From left to right
and top to down <strong>the</strong> eight instances <strong>of</strong> task 1 are shown, followed by eight instances <strong>of</strong> task 2 etc.
The black diagonal shows <strong>the</strong> comparison <strong>of</strong> a task with itself, resulting in a similarity <strong>of</strong> 1.
The figure shows that for <strong>the</strong> tasks 1, 3, 4 and 5 <strong>the</strong> mean similarity <strong>of</strong> instances <strong>of</strong> <strong>the</strong> same task is
notably higher than <strong>the</strong> mean similarity <strong>of</strong> a task instance with instances <strong>of</strong> o<strong>the</strong>r tasks. For tasks 1, 3
and 5 <strong>the</strong> topic was precisely defined in <strong>the</strong> task execution processes and a set <strong>of</strong> initial resources
was highlighted (e.g. application forms). Task 4 instances have smaller similarity value than task 1, 3
and 5 as no resources are proposed and <strong>the</strong> individuals only have a similar goal. Task 2 does not
have a high similarity among <strong>the</strong> task instances. As task 2 asked <strong>the</strong> participants to collect papers <strong>of</strong>
<strong>the</strong>ir research domain very different types <strong>of</strong> papers were searched, reviewed and selected.
Overall, instances <strong>of</strong> <strong>the</strong> same task have a high similarity value, in spite <strong>of</strong> different executors. The
detail <strong>of</strong> task description influences <strong>the</strong> similarity.
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
Figure 5: Similarity <strong>of</strong> each task instance with each o<strong>the</strong>r task instance. The darker a box, <strong>the</strong> higher
is <strong>the</strong> similarity.
3.3.2 Topic identification
The information, users select in a task execution process is organized by <strong>the</strong> goal <strong>of</strong> <strong>the</strong> user. A goal
initiates an information need that <strong>the</strong> user tries to meet by accessed resources. To make <strong>the</strong>
information requirement explicit <strong>the</strong> extraction <strong>of</strong> topics from interaction histories is presented. For
topic models, texts are considered as mixtures <strong>of</strong> topics, whereas a topic is a probability distribution
over words (Steyvers & Griffiths, 2010). To create a document, using <strong>the</strong> topic model assumption, a
distribution <strong>of</strong> topics is chosen. Following <strong>the</strong> topic word distribution respective words are chosen
randomly and added to <strong>the</strong> document. Latent Dirichlet Allocation (LDA) reverses <strong>the</strong> process (Blei,
Ng, & Jordan, 2003): matrices <strong>of</strong> documents and word counts (c.f. LSA) are input to extract a
specified number <strong>of</strong> topics with respective document distributions. To analyze <strong>the</strong> study with LDA an
input vector was calculated for each interaction history. Five topics were extracted as visible in table
7.In fact, each study task can be assigned to one topic. The input vectors were mixtures <strong>of</strong> tasks and
topics with <strong>the</strong> most relevant terms for each task were extracted by LDA. An extraction <strong>of</strong> clearly task
related topics shows that topics enable <strong>the</strong> distinction <strong>of</strong> tasks in a manner that is robust to task
switching without requiring any additional process information.
3.4 Study summary
The analysis <strong>of</strong> <strong>the</strong> study has shown that <strong>the</strong> textual content <strong>of</strong> work execution can be used to extract
important information about tasks. On <strong>the</strong> one hand, <strong>the</strong> comparison <strong>of</strong> <strong>the</strong> task instances has shown
that <strong>the</strong> textual content used for similar tasks has a high similarity among different study participants.
This shows that <strong>the</strong> access <strong>of</strong> different resources to solve a task not necessarily changes <strong>the</strong>
information included in <strong>the</strong> accessed texts. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> extraction <strong>of</strong> topics has shown that
tasks generate topics within <strong>the</strong> textual content that can be used to identify tasks and task switches in
work execution.
4. Conclusion
The paper has presented <strong>the</strong> analysis <strong>of</strong> two studies <strong>of</strong> work execution processes. The analysis <strong>of</strong>
application usage has shown core execution processes as mixtures <strong>of</strong> desktop operations and
knowledge actions that are connected in a hierarchical relation. Identification <strong>of</strong> recurring knowledge
actions as work techniques abstract from <strong>the</strong> actual execution process <strong>of</strong> a single task and addresses
a shared understanding <strong>of</strong> application usage for specific requirements. The results support an
understanding <strong>of</strong> task execution as hierarchical task decomposition that mixes well trained work
methods. In <strong>the</strong> working process <strong>the</strong> difference <strong>of</strong> tasks materializes in <strong>the</strong> textual content that is
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Benedikt Schmidt, Todor Stoitsev and Max Mühlhäuser
accessed and produced during work execution. The second study has shown that <strong>the</strong> textual content
for similar tasks was similar among <strong>the</strong> different users and that <strong>the</strong> textual content <strong>of</strong> <strong>the</strong> study
participants enabled <strong>the</strong> identification <strong>of</strong> topics that stand for <strong>the</strong> domain <strong>of</strong> <strong>the</strong> different tasks.
Process and content aspect are considered as important, complementary execution aspects. The
domain <strong>of</strong> a task manifests in <strong>the</strong> textual content whereas <strong>the</strong> execution is realized by <strong>the</strong> application
<strong>of</strong> work techniques, namely knowledge actions that are realized by desktop operations. From this
point <strong>of</strong> view, we understand tasks as realizations <strong>of</strong> working technique combinations for content
domains.
Table 7: Words with highest relevance per topic as LDA output <strong>of</strong> study data for 5 topics (probability
that a word belongs to <strong>the</strong> topic is given in brackets)
Topic1≈Task5 Topic2≈Task2 Topic3≈Task3 Topic4≈Task4 Topic5≈Task1
Business (0.00311) Knowledge
University (0.00244) Europe (0.00213) Participants
(0.00312)
(0.00167)
Computer (0.00233) Processes (0.00238) Master (0.00241) Develop (0.00185) Demand
(0.00144)
Implementation People (0.00228) Planning (0.00216) Component
Travel (0.00141)
(0.00232)
(0.00163)
Job (0.00221) Project (0.00193) Production
(0.00214)
Role (0.00143) Costs (0.00135)
Contact (0.0022) Analysis (0.00162) Transport (0.00198) Processing
(0.00142)
Flight (0.00132)
Systems (0.0021) Products (0.00157) Demand (0.00185) Application (0.00138) Trip (0.00131)
Resources
Distribution
Single (0.0018) Aviation (0.0012) Tool (0.0013)
(0.00208)
(0.00153)
International Activities (0.00147) Updated (0.00178) Services (0.00115) Address
(0.00185)
(0.00122)
Experience
(0.00181)
Task (0.00143) Orders (0.00177) Products (0.00114) Nights (0.00115)
Requirements Roles (0.00142) Stock (0.00157) Corporation
California
(0.0018)
(0.00113)
(0.00111)
Acknowledgement
Work presented in this paper has been partly funded by <strong>the</strong> German Federal Ministry <strong>of</strong> Education
and Research, grant no. 01IA08006.
References
Bannon, L., Cypher, A., Greenspan, S., & Monty, M. L. (1983). Evaluation and analysis <strong>of</strong> users activity
organization. <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> SIGCHI conference on Human Factors in Computing Systems (pp. 54-57).
ACM. Retrieved March 2, 2011, from http://portal.acm.org/citation.cfm?id=801580.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. (J. Lafferty, Ed.)Journal <strong>of</strong> Machine
Learning Research, 3(4-5), 993-1022. doi: 10.1162/jmlr.2003.3.4-5.993.
Czerwinski, M., Horvitz, E., & Wilhite, S. (2004). A diary study <strong>of</strong> task switching and interruptions. <strong>Proceedings</strong> <strong>of</strong>
<strong>the</strong> SIGCHI. Retrieved September 22, 2010, from http://portal.acm.org/citation.cfm?id=985715.
Ferreira, D., Zacarias, M., Malheiros, M., & Ferreira, P. (2007). Approaching process mining with sequence
clustering: experiments and findings. Business Process Management, (1), 360–374. Springer. Retrieved
March 24, 2011, from http://www.springerlink.com/index/744g104721811151.pdf.
Hädrich, T. (2008). Situation-oriented Provision <strong>of</strong> Knowledge Services. Dissertation, Martin Lu<strong>the</strong>r Universität
Halle-Wittenberg. Retrieved from http://deposit.ddb.de/cgibin/dokserv?idn=994624166&amp;dok_nam=abstract&amp;dok_ext=htm&amp;filename=994624166.htm.
Lokaiczyk, R., Faatz, A., Beckhaus, A., & Goertz, M. (2007). Enhancing Just-in-Time E-Learning Through
Machine Learning on Desktop Context Sensors. Context, 330-341.
Mark, G., Gonzalez, V. M., & Harris, J. (2005). No Task Left Behind ? Examining <strong>the</strong> Nature <strong>of</strong> Fragmented
Work. Stand.
Schultze, U. (2000). A confessional account <strong>of</strong> an ethnography about knowledge work. MIS Quarterly, 24(1), 3-
Stenmark, D. (2001). Leveraging Tacit Organisational Knowledge. Journal <strong>of</strong> Management, 17(3), 9-24.
Steyvers, M., & Griffiths, T. (2010). Probabilistic Topic Models. Latent Semantic Analysis: A Road to Meaning.
(Vol. 22, pp. 1028-1040). doi: 10.1109/TKDE.2009.122.
Veiga, G. M., & Ferreira, D. R. (2010). Understanding spaghetti models with sequence clustering for prom.
Business Process Management Workshops (p. 92–103). Springer. Retrieved March 24, 2011, from
http://www.springerlink.com/index/KM1T6130221611K6.pdf.
Walsham, G. (2005). Knowledge Management Systems : Representation and Communication in Context 1
Introduction 2 Knowledge , Representation and Communication. International Journal, 1(1), 6-18.
898

Applying Web Analytics Tools in <strong>the</strong> Context <strong>of</strong> Enterprise
Social S<strong>of</strong>tware
Alexander Schneider and Alexander Steinh<strong>of</strong>f
Technische Universität München, Munich, Germany
schneial@in.tum.de
steinh<strong>of</strong>@in.tum.de
Abstract: Web analytics tools are now in operation for about 15 years. While primarily developed to support <strong>the</strong>
operators <strong>of</strong> public commercial web sites, <strong>the</strong>y <strong>of</strong>fer a stable and mature set <strong>of</strong> general features to track <strong>the</strong>
behavior <strong>of</strong> users <strong>of</strong> arbitrary web based systems. Independently, in recent years web based social s<strong>of</strong>tware is
increasingly used to support collaboration and knowledge exchange in modern enterprises. This paper examines
how enterprise social s<strong>of</strong>tware can benefit from <strong>the</strong> features provided by state <strong>of</strong> <strong>the</strong> art web analytics
solutions.In <strong>the</strong> first part <strong>of</strong> <strong>the</strong> paper, we give an overview <strong>of</strong> <strong>the</strong> most common capabilities <strong>of</strong> commercial and
open source web analytics tools and examine how <strong>the</strong> data <strong>the</strong>y produce can be accessed and utilized by o<strong>the</strong>r
applications. Subsequently, illustrating how social s<strong>of</strong>tware users – be it as authors or consumers <strong>of</strong> content –,
site operators and even <strong>the</strong> s<strong>of</strong>tware developers can pr<strong>of</strong>it from this integration. To demonstrate <strong>the</strong> general
feasibility <strong>of</strong> <strong>the</strong> approach, we <strong>the</strong>n describe how one scenario was implemented in a commercial web based
enterprise collaboration platform using an open source web analytics tool. The current popularity <strong>of</strong> individual
documents, like wiki pages or blog posts, was measured using <strong>the</strong> number <strong>of</strong> recent visits for <strong>the</strong> respective web
pages. The resulting metric was <strong>the</strong>n used to improve <strong>the</strong> document scoring mechanism <strong>of</strong> <strong>the</strong> platform's search
component. Finally we show that <strong>the</strong> web analytics tool can in turn be used to track precisely how <strong>the</strong> search
functionality is used – and thus allows for an assessment <strong>of</strong> <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> abovementioned
improvement. It fur<strong>the</strong>r enables a qualitative analysis <strong>of</strong> user behavior that helps to find defects in <strong>the</strong> s<strong>of</strong>tware
and additionally motivates fur<strong>the</strong>r refinements and enhancements.
Keywords: Enterprise 2.0, Social S<strong>of</strong>tware, Web Analytics, Intranet Search
1. Introduction
Web analytics tools as well as social s<strong>of</strong>tware have reached a mature state over <strong>the</strong> last few years
and todays s<strong>of</strong>tware products in both categories range from commercial all-in-one solutions through to
free-to-use applications and open source projects. While web analytics in general has an enormous
potential regarding insights in social processes (Watts 2007), it is applied primarily in <strong>the</strong> e-commerce
sector. However, recently, <strong>the</strong> blogging community also discovered <strong>the</strong> potentials <strong>of</strong> web analytics:
wordpress (http://www.wordpress.com), for example, has an integrated stats system <strong>of</strong>fering basic
web analytics reports to blog authors. In this paper, we investigate how <strong>the</strong> capabilities <strong>of</strong> web
analytics tools can be exploited in <strong>the</strong> context <strong>of</strong> enterprise social s<strong>of</strong>tware, be it by automatically
processing tracking data to improve certain functionalities, like search – similar to (Agichtein et al.
2006; Joachims et al. 2007) who focus on web search – or by manually reviewing analysis reports to
identify potentials for improvements. The latter has found to be useful for developers for example to
discover usability issues (Weischedel and Huizingh 2006). We suggest that <strong>the</strong>re is a large variety <strong>of</strong>
stakeholders who can benefit from <strong>the</strong> integration <strong>of</strong> a web analytics tool with an Enterprise 2.0
solution.
Chapter 2 provides an overview <strong>of</strong> <strong>the</strong> foundations <strong>of</strong> web analytics as well as challenges arising
when <strong>the</strong>y are integrated with social s<strong>of</strong>tware. Chapter 3 depicts <strong>the</strong> results <strong>of</strong> a web analytics tool
analysis and describes <strong>the</strong> features that are relevant for social s<strong>of</strong>tware. Derived from this analysis,
Chapter 4 proposes opportunities how web analytics data can be used to support social s<strong>of</strong>tware
users. Subsequently, Chapter 5 depicts <strong>the</strong> prototypic implementation <strong>of</strong> one scenario and <strong>the</strong> results
<strong>of</strong> an evaluation period. Finally, Chapter 6 summarizes this paper and provides suggestions for fur<strong>the</strong>r
research.
2. Foundation <strong>of</strong> web analytics and social s<strong>of</strong>tware
This section gives an overview <strong>of</strong> <strong>the</strong> distinct knowledge domains <strong>of</strong> web analytics and enterprise
social s<strong>of</strong>tware and analyzes specific challenges for web analytics tools operating in an Enterprise 2.0
environment.
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2.1 Web analytics evolution
Alexander Schneider and Alexander Steinh<strong>of</strong>f
The goal <strong>of</strong> web analytics is to help companies to improve <strong>the</strong> quality <strong>of</strong> <strong>the</strong>ir websites (Weischedel
and Huizingh 2006; Kaushik 2007). The quality <strong>of</strong> websites has been <strong>the</strong> focus <strong>of</strong> many academic
studies and is typically concerned with <strong>the</strong> performance and usability <strong>of</strong> a website. A large number <strong>of</strong>
scales have been proposed and validated in empirical studies to measure website quality (Loiacono
2000; Kim and Stoel 2004; Aladwani and Palvia 2002). In order to enhance a website’s quality, its
operator must understand what users or customers need in order to optimize <strong>the</strong> website accordingly.
Using mature technologies it is possible to track users over time, measure campaign success and
figure out <strong>the</strong> return-on-investment. Web analytics draws attention primarily in <strong>the</strong> e-commerce field
because such firms generate <strong>the</strong>ir revenue mainly or exclusively with <strong>the</strong>ir website. Amazon, one <strong>of</strong>
<strong>the</strong> world’s largest e-commerce companies, developed a recommender-system based on web
analytics data (Chittoor 2010) which generates about 35% <strong>of</strong> Amazon’s revenue.
The technical evolution <strong>of</strong> web analytics tools proceeded in three stages (Croll and Power 2009).
First, data was extracted out <strong>of</strong> server log files. However, this server-based approach, which is still
implemented by some tools today, has some limitations. For example, a server is not able to
recognize page views served by proxies or caches. Fur<strong>the</strong>rmore, user-tracking based on IP
addresses is highly error-prone. In order to overcome <strong>the</strong>se obstacles, client-based approaches were
implemented in <strong>the</strong> second stage. Therefore, an invisible image is embedded in every page, <strong>the</strong> socalled
page tag. Every request <strong>of</strong> this image can <strong>the</strong>n be counted as a page view. It is also possible to
store <strong>the</strong> image at a provider’s server, which enables companies to outsource <strong>the</strong>ir web analytics
technologies. Fur<strong>the</strong>rmore, additional data can be ga<strong>the</strong>red if cookies and JavaScript are used. Such
client-side approaches are implemented and used by nearly all web analytics tools today, but one
limitation remains: a web page is only counted as a whole. Therefore, <strong>the</strong> third stage <strong>of</strong> web analytics’
evolution refines <strong>the</strong> second stage by allowing <strong>the</strong> site operators to define events for every userinteraction
<strong>the</strong>y want to track. For example, such events can be triggered if a user stayed more than
five minutes on a single web page. However, this approach works only in an appropriate environment.
If <strong>the</strong> client disables JavaScript and uses a text browser, she becomes invisible for client-side tracking
approaches.
In summary, web analytics tools provide lots <strong>of</strong> data for companies. After <strong>the</strong> three stages <strong>of</strong>
evolution: log file analysis, page tagging and event tracking, <strong>the</strong> web analytics technologies got
mature over <strong>the</strong> last 15 years. Never<strong>the</strong>less, <strong>the</strong> results provided by web analytics tools should
always be regarded carefully and questioned because <strong>the</strong>y suggest precision while technical
boundaries still limit <strong>the</strong> ability to track user behavior exactly.
2.2 Challenges for web analytics technologies in a social s<strong>of</strong>tware context
Social s<strong>of</strong>tware, as for example wikis, blogs or social networks, is increasingly used by many
enterprises to foster internal knowledge management, collaboration and communication (McAfee
2006). Such Enterprise 2.0 solutions rely heavily on user generated content and Web 2.0
technologies. AJAX,Flash and Mashups are common technologies to realize an interactive social
s<strong>of</strong>tware site generated on thy fly. The resulting pages, also known as Rich Internet Applications,
cannot be considered as a single page anymore, but form a collection <strong>of</strong> independent entities like
menu bars, media players or image galleries which have to be analyzed separately. Therefore, web
analytics tools are required to track user interaction on a very fine granularity. Event-tracking, as
mentioned before, is actually <strong>the</strong> only way to accomplish this, with <strong>the</strong> drawback <strong>of</strong> increasing effort
for its configuration.
On Enterprise 2.0 platforms, users can interact with almost any page and generate content every
time. Especially in micro-blogging applications, as for example Yammer or Twitter, a huge amount <strong>of</strong>
data is generated every minute. Web analytics tools are required to take such content immediately
into account if <strong>the</strong>ir analyses should support <strong>the</strong> users before <strong>the</strong> content is out <strong>of</strong> date. Such an
approach, toge<strong>the</strong>r with <strong>the</strong> capability to provide immediate feedback to <strong>the</strong> user, is called real-time
analysis and <strong>of</strong>ten essential in an enterprise social s<strong>of</strong>tware environment.
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3. Web analytics tool analysis
Alexander Schneider and Alexander Steinh<strong>of</strong>f
To provide social s<strong>of</strong>tware users with web analytics data, <strong>the</strong> actual capabilities <strong>of</strong> web analytics tools
have to be determined. The development <strong>of</strong> scenarios showing interesting use cases for web
analytics data in Enterprise 2.0 s<strong>of</strong>tware will be based on <strong>the</strong>se insights.
3.1 Tool comparison
Beside very mature but <strong>of</strong>ten expensive commercial s<strong>of</strong>tware from vendors like Omniture or
Webtrends, free tools are also available and successful. Google <strong>of</strong>fers a free web analytics tool
named Google Analytics which is actually <strong>the</strong> leading product in its market (Bright 2010; Hamel 2008).
However, <strong>the</strong> use <strong>of</strong> free tools has to be paid by sharing data with <strong>the</strong> vendor. Thereby, <strong>of</strong>fering
companies like Google or Yahoo get access to all analytics data, and in case <strong>of</strong> Google, use it to
enhance <strong>the</strong>ir o<strong>the</strong>r products as for example AdWords. Additionally, a third category <strong>of</strong> web analytics
tools got attention over <strong>the</strong> last few years: open source s<strong>of</strong>tware. Prominent representatives are Piwik,
Webalizer, and Open Web Analytics which are all evolving in an active community. Such open source
tools have to be hosted on own servers, but <strong>the</strong> data ga<strong>the</strong>red by <strong>the</strong> tool needs not to be shared with
o<strong>the</strong>r companies.
To examine <strong>the</strong> actual capabilities <strong>of</strong> web analytics tools, three tools will be analyzed in detail, one <strong>of</strong>
each category introduced before. The tools analyzed are: <strong>the</strong> open source tool Piwik, <strong>the</strong> free tool
Google Analytics, and <strong>the</strong> commercial tool SiteCatalyst by Omniture. Google’s and Omniture’s tools
have been chosen because at <strong>the</strong> beginning <strong>of</strong> 2010 <strong>the</strong>y had <strong>the</strong> greatest market share, toge<strong>the</strong>r
78% (Bright 2010). With respect to <strong>the</strong> prototypic implementation described in Chapter 5, Piwik has
been chosen because it promised to be a good prototype candidate. Unfortunately, <strong>the</strong> analysis <strong>of</strong>
SiteCatalyst had to rely on <strong>the</strong> descriptions and <strong>the</strong> feature list <strong>of</strong> Omniture due to its license costs.
Google Analytics has been evaluated by analyzing <strong>the</strong> data provided in its administration zone. A very
detailed analysis <strong>of</strong> ga<strong>the</strong>red data was possible with Piwik due to <strong>the</strong> available code.
3.2 Detailed data analysis
The following data is provided by all analyzed web analytics tools. Some e-commerce and advertising
related data is not shown here because it is not relevant for enterprise social s<strong>of</strong>tware analytics. Table
1 lists key figures about <strong>the</strong> number and type <strong>of</strong> visitors and <strong>the</strong>ir technical environment.
Table 1: User data <strong>of</strong> web analytics tools
Visits Loyalty Technical
Total visits Length <strong>of</strong> visit Browser type
Unique visitors Depth <strong>of</strong> visit Connection speed
Total page views Return rate Operating system
Average page views Recency <strong>of</strong> visits Screen settings
Bounce rate Time on site Flash settings
Location Java settings
In this context, unique visitors is <strong>the</strong> number <strong>of</strong> visiting persons who could be identified by a cookie. If,
for example, such a person visits <strong>the</strong> website in <strong>the</strong> morning for 10 minutes and in <strong>the</strong> evening for 15
minutes, for that day one unique visitor is counted, but <strong>the</strong>re are two total visits. A pageview, also
sometimes called a hit, is generated when a visitor loads a new page. If a visitor loads only one page
this is considered as a bounce. In Table 2, ga<strong>the</strong>red data about <strong>the</strong> context <strong>of</strong> <strong>the</strong> website is shown,
for example a list <strong>of</strong> search engines used to find <strong>the</strong> web page.
Table 2: Context data <strong>of</strong> web analytics tools
Traffic sources Content Internal search
Search engines Top content Search terms
Referring sites Top landing pages Start pages
Direct traffic Top exit pages Destination pages
In addition, Google Web Analytics and SiteCatalystlist all manually configured events with <strong>the</strong>ir
number <strong>of</strong> occurrence.
Differences between <strong>the</strong> analyzed web analytics tools exist, for instance, in <strong>the</strong>ir capabilities to
integrate <strong>the</strong>m with o<strong>the</strong>r applications. Integration becomes relevant, if online tracking data should be
combined with <strong>of</strong>fline data, or if reports should be enhanced in o<strong>the</strong>r desktop tools as for example
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Alexander Schneider and Alexander Steinh<strong>of</strong>f
Micros<strong>of</strong>t Excel. Regarding import options, nei<strong>the</strong>r Google Analytics nor Piwik <strong>of</strong>fer any possibilities to
import data from o<strong>the</strong>r sources. However, SiteCatalyst <strong>of</strong>fers different opportunities to import data, for
example, from customer relationship management (CRM) tools. Regarding export options, Google
Analytics <strong>of</strong>fers PDF, XML, CSV and TSV. In addition, Piwik <strong>of</strong>fers a JSON and RSS export <strong>of</strong>
reports. A native export to Micros<strong>of</strong>t Excel or Word is only included in SiteCatalyst. Ano<strong>the</strong>r advantage
<strong>of</strong> SiteCatalyst is <strong>the</strong> availability <strong>of</strong> a SOAP-based Web service API, and <strong>the</strong> ability to integrate its
reports with o<strong>the</strong>r reporting tools.
An important means to determine <strong>the</strong> success <strong>of</strong> a website is to measure conversions. The term
conversion refers to <strong>the</strong> transformation <strong>of</strong> a visitor to a customer (Stern 2002). Extensively used in ecommerce,
conversions can, for example, be defined as <strong>the</strong> purchase <strong>of</strong> a product. The presented
web analytics data analysis excludes all data and respective reports <strong>of</strong> conversions. The reason is
that conversions are difficult to define for social s<strong>of</strong>tware and not <strong>the</strong> focus <strong>of</strong> this paper.
4. Potential benefits for users
The integration <strong>of</strong> web analytics tools in enterprise social s<strong>of</strong>tware environments, as for example a
wiki-based intranet portal, can be beneficial for various kinds <strong>of</strong> stakeholders:
Consumers: users reading content
Authors: users generating new content
Site operators: people administering <strong>the</strong> s<strong>of</strong>tware
Developers: people developing and maintaining <strong>the</strong> s<strong>of</strong>tware
For each <strong>of</strong> <strong>the</strong>se roles we identify potential benefits which can result from <strong>the</strong> integration <strong>of</strong> web
analytics tools with Enterprise 2.0 systems.
4.1 Benefits for consumers
For every user <strong>the</strong>re are typically several contents that he visits frequently. Based on <strong>the</strong> web
analytics data, <strong>the</strong> system could recognize <strong>the</strong>se contents and provide quick navigation links. The
necessary tracking data can be provided by many web analytics tools given that <strong>the</strong> user can be
identified for more than one session, for example by setting a cookie.
Search engines have become an essential part <strong>of</strong> social s<strong>of</strong>tware since <strong>the</strong>y provide easy access to
many kinds <strong>of</strong> information. If <strong>the</strong> data ga<strong>the</strong>red by web analytics tools is understood as implicit
feedback <strong>of</strong> users (Agichtein et al. 2006), it can be used to significantly improve <strong>the</strong> ranking <strong>of</strong> <strong>the</strong>
search results which we will show in <strong>the</strong> next section.
Especially in enterprise social s<strong>of</strong>tware environments users <strong>of</strong>ten share same interests and so <strong>the</strong>y
are interested in <strong>the</strong> same content as o<strong>the</strong>rs. Because web analytics tools are able to track <strong>the</strong>
number <strong>of</strong> views for each page in real-time, this data can be used to easily implement some type <strong>of</strong>
recommender system based on <strong>the</strong> actual interests <strong>of</strong> <strong>the</strong> user base. Beside a search engine such a
feature can ease <strong>the</strong> user’s access to relevant content.
4.2 Benefits for content authors
Social s<strong>of</strong>tware relies strongly on users generating content. While many blogs or wikis allow o<strong>the</strong>r
users to provide explicit qualitative feedback in <strong>the</strong> form <strong>of</strong> comments or ratings, authors do not get
detailed quantitative feedback about <strong>the</strong>ir contributions. With web analytics tools this kind <strong>of</strong> feedback
can be provided. For example, information about <strong>the</strong> readers <strong>of</strong> <strong>the</strong> user’s generated content can be
interesting as well as motivating to generate more content. O<strong>the</strong>r figures <strong>of</strong> interest might be <strong>the</strong> time
o<strong>the</strong>r users spend for reading <strong>the</strong> content and <strong>the</strong>ir loyalty (return rate). These figures can be
presented as numbers or visualized as charts showing <strong>the</strong> progress for <strong>the</strong> last days. Ano<strong>the</strong>r
possible feature is <strong>the</strong> determination <strong>of</strong> a user’s most accessed and maybe most interesting content.
Topics currently interesting for <strong>the</strong> user base might attract authors to contribute to <strong>the</strong>m. With a
sophisticated integration <strong>of</strong> a web analytics tool such topics can be identified and recommended to
authors. In turn authors can also be categorized according to <strong>the</strong> topics <strong>the</strong>y have already contributed
to.
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4.3 Benefits for site administrators
Alexander Schneider and Alexander Steinh<strong>of</strong>f
With site administrators we mean users who are not only responsible for running <strong>the</strong> s<strong>of</strong>tware but also
for <strong>the</strong> organization and presentation <strong>of</strong> <strong>the</strong> available content. In an intranet system for example, <strong>the</strong>y
decide which contents are available on <strong>the</strong> starting page or <strong>the</strong> main menu. Considering <strong>the</strong> user
behavior data provided by web analytics tools, <strong>the</strong>se privileged users can easily identify parts <strong>of</strong> <strong>the</strong>
navigation which are not used constantly by users. Then, <strong>the</strong> navigation can be enhanced by
removing <strong>the</strong>se parts or replacing <strong>the</strong>m with o<strong>the</strong>r pages as for example pages which are <strong>of</strong>ten visited
via <strong>the</strong> search engine. Fur<strong>the</strong>rmore, it can be helpful for <strong>the</strong> administrators to know where users come
from, when <strong>the</strong>y visit <strong>the</strong> site and what browser <strong>the</strong>y use.
4.4 Benefits for developers
When developers have implemented a new feature for an enterprise social s<strong>of</strong>tware system, it is
difficult for <strong>the</strong>m to measure whe<strong>the</strong>r or to what extent <strong>the</strong> feature is adopted by users. With a web
analytics tool such evaluation can be realized easily by configuring a special event for <strong>the</strong> new feature
so that <strong>the</strong> frequency <strong>of</strong> its use is tracked.
In addition to <strong>the</strong> development <strong>of</strong> new features, a developer is also responsible for both, identifying
and fixing s<strong>of</strong>tware bugs. A web analytics tool can support <strong>the</strong> developer in two ways: by analyzing
<strong>the</strong> provided data, for example <strong>the</strong> sequence <strong>of</strong> pages viewed by users, <strong>the</strong> developer can identify
bugs or usability issues, for example when people frequently fail to submit a form. Additionally, after
fixing such an issue, <strong>the</strong> developer usually wants to test if <strong>the</strong> problem is really solved. Instead <strong>of</strong>
extensive tests in several browsers that usually do not cover all usage scenarios, <strong>the</strong> web analytics
tool can be used to track whe<strong>the</strong>r <strong>the</strong> problem occurs again in <strong>the</strong> future. This approach saves time
<strong>the</strong> developer would spend for testing and might also cover a wider scope <strong>of</strong> potential bug triggers,
because <strong>the</strong> actual users are employed as testers.
5. Prototypic implementation
To evaluate part <strong>of</strong> <strong>the</strong> potential benefits that we identified in <strong>the</strong> last section, we experimented with a
prototypical integration <strong>of</strong> a web analytics tool and a web-based enterprise collaboration platform. By
contrast to <strong>the</strong> well-known PageRank approach (Brin and Page 1998) which determines <strong>the</strong> relevance
<strong>of</strong> documents by analyzing <strong>the</strong> structural links between <strong>the</strong>m, we use <strong>the</strong> web analytics data to
improve <strong>the</strong> ranking mechanism <strong>of</strong> <strong>the</strong> search feature <strong>of</strong> <strong>the</strong> platform. After shortly describing
technical aspects <strong>of</strong> <strong>the</strong> implementation, we detail on our observations regarding <strong>the</strong> benefits for <strong>the</strong>
different stakeholders.
5.1 Extension <strong>of</strong> a collaboration platform
Our prototypical integration <strong>of</strong> an enterprise social s<strong>of</strong>tware system and a web analytics tool is based
on <strong>the</strong> commercial collaboration platform Tricia which is developed by <strong>the</strong> infoasset AG. The platform
includes a structured wiki, blogging functionality, a file share and it allows <strong>the</strong> flexible organization <strong>of</strong>
information. Additionally, this Java-based s<strong>of</strong>tware can be extended with a powerful plug-in
mechanism. It was used to implement a connector to <strong>the</strong> open source web analytics tool Piwik. Piwik
was chosen because it can be used free <strong>of</strong> charge and it provides easy access to <strong>the</strong> collected data
via a Restful web service. It requires a web server with PHP integration and a MySQL database.
The developed plug-in regularly connects to <strong>the</strong> Piwik server, downloads <strong>the</strong> most recent tracking
data and saves it in <strong>the</strong> Tricia data store. As a prerequisite, a short JavaScript snippet had to be
included in <strong>the</strong> header <strong>of</strong> <strong>the</strong> HTML files generated by Tricia to trigger <strong>the</strong> tracking by Piwik. The visits
to <strong>the</strong> URLs counted by Piwik are mapped to <strong>the</strong> documents in Tricia (wiki pages, blog posts,...).
These visits are used to influence <strong>the</strong> order <strong>of</strong> <strong>the</strong> search results <strong>of</strong> <strong>the</strong> full-text search <strong>of</strong> <strong>the</strong> platform.
Tricia internally uses <strong>the</strong> Lucene indexing and retrieval framework (http://lucene.apache.org/) that
comes with a default scoring mechanism which is based on <strong>the</strong> frequency <strong>of</strong> <strong>the</strong> search terms in <strong>the</strong>
documents. Based on <strong>the</strong> frequency <strong>of</strong> access in <strong>the</strong> last 30 days, a popularity factor is calculated for
each document. This factor is <strong>the</strong>n multiplied with <strong>the</strong> result <strong>of</strong> <strong>the</strong> default scoring mechanism <strong>of</strong>
Lucene when <strong>the</strong> ordering <strong>of</strong> results is determined.
The factor is determined as follows: If a document has not been accessed in <strong>the</strong> last 30 days, <strong>the</strong>
factor is set to one. All documents with at least one visit are assigned a factor between 2.5 and 5.0.
To avoid that only few very frequently visited documents get a very high score, <strong>the</strong> difference between
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Alexander Schneider and Alexander Steinh<strong>of</strong>f
<strong>the</strong> assigned factor and 2.5 is proportional to <strong>the</strong> logarithm <strong>of</strong> <strong>the</strong> number <strong>of</strong> visits. The respective
proportional factor is calibrated based on <strong>the</strong> most popular document each time new tracking data is
imported. The Lucene scoring mechanism cannot be covered here in detail. However, we can
compare <strong>the</strong> factors to o<strong>the</strong>r scoring factors specified by <strong>the</strong> Tricia platform: If a search term occurs in
<strong>the</strong> title <strong>of</strong> a document, its weight in <strong>the</strong> final score is 3 times as much as for keywords <strong>of</strong> <strong>the</strong>
document and 15 times as much as for words in <strong>the</strong> content.
Tricia features a quick-search control with type-ahead functionality on every page (see Figure 1).
While <strong>the</strong> user types, <strong>the</strong> system instantly shows matching documents by expanding <strong>the</strong> user input to
matching terms in <strong>the</strong> index and performing a search accordingly. The user can ei<strong>the</strong>r choose a
document from <strong>the</strong> list <strong>of</strong> documents determined this way or he can submit <strong>the</strong> query to get a more
detailed view <strong>of</strong> <strong>the</strong> results.
Figure 1: The quick-search control with auto-complete functionality that is shown on every Tricia page
in a prominent position
To assess if <strong>the</strong> quality <strong>of</strong> search results improved, we also used Piwik to track <strong>the</strong> usage <strong>of</strong> <strong>the</strong> quicksearch
control. Piwik is triggered each time a user activates <strong>the</strong> control, when a query is submitted
and when <strong>the</strong> user chooses one <strong>of</strong> <strong>the</strong> suggested results. In <strong>the</strong> last two cases it is also saved what
<strong>the</strong> user had typed in <strong>the</strong> input field. Additionally, for <strong>the</strong> selected results it is saved which rank <strong>the</strong>y
had in <strong>the</strong> result list. Since this is not natively supported by Piwik, we encoded <strong>the</strong> respective
information in artificial URLs <strong>of</strong> a dummy domain. For example, a visit to
http://dummydomain.com/quicksearchUsed is tracked each time <strong>the</strong> user activates <strong>the</strong> quick-search
control.
In contrast to (Agichtein et al. 2006), we directly use tracking data <strong>of</strong> <strong>the</strong> content URLs and not <strong>of</strong> user
behavior regarding <strong>the</strong> search functionality. However, <strong>the</strong> latter is used later in <strong>the</strong> evaluation. Taking
into account <strong>the</strong> research <strong>of</strong> Gonçalves and Ramasco (Gonçalves 2008), it seems reasonable that
using popularity directly will also yield better results.
5.2 Evaluation
To assess if <strong>the</strong> quality <strong>of</strong> search results improved for <strong>the</strong> users <strong>of</strong> <strong>the</strong> platform, we tracked usage <strong>of</strong>
<strong>the</strong> quick-search control on <strong>the</strong> intranet platform <strong>of</strong> <strong>the</strong> chair for S<strong>of</strong>tware Engineering for Business
Information Systems (sebis) <strong>of</strong> <strong>the</strong> Technische Universität München for four weeks. This Tricia
installation serves also as <strong>the</strong> chair’s public website. Only in <strong>the</strong> second half <strong>of</strong> <strong>the</strong> observation period,
<strong>the</strong> popularity <strong>of</strong> documents – i.e. <strong>the</strong> number <strong>of</strong> recent visits – was factored in for <strong>the</strong> ranking <strong>of</strong>
search results. In <strong>the</strong> first half, <strong>the</strong> ranking only depended on <strong>the</strong> document content and metadata
(name and keywords).
We tracked 662 uses <strong>of</strong> <strong>the</strong> search control in total. In 213 cases, users selected a search hit from <strong>the</strong>
auto-complete list in <strong>the</strong> first phase. In <strong>the</strong> second phase we tracked 181<strong>of</strong>such selections. In <strong>the</strong> rest
<strong>of</strong> <strong>the</strong> cases users aborted <strong>the</strong> search or submitted <strong>the</strong> input to get a more detailed view <strong>of</strong> <strong>the</strong>
results.
Figure 2 shows that users typed fewer characters in <strong>the</strong> second phase than in <strong>the</strong> first one before
<strong>the</strong>y selected an entry from <strong>the</strong> list. While in 95% <strong>of</strong> <strong>the</strong> cases 20 characters or less were sufficient for
<strong>the</strong> users to find what <strong>the</strong>y were looking for in <strong>the</strong> first phase, this dropped to 16 characters after <strong>the</strong>
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Alexander Schneider and Alexander Steinh<strong>of</strong>f
change. When very long inputs are ignored (more than 30 characters – apparently copied into <strong>the</strong>
input field, not typed) <strong>the</strong> average number <strong>of</strong> characters changes from 9.34 to 8.10.
We expected that users would select entries higher up in <strong>the</strong> result list in <strong>the</strong> second phase because
popular pages now get a better rank. However, Figure 3 shows that users chose <strong>the</strong> first entry most <strong>of</strong>
<strong>the</strong> time already in <strong>the</strong> first phase. In <strong>the</strong> second phase <strong>the</strong> first entry is chosen even more frequently
but this is compensated by a worse performance for <strong>the</strong> cases when users select entries <strong>of</strong> <strong>the</strong>
second half <strong>of</strong> <strong>the</strong> list. Apparently users prefer to refine <strong>the</strong>ir query ra<strong>the</strong>r than scanning <strong>the</strong> complete
list <strong>of</strong> search results.
Figure 2: Percentage <strong>of</strong> uses <strong>of</strong> <strong>the</strong> search control where n characters (x-axis) or less were typed
before <strong>the</strong> users selected a search hit. The solid line represents <strong>the</strong> data collected in <strong>the</strong> first phase,
<strong>the</strong> dashed line that <strong>of</strong> <strong>the</strong> second phase.
Figure 3: Percentage <strong>of</strong> uses <strong>of</strong> <strong>the</strong> search control where <strong>the</strong> n-th entry (x-axis) or an earlier one was
selected. The solid line represents <strong>the</strong> data collected in <strong>the</strong> first phase; <strong>the</strong> dashed line that <strong>of</strong> <strong>the</strong>
second phase.
While it was not evaluated in detail, <strong>the</strong> usage data <strong>of</strong> <strong>the</strong> quick-search control suggests that <strong>the</strong>re are
valuable hints for <strong>the</strong> site administrators regarding <strong>the</strong> organization <strong>of</strong> contents. For some general
topics (e.g. "bachelor<strong>the</strong>sis") <strong>the</strong>re seem to be no appropriate links on <strong>the</strong> starting page – o<strong>the</strong>rwise
people would ra<strong>the</strong>r click <strong>the</strong>se links than type <strong>the</strong> search term. If even no appropriate document is
available via <strong>the</strong> quick-search, this indicates that a page for this topic should be created or a keyword
should be added to <strong>the</strong> right page respectively. This can be compared to <strong>the</strong> approach <strong>of</strong> (Happel
2009).
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Alexander Schneider and Alexander Steinh<strong>of</strong>f
As it was also reported by (Weischedel and Huizingh 2006), for <strong>the</strong> developers <strong>of</strong> <strong>the</strong> platform <strong>the</strong>
evaluation <strong>of</strong> <strong>the</strong> tracking data was found to be very valuable. After <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong> popularity
based ranking, additional small changes to <strong>the</strong> quick-search control were made. These changes could
in turn be evaluated using <strong>the</strong> Piwik tracking data. Additionally, a bug in <strong>the</strong> JavaScript code <strong>of</strong> <strong>the</strong>
control was found: Some users tried to select a search result from <strong>the</strong> auto-complete list but instead a
detailed search for <strong>the</strong> current content <strong>of</strong> <strong>the</strong> input field was performed. After <strong>the</strong> bugfix was applied,
<strong>the</strong> tracking data could be used to assert that this problem did not occur anymore. Finally, <strong>the</strong> usage
data motivates fur<strong>the</strong>r improvements to <strong>the</strong> control, like shortening <strong>the</strong> list <strong>of</strong> visible search results,
since users rarely select an entry below <strong>the</strong> 5th entry in <strong>the</strong> result list.
6. Conclusion
In this paper, we first gave an overview over web analytics tools in general and <strong>the</strong>n pointed out that
many opportunities arise from <strong>the</strong> application <strong>of</strong> <strong>the</strong>se powerful tools in an enterprise social s<strong>of</strong>tware
context. We fur<strong>the</strong>r gave particular examples for how different kinds <strong>of</strong> users as well as developers
can pr<strong>of</strong>it from this combination. Our experiences with a concrete scenario affirm this. It could be
observed that <strong>the</strong>re are considerable benefits for both, users and developers, when a web analytics
tool is connected to <strong>the</strong> s<strong>of</strong>tware. In our case, users could be provided with more relevant search
results and developers could easily monitor <strong>the</strong> quality <strong>of</strong> <strong>the</strong> s<strong>of</strong>tware.
The suggested scenarios and our prototypic implementation are only a first step towards <strong>the</strong> usage <strong>of</strong>
web analytics tools for social s<strong>of</strong>tware platforms. To loosen <strong>the</strong> coupling between <strong>the</strong> web analytics
tool and <strong>the</strong> social s<strong>of</strong>tware, a common interface between <strong>the</strong>m would be necessary. Fur<strong>the</strong>r research
is needed to verify <strong>the</strong> user benefits introduced in this paper and to evaluate whe<strong>the</strong>r additional
benefits can be realized. In addition, empiric research should also determine how users change <strong>the</strong>ir
behavior when web analytics data is integrated in social s<strong>of</strong>tware.
References
Agichtein, Eugene; Brill, Eric and Dumais, Susan(2006)“Improving web search ranking by incorporating user
behavior information”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 29th annual international ACM SIGIR conference on Research
and development in information retrieval (SIGIR '06). ACM, New York, NY, USA, pp 19-26.
Aladwani, A. M. and Palvia, P. C. (2002)“Developing and validating an instrument for measuring user-perceived
web quality”, Information and Management, Vol 39, No. 6, pp 467–476.
Bright, D. (2010) “E-Commerce Web Analytics Market Share”, [online], istobe,
http://istobe.com/blog/2010/01/25/e-commerce-web-analytics-market-share-january-2010.
Brin, S. and Page, L. (1998) “The Anatomy <strong>of</strong> a Large-Scale Hypertextual Web Search Engine”, Computer
Networks and ISDN Systems, Vol 30, No. 1-7, pp 107-117.
Chittoor, V. (2010) “Online Retail: Getting <strong>the</strong> Right Product in Front <strong>of</strong> Your Customers”, [online], Inc. Magazine,
http://www.inc.com/internet/articles/201002/chittoor.html.
Croll, A. and Power, S. (2009) Complete Web Monitoring, O’Reilly Media, Inc., Sebastopol.
Gonçalves, Bruno and Ramasco, José J. (2008) “Human dynamics revealed through Web analytics” Physical
Review E, Vol. 78, No. 2.
Hamel, S. (2008) “Web Analytics vendors market shares”, [online], immeria, http://blog.immeria.net/2008/01/webanalytics-vendors-market-shares.html.
Happel, H. (2009) “Social search and need-driven knowledge sharing in Wikis with Woogle”, <strong>Proceedings</strong>
<strong>of</strong> <strong>the</strong> 5th International Symposium on Wikis and Open Collaboration, WikiSym '09, ACM, New York.
Joachims, Thorsten; Granka, Laura; Pan, Bing; Hembrooke, Helene; Radlinski, Filip and Gay, Geri.
(2007)“Evaluating <strong>the</strong> accuracy <strong>of</strong> implicit feedback from clicks and query reformulations in Web search”,
ACM Transactions on Information systems, Vol 25, No.2.
Kaushik, A. (2007) Web Analytics: An Hour a Day, Wiley Publishing, Indianapolis.
Kim, S. and Stoel, L. (2004) “Dimensional hierarchy <strong>of</strong> retail website quality”, Information Management, Vol 41,
No. 5, pp 619–633.
McAfee, A.P. (2006) “Enterprise 2.0: The Down <strong>of</strong> Emergent Collaboration”, MIT Sloan Management Review, Vol
47, No. 3, pp 21-28.
Loiacono, E.T. (2000) WebQual: a website quality instrument, PhD <strong>the</strong>sis, University <strong>of</strong> Georgia.
Stern, J. (2002) Web Metrics: Proven Methods for Measuring Web Site Success, John Wiley & Sons Inc., New
York.
Watts, Duncan J. (2007) “A twenty first century science”, Nature, Vol 445, No. 1, p 489. Nature Publishing Group.
Weischedel, B. and Huizingh, E. K. (2006) “Website optimization with web metrics: a case study”, ICEC ’06:
<strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 8th international conference on Electronic commerce, pp 463–470, ACM, New York.
906

Intangible Assets: From Evaluation to Valuation
Camilo Augusto Sequeira and Eloi Fernández y Fernández
Institute <strong>of</strong> Energy, Dept. <strong>of</strong> Mechanical Engineering, Catholic University, Rio
de Janeiro, Brazil
camilo@esp.puc-rio.br
eloi@puc-rio.br
Abstract: The purpose <strong>of</strong> this paper is to show, based on an actual example <strong>of</strong> <strong>the</strong> application <strong>of</strong> <strong>the</strong> Intangible
Assets Management method, how <strong>the</strong> process <strong>of</strong> intangible asset assessment should be conducted in order to
facilitate <strong>the</strong> subsequent process, i.e., appraisal <strong>of</strong> intangible assets. In particular, this method splits from that <strong>of</strong>
internal values – associated with <strong>the</strong> organization's financial performance – and provides a valuation <strong>of</strong> each
intangible asset. This is important since <strong>the</strong> methods that encompass valuation generally start out with company
market value to generate an appraisal <strong>of</strong> intangible assets. Although <strong>the</strong> method analyzed in this paper requires
some effort on <strong>the</strong> part <strong>of</strong> specialists during <strong>the</strong> appraisal procedure, such effort is compensated by <strong>the</strong> fact that
<strong>the</strong> resulting values suffer less from volatility, i.e., <strong>the</strong>y are less prone to market reactions. Ano<strong>the</strong>r important
point made here is that <strong>the</strong> method outlined can be applied to any organization, regardless <strong>of</strong> whe<strong>the</strong>r its shares
are publicly traded or not, since <strong>the</strong> method does not rely on market value to appraise intangible assets. The
method attempts to show that <strong>the</strong> best way to handle risks and intrinsic uncertainties <strong>of</strong> intangibles during <strong>the</strong>
appraisal <strong>of</strong> assets is through <strong>the</strong> concomitant application <strong>of</strong> Real Options Analysis (ROA), as well as o<strong>the</strong>r
methods, such as Discounted Cash Flow (DCF). Although <strong>the</strong> method has been developed and applied in an
organization that belongs to a preeminent energy sector company, it could be applied in business units,
departments, divisions or o<strong>the</strong>r organizations belonging to different sectors <strong>of</strong> activity.
Keywords: Intellectual capital; intangible assets; valuation; real options; organizational competencies; business
processes.
1. Introduction
Managers view <strong>the</strong> valuation <strong>of</strong> intangibles as a way <strong>of</strong> presenting <strong>the</strong> results <strong>of</strong> <strong>the</strong>ir management.
Importantly, <strong>the</strong>y also see it as a support for strategy formulation, since <strong>the</strong> value created by
intangible assets has been demonstrated to be more relevant than that created by tangible or physical
assets.
An important recent method (Alwert, K., Bornemann, M., and Will, M. (2007)), based on a similar
conceptual footing, does not focus on valuation per se as an objective. Therefore, <strong>the</strong> aim <strong>of</strong> this
paper is to present, through a real case, <strong>the</strong> process <strong>of</strong> valuation <strong>of</strong> intangible assets and its
importance as a tool to support decision making. The comparison in details <strong>of</strong> <strong>the</strong> two methods will be
<strong>the</strong> object <strong>of</strong> future paper.
The first pilot application <strong>of</strong> <strong>the</strong> method Intangible Assets Management was conducted in <strong>the</strong> area
responsible for Health, Safety and Environment (HSE) policies within a large corporation in <strong>the</strong> energy
sector. Based on this real case, procedures to identify and map <strong>the</strong> organization's intangible assets
will be presented, such procedures being a part <strong>of</strong> <strong>the</strong> evaluation <strong>of</strong> intangibles.
The subsequent stage, valuation <strong>of</strong> identified intangible assets, will be described in more detail. But,
firstly, <strong>the</strong> importance <strong>of</strong> concepts will be commented on, including such concepts as organizational
processes and skills through which <strong>the</strong> creation <strong>of</strong> value occurs within organizations. We also
emphasize an integrated view <strong>of</strong> tangible/intangible assets, and <strong>the</strong> importance <strong>of</strong> meeting <strong>the</strong>
expectations <strong>of</strong> interested parties (stakeholders) in order to sanction <strong>the</strong> valuation process.
Next, we describe <strong>the</strong> evaluation procedure, highlighting <strong>the</strong> important points <strong>of</strong> this phase and
explaining how <strong>the</strong>y are vital to <strong>the</strong> success <strong>of</strong> valuation. We shall also describe <strong>the</strong> procedure for
valuation, using one <strong>of</strong> <strong>the</strong> identified assets, during pilot application, for illustration purposes.
Finally, we describe <strong>the</strong> results <strong>of</strong> <strong>the</strong> valuation and identify tasks for future development.
2. Tangible and intangible resources: <strong>the</strong> need for an integrated vision
In general, <strong>the</strong> flow <strong>of</strong> resources and allocation <strong>of</strong> activities must meet three demands <strong>of</strong> value:
Create value for customers to keep <strong>the</strong>m satisfied and loyal.
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Camilo Augusto Sequeira and Eloi Fernández y Fernández
Create results for internal stakeholders, to whom <strong>the</strong> organization answers directly. Internal
stakeholders are <strong>the</strong> parties most directly involved, for example, company <strong>of</strong>ficers, presidents or
high level <strong>of</strong>ficials to whom <strong>the</strong> organization responds.
Create competitive value to ensure <strong>the</strong> medium and long-term survival <strong>of</strong> <strong>the</strong> organization.
This requires an integrated view <strong>of</strong> <strong>the</strong> application <strong>of</strong> tangible/intangible resources and constant
optimization <strong>of</strong> activities through organizational processes and capabilities. If managers restrict <strong>the</strong>ir
energies to just one <strong>of</strong> those demands, <strong>the</strong> risk <strong>of</strong> failure and unbalanced results is high. For example,
cost reduction programs, from <strong>the</strong> singular perspective <strong>of</strong> creating immediate results for stakeholders,
will be reported by managers as successful if traditional models using income statements are used.
But cost reductions can substantially reduce <strong>the</strong> value created for customers and obliterate
competitive value, rendering <strong>the</strong> organization vulnerable. This occurs when cost reductions consider
intangible assets to be unimportant – as unworthy <strong>of</strong> preservation.
Likewise, if managers take care <strong>of</strong> value creation only from <strong>the</strong> perspective <strong>of</strong> customers, without
rewarding investment, <strong>the</strong> organization will face repercussions from internal stakeholders. Achieving
equilibrium requires managers' efforts to balance <strong>the</strong> application <strong>of</strong> resources and an ongoing
assessment <strong>of</strong> <strong>the</strong> return on investments in intangible assets (including human resources training,
talent retention, streng<strong>the</strong>ning <strong>of</strong> <strong>the</strong> brand, new technologies, etc.) by comparing respective returns
with those from tangible assets. In this sense, <strong>the</strong> concept <strong>of</strong> organizational-competencies portfolio
management is an important tool to ensure a balanced application <strong>of</strong> organizational resources.
Fur<strong>the</strong>rmore, <strong>the</strong> classification <strong>of</strong> intangible assets in categories such as operational, tactical and
competitive value creators helps prioritize resources – a key objective <strong>of</strong> <strong>the</strong> method.
To develop a model based on an integrated view <strong>of</strong> all organizational resources, it is necessary to
start with concepts that are both robust and accepted by <strong>the</strong> academic/business world. In studying <strong>the</strong>
concept <strong>of</strong> organizational competencies, and, in particular, core competencies, we noticed that <strong>the</strong>
integrated view <strong>of</strong> resources could be represented using a concept developed by Hamel and Prahalad
(in Prahalad, et al., 2004). However, <strong>the</strong> concept <strong>of</strong> those authors, as originally conceived, is not
sufficiently rigorous to differentiate intangible assets from <strong>the</strong> o<strong>the</strong>r organizational resources.
Therefore, drawing on <strong>the</strong> work <strong>of</strong> Andriessen (2004) on <strong>the</strong> definition <strong>of</strong> core competencies, we have
established additional criteria to implement such rigor.
Although <strong>the</strong> method developed by Andriessen (2004) is innovative, it still fails – at least in an
integrated way – to account for two important concepts: organizational processes and organizational
skills, both <strong>of</strong> which are critical during <strong>the</strong> stages <strong>of</strong> identification and mapping <strong>of</strong> <strong>the</strong> intangible assets
<strong>of</strong> an organization. The InCaS (Intellectual Capital Statement) method, described by Alwert, K.,
Bornemann, M., and Will, M. (2007), partially fills this gap by highlighting business processes and
knowledge processes. Academic and business communities, in general, regard this method as a
candidate for a universal standard. The <strong>European</strong> Economic Community, for one, has demonstrated
its commitment to such standardization.
The InCaS method represents an advance in <strong>the</strong> use <strong>of</strong> business/knowledge processes as a
conceptual base. However, like <strong>the</strong> o<strong>the</strong>rs, it still does not address <strong>the</strong> process <strong>of</strong> valuation. Firms are
increasingly feeling <strong>the</strong> need to implement a method for managing intangible assets that includes a
way to appraise such assets, despite <strong>the</strong> difficulties in obtaining such an appraisal.
Based on <strong>the</strong>se concepts and assumptions, and by combining organizational processes and
competencies, a model was developed with <strong>the</strong> intent to establish a new paradigm – that <strong>of</strong> enabling
managers to concentrate on intangible resources – and transcending <strong>the</strong> culture that assumes
financial and physical assets to be <strong>the</strong> main factors that produce value in <strong>the</strong> organization. Managers
want more than to merely evaluate intangibles: <strong>the</strong>y need tools to support <strong>the</strong>ir decision-making, i.e.,
to diagnose which intangibles have <strong>the</strong> highest probability <strong>of</strong> yielding economic benefits.
3. The role <strong>of</strong> processes, organizational competencies and stakeholder
expectations
Processes express, in a sense, <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong> organization, dynamics that manifest <strong>the</strong>mselves
through organizational competencies. Therefore, competencies are closely linked to processes. While
processes enable <strong>the</strong> flow <strong>of</strong> available resources, organizational competencies represent <strong>the</strong> activities
that use <strong>the</strong> resources to create value.
908

Camilo Augusto Sequeira and Eloi Fernández y Fernández
We define Organizational Competence (Sequeira, C.; Fernández, E. F; Borges, M. C. (2009)) as a set
<strong>of</strong> intangible assets, backed by tangible assets, in a manner both interrelated and synergistic, and
with <strong>the</strong> potential to create value. Although <strong>the</strong> focus <strong>of</strong> organizational competence is <strong>the</strong> organization
itself, <strong>the</strong> approach is comprehensive, i.e., it includes relationships with suppliers, partners, customers
and o<strong>the</strong>r interested parties (stakeholders).
Organizational Competence is made up <strong>of</strong> two basic components: a) <strong>the</strong> component formed by <strong>the</strong>
group <strong>of</strong> individuals, who instantiate <strong>the</strong> competence, and who contribute with skills, knowledge and
attitudes; b) <strong>the</strong> component formed by <strong>the</strong> internal and external context, i.e., available technologies,
work infrastructure, explicit knowledge, norms, values, management processes, environment, etc.,
fostering <strong>the</strong> development <strong>of</strong> organizational competencies to a greater or lesser degree.
The method considers processes and organizational competencies in synergy ra<strong>the</strong>r than in isolation.
As noted earlier, some management methods tend to focus only on processes or competencies.
Ano<strong>the</strong>r example is that <strong>of</strong> business process re-engineering, which guides redesign efforts to reduce
costs. While this may be necessary, it is not sufficient by itself because it can lead <strong>the</strong> organization to
destroy value by excluding important sources <strong>of</strong> added value.
Therefore, to start <strong>the</strong> qualitative analysis (evaluation), <strong>the</strong> method regards as essential <strong>the</strong>
stakeholder expectations vis-à-vis a particular future scenario; and <strong>the</strong> present status <strong>of</strong> <strong>the</strong>
organization.
The concepts <strong>of</strong> assessment and valuation <strong>of</strong> assets are not always clear, particularly among
managers far from <strong>the</strong> financial area. To value tangible or physical assets, different methods (Titman,
2008; Koller, 2005; Copeland, 2001) already exist that are well consolidated and accepted by both
parties, i.e., <strong>the</strong> sellers and buyers <strong>of</strong> such assets. Still, <strong>the</strong> valuation <strong>of</strong> tangible assets is a task that
requires a certain degree <strong>of</strong> discretion and great effort. And, when it comes to intangible assets, <strong>the</strong>
difficulty is magnified because, in general, <strong>the</strong>re is not always a market for intangible assets:
intangibles are useful or attractive, mainly, to <strong>the</strong> organization to which <strong>the</strong>y belong.
In order to clarify <strong>the</strong> concepts <strong>of</strong> value, assessment and valuation we will use <strong>the</strong> description <strong>of</strong>
Andriessen (2004) as regards Valuation and Measurement <strong>of</strong> Intangibles. Andriessen (2004 pp. 11-
15) outlines what is meant by Financial Valuation, Value Measurement, Value Assessment and
Measurement, using a procedure that was adapted and illustrated in Figure 1:
Financial Valuation,
Value Measurement, Value Assessment and Measurement
Is <strong>the</strong>re a value scale
we can use that reflects
usefulness or desirability?
YES
Is money <strong>the</strong> unit used
on <strong>the</strong> value scale?
YES
FINANCIAL VALUATION
Figure 1: Valuation and Evaluation
NO Is <strong>the</strong>re a variable whose
state can be observed?
NO
NO
MEASUREMENT
Can <strong>the</strong> value be translated
into observable criteria?
909
YES
YES
VALUE MEASUREMENT
NO
EXIT
VALUE ASSESSMENT

Camilo Augusto Sequeira and Eloi Fernández y Fernández
This procedure, with which we agree, helps us to understand <strong>the</strong> terms evaluation and valuation. We
make one caveat: when we refer to valuation we follow strictly <strong>the</strong> concept adopted in finance (Koller,
2005), i.e., Financial Valuation. So we will reserve <strong>the</strong> word evaluation for <strong>the</strong> o<strong>the</strong>r cases: Value
measurement, Value assessment, and Measurement as described above.
Also according to Andriessen (2004, p. 11) <strong>the</strong> concept <strong>of</strong> value is defined as follows:
“The degree <strong>of</strong> usefulness or desirability <strong>of</strong> something, especially in comparison with
o<strong>the</strong>r things.”
Andriessen uses <strong>the</strong> term usefulness to emphasize <strong>the</strong> utilitarian purpose <strong>of</strong> valuation. But he adds
<strong>the</strong> term desirability to complement <strong>the</strong> concept <strong>of</strong> value, arguing that a product or service may have
value simply by being beautiful, pleasant, or by having o<strong>the</strong>r characteristics that appeal to
stakeholders’ desires.
If we can translate value into an observable phenomenon and if we use money as a measure <strong>of</strong>
value, <strong>the</strong>n we will be moving towards its valuation. If money is not used for purposes <strong>of</strong>
measurement, <strong>the</strong>n we must have o<strong>the</strong>r criteria or yardsticks to make such assessment. According to
Andriessen, we cannot always translate value into observable phenomena and, <strong>the</strong>refore, some
assessments are personal.
4. An Overview <strong>of</strong> <strong>the</strong> method
As stated earlier, <strong>the</strong> assessment phase, which will be described below, is fundamental for <strong>the</strong>
process <strong>of</strong> valuation <strong>of</strong> identified assets to be adequately performed.
The method was designed and specified by creating a simulator in Excel, complemented by a
simulator in a web environment. In this way, we ordered <strong>the</strong> steps <strong>the</strong> organization must follow to
obtain <strong>the</strong> mapping <strong>of</strong> intangible assets and sources <strong>of</strong> added value. Figure 2 illustrates <strong>the</strong> method in
eight steps:
Organizational Identity;
Contextual position;
Value expectations;
Value Creation
Mapping <strong>of</strong> Intangibles
Sources <strong>of</strong> value
Valuation and
Action Plan, Analyses and Reports
These eight steps are grouped into blocks (WP - WorkPackage) where <strong>the</strong> worksheet folders are
identified that support <strong>the</strong> application <strong>of</strong> <strong>the</strong> method, for example, 2. Identification; 3. Contexts; and so
forth.
During <strong>the</strong> first six steps (1 – Organizational Identity, to 6 – Sources <strong>of</strong> value) <strong>the</strong> stages <strong>of</strong>
organization appraisal are conducted. This is a qualitative analysis that starts with <strong>the</strong> statement <strong>of</strong>
<strong>the</strong> value creation model, and <strong>the</strong>n proceeds through analysis <strong>of</strong> internal and external contexts,
identification <strong>of</strong> stakeholders, stakeholder expectations, and critical success factors and actions
necessary to meet <strong>the</strong>se expectations.
At <strong>the</strong> end <strong>of</strong> this cause-and-effect sequence, <strong>the</strong> Intangible Assets (IAs), necessary to perform <strong>the</strong>
actions and attain <strong>the</strong> critical success factors, are identified; <strong>the</strong> goal is to meet stakeholder
expectations, always taking into considering <strong>the</strong> organization's value creation model. These stages
are supplemented by <strong>the</strong> mapping <strong>of</strong> <strong>the</strong> Sources <strong>of</strong> Added Value (SAV) related to each Intangible
Asset, as shown in Figure 3.
910

Figure 2 : Step-by-step method
STAKEHOLDER
AND
STAKEHOLDER
EXPECTATIONS
UNDERLYING
ASSET
INTANGIBLE
ASSET
MANAGEMENT OF PORTFOLIO
OF INTANGIBLE ASSETS
SOURCES OF
ADDED VALUE
Camilo Augusto Sequeira and Eloi Fernández y Fernández
SAV11
Figure 3: Conceptual Basis
Mapping <strong>of</strong> Intangible Assets and Their Sources
Stakeholder 1 Stakeholder 2 Stakeholder n
Expectation 1 Expectation 2 Expectation n
IA1
> > >
UA1
SAV1n
> > >
UA2 UAn
IA2 > > >
IAn
SAV21 > > > SAV2n
> > >
SAVn1 > > > SAVnn
Also during <strong>the</strong> evaluation stages, complementary activities are carried out, including <strong>the</strong> classification
<strong>of</strong> intangible assets into three categories: Tactical, Operational and Competitive (TOC). In order to
define <strong>the</strong>se categories, a set <strong>of</strong> twenty-seven questions must be answered, which have been
911

Camilo Augusto Sequeira and Eloi Fernández y Fernández
previously established and cross-referenced with <strong>the</strong> three categories. When answered, <strong>the</strong> questions
provide a percentage indication that points to <strong>the</strong> category to which <strong>the</strong> asset is most closely related.
The value contribution level is also analyzed; it will facilitate <strong>the</strong> establishment <strong>of</strong> priorities during <strong>the</strong>
management <strong>of</strong> intangibles, jointly with <strong>the</strong> classification <strong>of</strong> intangible asset in one <strong>of</strong> five categories <strong>of</strong>
Intellectual Capital, i.e., Human, Organizational, Relationship, Technology Domain and
Environmental, in accordance with <strong>the</strong> description in <strong>the</strong> Intangible Assets Management Report,
IEPUC
(2009). Intangible assets that have been identified as necessary to achieve actions are now
classified as existing, non-existing or partially-existing.
As mentioned, <strong>the</strong> weighting <strong>of</strong> each intangible asset (previously identified and mapped) serves to
prioritize <strong>the</strong> most important assets. The calculation <strong>of</strong> this weighting is performed based on
responses to six questions considered essential to identifying core assets. These questions follow <strong>the</strong>
same paradigm as core competence. The response results indicate <strong>the</strong> level <strong>of</strong> contribution <strong>of</strong> <strong>the</strong>
intangible asset. Following this procedure, we can prioritize assets with greater potential to contribute
and continue to <strong>the</strong> next steps <strong>of</strong> <strong>the</strong> qualitative assessment. Here too, <strong>the</strong> potential contribution is
identified <strong>of</strong> each source and its weighting (marginal, participatory, contributing, beacon, fundamental)
in creating value for intangible asset to which it is associated. These weightings are self-explanatory:
Marginal source has a weighting <strong>of</strong> 1, which is lower than <strong>the</strong> Fundamental source, with a weighting
<strong>of</strong>
5. Also defined is <strong>the</strong> impact <strong>of</strong> <strong>the</strong> intangible asset on organizational results through <strong>the</strong> most
impacted financial indicators – so-called Underlying Assets (UA) – as shown in Figures 3 and 4.
The calculation <strong>of</strong> <strong>the</strong> potential contribution <strong>of</strong> each source is directly associated with <strong>the</strong> type <strong>of</strong>
underlying asset to which <strong>the</strong> intangible asset, representing <strong>the</strong>se sources, is correlated. For
example, for <strong>the</strong> underlying asset whose indicator is CAPEX (CAPital EXpenditure), <strong>the</strong>re are three
specific questions
to be answered: Prevents <strong>the</strong> root causes <strong>of</strong> harm? Contributes to performance?
Ensures final quality? For each underlying asset, a specific set <strong>of</strong> questions is listed (Final Report,
IEPUC (2009)).
5. Pilot application
he first workshop <strong>of</strong> <strong>the</strong> pilot application begins by filling in <strong>the</strong> fields relative to Organizational
Identification. Among <strong>the</strong>se,
<strong>the</strong> main fields relevant for <strong>the</strong> analysis <strong>of</strong> intangibles are Declaration <strong>of</strong>
Value,
Value Creation Model, Primary Supplier, Secondary Supplier, Primary Customer and
Secondary Customer.
The Statement <strong>of</strong> Value and <strong>the</strong> Value Creation Model contribute to focus <strong>the</strong> work that results from
<strong>the</strong> identification <strong>of</strong> Primary and Secondary Suppliers, Primary and Secondary Customers, and
especially, in <strong>the</strong> identification <strong>of</strong> organizational stakeholders. It is important to note that, as a pilot
application, stakeholders were not surveyed directly, and, <strong>the</strong>refore,
<strong>the</strong> statements in <strong>the</strong> model are
<strong>the</strong>
expression <strong>of</strong> <strong>the</strong> perceptions <strong>of</strong> those present on <strong>the</strong> day <strong>of</strong> <strong>the</strong> workshop. The next step was to
identify <strong>the</strong> assets needed to support <strong>the</strong> required actions.
The internal and external contexts are analyzed from <strong>the</strong> perspective <strong>of</strong> a set <strong>of</strong> parameters.
Organizational Competencies, Managerial Dynamics and Intangible Development Potential are <strong>the</strong>
parameters through which <strong>the</strong> internal context is evaluated. For each parameter, <strong>the</strong>re is a set <strong>of</strong>
related questions which, when answered, provide <strong>the</strong> degree <strong>of</strong> conformity vis-à-vis each parameter
<strong>of</strong> <strong>the</strong> organization. This degree <strong>of</strong> conformity, which varies from 0.5 to 1.5, is <strong>the</strong> resilience factor.
Resilience indicates <strong>the</strong> organization's propensity to create intangible value. The external context is
analyzed using <strong>the</strong> following parameters: Customer Vision, Relationships, Sustainability, Institutional
Politics, Regulation, Technology and Economy. The parameters <strong>of</strong> each context are also weighted
according to <strong>the</strong>ir degree <strong>of</strong> influence. Factors range from Fundamental (weight 5) to Marginal (weight
1), passing down through Beacon (weight 4), Contributor (weight 3) and Participatory (weight 2). The
analysis <strong>of</strong> contexts gives a snapshot view <strong>of</strong> <strong>the</strong> organization. The parameters that require more
attention
are Economy and Regulation. In analyzing <strong>the</strong> sub-dimensions managers can, in <strong>the</strong> future,
devise strategies to change this factor.
One <strong>of</strong> <strong>the</strong> objectives <strong>of</strong> this pilot application was to select <strong>the</strong> most important assets and to follow <strong>the</strong>
procedure through to <strong>the</strong> valuation stage. As such, four assets were chosen to be analyzed in this
pilot application. In this paper, we describe just one <strong>of</strong> <strong>the</strong>se assets in <strong>the</strong> following steps. The
classification <strong>of</strong> <strong>the</strong> asset as Tactical, Operational or Competitive (TOC) also contributes to ranking <strong>of</strong>
912

Camilo Augusto Sequeira and Eloi Fernández y Fernández
assets, within a set, in order to ascertain which are <strong>the</strong> most important. Usually, Competitive assets
are prioritized when it is time to prioritize future strategies. But if organizational focus is on <strong>the</strong>
present, <strong>the</strong>n Tactical or Operational assets have stronger weighting.
Everything depends on <strong>the</strong>
organization's current situation.
6. Identification <strong>of</strong> sources <strong>of</strong> added value (SAV)
Figure 4 shows <strong>the</strong> Value Creation Map for <strong>the</strong> stakeholder: Upper Management. As well as <strong>the</strong> Value
Statement and <strong>the</strong> Value Creation Model, <strong>the</strong> map also depicts
expectations, critical success factors,
actions
to meet expectations, actions to surpass critical success factors, intangible assets required to
support <strong>the</strong> actions, and Value Added Sources <strong>of</strong> each asset.
Only one Upper Management expectation was analyzed, whose critical factors and corresponding
actions were mapped, as illustrated in Figure 4.
1 – STATEMENT OF VALUE
AND VALUE CREATION
MODEL
2 –STAKEHOLDERS AND
THEIR EXPECTATIONS
3 – CRITICAL
SUCCESS
FACTORS
4 – ACTIONS TO
MEET
EXPECTATIONS
5 – NECESSARY
INTANGIBLE ASSETS
6 – SOURCES
OF ADDED
VALUE
Execute
benchmark
Foster integration and
uniformity <strong>of</strong> actions
within international
standards <strong>of</strong> HSE
excellence.
Foster excellence in
HSE corporate
performance
Establish objectives
and goals for
excellence in HSE
Foster alignment
with corporate
strategies and
status
Model for
HSE processes
and
methodologies
Identification process <strong>of</strong>
strategic competencies
Capacity to implement
aspects <strong>of</strong> HSE based on
strategic objectives.
Government and
relationship networks
Process to identify best
practices, gaps and obtain
feedback
Capacity to identify
standards <strong>of</strong> excellence -- benchmark;
relationship networks and information bases
Upper
Management
Promote
commitment and
representativeness
<strong>of</strong> areas
Figure 4: Upper Management: value creation map
Monitoring market tendencies,
producing/disseminating
corporate HSE advisories,
evaluating its implementation
and assessing cost/benefit
ratio.
Effective dissemination
and capacity building at all
levels
Governance structured for
validation
Critical analysis <strong>of</strong>
corporate HSE
performance
Valuation <strong>of</strong> benefits
Consolidate and
report HSE
performance
information
Systematize <strong>the</strong>
process <strong>of</strong> collection,
consolidation and
dissemination <strong>of</strong> HSE
performance
information
Technical/
up-to-date
competence in
HSE
Value Creation Map
Organization:
CORPORATE HSE
Developed
capacity for
critical analysis
<strong>of</strong> information
Implement HSE
Knowledge
Management
Three intangible assets related to Upper Management were identified and mapped: Model for HSE
processes and methodologies,
with eight Value Added Sources; Technical/Up-to-date competence in
HSE, with five Value Added Sources; and Management <strong>of</strong> relationship and information networks,
with
five Value Added Sources.
7. SAV analysis <strong>of</strong> Intangible Asset: "Model for HSE Processes and
Methodologies"
Maintain process <strong>of</strong>
Continual
improvement
Management
<strong>of</strong> relationship
and information
networks
As stated earlier, <strong>the</strong> results <strong>of</strong> just one <strong>of</strong> <strong>the</strong> assets are presented. The Intangible Asset selected,
"Model for HSE Processes and Methodologies," is associated with <strong>the</strong> stakeholder Upper
Management through <strong>the</strong> expectation "Foster excellence in corporate HSE performance". After a
detailed financial analysis <strong>of</strong> <strong>the</strong> impacts <strong>of</strong> this asset, it was concluded that <strong>the</strong> indicator <strong>of</strong> <strong>the</strong>
underlying asset is CAPEX, as illustrated in Figure 5. This means that this asset creates value by
releasing CAPEX,
i.e., it helps to make resources available to be used in activities requiring
913

Camilo Augusto Sequeira and Eloi Fernández y Fernández
investment. As an asset classified as "competitive," <strong>the</strong> value created is directly aligned with future
strategies.
STAKEHOLDER
AND STAKEHOLDER
EXPECTATIONS
UNDERLYING ASSET
INTANGIBLE ASSET
SOURCES OF ADDED VALUE
1.Identification
process <strong>of</strong>
strategic
competencies
Partial
2. Capacity to implement
aspects <strong>of</strong> HSE based on
strategic objectives.
Government and
relationship networks
Yes
Upper
Management
3. Process to
identify best
practices, gaps
and obtain
feedback Partial
Foster corporate
excellence in HSE
performance
Model for HSE Processes
and Methodologies
Organizational, Competitive
Figure 5:Intangible Asset, Underlying Asset, and SAVs
CAPEX
4 5 5 5 4 4 5
The intangible asset has a potential <strong>of</strong> 63%; this means it has room for 37 percentage points <strong>of</strong>
possible performance improvement for creation <strong>of</strong> value.
There are eight sources that create value for this asset. One <strong>of</strong> <strong>the</strong>m, "Valuation <strong>of</strong> Benefits," is
considered to be a Contributor with a weighting <strong>of</strong> three.
Four are ranked as Fundamental with a
weighting <strong>of</strong> five, and three are ranked as Beacon with weighting <strong>of</strong> four, as illustrated in figure 5.
8. Valuation stage and decision support
4. Capacity to identify
standards <strong>of</strong>
excellence -benchmark;
relationship networks
and information bases
Partial
5. Effective
dissemination and
capacity building at all
levels
Partial
Organization:
CORPORATE HSE
CONTRIBUTION
1- Marginal
2- Participatory
3- Contributing
4- Beacon
5- Fundamental
8.Valuation <strong>of</strong>
benefits No
7. Critical analysis
<strong>of</strong> corporate HSE
performance
Partial
6. Governance
structured for
validation
Yes
In order to understand <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong> application procedure as a whole, immediately following
<strong>the</strong> SAV-analysis <strong>of</strong> each asset, <strong>the</strong> information with respect to maintenance costs, projections <strong>of</strong>
future
value creation, investments required for growth <strong>of</strong> intangible assets, etc.,
are consolidated by
<strong>the</strong> organization's managers. From <strong>the</strong>re, <strong>the</strong> evaluation <strong>of</strong> source characteristics commences, mainly
with regard to ways <strong>of</strong> creating value, and managing risks and uncertainties.
Based on <strong>the</strong> financial information, <strong>the</strong> value <strong>of</strong> <strong>the</strong> impact is calculated and entered into <strong>the</strong> Excel
and web-based simulators. The simulators <strong>the</strong>n perform <strong>the</strong> respective valuation calculations. Here,
two scenarios are considered, A and B. The difference between <strong>the</strong> two scenarios stems from <strong>the</strong>
combination <strong>of</strong> resilience and volatility to produce <strong>the</strong> values <strong>of</strong> <strong>the</strong> two scenarios, as follows: The
greater
<strong>the</strong> resilience factor, <strong>the</strong> lower
<strong>the</strong> volatility <strong>of</strong> values with respect to <strong>the</strong> organization. And, <strong>the</strong>
lower <strong>the</strong> resilience, <strong>the</strong> greater <strong>the</strong> volatility <strong>of</strong> values. Thus, scenario A reflects greater volatility;
scenario B reflects lower volatility.
The valuation depends on how <strong>the</strong> assessment is performed. In this case, <strong>the</strong> aim is to derive a ratio
<strong>of</strong> <strong>the</strong> value <strong>of</strong> <strong>the</strong> intangible asset with <strong>the</strong> performance <strong>of</strong> <strong>the</strong> organization under assessment, and
unrelated to <strong>the</strong> market value <strong>of</strong> <strong>the</strong> corporation to which <strong>the</strong> organization belongs. The methods <strong>of</strong>
valuation using market value are more susceptible to systemic volatility: changes in <strong>the</strong> local or global
macroeconomic scenario affect business performance and, <strong>the</strong>refore, <strong>the</strong> valuation <strong>of</strong> intangibles will
also be so affected.
914
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4.17
3
POTENTIAL
LEVEL OF
CONTRIBUTION

Camilo Augusto Sequeira and Eloi Fernández y Fernández
Ano<strong>the</strong>r aspect <strong>of</strong> this valuation method is that it can be applied to any business or organization, even
those that have no explicit market value, i.e., companies not listed on <strong>the</strong> stock market. In summary,
<strong>the</strong>
valuation developed here depends more on structural uncertainties internal to <strong>the</strong> organization
than systemic uncertainties external to <strong>the</strong> organization.
As mentioned, during <strong>the</strong> application <strong>of</strong> <strong>the</strong> method <strong>the</strong> most relevant assets become evident, and <strong>of</strong>
<strong>the</strong>se, "Model for HSE processes and methodologies' stood out as one <strong>of</strong> <strong>the</strong> organization's core
competencies. The assessments pointed to <strong>the</strong> indicator CAPEX, shown in Figure 5, as being most
influenced by this intangible asset: as defined by <strong>the</strong> participants during <strong>the</strong> pilot application, <strong>the</strong>
main
function
<strong>of</strong> <strong>the</strong> organization being surveyed is to define, observe and enforce standards and
procedures, HSE being categorized as an organization within <strong>the</strong> ambit <strong>of</strong> compliance.
Thus, its creation <strong>of</strong> value is a function <strong>of</strong> <strong>the</strong> ability to avoid losses due to failures, accidents and
absenteeism
in health, safety and environment. Therefore, by reducing exposure to risk by <strong>the</strong>
compliance activities, provisions for contingencies to be reduced, thus freeing up investment capacity.
In fact, <strong>the</strong> impact
on CAPEX <strong>of</strong> this intangible asset can be observed by looking at <strong>the</strong> organization's
activities in <strong>the</strong> period 2000 to 2008. In particular,
we observe that four variables showed high
correlation:
Days-Away-from-Work Injury rate
(TFCA)
Oil Spill Volume per m
<strong>the</strong>
nterprise, TFCA and VVO decreased, with a concomitant decrease in PPC. Previously, HSE had not
3 (VVO)
Provision for Contingencies (PPC)
Start <strong>of</strong> HSE policies under <strong>the</strong> responsibility <strong>of</strong> corporate organization, i.e., centralized (HSE
CORP)
Thus, <strong>the</strong> intangible asset "Model for HSE processes and methodologies" was valued, presenting
creation <strong>of</strong> value as reflected in <strong>the</strong> variables TFCA, VVO and PPC. When adopting a centralized
policy <strong>of</strong> Corporate HSE, implementing <strong>the</strong> HSE model <strong>of</strong> processes and methodologies across
e
been a corporate-wide undertaking, and each business unit had implemented its own HSE policy.
Finally, in order to close <strong>the</strong> numbers, several comparative
analyses were conducted. Econometric
calculations
and correlation <strong>of</strong> <strong>the</strong> above variables were also performed to test <strong>the</strong> hypo<strong>the</strong>ses as to
<strong>the</strong>ir impact and measure <strong>the</strong>ir relative weighting.
From <strong>the</strong> standpoint <strong>of</strong> supporting decision-making, <strong>the</strong> organization's managers must define goals for
<strong>the</strong> coming period and, <strong>the</strong>reafter, take appropriate actions, within <strong>the</strong> ambit <strong>of</strong> <strong>the</strong> organization's
strategic requirements, which can be simulated by adjusting parameters in <strong>the</strong> spreadsheet, as done
previously during <strong>the</strong> application <strong>of</strong> <strong>the</strong> method. Real Options Analysis (ROA) method, (Copeland, T.,
Antikarov, V. (2001)) allows for an appraisal <strong>of</strong> strategic flexibility or ability to respond to future
uncertainties by measuring <strong>the</strong> benefit <strong>of</strong> investment in such assets in financial terms. Similarly, <strong>the</strong>
method allows us to measure <strong>the</strong> investment in intangibles, with an indication
<strong>of</strong> <strong>the</strong> level <strong>of</strong> risk or
cost
savings that can be expected. Using ROA requires <strong>the</strong> prior assessment <strong>of</strong> internal and external
uncertainties, as well as uncertainties associated with <strong>the</strong> asset itself.
Capabilities created by Human Resources practices and combinations <strong>the</strong>re<strong>of</strong> are options that can
address such uncertainties. In essence, <strong>the</strong>se capabilities are options because <strong>the</strong>y can lower <strong>the</strong>
cost <strong>of</strong> adjustments when changes occur, preserve value, and provide decision-making and
operational flexibility. Real Options <strong>the</strong>ory recommends that to manage uncertainties proactively
(Bhattacharya, M., Wright, P., 2004) a venture must develop capabilities, i.e. combinations <strong>of</strong>
resources and processes. While SAVs are related resources, capable <strong>of</strong> synergistically creating
future value (future benefits) and presenting uncertainties and associated risks, Real Options provide
a way to manage <strong>the</strong> uncertainties related to investments in assets. while valuation derived from
discounted cash flow (DCF), (Koller, T., Goedhart, M. Wessels, D., (2005)), does not engender
decision-making and operational flexibility, it is complemented by Real Options Analysis.
In o<strong>the</strong>r
words, <strong>the</strong> one does not preclude <strong>the</strong> o<strong>the</strong>r. In fact, valuation starts out with <strong>the</strong> application <strong>of</strong> DCF.
The method already includes
<strong>the</strong> basic tools to carry out a preliminary assessment.
915

9. Conclusions
Camilo Augusto Sequeira and Eloi Fernández y Fernández
The results <strong>of</strong> <strong>the</strong> first application <strong>of</strong> <strong>the</strong> method highlight its strengths, but also point to a number <strong>of</strong>
improvements that need to be incorporated, both with respect to <strong>the</strong> method itself and application
procedures/dynamics. One positive aspect is in providing executives with <strong>the</strong> ability to identify and
map <strong>the</strong> most important intangible assets, through a sequence <strong>of</strong> cause and effect, starting with
stakeholder expectations and ending with an inventory <strong>of</strong> intangible assets and related sources <strong>of</strong>
added value. In addition to identifying <strong>the</strong> most relevant intangible assets, <strong>the</strong> pilot application also
provided <strong>the</strong> opportunity to consolidate <strong>the</strong> information and data necessary for <strong>the</strong> valuation
procedure. Fur<strong>the</strong>r Aspects: Using this procedure, management can analyze in-depth <strong>the</strong> processes
and organizational competencies in <strong>the</strong>ir search <strong>of</strong> intangible assets, as well as <strong>the</strong> impacts on
financial results, as pointed to by <strong>the</strong> indicators most closely related to each asset. The concept <strong>of</strong>
Sources <strong>of</strong> Added Value provides a way <strong>of</strong> managing intangible assets using <strong>the</strong> very resources that
comprise
such sources. As such, <strong>the</strong> valuation procedure used by <strong>the</strong> method uses, as inputs, <strong>the</strong>
financial figures and information that are most closely related to <strong>the</strong> organization and its performance.
This approach contrasts with o<strong>the</strong>r methods that start with market value and, from <strong>the</strong>re, proceed to a
division and allocation <strong>of</strong> value to each <strong>of</strong> <strong>the</strong> organization's intangible assets (Haigh, D. (2009)).
One aspect to consider in future development is to balance and optimize <strong>the</strong> time commitment
required <strong>of</strong> an organization's executives with <strong>the</strong> work required <strong>of</strong> facilitators to process previously
existing organizational data, a task that is <strong>of</strong>ten an arduous one without <strong>the</strong> active collaboration <strong>of</strong>
executives. Ano<strong>the</strong>r finding that came to light during <strong>the</strong> application <strong>of</strong> <strong>the</strong> method concerns <strong>the</strong>
concept <strong>of</strong> valuation, which must be previously explained and aligned to avoid creating false
expectations as to results. The method highlights <strong>the</strong> difficulty <strong>of</strong> valuing assets to help establish an
objective link between <strong>the</strong> activities <strong>of</strong> identification and valuation, and mapping <strong>the</strong> link to financial
performance indicators and <strong>the</strong> organization's financial performance. This difficulty points to <strong>the</strong> need
for future work on this <strong>the</strong>me.
Acknowledgements
This project
was made possible thanks to <strong>the</strong> support and encouragement <strong>of</strong> <strong>the</strong> Petrobras Division <strong>of</strong>
Management Systems
Development and Knowledge Management. We would also like to
acknowledge and thank Washington Salles, Alexandre Korowajczuk, Antônio Sergio, and Irani
Varella.
References
Alwert, K.; Bornemann, M.; Will, M. (2007) “Does Intellectual Capital Reporting Matter to Financial Analysts?”
Paper read at <strong>the</strong> 8th <strong>European</strong> <strong>Conference</strong>
on Knowledge Management. Barcelona, Spain.
Andriessen, D. (2004) Making Sense <strong>of</strong> Intellectual Capital: Designing a Method for <strong>the</strong> Valuation <strong>of</strong> Intangibles.
Burlington, VT: Elsevier Butterworth-Heinemann.
Bhattacharya, M.; Wright, P. (2004) Managing Human Assets in an Uncertain World: Applying Real Options
Theory to HRM, Cornell University.
Cohen, Jeffrey A. (2005) Intangible Assets: Valuation and Economic Benefit. Hoboken, NJ: John Wiley & Sons.
Copeland, T.; Antikarov,
V. (2001), Real Options, Texere LLC, London.
Haigh, D. (2009) “Global Intangible Finance Tracker, An Annual Review <strong>of</strong> <strong>the</strong> World’s Intangible Value”,
Appendix 5: Valuation <strong>of</strong> Intangible Assets. www.brandfinance .com.
Prah y, V. (2004) O Futuro da Competição: Como desenvolver diferenciais inovadores
Sequ
Titma Art and Science <strong>of</strong> Corporate Investment Decisions. Boston,
Pearson Education Inc.
iedma, J. M. (2006) ‘In Search <strong>of</strong> an Intellectual Capital Comprehensive Theory’ In: <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 3 rd
Instituto de Energia da Universidade Católica, IEPUC (2009) Relatório Final de Gestão dos Ativos Intangíveis.
Rio de Janeiro.
Koller, T.; Goedhart, M.; Wessels, D.; (2005), Valuation, Measuring and Managing <strong>the</strong> Value <strong>of</strong> Companies,
McKinsey & Company, John Wiley & Sons, New Jersey, USA.
alad, C. K. & Ramaswam
em parcerias com os clientes. São Paulo: Editora Campus.
eira, C.; Fernández, E. F. (2010) ‘Putting Intangible Assets Management to Work’ In: <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong>
11th <strong>European</strong> <strong>Conference</strong> on Knowledge Management. Universidade Lusíada de Vila Nova de Famalicão,
Portugal. pp. 901-911.
Sequeira, C.; Fernández, E. F; Borges, M. C. (2009) The Best Returns Come from Intangible Resources: an
Integrated Approach, Journal Vine; volume: 39, issue: 1, page: 7-19. Emerald Group Publishing Limited.
n, S.; Martin, J. D.; (2008) Valuation: The
V
International <strong>Conference</strong> on Intellectual Capital and Knowledge Management. Santiago, Chile: Pontificia
Universidad Católica de Chile. pp. 1–16.
916

Intellectual Capital Evaluation: Relationship between
Knowledge Management Implementation and Company’s
Performance
Elena Shakina and Anna Bykova
State University Higher School <strong>of</strong> Economics, Perm, Russia
eshakina@hse.ru
abykova@hse.ru
Abstract: Knowledge management is becoming <strong>the</strong> most relevant and challenging issue <strong>of</strong> company’s strategy
implementation in <strong>the</strong> new economy. Intellectual capital identification and evaluation is one <strong>of</strong> <strong>the</strong> most important
issues in knowledge management. Our study focuses on <strong>the</strong> evaluating intellectual capital methods that allow
finding out <strong>the</strong> most efficient way <strong>of</strong> intellectual capital management, including investment decision making. We
suppose that <strong>the</strong> potential effectiveness <strong>of</strong> intellectual capital resources varies depending on a company size,
industry and country. The majority <strong>of</strong> <strong>the</strong> relevant researches are based on resource- and value-based
approaches that separately analyze <strong>the</strong> intellectual capital from a certain point <strong>of</strong> view, limiting <strong>the</strong> number <strong>of</strong>
problems at concurrence <strong>of</strong> <strong>the</strong>se concepts. Therefore, to solve problems <strong>of</strong> intellectual capital evaluation we
integrate two approaches that are relevant for studying <strong>the</strong> companies’ and industries’ behavior.
We seek to integrate two approaches to answer following questions:
Is <strong>the</strong>re a close relationship between an intellectual capital quality and a company performance: a creation
and destruction <strong>of</strong> <strong>the</strong> enterprise value due to intellectual capital employed?
What are <strong>the</strong> external and internal factors affecting this relationship? (country, industry, company size,
market dynamics, etc.)
Is <strong>the</strong>re a certain complementarity <strong>of</strong> <strong>the</strong> intellectual capital separate components (human, institutional and
market resources)?
Despite <strong>the</strong> large empirical background <strong>the</strong> intellectual capital management issues fundamentally are not well
studied. The purpose <strong>of</strong> this research is <strong>the</strong> development <strong>of</strong> cost-effectiveness tools for analyzing <strong>the</strong> company’s
intellectual resources. Several statistical methods should be provided for <strong>the</strong> empirical issues <strong>of</strong> this research,
including common cross-sectional and panel data analysis. The data base collected for this purpose will consist
<strong>of</strong> financial and economic indicators underlying <strong>the</strong> intellectual capital evaluation, for example, strategic
performance indicators (EVA, FGV, Q-Tobin).It should be emphasized, that a number <strong>of</strong> required data are quite
specific and hardly observed. Thus, <strong>the</strong> data base <strong>of</strong> this research founds on <strong>the</strong> annual statistical and financial
reports including <strong>the</strong> description <strong>of</strong> some qualitative characteristics <strong>of</strong> analyzed companies and industries: total
labor productivity, staff education level, customer loyalty, product range, R&D expenditures, participation in
business associations, co-operative innovation projects, localization and specialization coefficients, and o<strong>the</strong>rs.
This paper is devoted to <strong>the</strong> research problem identification and motivation and also presents some empirical
results.
Keywords: intellectual capital inputs and outcomes; economic value added
1. Introduction
The strategic role <strong>of</strong> <strong>the</strong> knowledge management for a company in <strong>the</strong> new economy is widely
discussed both on <strong>the</strong>oretical and practical levels (Ramelt, 1991; Stewart, 2001; Roos, 2006). The
intellectual capital identification and evaluation, as well as company’s performance measurement in
terms <strong>of</strong> value-added <strong>of</strong> <strong>the</strong> intellectual capital is one <strong>of</strong> <strong>the</strong> principal issues in <strong>the</strong> knowledge
management. .
Our study focuses on <strong>the</strong> methods <strong>of</strong> intellectual capital evaluation, in particular, on key value drivers’
identification and <strong>the</strong>ir relationship analysis. This approach allows finding out <strong>the</strong> most efficient way <strong>of</strong>
knowledge management, including making an investment decision. It should be emphasized that <strong>the</strong>
intellectual capital analysis in terms <strong>of</strong> investment evaluation is based on inputs and outcomes
identification and assessment. In o<strong>the</strong>r words, we need to study <strong>the</strong> mechanism <strong>of</strong> intellectual capital
transformation in a company’s performance.
Most <strong>of</strong> <strong>the</strong> relevant studies are based on resource- and value-based approaches that separately
analyze <strong>the</strong> intellectual capital from a certain point <strong>of</strong> view, limiting <strong>the</strong> number <strong>of</strong> problems at <strong>the</strong>
concurrence <strong>of</strong> <strong>the</strong>se concepts. Therefore, we’ve integrated two approaches to solve <strong>the</strong> problems <strong>of</strong>
intellectual capital evaluation that are relevant for <strong>the</strong> study <strong>of</strong> companies’ and industries’ behavior.
The idea <strong>of</strong> <strong>the</strong> intellectual capital research in <strong>the</strong> resource-based approach is associated with
917

Elena Shakina and Anna Bykova
P. Ramelt, who showed empirically <strong>the</strong> predominance <strong>of</strong> intra-over inter-sectoral differences in <strong>the</strong>
ratio <strong>of</strong> 7:1. This confirms <strong>the</strong> organizational factors dominance as a company’s competitive
advantage (Ramelt, 1991). B. Stewart in his research as part <strong>of</strong> a value-based approach draws
attention to <strong>the</strong> gap between <strong>the</strong> book and market value: while in 1978 <strong>the</strong> gap was about 5-10%, in
1998 <strong>the</strong> market value exceeded <strong>the</strong> book value by three times in average (Stewart, 2001). The
researches dealing with value-based concept refer this fact to <strong>the</strong> increasing role <strong>of</strong> <strong>the</strong> intellectual
capital in <strong>the</strong> new economy, calling it “<strong>the</strong> knowledge economy” (Stern, Stewart, 2001).
Several researches, analyzing <strong>the</strong> intellectual capital in terms <strong>of</strong> knowledge management
implementation, are trying to catch a connection between indirect characteristics <strong>of</strong> intellectual capital
and performance <strong>of</strong> a company. The most <strong>of</strong> empirical studies essentially assume that an indirect
assessment <strong>of</strong> intellectual capital could be provided by <strong>the</strong> financial statement analysis. Obviously,
<strong>the</strong> intangible characteristics <strong>of</strong> a company are very poorly expressed in financial terms. Therefore,
we need to use <strong>the</strong> information that could not be found in financial statements to assess <strong>the</strong>
intellectual capital inputs and knowledge management implementation. Despite <strong>the</strong> relevance <strong>of</strong> <strong>the</strong>
intellectual capital issues <strong>the</strong> existing studies show poor development and practical implementation <strong>of</strong>
measuring tools. This results from a number <strong>of</strong> limitations and shortcomings <strong>of</strong> <strong>the</strong> knowledge
management monitoring and assessment systems.
This paper is devoted to <strong>the</strong> problem identification and motivation, with some empirical results also
presented here. The purpose <strong>of</strong> this research is to develop cost-effective tools for identification <strong>of</strong>
knowledge management drivers. We suppose that <strong>the</strong> potential effectiveness <strong>of</strong> <strong>the</strong> intellectual capital
resources varies depending on a company size, industry and country.
2. Literature review
Analyzing <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> intellectual capital concept, we can conclude that, compared to a
common comprehension <strong>of</strong> this phenomenon in <strong>the</strong> classical economics, in <strong>the</strong> modern scientific and
applied studies <strong>the</strong> interpretation <strong>of</strong> <strong>the</strong> intellectual capital is yet diversified. That could be easily
explained by <strong>the</strong> multiple purposes <strong>of</strong> its study. Obviously, <strong>the</strong> intellectual capital phenomenon is
described by two categories: capital and intelligence (knowledge). The first <strong>of</strong> <strong>the</strong>se concepts reveals
<strong>the</strong> essence <strong>of</strong> <strong>the</strong> phenomenon, while <strong>the</strong> latter provides its basic definition.
Most <strong>of</strong> concepts are based exactly on <strong>the</strong> combination <strong>of</strong> <strong>the</strong> above mentioned attributes, such as
‘capital’ and ‘intelligence’. For instance: ‘Intellectual capital is <strong>the</strong> group <strong>of</strong> knowledge assets that are
attributed to an organization and most significantly contribute to an improved competitive position <strong>of</strong>
this organization by adding value to defined key stakeholders’(Marr, Schiuma, 2001). Analyzing this
definition, we can conclude that <strong>the</strong> intellectual capital is defined as a company’s resource that should
provide <strong>the</strong> additional value for stakeholders. That explains <strong>the</strong> simultaneous development <strong>of</strong> two
intellectual capital concepts: <strong>the</strong> resources-based and value-based approaches.
The ability to enhance effectiveness <strong>of</strong> o<strong>the</strong>rs resources including tangible assets is <strong>the</strong> key feature <strong>of</strong>
intellectual capital. Knowledge management provides <strong>the</strong> whole range <strong>of</strong> tools for <strong>the</strong> effective use <strong>of</strong>
intangibles. Despite specific features <strong>of</strong> intellectual resources <strong>the</strong>y should be considered as part <strong>of</strong> <strong>the</strong>
companies’ invested capital and characterized according to common approach to <strong>the</strong> capital
identification. Let us analyze <strong>the</strong> key attributes <strong>of</strong> <strong>the</strong> intellectual capital by associating it with tangible
assets (figure 1). According to <strong>the</strong> common approach, on <strong>the</strong> financial and economics basis <strong>the</strong>
invested capital is characterized by <strong>the</strong> following categories:
amount <strong>of</strong> capital employed;
return on capital employed;
cost <strong>of</strong> capital employed.
918

Return
on
Capital
Amount
<strong>of</strong> Capital
Capital
Elena Shakina and Anna Bykova
Cost
<strong>of</strong> Capital
Return
on IC
Quantity
and
Quality <strong>of</strong>
IC
Intellectual
Capital
Figure 1: Common characteristics <strong>of</strong> intellectual resources as part <strong>of</strong> company’s capital
Alternativ
e Cost <strong>of</strong>
IC
It should be noted that <strong>the</strong> intellectual capital is a heterogeneous resource. We need to split <strong>the</strong>
intellectual capital into components and analyze each <strong>of</strong> <strong>the</strong>m separately. A variety <strong>of</strong> options for
intangibles’ combinations are currently proposed and reasoned, including two- three-, four- and fivecomponent
structures (Edvinsson, Malone, 1997; Bontis, 1998, Stewart, 1997, Saint-Onge, 1996;
Sveiby, 1997; Van Buren, 1999; Roos, 1998; O'Donnell, O'Regan, 2000). We are following <strong>the</strong>
approach suggested by Roos and Stewart who identified three components <strong>of</strong> <strong>the</strong> intellectual capital:
human (HC), relational (RC) and structural resources (SC) – Fig.1. This division fits in <strong>the</strong> resourcebased
logic, as it separately describes key areas <strong>of</strong> a company management:
HC - human resource management;
RC - marketing (communication with customers, suppliers, partners and competitors);
SC - processes engineering, organizational culture, innovation and technology
RC
HC
Figure 2: Three-component structure <strong>of</strong> intellectual capital
All <strong>the</strong> intellectual capital components are strongly interconnected. Meanwhile, many studies
emphasize higher importance <strong>of</strong> <strong>the</strong> human capital, while <strong>the</strong> o<strong>the</strong>rs pay closer attention to <strong>the</strong>
structural capital. We suppose that significance <strong>of</strong> each component is associated with a variety <strong>of</strong>
factors, including those belonging to a particular industry and country.
Many researchers argue that <strong>the</strong> intellectual capital is becoming almost <strong>the</strong> only competitive
advantage <strong>of</strong> a company in <strong>the</strong> new economy. The economic pr<strong>of</strong>it or residual income concepts are
based on <strong>the</strong> fact that <strong>the</strong> competitive advantages <strong>of</strong> a particular company only provide additional
value creation. Therefore, <strong>the</strong> close connection <strong>of</strong> <strong>the</strong> modern value-based management concepts
and knowledge management becomes apparent.
Despite <strong>the</strong> obvious logical relation and <strong>the</strong>oretical reasonableness <strong>of</strong> <strong>the</strong> assumptions mentioned
above, testing <strong>of</strong> this hypo<strong>the</strong>sis brings out contradictory results in empirical studies. We suppose that
such results could be explained by shortcomings <strong>of</strong> <strong>the</strong> information field as well as unclear objective
setting and incorrect choice <strong>of</strong> research instruments. Our study is based on <strong>the</strong> critical analysis <strong>of</strong> <strong>the</strong>
relevant <strong>the</strong>oretical and empirical researches and seeks to take into account <strong>the</strong>ir experience for
drawing more precise conclusion.
919
SC

Elena Shakina and Anna Bykova
According to <strong>the</strong> applicable studies, <strong>the</strong> value created by a company, expressed in material form, now
depends largely on intangibles employed such as reputation and relationships with clients, staff
competence, etc.. In most researches, <strong>the</strong> intellectual capital is recognized as knowledge that can be
converted into value (Edvinsson, Malone, 1997; Zéghal, Maaloul, 2010).
Consideration <strong>of</strong> <strong>the</strong> value added as one <strong>of</strong> <strong>the</strong> key productivity and efficiency indicators <strong>of</strong> intellectual
capital has been an increasingly common subject <strong>of</strong> academic studies in recent years. Within <strong>the</strong>
value-based approach, <strong>the</strong> following tools are considered <strong>the</strong> proxy indicator <strong>of</strong> <strong>the</strong> intellectual capital:
economic value added (EVA © ), future growth value (FGV © ), Q-Tobin, real assets value enhancer
(RAVE © ) and value added <strong>of</strong> <strong>the</strong> intellectual capital (VAIC © ). Meanwhile, we are going to implement
<strong>the</strong> resource-based approach principles to obtain a comprehensive and complete description <strong>of</strong> all
intellectual capital components.
The intellectual capital, which allows companies to create <strong>the</strong> value added, is considered a long-term
growing point in <strong>the</strong> new economy (Riahi-Belkaoui, 2003; Youndt, 2004). Several empirical
researches were devoted to <strong>the</strong> intellectual capital and value added analysis, for instance, Kimura
who studied <strong>the</strong> Brazilian public companies (Kimura et al, 2010), Öztürk and Demirgüneş, (Öztürk,
Demirgüneş,, 2007), who examined companies listed on <strong>the</strong> ISE (Istanbul Stock Exchange), Díez who
analyzed <strong>the</strong> value creation by Spanish companies (Díez JM et al, 2010), as well as many o<strong>the</strong>rs. The
statistical analysis allowed <strong>the</strong>m to reveal a relationship between <strong>the</strong> intellectual capital and <strong>the</strong> value
added elements. However, it remains unclear which share <strong>of</strong> value is created by physical or
intellectual resources, respectively. Thus, with <strong>the</strong> plentiful works covering <strong>the</strong> issues mentioned
above, (Pulić, 2000; Stewart, 2002; Chen, Cheng, Hwang, 2005; Tan et al, 2007; Zéghal, Maaloul,
2010) this research problem remains relevant and interesting for fur<strong>the</strong>r studying.
The recent studies regard EVA as one <strong>of</strong> <strong>the</strong> key proxy indicators <strong>of</strong> intellectual capital (Lev, 1999,
Pohlman, 2000; Stern, 2001; Riahi-Belkaoui, 2003). They argue that <strong>the</strong> economic pr<strong>of</strong>it is <strong>the</strong> welfare
gains <strong>of</strong> <strong>the</strong> company through <strong>the</strong> effective use <strong>of</strong> resources. In <strong>the</strong> era <strong>of</strong> globalization and "<strong>the</strong> new
economy", <strong>the</strong> role <strong>of</strong> tangible assets recedes into <strong>the</strong> background since <strong>the</strong>y are unable to create <strong>the</strong>
company's competitive advantage. As a result <strong>of</strong> <strong>the</strong> growing importance <strong>of</strong> investments in intangible
assets in terms <strong>of</strong> value creation, <strong>the</strong> intellectual capital accumulation increases every year,
exceeding <strong>the</strong> amount <strong>of</strong> investments in <strong>the</strong> physical and financial capital in some countries. (Zéghal,
Maaloul, 2010). Many researchers associate <strong>the</strong>se changes <strong>of</strong> <strong>the</strong> investment strategy with <strong>the</strong>
transition to <strong>the</strong> knowledge economy (Stewart, 1997; Sveiby, 1997; Edvinsson, 1997; Lynn, 1998;
Zéghal, 2000).
Despite its sufficient <strong>the</strong>oretical validity, some researchers strongly criticize this approach to <strong>the</strong>
intellectual capital evaluation; this criticism is generally based on <strong>the</strong> empirical results. For instance, in
his research Fernandez has shown that EVA © indicator is unable to measure even if <strong>the</strong> creation or
destruction <strong>of</strong> value is expressed in <strong>the</strong> market capitalization (Fernandez, 2001). Meanwhile,
according to some researches, an impact <strong>of</strong> <strong>the</strong> intellectual capital and its components on <strong>the</strong>
company’s value explains <strong>the</strong> spread between market capitalization and book value (Steward, 1999;
Lev, 1999). The index, calculated as <strong>the</strong> ratio <strong>of</strong> market value <strong>of</strong> assets to <strong>the</strong>ir book value, is called
q-Tobin. The idea <strong>of</strong> this approach is, as follows: <strong>the</strong> higher this indicator is, <strong>the</strong> higher <strong>the</strong> spread
between invested capital and potential return is and <strong>the</strong> greater <strong>the</strong> intellectual capital employed is. If
we assume that <strong>the</strong> market capitalization reflects companies’ performance and especially intangible
outcomes, EVA © could not be used as an intellectual capital indicator. Fur<strong>the</strong>rmore, a number <strong>of</strong>
sound conclusions <strong>of</strong> existing relevant empirical studies regarding <strong>the</strong> low predicative power <strong>of</strong> almost
all value-based models were applied during <strong>the</strong> intellectual capital analysis. (Bontis, Dragonetti,
Jacobsen, Roos, 1999).
Value <strong>of</strong> future growth (FGV © ) is ano<strong>the</strong>r proxy indicator closely connected with economic pr<strong>of</strong>it.
FGV © assesses <strong>the</strong> share <strong>of</strong> market value attributed to <strong>the</strong> EVA © growth. Following J. Stern and B.
Stewart, FGV © can be driven by market expectations <strong>of</strong> productivity improvements, organic growth,
and value-creating acquisitions. Companies can tailor <strong>the</strong>ir incentive plan to performance targets tied
to <strong>the</strong> annual EVA © growth implied by FGV © . Fur<strong>the</strong>rmore, <strong>the</strong> FGV © component can be a useful tool
in benchmarking against <strong>the</strong> "growth plan" <strong>of</strong> competitors and evaluating investors' assessment <strong>of</strong> <strong>the</strong>
wealth creation potential <strong>of</strong> new strategies and opportunities (Stern, Stewart, 2010). Several studies
show that <strong>the</strong> share <strong>of</strong> <strong>the</strong> future growth value in <strong>the</strong> value <strong>of</strong> a number <strong>of</strong> companies increases
yearly, while in some industries it is characterized by innovative products implementation (Burgman,
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Elena Shakina and Anna Bykova
Roos, 2005). This approach suggests that <strong>the</strong> innovative behavior and investment policy focused on
<strong>the</strong> intellectual capital accumulation possess higher potential <strong>of</strong> <strong>the</strong> future growth. Obviously, FGV ©
has similar shortcomings and limitations like EVA © . However, this indicator provides clear
interpretation <strong>of</strong> <strong>the</strong> future opportunity <strong>of</strong> a particular company to increase its current value through
intellectual capital employment. Therefore, a share <strong>of</strong> FGV © in <strong>the</strong> market value could be considered
<strong>the</strong> intellectual capital outcome in terms <strong>of</strong> value creation.
3. Research design
As we have mentioned above we are trying herein to syn<strong>the</strong>size value- and resource-based
approaches in <strong>the</strong> intellectual capital study. However, we primarily focus on <strong>the</strong> value-based approach
goal setting. It means that <strong>the</strong> idea and main assumption <strong>of</strong> this research is closely connected with
relevant VBM models, in particular, with economic EVA © , FGV © , and Q-Tobin. These indicators are
considered <strong>the</strong> proxy indicators <strong>of</strong> intellectual capital outcomes in our research and present explained
variables. Meanwhile, we are going to implement <strong>the</strong> resource-based approach principles to obtain a
comprehensive and complete description <strong>of</strong> all intellectual capital components (intellectual capital
inputs). Moreover, we need to identify factors that support or impede <strong>the</strong> intellectual capital
transformation in <strong>the</strong> companies’ performance (Figure 3).
Any link between performance outcomes and intellectual capital components is unlikely to be simple.
Following this fact, four prominent hypo<strong>the</strong>ses were tested during <strong>the</strong> research:
Hypo<strong>the</strong>sis 1: Economic value added, future growth value and Q-Tobin are proxy
indicators <strong>of</strong> intellectual capital outcomes.
Hypo<strong>the</strong>sis 2: Intellectual capital inputs can be described by proxy indicators, based on
public available information about a company from its annual financial and statistical
reports.
Hypo<strong>the</strong>sis 3: There are internal (IC components configuration <strong>of</strong> <strong>the</strong> particular company
and age) and external (country, industry, location) factors that influence transformation <strong>of</strong>
intellectual capital in companies’ performance.
Hypo<strong>the</strong>sis 4: There is a complementarity between intellectual capital components that
impacts company performance.
With regard to <strong>the</strong>se assumptions and literature background we use <strong>the</strong> following research
framework: Given that all components <strong>of</strong> intellectual capital are interlinked (Figure 2) we need to
analyze some attributes <strong>of</strong> intangibles separately. A description <strong>of</strong> <strong>the</strong> intellectual capital attributes as
well as examples <strong>of</strong> some indicators are presented hereinafter (Figure 4). According to <strong>the</strong> abovesuggested
approach (Figure 1), five characteristics <strong>of</strong> intangibles should be identified. We suppose
that <strong>the</strong> all-around analysis would reveal important proxy characteristics to provide us with adequate
estimations <strong>of</strong> intellectual capital investments and knowledge management effectiveness. These
indicators present explanatory (dependent) variables in our study.
Figure 3: Framework <strong>of</strong> intellectual capital analysis (resource- and value-based approaches’
combination)
921

Elena Shakina and Anna Bykova
Figure 4: Description <strong>of</strong> <strong>the</strong> intellectual capital attributes
This study examines <strong>the</strong> local economic impact <strong>of</strong> intellectual capital components on Russian and
<strong>European</strong> companies’ performance. To assess <strong>the</strong> economic impact we use a number <strong>of</strong> different
dependent variables measured at <strong>the</strong> level <strong>of</strong> an individual company. Figure 5 provides a brief
description <strong>of</strong> variables used in our study, which were selected based on earlier studies and
<strong>the</strong>oretical models. In order to capture possible relationships and significant internal and external
factors, we also consider natural logarithms <strong>of</strong> <strong>the</strong> measures.
Figure 5: Variable list
Notes:
* Criteria:
If more than one third <strong>of</strong> directors have postgraduate level <strong>of</strong> qualification and more than 5 years
experience – 2 points.
922

Elena Shakina and Anna Bykova
If more than one third <strong>of</strong> directors have postgraduate level <strong>of</strong> qualification or more than 5 years
experience – 1 point.
Ano<strong>the</strong>r – 0.
** Criteria:
Availability <strong>of</strong> information for investors.
Multi-lingual information.
Amount <strong>of</strong> information.
Design.
Each categorical variable is transformed into a dummy variable for <strong>the</strong> linear regression analysis.
Before <strong>the</strong> empirical study results in <strong>the</strong> next section we will present <strong>the</strong> employed data.
4. Data and methodology
We have studied companies from Russia and a number <strong>of</strong> <strong>European</strong> countries including Serbia,
Great Britain, Ukraine, Turkey, Finland, Denmark, and Spain, based on a country position in
Knowledge Economy Index 2008 [http://data.worldbank.org/data-catalog/KEI]. Also, we have only
analyzed <strong>the</strong> companies from industries with <strong>the</strong> predominance <strong>of</strong> varied intellectual capital
components and <strong>the</strong>refore different intellectual capital configuration. That way, we have selected <strong>the</strong>
following industries: financial services, wholesale and retail trade, machinery and equipment
manufacture, chemical, and transport and communications. We have chosen <strong>the</strong>se particular
industries since <strong>the</strong>y represent a wide range <strong>of</strong> knowledge-intensive manufacturing and service
sectors.
The datasets in this study derive from combination <strong>of</strong> several detailed longitudinal databases FIRA
PRO and SPARK-INTERFAX for Russia and Bureau Van Dijk (Amadeus and Ruslana) for Europe
based on <strong>the</strong> companies’ annual statistical and financial reports. Owing to <strong>the</strong> intellectual capital
nature and in line with our objectives we have used multiple qualitative data from web-sites,
magazines, citation bases, data from patent bureaus, etc.
We have applied <strong>the</strong> following criteria when deciding on <strong>the</strong> inclusion <strong>of</strong> companies into <strong>the</strong> sample:
Number <strong>of</strong> employees should be no less than 500 and no more than 20,000 people.
A company should refer to <strong>the</strong> public company.
Accordingly, <strong>the</strong> Russian and <strong>European</strong> databases included information on 420 and 332 companies
over 2005-2009 years, respectively. The dataset compiled by <strong>the</strong> authors includes <strong>the</strong> following
information:
Common indicators – form and structure <strong>of</strong> ownership, company age, industry and enterprise
code, location, patents and licenses.
Economic indicators – costs, export, R&D expenditures, capital investments, and working capital
Financial indicators – operating pr<strong>of</strong>it, company pr<strong>of</strong>itability, economic value added, future gross
value and Q-Tobin coefficient, etc.
Specific intellectual capital indicators – VAIC, brands, quality <strong>of</strong> web-site, and o<strong>the</strong>rs.
Table 1 helps us to characterize type <strong>of</strong> <strong>the</strong> company that was used in our research. It presents
several descriptive statistics <strong>of</strong> <strong>the</strong> sample, where <strong>the</strong> mean and <strong>the</strong> standard deviation <strong>of</strong> <strong>the</strong>
variables are detailed:
923

Table 1: The sample descriptive statistics
Elena Shakina and Anna Bykova
As seen in Table 1, we can detect R&D investments only for 217 out <strong>of</strong> 1635 adjectives for <strong>the</strong>
<strong>European</strong> database. O<strong>the</strong>rs adjectives in <strong>the</strong> databases are classified as “system-missing”. Despite
<strong>the</strong> importance <strong>of</strong> <strong>the</strong>se indicators, we’ve decided to exclude <strong>the</strong>m from our research so as not to
reduce <strong>the</strong> sample.
Let us now turn to EVA and intellectual capital indicators for our sample. According to <strong>the</strong> established
approach to <strong>the</strong> competitiveness <strong>the</strong>ory and intellectual capital concept, <strong>the</strong> higher <strong>the</strong> intellectual
capital efficiency degree is, <strong>the</strong> more competitive and successful a company is, as measured by EVA,
FGV and o<strong>the</strong>r measures.
5. Empirical results
As mentioned earlier, <strong>the</strong> interest in <strong>the</strong> intellectual capital study results from <strong>the</strong>ir assumed ability to
enhance <strong>the</strong> value creation. Never<strong>the</strong>less, <strong>the</strong> empirical research <strong>of</strong>fers contradictory results that, on
occasions, call into question <strong>the</strong> statements made in <strong>the</strong> papers studying this link.
It should be made clear that we do not combine <strong>the</strong> Russian and <strong>European</strong> samples due to <strong>the</strong>
distinctions between countries and companies, respectively. Therefore, we have constructed separate
equations and provided different outcomes. Never<strong>the</strong>less, we try to use <strong>the</strong> standardized variables
wherever possible.
Our core econometric specification is, as follows:
Perf = α + (β1, ..., βn) HC + (δ1, ..., δm) SC + (δ1, ..., δk) RC + (λ1, ...,λt) Dummy + ε,
where
Perf is an indicator <strong>of</strong> <strong>the</strong> performance <strong>of</strong> companies (EVA; Q-Tobin; FGV as independent
variables);
HC is a vector <strong>of</strong> variables responsible for human capital component;
SC is a vector <strong>of</strong> variables responsible for structural capital component;
RC is a vector <strong>of</strong> variables responsible for relational capital component;
Dummy is a vector <strong>of</strong> dummy variables introduced in <strong>the</strong> analysis.
OLS method is used for regression equation coefficient estimation. There is no statistically significant
spatial correlation existing between <strong>the</strong> independent variables.
924

Elena Shakina and Anna Bykova
This model is developed in accordance with <strong>the</strong> concept <strong>of</strong> financial architecture based on
assumptions regarding <strong>the</strong> exogenous variables <strong>of</strong> <strong>the</strong> structure ownership and <strong>the</strong> capital structure.
In this case, <strong>the</strong> measurement <strong>of</strong> <strong>the</strong> companies’ performance was conducted in <strong>the</strong> context <strong>of</strong> three
indicators, which allowed reducing <strong>the</strong> human factor in deciding on an indicator, and also enabled
comparing <strong>the</strong> results. For each case, we have constructed three models: for quantitative and
qualitative factors separately, as well as for <strong>the</strong>ir combination for checking <strong>the</strong> robustness <strong>of</strong> our
results. Though we’ve tested different specifications <strong>of</strong> our general model for finding out <strong>the</strong> most
valuable <strong>of</strong> <strong>the</strong>m in terms <strong>of</strong> robustness and effectiveness <strong>of</strong> estimates, in this paper we show <strong>the</strong>
most significant <strong>of</strong> <strong>the</strong>m only. In case <strong>of</strong> <strong>the</strong> Hypo<strong>the</strong>sis 1 – 2 confirmations, we expect <strong>the</strong> statistical
significance <strong>of</strong> models, in general. For <strong>the</strong> Hypo<strong>the</strong>sis 3 confirmation, <strong>the</strong> variables reflecting <strong>the</strong>
intellectual capital components need to be statistically significant. The results <strong>of</strong> <strong>the</strong> regression
analyses for Russian companies are given in Table 2.
Table 2: Regression results for Russian companies
Notes: * Significant at p

Elena Shakina and Anna Bykova
Table 3: Regression results for <strong>European</strong> companies: qualitative factors 1
Notes: 1 – The model where Q-Tobin coefficient was seen as dependent variable is statistical
insignificant.
* Significant at p

Elena Shakina and Anna Bykova
Table 4 provides key results and evidence for two findings <strong>of</strong> this model. First, according to <strong>the</strong>
Hypo<strong>the</strong>sis 3 confirmation, <strong>the</strong>re are three intellectual component inputs’ indicators that can be used
in determination <strong>of</strong> <strong>the</strong> company’s intellectual capital configuration:'
Earnings per employee (human capital).
Intangible assets (structural capital).
Well-known brand (relational capital).
Second, both industry-(manufacture) and country- (Germany) specific features are associated with
higher intellectual capital outcomes.
Testing <strong>of</strong> Hypo<strong>the</strong>sis 4 both for Russian and <strong>European</strong> companies is <strong>the</strong> final stage <strong>of</strong> our research.
We assume that <strong>the</strong> IC components are interconnected not additively but multiplicatively. As a result
<strong>of</strong> testing <strong>of</strong> this assumption we have constructed <strong>the</strong> following model:
Perf = α *β1HC* δ1SC * λ1RC * ε
or (after logarithmic procedure) lnPerf = α + β1lnHC + δ1lnSC + λ1lnRC + ε
where
Perf is an indicator <strong>of</strong> <strong>the</strong> performance <strong>of</strong> companies (EVA; Q- Tobin; FGV as independent
variables);
HC is a variable responsible for human capital component;
SC is a variable responsible for structural capital component;
RC is a variable responsible for relational capital component.
We have used intellectual component inputs obtained in <strong>the</strong> previous model as independent variables
for human and structural capital except for <strong>the</strong> relation component. We had to replace <strong>the</strong> well-known
brand variable with commercial expenses share due to its nominal scale. The results are shown in
Table 5:
Table 5: Complementarity for <strong>the</strong> IC components for Russian and <strong>European</strong> companies
Notes: * Significant at p

Elena Shakina and Anna Bykova
It should be noticed that all our models were tested for autocorrelation and heteroskedasticity via
Durbin-Watson test and residual’s scatter-plot. As a result we can conclude that our estimates can be
considered as <strong>the</strong> effective and unbiased.
6. Conclusion
We can draw a number <strong>of</strong> conclusions based on <strong>the</strong>oretical and empirical parts <strong>of</strong> our research.
The high explanatory power <strong>of</strong> EVA and FGV indicators as indicators <strong>of</strong> <strong>the</strong> intellectual capital
outcomes was confirmed. Meanwhile, <strong>the</strong> widespread Q-Tobin indicator seems to be not so
adequate in explaining <strong>the</strong> transformation <strong>of</strong> <strong>the</strong> intellectual capital inputs in <strong>the</strong> company's value
even in developed markets.
A validity <strong>of</strong> intellectual capital proxy indicators use was proved. Specifically, we could obtain <strong>the</strong>
information on some company’s internal factors <strong>of</strong> knowledge management using publicly
available data. Many <strong>of</strong> <strong>the</strong> selected indicators showed high significance in <strong>the</strong> specified models
and are obviously interpreted in terms <strong>of</strong> <strong>the</strong>ory and practice <strong>of</strong> knowledge management.
Some significant internal and external factors <strong>of</strong> <strong>the</strong> intellectual capital transformation were
identified. For instance: company age, country (especially, Russia and <strong>European</strong> countries), and
industry. Significant differences between developed and developing markets were found out. The
relational and human capital showed higher significance in developed countries, while in Russia
<strong>the</strong> structural characteristics present a growing point in most <strong>of</strong> corporations.
However, <strong>the</strong> impact <strong>of</strong> some indicators on <strong>the</strong> company’s performance is not so observable. For
example, a negative correlation between R&D expenses and value added has been discovered in
Russia. This phenomenon could be explained by higher risk <strong>of</strong> this investment and lower
protection <strong>of</strong> intellectual property in Russia.
Higher complementarity <strong>of</strong> <strong>the</strong> intellectual capital components should be noticed. Moreover, <strong>the</strong>
combinations <strong>of</strong> interconnected elements are different for Russia and Europe. That could be also
explained by different level <strong>of</strong> <strong>the</strong>se components’ significance in <strong>the</strong> analyzed markets.
We can conclude that our results require fur<strong>the</strong>r thorough analysis. For example, we need to
assume a possible regressors’ endogeneity, as well as consider <strong>the</strong> lagged nature <strong>of</strong> some
intellectual capital inputs and outcomes. However, we have obtained certain preliminary results
that could be used for knowledge management design and implementation.
Therefore <strong>the</strong> goal <strong>of</strong> this research step was reached. We had confirmed that intellectual capital could
be analyzed on companies and industries levels. We realize that <strong>the</strong> statistical tools used in this paper
are not sophisticated enough. We need to use advanced methods like panel data analysis as well as
instrumental variables to solve endogeneity problem. Despite a number <strong>of</strong> <strong>the</strong> shortcomings <strong>of</strong> this
research we have revealed some relevant and interesting issues that expected to be studied
hereafter.
References
Bontis, N. (2000) “Assessing Knowledge Assets: A review <strong>of</strong> <strong>the</strong> models used to measure intellectual capital”,
Knowledge-Based Enterprises, Kingston, Canada: Queen’s Management Research Centre for KBE,
republished from International Journal <strong>of</strong> Management Reviews, pp. 1-24.
Bontis, N., Dragonetti, N. C., Jacobsen, K. and Roos G. (1999) “The Knowledge Toolbox: A Review <strong>of</strong> <strong>the</strong> Tools
Available to Measure and Manage Intangible Resources”. <strong>European</strong> Management Journal, August.
Chan K.H. (2009) “Impact <strong>of</strong> intellectual capital on organizational performance”, The Learning Organization.
Vol.16, No.1, pp.4-21.
Chen, M., Cheng, S. and Hwang, Y. (2005) “An Empirical Investigation <strong>of</strong> <strong>the</strong> Relationship Between Intellectual
Capital and Firms' Market Value and Financial Performance”, Journal <strong>of</strong> Intellectual Capital. Vol. 6, No. 2,
pp. 159-176.
Cooper, S. (2000) “Shareholder Wealth Maximisation : A Stakeholder Perspective”, G. Arnold & M. Davies (eds),
Value Based Management, London: Wileys, pp. 81-104.
Díez, J. M., Ochoa, M. L., Prieto M.B., Santidrián A. (2010) “Intellectual capital and value creation in Spanish
firms”, Journal <strong>of</strong> Intellectual Capital. Vol. 11, No. 3, pp. 348-367.
Edvinsson, L., Malone, M. (1997) Intellectual Capital: Realising Your Company’s True Value by Finding its
Hidden Brainpower, Harper Collins, New York, NY.
Firer, S. and Williams, S.M. (2003) “Intellectual Capital and Traditional Measures <strong>of</strong> Corporate Performance”,
Journal <strong>of</strong> Intellectual Capital. Vol. 4, No. 3, pp.348-360.
928

Elena Shakina and Anna Bykova
Huang C. Wang M.-C. (2008) “The Effects <strong>of</strong> Economic Value Added and Intellectual Capital on <strong>the</strong> Market Value
<strong>of</strong> Firms: An Empirical Study”, International Journal <strong>of</strong> Management. Vol. 25, No. 4, pp. 722 – 731.
Kimura H., Cruz Basso L.F., Nogueira S.G., Lucas de Barros Junior (2010) “The impact <strong>of</strong> intellectual capital on
value added <strong>of</strong> Brazilian companies traded at <strong>the</strong> BMF-Bovespa”, Journal <strong>of</strong> International Finance &
Economics.
Lev B. (1999) “R&D and Capital Markets”, Journal <strong>of</strong> Applied Corporate Finance, pp. 21-35.
Lynn B. (2000) “Intellectual Capital Key to Value Added Success”, Chartered Accountants Journal. Vol. 79 Issue
3, pp. 24-32.
Meek G. K., Sidney J. G. (1998) “The Value added statement: An innovation for <strong>the</strong> U.S. companies”, Accounting
Horizons, pp. 73-81.
Öztürk M. B.; Demirgüneş K. (2007) “Determination <strong>of</strong> Effect <strong>of</strong> Intellectual Capital on Firm Value via Value
Added Intellectual Coefficient Methodology: An Empirical Study on ISE-Listed Manufacturing Firms”, ISE
Review. Vol.10, is. 37, pp. 59-77.
Pulić A.(2000) “VAIC – an accounting tool for IC management”, International Journal <strong>of</strong> Technology
Management. Vol.20, No.5-8, pp.702-714.
Riahi-Belkaoui A. (2003) “Intellectual capital and firm performance <strong>of</strong> US multinational firms”, Journal <strong>of</strong>
Intellectual capital. Vol.4, No. 2.
Stewart T.A. (1997) Intellectual Capital: The new Wealh <strong>of</strong> organization, Nicholas Brealey, London.
Stewart T.A. (2002) “The case against knowledge management”, Business 2.0. Vol.3, No.2.
Strack R., Villis U. (2002) “RAVE: Integrated Value Management for Customer, Human, Supplier and Invested
Capital”, <strong>European</strong> Management Journal. Vol.20, No.2, pp. 147-158.
Sveiby, K. (1997) The New Organisational Wealth: Managing and Measuring Knowledge Based Assets, Berrett-
Koehler, San Francisco, CA.
Tan H.P., Plowman D., Hancock P. (2007) Intellectual capital and financial returns <strong>of</strong> companies”, Journal <strong>of</strong>
Intellectual capital. Vol.8, No.2.
Young, S. David, O'Byrne, Stephen F. (2000) EVA and Value-Based Management: A Practical Guide to
Implementation, McGraw-Hill Pr<strong>of</strong>essional Book Group, p. 511.
Youndt M., Subramaniam M., Snell S. (2004) “Intellectual capital pr<strong>of</strong>iles: An examination <strong>of</strong> investments and
returns”, Journal <strong>of</strong> Management Studies, Vol. 41, No.2, pp. 335-362.
Zéghal D., Maaloul A. (2010), “Analyzing value added as an indicator <strong>of</strong> intellectual capital and its consequences
on company performance”, Journal <strong>of</strong> Intellectual capital, Vol.11, No.1.
Acknowledgement: This study comprises research findings from <strong>the</strong> ‘Intellectual Capital Evaluation”
Project carried out within The Higher School <strong>of</strong> Economics’ 2011 Academic Fund Program.
929

The Major Challenges <strong>of</strong> Electronic Communities <strong>of</strong>
Practice in an Iranian Leading Virtual University: A
Qualitative Approach
Mehdi Shami Zanjani 1 , Hamid Rahimian 2 and Farnoosh Alami 2
1
Department <strong>of</strong> IT Management, Faculty <strong>of</strong> Management, University <strong>of</strong> Tehran,
Tehran, Iran
2
Department <strong>of</strong> Educational Administration, Faculty <strong>of</strong> Psychology and
Education, University <strong>of</strong> Allameh Tabataba’i, Tehran, Iran
mshami@ut.ac.ir
h.rahimian@atu.ac.ir
f.alami@st.atu.ac.ir
Abstract: This study aims to examine <strong>the</strong> major challenges <strong>of</strong> communities <strong>of</strong> practice (COPs) in an Iranian
leading virtual university. To achieve <strong>the</strong> main research goal; two-phase research strategy is employed in this
paper. At first, knowledge derived from an analysis <strong>of</strong> <strong>the</strong> COP literature is used in order to design <strong>the</strong> interview
guide which is needed in <strong>the</strong> next phase. The second phase consists <strong>of</strong> a qualitative case study <strong>of</strong> major
Challenges <strong>of</strong> e-COPs in “Mehralborz” virtual university. The university has almost 600 postgraduate students in
<strong>the</strong> field <strong>of</strong> management. Seven semi-structured interviews with <strong>the</strong> e-COPs leaders have been conducted. The
investigation led to <strong>the</strong> discovery <strong>of</strong> six major challenges that e-COPs struggling with <strong>the</strong>m. The <strong>the</strong>oreticalbased
case study provides practitioners with a complex perspective <strong>of</strong> <strong>the</strong> challenges <strong>of</strong> e-COPs in educational
environments and adds originality to <strong>the</strong> paper.
Keywords: Knowledge Management, Community <strong>of</strong> Practice, Virtual, University, Challenge, Iran
1. Introduction
The concept <strong>of</strong> community <strong>of</strong> practice, which was introduced by Lave and Wenger in 1990, has
received much attention in <strong>the</strong> area <strong>of</strong> knowledge management (KM). In particular, <strong>the</strong> concept is
deemed useful by both practitioners and researchers to account for <strong>the</strong> learning processes taking
place within an organization (cited in Dupou and Yıldızoglu, 2006).
A community <strong>of</strong> practice is best described as a group <strong>of</strong> people who interact, learn toge<strong>the</strong>r, build
relationships, and also develop a sense <strong>of</strong> shared commitment and belonging (Schrum et al., 2007).
More precisely, a community <strong>of</strong> practice is a group <strong>of</strong> individuals who shares <strong>the</strong>ir interests and
problems with a specific topic, and gains a greater degree <strong>of</strong> knowledge and expertise on a topic
through <strong>the</strong>ir regular interaction (Probst and Borzillo, 2008). COP is defined more by <strong>the</strong> knowledge
than by <strong>the</strong> task, and exists because participation is valuable to its members (Oberty and Saa´-
Pe´rez, 2006).
The main objective <strong>of</strong> establishing a community <strong>of</strong> practice is to share essential knowledge and
upgrade <strong>the</strong> knowledge levels <strong>of</strong> group members. A COP represents a process <strong>of</strong> social learning
whereby learners with a common interest in a subject collaboratively share ideas, find solutions, and
build innovations (Chang et al., 2008).
The literature tends to focus on <strong>the</strong> common features <strong>of</strong> COPs ra<strong>the</strong>r than differences between
different communities. There are many factors that could be used to distinguish between communities
<strong>of</strong> practice. For example, COPs differ in terms <strong>of</strong> size, permanence, geographical dispersion and <strong>the</strong>
degree <strong>of</strong> support received from beyond <strong>the</strong> community (Klein et al., 2005).
An increasing number <strong>of</strong> studies have debated whe<strong>the</strong>r organizations can play an active role in
constructing and supporting COPs. Initially, COPs were presented as spontaneous, self-organizing,
and fluid processes that management cannot intentionally establish. Later works, however, suggest
that COPs are amenable to manipulation and thus must receive institutional support for strategic
advantage. These studies have caused a growing tension in <strong>the</strong> literature regarding COP’s
manageability (Probst and Borzillo, 2008).
Regarding to <strong>the</strong> inherent capabilities <strong>of</strong> COPs in handling knowledge, organizations are applying this
concept to <strong>the</strong>ir knowledge management practices, attempting to cultivate such knowledge-embedded
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Mehdi Shami Zanjani, Hamid Rahimian and Farnoosh Alami
communities (Zhang and Watts, 2008). In numerous firms across <strong>the</strong> globe, managers are expending
considerable resources to support or even formalize and construct <strong>the</strong>se informal organizational forms
in <strong>the</strong> hope <strong>of</strong> improving <strong>the</strong> firm’s competitive advantage based on knowledge (Schenkel and
Teigland, 2008).
More recent studies suggest that while organizations need to foster and participate in COPs to
leverage <strong>the</strong>ir full potential, <strong>the</strong>y cannot fully own or control <strong>the</strong>m (Probst and Borzillo, 2008).
The early research on COPs was focusing on face-to-face and collocated communities. However, <strong>the</strong>
proliferation <strong>of</strong> online collaboration tools, coupled with rapid global expansion <strong>of</strong> business
organizations, resulted in <strong>the</strong> emergence <strong>of</strong> a new form <strong>of</strong> collective learning and knowledge sharing:
virtual communities <strong>of</strong> practice (Ardichvili, 2008). Using a variety <strong>of</strong> tools in COPs, including
electronic mailing, bulletin boards, blogs, wikis, multi-user dungeons (MUDs) and o<strong>the</strong>r collaborative
technologies are very common today (Akoumianakis, 2009).
Chiu, Hsu, and Wang (2006) defined virtual communities as “online social networks in which people
with common interests, goals, or practices interact to share information and knowledge, and engage
in social interactions.”
There are various types <strong>of</strong> virtual communities (Cheung & Lee, 2009). They are developed to serve
different purposes, such as mental support, information/knowledge exchange, entertainment,
collaboration and production. A lot <strong>of</strong> attention has been paid to <strong>the</strong> emergence <strong>of</strong> <strong>the</strong>se communities
and <strong>the</strong>ir applicability to learning and information exchange. Many researchers have focused on <strong>the</strong>
use <strong>of</strong> virtual communities in student learning. Preece (2001) found that many initially active virtual
communities have failed to retain <strong>the</strong>ir members and have become ‘cyber ghost towns’. It is thus
important to understand what motivates members to continue using <strong>the</strong> virtual community (Cheung &
Lee, 2009).
Despite <strong>the</strong> pr<strong>of</strong>usion <strong>of</strong> research about COPs, fewer studies tried to systematically analyze <strong>the</strong>
challenges <strong>of</strong> COPs, especially virtual COPs. Ardichvili (2008) argues that, currently, factors leading
to successful virtual COPs are not well understood.
Kerno and Mace (2010) believe that challenges confronting COPs are from <strong>the</strong> perspectives <strong>of</strong> time
demands and constraints, organizational hierarchies, and regional culture (socio-cultural
environment).
As highlighted by Loyarte and Rivera (2007), to integrate <strong>the</strong> COPs in organizations, it is important to
understand <strong>the</strong> four challenges for <strong>the</strong> cultivation <strong>of</strong> COPs which are: management challenge,
community challenge, technical challenge and personal challenge. (Cited in Loyarte and Rivera,
2007).
Huang et al. (2007) argued that it is important to identify factors hindering <strong>the</strong> implementation <strong>of</strong> <strong>the</strong>
community <strong>of</strong> practice to increase <strong>the</strong> possibility <strong>of</strong> successful knowledge sharing. They proposed a
ma<strong>the</strong>matical model to vividly demonstrate <strong>the</strong> process <strong>of</strong> knowledge diffusion activities in <strong>the</strong>
community <strong>of</strong> practice by considering various factors such as distance, knowledge gap, learning
ability and willingness to share.
Kimble and Hildreth (2005) mentioned that all members <strong>of</strong> COPs must have <strong>the</strong> desire, motivation
and will to work toge<strong>the</strong>r.
Ardichvili (2008) present a framework that includes key motivational factors, such as utilitarian
considerations, value-based considerations, and sense <strong>of</strong> community and belonging; barriers to
online knowledge sharing, including interpersonal factors, procedural and/or use <strong>of</strong> technology-related
factors, and cultural norms; and enablers <strong>of</strong> virtual COPs knowledge sharing, including supportive
organizational culture, presence <strong>of</strong> personal knowledge-based trust, and availability <strong>of</strong> adequate tools.
Probst and Borzillo (2008) conducted an investigation based on a questionnaire survey <strong>of</strong> 57 COP
leaders from major <strong>European</strong> and US companies. The results revealed ten governance mechanisms
linked to strategic objectives, an active collaboration between a ‘‘sponsor’’ from top management and
a COP ‘‘leader”, networking routines, a risk-free environment, and <strong>the</strong> measurement <strong>of</strong> a COP’s
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Mehdi Shami Zanjani, Hamid Rahimian and Farnoosh Alami
success. The results also revealed a number <strong>of</strong> reasons for failure: absence <strong>of</strong> a core group, weak
one-to-one connections between members, rigidity <strong>of</strong> competences, lack <strong>of</strong> identification with <strong>the</strong>
network, and practice intangibility.
Some literatures attributed <strong>the</strong> success <strong>of</strong> COPs in handling knowledge to <strong>the</strong> face-to-face
communication and <strong>the</strong> close connections fostered through <strong>the</strong> personal interactions (Zhang and
Watts, 2008).
The serial researches <strong>of</strong> Allen showed an inverse relationship between <strong>the</strong> distance among people
and <strong>the</strong> likelihood <strong>of</strong> knowledge flow. The proportion <strong>of</strong> interaction decreases with increasing distance
(Huang et al., 2007).
Virtual Communities <strong>of</strong> practice have attracted much attention from scholars and practitioners
interested in <strong>the</strong> area <strong>of</strong> knowledge management. Although e-COP has been discussed in various
circles, fewer studies tried to analyze <strong>the</strong> challenges systematically. Also, <strong>the</strong> literature has neglected
so far <strong>the</strong> challenges <strong>of</strong> e-COPs in university environments.
In order to bridge this gap in <strong>the</strong> literature, this paper aims to examine challenges <strong>of</strong> e-COPs in an
Iranian leading virtual university.
2. Research methodology
Since <strong>the</strong> <strong>the</strong>ory about knowledge management still represents an emergent and sometimes
confusing field, <strong>the</strong> case study method seems to be <strong>the</strong> most suitable research strategy in this area.
Due to its special characteristics, <strong>the</strong> case study method allows us to take a closer look at <strong>the</strong> unit
analyzed.
The case selection was based on a non-random sampling in which a case that could potentially
provide better learning opportunities was chosen. As a result, <strong>the</strong> most leading virtual university in
Iran was selected.
Mehralborz virtual university is a private and non-governmental institution began its operations in
2002 with <strong>the</strong> aim <strong>of</strong> training a pr<strong>of</strong>essional workforce in <strong>the</strong> fields <strong>of</strong> ICT management and by taking
into consideration <strong>the</strong> pr<strong>of</strong>essional needs <strong>of</strong> knowledge based economy. The mission <strong>of</strong> <strong>the</strong> university
is to <strong>of</strong>fer high quality, low cost educational services to all Farsi (Persian) speakers worldwide. The
university now has almost 600 postgraduate students in <strong>the</strong> field <strong>of</strong> management.
All courses <strong>of</strong> <strong>the</strong> university have its own e-COP in <strong>the</strong> educational portal with active participation <strong>of</strong>
students and teacher. Every teacher <strong>of</strong> <strong>the</strong> university has one assistant that acts as <strong>the</strong> course COP
leader. Members share best practices and practical experiences in <strong>the</strong>se e-COPs.
Seven semi-structured interviews with <strong>the</strong> e-COPs leaders (teacher assistants) have been conducted.
During <strong>the</strong> in-depth interview, we asked <strong>the</strong>ory-driven questions related to <strong>the</strong> challenges <strong>of</strong> <strong>the</strong>ir e-
COPs. The interviews lasted thirty minutes on average and all <strong>of</strong> <strong>the</strong>m were recorded, with <strong>the</strong>
interviewee’s consent, and later transcribed. Until <strong>the</strong> final report on each case was completed,
telephone and face to face contact was maintained in order to clarify any potential doubts that might
arise.
The data analysis in <strong>the</strong> case study was conducted in two phases. In <strong>the</strong> first phase, <strong>the</strong> authors used
<strong>the</strong> open-coding technique to code <strong>the</strong> interview transcripts and o<strong>the</strong>r information collected. Based
on <strong>the</strong> challenges identified in this first round <strong>of</strong> coding, an additional coder was recruited to recode all
<strong>the</strong> interview documents. Based on <strong>the</strong> results <strong>of</strong> this phase <strong>of</strong> coding, one <strong>of</strong> <strong>the</strong> challenges identified
in phase 1 were collapsed. One additional Challenge was identified in this second round <strong>of</strong> coding.
Finally, <strong>the</strong> investigation led to <strong>the</strong> discovery <strong>of</strong> six major challenges that e-COPs <strong>of</strong> <strong>the</strong> university
struggling with <strong>the</strong>m.
3. Case study findings
We discovered six challenges <strong>of</strong> best practices sharing in e-COPs <strong>of</strong> <strong>the</strong> university as bellow:
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Mehdi Shami Zanjani, Hamid Rahimian and Farnoosh Alami
• Lack <strong>of</strong> Willingness and Motivation to Share Knowledge
Our results show that <strong>the</strong> members’ shared interest, desire and motivation to share Knowledge and
best practices is <strong>the</strong> most crucial determinant factor <strong>of</strong> COPs success. Willingness to teach and
Motivation to learn are <strong>the</strong> preconditions <strong>of</strong> learning process and sharing knowledge. Some
interviewees highlighted that if students don’t feel usefulness & productivity <strong>of</strong> <strong>the</strong> e-COPs, <strong>the</strong>y don’t
have enough enthusiasm to participate.
• Ambiguity <strong>of</strong> Incentives
In most e-COPs, students cannot be possibly forced to share individual knowledge with o<strong>the</strong>rs;
<strong>the</strong>refore, incentives should be tactically utilized to gain <strong>the</strong> trust <strong>of</strong> knowledge owners and produce a
much more incentivized, motivated and committed COP members. Our results demonstrate that lack
<strong>of</strong> incentives like “a persuasive message from <strong>the</strong> teacher / <strong>the</strong> teacher assistant”, “introducing as <strong>the</strong>
best contributor <strong>of</strong> <strong>the</strong> e-COP”, “extra mark for active students in <strong>the</strong> e-COP” and “assigning additional
roles in <strong>the</strong> e-COP to active students” are challenging factors <strong>of</strong> e-COPs in <strong>the</strong> university.
• Weakness in Leadership
Initiating valuable topics, evaluating comments, sending feedback, summarizing and closing debates
are main responsibility <strong>of</strong> teacher assistants as e-COPs leaders. Our findings indicate that eCOPs
leadership is one <strong>of</strong> <strong>the</strong> most important challenges. Results suggest that capable e-COP leaders act
as drivers and control agents for monitoring whe<strong>the</strong>r or not <strong>the</strong> e-COP effectively develops and shares
best practices over a specific period <strong>of</strong> time. Our data reveal that in some e-COPs where <strong>the</strong> leader is
not fully able to activate <strong>the</strong> connections between members, it is less likely that <strong>the</strong> knowledge flows’
density will increase.
• Absence <strong>of</strong> Rich Interactions
Evidence also suggests that lack <strong>of</strong> one-to-one interaction between members, especially face-to-face,
is a serious challenge <strong>of</strong> e-COPs. Some interviewees highlighted that pure virtual COPs can’t work in
<strong>the</strong> university environments. They insist on complementary face-to-face interaction in order to make
friendlier and closer environment among community members. Our results show that because <strong>of</strong>
geographically dispersion <strong>of</strong> COPs members - <strong>the</strong>y meet each o<strong>the</strong>r only twice in a semester – e-
COPs are not warm enough.
• Inexperience Community Members
Majority <strong>of</strong> <strong>the</strong> university students have a few job experiences. When we talk about e-COPs for
sharing best practices and lesson learned, <strong>the</strong> role <strong>of</strong> job experiences is very important. Some
interviewees argue that one <strong>of</strong> <strong>the</strong> most challenging reasons <strong>of</strong> e-COPs failure is lack <strong>of</strong> valuable
stories to share with o<strong>the</strong>r members.
• Copy & Paste Culture
COPs leaders highlighted that “copy & paste culture” hurts effectiveness <strong>of</strong> some e-COPs, especially
e-COPs that member’s participation is mandatory. According to interviewees, <strong>the</strong> best case in an e-
COP happens when members tell personal stories to <strong>the</strong> o<strong>the</strong>rs. In <strong>the</strong> university, some students
participate in debate only because <strong>of</strong> its mark. As a result, <strong>the</strong>y don’t want to spend big energy on it.
From <strong>the</strong>ir viewpoint, <strong>the</strong> shortest road is searching in <strong>the</strong> Internet and finding something to copy on
e-COPs. This makes e-COPs repetitive, boring and unchallenging.
4. Conclusion
As e-learning continues to increase in importance, research on virtual communities <strong>of</strong> practice <strong>of</strong>fers
<strong>the</strong> potential to contribute significantly to our understanding <strong>of</strong> <strong>the</strong> new paradigm <strong>of</strong> learning.
E-COPs have been identified as playing a critical role in <strong>the</strong> promotion <strong>of</strong> collective learning in
educational environments. The purpose <strong>of</strong> knowledge diffusion activities in e-COPs is primarily to
transfer <strong>the</strong> knowledge that is preserved by members to <strong>the</strong> one who lacks by using information
technology infrastructure.
As a result <strong>of</strong> <strong>the</strong> research effort, six challenges <strong>of</strong> e-COPs in <strong>the</strong> University <strong>of</strong> Mehralborz were
recognized. The factors considered include “lack <strong>of</strong> willingness and motivation to share knowledge”,
“ambiguity <strong>of</strong> incentives”, “weakness in leadership”, “absence <strong>of</strong> rich interactions”, “inexperience
community members” and “copy & paste culture”.
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Mehdi Shami Zanjani, Hamid Rahimian and Farnoosh Alami
Three <strong>of</strong> <strong>the</strong> challenges discussed as being important for e-COPs <strong>of</strong> <strong>the</strong> investigated virtual university
included: “ambiguity <strong>of</strong> incentives”, “inexperience community members” and “copy & paste culture” is
not similar to those mentioned in <strong>the</strong> literature before and we can suppose <strong>the</strong>m as exclusive
challenges <strong>of</strong> e-COPs in virtual universities. On <strong>the</strong> o<strong>the</strong>r hand, it seems that ‘‘lack <strong>of</strong> willingness and
motivation to share knowledge’’, “weakness in leadership” and “absence <strong>of</strong> rich interactions” are
analogous to what various authors like Probst and Borzillo (2008), Zhang and Watts (2008), Loyarte
and Rivera (2007), Huang et al. (2007) and Kimble and Hildreth (2005) have stressed as crucial for
COPs and we can suppose <strong>the</strong>m as general challenges <strong>of</strong> e-COPs.
As a single-site case study, which was investigated only electronic communities <strong>of</strong> practice within one
virtual university; <strong>the</strong> study does not permit <strong>the</strong> extrapolation <strong>of</strong> results to a larger population.
It will remain for future research to refine and expand <strong>the</strong> proposed challenges <strong>of</strong> e-COPs in university
environments.
References
Ardichvili, A., (2008) “Learning and Knowledge Sharing in Virtual Communities <strong>of</strong> Practice: Motivators, Barriers,
and Enablers”. Advances in Developing Human Resources, 10(4): 541-554.
Akoumianakis, D., (2009) “Practice-oriented toolkits for virtual communities <strong>of</strong> practice”. Journal <strong>of</strong> Enterprise
Information Management, 22(3): 317-345.
Boh, W. F., (2007) “Mechanisms for sharing knowledge in project-based organizations”. Information and
Organization, 17(1): 27–58.
Chang, C.K., G.D. Chen and L.Y. Li, (2008) “Constructing a community <strong>of</strong> practice to improve coursework
activity”. Computers & Education, 50(1): 235–247.
Claver-Corte´s, E., P. Zaragoza-Sa´ez and E. Pertusa-Ortega, (2007) “Organizational structure features
supporting knowledge management processes. Journal <strong>of</strong> Knowledge Management”, 11(4):45-57.
Cheung, C. M. K., and Lee, M. K. O. (2009) “Understanding <strong>the</strong> sustainability <strong>of</strong> a virtual community: model
development and empirical test”. Journal <strong>of</strong> Information Science, 35 (3), 279–298.
Dupou, O. and M. Yıldızoglu, (2006) “Organizational performance in hierarchies and communities <strong>of</strong> practice”.
Journal <strong>of</strong> Economic Behavior & Organization, 6(4): 668–690.
Huang, N. T., C. C. Wei and W. K. Chang, (2007) “Knowledge management: modeling <strong>the</strong> knowledge diffusion in
community <strong>of</strong> practice”. Kybernetes, 36 (5/6): 607-621.
Kimble, C. and P. Hildreth, Dualities, (2005) “Distributed communities <strong>of</strong> practice and knowledge management”.
Journal <strong>of</strong> Knowledge Management, 9(4): 102-113.
Klein, J. H., N. Connell and E. Meyer, (2005). “Knowledge characteristics <strong>of</strong> communities <strong>of</strong> practice”. Knowledge
Management Research & Practice, 3(2): 106–114.
Kerno, S. J. and Mace, S. L. (2010) “Communities <strong>of</strong> Practice: Beyond Teams”. Advances in Developing Human
Resources, 12 (1), 78–92.
Loyarte, E. and O. Rivera, (2007) “Communities <strong>of</strong> practice: a model for <strong>the</strong>ir cultivation”. Journal <strong>of</strong> Knowledge
Management, 11 ( 3): 67-77.
Mittendorff K., F. Geijsel, A. Hoeve, M. de Laat and L. Nieuwenhuis, (2006) “Communities <strong>of</strong> practice as
stimulating forces for collective learning”. Journal <strong>of</strong> Workplace Learning, 18 (5): 298-312.
Oberty C. Z., and P. D. Saa´-Pe´rez, (2006) “Work teams to favor knowledge management: towards communities
<strong>of</strong> practice”. <strong>European</strong> Business Review, 18(1):60-76.
Probst, G. and S. Borzillo, (2008) ”Why communities <strong>of</strong> practice succeed and why <strong>the</strong>y fail”. <strong>European</strong>
Management Journal,25 (5):335-348.
Schenkel, A. and R. Teigland, (2008) “Improved organizational performance through communities <strong>of</strong> practice”.
Journal <strong>of</strong> Knowledge Management, 12 (1):. 106-118.
Schrum, L., M. D. Burbank and R. Capps, (2007) “Preparing future teachers for diverse schools in an online
learning community: perceptions and practice”. Internet and Higher Education, 10(3): 204–211.
Wong, K. Y., (2005) “Critical success factors for implementing knowledge management in small and medium
enterprises”. Industrial Management & Data Systems, 105(3): 261-279.
Zhang, W. and S. Watts, (2008) “Online communities as communities <strong>of</strong> practice: a case study”. Journal <strong>of</strong>
Knowledge Management, 12(4): 55-71.
934

Existing Dimensions <strong>of</strong> Absorptive Capacity and <strong>the</strong> Way
Foreword: The Mediating Functionality <strong>of</strong> Mental Models
Manifested by Entrepreneurs
Evangelia Siachou 1 , S<strong>of</strong>ia Daskou 1 and Peter Yannopoulos 2
1
Hellenic American University, Manchester, NH, USA
2
Brock University, Ontario, Canada
esiachou@hau.gr
sdaskou@hau.gr
pyannopoulos@brocku.ca
Abstract: The acquisition <strong>of</strong> knowledge (ei<strong>the</strong>r tacit or explicit) that is externally derived is one <strong>of</strong> <strong>the</strong> main best
practices undertaken by entrepreneurs to make <strong>the</strong> right decisions for expansion <strong>of</strong> <strong>the</strong>ir businesses. However,
along <strong>the</strong> lines with prior research work, scholars have posited out that, <strong>the</strong> effective implementation <strong>of</strong> <strong>the</strong>
incoming knowledge into <strong>the</strong> organizational daily practices and routine activities perquisites <strong>the</strong> absorptive
capacity performed by those involved in <strong>the</strong> knowledge acquisition process (Cohen and Levinthal, 1990;
Daghfous, 2004; Grunfeld, 2003; Lane and Lubatkin, 1998; Rocha, 1999; Stock et al., 2001). Absorptive
capacity’s concept has initially been conceptualised through a three-dimensional approach (recognition <strong>of</strong> <strong>the</strong>
value <strong>of</strong> <strong>the</strong> new knowledge, its assimilation and its implementation into commercial ends, Cohen and Levithal,
1989). In a way forward <strong>of</strong> this dominant conceptualization <strong>of</strong> <strong>the</strong> rationale behind <strong>the</strong> absorptive capacity we
estimate that <strong>the</strong> input <strong>of</strong> new mediators –i.e. mental models (MMs) - should have a lead over <strong>the</strong> three
aforementioned definitive dimensions <strong>of</strong> absorptive capacity. More specifically, we argue <strong>the</strong> functionality <strong>of</strong>
(MMs) in recognizing <strong>the</strong> value <strong>of</strong> external knowledge acquisition <strong>of</strong> entrepreneurs and propose that MMs
mediate <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs, who use <strong>the</strong>m to filter <strong>the</strong> value <strong>of</strong> new knowledge <strong>the</strong>y acquire
from <strong>the</strong> external environment. To explore this proposition, we used <strong>the</strong> Karakaya and Yannopoulos (2010)
typology <strong>of</strong> MMs applied in situations <strong>of</strong> decision making relevant to business growth. The findings <strong>of</strong> our study
reveal that <strong>the</strong> MMs <strong>of</strong> <strong>the</strong> sample seem to relate to <strong>the</strong> ability <strong>of</strong> <strong>the</strong> entrepreneur to appreciate and assimilate
incoming knowledge. Thus, <strong>the</strong> first and second considerations <strong>of</strong> absorptive capacity seem to be influenced by
MMs. Additionally, new research hypo<strong>the</strong>sis have been brought about concerning <strong>the</strong> potential distinct upshots <strong>of</strong>
<strong>the</strong> types <strong>of</strong> MMs on one or more <strong>of</strong> <strong>the</strong> specifically discussed dimensions <strong>of</strong> absorptive capacity.
Keywords: absorptive capacity, entrepreneurship, external knowledge acquisition, decision making, mental
models
1. Introduction
In <strong>the</strong> current era <strong>of</strong> business evolution <strong>the</strong> acquisition <strong>of</strong> external knowledge is one <strong>of</strong> <strong>the</strong> main best
practices undertaken by entrepreneurs to make <strong>the</strong> right decisions for expansion <strong>of</strong> <strong>the</strong>ir businesses.
The direct and positive effect <strong>of</strong> external knowledge (both in its tacit and explicit type) on <strong>the</strong>
organisational performance outcomes has already been investigated <strong>the</strong>oretically and/or empirically
by an enormous body <strong>of</strong> research work in <strong>the</strong> field <strong>of</strong> Knowledge Management (see for example,
Ancona and Caldwell, 1992; Inkpen and Dinur, 1998; Lu et al., 2006; Zander and Kogut, 1995).
In <strong>the</strong> networked economies <strong>the</strong> orientation skills <strong>of</strong> <strong>the</strong> firm contribute to organizational learning
(Gummesson, 2006; Murrey and Carter, 2005). Actually, “<strong>the</strong> knowledge produced by means <strong>of</strong>
interaction and dialogue feeds back to <strong>the</strong> participants, thus giving rise to a new cycle <strong>of</strong> knowledge
creation, dissemination and use’’ and this whole process can develop “in a relationship climate and
culture’’ (Tzokas and Saren, 2004:132). Fulford and Love (2004) suggest that <strong>the</strong> combination <strong>of</strong>
marketing practice and customer relationship management systems may lead to improved customer
service, thus <strong>the</strong> firm may be able to acquire knowledge sourced outside firm’s boundaries and
distribute it inside <strong>the</strong> firm. The incoming knowledge contributes to <strong>the</strong> decision making process
(Yannopoulos and Daskou, 2010). However it is worth asking what happens to <strong>the</strong> incoming
knowledge if/when is internalized by <strong>the</strong>se firms? Along <strong>the</strong> lines with this prior work, scholars have
posited out that an essential prerequisite for <strong>the</strong> effective implementation <strong>of</strong> <strong>the</strong> incoming knowledge
into <strong>the</strong> existing organisational practices and routine activities is <strong>the</strong> absorptive capacity performed by
those involved in <strong>the</strong> knowledge acquisition process (Cohen and Levinthal, 1990; Daghfous, 2004;
Grunfeld, 2003; Lane and Lubatkin, 1998; Rocha, 1999; Stock et al., 2001).
The following sections present <strong>the</strong> <strong>the</strong>oretical discussion <strong>of</strong> <strong>the</strong> dimensions <strong>of</strong> absorptive capacity, <strong>the</strong>
role <strong>of</strong> mental models (MMs) <strong>of</strong> business decision makers in <strong>the</strong>ir effort to expand <strong>the</strong>ir enterprises
and a conceptualization <strong>of</strong> <strong>the</strong> mediating functionality <strong>of</strong> MMs in <strong>the</strong> three dimensions <strong>of</strong> absorptive
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Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
capacity performed by entrepreneurs. The paper proceeds to present a qualitative research design <strong>of</strong>
exploring <strong>the</strong> aforementioned relationship, and submits findings that support <strong>the</strong> conceptualization <strong>of</strong>
<strong>the</strong> researchers.
2. State <strong>of</strong> <strong>the</strong> art
The sections that follow address <strong>the</strong> ontology <strong>of</strong> absorptive capacity <strong>of</strong> managers that deal with
external knowledge acquisition, <strong>the</strong> MMs that mediate managerial learning and decision making and a
conceptual model that links <strong>the</strong> two concepts.
2.1 Dimensions <strong>of</strong> absorptive capacity
The conceptualization <strong>of</strong> and <strong>the</strong> rationale behind <strong>the</strong> significance <strong>of</strong> an organizations’ absorptive
capacity have been investigated by many scholars <strong>of</strong> <strong>the</strong> disciplines <strong>of</strong> strategic management (Lane
and Lubatkin, 1998; Nahapiet and Ghoshal, 1998), <strong>of</strong> innovation and technology management
(Schilling, 1998) as well as <strong>of</strong> <strong>the</strong> discipline <strong>of</strong> business administration in an international context
(Kadia and Bhagat, 1998), whilst references <strong>of</strong> this concept can be also made in <strong>the</strong> economic field
(Glass and Saggi, 1998). Their aim is to describe and analyse an organization’s ability to absorb
intangible assets (resources) which in turn consist <strong>of</strong> dynamic capabilities for those <strong>of</strong> <strong>the</strong>
organizations which have <strong>the</strong> ability to exploit <strong>the</strong>m effectively and efficiently. The absorptive capacity
<strong>of</strong> an organization has been studied in consistency with innovation and learning and applies mainly on
<strong>the</strong> organization’s ability to absorb and assimilate knowledge that is externally derived.
The term absorptive capacity has been initially introduced in <strong>the</strong> context <strong>of</strong> knowledge management
by Cohen and Levinthal (1990), who claim that absorptive capacity is <strong>the</strong> organization’s ability to
recognise <strong>the</strong> value <strong>of</strong> new knowledge, assimilate it and apply it to commercial ends. The results <strong>of</strong>
<strong>the</strong>ir study, which are found to be definitive, indicate that an organization’s ability to absorb knowledge
that is derived from various external sources significantly influences <strong>the</strong> decision making about <strong>the</strong>
investments in new technologies which <strong>of</strong>ten aim at <strong>the</strong> development <strong>of</strong> innovative projects.
The established term <strong>of</strong> absorptive capacity has also been identified with <strong>the</strong> investments on research
and development (R&D) which, firstly, require access to new knowledge repositories and, secondly,
acquisition and assimilation <strong>of</strong> new amounts <strong>of</strong> knowledge. Thus, <strong>the</strong> investments on <strong>the</strong> mere
acquisition <strong>of</strong> patents or on <strong>the</strong> imitation <strong>of</strong> innovative products and services <strong>of</strong>fered by <strong>the</strong>
competitors have not provided as best practices able to increase <strong>the</strong> organizational performance.
Hence, <strong>the</strong> effect <strong>of</strong> absorptive capacity on organizations’ performance outcomes has intrigued later
researchers in <strong>the</strong> field <strong>of</strong> innovation and learning who have modified <strong>the</strong> concept <strong>of</strong> absorptive
capacity in relation to <strong>the</strong> subject and <strong>the</strong> level <strong>of</strong> <strong>the</strong> analysis conducted each time (organizational,
unit, individual).
Specifically, Mowery and Oxley (1995) define <strong>the</strong> term absorptive capacity as <strong>the</strong> spectrum <strong>of</strong>
capabilities based on which an organization can effectively manage <strong>the</strong> tacit or salient knowledge that
is externally derived by implementing it on daily routine tasks and abilities. Behind <strong>the</strong> concept <strong>of</strong>
organizational learning <strong>the</strong>ory, absorptive capacity has been defined by Kim (1998) as a consequence
<strong>of</strong> an organization’s ability to learn to deal effectively with <strong>the</strong> problem solving process and
techniques. Whilst, Zahra and George (2002), present <strong>the</strong> bi-dimensional nature <strong>of</strong> an organization’s
absorptive capacity by distinguishing between <strong>the</strong> potential and realized absorptive capacity. The first
term refers to an organization’s ability to acquire and continually assimilate new knowledge sourced
on its external environment whilst <strong>the</strong> later relates to transformation and exploitation <strong>of</strong> <strong>the</strong> incoming
knowledge. In a more recent perspective, Todorova and Dursin (2007), present <strong>the</strong> multidimensional
nature <strong>of</strong> absorptive capacity adding also <strong>the</strong> contribution <strong>of</strong> powerful relationships in <strong>the</strong> effective
exploitation <strong>of</strong> <strong>the</strong> incoming knowledge to various organizational policies, practices and routine tasks.
According to <strong>the</strong> existing literature, <strong>the</strong> concept <strong>of</strong> absorptive capacity is multidimensional, while
simultaneously, it is significantly affected by a spectrum <strong>of</strong> factors, which are differentiated in relation
to <strong>the</strong> level <strong>of</strong> <strong>the</strong> analysis which take place each time (i.e. organizational, unit and individual level <strong>of</strong>
analysis). Apart from <strong>the</strong> dimensions as well as <strong>the</strong> different factors which have been identified ei<strong>the</strong>r
through <strong>the</strong> empirical research or conceptually, scholars in this field support that <strong>the</strong> prerequisite for
an organization to develop its ability to absorb knowledge that is externally derived is <strong>the</strong> knowledge
stock which has been sourced in various repositories. Various organizational repositories include
experience, capabilities as well as a common organizational language implemented in certain daily
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Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
actions and routines. In certain circumstances, <strong>the</strong> knowledge stock possessed by an organization
consists <strong>of</strong> patents which are found to be <strong>the</strong> basis for development and innovation. Consequently,
<strong>the</strong> existing organizational knowledge is <strong>the</strong> appropriate cognitive background in order for <strong>the</strong> new
knowledge to be effectively assimilated and utilised aiming at <strong>the</strong> economic value (on <strong>the</strong> part <strong>of</strong>
organizations, Cohen and Levinthal, 1990). In an attempt to expand <strong>the</strong> existing <strong>the</strong>ory <strong>of</strong> absorptive
capacity we focus our analysis on individual level arguing <strong>the</strong> functionality <strong>of</strong> (MMs) <strong>of</strong> managers in
recognizing <strong>the</strong> value <strong>of</strong> external knowledge acquisition <strong>of</strong> entrepreneurs. Specifically we propose that
MMs mediate <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs, who use <strong>the</strong>m to filter <strong>the</strong> value <strong>of</strong> new
knowledge <strong>the</strong>y acquire from <strong>the</strong> external environment.
2.2 Mental models (MMs) adopted in decision making
Literature suggests that exploration <strong>of</strong> managerial MMs contribute in our understanding <strong>of</strong> how
managers make informed decisions (Yannopoulos and Daskou, 2010). MMs are cognitive structures
(Song et al., 1999) used by managers or entrepreneurs to make sense <strong>of</strong> environmental complexities,
draw inferences and make decisions (Kiesel and Sproull, 1982). They are simplified mental
representations (Thagard, 2005), belief systems (Simon, 1955) and filters (Daft and Weick, 1984) <strong>of</strong>
external knowledge used by entrepreneurs for <strong>the</strong> sake <strong>of</strong> cognitive economy in dealing with
environmental complexity. Managers use MMs to make evaluations (Song et al., 1999), scan <strong>the</strong>
environment and make strategic decisions (Stubbart, 1989).
Day and Nedungadi (1994) identified 4 types <strong>of</strong> managerial MMs and <strong>the</strong> MMs typology was later
fur<strong>the</strong>r conceptualized by Karakaya and Yannopoulos (2010) who propose that firms that
internationalize use <strong>the</strong>m in situations <strong>of</strong> foreign market entry. The typology is described in table 1.
Table 1: Types <strong>of</strong> Mental Models (MMs)
Type <strong>of</strong> MMs Description
Competitor
centered
Self (firm)
centered
Market driven
Customer
centered
Firm directly compares with competition and search <strong>the</strong> environment for information
about <strong>the</strong> competition (Day and Nedungadi, 1994). Firm responds to competitive
activities (Narver and Slater, 1990, lower prices and costs and improve products and
forms strategy in terms <strong>of</strong> competitive activities. The company forms strategy in terms
<strong>of</strong> competitive activities.
Limited responsiveness to customer needs and competitive moves (Maignain and
Lucas, 1997). Focus on selling a specific product to a specific market (Levit, 1960).
Firms accommodate <strong>the</strong>ir rivals ra<strong>the</strong>r than compete with <strong>the</strong>m. Limited attention to
customer needs, lower awareness <strong>of</strong> competitive environment, do not notice <strong>the</strong>
threatening signals <strong>of</strong> competitors (Day and Nedungadi, 1994).
Firm focuses on competitive activity and customer requirements, are open to
environmental information, do not oversimplify, use <strong>the</strong>ir knowledge to generate value
and satisfaction to customers, and are proactive ra<strong>the</strong>r than reactive to change (Day,
1994; Noble, Sinha and Kumar, 2002; Deshpande, Farley and Webster, 1993;
Gatington and Xuereb, 1997).
The firm notices customer preferences (Voss and Voss, 2000) and behaviors. Firm
places attention to customer retention, product innovation, good customer service,
and focused customer segments.
*Source: Typologies <strong>of</strong> MMs based on Karakaya and Yannopoulos, (2010) and fur<strong>the</strong>r developed by Daskou, Yannopoulos and
Koutoulas (2012).
In situations <strong>of</strong> uncertainty individuals may assume inductions which facilitate <strong>the</strong>m to learn from <strong>the</strong>ir
former choices (Heiner, 1983). More specifically, in many circumstances individuals may ga<strong>the</strong>r
information, organizing and interpreting <strong>the</strong>m ra<strong>the</strong>r daunting <strong>of</strong> this process (March, 1994). Given
individuals limited capacity to process information (Simon, 1955), <strong>the</strong>y occasionally choose to make
decisions that are simply quite good (in terms <strong>of</strong> <strong>the</strong>ir quality, March, 1994) ra<strong>the</strong>r than to decide
based on <strong>the</strong> outcome <strong>of</strong> a fully developed and informed learning process. Therefore, in such
situations, MMs service managers’ cognitive economy, by aiding <strong>the</strong>m to pay <strong>the</strong>ir attention on some
aspects <strong>of</strong> <strong>the</strong> incoming knowledge (ra<strong>the</strong>r than <strong>the</strong> totality <strong>of</strong> <strong>the</strong> external knowledge as a whole)
(Day, 1994; Walsh, 1995). Consequently, <strong>the</strong>y avoid becoming overwhelmed by <strong>the</strong> substantial
complexity <strong>of</strong> <strong>the</strong> incoming information <strong>the</strong>y would rationally acquire to aforementioned process
(Karakaya and Yannopoulos, 2010). That is <strong>the</strong> primary utility <strong>of</strong> MMs.
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3. The conceptualization and research proposition
MMs are viewed as mental stencils <strong>of</strong> organized knowledge about information fields that facilitate
managers to acquire, interpret, retain, and process information and develop activities in <strong>the</strong>
environment <strong>of</strong> those fields (Walsh, 1995). The translation <strong>of</strong> environmental information, <strong>the</strong>
development <strong>of</strong> shared meanings and MMs are outcomes <strong>of</strong> organizational interpretation <strong>of</strong> <strong>the</strong>
environment in which an organization operates (Daft and Weick, 1984). Organizations decide what
has effectively worked out (or not) in <strong>the</strong> past in order to develop memories <strong>of</strong> those past experiences
(Murrey and Carter, 2005) as well as to form insights based on earlier MMs and by <strong>the</strong>ir previous
learning (Weick, 1979). Drawing on <strong>the</strong> current literature <strong>of</strong> absorptive capacity but aiming to expand
this one step fur<strong>the</strong>r, we assume that (MMs) mediate <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs who
use <strong>the</strong> later to filter <strong>the</strong> value <strong>of</strong> new knowledge that is externally derived. Thus, <strong>the</strong> defined level <strong>of</strong>
our analysis is found to be <strong>the</strong> individual level <strong>of</strong> analysis.
The proposed conceptualization, along <strong>the</strong> lines <strong>of</strong> prior research on absorptive capacity estimates
that an essential prerequisite for <strong>the</strong> effective implementation <strong>of</strong> <strong>the</strong> incoming knowledge into <strong>the</strong>
existing organisational practices and routine activities is <strong>the</strong> absorptive capacity performed by those
involved in <strong>the</strong> knowledge acquisition process (Cohen and Levinthal, 1990; Daghfous, 2004; Grunfeld,
2003; Lane and Lubatkin, 1998; Rocha, 1999; Stock et al., 2001). In consistency, also, with prior
empirical work <strong>the</strong> concept <strong>of</strong> aabsorptive capacity has initially been conceptualised through a threedimensional
approach including: (i) <strong>the</strong> ability to recognise <strong>the</strong> value <strong>of</strong> <strong>the</strong> new knowledge sourced
outside an organizations’ boundaries, (ii) <strong>the</strong> ability to assimilate <strong>the</strong> incoming knowledge into <strong>the</strong>
existing tasks and activities undertaken by <strong>the</strong> organisation and (iii) <strong>the</strong> ability to apply it into
commercial ends (Cohen and Levithal, 1989). In a way forward <strong>of</strong> <strong>the</strong> dominant conceptualization <strong>of</strong>
<strong>the</strong> rationale behind <strong>the</strong> absorptive capacity we estimate that <strong>the</strong> input <strong>of</strong> new mediators – i.e. mental
models- may have a lead over <strong>the</strong> three aforementioned definitive dimensions <strong>of</strong> absorptive capacity.
The above rationale is depicted on <strong>the</strong> following framework (Figure 1) which conceptualises <strong>the</strong>
mediating functionality <strong>of</strong> MMs in <strong>the</strong> three dimensions <strong>of</strong> absorptive capacity performed by
entrepreneurs.
Figure 1: Relationship <strong>of</strong> mental models with <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs
Drawing upon <strong>the</strong> proposed conceptualization we estimate that MMs are used by entrepreneurs to
filter incoming knowledge in order to achieve cognitive economy and to facilitate <strong>the</strong>ir own decision
making. This is reflected in <strong>the</strong> following research proposition:
Research Proposition: MMs have a lead over <strong>the</strong> three main dimensions <strong>of</strong> absorptive capacity <strong>of</strong>
entrepreneurs in <strong>the</strong> decision making procedure.
Based on <strong>the</strong> existing MMs taxonomy discussed above we attempt to explore <strong>the</strong> potential variable
mediating effect <strong>of</strong> MMs on <strong>the</strong> capacity <strong>of</strong> entrepreneurs to absorb knowledge that is externally
derived.
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4. Methodology
Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
With <strong>the</strong> aim to explore <strong>the</strong> above mentioned proposition, <strong>the</strong> study adopted a qualitative approach
which aided <strong>the</strong> researchers to discover <strong>the</strong> potential role <strong>of</strong> MMs in <strong>the</strong> absorptive capacity <strong>of</strong> Greek
entrepreneurs. The research design was developed with <strong>the</strong> scope to undergo an exploratory study
which would help <strong>the</strong> researchers develop a survey later on. The purpose <strong>of</strong> <strong>the</strong> study is not to test a
hypo<strong>the</strong>sis but ra<strong>the</strong>r to help form one. Epistemologically <strong>the</strong> study was developed from <strong>the</strong> view point
<strong>of</strong> Realism, (<strong>the</strong> academic expertise <strong>of</strong> <strong>the</strong> researchers that also serves <strong>the</strong> research question) and
made use <strong>of</strong> semi-structured interviews with Greek entrepreneurs (owners/managers) <strong>of</strong> family owned
enterprises. The fieldwork was conducted in three (3) phases. In <strong>the</strong> first phase <strong>the</strong> participant firms
were pulled from two (2) formal networks representing <strong>the</strong> sectors <strong>of</strong> Construction Materials (CMN)
and Food & Beverages (FBN) and were pr<strong>of</strong>iled. In <strong>the</strong> second and third phases, twelve (12)
interviews with FBN entrepreneurs and fourteen (14) interviews with CMN entrepreneurs took place
and data were tabulated on protocols that serviced <strong>the</strong> interview guides. The samples were accessed
by permission and recommendation <strong>of</strong> <strong>the</strong>ir founding Institution (<strong>the</strong> Development Agency <strong>of</strong> Karditsa:
ANKA), since <strong>the</strong>y were <strong>the</strong> only formal business networks in <strong>the</strong> region. The census <strong>of</strong> <strong>the</strong> members
was selected, in order to acquire a pluralistic view <strong>of</strong> <strong>the</strong> data. Given <strong>the</strong> small number <strong>of</strong> <strong>the</strong>
population, <strong>the</strong> interviews were done at <strong>the</strong> census <strong>of</strong> <strong>the</strong> network members, ra<strong>the</strong>r than on a sample.
The pr<strong>of</strong>iles <strong>of</strong> phase one (1), were content analyzed and <strong>the</strong> findings <strong>of</strong> that analysis were used to
inform <strong>the</strong> researchers’ development <strong>of</strong> <strong>the</strong> interview protocols <strong>of</strong> phases two (2) and three (3). The
findings <strong>of</strong> <strong>the</strong> twenty-eight (26) interviews were content analyzed and were fur<strong>the</strong>r processed using
<strong>the</strong> analysis tools <strong>of</strong> Conceptually Thematic matrices (Miles and Huberman, 1994). The findings <strong>of</strong>
this exploratory study are only tentative to <strong>the</strong> study and by no means prescriptive or generalizable.
5. Findings
The findings <strong>of</strong> <strong>the</strong> study reflect <strong>the</strong> views <strong>of</strong> entrepreneurs who are members <strong>of</strong> <strong>the</strong> FBN and <strong>the</strong>
CMN operating in <strong>the</strong> Region <strong>of</strong> Thessaly in Greece. The Networks were formalized as a
consequence <strong>of</strong> <strong>the</strong> initiative <strong>of</strong> ANKA an NGO Development Agency that serves <strong>the</strong> local region. The
interviewees were all general managers/owners <strong>of</strong> <strong>the</strong> networked enterprises. The customer centered
MMs are conceptualized to address <strong>the</strong> thinking <strong>of</strong> managers which is focused around customer
preferences (Voss and Voss, 2000) and behaviors, and more particularly focus on good customer
service, customer retention, customer segmentation and product innovation (Karakaya and
Yannopoulos, 2010). Managers who adopt market driven MMs focus on issues <strong>of</strong> monitoring <strong>of</strong>
competitive activity and customer requirements (Day and Nendungadi, 1994) and anticipation <strong>of</strong>
competitive behavior and customer needs (Karakaya and Yannopoulos, 2010). Market driven
managers anticipate competition and that information is shared with <strong>the</strong> rest <strong>of</strong> <strong>the</strong> organization (Day,
1994). Managers who adopt competitor centered MMs analyze competitive activity and adopt
defensive action again competition (Karakaya and Yannopoulos, 2010). Self centered MMs are
characterized with limited responsiveness towards <strong>the</strong> completion and <strong>the</strong> needs <strong>of</strong> customers (Day,
1994; Maignan and Lukas, 1997). The findings are presented below in terms <strong>of</strong> each network <strong>of</strong>
entrepreneurs.
5.1 Construction Materials Networks (CMN)
The analyzed data revealed that at a majority <strong>the</strong> CMN members adopt ei<strong>the</strong>r customer centered or
market driven MMs. More specifically, half <strong>of</strong> <strong>the</strong> CMN sample declared that <strong>the</strong>ir strategic decision
making is driven by information that refers to activities and practices <strong>of</strong> <strong>the</strong> competition. The
knowledge <strong>the</strong>y receive about competitive practice (via intelligence, market research and networking)
is processed in terms <strong>of</strong> <strong>the</strong>ir crystallized views <strong>of</strong> how competitors generally behave in <strong>the</strong> market. In
monitoring <strong>the</strong> competition <strong>the</strong> CMN entrepreneurs seem to automatically review competitive activities
concerning product promotions, product mix and prices, but not much more beyond that. This adopted
behavior automates <strong>the</strong>ir ability to evaluate <strong>the</strong> market knowledge that is external to <strong>the</strong> firm, and so
information that flows inwards to <strong>the</strong> firm are to a certain degree took on board when management
decision are made. Some <strong>of</strong> <strong>the</strong> respondents <strong>of</strong> this sample also mentioned that <strong>the</strong>ir participation in
<strong>the</strong> CMN may <strong>of</strong>fer <strong>the</strong>m some access to market information relevant to new technologies and
cooperative learning.
Fur<strong>the</strong>rmore, when considering expanding <strong>the</strong>ir business in <strong>the</strong> region, <strong>the</strong> CMN entrepreneurs
consider external information about product mix demand, customer requirements and preferences. In
addition when <strong>the</strong> CMN entrepreneurs attempt to expand <strong>the</strong>ir business nationally or internationally,
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Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
<strong>the</strong>y also investigate information concerning prices and technologies <strong>of</strong> <strong>the</strong> competition and product
innovations available to prospect customers. All this learning drives many <strong>of</strong> <strong>the</strong>ir subsequent
managerial decision and practices in addition to <strong>the</strong>ir vision about <strong>the</strong> future plans <strong>of</strong> enterprise
development. This learning filters <strong>the</strong>ir decisions and determines many, but not most <strong>of</strong> <strong>the</strong>ir
commercial practices. The CMN entrepreneurs tend to later share some <strong>of</strong> this learning with o<strong>the</strong>r
members <strong>of</strong> <strong>the</strong> construction materials network and 43% <strong>of</strong> <strong>the</strong> sample shares <strong>the</strong>ir learning with
members <strong>of</strong> <strong>the</strong>ir families (who are commercially involved with <strong>the</strong> firms) and or some <strong>of</strong> <strong>the</strong> o<strong>the</strong>r
members <strong>of</strong> <strong>the</strong> firm. Overall, <strong>the</strong> CMN entrepreneurs are more extrovert than <strong>the</strong> members <strong>of</strong> <strong>the</strong>
FBN.
Thus, <strong>the</strong> MMs adopted filter <strong>the</strong>ir thinking <strong>of</strong> assessing <strong>the</strong> value and utility <strong>of</strong> <strong>the</strong> external
information and when that knowledge reaffirms <strong>the</strong>ir crystallized views <strong>the</strong>y experience dissonance.
When <strong>the</strong> information is new or unexpected, <strong>the</strong> lessons learned are assimilated to develop tasks that
drive strategy, specifically in terms <strong>of</strong> planning for exporting or business growth outside <strong>the</strong>
boundaries <strong>of</strong> <strong>the</strong> county or local region.
5.2 Food and Beverages Network (FBN)
The sample <strong>of</strong> <strong>the</strong> FBN members, at its majority indicates that <strong>the</strong>ir managerial thinking is generally
less complex than that that <strong>of</strong> <strong>the</strong> CMN members. The cognitive structures adopted by <strong>the</strong>se
entrepreneurs do not function based on a syn<strong>the</strong>sis <strong>of</strong> combinations <strong>of</strong> competitor and customer
centered issues, but ra<strong>the</strong>r develop independently on issues concerning <strong>the</strong> traditional organizational
issues <strong>of</strong> <strong>the</strong> firm and some basic information that regards competitive activity. Only 24% <strong>of</strong> <strong>the</strong> FBN
entrepreneurs, factor customer and competition relevant information in <strong>the</strong>ir planning for new
business growth, although 72% <strong>of</strong> that sample claim that <strong>the</strong>y collect market relevant information. This
finding suggests that <strong>the</strong>se entrepreneurs do not seem to absorb or assimilate valuable learning that
streams inwards <strong>the</strong> firm.
The thinking <strong>of</strong> <strong>the</strong> majority <strong>of</strong> <strong>the</strong> FBN entrepreneurs tends to focus more on operational issues <strong>of</strong>
<strong>the</strong> organizations, such as cost cutting and sales promotions that may lead to pr<strong>of</strong>it maximization and
some estimations <strong>of</strong> product demand. These issues tend to be <strong>the</strong> learning ground on which <strong>the</strong> FBN
entrepreneurs base <strong>the</strong>ir planning for growth. However, 67% <strong>of</strong> <strong>the</strong>se entrepreneurs don’t take into
account competitive practices and 66% don’t even develop product, price or positioning information
when <strong>the</strong>y make plans for <strong>the</strong> future. When <strong>the</strong>se entrepreneurs make business growth plans in <strong>the</strong>
region, nationally or internationally <strong>the</strong>y simply consider some product relevant information (in terms
<strong>of</strong> packaging and pricing) and some <strong>of</strong> <strong>the</strong> competitive prices and publicity. Only 33% <strong>of</strong> <strong>the</strong> sample
on occasion explores customer needs but <strong>the</strong>re is no evidence to suggest that <strong>the</strong>y actually
operationalize any <strong>of</strong> <strong>the</strong> information <strong>the</strong>y acquire. Generally <strong>the</strong> FBN entrepreneurs designate that
<strong>the</strong>y generally adopt self centered or even sometimes competition centered MMs that act as foci <strong>of</strong>
<strong>the</strong>ir thinking. None <strong>of</strong> <strong>the</strong> members <strong>of</strong> <strong>the</strong> FBN sample seem to take in information and learning <strong>the</strong>y
have access to ei<strong>the</strong>r from <strong>the</strong>ir formal network or from <strong>the</strong> broader external environment, simply
because <strong>the</strong>y don’t seem to be able to recognize its value for <strong>the</strong>ir firms. Some <strong>of</strong> <strong>the</strong> responses
referred to <strong>the</strong> fact that <strong>the</strong> relationships that develop between network members may <strong>of</strong>fer <strong>the</strong>m <strong>the</strong>
possibility <strong>of</strong> developing learning that may aid <strong>the</strong>m improve <strong>the</strong>ir distribution systems.
Finally, <strong>the</strong> limited knowledge that <strong>the</strong> FBN members acquire that may inform <strong>the</strong>ir managerial
decisions making is sourced after shared with some family members (that are pr<strong>of</strong>essionally involved
with <strong>the</strong> firms), with associated and personnel in <strong>the</strong> <strong>the</strong>se firms. About 37% <strong>of</strong> <strong>the</strong> sample shares <strong>the</strong>
possessed knowledge with no one. Based on <strong>the</strong> later data we note that although most <strong>of</strong> <strong>the</strong>m resist
new knowledge that is externally derived, <strong>the</strong>y tend to better assimilate some <strong>of</strong> it than many <strong>of</strong> <strong>the</strong>
CMN members do.
6. Inferences and concerns.
The submitted data indicate that <strong>the</strong> MMs <strong>of</strong> <strong>the</strong> sample seem to relate to <strong>the</strong> ability <strong>of</strong> <strong>the</strong>
entrepreneur to appreciate and assimilate incoming knowledge. Thus, <strong>the</strong> first and second
considerations <strong>of</strong> absorptive capacity seem to be influenced by MMs. Yet, given <strong>the</strong> composition <strong>of</strong>
<strong>the</strong> sample and <strong>the</strong> adopted method <strong>of</strong> investigation, any conclusion we could draw from this study,
can only be tentative to <strong>the</strong> sample, and to <strong>the</strong> region. In fact, <strong>the</strong> results <strong>of</strong> <strong>the</strong> study encourage us to
develop a hypo<strong>the</strong>sis that merits investigative attention, ra<strong>the</strong>r than drawing prescriptive conclusions.
What merits investigative attention is <strong>the</strong> exploration <strong>of</strong> <strong>the</strong> potential distinct upshots <strong>of</strong> <strong>the</strong> types <strong>of</strong>
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Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
MMs on one or more <strong>of</strong> <strong>the</strong> specifically discussed dimensions <strong>of</strong> absorptive capacity. Specifically, we
hypo<strong>the</strong>size that market driven and customer driven MMs adopted by entrepreneurs, aid <strong>the</strong>m to sort
out incoming knowledge, in terms <strong>of</strong> its utility, in <strong>the</strong>ir decision making. In o<strong>the</strong>r words, <strong>the</strong> absorptive
capacity <strong>of</strong> entrepreneurs who adopt customer driven or market driven MMs, is consequently
enriched, as <strong>the</strong>y are more able to value incoming knowledge, are also more able to assimilate that
knowledge and may be more apt to use it to generate business growth, than entrepreneurs who have
not developed <strong>the</strong>se particular types <strong>of</strong> MMs. Therefore <strong>the</strong> hypo<strong>the</strong>ses are stated as follows:
H1: Market driven MMs aid entrepreneurs to recognize <strong>the</strong> value <strong>of</strong> <strong>the</strong> incoming
knowledge.
H2: Customer driven MMs aid entrepreneurs to recognize <strong>the</strong> value <strong>of</strong> <strong>the</strong> incoming
knowledge.
H3: Market driven MMs aid entrepreneurs to assimilate incoming knowledge.
H4: Customer driven MMs aid entrepreneurs to assimilate incoming knowledge.
H5: Market driven MMs aid entrepreneurs to apply knowledge into commercial ends.
H6: Customer driven MMs aid entrepreneurs to apply knowledge into commercial ends.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> findings encourage us to hypo<strong>the</strong>size that self centered and competitor
centered MMs block <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs, in that <strong>the</strong>y prevent entrepreneurs from
valuing new knowledge, and limit <strong>the</strong>ir capacity to digest it, and even make use <strong>of</strong> it. Specifically we
can now hypo<strong>the</strong>size that:
H7: Self centered MMs block <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs at its whole.
H8: Competitor centered MMs block <strong>the</strong> absorptive capacity <strong>of</strong> entrepreneurs at its
whole.
At this point, what is <strong>of</strong> major importance is <strong>the</strong> negative effect that <strong>the</strong>se two types <strong>of</strong> MMs have on
<strong>the</strong> third dimension <strong>of</strong> adsorptive capacity, i.e. <strong>the</strong> ability <strong>of</strong> entrepreneurs to make useful application
<strong>of</strong> incoming knowledge to <strong>the</strong> daily routine and tasks <strong>of</strong> <strong>the</strong>ir managerial performance.
Fur<strong>the</strong>ring in <strong>the</strong> views <strong>of</strong> Mowery and Oxley (1995), that absorptive capacity reflects a spectrum <strong>of</strong>
organizational capabilities <strong>of</strong> effective utility <strong>of</strong> tacit knowledge, we content that <strong>the</strong> concept also
reflects similar capabilities at an individual level (entrepreneurial capabilities). Potentially, <strong>the</strong>
entrepreneurs who only espouse <strong>the</strong> types <strong>of</strong> competitor and self centered MMs, lack <strong>the</strong> opportunity
to adopt and effectively implement best practices, thus <strong>the</strong>y focus only on mere acquisition <strong>of</strong> patterns
or imitations <strong>of</strong> <strong>the</strong> <strong>of</strong>ferings <strong>of</strong> <strong>the</strong>ir competitors. In addition <strong>the</strong>se entrepreneurs appear not capable
<strong>of</strong> solving complex problems as <strong>the</strong> aforementioned MMs prevent <strong>the</strong>m from benefiting from <strong>the</strong>ir
learning experiences. This conclusion is consistent with <strong>the</strong> view <strong>of</strong> Kim (1998) who posits that
problem solving skills are consequent to absorptive capacity. Finally, consistent with <strong>the</strong> work <strong>of</strong>
Todorova and Durisin (2007) <strong>of</strong> <strong>the</strong> role <strong>of</strong> relationships in exploiting incoming knowledge, <strong>the</strong> study
findings permit us to infer that <strong>the</strong> relationships <strong>the</strong> entrepreneurs develop in <strong>the</strong> networks, may
facilitate <strong>the</strong>m to potentially become more able to absorb incoming knowledge.
The major limitation <strong>of</strong> <strong>the</strong> method was that <strong>the</strong> research design could not overcome <strong>the</strong> degree <strong>of</strong>
lack <strong>of</strong> cooperation between <strong>the</strong> interviewers and <strong>the</strong> subjects. The entrepreneurs were on occasion
unavailable, inpatient, and overly busy. In addition <strong>the</strong> limited funding was one <strong>of</strong> <strong>the</strong> main obstacles
<strong>of</strong> securing additional interviews, which would allow <strong>the</strong> researchers to triangulate <strong>the</strong> data submitted
by <strong>the</strong> entrepreneurs. One limitation <strong>of</strong> <strong>the</strong> study itself relies on <strong>the</strong> fact that <strong>the</strong> sample used was
localized in a specific region <strong>of</strong> <strong>the</strong> country. Although <strong>the</strong> sample comprised <strong>of</strong> <strong>the</strong> census <strong>of</strong> <strong>the</strong>
networks yet its size was ra<strong>the</strong>r limited. Therefore one <strong>of</strong> our considerations <strong>of</strong> future research is to
conduct a cross cultural analysis using data <strong>of</strong> samples <strong>of</strong> different national and international regions.
In those studies we hope to identify additional types <strong>of</strong> MMs as well as <strong>the</strong>ir distinct functionality in <strong>the</strong>
various defined dimensions <strong>of</strong> absorptive capacity <strong>of</strong> entrepreneurs. We are also particularly
interested in discovering <strong>the</strong> role <strong>of</strong> powerful network relationships in <strong>the</strong> degree and pace <strong>of</strong> new
knowledge absorption.
References
Ancona, D. and Caldwell, D. (1992) “Bridging <strong>the</strong> boundary: External activity and performance in organization
teams”, Administrative Science Quarterly, Vol. 37, No. 4, pp. 150-169.
941

Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
Cohen, W. and Levinthal, D. (1990) “Absorptive capacity: A new perspective on learning and innovation”,
Administrative Science Quarterly, Vol. 35, No. 1, pp. 128-152.
Daft, R.L. and Weick, K. E. (1984), "Toward a Model <strong>of</strong> Organizations as Interpretation Systems", Academy <strong>of</strong>
Management Review, Vol. 9, No. 2, pp. 284-95.
Daghfous, A. (2004) “Absorptive capacity and innovative enterprise systems: A two-level framework”,
International Journal <strong>of</strong> Innovation and Learning, Vol. 4, No. 1, pp. 60-73.
Daskou, S., Yannopoulos, P., and Koutoulas, G. (2012) "Mental models <strong>of</strong> Greek entrepreneurs: Evidence from
two networks", International Journal <strong>of</strong> Business and Economics Research, forthcoming.
Day, G.S. (1994) “Continuous learning about markets”, California Management Review, Vol. 36, No.4, Summer,
pp. 9-31.
Day, G.S. and Nedugandi, P. (1994) "Managerial representations <strong>of</strong> competitive advantage”, Journal <strong>of</strong>
Marketing, Vol. 58, No. 2, pp. 31-44.
Deshpande, R., Farley, J.U. and Webster, F.E. Jr. (1993) "Corporate culture, customer orientation, and
innovativeness in Japanese firms: a quadrad analysis," Journal <strong>of</strong> Marketing, Vol.57, No.1, pp.23-37.
Fulford, R. and Love, P.E.D. (2004) “Propagation <strong>of</strong> an alternative enterprise service application, adoption
model”, Industrial Management & Data Systems. Vol.104, No. 6, pp 450-456.
Gatignon, H.A., Anderson, E, and Helsen, K. (1989), "Competitive Reactions to Market Entry: explaining interfirm
differences", Journal <strong>of</strong> Marketing Research, Vol. 26, No.1, pp.44-55.
Glass, A.J. and Saggi, K. (1998) “International technology transfer and <strong>the</strong> technology gap”, Journal <strong>of</strong>
Development Economics, Vol. 55, No. 2, pp. 369-398.
Gummesson, E. (1996) “Relationship marketing and imaginary organizations: a syn<strong>the</strong>sis”, <strong>European</strong> Journal <strong>of</strong>
Marketing, Vol. 30, No. 2, pp 31-44.
Heiner, R.A. (1983) “The origin <strong>of</strong> predicatable behavior”, The American Economic Review, Vol.73, No.4, pp.
560-595.
Inkpen, A. and Dinur, A. (1998) “Knowledge management processes and international joint ventures”,
Organization Science, Vol. 9, No.4, pp. 454-468
Kadia, B.L. and Bhagat, R.S. (1998) ”Cultural constraints on transfer <strong>of</strong> technology across nations: Implications
for research in international and comparative management”, Academy <strong>of</strong> Management Review, Vol. 13, No.
4, pp. 559-571.
Karakaya, F. and Yannopoulos, P. (2010) “Defensive framework in global markets: A mental models approach,”
<strong>European</strong> Journal <strong>of</strong> Marketing, Vol. 44, No. 7/8, pp. 1077-1100.
Kiesler, S. and Sproull, L. (1982) "Managerial response to changing environments: Perspectives on problem
sensing from social cognition", Administrative Science Quarterly, Vol. 27, No. 4, pp 548-70.
Kim, L. (1998) “Crisis construction and organizational learning: Capability building in catching-up at Hyundai
Motor”, Organization Science, Vol. 9, No. 4, pp. 506-521.
Lane, P. and Lubatkin, M. (1998) “Relative absorptive capacity and interorganizational learning”, Strategic
Management Journal, Vol. 19, No. 5, pp. 461-477.
Levitt, T. (1960), "Marketing Myopia," Harvard Business Review, Vol.38, No.4, pp. 45-56.
Lu, L., Leung, K. and Koch, P. (2006) “Managerial knowledge sharing: The role <strong>of</strong> individual, interpersonal and
organizational factors”, Management and Organization Review, Vol. 2, No. 1, pp. 15-41.
Maignan, I. and Lukas, B.A. (1997), "Entry mode decisions: <strong>the</strong> role <strong>of</strong> managers’ mental models," Journal <strong>of</strong>
Global Marketing, Vol. 10, No. 4, pp 7-23.
March, J.G. (1994) A primer on decision making, The Free Press, New York.
Miles, M. B. and Huberman, A. M. (1994) Qualitative Data Analysis (2nd eds.) Sage, Thousand Oaks, CA.
Mowery, D.C. and Oxley, J.E. (1995) “Inward technology transfer and competitiveness. The role <strong>of</strong> national
innovation systems”, Cambridge Journal <strong>of</strong> Economics, Vol. 19, No. 1, pp. 67-93.
Murray, P. and Carter, L. (2005) “Improving marketing intelligence through learning systems and knowledge
communities in non-pr<strong>of</strong>it-workplaces”, Journal <strong>of</strong> Workplace Learning, Vol. 17, No. 7, pp. 421-435.
Nahapiet, J. and Ghoshal, S. (1998) “Social capital, intellectual capital, and <strong>the</strong> organizational advantage”,
Academy <strong>of</strong> Management Review, Vol. 23, No. 2, pp. 242–266.
Naver, J.C. and Slater, S.F. (1990) “The effect <strong>of</strong> market orientation on business pr<strong>of</strong>itability”, Journal <strong>of</strong>
Marketing, Vol. 54, No. 4, pp. 20-35.
Noble, C.H., Sinha, R.K. and Kumar, A. (2002) "Market orientation and alternative strategic orientations: A
longitudinal assessment <strong>of</strong> performance implications," Journal <strong>of</strong> Marketing, Vol. 66, No. 4, pp.25-39.
Rocha, F. (1999) “Inter-firm technological cooperation: Effects <strong>of</strong> absorptive capacity, firm-size and
specialization”. Economics <strong>of</strong> Innovation & New Technology, Vol. 8, No. 3, pp. 253-271.
Schilling, M. (1998) “Technological lockout: An integrative model <strong>of</strong> <strong>the</strong> economic and strategic factors driving
technology success and failure”. Academy <strong>of</strong> Management, Vol. 23, No.2, pp. 267-284.
Simon, H. A. (1955) "A Behavioral Model <strong>of</strong> Rational Choice", Quarterly Journal <strong>of</strong> Economics, Vol. 69, No. 1, pp
99-118.
Song, M., R., Calantone, J. and Di Benedetto, C. A. (1999) “Competitive forces and strategic choice decisions:
An experimental investigation in <strong>the</strong> United States and Japan”, Strategic Management Journal, Vol. 23,
No.10, pp. 969-978.
Stock, G., Greis, N. and Fischer, W. (2001) “Absorptive capacity and new product development”, Journal <strong>of</strong> High
Technology Management Research, Vol. 12, No. 1, pp. 77-91.
Stubbart, C.I. (1989), "Managerial cognition: A missing link in strategic management research", Journal <strong>of</strong>
Management Studies, Vol. 26, No. 4, pp. 325-47.
942

Evangelia Siachou, S<strong>of</strong>ia Daskou and Peter Yannopoulos
Thagard, P. (2005), Mind: Introduction to cognitive science, 2 nd edition, The MIT Press, Cambridge.
Todorova, G. and Durisin, B. (2007) “Absorptive capacity: Valuing a reconceptualization”, Academy <strong>of</strong>
Management Review, Vol. 32, No. 3, pp. 774-786.
Tzokas, N. and Saren, M. (2002) “Competitive advantage, knowledge and relationship marketing – where, what
and how? ”, Journal <strong>of</strong> Business and Industrial Marketing, Vol. 19, No.2, pp 124-135.
Voss, G.B. and Voss, G. Z. (2000) "Strategic orientation and firm performance in an artistic environment”, Journal
<strong>of</strong> Marketing, Vol. 64, No.1, pp. 67-83.
Walsh J.P., (1995) “Managerial and organizational cognition: A trip down <strong>the</strong> memory lane”, Organization
Science, Vol. 6, No.3, pp.280-321.
Weick, K. E. (1979) “Cognitive processes in organizations”, In Staw, B.W. (Ed.), Research in organizational
behavior, (Vol. 1, pp.41-74). CT: JAI Press, Greenwich.
Yannopoulos, P. and Daskou, S. (2010) “The role <strong>of</strong> mental models in managerial decision making”, The
International Journal <strong>of</strong> Arts and Sciences, Vol. 3, No.12, pp. 416-431.
Zahra, S. and George, G. (2002) “Absorptive capacity: A review, reconceptualization, and extension”, Academy
<strong>of</strong> Management Review, Vol. 27, No.2, pp. 185-203.
Zander, U. and Kogut, B. (1995) “Knowledge and <strong>the</strong> speed <strong>of</strong> <strong>the</strong> transfer and imitation <strong>of</strong> organizational
capabilities: An empirical test”, Organization Studies, Vol. 6, No. 1, pp. 76-92.
943

Relevance <strong>of</strong> Intellectual Capital for <strong>the</strong> Public Sector
Marta Christina Suciu, Luciana Picioruş, Cosmin Imbrişcă, Simona Busoi,
Ramona Spiridon and Magdalena Bunghez
Academy <strong>of</strong> Economic Studies <strong>of</strong> Bucharest, Bucharest, Romania
suciuchristina@yahoo.com
lusianapiciorus@yahoo.co.uk
imbrisca_c@yahoo.com
busois@yahoo.com
ramonaspiridon@yahoo.com
magda_ella@yahoo.com
Abstract: The public institutions <strong>of</strong> an economy have always been reluctant to change. In Romania, unlike in <strong>the</strong>
case <strong>of</strong> private enterprises, <strong>the</strong>re is little to no real competition, hardly a direct relationship between <strong>the</strong> price paid
by <strong>the</strong> individual and <strong>the</strong> services rendered and, <strong>the</strong> most important, <strong>the</strong>re are social and political factors that
must be taken into consideration when dealing with any public entity. All <strong>the</strong>se have created a thoroughly
bureaucratic and rigid system that responds slowly to top-down changes and, if ever, to bottom-up ones.
Never<strong>the</strong>less recent studies like those carried during 2001-2002 in Denmark, suggest an increased role <strong>of</strong>
intellectual capital in developing new public management thinking. The paper is divided into two parts. Firstly, it
deals with creating a context for <strong>the</strong> research. This will be done by analyzing <strong>the</strong> current perspective on
intellectual capital management in public enterprises and contextualizing <strong>the</strong>m for <strong>the</strong> Romanian system.
Secondly, <strong>the</strong> study opens a more direct approach and attempts to determine what are <strong>the</strong> agents <strong>of</strong> change,
how change is implemented, what <strong>the</strong> new perspective on intellectual capital is and what evaluation methods are
used. Due to <strong>the</strong> multifaceted nature <strong>of</strong> <strong>the</strong> topic a multi-method combination is used as research as it will start
with a questionnaire aimed at regular employees in order to determine both <strong>the</strong>ir level <strong>of</strong> interest and awareness
and this will be followed up with a series <strong>of</strong> in depth structured interviews with <strong>the</strong> purpose <strong>of</strong> obtaining more
qualitative information. The results should be able to create a general image <strong>of</strong> <strong>the</strong> present perception on
intellectual capital. In order to <strong>of</strong>fer a solid framework for this information, secondary data is used as well,
qualitative and quantitative, from o<strong>the</strong>r up to date studies on this topic. The findings suggest a few changes, as
deemed necessary, in order to improve <strong>the</strong> current organizational climate. The results are relevant because a
study <strong>of</strong> intellectual capital in public entities is mandatory for <strong>the</strong> Romanian public sector. Today, public
institutions are promoting <strong>the</strong>mselves as belonging to <strong>the</strong> business world as well, making use <strong>of</strong> intellectual
capital and <strong>the</strong> competitive advantage <strong>the</strong>y create, in order to promote <strong>the</strong>m on <strong>the</strong> ‘market’. The first things to do
when attempting this are to observe how <strong>the</strong>se institutions perceive <strong>the</strong>mselves, if and how are <strong>the</strong>y preparing for
<strong>the</strong>se coming changes. The governments’ approach must change with it or risk not only lagging behind but
downright hindering <strong>the</strong> private sector. In order to prevent <strong>the</strong> negative aspects it is necessary to understand
what <strong>the</strong> role <strong>of</strong> government in knowledge based economy is.
Keywords: Intellectual capital, public sector, knowledge based economy
1. Introduction
Intellectual Capital is playing an ever more important role in <strong>the</strong> modern economy. More and more
people are starting to take note <strong>of</strong> <strong>the</strong> changes in <strong>the</strong> economy, changes that encompass <strong>the</strong> growth
<strong>of</strong> <strong>the</strong> service sector, <strong>the</strong> importance <strong>of</strong> brands and image and more so <strong>the</strong> flow <strong>of</strong> information within
<strong>the</strong> company, in-between companies or with <strong>the</strong>ir clients or suppliers. And so individuals can
communicate almost instantaneously and work toge<strong>the</strong>r not just in <strong>the</strong> same building but in different
companies on opposite sides <strong>of</strong> <strong>the</strong> world. As a result <strong>of</strong> all <strong>the</strong>se changes ‘Intellectual Capital’ has
become a key focus for academics everywhere. However, most <strong>of</strong> <strong>the</strong> studies devoted to this topic
seem to deal with <strong>the</strong> private sector <strong>of</strong> <strong>the</strong> economy and neglect <strong>the</strong> fact that <strong>the</strong> public sector has its
own needs and structures that are very different than those in <strong>the</strong> private sector (Abeysekera, Jebeile
and Kamuruddin, 2010). The public sector can encompass anything from education, transport,
healthcare or defense along with <strong>the</strong> necessary administrative institutions that oversee <strong>the</strong>se
operations as well as <strong>the</strong>ir financing.
The study focuses on <strong>the</strong> importance <strong>of</strong> intellectual capital in <strong>the</strong> public sector <strong>of</strong> <strong>the</strong> economy, how it
is perceived and, more important, how it is adapting to create a framework onto which <strong>the</strong> private
sector can develop. In order to have an appropriate understanding <strong>of</strong> <strong>the</strong> topic a multi-method <strong>of</strong>
research was used for this paper. The first part aimed to create a thorough review <strong>of</strong> previous
research both through a literature review as well as empirical research studies, on <strong>the</strong> topic <strong>of</strong>
intellectual capital and <strong>the</strong> way this research translates to <strong>the</strong> public sector <strong>of</strong> <strong>the</strong> economy. However,
944

Marta Christina Suciu et al
it focuses mainly on <strong>the</strong> present Romanian public sector and <strong>the</strong> role played by intellectual capital in
this area <strong>of</strong> <strong>the</strong> Romanian economy.
2. Perspectives on intellectual capital
The first issue refers to what Intellectual Capital is and how it relates with <strong>the</strong> public economy.
However, even if it has been studied for several decades now, <strong>the</strong>re is no real consensus as to <strong>the</strong>
definition. Schneider and Samkin (2008) have grouped <strong>the</strong> numerous definitions under two distinct
frameworks: process and classification.
Table 1: Defining Intellectual Capital
Author Definition <strong>of</strong> Intellectual Capital Framework
Roos et al
(1997)
Sveiby
(1997)
Brooking
(1996)
Edvinsson
and
Malone
(1997)
Stewart
(1997)
Sullivan
(1999)
Abeysekera
(2008)
IC is classified as structural and human capital, thinking and non-thinking
asset. The authors make <strong>the</strong> distinction primarily on <strong>the</strong> premise that human
capital requires different management approaches than o<strong>the</strong>r types <strong>of</strong> capital.
IC consists <strong>of</strong> <strong>the</strong> invisible assets <strong>of</strong> <strong>the</strong> organizations which
include: employee competence (skills, education, and
experience) and <strong>the</strong>ir capacity to act in a wide variety <strong>of</strong>
situations; internal structure (management, structure patents,
concepts, models, research and development capability and s<strong>of</strong>tware); and
external structure (image, brands, customers and supplier relations).
IC has four components: market assets, human-centred assets, intellectual
property assets, and infrastructural assets.
IC implies human, system and market elements. Human capital refers to what
people can do individually and collectively. The system component represents
<strong>the</strong> knowledge in <strong>the</strong> firm which is independent <strong>of</strong> people and includes patents,
contracts, databases, and information and production technology. The market
component consists <strong>of</strong> <strong>the</strong> relationships between <strong>the</strong> organization and
outsiders, e.g. suppliers, distributors and customers.
IC is defined as intellectual material- knowledge, information, intellectual
property and experience- that can be put to use to create wealth.
IC is knowledge that can be converted into pr<strong>of</strong>its. It comprises two categories:
human capital and intellectual assets. Human capita consists <strong>of</strong> <strong>the</strong> firm’s
individual employees who posses skills, abilities, knowledge and know-how.
Within each employee resides <strong>the</strong> tacit (uncodified knowledge <strong>the</strong> firm seeks to
utilize). Intellectual assets are created whenever human capital is codified.
IC is “intellectual material that has been formalized, captured and leveraged to
produce a higher-valued asset” and if successfully managed leading to future
benefit that does not have a physical or financial embodiment.
Classification
Classification
Classification
Classification
Process
Process
Process
Source: “Researching for Intellectual Capital Management in New Public Sector Organisations for
Effectiveness, Efficiency, and Reputation”, Abeysekera, I., Jebeile, S., Kamuruddin, K. (2010),
[online],
http://www.afaanz.org/openconf/2010/modules/request.php?module=oc_program&action=view.php&i
d=41
As it can be seen from table 1, certain elements are common to all <strong>the</strong> definitions such as: <strong>the</strong>
emphasis on human resources, <strong>the</strong> value <strong>of</strong> knowledge and most important, <strong>the</strong> creation <strong>of</strong> value for
<strong>the</strong> organization and as a result for <strong>the</strong> client. Therefore, we can say that we have certain key
elements in all <strong>the</strong>se definitions (table 2):
Table 2: Common elements to Intellectual Capital definitions
Approach Elements Description
Internal
Capital
External
Capital
Human
Resources
Organizational
Capital
Structural Capital
Internal Relations
Customer Capital
Relational Capital
External Capital
Employee
Competence
Knowledge embedded in organizational structures and processes,
patents research and development, technology and systems.
Relationships with customers and suppliers, brand names, trademarks,
reputation.
Employee skills, training and education, experience.
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Marta Christina Suciu et al
Source: “Researching for Intellectual Capital Management in New Public Sector Organisations for
Effectiveness, Efficiency, and Reputation”, Abeysekera, I., Jebeile, S., Kamuruddin, K. (2010) [online],
http://www.afaanz.org/openconf/2010/modules/request.php?module=oc_program&action=view.php&i
d=41
The fact that all this research has been done on <strong>the</strong> private sector has had as a result a distinct focus
on <strong>the</strong> needs <strong>of</strong> private companies. However, as noted by Cinca et al (2003), <strong>the</strong> public sector has its
own set <strong>of</strong> needs and rules and <strong>the</strong> private sector concept <strong>of</strong> intellectual capital, while useful, is
insufficient due to <strong>the</strong> fact that <strong>the</strong> public sector concept involves more human and organizational
capital.
The methods for evaluating human capital have been grouped (Sveiby, 2000) into four distinct
categories: market capitalization, return on assets, direct intellectual capital methods, scorecard
methods. Of all <strong>the</strong>se, <strong>the</strong> first three focus on <strong>the</strong> financial aspects <strong>of</strong> <strong>the</strong> entity, but, for a public
enterprise, finance, while important, must compete with o<strong>the</strong>r objectives which can be even more
important such as responsiveness for a medical emergency from <strong>the</strong> service provider. A scorecard
method would prove adequate under <strong>the</strong>se circumstances as it allows <strong>the</strong> flexibility required by <strong>the</strong>
multiple concurrent objectives that need to be satisfied at <strong>the</strong> same time by <strong>the</strong> different public
entities.
The scorecard method is not without its faults though. In order for <strong>the</strong> method to be <strong>of</strong> use it needs to
have a solid foundation and must be fully supported by <strong>the</strong> authorities. However, <strong>the</strong>se same
authorities have shown <strong>the</strong>mselves to be very reluctant in <strong>the</strong> subject matter and so <strong>the</strong> accounting
systems have been rigid. At <strong>the</strong> moment <strong>the</strong> Romanian accounting system focuses on <strong>the</strong> historical
costs and reported transactions but <strong>the</strong>y focus very little on value creation and so human capital,
organizational and structural capital investments are instead treated as expenses and <strong>the</strong>re still exists
a strong focus on <strong>the</strong> physical assets possessed by an organization. As a result <strong>of</strong> <strong>the</strong> rise <strong>of</strong>
information technology, globalization, stressing innovation as a generator <strong>of</strong> competitive advantage
<strong>the</strong>re is a shift in perspective from <strong>the</strong> tangible to <strong>the</strong> intangible (More and Craig, 2008) but <strong>the</strong>
transformation is slow.
3. Empirical study and research methodology
For <strong>the</strong> purposes <strong>of</strong> this study an initial methodology was built based on <strong>the</strong> supposition that <strong>the</strong>re
already is some degree <strong>of</strong> familiarity with <strong>the</strong> topic <strong>of</strong> intellectual capital in public enterprises. Based
on this assumption a questionnaire was designed and structured around <strong>the</strong> three dimensions <strong>of</strong>
intellectual capital: human, relational and organizational components, having <strong>the</strong> purpose to
determine <strong>the</strong> current state <strong>of</strong> IC management in public enterprises.
The items were adapted from specific literature where possible, while new items were developed
according to <strong>the</strong> defined objectives <strong>of</strong> our study. The questionnaire was aimed at 150 employees
working in <strong>the</strong> Romanian public sector. However, <strong>the</strong> questionnaire was firstly tested and revised. But
after <strong>the</strong> responses were centralized, having a retrieval rate <strong>of</strong> 100%, <strong>the</strong> results were deemed
irrelevant. The main reasons were inconsistencies within <strong>the</strong> same questionnaire or downright
contradictions with <strong>the</strong> obvious practices.
As a result <strong>the</strong> methodology had to be adapted and was refocused on obtaining qualitative data
through <strong>the</strong> use <strong>of</strong> in-depth interviews with several employees that worked in <strong>the</strong> equivalent <strong>of</strong> middle
management in public organizations. These interviews followed <strong>the</strong> same basic premise and structure
<strong>of</strong> <strong>the</strong> questionnaire however <strong>the</strong>y allowed for significantly more room personal opinions, and
following an interview protocol.
The first section referred to identification, but in order to ensure anonymity only age and gender were
recorded on <strong>the</strong> <strong>of</strong>ficial documents registered in <strong>the</strong> research.
In <strong>the</strong> second section, <strong>the</strong>re were questions where respondents were asked to rank on a Likert scale,
with answers from 1 to 5 where 1 means “strong disagreement” and 5 “strong agreement”, a series <strong>of</strong>
statements concerning <strong>the</strong> human capital in <strong>the</strong>ir institution, along with supplying additional
argumentation for <strong>the</strong>ir choice. Such items referred to employees’ qualifications, level <strong>of</strong> training
courses <strong>of</strong>fered by <strong>the</strong> organization and <strong>the</strong>ir availability, and degree <strong>of</strong> correlation with <strong>the</strong>
employees’ needs. A separate part <strong>of</strong> <strong>the</strong> regarding pr<strong>of</strong>essional development opportunities and <strong>the</strong>
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performance and reward system was included; <strong>the</strong> questions within were open so that <strong>the</strong>y could
freely express <strong>the</strong>ir opinions.
In <strong>the</strong> organizational capital part <strong>the</strong> questions evaluated <strong>the</strong> different resources available to <strong>the</strong>
institution and to <strong>the</strong> employees by focusing on whe<strong>the</strong>r <strong>the</strong> information is easily available, useful,
updating system, knowledge management (tacit and codified knowledge, as determining <strong>the</strong>ir ability
in preserve knowledge even if workers leave <strong>the</strong> institution (Ministry <strong>of</strong> Finance, 2004)). This is very
important as it can <strong>of</strong>fer valuable information over <strong>the</strong> capacity <strong>of</strong> <strong>the</strong> public sector to transform and
reinvent itself toward reaching its final goal: a ‘companyzed’ (Brunsson, 1994), ‘accountingized’
(Power and Laughlin, 1994) public entity, according to <strong>the</strong> new public management concept.
Never<strong>the</strong>less, relational capital is as significant as <strong>the</strong> o<strong>the</strong>r two divisions in <strong>of</strong>fering an overall image
<strong>of</strong> a public sector <strong>of</strong> an economy. Here, <strong>the</strong> interview extracted information on <strong>the</strong> relationships that
<strong>the</strong> institution has built with o<strong>the</strong>r organizations, such as government agencies, universities and
research centers, pr<strong>of</strong>essional organizations, etc., as well as on <strong>the</strong> way it works on maintaining
<strong>the</strong>m. In this second segment, <strong>the</strong> interest was shift towards <strong>the</strong> management <strong>of</strong> <strong>the</strong> collected
information- s<strong>of</strong>tware, meetings for dissemination <strong>of</strong> knowledge, forums and blogs for <strong>the</strong>ir
employees.
This research method carried both advantages as well as disadvantages. The most important
shortcoming is obviously <strong>the</strong> bias that can appear both from <strong>the</strong> part <strong>of</strong> <strong>the</strong> interviewee as well as <strong>the</strong>
researcher especially involuntary bias due <strong>the</strong> way <strong>the</strong> information is perceived by <strong>the</strong> parties.
However, it meant addressing <strong>the</strong> same questions to all <strong>the</strong> interviewed employees, so that <strong>the</strong>
responses could be aggregated in a unitary manner. The order <strong>of</strong> <strong>the</strong> questions was also maintained
unchanged for all <strong>the</strong> respondents. As it is an in-depth interview, a non-structured part <strong>of</strong> <strong>the</strong> interview
was included as well. It was guided by <strong>the</strong> interviewer, allowing more freedom <strong>of</strong> expression from <strong>the</strong>
respondents and it was addressed in a less formal and rigid manner.
4. Romanian public sector
Romania is a republic with a democratically elected Parliament and President. The Parliament
represents <strong>the</strong> legislative branch <strong>of</strong> <strong>the</strong> system and, based on a mandate from Parliament, <strong>the</strong>
Government represents <strong>the</strong> executive branch and as a result oversees all <strong>the</strong> activities <strong>of</strong> <strong>the</strong> public
sector <strong>of</strong> <strong>the</strong> economy. The public system is <strong>the</strong>n divided between <strong>the</strong> central authorities and <strong>the</strong> local
ones.
The central authorities represent <strong>the</strong> Government, <strong>the</strong> fifteen Ministries and numerous o<strong>the</strong>r central
authorities that oversee a certain part <strong>of</strong> <strong>the</strong> economy or are tasked with a specific objective, <strong>the</strong>se
report ei<strong>the</strong>r to a ministry or directly to <strong>the</strong> prime-minister.
Local authorities are usually represented by <strong>the</strong> mayor and <strong>the</strong> mayor’s <strong>of</strong>fice along with a local
council and a prefect. It is <strong>the</strong>ir job to oversee local activities and as such <strong>the</strong>y usually take care <strong>of</strong> <strong>the</strong>
day to day activities <strong>of</strong> <strong>the</strong> local representatives <strong>of</strong> central authorities such as local police, healthcare
or schools. The exact specificities are considerably more complex when you take into consideration
who reports to whom, who provides <strong>the</strong> financing and how different events can alter this chain <strong>of</strong>
command.
The exact activities <strong>of</strong> all <strong>the</strong>se institution are thoroughly regulated by different laws <strong>of</strong> which some <strong>of</strong>
<strong>the</strong> most important are:
The Local Administration Law 215/2001
The Romanian Civil Servant Law 188/1999
The Health Care Law 95/2006
National Education Law 1/2011
The Public Acquisition Government Ordinance OUG 34/2006
Fiscal Code 571/2003 and OG 92/2003
The Penal Procedural Code
The Pension Law 263/2011
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Apart from <strong>the</strong>se laws a series <strong>of</strong> o<strong>the</strong>r more focused laws or <strong>of</strong> procedures formulated by <strong>the</strong> ministry
or by local authorities insure <strong>the</strong> groundwork for any <strong>of</strong> <strong>the</strong> daily activities.
But what is ano<strong>the</strong>r characteristic <strong>of</strong> <strong>the</strong> Romanian public sector is <strong>the</strong> fact it is perceived as not being
capable <strong>of</strong> performance, as in many o<strong>the</strong>r countries (Mouritsen et al, 2004). And this perspective, in
<strong>the</strong> context <strong>of</strong> a pr<strong>of</strong>ound and prolonged economic crisis, many times attributed to public sector
deficiencies that hinder recovery, is more and more spread in our country. Within <strong>the</strong>se
circumstances, intellectual capital could prove an effective instrument in evaluating and assessing <strong>the</strong>
public entity, especially if including a intellectual capital statement as a compulsory part <strong>of</strong> <strong>the</strong>
periodical evaluation process.
5. Results and interpretation
The initial survey, based on <strong>the</strong> three dimensions <strong>of</strong> intellectual capital, was supposed to allow for a
better understanding <strong>of</strong> how <strong>the</strong> system works, where <strong>the</strong> problems are and how <strong>the</strong> individuals cope
with changes. However, once part <strong>of</strong> <strong>the</strong> results were in, it became obvious that <strong>the</strong>y will be <strong>of</strong> little
use because <strong>the</strong>y shaped <strong>the</strong> picture <strong>of</strong> an ideal system that was working in perfect synchronization
with everyone involved, individuals and institutions. Also, <strong>the</strong> everyday reality showed that this was far
from <strong>the</strong> truth and as a result a more thorough analysis was required.
5.1 Human capital
Human resources are <strong>the</strong> key to <strong>the</strong> success <strong>of</strong> every organization even more so for an entity that
provides services like <strong>the</strong> ones in <strong>the</strong> public sector. Recruiting <strong>the</strong> best employee for <strong>the</strong> job is critical.
Starting from a formal level, <strong>the</strong> national legal procedure enforce indicates that it does receive <strong>the</strong>
attention it deserves.
The Romanian Civil Servant Law 188/1999 stipulates that a person can be hired only after passing an
exam for <strong>the</strong> position. However, since it first passed through Parliament it has been amended on a
number <strong>of</strong> occasions and at <strong>the</strong> present moment it allows for open positions to be filled not only
thorough advancement, recruitment or short term transfers under special circumstances but also for a
person to be assigned directly. The rationale behind this is to allow more flexibility in order to fill <strong>the</strong>
position temporarily so as not to disrupt <strong>the</strong> organization and permit more time to find <strong>the</strong> right
employee.
Additionally, according to article 51 <strong>of</strong> <strong>the</strong> Romanian Civil Servant Law, authorities have to obligation
to provide funding for employee trainings.
These can be short term, less than 90 days, or long term, in <strong>the</strong> country or abroad; for <strong>the</strong> duration <strong>of</strong>
<strong>the</strong>se programs <strong>the</strong>y are entitled to <strong>the</strong> normal salary yet are excused from regular work.
According to our findings from <strong>the</strong> initial survey, most employees found <strong>the</strong> training programs <strong>of</strong>fered
within <strong>the</strong> institution <strong>the</strong>y work for as satisfactory and this was backed up by <strong>the</strong> interviews’ results. In
fact, <strong>the</strong> major complaints were related to <strong>the</strong> short length <strong>of</strong> <strong>the</strong> course and <strong>the</strong> long period’s inbetween.
In terms <strong>of</strong> degree <strong>of</strong> correlation with <strong>the</strong>ir pr<strong>of</strong>essional and development needs things are
less positive: university and doctoral studies are not financed in any way by <strong>the</strong> employer, coupled
with <strong>the</strong> fact that should <strong>the</strong> employee take part in any training program that would last more than 90
days <strong>the</strong>y would have to sign an agreement to keep working as a civil servant for ano<strong>the</strong>r two to five
years, and that up until recently <strong>the</strong> monthly wage received by a civil servant was actually composed
<strong>of</strong> a salary along with benefits that at times represented just as much as <strong>the</strong> salary itself, create
disincentives for following <strong>the</strong>ir wants in terms <strong>of</strong> education and courses to attend.
The performance and reward system within <strong>the</strong> public sector s based on an evaluation <strong>of</strong> <strong>the</strong> civil
servant workforce. This is done annually in accordance with article 69 <strong>of</strong> <strong>the</strong> Romanian Civil Servant
Law, according to <strong>the</strong> methodology proposed by <strong>the</strong> National Civil Servant Agency and approved by
<strong>the</strong> Romanian Government. It is <strong>of</strong>ficially used in determining <strong>the</strong> salary evolution, promotion or
dismissal. During <strong>of</strong> <strong>the</strong> interviews it was shaped a strong view regarding <strong>the</strong> fact that in reality no one
is truly paying attention to <strong>the</strong> evaluation because salaries tend to follow a fairly rigid grid, benefits are
not linked to <strong>the</strong> annual evaluation, and promotion was ei<strong>the</strong>r after passing an exam or being
nominated for <strong>the</strong> position and dismissal was such a rare occurrence that no employee actually gave
it any thought.
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The resources accessible at institutional level for <strong>the</strong> entity and <strong>the</strong> employees were described as
insufficient, while information availability is reduced. Some <strong>of</strong> <strong>the</strong> reasons considered responsible <strong>the</strong>
insufficient number <strong>of</strong> employees, mostly reduced due to <strong>the</strong> economic crisis and <strong>the</strong> need to cut <strong>the</strong>
public budget, some, <strong>the</strong> IT systems which are not enough upgraded so that <strong>the</strong>y can face increasing
volume <strong>of</strong> information and necessities.
Ano<strong>the</strong>r delicate issue was knowledge management, <strong>of</strong> both tacit and codified knowledge. Consistent
with article 42 paragraph 2: “Supervising Civil Servants must support <strong>the</strong> propositions and initiatives <strong>of</strong>
those working under him in order to improve <strong>the</strong> activity <strong>of</strong> <strong>the</strong> public authority or public institution in
which <strong>the</strong>y work, as well as <strong>the</strong> quality <strong>of</strong> <strong>the</strong> service <strong>of</strong>fered to <strong>the</strong> citizen”. But this is perceived as
somewhat false affirmation. Knowledge remains many times tacit as many employees consider that
just a insignificant percentage <strong>of</strong> <strong>the</strong>ir proposal was turned into reality, while if anything did succeed
<strong>the</strong> superior would reap <strong>the</strong> benefit.
The phrases used for describing <strong>the</strong> system were thoroughly bureaucratic, very rigid and
cumbersome; a system where any decision takes very long time to be made.
5.3 Relational capital
Romanian public entities usually function as monopolies in <strong>the</strong>ir own area <strong>of</strong> expertise. As a result
<strong>the</strong>re is no competition in social areas such as national defense or justice and very limited private
competition in terms <strong>of</strong> education, healthcare, infrastructure or security.
The relations developed between different public entities, public entities and clients, educational and
research centers or pr<strong>of</strong>essional associations and o<strong>the</strong>r type <strong>of</strong> organization with which <strong>the</strong>y share a
common interest are weak. Most <strong>of</strong> <strong>the</strong> employees remember only a few campaigns aimed at raising
public awareness and involvement, and <strong>the</strong>se were usually aimed at improving <strong>the</strong>ir reputation and no
actually building anything similar to a “client base” as <strong>the</strong>se are already locked-in. They collaborate
sometimes with different universities in presenting jobs opportunities, but <strong>the</strong>se are rare events and
not always on a regular base.
As for <strong>the</strong> management <strong>of</strong> <strong>the</strong> information collected from <strong>the</strong>ir partners, this is hardly disseminated,
namely from two reasons: financial resources are not stipulated within <strong>the</strong> budget for this or only very
small amounts, and sometimes because this is not considered a productive practice for <strong>the</strong> institution
itself. Forums and blogs exist and some <strong>of</strong> <strong>the</strong> employees use <strong>the</strong>m, but not <strong>of</strong>ficially created by <strong>the</strong>
public organization but outside its <strong>of</strong>ficial framework.
5.4 The Romanian public enterprise reality
Analyzing <strong>the</strong> results <strong>of</strong> <strong>the</strong> research led to obviously to an interpretation regarding <strong>the</strong> dysfunctions in
<strong>the</strong> public system.
Firstly, <strong>the</strong> employees, as representatives <strong>of</strong> <strong>the</strong> human capital component <strong>of</strong> Intellectual Capital,
though satisfied with <strong>the</strong> trainings <strong>of</strong>fered, were not content with <strong>the</strong>ir length and <strong>the</strong> long periods
elapsing between. Also <strong>the</strong> fact that no public employer <strong>of</strong>fers financial support in order to graduate
from university or a PhD is upsetting. On <strong>the</strong> o<strong>the</strong>r hand, many <strong>of</strong> <strong>the</strong> higher hierarchy positions are
conditioned by tertiary education as well as increases in salary. And because <strong>of</strong> <strong>the</strong> generic
requirements stipulated in <strong>the</strong> law itself, this has created a political opportunity with certain key
positions being forever open and certain employees rotating between <strong>the</strong>m.
Knowledge and resources management fails as new technology is not yet methodically implemented
at national level, though hesitant initiatives exist. But an efficient knowledge management may be a
response and streng<strong>the</strong>n <strong>the</strong> organization by showing that knowledge comes first for it, that <strong>the</strong>y are
innovative and that humans come first as <strong>the</strong>y are <strong>the</strong> main sources <strong>of</strong> future codified knowledge that
is vital for <strong>the</strong> well functioning <strong>of</strong> <strong>the</strong> system.
The investments in relational capital are at best sporadic and it <strong>the</strong>ir degree <strong>of</strong> success is uncertain,
thing which is underlined too by <strong>the</strong> small number <strong>of</strong> campaigns and PR events or communication
instruments used. However, <strong>the</strong> negative implications are both for <strong>the</strong> people who work inside <strong>the</strong>
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public institution, and whose sense <strong>of</strong> belonging is weak, and for those outside (limited
communication though <strong>the</strong> boundaries <strong>of</strong> enterprises nowadays are more and more permeable).-
clients, suppliers, future employees, etc.
These details in particular, along with <strong>the</strong> evaluation system which is neglected by most <strong>of</strong> <strong>the</strong>m,
suggest that it is an urgent need for <strong>the</strong> public sector entities to reconsider <strong>the</strong>ir strategies, human,
organizational and relational, and think <strong>the</strong>m in an integrated and national manner, not fractioned and
focus more on intellectual capital. Therefore, an intellectual capital statement could be a solution. It is
a very useful instrument in filling <strong>the</strong> gap between <strong>the</strong> desired level <strong>of</strong> performance and <strong>the</strong> actual
one, streng<strong>the</strong>n <strong>the</strong> relations between organizations within <strong>the</strong> same economy, allowing also a more
flexible and efficient flow <strong>of</strong> resources and knowledge and information within <strong>the</strong> entities, while
employees could develop and become more aware <strong>of</strong> <strong>the</strong>ir educational and pr<strong>of</strong>essional needs.
6. Conclusions
The study <strong>of</strong> intellectual capital from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> Romanian public sector <strong>of</strong> <strong>the</strong> economy is
a very intricate issue due to <strong>the</strong> troubles <strong>of</strong> finding a conceptual framework on which to base <strong>the</strong> study
and to actually perform <strong>the</strong> analysis while taking into consideration all <strong>the</strong> national specificities.
First <strong>of</strong> all, intellectual capital has to find its own place in <strong>the</strong> public sector. The findings <strong>of</strong> this paper
leads us to consider it would be best for intellectual capital belonging to public entities to be seen and
analyzed as a process through which value is created for <strong>the</strong> citizens and <strong>the</strong> system itself, and that
<strong>the</strong> evaluation method would be to use a scoreboard system adapted to its specific needs. Until that
happens it is necessary to achieve a consensus in terms <strong>of</strong> what it is and how it can be recognized
and managed properly in a public institution.
In <strong>the</strong> case <strong>of</strong> Romania it is very hard to say that it follows a certain path, if any, in <strong>the</strong> evaluation and
management <strong>of</strong> intellectual capital.
The current system is trying to cope with high standards, obligatory at <strong>European</strong> Union or o<strong>the</strong>r
international bodies’ level, which it does not understand completely yet were imposed on it. In o<strong>the</strong>r
words it is trying to adapt while adequately fulfilling certain standards, fulfilling o<strong>the</strong>rs only at a formal
level and completely disregarding o<strong>the</strong>rs. It must be pointed out that, while <strong>the</strong> changes are in <strong>the</strong>
right direction, <strong>the</strong>y are asymmetrical in terms <strong>of</strong> speed and expectation and <strong>the</strong> system is burdened
by deeper problems that have less to do with management and more to do with corruption matters or
party politics.
Acknowledgments:
This article is a result <strong>of</strong> <strong>the</strong> projects PARTENERIATE 92116, project that had been obtained on
competition bases and coordinated by <strong>the</strong> first author <strong>of</strong> <strong>the</strong> paper and POSDRU/6/1.5/S/11 „Doctoral
Program and PhD Students in <strong>the</strong> education research and innovation triangle”. The last project is co
funded by <strong>the</strong> <strong>European</strong> Social Fund through The Sectorial Operational Programme for Human
Resources Development 2007-2013, coordinated by The Academy <strong>of</strong> Economic Studies Bucharest.
References
Abeysekera, I., (2008) ‘’Intellectual capital disclosure trends: Singapore and Sri Lanka’’, Journal <strong>of</strong> Intellectual
Capital, Vol. 9 Iss: 4, pp.723 – 737
Abeysekera, I., Jebeile, S., Kamuruddin, K. (2010) ‘‘Researching for Intellectual Capital Management in New
Public Sector Organisations for Effectiveness, Efficiency, and Reputation’’, Paper presented at <strong>the</strong> 2010
AFAANZ <strong>Conference</strong>, 4-6 July, Christchurch, New Zealand, [online],
http://www.afaanz.org/openconf/2010/modules/request.php?module=oc_proceedings&action=vie
w.php&a=Accept+as+Paper&id=41
Brooking, A., (1996) Intellectual Capital: Core Assets for <strong>the</strong> Third Millenium Enterprise, Thomson Business
Press, London
Brunsson, N. (1994), “Politicization and companyzation – on institution affiliation and confusion in <strong>the</strong>
organizational world”, Management Accounting Research, Vol. 5, pp. 323-35
Cinca, C. S.,Molinero, C. M. & Queiroz, A. B. (2003) ‘’The measurement <strong>of</strong> intangible assets in public sector
using scaling techniques’’, Journal <strong>of</strong> Intellectual Capital, Volume 4, Number 2, 2003 , pp. 249-275(27)
Edvinsson, L. and Malone, M. S. (1997) Intellectual Capital: Realizing Your Company's True Value by Finding Its
Hidden Brainpower, Harper Business, New York
Lege nr. 188/1999 privind Statutul functionarilor publici (The Law no. 188/1999 regarding <strong>the</strong> Statute <strong>of</strong> Civil
Servants), [online], http://legislatie.resurse-pentru-democratie.org/188_1999.php
950

Marta Christina Suciu et al
Ministry <strong>of</strong> Finance (2000), Knowledge Management and Intellectual Capital Statements in Government,’
Copenhagen.
Mouritsen, J., Thorbjørnsen, S., Bukh, P., Johansen, M. (2004) “Intellectual capital and new public management.
Reintroducing enterprise“, Learning Organization, Vol. 11 Iss: 4/5, pp.380 – 392
Power, M. and Laughlin, R. (1994), “Critical <strong>the</strong>ory and accounting”, in Alvesson, M. and Willmott, H. (Eds),
Critical Management Studies, Sage, London, pp. 113-35
Roos, J.,Roos, G.,Dragonetti, N. and Edvinsson, L. (1997) Intellectual Capital: Navigating <strong>the</strong> new Business
Landscape, Macmillan Press, London
Schneider, A. and Samkin, G. (2008) ‘’Intellectual capital reporting by <strong>the</strong> New Zealand local government sector’’,
Journal <strong>of</strong> Intellectual Capital, Vol. 9 Iss: 3, pp.456 - 486
Stewart, T. A., (1997) Intellectual Capital: The New Wealth Organizations, Doubleday Dell Publishing Group, Inc.,
New York
Sullivan, P. H. (1999) ‘’Pr<strong>of</strong>iting from intellectual capital’’, Journal <strong>of</strong> Knowledge Management, Vol. 3 Iss: 2,
pp.132 – 143
Sveiby, K. E., (1997) The New Organizational Wealth: Managing and Measuring Knowledge based Assets, C.A.,
Berret-Koehler Publishers, Inc., San Francisco
Sveiby, K., (2000) Measuring intangible assets - an emerging standards, in Morey, D., Maybury, M. and
Thuraisingham, B. (Eds.) Knowledge Management- Classic Contemporary Works, MIT Press, Cambridge
MA
951

Managing Intellectual Capital in SMEs in <strong>the</strong> Framework <strong>of</strong>
<strong>the</strong> Knowledge Based Society
Marta-Christina Suciu, Alexandru Ghiţiu-Brătescu Simona Busoi, Ramona
Spiridon, Cristiana Bolocan Protopopescu, Florin Sirbu and Irina Dumitrescu
Academy <strong>of</strong> Economic Studies Bucharest, Bucharest, Romania
suciuchristina@yahoo.com,
alexghitiu@yahoo.com,
busois@yahoo.com
ramonaspiridon@yahoo.com,
bolocan_proto@yahoo.com,
florin_sirbu@yahoo.com
irinadumitrescuupg@yahoo.com
Abstract: The aim <strong>of</strong> this article is to identify <strong>the</strong> operating systems that companies can adopt to manage
intellectual capital, especially in SMEs. Based on intellectual capital management and resourced based approach
for Knowledge and Intangible Assets Management, SMEs companies seeking to meet <strong>the</strong> needs <strong>of</strong> all staff, are
not investing in an adequate way in those pr<strong>of</strong>essions that might make a real difference. It is important to
implement an assessment aimed to bring out explicitly <strong>the</strong> contribution to <strong>the</strong> total value <strong>of</strong> intangible assets <strong>of</strong>
<strong>the</strong> organization. Our paper tries to look for an integrative approach dealing with intellectual capital management
that might be applied for more SMEs having a distinguished organizational culture. We like to demonstrate that
<strong>the</strong> ideal environment for Intellectual Capital Development (ICD) is to be pro-active, able to give direction to <strong>the</strong>
technological and organizational changes affecting <strong>the</strong> sector in which <strong>the</strong>y work an undertaking in which all
human resources are identified and where <strong>the</strong>re is awareness <strong>of</strong> <strong>the</strong>ir role and attitudes toward change.
Concentrated on <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> Balance Scorecard method, namely <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> processes <strong>of</strong>
learning and growth, we can understand how <strong>the</strong> development <strong>of</strong> intellectual capital is a challenge for <strong>the</strong>
leadership <strong>of</strong> <strong>the</strong> staff within SMEs: it is required to conduct and roles on <strong>the</strong> one hand aimed at facilitating <strong>the</strong>
process <strong>of</strong> knowledge accumulation, o<strong>the</strong>r mechanisms to enable more appropriate management and motivation
to help <strong>the</strong> knowledge generated to develop sharing and transfer. The paper presents briefly an exploratory
research used for debating <strong>the</strong> issue <strong>of</strong> intellectual capital (IC) in Romania’s SMEs. Our research is at a starting
point for possible future <strong>the</strong>oretical and empirical investigations. This paper seeks to develop in Romania a
framework <strong>of</strong> IC reporting by starting to learn and adapt.
Keywords: intellectual capital, intangible assets, knowledge based economy, intellectual capital management,
balance scorecard, competitive advantage
1. Introduction
Companies must realize how important it is to "know what <strong>the</strong>y know" and to be able to make
maximum use <strong>of</strong> knowledge. In terms <strong>of</strong> definitions, knowledge assets are knowledge regarding
markets, products, technologies and organizations, that a business holds or should hold in order to
enable its business processes to generate pr<strong>of</strong>its and add value.
On <strong>the</strong> o<strong>the</strong>r hand, knowledge management is not only about managing <strong>the</strong>se assets, but also on
managing <strong>the</strong> processes that act on <strong>the</strong>se assets.
In <strong>the</strong> knowledge based society and economy, intellectual capital provides a competitive advantage
and is fundamental to support management decisions in <strong>the</strong> allocation <strong>of</strong> investment and investors'
decision on value versus price. National policies should be implemented to begin to measure but also
to create a supplement to <strong>the</strong> annual financial records as <strong>the</strong> ratio <strong>of</strong> IC. (Suciu, C, 2010).
A correct evaluation <strong>of</strong> intellectual capital and <strong>the</strong> prevailing trends in <strong>the</strong> medium-long term is <strong>of</strong>
fundamental importance for realistic projections based on <strong>the</strong> model <strong>of</strong> sustainable development in all
its essential components: economic, socio-cultural and environmental. Given that <strong>the</strong> Lisbon Agenda
expired in 2010, <strong>the</strong> EU has prepared a new strategy for growth over 10 years to replace it, in an
effort to improve <strong>the</strong> process this time. This project has been named "Europe 2020", aimed at
delivering growth and jobs for <strong>the</strong> next decade.
In <strong>the</strong> case <strong>of</strong> Romania priorities should include <strong>the</strong> development <strong>of</strong> social and intellectual capital <strong>of</strong>
SME’s that have a real impact on <strong>the</strong> development <strong>of</strong> innovation and competitiveness.
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Intellectual capital is <strong>of</strong>ten described as <strong>the</strong> difference between market value and book value <strong>of</strong> an
organization. It is undisputed that intellectual capital is <strong>the</strong> most important asset <strong>of</strong> a knowledgebased
organization. This value is usually not stated in annual reports and does not appear in
conventional analytical accounting models because intellectual capital is intangible by definition and
<strong>the</strong> available measurements consist <strong>of</strong> proxy variables or indicators. Intellectual capital must be
turned into a knowledge resource to formulate an intellectual capital statement.
2. The role <strong>of</strong> Intellectual capital in <strong>the</strong> knowledge based society (KBS)
The total relevant organizational knowledge is <strong>of</strong>ten called "intellectual capital". IC does not only
include <strong>the</strong> knowledge as a single concept (as an individual human resource,), but as knowledge <strong>of</strong>
an organization that appears in <strong>the</strong> patents and in <strong>the</strong> specific process models and routines. Even <strong>the</strong>
organization's culture and relations with customers and suppliers are <strong>the</strong> intellectual capital.
Knowledge-based organizations (KBO) give a great importance to investment in human capital and
investment in education in particular. KBO are aware <strong>of</strong> <strong>the</strong> great importance that people have,
through <strong>the</strong>ir knowledge and skills (human capital) for a sustainable and competitive development <strong>of</strong>
<strong>the</strong> organization. But also important are <strong>the</strong> specific internal processes <strong>of</strong> an organization (structural
capital), and its reputation (relational capital). Toge<strong>the</strong>r human, structural and relational capitals are
considered to be <strong>the</strong> most important components <strong>of</strong> intellectual capital. These components are <strong>the</strong>
basis for assessing organizational performance based on Intellectual Capital Reports (ICR), as
evidenced in <strong>the</strong> case <strong>of</strong> Skandia, a multinational company that developed in 1997 <strong>the</strong> first ICR.
While many authors use <strong>the</strong> terms "intellectual asset" and "intellectual capital" interchangeably, <strong>the</strong>re
are subtle differences between <strong>the</strong> two meanings. In terms <strong>of</strong> <strong>the</strong> accounting balance, intellectual
assets are those items based on <strong>the</strong> knowledge that <strong>the</strong> organization has that will produce a future
stream <strong>of</strong> benefits to <strong>the</strong> organization. They are "flows" or individual elements which comprise
intellectual assets on <strong>the</strong> balance sheet, while <strong>the</strong> total stock <strong>of</strong> intellectual capital is <strong>the</strong> capital or
equity in <strong>the</strong> balance <strong>of</strong> <strong>the</strong> knowledge that <strong>the</strong> organization possesses. We have syn<strong>the</strong>sized seven
critical components in <strong>the</strong> cycle <strong>of</strong> learning that support <strong>the</strong> development <strong>of</strong> intellectual capital:
Preparation. Learning is more effective if people are ready, reading tasks, surveys, and
evaluations are frequently used.
Learning in <strong>the</strong> classroom. Time in class is used to clarify information, practice using new skills
and techniques, to reflect <strong>the</strong> culture and experience from different perspectives.
Action Plans. Learning needs opportunities to try new things. Participants are required to submit
plans for action after each session. The plan will describe how to apply learning in <strong>the</strong> workplace.
Applications. Participants apply concepts and techniques that are appropriate for <strong>the</strong>ir business
needs and projects.
Mentoring. Mentors help to make meaningful connections between classroom learning and <strong>the</strong>
everyday activity <strong>of</strong> <strong>the</strong> participant. Mentors discus <strong>the</strong> applications, provide feedback and enable
participants to reflect on <strong>the</strong>ir experiences.
Reflections. Learning also requires reflection on <strong>the</strong> applications and what went wrong, what is
not, and what needs to be improved. Participants write <strong>the</strong>ir reflections and submit <strong>the</strong>m before
<strong>the</strong> class facilitators.
Interrogation. Each class begins with a two-hour interrogation <strong>of</strong> <strong>the</strong> application. Interrogation
sessions teach and promote <strong>the</strong> use <strong>of</strong> inquiry, dialogue, reflection and exchange <strong>of</strong> experience.
InCaS is a project for <strong>European</strong> organizations which addresses <strong>the</strong> issue <strong>of</strong> intellectual capital and <strong>of</strong>
“know-how "and tries to fill <strong>the</strong> gap. InCaS is a collaboration between leading academic institutions
such as LSE, Fraunh<strong>of</strong>er and UPC, which have a proven track record in producing market-relevant
outputs and a number <strong>of</strong> associations <strong>of</strong> SMEs (small and medium enterprises), working in five
countries live with <strong>the</strong>se SMEs.
InCaS aims to develop a way for organizations to be able to understand and represent <strong>the</strong> value <strong>of</strong>
intellectual capital in <strong>the</strong> benefit <strong>of</strong> companies internally but also for outside groups such as <strong>the</strong>
financial community for a more optimal exploration.
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We consider that so far, all <strong>the</strong> ideas and methods <strong>of</strong> evaluation <strong>of</strong> IC are in parallel one next to <strong>the</strong>
o<strong>the</strong>r and so far <strong>the</strong>re has been no attempt to harmonize <strong>the</strong>se approaches scattered on a high
scientific level. This is where <strong>the</strong> project proceeds from: integration and consolidation <strong>of</strong> academic
perspectives and practical experiences leading to a harmonized and consistent assessment <strong>of</strong> <strong>the</strong>
implementation <strong>of</strong> an Intellectual Capital Statement. InCaS wants to consolidate all <strong>of</strong> <strong>the</strong> experiences
<strong>of</strong> intellectual capital, <strong>the</strong> method is based on earlier experiences in Sweden, Denmark, Austria and
o<strong>the</strong>r <strong>European</strong> countries, taking as a basis for methodology <strong>the</strong> German orientation. In 50 pilot
implementations <strong>the</strong> German method proved to be a practical method to be introduced and
implemented in SMEs (Will Wuscher, Bodderas 2006).
The Inca Procedural Model leads through <strong>the</strong> process <strong>of</strong> IC reporting and defines <strong>the</strong> steps to
implement an IC statement, meaning <strong>the</strong> methodology to be used in assessing and measuring <strong>the</strong>
factors <strong>of</strong> IC. The approach to achieving a IC statement is divided into five steps, each step builds on
<strong>the</strong> previous step. Step 1, Step 2 and Step 4 are supported by direct IC Trainers. Step 5 and Step 3
are prepared internally without direct participation <strong>of</strong> a trainer on site. The steps are:
Approaching <strong>the</strong> business model - management meeting;
IC Analysis - workshop 1;
Measurement - internal activity;
Clarify <strong>the</strong> strategy and <strong>the</strong> measures to be addressed - workshop 2;
The final IC reporting document - internal activity;
The statement <strong>of</strong> IC is a strategic management tool for assessing and developing <strong>the</strong> intellectual
capital <strong>of</strong> an organization. It shows how intellectual capital is linked to corporate objectives, business
processes and business success <strong>of</strong> an organization using indicators to measure <strong>the</strong>se elements
(<strong>European</strong> Commission, 2010). In particular, <strong>the</strong> InCaS method solves a dilemma in IC reporting
approaches. For internal management purposes, it is important to use an individualized approach,
namely to define <strong>the</strong> specific elements <strong>of</strong> <strong>the</strong> strategy and IC in accordance with <strong>the</strong> specifications <strong>of</strong>
<strong>the</strong> company's organizational model. On <strong>the</strong> o<strong>the</strong>r hand for external reporting, standardized
approaches are required allowing external interested parties such as customers, investors and o<strong>the</strong>r
partners to compare <strong>the</strong> IC situation for different companies.
3. The Balanced scorecard model
The Balanced Scorecard approach is a response to <strong>the</strong> problems arising from a traditional approach
to performance measurement and is one <strong>of</strong> <strong>the</strong> most effective strategic management systems to
implement long-term strategy and strategic alignment <strong>of</strong> resources with objectives, creating an
organizational environment structure that is more focused on learning which has no control. The
results incorporate internal measures (process and learning perspectives), <strong>the</strong> external resulting
measures (economic and financial prospects and customers), measurements <strong>of</strong> phenomena that
affect on short-term (mainly recognized <strong>the</strong> economic and financial prospects and customers)
measures <strong>the</strong> effects <strong>of</strong> phenomena characterized by medium and long term (mostly collected from
<strong>the</strong> perspectives <strong>of</strong> internal processes and learning), including financial and non-financial measures,
between lagging indicators (or ex-post indicators) and trend indicators (or "drivers" <strong>of</strong> performance).
Therefore <strong>the</strong> Balanced Scorecard retains traditional economic and financial measures, but those are
not able to simultaneously assess <strong>the</strong> value created and generated in <strong>the</strong> organization, at <strong>the</strong> same
time to provide useful information for <strong>the</strong> future prospective <strong>of</strong> <strong>the</strong> organization are a result <strong>of</strong> past
measures, which cannot give any clue as to how <strong>the</strong>y affect future results and so <strong>the</strong>y are
complemented with measures on <strong>the</strong> so-called drivers <strong>of</strong> future performance. The latter are trend
indicators, which indicate what you should do today to create value tomorrow.
Balanced Scorecard approach is used successfully by many companies. Although <strong>the</strong>re are
companies that have discovered <strong>the</strong> benefits <strong>of</strong> its application, we have a slight different view, finding
entry-based methods difficult and challenging scores:
Balanced Scorecard implies a "top down" control, where <strong>the</strong> strategies are formulated by top
management. It can only get a superficial commitment to <strong>the</strong> people in your organization and can
create some difficulties in achieving <strong>the</strong> alignment <strong>of</strong> individual behavior to strategic objectives.
Often middle management is involved in its construction in a bureaucratic way and <strong>the</strong>refore is
not adequately involved in its implementation.
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In choosing <strong>the</strong> indicators and <strong>the</strong>ir relationship with o<strong>the</strong>r systems <strong>of</strong> organizational control
issues may cause problems. It <strong>of</strong>ten happens that <strong>the</strong> indicators are not identified in accordance
with <strong>the</strong> organization's strategy, but are chosen in relation to <strong>the</strong>ir already existing business
indicators. Moreover, despite <strong>the</strong> new information technologies, systematic measurement <strong>of</strong> <strong>the</strong>se
indicators is <strong>of</strong>ten difficult, requiring <strong>the</strong> involvement and ongoing commitment <strong>of</strong> all functions <strong>of</strong>
<strong>the</strong> organization.
It is effective only if it is adapted to <strong>the</strong> needs <strong>of</strong> <strong>the</strong> company that intends to implement it.
Perspectives to be considered could be, <strong>the</strong>refore, different and more than planned. It is a great
need for customization <strong>of</strong> <strong>the</strong> tool which may require <strong>the</strong> organization considerable resources <strong>of</strong>
expertise, time and finance. If <strong>the</strong> company introduces this method, without making clear that it
intends to apply it and without <strong>the</strong> necessary resources (human, technological and financial) may
be confronted with a dramatic failure <strong>of</strong> <strong>the</strong> project from unforeseen consequences.
The method still tends to focus on economic and financial measures and to be focused on <strong>the</strong>
inside. Three <strong>of</strong> <strong>the</strong> four perspectives <strong>of</strong>fered are focused on internal resources, <strong>the</strong> cause and
effect relationship that characterizes <strong>the</strong> company's internal functioning.
There is an inherent difficulty <strong>of</strong> this method to identify, measure and interpret facts, especially
intangible assets and stock <strong>of</strong> knowledge. A limitation is its own human resources, in fact, <strong>the</strong>
model considers <strong>the</strong>m as integral part <strong>of</strong> learning and growth perspective, which <strong>of</strong>ten
underestimate <strong>the</strong> value and knowledge <strong>of</strong> individuals, particularly in companies operating in
those areas where pr<strong>of</strong>essional and management skills are extremely important .
Balanced Scorecard is designed as an internal document. This makes it very difficult to compare
with its competitors in similar documents.
4. An attempt to define a reference model for intellectual capital reporting in
Romanian SMEs
In recent years, several approaches have been identified for <strong>the</strong> identification, classification and
measurement <strong>of</strong> intellectual capital components. Although <strong>the</strong>re is an agreement meant to define <strong>the</strong>
components <strong>of</strong> intellectual capital (human, structural and relational), <strong>the</strong>re is no uniformity in <strong>the</strong>
elements that make up <strong>the</strong> three categories.
These elements are crucial in strategic terms, in a position to influence organizational structure, its
features and level <strong>of</strong> pr<strong>of</strong>essional services is essential to create value, develop and <strong>the</strong>n implement a
strategy based on knowledge.
Traditional accounting approaches fail to understand <strong>the</strong> total value <strong>of</strong> intellectual capital components,
especially regarding those intangible elements that are increasingly important on competitive level,
significantly determines <strong>the</strong> value <strong>of</strong> a company and its prospects for future growth. In many cases,
<strong>the</strong>se factors are beyond recognition in <strong>the</strong> budget, ei<strong>the</strong>r by nature and manner <strong>of</strong> acquisition <strong>of</strong>
<strong>the</strong>se resources or conventions governing <strong>the</strong> operation model. Based on this approach to be
constructed, placed as attachments to <strong>the</strong> accompanying financial statements, are able to provide a
more appropriate competitive sustainability <strong>of</strong> <strong>the</strong> organization.
Given <strong>the</strong> repeated observation that intellectual capital is an increasingly important component in
determining <strong>the</strong> success <strong>the</strong> undertaking should be accepted that, although many researchers have
developed various instruments designed to measure, we are still far from finding permanent solutions
and widely accepted. It is not yet defined a model that has <strong>the</strong> agreement <strong>of</strong> <strong>the</strong> business community
and scientific management.
There are some differences and fears to overcome, especially in what regards <strong>the</strong> culture <strong>of</strong> privacy
management and time to be cautious in disclosing information outside <strong>of</strong> <strong>the</strong> success factors,
strategies and implementation programs. These tools <strong>of</strong> analysis, <strong>the</strong>refore, must be known
entrepreneurs and company executives to become more open in providing such information and
understand that information about <strong>the</strong>se values may be an important factor <strong>of</strong> credibility and success
for <strong>the</strong> organization.
We have identified some characteristics, which should be a model for measuring intellectual capital
and can <strong>the</strong>n begin from <strong>the</strong> disadvantages that <strong>the</strong> Balanced Scorecard model and elements <strong>of</strong>
similarity and diversity <strong>of</strong> structure and conditions <strong>of</strong> various methods that scorecard are related. The
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aim is to define <strong>the</strong> basis for creating a report that combines accounting measures and those related
to intellectual capital:
4.1 1’st characteristic: (integrating <strong>the</strong> IC models for measuring)
The first element is <strong>the</strong> integration in <strong>the</strong> model <strong>of</strong> economic and financial reports <strong>of</strong> <strong>the</strong> traditional
evaluation <strong>of</strong> <strong>the</strong> intangible assets and intellectual property. Integration is useful for organizations in
<strong>the</strong> knowledge-era; such intangible assets are essential to business success than those traditional
and tangible. The new reporting system can <strong>the</strong>n be divided into two levels <strong>of</strong> information, each aimed
at valuing, namely past, present and future. And when it is necessary to highlight <strong>the</strong> tangible and
intangible assets, intellectual capital will be included.
4.2 2’nd characteristic: (elements taking in <strong>the</strong> model)?
Despite studies, <strong>the</strong>re is no common model, but <strong>the</strong>re is a common tendency to regard IC as
consisting <strong>of</strong> three main components: human capital, structural and relational. Intellectual capital
approaches recognize that every organization must develop its own system <strong>of</strong> intellectual capital that
will be refined over time and in accordance with <strong>the</strong> strategy. An effective model for intellectual capital
should carry out two tasks. The first is to identify and evaluate various components <strong>of</strong> intellectual
capital through <strong>the</strong> most appropriate indicators. The second is to guide behavior and strategies <strong>of</strong> <strong>the</strong>
company, providing a basis <strong>of</strong> legitimacy and support investments in human, relational and structural
capital. This approach is not only dynamic but also involves a continuous improvement process for <strong>the</strong>
conversion <strong>of</strong> intangible assets in financial gains.
Human capital must include <strong>the</strong> skills and motivation <strong>of</strong> people. Structural capital must take into
account <strong>the</strong> organizational structure, culture and climate. Relational Capital, finally, it should cover
contractual relations, trust and shared values. The risk that can come without a proper mapping and
measuring <strong>of</strong> relationships between elements is to create groups <strong>of</strong> indicators that have a lack <strong>of</strong>
coherence and, <strong>the</strong>refore, fail to ensure conditions for <strong>the</strong> creation and development <strong>of</strong> intellectual
resources.
4.3 3’rd characteristic (measurement perspectives)
The four perspectives (economic, financial, customer, internal processes and organizational learning
and growth) <strong>of</strong> <strong>the</strong> Balanced Scorecard method proposed by Kaplan and Norton should be
considered as a starting point for designing <strong>the</strong> model. In fact, you can change <strong>the</strong> original approach
and add additional perspectives to represent <strong>the</strong> most significant organizational reality.
The number <strong>of</strong> analytical perspectives should not be large because <strong>the</strong> complexity increases
exponentially with <strong>the</strong> number <strong>of</strong> linking variables, such as identifying indicators is necessary to make
choices to allow sufficient data to build, ensuring <strong>the</strong> right balance between relevance and availability
indicators data. The criterion to be followed in this operation is simply to introduce measures for
factors that significantly differentiate <strong>the</strong> organization that represents innovation and competition and
significant changes to <strong>the</strong> original situation <strong>of</strong> <strong>the</strong> company.
4.4 4’th characteristic (<strong>the</strong> need for indicators)
A turning point in designing <strong>the</strong> measurement model <strong>of</strong> intellectual capital is <strong>the</strong> definition <strong>of</strong> what is
measured, namely <strong>the</strong> identification for each element <strong>of</strong> Intellectual Capital, relevant indicators. The
most important is choosing <strong>the</strong> series <strong>of</strong> indicators to be used. A large number <strong>of</strong> indicators measuring
is too general, with <strong>the</strong> possibility <strong>of</strong> losing crucial resources for performance monitoring, leading to a
loss <strong>of</strong> focus on <strong>the</strong> management <strong>of</strong> users, reducing <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> adoption <strong>of</strong> indicators,
making it too complicated to be managed and understood while <strong>the</strong> presence <strong>of</strong> a very limited number
<strong>of</strong> indicators cannot capture <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> phenomenon. Therefore, it is important to find an
acceptable balance between <strong>the</strong> number <strong>of</strong> indicators chosen and <strong>the</strong> ease and accuracy and
consistency <strong>of</strong> interpretation.
The set <strong>of</strong> selected indicators should be simple, easy to read and meaningful to be able to investigate
and monitor a specific element and provide information <strong>of</strong> significant interest from both strategic and
management point, providing a simplified framework for analysis and all components <strong>of</strong> intellectual
capital accounts without favoring one section to <strong>the</strong> detriment <strong>of</strong> ano<strong>the</strong>r. They must be present in <strong>the</strong>
analysis <strong>of</strong> quantitative and qualitative indicators strictly appropriate and specific to each organization.
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After <strong>the</strong> analysis, we syn<strong>the</strong>sized that <strong>the</strong> indicators should have <strong>the</strong> following characteristics in our
opinion:
to cover cognitive areas <strong>of</strong> organizational system with strategic importance;
be relevant to end users
to provide useful management information,
be operational and manageable
understandable and communicable
be built on easily accessible database enterprise system
4.5 5’th characteristic (<strong>the</strong> possibility to compare)
It is necessary to find a model that would allow comparisons between different business realities. An
approach that can be taken into account is <strong>the</strong> IC index (Intellectual Capital Index), which focuses on
monitoring <strong>the</strong> dynamics <strong>of</strong> intellectual capital that aims to aggregate all <strong>the</strong> indicators into a single
index and correlate with changes in intellectual capital and changes in market, defined in order to
overcome <strong>the</strong> difficulties brought about by <strong>the</strong> use <strong>of</strong> different methods, characterized by inflexibility
and <strong>the</strong> difficulty <strong>of</strong> making comparisons.
4.6 6’th characteristic (organizational variables)
Changes should not only cover business strategies, but must also affect <strong>the</strong> organizational structure
and personnel management practices as key elements to nourish and support <strong>the</strong> ongoing review <strong>of</strong>
<strong>the</strong> company's market positioning. A good reporting system requires, <strong>the</strong>refore, organizational
integration, which focus on both <strong>the</strong> system's capacity to influence <strong>the</strong> behavior <strong>of</strong> different levels <strong>of</strong>
organization so as to align <strong>the</strong> behavior <strong>of</strong> all stakeholders in <strong>the</strong> ultimate goal <strong>of</strong> creating value, so
<strong>the</strong> links between performance measurement system, business strategy, management systems and,
but also in particular, <strong>the</strong> operational mechanisms <strong>of</strong> planning and control and incentive systems.
It is necessary to support organizational action to have a flexible organizational structure, an
integrated and strategic management through which businessmen can ensure a successful
completion. Emphasis should be geared to building an environment and a general climate, a
relationship, so that people can find <strong>the</strong>ir own values and aspirations and ambitions are brought in to
<strong>of</strong>fer <strong>the</strong>ir commitment and creativity. Since <strong>the</strong> strategy is shared by employees is necessary that
personal goals are aligned with organizational goals, you will need to build a model that gives
individuals an understanding <strong>of</strong> corporate strategy, explaining how each fits into <strong>the</strong> strategic map <strong>of</strong>
organizing and how it contributes to <strong>the</strong> strategic objectives. Involvement in decisions, awards for <strong>the</strong>
proper performance <strong>of</strong> duties, access to information, <strong>the</strong> encouragement to be creative and innovative
collaborations obtained from <strong>the</strong> bodies <strong>of</strong> staff general satisfaction in being part <strong>of</strong> <strong>the</strong> company, are
conditions that help to improve employee satisfaction, and <strong>the</strong>refore, <strong>the</strong>ir loyalty and productivity, so
that <strong>the</strong> reference point is <strong>the</strong> contribution <strong>of</strong> sustainable and consistent over time, that people can
lead to organizational success.
5. Intellectual capital management perspectives
Intellectual capital management system is an area <strong>of</strong> knowledge that involves a variety <strong>of</strong> intellectual
property pr<strong>of</strong>essionals, especially lawyers, business executives, financial planners and advisers.
Pr<strong>of</strong>essionals in <strong>the</strong> field <strong>of</strong> "intellectual property" tend to focus on knowledge that can be encrypted
and protected legally. Human resources and information technology pr<strong>of</strong>essionals considered most
important <strong>the</strong> exchange <strong>of</strong> knowledge. Those who occupy <strong>the</strong> largest part <strong>of</strong> innovation tend to look
for more effective results.Intellectual capital management is not a new concept. Since <strong>the</strong> fifties, is an
area <strong>of</strong> knowledge that involves various stakeholders and developing a variety <strong>of</strong> tools, some <strong>of</strong> which
are well known, such as TQM (Total Quality Management) and JIT (Just in Time). Many researchers
argue that <strong>the</strong> greatest part <strong>of</strong> resources in organizations in developed countries consist <strong>of</strong>
knowledge, intellectual capital, which inevitably have a major role in managing global affairs.
While <strong>the</strong> sector strategy and organization development program are what make up its intellectual
capital management, each company still needs a comprehensive strategy for managing intellectual
capital. The term "global" means an intellectual capital management model that goes beyond <strong>the</strong>
cycle <strong>of</strong> value creation. The needs <strong>of</strong> each organization in relation to this model, regardless <strong>of</strong> its
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requirements, resulting from strategic issues must be addressed by top management in <strong>the</strong>
knowledge economy in order to create competitive advantage.
In our opinion in an economy based on knowledge and intensive innovation, strategic management <strong>of</strong>
IC involves, among o<strong>the</strong>rs, <strong>the</strong> following questions:
What we know and what we should know?
How to acquire <strong>the</strong> knowledge resources needed to achieve a competitive position / strategy
required?
We develop <strong>the</strong>se resources internally, sharing and transferring knowledge, or externally, through
acquisitions and partnerships?
How to use our intellectual capacity to create competitive advantage through <strong>the</strong> radical or
incremental innovation?
How to use our intellectual capital to compete in existing markets and new markets?
What is <strong>the</strong> strategy for intellectual capital that will allow us to maintain our competitive
advantage?
Strategies on intellectual capital should have to create balance between <strong>the</strong> need to establish global
models and intellectual capital management while adapting to <strong>the</strong> organizational situation. If top
management does not understand <strong>the</strong> nature and value <strong>of</strong> Intellectual Capital Management, it cannot
fully understand where resources are needed to turn a new strategy into a success.
One <strong>of</strong> <strong>the</strong> main problems that arise in <strong>the</strong> management <strong>of</strong> intellectual capital and intellectual capital
management models <strong>the</strong>y <strong>of</strong>ten do not have a precise and systematic basis, thus providing only
partial solutions.
Choosing a measurement model <strong>of</strong> intellectual capital in SMEs in Romania must take into
consideration <strong>the</strong> following criteria (Choo, C. and Bontis, N. 2002):
information that can be audited and consistent (observers may not interfere with data, while <strong>the</strong>
sources <strong>of</strong> information should not be affected, following very strict requirements <strong>of</strong> <strong>the</strong> evaluation
system, whe<strong>the</strong>r it is a qualitative or quantitative measurement);
not impose a high cost <strong>of</strong> measurement (keep in mind <strong>the</strong> purpose <strong>of</strong> analysis, to improve <strong>the</strong>
organization, not to drain and to avoid loss <strong>of</strong> control over <strong>the</strong> collection and management <strong>of</strong> <strong>the</strong>
database used);
strategic and tactical management to help (<strong>the</strong> results should be useful);
generate information for shareholders and investors (intellectual capital reports and human capital
for specific purposes);
<strong>the</strong> perspective <strong>of</strong> future, past and present.
Knowledge plays an increasingly important role in processes that add value to <strong>the</strong> company. The
biggest challenges facing <strong>the</strong> current administration require that intangible assets become more
productive. These problems will force <strong>the</strong> firm to take appropriate steps and to define standards <strong>of</strong>
performance evaluation not only <strong>of</strong> human resources and corporate structure and relationships with
customers. Report on intellectual capital already proved to be an appropriate tool for representation
and standards. It is also useful as a resource that can be used to communicate strategic objectives,
not only within but also outside <strong>the</strong> enterprise. One <strong>of</strong> <strong>the</strong> most important application is to support staff
development programs.
6. Conclusions and recommendations
Human capital plays a major role for IC and it is built and managed on <strong>the</strong> base <strong>of</strong> many factors, from
education to training.
The <strong>the</strong>ory <strong>of</strong> <strong>the</strong> impact <strong>of</strong> <strong>the</strong> intellectual capital on <strong>the</strong> development <strong>of</strong> economies is still under
construction, but <strong>the</strong> practice shows, that organizations and countries, which have accepted this,
surpass o<strong>the</strong>rs in <strong>the</strong> development <strong>of</strong> <strong>the</strong> competitive advantage. That`s why <strong>the</strong> state should
concentrate on developing <strong>the</strong> quantified methods for measuring intellectual capital and its
components. These will allow us to make an assessment <strong>of</strong> intellectual capital comprising on
innovative potential <strong>of</strong> a company, in <strong>the</strong> national economy and in <strong>the</strong> whole Europe.
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Organizations in Romania should be encouraged to make reports containing views <strong>of</strong> <strong>the</strong> vision and
business goals, challenges and actions <strong>of</strong> management. We propose for fur<strong>the</strong>r research <strong>the</strong>
development <strong>of</strong> operational parameters in Romania and <strong>of</strong> empirical methodological studies based on
dialogue and dissemination <strong>of</strong> knowledge. We are interested what is <strong>the</strong> perception and awareness <strong>of</strong>
intellectual capital in our country and in particular <strong>the</strong> importance <strong>of</strong> intellectual capital reporting as
part <strong>of</strong> <strong>the</strong> annual statement. This part will be achieved through a case study, completed
questionnaires on <strong>the</strong> perception <strong>of</strong> intellectual capital in organizations in Romania.
Evaluating <strong>the</strong> references from <strong>the</strong> specific literature and considering <strong>the</strong> empirical study we propose
to carry out, <strong>the</strong> evaluation model for intellectual capital to be applied in reducing uncertainty and
increase competitiveness <strong>of</strong> human capital. Considering <strong>the</strong> characteristics <strong>of</strong> <strong>the</strong> Romanian
organizational system, should consider <strong>the</strong> arguments for an IC policy based on rate <strong>of</strong> return
analysis, which treats for example expenses with human capital as an investment that boosts
productivity and generates future monetary/non-monetary outflows, and a connection to economic
growth analysis. We consider that policy makers and stakeholders have to be actively involved to
design and monitor IC policy. All have to be subsumed to <strong>the</strong> goal <strong>of</strong> providing valuable information to
managers as well as for o<strong>the</strong>r stakeholders (direct beneficiaries, social and business community,
etc.). Periodical reports on intellectual capital evaluation could be valuable, bringing additional
investments at institutional level. This type <strong>of</strong> research specially dedicated to <strong>the</strong> topic <strong>of</strong> IC & IC
management is quite new for Romanian SMEs. But we consider that it is an important issue to be
developed and applied in our country. We intend to develop our research being involved in cross
disciplinary teams in <strong>the</strong> future.
Acknowledgments:
This article is a result <strong>of</strong> <strong>the</strong> projects PARTENERIATE 92116 (coordinated by <strong>the</strong> first author) and
POSDRU/6/1.5/S/11 „Doctoral Program and PhD Students in <strong>the</strong> education research and innovation
triangle” (co funded by <strong>the</strong> <strong>European</strong> Social Fund through <strong>the</strong> Sectorial Operational Programme for
Human Resources Development 2007-2013, coordinated by The Academy <strong>of</strong> Economic Studies
Bucharest).
References
Choo, C.W. and Bontis, N. (2002), The Strategic Management <strong>of</strong> Intellectual Capital and Organizaţional
Knowledge, Oxford University Press, New York, NY.
<strong>European</strong> Commission, (2010) “Intellectual Capital Statement - Made in Europe”, pp. 9, [online],
http://www.in<strong>the</strong>kzone .com/pdfs/Intellectual_Capital_Statement.pdf
Jose Manuel Barroso, (2010), “EUROPE 2020 A <strong>European</strong> strategy for smart, sustainable and inclusive growth”,
<strong>European</strong> Commission, 2010, [online], http://ec.europa.eu/eu2020/pdf/COMPLET% 20EN%20
BARROSO%20%20%20007%20-%20Europe%202020%20-%20EN%20version.pdf ;
Prepelita-Raileanu, Branduşa, (2010), “The Decentralization Strategy <strong>of</strong> Romanian Higher Education and <strong>the</strong>
Implementation <strong>of</strong> Quality Management System”, [online], <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 7 th WSEAS International
<strong>Conference</strong> on Latest Trends on Engineering Education (EDUCATION ‘10), ISSN: 1792-426X, ISBN: 978-
960-474-202-8, Corfu Island, Greece, July 22-24, 2010, pp. 116-118, http://www.wseas.us/elibrary/conferences/2008/crete/education/education66.pdf
Stern. G. .H. (December 1998) ‘’Do We Know Enough about Economics? ‘’, [online], Federal Reserve Bank <strong>of</strong>
Minneapolis, pp. 1, http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=3578
Suciu M.C., Ghiţiu-Brătescu A. (2010), “Implementing a framework for IC reporting in România”, The 21st
DAAAM International University <strong>of</strong> Zadar, Zadar, Croatia 2010-10-20/23, ISI Proceeding
Suciu, M.C. (2004). Economics. New economics & Knowledge-based society / Economie. Noua economie si
societatea bazata pe cunoastere, Part I and I, ASE Publishing House, 798 p.
Suciu, M.C. (2002). Knowledge based economy and <strong>the</strong> global civilization. Investing and hoping in people /
Economia cunoaşterii şi civilizaţia globală. Investiţia şi speranţa în om, ASE Publishing House, Bucharest,
289 p
Suciu, M.C. (2001). Investment in education / Investiţia în educaţie, Economică, Publishing House, 487 p
Swing, E. and Schriewer, J. (2000) Problems and prospects in <strong>European</strong> education, Greenwood Publishing
Group, U.S.A., pp. 1-2
Will, M.; Wuscher, S.; Bodderas, M. (2006): „Wissensbilanz – Made in Germany – Nutzung und Bewertung der
Wissensbilanz durch die Pilotunternehmen“, Recent study by Fraunh<strong>of</strong>er IPK, Berlin. Retrieved May 2nd,
2007, [online] http://www.akwissensbilanz.org/Projekte/KMU Wirkungstest Teil 2.pdf
Wilson, R. A., Briscoe, G. (2004) ‘’The impact <strong>of</strong> human capital on economic growth: a review’’, [online], in Descy,
P.; Tessaring, M. (eds) ‘’Impact <strong>of</strong> education and training Third report on vocational training research in
Europe: background report’’, Luxembourg: Office for Official Publications <strong>of</strong> <strong>the</strong> <strong>European</strong> Communities,
(Cedefop Reference series, 54), pp. 31-32
959

Universities as Knowledge Creation and Sharing
Institutions – Research Perspectives from Romania
Marta-Christina Suciu 1 , Klaus Bruno Schebesch 2 , Corina Grigore 1 , Simona-
Margareta Busoi 1 , Ramona Spiridon 1 , Valentin-Matei Şerbu 1
1
Academy <strong>of</strong> Economic Studies <strong>of</strong> Bucharest, Bucharest, Romania
2
Vasile Goldis University, Arad, Romania
suciu31@gmail.com
kbsbase@gmx.de
corinagrigore@gmail.com
busois@yahoo.com
ramona.spiridon@inspectiamuncii.ro
lmvaly@yahoo.com
Abstract: In <strong>the</strong> era <strong>of</strong> knowledge-based economy and society (KBES), human capital (HC), intellectual capital
(IC), creativity, innovation and education play an increasing role in <strong>the</strong> portfolio <strong>of</strong> assets owned by an
organization. The importance <strong>of</strong> knowledge is acknowledged through various papers and studies on knowledge
production and its impact on growth. In a knowledge-based economy characterized by <strong>the</strong> production,
transmission and dissemination <strong>of</strong> knowledge, <strong>the</strong> higher education institutions (universities) play a unique role in
all <strong>the</strong>se knowledge process steps. An efficient knowledge management in a university will ensure <strong>the</strong> best use <strong>of</strong>
available resources and best use <strong>of</strong> <strong>the</strong> knowledge as a key resource; creation / generation <strong>of</strong> new knowledge;
understanding and awareness; capturing, organizing, storing <strong>the</strong> knowledge at individual, group and organization
level; and knowledge transfer to o<strong>the</strong>r persons or o<strong>the</strong>r organizations. The aim <strong>of</strong> this paper is to analyse <strong>the</strong> role
<strong>of</strong> universities in supporting <strong>the</strong> development <strong>of</strong> knowledge economy in Romania and to highlight <strong>the</strong> extent to
which students consider that <strong>the</strong> Romanian economic university system is ready to face <strong>the</strong> demands <strong>of</strong> KBES.
The results are based on students’ perception on <strong>the</strong> role that <strong>the</strong> higher education institutions within <strong>the</strong> context
<strong>of</strong> KBES in Romania. The study is based on <strong>the</strong> answers <strong>of</strong> questioned students from <strong>the</strong> Faculty <strong>of</strong> Business
Administration with teaching in foreign languages (FABIZ) <strong>of</strong> <strong>the</strong> Academy <strong>of</strong> Economic Studies <strong>of</strong> Bucharest.
The study acknowledged <strong>the</strong> importance <strong>of</strong> knowledge management in education institutions and identified
challenges that <strong>the</strong> institutions will be facing. The paper is structured as following: <strong>the</strong> first part <strong>of</strong> <strong>the</strong> paper
introduces briefly <strong>the</strong> concepts <strong>of</strong> knowledge and knowledge-based economy and society; <strong>the</strong> second part
provides a short literature review on knowledge production and its importance for growth; <strong>the</strong> third part draws on
<strong>the</strong> role <strong>the</strong> higher education institutions can play as knowledge institutions; part four presents <strong>the</strong> case-study
assessing <strong>the</strong> perceived role <strong>of</strong> higher education institutions within <strong>the</strong> context <strong>of</strong> KBES in Romania, while <strong>the</strong>
last part concludes.
Keywords: knowledge management, knowledge-based economy, knowledge production, knowledge sharing,
higher education institutions (universities)
1. Introduction
In <strong>the</strong> era <strong>of</strong> knowledge-based economy and society (KBES), human capital (HC), intellectual capital
(IC), creativity, innovation and education play an increasing role in <strong>the</strong> portfolio <strong>of</strong> assets owned by an
organization. The real source <strong>of</strong> sustainable competitive advantage for universities resides in people,
by concentrating efforts on <strong>the</strong>ir knowledge and skills (M.C. Suciu, 2002). The change in <strong>the</strong> nature <strong>of</strong>
knowledge imposes new requirements on school systems in relation to KBES, which must provide
trained individuals able to operate with new cognitive tools, and it focuses on <strong>the</strong> harmonious
integration <strong>of</strong> traditional learning processes in institutions along formal-informal-non formal axis
(according to education prerogatives for lifelong learning LLL). It is necessary to identify and analyze
key competences specific to KBES. Investing in human capital and particularly in LLL is an investment
<strong>of</strong> strategic priority both at microeconomic (for every person and meso-economic level (region, field <strong>of</strong>
activity, local community) (M.C. Suciu, 2001). Universities are actively involved in <strong>the</strong> production,
transmission and dissemination <strong>of</strong> knowledge, essential processes for KBES. Through our own
research we tried to capture <strong>the</strong> perception on <strong>the</strong> role <strong>of</strong> universities in KBES in Romania.
2. Knowledge-based economy and society – conceptual and methodological
framework
Under <strong>the</strong> auspices <strong>of</strong> a knowledge-based society and economy, knowledge, creativity, innovation,
intangible assets, and intellectual capital represent key factors for a sustainable development.
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Marta Christina Suciu et al
In previous published works (M. C. Suciu, 2001) we highlighted that investing in education provides
several opportunities for streng<strong>the</strong>ning and developing competences along formal-informal-nonformal
axis with reference to KBES.
The international organisations, such as <strong>the</strong> OECD (Blöndal, 2002) or <strong>the</strong> World Bank are interested
in managing <strong>the</strong> competences <strong>of</strong> <strong>the</strong>se assets, and particularly in education and innovation policies in
KBES. For instance, Education for Knowledge Based Economy is a program initiated by <strong>the</strong> World
Bank. The World Bank Institute, through <strong>the</strong> Knowledge for Development program, developed a
knowledge assessment methodology (KAM) and created <strong>the</strong> index for knowledge economy (KEI).
This is an aggregate indicator referring to a country’s overall preparedness towards knowledge based
economy and society. According to <strong>the</strong> KAM, <strong>the</strong> main components <strong>of</strong> a knowledge based economy
are: economic and institutional regime; education and human resources competences; dynamic
informational infrastructure; and efficient national innovation systems. With reference to <strong>the</strong> latest KEI
in 2009, on a scale from 1 to 10, Denmark ranked <strong>the</strong> most advanced knowledge-based economy
with a KEI <strong>of</strong> 9.52. Sweden and Finland were taking <strong>the</strong> next two positions in <strong>the</strong> ranking. Romania
ranked 47 out <strong>of</strong> 145 countries in 2009 (with a score <strong>of</strong> 6.43) and it showed an improvement <strong>of</strong> KEI
performance for all <strong>the</strong> dimensions analysed (table 1 below).
Table 1 KEI in 1995 and 2009
Country
/score out
<strong>of</strong> 10
KEI Economic
and
institutional
regime
Innovation Education Information
Technology and
Communication
ICT
2009 1995 2009 1995 2009 1995 2009 1995 2009 1995
Romania 6.43 5.48 6.98 5.73 5.74 4.75 6.47 6.20 6.55 n/a
Source: adapted from <strong>the</strong> World Bank Statistics, KAM, Knowledge for Development, 2010
Based on <strong>the</strong> data provided in <strong>the</strong> table above, it can be noticed that education is one <strong>of</strong> <strong>the</strong>
components which has a significant contribution to <strong>the</strong> aggregate index value (KEI in 2009 for
Romania was 6.43 and <strong>the</strong> related education component score was 6.47). This case confirms <strong>the</strong>
World Bank's KAM methodology that education and especially higher education (university studies,
master, doctoral and postdoctoral programs, etc.) became a strategic investment priority <strong>of</strong> <strong>the</strong>
country contributing to improved performance in relation to <strong>the</strong> different benchmarking methods. LLL
is a real source <strong>of</strong> sustainable competitive advantage in <strong>the</strong> long term. As we pointed out in previous
papers (Suciu, 2004, 2002) emphasizing <strong>the</strong> importance <strong>of</strong> investment in education and <strong>the</strong>
increasingly role <strong>of</strong> intangible assets and intangible asset management.
3. Short literature review on knowledge production
The importance <strong>of</strong> knowledge is acknowledged through various papers and studies on knowledge
production and its impact on growth. The production <strong>of</strong> knowledge is at <strong>the</strong> heart <strong>of</strong> R&D (research
and development) based models <strong>of</strong> endogenous growth (e.g. Romer, 1990; Grossman and Helpman,
1991; Aghion and Howitt, 1992). In <strong>the</strong>se models, new knowledge raises productivity and stimulates
sustained capital formation. Continued technological progress - enabled by a constant allocation <strong>of</strong>
resources to <strong>the</strong> ideas-producing sector – and sustained capital accumulation, toge<strong>the</strong>r, bring about
long-run growth. Growth rates are typically predicted to be proportional to <strong>the</strong> level <strong>of</strong> R&D. The
technology is assumed to progress endogenously; self-motivated, rational, pr<strong>of</strong>it-maximizing agents
who respond to market incentives undertake R&D in search <strong>of</strong> innovation. Government policies
designed to encourage R&D investment influence <strong>the</strong> long-run rate <strong>of</strong> economic growth. These
models contrast sharply with <strong>the</strong> neoclassical growth model, proposed by Solow (1956), which
assumes that technological change and hence long-run growth are exogenously determined. Romer’s
(1990) seminal work on “endogenous technical change” states that <strong>the</strong> production (flow) <strong>of</strong> new ideas
depends on <strong>the</strong> amount <strong>of</strong> human capital devoted to <strong>the</strong> ideas-producing sector and on <strong>the</strong> stock <strong>of</strong>
knowledge available in that sector. Knowledge and ideas are intangible and difficult to measure.
Patent data are widely used to proxy <strong>the</strong>m. Griliches (1990) calls patents “a good index <strong>of</strong> inventive
activity”; Eaton and Kortum (1996) approve <strong>of</strong> patent data as a widely accepted measure <strong>of</strong>
innovation. However, patents are a ra<strong>the</strong>r “noisy” measure <strong>of</strong> innovations because: (i) <strong>the</strong>y cannot
discriminate between <strong>the</strong> quality <strong>of</strong> innovations, and (ii) not all innovations are patented. Patents differ
markedly in <strong>the</strong>ir technical and economic importance. Among <strong>the</strong> thousands <strong>of</strong> patents awarded each
year, only a few may embody valuable knowledge and ideas.
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Luintel, K. B. and M. Khan (2005) examine <strong>the</strong> dynamics <strong>of</strong> knowledge production for a panel <strong>of</strong> 19
OECD countries. A new and unique data set is used to proxy <strong>the</strong> domestic flows <strong>of</strong> “new-to-<strong>the</strong>-world”
knowledge and ideas. The cross-country heterogeneity in <strong>the</strong> production <strong>of</strong> knowledge and <strong>the</strong>
endogenous nature <strong>of</strong> <strong>the</strong> process are addressed in <strong>the</strong>ir paper. The parameters <strong>of</strong> <strong>the</strong> knowledge
production function point to large cross-country differences. Domestic and foreign stocks <strong>of</strong>
knowledge and ideas have a net positive effect on <strong>the</strong> production (flows) <strong>of</strong> new ideas. Countries with
a low domestic knowledge base appear to improve <strong>the</strong>ir total factor productivity considerably through
<strong>the</strong> accumulation <strong>of</strong> knowledge. This effect is very modest for countries that already have a sizeable
domestic knowledge base. Given <strong>the</strong> heterogeneous nature <strong>of</strong> knowledge production across OECD
countries, R&D policy will need to be adapted to <strong>the</strong> specific nature <strong>of</strong> each country as <strong>the</strong> one-sizefits-all
approach may not be effective.
An OECD (2009) study modeling a knowledge production function and using data from OECD
countries showed that investments in R&D have a positive impact on patent activity in all categories
considered (R&D expenditures by businesses, public sector, higher education institutions (HEI),
private non-pr<strong>of</strong>it sector). A surprising result may be <strong>the</strong> fact that R&D expenditures in HEI influences
patenting activities in a negative way and so does <strong>the</strong> effect <strong>of</strong> personnel in higher education –
however, this may reflect <strong>the</strong> lack <strong>of</strong> commercialization in HEI.
Gurmu and Shiferaw (2010) estimate a knowledge production function for university patenting using
an individual effects negative binomial model. They control for Research and Development
expenditures, research field, and <strong>the</strong> presence <strong>of</strong> a Technology Transfer Office. We distinguish
between three kinds <strong>of</strong> researchers: faculty, postdoctoral scholars (postdocs), and PhD students. For
<strong>the</strong> latter two, we also distinguish by visa status. We find patent counts to relate positively and
significantly to <strong>the</strong> number <strong>of</strong> PhD students and number <strong>of</strong> postdocs. Our results also suggest that not
all graduate students and postdocs contribute equally to patenting but that contribution is mediated by
citizenship and visa status
Ahrweiler, P (2011) applies <strong>the</strong> agent‐based SKIN model (Simulating Knowledge Dynamics in
Innovation Networks) to university‐industry links. The model builds on empirical research about
innovation networks in knowledge‐intensive industries with procedures relying on <strong>the</strong>oretical
frameworks <strong>of</strong> innovation economics and economic sociology. The experiments compared innovation
networks with and without university agents. Results showed that having universities in <strong>the</strong>
co‐operating population <strong>of</strong> actors raised <strong>the</strong> competence level <strong>of</strong> <strong>the</strong> whole population, increased <strong>the</strong>
variety <strong>of</strong> knowledge among <strong>the</strong> firms, and increased innovation diffusion in terms <strong>of</strong> quantity and
speed. Fur<strong>the</strong>rmore, firms interacting with universities are more attractive for o<strong>the</strong>r firms when new
partnerships are considered. These results can be validated against empirical findings. The simulation
confirms that university‐industry links improve <strong>the</strong> conditions for innovation diffusion and enhance
collaborative arrangements in innovation networks.
4. The role <strong>of</strong> higher education institutions
In a knowledge-based economy characterized by <strong>the</strong> production, transmission and dissemination <strong>of</strong>
knowledge, <strong>the</strong> universities play a unique role in all <strong>the</strong>se processes. An efficient knowledge
management in a university will ensure <strong>the</strong> best use <strong>of</strong> available resources and best use <strong>of</strong> <strong>the</strong>
knowledge as a key resource; creation / generation <strong>of</strong> new knowledge; understanding and
awareness; capturing, organizing, storing <strong>the</strong> knowledge at individual, group and organization level;
and knowledge transfer to o<strong>the</strong>r persons or o<strong>the</strong>r organizations. Knowledge management has <strong>the</strong>
opportunity to promote and establish a culture <strong>of</strong> innovation and learning within <strong>the</strong> institution. A
learning environment is very important for <strong>the</strong> new learning which takes place within <strong>the</strong> institution.
Universities have traditionally been involved in <strong>the</strong> development <strong>of</strong> new concepts through research.
Research performed by universities has been a contributor towards innovative ideas and products.
Through technology transfer, <strong>the</strong>se new discoveries can be transferred to practicing businesses or
o<strong>the</strong>r sectors. By remaining in one’s field, an innovator can take <strong>the</strong> research from <strong>the</strong> universities and
develop new concepts or products that may be brought to market. The first type <strong>of</strong> innovation is not
restricted to formal research as typically presented by a university. Many private laboratories and
research groups contribute to new findings also. Once a new discovery has been made it must be
made practical. This is <strong>the</strong> second phase <strong>of</strong> innovation. The applications <strong>of</strong> new concepts, <strong>the</strong> second
part <strong>of</strong> innovation is typically in <strong>the</strong> hands <strong>of</strong> practitioner innovators. By staying in business
management and technology advances, for example, individuals and groups have developed new
online services based on <strong>the</strong> researchers developing <strong>the</strong> concept <strong>of</strong> <strong>the</strong> Internet.
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An efficient knowledge management in a university will ensure <strong>the</strong> best use <strong>of</strong> available resources
and best use <strong>of</strong> <strong>the</strong> knowledge as a key resource; creation / generation <strong>of</strong> new knowledge;
understanding and awareness; capturing, organizing, storing <strong>the</strong> knowledge at individual, group and
organization level; and knowledge transfer to o<strong>the</strong>r persons or o<strong>the</strong>r organizations.
In <strong>the</strong> first half <strong>of</strong> April 2009, a study called "Corporate attitude toward a business-education
partnership" was conducted in Bucharest. The objective was to identify <strong>the</strong> companies view on
economic universities, <strong>the</strong>ir curricula and degree <strong>of</strong> openness to a potential partnership. Companies
that have completed <strong>the</strong> questionnaire came from a broad range <strong>of</strong> areas <strong>of</strong> activity. With respect to
<strong>the</strong> rate <strong>of</strong> employment, this was more than 80% in all <strong>of</strong> <strong>the</strong> companies investigated (120 in total).
This highlights <strong>the</strong> fact that responding companies are important "consumers" <strong>of</strong> "products" <strong>of</strong> higher
education graduates in person. Despite <strong>the</strong> fact that usually <strong>the</strong>re is no significant correlation between
<strong>the</strong>ory and practice, <strong>the</strong> percentage <strong>of</strong> responses in favuor <strong>of</strong> correlation, meaning that programs
reflect <strong>the</strong> practices <strong>of</strong> universities and private sector standards warrant, was high (42%) . All
respondents felt that practical training should take place in a business-education partnership. More
than half (58%) <strong>of</strong> surveyed companies had programs that were specifically addressed to students,
while 42% <strong>of</strong> respondents not.
5. The role <strong>of</strong> higher education institutions in Romania in <strong>the</strong> context <strong>of</strong> KBES
This section builds upon <strong>the</strong> results <strong>of</strong> a study conducted regarding <strong>the</strong> Economics and Business
higher education institutions. The aim <strong>of</strong> this case-study was to analyse students’ perception on <strong>the</strong>
role <strong>the</strong> higher education institutions, namely universities, have within <strong>the</strong> context <strong>of</strong> KBES in
Romania. The study is based on <strong>the</strong> perception <strong>of</strong> questioned students from <strong>the</strong> Faculty <strong>of</strong> Business
Administration with teaching in foreign languages (FABIZ) <strong>of</strong> <strong>the</strong> Academy <strong>of</strong> Economic Studies <strong>of</strong>
Bucharest. The main goal focused on identifying students' perceptions <strong>of</strong> <strong>the</strong> role <strong>of</strong> universities in
supporting <strong>the</strong> development <strong>of</strong> knowledge economy in Romania and to highlight <strong>the</strong> extent to which
students consider that <strong>the</strong> Romanian economic university system is ready to face <strong>the</strong> demands <strong>of</strong>
KBES.
5.1 Data collection
For this case study, a structured questionnaire was used. The questionnaire’s items targeted both
two-choice questions and statements measured on <strong>the</strong> 7-point Lickert scale. The questionnaire was
divided into five sections that focused on <strong>the</strong> essential characteristics <strong>of</strong> interest groups. It included an
introductory part in which some <strong>of</strong> <strong>the</strong> concepts were defined, particularly those that could raise
problems in understanding from <strong>the</strong> respondents’ side due to <strong>the</strong> novelty <strong>of</strong> <strong>the</strong> topic. The
questionnaire was distributed throughout November 2010 - January 2011, and it included a number <strong>of</strong>
200 respondents who have volunteered, representing students from <strong>the</strong> Academy <strong>of</strong> Economic
Studies, Faculty <strong>of</strong> Business Administration teaching in foreign languages (FABIZ). Out <strong>of</strong> <strong>the</strong> 200
questionnaires distributed, 175 questionnaires were fully completed, and <strong>the</strong>refore, <strong>the</strong>ir validation
rate was high. In terms <strong>of</strong> socio-demographic characteristics, we would like to emphasize that
respondents were randomly selected, representing a diverse sample, aged 19-36 and a favourable
gender distribution toward females (78% <strong>of</strong> respondents), compared to 12% males. The sample
included both freshmen students and people in <strong>the</strong>ir Master’s degree taught in English in "Business
Administration" within <strong>the</strong> same faculty, FABIZ. The share <strong>of</strong> foreign students who completed <strong>the</strong>
questionnaire was 10%.
5.2 Structure <strong>of</strong> <strong>the</strong> questionnaire
The questionnaire was made <strong>of</strong> 37 questions and some <strong>of</strong> <strong>the</strong> most significant issues questioned are
presented below: assessing <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> learning process, structural and organizational
changes needed in higher education in <strong>the</strong> economic programs <strong>of</strong> studies <strong>of</strong>fered to ensure a close
correlation with <strong>the</strong> demands <strong>of</strong> knowledge-based economy and society.
• Assessing <strong>the</strong> effectiveness <strong>of</strong> learning
The problem <strong>of</strong> assessing <strong>the</strong> economic institutions <strong>of</strong> higher education is increasingly important,
gradually overcoming <strong>the</strong> confusion stage with <strong>the</strong> simple "testing" on <strong>the</strong> knowledge accumulated.
From <strong>the</strong> perspective <strong>of</strong> lifelong learning and aligning personal and pr<strong>of</strong>essional development, it has
become obvious that it is insufficient to assess <strong>the</strong> performance requirements related to promoting an
exam only or <strong>the</strong> accumulation <strong>of</strong> a number <strong>of</strong> credit points to justify obtaining a certificate <strong>of</strong>
graduation. It becomes imperative for <strong>the</strong> individual to demonstrate that he or she truly learned and
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Marta Christina Suciu et al
holds a "skills portfolio" that not only gives more chances for getting a job, but also for managing and
developing <strong>the</strong>ir careers. Fur<strong>the</strong>rmore, it aims to assess not only students but also teachers and it
considers <strong>the</strong> whole process which can give universities <strong>the</strong> virtues <strong>of</strong> knowledge-based
organizations. Therefore, <strong>the</strong> evaluation is done not only at student level, but also at <strong>the</strong> level <strong>of</strong> <strong>the</strong>
education system. Ano<strong>the</strong>r aspect to be considered is related to specific forms <strong>of</strong> online distance
learning.
• Structural and organizational changes needed in higher education in <strong>the</strong> economic programs <strong>of</strong>
study <strong>of</strong>fered to provide a closer correlation with <strong>the</strong> demands <strong>of</strong> knowledge-based economy and
society
At <strong>the</strong> level <strong>of</strong> university education in general, and <strong>the</strong> economic one in particular, <strong>the</strong> number <strong>of</strong>
graduates has increased. Although universities are considered to be some entities specific to <strong>the</strong>
“business” sector, this statement does not hold as valid any longer in recent years. The
entrepreneurial university model promoted especially in <strong>the</strong> U.S.A. and <strong>the</strong> integration <strong>of</strong> <strong>the</strong>
corporate model into <strong>the</strong> structure <strong>of</strong> educational organizations, led to a convergence towards a
common area defined by <strong>the</strong>se two types <strong>of</strong> organizations. KBES exigencies require an integrated
treatment <strong>of</strong> knowledge-based organizations, no matter if <strong>the</strong>y belong to academia or <strong>the</strong> business
environment. In some cases, a list <strong>of</strong> public/private financed courses is being <strong>of</strong>fered in order to meet
<strong>the</strong> specific economic sector needs. This requires finding answers to questions about <strong>the</strong> cost,
efficiency and effectiveness in relation to <strong>the</strong> skills which are developed.
In some cases, <strong>the</strong> courses tend to be too expensive compared to <strong>the</strong> value added <strong>the</strong>y may
generate later.
Situations need to be analysed in details and case by case as <strong>the</strong> efficiency <strong>of</strong> educational investment
and rate <strong>of</strong> return follow a medium and long term approach (C. Suciu, 2001). Structuring <strong>the</strong> programs
should reflect <strong>the</strong> requirements <strong>of</strong> <strong>the</strong> knowledge-based economy and society. To a large extent it is
considered that this has been initiated in Romania by integrating in <strong>the</strong> curriculum <strong>of</strong> <strong>the</strong> reflexive
questioning and valuable dimension, which takes into account developing specific skills for critical
thinking and a practical dimension, following <strong>the</strong> formation <strong>of</strong> attitudes and practicing rational
behaviours. Expanding range <strong>of</strong> options in terms <strong>of</strong> economic training, may lead to <strong>the</strong> diversification
<strong>of</strong> skills and abilities <strong>of</strong> individuals, helping <strong>the</strong>m to be able to obtain a competitive advantage in <strong>the</strong>
labour market.
5.3 Main results
An important element <strong>of</strong> <strong>the</strong> questionnaire was to investigate <strong>the</strong> expectations <strong>of</strong> <strong>the</strong> beneficiaries <strong>of</strong>
higher education in economics and business education about <strong>the</strong> implications <strong>of</strong> promoting
knowledge-based economy and society. As international experience shows, <strong>the</strong> functionality and
efficiency <strong>of</strong> universities is an essential part <strong>of</strong> building an environment to cope with <strong>the</strong> demands <strong>of</strong>
<strong>the</strong> KBES. The questionnaire aims to identify <strong>the</strong> factors that may determine <strong>the</strong> success for a KBES.
The results showed that <strong>the</strong> students surveyed are generally satisfied with <strong>the</strong>ir university (70%), <strong>the</strong>
remaining being undecided or showing a degree <strong>of</strong> dissatisfaction. Some areas have enjoyed a
greater appreciation among students, such as: employees’ competence (teachers, administrative
staff), for which we obtained a weighted score <strong>of</strong> 4.68; universities attempt to provide better services
as compared with o<strong>the</strong>r similar educational institutions. The existence <strong>of</strong> appropriate information
infrastructure creates favourable conditions for developing individual skills, which can help <strong>the</strong> student
not only to accumulate knowledge, but also disseminate it and develop certain specific competences
for KBES. The importance <strong>of</strong> developing online education programs in universities in Romania is seen
as an urgent requirement for KBES The qualitative component is emphasised through <strong>the</strong> actual use
<strong>of</strong> information infrastructure and access to <strong>the</strong> databases <strong>of</strong> <strong>the</strong> university. Students gave a 5.1 score
to <strong>the</strong>se facilities, 63% <strong>of</strong> respondents being convinced that a better system than <strong>the</strong> current one
could be built. 57% <strong>of</strong> respondents noticed <strong>the</strong> existence <strong>of</strong> videoconferencing and teleconferencing
equipment required in universities, but only 34% participated at least once in activities which involved
using <strong>the</strong>se facilities.
In <strong>the</strong> same time, <strong>the</strong> online learning is gaining ground and students are encouraged to use <strong>the</strong>
available s<strong>of</strong>tware (with a score <strong>of</strong> 5.6).
The second part <strong>of</strong> <strong>the</strong> questionnaire sought to examine <strong>the</strong> extent to which specific principles <strong>of</strong>
knowledge-based economy and society are reflected in <strong>the</strong> economic institutions <strong>of</strong> higher education.
The main findings are presented below:
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Basic concepts <strong>of</strong> knowledge-based economy are treated many courses, especially in <strong>the</strong>
economic faculties. However, <strong>the</strong>re is a need for greater promotion <strong>of</strong> modern methods <strong>of</strong>
learning conducive to innovation and stimulating creative thinking.
Respondents considered that Romania will have to prepare university annual reports on
intellectual capital (average score is 6). Most students (with a rating <strong>of</strong> 6.4 out <strong>of</strong> 7) mentioned
<strong>the</strong>se reports as vital and highlighted that <strong>the</strong>se should be transparent and made publicly
available with a view to a real feedback. From <strong>the</strong> institutional perspective, students felt that
research and development departments in universities could be designated to make such reports.
86% <strong>of</strong> students acknowledged <strong>the</strong> existence <strong>of</strong> <strong>the</strong>se bodies <strong>of</strong> research within universities.
Higher education institutions manage to create an atmosphere that encourages students’
activities (average rating 5), but with <strong>the</strong> caveat that it is necessary to promote new and
innovative ideas. Please note that students considered appropriate making use <strong>of</strong> <strong>the</strong> expertise
that can be achieved by streng<strong>the</strong>ning links between universities in consortia and/or through
partnerships with <strong>the</strong> business environment.
realized
not realized
Figure 1: Correlating <strong>the</strong> universities’ educational <strong>of</strong>fer with <strong>the</strong> labour market demand in <strong>the</strong> context
<strong>of</strong> KBES
Figure 1 presents <strong>the</strong> students’ perception about <strong>the</strong> degree <strong>of</strong> compliance with <strong>the</strong> specific
requirements <strong>of</strong> KBES <strong>of</strong> higher education institutions (in <strong>the</strong> graph, <strong>the</strong>se requirements have been
encoded with capital letters from A to F):
Encouraging <strong>the</strong> development <strong>of</strong> intra- and interpersonal relations within and between different
groups <strong>of</strong> students;
Prepare students so that <strong>the</strong>y have <strong>the</strong> opportunity to know a number <strong>of</strong> practical aspects
required on <strong>the</strong> labour market;
The existence <strong>of</strong> a significant number <strong>of</strong> optional courses in which students can enrol for
specialization;
The existence <strong>of</strong> sufficient practical work in courses and seminars in which students practice a
wide range <strong>of</strong> skills;
Encouraging participation in scientific events organized in <strong>the</strong>ir area <strong>of</strong> interest;
Organization <strong>of</strong> events to facilitate <strong>the</strong> access <strong>of</strong> young people to <strong>the</strong> labour market.
Students expressed <strong>the</strong> need for practical work in courses and seminars, and training in universities
and matching <strong>the</strong>m with <strong>the</strong> labour market requirements. Only a proportion <strong>of</strong> 57% <strong>of</strong> subjects
surveyed believe that most teachers are involved both in <strong>the</strong> training process and in <strong>the</strong> development
<strong>of</strong> individual students and 67% <strong>of</strong> <strong>the</strong>m have at least one teacher as <strong>the</strong>ir mentor. The survey
recorded an average rating <strong>of</strong> 3.8 in terms <strong>of</strong> frequency <strong>of</strong> regular teachers-students meetings.
Students considered that <strong>the</strong> holding informal meetings would emphasize <strong>the</strong> pedagogical aspects <strong>of</strong>
education.
The last part <strong>of</strong> <strong>the</strong> questionnaire sought to identify <strong>the</strong> extent to which universities in Romania are
able to engage effectively in KBES, mainly taking into account to:
• Reduce inefficiencies in <strong>the</strong> organization <strong>of</strong> activities and projects that will include information
campaigns on <strong>the</strong> rights <strong>of</strong> students. Respect for <strong>the</strong> principles <strong>of</strong> diversity, equal opportunities
and social inclusion require <strong>the</strong> integration <strong>of</strong> all students, regardless <strong>of</strong> ethnicity, gender. 96% <strong>of</strong>
respondents believe that <strong>the</strong>re is no preferential treatment for certain groups <strong>of</strong> students
regarding access to facilities on campus. Progress has been made also in terms <strong>of</strong> diversifying
access to higher education, 77% <strong>of</strong> students found useful and transparent <strong>the</strong> use <strong>of</strong> online
learning programs - 84% for distance learning and 100% for exchange programs. Students
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Marta Christina Suciu et al
propose and encourage informal meetings to enhance communication skills and cross team spirit.
The negative effects <strong>of</strong> bureaucracy in universities are still being felt (5.1 rating).
• Grant awards and distinctions have been noted by students who consider that recognition <strong>of</strong>
merit is important.
6. Conclusions and recommendations
Within <strong>the</strong> context <strong>of</strong> a KBES, sustainable development needs to emphasize <strong>the</strong> endogenous sources
<strong>of</strong> knowledge, innovation, human capital, intangible assets, etc. In KBES, <strong>the</strong> role <strong>of</strong> universities and
higher education institutions in economics and business are <strong>the</strong> main investors in lifelong learning is
extremely important. Universities are actively involved in <strong>the</strong> production, transmission and
dissemination <strong>of</strong> knowledge, essential processes for KBES. Through our own research we tried to
capture <strong>the</strong> perception on <strong>the</strong> role <strong>of</strong> universities in KBES in Romania. Our study focused on
identifying students' perceptions <strong>of</strong> <strong>the</strong> role <strong>of</strong> universities in supporting <strong>the</strong> development <strong>of</strong>
knowledge economy in Romania and to highlight <strong>the</strong> extent to which students consider that <strong>the</strong>
Romanian economic university system is ready to face <strong>the</strong> demands <strong>of</strong> KBES. The results showed
that students are in general satisfied with <strong>the</strong>ir institutions but <strong>the</strong> key challenges for a knowledgebased
economy and society have been pointed out. The importance <strong>of</strong> developing online education
programs in universities in Romania is seen as an urgent requirement for KBES. Students expressed
<strong>the</strong> need for practical work in courses and seminars, and training in universities and matching <strong>the</strong>m
with <strong>the</strong> labour market requirements. In terms <strong>of</strong> <strong>the</strong> extent to which universities in Romania are able
to engage effectively in KBES, two points have been highlighted: to reduce inefficiencies in <strong>the</strong>
organization <strong>of</strong> activities and projects that will include information campaigns on <strong>the</strong> rights <strong>of</strong> students,
and to grant awards and distinctions in recognition to merit.However, <strong>the</strong> results <strong>of</strong> our research
represent only a point <strong>of</strong> view and we remain open to dialog and continuing <strong>the</strong> research by engaging
ourselves in trans- and inter-disciplinary research groups.
Acknowledgements
This article reflects some <strong>of</strong> <strong>the</strong> results obtained under <strong>the</strong> following projects: „Parteneriate 92116”,
financed by CNCSIS /UEFISCSU and coordinated by <strong>the</strong> first author <strong>of</strong> this paper;
POSDRU/88/1.5/S/55287 project on „Doctorat în economie la standardele Europei cunoa terii”
(DOESEC), co-financed by <strong>the</strong> Social <strong>European</strong> Fund through <strong>the</strong> Sectoral Operational Program on
Human Resources Development 2007-2013 and implemented by Academy <strong>of</strong> Economic Studies <strong>of</strong>
Bucharest in partnership with <strong>the</strong> West University <strong>of</strong> Timisoara.
References
Aghion, P. and Howitt P. (1992), “A Model <strong>of</strong> Growth through Creative Destruction”, Econometrica, March, 60(2),
Ahrweiler, P (2011). "A new model for university-industry links in knowledge-based economies". The
Journal <strong>of</strong> product innovation management (0737-6782), 28(2), p.218.
Blondal, S., Field, S. & Girouard, N. (2002). Investment in human capital through upper-secondary and tertiary
education. OECD Economic Studies No. 34, pp. 41–89.
Eaton, J. and Samuel K. (1996), “Trade in Ideas: Patenting and Productivity in <strong>the</strong> OECD”, Journal <strong>of</strong>
International Economics, May, Vol. 40 no. 3-4, pp. 251-278.
Griliches, Z. (1990), “Patent Statistics as Economic Indicators: A Survey”, Journal <strong>of</strong> Economic Literature,
December, vol. 28(4), pp. 1661-1707.
Grossman, G. M. and Helpman E. (1991), Innovation and Growth in <strong>the</strong> Global Economy, Cambridge, MA, and
London: MIT Press.
Gurmu, Shiferaw (2010). "The Knowledge Production Function For University Patenting.". Economic
inquiry (0095-2583), vol. 48(1), p.192.
Luintel, K. B. and M. Khan (2005), “An Empirical Contribution to Knowledge Production and Economic Growth”,
OECD Science, Technology and Industry Working Papers, 2005/10, OECD Publishing
Romer, P. M. (1990), “Endogenous Technological Change”, Journal <strong>of</strong> Political Economy, October, vol. 98(5), pp.
Suciu, M.C. (2004). Economics. New economics & Knowledge-based society/ Economie. Noua economie si
societatea bazata pe cunoastere, Part I and I, ASE Publishing House, 798 p.
Suciu, M.C. (2002). Economia cunoaşterii şi civilizaţia globală. Investiţia şi speranţa în om, ASE Publishing
House, Bucharest, 289 p
Suciu, M.C. (2001). Investiţia în educaţie, Economică, Publishing House, 487 p
OECD (2009) How Regions Grow: Trends and Analysis
966

Using Naturalistic Decision Making to Understand
Knowledge Barriers in Launching Telecommunication for
Public Safety
Kaj Suneson and Ilona Heldal
Chalmers University <strong>of</strong> Technology, Management and Economics,
Go<strong>the</strong>nburg, Sweden
suneson@chalmers.se
ilona.heldal@chalmers.se
Abstract: Based on an empirical study <strong>of</strong> <strong>the</strong> launch <strong>of</strong> an information and communication technology [ICT]
system for <strong>the</strong> main Public Safety Agencies [PSA] in Sweden, this paper combines concepts from knowledge
management [KM] with naturalistic decision-making [NDM] to gain a greater understanding <strong>of</strong> problems in <strong>the</strong>
implementation and adoption phase <strong>of</strong> <strong>the</strong> new system. The launch has been experienced as being much slower
than was originally envisaged by all <strong>the</strong> organizations involved and <strong>the</strong> system will not cover its own costs in <strong>the</strong>
near future. Implementation and adoption also required extra, unplanned, resources to develop processes for
collaboration and joint work by and between <strong>the</strong> organizations. In <strong>the</strong> public safety and emergency response
sector, <strong>the</strong> need for communication and co-operation between <strong>the</strong> organizations involved is regarded mostly as
self-evident by both <strong>the</strong> organizations involved and external organizations. To make co-operation within and
between <strong>the</strong> different organizations more efficient, a new radio communication system is being introduced in
Sweden. Adoption and use are presented as being <strong>the</strong> prerogative <strong>of</strong> <strong>the</strong> user organizations. In an earlier paper
we identified several problems, i.e. 'knowledge barriers' that prevented or slowed down adoption and thus <strong>the</strong>
launch process (Sunesson and Heldal, 2010). This paper takes a fur<strong>the</strong>r step to gain a deeper understanding <strong>of</strong>
what <strong>the</strong>se knowledge barriers mean by using terms from knowledge management [KM] combined with concepts
and frameworks from NDM. NDM provides a promising framework to understand <strong>the</strong>se situations, since <strong>the</strong>
situation in which <strong>the</strong> system will be used can be related to understanding technologies in complex environments,
i.e. solving ill-defined problems under time pressure and with high stakes combined with uncertain outcomes. By
analysing a set <strong>of</strong> five categories <strong>of</strong> knowledge barriers through concepts from <strong>the</strong> NDM framework, such as
'sensemaking', 'situation awareness', and 'mental models', this paper strives to increase understanding <strong>of</strong> <strong>the</strong>
obstacles that need to be overcome when launching large-scale ICT systems for parties with specific needs and
requirements. The main benefit <strong>of</strong> using knowledge management models is to provide a good overview <strong>of</strong> <strong>the</strong>
launch process, emphasizing relevant problem areas. The advantage <strong>of</strong> examining <strong>the</strong>se problem areas more
deeply by means <strong>of</strong> concepts and frameworks from NDM can be found in <strong>the</strong> possibility <strong>of</strong> acquiring a deeper
understanding and providing suggestions to overcome <strong>the</strong> identified problems for users, launchers and
influencers. This paper makes both practical and <strong>the</strong>oretical contributions. Practical contributions exemplify how
important mutual understanding is to co-operation and in doing so a more coherent mental model on which to
build co-operation. The <strong>the</strong>oretical contribution takes <strong>the</strong> form <strong>of</strong> suggestions to improve our understanding <strong>of</strong> <strong>the</strong>
benefits <strong>of</strong> combining KM models with frameworks and concepts from NDM.
Keywords: Co-operation, public safety, sensemaking, situation awareness, knowledge barriers, system roll-out
1. Introduction
Today, most communication between <strong>the</strong> main Public Safety Agencies [PSA], such as <strong>the</strong> police,
ambulance and fire/rescue departments, is via old analogue technologies with limited functionality.
Common problems are limited coverage and <strong>the</strong> problem <strong>of</strong> replacing, repairing or upgrading devices
and infrastructure. The technology is also simple to intercept by unauthorized users. Different PSAs
use different systems that are not compatible with each o<strong>the</strong>r. Due to <strong>the</strong> organizations' need to
interact in emergency situations <strong>the</strong>y <strong>of</strong>ten require access to and knowledge <strong>of</strong> <strong>the</strong> o<strong>the</strong>r agencies'
communication systems. The agencies have different regulations and different principals and several
<strong>of</strong> <strong>the</strong> stakeholders involved have identified <strong>the</strong> need for improved co-operation between agencies.
This paper aims to discuss experiences during <strong>the</strong> roll-out <strong>of</strong> a new broadband communication system
from <strong>the</strong> perspective <strong>of</strong> naturalistic decision-making [NDM] using concepts such as sensemaking,
situation awareness and mental models. The focus is on <strong>the</strong> users' and <strong>the</strong>ir organizations' view <strong>of</strong> <strong>the</strong>
implementation. We argue that by providing a better description <strong>of</strong> obstacles that need to be
overcome to launch this ICT system, <strong>the</strong> experiences gained will contribute to a better general
understanding <strong>of</strong> <strong>the</strong> launch <strong>of</strong> large-scale, multiuser ICT systems. The results also illustrate <strong>the</strong>
usefulness <strong>of</strong> concepts from <strong>the</strong> NDM framework in understanding social and technical premises for
adopting new technologies for co-operation within and between PSAs.
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Kaj Suneson and Ilona Heldal
Many research projects aim to examine collaboration for PSAs (e.g. Berlin and Carlström, 2009) or
examining technologies that support emergency and rescue operations (e.g. Landgren, 2007, Nemeth
et al., 2010). There are also studies and methods are also aimed at <strong>the</strong> use <strong>of</strong> technology or to obtain
predictions for 'proactive reasoning across domains as diverse as energy, security, <strong>the</strong> environment,
health and finance in order to maximize opportunities and counter adversities' (Sanfilippo et al., 2009,
p.1). O<strong>the</strong>r studies examine specific aspects, e.g. a common knowledge aid for decision quality
(Mosier, 1997) or, on <strong>the</strong> contrary, how it makes decisions slower (Wittenbaum et al., 2004). However,
as far as we are aware no previous work has explored <strong>the</strong> benefits <strong>of</strong> NMD when launching large
technical systems, especially for inter-organizational communication and collaboration.
2. Background
This section will first introduce telecommunication for public safety and <strong>the</strong> situation being studied
followed by descriptions <strong>of</strong> knowledge barriers and <strong>the</strong> NDM framework and associated concepts that
assure usefulness in <strong>the</strong> understanding and specification <strong>of</strong> knowledge.
2.1 Telecommunication in <strong>the</strong> Public Safety sector
When providing ICT systems to <strong>the</strong> Public Safety sector two <strong>the</strong>oretical possibilities are available. The
choice can ei<strong>the</strong>r be to use an old standard, already developed for tailored solutions for this sector, or
<strong>the</strong> launch can wait until a future commercial solution is suitable for PSAs.
Tailored solutions have been launched in several waves in <strong>European</strong> countries over <strong>the</strong> last 20 years
in <strong>the</strong> form <strong>of</strong> digital radio communications based on TETRA, TETRAPOL or P25 standards (Tetra,
2000). These serve <strong>the</strong> needs <strong>of</strong> local, regional and central government services involved in public
safety and order, such as <strong>the</strong> health and emergency services, police, ambulance, firefighting and
rescue services. Depending on <strong>the</strong> country, o<strong>the</strong>r user organizations, e.g. local, regional and
municipal services, social workers, nuclear power plants, probation and prison systems, could also be
included (Sjöblom et al., 2010). The systems may also extend to <strong>the</strong> armed forces, although many
countries have separate military networks and administrations.
Interestingly, <strong>the</strong> road map <strong>of</strong> tailored digital networks for emergency services has too <strong>of</strong>ten led
nowhere, with high costs, poor technical performance, a slow and difficult launch process and poor
reliability (Bohlin et al., 2005). There are various reasons for this, e.g. <strong>the</strong> digital technology not only
replaces an older technology but must be treated as a new innovation (Sjöblom et al., 2010) and
consequently a longer adaptation process needs to be considered. Berlin and Carlström (2009) points
out that differences between <strong>the</strong> collaborative partners, <strong>the</strong>ir organizational cultures and <strong>the</strong>ir working
routines might affect co-operation negatively. This can also be interpreted as ano<strong>the</strong>r reason for <strong>the</strong>
negative road map.
The currently available commercial alternatives on <strong>the</strong> market, such as GSM and 3G, do not satisfy
<strong>the</strong> specific requirements <strong>of</strong> <strong>the</strong> Public Safety sector. There are strong demands on functions such as
push-to-talk (PTT, allowing almost instant connection), data security and robustness. Using
commercial digital broadband for Public Safety <strong>of</strong>ten means "lost calls" due to lack <strong>of</strong> coverage,
overloaded systems in crisis and so on. Fur<strong>the</strong>rmore, at <strong>the</strong> present stage many <strong>of</strong> <strong>the</strong> commercial
networks are unable to <strong>of</strong>fer solutions for group communication, communication between handsets
without a network, or services that require exact positioning, which are also common demands from
PSAs.
2.2 The studied telecommunication roll-out
To solve <strong>the</strong> problems presented in <strong>the</strong> introduction and to increase potential co-operation between
PSAs, a new digital communication system, RAKEL (RAdioKommunikaktion för Effektiv Ledning –
Radio communication for effective management), is being introduced in Sweden (RAKEL, 2009).
Adoption and connection to RAKEL have been much slower than was originally envisaged and <strong>the</strong>
co-operation and collaboration organization has not been solved ei<strong>the</strong>r. Although thought <strong>of</strong> as a
business that should cover its own costs, <strong>the</strong> system will not achieve this goal in <strong>the</strong> near future
(Heldal et al. 2010).
The digital telecommunication system decided in Sweden and tailored to PSAs and o<strong>the</strong>r
organizations has an important role to play in society in case <strong>of</strong> crisis. In <strong>the</strong> long run <strong>the</strong> system is
aimed at a broad collection <strong>of</strong> organizations with <strong>the</strong> role <strong>of</strong> achieving stability during crises and major
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Kaj Suneson and Ilona Heldal
emergencies. To date, it is <strong>the</strong> traditional PSAs that are in part connected to <strong>the</strong> system. The Swedish
Civil Contingencies Agency (MSB) was granted consent by <strong>the</strong> government to set up and start <strong>the</strong>
system as well as <strong>the</strong> task <strong>of</strong> running it in <strong>the</strong> future. MSB sells services to approved organizations
and <strong>the</strong> system is regarded as being self-funding through fees. MSB only delivers <strong>the</strong> services and
access to <strong>the</strong> system. The influence that user organizations can exert on <strong>the</strong> system's design and
properties is to state <strong>the</strong>ir wishes although <strong>the</strong> actual decisions are taken by MSB on what are for
<strong>the</strong>m commercial grounds. MSB does not instruct <strong>the</strong> users on how to use RAKEL. In <strong>the</strong> set-up, <strong>the</strong>
main PSAs are seen as users buying <strong>the</strong> service. The PSAs in Sweden have divided responsibilities,
different principals and in part different alarm centres.
To support mobile communication up to now, <strong>the</strong> PSAs have used different, mostly analogue,
systems. This means that <strong>the</strong>y have had to manage many different systems in order to communicate
and collaborate with each o<strong>the</strong>r. One <strong>of</strong> <strong>the</strong> aims <strong>of</strong> <strong>the</strong> new, tailored system is that all organizations
involved in times <strong>of</strong> crisis will be able to use <strong>the</strong> same system. In <strong>the</strong> instructions for <strong>the</strong> new system,
increased co-operation between <strong>the</strong> organizations is also sought.
Reasons for <strong>the</strong> slow connection and <strong>the</strong> problems organizing communication and collaboration were
identified in an earlier study as "knowledge barriers" (Suneson and Heldal, 2010). While <strong>the</strong> concept
<strong>of</strong> "knowledge barriers" can identify <strong>the</strong> main problem areas, it does not in itself clarify how to <strong>the</strong>se
barriers can be overcome.
2.3 Knowledge barriers
In our view, knowledge barriers have <strong>the</strong>ir origin in Attewell (1992), showing that <strong>the</strong> adoption <strong>of</strong>
complex technological systems is not just a question <strong>of</strong> transferring knowledge <strong>of</strong> <strong>the</strong> technology
between providers and buyers. The adoption and use <strong>of</strong> <strong>the</strong> technology also demands sufficient
knowledge, i.e. a knowledge barrier, to master <strong>the</strong> use <strong>of</strong> that technology in organizations.
Tanriverdi and Iacono (1998) developed <strong>the</strong> view <strong>of</strong> knowledge barriers fur<strong>the</strong>r with a proposed
categorization <strong>of</strong> <strong>the</strong> knowledge barriers into four different categories to fit technology dissemination
for telemedicine. Their suggestion was that four types <strong>of</strong> knowledge barriers needed to be lowered to
facilitate dissemination: 1) technical barriers (cf. Attewell (1992)), 2) financial barriers – <strong>the</strong> need to
understand how value creation will benefit <strong>the</strong> stakeholder (adopter), 3) organizational barriers, i.e.
being able to understand how <strong>the</strong> new technology fits structures and processes within <strong>the</strong> adopting
organization, and 4) behavioural barriers as co-workers in <strong>the</strong> organization need to be convinced
about adopting <strong>the</strong> technology, e.g. by managing resistance. These four categories might help to
understand dissemination within one organization. In inter-organizational co-operation <strong>the</strong> complexity
increases, mainly due to <strong>the</strong> different contexts and lack <strong>of</strong> knowledge about each <strong>of</strong> <strong>the</strong> parties
involved. To describe this situation a fifth, inter-organizational knowledge barrier is proposed by
Suneson and Heldal (2010).
It is important to see a knowledge barrier as a lack <strong>of</strong> knowledge. A financial knowledge barrier is <strong>the</strong>
inability to see <strong>the</strong> financial model and <strong>the</strong> costs or benefits in using <strong>the</strong> technology for one's own
organization. To make a calculation that shows that <strong>the</strong> cost is too high or something similar is not a
knowledge barrier.
2.4 Naturalistic decision-making
Traditional decision-making <strong>the</strong>ories focus on how people make mistakes in judging alternatives and
making choices. The view <strong>of</strong> people is that <strong>the</strong>y are rational within certain limits (Bounded rationality,
(e.g. Simon, 2000)). Decision-making is seen as making choices between alternatives. This has also
been seen in <strong>the</strong> "common world" (outside science) as "decisions". The founders <strong>of</strong> NDM (e.g. Gary
Klein) saw this from a completely different point <strong>of</strong> view. In a series <strong>of</strong> studies <strong>the</strong> researchers asked
pr<strong>of</strong>essionals (such as fire-fighters and soldiers) how <strong>the</strong>y made decisions. The answer was that <strong>the</strong>y
did not make any decisions – <strong>the</strong>y just worked. In his view this could not be true because out <strong>of</strong> a
large set <strong>of</strong> options <strong>the</strong>y followed some or one <strong>of</strong> <strong>the</strong>m. The question <strong>the</strong>n was why <strong>the</strong>y felt that <strong>the</strong>y
did not make decisions.
The solution that Klein (e.g. 1989, 2008) arrived at was <strong>the</strong> <strong>the</strong>ory <strong>of</strong> "recognition-primed decisionmaking".
In this model making a choice between alternatives is not necessary. The decision-maker
does not find <strong>the</strong> optimum solution but finds a good enough solution. In <strong>the</strong> model <strong>the</strong> decision-maker
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recognizes "keys" in a situation that are compared to a solution and to see if <strong>the</strong> solution satisfies <strong>the</strong>
required goal. If this solution fails on a particular key, <strong>the</strong> next possible solution is tested and so on
until a "good enough" solution is found. Different versions <strong>of</strong> this model have been developed
although <strong>the</strong>y all converge in that <strong>the</strong> decision-maker is searching for a "good enough" or "working"
solution and not an optimum one. This process is not necessarily conscious or even deliberate.
This has a huge impact on what is important to understand in <strong>the</strong> work processes <strong>of</strong> <strong>the</strong> parties
involved. First <strong>of</strong> all <strong>the</strong>y need to recognize what keys to look for. This is an automated process and is
so heavily internalized that it is a part <strong>of</strong> <strong>the</strong>ir cognitive perception. A concept used to describe this
phenomenon is "situation awareness".
In a broader view <strong>the</strong>se parties also need a model <strong>of</strong> "reality” in which to use <strong>the</strong>se keys. In a way it
could be said that <strong>the</strong>y need a model <strong>of</strong> reality to test <strong>the</strong> keys against, a “mental model” <strong>of</strong> <strong>the</strong>
relevant part <strong>of</strong> <strong>the</strong> world and existence.
A mental model cannot be <strong>the</strong> same over time due to changing conditions and o<strong>the</strong>r inputs. There is
an ongoing process <strong>of</strong> changing, refining and choosing relevant parts <strong>of</strong> mental models in certain
situations. This process is called "sensemaking" (making sense out <strong>of</strong> situations). This concept has
also been used to understand organizations and organizational behaviour.
Different mental models and different situation awareness can make collaboration very difficult. In this
view concepts <strong>of</strong> knowledge sharing and knowledge transfer are important elements in <strong>the</strong> task <strong>of</strong>
understanding collaboration problems. Knowledge-sharing and knowledge transfer can be seen as
processes where mental models are shared and which affect possible collaboration/co-operation.
Sensemaking, situation awareness and mental models
The three concepts 'sensemaking', 'situation awareness', and 'mental models' are obviously
connected and are difficult to distinguish from each o<strong>the</strong>r (Sieck et al., 2007).
Sensemaking is usually defined as <strong>the</strong> set <strong>of</strong> processes needed to improve one's understanding <strong>of</strong> a
situation (Weick, 1995). In an organization this means:
(a) a process <strong>of</strong> externalization,
(b) embodied as written and spoken information,
(c) a media to shape behaviour (according to Weick et al. (2005) and Gioia et al (1994)).
Weick (2005) discusses sensemaking as a process in an organization. Sensemaking in Weick's
perspective is an ability to organize a stream <strong>of</strong> unstructured data from our perception. Out <strong>of</strong> this
stream we pay attention to, distinguish and study certain parts more thoroughly. Sensemaking is very
much about, from a retrospective view, putting words to what has happened. We cannot tell about,
structure and work with situations that have not yet happened but from a retrospective point <strong>of</strong> view
we are able to put toge<strong>the</strong>r <strong>the</strong> puzzle <strong>of</strong> what really happened. Sensemaking is a social phenomenon
and belongs to <strong>the</strong> system where <strong>the</strong> situation appears and is to a large extent about organizing by
communication. Sensemaking could also be stated as an organizational, evolutionary process where
identity in <strong>the</strong> form <strong>of</strong> a "self" and organizational belonging are created. This identity decides how
events are perceived and interpreted.
Endsley describes situation awareness on three levels: <strong>the</strong> first is perception <strong>of</strong> elements in <strong>the</strong>
environment, <strong>the</strong> second is comprehension <strong>of</strong> <strong>the</strong> perception <strong>of</strong> <strong>the</strong> elements in <strong>the</strong> situation, and <strong>the</strong>
third is projection <strong>of</strong> future status (Endsley, 1995).
A mental model is a broader view <strong>of</strong> how <strong>the</strong> previous relevant elements are connected. It can also be
seen as a memory representation <strong>of</strong> a specific situation (Endsley 2000). Burns described how mental
models are used through <strong>the</strong> three major stages towards achieving situational awareness, i.e.
describing, explaining and predicting a process that begins with internal representations and ends
with external actions (Lipschitz and Strauss, 1997, Burns, 2005).
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During <strong>the</strong> summer and autumn <strong>of</strong> 2009, 49 interviews (each <strong>of</strong> at least one hour's duration) were
performed with informants who represented different levels in <strong>the</strong> decision hierarchy within <strong>the</strong> launch
and user organizations. For fur<strong>the</strong>r information, see (Heldal et al., 2010).
All interviews were semi-structured with five prepared topics <strong>of</strong> interest:
Experiences <strong>of</strong> <strong>the</strong> roll-out
Experiences from early usage
Co-operation and collaboration
Culture and opinions regarding regulations
User expectations in general.
The interpretations <strong>of</strong> <strong>the</strong> informants' answers are developed from a hermeneutic process where
different aspects and statements from <strong>the</strong> interviews grow toge<strong>the</strong>r in discussions to form a picture <strong>of</strong>
<strong>the</strong> situation being examined. The major problem areas and important elements <strong>of</strong> <strong>the</strong> situations were
presented at a workshop with representatives (45 people) from <strong>the</strong> organizations involved. The
participants acknowledged <strong>the</strong> interpretations as being good descriptions focused on <strong>the</strong> major
problems in <strong>the</strong> launch process.
4. Knowledge barriers in a perspective taken from NDM
In this section we present certain findings for each part <strong>of</strong> <strong>the</strong> interviews, with identified knowledge
barriers and proposed links to sensemaking, situation awareness and mental models.
4.1 Experiences <strong>of</strong> <strong>the</strong> roll-out
The user informants stated that initially <strong>the</strong>y had high expectations <strong>of</strong> <strong>the</strong> new radio communication
system through extensive improvements in communication. Both <strong>the</strong> launching organizations and
user organizations wanted an 'open' process to adopt and implement functions to meet <strong>the</strong> users'
needs.
Originally, <strong>the</strong> system was set up via requirements laid down by <strong>the</strong> government authorities. Users felt
that <strong>the</strong> implementation required a great deal <strong>of</strong> time and effort in <strong>the</strong>ir day-to-day work. In most
organizations no extra resources were allocated for implementation. They did not perceive
<strong>the</strong>mselves as having sufficient knowledge <strong>of</strong> <strong>the</strong> system to find a good, working configuration
immediately. They had a difficult time describing requirements for <strong>the</strong> new system due to a lack <strong>of</strong>
knowledge <strong>of</strong> <strong>the</strong> technology (technical barrier) and <strong>of</strong> <strong>the</strong> technology as part <strong>of</strong> <strong>the</strong>ir everyday work
(organizational barrier). Their mental model for rescue operations did not include this new
communication system or <strong>the</strong> procedures for using it. The launch informants claimed that <strong>the</strong> users
who worked with <strong>the</strong> system should adjust configurations according to <strong>the</strong>ir needs. The launching
authority did not have sufficient knowledge or a good mental model to see how <strong>the</strong> specific set-up
could contribute to <strong>the</strong> user organizations' everyday work.
The work premises for <strong>the</strong> launching organization and <strong>the</strong> user organizations are quite different. User
organizations need to make a lot <strong>of</strong> decisions in a setting in which NDM was developed, with many
important decisions made using incomplete information, a weak structure, uncertain outcomes and
high stakes, while <strong>the</strong> personnel in <strong>the</strong> launching organizations are used to working with decisions
based on well-formulated goals and with quite a long time to evaluate different options (behavioural
and inter-organizational barriers). This does not make <strong>the</strong> possibility <strong>of</strong> creating a mental model that is
coherent and agreed upon easier. Even if knowledge bases were complementary, a common
knowledge base, needed for good performance, was lacking (e.g. Mosier 1997). The view <strong>of</strong> <strong>the</strong> rollout
and how it should be handled differed due to <strong>the</strong> differences in <strong>the</strong> normal sensemaking
processes and mental models. The launching organization devoted a great <strong>of</strong> interest to making
procedures that ensured <strong>the</strong> systems' security, even if it meant that bureaucratic processes were
needed.
Some <strong>of</strong> <strong>the</strong> effects <strong>of</strong> this – early on in <strong>the</strong> roll-out process – were that equipment (terminals) had to
be sent away to be configured/programmed centrally. This was to ensure that <strong>the</strong> systems' basic
functions for security would not be compromised. This required extra terminals, which were felt to be
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too expensive. The user informants also stated that <strong>the</strong> need for extra resources – a police
department for example needed to employ two administrators – would remain even after
implementation. This was not planned for or known in advance.
Likewise, <strong>the</strong> set-up <strong>of</strong> <strong>the</strong> system for each user organization required numerous meetings, efforts
and unplanned resources from <strong>the</strong> user side. This did not conform with <strong>the</strong>ir view <strong>of</strong> how <strong>the</strong> set-up
should be performed.
4.2 Use <strong>of</strong> <strong>the</strong> system
Users felt that <strong>the</strong> new functions presented to support communication in <strong>the</strong> day-to-day work were
complicated. New choices and new interfaces needed to be learned. At <strong>the</strong> same time, <strong>the</strong> interfaces
are considered to be old-fashioned compared to common touch phones (see Figure 1).
Figure 1: Ericsson (DEC) mobile phone from <strong>the</strong> 1980s, and <strong>the</strong> actual standard for RAKEL terminals
(EADS and Motorola)
This situation made potential future users thoughtful and caused <strong>the</strong>m to hold back on
implementation. The launching organization had to approve all equipment that would be connected to
<strong>the</strong> communication system. The basis for this was <strong>the</strong> stated requirements in <strong>the</strong> governmental plan
for <strong>the</strong> system. This made tailored solutions difficult to develop and <strong>the</strong> use <strong>of</strong> common interfaces
risked taking <strong>the</strong> focus away from <strong>the</strong> rescue situation. The focus on what is important in <strong>the</strong> situation
could be lost and situation awareness impaired.
User organizations struggle to maintain sufficient competence among personnel since <strong>the</strong>re are many
infrequent users, e.g. part-time firemen. Doctors were interested in saving lives and not learning about
radio communication with ambulances. The risk <strong>of</strong> impairing situation awareness with complex and
unfamiliar interfaces was also stated clearly by a police <strong>of</strong>ficer: "Just imagine a situation where you
are in a hostile emergency situation with a victim in an apartment and you have to get in by force with
a gun in your hand…. At <strong>the</strong> same time you have to pick up your radio and search through menus to
find <strong>the</strong> correct collaboration group to call for back-up…" These examples show that safety or <strong>the</strong>
quality <strong>of</strong> work could be impaired by switching <strong>the</strong> focus from making sense <strong>of</strong> a highly charged
situation to learning to communicate in a new way (both organizational and inter-organizational
barriers).
There is concern that <strong>the</strong> new system would put an extra burden on <strong>the</strong> user and cause him/her to
lose focus on <strong>the</strong> situation and <strong>the</strong>refore lose situation awareness, especially when using ano<strong>the</strong>r
extra system in top <strong>of</strong> <strong>the</strong> existing ones (see Figure 2).
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Kaj Suneson and Ilona Heldal
Figure 2: Different communication possibilities in a police car according to current standards
(separate GPS, analogue radio S70, RAKEL).
The priorities and focus can vary within one and <strong>the</strong> same organization. A function that facilitates <strong>the</strong>
work <strong>of</strong> one position might be a burden for ano<strong>the</strong>r. Within an organization, taking a photo and
sending it might help one person to improve <strong>the</strong>ir situation awareness but at <strong>the</strong> same time it could
disturb ano<strong>the</strong>r person. Examining sensemaking inter-organizationally would throw light on <strong>the</strong>se
problems.
4.3 Co-operation and collaboration
This new form <strong>of</strong> telecommunication promises to facilitate co-operation with functionalities across
organizational boundaries and longer distances. During <strong>the</strong> launch, development and implementation
<strong>of</strong> work processes for this were not ready (e.g. defining work groups, stating processes for how to cooperate
in certain situations). This raised <strong>the</strong> question <strong>of</strong> who is responsible for developing <strong>the</strong>se
processes. As stated in 4.1, <strong>the</strong> user organizations did not have sufficient knowledge to state
prerequisites and <strong>the</strong> launcher did not know enough about <strong>the</strong> users' work to know how to configure
work processes. Consequently, a Catch 22 situation arose, where arguments put forward by both
sides about not being able to assume responsibility for developing work processes seem reasonable
but result in a deadlock in <strong>the</strong> development <strong>of</strong> co-operation routines. This seems to highlight many
aspects <strong>of</strong> both organizational and behavioural barriers. The goal for a rescue operation need not be
shared completely between <strong>the</strong> different user organizations and it is <strong>the</strong>refore difficult for <strong>the</strong>m to
participate and understand <strong>the</strong> needs <strong>of</strong> <strong>the</strong> o<strong>the</strong>r organizations. The parties involved do not have
enough common knowledge to communicate and understand easily <strong>the</strong> different parties' goals in <strong>the</strong>
current situation. The possibility to take <strong>the</strong> initiative in order to search for an agreeable solution is not
possible if <strong>the</strong> views <strong>of</strong> <strong>the</strong> situation not are synchronized.
4.4 Culture and regulations
Regulations and legal systems mean that concept content is different in different organizations. An
example <strong>of</strong> this was that different regulations stipulated that organizations were required to control
'information privacy'. The police had to maintain 'information privacy' to protect investigations while <strong>the</strong>
ambulance service had to protect patient integrity. The fire brigade on <strong>the</strong> o<strong>the</strong>r hand did not perceive
any such restrictions. The effect <strong>of</strong> this was that <strong>the</strong> authorities could not speak freely to each o<strong>the</strong>r
even before <strong>the</strong> new communication system was launched. It is important to be able to identify <strong>the</strong>
inter-organizational barriers raised by different organizational cultures. These could be lowered by
being discussed, thus providing a mental model for <strong>the</strong> different PSAs that includes <strong>the</strong> o<strong>the</strong>r parties’
basic cultural background and ways <strong>of</strong> working.
4.5 Expectations <strong>of</strong> <strong>the</strong> users
At <strong>the</strong> launch, users regarded <strong>the</strong> system as 'ready for use'. MSB on <strong>the</strong> o<strong>the</strong>r hand launched what
<strong>the</strong>y regarded as a system that is 'ready to adjust'.
Users expected <strong>the</strong> system to have 100 per cent coverage from <strong>the</strong> start but experienced problems
with coverage or duplex calls. While <strong>the</strong> launching organizations thought: 'It’s just a matter <strong>of</strong> an extra
base station', users regarded this as problematic. They do not know how to go about obtaining new
base stations or on what basis <strong>the</strong> decision to invest in a new base station is taken. Although
technicians cannot see any technical barrier, users can see a huge organizational barrier. Describing
and examining situation awareness and sensemaking for actual situations would help solve this.
5. Concluding remarks
This study shows that even though systems involve organizations with needs that are relatively similar
<strong>the</strong>re are a lot <strong>of</strong> knowledge barriers that affect adaptation <strong>of</strong> <strong>the</strong> system. The adaptation process for
<strong>the</strong> private sector can <strong>of</strong>ten be seen as a period <strong>of</strong> trial and error – in this situation this could be very
expensive as lives are at stake. Co-operation that was previously founded on personal trust and
networks was one <strong>of</strong> <strong>the</strong> main improvements sought when deciding on <strong>the</strong> new system. However,
many problems hindering <strong>the</strong> roll-out process can be identified. These problems are within <strong>the</strong>
organizations involved and between <strong>the</strong> organizations.
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In this study we can see that many <strong>of</strong> <strong>the</strong> findings and identified knowledge barriers can also be
described in terms and concepts from NDM.
Technological knowledge barriers in this study show a link to situation awareness in <strong>the</strong> concerns
about complex interfaces detracting from <strong>the</strong> rescue task. There is also lack <strong>of</strong> mental models
containing <strong>the</strong> new ICT system and this is noticed in <strong>the</strong> problem <strong>of</strong> stating requirements related to
how to set up <strong>the</strong> systems' performance.
The study highlights organizational knowledge barriers with clear connections to sensemaking and
mental models, in <strong>the</strong> inability to present clear-cut requirements in <strong>the</strong> set-up. This is partly due to a
lack <strong>of</strong> technological knowledge <strong>of</strong> <strong>the</strong> system but also to not being able to foresee how <strong>the</strong> use will
affect work processes. These connections also became obvious when <strong>the</strong> respondents discussed
how to maintain <strong>the</strong> competence <strong>of</strong> infrequent users. The competence is to have mental models and
sensemaking processes that take <strong>the</strong> new and more complex ICT system into consideration.
The study also highlights <strong>the</strong> fact that different levels within an organization might be affected in
different ways by <strong>the</strong> new system. A picture might improve situation awareness and facilitate
sensemaking on one level (Management) but could be a disruptive element on ano<strong>the</strong>r level
(Operational fire-fighter).
Financial questions were discussed extensively during <strong>the</strong> study but it was difficult to obtain a clear
picture. This is a sign <strong>of</strong> a financial knowledge barrier. Many <strong>of</strong> those interviewed agreed that <strong>the</strong> new
ICT system could improve efficiency in operations and that <strong>the</strong>re was an overall gain but few <strong>of</strong> <strong>the</strong>m
could state how this gain would benefit <strong>the</strong> respondents' own organization or how/what new resources
would be needed by using <strong>the</strong> new system. In many cases <strong>the</strong> introduction has led to demand for
unplanned and unfinanced resources.
Behavioural barriers have a link to situation awareness in this study. Fear <strong>of</strong> having to switch <strong>the</strong>
focus from <strong>the</strong> central task to <strong>the</strong> equipment could harm situation awareness to such degree that task
performance might be impaired or personal safety put at risk.
Inter-organizational knowledge barriers in this study have clear links to mental models and
sensemaking processes. The PSAs are governed by different regulations and have different goals.
There were obvious problems in setting up co-operation routines due to <strong>the</strong> different mental models
and sensemaking processes. These were not well known to all <strong>of</strong> <strong>the</strong> parties and consequently each
party could not know and adjust to <strong>the</strong> needs <strong>of</strong> <strong>the</strong> o<strong>the</strong>rs.
Identifying and breaking down knowledge barriers into components, such as concepts from NDM, is
beneficial in examining <strong>the</strong> whole launch process, since more fine-tuned understanding <strong>of</strong> problems
can be achieved and directed. By using concepts such as sensemaking, situational awareness or
describing <strong>the</strong> mental models for system usage, certain basic obstacles can be described better for
both users and influencers. Although informative and useful to combine <strong>the</strong> <strong>the</strong>oretical frameworks in
this study can future research reveal clearer connections between knowledge barriers and NDM
concepts which will help to understand and lowering knowledge barriers.
Acknowledgements
We would like to thank Vinnova (The Swedish Governmental Agency for Innovation Systems) for
funding this research and we would also like to thank all <strong>the</strong> interviewees and our colleagues Gustav
Sjöblom, Bo Norrhem and Erik Bohlin for <strong>the</strong>ir input.
References
Attewell, P. (1992) “Technology diffusion and organizational learning: The case <strong>of</strong> business computing”,
Organization Science, Vol 3, No. 1, pp. 1–19.
Berlin, J. and Carlström, E. (2009) Samverkan på olycksplatsen. Om organisatoriska barriäreffekten,
[Cooperation on accident places. Organizational barriers] Högskolan Väst, Trollhättan.
Bohlin, E., Lindmark, S., Forge, S. and Blackman, C. (2005). “Implications <strong>of</strong> Alternative Wireless Technologies
for Europe and Policy Recommendations”, Report IPTS, Luxembourg.
Burns, K., (2005) Mental Models and Normal Errors. In: Montgomery, H., Raanan, L. and Berndt B, ed. How
pr<strong>of</strong>essionals make decisions. Mahwah, New Jersey: Lawrence Erlbaum Associates Inc,. Ch. 2.
Endsley, M.R. (1995) “Toward a Theory <strong>of</strong> Situation Awareness in Dynamic Systems”, Human Factors
Vol. 37, No. 1, pp. 32-64.
974

Kaj Suneson and Ilona Heldal
Endsley, M.R. (2000) “Situation Models: An Avenue to <strong>the</strong> Modeling <strong>of</strong> Mental Models” In: <strong>Proceedings</strong> <strong>of</strong> 14 th
Triennial Congress <strong>of</strong> <strong>the</strong> International Ergonomics Association and <strong>the</strong> 44 th Annual Meeting <strong>of</strong> <strong>the</strong> Human
Factors and Ergonomics Society, Santa Monica, 2000. Vol. 1, pp 61-64
Gioia, D. A., Thomas, J.B, Clark, S.M., and Chittipeddi, K. (1994) “Symbolism and strategic change in academia:
The dynamics <strong>of</strong> sensemaking and influence”, Journal <strong>of</strong> Organization Science, Vol. 5, No. 3, August, pp.
363–383.
Heldal, I., Sjöblom G., Forge, S., Bohlin, E., Suneson K. (2010). ”Taking <strong>the</strong> Old Standard or Waiting for Take<strong>of</strong>f?
Launching and Using Digital Radio for Public Safety”, In: <strong>Proceedings</strong> <strong>of</strong> <strong>European</strong> <strong>Conference</strong> on
Innovation Management, Rome, Italy, 18-22 May.
Klein, G. (1989) “Recognition-Primed Decisions”, In: W. B. Rouse (Ed.), Advances in Man-Machine Research,
Vol. 5, pp. 47-92, Greenwich, CT. JAI Press
Klein, G. (2008) “Naturalistic decision making”, Human Factors, Vol. 50, No. 3, pp 456-460.
Landgren, J. (2007) “Investigating <strong>the</strong> Tension between Information Technology Use and Emergency Response
Work”, In: <strong>Proceedings</strong> <strong>of</strong> ECIS2007, Jun 2007. St. Gallen, Switzerland: Association for Information
Systems, pp 1886-1897.
Lipschitz, R. and Strauss, O. (1997) “Coping with Uncertainty: A Naturalistic Decision-Making Analysis”,
Organizational Behavior and Human Decision Processes, Vol. 69, No 2, pp. 149–163.
Mosier, K.L. (1997) “Myths <strong>of</strong> Expert Decision Makin and Automated Decision Aids”, In: Zsambok, C.E., and
Klein, G.(eds). Naturalistic Decision Making. Mahwah, New Jersey: Lawrence Erlbaum Associates.
Nemeth, C., Walter, J. and Cook, R. (2010) “Resilience is not control: Healthcare, crisis management
and ICT”, In: Mendonça, D. (Ed). Information and Communications Technology in Crisis
Management. Technical University. Delft, Ne<strong>the</strong>rlands.
RAKEL (2009) “RAKEL” [Online] Available from: http://www.msb.se/en/Civil-contingencies/Support-systems-
/Rakel/ [Accessed 21 March 2011].
Sanfilippo, A., Cowell A.j., Malone l., Riensche R., Thomas J., Unwin S., Whitney P. And Wong P.C. (2009)
“Technosocial Predictive Analytics in Support <strong>of</strong> Naturalistic Decision Making”, In: The 9th bi-annual
International <strong>Conference</strong> on Naturalistic Decision Making, Covent Garden, London.
Sieck, W., Klein, G., Peluso, D.A., Smith, J.L. and Harris-Thompson, D. (2007) “FOCUS: A Model <strong>of</strong>
Sensemaking”, [Online] Technical report United States Army Research Institute for <strong>the</strong> Behavioral and
Social Sciences, http://www.au.af.mil/au/awc/awcgate/army/tr1200.pdf [Accessed 21 March 2011].
Simon, H.A. (2000) “Bounded rationality in social science: Today and Tomorrow”, Mind & Society, Vol. 1, No. 1,
pp. 25-39.
Sjöblom, G., Heldal, I., Bohlin, E. and Sunesson, K. (2010) “Path dependency, momentum, and current
challenges for public safety communications in <strong>the</strong> USA and Sweden”, In: Proceeding <strong>of</strong> ITS 2010, Tokyo:
International Telecommunications Society
Shattuck, G.L., Miller, N.L. (2006) “Extending Naturalistic Decision Making to Complex Organizations: A Dynamic
Model <strong>of</strong> Situated Cognition”, Organization Studies, Vol. 27, No. 7, pp. 989-1009.
Sunesson, K. and Heldal, I. (2010) “Knowledge Barriers in Launching new Telecommunications for Public
Safety”, In: <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 7th International <strong>Conference</strong> on Intellectual Capital, Knowledge
Management & Organizational Learning, November 2010, Hong Kong: Academic conferences international.
Tanriverdi, H. and Iacono, C.S. (1998) “Knowledge barriers to diffusion <strong>of</strong> telemedicine”, In: <strong>Proceedings</strong> at ICIS
'98 International <strong>Conference</strong> on Information Systems, Helsinki, Finland.
TETRA (2000) Terrestrial Trunked Radio [Online] http://searchmobilecomputing.techtarget.com/definition/TETRA
[Accessed 21 March 2011].
Weick, K.E. (1995) Sensemaking in organizations, Thousand Oaks, CA: Sage Publications.
Weick, K.E., Sutcliffe, K.M., and Obstfeld, D. (2005) “Organizing and <strong>the</strong> Process <strong>of</strong> Sensemaking”, Journal <strong>of</strong>
Organization Science, Vol. 16, No. 4, pp. 409–421.¨
Wittenbaum, G.M., Hollingshead, A.B. and Botero, I.C. (2004) “From Cooperative to Motivated Information
Sharing in Groups: Moving Beyond <strong>the</strong> Hidden Pr<strong>of</strong>ile Paradigm”, Communication Monographs, Vol. 71, No.
3, pp. 286–310.
975

Infrastructure <strong>of</strong> Innovative Universities Based on ICT and
KM for Building Smart Economy
Natalia Tikhomirova, Vladimir Tikhomirov, Yury Telnov and Valentina
Maksimova
Moscow State University <strong>of</strong> Economics, Statistics and Informatics, Russia
NTikhomirova@mesi.ru
VPT@mesi.ru
YTelnov@mesi.ru
VMarsimova@mesi.ru
Abstract: At present <strong>the</strong> world community faces <strong>the</strong> challenge <strong>of</strong> building <strong>the</strong> Smart economy. It is well
recognized that <strong>the</strong> economic progress comes from increased productivity, innovation, competitiveness,
economic renewal and creativity <strong>of</strong> <strong>the</strong> workforce. The goal <strong>of</strong> <strong>the</strong> smart economy is to restore and retain a
sustainable economic growth after a depressing crises, to provide a high-quality education system, to promote
innovative business environment, to ensure high productivity, to translate knowledge creation into economic
return. In this context innovative universities aim at training knowledge workers, forming <strong>the</strong> competences <strong>of</strong>
learners in line with <strong>the</strong> requirement <strong>of</strong> modern labor market in <strong>the</strong> Smart economy. The graduates face <strong>the</strong>
challenge to generate new ideas and to work in <strong>the</strong> knowledge- intensive industries that supply advanced ICT
devices, systems and networks. The universities in <strong>the</strong> smart economy need to transform <strong>the</strong> infrastructure to
meet <strong>the</strong> challenges. The paper presents <strong>the</strong> innovative infrastructure that facilitates a more full perception <strong>of</strong>
new knowledge, interoperability between internal and external knowledge, a high degree <strong>of</strong> knowledge and
innovation in universities.
Keywords: intellectual capital, human capital, innovative infrastructure, innovative universities, management <strong>of</strong>
academic knowledge, Smart economy.
1. Introduction
Nowadays in <strong>the</strong> Smart economy innovation and creation <strong>of</strong> new knowledge is recognized as key
enablers for enhancing competitive advantages at <strong>the</strong> individual and organizational levels. The Smart
economy implies high productivity, forward-looking investments in research and development,
intelligent infrastructure, widespread adoption <strong>of</strong> advanced technology, highly educated and skilled
workforce, high-quality business and living environment, production <strong>of</strong> knowledge- intensive goods
and services, <strong>the</strong> development <strong>of</strong> a green technology sector.
The key feature <strong>of</strong> <strong>the</strong> Smart economy is building an innovation component to transform valuable
ideas into new processes, products and services. The Smart economy can be developed by raising
labor productivity, incentivizing innovation investments and commercialization, promoting business,
cultural and social environment (2008, Building Ireland’s Smart Economy). The progress <strong>of</strong> <strong>the</strong> Smart
economy depends upon mutual reinforcing <strong>the</strong> human, physical and social capital. The human capital
captures explicit and implicit knowledge, pr<strong>of</strong>essional skills and creativity <strong>of</strong> people, generates new
ideas, new knowledge and innovation. The physical capital is a stock <strong>of</strong> infrastructure used to produce
new goods and services. The social capital embraces <strong>the</strong> networks, connections, shared values and
behaviors <strong>of</strong> <strong>the</strong> people to contribute to <strong>the</strong> cohesiveness <strong>of</strong> society to promote social environment.
In Europe a renewed Lisbon Strategy suggests guiding principles for economic policy coordination
based on a sustainable growth model and environmentally- and energy-efficient industrial base (2010,
Europe 2020). The research endeavors look forward to implement innovative technologies and to
carry out <strong>the</strong> market integration <strong>of</strong> technologies which is essential for <strong>the</strong> reorganization <strong>of</strong> Europe as
a business location.
The Smart economy can be built by:
• investing in human and intellectual capital;
stimulating innovations and commercialization <strong>of</strong> research outcomes;
implementing technologies <strong>of</strong> new generation and networks;
developing green economy;
enhancing <strong>the</strong> public sector performance;
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providing social security;
developing efficient ecosystem;
investing in critical infrastructure.
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Some studies showed that in building <strong>the</strong> Smart economy <strong>the</strong> efforts should be concentrated on a
much more balanced distribution <strong>of</strong> resources fostering, on <strong>the</strong> one hand, technical innovation based
on RCD, and, on <strong>the</strong> o<strong>the</strong>r hand, on non-technical innovations that are vital to <strong>the</strong> development <strong>of</strong>
knowledge-intensive services (2010, Preston, Sparviero).
In <strong>the</strong> Smart economy <strong>the</strong> universities require an ever greater concentration <strong>of</strong> <strong>the</strong>ir endeavors to
enable <strong>the</strong> higher education system to reach new levels <strong>of</strong> knowledge acquisition, knowledge
creation, knowledge transfer and sharing, knowledge application and innovation performance.
The Smart economy fosters <strong>the</strong> higher education institutions to unite all <strong>the</strong> efforts within <strong>the</strong>
framework <strong>of</strong> international education networks, consortium, associations as well as <strong>the</strong> integration <strong>of</strong>
science, education and business via technological parks, joint competence centers and o<strong>the</strong>r effective
channels <strong>of</strong> intercommunication.
In this context <strong>the</strong> management <strong>of</strong> academic knowledge in innovative Universities facilitates <strong>the</strong>
intensive and dynamic accumulation and implementation <strong>of</strong> intellectual capital to enhance <strong>the</strong> quality
<strong>of</strong> education research, to support and sustain a scientific and educational content, to develop
academic mobility <strong>of</strong> learners, teachers and researchers.
The adequate management <strong>of</strong> academic knowledge in Universities is indispensable without a sound
innovative infrastructure. The latter meets <strong>the</strong> requirements <strong>of</strong> smart innovation networks by using <strong>the</strong>
new generation <strong>of</strong> ICT, including smart grids.
2. Higher education faces new requirements in <strong>the</strong> Smart economy
The processes <strong>of</strong> creating, capturing, sharing and using knowledge and information are a critical
driver for economic and social development. The abilities <strong>of</strong> firms to open a rapid access to new
knowledge and to adapt it to concrete conditions promote <strong>the</strong>ir competitive advantages in <strong>the</strong> market.
Successful world companies are widely embedding advanced information and communication
technologies in <strong>the</strong>ir production processes including smart grid. IBM company (2009, IBM Business
Value) stands for <strong>the</strong> development <strong>of</strong> “Smarter Planet” based on <strong>the</strong> intelligence approach that
radically changes <strong>the</strong> relationships between an individual person, business and society via enhancing
interconnectedness, efficiency, effectiveness, flexibility and responsiveness (ECKM 2011).
The application <strong>of</strong> new ICT in various spheres <strong>of</strong> human activities engender new phenomenon:
The formation <strong>of</strong> new information culture based on communication in social networking, virtual
communities <strong>of</strong> practice.
The transformation <strong>of</strong> intellectual labor towards it collective and collaborative character.
The setting <strong>of</strong> horizontal flexible organizational structures to effectively realize projects and
processes.
The activities <strong>of</strong> firms as self-organizations in adopting <strong>the</strong>ir strategies to ongoing changes.
These novelties encourage new processes in producing knowledge- intensive goods and
services based on mass collaboration (2008, Tapscott D., Williams A., 2009, Fingar P., 2010,
Xin-jian Gu, Feng Dai, Lu-sha Zhen,Wei Zhang, Qiu-hao Yang,Ye Zhang, Chen-yong Du.).
The idea <strong>of</strong> collaborative editing in Internet captures people’s mind all over <strong>the</strong> world and provides
<strong>the</strong> realization <strong>of</strong> <strong>the</strong> following principles in new economic environment:
openness <strong>of</strong> companies to external ideas and human resources, open door to each talent from
various areas <strong>of</strong> <strong>the</strong> world;
peer production, self-organization <strong>of</strong> people into communities for creating new products and
services, collaborative knowledge, exchange <strong>of</strong> best practices;
provision <strong>of</strong> access to knowledge, <strong>the</strong> distribution <strong>of</strong> knowledge among <strong>the</strong> creators <strong>of</strong> knowledge,
exchange <strong>of</strong> best practices;
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global character <strong>of</strong> activities fostering <strong>the</strong> integration <strong>of</strong> national economies into a world economic
area and providing an access to new markets, ideas and technologies.
The above mentioned principles also concern <strong>the</strong> education system to full extent. Globalization <strong>of</strong>
education entails <strong>the</strong> joint endeavors <strong>of</strong> universities within <strong>the</strong> framework <strong>of</strong> international education
networks, consortiums and associations <strong>of</strong> educational institutions, as well as <strong>the</strong> integration <strong>of</strong>
science, education and business through technological parks, collaborative competence centers and
o<strong>the</strong>r alternative forms <strong>of</strong> interrelationship. To this end <strong>the</strong> system <strong>of</strong> knowledge management needs
to fur<strong>the</strong>r development to ensure an intensive and dynamic accumulation and use <strong>of</strong> intellectual
capital, to update a scientific and educational content, to make proper conditions for <strong>the</strong> development
<strong>of</strong> academic mobility <strong>of</strong> learners, researchers and teachers.
3. Transformation <strong>of</strong> Universities into innovation centers
The universities face new challenges in <strong>the</strong> Smart economy. The latter addresses <strong>the</strong> need to
reexamine institutional roles and organizational relationships in <strong>the</strong> higher education system to enable
<strong>the</strong> universities to reach new levels in teaching, learning, research and innovation performance.
(2008, Building Ireland's Smart Economy). The goal <strong>of</strong> <strong>the</strong> universities is not only to improve <strong>the</strong> skills
and capabilities <strong>of</strong> learners but to ensure that <strong>the</strong> graduates possess <strong>the</strong> competences required to
Smart economy. An expanding emphasis needs to be on endeavors to increase participation in
lifelong learning and to ensure required up-skilling and reskilling.
The universities face <strong>the</strong> challenge to design a new configuration <strong>of</strong> educational environment, to make
qualitative changes in <strong>the</strong> curricula, study programs and content, to alter teaching and learning
methods.
Higher education institutions are concerned with setting out new organizational arrangements that can
facilitate higher productivity, effectiveness and performance through more effective concentration <strong>of</strong>
expertise, knowledge capacity and investment. The concentration <strong>of</strong> intellectual potential helps <strong>the</strong>
universities to function as innovation centers.
Nowadays <strong>the</strong>re are dozens <strong>of</strong> innovative universities in <strong>the</strong> world and <strong>the</strong> number <strong>of</strong> <strong>the</strong>se
universities in Russia is about 30.
The key characteristic features <strong>of</strong> innovative research universities are <strong>the</strong> following:
1. Research and development take a leading role in <strong>the</strong> universities' activity; <strong>the</strong>y aim at developing
innovations to be implemented in economy, on <strong>the</strong> one hand, and, on <strong>the</strong> o<strong>the</strong>r, at designing a
modern educational content to teach <strong>the</strong> students <strong>the</strong> updated achievements in research,
science and practice. Various research structures are set up: research institutes, laboratories,
research and education centers at <strong>the</strong> chairs, departments and <strong>the</strong> university.
2. The innovation research universities sustain <strong>the</strong>ir integration with advanced companies and
research organizations and create joint small innovative enterprises, competence centers,
transfer technology centers, technological parks, business incubators.
3. The universities support cooperation with each o<strong>the</strong>r to form alliances for collaborative research
and education activities based on <strong>the</strong> principles <strong>of</strong> academic mobility.
4. Students are actively involved into research and innovation activity on <strong>the</strong> principle "learning by
doing". The method <strong>of</strong> learning facilitates <strong>the</strong> development <strong>of</strong> research skills, capabilities to team
work and entrepreneurship.
5. The innovative universities widely implement advanced networking, smart technologies for
integrating science, innovation and education. For instance, Innovative Research Universities
(2011, Innovative Research Universities) are a network <strong>of</strong> seven comprehensive universities
committed to conducting research <strong>of</strong> national and international standing. The network develops
some recommendations on higher education, research and innovation and experience to achieve
best practices in teaching, research culture and organizational setting. It provides suitable
facilities for students and partners through collaboration in <strong>the</strong>ir up-skilling.
The <strong>European</strong> Consortium <strong>of</strong> Innovative Universities (2011, <strong>European</strong> Consortium <strong>of</strong> Innovative
Universities) consists <strong>of</strong> eleven universities and three overseas associate partners. It is a <strong>European</strong>
network aiming at <strong>the</strong> exchange <strong>of</strong> experience and best practices with research, education and
regional development to enhance performance.
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The International Consortium “Electronic University” (Russia) (2011, Participants and Partners <strong>of</strong>
International Consortium) includes core universities with affiliated network: Moscow State University <strong>of</strong>
Economics, Statistics and Informatics (MESI), <strong>European</strong> Open Institute (EAOI), International
University <strong>of</strong> Business and New Technologies (IUBNT) and o<strong>the</strong>rs. The goal <strong>of</strong> innovative activities in
<strong>the</strong> Electronic University is to develop and share <strong>the</strong> e-learning tools based on advanced IC
technologies, to conduct research and innovation activities within <strong>the</strong> framework <strong>of</strong> Smart economy
and regional development <strong>of</strong> Russia.
The operation <strong>of</strong> <strong>the</strong> enumerated universities shows that <strong>the</strong>y are becoming <strong>the</strong> engines and
generators <strong>of</strong> ever growing, uninterrupted knowledge flow. The tendency <strong>of</strong> opening research
innovative universities can be considered as a key route for effective performance in <strong>the</strong> Smart
economy.
4. Principles <strong>of</strong> Smart education.
The methodology <strong>of</strong> creating research innovative universities incorporates <strong>the</strong> smart education
principles:
Smart education is a complaint and flexible learning in <strong>the</strong> interactive education space with an
open access to <strong>the</strong> world content based on ICT and cloudy technologies.
Smart education was initiated by digital device two. The digital device one entailed <strong>the</strong>
assessment <strong>of</strong> countries, nations and continents via implementation <strong>of</strong> ICT. It was found that <strong>the</strong>
countries possessed <strong>the</strong> huge amount <strong>of</strong> technologies demonstrated higher growth rates. The
digital device two engendered a new focus: many human functions have been transferred to
machines and people changed <strong>the</strong>ir focus on creation ideas and self-learning. The philosophy <strong>of</strong>
digital device two necessitates novel stimuli to getting new knowledge. The adequate use <strong>of</strong> smart
technologies increases awareness <strong>of</strong> learning to <strong>the</strong> access to open resources.
The framework <strong>of</strong> smart education allows a teacher to develop an individual approach to each
student with respect to option sources and help a learner to tailor his\her curriculum to shape <strong>the</strong>
required competences. The new education content facilities to obtain knowledge and skills in line
with a competence model.
Smart education needs <strong>the</strong> development <strong>of</strong> internet communities and social networking for <strong>the</strong>
teaching staff to exchange <strong>the</strong> content with detailed aspects <strong>of</strong> concrete discipline.
Smart education implies <strong>the</strong> transition from a passive to an interactive on-line content. Smart
education aims at concurrent by presenting knowledge from <strong>the</strong> web-resources to <strong>the</strong> study
materials, at enhancing <strong>the</strong> education quality and assurance, at paying more attention to
international standards, at improving learners` competitive advantages.
Smart education implies non-formal integration <strong>of</strong> education institutions to carry out <strong>the</strong>ir
collaborative activities in <strong>the</strong> internet and networking based on international standards and
agreements, to design a joint coherent, harmonious content. Universal <strong>European</strong> University
(UEU) is to provide a collective, collaborative study process via a repository <strong>of</strong> learning materials
and to move students from one institution to <strong>the</strong> o<strong>the</strong>r, if necessary, without additional exams. So
smart education unites <strong>the</strong> learners, teachers and knowledge all over <strong>the</strong> world. New approaches
to <strong>the</strong> learning process produce higher requirements to <strong>the</strong> pr<strong>of</strong>essional and ICT competences <strong>of</strong>
<strong>the</strong> teaching staff.
5. The building <strong>of</strong> universities` innovative infrastructure in Smart economy
The central place in <strong>the</strong> innovative infrastructure <strong>of</strong> an university belongs to <strong>the</strong> information
infrastructure that incorporates a set <strong>of</strong> information centers, databases and connection systems and
addresses an access <strong>of</strong> users to information resources. The information infrastructure should provide
<strong>the</strong> volume <strong>of</strong> information resources required to support an ever increasing rate <strong>of</strong> innovations.
To this end it is valuable to carry out a proper management <strong>of</strong> academic knowledge, to effectively
consume intellectual resources and to develop opportunities for academic mobility.
The management <strong>of</strong> academic knowledge (2010-2011, Tikhomirova N., Tikhomirov V., Telnov Y.,
Maksimova V.) in higher education institutions suggests <strong>the</strong> solution <strong>of</strong> <strong>the</strong> following tasks:
creation <strong>of</strong> communities <strong>of</strong> practices and database in <strong>the</strong> integrated research and educational
area to provide <strong>the</strong> integration <strong>of</strong> education, science and business;
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development <strong>of</strong> content in <strong>the</strong> innovative scientific educational programs in line with <strong>the</strong> needs <strong>of</strong>
international education markets, e-library and knowledge base;
development <strong>of</strong> innovative educational technologies on <strong>the</strong> basis <strong>of</strong> world adopted standards;
development <strong>of</strong> intellectual potential and intellectual resources, <strong>the</strong> system <strong>of</strong> improving <strong>the</strong>
qualification <strong>of</strong> <strong>the</strong> teaching staff within <strong>the</strong> integrated scientific education area;
connection to international education systems to provide internationalization <strong>of</strong> education and
enhance <strong>the</strong> export potential <strong>of</strong> a university and <strong>the</strong> national education system as a whole.
The management <strong>of</strong> academic knowledge is based upon <strong>the</strong> virtual learning environment (VLE)
(2007, Weller M., 2009, Virtual learning environments) constructed upon <strong>the</strong> principles <strong>of</strong> serviceoriented
architecture with its potential realization in cloud computing. The structure <strong>of</strong> <strong>the</strong> system <strong>of</strong>
academic knowledge management is shown in Fig.1.
Small innovative
enterprises
Research
labs
(Virtual learning environment)
(Provision <strong>of</strong> services)
Repository <strong>of</strong> scientific
and educational objects
(ICD)
Figure 1: The structure <strong>of</strong> academic knowledge management system
Study and Training
structural units
VLE is a system <strong>of</strong> ICT services designed to support teaching and learning and to provide research
innovation and knowledge management via computer- mediated communication.
VLE aims at facilitating higher integration and connectedness <strong>of</strong> remote university’s structural units to
provide research innovation and education activities. It helps to solve <strong>the</strong> following objectives:
to receive new research outcomes in <strong>the</strong> framework <strong>of</strong> laboratories, research centers and
research institutions;
to use commercialization research outcomes in <strong>the</strong> innovative activities <strong>of</strong> small innovative
enterprises and o<strong>the</strong>r innovation structures;
to implement research outcomes into teaching process and practical work at virtual educational
enterprises.
We consider that <strong>the</strong> virtual learning environment (VLE) incorporates a set <strong>of</strong> IT services, that
provides in a remote regime information and s<strong>of</strong>tware for research, innovative and educational
activity. The VLE aims at securing a higher degree <strong>of</strong> integration and interconnectedness <strong>of</strong>
distributed units, structures and partners <strong>of</strong> <strong>the</strong> university.
The full set <strong>of</strong> services includes two groups: general services that can be used for all kinds <strong>of</strong> activities
(research, innovative, educational) and services that are specific for each kind <strong>of</strong> activity.
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The general services <strong>of</strong> virtual learning environment provide normally work over <strong>the</strong> Internet and
provide a collection <strong>of</strong> tools such as those for assessment (particularly <strong>of</strong> types that can be marked
automatically, such as multiple choice), communication, uploading <strong>of</strong> content, return <strong>of</strong> students' work,
peer assessment, administration <strong>of</strong> student groups, collecting and organizing student grades,
questionnaires, tracking tools, etc. Features in <strong>the</strong>se systems include wikis, blogs, RSS and 3D virtual
learning spaces (2009, Virtual learning environments).
The foundation for VLE is <strong>the</strong> development <strong>of</strong> information centers <strong>of</strong> disciplines (ICD) incorporated
into <strong>the</strong> general system <strong>of</strong> organization and integration <strong>of</strong> knowledge and standing as repository <strong>of</strong>
scientific and educational objects (2010 In search <strong>of</strong> Knowledge Management). The ICD integrates
<strong>the</strong> knowledge created in research grants, scientific schools, dissertations, innovation projects <strong>of</strong>
competence centers and laboratories.
The effective operation <strong>of</strong> ICD depends upon <strong>the</strong> teaching staff <strong>of</strong> chairs, co-workers <strong>of</strong> research
structural units and partners, students, postgraduate students and distributed organizations.
6. Conclusion
The study <strong>of</strong> <strong>the</strong> innovative infrastructure in <strong>the</strong> universities shows <strong>the</strong> need <strong>of</strong> implementing smart
technologies and networking to create virtual learning environment for effective solving <strong>the</strong> following
tasks:
to obtain new results in intellectual activity;
to use <strong>the</strong> research outcomes on commercial basic within <strong>the</strong> framework <strong>of</strong> small innovative
enterprises;
to incorporate <strong>the</strong> results <strong>of</strong> intellectual activity into <strong>the</strong> teaching and learning process and to
attract students to innovative and enterprise activities;
The transformation <strong>of</strong> universities into innovative facilitates <strong>the</strong> generation <strong>of</strong> new knowledge on
uninterrupted basis to raise <strong>the</strong> higher education system to a new level and provide an effective
performance in Smart economy.
References
Building Ireland’s Smart Economy (2008). A Framework for Sustainable Economic Renewal // Dublin published
by <strong>the</strong> stationery <strong>of</strong>fice prn no: a8/1932, Government <strong>of</strong> Ireland, – http://www.taoiseach.gov.ie
Crosier D., Purser L., Smidt H. (2007). Trends V: Universities Shaping <strong>the</strong> <strong>European</strong> Higher Education. EUA.
Europe 2020 (2010). Proposals for <strong>the</strong> Post-Lisbon Strategy. Friedrich Ebert Stiftung.
<strong>European</strong> Consortium <strong>of</strong> Innovative Universities (2011). // From Wikipedia, <strong>the</strong> free encyclopedia
http://en.wikipedia.org/wiki/ECIU
Fingar P. (2009) DOT.CLOUD. The 21 st Centure Business Platform on Ckoud Compuing. Meghan-Kiffer Press
Tampa, Florida, USA,.
IBM Business Value (2009). Smart Earth. Oriental Press.
Innovative Research Universities (2011) // Wikipedia, <strong>the</strong> free encyclopedia http://en.wikipedia.org/wiki/IRUA
Participants and Partners <strong>of</strong> International Consortium «Electronic University» (2011). // http://econsortium.ru/organizations/partners
Preston P., Sparviero S. (2010). Smart if Small Economies. Dublin City University.
Tapscott D., Williams A.D.. Wikinomics (2009). How Mass Collaboration Changes Everything.
BestBusinessBooks.
Tikhomirova N., Tikhomirov V., Telnov Y., Maksimova V. (2010). The University’s Integrated Knowledge Space
in Knowledge Management. In: Annie Green, Linda Vandergriff, and Michael Stankosky (eds). In Search <strong>of</strong>
Knowledge Management: Pursuing Primary Principles. Emerald, UK.
Tikhomirov V., Tikhomirova N., Maksimova V., Telnov Y. The management <strong>of</strong> academic knowledge based on
organizational learning (2010). // <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 7th International <strong>Conference</strong> on Intellectal Capital,
Knowledge Management and Organizational Learning (Hong Kong, China).
Weller, M. (2007). Virtual Learning Environments: Using, choosing and eveloping your VLE - London. Routledge
Virtual learning environments: an evaluation <strong>of</strong> <strong>the</strong>ir development in a sample <strong>of</strong> educational settings (2009). //
The Office for Standards in Education, Children's Services and Skills (Ofsted), Reference no. 070251 –
http://www.<strong>of</strong>sted.gov.uk
Xin-jian Gu, Feng Dai, Lu-sha Zhen,Wei Zhang, Qiu-hao Yang,Ye Zhang, Chen-yong Du. Smarter Innovation
Networks in Enterprises (2010). // <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 7th International <strong>Conference</strong> on Intellectal Capital,
Knowledge Management and Organizational Learning (Hong Kong, China
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Measurement <strong>of</strong> customer knowledge value
Kamila Tislerova
University <strong>of</strong> JE Purkyne, Usti nad Labem, Czech Republic
Kamila.tislerova@ujep.cz
Abstract: This paper aims at expressing <strong>the</strong> customer value, particularly <strong>the</strong> value <strong>of</strong> his knowledge. There is a
gap in this field because researchers usually express <strong>the</strong> overall Customer Lifetime Value and similar to Capital
Asset Pricing Model; <strong>the</strong> result is finally discounted. The o<strong>the</strong>r parts <strong>of</strong> customers’ value are usually disregarded.
Is <strong>the</strong> separate item “customers´ knowledge” such a marginal item that it does not need to be counted (and
effectively exploited in favour <strong>of</strong> <strong>the</strong> business?). There are four research questions in this contribution:
Communication level (how <strong>the</strong> dialogue with <strong>the</strong> customer is conducted), deployment capacity (how companies
act upon customer knowledge), organizational sharing capacity (what is <strong>the</strong> system <strong>of</strong> integrating customer and
organisational knowledge assets and <strong>the</strong>n reproducing <strong>the</strong>m internally, etc.) and customers´ motivation (trust,
motivation tools, incentives, etc.). Both <strong>the</strong>oretical and empirical research methods were used. At first, <strong>the</strong>
<strong>the</strong>oretical approaches and findings from related disciplines were analyzed and compared (Customer Knowledge
Management, Customer Portfolio Management, Customer Equity Management, Customer Value Management,
etc.). Then some quantitative research was conducted in small and medium sized enterprises. Large companies
were not included in this research because previous research showed that <strong>the</strong>y are mostly focused on risk
management and individual customer evaluation includes <strong>the</strong> customers´ knowledge evaluation only rarely. It is
important for <strong>the</strong> business not only to keep in mind <strong>the</strong> “knowledge potential” <strong>of</strong> each customer but also to be
able to explore it. Issues such as <strong>the</strong>ir ability to motivate <strong>the</strong> customers to share <strong>the</strong>ir knowledge with <strong>the</strong>
business, managerial skills and marketing strategy towards generating pr<strong>of</strong>it from <strong>the</strong> customers’ base are also
discussed in this contribution. At <strong>the</strong> same time, this paper is accompanied by brief samples <strong>of</strong> customers’
knowledge exploitation in practice in order to demonstrate <strong>the</strong> potential <strong>of</strong> customers to help <strong>the</strong> business to gain
a competitive advantage. Although all <strong>the</strong> financial statements on <strong>the</strong> value <strong>of</strong> customers´ knowledge will always
be based just on rough estimations with a great deal <strong>of</strong> uncertainty, some findings in this contribution perhaps
demonstrate adequately <strong>the</strong> importance <strong>of</strong> customers´ knowledge for business development.
Keywords: Customer knowledge value; knowledge measurement; customer base; knowledge from customer;
three-dimensional approach
1. Introduction
This contribution is focused on <strong>the</strong> framework <strong>of</strong> Customer Knowledge Value from different points <strong>of</strong>
view. The main goal is to advocate a new approach to <strong>the</strong> customer: from considering him/her to be a
passive audience for a firm’s <strong>of</strong>fer to become active players and co-creators <strong>of</strong> knowledge and value.
2. Research questions and methodology
The research was carried out during November-December 2010 (focus groups and qualitative
research) and January-February 2011 (quantitative research.)
2.1 Research objectives:
The main objective is to acquire an overview <strong>of</strong> <strong>the</strong> level <strong>of</strong> using customers´ knowledge in favour <strong>of</strong>
<strong>the</strong> company in small and medium sized enterprises. Questions were arranged into blocks <strong>of</strong> topics:
Communication level (how <strong>the</strong> dialogue with <strong>the</strong> customer is conducted, if <strong>the</strong> customer is able to
speak <strong>the</strong> same language as <strong>the</strong> company, etc)
Deployment capacity (how companies act upon customer knowledge, what are <strong>the</strong> incentives for
employees, etc)
Organizational sharing capacity (what is <strong>the</strong> system <strong>of</strong> integrating <strong>the</strong> customer and
organisational knowledge assets and <strong>the</strong>n reproducing <strong>the</strong>m internally, etc.)
Customers´ motivation (trust, motivation tools, incentives, etc.)
2.1 Methodology
Both qualitative and quantitative research was carried out. Concerning <strong>the</strong> qualitative research: It was
managed in <strong>the</strong> form <strong>of</strong> conducted interviews with as a large scale <strong>of</strong> pr<strong>of</strong>essions as possible
(marketing managers, key account managers, general managers, sales forces, etc.) from various
types <strong>of</strong> business. The number <strong>of</strong> individuals I was talking to in person was 29. The interview was
focused on <strong>the</strong> main aspects <strong>of</strong> acquiring customers´ knowledge.
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Kamila Tislerova
Based on <strong>the</strong> results <strong>of</strong> <strong>the</strong> qualitative research, <strong>the</strong> body <strong>of</strong> <strong>the</strong> quantitative questionnaire was
structured into four parts corresponding with <strong>the</strong> research objectives. This sample cannot be regarded
as a representative one and <strong>the</strong> main goal was to acquire a solid base for <strong>the</strong> construction <strong>of</strong> a
quantitative questionnaire.
The method <strong>of</strong> electronic transmission <strong>of</strong> <strong>the</strong> questionnaire was chosen. There was no requirement
for a specific person to respond in <strong>the</strong> company, but a kind request for his/her identification
concerning <strong>the</strong> job position.
In <strong>the</strong> first period <strong>the</strong> questionnaire was sent to 1 223 units and <strong>the</strong> backflow was almost 50% (612).
3. How to understand customer knowledge
Customer knowledge is becoming an important topic. Unfortunately very <strong>of</strong>ten <strong>the</strong> approach is too
simplified; even pr<strong>of</strong>essionals and marketers supplied explanations such as:
„Customer knowledge can be approached from two ends. Firstly, you could say that customer
knowledge is <strong>the</strong> collection <strong>of</strong> information and viewpoints that an organisation has about its
customers. Using this definition, <strong>the</strong> role <strong>of</strong> customer knowledge management is to capture and
organise this data to allow it to be shared and discussed throughout <strong>the</strong> organisation. An alternative
definitive <strong>of</strong> customer knowledge is that it is <strong>the</strong> collection <strong>of</strong> information and insight that you
need to have to build stronger customer relationships. From this point <strong>of</strong> view what you currently
know about your customers may not be sufficient. You may need to put in processes and systems to
ga<strong>the</strong>r more information and data about who your customers are, what <strong>the</strong>y do and how <strong>the</strong>y think.“
Such an approach is derived from <strong>the</strong> web pages <strong>of</strong> dobney.com which regards itself as is a fullservice
international marketing research consultancy.
Some researchers suggest that ideally, customer knowledge should work at both <strong>the</strong> micro and
macro level. That is, it should include information about individuals that helps explain who those
individuals are, what <strong>the</strong>y do and what <strong>the</strong>y are looking for, and it should enable broader analysis <strong>of</strong>
<strong>the</strong> customer base as a whole.
To explain Customer Knowledge Value properly it is necessary to adopt a three-dimensional
approach where all attributes <strong>of</strong> <strong>the</strong> value are considered:
1) Knowledge for Customer:
Knowledge for <strong>the</strong> customer is a kind <strong>of</strong> knowledge (also data or information which can be analyzed,
interpreted and eventually converted to knowledge) that <strong>the</strong> target customer attains in order to know
us better (Garcia-Murillo, Annabi 2002). Often in <strong>the</strong> literature, sources that provide knowledge for <strong>the</strong>
customer are overlooked. Data, information or knowledge for customers can be gained from <strong>the</strong>
company´s customers, IT consulting institutes, competitors and <strong>the</strong> company itself to provide
information about <strong>the</strong> needs <strong>of</strong> customers. It should be taken into consideration that a company is not
able to manage all <strong>of</strong> <strong>the</strong>se knowledge flows (e.g. knowledge that is gleaned from competitors).
2) Knowledge from Customer:
Knowledge from <strong>the</strong> customer is a kind <strong>of</strong> knowledge (also data or information which can be
analyzed, interpreted and eventually converted to knowledge) that <strong>the</strong> company attains in order to
enhance its products and services (Garcia-Murillo, Annabi, 2002).
3) Knowledge about Customer:
Knowledge about a customer is a kind <strong>of</strong> knowledge (also data or information which can be analyzed,
interpreted and eventually converted into knowledge) that <strong>the</strong> company attains in order to get to know
its target customer better (Davenport, Harris, Kohli, 2001). Companies not only acquire knowledge
about customers but also purchase data, information and knowledge about customers (Bohler, 2002).
The suggested simplification <strong>of</strong> Customer Knowledge Value in this contribution is in <strong>the</strong> model
covering <strong>the</strong> three aspects referring to knowledge “for”, “from” and “about” <strong>the</strong> customer.
4. Deeper insight into Knowledge “from“ <strong>the</strong> customer
Interactions with customers are a strategic opportunity for companies to learn. These insights can be
thoughts or opinions on current products and services, future needs and ideas for product
innovations. It is interesting to note that product innovation ideas frequently emerge from end-users
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Kamila Tislerova
and customers, ra<strong>the</strong>r than from within <strong>the</strong> organization. Capturing customer knowledge and involving
customers in <strong>the</strong> innovation process can represent for <strong>the</strong> company a very pr<strong>of</strong>itable source.
4.1 Means <strong>of</strong> customer involvement
Customer involvement occurs in different phases <strong>of</strong> <strong>the</strong> new product development process. Some
customers are only involved in <strong>the</strong> initial stages <strong>of</strong> <strong>the</strong> process; o<strong>the</strong>rs in <strong>the</strong> final stages and o<strong>the</strong>rs
interact continuously with <strong>the</strong> provider during <strong>the</strong> entire course <strong>of</strong> development. Comparing different
methods for involvement requires a framework to which <strong>the</strong> methods can be related. Kaulio (1998)
proposes a framework based on two dimensions:
(1) The longitudinal dimension, which includes <strong>the</strong> issues <strong>of</strong> an interaction between customers and
<strong>the</strong> design process.
(2) The lateral dimension, which captures <strong>the</strong> level <strong>of</strong> customer involvement in <strong>the</strong> design process.
This dimension is fur<strong>the</strong>r divided into three different categories:
- design for, (<strong>the</strong> products are designed based on customer research but <strong>the</strong> customer is not
involved fur<strong>the</strong>r);
- design with (denotes an approach which, in addition to <strong>the</strong> above, also includes displays <strong>of</strong>
different concepts for <strong>the</strong> customer to react upon);
- design by (signifies an approach where customers are actively involved and partake in <strong>the</strong>
product design).
There are also o<strong>the</strong>r frameworks - Nijssen and Lieshout (1995) categorise <strong>the</strong> most popular tools for
new product development according to <strong>the</strong>ir purpose in four groups as follows:
(1) Idea generation, including creative tools such as brainstorming, syn<strong>the</strong>tics and morphological
analysis and non-creative tools such as focus groups, Delphi method, surveys, observation,
scenario and expert opinion.
(2) Product optimisation, including conjoint analysis, quality function deployment, concept testing,
prototype testing and pilot plant/in-home use test.
(3) Marketing mix optimisation, including simulated test marketing, mini-market, limited prediction,
including computer prediction models, diffusion models, and economic models.
(4) Prediction, including computer prediction models, diffusion models, and economic models, such
as ROI/BE – analysis and pay-back time.
4.2 Reasons for customer knowledge involvement
One <strong>of</strong> <strong>the</strong> most commonly mentioned reasons is cooperation in <strong>the</strong> innovation process. Successful
new product development requires an in-depth understanding <strong>of</strong> <strong>the</strong> customers, <strong>the</strong>ir situation, <strong>the</strong>ir
needs and <strong>the</strong>ir wishes (Lagrosen, 2001). This requires active interaction with <strong>the</strong> customers.
There are many authors confirming <strong>the</strong> value <strong>of</strong> involving <strong>the</strong> customers in new product development.
Proximity to customers is <strong>of</strong>ten ranked as one <strong>of</strong> <strong>the</strong> most important factors for new product
development success in business markets. Understanding customers is usually regarded in literature
as being crucial for successful new product development (Lagrosen, 2001). However, understanding
alone, is <strong>of</strong>ten not sufficient. The customer has to be motivated and activated. Von Hippel (2001) even
argues that by using user toolkits for innovation, companies can abandon <strong>the</strong>ir efforts to understand
customers’ needs and outsource need-related innovation tasks to <strong>the</strong>ir users.
4.3 The role <strong>of</strong> relationship marketing in involving customers
There has been a remarkable evolution in marketing trends from a ra<strong>the</strong>r limited focus on transactions
to a focus on <strong>the</strong> relationships that companies have with <strong>the</strong>ir customers and o<strong>the</strong>r actors. Research
has indicated that well-managed relations can moderate <strong>the</strong> effects <strong>of</strong> inadequate product
performance. Product development management involving customers is related to <strong>the</strong> companies’
customer relationship management processes. Developing products according to <strong>the</strong> customers’
needs and wants has long been promoted. However, <strong>the</strong> relationship marketing goes beyond a
distanced relation based on quantitative market research and upholds <strong>the</strong> interaction between <strong>the</strong>
parties <strong>of</strong> <strong>the</strong> relation as <strong>the</strong> most important source for market knowledge. This implies that product
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Kamila Tislerova
development should be seen as an interactive process involving both suppliers and customers. The
relations between suppliers and customers consist <strong>of</strong> <strong>the</strong> three crucial aspects <strong>of</strong> activity links,
resource ties and actor bonds. The level <strong>of</strong> closeness between <strong>the</strong>se aspects gives rise to three
different levels <strong>of</strong> relationships (Ford, McDowell, 1999):
(1) Transactional relationships, involving no integration between supplier and customer and <strong>the</strong>
supplier’s <strong>of</strong>fering is undifferentiated from that <strong>of</strong> o<strong>the</strong>rs.
(2) Facilitative relationships, in which <strong>the</strong> customer also wishes to acquire relative undifferentiated
products at <strong>the</strong> lowest possible cost but both parties are willing to invest in activity links and
resource ties to increase <strong>the</strong> cost benefits <strong>of</strong> <strong>the</strong> relationship.
(3) Integrative relationships, wherein <strong>the</strong> customer expects benefits beyond those <strong>of</strong> lower costs and
extending to <strong>the</strong> revenue benefits <strong>of</strong> enhanced performance <strong>of</strong> its own <strong>of</strong>fer. In <strong>the</strong>se
relationships, <strong>the</strong> supplier frequently has a representative on site, working with <strong>the</strong> customer’s
development staff with <strong>the</strong> aim <strong>of</strong> having his products designed into <strong>the</strong> customer’s product.
4.4 The indirect value <strong>of</strong> a customer
The indirect value <strong>of</strong> a customer is <strong>the</strong> additional value (over and above <strong>the</strong> direct financial value
measured by customer lifetime value/customer equity) that accrues to <strong>the</strong> firm through <strong>the</strong>ir
relationship with that customer. Probably <strong>the</strong> most-explored area is advocacy, or word <strong>of</strong> mouth,
identified very early as an important element in relationship marketing and associated not just with
satisfaction but also with <strong>the</strong> relationship duration. Advocacy has repeatedly been shown to have a
strong impact on purchasing behaviour and can be influenced by marketing strategies (Harrison-
Walker, 2001). Consequently, it is positively correlated with company growth). The advocacy concept
has been extended to include customer reputation or reference ability (Stahl, Matzler and Hinterhuber
2003).
Fur<strong>the</strong>r relational benefits from customer relationships include learning (Stahl, Matzler and
Hinterhuber 2003) and innovation (Thomke and von Hippel 2002). Learning and information benefits
contribute slightly differently to <strong>the</strong> value <strong>of</strong> <strong>the</strong> firm. Ra<strong>the</strong>r than reducing <strong>the</strong> costs <strong>of</strong> customer
acquisition. They enhance <strong>the</strong> overall competitiveness and revenues/efficiency <strong>of</strong> <strong>the</strong> firm (Cravens,
Piercy and Prentice 2000). It is difficult to account for indirect value. For example, <strong>the</strong> impact <strong>of</strong>
advocacy or reference benefits is to reduce <strong>the</strong> cost <strong>of</strong> customer acquisition, which increases <strong>the</strong>
financial value <strong>of</strong> <strong>the</strong> acquired customer but does not affect <strong>the</strong> value <strong>of</strong> <strong>the</strong> referring customer.
Learning and innovation benefits may or may not increase <strong>the</strong> financial value <strong>of</strong> <strong>the</strong> customer
generating <strong>the</strong>m, through increased sales or reduced costs, but <strong>the</strong>y can also increase <strong>the</strong> overall
revenues or improve <strong>the</strong> overall efficiency <strong>of</strong> <strong>the</strong> company
5. The research findings
5.1 Survey findings – commentary and recommendations
In 89% <strong>of</strong> companies (respondents for this research), top management recognises Customer
Knowledge Management as an important part <strong>of</strong> <strong>the</strong> business strategy. According to <strong>the</strong> following
findings unfortunately <strong>the</strong>re is a gap between <strong>the</strong> top management effort and <strong>the</strong> necessary results.
People in <strong>the</strong> organisation (just over 70%) are aware <strong>of</strong> <strong>the</strong> need to proactively manage customer
knowledge but at <strong>the</strong> same time almost 61% consider this activity to be too time-consuming at <strong>the</strong>
expense <strong>of</strong> <strong>the</strong>ir “regular” duties. It should be alarming for <strong>the</strong> companies that employees are able to
separate <strong>the</strong>ir work according to <strong>the</strong>ir job description and <strong>the</strong>ir involvement in customer knowledge
management and treat <strong>the</strong>se two items differently.
In 75% <strong>of</strong> <strong>the</strong> companies <strong>the</strong>re is hardly any duplication <strong>of</strong> effort in <strong>the</strong> organisation so probably <strong>the</strong>
main concept is implemented properly.
Marketing managers do not (71%) evaluate and improve <strong>the</strong>ir incentives for customers. Partly
because <strong>the</strong>y consider it “too continuous to be able to measure” and partly because <strong>the</strong>y share this
“duty” with customer service managers. The recommendation is to treat this with incentives similar to
standard marketing actions (start, end, evaluation).
Surprisingly more than half <strong>of</strong> <strong>the</strong> managers do not have <strong>the</strong> feedback from a product
modified/developed upon customer suggestion (managerial accounting – to express <strong>the</strong> amount
which was saved). This feedback on <strong>the</strong> following sales <strong>of</strong> <strong>the</strong> modified product can also be regarded
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Kamila Tislerova
as pretty poor (45%). The reason is that product technical specifications and improvement are out <strong>of</strong>
<strong>the</strong> focus <strong>of</strong> marketing managers.
Also monitoring at <strong>the</strong> level <strong>of</strong> customer involvement is not sufficient, more than 70% <strong>of</strong> managers are
“too flexible” with <strong>the</strong> expression <strong>of</strong> <strong>the</strong> current situation and trend evaluation. It requires sharper
evaluation than just “may be better than last year”.
Especially with <strong>the</strong> companies which already have mentioned some successful customer suggestion
implementation, it is difficult to recognize how <strong>the</strong>y work with all <strong>the</strong> customer portfolio as <strong>the</strong>y are too
focused on <strong>the</strong> co-operative customers. But <strong>the</strong>re is a large potential in <strong>the</strong> customer base and
communication with <strong>the</strong> customer from <strong>the</strong> marketing point <strong>of</strong> view, is moreover beneficial anyway.
One question was dedicated to “Idea Management” (<strong>the</strong> managerial system which enables actively
collect, evaluate and implement <strong>the</strong> ideas from own employees). This managerial tool is used in 32%
<strong>of</strong> companies and it is very easy to make an analogy to Customer Knowledge Management, as 25%
<strong>of</strong> <strong>the</strong>se companies showed high-level results in managing customers´ knowledge.
5.2 Suggestions <strong>of</strong> measurement
Based on <strong>the</strong> survey and its findings <strong>the</strong>re <strong>the</strong> most important recommendations were derived (in <strong>the</strong>
form <strong>of</strong> metrics) to be implemented as follows in Table 1:
Table 1: suggested metrics
Topic Measurement
Evaluation <strong>of</strong> <strong>the</strong> most popular incentive for co-creation value % <strong>of</strong> responses received
The most valuable suggestion from customer (efficiency) – IT testing, new € saved (derived from
brand name development, technical solution, etc.
Customisation: <strong>the</strong> number <strong>of</strong> customisable products sold to suit customer
requirements This measure can indicate a level <strong>of</strong> flexibility and also a level
<strong>of</strong> customer focus
standard prices)
% <strong>of</strong> total product sold
€ <strong>of</strong> total product sold
Level <strong>of</strong> co-creation with <strong>the</strong> customer (allows <strong>the</strong> trend description) Number <strong>of</strong> suggestions
implemented (incl. € value)
The strength <strong>of</strong> <strong>the</strong> relationship with customers in product/service design
(customer involvement)
∅ number <strong>of</strong> hours <strong>of</strong>
customer contact in this
process.
Customer feedback proceeding (evaluating, discussing, etc.) Number <strong>of</strong> occurrence
(agenda items on meetings)
Customer partnership in product design % <strong>of</strong> products designed by
Community feedback - provides information for monitoring <strong>of</strong> <strong>the</strong> process <strong>of</strong>
communication.
Source: own derivation
6. Mini case studies
customer
% <strong>of</strong> community that provide
feedback
(1) A young Czech singer, Ewa Farna announced in cooperation with <strong>the</strong> Company Oriflame a
contest „Be a copyrighter for Eva!“. Candidates were encouraged to write a text for <strong>the</strong> singer which
had to contain minimally three words linked with <strong>the</strong> company Oriflame. The three best song-texts
would be awarded by Oriflame products (valued 400 Euro) and by <strong>the</strong> opportunity <strong>of</strong> meeting Eva in
person.
The awards do not seem to be fantastic but a similar action was conducted by this singer two years
before and <strong>the</strong> number <strong>of</strong> ideas was amazing.
(2) McDonald´s announced a contest: young people are called to make a video demonstrating <strong>the</strong>ir
love for McDonald´s. Also similar ideas (why to eat <strong>the</strong>re, what is <strong>the</strong> best food <strong>the</strong>re, etc.) were
encouraged. Awards were pretty high: 10 authors would be given 300 Euro and <strong>the</strong> winner would
receive 8 000 Euro. Fur<strong>the</strong>rmore, every video would be awarded meal vouchers.
Although this Return on Investment might not seem to be so high, in comparison with <strong>the</strong> fact that
many unique, fresh and young customers tailored ideas <strong>the</strong>y received, <strong>the</strong> ROI is probably more than
satisfactory.
(3) Linux co-creates with its customers and <strong>the</strong>ir funs <strong>the</strong> s<strong>of</strong>tware development and application
testing. The economic benefit <strong>of</strong> <strong>the</strong> idea that Linux demonstrates - to turn its customers into free <strong>of</strong>
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Kamila Tislerova
charge working valuable capacities – this would make it worthwhile to create an extra contribution or
to organize an extra conference.
7. Conclusion
It is a mistake to understand Customer Knowledge Management just from <strong>the</strong> point <strong>of</strong> view <strong>of</strong> a data
mining discipline (particularly to be concerned with customers’ data collections). The framework <strong>of</strong>
Customer Value should be used in <strong>the</strong> three dimensional model (from, for, about customer). Both
researchers and practitioners should fully discover <strong>the</strong> potential <strong>of</strong> customer knowledge and attempt
to employ <strong>the</strong> customer as a knowledgeable agent who might be even happy to help to create a
pr<strong>of</strong>itable business.
Note
This contribution was supported by grant <strong>of</strong> University JE Purkyne in Usti nad Labem, Czech Republic
(IGA 43 201 15 0004 01).
References
Bohler, H. (2002): “Marketngmanagement und Unternehmensfuhrung“, Stuttgart, pp. 273-295.
Cravens, D.W., Piercy, N.F., Ashley Prentice, (2000) "Developing market-driven product strategies", Journal <strong>of</strong>
Product & Brand Management, Vol. 9, Iss: 6, pp.369 – 388
Davenport, T.H., Harris, J.G., Kohli, A.K.,(2001) “How do <strong>the</strong>y know <strong>the</strong>ir customers so well?“ MIT Sloan
Management Review Winter, pp. 63–73.
Ford, D., McDowell, R. (1999). “Managing business relationships by analyzing <strong>the</strong> effects and value <strong>of</strong> different
actions”. Industrial Marketing Management, Vol. 28, Iss. 5, pp. 429-442.
Garcia-Murillo, M. and Annabi, H. (2002) “Customer Knowledge Management“, Journal <strong>of</strong> <strong>the</strong> Operational
Research Society, pp. 875-884.
Harrison-Walker, J. (2001) “The Measurement <strong>of</strong> Word-<strong>of</strong>-Mouth Communication and an Investigation <strong>of</strong> Service
Quality and Customer Commitment As Potential Antecedents“, Journal <strong>of</strong> Service Research, Vol. 4, Iss.
1, pp. 60-75
Kaulio, M. (1998). “Customer, Consumer and User Involvement in Product Development: A Framework and a
Review <strong>of</strong> Selected Methods“.Total Quality Management, Vol. 9, Iss. 1, pp. 141-150.
Lagrosen, S. (2005) "Customer involvement in new product development: A relationship marketing perspective",
<strong>European</strong> Journal <strong>of</strong> Innovation Management, Vol. 8, Iss. 4, pp.424 – 436
Lagrosen, S. (2001) "Streng<strong>the</strong>ning <strong>the</strong> weakest link <strong>of</strong> TQM – from customer focus to customer understanding",
The TQM Magazine, Vol. 13 Iss. 5, pp. 348 – 354
Nijssen, E. J.; Lieshout, K. F. (1995). “Awareness, use and effectiveness <strong>of</strong> models and methods for new product
development“, <strong>European</strong> Journal <strong>of</strong> Marketing, Vol. 29, Iss. 10, pp. 27−44.
Stahl, H.K., Matzler, K., Hinterhuber, H.H. (2003) “Linking customer lifetime value with shareholder value”.
Industrial Marketing Management, Vol. 32, Iss. 4, pp. 267-279.
Thomke, S., and E. von Hippel (2002). "Customers As Innovators: A New Way to Create Value." Harvard
Business Review, Vol. 80, Iss.. 4
von Hippel, E. (2001). “Perspective: User toolkits for innovation” Journal <strong>of</strong> Product Innovation
Management, Vol. 18, Iss. 4, pp. 247-257.
987

Multi Perspective Framework to Improve <strong>the</strong> Knowledge
Flow
Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
University <strong>of</strong> Technology Sydney, Sydney, Australia
cyoo@it.uts.edu.au
igorh@it.uts.edu.au
kyeong@it.uts.edu.au
Abstract: This paper addresses issues <strong>of</strong> knowledge management processes and procedures including social
complexity in Australian government organizations. We investigated <strong>the</strong>se issues for building a multiple
perspective framework in designing a complex business structure. Particularly, we focused on a social
complexity, which possesses <strong>the</strong> role <strong>of</strong> <strong>the</strong> functional units and multiple stakeholders. To understand <strong>the</strong>
complex issues and significant changes in <strong>the</strong> past years, social complexity holds a specific fragmenting strength
which can lead to difficulty in effective communication (Conklin, 2005). Hence, social complexity needs tools that
are standardized to complex organizations. This notion <strong>of</strong> social complexity correlates to a project group involved
within a social network. The growing complexity <strong>of</strong> Information Systems results in a situation where system
design is not easily catered to identify new characteristics <strong>of</strong> system and managing <strong>the</strong> dimensions <strong>of</strong>
organizational performance by existing methodologies. The purpose <strong>of</strong> this study is to build a framework <strong>of</strong>
defining systems for <strong>the</strong> knowledge flow to ensure local intelligent time <strong>of</strong> <strong>the</strong> decision making process is effective
and helps to manage information system evolution. This paper suggests that to cater for <strong>the</strong>se characteristics
and characterize <strong>the</strong> communication paths, new open methods that look at o<strong>the</strong>r perspectives in <strong>the</strong> additional
process flow are required. These additional perspectives include social structure, business architecture, and
knowledge hub to identify <strong>the</strong> communities <strong>of</strong> practice and development <strong>of</strong> a model to support complex
organizations. In summary, this paper outlines a design method that uses a multi perspective framework as a tool
for modeling and will help to tackle <strong>the</strong> complex problem.
Keywords: Complexity, Multi-perspective, Social complexity, System evolution
1. Introduction
Information systems are becoming more complex as <strong>the</strong>y are increasingly needed to support what are
known as ‘wicked’ (Rittel and Webber 1973) problems. Rittel and Weber (1973) defined
characteristics <strong>of</strong> wicked problems as highly resistant to resolution; no definitive and no immediate
solution; every wicked problem can be considered to be a symptom <strong>of</strong> ano<strong>the</strong>r problem. Importantly, it
creates <strong>the</strong> social complexity which makes communication and interaction difficult among multiple
stakeholders. Most current modeling methods are suitable for structured systems, however needs to
explicitly address <strong>the</strong> new emerging structures to improve <strong>the</strong> collaborative process. It requires strong
knowledge shared networks and understanding <strong>of</strong> self-organization to work constructively in search <strong>of</strong>
innovative solutions to complex issues. The self-organization performs efficiently and its process
raises <strong>the</strong> idea <strong>of</strong> <strong>the</strong> importance <strong>of</strong> newly emerging structures that evolve multiple levels <strong>of</strong>
complexity (Merali 2006) in organization. In order to analyze and structure <strong>the</strong> problem <strong>of</strong> complexity,
this study raises research problem: What are <strong>the</strong> modeling methods needed for people to understand
wicked problems and how does this model cater for complex characteristics. To illustrate <strong>the</strong>se, this
paper approaches <strong>the</strong> issue from both empirical and <strong>the</strong>oretical viewpoints.
Organizational complexity is <strong>of</strong>ten defined as <strong>the</strong> measure <strong>of</strong> diversity in internal and environmental
factors (Mason 2007). Complexity as a <strong>the</strong>ory has been studied over many years. It is only lately that
complexity science has been used to interpret behavior on o<strong>the</strong>r fields, including organizational
studies (Maguire 2006). Organizations operate in a way that <strong>the</strong>y are more diverse and dispersed.
However, <strong>the</strong>re could be <strong>the</strong> appearance <strong>of</strong> ano<strong>the</strong>r view <strong>of</strong> complexity which possesses <strong>the</strong> essential
inferences needed for organizational architecture. Dynamics <strong>of</strong> complex <strong>the</strong>ory has progressed into
<strong>the</strong> highest point <strong>of</strong> study but has not systematically been applied <strong>the</strong> social networking communities.
The study <strong>of</strong> organizational complexity stands on <strong>the</strong>se disciplines that examine <strong>the</strong> change
requirements and understanding <strong>the</strong> terms <strong>of</strong> what complexity is, and what kinds <strong>of</strong> processes lead to
it (Heylighen 1999; Keirsey 1999)? Some <strong>of</strong> traditional research states that <strong>the</strong> complexity <strong>the</strong>ory only
tries finding <strong>the</strong> patterns by computational system. In contrast, <strong>the</strong> growing complexity is a result <strong>of</strong>
system evolution (Heylighen 1989).
The paper begins by examining previous research on <strong>the</strong> complexity <strong>the</strong>ory and self-organization
followed by using a multiple perspective framework to address <strong>the</strong> ways organizational structure
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Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
formalize, <strong>the</strong> process <strong>of</strong> change and <strong>the</strong>ir social interactions with communities in complex
organization.
2. Background
There is a need for <strong>the</strong> modeling method to analyze change management and improve adaptation.
The implications <strong>of</strong> this are that existing methodologies and organizational complexity have to be
examined. Developing <strong>the</strong> innovative method can be difficult compared to <strong>the</strong> existing models. The
subsequent emergence <strong>of</strong> <strong>the</strong> self-organizing social and economic networks has issues <strong>of</strong> complexity
(Merali 2006). For complexity and change, integrate multiple organizational perspectives literature
(Dagnino 2004; Head and Alford 2008; McElory 2000; Smith and Humphries 2004) highlights as key
issues. Much discussion <strong>of</strong> complexity issues implies that focus should be placed on modeling
method and how organization to adapt change environment for collaboration. Never<strong>the</strong>less, <strong>the</strong>
integration <strong>of</strong> perspectives increase complexity <strong>of</strong> social system dynamic which creating requirements
to improve <strong>the</strong> behavioural perspective.
Initially this section explains <strong>the</strong> new characteristics from dynamic complexity that identifies essential
criteria for new requirements. Modeling <strong>the</strong> multiple perspectives framework follows, which can be
guided for behavioral change.
2.1 Design criteria from complexity <strong>the</strong>ory
In emergence <strong>of</strong> structure <strong>the</strong> new characteristics resulting from complexity, for example, learning
organization which can learn from experiences; virtual organization for no boundaries; social network
structure for new knowledge; self-organizing system that requires persistent feedback from <strong>the</strong> rapidly
changing environment.
McElroy (2000) describes <strong>the</strong> importance <strong>of</strong> complexity <strong>the</strong>ory which <strong>of</strong>fers knowledge acquired in
human social systems. He also illustrates <strong>the</strong> relationship between <strong>the</strong> knowledge management,
organizational learning and complexity <strong>the</strong>ory in human organizations to adapt to changes quickly and
focus on second generation strategies to create new knowledge for organizational demand.
Moreover, Merali (2006) supports <strong>the</strong> argument <strong>of</strong> <strong>the</strong> new characteristics and an understanding <strong>of</strong>
<strong>the</strong> complex organization. He also, draws <strong>the</strong> significance <strong>of</strong> adaptation and self-organization in a
dynamic environment. However, a model to capture <strong>the</strong> emergent dynamics <strong>of</strong> complex system is
difficult and almost impossible to use traditional methodologies to analyze <strong>the</strong> complex issues.
Andriani (2005) points out, in practice, adaptive strategies are required for sensing change in <strong>the</strong>
environment and <strong>the</strong> importance <strong>of</strong> diversity and self-organization. In addition, Arthur et al. (2001)
highlights that internal diversity is met by a rapid reconfiguration <strong>of</strong> links based on self-organization.
2.2 Modeling <strong>the</strong> perspective
In <strong>the</strong> collaborative innovation network literature <strong>the</strong>re are several approaches dealing with <strong>the</strong>
different design models to support <strong>the</strong> new process implementation over self-organized business
processes. With value to collaborative innovation, dimensions <strong>of</strong> new multi perspective performance is
explored and examined that new ways <strong>of</strong> modeling can help in detecting <strong>the</strong> issues <strong>of</strong> new
organizational process implementation and quickly adapting to changes. A number <strong>of</strong> authors have
highlighted <strong>the</strong> importance <strong>of</strong> complexity <strong>the</strong>ory (Cohen 1999; McElroy 2000; Dagnino 2004) and
Smith and Humphries (2004) concluded that complexity <strong>the</strong>ory is difficult to translate into practice.
The way to develop a model combing holistic model is to use a number <strong>of</strong> perspectives to model <strong>the</strong>
complex system. This approach focuses on describing <strong>the</strong> behavioral perspective <strong>of</strong> social system
dynamic and organizational structures.
Recent authors have emphasized that challenges to tackle wicked problems <strong>of</strong>ten leads to social
complexity (Head and Alford 2008). They demonstrated <strong>the</strong> typology <strong>of</strong> problems and techniques to
manage <strong>the</strong> complex issues with wicked elements. Dealing with <strong>the</strong> wicked problems, <strong>the</strong>re is
collaborative diversity and takes many different shapes (Imperial, 2004) among multiple stakeholders.
This paper aims to identify and develop a multiple perspective framework to model complex services
across organizations. Consequently, it will support <strong>the</strong> collaborative structure effectively and
efficiently.
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2.3 Dynamics <strong>of</strong> Self-organization
The work <strong>of</strong> Heylighen (2001) and Andriani (2005) are valuable understanding <strong>the</strong> emergence <strong>of</strong> new
structure and adaptability in a dynamic environment. For examples, organization has option to control
with <strong>the</strong> importance <strong>of</strong> continual adaptation to a changing environment (Heylighen 2001), moreover
organization ensures that new structure emerge in a consistent way and <strong>the</strong>n achieves <strong>the</strong> benefits
associated with effective operation. In <strong>the</strong> current competitive environment coupled with <strong>the</strong> ever
changing environmental enterprise scene, <strong>the</strong> need for organizational change demands better
business processes and specially <strong>the</strong> social behavior which require sensitive assessment <strong>of</strong> <strong>the</strong>ir
impact on overall performance. However, <strong>the</strong> implications <strong>of</strong> adopting <strong>the</strong> new perspective models as
a management <strong>of</strong> system evolution are fragmentally addressed.
Design method for dynamic organization is a better approach to provide improvement in organization.
Research needs more emphasis on exploring and investigating new framework to evaluate design
model and show that it is more appropriate than using existing modeling method to identify <strong>the</strong> new
characteristics. In fact, social behavioral perspective is <strong>the</strong> main element <strong>of</strong> <strong>the</strong> complex processes
modeling and it evolves toward collaboration.
Leading researchers in IS emphasized <strong>the</strong> significance <strong>of</strong> self-organization (Arthur et al. 2001;
Andriani 2005; Heylighen 2008). On <strong>the</strong> o<strong>the</strong>r hand, a model to capture <strong>the</strong> emergent dynamics <strong>of</strong>
complex system is difficult and almost impossible to use traditional methodologies to analyze <strong>the</strong>
complex issues. In practice, adaptive strategies are required for sensing change in <strong>the</strong> environment
and <strong>the</strong> significance <strong>of</strong> diversity and self-organization.
3. Researh question and hypo<strong>the</strong>sis
This study seeks to address a central research question. Apart from <strong>the</strong> main question, <strong>the</strong>
subsequent sets <strong>of</strong> research hypo<strong>the</strong>ses were formulated to act as guiding questions throughout <strong>the</strong>
exploratory studies as following:
“Does a multiple perspectives framework improve <strong>the</strong> ability to manage change in
complex processes? “
Hypo<strong>the</strong>ses:
This research focuses on multi perspective modeling derived from <strong>the</strong> literature and <strong>the</strong> <strong>the</strong>oretical
foundations. The paper will test specific hypo<strong>the</strong>ses to address <strong>the</strong> research questions.
The proposed model is meant to identify <strong>the</strong> information flow in unforeseen
circumstances by measuring <strong>the</strong> user satisfaction. Therefore:
H1: Multi perspective model will help to identify knowledge flow for better decision
making.
The proposed model enables users to understand what to do in <strong>the</strong> unlikely event <strong>of</strong>
unforeseen circumstances by measuring <strong>the</strong> reliability <strong>of</strong> <strong>the</strong> model. Hence:
H2: The model helps organization to learn how to discover knowledge.
A multi perspective model will support users to facilitate <strong>the</strong> changes and collaboration to
unforeseen circumstances by measuring <strong>the</strong> adaptability <strong>of</strong> <strong>the</strong> model. Thus:
H3: Knowledge flow in organizations is influenced by significant change.
A multi perspective model will enhance <strong>the</strong> development <strong>of</strong> better relationships among stakeholders
and improve responsiveness to unforeseen circumstances by measuring <strong>the</strong> responsiveness <strong>of</strong> <strong>the</strong>
model. Therefore:
H4: The organization will respond to unforeseen circumstances needed for better
knowledge flow.
4. Approach and objective
System design framework is recognized prior to modeling a complex system at various points in <strong>the</strong>
organization's development and in managing system evolution. However, <strong>the</strong> framework would not
succeed without <strong>the</strong> full understanding <strong>of</strong> significant change across <strong>the</strong> organizations. The research
question rises as to whe<strong>the</strong>r <strong>the</strong> model based on a multi perspective approach allows an easier
management <strong>of</strong> system evolution. To illustrate this, we propose an approach that <strong>the</strong> modeling <strong>of</strong>
complex systems uses multiple perspectives framework to improve understanding <strong>of</strong> <strong>the</strong> complex
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relationships in such systems. The overall research design proposes a multiple case field study and
<strong>the</strong>oretical viewpoint, involving interview and examination <strong>of</strong> complex organization for knowledge
based systems in a real organizational setting.
This paper proposes that modeling <strong>of</strong> complex systems use multiple perspectives framework to
improve understanding <strong>of</strong> <strong>the</strong> complex relationships in such systems. This includes relationships
between organizational structure, <strong>the</strong> organizational units and communities <strong>of</strong> knowledge workers and
<strong>the</strong> organization <strong>of</strong> business activities. This paper is set out to gain a general understanding <strong>of</strong> <strong>the</strong>
activity knowledge network with organizations in <strong>the</strong>ir process relationship, and to identify possible
areas for improvement. The key objectives for this research are:
Developing a model made up <strong>of</strong> a number <strong>of</strong> perspectives to help and analyze <strong>the</strong> complex issues,
Identify some perspectives and show how <strong>the</strong>y are integrated into a holistic model, and;
Identify possible modeling constructs for <strong>the</strong> perspectives.
5. multiple perspectives framework
A holistic model consists <strong>of</strong> organization, business, knowledge and social perspectives. These were
chosen to describe <strong>the</strong> evolving environment and <strong>the</strong> self-organization needed to manage <strong>the</strong>
complex processes within that environment. The following section describes <strong>the</strong> use <strong>of</strong> this approach
to model <strong>the</strong> different dimensions <strong>of</strong> organization architectures for collaboration.
5.1 Multi Perspective Model
Multi perspective framework includes four dimensions namely; <strong>the</strong> first dimension, organization
perspective, refers to <strong>the</strong> positions in <strong>the</strong> supply chain; <strong>the</strong> second dimension is knowledge
perspective which illustrates <strong>the</strong> knowledge shared in <strong>the</strong> communities <strong>of</strong> practice; third dimension is
business perspective, which shows how business activities are formalized, <strong>the</strong> interaction with
different roles in <strong>the</strong> organizations. Consequently, <strong>the</strong> fourth dimension is social perspective that
illustrates <strong>the</strong> social complexity and process <strong>of</strong> change within and across o<strong>the</strong>r perspectives.
The subsequent section demonstrates some possibilities <strong>of</strong> modeling techniques used in <strong>the</strong> different
perspectives. These draw on existing methods amending <strong>the</strong>m to realize <strong>the</strong> modeling goals <strong>of</strong>
catering for evolution and self organization.
Figure 1: Different dimensions <strong>of</strong> multi perspective model
Innovative perspectives rise to identify and capture <strong>the</strong> different dimensions <strong>of</strong> emergent structure as
above Figure 1. The transformation <strong>of</strong> <strong>the</strong> self-organization (Ferlie 2007) to a new structure creates
dimensions <strong>of</strong> perspective. It shows that <strong>the</strong> collaborative infrastructure platform across all
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Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
organization and <strong>the</strong> knowledge flow from one to o<strong>the</strong>r perspectives. A multi perspective model
displays <strong>the</strong> relationship between <strong>the</strong> activities and <strong>the</strong> roles that change in a dynamic environment.
From an organizational perspective, policy can be referred to organizational rules and <strong>the</strong> actual
practices <strong>of</strong> government (Colebatch 2002). Successful organizations need to understand how each
functional unit interacts and how <strong>the</strong> policy derives from expert groups. In particular, recognition <strong>of</strong>
unique cultures is important for a change from an organizational perspective. For example,
organizational perspective approach helps to understand <strong>the</strong> way things are currently and easily
identify <strong>the</strong> culture change aligned with strategic direction. Moreover, this method provides an
organizational learning process and being used for enterprise relationships. It also recognises <strong>the</strong>
interaction between organizational units and <strong>the</strong>ir activity and responsibility.
Relating to a business perspective, activities can be categorized as services that are required to build
successful organizations. The objective <strong>of</strong> this method is to enable activity groups to understand how
<strong>the</strong>y achieve and deliver <strong>the</strong> key business goals. For example, business perspective method enables
change for business benefit and assists <strong>the</strong> stakeholders’ interactions. Moreover, it generates
knowledge to build better relationships between <strong>the</strong> government agencies.
Looking at a knowledge perspective point <strong>of</strong> view, second generation knowledge management
(McElroy 1999) can be defined as organizational practices that create <strong>the</strong> knowledge processes to
produce organizational learning. For example, this method supports unstructured communities and
enables to share knowledge between <strong>the</strong> communities including groups such as control, expert, policy
and governance. It also facilitates collaboration and innovation to support emergence. The knowledge
perspective uses rich pictures from s<strong>of</strong>t system methodologies to describe <strong>the</strong> knowledge sharing and
learning that takes place within collaborative environments.
Pralahad and Krishnan (2008) emphasize <strong>the</strong> importance <strong>of</strong> social networks that encourage
innovative behavior. Addressing it from a social perspective, roles can be identified as imperative
dynamic to adapt changes in organizational structures. For example, this method displays <strong>the</strong> way
people interact and knowledge created. Fur<strong>the</strong>rmore, it demonstrates how initial knowledge
infrastructure is being created and adapt to evolving systems. A model <strong>of</strong> <strong>the</strong> social perspective draws
on techniques used in social networking but extends <strong>the</strong>m into an organizational framework by
focusing on roles, <strong>the</strong>ir responsibilities and relationships between <strong>the</strong>ses roles. An enterprise social
network may support different types <strong>of</strong> responsibility such as coordinate decision making. It requires
<strong>the</strong> roles to use and create enterprise knowledge to continuously improve <strong>the</strong> performance <strong>of</strong>
organizational processes which manages <strong>the</strong> interactions among <strong>the</strong> members. Enterprise social
network modeling efforts can be categorized as <strong>the</strong>ir emphasis based models <strong>of</strong> roles, complexity <strong>of</strong>
activities (Schulz 2005) and organization.
As illustrated in Figure 1, multi perspective framework has <strong>the</strong> overall task in managing <strong>the</strong> processes
<strong>of</strong> organization and development <strong>of</strong> new knowledge through integration and adaptation to changes. It
is also capable <strong>of</strong> identifying <strong>the</strong> requirements and social complexities.
The proceeding section gives an approach <strong>of</strong> research that validates <strong>the</strong> multiple perspectives
framework by <strong>the</strong> case studies.
6. Research design
In <strong>the</strong> first instance, this research employed a multi perspective model deriving from <strong>the</strong> literature. The
model was <strong>the</strong>n reconstructed based on <strong>the</strong> collaborative design through a pilot case study, prior to
employing <strong>the</strong> multiple case study research method to fur<strong>the</strong>r specify <strong>the</strong> model. The case study
method is <strong>the</strong> most common qualitative method used in information systems (Orlikowski and Baroudi
1991; Alavi and Carlson 1992). It is an empirical inquiry that investigates an emerging area in which
multiple sources <strong>of</strong> evidence are used (Yin 2002). Progressively, <strong>the</strong> model will be validated through a
multiple case study involving four case studies across three Australian government agencies. The
resultant conceptual model can easily be evaluated as an analysis instrument designed to capture
and record evidence for fur<strong>the</strong>r validation <strong>of</strong> <strong>the</strong> successful model. The following section gives an
approach <strong>of</strong> research design that validates <strong>the</strong> model by <strong>the</strong> multiple case studies.
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6.1 Deriving <strong>the</strong> measurement model
In terms <strong>of</strong> selecting criteria, literature review was carried out and feedback from informal practitioners
interview that enabled to identify <strong>the</strong> important factors which are relevant to measure success model.
These relate to user satisfaction, model quality, model use and model content.
To devise a multi-dimensional concept <strong>of</strong> a multi perspective model success, a complete set <strong>of</strong>
measurement dimension is important (Kanellis et al. 1998). Bandara et al. (2006) describe success is
complex and due to <strong>the</strong> lack <strong>of</strong> IS success frameworks studies, adaptation <strong>of</strong> modeling success
dimensions deriving from <strong>the</strong> literature by conceptualizing <strong>the</strong> model success to measure four
dimensions as identified below Table 1. These extracted dimensions will correlate to <strong>the</strong> multiple
perspective contexts.
Table 1: Modeling success dimensions
User Satisfaction The extent to which <strong>the</strong> users believe multi
perspective modeling achieves <strong>the</strong> objectives
in unforeseen circumstances
Multi Perspective Model
Quality
Multi Perspective Model
Use
Multi Perspective Model
Content
*Adapted from Bandara et al. (2006)
6.2 Case study design
The extent to which <strong>the</strong> users believe multi
perspective modeling affects <strong>the</strong>
responsiveness in unforeseen circumstances
The extent to which <strong>the</strong> users believe multi
perspective modeling controls <strong>the</strong> adaptability
in unforeseen circumstances
The extent to which <strong>the</strong> users believe multi
perspective modeling delivers <strong>the</strong> reliability in
unforeseen circumstances
A pilot case study was performed and four subsequent multiple case studies are being carried out
with <strong>the</strong> modeling success dimensions which described in Table 1. Qualitative method is being
employed to examine <strong>the</strong> organizations to understand <strong>the</strong> complex processes and collaboration
issues facing participating organizations. Moreover, case study to evaluate <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong>
proposed framework allows an easier management <strong>of</strong> system evolution and in supporting multiple
stakeholders with coordination <strong>of</strong> complex projects.
6.3 Data collection
In recognition <strong>of</strong> various known case study method, participants are being asked questions about
complex project coordination across organizations. The focus <strong>of</strong> this research is semi-structured
interviews as a method <strong>of</strong> obtaining primary data on <strong>the</strong> working practice in dynamic organizations. In
particular, documenting all procedures relates to <strong>the</strong> interview findings and <strong>the</strong> analysis <strong>of</strong> data for
validation phases <strong>of</strong> <strong>the</strong> research project. All <strong>the</strong> interviews will instigate with two phases which are a
comparison <strong>of</strong> pre and post experimental questionnaires that will be used to collect information
regarding case study. Fur<strong>the</strong>rmore, research will be employing content analysis to review literature
and evaluation <strong>of</strong> multi perspective approaches is critically conducted.
6.4 Data analysis
The purpose <strong>of</strong> analysis is to understand <strong>the</strong> structure <strong>of</strong> what already exist, and to identify <strong>the</strong>
causes <strong>of</strong> any problems or restrictions. The data collected have subject to both analysis and syn<strong>the</strong>sis
in addressing research questions. Each case follows a standard format, reflecting <strong>the</strong> <strong>the</strong>oretical
motivation for <strong>the</strong> research, and includes as much <strong>of</strong> <strong>the</strong> common data as possible to facilitate
comparative analysis, such as constant comparison (Strauss 1987) and case study method (Yin
2002).
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Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
Literature is critically analysed to evaluate <strong>the</strong> concept, approaches, process <strong>of</strong> new characteristics <strong>of</strong>
complex organization
Results will be used to formulate research aims, research questions and to develop and
understanding for <strong>the</strong> interview and case study
Interviews
Responses will be analysed qualitatively whereby a search will be conducted to identify <strong>the</strong> common
<strong>the</strong>mes <strong>of</strong> problem and also comparison for similarities and differences
The analysis <strong>of</strong> <strong>the</strong> interview data and <strong>the</strong> content analysis from <strong>the</strong> literature will provide deeper
insights, understanding and knowledge required for developing multi perspective framework
6.4.2 Case Study
Responses from <strong>the</strong> questionnaire that <strong>the</strong> participants complete will be analysed qualitatively
Multiple criteria will also be used to measure <strong>the</strong> efficiency <strong>of</strong> <strong>the</strong> framework, strengths, weaknesses
<strong>of</strong> <strong>the</strong> self-organization and <strong>the</strong> effectiveness <strong>of</strong> easier to manage system evolution
With <strong>the</strong> use <strong>of</strong> multiple sources <strong>of</strong> evidence <strong>the</strong> validity <strong>of</strong> <strong>the</strong> concept will be reinforced within <strong>the</strong>
case study, <strong>the</strong>re will be an expansion <strong>of</strong> <strong>the</strong> results through <strong>the</strong> conduct <strong>of</strong> four multiple case studies.
6.5 Pilot case study findings
The initial research was able to retrieve innovative designs to improve <strong>the</strong> model due to <strong>the</strong> method
mentioned above while concurrently validating <strong>the</strong> framework. In <strong>the</strong> initial findings, a pilot case study
enables <strong>the</strong> identification <strong>of</strong> <strong>the</strong> most important factors in success model dimensions and <strong>the</strong>se are:
Responsiveness – users will respond to unforeseen circumstances and notify stakeholders
Reliability – users will know what to do in <strong>the</strong> unlikely event <strong>of</strong> unforeseen circumstances
Adaptability – a multi perspective model will help user facilitate <strong>the</strong> changes
Satisfaction – users able to express <strong>the</strong> unforeseen circumstances as; social, activity, knowledge
flow and organizational changes
A pilot case study shows that a proposed multi perspective model will satisfy success model
dimensions to measure and improve <strong>the</strong> knowledge flow in <strong>the</strong> unlikely event <strong>of</strong> unforeseen
circumstances. This research was carried out using interviews and set <strong>of</strong> questionnaires involving a
director and seven project managers for a period <strong>of</strong> two months. Research project is focusing on
social networking and aims to develop a multi perspective design method to model complex
organization and structures to provide support for collaborative system design. This method provides
a more systematic approach and includes social structure, business architecture, and knowledge hub
to identify <strong>the</strong> communities <strong>of</strong> practice and business activity workflow.
7. Discussion
The research was conducted using multiple case studies to an understanding <strong>of</strong> a complex issue and
identified new characteristics for a dynamic environment. The primary data is being collected and
stored for continuous analysis <strong>of</strong> a pr<strong>of</strong>ound influence on multi perspective views in <strong>the</strong> significance <strong>of</strong>
diversity and self-organization. By conducting <strong>the</strong> research and testing <strong>the</strong> validity <strong>of</strong> <strong>the</strong> model in
close cooperation with participants in <strong>the</strong> government agencies, <strong>the</strong>re is effective management <strong>of</strong>
wicked problems faced by organizations. This project places emphasis on bringing <strong>the</strong> newly
ga<strong>the</strong>red data to evaluate <strong>the</strong> development <strong>of</strong> a multi perspective model and all areas concerned with
<strong>the</strong> study <strong>of</strong> complexity, where organizations have to face emergent changes. The next phase <strong>of</strong> <strong>the</strong>
study will analyze <strong>the</strong> results ga<strong>the</strong>red from primary data, both newly collected and archived materials
to expand and test <strong>the</strong> success modeling dimensions for validity and reliability. Moreover, research is
also needed to investigate <strong>the</strong> behavioral perspective <strong>of</strong> social system dynamic with complex
processes which are to be extended to <strong>the</strong> support elements <strong>of</strong> <strong>the</strong> organization and for fur<strong>the</strong>r
adoption <strong>of</strong> system evolution.
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Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
Multi perspective model verification with efforts to improve <strong>the</strong> collaborative process requires
innovative solutions to complex issues. However, <strong>the</strong>ir effectiveness can be limited without some
additional tools to improve collaboration between <strong>the</strong> different dimensions <strong>of</strong> perspectives from <strong>the</strong>
<strong>the</strong>oretical to <strong>the</strong> practical. As a consequence, <strong>the</strong> result <strong>of</strong> model verification process is changeable.
Future research should <strong>the</strong>refore concentrate on <strong>the</strong> investigation <strong>of</strong> dynamic complexity which can
be evaluated to support higher levels <strong>of</strong> adaptability to manage system evolution.
8. Conclusion
The present study was designed to determine <strong>the</strong> effect <strong>of</strong> multi perspective framework. In this
investigation, <strong>the</strong> aim was to develop a methodology to support systems in complex environments.
This study outlines <strong>the</strong> management <strong>of</strong> self-organization and knowledge flow which creates a serious
challenge facing <strong>the</strong> public sector. According to Skyrme (2003), all governments are reluctant to be
responsive promptly to unforeseen circumstances and higher quality services both <strong>of</strong> which are
expected by its citizens. In contrast, knowledge management has played an important role in ensuring
organizational performance improvement in <strong>the</strong> private sector. The proposed framework characterizes
<strong>the</strong> unstructured knowledge flow for effective management <strong>of</strong> collaborative interactions between <strong>the</strong>
stakeholders. The model enables organizations to respond to a rapidly changing environment.
Moreover, it helps to manage system evolution and will have a significant impact in <strong>the</strong> public sector.
These outcomes will make a substantial contribution to both <strong>the</strong> social behavioral perspective
research and <strong>the</strong> modeling <strong>of</strong> multiple perspectives techniques. There are significant findings to prove
<strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> emergent structure for improving organization collaboration. The empirical
findings in this study present a new understanding <strong>of</strong> <strong>the</strong> model’s ability to manage system evolution
and provide a practical approach for integrating multi perspective views. The results from this study
make several contributions to <strong>the</strong> current literature. First, fur<strong>the</strong>r analysis <strong>of</strong> methods in relation to <strong>the</strong>
integrating complexity <strong>the</strong>ory (McElroy 2000) and second, addresses new characteristics <strong>of</strong>
organization, noted by Merali (2006).
This paper examined <strong>the</strong> strategy for improving complex processes which is significant to develop <strong>the</strong>
model and states <strong>the</strong> self-organization through <strong>the</strong> internal diversity practice crucial for <strong>the</strong>
organization accustom to a changed environment. Behavioral perspective <strong>of</strong> social system is not
entirely recognized in <strong>the</strong> existing models that are based on <strong>the</strong> cognitive approach. In contrast, <strong>the</strong>
multiple perspectives model attempts to combine <strong>the</strong> self-organization with different dimensions <strong>of</strong>
organization’s architecture to manage easily when significant changes occur. For example, an
organization tackles <strong>the</strong> complex problems by examining <strong>the</strong> business processes and strategic goals
ra<strong>the</strong>r than making an effort to change a behavioral relationship between <strong>the</strong> roles and activities that
take place in <strong>the</strong> environment. Fur<strong>the</strong>r experimental investigations are needed to indicate that <strong>the</strong>
extent <strong>of</strong> <strong>the</strong> external validity <strong>of</strong> <strong>the</strong> multiple perspectives framework is beneficial to <strong>the</strong> complex
organization.
Acknowledgements
I would like to thank Pr<strong>of</strong>essor Igor Hawryszkiewycz and Dr. Kyeong-Soon Kang from <strong>the</strong> School <strong>of</strong>
Information Systems and Management <strong>of</strong> leadership whose suggestions and encouragement helped
me in all <strong>the</strong> time <strong>of</strong> research and writing <strong>of</strong> this paper.
References
Alavi, M. and Carlson, P. (1992) "A review <strong>of</strong> MIS research and disciplinary development," Journal <strong>of</strong>
Management Information Systems (8:4), 1992, pp. 45-62.
Andriani, P. (2005) “Diversity, Knowledge and Complexity Theory: Some introductory issues”, International
Journal <strong>of</strong> Innovation Management, Vol. 5, No. 2 (June 2001) pp. 257–274.
Arthur, M. B., DeFillippi, R. J. & Lindsay, V. J. (2001) “Careers, communities, and industry evolution: links to
complexity <strong>the</strong>ory”, International Journal <strong>of</strong> Innovation Management, 2(2), forthcoming.
Bandara, W., Gable, G. & Rosemann, M. (2006) "Business Processing Modeling Success: An Empirically Tested
Measurement Model", ICIS 2006 <strong>Proceedings</strong>. Paper 57.
Cohen, M. (1999) “Commentary on <strong>the</strong> organization science special issue on complexity”, Organization Science,
Vol. 10 No. 3, pp. 373-6.
Colebatch, H. K. (2002) Policy,2nd edition. Open University Press, Buckingham
Conklin, J. (2005) “Dialogue Mapping: Building Shared Understanding <strong>of</strong> Wicked Problems”, Wiley, Chapter 1.
Dagnino, G. (2004) “Complex systems as key drivers for <strong>the</strong> emergence <strong>of</strong> a resource-and capability-based
interorganizational network”, Emergence: Complexity and Organization, Vol. 6 Nos 1-2, pp. 61-9.
995

Choon-Bae Yoo, Igor Hawryszkiewycz and Kyeong-Soon Kang
Ferlie, E. (2007) “Complex organizations and contemporary public sector organizations”. International Public
Management Journal, 10(2), 153-165.
Head, B and Alford, J. (2008) “Wicked Problems: The Implications for Public Management”, International
Research Society for Public Management, <strong>12th</strong> Annual <strong>Conference</strong>. Brisbane.
Hevner, A. R., March, S. T., Park, J. and Ram, S. (2004) “Design Science in Information Systems Research”,
MIS Quarterly, Vol. 28, No. 1, pp. 75-105.
Heylighen, F. (1989) “Self-organization, emergence and <strong>the</strong> architecture <strong>of</strong> complexity”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> First
<strong>European</strong> <strong>Conference</strong> on System Science. AFCET, Paris, 23-32.
Heylighen, F. (1999) “The growth <strong>of</strong> structural and functional complexity during evolution”, Heylighen, F., Bollen,
J. and Riegler, A. (eds.), The Evolution <strong>of</strong> Complexity, Kluwer Academic, Dordrecht-Boston-London. 17-43.
Heylighen, F. (2001) "The Science <strong>of</strong> Self-organization and Adaptivity", in: L. D. Kiel, (ed.) Knowledge
Management, Organizational Intelligence and Learning, and Complexity, in: The Encyclopedia <strong>of</strong> Life
Support Systems, Eolss Publishers, Oxford.
Heylighen, F. (2008) “Five Questions on Complexity”, in: C. Gershenson (ed.): Complexity: 5 questions,
Automatic Press.
Imperial, M.T. (2004) “Collaboration and Performance Management in Network Settings”, Report for IBM Center,
Washington DC: IBM Center for <strong>the</strong> Business <strong>of</strong> Government.
Kanellis, P., Lycett, M. & Paul, R. J. (1998) "An Interpretative Systems Success: From Concept to Practical
Application", Information Systems Success Measurement, Series in Information Technology Management,
Idea Group Publishing.
Keirsey, D. M. (1999) “Involution: On <strong>the</strong> structure and process <strong>of</strong> existence”, Heylighen, F., Bollen, J. and
Riegler, A. (eds.), The Evolution <strong>of</strong> Complexity, Kluwer Academic, Dordrecht-Boston-London. 45-57.
Maguire, S., Mckelvey, B., Mirabeau, L. and Oztas, N. (2006) “Complexity Science and Organization Studies, in
S. R. Clegg, C. Hardy and Tl Lawrence (eds.)”, The Sage Handbook <strong>of</strong> Organization Studies, 2 nd Ed,
Thousand Oaks, CA: Sage.
Mason, R. (2007) “The external environment’s effect on management and strategy. A complexity <strong>the</strong>ory
approach”. Management Decision, 45(1), 10-28.
McElroy, M. W. (1999) "The Second Generation <strong>of</strong> Knowledge Management", Knowledge Management, pp. 86-
88.
McElroy, M. W. (2000) “Integrating complexity <strong>the</strong>ory, knowledge management and organizational learning”,
Journal <strong>of</strong> Knowledge Manqgement, Vol. 4, No. 3, pp. 195-203.
Merali, Y. (2006) “Complexity and Information Systems: <strong>the</strong> emergent domain”, Journal <strong>of</strong> Information
Technology, London, Vol. 21, Iss. 4, pp. 216-241.
Orlikowski, W.J. & Baroudi, J.J. (1991) "Studying Information Technology in Organizations: Research
Approaches and Assumptions", Information Systems Research (2), pp. 1-28.
Prahalad, C. K. and Krishnan, M. S. (2008) “The new age <strong>of</strong> innovation: driving cocreated value through global
network”, McGraw-Hills Company.
Rittel, H. and Webber, M. (1973) "Dilemmas in a General Theory <strong>of</strong> Planning," pp. 155-169, Policy Sciences, Vol.
4, Elsevier Scientific Publishing Company, Inc., Amsterdam.
Schulz, K-P. (2005) “Learning in complex organizations: A model development and application from a <strong>the</strong>ory <strong>of</strong>
practice perspective”, Journal <strong>of</strong> Workplace Learning, Vol. 17, No. 8, pp. 493-507.
Skyrme, D. J. (2003) Public Sector - Public Knowledge, Ark Group, p.136.
Smith, A. and Humphries, C. (2004) “Complexity <strong>the</strong>ory as a practical management tool: a critical evaluation”,
Organization Management Journal, Vol. 1 No. 2, pp. 91-106.
Strauss, A. L. (1987) “Qualitative analysis for social scientists”. New York: Cambridge University Press.
Yin, R. K. (2002) “Case Study Research, Design and Methods”, 3rd ed. Newbury Park, Sage Publications.
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A S<strong>of</strong>t Systems Approach to Solving Knowledge
Management Problems in Organisational Change
Environments
John T Young
RMIT University, Melbourne Australia
john.young@rmit.edu.au
Abstract: Many organisations are undergoing imposed change in an attempt to become lean and more efficient.
Frequently this results in staff reduction and early retirements and rapid cultural change. Organisations may be
in danger <strong>of</strong> loosing <strong>the</strong>ir ‘memory’ as long serving employees with critical knowledge and experience move on.
Knowledge is considered <strong>the</strong> driver <strong>of</strong> contemporary organisations and although it may be managed by
structured systems, it is also heavily affected by human activity systems. This paper presents a practical
approach to solving knowledge management problems in organisations that are undergoing change. S<strong>of</strong>t
systems methodology is applied to a number <strong>of</strong> organisations to generate hi<strong>the</strong>rto un-recorded, unknown,
suppressed or forgotten information which may be captured in knowledge management systems. S<strong>of</strong>t system
methodology is an enquiring technique which seeks to provide input and perspective from participants in
situations that are complex, subjective and messy. It also seeks to establish a level <strong>of</strong> mutual accommodation in
<strong>the</strong> hearts and minds <strong>of</strong> <strong>the</strong> participants which can accelerate organisational learning and can contribute to <strong>the</strong>
motivation and commitment <strong>of</strong> <strong>the</strong> participants. Two case studies are presented which illustrate <strong>the</strong> use <strong>of</strong> s<strong>of</strong>t
system methodology in developing trust and creating a willingness to share information. They were selected
from a series <strong>of</strong> case studies that were conducted by <strong>the</strong> author across a broad range <strong>of</strong> industries using
qualitative research and <strong>the</strong> application <strong>of</strong> s<strong>of</strong>t system methodology. The first case presented here relates to <strong>the</strong>
imposed merger <strong>of</strong> two independent municipalities. Significant staff reduction occurred resulting in a knowledge
drain and <strong>the</strong> new merged entity, with new management, had to continue <strong>the</strong> operational processes <strong>of</strong> <strong>the</strong>
municipalities. Solutions which addressed <strong>the</strong> information and knowledge required for continued operation were
developed. The second case relates to family owned manufacturer <strong>of</strong> industrial clothing facing competitive and
logistics problems. Solutions were developed using knowledge generated from employees and participants o<strong>the</strong>r
than <strong>the</strong> management team.
Keywords: Knowledge management, S<strong>of</strong>t systems methodology, Problem solving, Organisational learning
1. Introduction
The management <strong>of</strong> knowledge in organisations in recent years has been enhanced by <strong>the</strong>
introduction <strong>of</strong> computer technology and <strong>the</strong> internet, which has provided both a vast source <strong>of</strong>
information and a place to store it. However, knowledge, resulting from information and rational
thinking, along with experiences is also stored in human memories, and in many cases is not
documented or stored in a way that is accessible to o<strong>the</strong>rs. In fact, certain elements <strong>of</strong> knowledge
may be retained by individuals as a form <strong>of</strong> power or security and jealously guarded.
As organisations grow, <strong>the</strong> management become more remote from <strong>the</strong> workforce, <strong>the</strong> divergence <strong>of</strong>
information and subsequent knowledge increases. New knowledge is being continuously created
adding to <strong>the</strong> challenge <strong>of</strong> knowledge management. Collecting, storing and managing this plethora <strong>of</strong>
knowledge can be difficult, especially at times <strong>of</strong> intense change, especially with <strong>the</strong> move to leaner
organisations. Individual may become more reluctant to share <strong>the</strong> basis <strong>of</strong> <strong>the</strong>ir security or sense <strong>of</strong>
self attainment. O<strong>the</strong>rs may leave <strong>the</strong> organisation under duress and without a plan for <strong>the</strong> transfer <strong>of</strong>
significant knowledge as part <strong>of</strong> a succession plan. These situations are not uncommon and create
risks <strong>of</strong> repeating errors or more seriously, making decisions without <strong>the</strong> benefit <strong>of</strong> vital circumstantial
knowledge. In every major system failure, <strong>the</strong>re is <strong>of</strong>ten an individual found, whose knowledge <strong>of</strong> <strong>the</strong>
circumstance could have prevented <strong>the</strong> failure, but <strong>the</strong>ir knowledge was not available to <strong>the</strong> decision
makers at <strong>the</strong> time.
In order to facilitate <strong>the</strong> sharing <strong>of</strong> knowledge and to break down barriers, ei<strong>the</strong>r real or perceived,
whilst maintaining <strong>the</strong> dignity and security <strong>of</strong> individuals, a process <strong>of</strong> trust building was applied to a
number <strong>of</strong> case studies. The process used was s<strong>of</strong>t systems methodology (SSM) and <strong>the</strong> outcome <strong>of</strong>
<strong>the</strong> process was to turn tacit knowledge into explicit knowledge and continuous learning. (Gao, Meng
et al. 2002)
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2. Research objective
John T Young
The objective <strong>of</strong> <strong>the</strong> research was to determine if s<strong>of</strong>t systems methodology could be successfully
applied in a business change environment to elicit new knowledge.
2.1 Structure <strong>of</strong> <strong>the</strong> Paper
The paper opens with a description <strong>of</strong> a knowledge management problem and its characterisations as
a s<strong>of</strong>t systems problem. The <strong>the</strong>ory underpinning <strong>the</strong> research is discussed in terms <strong>of</strong> s<strong>of</strong>t systems
thinking. The s<strong>of</strong>t systems research methodology applied is explained and two case studies from
recent research are presented. The paper concludes by showing <strong>the</strong> successful application <strong>of</strong> SSM
in turning tacit knowledge into explicit knowledge.
2.2 S<strong>of</strong>t Systems Methodology Overview
S<strong>of</strong>t systems methodology (SSM) is at its core, a cyclic iterative approach <strong>of</strong> enquiry for formulating
and structuring thinking about problems in a situation where people have diverse views <strong>of</strong> <strong>the</strong> world
(Checkland and Scholes 1998). Its process is <strong>the</strong> construction <strong>of</strong> conceptual models <strong>of</strong> solutions
based on root definitions <strong>of</strong> <strong>the</strong> ideal systems required, and drawn from a rich picture <strong>of</strong> <strong>the</strong>
expressed problem. The conceptual models are <strong>the</strong>n compared with reality and debate is stimulated
about <strong>the</strong> underlying assumptions and changes required to bring about a desirable solution. S<strong>of</strong>t
systems methodology is not about analysing systems found in <strong>the</strong> real world, but ra<strong>the</strong>r about
applying systems thinking to problems in complex situations to generate a series <strong>of</strong> intellectual
constructs that generate learning and understanding <strong>of</strong> <strong>the</strong> <strong>of</strong>ten hidden variables in <strong>the</strong> complex
situation.
2.3 The application <strong>of</strong> SSM to knowledge management problems
The strength <strong>of</strong> SSM in researching knowledge management is that it includes explicit modelling <strong>of</strong>
both <strong>the</strong> existing situation and <strong>the</strong> desired outcomes. This is important as it seeks agreement from
<strong>the</strong> participants on <strong>the</strong> identification <strong>of</strong> assumptions and <strong>the</strong> interpretation <strong>of</strong> world views. Its process
is iterative and is similar to <strong>the</strong> action research cycle <strong>of</strong> diagnosing, action planning, action taking,
evaluating and new learning as used in knowledge management research (Baskerville and Wood
Harper 1996). Fur<strong>the</strong>rmore, it is a cognitive process that relies on <strong>the</strong> interaction <strong>of</strong> <strong>the</strong> participants
with <strong>the</strong>ir environment(Baskerville and Wood Harper 1998).
The methodological approach consists <strong>of</strong> firstly characterising <strong>the</strong> problem as a system in a pictogram
known as a rich picture. Next an idealised system objective, known as <strong>the</strong> root definition, is
formulated to define what <strong>the</strong> system should do. Six key elements are considered in <strong>the</strong> development
<strong>of</strong> <strong>the</strong> root definition. Next a conceptual model <strong>of</strong> <strong>the</strong> ideal system is developed. This is compared with
<strong>the</strong> problem situation, possible and desirable changes are explored and a consensus on action to
improve <strong>the</strong> situation is sought.
2.4 S<strong>of</strong>t systems methodology as a seven stage process
The following figure 1 illustrates six <strong>of</strong> <strong>the</strong> seven stages. The seventh stage is <strong>the</strong> action stage where
a response is implemented. The process was initially presented as shown, however, more recent
research has presented a less structured more flexible approach which has broadened <strong>the</strong> application
<strong>of</strong> <strong>the</strong> methodology(Tsouvalis and Checkland 1996). For <strong>the</strong> purposes <strong>of</strong> this paper, stages 5-7 are
consolidated into <strong>the</strong> discussion.
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John T Young
Figure 1: The Process <strong>of</strong> S<strong>of</strong>t Systems Methodology
2.5 Stage1. The problem situation unstructured
This is sometimes described as <strong>the</strong> ‘finding out stage’. Here information is ga<strong>the</strong>red about <strong>the</strong>
problem situation from various sources. It may include written information, informal interviews,
observations, formal samples or information from any available source comprising both primary and
secondary data. Both formal and informal communication links are examined and any prevailing
issues or assumptions are identified. Organisational politics and taboos can be identified and
individual concerns or mistrusts can also be included (Fig 2)
Source:(Patching 1990)
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Figure 2: Exploring and expressing <strong>the</strong> situation
John T Young
2.6 Stage 2 The problem Situation Expressed
At this stage <strong>the</strong> situation is expressed pictorially. This known as a ‘rich picture’ and is a sketch,
diagram or illustration, usually hand drawn, which depicts <strong>the</strong> elements and issues <strong>of</strong> <strong>the</strong> situation
(Patching 1990). It is a collection <strong>of</strong> <strong>the</strong> perceptions or viewpoints <strong>of</strong> <strong>the</strong> participants. Its strength is
that it also indicates relationships. The resulting illustration (Fig 3) <strong>the</strong>n enables <strong>the</strong> participants to
address <strong>the</strong> issues from selected viewpoints and to be aware <strong>of</strong> <strong>the</strong> prevailing relationships.
Figure 3: Characteristics <strong>of</strong> a rich picture.
Source:(Patching 1990)
2.7 Stage 3 Formulating Root Definitions <strong>of</strong> Relevant systems
Here we move from real world considerations to modelling <strong>of</strong> virtual idealised systems that appear
relevant to <strong>the</strong> problem situation. One or more root definitions are <strong>the</strong>n developed which become <strong>the</strong>
basis for subsequent models. Flood and Jackson explain that root definitions are idealised views if
what <strong>the</strong> relevant systems should do. They explain that “<strong>the</strong> aim is to draw out <strong>the</strong> essence <strong>of</strong> what is
to be done, why it is to be done, who is to do it, who is to benefit or suffer and what environmental
constraints limit <strong>the</strong> actions and activities”(Flood & Jackson 1991, p175).
The mnemonic CATWOE is used to identify <strong>the</strong> following six elements that should encompass <strong>the</strong>
emergent properties <strong>of</strong> <strong>the</strong> system under consideration:
Customers; <strong>the</strong> victims or beneficiaries <strong>of</strong> <strong>the</strong> proposed activity or transformation.
Actors; those who do <strong>the</strong> activity.
Transformation Process; <strong>the</strong> conversion <strong>of</strong> an input process to an output
Weltanschauung; <strong>the</strong> world view that make <strong>the</strong> transformation meaningful
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Owners; those who have <strong>the</strong> power to modify or stop <strong>the</strong> system
Environmental Constraints; constraints within <strong>the</strong> environment <strong>of</strong> <strong>the</strong> system.
The above elements are used to develop <strong>the</strong> root definitions that describe <strong>the</strong> problem under
consideration.
2.8 Stage 4 Conceptual Models
Conceptual models are derived directly from <strong>the</strong> root definitions and will define <strong>the</strong> minimum activity
for <strong>the</strong> human activity system under consideration. Checkland (2000, p16) defines <strong>the</strong> building <strong>of</strong>
conceptual models as: “A systemic account <strong>of</strong> a human activity system, built on <strong>the</strong> basis <strong>of</strong> <strong>the</strong>
system’s root definition, usually in <strong>the</strong> form <strong>of</strong> a structured set <strong>of</strong> verbs in <strong>the</strong> imperative mood……”
His emphasis is on <strong>the</strong> minimum number <strong>of</strong> verbs necessary to enact <strong>the</strong> tasks described by <strong>the</strong> root
definition. All <strong>the</strong> elements identified in <strong>the</strong> CATWOE must be included for <strong>the</strong> transformation to be
complete. The models need to be kept relatively simple. Patching (1990) advises no more than ten
activities, with considerable emphasis on <strong>the</strong> ‘what’ ra<strong>the</strong>r than <strong>the</strong> ‘how’.
2.9 Stage 5: Comparing Models with Reality
At this stage a level <strong>of</strong> debate is introduced to compare <strong>the</strong> model or models with <strong>the</strong> real world
situation and to highlight possible changes. The discussion should focus on whe<strong>the</strong>r or not <strong>the</strong>
various perceptions are being met and if <strong>the</strong>re are any discrepancies. The comparison can be carried
out in a number <strong>of</strong> ways such as by interviews or benchmarking (Checkland and Scholes 1998).
2.10 Stage 6: Defining <strong>the</strong> Changes
At this stage <strong>the</strong> debate focuses on which changes are feasible, taking into consideration <strong>the</strong> cultural
and political environment. The participants <strong>the</strong>n need to come to an agreement or consensus as to
which changes are systemically desirable. This may require some iteration <strong>of</strong> <strong>the</strong> process and
revisiting earlier stages. Checkland suggests a return to <strong>the</strong> CATWOE elements to ensure <strong>the</strong>re are
no areas <strong>of</strong> contradiction or physical and financial constraints. A review <strong>of</strong> assumptions can also be
revealing in creating a larger range <strong>of</strong> possibilities. Checkland (2000) identifies three principal areas
<strong>of</strong> change, namely: structural change, procedural change and attitudinal change. He fur<strong>the</strong>r suggests
that <strong>the</strong> changes considered should meet two main criteria: <strong>the</strong>y must be systemically or logically
desirable and <strong>the</strong>y must be culturally feasible.
2.11 Stage 7: Taking Action
This is <strong>the</strong> stage where <strong>the</strong> accepted changes are put into action. The implementation may still
require convincing senior levels <strong>of</strong> authority, however, <strong>the</strong> participants will have gained confidence
from <strong>the</strong> continuous learning process <strong>of</strong> <strong>the</strong> methodology and from <strong>the</strong> reduced levels <strong>of</strong> uncertainty.
They will have clarified and explored future options and will have gained a broader worldview <strong>of</strong> <strong>the</strong>
organisation and its participants. As <strong>the</strong> changes are implemented, a new dynamic will result and <strong>the</strong>
process <strong>of</strong> continuous refinement can continue.
3. Selection <strong>of</strong> Cases and Data Ga<strong>the</strong>ring
Cases were selected on <strong>the</strong> basis <strong>of</strong> access to <strong>the</strong> organisation and <strong>the</strong> participants. The cases
presented in this paper reflect <strong>the</strong> successful application <strong>of</strong> SSM. Data for <strong>the</strong> study was ga<strong>the</strong>red by
two primary means, by interview to ga<strong>the</strong>r verbal data, and by direct observation to ga<strong>the</strong>r
behavioural data. The interview process used is <strong>the</strong> semi-structured interview process, which is
utilized to give a frank open and interactive mode <strong>of</strong> communication.
3.1 Case study 1
3.1.1 Background and Problem Identification
Local municipal authorities in <strong>the</strong> state <strong>of</strong> Victoria, Australia are democratically elected authorities for
<strong>the</strong> governance and management <strong>of</strong> <strong>the</strong>ir district. They are a subsidiary part <strong>of</strong> <strong>the</strong> Victorian state
government and are <strong>the</strong> responsibility <strong>of</strong> <strong>the</strong> Minister for Local Government. Traditionally <strong>the</strong>y had
existed in every major town or city. They operated from <strong>the</strong> town hall and provided a range <strong>of</strong>
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municipal services. The elected representatives are known as councillors and <strong>the</strong> municipal bodies
are known as councils in urban areas and shires in rural areas. The councillors sit on a number <strong>of</strong>
committees which focus on <strong>the</strong> management <strong>of</strong> a range <strong>of</strong> services such as town planning, refuse
collection, health and home services, road maintenance, parks and gardens etc.
As municipalities and populations grew, some towns developed and some diminished creating an
imbalance between neighbouring bodies. Their capacity to generate income and <strong>the</strong> extent <strong>of</strong> <strong>the</strong>ir
services was linked directly to <strong>the</strong>ir municipal boundaries. Many <strong>of</strong>fered duplicated and conflicting
services or were unable to <strong>of</strong>fer <strong>the</strong> depth <strong>of</strong> services called for by <strong>the</strong> communities. Development and
maintenance work was generally conducted on a cost plus basis resulting in municipal charges being
increased to cover costs.
In recent years, <strong>the</strong> advent <strong>of</strong> a newly elected State government was <strong>the</strong> catalyst for structural and
cultural change. The new state government forced amalgamation <strong>of</strong> two, three or so smaller
municipalities into larger bodies, dismissed all elected <strong>of</strong>ficials and appointed temporary
administrators, recruited new chief executives and decreed that rate revenue be reduced by 20%, and
that work be conducted on <strong>the</strong> basis <strong>of</strong> compulsory competitive tendering. Projected savings across
<strong>the</strong> state were expected to be $100 million. A large number <strong>of</strong> <strong>the</strong> elected councillors and <strong>the</strong>
municipal employees were <strong>of</strong> a different political persuasion to <strong>the</strong> new government and resented <strong>the</strong>
changes in <strong>the</strong>ir circumstances with some hostility. The government countered this by appointing
competent administrators and by delivering a positive supportive attitude to change.
3.1.2 Characteristics <strong>of</strong> <strong>the</strong> changes
These changes had a wide ranging affect on <strong>the</strong> local culture as well those employed by <strong>the</strong>
municipalities. In rural and semi rural areas, <strong>the</strong> local municipality provided a sense <strong>of</strong> identity for <strong>the</strong>
community. In many cases, <strong>the</strong> centre <strong>of</strong> government, i.e., <strong>the</strong> town hall was moved to ano<strong>the</strong>r
location, usually to a bigger town. This resulted in feelings <strong>of</strong> stress, anger and bewilderment among
<strong>the</strong> community, and in particular, <strong>the</strong> employees <strong>of</strong> <strong>the</strong> municipality.
Their primary and most immediate task was to recover <strong>the</strong>ir traditional work and source <strong>of</strong>
employment for <strong>the</strong> majority <strong>of</strong> blue-collar employees. This was now to be achieved by open
competitive tendering, as prescribed by new legislation. These workers were now exposed to <strong>the</strong>
uncertainty and risk <strong>of</strong> <strong>the</strong> tendering process. Most had diverse levels <strong>of</strong> education and were
unaccustomed to thinking or contributing ideas about <strong>the</strong>ir tasks. There had been no investment in
development <strong>of</strong> skills or knowledge <strong>of</strong> this part <strong>of</strong> <strong>the</strong> workforce.
In terms <strong>of</strong> system change, all staff was now more exposed to systems processes and to process
improvement. All operations were more process oriented with an emphasis on probity <strong>of</strong> accounting
and stewardship <strong>of</strong> resources. There was a realisation that employees needed to take responsibility
for outcomes and desire to improve efficiency so that <strong>the</strong>y would win tenders for work on <strong>the</strong> open
market. Improvements introduced by <strong>the</strong> chief executive were very much in <strong>the</strong> ‘pull’ category. The
concepts <strong>of</strong> quality management, service capability, leadership development and clear
communication lines were introduced with positive results.
Many difficulties were encountered. In one <strong>of</strong> <strong>the</strong> towns that had previously been independent, a
dichotomy <strong>of</strong> mindsets arose. Eventually all disaffected staff resigned, whilst <strong>the</strong> remaining staff,
which had in effect won <strong>the</strong> day, had a very positive attitude. Encouraging and accepting new ideas
from <strong>the</strong>m reinforced this attitude. A new culture developed that could accept bad news but not
cover-ups or undisclosed information. A staff survey was conducted which highlighted <strong>the</strong> need for
more information sharing.
The business unit had to start tendering from inception and winning a tender was a considerable
change lever. Not all tenders were won but <strong>the</strong>y became averse to loosing and made every effort to
win. Works supervisors were required to keep every job within budget. They found <strong>the</strong> change
process quite practical and were able to work well with it.
The first step is to explore and understand <strong>the</strong> situation, followed by expressing <strong>the</strong> unstructured
situation in a Rich Picture (Fig 4). Rich Pictures are pictorial representation <strong>of</strong> a messy problem
situation. They include a range <strong>of</strong> elements such as opinions, concerns, conflicts, constraints as well
as business processes and structural arrangements.
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Figure 4: Rich Picture <strong>of</strong> The Problem Unstructured - Amalgamated Local Government
4. Developing Systems Models
4.1 The CATWOE components.
The most important <strong>of</strong> <strong>the</strong>se elements is <strong>the</strong> Transformation, followed by <strong>the</strong> Worldview as <strong>the</strong>se need
to be determined before <strong>the</strong> o<strong>the</strong>r elements can be assigned identities. In fact, <strong>the</strong>se two element
need to be complimentary to ensure a correct understanding <strong>of</strong> <strong>the</strong> problem situation.
4.1.1 Transformation T
Provide works and services that are cost and time competitive with those available from outside
providers.
4.1.2 Worldview or Weltanshauung W
Development <strong>of</strong> business skills and culture can make <strong>the</strong> transition feasible.
4.1.3 The Environment E
The political environment which initiated <strong>the</strong> change
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4.1.4 The Customers C
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The customers are <strong>the</strong> community members served by <strong>the</strong> municipality.
4.1.5 The Actors A
The employees are <strong>the</strong> actors.
4.1.6 The Owners O
The elected or appointed directors <strong>of</strong> <strong>the</strong> municipality.
Statement <strong>of</strong> Root Definition
An organisational change, initiated by <strong>the</strong> state government, and put into practice by <strong>the</strong>ir appointed
representatives, to change <strong>the</strong> structure, culture and practices <strong>of</strong> <strong>the</strong> municipal authority, for <strong>the</strong> cost
and service benefit <strong>of</strong> <strong>the</strong> community, in an environment <strong>of</strong> forced amalgamation <strong>of</strong> municipal districts.
5. Development <strong>of</strong> conceptual models
Conceptual models (Fig 5) are used to illustrate <strong>the</strong> relationships between <strong>the</strong> elements defined in <strong>the</strong>
root definitions. It is an account <strong>of</strong> <strong>the</strong> activities which <strong>the</strong> system must do in order to be <strong>the</strong> system
defined in <strong>the</strong> root definition (Checkland 1999). Their construction may be iterative as part <strong>of</strong> <strong>the</strong>
process <strong>of</strong> analysis and <strong>the</strong>y are primarily concerned with expressing <strong>the</strong> objective ra<strong>the</strong>r than <strong>the</strong>
method <strong>of</strong> achieving <strong>the</strong> transformation.
Figure 5: Conceptual model <strong>of</strong> <strong>the</strong> problem - structured
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Comparison <strong>of</strong> <strong>the</strong> conceptual model with <strong>the</strong> real world is shown in <strong>the</strong> following table 1
6. Comparison <strong>of</strong> conceptual model with real world
Conceptual activity Reality Proposed knowledge
requirements
Establish which areas <strong>of</strong> <strong>the</strong>ir Uncertainty, resentment and fear CEO identifies works and
activity were in fact under threat <strong>of</strong> job losses. Large commercial services under threat.
from competitors.
competitors active and
pr<strong>of</strong>icient.
Identify skills required for No in-house experience Hire skilled managers. Map
tendering
processes
Establish costing metrics Loose system <strong>of</strong> cost-plus Introduce activity based costing
accounting
and IT based cost management
Train staff in <strong>the</strong> process Departments not responsible for Delegated responsibility for cost
cost accounting
control to operating level.
Expose staff to process
Amalgamate and consolidate No team culture. Separate Develop team work and team
resources
towns culture
culture
Define, cost and present Imposed urgency in achieving Focus attention <strong>of</strong> entire staff on
services
tendering pr<strong>of</strong>iciency
tendering process to retain jobs
Achieve an effective business Management lacked business Introduce management and
unit
skills. Blue collar workers leadership training. Involve blue
lacked incentive and initiative collar workers in activities and
communications
Win tenders, keep jobs and Some services already lost to Strong leadership and support
enhance service cost and
delivery
contractors
for positive outcomes
Monitor and control <strong>the</strong> process New CEO responsible for Costing metrics and controls in
positive outcomes
place
Table 1: SSM Comparison Stage – Case 1
To achieve this level <strong>of</strong> knowledge management, <strong>the</strong> following three areas <strong>of</strong> change were
addressed:
6.1 Attitudinal change
As <strong>the</strong> overall policy change was a coercive change, <strong>the</strong>re was considerable hostility to its
introduction. However, as <strong>the</strong>se changes were occurring in all areas <strong>of</strong> <strong>the</strong> state, a positive outcome
was <strong>the</strong> realisation that <strong>the</strong>re was no turning back. To retain jobs and to consolidate <strong>the</strong>ir future,
participants had to adopt a ‘can do’ attitude. This was reinforced by <strong>the</strong> firm direction and support <strong>of</strong>
<strong>the</strong> CEO. On <strong>the</strong> negative side, <strong>the</strong> new CEO found <strong>the</strong>re was a latent inertia in thinking, and also a
tendency to think as a group, particularly with blue-collar workers. This reduced <strong>the</strong> level <strong>of</strong>
spontaneity and initiative in generating new ideas and slowed down <strong>the</strong> renewal process. There was
also a reluctance to accept and follow <strong>the</strong> new staff induction program. Employees were now held
more accountable for <strong>the</strong> stewardship <strong>of</strong> <strong>the</strong>ir time and resources and were expected to take a more
holistic attitude to <strong>the</strong> overall operation <strong>of</strong> <strong>the</strong> organization.
Areas <strong>of</strong> conflict included <strong>the</strong> nurses’ team. This group <strong>of</strong> employees have a strong sense <strong>of</strong> moral
equity and pr<strong>of</strong>essionalism. They had difficulty taking direction from <strong>the</strong> CEO and relinquishing direct
response to community calls. The new IT system proved to be an area <strong>of</strong> conflict as many employees
were reluctant to learn how to use it, or apply it to <strong>the</strong>ir work processes. This also slowed <strong>the</strong> renewal
process. O<strong>the</strong>r areas <strong>of</strong> notable conflict were with <strong>the</strong> community, in areas such as <strong>the</strong> change from a
regional library to a library bus and <strong>the</strong> loss <strong>of</strong> <strong>the</strong> town hall to ano<strong>the</strong>r location.
6.2 Structural change
A major structural change was required in amalgamating multiple municipalities into one new authority
with a new CEO. Material and human resources had to be rationalised. This meant that <strong>the</strong> new
municipal centre was relocated to a central location, in this case, one <strong>of</strong> <strong>the</strong> existing town halls. This
<strong>the</strong>n affected o<strong>the</strong>r locations where <strong>the</strong>ir former town hall was redundant and was no longer <strong>the</strong> focal
point <strong>of</strong> that district causing community discontent.
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6.3 Procedural changes
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As <strong>the</strong> main change driver was <strong>the</strong> need to succeed at competitive tendering, mapping and refining <strong>of</strong>
all associated process was required. Staff were also exposed to quality tools and Total Quality
Management (TQM). Process improvement teams were established to improve efficiency and
outcomes. Teamwork was encouraged in all areas to stimulate involvement and initiative. Regular
work team briefings enhanced communication and consolidated involvement. The introduction <strong>of</strong> a
new IT system and its use in managing <strong>the</strong> information necessary for competitive tendering required
training and learning. Management and leadership training was introduced for senior staff and was
accepted with enthusiasm.
6.4 Lessons learnt
The most significant lesson learnt was <strong>the</strong> need to resolve <strong>the</strong> conflict between <strong>the</strong> politically imposed
urgency to succeed at competitive tendering and <strong>the</strong> lack <strong>of</strong> skills, information, and procedures for
achieving success. Mapping <strong>the</strong> processes, setting achievement standards and training <strong>the</strong> staff
bridged this gap. An associated problem was <strong>the</strong> need to have realistic costing information for <strong>the</strong>
tender process. This was addressed by hiring new management staff with experience in process
costing. The introduction <strong>of</strong> a new IT systems brought with it not only <strong>the</strong> problems <strong>of</strong> delays in
learning <strong>the</strong> s<strong>of</strong>tware but in this case, using and relying on IT as part <strong>of</strong> <strong>the</strong> work function. This was
addressed progressively with training and exposure to <strong>the</strong> system.
Ano<strong>the</strong>r problem was <strong>the</strong> considerable cultural divide between <strong>the</strong> existing bureaucratic culture and
<strong>the</strong> required business culture <strong>of</strong> responsibility and achievement
6.5 Points <strong>of</strong> Leverage
Winning <strong>the</strong> hearts and minds <strong>of</strong> <strong>the</strong> employees was part <strong>of</strong> this learning process. This was achieved
by giving genuine and unflagging support for <strong>the</strong> employees, providing skill development and
promoting a winning attitude. Tenders won meant jobs retained. Tenders lost meant redundancies.
Successful tendering was by far <strong>the</strong> most significant lever <strong>of</strong> change.
6.6 New knowledge gained
A significant change was <strong>the</strong> way <strong>the</strong> community related to <strong>the</strong> council and vice versa. To resolve
<strong>the</strong>se issues, <strong>the</strong> research indicated a public relations program to inform <strong>the</strong> community <strong>of</strong> <strong>the</strong> new
status <strong>of</strong> <strong>the</strong> municipality, its service delivery and its relation with <strong>the</strong> community. It became evident
that employees needed clarity <strong>of</strong> direction and some sense <strong>of</strong> stability in times <strong>of</strong> change. Information
from <strong>the</strong> staff and community generated by <strong>the</strong> SSM process led to new knowledge on resolving
<strong>the</strong>se problems.
7. Case Study No2
7.1 Logistics Changes in a Clothing Manufacturer
7.1.1 Background and Problem Identification
A large Australian work-wear and industrial clothing manufacturer was faced with a storage and
distribution problem <strong>of</strong> <strong>the</strong>ir product. In <strong>the</strong> present situation, <strong>the</strong> manufacturing facility and base
warehouse, which also served as <strong>the</strong> main distribution centre, were located in <strong>the</strong> same single story
industrial building, situated in an outer suburb. An additional small warehouse that stored industrial
wear was located ten kilometres across town. Distribution centres were also located in <strong>the</strong> five major
Australian capitol cites as well as at one <strong>of</strong> <strong>the</strong> large interstate open cut mines
The company faced increasing costs and complexity in managing stock level across <strong>the</strong> range <strong>of</strong>
location. Demand at some locations showed a stock-out, whilst o<strong>the</strong>r locations had surplus stock. To
fill some orders, stock had to be transferred from some branch stores back to <strong>the</strong> main warehouse. To
satisfy demand and delivery requirements, stock was increased at all sites to avoid lost sales to
competitors. This created national overstock position.
There was insufficient room at <strong>the</strong> base warehouse to store more products using <strong>the</strong> existing storage
methodology. Also, <strong>the</strong>re was congestion from inwards and outwards goods using <strong>the</strong> same access
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door. Interviews with management and staff revealed that <strong>the</strong> second warehouse had eased storage
problems but had not eased delivery problems. Stock control was fur<strong>the</strong>r exacerbated at <strong>the</strong> time <strong>of</strong>
<strong>the</strong> study by shortcomings in <strong>the</strong> computer network between <strong>the</strong> main warehouse and <strong>the</strong> secondary
one.
Manufacturing lead times were relatively short, usually a matter <strong>of</strong> days, however, not all products
were manufacturer simultaneously. Some stock was seasonal; some was bulk orders from large
customers, and o<strong>the</strong>rs were continuous demand. With continuous demand stock, various styles were
made in batch quantities and stock carried in <strong>the</strong> warehouse until sold or <strong>the</strong> next batch was
scheduled. This meant that a manufacturing cycle <strong>of</strong> many items occurred every three months. The
result was that <strong>the</strong> warehouse had to accommodate large quantities <strong>of</strong> finished goods in addition to
<strong>the</strong> regular merchandise stock. Contracts with large work-wear customers such as mining companies
required stocking <strong>of</strong> a wide range <strong>of</strong> sizes and fittings to accommodate all <strong>the</strong>ir employees. This
generated an abnormal number <strong>of</strong> slow moving items that had to be accommodated. Orders placed
on suppliers for finished goods also have to be accommodated. These consisted <strong>of</strong> large orders that
were typically three months supply.
These demands and constraints resulted in an overcrowded warehouse facility, which limited retrieval
and storage flexibility. Within this crowed environment, some space was poorly utilised by storing
slow moving items in standard sized stock keeping units (SKUs). In some case <strong>the</strong> quantities were
only one or two items. S<strong>of</strong>t systems methodology was applied to identify information and knowledge
<strong>of</strong> <strong>the</strong> logistical problems <strong>of</strong> storage, stock control and materials handling. A rich picture <strong>of</strong> <strong>the</strong>
problem situation is presented in <strong>the</strong> following diagram (Fig 6). The rich picture was established from
interview and observation with <strong>the</strong> management and <strong>the</strong> employees.
Figure 6: Rich Picture <strong>of</strong> Industrial clothing Manufacturer Logistics Problem – Unstructured
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8. The CATWOE Components
8.1 Transformation T
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Provide an efficient and effective inventory management system, in particular <strong>the</strong> storage and
distribution elements.
8.2 Worldview or Weltenshauung
A warehousing function capable <strong>of</strong> handling <strong>the</strong> quantity and range <strong>of</strong> products produced and
distributed.
8.3 The Environment E
The physical constraints <strong>of</strong> <strong>the</strong> business premises
8.4 The Customer C
The customers <strong>of</strong> <strong>the</strong> transformation are <strong>the</strong> company owners and mangers.
8.5 The Actors A
The actors are <strong>the</strong> company managers and those employees involved in <strong>the</strong> proposed process
change.
8.6 The Owners O
The owner is <strong>the</strong> chief executive <strong>of</strong> <strong>the</strong> company
Statement <strong>of</strong> Root Definition
An organisational change in warehouse logistics, initiated by <strong>the</strong> CEO to achieve a cost effective and
space effective storage and distribution facility.
Figure 7: Conceptual Model <strong>of</strong> Storage and Distribution System- Structured
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Comparison <strong>of</strong> <strong>the</strong> conceptual model (Fig 7) with <strong>the</strong> real world is shown in <strong>the</strong> following table 2
9. Comparing <strong>the</strong> Conceptual Model with Reality
Conceptual Activity Reality Proposed knowledge management
required
Identify Storage Capacity Required Storage dispersed across numerous Consolidate stock in one central
for:
Regular Orders
Seasonal and Bulk Orders
warehouses in different states distribution centre
Plan Storage Space for:
Congested flow <strong>of</strong> material Some Structure flow <strong>of</strong> material and work.
New Material
materials stored in aisles. Finished Separate inbound and outbound
Finished Product
product stored on trolleys not in stock. Install comprehensive slickrail
Work in process
locations. Hanging garment slickrail system. New door for outbound
Hanging Garments
system installed piecemeal with
limited coverage
goods
Plan Space for order picking, Packing presently centrally located, Located outbound activities toge<strong>the</strong>r.
consolidation and packing
not near exit. No room for order
consolidation
Include space for order consolidation
Plan Distribution Channels:
All orders received centrally but Close all o<strong>the</strong>r distribution centres
Distribution Centre
processed at individual distribution and consolidate distribution centrally
Direct Customers
Export Orders
centres
Plan Order Entry Model:
Order entry system incompatible Upgrade order entry system to
On line Orders
with computer system. Multiple include web based input, credit
Manual Orders, Phone, Email etc. crashes & confusion
control and generation <strong>of</strong> picking slips
Provide Customer Feedback
No established channel available for Develop customer feedback system
Channel
customer feedback
and encourage its use
Monitor and Control Production and sales reports to CEO Accurate costing <strong>of</strong> stock turnover,
distribution costs and productive use
<strong>of</strong> space. Customer feedback
available to management.
Table 2: SSM Comparison Stage – Case 2
The interviewees were asked to consider how <strong>the</strong>y would approach <strong>the</strong> task in a new greenfield site.
The conceptual diagram represents <strong>the</strong> ideal situation considered without <strong>the</strong> constraints <strong>of</strong> <strong>the</strong>
present facility. To achieve <strong>the</strong>se ideas, <strong>the</strong> following three areas <strong>of</strong> change were addressed:
9.1 Attitudinal change
The management had decided on <strong>the</strong> manufacture <strong>of</strong> a line <strong>of</strong> economy products with department
store branding. Although <strong>the</strong>se represented high volume orders, <strong>the</strong>ir total cost was questionable
when considering <strong>the</strong> need to manufacture and consolidate stock before delivery.Management, and
<strong>the</strong> CEO in particular, were reluctant to use <strong>the</strong> latest technology for stock control. Barcoded labels
were in use but RFID (Radio Frequency Identification) was not being used and could be considered.
Picking <strong>of</strong> individual items was all by manual means. No mechanisation was considered.
9.2 Structural change
A review <strong>of</strong> <strong>the</strong> warehouse floor plan and part <strong>of</strong> <strong>the</strong> manufacturing space indicated that all present
stock could be accommodated in <strong>the</strong> present warehouse with <strong>the</strong> use <strong>of</strong> a mezzanine floor. This
would obviate <strong>the</strong> need for a secondary warehouse across town and <strong>the</strong> associated difficulties with
data communications and stock transfers being experienced with that site. Although traditional
warehouse design calls for a single story building, <strong>the</strong> present site has sufficient height to
accommodate a mezzanine floor without infringing building regulation or OH&S regulations. Present
access methods to SKUs could be utilised on a mezzanine floor and also slick-rail can be routed to
upper levels such as mezzanines. Assess to <strong>the</strong> building by constructing ano<strong>the</strong>r door, providing
separate inward and outwards loading and unloading docks could improve associated material flow
9.3 Procedural Change
The concept <strong>of</strong> establishing and servicing distribution centres in each state was questioned. The
alternative was to hold all stock at <strong>the</strong> main warehouse and distribution centre and ship to retail
customers from <strong>the</strong>re. This would enable a reduction <strong>of</strong> <strong>the</strong> overall inventory held and an increase in
<strong>the</strong> service level <strong>of</strong> customer orders due to <strong>the</strong> elimination <strong>of</strong> stock fragmentation. Urgent customer
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orders could be airfreighted or sent by overnight road transport at less cost than having a local
distribution centre.
9.4 Lessons Learnt
As all business organisations depend on <strong>the</strong>ir external environment for survival, this manufacturer
found <strong>the</strong>mselves in a fortunate position. Their product consisted mainly <strong>of</strong> work wear, jeans and
outdoor casual wear. The market for <strong>the</strong>se products was relatively stable and was separated from <strong>the</strong>
fashion industry by being a service product ra<strong>the</strong>r than fashion apparel that changed frequently with
seasons or style. The product’s appeal was centred on quality, toughness and serviceability. They
also had an element <strong>of</strong> cultural and emotional advantage by being a traditional Australian made
product with a traditional Australian name. These advantages meant that <strong>the</strong>y could forecast
projected sales with relative accuracy and could safely consolidate <strong>the</strong>ir stock and supply retailer
direct without <strong>the</strong> threat <strong>of</strong> lost sales due to delays in distribution. This in turn allowed <strong>the</strong>m to adopt
a leaner inventory management scheme.
9.5 New knowledge gained
Apart from manufacturing costs, <strong>the</strong> o<strong>the</strong>r significant costs drivers are in storage, handling and
distribution. By adopting a more strategic approach to <strong>the</strong>ir in house and outbound logistics by
consolidating and refining <strong>the</strong> organization to a more strategically competitive structure, <strong>the</strong>y will be
able to maintain a competitive edge over <strong>the</strong>ir competitors. The experiential learning generated in this
case was in discussions with management and employees where <strong>the</strong>y were asked to give reasons
why processes were in place. Assumptions regarding customer requirements, management
expectations and alternative processes were raised and challenged. This process generated a
number <strong>of</strong> alternatives that were explored in order to arrive at <strong>the</strong> projected solution. Employees
highlighted many perceived constraints, much to <strong>the</strong> surprise <strong>of</strong> management, which were resolved in
<strong>the</strong> planning stage.
Conclusion
This research has shown that <strong>the</strong> SSM process can be successfully applied in a business change
environment to elicit new knowledge. It also shows that it is very flexible and can be applied to a
number <strong>of</strong> different organizational situations where problem ownership is increased and articulation <strong>of</strong>
issues creates new knowledge and learning. S<strong>of</strong>t system methodology has proved to be a powerful
tool in bringing <strong>the</strong> hearts and minds <strong>of</strong> <strong>the</strong> participants toge<strong>the</strong>r to form an accommodation <strong>of</strong> views
that can <strong>the</strong>n be harnessed to generate a conceptual model <strong>of</strong> <strong>the</strong> future. This tool for changing <strong>the</strong>
patterns <strong>of</strong> thought to deliver a congruent or acceptable view <strong>of</strong> <strong>the</strong> future allows <strong>the</strong> organization to
proceed with <strong>the</strong> support <strong>of</strong> <strong>the</strong> participants. The hidden benefits <strong>of</strong> <strong>the</strong> approach can be new
knowledge and new learning in <strong>the</strong> form <strong>of</strong> creative solutions not previously considered.
References
Baskerville, R. and A. Wood Harper (1996). "A critical perspective on action research as a method for information
systems research." Journal <strong>of</strong> Information technology(11): 235-246.
Baskerville, R. and A. Wood Harper (1998). "Diversity in information systems action research methods."
<strong>European</strong> journal <strong>of</strong> Information systems(7): 90-107.
Checkland, P. (1999). Systems Thinking, Systems Practice, Including a 30-year retrospective. Chichester, John
Wiley and Sons Ltd.
Checkland, P. (2000). Systems Thinking, Systems Practice, Including a 30-year retrospective. Chichester, John
Wiley and Sons Ltd.
Checkland, P. (2004). "S<strong>of</strong>t Systems Methodology." Retrieved 1/7/2004, 2004, from
http://www.onesixsigme.com/experience/white_papers/whitepaper_pages/peterchecklandssm1.php.
Checkland, P. and J. Scholes (1998). S<strong>of</strong>t Systems Methodology in Action. West Sussex England, Wiley.
Gao, F., L. Meng, et al. (2002). "Systems thinking on knowledge and its management: systems methodology for
knowledge management." Journal <strong>of</strong> knowledge management 6(1): 7-17.
Patching, D. (1990). Practical S<strong>of</strong>t System Analysis. Essex, Prentice Hall.
Tsouvalis, C. and P. Checkland (1996). "Reflecting on SSM: <strong>the</strong> Dividing Line Between 'Real world' and 'Systems
Thinking World'." Systems Research 13(1): 35-45.
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The Symbolic Innovation <strong>of</strong> <strong>the</strong> Bio-informatics Discipline:
A Political Networks Approach to IT development
Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
Department <strong>of</strong> Business Information Technology and Enterprise, Engineering
School, Central Avenue, Chatham Maritime, Kent, UK
a.k.k<strong>of</strong>inas@gre.ac.uk
Abstract: In knowledge intensive firms IT is viewed as a means <strong>of</strong> cutting across bureaucratic barriers,
organisational complexity, and a tool for enhancing knowledge-related activities. In <strong>the</strong> pharmaceutical industry
this IT-led transformation coincided with o<strong>the</strong>r socio-technological phenomena such as biotechnology and <strong>the</strong>
Human Genome Project. The organisational uncertainty arising from <strong>the</strong> challenges <strong>of</strong> absorbing <strong>the</strong> new
knowledge created an organisational vacuum. This vacuum was filled by entrepreneurial scientists
knowledgeable in both bio-science and IT and dubbed <strong>the</strong>mselves as bio-informaticians. In a romantic sense
<strong>the</strong>se corporate entrepreneurs were allowed to explore <strong>the</strong> “wild frontiers” <strong>of</strong> pharmaceutical R&D research. This
paper relies on actor-network <strong>the</strong>ory (ANT) as an interpretation lens for clarifying <strong>the</strong> diverse elements that
enabled <strong>the</strong> successful creation <strong>of</strong> that organisational space. An in-depth exploratory case study is used here
with a framework based on ANT used for coding, organising and interpreting <strong>the</strong> qualitative data. The findings
highlight <strong>the</strong> importance and challenges an established company faces in absorbing new knowledge and
technology and <strong>the</strong> importance <strong>of</strong> leveraging <strong>the</strong> entrepreneurial instincts <strong>of</strong> its employees in order to create and
absorb new knowledge.
Keywords: R&D, pharmaceutical industry, drug discovery, actor-network <strong>the</strong>ory, knowledge, action, social
networks
1. Introduction: Bio-informatics and Symbolism
Traditionally <strong>the</strong> pharmaceutical industry has been very conservative. The reasons are numerous;
some are industry-specific such as <strong>the</strong> long product development cycles (drug discovery and
development can take 8-12 years), or <strong>the</strong> high pr<strong>of</strong>it margins that <strong>the</strong> top innovators enjoy (that could
lead to innovative complacency) while o<strong>the</strong>rs are socio-political such as <strong>the</strong> tight controls by
governmental bodies and regulations, or <strong>the</strong> sensitive nature <strong>of</strong> <strong>the</strong> health needs <strong>of</strong> <strong>the</strong> public that
drugs are to meet (Lerer and Piper, 2003, Denning, 2005). The large pharmaceutical firms in <strong>the</strong> early
1990’s, populated mainly by chemists ra<strong>the</strong>r than bio-scientists or computational biologists (Hulse,
2004, Quere, 2003) were unsure about <strong>the</strong> potential <strong>of</strong> <strong>the</strong> Information Technology (IT). As a
consequence, <strong>the</strong> pharmaceutical industry has been notorious for its hesitance to embrace IT for its
operations and research processes (Lerer and Piper, 2003). It was <strong>the</strong> Human Genome Project data
that finally shook <strong>the</strong> pharmaceutical industry into a spur <strong>of</strong> innovation and <strong>the</strong> formation <strong>of</strong> an IT
discipline peculiar to this industry called Bioinformatics.
1.1 Human Genome Project
When HGP was completed in 2000 it finished ahead <strong>of</strong> schedule mainly because <strong>the</strong>re was a vast
improvement in computer technology and <strong>the</strong> techniques for DNA sequencing. New technologies,
such as high throughput screening, DNA blasting, and genomic databases, were first applied during
<strong>the</strong> HGP (Elrod-Erickson and Ford, 2000, Griffith, 2000). The vast data provided by HGP was
unstructured and uninformed. The tools for generating DNA sequences <strong>of</strong> <strong>the</strong> human genome were
formidable but <strong>the</strong> analytical ability to gain knowledge and understanding from this data was lacking. It
soon became paramount for <strong>the</strong> pharmaceutical industry to find new tools and knowledge to absorb
and usefully utilise this data (McMeekin and Harvey, 2002). As a result during <strong>the</strong> 1990’s many <strong>of</strong> <strong>the</strong>
top pharmaceutical innovators made strategic alliances with or strategic acquisitions <strong>of</strong> biotechnology
firms in a bid to acquire <strong>the</strong> domain knowledge that would assist <strong>the</strong>m in using bio-sciences and bioinformatics
successfully to discover potential <strong>the</strong>rapeutic drugs (Quere, 2003). Despite <strong>the</strong>se efforts,
from <strong>the</strong> mid-1990s up until <strong>the</strong> present <strong>the</strong> pharmaceutical corporate R&D has been producing fewer
and fewer drugs that would gain governmental approval (Arlington, 2000, Gassmann and Reepmeyer,
2005). At <strong>the</strong> same period <strong>the</strong> biotechnology industry maintained its innovativeness and has been
involved in an increasing proportion <strong>of</strong> <strong>the</strong> newer blockbuster drugs.
The uncertainty around IT adoption existed in o<strong>the</strong>r industries such as accounting, law, engineering
but what makes <strong>the</strong> pharmaceutical industry unique is <strong>the</strong> creation <strong>of</strong> bio-informatics as a wholly new
discipline. This new discipline is interdisciplinary; it required a bio-science researcher who was
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
comfortable with computer science. Such people were rare within <strong>the</strong> pharmaceutical companies <strong>of</strong>
<strong>the</strong> early 1990s and even now <strong>the</strong> early 21 st century. Internet-based genomics libraries such as
Incyte, or <strong>the</strong> publicly held ENSembl, electronic laboratory notebooks (Grieff, 2003), and o<strong>the</strong>r
informatics tools have facilitated innovation in <strong>the</strong> pharmaceutical R&D processes that would have
been unthinkable fifteen years ago but now <strong>the</strong> established everyday reality <strong>of</strong> pharmaceutical R&D
(Lerer and Piper, 2003, McMeekin and Harvey, 2002, Greco, 2007). This formation <strong>of</strong> new
organizational structures (Schmid and Smith, 2004, Ward, 2004, Studt, 2003) and network
arrangements intra- and inter-organizationally (Estades and Ramani, 1998, Kreiner and Schultz,
1993, Lerner and Merges, 1998) as a response to this new technology is an investment strategy that
is observed in times <strong>of</strong> extreme volatility and high risk (Nooteboom, 2002, Prabhu, 1999).
I am going to suggest in this paper with <strong>the</strong> help <strong>of</strong> a case study that <strong>the</strong> creation <strong>of</strong> bio-informatics as
a separate discipline has been a business and a political innovation ra<strong>the</strong>r than a technological
innovation. The new discipline allowed under its guise entrepreneurial teams <strong>of</strong> IT/biology
practitioners to usurp a conceptual vacuum where <strong>the</strong> bio-informaticians created <strong>the</strong> right capacities
within <strong>the</strong>ir corresponding pharmaceutical companies that allowed for <strong>the</strong> absorption <strong>of</strong> new
knowledge (Anyanwu, 2006, Datamonitor, 2004, Fodor, 2004, Katz and H<strong>of</strong>man, 2005, McMeekin
and Harvey, 2002, Ouzounis and Valencia, 2003).
This paper relies on actor-network <strong>the</strong>ory (ANT) (Doolin and Lowe, 2002, Elbanna, 2007, Latour,
1999a) as an interpretation lens to argue that <strong>the</strong> creation and interpretation <strong>of</strong> bio-informatics as a
separate discipline different from past pharmaceutical research practices during <strong>the</strong> 1990s is a
symbolic innovation on its own right. The <strong>the</strong>sis examined here is that PharmaCo, an established
company, created a symbolical organisational space that allowed for <strong>the</strong> deployment <strong>of</strong> new tools and
knowledge in order to absorb <strong>the</strong> new knowledge. The PharmaCo case study will illustrate <strong>the</strong>
complex reality <strong>of</strong> <strong>the</strong> actor-networks that are mobilized within <strong>the</strong> company to meet <strong>the</strong> external
challenges and to absorb new knowledge. The remainder <strong>of</strong> <strong>the</strong> literature review demonstrates <strong>the</strong>
power <strong>of</strong> actor network <strong>the</strong>ory (ANT) in explaining processes that would be relevant for <strong>the</strong> creation <strong>of</strong>
<strong>the</strong> bio-informatics department; <strong>the</strong> second section operationalizes <strong>the</strong> suggested framework within
<strong>the</strong> context <strong>of</strong> <strong>the</strong> case study while <strong>the</strong> last section discusses <strong>the</strong> findings and <strong>the</strong> tentative
conclusions from this case study.
1.2 Social & Philosophical Underpinnings: Actor-Network Theory
Gronevetter’s seminal article on social networks (Granovetter, 1973) did much to popularise <strong>the</strong> main
concepts behind social network <strong>the</strong>ories. Most <strong>of</strong> <strong>the</strong> empirical body on social networks has been
traditionally quantitative (Abrahamson and Rosenkopf, 1997, Hedstrom, 1994, Lemarie et al., 2001,
Yli-Renko et al., 2001). Social networks could be egocentric or closed systems. A closed system
means that <strong>the</strong> group we are investigating is enclosed within certain boundaries and <strong>the</strong> number <strong>of</strong>
actors is finite while an egocentric puts <strong>the</strong> main actors in <strong>the</strong> centre and we expand <strong>the</strong> network
tracking <strong>the</strong> relationships as <strong>the</strong>y develop from <strong>the</strong> centre (Wasserman and Faust, 1994). Analysis <strong>of</strong>
social networks can pinpoint issues within <strong>the</strong> social network such as information bottlenecks, points
<strong>of</strong> boundary transcendence where knowledge is transferred and points <strong>of</strong> boundary breakdown or
rigidity (Cross et al., 2003) that may highlight organisational trouble-spots.
Actor-network <strong>the</strong>ory is <strong>the</strong> chosen network <strong>the</strong>ory for this case study. Bruno Latour, one <strong>of</strong> <strong>the</strong>
founders <strong>of</strong> Actor-Network Theory (ANT), once stated “<strong>the</strong>re are four things that do not work with
actor-network <strong>the</strong>ory; <strong>the</strong> word actor, <strong>the</strong> word network, <strong>the</strong> word <strong>the</strong>ory and <strong>the</strong> hyphen!”(Latour,
1999a). Actors in ANT are presented to have <strong>the</strong>ir own reality, an essence that seems to defy
explanation. This is <strong>the</strong> principle <strong>of</strong> <strong>the</strong> in-determinability <strong>of</strong> <strong>the</strong> actors (Latour, 1999a, Latour, 1993,
Callon, 1999); <strong>the</strong> actors can only be described via <strong>the</strong>ir networks, <strong>the</strong> emphasis firmly on <strong>the</strong> second
part <strong>of</strong> <strong>the</strong> composite word; <strong>the</strong> work (Green et al., 1999, Latour, 1999a). For ANT <strong>the</strong>orists any entity
with a physical reality is an actor, whe<strong>the</strong>r human or non-human (Green et al., 1999). The emphasis
on non-human actors contrasts that <strong>of</strong> many o<strong>the</strong>r network <strong>the</strong>orists who focus mainly on human
social networks (Pan and Leidner, 2003, Hayes and Walsham, 2003). The hyphen between actors
and networks denotes <strong>the</strong> inherent conflict as actors work on <strong>the</strong> environment wittingly or unwittingly
and come in friction and conflict with <strong>the</strong> networks formed (Law, 1999). The word <strong>the</strong>ory is equally
problematic (Latour, 2004, Latour, 1999a, Latour, 1999b). For Latour ANT should be thought <strong>of</strong> as a
possible representation <strong>of</strong> <strong>the</strong> world (Latour, 2003). Thus ANT is denied prescriptive powers in its
<strong>the</strong>oretical body.
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
However ANT has two advantages over o<strong>the</strong>r philosophical and methodological analysis frameworks:
Due to its inclusion <strong>of</strong> inanimate actors in its actor-networks ANT readily lends itself to IT-based
case studies. By giving IT an equal role to human actors within <strong>the</strong> examined actor-networks it
allows <strong>the</strong> researcher to examine how IT can be used and is involved in IT-related case studies
A powerful descriptive capability and realist precepts and foundations empowering a researcher
who wishes to adopt a critical realist perspective in <strong>the</strong>ir research. Change is brought about by
actors and <strong>the</strong>ir ongoing interactions with o<strong>the</strong>r human and non-human actors, inevitably
becoming part <strong>of</strong> <strong>the</strong> network <strong>the</strong>y worked to form. In order to perform <strong>the</strong> necessary work to
create <strong>the</strong> networks that will promote <strong>the</strong>ir enterprise <strong>the</strong> actors have to mobilize human and nonhuman
actors in <strong>the</strong> enterprise.
The author has used Latour’s visualization <strong>of</strong> science flow (figure. 2) to describe <strong>the</strong> process <strong>of</strong>
creating a discipline such as Bio-informatics within PharmaCo
Figure 1: A visualization <strong>of</strong> an actor's network (Latour, 1999b)
The communication and work flows <strong>of</strong> an enterprise according to Latour consist <strong>of</strong> four interconnected
loops (fig. 2) and a central one, <strong>the</strong> fifth loop, that holds <strong>the</strong> enterprise toge<strong>the</strong>r. These five loops
represent five types <strong>of</strong> activities that any scientific project would need to undergo to describe with
some accuracy its evolution. The first refers to <strong>the</strong> mobilization <strong>of</strong> <strong>the</strong> world, by which Latour denotes
all those non-human actors that are loaded and mobilized through action and discourse. In <strong>the</strong> case
<strong>of</strong> Bio-informatics it includes actors such as DNA, scientists, genomic data, bio-informaticians, new IT
s<strong>of</strong>tware such as ENSembl, and <strong>the</strong> new organisational structures that <strong>the</strong> companies have to form in
order to accommodate <strong>the</strong> bio-informatics practitioners (Abbas and Holmes, 2004, Anyanwu, 2006,
Chalasani and Barber, 2004, Gladstone, 2005, Greco, 2007). The second loop is that <strong>of</strong>
autonomization whereupon <strong>the</strong> practitioner engages into convincing his fellow colleagues <strong>of</strong> <strong>the</strong> value
<strong>of</strong> <strong>the</strong>ir enterprise and thus creates a corps <strong>of</strong> people versed in her personal enterprise.
Autonomization is <strong>the</strong> force behind institutionalization and resembles <strong>the</strong> formation <strong>of</strong> scientific
paradigms (Kuhn, 1996). In <strong>the</strong> case <strong>of</strong> bio-informatics <strong>the</strong> process involved <strong>the</strong> creation <strong>of</strong> a subdiscipline
and <strong>the</strong> struggle for recognition (Hopkins et al., 2007, Hulse, 2004, Nightingale and Martin,
2004, Ouzounis and Valencia, 2003). The third loop concerns <strong>the</strong> mobilization <strong>of</strong> alliances where <strong>the</strong>
practitioner mobilizes a number <strong>of</strong> disparate and diverse groups and communities <strong>of</strong> practise to
support <strong>the</strong> new enterprise. This loop facilitates <strong>the</strong> placement <strong>of</strong> <strong>the</strong> enterprise into “a context that is
sufficiently large and secure to enable it to exist and endure” (Latour, 1999b). Bio-informatics
practitioners for example had to find allies within <strong>the</strong> current pharmaceutical R&D paradigm who were
<strong>of</strong>ten identical to <strong>the</strong> potential enemies: IT departments <strong>of</strong> each pharmaceutical company and
research departments within each company. These potential alliances will be clearly exemplified in
<strong>the</strong> PharmaCo case study. The fourth loop refers to public representation, <strong>the</strong> process <strong>of</strong> getting <strong>the</strong>
new enterprise accepted by <strong>the</strong> wider public and thus smoo<strong>the</strong>n <strong>the</strong> introduction <strong>of</strong> new elements in
<strong>the</strong> system <strong>of</strong> norms and beliefs held by <strong>the</strong> outside world. With Bio-informatics public representation
was only relevant within <strong>the</strong> context <strong>of</strong> <strong>the</strong> pharmaceutical industry. Bio-informatics groups proved to b
specialised, and small a community <strong>of</strong> practice to be widely noted by <strong>the</strong> general public outside <strong>the</strong>
pharmaceutical industry and its suppliers (Gladstone, 2005, Hopkins et al., 2007, McMeekin and
Harvey, 2002, Ng and Wong, 2004). In <strong>the</strong> fifth loop <strong>the</strong> actors involved bring all flows <strong>of</strong> activity and
knowledge toge<strong>the</strong>r; Latour terms <strong>the</strong> fifth loop as <strong>the</strong> links and knots <strong>of</strong> <strong>the</strong> enterprise. One could
think <strong>of</strong> it as <strong>the</strong> individual, or <strong>the</strong> group <strong>of</strong> people who hold <strong>the</strong> new enterprise toge<strong>the</strong>r. The fifth loop
is <strong>the</strong> sense-creating, sense-making centre <strong>of</strong> <strong>the</strong> enterprise and <strong>the</strong> drive for inter-linkages with <strong>the</strong>
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
o<strong>the</strong>r four loops; a flexible network <strong>of</strong> ideas, concepts and connections that are involved in <strong>the</strong>
propagation <strong>of</strong> <strong>the</strong> enterprise. In <strong>the</strong> case <strong>of</strong> bioinformatics <strong>the</strong> links and knots are situated with <strong>the</strong>
small groups <strong>of</strong> bio-informaticians that rose within <strong>the</strong> major pharmaceutical companies and public
research institutions. The <strong>the</strong>oretical framework developed here will be used to explain how <strong>the</strong>
concept <strong>of</strong> bioinformatics as a discipline created in PharmaCo <strong>the</strong> symbolic space needed to absorb
<strong>the</strong> new specialised knowledge derived from <strong>the</strong> genomic revolution. By incorporating <strong>the</strong> non-human
actors into <strong>the</strong> social networks ANT provides <strong>the</strong> means by which bioinformatics tools were utitlised in
<strong>the</strong> network interactions <strong>of</strong> <strong>the</strong> PharmaCo communities <strong>of</strong> practice and allowed organisational space
within which organisation knowledge was created.
2. Research Methodology
2.1 Research Design
The research paradigm utilized in this study is that <strong>of</strong> pragmatic critical realism (Collier, 1994, Sayer,
1992). The single case study research design is chosen because a. <strong>the</strong>re is considerable access to
sensitive and context-rich information that would not be utilized as effectively in <strong>the</strong> longitudinal,
cross-sectional or <strong>the</strong> experimental research designs (De Vaus, 2001) b. it can achieve high internal
validity by using a multi-methods approach and thus may elucidate complex phenomena. To ensure
external validity we rely on <strong>the</strong> logic <strong>of</strong> replication (Yin, 1989) whereby a single case study is used to
contribute to specific <strong>the</strong>oretical precepts and thus allowing for <strong>the</strong>oretical generalizations. Such a
design makes <strong>the</strong> strategic selection <strong>of</strong> case studies vital for <strong>the</strong> enhancement <strong>of</strong> <strong>the</strong> external validity
<strong>of</strong> our design. The case study is based on a large top ten Pharmaceutical company (PharmaCo) and
examines <strong>the</strong> creation, evolution and final “death” <strong>of</strong> <strong>the</strong>ir dedicated bio-informatics department. This
paper utilizes two main methods <strong>of</strong> data collection: documentation & interviews. The interviews were
semi-structured. Each individual actor underwent a one-hour interview during which detailed notes
were kept. Feedback on <strong>the</strong> notes was received by most <strong>of</strong> <strong>the</strong> interviewees to ensure internal
validity. The choice <strong>of</strong> interviewees was determined at large by <strong>the</strong> stakeholders <strong>of</strong> <strong>the</strong> bio-informatics
function. During a period <strong>of</strong> seven months <strong>the</strong> following interviews took place:
Table 1: Interview Map
Affiliation/Department Discipline Number
Corporate Information Systems (CIS) IT Specialists 5
DxIx Bioinformaticians 7
ETA Bioinformaticians 5
EUN Researchers with IT skills 7
Research Scientists Researchers 7
Total: 31
Fur<strong>the</strong>rmore <strong>the</strong> following data sources were utilized:
Documentation pertinent to <strong>the</strong> project management and development <strong>of</strong> Bx Tools;
Documentation relevant to marketing and public relations activities relevant to Bx Tools;
Access to <strong>the</strong> individuals responsible directly or indirectly to <strong>the</strong> development <strong>of</strong> Bx Tools;
Access to <strong>the</strong> users that benefited from Bx tools;
The interviews and documentation are used to outline <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> Bx function over time. At
<strong>the</strong> first level <strong>of</strong> analysis <strong>the</strong> data was coded using NVivo along <strong>the</strong> five work flows <strong>of</strong> <strong>the</strong> enterprise.
For this case study we will place <strong>the</strong> Bx employees in <strong>the</strong> middle <strong>of</strong> <strong>the</strong> enterprise (<strong>the</strong> links and knots
<strong>of</strong> <strong>the</strong> enterprise) and thus aim to make some aggregate descriptions <strong>of</strong> how <strong>the</strong> Bx Function handled
<strong>the</strong> interactions with its environment. The data collated this way is used for <strong>the</strong> second level <strong>of</strong>
analysis: <strong>the</strong> different phases in <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> Bio-informatics function within PharmaCo.
3. Findings
3.1 ANT analysis <strong>of</strong> <strong>the</strong> Bx Function
The total <strong>of</strong> material coded is presented below:
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Table 2: Number <strong>of</strong> words per category
Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
Mythology Quotes Political quotes
1 : Alliances 6379 7318
2 : Autonomization - Colleagues 4140 4914
3 : Links and knots Understanding 8687 8121
4 : Mobilization <strong>of</strong> <strong>the</strong> world 6372 5696
5 : Public Representation 2552 2817
Table 3: Number <strong>of</strong> references in <strong>the</strong> total <strong>of</strong> interviews
Mythology Quotes Political quotes
1 : Alliances 76 95
2 : Autonomization - Colleagues 51 61
3 : Links and knots Understanding 106 113
4 : Mobilization <strong>of</strong> <strong>the</strong> world 71 68
5 : Public Representation 30 39
The material was reiteratively coded first along <strong>the</strong> five ANT loops described in <strong>the</strong> literature review
and <strong>the</strong>n coded along <strong>the</strong> two main <strong>the</strong>mes <strong>of</strong> this paper: <strong>the</strong> political dimension and <strong>the</strong> mythological
dimension. The following quotes table indicates <strong>the</strong> kind <strong>of</strong> material coded under each <strong>of</strong> <strong>the</strong> cells <strong>of</strong>
<strong>the</strong> matrix:
Table 4: Type <strong>of</strong> material coded for each category
1 : Alliances
2 : Autonomization
Colleagues
3 : Links and knots
Understanding
Mythology Quotes Political quotes
“1997-8: In PharmaCo <strong>the</strong> infection people were
ahead <strong>of</strong> <strong>the</strong>ir times. They wanted to use <strong>the</strong>
genomes <strong>of</strong> various bacteria to perform
comparative genomics research.”
(1 st Director <strong>of</strong> Bx 1997-9)
Relevance:
The alliance between <strong>the</strong> infection people and
<strong>the</strong> newly formed Bx group was instrumental to
start <strong>the</strong> Bx Function and <strong>the</strong> myth that Bx tools
are ahead <strong>of</strong> <strong>the</strong>ir times is reinforced by
association (<strong>the</strong> association <strong>of</strong> Bx to infection)
“It is reminiscent <strong>of</strong> <strong>the</strong> soviet central planning
bureaucracies and everything takes too long.
The Bx group started because scientists were
not getting what <strong>the</strong>y wanted from CIS
department. Bx was about small “quick and
dirty” solutions whereas IS favours big projects
with lots <strong>of</strong> people involved. Should look at
what is important and focus on benefits but
with not so much bureaucracy”
(Bx scientist 2)
Relevance:
An interesting example <strong>of</strong> myth-making: this
bio-informatician sees a difference between
scientists, IS specialists and bio-informaticians
thus reinforcing <strong>the</strong> unique identity <strong>of</strong> <strong>the</strong> bioinformatician
which is getting lost.
“The DxIx matrix where everybody becomes
Mr Smith. The activity becomes a process.
Creative people are leaving. The IS mode is
taking over <strong>the</strong> Ix mode. It is <strong>of</strong>ten easier to
learn IS than to go through <strong>the</strong> demand and
1015
The stakeholder community is
bewildering - >60 just for <strong>the</strong> current
work in progress. DxIx, purchase,
RAs (global/local/regional) as <strong>the</strong>
same function (for example
purchasing) can be very different in
different parts <strong>of</strong> <strong>the</strong> world.
(2 nd Director <strong>of</strong> Bx 2004-5)
Relevance:
The demonstration <strong>of</strong> <strong>the</strong> complexity
that <strong>the</strong> Bx Function was facing at <strong>the</strong>
height <strong>of</strong> its power shows <strong>the</strong>
importance <strong>of</strong> political manoeuvring
for <strong>the</strong> Bio-informaticians.
“There is a blame culture in CIS and
PharmaCo and a lack <strong>of</strong> willingness to
take risks within CIS. It is strange cause
PharmaCo is a high-risk company so
why parts <strong>of</strong> <strong>the</strong> organisation are not
encouraged to take risks? The maturity
<strong>of</strong> CIS function should be about taking
more risks being more vocal.”
(CIS manager 1)
Relevance:
The differences between Bx and CIS
are highlighted to some degree. Also
<strong>the</strong> sense <strong>of</strong> <strong>the</strong> special culture within
CIS is highlighted here.
“What researchers need is quicker,
faster, experimental. DxIx can not cope
with this research and experimental
mentality. Can not wait for DxIx to
deliver after a long term. DxIx wants it

4 : Mobilization <strong>of</strong> <strong>the</strong>
world
5 : Public
Representation
Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
justification processes needed.”
(Researcher 1)
Relevance:
Here we have <strong>the</strong> use <strong>of</strong> a pop reference (mr.
Smith) to enhance <strong>the</strong> Bx myth <strong>of</strong> creativity
and innovation vs <strong>the</strong> CIS mode.
“The CIS guys have never liked Ensembl inhouse
as <strong>the</strong>re has been a public alternative.
There was a fight to keep it in-house, but a
positive development was that <strong>the</strong> users loved
it (much anecdotal evidence). However once
Genie appeared on <strong>the</strong> scene, support for
Ensembl from users disappeared. Despite all
<strong>the</strong>se <strong>the</strong> Ensembl initiative kept going.”
(Bx Scientist 3)
Relevance:
Dual myth making: focusing on <strong>the</strong> Ensembl
tool people focus on <strong>the</strong> use <strong>of</strong> <strong>the</strong> tool to
attract <strong>the</strong> users and create <strong>the</strong> sense <strong>of</strong>
success. The idea <strong>of</strong> conflict gives fur<strong>the</strong>r
credit to <strong>the</strong> myth. The us and against <strong>the</strong>m
(CIS vs Bx).
“Initially no RA scientists were involved with
Ensembl. The web-interface was added as an
afterthought. User feedback forms/Internal
help desks, training events and informal
meeting were used to enrich Ensembl. After
roll-out <strong>the</strong>re was focus on usability. This
evolution. reflects <strong>the</strong> changes in Bx where
from <strong>the</strong> “Power Users” we are moving to
“everyone uses”.
(Bx Scientist 1)
Relevance:
Systems are perceived as a way to engage <strong>the</strong>
wider community with <strong>the</strong> new tools and in
extension with <strong>the</strong> Bx function as an
organisational entity. That becomes part <strong>of</strong> <strong>the</strong>
Bx mythology: pioneers who bring relevant
data to <strong>the</strong> research community
in reverse. In research areas <strong>the</strong>y
would appreciate rapid response teams
that would give quick solutions. But Dx
Ix can not afford <strong>the</strong> staff. Immediately
put process in place instead <strong>of</strong> creating
rapid response teams. There should be
an interface between <strong>the</strong> two cultures
and not a struggle for domination.”
(Bx Scientist 2)
Relevance:
At <strong>the</strong> same time a quote that
demonstrates <strong>the</strong> political struggles that
<strong>the</strong> Bx function faced as well as <strong>the</strong>
mythology involved in <strong>the</strong> formation <strong>of</strong>
<strong>the</strong> Bx function with <strong>the</strong> emphasis on
<strong>the</strong> differences in <strong>the</strong> ways it operates.
“Ian and some o<strong>the</strong>r Bx members saw
a disconnect between <strong>the</strong> Bx group and
<strong>the</strong>ir user base.” “Ian had much conflict
over <strong>the</strong>re with issues <strong>of</strong> user
involvement. Conflict between
academic Bxs (doing things that are
more important in an academic Bx
sense) and more pragmatic Bxs. In
addition <strong>the</strong>re was a disconnect
between drug discovery people and
CIS people.”
(Research Scientist 1)
Relevance:
An example where <strong>the</strong> researcher
identifies <strong>the</strong> political struggle among
<strong>the</strong> main three actors from <strong>the</strong> way <strong>the</strong>y
treat and utilise <strong>the</strong> inanimate actor (TD
in this case) which becomes
instrumental in <strong>the</strong> political struggle
among <strong>the</strong> three groups.
“Much <strong>of</strong> <strong>the</strong> recent changes probably
had to do with changes in
management. A new manager comes
in… <strong>the</strong>re is (Bx Director’s) legacy
system and in order to prove that <strong>the</strong>re
has been some change he changes <strong>the</strong>
legacy system…”
(Bx Scientist 4)
Relevance:
The public’s perception <strong>of</strong> <strong>the</strong> Bx
function changes as <strong>the</strong> management
<strong>of</strong> <strong>the</strong> Bx Function changes. The
change from legacy to new system is
politics in action. One can already see
<strong>the</strong> shift in political power back in 2005
from Bx to <strong>the</strong> newly formed Discovery
Ix.
From <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> material coded we were able to derive <strong>the</strong> story <strong>of</strong> creating <strong>the</strong> Bx function in
PharmaCo and <strong>the</strong> importance <strong>of</strong> creating an organisational space to absorb <strong>the</strong> new knowledge
created from <strong>the</strong> Human Genome Project.
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
3.2 The “Life” and “Death” <strong>of</strong> PharmaCo’s Bio-informatics
PharmaCo is <strong>the</strong> result <strong>of</strong> a merger between two smaller pharmaceutical companies and is a top-10
multinational pharmaceutical company in terms <strong>of</strong> sales. It is a knowledge-intensive company with a
large R&D programme for drug discovery and development geared towards <strong>the</strong> creation <strong>of</strong> new drugs.
As early as 1995, PharmaCo formed a Bioinformatics group in <strong>the</strong> UK site originating from <strong>the</strong> science
research areas <strong>of</strong> <strong>the</strong> company. The founders <strong>of</strong> <strong>the</strong> original group were firmly rooted in <strong>the</strong> sciences
disciplines. Its first reincarnation was <strong>of</strong> a project group that would a. be involved in <strong>the</strong> absorption <strong>of</strong>
<strong>the</strong> first information technology tools, such as SRS, that were coming out <strong>of</strong> <strong>the</strong>
bioinformatics/biotechnology boom and b. make true <strong>the</strong> promise <strong>of</strong> knowledge value that <strong>the</strong> Human
Genome Project (HGP).
Figure 2: Bx Function in 1995-7
The Bx group quickly grew in size from five to an approximate twenty members in <strong>the</strong> UK site alone.
By 1997 <strong>the</strong> group consisted <strong>of</strong> about 15-20 employees who rose from <strong>the</strong> research areas and
toge<strong>the</strong>r with new recruits were catering to <strong>the</strong> bioinformatics needs <strong>of</strong> <strong>the</strong> research areas. Those first
two years were heady for <strong>the</strong>se first PharmaCo bio-informaticians. For <strong>the</strong> head <strong>of</strong> <strong>the</strong> group <strong>the</strong>
“biology nerds became <strong>the</strong> saviours” within <strong>the</strong> company, as <strong>the</strong>y were bringing in <strong>the</strong> research areas
those important new tools. As <strong>the</strong> group was quite informal “it was <strong>of</strong>ten difficult to see <strong>the</strong> edges”.
The membership was dynamic and ad hoc.
The Bx function foresaw <strong>the</strong> importance <strong>of</strong> collaborating with <strong>the</strong> corporate information systems (CIS)
department <strong>of</strong> PharmaCo. At <strong>the</strong> same time CIS saw <strong>the</strong> value in supporting <strong>the</strong> Bx function. The
move made sense both in terms <strong>of</strong> technical capability (bio-informaticians understood science while
CIS understood computing) and political manoeuvring as <strong>the</strong> small Bx function needed to collaborate
with CIS. The Bx function changed in 1999:
Figure 3: Bx Function in 1997-9
In 1999 Bio-Informatics Projects are managed co-jointly by Bx and CIS in order to serve <strong>the</strong> research
areas. In <strong>the</strong> turbulence <strong>of</strong> <strong>the</strong> PharmaCo merger, <strong>the</strong> UK Bx function, being larger and much more
developed, absorbed <strong>the</strong> smaller bio-informatics groups around <strong>the</strong> globe under its Bx flag. An
important consequence <strong>of</strong> <strong>the</strong> merger was a process <strong>of</strong> introspective re-organisation <strong>of</strong> <strong>the</strong> newly
streng<strong>the</strong>ned Bx group and its emergence as a formalised Bx function with its own budget, access to
<strong>the</strong> higher management <strong>of</strong> <strong>the</strong> Discovery Management Board, one step away from <strong>the</strong> board <strong>of</strong>
directors <strong>of</strong> PharmaCo. A position <strong>of</strong> immense power for a group that now numbered about 100
employees in a company <strong>of</strong> 40,000 employees.
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
The streng<strong>the</strong>ning <strong>of</strong> <strong>the</strong> Bx function in <strong>the</strong> early 2002 caused much uncertainty among <strong>the</strong> major Bx
stakeholders: <strong>the</strong> researchers, CIS and PharmaCo. A reaction from research areas was <strong>the</strong> formation
<strong>of</strong> new groups <strong>of</strong> bio-informaticians whose main aim was to assist in implementing and disseminating
<strong>the</strong> Bx tools developed by <strong>the</strong> Bx Function. These newly formed groups distinguished <strong>the</strong>mselves
from <strong>the</strong> Bx function both in name and mandate. The four groups <strong>of</strong> expert users, one for each <strong>of</strong> <strong>the</strong>
research sites, quickly formed a community <strong>of</strong> practice (hereinafter EUN). The EUN became <strong>the</strong>
gatekeepers for <strong>the</strong> Bio-informatics function which was perceived as an IS function.
Figure 4: Bx Function in 1999-2003
By 2003 <strong>the</strong> Bx function has grown to an independent department with its own budget and its own
operations. CIS has to collaborate with Bx to serve its internal clients: <strong>the</strong> Research Areas. The large
and expanded Bx group absorbed a section <strong>of</strong> CIS employees and has started its Awareness,
Training, Exploitation (ATE) network that is promoting bio-informatics to <strong>the</strong> internal research
community. By 2004 ATE has become an extensive training institution. Bio-informatics function
deliberately channelled resources into cultivating both ATE and EUN networks as <strong>the</strong>y were seen as
intermeshed with Bx’s goal <strong>of</strong> delivering value to <strong>the</strong> research functions <strong>of</strong> PharmaCo.
In 2004 PharmaCo faced harsh financial conditions in its business environment and CIS made its
move. Using <strong>the</strong> intra-organisational drive cost-cutting and re-structuring CIS management argued
that Bx Function should join <strong>the</strong> CIS instead <strong>of</strong> having a two separate functions (CIS & Bx). The
resulting organisation formed was a hybrid entity with <strong>the</strong> name <strong>of</strong> Discovery informatics (DxIx). The
hybrid nature <strong>of</strong> <strong>the</strong> emerging function was mainly on paper; many <strong>of</strong> <strong>the</strong> members <strong>of</strong> <strong>the</strong> old Bx
function left <strong>the</strong> company within a year after <strong>the</strong> merger occurred. With <strong>the</strong> Bx employees
outnumbered 5:1 and all senior management <strong>of</strong> <strong>the</strong> new structure belonging to ex-CIS members it
was clear that <strong>the</strong> new entity was CIS-dominated with a few “bio-informatician troublemakers at <strong>the</strong>
periphery”.
Figure 5: DxIx Function in 2004-2005
The following table summarises <strong>the</strong> organisational changes <strong>of</strong> <strong>the</strong> Bx function over time:
Table 5: Outline <strong>of</strong> each Stage <strong>of</strong> Development for <strong>the</strong> Bx Function
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Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
Stage <strong>of</strong> Development Political
manoeuvring
# <strong>of</strong> Employees Budget
Foundation (1995-7) Independence
from Research
Areas and CIS
10 -->20 From RAs
Growth (1997-9) Alignment with CIS 20 -->30 Resources from RAs
and CIS
Expansion (1999-2003) Formalisation and 30 --> 100 Independent Budget
growth <strong>of</strong> <strong>the</strong> Bx
from <strong>the</strong> higher
management
Absorption (2004-5) Annexation <strong>of</strong> <strong>the</strong> 100 --> 600 Part <strong>of</strong> <strong>the</strong> CIS and
Bx by CIS
budgt owned by CIS
4. Conclusion
What becomes clear from <strong>the</strong> findings <strong>of</strong> this case study is that <strong>the</strong> new knowledge generated from
<strong>the</strong> Human Genome Project seems to require a unique combination <strong>of</strong> IT and Bio-science skills.
PharmaCo struggled to find <strong>the</strong> right kind <strong>of</strong> people that actually combined <strong>the</strong> two. Clearly <strong>the</strong> Bioscientists
did not have <strong>the</strong> IT skills while <strong>the</strong> IT specialists did not understand <strong>the</strong> domain. The
organisation gap in skills was filled in an entrepreneurial fashion by a group <strong>of</strong> people who seem to
have had <strong>the</strong> combination <strong>of</strong> skills required. What makes this fascinating is <strong>the</strong> mythology created
around this newly formed group. And while it is true that <strong>the</strong>y did have a unique culture <strong>the</strong>y became
an orphan group as on <strong>the</strong> one hand <strong>the</strong> Scientists saw <strong>the</strong>m as representatives <strong>of</strong> <strong>the</strong> IS discipline
and <strong>the</strong> CIS specialists saw <strong>the</strong>m as scientists. Being essentially inter-disciplinary <strong>the</strong>y were in a
precarious position.
This case study indicates that in times <strong>of</strong> high turbulence and uncertainty an adaptive organisation will
form new organisational structures to accommodate for <strong>the</strong> absorption <strong>of</strong> new knowledge. These
organisational structures seem to operate in <strong>the</strong> periphery <strong>of</strong> <strong>the</strong> organisation and seem politically
vulnerable. The ease with which <strong>the</strong>y were eventually absorbed by CIS demonstrates <strong>the</strong> weak
position <strong>the</strong>y always held in PharmaCo.
References
Abbas, a. E. & holmes, s. P. 2004. Bioinformatics and management science: some common tools and
techniques. Operations research, 52, 165.
Abrahamson, e. & rosenkopf, l. 1997. Social network effects on <strong>the</strong> extent <strong>of</strong> innovation diffusion: a computer
simulation. Organization science, 8, 289-309.
Anyanwu, c. E. 2006. Bioinformatics and pharmacogenomics in drug discovery and development. Master <strong>of</strong>
science in bioinformatics, university <strong>of</strong> indiana.
Arlington, s. 2000. Pharma 2005. Pharmaceutical executive, 20, 74.
Callon, m. 1999. Actor-network <strong>the</strong>ory - <strong>the</strong> market test. In: law, j. & hassard, j. (eds.) Actor network <strong>the</strong>ory and
after. Oxford: blackwell publishers.
Chalasani, s. & barber, r. 2004. Architectures for java-based bioinformatics applications. Industrial management
+ data systems, 104, 578.
Collier, a. 1994. Critical realism: an introduction to roy bhaskar's philosophy, london, verso.
Cross, r., parker, a. & borgatti, s. P. 2003. Making invisible work visible: using social network analysis to support
strategic collaboration. In: cross, r., parker, a. & sasson, l. (eds.) Networks in <strong>the</strong> knowledge economy.
Oxford: oxford university press.
Datamonitor 2004. Addressing pharma's r&d productivity crisis. Datamonitor healthcare report. London:
datamonitor.
De vaus, d. A. 2001. Research design in social research, london, sage.
Denning, s. 2005. Why <strong>the</strong> best and brightest approaches don't solve <strong>the</strong> innovation dilemma. Strategy &
leadership, 33, 4.
Doolin, b. & lowe, a. 2002. To reveal is to critique: actor-network <strong>the</strong>ory and critical information systems research.
Journal <strong>of</strong> information technology, 17, 69.
Elbanna, a. R. 2007. Implementing an integrated system in a socially dis-integrated enterprise. Information
technology &amp; people, 20, 121.
Elrod-erickson, m. J. & ford, w. F. 2000. Economic implications <strong>of</strong> <strong>the</strong> human genome project. Business
economics, 35, 57.
Estades, j. & ramani, s. V. 1998. Technological competence and <strong>the</strong> influence <strong>of</strong> networks: a comparative
analysis <strong>of</strong> new biotechnology firms in france and britian. Technology analysis & strategic management, 10,
483.
Fodor, k. 2004. Bioinformatics on <strong>the</strong> brink. The scientist, 34, 34-6.
Gassmann, o. & reepmeyer, g. 2005. Organizing pharmaceutical innovation: from science-based knowledge
creators to drug-oriented knowledge brokers. Creativity and innovation management, 14, 233-245(13).
1019

Alexander K. K<strong>of</strong>inas and Abdallah Al-Shawakbeh
Gladstone, j. A. 2005. Using databases in designing drugs: current legal and interoperability considerations which
arise in pharmacogenomics. The business review, cambridge, 4, 218.
Granovetter, m. S. 1973. The strength <strong>of</strong> weak ties. The american journal <strong>of</strong> sociology, 78, 1360-1380.
Greco, j. F. 2007. The commercialisation <strong>of</strong> bioinformatics and <strong>the</strong> threat <strong>of</strong> open-source s<strong>of</strong>tware. Journal <strong>of</strong>
commercial biotechnology, 13.
Green, j. C., caracelli, v. J. & graham, w. F. 1989. Toward a conceptual framework for mixed method evaluation
designs. Educational evaluation and policy analysis, 11, 255-274.
Green, k., hull, r., mcmeekin, a. & walsh, v. 1999. The construction <strong>of</strong> <strong>the</strong> techno-economic: networks vs.
Paradigms. Research policy, 28, 777-792.
Grieff, e. 2003. Electronic laboratory notebook systems. Intellectual property & technology law journal, 15, 1.
Author. 2000. Mammoth task to untangle <strong>the</strong> genome: interpreting <strong>the</strong> genome financial times.
Hayes, n. & walsham, g. 2003. Knowledge sharing and icts: a relational perspective. In: easterby-smith, m. &
lyles, m. (eds.) The blackwell handbook <strong>of</strong> organizational learning and knowledge management. Blackwell.
Hedstrom, p. 1994. Contagious collectivities: on <strong>the</strong> spatial diffusion <strong>of</strong> swedish trade unions, 1890-1940.
American journal <strong>of</strong> sociology, 99, 1157-1179.
Hopkins, m. M., martin, p. A., nightingale, p., kraft, a. & mahdi, s. 2007. The myth <strong>of</strong> <strong>the</strong> biotech revolution: an
assessment <strong>of</strong> technological, clinical and organisational change. Elsevier.
Hulse, j. H. 2004. Biotechnologies: past history, present state and future prospects. Elsevier.
Katz, f. N. & h<strong>of</strong>man, k. J. 2005. Enabling genetics. Harvard international review, 27, 38.
Kreiner, k. & schultz, m. 1993. Informal collaboration in r & d. The formation <strong>of</strong> networks across organizations.
Organization studies, 14, 189.
Kuhn, t. S. 1996. The structure <strong>of</strong> scientific revolutions, <strong>the</strong> university <strong>of</strong> chicago press.
Latour, b. 1993. We have never been modern, harvester wheatsheaf.
Latour, b. 1999a. On recalling ant. In: law, j. & hassard, j. (eds.) Actor network <strong>the</strong>ory and after. Oxford: blackwell
publishers.
Latour, b. 1999b. Science's blood flow; an example from joliot's scientific intelligence. Pandora's hope: essays on
<strong>the</strong> reality <strong>of</strong> science studies. Cambridge, massachusetts, london: harvard university press.
Latour, b. 2003. The promises <strong>of</strong> constructivism. In: ihde, d. (ed.) Chasing technology: matrix <strong>of</strong> materiality.
Indiana university press.
Latour, b. 2004. A prologue in form <strong>of</strong> a dialog between a student and his (somewhat) socratic pr<strong>of</strong>essor [online].
Available: http://www.ensmp.fr/~latour/articles/article/090.html [accessed viewed in 09/12/2004 2004].
Law, j. 1999. After ant: complexity, naming and topology. In: law, j. & hassard, j. (eds.) Actor network <strong>the</strong>ory and
after. Oxford: blackwell publishers.
Lemarie, s., mangematin, v. & torre, a. 2001. Is <strong>the</strong> creation and development <strong>of</strong> biotech smes localised?
Conclusions drawn from <strong>the</strong> french case. Small business economics, 17, 61.
Lerer, l. & piper, m. 2003. Digital strategies in <strong>the</strong> pharmaceutical industry, new york, palgrave macmillan.
Lerner, j. & merges, r. P. 1998. The control <strong>of</strong> technology alliances: an empirical analysis <strong>of</strong> <strong>the</strong> biotechnology
industry. The journal <strong>of</strong> industrial economics, 46, 125.
Mcmeekin, a. & harvey, m. 2002. The formation <strong>of</strong> bioinformatic knowledge markets: an 'economies <strong>of</strong>
knowledge' approach. Cric working paper series. Manchester: cric, <strong>the</strong> university <strong>of</strong> manchester & umist.
Ng, s.-k. & wong, l. 2004. Accomplishments and challenges in bioinformatics. It pr<strong>of</strong>essional magazine, 6, 44.
Nightingale, p. & martin, p. 2004. The myth <strong>of</strong> <strong>the</strong> biotech revolution. Elsevier.
Nooteboom, b. Year. A cognitive <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm. In: international workshop on "<strong>the</strong> <strong>the</strong>ories <strong>of</strong> <strong>the</strong> firm", 2002
paris.
Ouzounis, c. A. & valencia, a. 2003. Early bioinformatics: <strong>the</strong> birth <strong>of</strong> a discipline-a personal view. Oxford univ
press.
Pan, s. L. & leidner, d. E. 2003. Bridging communities <strong>of</strong> practise with information technology in pursuit <strong>of</strong> global
knowledge sharing. Journal <strong>of</strong> strategic information systems, 12, 71-88.
Prabhu, g. N. 1999. Managing research collaborations as a portfolio <strong>of</strong> contracts: a risk reduction strategy by
pharmaceutical firms. International journal <strong>of</strong> technology management, 18, 207.
Quere, m. 2003. Knowledge dynamics: biotechnology's incursion into <strong>the</strong> pharmaceutical industry. Industry and
innovation, 10, 255.
Sayer, a. 1992. Method in social science: a realist approach, london, rootledge.
Schmid, e. F. & smith, d. A. 2004. Is pharmaceutical r&d just a game <strong>of</strong> chance or can strategy make a
difference? Drug discovery today, 9, 18-26.
Studt, t. 2003. Knowledge management is key to improving drug r&d cycles. R & d, 45, 18.
Tashakkori, a. & teddlie, c. 1998. Mixed methodology, thousand oaks, sage publications.
Ward, s. J. 2004. Match-maker: aligning information needs and it solutions in biopharmaceutical process
development. Pharmaceutical technology, s22.
Wasserman, s. & faust, k. 1994. Social network analysis: methods and applications, cambridge university press.
Yin, r. K. 1989. Case study research; design and methods, beverly hills and london, sage publications.
Yli-renko, h., autio, e. & sapienza, h. J. 2001. Social capital, knowledge acquisition, and knowledge exploitation in
young technology-based firms. Strategic management journal, 22, 587-613.
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Research
Papers
1021

1022

Measuring Utility <strong>of</strong> Geospatial maps for Information
Seeking: Findings <strong>of</strong> a Structured Literature Review and
Preliminary Think-Aloud-Study
Nadine Amende
Chair <strong>of</strong> Business Computing, University <strong>of</strong> Passau, Passau, Germany
nadine.amende@uni-passau.de
Abstract: Handling information and ever growing knowledge bases are still key tasks <strong>of</strong> <strong>the</strong> information society.
Many organisations deplore that co-workers invest much time into search and handling <strong>of</strong> information.
Information visualisation methods and tools can help to prepare and visually represent abstract information to
amplify cognition. However, a more widespread adoption <strong>of</strong> visualisation tools for information seeking is still
missing. Geovisualisation, a sub domain <strong>of</strong> information visualisation, is concerned with geospatial maps as visual
representations <strong>of</strong> information. With <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> WWW, geospatial maps are now easy to implement and
because <strong>of</strong> a more intuitive representation easy to understand. There is a high benefit potential for organisations,
due to that 80% <strong>of</strong> organisational data possess spatial characteristics and can be linked and displayed on
geospatial maps. Thus, research objective is to measure utility <strong>of</strong> geospatial maps for information seeking. This
paper presents a state-<strong>of</strong>-<strong>the</strong>-art in measuring information visualisation utility by means <strong>of</strong> a structured literature
review. The review examines 33 empirical papers and shows an absence <strong>of</strong> <strong>the</strong>oretical models and influencing
factors as a basis for in-depth utility analysis. Fur<strong>the</strong>rmore, <strong>the</strong> review identifies marginal analysis <strong>of</strong> geospatial
maps for information seeking. In order to gain more insight into user seeking behaviour and requirements a pilot
think aloud test and a survey was conducted. Thereto, 16 participants had to perform a web based real estate
search. The results indicate that <strong>the</strong>re is a need for geospatial map as tool for information seeking within spatial
contexts. The next step for future research is to conduct laboratory experiments based on <strong>the</strong>se findings to
analyse utility. Implications <strong>of</strong> this research are to make evident statements for using geospatial maps for
information seeking and to validate <strong>the</strong> used <strong>the</strong>oretical model for utility measurement.
Keywords: utility measurement, geospatial maps, information visualisation, structured literature review, think
aloud test, information seeking
1. Introduction
Handling information and ever growing knowledge bases are still key tasks <strong>of</strong> <strong>the</strong> information society.
Many organisations deplore that co-workers invest much time into search and handling <strong>of</strong> information
(LexisNexis 2008). Information visualisation methods and tools can help to prepare and visually
represent abstract and plenty <strong>of</strong> information. Card et al. define information visualisation as „… <strong>the</strong> use
<strong>of</strong> computer-supported, interactive, visual representations <strong>of</strong> abstract data to amplify cognition.“ (Card
et al., 1999, p.7)
With <strong>the</strong> evolution <strong>of</strong> information visualisation as a research discipline, many visualisation tools and
techniques exist, but a more widespread adoption <strong>of</strong> visualisation tools for information seeking is
missing. On <strong>the</strong> one hand, this can be traced back to a lack <strong>of</strong> usability <strong>of</strong> visualisation tools as well
as <strong>the</strong> absence <strong>of</strong> <strong>the</strong> user’s ability to understand visualisations, and on <strong>the</strong> o<strong>the</strong>r hand to missing
instruments for assessing <strong>the</strong> utility <strong>of</strong> information visualisation tools (Plaisant 2004). Organisations
need to justify <strong>the</strong>ir investments. Thus, it is crucial to analyse and understand utility and its influencing
factors.
Geovisualisation, a sub domain <strong>of</strong> information visualisation, is concerned with geospatial maps as
visual representations <strong>of</strong> data. „Geovisualisation is <strong>the</strong> visualisation <strong>of</strong> spatial data which requires <strong>the</strong>
use <strong>of</strong> maps or 3D displays where at least two display dimensions are utilised to represent <strong>the</strong>
physical space, which is different from information visualisation dealing with abstract data spaces.”
(ICA 2009)
With <strong>the</strong> evolution <strong>of</strong> <strong>the</strong> WWW, geospatial maps are now easy to implement and because <strong>of</strong> a more
intuitive representation easy to understand. There is a high benefit potential for organisations, due to
that 80% <strong>of</strong> organisational data possess spatial characteristics and can be linked and visualised on
geospatial maps. Dykes et al. (2005) and <strong>the</strong> Commission on Geovisualisation <strong>of</strong> <strong>the</strong> International
Cartographic Association (2009) claim more investigations on <strong>the</strong> explanation <strong>of</strong> functionalities and
conditions, e.g. how do different users react on geovisualisations, which cognitive processes are
important, and usability concepts. Thus, research objective is to analyse utility <strong>of</strong> geospatial maps for
information seeking.
1023

Nadine Amende
Figure 1 shows an example <strong>of</strong> using geovisualisation for information seeking. The geospatial map
displays a company’s marketing activities. Documents, news postings, websites etc. dealing with
marketing activities can be linked by means <strong>of</strong> a spatial attribute to <strong>the</strong> geomap.
Figure 1: Displaying a company’s marketing activities (BizSphere 2010)
The remainder <strong>of</strong> this paper is structured as follows. Below, we give an overview <strong>of</strong> <strong>the</strong> research
objective and agenda. We <strong>the</strong>n present a state-<strong>of</strong>-<strong>the</strong>-art in measuring information visualisation utility
by means <strong>of</strong> a structured literature review. In order to gain more insight into user seeking behaviour
and requirements a pilot think aloud test and a survey was conducted. Finally, we point out future
work and limitations <strong>of</strong> our research.
2. Research objective
Aim <strong>of</strong> this dissertation research is to analyse and describe utility <strong>of</strong> geospatial maps for information
seeking. Firstly, we analysed <strong>the</strong> state-<strong>of</strong>-<strong>the</strong>-art in information visualisation particularly
geovisualisation and its utility measurement, e.g. existing information visualisation techniques and
<strong>the</strong>ir utility, practices for measuring utility and appropriate constructs and metrics. Therefore, we
conducted a structured literature review to undertake validated research and present evident results
in utility or ra<strong>the</strong>r success measurement, which has been a permanent topic in IS research. Measuring
IS success has been popular for 25 years and is an ongoing issue in IS research with a plethora <strong>of</strong>
success definitions, e.g. individual or organisational performance, utility, user acceptance or user
satisfaction, and <strong>of</strong> models like <strong>the</strong> technology acceptance model by Davis (1986), <strong>the</strong> task
technology fit model by Goodhue (1995), Roger’s diffusion <strong>of</strong> innovations <strong>the</strong>ory (1995) or <strong>the</strong> most
famous DeLone and McLean IS success model (updated 2003). In <strong>the</strong> field <strong>of</strong> information
visualisation Cognitive Fit Theory by Vessey (1991) explains higher task performance by a match <strong>of</strong>
visual representation <strong>of</strong> a task and decision maker’s mental representation <strong>of</strong> a task.
Current evaluation practices <strong>of</strong> information visualisation are experiments, case studies and technical
usability studies (Gershon et al. 1998). To gain a better evaluation, adaption <strong>of</strong> IS success
measurement expertise to <strong>the</strong> information visualisation domain can be useful. Common IS success
models evaluate influencing factors and causal relationships in-depth. As information visualisation
systems are particular information systems, appliance <strong>of</strong> common IS success models is a beginning
for <strong>the</strong>oretical based analysis. These models consider analysed and validated causal relationships
and important influencing factors, which are a good prerequisite for analysis. Beyond, IS success
measurement research defines different units and perspectives <strong>of</strong> measurement (see 3.3.1).
To set up <strong>the</strong> state <strong>of</strong> <strong>the</strong> art <strong>the</strong> literature review addresses <strong>the</strong> following questions:
What information visualisation techniques were analysed in past empirical studies?
Which <strong>the</strong>oretical models or frameworks for measuring utility can be found in literature and were
used in past empirical studies?
Which influencing factors were analysed in past empirical studies?
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Which research designs were used in past empirical studies?
Secondly, we performed a pilot think aloud test and a survey to gain more insight into user seeking
behaviour and requirements and to show relevance <strong>of</strong> <strong>the</strong> research objective.
3. Findings <strong>of</strong> structured literature review
To follow <strong>the</strong> rigor and relevance debate in IS research (Hevner et al. 2004) on <strong>the</strong> one hand and a
call <strong>of</strong> Webster and Watson (2002) on <strong>the</strong> o<strong>the</strong>r hand, this research uses <strong>the</strong> structured literature
method as basis for a rigorous foundation. A structured literature review facilitates <strong>the</strong>ory
development, closes areas where plenty research exists and discovers new research areas
(Webster/Watson 2002). It summarizes and integrates prior research and elicits inconsistencies,
relations and gaps in research to help following a research objective.
Conducting a literature search in <strong>the</strong> IS domain is very complex and time consuming. This is caused
by a constantly increasing number <strong>of</strong> IS journals and conferences, e.g. 681 active IS journals are
listed in <strong>the</strong> index <strong>of</strong> Information Systems Journals (Lamp 2009). Therefore, a relatively
comprehensive number <strong>of</strong> relevant and qualitative articles is crucial for an evident literature analysis.
A structured literature review ensures that all relevant sources can be ga<strong>the</strong>red and analysed by
means <strong>of</strong> a scientific method (Fettke 2006). The process <strong>of</strong> review and <strong>the</strong> exclusion <strong>of</strong> sources are
documented. Because <strong>of</strong> this transparency and reliability <strong>of</strong> research, results <strong>of</strong> <strong>the</strong>se activities can be
understood and reused.
Figure 2: Process <strong>of</strong> structured literature review (Brocke et al. 2009)
Brocke and Riemer recommend a 5-phasing iterative literature review process (see figure 2), which is
<strong>the</strong> basis for this research objective and is described below.
3.1 Definition <strong>of</strong> review scope
The review scope defines <strong>the</strong> degree <strong>of</strong> coverage <strong>of</strong> sources and <strong>the</strong> period covered. The degree <strong>of</strong>
coverage <strong>of</strong> sources is crucial for reviewing. Cooper distinguishes between an exhaustive review, an
exhaustive review describing only a sample, a representative review typifying larger groups <strong>of</strong> articles
and a pivotal review illustrating central articles (Cooper 1988). In keeping with this paper’s objective
<strong>the</strong> state <strong>of</strong> <strong>the</strong> art <strong>of</strong> utility measurement in information visualisation was accomplished by means <strong>of</strong>
an exhaustive review.
The period covered can be restricted as well. A period can be selective, exhaustive or determines an
interval. The spread <strong>of</strong> information visualisation as an area <strong>of</strong> research began with some
developments at XEROX Palo Alto Research Center in <strong>the</strong> early 90’s and first information
visualisation conferences in 1995. IS success resp. utility measurement has been pushed since 1992
when DeLone and McLean published <strong>the</strong>ir IS success measurement model. Thus, an appropriate
interval between 1992 and 2010 was determined to focus on a wide range on relevant publications.
3.2 Literature search
A systematic selection <strong>of</strong> appropriate sources was carried out by means <strong>of</strong> IS journal rankings.
Multiple ranking lists (AIS 2006), (Fisher et al. 2007), (Willcocks et al. 2008), (Henning-Thurau et al.
2004) and (Holsapple 2009) were consolidated to identify top IS journals. Thus, identified top journals
do not represent <strong>the</strong> perception <strong>of</strong> one researcher and one ranking method. Journals were selected, if
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<strong>the</strong>y were listed in three out <strong>of</strong> <strong>the</strong> five ranking lists. In total, 13 top IS journals were identified. Several
specific information visualisation journals and geographic information systems journals were added to
receive more relevant articles despite a lower quality than top journals (Willcocks et al. 2008). The
selection <strong>of</strong> conference papers was rejected because <strong>of</strong> a restricted length and thus relatively
unspecific statements and a lower quality than journals (Brocke et al. 2009). Books were rejected as
well because <strong>of</strong> partially lacking review processes.
In a next step, appropriate key phrases were generated by combining keywords for information
visualisation, e.g. “map visualisation”, “tree visualisation” with keywords for measurement, e.g.
“evaluation”, “benefit” or “acceptance”. These key phrases were used for literature searches <strong>of</strong>
electronic databases, e.g. ScienceDirect, ProQuest, Emerald, Springer and Wiley Science or journal
websites. A precise key phrase excludes not necessarily relevant articles.
The search <strong>of</strong> databases was conducted in three steps. Initially, articles were retrieved by searching
in titles, abstracts, denoted article keywords and partially in full text. Results <strong>of</strong> this first step were
1018 “initial hits”.
In a second step, abstracts <strong>of</strong> <strong>the</strong>se initial hits were analysed in-depth in respect <strong>of</strong> <strong>the</strong>ir relevance
that is, consistency with <strong>the</strong> defined time period and review objectives – use <strong>of</strong> information
visualisation and its utility measurement. Finally, a roughly full text analysis <strong>of</strong> <strong>the</strong> 215 “abstract hits”
was conducted to select relevant literature for inclusion in <strong>the</strong> review. Articles were rejected for <strong>the</strong>
following reasons to ensure a certain quality <strong>of</strong> articles:
address no empirical investigation, e.g. only meta analysis, literature review or analytical
evaluation (Urbach et al. 2008),
address scientific visualisation <strong>of</strong> physical data, e.g. ozone concentration in <strong>the</strong> atmosphere,
instead <strong>of</strong> information visualisation <strong>of</strong> non-physical data, e.g. financial and business data (Card et
al. 1999),
do not describe research design,
do not define a utility construct, exogenous and endogenous variables for measurement,
is a duplicate (Petter et al. 2008) or
uses data from ano<strong>the</strong>r source (second hand).
The evaluation phases (step 2 and 3) limit <strong>the</strong> amount <strong>of</strong> literature to relevant articles. The resulting
list <strong>of</strong> “relevant hits” contains 33 articles, which became <strong>the</strong> basis for <strong>the</strong> literature review. Table 1
displays <strong>the</strong> results <strong>of</strong> literature search.
3.3 Literature analysis and syn<strong>the</strong>sis
The identified relevant articles were considered for an in-depth analysis and syn<strong>the</strong>sis. For evident
results <strong>the</strong> analysis and syn<strong>the</strong>sis process has to be conducted systematically. A framework was
developed to categorize <strong>the</strong> relevant articles and to structure <strong>the</strong> analysis process.
3.3.1 Framework for analysing literature
Several authors recommend a framework for arranging, discussing and syn<strong>the</strong>sizing prior research.
Hart and Webster/Watson provide a generic Framework with generic categories for any research
discipline (Hart 1998), (Webster/Watson 2002). A framework considering specifics <strong>of</strong> IS success
measurement was defined by DeLone and McLean (1992), Petter et al. (2008) and Urbach et al.
(2008). This preliminary work was adapted and amended to analyse and syn<strong>the</strong>size information
visualisation utility measurement systematically. The developed framework consists <strong>of</strong> 10 categories
displayed in figure 3.
Theoretical foundations for IS success measurement were determined to analyse <strong>the</strong>ir use for
information visualisation evaluation. Therefore, common IS success reference models and
frameworks e.g. <strong>the</strong> updated D&M model, TAM, TTF, diffusion <strong>of</strong> innovations <strong>the</strong>ory, End User
Computing Satisfaction – EUCS by Doll and Torkzadeh (1988) and SERVQUAL by Parasuraman et
al. (1988) were considered (Petter et al. 2008). The cognitive fit model emerged from information
visualisation research was added. Articles which referenced ano<strong>the</strong>r <strong>the</strong>ory were categorized as
“o<strong>the</strong>r” or which referenced no <strong>the</strong>ory were categorized as “n/a” (not applicable).
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Endogenous and exogenous variables were determined to analyse <strong>the</strong> underlying utility constructs indepth.
That is, it is crucial to know and analyse potential factors which can influence utility and to
clearly define utility. Endogenous variables were deduced from <strong>the</strong>oretical IS success models.
Exogenous variables describe influencing factors for successful visualisation and were deduced from
IS success models and information visualisation <strong>the</strong>ory (Plaisant 2004). Articles which used no
designated variables were categorized as “o<strong>the</strong>r”.
Table 1: Results <strong>of</strong> literature search
Journal
TOP IS Journals
Hits initial Hits abstract Hits full
text (relevant)
Management Information Systems Quarterly 3 2 1
Information Systems Research 0 0 0
Journal <strong>of</strong> Management Information Systems 8 5 0
Decision Support Systems 36 19 8
The DATABASE for Advances in Information
21 3 0
Systems
Information & Management 8 2 1
Information Systems Journal 2 0 0
Journal <strong>of</strong> Strategic Information Systems 1 0 0
Management Science 0 0 0
Decision Science 4 2 1
<strong>European</strong> Journal <strong>of</strong> Information Systems 9 0 0
Journal <strong>of</strong> <strong>the</strong> Association for Information Systems 2 0 0
Journal <strong>of</strong> Information Technology
Specific Information Visualisation Journals
2 0 0
Cartography and Geographic Information Science
77 23 2
(CaGIS)
Computer-Aided Design 3 0 0
Computers & Geosciences 345 37 0
Computers and Geotechnics 0 0 0
Computers & Graphics 46 15 0
Computer Graphics Forum 36 7 0
Computer Vision and Image Understanding
GeoInformatica – An International Journal on
2 0 0
Advances <strong>of</strong> Computer Science for Geographic 165 4 1
Information Systems
Information Visualisation 65 42 7
International Journal <strong>of</strong> Human-Computer Studies 44 20 9
Journal <strong>of</strong> Geographical Systems 88 9 0
Journal <strong>of</strong> Visual Languages & Computing 39 25 3
Journal on Multimodal User Interfaces 12 0 0
Σ 1018 215 33
The two categories visualisation type and objective <strong>of</strong> visualisation were determined to present <strong>the</strong>
object <strong>of</strong> analysis and show <strong>the</strong> focus <strong>of</strong> <strong>the</strong> investigated studies. Common visualisation types: tree,
network, conceptual map, spatial map, fisheye view, matrix, diagram, tilebar, perspective wall and
virtual environment visualisation were identified (Plaisant 2005), (Card et al. 1999), (Chen 2004).
O<strong>the</strong>r possible visualisation types were categorized as “o<strong>the</strong>r”. The objective <strong>of</strong> visualisation is to
visualise search results, support navigation in information space, support pattern recognition,
represent reality and support problem solving (Plaisant 2005) (Card et al. 1999), (Däßler 1999),
(Davis 1986).
Unit <strong>of</strong> observation means that a measurement can be conducted on different organisational levels.
Success resp. utility can be measured as individual, group or as organisational benefit. Measuring an
organisational benefit is <strong>of</strong>ten difficult because <strong>of</strong> <strong>the</strong> numerous influencing factors which do not allow
tracing it back to solely an information visualisation system. Thus, an individual perspective is
auxiliary, because an individual has to work with <strong>the</strong> information visualisation system. Explicit
conclusions can be drawn more easily (Hart 1998) (Webster/Watson 2002).
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Figure 3: Classification <strong>of</strong> reviewed literature
Nadine Amende
The evaluation perspective category defines different perspectives on success e.g. internal user, IS
personnel, IS executives, customers and suppliers (external stakeholders) or multi-stakeholders.
Because <strong>of</strong> different interests in evaluating IS, different users can generate different measurement
results. Considering different user perspectives is crucial for a comprehensive understanding <strong>of</strong>
information visualisation utility (Urbach et al. 2008).
Research strategy category describes <strong>the</strong> used methodology to ga<strong>the</strong>r data. Common empirical IS
methodologies are survey, case study, experiment, interview and observation.
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The category data analysis refers to frequently used techniques e.g. structural equation modelling,
regression analysis, factor analysis, variance analysis, cluster analysis and t-test (Urbach et al. 2008).
Both categories permit to analyse reliability, internal and external validity <strong>of</strong> <strong>the</strong> empirical studies.
3.3.2 Results <strong>of</strong> literature analysis
The results (see figure 3) show that use <strong>of</strong> consolidated IS success models is weak. Only 2 studies
used cognitive fit <strong>the</strong>ory and 1 <strong>the</strong> task technology fit model as foundation. The use <strong>of</strong> <strong>the</strong>oretical
models can improve analysing utility and its influencing factors. These models permit a structured and
holistic analysis <strong>of</strong> influencing factors and causal relationships. They depict already investigated and
validated influencing factors and causal relationships. Due to <strong>the</strong>ir popularity, many studies exist
presenting measures and approaches for evaluation. Measures <strong>of</strong> <strong>the</strong>se common models can be
adapted for information visualisation research.
A more in-depth analysis <strong>of</strong> influencing factors reveals that most studies only analyse influences by
varying visualisation types (31) and additionally task complexities (12). Most used endogenous
variables are effectiveness (29) and efficiency (29) measured by task correctness and time. O<strong>the</strong>r
exogenous and endogenous variables contained in IS success models like voluntariness, subjective
norm and satisfaction or acceptance etc. also have to be investigated for deeper understanding <strong>of</strong>
utility. Thus, validated IS success models support a rigor evaluation process and have to be used to
measure geovisualisation utility.
Fur<strong>the</strong>rmore, <strong>the</strong> literature analysis depicts that only conceptual maps (20) are frequently analysed
and visualisation <strong>of</strong> search results is weakly considered. These two issues substantiate <strong>the</strong> research
objective to analyse utility <strong>of</strong> geomaps for information seeking.
Dominant research perspective is <strong>the</strong> individual (31) user (23) perspective, which results from usercentered
information visualisation design strategy. However, measuring organisational benefit is hard.
There are many factors (e. g. market situation), which can influence organisational benefit. So it is
difficult to infer visualisation systems influence exclusively. Due to that, we will also focus on <strong>the</strong>
individual user perspective.
Most common research design is laboratory experiment (32) and variance analysis (21) or simple ttest
(14). Laboratory experiments are a good way to analyse influencing factors <strong>of</strong> utility and causal
relationships. Thus, this will be used to achieve <strong>the</strong> research objective.
4. Findings <strong>of</strong> preliminary think-aloud-study
The Think aloud method is more and more used in evaluation <strong>of</strong> human computer interfaces (Denning
et al. 1990). Being traditionally used as a psychological research method, participants are observed
whilst <strong>the</strong>y are using a system to perform various predetermined tasks (Ericsson/Simon, 1984). The
participants have to verbalise <strong>the</strong>ir thoughts and comments. These comments and thoughts are <strong>the</strong>n
analysed to identify problems in system usage behaviour.
We used a preliminary think-aloud-study and post session questionnaire to explore information
seeking behaviour and requirements and to show relevance <strong>of</strong> <strong>the</strong> research objective. Findings <strong>of</strong> this
explorative study will <strong>the</strong>n be used to formulate hypo<strong>the</strong>ses for <strong>the</strong> laboratory experiment and as
basic requirements for a geo map prototype. The following research questions were:
Are end users satisfied with current search functionalities?
Are end users satisfied with expenditure <strong>of</strong> time?
Which search criteria are important to find relevant apartments?
How helpful are geo maps?
Do geo maps improve effectiveness and efficiency to find relevant apartments?
The think aloud study involved 16 experienced computer users. They indicated that <strong>the</strong>y used
computers daily. The participants were randomly split into two groups. Participants were introduced to
<strong>the</strong> think-aloud-method and <strong>the</strong> task. The task involved using a common real estate web site to
search for apartments. The search results were visualised as a list. One group had to search for
apartments in a known city. The o<strong>the</strong>r group had to search for apartments in an unknown city. The
participants worked individually and were encouraged to think aloud. The examiner used questions
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Nadine Amende
such as “What are you doing?”, “Why are you doing this? and “What are you thinking?” to remind <strong>the</strong>
participants to think aloud. Audio recordings and transcripts <strong>of</strong> relevant passages were made.
All participants answered post session questions on a 5-point-Likert scale about relevance <strong>of</strong> common
search criteria such as price, number <strong>of</strong> bedrooms etc., and <strong>of</strong> spatial criteria such as street, district,
location, environment and distance. They also had to answer questions about relevance <strong>of</strong> geo maps
for real estate search, satisfaction with current search portals and efficiency and effectiveness <strong>of</strong>
information seeking by means <strong>of</strong> geo maps.
The think-aloud-study reveals that 13 participants tried to get some information about <strong>the</strong> location, 12
participants needed information about <strong>the</strong> distance to a specific place, e.g. place <strong>of</strong> work or city
centre, and 11 participants needed information about <strong>the</strong> environment to find relevant apartments.
These information were not directly and intuitively available. 8 <strong>of</strong> 16 participants used <strong>the</strong> district
criteria to find relevant apartments. A specific street as criteria to find relevant apartments was only
mentioned by 2 participants. Thus, a visual representation <strong>of</strong> this information is important for end
users to get an imagination <strong>of</strong> <strong>the</strong> location, e.g. where <strong>the</strong> city centre is, to know where districts are or
a specific street is, how <strong>the</strong> environment looks like and to know <strong>the</strong> distances to a specific place.
Results <strong>of</strong> <strong>the</strong> post session questionnaire also illustrate <strong>the</strong> importance <strong>of</strong> spatial criteria (see figure
4a and 4b).
Figure 4a: Results <strong>of</strong> spatial criteria, group 1: known city
Figure 4b: Results <strong>of</strong> spatial criteria, group 2: unknown city
Besides some slight differences between <strong>the</strong> groups, <strong>the</strong>re can be seen a trend towards importance
<strong>of</strong> <strong>the</strong> criteria environment, distance, location and district. The summarized frequency over both
groups and both values (very important and important) for every <strong>of</strong> <strong>the</strong>se criteria is 10 out <strong>of</strong> 16. The
street criterion is not important.
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The results <strong>of</strong> <strong>the</strong> remaining questions
“How helpful would be a geo map visualising all search results?”
“How helpful would be a geo map for comparing search results with <strong>the</strong> help <strong>of</strong> spatial criteria?”
“How effective would be performing a search task by means <strong>of</strong> a geo map?”
“How efficient would be performing a search task by means <strong>of</strong> a geo map?”
show high frequency values <strong>of</strong> 12 out <strong>of</strong> 16 for <strong>the</strong> former question, 13 for <strong>the</strong> second one, 14 for <strong>the</strong>
third one and 12 for <strong>the</strong> last one. The frequency values were summarized over both groups and both
values (very helpful and helpful resp. very effective and effective resp. very efficient and efficient).
8 <strong>of</strong> 16 participants are satisfied with current search functionalities. Only 2 participants are very
satisfied with <strong>the</strong> functionalities. 8 <strong>of</strong> 16 participants are satisfied with <strong>the</strong> expenditure <strong>of</strong> time for
current search portals. 5 participants are very satisfied.
However, <strong>the</strong> results depict a lack <strong>of</strong> a more effective, efficient and intuitive information seeking tool
by means <strong>of</strong> information visualisation techniques. Thus, geovisualisation, particularly geo maps, have
to be investigated for usage for information seeking.
5. Conclusion and outlook
Information visualisation research has been popular for nearly two decades, but a broad adoption is
missing, especially in <strong>the</strong> domain <strong>of</strong> information seeking. Geovisualisation can be useful, because <strong>of</strong>
its intuitive representation <strong>of</strong> information and due to that 80% <strong>of</strong> organisational data possess spatial
characteristics. We presented a state-<strong>of</strong>-<strong>the</strong>-art in measuring information visualisation utility by means
<strong>of</strong> a structured literature review to gain evident results.
Limitations <strong>of</strong> <strong>the</strong> structured literature review can be seen in <strong>the</strong> definition <strong>of</strong> review scope, <strong>the</strong>
selection <strong>of</strong> relevant journals and key phrases, <strong>the</strong> rejection <strong>of</strong> conference articles and omitted
backward and forward search in article references. This could have elided potentially relevant articles.
The literature review was conducted by one person. Thus, subjective interpretations could result, but
can be avoided by intercoder reliability tests.
The preliminary think-aloud-study was conducted to explore user seeking behaviour and requirements
in-depth. Limitations can be seen in using a small number <strong>of</strong> participants. This can be rejected,
because <strong>of</strong> an explorative character <strong>of</strong> this preliminary study.
The next step for future research is that findings <strong>of</strong> <strong>the</strong> structured literature review and <strong>the</strong> think-aloudstudy
will be used to formulate hypo<strong>the</strong>ses for <strong>the</strong> laboratory experiment and to define requirements
for a geo map prototype. Fur<strong>the</strong>rmore, a <strong>the</strong>oretical utility model and utility definition have to be
specified as basis for <strong>the</strong> experimental design. Implications <strong>of</strong> this research are to make evident
statements for using geospatial maps for information seeking and to validate <strong>the</strong> used <strong>the</strong>oretical
model for utility measurement.
References
AIS - Association for Information Systems (2006), [online], http://ais.affiniscape.com/displaycom
mon.cfm?an=1&subarticlenbr=432.
BizSphere (2010), [online], http://download.bizsphere.com/bz-content/SE_Resources/BizSphere_
SustainableKnowledgeManagement_Broschuere.pdf
vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R. and Cleven, A. (2009) “Reconstructing <strong>the</strong>
Giant: On <strong>the</strong> Importance <strong>of</strong> Rigour in Documenting <strong>the</strong> Literature Search Process”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 17th
<strong>European</strong> <strong>Conference</strong> on Information Systems (ECIS), Verona, Italy, June.
Card, S., Mackinlay, J. and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think,
Morgan Kaufmann.
Carpendale, S. (2008) Evaluating Information Visualizations, Information Visualization, Kerren, A., Stasko, J.T.,
Fekete, J.-D. and North, C., (Eds.), LNCS 4950, pp 19-45.
Chen, C. (2004) Information Visualization, 2nd edition, Springer, London.
Cooper, H. M. (1988) “Organizing Knowledge syn<strong>the</strong>ses: A taxonomy <strong>of</strong> literature reviews”, Knowledge in
Society, Vol. 1, pp 104-126.
Däßler, R. (1999) Informationsvisualisierung – Stand, Kritik und Perspektiven, Research Paper, Project group
Infoviz, UAS Potsdam.
1031

Nadine Amende
Davis, F. D. (1986) A technology acceptance model for empirical testing new end-user information systems:
Theory and results, Doctoral dissertation, Sloan School <strong>of</strong> Management, Massachusetts Institute <strong>of</strong>
Technology.
DeLone, W. H. and McLean, E. R. (1992) “Information Systems Success: The Quest for <strong>the</strong> Dependent Variable”,
Information Systems Research, Vol. 3, No. 1, pp 60-95.
DeLone, W. H. and McLean, E. R. (2003) “The DeLone and McLean Model <strong>of</strong> Information Systems Success: A
Ten-Year Update”, Journal <strong>of</strong> Management Information Systems, Vol. 19, No. 4, pp 9-30.
Denning, S., Hoiem, D., Simpson, M. and Sullivan, K. (1990) "The value <strong>of</strong> thinking-aloud protocols in industry: A
case study at Micros<strong>of</strong>t Corporation", <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> Human Factors Society 34th Annual Meeting,
Santa Monica, pp 1285 - 1289.
Doll, W. J. and Torkzadeh, G. (1988) “The measurement <strong>of</strong> end-user computing satisfaction”, MIS Quarterly,
Vol.12, No. 2, pp 259-274.
Ericsson K. A. and Simon H. A. (1984) Protocol Analysis: Verbal Reports as Data, Bradford Books/MIT Press,
Cambridge.
Fettke, P. (2006) “State-<strong>of</strong>-<strong>the</strong>-Art des State-<strong>of</strong>-<strong>the</strong>-Art: Eine Untersuchung der Forschungsmethode “Review”
innerhalb der Wirtschaftsinformatik”, WIRTSCHAFTSINFORMATIK, Vol. 48, No. 4, pp 257-266.
Fisher, J., Shanks, G. and Lamp, J. (2007) “A Ranking List for Information Systems Journals”, Australasian
Journal <strong>of</strong> Information Systems, Vol. 14, No. 1, pp 5-18.
Gershon, N., Eick, S. G. and Card, S. (1998) “Information Visualization”, Interactions, Vol. 5, No. 2, pp 9-15.
Goodhue, D. L. (1995) “Understanding user evaluations <strong>of</strong> information systems”, Management Science, Vol. 41,
No. 12, pp 1827-1844.
Hart, C. (1998) Doing a literature review: releasing <strong>the</strong> social science research imagination, Sage Publications,
London.
Hennig-Thurau, T., Walsh, G. and Schrader, U. (2004) „VHB-JOURQUAL: Ein Ranking von betriebswirtschaftlich
relevanten Zeitschriften auf der Grundlage von Expertenurteilen“, Zeitschrift für betriebswirtschaftliche
Forschung, Vol. 56, No. 1, pp 520-545.
Hevner, A. R., March, S. T., Park, J. and Ram, S. (2004) “Design science in Information Systems research”, MIS
Quarterly, Vol. 28, No. 1, pp 75-105.
Holsapple, C. W. (2009) “A New Map for Knowledge Dissemination Channels”, Communications <strong>of</strong> <strong>the</strong> ACM, Vol.
52, No. 3, pp 117-125.
Lamp, J. W. (2009) Index <strong>of</strong> Information Systems Journals. Geelong, Deakin University. [Online],
http://lamp.infosys.deakin.edu.au/journals/
Parasuraman, A., Berry, L. L. and Zeithaml, V. A. (1988) “A Conceptual Model <strong>of</strong> Service Quality and Its
Implications for Future Research”, Journal <strong>of</strong> Marketing, Vol. 49, No. 4, pp 41-50.
Petter, S., DeLone, W. and McLean, E. (2008) “Measuring Information Systems Success: Models, Dimensions,
Measures, and Relationships”, <strong>European</strong> Journal <strong>of</strong> Information Systems, Vol. 17, No. 3, pp 236-263.
Plaisant, C. (2004) “The Challenge <strong>of</strong> Information Visualization Evaluation”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> working
<strong>Conference</strong> on Advanced visual interfaces, Gallipoli, Italy, May.
Plaisant, C. (2005) Information Visualization and <strong>the</strong> Challenge <strong>of</strong> Universal Usability, Exploring Geovisualization,
Dykes, J., MacEachren, A. and Kraak, M. J., (Eds.), Elsevier, Oxford, pp 53-82.
Rogers, E. M. (1995) Diffusions <strong>of</strong> Innovations, 4th edition, Free Press, New York.
Urbach, N., Smolnik, S. and Riempp, G. (2008) “A Methodological Examination <strong>of</strong> Empirical Research on
Information Systems Success: 2003 to 2007”, <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 14th Americas <strong>Conference</strong> on Information
Systems (AMCIS), Toronto, ON, Canada, August.
Vessey, I. (1991) “Cognitive Fit: A Theory-Based Analysis <strong>of</strong> <strong>the</strong> Graph Versus Table Literature”, Decision
Sciences, Vol. 22, No. 2, pp 219-240.
Webster, J. and Watson, R. T. (2002) “Analyzing <strong>the</strong> Past to Prepare for <strong>the</strong> Future: Writing a Literature Review”,
MIS Quarterly, Vol. 26, No. 2, pp xiii-xxiii.
Willcocks, L., Whitley, E. A. and Avgerou, C. (2008) “The Ranking <strong>of</strong> top IS journals: a Perspective from <strong>the</strong>
London School <strong>of</strong> Economics”, <strong>European</strong> Journal <strong>of</strong> Information Systems, Vol. 17, pp 163–168.
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Knowledge Boundaries <strong>of</strong> <strong>the</strong> Firm in Russian Heavy
Engineering Companies
Evgeny Blagov
Graduate School <strong>of</strong> Management, Saint Petersburg University, Russian
Federation
evgeniy.blagov@gmail.com.
Abstract: The purpose <strong>of</strong> this research is to look at <strong>the</strong> factors influencing a company’s choice <strong>of</strong> technological
knowledge renovation sources in <strong>the</strong> Russian heavy engineering industry. The <strong>the</strong>oretical foundations <strong>of</strong> this
research are <strong>the</strong> transaction cost <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm, mainly continuing O. Williamson’s views on <strong>the</strong> boundaries <strong>of</strong>
<strong>the</strong> firm problem, and <strong>the</strong> knowledge-based view <strong>of</strong> <strong>the</strong> firm as a part <strong>of</strong> <strong>the</strong> resource-based view. The problem
addressed by <strong>the</strong> research is how a company chooses between different sources <strong>of</strong> technological knowledge
renovation (where <strong>the</strong> term “knowledge” refers to technological know-how) when <strong>the</strong> knowledge used for
developing <strong>the</strong> products <strong>of</strong> <strong>the</strong> company seems obsolete, thus creating a “knowledge gap” (or “knowledge
absence”). Continuing <strong>the</strong> “make-buy-or-ally” stream <strong>of</strong> literature, such variants <strong>of</strong> technological knowledge
renovation sources are considered as creation <strong>of</strong> necessary knowledge by own strength, focal firm’s acquisition
<strong>of</strong> <strong>the</strong> necessary knowledge provider, long-term alliance relationship creation with <strong>the</strong> necessary knowledge
provider, and, finally, non-recurring purchase <strong>of</strong> necessary knowledge from an external provider on <strong>the</strong> open
market. On <strong>the</strong> basis <strong>of</strong> <strong>the</strong> literature review in fields <strong>of</strong> boundaries <strong>of</strong> <strong>the</strong> firm and knowledge-based view <strong>of</strong> <strong>the</strong>
firm studies a set <strong>of</strong> factors is figured out <strong>the</strong> influence <strong>of</strong> which on <strong>the</strong> technological knowledge renovation
sources choice is analyzed in <strong>the</strong> quantitative study. These factors are <strong>the</strong> following: importance <strong>of</strong> <strong>the</strong>
knowledge resource involved in <strong>the</strong> focal transaction for <strong>the</strong> firm’s result, uniqueness (in comparison to <strong>the</strong>
respective market and industry) <strong>of</strong> <strong>the</strong> knowledge resource(s) involved in a focal transaction, <strong>the</strong> focal firm’s
technological knowledge as a whole, and <strong>of</strong> <strong>the</strong> focal firm’s production, <strong>the</strong> focal firm’s comparative orientation
on cost- or quality-based competition, congruence between <strong>the</strong> results <strong>of</strong> <strong>the</strong> focal firm and its (potential)
counterparty, focal firm’s ability to control <strong>the</strong> actions <strong>of</strong> that (potential) counterparty and, finally, necessity <strong>of</strong>
support and fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge resource. To check <strong>the</strong>se factors’ influence on <strong>the</strong>
companies’ technological knowledge renovation sources choice a set <strong>of</strong> hypo<strong>the</strong>ses is formulated, considering
both <strong>the</strong> direct influence <strong>of</strong> <strong>the</strong> factors’ importance on <strong>the</strong> firm’s technological knowledge renovation sources
choice and <strong>the</strong> mediating effect <strong>of</strong> such variables as “congruence between <strong>the</strong> results <strong>of</strong> <strong>the</strong> focal firm and its
(potential) counterparty”, “focal firm’s ability to control <strong>the</strong> actions <strong>of</strong> <strong>the</strong> counterparty” and “necessity <strong>of</strong> support
and fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge resource” on <strong>the</strong> direct interactions. The quantitative method
used for hypo<strong>the</strong>ses testing is <strong>the</strong> ordered probit regression with <strong>the</strong> influencing factors’ importance for <strong>the</strong> firm in
a knowledge renovation act as independent variables and four technological knowledge renovation source
variants as dependent variables. The questionnaire constructed for <strong>the</strong> quantitative study has been sent via email
to sample <strong>of</strong> 300 Russian companies <strong>of</strong> <strong>the</strong> industry classification “Heavy, energetic and transport
engineering” randomly selected by region with <strong>the</strong> response rate <strong>of</strong> 10%, giving <strong>the</strong> resulting sample <strong>of</strong> 30
companies.
Keywords: technological knowledge renovation, knowledge boundaries <strong>of</strong> <strong>the</strong> firm, transaction cost <strong>the</strong>ory <strong>of</strong> <strong>the</strong>
firm
1. Purpose <strong>of</strong> <strong>the</strong> research
The purpose <strong>of</strong> this research is to look at <strong>the</strong> factors influencing a company’s choice <strong>of</strong> technological
knowledge renovation sources in <strong>the</strong> Russian heavy engineering industry. Thus, <strong>the</strong> research
objectives are to suggest <strong>the</strong> main variants <strong>of</strong> technological knowledge renovation sources and <strong>the</strong>
main factors possibly exerting influence on <strong>the</strong> choice between <strong>the</strong>se sources, to suggest hypo<strong>the</strong>ses
about <strong>the</strong>se influences and to test <strong>the</strong> hypo<strong>the</strong>ses in an empirical setting <strong>of</strong> Russian heavy
engineering companies.
2. Theoretical framework
In <strong>the</strong> mainstream <strong>of</strong> contemporary economic and managerial sciences it is conventional to associate
<strong>the</strong> deliberate development <strong>of</strong> boundaries <strong>of</strong> <strong>the</strong> firm studies as a separate area <strong>of</strong> research with <strong>the</strong>
name <strong>of</strong> Ronald Coase, especially with his 1937’s work “Theory <strong>of</strong> <strong>the</strong> Firm”.
A main proposition <strong>of</strong> this work, forming <strong>the</strong> basis <strong>of</strong> such a branch <strong>of</strong> economic <strong>the</strong>ory as <strong>the</strong> New
Institutional Economics, is a proposition that <strong>the</strong> boundaries <strong>of</strong> <strong>the</strong> firm are determined by a balance
<strong>of</strong> <strong>the</strong> “costs <strong>of</strong> using <strong>the</strong> price mechanism” (later called “transaction costs” by a number <strong>of</strong> different
authors in 1950s; a good general definition <strong>of</strong> <strong>the</strong> transaction costs concept was given by Kenneth
Arrow in [Arrow, 1969], where <strong>the</strong>se were referred to as “<strong>the</strong> costs <strong>of</strong> using <strong>the</strong> economic system”)
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Evgeny Blagov
internal and external to <strong>the</strong> firm. Thus, a firm emerges as means to minimize <strong>the</strong> external transaction
costs, and reaches its growth limits when <strong>the</strong> intrafirm transaction costs level <strong>of</strong>f <strong>the</strong> external. This
<strong>the</strong>oretical proposition looks logical, but does not give clear definitions <strong>of</strong> what costs can be attributed
as transaction costs and how <strong>the</strong>se could be operationalized. One <strong>of</strong> <strong>the</strong> most successful attempts to
concretize <strong>the</strong> Coasian insights was made in <strong>the</strong> early 1970s by Oliver Williamson, who suggested <strong>the</strong>
main criteria influencing <strong>the</strong> transaction costs level and thus <strong>the</strong> firm boundaries to be <strong>the</strong> specificity <strong>of</strong>
<strong>the</strong> firm’s actives involved into <strong>the</strong> transaction, <strong>the</strong> frequency <strong>of</strong> <strong>the</strong> transaction and <strong>the</strong> presence <strong>of</strong>
uncertainty in <strong>the</strong> transaction.
On <strong>the</strong> basis <strong>of</strong> <strong>the</strong>se criteria, Williamson considers <strong>the</strong> following firm boundaries decision variants:
“market governance” for <strong>the</strong> non-specific transactions both <strong>of</strong> accidental and regularly occurring
nature, “trilateral governance” for transactions <strong>of</strong> accidental nature but connected with more
specialized (“mixed” or “highly specialized”) investments, “bilateral governance” for regular
transactions with mixed or highly specialized investments, and, finally, “shared governance” for
regularly occurring transactions with highly specialized (“idiosyncratic”) investments.
Respectively, according to this model, <strong>the</strong> increase in <strong>the</strong> investments specialization in <strong>the</strong> transaction
and <strong>the</strong> transaction frequency and uncertainty promote <strong>the</strong> shift from market to shared governance <strong>of</strong>
<strong>the</strong> transaction.
Studies <strong>of</strong> <strong>the</strong> boundaries <strong>of</strong> <strong>the</strong> firm within specific industrial contexts are a fairly significant part <strong>of</strong>
<strong>the</strong> empirical literature on this topic (see a review <strong>of</strong> such studies in, e.g., [Geyskens, Steenkamp,
Kumar, 2006] and [Baker, Hubbard, 2003]). However, this literature is lacking studies <strong>of</strong> heavy
engineering as a specific industry-level focus <strong>of</strong> attention, creating a significant knowledge gap to be
filled.
Ano<strong>the</strong>r actual gap in <strong>the</strong> boundaries <strong>of</strong> <strong>the</strong> firm literature is <strong>the</strong> study <strong>of</strong> peculiarities <strong>of</strong> knowledge
resources as <strong>the</strong> main boundary decisions object (a problem defined by K. Pavitt in [Pavitt, 1999] as
<strong>the</strong> “knowledge boundaries <strong>of</strong> <strong>the</strong> firm” problem). In this study <strong>the</strong> type <strong>of</strong> knowledge resources under
scrutiny is <strong>the</strong> technological knowledge reposed both in separate individuals’ tacit knowledge and in
more formalized nature <strong>of</strong>, e.g., technological documentation and o<strong>the</strong>r knowledge regarding <strong>the</strong>
technologies used by an organization that are included or potentially can be included into <strong>the</strong>
company’s physical and organizational infrastructure.
3. Research design and methodology
Hypo<strong>the</strong>ses
The formulations <strong>of</strong> <strong>the</strong> dependent variables <strong>of</strong> <strong>the</strong> research are in general based on <strong>the</strong>
understanding <strong>of</strong> <strong>the</strong> firm boundaries decision variants suggested in [Williamson, 1975], i.e., making
<strong>the</strong> necessary products or services by <strong>the</strong> firm’s own strength, non-recurring purchase <strong>of</strong> necessary
products or services from an external counterparty, acquisition <strong>of</strong> such a counterparty by a focal firm,
and, finally, creating <strong>of</strong> a long-term alliance relationship between <strong>the</strong> focal firm and <strong>the</strong> necessary
products or services provider.
As this research is dedicated to technological knowledge renovation, <strong>the</strong> “necessary products or
services” here refer to <strong>the</strong> technological knowledge needed by <strong>the</strong> company to sustain <strong>the</strong> competitive
advantage under rapid technological change conditions. Respectively, <strong>the</strong> dependent variable
variants formulations are <strong>the</strong> “creation <strong>of</strong> necessary knowledge by own strength”, “focal firm’s
acquisition <strong>of</strong> <strong>the</strong> necessary knowledge provider”, “long-term alliance relationship creation with <strong>the</strong>
necessary knowledge provider”, and, finally, “non-recurring purchase <strong>of</strong> necessary knowledge from an
external provider on <strong>the</strong> open market” (in <strong>the</strong> hypo<strong>the</strong>ses for <strong>the</strong> facility <strong>of</strong> understanding purpose
referred to as “…to buy necessary knowledge on <strong>the</strong> open market”).
What factors can be influencing <strong>the</strong> firms’ choice between <strong>the</strong>se variants? Following <strong>the</strong> Coasian and
Williamsonian logic it can be suggested that such factors must be exerting influence on <strong>the</strong> internal
and external transaction costs balance, on <strong>the</strong> comparative asset specificity between <strong>the</strong> focal firm
and its counterparties and <strong>the</strong> necessary knowledge renovation frequency and uncertainty.
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The first <strong>of</strong> such factors is suggested to be <strong>the</strong> importance <strong>of</strong> <strong>the</strong> knowledge resource involved in <strong>the</strong>
focal transaction for <strong>the</strong> firm’s result as it can lay in <strong>the</strong> foundation <strong>of</strong> <strong>the</strong> comparative bargaining
power advantages between <strong>the</strong> firm and its potential counterparties.
As <strong>the</strong> focal knowledge resource’s importance for <strong>the</strong> firm’s result can be suggested to increase <strong>the</strong>
external transaction costs <strong>of</strong> <strong>the</strong> focal firm, it can be supposed that <strong>the</strong> bigger this importance, <strong>the</strong>
more <strong>the</strong> firm would be inclined to choose <strong>the</strong> technological knowledge renovation sources oriented
on <strong>the</strong> external transaction costs minimization, and, respectively, <strong>the</strong> less inclined to choose <strong>the</strong>
sources oriented on <strong>the</strong> internal transaction costs minimization.
Consequently, such a hypo<strong>the</strong>sis can be suggested about this factor’s influence on <strong>the</strong> choice <strong>of</strong>
technological knowledge renovation variants:
Hypo<strong>the</strong>sis 1:
Increase in focal knowledge resource’s importance for <strong>the</strong> firm’s result would lead to <strong>the</strong>
increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to create necessary knowledge by own
strength, to acquire <strong>the</strong> necessary knowledge provider and to create a long-term alliance
relationship with <strong>the</strong> necessary knowledge provider (in case <strong>of</strong> increase in <strong>the</strong> ability to
control <strong>the</strong> potential counterparty’s actions) and, in turn, to decrease in probability <strong>of</strong> <strong>the</strong>
firm’s inclination to buy necessary knowledge on <strong>the</strong> open market.
It seems evident that <strong>the</strong> potential counterparties would never have an interest to get involved in a
transaction if <strong>the</strong> object <strong>of</strong> <strong>the</strong> transaction (in this research, <strong>the</strong> technological knowledge resource)
has a zero importance for <strong>the</strong> result <strong>of</strong> each <strong>of</strong> <strong>the</strong>m. Consequently, it can be suggested that <strong>the</strong>
comparative bargaining power <strong>of</strong> <strong>the</strong> focal firm and a (potential) counterparty depends on <strong>the</strong>
comparative degree <strong>of</strong> <strong>the</strong> focal knowledge resource’s importance for <strong>the</strong> economical results <strong>of</strong> each
<strong>of</strong> <strong>the</strong>m. Thus, <strong>the</strong> congruence between <strong>the</strong> transaction parties’ results seems to be an important
factor.
In [Bushman, Penno, Indjejikian, 2000; Datar, 2000] <strong>the</strong> congruence between <strong>the</strong> results <strong>of</strong> <strong>the</strong> focal
firm as a principal and a (potential) counterparty as an agent is divided into such two parts as <strong>the</strong>
congruence being a result <strong>of</strong> <strong>the</strong> principal’s intentional actions and <strong>the</strong> degree being not; an abstract
formulation <strong>of</strong> <strong>the</strong> former is more or less <strong>the</strong> focal firm’s ability to control <strong>the</strong> actions <strong>of</strong> <strong>the</strong> potential
counterparty. When looking on <strong>the</strong> congruence factor from <strong>the</strong> Williamsonian perspective it can be
suggested that <strong>the</strong> increase in <strong>the</strong> results congruence can decrease <strong>the</strong> potential uncertainty in <strong>the</strong>
counterparties’ relationships that must lead to <strong>the</strong> external transaction costs decrease and thus to <strong>the</strong>
increase in <strong>the</strong> firm’s probability to choose <strong>the</strong> technological knowledge renovation variants oriented
on <strong>the</strong> internal transaction costs minimization.
Thus, such a hypo<strong>the</strong>sis can be formulated:
Hypo<strong>the</strong>sis 2:
Increase in <strong>the</strong> congruence <strong>of</strong> results <strong>of</strong> <strong>the</strong> focal firm and a potential counterparty would
lead to <strong>the</strong> increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to buy necessary knowledge on
<strong>the</strong> open market and to create a long-term alliance relationship with <strong>the</strong> necessary
knowledge provider, and, in turn, to <strong>the</strong> decrease in probability <strong>of</strong> <strong>the</strong> firm’s inclination to
create necessary knowledge by own strength and to acquire <strong>the</strong> necessary knowledge
provider.
Most <strong>of</strong> <strong>the</strong> empirical studies <strong>of</strong> <strong>the</strong> firm boundaries based on <strong>the</strong> Williamsonian framework (see a
comparative analysis <strong>of</strong> such studies in [Geyskens, Steenkamp, Kumar, 2006]) show that <strong>the</strong> factor <strong>of</strong>
<strong>the</strong> framework mostly influential to <strong>the</strong> decision making is <strong>the</strong> asset specificity.
The formulation “asset specificity” is too vague to be used in an empirical study as is, so in most <strong>of</strong> <strong>the</strong>
studies proxies for it are created. In this research such proxies are <strong>the</strong> factors <strong>of</strong> uniqueness <strong>of</strong> <strong>the</strong>
firm’s technological knowledge resources as a whole, <strong>of</strong> <strong>the</strong> knowledge resource involved in a focal
transaction, and <strong>of</strong> <strong>the</strong> firm’s production in comparison with <strong>the</strong> respective market and industry.
These proxies are supposed to have a positive correlation with <strong>the</strong> asset specificity, i.e., <strong>the</strong> increase
in <strong>the</strong>se factors can be explained as <strong>the</strong> increase in specificity <strong>of</strong> assets involved in a (potential)
transaction. Thus it can be suggested that <strong>the</strong> increase in <strong>the</strong> uniqueness set <strong>of</strong> factors would lead to
<strong>the</strong> increase in firm’s probability to choose <strong>the</strong> technological knowledge renovation sources variants
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Evgeny Blagov
oriented on <strong>the</strong> external transaction costs minimization and to decrease <strong>the</strong> probability to choose <strong>the</strong>
variants oriented on <strong>the</strong> internal transaction costs minimization.
Thus, <strong>the</strong> hypo<strong>the</strong>ses concerning <strong>the</strong>se factors have such forms:
Hypo<strong>the</strong>sis 3:
Increase in <strong>the</strong> uniqueness <strong>of</strong> <strong>the</strong> focal knowledge resource in comparison with <strong>the</strong>
respective market and industry would lead to <strong>the</strong> increase in probability <strong>of</strong> <strong>the</strong> firm’s
inclination to create necessary knowledge by own strength, to acquire <strong>the</strong> necessary
knowledge provider and to create a long-term alliance relationship with <strong>the</strong> necessary
knowledge provider (in case <strong>of</strong> increase in <strong>the</strong> ability to control <strong>the</strong> potential
counterparty’s actions) and, in turn, to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firm’s inclination
to buy necessary knowledge on <strong>the</strong> open market.
Hypo<strong>the</strong>sis 4:
Increase in <strong>the</strong> uniqueness <strong>of</strong> <strong>the</strong> firm’s products in comparison with <strong>the</strong> respective
market and industry would lead to <strong>the</strong> increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to
create necessary knowledge by own strength, to acquire <strong>the</strong> necessary knowledge
provider and to create a long-term alliance relationship with <strong>the</strong> necessary knowledge
provider (in case <strong>of</strong> increase in <strong>the</strong> ability to control <strong>the</strong> potential counterparty’s actions)
and, in turn, to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firm’s inclination to buy necessary
knowledge on <strong>the</strong> open market.
Hypo<strong>the</strong>sis 5:
Increase in <strong>the</strong> uniqueness <strong>of</strong> <strong>the</strong> technological knowledge resources <strong>of</strong> <strong>the</strong> firm in <strong>the</strong>
whole in comparison with <strong>the</strong> respective market and industry would lead to <strong>the</strong> increase
in probability <strong>of</strong> <strong>the</strong> firm’s inclination to create necessary knowledge by own strength, to
acquire <strong>the</strong> necessary knowledge provider and to create a long-term alliance relationship
with <strong>the</strong> necessary knowledge provider (in case <strong>of</strong> increase in <strong>the</strong> ability to control <strong>the</strong>
potential counterparty’s actions) and, in turn, to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firm’s
inclination to buy necessary knowledge on <strong>the</strong> open market.
In <strong>the</strong> value creation logics analysis in [Thompson, 1967; Roos, Pike, Fernström, 2005] <strong>the</strong>
uniqueness <strong>of</strong> <strong>the</strong> firm's production in relation to <strong>the</strong> respective market and industry is connected to
<strong>the</strong> variable <strong>of</strong> <strong>the</strong> firm’s predominant orientation on <strong>the</strong> price- or cost-based competition, as <strong>the</strong>
orientation on cost-based competition can be practically embodied in <strong>the</strong> high level <strong>of</strong> standardization
<strong>of</strong> both <strong>the</strong> used technologies and <strong>the</strong> production <strong>of</strong> <strong>the</strong> firm aiming to reach <strong>the</strong> maximal scale
economy, while <strong>the</strong> orientation on quality-based competition can be embodied in lower levels <strong>of</strong>
standardization <strong>of</strong> <strong>the</strong> both aiming to reach <strong>the</strong> maximal possible supply diversity.
Thus, <strong>the</strong> following hypo<strong>the</strong>ses can be formulated:
Hypo<strong>the</strong>sis 6:
Increase in <strong>the</strong> firm’s orientation on <strong>the</strong> price-based competition would lead to <strong>the</strong>
increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to buy necessary knowledge on <strong>the</strong> open
market and to create a long-term alliance relationship with <strong>the</strong> necessary knowledge
provider (in case <strong>of</strong> increase in <strong>the</strong> ability to control <strong>the</strong> potential counterparty’s actions),
and, in turn, to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firm’s inclination to create necessary
knowledge by own strength and to acquire <strong>the</strong> necessary knowledge provider.
Hypo<strong>the</strong>sis 7:
Increase in <strong>the</strong> firm’s orientation on <strong>the</strong> quality-based competition would lead to <strong>the</strong>
increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to create necessary knowledge by own
strength, to acquire <strong>the</strong> necessary knowledge provider and to create a long-term alliance
relationship with <strong>the</strong> necessary knowledge provider (in case <strong>of</strong> increase in <strong>the</strong> ability to
control <strong>the</strong> potential counterparty’s actions) and, in turn, to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong>
<strong>the</strong> firm’s inclination to buy necessary knowledge on <strong>the</strong> open market.
The technological knowledge used by a firm under rapid technological change conditions can
undergo changes while being used, thus such an influencing factor can also be suggested as <strong>the</strong>
possible necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge resource after its
implementation. This factor can be supposed to increase <strong>the</strong> potential uncertainty and frequency <strong>of</strong>
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Evgeny Blagov
<strong>the</strong> transaction and thus to increase <strong>the</strong> firm’s probability to choose <strong>the</strong> variants <strong>of</strong> technological
knowledge renovation sources oriented on <strong>the</strong> external transaction costs minimization and to
decrease <strong>the</strong> probability to choose <strong>the</strong> variants oriented on <strong>the</strong> internal transaction costs minimization.
Thus, such a hypo<strong>the</strong>sis concerning this factor can be formulated:
Hypo<strong>the</strong>sis 8:
Increase in necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge
resource would lead to <strong>the</strong> increase in probability <strong>of</strong> <strong>the</strong> firm’s inclination to create
necessary knowledge by own strength, to acquire <strong>the</strong> necessary knowledge provider and
to create a long-term alliance relationship with <strong>the</strong> necessary knowledge provider (in
case <strong>of</strong> increase in <strong>the</strong> ability to control <strong>the</strong> potential counterparty’s actions) and, in turn,
to decrease <strong>of</strong> <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firm’s inclination to buy necessary knowledge on <strong>the</strong>
open market.
Ano<strong>the</strong>r suggestion based on <strong>the</strong> assumption <strong>of</strong> this factor’s positive influence on <strong>the</strong> potential
uncertainty <strong>of</strong> <strong>the</strong> knowledge renovation transaction is <strong>the</strong> one that <strong>the</strong> necessity <strong>of</strong> support and
fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge resource can act as a counterbalance to <strong>the</strong> influence <strong>of</strong>
<strong>the</strong> congruence factor on <strong>the</strong> uncertainty <strong>of</strong> <strong>the</strong> transaction.
So, a following hypo<strong>the</strong>sis can be formulated:
Hypo<strong>the</strong>sis 9:
Necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> focal knowledge resource would
diminish <strong>the</strong> influence <strong>of</strong> congruence <strong>of</strong> results <strong>of</strong> <strong>the</strong> focal firm and a potential
counterparty on <strong>the</strong> firm’s choice <strong>of</strong> <strong>the</strong> knowledge renovation source variants.
3.1 Empirical research design
To check each <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>ses four ordered probit models have been built with ordinal variables
characterizing <strong>the</strong> firm’s inclination to choose a variant <strong>of</strong> <strong>the</strong> technological knowledge renovation
sources.
An example <strong>of</strong> such a model could be a questionnaire item reflecting <strong>the</strong> dependent variable <strong>of</strong> <strong>the</strong>
probability <strong>of</strong> <strong>the</strong> firm’s inclination to create necessary knowledge by own strength. A question has a
following formulation:
How <strong>of</strong>ten in cases <strong>of</strong> technological knowledge renovation for <strong>the</strong> last 10 years <strong>the</strong> firm did create <strong>the</strong>
necessary knowledge by own strength?
(0 – it never did; 5 – <strong>the</strong> firm used this source in around 50% cases; 10 – <strong>the</strong> firm did use solely this
source <strong>of</strong> technological knowledge renovation).
An independent variable for that question was designed in <strong>the</strong> following logic:
y = {0, if <strong>the</strong> firm did never create necessary knowledge by own strength; 5, if <strong>the</strong> firm used this
source in around 50% cases; 10, if <strong>the</strong> firm did use solely this source <strong>of</strong> technological knowledge
renovation}.
Description <strong>of</strong> <strong>the</strong> o<strong>the</strong>r three dependent variables was done analogically.
As independent variables ordinal and binary variables characterizing <strong>the</strong> influencing factors were
used.
An example <strong>of</strong> an ordinal variable construction could be a question related to <strong>the</strong> factor <strong>of</strong> <strong>the</strong> focal
knowledge resource’s importance for <strong>the</strong> firm’s result.
A question has a following formulation:
How important for <strong>the</strong> firm in making decisions related to <strong>the</strong> technological knowledge
renovation sources for <strong>the</strong> last 10 years have been a factor <strong>of</strong> <strong>the</strong> focal knowledge
resource’s importance for <strong>the</strong> firm’s result?
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Evgeny Blagov
(0 – absolutely unimportant; 5 – relatively important among o<strong>the</strong>rs; 10 – this factor was one <strong>of</strong> <strong>the</strong> key
factors).
An independent variable for this question was designed using <strong>the</strong> following logic:
x = {0, if <strong>the</strong> factor have been absolutely unimportant; 5, if <strong>the</strong> factor have been relatively important
among o<strong>the</strong>rs; 10, if it have been one <strong>of</strong> <strong>the</strong> key factors}.
All o<strong>the</strong>r independent variables were described analogously except for those related to <strong>the</strong> factors <strong>of</strong>
orientation on cost- or quality-based competition that are not ordinal but binary.
A questionnaire item for <strong>the</strong>se questions is <strong>the</strong> following:
Was <strong>the</strong> firm in <strong>the</strong> last 10 years more oriented on price- or quality-based competition?
(0 – a firm was oriented primarily on cost-based competition; 5 – a firm was nearly equally oriented on
both; 10 – a firm was oriented primarily on quality-based competition).
The independent variables for <strong>the</strong>se factors were built using <strong>the</strong> following logic:
For <strong>the</strong> orientation on cost-based competition:
х = {0, if a firm was oriented primarily on quality-based competition; 1, if a firm was nearly equally
oriented on both or primarily on cost-based competition}.
For <strong>the</strong> orientation on quality-based competition:
х = {0, if a firm was oriented primarily on cost-based competition; 1, if a firm was nearly equally
oriented on both or primarily on quality-based competition}.
4. Results
The questionnaire constructed for <strong>the</strong> quantitative research was sent via e-mail to <strong>the</strong> sample <strong>of</strong> 300
Russian companies <strong>of</strong> <strong>the</strong> industry classification “Heavy, energetic and transport engineering”
randomly selected by region. The response rate was <strong>of</strong> 10%, thus giving <strong>the</strong> resulting sample <strong>of</strong> 30
companies.
At <strong>the</strong> 0.05 significance level most <strong>of</strong> <strong>the</strong> equations resulted to be not statistically significant, except
for <strong>the</strong> equation testing <strong>the</strong> hypo<strong>the</strong>sis 3 for a technological knowledge renovation source <strong>of</strong> creating
a long-term alliance relationship with a necessary knowledge provider (<strong>the</strong> hypo<strong>the</strong>sis is partly not
supported because <strong>the</strong> coefficient <strong>of</strong> <strong>the</strong> uniqueness variable is negative (-2,32) along with <strong>the</strong>
positive coefficient (2,07) <strong>of</strong> <strong>the</strong> ability to control variable) and all <strong>the</strong> equations testing <strong>the</strong> hypo<strong>the</strong>ses
8 and 9.
From <strong>the</strong> equations testing <strong>the</strong> hypo<strong>the</strong>sis 8, <strong>the</strong> equation for making necessary knowledge by owns
strength is supporting <strong>the</strong> hypo<strong>the</strong>sis due to <strong>the</strong> positive coefficient <strong>of</strong> <strong>the</strong> independent variable
(0.186). The equation for buying necessary knowledge on <strong>the</strong> open market is statistically significant
but <strong>the</strong> coefficient <strong>of</strong> <strong>the</strong> independent variable (0.053) is not significant. The equation for acquisition <strong>of</strong>
<strong>the</strong> necessary knowledge provider is not supporting <strong>the</strong> hypo<strong>the</strong>sis due to <strong>the</strong> negative coefficient <strong>of</strong>
<strong>the</strong> independent variable (-0.164). Finally, <strong>the</strong> equation for creating long-term alliance relationship
with <strong>the</strong> necessary knowledge provider is not supporting <strong>the</strong> hypo<strong>the</strong>sis due to <strong>the</strong> negative
coefficient <strong>of</strong> <strong>the</strong> independent variable <strong>of</strong> necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> focal
knowledge resource (-0.387) along with <strong>the</strong> positive coefficient <strong>of</strong> <strong>the</strong> variable <strong>of</strong> a focal firm’s ability to
control <strong>the</strong> actions <strong>of</strong> a potential counterparty (0,473).
From <strong>the</strong> equations testing <strong>the</strong> hypo<strong>the</strong>sis 9, <strong>the</strong> equation for making necessary knowledge by own
strength is not able to be used for <strong>the</strong> hypo<strong>the</strong>sis testing due to <strong>the</strong> insignificance <strong>of</strong> <strong>the</strong> independent
variables <strong>of</strong> <strong>the</strong> congruence factor (p-values for <strong>the</strong> results congruence and ability to control a
potential counterparty are respectively 0.084 and 0.414). The equation for buying necessary
knowledge on <strong>the</strong> open market is partly supporting <strong>the</strong> hypo<strong>the</strong>sis: <strong>the</strong> variable <strong>of</strong> <strong>the</strong> focal firm’s
ability to control <strong>the</strong> actions <strong>of</strong> <strong>the</strong> potential counterparty is not significant (<strong>the</strong> p-value <strong>of</strong> this variable
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Evgeny Blagov
is 0.573), while <strong>the</strong> coefficients for <strong>the</strong> results congruence and necessity <strong>of</strong> support and fur<strong>the</strong>r
development <strong>of</strong> <strong>the</strong> focal knowledge resource are significant with <strong>the</strong> coefficients <strong>of</strong> respectively 0.346
and -0.385. The equation for acquiring <strong>the</strong> necessary knowledge provider is partly supported because
<strong>the</strong> independent variable <strong>of</strong> <strong>the</strong> focal firm’s ability to control <strong>the</strong> actions <strong>of</strong> <strong>the</strong> potential counterparty is
not significant (with <strong>the</strong> p-value <strong>of</strong> 0.490) while <strong>the</strong> significant variables (results congruence and
necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> focal knowledge) have coefficients <strong>of</strong> respectively
0.485 and -0.074. Finally, <strong>the</strong> equation for creating a long-term alliance relationship with <strong>the</strong>
necessary knowledge provider is supporting <strong>the</strong> hypo<strong>the</strong>sis with <strong>the</strong> coefficients <strong>of</strong> 0.339, 0.441 and -
0.506 for <strong>the</strong> independent variables <strong>of</strong> <strong>the</strong> results congruence, ability to control and necessity <strong>of</strong>
support and fur<strong>the</strong>r development <strong>of</strong> focal knowledge resource respectively.
5. Discussion
Although <strong>the</strong> comparative analysis <strong>of</strong> empirical boundaries <strong>of</strong> <strong>the</strong> firm studies based on <strong>the</strong>
Williamson’s framework show that <strong>the</strong> most significant factor from those included in <strong>the</strong> framework is
<strong>the</strong> asset specificity, <strong>the</strong> results <strong>of</strong> this study are not directly supporting this. From <strong>the</strong> factors
analyzed in <strong>the</strong> study being direct proxies to asset specificity (three “uniqueness” factors) only one
(namely <strong>the</strong> uniqueness <strong>of</strong> <strong>the</strong> focal knowledge resource in comparison with <strong>the</strong> respective market
and industry) appears to exert statistically significant influence on <strong>the</strong> firms’ inclination to choose only
one <strong>of</strong> <strong>the</strong> technological knowledge renovation sources, namely, <strong>the</strong> long-term alliance relationship
creation with <strong>the</strong> necessary knowledge provider. However, this influence does not follow <strong>the</strong>
respective hypo<strong>the</strong>sis suggestion: even in <strong>the</strong> case <strong>of</strong> growth <strong>of</strong> <strong>the</strong> ability to control <strong>the</strong> actions <strong>of</strong> this
provider, this factor doesn’t show <strong>the</strong> positive influence <strong>of</strong> <strong>the</strong> firm’s inclination to create such
relationships.
It can be suggested that such result is caused by specific traits <strong>of</strong> knowledge resources that differ
<strong>the</strong>se from o<strong>the</strong>r kinds <strong>of</strong> resources. Several authors (e.g., D. Teece (see, e.g., [Teece, 1998], H.
Varian (e.g., [Varian, 2000]), G. Roos et al. ([Roos et al., 2005]) point out such properties <strong>of</strong> <strong>the</strong>
knowledge resources as <strong>the</strong>ir non-exclusivity and non-diminishing marginal revenue that sharpen <strong>the</strong>
time constraints <strong>of</strong> making decisions about <strong>the</strong> boundaries <strong>of</strong> <strong>the</strong> firm ([Teece, 1998]). This can be
supposed to lower <strong>the</strong> significance <strong>of</strong> asset specificity for such decisions, at least in <strong>the</strong> static view,
and make <strong>the</strong> asset specificity more an issue <strong>of</strong> time, for example, <strong>of</strong> time advantage <strong>of</strong> <strong>the</strong> firstmovers
in <strong>the</strong> commercial implementation <strong>of</strong> <strong>the</strong> (focal) knowledge resource.
In <strong>the</strong> results <strong>of</strong> this study this suggestion is supported by <strong>the</strong> significance <strong>of</strong> <strong>the</strong> factor <strong>of</strong> necessity <strong>of</strong>
support and fur<strong>the</strong>r development <strong>of</strong> focal knowledge resource. As <strong>the</strong> increase in this factor increases
<strong>the</strong> firm’s inclination to create necessary knowledge by own strength and counteracts <strong>the</strong> results
congruence factor’s pro-externalization influence, it can be suggested that in <strong>the</strong> dynamic context <strong>the</strong>
knowledge resources follow <strong>the</strong> transaction cost <strong>the</strong>ory considerations. So, <strong>the</strong> fur<strong>the</strong>r directions <strong>of</strong>
<strong>the</strong> research development could contain a deeper inquiry into <strong>the</strong> temporal aspects <strong>of</strong> <strong>the</strong>
technological knowledge renovation process, for example, <strong>the</strong> role <strong>of</strong> first-mover advantages and
standard-setting in <strong>the</strong> decisions <strong>of</strong> <strong>the</strong> heavy engineering companies about technological renovation
sources. Beside that, possible directions <strong>of</strong> fur<strong>the</strong>r development <strong>of</strong> this research could be a
comparative study <strong>of</strong> firms belonging to different industries to analyze possible influence <strong>of</strong> <strong>the</strong>
industrial specificity on <strong>the</strong> researched interdependencies.
6. Conclusions
Following <strong>the</strong> goal <strong>of</strong> studying <strong>the</strong> factors influencing a company’s choice <strong>of</strong> technological knowledge
renovation sources in <strong>the</strong> Russian heavy engineering industry a set <strong>of</strong> hypo<strong>the</strong>ses adapting R.
Coase’s and O. Williamson’s frameworks to <strong>the</strong> analysis <strong>of</strong> knowledge boundaries <strong>of</strong> <strong>the</strong> firm was
created and tested on a sample <strong>of</strong> 30 Russian heavy engineering firms. The main founding <strong>of</strong> <strong>the</strong>
study is that <strong>the</strong> factor mostly influencing <strong>the</strong> technological knowledge renovation sources choice from
<strong>the</strong> factors tested is <strong>the</strong> necessity <strong>of</strong> support and fur<strong>the</strong>r development <strong>of</strong> focal knowledge resource
that increases <strong>the</strong> probability <strong>of</strong> <strong>the</strong> firms’ inclination to internalization <strong>of</strong> <strong>the</strong> technological knowledge
renovation. This result can be interpreted as supporting <strong>the</strong> suggestions <strong>of</strong> R. Coase’s and O.
Williamson’s frameworks in <strong>the</strong> dynamic temporal settings, thus leading to such possible fur<strong>the</strong>r
directions <strong>of</strong> research development as deeper inquiry into <strong>the</strong> temporal aspects <strong>of</strong> <strong>the</strong> technological
knowledge renovation process, for example, <strong>the</strong> role <strong>of</strong> first-mover advantages and standard-setting
in <strong>the</strong> decisions <strong>of</strong> <strong>the</strong> heavy engineering companies about technological renovation sources.
1039

References
Evgeny Blagov
Arrow, K.J. (1969) “The Organization <strong>of</strong> Economic Activity: Issues Pertinent to <strong>the</strong> Choice <strong>of</strong> <strong>the</strong> Market versus
Non-Market Allocation”, Paper read at U.S. Joint Economic Committee, 91st Congress, 1st Session, The
Analysis and Evolution <strong>of</strong> Public Expenditure: The PPB System (1), Washington, D.C.
Baker, G., Hubbard, T. (2003) “Make versus buy in trucking: asset ownership, job design, and information”,
American Economic Review, Vol 93, No 3, pp 551–572.
Bushman, R.M., Penno, M.C., and Indjejikian, R.J. (2000) “Private Predecision Information, Performance
Measure Congruity, and <strong>the</strong> Value <strong>of</strong> Delegation”, Contemporary Accounting Research, Vol 17, No 4, pp.
561–587.
Coase, R.H. (1937) “The nature <strong>of</strong> <strong>the</strong> firm”, Economica, Vol 4, No 16, pp. 386–405.
Datar, S.M. (2000) “Discussion <strong>of</strong> "Private Predecision Information, Performance Measure Congruity, and <strong>the</strong>
Value <strong>of</strong> Delegation"”, Contemporary Accounting Research, Vol 17, No 4, pp. 589–94.
Geyskens, I., Steenkamp, J.-B. E. M., and Kumar, N. (2006) ”Make, buy, or ally: a transaction cost <strong>the</strong>ory metaanalysis”,
Academy <strong>of</strong> Management Journal, Vol 49, No. 3, pp. 519–543.
Pavitt, K. (1999) Technology, Management and Systems <strong>of</strong> Innovation, Edward Elgar, Cheltenham.
Roos, G., Pike, S., and Fernström, L. (2005) Managing intellectual capital in practice, Butterworth Heinemann,
London.
Thompson, J.D. (1967) Organizations in Action, McGraw-Hill, New York.
Varian, H.R. (2000) “Buying, Sharing and Renting Information Goods”, Journal <strong>of</strong> Industrial Economics, Vol 48,
Issue 4, pp. 473–488.
Wallace, D.P. (2007) Knowledge Management: Historical and Cross-Disciplinary Themes, Libraries Unlimited;
2nd edition.
Williamson, O.E. (1975) Markets and Hierarchies: Analysis and Antitrust Implications, Free Press, New York.
1040

Multipr<strong>of</strong>essional Communities <strong>of</strong> Practice in a Large-scale
Healthcare Collaboration: Formation, Identity Building and
Knowledge Sharing
Roman Kislov
Manchester Business School, The University <strong>of</strong> Manchester, UK
roman.kislov@postgrad.mbs.ac.uk
Abstract: While it is implicitly implied by policy-makers that healthcare pr<strong>of</strong>essionals and healthcare
organisations are able and willing to work toge<strong>the</strong>r in <strong>the</strong> improvement <strong>of</strong> healthcare services, <strong>the</strong>se assumptions
are not always grounded in sound scientific evidence. This study aims to address this issue by exploring <strong>the</strong>
reality <strong>of</strong> healthcare collaboration in <strong>the</strong> implementation strand <strong>of</strong> <strong>the</strong> Collaboration for Leadership in Applied
Health Research and Care (CLAHRC) for Greater Manchester. The study uses <strong>the</strong> <strong>the</strong>ory <strong>of</strong> communities <strong>of</strong>
practice (CoPs) to explore <strong>the</strong> process <strong>of</strong> joint working in primary care in <strong>the</strong> context <strong>of</strong> <strong>the</strong> English NHS and
reflect on how this process is influenced by <strong>the</strong> CLAHRC. It specifically focuses on <strong>the</strong> development <strong>of</strong>
multipr<strong>of</strong>essional and multi-organisational CoPs in healthcare organisations, reconciliation <strong>of</strong> pr<strong>of</strong>essional and
organisational identities in <strong>the</strong> process <strong>of</strong> collaborative working, and knowledge sharing across interpr<strong>of</strong>essional
and inter-organisational boundaries within and across CoPs. Underpinned by <strong>the</strong> critical realist approach, <strong>the</strong>
research uses a qualitative case study as an overarching research methodology, with specific methods
comprising documentary analysis, semi-structured interviews and participant observation. It adopts a purposeful
sampling strategy, with respondents including general practitioners, nurses, managers, hospital consultants,
knowledge transfer associates and management academics. The data is subjected to template analysis
facilitated by NVivo s<strong>of</strong>tware. Initial data analysis upon <strong>the</strong> completion <strong>of</strong> <strong>the</strong> first stage <strong>of</strong> fieldwork has led to <strong>the</strong>
formulation <strong>of</strong> <strong>the</strong> following <strong>the</strong>oretical propositions:
• The organisational landscape in <strong>the</strong> NHS primary care is represented by multiple co-existing and
overlapping CoPs, which are mainly centred around general practices, pr<strong>of</strong>essional groups and
geographical areas.
• Multipr<strong>of</strong>essional CoPs in primary care are characterised by such features as division <strong>of</strong> functions,
interconnectedness with co-existent unipr<strong>of</strong>essional communities, and operational proximity <strong>of</strong> its
members.
• Formation <strong>of</strong> emergent multipr<strong>of</strong>essional and multi-organisational CoPs is influenced by <strong>the</strong>ir
relationship with pre-existing communities and networks, enthusiasm <strong>of</strong> CoP participants and adequate
leadership.
• Identity formation in <strong>the</strong>se newly formed communities takes place in <strong>the</strong> context <strong>of</strong> multimembership in
several relevant CoPs and may be hampered by strong identification with existing pr<strong>of</strong>essional and
organisational communities.
• Knowledge sharing between CoPs can be promoted by a combination <strong>of</strong> external and internal facilitation
whereby knowledge brokers deploy a number <strong>of</strong> context-tailored boundary objects and boundary
interactions.
• Distinct boundaries between general practices, which are only partially permeable, as well as marked
barriers to <strong>the</strong> development <strong>of</strong> supra-organisational CoPs in primary care, underscore <strong>the</strong> importance <strong>of</strong>
external facilitation in <strong>the</strong> process <strong>of</strong> implementing change.
• Achieving and sustaining improvement introduced by healthcare collaboration may be influenced by <strong>the</strong>
degree <strong>the</strong> change is embedded in <strong>the</strong> routines <strong>of</strong> <strong>the</strong> organisation, <strong>the</strong> presence <strong>of</strong> knowledge sharing
channels between all CoPs involved and <strong>the</strong> clarity <strong>of</strong> goals agreed by <strong>the</strong> participants.
By fur<strong>the</strong>r exploring <strong>the</strong>se propositions in <strong>the</strong> next stage <strong>of</strong> <strong>the</strong> fieldwork, this study <strong>of</strong>fers <strong>the</strong> prospect <strong>of</strong> making
a contribution to analytic frameworks applied to <strong>the</strong> understanding <strong>of</strong> interpr<strong>of</strong>essional and inter-organisational
work, enhancing <strong>the</strong> understanding <strong>of</strong> barriers to joint working, and identifying effective knowledge management
strategies which could be utilised in <strong>the</strong> UK healthcare context.
Keywords: communities <strong>of</strong> practice; healthcare collaboration; multipr<strong>of</strong>essionality; knowledge sharing; identity
1. Background
With a policy turn towards collaboration and partnership working over <strong>the</strong> last decade, <strong>the</strong> issues
related to interpr<strong>of</strong>essional and inter-organisational joint working in UK healthcare require more
attention from researchers. While it is implicitly implied by policy-makers that healthcare pr<strong>of</strong>essionals
and healthcare organisations are able and willing to work toge<strong>the</strong>r in <strong>the</strong> improvement <strong>of</strong> healthcare
services, <strong>the</strong>se assumptions are not always grounded in sound scientific evidence. Fur<strong>the</strong>rmore, we
do not always understand what mechanisms and contextual factors underpin successful collaboration,
how <strong>the</strong> benefits and drawbacks <strong>of</strong> joint working are perceived by healthcare pr<strong>of</strong>essionals, and how
people from different pr<strong>of</strong>essional and organisational backgrounds learn to effectively communicate
with each o<strong>the</strong>r when brought toge<strong>the</strong>r in <strong>the</strong> healthcare field.
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Roman Kislov
This research looks at <strong>the</strong>se issues through <strong>the</strong> lens <strong>of</strong> communities <strong>of</strong> practice (CoPs), a <strong>the</strong>oretical
approach developed initially by Lave and Wenger (1991) as part <strong>of</strong> <strong>the</strong> <strong>the</strong>ory <strong>of</strong> situated learning. A
CoP is defined as ‘a group <strong>of</strong> people who share a concern, a set <strong>of</strong> problems, or a passion about a
particular topic, and who deepen <strong>the</strong>ir understanding and knowledge <strong>of</strong> this area by interacting on an
ongoing basis’ (Wenger et al. 2002, p. 4). CoPs can range in size; <strong>the</strong>y can be long or short lived, colocated
or distributed, homogeneous or heterogeneous, spontaneous or intentional, unrecognised or
institutionalised. Organisations can in turn be interpreted as <strong>the</strong> ‘communities <strong>of</strong> communities’ (Brown
and Duguid 1991), or ‘constellations <strong>of</strong> interconnected CoPs’ (Wenger 1998). Focusing on practice,
meaning, learning and identity, <strong>the</strong> CoP approach is being increasingly used in healthcare both as an
analytical tool to explore <strong>the</strong> dynamics <strong>of</strong> healthcare organisations and as a strategy to promote
partnership working, collaborative learning and knowledge mobilisation (le May 2009).
The paper reflects on work in progress and will be structured in <strong>the</strong> following way. The next section
will provide a brief overview <strong>of</strong> <strong>the</strong> relevant CoP literature, which is organised around <strong>the</strong> three main
<strong>the</strong>mes: (1) CoP development; (2) CoP identity building; and (3) CoP knowledge sharing. Section 3
will outline <strong>the</strong> research questions <strong>of</strong> <strong>the</strong> study. Design and methodology <strong>of</strong> <strong>the</strong> study will be
described in Section 4. Section 5 will present <strong>the</strong>oretical propositions, which have been formulated
upon <strong>the</strong> completion <strong>of</strong> <strong>the</strong> first stage <strong>of</strong> fieldwork. This will be followed by a concluding section,
reflecting on <strong>the</strong> main implications <strong>of</strong> <strong>the</strong> study for knowledge management in healthcare
collaboration.
2. Literature review
2.1 CoP development
Wenger (1998) formulates three defining characteristics <strong>of</strong> CoPs. Firstly, CoP members interact with
one ano<strong>the</strong>r, establishing relationships and negotiating meaning <strong>of</strong> <strong>the</strong>ir actions through mutual
engagement. Secondly, members are bound toge<strong>the</strong>r by an understanding <strong>of</strong> a sense <strong>of</strong> joint
enterprise, which entails a common set <strong>of</strong> tasks that CoP members can influence. Finally, <strong>the</strong>y
produce over time a shared repertoire <strong>of</strong> routines, words, tools, stories, symbols or concepts which
become part <strong>of</strong> <strong>the</strong> CoP practice. While <strong>the</strong>re is general agreement on <strong>the</strong>se defining characteristics,
<strong>the</strong> development <strong>of</strong> CoPs in organisations remains an area <strong>of</strong> debate.
CoPs as defined by Lave and Wenger (1991) cannot be deliberately designed by managers; a
business can only establish a team for a particular project, which may later emerge as a CoP (Roberts
2006). Wenger and Snyder (2000) suggest that managers cannot mandate CoPs since <strong>the</strong> organic,
spontaneous, and informal nature <strong>of</strong> <strong>the</strong> communities makes <strong>the</strong>m resistant to supervision and
interference. At <strong>the</strong> same time <strong>the</strong>y argue that CoPs may benefit from cultivation by managers, who
should identify communities which can potentially enhance <strong>the</strong> company’s strategic capabilities and
provide <strong>the</strong> infrastructure that will support such communities and enable <strong>the</strong>m to apply <strong>the</strong>ir expertise
effectively. Finally, more recent contributions suggest that CoPs can be created intentionally;
fur<strong>the</strong>rmore, <strong>the</strong>se deliberate communities might be more useful for an organisation than <strong>the</strong> organic
ones (Wenger et al. 2002; Saint-Onge and Wallace 2003).
There is some encouraging evidence on <strong>the</strong> use <strong>of</strong> CoPs to improve inter-agency and
interpr<strong>of</strong>essional working. For example, Lathlean and le May (2002) conclude that CoPs can be a
useful mechanism for <strong>the</strong> development <strong>of</strong> services spanning <strong>the</strong> interests <strong>of</strong> different stakeholders.
They describe <strong>the</strong> following key factors that influence <strong>the</strong> development, functioning and maintenance
<strong>of</strong> multipr<strong>of</strong>essional CoPs: (1) membership—selecting <strong>the</strong> members, <strong>the</strong> extent (active or passive)
and legitimacy <strong>of</strong> <strong>the</strong>ir involvement; (2) commitment to <strong>the</strong> desired goals; (3) relevance to local
communities and <strong>the</strong> existing services; (4) enthusiasm; (5) infrastructure to support <strong>the</strong> work <strong>of</strong> CoPs;
(6) skills in accessing and appraising evidence; and (7) resources.
Similarly, some studies report that deliberate CoPs improve communications and information transfer
and thus enhance interpr<strong>of</strong>essional clinical practice (White et al. 2008; Bentley et al. 2010). It should
be noted, however, that in <strong>the</strong> majority <strong>of</strong> studies using CoPs as an instrument <strong>of</strong> implementing
change, <strong>the</strong> possibility <strong>of</strong> <strong>the</strong>ir deliberate cultivation is <strong>of</strong>ten taken for granted. At <strong>the</strong> same time, it is
not clear how <strong>the</strong>se CoPs are formed, how <strong>the</strong>y develop and in what respects <strong>the</strong>y are different from
multidisciplinary teams which are so common in healthcare. Fur<strong>the</strong>rmore, fur<strong>the</strong>r research is required
to determine <strong>the</strong> extent to which <strong>the</strong>se communities could be created, facilitated and supported. It is
also unclear whe<strong>the</strong>r CoP cultivation should be directed by managers or take a bottom up approach:
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while it has been suggested that managers play a critical role in constructing, aligning or supporting
CoPs, <strong>the</strong>re is little empirical evidence for <strong>the</strong>se assertions (Swan et al. 2002).
Ano<strong>the</strong>r debated issue is <strong>the</strong> possibility <strong>of</strong> CoP formation in healthcare collaboratives which bring
toge<strong>the</strong>r representatives <strong>of</strong> different pr<strong>of</strong>essional and organisational groups (Bate and Robert 2002).
A number <strong>of</strong> factors may explain why <strong>the</strong> formation <strong>of</strong> CoPs in collaboratives is problematic.
Individuals, teams and organisations involved in collaboration may have different objectives (Øvretveit
et al. 2002); multiple internal and external barriers impede <strong>the</strong> development <strong>of</strong> trust and stable
working relationships (Johnson et al. 2003); <strong>the</strong>re is a discrepancy between <strong>the</strong> egalitarian ethos <strong>of</strong>
informal networks and <strong>the</strong> vertical ‘command and control’ structure <strong>of</strong> <strong>the</strong> National Health Service
(NHS) with its emphasis on meeting targets and managing performance (Currie and Suhomlinova
2006). In addition, an excessive legitimisation and formalisation <strong>of</strong> ‘organic’ CoPs can disrupt, ra<strong>the</strong>r
than support, <strong>the</strong>ir knowledge sharing capacity (Addicott et al. 2006).
2.2 CoP identity building
Wenger (1998) suggests <strong>the</strong> following characterisations <strong>of</strong> identity:
Identity as negotiated experience: negotiating <strong>the</strong> meanings <strong>of</strong> participating in a CoP with o<strong>the</strong>r
CoP members;
Identity as community membership: translation <strong>of</strong> <strong>the</strong> CoP membership into an identity as a form
<strong>of</strong> competence;
Identity as a learning trajectory: a coherent process <strong>of</strong> changing forms <strong>of</strong> participation within a
CoP over time;
Identity as nexus <strong>of</strong> multimembership: an experience <strong>of</strong> multimembership in various CoPs and
reconciliation <strong>of</strong> different identities to maintain one identity across boundaries;
Identity as a relation between <strong>the</strong> local and <strong>the</strong> global: negotiating local ways <strong>of</strong> belonging to
broader constellations <strong>of</strong> CoPs.
However, <strong>the</strong> processes <strong>of</strong> identity formation in multipr<strong>of</strong>essional and multi-organisational CoPs are
not specifically addressed in <strong>the</strong> seminal CoP literature. It might thus be useful to refer to <strong>the</strong> wider
literature on pr<strong>of</strong>essional and organisational identification.
Pr<strong>of</strong>essional identity is defined as ‘<strong>the</strong> relatively stable and enduring constellation <strong>of</strong> attributes, beliefs,
values, motives, and experiences in terms <strong>of</strong> which people define <strong>the</strong>mselves in a pr<strong>of</strong>essional role’
(Ibarra 1999, pp.764-765). Development <strong>of</strong> pr<strong>of</strong>essional identity in <strong>the</strong> process <strong>of</strong> healthcare
collaboration should be analysed in relation to pr<strong>of</strong>essional dominance, pr<strong>of</strong>essional collegiality and
pr<strong>of</strong>essional autonomy (Harrison and McDonald 2008). Firstly, interpr<strong>of</strong>essional collaboration may be
more difficult where <strong>the</strong>re are perceived status differentials between team members. Secondly,
dominant pr<strong>of</strong>essions have <strong>the</strong> capacity to become strong groups independent <strong>of</strong> employing
organisations and <strong>of</strong> o<strong>the</strong>r occupations. Finally, where members <strong>of</strong> a pr<strong>of</strong>ession have similar
perceptions, values and experiences, <strong>the</strong>n <strong>the</strong>re will be more agreement among members within a
pr<strong>of</strong>ession than between members <strong>of</strong> two different pr<strong>of</strong>essions (Hudson 2002).
Organisational identification is defined as a ‘form <strong>of</strong> psychological attachment that occurs when
members adopt <strong>the</strong> defining characteristics <strong>of</strong> <strong>the</strong> organisation as defining characteristics for
<strong>the</strong>mselves’ (Dutton et al. 1994, p. 242). A member’s level <strong>of</strong> organisational identification indicates <strong>the</strong>
degree to which his/her membership in an organisation is tied to <strong>the</strong> content <strong>of</strong> his/her self-concept
(Dukerich et al. 2002). Organisational identification is most likely to occur under conditions where <strong>the</strong>
boundaries between one’s own organisation and o<strong>the</strong>r organisations are salient, when membership in
<strong>the</strong> organisation is attractive, and when formal organisational structures optimally reflect similarities
and differences across individuals and groups (Pratt 1998).
A few studies are worth mentioning in this respect. In <strong>the</strong>ir research on interagency and
interpr<strong>of</strong>essional teams in <strong>the</strong> NHS, Robinson and Cottrell (2005) argue that in multipr<strong>of</strong>essional work,
pr<strong>of</strong>essional knowledge boundaries can become blurred and pr<strong>of</strong>essional identity can be challenged
as roles and responsibilities change. Team members may struggle to cope with <strong>the</strong> disintegration <strong>of</strong>
one version <strong>of</strong> pr<strong>of</strong>essional identity before a new version can be built. Baxter and Brumfitt (2008)
suggest that <strong>the</strong> depth <strong>of</strong> pr<strong>of</strong>essional knowledge and skills is perceived as <strong>the</strong> core element in
preserving pr<strong>of</strong>essional differences in interpr<strong>of</strong>essional collaboration; that <strong>the</strong>re is little evidence <strong>of</strong>
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role boundary blurring between pr<strong>of</strong>essions; and, finally, that <strong>the</strong>re is variation among staff whe<strong>the</strong>r
<strong>the</strong>y consider <strong>the</strong>mselves firstly as a member <strong>of</strong> a particular pr<strong>of</strong>ession, or mainly as a member <strong>of</strong> a
local team.
The formation <strong>of</strong> a multipr<strong>of</strong>essional and multi-organisational CoP implies that <strong>the</strong> CoP members will
have to build a new CoP identity which reconciles <strong>the</strong>ir pr<strong>of</strong>essional and organisational identities. It is
not clear, however, what trajectories <strong>the</strong>se new identities take within newly formed CoPs, to what
extent socialisation to a new CoP can override pr<strong>of</strong>essional and organisational differences amongst
staff, and whe<strong>the</strong>r <strong>the</strong>se new, ‘collaborative’ identities within a multipr<strong>of</strong>essional and multiorganisational
CoP are predetermined by dominance, power and status issues.
2.3 CoP knowledge sharing
The CoP concept emerged within <strong>the</strong> situated <strong>the</strong>ory <strong>of</strong> learning which views practice, i.e. a domain <strong>of</strong>
collective knowing and doing, as <strong>the</strong> means through which knowledge dynamics in an organisation
unfold (Wenger 2000). Apart from explicit, codifiable, ‘know-that’ knowledge, collective practice
generates a great deal <strong>of</strong> tacit, ‘know-how’ knowledge, which is embodied in <strong>the</strong> CoP members’
practical skills and expertise (Brown and Duguid 2001). As a result, homogeneous and wellestablished
CoPs create distinct epistemic cultures, i.e. cultures which ‘create and warrant
knowledge’, making up ‘how we know what we know’ (Knorr Cetina 1999, p. 1). Knowledge can flow
relatively easily within such cultures, while it can become sticky at <strong>the</strong> boundaries between <strong>the</strong>m
(Duguid 2005). These boundaries can be classified as syntactic (difference in language), semantic
(difference in meaning) and pragmatic (difference in practice), <strong>the</strong> latter being most difficult to
overcome (Carlile 2002).
The presence <strong>of</strong> distinct epistemic cultures and boundaries in healthcare has been confirmed by a
number <strong>of</strong> empirical studies. It has been shown, for instance, that doctors, nurses and managers have
different attitudes to organisational change which are deeply embedded in <strong>the</strong>ir pr<strong>of</strong>essional cultures
(Degeling et al. 2001; Hall 2005; Morgan and Ogbonna 2008) and that <strong>the</strong>re are multiple differences
<strong>of</strong> values, structures, education and relationships between <strong>the</strong> acute and primary care sectors
(Fitzgerald et al. 2002). Unipr<strong>of</strong>essional CoPs are predominantly unidisciplinary, tend to seal
<strong>the</strong>mselves <strong>of</strong>f from neighbouring pr<strong>of</strong>essional communities and are highly institutionalised. This
causes <strong>the</strong> ‘stickiness’ <strong>of</strong> knowledge across boundaries and hence retards <strong>the</strong> innovation spread
(Ferlie et al. 2005).
Wenger (1998; 2000) distinguishes <strong>the</strong> following types <strong>of</strong> bridges across boundaries:
Knowledge brokers—people who facilitate interaction and coordinate practice between
communities.
Boundary objects—artifacts, discourses and processes possessing interpretative flexibility which
allows <strong>the</strong>m to overcome boundaries and hence contribute to knowledge transfer across CoPs
(Star and Griesemer 1989; Swan et al. 2007).
Boundary interactions among people from different CoPs—<strong>the</strong>se include single or discrete
boundary encounters (e.g. meetings, visits and delegations) and longer-lived practice-based
connections.
Knowledge transfer in cross-disciplinary boundary interactions in healthcare has been explored in a
number <strong>of</strong> recent empirical studies. For example, Gabbay et al. (2003) show that knowledge transfer
in CoPs does not follow <strong>the</strong> model <strong>of</strong> evidence-based practice, but exhibits <strong>the</strong> more socially
determined pattern <strong>of</strong> collective sense-making which is shaped by personal, political and pr<strong>of</strong>essional
agendas. Tagliaventi and Mattarelli (2006) argue that operational proximity in combination with
holding common values regarding shared practice acts as a leveraging tool for knowledge transfer
between doctors, technicians, physicists and nurses in a multipr<strong>of</strong>essional CoP.
Finally, Oborn and Dawson (2010) highlight that while learning in unidisciplinary CoPs develops
through repetition, gaining legitimacy and achieving mastery, learning in multidisciplinary teams is
facilitated by <strong>the</strong> following practices: (1) organising discussions; (2) acknowledging o<strong>the</strong>r
perspectives, and (3) challenging assumptions. Knowledge brokers in multidisciplinary teams are
<strong>of</strong>ten central to <strong>the</strong>ir respective unidisciplinary CoPs and use boundary objects to make <strong>the</strong>ir practices
more visible, interrelate and bridge <strong>the</strong> neighbouring unidisciplinary communities, and hence enable a
shift in understanding across CoPs.
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Apart from <strong>the</strong>se studies, <strong>the</strong>re seems to be little discussion in healthcare literature about boundary
interactions between interconnected/co-located communities and within newly formed
multipr<strong>of</strong>essional CoPs in particular. Although CoPs are seen as a knowledge management tool
transferring tacit knowledge, it is not clear how knowledge transfer is shaped by multiple semantic,
syntactic and pragmatic boundaries within <strong>the</strong>se newly formed heterogeneous groupings, individual
role-identities <strong>of</strong> CoP members and <strong>the</strong>ir perceptions.
3. Research objectives
In light <strong>of</strong> <strong>the</strong> literature review presented above, this study aims to answer <strong>the</strong> following research
questions:
How does a large-scale healthcare collaboration influence <strong>the</strong> development <strong>of</strong> multipr<strong>of</strong>essional
and multi-organisational communities <strong>of</strong> practice (CoPs)?
How do <strong>the</strong> members <strong>of</strong> multipr<strong>of</strong>essional and multi-organisational CoPs reconcile <strong>the</strong>ir
pr<strong>of</strong>essional and organisational identities and a new, ‘collaborative’ CoP identity?
How does a large-scale healthcare collaboration programme influence <strong>the</strong> process <strong>of</strong> knowledge
sharing within and across multipr<strong>of</strong>essional and multiorganisational CoPs?
4. Design and methodology
4.1 Setting
This research is conducted within <strong>the</strong> implementation strand <strong>of</strong> <strong>the</strong> CLAHRC (Collaboration for
Leadership in Applied Health Research and Care) for Greater Manchester—a collaborative
partnership between <strong>the</strong> University <strong>of</strong> Manchester and twenty NHS Trusts across Greater Manchester
aiming to undertake high-quality patient-centred applied health research and to support <strong>the</strong> translation
<strong>of</strong> research evidence into clinical practice (NIHR 2011). In total, nine CLAHRCs were established
across <strong>the</strong> UK in 2008 to enhance knowledge transfer between academic researchers and NHS staff
and thus address <strong>the</strong> “second gap in translation”—a gap in <strong>the</strong> translation <strong>of</strong> new medical
interventions into everyday practice identified by Cooksey's (2006; p.99) Review <strong>of</strong> UK Health
Research Funding. Crossing inter-organisational boundaries, bringing toge<strong>the</strong>r people from different
pr<strong>of</strong>essional backgrounds and a relatively long life span <strong>of</strong> <strong>the</strong> initiative make CLAHRCs an optimal
setting for studying <strong>the</strong> formation <strong>of</strong> multi-organisational and multipr<strong>of</strong>essional CoPs.
4.2 Research participants
More specifically, <strong>the</strong> study looks at <strong>the</strong> following two CLAHRC implementation <strong>the</strong>mes: Chronic Heart
Disease and Chronic Kidney Disease, both <strong>of</strong> which aim to improve healthcare and reduce
inequalities in health for people with cardiovascular conditions through redesigning primary care in
line with existing scientific evidence. The purposeful sampling strategy has been deployed to recruit
research participants, including people actively involved in <strong>the</strong> above two <strong>the</strong>mes and representing a
number <strong>of</strong> pr<strong>of</strong>essional groups – GPs, GPs with special interest, specialist nurses, practice nurses,
hospital consultants, project managers, Primary Care Trust Medical Directors, Knowledge Transfer
Associates and management academics. Around 45-50 research participants will be involved in <strong>the</strong>
project.
4.3 Data collection and analysis
The study is underpinned by <strong>the</strong> ontological and epistemological assumptions <strong>of</strong> critical realism. The
critical realist approach sees <strong>the</strong> main aim <strong>of</strong> <strong>the</strong> social sciences in explaining <strong>the</strong> social world by
uncovering generative mechanisms that get activated in certain contexts and produce different social
phenomena. To uncover <strong>the</strong>se (<strong>of</strong>ten hidden) mechanisms and thus provide a scientific explanation,
<strong>the</strong> processes <strong>of</strong> building, testing and refining <strong>the</strong> <strong>the</strong>ory are usually combined (Outhwaite 1987;
Danermark et al. 2002). This is reflected in <strong>the</strong> design <strong>of</strong> <strong>the</strong> current study, which uses a qualitative
comparative case study as an overarching research methodology and will be comprised <strong>of</strong> <strong>the</strong>
following three stages.
Stage 1 is mainly concerned with <strong>the</strong> identification and description <strong>of</strong> <strong>the</strong> processes taking place in
multipr<strong>of</strong>essional CoPs, mapping <strong>the</strong> main <strong>the</strong>mes and, in <strong>the</strong> end, inducing a list <strong>of</strong> refined
<strong>the</strong>oretical propositions that could potentially be used for explaining <strong>the</strong> phenomenon under study.
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The following methods are used in this stage: (1) analysis <strong>of</strong> organisational documents to provide
background information about <strong>the</strong> projects implemented by <strong>the</strong> <strong>the</strong>mes, <strong>the</strong>ir structure and history; (2)
direct observation <strong>of</strong> <strong>the</strong> activities <strong>of</strong> multidisciplinary teams; and (3) semi-structured ‘orientation’
interviews to get a general overview <strong>of</strong> <strong>the</strong> research topic and formulate <strong>the</strong>oretical propositions to be
explored in <strong>the</strong> second stage <strong>of</strong> <strong>the</strong> project.
In Stage 2, <strong>the</strong>se <strong>the</strong>oretical propositions will be tested to uncover and explain <strong>the</strong> mechanisms which
underlie <strong>the</strong> processes taking place in multipr<strong>of</strong>essional CoPs. In addition to observation which will
continue throughout this stage, a series <strong>of</strong> more structured face-to-face interviews will be conducted
with <strong>the</strong> members <strong>of</strong> <strong>the</strong> multipr<strong>of</strong>essional teams and key informants identified in <strong>the</strong> previous stage.
The data obtained from <strong>the</strong> semi-structured interviews will be <strong>the</strong>n coded and analysed with <strong>the</strong> aid <strong>of</strong>
NVivo s<strong>of</strong>tware.
In Stage 3, template analysis (King 2004) and matrix analysis (Nadin and Cassell 2004) will be
applied to <strong>the</strong> datasets obtained with different methods and at different research sites. The findings
will be triangulated. In this phase, <strong>the</strong> researcher might need to revisit interviewees and research sites
as necessary, for purposes <strong>of</strong> checking and validation <strong>of</strong> initial findings. Initial <strong>the</strong>oretical propositions
developed in stage 1 will be critically revised in light <strong>of</strong> <strong>the</strong> stage 2 data and refined into a series <strong>of</strong>
conclusions related to <strong>the</strong> research questions.
5. Theoretical propositions
The following <strong>the</strong>oretical propositions have been formulated as <strong>the</strong> result <strong>of</strong> analysing <strong>the</strong> data
obtained from Stage 1 <strong>of</strong> fieldwork, which comprised 21 interviews and 32 hours <strong>of</strong> observation.
(1) The organisational landscape in <strong>the</strong> NHS primary care is represented by multiple co-existing and
overlapping CoPs, which are mainly centred around general practices, pr<strong>of</strong>essional groups and
geographical areas.
(2) Multipr<strong>of</strong>essional CoPs in primary care are characterised by such features as division <strong>of</strong>
functions, interconnectedness with co-existent unipr<strong>of</strong>essional communities, and operational
proximity <strong>of</strong> its members.
(3) Formation <strong>of</strong> emergent multipr<strong>of</strong>essional and multi-organisational CoPs is influenced by <strong>the</strong>ir
relationship with pre-existing communities and networks, enthusiasm <strong>of</strong> CoP participants and
adequate leadership.
(4) Identity formation in <strong>the</strong>se newly formed communities takes place in <strong>the</strong> context <strong>of</strong>
multimembership in several relevant CoPs and may be hampered by strong identification with
existing pr<strong>of</strong>essional and organisational communities.
(5) Knowledge sharing between CoPs can be promoted by a combination <strong>of</strong> external and internal
facilitation whereby knowledge brokers deploy a number <strong>of</strong> context-tailored boundary objects and
boundary interactions.
(6) Distinct boundaries between general practices, which are only partially permeable, as well as
marked barriers to <strong>the</strong> development <strong>of</strong> supra-organisational CoPs in primary care, underscore <strong>the</strong>
importance <strong>of</strong> external facilitation in <strong>the</strong> process <strong>of</strong> implementing change.
(7) Achieving and sustaining improvement introduced by healthcare collaboration may be influenced
by <strong>the</strong> degree <strong>the</strong> change is embedded in <strong>the</strong> routines <strong>of</strong> <strong>the</strong> organisation, <strong>the</strong> presence <strong>of</strong>
knowledge sharing channels between all CoPs involved and <strong>the</strong> clarity <strong>of</strong> goals agreed by <strong>the</strong>
participants.
6. Conclusion
While it is preliminary at this stage to make any definitive conclusions about using CoP approach as a
way <strong>of</strong> analysing and/or improving joint working in healthcare organisations, <strong>the</strong> literature review and
initial findings presented in <strong>the</strong> paper raise a number <strong>of</strong> important issues. Firstly, <strong>the</strong> evidence <strong>of</strong>
existence and effects <strong>of</strong> deliberately cultivated CoPs is sketchy and <strong>the</strong> possibility <strong>of</strong> <strong>the</strong>ir creation
from scratch remains questionable. Secondly, regardless <strong>of</strong> what approach to knowledge
management is taken by a collaborative organisation operating in healthcare, its successful
implementation is unlikely without addressing <strong>the</strong> issue <strong>of</strong> shared identity and sense <strong>of</strong> belonging.
Finally, successful utilisation <strong>of</strong> CoPs as a knowledge management tool in <strong>the</strong> NHS will depend on <strong>the</strong>
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availability <strong>of</strong> knowledge brokers enabling institutionalisation <strong>of</strong> change, negotiation <strong>of</strong> shared vision
and knowledge transfer between all CoPs involved.
By exploring <strong>the</strong> <strong>the</strong>oretical propositions outlined above in <strong>the</strong> next stages <strong>of</strong> <strong>the</strong> fieldwork, this study
<strong>of</strong>fers <strong>the</strong> prospect <strong>of</strong> making a contribution to analytic frameworks applied to <strong>the</strong> understanding <strong>of</strong>
interpr<strong>of</strong>essional and inter-organisational work, enhancing <strong>the</strong> understanding <strong>of</strong> barriers to joint
working, and identifying effective knowledge management strategies which could be utilised in <strong>the</strong> UK
healthcare context.
References
Addicott, R., McGivern, G. and Ferlie, E. (2006) “Networks, organizational learning and knowledge management:
NHS Cancer Networks”, Public Money & Management, Vol 26, No. 2, pp 87-94.
Bate, S. P. and Robert, G. (2002) “Knowledge management and communities <strong>of</strong> practice in <strong>the</strong> private sector:
Lessons for modernizing <strong>the</strong> National Health Service in England and Wales”, Public Administration, Vol 80,
No. 4, pp 643–663.
Baxter, S. K. and Brumfitt, S. M. (2008) “Pr<strong>of</strong>essional differences in interpr<strong>of</strong>essional working”, Journal <strong>of</strong>
Interpr<strong>of</strong>essional Care, Vol 22, No. 3, pp 239–251.
Bentley, C., Browman, G. P. and Poole, B. (2010) “Conceptual and practical challenges for implementing <strong>the</strong>
communities <strong>of</strong> practice model on a national scale—a Canadian cancer control initiative”, BMC Health
Services Research, Vol 10, No. 3.
Brown, J. S. and Duguid, P. (1991) “Organizational learning and communities-<strong>of</strong>-practice: Toward a unified view
<strong>of</strong> working, learning and innovation”, Organization Science, Vol 2, No. 1, pp 40-57.
Brown, J. S. and Duguid, P. (2001) “Knowledge and organization: A social-practice perspective”. Organization
Science, Vol 12, No. 2, pp 198-213.
Carlile, P. R. (2002) “A pragmatic view <strong>of</strong> knowledge and boundaries: Boundary objects in new product
development”, Organization Science, Vol 13, No. 4, pp 442-455.
Cooksey, D. (2006) A Review <strong>of</strong> UK Health Research Funding. Stationery Office, London.
Currie, G. and Suhomlinova, O. (2006) “The impact <strong>of</strong> institutional forces upon knowledge sharing in <strong>the</strong> UK
NHS: The triumph <strong>of</strong> pr<strong>of</strong>essional power and <strong>the</strong> inconsistency <strong>of</strong> policy”. Public Administration, Vol 84, No.
1, pp 1-30.
Danermark, B., Ekström, M., Jakobsen, L. and Karlsson, J. Ch. (2002) Explaining Society: Critical Realism in <strong>the</strong>
Social Sciences. Routledge, Oxon.
Degeling, P., Kennedy, J. and Hill, M. (2001) “Mediating <strong>the</strong> cultural boundaries between medicine, nursing and
management—<strong>the</strong> central challenge in hospital reform”, Health Services Management Research, Vol 14,
No. 1, pp 36-48.
Duguid, P. (2005) “The art <strong>of</strong> knowing”: Social and tacit dimensions <strong>of</strong> knowledge and <strong>the</strong> limits <strong>of</strong> <strong>the</strong> community
<strong>of</strong> practice”, The Information Society, Vol 21, No. 2, pp 109–118.
Dukerich, J. M., Golden, B. R. and Shortell, S. M. (2002) “Beauty is in <strong>the</strong> eye <strong>of</strong> <strong>the</strong> beholder: The impact <strong>of</strong>
organizational identification, identity, and image on <strong>the</strong> cooperative behaviors <strong>of</strong> physicians”, Administrative
Science Quarterly, Vol 47, No. 3, pp 507–537.
Dutton, J. E., Dukerich, J. M. and Harquail, C. V. (1994) “Organizational images and member identification”,
Administrative Science Quarterly, Vol 39, No. 2, pp 239-263.
Ferlie, E., Fitzgerald, L., Wood, M. and Hawkins, C. (2005) “The nonspread <strong>of</strong> innovations: The mediating role <strong>of</strong>
pr<strong>of</strong>essionals”, The Academy <strong>of</strong> Management Journal, Vol 48, No 1, pp 117-134.
Fitzgerald, L., Ferlie, E., Wood, M. and Hawkins, C. (2002) “Interlocking interactions: The diffusion <strong>of</strong> innovations
in health care”, Human Relations, Vol 55, No. 12, pp 1429-1449.
Gabbay, J., le May, A., Jefferson, H., Webb, D., Lovelock, R., Powell, J. and Lathlean, J. (2003) “A case study <strong>of</strong>
knowledge management in multiagency consumer-informed 'communities <strong>of</strong> practice': Implications for
evidence-based policy development in health and social services”, Health, Vol 7, No. 3, pp 283-310.
Hall, P. (2005) “Interpr<strong>of</strong>essional teamwork: Pr<strong>of</strong>essional cultures as barriers”, Journal <strong>of</strong> Interpr<strong>of</strong>essional Care,
Vol 19, Suppl 1, pp 188–196.
Harrison, S. and McDonald, R. (2008) The Politics <strong>of</strong> Healthcare in Britain. SAGE Publications, London.
Hudson, B. (2002) “Interpr<strong>of</strong>essionality in health and social care: The Achilles' heel <strong>of</strong> partnership?” Journal <strong>of</strong>
Interpr<strong>of</strong>essional Care, Vol 16, No. 1, pp 7–17.
Ibarra, H. (1999) “Provisional selves: Experimenting with image and identity in pr<strong>of</strong>essional adaptation”,
Administrative Science Quarterly, Vol 44, No. 4, pp 764-791.
Johnson, P., Wistow, G., Schulz, R. and Hardy, B. (2003) “Interagency and interpr<strong>of</strong>essional collaboration in
community care: The interdependence <strong>of</strong> structures and values”, Journal <strong>of</strong> Interpr<strong>of</strong>essional Care, Vol 17,
No. 1, pp 70–83.
King, N. (2004) “Using templates in <strong>the</strong> <strong>the</strong>matic analysis <strong>of</strong> text” In Cassell, C. and Symon, G. (eds.) Essential
Guide to Qualitative Methods in Organizational Research. SAGE Publications, London.
Knorr Cetina, K. (1999) Epistemic Cultures: How <strong>the</strong> Sciences Make Knowledge. Harvard University Press,
Cambridge, MA.
Lathlean, J. and le May, A. (2002) “Communities <strong>of</strong> practice: An opportunity for interagency working”, Journal <strong>of</strong>
Clinical Nursing, Vol 11, No. 3, pp 394-398.
1047

Roman Kislov
Lave, J. and Wenger, E. (1991) Situated Learning: Legitimate Peripheral Participation. Cambridge University
Press, Cambridge.
le May, A. (ed.) (2009) Communities <strong>of</strong> Practice in Health and Social Care. Wiley-Blackwell, Oxford.
Morgan, P. I. and Ogbonna, E. (2008) “Subcultural dynamics in transformation: A multi-perspective study <strong>of</strong>
healthcare pr<strong>of</strong>essionals”, Human Relations, Vol 61, No. 1, pp 39-65.
Nadin, S. and Cassell, C. (2004) “Using data matrices” In Cassell, C. and Symon, G. (eds.) Essential Guide to
Qualitative Methods in Organizational Research. SAGE Publications, London.
NIHR (National Institute for Health Research) (2011) “The CLAHRC for Greater Manchester”, [online],
http://clahrc-gm.nihr.ac.uk.
Oborn, E. and Dawson, S. (2010) “Learning across communities <strong>of</strong> practice: An examination <strong>of</strong> multidisciplinary
work”, British Journal <strong>of</strong> Management, Vol 21, No. 4, pp 843-858.
Outhwaite, W. (1987) New Philosophies <strong>of</strong> Social Science: Realism, Hermeneutics and Critical Theory.
Macmillan, London.
Øvretveit, J., Bate, P., Cleary, P., Cretin, S., Gustafson, D., McInnes, K., McLeod, H., Molfenter, T., Plsek, P.,
Robert, G., Shortell, S. and Wilson, T. (2002) “Quality collaboratives: Lessons from research”, Quality and
Safety in Health Care, Vol 11, No. 4, pp 345-351.
Pratt, M. G. (1998). “To be or not to be? Central questions in organizational identification” In Whetten, D. A. and
Godfrey, P. C. (eds.) Identity in Organizations: Building Theory through Conversations. SAGE Publications,
London.
Roberts, J. (2006). ”Limits to communities <strong>of</strong> practice”, Journal <strong>of</strong> Management Studies, Vol 43, No. 3, pp 623-
639.
Robinson, M. and Cottrell, D. (2005) “Health pr<strong>of</strong>essionals in multi-disciplinary and multi-agency teams: Changing
pr<strong>of</strong>essional practice”, Journal <strong>of</strong> Interpr<strong>of</strong>essional Care, Vol 19, No. 6, pp 547–560.
Saint-Onge, H. and Wallace, D. (2003) Leveraging Communities <strong>of</strong> Practice for Strategic Advantage.
Butterworth-Heinemann, Burlington, MA.
Star, S. L. and Griesemer, J. R. (1989). “Institutional ecology, 'translations' and boundary objects: Amateurs and
pr<strong>of</strong>essionals in Berkeley's Museum <strong>of</strong> Vertebrate Zoology, 1907-39”, Social studies <strong>of</strong> science, Vol 19, No.
3, pp 387–420.
Swan, J., Scarbrough, H. and Robertson, M. (2002) “The construction <strong>of</strong> `communities <strong>of</strong> practice' in <strong>the</strong>
management <strong>of</strong> innovation”, Management Learning, Vol 33, No 4, pp 477-496.
Swan, J., Bresnen, M., Newell, S. and Robertson, M. (2007) “The object <strong>of</strong> knowledge: The role <strong>of</strong> objects in
biomedical innovation”, Human Relations, Vol 60, No. 12, pp 1809-1837.
Tagliaventi, M. R. and Mattarelli, E. (2006) “The role <strong>of</strong> networks <strong>of</strong> practice, value sharing, and operational
proximity in knowledge flows between pr<strong>of</strong>essional groups”, Human Relations, Vol 59, No. 3, pp 291-319.
Wenger, E. (1998) Communities <strong>of</strong> Practice: Learning, Meaning and Identity. Cambridge University Press,
Cambridge.
Wenger, E. (2000). “Communities <strong>of</strong> practice and social learning systems”. Organization, Vol 7, No. 2, pp 225-
246.
Wenger, E. C. and Snyder, W. M. (2000) “Communities <strong>of</strong> practice: The organizational frontier”, Harvard
Business Review, Vol 78, No. 1, pp 139-146.
Wenger, E., McDermott, R. and Snyder, W. M. (2002) Cultivating Communities <strong>of</strong> Practice. Harvard Business
School Press, Boston, MA.
White, D., Suter, E., Parboosingh, I. J. and Taylor, E. (2008) “Communities <strong>of</strong> practice: Creating opportunities to
enhance quality <strong>of</strong> care and safe practices”, Healthcare Quarterly, Vol 11, pp 80–84.
1048

Evaluation <strong>of</strong> Knowledge Quality <strong>of</strong> Lessons Learned by
Asking Questions
Tony K. M. Lo and Patrick S.W. Fong
The Hong Kong Polytechnic University, Hong Kong
tonykmlo@gmail.com
bspafong@polyu.edu.hk
Abstract: Lessons learned, which are a special kind <strong>of</strong> knowledge obtained by people from experience, can help
enterprises avoid reinvention and standardize <strong>the</strong>ir best practices. Pr<strong>of</strong>essionals’ performance in various sectors
can also be improved. Lesson learning is <strong>the</strong> process <strong>of</strong> converting people’s experience <strong>of</strong> events into knowledge
for application in <strong>the</strong> future. While <strong>the</strong> process and IT systems <strong>of</strong> lesson learning have been widely studied, <strong>the</strong>re
is little research on <strong>the</strong> quality <strong>of</strong> <strong>the</strong> knowledge content <strong>of</strong> lessons learned. This paper discusses <strong>the</strong> concept <strong>of</strong>
knowledge quality and relationships among <strong>the</strong> context, experience, reflection asking and content <strong>of</strong> lessons
learned. Focusing on captured lessons, we propose <strong>the</strong> method <strong>of</strong> asking relevant questions to evaluate lessons
learned. The questions are based on qualitative content analysis from various facets, namely context, content
and experience. We have provided a checklist which consists <strong>of</strong> more than a dozen <strong>of</strong> questions as our first
findings. We illustrate <strong>the</strong> method by describing and analyzing an au<strong>the</strong>ntic case <strong>of</strong> lessons learned produced by
a public organization, namely <strong>the</strong> medication incidents bulletins published by <strong>the</strong> Hong Kong Hospital Authority.
More case studies can be conducted for o<strong>the</strong>r industries. We conclude this study with suggestions on possible
improvements to <strong>the</strong> quality <strong>of</strong> content <strong>of</strong> lessons learned. This paper provides a foundation for future research
by developing a framework for analyzing lessons learned. It also facilitates deeper understanding <strong>of</strong> <strong>the</strong>
evaluation <strong>of</strong> knowledge quality.
Keywords: lesson learned, knowledge quality, context, experience, reflection, content analysis
1. Introduction
1.1 Lessons learned and <strong>the</strong> lessons learning process
Lessons learned are a special kind <strong>of</strong> knowledge obtained by people from experiences, which can be
positive or negative, successes or failures. Existing lessons learned programs and systems are
implemented as knowledge bases, case retrieval systems or document repositories codified in texts,
sounds, images or videos. Among <strong>the</strong>m, textual documents remain <strong>the</strong> most available and durable
forms <strong>of</strong> lessons learned for knowledge sharing and are <strong>the</strong> major objects <strong>of</strong> study in this paper. The
crystallization <strong>of</strong> <strong>the</strong> experience into knowledge <strong>of</strong> lessons learned in <strong>the</strong> form <strong>of</strong> textual documents
reflects exactly <strong>the</strong> concept <strong>of</strong> <strong>the</strong> conversion <strong>of</strong> tacit knowledge to explicit knowledge, or
“externalization”, which is one <strong>of</strong> <strong>the</strong> four modes <strong>of</strong> knowledge conversion (Nonaka 94). Lessons
learned can help enterprises avoid unnecessary reinvention and standardize <strong>the</strong>ir best practices
(APQC 2010). Surveys and case studies show that effective lessons learned systems can improve
pr<strong>of</strong>essionals’ performance in various sectors, e.g. construction, engineering and project management
(Fong and Yip 2006; Carillo 2005). The publication <strong>of</strong> KM manuals, toolkits and handbooks covering
lessons learned practices by national and cross-national organizations, such as <strong>the</strong> Asian Productivity
Council, NATO, <strong>the</strong> Swiss Agency for Development and Cooperation, indicates <strong>the</strong> critical role <strong>of</strong>
lessons learned (APO 2010; NATO 2010; SDC 2007).
In this paper, <strong>the</strong> term “lessons learning” refers to <strong>the</strong> process <strong>of</strong> converting <strong>the</strong> experience <strong>of</strong> people
who have participated in some events into learners’ own knowledge. The process can be divided into
three stages:
Stage 1: Participants experience an event
Stage 2: Authors identify <strong>the</strong> event as a lesson and capture knowledge from it
Stage 3: Learners learn from <strong>the</strong> lesson
In <strong>the</strong> first stage, <strong>the</strong> potential sources <strong>of</strong> lessons are identified. The second stage is to extract
knowledge from <strong>the</strong> lessons created, while in <strong>the</strong> third stage <strong>the</strong> knowledge captured in <strong>the</strong> lessons is
applied in real situations. In this process, “participants” refers to those people who are directly
involved in <strong>the</strong> event and thus have obtained first-hand experience in a certain context. The term
“authors” has a broad sense, including those people who are responsible for identifying <strong>the</strong> lessons
from <strong>the</strong> event occurring in stage 1, collecting information <strong>of</strong> <strong>the</strong> lessons, judging <strong>the</strong> validity <strong>of</strong> <strong>the</strong>
information, analyzing <strong>the</strong> lessons, making recommendations, and finally compiling and writing <strong>the</strong>
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Tony K. M. Lo and Patrick S.W. Fong
content <strong>of</strong> <strong>the</strong> lessons learned documents. “Learners” represent <strong>the</strong> audiences addressed, <strong>the</strong>
readers <strong>of</strong> <strong>the</strong> lessons learned, <strong>the</strong> users <strong>of</strong> knowledge and <strong>the</strong> action-takers <strong>of</strong> <strong>the</strong> recommendations
proposed. Participants, authors and learners are among <strong>the</strong> main stakeholders <strong>of</strong> <strong>the</strong> lessons
learned.
1.2 Lessons learned and knowledge quality
While <strong>the</strong> term “quality” has many definitions in management, <strong>the</strong>re are two qualities in lesson
learning: process quality and content quality. Process quality refers to <strong>the</strong> quality pertaining to <strong>the</strong>
lessons learning process, such as <strong>the</strong> efficiency <strong>of</strong> recording <strong>the</strong> information <strong>of</strong> events or <strong>the</strong> reliability
<strong>of</strong> <strong>the</strong> IT infrastructure. Content quality, which is <strong>the</strong> main concern <strong>of</strong> this paper, is about what
elements make for good quality in <strong>the</strong> content <strong>of</strong> a lesson learned. Issues regarding data and
information quality have been extensively studied, and criteria for evaluating data and information
management systems have been suggested (Madnick 2009; Redman 2005; Pipino et al. 2005; Eppler
2006). By contrast, little research has been conduced on knowledge quality. Evaluations <strong>of</strong> lessons
learned systems in terms <strong>of</strong> knowledge quality have not been widely studied. Yoo et al. (2010)
describe <strong>the</strong> dimensions <strong>of</strong> knowledge quality and propose quantitative instruments to measure it.
Davenport & Prusak (1998) state that <strong>the</strong> thing delivered is more important than <strong>the</strong> delivery vehicle.
A case study reveals that lessons learned systems are seldom reused because <strong>of</strong> concerns over
lesson quality (Milton 2010). There exists a gap in <strong>the</strong> study <strong>of</strong> <strong>the</strong> knowledge quality and hence a
need for exploring <strong>the</strong> quality <strong>of</strong> lessons learned.
One <strong>of</strong> <strong>the</strong> approaches <strong>of</strong> evaluating <strong>the</strong> quality <strong>of</strong> lessons learned is by result, i.e. by determining
whe<strong>the</strong>r learners have learned anything from <strong>the</strong> lessons that has resulted in a change in behaviour.
In o<strong>the</strong>r words, we can say a lesson learned is <strong>of</strong> good quality if, when <strong>the</strong> events described in <strong>the</strong>
lessons happen again, learners can repeat <strong>the</strong> success or avoid making <strong>the</strong> same mistakes by reapplying
<strong>the</strong> knowledge <strong>of</strong> those lessons. It is a “fit-for-use” approach. However, in that case, we can
only judge a lesson learned after its application. Although modifications <strong>of</strong> <strong>the</strong> content are always
possible, it would be more useful if an acceptable level <strong>of</strong> lessons learned quality could be ensured
before <strong>the</strong>y are disseminated to learners. The o<strong>the</strong>r way is to construct a list <strong>of</strong> attributes as criteria to
assess <strong>the</strong> quality <strong>of</strong> lessons learned, such as comprehensiveness, relevance, viscosity (Holsapple
2003; Boisot 1998; Szulanski 2003; and Zhao & Anand 2009). While <strong>the</strong> attributes provide a basis for
evaluation, <strong>the</strong> actual meanings <strong>of</strong> <strong>the</strong> attribute are usually vague, subjective and difficult to operate.
Fur<strong>the</strong>rmore, unlike information, good knowledge is expected to <strong>of</strong>fer more substantial meanings in
terms <strong>of</strong> reflection or in-depth thinking. The question is: what are <strong>the</strong> proper dimensions for evaluating
<strong>the</strong> quality <strong>of</strong> knowledge?
2. Research design
2.1 Research approach and process
While quantitative instruments or metrics have been proposed to measure knowledge quality (Rech
2007; Yoo 2011), this research attempts a different approach by proposing a questions checklist for
judging <strong>the</strong> quality <strong>of</strong> <strong>the</strong> content <strong>of</strong> lessons learned.The questions in <strong>the</strong> checklist are qualitative in
nature, which means that <strong>the</strong>y are not measurable in terms <strong>of</strong> numerals. We do not hold that good
lessons learned have to give answers to all <strong>the</strong> questions in a checklist. Nei<strong>the</strong>r should <strong>the</strong> checklist
be exhaustive or definitive. Not all <strong>the</strong> questions in a checklist are relevant for a particular lesson
learned. The key is to provide a framework for assessment <strong>of</strong> <strong>the</strong> content from various facets.
Depending on <strong>the</strong> real situation, attention has to be paid to how to strike a balance between <strong>the</strong>
comprehensiveness and conciseness <strong>of</strong> <strong>the</strong> questions. There are many facets that can be employed
for evaluation. This paper selects a couple <strong>of</strong> <strong>the</strong>m that are considered important components related
to <strong>the</strong> content <strong>of</strong> lessons learned, namely, context richness, experience sharing and reflective
thinking. They will be described in <strong>the</strong> following sections. A few questions for each component will be
provided to form a checklist based on literature review in each <strong>of</strong> <strong>the</strong> components mentioned above.
With this checklist <strong>the</strong> content <strong>of</strong> an au<strong>the</strong>ntic lesson learned is <strong>the</strong>n analyzed. Finally <strong>the</strong> results are
discussed, toge<strong>the</strong>r with <strong>the</strong> conclusions and implications <strong>of</strong> our findings.
2.2 Qualitative content analysis
In this paper, we use <strong>the</strong> method <strong>of</strong> content analysis for case study. There are variations <strong>of</strong> <strong>the</strong>
definitions <strong>of</strong> content analysis. In <strong>the</strong> broadest sense, it is a technique that examines <strong>the</strong> content <strong>of</strong>
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human communication. While traditional content analysis has <strong>the</strong> primary goal <strong>of</strong> producing a
numerically based summary <strong>of</strong> <strong>the</strong> message (Neuendorf 2002), both quantitative and qualitative
approaches for content analysis are indispensable (Krippendorff 2004). This view follows that content
is always reader-dependent, with multiple meanings and subject to various interpretations, an intrinsic
property <strong>of</strong> human communication. Reflections <strong>of</strong> patterns (e.g. group cultures) and trend descriptions
are among <strong>the</strong> acceptable objectives <strong>of</strong> content analysis. Lesson learning involves communication <strong>of</strong>
messages containing knowledge. Qualitative content analysis is employed here to analyze <strong>the</strong>
patterns and <strong>the</strong> trend <strong>of</strong> documents <strong>of</strong> lessons learned. The validity and reliability <strong>of</strong> <strong>the</strong> research can
be enhanced by ensuring <strong>the</strong> au<strong>the</strong>nticity, truthfulness, dependability and consistency <strong>of</strong> <strong>the</strong> data and
results <strong>of</strong> this project. It can be achieved by triangulation which is a combination <strong>of</strong> multiple
methodological practices, empirical materials and perspectives (Denzin & Lincoln 2005). Specifically,
expert interviews and testing <strong>of</strong> <strong>the</strong> question checklist by users <strong>of</strong> lessons learned are among <strong>the</strong>
methods to ensure validity and reliability.
3. Context richness
3.1 Content and context
In lessons learning, <strong>the</strong> content <strong>of</strong> lessons manifests itself in a context and comprises stakeholders’
experience, as shown in Figure 1. The lessons learning process can be viewed as a transfer <strong>of</strong>
context and experience from <strong>the</strong> participants to <strong>the</strong> learners via <strong>the</strong> content written by <strong>the</strong> authors.
Experience
Content
Context
Figure 1: Content, context and experience <strong>of</strong> lessons learned
Context is <strong>the</strong> situation within which something exists or happens, and which can help you explain
and understand it. Maqsood et al. (2004) make <strong>the</strong> criticism that many lessons learned systems miss
contextual information. No reference on lessons should be taken out <strong>of</strong> context. Lessons learned
without context may lead to wrong conclusions, unsound judgments and improper applications.
Context can be ei<strong>the</strong>r content-independent or content-dependent, though <strong>the</strong> difference between
<strong>the</strong>m may be vague. Not touching on <strong>the</strong> subject matter <strong>of</strong> <strong>the</strong> lessons, content-independent context
covers, for example, <strong>the</strong> process <strong>of</strong> lessons identification and capturing, <strong>the</strong> accessibility <strong>of</strong> lessons,
<strong>the</strong> storage <strong>of</strong> lessons, confidentiality, <strong>the</strong> provision <strong>of</strong> links to o<strong>the</strong>r connected lessons, and so on.
Content-dependent context is about <strong>the</strong> subject matter <strong>of</strong> <strong>the</strong> lessons, including <strong>the</strong> time and location
<strong>of</strong> <strong>the</strong> event’s occurrence, <strong>the</strong> identities and interests <strong>of</strong> <strong>the</strong> stakeholders and <strong>the</strong>ir relationships, <strong>the</strong>
reasons why <strong>the</strong> authors identified <strong>the</strong> event as a lesson, and so on.
3.2 Relationships among stakeholders
The relationships among stakeholders (participants, authors and learners) are an essential part <strong>of</strong> <strong>the</strong>
context <strong>of</strong> lessons learned. Stakeholders <strong>the</strong>mselves may share <strong>the</strong> same or different identities. The
simplest case is that all stakeholders belong to a single group <strong>of</strong> people or just a single person. The
o<strong>the</strong>r extreme is that <strong>the</strong> identities <strong>of</strong> <strong>the</strong> three stakeholders are different. There are o<strong>the</strong>r possibilities
in between <strong>the</strong>se two extremes. If we use P, A and L to represent participants, authors and learners
respectively, we have <strong>the</strong> following typology, with examples as described in Table 1.
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Type
P=A=L
P=A, L
P, A=L
P=L, A
P, A, L
Example
Tony K. M. Lo and Patrick S.W. Fong
Suppose I mistakenly deleted a file in my computer because I misunderstood a
response from a pop-up message. I got frustrated and wrote down what I felt
and what I did wrong on a “post-it” on my desk. When I encountered <strong>the</strong> same
situation a week later, I knew how I could avoid repeating <strong>the</strong> error. I learned it
from my own lesson. In this case, I am <strong>the</strong> only participant, both author and
learner.
If my colleague John comes to use my computer and notices my “post-it”, he
can learn from my mistake without having to go through my frustrating
experience. In this case, <strong>the</strong> author is <strong>the</strong> same person as <strong>the</strong> participant (me)
but <strong>the</strong> learner (John) is different.
Psychology experiments conducted by psychologists are good examples <strong>of</strong> this
type. By writing reports on participants’ experience, <strong>the</strong> researchers <strong>the</strong>mselves
acquire new knowledge.
The use <strong>of</strong> after action review (AAR) by <strong>the</strong> US army, with <strong>the</strong> help <strong>of</strong> a
facilitator, falls within this type. The facilitator, who is usually not part <strong>of</strong> <strong>the</strong>
squad conducting <strong>the</strong> review, records and summarizes what <strong>the</strong> soldiers have
done, what should have been done, and actionable recommendations for <strong>the</strong>
soldiers <strong>the</strong>mselves to follow.
Many lessons learned in <strong>the</strong> form <strong>of</strong> <strong>of</strong>ficial reports investigating accidents by
public organizations are typical examples where <strong>the</strong> participants in <strong>the</strong>
incidents, <strong>the</strong> authors and <strong>the</strong> learners (such as <strong>the</strong> practitioners <strong>of</strong> <strong>the</strong> domain,
<strong>the</strong> monitoring bodies, or even <strong>the</strong> general public) are different entities.
Table 1: Typology <strong>of</strong> relationship among stakeholders <strong>of</strong> lesson learning
3.3 Possible questions checklist on context richness
Our assumption is: <strong>the</strong> richer <strong>the</strong> context <strong>of</strong> lessons learned, <strong>the</strong> better <strong>the</strong> quality <strong>of</strong> its content.
Hence <strong>the</strong> aim <strong>of</strong> asking questions is to examine <strong>the</strong> content for clues about <strong>the</strong> richness <strong>of</strong> <strong>the</strong>
context. Based on <strong>the</strong> analysis in sub-sections 2.1 and 2.2, possible questions that may be raised for
this purpose are described below.
What kinds <strong>of</strong> content-independent and content-dependent context does <strong>the</strong> lesson mention?
Does <strong>the</strong> lesson explain how <strong>the</strong> author identified and captured <strong>the</strong> knowledge?
Does <strong>the</strong> lesson describe <strong>the</strong> backgrounds <strong>of</strong> <strong>the</strong> stakeholders and <strong>the</strong> relationships among
<strong>the</strong>m?
Does <strong>the</strong> lesson state who <strong>the</strong> target learners are?
Does <strong>the</strong> lesson mention <strong>the</strong> normal or best practices that <strong>the</strong> participants should have taken?
Are <strong>the</strong>re any instructions for learners on how to use <strong>the</strong> knowledge properly?
Can <strong>the</strong> learners know for how long <strong>the</strong> knowledge is supposed to be valid?
4. Experience sharing
4.1 Experiential learning
Many lessons learned originate from reviews, debriefings, interviews, dialogues, evaluations and
learning histories. Participants share <strong>the</strong>ir experience with o<strong>the</strong>rs and make sense <strong>of</strong> it <strong>the</strong>mselves.
Lessons learned can be supported by narratives or stories that facilitate <strong>the</strong> transfer <strong>of</strong> context and
experience (Milton 2010). Experience consists <strong>of</strong> <strong>the</strong> total response <strong>of</strong> a person to a situation or event
and what <strong>the</strong>y think and conclude at <strong>the</strong> time (Boud 1985). It can also assist hearers’ understanding
and acceptance <strong>of</strong> <strong>the</strong> evidence collected from biographies and narratives (Gibbs 2007). Among
various learning <strong>the</strong>ories for education and training, <strong>the</strong> experiential learning <strong>the</strong>ory (ELT) emphasizes
<strong>the</strong> central role that experience plays in <strong>the</strong> learning process, which is in line with what has been
mentioned in this paper. Unlike cognitive learning <strong>the</strong>ories, which pay greater attention to cognition
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than effect, and behavioural learning <strong>the</strong>ories, which deny any role for subjective experience in
learning, ELT has a unique perspective on learning and development. It stresses <strong>the</strong> holistic model <strong>of</strong>
<strong>the</strong> learning process and a multi-linear model <strong>of</strong> adult development, both <strong>of</strong> which are consistent with
what we know about how people learn, grow, and develop (Kolb 1984). In ELT, learning is defined as
<strong>the</strong> process whereby knowledge is created through <strong>the</strong> transformation <strong>of</strong> experience and <strong>the</strong>
combination <strong>of</strong> grasping and transforming experience. The key is how to turn experience into learning.
4.2 Possible questions checklist on experience sharing
The prime objective here is to ask to what degree <strong>the</strong> content <strong>of</strong> <strong>the</strong> lessons learned has transferred
participants’ unique experience to <strong>the</strong> learners, ei<strong>the</strong>r directly or indirectly. In textual documents, while
words are naturally <strong>the</strong> major medium <strong>of</strong> experience sharing, pictures (if any) and <strong>the</strong> layouts <strong>of</strong> <strong>the</strong>
documents can achieve <strong>the</strong> purpose as well. As discussed above, experience is not limited to
emotions. Participants’ observations, understanding and judgments are also essential elements to
enable learners to share what <strong>the</strong> participants felt during <strong>the</strong> occurrence <strong>of</strong> <strong>the</strong> event. Here are some
possible questions to be asked.
Does <strong>the</strong> lesson tell you what <strong>the</strong> participants observed during <strong>the</strong> events?
Does <strong>the</strong> lesson convey <strong>the</strong> feelings <strong>of</strong> <strong>the</strong> participants?
Are participants’ immediate judgments made known in <strong>the</strong> lesson?
5. Reflective thinking
5.1 Learning through reflection
Reflection is involved in learning (Moon 2004). Being an active process <strong>of</strong> exploration and discovery,
reflection is <strong>the</strong> persistent and careful consideration <strong>of</strong> any belief or supposed form <strong>of</strong> knowledge.
Learners take control <strong>of</strong> <strong>the</strong>ir own learning with a deep approach by seeking an understanding <strong>of</strong> <strong>the</strong>
meaning and relating it to <strong>the</strong>ir previous experience (Boud 1985). Different frames <strong>of</strong> reference are
reviewed with new and meaningful ideas generated. Most lessons learned systems just focus on
which practices work or fail, but not on why <strong>the</strong>y work or fail. Julian (2008) recommends that
companies should ask more “why” questions so as to provide richer contextual information. Argyris et
al. (1985) argued that knowledge brings <strong>the</strong> chance <strong>of</strong> double-loop learning by triggering discussion <strong>of</strong>
conflicting issues. Learners need to be able to initiate deep reflection on strategic thinking after
reading <strong>the</strong> lesson learned documents. In <strong>the</strong> context <strong>of</strong> lessons learned, single-loop learning means
that learners try to seek feasible solutions, or operational change, for <strong>the</strong> existing problems in mind.
However, in double-loop learning, lessons learned inspire learners to reflect on <strong>the</strong> original frame <strong>of</strong>
problems, usually resulting in changes in organizations’ underlying norms, policies and objectives. It is
hard to overstate <strong>the</strong> significance <strong>of</strong> reflective thinking as a response to <strong>the</strong> present challenge and
strong demand for innovative ideas. Quality <strong>of</strong> knowledge can be reflected in employees’ ability to
make improvement on <strong>the</strong>ir tasks, discard old solutions and improvising new approaches (Erden et al
2009).
5.2 Possible questions checklist on reflective thinking
As explained, <strong>the</strong> knowledge quality <strong>of</strong> lessons learned depends on whe<strong>the</strong>r <strong>the</strong> content provides any
information leading to learners’ reflective thinking. The following questions are candidates for
evaluating <strong>the</strong> quality <strong>of</strong> <strong>the</strong> content.
Does <strong>the</strong> lesson give any reasons for <strong>the</strong> occurrence <strong>of</strong> <strong>the</strong> event?
Does <strong>the</strong> lesson mention alternatives and o<strong>the</strong>r possibilities? Does <strong>the</strong> lesson <strong>of</strong>fer multiple points
<strong>of</strong> view?
Does <strong>the</strong> lesson remind learners <strong>of</strong> <strong>the</strong> special conditions or exceptions where <strong>the</strong>
recommendations may not be applicable?
Does <strong>the</strong> lesson challenge any underlying assumptions?
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6. Case analysis
Tony K. M. Lo and Patrick S.W. Fong
6.1 The case: Medication Incidents Reporting Programme (MIRP) by Hong Kong
Hospital Authority (HKHA)
Established as a statutory body in 1990, <strong>the</strong> HKHA manages all <strong>the</strong> public hospitals in Hong Kong.
The MIRP started in 1994 with a view to identifying opportunities for quality improvement in
medication and to assisting healthcare pr<strong>of</strong>essionals in reducing <strong>the</strong> chance <strong>of</strong> reoccurrence <strong>of</strong>
medication incidents. Since its inception, <strong>the</strong> MIRP has published 25 bulletins, which are available for
download at <strong>the</strong> following website:
(http://www.ha.org.hk/visitor/ha_visitor_index.asp?Parent_ID=367&Content_ID=676). Under this
programme, public hospitals are required to submit quarterly statistical returns and information about
any medication incidents to HKHA’s head <strong>of</strong>fice for analysis and consolidation. It is basically a
voluntary self-reporting system. Nursing personnel, pharmacy staff, doctors and o<strong>the</strong>r staff coming to
<strong>the</strong> knowledge <strong>of</strong> medication incidents can make reports <strong>of</strong> incidents. In order to encourage reporting,
<strong>the</strong> identity <strong>of</strong> <strong>the</strong> staff and patients involved in <strong>the</strong> incident are not required to be disclosed. Reported
cases are directed to <strong>the</strong> Hospital Drug Committee and reviewed by a panel consisting <strong>of</strong> doctors,
pharmacists and nurses appointed by <strong>the</strong> Committee. The Committee identifies <strong>the</strong> underlying causes
<strong>of</strong> incidents and recommends appropriate preventive or remedial measures, which are <strong>the</strong>n published
in <strong>the</strong> MIRP bulletins distributed to HKHA’s staff and made accessible to <strong>the</strong> public. The MIRP
bulletins, written in English, are typical textual documents <strong>of</strong> lessons learned. While <strong>the</strong> style and
format <strong>of</strong> <strong>the</strong> MIRP bulletins have been modified over <strong>the</strong> years, <strong>the</strong> structure <strong>of</strong> its content remains
basically unchanged and consists <strong>of</strong> 3 main areas: <strong>the</strong> main topic, case sharing and statistics. An
issue is usually 4 to 5 pages long. The main topics cover a wide range <strong>of</strong> <strong>the</strong>mes from <strong>the</strong> mechanism
<strong>of</strong> <strong>the</strong> MIRP to <strong>the</strong> identification <strong>of</strong> high-risk areas in <strong>the</strong> drug administration process. Cases for
sharing are medication incidents or near misses <strong>of</strong> medication errors, e.g. drugs issued to <strong>the</strong> wrong
patient, dose miscalculation, unattended medication, etc. Statistics are numerical information
including <strong>the</strong> distribution <strong>of</strong> incidents (by error type, by attributed cause and by severity). There are
also o<strong>the</strong>r error reduction tips and news, and a message to readers.
6.2 Evaluation based on question checklist
We select MIRP bulletin issue no. 25, published in July 2010, as an example for analysis. In this
issue, five cases <strong>of</strong> medication errors are reported as events for lesson learning. For illustration
purposes, <strong>the</strong> questions suggested in <strong>the</strong> above sections are used to reveal <strong>the</strong> context richness,
experience sharing and reflective thinking <strong>of</strong> <strong>the</strong> content <strong>of</strong> this issue. The questions and our brief
analyses are shown in Table 2.
Question
Context richness
What kinds <strong>of</strong> content-independent and
content-dependent contexts does <strong>the</strong> lesson
mention?
Does <strong>the</strong> lesson explain how <strong>the</strong> author
identified and captured <strong>the</strong> knowledge?
Does <strong>the</strong> lesson state who <strong>the</strong> target
learners are?
Analysis
The processes <strong>of</strong> lesson identification, capturing and
sharing are not found in this issue (no. 25), but <strong>the</strong>
mechanisms <strong>of</strong> <strong>the</strong> MIRP were explained in detail in <strong>the</strong>
first few issues published in 1994 and 1995. Leaners
may not be aware <strong>of</strong> <strong>the</strong> mechanism if <strong>the</strong>y have not
read <strong>the</strong> first few issues. The mechanism is a bottom-up
process relying on submission <strong>of</strong> information from
individual hospitals. However, <strong>the</strong> criteria for identifying
potential lessons and how <strong>the</strong> knowledge is captured
(e.g. by interviews or by an inquiry committee) are not
mentioned.
The exact time and place <strong>of</strong> <strong>the</strong> occurrence <strong>of</strong> individual
events are not given in MIRP bulletin no. 25. Yet
learners can deduce from <strong>the</strong> content where <strong>the</strong> event
happened, say, in a ward or dispensary. It is also
reasonable to assume that <strong>the</strong> events happened within a
few months prior to <strong>the</strong> publication date.
The bulletin does not say explicitly who <strong>the</strong> main
audience is. Given that <strong>the</strong> content is written in English,
with many pr<strong>of</strong>essional terms, it is obvious that it is
intended for pr<strong>of</strong>essional staff such as medication
nurses, doctors and pharmacy staff, who are
responsible for medication.
1054

Question
Does <strong>the</strong> lesson describe <strong>the</strong> backgrounds
<strong>of</strong> <strong>the</strong> stakeholders and <strong>the</strong> relationships
among <strong>the</strong>m?
Does <strong>the</strong> lesson mention <strong>the</strong> normal or best
practices that <strong>the</strong> participants should have
taken?
Are <strong>the</strong>re any instructions for learners on
how to use <strong>the</strong> knowledge properly? Can <strong>the</strong>
learners find out for how long <strong>the</strong> knowledge
is supposed to be valid?
Experience sharing
Does <strong>the</strong> lesson tell you what <strong>the</strong>
participants observed during <strong>the</strong> events?
Does <strong>the</strong> lesson convey <strong>the</strong> feelings <strong>of</strong> <strong>the</strong>
participants?
Reflective thinking
Does <strong>the</strong> lesson give any reasons for <strong>the</strong>
occurrence <strong>of</strong> <strong>the</strong> event given?
Does <strong>the</strong> lesson mention alternatives and
o<strong>the</strong>r possibilities? Does <strong>the</strong> lesson <strong>of</strong>fer
multiple points <strong>of</strong> view?
Does <strong>the</strong> lesson remind learners <strong>of</strong> <strong>the</strong>
special conditions or exceptions where <strong>the</strong>
recommendations may not be applicable?
Does <strong>the</strong> lesson challenge <strong>the</strong> underlying
assumptions?
Tony K. M. Lo and Patrick S.W. Fong
Analysis
All <strong>the</strong> stakeholders (participants, authors and learners)
are supposed to be medical personnel <strong>of</strong> <strong>the</strong> HKHA who
are familiar with hospital settings and working
environments. However, <strong>the</strong> identities <strong>of</strong> participants in
<strong>the</strong> individual event are not disclosed in <strong>the</strong> bulletin.
The best practices or proper procedures are explicitly
stated and repeatedly frequently in <strong>the</strong> bulletin in <strong>the</strong>
form <strong>of</strong> recommendations. The texts related to <strong>the</strong>
recommendations are put in a separate box in eyecatching
fonts or icons so as to enhance <strong>the</strong> impression
made on <strong>the</strong> learners. A case <strong>of</strong> wrong dosage is shown
in Figure 2.
No instructions for use are given. Learners are
supposed to obtain knowledge through reading <strong>the</strong>
bulletins. It is possible that some <strong>of</strong> <strong>the</strong> knowledge in <strong>the</strong>
bulletin may be outdated after a certain period. For
example, <strong>the</strong> introduction <strong>of</strong> a computer system for
medication may reduce mistakes due to poor
handwriting.
Participants’ observations are only indirectly reported. In
<strong>the</strong> case given in Figure 2, in which a nurse
administered <strong>the</strong> wrong drug based on a physician’s
verbal order, <strong>the</strong>re is no information about what <strong>the</strong>
nurse thought she had heard. Learners do not know
whe<strong>the</strong>r <strong>the</strong> nurse struggled to obey or violated <strong>the</strong>
physician’s order.
In most cases, only objective facts can be found in <strong>the</strong>
content <strong>of</strong> <strong>the</strong> bulletin. Participants’ feelings, such as
hesitation or surprise, are not conveyed.
The possible reasons are ei<strong>the</strong>r too superficial or limited
to just a couple <strong>of</strong> categories, such as wrong drug,
wrong strength, wrong dosage form, etc. Underlying
reasons cannot be found in <strong>the</strong> lessons. Taking <strong>the</strong> case
in Figure 2 as an example, it would be useful if learners
could find out why <strong>the</strong> physician gave a verbal order.
Was it because <strong>of</strong> an urgent situation, fatigue, or simply
carelessness?
Participants’ own opinions are missing from <strong>the</strong> bulletin.
Only authors’ viewpoints are mentioned, in <strong>the</strong> form <strong>of</strong>
recommendations. Special conditions or exceptions
where <strong>the</strong> recommendations may not be applicable are
also not found in this issue.
The issue does not attempt to challenge <strong>the</strong> underlying
assumptions <strong>of</strong> operations or reasons for <strong>the</strong> occurrence
<strong>of</strong> errors.
Table 2: Questions and analysis <strong>of</strong> <strong>the</strong> content <strong>of</strong> MIRP bulletin no. 25
7. Discussion and conclusion
Judging from <strong>the</strong> content <strong>of</strong> this single issue (no. 25), we find that <strong>the</strong> MIRP bulletin is fairly rich in
context, with <strong>the</strong> relationships among <strong>the</strong> stakeholders and <strong>the</strong> domain <strong>of</strong> <strong>the</strong> knowledge to be shared
well defined. Since <strong>the</strong> backgrounds <strong>of</strong> <strong>the</strong> stakeholders, or <strong>the</strong> target audience <strong>of</strong> <strong>the</strong> bulletin, are
clear, learners should be able to absorb <strong>the</strong> knowledge captured in <strong>the</strong> lessons. Never<strong>the</strong>less, <strong>the</strong>
authors are too concerned with <strong>the</strong> fact that <strong>the</strong> whole process should be anonymous and nonpunitive.
While it is understandable that anonymity can encourage people’s reporting, much important
contextual information may have been lost. For example, in <strong>the</strong> case <strong>of</strong> <strong>the</strong> wrong dose, <strong>the</strong> exact
time <strong>of</strong> <strong>the</strong> occurrence <strong>of</strong> <strong>the</strong> event may provide inspiration to learners. Apart from <strong>the</strong> staff who made
<strong>the</strong> mistake, information regarding who else was working in <strong>the</strong> ward may also be helpful in enabling
1055

Tony K. M. Lo and Patrick S.W. Fong
learners to obtain substantive knowledge on why <strong>the</strong> incident happened. The specific context may be
a clue for double-loop learning and help learners connect to what <strong>the</strong>y have experienced.
Figure 2: A lesson learned - a case <strong>of</strong> medication error “wrong drug administered” (MIRP bulletin
issue no. 25, 2010, HKHA)
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> bulletin is weak in conveying experience. Most <strong>of</strong> <strong>the</strong> mistakes mentioned in
<strong>the</strong> bulletin are due to cognitive errors. Yet <strong>the</strong>re is a lack <strong>of</strong> explanation <strong>of</strong> <strong>the</strong> staff’s feelings, which
could have been useful in enabling learners to imagine what <strong>the</strong>y could have done if <strong>the</strong>y had been
<strong>the</strong> participants. Recommendations provided in <strong>the</strong> bulletin are <strong>of</strong>fered top-down. They are <strong>the</strong> major
messages that <strong>the</strong> author wants to convey. However, opportunities for reflective thinking by attacking
underlying assumptions are hard to find in <strong>the</strong> content <strong>of</strong> <strong>the</strong> bulletins. In <strong>the</strong> MIRP, <strong>the</strong> assurance <strong>of</strong>
independent, objective and trustful investigation results is <strong>of</strong> top priority; greater effort is <strong>the</strong>refore
necessary to transfer <strong>the</strong> knowledge and experience from <strong>the</strong> participants to <strong>the</strong> learners.
This paper does not claim to prove that lessons learned with rich context and experience must be
better than those without or with just a little. We propose an alternate approach to assessing <strong>the</strong>
quality <strong>of</strong> lessons learned besides <strong>the</strong> quantitative approach. In <strong>the</strong> future, <strong>the</strong> questions checklist can
be enriched and improved by interviewing with stakeholders or taking into consideration experts’
views which can make <strong>the</strong> questions more objective. Rubrics for assessment can be designed on <strong>the</strong>
basis <strong>of</strong> opinions obtained from stakeholders and experts, as well as from an analysis <strong>of</strong> literature and
o<strong>the</strong>r research results. More cases in safety can be studied as comparison, especially for those in
o<strong>the</strong>r industries like construction and security.
Acknowledgement
The work described in this paper was fully supported by a grant from <strong>the</strong> Research Grants Council <strong>of</strong>
<strong>the</strong> Hong Kong Special Administrative Region, China (Project no.: PolyU 5160/07E).
References
Argyris, C., Putnam, R. and Smith, D. M. (1985) Action science. San Francisco: Jossey-Bass.
Boud, D., Keogh, R. and Walker, D. (1985) Reflection, turning experience into learning. London: Kogan Page.
Carrillo, P. (2005) “Lessons learned practices in <strong>the</strong> engineering, procurement and construction sector”,
Engineering Construction & Architectural Management, 12(3), 236-250.
Davenport, T. H. and Prusak, L. (1998) Working knowledge: How organizations manage what <strong>the</strong>y know. Boston,
Mass: Harvard Business School Press.
Denzin, N. K. and Lincoln, Y. S. (2005) The sage handbook <strong>of</strong> qualitative research (3rd ed.). Thousand Oaks,
Calif.: Sage Publications.
Eppler, M. J. (2006) Managing information quality: Increasing <strong>the</strong> value <strong>of</strong> information in knowledge-intensive
products and processes (2nd ed.). Berlin: Springer.
Erden, Z., von Krogh, G. and Nonaka, I. (2008) “The quality <strong>of</strong> group tacit knowledge”, The Journal <strong>of</strong> Strategic
Information Systems, 17(1), 4-18.
1056

Tony K. M. Lo and Patrick S.W. Fong
Fong, P. S. W. and Yip, J. C. H. (2006) “An investigative study <strong>of</strong> <strong>the</strong> application <strong>of</strong> lessons learned systems in
construction projects”, Journal for Education in <strong>the</strong> Built Environment, 1(2), 27-38.
Gibbs, G. (2007). Analyzing qualitative data. London: SAGE Publications.
Holsapple, C. W. (2003) Handbook on knowledge management (1st ed.). Berlin: Springer-Verlag.
Julian, J. L. (2008) An exploratory study <strong>of</strong> project management <strong>of</strong>fice leaders and <strong>the</strong>ir role in facilitating crossproject
learning. (PhD dissertation, Columbia University).
Kolb, D. A. (1984) Experiential learning: Experience as <strong>the</strong> source <strong>of</strong> learning and development. Englewood
Cliffs, N.J.: Prentice-Hall.
Krippendorff, K. (2004) Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks, Calif.:
Sage Publications.
Madnick, S. E., Wang, R. Y., Lee, Y. W. and Zhu, H. (2009) “Overview and framework for data and information
quality research”, Journal <strong>of</strong> Data and Information Quality, 1(1), 1-22.
Maqsood, T., Walker, D. H. T. and Finegan, A. (2004) Project histories and project learning - a knowledge
management challenge. 1(561) 570.
Milton, N. J. (2010) The lessons learned handbook :Practical approaches to learning from experience. Oxford:
Chandos Publishing.
Moon, J. A. (2004) A handbook <strong>of</strong> reflective and experiential learning: Theory and practice. London; New York:
Routledge Falmer.
Neuendorf, K. A. (2002) The content analysis guidebook. Thousand Oaks, Calif.: Sage Publications.
Nonaka, I. (1994) A dynamic <strong>the</strong>ory <strong>of</strong> organizational knowledge creation. Organization Science, 5(1), pp. 14-37.
Pipino, L., Wang, Y. R., Kopcso, D. and Rybolt, W. (2005) Developing measurement scales for data-quality
dimensions. Information quality (1st ed.). Armonk, N.Y.: M.E. Sharpe.
Rech, J., Decker, B., Ras, E., Jedlitschka, A. and Feldmann, R. (2007) “The quality <strong>of</strong> knowledge: Knowledge
patterns and knowledge refactorings”, International Journal <strong>of</strong> Knowledge Management, 3(3), 74-103.
Redman, T. (2005) “Measuring data accuracy: A framework and review”, Information quality (1st ed., ). Armonk,
N.Y.: M.E. Sharpe.
SDC (Ed.). (2007) Knowledge management toolkit. Bern: Swiss Agency for Development and Cooperation,
Knowledge and Learning Processes Division.
Szulanski, G. (2003) Sticky knowledge: Barriers to knowing in <strong>the</strong> firm. London: Sage Publications.
Trees, L., and American Productivity & Quality Center. (2010) Cutting <strong>the</strong> cost <strong>of</strong> not knowing: Lessons learned
systems people really use. Houston, Tex.: Apqc.
Yoo, D. K., Vonderembse, M. A. and T.S. Ragu-Nathan. (2011) “Knowledge quality: Antecedents and
consequence in project teams”, Journal <strong>of</strong> Knowledge Management, 15(2), 329-343.
Zhao, Z. and Anand, J. (2009) “A multilevel perspective on knowledge transfer: Evidence from <strong>the</strong> chinese
automotive industry”, Strategic Management Journal, 30(9), 959.
1057

KM as a Solution for <strong>the</strong> Shortage <strong>of</strong> Competent
Employees in SMEs at <strong>the</strong> Developing Country (Case
study: Vietnam)
Thi Hai Hang Nguyen 1 , Lubor Homolka 2 , Zdenek Molnar 3 , and Roderick
J.Macdonald 4
1,2 Faculty <strong>of</strong> Management and Economics, Tomas Bata University in Zlin,
Czech Republic
3 Faculty <strong>of</strong> informatics and Statistics, University <strong>of</strong> Economics, Prague, Czech
Republic
4 Faculty <strong>of</strong> Management &Technology, School <strong>of</strong> Business, University <strong>of</strong>
Quebec and Montreal, Canada
nghaihang@fame.utb.cz
homolka@fame.utb.cz
zdenek.molnar@vse.cz
macdonald.roderick@uqam.ca
Abstract: This paper presents partial results from <strong>the</strong> first empirical study <strong>of</strong> KM in SMEs <strong>of</strong> Vietnam. A
preliminary survey had revealed that KM is an important issue for SMEs in Vietnam. It also identified <strong>the</strong> main
motivation for SMEs to implement KM-related activities (<strong>the</strong> search for solutions to a chronic shortage <strong>of</strong>
competent employees) as well as <strong>the</strong> two probable main causes for this shortage: (1) <strong>the</strong> departure <strong>of</strong> employees
from SMEs and/or (2) remaining employees not learning how to work effectively. Using statistical analysis and
fuzzy methodology, this paper formally verifies whe<strong>the</strong>r <strong>the</strong> inferences from <strong>the</strong> preliminary study really hold for
<strong>the</strong> general population <strong>of</strong> Vietnamese SMEs, and from <strong>the</strong>re proposes obvious actions to be taken to solve <strong>the</strong>
shortage <strong>of</strong> competent employees. The findings presented in this paper help us understand more about <strong>the</strong>
operations <strong>of</strong> SMEs in developing countries and suggest a feasible approach to initiate KM for SMEs.
Keywords: Knowledge, knowledge management (KM), SMEs, Vietnam
1. Introduction
Most KM research focuses on big and resource-rich corporation (Wong, 2005), and, after more than
30 years <strong>of</strong> development, studies about KM practices in SMEs are still rare. The present paper hopes
to fill that void in a small way both by its own limited empirical contribution and by exemplifying how
such research (knowledge management in small to mid-sized firms) may be conducted in <strong>the</strong>
particular context <strong>of</strong> developing countries. As well, <strong>the</strong> empirical research was conducted in Vietnam
partly with <strong>the</strong> desire as to find ways to implement KM for SMEs <strong>the</strong>re. KM is still a new and
unpopular concept in Vietnam (Vu, 2008) not only for businesses but also for government and o<strong>the</strong>r
regulatory authorities as well as consultant companies. Fur<strong>the</strong>r, previous studies in KM have focused
on companies/organizations that had already implemented KM. The present research does not
suppose such implementation.
A preliminary survey conducted at <strong>the</strong> beginning <strong>of</strong> 2010 revealed that, SMEs in Vietnam are doing
<strong>the</strong>ir best to deal with KM issues although unaware <strong>of</strong> <strong>the</strong> academic terminology (Nguyen et al.,
2010). This was surprising. SME’s in Vietnam tend to leave strategic management “for later”. Since
KM is part <strong>of</strong> <strong>the</strong> strategic management <strong>of</strong> <strong>the</strong> firm, because it is more closely linked to innovation
ra<strong>the</strong>r than <strong>the</strong> day-to-day business <strong>of</strong> survival, and finally because it is mostly intangible and difficult
to measure and justify, it is not a field to which such SMEs are likely to dedicate much effort. The
preliminary survey also identified <strong>the</strong> main motivation for SMEs to implement KM-related activities: <strong>the</strong>
search for solutions to a chronic shortage <strong>of</strong> competent employees. Observation and miniconsultations
with some SMEs business owners suggested two probable main causes for this
shortage: (1) <strong>the</strong> departure <strong>of</strong> knowledge employees from SMEs and/or (2) o<strong>the</strong>r employees don’t
learn how to work effectively.
This paper partly presents <strong>the</strong> empirical data collected from 87 SMEs in Ho Chi Minh City – <strong>the</strong>
largest city <strong>of</strong> Vietnam and <strong>the</strong> most important in economic terms. The findings presented in this
paper could help academics and practitioners understand more about <strong>the</strong> operations <strong>of</strong> SMEs in
Vietnam and in o<strong>the</strong>r developing countries as well as to suggest a feasible approach to initiate KM
1058

Thi Hai Hang Nguyen et al
within SMEs. The paper is divided into 6 sections including this introduction. The second section
briefly invokes <strong>the</strong> scarcity <strong>of</strong> <strong>the</strong> literature about knowledge management in small to mid-sized firms.
The third section presents <strong>the</strong> current situation <strong>of</strong> <strong>the</strong> Vietnamese economy as well as <strong>of</strong> SMEs in
Vietnam. The fourth section elaborates <strong>the</strong> methodology applied in <strong>the</strong> empirical research used in this
paper. The fifth section presents <strong>the</strong> findings <strong>of</strong> <strong>the</strong> research and discusses <strong>the</strong> results <strong>of</strong> <strong>the</strong> study. A
final discussion, <strong>the</strong> implications and <strong>the</strong> limitations <strong>of</strong> <strong>the</strong> research are included in <strong>the</strong> sixth section.
2. Theoretical background: Knowledge Management for SMEs
There are still very few studies that examine KM issues related to small business (Pillania,
2006;Wong, 2005) despite <strong>the</strong> large population <strong>of</strong> SMEs to be found in every national economy.
These studies recognized significantly different issues for implementing KM in SMEs. Wong and
Aspinwal (2005) identified eleven critical success factors for implementing KM in <strong>the</strong> SME sector
(Wong and Aspinwall, 2005). Sparrow (2005) proposed a model to guide knowledge projects in small
firms as well as identified <strong>the</strong> phases in <strong>the</strong> KM development process in SMEs.
Although <strong>the</strong>re have been some efforts to find <strong>the</strong> answer to <strong>the</strong> question <strong>of</strong> how to apply KM
effectively into an SME operation, all seeking <strong>the</strong> optimum manner to implement/deploy KM in SME
operations, little attention has been paid to developing countries, and <strong>the</strong>re is no study <strong>of</strong> SMEs in
Vietnam. Current thinking holds that <strong>the</strong> Vietnamese economy is at a very early stage <strong>of</strong>
development, and thus is not <strong>the</strong> ready for <strong>the</strong> application <strong>of</strong> advanced management tools such as
KM. However, this paper derives from a different and more proactive. As <strong>the</strong> “late-comers” to <strong>the</strong>
global market, businesses from developing countries should not play follow-<strong>the</strong>-leader in an effort to
“catch-up” with <strong>the</strong> competition. Businesses in resource-poor developing countries – starting from
zero – have no o<strong>the</strong>r choice than maximizing intelligent use <strong>of</strong> scarce resources and acquiring
increased knowledge in order to “leap-frog” <strong>the</strong> competition (Nguyen et al., 2010).
3. SMEs in Vietnam
3.1 The economic situation in Vietnam
In 1986 Vietnam launched a political and economic renewal campaign (Doi moi) that introduced
reforms intended to facilitate <strong>the</strong> transition from a centrally planned economy to a “socialist-oriented
market economy”.
Economic reform has made <strong>the</strong> country into one <strong>of</strong> <strong>the</strong> fastest-growing economies in <strong>the</strong> world. Still,
although <strong>the</strong> Vietnamese economy has achieved much on <strong>the</strong> road toward a free-market situation, it
still requires a lot <strong>of</strong> effort to make <strong>the</strong> economic panorama brighter. Inflation has been a persistent
problem for Vietnam (Figure 1). Last year <strong>the</strong> inflation rate hit 11.75 percent and it has been
increasing continuously since <strong>the</strong> beginning <strong>of</strong> 2011. The fact that Vietnam dong was devaluated
twice during last few months (<strong>the</strong> latest – in January 2011 - by 9.3%) is a signal <strong>of</strong> serious
weaknesses in <strong>the</strong> national economy and is an embarrassment for <strong>the</strong> central government.
Percentage
25
20
15
10
5
0
Inflation rate
2005 2006 2007 2008 2009 2010
Inflation rate
Figure 1: Vietnam inflation rate from 2005-2010. Source: General statistics <strong>of</strong>fice <strong>of</strong> Vietnam
In spite <strong>of</strong> this struggle with inflation, Vietnam is considered as an attractive place for international
investors because <strong>of</strong> comparatively low prices and stable political regime. On <strong>the</strong> o<strong>the</strong>r hand, this
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Thi Hai Hang Nguyen et al
competitive edge is threatened by a heavy dependence on imported materials (80 percent <strong>of</strong> products
use imported materials, mainly from China) and Vietnam is “too far downstream in <strong>the</strong> global supply
chain”, where <strong>the</strong> pr<strong>of</strong>it margin is very small (Porter, 2010).
Despite GDP growth reaching 7.26% per year over last 10 years, Vietnam growth quality, productivity,
efficiency and <strong>the</strong> competitiveness <strong>of</strong> <strong>the</strong> economy are still low and macro balances are unstable.
Export products from <strong>the</strong> country are mostly raw materials and industrial goods are mainly manually
made. Productivity is much lower than regional economies (e.g. 2.6 times lower than China and 4.3
times lower than Thailand) (Porter, 2010).
These macro-economic issues have caused a lot <strong>of</strong> difficulties for enterprises in Vietnam. In addition
to <strong>the</strong>se challenges, <strong>the</strong>re are still o<strong>the</strong>r factors that are hurdles to doing business in <strong>the</strong> country, such
as <strong>the</strong> limited access to financing, policy instability, inadequately educated workforce, inadequate
supply <strong>of</strong> infrastructure, foreign currency regulations, tax regulations, poor work ethic in national labor
force, corruption, tax rates, etc (Porter, 2010). It is not easy to start an SME in Vietnam, although
<strong>the</strong>re is a plethora <strong>of</strong> marginal and mostly illegal one or two person retail stores or restaurants, <strong>of</strong>ten
set up for a few hours on a sidewalk.
3.2 Vietnamese SMEs
SMEs are none<strong>the</strong>less now <strong>of</strong>ficially recognized as <strong>the</strong> driving force <strong>of</strong> <strong>the</strong> national economy <strong>of</strong>
Vietnam The business environment has improved and is more supportive but <strong>the</strong>re are still a lot <strong>of</strong>
obstacles for real business development in Vietnam (as mentioned in part 3.1), especially for
privately – owned SMEs (POE). But <strong>the</strong>re is still <strong>the</strong> discrimination between state-owned enterprise
(SOE) and POE. In comparison with SOE, POE’s access to financing is much more limited. And while
SOEs use public lands for very cheap rental fee, POEs have to struggle for space for <strong>the</strong>ir factories or
companies. In most cases, POEs rent infrastructure from SOEs.
The average size <strong>of</strong> SMEs in Vietnam is ra<strong>the</strong>r small with about 50 employees and less than 250,000
USD in assets. The most critical issue <strong>of</strong> SMEs in Vietnam is <strong>the</strong> low educational background <strong>of</strong> <strong>the</strong>
business owners who normally also work as <strong>the</strong> CEOs <strong>of</strong> <strong>the</strong> firms <strong>the</strong>y own. A survey conducted by
<strong>the</strong> Vietnamese authorities among 63.000 enterprises throughout Vietnam in 2009 revealed that 43%
<strong>of</strong> top managers (<strong>of</strong> SME’s) hadn’t graduated from high school. According to this survey, 51,3% <strong>of</strong>
SMEs had under 10 annual workers, 44% <strong>of</strong> SMEs employed from 10 to 200 annual workers, and
only 1,43% <strong>of</strong> enterprise employ more than 300 workers(VNexpress, 2009).
4. Methodology used in <strong>the</strong> empirical research
This paper presents <strong>the</strong> findings <strong>of</strong> <strong>the</strong> intermediate stage (stage 2) <strong>of</strong> a larger research project (see
figure 2). The general research approaches as well as <strong>the</strong> details <strong>of</strong> research design such as item
generation, hypo<strong>the</strong>ses development, and research instrument are stated in this section.
Figure 2: Empirical research flow
4.1 Background: <strong>the</strong> approach <strong>of</strong> stage 1
Taking into account all <strong>the</strong> related conditions for implementing <strong>the</strong> study and its objectives, <strong>the</strong>
grounded <strong>the</strong>ory approach was chosen for <strong>the</strong> orientation survey <strong>of</strong> this research (stage 1 - see
figure 2) to set up <strong>the</strong> direction and to develop <strong>the</strong> hypo<strong>the</strong>ses for <strong>the</strong> next steps (Glaser and Strauss,
1967). The findings from this orientation stage mainly determine <strong>the</strong> item generations and hypo<strong>the</strong>ses
development for <strong>the</strong> part <strong>of</strong> study which is presented in this paper. The grounded <strong>the</strong>ory is a
systematic methodology that is different from <strong>the</strong> traditional scientific model where <strong>the</strong> researcher
starts with a <strong>the</strong>oretical framework, and <strong>the</strong>n applies this model to <strong>the</strong> studied phenomenon. The
grounded <strong>the</strong>ory approach allows <strong>the</strong> researcher start with <strong>the</strong> data collection, <strong>the</strong>n by a specific
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Thi Hai Hang Nguyen et al
working process form categories, from which a <strong>the</strong>ory or engineered hypo<strong>the</strong>ses can be developed
(Allan, 2003;Martin and Turner, 1986).
4.2 Hypo<strong>the</strong>ses development in stage 2
The objective <strong>of</strong> this paper is to verify whe<strong>the</strong>r <strong>the</strong> inference <strong>of</strong> <strong>the</strong> authors really hold for <strong>the</strong> general
population <strong>of</strong> Vietnamese SMEs. The orientation survey had revealed that SMEs <strong>of</strong> Vietnam are
doing <strong>the</strong>ir best to deal with KM issues without being aware <strong>of</strong> <strong>the</strong> terminologies. They do this
primarily to maintain <strong>the</strong>ir business, and secondarily to develop it. The result also identified <strong>the</strong> main
motivation for SMEs in implementing KM-related activities, which is <strong>the</strong> search for solutions to a
chronic shortage <strong>of</strong> competent employees, as well as <strong>the</strong> two probable main causes for this shortage:
(1) <strong>the</strong> departure <strong>of</strong> knowledge employees from SMEs and/or (2) o<strong>the</strong>r employees don’t learn how to
work effectively. This raises issues that are not dealt with in <strong>the</strong> literature and, accordingly, lead to
hypo<strong>the</strong>ses not suggested by <strong>the</strong> literature. We focused on two main null hypo<strong>the</strong>ses:
H1: High staff turnover is not harmful for business in Vietnam SMEs.
H2: Employees <strong>of</strong> SMEs learn (how to do <strong>the</strong> work, how to do work better) effectively on
<strong>the</strong> job.
From <strong>the</strong> point <strong>of</strong> view <strong>of</strong> company management, <strong>the</strong> symptom <strong>of</strong> knowledge hoarding, an ineffective
record system to capture existing knowledge in <strong>the</strong> organization, an ineffective training policy are
determined as KM-related factors that would influence <strong>the</strong> learning effectiveness <strong>of</strong> employees in <strong>the</strong>
organization. In <strong>the</strong> framework <strong>of</strong> this research, to examine <strong>the</strong> existence <strong>of</strong> <strong>the</strong>se factors, four subhypo<strong>the</strong>ses
are set as follows (o<strong>the</strong>r factors were not focused upon in this study):
H2a: Company has an effective training policy
H2b: Knowledgeable people are willing to share <strong>the</strong>ir knowledge
H2c: Company’s record system captures knowledge <strong>of</strong> employees
H2d: Employees have recourse to <strong>the</strong> company’s record system
The inter-relationships <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>ses/sub-hypo<strong>the</strong>ses are illustrated in figure 2 as follows:
Figure 3: Possible causes for <strong>the</strong> shortage <strong>of</strong> competent employees in Vietnamese SMEs
4.3 The research instrument
The survey method was used for this study. A questionnaire for SME’s CEOs or business owners was
designed and presented both online and in a printable version (taking into account <strong>the</strong> fact that <strong>the</strong>re
is a big percentage <strong>of</strong> SME’s CEOs or business owners who are not familiar with using computer yet).
CEOs/business owners <strong>of</strong> SMEs gave <strong>the</strong>ir evaluation <strong>of</strong> <strong>the</strong> firm’s performance and <strong>the</strong>ir views on
firm management. Their responses were used as <strong>the</strong> primary data for analysis.
The questionnaire was developed based on <strong>the</strong> findings from an intensive review <strong>of</strong> previous studies.
A five point scale was used in this questionnaire.
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Thi Hai Hang Nguyen et al
The variable “harmfulness level <strong>of</strong> high staff turnover” (H1) was measured by a single question. The
variable “effectiveness <strong>of</strong> record system” was measured by average <strong>of</strong> two related questions basing
on <strong>the</strong> usage <strong>of</strong> job-related reports and <strong>the</strong> consulting sources for management in <strong>the</strong> firm. O<strong>the</strong>r
variables were measured by different constructs namely (1) “Knowledge sharing level <strong>of</strong> working
environment” (9 items adopted from Yang (2007), (2) “training effectiveness” (5 items); those are
needed to check <strong>the</strong> reliability. The full questionnaire is available from <strong>the</strong> contact author.
4.4 Contact method& sample size
In January 2010, <strong>the</strong> questionnaire were delivered by post and via emails to more than 500
companies randomly selected in <strong>the</strong> Yellow Pages <strong>of</strong> Ho Chi Minh City, but more than 50% <strong>of</strong> emails
failed to reach <strong>the</strong>ir destination (i.e. incorrect email addresses) and <strong>the</strong> general response rate was too
low. The researchers <strong>the</strong>n proceeded to carry out <strong>the</strong> survey via available networks to contact
CEOs/business owners <strong>of</strong> SMEs. In this second round, <strong>the</strong> questionnaires were sent to more than
300 CEOs/business owners, and <strong>the</strong> response rate was quite positive – 87 responses were received
(~29%). Of <strong>the</strong>se, 71 sets <strong>of</strong> data met requirements for analysis. With respondent permission, 25
visits to companies were made to obtain a better view <strong>of</strong> what was really going on inside those firms.
4.5 Data analysis methods
A fuzzy method and a chi-square test were employed to analyze collected data. Linear Discriminant
Analysis (LDA) was also used to classify <strong>the</strong> classes <strong>of</strong> firms in this research. While <strong>the</strong> chi-square
test and LDA are quite popular in economic and management research, fuzzy sets are still not used
<strong>of</strong>ten despite <strong>of</strong> <strong>the</strong>ir advantages such as providing a more nuanced representation <strong>of</strong> categorical
concepts by permitting degrees <strong>of</strong> membership in sets ra<strong>the</strong>r than binary in-or-out membership, and
<strong>the</strong> possibility <strong>of</strong> addressing both diversity and ambiguity in a systematic manner (Ragin and
Pennings, 2005). Most <strong>of</strong> <strong>the</strong> primary data collected from <strong>the</strong> survey in this research is categorical,
and somehow presents imprecise concepts such as <strong>the</strong> level <strong>of</strong> administrative management, or
sharing culture and working environment evaluation, etc. The rules to assess <strong>the</strong> criteria with
imprecise dependencies among variables in <strong>the</strong> research are fuzzy. Thus, <strong>the</strong> fuzzy method was
selected for this study.
5. Survey findings
5.1 Sample description
Primary data was obtained from 71 sets <strong>of</strong> responses. All firms in this study are ranked as SMEs
according to <strong>the</strong> Vietnamese Government <strong>of</strong>ficial definition. The industry types <strong>of</strong> SMEs are described
in table 1. The average age among firms in <strong>the</strong> sample is 6.3 years old. 58 out <strong>of</strong> 71 firms are locally
owned firms (81.6%). Eleven firms are owned and operated by foreigners. And only two <strong>of</strong> <strong>the</strong> 71
firms are state-owned.
Industry types Number <strong>of</strong> firms Percentage
Service 54 79.2%
Construction & Real Estate 06 8.5%
IT 06 8.5%
Trading 05 7.0%
Forwarding 16 22.5%
Tourist agency 12 16.9%
Consultancy 02 2.8%
O<strong>the</strong>r 07 9.9%
Manufacturing or Mixed 17 23.8%
Garment& textile 04 5.6%
Furniture & handicraft 03 4.2%
Construction materials 04 5.6%
Food processing 03 4.2%
O<strong>the</strong>r 03 4.2%
Table 1: Description <strong>of</strong> SMEs in <strong>the</strong> sample
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5.2 Reliability
Thi Hai Hang Nguyen et al
The reliability <strong>of</strong> related constructs in <strong>the</strong> questionnaire was evaluated by calculating Cronbach’s
coefficient alpha while face validity was conducted during <strong>the</strong> pilot test to modify <strong>the</strong> wording and
scales. The results are presented in Table 2. With <strong>the</strong> alpha value >0.7, <strong>the</strong> two main constructs in <strong>the</strong>
questionnaire are proven internally consistent and reliable.
Construct No. <strong>of</strong> items Alpha value
Knowledge sharing level <strong>of</strong>
working environment
9 0.829
Training effectiveness 5 0.780
Table 2: Summary results from reliability and validity tests (SPSS ver.16)
5.3 Findings
The companies are classified into 3 different classes in term <strong>of</strong> general administrative management
practice (named Good, Moderate, and Bad) basing on five criteria: (1) The level <strong>of</strong> applied IT, (2) The
level <strong>of</strong> applied management tools, (3) The knowledge sharing working culture & environment, (4) The
effectiveness <strong>of</strong> learning <strong>of</strong> employees at <strong>the</strong> firm, and (5) The vision <strong>of</strong> top management (long-term
vs. short term).
A chi-square test was employed to test <strong>the</strong> first hypo<strong>the</strong>sis: High staff turnover is not harmful for
business in Vietnam SMEs. Analysis <strong>of</strong> <strong>the</strong> data brought out <strong>the</strong> following result:
X^2 (Chi Square) Test value Degree <strong>of</strong> freedom P-Value
Likelihood Ratio 8.85 8 0.37549
Pearson 8.98 8 0.36825
According to this result, we cannot reject <strong>the</strong> null hypo<strong>the</strong>sis H1 as <strong>the</strong>re is no relationship between
turnover <strong>of</strong> employees and <strong>the</strong> company class. The obvious interpretation is that we cannot make a
conclusion as to whe<strong>the</strong>r <strong>the</strong> rate <strong>of</strong> staff turnover negatively affects <strong>the</strong> performance <strong>of</strong> <strong>the</strong> firm or
not. This may be caused by (1) <strong>the</strong> sample not being purely random (as explained in 4.3) or/and (2)
<strong>the</strong> sample size not being big enough. It also may be a simple truth about businesses in Vietnam:
among <strong>the</strong> companies that were visited, very few confirmed having difficulties with a high staff
turnover rate. In SMEs, <strong>the</strong> turnover rate is high mostly in <strong>the</strong> manufacturing sector and this happens
with workers who are not competent enough or who are dealing with simple tasks in factories and are
easily replaced by new employees. Normally, SMEs don’t have any problem at <strong>the</strong> management level.
This finding rejected <strong>the</strong> first intuition <strong>of</strong> <strong>the</strong> researchers but is congruent with a finding by Desouza
and Awazu (2006) that SMEs never consider knowledge loss caused by <strong>the</strong> departure <strong>of</strong> skilled
employees a real problem or issue as it is in large organizations. Only a few mature SMEs had some
deliberate mechanism in <strong>the</strong> firms to prevent knowledge loss (Desouza and Awazu, 2006). Reasons
may vary, but CEOs are also <strong>the</strong> business owners and <strong>the</strong> people who possess <strong>the</strong> key knowledge in
SMEs. And as <strong>the</strong>ir business is small, <strong>the</strong>y can easily find sufficient new workers to be trained to fill
vacancies. Ano<strong>the</strong>r fact that can somehow explain this symptom is that in SMEs most management
positions or key positions are assigned to persons in <strong>the</strong> same family or close friends who have a
good relationship with <strong>the</strong> CEOs/business owners. The CEOs <strong>of</strong> some o<strong>the</strong>r SMEs revealed that a
high staff turnover rate is considered as part <strong>of</strong> <strong>the</strong> nature <strong>of</strong> business in that industry, and <strong>the</strong>y just
purposefully limit <strong>of</strong> business to a size <strong>the</strong>y can handle.
Fuzzy logic was used to set up <strong>the</strong> scale, <strong>the</strong> rule <strong>of</strong> assessment to test hypo<strong>the</strong>sis 2. The following
steps describe <strong>the</strong> fuzzy rule:
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Thi Hai Hang Nguyen et al
The results showed that only 56% <strong>of</strong> companies in <strong>the</strong> sample have an effective training policy (H2a).
Then, according to <strong>the</strong> set standards, we cannot decide if SMEs in Vietnam has an effective training
policy or not. For H2b, <strong>the</strong> result is ra<strong>the</strong>r good: 66% <strong>of</strong> <strong>the</strong> sample set evaluated <strong>the</strong>ir working
environment and <strong>the</strong> willingness <strong>of</strong> people to share <strong>the</strong>ir experience and know-how. 24% <strong>of</strong>
companies in <strong>the</strong> survey considered <strong>the</strong>ir record system as effective (H2c) and 17% <strong>of</strong> companies
asserted that <strong>the</strong>ir people have recourse to <strong>the</strong> company’s record system (H2d). Accordingly, <strong>the</strong>
second hypo<strong>the</strong>sis (H2) with <strong>the</strong> value 0,4 was rejected based on <strong>the</strong> set standard:
Then we can make <strong>the</strong> conclusion that employees in most SMEs in Vietnam don’t learn how to work
effectively on <strong>the</strong> job. This conclusion reflects what is really happening in SMEs in Vietnam. O<strong>the</strong>r
results from <strong>the</strong> survey supported this finding. While 52% (37/71) <strong>of</strong> companies complained about <strong>the</strong>
serious shortage <strong>of</strong> competent employees for <strong>the</strong>ir businesses, only a little more than 35% (25/71) <strong>of</strong>
companies claimed <strong>the</strong>y have a training policy for <strong>the</strong>ir employees, <strong>the</strong> rest admitting that <strong>the</strong>y don’t
have any training policy yet, with <strong>the</strong> decision whe<strong>the</strong>r to train certain employees or not made on a
case-by-case basis. Answering <strong>the</strong> question as to <strong>the</strong> reason for <strong>the</strong> business owner not to train
employees, 29 CEOs (~41%) stated lack <strong>of</strong> commitment from <strong>the</strong> employees as <strong>the</strong> most frequent
(and decisive) reason. Regarding <strong>the</strong> record system, more than 46% companies (33/71) revealed that
<strong>the</strong>y don’t use any job-related records or if <strong>the</strong>y had records, <strong>the</strong>y were simply kept in case <strong>of</strong>
necessity. This fact can be considered as a most significant weakness <strong>of</strong> SME management. This is
understandable when 43% <strong>of</strong> SMEs’ CEOs have not graduated from high school. The result <strong>of</strong> H2d is
closely connected to this fact: Very few people can rely on a weak record system in performing <strong>the</strong>ir
tasks.
6. Discussion, limitations and implications
Analysis <strong>of</strong> <strong>the</strong> data reveals that (1) <strong>the</strong>re is almost no connection between <strong>the</strong> high staff turnover and
<strong>the</strong> Vietnamese SME performance in general, and fur<strong>the</strong>r (2) employees don’t seem to have access
to proper training at work. The shortage <strong>of</strong> competent employees may very well be attributed to this
latter reason. The results indicate that people don’t learn well at work mainly because <strong>of</strong> (1) a weak
record system (or <strong>the</strong> absence <strong>of</strong> record system), (2) limited access to <strong>the</strong> record system (if any), and
(3) lack <strong>of</strong> company training policy. This implies that when firms set up a KM system, one should give
special priority to building <strong>the</strong> record system and ensuring that people can assess this record system
to learn. 56% <strong>of</strong> companies evaluated <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong>ir training system positively, yet only
35% <strong>of</strong> companies confirmed that <strong>the</strong>y really have a training policy! Did CEOs not tell <strong>the</strong> truth, or was
this just a question <strong>of</strong> <strong>the</strong> way <strong>the</strong>y perceived <strong>the</strong> issue? The result from ano<strong>the</strong>r question supported
<strong>the</strong> latter explanation: 55% CEOs <strong>of</strong> companies revealed in different ways that how <strong>the</strong>y really don’t
think <strong>the</strong> training <strong>of</strong> employees is important for business (because <strong>the</strong> job is simple, or just because
<strong>the</strong>y don’t have time nor money to train employees), and some <strong>of</strong> <strong>the</strong>se CEOs believed that <strong>the</strong>ir
employees automatically learn how to do job well while working in <strong>the</strong>ir firms. This phenomenon
confirms <strong>the</strong> existence <strong>of</strong> an urgent need to improve <strong>the</strong> management background <strong>of</strong> CEOs <strong>of</strong> SMEs
in a very practical way.
The research could not avoid some limitations. First, for objective reasons, <strong>the</strong> sample size is not big
enough and not purely random (see 4.3). This limitation may somehow bias <strong>the</strong> results <strong>of</strong> <strong>the</strong>
research. Second, <strong>the</strong> framework <strong>of</strong> this research was developed focusing on KM-related
determinants while in reality <strong>the</strong>re may be o<strong>the</strong>r factors that can lead to <strong>the</strong> same phenomenon.
As <strong>the</strong> first empirical research in KM in <strong>the</strong> country, <strong>the</strong> authors believe that <strong>the</strong> findings presented in
this paper could help academics and practitioners understand more about <strong>the</strong> operations <strong>of</strong> SMEs in
Vietnam as well as in o<strong>the</strong>r developing countries, as well as suggest directions for research about KM
in developing country SMEs, and suggest a feasible approach to initiate KM for SMEs.
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References
Thi Hai Hang Nguyen et al
Allan, G. (2003). "A critique <strong>of</strong> using grounded <strong>the</strong>ory as a research method", Electronic Journal <strong>of</strong> Business
Research Methods,Vol. 2 No. 1, pp. 1-10.
Desouza, K. C. & Awazu, Y. (2006). "Knowledge management at SMEs: five peculiarities ", Journal <strong>of</strong> Knowledge
Management,Vol. 10 No. 1, pp. 32-43.
Glaser, B. & Strauss, A. (1967). The Discovery <strong>of</strong> Grounded Theory: Strategies for Qualitative Research, Aldine
De Gruyter.New York
Martin, P. Y. & Turner, B. A. (1986). "GROUNDED THEORY AND ORGANIZATIONAL RESEARCH", Journal <strong>of</strong>
Applied Behavioral Science,Vol. 22 No. 2, pp. 141-157.
Nguyen, T. H. H., Molnar, Z. & Pham, L. C. (2010). Knowledge management for SMEs in deveploping countries -
How feasible is it? In: The 10th <strong>European</strong> <strong>Conference</strong> for Knowledge Management Facilicao - Portugal.
ACI, London.
Pillania, R. K. (2006). "Leveraging knowledge for sustainable competitiveness in SMEs", International Journal <strong>of</strong>
Globalisation and Small Business,Vol. 1 No. 4, pp. 393-406.
Porter, M. (2010). Vietnam Competitiveness Report. Central Institute for Economic Management (Vietnam); Asia
Competitiveness Institute (Singapore).
Ragin, C. C. & Pennings, P. (2005). "Fuzzy sets and social research", Sociological Methods & Research,Vol. 33
No. 4, pp. 423-430.
VNexpress (2009). "43% lanh dao doanh nghiep chua tot nghiep cap 3" .[Online]
http://www.vnexpress.net/GL/Kinh-doanh/Kinh-nghiem/2009/08/3BA1214D/ [Accessed 2009].
Vu, H. D. (2008). Knowledge Management in Vietnam. In: TALISAYON, S. D. (ed.) Knowledge Management in
Asia: Experiences and Lessons - Report <strong>of</strong> <strong>the</strong> APO Survey on <strong>the</strong> Status <strong>of</strong> Knowledge Management in
Member Countries. Tokyo: Asian Productivity Organization.
Wong, K. Y. (2005). "Critical success factors for implementing knowledge management in small and medium
enterprises", Industrial Management & Data Systems,Vol. 105 No. 3-4, pp. 261-279.
Wong, K. Y. & Aspinwall, E. (2005). "An empirical study <strong>of</strong> <strong>the</strong> important factors for knowledge-management
adoption in <strong>the</strong> SME sector", Journal <strong>of</strong> Knowledge Management,Vol. 9 No. 3, pp. 64-82.
1065

Knowledge Base Development <strong>of</strong> Companies in R&D
Consortia; An Organisational Process and Social
Interaction Perspective on Learning
Omid Omidvar
MIoIR/ MSB, UK
omid.omidvar@postgrad.mbs.ac.uk
Abstract: R&D consortia are becoming one <strong>of</strong> <strong>the</strong> prominent forms <strong>of</strong> collaborations among organisations aimed
at triggering knowledge transferring among participating firms. In such settings, learning is not exclusively limited
to mutual or dyadic relations, but is mainly determined by multilateral ones. However, much current work on interorganisational
learning literature rest on bilateral knowledge transfer models (sender-receiver) in partnerships
which can hardly address <strong>the</strong> complexity <strong>of</strong> inter-organisational learning in R&D consortia. To explore <strong>the</strong>
learning process in R&D consortia, I will devise social capital (SC) and absorptive capacity (AC) <strong>the</strong>ories. To gain
knowledge, on <strong>the</strong> one hand, organisations rely on <strong>the</strong> processes and capabilities enabling <strong>the</strong>m to acquire and
exploit external knowledge represented by AC. It enables organisations to identify, assimilate and apply external
knowledge to commercial ends. On <strong>the</strong> o<strong>the</strong>r hand, organisations rely on <strong>the</strong> very social context <strong>of</strong> <strong>the</strong>ir
interpersonal and inter-group knowledge exchange and communications which can be achieved through SC. By
nurturing goodwill in <strong>the</strong> social context <strong>of</strong> R&D collaborations, SC facilitates knowledge sharing, exchanging and
combining among parties. Based on this <strong>the</strong>oretical lens, first I will explore how SC and AC contribute to interorganisational
learning in R&D consortia. Secondly, I will investigate how SC and AC (co)develop in such
settings. Driven by critical realism epistemological stance, this research will use case study as <strong>the</strong> research
strategy in 4-5 companies engaged in R&D consortia. It also adopts qualitative methodology and research
methods will mainly involve semi-structured interviews. The results are expected to enhance our understanding
<strong>of</strong> how AC and SC develop in R&D consortia. They are, moreover, expected to give some managerial
implications on how to increase <strong>the</strong> learning outcomes in R&D collaborations in general and in R&D consortia in
particular.
Keywords: Absorptive capacity, social capital, organisational learning, r&d consortium, intra and interorganisational
settings, case study
1. Introduction
In <strong>the</strong> highly competitive business environments, R&D partnerships are becoming one <strong>of</strong> <strong>the</strong> most
important sources <strong>of</strong> competitive advantage aimed at enlarging <strong>the</strong> knowledge- base <strong>of</strong> <strong>the</strong> individual
firms. Firms participate in different collaborations to gain knowledge, to achieve innovative products,
or to access partners’ complimentary capabilities. However, <strong>the</strong> complexity <strong>of</strong> inter-organisational
relations constrains effective learning in R&D partnerships and companies fail to achieve <strong>the</strong>ir
predetermined goals unless <strong>the</strong>y are equipped with <strong>the</strong> sufficient resources and capabilities.
Managing <strong>the</strong> inter-organisational learning becomes even more difficult when <strong>the</strong>re are more than two
companies collaborating. The fact that many R&D consortia fail and managers face structural and
coordination problems indicates <strong>the</strong> difficulty <strong>of</strong> handling inter-organisational learning in such settings.
Literature suggests that absorptive capacity and social capital are two <strong>of</strong> <strong>the</strong> very important sources <strong>of</strong>
learning in organisations in general (Thorpe et al., 2005) and in R&D collaborations in particular.
Originally defined as <strong>the</strong> organisational ability to identify, assimilate, and apply external knowledge to
commercial ends (Cohen and Levinthal, 1990), absorptive capacity is considered as <strong>the</strong> source <strong>of</strong>
organisational competitiveness (Zahra and George, 2002) and comprises organisational processes
and capabilities to absorb knowledge (Lewin et al., 2011; Lane et al., 2006). On <strong>the</strong> o<strong>the</strong>r hand,
‘Social capital is <strong>the</strong> goodwill available to individuals or groups. Its source lies in <strong>the</strong> structure and
content <strong>of</strong> <strong>the</strong> actor's social relations. Its effects flow from <strong>the</strong> information, influence, and solidarity it
makes avail- able to <strong>the</strong> actor’ (Adler and Kwon, 2002). It determines <strong>the</strong> very social context <strong>of</strong> <strong>the</strong>
interpersonal, inter-group and inter-organisational knowledge exchange and communications which is
crucial in inter-organisational learning.
Given <strong>the</strong> importance <strong>of</strong> social capital (SC) and absorptive capacity (AC) to learning in interorganisational
settings, current work aims to contribute to <strong>the</strong> management literature in two major
ways. First, it aims to improve <strong>the</strong> understanding <strong>of</strong> <strong>the</strong> ways AC and SC contribute to interorganisational
learning. In doing so, SC and AC will be taken into consideration at two stages. First,
prior to a consortium formation, <strong>the</strong> pre-existing AC and SC <strong>of</strong> <strong>the</strong> focal company will be analysed to
identify <strong>the</strong>ir role in R&D partnership formation. Second, when a consortium is running, I will explore
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how SC and AC contribute into inter-organisational learning. In this regard, I will question whe<strong>the</strong>r and
how SC as a collective good lubricates knowledge transfer among parties. On <strong>the</strong> o<strong>the</strong>r hand, current
work examines how AC at <strong>the</strong> inter-organisational (consortium) level will contribute to learning.
To this end, <strong>the</strong> AC concept must be developed in order to embrace network conditions. Current work
on AC has ei<strong>the</strong>r focused on absolute AC(Cohen and Levinthal, 1990, Kim, 1998, Lichtenthaler, 2009)
or on dyadic inter-organisational relations referred as relative AC(Lane and Lubatkin, 1998). Although
relative AC is a developed concept in <strong>the</strong> extant literature, <strong>the</strong>re is not much work addressing <strong>the</strong>
network view <strong>of</strong> AC.
How do SC and AC <strong>of</strong> <strong>the</strong> focal firm affect its participation in an R&D partnership/ consortium?
How do SC and AC influence learning in R&D consortia?
On <strong>the</strong> o<strong>the</strong>r hand, extant literature implies that AC and SC interact and have mutual impact.
However, <strong>the</strong> mechanisms <strong>of</strong> interactions between AC and SC prior to as well as throughout R&D
partnerships need fur<strong>the</strong>r investigations. Hence, <strong>the</strong> following research question:
How do SC and AC interact in R&D consortia?
AC literature suggests that it is important to move away from AC as a static capability <strong>of</strong> an
organisation into a process view(Lane et al., 2006, Zahra and George, 2002). Volberda et al. (2010)
identify realizing <strong>the</strong> processes <strong>of</strong> AC and its changes over time, as a conspicuous research gap.
However, very few <strong>the</strong>oretical studies try to shed some light on how AC unfolds in organisations, let
alone empirical ones. Thus far, <strong>the</strong> majority <strong>of</strong> empirical studies are conducted quantitatively, which
do not enhance our understanding <strong>of</strong> <strong>the</strong> mechanisms <strong>of</strong> AC development in organisations.
Therefore, I propose <strong>the</strong> following research question:
How does AC unfold in R&D consortia?
Finally, SC is usually considered as a given phenomenon in <strong>the</strong> management literature, and with few
exceptions, SC literature has not addressed <strong>the</strong> development <strong>of</strong> SC in organisations. This is largely
because <strong>of</strong> two important presumptions in understanding <strong>of</strong> SC. First, SC literature has usually
emphasized <strong>the</strong> social network perspective at <strong>the</strong> expense <strong>of</strong> s<strong>of</strong>t aspects <strong>of</strong> social capital (cf. Ahuja,
2000). Second, <strong>the</strong> majority <strong>of</strong> research on SC in management literature has followed a quantitative
approach which can hardly explain <strong>the</strong> development mechanisms <strong>of</strong> SC(cf. Tsai and Ghoshal, 1998).
Consequently, <strong>the</strong> last research question is:
How does SC unfold in R&D consortia?
To answer <strong>the</strong>se questions, <strong>the</strong> study will distinguish between relative and absolute forms <strong>of</strong>
absorptive capacity, as well as, internal and external forms <strong>of</strong> social capital in R&D partnerships.
Making such distinctions is essential for analysing <strong>the</strong> knowledge flow across <strong>the</strong> organisational
boundaries. Whereas absolute absorptive capacity refers to a company’s general ability to absorb
relevant external knowledge, relative absorptive capacity reveals its ability to absorb knowledge from
a particular partner in R&D partnerships. Literature suggests that individuals’ knowledge base,
general experience <strong>of</strong> alliance management, incentives structure and <strong>the</strong> firms’ strategy are important
antecedents <strong>of</strong> absolute AC (cf. Jansen et al., 2005; Lane et al., 2006). Relative AC helps partners
increase <strong>the</strong> cognitive proximity, which in turn enhances <strong>the</strong> knowledge absorption capability.
Literature indicates that <strong>the</strong> history <strong>of</strong> <strong>the</strong> relationship with a particular partner along with <strong>the</strong> very
governance and <strong>the</strong> structure <strong>of</strong> <strong>the</strong> alliances are two major variables contributing to <strong>the</strong> relative AC
(Lane and Lubatkin, 1998, Dyer and Singh, 1998).
Likewise, while internal social capital refers to <strong>the</strong> resources inside a social boundary and is a
collective good in nature, external social capital is located beyound such boundaries and is an
individual possession. Inside a group, unit or an organisation, social capital is shared and is available
to all actors in <strong>the</strong> boundary regardless <strong>of</strong> <strong>the</strong>ir resources as input to <strong>the</strong> community. Therefore, <strong>the</strong>re
is no competition among actors to gain it (cf. Coleman, 1990, Adler and Kwon, 2002). On <strong>the</strong> o<strong>the</strong>r
hand, external social capital represents <strong>the</strong> social resources available to individual actors and it
depends on <strong>the</strong>ir social relations and resources <strong>the</strong>y share with o<strong>the</strong>rs through <strong>the</strong>se social relations
(Adler and Kwon, 2002). Those actors who can better manage <strong>the</strong>ir social resources are in a superior
position in comparison with <strong>the</strong> ones who have less access to such resources. Here, actors can be
individuals, groups or organisations. In R&D collaborations, <strong>the</strong> internal social capital is inside <strong>the</strong>
R&D units and <strong>the</strong> external social capital relates to <strong>the</strong> external boundaries and interfaces between
companies.
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2. Literature review
2.1 AC: Theoretical and empirical issues
Omid Omidvar
Originally defined as <strong>the</strong> organisational ability to identify, assimilate, and apply external knowledge to
commercial ends (Cohen and Levinthal, 1990), AC is considered as <strong>the</strong> source <strong>of</strong> organisational
competitiveness (Zahra and George, 2002). However, this dynamic capability (Zahra and George,
2002) has gone under continuous controversies and revisions since its initiation. Lane et al. (2006)
showed that AC has become reified in its academic applications, and called for a considerable
reinvestigation <strong>of</strong> <strong>the</strong> concept. As <strong>the</strong>y indicate, AC ‘promised to explain <strong>the</strong> process through which
firms learn, develop, and assimilate new knowledge necessary for competitive advantage’ (Lane et
al., 2006). Moreover, it could explain <strong>the</strong> differences in competitive advantage <strong>of</strong> firms in accordance
with <strong>the</strong> resource based view <strong>of</strong> <strong>the</strong> firm (Penrose, 1959). Accordingly, researchers have <strong>of</strong>ten used it
in order to provide a taken for granted concept without discussing it deeply.
AC literature can be divided into two distinct branches; those who have considered it as an
organizational capability (Cohen &Levinthal, 1990), and those defining it from a process-oriented
perspective (Van den Bosch et al. 1999; Lane et al. 2006; Zahra and George 2002, Lewin et al.
2011). These two different approaches have led into different conceptualizations. The first group have
applied AC as a static capability-which leads into certain outputs needed for organisational survival. In
this sense, AC is operationalized as knowledge stock (e.g. patents, and R&D intensity), or o<strong>the</strong>r
proxies like age, size, and knowledge routines (see Lane and Lubatkin, 1998). Accordingly, it is
argued that whe<strong>the</strong>r it is right to use proxies instead <strong>of</strong> operationalizing AC itself (see Lane et al.,
2006, Lewin et al., 2011). This reminds us <strong>of</strong> <strong>the</strong> controversies occurring in <strong>the</strong> social science
research methods; while Prime and Butler (2001) believe that testing an unobserved capability is
impossible, Barney (2001) argues that a capability needs not to be directly measured if <strong>the</strong> <strong>the</strong>ory
explains its origins and outcomes. However, <strong>the</strong> o<strong>the</strong>r group have defined it as a dynamic capability,
which is <strong>the</strong> change drive in organisations (Teece et al., 1997). It means that it is an organisational
dynamic capability enabling it to create and deploy <strong>the</strong> knowledge needed for o<strong>the</strong>r organisational
capabilities (Todorova and Durisin, 2007, Lewin et al., 2011, Zahra and George, 2002, Lane et al.,
2006). In this stream, Zahra and George (2002) reconceptualised AC as a ‘set <strong>of</strong> organisational
routines and processes by which firms acquire, assimilate, transform and exploit knowledge to
produce a dynamic organisational capability’. According to Lane et al. (2006), defining AC in a
process-oriented mode helps to avoid becoming limited to knowledge content <strong>of</strong> a firm, or being
trapped in defining AC as a simple outcome <strong>of</strong> R&D efforts <strong>of</strong> <strong>the</strong> firm.
These <strong>the</strong>oretical and empirical confusions resulted from different viewpoints to AC and its reification
(Lane et al., 2006) have led to inconsistent results. For instance, although many studies corroborate
that AC increases learning and innovation potential <strong>of</strong> <strong>the</strong> firm (e.g. Tsai, 2001), Mowery et al. (1996)
cannot come into conclusive results about <strong>the</strong> significance <strong>of</strong> AC in inter-organisational learning.
In order to settle <strong>the</strong>se challenges, many authors have tried to clarify <strong>the</strong> underlying routines (Zahra
and George, 2002), antecedents (Van den Bosch et al., 1999), processes (Lane et al., 2006), or meta
routines (Lewin et al., 2011) involved in AC. These contributions have tried to both illuminate <strong>the</strong>
operationalization as well as <strong>the</strong> concept development <strong>of</strong> AC.
Zahra and George (2002), by differentiating between potential absorptive capacity (PACAP) and
realized absorptive capacity (RACAP), discuss for an outstanding need <strong>of</strong> making balance between
<strong>the</strong>se dimensions in companies. While PACAP is oriented to acquire and assimilate external
knowledge, RACAP is <strong>the</strong> ability to transform this knowledge into organisational outputs. In o<strong>the</strong>r
words, RACAP underlines <strong>the</strong> organisational capability to transform acquired knowledge into valuable
products or services.
Van den Bosch et al. (1999) discuss social, coordination, and systems capabilities as <strong>the</strong> three main
antecedents <strong>of</strong> AC. In <strong>the</strong>ir discussion, in addition to prior knowledge resources (cognitive base),
organisations must develop combinative capabilities (Kogut and Zander, 1992) in order to absorb
knowledge effectively.
Lewin et al. (2010) suggest internal and external meta-routines as <strong>the</strong> basis for operationalizing AC.
While external AC routines are those routines enabling firms to scan <strong>the</strong> environmental knowledge,
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Omid Omidvar
and assimilate it, internal AC routines are to make enquiries inside <strong>the</strong> organisational boundaries in
order to absorb <strong>the</strong> previously unabsorbed internal knowledge. They argue that although
organisations may practice different routines, <strong>the</strong>se routines can be categorized into certain metaroutine.
However, as <strong>the</strong>y explain, AC is still far from being operationalized robustly.
2.2 SC: Theoretical and empirical issues
Adler and Kwon (2002), by reviewing <strong>the</strong> previous work done on SC, propose a new SC <strong>the</strong>ory, which
locate ability, motivation, and opportunity as <strong>the</strong> sources <strong>of</strong> SC, and define it as <strong>the</strong> goodwill available
to individuals or groups.
Being interdisciplinary (e.g. sociology, political sciences, development studies and management), SC
literature has experienced considerable fragmentations in terms <strong>of</strong> definitions, approaches, and
underlying philosophies <strong>of</strong> social sciences rendering it an umbrella concept (Adler and Kwon, 2002).
One <strong>of</strong> <strong>the</strong> major discrepancies in <strong>the</strong> SC literature is <strong>the</strong> way it is related to social structures and
social networks. Some authors have equated SC with <strong>the</strong> structure <strong>of</strong> social networks through which
actors interact (Borgatti et al., 1998, Cross et al., 2002). In this strand, SC is formulated in terms <strong>of</strong>
those opportunities provided for or withdrawn from actors in <strong>the</strong>ir social relations. For example, Burt
(2005) discusses that actors located at <strong>the</strong> bridging position are in advantage to those who are
lacking such opportunities. On <strong>the</strong> o<strong>the</strong>r hand, Coleman (1988) advocates network closure as a sign
<strong>of</strong> higher levels <strong>of</strong> SC due to reinforced norms and social identity in a group. Underpinning
assumption in this type <strong>of</strong> research is that actors are governed by <strong>the</strong>ir encompassing structures, and
<strong>the</strong>ir SC is captured by <strong>the</strong>ir network position. Empirically, SC research has usually adopted <strong>the</strong>
structural framework for analysing SC at a certain level <strong>of</strong> analysis. Here, research is usually informed
by social network analysis methods, and SC is operationalized through network measures (e.g.
centrality, density, etc.). This type <strong>of</strong> research can be enormously found in inter-organisational
knowledge transfer, and inter-organisational learning literature (eg. Xiao and Tsui, 2007, Tsai, 2001).
However, social network analysis and social network positions can merely reveal one aspect <strong>of</strong> SC
referred as <strong>the</strong> opportunity <strong>of</strong> gaining SC (Adler and Kwon, 2002).Conversely, many o<strong>the</strong>r authors
situate qualitative dimensions <strong>of</strong> SC at <strong>the</strong> heart <strong>of</strong> <strong>the</strong>ir SC <strong>the</strong>ory. In this research stream,
Fukuyama (1995) equates trust with SC, and Putnam (1995) underlines those social norms
contributing to collective good as <strong>the</strong> basis <strong>of</strong> social capital. Empirical studies have usually
operationalized SC just as trust in social relations (Tsai and Ghoshal, 1998, Subramaniam and
Youndt, 2005). However, it should be notified that although <strong>the</strong>se approaches do not tell whole <strong>the</strong>
story, <strong>the</strong>y reveal its important aspects.
The second difficulty arises from what Adler and Kwon (2002) identify as broad vs. narrow definitions
<strong>of</strong> SC. While in broad definitions (e.g. Leenders and Gabbay, 1999) any type <strong>of</strong> resources shared
through social structures (i.e. market relations, hierarchy relations, and social relations) can be
considered as <strong>the</strong> sources <strong>of</strong> SC, in narrow approaches, only those resources which are distributed
through social relations are considered as its sources (Adler and Kwon, 2002). In broad definitions,
SC overlaps with many o<strong>the</strong>r forms <strong>of</strong> organisational capital. For example, Nahapiet and Ghoshal
(1998) define social capital through its three dimensions namely cognitive, relational, and structural
dimensions. Structural capital is defined as <strong>the</strong> impersonal configuration <strong>of</strong> linkages between actors;
relational capital is defined as <strong>the</strong> nature and quality <strong>of</strong> <strong>the</strong> interactions; and cognitive capital is
defined as those resources providing shared representations, interpretations, and systems <strong>of</strong>
meaning among parties (Nahapiet&Ghoshal, 1998). In this format, SC overlaps with many o<strong>the</strong>r
organisational dynamics like organisational culture, cognitive base, etc. In <strong>the</strong> narrow form, however,
this problem is resolved, since SC refers to particular sources in social relations. Yet, narrow form is
not applied in <strong>the</strong> SC research broadly.
Likewise, SC literature shows many inconsistencies in terms <strong>of</strong> research outcomes. Yli-Renko et al.
(2001) examine <strong>the</strong> impact <strong>of</strong> SC on knowledge acquisition and exploitation in young technologybased
firms. This research shows that social interactions increase <strong>the</strong> knowledge acquisition.
However, <strong>the</strong>ir findings reveal that <strong>the</strong> relational dimension <strong>of</strong> SC is negatively associated with
knowledge acquisition. This is in contrast to Tsai and Ghoshal’s (1998) findings that all dimensions <strong>of</strong>
SC support <strong>the</strong> knowledge exchange capabilities.
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2.3 The inter-relations between SC and AC
Omid Omidvar
External knowledge is increasingly contributing to organisational growth, innovation, and
performance. While many organisations have been able to convert <strong>the</strong>ir external knowledge into
beneficiary outcomes, <strong>the</strong>re are many instances <strong>of</strong> inability in taking advantage <strong>of</strong> <strong>the</strong>se knowledge
sources. Due to its routinized nature, AC (Lewin et al., 2011) is hardly replicable by organisational
rivals, and is found as <strong>the</strong> major source <strong>of</strong> organisational competitiveness (Zahra and George, 2002,
Tsai, 2001). However, organisations are unable to take advantage <strong>of</strong> <strong>the</strong>ir AC unless <strong>the</strong>y are
equipped with <strong>the</strong> social relations providing <strong>the</strong>m with access to external sources <strong>of</strong> knowledge (Uzzi,
1996, Yli-Renko et al., 2001), or possess internal social capabilities to share knowledge and expertise
inside <strong>the</strong> organisational boundaries (Cohen and Levinthal, 1990) called SC. On <strong>the</strong> o<strong>the</strong>r hand, it is
suggested that <strong>the</strong> values driven by SC can augment in <strong>the</strong> presence <strong>of</strong> complementary capabilities
(Teece et al., 1997), which brings valuable sources into social relations (Adler and Kwon, 2002). This
duality has raised confusions in <strong>the</strong> current body <strong>of</strong> research. We know little about <strong>the</strong> nature <strong>of</strong> <strong>the</strong>
interrelations between AC and SC.
Ample research findings suggest that social dimension is one <strong>of</strong> <strong>the</strong> major aspects <strong>of</strong> AC (Lane et al.,
2006, Lewin et al., 2010, Zahra and George, 2002, Jansen et al., 2005). For instance, although
Jansen et al. (2005, 2006) investigate <strong>the</strong> role <strong>of</strong> social capabilities in organisational learning and AC,
<strong>the</strong>y call future research to consider those aspects <strong>of</strong> social capabilities, which are not limited to <strong>the</strong>
network dimension <strong>of</strong> SC (i.e. centrality or degree <strong>of</strong> connectedness), and more concerned with <strong>the</strong>
quality <strong>of</strong> relations (e.g. trust).
The inter-relations between SC and AC are examined in some research, though <strong>the</strong>y might have
adopted different terms in <strong>the</strong>ir studies. For instance, Tsai (2001) argues that AC moderates <strong>the</strong>
innovative performance <strong>of</strong> an organisation located in a network. Jansen et al (2005) discuss that
socialisation capabilities, as one form <strong>of</strong> <strong>the</strong> combinative capabilities (Kogut and Zander,
1992),enhance realized and Potential AC (Zahra and George, 2002). However, as <strong>the</strong>y show in <strong>the</strong>ir
later article (Jansen et al., 2006), <strong>the</strong>y have not addressed relational aspects <strong>of</strong> SC (Nahapiet and
Ghoshal, 1998, Tsai and Ghoshal, 1998), and <strong>the</strong>ir study was mainly limited to <strong>the</strong> structural
dimension like connectedness, and network density.
3. Methodology
Being driven by critical realist epistemological stance (Sayer, 1992), this research will use case study
as <strong>the</strong> research strategy in 4-5 R&D partnership cases where <strong>the</strong> unit <strong>of</strong> analysis will be collaborative
R&D projects. Although case studies are not fashionable in <strong>the</strong> R&D alliance research, <strong>the</strong>y are
appropriate for this study because <strong>of</strong> two main reasons. First, research questions necessitate an indepth
analysis <strong>of</strong> R&D partnerships, a comprehensive analysis <strong>of</strong> <strong>the</strong> context <strong>of</strong> <strong>the</strong> study and a rich
picture <strong>of</strong> life and behaviour in R&D units as well as <strong>the</strong> inter-organisational settings (Easterby-Smith
et al., 2008) 2) <strong>the</strong> high level <strong>of</strong> complexity <strong>of</strong> inter-organizational learning processes are not possible
to be realized through quantitative methods and surveys. Following from that, this study adopts
qualitative methodology where research methods mainly involve semi-structured interviews and
observations.
Case studies will involve two phases <strong>of</strong> empirical work. At <strong>the</strong> first stage, semi-structured interviews
with R&D project managers and R&D scientists, observation <strong>of</strong> meetings, and document analysis will
be conducted in order to achieve a basic understanding <strong>of</strong> <strong>the</strong> context <strong>of</strong> <strong>the</strong> research and clarifying
<strong>the</strong> relevance <strong>of</strong> <strong>the</strong> research questions. Then, a set <strong>of</strong> propositions will be derived which will be
tested in <strong>the</strong> second phase <strong>of</strong> <strong>the</strong> research.
Interviews will be around 90 minutes and all will be taped, transcribed, and sent back to <strong>the</strong>
interviewees for <strong>the</strong>ir feedback. In <strong>the</strong> case <strong>of</strong> discrepancies, I will go back to <strong>the</strong> respondents for
asking for fur<strong>the</strong>r clarification. They will be conducted in a retrospective way to reduce <strong>the</strong> risk <strong>of</strong> data
overload and collecting much unusable data. However, retrospective data ga<strong>the</strong>ring has its own
costs. For instance, respondents may have <strong>the</strong> tendency to filter out those events that do not fit or
may render <strong>the</strong>ir story less coherent. Therefore, by triangulating <strong>the</strong> data I will try to mitigate <strong>the</strong>
potential risk. Finally, template analysis will be <strong>the</strong> basis for analysing <strong>the</strong> qualitative data and drawing
conclusions.
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4. Discussion and conclusion
Omid Omidvar
As indicated above, this research originates from <strong>the</strong> problems associated with managing interorganisational
learning in R&D consortia as one <strong>of</strong> <strong>the</strong> dominant forms <strong>of</strong> R&D collaborations in <strong>the</strong>
last decade. In such settings, firms usually fail to effectively achieve <strong>the</strong>ir predetermined goals.
However, R&D collaboration literature has paid less attention to R&D consortia in comparison with
o<strong>the</strong>r forms <strong>of</strong> R&D collaborations leaving <strong>the</strong> academic understanding <strong>of</strong> inter-organisational learning
as well as practitioners’ ability to manage R&D consortia limited.
This research aims to address this gap through <strong>the</strong> <strong>the</strong>oretical lens <strong>of</strong> SC and AC. Having reviewed
<strong>the</strong> literature, several research gaps stand out. First, SC and AC literature have mainly remained
restricted to quantitative research methods. This has prevented deeper understanding <strong>of</strong> <strong>the</strong> ways AC
and SC develop. In respect to SC, moreover, a large body <strong>of</strong> work has employed network view to SC
dismissing its s<strong>of</strong>t aspects which has led to more opportunity rationales for SC.
Regarding AC, <strong>the</strong>re are both conceptual as well as empirical difficulties in <strong>the</strong> extant literature. First,
AC literature has ei<strong>the</strong>r employed absolute or relative forms <strong>of</strong> AC (focusing on dyadic relations
among organisations) opening a research avenue for detailed investigation <strong>of</strong> network AC. Second,
Ac is operationalised in inconsistent ways and <strong>the</strong> more dynamic conceptualisations are rarely applied
in empirical studies. While some have used static approach and applied knowledge stock and R&D
intensity (Capron and Mitchell, 2009), o<strong>the</strong>rs have developed <strong>the</strong> concept into more process based
approaches. In <strong>the</strong> latter form, <strong>the</strong>re is not much empirical work conducted and <strong>the</strong> process under
which AC unfolds is not fully understood(cf. Lichtenthaler, 2009 for an exception). By applying
process operationalisations, this research aims to improve <strong>the</strong> understanding <strong>of</strong> AC development in
organisations as well as consortia.
Finally, <strong>the</strong> inter-relation <strong>of</strong> AC and SC is still missing in <strong>the</strong> literature. Whe<strong>the</strong>r <strong>the</strong>y affect each o<strong>the</strong>r,
co-develop and trigger inter-organisational learning is an open question located at <strong>the</strong> core <strong>of</strong> this
PhD project.
The next steps <strong>of</strong> <strong>the</strong> research is identifying collaborative R&D projects as <strong>the</strong> cases and <strong>the</strong>n
researching <strong>the</strong>ir knowledge transfer mechanisms which will lead into <strong>the</strong> identification <strong>of</strong> <strong>the</strong> roles <strong>of</strong>
AC and SC in learning as well as <strong>the</strong>ir development mechanism.
Acknowledgement
I would like to thank my supervisors Pr<strong>of</strong>. Edler and Dr. Malik for <strong>the</strong>ir persistent support and <strong>the</strong>ir
genuine intellectual contribution to <strong>the</strong> research project.
References
Adler, P. and Kwon, S. 2002. 'Social Capital: Prospects for a New Concept'. The Academy <strong>of</strong> Management
Review, Vol 27, No (1), pp 17-40.
Ahuja, G. 2000. 'The Duality <strong>of</strong> Collaboration: Inducements and Opportunities in <strong>the</strong> Formation <strong>of</strong> Interfirm
Linkages'. Strategic Management Journal, Vol 21, No (3), pp 317-343.
Borgatti, S. P., Jones, C. and Everett, M. G. 1998. 'Network Measures <strong>of</strong> Social Capital'. Official Journal <strong>of</strong> <strong>the</strong>
International Network for Social Network Analysis, Vol 21, No (2), pp 27-36.
Burt, R. 2005. Brokerage and Closure: An Introduction to Social Capital, Oxford University Press, USA.
Capron, L. and Mitchell, W. 2009. 'Selection Capability: How Capability Gaps and Internal Social Frictions Affect
Internal and External Strategic Renewal'. Organization Science, Vol 20, No (2), pp 294-312.
Cohen, W. M. and Levinthal, D. A. 1990. 'Absorptive Capacity: A New Perspective on Learning and Innovation'.
Administrative Science Quarterly, Vol 35, No (1), pp 128-152.
Coleman 1990. Foundations <strong>of</strong> Social Theory, Cambridge, The Belknap press <strong>of</strong> Harvard university press.
Coleman, J. S. 1988. 'Social Capital in <strong>the</strong> Creation Of'. American Journal <strong>of</strong> Sociology, Vol 94, No S95-S120.
Cross, R., Borgatti, S. P. and Parker, A. 2002. 'Making Invisible Work Visible: Using Social Network Analysis to
Support Strategic Collaboration'. California Management Review, Vol 44, No (2), pp 25-46.
Dyer, J. H. and Singh, H. 1998. 'The Relational View: Cooperative Strategy and Sources <strong>of</strong> Interorganizational
Competitive Advantage'. The Academy <strong>of</strong> Management Review, Vol 23, No (4), pp 660-679.
Easterby-Smith, M., Thorpe, R. and Jackson, P. R. 2008. London, Sage
Fukuyama, F. 1995. Trust: Social Virtues and <strong>the</strong> Creation <strong>of</strong> Prosperity., London, Hamish Hamilton.
Jansen, J. J. P., Van Den Bosch, F. A. J. and Volberda, H. W. 2005. 'Managing Potential and Realized
Absorptive Capacity: How Do Organizational Antecedents Matter?'. Academy <strong>of</strong> Management Journal, Vol
No.
1071

Omid Omidvar
Jansen, J. J. P., Van Den Bosch, F. A. J. and Volberda, H. W. 2006. 'Exploratory Innovation, Exploitative
Innovation, and Performance: Effects <strong>of</strong> Organizational Antecedents and Environmental Moderators'.
Management Science, Vol 52, No (11), pp 1661–1674.
Kim, L. 1998. 'Crisis Construction and Organizational Learning: Capability Building in Catching-up at Hyundai
Motor'. Organization Science, Vol 9, No (4), pp 506-521.
Kogut, B. and Zander, U. 1992. 'Knowledge <strong>of</strong> <strong>the</strong> Firm,Combinative Capacities and <strong>the</strong> Replication <strong>of</strong>
Technology'. Organization Science, Vol 3, No 383-397.
Lane, P. J., Koka, B. R. and Pathak, S. 2006. 'The Reification <strong>of</strong> Absorptive Capacity: A Critical Review and
Rejuvenation <strong>of</strong> <strong>the</strong> Construct'. Academy <strong>of</strong> Management Review, Vol 31, No (4), pp 833-863.
Lane, P. J. and Lubatkin, M. 1998. 'Relative Absorptive Capacity and Interorganizational Learning'. Strategic
Management Journal, Vol 19, No 461-477.
Leenders, R. T. A. J. and Gabbay, S. M. 1999. Corporate Social Capital and Liability, Boston:Kluwer Academic
Publishers.
Lewin, A. Y., Massini, S. and Peeters, C. 2011. 'Micr<strong>of</strong>oundations <strong>of</strong> Internal and External Absorptive Capacity
Routines'. Organization Science, Vol 22, No (1), pp 81-98.
Lichtenthaler, U. 2009. 'Absorptive Capacity, Environmental Turbulance, and <strong>the</strong> Complementarity <strong>of</strong>
Organizational Learning Processes'. Academy <strong>of</strong> Management Journal, Vol 52, No (4), pp 822–846.
Mowery, D. C., Oxley, J. E. and Silverman, B. S. 1996. 'Strategic Alliances and Interfirm Knowledge Transfer'.
Strategic Management Journal, Vol 17, No 77-91.
Nahapiet, J. and Ghoshal, S. 1998. 'Social Capital, Intellectual Capital, and <strong>the</strong> Organizational Advantage'.
Academy <strong>of</strong> Management, Vol 23, No (2), pp 242-266.
Penrose, E. 1959. The Theory <strong>of</strong> <strong>the</strong> Growth <strong>of</strong> <strong>the</strong> Firm, Oxford, Basil Blackwell.
Putnam, R. D. 1995. 'Bowling Alone: America's Declining'. Journal <strong>of</strong> Democracy, Vol No 65-78.
Sayer, A. 1992. Method in Social Science, a Realist Approach, London, Routledge.
Subramaniam, M. and Youndt, M. A. 2005. 'The Influence <strong>of</strong> Intellectual Capital on <strong>the</strong> Types <strong>of</strong> Innovative
Capabilities'. Academy <strong>of</strong> Management Journal, Vol 48, No (3), pp 450-463.
Teece, D. J., Pisano, G. and Shuen, A. 1997. 'Dynamic Capabilities and Strategic Management'. Strategic
Management Journal, Vol 18, No (7), pp 509-533.
Thorpe, R., Holt, R., Macpherson, A. and Pittaway, L. 2005. 'Using Knowledge within Small and Medium-Sized
Firms: A Systematic Review <strong>of</strong> <strong>the</strong> Evidence'. International Journal <strong>of</strong> Management Reviews, Vol 7, No (4),
pp 257-281.
Todorova, G. and Durisin, B. 2007. 'Absorptive Capacity: Valuing a Reconceptualization'. Academy <strong>of</strong>
Management Review, Vol 32, No (3), pp 774-786.
Tsai, W. 2001. 'Knowledge Transfer in Intraorganizational Networks: Effects <strong>of</strong> Network Position and Absorptive
Capacity on Business Unit Innovation and Performance'. The Academy <strong>of</strong> Management Journal, Vol 44, No
(5), pp 996-1004.
Tsai, W. and Ghoshal, S. 1998. 'Social Capital and Value Creation: The Role <strong>of</strong> Intrafirm Networks'. The
Academy <strong>of</strong> Management Journal, Vol 41, No (4), pp 464-476.
Van Den Bosch, F. A. J., Volberda, H. W. and De Boer, M. 1999. 'Coevolution <strong>of</strong> Firm Absorptive Capacity and
Knowledge Environment: Organizational Forms and Combinative Capabilities'. Organization Science, Vol
44, No 551-568.
Volberda, H. W., Foss, N. J. and Lyles, M. A. 2010. 'Absorbing <strong>the</strong> Concept <strong>of</strong> Absorptive Capacity: How to
Realize Its Potential in <strong>the</strong> Organization Field'. Organization Science, Vol 21, No (4), pp 931-951.
Xiao, Z. and Tsui, A. S. 2007. 'When Brokers May Not Work: The Cultural Contingency <strong>of</strong> Social Capital in
Chinese High-Tech Firms'. Administrative Science Quarterly, Vol 52, No (1), pp 1.
Yli-Renko, H., Autio, E. and Sapienza, J. 2001. 'Social Capital, Knowledge Acquisition, and Knowledge
Exploitation in Young Technology-Based Firms'. Strategic Management Journal, Vol 22, No (6-7), pp 587.
Zahra, S. and George, G. 2002. 'Absorptive Capacity: A Review, Reconceptualization, and Extension'. Academy
<strong>of</strong> Management Review, Vol 27, No (2), pp 185-203.
1072

The Mutual Transmission <strong>of</strong> Knowledge and Competencies
between Generations: an enabler <strong>of</strong> Dynamic Capabilities
Sakura Shimada
Paris Dauphine University, France
Sakura.shimada@gmail.com
Abstract:: The purpose <strong>of</strong> this article is to propose a generational view <strong>of</strong> <strong>the</strong> organization in order to think about
<strong>the</strong> firm evolution between continuity and adaptation. Especially, we suggest that <strong>the</strong> “intergenerational
transmission <strong>of</strong> knowledge and competencies” may be an enabler <strong>of</strong> assets reconfiguration in <strong>the</strong> ambit <strong>of</strong>
dynamic capability. Since <strong>the</strong> 1980’s, <strong>the</strong> resource-based view <strong>of</strong> <strong>the</strong> firm (RBV) (Penrose 1959; Wernerfelt 1984;
Barney 1986) has pointed out <strong>the</strong> role <strong>of</strong> internal and idiosyncratic resources <strong>of</strong> <strong>the</strong> firm to gain in competitive
advantage. Knowledge and competencies are regarded as being <strong>the</strong> main strategic assets that <strong>the</strong> firm should
identify, protect, exploit and renew. We sum up <strong>the</strong>se strategic assets in a four-stage systemic model, ranging
from <strong>the</strong> 1) individual knowledge and 2) competencies, to <strong>the</strong> 3) collective competencies and 4) organizational
capabilities. Yet, in a changing environment, firm has to evolve and transform itself in order to sustain its
competitive advantage. The ability <strong>of</strong> a firm to extend and modify its strategic assets to do so is called dynamic
capability (Teece and al. 1997; Helfat and al. 2007). However, <strong>the</strong> question about how, concretely, <strong>the</strong> assets
reconfiguration manifest itself in a local level has not been answered yet. This is an important issue, considering
<strong>the</strong> systemic and socially embedded nature <strong>of</strong> knowledge and competencies: <strong>the</strong>se are sticky assets (Szulanski
1996) that hardly evolve. Recently, <strong>the</strong> on-going retirement <strong>of</strong> baby boomers has emphasized <strong>the</strong> need <strong>of</strong> an
intergenerational transmission: it is fundamental to retain <strong>the</strong>ir knowledge and competencies which represent <strong>the</strong>
strategic assets <strong>of</strong> <strong>the</strong> firm. However, <strong>the</strong> one-way transmission from <strong>the</strong> elder to <strong>the</strong> younger is not sufficient to
ensure <strong>the</strong> survival <strong>of</strong> <strong>the</strong> firm. The strategic assets <strong>of</strong> <strong>the</strong> firm also need adjusting and updating through time.
Therefore, <strong>the</strong> intergenerational transmission should be seen as a dynamic capability. Based on this parallelism,
we put forward that <strong>the</strong> concrete functioning <strong>of</strong> strategic assets reconfiguration may be observed through <strong>the</strong>
analysis <strong>of</strong> <strong>the</strong> intergenerational transmission <strong>of</strong> knowledge and competencies. Then, we try to define <strong>the</strong>
concept <strong>of</strong> generation so as to explain <strong>the</strong> issues related to <strong>the</strong> strategic assets reconfiguration. Two generational
concepts in management are identified and rejected: <strong>the</strong> “societal generation” and <strong>the</strong> “life cycle generation”. A
third and new concept <strong>of</strong> generation is proposed: <strong>the</strong> “generation <strong>of</strong> knowledge and competences” which refers to
a group <strong>of</strong> people representing a homogeneous area <strong>of</strong> knowledge and competencies. Building on this definition,
we advocate that <strong>the</strong> intergenerational transmission may act as an enabler <strong>of</strong> firm assets reconfiguration between
continuity and adaptation. Then, a better understanding <strong>of</strong> <strong>the</strong> former could contribute to <strong>the</strong> management <strong>of</strong> <strong>the</strong>
latter. Implications for future research are discussed.
Keywords: generation, knowledge, competence, dynamic capability, <strong>the</strong> resource-based view
1. Introduction
Since <strong>the</strong> 1980’s, <strong>the</strong> resource-based view <strong>of</strong> <strong>the</strong> firm (RBV) (Penrose 1959, Wernerfelt 1984; Barney
1986) has pointed out <strong>the</strong> role <strong>of</strong> internal and idiosyncratic resources <strong>of</strong> <strong>the</strong> firm to gain in competitive
advantage. In <strong>the</strong> 1990’s, <strong>the</strong> RBV evolved in two directions. One stream <strong>of</strong> research developed
focusing on <strong>the</strong> resource knowledge, while ano<strong>the</strong>r turned to <strong>the</strong> resource competencies. The vision
<strong>of</strong> <strong>the</strong> competitive advantage increased in dynamism, and <strong>the</strong> collective and tacit dimensions <strong>of</strong> <strong>the</strong>
strategic assets were emphasized. The renewal and update <strong>of</strong> knowledge and competencies are
recognized as being as important as <strong>the</strong>ir acquisition and protection, and <strong>the</strong> notion <strong>of</strong> dynamic
capability (Teece and al. 1997) has become a key topic in <strong>the</strong> strategic management field <strong>of</strong> research.
It refers to <strong>the</strong> ability <strong>of</strong> a firm to extend, reconfigure and modify its strategic assets so as to adapt to
its changing environment (Helfat and al. 2007).
The notion <strong>of</strong> dynamic capability was first popularized by Teece and al. (1997). Since <strong>the</strong>ir article,
specifications and alternative definitions have been given (Eisenhardt and al. 2000; Zollo & Winter
2002; Wang and Ahmed 2007), and <strong>the</strong> concept is becoming key to <strong>the</strong> strategic management field <strong>of</strong>
research. Helfat and al. (2007: 4) proposed a syn<strong>the</strong>sis definition by discussing previous research: "A
dynamic capability is <strong>the</strong> capacity <strong>of</strong> an organization to purposefully create, extend, or modify its
resource base”. Empirical studies keep extending and examples <strong>of</strong> organizational and managerial
processes which constitute dynamic capabilities have been illustrated (see Helafat and al. 2007,
Teece 2007).
Yet, beginning in <strong>the</strong> 1990s, research in dynamic capabilities is relatively new (Helfat and Peteraf
2009; Di Stefano and al. 2010). Thus, research has been mainly dealing with <strong>the</strong> <strong>the</strong>oretical
foundations <strong>of</strong> <strong>the</strong> approach: <strong>the</strong> identification <strong>of</strong> different types <strong>of</strong> dynamic capabilities (Teece 2007)
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and that <strong>of</strong> <strong>the</strong> underlying organizational and managerial processes (Helfat and al. 2007). Dynamic
capabilities are analysed in a macro organizational point <strong>of</strong> view, in a content approach answering to
<strong>the</strong> question “what”, while <strong>the</strong> question “how” about <strong>the</strong> concrete functioning (process) <strong>of</strong> <strong>the</strong>m is
hardly considered (Regnér 2008; Maritan and Helfat 2007; Ambrosini and Bowman 2009). Thus, <strong>the</strong>
question about how, concretely, <strong>the</strong> assets reconfiguration manifest itself in a local level has not been
answered yet. This is an important issue, considering <strong>the</strong> systemic and socially embedded nature <strong>of</strong>
knowledge and competencies: <strong>the</strong>se are sticky assets (Szulanski 1996) that hardly evolve.
Recently, <strong>the</strong> on-going retirement <strong>of</strong> <strong>the</strong> baby boomers generation has emphasized <strong>the</strong> need <strong>of</strong> an
intergenerational transmission. This generation is said to be leaving without having transmitted its
knowledge and competencies to <strong>the</strong> next generations. The intergenerational transmission is an
essential requirement. However, <strong>the</strong> one-way transmission from <strong>the</strong> elder to <strong>the</strong> younger is not
sufficient to ensure <strong>the</strong> survival <strong>of</strong> <strong>the</strong> firm. Some assets have to be preserved, while o<strong>the</strong>rs should be
abandoned. Therefore, <strong>the</strong> intergenerational transmission <strong>of</strong> knowledge and competencies should be
seen as a dynamic capability. Based on this parallelism, we put forward that <strong>the</strong> concrete functioning
<strong>of</strong> strategic assets reconfiguration may be observed through <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> intergenerational
transmission <strong>of</strong> knowledge and competencies.
The paper is organized as follows. The next section analyses <strong>the</strong> RBV literature to identify <strong>the</strong> main
categories making up <strong>the</strong> strategic assets <strong>of</strong> an organization. We propose a four-stage systemic view
<strong>of</strong> <strong>the</strong> strategic assets. Dynamic capability consists in <strong>the</strong> reconfiguration <strong>of</strong> this strategic assets
system. Referring to <strong>the</strong> socially embedded nature <strong>of</strong> <strong>the</strong>se assets, we highlight <strong>the</strong> need to adopt a
generational view <strong>of</strong> <strong>the</strong> organization during its reconfiguration. Then, <strong>the</strong> third section investigates
<strong>the</strong> notion <strong>of</strong> generation used in managerial literature. Two main concepts <strong>of</strong> generation in use are
identified, and a third definition is proposed. As a syn<strong>the</strong>sis, <strong>the</strong> fourth section proposes <strong>the</strong>
intergenerational transmission <strong>of</strong> knowledge and competencies as an enabler <strong>of</strong> <strong>the</strong> dynamic
capability. Methodological considerations and <strong>the</strong>oretical points to be broadened are discussed in <strong>the</strong>
fifth section.
2. The strategic assets <strong>of</strong> an organization and dynamic capability
In <strong>the</strong> strategic management and organization’s fields <strong>of</strong> research, <strong>the</strong> maintenance <strong>of</strong> knowledge and
competencies is a key issue. According to <strong>the</strong> RBV, knowledge and competencies are <strong>the</strong> main
strategic assets that allow a sustainable competitive advantage to <strong>the</strong> firm.
At <strong>the</strong> early stage <strong>of</strong> <strong>the</strong> <strong>the</strong>ory, <strong>the</strong> resources referred “anything which could be thought <strong>of</strong> as a
strength or weakness <strong>of</strong> a given firm” (Wernerfelt 1984: 172). Barney (1986; 1991) defined <strong>the</strong> VRIN
criteria to identify “strategic resources”: a strategic resource is valuable, rare, inimitable and nonsubstitutable.
The strategic mission assigned to <strong>the</strong> firm is to identify its strategic resource, to optimize
<strong>the</strong>ir internal allocation and to protect <strong>the</strong>m.
In <strong>the</strong> 1990’s, a stream <strong>of</strong> <strong>the</strong> RBV focused progressively on one <strong>of</strong> <strong>the</strong> only resource said to be
inimitable, knowledge, developing a Knowledge-Based View (KBV) (Kogut and Zander 1992; Grant
1996; Nonaka and Takeuchi 1995). The KBV considers knowledge as <strong>the</strong> main strategic resource,
and advises to capture and to leverage its value. The discussion about <strong>the</strong> nature <strong>of</strong> knowledge
centered mainly around two questions: <strong>the</strong> epistemological question about <strong>the</strong> explicit/tacit dimension
<strong>of</strong> knowledge, and <strong>the</strong> ontological question about <strong>the</strong> individual/collective aspect <strong>of</strong> knowledge.
Explicit knowledge can be transferred and stocked in written or oral form without losing much <strong>of</strong> its
value, whereas tacit knowledge can hardly be transferred without being altered or distorted (Polanyi
1983; Nonaka 1991). The general tendency is to admit a greater value to tacit knowledge, because <strong>of</strong>
its complexity and contextual embeddedness which make it hardly imitable (see Nonaka and Takeuchi
1995; Szulanski 1996). However, <strong>the</strong> explicit and tacit dimensions <strong>of</strong> knowledge tend to be considered
as <strong>the</strong> ends <strong>of</strong> a continuum, ra<strong>the</strong>r than as two distinct and opposed things.
The distinction between <strong>the</strong> individual and collective dimensions <strong>of</strong> knowledge (Nonaka and Takeuchi
1995; Spender 1996) questions <strong>the</strong> existence <strong>of</strong> collective/organizational knowledge. The debate is
not closed, whereas it is generally agreed that collective knowledge can be more or less than <strong>the</strong> sum
<strong>of</strong> individual knowledge (Laroche and Nioche 1998). In any case, knowledge can be organizational, to
<strong>the</strong> extent that it is socially constructed and that it is used in <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> organization.
Around <strong>the</strong> same period, ano<strong>the</strong>r branch <strong>of</strong> <strong>the</strong> RBV developed: <strong>the</strong> Competence-Based View
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(Prahalad and Hamel 1990; Teece et al. 1997; Sanchez and al. 1997). This stream <strong>of</strong> research puts
that <strong>the</strong> source <strong>of</strong> <strong>the</strong> competitive advantage resides in <strong>the</strong> way a firm uses resources, ra<strong>the</strong>r than in
<strong>the</strong> resources <strong>the</strong>mselves. A distinction between <strong>the</strong> “having capabilities” and <strong>the</strong> “doing capabilities”
(Hall 1993) is made. The whole strategic assets <strong>of</strong> a firm are made <strong>of</strong> strategic resources and
organizational capabilities (Amit and Schoemaker 1993).
Individual competency is <strong>the</strong> mobilization <strong>of</strong> cognitive resources deployed in relation to <strong>the</strong> activity
demand and to its rationalization (Raoult 2006: 163). It is <strong>the</strong> aptitude <strong>of</strong> a person to deploy
knowledge in a coordinated manner, in order to reach a given goal (Le Boterf 2000). This involves <strong>the</strong>
ability <strong>of</strong> <strong>the</strong> person to act and to react to each situation, in a socially accepted and determined way.
In addition, to be seen as a competent person, <strong>the</strong>y should be able to mobilize and combine <strong>the</strong>ir own
knowledge, but those from o<strong>the</strong>rs too (ibid.). As a result, individual competencies are also dependent
on <strong>the</strong>ir social context, because <strong>the</strong> goal, <strong>the</strong> constraints, <strong>the</strong> resources-base and <strong>the</strong> social norms,
etc. are specific to each context.
Originating in <strong>the</strong> evolutionary <strong>the</strong>ories (Nelson and Winter 1982), <strong>the</strong> CBV considers that a firm
develops competencies through learning-by-doing, and stabilizes <strong>the</strong>m as organizational routines and
processes which work as an organizational memory. Routines and organizational competencies (also
called capabilities) refer to <strong>the</strong> “way <strong>of</strong> doing things” <strong>of</strong> an organization, a repeated patterns <strong>of</strong>
activities (ibid). They are complex social phenomena involving a lot <strong>of</strong> people, <strong>the</strong>ir knowledge and
competencies, as well as <strong>the</strong>ir explicit and tacit coordination patterns (procedures and routines). They
are composed <strong>of</strong> management process and organizational structure, but are also led by a strategic
vision and an organizational culture (identity) (Durand 2006).
While <strong>the</strong> CBV advocates <strong>the</strong> identification and development <strong>of</strong> <strong>the</strong> core-competencies <strong>of</strong> <strong>the</strong>
organization (Prahalad and Hamel 1990), <strong>the</strong> risk is organizational inertia, or <strong>the</strong> core-rigidity <strong>of</strong> <strong>the</strong>
organization (Leonard-Barton 1992). In order to maintain a sustainable competitive advantage in a
dynamic environment, firms have to extend and to reconfigure <strong>the</strong>ir resources and competencies. This
ability <strong>of</strong> <strong>the</strong> firm is called <strong>the</strong> dynamic capability (Teece and al. 1997; Zollo and Winter 2002; Wang
and Ahmed 2007; Helfat and al. 2007).
In sum, organizational competencies refer to <strong>the</strong> capacity <strong>of</strong> <strong>the</strong> firm to deploy its resources in a
coordinated way, in order to reach a goal (Amit and Shomaker 1993, Sanchez and al. 1997).
To sum up, <strong>the</strong> following four points can be put forward about <strong>the</strong> knowledge and competencies <strong>of</strong> a
firm:
The existence <strong>of</strong> different levels <strong>of</strong> strategic assets, ranging from <strong>the</strong> individual level to <strong>the</strong>
collective and organizational ones.
The strategic assets form a system in which <strong>the</strong> distinction between “resources” and <strong>the</strong>ir
“deployment” are repeated at each level in a nested structure
The coexistence <strong>of</strong> both explicit and tacit dimensions at each level.
The dynamic capability consists in restructuring <strong>the</strong> strategic assets system <strong>of</strong> <strong>the</strong> firm, in order to
face <strong>the</strong> changing environment.
The strategic assets <strong>of</strong> a firm can be summarized in a four-stage systemic model as shown in figure 1.
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Figure 1: Strategic assets <strong>of</strong> an organization: a systemic view
Because <strong>of</strong> <strong>the</strong> socially embedded nature <strong>of</strong> <strong>the</strong> strategic assets, <strong>the</strong> reconfiguration process <strong>of</strong>
dynamic capabilities may not proceed as smoothly as expected: people, knowledge and
competencies cannot instantaneously adapt. Thus, during a certain time after <strong>the</strong> change, <strong>the</strong>
organization is in a period <strong>of</strong> transition. Then, this may result in a gap between: 1) a group <strong>of</strong> people
perpetuating <strong>the</strong> old knowledge and competencies, and 2) <strong>the</strong> group made <strong>of</strong> people who have
adapted to <strong>the</strong> new knowledge and competencies, or who arrived after <strong>the</strong> modification <strong>of</strong> strategic
assets. Thus, a gap in strategic assets risks to be observed between two generations within <strong>the</strong> same
firm, during <strong>the</strong> reconfiguration process <strong>of</strong> <strong>the</strong> dynamic capability in action.
3. The concept <strong>of</strong> generation
The notion <strong>of</strong> generation is a social construct depending on <strong>the</strong> context. We identify two main
meanings and logic <strong>of</strong> categorization: <strong>the</strong> generation as a social group, and <strong>the</strong> generation as a stage
in a life cycle process. We will call <strong>the</strong>m respectively 1) <strong>the</strong> societal generation, and 2) <strong>the</strong>
pr<strong>of</strong>essional life cycle generation.
The societal generation is a group <strong>of</strong> people born in a same period, and thus who grew up in a
particular political, economic, social and cultural context. Because <strong>the</strong>y share <strong>the</strong> same part <strong>of</strong> history
and experienced <strong>the</strong> same landmark events <strong>of</strong> a community, <strong>the</strong>y are considered as sharing similar
values, worldview, behaviour, etc. In sum, a societal generation represents a culture, and <strong>the</strong>
“intergeneration” refers to cultural differences.
The most well-known names for this societal generation in Western countries are probably <strong>the</strong> “babyboomers”,
<strong>the</strong> “gen X” and <strong>the</strong> “gen Y”. Obviously, <strong>the</strong> boundaries <strong>of</strong> a generation depend on <strong>the</strong>
events that have marked <strong>the</strong> contextual history. The baby-boomer generation is mostly common to
Western countries and Japan, because <strong>the</strong>se countries share similar historical events and context:
<strong>the</strong> Second World War and <strong>the</strong> boom <strong>of</strong> birth following it, <strong>the</strong> democratization <strong>of</strong> education, <strong>the</strong>
student movements around 1968, etc. But different names and more detailed contextual
categorizations do exist (see Chauvel 1998, Excousseau 2000, Préel 2001, 2005).
Within a company, <strong>the</strong> societal generation is <strong>of</strong>ten used to point out <strong>the</strong> intergenerational difference <strong>of</strong>
work attitude, <strong>the</strong> gap in <strong>the</strong> way <strong>of</strong> doing things. Often, <strong>the</strong> expression “generation gap” refers to <strong>the</strong>
relationships between <strong>the</strong>se age groups. But, this logic <strong>of</strong> categorization seems to be irrelevant,
compared with our intent: <strong>the</strong>re is no relationship between <strong>the</strong> generational segmentation and <strong>the</strong>
area <strong>of</strong> knowledge and competencies <strong>the</strong>y represent.
The pr<strong>of</strong>essional life cycle generation refers to an age group which corresponds to a life cycle
process’ stage. Each stage is associated with a social role or statute, and <strong>the</strong> “intergeneration” refers
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to <strong>the</strong> distribution <strong>of</strong> social responsibilities, <strong>of</strong> wealth and charges among <strong>the</strong>ses age groups.
Boundaries among age groups are <strong>of</strong>ten fixed according to custom (age to get a job, to be married, to
have children) or law (age <strong>of</strong> majority and <strong>of</strong> retirement). The most simple and traditional
segmentation is ternary, with childhood (learning period), middle age (working period) and old age
(resting period). These stages are reproduced at a firm-wide scale, <strong>of</strong>ten with specific management
associated: <strong>the</strong> young (integration and learning program), <strong>the</strong> middle-aged (career management) and
<strong>the</strong> senior (end-<strong>of</strong>-career management).
This logic <strong>of</strong> categorization is interesting because <strong>of</strong> <strong>the</strong> reference made between age groups and <strong>the</strong>
evolution <strong>of</strong> <strong>the</strong>ir knowledge and competencies: <strong>the</strong> young is poor in experience, and she/he will
evolve to be ei<strong>the</strong>r a senior expert, or an old worker out-dated. However, this categorization is no
more pertinent today in our western countries. The traditional landmarks for age group boundaries are
desynchronized, including periods <strong>of</strong> employment, unemployment and training periods (Gaullier 1997;
Marbot and Peretti 2006; Labruffe 2007; Raoult and al. 2007; Vendramin and Cultiaux 2008).
Moreover, valuable knowledge and competencies can turn out-dated very quickly, because <strong>of</strong>
technological changes, market evolution or <strong>the</strong> strategic repositioning <strong>of</strong> <strong>the</strong> firm.
Figure 2 sums up <strong>the</strong> societal and pr<strong>of</strong>essional life cycle generations.
Figure 2: The « societal generation » and <strong>the</strong> « pr<strong>of</strong>essional life cycle generation »
We now <strong>of</strong>fer a new concept <strong>of</strong> generation, based on organizational knowledge and competencies,
ra<strong>the</strong>r than on people’s age range or shared values.
Newcomers in an organization acquire and develop <strong>the</strong> knowledge and competencies in force in <strong>the</strong>
organization. They take part to <strong>the</strong> dynamics <strong>of</strong> cooperation in operation, and will be considered as
“competent” as <strong>the</strong>y become able to meet <strong>the</strong> needs <strong>of</strong> <strong>the</strong> organization, in <strong>the</strong> way established by <strong>the</strong>
organization. Then, we say that newcomers are integrated in a body <strong>of</strong> articulated strategic assets. At
<strong>the</strong> same time, <strong>the</strong>se people participate in <strong>the</strong> development <strong>of</strong> <strong>the</strong>se knowledge and competencies.
People are influenced by <strong>the</strong> organizational assets, at <strong>the</strong> same time <strong>the</strong>y participate in <strong>the</strong>ir creation.
We call generation <strong>of</strong> knowledge and competencies, this group <strong>of</strong> people who have been influenced
by, and who have influenced a given configuration <strong>of</strong> knowledge and competencies during a period <strong>of</strong>
time. Thus, <strong>the</strong> members <strong>of</strong> a generation represent a set <strong>of</strong> articulated knowledge with individual
competencies, collective competencies and organizational capabilities. In this way, we can match <strong>the</strong>
definition <strong>of</strong> <strong>the</strong> generation with <strong>the</strong> systemic view <strong>of</strong> <strong>the</strong> organizational strategic assets. Then, <strong>the</strong>
“intergeneration” refers to a question <strong>of</strong> balance between continuity and discontinuity <strong>of</strong> inheritance. A
new group appears when a group <strong>of</strong> people representing new knowledge and competencies is
identified. Thus, <strong>the</strong> key factor here is less <strong>the</strong> age <strong>of</strong> people than <strong>the</strong>ir tenure/seniority in <strong>the</strong> firm, <strong>the</strong>
job or <strong>the</strong> activity.
The discontinuity in knowledge and competencies may be <strong>of</strong> varying scale, occurring in any level. It
may be a technological change affecting <strong>the</strong> required explicit knowledge and know-how, as well as an
M&A which affects <strong>the</strong> corporate organizational structure <strong>of</strong> <strong>the</strong> firm. But knowing <strong>the</strong> systemic
structure <strong>of</strong> <strong>the</strong> strategic assets, we can assume that a change in one <strong>of</strong> its component will have
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broader repercussion on <strong>the</strong> system. Note that <strong>the</strong> strategic assets system never being static, <strong>the</strong>
determination <strong>of</strong> intergenerational boundaries is somewhat arbitrary. If generations <strong>of</strong> knowledge and
competencies can be identified, it is necessarily in respect to a specific aspect. Depending on which
criteria we focalize on, <strong>the</strong>re may or not be a distinction between two generations. The following figure
3 summarizes <strong>the</strong> concept <strong>of</strong> <strong>the</strong> generation <strong>of</strong> knowledge and competencies.
Figure 3: The generation <strong>of</strong> knowledge and competencies
4. The intergenerational transmission <strong>of</strong> knowledge and competencies:
toward a smooth reconfiguration <strong>of</strong> strategic assets
In <strong>the</strong> previous sections, we clarified our vision <strong>of</strong> organizational strategic assets, and <strong>the</strong> definition <strong>of</strong>
generation in relation to <strong>the</strong>m. In our view, a significant change in <strong>the</strong> organizational strategic assets
results in <strong>the</strong> emergence <strong>of</strong> two distinct “generations <strong>of</strong> knowledge and competencies” within <strong>the</strong>
organization. The coexistence <strong>of</strong> different generations is a problem for <strong>the</strong> organization, as this means
a discontinuity and a disparity in its strategic assets, and in <strong>the</strong> worst case, a mismatch among <strong>the</strong>m.
The old generation still participates in <strong>the</strong> strategic assets <strong>of</strong> <strong>the</strong> organization, but henceforth, a part <strong>of</strong>
<strong>the</strong> knowledge and competencies it represents is out-dated.
Then, different options are available for <strong>the</strong> firm.
First, <strong>the</strong> firm may leave <strong>the</strong> renewal <strong>of</strong> <strong>the</strong>se out-dated strategic assets to <strong>the</strong> “natural” turnover <strong>of</strong> its
employee due to <strong>the</strong> retirement. The old generation would be assigned to <strong>the</strong> old knowledge and
competencies without future, and <strong>the</strong> new generation to <strong>the</strong> developing strategic assets. In this
generational division <strong>of</strong> labor, firm not only keeps <strong>the</strong> obsolete strategic assets, but <strong>the</strong>se assets may
also contradict <strong>the</strong> new ones. Moreover, <strong>the</strong> different patterns <strong>of</strong> work between generations risks to
degenerate into some intergenerational conflicts (Beaud and Pialoux 1999; Cultiaux 2006; Le Roux
2006; Flamant 2005; Delay 2008; Vendramin and Cultiaux 2008).
Second, <strong>the</strong> firm may lay <strong>of</strong>f <strong>the</strong> members <strong>of</strong> <strong>the</strong> old generation. However, strategic assets are rarely
modified as a whole, so <strong>the</strong> old generation probably excels in o<strong>the</strong>r fields: a generation is older than
ano<strong>the</strong>r, in reference to a specific domain <strong>of</strong> knowledge and competencies. Even after a major
change, <strong>the</strong> new generation still needs to learn from its elder to preserve <strong>the</strong> continuity <strong>of</strong> <strong>the</strong>
organizational strategic assets. If <strong>the</strong> firm let go <strong>the</strong> old generation, it will not only lose a part <strong>of</strong> its
strategic assets, but it will also lose <strong>the</strong> opportunity to pass <strong>the</strong> knowledge and competencies on to
<strong>the</strong> next generation. Moreover, <strong>the</strong>se two options are not tenable solutions in a rapidly and constantly
changing environment, both from an economic and social point on view.
Finally, <strong>the</strong> firm can choose to proceed to an intergenerational transmission <strong>of</strong> knowledge and
competencies, in order to preserve traditional strategic assets while adapting <strong>the</strong> old ones. Then, <strong>the</strong>
transmission cannot be reduced to a replacement <strong>of</strong> <strong>the</strong> old by <strong>the</strong> new. When new knowledge or
competencies are introduced into <strong>the</strong> organization, <strong>the</strong>re is no warranty that <strong>the</strong>y will integrate well
into <strong>the</strong> rest <strong>of</strong> <strong>the</strong> system. In order to ensure <strong>the</strong> global coherence <strong>of</strong> <strong>the</strong> system, any change in <strong>the</strong>
strategic assets requires at least some minor adaptations <strong>of</strong> o<strong>the</strong>r elements. There needs to be a coconstruction
<strong>of</strong> a new strategic asset system by <strong>the</strong> two generations. The intergenerational
transmission <strong>of</strong> knowledge and competencies is dialectic between stability and change. It enables
organizational adaptation in its continuity.
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Thus, our proposition is to consider <strong>the</strong> intergenerational transmission <strong>of</strong> knowledge and
competencies as a concrete manifestation <strong>of</strong> <strong>the</strong> assets reconfiguration in <strong>the</strong> ambit <strong>of</strong> a dynamic
capability. Then, its analysis may allow clarifying how dynamic capabilities work concretely. In
particular, <strong>the</strong> identification <strong>of</strong> <strong>the</strong> enablers and hindrances <strong>of</strong> <strong>the</strong> intergenerational transmission may
contribute to a better management <strong>of</strong> <strong>the</strong> dynamic capability in its reconfiguration phase.
5. Conclusion and implication for future researches
This article proposes to adopt a generational view <strong>of</strong> <strong>the</strong> organization, in order to analyze <strong>the</strong> firm
adaptation and continuity through time. Especially, we suggest to analyze <strong>the</strong> “intergenerational
transmission <strong>of</strong> knowledge and competencies” in order to understand <strong>the</strong> assets reconfiguration
process <strong>of</strong> dynamic capabilities. Our intent is to clarify <strong>the</strong> concrete functioning <strong>of</strong> <strong>the</strong> dynamic
capability in <strong>the</strong> daily activities <strong>of</strong> <strong>the</strong> “average men and women”.
For this, we will adopt <strong>the</strong> strategy-as-practice approach. This approach proposes to analyze strategy
as social practices, with <strong>the</strong> aim to “humanize” research in strategic management (Golsorkhi 2006). Its
question is about how people “do strategy”, and not about what <strong>the</strong> organization “have” as strategy
(Whittington 1996). The level <strong>of</strong> analysis adopted is <strong>the</strong> micro or meso level, with a connexion created
between <strong>the</strong> concrete daily practices <strong>of</strong> people on <strong>the</strong> one hand, and <strong>the</strong> corporate strategic practices
on <strong>the</strong> o<strong>the</strong>r. Thus, every actor is a potential “strategist”, not only <strong>the</strong> top management.
Ano<strong>the</strong>r point we have insisted on is <strong>the</strong> contextual embeddedness <strong>of</strong> this research <strong>the</strong>me. We have
heavily emphasized on <strong>the</strong> contextual dependence <strong>of</strong> knowledge, competencies and generation. To
take into account this contextual dependence, we propose to carry out comparative research between
France and Japan. These two countries are industrialized economies which have achieved <strong>the</strong>ir
demographic transition. Their firms have to cope with <strong>the</strong> same dilemma <strong>of</strong> balancing <strong>the</strong> adaptation
and continuity <strong>of</strong> <strong>the</strong>ir organizational strategic assets. However, <strong>the</strong>y differ considerably in <strong>the</strong>ir
historical, cultural, and national industrial contexts. We believe <strong>the</strong> comparison would allow us o
highlight some significant contextual enablers and hindrances to <strong>the</strong> intergenerational transmission <strong>of</strong>
knowledge and competencies, especially in <strong>the</strong> intergenerational relationships. We intend to carry out
two case studies, one in a French firm and <strong>the</strong> o<strong>the</strong>r in a Japanese one. We are currently thinking
about <strong>the</strong> criteria <strong>of</strong> <strong>the</strong> firms to analyze.
On <strong>the</strong> <strong>the</strong>oretical plan, we still need to consolidate our strategic assets system with fur<strong>the</strong>r literature.
Especially, <strong>the</strong> relationships between <strong>the</strong> individual, collective and organizational level <strong>of</strong>
competencies have to be clarified. This is core to our framework and thus, must be considered
carefully. Then, we will have to face <strong>the</strong> challenge <strong>of</strong> <strong>the</strong> operationalization <strong>of</strong> <strong>the</strong>ses concepts.
References
Ambrosini, V. R. and Bowman C. (2009) “What are dynamic capabilities and are <strong>the</strong>y a useful construct in
strategic management?”, International Journal <strong>of</strong> Management Reviews, Vol.11, No.1, pp.29-49.
Amit, R. and Schoemaker, P.J.H. (1993) “Strategic assets and organizational rent”, Strategic management
journal, Vol.14, pp.33–33.
Barney, J. (1991) “Firm Resources and Sustained Competitive Advantage”, Journal <strong>of</strong> Management, Vol. 17, No.
1, pp.99-120.
Barney, J.B. (1986) “Strategic factor markets: expectations, luck, and business strategy”, Management science,
Vol. 32, No. 10, pp.1231–1241.
Beaud, S. and Pialoux M. (1999) Retour sur la condition ouvrière, Enquête aux usines Peugeot de Sochaux-
Montbélirad, Fayard, Paris.
Chauvel, L. (1998) Le Destin des générations : Structure sociale et cohortes en France au XXe siècle, PUF,
Paris.
Cultiaux, J. (2006) « Qu'en est-il des conflits des générations? Une différenciation des attitudes et des attentes à
l’égard du travail », Note éducation permanente, 21, Association pour une Fondation Travail-Université
(FTU).
Di Stefano, G. and Peteraf M. and Verona G. (2010) “Dynamic capabilities deconstructed: a bibliographic
investigation into <strong>the</strong> origins, development, and future directions <strong>of</strong> <strong>the</strong> research domain”, Industrial &
Corporate Change, Vol. 19, No. 4, pp.1187-1204.
Dosi, G., Nelson, R.R. and Winter, S. (2000) The Nature and Dynamics <strong>of</strong> Organizational Capabilities, Oxford
University Press, NY.
Durand, T. (2006) « L’alchimie de la compétence », Revue française de gestion, No.1, pp.261–292.
Eisenhardt, K.M. andMartin, J.A. (2000) “Dynamic Capabilities: What are <strong>the</strong>y?” Strategic Management Journal,
Vol.21, No.10, pp.1105–1121.
1079

Sakura Shimada
Excousseau, J-L. (2000) La mosaïque des générations: comprendre les sensibilités et les habitudes de
consommation des Français, Organisation, Paris.
Gaullier, X. (1997) « Emploi et retraite. Du risque “fin de carrière” à la pluriactivité à tout âge », Lien social et
Politiques–RIAC No.38, pp.63–70.
Grant, R.M. (1996) “Toward a Knowledge-Based Theory <strong>of</strong> <strong>the</strong> Firm”, Strategic Management Journal, Vol.17,
Special Issue, pp.109-122.
Golsorkhi, D. (2006) La fabrique de la stratégie : Une perspective multidimensionnelle, Vuibert, Paris.
Hall, R. (1993) “A framework linking intangible resources and capabilities to sustainable competitive advantage”,
Strategic Management Journal, Vol.14, No.8, pp.607–618.
Helfat, C. E., Finkelstein, S. Mitchell, W., Peteraf, M., Singh, H., Teece, D. and Winter, S. (2007) Dynamic
Capabilities: Understanding Strategic Change In Organizations, Wiley-Blackwell.
Helfat, C. E. and M. A. Peteraf (2009) “Understanding dynamic capabilities: progress along a developmental
path”, Strategic Organization, Vol.7, pp. 91-102.
Kogut, B. and Zander, U. (1992) “Knowledge <strong>of</strong> <strong>the</strong> firm, combinative capabilities, and <strong>the</strong> replication <strong>of</strong>
technology”, Organization science, Vol.3, No. 3, pp.383–397.
Labruffe, A. (2007) Seniors: Talents et compétences dans l'entreprise, AFNOR, La Plaine Saint Denis.
Laroche, H. & Nioche. J-P. (1998), Repenser la stratégie, fondements et perspectives, Vuibert, Paris.
Le Boterf, G. (2000) Construire les compétences individuelles et collectives, Organisation, Paris.
Leonard-Barton, L. (1992) “Core Capabilities and Core Rigidities: A Paradox in Managing New
Product Development”, Strategic Management Journal, Vol. 13, pp.111-125.
Le Roux, D. (2006) “Les processus sociaux de la transmission intergénérationnelle des compétences : le cas
d'une centrale nucléaire », Sociologies pratiques, Vol.12, No.1, pp.23-36.
Marbot, E. and Peretti J. M. (2006) Les Seniors dans l’entreprise, 2 éd., Village Mondial, Paris.
Maritan C. A. and Helfat C. (2007) Dynamic Capabilities and Organiszational Porcesses in Helfat, C. E., S.
Finkelstein, W. Mitchell, M. Peteraf, H. Singh, D. Teece et S. Winter (dir.), Dynamic Capabilities:
Understanding Strategic Change In Organizations, Wiley-Blackwell, chap.3, 30-45.
Nelson, R. and Winter, S. (1982) An evolutionary <strong>the</strong>ory <strong>of</strong> economic change, Cambridge Mass., Harvard
University Press, London.
Nonaka, I. (1991) “The Knowledge-Creating Company”, Harvard Business Review, Vol. 69, No. 6, pp.96-104.
Nonaka, I. and Takeuchi, H. (1995) The knowledge-creating company: how Japanese companies create <strong>the</strong>
dynamics <strong>of</strong> innovation, Oxford university press, New York Oxford.
Penrose, E. (1959) The Theory <strong>of</strong> <strong>the</strong> Growth <strong>of</strong> <strong>the</strong> Firm, Oxford University Press, USA.
Polanyi, M. (1983) The Tacit Dimension, Peter Smith, US.
Prahalad, C.K. and Hamel, G. (1990) “The Core Competencies <strong>of</strong> <strong>the</strong> Corporation”, Harvard Business Review,
Vol. 68, No. 3, pp.79-91.
Préel, B. (2000) Le choc des générations, La Découverte, Paris.
———. ( 2005) Les Générations Mutantes, La Découverte, Paris.
Raoult, N. (2006) Changements et expériences, expérience des changements : gestion des âges valorisation des
expériences, L’Harmattan, Paris.
Raoult, N., Delay B. and Marchand A. (2007) Dessine-moi une trajectoire. Construire sa place dans l’entreprise
aux différents âges de la vie, ANACT.
Regnér, P. (2008) “Strategy-as-practice and dynamic capabilities: steps towards a dynamic view <strong>of</strong> strategy”,
Human Relations, Vol. 61, No. 4, pp.565-588.
Sanchez, R., Heene, A., and Thomas, H. (1996) Dynamics <strong>of</strong> competence-based competition: <strong>the</strong>ory and
practice in <strong>the</strong> new strategic management, Elsevier.
Spender, J.C. (1996) “Making knowledge <strong>the</strong> basis <strong>of</strong> a dynamic <strong>the</strong>ory <strong>of</strong> <strong>the</strong> firm”, Strategic management
journal, No.17, pp.45–62.
Szulanski, G. (1996) “Exploring Internal Stickiness: Impediments to <strong>the</strong> Transfer <strong>of</strong> Best Practice Within <strong>the</strong>
Firm”, Strategic Management Journal, Vol. 17, Special Issue, pp.27-43.
Teece, D. J, Pisano, G. and Shuen, A. (1997) “Dynamic capabilities and strategic management”, Managing
Strategic Innovation and Change, Vol. 18, No. 7, pp.509–533.
Teece, David J. (2007) “Explicating dynamic capabilities: <strong>the</strong> nature and micr<strong>of</strong>oundations <strong>of</strong> (sustainable)
enterprise performance”, Strategic Management Journal, Vol. 28, No. 13, pp.1319-1350.
Vendramin, P. and Cultiaux J. (2008) Les générations face aux mutations du rapport au travail, Dans Actes du
Colloque international de l'AISLF, pp.475-484. Brest, Mai 29.
Wang, C.L. and Ahmed, P.K. (2007) “Dynamic capabilities: A review and research agenda”, International Journal
<strong>of</strong> Management Reviews, Vol. 9, No. 1, pp.31-51.
Wernerfelt, B. (1984) “A Resource-based View <strong>of</strong> <strong>the</strong> Firm”, Strategic Management Journal, Vol. 5, No. 2,
pp.171-180.
Zollo, M. & Winter, S.G. (2002) “Deliberate learning and <strong>the</strong> evolution <strong>of</strong> dynamic capabilities”, Organization
science, Vol. 13, No. 3, pp.339–351.
1080

Organizational Learning through University-Industry
Research Cooperation
Simon Woll
Chair for Organizational Theory and Human Resource Management, Ingolstadt
School <strong>of</strong> Management, Catholic University Eichstaett-Ingolstadt, Germany
Simon.Woll@ku-eichstaett.de
Abstract: The purpose <strong>of</strong> this paper is to describe a PhD research work in progress, which empirically
determines organizational learning processes in University-Industry Research Cooperation (UI). Since
knowledge is considered to be <strong>the</strong> most valuable resource <strong>of</strong> an organization, <strong>the</strong> management <strong>of</strong> knowledge has
become a central issue in <strong>the</strong> scientific as well as in <strong>the</strong> practical field. For industrial companies, one promising
source <strong>of</strong> new knowledge creation is <strong>the</strong> research cooperation with universities. But still <strong>the</strong> preservation and
transfer <strong>of</strong> (tacit) knowledge is a major challenge for organizations. Since project work is commonly applied in UI
research cooperation, <strong>the</strong> documentation <strong>of</strong> tacit knowledge and <strong>the</strong> transfer <strong>of</strong> procedural knowledge to o<strong>the</strong>r
parts <strong>of</strong> <strong>the</strong> organization are considered as essential steps towards a learning organization. While research
projects bear a high learning potential for individuals and <strong>the</strong> whole organization, <strong>the</strong> practical realization <strong>of</strong> this
idea is not fully accomplished yet. One reason is <strong>the</strong> unanswered question <strong>of</strong> how to design project work <strong>of</strong> UI
cooperations in a reflexive manner for <strong>the</strong> purpose <strong>of</strong> organizational learning. Therefore <strong>the</strong> research question
can be formulated as follows: How should project work in UI research cooperation be designed to foster
organizational learning? To answer this research question <strong>the</strong> author refers to various <strong>the</strong>oretical fields <strong>of</strong>
research (organizational learning, knowledge management, project management). Since social interaction is
considered to be an essential part in project work and tacit knowledge transfer, a qualitative research approach in
form <strong>of</strong> a single case study seems to be adequate. Object <strong>of</strong> determination will be <strong>the</strong> institutionalized program <strong>of</strong>
an industry organization which undertakes diverse research and development projects in cooperation with
several universities in Germany. In conducting interviews with participants from both organizations and in
observing <strong>the</strong> generation, use and transfer <strong>of</strong> <strong>the</strong> project knowledge, <strong>the</strong> author develops a model <strong>of</strong> reflexive
project work, which can be seen as an advanced version <strong>of</strong> actual project management in regards <strong>of</strong> a
sustainable use <strong>of</strong> organizational knowledge. Research results will <strong>of</strong>fer interesting insights into <strong>the</strong> design <strong>of</strong>
cooperative UI research projects. Implications derived from <strong>the</strong> research may be useful for scientists in <strong>the</strong> field
<strong>of</strong> knowledge management and project management as well as to practitioners like project managers or
consultants.
Keywords: organizational learning; university-industry research projects; project work; reflexion; case study
1. Introduction/Background
Once knowledge had been recognized as a central economic factor, <strong>the</strong> organization with its learning
mechanisms and its ability to create new and mobilize existing stocks <strong>of</strong> knowledge is increasingly
investigated. The objective <strong>of</strong> many companies today is to advance towards a learning organization,
what can be explained by increasing international competition, technological developments and a
shortening half-life <strong>of</strong> knowledge (cf. Pedler et al. 1991). Industrial organizations are trying to create
relevant knowledge and innovations as well as to develop <strong>the</strong>ir organizational competences through
cooperative projects with scientific institutions, e.g. universities (cf. Schelle 2008; Hemmert 2009).
Thereby <strong>the</strong> actors are confronted by various challenges specific for collaborative projects between
universities and industrial companies (UI) due to boundary-crossing activities in general and to
different cultures <strong>of</strong> knowledge and learning and <strong>the</strong>ir objectives in particular (cf. Sherwood/Covin
2008). On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong>se collaborative projects <strong>of</strong>fer great opportunities for organizational
learning to <strong>the</strong> participating organizations (cf. Barnes et al. 2002; Cyert/Goodman 1997;
Hermans/Castiaux 2007). Particularly decisive for organizational learning in <strong>the</strong> context <strong>of</strong> cooperative
UI projects seem to be <strong>the</strong> transfer <strong>of</strong> tacit knowledge, experience and process knowledge as well as
an appropriately designed knowledge- and learning-oriented project work, which involves and fosters
processes <strong>of</strong> reflexion necessary for collective learning (cf. Agrawal 2001; Barbolla/Corredera 2009).
The starting point <strong>of</strong> this research is <strong>the</strong> idea to consider and apply aspects <strong>of</strong> <strong>the</strong> organizational
learning concept in a specific and practical context - project work in UI research cooperations.
Therefore UI research projects will be regarded from a learning <strong>the</strong>ory perspective to estimate <strong>the</strong>ir
contribution to organizational learning. Based on <strong>the</strong> findings implications will be drawn for a
knowledge- and learning-oriented and thus reflexive project work in UI research cooperations. Finally
options for a successful implementation will be discussed.
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Simon Woll
The research topic can be located in an overlap <strong>of</strong> various research areas which each one <strong>of</strong>fers a
specific perspective on <strong>the</strong> topic and will <strong>the</strong>refore be considered in more detail. First, <strong>the</strong> literature on
UI research cooperation will provide an overview <strong>of</strong> best practices and specific characteristics <strong>of</strong> this
form <strong>of</strong> cooperation, while <strong>the</strong> relevant literature on organizational learning <strong>of</strong>fers a basic <strong>the</strong>oretical
understanding. The project management literature is used for <strong>the</strong> development <strong>of</strong> a processual
framework, and toge<strong>the</strong>r with <strong>the</strong> knowledge management literature, <strong>the</strong>y are considered to identify
methods that support processes <strong>of</strong> collective reflexion which are crucial for organizational learning.
2. Research objectives
Hermans and Castiaux (2007) doubt that knowledge transfer takes place through mere relationship
between university and enterprise without any fur<strong>the</strong>r activities. They consider collaborative UI
research projects as a special form <strong>of</strong> inter-organizational knowledge transfer and as an opportunity
for organizational learning. Particularly with regard to <strong>the</strong> assessment <strong>of</strong> UI research projects also
Cyert and Goodman (1997) emphasize: "<strong>the</strong> essential issue is whe<strong>the</strong>r <strong>the</strong>se relationships (UI
alliances) create learning for a few individuals or whe<strong>the</strong>r <strong>the</strong> learning's are diffused throughout <strong>the</strong>
organization" (p. 52). This view is shared in <strong>the</strong> research. Therefore <strong>the</strong> objective is an active
influence and promotion <strong>of</strong> organizational learning by reflexive project work in UI research
cooperations. Through <strong>the</strong> dissemination <strong>of</strong> generated knowledge in o<strong>the</strong>r parts <strong>of</strong> <strong>the</strong> organization
<strong>the</strong> first step towards organizational learning is taken. In this case, <strong>the</strong> transfer <strong>of</strong> tacit and processual
knowledge is very important. Project work, i.e. <strong>the</strong> practical design and implementation <strong>of</strong> projects, is
supposed to be an effective tool for securing this transfer. Since reflexion can be considered as an
important premise for organizational learning project work has to contain practices and components to
initiate and support reflexive processes. Therefore <strong>the</strong> objective is to build a model <strong>of</strong> reflexive project
work that fosters organizational learning in UI research cooperations. The next section will give a
short introduction to <strong>the</strong> relevant research areas.
3. Introduction to relevant research areas
3.1 Organizational learning and knowledge management
The concepts <strong>of</strong> organizational learning and <strong>the</strong> learning organization consider <strong>the</strong> question <strong>of</strong> how
organizations can optimize <strong>the</strong>ir learning processes (cf. Probst et al. 2010). In <strong>the</strong> scientific discussion
on organizational change, <strong>the</strong>y seem to have largely replaced <strong>the</strong> concept <strong>of</strong> organization
development. In <strong>the</strong> 1970s <strong>the</strong>se concepts were mentioned for <strong>the</strong> first time. While <strong>the</strong>y have been
partly neglected in <strong>the</strong> 1980s, <strong>the</strong> literature boom is continuing since <strong>the</strong> late 1990s. There is no
unique definition <strong>of</strong> organizational learning, as it is shown by numerous and diverse examples in <strong>the</strong>
literature (cf. Wiegand 1998). For this study three <strong>of</strong>ten mentioned aspects <strong>of</strong> organizational learning
are especially interesting: <strong>the</strong> distinction between various levels <strong>of</strong> learning (cf. Nonaka/Takeuchi
1995), <strong>the</strong> assumption <strong>of</strong> organizational learning as learning from experience (cf. Kolb 1984) and
learning as a change <strong>of</strong> <strong>the</strong> organizational knowledge base (cf. Pautzke 1989; Reinhardt 1995).
The relationship between individual and organizational learning is significant in <strong>the</strong> discussion on
organizational learning. The individual is <strong>of</strong>ten referred to as agent <strong>of</strong> organizational learning (cf.
Wiegand 1998). Active behavior thus is a premise for reflexion that in turn allows someone to learn.
Reflexion is understood as “(…) a mental activity aimed at investigating one's own action in a certain
situation and involving a review <strong>of</strong> <strong>the</strong> experience, an analysis <strong>of</strong> causes and effects, and <strong>the</strong> drawing
<strong>of</strong> conclusions concerning future action" (Woerkom 2003, p. 40). This definition goes along with Kolb’s
(1984) model <strong>of</strong> a learning circle which will be used for an introduction to reflexion processes in
learning from experience. As a social process reflexion <strong>of</strong>fers <strong>the</strong> opportunity to learn collectively and
supports <strong>the</strong> transfer <strong>of</strong> tacit knowledge and experiences. Therefore reflexion can bridge <strong>the</strong> gap
between individual and organizational learning (cf. Hoyrup 2004). According to Pautzke (1989)
organizational learning is defined as processes for <strong>the</strong> transformation <strong>of</strong> <strong>the</strong> organizational knowledge
base. This implies a wide understanding <strong>of</strong> all knowledge which is available to <strong>the</strong> members <strong>of</strong> an
organization. This idea <strong>of</strong> organizational learning as a transformation <strong>of</strong> <strong>the</strong> organizational knowledge
base is addressed in <strong>the</strong> research and will be applied to UI research projects.
In contrast to <strong>the</strong> aforementioned concept <strong>of</strong> organizational learning, which is more focused in <strong>the</strong>
process <strong>of</strong> acquiring knowledge and developing <strong>the</strong> organizational knowledge base, knowledge
management aims at an intervention and <strong>of</strong>ten refers to <strong>the</strong> product <strong>of</strong> learning, i.e. knowledge as a
resource (cf. Schiersmann/Thiel 2010; Probst et al. 2010). It can be regarded as a relatively concrete
concept <strong>of</strong>fering practical tools and instruments. Both concepts can complement one ano<strong>the</strong>r in <strong>the</strong>ir
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attempt to achieve <strong>the</strong> joint objective <strong>of</strong> an effective organization <strong>of</strong> learning processes. Nonaka and
Takeuchi (1995) introduced <strong>the</strong>ir prominent model <strong>of</strong> knowledge creation, which describes <strong>the</strong>
development <strong>of</strong> knowledge as a spiral learning process through transfer <strong>of</strong> tacit knowledge into
explicit knowledge and back into tacit knowledge. The model provides four transmission modes <strong>of</strong>
knowledge: socialization, externalization, combination and internalization (SECI). Tacit knowledge is
shared, discussed, combined with <strong>the</strong> knowledge <strong>of</strong> o<strong>the</strong>rs and communicated through shared
experience, which results in new knowledge. The use <strong>of</strong> <strong>the</strong> SECI model to UI research collaborations
is considered to result in interesting implications for <strong>the</strong> design <strong>of</strong> <strong>the</strong> project work in UI research
collaborations (cf. Feller et al. 2005).
The organizational learning concept is <strong>of</strong>ten confronted with <strong>the</strong> criticism that key issues and
approaches are too <strong>the</strong>oretical for practical use. Toge<strong>the</strong>r with <strong>the</strong> call for a transformation <strong>of</strong>
organizations to become learning organizations, <strong>the</strong> author understands it as a plea for a knowledge-
and learning-oriented design <strong>of</strong> project work in UI cooperation. In this research organizational learning
is understood as an expansion <strong>of</strong> <strong>the</strong> organizational knowledge base caused by collective reflexion
and knowledge transfer processes.
3.2 University-industry research cooperation
The number <strong>of</strong> UI research collaborations in Germany is currently increasing since <strong>the</strong> German
economic production is very research intensive - above-average compared to o<strong>the</strong>r <strong>European</strong>
countries (cf. Schasse et al. 2011). In addition companies and universities have recognized <strong>the</strong>
mutual benefits <strong>of</strong> cooperating in research and development activities, e.g. <strong>the</strong> fast and cost-effective
development <strong>of</strong> innovations. Many companies also consider UI cooperations as additional occasion to
recruit highly qualified pr<strong>of</strong>essionals, what should increase in importance with regard to demographic
changes in Germany. From a university perspective cooperating companies can be an additional
source <strong>of</strong> funding <strong>of</strong> <strong>the</strong>ir research activities. Ano<strong>the</strong>r positive aspect for universities is <strong>the</strong> assignment
<strong>of</strong> a new role for <strong>the</strong> knowledge society (cf. Garrick et al. 2004).
The UI research field goes back to <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> 1990s. Reasons for a scientific discussion <strong>of</strong>
UI research collaborations is, in addition to <strong>the</strong>ir observable increase in number, <strong>the</strong> decline <strong>of</strong> public
resources for research activities, along with an increasing global demand for innovation. So far, <strong>the</strong>
research focus had mainly been on management practices and contextual factors for a successful
completion <strong>of</strong> <strong>the</strong> project and an effective technology transfer between university and industry.
Quantitative approaches, e.g. for quantitative assessments <strong>of</strong> patent and publication activities, were
commonly used (cf. OECD 2003). Only few recent studies which are introduced in <strong>the</strong> following
focused on qualitative aspects and research methods.
Barnes, Pashby and Gibbons (2002) examined management practices within six UI collaborative
research projects <strong>of</strong> an university research centre to form a good practice model. They conducted
interviews and a cross-case analysis with <strong>the</strong> aim <strong>of</strong> identifying factors <strong>of</strong> influence for perceived
success <strong>of</strong> <strong>the</strong> cooperation. Besides <strong>the</strong> choice <strong>of</strong> partner and environmental factors, <strong>the</strong> results
indicated project management practices as most influential for successful research cooperations.
These findings are not fur<strong>the</strong>r concretized, what can be seen as a weakness <strong>of</strong> <strong>the</strong> study. Henderson,
McAdam and Leonard (2006) conducted a longitudinal case study for four years on an UI research
partnership. The collaboration was based on action learning, participatory observation and practitioner
reflection as working methods with <strong>the</strong> aim <strong>of</strong> implementing a TQM in <strong>the</strong> organization to foster
organizational learning. The findings stress transfer <strong>of</strong> learning and reflexion as important methods in
UI cooperation, although it is time-consuming and work-intensive and <strong>the</strong>refore <strong>of</strong>ten neglected.
Sherwood and Covin (2008) conducted a survey with industrial managers to explore <strong>the</strong> effects <strong>of</strong>
organizational knowledge interface factors on knowledge acquisition in UI cooperations, referring to
situated-learning <strong>the</strong>ory. The results <strong>of</strong> a multiple regression analysis indicate, that trust between <strong>the</strong>
cooperation partners is strongly predicting <strong>the</strong> successful acquisition <strong>of</strong> tacit knowledge. In <strong>the</strong>ir
interview study <strong>of</strong> technology transfer in UI research projects Barbolla and Corredera (2009) provide
an analytical framework and identify factors <strong>of</strong> influence. In addition to structural factors at <strong>the</strong> project
level, both <strong>the</strong> skills and motivation <strong>of</strong> <strong>the</strong> university research team and <strong>the</strong> cooperation experience
and absorptive capacity <strong>of</strong> <strong>the</strong> company seem important. Formal as well as informal relationship
between <strong>the</strong> parties appears crucial to <strong>the</strong> success <strong>of</strong> <strong>the</strong> cooperation. Beside social aspects <strong>the</strong>se
also contain methods for promoting reflection.
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UI research projects are characterized by a specific form <strong>of</strong> cooperation and related challenges
through <strong>the</strong> constellation <strong>of</strong> different actors and organizations (cf. Agrawal 2001). In <strong>the</strong> literature <strong>the</strong>
difference <strong>of</strong> missions, objectives and cultures <strong>of</strong> universities and industrial companies is <strong>of</strong>ten seen
as obstacle for a successful cooperation (cf. Sherwood/Covin 2008). Therefore Barnes et al. (2002)
argue for a balance <strong>of</strong> industrial and academic needs to secure a mutual success <strong>of</strong> <strong>the</strong> research
project. There is an observable trend in companies to permanently institutionalize research
cooperations with universities. The benefits are seen in <strong>the</strong> following aspects (cf. Hermans/Castiaux
2007; Sherwood/Covin 2008):
A formal shape with a clear definition <strong>of</strong> goals and responsibilities
Confidence-building effects through long-term cooperation
Opportunities for improving <strong>the</strong> efficiency <strong>of</strong> R&D
Transfer <strong>of</strong> tacit knowledge and experience through regular personal contact
One common form <strong>of</strong> UI research cooperation is contract research which can be considered as an
important knowledge transfer mechanism, especially in <strong>the</strong> case <strong>of</strong> a knowledge-based application
orientation (cf. Barbolla/Corredera 2009; Bekkers/Freitas 2008). This form <strong>of</strong> cooperation is also
applied in <strong>the</strong> case study projects and will be considered in <strong>the</strong> project work design. UI research very
<strong>of</strong>ten applies project work as practical mode <strong>of</strong> collaboration. Since this can be regarded as a general
characteristic <strong>of</strong> UI collaboration, a processual perspective <strong>of</strong> project work <strong>of</strong>fers a conceptual
framework for fur<strong>the</strong>r investigation. In <strong>the</strong> following section <strong>the</strong> project work is <strong>the</strong>refore considered in
more detail as <strong>the</strong> dominant form <strong>of</strong> collaboration in UI research projects.
3.3 Project work
Project work is more commonly used in different sectors as an appropriate approach for increasingly
complex problems (cf. Gleich et al. 2006). By 2020 a proportion <strong>of</strong> total economic value <strong>of</strong> 15% is
forecasted for inter-organizational cooperation projects (cf. Schelle 2008). UI research projects can be
regarded as a promising form <strong>of</strong> knowledge creation and ultimately <strong>of</strong> organizational learning (cf.
Schiersmann/ Thiel 2000). Organizations also learn by experience from past UI research projects and
can draw consequences for future projects, i.e. inter-project learning (cf. Prange 1996). In particular
aspects <strong>of</strong> reflexion and sharing <strong>of</strong> knowledge and experiences seem to be <strong>of</strong> high importance for <strong>the</strong>
preservation, dissemination and use <strong>of</strong> knowledge gained through collaborative research projects.
The project management literature <strong>of</strong>fers single processes supporting reflexion, i.e. lessons learned or
post-project reviews. These are, however, <strong>of</strong>ten neglected or even suppressed in current practice (cf.
Stauber 2002). One reason for this is seen by project managers in <strong>the</strong> absence <strong>of</strong> guidelines for
practical implementation <strong>of</strong> <strong>the</strong>se components in existing processes without creating much additional
workload. The study intends to address this deficit in regard <strong>of</strong> <strong>the</strong> specific application <strong>of</strong> project work
in UI research projects and promotes a systematic use <strong>of</strong> <strong>the</strong> aforementioned components to
establish a reflexive design <strong>of</strong> project work.
Figure 1: Relevant research areas <strong>of</strong> <strong>the</strong> study
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4. Research design and methods
Simon Woll
The research design is oriented on <strong>the</strong> tradition <strong>of</strong> action research. Here, <strong>the</strong> knowledge is gained
through a mix <strong>of</strong> research and practice, which takes into account both <strong>the</strong>oretical and practical needs.
At <strong>the</strong> same time a high practical relevance <strong>of</strong> <strong>the</strong> results is assured. In an iterative process <strong>of</strong>
examination, <strong>the</strong> research object is increasingly investigated (cf. Probst/Raub 1995).
The studied UI research projects will be considered from a learning <strong>the</strong>ory perspective on <strong>the</strong>ir
contribution to organizational learning. Based on <strong>the</strong> examination results implications for a reflexive
and thus learning-oriented project work will be develop and options for implementation will be
presented securing a sustainable use <strong>of</strong> project results for a wide part <strong>of</strong> <strong>the</strong> organization. The project
management literature will be screened for concepts <strong>of</strong> planned reflexion in projects, which will be
systematized in regard to organizational learning. First, project aspects are to be identified, which can
be used to examine reflexive processes taking place in UI research projects. Based on <strong>the</strong> results
design principles <strong>of</strong> a reflexive project work will be developed, which can lead to an optimized use and
preservation <strong>of</strong> knowledge and skills in UI projects. Fur<strong>the</strong>rmore, a framework for organizational
learning by reflexive project work will be developed which integrates <strong>the</strong> relevant aspects <strong>of</strong> <strong>the</strong>
previously discussed research areas. In an extension to knowledge management providing relevant
knowledge "in <strong>the</strong> right place at <strong>the</strong> right time", <strong>the</strong> goal <strong>of</strong> a reflexive project work is to support project
workers in <strong>the</strong> generation and application <strong>of</strong> new knowledge and <strong>the</strong> dissemination <strong>of</strong> resulting
experiences to o<strong>the</strong>r parts <strong>of</strong> <strong>the</strong> organization, i.e. organizational learning. To meet and sustain <strong>the</strong>se
objectives and to put <strong>the</strong>m into practice, jointly developed organizational learning goals <strong>of</strong> <strong>the</strong> UI
research cooperation will be set in a corporate learning strategy.
The author holds a view <strong>of</strong> business administration as an applied social science and tries to examine
<strong>the</strong> presented issue by combining desk research and field research. Desk research understood as
literature analysis should provide an overview <strong>of</strong> <strong>the</strong> current status <strong>of</strong> UI research cooperation as well
as <strong>the</strong> <strong>the</strong>oretical basis to build a contextual framework for a reflexive project work. Several scientific
databases in German and English language (WISO, EBSCO) as well as reports <strong>of</strong> German research
institutions (e.g. Deutsches Institut für Wirtschaftsforschung) are considered.
The field research approach ensures a high practical relevance <strong>of</strong> <strong>the</strong> study. An inductive approach is
chosen to generate <strong>the</strong> empirical basis, using qualitative research methods in a single case study (cf.
Hermans/Castiaux 2007). Object <strong>of</strong> investigation is an institutionalized corporate program for UI
research projects. To get a holistic and realistic picture <strong>of</strong> reality as many dimensions as possible are
to be captured (cf. Lamnek 1995). The advantage <strong>of</strong> a single case study is seen in <strong>the</strong> intense
examination <strong>of</strong> <strong>the</strong> object <strong>of</strong> investigation leading to large and complex results. The case study can
<strong>the</strong>refore be divided into different sections. In <strong>the</strong> first section a model <strong>of</strong> reflexive project work in UI
cooperations will be created based on <strong>the</strong>oretical findings and <strong>the</strong> results <strong>of</strong> exploratory interviews.
These interviews will be conducted with participants involved in a U-I-project, i.e. <strong>the</strong> senior university
researcher, <strong>the</strong> PhD candidate and <strong>the</strong> person in charge on <strong>the</strong> corporate side. The interviews will be
evaluated using qualitative content analysis (cf. Mayring 2000; Gläser/Laudel 2006). In more detail,
<strong>the</strong> current project work will be described and analyzed for processes <strong>of</strong> collective reflexion and
knowledge transfer. Based on <strong>the</strong> findings <strong>of</strong> a literature review and <strong>of</strong> exploratory interviews, a model
will be established, which integrates collective learning and reflexion processes in project work and
shows its connection to organizational learning.
The second section <strong>of</strong> <strong>the</strong> case study uses focus groups with experts for conceptual considerations in
regard <strong>of</strong> <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> previously created model <strong>of</strong> reflexive project work (cf.
Bogner/Leuthold 2005). The workshop results will be evaluated using qualitative content analysis as
well.
In <strong>the</strong> third section <strong>of</strong> <strong>the</strong> case study a learning strategy for <strong>the</strong> corporate UI research cooperation
program will be developed by integrating learning goals and knowledge goals into <strong>the</strong> project goals.
Fur<strong>the</strong>r, an appropriate guideline for a sustained promotion <strong>of</strong> a reflexive project work will be
generated and tested in a pilot project to validate previous assumptions.
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Figure 2: Research design and methods
References
Simon Woll
Agrawal, A. (2001) “University-to-industry knowledge transfer”, International Journal <strong>of</strong> Management Reviews,
Vol. 3, No. 4, pp. 285–302.
Barbolla, A. M. B./ Corredera, J. R. C. (2009) “Critical factors for success in university-industry research projects”,
Technology Analysis & Strategic Management, Vol. 21, No. 5, pp. 599–616.
Barnes, T./ Pashby, I./ Gibbond, A. (2002) “Effective University-Industry Interaction”, <strong>European</strong> Management
Journal, Vol. 20, No. 3, pp. 272–285.
Bekkers, R./ Freitas, I. M. B. (2008) “Analysing knowledge transfer channels between universities and industry:
To what degree do sectors also matter?”, Research Policy, Vol. 37, pp. 1837–1853.
Bogner, A./ Leuthold, M. (2005) "Was ich noch dazu sagen wollte…" - Die Moderation von Experten-
Fokusgruppen, in Bogner, A./ Littig, B./ Menz, W. (ed. 2005) Das Experteninterview, Wiesbaden, pp. 155–
172.
Bogner, A./ Littig, B./ Menz, W. (2005) Das Experteninterview: Theorie, Methode, Anwendung, 2. Aufl.,
Wiesbaden.
Cyert, R. M./ Goodman, P. S. (1997) “Creating Effective University-Industry Alliances: An Organizational
Learning Perspective”, Organizational Dynamics, Vol. 25, No. 4, pp. 45–57.
Feller, J./ Hirvensalo, A./ Smeds, R. (2005) “Inter-partner process learning in collaborative R&D - a case study
from <strong>the</strong> telecommunications industry”, Production Planning and Control, Vol. 16, No. 4, pp. 388–395.
Garrick, J./ Chan, A./ Lai, J. (2004) “University-Industry Partnerships”, Journal <strong>of</strong> <strong>European</strong> Industrial Training,
Vol. 28, No. 2/3/4, pp. 329–338.
Gläser, J./ Laudel, G. (2006) Experteninterviews und qualitative Inhaltsanalyse, 2. Aufl., Wiesbaden.
Gleich, R./ Gschendtner, M./ Lindner, F./ Mayer, T. L./ Wald, A. (2008) „Status quo und Gestaltungsfaktoren im
Projektwissensmanagement“, Projektmanagement aktuell, Vol. 5, pp. 49–58.
Hemmert, M. (2009) „Forschungskooperationen zwischen Unternehmen und Universitäten: Eine vergleichende
Studie zu den USA, Deutschland, Japan und Korea“. Available at:
videoonline.edu.lmu.de/files/videoonline/data/1110/files/bwl090128.pdf, [1 March 2011].
Henderson, J./ McAdam, R./ Leonard, D. (2006) “Reflecting on a TQM based university/industry partnership.
Contributions to research methodology and organisational learning”, Management Decision, Vol. 44, No. 10,
pp. 1422–1440.
Hermans, J./ Castiaux, A. (2007) “Knowledge creation through university-industry collaborative research
projects”, The Electronic Journal <strong>of</strong> Knowledge Management, Vol. 5, No. 1, pp. 43–54.
Hoyrup, S. (2004) “Reflection as a core process in organisational learning”, The Journal <strong>of</strong> Workplace Learning,
Vol. 16, No. 8, pp. 442–454.
Kolb, D. A. (1984) Experimental Learning. Experiences as <strong>the</strong> Source <strong>of</strong> Learning and Development, New York.
Lamnek, S. (1995) Qualitative Sozialforschung. Methoden und Techniken, 3. Aufl., München.
Mayring, Ph. (2000). Qualitative Inhaltsanalyse. FQS, Vol. 1(2), Art. 20.
1086

Simon Woll
Nonaka, I./ Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation, New York.
OECD (2003) „Kurzfassung - Verwertung wissenschaftlicher Ergebnisse: Patentverwertung und Lizenzvergabe
durch öffentliche Forschungseinrichtungen“. Available at: www.oecd.org/ dataoecd /18/25/16231038.pdf,
[21 March 2011].
Pautzke, G. (1989) Die Evolution der organisatorischen Wissensbasis: Bausteine zu einer Theorie des
organisatorischen Lernens, Herrsching.
Pedler, M./ Boydell, T./ Burgoyne, J. (1991) „Auf dem Weg zum "Lernenden Unternehmen"“, in Sattelberger, T.
(ed. 1991) Die lernende Organisation, Wiesbaden, pp. 57–66.
Prange, C. (1996) „Interorganisationales Lernen: Lernen in, von und zwischen Organisationen“, in Schreyögg, G./
Conrad, P. (ed. 1996) Wissensmanagement, Berlin, pp. 163–189.
Probst, G. J. B./ Raub, S. P. (1995) „Action Research - Ein Konzept angewandter Managementforschung“, Die
Unternehmung, Vol. 1, pp. 3–19.
Probst, G. J. B./ Raub, S. P./ Romhardt, K. (2010) Wissen managen: Wie Unternehmen ihre wertvollste
Ressource optimal nutzen, 6. überarb. u. erw. Aufl., Wiesbaden.
Reinhardt, R. (1995) Das Modell Organisationaler Lernfähigkeit und die Gestaltung Lernfähiger Organisationen,
2. Aufl., Wien.
Sattelberger, T. (ed. 1991), Die lernende Organisation, Wiesbaden.
Schasse, U./ Krawczyk, O./ Gehrke, B./ Stenke, G./ Kladroba, A. (2011) „FuE-Aktivitäten von Wirtschaft und
Staat im internationalen Vergleich“. Available at: http://www.e-fi.de/fileadmin/Studien/StuDIS_2011/
StuDIS_2_2011.pdf, [21 March 2011].
Schelle, H. (2008) „Deutschland im Jahr 2020: Neue Herausforderungen für ein Land auf Expedition“,
Projektmanagement aktuell, Vol. 3 pp. 40–41.
Schiersmann, C./ Thiel, H. U. (2000) Projektmanagement als organisationales Lernen: Ein Studien- und
Werkbuch (nicht nur) für den Bildungs- und Sozialbereich, Opladen.
Schiersmann, C./ Thiel, H. U. (2010) Organisationsentwicklung: Prinzipien und Strategien von
Veränderungsprozessen, Wiesbaden.
Schreyögg, G./ Conrad, P. (1996), Wissensmanagement, Vol. 6, Berlin.
Sherwood, A. L./ Covin, J. G. (2008) “Knowledge Acquisiton in University-Industry Alliances: An Empirical
Investigation from a Learning Theory Perspective”, The Journal <strong>of</strong> Product Innovation Management, Vol. 25,
pp. 162–179.
Stauber, E. (2002) „In und aus Projekten lernen“, Projektmanagement, Vol. 3, pp. 29–38.
Wiegand, M. (1998) Prozesse organisationalen Lernens, Wiesbaden.
Woerkom, M. (2003) "Critical reflection at work. Bridging individual and organisational learning", PhD <strong>the</strong>sis,
Twente University, Enschede/Print Partners, Ipskamp.
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Academic
Papers
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Institutional Leadership: An Inconvenient Truth
Chris Blodgett
Canadian Air Force, Knowledge Management Officer (KMO), NATO School
Oberammergau, Germany
blodgett.chris@NATOschool.NATO.int
“The world is getting smaller, time is getting shorter, networks are getting larger and
more complex, and your stakeholders are demanding more…Do you thrive in this
environment?”(Carla O’Dell) The Executives’ Role in KM (2004)
Abstract: The views expressed in this paper are those <strong>of</strong> <strong>the</strong> author and do not represent <strong>the</strong> <strong>of</strong>ficial policy <strong>of</strong>
Canada DND/CF, NATO or NATO School. “Demoralized by leadership failures and struggling to prioritize
its workload, <strong>the</strong> Mounties [Royal Canadian Mounted Police – RCMP] have been waiting for months
for <strong>the</strong> federal government to appoint a new commander. Under <strong>the</strong> status quo, a proliferation <strong>of</strong>
fiefdoms within <strong>the</strong> RCMP leaves commanders raiding each o<strong>the</strong>r’s budgets for resources, and
starving some important programs to feed o<strong>the</strong>rs.” This review <strong>of</strong> <strong>the</strong> Auditor General’s annual report
to <strong>the</strong> Canadian parliament (Globe & Mail, 10 June 2011) is a good example <strong>of</strong> a public service
organization that is struggling to effectively integrate <strong>the</strong> top-down achievement <strong>of</strong> organizational
objectives with satisfying <strong>the</strong> bottom-up operational demands <strong>of</strong> <strong>the</strong> organization. While <strong>the</strong> RCMP
may not have had a commander/CEO in place, <strong>the</strong>re are many organizations that do and yet <strong>the</strong>y still
suffer <strong>the</strong> same ‘workload-fiefdom’ fate because <strong>the</strong>y lack <strong>the</strong> institutional leadership to pull it all
toge<strong>the</strong>r. There does exist a ‘private sector’ organizational development discipline that can address
this phenomenon - Knowledge Management (KM). KM is not so much a discipline unto itself as it is
<strong>the</strong> namesake for all <strong>the</strong> multi-disciplined approaches that make up <strong>the</strong> institutional performance
dichotomy <strong>of</strong> making sense <strong>of</strong> <strong>the</strong> world so that we can act more effectively in it. These two
fundamentally different ways <strong>of</strong> “seeing <strong>the</strong> world” are part <strong>of</strong> <strong>the</strong> challenge <strong>of</strong> KM because right from
<strong>the</strong> outset each paradigm takes <strong>the</strong> KM pr<strong>of</strong>essional down a completely different path to defining what
KM is all about. Like most organizations, <strong>the</strong> North Atlantic Treaty Organization (NATO) has also
struggled to make sense <strong>of</strong> <strong>the</strong> complexities <strong>of</strong> <strong>the</strong> new Information Age at a time when <strong>the</strong>
operational perspectives, perceptions and different organizational maturity models <strong>of</strong> 28 different
nations, not to mention <strong>the</strong> different cultures and sub-cultures <strong>of</strong> <strong>the</strong>ir armies, navies and air forces
<strong>of</strong>ten vary. NATO has recently coined a new term called Information and Knowledge Management
(IKM) and produced an IKM Vision/Concept that envisions transforming into a truly 21 st Century
Knowledge Centric Organization (KCO). The title <strong>of</strong> this paper is intended to solicit interest in a holistic
approach to knowledge transfer and information management that will bring toge<strong>the</strong>r both <strong>the</strong>
organizational and operational perspectives <strong>of</strong> KM. This paper introduces a holistic framework that
integrates both KM perspectives in order to ensure better decision-making throughout all levels <strong>of</strong> <strong>the</strong>
organization; ie, <strong>the</strong> simultaneous achievement <strong>of</strong> long-term ‘organizational’ objectives and short-term
‘operational’ requirements. This KM Institutional Effectiveness Framework forms <strong>the</strong> basis for <strong>the</strong> IKM
Blueprint for Action that is intended to provide <strong>the</strong> institutional leader with a big picture ‘tool’ for
implementing <strong>the</strong> tenets <strong>of</strong> KM. The ‘organizational’ component <strong>of</strong> <strong>the</strong> framework has a heavy topdown
organizational strategic change focus to it, while <strong>the</strong> ‘operational’ component draws upon
bottom-up business requirements necessary to maximize overall organizational and operational
efficiency and effectiveness (MOOEE). For this integrated KM framework and accompanying blueprint
to actually improve overall organizational performance in complex environments, leaders across <strong>the</strong>
organization will have to face <strong>the</strong> fact that institutional leadership is an inconvenient but vital truth <strong>of</strong>
<strong>the</strong> 21 st Century!
Keywords: IKM, decision-making, knowledge transfer, OD
1. Introduction
Knowledge Management (KM) is a term that ei<strong>the</strong>r attracts people or sends <strong>the</strong>m running in fear
because <strong>the</strong>y simply do not understand <strong>the</strong> term. When KM practitioners use terms like epistemology,
cognition, taxonomy, ontology and semantic web, we <strong>of</strong>ten lose <strong>the</strong> attention <strong>of</strong> our target audience
before we have begun to tell <strong>the</strong> KM story. Colleagues <strong>of</strong>ten lament that while <strong>the</strong>y are unable to
define KM, <strong>the</strong>y believe <strong>the</strong>y have been doing it for most <strong>of</strong> <strong>the</strong>ir pr<strong>of</strong>essional lives. The proposed KM
Institutional Effectiveness Framework (KM IEF) in this paper is <strong>the</strong> first step <strong>of</strong> <strong>the</strong> institutional
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leadership journey towards maximizing <strong>the</strong> organizational and operational effectiveness and efficiency
(MOOEE) for organizations over <strong>the</strong> long-term. This paper will:
demonstrate that knowledge management really is a two-sided coin,
articulate a specific public sector institutional perspective <strong>of</strong> knowledge management, and
present a KM Institutional Effectiveness Framework that embraces both yin-yang KM
perspectives while incorporating <strong>the</strong> multi-disciplinary tenets <strong>of</strong> KM.
1.1 Organizations equal people
Knowledge or justifiable true belief is a deeply human construct that mankind has been struggling to
define for millennia. For <strong>the</strong> purposes <strong>of</strong> this paper, ‘knowledge’ is considered to be what people
‘know’ and ‘information’ is how people ‘explain’ what <strong>the</strong>y know – one person’s knowledge is ano<strong>the</strong>r
person’s information. The term ‘management’ implies a systematic/disciplined approach to a given
discipline. For individuals life is a knowledge management journey where people manage <strong>the</strong>ir own
personal knowledge - but can we truly manage what o<strong>the</strong>r people know? This is <strong>the</strong> organizational
query <strong>of</strong> <strong>the</strong> century.
The most universal example <strong>of</strong> an organization is <strong>the</strong> family unit. Like any 21 st Century organization it
will mature over time; however, as a family unit it will have to interact with o<strong>the</strong>r families and o<strong>the</strong>r
organizations all <strong>of</strong> which will have <strong>the</strong>ir own levels <strong>of</strong> organizational maturity, perspectives,
perceptions and organizational goals. Time marches on, <strong>the</strong> world changes (socially and technically)
and <strong>the</strong> family unit as an organization grows, adapts, matures, evolves and hopefully finds success in
<strong>the</strong> achievement <strong>of</strong> its hopes and dreams… but hope is not a method! If <strong>the</strong> parents (family unit
‘institutional’ leaders) don’t make sense <strong>of</strong> <strong>the</strong> world, come up with a plan on how to act effectively in it
and actually guide its implementation, <strong>the</strong> family unit may be doomed to fail despite <strong>the</strong> best
intentions <strong>of</strong> all involved. Without institutional leadership, <strong>the</strong> parents/family leaders will end up
conducting lots <strong>of</strong> family/team meetings and when required seek advice from family subject matter
experts (SMEs) like grandparents who have lived <strong>the</strong> dream (free contractors) and pr<strong>of</strong>essional
<strong>the</strong>rapists who have studied cognitive science/human behaviour (not free contractors): such is <strong>the</strong>
fate <strong>of</strong> many organizations.
The fundamental KM principles are <strong>the</strong> same whe<strong>the</strong>r one is from <strong>the</strong> family, private, public or not-forpr<strong>of</strong>it
sectors. While perspectives vary according to <strong>the</strong> ‘sector’ within which we find ourselves, all
organizations do have <strong>the</strong> same ingredient, people! Traditional organizational <strong>the</strong>ory models
recognize that despite different sector perspectives, organizations still view <strong>the</strong>mselves in terms <strong>of</strong>
people: corporate leaders, business managers and workers. Managers/workers confront issues every
day that cause <strong>the</strong>m to wonder if <strong>the</strong>y are doing things right; ie, how can <strong>the</strong>y work more efficiently
(save resources, time and/or effort) in order to pursue o<strong>the</strong>r life goals. The vast majority <strong>of</strong> us possess
this perspective as we are all <strong>the</strong> centers <strong>of</strong> our own universe.
The forgotten question is, are we doing <strong>the</strong> right things? This is <strong>the</strong> domain <strong>of</strong> institutional leaders
who workers depend upon to ensure that <strong>the</strong>ir efforts are vertically aligned with organizational goals
and horizontally synchronized over time. Institutional leadership ensures that organizational and
operational perspectives are not stove-piped and are seen as two yin-yang halves that make up <strong>the</strong>
whole institution.
1.2 Public Sector KM
The world is ‘seen’ differently by those in <strong>the</strong> public sector. As a public/sworn servant and member <strong>of</strong>
<strong>the</strong> Canadian Department <strong>of</strong> National Defence/Canadian Forces (DND/CF), it is <strong>the</strong> maximizing <strong>of</strong>
organizational and operational effectiveness and efficiency (MOOEE) in support <strong>of</strong> operations that
drives me and not private sector shareholder value. Shareholder value is essentially replaced by
‘mission success’ or ‘stakeholder value’ as defined by <strong>the</strong> citizen, <strong>the</strong> government, <strong>the</strong> defence
department and <strong>the</strong> partners/suppliers/consumers <strong>of</strong> defence related knowledge and information.
1.3 Institutional Leadership
Institutional leadership is different from <strong>the</strong> leadership that we grew up with. “Leadership at <strong>the</strong> tactical
level is primarily about accomplishing missions and tasks through direct influence on o<strong>the</strong>rs – that is,
leading people. Institutional leadership, on <strong>the</strong> o<strong>the</strong>r hand, takes place predominantly in higher
headquarters at <strong>the</strong> operational, or <strong>the</strong>atre level and at <strong>the</strong> strategic level… [still] an institutional
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leader is not necessarily synonymous with a senior leader. Although senior leaders do have unique
responsibilities for leading <strong>the</strong> instruction, members in staff positions who assist and influence senior
<strong>of</strong>ficers, or liaise with external agencies on behalf <strong>of</strong> a senior appointment, also play a significant role
in leading <strong>the</strong> institution” (Leading <strong>the</strong> Institution 2007). Human behaviour draws us all to action and
while seeing <strong>the</strong> results <strong>of</strong> our labour may be rewarding, complex organizational issues are still not
easily understood nor readily apparent over <strong>the</strong> short-term. Institutional leaders must ensure <strong>the</strong>y
maintain <strong>the</strong> macro long-term sense-making view.
2. KM: Two Perspectives vs a Single Definition (?)
Models are <strong>of</strong>ten used as tools to explain what we know – to make tacit knowledge explicit. Still, <strong>the</strong>
KM heuristic that all models are wrong, some are useful must always be in our minds. David
Snowden has demystified KM for many with his simple statement that KM is nothing more than people
attempting to make sense <strong>of</strong> <strong>the</strong> world so that <strong>the</strong>y can act more effectively in it. Unfortunately, <strong>the</strong>
poor integration <strong>of</strong> <strong>the</strong>se two perspectives does result in organizational friction.
With well over 100 published definitions <strong>of</strong> knowledge management, it is no wonder <strong>the</strong> uninitiated
find reasons to stay away from KM. KM practitioners recognize that <strong>the</strong>re are three distinct
perspectives for KM: cognitive/knowledge science, business, and process/technology perspectives
(Dalkir 2005). These three perspectives can also be ‘explained’ by <strong>the</strong> IKM integration <strong>of</strong> people,
process and technology (Figure 1). Note that <strong>the</strong> three circles are constantly in motion depending on
<strong>the</strong> organization’s leadership, management and knowledge-worker response to <strong>the</strong> ever-changing
internal and external environments. These three ‘domains’ must be successfully integrated for
NATO’s Military Structure to successfully transform into a truly knowledge centric organization (IKM
Vision/Concept 2007).
Figure 1: PEOPLE, PROCESS & information technology Domains
In recent years, <strong>the</strong> predominant KM view within NATO has been a bottom-up ‘knowledge creation’
view as shown by Jacob Needleman’s knowledge pyramid (Figure 2). This model is useful for
describing how individual understanding is built upwards upon <strong>the</strong> foundations <strong>of</strong> data, information
and knowledge (Foley/Kivowitz 2001). Individual knowledge creation is achieved by aggregating and
contextualizing data to give it meaning (information), interpreted through experience (real or learned)
and <strong>the</strong> application <strong>of</strong> a cognitive process in order to have sufficient knowledge to make a ‘wise’
decision. It is why many <strong>of</strong> <strong>the</strong> military communities associated with Command, Control,
Communication, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) communities
gravitate towards <strong>the</strong> Needleman model.
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Figure 2: Needleman’s Knowledge Pyramid
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Most KM practitioners see two kinds <strong>of</strong> knowledge: <strong>the</strong> tacit in-your-head cognitive ‘knowings’; and <strong>the</strong>
explicit knowledge that we ‘explain’ through words, pictures and/or actions. I know more than I can
say and I can say more than I can write down. One <strong>of</strong> <strong>the</strong> best models for explaining knowledge
transfer in terms <strong>of</strong> tacit and explicit knowledge is <strong>the</strong> Socialization Equalization, Combination and
Internalization (SECI) model shown in Figure 3 (Nonaka/Takeuchi 1995). While multiple definitions for
KM do abound because <strong>of</strong> <strong>the</strong>ir direct connection to organizational business goals, NATO has defined
KM as “a multi-disciplined approach to achieving organizational objectives by making <strong>the</strong> best use <strong>of</strong>
information, expertise, insights and best practices” (IKM Directive 2008).
Figure 3: Tacit-Explicit SECI Transfer Model
3. NATO Information & Knowledge Management (IKM)
NATO’s 28 nations operate in an extremely complex political/security environment. In its quest to
become a Knowledge Centric Organization (KCO), NATO has defined KM as “a multi-disciplined
approach to achieving organizational objectives by making <strong>the</strong> best use <strong>of</strong> information, expertise,
insights and best practices” (IKM Directive 2008). Unfortunately, few orgs have articulated <strong>the</strong>ir
organizational objectives.
NATO does have a robust IM community who sees IM as “a discipline that directs and supports <strong>the</strong>
handling <strong>of</strong> information throughout its life-cycle [Figure 4] ensuring it becomes <strong>the</strong> right information in
<strong>the</strong> right form and <strong>of</strong> adequate quality to satisfy <strong>the</strong> demands <strong>of</strong> <strong>the</strong> organization” (NIMP 2008).
Lacking a NATO definition for IKM, a good ‘explanation’ is <strong>the</strong> one currently used to define <strong>the</strong>
NATO’s Network Centric Enabled Capability (NNEC): “The Alliance’s cognitive and technical ability to
federate <strong>the</strong> various components <strong>of</strong> <strong>the</strong> operational environment, from <strong>the</strong> strategic level (including
NATO HQ) down to <strong>the</strong> tactical levels, through a networking and information infrastructure” (NNEC
FAQ 2010). The NATO Joint Warfare Centre recently published an article on NATO IKM that provides
a good overview <strong>of</strong> <strong>the</strong> current IKM challenge in NATO (Three Swords 2011); unfortunately, it focuses
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<strong>the</strong> military mind on Needleman’s “staff <strong>of</strong>ficer” pyramid and not on <strong>the</strong> institutional leadership
perspective.
Figure 4: NATO Information Management Life-Cycle
3.1 Knowledge Management Champions
While <strong>the</strong>re is no NATO KM Champion, IM organizations and <strong>the</strong>ir champions have produced a robust
set <strong>of</strong> NATO IM doctrinal/policy documents, formal directives and formal training (NATO IKM Course
at NATO School, Oberammergau). NATO’s two Bi-SC headquarters (HQs) do have sub-organizations
to address KM sense-making (<strong>the</strong> identification <strong>of</strong> organizational objectives): <strong>the</strong> Strategic
Headquarters Allied Powers Europe (SHAPE) has an Organizational Development cell and <strong>the</strong>
Supreme Allied Command Transformation (SACT) has a Strategic Plans and Policy section. While
both <strong>of</strong> <strong>the</strong>se organizations support <strong>the</strong> institutional top-down leadership responsibility for sensemaking,
<strong>the</strong> associated ‘institutional leadership’ competencies are <strong>of</strong>ten not cascaded down into <strong>the</strong>
subordinate organizations; ie, each child organization should be taking <strong>the</strong> desired objectives <strong>of</strong> its
parent as an input into sense-making for its own strategic change management planning.
3.2 Knowledge Creation = Knowledge Development (KD)
Knowledge creation is <strong>the</strong> first part <strong>of</strong> <strong>the</strong> action-oriented Integrated KM Cycle (Figure 5) that focuses
on <strong>the</strong> operational requirements <strong>of</strong> <strong>the</strong> organization’s information holdings (Dalkir 2005). Newcomers
to NATO KM completely ignore <strong>the</strong> top-down KM definition (multi-disciplined approach to achieve
organizational objectives) and myopically focus on <strong>the</strong> bottom-up operational requirement.
Figure 5: Integrated KM Cycle
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Analysts turn information into knowledge and <strong>the</strong>n take that knowledge and disseminate/share it with
<strong>the</strong> next decision-maker in <strong>the</strong> decision-making chain. Using <strong>the</strong> INPUT-OUTPUT analysis model
(Figure 6), staff <strong>of</strong>ficers receive information via a variety <strong>of</strong> INPUTS and based on <strong>the</strong>ir personnel
knowledge, experience and cognition fuse those inputs into something that will add value in support <strong>of</strong>
decision-making. As soon as <strong>the</strong> analysts/staff <strong>of</strong>ficers attempt to explain <strong>the</strong>ir ‘findings’ <strong>the</strong> ‘tacit’
knowledge becomes ‘explicit’ information. This OUTPUT is information (albeit richer information) that
will now be made available to <strong>the</strong> decision-making value chain as ano<strong>the</strong>r INPUT.
Figure 6: INPUT-OUTPUT Analysis: The Black Box <strong>of</strong> Knowledge Creation
In <strong>the</strong> absence <strong>of</strong> any clear top-down NATO guidance with regards to KM, NATO’s intelligence
community (J2) are championing <strong>the</strong>ir own bottom-up requirements for KM and knowledge
development (KD). “The core function and objective <strong>of</strong> KD is to examine <strong>the</strong> complex operational
environment using all available sources <strong>of</strong> info, applying systems analysis techniques and considering
<strong>the</strong> inter-relationships and influences <strong>of</strong> all <strong>the</strong> PMESII (Political, Military, Economic, Social,
Informational, Infrastructural) domains to support <strong>the</strong> commander and staff with comprehensive
understanding for more effective operational planning, execution and assessment” (Three Swords
2010).
4. KM Institutional Effectiveness Framework (KM IEF)
Any KM Institutional Effectiveness Framework must address two perspectives: ‘organizational
cognition’; and, <strong>the</strong> individual ‘operational process/technical’ perspective. It is <strong>the</strong> institutional
leadership that must meld <strong>the</strong> two toge<strong>the</strong>r. The ‘organizational objectives’ perspective draws it
elements from <strong>the</strong> Canadian Forces Effectiveness Framework developed by <strong>the</strong> Canadian Defence
Academy that really espouses <strong>the</strong> importance <strong>of</strong> institutional leadership, strategy/change
management and systems thinking. The ‘operational process/technical’ perspective draws some <strong>of</strong> its
elements from <strong>the</strong> integration <strong>of</strong> <strong>the</strong> people, processes and technology domains identified in <strong>the</strong>
APQC approach to KM (O’Dell 2004). The Kaplan & Norton (K&N) Balanced Scorecard Strategy
Management System serves as an excellent integrator <strong>of</strong> <strong>the</strong> two, because it also incorporates <strong>the</strong>
same underlying foundational perspectives <strong>of</strong> employee value, internal integration and external
adaptability (K&N 2001); specifically:
Mission/Stakeholder Success,
External Stakeholder/Customer Focus,
Internal Input-Output Processes/Operational Focus, and
Learning & Growth/Employee Focus.
This KM Institutional Effectiveness Framework (Figure 7) embraces several organizational
development disciplines <strong>the</strong>reby contributing to a more holistic view <strong>of</strong> <strong>the</strong> KM problem set. “From an
early age, we’re taught to break apart problems in order to make complex tasks and subjects easier to
deal with. But this creates a bigger problem we lose <strong>the</strong> ability to see <strong>the</strong> consequences <strong>of</strong> our
actions, and we lose a sense <strong>of</strong> connection to a larger whole” (Senge 1999).
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Figure 7: KM Institutional Effectiveness Framework (KM IEF)
4.1 Institutional Knowledge Development (IKD)
The left side <strong>of</strong> <strong>the</strong> KM IEF embraces <strong>the</strong> Institutional Knowledge Development (IKD) domain that
focuses on top-down sense-making where strategic concepts are translated into organizational
objectives and desired effects. This in turn creates <strong>the</strong> organizational context where both <strong>the</strong> parent
and child organizations must operate; unfortunately, few military leaders are taught this skill and <strong>the</strong>y
reach out to heroic amateurs - staff <strong>of</strong>ficers who draw upon <strong>the</strong>ir own operational/tactical training and
planning experience. This is similar to <strong>the</strong> Effects Based Approach to Operations (EBAO) - “even
though <strong>the</strong> term ‘EBAO’ is no longer being used, <strong>the</strong> core functions remain” (Three Swords 2010).
Top-down sense-making does require worker engagement. “We’ve all met managers who try to keep
<strong>the</strong>ir employees focused on <strong>the</strong> work and let some strategic planning group think about <strong>the</strong> external,
competitive world. But <strong>the</strong> attention psychology literature suggests that employees will pay more
attention to <strong>the</strong>ir work if <strong>the</strong>y understand it in <strong>the</strong> context <strong>of</strong> <strong>the</strong> competitive world” (Davenport/Beck
2001).
4.2 Knowledge is Contextual
Truly adept leaders know how to identify <strong>the</strong> context within which <strong>the</strong>y are working and how to change
<strong>the</strong>ir behaviour to match. Institutional leadership implies a contextual perspective that is foreign to
many military leaders because <strong>the</strong> operational/tactical ‘doing’ is so entrenched into <strong>the</strong> military
psyche. David Snowden’s Cynefin Framework (Figure 8) shows complexity as a way <strong>of</strong> thinking
about <strong>the</strong> world in that different contexts make knowledge simple, complicated, complex or chaotic
(Snowden/Boone 2007). Simple knowledge is characterized by stability where <strong>the</strong> cause-and-effect
relationships are reasonably evident: sense-categorize-respond. Those working in this context
typically reside in <strong>the</strong> ‘worker’ domain looking to higher level guidance for section Terms <strong>of</strong> Reference
(TOR), individual job descriptions and job aides.
Complicated ‘knowledge’ typically has more than one response option and lends itself more to
systems thinking where <strong>the</strong> cause-and-effect relationships are not so evident; however, given enough
time, people and money any issue can be resolved: decision-makers must sense-analyse-respond.
Those working in this context typically reside in <strong>the</strong> ‘management’ domain where experts and heroic
amateurs (staff <strong>of</strong>ficers) look to better integrate <strong>the</strong> internal stove-piped functional perspectives <strong>of</strong> <strong>the</strong>
organization and/or external stakeholder organizations.
A key ‘complexity’ characteristic is that <strong>the</strong> interactions <strong>of</strong> large numbers <strong>of</strong> interacting elements are
not linear so minor changes can produce disproportionately major consequences; ie, “most situations
and decisions in organizations are complex because some major change…introduces unpredictability
and flux” (Snowden/Boone 2007). This means that ‘leaders’ must fight <strong>the</strong> urge to ‘solve <strong>the</strong> symptom’
and wait for <strong>the</strong> answer to <strong>the</strong> problem to make itself apparent. They must be prepared to probesense-respond.
Most organizations that are unable to effectively deal with complexity seem to
continuously bounce back and forth between <strong>the</strong> complicated context and <strong>the</strong> chaotic context at <strong>the</strong>
whims <strong>of</strong> changing internal and external environments. “Leaders who try to impose order in a
complex context will fail, but those who set <strong>the</strong> stage, step back a bit, allow patterns to emerge, and
determine which ones are desirable will succeed” (Snowden/Boone 2007).
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Figure 8: Cynefin Framework
Chris Blodgett
5. Knowledge transfer frameworks (KTFs)
When it comes to KM sense-making, it is <strong>the</strong> Knowledge Transfer Frameworks (KTFs) that can
actually be ‘managed’; ie, KTF disciplines like strategy, change, risk, communication and human
resource management. The challenge for institutional leaders at all levels is to determine which KTFs
are best suited for <strong>the</strong>ir organization.
5.1 Strategy management
While many different schools <strong>of</strong> strategy exist in management literature, <strong>the</strong>re is no single prevailing
body <strong>of</strong> knowledge in this field. Still, it is generally accepted that organizations have three levels <strong>of</strong>
strategy: <strong>the</strong> corporate strategy, <strong>the</strong> business/divisional strategies and <strong>the</strong> strategy within <strong>the</strong>
businesses (Hrebiniak, 2005). For most military personnel, strategy is associated with a plan or series
<strong>of</strong> actions designed to achieve a specific objective. But what if your goal is to maximize operational
and organizational efficiency and effectiveness (MOOEE)?
The inconvenient truth is that institutional leaders need to ‘make <strong>the</strong> time’ to acquire <strong>the</strong>
organizational competency to formulate strategy at all levels. The K&N Balanced Scorecard
approach <strong>of</strong>fers a systems approach to integrating common tacit goals with sound performance
management. It is a descriptive (not prescriptive) framework that embraces quality initiatives like ISO
and Six-Sigma. The Balanced Scorecard design process builds upon <strong>the</strong> premise <strong>of</strong> “strategy as a
hypo<strong>the</strong>sis, where <strong>the</strong> key for implementing strategy is to have everyone in <strong>the</strong> organization
understand <strong>the</strong> underlying hypo<strong>the</strong>ses, align resources with <strong>the</strong> hypo<strong>the</strong>ses, test <strong>the</strong> hypo<strong>the</strong>ses
continually and adapt as required in real time” (K&N 2001).
In NATO <strong>the</strong>re are at least two organizations that employ <strong>the</strong> K&N Balanced Scorecard system: <strong>the</strong>
Supreme Allied Command Transformation (SACT) Headquarters and <strong>the</strong> NATO Consultation,
Command and Control Agency (NC3A). The NC3A two-year strategic plan uses a strategic
methodology similar to <strong>the</strong> KM IEF introduced in this paper; ie, a concurrent strategic top-down and
bottom-up operational approach (NC3A Strategic Plan 2009).
5.2 Change management
Strategy may be important, but execution is everything! A study <strong>of</strong> almost 300 managers revealed that
<strong>the</strong> ability to execute strategy was more important than <strong>the</strong> quality <strong>of</strong> <strong>the</strong> strategy itself (Kaplan/Norton
2001). Execution in simple terms represents a disciplined process or logical set <strong>of</strong> connected activities
that enables an organization to take a strategy and make it work. Not only is strategy execution
critical for success, it is more difficult to do than <strong>the</strong> actual task <strong>of</strong> developing <strong>the</strong> strategy (Hrebiniak
2005). The institutional leader will require formal training in this discipline.
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5.3 Communication management
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Communication management is not just about marketing <strong>the</strong> organization’s strategy but
communicating <strong>the</strong> cultural nuances that are necessary to implement it. While strategic
communication plans tend to have an external stakeholder focus, <strong>the</strong>y should also have an internal
component. Internal communications are perhaps <strong>the</strong> one single vehicle for ensuring that
commander’s intent is realized and internalized by <strong>the</strong> people that make up <strong>the</strong> larger organizational
team. The commander’s critical information requirements (CCIRs) for change must be clearly
articulated and <strong>the</strong> supporting internal integration processes put in place to balance virtual and faceto-face
communication exchanges. Organizations need to understand how people collaborate, share
what <strong>the</strong>y know and how <strong>the</strong>y intend to balance push-pull information requirements.
5.4 Risk Management and Organizational Agility
Risk management is all about uncertainty; ie, balancing <strong>the</strong> likelihood <strong>of</strong> something happening (or not
happening) and <strong>the</strong> resultant negative impact on <strong>the</strong> organization, its people and external
stakeholders. Risk assessments require informed cognitive assessments that are based on
assumptions about intangibles like individual human behaviour, human relations and <strong>the</strong> future. The
quantum leap in 21 st Century complexity is why organizational sense-making KTFs associated with
strategy, change, communication and organizational performance management disciplines are
becoming <strong>the</strong> new skill sets for <strong>the</strong> institutional leader.
One way to reduce risk and <strong>the</strong> inherent stress <strong>of</strong> being distracted from our normal duties is to reduce
confusion by reducing <strong>the</strong> organizational ‘noise’; ie, what we perceive to be complex may simply be a
collection <strong>of</strong> complicated and simple ‘knowledge artefacts’ that we have not yet sorted out. “The lack
<strong>of</strong> agility inherent in Industrial Age organization is more than simply a result <strong>of</strong> a systemic lack <strong>of</strong>
interoperability, although a lack <strong>of</strong> interoperability significantly impacts <strong>the</strong> agility <strong>of</strong> an organization.
This lack <strong>of</strong> agility stems directly from an Industrial Age belief in optimization and centralized planning”
(Albert/Hayes 2004).
5.5 Human Resource (HR) Management
For many, HR management is associated with <strong>the</strong> transactional stovepipe disciplines like job analysis,
staffing, health/safety, compensation/benefits, employee relations and administrative support. While
all <strong>of</strong> <strong>the</strong>se are important, 21 st Century institutions are demanding much more from <strong>the</strong> HR leadership
in terms <strong>of</strong> an overall HR strategy, change management execution and effective strategic
communication with employees. HR needs to evolve beyond its 1900s transactional role.
Given that most military organizations have a three-year annual posting cycle (100% turnover in
military personnel every three years), good institutional leaders will have to make sure that <strong>the</strong>y have
some sort <strong>of</strong> KM IEF in place to help <strong>the</strong> organization deal with complexity, staff turnover and
business continuity. Statements like ‘our employees are our most valuable asset’ roll easily <strong>of</strong>f <strong>the</strong>
tongue, however, many organizations could not provide a formal cascaded HR strategy if asked to do
so. Integrating people, processes and technology is a human factor endeavour that will require
specific education and training when it comes to 21 st Century collaboration.
6. From KM Framework to IKM Blueprint for Action (BA)
The right side <strong>of</strong> <strong>the</strong> KM IEF is action focused on <strong>the</strong> operational information and knowledge
exchange requirements. It is <strong>the</strong> defining <strong>of</strong> an organizational ‘architecture’ that refers to <strong>the</strong> activities
<strong>of</strong> designing and maintaining a representation <strong>of</strong> components <strong>of</strong> a business (organization, processes,
information, technology) and <strong>the</strong>ir relationships in order to understand where, when and why
information is required (NIMP 2008). “While <strong>the</strong>re are plenty <strong>of</strong> people who treat knowledge
management as a religion, real knowledge management is practical and action oriented, not
ideological and <strong>the</strong>oretical. If done right, it produces bottom-line results – always a sure way to
guarantee sustainability” (O’Dell/Grayson, 1998). The premise for this action oriented IKM BA is that
organizations behave as a set <strong>of</strong> adaptive systems, <strong>the</strong> last <strong>of</strong> which is <strong>the</strong> control mechanism known
as <strong>the</strong> ‘management system’. It is management that interprets and reacts to internal and external
feedback so that <strong>the</strong> organization remains in balance with <strong>the</strong> external environment. The intelligent
use <strong>of</strong> this feedback is what has become known as Peter Senge’s ‘learning organization’
(Rummler/Brache 1995). The role <strong>of</strong> <strong>the</strong> institutional leader is not to manage <strong>the</strong> IKM BA for <strong>the</strong>ir
organization, but to make sure that <strong>the</strong>ir organization has one. The following ‘levels’ constitute <strong>the</strong>
blueprint elements for <strong>the</strong> IKM BA:
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Chris Blodgett
IKM Level 1: Information Management Planning
IKM Level 2: Internal Integration Planning
IKM Level 3: External Adaptability Planning
IKM Level 4: Continuous Improvement Planning
6.1 IKM Level 1: Information Management (IM) Planning
The Information Age has clearly taken over from <strong>the</strong> Industrial Age where information has now
become <strong>the</strong> primary resource <strong>of</strong> <strong>the</strong> new age for all organizations. Efficient and effective IM is now a
critical success factor for achieving organizational goals and objectives. The purpose <strong>of</strong> IM planning
is to produce and communicate effective workable plans necessary to effectively and efficiently
manage information. ‘Information needs’ must be determined as part <strong>of</strong> <strong>the</strong> planning and architecture
processes in order to support <strong>the</strong> organizations intended activities. CCIRs are essential to this
endeavour because no matter what level <strong>of</strong> maturity a particular organization may possess, it must
ensure <strong>the</strong> provision <strong>of</strong> a safe work environment and <strong>the</strong> efficient and effective conduct <strong>of</strong> <strong>the</strong> mission.
Successful IM implementation will require <strong>the</strong> coordination <strong>of</strong> multiple parallel work streams, many <strong>of</strong>
which will cross traditional functional and administrative boundaries. Here are a few areas that an IM
Plan should address:
IM policy framework,
IM structures and responsibilities architecture,
Information needs assessment, and
Retention, disposition and disposal <strong>of</strong> records and documents.
6.2 IKM Level 2: Internal Integration
IKM Internal Integration planning provides a systematic and disciplined approach to managing <strong>the</strong>
organizational architecture and is where <strong>the</strong> leaders and managers have real control to affect change.
It is <strong>the</strong> organizational ‘space’ where <strong>the</strong> leaders must come toge<strong>the</strong>r to balance organizational
objectives with operational requirements through sound hands-on leadership. This is difficult to do
because we all live and work in an attention economy where <strong>the</strong>re is a plentiful supply <strong>of</strong> information
but a shortage <strong>of</strong> human attention. “Understanding and managing attention is now <strong>the</strong> single most
important determinant <strong>of</strong> business success” (Davenport/Beck 2001). Choosing a particular maturity
model will help frame <strong>the</strong> internal integration approach taken. While <strong>the</strong>re are many to choose from,
<strong>the</strong> one relevant to <strong>the</strong> military command/control culture is <strong>the</strong> NATO Network Centric Enabled
Capability (NNEC) Command and Control Maturity Model (N2C2M2). The four main levels in order <strong>of</strong>
increasing levels <strong>of</strong> organizational maturity are: de-confliction, coordination, collaboration and selfsynchronization
(Alberts/Huber/M<strong>of</strong>fat 2010).
Internal Integration truly requires hands-on leadership because today’s generation <strong>of</strong> leaders will be
tempted to fall back to <strong>the</strong> traditional Industrial Age hierarchical command & control management
styles. The NNEC tenets foresee a NATO robustly networked force with improved information sharing
and collaboration, enhanced quality <strong>of</strong> information and shared situational awareness which enable
collaboration and self-synchronization and <strong>the</strong>reby increased mission effectiveness. “In a robust
network, <strong>the</strong> burden <strong>of</strong> ensuring proper distribution is shifted toward <strong>the</strong> users <strong>of</strong> information, who
must be empowered through training and tools to know what information is relevant to <strong>the</strong>ir situation,
where <strong>the</strong>y can find it, and how value added services can be used to support <strong>the</strong>m…groups that have
worked toge<strong>the</strong>r over time and across situations prove to be much faster without having to sacrifice
decision quality” (Alberts/Hayes 2004). Having a robust governance architecture in place should
actually make <strong>the</strong> managing <strong>of</strong> <strong>the</strong> whitespaces on <strong>the</strong> org chart much simpler for leaders and
managers at all levels. Here are a few issues that an Internal Integration Plan should address:
internal governance framework and business battle rhythm,
policy framework and standards,
maintenance <strong>of</strong> good organizational situational awareness, and
maintenance <strong>of</strong> a common operating picture with regards to <strong>the</strong> changing task priorities.
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6.3 IKM Level 3: External Adaptability
Chris Blodgett
External Adaptability also requires good situational awareness and common operating picture to
ensure that organizations understand <strong>the</strong> ‘value propositions’ for all external stakeholders. “The core
<strong>of</strong> any business strategy – connecting a company’s internal processes to improved outcomes with
customers – is <strong>the</strong> ‘value proposition’ delivered to <strong>the</strong> customer. … [Kaplan & Norton] have found that
approximately 75% <strong>of</strong> executive teams do not have a clear consensus around <strong>the</strong> customer value
proposition. The development <strong>of</strong> this layer <strong>of</strong> <strong>the</strong> strategy map forces <strong>the</strong> team to clarify its
understanding <strong>of</strong> <strong>the</strong> customer and is one <strong>of</strong> <strong>the</strong> most valuable parts <strong>of</strong> a Balanced Scorecard design
process” (K&N 2001). It is important to remember that <strong>the</strong> external environment is beyond <strong>the</strong> control
<strong>of</strong> your organization and at best you may be able to influence external stakeholders in some way.
Here are a few issues to consider when building external stakeholder connectivity plans:
Is your own business battle rhythm linked to <strong>the</strong> business battle rhythms <strong>of</strong> <strong>the</strong> external
stakeholders?
What are <strong>the</strong> external stakeholder expectations in terms <strong>of</strong> work flow and turn around time?
Who is responsible for <strong>the</strong> ‘connectivity’ issues with <strong>the</strong> stakeholder organizations? and
Do Subject Matter Expertise Locator Systems (SMELS) exist?
6.4 IKM Level 4: Continuous Improvement
This final element <strong>of</strong> <strong>the</strong> IKM BA promotes self-synchronization through <strong>the</strong> development <strong>of</strong> a culture
<strong>of</strong> continuous improvement and is drawn directly from words used in <strong>the</strong> NATO KM definition “… by
making <strong>the</strong> best use <strong>of</strong>… insights and best practices.” To that end, <strong>the</strong>re are three specific
components that should be included in <strong>the</strong> development <strong>of</strong> any continuous improvement plan: lessons
learned, best practices and innovation. The challenge for all organizations is to continuously work at
narrowing <strong>the</strong> Performance-Knowledge Gap (Figure 9). The capability-performance gap is <strong>the</strong>
difference between what your organization is able to do and what it must do. From an ‘organizational
knowledge’ perspective <strong>the</strong>re is an accompanying knowledge gap; ie, <strong>the</strong> difference between what we
know (what we can do) and what we must know (what we must do).
Figure 9: The Performance-Knowledge Gap
As an organization, we try to narrow this knowledge-performance gap by considering <strong>the</strong> following:
finding and making explicit existing internal knowledge and experience,
capturing Lessons Learned,
seeking out Best Practices through informal and formal opportunities, and
promoting innovative approaches for emerging internal and external issues.
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7. Conclusion
Chris Blodgett
Given <strong>the</strong> multi-disciplinary nature <strong>of</strong> KM, it is important to remind ourselves that <strong>the</strong>re is no single KM
right answer; however, <strong>the</strong>re are specific organizational disciplines identified in this paper that can
provide <strong>the</strong> institutional leadership with <strong>the</strong> proper frameworks for enhanced decision-making that will
maximize organizational and operational effectiveness and efficiency (MOOEE). The aim <strong>of</strong> this
paper was to assert that KM is all about <strong>the</strong> institutional leadership ability to balance and integrate <strong>the</strong>
competing yin-yang KM perspectives <strong>of</strong> organizational and operational life. Framing knowledge
management into its two perspectives <strong>of</strong> organizational sense-making (are we doing <strong>the</strong> right things?)
and organizational action (are we doing things right?) will prove useful to <strong>the</strong> 21st Century institutional
leaders wherever <strong>the</strong>y may sit in <strong>the</strong>ir organization. While some KM practitioners may argue that a
particular KM sub-component belongs within a different element <strong>of</strong> <strong>the</strong> KM IEF, <strong>the</strong> point is not about
<strong>the</strong> positioning <strong>of</strong> a KM issue within <strong>the</strong> Framework but that fact that it is actually captured in <strong>the</strong>
blueprint for action. Wherever it fits best in <strong>the</strong> IKM BA is up to you and your organization - capture it
and move out! For <strong>the</strong> institutional leader <strong>the</strong> world is a complex place and organizational success in
terms <strong>of</strong> sustained organizational change will depend on <strong>the</strong> institutional leadership ability to integrate
and embrace <strong>the</strong> full multi-disciplinary nature <strong>of</strong> KM… an inconvenient, but vital truth <strong>of</strong> <strong>the</strong> 21st
Century!
References
Alberts, D.S & Hayes, R.E., 2004. Power to <strong>the</strong> Edge: Command & Control in <strong>the</strong> Information Age, Information
Age Transformation Series, [online]. Available at: www.dodccrp.org/files/Alberts_Power.pdf [Accessed 22
Mar 11]
Alberts, D.S., Huber, R.K. & M<strong>of</strong>fat, J., 2010. NATO NEC C2 Maturity Model, CCRP Publication Series, [online]
Available at: www.dodccrp.org/files/N2C2M2_web_optimized.pdf [22 Mar 11]
Atkinson, S.R. & M<strong>of</strong>fat, J., 2005. The Agile Organization, Information Age Transformation Series, [online]
Available at: www.dodccrp.org/files/Atkinson_Agile.pdf [Accessed 22 Mar 11]
Canadian Defence Academy , 2008. Institutional Leadership in <strong>the</strong> Canadian Forces. [online] Available at:
http://www.cda-acd.forces.gc.ca/cfli-ilfc/lea/index-eng.asp [Accessed 9 Apr 11]
Dalkir, K. (2005) Knowledge Management in Theory & Practice, Elsevier Butterworth-Heinemann, Oxford.
Davenport, T. & Beck, J. (2001) The Attention Economy, Harvard Business School Press, Boston
Foley, K. & Kivowitz, B. (2001) The Manager's Pocket Guide to Knowledge Management, Human Resource
Development Press, Amherst.
Hrebiniak, L.G. (2005) Making Strategy Work: Leading Effective Execution and Change, Wharton School
Publishing, Upper Saddle River.
Hutson, P., 2011. Information & Knowledge Management: Framing <strong>the</strong> Challenge. The Three Swords, Issue #
19, pp.46-49.
Johnson, T.F., 2010. The Comprehensive Approach & <strong>the</strong> Death <strong>of</strong> <strong>the</strong> Term “EBAO”. The Three Swords, Issue
# 17, pp. 8-13.
Kaplan, R.S. and Norton, D.P. (2001) The Strategy Focused Organization: How Balanced Scorecard Companies
Thrive in <strong>the</strong> New Business Environment, Harvard Business School Press, Watertown.
NATO Bi-SC IKM Directive 25-1 (2008)
NATO Bi-SC IKM Vision & Concept (2007)
NATO Information Management Policy – NIMP (2008)
NATO NC3A Strategic Plan 2010-2012 (2009), NATO C3 Agency, [online] Available at:
http://www.nc3a.nato.int/SiteCollectionDocuments/NC3A_Strategic_Plan_2010-2012.pdf [Accessed
22 Jan 2011]
NATO Networked Enabled Capability (NNEC). [online] Available at:
http://www.nato.int/cps/en/natolive/topics_54644.htm [Accessed 29 May 2011]
NATO Networked Enabled Capability (NNEC) Frequently Asked Questions (FAQ), Allied Command
Transformation (ACT) March, 2010
Nonaka, Ikujiro & Takeuchi, Hirotaka (1995) The Knowledge-Creating Company, Oxford University Press, New
York.
O’Dell, C. (2004) The Executive's Role in Knowledge Management, American Productivity and Quality Centre,
Houston.
O’Dell, C. & Grayson, C.J. (1998) If Only We Knew What We Know: The Transfer <strong>of</strong> Internal Knowledge and Best
Practice, The Free Press, New York.
Rummler, G.A & Brache, A.P. (1995) Improving Performance: How to Manage <strong>the</strong> White Space in <strong>the</strong>
Organization Chart, Jossey-Bass, San Francisco.
Senge, P.M., Kleiner, A., Roberts, C., Roth, G., Ross, R., and Smith, B. (1999) The Dance <strong>of</strong> Change: The
Challenges to Sustaining Momentum in Learning Organizations, Random House, New York.
Snowden, D.J. and Boone, M.E., 2007. Leader's Framework for Decision Making, Harvard Business Review,
[online] Available at: www.mpiweb.org/CMS/uploadedFiles/Article%20for%20Marketing%20-
%20Mary%20Boone.pdf [Accessed 8 May 11]
1102

Can Knowledge Management Survive Without Information
Technologies?
Stefanie Dannemann
Communication and Knowledge Management Pr<strong>of</strong>essional, Nyon, Switzerland
stefanie.dannemann@gmail.com
Abstract: The objective <strong>of</strong> Knowledge Management (KM) is to facilitate and encourage an exchange <strong>of</strong>
experiences mainly connected to activities which involve people, process and technology. What would happen if
this exchange was only limited to people and processes and without or with limited technology to facilitate
knowledge exchange?. Knowledge processes usually evolve from inside <strong>the</strong> human who is accustomed to
specific cultural behavior aimed at increasing <strong>the</strong> use <strong>of</strong> information across a group or community. Recently,
<strong>the</strong>re has been an increasing tendency to use social media and new technologies in human information
exchange. This lures us into forgetting that knowledge is something that we “construct” first in our mind and only
later share in conversations or in written statements. Would it be easier for an organization to encourage its
people to share <strong>the</strong>ir knowledge if it first focused on <strong>the</strong> human exchanges? What if it intentionally put less
emphasis on searching and using <strong>the</strong> best kinds <strong>of</strong> trends in technology for knowledge exchange, which would
best match its organizational culture? What are <strong>the</strong> factors which make people share and trigger <strong>the</strong>m to
exchange knowledge, to change peoples’ disinclinations to enthusiasm for communication? These can be
external influences and enticements, such as remuneration, recognition and job promotion. But <strong>the</strong> most
successful factor to initiate a knowledge sharing culture is to simply make people comfortable with sharing <strong>the</strong>ir
knowledge with o<strong>the</strong>rs; imposing a new technology and tools might not always be <strong>the</strong> best choice for this. How
do we achieve this knowledge sharing?. With this paper, I will review various ways <strong>of</strong> stimulating interest and
comfort to share knowledge face-to-face, and <strong>of</strong> ensuring that knowledge sharing is understood to be a part <strong>of</strong> an
employee’s responsibilities. Additionally, I will have a more detailed look at organizational techniques and
methods that can encourage a knowledge sharing culture. It can start out with a small gesture which, in <strong>the</strong> end,
can have a huge impact on <strong>the</strong> organizational culture, provided that <strong>the</strong>re is buy-in not only from management
but also from your peers.
Keywords: knowledge, information technology, face-to-face, organizational culture, recognition
1. Introductions
Today I read a friend’s quote in Facebook: “Sometimes you happen to stumble across people in your
life who are truly excellent at what <strong>the</strong>y do. And by simply doing <strong>the</strong>ir job and sharing <strong>the</strong>ir knowledge,
<strong>the</strong>y manage to inspire and affect your life in a very positive way…”. It was an effective description <strong>of</strong>
“sharing knowledge” or knowledge management in general. The literature lacks a core explanation for
<strong>the</strong> term ‘knowledge’ and ‘knowledge management’, and this was it.
Generally, articles state ei<strong>the</strong>r that <strong>the</strong>re are too many good definitions <strong>of</strong> Knowledge Management
(Knowledge Management, n.d.), or point out that disciplines such as social sciences, business
administration or psychology leave <strong>the</strong> topic <strong>of</strong> KM still unexplored (Bode, 2006). Only philosophy has
explored <strong>the</strong> term for hundreds <strong>of</strong> years, producing several disputes and philosophical discourses.
Knowledge is described as a justified and real belief and as all cognitive skills and abilities which
individuals apply to solve problems and tasks. Plato (in Bode, 2006) states: “A good decision is based
on knowledge and not on numbers”. Therefore, management and proper handling <strong>of</strong> knowledge are
essential for making good decisions in private and pr<strong>of</strong>essional lives. Knowledge should be used to
facilitate and encourage an exchange <strong>of</strong> experiences and lessons learnt from activities that involve
people, process and technology in an organization. The main objectives <strong>of</strong> Knowledge Management
are to facilitate this exchange process and to evaluate various methods and approaches to a
successful generation, exchange and retention <strong>of</strong> knowledge. Increasingly, companies and non-pr<strong>of</strong>it
organizations realize its importance for maintaining an effective and innovative workplace. Resources
get spent, dedicated to internal KM efforts, <strong>of</strong>ten as a part <strong>of</strong> <strong>the</strong> 'business strategy', 'information
technology', or 'human resource management' departments. (Addicott, McGivern, and Ferlie, 2006).
Sometimes expertise is hired from consultants who advise on strategic and implementation decisions
related to KM.
With <strong>the</strong> fast development <strong>of</strong> technology and <strong>the</strong> creation <strong>of</strong> Web 2.0, Knowledge Management and
its concept have changed to emphasize people’s participation and technology use. This evolution has
been termed Enterprise 2.0 (McAfee, 2006). It has been questioned (Lakhani and McAfee, 2007)
whe<strong>the</strong>r this indeed is <strong>the</strong> future <strong>of</strong> knowledge management (Davenport, 2008) and whe<strong>the</strong>r KM will
become “technologized” as a discipline.
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Stefanie Dannemann
However, what would happen, if <strong>the</strong> elements <strong>of</strong> knowledge exchange were limited to people and
organizational processes - and <strong>the</strong>re was no or limited technology available to facilitate this
exchange?
We live in <strong>the</strong> era <strong>of</strong> communication and information technology (IT). People are used to receiving
instant information, are up-to-date with <strong>the</strong> latest tools and gadgets, and know how to use <strong>the</strong>se tools
to process information. Knowledge processes usually evolve from inside <strong>the</strong> human who is
accustomed to specific cultural behavior, which aims to increase use <strong>of</strong> information across a group or
community. Recently, <strong>the</strong>re has been an increasing tendency to use social media and new
technologies in human information exchange. This lures us into forgetting that knowledge is
something that we “construct” first in our mind and only later share in conversations or written
statements. We tell "stories", with a specific skill where <strong>the</strong> narrator draws <strong>the</strong> audience into a good
story. Izuzi (2007) has blogged that “<strong>the</strong> way a story is told counts. Previous knowledge and
internalization ability in <strong>the</strong> audience counts. Not to mention <strong>the</strong> action taken or lesson learned from
<strong>the</strong> story itself.”
Would it be easier to implement Knowledge Management practices and culture in an organization if
we first focused on <strong>the</strong> human exchanges and intentionally put less emphasis on <strong>the</strong> search and use
<strong>of</strong> <strong>the</strong> best technology for knowledge exchange? Or would we first focus on finding <strong>the</strong> “right”
technology and <strong>the</strong>n train people to use it, facilitating <strong>the</strong> change in communication culture? What are
<strong>the</strong> factors which make people share and trigger <strong>the</strong>m to exchange knowledge, to change peoples’
disinclinations to enthusiasm for communication? In my opinion, <strong>the</strong>se can be external influences and
enticements, such as remuneration, recognition and job promotion. But <strong>the</strong> most successful factor to
initiate a knowledge sharing culture is to simply make people comfortable with sharing <strong>the</strong>ir
knowledge with o<strong>the</strong>rs, regardless <strong>of</strong> technology. I would even argue that imposing a new technology
and tools might not always be <strong>the</strong> best choice as a primary tool to achieve knowledge sharing.
There are several knowledge management models that describe <strong>the</strong> process <strong>of</strong> managing <strong>the</strong>
knowledge flow in an organization. Some refer to knowledge as being mechanistic and socially
constructed (Nonaka and Hedlund, 1993), some see knowledge as a strategic source <strong>of</strong> competitive
advantage (Kogut and Zander, 1996), and o<strong>the</strong>rs describe it as a framework for leadership,
organizational structure, and IT infrastructure. They see it as a learning process which is an important
foundation for knowledge management strategy in an organization. (Stankosky and Baldanza, 2001).
While Knowledge Management models have evolved over time, it is still difficult to translate KM into
practical activities to be executed in an organization.
Nonaka and Takeuchi (1995) state that <strong>the</strong> main element <strong>of</strong> knowledge transfer is <strong>the</strong> ‘implicit’
knowledge <strong>of</strong> a person, and its processing and expression into ‘explicit’ knowledge that makes it
accessible and useful to o<strong>the</strong>rs. The authors point out that western philosophy is focusing too hard
on <strong>the</strong> management <strong>of</strong> external knowledge which can be measured and articulated, while personal
convictions, values and intuitions are ignored.
None<strong>the</strong>less, all KM models show that communication ultimately depends on human contact and
interaction, regardless <strong>of</strong> technology in use. Any communication process needs at least one “sender”
and one “receiver” and at least one “channel or medium”.
So what are various ways <strong>of</strong> stimulating interest and comfort with sharing knowledge face-to-face, and
how can we ensure that knowledge sharing becomes a part <strong>of</strong> an employee’s own pr<strong>of</strong>essional
responsibilities in <strong>the</strong> workplace? First <strong>of</strong> all, <strong>the</strong>re is a need for communal trust, respect and honesty.
The best way to build <strong>the</strong>se values between two or more people in a workplace is to cultivate a
regular exchange <strong>of</strong> opinions, even confrontations, in which problems, situations or events can be
discussed. These can be in a form <strong>of</strong> staff development dialogues, regular team meetings or c<strong>of</strong>fee
meetings, as long as this exchange also includes an update on issues from all participants.
Knowledge sharing should be a part <strong>of</strong> <strong>the</strong> working culture and collaborative problem-solving is a
building block that should be encouraged.
Several techniques can be applied to keep <strong>the</strong> exchange ongoing in an organization, department or
team. Two I find useful are “wall <strong>of</strong> problems” and “usability testing”. Both encourage involvement <strong>of</strong>
all participants. The “wall <strong>of</strong> problems” invites people to share problems and <strong>of</strong>fer <strong>the</strong>ir skills in
collective troubleshooting. Problems are posted on a wall (poster) and team members are asked to
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Stefanie Dannemann
help and share ideas/knowledge to solve <strong>the</strong>m. “Usability testing” comes from <strong>the</strong> IT pr<strong>of</strong>ession;
people are invited to test a (any) product and to collect feedback and suggestions. I have used both
techniques to keep up with changing business environment and stakeholder demands and to
encourage team building bonds within my team. In addition, “working groups/task forces” on specific
<strong>the</strong>mes/topics can help facilitate regular discussion. Provided <strong>the</strong>ir facilitation and terms <strong>of</strong> reference
are clear, this type <strong>of</strong> organized discussion can enable people and departments to make suggestions,
share <strong>the</strong>ir best practices or lessons learnt, and build community buy-in for <strong>the</strong> practice. O<strong>the</strong>r
techniques I find useful are “workshops”, “brown bag lunches”, “quizzes”, “questionnaires”, “surveys”,
“visitor presentations” and “newcomer presentations”. I personally like “brown bag lunches”,
“workshops” and “quizzes” most because <strong>the</strong>y all encourage ei<strong>the</strong>r a face-to-face relationships or at
least a more involved exchange with <strong>the</strong> customer/client. They aim at a specific topic or goal, consist
<strong>of</strong> exercises and presentations that get everyone relaxed and involved, and end with a follow-up that
helps to improve <strong>the</strong> next similar event.
“Brown bag lunches” or “Brown bag seminars” are sessions or lunches set in an informal workplace
environment. They are organized at lunch time, or as c<strong>of</strong>fee breaks. The sessions are kept informal
by participants eating <strong>the</strong>ir own lunch packs, or simple food is provided by <strong>the</strong> host. In <strong>the</strong> States,
home-made or take-away lunch is <strong>of</strong>ten packed in brown paper bags to bring to work, hence <strong>the</strong>
name. Brown bag seminars normally run an hour or two (Brown Bag Seminars, n.d.). They are
intended to break <strong>the</strong> habit <strong>of</strong> employees sitting at <strong>the</strong>ir desk, surfing <strong>the</strong> web, and eating <strong>the</strong>ir lunch
in solitary. The technique has already become part <strong>of</strong> Knowledge Management toolkits, described by
Dalton (2009) as “a structured social ga<strong>the</strong>ring during an organizational lunch time period which is
used specifically for <strong>the</strong> purpose <strong>of</strong> transferring knowledge, building trust, social learning, problem
solving, establishing networking or brain storming.” A series <strong>of</strong> sessions can be used to provide for an
open and interactive exchange <strong>of</strong> opinions and discussions. Such discussion is a learning process
which <strong>of</strong>fers an individual or a group <strong>the</strong> opportunity to identify skills amongst each o<strong>the</strong>r that are
relevant to <strong>the</strong> organization and <strong>the</strong>ir own jobs. Usually, it is a great opportunity to receive feedback
from colleagues who might o<strong>the</strong>rwise not be involved in discussions. Generally, <strong>the</strong>se sessions are a
wonderful opportunity to cultivate a sharing working environment and allow people to learn with and
from <strong>the</strong>ir colleagues. Though it takes some exciting discussions and several good presentations to
gain more audience, <strong>the</strong>se sessions can be a good promoter <strong>of</strong> knowledge sharing.
An efficient workshop must focus on <strong>the</strong> needs <strong>of</strong> <strong>the</strong> audience and must have clear objectives. It is a
mix <strong>of</strong> ice breaker, brainstorming techniques, and good presentation skills (for example, avoiding
endless power point slides presented with a monotonous voice). The ultimate aim <strong>of</strong> a workshop is to
support a learning process for <strong>the</strong> participating group, elicit reactions, and facilitate <strong>the</strong> group work
towards <strong>the</strong> workshop objectives. Facilitation <strong>of</strong> this exchange <strong>of</strong> ideas and experiences creates an
environment where <strong>the</strong> learning and knowledge sharing is successfully achieved. The relationships
which develop in a workshop can establish understanding and knowledge exchange and can have
long-term effects on participants’ pr<strong>of</strong>essional collaboration.
The third technique I like to use is quizzes. Most people remember quizzes from school as a means <strong>of</strong>
checking students’ knowledge, but few recognize this tool’s rich features and potential. Used
appropriately, it can introduce learning opportunities for all employees, in an amusing way. Sending
<strong>of</strong>f a weekly short quiz to all staff, asking relevant questions about working areas, new developments
or changes, can reach out to more people than a two-hour all-staff meeting where most people doze
<strong>of</strong>f after 15 min.
In summary, <strong>the</strong>se techniques can enable an organization to establish a fluent communication and
knowledge exchange on an in-person, face-to-face basis without any use <strong>of</strong> technology. They should
be <strong>the</strong> first tools when selecting dynamic tools to implement an internal knowledge sharing strategy,
aiming to become more effective and productive. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong>se techniques, cannot
counter <strong>the</strong> pervasive organizational culture on <strong>the</strong>ir own; promoting knowledge sharing practice will
be much less successful in workplaces that value competitiveness and self-promotion over
communication and problem-solving. Moreover, while successful knowledge sharing requires
participation <strong>of</strong> all staff, <strong>the</strong>se techniques won’t work ei<strong>the</strong>r if <strong>the</strong> top management does not believe in
<strong>the</strong>m or refuses to support <strong>the</strong> practice.
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Stefanie Dannemann
In modern economy, <strong>the</strong> biggest competitive advantage organization can hold is <strong>the</strong> knowledge <strong>the</strong>ir
employees generate and share, and <strong>the</strong> resulting potential for innovation and creativity. Knowledge
itself is seen as a dynamic and powerful asset with a static element that needs to be identified face-t<strong>of</strong>ace
and channeled through available tools. Generally, <strong>the</strong>re is no need to use <strong>the</strong> newest technology
in knowledge sharing, if <strong>the</strong> identified channel is properly used. Reinhild Bode (2006) concludes in a
case analysis that “<strong>the</strong> verb “knowing” is much more likely to express <strong>the</strong>se characteristics; …due to
its tacit dimension, knowledge is context and person specific and needs to be shared in order to
become (at least partially) explicit and useful for o<strong>the</strong>rs; …creating knowledge means learning and
hence inducing change, which is not free <strong>of</strong> interests and independent <strong>of</strong> power relations; …thus,
solving <strong>the</strong> problem <strong>of</strong> managing knowledge by a pure technological approach is only <strong>the</strong> tip <strong>of</strong> <strong>the</strong>
iceberg; …as it is more likely to be a socio-organizational and cultural issue, approaches to
organizational communication and learning are required”.
Face-to-face knowledge exchange gives one <strong>the</strong> opportunity to put one’s point across while being
able to react to <strong>the</strong> feedback from <strong>the</strong> audience, to reinforce <strong>the</strong> team spirit, to capture attention, and
to inspire a positive climate to catalyze collaboration, innovation and performance. In <strong>the</strong> end, after all,
one need not depend on information systems to do this. A communication approach that is
independent <strong>of</strong> any IT platform most likely leads to a workplace which is open to sharing knowledge. It
supports in its core a culture <strong>of</strong> collegiality and teamwork. By practicing open knowledge sharing, staff
become champions <strong>of</strong> <strong>the</strong> practice internally in <strong>the</strong> organization. In <strong>the</strong> end, this will positively impact
acceptance <strong>of</strong> any IT platforms intended to exchange information, should <strong>the</strong> company decide to
implement <strong>the</strong>m. Many KM IT platforms fail because <strong>of</strong> resistance by staff and inflexible organizational
processes. As in any launch <strong>of</strong> an IT platform, user acceptance and system promotion can become a
bigger challenge to a KM platform than <strong>the</strong> effort that had been put into its development. Successful
companies can be flexible and great business innovators because <strong>of</strong> both: having a great mechanism
for knowledge exchange and fostering collaboration, and using good tools for knowledge exchange
and business practice
Acknowledgements
I would like to thank Tina Purnat, Cristian Avram and Olivia Carstocea for <strong>the</strong>ir encouragement,
guidance and support.
References
Addicott, R., McGivern, G., and Ferlie, E. (2006) ‘Networks, Organizational Learning and Knowledge
Management: NHS Cancer Networks’, Public Money & Management, vol. 26, no. 2, pp. 87–94.
Bode, R. (2006) ‘Knowledge Management, Learning and Communication in Value Chains: A Case
Analysis <strong>of</strong> <strong>the</strong> specialty c<strong>of</strong>fee Value Chain <strong>of</strong> FAPECAFES, Ecuador. (Final Report)’, GTZ-
CIAT-BMZ.
Brown Bag Seminars, [Online], Available: http://en.wikipedia.org/wiki/Brown_bag_seminars [May 20, 2011]
Dalton, B. (2009) Brown Bag Lunch Strategy, [Online], Available: http://www.slideshare.net/Loggy/brownbag-lunch-strategy
[June 03, 2011]
Davenport, T. (2008) Enterprise 2.0: The New, New Knowledge Management, [Online], Available:
http://blogs.hbr.org/davenport/2008/02/enterprise_20_<strong>the</strong>_new_new_know_1.html [June 03, 2011]
Izuzi, M. (2007) Storytelling and KM, [Online], Available: http://www.kmtalk.net/blog/?p=32 [May 18,
2011]
Knowledge Management, [Online], Available:
http://www.google.com/search?q=definition+<strong>of</strong>+knowledge+managemet#sclient=psy&q=are+<strong>the</strong>r
e+good+definitions+<strong>of</strong>+knowledge+management [May 18, 2011]
Kogut, B. and Zander, U. (1996) ‘What Firms Do? Coordination, Identity, and Learning’, Organization Science,
vol. 7, no. 5, pp. 502-523.
Lakhani, K. and McAfee, R. (2007) Case study on deleting "Enterprise 2.0" article, Harvard Business
School: Courseware #9-607-712.
McAfee, A. (2006) ‘Enterprise 2.0: The Dawn <strong>of</strong> Emergent Collaboration’, Sloan Management
Review, vol. 47, no. 3, pp. 21–28.
Nonaka, I. and Hedlund, G. (1993) ‘Models <strong>of</strong> Knowledge Management in <strong>the</strong> West and Japan’, in Lorange, B.,
Chakravarthy, B., Roos, J. and Van de Ven, H. (Eds) Implementing Strategic Process, Change, Learning
and Cooperation, London: Macmillan.
Nonaka, I. and Takeuchi, H. (1995) The Knowledge Creating Company: How Japanese companies create <strong>the</strong>
dynamics <strong>of</strong> innovation, New York: Oxford University Press.
1106

Enterprise 2.0: Knowledge Management for Decision
Support
Mohamed Farid
Egyptian cabinet, Information and Decision Support Center, Cairo, Egypt
m<strong>of</strong>arid@idsc.net.eg
Abstract: Think Tanks are concerned with <strong>the</strong> process <strong>of</strong> national and global development. They build viable
relations among external actors across <strong>the</strong> board, from civil society to decision makers. Despite <strong>of</strong> <strong>the</strong> large
volume <strong>of</strong> literature describing <strong>the</strong> role <strong>of</strong> Knowledge Management (KM) to <strong>the</strong> corporate sector, <strong>the</strong>re continues
to be a lack <strong>of</strong> information discussing how exactly KM helps <strong>the</strong>se institutes and organisations. The aim <strong>of</strong> this
paper, an action research, is to address actions taken toward developing an appropriate Knowledge
Management System (KMS) inside IDSC, an Egyptian think tank working in <strong>the</strong> field <strong>of</strong> decision support. Unless
<strong>the</strong>re is no common systematic Knowledge Management System Development (KMSD) approach to follow,
action research has been considered to take place in this paper to identify opportunities for KM applications and
<strong>the</strong>ir corresponding Enterprise 2.0 enabling technologies for <strong>the</strong> organisation’s KM investment.
Keywords: Knowledge management, knowledge management system, think tank, enterprise 2.0, action
research
1. Introduction
Think Tanks normally work to support decision makers in various fields, such as political, economic,
social and technology issues. Dealing with decision makers requires providing right knowledge from
<strong>the</strong> right people at <strong>the</strong> right time" (Madon 2000). To accomplish its mission, organization struggling to
retain its knowledge, find new approaches to acquire more knowledge and circulate among its
workers.
IDSC was founded by Egyptian Cabinet in 1985 supporting decision makers over issues <strong>of</strong> relevance
to economic, social and political reform. Its mission impartially is to support <strong>the</strong> government <strong>of</strong> Egypt
by advising <strong>the</strong> best policy mix and delivering analytical policy research to improve <strong>the</strong> socioeconomic
wellbeing <strong>of</strong> <strong>the</strong> Egyptian society. IDSC processes <strong>the</strong> data provided by its stakeholders
using an information and trend analysis approach. Analysis and research depend on a wide variety <strong>of</strong>
tools ei<strong>the</strong>r quantitative or qualitative such as information reports, field surveys, economic modelling,
and indicators. In sum <strong>of</strong> its experience, IDSC has dominated knowledge in many various areas <strong>of</strong>
interests, with current 600 knowledge workers supported with expertise in both academia and
business, performing its experienced heritage <strong>of</strong> knowledge assets.
It is challenging to an organisation to keep and disseminate its knowledge assets, were that explicit
knowledge, represented in documents, reports/databases, or tacit (implicit) knowledge as what
knowledge workers' ideas and thoughts. At <strong>the</strong> time explicit knowledge could be codified and
managed by information systems, essential tacit knowledge still hard to be transformed into
searchable, sharable, and actionable knowledge assets (Tiwana 2002).
Based on action research, Enterprise 2.0 paradigm and its capable technology tools will be introduced
in this paper, in approach to develop Knowledge Management System (KMS) for The Egyptian Think
Tank, IDSC.
The paper will start by briefly describes <strong>the</strong> paper's research methodology and goes through <strong>the</strong>
action research stages starting by diagnosing <strong>the</strong> current situation inside <strong>the</strong> organization, "AS-IS"
model, to <strong>the</strong> desired stage, "TO-BE" model, for implementing KMS architecture. The Binney’s KM
spectrum (Binney 2001) will be introduced in this paper in order to identify in which areas <strong>the</strong>
organisation should direct its KM investment. Finally, <strong>the</strong> developed enabling Enterprise 2.0 KM
application will be examined against important KM <strong>the</strong>ory, Nonaka & Takeuchi's spiral model <strong>of</strong>
knowledge conversion between both tacit and explicit knowledge, <strong>the</strong> model describe <strong>the</strong> knowledge
creation through four types: socialisation, externalisation, combination and internalisation, SECI
model.
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2. Research design and methodology
Mohamed Farid
The paper carries out action research in information systems, to develop KMS as a solution to <strong>the</strong>
current situation. In its quest for a solution, <strong>the</strong> research will try to answer <strong>the</strong> question “What are <strong>the</strong>
methodologies and activities carried out in order to develop KMS to IDSC?". Action research requires
high involvement into <strong>the</strong> organisation’s business process and direct interaction with <strong>the</strong> members <strong>of</strong>
<strong>the</strong> organisation. Fur<strong>the</strong>rmore, action research process could be iterated to optimise <strong>the</strong> solution.
Action research, will goes in two stages <strong>of</strong> processes, similar to what introduced by (Blum 1955),
diagnosing (diagnostic) stage and designing and implementing KMS stage (<strong>the</strong>rapeutic).
In <strong>the</strong> first stage, <strong>the</strong> diagnosing stage, we analyse <strong>the</strong> current situation,” AS-IS” model, identifying
IDSC’s missions and objectives in adopting KM to its environment. We analyse <strong>the</strong> business process
dealing with knowledge entities, and carry out <strong>the</strong> process <strong>of</strong> envisioning knowledge work behaviour.
Following this, we examine <strong>the</strong> current problems that <strong>the</strong> organisation faces to retain its knowledge
assets and make <strong>the</strong>m available to all its knowledge workers. KM Spectrum (Binney 2001) used to
identify KM application inside IDSC and its corresponding enabled information systems.
Stage two, designing and implementing KMS stage, responsible to design and implement <strong>the</strong>
desirable KMS to <strong>the</strong> organisation, “To-Be” model, to improve <strong>the</strong> situation inside <strong>the</strong> organisation.
Figure 1: Action Reseach cycle
After, <strong>the</strong> paper will discuss <strong>the</strong> KM <strong>the</strong>ories behind <strong>the</strong> developed KMS to IDSC, and also try to
improve <strong>the</strong> efficiency <strong>of</strong> <strong>the</strong> developed KMS by detecting any required future changes to improve <strong>the</strong>
organisation's work business process.
3. Diagnosis
3.1 Current Situation
In <strong>the</strong> aim <strong>of</strong> analysing <strong>the</strong> current situation inside <strong>the</strong> organisation, <strong>the</strong> AS-IS model, <strong>the</strong> process <strong>of</strong>
envisioning knowledge work behaviour is carried out to understand <strong>the</strong> nature <strong>of</strong> <strong>the</strong> knowledge inside
<strong>the</strong> IDSC. This analysis shall try finding out <strong>the</strong> knowledge sources and assets, discovering <strong>the</strong>
internal business workflow which deals with <strong>the</strong> knowledge and assessing <strong>the</strong> organisation’s current
KM applications for <strong>the</strong> future KM investment planning.
3.2 Knowledge inside IDSC
The accumulated explicit knowledge assets are mainly stored in IDSC's Information Technology (IT)
infrastructure, which mainly consists <strong>of</strong> databases, data warehouse, information directories, internal
information systems, intranet, IDSC’s external websites, and scattered documents along web servers
and file systems. Figure 2 demonstrates types <strong>of</strong> explicit knowledge inside IDSC.
The various knowledge sources are important ingredients in <strong>the</strong> knowledge assets, but still knowledge
workers find it hard to find <strong>the</strong> information <strong>the</strong>y are looking for, even though this knowledge is
distributed over searchable scattered information systems. Moreover, some o<strong>the</strong>r workers do not
know about some or most <strong>of</strong> those knowledge locations, i.e. <strong>the</strong>y do not know if such information is
managed by particular system, many o<strong>the</strong>r workers do not know even if such information system do
exist.
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Figure 2: Explicit knowledge inside IDSC
Mohamed Farid
3.3 Observation for IDSC internal work process
IDSC internal business work schemes across its departments, as shown in figure 3 below, rely on
planned projects, ad-hoc activities or ordinary administrative business processes. Our focus in this
paper will be on both planned projects and ad-hoc activities as <strong>the</strong>y constitute <strong>the</strong> core work <strong>of</strong> IDSC.
Figure 3: IDSC internal work schemes.
The planned projects have predefined start and end dates, always managed by projects tracking
system, and go through sequential processes until <strong>the</strong>y are approved finally by a “Quality Assurance”
process, as shown in figure 3. As a real example inside IDSC, development <strong>of</strong> economic or social
study should be assigned to a primary responsible department, <strong>the</strong> responsible department start
assigning <strong>the</strong> work to an internal team, this project could require data and information to be ga<strong>the</strong>red
from internal and external sources, as later on this data should be analysed to support <strong>the</strong>
development <strong>of</strong> <strong>the</strong> concerned study. In most cases, communications with o<strong>the</strong>r departments are
established to collaborate in necessary activities related to <strong>the</strong> work in progress. After <strong>the</strong> work has
been achieved, <strong>the</strong> output study will be handled by <strong>the</strong> “Quality Assurance” department to be revised.
In most cases, <strong>the</strong> output is re-sent again to <strong>the</strong> responsible department to apply changes required
corresponding to revision comments. The process <strong>of</strong> “Quality Assurance” goes through an archiving
information system to exchange all related documents between <strong>the</strong> responsible department and
“Quality Assurance” department. Finally <strong>the</strong> output will be registered in <strong>the</strong> project tracking system.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> ad-hoc activity is carried out as a response to an external demand from a
decision maker that must be supplied in a short time, its output maybe registered or not, depending
on <strong>the</strong> situation. Not to mention, this work never goes through any collaborative environment system.
Figure 4: The internal work process <strong>of</strong> a planned project inside IDSC
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3.4 KM Applications in IDSC
Mohamed Farid
The Binney’s KM spectrum (Binney 2001), based on <strong>the</strong>ories and practices, provides checklist advice
<strong>the</strong> management <strong>of</strong> <strong>the</strong> organisation how to direct its KM strategic goals. Binney (2001) in his
literature try to answer <strong>the</strong> always asked question, “Where should I make my KM investment,
balancing <strong>the</strong> option presented to me in <strong>the</strong> literature?”
The KM spectrum has been grouped into six categories ‘elements’ as follows: transactional, analytical
asset management, process based, developmental and innovation and creation KM, each element
describes a field <strong>of</strong> KM.
Figure 5, which draw on KM spectrum, lists <strong>the</strong> KM applications in IDSC and indicates <strong>the</strong>ir posited
relevance to KM activities. As observed from <strong>the</strong> table, most <strong>of</strong> KM applications that exist in IDSC are
grouped under analytical applications, such data warehouse, business intelligence, decision support,
and o<strong>the</strong>r data analytical tools. Also <strong>the</strong> figure shows fewer KM applications existing under
transactional applications and innovation and creation applications, and less few in o<strong>the</strong>rs. Even <strong>the</strong>re
as an intranet and general discussion forums, still <strong>the</strong>re is no collaborative information system
allowing <strong>the</strong> employees to share <strong>the</strong>ir knowledge or to achieve common work in.
Figure 5: KM applications inside IDSC
As an observation to <strong>the</strong> KM spectrum, shown in figure 5 which was extracted from his research,
Binney (2001) argues that <strong>the</strong> first three KM applications in <strong>the</strong> spectrum, transactional, analytical,
and asset management are more interested by “technologists” while “Management Theorists” are
more focus on <strong>the</strong> rest <strong>of</strong> KM applications <strong>of</strong> <strong>the</strong> spectrum.
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Mohamed Farid
Figure 6: Observations mapped to <strong>the</strong> KM spectrum
He also shows that <strong>the</strong> level <strong>of</strong> explicit knowledge decreases from left to right among <strong>the</strong> KM
applications, while transactional applications deal more with explicit knowledge. “Asset management”
and “process” based KM applications are concerned with transforming tacit knowledge into explicit
knowledge to be shared among <strong>the</strong> organisation, while “innovation and creation” KM applications help
to circulate tacit knowledge among its workers.
To illustrate <strong>the</strong> impact <strong>of</strong> existing KM Applications on <strong>the</strong> knowledge culture inside IDSC, based on
Binney’s KM spectrum observation (Binney 2001), <strong>the</strong> author <strong>of</strong> this dissertation had developed a
“radar diagram” to assess <strong>the</strong> KM inside <strong>the</strong> organisation. As shown in figure 7, <strong>the</strong> radar scales <strong>the</strong>
amount <strong>of</strong> KM applications found in IDSC, <strong>the</strong> number <strong>of</strong> applications does not represent <strong>the</strong> number
<strong>of</strong> IT systems deployed in behalf <strong>of</strong> <strong>the</strong> KM application, in o<strong>the</strong>r words KM applications are grouped
toge<strong>the</strong>r. The volume <strong>of</strong> KM application plotted on each KM application axis, connecting lines
between each axis in corresponding points drawing a hexagon shape.
Figure 7: Radar illustrates knowledge Characteristic inside IDSC
As <strong>the</strong> result shown in <strong>the</strong> radar figure, more areas <strong>of</strong> <strong>the</strong> hexagon covered in <strong>the</strong> left side, most
knowledge assets addressed by knowledge applications are explicit knowledge compared with much
less tacit knowledge are addressed inside <strong>the</strong> organisation. Most <strong>of</strong> KM applications coming from
technologists point <strong>of</strong> view inside <strong>the</strong> organisation, less interest from management <strong>the</strong>orists.
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Mohamed Farid
3.5 Problems with <strong>the</strong> current IDSC business process
Reinvention <strong>of</strong> <strong>the</strong> same work, unless most if not all <strong>the</strong> projects are registered in projects tracking
system, but still “Reinvention <strong>of</strong> <strong>the</strong> Wheel” is highly recognized inside <strong>the</strong> organisation due <strong>the</strong> lack <strong>of</strong>
information <strong>of</strong> certain work that have been developed in <strong>the</strong> past.
Repeated mistakes, as <strong>the</strong>re is no logs for previous similar works exist; mistakes could occur again,
learned lessons get lost.
Inconsistence <strong>of</strong> multiple knowledge versions, as <strong>the</strong>re is no common workspace for projects
members that centralise knowledge outputs exist, redundant versions <strong>of</strong> explicit knowledge could be
generated, scattered across <strong>the</strong> organisation’s file system or emails.
The loss <strong>of</strong> tacit knowledge, knowledge experienced by employees considered as hard to be
codified, articulated and transferred to <strong>the</strong> o<strong>the</strong>rs. Due to high turnover inside <strong>the</strong> organisation, this
tacit knowledge could be completely lost.
The absence <strong>of</strong> knowledge transfer across organisational units, while transferring knowledge
between unit members could be easier as <strong>the</strong>y are working toge<strong>the</strong>r in <strong>the</strong> same location sharing <strong>the</strong>
same domain’s problems, sharing across o<strong>the</strong>r units could be more difficult.
3.6 Recommendations for action
As a result <strong>of</strong> investigations, provisions to IDSC work process and to <strong>the</strong> organisation culture,
numbers <strong>of</strong> recommendations were proposed concerning <strong>the</strong> development <strong>of</strong> KMS to improve <strong>the</strong>
organisational learning:
Knowledge repository: a central repository that handles all codified knowledge <strong>of</strong> workers, or at
least refer to <strong>the</strong>ir locations, will simplify retrieving <strong>the</strong> past created knowledge, best practices, learned
lessons and How-to’s in <strong>the</strong> context <strong>of</strong> work.
Community <strong>of</strong> practice (CoP): virtual platform that community <strong>of</strong> practice members can share <strong>the</strong>ir
ideas, work process, and handle <strong>the</strong>ir documents and organise <strong>the</strong>ir events through. Levels <strong>of</strong>
permissions should be declared to identify each member’s role in <strong>the</strong> community.
Knowledge communication and network: communication channels that member <strong>of</strong> community can
exchange <strong>the</strong>ir ideas on, i.e. discussion boards, forums and instant messaging. This network also
provides a mechanism that allows individuals to find particular communities <strong>of</strong> interest to join.
Organisational routine for knowledge sharing: simply, related “know-how” needs to be codified in
order to be used by o<strong>the</strong>rs, especially for new comers to <strong>the</strong> organisation.
Search <strong>the</strong> knowledge asset <strong>of</strong> <strong>the</strong> organisation: single point interface to <strong>the</strong> knowledge workers
as <strong>the</strong>y could search all knowledge assets inside <strong>the</strong> organisation.
Integration to o<strong>the</strong>r existing IT systems: <strong>the</strong> developed KMS should be integrated and correlated to
<strong>the</strong> existing IT tools and infrastructure, to guarantee <strong>the</strong> flow <strong>of</strong> information and provide integrity <strong>of</strong>
data.
Codifying <strong>of</strong> tacit knowledge: knowledge workers experience, business consultants, and academia
expertise should be codified.
4. Designing and Implementing KMS to IDSC
In our research to find <strong>the</strong> desired solution, <strong>the</strong> To-Be mode, we ga<strong>the</strong>red demanded requirements,
explored experienced practices, made research over available solutions in <strong>the</strong> market and examined
new enabled technologies for <strong>the</strong> new IDSC’s KMS design. A platform, named “MyIDSC”, has been
decided to be developed in IDSC. “MyIDSC” basically based on two major subsystems, an (1)
enterprise search engine and (2) enterprise collaboration platform based on Enterprise 2.0. The
platform design is shown in figure 8.
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Mohamed Farid
4.1 “MyIDSC” Architecture and Technical Features
Figure 8: The proposed KMS design
The internal search engine has a primary function <strong>of</strong> indexing and searching all knowledge assets
inside <strong>the</strong> organisation; those assets vary from databases, data warehouse, information directories,
internal information systems, intranet, IDSC external websites and scattered documents along with
web servers and file systems.
The search engine is designed to have web crawler agents, with support to different data source
formats, i.e. database engine, text files, html files, etc. Scheduled to crawl interesting internal and
external websites, databases records, and any textual based files which after will be indexed. The
search engine has a web interface as a single point <strong>of</strong> access to <strong>the</strong> user, as shown in figure 9.
The search engine is powered by <strong>the</strong> open source project “Apache Lucene”, a high-performance, fullfeatured
text search engine library written entirely in Java platform. The solution supports both <strong>the</strong>
Arabic and English languages.
Documents.
PDF, DOC, XLS
Web Sites
Data Base
Figure 6: Enterprise search engine architecture
Crawler
Web, Document,
Database
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Indexer
Reverse Word
Index
Database
Web Index
Search
User Interface
Web Services
HTTP Server

Mohamed Farid
“Enterprise collaborative platform” has been developed in order to support collaborative activities
between individuals among <strong>the</strong> organisation. The platform supports <strong>the</strong> creation <strong>of</strong> a “virtual
community”, tackling <strong>the</strong> KM <strong>the</strong>ories <strong>of</strong> “Community <strong>of</strong> Practice” and “Community <strong>of</strong> Interest”, in
addition to presenting <strong>the</strong> business unit inside <strong>the</strong> physical hierarchy <strong>of</strong> <strong>the</strong> organisation, i.e.
departments, sectors, divisions and teamwork.
Each virtual community is identified by one <strong>of</strong> three types, open, closed or restricted community, and
each type declares its access roles. Open community allows for any o<strong>the</strong>r platform’s users to join <strong>the</strong>
community without any previous permission from <strong>the</strong> admin or creator <strong>of</strong> <strong>the</strong> community, while
restricted one allows o<strong>the</strong>rs to join through requesting an online membership permission. The closed
community is a fixed group <strong>of</strong> members determined by <strong>the</strong> creator or <strong>the</strong> admin <strong>of</strong> <strong>the</strong> community,
even o<strong>the</strong>r users cannot request membership, and <strong>the</strong> community cannot even be searchable or
visible to o<strong>the</strong>r users. Screenshot to communities’ directory in MyIDSC is shown in figure 10.
In addition to <strong>the</strong> fact that <strong>the</strong> community represents a group <strong>of</strong> members, <strong>the</strong> platform by default also
allows <strong>the</strong> user <strong>of</strong> <strong>the</strong> system to have his/her own private workspace, i.e. “My Community”, to manage
his/her own works, documents, events, etc.
The “virtual community” enables its member to share various types <strong>of</strong> documents and multimedia files
through a content management system (CMS) with support <strong>of</strong> versioning control, also <strong>the</strong>y are able to
organise events and outline <strong>the</strong>ir meetings through a shared calendar, and make discussion through
an internal discussion board. Even though, members <strong>of</strong> <strong>the</strong> community are able to externalise <strong>the</strong>ir
knowledge to o<strong>the</strong>rs in forms <strong>of</strong> news, articles, documents and mini blogs. MyIDSC users also can
create <strong>the</strong>ir own blogs to capture <strong>the</strong>ir own ideas and experience and share with o<strong>the</strong>rs, getting <strong>the</strong>
ability to have feedback and comments from <strong>the</strong> community.
MyIDSC which is also based on an open source project, Liferay portal, supports internal search
capacities, and dynamic tagging enables its user to tag any knowledge content, i.e. web content or
document, leads to dynamically sharing <strong>of</strong> important or interesting content with <strong>the</strong> o<strong>the</strong>rs.
Figure 10: Screenshot to MyIDSC, directory <strong>of</strong> communities
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The overall design <strong>of</strong> <strong>the</strong> system is based on social networking mechanism and Enterprise 2.0
technologies, it gives opportunities to individuals and communities <strong>of</strong> workers to interact in circles <strong>of</strong>
knowledge sharing, keep <strong>the</strong>m updated by o<strong>the</strong>rs activities, and o<strong>the</strong>r department’s on-going projects.
MyIDSC provides channels <strong>of</strong> communication between its users, i.e. discussion boards. The features
<strong>of</strong> MyIDSC, complying with Enterprise 2.0, and its basic components are shown in figure 11.
One <strong>of</strong> <strong>the</strong> most promising Enterprise 2.0 features in “MyIDSC” system is Wiki. Wiki is defined as one
<strong>of</strong> Enterprise 2.0 paradigm to articulate and share knowledge. Wiki, as some like to say, stands for
“What I Know Is”, gets its popularity through <strong>the</strong> Wikipedia website. Wiki here is designed to articulate
certain development issues, lesson learned from previous works, and <strong>the</strong> organisation’s know-how.
Wiki’s editing mechanism is bottom-up, knowledge workers are to create articles in context <strong>of</strong> work,
allow o<strong>the</strong>rs easily to edit, modify and add <strong>the</strong>ir contribution <strong>of</strong> knowledge and experience. Wiki, in
contrast to any casual web page, allows creating articles through following hyperlink tag to suggest
new article as well as viewing interested articles. In o<strong>the</strong>r words, wiki enables article visitor not only
viewing articles, but also sharing <strong>the</strong>ir added knowledge, auditing and judging articles. Such kind <strong>of</strong>
sharing articles encourage knowledge workers to share <strong>the</strong>ir own ideas; as well it keeps <strong>the</strong>ir rights <strong>of</strong>
“ownership” <strong>of</strong> <strong>the</strong>ir introduced knowledge besides perceiving <strong>the</strong>ir own intellectual capital (IP).
“Intellectual property (IP) matter is very important, individuals at <strong>the</strong> IDSC don't tend to release <strong>the</strong>ir
ideas for fear <strong>of</strong> missing <strong>the</strong> chance <strong>of</strong> being awarded for <strong>the</strong>ir work and <strong>the</strong>se ideas are captured by
o<strong>the</strong>rs”, as IDSC’s CEO declared.
Figure 7: MyIDSC and its Enterprise 2.0 technical features
5. Discussion and fur<strong>the</strong>r implementation
5.1 KM <strong>the</strong>ories behind Enterprise 2.0
Nonaka & Takeuchi (Nonaka & Takeuchi 1995) propose a spiral model <strong>of</strong> knowledge conversion
between both tacit and explicit knowledge, <strong>the</strong> model describes <strong>the</strong> knowledge creation through four
types: socialisation, externalisation, combination and internalisation, known as <strong>the</strong> SECI model, as
shown in figure 12.
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Mohamed Farid
The design <strong>of</strong> MyIDSC follows this model <strong>of</strong> knowledge conversion; figure 13 shows examples for
each type <strong>of</strong> conversion taking place in MyIDSC’s enterprise collaborative platform subsystem.
Knowledge socialisation refers to <strong>the</strong> creation <strong>of</strong> tacit knowledge as a result <strong>of</strong> a tacit knowledge
conversion. Tacit knowledge could be transferred through human activities, as well as sharing ideas,
dialogue conversation, observation, imitation or lesson learning. In context <strong>of</strong> an organisation,
knowledge socialisation could occur through knowledge sharing in conferences, workshops, training
sessions or even trough regular direct dialogue with colleagues in work.
MyIDSC provides social networking capability that allows individuals and team member's to
communicate over <strong>the</strong> system, as a result, <strong>the</strong> tacit knowledge presented over discussion boards will
be codified in searchable assets. In <strong>the</strong> same time, communications could be anytime and anywhere,
leading to deliver <strong>the</strong> right knowledge at <strong>the</strong> right time.
Figure 8: Process <strong>of</strong> Knowledge Spiral model
Source: extracted from (Takeuchi 2006), adopted from (Nonaka & Takeuchi 1995).
The knowledge externalisation, describes <strong>the</strong> process <strong>of</strong> converting tacit knowledge to an explicit
one, MyIDSC utilises <strong>the</strong> best <strong>of</strong> Enterprise 2.0 technologies, wiki and blog, that supports individuals,
teams and departments to share and accumulate knowledge through writing articles. The acquired
knowledge externalised in <strong>the</strong> form <strong>of</strong> best practice, know-how and learned lessons.
Vice versa, knowledge internalisation takes place when employees learn from such articles and
content, showing explicit to tacit conversion.
Finally, knowledge combination could be shown by circulating explicit knowledge through <strong>the</strong>
document management system in MyIDSC platform. Employees could search, share and exchange
<strong>the</strong>ir documents, images or any o<strong>the</strong>r types <strong>of</strong> codified knowledge.
5.2 Future planning
While MyIDSC provides collaboration tools to knowledge workers inside <strong>the</strong> organisation, still <strong>the</strong>y are
not used to share knowledge over. As a conclusion from <strong>the</strong> first prototype, <strong>the</strong> system is strongly
recommended to be in middle <strong>of</strong> <strong>the</strong> business process workflow <strong>of</strong> IDSC. No one will do a volunteer
work without support from top management, even to be done obligatory in order to achieve such
work.
As described earlier for planned projects workflow in <strong>the</strong> IDSC work schema, section 3-3, MyIDSC
could take place in <strong>the</strong> stage <strong>of</strong> “collaboration” to achieve a particular required output, <strong>the</strong> new
redesign introduced in figure 14, presents <strong>the</strong> function <strong>of</strong> MyIDSC to take place within IDSC business
workflow in dotted border.
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Mohamed Farid
The impact <strong>of</strong> <strong>the</strong> proposed workflow should help <strong>the</strong> quality assurance department to influence <strong>the</strong>
project development process in early time, project team members plus <strong>the</strong> assurance quality
department’s members could collaborate toge<strong>the</strong>r over virtual workspace in MyIDSC. The new
redesign will help for better quality and will reduce <strong>the</strong> time required to achieve such work.
Figure13: Example <strong>of</strong> knowledge creation in MyIDSC
Figure 14: Proposed work process <strong>of</strong> planned project inside IDSC
Up to IDSC's heritage <strong>of</strong> national knowledge and information, IDSC could externalise financial, social
and political issues, in forms <strong>of</strong> articles in WIKI to <strong>the</strong> society, as readers can contribute to <strong>the</strong> articles,
maximising <strong>the</strong> produced knowledge outside <strong>the</strong> border <strong>of</strong> <strong>the</strong> organisation. This process could be
carried out through a top-down approach, i.e. articles should be determined by a top community <strong>of</strong>
knowledge inside IDSC, <strong>the</strong>y also should tackle <strong>the</strong> auditing and <strong>the</strong> reviewing <strong>of</strong> those articles again.
6. Conclusion
IDSC, <strong>the</strong> Egyptian think tank working in <strong>the</strong> field <strong>of</strong> decision support, decided to adopt KM to
circulate knowledge inside <strong>the</strong> organisation and to strength its organisational learning. The aim <strong>of</strong> <strong>the</strong>
paper is to address <strong>the</strong> taken actions in order to develop an appropriate KMS to achieve IDSC's goal.
The paper is based on “Action Research”, which goes in two fundamental stages, similar to what
introduced by (Blum 1955), diagnosing (diagnostic) stage and designing and implementing KMS
(<strong>the</strong>rapeutic) stage. Action research required high involvement to <strong>the</strong> organisation's business
processes, direct interaction with its members. Fur<strong>the</strong>rmore, <strong>the</strong> result evaluated and action research
processes could be iterated to optimise <strong>the</strong> KMS solution.
Diagnosing stage, which explores <strong>the</strong> current situation, <strong>the</strong> AS-IS model in IDSC, analyses <strong>the</strong>
business process dealings with knowledge entities, and carry out <strong>the</strong> process <strong>of</strong> envisioning <strong>the</strong>
knowledge work behaviour. KM Spectrum (Binney 2001) used to identify <strong>the</strong> current KM applications
inside IDSC and its corresponding enabled information systems.
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For <strong>the</strong> <strong>the</strong>rapeutic stage to design <strong>the</strong> desired KMS, <strong>the</strong> TO-BE model, Enterprise 2.0 paradigms
features have been used to build <strong>the</strong> KMS, along with building enterprise search engine to manipulate
<strong>the</strong> knowledge assets inside <strong>the</strong> organisation. The KMS, which is named MyIDSC, is basically
developed to articulate <strong>the</strong> knowledge inside <strong>the</strong> organisation and to support collaboration between
individuals and business units.
KM <strong>the</strong>ories have been examined against Enterprise 2.0 features used in MyIDSC. In this paper we
highlighted Nonaka & Takeuchi (Nonaka & Takeuchi 1995) spiral model, <strong>the</strong> model describes <strong>the</strong>
knowledge creation through four types: socialisation, externalisation, combination and internalisation,
known as <strong>the</strong> SECI model, which also identifies <strong>the</strong> dialectical relationship between tacit and explicit
knowledge.
In process <strong>of</strong> evaluating <strong>the</strong> impact <strong>of</strong> <strong>the</strong> designed KMS, a proposal has been introduced for <strong>the</strong>
future redesigning <strong>the</strong> current business process inside <strong>the</strong> organisation, so <strong>the</strong> system can play <strong>the</strong>
effective role <strong>of</strong> allowing <strong>the</strong> co-workers to work in a collaborative manner and in an effective way.
Moreover, <strong>the</strong> paper discussed <strong>the</strong> future <strong>of</strong> <strong>the</strong> system in order to maximise its usage in terms <strong>of</strong>
disseminating <strong>the</strong> knowledge inside and outside <strong>the</strong> organisation utilising its “Wiki” subsystem.
As a final comment, I would claim that <strong>the</strong> action research carried out in context <strong>of</strong> “technology-push”
model (Malhotra 2005), which deliver <strong>the</strong> success factor for IT enabler to develop KMS to <strong>the</strong>
organisation. These technologies should be backed <strong>of</strong>f by a clear strategy, “strategy-pull” model
(Malhotra 2005) from <strong>the</strong> top management <strong>of</strong> <strong>the</strong> organisation, to guarantee <strong>the</strong> knowledge flow and
to achieve <strong>the</strong> ultimate benefits from <strong>the</strong> driven business performance in <strong>the</strong> enterprise.
References
Binney, D 2001, 'The knowledge management spectrum - understanding <strong>the</strong> KM landscape', Journal <strong>of</strong>
Knowledge Management, vol 5, no. 1, pp. 33-42.
Blum, F 1955, '"Action Research - A scientific Approach?,"', Philosophy <strong>of</strong> Science, 1955, pp. 1-7.
Madon, S 2000, International NGOs: Networking, information flows and learning’, Institute <strong>of</strong> Development Policy
and Management, University <strong>of</strong> Manchester, Manchester.
Malhotra, Y 2005, 'Integrating Knowledge management technologies in organizational business process: getting
real time enterprise to deliver real business performance', journal <strong>of</strong> knowledge management, vol 9, no. 1,
pp. 7-28.
Nonaka, I & Takeuchi, H 1995, The Knowledge-Creating Company: How Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation , Oxford University Press, New York.
Takeuchi, H 2006, 'The New Dynamism <strong>of</strong> <strong>the</strong> Knowledge Creating Company', in Japan, Moving Toward a More
Advanced Knowledge Economy: Advanced Knowledge Creating Companies, World Bank Publications.
Tiwana, A 2002, The Knowledge Management Toolkit, Prentice Hall, Upper Saddle River, NJ.
1118

Capturing Lessons That Should be Learned: An After
Event Review for Whole-<strong>of</strong>-Government Security Planning
and Operations
Susan McIntyre and Kate Kaminska
Defence Research and Development Canada, Centre for Security Science,
Ottawa, Ontario Canada
susan.mcintyre@drdc-rddc.gc.ca
kate.kaminska@drdc-rddc.gc.ca
Abstract: In February 2010, Canada hosted <strong>the</strong> Vancouver 2010 Winter Games. To ensure a Safe and Secure
Games, multiple safety and security agencies at three levels <strong>of</strong> government had to work in an unprecedented
partnership for security planning and operations. This whole-<strong>of</strong>-government approach to domestic safety and
security, <strong>of</strong>ten bringing toge<strong>the</strong>r non-traditional partners, provided a unique opportunity to analyze collective
strengths and weaknesses and to make suggestions for process improvements in future domestic interorganizational
public safety or security events. Defence Research and Development Canada – Centre for
Security Science undertook an After Event Review which incorporated qualitative operational research methods
into a Lessons Learned process. Typically a Lessons Learned cycle involves five steps: preparation, collection,
analysis, endorsement, and change. Often, <strong>the</strong> process relies upon initial observations without <strong>the</strong> benefit <strong>of</strong>
independent analysis. The After Event Review refined <strong>the</strong> preparation, collection and analysis stages by focusing
on analytical methodologies and inserting subject matter expert validation throughout <strong>the</strong> process. Beginning
with a mission analysis in <strong>the</strong> preparation phase, <strong>the</strong> researchers were able to determine <strong>the</strong> expected outcomes
against which to appraise <strong>the</strong> ability <strong>of</strong> <strong>the</strong> partners’ collective success. It was also necessary to determine a
capability framework against which to assess <strong>the</strong> overall planning and operations. For this purpose, <strong>the</strong> US
Department <strong>of</strong> Homeland Security’s Target Capability List was selected. During <strong>the</strong> collection phase, interview
surveys, a social network analysis and case studies were employed and resulted in <strong>the</strong> identification <strong>of</strong> nine
critical issues for analysis. Using <strong>the</strong> capability assessment approach, <strong>the</strong> researchers were <strong>the</strong>n able to identify
best practices and corrective actions which could be applied to future domestic security operations. Subject
matter experts were consulted throughout to determine if <strong>the</strong> findings were indicative <strong>of</strong> operational realities. The
resulting conclusions could lead to recommendations for <strong>the</strong> consideration <strong>of</strong> change authorities within <strong>the</strong>
federal security community. The enhanced Lessons Learned approach gave <strong>the</strong> final results a level <strong>of</strong> validation
that may not normally be recognized through a less thorough process.
Keywords: Lessons Learned, after event review, social network analysis, v2010, security planning, operational
research
1. Background
In February 2010, Canada hosted <strong>the</strong> Vancouver 2010 Winter Games (V2010). To ensure a “safe
and secure Games,” multiple safety and security agencies at three levels <strong>of</strong> government had to work
in an unprecedented partnership for security planning and operations. This whole-<strong>of</strong>-government 1
approach to domestic safety and security, <strong>of</strong>ten bringing toge<strong>the</strong>r non-traditional partners, provided a
unique opportunity to analyze collective strengths and weaknesses. Defence Research and
Development Canada – Centre for Security Science (DRDC CSS) undertook an After Event Review
(AER) which incorporated qualitative operational research methods into a Lessons Learned process.
The objective <strong>of</strong> <strong>the</strong> AER was to identify and analyze critical issues common to <strong>the</strong> federal security
partners and to make suggestions for best practices and process improvements in future domestic
inter-organizational public safety or security events.
The objective <strong>of</strong> a Lessons Learned process is to achieve performance improvement. Lessons are
only “learned” through an intentional process which results in changed doctrine, operating procedures
and behaviour. Organizational learning is challenging for most institutions and is only made more
complex in meta-organizational (i.e., an organization <strong>of</strong> organizations) situations as is experienced in
whole-<strong>of</strong>-government collaboration. One <strong>of</strong> <strong>the</strong> greatest challenges in making a Lessons Learned
process an effective organizational learning tool is in how to perform an analysis that produces
1 “Whole <strong>of</strong> government” denotes public service agencies working across portfolio boundaries to achieve a
shared goal and an integrated government response to particular issues. Approaches can be formal and informal.
They can focus on policy development, program management and service delivery.” Australian Public Service
Commission, 2004.
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reliable results. The AER incorporated operational research and analysis techniques into a Lessons
Learned process.
2. Lessons learned cycle
The Lessons Learned Cycle is based upon <strong>the</strong> premises <strong>of</strong> continuous learning as demonstrated in
<strong>the</strong> “OODA Loop” (Boyd, 1987). The four steps <strong>of</strong> <strong>the</strong> loop are to:
• Observe actions and <strong>the</strong>ir effects;
• Orientate and analyze <strong>the</strong>m to determine <strong>the</strong> basic causes and impacts and consider options
for behavioural or systemic change;
• Decide by accepting or rejecting <strong>the</strong> options; and
• Act by implementing <strong>the</strong> preferred course <strong>of</strong> action.
The OODA Loop is essentially a problem solving and decision making model that allows iterative but
deliberate change. Each <strong>of</strong> <strong>the</strong>se steps can be embedded into a broader Lessons Learned process
as illustrated in Figure 1 (adapted from Eaton et al., 2006).
Figure 1: Lessons Learned Cycle
The Lessons Learned Cycle normally begins in anticipation <strong>of</strong> an event, such as an exercise or
operation, as a result <strong>of</strong> a milestone in a process, or in <strong>the</strong> aftermath <strong>of</strong> an event. The process has six
steps: preparation, collection, analysis, endorsing and directing change, implementation, and
monitoring and validating change.
2.1 Preparation
Preparation includes identifying <strong>the</strong> functions and activities to be analyzed, <strong>the</strong> critical issues to be
explored, and <strong>the</strong> method and details <strong>of</strong> data collection. In anticipation <strong>of</strong> <strong>the</strong> event, a change leader
(<strong>the</strong> person responsible for <strong>the</strong> guiding documentation, process or event) will identify, in consultation
with o<strong>the</strong>r planners, <strong>the</strong> main issues which should be targeted for observation, e.g., are <strong>the</strong>
communications protocols between organizations functioning as required? Once <strong>the</strong> main issues are
identified, <strong>the</strong> methods <strong>of</strong> data collection should be identified and prepared. This stage should involve
a mission analysis to determine <strong>the</strong> objectives <strong>of</strong> <strong>the</strong> process or event.
2.2 Collection
Collection is <strong>the</strong> process <strong>of</strong> observing, collecting data, and identifying lessons. This involves <strong>the</strong>
actual data collection during or post-event by employing ei<strong>the</strong>r observers or participants. Collection
can occur through observation and check-lists, surveys, interviews, data collection and after action
reviews. Ideally, it can occur during <strong>the</strong> event, but may require post-event collection as well.
2.3 Analysis
Analysis is <strong>the</strong> systematic identification <strong>of</strong> issues, <strong>the</strong> objective analysis <strong>of</strong> <strong>the</strong> data, and <strong>the</strong> validation
<strong>of</strong> results with subject matter experts (SMEs). Whe<strong>the</strong>r scientifically rigorous or not, analysis is
important in processing subjective observations and dismissing <strong>the</strong> spurious assumptions to find
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au<strong>the</strong>ntic solutions. An important portion <strong>of</strong> this process is validation, whereby SMEs at various levels
determine if changes and solutions are workable. This can be accomplished through individual
consultation or in an organized conference <strong>of</strong> stakeholders.
2.4 Endorsing and directing change
Endorsing and directing change is <strong>the</strong> stage at which recommendations for corrective actions and
best practices are made to <strong>the</strong> change authorities (decision makers), who in turn accept or reject <strong>the</strong>
proposed changes. This occurs when <strong>the</strong> change authority considers <strong>the</strong> options and ei<strong>the</strong>r rejects or
endorses recommendations. It is <strong>the</strong>n <strong>the</strong> responsibility <strong>of</strong> <strong>the</strong> change agent or o<strong>the</strong>r <strong>of</strong>ficials to enact
<strong>the</strong> change.
2.5 Implementation
Implementation involves integrating <strong>the</strong> identified lessons into existing documentation, training, and
processes. This systemic change should beget behavioural change. During implementation, it is <strong>the</strong>
responsibility <strong>of</strong> <strong>the</strong> designated or multiple change agents to initiate and manage <strong>the</strong> recommended
best practices or corrective measures.
2.6 Monitoring and validating change
Monitoring and validating change is <strong>the</strong> confirmation that <strong>the</strong> recommendations have been
implemented and verification that <strong>the</strong> changes are effective. The change agent reports back to <strong>the</strong>
decision-maker and stakeholders as to <strong>the</strong> implementation and effectiveness <strong>of</strong> <strong>the</strong> change. It is at
this stage that <strong>the</strong> Lessons Learned Cycle begins again.
3. Methodology
3.1 Mission analysis
Clarifying mission objectives is an important aspect <strong>of</strong> <strong>the</strong> preparation stage in a Lessons Learned
process. It provides a baseline from which to ascertain whe<strong>the</strong>r <strong>the</strong> mission was successfully
achieved and what facilitated or hindered that. It also enables stakeholders to learn how to better
perform in future situations. In <strong>the</strong> case <strong>of</strong> whole-<strong>of</strong>-government security planning for V2010, <strong>the</strong>
mission was clear: a Safe and Secure Games. The mission essential components, (indicators <strong>of</strong> a
successful mission), or <strong>the</strong> operational objectives from a whole-<strong>of</strong>-government point <strong>of</strong> view,(how <strong>the</strong>
mission was to be accomplished), were not as clearly articulated.
It was necessary, <strong>the</strong>refore, to retrospectively determine what <strong>the</strong>se components and objectives were
in order to frame <strong>the</strong> lessons learned process overall. For example, one mission essential component
was deemed to be <strong>the</strong> need to provide a safe and secure environment for a successful international
sporting event – including regional celebrations and <strong>the</strong> protection <strong>of</strong> Internationally Protected
Persons and o<strong>the</strong>r dignitaries. Likewise, an operational objective for <strong>the</strong> whole-<strong>of</strong>-government
approach would be to test and confirm federal operational interoperability and readiness for V2010.
This mission analysis created <strong>the</strong> structural basis for scoping <strong>the</strong> overall project.
3.2 Critical issues
The first step in <strong>the</strong> methodology <strong>of</strong> <strong>the</strong> AER process was <strong>the</strong> identification <strong>of</strong> critical issues, which
fur<strong>the</strong>r aided <strong>the</strong> development and structuring <strong>of</strong> <strong>the</strong> rest <strong>of</strong> <strong>the</strong> methodology, as well as guided and
focused data collection. In order to identify <strong>the</strong> critical issues, V2010 planning phase documentation,
lessons learned reports from <strong>the</strong> exercise program, and post-event reports from individual partner
departments and agencies were reviewed and analyzed. Through this process, five main critical
issues arising out <strong>of</strong> federal whole-<strong>of</strong> government security planning and operations were identified for
exploration. These included such issues such as information sharing between partners. Based on
<strong>the</strong> critical issues, research questions were developed. The questions were used to guide <strong>the</strong>
structured portion <strong>of</strong> <strong>the</strong> interviews which followed.
3.3 Data collection
The data collection was comprised <strong>of</strong> four analytical processes:
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Collection <strong>of</strong> documentation and reports from <strong>the</strong> planning phase, <strong>the</strong> exercise program, and
post-event. These were primarily used to guide <strong>the</strong> identification <strong>of</strong> critical issues and later to
validate preliminary findings.
Interviews <strong>of</strong> senior <strong>of</strong>ficials who occupied pivotal positions at <strong>the</strong> strategic-operational interface
during V2010 planning and operations.
Collection <strong>of</strong> social network analysis data. The first phase data was collected as part <strong>of</strong> <strong>the</strong>
interviews while second phase data was collected by means <strong>of</strong> a web-based survey.
Collection <strong>of</strong> case studies data in three domain areas <strong>of</strong>: Critical Infrastructure Protection,
Chemical, Biological, Radiological, Nuclear and Explosives (CBRNE) preparedness, and interagency
cooperative information sharing as examined in <strong>the</strong> Olympic Marine Operations Centre.
The case study data was collected as part <strong>of</strong> <strong>the</strong> initial interviews as well as through follow-up
interviews <strong>of</strong> key stakeholders who were directly involved, at both <strong>the</strong> strategic and operational
levels, in each <strong>of</strong> <strong>the</strong> three domains.
3.4 Assessment criteria
To provide scientific rigour to <strong>the</strong> data analysis, appropriate benchmarks were required against which
results could be assessed. Currently, Canada does not have comprehensive, accepted and
standardized national benchmarks against which to assess capabilities. Given this, <strong>the</strong> Target
Capabilities List (TCL) developed by <strong>the</strong> Federal Emergency Management Agency was used as a
benchmark for <strong>the</strong> assessment (US Department <strong>of</strong> Homeland Security, 2007). In instances where <strong>the</strong>
subject was not within <strong>the</strong> scope <strong>of</strong> <strong>the</strong> TCL, o<strong>the</strong>r relevant best practices, extracted from literature,
were applied in <strong>the</strong> assessment (Lemyre, 2010)
3.5 Limitations and constraints
It is important to acknowledge <strong>the</strong> limitations and constraints <strong>of</strong> <strong>the</strong> methodology and process which
were implemented in <strong>the</strong> AER. The main constraint, given a study with such a broad scope, was
keeping with tight time-deadlines while at <strong>the</strong> same time ensuring scientific rigour and integrity. The
time deadlines were implemented to keep <strong>the</strong> information current and relevant and to ensure timely
feedback to <strong>the</strong> relevant stakeholders. However, given <strong>the</strong> tight timelines, not all issues were explored
in-depth and instead <strong>the</strong> AER focused on providing a broad overview <strong>of</strong> <strong>the</strong> main issues.
The second limitation was associated with <strong>the</strong> sole reliance on post-event data. Since <strong>the</strong> study was
initiated after V2010, this precluded any opportunity for in situ, real-time data collection during
operations. This limitation meant that <strong>the</strong> analysts had to rely on <strong>the</strong> interviewees’ recall, and
<strong>the</strong>refore a possibility existed that some issues may have been missed simply because <strong>the</strong>
interviewees may have forgotten about <strong>the</strong>m. This was mitigated by <strong>the</strong> relatively large number <strong>of</strong>
interviewees, many with overlapping experiences, and by employing data validation methods as
outlined below.
Finally, since <strong>the</strong> analysis employed in this study consisted largely <strong>of</strong> <strong>the</strong> syn<strong>the</strong>sis <strong>of</strong> <strong>the</strong> experiences
and opinions expressed by <strong>the</strong> interviewees, <strong>the</strong>re was a danger <strong>of</strong> “confirmation bias”, or <strong>the</strong>
tendency to form early opinions and selectively favour data that validated those opinions. In order to
avoid this phenomenon, <strong>the</strong> analysts continuously challenged one ano<strong>the</strong>r’s assumptions and
collectively identified any major <strong>the</strong>mes arising from <strong>the</strong> interviews, thus eliminating issues which were
not broadly acknowledged. Fur<strong>the</strong>rmore, where possible, analysis and conclusions were validated by
SMEs and compared with any available literature and supporting documentation. The final results
were also peer-reviewed.
3.6 Interviews
As <strong>the</strong> AER was focused on strategic issues related to V2010 security planning and operations, <strong>the</strong>
interviewees were selected from <strong>the</strong> population <strong>of</strong> senior <strong>of</strong>ficials representing all <strong>of</strong> <strong>the</strong> main partner
organizations and who operated at <strong>the</strong> strategic-operational interface. The interviewees held a unique
perspective, which enabled <strong>the</strong>m to provide views on <strong>the</strong> impact <strong>of</strong> strategic decisions on operations,
as well as <strong>the</strong> influence <strong>of</strong> operations on strategy. Initially, 30 potential interviewees were selected
based on <strong>the</strong>ir knowledge <strong>of</strong> <strong>the</strong>ir respective departments and agencies, as well as <strong>the</strong>ir personal
involvement in planning and operations. The target group <strong>of</strong> interviewees was eventually expanded to
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41, when it was determined that <strong>the</strong> breadth <strong>of</strong> partner departments and agencies, as well as <strong>the</strong>
diversity <strong>of</strong> roles, could not be covered by a smaller sample.
The criteria for <strong>the</strong> choice <strong>of</strong> analysts who conducted <strong>the</strong> interviews and performed <strong>the</strong> subsequent
analysis was also important. The interviewers consisted <strong>of</strong> three operational research scientists and
<strong>the</strong> primary author <strong>of</strong> this paper with a background in knowledge management. Although all <strong>the</strong>
interviewers came from Defence R&D Canada, an organization which was heavily involved in V2010
security planning and operations, none <strong>of</strong> <strong>the</strong> individuals chosen for <strong>the</strong> AER work were involved in
those activities. As such, <strong>the</strong> interviewers had little prior knowledge <strong>of</strong> <strong>the</strong> topic <strong>of</strong> <strong>the</strong> study, thus
ensuring external, independent and unbiased analysis.
The interviews were guided by a questionnaire which was developed through analysis <strong>of</strong> critical
issues as described above. The interviewees were asked to:
Please reflect on <strong>the</strong> strategic and operational experience that you gained from your involvement in
planning and operations for V2010 when answering <strong>the</strong> following questions.
From a whole-<strong>of</strong>-government perspective, what did you learn about planning and operations for
major events?
Do you have any particular suggestions or observations pertaining to federal whole-<strong>of</strong>government
major event planning and operations in <strong>the</strong> following areas:
a) Governance?
b) Inter-agency relationships?
c) Sensitive information sharing?
d) Assessment and protection <strong>of</strong> national, provincial and regional critical infrastructure?
How do existing federal government mandates enable or hinder <strong>the</strong> whole-<strong>of</strong>-government public
safety and security approach for major events?
Can you describe an example <strong>of</strong> learning or innovation that occurred within your organization as a
result <strong>of</strong> inter-agency involvement in planning and operations for <strong>the</strong> Games?
How will your organization make use <strong>of</strong> what it has learned for future, major inter-agency events?
Most <strong>of</strong> <strong>the</strong> interviews were recorded and all <strong>of</strong> <strong>the</strong> recordings were transcribed. Both <strong>the</strong> recordings
and <strong>the</strong> transcriptions were labeled using codes to protect <strong>the</strong> confidentiality <strong>of</strong> <strong>the</strong> respondents.
Once transcribed, <strong>the</strong> recordings were destroyed.
Initially, <strong>the</strong> interviews were quite structured, closely following <strong>the</strong> questionnaire; however, over time
<strong>the</strong>y evolved to a less structured format. More probing questions emerged as <strong>the</strong> interviewer’s
experience and level <strong>of</strong> comfort with <strong>the</strong> topics at hand grew. Fur<strong>the</strong>rmore, it became apparent that
flexibility and adaptability was needed, based on <strong>the</strong> particular experiences <strong>of</strong> individual interviewees
as well as <strong>the</strong>ir personalities, and <strong>the</strong> culture and operational model <strong>of</strong> <strong>the</strong>ir organizations. For
example, interviewees who came from “uniformed” police and military organizations, which tend to
have more “tight” and formalized cultures, generally preferred <strong>the</strong> structured interview format and
rarely ventured beyond <strong>the</strong> scope <strong>of</strong> <strong>the</strong> questionnaire. On <strong>the</strong> o<strong>the</strong>r hand, some interviewees
preferred to “tell stories” ra<strong>the</strong>r than strictly follow <strong>the</strong> questionnaire.
The analysis <strong>of</strong> <strong>the</strong> interview data consisted <strong>of</strong> coding <strong>the</strong> interview transcripts using QSR NVIVO
s<strong>of</strong>tware. The codes were developed based on <strong>the</strong> initially defined critical issues as well as major
<strong>the</strong>mes which emerged through <strong>the</strong> interview process. Seven main <strong>the</strong>mes were identified through
extensive discussion and collective analysis <strong>of</strong> <strong>the</strong> transcripts by <strong>the</strong> analysts. These included topics
such as governance, exercises and <strong>the</strong> roles <strong>of</strong> culture and personality.
To ensure that all analysts consistently interpreted <strong>the</strong> codes, some <strong>of</strong> <strong>the</strong> transcripts were analyzed
by more than one individual, and <strong>the</strong>n compared for uniformity <strong>of</strong> coding. Even after <strong>the</strong> coding work
was divided up among <strong>the</strong> analysts, frequent meetings were held to discuss <strong>the</strong> findings and progress
and to ensure consistency <strong>of</strong> <strong>the</strong> analysis approach. Once all <strong>the</strong> transcripts were coded, <strong>the</strong>
information was sorted according to individual codes and divided up among <strong>the</strong> analysts responsible
for <strong>the</strong>ir respective issues.
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Throughout <strong>the</strong> interviews and <strong>the</strong> subsequent analysis, care was taken to protect <strong>the</strong> privacy <strong>of</strong> <strong>the</strong>
interviewees. It was important for <strong>the</strong> study that <strong>the</strong> interviewees spoke on <strong>the</strong> condition <strong>of</strong> anonymity,
with <strong>the</strong> intention that this would provide more candid information and allow individuals to address
issues which <strong>the</strong>y may o<strong>the</strong>rwise not be comfortable discussing. To ensure that <strong>the</strong> identity <strong>of</strong> <strong>the</strong>
interviewees was protected, all electronic files were labeled with randomized codes and precautions
were taken with data storage and sharing. All <strong>the</strong> recordings, transcriptions, and notes taken by <strong>the</strong>
interviewers were destroyed after <strong>the</strong>y were analyzed and <strong>the</strong> relevant information extracted.
Fur<strong>the</strong>rmore, <strong>the</strong> interview results were anonymized to <strong>the</strong> point where <strong>the</strong>y could not be attributed to
any single individual. For example, any direct quotations used in <strong>the</strong> analysis were stripped <strong>of</strong> any
detail which could potentially indentify an individual.
3.7 Social network analysis
Social network analysis (SNA) is <strong>the</strong> study <strong>of</strong> relationships between people and organizations. By
compiling individual relationship links between members <strong>of</strong> a group, a network is derived that can be
analyzed to understand interactions and positions within a social group. An SNA was undertaken as
part <strong>of</strong> <strong>the</strong> AER to identify information-sharing relationships that existed between <strong>the</strong> people who were
involved in V2010 security planning and operations. Given that individuals represented different
departments and agencies, <strong>the</strong> SNA provided a means <strong>of</strong> assessing <strong>the</strong> state <strong>of</strong> whole-<strong>of</strong>-government
cooperation.
The following overarching research questions were used to guide <strong>the</strong> SNA and provide a structure for
<strong>the</strong> analysis:
Are <strong>the</strong> people and organizations that should be contact with one ano<strong>the</strong>r actually in contact to
facilitate major events security planning?
Are <strong>the</strong> interactions <strong>the</strong> organizations reported in <strong>the</strong> social network analysis consistent with <strong>the</strong>
interview findings?
Which are <strong>the</strong> central organizations in <strong>the</strong> network?
Forty interviewees completed <strong>the</strong> SNA portion <strong>of</strong> <strong>the</strong> study. The interviewees were asked to record as
many names as desired based on <strong>the</strong>ir response to <strong>the</strong> following question: “Who were <strong>the</strong> key people
with whom you shared information on V2010 planning and operations?” Since <strong>the</strong> question was
posed during <strong>the</strong> interviews it was designed to be very general in order not to limit free recall. While
information sharing typically implies both transmitting and receiving information, <strong>the</strong> question did not
specify this directly nor did it ask <strong>the</strong> respondents to specify <strong>the</strong> frequency <strong>of</strong> interaction. The
interpretation was left up to <strong>the</strong> individual interviewees, and <strong>the</strong>refore <strong>the</strong> only thing that could be
established with certainty from <strong>the</strong> data collected, was whe<strong>the</strong>r an information sharing relationship
was present or not. The 40 respondents nominated a total <strong>of</strong> 341 key contacts that were assembled
into a social network which is shown in Figure 2.
The person-to-person information sharing network gave insight into relationships between V2010
security and safety stakeholders and analysis <strong>of</strong> <strong>the</strong> positioning <strong>of</strong> individuals in <strong>the</strong> network provided
valuable information about that <strong>the</strong> different roles <strong>the</strong> individuals played in <strong>the</strong> network. For example,
analysis revealed that some individuals served as a bridge, or information sharing conduit, connecting
o<strong>the</strong>rwise disconnected individuals. The SNA also revealed that approximately 55% <strong>of</strong> <strong>the</strong>
interviewees appeared as central figures in <strong>the</strong> network, confirming <strong>the</strong> appropriate choice <strong>of</strong> <strong>the</strong>
sample population. A separate analysis was undertaken that looked at key information sharing
relationships between <strong>the</strong> organizational entities, as opposed to individuals. This analysis revealed
which organizations served as intermediaries, linking toge<strong>the</strong>r different communities, and which
operated more on <strong>the</strong> periphery <strong>of</strong> <strong>the</strong> network.
While <strong>the</strong> data collected through <strong>the</strong> SNA proved very valuable it is important to recognize its
limitations. Firstly, data was obtained from <strong>the</strong> 40 interviewees only <strong>the</strong>refore <strong>the</strong> scope <strong>of</strong> <strong>the</strong>
analysis was necessarily limited to a specific category <strong>of</strong> individual. Secondly, <strong>the</strong> data was collected
at <strong>the</strong> level <strong>of</strong> <strong>the</strong> individual and <strong>the</strong>refore <strong>the</strong>re was a possibility that some interviewees may have
overstated <strong>the</strong>ir connections in order to portray <strong>the</strong>mselves in a positive light. To counteract this
tendency, during <strong>the</strong> interviews, <strong>the</strong> interviewers sought to limit <strong>the</strong> appearance <strong>of</strong> expectations
concerning <strong>the</strong> number <strong>of</strong> identified contacts and emphasized <strong>the</strong> need to convey only key contacts.
Ano<strong>the</strong>r limitation is that missing or incomplete data was especially problematic as it could lead to
skewed results with some individuals being ei<strong>the</strong>r underrepresented or overemphasized. To address
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this issue, a follow-up on-line survey was administered to ga<strong>the</strong>r social network data from 72
additional individuals who were not interviewed, but who were identified by more than one <strong>of</strong> <strong>the</strong>
interviewees as being key contacts. Notwithstanding <strong>the</strong> limitations <strong>of</strong> <strong>the</strong> data, <strong>the</strong> SNA portion <strong>of</strong> <strong>the</strong>
AER provided valuable insight into <strong>the</strong> information sharing relationships which existed within <strong>the</strong>
security and safety communities during V2010 planning and operations. The results both
complemented and supported <strong>the</strong> interview data.
Figure 2: Social network diagram representing key person-to-person information sharing relationships
during V2010 security planning and operations.
3.8 Case studies
Three case studies were selected as topics which would provide a more in-depth exploration <strong>of</strong> <strong>the</strong>
critical issues. Both CBRNE preparedness and Critical Infrastructure Protection were challenging
cross-cutting topics which involved multiple organizations and inter-jurisdictional discussions. By
exploring <strong>the</strong>m in more detail, it was anticipated that <strong>the</strong> analysis could indicate how <strong>the</strong>se topics
might be managed in future event planning and in normal operations. The Olympic Marine
Operations Centre was selected as a case study illustrating inter-agency cooperative information
sharing because it was one <strong>of</strong> <strong>the</strong> operational centres which were considered to be particularly
successful in managing organizational information sharing barriers. Although more in-depth, <strong>the</strong> case
studies followed <strong>the</strong> same analytical pattern as <strong>the</strong> issues analysis, including a capability analysis.
Some <strong>of</strong> <strong>the</strong> case study data was obtained during <strong>the</strong> initial interviews with <strong>of</strong>ficials who were directly
involved in <strong>the</strong> three chosen topics, however many follow-up interviews were scheduled with o<strong>the</strong>r
pertinent individuals who were not a part <strong>of</strong> <strong>the</strong> original group <strong>of</strong> 41 interviewees. One issue which
arose, because <strong>of</strong> <strong>the</strong> large number <strong>of</strong> individuals from whom input was solicited, was that at times
diverging accounts and opinions <strong>of</strong> certain outcomes were recalled. This put added pressure on <strong>the</strong>
analysts to fact-check and validate all accounts. Ano<strong>the</strong>r challenge was that DRDC staff had been
heavily involved in each <strong>of</strong> <strong>the</strong> three case study areas in <strong>the</strong> planning leading up to V2010 and it was
recognized that <strong>the</strong>re might be a danger <strong>of</strong> bias in <strong>the</strong> analysis. To compensate for this, <strong>the</strong> analysts
were required to validate <strong>the</strong>ir findings with non-DRDC SMEs.
4. Discussion
While <strong>the</strong> conclusions <strong>of</strong> <strong>the</strong> study have not been disseminated and cannot be shared outside <strong>of</strong> <strong>the</strong>
stakeholder group, <strong>the</strong> results have shown that <strong>the</strong> challenges <strong>of</strong> whole-<strong>of</strong>-government initiatives most
<strong>of</strong>ten involve complex or systemic issues that are not easily resolved. The AER itself, however,
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illustrated both benefits and challenges to <strong>the</strong> Lessons Learned process as it pertains to metaorganizational
learning.
Firstly, <strong>the</strong> validity <strong>of</strong> <strong>the</strong> conclusions <strong>of</strong> a Lessons Learned process is heavily dependent on <strong>the</strong>
reliability <strong>of</strong> <strong>the</strong> analysis. The AER’s concentration on <strong>the</strong> analytical approach was able to produce
results that were considered rigorous and reliable by <strong>the</strong> stakeholders. The analysis relied upon <strong>the</strong>
“evidence” provided by <strong>the</strong> subjects; <strong>the</strong> views had to be validated, first by complimentary evidence
from o<strong>the</strong>r subjects and secondly by independent SMEs. Similarly, potential solutions posited by <strong>the</strong>
analytical team had to be verified and confirmed by a diversity <strong>of</strong> SMEs. This approach provided <strong>the</strong>
stakeholders with confidence that <strong>the</strong> analysis was reliable and not subjective.
Secondly, <strong>the</strong> capability assessment provided a mechanism against which to compare, even
benchmark, whole-<strong>of</strong>-government capability. Without <strong>the</strong> use <strong>of</strong> such tools as <strong>the</strong> TCL, it would have
been difficult to determine how future initiatives might be sustained or improved. The TCL is a
comprehensive list <strong>of</strong> best practices in <strong>the</strong> public safety and security domain and was able to serve as
a de facto standard for some <strong>of</strong> <strong>the</strong> issues, e.g., information sharing, planning and exercises.
Unfortunately, such capability lists are not available for all aspects <strong>of</strong> meta-organizational issues and
literature reviews were undertaken to provide guidance for o<strong>the</strong>r topics, e.g., governance, culture and
personality (Lemyre, 2010).
The most significant challenge to <strong>the</strong> AER as a meta-organizational Lessons Learned process was
<strong>the</strong> need to engage all <strong>of</strong> <strong>the</strong> partner agencies in a non-threatening way. The participant
organizations needed to be assured that <strong>the</strong>y were not be evaluated or judged. This was
accomplished through extensive communications in explaining <strong>the</strong> objective <strong>of</strong> a Lessons Learned
process to be “no fault” and to search for issues <strong>of</strong> mutual concern. The consultation and
engagement <strong>of</strong> SMEs throughout <strong>the</strong> process ensured that delicate issues were handled sensitively
and that assumptions were appropriately questioned.
In terms <strong>of</strong> <strong>the</strong> challenges associated with integrating Lessons Learned processes in a whole-<strong>of</strong>government
context, while similar to those <strong>of</strong> individual organizations, <strong>the</strong>y are exacerbated by <strong>the</strong>
complexity posed in meta-organizations. For lessons to be truly learned, organizational and
behavioural change can only result when a change authority accepts <strong>the</strong> proposed best practices or
corrective actions and directs that <strong>the</strong>y are implemented. It may be difficult to determine who <strong>the</strong>
appropriate change authority is in an individual organization and even more difficult to convince that
individual <strong>of</strong> <strong>the</strong> need for implementation. In a whole-<strong>of</strong>-government lessons learned process, it is
more complicated to determine who should, or even can, influence or direct change. Solid analysis,
<strong>the</strong>refore, is critical to positioning <strong>the</strong> results for endorsement by <strong>the</strong> change authorities in situations
where <strong>the</strong>y could impact many organizations.
5. Conclusion
The AER process described here covered only <strong>the</strong> first three stages <strong>of</strong> <strong>the</strong> Lessons Learned Cycle.
Each stage <strong>of</strong> <strong>the</strong> Cycle poses its own challenges and <strong>the</strong> analysis phase can <strong>of</strong>ten be overlooked or
underestimated in traditional Lessons Learned processes. The AER applied a robust approach to <strong>the</strong>
analysis stage with a four methodology approach: document and literature review, interviews, social
network analysis and case studies. The capability analysis provided an additional benchmarking
framework. It is through this robust analytical process that stakeholders can be assured <strong>of</strong> <strong>the</strong>
thoroughness <strong>of</strong> <strong>the</strong> results and can <strong>the</strong>refore make informed decisions on complex issues.
References
Boyd, J. (1987) A discourse on winning and losing, Maxwell Air force Base AL: Air University Library Document
No. M-U 43947 (Briefing Slides).
Eaton, Jacqueline, Redmayne, John, and Thordsen, Marvin (2006) Joint Analysis Handbook (2 nd ed.), NATO
Joint Analysis and Lessons Learned Centre, Lisbon.
Lemyre, Louise, et al. (2010) Literature Review on Best Practices in Collective Learning. Defence R&D Canada –
Centre for Security Science Contractor Report 2010-11, November 2010.
Lemyre, Louise, et al. (2010) Literature Review on Best Practices in Meta-Organizational Governance. Defence
R&D Canada – Centre for Security Science Contractor Report 2010-12, November 2010.
US Department <strong>of</strong> Homeland Security (2007) Target Capabilities List: A Companion to <strong>the</strong> National
Preparedness Guidelines. Retrieved from https:///www.rkb.us/pspd8.cfm.
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Knowledge Ontology in Labor Outsourcing Environments
Óscar Arias Londoño
Student <strong>of</strong> PhD in Administration, Universidad Eafit, Colombia
oariaslo@eafit.edu.co
Abstract: The purpose <strong>of</strong> this paper is to show progress on ontological aspects <strong>of</strong> tacit knowledge, found in
Colombia's textile companies where outsourcing occurs. It is a qualitative study based on four concepts that
are analized in a spiral: identity, autonomy, tacit knowledge and ontology; citing authors such as
Foucault, Kant, Nonaka-Takeuchi, and Bédard. Partial results show that changes in <strong>the</strong> tacit
knowledge occur when workers move from having a direct contract to be subcontracted; is also
observed that <strong>the</strong>se changes occur in parallel in <strong>the</strong> ontological dimension, identity and autonomy
<strong>of</strong> workers. The idea <strong>of</strong> focusing <strong>the</strong> analysis on <strong>the</strong> ontological dimension arises from <strong>the</strong> need for a
debate on a general presumption in <strong>the</strong> academic literature, where <strong>the</strong> new knowledge in companies
must be managed through strategically designed programs. This paper shows that tacit knowledge is
also a result <strong>of</strong> contingencies arising from decisions on contract workers. The value and usefulness <strong>of</strong>
<strong>the</strong> partial results for <strong>the</strong> future <strong>of</strong> this research is to provide criteria to broaden understanding <strong>of</strong> tacit
knowledge in textile companies, based on analysis <strong>of</strong> current policies <strong>of</strong> labor flexibility in today's
world.
Keywords: Ontology, tacit knowledge, identity, autonomy, labor outsourcing.
1. Problem’s context
The decline <strong>of</strong> <strong>the</strong> big enterprises starts at <strong>the</strong> end <strong>of</strong> <strong>the</strong> eighties; this fact comes along with <strong>the</strong>
surge <strong>of</strong> re engineering and outsourcing <strong>of</strong> industrial processes. In Colombia, <strong>the</strong> textile sector
subcontracts workers from cooperatives which had been created by employees who formerly
belonged to <strong>the</strong> textile companies; <strong>the</strong>se workers were sometimes obliged to create <strong>the</strong>se
cooperatives in order to avoid being dismissed.
There are two main reasons causing outsourcing in this sector: a) <strong>the</strong> separation <strong>of</strong> complementary
productive areas and, b) <strong>the</strong> removal <strong>of</strong> structural areas within organizations. Consequently, two
situations emerged: In one situation, <strong>the</strong> employees took over management <strong>of</strong> <strong>the</strong> companies as an
alternative solution to bankruptcy or liquidation. In <strong>the</strong> o<strong>the</strong>r one, determined <strong>the</strong> creation <strong>of</strong>
cooperatives derived from rationality <strong>of</strong> capital investment.
The current debate in <strong>the</strong> country is about <strong>the</strong> convenience <strong>of</strong> subcontracting cooperatives since, on
one hand, <strong>the</strong>ir contractual terms represent forms <strong>of</strong> labor exploitation and undermining and on <strong>the</strong>
o<strong>the</strong>r hand, <strong>the</strong>se contractual practices represent relevant economical benefits for exporting
companies and consequently, for <strong>the</strong> country. Based on <strong>the</strong> previous context, <strong>the</strong> problem <strong>of</strong> research
is: What are <strong>the</strong> ontological aspects <strong>of</strong> tacit knowledge emerging in <strong>the</strong> textile sector companies in
Colombia, which use labor outsourcing?
2. Theoretical references
The labor outsourcing environments in <strong>the</strong> textile sector in Colombia originated as a consequence <strong>of</strong>
<strong>the</strong> hiring companies’ managerial decisions, <strong>the</strong>n <strong>the</strong> ethical aspects included in <strong>the</strong>se decisions
become important for this analysis since <strong>the</strong>y influence human behavior and consequently,
knowledge management. This leads to consider four aspects:
Employees’ behavior is reflected between conviction and / or convenience and <strong>the</strong>se can lead to
establish autonomy levels. Kant stated that autonomy is only possible if people express <strong>the</strong>ir will
to think and act freely; this represents morality’s main principle which supports every action
rational beings take; freedom, autonomy and morality’s main principle are closely linked concepts
(Kant, 1999: p. 42)
Behavioral manifestations represent actions which define employees’ identity since <strong>the</strong>se actions
lead <strong>the</strong>m to achieve a singular truth; and this one risks <strong>the</strong>ir own individuality (Foucault, 2006:
33).
Tacit knowledge can be identified if all aspects related to subcontracted employees (and <strong>the</strong>ir
contractual terms influencing <strong>the</strong>ir behavior) are studied. Nonaka and Takeuchi (1999: 65)
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Óscar Arias Londoño
mention that knowledge creation involves two dimensions: <strong>the</strong> ontological and <strong>the</strong>
epistemological. They say that <strong>the</strong> first one states that knowledge is solely created by individuals.
The second one is explained based on Polanyi (1966), who establishes differences between <strong>the</strong>
tacit and <strong>the</strong> explicit knowledge. Tacit knowledge is specific, personal and it is difficult to formalize
and express; it includes cognitive elements - mental models like schemes, paradigms, beliefs and
point <strong>of</strong> view, know-how, specific skills (Nonaka y Takeuchi, 1999: 66). In brief, tacit knowledge
implies that in each activity <strong>the</strong>re are two dimensions present: one that focuses on <strong>the</strong> target
object; and <strong>the</strong> o<strong>the</strong>r is <strong>the</strong> knowledge used to interpret that target.
The ontological dimension represents support for tacit knowledge since it deals with concept
fundamentals <strong>of</strong> reality; from <strong>the</strong>se, human behavior is explained. Ontology is <strong>the</strong> deepest
component <strong>of</strong> <strong>the</strong> framework; it is <strong>the</strong> soil in which people’s ideas and concepts are rooted; due to
this, <strong>the</strong>y are difficult to explore and provide great guidelines by allowing individuals orientation,
assuring base intelligibility, and promote fundamental certainties. It includes founding paradigms
like common points <strong>of</strong> view shared by several individuals about a topic at a certain time and place
(Bédard, 2003: 83).
3. Objective
Identify <strong>the</strong> tacit knowledge ontological aspects emerging in labor outsourcing environments.
4. Methodology
Definition <strong>of</strong> research problem: What are <strong>the</strong> ontological aspects <strong>of</strong> tacit knowledge emerging in
<strong>the</strong> textile sector companies in Colombia, which use labor outsourcing?
Review <strong>of</strong> <strong>the</strong> <strong>the</strong>oretical aspects: Identity, autonomy, tacit knowledge and ontology.
Selection <strong>of</strong> <strong>the</strong> companies under study: The three major textile companies in <strong>the</strong> country.
Selection <strong>of</strong> informants: Identification by qualitative sampling <strong>of</strong> subcontracted workers, and <strong>the</strong>
managers <strong>of</strong> three companies.
Design data collection instruments: Ethnographic method and documental.
Fieldwork: Performing in-depth interviews and document review.
Information analysis: Application <strong>of</strong> matrices to classify and correlate data recorded.
5. Partial findings
5.1 Contractual relationship
Diverse types <strong>of</strong> contracts are used and <strong>the</strong>ir purpose is to partially or fully outsource different
production processes:
Production: <strong>the</strong> hiring company can transfer its technology to its employees in order to guarantee
<strong>the</strong> quality <strong>of</strong> <strong>the</strong> outsourced process.
Mandate contract: The hiring company works as a mandator; it entrusts productive processes to a
mandatary cooperative. The hiring company takes on <strong>the</strong> resulted produce.
Service contract: For short-term production processes; <strong>the</strong>re is no direct labor link or
subordination or authority relationships between <strong>the</strong> hiring and hired companies.
Contract for a specific project or service: There are two kinds: 1) when <strong>the</strong> execution <strong>of</strong> <strong>the</strong>
process is time predetermined; and even though, its duration is initially limited, it is uncertain. 2)
when hiring companies face unexpected market variations.
Commercial <strong>of</strong>fer: It is also called service leasing; <strong>the</strong> subcontracted worker is considered as an
entrepreneur (owner) so a parity relationship is established between <strong>the</strong> parts.
5.2 Employees’ identity (See: Table 1)
From <strong>the</strong> previous contractual terms, <strong>the</strong> changes observed in <strong>the</strong> subcontracted employees’ identity
are determined by <strong>the</strong> following circumstances:
Before being an associate worker at <strong>the</strong> cooperative, he/she was a direct employee <strong>of</strong> <strong>the</strong> company
that is currently subcontracting him/her.
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Óscar Arias Londoño
Before being subcontracted, <strong>the</strong> employee earned a fixed salary regulated by <strong>the</strong> Código
Sustantivo del Trabajo (a Colombian state law that regulates salaries); now he/she gets a
regulated compensation determined by a special compensations system.
Before being subcontracted, <strong>the</strong> employee had a stable job, now it is uncertain since its continuity
depends on maker variations.
Before being subcontracted, <strong>the</strong> employee was a subordinate, now he/she is called a freelance
entrepreneur but he/she depends on <strong>the</strong> subcontracting company.
Before being subcontracted, <strong>the</strong> employee used technology belonging to <strong>the</strong> hiring company, now
that <strong>the</strong> employee is an associate at <strong>the</strong> cooperative, this technology belongs to <strong>the</strong> hiring
company despite <strong>of</strong> <strong>the</strong> fact that <strong>the</strong> employee actually owns <strong>the</strong> production means.
Table 1: Employees’ identity
5.3 The employees’ autonomy (See: Table 2)
5.3.1 Aspects that favor autonomy. When <strong>the</strong> associated employees have <strong>the</strong> possibility <strong>of</strong> being
autonomous, it happens because <strong>the</strong>ir cooperatives present <strong>the</strong> following characteristics:
The general manager’s education and experience are aligned to <strong>the</strong> cooperative corporate
mission statement.
There was auto-determination from <strong>the</strong> employees in order to create <strong>the</strong> cooperative.
There was capacity for technological appropriation.
5.3.2 Aspects that limit autonomy. There are limitations in <strong>the</strong> most number <strong>of</strong> cooperatives for
employees to exert autonomy due to <strong>the</strong> following reasons:
There was influence from <strong>the</strong> hiring company in <strong>the</strong> creation <strong>of</strong> <strong>the</strong> cooperative.
There are unfavorable aspects in corporate life: low participation in <strong>the</strong> cooperative’s
administration and low educational level <strong>of</strong> <strong>the</strong> associates.
There are unequal power relationships against <strong>the</strong> hiring company.
There is incapacity for technological appropriation.
Table 2: The employees’ autonomy
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5.4 Tacit Knowledge
Óscar Arias Londoño
5.4.1 Cognitive elements. The following trends were found among <strong>the</strong> employees:
Scheme: They have <strong>the</strong> capacity to understand authority and hierarchical roles between <strong>the</strong> hiring
company and <strong>the</strong> cooperative; this show <strong>the</strong>m <strong>the</strong> unbalances in <strong>the</strong> power relationships.
Paradigm: They express that whereas capitalism rules <strong>the</strong> economical relations, it will not be
possible to overcome <strong>the</strong> current poverty levels; trying to survive becomes <strong>the</strong> only alternative.
Belief: They consider that outsourcing is a mechanism for labor exploitation.
Opinion: They want <strong>the</strong> government and hiring companies think <strong>of</strong> <strong>the</strong>ir lifestyle quality and <strong>the</strong>ir
families’.
5.4.2 Technical elements. The following aspects have been found among <strong>the</strong> employees:
Know-how: They call <strong>the</strong>mselves experts due to <strong>the</strong> previous labor experience, <strong>the</strong>ir learning
skills and capacity for transferring <strong>the</strong>ir knowledge to o<strong>the</strong>rs.
The Job: They highly value <strong>the</strong>ir performance since it is crucial for <strong>the</strong> company’s progress and
for <strong>the</strong>mselves as employees.
Skills: They work hard on mastering <strong>the</strong>ir technical performance in order to improve in <strong>the</strong>ir field
and get promoted in <strong>the</strong>ir companies or elsewhere seeking for better salaries.
Competences: They do not want to decrease <strong>the</strong>ir current performance by demonstrating <strong>the</strong>ir
knowledge on <strong>the</strong>ir fields even though contractual terms are not a motivation.
Table 3: Tacit knowledge aspects and characteristics
5.5 Ontological aspects related to tacit knowledge (See: Table 4)
5.5.1 Related to <strong>the</strong> cognitive. Three aspects have been found:
A duality between <strong>the</strong> autonomy sense as owners <strong>of</strong> <strong>the</strong> cooperative and <strong>the</strong> sense o heteronomy
derived from outsourcing.
Self–puzzlement feeling for being part <strong>of</strong> a cooperative company and <strong>of</strong> ano<strong>the</strong>r one that
competes.
A feeling <strong>of</strong> being temporarily working for <strong>the</strong> cooperative and wish for being directly hired by <strong>the</strong>
hiring company.
5.5.2 Related to <strong>the</strong> technical. Two aspects have been found.
A deeply rooted wish and conviction <strong>of</strong> finding new and better labor conditions leading to improve
<strong>the</strong>ir quality <strong>of</strong> life.
Moral conflict in terms <strong>of</strong> commitment to <strong>the</strong> cooperative or <strong>the</strong> hiring company.
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Table 4: Ontologies <strong>of</strong> tacit knowledge
6. Conclusion
Óscar Arias Londoño
Labor outsourcing in <strong>the</strong> textile sector in Colombia has caused spiral-shaped transformations on
identity, autonomy, tacit knowledge creation and <strong>the</strong> ontological dimension <strong>of</strong> employees. (See:
Graph 1). The companies that outsource are currently incorporating knowledge management
methodologies conceived for cultural realities where labor stability seem to be a fairytale. The paradox
is that while companies make efforts towards knowledge management, employees experience a
different reality, full <strong>of</strong> contradictions. A deeper analysis <strong>of</strong> information is <strong>the</strong> challenge that research
faces in <strong>the</strong> future; an analysis to design a proposal leading to a better knowledge management in
textile companies starting from a pr<strong>of</strong>ound comprehension <strong>of</strong> human relationships (Von Krogh, Ichijo
Nonaka, 2001: 28) in a world <strong>of</strong> labor outsourcing.
Graph 1: Spiral-shaped transformations
References
Bédard, R. (2003). Los fundamentos del pensamiento y las prácticas administrativas. El rombo y las cuatro
dimensiones filosóficas, En: Revista ADMINISTER, N°3, Universidad Eafit, Medellín, Colombia.
Foucault, M. (2006). La hermenéutica del sujeto, Fondo de Cultura Económica, México D.F.
Kant, I. (1980). Fundamentación de la metafísica de las costumbres, Publicación de la Biblioteca Virtual Miguel
de Cervantes, 1999. Edición digital basada en la 6ª edición, Madrid, Espasa-Calpe, 1980. Traducción:
Miguel García Morente.
Nonaka, I. y Takeuchi, H. (1999). La organización creadora de conocimiento, Oxford University Press, México D.
F.
Von Krogh, G., Ichijo, K. y Nonaka, I. (2001). Facilitar la creación de conocimiento. Cómo desentrañar el misterio
del conocimiento tácito y liberar el poder de la innovación, Oxford University Press, México D.F.
1133

Proposal For A 2.0 Knowledge Management Model for
“Medellin, Cluster City (Medellin, City <strong>of</strong> Knowledge)
Lillyana María Giraldo Marín 1 , Jenny Martínez Crespo 2 , José Alfredo Vásquez
Paniagua 2 and Luis Joyanes Aguilar 3
1
School <strong>of</strong> Engineering, Universidad de Medellín, Colombia
School <strong>of</strong> Economic and Managerial Sciences, Universidad de Medellin,
Colombia
3
Universidad Pontificia de Salamanca, Madrid, España
lmgiraldo@udem.edu.co
jmartinez@udem.edu.co
javasquez@udem.edu.co
luis.joyanes@upsam.net
Abstract: According to studies conducted in emerging economies, clusters are defined as strategies leading to
power regional economic growth processes around <strong>the</strong> globe. This happens through knowledge creation,
appropriation, transfer and distribution. Agents belonging to diverse sectors are involved into clusters such as <strong>the</strong>
governmental sector, private, gild, entrepreneurial and <strong>the</strong> educational. Clusters are nowadays a fundamental
tool to stimulate entrepreneurial growth in cities since it streng<strong>the</strong>ns competitiveness in <strong>the</strong> regions and in <strong>the</strong>
economies. Clusters allow companies from a certain sector to build business networks; spot new trade niches
and become more competitive and more sustainable. As a top priority <strong>of</strong> its strategic plan <strong>of</strong> development,
Medellin has given emphasis to <strong>the</strong> project “Medellin, a Cluster City”. This project makes part <strong>of</strong> a bigger plan
called “Medellin, City <strong>of</strong> Knowledge”. Setting up and supporting clusters demand from <strong>the</strong> city proper knowledge
infrastructure which leads it to achieve diverse objectives; objectives aiming at creating innovation, a chrysalis
adapted to <strong>the</strong> city’s context. This article presents a knowledge transfer model supported by Web 2.0 platforms
which assists collaboration and interaction (generating associativity and cooperation) as fundamental
components <strong>of</strong> clusters. Under <strong>the</strong>se approaches and <strong>the</strong> supportive technologies, collective intelligence is
expected to surge. The knowledge transfer model is based on Nonaka and Takeuchi’s knowledge creation
model.
Keywords: Cluster, Knowledge transfer model, Web 2.0, collective intelligence
1. Introduction
Knowledge Management (KM) has become a great interest subject recently; knowledge and its
management have been associated to <strong>the</strong> definition <strong>of</strong> strategy and corporate performance. In this
sense, <strong>the</strong> impact <strong>of</strong> KM on companies can be verified through <strong>the</strong> reports issued by <strong>the</strong> Organisation
for Economic Co-operation and Development (OCED) since 1999; according to <strong>the</strong>se, economies
based on KM have achieved competitive advantages in innovation, science, technology, human
resources management and investment in research (Riesco, 2006).
Having those advantages derived from KM as starting points, new organizational structures have
emerged to support creation and knowledge transfer processes; among those new organizational
structures, clusters have prevailed.
Clusters are defined as strategies leading to power regional economic growth processes around <strong>the</strong>
globe through knowledge creation, appropriation, transfer and distribution. Porter (1991) states that
clusters are groups <strong>of</strong> companies linked in a value system (chain value) involving diverse agents such
as <strong>the</strong> governmental sector, private, gild, entrepreneurial and <strong>the</strong> educational. A cluster is a
development strategy that permits companies from a certain sector to build business networks;
identify new commercial possibilities and become more competitive and sustainable. Interaction
among <strong>the</strong> agents inside <strong>the</strong> value system (local, regional or global) generates collective competitive
advantages. Thus, cooperation and associativity processes are born among <strong>the</strong> companies leading to
<strong>the</strong> cluster’s genesis and consolidation.
Cooperation and associativity among <strong>the</strong> companies belonging to <strong>the</strong> cluster require means to
support collaboration and interaction. These processes can be supported by Web 2.0 technologies
which provide diverse alternatives to promote interactivity among <strong>the</strong> agents. These means favor
knowledge creation, transfer and distribution processes leading to opportunities for knowledge
socialization, exteriorization, combination and interiorization momentums; <strong>the</strong>se are <strong>the</strong> stages
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Lillyana María Giraldo Marín et al
composing Nonaka and Takeuchi’s knowledge creation model (1995, 2002). Each <strong>of</strong> <strong>the</strong> stages
includes <strong>the</strong>ir own knowledge transfer processes. This continuous knowledge creation and transfer
inside <strong>the</strong> organization assists <strong>the</strong> emergence <strong>of</strong> ‘collective intelligence’; that is to say, intelligence
spread throughout <strong>the</strong> organization powered by cooperation and associativity (Lévy, 2004).
2. Methodology
The Knowledge Transfer Model is <strong>the</strong> result <strong>of</strong> a qualitative, reflexive research project whose
methodological strategy is Ethnography, particularly <strong>the</strong> one from Werner and Schoepfle (1987a).
Documental research (Mac Donald y Tipot, 1997) was conducted; also interviews with experts were
held and literature was reviewed. The interviews and <strong>the</strong> documental research were accompanied by
experts in clusters, knowledge management and knowledge transfer processes.
2.1 The 2.0 knowledge transfer model
Companies usually manage knowledge through technology and information systems; this requires
identifying <strong>the</strong> knowledge available within <strong>the</strong> organization; an inventory <strong>of</strong> <strong>the</strong> patented technology,
<strong>the</strong> company’s know- how and <strong>the</strong> intangible assets. The Japanese school <strong>of</strong> <strong>the</strong> <strong>the</strong>ory based on
knowledge (Nonaka, 1991; Nonaka and Takeuchi, 1995); and o<strong>the</strong>r authors like Garud and Navyar
(1994) and Kogut and Zander (1992, 1995) consider that KM in organizations should be studied from
<strong>the</strong>ir internal processes, that is to say, identifying how companies create and transfer knowledge,
determining <strong>the</strong>ir knowledge stock and how it is used in <strong>the</strong> creation <strong>of</strong> new knowledge. According to
this, <strong>the</strong> transfer stands for one <strong>of</strong> <strong>the</strong> links in <strong>the</strong> process <strong>of</strong> KM.
According to Zapata (2008), knowledge transfer can be understood as <strong>the</strong> analysis <strong>of</strong> collecting <strong>the</strong>
benefits from knowledge exchange momentums and <strong>the</strong> technical processes or platforms that make
this possible.
Knowledge transfer refers to <strong>the</strong> knowledge that is transferred from an agent to ano<strong>the</strong>r. This process
occurs from individual to individual, among groups and throughout <strong>the</strong> organization. Even though it
has always existed (normally associated to informal communication mechanisms), KM occurs today.
Tacit knowledge transfer happens through relations based on personal and /or social interaction plus
an organizational structure where <strong>the</strong> culture, <strong>the</strong> design and <strong>the</strong> strategy are fundamental
components <strong>of</strong> <strong>the</strong> transfer process and its analysis (Zapata, 2008). Thus, KM implies understanding
complex processes <strong>of</strong> knowledge creation and transfer.
In knowledge transfer processes, <strong>the</strong> ontological issues are fundamental (Bédard, 2003, 2004); that is
why appropriate conditions favoring human activities (work), human interaction with o<strong>the</strong>r people and
particularly, <strong>the</strong> knowledge creation processes (individual or collectively) are required. People’s
interest for interacting in <strong>the</strong> quest for a common objective implies its setting up as an organization, as
a collective or as a group. Sustaining this group <strong>of</strong> people through time in order to achieve <strong>the</strong>
proposed goals requires a deliberate interest for keeping minimum associativity standards. This level
<strong>of</strong> associativity not only permits <strong>the</strong> groups genesis but also its sustenance. Achieving <strong>the</strong> projected
goals demands from <strong>the</strong> group’s members true commitment according to <strong>the</strong>ir capabilities; it means
that each individual is required to heartedly cooperate. In order to develop collective creation
processes like in <strong>the</strong> Nonaka and Takeuchi’s model, associativity and cooperation within <strong>the</strong>
organization are required.
In knowledge creation and transfer environments, associativity and cooperation require support from
collaboration and interactivity processes; among <strong>the</strong> means that help provide this support,
technological platforms such as <strong>the</strong> Web 2.0 are currently available. Web 2.0 was defined by Tim
O’Reilly (2004), as “a group <strong>of</strong> economic, social and technological tendencies which compose <strong>the</strong>
fundamentals <strong>of</strong> <strong>the</strong> next generation <strong>of</strong> <strong>the</strong> Internet; this is characterized by <strong>the</strong> user’s involvement
and <strong>the</strong> network’s effects. Even though this first approach could be associated with <strong>the</strong> definition <strong>of</strong>
technology, subsequent definitions provided by O’Reilly (2005a, 2005b) determined <strong>the</strong> fundamental
principles <strong>of</strong> it: sharing, recycling, continuous improvement, trust, taking advantage <strong>of</strong> collective
intelligence and considering users as <strong>the</strong> main source <strong>of</strong> information. These and o<strong>the</strong>r principles have
promoted <strong>the</strong> establishment <strong>of</strong> a 2.0 attitude, making technology move over (Margaix, 2007, 96). In
this context, Web 2.0 can currently be understood as design patterns and business modeling
(O’Reilly, 2006). This Web 2.0 universe is particularly important for economies since it can affect
competition strategies or stimulate <strong>the</strong> active role <strong>of</strong> stakeholders; this finally can affect an
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Lillyana María Giraldo Marín et al
organization’s processes, products or services (creating or modifying <strong>the</strong> existing ones) through
personalizing <strong>the</strong> Web interface and helping reusing contents from personal websites (Corrocher,
2011).
This article proposes a “2.0 knowledge transfer model for an IT and communication cluster” based on
Nonaka and Takeuchi’s “knowledge creation” model and it is structured in four stages: socialization
exteriorization, combination and interiorization (1999, 2002). The proposed model focuses on <strong>the</strong>
knowledge transfer process supported by two fundamental principles <strong>of</strong> a cluster: associativity and
cooperation. They facilitate an encounter <strong>of</strong> <strong>the</strong> involved agents in which knowledge creation and
exchange is possible. Web 2.0 technologies will also make possible interaction, collaboration and
communication among agents to allow <strong>the</strong> emergence <strong>of</strong> collective intelligence among members <strong>of</strong>
<strong>the</strong> cluster. Figure 1 shows <strong>the</strong> knowledge transfer model for <strong>the</strong> IT cluster.
The application <strong>of</strong> <strong>the</strong> model (Figure 1) in a cluster requires <strong>the</strong> following steps:
- To define <strong>the</strong> work team.
- To identify <strong>the</strong> processes <strong>of</strong> <strong>the</strong> cluster in which <strong>the</strong> model would be applied.
- To design knowledge map <strong>of</strong> <strong>the</strong> selected process.
- To identify <strong>the</strong> flows, sources and knowledge transfer processes within <strong>the</strong> selected process.
- To determine <strong>the</strong> ways in which associativity and cooperation processes are evidenced at <strong>the</strong>
studied process.
- To define strategies for knowledge creation supported by Web 2.0 technologies to be
implemented.
Figure 1: 2.0 Knowledge Transfer Model.
3. Results and conclusions
Among <strong>the</strong> expected results from this 2.0 Knowledge Transfer Model we have <strong>the</strong> following:
To define and establish conscious strategies <strong>of</strong> knowledge transfer for individuals, groups and
clusters based on Nonaka and Takeuchi’s model.
To establish methods and techniques to record individual tacit knowledge so it can be transferred to
o<strong>the</strong>rs.
To support <strong>the</strong> creation and sustenance <strong>of</strong> associativity and cooperation processes with <strong>the</strong> cluster.
To promote organizational change is diverse aspects such as: culture, structure and strategy so it
favors knowledge transfer among <strong>the</strong> members <strong>of</strong> <strong>the</strong> cluster.
To support <strong>the</strong> knowledge transfer flows and sources within <strong>the</strong> cluster through technological
platforms favoring communication, interactivity and cooperation among <strong>the</strong> agents.
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Lillyana María Giraldo Marín et al
From <strong>the</strong> implementation <strong>of</strong> this model within <strong>the</strong> cluster, it is assumed that proper conditions for
emerging collective intelligence are given.
References
Bédard, R. (2003). Los fundamentos del pensamiento y prácticas administrativas. El rombo y las cuatro
dimensiones filosóficas. Administer, 68-88.
Bédard, R. (2004). Los fundamentos del pensamiento y las prácticas administrativas. La trilogía administrativa. .
Administer , 80-108.
Corrocher, N. (2011). The adoption <strong>of</strong> Web 2.0 services: An empirical investigation. En Technological
Forecasting and Social Change, Vol. 78, Issue 4, 547-558.
Lévy, P. (2004). Inteligencia colectiva para una antropología del ciberespacio. . Washington: Organización
Panamericana de la Salud (OPS) - Unidad de Promoción y Desarrollo de la Investigación y el Centro
Latinoamericano y del Caribe de Información y Ciencias de la Salud BIREME.
MacDonald, K., & Tipot, C. (1995). Using Documents. In Gilbert, Nigert (ed.), Researching social life. London:
Sage publications.
Margaix, D. (2007). Conceptos de web 2.0 y biblioteca 2.0: origen, definiciones y retos para las bibliotecas
actuales. En El pr<strong>of</strong>esional de la información, marzo-abril, vol. 16, no. 2, 95-106.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge Creating Company: How Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation. Nueva York: Oxford University Press.
Nonaka, I., & Takeuchi, H. (2002 (1999)). La organización creadora del conocimiento. Oxford: Editorial Oxford.
O’Reilly, T. (2006). Que es Web 2.0. Patrones del diseño y modelos del negocio para la siguiente generación del
s<strong>of</strong>tware. En Tribuna, Telefónica,
http://sociedadinformacion.fundacion.telefonica.com/DYC/SHI/seccion=1188&idioma=es_ES&id=20091001
16300061&activo=4.do?elem=2146 [01-02-2011, 10:45]
Porter, M. (1991). La ventaja competitiva de las naciones. Barcelona: Plaza y Janés.
Riesco, M. (2006). El negocio es el conocimiento. España: Ediciones Días de Santos.
Werner, O. y Schoepfle, G.M. (1987a). Systematic fieldwork: Foundations <strong>of</strong> ethnography and interviewing
(vol.1). Newbury Park, CA: Sage.
Zapata, L. (2008). El aprendizaje organizacional y la administración del conocimiento. En Aprendizaje
organizacional, Laura Zapata (ed.). México: McGraw-Hill. Pp. 141-164.
1137

Similarity and Accuracy <strong>of</strong> Shared Mental Models and its
Impact on Process Stability in Steel Production: First
Results <strong>of</strong> a Knowledge Audit Methodology
Nina Groß 1 , Annette Kluge 1 , Gabriele vom Ende 2 , and Thomas Schneeberger 2
1
University Duisburg-Essen, Department Computer and Cognitive Sciences,
Organisation and Economic Psychology, Duisburg, Germany
2
Hüttenwerke Krupp Mannesmann, Department <strong>of</strong> Vocational Training,
Duisburg, Germany
nina.gross@uni-due.de
Abstract: Based on <strong>the</strong> assumption that a higher degree <strong>of</strong> similarity and accuracy <strong>of</strong> <strong>the</strong> four types <strong>of</strong> Shared
Mental Models (SMM) lead to more stable processes, a Knowledge Audit (KA) has been developed for
Hüttenwerke Krupp Mannesmann (HKM), a steel producing company in Duisburg, Germany, which is based on a
SMM measurement approach. This approach fits <strong>the</strong> HKM specific needs (e.g. rotating shift, decentralized shift
over) based on <strong>the</strong> assumption that SMM are regarded as essential elements to coordinate <strong>the</strong> adaptability <strong>of</strong>
work groups which allows <strong>the</strong>m to act purposefully and mindfully as a unit. In <strong>the</strong> present study, SMM are
selected to develop an innovative approach to measure within a KA what is assumed to improve team
performance which leads to a more stable (slap casting) process.
Keywords: Shared Mental Models; Knowledge Management; Experience Management; Stable Processes;
Measuring
1. Introduction
Organizational knowledge originates in working processes; it is embodied individually or is socially
bound in working communities (Brödner 2006). But knowledge and experience might get lost by
employees reaching <strong>the</strong> retirement .t age, by information flow gaps and by relevant intuitions about
processes and <strong>the</strong>ir sub-processes between work groups that remain unshared. Towards this
organizations need to identify recurring process errors early to increase <strong>the</strong> reaction rate and to
assure robust processes. Without continuous access to <strong>the</strong> experience and strategies <strong>of</strong> <strong>the</strong>
employees, <strong>the</strong> aims can hardly be realised. These challenges were also encountered by HKM, a
steel producing company in Duisburg, Germany. The model and study presented in this paper has
been developed in co-operation with HKM to serve <strong>the</strong> special needs <strong>of</strong> a production plant, in which
Knowledge and Experience Management needs to be applicable in everyday face-to-face
communication based teamwork. This paper provides a methodology which <strong>of</strong>fers a fundamentally
integration into <strong>the</strong> every day business and allows to expect a high acceptance and use through <strong>the</strong>
high involvement <strong>of</strong> <strong>the</strong> employees.
2. The <strong>the</strong>oretical background
2.1 Shared mental models
In <strong>the</strong> present study, we refer to <strong>the</strong> concept <strong>of</strong> SMM and its roots in mental model-research (MM).
MM serve three important purposes: 1) MM enable people to understand <strong>the</strong> process, 2) <strong>the</strong>y support
a person to act in <strong>the</strong> appropriate way, and 3) <strong>the</strong>y allow predictions <strong>of</strong> system states (Kluwe 2006). In
many occupations, industrial processes are steered by groups who possess some kind <strong>of</strong> SMM <strong>of</strong>
<strong>the</strong>ir working context. SMM comprise <strong>the</strong> shared knowledge <strong>of</strong> all group members and enable each
person first to identify and internalize <strong>the</strong> demands <strong>of</strong> <strong>the</strong>ir work and <strong>the</strong>ir team role and secondly to
act accordingly (Friesdorf 2006). Four types <strong>of</strong> SMM are assumed (Cannon-Bowers 1993, Mathieu
2000): <strong>the</strong> Equipment-, <strong>the</strong> Task-, <strong>the</strong> Interaction-, and <strong>the</strong> Team-Model. The Equipment- Model
comprises <strong>the</strong> shared knowledge with respect to tools and technologies, for example <strong>the</strong> user
knowledge <strong>of</strong> <strong>the</strong> group- and s<strong>of</strong>tware, equipment-functioning or operating procedures which can be
used for specific tasks by <strong>the</strong> team. It also comprises system limitations and probable malfunctions.
The Task-Model includes <strong>the</strong> group members’ knowledge <strong>of</strong> <strong>the</strong> working steps, in terms <strong>of</strong> <strong>the</strong> task
strategies, procedures, <strong>the</strong> contingencies or problems with <strong>the</strong> working execution as well as <strong>the</strong>
environmental conditions or constrains. The Interaction-Model comprises structured knowledge <strong>of</strong> <strong>the</strong>
roles and responsibilities <strong>of</strong> <strong>the</strong> group members, <strong>of</strong> <strong>the</strong>ir communication channels, and <strong>of</strong> mutual
dependencies. The Team-Model embodies <strong>the</strong> team members’ knowledge <strong>of</strong> <strong>the</strong> competencies,
abilities, preferences, and settings <strong>of</strong> <strong>the</strong> single members (Mathieu 2000). Several researchers
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identified two fundamental characteristics <strong>of</strong> SMM which are required to benefit team effectiveness:
similarity (“overlap in team mental models”, Uitdewilligen 2010, pp. 298) and accuracy (“<strong>the</strong> extent to
which <strong>the</strong> mental models <strong>of</strong> <strong>the</strong> team members adequately represent <strong>the</strong> structure <strong>of</strong> <strong>the</strong> system it
models”, ib.). Within <strong>the</strong> present study, SMM within work groups are <strong>the</strong> knowledge assets which are
to be addressed by <strong>the</strong> Knowledge Audit.
2.2 Knowledge auditing
Knowledge Audits are assumed to play an important role within <strong>the</strong> successful implementation <strong>of</strong>
knowledge management (Perez-Soltero 2006). KAs are proposed to be one <strong>of</strong> <strong>the</strong> most central
stages <strong>of</strong> a KM initiative and are described as an assessment <strong>of</strong> <strong>the</strong> potential “stores” <strong>of</strong> <strong>the</strong>
organizational knowledge (Liebowitz 2000). Although KAs are viewed as vital, <strong>the</strong> KA content and
what needs to be audited remains open. In <strong>the</strong> present study, we aim at specifying precisely <strong>the</strong>
essential organizational knowledge elements supposed to be audited: <strong>the</strong> team members’ knowledge
structures which is represented in MM. SMM-<strong>the</strong>ory was <strong>the</strong>refore integrated into <strong>the</strong> seven step KA
methodology to cover <strong>the</strong> complexity <strong>of</strong> organisational environments and with a strong embedding
assessing <strong>the</strong> complementary organized knowledge structures <strong>of</strong> key elements: task, equipment,
interaction and team (Klimoski & Mohammed, 1994). The KA methodology will be described below.
3. The research gap: KA Methodology with emphasis on SMM
The Knowledge Audit-Model used in <strong>the</strong> present study consists <strong>of</strong> seven steps which are based on
<strong>the</strong> work by Perez-Soltero (2006). These adapted model describes an integrated concept within <strong>the</strong>
scope <strong>of</strong> KM with <strong>the</strong> objective to 1) implement KM in an organization, 2) to support a widespread use
<strong>of</strong> knowledge and experience gradually within working teams, and 3) to measure <strong>the</strong> success <strong>of</strong> KM
initiatives. Figure 1 shows an overview <strong>of</strong> <strong>the</strong> KA methodology:
Figure 1: KA Methodology with emphasis on SMM (adapted on Perez-Soltero 2006, Probst 2006)
First and foremost after choosing <strong>the</strong> participating process (“slab casting” in <strong>the</strong> present paper) is
defining <strong>the</strong> Knowledge Strategy. For <strong>the</strong> implementation <strong>of</strong> this initial stage <strong>of</strong> <strong>the</strong> KA at HKM, two
workshops (2 hours each) with line managers were conducted to develop a common understanding <strong>of</strong>
KM. Defining <strong>the</strong> Knowledge Strategy means what organizations wants to achieve by using KM in
general (Gronau 2009), e.g. to retain, share, and expand knowledge as it was <strong>the</strong> case for HKM. The
second step is analysing <strong>the</strong> process. In this step <strong>the</strong> deficiencies in <strong>the</strong> information flow and <strong>the</strong> nonuse
<strong>of</strong> existing knowledge will be analysed (based on Hacker 2009). Integrated in this step is a pre
SMM-Assessment focusing <strong>the</strong> similarity and accuracy <strong>of</strong> <strong>the</strong> SMMs. In June 2011 <strong>the</strong> work group
SMMs will be assessed. Step three is eliciting <strong>the</strong> knowledge which should be shared within a work
group. For realizing step three team-members (n = 40) <strong>of</strong> <strong>the</strong> slab-casting process will be interviewed
(June - August 2011). Therefore interviews are structured according to <strong>the</strong> four SMM types in order to
provide cues, strategies and knowledge assets to cover <strong>the</strong> specific demands <strong>of</strong> <strong>the</strong> task
environment. Step four is supposed to discuss <strong>the</strong> elicited knowledge with line managers in order to
develop a shared understanding <strong>of</strong> <strong>the</strong> kind <strong>of</strong> SMM that is appreciated and valuable to be shared and
passed to <strong>the</strong> whole work group. In addition, <strong>the</strong> elicited knowledge will be prioritized and used as a
preparation for step five, <strong>the</strong> SMM-intervention. The SMM-intervention will be a Task-oriented
Knowledge-Transfer (TOKT) based on <strong>the</strong> results <strong>of</strong> steps two, three and fours. Step six includes KA
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Nina Groß et al
reporting and control to pro<strong>of</strong> <strong>the</strong> outcome. Integrated in that step is <strong>the</strong> post SMM-Assessment. Step
seven is Reauditing to retain or adapt <strong>the</strong> Knowledge Strategy. Recently <strong>the</strong> information flow analysis
<strong>of</strong> step two <strong>of</strong> <strong>the</strong> KA has been carried out and will be described in <strong>the</strong> following.
4. First Results and next steps
An observation throughout all shift teams <strong>of</strong> a slab casting process has been carried out. Therefore all
5 production teams (each consisting <strong>of</strong> 17 teammates, altoge<strong>the</strong>r 85 people) have been escorted on
rotating shifts. The teams 1, 2, and 3 were observed during two morning shifts. Teams 4 and 5 were
both observed during two early, two late, and two night shifts each. Therefore every task <strong>of</strong> <strong>the</strong> slab
casting process and <strong>the</strong> associated working steps were captured. Step 3a <strong>of</strong> <strong>the</strong> KA concerned <strong>the</strong>
accuracy and completeness <strong>of</strong> information, and was clustered into “content”, “whereto”, “wherefrom”,
and “frequency”. Lacks <strong>of</strong> <strong>the</strong> four investigated deficiencies were identified and classified into <strong>the</strong>
clusters knowledge, qualification, and process. In <strong>the</strong> present study, only <strong>the</strong> identified lacks <strong>of</strong>
information in <strong>the</strong> cluster knowledge were required because <strong>of</strong> <strong>the</strong> connection to limiting interactionrelated
knowledge. These results <strong>of</strong> <strong>the</strong> information flow analysis were prioritized by <strong>the</strong> line
managers and will constitute <strong>the</strong> basis for eliciting <strong>the</strong> knowledge. Next steps are <strong>the</strong> specification <strong>of</strong>
recurring process errors (e.g. operator errors, loss <strong>of</strong> strand) to get measurable indicators for a stable
process, and an assessment <strong>of</strong> <strong>the</strong> task-, equipment-, and team-related knowledge. The similarity and
accuracy <strong>of</strong> <strong>the</strong> mental models before and after <strong>the</strong> intervention will be compared in a pre-post-test
design. The first assessment will be in step 3b, before any TOKT has been carrying out. The second
assessment (step “Report and Control”, see Figure 1) will be executed three month weeks after
starting TOKT to investigate <strong>the</strong> changes in <strong>the</strong> degree <strong>of</strong> similarity and to infer <strong>the</strong>ir impact on
process stability (e.g. reducing <strong>the</strong> recurrent process errors, enhancing reaction rates).
5. Significance and discussion
The presented conception <strong>of</strong> a specified KA approach aims at demonstrating <strong>the</strong> impact <strong>of</strong> a higher
similarity and accuracy <strong>of</strong> <strong>the</strong> SMM in a production plant and is expected to answer <strong>the</strong> question,
whe<strong>the</strong>r this higher similarity and accuracy results in more stable processes. Regarding organizational
KM, <strong>the</strong> <strong>the</strong>ory <strong>of</strong> SMM is suited to share experience, strategies and knowledge <strong>of</strong> individuals, teams,
processes or <strong>the</strong> whole organization by a Task oriented knowledge transfer. The developed method
supports a continuous access to <strong>the</strong> cues and strategies <strong>of</strong> <strong>the</strong> employees, with a strong emphasis on
measuring <strong>the</strong> effectivity <strong>of</strong> KM activities based on <strong>the</strong> <strong>the</strong>ory <strong>of</strong> SMM. More stable processes will be
an indication for success.
References
Cannon-Bowers, J., Salas, E. and Converse, S. (1993) “Shared mental models in expert decision making in
expert team decision making”, in Castellan, N., edt.: Individual and group decision making. Lawrence
Erlbaum, Hillsdale, pp. 221-246.
Brödner, P. (2006): Betriebliche Rationalisierungsstrategien und Einsatz technischer Systeme. Zimolong, B.;
Konradt, U.: Ingenieurpsychologie (Engineering Psychology). Hogrefe Verlag, Göttingen u.a., S. 943-980
Gronau, N. (2009) Prozessorientiertes Wissensmanagement (Processoriented Knowledge Management).
Oldenbourg, München.
Friesdorf, W., Göbel, M. and Buß, B. (2006): “Gestaltung komplexer klinischer Arbeitssysteme” („Creation <strong>of</strong>
complex clinical work environments“). Zimolong, B. and Konradt, U.: Ingenieurpsychologie (Engineer
Psychology). Hogrefe, Göttingen, pp. 907-947.
Hacker, W. (2009) Informationsflussgestaltung als Arbeits- und Organisationsoptimierung – Jenseits des
Wissensmanagement (Information flow design – beyond knowledge management). Vdf Hochschulverlag,
Klimoski, R., & Mohammed, S. (1994). Team mental model: Construct or metaphor?. Journal <strong>of</strong> Management,
Kluwe, R. (2006) „Informationsaufnahme und Informationsverarbeitung“ („Information Reception and Information
Processing“), Zimolong, B. and Konradt, U. (Hrsg.): Ingenieurpsychologie (Engineer Psychology) Hogrefe
Verlag, Göttingen u.a., 2006; pp. 35-70
Liebowitz J, Rubenstein-Montano B, Mccaw D, Buchwalter J, Browning C. 2000. The knowledge audit.
Knowledge and Process Management 7(1): 3–10.
Mathieu, J., Goodwin, G., Heffner, T., Salas, E. and Cannon-Bowers, J. (2000) “The Influence <strong>of</strong> Shared Mental
Models on Team Process and Performance”, Journal <strong>of</strong> Applied Psychology, Vol. 85, No. 2, pp 273-283.
Perez-Soltero, A., Barcelo-Valenzuela, M., Sanchez-Schmitz, G., Martin-Rubio, F. and Palma-Mendez, J. (2006)
“Knowledge Audit Methodology with emphasis on core processes”, Paper presented at <strong>European</strong> and
Mediterranean <strong>Conference</strong> on Information Systems (EMCIS), Alicante, Spain.
Probst, G., Raub, S., Romhardt, K. (2006). Wissen managen (Managing Knowledge). Gabler, Wiesbaden.
Uitdewilligen, S., Waller, M., Zijlstra, F. (2010). “Team Cognition and Adaptability in Dynamic Settings:
A Review <strong>of</strong> Pertinent Work”. International Review <strong>of</strong> Industrial and Organizational Psychology 2010,
1140

What’s your Strategy for Measuring IT & Non-IT for
Knowledge Management in an Organisation?
Ravinder Singh Kahlon and Man-Chie Tse
Middlesex University, School <strong>of</strong> Engineering and Information Sciences
The Burroughs, London, UK
RK350@live.mdx.ac.uk
MT631@live.mdx.ac.uk
Abstract: This paper presents and describes a new pragmatism orientation technique for identifying and
measuring knowledge management to grow an organisation. The technique brings toge<strong>the</strong>r Dilip Bhatt KM
components. The empirical study presents results, a Kiviat diagram, highlights <strong>the</strong> principal significance for
comparison alignment between two KM models (Non-IT and IT connections) using Composite Features
Diagramming methodology. This method illustrates an abundance characteristics component results in visual
qualitative technique that also enables conducted quantitative evaluation to fundamentally analyse, measure and
transform an organisation success level retrospectively. It studies <strong>the</strong>se links in a Healthcare organisation
context, where KM is a strategic concern. Finally, fur<strong>the</strong>r recommendation is subsequently addressed for future
exploration works.
Keywords: Knowledge Management (KM), Composite Features Diagramming (CFD), Knowledge Management
Systems (KMS), Knowledge Management Models (KMM), Information Technology (IT) Models Metrics, Kiviat.
1. Introduction
How can organisations measure Knowledge Management (KM)? Where and what are <strong>the</strong> issues and
problems, and how are <strong>the</strong>y linked? KM is becoming a prominent aspect <strong>of</strong> re-engineering in
organisations (Malhotra, 2000). However, <strong>the</strong> necessity <strong>of</strong> measuring and evaluating KM is hardly
questioned. The term KM Models (KMM) originated from within s<strong>of</strong>tware development, several
fragmented models exist for KM in <strong>the</strong> context <strong>of</strong> IT based perspective, and <strong>the</strong>se models established
two categories used for system development. These categories are Non-IT and IT KM discipline
models. Non-IT follows a generalised reiterative single system application approach. On <strong>the</strong> o<strong>the</strong>r
hand, IT has a linear iterative nature which is based on combination where each component is
repeated. Organisations postulating towards Non-IT usually follow an explicated implicit traditional
form which bounds within a structured organisation. The IT approach, however follows an
unstructured combined with dependent Non-IT to support <strong>the</strong> foundations and predefined
requirements for an organisation (Kahlon, 2009). The objective <strong>of</strong> this paper begins with a discussion
<strong>of</strong> <strong>the</strong>se KMM (Non-IT and IT). This is followed by critically examining <strong>the</strong> two models and provides a
recommendation based on <strong>the</strong> application <strong>of</strong> findings. The background research is based on
publications, practitioners’ reports from a healthcare context. The evaluation and selection was based
on participant’s opinions. The paper is divided into three sections. The first section provides an
overview <strong>of</strong> <strong>the</strong>se models. The second section explains <strong>the</strong> CFD technique methodology and
discusses <strong>the</strong> results attached to it. The last section provides an outline <strong>of</strong> analysis recommendation
and conclusion.
2. Overview <strong>of</strong> <strong>the</strong> models
There are vast amounts <strong>of</strong> literature on KM and <strong>the</strong> subject area attracted much attention. Past
literatures led focus on associating purely with technology as facilitating and transmitting, a platform
giving wonders for capability to spread different dimensions <strong>of</strong> knowledge representation, whe<strong>the</strong>r it is
internal or external for an organisation. Yet, organisations fail to understand <strong>the</strong> surreal technology
platform and <strong>the</strong> potential gradient value. The work <strong>of</strong> <strong>the</strong> two authors presents advancement,
evolved from Dilip Bhatt (2000), best known practitioner <strong>of</strong> KM, who derived three key KM
components being People, Process and Technology, as connecting sub-elements. As a focal point for
KM, <strong>the</strong>se three elements were integrated and embedded accordingly to Non-IT and IT approaches.
2.1 Non-IT Approach
Non-IT accomplishes high level <strong>of</strong> constructed intellectual knowledge task in traditional forms (face to
face, direct communication, etc). In spite <strong>of</strong> progress to simplify Non-IT, <strong>the</strong>y remain to be powerful
tools required to balance 70% and 30%, o<strong>the</strong>rwise lack <strong>of</strong> implicit way can impede and hinder an
organisation knowledge communication flow (Bhatt, 2000). Non-IT model are essential for any
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Ravinder Singh Kahlon and Man-Chie Tse
organisation ei<strong>the</strong>r small or large because implicit communication will always be implicitly vital. The
strength <strong>of</strong> this model follows a similar document, process or code driven. The disadvantage <strong>of</strong> this
model is that <strong>the</strong>re is a variety <strong>of</strong> practices exist which may not be included as part <strong>of</strong> <strong>the</strong> attributes
within <strong>the</strong> model, which can impede and impact <strong>the</strong> evaluation and <strong>the</strong> overall analysis in an
organisation.
2.2 IT Approach
Early adopters <strong>of</strong> incorporating conventional IT reported encouraging results such as increased
productivity or systematically identifying cultivated knowledge. IT approaches attempts to make
complex and conceptual essence <strong>of</strong> infrastructure technology to support and make knowledge
incrementally explicit. Ultimately, a retrospective from past literatures shows IT model solutions are
ideal for medium to large sized organisations. The strength <strong>of</strong> this model supports technology transfer
and aid development towards dynamic system thinking (Kahlon, 2009; Tse, 2009).
3. Methodology
A Composite Features Diagramming (CFD) Framework developed by Georgiadou (2003) was
adopted for <strong>the</strong> comparative evaluation for Non-IT and IT. The first initial step was to list <strong>the</strong> primary,
secondary, and tertiary attributes for an ‘Ideal Model’ (See Appendix A) to measure KM in an
organisation. The primary feature attributes were devised using <strong>the</strong> model components encompassing
<strong>of</strong> Bhatt (2000) People, Process and Technology. Expanding on from <strong>the</strong> primary features, <strong>the</strong> work
<strong>of</strong> Alavi and Leider (1999) was incorporated, which derived <strong>the</strong> primary, secondary and tertiary
attributes. Therefore, this model acted as a questionnaire, depicted to enable building visual
representation taxonomy. These attributes were chosen because <strong>the</strong>y illustrate an overall constituent
perspective <strong>of</strong> KM features in an organisation, thus <strong>the</strong> ideal model can be validated to be applied to
any organisation. The gradual decomposition <strong>of</strong> composite features to sub features into lower levels
enabled <strong>the</strong> construction <strong>of</strong> multiple taxonomies and visual representation shown in Appendix A
(secondary and tertiary). This ideal model mechanism was <strong>the</strong>n used for mapping Non-IT (Figure 1)
and IT (Figure 2) applicable valid attributes. Values (data) for each leaf branch were to be collected
from a survey sample size <strong>of</strong> 50 participants to compare which contextually reflected <strong>the</strong> healthcare
organisation articulation, to help select or reject and evolve. Table 1 (see Appendix B) shows <strong>the</strong>
attributes and <strong>the</strong>ir gradual decomposition to lower layers. This tabular comparison shows <strong>the</strong>
presence and absence <strong>of</strong> features in <strong>the</strong> two KMM.
4. Mapping Features to <strong>the</strong> Ideal CFD
4.1 Qualitative/Visual Comparison
Figure 1: The CFD <strong>of</strong> <strong>the</strong> Non-IT Model
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Figure 2: The CFD <strong>of</strong> <strong>the</strong> IT Model
Ravinder Singh Kahlon and Man-Chie Tse
4.2 Quantitative and participants evaluation
A survey size <strong>of</strong> 12 respondents participated to undertaken <strong>the</strong> questionnaire, assigning values <strong>of</strong> 0
(lowest) – 5 (highest) on each highlighted branches. By adopting Georgiadou (2003) algorithm
allowed calculation <strong>of</strong> values to determine <strong>the</strong> strength <strong>of</strong> each branches.
Participant’s evaluation was acquired to help analyse and measure <strong>the</strong> organisation in <strong>the</strong> business
environment context. This was highly useful because data can be captured to help aid and support
decision making. This aided valid response as biased opinion was avoided.
4.3 Data collection analysis
Data collection analysis was conducted to contrast <strong>the</strong> two KMM in comparison against <strong>the</strong> set
threshold through calculated averages. The survey values calculated <strong>the</strong> averages exemplified in
Table 2, <strong>the</strong>se were assigned to each primary attribute <strong>of</strong> <strong>the</strong> respective model. The threshold value
were set at 2, <strong>the</strong> decision for this is because 1 was far too low representation whilst, 3, 4 and 5 were
too high, <strong>the</strong>refore <strong>the</strong> value was set in line according to <strong>the</strong> minimal expectation.
Table 2: Values from <strong>the</strong> survey and calculated threshold (primary Level)
4.4 Visual & Quantitative Comparison
The data from Table 2 was used to construct a Kiviat diagram (Figure 3) to allow visual representation
comparison <strong>of</strong> qualitative information. As shown below in Figure 3, <strong>the</strong> Kiviat diagrams reflects <strong>the</strong>
organisation level <strong>of</strong> feasibility for KM in terms <strong>of</strong> Non-IT and IT context, in specific, <strong>the</strong> comparative
analysis helps us as a starting point to determine <strong>the</strong> strengths and weaknesses.
The Non-IT strongest attribute was processes, followed by technology; people received <strong>the</strong> weakest
overall, possibly because participants expressed <strong>the</strong>ir personal feelings. In contrast, technology
received <strong>the</strong> highest strength with people <strong>the</strong>n process in <strong>the</strong> IT model.
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Figure 3: Non-IT and IT Kiviat Diagram
Ravinder Singh Kahlon and Man-Chie Tse
Figure 3 can be excelled fur<strong>the</strong>r by going in-depth into <strong>the</strong> second layer as presented overleaf Figure
4. It shows a clear illustration <strong>of</strong> Non-IT suppressing past <strong>the</strong> threshold value whilst IT by passes a
few attributes <strong>of</strong> which is linked to human behaviour. The total area was also calculated and Non-IT
had 75.676% whereas IT had 52.854%.
5. Analysis recommendations
Based on evaluation and analysis, Non-IT surpasses <strong>the</strong> threshold <strong>the</strong> most, whilst IT had <strong>the</strong> least.
This illustrates that although <strong>the</strong> healthcare organisation lacks IT, Non-IT is <strong>the</strong> strongest component
within <strong>the</strong> environment at present. The results give some evidence to suggest <strong>the</strong> organisation have a
high correlation to Non-IT practice if this was contrasted and compared to <strong>the</strong> marked scale out <strong>of</strong>
100%. Also, IT received merely 52% which has just only passed <strong>the</strong> 50% barrier, since IT received
<strong>the</strong> lowest in value; this recommends an implementation with starting <strong>of</strong> low IT with progression
towards medium. Following on previous, one aspect was that <strong>the</strong> Kiviat above (Figure 3) provides an
illustration purpose <strong>of</strong> <strong>the</strong> overall strength <strong>of</strong> <strong>the</strong> primary features, <strong>the</strong>refore to go deeper and explore
<strong>the</strong> applicable points; <strong>the</strong> next secondary level layer findings was reviewed. This gained a greater
supportive comprehension, illustrated visually in Figure 4 below.
Figure 4: Second Layer Kiviat Diagram
In reference to <strong>the</strong> above, <strong>the</strong> empirical study investigation evidently justifies <strong>the</strong>re is a gap between
Non-IT and IT in <strong>the</strong> healthcare organisation context. The model discussed, <strong>of</strong>fers a potential
1144

Ravinder Singh Kahlon and Man-Chie Tse
contribution to describe and examine visually <strong>the</strong> evolution <strong>of</strong> where difficulty arises more precisely in
an organisation:
Sharpens visual strength between establishment <strong>of</strong> Non-IT and IT and where gap lies
Outlines indication and determinant perceived
To simulate and focus on what is important, alongside justification <strong>of</strong> effectiveness
Allows identification <strong>of</strong> where alignment can take place, giving useful results as indicators for
starting point
Provides a basis for organisation valuation
Fur<strong>the</strong>r enhancing and opportunities for integration and development <strong>of</strong> pr<strong>of</strong>iles
6. Conclusion
The purpose <strong>of</strong> this paper highlighted and explored a multiple dual phase, a new potential centricity to
exploring Non-IT and IT dimensions exhibited in an organisation. This research attempts to <strong>of</strong>fer
streng<strong>the</strong>ning contribution to <strong>the</strong> process <strong>of</strong> <strong>the</strong>ory and practice between measuring and comparative
analysis <strong>of</strong> Non-IT and IT data <strong>of</strong> KM and better identification to an organisation. The contextualised
Kiviat diagram provides an important rich base <strong>of</strong> formal comparative analysis paradigm between <strong>the</strong>
two, findings <strong>of</strong> <strong>the</strong>se suggest <strong>the</strong> framework provides vital concrete measures to support KM
implementation for management decision to experiment. It is important to note <strong>the</strong> fore mentioned
features as well as <strong>the</strong> threshold values are specific to groups <strong>of</strong> stakeholders and particular
organisations. Future work will involve industrially based evaluations where practitioners can provide
fur<strong>the</strong>r insights to explore <strong>the</strong> field deeper and grasp an even greater picture.
References
Alavi, M. & Leidner, D. E. (1997) Knowledge Management System: Issues, Challenges and Benefits [Online].
Available: http://www.belkcollege.uncc.edu/jpfoley/Readings/artic07.pdf [2009, 11/09/2009].
Bhatt, D. (2000). EFQM Excellence Model and Knowledge Management Implications. [Online]. Available:
http://www.eknowledgecenter.com/articles/1010/1010.htm [2009, 14/10/2009].
Georgiadou, E. (2003). GEQUAMO - A Generic, Multilayered, Customisable, S<strong>of</strong>tware Quality Model. S<strong>of</strong>tware
Quality Journal. Vol.11, No.4, Pp.313-323.
Kahlon, R. S. (2009). Ravi's Knowledge Management Blog [Ravi KM - Blogger Google Inc], 19/02/2010-last
update [Online]. Available: http://rsk666.blogspot.com/ [2009, 20/10/2009].
Malhotra, Y. (2000). From Information Management to Knowledge Management: Beyond The 'Hi'Tech
Hidebound' Systems. [Online]. Available: http://www.brint.org/IMtoKM.pdf [2011, 22/01/2011].
Tse, M. C. (2009). MC Knowledge Management [Online]. Available: http://mc-tse.blogspot.com [2009,
15/11/2009].
Appendix A
Figure 5: A Composite Features Diagram <strong>of</strong> an Ideal Model
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Appendix B
Ravinder Singh Kahlon and Man-Chie Tse
1146

Scope <strong>of</strong> Knowledge Management for Improving
Performance in Call Centre Service Delivery
Pushkal Pandey, Sandra M<strong>of</strong>fett and Rodney McAdam
University <strong>of</strong> Ulster, United Kingdom
Pandey-p@email.ulster.ac.uk
Abstract: As in o<strong>the</strong>r services, call centres may primarily be characterized by <strong>the</strong>ir interface, <strong>the</strong> point where <strong>the</strong>
customer and provider meet (Teboul, 1991). The interaction plays a major role in forming <strong>the</strong> customer’s
perception <strong>of</strong> <strong>the</strong> company and hence will determine its success (Bitner, 1990; Bitner, Booms, & Tetrault, 1990).
Performance in a call centre service interaction is influenced by three interacting dyads, namely customer-agent,
agent-knowledgebase, agent-management. As shown in Figure 1 customer encounter acts as <strong>the</strong> point <strong>of</strong>
confluence, where each <strong>of</strong> <strong>the</strong>se dyads interacts.
Figure 1: Unit <strong>of</strong> analysis
It is to be noted that an agent is <strong>the</strong> common actor in each <strong>of</strong> <strong>the</strong>se dyads, as he/she is <strong>the</strong> point <strong>of</strong> contact for
<strong>the</strong> customer. Figure 2 is a representation <strong>of</strong> <strong>the</strong> service interaction in a call centre fur<strong>the</strong>r broken down:
Figure 2: Service interaction in call centers
When a customer is calling a call centre, his/her first interaction is with <strong>the</strong> Interactive voice response system,
which is basically a automatic knowledge repository designed to help customers in solving basic queries; if <strong>the</strong>
customers queries are not resolved here, <strong>the</strong> next stage is <strong>the</strong> interaction with <strong>the</strong> agent. The agents interact with
<strong>the</strong>ir reference database on <strong>the</strong> desktop to obtain information on <strong>the</strong> customer and his query. Management has to
interact with <strong>the</strong> agent in terms <strong>of</strong> providing consistent on/<strong>of</strong>f floor training/support, monitoring and appraisal in
handling customer calls. The goals <strong>of</strong> a successful customer encounter would be forfeited only when <strong>the</strong> agent is
able to communicate with its counterparts in all three dyads, in <strong>the</strong> most effective and efficient manner.
Therefore, <strong>the</strong> questions this research is asking are:
What is required <strong>of</strong> each <strong>of</strong> <strong>the</strong>se interactions in order to be effective? and
How can <strong>the</strong> available knowledge resources be utilised to <strong>the</strong> optimum to remove unwanted glitches so that
appropriate efficiency in <strong>the</strong> processes can be achieved?
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Pushkal Pandey, Sandra M<strong>of</strong>fett et al
To answer <strong>the</strong> first question <strong>the</strong> research will involve an analysis <strong>of</strong> <strong>the</strong> customer-agent interaction, regarding
what constitutes an effective service encounter from <strong>the</strong> customer’s stand point. This will involve a qualitative
investigation into what factors influence customer satisfaction (involving literature review, interviews and focus
groups with call centre staff) contributing towards forming a survey to be used to analyse customer perception <strong>of</strong>
<strong>the</strong>se factors with regards to <strong>the</strong>ir influence on overall satisfaction with <strong>the</strong> interaction.
The second research question will involve a content analysis <strong>of</strong> <strong>the</strong> available knowledge base to see if <strong>the</strong>y
contribute to <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> service as identified from <strong>the</strong> first research question, and also path analysis
<strong>of</strong> <strong>the</strong> communication system in a call centre with attention to identifying hurdles and ensuring efficient flow <strong>of</strong>
information in between all <strong>the</strong> actors as identified in <strong>the</strong> figure 2.
This research contributes towards both <strong>the</strong>ory and practice <strong>of</strong> performance management in understanding <strong>the</strong>
needs that call centre communication must meet so as to produce positive experiences for <strong>the</strong>ir customers.
Keywords: Performance management, Knowledge management, call centre service
1. Introduction
The main benefits <strong>of</strong> <strong>the</strong> call center are convenience, lower costs, flexibility and customization
(Bennington et al. 2000). Prabhaker et al. (1997) see call centers as an information gateway, where
every call is an opportunity to obtain or disseminate knowledge. Communication in call centers is
<strong>of</strong>ten very task-oriented. An analogy for type <strong>of</strong> customer-agent interaction in a call center could be
drawn from <strong>the</strong> service relationship between <strong>the</strong> big city cab driver and his fare. Davis (1959)
acknowledges that due to <strong>the</strong> way <strong>the</strong> cab driver and its fare come into contact with each o<strong>the</strong>r this
relationship is especially subject to structural weakness. The relationship is characterized by him as
being “random, fleeting, unrenewable, and largely devoid <strong>of</strong> social integrative features which in o<strong>the</strong>r
client and patron oriented services help sustain a wider range <strong>of</strong> constraints and controls between <strong>the</strong>
parties to <strong>the</strong> transaction.”
2. Customer encounter model <strong>of</strong> service process in call centers
Taking <strong>the</strong> service encounter as <strong>the</strong> unit <strong>of</strong> analysis, this model takes into account <strong>the</strong> process <strong>of</strong>
service delivery in a call center. Customer encounter is <strong>the</strong> most important incident from <strong>the</strong>
customer’s perspective in judging performance <strong>of</strong> a call center. Therefore for efficient and effective
handling <strong>of</strong> customer’s calls it is vital to consider all factors which may have an influence at this time.
Recognizing <strong>the</strong> importance <strong>of</strong> service interaction between <strong>the</strong> agent and <strong>the</strong> customer and based on
<strong>the</strong> above arguments <strong>the</strong> following triangular model is proposed to look at all <strong>the</strong> constituents <strong>of</strong>
service process in call center service delivery:
The Performance <strong>of</strong> an agent during <strong>the</strong> service interaction with <strong>the</strong> customer is guided solely by
three factors, designated as co-ordinates in <strong>the</strong> above model: Context coordinate- The effectiveness
<strong>of</strong> each encounter is primarily associated with <strong>the</strong> content <strong>of</strong> <strong>the</strong> reference interaction. The service
delivery in call centers has been classified into four distinct categories based upon <strong>the</strong> degree <strong>of</strong>
customer involvement and degree <strong>of</strong> standardization inherent in <strong>the</strong> interaction (figure 3). The arrow
to <strong>the</strong> left shows <strong>the</strong> Degree Of Standardization
Figure 3: Customer process model <strong>of</strong> service performance in call centers
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Pushkal Pandey, Sandra M<strong>of</strong>fett et al
Figure 4: Classification <strong>of</strong> call center service delivery
Agent performance is judged by <strong>the</strong> customer on <strong>the</strong> basis <strong>of</strong> his/her behavioral simulation <strong>of</strong> <strong>the</strong>
expected service script, which is determined by under which specific category does <strong>the</strong> particular
interaction fall. For example, it will not be beneficial to focus on rapport building and elaborate
processes on encounters which are <strong>of</strong> basic type i.e. requiring general information as <strong>the</strong>y fall into low
involvement and low standardization type <strong>of</strong> interactions. Therefore, to achieve optimum performance
it is essential that <strong>the</strong> call center management look into segregating <strong>the</strong>ir <strong>of</strong>fering into its proper type
according to <strong>the</strong> model. Technology coordinate- This research explores <strong>the</strong> role <strong>of</strong> technology as a
medium <strong>of</strong> communication between <strong>the</strong> customer-agent, agent-management and agentknowledgebase.
The emphasis on seeing communication aspect <strong>of</strong> technology is influenced by <strong>the</strong>
fact that call center service delivery is essentially a technology mediated encounter. Therefore, task
resolution requires effective employment <strong>of</strong> this resource. Technology has a major role in influencing
agent performance, such as during automated call routing it allows generating an exact match
between <strong>the</strong> customers query and <strong>the</strong> agent so as to be efficiently directed, <strong>the</strong> Interactive voice
response technology which allows automatic customer support for basic queries without <strong>the</strong> need <strong>of</strong>
<strong>the</strong> customer to speak to an agent thus making agents available for more complex queries from o<strong>the</strong>r
customers. Fur<strong>the</strong>r, researchers have established that certain types <strong>of</strong> tasks are better suited for
certain medium, for this purpose it will be better to understand <strong>the</strong> compatibility between <strong>the</strong> medium
and <strong>the</strong> task (McGrath and Hollingshead, 1993). Technology also acts as a chief medium <strong>of</strong>
communication between <strong>the</strong> management and <strong>the</strong> agent with regards to providing on <strong>the</strong> floor
support, and o<strong>the</strong>r essential communication relating to changes in policies and products. Call centers
could benefit from understanding how to effectively employ this communication channel to increase
agent productivity. Similarly, agent has to access a lot <strong>of</strong> information from his/her desktop. An
emphasis on making it easier for <strong>the</strong> agent to draw on this resource <strong>of</strong> information would contribute
towards efficiency in call resolution. Primarily, through an in-depth understanding <strong>of</strong> <strong>the</strong> socioemotional
nature <strong>of</strong> <strong>the</strong> technology-mediated interaction we can achieve maximum benefit out <strong>of</strong> it.
Therefore, it is through effective technology management that we can attain improved performance in
call centers.
Knowledge base coordinate- Knowledge bases allow customer representatives to be more efficient
while making consumers more self reliant by providing valuable resources and information for users.
Therefore, <strong>the</strong> efficiency <strong>of</strong> a service encounter is determined by <strong>the</strong> efficiency in assessing and
sufficiency <strong>of</strong> <strong>the</strong>se varied knowledge bases (Frequently Asked or Answered Questions - FAQs, and
pre-defined answer scripts, question taxonomies) specific to <strong>the</strong> content <strong>of</strong> <strong>the</strong> reference interaction.
Pomerantz (2005) suggests that if each transaction was unique in terms <strong>of</strong> meeting <strong>of</strong> a user,
provider, and question <strong>the</strong>n it would not be useful to achieve efficiencies <strong>of</strong>fered by knowledge bases.
This is not <strong>the</strong> case in call centre service interactions. As has <strong>of</strong>ten been noted, <strong>the</strong> customer query
for particular types <strong>of</strong> service centres is mostly repetitive. In relation to efficiency provided by
knowledge bases in call centres to work well it is necessary that <strong>the</strong> affective emotional expectations
<strong>of</strong> consumers and <strong>the</strong> content <strong>of</strong> <strong>the</strong> questions need to be somewhat homogeneous across multiple
interactions. Fortunately, as shown in prior research (Dean, 2004), and our own preliminary study, this
is so in call centres. However, it is important to readily examine <strong>the</strong> assumptions behind knowledge
bases when <strong>the</strong> inevitable miscommunication happens in <strong>the</strong> course <strong>of</strong> service interaction. Hence,
while agents should certainly maintain and draw upon such answer repositories, customers should
receive customized service when <strong>the</strong>ir needs do not fully match predetermined answers. This is not to
say that every encounter must take into account a customized approach towards <strong>the</strong> customer but
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Pushkal Pandey, Sandra M<strong>of</strong>fett et al
ra<strong>the</strong>r that sufficient acknowledgement <strong>of</strong> <strong>the</strong> individual should be made so that <strong>the</strong> customer feels
“as if” his or her individual needs have been addressed.
It is useful to note that aspects <strong>of</strong> performance associated with each <strong>of</strong> <strong>the</strong>se coordinates (figure 3)
are not independent <strong>of</strong> one ano<strong>the</strong>r, but are all interrelated. For example, <strong>the</strong> efficiency associated
with <strong>the</strong> ready reference databases is related to <strong>the</strong> context <strong>of</strong> <strong>the</strong> reference interaction i.e. a low
involvement and low standardization encounter will have shorter time duration, generally, as
compared to a high involvement and high standardization encounter. It is through well maintained
knowledge bases that characteristics <strong>of</strong> <strong>the</strong> provider can be matched with characteristics <strong>of</strong> <strong>the</strong>
customer query, hence allowing <strong>the</strong> generation <strong>of</strong> a one-to-one match between <strong>the</strong> customer and <strong>the</strong>
agent to be used as basic guideline for designing efficient- Automated call routing systems. Similarly,
some types <strong>of</strong> interactions (figure 4) are better handled through a specific mode <strong>of</strong> communication
(chat, telephone, email). For example, it will be highly frustrating for a customer to discuss warranty
claims over a chat service, whereas, a customer requiring to know <strong>the</strong> balance on his bank account
would prefer such medium. The above discussion highlights <strong>the</strong> distinguishing characteristics <strong>of</strong> call
centres and draws on all <strong>the</strong> actors (namely technology, context and knowledgebase) which have a
role in effective and efficient query resolution, which are <strong>the</strong>n used to develop a conceptual model.
References
Bennington, L., Cummane, J. and Conn, P. (2000), Customer satisfaction and call centers: an Australian study,
International Journal <strong>of</strong> Service Industry Management, Vol. 11, No. 2, pp. 162-73.
Davis, F, (1959), “The cab driver and his fare”, American journal <strong>of</strong> sociology, 45, pp. 158-165.
Dean, Alison M. (2004), Rethinking customer expectations <strong>of</strong> service quality, Journal <strong>of</strong> Services marketing, Vol.
El Sawy, O. A., & Bowles, G. (1997), Redesigning <strong>the</strong> customer support process for <strong>the</strong> electronic economy:
Insights from Storage Dimensions. MIS Quarterly, Vol. 27(4), pp. 457-483.
Froehle, C. M. (2006), Service personnel, technology, and <strong>the</strong>ir interaction in influencing customer satisfaction.
Froehle, C. M., & Roth, A. V. (2004). New measurement scales for evaluating perceptions <strong>of</strong> <strong>the</strong> technologymediated
customer service experience. Journal <strong>of</strong> Operations Management, 22(1), 1–22.
McGrath, J. E., and A. B. Hollingshead. (1993). Putting <strong>the</strong> ‘Group’ Back in Group Support Systems: Some
Theoretical Issues About Dynamic Processes in Groups With Technological Enhancements, in L. M. Jessup
and J. S. Valacich (eds.), Group Support Systems: New Perspectives. New York: Macmillan, pp. 78–96.
Pomerantz, J. (2005). A linguistic analysis <strong>of</strong> question taxonomies. Journal <strong>of</strong> <strong>the</strong> American Society for
Information Science and Technology, 56(7), 715−728.
Prabhaker, P.R., Sheehan, M.J. and Coppett, J.I., (1997). The power <strong>of</strong> technology in business selling: call
centers. J. Bus. Ind. Mark. Vol. 12, pp. 222–235.
Ray, G., Muhanna, W., and Barney, J. (2005), Information technology and <strong>the</strong> performance <strong>of</strong> <strong>the</strong> customer
service process: A resource-based assessment. MIS Quarterly, Vol.29 (4), pp. 625--653.
Reynolds, R. and Brannick, M.T., (2009) Effect <strong>of</strong> communication media on developmental relationships: Selfreported
and observed behaviors. Computers in Human Behavior, 25 (1), 233-243.
Schumann, J. H., N. V. Keller, F. v. Wangenheim ; H. H. Holzmüller (2007), “Technology Mediation in Service
Delivery: A new Typology and an Agenda for Managers and Academics“, AMA Winter Educator’s
<strong>Conference</strong> <strong>Proceedings</strong>, 44-45.
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A Model for Risk Analysis in <strong>the</strong> Process <strong>of</strong> Building a
Knowledge Cluster in Colombia
José Vásquez Paniagua
Facultad de Ciencias Económicas y Administrativas, Universidad de Medellín,
Colombia, and Universidad EAFIT, Colombia
javasquez@udem.edu.co
Abstract: In such a competitive world, where companies face new challenges every day, clusters, defined as
strategies that dynamize national, regional or world economic growth, have become a space leading to
knowledge creation, ownership, transferring and diffusion inside companies. This strategy represents a growing
source for companies and cities as much as <strong>the</strong>y allow companies belonging to a common sector group into
business networks, also, <strong>the</strong>y can spot new business opportunities and become more competitive and
sustainable. Knowledge clusters involve different agents from diverse nature such as governmental, private,
industrial, educational or entrepreneurial. In o<strong>the</strong>r words, <strong>the</strong>se represent a space where demandants
(companies) and knowledge <strong>of</strong>ferors (universities, research institutes, consulting groups, etc) interact.These
clusters’ main goal is to support knowledge creation, ownership, and application processes which can lead to a
better development <strong>of</strong> competitiveness among companies by means <strong>of</strong> creating a dynamic exchange <strong>of</strong>
information among <strong>the</strong> companies belonging to <strong>the</strong> cluster. This paper aims at presenting a model for risk
analysis in <strong>the</strong> process <strong>of</strong> building a knowledge cluster between private companies and high education
institutions (IES*) from a qualitative point <strong>of</strong> view. This methodology has been used in a process <strong>of</strong> building a
knowledge cluster in Colombia.
Keywords: Knowledge cluster, risk, knowledge network, philosophy
1. Introduction
According to Letreros (2004), a knowledge cluster is an interactive and cooperative management
formation structured by collective learning processes and knowledge interchange among actors from
<strong>the</strong> world <strong>of</strong> management. Sapporo says that a knowledge cluster is a technology innovation system
created through local initiatives leading to increase knowledge assets, including human knowledge,
culture and society in a way that <strong>the</strong> use <strong>of</strong> this knowledge would allow achieving a higher competitive
advantage for <strong>the</strong> companies involved (Sapporo, 2003).
Nowadays, <strong>the</strong> processes <strong>of</strong> creation, transmission and appropriation <strong>of</strong> knowledge within an
organization have become highly relevant. This fact has outlined <strong>the</strong> role <strong>of</strong> <strong>the</strong> human nature. The
human being plays a fundamental part because thanks to him, <strong>the</strong>se processes inside knowledge
clusters would never take place.
In this sense, Sapporo’s statement about <strong>the</strong> cluster’s purpose <strong>of</strong> increasing human knowledge could
be crucial if we consider that this would bring <strong>the</strong> elements to understand how people’s behavior
determine <strong>the</strong> processes <strong>of</strong> creation, transmission and appropriation <strong>of</strong> knowledge within an
organization
The former statements highlight <strong>the</strong> relevance <strong>of</strong> having an instrument or method to identify <strong>the</strong>
possible occurrence <strong>of</strong> undesired events related to knowledge generation within an organization as a
consequence <strong>of</strong> human factors. This would be particularly important for organizations such as
knowledge clusters whose main objective is transmission and appropriation <strong>of</strong> knowledge.
2. Theoretical framework
Concerning management thinking, Renée Bédard has proposed a scheme called <strong>the</strong> Philosophical
Diamond. This conceives organizations from a humanist point <strong>of</strong> view widely differentiated from <strong>the</strong>
traditional management <strong>the</strong>ories and practices. The Philosophical Diamond states four philosophical
dimensions which dialectically interact and are well articulated; Praxeology deals with everything
surrounding individual human action (how things are done); Epistemology talks about validity criteria
<strong>of</strong> human practices (validity <strong>of</strong> knowledge); Axiology refers to social, individual and cultural system <strong>of</strong>
values that outlines choices and support individual and collective preferences (legitimacy); Ontology
(modes <strong>of</strong> existence and issues <strong>of</strong> identity) is about <strong>the</strong> nature <strong>of</strong> beings, <strong>the</strong>ir way <strong>of</strong> experiencing
reality, <strong>of</strong> finding direction, how individuals relate with <strong>the</strong> sacred (Bédard, 2003). This model clearly
states a renewed position <strong>of</strong> human beings within management fields and practices.
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According to Bédard, all four philosophical dimensions are fully integrated and complement each
o<strong>the</strong>r. Based on <strong>the</strong>se dimensions, she has built a philosophical model on people’s behaviors within
organizations. Bédard classifies into four <strong>the</strong>se behaviors: <strong>the</strong> Pragmatical: It is practice-centered, <strong>the</strong>
results; <strong>the</strong> Systematic: It is based on orderliness, validity. The Relational: This behavior considers
concert, equity, comprehension and relationships with o<strong>the</strong>rs as fundamental elements. The Mythical:
This one refers to behaviors characterized by valuing achievements, symbols, tradition and memory;
myths, generosity and loyalty. In this way, Bédard’s Philosophical Diamond seeks for deep thinking in
order to understand organizations from a behavioral point <strong>of</strong> view.
Knowledge creation, transmission and appropriation can be favored or inhibited by people’s
behaviors; this is why <strong>the</strong>y are so crucial for almost any organization. Thus, people’s behaviors could
promote <strong>the</strong> occurrence <strong>of</strong> events influencing <strong>the</strong> birth <strong>of</strong> an organization or even its future
performance in terms <strong>of</strong> its achievements; this consequently leads to analyze <strong>the</strong> concept <strong>of</strong> risk.
According to Kaplan and Garrick (1981) say that a risk is a group <strong>of</strong> scenarios which has a probability
<strong>of</strong> occurrence and a consequence. To Correia et al (1989) and to Remenyi and Heafield (1996) risk
is usually used in <strong>the</strong> context <strong>of</strong> <strong>the</strong> possibility <strong>of</strong> an unfortunate outcome resulting from a given
action. To Rosa (1998), A risk is a situation or event in which something valuable for humans (human
beings <strong>the</strong>mselves included) is in danger and its outcome is uncertain. Renn, (1998) defines risk as
<strong>the</strong> possibility <strong>of</strong> occurrence <strong>of</strong> an event (natural or man-induced) and <strong>the</strong> consequences <strong>of</strong> that event
on human values. Bernard, et al (2002) states that risk is a event or conditions <strong>the</strong> occurrence <strong>of</strong>
which causes positive (chance) or negative (threats) deviations from an expected outcome. Finally
Rivera (2007) states that <strong>the</strong> possibility <strong>of</strong> predicting is a characteristic <strong>of</strong> a risk, that is to say, risk
occurrence could be predicted.
Having in mind how complex predicting could be (such as predicting human behavior and <strong>the</strong>
occurrence <strong>of</strong> events derived from interpersonal relationships within an organization), Renn’s concept
<strong>of</strong> risk is adopted and it is complemented by Rosa’s in <strong>the</strong> sense <strong>of</strong> assuming <strong>the</strong> occurrence <strong>of</strong>
uncertain and unexpected results influencing an organization’s objective-achieving.
3. Methodology
In this study, social research methods have been used to obtain information to identify feasible risk
events that could affect <strong>the</strong> process <strong>of</strong> building a knowledge cluster. During <strong>the</strong> social research,
interviews and documentary research were employed. The findings were complemented with <strong>the</strong>
existing literature on <strong>the</strong> subject This method is <strong>the</strong> result <strong>of</strong> a study conducted in four research
centers; it was enhanced by interviews with managers and researchers <strong>of</strong> state and private
institutions.
Renée Bédard’s <strong>the</strong>ories, her philosophical diamond and classification <strong>of</strong> behaviors were used to
construct <strong>the</strong> method <strong>of</strong> risk identification; from <strong>the</strong>se, analysis parameters for <strong>the</strong> different behaviors
<strong>of</strong> people <strong>of</strong>fering and demanding knowledge at <strong>the</strong> knowledge cluster were defined. The findings
were contrasted to an ideal <strong>the</strong>oretical model <strong>of</strong> <strong>the</strong> cluster’s philosophical behavior according to its
objectives, mission and vision from where it is possible to identify <strong>the</strong> risks for <strong>the</strong> organization. The
proposed methodology for human risk factors identification in knowledge cluster building is presented:
4. The Process
4.1 Determination <strong>of</strong> <strong>the</strong> Knowledge Cluster’s Philosophical Behavior: The Cluster’s
Ideal Behavior
Based on its main objective <strong>of</strong> transmitting beneficial knowledge to its participants, <strong>the</strong> ontology or
behavior <strong>of</strong> a knowledge cluster should be, from a philosophical viewpoint, <strong>of</strong> <strong>the</strong> relational type so
community can be formed from <strong>the</strong> common interest <strong>of</strong> sharing knowledge.
4.2 Identification <strong>of</strong> <strong>the</strong> Cluster’s behavior (according to its specific nature)
This identification is obtained from a philosophical analysis <strong>of</strong> <strong>the</strong> behavior <strong>of</strong> people <strong>of</strong>fering and
demanding knowledge at <strong>the</strong> cluster. In order to do this, surveys were designed; <strong>the</strong>se surveys
included aspects related to each <strong>of</strong> <strong>the</strong> four philosophical dimensions Renée Bédard proposes in her
Philosophical Diamond. The interviews were elaborated according to knowledge <strong>of</strong>fering and
demanding people’s pr<strong>of</strong>iles and <strong>the</strong> activities <strong>the</strong>y perform; for example, employees, researchers,
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José Vásquez Paniagua
entrepreneurs, teachers, consultants etc. The questionnaires were applied to each member <strong>of</strong> <strong>the</strong>
groups (knowledge <strong>of</strong>fering and demanding).
4.3 Identification <strong>of</strong> knowledge <strong>of</strong>fering and demanding people’s behavior
Thanks to <strong>the</strong> interview model, identification <strong>of</strong> <strong>the</strong> philosophical behavior <strong>of</strong> each member
(knowledge <strong>of</strong>fering and demanding people) <strong>of</strong> <strong>the</strong> group was possible. By means <strong>of</strong> evaluating each
minor group’s interview results, <strong>the</strong> empirical behavior <strong>of</strong> <strong>the</strong> group <strong>of</strong> people belonging to <strong>the</strong>
knowledge cluster can be identified, based on <strong>the</strong>ir characteristics in terms <strong>of</strong> <strong>the</strong> four levels in <strong>the</strong>
Philosophical Diamond. Table 1 and Table 2 shows <strong>the</strong> process.
Table 1: Identification <strong>of</strong> individual behavior: Member <strong>of</strong> <strong>the</strong> Cluster
Table 2: Identification <strong>of</strong> group’s behaviour: Knowledge <strong>of</strong>fering and demanding
4.4 Comparative Analysis between both models (<strong>the</strong>oretical - empirical)
The Cluster’s behavior as an organization is determined by evaluating <strong>the</strong> results in Table 1 and
Table 2. Although <strong>the</strong> knowledge cluster as an organization is composed <strong>of</strong> two agents (knowledge
<strong>of</strong>fering and demanding), this should, <strong>the</strong>oretically speaking, form one balanced unit in terms <strong>of</strong> an
ideal relational –type behavior. Identifying <strong>the</strong> main differences between <strong>the</strong>se two groups is possible
by means <strong>of</strong> a comparative analysis. In order to carry out this analysis, some transformation functions
have to be set; <strong>the</strong>se can represent <strong>the</strong> percentage in terms <strong>of</strong> similarities between both <strong>the</strong>oretical -
empirical behaviors. The functions created for each <strong>of</strong> <strong>the</strong> four dimensions <strong>of</strong> <strong>the</strong> Philosophical
Diamond (praxeological, epistemological, axiological, ontological) Thanks to <strong>the</strong>se functions, it is
possible to represent <strong>the</strong> empirical level <strong>of</strong> each behavioral dimension <strong>of</strong> <strong>the</strong> organization in units <strong>of</strong>
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<strong>the</strong> same <strong>the</strong>oretical model dimension. Figure 1 represent one transformational function
corresponding to <strong>the</strong> praxeological level.
Figure1: Transformational function: Praxeological Level
4.5 Risk identification within <strong>the</strong> Organization (Cluster)
The difference between <strong>the</strong> cluster’s ideal behavior (<strong>the</strong>oretical model) and <strong>the</strong> group’s empirical
behavior that makes part <strong>of</strong> <strong>the</strong> organization will allow determining <strong>the</strong> behavior’s philosophical
dimensions <strong>of</strong> <strong>the</strong> group which might strain <strong>the</strong> relationships among <strong>the</strong> people belonging to <strong>the</strong>
organization.
The differences between <strong>the</strong> empirical behavior and <strong>the</strong> <strong>the</strong>oretical one are determined by means <strong>of</strong> a
comparative analysis <strong>of</strong> <strong>the</strong> knowledge cluster’s <strong>the</strong>oretical model (ideal) and <strong>the</strong> resultant empirical
model from <strong>the</strong> persons belonging to <strong>the</strong> cluster. According to <strong>the</strong> dissimilarity percentage between
both behaviors, risk level scales are defined: Acceptable, moderate, severe and critical. The first level
(acceptable) includes percentages up to, or below 25% and in <strong>the</strong> last level (critical) percentages
surpassing 75% are included.
From <strong>the</strong>se results, an organization’s philosophical behavior risk map is created. Table 4 indicates a
risk map scheme that could be derived from <strong>the</strong> results presented in Table 3.
Table 3: Risk Map
From <strong>the</strong> analysis <strong>of</strong> this risk map, <strong>the</strong> possible adverse effects generated as a consequence <strong>of</strong> <strong>the</strong>
organization people’s behaviors at <strong>the</strong> knowledge cluster could be derived.
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5. Results and conclusions
José Vásquez Paniagua
The proposed methodology for <strong>the</strong> qualitative risk analysis is basically characterized by:
It allows indentifying philosophical aspects <strong>of</strong> knowledge <strong>of</strong>fering and demanding people’ behavior at
a knowledge cluster; <strong>the</strong>se aspects might become obstacles to an organization’s main processes.
It turns into a supporting element in order to define, in philosophical terms, <strong>the</strong> identity (behavior) <strong>of</strong>
<strong>the</strong> agents or <strong>the</strong> cluster’s interest groups; this leads to determine <strong>the</strong> groups’ possible risk factors for
<strong>the</strong>ir performance inside an organization (independently from <strong>the</strong>ir relation with <strong>the</strong> o<strong>the</strong>r members <strong>of</strong>
<strong>the</strong> cluster).
It makes possible directing <strong>the</strong> measurements for managing possible risks towards <strong>the</strong> organization’s
vulnerability level at each philosophical dimension <strong>of</strong> its behavior.
References
Abel,C.F. (2005) Beyond <strong>the</strong> mainstream: Foucault, power and organization <strong>the</strong>ory. International Journal <strong>of</strong>
Organization Theory and Behavior, 8(4), 2005, p. 495-
Bédard, R. (2003) Los fundamentos del Pensamiento y las Prácticas Administrativas. El rombo y las
cuatro dimensiones filosóficas. Revista Ad-Minister. Universidad EAFIT, Medellín, Nro.3
Correia,C, Flynn, D, Uhana, E and Wormald, M (1989) Financial Management 2nd Edn, Juta and
Co. Ltd. Cape Town.
Kaplan, S and Garrick , B.J., (1981) On <strong>the</strong> quantitative definition <strong>of</strong> risk, Risk Analysis, 1, 11-27.
Letreros. (2004). Clusters, aspectos básicos de algo novedoso.
Recuperado octubre 20 de 2009.http://www.revistaletreros.com/pdf/73-074a076.pdf
Bernard., J.. , and Aubert. A.B.. et al. (2002). Le risque: un model conceptuel d'integration, Project
report No. 2002RP-16, Montreal
Remeyi, D, Healfield, A. (1996) Business process re-engineering: some aspects <strong>of</strong> how to evaluate
and manage <strong>the</strong> risk exposure. Elsevier Science Ltd and IPMA.
Renn, O. (1998). The role <strong>of</strong> risk perception for risk management. Reliability Enginnering and System
Safety, 59. pag 49-62.
Rivera, J. (2007). La borrosa distinción Riesgo-Incertidumbre. Instituto Tecnológico Metropolitano.
Revista Tecno-Lógicas. 19. pag 713-46. Medellín. Colombia.
Rosa, E. A. (1998) Meta<strong>the</strong>oretical Foundations for Post- Normal Risk. Journal <strong>of</strong> Risk Research, 1,
15-44.
Sapporo. (2003). Sapporo IT Carrozzeria Project. Recuperado octubre 30 de 2009.
http://www.itcluster.jp/english/index.html
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