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 one
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.
xxii

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

Developing an Innovative Knowledge Management
Implementation
Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
University <strong>of</strong> Greenwich, London, UK
A.A.F.Al-Shawabkeh@greenwich.ac.uk
a.k.k<strong>of</strong>inas@gre.ac.uk
M.W.Sharp@greenwich.ac.uk
Abstract: Knowledge management (KM) has been considered a key success factor in <strong>the</strong> success <strong>of</strong> an
organisation, especially in <strong>the</strong> current economic climate. Never<strong>the</strong>less, many KM initiatives have been unable to
manage knowledge resulting in failure. Companies and corporations hope that KM may somehow solve all <strong>the</strong>ir
existing and future problems <strong>of</strong>ten without defining those problems’ root causes. Starting a KM system with no
aim will end up in failure. Using different case studies from <strong>the</strong> literature, <strong>the</strong> analysis presented in this paper
focuses on <strong>the</strong> challenges <strong>of</strong> KM implementation and is based on previous research done. The outcome <strong>of</strong> this
analysis and experience has led to <strong>the</strong> formulation <strong>of</strong> a new KM approach which has been subsequently tested
and verified. Using this newly formed KM approach, this paper provides a <strong>the</strong>oretical understanding <strong>of</strong> <strong>the</strong>
processes needed to establish a knowledge management system in organisations.
Keywords: knowledge management (km), knowledge management approach/framework, knowledge
management success factors, people & culture and information technologies (it)
1. Introduction
According to researchers (Storey and Barnett, 2000; Wang and Wang, 2008), many Knowledge
Management (KM) systems fail to deliver on promises rendered. The reasons cited for <strong>the</strong>se failures
are a lack <strong>of</strong> careful understanding <strong>of</strong> knowledge, knowledge management and its processes (Carlile,
2004, Newell et al., 2004).
Some projects fail to account for <strong>the</strong> organisational culture; o<strong>the</strong>r projects do not develop <strong>the</strong> right
technological infrastructure while o<strong>the</strong>r projects fail to develop <strong>the</strong> human resources required
(Alvesson & Karreman, 2001; Cabrera & Cabrera, 2002; Storey and Barnett, 2000). According to
Wong and Aspinwall (2004), implementation <strong>of</strong> KM may fail without proper guidance and a systematic
implementation that meets <strong>the</strong> diverse needs <strong>of</strong> KM systems. There is a great necessity for creating
and adapting systematic practices <strong>of</strong> managing knowledge building towards an effective KM approach
(Drucker, 1993).
A new KM approach should appraise <strong>the</strong> KM implementation process easing <strong>the</strong> transition from
<strong>the</strong>ory to practice. The challenge for managers facing <strong>the</strong> task <strong>of</strong> a KM implementation is <strong>the</strong> lack <strong>of</strong>
thorough understanding <strong>of</strong> <strong>the</strong> complexities <strong>of</strong> a KM system and <strong>the</strong> dismissal <strong>of</strong> its components and
structure. The suggested KM approach/framework aims to include all <strong>the</strong>se essential components,
and suggest <strong>the</strong> relevant connections among <strong>the</strong>m in order to facilitate <strong>the</strong> implementation <strong>of</strong> a KM
system.
The processes for managing knowledge are <strong>the</strong> focus <strong>of</strong> much <strong>of</strong> <strong>the</strong> knowledge management
literature. For example Alavi and Leidner (2001) suggest that <strong>the</strong>re are four main knowledge
management processes: i. knowledge sharing, ii. knowledge storage, iii. knowledge transfer/sharing
and iv. knowledge application. I am going to add two knowledge management processes as
suggested by Hendriks and Vriens (1999): v. assessing <strong>the</strong> current position <strong>of</strong> intra-organisational
knowledge (A, B &C) and, vi. <strong>the</strong> process <strong>of</strong> evaluating knowledge gained (H). Thus I suggest in
Figure 2.1 that knowledge management encompasses <strong>the</strong> following six processes that here I have
presented <strong>the</strong>n in a circular progression from knowledge assessment to knowledge creation, storage,
transfer, application and evaluation.
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Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
Figure 2.1: The cycle <strong>of</strong> knowledge management processes
1.1 KM Implementation Frameworks
As it could be seen to Figure-2-2, Nonaka & Takeuchi (1995) articulated <strong>the</strong> process <strong>of</strong> knowledge
management within and across corporations, highlighting <strong>the</strong> effect <strong>of</strong> knowledge management to
innovation. In <strong>the</strong>ir typology, knowledge has two dimensions: explicit and tacit. The former focuses on
codifiable knowledge while <strong>the</strong> latter focuses on bespoke, uncodifiable and personalised knowledge
that are interminably exchanged between individuals and groups to form <strong>the</strong> spiral <strong>of</strong> knowledge
creation. In <strong>the</strong>ir model <strong>the</strong>re are four types <strong>of</strong> knowledge conversion and each is <strong>the</strong> result <strong>of</strong> a
conversion process between <strong>the</strong> tacit/explicit dimensions. According to this model <strong>of</strong> knowledge
creation knowledge shifts from individuals to groups back to individuals, is ceaselessly inter-converted
making <strong>the</strong> management <strong>of</strong> knowledge <strong>the</strong> necessary precondition <strong>of</strong> innovation and innovation itself
is <strong>the</strong> outcome <strong>of</strong> knowledge creation processes (Nonaka and Takeuchi 1995).
Figure 2.2: The Spiral <strong>of</strong> Knowledge (Nonaka and Takeuchi 1995)
Blackler (1995)suggested a more extensive typology where knowledge is embrained, embodied,
encultured, embedded or encoded. Embrained knowledge refers to individual conceptual skills and
cognitive abilities (e.g. ma<strong>the</strong>maticians). Embodied knowledge focuses on those activities <strong>of</strong> an
individual that are <strong>the</strong> result <strong>of</strong> extensive practice. They <strong>of</strong>ten involve physical action (e.g. skilled craft
workers). Encultured knowledge describes <strong>the</strong> process <strong>of</strong> shared understanding and is knowledge
that can occur at all levels; groups, organizations and societies. Embedded knowledge here relates to
taken-for-granted routines and interactions that enable shared action on <strong>the</strong> part <strong>of</strong> a team. Finally
encoded knowledge refers to <strong>the</strong> explicit knowledge representations found in concrete artifacts such
as books, databases and web-sites. Such a view <strong>of</strong> knowledge has similarities to Nonaka et al’s
perspective (1995) and <strong>the</strong>re seem to be some direct links. Encoded knowledge seems similar to
explicit while encultured, and embedded seems to describe <strong>the</strong> processes <strong>of</strong> externalization and
combination in Nonaka & Takeuchi’s framework. Then he goes on to suggest a typology along two
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Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
dimensions; <strong>the</strong> first utilises <strong>the</strong> individual-social perspective <strong>of</strong> knowledge and <strong>the</strong> second dimension
focuses on problem solving.
Figure 2.3: Organizations and Knowledge Types
In <strong>the</strong> same article Blackler (1995)proposes an alternative <strong>the</strong>ory <strong>of</strong> knowledge whereupon knowledge
becomes “knowing” and is analyzed as an active process that is “mediated, situated, provisional,
pragmatic and contested”. His approach emphasizes <strong>the</strong> importance <strong>of</strong> investigating knowledge in
context <strong>of</strong> action. This concept <strong>of</strong> action and its importance in generating knowledge is implicit in o<strong>the</strong>r
<strong>the</strong>ories <strong>of</strong> organizational knowledge (Spender 1996; Tsoukas 1996). Blackler adopts <strong>the</strong> cultural
historical activity <strong>the</strong>ory (Kaptelinin and Nardi 1997; Engestrom 2000) as <strong>the</strong> foundation <strong>of</strong> this
alternative knowledge-as-action perspective. In activity <strong>the</strong>ory knowledge is acquired via continuous
iterations <strong>of</strong> actors with <strong>the</strong> context and <strong>the</strong> social practices <strong>of</strong> <strong>the</strong>ir activity systems. It is through
activity that knowledge is created, defined, managed and distributed (Blackler, 1995, Spender, 1996).
Cultural-Historical activity <strong>the</strong>ory (CHAT) encompasses artefacts in its <strong>the</strong>ory as part <strong>of</strong> <strong>the</strong>
epistemology <strong>of</strong> knowledge (Engestrom and Blackler, 2005, Miettinen and Virkkunen, 2005,
Macpherson and Jones, 2008). Meanwhile <strong>the</strong> actors/participants <strong>of</strong> <strong>the</strong> activity are going through a
cycle <strong>of</strong> expansive learning that facilitates new knowledge creation and continuous adjustment to <strong>the</strong>
environment (Engestrom 2001). For activity <strong>the</strong>ory <strong>the</strong> locus <strong>of</strong> knowledge creation and learning occur
in a changing mosaic <strong>of</strong> interconnected activity systems which are energized by <strong>the</strong>ir inner
contradictions (Engestrom, 2001, p. 140). Although <strong>the</strong> learning process is reminiscent <strong>of</strong> Argyris’s
<strong>the</strong>ory <strong>of</strong> knowledge creation (Argyris 2004), it has an important difference; it is not interested in <strong>the</strong>
egocentric, individualistic cognitive processes that people undergo during <strong>the</strong> expansive learning
cycle. This is clarified in Engestrom’s discussion <strong>of</strong> <strong>the</strong> fourth umpire who views his experience and
learning processes as a node within a social system (Engestrom, 2000).
In contrast Tsoukas’s conception <strong>of</strong> knowledge (1996) is akin to community-based understandings <strong>of</strong>
knowledge creation (Kuhn, 1996, Gherardi, 2003, Hayes and Walsham, 2003). Tsoukas (1996) views
knowledge as indeterminate and continually emerging; <strong>the</strong> company is viewed as a distributed
knowledge system and <strong>the</strong> knowledge stock <strong>of</strong> each employee is derived from three sources: “(a)
role-related normative expectations; (b) dispositions, which have been formed in <strong>the</strong> course <strong>of</strong> past
socializations; and (c) local knowledge <strong>of</strong> particular circumstances <strong>of</strong> time and place”. There is little
emphasis here on explicit knowledge and almost no regard for artefacts encoding knowledge.
However in Blackler’s (date?) conceptualization <strong>the</strong> knowing via action (or praxis) becomes
paramount while Tsoukas (1996) seems to put much more emphasis on <strong>the</strong> context <strong>of</strong> knowledge. It
is within such community-based understanding <strong>of</strong> knowledge management that Gibbons et al (1994)
suggested <strong>the</strong>ir own typology <strong>of</strong> knowledge management. In <strong>the</strong>ir work <strong>the</strong>y argued that <strong>the</strong>re is a
new knowledge co-production mode called mode 2 that started in <strong>the</strong> mid-20 th century and is now in
full-swing. Before that starting from <strong>the</strong> 19 th century <strong>the</strong>re was mode 1 as exemplified by Kuhn (1997)
with communities <strong>of</strong> practitioners within a discipline creating knowledge but with <strong>the</strong> rise <strong>of</strong> <strong>the</strong>
knowledge economy and innovation we have <strong>the</strong> creation <strong>of</strong> interdisciplinary groups that transcend
<strong>the</strong> original disciplinary boundaries and disciplinary project groups. Mode 1 tended to focus on
problems defined within <strong>the</strong> discipline or in <strong>the</strong> boundaries <strong>of</strong> disciplines but as <strong>the</strong> society became
increasingly complex so did <strong>the</strong> issues thus leading to a Mode 2 approach that pays attention to
context (Gibbons et al., 1994). Beyond a <strong>the</strong>ory <strong>of</strong> KM <strong>the</strong> Gibbons, Limoges et al (1994)work is also
a <strong>the</strong>ory <strong>of</strong> innovation management. It is tempting however to see this mode 2 <strong>of</strong> knowledge
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Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
production as a temporary phenomenon that leads to yet ano<strong>the</strong>r paradigm or discipline; in accord
with Kuhn’s (1997)understanding <strong>of</strong> paradigm shifts in knowledge creation and scientific progress.
Much <strong>of</strong> this literature is focused on knowing and action: Blackler’s (1995) alternative view <strong>of</strong>
knowledge rooted in action and Tsoukas’s version (1996) <strong>of</strong> knowledge in distributed knowledge
systems and Engestrom’s (Engestrom 2001) or Argyris’s (2004) conceptions <strong>of</strong> knowledge creation
can be seen as representative <strong>the</strong>ories <strong>of</strong> <strong>the</strong> relational perspective (Hayes and Walsham, 2003).
Social capital <strong>the</strong>ory (Nahapiet and Ghoshal 1998) syn<strong>the</strong>sises a framework <strong>of</strong> organizational
knowledge creation that provides a macro level analysis <strong>of</strong> <strong>the</strong> process <strong>of</strong> KM. Intellectual capital
(organizational knowledge accumulated and used as a resource) and its creation are achieved
through <strong>the</strong> combination and exchange <strong>of</strong> its underlying conditions: access to parties for
combining/exchanging intellectual capital, anticipation <strong>of</strong> value through combining/exchanging
intellectual capital, motivation to combine/exchange intellectual capital and combination capability.
This framework is quite reminiscent <strong>of</strong> <strong>the</strong> knowledge transfer framework developed by Nonaka and
Takeuchi (1995). The new in social capital <strong>the</strong>ory is <strong>the</strong> underlying dimensions that facilitate <strong>the</strong>
creation <strong>of</strong> <strong>the</strong> intellectual capital. As I demonstrate in Figure 2-4 <strong>the</strong>se dimensions are <strong>the</strong> structural,
<strong>the</strong> cognitive and <strong>the</strong> relational dimension and make up <strong>the</strong> social capital <strong>of</strong> <strong>the</strong> organization. Social
capital comprises both <strong>of</strong> <strong>the</strong> network and <strong>the</strong> actors that are mobilized through that network
(Nahapiet and Ghoshal 1998). The structural dimension concerns <strong>the</strong> configuration <strong>of</strong> linkages
between actors or units <strong>of</strong> analysis (Newell, Tansley et al. 2004). The relational dimension refers to
<strong>the</strong> nature <strong>of</strong> <strong>the</strong> relationships that actors have developed with each o<strong>the</strong>r through a history <strong>of</strong>
interactions. The focus within this dimension is on trust, respect and friendship. The third dimension is
<strong>the</strong> cognitive dimension that refers to <strong>the</strong> resources that facilitate <strong>the</strong> formation <strong>of</strong> shared norms,
representations, understanding and meaning that reside within each node <strong>of</strong> <strong>the</strong> social network;
Figure 2.4: Social Capital and Knowledge Creation (Nahapiet and Ghoshal 1998)
Social capital <strong>the</strong>ory provides management with <strong>the</strong> means to manage knowledge by exercising
control over <strong>the</strong>se three dimensions. It is an ambitious framework and captures many <strong>of</strong> <strong>the</strong>
components <strong>of</strong> <strong>the</strong> o<strong>the</strong>r typologies mentioned above.
O<strong>the</strong>r frameworks that have been introduced to answer <strong>the</strong> question "What is KM?" are focusing on
describing knowledge cycle activities (Rubinstein-Montano et al, 2001). The main concentration <strong>of</strong>
those frameworks emphasises on knowledge flow from creation to application, but not guidance on
how to implement KM. O<strong>the</strong>r KM frameworks have been introduced to investigate and analyse <strong>the</strong> KM
initiatives that were performed in particular industries. A well known framework <strong>of</strong> this category
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Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
developed by Apostolou and Mentzas (1998), and Lai and Chu (2002).Wong and Aspinwall (2004)
have stated <strong>the</strong> need <strong>of</strong> KM frameworks that provide more clear guidance on <strong>the</strong> implementation
phase, which require not only answering "What is KM?" but also describing and advising how to
implement KM.
1.2 Why a KM Implementation Approach is needed
Implementing a knowledge management approach that only concentrates on one particular aspect
such as information technology leaving out o<strong>the</strong>r aspects, would not bring out <strong>the</strong> desired outcome for
<strong>the</strong> organisation. KM is a complex complementary process (Arora, 2002). For example, managing
explicit knowledge without giving attention to tacit knowledge may end up <strong>the</strong> whole process with
failure. An appropriate KM approach must consider all different aspects, and must be aligned with
organisational strategy for accomplishing <strong>the</strong> successful implementation (Arora, 2002). According to
Wong and Aspinwall (2004), <strong>the</strong> main reasons for developing a comprehensive KM framework are:
To provide <strong>the</strong> clarification <strong>of</strong> KM domain increasing <strong>the</strong> awareness, helping to understand <strong>the</strong>
processes and activities concerned.
To help understand <strong>the</strong> KM in a broader perspective <strong>of</strong>fering a holistic view <strong>of</strong> KM.
To enable communication <strong>of</strong> KM in <strong>the</strong> organisation providing <strong>the</strong> common language for people.
Make easier <strong>the</strong> communication between managers and employees.
To outline <strong>the</strong> elements, processes and influences, showing <strong>the</strong> scope and phases <strong>of</strong> <strong>the</strong> tasks
that need to be done.
To help <strong>the</strong> managers in auditing <strong>the</strong> process <strong>of</strong> KM implementation as it provides a detailed
checklist to be followed.
To help <strong>the</strong> managers to control <strong>the</strong> whole process <strong>of</strong> KM implementation, ensuring that all
organisational efforts are coordinated in systematically.
In <strong>the</strong> same context, Wong has proposed four main elements to have to be remembered when
developing a KM framework, <strong>the</strong>se are:
The Structure.
Knowledge types or knowledge resources.
KM processes or activities.
KM influences or factors.
A clear framework should include all milestones and tasks in an order that would sustain a successful
implementation <strong>of</strong> KM. Thus it is necessary to organise and divide <strong>the</strong> activities into separate
segments and different stages as needed (Wiig et al., 1997; and Rubenstein-Montano et al., 2001).
The framework should make clear identification <strong>of</strong> different types <strong>of</strong> knowledge in <strong>the</strong> organisation
giving <strong>the</strong> opportunity to align different management strategies for explicit and tacit knowledge.
Managing explicit knowledge which is created by individuals and can be obtained from outer sources
can be done via different tools such as a knowledge map; lessons learnt databases, groupware and
electronic data interchange. On <strong>the</strong> o<strong>the</strong>r hand, managing tacit knowledge can be done through listing
<strong>of</strong> <strong>the</strong> experts possessing this tacit knowledge, group meeting, face to face conversations and
practice forums (Sanghani, 2009).
The next logical step in KM framework is to encompass <strong>the</strong> process and activities that effectively
handle knowledge assets, as <strong>the</strong>se processes are considered <strong>the</strong> main activities in <strong>the</strong> knowledge
cycle (Rubenstein-Montano et al., 2001). Holsapple and Joshi (2002) assert that one <strong>of</strong> <strong>the</strong> main
tasks <strong>of</strong> KM is to manage organizational knowledge resources to boost organizational effectiveness.
Both organizational knowledge-related efficacy and return from knowledge assets depends on KM as
it manages effective knowledge processes (Wiig, 1997). Additionally, <strong>the</strong> awareness about KM
enablers and disablers is vital for successful KM implementation (Sanghani, 2009). Obviously, <strong>the</strong>
acknowledgement <strong>of</strong> those factors will help to make <strong>the</strong> optimal decisions, which is about <strong>the</strong>
measures and future course <strong>of</strong> actions directed to use <strong>the</strong> enablers for <strong>the</strong> organisational advantage
and to decrease <strong>the</strong> effect <strong>of</strong> <strong>the</strong> disablers (Wong and Aspinwall, 2004).
Organisational culture has been considered as <strong>the</strong> crucial aspect that influence <strong>the</strong> process <strong>of</strong> KM
implementation (Jarrar, 2002; Apostolou and Mentzas, 1998; Liebowitz, 2000) through ei<strong>the</strong>r enabling
5

Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
it to be successful or causing it to fail. The proceses <strong>of</strong>creating, sharing and distributing knowledge is
intricately linked to aspects <strong>of</strong> organisational culture.
Technological support plays a significant role in KM implementation (Jarrar, 2002). Based on that, KM
framework should support <strong>the</strong> balance between a technological and a social approach to ensure
successful KM implementation (Gao et al., 2002;and Offsey, 1997). According to Carter and
Scarbrough (2001), most KM initiatives that were focused on <strong>the</strong> technological side and ignored <strong>the</strong>
social and organisational aspects have failed. Concurrently, <strong>the</strong> humans cannot manage <strong>the</strong>
knowledge in an adequate manner as <strong>the</strong>y could do with using <strong>the</strong> necessary media and
technologies.
1.3 Forming an Innovative KM approach
The following KM approach is proposed which contains steps to ensure a successful KM
implementation. Figure 1 describes how to apply this approach.
Processes
Evaluate Knowledge
Focus
Figure 2.5: Inn-KM Approach Cycle
People &
Culture
Identify
Control
Control
Improve
Organisational
Strategy
Measure
Analyse KM
Information
Technology
1.3.1 Step 1: Identifying <strong>the</strong> Challenges
We must identify <strong>the</strong> problem accurately first: addressing a legitimate, recognised and relevant
problem is important as it provides <strong>the</strong> incentive for employees and management to implement <strong>the</strong>
KM solution and commit <strong>the</strong>mselves to make it work. The following are some <strong>of</strong> <strong>the</strong> processes
involved:
Brainstorm and understand <strong>the</strong> impact <strong>of</strong> <strong>the</strong> problem
Define a process to investigate <strong>the</strong> problem
Choose a proper research methodology (i.e case study, empirical research...)
Use suitable research methods (i.e surveys and/or questionnaires, interviews...)
1.3.2 Step 2: Measuring <strong>the</strong> Performance
It is important to have <strong>the</strong> systems in place to collect relevant data. Only this way we can measure
current performance and make decisions for <strong>the</strong> future. An accurate map <strong>of</strong> <strong>the</strong> existing areas <strong>of</strong> <strong>the</strong>
organisation helps pinpoint <strong>the</strong> areas that cause organisational problems. Such clarity in <strong>the</strong> state <strong>of</strong>
<strong>the</strong> organisation forms <strong>the</strong> basis <strong>of</strong> organisational problem-solving.
1.3.3 Step 3: Analysis
Once data is collated it is in this step, that <strong>the</strong> context <strong>of</strong> <strong>the</strong> KM issues facing <strong>the</strong> organisation.
Problems can be statistically analysed from KM point <strong>of</strong> view. This includes:
Identify <strong>the</strong> problem’s root causes
6

Find out current KM status
Analyse reasons <strong>of</strong> <strong>the</strong> problem
Define KM gaps
Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
Identify KM barriers and enablers
1.3.4 Step 4: Improve upon existing KM systems
Improvements for potential causes, identified in ‘Analysis’ step 3 are carried out in this step through
proposing a KM solution, this includes:
Recommend and establish a KM Solution: improvements for potential problem causes identified
in step 3 are carried out in this step. The recommended KM Solution needs to be put in <strong>the</strong>
context <strong>of</strong> <strong>the</strong> recognised problem
Build a culture that accepts <strong>the</strong> proposed solution, this contains building an environment that
facilitates:
Knowledge creation
Knowledge dissemination
Knowledge sharing, and
Knowledge implementation
1.3.5 Step 5: Evaluate <strong>the</strong> KM system
You must collect related data and appraise/evaluate <strong>the</strong> system by measuring its performance against
developed deliverables Monitor continuously <strong>of</strong> whe<strong>the</strong>r <strong>the</strong> improved process is well maintained.
1.4 Control
Control should exist at all steps before, during, and after <strong>the</strong> KM implementation. This includes
activities involved in ensuring a process is predictable, stable, and consistently operating at <strong>the</strong> target
level <strong>of</strong> performance with only normal variation.
1.5 Knowledge Focus
Knowledge Focus includes managing different types <strong>of</strong> knowledge. Since explicit knowledge is
created by individuals and can be attained from outer channels a knowledge map, lessons-learnt
databases, groupware and electronic data interchange can be <strong>the</strong> appropriate tools for managing this
type <strong>of</strong> knowledge. On <strong>the</strong> o<strong>the</strong>r hand, when it comes to deal with <strong>the</strong> tacit knowledge <strong>the</strong>n a listing <strong>of</strong>
<strong>the</strong> experts possessing a tacit knowledge, group meetings, face-to-face conversations and practice
forums can be <strong>the</strong> way <strong>of</strong> managing tacit knowledge (Wong and Aspinwall, 2004).
1.6 Organisational Strategy Focus
All KM processes must be aligned with business strategy and goals. At each step <strong>of</strong> <strong>the</strong> framework
<strong>the</strong>re should be a possibility to refer to <strong>the</strong> organisational strategy and goals and ensure that <strong>the</strong>y are
integrated in <strong>the</strong> core <strong>of</strong> all decisions.
As a guide to implement this Inn-KM approach, Table 1 describes its contents:
7

Table 1: Inn-KM approach contents
2. Conclusion
Abdallah Al-Shawabkeh, Alexander K<strong>of</strong>inas and Mike Sharp
When organisations do not have <strong>the</strong> support <strong>of</strong> a solid foundation and <strong>the</strong>oretical underpinning as
guidance, <strong>the</strong>n it is more likely <strong>the</strong>y will be struggling to comprehend <strong>the</strong> full potential <strong>of</strong> <strong>the</strong>ir KM
practices. The aim <strong>of</strong> this paper was to build a new KM implementation approach which gives a
proper guidance with regards to <strong>the</strong> stages and processes <strong>of</strong> <strong>the</strong> KM system in order to ensure
systematic practices <strong>of</strong> managing knowledge.
Using <strong>the</strong> KM new approach, a <strong>the</strong>oretical understanding <strong>of</strong> <strong>the</strong> processes needed to establish a
knowledge management system in organisations. Also, a guide on how to implement this approach
has been produced.
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9

Knowledge Dynamics and Organisational Learning Cycles
Eckhard Ammann
School <strong>of</strong> Informatics, Reutlingen University
Eckhard.Ammann@Reutlingen-University.de
Abstract: Knowledge development in an enterprise is about approaches, methods, techniques and tools, which
will support <strong>the</strong> advancement <strong>of</strong> individual and organisational knowledge for <strong>the</strong> purpose <strong>of</strong> an improvement <strong>of</strong>
businesses. As a basis, conceptions <strong>of</strong> knowledge and <strong>of</strong> knowledge conversions are needed. Here knowledge
dynamics is understood to cover all <strong>of</strong> acquisition, conversion, transfer and usage <strong>of</strong> knowledge. Conceptions <strong>of</strong>
knowledge and <strong>of</strong> knowledge conversions are provided in this paper, which introduce three dimensions <strong>of</strong>
knowledge and general conversions between knowledge assets, respectively. Knowledge is represented by a
three-dimensional model <strong>of</strong> knowledge with types, kinds and qualities. General knowledge conversions between
<strong>the</strong> various knowledge assets are introduced as a model for knowledge dynamics in <strong>the</strong> enterprise. First a basic
set <strong>of</strong> such conversions is defined. Building on this set general knowledge conversions can be defined, which
reflect knowledge transfers and development. In effect, <strong>the</strong> well-known SECI model for knowledge development
is as well extended as generalised in this approach. While organisational learning is not merely a multiplicity <strong>of</strong>
individual learning efforts <strong>of</strong> its members, organisations learn through experience and activities <strong>of</strong> individuals to a
large extent. Built on <strong>the</strong> presented conception <strong>of</strong> knowledge development, organisational learning scenarios
involving teams <strong>of</strong> members and <strong>the</strong> organisational memory are identified and described in this paper. Three
basic learning cycles are identified, which are closely related with appropriate combinations <strong>of</strong> basic and general
knowledge conversions. Through appropriate combinations <strong>of</strong> such basic learning cycles, important learning
scenarios in an organisation can be described. Especially, important known organisational learning types are
covered by this approach, including single-loop learning and double-loop learning. In order to validate <strong>the</strong>
approach to knowledge development and organisational learning, an example <strong>of</strong> an organisational learning
scenario is given, namely a supervised learning-by-doing scenario in a team.
Keywords: Conception <strong>of</strong> knowledge, knowledge dynamics, organisational learning, learning cycles,
organisational memory, single-loop and double-loop learning
1. Introduction
Knowledge development in an enterprise is about approaches, methods, techniques and tools, which
will support <strong>the</strong> advancement <strong>of</strong> individual and organisational knowledge for <strong>the</strong> purpose <strong>of</strong> an
improvement <strong>of</strong> businesses. As a basis, a conception <strong>of</strong> knowledge and <strong>of</strong> knowledge dynamics is
needed. Here, knowledge dynamics is understood to cover all <strong>of</strong> acquisition, conversion, transfer and
usage <strong>of</strong> knowledge. While organisational learning is not merely a multiplicity <strong>of</strong> individual learning
efforts <strong>of</strong> its members, organisations learn through experience and activities <strong>of</strong> individuals to a large
extent. Built on a conception <strong>of</strong> knowledge development, organisational learning scenarios involving
teams <strong>of</strong> members and <strong>the</strong> organisational memory are identified and described in this paper.
A number <strong>of</strong> approaches for knowledge management exist, including <strong>the</strong> classic asset-oriented
approach, <strong>the</strong> process-oriented approach, <strong>the</strong> knowledge-intensive process-oriented approach, and
finally <strong>the</strong> community-oriented approach, see (Ammann 2009, Gronau 2006, Lehner 2009). While <strong>the</strong>
management aspect <strong>of</strong> knowledge management seems to be ra<strong>the</strong>r well understood and practised in
many companies (Lehner 2009), <strong>the</strong>re is no common concept and understanding <strong>of</strong> knowledge and <strong>of</strong>
knowledge development as basis <strong>of</strong> it.
There exist several approaches, <strong>of</strong> course. The knowledge development model by Nonaka/Takeuchi
(Nonaka 1995) is built on <strong>the</strong> distinction between tacit and explicit knowledge and on four
fundamental knowledge conversions between those knowledge types (SECI-model). However, many
discussions exist, whe<strong>the</strong>r to interpret <strong>the</strong> explicit knowledge part as still bound to <strong>the</strong> human being,
or as already detached from him. Ano<strong>the</strong>r important work is <strong>the</strong> introduction <strong>of</strong> <strong>the</strong> type/quality
dimensions <strong>of</strong> knowledge in (De Jong 1996). Finally, important distinctions <strong>of</strong> implicit knowledge are
given in (Hasler Rumois 2007), which partly resemble <strong>the</strong> philosophy-based knowledge concept given
in (Williams 2001). A different approach <strong>of</strong> knowledge development is given by Boisot (see Boisot
1998), where knowledge passes through <strong>the</strong> three dimension <strong>of</strong> <strong>the</strong> so-called information space (Ispace)
in a social learning cycle, while changing its characteristics <strong>of</strong> abstraction, codification, and
diffusion. The research on knowledge transfer, which is an important part <strong>of</strong> knowledge development,
is reviewed in (Ling 2007). The approaches by Nonaka/Takeuchi and by Boisot are also included in
10

Eckhard Ammann
this work. In (Wahab 2009) a review on technologie transfer models, which also includes knowledgebased
models on technology transfer, is provided.
In this paper, we introduce a conception <strong>of</strong> knowledge, which combines and resembles parts <strong>of</strong> <strong>the</strong>
existing approaches and extends <strong>the</strong>m substantially. It is represented by a knowledge cube, a threedimensional
model <strong>of</strong> knowledge with types, kinds and qualities. The type dimension addresses <strong>the</strong>
internal-external aspect <strong>of</strong> knowledge, seen from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> human being. Here explicit
knowledge is a kind <strong>of</strong> interface between those two types, which drives human interaction and
knowledge externalisation. The type dimension is <strong>the</strong> most important for knowledge development in a
company. It categorizes knowledge according to its presence and availability. Is it only available for
<strong>the</strong> owning human being, or can it be communicated, applied or transferred to <strong>the</strong> outside, or is it
externally available in <strong>the</strong> company’s organisational memory? It is crucial for <strong>the</strong> purposes <strong>of</strong> <strong>the</strong>
company, and hence a main goal <strong>of</strong> knowledge management activities, to make as much as possible
knowledge available to a company, i.e. let it be converted from internal to more external types. As <strong>the</strong>
two o<strong>the</strong>r dimensions <strong>of</strong> knowledge, <strong>the</strong> kind dimension distinguishes various knowledge kinds,
namely propositional, procedural, strategic knowledge and familiarity. Finally, in <strong>the</strong> quality dimension,
several quality measures <strong>of</strong> knowledge are given.
Using this conception we introduce general knowledge conversions between <strong>the</strong> various knowledge
variants. First a basic set <strong>of</strong> such conversions is defined, which extends <strong>the</strong> set <strong>of</strong> <strong>the</strong> four
conversions <strong>of</strong> <strong>the</strong> well-known SECI-model (Nonaka 1995). Building on this set, general knowledge
conversions can be defined, which reflect knowledge transfers and development more realistically
and do not suffer from <strong>the</strong> restrictions <strong>of</strong> <strong>the</strong> SECI-model. These general knowledge conversions take
effect as well in <strong>the</strong> form <strong>of</strong> changes <strong>of</strong> knowledge <strong>of</strong> a person as in <strong>the</strong> form <strong>of</strong> transfers <strong>of</strong>
knowledge between persons. This conception <strong>of</strong> knowledge dynamics can also be naturally applied
as base level <strong>of</strong> intellectual capital advancement in a company, see (Ammann 2011).
Built on <strong>the</strong>se conceptions, organisational learning scenarios are described. Three basic learning
cycles are identified, which are closely related with appropriate combinations <strong>of</strong> basic and general
knowledge conversions. Through appropriate combinations <strong>of</strong> such basic learning cycles, important
learning scenarios in an organisation can be described. Especially, important known organisational
learning types are covered by this approach, including single-loop learning and double-loop learning
(Argyris 1978 and 1996). In order to validate <strong>the</strong> approach to knowledge development and
organisational learning, an example <strong>of</strong> an organisational learning scenario is given, namely a
supervised learning-by-doing scenario in a team.
The structure <strong>of</strong> <strong>the</strong> paper is as follows. After an introduction, <strong>the</strong> two sections 2 and 3 will introduce
<strong>the</strong> conceptions <strong>of</strong> knowledge and <strong>of</strong> knowledge dynamics, respectively. Section 4 applies <strong>the</strong>se
conceptions to organisational learning. Basic organisational learning cycles are identified. Appropriate
combinations <strong>of</strong> <strong>the</strong>se basic cycles lead to important organisational learning scenarios, among <strong>the</strong>m
<strong>the</strong> well-known single-loop and double-loop learning. The following section 5 provides an example <strong>of</strong>
an organisational learning scenario. A supervised learning-by-doing scenario is described. Finally,
section 6 concludes <strong>the</strong> paper.
2. Conception <strong>of</strong> knowledge
2.1 General understanding <strong>of</strong> knowledge
In this section we provide a conception <strong>of</strong> knowledge, and <strong>of</strong> knowledge types, kinds and qualities. As
our base notion knowledge is understood as justified true belief, which is (normally) bound to <strong>the</strong>
human being, with a dimension <strong>of</strong> purpose and intent, identifying patterns in its validity scope, brought
to bear in action and with a generative capability <strong>of</strong> new information, see (Hasler Rumois 2007,
Lehner 2009, and Williams 2001). It is a perspective <strong>of</strong> “knowledge-in-use” (De Jong 1996) because
<strong>of</strong> <strong>the</strong> importance for its utilisation in companies and for knowledge management. In contrast,
information is understood as data in relation with a semantic dimension, but is lacking <strong>the</strong> pragmatic
and pattern-oriented dimension, which characterises knowledge.
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Eckhard Ammann
We distinguish three main dimensions <strong>of</strong> knowledge, namely types, kinds and qualities, and describe
those in <strong>the</strong> following three sub-sections. The whole picture leads to <strong>the</strong> three-dimensional knowledge
cube, which is introduced at <strong>the</strong> end <strong>of</strong> this section.
2.2 Type Dimension <strong>of</strong> knowledge
The type dimension is <strong>the</strong> most important for knowledge management in a company. It categorizes
knowledge according to its presence and availability. Is it only available for <strong>the</strong> owning human being,
or can it be communicated, applied or transferred to <strong>the</strong> outside, or is it externally available in <strong>the</strong>
company’s organisational memory, detached from <strong>the</strong> individual human being? It is crucial for <strong>the</strong>
purposes <strong>of</strong> <strong>the</strong> company, and hence a main goal <strong>of</strong> knowledge management activities, to make as
much as possible knowledge available, i.e. let it be converted from internal to more external types <strong>of</strong>
knowledge.
Our conception for <strong>the</strong> type dimension <strong>of</strong> knowledge follows a distinction between <strong>the</strong> internal and
external knowledge types, seen from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> human being. As third and intermediary
type, explicit knowledge is seen as an interface for human interaction and for <strong>the</strong> purpose <strong>of</strong>
knowledge externalisation, <strong>the</strong> latter one ending up in external knowledge. Internal (or implicit)
knowledge is bound to <strong>the</strong> human being. It is all that, what a person has “in its brain” due to
experience, history, activities and learning. Explicit knowledge is “made explicit” to <strong>the</strong> outside world
e.g. through spoken language, but is still bound to <strong>the</strong> human being. External knowledge finally is
detached from <strong>the</strong> human being and may be kept in appropriate storage media as part <strong>of</strong> <strong>the</strong>
organisational memory. Figure 1 depicts <strong>the</strong> different knowledge types.
Figure 1: Conception <strong>of</strong> knowledge types
Internal knowledge can be fur<strong>the</strong>r divided into tacit, latent and conscious knowledge, where those
subtypes do partly overlap with each o<strong>the</strong>r, see (Hasler Rumois 2007). Conscious knowledge is
conscious and intentional, is cognitively available and may be made explicit easily. Latent knowledge
has been typically learning as a by-product and is not available consciously. It may be made explicit,
for example in situations, which are similar to <strong>the</strong> original learning situation, however. Tacit knowledge
is built up through experiences and (cultural) socialisation situations, is specific in its context and
based on intuition and perception. Statements like “I don’t know, that I know it” and “I know more, than
I am able to tell” (adapted from Polanyi 1996) characterise it.
2.3 Kind dimension <strong>of</strong> knowledge
In <strong>the</strong> second dimension <strong>of</strong> knowledge, four kinds <strong>of</strong> knowledge are distinguished: propositional,
procedural and strategic knowledge, and familiarity. It resembles to a certain degree <strong>the</strong> type
dimension as described in (De Jong 1996). Propositional knowledge is knowledge about content,
facts in a domain, semantic interrelationship and <strong>the</strong>ories. Experience, practical knowledge, and <strong>the</strong>
knowledge on “how-to-do” constitute procedural knowledge. Strategic knowledge is meta-cognitive
knowledge on optimal strategies for structuring a problem-solving approach. Finally, familiarity is
12

Eckhard Ammann
acquaintance with certain situations and environments, it also resembles aspects <strong>of</strong> situational
knowledge, i.e. knowledge about situations, which typically appear in particular domains (De Jong
1996).
2.4 Quality dimension <strong>of</strong> knowledge
The quality dimension introduces five characteristics <strong>of</strong> knowledge with an appropriate qualifying and
is independent <strong>of</strong> <strong>the</strong> kind dimension, see [4].
The level characteristics aims at overview vs. deep knowledge, structure distinguishes isolated from
structured knowledge. The automation characteristic <strong>of</strong> knowledge can be step-by-step-doing by a
beginner in a domain <strong>of</strong> work or automated fast acting by an expert. All <strong>the</strong>se qualities measure work
along an axis and can be subject to knowledge conversions, see section 3. Modality as <strong>the</strong> fourth
quality asks for <strong>the</strong> representational form <strong>of</strong> knowledge, be it words versus pictures in situational
knowledge kinds, or propositions versus pictures in procedural knowledge kinds. Finally, generality
differentiates general versus domain-specific knowledge. Knowledge qualities apply to each
knowledge asset.
2.5 The knowledge cube
Bringing all three dimension <strong>of</strong> knowledge toge<strong>the</strong>r, we gain an overall picture <strong>of</strong> our knowledge
conception. It can be represented by <strong>the</strong> knowledge cube, as is shown in Figure 2.
Figure 2: The knowledge cube
Note, that <strong>the</strong> dimensions in <strong>the</strong> knowledge cube behave different. In <strong>the</strong> type and kind dimensions,
<strong>the</strong> categories are mostly distinctive (with <strong>the</strong> mentioned exception in <strong>the</strong> sub-types), while in <strong>the</strong>
quality dimension each <strong>of</strong> <strong>the</strong> given five characteristics are always present for each knowledge asset.
3. Knowledge conversions
In this section we give a conception <strong>of</strong> knowledge conversions. The transitions between <strong>the</strong> different
knowledge types, kind and qualities are responsible to a high degree for knowledge development in
an organisation.
Most important for knowledge management purposes are conversions between <strong>the</strong> knowledge types
and <strong>the</strong>y will be <strong>the</strong> focus in <strong>the</strong> following. Among those, especially those conversions making
individual and internal knowledge <strong>of</strong> employees usable for a company, are crucial for knowledge
management. The explicitation and externalisation conversion described in this section achieve this.
Implicitly socialisations between tacit knowledge <strong>of</strong> different people also may contribute to this goal.
Conversions in <strong>the</strong> kind dimension <strong>of</strong> knowledge are seldom, normally <strong>the</strong> kind dimension <strong>of</strong>
knowledge remains unchanged in a knowledge conversion changing <strong>the</strong> type dimension. Those in <strong>the</strong>
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Eckhard Ammann
quality dimension are mostly knowledge developments aiming at quality improvement and will not
change <strong>the</strong> type and kind dimensions <strong>of</strong> <strong>the</strong> involved knowledge assets.
Five basic knowledge conversions (in <strong>the</strong> type dimension) are distinguished here: Socialisation,
explicitation, externalisation, internalisation and combination. Basic conversion means, that exactly
one source knowledge asset is converted into exactly one destination knowledge asset. More
complex conversions may be easily gained by building on this set as described later in this section.
They will consist <strong>of</strong> m-to-n-conversions and include information assets in addition.
Socialisation converts tacit knowledge <strong>of</strong> a person into tacit knowledge <strong>of</strong> ano<strong>the</strong>r person. For
example, this succeeds by exchange <strong>of</strong> experience or in a learning-by-doing situation under
supervision <strong>of</strong> an experienced person. Explicitation is <strong>the</strong> internal process <strong>of</strong> a person, to make
internal knowledge <strong>of</strong> <strong>the</strong> latent or conscious type explicit, e.g. by articulation and formulation (in <strong>the</strong>
conscious knowledge type case) or by using metaphors, analogies and models (in <strong>the</strong> latent type
case). Externalisation is a conversion from explicit knowledge to external knowledge or information
and leads to detached knowledge as seen from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> human being, which can be
kept in organisational memory systems. Internalisation converts ei<strong>the</strong>r external or explicit knowledge
into internal knowledge <strong>of</strong> <strong>the</strong> conscious or latent types. It leads to an integration <strong>of</strong> experiences and
competences in your own mental model. Finally, combination combines existing explicit or external
knowledge in new forms. These five basic knowledge conversions are shown in Figure 3.
Figure 3: Knowledge conversions in <strong>the</strong> type dimension
The Nonaka/Takeuchi-model (Nonaka 1995) uses four basic knowledge conversions in <strong>the</strong> sense
defined above, which interact in a spiral <strong>of</strong> knowledge creation, which itself becomes larger in scale
as it moves up <strong>the</strong> ontological dimension from <strong>the</strong> individual to groups and <strong>the</strong> whole organisation.
This limiting linearity <strong>of</strong> its knowledge development spiral concept and <strong>the</strong> restriction to basic
conversions has been criticised, besides <strong>the</strong> discussions on <strong>the</strong> meaning <strong>of</strong> explicit knowledge.
Our conception allows <strong>the</strong> generalisation <strong>of</strong> <strong>the</strong> basic five knowledge conversions described above.
General knowledge conversions are modeled converting several source assets (possibly <strong>of</strong> different
types, kinds and quality) to several destination assets (also possibly different in <strong>the</strong>ir knowledge
dimensions). In addition, information assets are considered as possible contributing or generated
parts <strong>of</strong> general knowledge conversions.
For example, in a learning-by-doing situation seen as a complex knowledge conversion, a new
employee may extend his tacit and conscious knowledge by working on and extending an external
knowledge asset in a general conversion, using and being assisted by <strong>the</strong> tacit and conscious
knowledge <strong>of</strong> an experienced colleague. A piece <strong>of</strong> relevant information on <strong>the</strong> topic may also be
available on <strong>the</strong> source side <strong>of</strong> <strong>the</strong> conversion. Here on <strong>the</strong> source side <strong>of</strong> <strong>the</strong> general conversion we
have two tacit, two conscious and one external knowledge assets plus one information asset, while on
<strong>the</strong> destination side one tacit, one explicit and one external knowledge asset (i.e. <strong>the</strong> resulted
enriched external knowledge) arise.
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Eckhard Ammann
Completing this section, we shortly mention knowledge conversions in <strong>the</strong> quality dimension <strong>of</strong>
knowledge. In three out <strong>of</strong> <strong>the</strong> five quality measures, basic conversions can be identified, which are
working gradually. Those are, firstly, a deepening conversion, which converts overview knowledge
into a deeper form <strong>of</strong> this knowledge. Secondly, <strong>the</strong>re is a structuring conversion, which performs
improvement in <strong>the</strong> singular-versus-structure scale <strong>of</strong> <strong>the</strong> structural measure. Finally, conscious and
step-by-step-applicable knowledge may convert into automated knowledge in an automation
conversion, which describes a process from beginner to expert in a certain domain. The remaining
two quality measures <strong>of</strong> knowledge, namely modality and generality, do not lend <strong>the</strong>mselves to
knowledge conversions. They just describe unchangeable knowledge qualities.
4. Organisational learning cycles and scenarios
Learning is about a result (i.e. something, which has been learned) or a process, which leads to this
result. An organisation is learning, if it acquires “… information, knowledge, understanding, know-how,
techniques, or practises <strong>of</strong> any kind and by whatever means” (Argyris 1996). This organisational
learning is performed by <strong>the</strong> individual employees, but enhances organisational knowledge through
(inter-) actions along <strong>the</strong> organisational processes within <strong>the</strong> framework <strong>of</strong> <strong>the</strong> organisational
environment. Argyris and Schön conceptualised organisational learning processes in <strong>the</strong> sense <strong>of</strong> a
<strong>the</strong>ory <strong>of</strong> action (Argyris 1978). Organisational learning <strong>the</strong>n is, to discover problems and mismatching
situations, to correct <strong>the</strong>m, and to change <strong>the</strong> organisational knowledge base in a way, that reflect
new problem solving and action competences. Learning processes enhance <strong>the</strong> organisational
knowledge, which itself may be represented in <strong>the</strong> minds <strong>of</strong> organisational members (normally only a
certain part per employee) or stored in <strong>the</strong> organisational memory. Earlier in this paper we already
stated <strong>the</strong> importance for knowledge management <strong>of</strong> making implicit knowledge explicit and available
for <strong>the</strong> organisation and <strong>of</strong> possibly storing it in <strong>the</strong> organisational memory (system) as external
knowledge. In this section we start by identifying basic organisational learning cycles. Through
appropriate combinations <strong>of</strong> such learning cycles more complex learning scenarios in an organisation
can be described. Especially, important known organisational learning types are covered by this
approach, including single-loop learning and double-loop learning (Argyris 1978 and 1996, Vlismas
2010).
4.1 Basic organisational learning cycles
Built on our conception <strong>of</strong> knowledge and <strong>of</strong> knowledge dynamics as presented in sections 2 and 3,
we can identify three basic cycles <strong>of</strong> organisational learning. They are shown in Figure 4.
Figure 4: Basic organisational learning cycles
Cycle I is intentional individual learning <strong>of</strong> a single member <strong>of</strong> <strong>the</strong> organisation in <strong>the</strong> first instance.
This <strong>of</strong> course is influential on <strong>the</strong> whole organisation or a part <strong>of</strong> it. The cycle consists <strong>of</strong> explicitation
<strong>of</strong> internal knowledge, possibly following combination conversions between <strong>the</strong> explicit knowledge <strong>of</strong>
<strong>the</strong> employee and that <strong>of</strong> o<strong>the</strong>r employees or external knowledge, and a following internalisation <strong>of</strong>
acquired knowledge and/or generated information.
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Eckhard Ammann
The second cycle, Cycle II, covers individual learning through socialisation. Employees learn by taking
problem solving behaviour <strong>of</strong> o<strong>the</strong>r employees as example. Finally, Cycle III represents a combined
individual and organisational learning cycle. Through combination conversions between explicit and
external knowledge and possibly information, individuals extend <strong>the</strong>ir (explicit) knowledge as well as
<strong>the</strong> organisation itself learns by extending <strong>the</strong> organisational knowledge base and (indirectly) by <strong>the</strong>
individual learning part <strong>of</strong> <strong>the</strong> cycle. Note, that <strong>the</strong> learning cycles I and III as described here are not
disjoint. The optional middle part <strong>of</strong> Cycle I may consist <strong>of</strong> instantiations <strong>of</strong> Cycle III.
Through appropriate combinations <strong>of</strong> <strong>the</strong>se basic learning cycles, important learning scenarios in an
organisation can be described. Two important scenarios are described in <strong>the</strong> following sub-section.
4.2 Important organisational learning scenarios
In this sub-section we focus on two important and well-known organisational learning scenarios,
namely single-loop learning and double-loop learning. They have been first introduced and described
by Argyris and Schön (Argyris 1978 and 1996).
Figure 5: Single-loop and double-loop learning
Single-loop learning is adaption learning within a given frame <strong>of</strong> governing variables <strong>of</strong> <strong>the</strong>
organisation. Governing variables are understood as <strong>the</strong> “<strong>the</strong>ory-in-use”, interpretation systems and
frames <strong>of</strong> reference, i.e. <strong>the</strong> organisational rules, norms, and procedures to give a more concrete
description. Corresponding organisational actions intend to eliminate detected gaps and mismatches
(between organisational expectations and outcomes) under <strong>the</strong> guidance <strong>of</strong> <strong>the</strong>se governing
variables, but without changing <strong>the</strong>se. Figure 5 depicts this kind <strong>of</strong> organisational learning.
Double-loop learning can be described as transformation learning, where in addition to <strong>the</strong> actions in
<strong>the</strong> single-loop case <strong>the</strong> governing variables in <strong>the</strong> organisation are revised and eventually adjusted. If
<strong>the</strong> environment <strong>of</strong> <strong>the</strong> organisation provides a challenging feedback to <strong>the</strong> assumptions <strong>of</strong> <strong>the</strong>
organisation as provided by <strong>the</strong> governing variables, <strong>the</strong>n <strong>the</strong>se assumptions have to be changed,
redefined or altered completely in order to fit to <strong>the</strong> demand from <strong>the</strong> environment. This second loop
compared to <strong>the</strong> single-loop learning situation causes <strong>the</strong> denotation double-loop learning. Doubleloop
learning is also shown in Figure 5.
A third learning type called deutero learning exists, which in fact is a kind <strong>of</strong> meta-learning. Subject <strong>of</strong>
this organisational learning type is to learn how to (better) learn in <strong>the</strong> organisation. We do not fur<strong>the</strong>r
elaborate on this learning type here, see (Argyris 1996 and Vlismas 2010) for details.
The single-loop and double-loop learning types can be modelled with <strong>the</strong> help <strong>of</strong> <strong>the</strong> three basic
organisational learning cycles as introduced in sub-section 4.1 and with combinations <strong>of</strong> <strong>the</strong>m. Note,
that <strong>the</strong>se cycles and combinations <strong>the</strong>mselves are based on <strong>the</strong> conception <strong>of</strong> knowledge and
knowledge dynamics as presented in section 2 and 3.
Figures 6 and 7 illustrate <strong>the</strong> coverage <strong>of</strong> single-loop and double-loop learning by our approach,
respectively. In <strong>the</strong>se figures ellipses denote general knowledge conversions with incoming and
outgoing arrows for <strong>the</strong>ir source and destination knowledge assets, grey-shaded rectangles represent
Knowledge or information. An internal or explicit knowledge asset is associated with an employee.
See (Ammann 2009) for more details on this graphical notation for knowledge-intensive processes.
Governing variables in <strong>the</strong> learning loop scenarios are modeled as external knowledge. The detected
problem or mismatch in <strong>the</strong> organisation is represented by an information asset. In <strong>the</strong> single-loop
learning case as shown in Figure 6, <strong>the</strong> problem is solved (new information is generated as
representation <strong>of</strong> this), but <strong>the</strong> governing variables remain unchanged and valid. This action occurs
through utilisation <strong>of</strong> <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> employee, which is extended during <strong>the</strong> action.
16

Eckhard Ammann
Figure 6: Single-loop learning with a general knowledge conversion
Figure 7: Double-loop learning with a general knowledge conversion
In <strong>the</strong> double-loop learning case, <strong>the</strong> external knowledge representing <strong>the</strong> governing variables have to
be changed in order to cover with <strong>the</strong> problem. This is shown in Figure 7.
5. An Example <strong>of</strong> an organisational learning scenario
As an example for an organisational learning scenario, we describe a supervised learning-by-doing
scenario. It is important to note, that this scenario is different from <strong>the</strong> much simpler example given in
section 3, which demonstrated a single general knowledge conversion. Here a new employee is able
to extend his explicit and tacit knowledge by working on and extending an information asset in two
general conversions. This information asset represents <strong>the</strong> task at hand to be performed or a yet
unsolved problem. He is using <strong>the</strong> explicit and tacit knowledge <strong>of</strong> an experienced colleague, who is
assisting him. The overall activity is governed by external knowledge, which represents organisational
norms and rules.
17

Figure 7: Supervised learning-by-doing
Eckhard Ammann
Figure 7 displays this scenario. Again ellipses denote general knowledge conversions, while <strong>the</strong>
rectangles stand for knowledge assets. The fill colour <strong>of</strong> <strong>the</strong> rectangles varies from dark grey (for
internal knowledge, here tacit knowledge) to middle grey for explicit knowledge, light grey for external
knowledge and while for information assets. See (Ammann 2009) for more details on this notation,
which allows for modeling <strong>of</strong> knowledge-intensive business processes.
The results <strong>of</strong> <strong>the</strong> two general conversions in Figure 7 are <strong>the</strong> following. General conversion 1 models
<strong>the</strong> task-solving or problem-solving activity, which as well leads to <strong>the</strong> problem solution as to extended
explicit knowledge <strong>of</strong> <strong>the</strong> new employee. The new employee is more skilled and experienced than
before. General conversion 2 denotes <strong>the</strong> extended or new tacit knowledge, which arose at <strong>the</strong> new
employee during <strong>the</strong> activity by observing <strong>the</strong> actions and advises <strong>of</strong> <strong>the</strong> experienced colleague. This
is a more unconscious side effect <strong>of</strong> <strong>the</strong> activity, which advances <strong>the</strong> abilities <strong>of</strong> <strong>the</strong> new employee in
addition. In an overall view, <strong>the</strong> scenario establishes a single-loop learning cycle when seen from <strong>the</strong>
perspective <strong>of</strong> <strong>the</strong> new employee. It is single-loop, because <strong>the</strong> governing variables represented by
<strong>the</strong> external knowledge asset are not changed during and by <strong>the</strong> activity.
6. Summary and conclusion
Conceptions <strong>of</strong> knowledge and <strong>of</strong> knowledge dynamics are introduced in this paper, which can be
seen as basis for knowledge development in a company. For <strong>the</strong> knowledge development part, <strong>the</strong>
well-known SECI-model is as well extended as generalised by this approach.
These conceptions <strong>of</strong> knowledge and knowledge dynamics are <strong>the</strong>n applied to <strong>the</strong> area <strong>of</strong>
organisational learning. Three basic organisational learning cycles have been identified. They can be
combined appropriately to cover important organisational learning scenarios. Especially, <strong>the</strong> wellknown
single-loop and double-loop learning cycles are covered by this approach. In order to validate
<strong>the</strong> approach to knowledge development and organisational learning, an example <strong>of</strong> an organisational
learning scenario is given, namely a supervised learning-by-doing scenario in a team.
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19

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> 22th
district <strong>of</strong> Tehran
Manouchehr Ansari 1 , Hossein Rahmany Youshanlouei 2,1 Mohammad
Mirkazemi Mood 1 , Nima sarabi1, and Younis Jabarzadeh 1
1
University <strong>of</strong> Tehran, Tehran, Iran
2
Member <strong>of</strong> Young Researchers Club, Islamic Azad University, Salmas Branch
mansari@ut.ac.ir
rahmani82@gmail.com
mohamad_mirkazemi@yahoo.com
nimasarabi@yahoo.com
yjabarzade@gmail.com
Abstract: Today, one <strong>of</strong> <strong>the</strong> most important ways to achieve competitive advantage is <strong>the</strong> implementation <strong>of</strong>
knowledge management. To be successful in this process, we should know and manage critical success factors
<strong>of</strong> knowledge management. In this paper, after reviewing research literature and <strong>the</strong>n using Delphi method, we
reached to a list <strong>of</strong> 21 components which were categorized into 5 groups that make our final list <strong>of</strong> critical
success factors. These are: organizational culture, organizational structure, leadership and strategy, IT
infrastructure, and human resource. Finally, using <strong>the</strong> Friedman test <strong>the</strong> relative importance <strong>of</strong> factors was
studied and <strong>the</strong> results indicate that <strong>the</strong> relative importance <strong>of</strong> factors is different.
Keywords: knowledge management, critical success factors, culture, structure, Delphi
1. Introduction
Emergence <strong>of</strong> ICT and its influence on all aspects <strong>of</strong> human life have brought about new paradigms
and change in human life (Glaser 2003). Organizations are not excluded from this and <strong>the</strong>y have
faced many changes and developments too. In <strong>the</strong> era <strong>of</strong> rapid changes and growing complexities,
Organizations, in order to achieve competitive advantages and continue to survive and deal with
changing environmental conditions, are using modern management tools, techniques and principles
(Nonaka and Takeuchi 1995).
Knowledge management as one <strong>of</strong> <strong>the</strong> latest organizational issues has been introduced to help
organizations. In <strong>the</strong> realm <strong>of</strong> management, Knowledge management encompasses a variety <strong>of</strong>
topics. This is due to <strong>the</strong> transformation and movement <strong>of</strong> industry-based economic systems toward
knowledge-based communities and knowledge-based economic development (Davenport and Grover
2001). Knowledge as one <strong>of</strong> <strong>the</strong> main factors <strong>of</strong> competitive advantage has been known in large and
small industrial and service enterprises. If organizations consider <strong>the</strong> issue <strong>of</strong> knowledge carefully,
<strong>the</strong>y will realize <strong>the</strong> fact that <strong>the</strong> existing knowledge in <strong>the</strong>ir organization is more than what <strong>the</strong>y have
already recognized (Ibid).
Knowledge management is a process that organizations produce wealth <strong>of</strong> knowledge and intellectual
capital (Nonaka and Takeuchi 1995). Very simply, knowledge management is a process in which
organizations create value from intellectual and knowledge - based assets. Often, creating value
requires knowledge sharing between employees, organizational units or even among o<strong>the</strong>r
organizations. Successful managers always use intellectual assets in <strong>the</strong>ir organizations and have
realized its importance; but most <strong>of</strong> <strong>the</strong>se activities have been done unsystematically with no defined
structure so that <strong>the</strong> managers could not be assure <strong>of</strong> spreading and deploying gained knowledge in
<strong>the</strong> organization. It is estimated that 85 percent <strong>of</strong> organizational knowledge assets have been placed
in e-mails, word processing s<strong>of</strong>tware and presentation files ra<strong>the</strong>r than storing in organization
knowledge databases (Turban 2003).
According to ano<strong>the</strong>r definition, knowledge management is a process or activity for creating,
obtaining, capturing, sharing and applying knowledge to increase learning and performance <strong>of</strong>
organizations (Swan 1999). Considering all issues, in simple words, knowledge management can be
used as <strong>the</strong> optimized processing <strong>of</strong> intellectual capital to achieve organizational goals. Today,
20

Manouchehr Ansari et al
considering <strong>the</strong> competitive environment in Iran, organizations and companies concentrate on
obtaining more competitive advantages compared to <strong>the</strong>ir competitors. One <strong>of</strong> <strong>the</strong> main leverages
with this respect, according to experts and scholars, is <strong>the</strong> implementation <strong>of</strong> knowledge
management. In order to be successful in this, we must know that areas in which results, if <strong>the</strong>y are
satisfactory, will ensure successful implementation <strong>of</strong> knowledge management. This is a new issue,
particularly in Iranian companies and organizations. These areas which are known as success factors
are those few key areas where things must go right for <strong>the</strong> successful accomplishment <strong>of</strong> our goals.
They are variables which management can influence through its decisions. The purpose <strong>of</strong> this study
is to identify those affecting factors –success factors- related to <strong>the</strong> successful implementation <strong>of</strong>
knowledge management in order to reach a ranking <strong>of</strong> affecting factors on success <strong>of</strong> KM
implementation. For this, we chose Municipality <strong>of</strong> 22th district <strong>of</strong> Tehran. It is one <strong>of</strong> <strong>the</strong> leading
public organizations in Iran in <strong>the</strong> adoption <strong>of</strong> new ICT technologies and knowledge management
implementation.
Research flowchart is shown in figure1:
Figure 1: Research flowchart
The remaining <strong>of</strong> <strong>the</strong> paper is organized as follows:
In <strong>the</strong> next section, initially, <strong>the</strong> definitions <strong>of</strong> <strong>the</strong> success factors and also past researches in this field
will be presented. Then <strong>the</strong> related literature to <strong>the</strong> success factors <strong>of</strong> knowledge management
implementation will be reviewed. After this, <strong>the</strong> process for identifying and weighting <strong>the</strong> related
success factors are described which was done through Delphi method. Finally conclusion and
recommendations for fur<strong>the</strong>r researches will be presented.
2. Critical Success Factors in Implementation <strong>of</strong> KM
Critical success factors are used for identifying and prioritizing <strong>the</strong> classified technical systems <strong>of</strong>
business necessities (Flynn and Arce 1997). As Somers and Nelson assert, <strong>the</strong>se factors are
constant examples that will help <strong>the</strong> process improvement and if <strong>the</strong> process has been considered
appropriate, it will have a large impact (Somers and Nelson 2001). Using knowledge management is
not a one-day activity but also it needs coherent programs and changes in strategy and processes. In
knowledge management efforts, like o<strong>the</strong>r business activities, significant results in a series <strong>of</strong> limited
areas is leading to successful performances.
These areas are named as Critical Success Factors <strong>of</strong> KM (Stankosky and Carolyn 2001).
According to this study, knowledge management is a set <strong>of</strong> factors that leads to project success by
coordinating and effective interaction with each o<strong>the</strong>r. Malhotra& Galletta (2003), while studying <strong>the</strong>
21

Manouchehr Ansari et al
users <strong>of</strong> a Knowledge Management Systems implemented in a health organization, realized <strong>the</strong>
importance <strong>of</strong> user's commitment and motivation. They found that giving only reward does not
guarantee <strong>the</strong> success <strong>of</strong> knowledge management systems. Therefore, a tool for measuring user
engagement and motivation is created.
Chourides, Longbottom & Murphy (2003) identified various critical factors in successful
implementation <strong>of</strong> KM in five functional areas <strong>of</strong> organization: strategy, human resources
management, information technology, quality and marketing. However, <strong>the</strong> outcome was <strong>the</strong> list which
should complete <strong>the</strong> activities such as improving product delivery time to market and increasing
momentum <strong>of</strong> <strong>the</strong> organization in responding to customers. In ano<strong>the</strong>r study Yu, Kim & Kim (2004)
investigated <strong>the</strong> relationship between organizational culture and knowledge management success by
studying 66 Korean companies. They found that KM stimulants such as learning culture, knowledge
sharing efforts, <strong>the</strong> quality <strong>of</strong> knowledge management systems, rewards and <strong>the</strong> actions <strong>of</strong> knowledge
management team significantly have influence on <strong>the</strong> performance <strong>of</strong> knowledge management, and
finally, 9 knowledge management stimulants were divided into three categories: Organizational
characteristics, Information Technology and Administrative supports.
Kavindra Msy (2004) in his MA <strong>the</strong>sis titled "Key success factors <strong>of</strong> knowledge management", after
qualitative and quantitative analysis <strong>of</strong> his collected data, identified five major factors that success <strong>of</strong>
knowledge management systems is depending on. The output <strong>of</strong> his research was identifying and
prioritizing <strong>the</strong> factors that play an important role in successful implementation <strong>of</strong> knowledge
management. He named five factors as: organizational culture, knowledge management strategy,
systems and infrastructure, systematic and effective processes and measurement criteria which are
most relevant factors and stimulants <strong>of</strong> <strong>the</strong> implementation <strong>of</strong> knowledge management.
from <strong>the</strong> Msy's point <strong>of</strong> view, a culture <strong>of</strong> knowledge sharing, existence <strong>of</strong> leaders for implementation
<strong>of</strong> knowledge management and pr<strong>of</strong>essional teams for managing knowledge assets, creating social
and electronic networks for using <strong>the</strong> organizational knowledge leverage, creating systematic and
effective processes that can create environmental knowledge, Strategy development, systems and
infrastructure for integrating knowledge management elements and finally, considering criteria for
success <strong>of</strong> knowledge management are factors that lead to <strong>the</strong> successful implementation <strong>of</strong>
knowledge management. In The o<strong>the</strong>r study conducted by Wong (2005) eleven success factors
have been identified: Leadership and management support, culture, information technology, strategy
and purpose, assessment, organizational infrastructures, processes and activities, motivation,
resources, education and training and human resources management. Similarly, Chong and Choi
(2005) determined eleven key factors in successful implementation <strong>of</strong> knowledge management.
These factors are staff training, staff involvement, teamwork, conferring <strong>of</strong> responsibilities, senior
management leadership and commitment, willingness to remove <strong>the</strong> organizational constraints,
information systems infrastructure, knowledge-based performance criteria, knowledge-friendly culture,
comparative study and <strong>the</strong> structure <strong>of</strong> knowledge.
Wong and Aspinwall (2005) studied eleven success factors <strong>of</strong> knowledge management in <strong>the</strong>ir paper
and arranged <strong>the</strong>m in terms <strong>of</strong> importance in <strong>the</strong> medium and small size organizations. The eleven
factors were leadership and management support, culture, strategy and purpose, resources,
processes and activities, education and training, human resources management, information
technology, motivational moods, organizational infrastructure and evaluation. Huang, Lin and Tsai
(2005) considered <strong>the</strong> top management commitment and leadership, employee involvement, reliable
teamwork, employee involvement, information systems infrastructure and performance measurement
as success factors <strong>of</strong> knowledge management.
As we can see, <strong>the</strong>re is a great commonness among <strong>the</strong> identified factors in various studies. In <strong>the</strong>
next sections, based on this, we are going to describe and investigate some <strong>of</strong> <strong>the</strong>se factors in depth
in order to have a common sense towards <strong>the</strong>m.
2.1 Organizational culture
Organizational culture consists <strong>of</strong> shared values, beliefs and common norms <strong>of</strong> organization's
members (Huener 2001). Organizational culture determines <strong>the</strong> style <strong>of</strong> work accomplishment in <strong>the</strong>
organization. The same perceptions that is observed among all members <strong>of</strong> <strong>the</strong> organization,
indicates a common and stable pr<strong>of</strong>ile that separates each organization from o<strong>the</strong>r organizations. In
o<strong>the</strong>r words, organizational culture defines <strong>the</strong> social identity <strong>of</strong> each organization (Robbins 1996). In
Davenport and Pursak study, eight criteria have been introduced for assessing <strong>the</strong> success <strong>of</strong>
22

Manouchehr Ansari et al
organizations in knowledge management projects. The study <strong>of</strong> <strong>the</strong>se criteria shows that, <strong>the</strong>
organizational culture is considered as <strong>the</strong> fundamental indicator <strong>of</strong> success measurement in
organizations (Davenport & Pursak 2000).
2.2 Organizational Structure
The rate <strong>of</strong> increased competition and rapid technological changes requires appropriate knowledge
transfer between functional boundaries <strong>of</strong> organizations (Gopalakrishnau and Sontoro 2004, Walczak
2005). Gold and o<strong>the</strong>rs (Gold et al, 2001) discuss that <strong>the</strong> organizational structure is a critical factor
in applying new technologies. Structure can be defined as a set <strong>of</strong> solutions in which individuals are
divided into different tasks and describe how to coordinate <strong>the</strong>se tasks (Andersson and Westterlind,
2000; Mintzberg 1983). Organizational structure determines <strong>the</strong> ways <strong>of</strong> decision-making and also
determines responsibilities for <strong>the</strong> processes <strong>of</strong> material, resources and human resources.
Organizational structure should be flexible to increase spreading <strong>of</strong> knowledge and cooperation
between organization's traditional boundaries towards <strong>the</strong> creative frontiers <strong>of</strong> knowledge (Walczak
2005). Studies have shown that formalization factors and lack <strong>of</strong> focus have great influence on
coordination and cooperation within <strong>the</strong> company and also on <strong>the</strong> creation and separation <strong>of</strong>
knowledge. (Adenfelt and Lagerstro 2005; Andersson and Westterlind 2000; Lee and Choi 2003).
Centralized structure prevents communications between units, distribution <strong>of</strong> ideas and also due to
time consuming communicational channels, it causes deviation and discontinuity <strong>of</strong> ideas (Lee and
Choi 2003). In order to be sure <strong>of</strong> proper knowledge distribution, it is necessary to have flowing
communicational channels (Gupta 2000).
2.3 Human Resources
People in organizations enable implementation <strong>of</strong> knowledge management and social instruments. So
<strong>the</strong>y are named as <strong>the</strong> main creators and distributors <strong>of</strong> knowledge (Adenfelt and Lagerstro 2005).
McDermott (1999) emphasized on <strong>the</strong> importance <strong>of</strong> people role in <strong>the</strong> processes <strong>of</strong> sharing
knowledge that should be raised to be involved in an activity. It needs additional time and is
threatened to lose <strong>the</strong>ir position <strong>of</strong> power (Porter 1985). People are important because any changes
or entry <strong>of</strong> new technology has effects on <strong>the</strong> labors inside <strong>the</strong> organization; so it is necessary to
evaluate <strong>the</strong> organizational culture and individual readiness for adoption <strong>of</strong> innovative and new
technology (Ruikar et al. 2005). In general, people in <strong>the</strong> organization perform <strong>the</strong> human skills which
include: knowledge, motivation and self-reaction, streng<strong>the</strong>ning, learning networks, social contacts,
coordination and creativity (M<strong>of</strong>fett et al. 2002). Individuals are knowledge innovators in <strong>the</strong>
organizations and <strong>the</strong> significant part <strong>of</strong> organizational knowledge exist in <strong>the</strong>ir mind. For this reason,
small mutations in work places can make major effect on overall performance (ESCFWA 2003).
Ruggles states that around 50% focus <strong>of</strong> time and knowledge management budget should be on
people (Ruggles 1998). Changes in organizational culture require a change in attitudes and traditions,
so inside <strong>the</strong> organizational changes that have stem from organizational knowledge management
implementation, human issues should be considered as a main factor. This kind <strong>of</strong> considerations in
<strong>the</strong> organizations will raise <strong>the</strong> topic <strong>of</strong> knowledge workers (ESCFWA, 2003).
2.4 Information Technology Infrastructure
Information technology includes a wide range <strong>of</strong> inventions and <strong>the</strong> communicational media that
connect information systems and people to each o<strong>the</strong>r and includes: voice posts, emails, audio
conferences, video conferences, Internet, S<strong>of</strong>tware, Hardware and <strong>the</strong> like. Information systems and
information technologies are <strong>of</strong>ten too complex and usually, <strong>the</strong> term <strong>of</strong> information technology refers
to all technologies (Tsang 2002). Knowledge management is a method ra<strong>the</strong>r than a product or
technology, which is used for business activities. However, information technology is critical for
successful knowledge management systems (Turban et al. 2006).
In fact, <strong>the</strong> role <strong>of</strong> information technology is for supporting knowledge management, repository <strong>of</strong>
knowledge, increasing access to and transfers <strong>of</strong> knowledge and can be provide individual, group and
organizational interactions. The information technology empowers processes <strong>of</strong> knowledge creation in
academic environments (Tian et al. 2009). Information technology and knowledge management are
mixed with each o<strong>the</strong>r, because both contribute to <strong>the</strong> promotion <strong>of</strong> structured knowledge (Alavi and
Leidner, 2001). Although <strong>the</strong> information technology provides a ground for powerful knowledge
management, it should serve <strong>the</strong> people too. Therefore, it should be simple and usable and
employees been able to take advantages <strong>of</strong> it without help <strong>of</strong> pr<strong>of</strong>essionals.
23

2.5 leadership and strategy
Manouchehr Ansari et al
Knowledge management in any organization is guided by <strong>the</strong> organizational strategy and it originates
from different principles and programs <strong>of</strong> that organization. Leadership depends on <strong>the</strong> concept <strong>of</strong>
knowledge management strategy and applying knowledge management requires top management
supports (in order to allocate resources and time for knowledge management projects and programs)
(Gaffoor 2008). In fact, an essential principle for <strong>the</strong> success <strong>of</strong> knowledge management programs is
creating strategic commitment for continuous management <strong>of</strong> knowledge by top and middle managers
to indicate particular features that lead to knowledge management (Rylatt 2003).
2.6 Delphi process
We used Delphi method in order to collect <strong>the</strong> most important factors in successful implementation <strong>of</strong>
knowledge management about which related experts have consensus. Delphi method was <strong>the</strong> result
<strong>of</strong> studies by RAND Corporation in 1950s. The purpose <strong>of</strong> study was to develop a method to obtain
<strong>the</strong> most reliable consensus <strong>of</strong> expert panel (Okoli & Pawalwski 2004). Researchers use Delphi
method, at <strong>the</strong> first step, where <strong>the</strong> information judgment is essential and usually use a series <strong>of</strong>
questionnaires with controlled feedback comments (Okoli & Pawalwski 2004). In this study we have
taken advantages <strong>of</strong> a panel <strong>of</strong> experts and consultants in <strong>the</strong> field <strong>of</strong> knowledge management
implementation. Experts were selected based on <strong>the</strong>ir work experience in KM. we asked managing
directors <strong>of</strong> companies active in <strong>the</strong> field <strong>of</strong> knowledge management to introduce KM experts <strong>of</strong> <strong>the</strong>ir
companies or o<strong>the</strong>r experts from <strong>the</strong> outside. Among <strong>the</strong> 20 candidates with whom we contacted, 14
people accepted to participate and complete our questionnaire. 7 out <strong>of</strong> 14 participants were
implementation experts <strong>of</strong> <strong>the</strong> organization under study (Municipality <strong>of</strong> 22th district) and <strong>the</strong> rest were
consultants in information systems and knowledge management.
The former group has average related work experiences <strong>of</strong> eight years, while <strong>the</strong> later has about 20
years work experience.
In <strong>the</strong> first questionnaire, panel members were asked to express factors that <strong>the</strong>y believe are effective
in implementing successful knowledge management based on <strong>the</strong>ir experiences. They also were
asked to mention a brief explanation about each <strong>of</strong> <strong>the</strong> expressed factors to provide a better
classification. 21 components were collected in questionnaires, and <strong>the</strong>n <strong>the</strong> five derived factors were
classified as follows: organizational culture, human resources, IT infrastructure, strategy and
leadership. In <strong>the</strong> second questionnaire, we rewrite <strong>the</strong> factors according to our classifications and
post <strong>the</strong>m to <strong>the</strong> each expert with a copy <strong>of</strong> his or her answers. The experts were requested to
confirm or modify his or her own responds considering comments and if necessary modify or amend
<strong>the</strong> provided classification. According to Schmidt "without leaving behind this stage, it cannot be
claimed that an integrated index is made valid"
Among <strong>the</strong> 21 initial factors, 5 factors were identified as "success factors in implementing knowledge
management" by more than 86 percent <strong>of</strong> <strong>the</strong> experts. In <strong>the</strong> third questionnaire, <strong>the</strong> experts were
asked to mention at least 21 indicators (<strong>the</strong> five categories for <strong>the</strong> presentation <strong>of</strong> various factors)
which <strong>the</strong>y believe are <strong>the</strong> most effective factors for successful implementation <strong>of</strong> knowledge
management (but not scoring) and we did this stage in two rounds.
Terms <strong>of</strong> consensus on selected final factors were defined so that <strong>the</strong> factors which in two
consecutive rounds have 7 votes or more (50% <strong>of</strong> all votes), or those which gained more than 85% <strong>of</strong>
votes were selected.
Finally, 21 indicators were achieved at this stage. It is important to mention that <strong>the</strong> conditions for
achieving consensus in Delphi method is determining by those who conduct <strong>the</strong> research and <strong>the</strong>re is
no specific rule. But <strong>the</strong> more number <strong>of</strong> rounds are, <strong>the</strong> more difficult <strong>the</strong> consensus conditions are,
and <strong>the</strong> results <strong>of</strong> Delphi method have more validity (Fink 1984). In <strong>the</strong> last questionnaire we asked
<strong>the</strong> participants to score each <strong>of</strong> <strong>the</strong>se indicators from number 1, means absolutely unimportant to
number 7, means absolutely important. Then <strong>the</strong> average degree <strong>of</strong> importance <strong>of</strong> each index was
calculated. The Condition <strong>of</strong> consensus at this stage <strong>of</strong> <strong>the</strong> Delphi method was determined in a way
that indicators with importance degree <strong>of</strong> more than 4 will be selected as <strong>the</strong> final factors. Therefore,
<strong>the</strong> factor <strong>of</strong> "humanitarianism culture" with <strong>the</strong> average <strong>of</strong> 3.95 was removed from <strong>the</strong> list .Final
factors with average degree <strong>of</strong> importance are seen in Table 1.
24

Table 1: Research flowchart
3. Ranking
Factors
Organizational culture
Manouchehr Ansari et al
Trust 4.6
Cooperation 5.67
Open atmosphere 6.03
Learning from mistakes 5.54
Innovation 5
Knowledge share 5.98
Organizational structure
decentralization 6
low formalization 5.90
Communicational channels 5.03
Team work 5.45
Human resources
Creativity 4
Verbal skills 4.5
T – Shape skills 3.45
IT Infrastructure
Access to network and hardware infrastructure 5.07
Availability <strong>of</strong> s<strong>of</strong>tware 5.5
Flexibility 4.75
Information Technology Staff 4.5
Collaboration technologies 4
Strategy & Leadership
Knowledge Strategy 5.03
reward policies 4.63
Top management support 6.73
The average <strong>of</strong> importance
degree
After analyzing <strong>the</strong> relative importance <strong>of</strong> indexes in each category (factor) we identified <strong>the</strong> ranking <strong>of</strong>
<strong>the</strong> factors too. Using Fridman Test we can see that <strong>the</strong>re is a significant difference in <strong>the</strong> importance
<strong>of</strong> <strong>the</strong> factors according to <strong>the</strong> experts participated in <strong>the</strong> panel. Table 2 shows <strong>the</strong> results <strong>of</strong> <strong>the</strong>
25

Manouchehr Ansari et al
conducted statistical test and also <strong>the</strong> ranking <strong>of</strong> <strong>the</strong> factors according to <strong>the</strong>ir relative importance in
affecting knowledge management implementation. As we can see, “organizational structure” is <strong>the</strong>
most important factor and o<strong>the</strong>r factors have <strong>the</strong>ir position respectively.
Table 2: Friedman test
factors Average Test Statistics
Organizational structure 7.92 Numbers 14
Organizational culture 6.73 Chi-Square 204.514
Strategy & Leadership 5.03 Degrees <strong>of</strong> freedom 4
IT Infrastructure 4.95 Significant level 0.000
Human resources 4.90 a. Friedman Test
The second part <strong>of</strong> Table 2 shows <strong>the</strong> number <strong>of</strong> sample, Value <strong>of</strong> chi square statistic (X2), Degree <strong>of</strong>
freedom and sig. index. Because <strong>the</strong> sig. index is less than 0.05, <strong>the</strong> relative importance <strong>of</strong> <strong>the</strong>se five
factors is different.
4. Conclusion
knowledge management implementation is one <strong>of</strong> <strong>the</strong> issues that nowadays organizations and
mangers are engaged in more than any time before. This process does not always lead to successful
accomplishment and <strong>the</strong>re are lots <strong>of</strong> organizations that do not consider <strong>the</strong>ir efforts so successful
and even some <strong>of</strong> <strong>the</strong>m admit <strong>the</strong>ir fail in knowledge management implementation. Because <strong>of</strong> this
we must identify and <strong>the</strong>n try to manage those critical factors that contribute to <strong>the</strong> success <strong>of</strong>
knowledge management implementation. In this research, we have tried to do such a work and
identify critical success factors <strong>of</strong> knowledge management implementation. After reviewing literature
and conducting Delphi method, we reached to a list <strong>of</strong> 5 critical success factors with 21 components.
As we can note, <strong>the</strong> extracted factors and components have a high degree <strong>of</strong> similarity with those
mentioned in <strong>the</strong> literature (Jalaldeen et al. (2009); Quin et al.(2005); Davenport et al. (1998); Holt
(2000); Mohammadi et al. (2009); Ross et al. (2005); Choi (2000); Hasanali (2002))
We also tried to analyze <strong>the</strong> relative importance <strong>of</strong> <strong>the</strong> identified factors and <strong>the</strong> result <strong>of</strong> <strong>the</strong> statistical
test shows that <strong>the</strong>re is a significant difference among factors’ importance in affecting knowledge
management implementation. The most important factors according to <strong>the</strong> participants are <strong>the</strong>
organizational structure and organizational culture. This is not surprising because <strong>the</strong>y are <strong>the</strong> most
influential factors in Iranian public sector organizations. Unlike private sector, individuals, both
managers and employees (human resource) do not have such an effect and role in <strong>the</strong> organization
that could influence <strong>the</strong> structure and goals greatly. Usually <strong>the</strong>y work in a predetermined structure
with strategies and objectives that defined from outside. But this does not mean to exclude
individuals and leadership role from consideration. The issue is a matter <strong>of</strong> relative importance.
Fur<strong>the</strong>r researches can be done in private sector to identify <strong>the</strong> critical success factors <strong>of</strong> KM
implementation. Also <strong>the</strong>re can be researches to assess KM implementation success by identifying
and weighting related success criteria. Advanced works is possible in designing models that relate
critical success factors to <strong>the</strong> success criteria with techniques like Structural Equations Model or path
analysis. The results <strong>of</strong> our study are useful for managers and practitioners in <strong>the</strong> KM field to focus
<strong>the</strong>ir efforts and considerations on <strong>the</strong> identified success factors.
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27

Serious Games in <strong>the</strong> Context <strong>of</strong> Organizational
Knowledge Management Practices
Albena Antonova 1 and Anandasivakumar Ekambaram 2
1
S<strong>of</strong>ia University, S<strong>of</strong>ia, Bulgaria
2
SINTEF – Technology and Society, Productivity and Project Management,
Trondheim, Norway
a_antonova@fmi.uni-s<strong>of</strong>ia.bg
siva@sintef.no
Abstract: Serious games (SG) are increasingly gaining popularity in various fields such as, education, business,
entertainment and research, and <strong>the</strong>y capture attention <strong>of</strong> learners, researchers and <strong>the</strong> business community.
Serious games are commonly defined as digital games used for purposes o<strong>the</strong>r than mere entertainment or fun.
SGs are considered as effective tools for enhancing knowledge building and testing <strong>of</strong> s<strong>of</strong>t skills and complex
competences. With <strong>the</strong>se characteristics, SG can lead to better visualization and understanding <strong>of</strong> reality and
high-order learning. Thus, being recognized as an enhanced learning platform, SG has entered in <strong>the</strong> field <strong>of</strong>
corporate training and competence development. The development and <strong>the</strong> widespread implementation <strong>of</strong> SG
can influence knowledge management (KM) processes and knowledge flows in organizations. The present
research objectives are to investigate <strong>the</strong> impact <strong>of</strong> SG implementation on different organizational practices <strong>of</strong>
KM. SG directly influence individuals’ (employees’) attitudes toward knowledge acquisition, competence building,
and cooperation and motivation. Fur<strong>the</strong>rmore, SGs contribute to improve organizational processes and pave <strong>the</strong>
way to enhance KM practices in organizations. And at <strong>the</strong> same time focus on KM can also improve <strong>the</strong>
processes <strong>of</strong> developing and implementing SG in companies. The paper first presents a <strong>the</strong>oretical overview <strong>of</strong>
issues related to KM and SG. Followed by this presentation, <strong>the</strong> paper describes <strong>the</strong> connection between KM and
SG. This description will incorporate both <strong>the</strong> individual dimension <strong>of</strong> KM and <strong>the</strong> organizational dimension <strong>of</strong> KM
with respect to SG. The following discussion provides recommendations for developing or adopting SG for KM
purposes. Finally, conclusion winds up <strong>the</strong> whole discussion.
Keywords: Serious games, knowledge flows, organizational knowledge management
1. Introduction
During <strong>the</strong> last few years, serious games (SG) and virtual worlds (VW) become a part <strong>of</strong> <strong>the</strong> modern
organizational world and gained influence in education and business. Serious games are used to
facilitate and improve learning, competence and skills building, but <strong>the</strong>ir impact is constantly
increasing in various contexts. Serious games represent an emerging field with wide application in, for
example, education, business, simulation and research (Antonova & Martinov, 2010).
In contrast to development <strong>of</strong> SG, knowledge management still remains isolated from o<strong>the</strong>r practices
in companies. Some reports highlight that KM have never succeeded to integrate with o<strong>the</strong>r
organizational systems and practices such as e-learning, HRM and innovations systems
(Learnovation, 2008). Main obstacles for KM include cultural obstacles, political obstacles (lack <strong>of</strong>
management support), evaluation obstacles (lack <strong>of</strong> appropriate metrics for KM) and technological
obstacles (lack <strong>of</strong> appropriate infrastructure) (Learnovation, 2008). Ano<strong>the</strong>r report (KPMG, 2004)
points out <strong>the</strong> following KM barriers: lack <strong>of</strong> time to share knowledge, information overload,
reinventing <strong>the</strong> wheel, not using technology to share knowledge, not using ideas to improve <strong>the</strong>
functioning <strong>of</strong> <strong>the</strong> organization and lost market opportunities. According to CWA (2004), some <strong>of</strong> <strong>the</strong>
main obstacles for KM are: time for sharing knowledge, lack <strong>of</strong> management support for KM,
“knowledge is power”, apathy about sharing knowledge, “not-invented here” syndrome, no appropriate
reward systems, different cultures and sub-cultures, organizational amnesia and over-emphasis on
technologies.
Based on <strong>the</strong>se descriptions, it can be concluded that though KM is a useful and important strategic
management approach, it is not widely and systematically implemented in companies. This lack <strong>of</strong>
implementation can be observed especially in small and medium enterprises (SMEs). Moreover, it is
evident that success <strong>of</strong> KM depends largely on social and organizational factors, and is influenced by
organizational culture and people’s attitudes and willingness to share knowledge. This condition
requires companies to explore different approaches in order to ensure an effective use <strong>of</strong> KM.
The objective <strong>of</strong> <strong>the</strong> present research is to describe how SG can improve KM processes in practice.
As SG represent an emerging and popular medium, <strong>the</strong> study will identify how SG can directly
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influence knowledge management on individual and organizational level. In this regard, this paper will
discuss how individuals build knowledge and become experts, and identify different knowledge
management processes on organizational level. This discussion will also include identification <strong>of</strong> main
KM processes and a description <strong>of</strong> corporate processes that are enabled by SG.
The paper will first present a <strong>the</strong>oretical overview <strong>of</strong> issues related to KM and SG. Followed by this
presentation, we shall describe <strong>the</strong> connection between KM and SG. This description will incorporate
both <strong>the</strong> individual dimension <strong>of</strong> KM and <strong>the</strong> organizational dimension <strong>of</strong> KM with respect to SG. The
following discussion will provide recommendations for developing or adopting SG for KM purposes.
Finally, conclusion will wind up <strong>the</strong> whole discussion.
2. Theoretical overview
Here, we shall first present <strong>the</strong> concept <strong>of</strong> knowledge and KM processes. In this regard, we shall look
at individual knowledge and organizational knowledge processes. And <strong>the</strong>n, we shall provide a
<strong>the</strong>oretical description <strong>of</strong> SG.
2.1 Knowledge, knowledge management and knowledge management processes
One <strong>of</strong> <strong>the</strong> popular definitions <strong>of</strong> knowledge is given by Davenport et al (1998, p. 5). They say:
“knowledge is 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 [...]¨.
Fur<strong>the</strong>rmore, <strong>the</strong> authors point out that information can be transformed to knowledge by means <strong>of</strong>
comparison, consequences, connections and conversations.
According to Popper (1999), knowledge is only “expectation or assumption about <strong>the</strong> reality, acquired
trough number <strong>of</strong> trial and errors experiences”.
Bellini et al. (2008) state that <strong>the</strong> creation, diffusion and application <strong>of</strong> knowledge are situated, and
hence <strong>the</strong> knowledge is heavily influenced by <strong>the</strong> context <strong>of</strong> practice. Therefore, proper understanding
and appropriate application <strong>of</strong> knowledge is important. The focus on context points out <strong>the</strong> necessity
to perceive and study various kinds <strong>of</strong> situations – several <strong>of</strong> <strong>the</strong>m can be complex.
Personal experience and method <strong>of</strong> trial and errors represent substantial component <strong>of</strong> knowledge
acquisition. The issues <strong>of</strong> context specificity, sticky knowledge and personal experiences that are
mentioned above lead to a categorization <strong>of</strong> knowledge: tacit and explicit knowledge. Tiwana (1999)
says that tacit knowledge is context-specific, personal knowledge that is difficult to formalize, record,
or articulate; it is stored in <strong>the</strong> heads <strong>of</strong> people. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> explicit knowledge is <strong>the</strong>
component <strong>of</strong> knowledge that can be codified and transmitted in a systematic and formal language;
for example in <strong>the</strong> form <strong>of</strong> documents, databases, web pages, emails, charts, etc. A number <strong>of</strong>
components <strong>of</strong> knowledge are listed by Tiwana (1999) including intuition, ground truth, judgment,
experience, values, assumptions, beliefs, and intelligence. The author suggests that knowledge
management must approach all <strong>the</strong>se components.
As we have seen in <strong>the</strong> above description, <strong>the</strong>re is not one single definition <strong>of</strong> knowledge
management that can be suitable for all contexts. This situation is due to <strong>the</strong> complex nature <strong>of</strong>
knowledge and its intrinsic character. The various definitions <strong>of</strong> knowledge management provided by
researchers and practitioners however focus generally on <strong>the</strong> ability <strong>of</strong> <strong>the</strong> company to adopt a unified
approach to increase value from knowledge resources, in such a way to realize gains and increase
performance <strong>of</strong> knowledge assets. In this regard, we would like to present <strong>the</strong> following definition to
support our general view <strong>of</strong> KM with respect to this paper: KM is
“[...] a business optimization strategy that identifies, selects, organizes, distills, and
packages information essential to <strong>the</strong> business <strong>of</strong> <strong>the</strong> company in a way that improves
employee performance and corporate competitiveness” (Bergeron, 2003).
2.1.1 A. Individual knowledge and expertise
One <strong>of</strong> <strong>the</strong> main goals <strong>of</strong> KM processes in organizations is to support individuals to develop <strong>the</strong>ir
expertise within a shorter period <strong>of</strong> time. Experts perform more efficiently than novices, and <strong>the</strong>y
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execute complex cognitive tasks substantially better than novices. According to Siedentop & Eldar,
(1998), expertise is performance-oriented, and it is highly specific to context and subject domain.
Usually expertise is developed over long period <strong>of</strong> time. Experts have finer discrimination (based on
meta-cognitive capabilities). They respond more quickly (automation) to different circumstances, and
have highly differentiated response repertoire. Experts are under control <strong>of</strong> more complex elements <strong>of</strong>
stimulus field, and <strong>the</strong>y are plan-independent and can reflect on <strong>the</strong>ir own performance (Siedentop &
Eldar, 1998).
Millitelo et al. (1998) point out <strong>the</strong> following characteristics <strong>of</strong> experts:
Past & future: Anticipate future better based on meaningful information from <strong>the</strong> past
Big picture: See <strong>the</strong> whole problem
Noticing: Identify low signals
Work smart: Know how to perform job efficiently
Opportunities/improvising: Can take risks
Self-monitoring: Know <strong>the</strong>ir own performance
Anomalies: Catch anomalies quick
Judging <strong>the</strong> usage <strong>of</strong> equipments: Are aware <strong>of</strong> <strong>the</strong> fact that equipment can mislead
Experts recognize patterns, detect anomalies, keep <strong>the</strong> big picture, and understand <strong>the</strong> way things
work, observe opportunities, are able to improvise, relate to past, present and future, pick up on very
subtle differences and address <strong>the</strong>ir own limitations (Quoetone, 2003).
The above description <strong>of</strong> <strong>the</strong> characteristics <strong>of</strong> experts not only illustrates <strong>the</strong> importance <strong>of</strong> acquiring
knowledge and skills, but also indicates – at least, to a certain extent – what kind <strong>of</strong> learning process
is needed for a non-expert to become an expert.
2.1.2 B. Organizational knowledge processes
On organizational level, we are going to present several categorizations <strong>of</strong> KM processes. Firestone
et al. (2003) mention two KM processes: knowledge production and knowledge integration. Ruggles
(1997) presents three KM processes: knowledge generation, codification and transfer. Alavi & Leidner
(2001) identify four main KM processes <strong>of</strong> knowledge conversion, including knowledge creation,
knowledge storage/retrieval, and knowledge transfer and knowledge application. Wiig (1993), Probst
(2002) and Beckman (1997) propose KM life-cycle model <strong>of</strong> tree-to-eight steps/phases.
Nonaka et al. (1995) discuss knowledge creation in <strong>the</strong> organizations by focusing on tacit and explicit
knowledge. In this regard, <strong>the</strong>y present a model (SECI model) that incorporates four processes
depicting conversion between tacit and explicit knowledge. The processes are: socialization,
externalization, combination and internalization. Consideration on both explicit and tacit aspects <strong>of</strong>
knowledge is important in <strong>the</strong> context <strong>of</strong> SG.
2.2 Serious games (SG)
In literature, <strong>the</strong>re are many definitions <strong>of</strong> SG, <strong>of</strong>ten overlapping or extending <strong>the</strong> terms <strong>of</strong> e-learning,
edutainment (combining education and entertainment), and game-based learning (Susi et al., 2007;
de Freitas, 2008). Wouters et al,(2008) define SG as learning environments in which learners are
engaged in a goal-driven, competitive activity, within a framework <strong>of</strong> agreed rules. Serious games
usually refer to games used for training, advertising, simulation, or education, and are designed to run
on personal computers or video game consoles. Nowadays <strong>the</strong> emergence <strong>of</strong> sophisticated mobile
applications provides even more dynamic mobile gaming experiences.
There are different classifications <strong>of</strong> computer games. A classification given by Claro (2007) includes
<strong>the</strong> following aspects:
Concerning idea: action games, adventure games, fighting games, puzzles, role-playing,
simulations, sport games and strategy games
Concerning players: individual or multiplayer game
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Concerning <strong>the</strong>ir development: mainstream, or commercial entertainment games, commercial
educational games, and research based educational games
Leyland (1996) categorizes SG based on <strong>the</strong>ir primary market - military games, government games,
educational games, corporate games, healthcare games, and political, religious and art games.
There are several advantages <strong>of</strong> using SG. Some <strong>of</strong> <strong>the</strong>m are (Frederik et al., 2010): fun and
challenge, immersive, stimulate learning, interactive, personalized, contextualized, ability to provide
feedback. There have been a number <strong>of</strong> studies which have demonstrated that games accelerate
learning, increase motivation and support <strong>the</strong> development <strong>of</strong> higher level cognitive skills (deFreitas &
Jarvis, 2006).This makes SG suitable for learning purposes and especially for active learning (focused
on end-learner), testing competences and skills, developing critical thinking, testing scenarios and
strategy <strong>of</strong> actions, pr<strong>of</strong>iling, and self-learning. Moreover, games are very good at using drama,
storyline, humor and characters to create a compelling experience which, from a training point <strong>of</strong> view,
develops memory hooks and means that learners not only remember what happened but also why it
happened. Weaknesses <strong>of</strong> SG are, among o<strong>the</strong>r things, technical limitations, balance between game
and learning, and syn<strong>the</strong>tic environment (Frederik et al., 2010). Ano<strong>the</strong>r weakness is that SG cannot
easily integrate <strong>the</strong>oretical knowledge. Thus, on many occasions, SG are accompanied by standard
e-learning tools and knowledge bases (for example, <strong>the</strong> games <strong>of</strong> Yentles@ and InTeLS@).
As we have seen, learning is connected to SG. However, many popular entertainment games (for
example, simulations, strategy games, and role playing) allow players to extend <strong>the</strong>ir skills and
knowledge base. Some examples <strong>of</strong> simulation games include city-building games (as Sim City@),
business simulation games, and life-simulation games, where players need to design and achieve
balance in complex ecosystems, and meeting some pre-defined goals. When it comes to more
intensive game experiences, <strong>the</strong>re are "exergames" or active games simulations as flight simulation,
racing games and space flight simulation, and o<strong>the</strong>r applications such as music and sport games. In
<strong>the</strong>se games, players actively perform actions and develop motor skills in contexts that tend to reflect
<strong>the</strong> reality. Therefore, it is not always easy to distinguish between SG and entertainment games with
respect to learning.
3. Serious games and Knowledge management
As described by Lohmann & Niesenhous (2008), a vast range <strong>of</strong> knowledge can be transmitted
through a serious game. Knowledge is integrated into <strong>the</strong> game in a number <strong>of</strong> different ways; ei<strong>the</strong>r
directly integrated into <strong>the</strong> game play (game mechanics) and scenarios, or made available as an
external resource. However, <strong>the</strong> knowledge in SG is not represented in explicit form. Players need to
obtain it through a number <strong>of</strong> trial and error methods. This makes <strong>the</strong>m to go through a number <strong>of</strong>
first-hand experiences, improve <strong>the</strong>ir competence, enable <strong>the</strong>m to build complex cognitive models
and become active learners. Moreover, <strong>the</strong> SG provide context <strong>of</strong> interaction, and create opportunities
for players to improve <strong>the</strong>ir social and cognitive skills and <strong>the</strong>ir ability to deal effectively with
complexity.
The following sections will describes how serious games can facilitate and improve knowledge
management in companies. This description is divided into two main parts. They are: SG for
individual-centered KM and SG for organizational-centered KM.
3.1 SG for Individual-centered KM
SG allow individuals to build expertise easily and quickly by adopting <strong>the</strong> concept <strong>of</strong> rapid
competence development. Based on literature studies, Wouters et al. (2008) present a classification
<strong>of</strong> learning outcomes <strong>of</strong> SG and address 4 main classes <strong>of</strong> skills. They are: cognitive skills
(knowledge and skills as problem solving, decision making, and situational awareness), motor skills
(acquisition and compilation <strong>of</strong> knowledge), affective skills (attitude and motivation) and
communicative skills (communicate, cooperate, and negotiate). SG can successfully integrate
immersive scenarios, transfer <strong>the</strong> knowledge <strong>of</strong> contexts and address particular knowledge domain.
SG are designed around context realistic scenarios that teach and facilitate individuals / employees to
take decisions, make forecasts and develop business-specific skills, when <strong>the</strong>y execute various
business tasks. Moreover, SG can enhance employees to develop specific competences in finance,
marketing, business planning, sales, forecast methods and o<strong>the</strong>r business issues and test <strong>the</strong>m in
close-to-real contexts. SG can fur<strong>the</strong>r train individuals to develop more complex s<strong>of</strong>t skills (for
example, communication and negotiation, decision-making and planning).
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SG provide effective methods for building expertise. SG can support various methods to train
individuals in pattern recognition, identifying cause-effect relationships, obtaining a big picture vision
(zoom in and out), looking for opportunities and taking risks in a risk-free environment, understanding
past, present and anticipating <strong>the</strong> future and noticing very subtle differences. By simulating critical
incidents, SG can improve building mental models in connection with how to deal with complex
incidents that rarely occur in real situation. This kind <strong>of</strong> a training and skill development is important to
deal successfully with varying demands <strong>of</strong> <strong>the</strong> modern organizational world. Hence, it can be said that
SG is an effective, life-long-learning mechanism for individuals in organizations.
3.2 SG for organizational-centered KM
Here, we shall describe SG with respect to barriers to KM, KM processes, knowledge audit and
organizational business functions.
A. SG and KM barriers
Apart from individual learning, serious games can also facilitate more complex, organizational
knowledge management processes. By looking at major obstacles and barriers to KM, it seems that in
practice companies do not apply KM practices effectively and efficiently, because companies lack an
integrative understanding <strong>of</strong> KM and employees <strong>of</strong>ten oppose to top-down KM practices. SG can
contribute to make positive influence on this situation by:
Improving awareness <strong>of</strong> short-term and long-term effects and benefits <strong>of</strong> KM, and <strong>the</strong>refore
discovering in practice how KM and knowledge sharing (KS) can enhance survival and success <strong>of</strong>
organizations. This can enhance self-motivation to contribute to KM processes (including KS),
and in turn enable organizations to overcome organizational amnesia.
Improving corporate culture for sharing knowledge (through simulations and context-specific
scenarios), and hence contributing to ensure management support for KM practices, and to avoid
undesirable political maneuvers (“knowledge is power”), <strong>the</strong> “not invented here” syndrome and
apathy <strong>of</strong> sharing knowledge.
Improving specific skills and competences related to KM (time management, change
management, IT infrastructure mastering) and KS (communication skills, interpersonal skills,
multicultural skills, language skills etc).
With <strong>the</strong> help <strong>of</strong> successful simulations and SG – including fun and entertainment elements –
companies can encourage and direct <strong>the</strong>ir employees to accept and adopt KM as a natural part <strong>of</strong>
<strong>the</strong>ir daily routines. Developing and ensuring a proper culture are core elements in understanding <strong>the</strong>
importance <strong>of</strong> KM and implementing proper KM practices in organizations.
3.2.1 B. KM processes
SG can improve knowledge management processes. By studying <strong>the</strong> SECI model in <strong>the</strong> context <strong>of</strong>
SG application, we present <strong>the</strong> following positive influence <strong>of</strong> SG:
Socialization – SG can encourage employees to socialize better and transfer <strong>the</strong>ir tacit knowledge
better. SG allow <strong>the</strong>m to develop unique relationships <strong>of</strong> interdependence, trust and relationships
that create new communication culture and channel (in <strong>the</strong> case <strong>of</strong> multiplayer games).
Externalization – SG allow employees to externalize a part <strong>of</strong> <strong>the</strong>ir knowledge by allowing <strong>the</strong>m to
observe <strong>the</strong>ir behavior and mental models, and to discover and conceptualize useful tacit
knowledge. By getting feedback on <strong>the</strong>ir performance, employees and company can identify <strong>the</strong>ir
hidden competences. These competencies can <strong>the</strong>n be formally described and become
accessible to o<strong>the</strong>rs.
Combination – SG allow combination <strong>of</strong> various explicit sources <strong>of</strong> knowledge in order to succeed
in challenging scenarios. These skills can be very useful in real situation, as employees will
discover how and where to find and combine explicit knowledge that is available.
Internalization – SG improve internalization <strong>of</strong> knowledge and processes <strong>of</strong> knowledge
acquisition, learning, and expertise building by using emotions, immersive scenarios and memory
hooks that enable individuals to perform better in <strong>the</strong> future.
By considering <strong>the</strong> four main KM processes that Alavi & Leidner (2001) present, we can describe <strong>the</strong>
role <strong>of</strong> SG in improving organizational KM:
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Creation <strong>of</strong> Knowledge: SG can facilitate learning as well as stimulate different knowledge
discovery and creative processes. Using entertainment and fun can unlock creative thinking and
facilitate employees to become more innovative and creative.
Knowledge retrieval/storage – By implementing best practices, company stories and lessons
learned in appealing game scenarios, companies can provide not only access to existing
knowledge, but also opportunities for <strong>the</strong>ir employees to gain experience on <strong>the</strong> knowledge before
<strong>the</strong>y apply it. In this way, knowledge that is stored in SG can be active, live and easy to
understand and transfer.
Knowledge transfer –SG can facilitate knowledge transfer by different methods. SG can improve
skills that are required for KS (communication skills). SG can build awareness <strong>of</strong> KS and change
attitude and prejudices with respect to KS.
Knowledge application – SG can provide safe environment for learning and skill acquisition –
environment where different simulations can take place, including strategies testing, improving
understanding <strong>of</strong> situations, and discovering complex models for decision making.
3.2.2 C. Knowledge audit
Serious games can improve perception <strong>of</strong> employees, and in a way, guide <strong>the</strong>m to know and become
familiar with valuable knowledge resources and important knowledge flows in <strong>the</strong>ir company. By
following <strong>the</strong> process <strong>of</strong> knowledge audit, we can look at how SG can contribute to organizational
awareness, and assessment and mapping <strong>of</strong> knowledge resources and flows (Antonova et al., 2007).
SG can provide effective tools to identify knowledge, apply knowledge audit, and develop knowledge
maps and to execute social network analysis. SG can help companies to identify not only explicit and
<strong>of</strong>ficial knowledge flows, but also real (including un<strong>of</strong>ficial) knowledge flows, knowledge inventory,
knowledge gaps and duplication <strong>of</strong> knowledge.
3.2.3 D. Organizational Business Functions
SG can successfully support <strong>the</strong> following key business functions:
Marketing, branding and customer services: Nowadays people prefer to spend more time on
playing computer games (including SG) compare to <strong>the</strong> use <strong>of</strong> o<strong>the</strong>r media, such as TV,
newspapers and radio. Knowledge flows in marketing can be seen in two-folds. Firstly, companies
can use SG to reach potential clients. Thus, <strong>the</strong>y develop SG application in order to improve
visibility <strong>of</strong> <strong>the</strong>ir corporate brand, expand services to clients, and improve clients’ awareness <strong>of</strong>
<strong>the</strong> companies’ products and services. Secondly, companies can utilize and improve knowledge
in-flows from <strong>the</strong>ir clients – understanding clients’ needs, preferences and habits. In addition,
communities can be developed around SG (for example, product based and branch based
communities). SG can be used successfully to capture data from customers, make pr<strong>of</strong>iles, and
investigate customers’ primary needs.
Human resource management and training: many organizations employ SG in <strong>the</strong>ir recruitment
process. For example, US Army uses its popular game (America’s Army@) to identify best
players and to recruit <strong>the</strong>m. Fur<strong>the</strong>rmore, companies use specially designed SG in order to test
skills and competences <strong>of</strong> employees or candidates, to identify <strong>the</strong>ir "s<strong>of</strong>t" skills and abilities,
discover patterns, skill gaps, build pr<strong>of</strong>iles <strong>of</strong> candidates, identify <strong>the</strong>ir leadership potential or
perform annual employee assessment. HRM can adopt SG to train and assess employees'
abilities, skills and knowledge, motivate people to work in team, help <strong>the</strong>m to socialize and
improve s<strong>of</strong>t skills, identify candidates for promotion and select successful carrier paths. SG can
support new employees to obtain a better understanding <strong>of</strong> organizational practices and make
<strong>the</strong>m efficient in a shorter period <strong>of</strong> time.
Innovations and R&D: SG can foster creativity, experiments, simulations, and improve abilities <strong>of</strong>
employees to generate new ideas and out-<strong>of</strong>-box solutions. Employing visual effects and an
environment, which is without limitations <strong>of</strong> time, location, money and physical constraints, can
additionally enhance creativity and innovation. SG can also improve collaborative skills that
contribute to efficient group work in innovative projects.
Collaboration and cooperation: SG improve personal abilities to work in team and perform
complex operations toge<strong>the</strong>r. SG are suitable tools to improve knowledge sharing skills and
create arenas for formal and informal discussions.
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Knowledge sharing functions: SG can improve all major knowledge processes within companies.
SG can facilitate formation <strong>of</strong> communities <strong>of</strong> practice, knowledge sharing and teamwork, visibility
<strong>of</strong> <strong>the</strong> learning process, training systems and networking within and across companies.
4. Discussion
Serious games can be successfully employed in order to support various knowledge management
practices in organizations. They can enhance organizations to overcome some <strong>of</strong> <strong>the</strong> main barriers to
KM; improve KM processes on individual and organizational level, enable better identification <strong>of</strong>
knowledge assets and knowledge flows, and facilitate KM in different organizational business
functions.
Lohmann & Niesenhaus (2008) discuss in details <strong>the</strong> knowledge management processes in <strong>the</strong>
construction <strong>of</strong> digital games, presenting different stages <strong>of</strong> game development. In practice, it is
difficult to develop or implement a single game that can perform all KM tasks that are mentioned
above. However, in <strong>the</strong> present research, we have identified that SG can address different aspects <strong>of</strong>
practical knowledge management in companies.
Here are some possible recommendations for developing or adopting SG for KM purposes:
Context <strong>of</strong> <strong>the</strong> game: In order to create more enjoyable game, <strong>the</strong> game environment could adopt
unexpected place, time, culture, species, objects and perspective <strong>of</strong> <strong>the</strong> activities. The game
should not recreate <strong>the</strong> same company environment. In this way, <strong>the</strong> employee or <strong>the</strong> learner will
act more freely, and will immerse better with his / her role, position and tasks.
Story <strong>of</strong> <strong>the</strong> games: Different stories could be developed. But, <strong>the</strong> important factor is to include
real lessons-learned, best cases and failure cases <strong>of</strong> <strong>the</strong> company. Thus, SG can transfer
organizational experience and culture to new employees in an appealing manner. Adequate
background information should be provided with narratives.
Knowledge domains: The explicit knowledge domains should be carefully identified and
addressed.
Objectives <strong>of</strong> <strong>the</strong> game: As presented in <strong>the</strong> description above, SG can successfully improve
different knowledge management processes. Careful analysis <strong>of</strong> <strong>the</strong> concrete needs associated
with both individual and organizational level can lead to define SG objectives.
Characters: The game characters should be complex and believable. Therefore, <strong>the</strong>y are to be
fur<strong>the</strong>r described, including <strong>the</strong>ir competences and skills as well as <strong>the</strong>ir personal histories and
motivations. The characters can have different positions and roles in <strong>the</strong> game play, supporting or
hindering success <strong>of</strong> <strong>the</strong> player.
Challenge: The challenge should be meaningful for <strong>the</strong> player. Challenge should be defined as
opportunity for research, taking risks, trying different approaches, forming alliances, stimulating
decision making and investigating fur<strong>the</strong>r strategies. The game should lead to different knowledge
acquisition options, identification <strong>of</strong> knowledge flows and motivation for fur<strong>the</strong>r learning.
Knowledge flows and knowledge assets: The game design should explicitly show what knowledge
is necessary for achieving <strong>the</strong> intended objectives, and how this knowledge will evolve during <strong>the</strong>
scenario(s). Knowledge assets (explicit and tacit) and knowledge flows should be mapped or
simulated in order to improve player capabilities.
Complexity and game control: SG can provide very complex and perplexing information-rich
environment. It has to be taken into account that learners should confront with different facts,
textual, visual and audio information, animations and task signals for limited time. As <strong>the</strong>re can be
different kinds <strong>of</strong> players – with respect to age, game experience, mentality, cognitive skills – it
will be advisable that SG provide options for user control, including speed, complexity (media
channels) and difficulty (cognitive level) regulation.
Feedback: SG should improve learning and <strong>the</strong>refore improve visibility <strong>of</strong> player performance. SG
can easily map player performance and compare it with general statistics. But, it is important that
SG should highlight strengths and weaknesses <strong>of</strong> <strong>the</strong> player’s performance, <strong>the</strong> players’
knowledge gaps and his / her preferred knowledge functions.
Technologies for SG continue to develop and provide even more sophisticated functionality, extended
dynamics and immersive effects. As it is highlighted in <strong>the</strong> report “Trend Universe 2012”, computer
games are among <strong>the</strong> key 15 technology trends to be paid attention to for <strong>the</strong> next few years.
Emerging or developing technologies include among o<strong>the</strong>r things <strong>the</strong> following types <strong>of</strong> games:
34

Albena Antonova and Anandasivakumar Ekambaram
alternate reality games, augmented reality games, in-game advertising, micro-games, mobile games,
multiplayer games, online games, serious games and social games (TrendUniverse, 2011).
It can be expected that soon companies or even individuals will be in <strong>the</strong> position to compose <strong>the</strong>ir
own computer games based on <strong>the</strong>ir needs and preferences. This possibility can revitalize many <strong>of</strong>
<strong>the</strong> KM processes and functions in <strong>the</strong> companies, and improve some substantial KM activities.
5. Conclusions
SG provide complex platform and media to improve some <strong>of</strong> <strong>the</strong> main KM practices. The present
research has pointed out some <strong>of</strong> <strong>the</strong> possible contributions <strong>of</strong> SG to KM. The fur<strong>the</strong>r work has to
extend <strong>the</strong> <strong>the</strong>oretical framework and develop real case studies and evidences for successful SG
applications. Nowadays SG are still in <strong>the</strong>ir infancy and are far behind commercial casual games.
Therefore, organizations do not need to focus on classification <strong>of</strong> games, but <strong>the</strong>y can look at how
different games elements (casual or serious) can improve <strong>the</strong>ir KM processes. Firstly, <strong>the</strong>re should be
investigations to find out whe<strong>the</strong>r target groups (employees, clients) have preferences for specific
computer games. Secondly, analyses can be performed regarding various aspects <strong>of</strong> popular games
to find out <strong>the</strong>ir (popular games) impact on KM processes – on individual and organizational level.
These analyses can become <strong>the</strong> first major step to organizational game-based KM strategy.
Traditional methodologies <strong>of</strong> learning and working evolve, partially forced by technology advancement
and increased demand for knowledge (expertise). Game-based learning is a part <strong>of</strong> <strong>the</strong> human
nature, and it provides many benefits with respect to knowledge acquisition and skills building.
Therefore, exploring (serious) games in organizational contexts can improve many <strong>of</strong> <strong>the</strong> KM
processes that an organization has and its efficiency in task performance.
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36

Sharing and Transferring Knowledge – how to increase
efficiency <strong>of</strong> s<strong>of</strong>t techniques for KS
Albena Antonova 1 and Aniko Csepregi 2
1 S<strong>of</strong>ia University, S<strong>of</strong>ia, Bulgaria
2 University <strong>of</strong> Pannonia, Veszprem, Hungary
a_antonova@fmi.uni-s<strong>of</strong>ia.bg
csepregia@gtk.uni-pannon.hu
Abstract: Knowledge sharing (KS) is one <strong>of</strong> <strong>the</strong> main concerns for organizational knowledge management.
Knowledge sharing contributes for better visibility <strong>of</strong> knowledge flows, improves access to knowledge assets, and
leads to increased organizational efficiency. However, <strong>the</strong> recent analysis <strong>of</strong> Antonova et al. (2011) has
demonstrated that in practice organizations predominantly pay attention on <strong>the</strong> processes <strong>of</strong> knowledge
internalization and neglect <strong>the</strong> processes <strong>of</strong> externalizing knowledge. This means that organizations use
motivational practices, s<strong>of</strong>t techniques and IT tools that stimulate employees to use (learn, search for information
and experts etc.) but not to share knowledge (to externalize its own knowledge in IS or to share it appropriately
for fur<strong>the</strong>r use). The application <strong>of</strong> <strong>the</strong> extended model <strong>of</strong> KS/KT (Antonova et al., 2011) discovered that coherent
organizational vision is lacking and all s<strong>of</strong>t and IT-enabled KS methods should address both <strong>the</strong> processes <strong>of</strong> KS
and KT combining externalization and internalization <strong>of</strong> knowledge. The present paper aims to investigate fur<strong>the</strong>r
how knowledge sharing and knowledge transfer can be improved in organizational context. Based on <strong>the</strong> KS/KT
model, <strong>the</strong> main problems for KS and KT in practice have been identified. Since little attention has been paid on
externalizing knowledge, additional efforts and focused incentives for its improvement in organizations will be
proposed. Therefore, by applying <strong>the</strong> model <strong>of</strong> KS/KT different knowledge flows in organizations will be analyzed
in <strong>the</strong> context <strong>of</strong> <strong>the</strong> most popular s<strong>of</strong>t techniques for KS (as groupwork, trainings, informal meetings, and o<strong>the</strong>rs).
Besides empirical data, different models for s<strong>of</strong>t techniques enhanced with ICT will be discussed. Finally <strong>the</strong>
conclusions will provide recommendations and practical considerations for implementation <strong>of</strong> KS/KT model in
organizations.
Keywords: knowledge sharing, knowledge transfer, s<strong>of</strong>t techniques, KS/KT model, KS in organizations
1. Introduction
Knowledge sharing is indispensable for organizations and for individuals. The act <strong>of</strong> sharing
knowledge is very important since an individual’s knowledge will not have much impact on <strong>the</strong>
organization unless it is made available to o<strong>the</strong>r individuals (Law, 2007). Therefore, knowledge has
value when it is used, when it is shared and when it contributes to organizational performance.
There are numbers <strong>of</strong> reasons why knowledge sharing and knowledge transfer are vital for
organizations. Murray et al. (2007) identified four basic benefits <strong>of</strong> KT: shared knowledge resources
lead to cost advantages, knowledge transfer allows organizations to respond faster to business
challenges, it enables individuals to obtain more complete information and to make better decisions,
and finally, organizations create new knowledge by combining individually held knowledge. However,
<strong>the</strong> sharing <strong>of</strong> knowledge is not an automated process. Individuals are in <strong>the</strong> core <strong>of</strong> all knowledge
sharing mechanisms (Sirkemaa, 2008). Fur<strong>the</strong>rmore Sirkemaa (2008) stated that in knowledge
sharing both <strong>the</strong> information that is shared and <strong>the</strong> characteristics <strong>of</strong> those who share information are
relevant. Szulanski (1996) discovered that knowledge sharing is inhibited by three major factors: lack
<strong>of</strong> absorptive capacity <strong>of</strong> <strong>the</strong> recipient, casual ambiguity concerning <strong>the</strong> knowledge itself, and an
arduous relationship between <strong>the</strong> sender and <strong>the</strong> receiver. Szulanski (1996) also revealed that
motivational factors play only a minor role in connection with <strong>the</strong> transferability <strong>of</strong> knowledge.
The recent study <strong>of</strong> Antonova et al. (2011) discovered that organizations and managers emphasize
incentives, knowledge sharing practices and methods that enhance and propagate knowledge
internalization. This means that organizational culture and practices stimulate individuals to internalize
knowledge (to learn, to explore documents, to absorb o<strong>the</strong>r´s knowledge) and not to externalize
knowledge (to share, to explain, to contribute to organizational knowledge). These practices are
reported as important barriers for knowledge sharing, limiting <strong>the</strong> use <strong>of</strong> sophisticated information
systems and organizational knowledge management instruments.
The present research aims to focus on s<strong>of</strong>t techniques for knowledge sharing. On <strong>the</strong> one hand, s<strong>of</strong>t
techniques and methods are <strong>of</strong>ten neglected in comparison to technology-enabled methods. On <strong>the</strong>
o<strong>the</strong>r hand, <strong>the</strong>re still lack understanding what factors should be considered when evaluating s<strong>of</strong>t KS
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Albena Antonova and Aniko Csepregi
techniques. While different researchers investigate knowledge types, organizational types or KS
instruments, we propose a new assessment framework. It is based on <strong>the</strong> developed KS/KT model
(Antonova et al. 2011) and is composed <strong>of</strong> 4 layers – initiator, focus, process and purpose <strong>of</strong> KS.
Therefore this framework is applied on empirical data, obtained from a survey among 640 middle
managers in Bulgaria and Hungary. It will be discussed how individuals are involved in KS activities
and what <strong>the</strong>ir role is during <strong>the</strong> internalization and externalization <strong>of</strong> knowledge. Analysis <strong>of</strong> empirical
results will enable us to identify popular practices for KS. The proposed model will enhance <strong>the</strong>
understanding <strong>of</strong> KS practices and will visualize <strong>the</strong> role <strong>of</strong> knowledge internalization and
externalization for s<strong>of</strong>t techniques. It will propose a practical approach to improve organizational s<strong>of</strong>t
techniques for knowledge sharing.
2. Theoretical overview
2.1 Understanding Knowledge sharing and Knowledge transfer
In <strong>the</strong> first section we will provide a short overview <strong>of</strong> <strong>the</strong> concepts <strong>of</strong> knowledge sharing and
knowledge transfer. According to Mughal (2010) in <strong>the</strong> epistemological literature <strong>of</strong> knowledge
management <strong>the</strong> terms <strong>of</strong> knowledge sharing and transfer have been used interchangeably.
However, it can be noticed that KS is used in social and organizational research texts, highlighting <strong>the</strong>
role <strong>of</strong> individual and organizational factors for KS (Lin, 2007; Hendriks,1999). On opposite, <strong>the</strong> term
KT is applied ra<strong>the</strong>r on abstract level, designating <strong>the</strong> process <strong>of</strong> knowledge transfer, media and ICT
technologies implementation.
Christensen (2007, pp. 37) believes that “<strong>the</strong> goal <strong>of</strong> KS can ei<strong>the</strong>r be to create new knowledge by
differently combining existing knowledge or to become better at exploiting existing knowledge”.
Knowledge sharing is <strong>the</strong> voluntary dissemination <strong>of</strong> acquired skills and experience to <strong>the</strong> rest <strong>of</strong> <strong>the</strong>
organization (Law, 2007). Bosua and Scheepers (2007) insist on <strong>the</strong> dual process <strong>of</strong> KS for enquiring
and contributing to knowledge through activities such as learning by- observation, listening and
asking, sharing ideas, giving advice, recognizing cues, and adopting patterns <strong>of</strong> behaviour. From <strong>the</strong>
point <strong>of</strong> view <strong>of</strong> Yang et al. (2007, pp.96) KS is “a set <strong>of</strong> behaviours about knowledge exchange which
involves actors, knowledge content, organizational context, appropriate media, and social
environment”. The two sides <strong>of</strong> <strong>the</strong> knowledge sharing process are discussed differently in <strong>the</strong>
literature and are assumed to have different nature and are influenced by different factors (van den
Ho<strong>of</strong>f, de Ridder 2004). Ardichvili et al (2003) distinguish between supplying and demanding for
knowledge, while van den Ho<strong>of</strong>f and de Ridder (2004) determine knowledge donating and knowledge
collecting.
Ipe (2003) draws a distinction between knowledge sharing and knowledge transfer. KS is “<strong>the</strong> process
by which knowledge held by an individual is converted into a form that can be understood, absorbed,
and used by o<strong>the</strong>r individuals” (Ipe 2003:34) and as a result <strong>the</strong> sender and <strong>the</strong> recipient has a joint
ownership <strong>of</strong> <strong>the</strong> knowledge. Meanwhile according to Ipe (2003) KT mainly illustrates <strong>the</strong> knowledge
movement that occurs between larger organizational entities (departments, divisions or organizations)
described by Chakravarthy et al. (1999) or Lam (1997). Following <strong>the</strong> organizational point <strong>of</strong> view KT
is <strong>of</strong>ten considered as “an event through which one organization learns from <strong>the</strong> experience <strong>of</strong>
ano<strong>the</strong>r” (Darr, Kurtzberg 2000, pp. 29) or it “occurs when experience in one unit <strong>of</strong> an organization
affects ano<strong>the</strong>r unit” (Argote, Ingram 2000, pp. 154).
From ano<strong>the</strong>r perspective that investigates individual level, knowledge transfer (KT) assumes all
activities <strong>of</strong> exchanging explicit or tacit knowledge between two agents, during which one agent
receives and applies <strong>the</strong> knowledge provided by <strong>the</strong> o<strong>the</strong>r agent (Kumar, Ganesh, 2009). Knowledge
transfer consists <strong>of</strong> movements <strong>of</strong> knowledge between its origin and destination within a specific
context (Bosua, Scheepers, 2007). Thus KT is <strong>the</strong> act <strong>of</strong> transmitting knowledge from one source to
ano<strong>the</strong>r source and using <strong>the</strong> transmitted knowledge in a general way. Following KT, <strong>the</strong> user should
be able to interpret knowledge, and at <strong>the</strong> end <strong>of</strong> <strong>the</strong> KT <strong>the</strong> user himself/herself has to show <strong>the</strong>
same knowledge ability owned by <strong>the</strong> source (Garavelli et al., 2002).
The KS process consists <strong>of</strong> two main activities: transmission and absorption, between <strong>the</strong> knowledge
owner (who possesses knowledge) and <strong>the</strong> knowledge perceiver (who acquires knowledge)
(Hendriks, 1999). First, <strong>the</strong> knowledge owner communicates knowledge in some form (written, verbal)
and after that <strong>the</strong> knowledge receiver has to be able to perceive expression <strong>of</strong> this knowledge and
make sense <strong>of</strong> it. Thus <strong>the</strong> KS process includes both <strong>the</strong> externalization (through <strong>the</strong> competences <strong>of</strong>
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Albena Antonova and Aniko Csepregi
codification and presentation) and <strong>the</strong> internalization <strong>of</strong> knowledge (through <strong>the</strong> capabilities <strong>of</strong>
reading, learning, interpreting, and absorbing). In illustration <strong>of</strong> this process, Hendriks (1999)
proposed a simplified model <strong>of</strong> knowledge sharing, including knowledge owners and knowledge
reconstructures and <strong>the</strong> two basic processes for KS.
As KT is used mainly in IT literature (Garvelli et al., 2002) , it relies on abstract conceptual model. Its
main focus is to show how knowledge can be transferred from person to IT system/or object (or
externalizing knowledge, making it explicit through writing, codifying etc). As Garavelli et al. (2002)
describe in <strong>the</strong>ir model, using technology to support <strong>the</strong> knowledge transfer means to support both
<strong>the</strong> knowledge codification that is <strong>the</strong> knowledge extraction from <strong>the</strong> source and its representation in
an object, and <strong>the</strong> knowledge interpretation <strong>of</strong> <strong>the</strong> user.
Combining and extending <strong>the</strong> models for KT (Garavelli et al., 2002) and for KS (Hendriks, 1999), a
joint model <strong>of</strong> KS and KT processes within an organization is proposed in Figure 1. (Antonova et al.,
2011). The model corresponds on <strong>the</strong> complex nature <strong>of</strong> KS and KT and contributes to better
visualization <strong>of</strong> bilateral process <strong>of</strong> knowledge externalization and internalization. It combines both
individual and organizational processes for KT and KS, and factors enabling knowledge
externalization and internalization. On <strong>the</strong> individual level key KS competences are identified in both
phases and <strong>the</strong> participant <strong>of</strong> <strong>the</strong> KS process are called knowledge owners and knowledge
perceivers/reconstructors. KS includes both externalization (through <strong>the</strong> competences <strong>of</strong> codification
and presentation) and internalization <strong>of</strong> knowledge through <strong>the</strong> capabilities <strong>of</strong> reading, learning,
interpreting, and absorbing. On <strong>the</strong> organizational level <strong>the</strong> main KS processes, including media (IT)
and ‘s<strong>of</strong>t’ organizational techniques (KS incentives, practices and motivational policy) are considered.
Figure 1: A model for KS and KT, combining KS and KT processes in organizational context
(Antonova et al., 2011)
2.2 KS s<strong>of</strong>t techniques – types and classifications
A number <strong>of</strong> knowledge sharing s<strong>of</strong>t techniques, activities and incentives exist in companies. Boh
(2007) stated that knowledge sharing mechanisms are formal and informal mechanisms for sharing,
integrating, interpreting and applying know-what, know-how, and know-why embedded in individuals
and groups to aid <strong>the</strong>ir performance. Sirkemaa (2008) explored two dimensions for KS mechanisms,
including aggregation <strong>of</strong> knowledge and degree <strong>of</strong> its articulation; he also proposes a framework
combining tacit versus explicit knowledge (personalization versus codification) and individual versus
collective knowledge sharing. Murray and Peyrefitte (2007) identified three groups <strong>of</strong> knowledge
sharing activities and methods, including technology-assisted communication methods, meetings and
training methods. They also revealed that usually individuals chose complex media tools to transfer
complex knowledge (Murray, Peyrefitte, 2007). Boh (2007) has confirmed that <strong>the</strong> greater <strong>the</strong>
complexity <strong>of</strong> information is, <strong>the</strong> more personal interactions are required to understand it.
Mughal (2010) stipulated that effective knowledge sharing mechanisms constitute two broad
dimensions consisting <strong>of</strong> personalized versus codified and individualized versus institutionalized
mechanisms which facilitate <strong>the</strong> transfer <strong>of</strong> knowledge. The transference and maximum utilization <strong>of</strong>
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Albena Antonova and Aniko Csepregi
knowledge requires <strong>the</strong> following knowledge sharing mechanisms: Individualized – Personalized
Mechanisms, Individualized – Codified Mechanisms, Institutionalized – Personalized Mechanisms and
Institutionalized – Codified Mechanisms.
2.3 Classification <strong>of</strong> Knowledge sharing tools and practices
Applying on practice <strong>the</strong> model <strong>of</strong> KS/KT, <strong>the</strong> following characteristics <strong>of</strong> knowledge sharing
mechanisms and s<strong>of</strong>t techniques have been outlined. Table 1 shows <strong>the</strong> main dimensions and factors
for KS, that enables <strong>the</strong> identification and analysis <strong>of</strong> knowledge sharing s<strong>of</strong>t techniques.
Table 1: Factors for identification <strong>of</strong> KS s<strong>of</strong>t techniques
Factor Description <strong>of</strong> s<strong>of</strong>t techniques
Initiator for KS
Focus on knowledge
exchange
Process <strong>of</strong> KS
Purpose <strong>of</strong> KS
Pull techniques - initiated by knowledge owner
Push techniques - initiated by knowledge perceiver
Active techniques – efforts to externalize knowledge
Passive techniques – efforts to internalize knowledge
Primary techniques - designed for sharing knowledge
Secondary techniques - resulting as consequence in KS
Individual – increase individual Knowledge
Organizational – increase organizational knowledge
The role <strong>of</strong> individual in KS method forms <strong>the</strong> first dimension for analysis <strong>of</strong> knowledge sharing s<strong>of</strong>t
techniques. As identified by Teng (2011) two processes for knowledge sharing can be described:
voluntary and solicit. Solicit knowledge sharing occurs when individuals are asked (by o<strong>the</strong>rs or by
organizations) to share knowledge, while voluntary knowledge sharing is initiated by <strong>the</strong>m. Therefore,
two main mechanisms for knowledge sharing can be distinguished: pull techniques (initiated by
knowledge owner) and push techniques (initiated by knowledge perceiver).
The second group <strong>of</strong> factors differentiates active and passive KS techniques. Active techniques
require substantial efforts to externalize knowledge, for example ability for codification and expression
<strong>of</strong> knowledge. Passive techniques focus on knowledge internalization, as decoding knowledge,
obtaining knowledge, learning, receiving knowledge. Many KS s<strong>of</strong>t techniques require both efforts for
externalization and internalization <strong>of</strong> knowledge.
The third group <strong>of</strong> factors include primary and secondary techniques for knowledge sharing. In <strong>the</strong>
case <strong>of</strong> primary KS process, knowledge sharing is expected and planned and <strong>the</strong> result <strong>of</strong> <strong>the</strong>
process is knowledge exchange. The secondary KS process has different primary purpose (for
example teamwork). In this case <strong>the</strong> knowledge sharing is not <strong>the</strong> final output and result <strong>of</strong> <strong>the</strong>
process.
The last factor for KS outlines <strong>the</strong> purpose <strong>of</strong> KS technique. This is determined if KS results in
increasing individual or organizational knowledge base. These criteria will form <strong>the</strong> framework for
evaluation <strong>of</strong> s<strong>of</strong>t techniques for KS.
Some <strong>of</strong> <strong>the</strong> most popular KS techniques in practice can be summarized in <strong>the</strong> following groups:
training techniques: seminars, workshops, lectures, training-on-<strong>the</strong>-job;
meetings: workshop, formal meetings, informal meetings, conversations, group discussions;
work in group: teamwork, quality groups, project teams, focus groups;
conferences: company visits, tradeshows, internships;
group creativity techniques: brainstorming and o<strong>the</strong>r techniques for group knowledge creation.
3. Practical implementation
In order to illustrate <strong>the</strong> KS/KT model and <strong>the</strong> proposed framework for KS s<strong>of</strong>t techniques in practice,
<strong>the</strong> outputs <strong>of</strong> a KS survey will be investigated. It was conducted during <strong>the</strong> last 2 years in parallel in
Hungary (HU) and in Bulgaria (BG). The survey was done among Hungarian (2006-2010) and
Bulgarian (2009-2010) middle managers at medium- and large-sized enterprises. A database <strong>of</strong> 321
questionnaires has been collected in BG which is compared with <strong>the</strong> 320 questionnaires collected in
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Albena Antonova and Aniko Csepregi
HU during <strong>the</strong> same period. Based on <strong>the</strong> observed results, it can be revealed how middle managers
consider knowledge sharing s<strong>of</strong>t techniques in <strong>the</strong> investigated countries. These employees can be
found in <strong>the</strong> mid-level management between <strong>the</strong> top-level and <strong>the</strong> first-level (operational level)
management. This results in being a role model for <strong>the</strong> employees <strong>of</strong> <strong>the</strong>ir department or group,
having a key position in vertical communication, being responsible for achieving business objectives
by setting goals for <strong>the</strong>ir own department or group, providing suggestion and feedback to <strong>the</strong> top
management for helping <strong>the</strong> improvement <strong>of</strong> <strong>the</strong> organization.
According to <strong>the</strong> survey results, middle managers have discovered <strong>the</strong> following knowledge sharing
techniques as most popular and useful ones:
Figure 2: Most popular KS techniques according to Bulgarian and Hungarian middle managers
3.1 Analysis <strong>of</strong> results
As it can be observed on Figure 2 <strong>the</strong> middle managers from both countries ranged <strong>the</strong> s<strong>of</strong>t
techniques for KS differently. We will analyze first <strong>the</strong> most popular techniques in both countries, and
<strong>the</strong>n <strong>the</strong> differences will be highlighted. Based on <strong>the</strong> empirical results, <strong>the</strong> framework for evaluation
<strong>of</strong> <strong>the</strong> most popular KS s<strong>of</strong>t techniques will be composed (Table 2).
According to <strong>the</strong> survey data, middle managers from both countries considered teamwork as <strong>the</strong> most
popular and useful s<strong>of</strong>t technique for KS (50% BG, 49% HU). Teamwork is defined as activity <strong>of</strong>
“individuals working toge<strong>the</strong>r in a cooperative environment to achieve common team goals through
sharing knowledge and skills” (Tarricone, 2002, pp 641). Teamwork is incorporated in organizational
practices, and some <strong>of</strong> <strong>the</strong> most important factors for teamwork success include: commitment to team
success and shared goals, interdependence, interpersonal skills, open communication, appropriate
team composition, and finally commitment to <strong>the</strong> team processes (Tarricone et al., 2002). Therefore,
we can identify that teamwork is both push and pull KS technique. The persons involved in teamwork
are both knowledge owners and knowledge perceivers. In <strong>the</strong> same time it can be considered that
teamwork is an active KS technique, because its success depends from <strong>the</strong> efforts for externalization
<strong>of</strong> knowledge and its implementation in final outputs. Teamwork is a secondary KS process, as its
main purpose is delivery <strong>of</strong> final team results. The role <strong>of</strong> knowledge sharing is just to facilitate and
support <strong>the</strong> delivery <strong>of</strong> team results. Teamwork is organizational s<strong>of</strong>t technique leading to increased
organizational performance and lead to increased knowledge in organizations.
Hungarian middle managers consider mainly formal trainings and knowledge building methods as
most important instruments and s<strong>of</strong>t techniques for KS. Trainings are selected as leading KS
technique for <strong>the</strong>m (53%). Trainings are usually initiated by knowledge seekers, and represent a push
KS technique. The main efforts here are put into knowledge internalization (absorption and retention
<strong>of</strong> knowledge). Trainings are formal and company-specific methods for increasing personal
knowledge and lead to long-term knowledge building. Not last, trainings contribute to personal
knowledge improvement.
41

Albena Antonova and Aniko Csepregi
A large part <strong>of</strong> Hungarian middle managers selected lectures on a third place (45%). Lectures are
more <strong>the</strong>ory-oriented than trainings and represent ano<strong>the</strong>r formal KS technique. Lectures are
delivered as pull techniques as usually invited speakers initiate and share with o<strong>the</strong>rs specific aspects
<strong>of</strong> <strong>the</strong>ory and practice. Lectures are active and primary KS technique, oriented to organizational
practices. Lectures increase organizational knowledge base.
Learning through projects (39%, HU) is a process <strong>of</strong> learning and knowledge sharing that is initiated
by project leader and knowledge owner. The knowledge owner shares along <strong>the</strong> project-work <strong>of</strong> his
ideas, knowledge and information with his teammates. Therefore, learning through projects is a kind
<strong>of</strong> group work and team work, leading to <strong>the</strong> delivery <strong>of</strong> specific final results. It is an active method, as
it requires efforts for proper externalization <strong>of</strong> knowledge. On <strong>the</strong> o<strong>the</strong>r side as any teamwork, it is a
secondary KS activity, as KS is complementary to <strong>the</strong> final output. Learning through projects leads to
increased personal knowledge.
According to <strong>the</strong> observed results, BG middle managers prefer o<strong>the</strong>r methods for KS than Hungarian
managers. On <strong>the</strong> second place, BG managers point out Informal talks (39%). Informal networks are
important devices for promoting communication within and between organizations (Jewels, 2002).
Among <strong>the</strong> topics <strong>of</strong> discussion for informal talks, <strong>the</strong> followings can be identified: mutual respect, time
to talk and reflect, listening o<strong>the</strong>rs, acceptance, conversations streng<strong>the</strong>n relationships, exploration <strong>of</strong>
questions that “matter”, develop shared meaning, learning new things, mutual commitment (Brown,
Issaacs 1997). Therefore, informal conversations can be characterized as both pull and push
techniques. Following development <strong>of</strong> large set <strong>of</strong> powerful social network technologies, many
researches prove that informal social networks can play increasingly important role for any
organization (Boh, 2007). Informal conversations can be both described as ad-hoc mechanisms for
sharing news, last up-dates, feelings, fears, personal insights and considerations, but <strong>the</strong>y can be as
well systematically oriented to knowledge sharing (request for help, for advice etc). Informal
conversations are active KS technique as people need to share and to externalize knowledge (that
should be explained and verbalized). On <strong>the</strong> o<strong>the</strong>r hand informal conversations are primary KS
technique, as information and knowledge exchange constitute <strong>the</strong> main outputs <strong>of</strong> this interaction.
Finally informal conversations are highly individual KS technique.
Table 2: Assessment framework <strong>of</strong> most popular KS methods
Push-pull
Active -
Passive
Primary –
Secondary
Organizational-
Individual
Trainings X X X X
Teamwork X X X X X
Lectures X X X X
Learning through projects X X X X
Informal conversations X X X X X
4. Discussion
Based on <strong>the</strong> findings from <strong>the</strong> survey <strong>of</strong> middle managers in both countries, it can be stated that <strong>the</strong>
most popular KS s<strong>of</strong>t techniques are country-specific. While in Hungary, middle managers prefer
formal and institulized KS s<strong>of</strong>t techniques for training and teamwork, in Bulgaria <strong>the</strong> mostly used KS
techniques are teamwork and informal meetings and conversations. Therefore, <strong>the</strong> use and
expansion <strong>of</strong> KS techniques reflect both national and organizational specifics.
Analyzing <strong>the</strong> assessment framework, it can be highlighted that pull techniques play increasing role
as most instruments require active participation and personal involvement <strong>of</strong> knowledge owners –
experts who have pr<strong>of</strong>essional knowledge. Knowledge seekers are in <strong>the</strong> position to support KS
process (in case <strong>of</strong> teamwork) or to identify appropriate methods to acquire new knowledge (trainings,
learning by doing). Therefore, <strong>the</strong> main player in KS process is <strong>the</strong> knowledge-owner. Organizations
can improve knowledge externalization and knowledge sharing paying attention and training
knowledge owners and extending <strong>the</strong> use <strong>of</strong> appropriate IT technologies.
The second conclusion is that most popular techniques for KS require active involvement from
participants. Active methods <strong>of</strong> KS include teamwork, learning through projects, meetings and
42

Albena Antonova and Aniko Csepregi
conversations, brainstorming and o<strong>the</strong>r group KS techniques. Therefore KS should be considered as
active ra<strong>the</strong>r than passive instrument. In o<strong>the</strong>r words, people should be aware that KS is about
externalization <strong>of</strong> knowledge and its expression in appropriate form and manner.
Next, both primary and secondary KS s<strong>of</strong>t techniques are popular and used in practice. This means
that even if some processes are not considered as primary KS (or aiming just to transfer or result in
new knowledge), <strong>the</strong>re should be paid attention <strong>of</strong> o<strong>the</strong>r KS aspects.
The final considerations include organizational versus individual knowledge sharing. While individuals
play <strong>the</strong> main role in every KS process, organizations can benefit from active KS as well. Via
appropriate mechanisms, every single process <strong>of</strong> KS can be replicated in IT systems and reused in
case <strong>of</strong> need. In KS scenarios both organizations and individuals win.
5. Conclusions
Knowledge sharing is largely discussed in literature and different models for its measurement and
practical implementation are proposed (Cummings 2004; Cabrera et al. 2006; Lin 2007; Jiacheng
et al 2010). However, <strong>the</strong> present research has proposed a different approach for analyzing KS s<strong>of</strong>t
techniques. We investigated some <strong>of</strong> <strong>the</strong> key elements in <strong>the</strong> KS/KT model to establish a new
assessment framework for middle managers. Based on <strong>the</strong> analysis, we found that knowledge owners
(middle managers) play <strong>the</strong> main role in <strong>the</strong> process <strong>of</strong> Knowledge sharing. O<strong>the</strong>r conclusions say
that KS s<strong>of</strong>t techniques require active knowledge externalization, and that many techniques can be
classified as secondary. These findings improved our understanding how KS processes can be
practically improved. Moreover, <strong>the</strong>y enhanced <strong>the</strong> ideas <strong>of</strong> <strong>the</strong> KS policy that companies need to
perform in order to increase <strong>the</strong> benefits <strong>of</strong> KS.
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44

The role <strong>of</strong> Critical Success Factors in Acquiring
Competitive Advantages in Two Industrial Factories,
Tehran ,Iran
Seyed Esmaeil Asgharpour 1 and Gholamreza Taleghani 2
1
Islamic Azad University <strong>of</strong> Shahre Rey Branch, Tehran, Iran
2
University <strong>of</strong> Tehran, Tehran, Iran
e_asgharpour12@yahoo.com
ghtaleghani@yahoo.com
Abstract: Knowledge management (KM) is one <strong>of</strong> <strong>the</strong> most significant initiatives in dealing with global
competition and new business challenges. KM efforts typically to focus on organizational objectives such as
improved performance, competitive advantage, innovation, <strong>the</strong> sharing <strong>of</strong> lessons learned, integration and
continuous improvement <strong>of</strong> <strong>the</strong> organization. “Most managers now seem to understand that <strong>the</strong>y will find
competitive advantage by tapping employees” most essential humanity, <strong>the</strong>ir ability to create, judge, imagine and
build relationships. “The champion managers <strong>of</strong> <strong>the</strong> InfoTech Age will be those who do it fastest and best” Stated
by Ge<strong>of</strong>frey Colvin.This paper is based on <strong>the</strong> role <strong>of</strong> critical success factors <strong>of</strong> knowledge management in
acquiring competitive advantage in two industrial bread producing factories which is located in Tehran province.In
this research, researcher considers five elements <strong>of</strong> Critical Success Factors <strong>of</strong> knowledge management and <strong>the</strong>
role <strong>of</strong> <strong>the</strong>se elements in gaining competitive advantage in <strong>the</strong> mentioned factories. The independent variables
are: attitude <strong>of</strong> senior management, knowledge sharing culture, learning-training, information technology and
document information repository .The dependent variables are: increasing market share ,innovation, quality <strong>of</strong>
products ,decrease <strong>of</strong> product delivery time and reducing production cost.This study is based on survey method,
library studies and experience <strong>of</strong> <strong>the</strong> author. The Knowledge Management Assessment Tools (KMAT)
questionnaire was distributed among <strong>the</strong> experts, skill and semi-skill workers in both factory <strong>the</strong>n data ga<strong>the</strong>red
from <strong>the</strong>se questionnaires were analyzed to get favorable results. The result <strong>of</strong> <strong>the</strong> research indicates that <strong>the</strong>re
are no significant differences between critical success factors <strong>of</strong> knowledge management and gaining competitive
advantage in two factories.
Keywords: critical, knowledge management, competitive, innovation, culture, success, strategy
1. Introduction
Knowledge management that fit well with <strong>the</strong> technological and economic environment may obtain
critical organizational capability (Sadat Bid Gholi 2009). Knowledge management plays an important
role in <strong>the</strong> global economic competition. Organizations applying knowledge management in <strong>the</strong>ir
organizations can obtain good results. It is extremely important for managers to understand, exploit
and smo<strong>the</strong>r <strong>the</strong> political climate in <strong>the</strong> company to maximize <strong>the</strong> organizational outcome and
satisfaction levels <strong>of</strong> <strong>the</strong> people. In o<strong>the</strong>r words, <strong>the</strong>ir outputs are exceeded to inputs, when much
money was invested on knowledge development in organization, it can be considered as winners <strong>of</strong>
economy and industry. Similarly, <strong>the</strong> knowledge assets existing at any given time provide an
opportunity for sustainable competitive advantage. Therefore, <strong>the</strong> author selected two bread firms for
his research in order to find out how <strong>the</strong>se five critical success factors <strong>of</strong> KM can affect on gaining
competitive advantage. The knowledge is based on <strong>the</strong> view <strong>of</strong> <strong>the</strong> firm that can explain convincingly
why certain firms are more competitive under <strong>the</strong> same market conditions (Seyed Javadin 2005). KM
is a policy within an organization <strong>of</strong> keeping information about every one’s knowledge and training.
Therefore, establishing KM in <strong>the</strong> organization is an essential part <strong>of</strong> organizational strategy and
stated as thought investment in <strong>the</strong> organization. Competitive strategy is an outline <strong>of</strong> how a business
intends to compete with o<strong>the</strong>r firms in <strong>the</strong> same industry. There are ten management ideas <strong>of</strong> <strong>the</strong>
twenty-first century: scientific management, assembly line manufacturing, <strong>the</strong> modern corporation,
leadership, <strong>the</strong> management guru, labor rights managing by <strong>the</strong> numbers, quality, re-engineering, and
knowledge management. The most important element <strong>of</strong> managerial work is <strong>the</strong> efficient skills to carry
out basic functions and fill fundamental roles. These are technical, interpersonal, conceptual, and
diagnostic skills. Technical skills are <strong>the</strong> skills necessary to accomplish specific tasks within <strong>the</strong>
organization .Interpersonal skills are skills that managers use to communicate with, understand, and
motivate individuals and groups .Diagnostic skills allow managers to better understand cause and
effect relationships and recognize <strong>the</strong> optimal solutions to problems.
Knowledge Management efforts can help individuals and groups to share valuable organizational
insights to reduce redundant work. In a general sense, knowledge management can be exerted in <strong>the</strong>
45

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
organization by ga<strong>the</strong>ring, sharing, and using knowledge as an organizational capital for access to <strong>the</strong>
organization’s goal.
2. Research method
This research is based on <strong>the</strong> questionnaires which were distributed among <strong>the</strong> skill and semi skill
workers <strong>of</strong> two firms. Questionnaires are consisting <strong>of</strong> two parts; <strong>the</strong> first part had 30 questions that is
based on critical factors <strong>of</strong> success <strong>of</strong> KM and <strong>the</strong> second part had 25 questions concerning to
variances <strong>of</strong> competitive advantages. For <strong>the</strong>se questionnaires, five variances were considered, each
variance consisting five items.
2.1 Definition <strong>of</strong> knowledge management
The primary definition for knowledge management is finding a way for creating, identifying, and
detecting, distributing and transferring organizational knowledge to <strong>the</strong> persons who need for <strong>the</strong>ir
promotion. O<strong>the</strong>r definitions <strong>of</strong> knowledge management are as follows:
Knowledge management is process <strong>of</strong> creating, confirming, presenting and applying knowledge
(Afrazeh 2009).
Knowledge management is a process through which organization produces wealth though
knowledge or his mental capital (Mousavinejad 2009).
Simply, knowledge management is a process through which organization produces value <strong>of</strong>
intellectual and knowledge –based property (Mousavinejad 2009).
In accordance with ano<strong>the</strong>r definition, knowledge management is a process or activity <strong>of</strong> creating,
obtaining, possessing, sharing and applying any knowledge, if available, for increase <strong>of</strong> learning
and performance in organizations (Swan 1999) .In order to understand meaning <strong>of</strong> knowledge
management, at first we should study concepts <strong>of</strong> data, information, knowledge, wisdom and
difference and relationship between <strong>the</strong>m. Terms <strong>of</strong> information and data are applied instead <strong>of</strong>
knowledge but <strong>the</strong>y have different meanings (Sadat Bid Gholi 2009).
Knowledge management is <strong>the</strong> result <strong>of</strong> learning, which provides <strong>the</strong> only sustainable pooled
expertise, special relationships and alliances. Km is value-added behavior and activities.
Knowledge Management is <strong>the</strong> management <strong>of</strong> <strong>the</strong> organization towards <strong>the</strong> continuous renewal
<strong>of</strong> <strong>the</strong> organizational knowledge base-this means e.g. creation <strong>of</strong> supportive organizational
structure, facilitation <strong>of</strong> organizational members ,putting IT instruments with emphasis on
teamwork and diffusion <strong>of</strong> knowledge (as e .g .group wave )into place.
KM (Knowledge Management) efforts typically focus on organizational objectives such as
improved performance, competitive advantage, innovation, <strong>the</strong> sharing <strong>of</strong> lessons learned,
integration and continuous improvement <strong>of</strong> <strong>the</strong> organization.
3. Research Hypo<strong>the</strong>ses
Hypo<strong>the</strong>sis refers to what <strong>the</strong> researcher seeks to answer and should be mentioned as claim so that it
can be tested (confirmation or rejection).
Hypo<strong>the</strong>sis or logical guess which expresses expected relationship between two or more variables.
Hypo<strong>the</strong>sis is called test because its au<strong>the</strong>nticity should be studied by performing test. Hypo<strong>the</strong>sis
may be accurate and essential and supported and confirmed with a set <strong>of</strong> facts or may be inaccurate
prediction to be rejected.
Research questions are <strong>the</strong> scientific and reasonable questions which are raised for <strong>the</strong> researcher
and should be expressed in such a manner that <strong>the</strong>y can limit domain <strong>of</strong> research.
3.1 Definition <strong>of</strong> Technical Terms
Data: Things known or assumed, facts or figures from which conclusions can be inferred. Data is a
series <strong>of</strong> raw and meaningless facts and a set <strong>of</strong> actual and abstract facts about <strong>the</strong> events. Data is
special facts or observations which are generally about physical phenomena or business exchanges
(Seyed Javadin 2005).
46

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
Knowledge: Includes data and information which has been organized or processed to contribute to
understanding, experience, collective and specialized learning. It also was defined as a combination
<strong>of</strong> organized experiences, values and information and expertise attitudes which give framework for
values and enjoying new experiences and information (Khan Sari 2006).
Knowledge is a set <strong>of</strong> <strong>the</strong> organized information, its related strategies, results <strong>of</strong> its application in
different decisions and its related education. Knowledge can be defined as insights <strong>of</strong> information and
data. It can be effective and dividable in different methods under different conditions. It is an
understanding through experience, argument, direct understanding and learning. Knowledge is an
awareness <strong>of</strong> person which is obtained from thought.
Organization: Organization is a structured social system which includes groups and individuals who
try to acquire desired goals. On <strong>the</strong> o<strong>the</strong>r hand, organization includes structured social units such as
individuals and working groups which work in order to achieve a common goal such as production,
sale, services and gain definite pr<strong>of</strong>it (Seyed Javadin 2005).
Education and learning: Refers to special activities (processes) for learning inside <strong>the</strong> organization.
The author believes that learning from experience is <strong>the</strong> key .It is true that we might get some insights
from education, but what really changes us in long-term, is <strong>the</strong> work experience to be intellectually
competitive (Bebenroth 2011).
Learning organization: Is an organization in which learning is mixed with its basic rules, <strong>the</strong>
organization <strong>of</strong> which different groups are increasing <strong>the</strong>ir knowledge continuously (Davenport 2001).
From Peter Sanger's point <strong>of</strong> view, learning organization is a place which persons increase <strong>the</strong>ir
abilities to create results which <strong>the</strong>y seek to find. Learning organization is an organization which is
developed by creating and nourishing capabilities required for access to <strong>the</strong> future successes (Grant
1996).
Innovation: Makes new and innovative thoughts resulting from creativity practical. Innovation
converts new creativity or new ideas to practice and result. In ano<strong>the</strong>r definition, innovation is defined
as creative idea and its conversion to product <strong>of</strong> services and new methods <strong>of</strong> operations (Akhavan
2007).
Information: Is defined as an interrelated set <strong>of</strong> data which can be transferred as a message and
interpretation and is basis <strong>of</strong> decision making in organizations. Adding ground and interpretation to
<strong>the</strong> data and <strong>the</strong>ir interrelation causes to form information. Information is combined data with its
ground and interpretation
Wisdom and awareness: The last stage is movement from knowledge to wisdom and perfection.
Wisdom is application <strong>of</strong> knowledge. If a person knows that eating fatty food will result in obesity but
he cannot help eating it, he will not be considered a wise man because he has been aware <strong>of</strong>
knowledge which he has not applied (Sadat Bid Gholi 2009).
Figure 1 shows that knowledge management is closely related to human resource management as it
deals with people and <strong>the</strong> process <strong>of</strong> how knowledge is transferred. Also how <strong>the</strong>se five critical factors
<strong>of</strong> KM ;such as attitude <strong>of</strong> senior management ,knowledge sharing ,education and learning,
knowledge reservoir ,and information technology affect on acquiring competitive advantage in <strong>the</strong>se
two bread factories.
47

Attitude <strong>of</strong>
Senior
Market
share
Increase
Seyed Esmaeil Asgharpour and Gholamreza Taleghani
Knowledge
Sharing
Innovation
Knowledge Management
Education &
Learning
Product
Quality
Competitive advantage
Knowledge
Reservoir
Supply to
Customers
Information
Technology
Production
Cost
Figure 1: Relation between knowledge management critical success factors in acquiring competitive
advantage
4. Knowledge pyramid:
With regard to <strong>the</strong> above definitions and concepts, we can draw knowledge pyramid. Data is in <strong>the</strong>
lowest level and wisdom is at top <strong>of</strong> pyramid. There are differences <strong>of</strong> opinions about details. But,
<strong>the</strong>re is general agreement on movement and general combination <strong>of</strong> knowledge pyramid (Figure 2).
Algorithm
Figure 2: Knowledge Pyramid
Non- Algorithm
Awareness
Knowledge
Information
Data
48
High
Low

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
4.1 Knowledge management and human resources management:
It has been fully accepted that successful implementation <strong>of</strong> knowledge management requires
interaction <strong>of</strong> knowledge and human resources management.
4.1.1 Critical success factors:
Any activity factor which is necessary for success <strong>of</strong> <strong>the</strong> organization in future is called success factor.
Critical success factors can be defined as those issues which are seemed important to <strong>the</strong>
organization with regard to its present and future performance. These factors may be changed over
time. Success factors can include some subjects such as quality <strong>of</strong> product, attitude <strong>of</strong> staff or flexible
production.
Word ''critical '' has different meanings such as ''important'', ''key'', ''determining '', ''fundamental'' ,
''strategic '', ''alarming '' and ''anxious '' but we mean generally any factor which has undeniable role in
access <strong>of</strong> <strong>the</strong> organization to success. The organization will fail in case <strong>of</strong> lack <strong>of</strong> access to its related
goals. Term'' critical factors <strong>of</strong> success'' was proposed by R.H. Daniel for <strong>the</strong> first time in educational
organizations and <strong>the</strong>n accepted by F.Rockert and gradually applied in non-educational organizations
(Morrison 2010).
Application <strong>of</strong> effective knowledge management is one <strong>of</strong> <strong>the</strong> most important factors under
competitive conditions and in wisdom era. This subject is so important that many organizations
measure <strong>the</strong> available knowledge and reflect it as intellectual capital <strong>of</strong> <strong>the</strong> organization as well as
index for ranking organizations in <strong>the</strong>ir reports. These institutes regard settlement <strong>of</strong> knowledge
management in <strong>the</strong> organization as part <strong>of</strong> organizational strategy (Jafari Moghadam 2004).
Regarding to <strong>the</strong> critical factors <strong>of</strong> success in knowledge management, many beliefs have been
expressed and different classifications have been provided .They can be classified into five groups
(Mousavinejad 2009):
Leadership
Culture
Structure , role and responsibilities
Substructures <strong>of</strong> information technology
Testing result
A: leadership: undoubtedly, leadership plays a major role in success <strong>of</strong> each activity in an
organization. This role is very important for knowledge management due to newness <strong>of</strong> <strong>the</strong> subject. In
organizations, no factor can be more effective than senior management attitude in supporting <strong>the</strong>
success <strong>of</strong> that special activity. Although supporting senior management plays key role in success <strong>of</strong>
actions <strong>of</strong> an organization.
B: culture: culture is a set <strong>of</strong> common history, expectations, unwritten regulations and social habits
which have effect on behavior <strong>of</strong> persons. Culture is composed <strong>of</strong> fundamental beliefs which are
important in understanding activities and communication <strong>of</strong> <strong>the</strong> personnel, though unwritten.
If <strong>the</strong>re is tendency to knowledge sharing in your organization, knowledge sharing will be simpler.
Most people tend to share knowledge. They like to let o<strong>the</strong>rs know <strong>the</strong>ir knowledge.
C: structure, role, responsibility: although <strong>the</strong>re are different methods for founding knowledge
management, American Productivity and Quality Center has applied common method in all
organizations with <strong>the</strong> best practice: a directing committee, a knowledge management supporting
group, consultants and owners <strong>of</strong> company.
Directing committee is composed <strong>of</strong> high rank managers <strong>of</strong> <strong>the</strong> organization whose duties are
promotion <strong>of</strong> concept, support and provision <strong>of</strong> necessary guidance. Supporting group is composed <strong>of</strong>
three or four persons aiming at supporting <strong>the</strong> implemented knowledge management plans.
49

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
4.2 Factors which have a critical role in success <strong>of</strong> knowledge management
Today, capitals <strong>of</strong> an organization are not only financial and physical capitals, but also capital <strong>of</strong>
knowledge, which is more important than <strong>the</strong> o<strong>the</strong>r two capitals. For execution <strong>of</strong> knowledge
management in an organization, time interval is needed in order to provide intellectual, cultural
circumstances. Also educational and technologic skills should be provided.
Leadership: like most plans, knowledge management projects should benefit from senior
management support. Performing knowledge management in an organization requires conscious and
effective leadership in <strong>the</strong> organization. In such organizations, knowledge is regarded as a valuable
source and <strong>the</strong> activities which are performed for maximizing <strong>the</strong>se sources are greatly considered.
Culture: culture and organizational structure should support knowledge management system in
<strong>the</strong> organization. Suitable culture and structure in an organization in which creativity and
innovation are regarded as a value, are driving factors <strong>of</strong> knowledge management. Culture means
a set <strong>of</strong> beliefs and images <strong>of</strong> <strong>the</strong> organization members. Values <strong>of</strong> <strong>the</strong> organization, unwritten
principles and regulations and procedures compose cultural sources <strong>of</strong> <strong>the</strong> knowledge. Culture
content <strong>of</strong> <strong>the</strong> organization as knowledge source can be reinforced by <strong>the</strong> persons, manuals,
procedure and computers. Therefore, learning and personal and organizational growth should be
regarded as value. All personnel <strong>of</strong> <strong>the</strong> organization, in different levels and positions believe that
<strong>the</strong>y should share <strong>the</strong>ir knowledge and information for growth and success <strong>of</strong> <strong>the</strong>ir organization.
And this kind <strong>of</strong> thought is needed to be encouraged and supported in <strong>the</strong> organization. In <strong>the</strong>
organization, all persons seek to learn and transfer <strong>the</strong>ir lessons to o<strong>the</strong>rs.
Measurement method: is one <strong>of</strong> <strong>the</strong> o<strong>the</strong>r parameters driving knowledge management is
measurement <strong>of</strong> effect <strong>of</strong> services and activities in <strong>the</strong> field <strong>of</strong> knowledge management. For
development <strong>of</strong> knowledge management in <strong>the</strong> organization, its changes should be systematically
identified, measured and supported.
Technological substructure: ano<strong>the</strong>r field which <strong>the</strong> organization should consider for success in
knowledge management is technological substructures. One <strong>of</strong> <strong>the</strong> subjects relating to
knowledge management is astonishing advances <strong>of</strong> information technology. This causes
considerable investment <strong>of</strong> <strong>the</strong> organizations. In this regard, it is difficult for <strong>the</strong> organizations to
invest in it unilaterally and without considering all dimensions and fields.
Increase in number <strong>of</strong> knowledgeable persons and <strong>the</strong>ir skills: knowledge management
without cooperation <strong>of</strong> all personnel will be useless. Public education should be executed in <strong>the</strong>
organization and all personnel supposed to learn it. Managers <strong>of</strong> <strong>the</strong> organization should be
obliged to learn principles and techniques <strong>of</strong> knowledge management in order to apply <strong>the</strong>m in
<strong>the</strong> organization.
4.2.1 Eight critical success factors <strong>of</strong> knowledge management (Khalily 2010)
Linking with economic performance and industry
Technical and organizational substructure
Standard and flexible structure
Knowledge love culture
Clear language
Motivational operations
Creating channel and way for knowledge transfer
Support <strong>of</strong> senior management
In <strong>the</strong> research which was performed by Mathi in Germany, in 2004, key factors <strong>of</strong> knowledge
management were introduced as:
Knowledge-based organization
Culture
Strategy
Systems and substructure <strong>of</strong> information technology
Systematic and effective processes
50

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
Units <strong>of</strong> measurement (Khansari 2006)
Khalifa&Liu performed a research in Hong Kong city university in 2003 called ‘’knowledge
management success model ‘’. Main question <strong>of</strong> this research was: what factors are important for
success <strong>of</strong> knowledge management plans? Research hypo<strong>the</strong>ses were:
Organizational factors have direct and significant effect on success <strong>of</strong> knowledge management.
Information technology has limited effect on success <strong>of</strong> knowledge management plans.
Knowledge Management processes have direct and significant effect on success <strong>of</strong> knowledge
management plans.
Information technology has direct and significant effect on processes <strong>of</strong> knowledge management.
Effective Organizational factors on knowledge management include:
Knowledge management strategy
Knowledge leadership
Organizational culture
Knowledge attitudes.
Knowledge management processes include: creation, transfer, application and preservation <strong>of</strong>
knowledge. It was specified that organizational factors and knowledge management processes have
<strong>the</strong> most effect on success <strong>of</strong> knowledge management plans. Information technology is as effective
as transfer and preservation <strong>of</strong> knowledge due to its effect on knowledge management processes
(Fayerstone 1995).
4.3 Key factors <strong>of</strong> success in small and large companies
In a study that performed by Kuan Yew Wong in 2005, eleven key factors <strong>of</strong> success in
implementation <strong>of</strong> knowledge management were identified in small and large companies including:
Senior manager’s support
Culture
Information technology
Strategy and goal
Unit <strong>of</strong> measurement
Organizational substructure
Activities and processes
Motivational help
Sources
Education
Human resources management
Identification <strong>of</strong> <strong>the</strong>se 11 factors is result <strong>of</strong> systematic effort which has been made in comprehensive,
integrated and holistic way.
4.4 Competitive advantage
Competitive advantage is one <strong>of</strong> <strong>the</strong> most important issues in <strong>the</strong>ories <strong>of</strong> strategic management
because competitive advantage in an organization means better performance in comparison to <strong>the</strong>
competitors and guarantees pr<strong>of</strong>itability in short term and survival and growth <strong>of</strong> <strong>the</strong> organization in
long term. Therefore, competitive advantage is not only increased, but also it has become more
critical .According to Porter, strategies allow organization to gain competitive advantage from three
different bases :Cost leadership, differentiation ,and focus. Porter calls <strong>the</strong>se bases "generic
strategies" (Fred 1995). Generally, components <strong>of</strong> competitive advantage include value , market
focus, customer satisfaction, recognition <strong>of</strong> potential ability , moving actual power , motivation ,
making skill, suitable price, accountability and innovation (Zar Abadipour 2006).
51

4.5 Comparative Advantage
Seyed Esmaeil Asgharpour and Gholamreza Taleghani
Theory <strong>of</strong> absolute advantage <strong>of</strong> Adam Smith brought some questions to economist’s minds after
Adam Smith. For example, in case that a country has advantage over o<strong>the</strong>r countries in production <strong>of</strong>
all goods, what role can it have in international trade<strong>of</strong>fs? Adam Smith’s attitude was that such
country cannot benefit from foreign trade<strong>of</strong>fs. Therefore, it will not participate in such trade<strong>of</strong>fs.
Ricardo in book <strong>of</strong> political economics principles which was published in 1817 rejected claim <strong>of</strong> Adam
Smith and mentioned that when a country can have absolute advantage over o<strong>the</strong>r countries in
production <strong>of</strong> all goods, it can produce and export <strong>the</strong> goods which have <strong>the</strong> highest efficiency and
import <strong>the</strong> goods which have <strong>the</strong> least efficiency from o<strong>the</strong>r countries. Execution <strong>of</strong> such policy will
benefit from foreign trade<strong>of</strong>f. This attitude which was mentioned with stronger economic arguments
was known as comparative advantage <strong>the</strong>ory.
4.6 Preceding studies
Regarding to this paper, critical success factors <strong>of</strong> knowledge management and competitive
advantage, several similar researches have been performed in different division <strong>of</strong> industries in Iran.
Also, a lot <strong>of</strong> papers have been published on <strong>the</strong> international magazines .The author tries to point out
briefly to some <strong>of</strong> <strong>the</strong>m.
One research entitled "The role <strong>of</strong> KM in acquiring competitive advantage in Alborz insurance
company was done in Mazanderan province in 2009.The results showed that six variable elements
related to KM such as: knowledge creation, knowledge acquirement, organizational learning
,knowledge transferring, applied knowledge and knowledge reserving comparing with variable
elements <strong>of</strong> competitive advantage .The results suggested that significant differences were seen.
Ano<strong>the</strong>r study which was done by Tehran university in 2008 on <strong>the</strong> branches <strong>of</strong> national bank, entitled
"Key factor for obtaining competitive advantage" with emphasizes on communication management
with bank customers. The results indicated that <strong>the</strong> effect <strong>of</strong> KM on competitive elements was not
considerable .Ano<strong>the</strong>r research has been performed on two electric industries in 2007 entitled "The
role <strong>of</strong> critical success factors <strong>of</strong> KM in acquiring competitive advantage. Its results suggested that <strong>the</strong>
only considerable significant difference was based on using information technology in <strong>the</strong>se two
industries .On <strong>the</strong> o<strong>the</strong>r items, <strong>the</strong>y had <strong>the</strong> similar situation.
4.7 Voluminous and semi-voluminous breads
As it was mentioned, rate <strong>of</strong> bread wastes in Iran is very considerable. The high percentage relates to
traditional breads. In this case, bread wastes can be reduced and traditional bread production to be
converted into industrial bread. In order to replace traditional breads with voluminous and semivoluminous
breads, effective steps must be taken. The first step is culture building and informing
people about voluminous and semi-voluminous breads. We should note that some traditional bread
such as Sangak bread has more advantages to <strong>the</strong> o<strong>the</strong>r bread. It should be preserved.
Bread is divided into some classes in terms <strong>of</strong> thickness: Flat bread such as Lavash, Taftoon,
Barbari, Sangak and some kinds <strong>of</strong> bread were produced by machine. Lavash bread is mostly
consumed (45%) due to its durability. Sangak bread is not consumed a lot due to production problem
and low income for <strong>the</strong> producer (8%). Voluminous and semi-voluminous breads are <strong>the</strong> breads which
are baked from flour, water, salt, leaven, and additives. In case that thickness <strong>of</strong> bread is between 2.5
and 5 cm, it is regarded as semi-voluminous. Voluminous and semi-voluminous breads have <strong>the</strong>ir
specifications in terms <strong>of</strong> shape, crust, porosity, elasticity, odor, and flavor. Nearly, 137 kg voluminous
bread is produced out <strong>of</strong> 100 kg flour.
5. Results
The collected data which extracted from questionnaires, were analyzed and <strong>the</strong>ir results were
summarized in tables 1,2 and 3. By analyzing (tables 1 and 2), we realized that differences among
five critical factors for success <strong>of</strong> knowledge management were insignificant.
52

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
Table 1: Ranking <strong>the</strong> critical factors for success <strong>of</strong> knowledge management in Aran firm
Factors Numbers Average Deviation <strong>of</strong> scale
Attitude <strong>of</strong> senior management 25 21.64 1.91
Culture & information sharing 25 21.40 4.28
Using information technology 25 20.20 3.29
Educating & learning 25 19.64 2.64
A Center for knowledge reserving 25 17.39 2.99
Table 2: Ranking <strong>the</strong> critical factors for success <strong>of</strong> knowledge management in Delapar firm
Factors Numbers Average Deviation <strong>of</strong> scale
Culture & information sharing 32 23.21 3.88
Attitude <strong>of</strong> senior management 32 20.25 2.92
Educating & learning 32 20.15 1.98
Using information technology 32 20.06 2.38
A center for knowledge reserving 32 17.46 2.61
Table 3:Comparing <strong>the</strong> critical factors for success <strong>of</strong> KM in both firms
Rank Aran firm Delapar firm
1 Attitude <strong>of</strong> senior management Culture & information sharing
2 Culture & information sharing Attitude <strong>of</strong> senior management
3 Using information technology Attitude <strong>of</strong> senior management
4 Educating & learning Using information technology
5 A center for knowledge reserving A center for knowledge Reserving
Table 3 shows that in Aran firm, <strong>the</strong> highest rank related to attitude <strong>of</strong> senior management and <strong>the</strong>
lowest rank to center <strong>of</strong> knowledge reserving whereas Delpar firm, has <strong>the</strong> highest rank in culture
sharing , <strong>the</strong> lowest rank is related to knowledge reserving center, <strong>the</strong> same as Aran firm.
Table 4: A Comparison between <strong>the</strong> five critical success factors <strong>of</strong> KM in two bread factories by using
<strong>the</strong> T-test
Critical
success
factors
1.Attitude <strong>of</strong>
Senior
Management
2.Knowledge
& Culture
sharing
3.Education &
Learning
Firm Average
Aran
Delapar
Aran
Delapar
Aran
Delapar
64.21
25.20
21.4
23.21
64.19
15.20
Standard
deviation
2.99
2.61
2.64
1.98
3.29
2.92
t Freedom Significant
difference
0.122 55 1.123
2.05
55 0.015
0.43 55 0.648
4. Information
Technology
Aran
Delapar
20.2
20.06
4.28
3.88
0.09 55 0.985
5. Knowledge
Reserving
Aran
Delapar
17.36
17.46
1.91
2.38
0.125 55 0.903
As <strong>the</strong> table 4 shows, <strong>the</strong> main significant difference between <strong>the</strong> two companies is knowledge &
culture sharing.
Clearly firms have always had important connections with o<strong>the</strong>r organizations ,especially ,customers
and suppliers .Assumption is now changing as firms recognize that many <strong>of</strong> <strong>the</strong> skills and resources
essential to <strong>the</strong>ir future success lie outside <strong>the</strong> firm`s boundaries and outside <strong>the</strong>ir direct control.
Competitive challenge is an important factor in producing firms, so applying knowledge management
in <strong>the</strong>se two industrial bread firms has increased productivity. Knowledge management has high
potential ability for dynamic organizational survival .It can promote innovation, strategic planning,
learning ability, product quality and productivity. Consequently, competitive advantage can be
obtained. Regarding to <strong>the</strong> mentioned discussions,<strong>the</strong> critical factors that were surveyed by T-test for
KM in <strong>the</strong>se two firms .It was inferred that <strong>the</strong> only significant difference which observed in <strong>the</strong>se two
bread firms ,was knowledge and culture sharing that <strong>the</strong>y have .In comparing <strong>the</strong> o<strong>the</strong>r four critical
factors that were discussed in this paper, have similar situation to each o<strong>the</strong>r
53

Seyed Esmaeil Asgharpour and Gholamreza Taleghani
In general sense, <strong>the</strong> author can state that differences between in <strong>the</strong>se two firms were insignificant
but Delapar firm has higher priority on skilled and semi-skilled workers to Aran firm.
6. Recommendations for providing favorable bread for managers <strong>of</strong> Aran and
Delapar
Raw materials with good quality (flour, liquid, salt, leaven) for bread production are needed.
For providing dough, flour and water should be mixed correctly.
Chemical materials should not be added to <strong>the</strong> dough.
Use food additives in providing dough.
Adequate time must be given for fermentation.
Cake <strong>of</strong> dough should be flattened well.
Hygienic conditions for bakery and bakers are obligatory.
Standard machinery must be used in bread production.
A suitable heating system for bread baking is required.
Experienced workers are needed for bread production.
7. Suggestions to <strong>the</strong> senior managers <strong>of</strong> <strong>the</strong> bread factories
The following points are significant issues on knowledge management. It is strongly recommended to
senior managers to apply an effective management in <strong>the</strong>ir firms.
Having a distinctive strategy for determining <strong>the</strong> organizational’s goals in long and short terms.
Also a suitable way should be provided for allocation <strong>of</strong> sources and organizational performance
measurement
Education &learning for human resources development should be considered. .Efficient human
resources and knowledge experience are <strong>the</strong> main critical factors for success <strong>of</strong> bread factories.
The bread firms can achieve to <strong>the</strong> more competitive advantages <strong>of</strong> market if <strong>the</strong>y have high
potential man powers.
Managers supposed to pay more attention to information technology and infrastructures.
Advanced computer technology can be applied for collecting and analyzing data
Creating a situation in which valuable knowledge sharing to be exchanged on management level,
knowledge sharing and knowledge exchanging are essentially to be considered. Organizational
culture which supports knowledge exchanging among different companies in which people have
no fear to present <strong>the</strong>ir own knowledge to each o<strong>the</strong>r .Educational training courses and
symposiums are held by specialists for improvement <strong>of</strong> workers’ knowledge in bread factories.
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55

Contextual Adaptive Visualization Environments: a
Knowledge Creation, Transfer and Sharing Platform
Xiaoyan Bai, David White and David Sundaram
Department <strong>of</strong> Information Systems and Operations Management, University <strong>of</strong>
Auckland, Auckland, New Zealand
xbai008@aucklanduni.ac.nz
d.white@auckland.ac.nz
d.sundaram@auckland.ac.nz
Abstract: As an essential component <strong>of</strong> Knowledge Management Systems, visualizations assist in creating,
transferring and sharing knowledge in a wide range <strong>of</strong> contexts where knowledge workers need to explore,
manage and get insights from tremendous volumes <strong>of</strong> data. Knowledge visualization context may involve any
information in regard to <strong>the</strong> decisional problem context within which visualizations are applied, e.g. relevant
problem situations, time, and knowledge visualization tasks and requirements. It may also incorporate <strong>the</strong>
visualization pr<strong>of</strong>iles <strong>of</strong> knowledge workers such as <strong>the</strong>ir cognitive characteristics, personal preferences, prior
knowledge <strong>of</strong> <strong>the</strong> problem domain, etc. Due to <strong>the</strong>ir inherent dynamic nature, <strong>the</strong>se contextual factors may cause
<strong>the</strong> changing visualization requirements and difficulties in maintaining <strong>the</strong> effectiveness <strong>of</strong> a knowledge
visualization when contextual changes occur. To address <strong>the</strong> above contextual complexities, visualization
systems to support knowledge management need to provide flexible support for <strong>the</strong> creation, manipulation,
transformation, and improvement <strong>of</strong> visualization solutions. Fur<strong>the</strong>rmore, <strong>the</strong>y should be able to sense, analyze
and respond to <strong>the</strong> contextual changes so as to support in maintaining <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> solutions. In
addition, <strong>the</strong>y need to possess <strong>the</strong> capability to mediate between <strong>the</strong> problem and <strong>the</strong> knowledge workers
through provision <strong>of</strong> action and presentation languages.However, many visualization systems tend to provide
weak support for fulfilling <strong>the</strong>se system requirements. They do not provide adequate flexibility for adapting <strong>the</strong>
visualizations to fit different knowledge visualization contexts. This motivated us to propose and implement a
flexible visualization system for better aiding knowledge creation, transfer and sharing, namely, Contextual
Adaptive Visualization Environment (CAVE). CAVE provides flexible support for (1) sensing and being aware <strong>of</strong>
<strong>the</strong> changes in <strong>the</strong> problem and/or knowledge worker contexts, (2) interpreting <strong>the</strong> changes through relevant
analysis and (3) responding to <strong>the</strong> changes through appropriate re-design and re-modelling <strong>of</strong> visual
compositions to address <strong>the</strong> problem. In order to fulfil <strong>the</strong> requirements posed above, we developed and
proposed conceptual models and framework which are fur<strong>the</strong>r elucidated through system-oriented architectures
and implementations.
Keywords: knowledge visualization, knowledge visualization context, knowledge creation and sharing, cave
model, cave framework, and cave implementation
1. Introduction
Knowledge visualization is concerned with designing, implementing and applying appropriate visual
representations to create, transform and communicate knowledge. Knowledge visualization is playing
an increasingly important role in knowledge management systems (Burkhard, 2004; Cañas et al.,
2005; Pinaud, Kuntz, Guillet & Philippé, 2006; Eppler & Burkhard, 2007; Bresciani & Eppler, 2009;
Bresciani & Eppler, 2010; Eppler & Burkhard, 2011). Knowledge visualizations can be designed and
developed by leveraging extensive visualization techniques and systems in <strong>the</strong> field <strong>of</strong> information
visualization. The existing visualization techniques have been reviewed and categorized by
researchers and practitioners according to <strong>the</strong>ir features such as data types that visualizations
support, purposes that visualizations fulfill, and problem domains where visualizations are applied
(Card, Mackinlay & Shneiderman, 1999; Chi, 2000; Chen, 2006; Spence, 2007; Heer, Bostock &
Ogievetsky, 2010).
Visualizations can be applied to a wide range <strong>of</strong> contexts where people need to explore, create,
represent, present, transfer and/or share knowledge. In general, knowledge visualization context
incorporates <strong>the</strong> decisional problem context where knowledge visualizations are deployed as well as
<strong>the</strong> visualization pr<strong>of</strong>iles <strong>of</strong> knowledge workers. More specifically, <strong>the</strong> decisional problem context may
involve relevant problem situations, physical surroundings, time, knowledge visualization tasks and
requirements, and social and technological contexts. The knowledge worker context may cover <strong>the</strong>
knowledge workers’ cognitive styles, personal preferences, prior knowledge <strong>of</strong> relevant problem
domain(s), skill acquisition abilities, age, gender, etc.
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Xiaoyan Bai, David White and David Sundaram
These contextual factors are diverse and dynamic, which, in turn, may cause huge complexity
inherent in knowledge visualization context. As a result <strong>of</strong> this, <strong>the</strong> visualization requirements for
solving <strong>the</strong> same decisional problem may vary when contextual changes occur. The same knowledge
visualizations that are appropriate under particular problem and knowledge worker contexts might not
even be relevant when certain contextual changes take place. For instance, knowledge workers <strong>of</strong> <strong>the</strong>
same knowledge visualization may vary over time. Different knowledge workers may have different
visualization preferences such as color, shape and interaction styles. Even for <strong>the</strong> same knowledge
worker, <strong>the</strong> visualization requirements may change when <strong>the</strong> knowledge worker becomes more
familiar with <strong>the</strong> relevant problem domain and <strong>the</strong> visualization system in use. A beginner-level
knowledge worker <strong>of</strong>ten needs step-by-step support for how to manipulate visualizations while an
expert-level knowledge worker may need more support for customizing visualization to complete
sophisticated tasks.
Knowledge visualization context is complex and dynamic in nature, which may cause two major
problems with developing effective knowledge visualizations. Firstly, many visualization systems to
support knowledge management <strong>of</strong>ten have little concern on knowledge visualization context. Context
complexity can significantly affect <strong>the</strong> effectiveness <strong>of</strong> a knowledge visualization in terms <strong>of</strong> how well it
can support a knowledge worker to solve <strong>the</strong> decisional problem <strong>of</strong> interest and achieve <strong>the</strong> intended
purpose. The lack <strong>of</strong> concerns on such impact may incur issues with ineffective knowledge
visualization design and even visualization misuse. Secondly, <strong>the</strong>re is a lack <strong>of</strong> support for developing
and/or adapting knowledge visualizations to address <strong>the</strong> changing requirements caused by
visualization context complexity. Though a knowledge visualization could be designed for a particular
context, it can very soon get out <strong>of</strong> sync with respect to <strong>the</strong> context. Maintaining visualization
effectiveness across contexts is a big challenge.
To address <strong>the</strong> above context-related problems, visualization systems to support knowledge
management need to provide flexible support for creating, manipulating, transforming, improving and
disposing visualization solutions. Meanwhile, <strong>the</strong>y should support knowledge workers to flexibly adapt
visualizations to address context dynamics and maintain <strong>the</strong> visualization effectiveness. However,
many existing knowledge management systems and <strong>the</strong>ir visualizations tend to provide weak support
for <strong>the</strong>se requirements.The above problems, issues and requirements associated with knowledge
visualization context motivated us to propose and implement a flexible system for better aiding
knowledge creation, transfer and sharing, namely, Contextual Adaptive Visualization Environment
(CAVE). As illustrated in Figure 1, CAVE is a context-sensitive, adaptive platform that can provide
flexible support for continuously sensing <strong>the</strong> dynamic problem and knowledge worker contexts. It
assists knowledge workers to define <strong>the</strong> contextual changes through proper analysis and identify <strong>the</strong>
associated visualization requirement changes. Also, CAVE helps <strong>the</strong> knowledge workers to respond
to <strong>the</strong> changes and requirements through appropriate re-design and re-modelling <strong>of</strong> visual
compositions to address <strong>the</strong> problem <strong>of</strong> interest.
Figure 1: A high-level sense and response model <strong>of</strong> CAVE
In this paper, we introduce a formal definition <strong>of</strong> knowledge visualization context in section 2. We <strong>the</strong>n
proceed to explicate <strong>the</strong> definition <strong>of</strong> CAVE and its high-level functional requirements in section 3.
Next, in section 4 we propose a conceptual model to deepen <strong>the</strong> understanding <strong>of</strong> CAVE definition
and how it can address contextual complexities and <strong>the</strong> subsequent changing requirements. After
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Xiaoyan Bai, David White and David Sundaram
this, a framework is pr<strong>of</strong>fered to guide <strong>the</strong> design and development <strong>of</strong> CAVE in section 5. In order to
prove <strong>the</strong> validity <strong>of</strong> our proposed concepts, models and framework, we implemented a prototypical
system to demonstrate how CAVE can adapt to both macro-level and micro-level contextual changes
in section 6.
2. Knowledge visualization contexts
Context has been articulated and categorized in different application domains by many researchers
such as Dey (2001), Schmidt et al. (2000), Chen and Kotz (2000), Schilit, Adams and Want (1994),
and Wu and Chen (2009). In <strong>the</strong> domain <strong>of</strong> visualization, knowledge visualization context involves <strong>the</strong>
information <strong>of</strong> any environmental entities that influence knowledge visualization design, development,
application and evaluation. By reviewing and syn<strong>the</strong>sizing <strong>the</strong> existing contextual classifications and
examples (e.g. Shneiderman (1996), Dreyfus and Dreyfus (1986), IBM Many Eyes (2011), Card,
Mackinlay and Shneiderman (1999), Eppler and Burkhard (2007), Lee, Lee and Lee (2009), Stanford
(2001), Donald, Blake, Girault, Datt and Ramsay (2009)), we identified two fundamental contextual
dimensions, that is, problem and knowledge worker contexts. A brief summary <strong>of</strong> typical contextual
factors involved in <strong>the</strong>se dimensions is provided in Table 1. Detailed information about <strong>the</strong>se
contextual dimensions and <strong>the</strong>ir potential impact on knowledge visualization design and
implementation are presented in sub-sections 2.1-2.3.
Table 1: A classification scheme <strong>of</strong> knowledge visualization context
Contextual
Dimension
Problem
Context
Knowledge
Worker
Context
Contextual Categories Description & Example
E.g. Statistical and categorical data management, digital library
management, personal services support, complex documents
Problem Situation
management, history management, classifications management, networks
management, etc.
Declarative knowledge, procedural knowledge, experiential knowledge,
Knowledge Types people-related knowledge, location-based knowledge, scenario-based
knowledge, and normative/value-based knowledge
Knowledge Management Knowledge creation, codification, transfer, identification,
Tasks
application/learning, measurement/assessment, and signaling
Visualization Tasks Overview, zoom, filter, details-on-demand, relate, history, and extract
Knowledge Generation Data transformation requirements
Knowledge
Representation
Knowledge Presentation
Problem-related Purpose
Problem-related Time
Data type, data quality, data volume, and relevant techniques (e.g.
structured text/tables, mental images/stories, heuristic sketch, conceptual
diagram, image/visual metaphor, knowledge map, etc.)
Semantic layer, animation, interaction, output device (size, resolution), input
device (touch panel, keyboard, mouse, etc.), network connectivity, and
communication costs/bandwidth
The domain perspective (e.g. to support statistical data analysis, to manage
digital libraries, to provide personal services support, to manage complex
documents, to aid historical data management, to manage classifications,
to visualize networks, etc.);
The task perspective (e.g. to discovery relationships/patterns from a large
volume <strong>of</strong> data points, facilitate data comparison, track/display trends over
time, illustrate structure or composition, analyze words/texts, and explore
geographical data)
Time-series data involved in a decisional problem, when <strong>the</strong> effectiveness
<strong>of</strong> a visualization solution is confirmed, when a purpose becomes relevant,
etc.
Location E.g. latitude, longitude, altitude, city, suburb, country, etc.
Physical Surroundings
Lighting, temperature, surrounding landscape, wea<strong>the</strong>r conditions, noise
levels, etc.
Movement State E.g. Speed
Knowledge Worker Types E.g. individual, team, community <strong>of</strong> practice, organization and <strong>the</strong> public
Knowledge Worker
Pr<strong>of</strong>ile
Knowledge Worker Ability
Knowledge Worker
Related Purpose
Cognitive styles, personal characteristics and preferences, educational
background, culture and social background (faith, nationality, etc.),
personality (introversive/extroversive), physical condition (disability,
left/right hands, etc.), age, gender, mood, etc.
Prior knowledge (e.g. knowledge in <strong>the</strong> problem domain, past experience
with manipulating <strong>the</strong> visualization, past experience with using <strong>the</strong>
visualization system), skill acquisition ability (i.e. novice, advanced
beginner, competent, pr<strong>of</strong>icient, expert, and master levels), etc.
E.g. to support financial analysis <strong>of</strong> last year, to support education and Elearning
in <strong>the</strong> University <strong>of</strong> Auckland, to support military debriefing, etc.)
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2.1 Problem context
Xiaoyan Bai, David White and David Sundaram
This decisional problem dimension is concerned with <strong>the</strong> contextual information with regard to <strong>the</strong>
problem situation to be supported and potential solutions. Knowledge visualizations nowadays may
be employed in many problem domains and/or disciplines to support diverse user purposes and tasks
involved in information/knowledge navigation, retrieval, query, discovery and/or interpretation. For
example, Card et al. (1999) have identified seven representative domains, namely, statistical and
categorical data management, digital library management, personal services support, complex
documents management, history management, classifications management, and networks
management. Quite <strong>of</strong>ten, real-world decisional problems span multiple application domains, instead
<strong>of</strong> merely residing within a single domain. For example, in a large utility (e.g. electricity and gas)
infrastructure company, <strong>the</strong> senior management may be interested in exploring and visualizing <strong>the</strong>
patterns and/or trends embedded in <strong>the</strong> problematic gas and electricity connections which have
incurred exceptionally high maintenance costs. This issue covers three typical application domains,
that is, statistical and categorical data management, complex documents management, and networks
management. More specifically, <strong>the</strong> application domain <strong>of</strong> statistical and categorical data
management is involved due to <strong>the</strong> need <strong>of</strong> visualizing accounting data (i.e. maintenance costs <strong>of</strong>
electricity connections and gas pipelines). Complex documents management is needed to handle <strong>the</strong>
reports <strong>of</strong> electricity connection and gas pipeline faults. Networks management is a necessity for
effectively generating map-based electricity and gas networks with problematic connections
highlighted.
2.2 Knowledge worker context
Knowledge worker context incorporates any stakeholder-related aspects that can affect <strong>the</strong> design,
development, cognition, interpretation and/or evaluation <strong>of</strong> a visualization by different kinds <strong>of</strong>
stakeholders. Along <strong>the</strong> way <strong>of</strong> accomplishing various user tasks involved in <strong>the</strong> associated problem
domains, knowledge workers may go through six learning development stages through which <strong>the</strong>y
progress to achieve higher levels <strong>of</strong> pr<strong>of</strong>iciency and/or expertise (Dreyfus & Dreyfus, 1986). These
stages are novice, advanced beginner, competent, pr<strong>of</strong>icient, expertise and master. Each learning
development stage is also associated with six mental functions, i.e. similarity recognition, aspect
recognition, decision paradigm, perspective, commitment, and monitoring. These learning
development stages and mental functions decide <strong>the</strong> abilities, cognition styles, and visualization
preferences and requirements <strong>of</strong> <strong>the</strong> knowledge workers.
Knowledge workers at different learning development levels may have different sets <strong>of</strong> tasks to
complete so as to address <strong>the</strong>ir interested problems and achieve <strong>the</strong>ir intended purpose(s), which can
lead to different requirements for knowledge visualization. More specifically, according to Dreyfus and
Dreyfus (1986), people at a beginner level are more likely to perceive and understand simple clues
appeared in a problem context and recognize very limited similar features to <strong>the</strong>ir experienced
problems. They have to depend on <strong>the</strong> available relevant rules and directions for guiding <strong>the</strong>ir
activities as well as on deliberately monitoring <strong>the</strong>ir own performance and getting feedback. The lack
<strong>of</strong> guidance on performing certain tasks or <strong>the</strong> lack <strong>of</strong> prior experiences for solving relevant problems
may cause <strong>the</strong>m to present low performance. In contrast, people with higher levels <strong>of</strong> expertise <strong>of</strong>ten
have stronger capabilities to understand and resolve problems through basing <strong>the</strong>ir judgments against
past experiences and relevant knowledge, which <strong>of</strong>ten leads to a better performance (Dreyfus &
Dreyfus, 1986). They are more likely to deal with complex problems and see through complicated
situations, decide task requirements for resolving <strong>the</strong> problems, and perform <strong>the</strong> tasks with less
monitoring efforts and more commitment to problem solving activities.
2.3 Contextual Impact on knowledge visualization design and implementation
The changing and dynamic problem and knowledge worker contexts may lead to changing
visualization requirements. For example, knowledge workers at beginner levels can generally deal
with smaller chunks <strong>of</strong> data at one time and thus normally requires visualization designs containing
<strong>the</strong> support/guidance for basic operations to accomplish a particular task. Compared to <strong>the</strong>m,
knowledge workers with higher levels <strong>of</strong> expertise are <strong>of</strong>ten able to process relatively large chunks <strong>of</strong>
data. They may not need visualizations to provide basic operation guidance but ra<strong>the</strong>r <strong>the</strong> support for
more complicated tasks such as advanced information analysis.
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Xiaoyan Bai, David White and David Sundaram
Fur<strong>the</strong>rmore, <strong>the</strong> problem and knowledge contexts may significantly influence <strong>the</strong> design and
implementation <strong>of</strong> knowledge visualizations in KMS. For instance, knowledge visualization
development is intimately coupled with mental tasks and attributes associated with different learning
development stages. Knowledge visualization design and implementation should concern to what
extent <strong>the</strong> knowledge workers rely on clearly defined decision making rules or task instructions, how
well <strong>the</strong>y are aware <strong>of</strong> <strong>the</strong> underlying problem situations, how easily <strong>the</strong>y can recognize similarities
between <strong>the</strong> problem under investigation and <strong>the</strong> problems that <strong>the</strong>y resolved in <strong>the</strong> past, how
accurately <strong>the</strong>y may identify and understand <strong>the</strong> relevant task requirements from <strong>the</strong> similarities, and
how effectively <strong>the</strong>y can monitor <strong>the</strong>ir own performance. In addition, visualization system involved in
knowledge management should <strong>of</strong>fer adequate support for personalization and customization so as to
better serve different knowledge workers. KMS should also provide appropriate adaptability
mechanisms to assist <strong>the</strong> knowledge workers with <strong>the</strong>ir transition from beginners through to
masters/experts.
To address <strong>the</strong> complexities involved in knowledge visualization context, we introduce contextual
adaptive visualization environment in <strong>the</strong> following section.
3. Contextual Adaptive Visualization Environment (CAVE)
We define Contextual Adaptive Visualization Environment as a context-sensitive, adaptive platform
that helps knowledge workers to continuously monitor <strong>the</strong> dynamic context <strong>of</strong> <strong>the</strong>ir interested problem,
sense and analyze <strong>the</strong> changes in <strong>the</strong> context, and respond to <strong>the</strong> problem by utilizing data, models
(problem and visual), solvers and scenarios to create and manage effective visual compositions
(Figure 2). The responses by <strong>the</strong> system and by <strong>the</strong> knowledge workers could be at different levels. It
could be a parametric change (single loop learning), introduction/modification/deletion <strong>of</strong> variables <strong>of</strong>
model (double loop learning), and/or transformational changes at a deep and broad level (triple loop
learning). The key purpose <strong>of</strong> CAVE is to sense, analyze and respond to <strong>the</strong> changes in knowledge
visualization context. Fur<strong>the</strong>rmore, CAVE mediates between <strong>the</strong> problem and <strong>the</strong> knowledge workers
through <strong>the</strong> explicit provision <strong>of</strong> action and presentation languages.
Figure 2: Contextual adaptive visualization environment model
To address <strong>the</strong> contextual dynamics and complexities, CAVE provides flexible support for (1)
creating/manipulating/transforming/improving/disposing visualization solutions and (2) maintaining <strong>the</strong>
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Xiaoyan Bai, David White and David Sundaram
effectiveness <strong>of</strong> <strong>the</strong> solutions within <strong>the</strong> dynamic contexts. More specifically, to adapt visualizations to
suit <strong>the</strong> changing knowledge visualization context, new visualizations are <strong>of</strong>ten required to be created.
Accordingly, visualization systems to support knowledge management need to enable a knowledge
worker to build new visualizations in a flexible fashion. The knowledge worker should be able to
develop new visualizations ei<strong>the</strong>r from scratch or based on existing reusable visualization
components. A visualization, which can fulfil a particular purpose at one point in time, may not be able
to achieve <strong>the</strong> same level effectiveness when <strong>the</strong> underlying problem and knowledge worker contexts
change over time and space. Thus, <strong>the</strong> visualization systems need to <strong>of</strong>fer users <strong>the</strong> capabilities <strong>of</strong>
flexibly modifying, customizing and enhancing visualizations to suit <strong>the</strong> changing requirements.
These system requirement can be fur<strong>the</strong>r clarified by applying Chi and Riedl (1998)’s data state
model. Chi (2000) opined that a visualization technique can be decomposed into a set <strong>of</strong> data stages
and operations. Data operations are composed <strong>of</strong> within-stage operators (i.e. value, analytical and
visualization stage operators) and between-stage transformations (i.e. data, visualization and visual
mapping transformations). Visualization creation is about selecting and integrating appropriate withinstage
operators accompanied with proper transformations. Visualization modification/customization/
enhancement can be conducted through adjusting <strong>the</strong>se within-stage and between-stage operations,
e.g. selecting <strong>the</strong> desired visual representations, changing <strong>the</strong> colour or <strong>the</strong> hue, adjusting
transformation parameters, etc.
In order to fulfil <strong>the</strong> requirements posed above, we developed and proposed a framework (section 4)
which is fur<strong>the</strong>r elucidated through an implementation (section 5).
4. Contextual Adaptive Visualization Environment Framework
The Contextual Adaptive Visualization Environment (CAVE) framework builds upon <strong>the</strong> CAVE model
discussed in <strong>the</strong> previous section. As illustrated in Figure 3, a knowledge visualization solution
comprises four fundamental building blocks, that is, data, models, solvers and scenarios. These
building blocks toge<strong>the</strong>r assist a knowledge worker in translating a decisional problem into a form that
is recognisable and manageable by CAVE and ultimately by <strong>the</strong> knowledge worker. This
understanding enables <strong>the</strong> knowledge worker to create visualization oriented data, models, solvers
and scenarios and adapt <strong>the</strong>m into a form that effectively responds to <strong>the</strong> contextual changes. These
components are managed and connected toge<strong>the</strong>r by a central component – kernel – which enables
<strong>the</strong> communication among different components. All <strong>the</strong>se components cooperate toge<strong>the</strong>r to assist
with various tasks involved in knowledge generation, knowledge representation, knowledge
presentation, visualization interaction and visualization evaluation.
CAVE may incorporate two broad types <strong>of</strong> data, that is, user data required by <strong>the</strong> system execution,
and <strong>the</strong> data depicting <strong>the</strong> characteristics <strong>of</strong> problem and knowledge worker contexts. They also
involve two essential groups <strong>of</strong> models for accomplishing knowledge creation and visualization.
Accordingly, <strong>the</strong>re are two types <strong>of</strong> solvers for manipulating <strong>the</strong>ir corresponding type <strong>of</strong> models. Data,
model and solver can be integrated to form a scenario. Among <strong>the</strong>se CAVE components, <strong>the</strong> problem
related data, models, solvers and scenarios are used to generate knowledge while <strong>the</strong> visualization
technique related components manages <strong>the</strong> representation and presentation <strong>of</strong> <strong>the</strong> knowledge. More
specifically, <strong>the</strong> problems related components are responsible for enhancing <strong>the</strong> quality, relevance
and effectiveness <strong>of</strong> <strong>the</strong> source data in terms <strong>of</strong> how well <strong>the</strong>y can address <strong>the</strong> decisional problem <strong>of</strong>
interest. In contrast, <strong>the</strong> visualization technique related components define and manage <strong>the</strong> way <strong>of</strong>
how <strong>the</strong> ready to be visualized data sets are transformed into appropriate views so as to adapt to <strong>the</strong>
dynamic contexts. A knowledge visualization solution is made up <strong>of</strong> appropriate problem and
visualization technique scenarios.
This framework is used to guide <strong>the</strong> design and implementation <strong>of</strong> a contextual adaptive visualization
environment, which is fur<strong>the</strong>r elucidated in <strong>the</strong> subsequent section.
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Xiaoyan Bai, David White and David Sundaram
Figure 3: Contextual adaptive visualization environment framework
5. Implementation
To validate <strong>the</strong> concepts, models and framework <strong>of</strong> CAVE, we implemented a vertical prototypical
system against <strong>the</strong> CAVE framework through utilizing a set <strong>of</strong> Micros<strong>of</strong>t technologies such as Bing
map, WPF, ADO.NET entity framework, and SQL Server. To help with demonstrating <strong>the</strong> support <strong>of</strong>
<strong>the</strong> prototype, we introduce two cases, that is, Napoleon’s army march to Russia, and child statistics.
The former case resides more in <strong>the</strong> domain <strong>of</strong> historical data management while <strong>the</strong> latter is mainly
about statistical data analysis. In <strong>the</strong> Napoleon’s march case, we focus on exploring <strong>the</strong> relationships
between army size reduction and its potential causing factors such as temperature, speed, location
altitude, enemy size, available resources at each location, etc. In <strong>the</strong> child statistics case, we
concentrate on discovering patterns that exist among a variety <strong>of</strong> education related indicators in
different countries, e.g. primary school completion rate, expenditure per student, and literacy rate <strong>of</strong>
adult. Both cases require visualizing spatial temporal multi-dimensional data.
When <strong>the</strong> problem situation changes from one case to ano<strong>the</strong>r, <strong>the</strong> CAVE prototype allows
knowledge workers to create different problem and visualization scenarios for different cases. For
visualizing <strong>the</strong> invasion and retreat related information <strong>of</strong> Napoleon’s main troop, in 1869 Charles
Joseph Minard published a map to portray <strong>the</strong> defeat <strong>of</strong> Napoleon’s army in Russia (Tufte, 2006).
Building on top <strong>of</strong> Minard’s work, we created an integrated problem-visualization scenario (Figure 4)
to illustrate how <strong>the</strong> army size (indicated by <strong>the</strong> width <strong>of</strong> <strong>the</strong> route band) diminishes as <strong>the</strong>
temperature and moving speed vary along <strong>the</strong> route in an animated fashion (Figure 5). In contrast, <strong>the</strong>
problem-visualization scenario (Figure 6) we created for <strong>the</strong> child statistics case presents <strong>the</strong> trends <strong>of</strong>
multiple education indicators in a static way (Figure 7).
Figure 4: An integrated problem-visualization scenario for Napoleon’s march case
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Xiaoyan Bai, David White and David Sundaram
Figure 5: An animated visualization for exploring causes for Napoleon’s army death
Figure 6: An integrated problem-visualization scenario for child statistics case
Figure 7: A static visualization for aiding <strong>the</strong> pattern discovery <strong>of</strong> child statistical data
6. Conclusion
Visualisations are integral for <strong>the</strong> creation, transfer and sharing <strong>of</strong> knowledge. Knowledge
visualization context is complex and dynamic in nature. Such complexity is caused by <strong>the</strong> extensive
diverse and changing factors involved in <strong>the</strong> problem and stakeholder contexts. The dynamic and
changing problem and knowledge worker contexts <strong>of</strong>ten lead to changing visualization requirements
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Xiaoyan Bai, David White and David Sundaram
that are ill supported by <strong>the</strong> visualizations systems involved in knowledge management. One major
challenge brought by <strong>the</strong> context complexity is how to enable knowledge workers to flexibly adapt
knowledge visualizations to <strong>the</strong> changing and evolving knowledge visualization context and maintain
<strong>the</strong>ir effectiveness over time and space. To help with addressing contextual dynamics and complexity,
we delineated knowledge visualization context and proposed <strong>the</strong> concept <strong>of</strong> a contextual adaptive
visualization environment. The ideas involved in CAVE were fur<strong>the</strong>r explicated through CAVE models
and framework. These proposed artefacts are validated through <strong>the</strong> implementation <strong>of</strong> CAVE. The
CAVE prototype is demonstrated through how it supports contextual changes at both macro and
micro levels. It deserves to be that <strong>the</strong> current prototype has only been tested against a limited
number <strong>of</strong> knowledge visualization context changes. Identifying and categorizing representative
contextual changes as well as exploring and improving <strong>the</strong> support <strong>of</strong>fered by <strong>the</strong> CAVE prototype will
be accomplished in our future research.
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in <strong>Proceedings</strong> <strong>of</strong> <strong>the</strong> 24th International Cartographic <strong>Conference</strong>s, Santiago.
64

Knowledge Production and Transfer: Advantages and
Costs
Simona-Clara Bârsan 1 , Mihaela-Georgia Sima 2 and Dan Săvescu 3
1
Research Institute for Analytical Instrumentation Cluj-Napoca, Technology
Transfer Centre
2
Bucharest Academy <strong>of</strong> Economic Studies
3
Transilvania University <strong>of</strong> Braşov, Faculty <strong>of</strong> Product Design and Environment
simonaclara_barsan@yahoo.com;
georgiasima@yahoo.com;
dsavescu@unitbv.ro;
Abstract: Since intellectual capital is a key factor in company’s pr<strong>of</strong>itability and, on <strong>the</strong> o<strong>the</strong>r hand, knowledge
assets provides a sustainable competitive advantage, it is fundamental to know how to evaluate <strong>the</strong>se intangible
assets in order to be able to <strong>of</strong>fer a support for <strong>the</strong> management’s decision whe<strong>the</strong>r to allocate investments into
achieving <strong>the</strong>m or to continue <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> market for o<strong>the</strong>r assets. It is also important for <strong>the</strong> inventor,
willing to sell his invention, since he or she needs a correct value diagnosis versus <strong>the</strong> alleged market price. In
this paper, <strong>the</strong> authors have decided to present some methods <strong>of</strong> knowledge evaluation, knowledge converted
into industrial property (IP) as part <strong>of</strong> a company’s intangible assets. Through IP registration and technology
transfer, this knowledge ends up producing benefits for its creators, as well as for <strong>the</strong>ir buyers, so, it should be
evaluated properly. We intended to write a practical paper, so we have chosen to present a case study consisting
<strong>of</strong> a technology transfer process <strong>of</strong> 5 knowledge production that we have selected from our portfolio. These are
recuperative and eco-friendly technologies belonging to a research institute and transferred to economic agents
willing to develop <strong>the</strong>ir business by applying <strong>the</strong> above mentioned technologies. Related to <strong>the</strong> 5 recuperative
and eco-friendly technologies, <strong>the</strong> paper presents <strong>the</strong> production costs, <strong>the</strong>ir preparation for transfer stage
(negotiation phase), finally being sold under only one tranche <strong>of</strong> royalties, as well as <strong>the</strong> main advantages <strong>of</strong>
achieving and utilizing <strong>the</strong> technologies, including <strong>the</strong> estimation <strong>of</strong> <strong>the</strong> future pr<strong>of</strong>its for <strong>the</strong> buyer companies and
<strong>the</strong> figures obtained. One <strong>of</strong> <strong>the</strong> most interesting things to be observed is <strong>the</strong> way <strong>the</strong> negotiation phase evolves:
<strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> buyer that is aware <strong>of</strong> <strong>the</strong> technology’s value (since he had all <strong>the</strong> necessary evaluation
means), <strong>the</strong> seller’s lack <strong>of</strong> knowledge <strong>of</strong> <strong>the</strong> seller, willing to sell for less than he could, happy he has found a
buyer and being in need <strong>of</strong> recovering his initial investment (<strong>the</strong> amount <strong>of</strong>fered was an important one, but still
not <strong>the</strong> right one), <strong>the</strong> role <strong>of</strong> <strong>the</strong> technology transfer intermediary in balancing <strong>the</strong> positions and finally in
transferring <strong>the</strong> inventions. Last but not least, we emphasize <strong>the</strong> role <strong>of</strong> funds sustaining knowledge production.
Keywords: knowledge, evaluation methods, intellectual property, technology transfer
1. Introduction
“Intellectual capital is intellectual material – knowledge, information, intellectual property, experience –
that can be put to use to create wealth” (Stewart, 1997). It is formed by its human capital (<strong>the</strong> knowhow
<strong>of</strong> its employees) and by its structural capital (its organization and intellectual property).
The major components <strong>of</strong> intellectual capital are <strong>the</strong> human capital (which is <strong>the</strong> potential <strong>of</strong> <strong>the</strong>
company’s intangible value) and <strong>the</strong> intellectual assets and intangible properties.
The human capital is represented by <strong>the</strong> sum <strong>of</strong> knowledge, abilities and qualities <strong>of</strong> <strong>the</strong> employees <strong>of</strong>
a company, as well as by <strong>the</strong> culture, values and principles developed within that company.
In what <strong>the</strong> structural capital is concerned, it includes <strong>the</strong> whole complex <strong>of</strong> data bases, s<strong>of</strong>tware,
organizational structures, trademarks, patents, as well as o<strong>the</strong>r factors and assets supporting <strong>the</strong>
productivity <strong>of</strong> <strong>the</strong> employees <strong>of</strong> <strong>the</strong> company.
For instance, <strong>the</strong> structural capital is composed <strong>of</strong> <strong>the</strong> capital represented by <strong>the</strong> clients’ portfolio and
<strong>the</strong> organizational capital <strong>of</strong> <strong>the</strong> company, <strong>the</strong> latter being composed <strong>of</strong> <strong>the</strong> company’s innovation
capital (intellectual property, o<strong>the</strong>r intellectual assets) and <strong>the</strong> capital invested in <strong>the</strong> production
processes (Baruch 2001).
A distinctive category is represented by those intangible assets <strong>the</strong> existence <strong>of</strong> which is legally
protected, such as intellectual property: invention or innovation patents, recipes, industrial designs,
utility models, computer s<strong>of</strong>tware, know-how, brands and trademarks, copyright, franchise and
licensing agreements etc. Their acknowledgement and legal protection represents a competitive
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advantage for <strong>the</strong> owner company and <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> value creation for <strong>the</strong> company
shareholders is performed from this perspective.
These assets are generally <strong>the</strong> result <strong>of</strong> <strong>the</strong> research and development programs conceived by <strong>the</strong>
company. However, not all research and development expenditures made by <strong>the</strong> company may result
in <strong>the</strong> generation <strong>of</strong> future benefits. Thus, <strong>the</strong> problem <strong>of</strong> evaluating <strong>the</strong> surplus value brought by
<strong>the</strong>se expenses to <strong>the</strong> company is raised.
The research and development activity generally implies two stages: <strong>the</strong> stage <strong>of</strong> generating
knowledge and <strong>the</strong> stage <strong>of</strong> technological progress, generated during <strong>the</strong> first stage. The second
stage will become applicable by implementing <strong>the</strong> generated knowledge into <strong>the</strong> products or services
made by <strong>the</strong> company or into its production processes. The transfer <strong>of</strong> <strong>the</strong> knowledge generated
within <strong>the</strong> research process into <strong>the</strong> products or <strong>the</strong> production processes <strong>of</strong> <strong>the</strong> company is nei<strong>the</strong>r
simultaneous nor complete. In some cases, this transfer cannot take place and <strong>the</strong> only way <strong>of</strong> turning
this obtained knowledge into money remains to sell it as know-how.
For estimating <strong>the</strong> value <strong>of</strong> <strong>the</strong> research and development projects that are in <strong>the</strong> transfer stage
knowledge generation, an evaluation method based on costs is generally used and consists <strong>of</strong>
estimating <strong>the</strong> total necessary costs for recreating <strong>the</strong> technology (Articles 54 and 55 <strong>of</strong> International
Accounting Standards - IAS 38).
All <strong>the</strong> costs implied by <strong>the</strong> research performance are taken into account: <strong>the</strong> wage costs with those
who are directly involved into <strong>the</strong> technology development; running expenditures representing <strong>the</strong> raw
material and components purchase, services supplied by third parties that are necessary for
technology development; <strong>the</strong> cost <strong>of</strong> <strong>the</strong> prototypes made outside <strong>the</strong> company that are necessary to
recreate <strong>the</strong> technology etc.
The evaluation <strong>of</strong> <strong>the</strong> research projects from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> future pr<strong>of</strong>it <strong>the</strong>y might generate
for <strong>the</strong> company is difficult. From <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> management’s decision to invest, <strong>the</strong>
techniques developed for <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong> actual options can be used with <strong>the</strong> purpose <strong>of</strong>
evaluating <strong>the</strong> value <strong>of</strong> <strong>the</strong> research and development projects.
The research and development projects resulting in intangible assets like: invention patents, computer
s<strong>of</strong>tware know-how, brand and trademarks, copyrights etc. are evaluated by taking into account <strong>the</strong>
surplus value or <strong>the</strong> cost savings that can be obtained by <strong>the</strong> company. For example, <strong>the</strong> invention
patents can be evaluated by taking into account <strong>the</strong> royalty savings obtained by <strong>the</strong> company that
owns <strong>the</strong>se patents. The method <strong>of</strong> evaluating <strong>the</strong> intangible assets on <strong>the</strong> basis <strong>of</strong> royalty or o<strong>the</strong>r
cost savings (made in order to attract new clients, to keep <strong>the</strong> key staff, for advertising, financing,
personnel training etc.) can also be used in <strong>the</strong> case <strong>of</strong> trademarks, lists <strong>of</strong> clients, labour
agreements, non-competition agreements, assets like favourable financing conditions, skilled labour.
2. Evaluation methods
Inventions, as objects <strong>of</strong> patent applications or granted ones, may be commercialized as well as any
o<strong>the</strong>r material goods or properties.
No matter how great <strong>the</strong> market need for a product is, it is not acceptable that <strong>the</strong> patent holder,
toge<strong>the</strong>r with its inventor should agree to rebate <strong>the</strong> amount <strong>of</strong> use, which must satisfy <strong>the</strong>
expectations <strong>of</strong> <strong>the</strong> market.
An important role in determining whe<strong>the</strong>r <strong>the</strong> commercialization <strong>of</strong> a product leads to market success
has <strong>the</strong> comparison <strong>of</strong> <strong>the</strong> product estimated sales price with <strong>the</strong> estimated manufacturing costs and,
if <strong>the</strong>re is any pr<strong>of</strong>it as well, <strong>the</strong>n <strong>the</strong>re is a real hope for having success.
Success shows up only when <strong>the</strong> new product has obvious and important technical advantages as
compared with <strong>the</strong> existing products on <strong>the</strong> market.
2.1 Analysis <strong>of</strong> a patent
Usually, <strong>the</strong> analysis <strong>of</strong> a patent consists <strong>of</strong>:
evaluation <strong>of</strong> <strong>the</strong> benefit obtained or estimated regarding <strong>the</strong> application <strong>of</strong> <strong>the</strong> patented invention;
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evaluation <strong>of</strong> <strong>the</strong> patenting costs, starting with <strong>the</strong> deposit data <strong>of</strong> <strong>the</strong> patent application and
continuing with <strong>the</strong> whole period <strong>the</strong> patent is in force;
evaluation <strong>of</strong> <strong>the</strong> income resulted from patenting;
analysis <strong>of</strong> <strong>the</strong> patent life;
evaluation <strong>of</strong> market.
2.2 Methods <strong>of</strong> calculation
During <strong>the</strong> evaluation <strong>of</strong> patent rights, <strong>the</strong> technical superiority <strong>of</strong> <strong>the</strong> invention reported to <strong>the</strong> known
state <strong>of</strong> <strong>the</strong> art is less important, <strong>the</strong> economic criteria standing in <strong>the</strong> forefront and attention being
paid to <strong>the</strong> generated pr<strong>of</strong>it.
One presents below an evaluation example <strong>of</strong> patent rights, by using equation (1) with <strong>the</strong> help <strong>of</strong>
which <strong>the</strong> material value <strong>of</strong> <strong>the</strong> rights deriving from a patent can be estimated (Savescu, 2009):
Vdr.br. = (Pn . TVP - ∑ C). KX (1)
in which:
Vdr.br. – material value <strong>of</strong> <strong>the</strong> rights arising from a patent;
Pn – net pr<strong>of</strong>it;
TVP – application time <strong>of</strong> <strong>the</strong> patented invention;
∑ C – <strong>the</strong> sum <strong>of</strong> <strong>the</strong> costs that form <strong>the</strong> invention commercial value, namely R&D, patenting,
advertising and publicity, market promotion up to <strong>the</strong> patented invention commercialization and those
for possible litigations.
KX – coefficient that represents <strong>the</strong> weighted average <strong>of</strong> <strong>the</strong> influence <strong>of</strong> some factors, such as:
invested capital;
social credit <strong>of</strong> <strong>the</strong> company;
management ability;
technical level <strong>of</strong> <strong>the</strong> specialized personnel;
ability to sell;
attractiveness and impression on public <strong>of</strong> design and quality assurance <strong>of</strong> <strong>the</strong> product promoted
through its brand;
influence <strong>of</strong> <strong>the</strong> superior technical characteristics <strong>of</strong> <strong>the</strong> patented product on <strong>the</strong> market (as
compared with similar existing products).
Ano<strong>the</strong>r evaluation method is <strong>the</strong> income capitalization method, whose equation is presented below
(Savescu, 2009):
Patent value = (∑ Operating revenues) / Capitalization rate (2)
The capitalization rate depends on <strong>the</strong> application field <strong>of</strong> <strong>the</strong> patented invention.
For dynamic fields, such as informatics, biotechnologies, nanotechnologies, <strong>the</strong> capitalization rate has
relatively small values, <strong>of</strong> 4...10%, while as for less dynamic fields (automotive, domestic appliances)
<strong>the</strong> capitalization rate is 15...25%. For construction materials, metallurgy and petrochemical industry,
<strong>the</strong> capitalization rate is even bigger, respectively 20...35% (Andriessen, 2004).
2.3 IP rights evaluation deriving from <strong>the</strong> patent
Some considerations regarding <strong>the</strong> patent rights evaluation are presented below:
<strong>the</strong> retail price <strong>of</strong> a product is based on <strong>the</strong> company level price, plus N% <strong>of</strong> it and, in this case,
<strong>the</strong> compensation is given by <strong>the</strong> value <strong>of</strong> "N ";
statistical method might be applied for obtaining standard compensations that are specific to each
area <strong>of</strong> activity and which may be, for example, 2 ... 5% in <strong>the</strong> chemical industry, 2 … 10% in <strong>the</strong>
pharmaceutical industry and 0,5 ... 5% in <strong>the</strong> electric industry;
to set up reasonable financial rights represents a lay intuition, being based on reasonable and
wise policies in business. The rule governing such evaluations and guidance can be obtained
through in-depth research on <strong>the</strong>se topics.
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2.4 Rights transfer
Simona-Clara Bârsan, Mihaela-Georgia and Dan Săvescu
The rights have been fully transmitted: <strong>the</strong> right to <strong>the</strong> patent and <strong>the</strong> IP rights deriving from owning
<strong>the</strong> patent, through exclusive licensing.
In addition to <strong>the</strong> granted patent, one has negotiated that <strong>the</strong> patent holder should provide o<strong>the</strong>r
technical information as well, such as information and/ or technical knowledge required to attain a
certain level <strong>of</strong> performance, alone or associated with:
know-how;
formulas, recipes, technical conditions and dates;
work instructions;
technical assistance;
education aimed at executive personnel qualification;
technological capacities.
3. Technology transfer – case study
In this study, we decided to analyze <strong>the</strong> importance <strong>of</strong> knowledge creation and transfer, and
especially if finally presents such advantages that overcome by far <strong>the</strong> costs and <strong>the</strong> efforts involved.
3.1 Background-entry data
The technology transfers have been carried out between: a research institute, specialised in
electrochemistry and having among its main activities <strong>the</strong> denocivization and treatment <strong>of</strong> hasardous
and non-hasardous waste and an association <strong>of</strong> 3 economic agents, working in <strong>the</strong> fields <strong>of</strong>
manufacturing <strong>of</strong> special concrete bricks, chemical products packaging and galvanization.
The research institute is <strong>the</strong> author <strong>of</strong> <strong>the</strong> transferred technologies, that address to <strong>the</strong> internal market
and were elaborated and perfectioned due to <strong>the</strong> funds provided by Romanian R&D programes.
From <strong>the</strong> beginning, <strong>the</strong> target groups for <strong>the</strong> elaborated technologies were thought those activating in
<strong>the</strong> electric, electrotechnical, electronic, chemical and electrochemical industries, considered as
significant envirnomental pollutants.
Until making <strong>the</strong> technology transfers from <strong>the</strong> research institute, <strong>the</strong> companies were highly poluting
<strong>the</strong> environment with <strong>the</strong> resulted wastes <strong>of</strong> <strong>the</strong>ir production activities, namely <strong>the</strong> diluted liquid waste
with heavy metal ions content (copper and silver), galvanic slums and solid waste with high contents
<strong>of</strong> copper.
The technologies transferred refer to:
recovery from solid and liquid waste <strong>of</strong> some relatively expensive and limited as natural resource
materials (copper and silver), as well as
turning into hazardous waste <strong>of</strong> <strong>the</strong> galvanic sludge resulted from specific industrial activities.
Toge<strong>the</strong>r with <strong>the</strong> technologies that were transferred, four corresponding installations were set up as
well, that allowed both a correlated and an independent functioning <strong>of</strong> <strong>the</strong> four technologies. After <strong>the</strong>
installations became functional, checking <strong>the</strong> operational parameters as well, <strong>the</strong> „zero” series,
according to <strong>the</strong> transferred technologies, followed.
In order to assess <strong>the</strong> impact that <strong>the</strong> technologies implementation would have on environment, an
environmental impact study was made. The measured global pollution index (GPI) had a value
ranging between 1 and 2, meaning that „<strong>the</strong> setting-up <strong>of</strong> <strong>the</strong> proposed objective will affect <strong>the</strong>
environment within acceptable limits”. The conclusion <strong>of</strong> <strong>the</strong> carried-out study was that <strong>the</strong> possibility
<strong>of</strong> affecting <strong>the</strong> environmental factors and population health because <strong>of</strong> applying <strong>the</strong> four technologies
was reduced, even negligible. So, <strong>the</strong> negative effects upon environment are minor, while as <strong>the</strong>
positive effects (reflected in low operational costs and a large range <strong>of</strong> utilization possibilities <strong>of</strong> <strong>the</strong>
transferred technologies) are considerable.
The transferred technologies are briefly presented below.
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3.2 Technology for obtaining high purity copper from solid waste with high content
<strong>of</strong> copper
The utilised raw material is represented by solid waste with high content <strong>of</strong> copper (metal residues,
bar ends, winding wires etc.), finally resulting, by applying <strong>the</strong> above mentioned technology,
electrolytic copper cathodes with purities <strong>of</strong> 99% or even higher (Linder et al, 1991). These cathodes
are commonly used in <strong>the</strong> electrical and electro technical industry, in electronics and
telecommunication, electrochemistry.
The carried out market studies have showed that <strong>the</strong> internal market necessary <strong>of</strong> electrolytic copper
is around tens <strong>of</strong> thousands annually. At <strong>the</strong> same time, in Romania <strong>the</strong>re is an important amount <strong>of</strong>
untapped copper waste. The lack <strong>of</strong> a domestic manufacturer determines <strong>the</strong> cover <strong>of</strong> <strong>the</strong> necessary
<strong>of</strong> electrolytic copper from external sources, while <strong>the</strong> emergence <strong>of</strong> a domestic manufacturer <strong>of</strong>
electrolytic copper cathodes would allow acquisitions at lower prices, under <strong>the</strong> conditions <strong>of</strong> <strong>the</strong>ir
manufacturing at <strong>the</strong> same quality with those <strong>of</strong> imported cathodes.
The technology for obtaining electrolytic copper from solid waste with high content <strong>of</strong> copper is based
on <strong>the</strong> electrolysis <strong>of</strong> some acid copper sulphate solutions with soluble anodes. One works in batches
(<strong>the</strong> medium time is approx. 12 days, out <strong>of</strong> which <strong>the</strong> first 2 days are for preparing <strong>the</strong> batch). The
quantity <strong>of</strong> electrolytic copper / batch may vary depending on <strong>the</strong> market and may be adjusted by
modifying <strong>the</strong> number <strong>of</strong> electrodes existing in each vat and, respectively, <strong>the</strong> number <strong>of</strong> vats in a
batch. The copper sulphate (bluestone) is <strong>the</strong> resulted by-product that can be used in various fields
(agriculture – viticulture, fruit trees, vegetable growing, chemical industry, electrochemical industry –
electroplating, textile and lea<strong>the</strong>r industry).
As regarding costs, this technology has two major advantages:
<strong>the</strong> raw material (solid waste with high content <strong>of</strong> copper) presents a copper content / costs ratio
superior to that corresponding to <strong>the</strong> raw material used in classical technologies (copper ore), that
leads to lower raw material costs;
by using titanium anodic baskets (which also represent <strong>the</strong> main original element <strong>of</strong> this
technology), one eliminates <strong>the</strong> stage <strong>of</strong> previous melting <strong>of</strong> <strong>the</strong> copper waste, which requires
high energy consumption.
3.3 Technology for recovering copper ions from diluted solutions
In <strong>the</strong> case <strong>of</strong> this technology, <strong>the</strong> raw material is represented by <strong>the</strong> diluted solutions that contain
copper ions (for example, <strong>the</strong> washing waters in <strong>the</strong> electroplating process). This industrial waste
cannot be discharged in environment before a preliminary processing, so as to remove those ions.
The dilution <strong>of</strong> <strong>the</strong> respective solutions (that contain less than 10 g/l copper ions) generates two sets
<strong>of</strong> problems: on <strong>the</strong> one hand, <strong>the</strong> ions extraction is difficult and expensive (very low productivity) and,
on <strong>the</strong> o<strong>the</strong>r hand, a very import amount <strong>of</strong> used solution is necessary to be transported, reported to
<strong>the</strong> metal ions content (Platona, 1997). The solution <strong>of</strong>fered to this problem by this technology is
represented by <strong>the</strong> purification installation, which is a mobile one, being thus able to be moved to <strong>the</strong>
client’s headquarters, thus avoiding <strong>the</strong> transport <strong>of</strong> some large amounts <strong>of</strong> used solutions, potentially
dangerous. The diluted solution containing copper ions is circulated in <strong>the</strong> installation and <strong>the</strong> resulted
purified solution is recycled in <strong>the</strong> client’s technological process. The processing time depends on <strong>the</strong>
concentration and composition <strong>of</strong> <strong>the</strong> respective solution.
The resulted copper powder can be utilised for sintering various parts (strip conductors, bearings).
By applying this technology, one ensures <strong>the</strong> management <strong>of</strong> <strong>the</strong> diluted liquid waste, containing
copper ions, due to <strong>the</strong> original and very ingenious method <strong>of</strong> advanced purifying diluted solutions.
3.4 Technology for recovering silver ions from diluted solutions
In this case, <strong>the</strong> utilised raw material is represented by diluted solutions containing silver ions, such as
<strong>the</strong> washing waters in photo labs (Florea, 2010). As already presented in <strong>the</strong> previous technology,
nei<strong>the</strong>r this waste can be discharged in environment, before a preliminary processing so as to remove
<strong>the</strong> silver ions. The dilution <strong>of</strong> <strong>the</strong> respective solutions (containing less than 10 g/l silver ions)
generates special problems, <strong>the</strong> ion extraction being difficult and expensive (productivity is very low
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and <strong>the</strong> solutions have a complex composition) (Dubitski, 1992).
The technology is based on <strong>the</strong> electrolysis <strong>of</strong> <strong>the</strong> solutions that are to be decontamination, collected
from current clients, after a preliminary treatment. The methods used are electrochemical, specific for
accelerating <strong>the</strong> electrolysis process. After an advanced recovery <strong>of</strong> <strong>the</strong> silver ions, <strong>the</strong> ions remained
in <strong>the</strong> solution precipitate and are removed through filtration. One works in batches (<strong>the</strong> average time
does not exceed 8 hours and depends on <strong>the</strong> concentration and composition <strong>of</strong> <strong>the</strong> respective
solution). The purified solution is finally discharged into <strong>the</strong> sewage system.
The application <strong>of</strong> this technology provides a superior management <strong>of</strong> <strong>the</strong> diluted liquid waste,
containing silver ions, due to <strong>the</strong> original advanced purification method <strong>of</strong> <strong>the</strong> solution. The resulted
metallic silver (small amounts) can be sold as such.
3.5 Technology for processing galvanic sludge in hazardous waste (19 03 05
category)
The high toxic potential <strong>of</strong> <strong>the</strong> galvanic sludge makes <strong>the</strong> latter improper for direct discharge or to be
deposited as such in environment and <strong>the</strong>ir very complex composition requires a complicated,
laborious and expensive processing so as to be decontamination. Thus, <strong>the</strong> galvanic sludge collected
from clients, toge<strong>the</strong>r with <strong>the</strong> 3 previous presented technologies, are neutralized (are brought to a
neutral pH), <strong>the</strong>n are embedded (toge<strong>the</strong>r with o<strong>the</strong>r components) in <strong>the</strong> composition <strong>of</strong> some special
concrete tiles (Untea, 2002).
The galvanic sludge processing technology in hazardous waste is original, simple and cheap and
involves <strong>the</strong> embedment <strong>of</strong> <strong>the</strong> galvanic sludge in stable structures, namely, special concrete tiles,
which can be used, for example, to stabilize and fix <strong>the</strong> waste or tailing dumps.
3.6 Transfer <strong>of</strong> <strong>the</strong> photonic field heating / drying technology, with separation <strong>of</strong>
mediums
Nowadays, <strong>the</strong> economic agents working in <strong>the</strong> shipbuilding (shipyards) or in <strong>the</strong> machinery and
equipments field, as well as those from <strong>the</strong> service stations, bodywork / paint etc., use hot air for
drying <strong>the</strong> fresh painted surfaces.
The technology <strong>of</strong> heating / drying in photonic field, with separation <strong>of</strong> mediums, is mainly based on
<strong>the</strong> process <strong>of</strong> focusing <strong>the</strong> radiation with colour temperature <strong>of</strong> a halogen lamp filament (Untea,
2002).
Thus, one can heat / dry at high temperatures materials <strong>of</strong> surfaces with no direct contact between
<strong>the</strong> heating element and <strong>the</strong> heated object. The heating element can be situated outside <strong>the</strong> working
area, being in this way separated from <strong>the</strong> possible existing reactive / oxidant atmosphere. According
to <strong>the</strong> clients’ necessities, <strong>the</strong> technology can be very easily adapted for o<strong>the</strong>r drying / heating
processes, such as preparing various surfaces for technological processes, drying / heating in food
industry.
Outline:
So as to assess <strong>the</strong> impact that <strong>the</strong> technologies implementation would have on environment, an
environmental impact study was made. The conclusion <strong>of</strong> <strong>the</strong> carried-out study was that <strong>the</strong> possibility
<strong>of</strong> affecting <strong>the</strong> environmental factors and population health because <strong>of</strong> setting-up <strong>the</strong> photonic field
generators workshop, as well as <strong>the</strong> applying, at industrial scale, <strong>of</strong> <strong>the</strong> technology for surfaces
heating / drying in photonic field was reduced, even negligible. So, <strong>the</strong> negative effects upon
environment are minor, while as <strong>the</strong> positive effects, reflected in reduced operational costs and large
range <strong>of</strong> utilization possibilities <strong>of</strong> utilization <strong>of</strong> <strong>the</strong> product and transferred technology, are
considerable.
As a result, it is advisable to accept, even to encourage <strong>the</strong> promotion <strong>of</strong> <strong>the</strong> technology for heating /
drying <strong>of</strong> painted surfaces in photonic field, as well as <strong>the</strong> production <strong>of</strong> <strong>the</strong> necessary equipment,
namely, <strong>the</strong> photonic field generator. Its maintenance and reparations are simple, with relatively
reduced costs. The main problem that may occur in <strong>the</strong> generator functioning is represented by <strong>the</strong>
mirrors distortion (because <strong>of</strong> <strong>the</strong> high operating temperature, as well as <strong>the</strong> great temperature
variations) and <strong>the</strong> loss <strong>of</strong> <strong>the</strong>ir luster, which leads to an imprecise focalisation <strong>of</strong> <strong>the</strong> photonic field
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Simona-Clara Bârsan, Mihaela-Georgia and Dan Săvescu
and, implicitly, to a bigger energy consumption. But, both <strong>the</strong> shape and <strong>the</strong> luster <strong>of</strong> <strong>the</strong> used mirrors
are relatively easy to be remade (Gay, 1997).
According to <strong>the</strong> economic agent needs, <strong>the</strong> technology can be easily adapted for o<strong>the</strong>r heating /
drying processes as well (preparing various surfaces for technological processes, heating / drying in
food industry, in constructions).
4. Transfer process
4.1 Outlines <strong>of</strong> <strong>the</strong> negotiation phase:
<strong>the</strong> team <strong>of</strong> <strong>the</strong> buyer presented itself very well documented, well prepared, having specialists in
<strong>the</strong> technical, economic, legal and communication field;
<strong>the</strong> inventor, on <strong>the</strong> o<strong>the</strong>r side, was not so well prepared from <strong>the</strong> economic point <strong>of</strong> view; he
started from <strong>the</strong> idea <strong>of</strong> recuperating <strong>the</strong> costs <strong>of</strong> production and 20% more, meaning <strong>the</strong>
minimum registered administration costs; he did not take into account <strong>the</strong> marketing expenses,
motivating that he, personally, did not put any money into it;
<strong>the</strong> representative <strong>of</strong> <strong>the</strong> technology transfer centre had an important role in all this process: first
<strong>of</strong> all, he managed to find a buyer for <strong>the</strong> inventions, evaluated <strong>the</strong> real costs <strong>of</strong> each invention,
using a proper methodology and finally proposed a correct selling price; <strong>the</strong>n, he managed to
sustain <strong>the</strong> inventor, during <strong>the</strong> discussions, providing arguments for <strong>the</strong> requested amount,
uprising it with approximately 26% from <strong>the</strong> initial <strong>of</strong>fer.
Last, but not least, he obtained a supplementary amount for <strong>the</strong> service provided and a draft <strong>of</strong>
future collaboration, for ano<strong>the</strong>r invention.
4.2 Cost estimation – obtained figures
For each technology, an estimation <strong>of</strong> <strong>the</strong> life cycle was made, compared to <strong>the</strong> experience in <strong>the</strong>
respective activity field; <strong>the</strong> obtained results allowed <strong>the</strong> estimation <strong>of</strong> <strong>the</strong> periods in which <strong>the</strong>
maximum and minimum pr<strong>of</strong>it could be obtained. The estimated was 2 years and 7 months, <strong>the</strong>
obtained was shorted with 2 months.
Market study: identification <strong>of</strong> possible clients; market trend; <strong>the</strong> estimated value to be obtained;
Knowledge phase <strong>of</strong> <strong>the</strong> invention at <strong>the</strong> moment <strong>of</strong> market analysis: exposure stage <strong>of</strong> <strong>the</strong> patent
application, laboratory stage, prototype stage or production stage;
Evaluation <strong>of</strong> production costs;
The production costs for <strong>the</strong> presented technologies were approximately 48% <strong>of</strong> <strong>the</strong> final obtained
amount.
The royalty obtained for each technology was:
for technologies 1- 4, approximately 25.000 Euro;
for technology 5, approximately 100.000 Euro.
These prices included all involved costs and <strong>the</strong> pr<strong>of</strong>it <strong>of</strong> <strong>the</strong> inventor.
4.3 Main advantages obtained by <strong>the</strong> SMEs:
Technologies no. 1-4:
very good costs / quality ratio, being thus very advantageous and assuring a great mobility <strong>of</strong> <strong>the</strong>
production capacity (<strong>the</strong> technologies are modular, <strong>the</strong> modules number depending on
necessities);
<strong>the</strong> electrolytic copper cathodes will be purchased on <strong>the</strong> internal market with lower prices than
those practiced by importers;
<strong>the</strong> copper and silver ions recovery from diluted solutions will have as effect, on <strong>the</strong> one hand, <strong>the</strong>
decontamination <strong>of</strong> used waters and, on <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> possibility <strong>of</strong> revaluating some ra<strong>the</strong>r
expensive materials;
<strong>the</strong> solid waste (galvanic sludge) management will assure <strong>the</strong> valorisation <strong>of</strong> <strong>the</strong> toxic sludge
resulted in electroplating workshops;
71

Simona-Clara Bârsan, Mihaela-Georgia and Dan Săvescu
besides solving <strong>the</strong> problem <strong>of</strong> <strong>the</strong> diluted liquid waste management, containing heavy metal
(silver and copper) ions, by recovering <strong>the</strong> resulted by-products (metallic copper and silver), one
can cover a part <strong>of</strong> <strong>the</strong> costs involved by <strong>the</strong> process <strong>of</strong> liquid waste purification;
besides solving <strong>the</strong> problem <strong>of</strong> galvanic sludge management, one can obtain, as by-product, <strong>the</strong>
special concrete tiles, whose selling can cover a part <strong>of</strong> <strong>the</strong> costs involved by <strong>the</strong> decontamination
process.
Technology no. 5:
according to <strong>the</strong> client’s needs, <strong>the</strong> photonic field generator can be produced in a large range <strong>of</strong>
dimensions and configurations, due to <strong>the</strong> fact that, when drawing-up <strong>the</strong> final product, one starts
from <strong>the</strong> photonic field generator unit, which is multiplied and set in a certain configuration. A
generator unit is made up from a halogen lamp and a flat-elliptical or a parabolic mirror. The
number <strong>of</strong> units depends on <strong>the</strong> surface <strong>of</strong> <strong>the</strong> heating / drying object while <strong>the</strong> configuration
(geometry) <strong>of</strong> <strong>the</strong> photonic field generation depends on <strong>the</strong> shape <strong>of</strong> <strong>the</strong> heating / drying object;
<strong>the</strong> heating may be carried out within a temperature range between room temperature and
approx. 1000° C;
temperature control is simple and easy (by controlling <strong>the</strong> electric current that crosses <strong>the</strong>
halogen lamp);
<strong>the</strong>re is no physical contact between <strong>the</strong> photonic field generator and <strong>the</strong> heated object;
<strong>the</strong> photonic field generator has no moving parts;
according to <strong>the</strong> heating / drying surface, <strong>the</strong>re are two possible variants. Thus, <strong>the</strong> generator can
be mobile (in this case, it is moved along <strong>the</strong> piece which is to be heated / dried. Example: at <strong>the</strong>
quick drying <strong>of</strong> ships, after painting) or fixed (in which case <strong>the</strong> pieces to be dried are moved on a
conveyor belt - for example, through a tunnel-shaped generator);
<strong>the</strong> drying process is rapid, uniform and does not lead to changes in coating layer properties
(subject to specified temperature range);
<strong>the</strong> conversion yields <strong>of</strong> electric energy into <strong>the</strong>rmal energy and <strong>of</strong> its transfer are much higher,
leading to a very advantageous costs-quality ratio as well as to significantly lower operating costs;
low operating costs;
non-pollutant technology.
5. Conclusions
The creation <strong>of</strong> greater transparency in <strong>the</strong> process <strong>of</strong> identifying and reporting <strong>of</strong> <strong>the</strong> Intellectual
Capital can improve <strong>the</strong> quality <strong>of</strong> <strong>the</strong> dialogue between investors and research intensive enterprises,
as well <strong>the</strong> internal process <strong>of</strong> managing resource allocation and augment <strong>the</strong> finance available to
carry out Research, Development and Innovation.
With this paper, <strong>the</strong> authors intended to emphasize <strong>the</strong> importance <strong>of</strong> creating and transferring
Knowledge from a potent knowledge creator to those who can actually benefit from implementing it:
<strong>the</strong> SMEs, <strong>the</strong> environment and <strong>the</strong> human beings living in a less polluted world.
The authors also mentioned that this was a national founding supported project and <strong>the</strong>refore, <strong>the</strong>
costs implied for SMEs were smaller (45% <strong>of</strong> <strong>the</strong> total amount). This is highly important since
articulating <strong>the</strong> value <strong>of</strong> Intellectual Capital is problematic and <strong>the</strong>refore, attracting capital for
investments is much harder to achieve than for investments in tangible assets.
The presented technologies have a lot <strong>of</strong> advantages, both for <strong>the</strong> buyer and <strong>the</strong> environment. The
results obtained from <strong>the</strong>ir implementation were far more important than <strong>the</strong> initially expected ones:
<strong>the</strong> impact on <strong>the</strong> environment was consistently improved, in <strong>the</strong> way <strong>of</strong> diminishing <strong>the</strong> bad one and,
on <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> costs involved were at forecast level, with a variation <strong>of</strong> +0,43%, which did not
represent a significant increase.
The history <strong>of</strong> economic evolution has showed that <strong>the</strong>re is no guarantee <strong>of</strong> a continuous evolution by
producing only what is vendible at <strong>the</strong> moment, but through investments in innovative products,
especially in an economy in stagnation or decrease situation.
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Simona-Clara Bârsan, Mihaela-Georgia and Dan Săvescu
Since we live in a society that needs to become more and more responsible towards environment, it is
advisable to sustain this kind <strong>of</strong> research, by providing funds from <strong>the</strong> private area <strong>of</strong> business,
complementary to those from <strong>the</strong> state department <strong>of</strong> research. O<strong>the</strong>rwise, we risk bending innovation
from blooming, which will inevitably lead to industrial stagnation and continuous increase <strong>of</strong> pollution
indicators. And this will affect all <strong>of</strong> us.
References
Andriessen, D. (2004) Making sense <strong>of</strong> Intellectual Capital – Designing a method for <strong>the</strong> valuation <strong>of</strong> intangibles,
Elsevier Butterworth-Heinemann, MA, USA.
Andriessen, D., Tissen, R. (2000) Weightless wealth: find your real value in a future <strong>of</strong> intangibles assets,
Financial Times Prentice Hall, London.
Baruch, L. (2001). Intangible: Management, measurement and reporting, Brookings Institution Press, Washington
D.C.
Dubitski, A., Braun, J., Brandwey, H. (1992) “Biotechniques”, Vol 13, rev. Chimia nr. 58, Bucharest
Edvinsson, L (2002) Corporate Longitude - Navigating <strong>the</strong> Knowledge Economy, Book House Publishing,
Sweden
Edvinsson, L., Malone, M.S. (1997) Intellectual Capital: Realizing your Company’s True Value by Finding Its
Hidden Brainpower, Harper Business, New York.
Florea, A. (2010) “Preliminary studies <strong>of</strong> silver coatings formation from choline chloride based ionic liquids”.
Scientific Bulletin <strong>of</strong> UP Bucharest, Series B, Vol 72, pp. 115-126.
Gay, D. (1997) Materiaux composites. Edition Hermes, Paris
Linder, M.C., Goode, C. (1991) Biochemistry <strong>of</strong> Copper, Plenium Press, New York
Platona, F., (1997) Process and installation for recovering electrolytic copper from waste, Patent 97- 02189/1997.
Savescu, D., Budala, A (2009) Proprietatea intelectuala in Romania si unele tari din UE, Editura Lux Libris
Brasov
Stewart, T.A. (1997). Intellectual Capital – The New Wealth <strong>of</strong> Organizations, Doubleday, New York
Untea, I. (2002) Purificarea gazelor reziduale, Editura Printech, Bucuresti
*** International Accounting Standards 38 (IAS 38), available at:
http://www.scritube.com/economie/contabilitate/Standardul-International-de-Co21338212.php
*** National Authority <strong>of</strong> Scientific Research (NASR), (2009), Governmental politics on research, developing and
innovation in Romania, available at: http://www.ancs.ro/index.php?action=view&idcat=21
73

A Framework for <strong>the</strong> Assessment <strong>of</strong> KM Readiness <strong>of</strong> an
Organization While Transferring into a Learning
Organization
Leila Beig, Maryam Mirian, Tahereh MirSaeed Ghazi and Mahmood Kharrat
Education and Research Institute for ICT, Tehran, Iran
l.beig @itrc.ac.ir
mirian@itrc.ac.ir
ghazi@itrc.ac.ir
kharrat@itrc.ac.ir
Abstract: In <strong>the</strong> knowledge era, learning organizations (LO) are being emerged and knowledge is <strong>the</strong> key fuel for
such organizations where Knowledge Management (KM) plays a critical role in learning more rapidly than <strong>the</strong>
competitors. However, implementing KM requires a number <strong>of</strong> steps to be taken. These steps usually lead to
significant changes in organizations especially in three main aspects including infrastructure, organizational
processes and <strong>the</strong> organizational culture. In order to effectively manage <strong>the</strong> changes, managers need to
comprehensively examine <strong>the</strong>ir organization’s readiness. This process will reduce excessive costs, avoid
redundancy and repetitive actions, make a clear understanding <strong>of</strong> <strong>the</strong> current state <strong>of</strong> <strong>the</strong> organization, and
introduce improvement actions instead <strong>of</strong> starting from scratch. In addition, identifying <strong>the</strong> gaps between <strong>the</strong>
current and desirable state will provide a comparative tool for mangers to monitor organization status as <strong>the</strong>y
plan and implement KM initiatives over time. The key idea <strong>of</strong> this paper is to propose an integrated framework for
measuring KM readiness in learning organizations. The framework sketches a holistic and concise description <strong>of</strong>
<strong>the</strong> major elements <strong>of</strong> KM assessment. This research seems to be significant since few frameworks currently
exist for a holistic KM assessment in learning organizations. However, <strong>the</strong> proposed framework can be used to
assess overall organizational KM to avoid probable project failures in cases in which <strong>the</strong> failure is related to <strong>the</strong>
lack <strong>of</strong> awareness about <strong>the</strong> current KM status <strong>of</strong> <strong>the</strong> organization.
Keywords: KM readiness assessment, learning organization, KM process
1. Introduction
Knowledge is considered a crucial driving power for accomplishment <strong>of</strong> any business. Organizations
are becoming more knowledge-oriented and awareness about <strong>the</strong> value <strong>of</strong> knowledge is being
increased. Therefore, knowledge has been systematically treated as o<strong>the</strong>r concrete resources and
many organizations are exploring <strong>the</strong> field <strong>of</strong> KM (Wong 2005).
Although, today, <strong>the</strong>re is a great deal <strong>of</strong> interest in KM, no universally accepted definition <strong>of</strong> KM
exists. Hung, Y.C. et al. (2005) defines KM as a systemized and integrated managerial strategy,
which combines information technology with <strong>the</strong> organizational process. KM refers to any activity <strong>of</strong>
creating, acquiring, capturing, sharing and employing knowledge wherever it resides, to enhance
learning and performance in organizations. Bounfour (2003) considers KM as a set <strong>of</strong> tools, designed
towards creating, sharing knowledge in inter and intra-organization levels (Nianti Mohd Zin & Charles,
2010).
With <strong>the</strong>se definitions in mind, KM might be proposed as a managerial activity which stores, transfers,
shares, applies and creates knowledge mostly through information technologies, as well as provides
<strong>the</strong> right knowledge at <strong>the</strong> right time to <strong>the</strong> right members <strong>of</strong> <strong>the</strong> organization to react and make <strong>the</strong>
right decisions, in order to reach <strong>the</strong> organization’s goals.
One <strong>the</strong> most important organization’s goals that can be attained by KM is organizational learning
(OL) improvement and consequently transferring into <strong>the</strong> learning organization through OL.
It is important to note <strong>the</strong> difference between <strong>the</strong> terms OL and LO: organizational learning refers to
<strong>the</strong> process <strong>of</strong> learning or learning activities in an organization while <strong>the</strong> concept <strong>of</strong> learning
organization refers to <strong>the</strong> organizations that are good at organizational learning (Aggestam 2006).
In <strong>the</strong> better words, in discussing learning organizations <strong>the</strong> focus is on what—<strong>the</strong> systems, principles,
and characteristics <strong>of</strong> organizations that learn and produce as a collective entity while in discussing
organizational learning we are concerned with how organizational learning occurs—<strong>the</strong> skills and
processes <strong>of</strong> building and utilizing knowledge.
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Leila Beig et al
Marquardt (2002) considers organizational learning as just one feature <strong>of</strong> a learning organization.
Learning organizations have <strong>the</strong> ability to learn and facilitate all facets <strong>of</strong> <strong>the</strong> learning process and
thus continuously transform <strong>the</strong>mselves. Such organizations consist <strong>of</strong> teams with balanced learning
styles, and people whose personal ambition corresponds to that <strong>of</strong> <strong>the</strong> organization. Therefore, <strong>the</strong>y
have a positive attitude towards improving, changing and learning. Learning organizations also
consist <strong>of</strong> people who constantly learn from <strong>the</strong>ir own mistakes, share knowledge and communicate
openly with each o<strong>the</strong>r. As it is mentioned in Marquardt (2002) “<strong>the</strong>se organizations have leaders who
coach, help, inspire, motivate, stimulate and intuitively make decisions, and have processes that are
constantly reviewed based on performance measures and feedback”.
KM and LO cannot survive without each o<strong>the</strong>r. An organization that wants to become a learning
organization has to pay attention to both at <strong>the</strong> same time. This requires both an understanding <strong>of</strong>
<strong>the</strong>ir different properties and goals, and <strong>of</strong> <strong>the</strong>ir relationships as well (Aggestam 2006). Creation <strong>of</strong>
new knowledge is a critical component <strong>of</strong> an organization’s ability to learn and adapt. A LO <strong>the</strong>refore
requires to effectively manage knowledge, and effective KM is what to do in order to fulfill this
requirement while KM aims to support distribution <strong>of</strong> knowledge, <strong>of</strong> course in turn aiming to support
learning. LO requires KM, and KM assumes a LO. Work processes must enable learning and
integrate every aspect <strong>of</strong> knowledge. A LO can be regarded as <strong>the</strong> system, which includes <strong>the</strong>
subsystem KM. Finally, it can be summarized that <strong>the</strong>y are two inseparable parts when an
organization wants to become a learning organization since KM is a prerequisite for a LO (Aggestam
2006) .
This paper is organized as follows. In <strong>the</strong> next section, a review <strong>of</strong> <strong>the</strong> KM readiness and LO
readiness for successful KM implementation in a LO will be presented. It will be followed by a
proposed framework for <strong>the</strong> assessment <strong>of</strong> <strong>the</strong> KM readiness <strong>of</strong> an organization while transferring into
<strong>the</strong> LO. Finally, in <strong>the</strong> last section by using <strong>the</strong> lessons learned from <strong>the</strong> literature review some
conclusions and recommendations for <strong>the</strong> benefit <strong>of</strong> <strong>the</strong> organizations transferring into LOs will be
presented.
2. A review <strong>of</strong> readiness models
Organizational readiness is now a popular and widely used term with varying definitions. The general
definition supplied in <strong>the</strong> existing literature use <strong>the</strong> word “readiness” as a necessary pre-condition for
a person or an organization to succeed in facing organizational change (D. T. Holt 2000). In following
a comparison <strong>of</strong> different KM readiness models and LO readiness models will be presented.
2.1 KM readiness models
According to Mohammadi et al. (2009), KM readiness is <strong>the</strong> ability <strong>of</strong> an organization, department or
work group to successfully adopt, use and benefit from KM. Readiness is an essential part <strong>of</strong> it and
needs to be administered in <strong>the</strong> early planning phase <strong>of</strong> KM initiatives. The following are some
examples from previous studies done by o<strong>the</strong>r authors regarding organizational readiness.
Trent, M. R. (2003) in his <strong>the</strong>sis about assessing <strong>the</strong> readiness <strong>of</strong> <strong>the</strong> USA air force for successful KM
implementation introduces Change Content variables (what is being changed), Process Variables
(how <strong>the</strong> change is being implemented), Contextual Variables (circumstances under which <strong>the</strong>
change is occurring) and Individual Variables (characteristics <strong>of</strong> those being asked to change as <strong>the</strong>
main elements <strong>of</strong> his proposed model.
Holt et al. (2004) conducted a study to develop an instrument for assessing KM readiness. This
particular study draws on <strong>the</strong> literature dealing with KM and organizational change to propose a
synergistic tool to measure readiness for KM and apply it in an organizational setting. This tool or
instrument considers individual, context, content, process measures and KM attitudes (Daniel T. Holt
et al. 2004).
John Blackwell associates (2007), proposed a KM readiness tool in <strong>the</strong> form <strong>of</strong> a questionnaire that
assess ten aspect <strong>of</strong> an organization including awareness and commitment, strategy, culture, external
focus, incentives, IT, maintenance and protection, ongoing assessment, organization and using and
applying knowledge. Every aspect is assessed through 7 questions that is scored from critical
(highest) to not improving (lowest) (JB associates 2007)
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Leila Beig et al
Additionally, Wei et al. (2009) have investigated <strong>the</strong> readiness <strong>of</strong> Malaysian telecommunication
industry to adopt KM by investigating <strong>the</strong> perceived importance and actual level <strong>of</strong> implementation <strong>of</strong>
five success factors (business strategy, organizational structure, KM team, K-Map and K-Audit), four
KM strategies (organizational culture, leadership support, technological infrastructure, performance
measurement) and three KM processes (construction, embodiment and deployment). This paper
focuses on KM implementation ra<strong>the</strong>r than on learning and knowledge utilization. It attempts to create
a snapshot <strong>of</strong> an effective implementation <strong>of</strong> KM strategies and create a basic topology <strong>of</strong> strategic
approaches towards knowledge. It <strong>the</strong>refore establishes a baseline description <strong>of</strong> preliminary KM
implementation and practices in <strong>the</strong> telecommunication industry (Wei et al. 2009).
Mohammadi et al. (2009) fur<strong>the</strong>r developed a systematic study to determine KM readiness
implementation in SME sector. They provide five organizational antecedents for effectiveness such as
vision for change, infrastructure, structure, support for change and culture <strong>of</strong> knowledge.
Meanwhile, Razi Jalaldeen et al. (2009) proposed a model to assess <strong>the</strong> organizational readiness
and its contributing factors for KM process adoption by integrating KM infrastructure and unified
<strong>the</strong>ory <strong>of</strong> acceptance and use <strong>of</strong> technology. The authors suggest that organizational readiness be
assessed by taking into consideration both organizational and individual factors (Razi Jalaldeen et al.
2009)
In table 1.a comparison <strong>of</strong> <strong>the</strong> mentioned models has been presented regarding <strong>the</strong> elements, sub
elements and context <strong>of</strong> <strong>the</strong> proposed models.
As <strong>the</strong> table shows, most <strong>of</strong> <strong>the</strong> models consider culture, IT, organization structure, context, content,
process, people and top level support as <strong>the</strong> main factors <strong>of</strong> <strong>the</strong> KM readiness assessment.
Table 1: KM readiness models
Elements Sub elements Context Source
Intention to adopt KM
Readiness to adopt KM processes
process
Performance expectancy <strong>of</strong> Perceived usefulness
adopting KM process
Job-fit
Effort expectancy <strong>of</strong> adopting KM Perceived ease <strong>of</strong> use
processes
Complexity
Collaboration
Organizational culture
Trust
Learning
Centralization
Organizational structure
Formalization
IT infrastructure IT support
Appropriateness
Change content
Valence
Semantic differential scale
Management support
Participation
Process
Communication climate
Quality <strong>of</strong> information
Perceived organizational
support
Context
Discrepancy
Principal support
Positive effect
Negative effect
Efficacy
Innovativeness
Individual
Job satisfaction
Turn over intention
Change anxiety
Pessimism
Business strategy
Organizational structure
KM preliminary success factors
KM team
Knowledge audit
76
Assessing
organizational
readiness for KM
process adoption
Assessing <strong>the</strong>
readiness <strong>of</strong> <strong>the</strong>
USA air force for
successful KM
implementation
Malaysian
telecommunication
industry readiness
for KM
(Razi Jalaldeen et
al. 2009)
(Trent 2003)
(Wei et al. 2009)

Leila Beig et al
Elements Sub elements Context Source
KM strategies
KM process
Awareness and commitment
Strategy
Culture
External focus
Incentives
IT
Maintenance and protection
Ongoing assessment
Organization
Using and applying knowledge
Individual
Context
Content
process measures
KM attitudes
vision for change
Infrastructure
Structure
support for change
culture <strong>of</strong> knowledge
KM process
Leadership in KM
KM culture
KM technology
KM measurement
2.2 LO readiness models
Knowledge map implementation
Technology
Culture
Leadership
Measurement
Construction
Embodiment
Deployment
A sum <strong>of</strong> questions
A sum <strong>of</strong> questions
A sum <strong>of</strong> questions
A sum <strong>of</strong> questions
KM readiness
assessment in any
organization
Measure readiness
for applying KM in
any organizational
setting
Readiness <strong>of</strong> <strong>the</strong>
organizations to
implement KM
strategy in <strong>the</strong>
constructive
industry
KM Selfassessment
tool for
an organization
(JB associates
2007)
(Daniel T. Holt et
al. 2004)
(Mohammadi et
al. 2009)
(American
Productivity &
Quality Center
2001)
The concept <strong>of</strong> a learning organization is <strong>of</strong> major significance at <strong>the</strong> worldwide level. The model <strong>of</strong> a
learning organization is replacing <strong>the</strong> beaurocratic model that has prevailed for a long time.
Competition among various organizations is very intense; <strong>the</strong>refore, <strong>the</strong>y must make substantial
changes that are to apply a new organizational model in order to survive. Marquardt 2002, in his book
“building <strong>the</strong> learning organization” introduces five subsystems <strong>of</strong> <strong>the</strong> systems learning organization
model including learning dynamics, organizational transformation, people empowerment, KM, and
technology application
In chapter eight <strong>of</strong> his book, becoming a learning organization, he addresses <strong>the</strong> importance <strong>of</strong> a
comprehensive, systematic examination <strong>of</strong> an organizations’ learning competence. He considers an
organization’s capability assessment as a critical early step to identify existing strengths as well as
weaknesses, resources and gaps <strong>of</strong> an organization while moving toward a learning one. He
introduced Learning Organization Pr<strong>of</strong>ile (LOP) as a tool for assessing a company’s status. The LOP
consists <strong>of</strong> 10 questions that address each <strong>of</strong> <strong>the</strong> five subsystems <strong>of</strong> <strong>the</strong> systems learning
organization model. Individuals or teams rate <strong>the</strong>ir organizations on a Likert scale from 4 (highest) to 1
(lowest) on each question.
Meanwhile, Rampersad, H. (2002), introduced a KM audit, shown in table 2, consisting <strong>of</strong> 50
statements regarding an organization’s knowledge and learning orientation, divided into five
dimensions: general, leadership style, strategic vision, internal processes and human resources.
Similar to Marquardt’s model, in this model every question also can get <strong>the</strong> scores 1 to 4. The scores
should be added vertically. The closer <strong>the</strong> total score gets to 200, <strong>the</strong> more knowledge intensive <strong>the</strong>
organization is. A comparably high score is related to a learning organization with a large learning
ability. The closer <strong>the</strong> total score is to 50, <strong>the</strong> smaller <strong>the</strong> organizational learning ability.
77

Leila Beig et al
The proposed KM audit quick scan was completed by <strong>the</strong> executive team <strong>of</strong> Business Jet (an airline
company for business men). The total score seemed to be 138 points; this implies that, in <strong>the</strong> area <strong>of</strong>
KM, something still needs to be done before Business Jet can be classified as a full-fledged learning
organization.
Nejat, H. et al., (2007) assessed <strong>the</strong> elements <strong>of</strong> <strong>the</strong> learning organization by seven dimensions or
action imperatives including: continuous learning, dialogue and inquiry, team learning, embedded
systems, empowerment, systems connections and leadership support. In table 2 <strong>the</strong> sub elements <strong>of</strong>
each element has been shown.
Britton, (1998), introduced supportive culture, ga<strong>the</strong>ring internal experience, accessing external
learning, communication systems, mechanisms for drawing conclusions, developing an organizational
memory, integrating learning into strategy and policy and applying <strong>the</strong> learning as <strong>the</strong> functions <strong>of</strong> a
LO that should be considered while assessing its readiness for learning.
Table 2. depicts a comparison <strong>of</strong> <strong>the</strong> mentioned models considering <strong>the</strong> elements, sub elements and
context <strong>of</strong> <strong>the</strong>m and also <strong>the</strong> corresponding KM elements.
As our literature review shows <strong>the</strong>re are several models for KM readiness assessment but <strong>the</strong> models
<strong>of</strong> LO readiness assessment are rare. Even <strong>the</strong> Rampersad’s model that is somehow close to our
survey also focuses mostly on <strong>the</strong> KM readiness assessment. The only difference <strong>of</strong> this model with
<strong>the</strong> mentioned KM readiness assessment model is that <strong>the</strong> basis <strong>of</strong> this model is on organizational
learning.
It can be concluded that several researches have been done in <strong>the</strong>se two fields separately but <strong>the</strong>re
is not any integrated framework or model about <strong>the</strong> KM readiness assessment <strong>of</strong> an organization
moving toward a learning one, yet. This issue simply implies <strong>the</strong> novelty <strong>of</strong> our framework that is going
to be presented in this paper.
Table 2: LO readiness models and corresponding KM elements
source context Elements Sub element
Marquardt,
M. J., 2002
Rampersad
, H. 2002
Learning
organization
pr<strong>of</strong>ile for
assessing an
organization
capacities
KM audit
quick scan
based on OL,
tested in an
airline
company
learning
dynamics
organization
transformatio
n
people
empowermen
t
KM
General
Leadership
style
Individual
Group or Team
Organization
Vision
Culture
Strategy
Structure
Manager
Employee
Customer
Partners
Suppliers
Community
Acquisition
Creation
Storage
Transfer
Utilization
culture
Integrated MIS and network <strong>of</strong>
knowledge workers
Easy access to knowledge
Skillful employees in KM
management
A simple organizational structure
An active program for developing
ideas
Top management commitment in
learning
Knowledgeable leaders
The ability to coaching, inspiring and
78
Corresponding KM
elements
Individual
Culture
Strategy
Structure
External focus
KM Process
Culture
Structure
Leadership support
Individual
Infrastructure
Leadership

Leila Beig et al
source context Elements Sub element
Nejat, H. et
al., 2007
Dimensions
<strong>of</strong> <strong>the</strong>
Learning
organization
tested in
Turkish
organizations
Strategic
vision
Internal
process
Human
resource
Continuous
learning
Dialogue and
Inquiry
Team
Learning
Embedded
Systems
Empowermen
t
serving
KM as a strategic <strong>the</strong>me
Continuous collective learning
Relating knowledge & learning
objectives to performance measures
Customer information as
strategically valuable capital
knowledge exchanging
mapping out <strong>of</strong> knowledge gaps
Transferring relevant implicit
knowledge into explicit
Communication and information
systems
Documentation <strong>of</strong> obtained and
developed knowledge
Rotation <strong>of</strong> employees
Constant incorporation <strong>of</strong> knowledge
into new products, services and
processes
Systematic benchmarking to gain
knowledge
Constant measurement <strong>of</strong>
knowledge & learning
Linking job appraisal to personal
ambition <strong>of</strong> individuals
Developing employee’s knowledge
constantly
A proactive competence
development policy
Passing <strong>the</strong> knowledge <strong>of</strong> departing
employees to successor
Rewarding based on collective
learning
Willingness to apply, share and
exchange knowledge
A suitable organizational structure
Openly discussing mistakes
Identifying <strong>the</strong> skills people need for
future work tasks
Helping each o<strong>the</strong>r learn
Financial incentives for learning
Problems as an opportunity to learn
Giving open and honest feedback
Treating with respect
Trust
The freedom to adapt goals
Equality
Rewarding for achievements as a
team/group
Organization action based on <strong>the</strong>
teams recommendations
Two-way communication
Quick access to needed information
An up-to-date data base <strong>of</strong>
employee skills
Systems to measure gaps between
current and expected performance
Access to lessons learned
Giving people choices in <strong>the</strong>ir work
assignments
Inviting people to contribute to <strong>the</strong>
organization's vision
Supporting risky employees
Building alignment <strong>of</strong> visions across
79
Corresponding KM
elements
Strategy
Process
measurement
culture
Incentives
Culture
organizational
structure
------
culture
-------
IT infrastructure
Individual

Leila Beig et al
source context Elements Sub element
B.Britton,
1998
The Key
functions <strong>of</strong> a
learning
organization
System
Connections
Leadership
Support
Supportive
Culture
Ga<strong>the</strong>ring
internal
experience
Accessing
external
learning
Communicati
on systems
Mechanisms
for drawing
conclusions
Developing
an
organizationa
l memory
Integrating
learning into
strategy and
policy
different levels and work groups
Balance between work and family
Thinking from a global perspective
Applying <strong>the</strong> customers' views in
decision making
Considering <strong>the</strong> impact <strong>of</strong> decisions
on employee morale
Working toge<strong>the</strong>r with <strong>the</strong> outside
community
Supporting learning opportunities
and training
Sharing up to date information
Organization's vision
Leaders as mentor and coach
Looking for opportunities to learn
Consistency <strong>of</strong> <strong>the</strong> organization's
actions with its values
Rewarding based on learning
High priority for sharing experience
and knowledge
Encouraging <strong>the</strong> experimentation
Easy access to resources and
facilities
Systematic procedures for <strong>the</strong>
regular monitoring and evaluation
Drawing lesson learned from work
and sharing it
Ga<strong>the</strong>ring and sharing relevant
information with o<strong>the</strong>rs
Co-operation with o<strong>the</strong>r
organizations to learn from <strong>the</strong>ir
experience
Free flowing <strong>of</strong> information
throughout <strong>the</strong> organization
Wide range <strong>of</strong> mechanisms for
sharing experience
Easy access to information on <strong>the</strong>
lessons
Encouraging sharing information
using electronic media
Skilled Staff at sharing <strong>the</strong>ir personal
knowledge and wisdom
Learning from experience as <strong>the</strong>
responsibility <strong>of</strong> everyone
Analyzing Monitoring and evaluation
reports routinely
Skilled organization at Converting
raw information from internal
evaluations and research into
useable knowledge
Using a continuous improvement
approach
easily accessible databases,
resource/information centers and
data retrieval systems
key documents are cross-referenced
and easily accessible to all staff
systematic database <strong>of</strong> all project
and program work
Strategy development as a learning
process
Policy making involves all people
All management processes
80
Corresponding KM
elements
-----
Leadership Support
culture
KM Process
------
IT infrastructure
-----
----
Knowledge strategy

Leila Beig et al
source context Elements Sub element
3. Proposed framework
Applying <strong>the</strong>
learning
organized to assist learning
Systems and operational procedures
for sharing <strong>the</strong> lessons learned
Continuous learning
Publishing experience for a wider
readership
Improving effectiveness through
changing practices to reflect new
knowledge
building capacity and innovating
based on learning
Corresponding KM
elements
KM Process
When an organization is moving towards a learning state, it requires a set <strong>of</strong> specific characteristics to
be ei<strong>the</strong>r generated or enhanced. These characteristics are described as “LO characteristics” that can
be classified into cultural values, leadership commitment and empowerment, communication,
knowledge transfer, employee characteristics and performance upgrading. Each characteristic builds
up organizations’ capability to become learning organizations (Sudharatna & Li 2004).
That’s <strong>the</strong> same for an organization while implementing KM. KM implementation in an organization is
not easy. There are several things that must be considered and prepared before implementing KM,
such as identifying proper stages and top management support.
Since in both cases mentioned most <strong>of</strong> <strong>the</strong> aspects <strong>of</strong> <strong>the</strong> organization will be changed and new
features will be replaced, <strong>the</strong> organization needs to master <strong>the</strong> change management dimensions.
Most <strong>of</strong> <strong>the</strong> researches introduce seven attributes including business environment recognition,
leadership, organizational culture, management practices, skill and job matching, reward and
recognition and organizational structure for assessing <strong>the</strong> readiness <strong>of</strong> an organization for change
(Sudharatna & Li 2004).
Many researches show that implementing KM in an organization without considering <strong>the</strong> specified
states <strong>of</strong> organization at <strong>the</strong> specific time results in failure. According to research institutions in UK
(British Telecommunications PLC), <strong>the</strong> failure rate <strong>of</strong> <strong>the</strong> project management knowledge is 70% while
<strong>the</strong> KM implementation risk can be reduced if organization is ready to implement KM. It is necessary
to do assessment to find out, whe<strong>the</strong>r <strong>the</strong> organization is actually ready to implement KM (Sudharatna
& Li 2004).
Since KM plays a key role in this kind <strong>of</strong> organizations and as Marquardt, M. J., et al. (2002) and
Sudharatna, Y. et al. (2004) showed in <strong>the</strong>ir surveys, KM is one <strong>of</strong> <strong>the</strong> special characteristics and
subsystems <strong>of</strong> LO organizations, <strong>the</strong> readiness assessment <strong>of</strong> KM in this kind <strong>of</strong> organizations is even
more important in order to reduce or avoid probable failures.
In fig. 1 that presents a system model <strong>of</strong> a LO and in fig. 2 that shows <strong>the</strong> characteristics <strong>of</strong> a LO, <strong>the</strong>
relationship between KM and LO has been illustrated. As <strong>the</strong> figures show KM has been considered
as a part <strong>of</strong> a LO.
A precise review <strong>of</strong> KM, LO and change management leads us to this conclusion that many factors
play important roles on <strong>the</strong> implementation <strong>of</strong> KM in an organization transferring into LO. Therefore,
regarding <strong>the</strong> fact that different scholars have used various terms or phrases to describe <strong>the</strong>se factors
in <strong>the</strong>ir studies, <strong>the</strong>y can be categorized into 10 main dimensions based on <strong>the</strong>ir main contents and
sub-elements (table 3). All <strong>the</strong>se factors were repeatedly emphasized in <strong>the</strong> research literature <strong>of</strong> KM,
LO and change management multiple times and also <strong>the</strong>y were described in different studies and
contexts.
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Leila Beig et al
Figure 1: Learning organization system model (Marquardt, M. J., 2002)
Figure 2: LO characteristics (Sudharatna, Y. et al., 2004)
The proposed dimensions are those that had <strong>the</strong> highest amplitude <strong>of</strong> papers, articles, essays etc
which are justified in <strong>the</strong> last column. These factors show overwhelming substantial areas required to
successfully implement KM in an organization moving to a LO because <strong>the</strong>y have been categorized
based on subject oriented studies.
As <strong>the</strong> table below shows, for assessing <strong>the</strong> KM readiness <strong>of</strong> an organization while transferring into a
LO, 10 dimensions should be considered including:
Knowledge and learning strategy
Learning dynamics
Organizational culture
Technological infrastructure
Organizational structure
Customers, partners and suppliers (external sources <strong>of</strong> knowledge)
Tacit and explicit knowledge process
Incentives based on collective learning , performance and sharing knowledge
Continuous evaluation <strong>of</strong> KM benefits towards performance upgrading
Leadership supports
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Leila Beig et al
Table 3: A classification <strong>of</strong> <strong>the</strong> main elements <strong>of</strong> an organization readiness for KM, LO and change
Readiness
Models
Elements
IT infrastructure
Infrastructure
IT
KM technology
Technology application
Organizational culture
Culture
Communication climate
KM culture
Culture <strong>of</strong> knowledge
KM attitudes
Organizational structure
Organization
Structure
KM process
Process
Using and applying
knowledge
Knowledge transfer
KM
Internal process
KM strategies
Vision for change
Learning strategy
Knowledge strategy
Incentives
Rewarding system
Ongoing assessment
Process measures
KM measurement
Support for change
Management support
Leadership support
Perceived organizational
support
Principal support
Individual
Skill and job matching
Skillful employees
Human resource
Organization
al readinessto-change
LO
readines
s
× × ×
× × ×
× × ×
× ×
× ×
× × ×
× × ×
× × ×
× × ×
people empowerment × ×
83
KM
readines
s
Description <strong>of</strong> <strong>the</strong> alternative
terms used in <strong>the</strong> first column
Different hardware and
s<strong>of</strong>tware used for doing a
process in an organization.
Attitudes, experiences, beliefs,
shared mental assumption,
values and norms <strong>of</strong> an
organization that guide
interpretation and action in
organizations.
Activities such as task
allocation, coordination and
supervision, which are
directed towards <strong>the</strong>
achievement <strong>of</strong> organizational
aims in implementing KM and
moving an organization toward
a LO.
The act <strong>of</strong> collecting, storing,
sharing and creating
knowledge in an organization
for different purposes such as
organizational learning.
A plan <strong>of</strong> action designed to
achieve a particular goal such
as KM implementation and
LO.
Factors (financial or nonfinancial)
that enable or
motivate a particular course <strong>of</strong>
action such as knowledge
sharing and continuous
learning.
Determination <strong>of</strong> <strong>the</strong> indicators
associated with <strong>the</strong> continuous
assessment <strong>of</strong> an organization
regarding <strong>the</strong> performance,
learning and knowledge
management in an
organization.
Leadership actions for
supporting an action or plan in
an organization.
The characteristics and skills
<strong>of</strong> <strong>the</strong> members <strong>of</strong> an
organization for achieving a
specific goal such as KM and
OL in an organization.
Involving <strong>the</strong> customers,
partners, suppliers and o<strong>the</strong>r
sources <strong>of</strong> knowledge in KM
process and transferrin an
organization into a LO.

Leila Beig et al
Figure 3 visually illustrates <strong>the</strong> mentioned elements. In <strong>the</strong> following, more details about <strong>the</strong> elements
will be presented.
Figure 3: Proposed framework for KM readiness <strong>of</strong> an organization while transferring into a LO
3.1 Knowledge and learning strategy
It is critical that <strong>the</strong> KM strategy be tightly coupled to <strong>the</strong> LO strategy. KM should never be
implemented as an standalone end. In <strong>the</strong> o<strong>the</strong>r words, <strong>the</strong>re must be a vision that links <strong>the</strong>
organization’s objectives and strategies, to <strong>the</strong> knowledge strategy.
From a strategic perspective, when an organization transferring into a LO seems ready for KM that
<strong>the</strong>re exists an explicit alignment between <strong>the</strong> organization strategy form on hand and <strong>the</strong> knowledge
and learning strategy on <strong>the</strong> o<strong>the</strong>r hand. This strategy should be spread throughout <strong>the</strong> organization
and becomes updated considering <strong>the</strong> feedbacks from different layers and parts <strong>of</strong> <strong>the</strong> LO.
3.2 Learning dynamics
Learning dynamics include scope <strong>of</strong> learning (individual, team and organizational), skills and types <strong>of</strong>
learning. The identification <strong>of</strong> <strong>the</strong> current state <strong>of</strong> <strong>the</strong> learning dynamic is very important for <strong>the</strong>
organizations moving towards LO because learning performs as <strong>the</strong> heart <strong>of</strong> a LO.
The ability to find what a member must know; <strong>the</strong> capacity to encourage learning in all three levels;
developing <strong>the</strong> necessary tools and techniques for capturing <strong>the</strong> intellectual capital that will ensure <strong>the</strong>
organization success; responsibility for continuous learning <strong>of</strong> each member; each person’s
commitment and ability to learn; constant learning <strong>of</strong> members and teams; <strong>the</strong> ability <strong>of</strong> a work team
to think, create new knowledge, and learn effectively as an entity; self-managed learning creativity
and <strong>the</strong> free flow <strong>of</strong> ideas in teams; innovative, coordinated actions in teams; <strong>the</strong> ability to encourage
and stimulate learning in o<strong>the</strong>r teams and organizational memory constitutes all factors that should be
considered in assessing KM readiness in an organization while transferrin into a LO. Through this
assessment, <strong>the</strong> role <strong>of</strong> KM in facilitating <strong>the</strong> process <strong>of</strong> learning based on <strong>the</strong> knowledge and
learning strategies and also organization strategy will become clearer and <strong>the</strong> introducing
corresponding actions will be validated.
3.3 Organizational culture
Culture refers to an organization’s values, beliefs, practices and customs. Effective KM requires
creating a supportive and collaborative working environment. In a learning organization, <strong>the</strong> corporate
culture is one in which learning is recognized as absolutely critical for business success(Marquardt
2002).
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From a cultural view, when an organization transferring into a LO is ready for KM that elements <strong>of</strong>
such a culture including freely sharing knowledge, trust, developing collaborative relationships,
flexibility, risk taking, high expectation for performance, team working, learning and enthusiasm for job
are all in acceptable level. This issue facilitates <strong>the</strong> KM implementation in such organizations.
3.4 Technological infrastructure
Technological infrastructure refers to <strong>the</strong> tools, systems, platforms, and automated solutions that
promotes <strong>the</strong> creating, application, and sharing <strong>of</strong> organizational knowledge. Technology support
(information systems, databases, communication technologies, web technologies, and e-mails) and
equipment (groupware, white boards, video conferencing equipment, and flexible manufacturing
systems) are some <strong>of</strong> <strong>the</strong> key enablers for a successful KM implementation. However, some
researches warned that KM is less <strong>of</strong> a technical issue, and more <strong>of</strong> a cultural one. Technology can
help in a well-established KM initiative, but it will not succeed only based on technology (Park et al.
2004).
Assessing <strong>the</strong> current state <strong>of</strong> technological infrastructure <strong>of</strong> an organization transferrin into a LO, not
only avoids wastages but also facilitates designing and integrating <strong>the</strong> new systems into <strong>the</strong> existing
ones.
3.5 Organizational structure
Moving to a culture that values and encourages innovation, openness, teamwork and knowledge
sharing, requires changes in relationships, organizational structures as well. The structure <strong>of</strong> an
organization is what follows from a division <strong>of</strong> work, tasks and responsibilities, both horizontally and
vertically (Plessis 2007).
The elements <strong>of</strong> <strong>the</strong> culture mentioned before, such as sharing knowledge freely in an organization
moving to a LO, is likely to occur within a larger group <strong>of</strong> individuals in more decentralized
organizations. Moreover, matrix structures and an emphasis on leadership instead <strong>of</strong> management
also facilitate culture issues primarily by cutting across traditional departmental boundaries.
3.6 Customers, partners and suppliers (external sources <strong>of</strong> knowledge)
Customers, partners and suppliers are all part <strong>of</strong> an overall learning environment (Marquardt 2002).
External sources <strong>of</strong> knowledge such as knowledge from Customers, partners and suppliers are as
important for a LO as are <strong>the</strong> internal sources. The knowledge <strong>of</strong> customers can provide up-to-date
product information, competitive comparisons, insights into changing preferences, and immediate
feedback about service and patterns <strong>of</strong> use. The knowledge and learning from <strong>the</strong> partners help <strong>the</strong>
organization in signing new agreements. The short-term partnership <strong>the</strong>refore may become a
pr<strong>of</strong>itable, long-term investment that can be used to leverage future successes. By assessing <strong>the</strong>
current state <strong>of</strong> <strong>the</strong> process or <strong>the</strong> way <strong>of</strong> managing <strong>the</strong> external source <strong>of</strong> knowledge, <strong>the</strong> role <strong>of</strong> KM
in ga<strong>the</strong>ring, sharing and creating new useful knowledge will be identified and <strong>the</strong> future steps in <strong>the</strong>
KM implementation regarding <strong>the</strong> external sources <strong>of</strong> knowledge will be defined, as well.
3.7 Tacit and explicit knowledge process
Tacit and explicit KM processes are crucial for KM implementation in any organization (Plessis 2007).
It is important to understand <strong>the</strong> current state <strong>of</strong> <strong>the</strong> organization about how knowledge is captured,
evaluated, cleansed, stored, provided and used, and how <strong>the</strong> organization can improve <strong>the</strong>m to reach
<strong>the</strong> desired state.
The more an organization is performing KM processes in a favorable manner, <strong>the</strong> more ready is that
organization for implementing KM. It should be mentioned that although explicit KM is seen important,
in <strong>the</strong> case <strong>of</strong> an organization transferring into a LO since continuous learning from experience is a
main characteristic <strong>of</strong> LO, tacit KM gains <strong>the</strong> same or even more importance.
3.8 Incentives based on collective learning, performance and knowledge sharing
Reward and recognition are recognized as key success factors for any change implementation in an
organization (Sudharatna & Li 2004). Clearly, rewards and incentives are also crucial to <strong>the</strong> success
<strong>of</strong> KM that is confidently an important change in many organizations. By rewarding <strong>the</strong> employees
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Leila Beig et al
based on collective learning and sharing knowledge, a climate <strong>of</strong> cooperation, learning and innovation
will be created and positive behaviors and attitudes required for KM will be encouraged.
From this perspective, when an organization transferring into a LO is on a high level <strong>of</strong> readiness for
KM that <strong>the</strong>re are suitable mechanisms and systems <strong>of</strong> performance appraisal and mutually a proper
rewarding system. The performance monitoring and evaluation <strong>of</strong> <strong>the</strong> employees should be based on
learning, knowledge sharing, <strong>the</strong> pace <strong>of</strong> learning; learning acquired and also distributed, innovation
and etc. The incentives and rewarding system <strong>of</strong> <strong>the</strong> organization should be designed in a way that
encourages <strong>the</strong> mentioned performance indicators.
3.9 Continuous evaluation <strong>of</strong> KM benefits towards performance upgrading
Jennex and Zakharova (2005) believe that KM should be linked to <strong>the</strong> economic performance <strong>of</strong> <strong>the</strong>
organization. They also believe that KM impact has to be measured to enable tracking <strong>of</strong> successes
and failures (Plessis 2007). Since KM is not a static activity and grows with <strong>the</strong> organization changes,
its impact should be continuously assessed to ensure that KM is keeping up with current needs in <strong>the</strong>
organization and results in <strong>the</strong> expected performance.
An organization with a continuous evaluation <strong>of</strong> KM benefits and rewarding culture based on <strong>the</strong>
results, shows more readiness for KM implementation, whe<strong>the</strong>r it is a learning organization or moving
towards it. The difference is on <strong>the</strong> selected performance indicators in each organization. In a LO, <strong>the</strong>
evaluation mostly is based on learning rate, knowledge creation, innovation and etc.
3.10 Leadership supports
Leadership support is regarded to have an important influence on readiness for any change in
organizations whe<strong>the</strong>r it is moving towards a LO or implementing KM. The only difference is in <strong>the</strong>
area <strong>of</strong> support. In <strong>the</strong> implementation <strong>of</strong> KM in an organization transferring into a LO, since leaders
have undeniable influence on <strong>the</strong> employees’ opinions and acceptance, <strong>the</strong>y should give complete
participation and guidance to <strong>the</strong> entire processes.
In a better word, <strong>the</strong>y should provide a motivation toward organizational culture for KM, show
willingness to share information and knowledge freely and to learn from o<strong>the</strong>rs in organization. Their
positive attitude reflects this fact that knowledge and learning solve organizational problems and
improve <strong>the</strong> organizations effectiveness. The more <strong>the</strong> leadership support, <strong>the</strong> more ready for KM
implementation is an organization transferring into LO.
As mentioned above, <strong>the</strong> proposed framework for <strong>the</strong> assessment <strong>of</strong> KM readiness <strong>of</strong> a learning
organization in this paper is based on <strong>the</strong> analytical reasoning <strong>of</strong> elements and issues in current well-
known organizational readiness models (see table 3). In this analysis, <strong>the</strong> lack <strong>of</strong> awareness about
<strong>the</strong> current KM status in each model, is covered by <strong>the</strong> issues <strong>of</strong> <strong>the</strong> o<strong>the</strong>r model and <strong>the</strong> dimensions
<strong>of</strong> proposed framework contain all <strong>of</strong> <strong>the</strong> required issues. Therefore, <strong>the</strong> claim <strong>of</strong> proposing a holistic
framework seems reasonable.
4. Conclusion and future work
In this paper, a novel framework was proposed to connect two well-known concepts <strong>of</strong> KM and LO by
explaining <strong>the</strong> level <strong>of</strong> readiness that should be assessed in an organization when it is going to be
transferred to a higher state from <strong>the</strong> viewpoint <strong>of</strong> learning from past experiences. It has been
discussed that for assessing <strong>the</strong> KM readiness <strong>of</strong> an organization while transferring into a LO, <strong>the</strong>
following dimensions should be considered and assessed: 1) Knowledge and learning strategy, 2)
Learning dynamics, 3) Organizational culture, 4) Technological infrastructure, 5) Organizational
structure, 6) Customers, partners and suppliers (external sources <strong>of</strong> knowledge), 7) Tacit and explicit
knowledge process, 8) Incentives based on collective learning , performance and sharing knowledge,
9) Continuous evaluation <strong>of</strong> KM benefits towards performance upgrading and 10) Leadership
supports.
The superiority <strong>of</strong> this framework is mainly in its integrated view in considering various dimensions
from both KM and LO perspectives when it makes a tight connection between <strong>the</strong>m.
One <strong>of</strong> <strong>the</strong> main issues to continue <strong>the</strong> current research that focused on "what should be assessed in
an organization before transforming into LO form <strong>the</strong> KM point <strong>of</strong> view?" is to answer <strong>the</strong> question
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Leila Beig et al
“how to practically assess <strong>the</strong>se aspects in a LO organization”. Ano<strong>the</strong>r issue is to explore <strong>the</strong> current
state <strong>of</strong> an organization and provide several recommendations based on <strong>the</strong> maturity level <strong>of</strong> an
organization in order to considerably improve it.
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87

Experiential Knowledge Creation Processes in <strong>the</strong> Higher
Education Teaching-Learning Process
Didiosky Benítez ¹, Dalgys Pérez², Frederik Questier³ and Chang Zhu³
¹Facultad de Ciencias de la Información y de la Educación, Departamento de
Tecnología Educativa, Universidad Central “Marta Abreu” de Las Villas, Santa
Clara, Villa Clara, Cuba
²Facultad de Ciencias de la Información y de la Educación, Centro de Estudios
de Educación, Universidad Central “Marta Abreu” de Las Villas, Santa Clara,
Villa Clara, Cuba
³Faculty <strong>of</strong> Psychology and Educational Sciences, Department <strong>of</strong> Educational
Sciences, Vrije Universiteit Brussel, Brussels, Belgium
didiosky@uclv.edu.cu
dalgys2@uclv.edu.cu
fquestie@vub.ac.be
chang.zhu@vub.ac.be
Abstract: Higher education institutions, as knowledge-intensive organizations, produce huge volumes <strong>of</strong>
knowledge through direct teaching-learning experiences. However, considering that <strong>the</strong> application <strong>of</strong> knowledge
management in <strong>the</strong> higher education teaching-learning process is a relatively new area for this context, much <strong>of</strong>
<strong>the</strong> knowledge produced is lost when stakeholders decide to leave. In order to contribute to <strong>the</strong> effective
management <strong>of</strong> knowledge in this particular area, this paper presents a <strong>the</strong>oretical model <strong>of</strong> experiential
knowledge creation processes in <strong>the</strong> higher education teaching-learning process. Building on <strong>the</strong> foundational
works <strong>of</strong> Kolb, Nonaka, Wenger, Eraut and o<strong>the</strong>rs, <strong>the</strong> model describes individual and group processes that
underlie <strong>the</strong> creation <strong>of</strong> experiential knowledge through <strong>the</strong> transformation <strong>of</strong> teaching-learning objects <strong>of</strong>
attention, as well as <strong>the</strong> enabling conditions that promote a more favorable climate for experiential knowledge
creation in <strong>the</strong> HE teaching-learning process. In addition to this, we describe how <strong>the</strong> proposed <strong>the</strong>oretical model
can serve as a useful framework for three main activities connected to innovation in higher education: (1) <strong>the</strong>
design and implementation <strong>of</strong> teaching-learning approaches; (2) <strong>the</strong> development <strong>of</strong> information and
communication technologies and; (3) <strong>the</strong> design and implementation <strong>of</strong> assessment measures and methods for
academic programs.
Keywords: knowledge creation, experiential knowledge, teaching/learning process, innovation, higher education
1. Introduction
Knowledge acquired through life experiences is one <strong>of</strong> <strong>the</strong> most important sources for innovation in
organizations. Since what people learn as users <strong>of</strong> technologies is an indirect but powerful way to
innovate (OECD 2004), <strong>the</strong> effective management <strong>of</strong> experiential knowledge has become a priority for
organizations that need to innovate in today’s knowledge economy, including higher education
institutions.
Higher education institutions, as knowledge-intensive organizations, have been in <strong>the</strong> knowledge
business for a long time (Rowley 2000). However, in spite <strong>of</strong> <strong>the</strong> requirements for constant innovation
<strong>of</strong> curriculums and teaching-learning methods, <strong>the</strong> application <strong>of</strong> knowledge management in higher
education (HE) constitutes a recent research field (Cranfield and Taylor 2008). As knowledge is <strong>the</strong>
major product <strong>of</strong> universities, including experiential knowledge in <strong>the</strong> form <strong>of</strong> personnel (Wijetunge
2002), questions arise about what processes underlie <strong>the</strong> creation <strong>of</strong> knowledge derived from
teaching and learning experiences in <strong>the</strong> HE teaching-learning process; but also, how <strong>the</strong>y are
connected to <strong>the</strong> innovation <strong>of</strong> such process.
In finding answers to <strong>the</strong> above questions, this paper presents a <strong>the</strong>oretical model <strong>of</strong> experiential
knowledge creation processes in <strong>the</strong> HE teaching-learning process, and how it can be used as useful
framework to encourage innovation in such process. The paper begins with a brief introduction to
experiential knowledge related concepts and <strong>the</strong>ories; <strong>the</strong>n, connects <strong>the</strong>se foundations to <strong>the</strong> HE
teaching-learning process; and finally, presents <strong>the</strong> <strong>the</strong>oretical model and some <strong>of</strong> <strong>the</strong> propositions for
innovation in higher education that emerged from <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> enabling conditions for
experiential knowledge creation, as well as its underlying processes.
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2. Basic concepts on experiential knowledge
All people learn from <strong>the</strong>ir own experiences. But experiential learning refers to situations where
experience is initially apprehended at <strong>the</strong> level <strong>of</strong> simple impressions, thus requiring a fur<strong>the</strong>r period
<strong>of</strong> reflection before it is assimilated. Experience refers to whatever humans perceive or understand
from <strong>the</strong>ir interactions with <strong>the</strong> environment, whereas knowledge refers to <strong>the</strong> content <strong>of</strong> <strong>the</strong> human
representation <strong>of</strong> experiences. Thus, in this paper <strong>the</strong> term ‘experiential knowledge’ is used to refer
things perceived from concrete experiences, things tacitly (or implicitly) learned or acquired after a
period <strong>of</strong> reflection on experiences (Eraut 1994; Storkerson 2009).
Experiential knowledge is gained through personal participation in or observation <strong>of</strong> concrete
experiences. The reasons why it remains initially at <strong>the</strong> level <strong>of</strong> impressions may be linked to <strong>the</strong>
learning intent or <strong>the</strong> need for simultaneous action at <strong>the</strong> moment <strong>of</strong> experiences. However, <strong>the</strong>re is
not much evidence about what is learned from experience. What we know is that experiential
knowledge depends on what is perceived, and on time dedicated to reflection (Eraut 1994).
2.1 Experiential knowledge categories
According to Eraut's (1994) knowledge categories, experiential knowledge can be classified into:
knowledge <strong>of</strong> people, situational knowledge, knowledge <strong>of</strong> practice, conceptual knowledge, process
knowledge, and control knowledge. This typology, as Eraut (1994) suggests, is a powerful tool for
thinking about <strong>the</strong> nature <strong>of</strong> experiential knowledge in educational contexts and how it might be
fur<strong>the</strong>r developed.
Knowledge <strong>of</strong> people refers to knowledge acquired unintentionally as a by-product <strong>of</strong> encounters that
have o<strong>the</strong>r purposes. These may be direct encounters or encounters with third parties who provide
indirect information about <strong>the</strong> person concerned. On <strong>the</strong> o<strong>the</strong>r hand, situational knowledge is
concerned with how people describe <strong>the</strong> situations in which <strong>the</strong>y find <strong>the</strong>mselves. This includes
significant features <strong>of</strong> situations, aspects susceptible to change, influences to certain decisions and
events. In addition to this, knowledge <strong>of</strong> practice covers <strong>the</strong> whole repertoire <strong>of</strong> possible practices
from which educators are able to choose. This includes not only some awareness <strong>of</strong> <strong>the</strong> existence <strong>of</strong>
those practices, but also its advantages, limitations, feasibility, and desirability. Closely related to this
knowledge <strong>of</strong> practice is process knowledge, which is knowledge <strong>of</strong> how to do things and how to get
things done. It usually requires a combination <strong>of</strong> knowledge and skills. It is partly a matter <strong>of</strong> knowing
all <strong>the</strong> things that one has to do and partly a matter <strong>of</strong> possessing and using practical skills.
Conceptual knowledge is defined as that set <strong>of</strong> concepts, <strong>the</strong>ories and ideas that a person has
consciously stored in memory. It includes not only <strong>the</strong> concepts <strong>the</strong>mselves but also its links to
particular contexts <strong>of</strong> use. Finally, control knowledge refers to knowledge that is important to control
one’s owns behaviors. It includes self-awareness and sensitivity; self-knowledge about one’s
strengths and weaknesses; self-management in <strong>the</strong> use <strong>of</strong> time, prioritization and delegation. In
general, even though <strong>the</strong>se categories seem different types <strong>of</strong> knowledge, most important educational
processes involve <strong>the</strong>ir combined use. However, as <strong>the</strong>se categories recognize experiential
knowledge as somewhat fallible, validity problems must have to be taken into account in any use <strong>of</strong>
experiential knowledge (Eraut 1994).
2.2 Experiential learningThere are several works linked to experiential learning, but as Lewis and
Williams (in Beaudin and Quick 1995:11) agree, Kolb’s experiential learning <strong>the</strong>ory is one <strong>of</strong> <strong>the</strong> most
influential works in this field. The <strong>the</strong>ory stands from six foundational propositions with regard to
learning: (1) learning is best conceived as a process, not in terms <strong>of</strong> outcomes; (2) all learning is relearning;
(3) learning requires <strong>the</strong> resolution <strong>of</strong> conflicts between dialectically opposed modes <strong>of</strong>
adaptation to <strong>the</strong> world; (4) learning is a holistic process <strong>of</strong> adaptation; (5) learning results from
synergetic transactions between <strong>the</strong> person and <strong>the</strong> environment and (6) learning is <strong>the</strong> process <strong>of</strong>
creating knowledge.
From Kolb’s perspective concrete experiences are <strong>the</strong> base for observations and reflections. These
reflections are assimilated into abstract concepts from which implications for action can be drawn.
Finally, <strong>the</strong>se implications can be actively tested and serve as guides in creating new experiences
conforming <strong>the</strong> experiential learning cycle, as shown in Figure 1.
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Figure 1: Experiential Learning Cycle (Kolb and Kolb 2008)
3. The higher education teaching-learning process
Instruction is one <strong>of</strong> <strong>the</strong> major functions <strong>of</strong> higher education institutions. As an institutional function, it
addresses <strong>the</strong> teaching-learning process at universities as well as its most important underlying
activities: teaching and learning. In formal higher education curriculum and teaching are formally
organized, whereas learning is intentional and explicit designed in terms <strong>of</strong> learning objectives, time
and support (Werquin 2010). As far as ‘education in a formal setting’ is concerned, teaching is <strong>the</strong>
activity performed by teachers in <strong>the</strong> teaching-learning process while learning, as a process, <strong>the</strong>
activity developed by learners.
In higher education <strong>the</strong> teaching-learning process is structured in terms <strong>of</strong> courses, fields <strong>of</strong> study,
subjects, topics, lessons and tasks. They conform a structural hierarchy where tasks, with <strong>the</strong> lowest
level <strong>of</strong> complexity, are located at <strong>the</strong> bottom <strong>of</strong> this hierarchy whereas courses are at <strong>the</strong> top. But,
even though <strong>the</strong> teaching-learning process is projected throughout <strong>the</strong> mentioned hierarchy, it is only
at <strong>the</strong> task level where it is personalized. Thus, from an individual perspective, <strong>the</strong> HE teachinglearning
process can be seen as successive sequence <strong>of</strong> tasks.
3.1 Experiential knowledge categories in <strong>the</strong> higher education teaching-learning
processEraut's (1994) categories are not directly connected to <strong>the</strong> teaching-learning process. They
are general categories that cover those areas <strong>of</strong> knowledge usually found in education where
teaching and learning are included. In accordance with this, Eraut’s categories can be used to
describe <strong>the</strong> experiential knowledge produced by participants <strong>of</strong> <strong>the</strong> HE teaching-learning process.
As teaching and learning are <strong>the</strong> two major activities in <strong>the</strong> HE teaching-learning process, experiential
knowledge categories can be mapped to <strong>the</strong> particular context <strong>of</strong> each activity, as is shown in Figure
2.
Figure 2: Experiential knowledge categories in <strong>the</strong> higher education teaching-learning process
In teaching, knowledge <strong>of</strong> people is linked to judgments or opinions about teaching participants. It’s
acquired through direct and indirect encounters with those people whom are in <strong>the</strong> role <strong>of</strong> teacher.
Situational knowledge is mapped with <strong>the</strong> teaching situation, with what is considered significant or
susceptible to change. On <strong>the</strong> o<strong>the</strong>r hand, knowledge <strong>of</strong> practice is connected to teaching
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approaches, but also its advantages, limitations, feasibility and desirability. Accordingly, process
knowledge is connected to get teaching approaches done, including <strong>the</strong> knowledge and necessary
skills to implement <strong>the</strong>m. In addition, conceptual knowledge is linked to <strong>the</strong> practical use <strong>of</strong> teaching
concepts and <strong>the</strong>ories, while control knowledge with knowledge that teachers use to control <strong>the</strong>ir own
behaviors in teaching.
Similarly, in learning, knowledge <strong>of</strong> people is mapped to judgments or opinions about learning
participants; situational knowledge to those aspects that are more susceptible to change in learning
situations; knowledge <strong>of</strong> practice to advantages, limitations, feasibility and desirability <strong>of</strong> learning
strategies; process knowledge to how to get learning strategies done; conceptual knowledge to <strong>the</strong>
practical use <strong>of</strong> learning concepts and, ideas; and control knowledge to knowledge that learners use
to control <strong>the</strong>ir own behaviors in learning.
4. Experiential knowledge creation processes in <strong>the</strong> higher education
teaching-learning process
Kolb’s (1984) experiential learning <strong>the</strong>ory suggests that experiential knowledge is created through <strong>the</strong>
transformation <strong>of</strong> <strong>the</strong> experience at <strong>the</strong> individual level. On <strong>the</strong> o<strong>the</strong>r hand, Nonaka's (1994) <strong>the</strong>ory <strong>of</strong>
organizational knowledge creation describes a process that, at <strong>the</strong> organizational level, amplifies <strong>the</strong>
knowledge created by individuals. As experiential knowledge is just a particular type <strong>of</strong> knowledge,
this paper considers Nonaka’s <strong>the</strong>ory as <strong>the</strong> <strong>the</strong>oretical foundation for an organizational perspective
<strong>of</strong> such knowledge.
In relation to organizational knowledge, Mcelroy (2000) distinguished two general processes:
knowledge production and knowledge integration. In this sense, experiential knowledge creation
processes can be understood as <strong>the</strong> integration <strong>of</strong> Kolb and Nonaka’s <strong>the</strong>ories production side in <strong>the</strong>
context <strong>of</strong> <strong>the</strong> HE teaching-learning process. As a result, experiential knowledge creation processes
are linked to knowledge production in HE institutions and comprises: constructing teaching-learning
objects <strong>of</strong> attention, socialization <strong>of</strong> stories, development <strong>of</strong> <strong>the</strong>ories and active experimentation.
Experiential knowledge creation processes can be seen as natural knowledge processes since
knowledge, in educational organizations, is <strong>the</strong> product <strong>of</strong> natural innovation schemes (Mcelroy 2000;
Petrides and Nodine 2003). They describe a holistic approach where teachers and students, referring
to teaching or learning activity, construct objects <strong>of</strong> attention; socialize stories; develop new <strong>the</strong>ories
and actively test <strong>the</strong>m (see Figure 3). In addition to this, <strong>the</strong> mentioned processes can have different
competing views, one <strong>of</strong> which –innovation in higher education- is addressed in later sections.
Figure 3: Experiential knowledge creation processes in <strong>the</strong> higher education teaching-learning
process
In what follows experiential knowledge processes are discussed, in addition to <strong>the</strong> enabling conditions
that promote a more favorable climate for <strong>the</strong> occurrence <strong>of</strong> such processes.
4.1 Construction <strong>of</strong> teaching-learning objects <strong>of</strong> attentionPeople are embedded in a
continuous flow <strong>of</strong> experience throughout <strong>the</strong>ir lives. At times, discrete experiences in this flow acquire
significant meaning or relevance, constituting <strong>the</strong> basic schemes that we use to interpret new
experiences. A discrete experience becomes meaningful after we attended and reflect upon it. This
‘act <strong>of</strong> attention’, brings particular experiences into <strong>the</strong> area <strong>of</strong> conscious thought conferring different
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meanings according to <strong>the</strong> moment. Discrete experiences also become subsumed within a higher
level ‘object <strong>of</strong> attention’, when separate acts <strong>of</strong> attention are considered to construct a higher level <strong>of</strong>
meaning. However, <strong>the</strong> construction <strong>of</strong> higher level objects <strong>of</strong> attention, even from o<strong>the</strong>r low level
objects, always requires that <strong>the</strong>y can be apprehended as single entities (Eraut 1994).
As basic experiences can be shared simultaneously with ‘o<strong>the</strong>rs’ in a common time, <strong>the</strong>re are many
experiences that not felt as my or your experience but as our experience. This experience, whose
nature is symbolic, permits us to focus on <strong>the</strong> meaning <strong>of</strong> common actions if we attend and reflect on
it (Muzzetto 2006). Therefore, as acts <strong>of</strong> attention could be focused on personal or common
experiences, <strong>the</strong> construction <strong>of</strong> objects <strong>of</strong> attention encompass ga<strong>the</strong>red toge<strong>the</strong>r into a higher
syn<strong>the</strong>sis: separated personal acts <strong>of</strong> attention, common acts <strong>of</strong> attention, a mix <strong>of</strong> personal and
common acts, or low level objects <strong>of</strong> attention.
In educational institutions, teachers and students are embedded in a continuous flow <strong>of</strong> teachinglearning
experiences. These personal or common experiences come from teaching-learning situations
where teachers and students participate or observe. As <strong>the</strong> HE teaching-learning process is
structured in terms <strong>of</strong> courses, fields <strong>of</strong> study, subjects, topics, lessons and tasks; teachers and
students can construct objects <strong>of</strong> attention as a higher syn<strong>the</strong>sis <strong>of</strong> <strong>the</strong>ir experiences in a particular
course, subject or any <strong>of</strong> <strong>the</strong> o<strong>the</strong>r structures. Such objects <strong>of</strong> attention, derived from <strong>the</strong> structural
units <strong>of</strong> <strong>the</strong> HE teaching-learning process, are what we called ‘teaching-learning objects <strong>of</strong> attention’.
Teachers or students construct teaching-learning objects <strong>of</strong> attention at <strong>the</strong> individual level. They
focus on experiences connected to <strong>the</strong> teaching-learning process, in order to assimilate whatever
<strong>the</strong>y have learned ei<strong>the</strong>r in tacit or implicit way. As knowledge is created by individuals, this process <strong>of</strong>
objects construction constitutes <strong>the</strong> base to articulate and amplify experiential knowledge in <strong>the</strong> HE
teaching-learning process.
4.2 Socialization <strong>of</strong> stories
As people acquire knowledge from <strong>the</strong>ir experiences, experiential knowledge is linked to things that
we learned or acquired tacitly or implicitly. But, as Baumeister and Newman (in Storkerson 2009:5)
suggested, in order to make sense <strong>of</strong> experiences people nearly always construct <strong>the</strong>m in story form.
Choo (in Stenmark 2000:14) agree that stories, as o<strong>the</strong>r rich modes <strong>of</strong> discourse, can be used to
reveal <strong>the</strong> tacitness nature <strong>of</strong> experiential knowledge. Therefore, teachers and students can construct
teaching and learning stories to express what <strong>the</strong>y have learned from experiences after focussing on
particular teaching-learning objects <strong>of</strong> attention.
Successful teaching and learning stories as well as negatives can teach important lessons. From a
teaching story we can learn how particular teaching concepts like ‘student-centered approach’, could
be implemented in practice, but also which teaching approaches are more feasible for its
accomplishment. On <strong>the</strong> o<strong>the</strong>r hand, it is possible to learn from a learning story which negative
results suggest that <strong>the</strong> learning approach followed present serious practical limitations. In both cases
we can learn and socialize what have been learned.
Through <strong>the</strong> socialization <strong>of</strong> stories in informal communities around learning and teaching practice,
experiential knowledge reaches a social context within which it can be amplified. In this sense,
individual stories conveying various form <strong>of</strong> experiential knowledge are converted to form a common
base for understanding. As members <strong>of</strong> informal communities <strong>of</strong> practice, teachers and students
share <strong>the</strong>ir experiences in form <strong>of</strong> stories and build <strong>the</strong> mutual trust and a common perspective which
are required for a fur<strong>the</strong>r development <strong>of</strong> ‘<strong>the</strong>ories’ (Nonaka 1994).
4.3 Development <strong>of</strong> <strong>the</strong>ories
Once mutual trust and a common perspective have been formed in informal communities, its
members can engage in <strong>the</strong> co-development <strong>of</strong> ‘<strong>the</strong>ories’ (ideas and abstract concepts) that proceed
from <strong>the</strong>ir stories. In this process members participate in continuous dialogues where teaching and
learning <strong>the</strong>ories are built in cooperation with o<strong>the</strong>rs (Nonaka 1994).
Teaching and learning <strong>the</strong>ories derived from stories are domain specific, and sometimes holistic.
According to this, a teaching <strong>the</strong>ory can refer to how to teach a particular content <strong>of</strong> a subject or to
which specific conditions using rounds –as a technique that encourages participation- are more
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effective than o<strong>the</strong>rs to encourage students’ participation in class. On <strong>the</strong> o<strong>the</strong>r hand, a learning
<strong>the</strong>ory around problem-based scenarios could state which reading and research are required in order
to individually solve a particular problem assigned in class.
Through <strong>the</strong> articulation <strong>of</strong> teaching and learning <strong>the</strong>ories that proceed from story forms <strong>of</strong> knowledge,
experiential knowledge acquired a more concise and logical form. As a result, members <strong>of</strong> <strong>the</strong>
informal communities can construct implications from developed <strong>the</strong>ories in order to test and validate
<strong>the</strong>m through experimentation.
4.4 Active Experimentation
Active experimentation is <strong>the</strong> process through which members <strong>of</strong> informal communities test and
validate <strong>the</strong> <strong>the</strong>ories that have been developed. In this sense, <strong>the</strong>y construct implications for action
that serve as a guide in creating new experiences. As implications may be seen as hypo<strong>the</strong>sizes and
<strong>the</strong> required knowledge to test <strong>the</strong>m (Kolb 1984), <strong>the</strong> dominance mode <strong>of</strong> knowledge conversion in its
construction is combination while actually testing <strong>the</strong>m result in <strong>the</strong> dominance <strong>of</strong> internalization.
As a result <strong>of</strong> active experimentation on teaching and learning <strong>the</strong>ories, individuals create new
experiences that lead to <strong>the</strong> transformation <strong>of</strong> <strong>the</strong>ir teaching-learning objects <strong>of</strong> attention. Thus, from
<strong>the</strong>se teaching-learning objects <strong>of</strong> attention, new stories can be created, as well as new <strong>the</strong>ories to
test, forming a process where individuals continuously learn from <strong>the</strong>ir own experiences.
4.5 Enabling conditions
Even though experiential knowledge follows a natural evolution in <strong>the</strong> HE teaching-learning process,
some conditions enable a more favorable climate for its creation. As experiential knowledge
processes can be seen from different competing views, new conditions may emerge in addition to:
individual commitment, institutionalization <strong>of</strong> reflection in action, redundancy, requisite variety, support
<strong>of</strong> communities, and learners’ competencies.
At <strong>the</strong> core <strong>of</strong> experiential knowledge creation processes are individuals who continuously create and
recreate experiential knowledge from <strong>the</strong>ir natural adaptation to <strong>the</strong> environment. Thus, in <strong>the</strong> HE
teaching-learning processes, individual commitment underlies teacher and student motivations to form
new knowledge; <strong>the</strong>ir judgments about <strong>the</strong> knowledge perceived or created; and <strong>the</strong>ir attitudes
(Nonaka 1994). On <strong>the</strong> o<strong>the</strong>r hand, individual competencies refer to skills related with acting, valuing,
thinking and decision (Kolb, Boyatzis et al. 2001).
As much as individuals, <strong>the</strong> environment has a significant influence on <strong>the</strong> experiential knowledge
creation processes. In this sense, <strong>the</strong> institutionalization <strong>of</strong> reflection in action, redundancy, requisite
variety and support <strong>of</strong> communities are factors that must be taken into account in order to foster such
processes. Thus, in <strong>the</strong> HE teaching-learning process, institutionalization <strong>of</strong> reflection in action
considers reflection as a planned action in every teaching-learning activity, as <strong>the</strong> product <strong>of</strong> <strong>the</strong>
intentionality; while redundancy refers to sharing extra information, adopting overlapping approaches
and implementing strategic rotations. Redundancy increases <strong>the</strong> amount <strong>of</strong> information that can be
processed in <strong>the</strong> teaching-learning process. Requisite variety refers to <strong>the</strong> existence <strong>of</strong> different
approaches to facilitate <strong>the</strong> processing <strong>of</strong> such information. Support <strong>of</strong> communities refers to <strong>the</strong>
institutional efforts to: give members <strong>the</strong> time and <strong>the</strong> space to participate in informal communities <strong>of</strong>
practice; pay attention to what emerges from <strong>the</strong>se communities; foster <strong>the</strong>ir formation; adapt rewards
systems and so on (Nonaka 1994; Wenger 1998).
5. Experiential knowledge creation processes in <strong>the</strong> innovation <strong>of</strong> higher
education
Innovation can be understood as a ‘process in which <strong>the</strong> organization creates and defines problems
and <strong>the</strong>n actively develops new knowledge to solve <strong>the</strong>m’ (Nonaka 1994:14). According to this, new
knowledge serves to fuel <strong>the</strong> development <strong>of</strong> organizations, in particular new knowledge derived from
<strong>the</strong> collaboration between users and/or doers (OECD 2004). As teachers and students are <strong>the</strong> main
‘users’ <strong>of</strong> knowledge in <strong>the</strong> HE teaching-learning process, <strong>the</strong> experiential knowledge coming from
<strong>the</strong>ir collaboration throughout experiential knowledge creation processes become an important source
<strong>of</strong> innovations in higher education.
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In previous sections, experiential knowledge creation processes were described from a general
perspective. Next subsections will focus on experiential knowledge creation processes as a useful
framework to encourage innovation through teaching-learning approaches, information and
communication technologies, and academic program assessments.
5.1 Innovation through teaching-learning approachesThere are a number <strong>of</strong> teacherlearning
approaches in higher education (e.g. problem solving, collaborative/cooperative learning and
independent learning) that is impossible to describe each <strong>of</strong> <strong>the</strong>m in relation to experiential knowledge
creation processes. However, even though all approaches are different, three general design and
implementation principles can be distinguished in order to encourage innovation through teachinglearning
approaches.
Teaching-learning approaches should develop learner’s adaptive competencies and commitment.
This is a principle aligned with <strong>the</strong> current trends in higher education. It refers to <strong>the</strong> design and
implementation <strong>of</strong> learning situations oriented to facilitate <strong>the</strong> development <strong>of</strong> skills and abilities for
action taking, sense-making, information processing and decision-making; as well as situations that
motivate students, capture <strong>the</strong>ir attention, question <strong>the</strong>ir attitudes, but also <strong>the</strong>ir frames to judge <strong>the</strong>
value <strong>of</strong> knowledge perceived or created.
Teaching-learning approaches should plan reflection activities. This principle considers reflection on
teaching-learning experiences an integral part <strong>of</strong> every teaching-learning activity, in particular, as a
planned activity that facilitates learning from those experiences; in addition to <strong>the</strong> design and
implementation <strong>of</strong> overlapping and rotation activities to fuel reflection processes.
Teaching-learning approaches should support <strong>the</strong> development <strong>of</strong> communities. As communities are
everywhere, this principle is regarding to promote <strong>the</strong> development <strong>of</strong> communities that emerges from
<strong>the</strong> above mentioned reflection processes. It means, given time and space to participate in those
communities, but also to foster <strong>the</strong>ir formation and participation through existent reward systems like
grades.
5.2 Innovation through information and communication technologies
Information and communication technologies (ICT) enable us to enhance invention in organizations.
They can affect knowledge creation, reduce <strong>the</strong> need for physical proximity and facilitate new modes
<strong>of</strong> knowledge production (OECD 2004). Therefore, as a source <strong>of</strong> innovation, ICT <strong>of</strong>fers a number <strong>of</strong>
opportunities to move innovation forward in higher education.
Experiential knowledge creation processes can be used as a framework to develop new information
and communications technologies that support teaching-learning approaches looking for innovations.
As technologies facilitate human processes, new technologies based on <strong>the</strong> experiential knowledge
creation processes can be oriented to support <strong>the</strong>ory building from shared learning and teaching
stories. As a general principle, stories and <strong>the</strong>ories should be considered <strong>the</strong> foundational concepts to
take into account in <strong>the</strong> development <strong>of</strong> such technologies, in addition to story and <strong>the</strong>ory networks
(see Figure 4). Such elements, altoge<strong>the</strong>r, can be seen as <strong>the</strong> base for an open construction <strong>of</strong>
teaching and learning <strong>the</strong>ories supported by ICT.
Figure 4: Story and <strong>the</strong>ory networks
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Besides <strong>the</strong> foundational concepts mentioned above, technologies based on experiential knowledge
creation processes would embrace well known social tools like tagging, rating and commentary
without excluding o<strong>the</strong>rs like recommendation engines. Additionally, <strong>the</strong>y would manage different
digitals formats <strong>of</strong> stories and provide facilities and services to connect stories and <strong>the</strong>ories with
teachers and students. In general, ICT can’t innovate by <strong>the</strong>mselves, but taking into account
foundational concepts linked to innovation in <strong>the</strong> HE teaching-learning process, <strong>the</strong>y enhance <strong>the</strong>
opportunities to innovate in such process.
5.3 Innovation through academic program assessments
Innovations in higher education are closely related to academic program assessment as <strong>the</strong>y can be
<strong>the</strong> product <strong>of</strong> assessment cycles. As regard to academic programs, assessment is oriented to
improve student learning outcomes and follows a cycle <strong>of</strong> goal setting, measures <strong>of</strong> outcomes,
interpretation <strong>of</strong> results, and improvement <strong>of</strong> programs (Bridgewater-State-College 2010). However, a
focus on student learning without considering what <strong>the</strong> institution learns as an organization, limits our
capacity to produce innovations in higher education through academic program assessments.
A focus on what <strong>the</strong> university learns from <strong>the</strong> teaching-learning process, could lead to assessment
goals, methods and measures on whatever is commonly known by teachers and/or students (e.g.
values, beliefs, practices and assumptions). As organizational stories emerge from real life
experiences and reflect norms, values and culture <strong>of</strong> organizations (Çelik 2010), experiential
knowledge creation processes can be seen as a useful framework to develop assessment measures
and methods enhancing <strong>the</strong> opportunities to innovate through academic program assessments. Thus,
through <strong>the</strong> use <strong>of</strong> different methods followed in analyzing stories and taking into account experiential
knowledge creation processes new assessment measures and methods could be developed.
As a general principle academic program assessments should consider <strong>the</strong> use <strong>of</strong> measures and
methods supported on <strong>the</strong> social network analysis <strong>of</strong> stories and <strong>the</strong>ories. According to this, for
instance, story network (as a method) might be used to support <strong>the</strong> social network analysis <strong>of</strong>
teaching and learning stories; whereas network measures like <strong>the</strong> degree <strong>of</strong> centrality (‘most
important’) and betweenness (‘important’), might be used to reflect how important particular practices
are in relation to <strong>the</strong> expected outcomes.
6. Conclusions
This paper has attempted to fill existing gaps around <strong>the</strong> creation <strong>of</strong> knowledge derived from teaching
and learning experiences in <strong>the</strong> context <strong>of</strong> Higher Education. From a <strong>the</strong>oretical perspective, <strong>the</strong>
paper described <strong>the</strong> construction <strong>of</strong> teaching-learning objects <strong>of</strong> attention, socialization <strong>of</strong> stories,
development <strong>of</strong> <strong>the</strong>ories and active experimentation as <strong>the</strong> processes that underlie <strong>the</strong> experiential
knowledge creation in <strong>the</strong> HE teaching-learning process. As experiential knowledge is created by
individuals, teachers’ and learners’ commitment and competencies were outlined in <strong>the</strong> paper as
factors (or enabling conditions) that promote a more favorable climate for <strong>the</strong> experiential knowledge
creation in <strong>the</strong> HE teaching-learning process. In addition, factors derived from <strong>the</strong> influence <strong>of</strong> <strong>the</strong>
environment like institutionalization <strong>of</strong> reflection in action, redundancy, requisite variety and support <strong>of</strong>
communities were also described in <strong>the</strong> paper.
Even although fur<strong>the</strong>r research is required to provide empirical evidences <strong>of</strong> <strong>the</strong> initial findings <strong>of</strong> <strong>the</strong>
paper, <strong>the</strong> knowledge creation processes outlined in this paper can provide a framework for
understanding how and where to focus our energy for innovation in higher education, in particular,
from knowledge gained through directed teaching-learning experiences. Thus, from using <strong>the</strong> above
mentioned processes as a <strong>the</strong>oretical framework to encourage innovation in higher education, <strong>the</strong>
paper suggested as general principles that:
Teaching-learning approaches should develop learner’s adaptive competencies and commitment.
Teaching-learning approaches should plan reflection activities.
Teaching-learning approaches should support <strong>the</strong> development <strong>of</strong> communities.
Teaching and learning stories (as detailed narratives), ‘<strong>the</strong>ories’ (derived from stories), and social
networks (around stories and <strong>the</strong>ories) should be considered foundational concepts for <strong>the</strong>
development <strong>of</strong> ICT that support teaching-learning approaches looking for innovation.
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Academic program assessments should consider <strong>the</strong> use <strong>of</strong> measures and methods supported on
<strong>the</strong> social network analysis <strong>of</strong> stories and <strong>the</strong>ories.
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96

Meta-Analysis <strong>of</strong> Publications on Web 2.0: Impact,
Productivity, Prevalent Topics and Research Agendas
Pavel Bogolyubov
Lancaster University Management School, Lancaster, UK
p.bogolyubov1@lancaster.ac.uk
Abstract: Web 2.0 is a relatively young field <strong>of</strong> study and <strong>the</strong> body <strong>of</strong> publications on it is still developing. The
paper <strong>of</strong>fers an in-depth analysis <strong>of</strong> <strong>the</strong> prevalent topics discussed in <strong>the</strong> business-oriented literature in relation to
Web 2.0 as well as <strong>the</strong> understanding <strong>of</strong> where <strong>the</strong> research is done (institution/country) and what <strong>the</strong> citation
impact is – in o<strong>the</strong>r words, <strong>the</strong> locale <strong>of</strong> <strong>the</strong> research, <strong>the</strong> quality <strong>of</strong> publications and <strong>the</strong> prominent research
topics. Even more importantly, it identifies emerging debates in <strong>the</strong> key areas relevant to <strong>the</strong> overall conference
<strong>the</strong>me. To assess <strong>the</strong> productivity and impact, ABI/Inform database was interrogated to compile <strong>the</strong> list <strong>of</strong> papers
(search for “Web 2.0” string, all fields and texts, scholarly journals only). For <strong>the</strong> search results, <strong>the</strong> following
information was ga<strong>the</strong>red: <strong>the</strong> number <strong>of</strong> citations (found via Google in databases such as SpringerLink, Emerald
Insight or Google Scholar), authors’ and institutions names and locations as well as keywords – from ABI/Inform,
o<strong>the</strong>r databases or from papers <strong>the</strong>mselves depending on <strong>the</strong> information availability. The resulting numbers
were subjected to simple statistical analysis: for each institution and each country three numbers were calculated:
total number <strong>of</strong> “hits” (people (co-)authoring papers), total number <strong>of</strong> citations per hit (time-normalised) and <strong>the</strong>
ratio between <strong>the</strong> two. The keywords (tags) were counted by <strong>the</strong> number <strong>of</strong> appearances on <strong>the</strong> list, and <strong>the</strong>
analysis highlighted a variety <strong>of</strong> topics that are discussed <strong>the</strong> most, with a number <strong>of</strong> trends highlighted. The full
sets <strong>of</strong> results are shown in <strong>the</strong> paper in two ways: using “league tables” and tag clouds. The publications
dedicated to aspects <strong>of</strong> Web 2.0 in KM are looked at in more detail in order to identify emerging <strong>the</strong>mes and
research agendas.
Keywords: Web 2.0, meta-analysis, knowledge management
1. Introduction
Web 2.0 – <strong>the</strong> “new” version <strong>of</strong> <strong>the</strong> Internet - is a comparatively young phenomenon: subject to some
debate, <strong>the</strong> term was coined by Darcy DiNucci in 1999 (Ruiz 2008), and <strong>the</strong> technological paradigm
per se has gained prominence from mid-2000s with <strong>the</strong> launch <strong>of</strong> a number <strong>of</strong> platforms and major
websites, e.g., Wikipedia (2001) and Facebook (2004) and <strong>the</strong> appearance <strong>of</strong> <strong>the</strong> first publications
dedicated to it in 2005 (Fig 1.). The term itself refers to what Andrew McAfee described as “new digital
platforms for generating, sharing and refining information” that incorporate a number <strong>of</strong> key
components: facilities to search <strong>the</strong> context, create links and author and tag content, as well as
extensions and notification signals - SLATES (McAfee 2006). In essence, we are talking about tools
like Wikis, blogs, social networking platforms and so on, <strong>the</strong> fundamental difference in comparison to
Web 1.0 being <strong>the</strong>ir interactive and participative nature.
The young age <strong>of</strong> <strong>the</strong> field, perhaps along with <strong>the</strong> dynamism <strong>of</strong> its development and <strong>the</strong> hype around
such phenomena as social networking - to <strong>the</strong> best <strong>of</strong> my knowledge, Facebook remains <strong>the</strong> only real
website with an award winning feature-length movie dedicated to it - <strong>the</strong> body <strong>of</strong> publications about
Web 2.0 appears to be at a development stage, both in quantity and quality. As per November 2010,
<strong>the</strong>re were <strong>the</strong> total <strong>of</strong> 8210 publications listed in ABI/Inform Global database as containing <strong>the</strong> “Web
2.0” phrase, with as little as 17% scholarly and <strong>the</strong> rest popular; in comparison, KM had 28703
records in total with 59% scholarly (ABI/INFORM 2010).
Although it is a fairly focussed subject area, <strong>the</strong> list <strong>of</strong> publications’ topics ranges from generalist
pessimistic predictions along <strong>the</strong> lines <strong>of</strong> “It’s ano<strong>the</strong>r bubble and it’s going to burst any time now”
(e.g., Rapoza 2007) to a variety <strong>of</strong> ra<strong>the</strong>r narrow <strong>the</strong>mes such as improving educational accountability
in Colorado (Wenning and Betebenner 2010). Similarly, <strong>the</strong> contributions come from a wide variety <strong>of</strong>
institutions, both commercial and academic from authors all over <strong>the</strong> world.
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Pavel Bogolyubov
Figure 1: number <strong>of</strong> publications on Web 2.0; source: ABI/INFORM, 2010
So, what did <strong>the</strong> field develop into in <strong>the</strong> six years that have passed since it became a publishable
research topic? Apart from bubbles that still don’t appear to be bursting, are <strong>the</strong>re any prevalent
research and discussion topics? Is <strong>the</strong> research led by <strong>the</strong> industry or <strong>the</strong> academia, and are <strong>the</strong>re
any discernible leaders? Taking into account that Web 2.0 giants such as Facebook, Wikipedia,
Twitter and YouTube among many o<strong>the</strong>rs are American, is <strong>the</strong>re similar geographical bias in <strong>the</strong>
research locale, or is W2.0 a worldwide topic?
The paper is dedicated to answering all <strong>the</strong>se questions.
2. Literature review
Various kinds <strong>of</strong> meta-analyses are a type <strong>of</strong> secondary research widely used in many disciplines,
ranging from systematic works such as Cochrane Reviews summarising primary research on specific
topics related to - in this case - healthcare (Anonymous 2011), to qualitative overviews <strong>of</strong> key
research agendas and emerging debates (e.g., Easterby-Smith, Crossan et al. 2000).
Somewhere in <strong>the</strong> middle are productivity and impact analyses, such as Serenko and Bontis’ papers
dedicated to analysing <strong>the</strong> multitude <strong>of</strong> publications on Knowledge Management (Serenko and Bontis
2004; Serenko, Bontis et al. 2010) or ranking <strong>the</strong> KM journals (Serenko and Bontis, 2009), analysing
patterns in personal productivity (Chung 1990) and assessing research institutions impact (e.g.,
Dusansky 1998 and Erkut 2002). The whole field <strong>of</strong> study exists called bibliometrics dedicated to
carrying out statistical analysis <strong>of</strong> published works, with two journals dedicated entirely to it
(Cybermetrics: International Journal <strong>of</strong> Scientometrics, Informetrics and Bibliometrics, and Science
Watch).
Even though <strong>the</strong> range <strong>of</strong> subjects in bibliometrics is quite varied even within <strong>the</strong> boundaries <strong>of</strong>
business and management studies – e.g., <strong>the</strong>re have been studies carried out on information
management, operations research and jurisprudence to name a few (Serenko and Bontis 2004),
however, most <strong>of</strong> <strong>the</strong>m share one commonality: <strong>the</strong>y are dedicated to counting how many papers
have been published and how <strong>of</strong>ten <strong>the</strong>y are cited (by author, institution, country, journal and so on).
The latter is considered to be a long-standing measure <strong>of</strong> publications impact, although not without
equally long-standing discussions challenging <strong>the</strong> correlation between <strong>the</strong> quality <strong>of</strong> cited research
and <strong>the</strong> citation frequency (see, for example, Garfield 1955 and Garfield 1973).
I shall follow <strong>the</strong> established pattern and analyse both productivity and impact <strong>of</strong> relevant publications,
however, providing a series <strong>of</strong> numbers would make little contribution to assessing <strong>the</strong> overall state <strong>of</strong>
<strong>the</strong> field <strong>of</strong> study, and I shall begin with analysing <strong>the</strong> publications content.
3. Data ga<strong>the</strong>ring and research methodology
The list <strong>of</strong> articles to be processed was generated by running a keyword search on ABI/INFORM in
November 2010 (“Web 2.0” search phrase, all fields and text, scholarly sources only, starting from
2005). Among about fifteen hundred returned matches, roughly two hundred had to be discarded –
various calendars, popular news bulletins and digests, conference programmes and incomplete
records in languages I don’t speak. For <strong>the</strong> remaining 1323 papers <strong>the</strong> following data was collected:
<strong>the</strong> year <strong>of</strong> publication, authors’ names, affiliation and country, number <strong>of</strong> citations, and keywords. All
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Pavel Bogolyubov
fields but year and names more <strong>of</strong>ten than not involved extensive search on o<strong>the</strong>r databases such as
Emerald Insight and SpringerLink or using Google, since original records were <strong>of</strong>ten incomplete.
Citations were taken exclusively from Google Scholar: in general, <strong>the</strong>re was a well pronounced
disparity between <strong>the</strong> numbers on ABI and Google, sometimes by <strong>the</strong> factor <strong>of</strong> ten, and in all but two
or three cases <strong>the</strong> citation numbers on ABI were lower; at <strong>the</strong> same time, Google’s figures were
consistent with o<strong>the</strong>r databases.
Keywords, providing a basis for fur<strong>the</strong>r contents analysis, were predominantly ABIs for <strong>the</strong> sake <strong>of</strong>
consistency, however, in a limited number <strong>of</strong> cases <strong>the</strong>y had to be taken from o<strong>the</strong>r databases or
suggested upon reading <strong>the</strong> papers.
It is worth mentioning that all figures discussed in this paper were valid in November 2010; <strong>the</strong>y tend
to change fairly rapidly as I have seen several times during <strong>the</strong> few months <strong>of</strong> working on <strong>the</strong> paper,
and this is effectively a snapshot in time.
As far as processing <strong>the</strong> numbers is concerned, <strong>the</strong> intention was to use <strong>the</strong> aforementioned paper by
Serenko and Bontis dedicated to meta-analysis <strong>of</strong> <strong>the</strong> literature on Knowledge Management and
Intellectual Capital (Serenko and Bontis 2004) as a general template, hence <strong>the</strong> choice <strong>of</strong> <strong>the</strong>
database. The fundamental principle <strong>of</strong> assessing <strong>the</strong> productivity and impact as key indicators
remained in force, but a few things were done differently.
First <strong>of</strong> all, instead <strong>of</strong> limiting <strong>the</strong> search to a number <strong>of</strong> leading journals, I have included all available
records, for two reasons: from a technical point <strong>of</strong> view, my set was <strong>of</strong> a manageable size, and
besides, full search would provide a more comprehensive assessment <strong>of</strong> <strong>the</strong> entire field.
Second, I have chosen a straight count method <strong>of</strong> calculating <strong>the</strong> productivity – in o<strong>the</strong>r words, every
author gets a point for (co-) authoring a paper regardless <strong>of</strong> how many people are involved. It is worth
mentioning that <strong>the</strong>re are several o<strong>the</strong>r ways <strong>of</strong> doing it, such as based on author position on <strong>the</strong> list
(<strong>the</strong> fur<strong>the</strong>r down, <strong>the</strong> lower <strong>the</strong> proportion <strong>of</strong> credit is attributed), equal credit (everyone gets an equal
portion <strong>of</strong> <strong>the</strong> total <strong>of</strong> one point) and normalised page size (<strong>the</strong> bigger <strong>the</strong> paper, <strong>the</strong> better), all <strong>of</strong>
<strong>the</strong>m, however, with <strong>the</strong>ir own drawbacks and limitations. Fur<strong>the</strong>rmore, I believe that straight count
provides an answer to <strong>the</strong> question <strong>of</strong> how many research-active authors are in each institution and in
each country - in o<strong>the</strong>r words, it helps to identify <strong>the</strong> research locale.
Third, even though authors’ names were ga<strong>the</strong>red too, I have decided to discard <strong>the</strong>m at least for <strong>the</strong>
time being and not to calculate productivity and impact by name as adding little to answering <strong>the</strong> key
questions.
The citation impact was calculated starting from straight count, however, bearing in mind that citations
accumulate over <strong>the</strong> time, individual numbers were time-normalised to generate <strong>the</strong> Normalised
Citation Impact Index – NCII (Holsapple, Johnson et al. 1994) – simply put, <strong>the</strong> number <strong>of</strong> citations
divided by <strong>the</strong> paper’s age in years. Naturally, <strong>the</strong> methods has its own weaknesses – for instance, a
paper published in December 2009 would get twice less credit than one published a month later,
however, taking papers’ longevity into account in a more accurate way would be unrealistic given <strong>the</strong>
volume <strong>of</strong> data processed.
Publication counts were added up by institution and country to generate total numbers, whereas total
NCIIs were divided by corresponding total publication numbers to show average time-normalised
number <strong>of</strong> citations per author per paper.
As a final point, it is worth mentioning that given <strong>the</strong> amount <strong>of</strong> manual data processing involved, <strong>the</strong>
analysis has proven to be not a pastime for <strong>the</strong> fain<strong>the</strong>arted. An extensive amount <strong>of</strong> pro<strong>of</strong>reading
was involved to avoid duplication, especially in institutions’ names: for example, Oxford University
could be listed in three different ways (Oxford University, University <strong>of</strong> Oxford and The University <strong>of</strong>
Oxford – all three appearing in different places on <strong>the</strong> lists), not to mention <strong>the</strong> non-English names
that could be spelt differently in a number <strong>of</strong> languages. I regret to admit that despite multiple
accuracy checks, some minor errors and omissions may still remain due to a high volume and varied
format <strong>of</strong> data as well as <strong>the</strong> variety <strong>of</strong> sources involved. I would like to apologise in advance for any
errors, should <strong>the</strong>y come to light.
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4. Results and discussion
Pavel Bogolyubov
The starting point <strong>of</strong> developing an overview <strong>of</strong> <strong>the</strong> research topics was to analyse <strong>the</strong> keywords by
frequency <strong>of</strong> <strong>the</strong>ir appearance, and <strong>the</strong> results are shown in Table 1 and on Fig. 2., respectively (<strong>the</strong>
words appearing more than 20 times are shown in <strong>the</strong> table, and “studies” were excluded from <strong>the</strong>
figure to improve visibility). Most notably – and somewhat disappointingly – <strong>the</strong> list is dominated by a
very predicable set: Studies, Internet, Information technology, Web Sites and WWW, all <strong>of</strong> <strong>the</strong>m being
so obviously inherent to any scholarly article concerning a web paradigm <strong>the</strong>y don’t really say
anything - I find it hard to imagine a paper dedicated to Web 2.0 that could be said not to cover, for
example, Internet-related matters – after all, Web 2.0 is an Internet phenomenon. This is, however, a
matter <strong>of</strong> indexing relevance, and it would have to be put aside.
A look fur<strong>the</strong>r down <strong>the</strong> table reveals a number <strong>of</strong> interesting trends. First <strong>of</strong> all, Knowledge
Management appears on a respectable 7 th place immediately after <strong>the</strong> “predictables”. Library-related
terms (Libraries, Academic Libraries, Librarians and so on – altoge<strong>the</strong>r 15 variations) in total give
back 178 matches, which would place <strong>the</strong> group on <strong>the</strong> 3 rd place. Various Web 2.0 technologies are
mentioned – Social Networks leading <strong>the</strong> way (215) followed by blogs (64), tagging (8), mashups (4),
RSS (3) and Wikis (3) as well as Fllickr (2) (photography is mentioned 5 times as well) and Twitter (2).
Finally, 41 different education-related terms excluding <strong>the</strong> corporate development yield as many as
270 results. Based on <strong>the</strong> keyword count it could be summarised that <strong>the</strong> topics receiving <strong>the</strong> most
attention are W2.0 technologies, education, library science and knowledge management, and one
couldn’t help noticing a certain bias towards education or, at <strong>the</strong> very top level, matters <strong>of</strong> knowledge:
even though business- or public sector-related terms appear on <strong>the</strong> list as well, <strong>the</strong>y are clearly in <strong>the</strong>
minority.
Is it because W2.0 is – or is seen to be – a knowledge-related paradigm? It is not necessarily so by
default: although some W2.0 technologies, such as Wikis, are indeed predominantly knowledge
repositories, o<strong>the</strong>rs have different purposes – e.g., social networks are <strong>the</strong>re to facilitate social
interaction. Or, is it ra<strong>the</strong>r because researchers dedicate more attention to what is “closer to home” for
<strong>the</strong>m – libraries, colleges and matters <strong>of</strong> knowledge creation, storage and dissemination?
Unfortunately, <strong>the</strong> question would have to remain open for now, although it would be an interesting
point for debate.
Table 1: Keywords count
100

Figure 2: Keywords tag cloud
Pavel Bogolyubov
Given <strong>the</strong> overall <strong>the</strong>me <strong>of</strong> <strong>the</strong> conference, it would be interesting to look at what is being discussed in
papers residing within <strong>the</strong> “twilight zone” between KM and Web 2.0 – i.e., papers with KM listed as a
keyword and containing a reference to W2.0.
Sixty-odd relevant articles can be grouped into 20 streams sometimes represented by an isolated
one, such as business benefits (Andriole 2010) or syn<strong>the</strong>tic worlds (Burley, Savion et al. 2010); o<strong>the</strong>r
topics discussed are various s<strong>of</strong>tware and platforms (e.g., Grossman 2008 – iBridge and Donnelly
2010 - GIS in libraries), tagging and folksonomy (Grinham 2007; Parise, Guinan et al. 2009; Lee and
Ge 2010; Mat<strong>the</strong>ws, Jones et al. 2010 and Wu, Gordon et al. 2010), Wikis (Grace 2009; Yates,
Wagner et al. 2010) and so on, however – not surprisingly given <strong>the</strong> collaborative nature <strong>of</strong> Web 2.0
and a high number <strong>of</strong> users implied – <strong>the</strong> matters <strong>of</strong> collaboration and knowledge sharing stand out as
receiving <strong>the</strong> most attention.
I shall start from two papers that provide a general overview <strong>of</strong> <strong>the</strong> role W2.0 plays in knowledge
sharing as well as considerations related to it. Patrick and Dotsika (2007) are discussing such issues
as difficulties with knowledge modelling in W2.0 context, <strong>the</strong> matter <strong>of</strong> standardization, security
concerns and <strong>the</strong> question <strong>of</strong> maintenance, but perhaps more importantly, <strong>the</strong>ir key idea is that W2.0
provides an opportunity for knowledge sharing systems to be developed “from within”, i.e., through <strong>the</strong>
empowerment <strong>of</strong> <strong>the</strong> end-user via a bottom-up process, effectively meaning a shift from an approach
focussed on IT systems to <strong>the</strong> one “building on <strong>the</strong> information and knowledge stored within <strong>the</strong>
organisation” (p. 398), which is an interesting point since in <strong>the</strong> early day <strong>of</strong> KM its understanding was
leaning heavily towards <strong>the</strong> ICT to <strong>the</strong> degree <strong>of</strong> 70% <strong>of</strong> all publications being dedicated to technical
aspects ra<strong>the</strong>r than <strong>the</strong> “s<strong>of</strong>ter” side <strong>of</strong> <strong>the</strong> matter (Easterby-Smith, Crossan et al. 2000).
Ano<strong>the</strong>r paper, written by Paroutis and Saleh (2009), and looking at <strong>the</strong> success factors for W2.0 in
knowledge sharing from a somewhat more managerial perspective, identifies five key “determinants”:
history (previous ways <strong>of</strong> doing things), outcomes (perceived benefits and rewards),
organizational/management support , and trust. Linked to <strong>the</strong> latter point, also referred to briefly in <strong>the</strong>
papers by McNamee, Schoch et al. (2010) and Schneckenberg (2009), found that <strong>the</strong> degree <strong>of</strong> using
W2.0 for organizational learning and knowledge sharing depends on such factors as <strong>the</strong> degree <strong>of</strong>
freedom, openness and employee empowerment, pointing out that <strong>the</strong> social setting differs in those
respects from <strong>the</strong> corporate environment. These findings are supported by <strong>the</strong> conclusions <strong>of</strong>
Prasarnphanich and Wagner (2009), who found that altruism is a prevalent motivating factor for Wiki
collaborators, and thus it can be argued that a closed, tightly controlled and disempowered culture will
be less conductive to collaboration using such W2.0 platforms as Wikis.
Overall, this group <strong>of</strong> papers clearly opens up a discussion about <strong>the</strong> role <strong>of</strong> <strong>the</strong> end user in sharing
knowledge through W2.0, shifting from <strong>the</strong> IT-centric view towards such consideration as trust,
empowerment and culture in general, a topic that is being continuously discussed in KM literature but
not widely researched in relation to W2.0 in KM context in particular.
Ano<strong>the</strong>r group <strong>of</strong> papers can be broadly placed within Nonaka and Takeuchi’s model (e.g., Nonaka
and Takeuchi 1995) – e.g., Martin-Niemi and Greatbanks (2010), have identified ten enabling
conditions making blogs a successful ba (environment) for knowledge conversion according to
Nonaka’s SECI model, including – yet again – mutual trust, and being in general purely behavioural
ra<strong>the</strong>r than ICT-related. Two o<strong>the</strong>r papers in <strong>the</strong> same group are dedicated to <strong>the</strong> matter <strong>of</strong> Personal
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Pavel Bogolyubov
Knowledge Management – PKM: Zhang (2009) refers to W2.0 as <strong>of</strong>fering an opportunity for
organizations to tap into personal knowledge (i.e., to facilitate its conversion), similar to Razmerita,
Kirchner et al. (2009) who highlight a number <strong>of</strong> ways how W2.0 can be used to enhance PKM
through making it more dynamic and increase <strong>the</strong> utilization <strong>of</strong> its tacit component.
Finally, ano<strong>the</strong>r group <strong>of</strong> articles is dedicated to particular projects and applications related to
collaboration and knowledge sharing: innovation management (Ribiere and Tuggle 2010), pan-
<strong>European</strong> collaborative projects (Siakas, Georgiadou et al. 2010, which suggests that W2.0 can be an
enabler for cross-cultural collaboration), healthcare, ERP and military (Aherne and Pereira 2008; Wu
and Cao 2009 and Mittu, Guleyupoglu et al. 2008, respectively), e-learning (Kane, Robinson-Combre
et al. 2010), <strong>the</strong> concept <strong>of</strong> open knowledge (García-Peñalvo, Figuerola et al. 2010; Llorens, Bayona
et al. 2010) and data mining (Wang and Wang 2008).
As a whole, <strong>the</strong>se papers appear to suggest that W2.0 can facilitate knowledge creation, conversion
and sharing processes as well as collaboration in organizations via a variety <strong>of</strong> relatively new, more
social approaches, which again, signifies a departure from KM being merely an incarnation <strong>of</strong> data
management in <strong>the</strong> direction <strong>of</strong> tapping into <strong>the</strong> tacit and <strong>the</strong> contextual.
The bibliometrical analysis – an attempt to measure <strong>the</strong> level <strong>of</strong> activity and localize it – shows that on
average, <strong>the</strong>re are 1.26 papers per active institution and 24.05 per country published , with 2.29 and
44.38 publishing authors (“Hits”) respectively. The top institutions are represented in Table 2 (<strong>the</strong>
institutions with 10 hits or above are shown).
Table 2: top institutions and countries by productivity (number <strong>of</strong> active authors)
Answering <strong>the</strong> question about whe<strong>the</strong>r W2.0 is an academic or a practitioner-led topic, <strong>the</strong> results
show that <strong>the</strong> list is dominated by universities in general, but <strong>the</strong>y are in <strong>the</strong> minority in <strong>the</strong> top five,
Micros<strong>of</strong>t and IBM being quite prolific (o<strong>the</strong>r non-academic institutions appearing too, but much fur<strong>the</strong>r
down <strong>the</strong> full list); <strong>the</strong>re are also a number <strong>of</strong> authors without any discernible affiliation (freelance);
ABA Banking Journal, although high on number <strong>of</strong> publications, is <strong>the</strong>re mostly due to a high number
<strong>of</strong> editorials not attracting citations (total NCII <strong>of</strong> 0.2). These findings are interesting in <strong>the</strong> sense that
despite <strong>the</strong> overall American dominance on <strong>the</strong> list, quite a few o<strong>the</strong>r regions (Europe, Asia, Australia)
are represented to a noticeable degree too. The findings are also shown on Fig. 3 using a tag cloud.
The top-twenty “league table” for countries by <strong>the</strong> number <strong>of</strong> hits can be seen in Table 3, and <strong>the</strong>
corresponding tag cloud – on Fig. 4 (USA has had to be removed from <strong>the</strong> picture to enhance o<strong>the</strong>r
countries’ visibility). It is dominated by English-speaking countries, with as much as 53% <strong>of</strong> all
published by American and English institutions, <strong>the</strong> Commonwealth countries represented as well,
which repeats to a high degree <strong>the</strong> findings <strong>of</strong> Serenko and Bontis regarding KM (US and UK totalling
50% and being followed by Australia, Canada and Spain; also similar to <strong>the</strong>ir results, 49% <strong>of</strong> all
papers are sole-authored, which is, according to <strong>the</strong>m, a sign <strong>of</strong> <strong>the</strong> field being relatively young with
little collaborative links established within <strong>the</strong> subject community).
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Pavel Bogolyubov
Figure 3: Tag cloud for <strong>the</strong> top forty-five institutions by <strong>the</strong> number <strong>of</strong> hits
It is not clear whe<strong>the</strong>r <strong>the</strong> Anglo-Saxon dominance is a true representation <strong>of</strong> <strong>the</strong> state <strong>of</strong> affairs or
ra<strong>the</strong>r <strong>the</strong>re is a bias perhaps due to <strong>the</strong> way non-English articles are handled by ABI, or in a broader
sense, <strong>the</strong> way research carried out in o<strong>the</strong>r languages is published.
The results <strong>of</strong> <strong>the</strong> impact assessment via NCII per hit, by institution, are shown in Table 4. It is
interesting to note that none <strong>of</strong> <strong>the</strong> most active institutions lead <strong>the</strong> impact table (with <strong>the</strong> exceptions
<strong>of</strong> <strong>the</strong> University <strong>of</strong> California, University College London and MIT) – in fact, those three excluded, <strong>the</strong>
top fifty NCII institutions have shown an average <strong>of</strong> 2.3 hits each; thus, no correlation is evident
between <strong>the</strong> level <strong>of</strong> activity and <strong>the</strong> impact, and <strong>the</strong> most cited papers are produced by relatively less
active research centres. Similarly, <strong>the</strong> by-country NCII results shown in Table 4 (only countries with
cited papers are included) demonstrate that <strong>the</strong>re is little correlation between <strong>the</strong> activity and <strong>the</strong>
impact: in fact, <strong>the</strong> Anglo-Saxon activity leaders appear outside <strong>the</strong> top ten, whereas, for example,
Iran’s position as number one is explained by two authors partaking in published research, one <strong>of</strong> <strong>the</strong>
papers being relatively highly impactful.
5. Conclusions
To summarise a few conclusions made on <strong>the</strong> basis <strong>of</strong> <strong>the</strong> analysis described above, it can be said
that Web 2.0 is a developing field that follows some patterns found by o<strong>the</strong>r authors in case <strong>of</strong> KM.
The bulk <strong>of</strong> <strong>the</strong> research activity is concentrated in <strong>the</strong> English-speaking world, however, on average
most impactful articles are produced elsewhere, and similarly, <strong>the</strong> most impactful institutions do not
necessarily appear high on <strong>the</strong> activity list: it seems that research quantity does not correlate at all
with <strong>the</strong> research quality. Fur<strong>the</strong>rmore, despite academia dominating both lists by <strong>the</strong> number <strong>of</strong>
institutions, industry is represented to a notable degree too: IBM, Micros<strong>of</strong>t and Hewlett-Packard are
but three examples <strong>of</strong> commercial firms actively involved in Web 2.0 research.
The keyword analysis shows that a number <strong>of</strong> topics such as <strong>the</strong> role <strong>of</strong> Web 2.0 in KM, libraries and
education stand out as receiving a high proportion <strong>of</strong> attention, <strong>the</strong> most prominent <strong>the</strong>me in KM being
collaboration and knowledge sharing. The common motif <strong>the</strong> reviewed articles share is <strong>the</strong> focus on
<strong>the</strong> “s<strong>of</strong>t” side <strong>of</strong> <strong>the</strong> matter, i.e., such aspects as empowerment, trust and relationship between
personal and collective knowledge; in o<strong>the</strong>r words, <strong>the</strong>re is relatively little attention given to <strong>the</strong>
systems side <strong>of</strong> <strong>the</strong> area, which is an interesting development.
The research could be extended onto o<strong>the</strong>r topics to cover <strong>the</strong> role <strong>of</strong> Web 2.0 in subject areas such
as <strong>the</strong> aforementioned two as well as marketing, R&D and so on; unfortunately, <strong>the</strong> mere size <strong>of</strong> <strong>the</strong>
task and <strong>the</strong> limitations on <strong>the</strong> size <strong>of</strong> <strong>the</strong> paper did not allow me to venture into it this time.
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Pavel Bogolyubov
Table 3: Countries “League table” by <strong>the</strong> number <strong>of</strong> hits
Figure 4: Tag cloud for <strong>the</strong> countries by <strong>the</strong> number <strong>of</strong> hits
Table 4: NCII per hit, by institution (twenty highest results shown).
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Pavel Bogolyubov
Table 5: NCII per hit, by country, twenty highest scores
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106

Communities <strong>of</strong> Practice: Comparing Experiences <strong>of</strong>
Private Companies and Public Organisations
Ettore Bolisani 1 , Francesca Gambarotto 2 and Enrico Scarso 1
1
Department <strong>of</strong> Management and Engineering - University <strong>of</strong> Padua, Vicenza,
Italy
2
Department <strong>of</strong> Economic Sciences - University <strong>of</strong> Padua, Padova, Italy
ettore.bolisani@unipd.it
francesca.gambarotto@unipd.it
enrico.scarso@unipd.it
Abstract: Since <strong>the</strong> seminal work <strong>of</strong> Wenger et al. (2002), <strong>the</strong>re has been a flourishing literature about
Communities <strong>of</strong> Practice (CoPs). While CoPs were initially conceptualized as a spontaneous phenomenon,
marked by informal nature and lack <strong>of</strong> regulation, shortly afterwards it has become clear that <strong>the</strong>y need to be
cultivated and managed. In particular <strong>the</strong>y need an enabling infrastructure and a governance system that
represent its key components and distinguish <strong>the</strong>m from a purely informal network. Nowadays, CoPs are <strong>of</strong>ten a
core part <strong>of</strong> KM programmes: many corporations have adopted <strong>the</strong>m as means to promote <strong>the</strong> sharing <strong>of</strong>
knowledge among <strong>the</strong>ir units. In view <strong>of</strong> <strong>the</strong> success that CoPs have obtained among private companies, public
organisations have also begun to adopt <strong>the</strong>m for managing <strong>the</strong>ir knowledge assets. As underlined by <strong>the</strong>
literature, KM practices in public and private sectors clearly differ due to <strong>the</strong> underlying differences in goals and
objectives, organizational environment, and processes. However, how CoPs can be created and managed in <strong>the</strong>
public sector is still debated, and little evidence has been published. The paper contributes to <strong>the</strong> analysis <strong>of</strong>
CoPs’ formation and management in <strong>the</strong> public sector by comparing two cases: a private corporation and a state
university. The comparison is based on four dimensions indicated in <strong>the</strong> interpretative framework previously
proposed in Scarso and Bolisani (2008): organizational, cognitive, economic, and technological dimension. The
two CoPs have been analysed though a case-study approach. Similarities and differences <strong>of</strong> <strong>the</strong> two cases are
highlighted, and implications for <strong>the</strong> management <strong>of</strong> CoPs in public organizations are discussed.
Keywords: KM; communities <strong>of</strong> practice; public sector; case study.
1. Introduction
Since <strong>the</strong> seminal work <strong>of</strong> Wenger et al. (2002), <strong>the</strong>re has been a flourishing literature about
Communities <strong>of</strong> Practice (CoPs). CoPs can play a strategic role for knowledge sharing and
organizational change when static efficiency is pursued, but can also be an excellent organizational
tool for new knowledge exploration in dynamic environments. They can be regarded as a
manifestation <strong>of</strong> a human-oriented approach to KM (Newell et al. 2006) where knowledge is
constructed through shared experience in social networks, and is characterised by a prevalently tacit
dimension. While CoPs were initially considered a spontaneous phenomenon, marked by informal
nature and lack <strong>of</strong> regulation, later it became clear that <strong>the</strong>y should be cultivated and managed. In
particular, differently from purely informal networks, <strong>the</strong>y need an enabling infrastructure and a
governance system.
CoPs are frequently a core part <strong>of</strong> KM programmes <strong>of</strong> large corporations (like for example: Shell, BP,
Allianz, Caterpillar, Accenture, IBM, HP, Siemens, and IKEA). Considering <strong>the</strong>ir popularity and
success among private companies, public organisations (namely: national and international agencies,
government departments, health care organisations, universities) have started to adopt CoPs as a
tool to manage <strong>the</strong>ir pool <strong>of</strong> knowledge. Never<strong>the</strong>less, how CoPs are implemented and more
generally how KM is applied in <strong>the</strong> public sector is not well understood due to little evidence being
published (Riege and Lindsay 2006). The practices and programmes in public organisations and
private companies clearly differ due to <strong>the</strong> underlying differences in goals and objectives,
organisational environment, and processes (Chawla and Joshi, 2010). This study contributes to
analysing CoP development in public sector by making a comparison with private companies.
Similarities and differences <strong>of</strong> two cases are highlighted, and implications for <strong>the</strong> management <strong>of</strong>
CoPs in public organisations are discussed.
2. KM in public organisations
The recognised importance <strong>of</strong> knowledge has encouraged public organisations to adopt KM practices
(Seba and Rowley 2010; OECD 2003) for driving efficiencies, resolving problems faster, and
delivering cost-effective and timely services (Riege and Lindsay 2006). However, compared to private
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Ettore Bolisani, Francesca Gambarotto and Enrico Scarso
companies, <strong>the</strong>y are late in adopting KM practices. A possible explanation can be that private
companies are induced to invest in KM for reasons <strong>of</strong> pr<strong>of</strong>it (Chawla and Joshi 2010) while in <strong>the</strong>
public sector <strong>the</strong>re is little awareness <strong>of</strong> <strong>the</strong> potential benefits <strong>of</strong> such practices.
In point <strong>of</strong> fact, <strong>the</strong> public sector has to face several challenges when approaching KM (Seba and
Rowley 2010). In particular, it is difficult to express KM goals clearly. In addition, employees in <strong>the</strong>
public sector may see knowledge as <strong>the</strong>ir private property, closely associated with power and career
progress. Bureaucracy can also be an obstacle. On <strong>the</strong> o<strong>the</strong>r hand, a possible advantage <strong>of</strong> public
organisations is that <strong>the</strong>y are less worried about disclosing trade secrets and o<strong>the</strong>r sensible
information. In some cases, since people are not strongly pr<strong>of</strong>it-motivated, knowledge sharing can be
viewed as a social benefit.
Empirical evidence about KM in <strong>the</strong> public sector is still scarce, because research has mostly
regarded (large) private companies. Therefore <strong>the</strong>re is <strong>the</strong> need to better understand how to
implement KM in <strong>the</strong> public sector successfully. This study contributes to this topic by comparing <strong>the</strong>
experience <strong>of</strong> a private corporation with that <strong>of</strong> a public organisation. The idea is that this comparison
may provide useful suggestions to public organizations interested in adopting CoPs for improving <strong>the</strong>ir
3. Research method
This paper compares two cases <strong>of</strong> CoP: one <strong>of</strong> a private company (Eni) and <strong>the</strong> o<strong>the</strong>r <strong>of</strong> a public
organisation (University <strong>of</strong> Padua). A case study approach well fits <strong>the</strong> descriptive and exploratory
nature <strong>of</strong> this research. In particular, it can be seen as a “revelatory case study” in Yin’s terminology
(Yin, 2003), because it <strong>of</strong>fers <strong>the</strong> opportunity for an in-depth analysis <strong>of</strong> internal managerial aspects
that are generally less accessible to researchers. As regards <strong>the</strong> generalisation <strong>of</strong> findings, <strong>the</strong>
purpose is not to draw general conclusions, but ra<strong>the</strong>r to highlight useful points for <strong>the</strong> management
<strong>of</strong> CoPs in <strong>the</strong> public sector. The units <strong>of</strong> analysis are <strong>the</strong> CoP programmes developed by <strong>the</strong> two
organisations. These organizations were selected for various reasons. First, both have a relatively
large dimension in <strong>the</strong>ir respective areas (especially Eni has implemented one <strong>of</strong> <strong>the</strong> most significant
KM programs in <strong>the</strong> world). There is also a matter <strong>of</strong> opportunity, since it was possible to have direct
access to relevant data thanks to <strong>the</strong> contribution <strong>of</strong> <strong>the</strong> programme leaders <strong>of</strong> both <strong>the</strong> CoPs. The
cases are illustrated in accordance with <strong>the</strong> four interpretative dimensions suggested by <strong>the</strong>
framework described in Scarso and Bolisani (2008), and summarised in Table 1.
Table 1: Key dimensions <strong>of</strong> a CoP (from: Scarso and Bolisani, 2008)
Dimensions Components
size (number <strong>of</strong> members)
degree <strong>of</strong> transverseness across organization
relationship with existing structure
formal acknowledgement
Organizational
governance
local vs. centralized management
roles <strong>of</strong> members and supporting functions
kind <strong>of</strong> leadership
nature <strong>of</strong> shared knowledge
cultural proximity <strong>of</strong> members
knowledge gaps between members
Cognitive
knowledge domain
KM processes and knowledge flows
mechanisms for establishing trust
mechanisms for evaluating costs and benefits
Economic budgeting, resources allocation, accounting
systems to reward participation
kind <strong>of</strong> technological platform
user-friendliness
Technological KM processes underpinned
relations with social/organisational context
intensity <strong>of</strong> use across <strong>the</strong> CoP
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4. Case studies
4.1 Eni
Ettore Bolisani, Francesca Gambarotto and Enrico Scarso
Eni is an oil multinational with more than 70,000 people in 70 countries. The business includes
several activities from exploration <strong>of</strong> reserves to refinery, distribution, and accessory services. This
implies a variety <strong>of</strong> activities and pr<strong>of</strong>essional skills and competencies: technical, economicmanagerial,
legal, and political skills. Eni’s business combines routines (day-by-day extraction <strong>of</strong> oil
from a well) that call for efficiency and standardization, with project-based activities (discovery <strong>of</strong> a
new reserve, or implementation <strong>of</strong> a new production site), where <strong>the</strong> research for new solutions is
<strong>of</strong>ten required. The Exploration and Production (E&P) Division (where <strong>the</strong> KM programme started)
deals with <strong>the</strong> search for new reserves and <strong>the</strong> production <strong>of</strong> oil. These risky and capital intensive
activities, characterised by long cycle time and leading-edge technology, imply several KM
challenges:
different competences have to be combined in any new project. To do this numerous “islands” <strong>of</strong>
specific knowledge must be connected;
efficiency and innovativeness must be balanced. Even when new projects differ from <strong>the</strong> old
ones, <strong>the</strong>y always call for past knowledge as well;
employees are <strong>of</strong> different nationalities. Cultural richness is a value, but hinders <strong>the</strong> effective
sharing <strong>of</strong> knowledge;
despite <strong>the</strong> increasing use <strong>of</strong> scientific methods and ICT applications, pr<strong>of</strong>essional experience is
still essential for interpreting uncertain data and taking decisions;
project life cycle extends over decades. To deal with retiring workforce, it is needed to capture <strong>the</strong>
people’s experience and avoid <strong>the</strong> impoverishment <strong>of</strong> company’s knowledge.
In light <strong>of</strong> <strong>the</strong>se challenges, Eni started a CoP-based KM programme aimed to:
create an environment that facilitates knowledge exchanges between operating lines and
experienced pr<strong>of</strong>essionals;
capture individual experience and transform it in established company assets;
increase interactions among different pr<strong>of</strong>essional groups.
The pilot project started in 2004, and by 2005 <strong>the</strong> system had covered 90% <strong>of</strong> subsidiaries. Initial
investments amounted to 5 Million €. Training involved 1,900 pr<strong>of</strong>essionals for a total <strong>of</strong> 450 meeting
hours, for illustrating potential benefits for both <strong>the</strong> company and <strong>the</strong> employees. Initially, five CoPs
were created, each one focusing on a core activity. O<strong>the</strong>r six communities have been established
next, and later five more.
The CoP system was designed to support <strong>the</strong> company’s “knowledge cycle”. CoPs are made around
“experts” having decades <strong>of</strong> service, who are <strong>the</strong> main source <strong>of</strong> knowledge and <strong>of</strong>fer solutions to <strong>the</strong>
problems encountered by line pr<strong>of</strong>essionals operating “on <strong>the</strong> ground”. The qualification <strong>of</strong> expert is
assigned on request, based on a “peer assessment” by colleagues. Currently, experts <strong>of</strong> a CoP are
between 10 and 40, are geographically dispersed, and are trained specifically for KM. There is no
rigid hierarchy, so everybody can contribute with a “good idea”. To speed up operations and avoid
“anarchy”, a Facilitator was designated with <strong>the</strong> task to improve relationships among experts and <strong>the</strong>
o<strong>the</strong>r members, and to connect a CoP with <strong>the</strong> o<strong>the</strong>rs. Facilitators also monitor <strong>the</strong> respect <strong>of</strong>
participation rules, and <strong>the</strong> response time to a request <strong>of</strong> advice. CoPs are assisted by an Enabling
Team, directed by a Chief Knowledge Officer (CKO). CKO, Enabling Team and communities are
supported by a KM Team, who specialise in communication and ICT: <strong>the</strong>y run a Web Portal, manage
document repositories, organise meetings and training sessions, etc.
To facilitate <strong>the</strong> acceptance by <strong>the</strong> formal organisation, a non-invasive structure was chosen. All KM
roles were defined formally, but not included in <strong>the</strong> organizational chart. They overlap existing
organisational levels. Due to <strong>the</strong> transverseness <strong>of</strong> CoPs and <strong>the</strong> intrinsically “intangible” nature <strong>of</strong>
<strong>the</strong>ir activities, flexible membership and voluntary participation is allowed. CKO and his collaborators
compound <strong>the</strong> only staff completely assigned to KM, while CoPs’ members continue to carry out <strong>the</strong>ir
“business as usual”. Their roles and hierarchical dependency are established by <strong>the</strong> conventional
chart, but for KM <strong>the</strong>y refer to a different and self-governed structure. CoP experts can devote up to
10% <strong>of</strong> <strong>the</strong>ir working time to KM activities, during which <strong>the</strong>y respond to <strong>the</strong> Enabling Team. Each
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Ettore Bolisani, Francesca Gambarotto and Enrico Scarso
community was left free to set specific internal arrangements, but a common set <strong>of</strong> practices and rules
was issued.
CoPs have <strong>the</strong> tw<strong>of</strong>old function <strong>of</strong> helping <strong>the</strong> line in <strong>the</strong>ir usual activities and preserving <strong>the</strong> company
knowledge base. Accordingly, knowledge domains <strong>of</strong> each Cop strictly concern everyday business
operations. A line pr<strong>of</strong>essional can resort to a particular Community when he has a problem to solve
(Figure 1): he emails a question to <strong>the</strong> Community, and <strong>the</strong> question is submitted to <strong>the</strong> CoP experts
who can suggest a solution. The Facilitator coordinates and syn<strong>the</strong>sises <strong>the</strong> response. Then <strong>the</strong> line
pr<strong>of</strong>essionals autonomously decide if and how to employ <strong>the</strong> response, but <strong>the</strong>y have to write an
“after action report” <strong>of</strong> <strong>the</strong> effects <strong>of</strong> <strong>the</strong>ir decision. The CoP experts analyse <strong>the</strong> report, validate it, and
upload it into <strong>the</strong> public knowledge library. The final report is structured following a rigid template for
facilitating classification and retrieval.
The focalisation <strong>of</strong> each CoP on a specific domain allows improving efficiency. Too wide communities
can lead to ineffective knowledge sharing, and can also cause <strong>the</strong> difficult codification and storage <strong>of</strong>
knowledge. However, since E&P projects also require <strong>the</strong> interaction among different areas,
interconnection between distinct communities is allowed; this requires that <strong>the</strong>ir structures replicate a
common model.
The KM team monitors <strong>the</strong> intensity <strong>of</strong> use <strong>of</strong> <strong>the</strong> system: number <strong>of</strong> processed issues or answers
given, communities involved, level <strong>of</strong> participation, number and duration <strong>of</strong> accesses to <strong>the</strong> knowledge
portal. To budget resources and set goals, costs are also evaluated. During <strong>the</strong> first two years, a
special budget was allocated to KM, but later KM became “a business practice” with a specific annual
budget.
Help <strong>the</strong> line
Experts
Pr<strong>of</strong>essional Answers on
experience and
knowledge
Knowledge library
CoP Mailbox
Uploading
Facilitator
Maintain enterprise knowledge
Validation
Facilitator
Answers on
experience,
search in stored
knowledge
Experts
Pr<strong>of</strong>essional
Action
AAR Submission
Facilitator
Results
Figure 1: ENI’s CoPs processes
To induce people to share knowledge, no economic or career incentives are provided. For experts,
<strong>the</strong> reward for CoP participation is <strong>the</strong> recognition <strong>of</strong> <strong>the</strong>ir prominent role in <strong>the</strong> company. For <strong>the</strong>
users, it comes from <strong>the</strong> benefits <strong>the</strong>y have to <strong>the</strong>ir work by getting useful advice. Based on <strong>the</strong> fact
that this increases <strong>the</strong> economic outcomes <strong>of</strong> <strong>the</strong> company, <strong>the</strong> most active people are compensated
through standard reward systems in relation to <strong>the</strong>ir overall contribution to <strong>the</strong> company’s goals.
To make <strong>the</strong> collaboration among people easy and immediate, emails and file-sharing systems are
used as a support <strong>of</strong> knowledge exchange. The use <strong>of</strong> phone calls is also allowed. A Knowledge
Portal has been implemented: it is a simple website containing information about <strong>the</strong> KM system and
CoPs’ activities, and links to internal and external knowledge sources. The portal is user-friendly,
conceived to enable <strong>the</strong> quick retrieval <strong>of</strong> <strong>the</strong> desired resource and to be accessed from mobile
locations. More than 2000 employees are regularly delivered a newsletter that illustrates materials <strong>of</strong>
general interest in simple terms. The content management is responsibility <strong>of</strong> <strong>the</strong> Enabling Team.
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The choice <strong>of</strong> a light technology tool was consistent with <strong>the</strong> o<strong>the</strong>r settings. The ICT structure is
flexible, allows <strong>the</strong> exchange <strong>of</strong> many types <strong>of</strong> knowledge, and is user-friendly and non-invasive for
<strong>the</strong> “day-by-bay business”. Simplicity has, however, some drawbacks. In particular, <strong>the</strong> system may
become inadequate to manage <strong>the</strong> complex flows <strong>of</strong> a growing bundle <strong>of</strong> communities.
The existing “legacy” applications were also a problem. The KM system was based on a Lotus Notes
mailing system, already used in <strong>the</strong> company but not tailored to KM. New solutions have been
studied, but <strong>the</strong>ir selection is difficult. Conversely, although <strong>the</strong> KM system has been designed for <strong>the</strong>
E&P Division, it may be <strong>of</strong> use to extend <strong>the</strong> CoP model to a broader corporate level. The problem is
to find a proper balance between specific applications on <strong>the</strong> one hand, and general shared models
on <strong>the</strong> o<strong>the</strong>r.
4.2 University <strong>of</strong> Padua
The University <strong>of</strong> Padua is one <strong>of</strong> <strong>the</strong> largest in Italy. Its structures (13 Faculties and 66 Departments)
are scattered all around <strong>the</strong> town and o<strong>the</strong>r urban sites in <strong>the</strong> Venetian region. This geographical
configuration hinders knowledge sharing and innovation production and also isolates individuals to
<strong>the</strong>ir workplace.
The University has 2,319 pr<strong>of</strong>essors and researchers, 2,300 administrative employees and
technicians, and 60,900 students. ICT technicians are 160, half <strong>of</strong> <strong>the</strong>m belong to a central ICT
department while <strong>the</strong> o<strong>the</strong>rs work in <strong>the</strong> various academic structures. The University provides ICT
infrastructures and services for all <strong>the</strong> stakeholders. The needed competences for <strong>the</strong> functioning <strong>of</strong>
<strong>the</strong> ICT network include design, production, implementation and management <strong>of</strong> ICT activities.
However <strong>the</strong>re is a strong ICT division <strong>of</strong> labour that splits hardware and s<strong>of</strong>tware activities (e.g.
s<strong>of</strong>tware platforms, email service, wiring) from activities <strong>of</strong> procurement, individual workspace
assistance, and assistance to ICT laboratories. This division has introduced some rigidity inside <strong>the</strong>
academic organization that negatively affected co-operation and knowledge sharing among ICT
technicians. Also, it has caused conflicts between employees <strong>of</strong> <strong>the</strong> central ICT department and those
<strong>of</strong> <strong>the</strong> academic departments. Today, technicians <strong>of</strong>ten do not share proposals or solutions. This
situation has a negative effect on <strong>the</strong> quality <strong>of</strong> ICT services delivered to students and pr<strong>of</strong>essors,
who have to use different web devices and protocols when <strong>the</strong>y move over <strong>the</strong> academic
organisation.
A change begun in 2007 with <strong>the</strong> new ICT management that challenged <strong>the</strong> established organization
with new aims and strategies. They approved <strong>the</strong> request <strong>of</strong> a group <strong>of</strong> ICT employees concerning <strong>the</strong>
transformation <strong>of</strong> tacit to explicit knowledge, and <strong>the</strong> start up <strong>of</strong> innovation production for <strong>the</strong> delivered
ICT services. Nobody realised that this organizational shift, addressed to best practices and
innovation, was <strong>the</strong> first step towards a KM project. This “unrevealed” project concerned:
creation <strong>of</strong> a knowledge network to share and capitalize experiences embodied in ICT
technicians;
increase in <strong>the</strong> quality <strong>of</strong> supplied ICT services;
increase in creativity <strong>of</strong> ICT technicians for stimulating exploration <strong>of</strong> new ICT solutions;
new ways <strong>of</strong> working, particularly <strong>the</strong> co-operative ones.
This project is a bottom-up process stemming from a public event called “InNova” where employees
discussed <strong>the</strong> structural deficits <strong>of</strong> university ICT system and <strong>the</strong>y suggest, for <strong>the</strong> first time, new
solutions. What followed was: a) <strong>the</strong> creation <strong>of</strong> a wiki and an online forum to create connection and
exchange experiences, b) <strong>the</strong> appointment <strong>of</strong> an academic group that re-connect <strong>the</strong> community and
<strong>the</strong> ICT Centre. This first project - called Dreams - was not funded, but ano<strong>the</strong>r project <strong>of</strong> ICT
innovation was presented later and funded with 30,000 €.
The CoP “Dreams” was an informal organization with voluntary participation. It was populated thanks
to a word-<strong>of</strong>-mouth process, even though a formal communication was posted to <strong>the</strong> ICT Centre
website. There is a kernel <strong>of</strong> active technicians that keep alive <strong>the</strong> community with technical solutions
and suggestions and call for new ideas. On <strong>the</strong> contrary, <strong>the</strong> CoP co-ordination group was designated
by academic bodies and it has a political more than technical role.
At <strong>the</strong> beginning, <strong>the</strong> CoP was organised in seven working groups. Leaders were selected with a peer
evaluation process based on competence and social reputation. Each group defined its goals and
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processes to manage its activities. To incentive participation and increase members’ effort to attain
goals, a formal project aggregating all <strong>the</strong> seven work groups was presented to <strong>the</strong> university
administration board. The project results, i.e. s<strong>of</strong>tware products and new procedures, were collected
in a white paper published online with creative commons copyright to share experience and
knowledge with everybody interested in <strong>the</strong> project. However, <strong>the</strong> enthusiasm and motivations <strong>of</strong> <strong>the</strong>
participants hit with an organizational deficit: <strong>the</strong> university management did not understand <strong>the</strong>
innovativeness <strong>of</strong> this organizational experience and <strong>the</strong>y did not promote <strong>the</strong> embeddedness <strong>of</strong> <strong>the</strong>
CoP. Identity is a lacking factor still now, even though it has been decided that ICT technicians can
dedicate one day and half <strong>of</strong> <strong>the</strong>ir monthly working time to <strong>the</strong> CoP.
The CoP is intensely used for problem-setting and problem-solving. Members <strong>of</strong> <strong>the</strong> CoP post <strong>the</strong>ir
issues, and o<strong>the</strong>rs interact sharing <strong>the</strong>ir experience or knowledge. In this way <strong>the</strong>y have begun to
recognize each o<strong>the</strong>r, to share tacit knowledge and build up a cognitive alignment. The selection
process <strong>of</strong> best practices stems from discussions and testing. However <strong>the</strong>re is no repository <strong>of</strong> all <strong>the</strong>
shared solutions and ideas. The community functioning is very similar to a social network, and <strong>the</strong>re is
lack <strong>of</strong> shared goals in terms <strong>of</strong> KM. A new idea or project can be shared with <strong>the</strong> co-ordination group
and submitted to <strong>the</strong> ICT academic group. Face-to-face meeting can be used beside <strong>the</strong> Internet
communication.
At <strong>the</strong> beginning, <strong>the</strong> CoP did not receive any funding. One year later it received funds by means <strong>of</strong> a
University project including <strong>the</strong> seven working teams that have defined goals. The monthly working
hours (around 10%) formally recognized to technicians for participating in <strong>the</strong> CoP by <strong>the</strong> university
management can be considered an implicit recognition <strong>of</strong> <strong>the</strong> economic value <strong>of</strong> <strong>the</strong> community.
However, <strong>the</strong>re is no explicit economic assessment <strong>of</strong> costs and benefits <strong>of</strong> <strong>the</strong> CoP. Moreover, <strong>the</strong>
time dedicated to CoP activities produces an uncertain effect: it is positive because it can incentive
ITC technicians to share or acquire knowledge, but on <strong>the</strong> o<strong>the</strong>r hand it is negative because it can
increase participation <strong>of</strong> free riders that de-motivate smart individuals to participate to <strong>the</strong> CoP. There
is no rule system to monitor and assess <strong>the</strong> participation to <strong>the</strong> CoP. This is partly due to <strong>the</strong> weak
interest <strong>of</strong> <strong>the</strong> management and partly to <strong>the</strong> informal nature <strong>of</strong> <strong>the</strong> CoP.
To communicate and exchange information <strong>the</strong> CoP adopted different tools: mailing list, website, and
wiki. However, as a natural selection, only <strong>the</strong> mailing list succeeded. Additional open source s<strong>of</strong>tware
supports <strong>the</strong> mailing list for scheduling activities, meetings or to test preferences about particular
issues. The choice <strong>of</strong> <strong>the</strong> mailing list may depend on <strong>the</strong> fact that <strong>the</strong>re wasn’t a content manager or a
formal facilitator. Moreover, this choice can be interpreted as <strong>the</strong> social need to meet colleagues and
to come out <strong>of</strong> isolation.
The social outcome is apparently important as much as <strong>the</strong> technological one. The mailing list was
very useful to discover technical attitudes and interests <strong>of</strong> colleagues scattered around all <strong>the</strong>
academic departments, and it has stimulated co-operation for exploiting and exploring knowledge.
5. Conclusion
We contrasted a CoP created by a private company and one made in a public institution. It is always
difficult to draw general conclusions, but our purpose is to provide food for thought to planners and
managers <strong>of</strong> CoPs in public organisations hoping that <strong>the</strong>y can get inspiration from <strong>the</strong> experience <strong>of</strong>
a leading company compared to <strong>the</strong> case <strong>of</strong> a state institution.
On <strong>the</strong> whole, <strong>the</strong> study addresses <strong>the</strong>se questions: why Eni’s CoP can be considered a success
while University CoP is more problematic? Can this be associated to some characteristics <strong>of</strong> <strong>the</strong>
public environment? What elements can shed some light on this? Our analysis was conducted based
on separate points that we report below (table 2).
5.1 Organisational aspects
As regards <strong>the</strong> relationship with <strong>the</strong> existing organisational structure, while it is important for a CoP to
avoid conflicts with <strong>the</strong> exiting system <strong>of</strong> authorities, this can reduce its visibility and recognition by <strong>the</strong>
rest <strong>of</strong> <strong>the</strong> organisation. This conflict exists in both <strong>the</strong> cases, but <strong>the</strong> situation appears much more
complicated for <strong>the</strong> university. This is probably due to <strong>the</strong> fact that this structure is more bureaucratic
and formal: it is harder for a CoP to remain in <strong>the</strong> grey area between formal structuring and
completely informal organisation (like at Eni). To avoid conflicts with <strong>the</strong> bureaucratic system, <strong>the</strong>
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activity <strong>of</strong> <strong>the</strong> university CoP was mostly kept in <strong>the</strong> shadow, which however raised problems <strong>of</strong>
recognition and effectiveness.
A second point is <strong>the</strong> strong support by top management, surely more difficult in <strong>the</strong> case <strong>of</strong> a public
institution where this would require a number <strong>of</strong> formal decisions by internal <strong>of</strong>fices. Also, it is likely
that all potential stakeholders (directors, employees, trade unions, etc.) have to be consulted, while in
a private company all appears to be more fluid. It is also hard to imagine a CoP without leadership
and clear rules. A purely voluntary approach to CoP building may not be enough. At Eni, even though
<strong>the</strong> free participation is promoted, a governance structure and a set <strong>of</strong> rules have been established.
5.2 Cognitive aspects
A common element <strong>of</strong> <strong>the</strong> two cases is <strong>the</strong> necessity to define <strong>the</strong> domain <strong>of</strong> a CoP. As <strong>the</strong> Eni case
shows, it is important that a CoP underpins <strong>the</strong> relevant KM processes to <strong>the</strong> organisation. This is
necessary for making CoPs a tool for <strong>the</strong> “everyday work”. As <strong>the</strong> university CoP shows, supplying a
pure discussion platform for sharing contents can be <strong>of</strong> some use, but <strong>the</strong> efficacy <strong>of</strong> <strong>the</strong> system
becomes questionable and, especially, can be only based on some “enthusiasts” that voluntary keep
<strong>the</strong> CoP active. In o<strong>the</strong>r words, not giving “organisational reasons” to participate can limit <strong>the</strong>
effectiveness <strong>of</strong> CoPs.
5.3 Economic aspects
There are major differences here that directly relate to <strong>the</strong> different context <strong>of</strong> application. For a
private company, it is necessary to assess costs and benefits <strong>of</strong> CoPs. This is difficult and <strong>the</strong>re is no
standard method to do that. However, <strong>the</strong> effort is worthwhile, because it allows to budget funds and
expenses in a reasonable way.
This is more complicated in <strong>the</strong> case <strong>of</strong> a public institution. The accounting system is different from
private companies, and is <strong>of</strong>ten subjected to specific regulations that may not fit <strong>the</strong> case <strong>of</strong> a CoP
and its intangible nature. Secondly, budgeting is less relevant, unless a project is formally framed into
a bureaucratic system (i.e. an <strong>of</strong>fice, a cost centre, etc.). The lack <strong>of</strong> accounting and budgeting may
however reduce <strong>the</strong> recognition <strong>of</strong> <strong>the</strong> role <strong>of</strong> a CoP.
The same can be said <strong>of</strong> <strong>the</strong> reward system. Free participation in a CoP is welcome, but individuals
must get some recognition for <strong>the</strong>ir participation. As <strong>the</strong> Eni case shows, it is not easy to find <strong>the</strong>
proper solution which fits organisational settings and corporate culture. But neglecting this aspect can
reduce <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> system. Unfortunately, <strong>the</strong> public environment is too rigid to allow <strong>the</strong>
implementation <strong>of</strong> a reward mechanism for an innovative system like a CoP: <strong>the</strong>re may be resistances
from both management and employees.
5.4 Technological aspect
In both cases technology has an essential function: accessibility. Users need to access <strong>the</strong> system
from everywhere and in a user-friendly way. However <strong>the</strong>re are some differences between <strong>the</strong> two
organisations, in terms <strong>of</strong> <strong>the</strong>ir structural characteristics. In particular, although <strong>the</strong>ir overall size is
comparable in terms <strong>of</strong> <strong>the</strong> people that can be potential beneficiaries (Eni has around 70,000
employees, <strong>the</strong> University about 70,000 people including students and pr<strong>of</strong>essors), Eni CoPs cover all
world while university’s people are located in one place, which affected <strong>the</strong> technological choices.
User-friendliness <strong>of</strong> technology is especially important in <strong>the</strong> case <strong>of</strong> tacit knowledge, but explicit
contents are relevant as well: Eni’s CoPs show that structuring <strong>the</strong> knowledge collected from
pr<strong>of</strong>essionals is important for its subsequent retrieval and reuse. Again, this requires a process <strong>of</strong>
analysis and formalisation <strong>of</strong> KM processes, and resources assigned to <strong>the</strong>m. Conversely, CoPs
limited to a pure knowledge sharing platform (like <strong>the</strong> case <strong>of</strong> university) appear less costly and
easier, but restricted in scope.
113

Table 2: Syn<strong>the</strong>sis <strong>of</strong> results
Ettore Bolisani, Francesca Gambarotto and Enrico Scarso
Dimension
Organisational
Eni University
Size
Around 500 expert members (40% active). Line
pr<strong>of</strong>essionals are mostly “users”
180 people (40 active members)
Trasverseness
CoPs specialise in different fields, but cover many
areas
Members specialise in <strong>the</strong> same area, but belong
to different units/departments
Relationship with existing
structure; formal
acknowledgement
CoPs aren’t included in <strong>the</strong> organisational chart, but
overlap <strong>the</strong> existing structure. People can participate
and do <strong>the</strong>ir usual business at <strong>the</strong> same time
Very loose. CoP is not integrated with <strong>the</strong> formal
structure. Formal recognition is very difficult
Governance
Central dedicated team (CKO and supporting people)
plus self-governance <strong>of</strong> each community
A first government group was nominated by <strong>the</strong>
University board. Later, this board was based on
self-candidates.
CoP not considered a resource by central
Local vs. centralized
CoPs are autonomous but have common rules. Partly University board, but just a possible support for
management
independent from <strong>the</strong> company direction
solving technical problems. It is recognised only by
technical staff.
Roles <strong>of</strong> members
Peer-to-peer participation, but with a differentiation <strong>of</strong>
roles (e.g., experts, line pr<strong>of</strong>essionals, governing
team)
Peer-to-peer participation. Some members are
very active while o<strong>the</strong>rs remain silent
Leadership
Cognitive
Partially emerging from <strong>the</strong> bottom, but with <strong>the</strong> strong
support <strong>of</strong> top management
From <strong>the</strong> bottom (those who took <strong>the</strong> initiative or
were most active)
Nature <strong>of</strong> knowledge Mostly technical Technical and partly organisational
Cultural proximity <strong>of</strong> members
Participants have homogeneous views <strong>of</strong> <strong>the</strong>
company’s goals and ways <strong>of</strong> working. CoP’s experts
and line pr<strong>of</strong>essionals can have different backgrounds
Participants have different views <strong>of</strong> both ICT
systems and ways <strong>of</strong> working. Some participants
remain silent because <strong>the</strong>y are reluctant to show
<strong>the</strong>ir incompetence
Knowledge domain(s) Focalised, but different across communities Focalised (ICT applications)
Acquisition, storage, retrieval, codification, delivery. Knowledge sharing. Process substantially not
KM processes and flows Knowledge flows from experts to line pr<strong>of</strong>essionals, formalised, and implemented by means <strong>of</strong> <strong>the</strong>
with feedbacks from <strong>the</strong> latter
discussion list
Trust mechanisms
Economic
Reputation <strong>of</strong> experts based on <strong>the</strong>ir experience and
past results. Peer-review
Costs: working hours <strong>of</strong> participants and material
Participation and knowledge sharing <strong>the</strong>mselves
Economic assessment expenses; benefits: contribution to overall company’s
results
No assessment
Budgeting and accounting
Formal budget; allocation <strong>of</strong> a number <strong>of</strong> hours for
participating in <strong>the</strong> CoP (experts); accounting efficacy
is limited by <strong>the</strong> intangible and transversal nature <strong>of</strong>
KM
Nei<strong>the</strong>r budgeting nor accounting.
Very late decision to allocate monthly working
hours for participating in <strong>the</strong> CoP
Rewards
Technological
No direct reward system for contributions; participants
rewarded based on <strong>the</strong>ir overall contribution to <strong>the</strong>
company
No reward is established. Top management still
does not see <strong>the</strong> reason
Platform Email-based Q&A system; knowledge repository Mailing list and discussion forum.
User-friendliness High. Simple system based on standard emailing tools
Simple design and high flexibility. People have
only to register
KM processes
All, but unstructured communications can also use
traditional channels
Sharing
Relationship with socialorganisational
context
Attempt to define a KM system that fits <strong>the</strong> daily
activities as an ordinary working tool
Easy and self-organised system
Intensity <strong>of</strong> use
Depending on <strong>the</strong> circumstances, but CoPs have a
continuous flow <strong>of</strong> questions. Fixed deadline for
answers
Depending on <strong>the</strong> circumstances. No facilitator that
keeps <strong>the</strong> discussion alive
References
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http://www.oecd.org/LongAbstract/0,2546,en_2649_201185_33709749_1_1_1_1,00.html
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public policy development”, Journal <strong>of</strong> Knowledge Management, Vol 10, No. 3, 24-39.
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Wenger, E., McDermott, R. and Snyder, W. (2002) Cultivating Communities <strong>of</strong> Practices, Harvard Business
School Press, Boston, MA.
Wiig, K.M. (2002) “Knowledge management in public administration”, Journal <strong>of</strong> Knowledge Management, Vol 6,
No. 3, 224-239.
Yin, R.K. (2003) Case Study Research. Design and Methods, Thousand Oaks, SAGE Publications, Third Edition.
114

KM Issues in KIBS Companies
Ettore Bolisani 1 , Marco Paiola 2 and Enrico Scarso 1
1
Department <strong>of</strong> Management and Engineering - University <strong>of</strong> Padua, Vicenza,
Italy
2
Department <strong>of</strong> Economics and Management - University <strong>of</strong> Padua, Padova,
Italy
ettore.bolisani@unipd.it
marco.paiola@unipd.it
enrico.scarso@unipd.it
Abstract: This paper investigates <strong>the</strong> issue <strong>of</strong> adoption and use <strong>of</strong> Knowledge Management (KM) practices by
Knowledge-Intensive Business Services (KIBS) companies, i.e. private companies whose job consists <strong>of</strong>
collecting, generating, analysing, and distributing knowledge with <strong>the</strong> purpose <strong>of</strong> developing customized services
or solutions to satisfy <strong>the</strong> needs <strong>of</strong> business clients. KIBS companies rely on highly educated pr<strong>of</strong>essionals,
experts on specific technical disciplines or functional domains, and supply knowledge-based services. Knowledge
is <strong>the</strong>ir major resource that allows developing new competencies and constitutes a precondition for <strong>the</strong> delivery <strong>of</strong>
new services. This is <strong>the</strong> reason why <strong>the</strong> issue <strong>of</strong> KM applied to KIBS should have a central relevance for
scholars and practitioners. Never<strong>the</strong>less, <strong>the</strong> research on this topic is still scarce. Fur<strong>the</strong>rmore, one would expect
that KIBS firms accurately manage <strong>the</strong>ir knowledge base, also by means <strong>of</strong> formal practices, but <strong>the</strong> (few)
empirical studies indicate that KIBS companies <strong>of</strong>ten lack systematic methods for managing <strong>the</strong>ir knowledge.
Consequently, <strong>the</strong>re is <strong>the</strong> need to analyse <strong>the</strong> issue <strong>of</strong> KM in KIBS more thoroughly. In light <strong>of</strong> this, <strong>the</strong> paper
discusses <strong>the</strong> findings <strong>of</strong> a survey involving 471 KIBS located in North-Eastern Italy and operating in three
different subsectors: ICT Services, Design and Communication, Pr<strong>of</strong>essional Services. In particular, <strong>the</strong> study: a)
analyses how internal knowledge is handled by <strong>the</strong> surveyed companies; b) investigates <strong>the</strong> main sources <strong>of</strong>
external knowledge <strong>the</strong> companies make use <strong>of</strong>; c) examines <strong>the</strong> relationship between <strong>the</strong> way KIBS companies
manage <strong>the</strong>ir knowledge assets and <strong>the</strong> kind <strong>of</strong> service <strong>the</strong>y supply. The outcomes <strong>of</strong> <strong>the</strong> investigation may have
important implications both from an academic and a managerial viewpoint. Especially, as regards <strong>the</strong> latter, it can
provide KIBS firms with food for though concerning <strong>the</strong> use <strong>of</strong> appropriate KM approaches for sustaining <strong>the</strong>ir
business activity.
Keywords: KIBS; knowledge management; survey; Italy
1. Introduction
The term Knowledge-Intensive Business Services (KIBS) denotes private companies whose job
consists <strong>of</strong> collecting, generating, analysing, and distributing knowledge with <strong>the</strong> purpose <strong>of</strong>
developing customised service solutions to satisfy <strong>the</strong> needs <strong>of</strong> business clients (Miles 2005; Freel
2006). KIBS companies rely on highly educated pr<strong>of</strong>essionals, experts on specific technical
disciplines or functional domains, and supply knowledge resources or o<strong>the</strong>r knowledge-based
services to clients. Three core features denote KIBS companies (Strambach 2008; Muller and
Doloreux 2009): a) knowledge is not only <strong>the</strong>ir key production factor, but also <strong>the</strong> kind <strong>of</strong> “goods” <strong>the</strong>y
sell; b) <strong>the</strong> provision <strong>of</strong> knowledge-intensive services requires an in-depth interaction between
supplier and client, who are both involved in mutual cumulative learning processes; and c) all KIBS
firms perform activity <strong>of</strong> consulting, under <strong>the</strong> form <strong>of</strong> a process <strong>of</strong> problem solving in which <strong>the</strong>y adapt
<strong>the</strong>ir expertise and knowledge to <strong>the</strong> specific requirements <strong>of</strong> <strong>the</strong> individual client firm. To sum up,
knowledge is <strong>the</strong> most important competitive resource <strong>of</strong> KIBS companies especially because it
allows to develop novel competencies and constitutes a precondition for <strong>the</strong> delivery <strong>of</strong> innovative
services (Kock and Strotmann 2008; Doloreux and Sheamur 2010). This is <strong>the</strong> reason why <strong>the</strong>
management <strong>of</strong> knowledge asset is a crucial matter for KIBS, and <strong>the</strong> issue <strong>of</strong> KM approaches should
be worthy <strong>of</strong> particular attention by scholars and practitioners. For instance, as underlined by Landry
et al. (2008), KIBS companies are challenged by <strong>the</strong> transfer <strong>of</strong> knowledge form a project to ano<strong>the</strong>r,
and this should induce <strong>the</strong>m to invest resources into KM practices and advanced technologies to
enhance knowledge capturing and sharing both within <strong>the</strong>ir organizations and with <strong>the</strong>ir clients. Thus,
one would expect that KIBS firms accurately manage <strong>the</strong>ir knowledge base, also through <strong>the</strong> use <strong>of</strong>
formal KM practices. However, not only available empirical evidence is still scarce, but also <strong>the</strong>se
studies reveal that KIBS companies commonly lack proper methods for managing knowledge (Aurum
et al. 2008; Scarso and Bolisani 2009). All this sharpens <strong>the</strong> problem <strong>of</strong> how such companies can
capitalise and exploit <strong>the</strong>ir cognitive assets, and <strong>the</strong>refore deserves a thorough analysis.
115

Ettore Bolisani, Marco Paiola and Enrico Scarso
In light <strong>of</strong> this, <strong>the</strong> paper aims at illustrating and discussing <strong>the</strong> findings <strong>of</strong> a survey involving 471 KIBS
companies located in North-Eastern Italy and operating in three different KIBS subsectors. In
particular, <strong>the</strong> study: a) analyses how internal knowledge is currently managed by <strong>the</strong> surveyed
companies; b) investigates <strong>the</strong> main sources <strong>of</strong> external knowledge <strong>the</strong>y make use <strong>of</strong>; c) examines
<strong>the</strong> relationship between <strong>the</strong> way KIBS companies manage <strong>the</strong>ir knowledge assets and <strong>the</strong> kind <strong>of</strong>
service <strong>the</strong>y supply. The outcomes <strong>of</strong> <strong>the</strong> investigation may have important implications both from an
academic and a managerial viewpoint. In particular, as regards <strong>the</strong> latter, <strong>the</strong> study can provide
practitioners with food for thought concerning <strong>the</strong> use <strong>of</strong> appropriate KM approaches for sustaining
<strong>the</strong>ir business.
The paper is articulated as follows. In <strong>the</strong> next section <strong>the</strong> main issues concerning <strong>the</strong> management <strong>of</strong>
knowledge by KIBS companies are discussed. Section three describes <strong>the</strong> research method adopted
and <strong>the</strong> empirical data used. Section four summarises <strong>the</strong> main findings <strong>of</strong> <strong>the</strong> empirical survey. The
last section proposes some concluding remarks about <strong>the</strong> most significant pieces <strong>of</strong> evidence drawn
from <strong>the</strong> study.
2. Knowledge management and KIBS
The literature on KIBS underlines that three relevant dimensions characterise such companies and
are strongly related to each o<strong>the</strong>r (Muller and Doloreux 2009): knowledge, innovation, and spatial
proximity. In spite <strong>of</strong> this, previous research has been conducted mainly adopting an innovation
perspective, while an explicit focus on knowledge processes leading to <strong>the</strong> development <strong>of</strong> new
services has been lacking (Leiponen 2006; Strambach 2008). In particular, even though some general
aspects have been already discussed, <strong>the</strong> specific nature and characteristics <strong>of</strong> <strong>the</strong>se processes have
not been clearly understood to date, and <strong>the</strong>y still deserve fur<strong>the</strong>r examination.
This gap seems to be very important, not only because knowledge is <strong>the</strong> main asset <strong>of</strong> KIBS
companies, but also because <strong>the</strong> delivery <strong>of</strong> <strong>the</strong>ir services is intrinsically a knowledge processing
activity. Actually, as underlined by Scarso and Bolisani (2009), <strong>the</strong> supplying <strong>of</strong> knowledge-intensive
services can be regarded as <strong>the</strong> result <strong>of</strong> a five-stages cognitive process consisting <strong>of</strong> <strong>the</strong> following
activities (Figure 1): (1) access – filling <strong>the</strong> gap between <strong>the</strong> external available knowledge and <strong>the</strong> one
that could be useful to provide solutions to <strong>the</strong> end users’ problems; (2) acquisition - absorption <strong>of</strong>
externally acquired knowledge and combination with <strong>the</strong> one already possessed; (3) storage –
memorisation <strong>of</strong> <strong>the</strong> new knowledge so that it can be applied when needed; (4) retrieval – locating
and using <strong>the</strong> stored knowledge to provide new services; (5) delivery <strong>of</strong> knowledge to customers.
These activities nearly recall <strong>the</strong> typical KM processes (Wong and Aspinwall, 2004), in that <strong>the</strong>y imply
<strong>the</strong> capability to manage knowledge, exchange it with external organisations (e.g., o<strong>the</strong>r suppliers,
R&D labs, universities, public agencies), and apply it to solve <strong>the</strong> problems <strong>of</strong> clients. Therefore,
deliberately or more <strong>of</strong>ten implicitly, KIBS firms find <strong>the</strong>mselves facing and trying to solve KM-related
issues.
Figure 1: Cognitive processes characterising <strong>the</strong> supplying <strong>of</strong> knowledge-intensive services
Hence, in order to identify and better understand <strong>the</strong> issues raised by a proper management <strong>of</strong>
knowledge by KIBS companies, we need to go inside <strong>the</strong> “black box” <strong>of</strong> <strong>the</strong> cognitive processes that
characterise <strong>the</strong> delivery <strong>of</strong> knowledge-intensive services. In particular we need to analyse some key
aspects <strong>of</strong> such processes and <strong>the</strong> relevant KM implications. To do this we will try to answer <strong>the</strong>
following questions: what are <strong>the</strong> knowledge sources that KIBS companies usually exploit? What is
<strong>the</strong> importance <strong>of</strong> <strong>the</strong>se sources? How do KIBS companies manage <strong>the</strong> owned knowledge, and,
specifically, its internal sharing? Is <strong>the</strong> reuse <strong>of</strong> old knowledge crucial when delivering a new service?
What are <strong>the</strong> methods that KIBS use to protect <strong>the</strong>ir cognitive assets? What are <strong>the</strong> technologies that
<strong>the</strong>y employ to support KM activities? What are <strong>the</strong> factors (e.g., size, sector, kind <strong>of</strong> products,
external relationships with clients and suppliers) that mainly affect <strong>the</strong> KM issues faced by KIBS?
116

3. Research method and data
Ettore Bolisani, Marco Paiola and Enrico Scarso
Data are drawn from a quantitative analysis on KIBS in <strong>the</strong> Veneto region (North-Eastern Italy),
conducted in July 2009. This study contributes to a broad research project aimed at analyzing
entrepreneurial dynamics, strategic models, organizations and forms <strong>of</strong> governance <strong>of</strong> KIBS
belonging to three different subsectors: ICT Services (ICT), Design and Communication (D&C),
Pr<strong>of</strong>essional Services (PFS). The sample <strong>of</strong> 471 observations was obtained selecting firms randomly
from <strong>the</strong> overall list <strong>of</strong> firms included in <strong>the</strong> <strong>of</strong>ficial register <strong>of</strong> <strong>the</strong> Italian Chambers <strong>of</strong> Commerce, in
accordance with <strong>the</strong> areas <strong>of</strong> activity that are relevant to our research.
Data were collected by a specialised research firm through a semi-structured phone-based
questionnaire composed <strong>of</strong> 36 multiple-choice questions, divided into three main sections. Section A
regarded firm’s general characteristics: type <strong>of</strong> activity (ATECO code), employment structure, overall
turnover and contribution <strong>of</strong> different geographical areas, number <strong>of</strong> branches. Section B was aimed
at ga<strong>the</strong>ring information regarding <strong>the</strong> entrepreneurial and organizational aspects: entrepreneurial
initiative, entrepreneurs’ competences, organizational structure, performance management systems,
and networks <strong>of</strong> collaboration. Finally, section C was directed to collect information concerning
services and service relationships: areas <strong>of</strong> specialisation, type <strong>of</strong> product and services (particularly:
level <strong>of</strong> standardisation), type <strong>of</strong> customer, market localisation, firm’s performances (turnover trend,
financial indicators trend, rate or number <strong>of</strong> innovations, etc.), type <strong>of</strong> competitors and competition,
sources <strong>of</strong> knowledge, knowledge management systems, level <strong>of</strong> innovativeness.
Data resulting from <strong>the</strong> questionnaires were cleaned, eliminating mistakes and incomplete records
from <strong>the</strong> database. Afterwards, data were coded and prepared for <strong>the</strong> statistical elaborations,
conducted using a standard statistical s<strong>of</strong>tware package (SPSS 18).
A general picture <strong>of</strong> <strong>the</strong> surveyed firms is given in Table 1 where <strong>the</strong> sample composition by size <strong>of</strong>
firms and KIBS subsector is illustrated. As <strong>the</strong> table shows, <strong>the</strong> sample is uniformly subdivided over
<strong>the</strong> three subsectors, and it is mainly formed by micro and very small enterprises: <strong>the</strong> average size <strong>of</strong>
firms is 5.2 employees. It is worth underlining that <strong>the</strong> sample fully represents <strong>the</strong> reality <strong>of</strong> local KIBS
companies that generally are very small. There are some differences between subsectors, given that
D&C companies are <strong>the</strong> smallest ones (average size 3.34 employees), while ICT <strong>the</strong> largest (average
size 7.06 employees).
Table 1: Sample composition (absolute values and percentages)
Size class
(n° <strong>of</strong> employees)
1-4
5-9
≥10
Total
ICT
KIBS subsector
D&C PFS
Total
87 120 85 292
56.5% 75.0% 54.1% 62.0%
37 31 54 122
24.0% 19.4% 34.4% 25.9%
30 9 18 57
19.5% 5.6% 11.5% 12.1%
154 160 157 471
100.0% 100.0% 100.0% 100.0%
As regards <strong>the</strong> nature <strong>of</strong> <strong>the</strong> delivered services, two thirds <strong>of</strong> <strong>the</strong> investigated companies affirm that
<strong>the</strong>ir <strong>of</strong>fer mainly consists <strong>of</strong> personalised services. This situation is however different from one
subsector to ano<strong>the</strong>r (Table 2). In particular, while personalisation is strongly prevalent among D&C
companies, <strong>the</strong> situation appears more balanced among ICT firms, while PFS firms lying in <strong>the</strong>
middle. Companies mainly serve local markets: for <strong>the</strong> majority <strong>of</strong> companies (63.8%) <strong>the</strong> most part <strong>of</strong>
sales (75% or more) come from <strong>the</strong> regional market. Only 10.8% <strong>of</strong> <strong>the</strong> companies export <strong>the</strong>ir
services, and in any case this is a small part <strong>of</strong> total revenues (25% or less).
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Ettore Bolisani, Marco Paiola and Enrico Scarso
Table 2: Types <strong>of</strong> services delivered by KIBS subsector
4. Main findings
Type <strong>of</strong> services
delivered
KIBS subsector
ICT D&C PFS
Total
ad hoc 46.8% 87.5% 65.6% 66.9%
standard 53.2% 12.5% 34.4% 33.1%
4.1 Suppliers as sources <strong>of</strong> knowledge
The questionnaire asked how important suppliers were perceived as sources <strong>of</strong> four kinds <strong>of</strong>
knowledge, i.e. basic technological, new product development, production process and market
knowledge. We used a 1-to-5 Likert scale to indicate such importance, and grouped <strong>the</strong> companies in
three different classes on <strong>the</strong> basis <strong>of</strong> <strong>the</strong> sum <strong>of</strong> <strong>the</strong> scores given to <strong>the</strong> four kinds <strong>of</strong> knowledge
(namely: 4-9 = low importance; 10-15 = medium importance; 16-20 = high importance). Then we
analysed how this variable varied across subsectors and in relations to size.
Table 3: Importance <strong>of</strong> suppliers as sources <strong>of</strong> knowledge by KIBS subsector
Importance <strong>of</strong> suppliers as
sources <strong>of</strong> knowledge
Subsector
ICT D&C PFS
Total
Low 42.4% 24.1% 48.4% 38.1%
Medium 37.7% 56.3% 40.6% 45.0%
High 19.9% 19.6% 11.0% 16.8%
In general, <strong>the</strong> importance ascribed to suppliers as knowledge sources is quite low; <strong>the</strong> highest values
are with D&C and ICT companies (Table 3). There is an unclear correlation with size: for example, <strong>the</strong>
largest firms tend to be located on <strong>the</strong> two extremes more than <strong>the</strong> medium and smallest ones (Table
4).
Table 4: Importance <strong>of</strong> suppliers as sources <strong>of</strong> knowledge by size class
Importance <strong>of</strong> suppliers as Size class
Total
sources <strong>of</strong> knowledge
1-4 5-9 ≥10
Low 35.4% 38.7% 50.9% 38.1%
Medium 48.6% 46.2% 24.6% 45.0%
High 16.0% 15.1% 24.6% 16.8%
4.2 Networking (excluding suppliers and customers)
Ano<strong>the</strong>r question regarded how much companies resort to external collaborations for designing or
delivering <strong>the</strong>ir services. The large majority (63.2%) declares that <strong>the</strong>y don’t use external resources
(or, occasionally, <strong>the</strong>y resort to temporary collaborators). There are no appreciable differences across
subsectors, although D&C companies make use <strong>of</strong> occasional collaborators more frequently than <strong>the</strong>
o<strong>the</strong>r companies (Table 5). Two o<strong>the</strong>r points deserve a comment: first, inter-sectorial collaboration is
almost absent; second, ICT companies are <strong>the</strong> least networked. In conclusion, data seem to indicate
that managing internal knowledge resources is much more critical than managing external
contributions.
Table 5: Resort to external collaboration by KIBS subsector
KIBS subsector
Collaborations and networking
Total
ICT D&C PFS
Only internal resources 39.0% 28.8% 34.0% 33.8%
Internal resources and occasional collaborators 26.6% 33.8% 28.2% 29.5%
Collaboration with companies in <strong>the</strong> same subsector 31.2% 26.2% 27.6% 28.3%
Collaboration with companies <strong>of</strong> o<strong>the</strong>r (sub)sectors 3.2% 11.2% 10.2% 8.3%
Data also suggest that large companies are more “self-sufficient” than <strong>the</strong> small ones (Table 6). In
particular 45.6% <strong>of</strong> companies with ten employees or more deliver <strong>the</strong>ir services only through <strong>the</strong> use
<strong>of</strong> internal resources. Companies that declare some kind <strong>of</strong> external networking indicate a high variety
<strong>of</strong> partners: ICT consultants (57.8%); pr<strong>of</strong>essional services (48.0%); marketing, communication or
design consultants (45.1%); technical designers or laboratories (26.6%); public agencies (12.7%),
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Ettore Bolisani, Marco Paiola and Enrico Scarso
universities (13.9%), scientific parks (6.4%), o<strong>the</strong>rs (26.0%). It is worth noting that <strong>the</strong> geographical
context <strong>of</strong> collaboration is essentially regional.
Table 6: Resort to external collaboration by size class
Collaborations and networking
Size class
1-4 5-9 ≥10
Total
Only internal resources 31.2% 34.4% 45.6% 33.8%
Internal resources and occasional collaborators 30.9% 29.5% 22.8% 29.5%
Collaboration with companies in <strong>the</strong> same subsector 28.9% 29.5% 22.8% 28.3%
Collaboration with companies <strong>of</strong> o<strong>the</strong>r (sub)sectors 8.9% 6.6% 8.8% 8.3%
4.3 Modalities <strong>of</strong> knowledge exchange or sharing within companies
This question clearly excluded individual firms (84 in total), because <strong>the</strong>y are irrelevant. The results
are shown in terms <strong>of</strong> number <strong>of</strong> companies that declare to use always or very <strong>of</strong>ten a specific
modality.
Table 7: Modalities <strong>of</strong> internal knowledge sharing by KIBS subsector
Modalities <strong>of</strong> knowledge
exchange/sharing
KIBS subsector
ICT D&C PFS
Total
Documents through internal ICT network 57.6% 42.7% 61.8% 55.5%
Electronic Files 86.1% 82.6% 73.6% 81,6%
Face-to-face communication 93.0% 96.0% 95.1% 94.6%
Traditional communications (i.e., face-to-face) still prevail (Table 7). Exchange <strong>of</strong> electronic files is <strong>the</strong>
second option, and this especially regards subsectors where knowledge can be encoded in an
electronic format. Exchange <strong>of</strong> documents through internal network is also <strong>of</strong>ten used, although D&C
companies employ this modality less frequently.
4.4 Qualification <strong>of</strong> employees
As one can expect, <strong>the</strong> qualification level is, on average, high, with a very marginal presence <strong>of</strong>
unskilled employees. Graduated people are numerous: 56.9% <strong>of</strong> companies have at least one
employee with a master degree, and 36% <strong>of</strong> companies declare that more than one half <strong>of</strong> <strong>the</strong>ir
employees has a master degree. Lastly, 6.1% <strong>of</strong> companies have people with an MBA.
4.5 Knowledge reuse
This question addressed <strong>the</strong> number <strong>of</strong> times <strong>the</strong> knowledge already possessed is reused by <strong>the</strong>
company to deliver new (for <strong>the</strong> company or <strong>the</strong> client) services. It is a crucial matter in KM, since a
high knowledge reuse should imply a high need <strong>of</strong> formal methods for capturing and storing <strong>the</strong>
owned knowledge. On <strong>the</strong> average, companies declare very low levels <strong>of</strong> knowledge reuse, especially
<strong>the</strong> smaller ones (Table 9). There is some difference across subsectors, with D&C (which includes <strong>the</strong>
smallest companies <strong>of</strong> <strong>the</strong> sample) that reuse knowledge even less than <strong>the</strong> o<strong>the</strong>r subsectors (Table
8).
Table 8: Level <strong>of</strong> knowledge reuse by KIBS subsector
Reuse classes (% <strong>of</strong> KIBS subsector
Total
reused knowledge) ICT D&C PFS
0-24% 59.1% 76.4% 36.8% 57.5%
25-49% 13.6% 8.3% 15.5% 12.4%
50-74% 12.3% 8.9% 23.2% 14.8%
75%- 14.9% 6.4% 24.5% 15.2%
Table 9: Level <strong>of</strong> knowledge reuse by size class
Reuse classes (% <strong>of</strong>
reused knowledge)
Size class
1-4 5-9 ≥10
Total
0-24% 63.3% 47.5% 49.1% 57.5%
25-49% 12.5% 11.7% 14.0% 12.4%
50-74% 11.1% 20.0% 22.8% 14.8%
75%- 13.1% 20.8% 14.0% 15.2%
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Ettore Bolisani, Marco Paiola and Enrico Scarso
Conversely, <strong>the</strong>re is apparently a direct relationship with <strong>the</strong> characteristic <strong>of</strong> services provided: as
one would expect <strong>the</strong> more services are standardised, <strong>the</strong> more knowledge is reused.
Table 10: Level <strong>of</strong> knowledge reuse by type <strong>of</strong> services delivered
Type <strong>of</strong> services
delivered
Reuse classes (% <strong>of</strong> reused knowledge)
0-24% 25-49% 50-74% ≥75%
Total
Ad hoc 79.5% 69.0% 55.1% 28.2% 66.7%
Standard 20.5% 31.0% 44.9% 71.8% 33.3%
4.6 Use <strong>of</strong> ICT
In this context, it was not appropriate to talk about <strong>the</strong> use <strong>of</strong> sophisticated knowledge management
systems. However, <strong>the</strong> study aimed to understand <strong>the</strong> intensity <strong>of</strong> use <strong>of</strong> ICT systems as a possible
prerequisite for <strong>the</strong> adoption <strong>of</strong> more advanced tools or methods.
Table 11: Use <strong>of</strong> ICT tools by KIBS subsector
Use <strong>of</strong> ICT tools
KIBS subsector
Total
(% <strong>of</strong> positive answers)
ICT D&C PFS
ERP 27.9% 4.4% 28.0% 20.0%
Groupware 11.0% 11.4% 19.1% 13.8%
DSS 10.4% 1.9% 10.8% 7.7%
Datawarehouse and data mining 31.2% 6.9% 23.6% 20.5%
E-learning 12.3% 3.2% 11.5% 8.9%
Intranet – LAN 57.1% 28.5% 65.0% 50.1%
Supply chain management 11.7% 0.6% 2.5% 4.9%
Electronic Commerce 20.8% 6.3% 1.9% 9.6%
Website 43.5% 34.1% 42.7% 40.0%
ICT systems are not diffusely employed, with two remarkable exceptions: intranet and local networks
on <strong>the</strong> one hand, and company websites on <strong>the</strong> o<strong>the</strong>r hand (Table 11). In any case, <strong>the</strong> situation is
variegated, and in particular D&C companies are less advanced than <strong>the</strong> o<strong>the</strong>rs. Each subsector
shows different attitudes towards different technologies. For example, ICT companies use ERP
modules and Business Intelligence systems more intensely than <strong>the</strong> o<strong>the</strong>r KIBS, which probably
reflects <strong>the</strong> fact that <strong>the</strong>se firms have a more industrial organisation.
The use <strong>of</strong> ICT systems considerably grows with size for all kinds <strong>of</strong> technology (Table 12). The large
majority <strong>of</strong> companies with more than 9 employees use Intranet/LAN and website. This confirms <strong>the</strong>
fact that <strong>the</strong> use <strong>of</strong> internal email and file exchange as a method <strong>of</strong> internal communication is popular,
especially among <strong>the</strong> largest companies.
Table 12: Use <strong>of</strong> ICT tools by size class
Use <strong>of</strong> ICT tools
(% <strong>of</strong> positive answers)
Size class
1-4 5-9 ≥10
Total
ERP 9.6% 34.7% 42.1% 20.0%
Groupware 6.9% 27.3% 21.1% 13.8%
DSS 2.4% 14.9% 19.3% 7.7%
Datawarehouse and data mining 11.0% 34.7% 38.6% 20.5%
E-learning 5.1% 14.0% 17.5% 8.9%
Intranet – LAN 36.1% 72.7% 73.7% 50.1%
Supply chain management 1.7% 9.1% 12.3% 4.9%
Electronic Commerce 6.5% 9.9% 24.6% 9.6%
Website 29.5% 50.4% 71.9% 40.0%
It is also possible to classify ICT applications based on <strong>the</strong>ir capability to automatically handle highly
structured data (i.e. ERP, DSS, DW, SCM, Electronic commerce) ra<strong>the</strong>r than support unstructured or
varied communications (Groupware, e-learning, Intranet/LAN, Website). The data confirm that 65.2%
<strong>of</strong> companies use at least one unstructured ICT system, and 33.7% at least one structured.
Apparently <strong>the</strong> kind <strong>of</strong> service <strong>of</strong>fered has no influence here.
4.7 Knowledge protection
Since protecting <strong>the</strong> owned knowledge is ano<strong>the</strong>r important issue in <strong>the</strong> KM field, we also investigated
<strong>the</strong> level <strong>of</strong> use <strong>of</strong> knowledge protection mechanisms.
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Ettore Bolisani, Marco Paiola and Enrico Scarso
Table 13: Use <strong>of</strong> knowledge protection methods by KIBS subsector
Modality (% <strong>of</strong> positive responses)
KIBS subsector
ICT D&C PFS
Total
Registered Brands 14.9% 27.0% 21.7% 21.3%
Patents 7.1% 8.2% 5.1% 6.8%
Registered Designs/Models 0.0% 10.1% 3.8% 4.7%
Copyright 1.3% 9.4% 3.2% 4.7%
Industrial secret 4.5% 7.5% 3.2% 5.1%
O<strong>the</strong>r 1.9% 7.5% 4.5% 4.7%
None 77.3% 54.1% 72.0% 67.6%
There are differences across subsectors and in relation to <strong>the</strong> kind <strong>of</strong> service <strong>of</strong>fered (which can
influence <strong>the</strong> possibility <strong>of</strong> protecting knowledge). But, as can be noted, <strong>the</strong> adoption <strong>of</strong> measures to
protect knowledge is uncommon, and with <strong>the</strong> exception <strong>of</strong> registered brands <strong>the</strong> o<strong>the</strong>r methods are
only marginally employed (Table 13).
Table 14: Use <strong>of</strong> knowledge protection methods by size class
Modality
Size class
Total
(% <strong>of</strong> positive responses)
1-4 5-9 ≥10
Registered Brands 18.2% 22.1% 35.1% 21.3%
Patents 5.8% 6.6% 12.3% 6.8%
Registered Designs/Models 5.1% 4.1% 3.5% 4.7%
Copyright 5.8% 2.5% 3.5% 4.7%
Industrial secret 5.1% 4.9% 5.3% 5.1%
O<strong>the</strong>r 5.1% 4.9% 5.3% 4.7%
None 70.4% 66.4% 56.1% 67.6%
As one can expect, <strong>the</strong> use <strong>of</strong> protection measures is more common for larger companies, although
<strong>the</strong> general pattern remains <strong>the</strong> same (Table 14).
4.8 Clients as sources <strong>of</strong> knowledge
The importance <strong>of</strong> clients as sources <strong>of</strong> key knowledge was measured following <strong>the</strong> same procedure
used in <strong>the</strong> case <strong>of</strong> suppliers. Comparing data <strong>of</strong> Table 15 with those <strong>of</strong> Table 3 reveals that clients
are considered more important as knowledge sources than suppliers. This is in line with what <strong>the</strong>
literature generally affirms (Kuusisto 2008), i.e. KIBS companies are characterized by a strict
collaboration with clients that are precious sources <strong>of</strong> knowledge for learning and developing new
services. In particular 26.2% <strong>of</strong> companies consider clients having a high importance, while this value
is 16.8% in <strong>the</strong> case <strong>of</strong> suppliers. This is mainly due to PFS and ICT companies, since <strong>the</strong> percentage
<strong>of</strong> companies that assign high importance increases from 11.0% to 24.2% and from 19.9% to 30.5%
respectively passing form suppliers to clients.
Table 15: Importance <strong>of</strong> clients as sources <strong>of</strong> knowledge by KIBS subsector
Importance <strong>of</strong> clients as Subsector
Total
sources <strong>of</strong> knowledge
ICT D&C PFS
Little 24.7% 31.0% 31.2% 29.0%
Medium 44.8% 44.9% 44.6% 44.8%
High 30.5% 24.1% 24.2% 26.2%
Also in this case, size doesn’t seem to be clearly correlated with this item (Table 16). As in <strong>the</strong> case <strong>of</strong>
suppliers, opinions <strong>of</strong> <strong>the</strong> larger companies are more polarised than those <strong>of</strong> <strong>the</strong> smaller ones.
Table 16: Importance <strong>of</strong> clients as sources <strong>of</strong> knowledge by size class
Importance <strong>of</strong> clients as Size class
Total
5. Conclusion
sources <strong>of</strong> knowledge
1-4 5-9 ≥10
Little 26.1% 32.2% 36.8% 29.0%
Medium 48.1% 43.0% 31.6% 44.8%
High 25.8% 24.8% 31.6% 26.2%
The aim <strong>of</strong> this paper was to investigate <strong>the</strong> issue <strong>of</strong> adoption and use <strong>of</strong> KM practices by Knowledge-
Intensive Business Services (KIBS) companies. The survey used a stratified sample statistically
representative <strong>of</strong> KIBS operating in <strong>the</strong> Veneto region (North-Eastern Italy) and belonging to three
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Ettore Bolisani, Marco Paiola and Enrico Scarso
subsectors (ICT Services, Design and Communication, and Pr<strong>of</strong>essional Services). The resulting
picture is, <strong>the</strong>refore, <strong>of</strong> some general significance.
Data <strong>of</strong>fer an initial description <strong>of</strong> some crucial aspects regarding, firstly, KM practices especially
adopted in small and medium sized KIBS. Secondly, <strong>the</strong>y provide insights into <strong>the</strong> limitations <strong>of</strong>
studying this sector following traditional approaches.
Despite one would expect KIBS consider knowledge management a critical issue, and, consequently,
a consistent use <strong>of</strong> relevant KM practices would be in place, our findings show an articulated and
heterogeneous picture, which highlights that this issue is very complex and deserves a thorough
analysis.
A key point regards <strong>the</strong> way KIBS manage internal knowledge, and, in particular, its reusing cycle.
This aspect is important, because it can explain <strong>the</strong> propensity or aversion <strong>of</strong> firms to structured KM
approaches and technologies for storing and retrieving knowledge. In this regard, it is important to
note that <strong>the</strong> large majority <strong>of</strong> companies do not consider <strong>the</strong> problem <strong>of</strong> knowledge reuse as a
cogent question. As was predictable, we found some relationship with <strong>the</strong> level <strong>of</strong> standardisation <strong>of</strong>
<strong>the</strong> services provided: companies that deem reuse as crucial are generally those that provide
standardised services. Instead, <strong>the</strong>re are no clear differences between small KIBS with one or very
few employees, and <strong>the</strong> large ones (which might have, in principle, both more opportunities to reuse
<strong>the</strong> knowledge <strong>the</strong>y produce, and more resources to manage this process in a structured way). Since
it is difficult to imagine that companies use brand new knowledge for any new client or project, this
circumstance is somewhat counterintuitive. An explanation <strong>of</strong> this finding could be that <strong>the</strong> large
majority <strong>of</strong> KIBS – that have a small or medium size – do reuse knowledge, but <strong>the</strong>y don’t recognize
this as an explicit and deliberate act as it could be perceived by a KM pr<strong>of</strong>essional. All this has two
important implications. Firstly, if that was <strong>the</strong> case, this would mean that <strong>the</strong>re is <strong>the</strong> need to
thoroughly investigate <strong>the</strong> way KIBS companies actually perceive <strong>the</strong>ir knowledge resources: to
understand that, <strong>the</strong>re may be <strong>the</strong> need to adapt <strong>the</strong> classic terminology and conceptual schemes that
we traditionally use for KM research. Secondly, before trying to spread KM practices in this sector,
KIBS companies may need to be made aware that knowledge is <strong>the</strong>ir real core business resource.
Ano<strong>the</strong>r important point is that KIBS companies exchange less knowledge with external partners than
<strong>of</strong>ten argued (den Hertog 2000; Ojanen 2007). In particular, inter-organisational knowledge
exchanges with o<strong>the</strong>r KIBS in <strong>the</strong> same subsector are <strong>of</strong> little importance, and even less across
different subsectors. In addition, only few companies consider <strong>the</strong> exchange <strong>of</strong> knowledge with
suppliers critical. This can be related to <strong>the</strong> fact that <strong>the</strong>y <strong>of</strong>ten acquire standard products or services.
Paradigmatic is <strong>the</strong> example <strong>of</strong> ICT companies: <strong>the</strong>y <strong>of</strong>ten acquire <strong>the</strong> license <strong>of</strong> a standard s<strong>of</strong>tware
module and <strong>the</strong>y <strong>the</strong>n customise and re-sell it. In this case, knowledge is still exchanged between
supplier and KIBS, but is embedded in an artifact (i.e. <strong>the</strong> s<strong>of</strong>tware) and thus becomes “hidden” in it.
Fur<strong>the</strong>rmore, <strong>the</strong>re is no need to commit into intense and interactive processes <strong>of</strong> knowledge
exchange with suppliers.
Conversely, <strong>the</strong> research confirms that interactions with clients are very important. This well fits <strong>the</strong>
definition <strong>of</strong> KIBS, and also confirms that <strong>the</strong> core business <strong>of</strong> <strong>the</strong>se companies is to provide
knowledge bundles to customers, and to do that <strong>the</strong>y also need to learn about <strong>the</strong>m or in o<strong>the</strong>r words
to acquire knowledge from <strong>the</strong>m.
The picture provided is consistent with <strong>the</strong> actual tools and methods <strong>the</strong> companies adopt to manage
knowledge and knowledge exchanges. As regards <strong>the</strong> modes <strong>of</strong> knowledge exchange, even though
<strong>the</strong>re is a slight difference between subsectors, <strong>the</strong>y are mostly traditional and based on face to face
interactions. Similarly, although <strong>the</strong> use <strong>of</strong> ICT systems is quite common, few companies use highly
structured applications and <strong>the</strong> most popular are those allowing unstructured communications. This
could be explained by <strong>the</strong> prevalent tacit nature <strong>of</strong> <strong>the</strong> knowledge typically used by KIBS companies
(Muller and Doloreux 2009). Probably for <strong>the</strong> same reason IPR protection is very scarce, and is
mostly based on a generic protection <strong>of</strong> <strong>the</strong> company’s brand. Technical and legal measures to
protect knowledge are generally considered inappropriate, while <strong>the</strong> building <strong>of</strong> a trustworthy
relationship and market reputation are regarded as much more important.
Finally, our study shows that an impressive number <strong>of</strong> companies are composed by one or very few
people. Since <strong>the</strong> space for adopting KM practices and tools generally proposed in <strong>the</strong> literature is
limited in such small-sized companies, <strong>the</strong> research on KM in KIBS (to date heavily focused on large
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Ettore Bolisani, Marco Paiola and Enrico Scarso
companies) should consider this aspect. Similarly, we register that <strong>the</strong> sectorial classification <strong>of</strong> KIBS
is rarely a clear discriminating factor for understanding KM issues. As well underlined in o<strong>the</strong>r recent
studies (Horgos and Koch 2008; Rodriguez and Camacho 2009; Consoli and Elche-Hortelano 2010),
we need new approaches to analyse this sector, and particularly to consider <strong>the</strong> specific cognitive
features <strong>of</strong> smaller units and individual firms, as for instance <strong>the</strong> particular kind <strong>of</strong> knowledge base
<strong>the</strong>y possess. In o<strong>the</strong>r words, focalising only on <strong>the</strong> “big KIBS multinationals” (like for instance big
business consultants or large ICT providers) may be insufficient to understand <strong>the</strong> articulated picture
<strong>of</strong> this increasingly important sector <strong>of</strong> <strong>the</strong> economy.
6. Acknowledgements
This research has been supported and funded by “Fondazione Cariparo, Cassa di Risparmio di
Padova e Rovigo” (Padua, Italy) within <strong>the</strong> project "Progetti di Eccellenza 2007/2008".
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123

Strategies for Increasing Knowledge Retention in
Universities Through Intergenerational Knowledge
Transfer
Constantin Bratianu, Adriana Agapie and Ivona Orzea
Academy <strong>of</strong> Economic Studies, Bucharest, Romania
cbratianu@yahoo.com
Adriana.agapie@clicknet.ro
Ivona.orzea@gmail.com
Abstract: The new Law <strong>of</strong> Education promulgated recently in Romania enforces pr<strong>of</strong>essors retirement at <strong>the</strong> age
<strong>of</strong> 65. The previous law had a provision for full pr<strong>of</strong>essors to remain in <strong>the</strong> academic life up to <strong>the</strong> age <strong>of</strong> 70, if
<strong>the</strong>ir universities needed <strong>the</strong>m. The new legislation produces an earthquake in our universities due to <strong>the</strong>
elimination from <strong>the</strong> academic life <strong>of</strong> a very important segment <strong>of</strong> knowledge and wisdom owners. This dramatic
situation shows how important it is to develop strategies for increasing knowledge retention in universities. Since
a university is by its own nature a multigenerational workforce, <strong>the</strong> best strategies should be based on <strong>the</strong>
intergenerational knowledge transfer. The purpose <strong>of</strong> our paper is to investigate two main strategies for <strong>the</strong>
intergenerational knowledge transfer: a) encouraging cooperation through teamwork, and b) encouraging
individual competition. The first strategy is emphasizing <strong>the</strong> synchronic knowledge transfer, while <strong>the</strong> second one
emphasizes <strong>the</strong> non-synchronic knowledge transfer. Each <strong>of</strong> <strong>the</strong>se strategies can be implemented through
different mechanisms specific for academic life. We have considered in our research three <strong>of</strong> <strong>the</strong>m: 1) research
grants; 2) writing books, and 3) writing papers for scientific journals. Our investigation is based on <strong>the</strong> Analytic
Hierarchy Process (AHP) method, and on using questionnaires structured according to <strong>the</strong>se strategies
mentioned above. We distributed 500 questionnaires to all <strong>the</strong> faculties <strong>of</strong> economics and business from <strong>the</strong> state
universities, and we have received 223 valid questionnaires. Based on <strong>the</strong>m we computed using AHP <strong>the</strong> priority
vectors for each strategy and activity considered. Numerical results show <strong>the</strong> importance <strong>of</strong> <strong>the</strong> cooperation
strategy in <strong>the</strong> intergenerational knowledge transfer, for increasing <strong>the</strong> knowledge retention in <strong>the</strong> university.
Keywords: analytical hierarchy process, knowledge transfer, knowledge sharing, intergenerational knowledge
transfer
1. Introduction
The Romanian Parliament promulgated recently a new Law <strong>of</strong> Education that introduces a
compulsory retirement at 65 for all pr<strong>of</strong>essors. The previous law considered <strong>the</strong> age 65 only as an
optional age for full pr<strong>of</strong>essors. They could continue <strong>the</strong>ir academic duties until <strong>the</strong> age <strong>of</strong> 70 if <strong>the</strong>
university needed <strong>the</strong>m. Thus, <strong>the</strong> new legislation contributed to <strong>the</strong> elimination from <strong>the</strong> academic life
<strong>of</strong> a significant number <strong>of</strong> very knowledgeable people. Considering all 56 state universities existing in
<strong>the</strong> Romanian higher education system, we may approximate that only in this year will be forced to
retire about 1000 pr<strong>of</strong>essors, which amount to a huge loss <strong>of</strong> knowledge and <strong>of</strong> discontinuities in <strong>the</strong>
academic life. The direct impact is mostly on <strong>the</strong> doctoral programs since many <strong>of</strong> <strong>the</strong>se pr<strong>of</strong>essors
have been involved as academic advisers for <strong>the</strong> doctoral students. This legislation has been based
on some political grounds, and it is against <strong>the</strong> education policies implemented in o<strong>the</strong>r countries. We
shall mention only <strong>the</strong> fact that in <strong>the</strong> USA <strong>the</strong>re is no imposed age for academic retirement since
knowledge and wisdom are not proportional with <strong>the</strong> biological age. Academic and scientific values
cannot be strictly related to <strong>the</strong> biological age. However, our politicians think primarily in a
deterministic and linear way which is <strong>the</strong> effect <strong>of</strong> <strong>the</strong> inertial socialist culture (Bratianu & Murakawa,
2004; Bratianu & Vasilache, 2009; Bratianu & Vasilache, 2010).
Since universities are knowledge intensive organizations (Bratianu, 2011a; Duderstadt, 2000; Marga,
2011;, Ruegg, 2004; Shattock, 2003) <strong>the</strong> loss <strong>of</strong> knowledge contributes directly to decreasing <strong>the</strong>ir
competitive advantage. In <strong>the</strong> new legislative context, universities should conceive strategies for
enhancing knowledge retention through intergenerational knowledge transfer, filming <strong>the</strong> old masters
classes, promoting special conferences for <strong>the</strong> benefit <strong>of</strong> younger generations, making <strong>the</strong>m scientific
Journals Editors, and inviting <strong>the</strong>m as consultants for <strong>the</strong> academic life. Universities are multilayer age
nested structures where intergenerational knowledge fluxes are generated by <strong>the</strong> strong
nonuniformity and nonlinearity <strong>of</strong> <strong>the</strong> organizational intellectual capital (Bratianu, 2011b; Bratianu,
2011c). Knowledge creation and knowledge transfer are intertwined processes, and both <strong>of</strong> <strong>the</strong>m are
strongly influenced by <strong>the</strong> age scale.
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Considering <strong>the</strong> Nonaka’s knowledge dynamics model we may say that a university is a nested Ba:
“The essence <strong>of</strong> Ba is <strong>the</strong> contexts and <strong>the</strong> meanings that are shared and created through
interactions which occur at a specific time and in a specific space, ra<strong>the</strong>r than a space itself. Ba also
means relationships <strong>of</strong> those who are at <strong>the</strong> specific time and <strong>the</strong> specific space” (Nonaka &
Takeuchi, 1995, p.23). A university is by its own nature a nested Ba, where <strong>the</strong> inner most layers are
represented by older pr<strong>of</strong>essors who concentrate <strong>the</strong> fundamental structures <strong>of</strong> knowledge, and <strong>the</strong>
outer layers are represented by students in <strong>the</strong>ir different cycles. Thus, <strong>the</strong> intergenerational
knowledge transfer becomes a core competence <strong>of</strong> <strong>the</strong> university able to contribute substantially to its
competitive advantage (Carpenter & Sanders, 2007; Dess, Lumpkin & Eisner, 2006).
The practice <strong>of</strong> our academic life showed that <strong>the</strong> most effective ways for implementing strategies <strong>of</strong>
knowledge transfer are through performing research grants, writing scientific papers and writing
books. The purpose <strong>of</strong> our paper is to investigate <strong>the</strong> main strategies for <strong>the</strong>se intergenerational
knowledge transfer: a) encouraging cooperation through teamwork, and b) encouraging individual
competition. The first strategy is emphasizing <strong>the</strong> synchronic knowledge transfer, while <strong>the</strong> second
one emphasizes <strong>the</strong> non-synchronic knowledge transfer. We defined in our research three activities
important for intergenerational knowledge transfer: 1) working toge<strong>the</strong>r in realizing research grants; 2)
working toge<strong>the</strong>r in writing textbooks, and 3) writing papers for scientific journals. Our investigation is
based on <strong>the</strong> Analytic Hierarchy Process (AHP) method, and on using questionnaires structured
accordingly. We distributed 500 questionnaires to all <strong>the</strong> faculties <strong>of</strong> economics and business from <strong>the</strong>
Romanian state universities, and we have received 223 valid questionnaires. Processing <strong>the</strong>se
questionnaires we computed according to <strong>the</strong> AHP method <strong>the</strong> priority vectors for each strategy and
activity considered. Empirical research results show <strong>the</strong> dominance <strong>of</strong> <strong>the</strong> cooperation strategy, and
<strong>the</strong> teamwork for research grants in <strong>the</strong> intergenerational knowledge transfer, for increasing <strong>the</strong>
knowledge retention in <strong>the</strong> university.
2. Intergenerational knowledge transfer
Intergenerational knowledge transfer refers to any knowledge flow across generations in a specific
Ba, as a result <strong>of</strong> <strong>the</strong> knowledge gradients in <strong>the</strong> organizational intellectual capital <strong>of</strong> <strong>the</strong> university.
Knowledge fluxes contain both explicit and tacit knowledge (Geisler & Wickramasinghe, 2009;
Nonaka & Takeuchi, 1995; Nonaka & Toyama, 2007). Explicit knowledge is that form <strong>of</strong> knowledge
that we can transfer through language and ma<strong>the</strong>matical modeling. It is implicitly contained in all kind
<strong>of</strong> knowledge types: know-what, know-why, know-how, and know-who. Explicit knowledge is a direct
result <strong>of</strong> <strong>the</strong> externalization and combination processes from <strong>the</strong> Nonaka’s model <strong>of</strong> knowledge
creation. University teaching and learning are based on explicit knowledge. Actually, <strong>the</strong> largest part
<strong>of</strong> all knowledge transfer in <strong>the</strong> academic environment is done using explicit knowledge. This form <strong>of</strong>
knowledge is highly rational and in most cases it is a result <strong>of</strong> our metaphorical thinking (Andriessen,
2008; Bratianu & Andriessen, 2008; Lak<strong>of</strong>f & Johnson, 1999; Pinker, 2007).
As Nonaka and Takeuchi (1995, p.8) emphasize, “Tacit knowledge is highly personal and hard to
formalize, making it difficult to communicate or to share with o<strong>the</strong>rs. Subjective insights, intuitions, and
hunches fall into this category <strong>of</strong> knowledge. Fur<strong>the</strong>rmore, tacit knowledge is deeply rooted in an
individual’s action and experience, as well as in <strong>the</strong> ideals, values, or emotions he or she embraces”.
Tacit knowledge contains two components: a technical component that reflects <strong>the</strong> know-how <strong>of</strong>
pr<strong>of</strong>essional activities, and a cognitive component that reflects mental models, beliefs and perceptions
as a result <strong>of</strong> many performed similar actions. Tacit knowledge embraces also highly subjective
insights, intuitions and hunches. Leaders usually make use <strong>of</strong> <strong>the</strong>se fine ingredients <strong>of</strong> tacit
knowledge, being able to inspire and motivate <strong>the</strong>ir followers (Schein, 2004; Bass & Riggio, 2006;
Northouse, 2007).
Unlike explicit knowledge that can be easily codified and transferred, tacit knowledge is sticky.
According to Szulanski (2000, p.10), knowledge transfer is seen as “a process in which an
organization recreates and maintains a complex, causally ambiguous set <strong>of</strong> routines in a new setting.
Stickiness connotes difficulty experienced in that process”. In o<strong>the</strong>r words, a knowledge transfer is
perceived as being difficult or sticky when efforts to fluidize <strong>the</strong> flow <strong>of</strong> knowledge become noteworthy.
The tacit knowledge transfer also depends significantly on <strong>the</strong> dispositions and abilities <strong>of</strong> <strong>the</strong> source
and recipient, and on <strong>the</strong> context. An organizational context that facilitates <strong>the</strong> inceptions and
development <strong>of</strong> transfer is frequently referred to as fertile. In contrast, a context that hinders <strong>the</strong>
gestation and evolution <strong>of</strong> transfer is said to be barren (Szulanki, 2000; Jensen & Szulanski, 2007).
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According to Szulanski (2000) <strong>the</strong> main stages <strong>of</strong> <strong>the</strong> knowledge transfer process are <strong>the</strong> following:
initiation, implementation, ramp-up, and integration. Each <strong>of</strong> <strong>the</strong>se stages contains barriers that slow
down <strong>the</strong> flow <strong>of</strong> knowledge. If <strong>the</strong> university leaders want to enhance intergenerational knowledge
transfer, <strong>the</strong>n <strong>the</strong>re should be implemented a strategy to motivate <strong>the</strong> source for knowledge sharing
by decreasing <strong>the</strong> stickiness <strong>of</strong> tacit knowledge in <strong>the</strong> initiation phase. From academic experience we
may say that it can be done more easily by encouraging teamwork in complex research projects. This
working context seems to be suitable also for <strong>the</strong> implementation, ramp-up and integration phases.
Motivation can be developed by setting up specific criteria for academic staff promotion based on <strong>the</strong>
number, importance and scientific value <strong>of</strong> <strong>the</strong>se research grants obtained through national or
international competition.
3. Research design
We considered for this research all <strong>the</strong> faculties <strong>of</strong> economics and business from <strong>the</strong> Romanian
academic consortium composed <strong>of</strong> <strong>the</strong> following universities: University <strong>of</strong> Bucharest, University
“Al.I.Cuza” <strong>of</strong> Iasi, University “Babes-Bolyai” <strong>of</strong> Cluj-Napoca, The West University <strong>of</strong> Timisoara, and
Academy <strong>of</strong> Economic Studies <strong>of</strong> Bucharest. We addressed 500 questionnaires, and we received 223
valid questionnaires from all <strong>of</strong> <strong>the</strong>se universities. It is important to emphasize <strong>the</strong> fact that in this kind
<strong>of</strong> ma<strong>the</strong>matical analysis it is important <strong>the</strong> significance <strong>of</strong> people participating in <strong>the</strong> quantitative
evaluation, and not <strong>the</strong>ir number. That is primarily from <strong>the</strong> main hypo<strong>the</strong>sis we made that <strong>the</strong>
knowledge field is asymmetric and highly nonuniform throughout <strong>the</strong> university. Its nonuniformity is
generating all <strong>the</strong> knowledge flows (Nissen, 2006), out <strong>of</strong> which we are interested especially in those
that represent intergenerational knowledge transfer.
The qualitative component <strong>of</strong> our research consists in structuring <strong>the</strong> knowledge transfer field within
<strong>the</strong> university and in defining <strong>the</strong> basic strategies for enhancing knowledge retention in universities,
and activities that are significant for intergenerational knowledge transfer. The whole transfer
dynamics is structured into three levels: <strong>the</strong> top level is for defining <strong>the</strong> goal <strong>of</strong> this evaluation. The
goal is enhancing knowledge retention in <strong>the</strong> university through intergenerational learning and
knowledge transfer. The next lower level is for <strong>the</strong> main strategies used for identifying <strong>the</strong> priorities <strong>of</strong>
knowledge transfer attitudes. These strategies are <strong>the</strong> following: (S1) – <strong>the</strong> strategy to stimulate <strong>the</strong>
faculty staff attitude toward cooperation; (S2) – <strong>the</strong> strategy to stimulate <strong>the</strong> faculty staff attitude
toward individual competition. The lowest level is for defining <strong>the</strong> main activities considered in this
research: (A1) – <strong>the</strong> activity done for research grants; (A2) – <strong>the</strong> activity done for writing books; (A3) –
<strong>the</strong> activity done for writing papers to be published in scientific journals. The graphical illustration <strong>of</strong>
this model is presented in figure 1.
The analytical hierarchy process includes judgments on pairs <strong>of</strong> elements throughout <strong>the</strong> hierarchy,
one level at a time beginning at <strong>the</strong> top, based on <strong>the</strong> respondent’s knowledge and according to <strong>the</strong>irs
perceived relative importance <strong>of</strong> <strong>the</strong> factors involved. The most heavily weighted alternative outcome
in <strong>the</strong> bottom level is <strong>the</strong> most likely one. A survey designed according with <strong>the</strong>se principles was
electronically distributed among <strong>the</strong> academic staff <strong>of</strong> <strong>the</strong> universities from <strong>the</strong> consortium mentioned
above. In order to understand how this was processed, a short presentation <strong>of</strong> <strong>the</strong> way in which <strong>the</strong>
questions were formulated in this survey and processed <strong>the</strong>reafter will follow. Numerical results and
interpretations will be presented in <strong>the</strong> next section <strong>of</strong> this paper.
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Activity (A1)
Constantin Bratianu, Adriana Agapie and Ivona Orzea
Strategy (S1)
Goal
Activity (A2)
Strategy (S2)
Activity
Figure 1: AHP model
We shall present <strong>the</strong> specific type <strong>of</strong> questions we used, since <strong>the</strong>y are <strong>the</strong> key for this kind <strong>of</strong>
analysis. In <strong>the</strong> first page were asked general information about <strong>the</strong> position <strong>of</strong> <strong>the</strong> respondent in <strong>the</strong>
university: <strong>the</strong> academic status (full pr<strong>of</strong>essor, associate pr<strong>of</strong>essor, assistant pr<strong>of</strong>essor, university
assistant or PhD student), <strong>the</strong> Department and <strong>the</strong> affiliation to a certain Faculty. The second page
was devoted to <strong>the</strong> determination <strong>of</strong> <strong>the</strong> priority vectors <strong>of</strong> <strong>the</strong> two strategies in determining <strong>the</strong> mode
<strong>of</strong> <strong>the</strong> knowledge transfer. The scale considered for this research is from 1 (equally important) to 9
(extremely important). Questions are formulated in comparative terms, as shown below:
1.a) Given <strong>the</strong> goal, what do you think is more important: <strong>the</strong> strategy to stimulate cooperation
(S1), or <strong>the</strong> strategy <strong>of</strong> stimulating <strong>the</strong> individual competition (S2)?
b) Please indicate, on a scale from 1 to 9 to what extent you consider that your previous choice
is more important than <strong>the</strong> o<strong>the</strong>r one.
The survey’s next module was devoted to <strong>the</strong> determination <strong>of</strong> <strong>the</strong> priority vectors <strong>of</strong> <strong>the</strong> alternative
activities: (A1) – <strong>the</strong> activity done for research grants; (A2) – <strong>the</strong> activity done for writing books; (A3) –
<strong>the</strong> activity done for writing papers to be published in scientific journals. The set <strong>of</strong> <strong>the</strong>se three
activities is considered for each strategy, individually. For <strong>the</strong> strategy considered for stimulating
intergenerational knowledge transfer through cooperation, questions were formulated as follows:
2.a) Given <strong>the</strong> strategy for stimulating faculty staff cooperation (S1), what do you think is more
important: <strong>the</strong> activity done for research grants (A1), or <strong>the</strong> activity done for writing books (A2)?
b) Please indicate, on a scale from 1 to 9 to what extent you consider that your previous
choice is more important than <strong>the</strong> o<strong>the</strong>r one.
3.a) Given <strong>the</strong> strategy for stimulating faculty staff cooperation (S1), what do think is more
important: <strong>the</strong> activity done for writing books (A2), or <strong>the</strong> activity done for writing papers to be
published in scientific journals (A3)?
b) Please, indicate, on a scale from 1 to 9 to what extent you consider that your previous choice
is more important than <strong>the</strong> o<strong>the</strong>r one.
4.a) Given <strong>the</strong> strategy for stimulating faculty staff cooperation (S1), what do you think is more
important: <strong>the</strong> activity done for writing papers to be published in scientific journals (A3), or <strong>the</strong>
activity done for research grants (A1)?
b) Please, indicate, on a scale from 1 to 9 to what extent you consider that your previous choice
is more important than <strong>the</strong> o<strong>the</strong>r one.
Similar questions were formulated for <strong>the</strong> strategy (S2) aiming at encouraging individual competition.
5.a) Given <strong>the</strong> strategy for stimulating faculty staff competition (S2), what do you think is more
important: <strong>the</strong> activity done for research grants (A1), or <strong>the</strong> activity done for writing books (A2)?
b) Please indicate, on a scale from 1 to 9 to what extent you consider that your previous
choice is more important than <strong>the</strong> o<strong>the</strong>r one.
6.a) Given <strong>the</strong> strategy for stimulating faculty staff competition (S2), what do think is more
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Constantin Bratianu, Adriana Agapie and Ivona Orzea
important: <strong>the</strong> activity done for writing books (A2), or <strong>the</strong> activity done for writing papers to be
published in scientific journals (A3)?
b) Please, indicate, on a scale from 1 to 9 to what extent you consider that your previous choice
is more important than <strong>the</strong> o<strong>the</strong>r one.
7.a) Given <strong>the</strong> strategy for stimulating faculty staff competition (S2), what do you think is more
important: <strong>the</strong> activity done for writing papers to be published in scientific journals (A3), or <strong>the</strong>
activity done for research grants (A1)?
b) Please, indicate, on a scale from 1 to 9 to what extent you consider that your previous choice
is more important than <strong>the</strong> o<strong>the</strong>r one.
Paired comparison judgments in <strong>the</strong> AHP are applied to pairs <strong>of</strong> homogeneous elements and
summarized in a matrix <strong>of</strong> judgments. Scoring is applied to rank <strong>the</strong> two alternative strategies. Matrix
<strong>of</strong> judgments is determined assuming values equal to one on <strong>the</strong> main diagonal and also reversibility
<strong>of</strong> <strong>the</strong> preferences-so that if S1 is preferred to S2 at a corresponding absolute value <strong>of</strong> 5, <strong>the</strong>n <strong>the</strong> S2
will be preferred to S1 at an absolute value <strong>of</strong> 1/5, which is 0.2. The corresponding vector <strong>of</strong> priorities
is computed in an eigenvalue formulation. The solution is obtained by raising <strong>the</strong> matrix to a
sufficiently large power, <strong>the</strong>n summing over <strong>the</strong> rows and normalizing to obtain <strong>the</strong> priority vector. The
process is stopped when <strong>the</strong> difference between components <strong>of</strong> <strong>the</strong> priority vector obtained at <strong>the</strong> (k)
power and at <strong>the</strong> (k+1) power is less than some predetermined small value. The vector <strong>of</strong> priorities is
<strong>the</strong> derived scale associated with <strong>the</strong> matrix <strong>of</strong> comparisons (Saaty, 1994). After setting priorities for
<strong>the</strong>se strategies, pair wise comparisons are also made ratings <strong>the</strong>mselves to set priorities for <strong>the</strong>m
under each strategy and dividing each <strong>of</strong> <strong>the</strong>ir priorities by <strong>the</strong> largest rated intensity to get <strong>the</strong> ideal
intensity. Finally, alternatives are scored by checking <strong>of</strong>f <strong>the</strong>ir respective ratings under each criterion
and summing <strong>the</strong>se ratings for all strategies. This produces a ratio scale score for <strong>the</strong> alternative. The
scores thus obtained <strong>of</strong> <strong>the</strong> alternatives can in <strong>the</strong> end be normalized by dividing each one by <strong>the</strong>ir
sum.
4. Numerical results and discussion
The survey was delivered to academics from <strong>the</strong> faculties <strong>of</strong> economics and business <strong>of</strong> <strong>the</strong>
consortium <strong>of</strong> <strong>the</strong> main comprehensive universities <strong>of</strong> <strong>the</strong> country, mentioned above. Finally we got
223 valid questionnaires. Once again, we underline <strong>the</strong> fact that in this type <strong>of</strong> research <strong>the</strong>
significance <strong>of</strong> respondents’ position is important and not <strong>the</strong> total number <strong>of</strong> respondents since <strong>the</strong>
knowledge transfer field is not homogeneous. The respondents represent quite well <strong>the</strong> three main
generations we considered in <strong>the</strong> academic staff: pr<strong>of</strong>essors – in <strong>the</strong> upper age level, associate
pr<strong>of</strong>essors – in <strong>the</strong> middle age level, and <strong>the</strong> young assistants – in <strong>the</strong> lower age level. The priority
vector <strong>of</strong> <strong>the</strong> strategy considered to influence <strong>the</strong> intergenerational knowledge transfer was calculated
as an average on <strong>the</strong> individual vectors <strong>of</strong> priority. Results weighted for all <strong>the</strong> respondents are
summarized in Table 1.
Table 1: Syn<strong>the</strong>sis in <strong>the</strong> Distributive Mode
Strategies S1 S2
Priority vector 0.786 0.214
Activity A1 0.596 0.600
Activity A2 0.230 0.245
Activity A3 0.174 0.155
Analyzing <strong>the</strong> structure <strong>of</strong> <strong>the</strong> priority vector <strong>of</strong> strategies becomes evident <strong>the</strong> fact that most <strong>of</strong> <strong>the</strong>
academic staff consider cooperation much more important (0.786) than individual competition (0.214).
Considering cooperation as <strong>the</strong> main strategy, respondents appreciate that team work for research
grants (0.596) is much more important as writing books (0.230), or writing papers for scientific journals
(0.174) in <strong>the</strong> perspective <strong>of</strong> intergenerational knowledge transfer. It is a ra<strong>the</strong>r interesting result
knowing <strong>the</strong> preference for competition in <strong>the</strong> past. It is a change in <strong>the</strong> university strategy followed by
a change in <strong>the</strong> organizational culture. Analyzing now <strong>the</strong> priority vector for each strategy individually,
it is clear that <strong>the</strong> most important mode <strong>of</strong> knowledge sharing is working toge<strong>the</strong>r for <strong>the</strong> research
grants (0.596 for strategy S1, and 0.600 for strategy S2). In second place comes knowledge sharing
through work for writing textbooks (0.230 for strategy S1, and 0.245 for strategy S2). In <strong>the</strong> teamwork
for research we can integrate both tacit and explicit knowledge; in writing books <strong>the</strong> most important
contribution is done by explicit knowledge. Writing papers for scientific journals remains less important
for intergenerational knowledge transfer (0.174 for strategy S1, and 0.155 for strategy S2). This score
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Constantin Bratianu, Adriana Agapie and Ivona Orzea
shows that writing papers is a ra<strong>the</strong>r individual process, even if many published papers have usually
several authors.
In order to establish <strong>the</strong> composite or global priorities <strong>of</strong> <strong>the</strong> alternatives considered we lay out in a
matrix <strong>the</strong> local priorities <strong>of</strong> <strong>the</strong> alternatives with respect to each strategy and multiply each column <strong>of</strong>
vectors by <strong>the</strong> priority <strong>of</strong> <strong>the</strong> corresponding strategy and <strong>the</strong>n add across each row, which results in
<strong>the</strong> composite or global priority vector <strong>of</strong> <strong>the</strong> alternatives. Corresponding results are presented in
Table 2.
Table 2: Syn<strong>the</strong>sis
Activities S1 S2 Global values
Activity A1 0.469 0.128 0.597
Activity A2 0.181 0.052 0.233
Activity A3 0.136 0.034 0.170
Total 0.786 0.214 1.000
These results streng<strong>the</strong>ns <strong>the</strong> distributive mode results demonstrating that <strong>the</strong> main strategy for
enhancing intergenerational knowledge transfer in <strong>the</strong> academic community is by working toge<strong>the</strong>r for
research grants (0.597 by comparison with 0.233 and 0.170). Complex projects stimulate team
member interactions, and <strong>the</strong> common responsibility drives a cooperation attitude from each member.
5. Discussion and conclusions
In <strong>the</strong> academic life <strong>the</strong>re is a strong tradition for competition, especially with <strong>the</strong> new Humboldtian
university model, established in <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> 19 th century in Berlin. This Humboldtian paradigm
<strong>of</strong> <strong>the</strong> university is based on <strong>the</strong> conception that teaching and research should be integrated in <strong>the</strong>
academic work (Marga, 2011). Knowledge creation in science means mostly individual work, and
individual performances. Intergenerational knowledge transfer is done through writing books and
papers by <strong>the</strong> some pr<strong>of</strong>essors, and, respectively, by studying <strong>the</strong>m by <strong>the</strong> younger faculty staff.
Today, research is performed mostly in teams, and its aim is to find affordable solutions to <strong>the</strong>
practical complex problems <strong>of</strong> society. Team work means a common vision and objective shared by
each member <strong>of</strong> <strong>the</strong> team, and shared accountability. Also, it means a specific context given by a
specific spatial and temporal locations. In Nonaka’s <strong>the</strong>ory this is a Ba, a dynamic context for
knowledge interactions (Nonaka & Takeuchi, 1995; Nonaka & Toyama, 2007). Team work becomes
possible only through a stimulating attitude <strong>of</strong> cooperation and knowledge sharing, that is a
synchronous process. Thus, knowledge retention in <strong>the</strong> university is much more efficient through
cooperation than through competition. University leaders who understand <strong>the</strong> importance <strong>of</strong>
knowledge retention will elaborate specific strategies for encouraging faculty staff cooperation, by
eliminating or reducing some <strong>of</strong> <strong>the</strong> traditional barriers in knowledge sharing.
Intergenerational knowledge transfer is becoming an important process in <strong>the</strong> academic environment
due especially to <strong>the</strong> age multilayered structure <strong>of</strong> universities, and to <strong>the</strong> asymmetric distribution <strong>of</strong>
knowledge. Intergenerational knowledge transfer is a very complex process based especially on
motivational driving forces that are impossible to quantify using direct methods. The complexity <strong>of</strong> this
process derives also from <strong>the</strong> fact that knowledge is transferred in both its fundamental forms: tacit
knowledge and explicit knowledge. The method we used in this analysis is <strong>the</strong> known Analytic
Hierarchy Process developed by Saaty, and used in managerial decision making. According to this
method <strong>the</strong> field <strong>of</strong> knowledge or knowledge transfer is structured in several levels, on <strong>the</strong> top being
<strong>the</strong> goal <strong>of</strong> research. In this present research <strong>the</strong> goal is to find out <strong>the</strong> priorities academics have in
<strong>the</strong> process <strong>of</strong> intergenerational knowledge transfer. The level <strong>of</strong> strategies has been structured in
attitudes for cooperation, competition and innovation. The level <strong>of</strong> alternative activities has been
structured in performing research grants, writing books and elaborating papers for scientific journals.
Our research has been designed to evaluate <strong>the</strong> relative importance <strong>of</strong> cooperation and competition
in <strong>the</strong> field <strong>of</strong> economics and business studies in <strong>the</strong> Romanian university system, and to reveal some
priorities in intergenerational knowledge transfer. Our research demonstrates that most academics
give priority to knowledge transfer through cooperation, and within this perspective to <strong>the</strong> activity <strong>of</strong>
working toge<strong>the</strong>r for <strong>the</strong> research grants. Knowing this kind <strong>of</strong> priority, <strong>the</strong> university management can
develop strategies to encourage and reward this type <strong>of</strong> activity that is an important component <strong>of</strong> <strong>the</strong>
financing mechanism. However, <strong>the</strong>re are some o<strong>the</strong>r methods used for this intergenerational
knowledge transfer, like: filming old masters classes, promoting conferences for <strong>the</strong> younger
generations, asking <strong>the</strong>se pr<strong>of</strong>essors to become Journal Editors, and inviting <strong>the</strong>m as external
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Constantin Bratianu, Adriana Agapie and Ivona Orzea
consultants in improving <strong>the</strong> academic life. From <strong>the</strong> best practice point <strong>of</strong> view in our universities
<strong>the</strong>se last methods are less used, <strong>the</strong>ir impact being much less important. Also, we would like to
mention that in <strong>the</strong> evaluation procedure <strong>of</strong> our faculty staff <strong>the</strong>se last methods play no importance.
Intergenerational knowledge transfer through research grants and writing papers remain <strong>the</strong> most
valuable for <strong>the</strong> time being.
Acknowledgement
We would like to acknowledge <strong>the</strong> support received from CNCSIS-UEFISCSU through <strong>the</strong> research
grant PN II-IDEI-1812/2008.
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130

Immersive Knowledge Communication in 3D Virtual
Environments
Klaus Bredl, Amrei Groß, Julia Hünniger and Jane Fleischer
University <strong>of</strong> Augsburg, Germany
bredl@phil.uni-augsburg.de
amreigross@t-online.de
julia.huenniger@phil.uni-augsburg.de
jane.fleischer@phil.uni-augsburg.de
Abstract: The rapid development <strong>of</strong> virtual worlds has created new possibilities for supporting formal and
informal knowledge acquisition and learning processes online. Consequently, greater immersion <strong>of</strong> “knowledge
workers” in cooperation and communication tasks in social virtual worlds should be a more prominent topic in
sociological and cognitive-psychological research designs. The relatively new social potential <strong>of</strong> virtual worlds
can be examined by using <strong>the</strong>oretical models that describe <strong>the</strong> use and assessment <strong>of</strong> virtual world technologies
in contexts <strong>of</strong> knowledge acquisition and exchange. In this paper, three scenarios created by social science
students will be described to help demonstrate how <strong>the</strong> use <strong>of</strong> <strong>the</strong> popular virtual world, Second Life, can be used
to explore new forms <strong>of</strong> interaction in (virtual) social contexts. These scenarios and <strong>the</strong> results <strong>of</strong> <strong>the</strong> avatarbased
ethnographic investigation during <strong>the</strong> process <strong>of</strong> co-creation and collaboration will be introduced and used
to reflect on <strong>the</strong> 3D projects. The paper will end with suggestions for fur<strong>the</strong>r research concerning <strong>the</strong> effects <strong>of</strong>
immersion during collaboration and education in virtual worlds.
Keywords: Virtual worlds, immersion, knowledge exchange social s<strong>of</strong>tware, knowledge management, web 2.0,
second life
1. Introduction
In light <strong>of</strong> new architectures for virtual worlds (Multiverse 2008) and open standards like OpenSim
(Open Simulator 2011) <strong>the</strong> probability that internet users will have some sort <strong>of</strong> presence in avatarbased
virtual worlds or MUVEs (Multi User Virtual Environments), in <strong>the</strong>ir pr<strong>of</strong>ession or o<strong>the</strong>rwise,
seems high (Gartner Group 2007). These digital virtual environments as part <strong>of</strong> <strong>the</strong> emerging Web3D
could confirm <strong>the</strong> possibility <strong>of</strong> stronger internalization <strong>of</strong> IT-supported knowledge processes and new
possibilities for formal and informal processes <strong>of</strong> knowledge acquisition and education to support a
personalized knowledge management (KM) strategy (Hansen, Nohria and Tierney 1999).
Fur<strong>the</strong>rmore, 3D worlds <strong>of</strong>fer a wide range <strong>of</strong> tools that provide users with many opportunities to
communicate and connect.
Additionally, MUVEs seem to possess a special motivational immersive character. The use <strong>of</strong> social
virtual worlds could help achieve <strong>the</strong> <strong>of</strong>ten demanded higher immersion <strong>of</strong> “knowledge workers” in
cooperation and communication tasks in information and communication systems. The relatively
newly recognized social potential <strong>of</strong> virtual worlds in supporting knowledge-based processes can be
examined using <strong>the</strong>oretical models <strong>of</strong> social presence and immersion and analyzing <strong>the</strong> use and
assessment <strong>of</strong> virtual world technologies in <strong>the</strong> context <strong>of</strong> knowledge acquisition and exchange. In
this context, an effort is being made to support research on “Information Space” by <strong>the</strong> Sociable
Media Group at MIT (Harry and Donath 2008).
Not only <strong>the</strong> perception <strong>of</strong> <strong>the</strong> presence <strong>of</strong> one’s own avatar in <strong>the</strong> learning and collaboration space,
but also <strong>the</strong> perception <strong>of</strong> <strong>the</strong> presence <strong>of</strong> o<strong>the</strong>rs contributes to a higher degree <strong>of</strong> immersion (Bredl &
Herz 2010, Davis et al. 2009). The possibility <strong>of</strong> <strong>of</strong>fering a higher degree <strong>of</strong> social presence and
<strong>the</strong>refore a higher degree <strong>of</strong> immersion raises fur<strong>the</strong>r community-related questions: Could <strong>the</strong>
requirements <strong>of</strong> <strong>the</strong> social constructivist and <strong>the</strong> newer connectivistic paradigm (Siemens 2005) be
fulfilled through linked 3D environments?.
Virtual worlds could provide a constructivistic and connectivistic environment because <strong>the</strong>y are
flexible, informal and non-structured, at least at a first glance. This is a topic that should be discussed
in sociological and cognitive-psychological research designs.
Apart from functions such as socializing via chat, gaming, role playing and movie showing, <strong>the</strong>
possibilities that 3D virtual worlds <strong>of</strong>fer are unlimited and <strong>the</strong>ir application in education especially in
131

Klaus Bredl, Amrei Groß and Jane Fleischer
<strong>the</strong> age range from 6-18 years is increasing (KZERO 2011). This will create a new generation <strong>of</strong>
experienced avatar users to work pr<strong>of</strong>essionally with virtual worlds in <strong>the</strong> next decade.
2. Theoretical Approach for Immersive Knowledge-Based Virtual
Environments
Game research points to an increase in efficiency <strong>of</strong> <strong>the</strong> knowledge acquisition process due to <strong>the</strong>
flow effect (Csikszentmihalyi 1993) fostered by virtual environments (Fritz 2004). According to
Csikszentmihalyi and Rathunde (1993), flow in virtual environments can be characterized by ten
factors. One such factor is losing oneself in one’s activity, or loss <strong>of</strong> consciousness <strong>of</strong> self. It should
be noted, however, that not all ten factors are needed to experience flow. Consequently, it can be
assumed that <strong>the</strong> flow effect can be achieved at <strong>the</strong> peak <strong>of</strong> immersion (Krause 2008).
Moreover, <strong>the</strong>re are indications for <strong>the</strong> connection <strong>of</strong> virtual worlds and intrinsic motivation (Deci and
Ryan 2004). Typical forms <strong>of</strong> communication in 3D virtual worlds are supposed to cause a higher
degree <strong>of</strong> immersion (Fromme 2006) and <strong>the</strong>refore lead to a more intense participation and a higher
degree <strong>of</strong> integration within a group <strong>of</strong> online participants. Increasing seriousness and comprehension
cause a higher estimation <strong>of</strong> <strong>the</strong> effectiveness <strong>of</strong> learning scenarios (Nattland 2008). Thus, immersion
is always influenced by a user’s subjective perception and personal attitude toward <strong>the</strong> virtual
construct (Bartle 2003).
Participants <strong>of</strong> social knowledge-based processes in virtual worlds seek knowledge exchange and
participation. Relationships develop and users begin to see <strong>the</strong>mselves as parts <strong>of</strong> a community.
Building on previously known digital social networks, a dimension <strong>of</strong> high immersion is added to <strong>the</strong>se
new 3D “knowledge spaces” (Castronova 2005) – a factor favoring <strong>the</strong> development <strong>of</strong> communities
<strong>of</strong> practice (see Wenger 1998; Wenger et al. 2005).
Therefore, an avatar-based 3D environment could serve as a platform for user-centered knowledge
acquisition and cooperation scenarios. Work-based social role-playing, simulations, and productbased
experimental grounds are now easily practicable. It has become possible to use virtual worlds
as instruments to exchange implicit knowledge and enable an informal and constructivist knowledge
exchange, based on <strong>the</strong> new learning paradigm <strong>of</strong> connectivism (Siemens 2005).
As a consequence <strong>of</strong> <strong>the</strong> developments in modern technology, <strong>the</strong> borders between real and virtual
worlds are becoming increasingly more blurry.
Technological developments enable pr<strong>of</strong>essionals to create photorealistic virtual environments, trueto-life
sound quality as well as <strong>the</strong> ability to input devices that provide haptic feedback and capture <strong>the</strong>
player’s movements (for example <strong>the</strong> Nintendo Wii). As a result, modern technology allows users to
interact virtually in a way that very closely resembles “real” interaction. This increases <strong>the</strong> interactivity
<strong>of</strong> modern computer games – <strong>the</strong> player experiences a higher level <strong>of</strong> presence: He has <strong>the</strong> sense <strong>of</strong>
actually being in a virtual environment (Steurer 1993).
Depending on how large and how vivid a virtual world is, <strong>the</strong> user can immerse more or less deeply
into <strong>the</strong> world (Pietschmann 2009). A well-designed and complex environment has <strong>the</strong> potential to put
<strong>the</strong> users into a state <strong>of</strong> mind where <strong>the</strong>y are surrounded “by a completely o<strong>the</strong>r reality […] that takes
over all [<strong>the</strong>ir] attention, [<strong>the</strong>ir] whole perceptual apparatus. [They] enjoy <strong>the</strong> movement out <strong>of</strong> our
familiar world, <strong>the</strong> feeling <strong>of</strong> alertness that comes from being in this new place, and <strong>the</strong> delight that
comes from learning to move within it” (Murray 1997: 98ff). The real world outside becomes irrelevant.
All <strong>of</strong> <strong>the</strong> user’s thoughts are focused on <strong>the</strong> virtual reality around <strong>the</strong>m.
Immersive virtual environments enable <strong>the</strong> user not only to learn, but also to use and improve what
<strong>the</strong>y have learned – to gain experience through action. According to Gee (2009: 70), “people primarily
think and learn through experiences <strong>the</strong>y have had. They store <strong>the</strong>se experiences in memory […] and
use <strong>the</strong>m to run simulations in <strong>the</strong>ir minds to prepare for action and problem solving in new situations.
These simulations help <strong>the</strong>m form hypo<strong>the</strong>ses about how to proceed in <strong>the</strong> new situation based on
past experiences.“ In o<strong>the</strong>r words: What people have learned online and in a virtual world can help
<strong>the</strong>m solve real-world exercises and problems.
132

3. Social Virtual Worlds In Enterprises
Klaus Bredl, Amrei Groß and Jane Fleischer
Contrary to <strong>the</strong> rigid codification <strong>of</strong> knowledge elements in systems and platforms, <strong>the</strong> strategy <strong>of</strong>
personalization within knowledge management (Hansen, Nohria and Tierney 1999) is gaining
importance. Due to <strong>the</strong> introduction <strong>of</strong> Web 2.0 technology and social s<strong>of</strong>tware, this development,
known as Enterprise 2.0 (Koch and Richter 2007), corresponds with a strategy <strong>of</strong> personalization,
which is seeing an increase within enterprises.
E.g. <strong>the</strong> staff <strong>of</strong> IBM (Fray Carey 2009) is already using <strong>the</strong> Second Life GRID-Engine for internal
cooperation and learning processes. In <strong>the</strong> future, “Immersive Business Collaboration Spaces (IBCS)“
(Multiverse 2008) and “Enterprise Immersive Platforms” (Driver and Driver 2008) could be used
enterprise-wide – not only by IBM, but by various o<strong>the</strong>r enterprises as well.
In Fig. 1, you can see how <strong>the</strong> factor <strong>of</strong> immersion could be embedded in a social s<strong>of</strong>tware model for
use in organizations (Bredl 2009).
Low Degree
<strong>of</strong>
Immersion
Information
Content and
Information
Management
Strategy<strong>of</strong>
Codifcation
Strategy <strong>of</strong>
Codifcation
CMS,
DMS
Wiki
Communication
Management
Weblog
Online-Forum
Collaboration
Groupware
Social
Tagging
Virtual
Community
<strong>of</strong> Practice
Digital
Social
Network
Community
Management
Avatar
Virtual
Worlds
Strategy<strong>of</strong>
Personalisation
Strategy <strong>of</strong>
Personalisation
Human
(Social)
Presence
Management
Figure 1: Crystal (Xtal-)-Model – Immersion in Social Media
Research Approaches for Immersive Knowledge-Based Virtual Environments
High Degree
<strong>of</strong>
Immersion
The advantages <strong>of</strong> immersion and networking and <strong>the</strong> actual knowledge gain through <strong>the</strong> use <strong>of</strong>
virtual worlds exceed <strong>the</strong> potential costs, alone because <strong>of</strong> <strong>the</strong> amount <strong>of</strong> time and travel that are
saved. To be considered for <strong>the</strong> application <strong>of</strong> MUVES, according to Bartle (2003):
The degree <strong>of</strong> (social) distance between <strong>the</strong> participants in a learning environment could be
regarded as diminishing thanks to a growing immersion in 3D environments.
The more or less unlimited possibilities <strong>of</strong> interaction compared to conventional information and
communication systems could be fur<strong>the</strong>r objectives <strong>of</strong> research.
Changing <strong>the</strong> user’s own identity by appearance <strong>of</strong> diversity (ethnicity, gender) brings <strong>the</strong>
possibility <strong>of</strong> anonymity and <strong>the</strong>refore equality in <strong>the</strong> knowledge process.
The feeling <strong>of</strong> being present toge<strong>the</strong>r with o<strong>the</strong>r learners in <strong>the</strong> learning field is possibly <strong>the</strong>
strongest factor in how immersion is perceived in <strong>the</strong> learning setting <strong>of</strong> a virtual world. This is
also potentiated by <strong>the</strong> o<strong>the</strong>r users’ reactions to <strong>the</strong> digital self, which increases one’s own
presence and participation within <strong>the</strong> MUVE.
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Klaus Bredl, Amrei Groß and Jane Fleischer
4. Scenarios for <strong>the</strong> Application <strong>of</strong> Knowledge and Learning Settings in a 3D
Virtual Environment
The following scenarios were co-created by media students as prototypical environments to show <strong>the</strong>
prospects <strong>of</strong> combining contexts <strong>of</strong> game-based learning and simulations within virtual worlds.
Initial motivation for developing <strong>the</strong> scenarios came from studying <strong>the</strong> recognized limits <strong>of</strong> e-learning
and knowledge management via a traditional learning platform (like Stud.IP or Moodle) and
information and knowledge management systems − especially <strong>the</strong> limitations <strong>of</strong> synchronous
interactions with <strong>the</strong> participants <strong>of</strong> online courses.
The infrastructure, implementation, potential benefits as well as challenges will be described in more
detail in <strong>the</strong> following section.
4.1 Surrounding <strong>the</strong> Case Study
The infrastructure for <strong>of</strong>fering a knowledge space through a social virtual world is based on a location
in a region in Second Life on a virtual “island”. Thus, it was beneficial for <strong>the</strong> project to search for “a
space” in an academic surrounding. Thus, our university’s virtual environment was placed on <strong>the</strong>
<strong>European</strong> University and on <strong>the</strong> <strong>European</strong> Science Island (Simteach 2011).
Scenario 1 - Learning adventure
In <strong>the</strong> first project, a game accompanying <strong>the</strong> course “Introduction to <strong>the</strong> Methods <strong>of</strong> Empirical
Communication Research” was created. It was designed just like an adventure game and fully
implemented in Second Life. Its framing story reminds somewhat <strong>of</strong> <strong>the</strong> one featured in <strong>the</strong> film
“Shrek”, following well-known fairy tales. The scenarios allows students to explore a “Magic Garden”,
“Sleeping Beauty's Castle”, a “Gingerbread House” and “Rapunzel's Tower”, while answering
questions about empirical communication research and completing different tasks (Bitzer et al. 2010;
Bitzer and Bredl 2010).
While picturing education at a German university, one might think <strong>of</strong> hundreds <strong>of</strong> students sitting in a
class and a pr<strong>of</strong>essor lecturing at <strong>the</strong> front <strong>of</strong> <strong>the</strong> room. One may also think <strong>of</strong> a seminar where
students hold presentations one by one, <strong>the</strong>ir only motivation being to become better presenters.
These students feel more like <strong>the</strong>y have to learn <strong>the</strong> subjects ra<strong>the</strong>r than wanting to learn <strong>the</strong>m.
Most parents want <strong>the</strong>ir children to have fun while learning, but not everyone knows how to go about
this. One possibility is providing children with learning games. A large range <strong>of</strong> games are <strong>of</strong>fered for
students, many <strong>of</strong> which make learning a more attractive venture. Unfortunately, <strong>the</strong>re are only a very
small number <strong>of</strong> equally effective products for university students, since a game cannot be
programmed for only one course. A good alternative would be to use a platform where you can build
whatever you want to without needing to know how to program and without having to invest a lot <strong>of</strong>
money - for example, a virtual world like Second Life. In Second Life, you can easily build on your
ideas and bring your own game concept to life.
Second Life has a lot <strong>of</strong> potential in <strong>the</strong> e-learning field and many different approaches can be made
depending on your instructional design. Moreover, virtual worlds have <strong>the</strong> potential to be highly
immersive and <strong>the</strong> various communication channels <strong>of</strong>fer advanced collaboration possibilities. At <strong>the</strong>
moment, <strong>the</strong>re are two main e-learning settings that can be found in Second Life. On <strong>the</strong> one hand,
<strong>the</strong>re are virtual classrooms where students or people meet and can listen to a presentation or similar.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong>re are complex system simulations and premade parts. But <strong>the</strong>re are so many
more possibilities for <strong>the</strong> use <strong>of</strong> virtual worlds for education.
One such option is to connect Second Life with game-based learning and so-called “serious games”
(Abt 1970; Gee 2003).
This is a new and improved concept for an accompanying game to <strong>the</strong> course “Introduction to <strong>the</strong>
Methods <strong>of</strong> Empirical Communication Research” for students <strong>of</strong> Media and Communication. Students
<strong>of</strong> Media and Communication by definition have a high affinity to digital media, and some students are
already acquainted with Second Life –a good premise for learning and creating in virtual worlds.
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Klaus Bredl, Amrei Groß and Jane Fleischer
The seminar “Introduction to <strong>the</strong> Methods <strong>of</strong> Empirical Communication Research” is a blendedlearning
seminar with twelve face-to-face meetings (one every week <strong>of</strong> <strong>the</strong> semester) with different
<strong>the</strong>oretical focuses. During <strong>the</strong> course <strong>of</strong> this seminar, students gain basic knowledge that allows
<strong>the</strong>m to work on <strong>the</strong> exercises in <strong>the</strong> game. Continuative information is communicated in Second Life.
Every meeting introduces a new level. The next level is always more complex than <strong>the</strong> previous one.
Students work toge<strong>the</strong>r in groups between face-to-face meetings.
The game fits into <strong>the</strong> adventure genre and is fully implemented in Second Life with a framing story
that resembles a fairy tale. Students have to unravel hidden secrets and do exercises in order to
consolidate <strong>the</strong>ir knowledge. The funny and fantastical nature <strong>of</strong> <strong>the</strong> stories incorporated into <strong>the</strong>
game should make <strong>the</strong> work more motivating. At <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> exercise, students are asked to
work as researchers at <strong>the</strong> “Magic Wood Research Institute”. Every level is represented by a new fairy
tale. Once <strong>the</strong>re, students have to answer questions that draw on <strong>the</strong> topic <strong>of</strong> <strong>the</strong> last meeting and
afterwards <strong>the</strong>re is a final practical exercise. Examples <strong>of</strong> some <strong>of</strong> <strong>the</strong> exercises are interviewing
people, creating a questionnaire and so on. When a problem can’t fully be solved in Second Life (for
example, because <strong>the</strong> student has to use a statistical s<strong>of</strong>tware like SPSS) <strong>the</strong> problem has to be
worked out in <strong>the</strong> real world and <strong>the</strong> results presented in Second Life.
As to <strong>the</strong> different levels in <strong>the</strong> virtual learning environment - participants jump from level to level.
Each student has to solve <strong>the</strong> problems in each level in <strong>the</strong> right order. After <strong>the</strong> next meeting, if <strong>the</strong>
exercise is correct, <strong>the</strong>y will get <strong>the</strong> password for <strong>the</strong> next level and can teleport <strong>the</strong>re to advance.
Scenario 2 - Knowledge acquisition in disaster training
In <strong>the</strong> second project, students built a prototypical disaster training site to show how real world
pr<strong>of</strong>essionals have <strong>the</strong> possibility to train at <strong>the</strong> point <strong>of</strong> need, practicing <strong>the</strong> management <strong>of</strong> real
world emergencies without all <strong>the</strong> limitations <strong>of</strong> real world training, such as time and place (Groß et al.
2011).
Real world disaster training is extremely time-consuming, expensive and difficult to organize. Because
<strong>of</strong> this, real disaster training exercises are rare and <strong>the</strong>re is a great need for efficient and affordable
solutions to make training available at <strong>the</strong> point <strong>of</strong> need. In a project by <strong>the</strong> Digital Media division <strong>of</strong>
<strong>the</strong> Institute <strong>of</strong> Media and Educational Technology at <strong>the</strong> University <strong>of</strong> Augsburg, a prototype for an
online training scenario was created in Second Life, simulating a cargo plane crash over a suburban
area on <strong>European</strong> University Island. Within this disaster site, <strong>the</strong> individual player has to perform a
triage with various injured persons and manage multiple spreading fires.
When disaster hits, it hits unexpectedly. And when it does, well-trained emergency pr<strong>of</strong>essionals
within <strong>the</strong> police force, <strong>the</strong> fire brigade and rescue services, have to cooperate across unit borders
and make prudent decisions in <strong>the</strong> midst <strong>of</strong> chaos in order to save lives. But how can <strong>the</strong>se
pr<strong>of</strong>essionals prepare for large scale emergencies when hundreds <strong>of</strong> injured persons need to be
taken care <strong>of</strong> by far too few emergency physicians; when important infrastructures are destroyed,
damaged or flooded? Regular training cannot prepare emergency pr<strong>of</strong>essionals for all <strong>of</strong> this. Efficient
disaster training requires a large number <strong>of</strong> emergency pr<strong>of</strong>essionals and actor “injured persons” to
be realistic. Preparing such training exercises tends to be extremely time-consuming, difficult and
expensive: The German Disaster Management Exercise “Lükex”, for example, simulated 14 different
disaster sites all across <strong>the</strong> nation, uniting tens <strong>of</strong> thousands <strong>of</strong> emergency pr<strong>of</strong>essionals in one large
scale training exercise − a training exercise that took two years <strong>of</strong> planning and that cost almost one
million Euros, since each participating unit had to remain deployable during <strong>the</strong> whole 36-hour training
session, in case <strong>of</strong> a real emergency.
Because <strong>of</strong> this, real disaster training exercises are rare and <strong>the</strong>re is a great need for efficient and
affordable solutions. A project group <strong>of</strong> <strong>the</strong> Digital Media division <strong>of</strong> <strong>the</strong> Institute <strong>of</strong> Media and
Educational Technology at our University began working on finding a solution: They wanted to know
whe<strong>the</strong>r tactical and activity oriented knowledge <strong>of</strong> disaster management could be learned within
virtual worlds – and <strong>the</strong>y created a prototype in Second Life, simulating a cargo plane crash over a
suburban area on <strong>European</strong> University Island.
In this training exercise, <strong>the</strong> individual player is confronted with various casualties and multiple
spreading fires. He needs to decide quickly where to begin fighting <strong>the</strong> fire in order to prevent fur<strong>the</strong>r
damage to <strong>the</strong> injured, and he has to perform a triage: Which individuals need to be taken care <strong>of</strong>
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Klaus Bredl, Amrei Groß and Jane Fleischer
immediately, which can wait until <strong>the</strong> severely injured have been treated? To guide <strong>the</strong> individual
player in making <strong>the</strong> correct treatment decision, every injured person within <strong>the</strong> simulated disaster site
has a notecard. This notecard describes <strong>the</strong> individual’s injuries and afflictions. After making a
treatment decision, <strong>the</strong> player has to put up <strong>the</strong> appropriate triage sign and move on.
All <strong>of</strong> <strong>the</strong> player’s actions during Second Life disaster training may be filmed with screen capturing
tools such as Fraps and can be evaluated afterwards inworld or in <strong>the</strong> real world.
Scenario 3 - Counseling in virtual worlds
Virtual worlds are increasingly becoming a part <strong>of</strong> our reality and have an increasing influence even in
<strong>the</strong> area <strong>of</strong> psychosocial counseling. But how can virtual worlds support clients <strong>of</strong> psychosocial
counseling? In this context, virtual environments could enhance <strong>the</strong> clients’ own identity and provide
many possibilities for communication and interaction. Today, implemented communication tools are
mostly asynchronous. There are very few efforts beings made to use immersive virtual environments
for counseling in <strong>the</strong> new Web3D.
The central social research concepts <strong>of</strong> virtual worlds are trust, social presence, representation and
<strong>the</strong>refore immersion.
Especially systemic counseling with its knowledge-oriented constructivist world view seems
appropriate for implementation in a virtual world. In virtual worlds, it is possible to <strong>of</strong>fer counseling to
clients who would not o<strong>the</strong>rwise go to see a counselor.
In 2009, a virtual psychological counseling ambulance, integrated into a German virtual university,
was created in Second Life. It is a prototypical 3D counseling environment (Bräutigam et al 2011).
It consists <strong>of</strong> an information area and three different counseling settings in various skyboxes which
are accessible via teleportation. In order to get <strong>the</strong> first ‘”real” anonymous clients, a campaign across
various social media services was started.
The primary objective <strong>of</strong> <strong>the</strong> project was an exploratory study about <strong>the</strong> possibilities and limitations <strong>of</strong>
3D virtual psychosocial counseling.
A special focus was set on <strong>the</strong> atmosphere <strong>of</strong> <strong>the</strong> counseling environment in order to allow for an
adequate constructive counseling setting. The chosen method <strong>of</strong> counseling in this project was
systemic counseling. During counseling, <strong>the</strong> client and <strong>the</strong> counselor were assessed by a team <strong>of</strong>
students to reflect on <strong>the</strong> possibilities <strong>of</strong> counseling in an innovative setting. Before starting
counseling, clients with deeper problems and those suffering from psychiatric illnesses were excluded
from <strong>the</strong> study via sounding interviews. To guarantee anonymity, <strong>the</strong> use <strong>of</strong> <strong>the</strong> local voice chat was
avoided.
4.2 Lessons Learned in Projects
Generally, students responded positively to <strong>the</strong> projects, as <strong>the</strong>y were very motivated to learn and
interact in <strong>the</strong> 3D virtual environment and <strong>the</strong>y developed many new ideas about how to use virtual
worlds for educational and cooperative purposes in <strong>the</strong> process. The fact that students <strong>of</strong>ten stayed in
<strong>the</strong> virtual environment considerably longer than <strong>the</strong> time set aside for <strong>the</strong> project to continue <strong>the</strong>ir
discussions was very remarkable. This streng<strong>the</strong>ned <strong>the</strong> hypo<strong>the</strong>sis <strong>of</strong> <strong>the</strong> existence <strong>of</strong> processes <strong>of</strong>
immersion and flow (Csikszentmihalyi and Rathunde 1993).
However, <strong>the</strong>re are some limitations to <strong>the</strong> use <strong>of</strong> virtual worlds, such as <strong>the</strong> lack <strong>of</strong> possibilities to
combine <strong>the</strong> established learning and communication platforms with <strong>the</strong> new possibilities <strong>of</strong> e-learning
in 3D environments. The projects showed that more time should be invested in instruments that take
advantage <strong>of</strong> <strong>the</strong> steady persistence <strong>of</strong> <strong>the</strong> learning, simulation and counseling environment when
nei<strong>the</strong>r teacher nor tutors are present in <strong>the</strong> virtual world with <strong>the</strong>ir avatars. One such instrument could
be an automatic Second Life Bot (SL Bot that answers questions from members <strong>of</strong> <strong>the</strong> learning
community automatically.
Despite more interactive possibilities, <strong>the</strong> need for <strong>the</strong> possibility to create and share content became
evident during <strong>the</strong> projects. Fur<strong>the</strong>rmore, learning and knowledge objects created within conventional
web applications could not easily be transferred into virtual worlds and vice versa. The project
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Klaus Bredl, Amrei Groß and Jane Fleischer
Sloodle, which combines Second Life and <strong>the</strong> open source platform Moodle, is an exception (Kemp
and Livingstone 2006). Thus, a sub-project for bridging <strong>the</strong> gap between our virtual campus
environment and our Moodle-platform was started. It was a combination <strong>of</strong> <strong>the</strong> 2D-web and <strong>the</strong> 3Dweb-environment;
a Second Life and Moodle mash-up.
5. Conclusion and Prospects
The goal <strong>of</strong> this paper was to describe <strong>the</strong> research phenomena in regard to social, cognitive and
personal competencies in teams <strong>of</strong> knowledge workers that could be observed, and to explore new
forms <strong>of</strong> interaction in virtual social contexts. Three scenarios were created to study <strong>the</strong> potential <strong>of</strong>
virtual worlds for supporting knowledge and collaborative processes.
The projects incorporated peer learning into <strong>the</strong>ir concepts. Peer education is predestined for use in
information technology because it creates effective cooperative learning via face-to-face interaction
and in virtual environments. Thanks to multimodal communication, <strong>the</strong> group can interact and
communicate online in various ways. Heterogeneous groups additionally benefit from peer learning,
resulting in <strong>the</strong> knowledge workers learning from each o<strong>the</strong>r as well as teaching one ano<strong>the</strong>r.
Based on technology with open standards (e.g. OpenSim, Croquet or Sun Wonderland), some
prototypical platforms for closed intraworlds in 3D are currently being developed. It is conceivable that
virtual worlds designed especially for knowledge work could increase <strong>the</strong> quality <strong>of</strong> fur<strong>the</strong>r immersive
knowledge processes. Corresponding with development initiatives, <strong>the</strong>re should be fur<strong>the</strong>r effort to
create interfaces between real and web-based environments.
The conclusion may be drawn that immersive education tends to be more engaging than text- or
video-based online communication. This leads to <strong>the</strong> hypo<strong>the</strong>sis that <strong>the</strong> phenomena <strong>of</strong> immersion
could increase <strong>the</strong> motivation and <strong>the</strong> learning capacities <strong>of</strong> avatar-based co-workers and learners.
Therefore, <strong>the</strong> phenomena <strong>of</strong> immersion and community-building should be studied more extensively
by means <strong>of</strong> reasonable operationalization in experiments with prototypical scenarios in virtual worlds.
To conclude this paper concerning <strong>the</strong> future <strong>of</strong> knowledge-oriented work, let us refer to an analogy
“Social Virtual Worlds could be <strong>the</strong> message” (McLuhan 1967).
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138

Assessing <strong>the</strong> Knowledge Economy’s Performance in
Romania
Camelia Burja and Vasile Burja
The Faculty <strong>of</strong> Sciences, “1 Decembrie 1918” University <strong>of</strong> Alba Iulia, Romania
cameliaburja@yahoo.com
vasileburja@yahoo.com
Abstract: Knowledge economy will be competitive if it achieves high economic performance. The performance <strong>of</strong>
an economy may be assessed by comparing <strong>the</strong> obtained results with predetermined standards, taking into
account aspects related to <strong>the</strong> use <strong>of</strong> available resources. The current paper evaluates <strong>the</strong> performance <strong>of</strong>
knowledge economy in Romania in comparison to <strong>the</strong> recently joined countries to <strong>the</strong> EU. The used methodology
is Data Envelopment Analysis, which allows assessing performance against a reference system. Characterizing
<strong>the</strong> performance <strong>of</strong> knowledge economy relied on representative indicators from education and training, science,
technology and innovation, and information society, which contributes to <strong>the</strong> economic growth by increasing
gross domestic product (GDP) per capita. The dynamic analysis was used in order to assess <strong>the</strong> recorded
progresses. The results <strong>of</strong> <strong>the</strong> analysis show <strong>the</strong> modest position occupied by Romania in comparison with o<strong>the</strong>r
countries, which indicates that <strong>the</strong>re are many area where action should be taken so that <strong>the</strong> results <strong>of</strong>
implementing knowledge economy might be as expected.
Keywords: Knowledge economy, performance, competitiveness, DEA
1. Introduction
In modern economy, <strong>the</strong> performance <strong>of</strong> an economic system is decisively influenced by intangibles
assets policy and <strong>the</strong>ir utilisation. These are <strong>the</strong> result <strong>of</strong> <strong>the</strong> intellectual capital used as a factor <strong>of</strong>
production in order to obtain new value by accumulating knowledge. This is why lately <strong>the</strong> concept <strong>of</strong>
knowledge economy (KE) has been generalized, because it is an economy where knowledge is
acquired, created, disseminated and used effectively to enhance economic development (Chen et al.
2005).
Although concerns about defining Knowledge economy started to manifest more than 50 years ago, it
may be said that <strong>the</strong>re is still no unanimously accepted definition and <strong>the</strong> notion seems ra<strong>the</strong>r like a
rhetoric metaphor than a concept that expresses an analytical reality (Smith 2002). The concept <strong>of</strong>
Knowledge economy became known in literature thanks to Peter Drucker (1969), and was
subsequently used by o<strong>the</strong>r economists and included in <strong>of</strong>ficial documents <strong>of</strong> national and
international bodies, with reference to current and future realities <strong>of</strong> economy.
The expansion <strong>of</strong> KE in Europe was stated with <strong>the</strong> occasion <strong>of</strong> <strong>the</strong> EU Lisbon Strategy (2000), which
prepared <strong>the</strong> ground for <strong>the</strong> transition to a competitive, dynamic, knowledge-based economy. More
recently, Aho Report stressed <strong>the</strong> need for change and real orientation <strong>of</strong> <strong>the</strong> EU members towards
developing <strong>the</strong> knowledge society. The strategy <strong>of</strong> achieving an economically and socially competitive
Europe should place emphasis on <strong>the</strong> quality <strong>of</strong> educational systems, on efficient use <strong>of</strong> human
capital, on developing innovation, on research and entrepreneurship and <strong>the</strong>ir efficiency (Aho 2006).
By joining <strong>the</strong> <strong>European</strong> Union, Romania has adopted <strong>the</strong> <strong>European</strong> model <strong>of</strong> economic growth. To
achieve this model, extensive actions were taken to transform legislation, institutions and <strong>the</strong> structure
<strong>of</strong> <strong>the</strong> entire society, which was an opportunity to create a favourable framework for <strong>the</strong>
implementation <strong>of</strong> knowledge economy (SOPIEC 2007; NSRDI 2007). Knowing <strong>the</strong> exact results and
<strong>the</strong> efforts put in by Romanian society on <strong>the</strong> implementation road <strong>of</strong> KE requires evaluating its main
characteristic aspects.
This paper has as objective <strong>the</strong> assessment <strong>of</strong> <strong>the</strong> Romanian actions for transition towards
knowledge economy and promoting <strong>the</strong> values <strong>of</strong> <strong>the</strong> <strong>European</strong> Union countries. The analysis was
made in comparison with <strong>the</strong> countries which recently joined <strong>the</strong> EU. These countries have an
economic-social development level more like Romania: Cyprus, <strong>the</strong> Czech Republic, Estonia,
Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia and Bulgaria.
The method used is Data Envelopment Analysis and it was preferred because it allowed <strong>the</strong>
determination <strong>of</strong> performance for economic systems expressed through efficiency coefficients, finding
out <strong>the</strong> systems with <strong>the</strong> highest efficiency and ranking <strong>the</strong>m.
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Camelia Burja and Vasile Burja
Appreciation <strong>of</strong> <strong>the</strong> efforts made by Romania in its actions to implement KE in comparison with <strong>the</strong>
countries from <strong>the</strong> considered group was made with some economic-social indicators that through
<strong>the</strong>ir economic content express <strong>the</strong> main aspects <strong>of</strong> <strong>the</strong> Knowledge Economy. They are structured on
directions: education and training system, science, technology and innovation system and information
and communication technology system.
In order to appreciate <strong>the</strong> effects obtained by <strong>the</strong> implementation in Romania <strong>of</strong> <strong>the</strong> measurements
necessary for transition toward KE we used <strong>the</strong> indicator Gross Product Domestic (GDP) per capita.
GDP is a syn<strong>the</strong>tic indicator that highlights <strong>the</strong> efficiency <strong>of</strong> factors that are engaged in development
<strong>of</strong> a country. In its expression as GDP per capita it allows comparing <strong>the</strong> development stage and
growth paces for economies studied.
The indicators used in analysis derived from <strong>the</strong> <strong>European</strong> Union database Eurostat and <strong>the</strong>y refer to
<strong>the</strong> 2004-2008 period.
The obtained results highlight <strong>the</strong> impact <strong>of</strong> some important factors for <strong>the</strong> KE performance in
Romania and <strong>the</strong> group <strong>of</strong> countries that recently joined <strong>the</strong> EU. The conclusions <strong>of</strong> <strong>the</strong> analysis
suggest <strong>the</strong> characteristic aspects <strong>of</strong> KE in Romania, <strong>the</strong> efficiency differences compared to o<strong>the</strong>r
recent EU member countries and <strong>the</strong> necessary courses <strong>of</strong> action for improving <strong>the</strong> performance <strong>of</strong>
knowledge economy.
2. Methodology <strong>of</strong> analysis
Performance is defined in <strong>the</strong> Business dictionary as “accomplishment <strong>of</strong> a given task measured
against preset standards <strong>of</strong> accuracy, completeness, cost, and speed”. Economic performance is
<strong>of</strong>ten considered as being economic efficiency, but <strong>the</strong>se two concepts are not synonymous.
Economic efficiency results from a comparison between effects and efforts, while performance
involves reaching a desired efficiency in comparison with a reference system.
Information about <strong>the</strong> performance <strong>of</strong> an economic system is useful for assessing <strong>the</strong> potential
changes <strong>of</strong> resources, which may be controlled to achieve <strong>the</strong> targeted objectives, or to adapt
objectives to available resources. From this perspective, evaluating knowledge economy performance
in Romania provides decision makers with <strong>the</strong> possibility to correct substantiate <strong>the</strong>ir decisions
regarding economic policies applied so that <strong>the</strong> expected results might become reality.
The performance <strong>of</strong> knowledge economy may be assessed through a system <strong>of</strong> indicators
characterizing <strong>the</strong> reached stage and <strong>the</strong> results recorded when achieving sustainable economic
growth by transforming knowledge in economic goods. There are two types <strong>of</strong> indicators that
characterize <strong>the</strong> stage <strong>of</strong> knowledge economy (Radosevic 2007):
simple: indicators for research and development and IT, as well as indicators for <strong>the</strong> Knowledge
Intensive Business Services (KIBS) analysis. These indicators are considered decisive elements for
enhancing knowledge and information and for improving productivity in all <strong>the</strong> sectors <strong>of</strong> economy;
aggregate on <strong>the</strong> basis <strong>of</strong> a large number <strong>of</strong> indicators that correspond to important areas upon which
performance and competitiveness depend, such as <strong>European</strong> Innovation Scoreboard, EU Global
Innovation Scoreboard, World Bank Knowledge Economy Index, WEF Global Competitiveness Index,
WEF Business Competitiveness Index, National Innovation Capacity Indexes etc.
The simple indicators characterize some individual aspects from education, science, technology, IT,
research and development. They provide a significant image only for <strong>the</strong> respective domains. The
aggregate indicators can give an assembly picture on <strong>the</strong> KE stage.
The analysis <strong>of</strong> knowledge economy performance intends not only to show <strong>the</strong> level <strong>of</strong> indicators that
characterized it. The goal <strong>of</strong> KE is to achieve a competitive and dynamic economy. Knowledge
economy will be efficient if <strong>the</strong> achieved results compared with <strong>the</strong> benchmark ones are those
expected.
In this study <strong>the</strong> output indicator that intervenes in appreciating <strong>the</strong> KE performance is GDP/capita. It
facilitates achieving <strong>the</strong> research objective due to its higher informative power concerning <strong>the</strong>
economic development and quality <strong>of</strong> life.
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Camelia Burja and Vasile Burja
In order to assess <strong>the</strong> knowledge economy’s stage we used in this paper 11 analytical indicators
related to educational system (3 items), impact <strong>of</strong> science, technology, innovation (4 items), and
information society (4 items). These indicators are input variables in <strong>the</strong> analysis model and reflect
efforts made for achieving <strong>the</strong> KE goal. The selected indicators characterize significant aspects <strong>of</strong> <strong>the</strong>
objectives stated in <strong>the</strong> Lisbon Strategy and National Strategy for Research, Development and
Innovation 2007-2013, for Romania.
To ensure comparability <strong>of</strong> data, a series <strong>of</strong> factors <strong>of</strong> average or relative level have been considered.
They express <strong>the</strong> manifestation intensity <strong>of</strong> <strong>the</strong> phenomena present in each economy.
Highlighting <strong>the</strong> position occupied by Romania in <strong>the</strong> group <strong>of</strong> recent EU members from <strong>the</strong>
perspective <strong>of</strong> <strong>the</strong> performance recorded in knowledge economy may be achieved with <strong>the</strong> Data
Envelopment Analysis (DEA) method. This methodology establishes an efficiency score in relative
size for each analyzed system. The score evaluates <strong>the</strong> system’s distance from <strong>the</strong> efficiency frontier,
which consists <strong>of</strong> <strong>the</strong> entities with <strong>the</strong> best performances, considered benchmarks, in this regard.
DEA is an alternative method to <strong>the</strong> regression analysis, which establishes an optimal allocation <strong>of</strong>
resources or achieves results, by making a comparison with one or several reference systems
identified as having maximum efficiency. Efficient units are part <strong>of</strong> a production frontier that envelops
inefficient units.
The DEA approach doesn’t need to know <strong>the</strong> functional relationship that correlates consumed
resources with outputs, being considered a non-parametrical measuring method <strong>of</strong> <strong>the</strong> efficiency <strong>of</strong>
DMUs. Ano<strong>the</strong>r advantage <strong>of</strong> using DEA against regression is <strong>the</strong> low volume <strong>of</strong> information needed
to study multiple inputs and outputs. At <strong>the</strong> same time, <strong>the</strong> flexibility feature <strong>of</strong> <strong>the</strong> method allows
using a wide range <strong>of</strong> indicators to express input variables or outputs, which increases <strong>the</strong>
applicability range <strong>of</strong> <strong>the</strong> method (Thanassoulis 2009). DEA models provide for each organization a
measurement <strong>of</strong> overall performance, which ensures <strong>the</strong> possibility <strong>of</strong> ranked alternative valuations
that may be useful for some decision makers (Sarkis 2000).
Initially, DEA was used as a model that has constant returns to scale (CRS), called also CCR after its
creators Charnes, Cooper and Rhodes (1978). Subsequently, o<strong>the</strong>r models were developed by
Banker, Charnes and Copper (1984), which are applicable to technologies with variable returns to
scale (VRS). Analysis models can be input or output-oriented models and lastly <strong>the</strong>y become linear
programming problems (Cooper et al. 2004; Ray 2004).
Being widely used in <strong>the</strong> analysis <strong>of</strong> technical efficiency, <strong>the</strong> CCR model considers k decision making
units that have n inputs and m outputs.
The efficiency <strong>of</strong> decision making unit k is:
Ek
=
subject to:
m
∑ v j ykj
j=
1
n
∑ ui
xki
i=
1
m
∑ v j ykj
j=
1
≤ 1
n
∑ ui
xki
i=
1
(1)
(2)
141

∀ , i ≥ 0 v u i
where: ui represents inputs weight (x);
vj represents outputs weight (y).
Camelia Burja and Vasile Burja
This system <strong>of</strong> relations can be transformed into a linear programming model that includes a set <strong>of</strong>
restrictions and <strong>the</strong> minimization objective (inputs orientated models) or <strong>the</strong> maximization objective
(output orientated models), where unknown elements are <strong>the</strong> given weight to inputs and outputs. For
each decision making unit k, <strong>the</strong> following model is developed:
m
max ∑ v j ykj
j=1
n
∑ ui
xki
= 1
i=
1
s.t. (3)
m n
∑ v j ykj−
∑ ui
xki
≤ 0
j= 1 i=
1
∀ui , v j ≥ 0
Solving <strong>the</strong> model leads to finding out <strong>the</strong> single score <strong>of</strong> general efficiency (for all inputs and outputs)
for each DMU. We will identify one or several efficient DMUs (score 1) that create an efficiency
frontier for production and play <strong>the</strong> role <strong>of</strong> reference systems for <strong>the</strong> o<strong>the</strong>r units with a low score. For
inefficient decision making units, weights represent <strong>the</strong> target adjustment values <strong>of</strong> inputs and
outputs, which, if achieved, will ensure <strong>the</strong>ir positioning on <strong>the</strong> production frontier where efficient
DMUs are placed (Andree et al. 2010).
There are numerous studies that analyze <strong>the</strong> efficiency <strong>of</strong> research and development (R&D) and <strong>of</strong>
knowledge economy by using DEA (Wang and Hung 2007; Sharma and Thomas 2008; Cullman et al.
2009; Roman 2010). In <strong>the</strong>se studies efficiency is analyzed by using as inputs different components
<strong>of</strong> R&D or KE, and <strong>the</strong> outputs are granted and publications counts. We believe that knowledge is an
important production factor for contemporary economy and <strong>the</strong> efficiency <strong>of</strong> its use is directly found in
economic growth. This is why in this paper we used Gross domestic product indicator as output for
DEA.
The conclusions <strong>of</strong> <strong>the</strong> analysis suggests <strong>the</strong> characteristic aspects <strong>of</strong> KE in Romania, <strong>the</strong>
development differences compared to o<strong>the</strong>r recent EU member countries and <strong>the</strong> necessary courses
<strong>of</strong> action for improving <strong>the</strong> performance <strong>of</strong> knowledge economy.
3. The performance <strong>of</strong> knowledge economy in Romania: Results
3.1 Education system-related KE performance
The direct connection between <strong>the</strong> quality <strong>of</strong> labour and economic growth has been acknowledged for
a long time (Denison 1962). Subsequently <strong>the</strong> analysis <strong>of</strong> this relation led to contrary conclusions.
Numerous studies highlighted that education and population growth do not explain international
variation in income per capita (Mankiw, Romer and Weil 1992; Bils and Klenow 2001; Pritchett 2001).
Recently Sterlacchini and Venturini (2009) showed that <strong>the</strong>re is a strength relation between
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Camelia Burja and Vasile Burja
knowledge endowments and per capita GDP growth in <strong>European</strong> regions. Cohen and Soto (2001)
also asserted <strong>the</strong> role <strong>of</strong> human capital for <strong>the</strong> economic growth and suggested that <strong>the</strong> differences <strong>of</strong>
opinions on human capital’s role derived from <strong>the</strong> quality <strong>of</strong> data used in analysis.
In <strong>the</strong>se context, in order to study <strong>the</strong> link between <strong>the</strong> performance <strong>of</strong> KE and <strong>the</strong> educational
system, we used as input variables <strong>the</strong> indicators that show <strong>the</strong> size <strong>of</strong> <strong>the</strong> labour force trained for <strong>the</strong>
active integration on <strong>the</strong> labour market in <strong>the</strong> areas that ensure social progress, and appropriate
investment efforts: students (ISCED 1_6) aged 15-24 years - as % <strong>of</strong> corresponding age population
(capacity <strong>of</strong> economy to absorb, develop and diffuse Knowledge), tertiary graduates in science and
technology (innovative potential <strong>of</strong> economy) and public expenditure on education.
Although <strong>the</strong> evaluation indicators <strong>of</strong> <strong>the</strong> educational system are more numerous, <strong>the</strong> DEA
methodology has imposed a restriction on <strong>the</strong>ir number. For <strong>the</strong> results to be relevant, it’s necessary
that <strong>the</strong> total number <strong>of</strong> input and output variables used in <strong>the</strong> analysis should be lower than <strong>the</strong>
number <strong>of</strong> studied entities (Cooper et al. 2006).
The lack <strong>of</strong> data for a few indicators in Malta led to confining <strong>the</strong> analysis to a group <strong>of</strong> 11 countries
that joined <strong>the</strong> EU recently. The EU statistics are mostly updated until 2008, and 2004 marked <strong>the</strong>
completion <strong>of</strong> negotiations for Romania’s accession to <strong>the</strong> EU, which is why <strong>the</strong> progresses recorded
since <strong>the</strong>n in terms <strong>of</strong> Knowledge economy are interesting.
In Romania, a part <strong>of</strong> <strong>the</strong> indicators that characterize education and training have recorded important
progresses during <strong>the</strong> analyzed period. There are still gaps in comparison with most countries that
recently joined <strong>the</strong> EU in terms <strong>of</strong> Students aged 15-24 years and Public expenditure on education.
The gross increase <strong>of</strong> domestic product per capita is also important, but GDP remained low in 2008
compared with o<strong>the</strong>r countries, except Bulgaria (table 1).
Table 1: Education and training, 2004-2008
Ţări GDP at market prices;
PPS per inh.
Euro
Students (ISCED 1_6)
aged 15-24 years –
as % <strong>of</strong> corresponding
age population
Tertiary graduates
in science
and technology
per 1 000 <strong>of</strong>
population aged
20-29 years
Public expenditure
on education
% <strong>of</strong> GDP
2004 2008 2004 2008 2004 2008 2004 2007
Bulgaria 7500 10900 48,9 51,9 8,5 9,1 4,51 4,13
Czech Republic 16300 20200 59,2 61,6 7,4 15,0 4,37 4,20
Estonia 12400 17000 63 60,8 8,9 11,4 4,92 4,85
Cyprus 19600 24400 42,3 43,6 4,2 4,0 6,70 6,93
Latvia 9900 14100 64,8 61,0 9,4 8,8 5,07 5,0
Lithuania 10900 15300 69,1 69,0 17,5 17,8 5,19 4,67
Hungary 13700 16200 59,7 64,5 5,1 6,1 5,41 5,20
Poland 11000 14100 68,6 70,3 9,4 14,1 5,41 4,91
Romania 7400 11700 47,4 56,9 9,8 15,2 3,28 4,25
Slovenia 18700 22800 67,6 71,0 9,3 10,7 5,76 5,19
Slovakia 12300 18100 52,1 56,5 9,2 15,0 4,20 3,62
Source: Eurostat database
* 2005 **Students at ISCED levels 5-6 enrolled in <strong>the</strong> following fields: science ma<strong>the</strong>matics
computing engineering manufacturing construction - as % <strong>of</strong> all students
Applying <strong>the</strong> DEA model in dynamic, as constant return to scale (CRS) and output-orientated, leads to
a distribution <strong>of</strong> <strong>the</strong> countries in <strong>the</strong> analyzed group in accordance with <strong>the</strong> performance level <strong>of</strong> <strong>the</strong>
educational system (table 2).
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Camelia Burja and Vasile Burja
Table 2: The Economic performance <strong>of</strong> Romanian education in KE, 2004-2008
Country Performance Country Performance
score, 2004
score, 2008
Czech Rep. 1.000 Czech Rep. 1.000
Cyprus 1.000 Cyprus 1.000
Slovenia 0.963 Slovenia 1.000
Slovakia 0.876 Slovakia 1.000
Hungary 0.831 Hungary 0.805
Estonia 0,684 Estonia 0.801
Lithuania 0.601 Lithuania 0.673
Poland 0.580 Latvia 0.669
Latvia 0.531 Poland 0.621
Bulgaria 0.507 Bulgaria 0.607
Romania 0.498 Romania 0.592
Source: results from DEAP s<strong>of</strong>t version 2.1
A main conclusion that emerges from <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> obtained results is that <strong>the</strong> Romanian
educational system is inefficient compared with o<strong>the</strong>r countries. The labour force, seen as <strong>the</strong> end
result <strong>of</strong> <strong>the</strong> educational system, doesn’t sufficiently exploit <strong>the</strong> creation and production potential, and
<strong>the</strong>refore, its contribution to GDP is modest. In 2008, Romania was placed on one <strong>of</strong> <strong>the</strong> last places,
surpassing only Bulgaria. Compared with countries that have efficient educational systems (Cyprus,
Slovenia, Slovakia), <strong>the</strong> education’s performance in Romania is lower by approximately 33.3%.
Despite a long series <strong>of</strong> reforms in education, <strong>the</strong> general performance <strong>of</strong> Romanian education is still
on a downward curve. This aspect is also demonstrated by <strong>the</strong> low skills <strong>of</strong> students. A recent study
conducted in <strong>the</strong> Program for International Student Assessment (PISA) shows that about 70 percent
15- year-old Romanian students performed below a level that is required to function in a modern
workplace, compared to 37 percent <strong>of</strong> 15-year-old EU students (World Bank 2007). Even if Romanian
higher education produces competitive experts in some areas, <strong>the</strong> vast majority <strong>of</strong> <strong>the</strong>m choose to
emigrate. In <strong>the</strong>se circumstances, education and training are an important reserve for economic
growth for Romania if this area is efficiently managed.
3.2 The KE performance related to science, technology and innovation system
In literature, <strong>the</strong> science, technology and innovation (STI) system is considered an important element
that ensures improving <strong>of</strong> <strong>the</strong> productivity, technical progress and economic development (Cohen and
Soto 2001; Fageberg 2006; Huang and Gerami 2007).
The results <strong>of</strong> studies on impact <strong>of</strong> <strong>the</strong> innovative processes for companies, sectors and countries
found out that economic-social development does not result only from <strong>the</strong> investments made in public
R&D infrastructure but more from investments in science, technology and innovation <strong>of</strong> business
environment. Technological and organizational changes can more rapidly put into practice <strong>the</strong>
innovative ideas and stimulate development (Fageberg 2006). In 2009 at yhe EU 27 level over 55% <strong>of</strong>
R&D expenditure were financed by <strong>the</strong> business enterprises sector, which means that <strong>the</strong>re is an
insufficient implication <strong>of</strong> business environment in financing <strong>the</strong> innovative processes and R&D, <strong>the</strong>
level indicated by <strong>the</strong> Lisbon Strategy being 70% (<strong>European</strong> Commission 2011).
To assess <strong>the</strong> impact <strong>of</strong> STI over economic growth in Romania and its position within <strong>the</strong> group <strong>of</strong>
countries that joined <strong>the</strong> EU recently, we take into account as output <strong>the</strong> GDP per capita indicator
(same as in table 2) and as inputs <strong>the</strong> indicators related to production and application <strong>of</strong> innovative
knowledge, such as: <strong>the</strong> enterprise’s expenditure on research and development, <strong>the</strong> employees
involved in <strong>the</strong> R&D activity, <strong>the</strong> labour force working in ST and <strong>the</strong> enterprises with innovative
technologies (table 3).
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Camelia Burja and Vasile Burja
Table 3: Science, technology, innovation, 2004-2008
Country Business
enterprise R&D
expenditure
(BERD),
euro/inh
Total R&D
personnel, %
<strong>of</strong> active
population
(full time eq)
Human resources in
science and
technology, % <strong>of</strong>
labour force
Shares <strong>of</strong>
enterprises
with
innovation
activities, %
2004 2008 2004 2008 2004 2008 2004 2006
Bulgaria 3 6,8 0,47 0,48 31,2 31,0 16,1 20,2
Czech R 67,3 129,3 0,56 0,97 32,8 37,1 38,34 25
Estonia 23,9 67 0,72 0,73 41,5 45,6 48,67 48,2
Cyprus 13,6 22,8 0,29 0,33 39,7 43,7 46,09 39,5
Latvia 9 15,6 0,45 0,54 31,0 39,9 17,5 16,2
Lithuania 8,5 18,2 0,65 0,78 34,6 42,5 28,5 22,3
Hungary 29,3 55,4 0,55 0,65 31,8 33,2 20,84 20,1
Malta 39,8 49 0,45 0,53 29,9 32,1 20,72 28
Poland 8,6 17,8 0,46 0,44 28,3 33,4 24,75 23
Romania 6 11,3 0,34 0,31 21,2 23,8 19,52 20,7
Slovenia 127,3 198,1 0,71 1,11 35,8 40,1 26,87 35,1
Slovakia 15,9 24,2 0,54 0,58 28,8 32,0 22,87 24,9
Source: Eurostat database
We note that most indicators that characterize <strong>the</strong> STI system in Romania are below <strong>the</strong> level or <strong>the</strong>
o<strong>the</strong>r countries, although some <strong>of</strong> <strong>the</strong>m have made progresses during <strong>the</strong> analyzed period, especially
Business enterprise R&D expenditure.
Applying <strong>the</strong> DEA method provides <strong>the</strong> necessary information to assess <strong>the</strong> performance <strong>of</strong>
Romanian economy as a result <strong>of</strong> <strong>the</strong> impact <strong>of</strong> science, technology and innovation in 2004 and 2008
(table 4).
Table 4: The Economic performance <strong>of</strong> Romanian STI system in KE, 2004-2008
Country Performance Country Performance
score, 2004
score, 2008
Cyprus 1.000 Cyprus 1.000
Malta 1.000 Latvia 1.000
Slovenia 0.995 Slovenia 0.946
Hungary 0.807 Malta 0.909
Latvia 0.693 Hungary 0.904
Bulgaria 0.685 Slovakia 0.850
Slovakia 0.665 Czech Rep. 0.767
Czech Rep. 0.605 Bulgaria 0.723
Poland 0.566 Poland 0.722
Lithuania 0.498 Lithuania 0.720
Estonia 0.400 Romania 0.602
Romania 0.328 Estonia 0.528
Source: results from DEAP s<strong>of</strong>t version 2.1
The analysis <strong>of</strong> <strong>the</strong> correlation between economic growth and indicators specific for <strong>the</strong> science,
technology and innovation system highlights <strong>the</strong> lagging <strong>of</strong> economy for both periods. Compared to
countries that in 2004 were at <strong>the</strong> frontier <strong>of</strong> efficiency, Romania has an inefficient STI system, whose
performance is lower by 67.2%. Although in 2008 <strong>the</strong> situation improved, <strong>the</strong> STI performance is still
low. The performance gap (39.8%) places Romania on <strong>the</strong> penult place in <strong>the</strong> group <strong>of</strong> analyzed
countries. The science, technology and innovation system is an important resource, which may lead
to sustainable economic growth in Romania if used efficiently.
3.3 The KE performance related to information and communication technology
The activities <strong>of</strong> creation, adaptation and wide use <strong>of</strong> new knowledge in economy should be
supported by an adequate system <strong>of</strong> information and communication technology (ICT). The
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Camelia Burja and Vasile Burja
information infrastructures are various combinations between hardware, s<strong>of</strong>tware and people (Bowker
2004). The ICT system contributes consistently to increasing <strong>the</strong> productivity <strong>of</strong> production factors,
transactions, competitiveness and accomplishment <strong>of</strong> sustainable economic development (Lorenz
2008).
To study <strong>the</strong> connection between ICT and economic development by using DEA, we used <strong>the</strong>
following as input indicators: Enterprises having Internet access, E-government usage by enterprises,
Enterprises using computers and Enterprises using s<strong>of</strong>tware solutions, like CRM. Because ICT
advances quickly and <strong>the</strong> statistics provide us with recent data about this area, we conducted a
dynamic analysis <strong>of</strong> <strong>the</strong> influences <strong>of</strong> this system over <strong>the</strong> economic performance <strong>of</strong> Knowledge
economy based on <strong>the</strong> data from 2006 and 2009 (table 5).
Table 5: Information society
Country GDP at market
prices, euro/inh
Enterprises
having
Internet
access, % <strong>of</strong>
enterprises
Egovernment
usage by
enterprises,
%
Enterprises
using
computers,
%
3.4 Enterprises
using s<strong>of</strong>tware
solutions, like
CRM **
2006 2009 2006 2009 2006 2009 2006 2009 2007 2009
Bulgaria 7500 10900* 75 84 46 60 89 89 9 9
Czech R 16300 19200 95 96 76 66 97 97 15 14
Estonia 12400 1500 92 95 69 79 94 96 10 10
Cyprus 19600 23200 86 92 44 72 95 95 14 16
Latvia 9900 12200 80 88 40 64 92 94 10 11
Lithuania 10900 12900 88 95 76 91 92 97 9 9
Hungary 13700 15300 80 88 45 68 89 90 5 6
Malta 16700 19000 90 95 67 79 94 96 22 20
Poland 11000 14300 89 91 61 61 93 93 12 14
Romania 7400 10900 57 73 39 41 76 81 14 13
Slovenia 18700 20700 96 96 75 89 97 98 14 12
Slovakia 12300 17200 93 98 77 92 97 99 11 19
Source: Eurostat database
*year 2008
**Percentage <strong>of</strong> enterprises with at least 10 persons employed in <strong>the</strong> given NACE sectors
CRM=Customer Relationship Management
The results obtained by using <strong>the</strong> DEA s<strong>of</strong>t are available in table 6. We note that <strong>the</strong> efficiency <strong>of</strong> <strong>the</strong>
informational infrastructure, which contributes to communication, dissemination and processing <strong>of</strong>
information and knowledge, is modest in Romania compared with o<strong>the</strong>r countries if we consider <strong>the</strong>
level <strong>of</strong> economic development. The performance score recorded in 2006 is 43% below maximum
performance, Romania being ranked 11 th out <strong>of</strong> 12 countries that joined <strong>the</strong> EU recently. However, in
2009 <strong>the</strong> situation improved and Romania grew closer by 17.5% to <strong>the</strong> efficient frontier, occupying a
much more favourable position.
Table 6: The economic performance <strong>of</strong> Romanian ICT system in KE, 2006-2009
Country Performance Country Performance
score, 2006
score, 2009
Cyprus 1.000 Cyprus 1.000
Hungary 1.000 Hungary 1.000
Slovenia 0.939 Slovenia 1.000
Czech Rep. 0.814 Czech Rep. 0.924
Malta 0.751 Romania 0.825
Estonia 0.681 Malta 0.810
Slovakia 0.647 Estonia 0.791
Latvia 0.634 Poland 0.728
Lithuania 0.620 Slovakia 0.711
Poland 0.589 Lithuania 0.703
Romania 0.570 Bulgaria 0.673
Bulgaria 0.474 Latvia 0.667
Source: results from DEAP s<strong>of</strong>t version 2.1
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Camelia Burja and Vasile Burja
The results obtained by applying <strong>the</strong> DEA methodology for all directions <strong>of</strong> development <strong>of</strong> <strong>the</strong>
knowledge economy place Romania in a modest position in <strong>the</strong> group <strong>of</strong> countries that joined EU
recently. Its position coincides with <strong>the</strong> ranking made by <strong>the</strong> World Bank with <strong>the</strong> Knowledge
Economy Index established on <strong>the</strong> basis <strong>of</strong> some criteria such as: Economic Incentive and
Institutional Regime; Innovation; Education; Information Communication Technology. In 2009
Romania is ranked in <strong>the</strong> last place <strong>of</strong> <strong>the</strong> EU (World Bank, 2010). This shows that Romania still has
important steps to perform for Knowledge economy to become functional and efficient.
4. Conclusions
Economic and social development has always been determined by <strong>the</strong> progresses recorded by
knowledge. In contemporary economy, using knowledge as a production factor has been generalized
by transition from <strong>the</strong> classic model <strong>of</strong> economic growth based on capital to knowledge-based
economy, where education, science, innovation and computerization have a key role in economic
growth.
Implementing KE is a priority strategic objective for <strong>the</strong> <strong>European</strong> Union. Romania and <strong>the</strong> recently
joined countries to <strong>the</strong> EU have undertaken this objective in order to achieve <strong>the</strong> <strong>European</strong> model <strong>of</strong>
sustainable economic growth based on economic competitiveness, social welfare and environmental
protection.
Evaluating <strong>the</strong> performance <strong>of</strong> knowledge economy is important for underlying <strong>the</strong> policies <strong>of</strong> efficient
allocation and use <strong>of</strong> resources, which produce, use and disseminate economy knowledge in order to
produce goods and provide services.
This paper presented <strong>the</strong> performance <strong>of</strong> Knowledge economy in Romania in comparison with
countries that joined <strong>the</strong> EU recently. The Data Envelopment Analysis methodology allowed
assessing <strong>the</strong> performances <strong>of</strong> <strong>the</strong> economic systems by making comparisons with <strong>the</strong> most efficient
systems <strong>of</strong> <strong>the</strong> group <strong>of</strong> countries analyzed (production frontier).
The results <strong>of</strong> <strong>the</strong> analysis show that Romania, during <strong>the</strong> considered period, registered a major
progress in developing knowledge economy, but <strong>the</strong>re are still gaps from o<strong>the</strong>r countries for a series
<strong>of</strong> important indicators, and <strong>the</strong> low economic performance shows that <strong>the</strong> potential <strong>of</strong> this type <strong>of</strong>
economy is not capitalized efficiently.
Research may be developed by taking into account o<strong>the</strong>r indicators that reflect <strong>the</strong> effort put into
implementing KE in those countries and <strong>the</strong> obtained results. The analysis may also be rounded by
using o<strong>the</strong>r methods.
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148

The Relationship between <strong>the</strong> <strong>European</strong> Social Fund and
Knowledge Management in Romanian Organizations
Adriana Schiopoiu Burlea
Management Marketing Department, University <strong>of</strong> Craiova, Romania
adriana_burlea@yahoo.com
Abstract: The aim <strong>of</strong> this paper is to discuss <strong>the</strong> relationship between <strong>the</strong> <strong>European</strong> Social Fund (ESF) and
Knowledge Management (KM) in <strong>the</strong> Romanian organizations based on public information and <strong>the</strong> data collected
from <strong>the</strong> rumours market. The main objectives <strong>of</strong> our research address, on <strong>the</strong> one hand, <strong>the</strong> extent to which <strong>the</strong>
lack <strong>of</strong> national human resources strategy influences KM strategies, and, on <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> relationship
between <strong>the</strong> intrinsic motivation <strong>of</strong> <strong>the</strong> trainer and potential results <strong>of</strong> <strong>the</strong> KM process. The quantitative and
qualitative research is scientifically well grounded, being used for demonstrate <strong>the</strong> interdependence <strong>of</strong> <strong>the</strong> lack <strong>of</strong>
transparency in public information relating ESF and <strong>the</strong> barrier for knowledge transfer to Romanian organizations.
Our concept cards for each code allowed us to draw constant comparisons between information obtained via
informal verbal exchanges, i.e. <strong>the</strong> rumours market, and information available from SOP HRM agencies. In order
to establish <strong>the</strong> ESF role in promoting knowledge management in organizations at different levels, it is necessary
to carefully answer <strong>the</strong> question <strong>of</strong> what knowledge represents for <strong>the</strong> Romanian organizations and what
happens when <strong>the</strong> ethical principles are non-observed by Romanian SOP HRM agencies. Results have shown
that <strong>the</strong> KM process is hindered both by national and regional HRM SOP Agencies and by <strong>the</strong> lack <strong>of</strong> a strategy
in <strong>the</strong> human resources field. Limitations <strong>of</strong> this study refer to lack <strong>of</strong> information as to partnerships, objectives <strong>of</strong>
<strong>the</strong> projects, and results <strong>of</strong> <strong>the</strong> Sectoral Operational Programme for Human Resources Development 2007-2013
(SOP HRM). The focus <strong>of</strong> <strong>the</strong> research is on <strong>the</strong> importance <strong>of</strong> knowledge management in every phase <strong>of</strong> <strong>the</strong>
access and implementation <strong>of</strong> <strong>the</strong> ESF in Romanian Organizations. The value <strong>of</strong> <strong>the</strong> paper is tw<strong>of</strong>old. First, it
consists in <strong>the</strong> presentation and discussion <strong>of</strong> <strong>the</strong> different barriers in managing knowledge transfer processes
related to lack <strong>of</strong> information, tangible results, best practices, and a negative attitude on <strong>the</strong> part <strong>of</strong> <strong>the</strong> national
and regional SOP HRM agencies. Therefore, <strong>the</strong> informational barriers that were identified are related to
creation, sharing, access, usage, and transfer <strong>of</strong> knowledge in <strong>the</strong> organizations. Secondly, we introduce <strong>the</strong>
concept <strong>of</strong> dissipation <strong>of</strong> responsibilities and waste <strong>of</strong> knowledge in order to identify informational and nonpr<strong>of</strong>essional
barriers that may prevent KM development in Romania, as a developing country.
Keywords: <strong>European</strong> Social Fund, Knowledge Management, Romanian organizations, Transparency
1. Introduction
The literature concerning national policies for human resource strategies in developing countries is
very rich and many comparative studies have been conducted (Burlea Schiopoiu et al., 2009; Cho
and McLean, 2004; Cox et al., 2006; Cunningham et al., 2006; Hasler et al., 2006; Mores, 2005;
United Nations, 2006; Wang and Swanson, 2008; Wang et al., 2008).
The relationship between intrinsic motivation and <strong>the</strong> quality <strong>of</strong> KM was considered by some authors
as a direct and positive relationship (Cohen and Levinthal, 1990; Szulanski, 1996, 2003), but in our
model it appears to be a barrier, even though, seemingly, it does not adversely affect <strong>the</strong> KM process
– in practice, it definitely affects this process in a negative manner, resulting in loss <strong>of</strong> valuable
knowledge.
Paradoxically, a national HR strategy cannot be broken down by o<strong>the</strong>r regional, local and
organizational strategies, because <strong>the</strong> national strategy applies nationwide.
In <strong>the</strong> case <strong>of</strong> Romania, a Member State, <strong>the</strong> <strong>European</strong> Social Fund (ESF) role in implementing and
developing Knowledge Management in organizations can be considered <strong>of</strong> paramount importance.
Admittedly, <strong>the</strong> paper aims to demonstrate and critically present <strong>the</strong> relationship between <strong>the</strong>
<strong>European</strong> Social Fund (ESF) and Knowledge Management (KM) in Romanian organizations, based
on public information and on <strong>the</strong> data collected from <strong>the</strong> rumours market.
The rumours market is influenced by <strong>the</strong> number <strong>of</strong> actors. Therefore, <strong>the</strong> rumours market is a place
where <strong>the</strong> researchers (<strong>the</strong> buyers) meet <strong>the</strong> social actors (sellers) in order to purchase some topic <strong>of</strong>
research that is not yet on <strong>the</strong> scientific market, but which can impact on <strong>the</strong> findings <strong>of</strong> fur<strong>the</strong>r
research. For example, in our research we used <strong>the</strong> rumours market to provide an original context,
<strong>the</strong> purpose being to investigate how <strong>the</strong> KM process may be implemented and developed at <strong>the</strong>
national, regional, local and organizational levels.
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Adriana Schiopoiu Burlea
With respect to <strong>the</strong> <strong>European</strong> Commission and for Romanian SOP HRM agencies efforts to promote
ESF in Romania, <strong>the</strong> paper caters some answers and solutions to <strong>the</strong> problems <strong>the</strong>y have faced so
far.
Scholars involved in developing conceptual models can use <strong>the</strong> principles <strong>of</strong> identifying tacit and
explicit knowledge and informational barriers presented in this paper to bridge some gaps due to <strong>the</strong>
non-availability <strong>of</strong> information that is considered classified.
Therefore, we introduce and develop <strong>the</strong> concept <strong>of</strong> dissipation <strong>of</strong> responsibilities and waste <strong>of</strong>
knowledge in order to identify informational and non-pr<strong>of</strong>essional barriers that may prevent KM
development in Romania, as a developing country. Romanian organizations should consider <strong>the</strong> fact
that ESF might constitute a strong mechanism used to transform knowledge in a source <strong>of</strong>
competitive advantage as well as a development strategy toolkit.
In order to establish <strong>the</strong> ESF role in promoting KM in organizations at <strong>the</strong> local, regional, and national
level, it is necessary to carefully answer <strong>the</strong> question <strong>of</strong> what knowledge represents for <strong>the</strong> Romanian
organizations and what happens when <strong>the</strong> ethical principles are non-observed by Romanian SOP
HRM agencies.
The ESF is a financial tool having <strong>the</strong> role to create, share, use and transfer knowledge KM) to <strong>the</strong>
human resources field (HR). The positive results <strong>of</strong> <strong>the</strong> ESF are represented in Figure 1.
ESF
Knowledge
creation
Knowledge
sharing
Knowledge
usage
Knowledge
transfer
Knowledge Management
Tacit
Knowledge
Explicit
Knowledge
Figure 1: The relationship between ESF and KM output
In this context, <strong>the</strong> ESF role is to adapt knowledge to human and technological resources existing at
each level at a specified period <strong>of</strong> time. The efficiency <strong>of</strong> <strong>the</strong> KM process depends on <strong>the</strong> extent to
which basic knowledge is circulated and used. The weaknesses that exist in <strong>the</strong> literature <strong>of</strong> KM at <strong>the</strong>
national level have led us to <strong>the</strong> approach underpinning our analysis by focusing on factors that
influence KM, ra<strong>the</strong>r than on <strong>the</strong> processes through which KM develops in organizations (Burlea
Schiopoiu, 2007).
Stiglizt (1998) considers that <strong>the</strong> transformation <strong>of</strong> society and <strong>of</strong> people needs a systemic effort that
presupposes a strong correlation between <strong>the</strong> HR strategy at <strong>the</strong> national level and rational policies.
HR at <strong>the</strong> national level may be regarded as a subsystem <strong>of</strong> <strong>the</strong> socio-economic system and <strong>the</strong> role
and implications <strong>of</strong> <strong>the</strong> ESF in designing <strong>the</strong> HRD national strategy are discussed.
There are two types <strong>of</strong> strategies in HR:
* <strong>the</strong> HR growth strategy, which is a quantitative form and it is based on <strong>the</strong> number <strong>of</strong>
<strong>the</strong> population, age, gender, urban and rural areas, migration, and employment rate;
* <strong>the</strong> HR development strategy is conceptualized as qualitative changes in <strong>the</strong> structure
<strong>of</strong> <strong>the</strong> human capital, such as competencies and knowledge.
Therefore, one <strong>of</strong> <strong>the</strong> strategies used by <strong>the</strong> <strong>European</strong> Union and <strong>the</strong> Romanian Government in order
to reduce <strong>the</strong> effects <strong>of</strong> <strong>the</strong> financial crisis is oriented towards human resources strategies and <strong>the</strong>
Sectoral Operational Programme for Human Resources Development (SOP HRD), which can be a
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Adriana Schiopoiu Burlea
catalyst for diminishing <strong>the</strong> effects <strong>of</strong> <strong>the</strong> financial crisis, leading to <strong>the</strong> development <strong>of</strong> human
resources competencies.
The main objectives <strong>of</strong> our research address, on <strong>the</strong> one hand, <strong>the</strong> extent to which <strong>the</strong> lack <strong>of</strong> national
human resources strategy influences <strong>the</strong> KM strategies, and on <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> relationship
between <strong>the</strong> intrinsic motivation <strong>of</strong> <strong>the</strong> trainer and potential results <strong>of</strong> <strong>the</strong> KM process.
In this context, we must provide practical directions and recommendations to implement <strong>the</strong> KM
mechanism in Romanian organizations. An analysis <strong>of</strong> <strong>the</strong> management process revealed <strong>the</strong>
different stakeholders’ realistic perspective <strong>of</strong> KM in Romanian organizations.
The paper is homogeneously and rigorously structured. In <strong>the</strong> first section, we discuss <strong>the</strong> role <strong>of</strong>
knowledge management as <strong>the</strong> core <strong>of</strong> human resources strategy. In <strong>the</strong> second section, we present
<strong>the</strong> case study using <strong>the</strong> grounded <strong>the</strong>ory and rumours market (Corbin and Strauss, 1990). Finally, we
advance some suggestions for improving <strong>the</strong> KM process in Romanian organizations taking into
consideration <strong>the</strong> financial and informational contribution <strong>of</strong> <strong>the</strong> ESF.
2. Is Knowledge Management <strong>the</strong> core <strong>of</strong> human resources strategy?
Nonaka (1991) considers that knowledge management is <strong>the</strong> core <strong>of</strong> human resources strategy.
Admittedly, <strong>the</strong> complex structure <strong>of</strong> <strong>the</strong> human resources strategy consists <strong>of</strong> two knowledge
management strategies that support each o<strong>the</strong>r in <strong>the</strong> process <strong>of</strong> transformational knowledge
(Hansen et al., 1999). These two knowledge management strategies have <strong>the</strong> role to enhance <strong>the</strong>
competitive advantage <strong>of</strong> <strong>the</strong> organization and are based on personalization and codification (Ajmal et
al., 2010).
We must pay attention to systematic <strong>the</strong>oretical arguments so as to achieve a definitive approach.
To implement and, fur<strong>the</strong>rmore, to develop a HR national strategy, it is necessary to develop and
implement Knowledge Management strategies at <strong>the</strong> national level with ESF support (see Figure 2).
Figure 2: The conceptual model <strong>of</strong> <strong>the</strong> relationship between Human Resources Strategies and
Knowledge Management Strategies
The regional, local and organizational strategies are customized and have <strong>the</strong> mission to support <strong>the</strong>
achievement <strong>of</strong> <strong>the</strong> national human resources strategy.
The KM in Romanian organizations, even if it is taken into consideration, cannot lead to a real
competitive advantage without development and improvement.
The literature <strong>of</strong> <strong>the</strong> KM process at <strong>the</strong> national and regional level did not connect knowledge with
strategies; thus, knowledge was not understood as enhancing progress.
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Adriana Schiopoiu Burlea
3. ESF role in promoting KM in Romanian organizations
3.1 Methodology
The well-grounded quantitative and qualitative research is used to demonstrate <strong>the</strong> interdependence
between <strong>the</strong> lack <strong>of</strong> transparency in public information about ESF and <strong>the</strong> barrier for knowledge
transfer to Romanian organizations.
With a view to identifying and explaining different factors that influence <strong>the</strong> success or failure <strong>of</strong> KM
initiatives in every phase <strong>of</strong> <strong>the</strong> access and implementations <strong>of</strong> <strong>the</strong> ESF in Romanian Organizations, it
is necessary to understand <strong>the</strong> realities <strong>of</strong> <strong>the</strong> Romanian organization in this respect.
The data have been collected from multiple sources concerning documents and data – public report
websites and informal interviews with a sample <strong>of</strong> anonymous interviewees, in order to evaluate <strong>the</strong>
KM at <strong>the</strong> national, regional, local and organizational levels. The o<strong>the</strong>r purpose was to identify <strong>the</strong>
stakeholders linked through KM to <strong>the</strong> ESF.
We did not use a random sample technique because <strong>the</strong> nature <strong>of</strong> information required a reputation
sampling technique (Scott, 1991); consequently, we selected <strong>the</strong> projects and <strong>the</strong> stakeholders
according to <strong>the</strong>ir importance in KM strategies in <strong>the</strong> academic environment.
The aim was to discover <strong>the</strong> rules starting from data (Glaser & Strauss, 1967) taking into
consideration that qualitative research is limited in capturing <strong>the</strong> details from <strong>the</strong> data provided by
SOP HRM agencies, but it is rich in details provided by <strong>the</strong> rumours market and individuals’ stories
and discussions. Therefore, <strong>the</strong> empirical research is based on a case study, rumours market and it
uses an exploratory ra<strong>the</strong>r than a confirmatory hypo<strong>the</strong>sis-testing approach.
3.1.1 Artificial Barriers to knowledge management process in <strong>the</strong> Romanian organizations
Artificial barriers are raised in front <strong>of</strong> <strong>the</strong> knowledge management process by absurd regulations
such as <strong>the</strong> imposition <strong>of</strong> trainer <strong>of</strong> trainers’ certificate.
The <strong>European</strong> Commission does not require such a certificate; it is a national law that, in order to
prevent unauthorized trainers to disseminate knowledge, it shows, in fact, <strong>the</strong> disguised mercantilist
purpose to create a market for companies providing <strong>the</strong>se certificates.
This requirement did not lead to a smooth running <strong>of</strong> <strong>the</strong> KM process, but ra<strong>the</strong>r to its overloading,
because even academics, who are trainers by definition, are required to obtain such a certificate from
a "specialized provider".
Out <strong>of</strong> <strong>the</strong> 311 projects (see table 1), which have as main beneficiary universities, we were able to
have informal discussions with people (non-teaching staff, teaching staff, students, and o<strong>the</strong>r
stakeholders) from 16 universities involved in 115 projects. As a first step in using grounded <strong>the</strong>ory we
followed <strong>the</strong> model <strong>of</strong> Miles and Huberman (1994) for processing informal discussions and open
coding (Strauss & Corbin, 1990; Turner, 1981), examining each statement in <strong>the</strong> light <strong>of</strong> responses to
questions such as:
Do you consider that <strong>the</strong> imposition <strong>of</strong> holding a trainer certificate provided by some companies
for academic staff represents a violation <strong>of</strong> ethics and pr<strong>of</strong>essional ethics?
Do you think that you should achieve a trainer certificate to be able to involve yourself or to
continue to work in a project financed by ESF?
As a director <strong>of</strong> a project funded by ESF, do you consider that people involved in your project
must have a trainer certificate?
Do you consider <strong>the</strong> fact that you were forced to obtain a trainer certificate to be demotivating in
<strong>the</strong> process <strong>of</strong> knowledge dissemination?
Arising from <strong>the</strong>se informal discussions, several conclusions have been drawn:
A first conclusion is that a large number <strong>of</strong> teaching staff has refused to get involved in ESF
projects because <strong>of</strong> this constraint, even if <strong>the</strong>y could acquire this certificate very simply and fast.
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There were also beneficiaries who have assumed <strong>the</strong> risk to be involved in a project with teaching
staff displaying valuable teaching expertise and solid scientific research work even if <strong>the</strong>y did not
hold any trainer certificates.
To avoid <strong>the</strong> possibility that, at a certain point, <strong>the</strong> project will not be funded, many beneficiaries
have agreed to engage in <strong>the</strong> project only people who have trainer certificates.
Table 1: Project approved on 30 September 2010 at <strong>the</strong> national level according to <strong>the</strong> priority axis
and category <strong>of</strong> beneficiaries - Universities:
Priority Axis Number <strong>of</strong> projects Value (RON)
1 2 2
Priority Axis 1 Education and training in
support for growth and development <strong>of</strong> a
knowledge based society
194 2,000,402,440
Priority Axis 2 Linking lifelong learning and
labor market
54 425,570,273
Priority Axis 3 Increasing adaptability <strong>of</strong>
workers and enterprises
51 267,953,507
Priority Axis 5: Promoting active employment
measures
4 31,923,539
Priority Axis 6: Promoting social inclusion 8 98,685,433
Total
* Source: Adaptation from:
311 2,825,535,192
http://www.fseromania.ro/index.php?option=com_content&task=view&id=38&Itemid=44#p1
From Table 1 we can notice that 194 projects are approved on Priority Axis 1 Education and training
in support for growth and development <strong>of</strong> a knowledge based society. The lack <strong>of</strong> Human Resources
strategy at <strong>the</strong> national level is also visible in <strong>the</strong> distribution <strong>of</strong> <strong>the</strong>se projects to universities located in
different regions <strong>of</strong> <strong>the</strong> country. The three-dimensional relationship between beneficiaries - value <strong>of</strong>
<strong>the</strong> project - knowledge management outputs is disproportionate and inequitable.
By analyzing <strong>the</strong> distribution <strong>of</strong> 194 projects from Priority Axis 1 to <strong>the</strong> beneficiaries <strong>of</strong> <strong>the</strong> various
regions <strong>of</strong> <strong>the</strong> country (universities and research institutes), <strong>the</strong> following conclusions are drawn:
The universities and research institutes from Bucharest benefited from 84 projects (43.3%) –
amounting to a total value <strong>of</strong> 1,019,282,375 RON (36 %).
1. The academic centre <strong>of</strong> Cluj Napoca – 30 projects (15.5%).
2. The academic centre <strong>of</strong> Iasi – 21 projects (10.8%).
3. The academic centre <strong>of</strong> Timisoara – 13 projects (6.7%).
4. The academic centre <strong>of</strong> Craiova – 9 projects (4.6%).
5. O<strong>the</strong>r universities and research institutes– 37 projects (19.1%).
Computing <strong>the</strong> above data, we noticed that large academic centres - Bucharest, Cluj Napoca, Iasi,
Timisoara and Craiova “captured” an important number <strong>of</strong> projects – 157 projects (80.9%).
If academic teaching staff involvement in <strong>the</strong> process <strong>of</strong> creating and sharing knowledge is limited,
<strong>the</strong>n <strong>the</strong> likelihood <strong>of</strong> a barrier in knowledge achievement by students and, accordingly, <strong>the</strong> waste <strong>of</strong>
knowledge are very high.
We argue that <strong>the</strong> trainer has <strong>the</strong> mission to transform implicit knowledge into explicit knowledge,
based on his pedagogical and <strong>the</strong>oretical expertise and experience, not only on <strong>the</strong> certificate that is
almost readily available.
If this trainer certificate is not supported by real <strong>the</strong>oretical and practical competencies, it has no value
and, in this case, it becomes a demotivating factor for <strong>the</strong> o<strong>the</strong>r trainers that consider this legal abuse
an instrument for dissipation <strong>of</strong> responsibilities and waste <strong>of</strong> knowledge.
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Adriana Schiopoiu Burlea
In <strong>the</strong> last years, <strong>the</strong> KM strategies have been focused on promoting tacit knowledge between people
and this KM strategy allows transfer <strong>of</strong> knowledge to become a more flexible process that improves
<strong>the</strong> knowledge worker’s performance.
Taking into consideration <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> human factor, we can state that rigid legislation
concerning <strong>the</strong> mandatory imposition <strong>of</strong> <strong>the</strong> certificate trainer, combined with <strong>the</strong> lack <strong>of</strong> human
resources strategies at different levels, produces artificial barriers in <strong>the</strong> KM process and prevent <strong>the</strong>
development and implementation <strong>of</strong> KM strategies (see Figure 3).
Figure 3: The relationship between Human Resources strategies and Knowledge Management
Strategies in Romania
The ESF was designed as a tool used to adapt knowledge to human and technological resources
existing at each level at a specific time and to disseminate this knowledge to target groups, requiring
not only a certificate, but this type <strong>of</strong> knowledge as well.
In this context, KM no longer prevails, but, on <strong>the</strong> contrary, negative effects occur and propagate in a
chain from one level to ano<strong>the</strong>r and return <strong>the</strong> feeling <strong>of</strong> futility and waste <strong>of</strong> knowledge, reinforcing
negative energy more powerfully than initially.
3.1.2 The relationship between transparency and knowledge management output
On <strong>the</strong> rumours market <strong>the</strong>re was a rumor that it would be possible to be involved in many activities,
some <strong>of</strong> <strong>the</strong>m representing a potential conflict <strong>of</strong> interests.
On <strong>the</strong> Managing Authority for Sectoral Operational Programme Human Resources Development
(MASOPHRD) website, we only found <strong>the</strong> following sentence on <strong>the</strong> conflict <strong>of</strong> interests: “Any false
information or potential conflict <strong>of</strong> interests will cause <strong>the</strong> elimination <strong>of</strong> <strong>the</strong> person from <strong>the</strong> database”
(MASOPHRD, 2010-2011).
Following <strong>the</strong> rumours market information, we came up with <strong>the</strong> conclusion that <strong>the</strong>re are people
being at <strong>the</strong> same time:
Manager and member in many projects at <strong>the</strong> university level,
Manager and member in o<strong>the</strong>rs projects at <strong>the</strong>ir own consulting company level,
Member in many projects <strong>of</strong> <strong>the</strong>ir associations – o<strong>the</strong>r than <strong>the</strong> university and consulting
company,
Independent evaluator for <strong>the</strong> projects funded by ESF,
Member in monitoring teams for <strong>the</strong> projects funded by ESF.
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Adriana Schiopoiu Burlea
The data found on <strong>the</strong> websites <strong>of</strong> <strong>the</strong> universities, consulting companies and associations proved us
that <strong>the</strong> rumours were true.
Because <strong>the</strong> MASOPHRD did not answer to our repeated emails, we cannot check <strong>the</strong> aspect
concerning <strong>the</strong> role <strong>of</strong> <strong>the</strong> independent evaluator and member in monitoring teams in <strong>the</strong> case <strong>of</strong> <strong>the</strong>
projects funded by ESF.
Consequently, <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong>se assumptions was confirmed by <strong>the</strong> informal discussions with
o<strong>the</strong>r beneficiaries and stakeholders that were monitored by <strong>the</strong>se persons.
Who guarantees that <strong>the</strong>se persons did not take advantage <strong>of</strong> <strong>the</strong>ir position <strong>of</strong> evaluators and
monitors to get hold <strong>of</strong> o<strong>the</strong>r people’s projects and, subsequently, to be funded for <strong>the</strong>m?
The copy-paste type <strong>of</strong> submitting projects and <strong>the</strong>ir allocation to o<strong>the</strong>r users who are unable to carry
out KM activities in <strong>the</strong> project and who have not <strong>the</strong> vision and strategy <strong>of</strong> <strong>the</strong> initial authors <strong>of</strong> <strong>the</strong>
project can be an explanation <strong>of</strong> how poorly <strong>the</strong> acquisition <strong>of</strong> tacit and explicit knowledge is
conducted, as a result <strong>of</strong> <strong>the</strong> KM process generated by ESF (see Figure 4).
Figure 4: The output <strong>of</strong> KM process in Romanian organizations
The informal discussions with <strong>the</strong> human resources that attend some courses in <strong>the</strong> framework <strong>of</strong> <strong>the</strong>
projects funded by ESF proved us <strong>the</strong> failure <strong>of</strong> <strong>the</strong> KM process and a very weak output. The subjects
told us that <strong>the</strong> explicit and tacit knowledge acquired during <strong>the</strong> learning process were insignificant
and <strong>the</strong>y perceived <strong>the</strong> courses as a waste <strong>of</strong> time. This negative output is not due to <strong>the</strong> content <strong>of</strong>
<strong>the</strong> courses, but to <strong>the</strong> quality <strong>of</strong> <strong>the</strong> trainers. Therefore, <strong>the</strong> trainers’ certificate in not a real instrument
for guaranteeing <strong>the</strong> quality <strong>of</strong> <strong>the</strong> KM process due to trainers.
Our study confirmed <strong>the</strong> rumours market that lack <strong>of</strong> transparency combined with corruption suspicion
negatively affects <strong>the</strong> output <strong>of</strong> <strong>the</strong> knowledge management process. Traditional economic <strong>the</strong>ory -
Granovetter (1985) - endorses that knowledge management process is based on systems <strong>of</strong> norms
and values that have <strong>the</strong> role <strong>of</strong> improving <strong>the</strong> human relations in knowledge management actions.
We tried to demonstrate how our model <strong>of</strong> knowledge management process explains <strong>the</strong> importance
<strong>of</strong> human resources strategy at <strong>the</strong> national level and how unethical decision-making generated
unethical behaviour both in <strong>the</strong> case <strong>of</strong> <strong>the</strong> beneficiaries <strong>of</strong> <strong>the</strong> knowledge management process and
in <strong>the</strong> case <strong>of</strong> <strong>the</strong> beneficiaries <strong>of</strong> ESF.
Hence, <strong>the</strong> lack <strong>of</strong> a HR strategy can lead to corruption and it is also reflected in <strong>the</strong> existing conflicts
<strong>of</strong> interests, which proves that <strong>the</strong> elaboration <strong>of</strong> a strategy for HR is an activity requiring a detailoriented
analysis <strong>of</strong> human resource aspects, not a routinised one (Burlea Schiopoiu et al., 2010,
Levy et al., 2010).
Our analysis highlights <strong>the</strong> fact that, in <strong>the</strong> absence <strong>of</strong> a national and/or regional HR strategy, <strong>the</strong>
knowledge management process is inefficient and ESF cannot be used.
To sum up, <strong>the</strong> ESF role in Romania can be seen as a set <strong>of</strong> tools for human resources development
and, implicitly, for <strong>the</strong> development <strong>of</strong> human capital.
155

3.2 Discussions
Adriana Schiopoiu Burlea
To draw a KM model in <strong>the</strong> process <strong>of</strong> implementing POSDRU projects, <strong>the</strong> following logical steps
should be considered:
1. Develop a pr<strong>of</strong>essional training strategy at <strong>the</strong> national level. This national strategy should be
differentiated according to several items <strong>of</strong> knowledge:
* A first distinction has to be made by development regions according to <strong>the</strong> economic and social
potential <strong>of</strong> each region
* A second distinction will be based on <strong>the</strong> absorption rate <strong>of</strong> EU funds by training agencies - which
will play <strong>the</strong> role <strong>of</strong> sharing knowledge
* A third difference has to take into account <strong>the</strong> existing human potential <strong>of</strong> each region and <strong>the</strong>
possibility <strong>of</strong> higher capitalization <strong>of</strong> <strong>the</strong> knowledge acquired upon <strong>the</strong> completion <strong>of</strong> <strong>the</strong> training
course during <strong>the</strong> project
* A fourth difference has to consider <strong>the</strong> possibility <strong>of</strong> using trainers in training programmes
The correct assessment <strong>of</strong> <strong>the</strong> costs <strong>of</strong> training and <strong>the</strong> development <strong>of</strong> standards so as to calculate
<strong>the</strong> minimum and maximum costs for specific training processes would be a prerequisite that would
not allow any more financial differences between <strong>the</strong> same types <strong>of</strong> projects, with similar objectives –
<strong>the</strong> present state indicates significant differences in funding policies.
2. Streng<strong>the</strong>ning <strong>the</strong> institutional role <strong>of</strong> <strong>the</strong> Romanian SOP HRM agencies taking into consideration
that <strong>the</strong> role <strong>of</strong> institutions (cultural, economic and political) is tw<strong>of</strong>old – on <strong>the</strong> one hand, to develop
human capital with respect to ethnic makeup and technological progress, and on <strong>the</strong> o<strong>the</strong>r hand, to
create <strong>the</strong> material conditions necessary to enhance progress.
Therefore, <strong>the</strong> role <strong>of</strong> SOP HRM agencies is:
* to facilitate <strong>the</strong> participation <strong>of</strong> <strong>the</strong> most viable stakeholders in specific ESF projects,
* to promote a real partnership between stakeholders so as to incorporate regional, local and
organizational KM strategies into <strong>the</strong> national human resources strategy,
* to promote and to defend <strong>the</strong> ethical principle so as to avoid <strong>the</strong> dissipation <strong>of</strong> responsibilities and
<strong>the</strong> waste <strong>of</strong> knowledge,
* to enhance <strong>the</strong> regional, local and organizational levels manpower to manage new knowledge and
high technologies. This characteristic allows quick adaptation to emerging demands for new skills in
parallel with <strong>the</strong> development <strong>of</strong> a culture <strong>of</strong> innovation.
4. Conclusions
In this paper, we have examined how ESF can be used by Romanian organizations, reshaping <strong>the</strong>ir
KM and overcoming barriers.
We argued that <strong>the</strong> KM coupled with a realistic HR strategy should be considered not just as a
demonstrative <strong>the</strong>ory, but also as a mechanism for obtaining a competitive advantage.
Therefore, notions such as dissipation <strong>of</strong> responsibilities and waste <strong>of</strong> knowledge have been used in
order to understand <strong>the</strong> artificial barriers that bodies designed to manage <strong>the</strong>se funds raise against
o<strong>the</strong>r stakeholders involved in this process.
In order to investigate how <strong>the</strong> national and regional SOP HRM agencies and o<strong>the</strong>r stakeholders use
KM to optimize <strong>the</strong> process <strong>of</strong> acquiring and developing new competencies, an in-depth analysis <strong>of</strong>
<strong>the</strong> public information and rumours market has been carried out.
Results have shown that <strong>the</strong> KM process is hampered both by national and regional HRM SOP
Agencies and by <strong>the</strong> lack <strong>of</strong> a strategy in <strong>the</strong> human resources field.
Therefore, <strong>the</strong> correlation between <strong>the</strong> data found on <strong>the</strong> websites and <strong>the</strong> rumors market is close and
shows a dissipation <strong>of</strong> responsibilities and a waste <strong>of</strong> knowledge at national and regional levels.
Reduced transparency and lack <strong>of</strong> communication, correlated with a superficial strategy for human
resources and knowledge management leads to <strong>the</strong> conclusion that <strong>the</strong> challenges <strong>of</strong> <strong>the</strong> ESF in
Romania do not display viable results, measurable through indicators <strong>of</strong> economic and social
efficiency.
156

Adriana Schiopoiu Burlea
The research limitations arise from <strong>the</strong> sensitivity <strong>of</strong> <strong>the</strong> subject – no answers received to emails –
and from <strong>the</strong> lack <strong>of</strong> interest – informal discussions with <strong>the</strong> staff involved in this process were
relevant, but cannot be considered defining for our research because <strong>the</strong> staff refused to fill in any
questionnaire and to participate in a formal interview.
Therefore, our study is clearly marked by <strong>the</strong> lack <strong>of</strong> information related to partnerships, objectives <strong>of</strong>
<strong>the</strong> projects, and results <strong>of</strong> <strong>the</strong> Sectoral Operational Programme for Human Resources Development
2007-2013 (SOP HRM).
The involvement <strong>of</strong> beneficiaries and employees who described different aspects <strong>of</strong> <strong>the</strong> relationship
between ESF and KM process raised questions related to <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong> information, thus this
sample might be considered as one <strong>of</strong> <strong>the</strong> weak points <strong>of</strong> our study. Anyway, <strong>the</strong> consistency <strong>of</strong> <strong>the</strong>
rumours market combined with <strong>the</strong> stories, informal interviews, and <strong>the</strong> information found on <strong>the</strong> site
<strong>of</strong> national and regional SOP HRM agencies lends credibility and validity to our findings.
In this context, it is very difficult to determine an efficient SOP HRM strategy because <strong>of</strong> multiple
social and economic variables, which require <strong>the</strong> improvement <strong>of</strong> <strong>the</strong> national and regional SOP HRM
agencies performance and transparency.
To develop KM in Romanian organizations, SOP HRM agencies should be concerned with <strong>the</strong> human
resources strategy at <strong>the</strong> national level, while paying due attention to knowledge-based activities
according to <strong>the</strong> economic power <strong>of</strong> every region.
5. Acknowledgment
This paper is realized with <strong>the</strong> financial support <strong>of</strong> <strong>the</strong> grant: Identification <strong>of</strong> some sustainable
strategies applicable to <strong>the</strong> SME from <strong>the</strong> Developing South-West Oltenia Region, for obtaining a
competitive position on <strong>the</strong> national and international market, ID_412, Contract no. 826/19.01.2009
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158

Organizational Strategy and Research Productivity: A
Comparison <strong>of</strong> two Academic Institutions
Simon Cadez and Vlado Dimovski
Faculty <strong>of</strong> Economics, University <strong>of</strong> Ljubljana, Slovenia
Simon.cadez@ef.uni-lj.si
Vlado.dimovski@ef.uni-lj.si
Abstract: Universities and o<strong>the</strong>r academic institutions play a central role in knowledge development and its
transfer to productive use. Academic researchers usually transfer new knowledge to <strong>the</strong> wider public by
publishing scientific articles. The number <strong>of</strong> publications is usually regarded as a robust indicator <strong>of</strong> <strong>the</strong> creation
<strong>of</strong> new knowledge as well as its transfer into productive use (direct transfer to companies and indirect transfer to
students). In <strong>the</strong> present study we examine <strong>the</strong> impact <strong>of</strong> organizational strategy on research productivity in
academic institutions. The method deployed is a case study. For comparative reasons we investigate two
academic institutions from <strong>the</strong> same discipline, i.e. business, but different with respect to <strong>the</strong> process <strong>of</strong> interest,
i.e. organizational strategy and organizational culture. The Faculty <strong>of</strong> Economics <strong>of</strong> <strong>the</strong> University <strong>of</strong> Ljubljana is a
business school with a long tradition, dominant position within Slovenia and international recognition. The Faculty
<strong>of</strong> Management <strong>of</strong> <strong>the</strong> University <strong>of</strong> Primorska on <strong>the</strong> o<strong>the</strong>r hand is a young school without tradition, but aiming to
secure its recognition both domestically and internationally. Organizational strategy was deduced from published
documents <strong>of</strong> both institutions. Research productivity was measured using a bibliometric method, operationalized
as articles published in journals included in <strong>the</strong> SCI/SSCI database in <strong>the</strong> period from 2000 to 2009. The main
findings <strong>of</strong> <strong>the</strong> study are: (1) research productivity in <strong>the</strong> period examined increased in both institutions, (2) vast
differences exist in research productivity and research quality across institutions and researchers, (3) research
output seems to be relatively consistent with organizational strategy in both schools (i.e. regional
leadership/recognition), and (4) organizational culture does not seem to be an important factor affecting research
productivity.
Keywords: Organizational strategy, organizational culture, research productivity, bibliometric method, academic
institutions.
1. Introduction
In <strong>the</strong> current turbulent environment, <strong>the</strong> ability to create and apply knowledge is <strong>the</strong> most important
source <strong>of</strong> <strong>the</strong> sustainable competitive advantage <strong>of</strong> companies, organizations, and entire economies
(Lanciano-Morandat, 2006; Nonaka and Takeuchi, 1995). Due to market changes, new technologies,
and new competitors, products or services can become redundant literally overnight. In such
circumstances only those companies can be successful that constantly create new knowledge and
successfully apply such in <strong>the</strong> form <strong>of</strong> new products and services (Nonaka and Takeuchi, 1995).
Being aware <strong>of</strong> <strong>the</strong> importance <strong>of</strong> knowledge for <strong>the</strong> competitiveness <strong>of</strong> <strong>the</strong>ir economies, many
developed countries have developed national research infrastructure for <strong>the</strong> development and transfer
<strong>of</strong> new knowledge (Abramo and D'Angelo, 2009). Universities and research institutes play <strong>the</strong> leading
role in such research infrastructure (Colyvas et al., 2002; David, 2006; Lanciano-Morrandat, 2006),
representing, by means <strong>of</strong> <strong>the</strong>ir processes entailing <strong>the</strong> creation and transfer <strong>of</strong> knowledge, <strong>the</strong>
cornerstone <strong>of</strong> societal progress and development (Tian et al., 2009). Researchers at universities and
public research institutes usually transfer new knowledge to <strong>the</strong> wider public by publishing scientific
articles (Van Raan, 2005). O<strong>the</strong>r codified (e.g. patents, databases) and non-codified (e.g.
consultation) methods exist to transfer new knowledge, but in <strong>the</strong> EU and <strong>the</strong> USA more than 90
percent <strong>of</strong> all newly created knowledge is transferred in <strong>the</strong> form <strong>of</strong> scientific publications (Abramo et
al., 2008). Research shows <strong>the</strong> existence <strong>of</strong> a positive correlation between <strong>the</strong> number <strong>of</strong> articles
published and <strong>the</strong> number <strong>of</strong> patents (Abramo et al., 2008; Van Looy et al., 2006), as well as between
<strong>the</strong> quality <strong>of</strong> research and academic work (Arnold, 2008; Bonaccorsi et al., 2006, Lindsay et al.,
2002), which is why many researchers are <strong>of</strong> <strong>the</strong> opinion that <strong>the</strong> number <strong>of</strong> articles published can
represent a strong indicator <strong>of</strong> both <strong>the</strong> creation <strong>of</strong> new knowledge as well as its transfer into
productive use (direct transfer to companies and indirect transfer to students).
The purpose <strong>of</strong> this study is to examine how organizational strategy affects <strong>the</strong> research productivity
<strong>of</strong> academic institutions. The method deployed is a case study. For comparative reasons we
investigate two academic institutions from <strong>the</strong> same discipline, i.e. business, but different with respect
to <strong>the</strong> process <strong>of</strong> interest, i.e. organizational strategy and organizational culture. The Faculty <strong>of</strong>
Economics <strong>of</strong> <strong>the</strong> University <strong>of</strong> Ljubljana is a business school with a long tradition, dominant position
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Simon Cadez and Vlado Dimovski
within Slovenia and international recognition. The Faculty <strong>of</strong> Management <strong>of</strong> <strong>the</strong> University <strong>of</strong>
Primorska on <strong>the</strong> o<strong>the</strong>r hand is a young school without tradition, but aiming to secure its recognition
both domestically and internationally.
The remainder <strong>of</strong> <strong>the</strong> paper is organized as follows. First, <strong>the</strong> concepts <strong>of</strong> organizational strategy,
organizational culture and research productivity are outlined. Next, research methodology is
presented and <strong>the</strong> findings are revealed. A discussion and conclusion are provided in <strong>the</strong> final
section, toge<strong>the</strong>r with a number <strong>of</strong> pointers for future research.
2. Literature review
2.1 Organizational strategy in academia
Contemporary society is increasingly obsessed with efforts to rank <strong>the</strong> best universities, schools, and
researchers (van Raan, 2005). The most well-known is <strong>the</strong> so-called Shanghai Ranking <strong>of</strong> <strong>the</strong> 500
best universities in <strong>the</strong> world, which is prepared every year by <strong>the</strong> Shanghai Jiao Tong University. The
criteria used is exclusively bibliometric, namely: Nobel Prizes and <strong>the</strong> Field Prizes for researchers and
alumni (30% weight), a high citation rate for <strong>the</strong> university's researchers (20%), articles published in
<strong>the</strong> magazines Science and Nature (20%), <strong>the</strong> citation index in <strong>the</strong> SCI and SSCI databases (20%),
and <strong>the</strong> institution's per capita academic performance with regard to all indicators listed above (10%).
There is also a number <strong>of</strong> initiatives to rank <strong>the</strong> best business schools. Jiao Tong University prepares
a special ranking <strong>of</strong> <strong>the</strong> 100 best business schools, wherein instead <strong>of</strong> articles published in <strong>the</strong>
magazines Nature and Science, articles in journals and magazines from <strong>the</strong> field <strong>of</strong> economics and
business sciences in <strong>the</strong> SSCI database are taken into account. Very influential listings are also
provided by business magazines or newspapers such as Financial Times, Business Week, and
Forbes (Bonner, Hesford, Van der Stede, & Young, 2006). Financial Times listing is compiled on <strong>the</strong>
basis <strong>of</strong> three groups <strong>of</strong> criteria, i.e. <strong>the</strong> career progress <strong>of</strong> alumni, <strong>the</strong> international nature <strong>of</strong> <strong>the</strong>
programme, and <strong>the</strong> research achievements <strong>of</strong> <strong>the</strong> institution (Gendron, 2008). The latter criterion is
bibliometric and is based on <strong>the</strong> articles <strong>of</strong> researchers published in <strong>the</strong> 40 most reputable journals
and magazines in <strong>the</strong> field <strong>of</strong> economics and business sciences (Biehl et al., 2006; Bonner et al.,
2006). In just a few years since its first publication in 1999, this ranking has become an important
factor in <strong>the</strong> scientific research work <strong>of</strong> many prestigious business schools, which in a planned
manner encourage <strong>the</strong>ir researchers to publish in <strong>the</strong>se journals and magazines (Gendron, 2008).
A recent uprise <strong>of</strong> <strong>the</strong> bibliometric paradigm has important implications for <strong>the</strong> research strategy <strong>of</strong>
academic institutions as well as individual researchers (Reidpath and Allotey, 2010). Many
universities and faculties strive for a (high) placement in <strong>the</strong> rankings mentioned above, and for this
objective <strong>the</strong>y have also adapted <strong>the</strong>ir systems <strong>of</strong> employing and rewarding researchers by applying
<strong>the</strong> “publish or perish” paradigm (Gendron, 2008; Long et al., 2009). Publication in reputable journals
is becoming <strong>the</strong> main factor in <strong>the</strong> successfulness <strong>of</strong> an individual's academic career since it affects
his reputation and chances <strong>of</strong> employment, promotion, and financial reward (Bonner et al., 2006). For
example, Long et al. (2009) stated that in <strong>the</strong> USA having just one article published in a top journal
can result in an annual salary difference amounting to $20,000 over a longer period <strong>of</strong> time.
2.2 Research productivity
Research productivity evaluation methods can generally be divided into <strong>the</strong> following two groups
(Abramo et al., 2008; Geuna and Martin, 2003): a bibliometric group and a peer-review group.
Bibliometric methods examine research productivity by <strong>the</strong> number <strong>of</strong> publications and <strong>of</strong>ten also
citations (Long et al., 2009; Van Raan, 2005). Peer-review methods for <strong>the</strong> evaluation <strong>of</strong> research
productivity are based on reviewing procedures. When applying <strong>the</strong>se methods, <strong>the</strong> evaluator selects
a panel <strong>of</strong> reviewers, typically pr<strong>of</strong>essionals in a certain research field, who <strong>the</strong>n assess <strong>the</strong> research
productivity <strong>of</strong> individual institutions or individuals (Horrobin, 1990; Moxham and Anderson, 1992).
Both types <strong>of</strong> methods have advantages and disadvantages, which have been analysed in detail (see
Abramo et al., 2008; Horrobin, 1990; Moxham and Anderson, 1992; Van Raan, 2005) and mainly fall
into three categories, i.e. cost effectiveness, time consumption, and <strong>the</strong> objectivity <strong>of</strong> <strong>the</strong>
measurement. From <strong>the</strong> perspectives <strong>of</strong> cost effectiveness and time consumption, bibliometric
methods definitively have an advantage over peer-review ones, since it is possible, with appropriate
technical support, to assess <strong>the</strong> research productivity <strong>of</strong> an institution or individual with only a few
clicks in appropriate information databases. A comparison <strong>of</strong> <strong>the</strong> methods from <strong>the</strong> perspective <strong>of</strong> <strong>the</strong>
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Simon Cadez and Vlado Dimovski
objectivity <strong>of</strong> <strong>the</strong> measurement is more complicated. In general, bibliometric methods are considered
to be more objective and universal since <strong>the</strong> number <strong>of</strong> articles published as well as citations are
measurable and internationally comparable categories and which additionally comprise a qualityrelated
dimension. Contrary to quantity, quality can not be measured directly, but can, however, be
assessed through various indicators. The two most frequent indicators <strong>of</strong> quality are <strong>the</strong> following: <strong>the</strong>
quality <strong>of</strong> <strong>the</strong> journal in which an article is published and <strong>the</strong> number <strong>of</strong> times a published article is
cited. Despite its advantages, this method has at least two drawbacks. The first one is its
presupposition that published articles can serve as an approximation <strong>of</strong> research productivity. The
second disadvantage <strong>of</strong> bibliometric methods is related to <strong>the</strong> manner <strong>of</strong> measuring bibliographies
and citations. For this purpose, <strong>the</strong> ISI Web <strong>of</strong> Knowledge database is <strong>of</strong>ten used, which is managed
by <strong>the</strong> ThomsonReuters company. Although <strong>the</strong> database includes approximately 12,000 journals
from all scientific fields, its representativeness differs greatly across disciplines (Van Raan, 2005).
Although bibliometric methods were traditionally used primarily in <strong>the</strong> Anglo-Saxon world (Geuna and
Martin, 2003; Van Raan, 2005), <strong>the</strong> bibliometric paradigm has also seen a significant increase in o<strong>the</strong>r
countries lately (Abramo et al., 2008). Bibliometric methods also prevail in Slovenia in <strong>the</strong> evaluation
<strong>of</strong> research work, which is evident from <strong>the</strong> universities' conditions for habilitation, as well as from <strong>the</strong>
methodology <strong>of</strong> <strong>the</strong> Slovenian Research Agency for assigning projects and allocating research funds.
2.3 Research questions
In accordance with <strong>the</strong> established objectives <strong>of</strong> this study, we are interested in <strong>the</strong> following research
questions:
1. What is <strong>the</strong> research productivity <strong>of</strong> individual schools and individual researchers?
2. Is research output consistent with organizational strategy?
With regard to <strong>the</strong> first research question, we are interested in <strong>the</strong> total number <strong>of</strong> articles published in
<strong>the</strong> period examined, according to individual years. We expect an increasing trend in <strong>the</strong> number <strong>of</strong>
articles published for at least two reasons: (1) habilitation conditions at all three faculties are
becoming ever stricter, including formal requirements for publishing articles in journals in <strong>the</strong>
SCI/SSCI database, and (2) every year more journals are included in <strong>the</strong> SCI/SSCI database,
<strong>the</strong>refore being published in <strong>the</strong>se journals is becoming easier. Fur<strong>the</strong>r, we expect differences to exist
across schools and even more so across researchers. Some publish a lot, o<strong>the</strong>rs not much, some not
at all. In our study we have defined those individuals who had at least one article published in a
journal included in <strong>the</strong> SCI/SSCI database in <strong>the</strong> period 2000 – 2009 as “active in <strong>the</strong> field <strong>of</strong>
research”, which is, <strong>of</strong> course, a very undemanding criterion with regard to demonstrating “activity in
<strong>the</strong> field <strong>of</strong> research”.
Second, we are interested whe<strong>the</strong>r organizational strategy affects research productivity. The field <strong>of</strong>
economics and business sciences is very broad and includes many sub-disciplines (e.g. <strong>the</strong> field <strong>of</strong>
business sciences includes sub-disciplines such as finance, accounting, marketing, entrepreneurship,
management, and o<strong>the</strong>rs). We wish to examine in which fields Slovene researchers most actively
create new knowledge. The SSCI database comprises 55 social science categories, <strong>of</strong> which four
directly refer to economics and business sciences. These categories are (1) economics
(approximately 300 journals), (2) business sciences (approximately 100 journals), (3) finance
(business, finance, approximately 90 journals), and (4) management (approximately 150 journals).
Although <strong>the</strong>re also exist o<strong>the</strong>r categories in addition to <strong>the</strong>se “primary” economics-business
categories that at least partially cover <strong>the</strong> field <strong>of</strong> economics and business sciences, we expect that
<strong>the</strong> majority <strong>of</strong> articles are published in journals from <strong>the</strong> four mentioned categories. Fur<strong>the</strong>r, we wish
to examine <strong>the</strong> quality <strong>of</strong> publications. Typically, <strong>the</strong> best scientific research is published in <strong>the</strong> most
prestigious scientific journals (Bonner et al., 2006; Reidpath and Allotey, 2010). The quality <strong>of</strong> journals
can be measured in two ways. The first is qualitative and is usually based on perception i.e. <strong>the</strong>
perception <strong>of</strong> researchers regarding <strong>the</strong> quality <strong>of</strong> a journal within a certain category or field. The
second is quantitative and is usually based on <strong>the</strong> number <strong>of</strong> citations <strong>of</strong> an individual journal (Bonner
et al., 2006).
3. Research methodology
The method deployed is a multiple case study. For comparative reasons we investigate two business
schools which are different with respect to <strong>the</strong> process <strong>of</strong> interest, i.e. organizational strategy. The
analysis is conducted exclusively using publicly available data hence we are allowed to use real
names <strong>of</strong> case organizations. The Faculty <strong>of</strong> Economics <strong>of</strong> <strong>the</strong> University <strong>of</strong> Ljubljana is a business
161

Simon Cadez and Vlado Dimovski
school with a long tradition, dominant position within Slovenia and international recognition. The
Faculty <strong>of</strong> Management <strong>of</strong> <strong>the</strong> University <strong>of</strong> Primorska on <strong>the</strong> o<strong>the</strong>r hand is a young school without
tradition, but aiming to secure its recognition both domestically and internationally.
A bibliometric method was used to measure research productivity. As in many prior studies (Abramo
et al., 2008; Van Raan, 2005), it has been operationalized by measuring articles published in <strong>the</strong>
period 2000 –2009 in journals included in <strong>the</strong> SCI/SSCI database. Articles published in journals
included in <strong>the</strong> SCI/SSCI database are available in <strong>the</strong> ISI Web <strong>of</strong> Knowledge database, which is
owned by ThomsonReuters and from which also <strong>the</strong> SICRIS database (Slovenian Current Research
Information System) obtains data. In this study a two-phase approach was applied for data capture.
First, at <strong>the</strong> beginning <strong>of</strong> 2010 we identified all researchers employed at <strong>the</strong> business schools in
question. This was done on <strong>the</strong> basis <strong>of</strong> <strong>the</strong> publicly available data on <strong>the</strong> websites <strong>of</strong> <strong>the</strong> individual
faculties about <strong>the</strong> teachers employed <strong>the</strong>reby. The data on <strong>the</strong> number <strong>of</strong> employees are presented
in Table 1. The second step involved looking up each individual researcher in <strong>the</strong> Web <strong>of</strong> Knowledge
database and, in order to confirm <strong>the</strong> data, also in <strong>the</strong> SICRIS database. From <strong>the</strong> databases a list
was compiled <strong>of</strong> all articles that individual researchers published in <strong>the</strong> examined period 2000 –2009
in journals included in <strong>the</strong> SCI/SSCI database; with regard to such, <strong>the</strong> following characteristics were
extracted: <strong>the</strong> title <strong>of</strong> <strong>the</strong> article, <strong>the</strong> year <strong>of</strong> publication, <strong>the</strong> name <strong>of</strong> <strong>the</strong> journal in which <strong>the</strong> article
was published, and <strong>the</strong> authors <strong>of</strong> <strong>the</strong> article.
Table 1: The number <strong>of</strong> researchers employed at <strong>the</strong> respective faculties at <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> year
2010
Faculty Number <strong>of</strong>
researchers
employed in 2010
FE UL 147
FM UP 97
Total 244
Source: The websites <strong>of</strong> <strong>the</strong> respective faculties.
A methodological limitation regarding <strong>the</strong> approach applied has to be taken into account: since <strong>the</strong>
number <strong>of</strong> employees at all <strong>the</strong> faculties changes from year to year, we did not include in <strong>the</strong> analysis
<strong>the</strong> published articles <strong>of</strong> those researchers who were no longer employed at <strong>the</strong> business schools
examined when <strong>the</strong> researchers employed <strong>the</strong>reby were identified, even if in <strong>the</strong> past <strong>the</strong>y had
published in journals included in <strong>the</strong> SCI/SSCI database. However, <strong>the</strong> analysis does include <strong>the</strong>
published articles <strong>of</strong> researchers who had been employed at a different institution during some <strong>of</strong> this
period. Despite this limitation regarding <strong>the</strong> methodology, <strong>the</strong> approach described provides a credible
summary <strong>of</strong> knowledge production in Slovenia and at individual faculties for several reasons: firstly,
<strong>the</strong> majority <strong>of</strong> researchers leaving <strong>the</strong> population examined resulted from retirements, with regard to
which older researchers in general do not publish much in <strong>the</strong> journals included in <strong>the</strong> SCI/SSCI
database; secondly, <strong>the</strong> majority <strong>of</strong> <strong>the</strong> researchers entering into this population resulted from <strong>the</strong>
employment <strong>of</strong> young teaching assistants, who in general do not publish much in journals included in
<strong>the</strong> SCI/SSCI database; thirdly, in Slovenia, those employed in academia are not very mobile since
<strong>the</strong> majority <strong>of</strong> teachers in higher education remain employed for <strong>the</strong>ir entire career at <strong>the</strong> same
institution; fourthly, a similar approach is used in o<strong>the</strong>r countries as well (see Reidpath and Allotey,
2010).
Organizational strategy was deduced from <strong>the</strong> published statements.
FM UP
The Faculty <strong>of</strong> Management Koper will become one <strong>of</strong> Europe's top quality business schools, with a
recognisable impact on economic success as well as on <strong>the</strong> wider socio-economic environment <strong>of</strong> <strong>the</strong>
Central and South-Eastern Europe, and <strong>the</strong> Mediterranean.
FE UL
By 2013 <strong>the</strong> Faculty <strong>of</strong> Economics will be internationally recognised for excellence and be <strong>the</strong> best
and most prominent international centre for students, teaching staff, researchers and <strong>the</strong> business
community involved in business and economics in Slovenia and South-East Europe.
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4. Results
Simon Cadez and Vlado Dimovski
Table 2 presents <strong>the</strong> number <strong>of</strong> published papers for each year and for each business school. In <strong>the</strong>
first half <strong>of</strong> <strong>the</strong> period examined, <strong>the</strong> two faculties were quite equal with regard to <strong>the</strong> number <strong>of</strong>
publications. However, in 2005 <strong>the</strong> FE UL significantly “accelerated” and this was especially true for
<strong>the</strong> last two years <strong>of</strong> <strong>the</strong> period examined.
Table 2: The number <strong>of</strong> articles published according to faculty in <strong>the</strong> period 2000 – 2009
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 TOTAL
FE UL 6 7 6 8 7 23 18 26 50 46 197
FM UP 4 9 4 5 7 8 11 17 22 23 110
TOTAL 10 16 10 13 14 31 29 43 72 69 307
Table 3 presents <strong>the</strong> share <strong>of</strong> active researchers and <strong>the</strong> number <strong>of</strong> articles published per active
researcher.
Table 3: The share <strong>of</strong> active researchers and <strong>the</strong> number <strong>of</strong> articles published per active researcher
Faculty The number
<strong>of</strong> articles
published
The number
<strong>of</strong>
researchers
employed
The number
<strong>of</strong> active*
researchers
The
percentage
<strong>of</strong> active*
researchers
The number
<strong>of</strong> articles per
researcher
The
number <strong>of</strong>
articles per
active
researcher
FE UL 197 147 94 63.9 1.34 2.10
FM UP 110 97 34 35.0 1.13 3.24
Total 307 244 128 53.0 1.26 2.40
* Active researchers are those who published at least one article in <strong>the</strong> period examined.
The share <strong>of</strong> active researchers at <strong>the</strong> three faculties barely exceeds one half, which means that
almost one half <strong>of</strong> <strong>the</strong> researchers employed did not have even one article published in a journal from
<strong>the</strong> SCI/SSCI database in <strong>the</strong> ten-year period examined. However, such share differs greatly
according to faculty. While almost two thirds <strong>of</strong> researchers at <strong>the</strong> FE UL are active researchers, <strong>the</strong>
share at FM UP amounts to only one third. For <strong>the</strong> sake <strong>of</strong> comparison, we also calculated <strong>the</strong>
number <strong>of</strong> articles per active researcher, according to which <strong>the</strong> FM UP was <strong>the</strong> most successful, with
its few active researchers being, in relative terms, <strong>the</strong> most productive. The 307 published papers
were published in a range <strong>of</strong> 132 different journals from <strong>the</strong> SCI/SSCI database. The researchers
published only one article in 73 <strong>of</strong> <strong>the</strong>se journals, while in <strong>the</strong> remaining 59 journals <strong>the</strong>y published at
least 2 articles. Table 4 presents <strong>the</strong> journals in which <strong>the</strong> researchers most frequently published.
Those journals are listed in which researchers published at least 6 articles in <strong>the</strong> period examined.
Table 4: The number <strong>of</strong> articles published by individual journals
Name <strong>of</strong> <strong>the</strong> journal Percentag
e <strong>of</strong> all
articles
Category according to
SCI/SSCI*
Impact
factor
2009
Rank
within
category
EASTERN EUROPEAN ECONOMICS 8.67% economics 0.19 224/245
KYBERNETES 6.51% computer science, cybernetics 0.31 18/19
POST-COMMUNIST ECONOMIES 5.06% economics 0.20 222/245
EKONOMICKÝ ČASOPIS 3.61% economics 0.24 215/245
INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2.41% computer science, interdisc. 1.53 34/95
DRUŠTVENA ISTRAŽIVANJA 2.17% social issues 0.06 34/35
TRANSFORMATIONS
ECONOMICS
IN BUSINESS & 2.17% business 1.20 49/87
CYBERNETICS AND SYSTEMS 1.93% computer science, cybernetics 0.78 12/19
STROJNIŠKI VESTNIK-JOURNAL OF 1.69% engineering, mechanical 0.53 70/115
MECHANICAL ENGINEERING
INTERNATIONAL JOURNAL OF PRODUCTION 1.45% oper. res. & management 2.07 12/73
ECONOMICS
science
JOURNAL FOR EAST EUROPEAN 1.45% management 0.35 97/112
MANAGEMENT STUDIES
ZBORNIK RADOVA EKONOMSKOG 1.45% business 0.19 81/87
FAKULTETA U RIJECI
*According to <strong>the</strong> SCI/SSCI classification system, some journals are listed in two or even three categories. For
example, <strong>the</strong> journal Europe-Asia Studies is listed in <strong>the</strong> categories economics, political science, and area
studies.
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Simon Cadez and Vlado Dimovski
It is apparent from Table 5 that researchers primarily publish in journals in <strong>the</strong> field <strong>of</strong> economics and
business sciences, while journals from <strong>the</strong> field <strong>of</strong> computer science are also strongly represented.
Quite highly ranked in second position is <strong>the</strong> journal Kybernetes, in which 6.5% <strong>of</strong> all <strong>the</strong> articles in
<strong>the</strong> period in question were published. The list also shows a considerable geographic orientation <strong>of</strong>
<strong>the</strong> research, as a considerable number <strong>of</strong> journals have a (south)eastern <strong>European</strong> orientation,
evident from ei<strong>the</strong>r <strong>the</strong> name <strong>of</strong> <strong>the</strong> journal (e.g. Eastern <strong>European</strong> Economics, Post-Communist
Economies, etc.) or <strong>the</strong> country <strong>of</strong> <strong>the</strong> publisher (e.g. Ekonomický Časopis – Slovakia,
Transformations in Business and Economics – Lithuania, etc.). Fur<strong>the</strong>r, <strong>the</strong> relative rank within
category shows that most <strong>of</strong> <strong>the</strong>se journals can not be regarded as high quality.
5. Conclusion and discussion
A comparison <strong>of</strong> <strong>the</strong> faculties shows that <strong>the</strong> FE UL leads in knowledge creation, which is, however, a
consequence <strong>of</strong> <strong>the</strong> fact that it employs <strong>the</strong> largest number <strong>of</strong> researchers. Calculated per researcher,
past research productivity does not differ significantly between <strong>the</strong> two schools. Of significant concern
is research productivity <strong>of</strong> individual researchers. Almost one half <strong>of</strong> <strong>the</strong>m did not have even one
article published in a journal from <strong>the</strong> SCI/SSCI database in <strong>the</strong> ten-year period. However, <strong>the</strong>
percentage differs significantly according to faculty. While at <strong>the</strong> FE UL one third <strong>of</strong> <strong>the</strong> researchers
were inactive, at <strong>the</strong> FM UP <strong>the</strong> share <strong>of</strong> such is two thirds. At <strong>the</strong> FE UL inactive researchers are
primarily comprised <strong>of</strong> teaching assistants who are at <strong>the</strong> beginning <strong>of</strong> <strong>the</strong>ir research career, whereas
at <strong>the</strong> FM UP even some researchers employed as docents and pr<strong>of</strong>essors are inactive. It is possible
to conclude from this that at <strong>the</strong> FM UP habilitation conditions are less strict than at <strong>the</strong> FE UL, and
fur<strong>the</strong>rmore that habilitation conditions represent an important factor for <strong>the</strong> research productivity <strong>of</strong>
individual researchers and institutions.
Researchers had articles published in 164 different journals from <strong>the</strong> SCI/SSCI database. The most
common outlets were Eastern <strong>European</strong> Economics, Kybernetes, Post-Communist Economies,
Ekonomicky Casopis, and Industrial Management & Data Systems. It seems that that <strong>the</strong> knowledge
created is to a large degree limited to <strong>the</strong> Eastern <strong>European</strong> region which is relatively consistent with
<strong>the</strong> organizational strategies <strong>of</strong> <strong>the</strong> two schools. Also <strong>of</strong> great concern is <strong>the</strong> quality <strong>of</strong> <strong>the</strong> knowledge
created becouse most papers were published in journals <strong>of</strong> relatively low quality.
The findings <strong>of</strong> this research have very important implications for <strong>the</strong> research strategies <strong>of</strong> individual
faculties as well as <strong>of</strong> <strong>the</strong> entire public research system. The major problems are inactivity and <strong>the</strong>
quality <strong>of</strong> newly created knowledge. Such conduct could lead to a conclusion that in Slovenia a
publication inflation effect is present, such that articles lack quality and thus have no significant
influence on <strong>the</strong> international scientific community (Reidpath and Allotey, 2010; Van Raan, 2005).
The implications <strong>of</strong> <strong>the</strong> findings <strong>of</strong> this research might be even more important with regard to <strong>the</strong>
competitiveness <strong>of</strong> organizations in Slovenia and <strong>of</strong> <strong>the</strong> entire state. With regard to <strong>the</strong> <strong>of</strong>ten
confirmed connection between <strong>the</strong> quality <strong>of</strong> research work and <strong>the</strong> competitiveness <strong>of</strong> companies
and economies (Marrano et al., 2009; Moed, 2008), it is a matter <strong>of</strong> significant concern that Slovene
companies only rarely obtain <strong>the</strong> knowledge <strong>the</strong>y need from <strong>the</strong> faculties, but that <strong>the</strong>y ra<strong>the</strong>r rely on
<strong>the</strong>ir own brainpower as well as on institutes (Gaspari, 2010). This entails that companies do not
perceive knowledge created at faculties as useful and beneficial, which fur<strong>the</strong>r supports <strong>the</strong> findings
<strong>of</strong> this research. Also <strong>of</strong> great importance are <strong>the</strong> implications <strong>of</strong> this study for <strong>the</strong> higher education
system. Empirical studies have determined a positive correlation between <strong>the</strong> quality <strong>of</strong> research work
and academic work (Arnold, 2010; Bonaccorsi et al., 2006, Lindsay et al., 2002), which is why low
quality research work can entail that teachers in higher education are not very competent at
transferring knowledge to students, who are <strong>the</strong> future creators <strong>of</strong> added value.
The results <strong>of</strong> <strong>the</strong> study must be interpreted in <strong>the</strong> light <strong>of</strong> its limitations. Content-related and technical
limitations <strong>of</strong> <strong>the</strong> bibliometric methods have been discussed at length in o<strong>the</strong>r studies (Abramo et al.,
2008; Horrobin, 1990; Moxham and Anderson, 1992; Van Raan, 2005) and will, <strong>the</strong>refore, not be
repeated here. A specific technical limitation <strong>of</strong> this study is related to <strong>the</strong> fact that <strong>the</strong> data was
captured in two phases, <strong>the</strong> reasons for which were explained in <strong>the</strong> chapter on methodology.
The study has pointed to a number <strong>of</strong> challenges regarding future research, <strong>of</strong> which here we will list
only a few. Firstly, why is such a large portion <strong>of</strong> researchers not active in <strong>the</strong> field <strong>of</strong> research?
Secondly, which are <strong>the</strong> productivity factors <strong>of</strong> individual researchers? Thirdly, why do Slovene
researchers not create more quality and cutting-edge knowledge? Fourthly, why are <strong>the</strong>re important
164

Simon Cadez and Vlado Dimovski
differences in knowledge production between <strong>the</strong> faculties? The final objective <strong>of</strong> such research could
be to find a systemic solution for fostering <strong>the</strong> creation and transfer <strong>of</strong>, above all, quality and cuttingedge
knowledge, which represent <strong>the</strong> only path to long-term competitiveness and welfare in a
knowledge-based society.
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165

Why Should I Share my new ideas? Cultural Barriers to
Innovation Spreading
Francesco Calza, Rossella Canestrino and Chiara Cannavale
Department <strong>of</strong> Business Administration, Università degli Studi di Napoli
Par<strong>the</strong>nope, Italy
francesco.calza@unipar<strong>the</strong>nope.it
rossella.canestrino@unipar<strong>the</strong>nope.it
chiara.cannavale@unipar<strong>the</strong>nope.it
Abstract: Both globalisation and <strong>the</strong> fast growth <strong>of</strong> international competition impel firms to continuously innovate
in order to sustain <strong>the</strong>ir own competitive advantage. Firms’ innovative capacity depends on <strong>the</strong>ir ability to grasp
new knowledge through cooperative arrangements, thus <strong>the</strong> effectiveness <strong>of</strong> knowledge transfer became more
and more important in allowing both organizations’ and local contexts growth. When firms are deeply embedded
in a given context, <strong>the</strong>y may relate with local actors; complex learning process can arise thanks to institutional
support, and cultural homogeneity, thus fostering <strong>the</strong> diffusion <strong>of</strong> new and valuable knowledge and <strong>the</strong> growth <strong>of</strong>
local context, at least. On <strong>the</strong> contrary, when an effective knowledge transfer among <strong>the</strong> parties does not exist,
an innovation does not spread its effects over <strong>the</strong> context, thus advantage <strong>the</strong> only firm that realised it.Not
surprising, <strong>the</strong>refore, firms usually locate into those areas where a positive entrepreneurial atmosphere already
exists. An existing asset <strong>of</strong> “contextual knowledge” attract firms; after located in given knowledge and relational
systems, firms may be able to support local growth by introducing, not only financial resources, but also new
knowledge and o<strong>the</strong>r valuable intangibles (i.e.: notoriety). Knowledge transfer is a very complex and <strong>of</strong>ten
misunderstood process. Its effectiveness depends on <strong>the</strong> willingness <strong>of</strong> <strong>the</strong> involved parties to transfer and
acquire knowledge. Both <strong>the</strong> propensity to transfer and to acquire new knowledge is culturally embedded; as a
consequence innovation spreading is affected by culture, too. National culture influences individuals’ inclination to
take risks, to share responsibilities and to accept o<strong>the</strong>rs’ idea, determining consequently firms’ capacity to find
and evaluate external opportunities.According to <strong>the</strong> above considerations, our paper aims at analyzing in depth
<strong>the</strong> influence <strong>of</strong> culture on innovation spreading and particularly on donor’s intentionality, and receiver’s
inclination to transfer and share <strong>the</strong>ir knowledge. As a result, a new interpretative model <strong>of</strong> innovation spreading
systems will be proposed.
Keywords: innovation spreading, knowledge transfer, culture, innovation systems
1. Introduction
Globalisation and <strong>the</strong> fast growth <strong>of</strong> international competition emphasise <strong>the</strong> linkage between
innovativeness and competitiveness <strong>of</strong> both firms and contexts (territory). Firms are expected to
continuously innovate, but innovating alone is very difficult, besides that costly. Cooperation becomes
consequently very important. It gives firms <strong>the</strong> opportunity to share risks and costs, as well as to
reduce <strong>the</strong> innovation time, through <strong>the</strong> sharing <strong>of</strong> resources.
When firms are deeply embedded in a given context <strong>the</strong>y may advantage <strong>of</strong> <strong>the</strong> knowledge locally
developed. At same time, <strong>the</strong>y may also support <strong>the</strong> competitive growth <strong>of</strong> <strong>the</strong> local system by
fostering <strong>the</strong> emergence <strong>of</strong> a “virtuous knowledge cycle”. Firms’ relationship with local actors,
institutional support, and cultural homogeneity (Lundvall, 1992) promote <strong>the</strong> arising <strong>of</strong> complex
learning process, driving firms to easily innovate. Although knowledge transfer is very important, it is a
very complex process, not automatically or easy to start. It depends on <strong>the</strong> capacity <strong>of</strong> local actors to
establish relationships, as well as on <strong>the</strong>ir ability to recognise, acquire, and to exploit <strong>the</strong> knowledge
coming out from external sources (Wang and Blomstrom, 1992; Cohen and Levinhal, 1989).
As a matter <strong>of</strong> fact, innovation and knowledge are strongly related: local availability <strong>of</strong> know-how is
<strong>the</strong> background for setting up <strong>the</strong> learning process; which can lead to innovation. Every innovation
arises and includes certain knowledge; at same time, every innovation is responsible for new
knowledge diffusion (Grant, 1991). From a systemic perspective, once generated, an innovation
has to be transfer by <strong>the</strong> donor, absorbed by <strong>the</strong> receiver and locally diffused. Innovation
diffusion is a sine qua no condition for <strong>the</strong> emergence <strong>of</strong> a competitive innovation system able to
survive over <strong>the</strong> time: <strong>the</strong> pre-existence, in a given geographical area, <strong>of</strong> firms’ networks able to
transfer knowledge one to each o<strong>the</strong>r, provide <strong>the</strong> best background for new entrepreneurial
activities, as well as for <strong>the</strong> growth <strong>of</strong> local system competitiveness.
According to <strong>the</strong> above considerations, our paper aims at investigating <strong>the</strong> conditions under
which knowledge transfer and diffusion may be limited at local level. The effectiveness <strong>of</strong>
knowledge transfer depends on two main factors: <strong>the</strong> donor’s intentionality to transfer his own
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knowledge, and receiver’s inclination to acquire donor’s knowledge. Both <strong>the</strong>se factors are
strongly influenced by culture. However, as far as many contributions exist on <strong>the</strong> linkage between
culture and innovation, nobody has still defined which cultural dimensions affect donor’s intentionality,
and receiver’s inclination. According to <strong>the</strong> above considerations, we aim at deeply investigate how
Future Orientation and Uncertainty Avoidance affect Knowledge Transfer between donor and
receiver.
We particularly support <strong>the</strong> following hypo<strong>the</strong>ses:
Hp.1: Future Orientation has a positive effect on donor’s intentionality to knowledge
transfer.
Hp.2: Uncertainty Avoidance has a positive effect on receiver’s learning inclination.
Our hypo<strong>the</strong>ses have been tested through an explorative analysis based on <strong>the</strong> GLOBE project
(House et al., 2004). More precisely, we verified <strong>the</strong> existence <strong>of</strong> a positive relation between: a) future
orientation and donor’s intentionality to transfer his own knowledge, and b) uncertainty avoidance and
receiver’s inclination to accept donor’s knowledge.
According to our research an interpretative model <strong>of</strong> <strong>the</strong> innovation spreading systems has been
developed, at least. Our model arises from <strong>the</strong> crossing <strong>of</strong> both donor’s intentionality, and receiver’s
inclination to transfer knowledge, from which 4 innovative systems may arise: Fruitless systems,
milieu innovateur, never-born systems and catching up systems.
The developed model helps us to map different countries by taking back, each <strong>of</strong> <strong>the</strong>m to <strong>the</strong> right
quadrant (innovative system). At a moment, we have been mapped 15 countries, belonging to <strong>the</strong>
GLOBE 1 project, but new countries would be added in <strong>the</strong> future.
2. The obstacles to innovations’ diffusion
After created, by internal learning process or by acquisition from external sources, innovation needs
to be transferred, in order to be locally diffused. However, while very important, knowledge transfer is
difficult to manage because <strong>of</strong> both internal, and external features. Both <strong>the</strong> nature <strong>of</strong> knowledge, tacit
vs. explicit, and casual ambiguity have a deep impact on knowledge transfer: Innovations refer not
only to technological, patentable or codified knowledge; on <strong>the</strong> contrary, <strong>the</strong>y <strong>of</strong>ten lie in tacit
knowledge, <strong>the</strong> last one embedded in social norms and practices. Very <strong>of</strong>ten, knowledge is deeply
embedded in firms’ products (Inkpen, 1998); it cannot be easily reproduced, because <strong>of</strong> <strong>the</strong> “casual
ambiguity” that characterizes <strong>the</strong> way inputs interact one to each o<strong>the</strong>r (Lippman and Rumelt, 1982;
Nonaka, 1994; Nonaka e Takeuchi, 1995; Szulanski, 1996; Spender, 1996).
Toge<strong>the</strong>r with knowledge characteristics, innovation transfer and diffusion may be also affected by
actors’ propensity to transfer and share what <strong>the</strong>y already know; individuals’ inclination to transfer and
share <strong>the</strong>ir knowledge is influenced by <strong>the</strong> kind <strong>of</strong> culture prevailing in a given context (Van
Everdingen and Waarts, 2003; Steensma et al., 2000; Abegglen and Stalk, 1998; Calvelli, 1998;
Tiessen, 1997).
2.1 Donor intentionality and <strong>the</strong> “not invented here” syndrome
Firms’ competitive advantage depends on <strong>the</strong> presence, inside <strong>the</strong> organization, <strong>of</strong> unique and
valuable know-how. This is <strong>the</strong> reason why both managers and firms tend to retain <strong>the</strong>ir own
knowledge and to do not diffuse <strong>the</strong> innovations <strong>the</strong>y have already developed. Many managers look
at knowledge as a source <strong>of</strong> power; thus limiting its diffusion both within and outside <strong>the</strong> firm. Unique
and valuable knowledge is leveraged in order to get personal advantages, like <strong>the</strong> continuity <strong>of</strong> <strong>the</strong>ir
career or <strong>the</strong> access to new and more powerful job positions.
At same time, a lot <strong>of</strong> firms limit knowledge transfer outside <strong>the</strong> organization, in order to avoid
imitation and <strong>the</strong> decline <strong>of</strong> monopolistic advantages. Very <strong>of</strong>ten firms under-evaluate <strong>the</strong> advantages
arising from collaboration and knowledge sharing. Every cooperative arrangement is difficult to start
1 The Global Leadership and Organizational Behaviour Effectiveness Research Project (GLOBE) is a multi-phase, multi-method
project in which researchers investigate 62 countries, grouped into ten cultural clusters, in order to analyse in depth <strong>the</strong>ir
different cultures. Cultural contexts are examined through nine dimensions (power distance, uncertainty avoidance, institutional
collectivism, in-group collectivism, gender egalitarianism, performance orientation, future orientation, human orientation,
assertiveness), which explain <strong>the</strong> different perception and acceptance <strong>of</strong> leadership within each context. Each dimension is
studied at two levels, in order to understand <strong>the</strong> practices and <strong>the</strong> values prevailing in each context, in order to highlight <strong>the</strong>
main cultural tendencies emerging within <strong>the</strong>m.
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and manage; it <strong>of</strong>ten require a lot <strong>of</strong> time to be established and it may be very expensive for all <strong>the</strong>
involved parties, so that managers consider economic disadvantages more important than <strong>the</strong>
benefits <strong>the</strong>y could obtain in <strong>the</strong> long time.
The “knowledge is power” syndrome is not <strong>the</strong> only obstacle to knowledge transfer and to <strong>the</strong>
innovation diffusion in a given context. Even when all <strong>the</strong> actors agree in sharing <strong>the</strong>ir knowledge, in
fact, <strong>the</strong> diffusion <strong>of</strong> innovations may be limited by <strong>the</strong> receiver’s capacity to accept and absorb <strong>the</strong>
knowledge coming from outside. We particularly refer to <strong>the</strong> so-called “not invented here” syndrome,
which may lead individuals to adopt defensive or inertial behavior, as well as to refuse external
knowledge, to hidden sabotage, and to reject both <strong>the</strong> adoption and <strong>the</strong> implementation <strong>of</strong> new
knowledge (Katz e Allen, 1982; Zaltman, Duncan e Holbeck, 1973).
The “not invented here” syndrome is <strong>of</strong>ten due to users’ disappointment and dissatisfaction:
individuals give little value to what <strong>the</strong> o<strong>the</strong>rs do, thus adopting inertial behaviors and obsolete
practices, <strong>the</strong> last ones inadequate to sustain firm’s competitive advantage, according to <strong>the</strong> new and
variable environmental conditions.
2.2 The role <strong>of</strong> <strong>the</strong> Absorptive Capacity
The Absorptive Capacity plays a very important role in every knowledge transfer process. It affects
receivers’ capability to acquire new knowledge, and to implement it. The concept was introduced by
Cohen and Levinthal (1990) to label <strong>the</strong> ability <strong>of</strong> <strong>the</strong> firm to evaluate, assimilate and use
outside knowledge for commercial ends. They defined <strong>the</strong> absorptive capacity construct as
“The firm’s ability to identify, assimilate and exploit knowledge from <strong>the</strong> environment” (1989, p.
569).Recognizing <strong>the</strong> relevance <strong>of</strong> a defined resource is <strong>the</strong> pre-condition for <strong>the</strong> absorption <strong>of</strong>
those information, that will be relevant for gaining a competitive advantage. According to <strong>the</strong>
authors, new knowledge adds to <strong>the</strong> old one through an association mechanism and events are
stored in memory according to <strong>the</strong>ir homogeneity with <strong>the</strong> already existing know-how (Van Wijk, Van
den Bosch e Volberda, 1999). On <strong>the</strong> contrary, learning is more difficult in novel domain, and when
<strong>the</strong> object <strong>of</strong> learning is not related to what individuals or organizations already know (Amburgey e Al-
Laham, 2001).
The underlined perspective avoids <strong>the</strong> possibility that cognitive asset may change over time, implying
a static approach to learning. In a more dynamic perspective, <strong>the</strong> relationship between absorptive
capacity and <strong>the</strong> know-how to be assimilated develops like a “virtuous cycle”, since <strong>the</strong> diversity
between new and old knowledge makes <strong>the</strong> learning process more difficult to develop; but learning
process itself widen one’s knowledge, thus fostering <strong>the</strong> adoption <strong>of</strong> new initiatives (Calvelli, 1998). In
addition, <strong>the</strong> absorptive capacity may be stimulated by firm’s R&D investments: R&D investments
allow cognitive spillover, supporting <strong>the</strong> “virtuous cycle” between absorptive capacity itself and
learning process. This is also coherent with Mowery, Oxley and Silverman’s (1996) perspective,
according to which <strong>the</strong> absorptive capacity come out by constant and long term firms’ knowledge
investments: when firms do not invest in R&D, <strong>the</strong>ir absorptive capacity cannot widen, and <strong>the</strong>ir
propensity to invest in <strong>the</strong> future will be limited too. As a consequence firms with a high absorptive
capacity tend to be more proactive than <strong>the</strong> o<strong>the</strong>rs.
3. National culture and innovation wide spreading
National culture influences individuals’ inclination to take risks, to share responsibilities and to accept
o<strong>the</strong>rs’ idea, determining firms’ capacity to find and evaluate opportunities. Many authors have
actually focused on <strong>the</strong> linkage between national culture and firms’ innovativeness.
Van Everdingen and Waarts (2003, 2005) focused on individualism vs. collectivism, power distance,
uncertainty avoidance, and masculinity vs. femininity (H<strong>of</strong>stede, 1980) and analyzed <strong>the</strong> innovative
behavior <strong>of</strong> 2000 <strong>European</strong> medium size firms, and particularly <strong>the</strong>ir inclination to adopt Enterprise
Resource Planning (ERP) systems. They found out that high level <strong>of</strong> uncertainty avoidance,
masculinity, and power distance negatively impact on <strong>the</strong> adoption <strong>of</strong> ERP systems.
On <strong>the</strong> same hand, Tiessen (1997), Calvelli (1998), and Abegglen and Stalk (1998) explained that
international cooperation can easily start in individualistic context, where <strong>the</strong> necessity to accept <strong>the</strong>
foreigner is justified by <strong>the</strong> perception <strong>of</strong> a great opportunity – thus reducing <strong>the</strong> risk <strong>of</strong> a closure
towards diversity, but that in collectivistic contexts co-operations, even if more difficult to start, are
longer and much stronger. These studies highlight <strong>the</strong> influence <strong>of</strong> National culture on knowledge
transfer and consequently on <strong>the</strong> widespread <strong>of</strong> innovation, which is our main interest. Even if easier
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to start, relationships involving individualistic players are unstable because <strong>the</strong>y tend to abandon <strong>the</strong>
cooperation when <strong>the</strong>y have reached <strong>the</strong>ir own interests.
O<strong>the</strong>r inquiries have highlighted a direct relationship between <strong>the</strong> implementation <strong>of</strong> s<strong>of</strong>tware and
individuals’ inclination to communicate in a formal way, communication style typical <strong>of</strong> low contexts
societies (Tosi, 1994). The difference between high context and low context societies is actually
dependent on <strong>the</strong> way people communicate and interact; high contexts societies, such as Italy, Japan
or China give a great importance to interpersonal relationships, which are considered <strong>the</strong> best way to
transfer and share information. On <strong>the</strong> contrary, in low contexts societies such as Germany, Swiss
and USA, information flows through formal mechanisms, such as reports, database, and Internet
(Morden, 1999). That’s why low context societies are more suitable to <strong>the</strong> adoption <strong>of</strong> ERP systems,
as noted by Van Everdingen and Waarts (2003).
4. An interpretative model
The reviewed literature is <strong>the</strong> starting point for fur<strong>the</strong>r researches. As far as innovation spreading is
very important, cross-cultural studies do not focus on this process, but on innovation itself. On <strong>the</strong>
contrary, we aim at understanding if national culture impacts on firms’ inclination to spread innovation,
and on <strong>the</strong>ir ability to accept, and implement outside-born innovations. The results <strong>of</strong> innovation
spreading depend on two subjective factors: donor’s intentionality to transfer his knowledge, and
receiver’s inclination to learn from <strong>the</strong> donor. Both donor’s intentionality and receiver’s propensity to
learn are shaped by national culture.
National culture can be studied according to different model and through many cultural dimensions.
Among <strong>the</strong> o<strong>the</strong>rs future orientation, and uncertainty avoidance (House et al., 2004) seem to affect
<strong>the</strong> effectiveness <strong>of</strong> a knowledge transfer process. According to <strong>the</strong> authors <strong>of</strong> <strong>the</strong> GLOBE project,
“Future orientation" can be meant as “<strong>the</strong> degree to which a collectivity encourages and rewards
future-oriented behaviors such as planning and delaying gratification” [p. 282]. Future oriented
societies tend to pursue economic success, evaluate success in <strong>the</strong> long term, have and pursue
strategic aims.
Uncertainty avoidance is "<strong>the</strong> extent to which a society, organization, or group relies on social norms,
rules, and procedures to alleviate <strong>the</strong> unpredictability <strong>of</strong> future events” [p. 30]. In uncertainty avoidant
societies people tend to avoid ambiguous situations, and to take calculated risks. Uncertainty
avoidance enables learning because learning is meant as <strong>the</strong> way to reduce uncertainty and
unknown.
Both <strong>the</strong>se dimensions are important for innovation wide spreading. Future orientation leads people to
plan and to scarify short-term goals in order to achieve bigger results in <strong>the</strong> long term. Uncertainty
avoidance leads people to learn and to develop innovation externally in order to reduce risks. Future
orientation and uncertainty avoidance speed up innovation wide spread, with positive effects for <strong>the</strong>
creation <strong>of</strong> local innovation systems.
According to <strong>the</strong> dimensions meaning, our hypo<strong>the</strong>ses are:
H.1: Future orientation has a positive effect on donor’s intentionality to knowledge
transfer.
H.2: Uncertainty avoidance has a positive effect on receiver’s learning inclination.
Future orientation and uncertainty avoidance are related to <strong>the</strong> main obstacles to innovation diffusion.
The knowledge is power syndrome can be reduced by donor’s awareness <strong>of</strong> <strong>the</strong> long-term
advantages deriving from <strong>the</strong> knowledge transfer. Future oriented people are more inclined to invest
in <strong>the</strong> long-term. They are able to evaluate <strong>the</strong> cognitive advantages and <strong>the</strong> innovation opportunities
coming out by cooperation.
Uncertainty avoidance reduces in stead <strong>the</strong> not invented here syndrome. If people are afraid <strong>of</strong> <strong>the</strong>
unknown and feel uncomfortable with risky activities, <strong>the</strong>y will be more inclined to learn, even when
learning implies <strong>the</strong> acceptance <strong>of</strong> o<strong>the</strong>rs’ knowledge. Catching knowledge is <strong>the</strong> most suitable way to
reduce uncertainty and to manage risks, even if new knowledge comes from an external source.
Summing up <strong>the</strong> underlined considerations, we argue that a) <strong>the</strong> presence <strong>of</strong> a high level <strong>of</strong> Future
Orientation turns into a high individual’s intentionality to knowledge transfer (a low Future Orientation
means low propensity to transfer knowledge); b) <strong>the</strong> presence <strong>of</strong> a high level <strong>of</strong> Uncertainty
Avoidance turns into a high individual’s inclination to accept (and absorb) new knowledge.
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Donor’s
intentionality
High
Low
Fruitless systems
Learning break down
Never‐born systems
Explicit knowledge
transfer
Low
R&D
Investments
Receiver’s inclination
Milieu innovateur
Local innovative spill over
“Catching up” systems
Reverse engeneering and
imitation
Figure 1: The drivers <strong>of</strong> innovation spreading: From <strong>the</strong> fruitless systems to <strong>the</strong> milieu innovateurs
When a high donor’s intentionality crosses with a high propensity to acquire knowledge <strong>the</strong> best
conditions develop for <strong>the</strong> spreading <strong>of</strong> innovation. The last may be very wide, thus fostering <strong>the</strong>
arising <strong>of</strong> a milieu innovateur. This situation arises when both <strong>the</strong> parties involved into a knowledge
transfer process show a high propensity to transfer and to absorb knowledge: donor is future oriented,
so he can understand all <strong>the</strong> advantages connected to knowledge transfer, by considering receiver’s
learning as a long-term investment. At <strong>the</strong> same time, uncertainty avoidance leads <strong>the</strong> receiver to
acquire new knowledge, in order to reduce <strong>the</strong> risks coming out from an innovative activity. As a
consequence, <strong>the</strong> receiver accepts <strong>the</strong> know-how coming from external sources, without rejecting it.
The second quadrant is characterized by a high intentionality <strong>of</strong> <strong>the</strong> donor, and a low inclination <strong>of</strong> <strong>the</strong>
receiver. The cross <strong>of</strong> <strong>the</strong>se two factors leads to what may be reasonably considered as a sterile
network: even if <strong>the</strong> donor wants to transfer his knowledge, an effective learning process is limited by
<strong>the</strong> receiver low capacity to accept and absorb <strong>the</strong> external information. The receiver may be also
unable to understand <strong>the</strong> importance <strong>of</strong> knowledge itself and <strong>the</strong> lack <strong>of</strong> learning poses a barrier to
innovation implementation and improvements.
The weak solution is shown by <strong>the</strong> third quadrant, where a low intentionality crosses with a low
learning inclination letting a never-born system arises. The creation <strong>of</strong> a real system is avoided by <strong>the</strong>
lack <strong>of</strong> future orientation, and <strong>of</strong> uncertainty avoidance. The donor does not recognize <strong>the</strong> value <strong>of</strong>
knowledge transfer, he protect his innovation and wants to exploit it himself. At <strong>the</strong> same time, <strong>the</strong>
receiver is not inclined to learn from <strong>the</strong> outside. Ei<strong>the</strong>r he has a strong belief in his capability, ei<strong>the</strong>r
he is low inclined to learn. In <strong>the</strong> last case, knowledge is not recognized as a core competence. Under
<strong>the</strong> underlined circumstances a virtuous cycle knowledge- innovation does not arise; local context
does not experience <strong>the</strong> development opportunities coming from innovation spreading and it is not
able to attract new investments.
The fourth quadrant (low intentionality, high learning inclination) is interesting to understand <strong>the</strong>
dangers deriving from a lack <strong>of</strong> future orientation. In this case, <strong>the</strong> donor has a low intentionality to
knowledge transfer. He would like to keep <strong>the</strong> advantages deriving from <strong>the</strong> innovation and he does
not believe in <strong>the</strong> system support. Probably, he looks at <strong>the</strong> relationship with <strong>the</strong> receiver in an
opportunistic way. He transfers to <strong>the</strong> receiver <strong>the</strong> minimum knowledge he can, in order to preserve
<strong>the</strong> control. However, <strong>the</strong> receiver is very inclined to learn. He refuses uncertainty and risks and
considers learning as <strong>the</strong> best way to overcome <strong>the</strong> unknown. He wants to fill <strong>the</strong> knowledge gap he
has with respect to <strong>the</strong> donor, and this is a big push to learn. The cross <strong>of</strong> <strong>the</strong>se two factors can
generate catching up systems, typical <strong>of</strong> emerging countries, where local players learn simply by
observing foreign investors, and by imitating <strong>the</strong>ir products.
The different situations explained by our interpretative model have been empirically verified, by using
<strong>the</strong> Globe’s country classification. According to <strong>the</strong> Globe’s scores, each country shows a different
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High

Francesco Calza, Rossella Canestrino and Chiara Cannavale
level <strong>of</strong> Future Orientation and Uncertainty Avoidance. Since <strong>the</strong> existing relation between <strong>the</strong>
mentioned cultural dimensions and <strong>the</strong> propensity <strong>of</strong> <strong>the</strong> donor and <strong>the</strong> receiver to transfer and
acquire knowledge, each country may reasonably represent different conditions for innovation
systems, as well as for <strong>the</strong> arising <strong>of</strong> a different innovative system. The following table shows
GLOBE’s “as is” scores for both Future Orientation, and Uncertainty Avoidance.
Table 1:GLOBE’s findings – as is scores
Future orientation Uncertainty
avoidance
Countries As is scores As is scores
Denmark 4.44 5.22
Finland 4.24 5.02
Sweden 4.39 5.32
Germany 4.27 5.16
Brazil 3.81 3.60
Egypt 3.86 4.06
Philippines 4.15 3.89
Hungary 3.21 3.12
Turkey 3.74 3.63
Greece 3.40 3.39
Morocco 3.26 3.65
Italy 3.25 3.79
China 3.75 4.94
France 3.48 4.43
New Zeeland 3.47 4.75
Mean 3.85 4.16
Standard
0.46 0.60
deviation
Source: House et al., 2004
The characteristics <strong>of</strong> innovative systems can be found in Denmark, Sweden, Finland, and Germany,
which according to <strong>the</strong> GLOBE have a high level <strong>of</strong> both future orientation and uncertainty avoidance.
According to <strong>the</strong> <strong>European</strong> Innovation Scoreboard 2009, <strong>the</strong>se countries are rich <strong>of</strong> innovation
systems, too. They are characterized by a high presence <strong>of</strong> innovative firms and important spillovers,
so that in specific geographical area, a high concentration <strong>of</strong> innovative firms exists.
Fruitless systems exist in stead in Brazil, Philippines, and Egypt, all countries characterized by a high
level <strong>of</strong> future orientation, and a low level <strong>of</strong> uncertainty avoidance. Even if government attracts
foreign firms and high tech multinationals, spillovers are very limited. R&D investments could reach
better results if local firms gave more importance to learning and cooperation, and policy maker
should look for <strong>the</strong> right mechanism to improve receivers’ inclination.
The worst situation is that <strong>of</strong> <strong>the</strong> never-born systems, that is areas in which innovative systems
difficulty arise because firms are not inclined to transfer <strong>the</strong>ir knowledge and to cooperate. Even <strong>the</strong>se
areas can present high R&D investments, but <strong>the</strong>y are above all public investments and reach low
results. This kind <strong>of</strong> situation can be observed in Hungary, Turkey, Greece, Morocco, and Italy. All
<strong>the</strong>se countries show a preference towards low tech activities, <strong>the</strong>y keep focused on traditional
sectors. Their belonging to this quadrant is coherent with Globe’s findings. All <strong>the</strong> three countries
show actually a low level <strong>of</strong> both future orientation, and uncertainty avoidance.
Last, but not least <strong>the</strong> hypo<strong>the</strong>sis that catching up systems arise in countries characterized by a low
level <strong>of</strong> future orientation, and a high level <strong>of</strong> uncertainty avoidance is confirmed, too. This kind <strong>of</strong>
systems is connected more to receiver’s capability to catch knowledge, than to donor’s intention to
transfer it. This is typical <strong>of</strong> areas characterized by a high level <strong>of</strong> entrepreneurship, by a high birth
rate <strong>of</strong> firms, even small firms, which develop as followers or suppliers <strong>of</strong> large innovative firms, such
as in France, and in New Zeland. On <strong>the</strong> same hand, catching up systems arise when international
activities delocalize <strong>the</strong>ir activities in countries, and local firms start to produce <strong>the</strong>ir products
independently, to learn foreigners’ technology, instead <strong>of</strong> producing only on demand and keeping
focused on <strong>the</strong> final output, such as in China.
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Donor’s
intentionality
High
Low
Fruitless systems
Brazil, Philippines, Egypt
Never‐born systems
Hungary, Turkey, Greece,
Morocco, Italy
Low
R&D
Investments
Receiver’s inclination
Figure 2: The drivers <strong>of</strong> innovation spreading: A country perspective
5. Conclusions
Milieu innovateur
Denmark, Finland,
Sweden, Germany
“Catching up” systems
China, France, New
Zeland
The capability to innovate can be rightly considered as <strong>the</strong> main source <strong>of</strong> competitive advantage,
both for firms and countries. Because <strong>of</strong> <strong>the</strong> increasing competition and <strong>the</strong> high level <strong>of</strong>
environmental turbulence, firms have to fasten in developing innovation and in deriving pr<strong>of</strong>its from its
spreading. Above all for territories, <strong>the</strong> spreading <strong>of</strong> innovation is a unique guarantee <strong>of</strong> welfare and
productivity.
In order to better understand <strong>the</strong> nature <strong>of</strong> innovation spreading and it functioning, our research has
been focused on <strong>the</strong> players <strong>of</strong> <strong>the</strong> process more than on mechanism. We are interested in
understanding <strong>the</strong> conditions upon which an effective knowledge transfer may develop, thus fostering
innovation systems born. As we noted above, knowledge transfer is a very complex and <strong>of</strong>ten
misunderstood process. Its effectiveness depends not only on <strong>the</strong> existence <strong>of</strong> knowledge “casual
ambiguity”, but also on <strong>the</strong> willingness <strong>of</strong> <strong>the</strong> involved parties to transfer and acquire knowledge. Both
<strong>the</strong> propensity to transfer and to acquire new knowledge is culturally embedded; as a consequence
innovation spreading is affected by culture, too.
As far as many contributions exist on <strong>the</strong> linkage between culture and innovation, nobody has defined
cultural dimensions, which could speed up, or avoid, innovation widespread. Given that innovation
spreading depends on two dimensions: donor’s intentionality and receiver’s inclination to learn, our
research aimed to highlight <strong>the</strong> cultural dimensions, which mainly affect this process. We focused on
two dimensions <strong>of</strong> <strong>the</strong> Globe project: Future Orientation, and Uncertainty Avoidance.
Future Orientation impels people to plan and to consider long terms benefits more important than <strong>the</strong>
short terms ones.
Uncertainty avoidance leads people to refuse ambiguity. It is positively related to learning, since
people used to consider knowledge as a way to reduce <strong>the</strong> uncertainty <strong>of</strong> what <strong>the</strong>y do not know, as
well as <strong>the</strong> better way for managing risks.
We hypo<strong>the</strong>sized a positive relationship between future orientation and donor’s intentionality, on one
side, and between uncertainty avoidance and receiver’s inclination to learn from <strong>the</strong> donor, on <strong>the</strong>
o<strong>the</strong>r.
Crossing donor’s intentionality and receiver’s inclination to learn, we obtain four kinds <strong>of</strong> innovation
systems. After developed our <strong>the</strong>oretical model, we used <strong>the</strong> Globe country classification in order to
identify those countries that show a high (or low) donor intentionality to transfer knowledge and which
ones present a high (or low) propensity <strong>of</strong> local players to accept knowledge. After considering <strong>the</strong>
cultural characteristics <strong>of</strong> <strong>the</strong> selected countries, and <strong>the</strong>ir impact on <strong>the</strong> effectiveness <strong>of</strong> local
knowledge transfer, we used <strong>the</strong>m for giving an empirical support to our <strong>the</strong>oretical model. Each
country has been putted into one <strong>of</strong> <strong>the</strong> innovative situation presented into our model.
The validity <strong>of</strong> our choice has been <strong>the</strong>n verified by considering <strong>the</strong> level <strong>of</strong> innovation for each <strong>of</strong> <strong>the</strong>
mentioned countries. We particularly referred to <strong>European</strong> Union sources, and Eurostat data reported
into <strong>the</strong> “<strong>European</strong> Innovation Scoreboard 2008. Competitive Analysis <strong>of</strong> innovation performance”.
172
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Francesco Calza, Rossella Canestrino and Chiara Cannavale
Data confirm <strong>the</strong> possibility to locate <strong>the</strong> countries in <strong>the</strong> hypo<strong>the</strong>sized quadrant and, that is <strong>the</strong> lack
<strong>of</strong> future orientation and <strong>the</strong> lack <strong>of</strong> uncertainty avoidance can represent important obstacles to
innovation spreading.
According to our model, effective milieux innovateurs arise when both <strong>the</strong> dimensions are at a high
level. It represents <strong>the</strong> best solution: innovation spreading is guaranteed by <strong>the</strong> existence <strong>of</strong>
innovative firms (donors) and follower firms strongly learning oriented (receivers). This is <strong>the</strong> case <strong>of</strong>
Denmark, Sweden, Finland, all countries characterized by a high percentage <strong>of</strong> innovative firms and
competitive innovation systems. On <strong>the</strong> contrary, when a low level <strong>of</strong> uncertainty avoidance crosses
with a low level <strong>of</strong> future situation innovation spreading is very limited. This is <strong>the</strong> case <strong>of</strong> <strong>the</strong> never
born systems and examples <strong>of</strong> <strong>the</strong>m can be easily found in Hungary, Turkey, Greece, Morocco, and
Sou<strong>the</strong>rn Italy.
The model gives some highlight in understanding innovation spreading through <strong>the</strong> different countries.
It helps in highlighting how culture affects <strong>the</strong> process and to understand which kind <strong>of</strong> problems can
arise in <strong>the</strong> four different situations. However, <strong>the</strong> research has still to go on. First <strong>of</strong> all, <strong>the</strong>
correlation among <strong>the</strong> different Globe’s dimensions should be evaluated, in order to test if o<strong>the</strong>r
dimensions impact on <strong>the</strong> model. Secondly, given that innovation systems can arise in particular
areas and that countries not always present homogeneous characteristics, <strong>the</strong> model would be more
useful if it could help in understanding <strong>the</strong> innovation spreading opportunities existing in particular
context, at a under-national level. In order to satisfy this exigency, fur<strong>the</strong>r researches will try to
reproduce Globe’s dimensions, namely Future Orientation and Uncertainty Avoidance, at an undernational
level, in order to highlight <strong>the</strong> differences existing within a country and to verify if <strong>the</strong>y affect
innovation spreading. Finally, in order to analyze <strong>the</strong> possibility to improve innovation spreading and
to create successful innovation systems, <strong>the</strong> analysis should be conducted according to a dynamic
perspective. Highlighting <strong>the</strong> values, which impact on innovation spreading could be very useful
indeed. To understand this level, <strong>the</strong> use <strong>of</strong> Globe’s “should be” scores is necessary.
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174

The Roles <strong>of</strong> Tacit Knowledge and Knowledge Management
Systems for Writing Academic Papers: A Research Case
José Manuel Cardenas and Mauro Spinola
Production Engineering Department, São Paulo University, Sao Paulo, Brazil
ppcardenas777@gmail.com
Abstract: Inside <strong>the</strong> knowledge management arena, two forms <strong>of</strong> knowledge have been considered with <strong>the</strong> goal
<strong>of</strong> choosing <strong>the</strong> ad hoc information system and align it with <strong>the</strong> available resources: explicit knowledge and tacit
knowledge. In this sense, resources based on information technology have demonstrated a good alignment and
have been a great tool for storing explicit knowledge and, in many cases, for recovering it. However, when it
comes to storing, distributing or sharing tacit knowledge, <strong>the</strong> same information systems could <strong>of</strong>fer significant
limitations. This paper strives to present <strong>the</strong> characteristics <strong>of</strong> tacit knowledge, which may <strong>the</strong>n be contained into
information systems in order to seek an alignment with <strong>the</strong> technology and <strong>the</strong> way in which this knowledge could
be understood at machine level; or, if it is possible, store it and <strong>the</strong>n share it. The case research consists <strong>of</strong> an
experience based on <strong>the</strong> observation <strong>of</strong> researchers seeking to write academic papers and <strong>the</strong>ir alignment with
support information systems that <strong>the</strong>y use. We conducted some interviews with researchers from different levels
<strong>of</strong> experience in writing academic papers, all belonging to <strong>the</strong> production engineering faculty (alumni and
practitioners). Thus, on a first exploratory round, it was discovered that <strong>the</strong> interviews could be divided into two
types: a) radical paper, when <strong>the</strong> topic is just emerging, or when it was just an idea in <strong>the</strong> mind <strong>of</strong> <strong>the</strong> researcher
and, b) incremental paper, when <strong>the</strong> article already had more than one version, or <strong>the</strong> contribution <strong>of</strong> more than
one author. This small difference permitted us to observe <strong>the</strong> use and adequacy <strong>of</strong> knowledge management
systems, and <strong>the</strong> role <strong>of</strong> experience, in order to write an academic paper. With that, preliminarily, it is possible to
affirm that <strong>the</strong> tacit knowledge—ga<strong>the</strong>red from <strong>the</strong> Knowledge Management arena—has differences from tacit
information, which can be stored at <strong>the</strong> information systems level. Results indicate that within <strong>the</strong> process <strong>of</strong>
formulation and adaptation with a specific knowledge management system, <strong>the</strong> tacit knowledge at <strong>the</strong> individual
level complements <strong>the</strong> limitations <strong>of</strong> information technology for tacit information storage. However, when it tried
storing <strong>the</strong> group tacit knowledge; tacit information could only be captured by <strong>the</strong> system manager and <strong>the</strong>
mediums to retrieve such information are not interesting to researchers. In o<strong>the</strong>r words, it is necessary to create a
repository <strong>of</strong> experience for each time when a new project starts to make sure everyone benefits from <strong>the</strong>
generated content for <strong>the</strong> researchers in a group. Finally, it is possible to affirm that <strong>the</strong> choice <strong>of</strong> information
technology tools intended to manage <strong>the</strong> resource "experience" will fit <strong>the</strong> user’s needs once those tools have
been explained and are clarified before starting <strong>the</strong> process <strong>of</strong> knowledge generation.
Keywords: tacit knowledge, knowledge management system, tacit information
1. Introduction
Currently, <strong>the</strong> role <strong>of</strong> information technology (IT) within <strong>the</strong> context <strong>of</strong> knowledge management (KM) is
taking a special significance since a large part <strong>of</strong> communication uses technology as a channel for
information transfer. However, some limitations <strong>of</strong> <strong>the</strong> technologies have been noted throughout <strong>the</strong>
process. For example, McDermot states: "The great trap in knowledge management is using
information management tools and concepts to design knowledge management systems" (Mcdermott
1999). Referring <strong>the</strong> fact that "knowing" is a merely human act and cannot be piped by computers or
o<strong>the</strong>r technologies. KM, as a discipline, involves human decisions and IT support for storing and
encoding <strong>the</strong> surrounding information, produced within an organization as part <strong>of</strong> its own development
<strong>of</strong> non-routine and routine tasks. Thus, <strong>the</strong> weight <strong>of</strong> decisions within <strong>the</strong> management <strong>of</strong> knowledge
remains under human responsibility (Walsham 2001). Moreover, one <strong>of</strong> those responsibilities in <strong>the</strong>
decision-making, involves <strong>the</strong> ability to formulate appropriate strategies when <strong>the</strong> use <strong>of</strong> certain or
specific technology is aimed to connect properly to those who produce <strong>the</strong> information (or encode it)
with <strong>the</strong> people whom really need it and know what to do with such information (or how to decode it)
(Jennex, Olfman & Addo 2003). In this sense, technologies for KM are intended to play two key roles:
1) storing data and information, and 2) retrieving data and information. And, <strong>the</strong> planned investment
for implementation <strong>of</strong> a particular IT will be closely linked with <strong>the</strong> expected support from it.
On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> activities <strong>of</strong> KM suggest traditionally two dimensions <strong>of</strong> knowledge: tacit and
explicit knowledge (Polanyi 1966; Nonaka 2007). Explicit knowledge is that one that it is easy to code
by putting it on paper, like making images, pictures, drawings or o<strong>the</strong>r media; hence, it is possible to
transfer this dimension <strong>of</strong> knowledge through detailed explanations. And, tacit knowledge is seen as
one that people have internalized; so, it is difficult to transfer because it involves some practices
developed and experienced over time. Tacit knowledge is <strong>the</strong> core interest <strong>of</strong> this paper.
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José Manuel Cardenas and Mauro Spinola
The goal is to describe a process <strong>of</strong> knowledge transfer and transformations by observing <strong>the</strong> role <strong>of</strong>
IT in support <strong>of</strong> this transfer/transformation. To do this, we observed <strong>the</strong> process <strong>of</strong> non-routine work:
<strong>the</strong> task/process <strong>of</strong> writing an academic paper. The concepts related to <strong>the</strong> assessment were: tacit
knowledge and tacit information. Tacit knowledge is <strong>the</strong> owned capability or skill from each researcher
to write an article individually and within a workgroup. In this sense, <strong>the</strong> knowledge
transfer/transformation could be observed through two types <strong>of</strong> scenarios and through <strong>the</strong> use <strong>of</strong> IT
tools related to KM in individual and group levels. The proposition <strong>of</strong> this research is that <strong>the</strong>
formulation <strong>of</strong> a knowledge management system (KMS) demands a level <strong>of</strong> understanding from <strong>the</strong>
tacit information in order to be contained within <strong>the</strong> process <strong>of</strong> designing such a system (system in <strong>the</strong>
broad sense <strong>of</strong> <strong>the</strong> word). Results are divided in: a) radical paper and b) incremental paper, according
to <strong>the</strong> grade <strong>of</strong> novelty and <strong>the</strong> collaboration embedded in its redaction. Both results are linked with
<strong>the</strong> dimension <strong>of</strong> tacit knowledge used with <strong>the</strong> goal <strong>of</strong> producing an academic paper. Concluding
that, it is necessary to understand how to produce experience repositories in order to reuse <strong>the</strong>
created tacit knowledge for enhancing this activity.
2. Explicit knowledge and tacit information
There is a kind <strong>of</strong> consensus about <strong>the</strong> fact that KM is focused on <strong>the</strong> ability to deal with two
dimensions <strong>of</strong> knowledge: tacit and explicit (Nevo, Furneaux & Wand 2007; Nonaka 2007). From
<strong>the</strong>se dimensions, tacit knowledge <strong>of</strong>fers more difficulty with evaluation once it belongs to a part <strong>of</strong> a
very personal dimension and it is difficult to formalize (Wagner & Sternberg 1985).Explicit knowledge
is that which can be easily transformed and articulated into words, symbols, images or any o<strong>the</strong>r
explanation that can be easily understood by <strong>the</strong> message receiver. According to Nonaka (1994), this
dimension <strong>of</strong> knowledge becomes tacit knowledge, that which is not easily expressed verbally. This
knowledge is again converted explicitly through interactions among <strong>the</strong> knowledge repositories; <strong>the</strong>n,
new tacit knowledge is produced by those interactions. And, this generates a cycle that shapes a
spiral <strong>of</strong> knowledge. This process <strong>of</strong> knowledge transfer seems relatively simple because people have
<strong>the</strong> ability to decode information from prior knowledge (Izquierdo, Bevilaqua & Cammarota 2006)
through a process <strong>of</strong> experience and use, learning by trial and error, or testing and failure. However,
when <strong>the</strong> objective is trying to transfer information from one person to a storage medium, or
technology designed for this purpose, it could be said that <strong>the</strong> next cycle will not be created because
<strong>the</strong> "storage medium" does not have <strong>the</strong> skills to generate tacit knowledge from <strong>the</strong> information
received. But, it could be said also that <strong>the</strong>re was an effective transfer <strong>of</strong> information, since <strong>the</strong>
message was passed without changes and may be assumed that information will be passed out to
o<strong>the</strong>r users without modification.
This way, if considered <strong>the</strong> premise that using a particular IT strives to properly transfer information,
like specific KM, two strategies can be considered. (Hansen, Nohria & Tierney 1999) formulate <strong>the</strong>
possibility <strong>of</strong> using: a) personalization and b) codification as strategies for use <strong>of</strong> IT tools. The former
implies that <strong>the</strong> organization seeks to primarily use <strong>the</strong> tacit knowledge <strong>of</strong> those involved and <strong>the</strong>
dissemination <strong>of</strong> this knowledge in order to develop an organizational learning that involves sharing
<strong>the</strong> experiences <strong>of</strong> each element <strong>of</strong> <strong>the</strong> organization from <strong>the</strong> problems or successes that happen on
a day-to-day basis. Personalization means to streng<strong>the</strong>n existing links within <strong>the</strong> organization in order
to let <strong>the</strong> social networks be more dynamic and enhance <strong>the</strong> process <strong>of</strong> dissemination and sharing <strong>of</strong>
experiences from each member as <strong>of</strong>ten as possible.
Strategy <strong>of</strong> codification involves <strong>the</strong> use <strong>of</strong> encryption technologies for storing information, creating
repositories and allowing access to stored data continuously in order to enhance <strong>the</strong> process <strong>of</strong>
learning from <strong>the</strong> people who need to use knowledge as a resource for personal development within
<strong>the</strong> scope <strong>of</strong> <strong>the</strong> organization. And, within <strong>the</strong> encoding process; activities, tasks and processes
should be as documented as possible, in order to be reviewed in <strong>the</strong> future and modify, eliminate or
improve <strong>the</strong> information. The process <strong>of</strong> choosing <strong>the</strong> most appropriate alternative depends on <strong>the</strong>
organization's ability to identify <strong>the</strong>ir needs for creating, processing and using <strong>the</strong>ir explicit knowledge
or <strong>the</strong>ir tacit information.That is, if it is needed to manage explicit knowledge through IT, <strong>the</strong>
organization should think in terms <strong>of</strong> knowledge that this "explicitly" expressed in <strong>the</strong> way that it is
able to be contained into physical mechanisms, stored in databases and accessible through
information systems that allow <strong>the</strong> recovery <strong>of</strong> such data when necessary. Thus, <strong>the</strong> role <strong>of</strong> IT within
<strong>the</strong> spiral <strong>of</strong> knowledge focuses on <strong>the</strong> transfer <strong>of</strong> a coded message for this purpose. However,
consider also <strong>the</strong> existence <strong>of</strong> <strong>the</strong> "tacit information," which is also produced as part <strong>of</strong> <strong>the</strong> process <strong>of</strong>
transforming knowledge. According to Falconer (2006), <strong>the</strong> information remains tacit for different
reasons. One reason is because it is part <strong>of</strong> <strong>the</strong> organizational culture that does not share all known
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José Manuel Cardenas and Mauro Spinola
information. It is not possible to explicit or express all information, and also by <strong>the</strong> many limitations
that IT has to store tacit information. Additionally, consider that <strong>the</strong> formulation <strong>of</strong> explicit knowledge
appears, by <strong>the</strong> prior existence <strong>of</strong> additional information, to be sometimes tacit, or meta-knowledge.
That is, knowledge about <strong>the</strong> knowledge that is being created (Nevo 2003). Finally, <strong>the</strong> information
that has been expressed in this paper, or by means <strong>of</strong> ideas, creates "tacit information," in parallel.
And, one part <strong>of</strong> this tacit information is explicit knowledge, in terms <strong>of</strong> KM; but, this cannot be stated
to be included within <strong>the</strong> IT tools, per se. At <strong>the</strong> same time, it is difficult to formulate a KMS that
involves a connection <strong>of</strong> people and IT, in an appropriate way.
3. Tacit knowledge
From <strong>the</strong> definition <strong>of</strong> Polanyi (1966) about tacit knowledge: "'We can know more than we can tell,"
<strong>the</strong> tacit knowledge has been seen from o<strong>the</strong>r opticals and brought inside <strong>the</strong> process <strong>of</strong> KM. In <strong>the</strong>
view <strong>of</strong> Collins (2001), tacit knowledge is made up <strong>of</strong> scientific information that has not been
transferred, but only via meetings and also formulas or ma<strong>the</strong>matical models. It also appears in <strong>the</strong>
form <strong>of</strong> knowledge, used to cope with specific situations (or Practical Intelligence) (Wagner &
Sternberg 1985; Mat<strong>the</strong>w & Sternberg 2009). This allows <strong>the</strong>re to be an open discussion about what
is and what is not tacit knowledge, besides <strong>the</strong> characteristics that may be appropriate or not to this
type <strong>of</strong> knowledge (Castillo 2002; Tsoukas 2003). That means that organizations are faced with a
difficult dare to manage this dimension <strong>of</strong> knowledge in its inner (Taylor 2007).
Castillo (2002) suggests that tacit knowledge can be individual or collective, and proposes <strong>the</strong>
following typology:
a) Nonepistle tacit knowledge: This type <strong>of</strong> knowledge is that which people cannot put on
paper, referring to <strong>the</strong> kind <strong>of</strong> letter that cannot be written because <strong>the</strong> individual cannot express
in words <strong>the</strong> characteristics that are intrinsic to him/her.
b) Sociocultural tacit knowledge: Part <strong>of</strong> <strong>the</strong> collective tacit knowledge that <strong>the</strong> individual
shares with <strong>the</strong> organization to which he/she belongs. As a neuron that just cannot express a
concept, but needs to link itself up with o<strong>the</strong>r neurons to reflect a notion.
c) Semantic tacit knowledge: The kind <strong>of</strong> tacit knowledge that can be conveyed through
gestures due <strong>the</strong> number <strong>of</strong> times that a process has been repeated; or <strong>the</strong> familiarity <strong>of</strong>
individuals with specific practice.
d) Sagacious tacit knowledge: The tacit knowledge that individuals acquire because <strong>of</strong> <strong>the</strong> skills
that he/she has developed on a personal level.
More generally, tacit knowledge - ei<strong>the</strong>r individually or collectively - demands for elicitation methods in
order to be found, which are in <strong>the</strong> form <strong>of</strong> psychological models (mental models) and assessments <strong>of</strong>
individual and group behavior. But, <strong>the</strong>se methods are not part <strong>of</strong> <strong>the</strong> purpose <strong>of</strong> this article.
4. Knowledge Management Systems
As mentioned, <strong>the</strong>re is a consensus on <strong>the</strong> fact that KM seeks to address <strong>the</strong> two basic types <strong>of</strong>
knowledge: tacit and explicit (Nevo, Furneaux & Wand 2007). Thus, research relating to <strong>the</strong> topic <strong>of</strong>
KM focus on <strong>the</strong> interest in <strong>the</strong> question <strong>of</strong> <strong>the</strong> existence <strong>of</strong> <strong>the</strong>se two dimensions <strong>of</strong> knowledge and
how <strong>the</strong>y affect organizational aspects. The goal <strong>of</strong> KM, in this sense, is to manage <strong>the</strong>se two types <strong>of</strong>
knowledge that seek gains in efficiency and effectiveness <strong>of</strong> routine and non-routine processes.
Knowledge Management Systems (KMS) are considered as information systems used specifically to
assist in <strong>the</strong> processes <strong>of</strong> KM within organizations (Alavi & Leidner 2001). However, <strong>the</strong> word
“system” refers to a junction <strong>of</strong> elements that aim to achieve a goal or develop a specific process/task
and later create a continuum from <strong>the</strong> system. Then, a KMS must possess <strong>the</strong> ability to aid and
manage <strong>the</strong> production activities and knowledge sharing, i.e., joint users with <strong>the</strong> appropriate
technology to generate and use knowledge. It is possible to note two basic functionalities <strong>of</strong> a KMS: a)
store and disseminate explicit knowledge, and b) serve as a platform in order to share/transfer tacit
knowledge. But, <strong>the</strong> research on tacit knowledge, as previously seen, has presented different points
<strong>of</strong> view and has increased difficultly in <strong>the</strong> role <strong>of</strong> IT for support processes and storage <strong>of</strong> this
knowledge. For this reason, this study was conducted as an experience to observe what <strong>the</strong>
limitations <strong>of</strong> IT are, and how to deal with tacit knowledge. In <strong>the</strong> review by Alavi & Leidner (2001), IT
has three applications within <strong>the</strong> scope <strong>of</strong> KM: a) coding and sharing <strong>of</strong> best practices, b) creating
corporative directories <strong>of</strong> knowledge and c) creating knowledge networks. These authors also build a
framework linking social processes with <strong>the</strong> processes <strong>of</strong> knowledge through: a) creation, b)
storage/retrieval, c) transfer and d) application. The latter more closely relates to <strong>the</strong> application <strong>of</strong>
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José Manuel Cardenas and Mauro Spinola
experience, or <strong>the</strong> use <strong>of</strong> tacit knowledge. Both insights suggest <strong>the</strong> need to observe <strong>the</strong> correlations
between <strong>the</strong> individual who uses <strong>the</strong> tools and <strong>the</strong> goals that he/she hopes to achieve from <strong>the</strong> use <strong>of</strong>
KMS. It is important to understand IT’s role in <strong>the</strong> processing and transferring <strong>of</strong> knowledge, and <strong>the</strong>
role that technology plays within <strong>the</strong>se processes.
Here, it is suggested that KMS are formed at both individual and collective (or corporative) levels. But,
<strong>the</strong> management <strong>of</strong> each <strong>of</strong> <strong>the</strong>se types involves <strong>the</strong> use <strong>of</strong> tacit knowledge at <strong>the</strong> individual level and
<strong>the</strong> management <strong>of</strong> explicit knowledge at <strong>the</strong> collective level. The verification <strong>of</strong> this proposition is part
<strong>of</strong> <strong>the</strong> verification <strong>of</strong> <strong>the</strong> role <strong>of</strong> tacit knowledge within KMSs.
5. Methodology
We used an interpretive methodology in order to verify <strong>the</strong> relationships <strong>of</strong> <strong>the</strong> knowledge generated
from a collective learning experience, as explained in articles and o<strong>the</strong>r materials (i.e: papers, forums,
internal discussions, etc). The case raised in our study corresponds to meetings with researchers that
have maintained some experience in <strong>the</strong> publication <strong>of</strong> scholarly articles. We did not considered <strong>the</strong>
impact <strong>of</strong> publications and/or <strong>the</strong> number <strong>of</strong> publications produced, because it is does not influence
<strong>the</strong> object <strong>of</strong> this study. A similar method was used by (Taylor 2007). The interviews deepened in <strong>the</strong>
understanding <strong>of</strong> <strong>the</strong> formation <strong>of</strong> tacit knowledge with <strong>the</strong> use <strong>of</strong> <strong>the</strong> deep interview method;
suggested by (Gephard 2004). Looking for <strong>the</strong> origins <strong>of</strong> what researchers have done from knowledge
acquired through social and practical conversations. In fact, a full article could be treated as explicit
knowledge. But, this is not so, because unlike <strong>the</strong> concept <strong>of</strong> explicit knowledge, an article is not a
source <strong>of</strong> information that is easy to explain and, <strong>the</strong> time that it takes to appear is a mixture <strong>of</strong> tacit
knowledge, experiences, information encoded (also not codified) and tacit information. Thus, it is
considered as a premise that tacit knowledge emerges from <strong>the</strong> constant practice <strong>of</strong> <strong>the</strong> exercise <strong>of</strong> a
non-routine activity. O<strong>the</strong>r assumptions:
It is assumed that a researcher with extensive experience also includes an organizational memory
and larger databases, which may not necessarily be more orderly.
Consider also that a text involves prior knowledge. It is not <strong>the</strong> focus <strong>of</strong> <strong>the</strong> article to understand
how such knowledge appeared. But, checking out <strong>the</strong> way that one researcher remembers <strong>the</strong>
process <strong>of</strong> writing his/her first paper helps <strong>the</strong> reader to understand <strong>the</strong> tacit knowledge formation
process. And, when appropriate, if he/she has already written many articles, is also interesting to
assess how and what he/she remembers about writing a former paper, or even his/her first paper.
So, <strong>the</strong> object <strong>of</strong> research used for this work was <strong>the</strong> "academic paper" with two connotations: few
developed articles and a mature article. This is because (based on <strong>the</strong> researcher's view), <strong>the</strong> more
recent articles in his/her KMS, <strong>the</strong> less complexity in <strong>the</strong> relationships with references in <strong>the</strong> text. And,
in turn, <strong>the</strong> tacit knowledge is also more personal. So, in a first approximation, <strong>the</strong> object <strong>of</strong> this paper
was obtained through two key concepts <strong>of</strong> object <strong>of</strong> research:
Incremental paper: The type <strong>of</strong> article that is in <strong>the</strong> process <strong>of</strong> improvement. It already has one
or several previous versions and is being added to by one or more researchers. So, that also
leaves behind it an organizational memory that can give support to <strong>the</strong> different stages <strong>of</strong> writing.
Radical paper: This paper coincides with <strong>the</strong> birth <strong>of</strong> an idea that aims to become one
publication. There are no data records that can append it. This type <strong>of</strong> article starts as a solitary
development. It does not gain support until its consolidation.
In a second approach, we explored <strong>the</strong> IT tools that researchers use as support, and variations that
<strong>the</strong>y develop from <strong>the</strong>se tools in order to gain ano<strong>the</strong>r uses for <strong>the</strong>se tools to meet different needs.
For example, you can use a spreadsheet to store electronic databases, regarding when a publication
was submitted or control <strong>the</strong> deadline <strong>of</strong> an upcoming event. If a specific IT tool exists to support
research activities, <strong>the</strong> review <strong>of</strong> <strong>the</strong>se tools has been part <strong>of</strong> this work. However, it does not appear
in this article, but it was necessary to have a level <strong>of</strong> comparison with current tools.
The interviews that reference to <strong>the</strong> tacit knowledge were done in two ways:
Individual tacit: Refers to memory that each person develops from <strong>the</strong> perception that <strong>the</strong>y
individually create from observation and assessment <strong>of</strong> each experience. This knowledge comes
from learning and usually does not have a notion <strong>of</strong> its existence (Wagner & Sternberg 1985).
These interviews were conducted in accordance with <strong>the</strong> strategies suggested by <strong>the</strong> Mat<strong>the</strong>w &
Sternberg (2009) using a methodology where <strong>the</strong> respondents expect to report <strong>the</strong>ir history, like
one who tells a anecdote or through storytelling (Taylor 2008).
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Tacit group: In <strong>the</strong> transformation process <strong>of</strong> knowledge, individuals depend on o<strong>the</strong>rs to share
<strong>the</strong>ir own learning. The tacit group denotes those concepts that can appear to be in <strong>the</strong> shared
experience <strong>of</strong> group, with <strong>the</strong> conditioning that such knowledge provides value to <strong>the</strong> group.
However, it is information that remains implicit, in strict sense; in o<strong>the</strong>r words, <strong>the</strong> type <strong>of</strong>
information that is accepted as true by everyone, but that has never been presented explicitly.
One example <strong>of</strong> this is that all members <strong>of</strong> a research group use <strong>the</strong> same word processor, since
someone suggested that this is <strong>the</strong> best method to use to work on writing articles.
In <strong>the</strong> course <strong>of</strong> investigating, <strong>the</strong> tacit group used <strong>the</strong> view <strong>of</strong> Erden, VonKrogh & Nonaka (2008), in
<strong>the</strong> sense that <strong>the</strong> construction <strong>of</strong> tacit group goes through four levels (Fig. 1). These levels begin with
a group <strong>of</strong> assemblages, when crew members are getting to know each o<strong>the</strong>r, and <strong>the</strong>re is not an real
sharing <strong>of</strong> ideas and confidence has not been consolidated. An intermediate level <strong>of</strong> sharing is <strong>the</strong>
Collective Action. When <strong>the</strong> group is consolidated it passes to a stage <strong>of</strong> Phronesis or "prudence,”
where actions are taken based on <strong>the</strong> tacit knowledge shared at <strong>the</strong> previous levels and finally; <strong>the</strong>
maximum level that can reach <strong>the</strong> group tacit knowledge. Collective improvisation, is a level that
indicates a need to innovate all <strong>the</strong> time, and <strong>the</strong> group responds positively to all <strong>of</strong> <strong>the</strong> opportunities
to share and develop common goals.
Figure 1: Different levels in <strong>the</strong> quality <strong>of</strong> group tacit knowledge. Font: (ERDEN ET AL, 2008)
During <strong>the</strong> development <strong>of</strong> research, <strong>the</strong> methodology for <strong>the</strong> discovery <strong>of</strong> individual tacit knowledge
was also applied individually to discover <strong>the</strong> tacit group. The difference here was that <strong>the</strong> concern for
storytelling had to be related to <strong>the</strong> group goals, and <strong>the</strong> perception <strong>of</strong> how critics or contributions
about <strong>the</strong> articles were being sent. This way, <strong>the</strong> role <strong>of</strong> information technology in this process could
be accurately evaluated.
6. Results
The results <strong>of</strong> observations and interviews were processed using <strong>the</strong> concepts <strong>of</strong> <strong>the</strong> <strong>the</strong>oretical
references and verifying <strong>the</strong> adherence <strong>of</strong> those concepts with <strong>the</strong> stories told by interviewees. The
first result is that <strong>the</strong> level <strong>of</strong> trust <strong>of</strong> individuals positively influences <strong>the</strong> generation <strong>of</strong> tacit knowledge
and transfer/transformation <strong>of</strong> it since socialization is <strong>the</strong> most prominent part <strong>of</strong> learning and creation
<strong>of</strong> tacit knowledge and tacit information. Ano<strong>the</strong>r way that this happens is through "trial and error".
To determine <strong>the</strong> role <strong>of</strong> IT in <strong>the</strong> transformation/transfer <strong>of</strong> knowledge, <strong>the</strong> following data was
collected:
Individual tacit: The KMS are formed at <strong>the</strong> individual level from <strong>the</strong> knowledge or unfamiliarity <strong>of</strong>
tools, or information systems, needed to perform specific functions <strong>of</strong> storing and indexing
databases. Namely, it is defined as KMS individual, such as instrumentation created by <strong>the</strong> user
for managing <strong>the</strong> explicit knowledge that he/she possesses. However, <strong>the</strong> individual handles <strong>the</strong>
sources <strong>of</strong> knowledge in accordance with aspects that are quite tacit. As an example, <strong>the</strong> criterion
<strong>of</strong> a citation <strong>of</strong> an author may be based, not only on <strong>the</strong> number <strong>of</strong> quotations, but based more on
<strong>the</strong> references to o<strong>the</strong>r researchers in <strong>the</strong> same line.
Tacit group: In this case, <strong>the</strong>re was always a leadership or governance for determining <strong>the</strong>
context <strong>of</strong> work. But, even if <strong>the</strong>re is a consensus in <strong>the</strong> use <strong>of</strong> technological tools to share
knowledge and <strong>the</strong> ways in which it will be disseminated, <strong>the</strong> user has to fit <strong>the</strong> information
system chosen. Therefore, this leads to tacit information in <strong>the</strong> process <strong>of</strong> junction <strong>of</strong> <strong>the</strong> people
who share knowledge. This is from <strong>the</strong> adherence <strong>of</strong> <strong>the</strong> user with a KMS, or <strong>the</strong> lack <strong>of</strong> it. So that
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trust that members have with each o<strong>the</strong>r within a group <strong>of</strong> research, generates information that
cannot be contained within <strong>the</strong> system characteristics. In hypo<strong>the</strong>sis, <strong>the</strong> group established a
culture in which <strong>the</strong>ir coexistence cannot be absorbed by new members through <strong>the</strong> current use
<strong>of</strong> a determined information system, but only by accumulated experiences.
7. Conclusions
This paper was written with <strong>the</strong> goal <strong>of</strong> verify some links between how <strong>the</strong> tacit knowledge appears in
<strong>the</strong> daily form <strong>of</strong> a group <strong>of</strong> researchers, and striving to seek links with <strong>the</strong> IT tools used in order to
support <strong>the</strong> process/task <strong>of</strong> writing an academic paper. The different kinds <strong>of</strong> academic papers, and
<strong>the</strong>ir goals <strong>of</strong> publication have not been considered because it wasn’t needed to understand <strong>the</strong>
characteristics <strong>of</strong> <strong>the</strong> knowledge production on <strong>the</strong> individual and collective levels.
Thus, it is possible to conclude that tacit knowledge dimension disagrees with <strong>the</strong> IT limitations in <strong>the</strong>
sense that is so difficult to align <strong>the</strong> properties <strong>of</strong> <strong>the</strong> specific knowledge and information that is not
codified. It is suggested to consider enhancing <strong>the</strong> methodology to better observe <strong>the</strong> behavior <strong>of</strong> <strong>the</strong>
tacit knowledge because it is so difficult to assess <strong>the</strong> role <strong>of</strong> technology in support <strong>the</strong> user needs in
order to take advantage from <strong>the</strong> tacit knowledge. The importance <strong>of</strong> this point is that each case is a
different case from o<strong>the</strong>r; considering <strong>the</strong> user’s view, but within a tacit group, usually, <strong>the</strong> IT tools are
<strong>the</strong> same.
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180

Knowledge Sources, Innovation and Organizational
Learning in Small Firms
Vincenzo Cavaliere and Daria Sarti
Department <strong>of</strong> Business Administration, University <strong>of</strong> Florence (Italy)
vincenzo.cavaliere@unifi.it
daria.sarti@unifi.it
Abstract: Current academic debate has shown <strong>the</strong> value <strong>of</strong> knowledge as <strong>the</strong> most critical intangible asset for
organizations and its essential role in <strong>the</strong> achievement <strong>of</strong> a competitive advantage. This paper provides evidence
<strong>of</strong> <strong>the</strong> complexity involved with <strong>the</strong> set <strong>of</strong> knowledge assets owned by firms in innovation strategy. In particular,
according to <strong>the</strong> Knowledge Based Theory <strong>of</strong> <strong>the</strong> Firm, <strong>the</strong> paper considers that intangible resources are key
elements for organizational development and innovation. That is, organizations must be aware that
understanding <strong>the</strong>ir own knowledge base can lead to a set <strong>of</strong> capabilities enhancing <strong>the</strong> chances for <strong>the</strong>ir growth
and competitive advantage. The context <strong>of</strong> <strong>the</strong> study is <strong>the</strong> small enterprises, i.e. firms with less than 50
employees according to <strong>the</strong> wide accepted small enterprise category reported in <strong>the</strong> last EU recommendation
(2003). The research analyzes <strong>the</strong> effect <strong>of</strong> knowledge sources importance and organizational knowledge base
on two different types <strong>of</strong> innovation: product and process innovation. To do this we estimated four factors: one
representing <strong>the</strong> breadth <strong>of</strong> knowledge owned by <strong>the</strong> firm and three o<strong>the</strong>r factors. One, considering human
capital as source <strong>of</strong> knowledge and two external knowledge sources such as: cooperation with non industrial
agents and interaction with actors <strong>of</strong> <strong>the</strong> supply chain. Analysis is based on a sample <strong>of</strong> 144 small firms operating
in mechanical sector in central Italy. The results have important practical implications for small firms
management; <strong>the</strong>y indicate that for <strong>the</strong> two processes <strong>of</strong> innovation investigated (process and product
innovation), <strong>the</strong> importance attached to knowledge sources and to <strong>the</strong> use <strong>of</strong> different cognitive assets play a
different role.In particular we find a positive relation between product innovation and prior organizational
knowledge base and learning by human capital (internal staff and learning by hiring). The results also suggest
positive relations between process innovation and learning by knowledge networking (Universities and Research
Centers, o<strong>the</strong>r Companies and Groups, Associations), learning by HC and learning by competitive functioning
(Customers, Suppliers and Competitors).
Keywords: Knowledge sources, knowledge base, process and product innovation, small enterprises
1. Introduction
Many scholars have emphasized <strong>the</strong> importance <strong>of</strong> organizational learning and a consistent
knowledge management activity to improve <strong>the</strong> organizational performance and innovation. Building
on <strong>the</strong> knowledge-based view <strong>of</strong> <strong>the</strong> firm, <strong>the</strong> purpose <strong>of</strong> this work is to shed light, within <strong>the</strong>
innovation debate, on <strong>the</strong> importance <strong>of</strong> different knowledge sources and knowledge assets in small
enterprises (SEs).
The aim <strong>of</strong> <strong>the</strong> paper is to shed fur<strong>the</strong>r light on a topic <strong>of</strong> which nowadays relatively few is known
(Caloghirou et al., 2004). Determinants <strong>of</strong> innovation are “many and varied” and may change over
time. In this paper we investigate <strong>the</strong> extent to which <strong>the</strong> existing organizational knowledge base and
<strong>the</strong> importance that SEs give to different sources <strong>of</strong> knowledge can affect innovation. Specifically, two
different kinds <strong>of</strong> innovation are under investigation: product and process innovation.
The first part <strong>of</strong> <strong>the</strong> paper presents a <strong>the</strong>oretical review <strong>of</strong> <strong>the</strong> main contributions on knowledge <strong>the</strong>ory
and innovation. In <strong>the</strong> second part an empirical analysis is developed. The analysis was carried out in
2009 on a sample <strong>of</strong> SEs (N = 144) operating in <strong>the</strong> mechanical and machine industry in <strong>the</strong> centre <strong>of</strong>
Italy.
The study tried to test <strong>the</strong> importance, for <strong>the</strong> innovation processes (i.e. process and product
innovation) <strong>of</strong> both knowledge sources (e.g. suppliers, research centers, human capital, universities)
and prior organizational knowledge.
Methodologically, an analysis <strong>of</strong> variance <strong>of</strong> two independent groups <strong>of</strong> randomly selected firms was
conducted, i.e. firms which realized an innovation and firms that did not, both in terms <strong>of</strong> product and
process innovation. Next, a factor analysis, highlights four distinct organizational processes (factors)
that we hypo<strong>the</strong>size as influencing <strong>the</strong> innovation. These are: Prior Organizational Knowledge,
Learning by Human Capital, Learning by Knowledge Networking, learning by Competitive
Functioning.
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Vincenzo Cavaliere and Daria Sarti
The results provide evidence <strong>of</strong> <strong>the</strong> complexity involved with <strong>the</strong> set <strong>of</strong> knowledge assets owned by
firms in innovation strategy and <strong>the</strong>y show that for <strong>the</strong> two processes <strong>of</strong> innovation, <strong>the</strong> importance
attached to knowledge sources and to <strong>the</strong> use <strong>of</strong> different cognitive assets play a different role.
2. Some prior insights in knowledge and organizational structure <strong>of</strong> small
enterprises
Scholars acknowledge that a small enterprise’s “knowledge structure” differs from that <strong>of</strong> larger firms.
In SEs, in fact, knowledge is strongly based on <strong>the</strong> owner-manager’s technical know-how and
expertise while managerial skills or sophisticated and formalized managerial practices are missing
(Maes et al., 2004).
In addition, entrepreneurs recognize environmental opportunities that are related to information and
knowledge <strong>the</strong>y already possess (Shane, 2000). In o<strong>the</strong>r words, <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> firm – including
<strong>the</strong> prior knowledge <strong>of</strong> <strong>the</strong> entrepreneur - affects <strong>the</strong> way SEs give a response to external stimuli.
Small businesses show best chances with production development process since that is <strong>the</strong> field <strong>the</strong>y
know best. In terms <strong>of</strong> “knowledge structure”, we can <strong>the</strong>refore consider SEs as highly specialized
organizations since <strong>the</strong>y show a highly specific set <strong>of</strong> knowledge and fur<strong>the</strong>rmore <strong>the</strong>y are <strong>of</strong>ten
specialized in specific stages <strong>of</strong> <strong>the</strong> supply chain.
Compared with large companies, SEs are unfettered by bureaucracy, hierarchical thinking, and
expensive existing information systems; SEs are also more innovative as <strong>the</strong>y can adapt, internalize
and crystallize information in a more efficient way across <strong>the</strong> entire firm (Liao et al., 2003).
In order to fulfill this expected innovation function it is important for SEs to manage inward technology
transfer effectively, to be able to implement new knowledge, to disseminate it internally and to make
use <strong>of</strong> new cognitive resources. This means to apply in an effective way <strong>the</strong>ir absorptive capacity and
governance <strong>of</strong> different knowledge sources in order to gain competitive advantage and be innovative.
3. Knowledge base, knowledge sources and innovation
3.1 The importance <strong>of</strong> knowledge perspective for innovation in <strong>the</strong> current
managerial debate
Recent contributions in <strong>the</strong> field <strong>of</strong> management and organizational studies have focused on a
knowledge-creating view <strong>of</strong> <strong>the</strong> firm. Knowledge is seen as <strong>the</strong> most valuable resource that provides
sustainable competitive advantage (Kogut, Zander, 1992; Teece, 1998) and leads to innovation
(Carneiro, 2000).
In this paper, two different kinds <strong>of</strong> innovation are under investigation: product and process
innovation. According to current studies on innovation, <strong>the</strong> former “may include goals such as <strong>the</strong>
development <strong>of</strong> a completely new product or <strong>the</strong> improvement <strong>of</strong> an existing product”, while <strong>the</strong> latter
“involves goals such as reduction <strong>of</strong> labour costs or improved manufacturing flexibility” (Leiponen,
Helfat, 2010: 225). Never<strong>the</strong>less, most studies investigating knowledge sources and prior knowledge
base as determinants <strong>of</strong> innovation typically consider only <strong>the</strong> product innovation (Cohen, Levinthal,
1990; Vega-Jurado et al., 2008).
According to Reichstein and Salter (2006), investigating process innovations and understanding its
sources is important because <strong>the</strong>y represent a critical source <strong>of</strong> increased productivity and a key
element in government innovation policy.
Fur<strong>the</strong>r, most authors suggest that both kind <strong>of</strong> innovation (process and product) lead to an increase
in <strong>the</strong> output produced by <strong>the</strong> firm; even though, <strong>the</strong> forces behind this output could be different and
<strong>the</strong>refore it is worth to carry out a specific and separate analysis (Ornaghi, 2006).
According to <strong>the</strong> Knowledge Theory <strong>of</strong> <strong>the</strong> Firm, cognitive processes within organizations are
cumulative, idiosyncratic and path-dependent processes and <strong>the</strong> prolonged process <strong>of</strong> knowledge
accumulation determine <strong>the</strong> capability <strong>of</strong> firms to absorb new knowledge and develop new products,
processes or service (Cohen Levinthal, 1990). In this sense, we introduce <strong>the</strong> concept <strong>of</strong> absorptive
capability. To date, most literature argue that firms with a high level <strong>of</strong> absorptive capacity show a
high aptitude for organizational learning (Wang et al., 2007), reach high performance and reveal to
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possess “<strong>the</strong> ability to produce more innovations” (Tsai, 2001: 998). Innovation is an outcome <strong>of</strong>
organizational learning and it also has a relationship with absorptive capacity (Lane et al., 2006).
Since <strong>the</strong> seminal work by Cohen and Levinthal (1989: 1990) absorptive capacity is used as predictor
<strong>of</strong> innovative activity. Fur<strong>the</strong>rmore, some authors consider absorptive capacity in SMEs as a
preliminary condition for firms’ growth and innovation (Gray, 2006; Wang et al., 2010).
Absorptive capacity, and <strong>the</strong>refore <strong>the</strong> process <strong>of</strong> organizational learning that leads to <strong>the</strong> exploration
and exploitation <strong>of</strong> knowledge (March, 1991), relies on two important elements: <strong>the</strong> existing
knowledge base and <strong>the</strong> intensity <strong>of</strong> efforts made for <strong>the</strong> development <strong>of</strong> technological capabilities. In
this sense, this paper considers both <strong>the</strong> prior organizational knowledge and <strong>the</strong> importance given to
<strong>the</strong> different knowledge sources for firm’s innovation. Considering <strong>the</strong> former as determinant to
assimilate new knowledge and exploit it; and <strong>the</strong> latter, as <strong>the</strong> importance given to different sources <strong>of</strong>
knowledge (suppliers, universities, staff etc.) for organizational learning, in order to feed <strong>the</strong>
organizational knowledge base.
Most contributions about knowledge sources for firm’s innovation show that enterprises can acquire
knowledge via interactions with o<strong>the</strong>r actors, through cooperative agreements, where innovation
efforts are made jointly with private firms and/or public institutions and universities (Caloghirou et al.,
2004; Kogut, 1988) or “functioning” relations with actors in <strong>the</strong> “supply chain” (Lorenzoni, Lipparini,
1999; Fritsch, Lukas, 2001). In this sense, <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> knowledge transfer process will
primarily depend on <strong>the</strong> characteristics <strong>of</strong> <strong>the</strong> transferred knowledge in terms <strong>of</strong> tacitness, complexity,
ambiguity (Winter, 1987; Zander, Kogut, 1995; Suzlanski, 1996) and on <strong>the</strong> absorptive capacity <strong>of</strong> <strong>the</strong>
target unit (Gupta, Govindarajan, 2000; Foss, Pedersen, 2002).
Finally, ano<strong>the</strong>r critical source through which organizations can acquire knowledge is via human
capital (C<strong>of</strong>f, 1997; Hitt et al., 2001).
3.2 The importance <strong>of</strong> knowledge base for innovation processes
The ability <strong>of</strong> firms to absorb new external knowledge depends, first <strong>of</strong> all, on <strong>the</strong>ir levels <strong>of</strong> prior
related knowledge since “it permits <strong>the</strong> assimilation and exploitation <strong>of</strong> new knowledge” (Cohen,
Levinthal, 1990: 135-6). Fur<strong>the</strong>r, according to Kogut and Zander (1992: 384), is very important “to
understand <strong>the</strong> knowledge base <strong>of</strong> a firm as leading to a set <strong>of</strong> capabilities that enhance <strong>the</strong> chances
for growth and survival”.
The existing knowledge base increases <strong>the</strong> ability to search, recognize and represent a problem as
well as assimilate new knowledge for problem solving, “provides a more robust basis for learning” and
“facilitates <strong>the</strong> innovative process” (Cohen, Levinthal, 1990: 134-5).
Vega-Jurado and colleagues (2008) suggest two possible effects <strong>of</strong> internal competencies on <strong>the</strong>
firm’s innovative performance: a direct positive effect on innovation output and an indirect effect from
increased absorptive capacity. In <strong>the</strong>ir findings about Spanish manufacturing SMEs, <strong>the</strong>y suggest that
<strong>the</strong> development <strong>of</strong> technological competences is <strong>the</strong> most important factor for <strong>the</strong> introduction <strong>of</strong> new
products into <strong>the</strong> market (i.e. product innovation). Fur<strong>the</strong>r, <strong>the</strong>y found that internal competencies and
<strong>the</strong> use <strong>of</strong> external sources <strong>of</strong> knowledge do not play in a complementary manner but ra<strong>the</strong>r result as
substitutes. They conclude that “cooperation with scientific agents does not constitute a key factor to
develop new products” (Vega-Jurado et al., 2008:630).
On <strong>the</strong> o<strong>the</strong>r hand, no empirical evidence seems to support a relation between internal knowledge
base and process innovation. Ra<strong>the</strong>r, evidence suggests that process innovation is much more
coherent with <strong>the</strong> use <strong>of</strong> sophisticated channels for imitation from external sources such as
recruitment <strong>of</strong> experts from o<strong>the</strong>r firms (Ornaghi, 2006).
Considering, <strong>the</strong>se empirical evidences, we posit that:
HP1a: A strong knowledge base is not related to process innovation.
HP1b: A strong knowledge base is positively related to product innovation.
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3.3 The importance <strong>of</strong> cooperation strategies and interaction with o<strong>the</strong>r actors for
innovation
The literature on <strong>the</strong> role <strong>of</strong> external communication and networking as determinants <strong>of</strong> innovation,
distinguishes two attempts to create valuable knowledge flows: scanning external information and
cooperating with external organizations (Souitaris, 2001). As Lundvall (1994) points out, strategic
know-how and competences are <strong>of</strong>ten developed interactively and shared within networks. Fur<strong>the</strong>r,
Inkpen (1998) claims that alliances facilitate firms to leverage <strong>the</strong>ir strengths. Referring to <strong>the</strong> learning
effects <strong>of</strong> <strong>the</strong> cooperation, Håkansson (1987) emphasizes that by combining experience, new ideas
can emerge. Explaining <strong>the</strong> motivation to joint ventures, Kogut (1988) considers collaboration a
means by which knowledge is transferred and firms learn from each o<strong>the</strong>r.
Some authors consider in specific <strong>the</strong> distinction between product and process innovation. Comparing
<strong>the</strong> complexity <strong>of</strong> imitation for process and product innovations through interaction with o<strong>the</strong>r
organizations, Ornaghi (2006) suggests that while a product innovation can be developed through
imitation (i.e. reverse engineering), <strong>the</strong> achievement <strong>of</strong> a process innovation may require more
sophisticated channels (i.e. industrial espionage; recruitment <strong>of</strong> experts from rival firms). Similarly,
Levin et al. (1987) found that <strong>the</strong> distinctive features <strong>of</strong> a process innovation are more difficult to
define; consequently, new processes can be kept secret more easily. Fur<strong>the</strong>r, while product
innovations don’t ask for complex choice and governance <strong>of</strong> knowledge sources, <strong>the</strong> transfer <strong>of</strong>
knowledge about process innovations requires higher degrees <strong>of</strong> difficulty, since it involves a more
hidden knowledge. Additionally, Rouvinen (2002) suggests that <strong>the</strong> use <strong>of</strong> external sources <strong>of</strong>
knowledge and cooperation with nonacademic partners is <strong>of</strong>ten associated with process innovation.
Several researches highlight <strong>the</strong> importance <strong>of</strong> “functioning” actors - within <strong>the</strong> supply chain - for
innovation. McDermott and Handfield (2000) state that external sources <strong>of</strong> innovation acquired from
suppliers can provide interaction and synergies that lead to <strong>the</strong> development <strong>of</strong> innovative products.
Von Hippel (1988) argues that firms <strong>of</strong>ten need to work closely with <strong>the</strong>ir suppliers in order to exploit
<strong>the</strong> full potential <strong>of</strong> new technology to innovate effectively. Rouvinen (2002) finds that process
innovation benefits from upstream sources <strong>of</strong> capital-embodied knowledge, such as suppliers. As
Fritsch and Lukas (2001) confirmed, client collaboration plays a critical role in <strong>the</strong> achievement <strong>of</strong>
product innovations. Indeed, <strong>the</strong>re is strong evidence that getting more market information from
clients and building strong interfaces between R&D personnel and counterparts in customer
organization leads to successful new product development (Nieto, Santamaria, 2007).
Within <strong>the</strong> strategic management debate innovation is related to <strong>the</strong> knowledge transfer perspective.
According to Hansen (2002), <strong>the</strong> more a knowledge is complex <strong>the</strong> more it is important to invest in
interaction with o<strong>the</strong>r actors for its effective transfer. Due to specific nature <strong>of</strong> knowledge, we can
suggest that while product innovation does not ask for high interaction, process innovation requires a
higher investment in communication with o<strong>the</strong>r actors.
The above discussion leads us to formulate <strong>the</strong> following hypo<strong>the</strong>sis:
HP2a: Process innovation is positively affected by sources <strong>of</strong> knowledge linked to
cooperation strategies and organizational functioning.
HP2b: Product innovation is not affected by sources <strong>of</strong> knowledge linked to cooperation
strategies and organizational functioning.
3.4 The importance <strong>of</strong> knowledge sources linked to human capital for innovation
People represents <strong>the</strong> organization’s most vital resource and intellectual capital is considered as one
<strong>of</strong> <strong>the</strong> main source <strong>of</strong> innovation (Gloet, Terziovski, 2004). Considering this, successful innovationbased
companies have learned how to manage, motivate and reward people (Gupta, Singhal, 1993).
In <strong>the</strong>ir research, Gimeno and collegues (1997) found a positive relation existing between overall level
<strong>of</strong> human capital and economic performance <strong>of</strong> <strong>the</strong> firm. Fur<strong>the</strong>r, o<strong>the</strong>r authors, proposed that
investment in on-<strong>the</strong>-job training and education lead to increase in organizational competitiveness
(Black, Lynch, 1996).
Human capital represents a critical knowledge source, this both in order to improve firm’s ability to
explore sources <strong>of</strong> technical knowledge outside its boundaries (external sources) and exploit
knowledge within <strong>the</strong> organization.
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Where <strong>the</strong> knowledge required is strongly tacit, and <strong>the</strong>refore difficult to transfer, its acquisition from
external sources can be granted through <strong>the</strong> recruitment <strong>of</strong> experienced individuals from o<strong>the</strong>r firms
(learning by hiring). As suggested by Song et al. (2003), since this knowledge is strongly embedded in
<strong>the</strong> human resources, <strong>the</strong> transfer <strong>of</strong> <strong>the</strong>se individuals from one organization to ano<strong>the</strong>r is an
excellent opportunity to gain knowledge o<strong>the</strong>rwise inaccessible. Moving to new organizations, such
individuals can apply <strong>the</strong>ir knowledge in new contexts, ensuring an effective transfer <strong>of</strong> knowledge
between firms.
The above discussion leads us to consider this source as critical for both kind <strong>of</strong> innovation. Therefore
<strong>the</strong> hypo<strong>the</strong>sis are:
HP3a: Process innovation is positively affected by sources <strong>of</strong> knowledge linked to human
capital assets.
HP3b: Product innovation is positively affected by sources <strong>of</strong> knowledge linked to human
capital assets.
4. Method
4.1 Sample and data collection
To test our hypo<strong>the</strong>ses, data were collected from SEs operating in <strong>the</strong> mechanical and machine
industry in <strong>the</strong> province <strong>of</strong> Pistoia (Central Italy). We chose this industry - composted mainly by micro
and small firms - because it is highly rooted in <strong>the</strong> manufacturing historical tradition <strong>of</strong> this area. In <strong>the</strong>
last century <strong>the</strong> highly skilled workforce available in this province was <strong>of</strong> attraction for a big Italian
transit manufacturer which moved to <strong>the</strong> area and which nowadays produces technologically
advanced rolling stock for railway and urban transit systems holding one <strong>of</strong> <strong>the</strong> top market position
between world producers.
The sample was randomly selected from a database provided by <strong>the</strong> local Chamber <strong>of</strong> Commerce.
Data was collected through <strong>the</strong> administration <strong>of</strong> face-to-face questionnaire. At <strong>the</strong> end <strong>of</strong> <strong>the</strong> survey
144 questionnaires have been fully filled. The coverage <strong>of</strong> <strong>the</strong> sample on 19.7% <strong>of</strong> <strong>the</strong> whole
population makes <strong>the</strong> investigation results significant for <strong>the</strong> manufacturing reality <strong>of</strong> Pistoia area.
4.2 Measurements
Dependent variable. Two measures are used to gauge innovation. First, we capture product
innovation through <strong>the</strong> question “Did your company realized a product innovation that distinguished it
from competitors?”. In addition, we use <strong>the</strong> question “Did your company realized a process innovation
that distinguished it from competitors?” to measure process innovation. Both are yes or no questions.
Independent variables. Consistent with prior studies (Foss and Pedersen, 2002 for <strong>the</strong> prior
knowledge base; Cohen and Levinthal, 1990 for knowledge sources), <strong>the</strong> analysis captured SE’s
prior knowledge base and knowledge sources using respectively 6 survey items and 8 survey items
derived from <strong>the</strong> literature. We asked to respondents to indicate <strong>the</strong> extent to which <strong>the</strong>ir companies
emphasized each item, using a seven-point likert-type response format (7=very relevant; 1=not
relevant at all).
As <strong>the</strong> comparison between companies that innovate and companies originated small sub-samples,
we decided to proceed with a factor analysis. Two orthogonal factor analysis, one for prior knowledge
and <strong>the</strong> o<strong>the</strong>r for knowledge sources, with varimax rotation produced four significant factors.
The first factor analysis produced one factor (eigenvalue= 3,189; 53% <strong>of</strong> <strong>the</strong> variance captured;
alpha= 0,828). The second factor analysis produced three factors (KMO= 0,685). The first factor
included 3 items (eigenvalue= 2,507; 31,34% <strong>of</strong> <strong>the</strong> variance captured; alpha= 0,656) and
emphasized <strong>the</strong> importance for organizational learning played by research center and universities, <strong>the</strong>
belonging to formal and informal groups <strong>of</strong> firms and <strong>the</strong> relation with associations. Considering <strong>the</strong>
knowledge sources here identified, firms that have a high level <strong>of</strong> this factor devote efforts for
establishing channels <strong>of</strong> knowledge flows and linkages through processes <strong>of</strong> cooperation with
external organizations and institutions. We labeled this factor “Learning by knowledge networking”.
The second factor, labeled “Learning by HC” (HC=human capital) , included two items and
represented <strong>the</strong> importance SEs give to human capital, both in term <strong>of</strong> internal staff and learning by
185

Vincenzo Cavaliere and Daria Sarti
hiring (eigenvalue= 1,400; 17,5% <strong>of</strong> <strong>the</strong> variance captured; alpha= 0,696). The third factor
(eigenvalue= 1,010; 12,63% <strong>of</strong> <strong>the</strong> variance captured; alpha= 0,506) captured importance given by
SEs to customers, suppliers and competitors. We named this factor “Learning by competitive
functioning”. The two factors “Learning by knowledge networking” and “Learning by competitive
functioning” represent toge<strong>the</strong>r <strong>the</strong> variable “cooperation strategies and organizational functioning” to
verify hypo<strong>the</strong>sis 2. Toge<strong>the</strong>r, <strong>the</strong> three factors explained 61,5% <strong>of</strong> <strong>the</strong> variance (Table 1).
Table 1: Factor analysis <strong>of</strong> importance given by SEs to knowledge sources.
Importance <strong>of</strong> knowledge sources
Factors
1 – Learning by knowledge
networking 2 Learning by HC
3 – Learning by
competitive functioning
Clients ,110 ,024 ,744
Suppliers ,011 -,099 ,771
Universities and Research Centres ,610 ,357 ,061
Competitors ,150 ,204 ,580
Networks and groups ,794 ,171 ,104
Associations ,824 -,014 ,132
Internal staff ,120 ,860 ,071
Hiring external staff ,174 ,829 ,012
Eigenvalue 2,507 1,400 1,010
% <strong>of</strong> variance explained 31,34% 17,50% 12,63%
% <strong>of</strong> cumulative variance explained 31,34% 48,84% 61,47%
5. Analysis and results
An independent-samples t-test was conducted to compare firms that did innovation and firms that did
not. Results show that firms that did process innovation give more importance to clients
(t(142)=3,124; p

Vincenzo Cavaliere and Daria Sarti
Table 2: Results <strong>of</strong> logistic regression analysis by innovation.
Variable
Model for Product Innovation Model for Process Innovation
b Exp (b) b Exp (b)
Intercept - 1,339*** 0,262 - 2,709*** 0,067
Prior organizational knowledge base 1,507*** 4,512 - 0,644 0,525
Learning by knowledge networking 0,036 1,037 0,526* 1,692
Learning by HC 1,171*** 3,225 1,390*** 4,015
Learning by competitive functioning 0,190 1,209 0,944** 2,570
Model Chi-square
Degrees <strong>of</strong> Freedom
Note: *p

Vincenzo Cavaliere and Daria Sarti
a specific problem <strong>of</strong> <strong>the</strong> client. In this sense, learning-by-doing play an essential role and in this
sense it is <strong>the</strong> knowledge internal to <strong>the</strong> firm and to individuals that lead to product innovation in SEs.
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189

National Knowledge Management Strategy for <strong>the</strong> TRNC:
Recommendations for Small Island Economies
Behiye Çavuşoğlu and Mustafa Sagsan
Near East University, Nicosia, Turkish Republic <strong>of</strong> Nor<strong>the</strong>rn Cyprus
behiyet@yahoo.com
msagsan@gmail.com
Abstract: In order to make national knowledge management strategy (NKMS) in particular small island
economies within developing countries, not only <strong>the</strong> macro economic indicators but also knowledge economy
variables should be considered. The paper is trying to find answer by questioning “how national knowledge
management strategies could be applied by <strong>the</strong> small island economies” based on <strong>the</strong> knowledge economy
variables such as gross domestic investment to GDP ratio, trade to GDP ratio, higher education enrollment ratio,
telephone main lines, <strong>the</strong> mobile subscribers, and <strong>the</strong> number <strong>of</strong> Internet users. At this point, <strong>the</strong> empirical part <strong>of</strong>
<strong>the</strong> study investigates <strong>the</strong> availability <strong>of</strong> knowledge economy variables <strong>of</strong> TRNC, as a small island. The results
show us that, as a developing country, TRNC has inadequate knowledge economy variables for making NKMS.
In conclusion, <strong>the</strong> advantages and disadvantages <strong>of</strong> <strong>the</strong>se inadequacies were discussed and proposed
suggestions for TRNC which is at <strong>the</strong> developing stage <strong>of</strong> economic growth.
Keywords: Knowledge economy, small island economies, national knowledge management strategy, Turkish
Republic <strong>of</strong> Nor<strong>the</strong>rn Cyprus
1. Introduction
Although knowledge management is a s<strong>of</strong>t science, one <strong>of</strong> <strong>the</strong> hardest topics <strong>of</strong> <strong>the</strong> field is knowledge
assets, which could be measured by some <strong>of</strong> <strong>the</strong> techniques, contributed to <strong>the</strong> organizational
effectiveness and based on knowledge economy indicators. Knowledge assets are <strong>the</strong> intangibles
and a part <strong>of</strong> intellectual capital (IC) within <strong>the</strong> literature. Based on <strong>the</strong> knowledge economy indicators
such as gross domestic investment to GDP ratio, trade to GDP ratio, higher education enrollment
ratio, telephone main lines, <strong>the</strong> mobile subscribers, and <strong>the</strong> number <strong>of</strong> Internet users were considered
in this study. The question on how national knowledge management strategies could be applied by
<strong>the</strong> small island economies based on <strong>the</strong>se indicators is attempted to answer. We also would like to
find answer ano<strong>the</strong>r question: what indicator exactly allows us to understand knowledge based
economy within <strong>the</strong> small island. These two fundamental research questions enable us to select a
country, which has a small island economy, for example TRNC based on <strong>the</strong> NKMS.
2. What encourages to making ‘national knowledge management strategy’ in
terms <strong>of</strong> knowledge economy?
There are four main concepts that need to be explained by defining national knowledge management
strategy: knowledge economy, intellectual capital, and information policy and knowledge assets.
2.1.1 Knowledge Economy
According to Lor and Britz (2006), a knowledge society operates within <strong>the</strong> paradigm <strong>of</strong> <strong>the</strong>
economics <strong>of</strong> information and <strong>the</strong> human capital is <strong>the</strong> prime input to production and innovation. They
also defined <strong>the</strong> knowledge as not <strong>the</strong> result <strong>of</strong> collection, processing and distribution <strong>of</strong> data;
knowledge requires application experience and <strong>the</strong> exercise <strong>of</strong> judgment. The growing codification <strong>of</strong>
knowledge and its transmission through communications and computer networks has led to <strong>the</strong>
emerging information society (Dutton, 2004). By <strong>the</strong> help <strong>of</strong> <strong>the</strong> above definitions we have a chance to
separate <strong>the</strong> knowledge society and information society. An information society besides from
knowledge society is to use, to create, to distribute <strong>the</strong> information is significant economic activity
judgment is not important.
According to Asgeirsdottir (2006: 18-22) <strong>the</strong>re are four important factors that need to be taken into
account when countries and <strong>the</strong>ir institutions want to promote <strong>the</strong>ir knowledge economy. The first one
is based on <strong>the</strong> economic fundamentals, which entails stable macroeconomic policies; wellfunctioning
labor, product and capital markets; efficient training policies in order to protect knowledge
divide; competition policies on low cost technology; liberalization <strong>of</strong> telecommunications policies; and
openness to trade and foreign direct investment. The second one refers to knowledge economy,
which includes four main issues: innovation (R&D expenditure, patents, etc), new technologies (ICT
investment in terms <strong>of</strong> its contribution to GDP growth, nanotechnology\bio technology), human capital
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(knowledge, skills and competencies, labor productivity, knowledge-intensive employment) and
enterprise dynamics (expand <strong>the</strong> boundaries across economics activity, shift resources and adjust
organizational structure based on <strong>the</strong> customers demands). Globalization is <strong>the</strong> third factor that
affects innovation, ICT, highly skilled workers on international mobility and low turnover rate <strong>of</strong>
organizations. The last and fourth factor requires new organizational form based on investing high ICT
and innovative capacity, supports teamwork and involves employee loyalty to adopt work practices
and respond customer demand timely. Information revolution, flexible organization, being
knowledgeable, skilled employee and openness to learning, creating innovation and knowledge
networks, enabling organizations to increase learning and innovation systems, competing globally,
integrating into complex chains <strong>of</strong> creation, production and distribution systems, concentrating on
network and learning economies are <strong>the</strong> most <strong>of</strong> <strong>the</strong> enablers that makes <strong>the</strong> emergence <strong>of</strong>
knowledge economy important (Houghton and Sheenan, 2000: 10-15). Two important forces
stimulate knowledge economy: <strong>the</strong> rise in knowledge intensity <strong>of</strong> economic activities (which is being
driven by <strong>the</strong> combined forces <strong>of</strong> <strong>the</strong> IT technology revolution) and <strong>the</strong> increasing globalization <strong>of</strong>
economic affairs (which is driven by national and international deregulation) (Houghton and Sheenan,
2000: 2; see also Sheenan and Tegart, 1998).
There is also alternative definition on knowledge economy which refers to libraries, archives,
museums, galleries, zoos, aquaria, and o<strong>the</strong>r systematic collection that enable individuals and
societies to know what <strong>the</strong>y know and to do what <strong>the</strong>y do based on epidemic perspective (Hedstorm
and King, 2006: 113). Porter (1999) also points out <strong>the</strong> term <strong>of</strong> ‘clusters’ which could be defined
geographic concentrations <strong>of</strong> interconnected companies and institutions in a particular filed. The
clusters might compete with each o<strong>the</strong>r if <strong>the</strong>y can access to specialized information, can harmonize
and construct a linkage <strong>the</strong> o<strong>the</strong>r cluster, can access to institutions and public goods, can provide
better motivation and performance measurement systems. The clusters can easily innovate in order to
enhance productivity and have a positive feedback loop to form a new business model. Finally,
choosing locations such as ease <strong>of</strong> transportation and communication, engaging locally like easy to
access important sources and information, working collectively are some <strong>of</strong> <strong>the</strong> important clues for <strong>the</strong>
clusters in <strong>the</strong> new economy.
2.1.2 Intellectual Capital
In order to make national knowledge management strategy, we need to determine national knowledge
assets, which is an important part <strong>of</strong> intellectual capital. Before explaining knowledge asset, it is
needed to express intellectual capital. A lot <strong>of</strong> definitions could be seen in <strong>the</strong> literature on intellectual
capital but one <strong>of</strong> <strong>the</strong> most popular and earlier explanations come from Edwinsson (1997), Streward
(1997), Brooking (1997), Sveiby (1997), and Ross, (1998) which all could be categorized within <strong>the</strong>
first generation <strong>of</strong> IC development, called ‘Raising and Awareness’ (Catasus and Chaminade, 2007).
The second one is ‘Simplifying’ and <strong>the</strong> third one is ‘Questioning and Expanding’. The fundamental
concept <strong>of</strong> knowledge assets emerges from <strong>the</strong> IC and belongs to <strong>the</strong> first generation. The seeking a
suitable and a new place <strong>of</strong> IC among <strong>the</strong> social sciences and <strong>the</strong> classifying <strong>of</strong> <strong>the</strong> field into <strong>the</strong> three
main sections (human, structural/organizational and social/relational capital) were discussed within
<strong>the</strong> second generation. The third generation underlines <strong>the</strong> IC measurement criteria, by considering
<strong>the</strong> main problematic issues on how could it be applied with and organization and society.
NKMS is required to be knowledge-intensive society. There is no doubt that knowledge and
intellectual capital are leveraged as <strong>the</strong> most important resources for growth and competitiveness and
it is an idea <strong>of</strong> a transformation form an industrial society to a knowledge society (Kline, 2006).
Intellectual capital is also considered instrumental in <strong>the</strong> determination <strong>of</strong> enterprise value and
national economic performance (Petty and Guthrie, 2000).
National knowledge management strategy requires activating innovative systems <strong>of</strong> any groups,
networks or society. It entails shared problem solving, implementing and integrating a new technical
processes and tools, experimenting and prototyping, importing and absorbing technological
knowledge from outside <strong>of</strong> <strong>the</strong> firm and learning from <strong>the</strong> market, and transferring product or services
development capabilities into developing nations (Leonard, 1995).
2.1.3 Information Policy
Information policy <strong>of</strong> a nation is ano<strong>the</strong>r important indicator that helps us to understand NKMS. In
o<strong>the</strong>r words, information policy studies are improving a parallel with NKMS based on <strong>the</strong>ir context.
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The main purpose <strong>of</strong> information policy is to show appropriate targets and <strong>the</strong> ways to <strong>the</strong> decision
makers, politicians, scholars, researchers, managers and citizens how to provide data and current
information timely and reliably (Çapar, 1997: 17). We need to determine real information users to
direct information policy in terms <strong>of</strong> qualification, quantification, identification, distributions, and user
surveys for <strong>the</strong> nations. The standards and regulatory on information should be considered by
designing strategic plan at <strong>the</strong> national level on information policy. Manpower planning should be
carried out by <strong>the</strong> politicians who are responsible for constructing national information policy within <strong>the</strong>
information sector. Technological infrastructure, s<strong>of</strong>tware and hardware industries are o<strong>the</strong>r related
issues while making information policy. We need to corporate institutions which also operate within
<strong>the</strong> information industry and eventually cost and benefits should be analyzed in providing, distributing,
creating, organizing and auditing information at <strong>the</strong> national level (Sagsan, 2000).
2.1.4 Knowledge assets
“Stocks <strong>of</strong> knowledge from which services are expected to flow for a period <strong>of</strong> time that may be hard
to specify in advance” (Boisot, 1998: 3) is one <strong>of</strong> <strong>the</strong> most popular definitions in knowledge assets. It
differs from physical assets and requires understanding in terms <strong>of</strong> quality and content <strong>of</strong> performance
outcomes (Malhotra, 2003: 3).
According to Malhotra (2000, 2003, 2004) knowledge assets can be thought <strong>of</strong> as a subset <strong>of</strong>
dispositions to act, or ‘potential for action’ embedded in individuals, groups or socio physical systems
with future prospects <strong>of</strong> value creation. National knowledge assets are <strong>the</strong> ‘intangible assets <strong>of</strong> a
country that have significant implications for future national growth and future value <strong>of</strong> <strong>the</strong> country to
various stakeholders. There is growing realization about KM as <strong>the</strong> enabler <strong>of</strong> innovation and learning
as well as national gross domestic product (GDP).
2.2 The variables <strong>of</strong> knowledge economy
In <strong>the</strong> light <strong>of</strong> <strong>the</strong>se explanations, it is not possible to make NKMS without implementing <strong>the</strong> variables
<strong>of</strong> knowledge economy. In <strong>the</strong> literature <strong>the</strong> main variables are listed as: ICT readiness, electronic
government index, <strong>the</strong> capacity <strong>of</strong> Internet infrastructure and usage, investment on R&D for
organizations, etc. The OECD economies are increasingly based on knowledge and information
(OECD, 1996). Knowledge is now recognized as <strong>the</strong> driver <strong>of</strong> productivity and economic growth.
While constructing <strong>the</strong> NKMS for TRNC, <strong>the</strong> main criteria for <strong>the</strong> knowledge economy are also taken
into consideration, such as gross domestic investment to GDP ratio, trade to GDP ratio, higher
education enrolment ratio, telephone main lines, <strong>the</strong> mobile subscribers and <strong>the</strong> number <strong>of</strong> Internet
users. In o<strong>the</strong>r words, TRNC, which is one <strong>of</strong> <strong>the</strong> small island economies, also helps us to understand
<strong>the</strong> effects <strong>of</strong> <strong>the</strong>se indicators on economic performance <strong>of</strong> <strong>the</strong> country. Small island economies are
sharing <strong>the</strong> main characteristics; NKMS for TRNC will be an important research for small islands as a
general.
2.3 Small island economy
When defining <strong>the</strong> small island economies, it is needed to understand that <strong>the</strong> island economy itself
does not necessarily imply a small economy. Smallness can be defined in terms <strong>of</strong> <strong>the</strong> physical size
(land area), population and GNP (GDP) or a combination <strong>of</strong> <strong>the</strong>se variables (Taylor, 1969: 183-202).
According to Kuznets (1955), population <strong>of</strong> ten million as <strong>the</strong> economically significant border between
small and large economies. However, in literature most <strong>of</strong> <strong>the</strong> arguments favor to use national income
to identify <strong>the</strong> size <strong>of</strong> <strong>the</strong> economy. Shand (1969: 3) systematic classification is also identified that
<strong>the</strong>re are three size indicators which diffuse <strong>the</strong> countries in debt classification such as small, very
small and micro categories. To make better analysis on small economies, we have to consider <strong>the</strong>se
three measurement criteria <strong>of</strong> smallness toge<strong>the</strong>r.
Kakazu (1994: 4) mentioned that smallness is a relative and not an absolute idea and those small
economies present unique development problems as a general characteristic. To understand <strong>the</strong>se
small island economies better let’s look <strong>the</strong>ir general characteristics and problems:
Due to <strong>the</strong>ir narrow range <strong>of</strong> human and nonhuman resources and market, economic activities
are less diversified than large economies (Kindleberger, 1968).
For economic development <strong>of</strong> <strong>the</strong> country, <strong>the</strong>re are not many choices; domestic market is very
small, limited arable land and limited population. Marshall (1927: 25) stated that a small country
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<strong>of</strong>ten had a relatively large foreign trade. The trade to GDP ratio used to measure <strong>the</strong>
dependency <strong>of</strong> <strong>the</strong> small economies on world market and also define <strong>the</strong> openness to <strong>the</strong> world
market. We can easily say that <strong>the</strong>se small economies mainly dependent upon foreign trade.
Most <strong>of</strong> <strong>the</strong> small island economies facing with chronic trade deficits which generally financed by
<strong>the</strong> foreign aids.
Diseconomies <strong>of</strong> scale in production, investment, education and administrative sciences are also
problems.
High rate <strong>of</strong> transportation cost.
Heavy dependency on government activities as a major source <strong>of</strong> income and employment.
Some small island countries are also dependent on <strong>the</strong> monetary authorities <strong>of</strong> industrial
countries, such as <strong>the</strong>y do not have an independent currency and monetary policies.
While listing <strong>the</strong> main characteristics and problems <strong>of</strong> small island economies, <strong>the</strong> paper also pointed
out <strong>the</strong> economic system <strong>of</strong> TRNC as a small island economy. In order to determine NKMS based on
knowledge economy with <strong>the</strong>ir indicators, <strong>the</strong> economic growth models allow us to understand small
island economy.
2.4 Economic growth models and <strong>the</strong> relationship with knowledge economy
In history <strong>of</strong> economic thought, growth models were favored by <strong>the</strong> both historians and economists.
While investigating <strong>the</strong> growth models, it is inevitable to recognize <strong>the</strong> technology is a common factor
for economic growth. To increase <strong>the</strong> productivity <strong>of</strong> <strong>the</strong> firm is only possible to adopt <strong>the</strong> new
technological improvements to <strong>the</strong> production process <strong>of</strong> <strong>the</strong> firm. The term 'new economy' which also
called 'knowledge economy' always mentions <strong>the</strong> knowledge is <strong>the</strong> most important production factor
which enable firm to improve its technological infrastructure and increase <strong>the</strong> production capacity.
The view taken by <strong>the</strong> contemporary literature on economic growth (Solow, 1956; Romer, 1990;
Schumpeter, 1911), technology considered ei<strong>the</strong>r endogenous or exogenous factor in production
process. As pointed out by Solow (1956), technological improvement is an exogenous model while
Romer (1990) advocated production process itself produces technology automatically, which
assumed that technological is endogenous factor. As Schumpeter (1911) stated technology is
propulsive force for economic growth and development <strong>of</strong> <strong>the</strong> countries.
According to Chavula (2010), <strong>the</strong> knowledge is at <strong>the</strong> heart <strong>of</strong> <strong>the</strong> development, which increases <strong>the</strong>
ability to take advantage <strong>of</strong> existing technologies and innovations, enhances competitiveness and
productivity. However, <strong>the</strong> author also mentioned this will dependent on <strong>the</strong> economic environment
such as educated labor force, modern information infrastructure, efficient innovations system and an
economic and institutional regime.
For economic success, while converting knowledge into innovation/technology and reach to growth,
<strong>the</strong> key item is <strong>the</strong> human capacity development. For Dahlman, Jorma, Pekka (2005), education is <strong>the</strong>
key element <strong>of</strong> <strong>the</strong> knowledge-based, innovation driven economy. It affects both <strong>the</strong> supply <strong>of</strong> and
demand for innovation. New technologies cannot be adopted in production without educated labor
force. Also, demand side is ano<strong>the</strong>r important factor that may not possible to being alive in <strong>the</strong>
absence <strong>of</strong> <strong>the</strong> educated customers. At this point <strong>of</strong> view, advanced skills and education allows
people to use physical capital more efficiently and also improve <strong>the</strong> capacity to adopt new
technologies. However, education itself does not enough. For developing countries, enhancing
knowledge is also crucial parallel <strong>of</strong> ICTs usage. ICTs allowed countries to gain competitive
advantage through innovations and technologies that creates global economic systems through
storing, sharing and analyzing <strong>the</strong> knowledge too.
3. The inferences for <strong>the</strong> TRNC based on <strong>the</strong> small island economy and <strong>the</strong>
knowledge economy
Before giving information on knowledge and small island economy for <strong>the</strong> TRNC, we would like to
mention about TRNC. TRNC is young and a new country. The root <strong>of</strong> Turkish Cypriot comes from <strong>the</strong>
Anatolia during <strong>the</strong> Ottoman Empire period (1571-1878). Cyprus is <strong>the</strong> third largest island in <strong>the</strong>
Mediterranean Sea, after Sicily and Sardinia; it is <strong>the</strong> biggest island in <strong>the</strong> Eastern Mediterranean. It
lies 65 km to <strong>the</strong> south <strong>of</strong> Turkey and 112 km to <strong>the</strong> west <strong>of</strong> Syria, its closest neighbors. After an
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unsuccessful and violent cohabitation experience under an unworkable constitution (Mallinson, 2005:
5-9), <strong>the</strong> Turkish Cypriot Community established its own state in 1983 under <strong>the</strong> name <strong>of</strong> <strong>the</strong> Turkish
Republic <strong>of</strong> Nor<strong>the</strong>rn Cyprus (TRNC, or North Cyprus), which is recognized only by Turkey.
International diplomatic efforts are underway in order to find a peaceful solution to <strong>the</strong> Cyprus
problem. The TRNC covers an area <strong>of</strong> 3,355 km2, which is approximately one-third <strong>of</strong> <strong>the</strong> island <strong>of</strong>
Cyprus. The population <strong>of</strong> <strong>the</strong> TRNC is 265,000 according to <strong>the</strong> 2006 Census. The political system <strong>of</strong>
<strong>the</strong> TRNC is parliamentary democracy (Sagsan and Yildiz, 2009: 411). TRNC has a small economy
and <strong>the</strong> country is a suitable based on our cases. There are also o<strong>the</strong>r countries which have a small
island economy such as Malta, Ireland, etc.
There are some reasons we have selected TRNC. The first one is TRNC have capacity to represent
<strong>the</strong> characteristic <strong>of</strong> small island economy. For economic growth, <strong>the</strong> country also needs to develop
appropriate NKMS based on <strong>the</strong> knowledge economy because it is also evaluated such a country
which is at <strong>the</strong> development stage. The characteristic <strong>of</strong> being small island economy is extremely
supported by TRNC. For example, trade to GDP ratio (export + import to GDP) in TRNC was 75,6% in
1990 and decreased to 45,1% in 2007 (State Planning Organization, 2010). TRNC is one <strong>of</strong> <strong>the</strong> small
island economies which always run trade deficits. The main financial instrument <strong>of</strong> <strong>the</strong> trade deficit <strong>of</strong>
TRNC is <strong>the</strong> foreign aids coming from Turkey. Because <strong>of</strong> <strong>the</strong> special condition <strong>of</strong> <strong>the</strong> TRNC
(isolation, embargoes ...), Turkey is <strong>the</strong> main country who send foreign aid. On February 2006, <strong>the</strong> EU
approved an aid regulation for <strong>the</strong> benefit <strong>of</strong> <strong>the</strong> Turkish Community, aimed at putting an end to <strong>the</strong>
isolation <strong>of</strong> <strong>the</strong> country and helping prepare for <strong>the</strong> reunification <strong>of</strong> <strong>the</strong> island. Unfortunately this aid
used for upgrading <strong>the</strong> infrastructure <strong>of</strong> SMEs, which was not enough to increase <strong>the</strong> competitiveness
<strong>of</strong> those firms. In TRNC 37,5% <strong>of</strong> <strong>the</strong> working population is working for government (State Planning
Organization, 2010). TRNC is using Turkey’s currency Turkish Lira, country not have its own
monetary policies.
3.1 Research methodology
The study assumed that <strong>the</strong> wealth <strong>of</strong> <strong>the</strong> nations depends on three important inputs which are
knowledge, technology and human resources. While measuring <strong>the</strong> impact <strong>of</strong> knowledge on
economic development <strong>of</strong> countries, <strong>the</strong> paper used knowledge variables such as economic incentive
regime, innovation, education capacity in <strong>the</strong> knowledge economy and information structure.
To measure <strong>the</strong> economic incentive regime, <strong>the</strong> model includes <strong>the</strong> factors which show <strong>the</strong> readiness
<strong>of</strong> <strong>the</strong> economic system to <strong>the</strong> absorption <strong>of</strong> <strong>the</strong> knowledge. At this point <strong>of</strong> view, cross sectional
endogenous growth model by Barro(1991) simplified and adapted to TRNC. While determining <strong>the</strong>
knowledge variables which affect <strong>the</strong> economy <strong>of</strong> TRNC, general knowledge variables tried to impose
but because <strong>of</strong> <strong>the</strong> lack <strong>of</strong> statistical data model is restricted. Trade openness, interest rate spread,
domestic credit for <strong>the</strong> private sector and tariff barriers. To measure <strong>the</strong> innovation, <strong>the</strong> model used
FDI inflows and outflows. Education capacity <strong>of</strong> country is measured by <strong>the</strong> literacy rate, gross
secondary enrolment ratio and gross territory enrolment ratio. The information infrastructure
measured by <strong>the</strong> help <strong>of</strong> fixed telephone lines per 100 population, mobile users per 100 population
and internet users per 1000 population.
3.2 Research design and data collection
The study assumed that countries wealth mainly depends on <strong>the</strong> independent inputs which are
knowledge, technology and human resources besides human capital. Knowledge variables should be
able to measure <strong>the</strong> knowledge use <strong>of</strong> <strong>the</strong> country. The study limited with <strong>the</strong> measuring <strong>the</strong> impact <strong>of</strong>
<strong>the</strong> knowledge variables on GDP per capita in TRNC and recommend <strong>the</strong> NKMS which will assist <strong>the</strong>
economic development path <strong>of</strong> <strong>the</strong> TRNC. Data collected annually from State Planning Organization
under <strong>the</strong> ro<strong>of</strong> <strong>of</strong> TRNC Prime Ministry. The main problem <strong>of</strong> <strong>the</strong> data collection was <strong>the</strong> availability <strong>of</strong>
data. The study cannot use all <strong>of</strong> <strong>the</strong> knowledge variables because <strong>of</strong> <strong>the</strong> lack <strong>of</strong> statistics.
3.3 The model
Cross-sectional endogenous growth model by Barro (1991) was analyzed <strong>the</strong> impact <strong>of</strong> knowledge on
<strong>the</strong> long term average growth rate <strong>of</strong> <strong>the</strong> African economies. The case <strong>of</strong> Barro (1991) concentrates
on <strong>the</strong> convergence in income levels. However, we are not primarily interested in convergence;
instead, we are interested in finding out <strong>the</strong> knowledge variables for <strong>the</strong> TRNC and examining <strong>the</strong>
effects <strong>of</strong> <strong>the</strong>m on economic growth <strong>of</strong> <strong>the</strong> island. Barro (1991) model imposed on 49 African
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countries, but <strong>the</strong> model for this analysis constructed and imposed only for <strong>the</strong> TRNC on <strong>the</strong> base <strong>of</strong>
availability <strong>of</strong> data and variables. To investigate <strong>the</strong> impact <strong>of</strong> knowledge on economic growth, <strong>the</strong>
study used <strong>the</strong> following model:
Yi = α0+ α1 X1İ + α2X2i + α3 X3i +α4 X4İ +α5X5i + α6X6i +u
Where Y is TRNC’s average per capita GDP between 1990 and 2007, X1 is average gross domestic
investment to GDP ratio, X2 is trade (export +import) to GDP ratio, X3 is higher education enrolment
ratio, X4 is telephone main lines per 1000 people between 1990 and 2007, X5 is <strong>the</strong> mobile
subscribers between 1994 and 2007, X6 is <strong>the</strong> number <strong>of</strong> internet users from 1996 to 2007.
The study not used exactly <strong>the</strong> same variables with Barro (1991) because <strong>of</strong> <strong>the</strong> lack <strong>of</strong> statistical
data. The study used limited variables such as, per capita GDP as a measure <strong>of</strong> people’s living
standards, investment to GDP ratio to measure capital wealth, gross higher education enrolment as a
measure <strong>of</strong> human capital, and telephone main lines, mobile subscribers, and <strong>the</strong> number <strong>of</strong> Internet
users as <strong>the</strong> knowledge variables. The model is tried to test <strong>the</strong> impact <strong>of</strong> those knowledge variables
on growth <strong>of</strong> <strong>the</strong> TRNC’s economy.
3.4 Empirical results and findings
To measure <strong>the</strong> impact <strong>of</strong> <strong>the</strong> different knowledge variables on economic growth <strong>of</strong> <strong>the</strong> TRNC, <strong>the</strong>
study used Ordinary Least Square Method. The estimated results are presented in Table 1 below.
Table 1: Knowledge Impact on Per Capita GDP in TRNC (1990-2007)
Dependent Variable, Y
Method, Least Squares
Sample 1990-2007
Included Observations 18
C (constant)
X1
X2
X3
X4
X5
X6
R-squared
Adjusted R-squared
S.E. <strong>of</strong> regression
Durbin-Watson statistics
F-statistics
Prob(F-statistics)
Coefficient
-6323.4005
848.1343
-61.8029
159.0038
-28.2369
3.8915
1.5016
0.73016
0.71316
689.1882
1.566947
17.51896*
0.0000019
Std. Error
3676.0996
138.4263
290.9103
21.12449
59.50319
1.012642
0.9857
t-statistic
-0.715696
3.143628*
-0.879112
3.475670*
-2.016792**
3.468754*
2.7091**
( * = Significant at 1% significant level; ** = Significant at 5% significant level)
p-value
0.4868
0.0078
0.3953
0.0041
0.0015
0.0042
0.0007
The table reports <strong>the</strong> results <strong>of</strong> <strong>the</strong> OLS regression on <strong>the</strong> five variables with necessary diagnostic
tests which show that <strong>the</strong> model is statistically significant with conventional levels <strong>of</strong> significance. The
result show that average gross domestic investment to GDP ratio, higher education enrolment ratio,
mobile subscribers and <strong>the</strong> number <strong>of</strong> Internet users are positively related; while trade openness and
telephone mobile lines negatively related with GDP per capita. Average gross domestic investment to
GDP ratio has <strong>the</strong> highest impact on GDP per person, means that TRNC is new and young country
and economy is in <strong>the</strong> factor based stage. An additional investment creates huge amount <strong>of</strong> impact on
GDP per person. O<strong>the</strong>r variable which represents trade openness have a negative relationship
between GDP with insignificant t-value. In this case, we do not pay too much attention on this result. It
may result <strong>of</strong> <strong>the</strong> huge and chronic trade deficit <strong>of</strong> <strong>the</strong> country. Higher education is ano<strong>the</strong>r significant
variable for <strong>the</strong> explanation <strong>of</strong> GDP per capita. As a result <strong>of</strong> <strong>the</strong> regression analysis, when <strong>the</strong>
enrolment ratio to higher education increase GDP per capita is also increase and <strong>the</strong> impact <strong>of</strong> this
variable on GDP per capita is considerable high. Since, enrolment ratio <strong>of</strong> higher education is <strong>the</strong>
variable which represents knowledge, <strong>the</strong> importance <strong>of</strong> <strong>the</strong> improvement in knowledge for <strong>the</strong>
economic development <strong>of</strong> TRNC has been increased. According to <strong>the</strong> regression results, telephone
main lines and GDP per capita is negatively related, and <strong>the</strong> numbers <strong>of</strong> Internet users are statistically
significant. The results show that continued enhancements <strong>of</strong> <strong>the</strong> information infrastructure with
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Behiye Çavuşoğlu and Mustafa Sagsan
investments in ICT would play a crucial role to improve <strong>the</strong> TRNC‘s per capita income. One <strong>of</strong> <strong>the</strong>
most important factors which effects GDP per capita is domestic investment to GDP ratio as well as
knowledge variables has a positive impact besides telephone main line on GDP per capita. In this
case, TRNC policy makers should consider that <strong>the</strong> investments which creates directly positive effect
on GDP per capita direct to <strong>the</strong> knowledge economy variables in order to make NKMS.
The result from <strong>the</strong> analysis suggests that TRNC’s economy is not a knowledge economy yet, but
main knowledge variables have a positive and significant effect on GDP per capita <strong>of</strong> <strong>the</strong> country. By
this point <strong>of</strong> view, in order to make NKMS for <strong>the</strong> country, <strong>the</strong> decision makers need to improve
educational capacity, information literacy, knowledge assets, technological infrastructure, innovative
and creative skills for <strong>the</strong> pr<strong>of</strong>essionals. Fur<strong>the</strong>rmore, <strong>the</strong> capacity <strong>of</strong> intellectual usage and economic
incentive regime, those also indicate for being information and knowledge society, are important
factors to make NKMS for <strong>the</strong> TRNC.
4. Conclusion and directions
In order to make NKMS for <strong>the</strong> TRNC, which indicates <strong>the</strong> characteristic <strong>of</strong> small island economy, <strong>the</strong>
model helps us to focus on <strong>the</strong> specific variables. It is obviously seem that, <strong>the</strong> investments play
important role for <strong>the</strong> GDP per capita. If <strong>the</strong>se investment can directed on knowledge variables by <strong>the</strong>
policy makers, economic growth <strong>of</strong> <strong>the</strong> country will be accelerated by considering NKMS. Although <strong>the</strong>
country has sufficient capacity on knowledge economy, for example, 100 % literacy rate, 81% higher
education enrolment ratio and $16, 158 GDP per capita, <strong>the</strong>re is no specific NKMS within <strong>the</strong> country.
The lack <strong>of</strong> awareness about making long term objective policy on information and knowledge, <strong>the</strong>
lack <strong>of</strong> pr<strong>of</strong>essional employees on knowledge management, inadequate domestic manufacturing
industry and <strong>the</strong> existence <strong>of</strong> isolations are some <strong>of</strong> <strong>the</strong> barriers that <strong>the</strong> TRNC should overcome in
order to adopt knowledge economy. More specifically, TRNC has to:
follow efficient training policies to protect knowledge divide and intellectual capital
make stable macroeconomic policies in particularly on investments by considering <strong>the</strong> knowledge
assets such as patents, trademarks, copyrights, etc.
follow low-cost leadership strategy in order to produce technological hardware and s<strong>of</strong>tware
support openness to trade and foreign direct investments
create awareness on <strong>the</strong> knowledge based economy indicators at <strong>the</strong> governmental level by
using e-government and e-governance index
ga<strong>the</strong>r more data based on <strong>the</strong> knowledge economy indicator from <strong>the</strong> society and related
organizations periodically
work collaboratively benefiting from <strong>the</strong> best practices <strong>of</strong> countries which have small island
economy
establish a ministry as soon as possible, related to information and knowledge works in <strong>the</strong>
country in cooperating with <strong>the</strong> o<strong>the</strong>r ministries
has to improve e-government efforts in order to get positive feedback from <strong>the</strong> citizens based on
<strong>the</strong> knowledge economy variables
focus on <strong>the</strong> tacit dimension <strong>of</strong> knowledge; because it requires lateral hierarchy, which allows
public organizations to create more knowledge assets within <strong>the</strong> socialization environment based
on <strong>the</strong> NKMS.
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197

Implementing a work-life balance culture in SMEs though
relational learning
Juan-Gabriel Cegarra-Navarro 1 , Mª Eugenia Sánchez-Vidal 1 and David Cegarra-
Leiva 2
1
Universidad Politécnica de Cartagena, Spain
2
Universidad de Murcia, Spain
juan.cegarra@upct.es
meugenia.sanchez@upct.es
davidcegarra@um.es
Abstract: A possible explanation for <strong>the</strong> numerous failures in <strong>the</strong> implementation and use <strong>of</strong> work life balance
(WLB) practices may relate to <strong>the</strong> fact that <strong>the</strong> majority <strong>of</strong> companies have only introduced <strong>the</strong>m without exploring
<strong>the</strong> market. This paper analyses <strong>the</strong> relationships between relational learning and WLB culture and tries to
identify whe<strong>the</strong>r WLB culture impacts on <strong>the</strong> business performance through an empirical study <strong>of</strong> 229 SMEs in
<strong>the</strong> Spanish metal industry. Our findings show that in order to support a positive attitude toward WLB, managers
need to provide and support a relearning process. These findings provide interesting insights into <strong>the</strong> drivers <strong>of</strong>
organisational performance for SMEs using a WLB-supporting culture.
Keywords: learning context, work-life balance culture, and organisational performance.
1. Introduction
In recent years, work life balance (WLB) practices have ranked among <strong>the</strong> most popular benefits for
employees, as <strong>the</strong>y can help foster employees’ quality <strong>of</strong> life and, as a consequence, workers will be
more satisfied, motivated and committed to <strong>the</strong> company (Allen, 2001). However, scholars have
highlighted that <strong>the</strong> existence <strong>of</strong> WLB practices does not guarantee <strong>the</strong>ir use. Research has revealed
that organisational support for WLB (operationalised in this paper as WLB culture) has greater impact
on employees´ and organisational outcomes than <strong>the</strong> implementation <strong>of</strong> formal WLB initiatives. The
importance <strong>of</strong> <strong>the</strong> WLB culture is even higher in SMEs, as WLB practices are not formalised but
individually negotiated between <strong>the</strong> manager and <strong>the</strong> employees. The key benefits <strong>of</strong> encouraging a
WLB culture in SMEs are clear; it enables <strong>the</strong>m to retain skilled workers, reduces costs through better
productivity and efficiency (improving services to customers) and increases pr<strong>of</strong>itability (Hughes and
Bozionelos, 2007).
However, despite <strong>the</strong> opportunities that a WLB culture will <strong>of</strong>fer as a result <strong>of</strong> its influence on
productivity, turnover and absenteeism, <strong>the</strong> fact is that SMEs have been slow to adopt WLB cultures
(Kirby and Krone, 2002). This is largely because very few organisations are prepared to face this
challenge (Poelmans et al., 2003). Regarding this, previous studies in business management confirm
that <strong>the</strong> numerous failures in <strong>the</strong> implementation <strong>of</strong> WLB cultures are a consequence <strong>of</strong> ignorance
(Poelmans et al., 2003). It should be noted, however, that creating new a new culture frequently
generates internal problems when it conflicts with <strong>the</strong> current culture in terms <strong>of</strong> <strong>the</strong> beliefs, habits and
things individuals take for granted (Argyris and Schön, 1978).
The ideas outlined above provide an illustration that, in order to streng<strong>the</strong>n a WLB culture and thus
positively influence employee satisfaction, an SME must be flexible when configuring (combining)
knowledge in a way that is appropriate for delivering value to <strong>the</strong> company and be effective in
updating <strong>the</strong> actual knowledge <strong>of</strong> its staff. Under this framework, <strong>the</strong> presence <strong>of</strong> an internal context
that fosters <strong>the</strong> replacement <strong>of</strong> old knowledge could be essential for SMEs that try to create new
knowledge (e.g. a change <strong>of</strong> attitude). The relational learning process represents <strong>the</strong> mechanism by
which <strong>the</strong> organization transforms <strong>the</strong> tacit and explicit knowledge <strong>of</strong> <strong>the</strong> environment into valuable
knowledge (Cegarra & Rodrigo, 2003). At its heart, this process attempts to re-orientate
organisational values, norms and/or behaviour by changing cognitive structures and core
assumptions which guide behaviour (Cegarra, 2007). Thus, <strong>the</strong> contribution <strong>of</strong> that process is related
to its ability to pave <strong>the</strong> way for new knowledge.
There is no empirical evidence, particularly in relation to SMEs, to support <strong>the</strong> concept <strong>of</strong> relational
learning and how it relates to a WLB culture, or to <strong>the</strong> improvement <strong>of</strong> organisational performance
(Lavoie, 2004). In this paper we test our postulated hypo<strong>the</strong>ses about such relationships, <strong>the</strong>
relational learning, <strong>the</strong> WLB culture and <strong>the</strong> improvement <strong>of</strong> organisational performance. These
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Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
relationships are examined through an empirical investigation <strong>of</strong> 229 SMEs in <strong>the</strong> Spanish metal
sector. The processes that characterise <strong>the</strong> relational learning process and <strong>the</strong> WLB culture are
discussed in detail in <strong>the</strong> following section. Section 2 also investigates <strong>the</strong> development <strong>of</strong> hypo<strong>the</strong>ses
as to how <strong>the</strong> relational learning and <strong>the</strong> WLB culture contribute to business performance. Details <strong>of</strong>
<strong>the</strong> survey which was used to collect appropriate data to test <strong>the</strong> models is presented in section 3,
whilst <strong>the</strong> results <strong>of</strong> testing <strong>the</strong> models are presented in section 4, followed by a discussion in section
5.
2. Conceptual framework
WLB initiatives are considered among <strong>the</strong> human resource practices that are believed to be more
relevant today (Fleetwood, 2007). Although <strong>the</strong>re is no clear definition <strong>of</strong> WLB, it could be described as
<strong>the</strong> “individuals´ ability <strong>of</strong> finding a life rhythm that allows <strong>the</strong>m to combine <strong>the</strong>ir work with o<strong>the</strong>r
responsibilities, activities or aspirations, irrespective <strong>of</strong> age and gender” (Felstead et al., 2002). In
relevant literature, authors have considered two different domains regarding WLB: “WLB practice” and
“WLB culture”. Several academics stress, for example, that formal WLB organisational policies have
less impact on organisations and employees than informal organisational support for WLB (Behson,
2005). O<strong>the</strong>r authors also affirm that WLB efforts have historically focused on implementing WLB
practices ra<strong>the</strong>r than changing <strong>the</strong> WLB culture within organisations (Harrington and Ladge, 2009).
WLB culture is defined as <strong>the</strong> “shared assumptions, beliefs and values regarding <strong>the</strong> extent to which
an organisation supports and values <strong>the</strong> integration <strong>of</strong> employees´ work and family lives” (Thompson et
al., 1999). Allen (2001) calls firms with high levels <strong>of</strong> WLB values ‘Family-Supportive Organisations’
(FSO), and Cappelli (2000) fur<strong>the</strong>r emphasises that a WLB culture is a key element <strong>of</strong> an
organisation’s employee retention strategies. There are several studies in relevant literature examining
which aspects characterise a WLB culture. One <strong>of</strong> <strong>the</strong> predominant approaches in <strong>the</strong> literature is <strong>the</strong>
one <strong>of</strong> Thompson et al., (1999) that divides WLB culture into three sub-dimensions: managerial
support for WLB, perceived negative career consequences <strong>of</strong> using work-family benefits and
organisational time expectations.
This approach has been used by o<strong>the</strong>r researchers, such as O´Neill et al. (2009) who found that <strong>the</strong>
managerial support for WLB was <strong>the</strong> most important factor <strong>of</strong> WLB culture and <strong>the</strong> main determinant <strong>of</strong>
lower turnover intentions and employee commitment. Managerial support for WLB indicates how
managers empathise with employees´ efforts to balance <strong>the</strong>ir work and life and are sensitive to <strong>the</strong>ir
family responsibilities. We consider that in SMEs, <strong>the</strong> managerial support for WLB is <strong>the</strong> factor to be
considered, as managers are <strong>the</strong> key decision makers who have <strong>the</strong> last word in deciding who enjoy
WLB benefits. Moreover, as workers <strong>of</strong> SMEs normally negotiate <strong>the</strong> use <strong>of</strong> WLB practices with <strong>the</strong>ir
managers, <strong>the</strong> positive attitude <strong>of</strong> managers towards WLB becomes key to support and encourage a
WLB culture in small organisations. Hence, in this paper, <strong>the</strong> term WLB culture is used to describe
companies in which WLB values are supported and encouraged by management.
The creation <strong>of</strong> a WLB culture supposes, in each case, <strong>the</strong> reactivation and development <strong>of</strong> new
values which, in turn, foster learning and <strong>the</strong> integration <strong>of</strong> new knowledge in members <strong>of</strong> <strong>the</strong>
organisation. For example, managers’ strategies which endeavour to address <strong>the</strong> lack <strong>of</strong> harmony
between work demands and life commitments include: employee compliance with new practices; <strong>the</strong>
exchange <strong>of</strong> information and sharing <strong>of</strong> personal concerns; acceptance <strong>of</strong> constraints related to
responsibilities in life; and collaboration with colleagues to accommodate <strong>the</strong> work/life needs <strong>of</strong> a
diverse workforce (Joplin & Daus 1997; Dass & Parker, 1999). It is worth noting that managers
implement <strong>the</strong>se WLB initiatives based on concerns expressed by employees or o<strong>the</strong>r stakeholders
(groups affected by <strong>the</strong> organisation’s values and practices) or through observing <strong>the</strong> benefits
achieved by o<strong>the</strong>r companies through <strong>the</strong>ir WLB programmes or because <strong>of</strong> <strong>the</strong>ir personal preferences
(Carlson et al., 2006).
However, creating new knowledge frequently generates internal problems when it conflicts with
current knowledge in terms <strong>of</strong> <strong>the</strong> beliefs, habits and things individuals take for granted (Argyris &
Schön, 1978). As Macinnes (2005) stated, one <strong>of</strong> <strong>the</strong> factors preventing WLB culture is managers’
negative attitude towards WLB practices due to perceived ambiguous results (e.g. <strong>the</strong> perceived cost
<strong>of</strong> implementing some initiatives). As noted above, WLB practices demand different organisational
architectures and <strong>the</strong>y change <strong>the</strong> economics <strong>of</strong> <strong>the</strong> industry (Poelmans and Beham, 2008).
Consequently, <strong>the</strong>y destroy existing company competences, create new value networks in which to
compete and challenge some <strong>of</strong> <strong>the</strong> managerial functions (e.g. commanding, organising, coordinating,
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Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
and controlling) with highly uncertain outcomes. Under this framework, <strong>the</strong> presence <strong>of</strong> an internal
context that fosters <strong>the</strong> replacement <strong>of</strong> old knowledge could be essential for organisations that try to
implement a WLB culture.
Bearing in mind <strong>the</strong>se considerations, <strong>the</strong> relational learning process represents <strong>the</strong> mechanism by
which <strong>the</strong> organization transforms <strong>the</strong> tacit and explicit knowledge <strong>of</strong> <strong>the</strong> environment into valuable
knowledge. This learning process is enhanced by <strong>the</strong> development <strong>of</strong> conscious knowledge (i.e.
language, conversation and dialogue, teamwork, polyvalence, and social relationships). This
encourages open sharing <strong>of</strong> information and removes constraints on information and communication
flows (Woodman et al. 1993). This is especially true for SMEs; a small business consists <strong>of</strong>
‘individuals whose personal constructs and personal understanding may in part be anchored to a
collective construct <strong>of</strong> meaning to be used for <strong>the</strong> good <strong>of</strong> <strong>the</strong> members <strong>of</strong> <strong>the</strong> organisation and for <strong>the</strong>
good <strong>of</strong> <strong>the</strong> organisation itself’ (Wyer and Mason, 1998:9). The process <strong>of</strong> learning for SMEs can take
place in many forms, such as face to face meetings, telephone conversations, email or creating
communities <strong>of</strong> practice where customers and vendors interact and work toge<strong>the</strong>r for <strong>the</strong> achievement
<strong>of</strong> a particular objective (Gibb, 1997; Cope and Watts, 2000).
The considerations above lead us to argue that for a given organisation ‘knowledge’, both external
and internal to <strong>the</strong> organization, needs to be critically examined since it may be relevant (Van den
Bent et al., 1999). As indicated above, to get an updated view <strong>of</strong> work-life conflict and to understand
its effects, managers have to examine <strong>the</strong> phenomena from a number <strong>of</strong> different angles. If <strong>the</strong>y come
to rely uncritically on internal knowledge, <strong>the</strong>y are likely to become less creative (Sinkula et al., 1997).
Under <strong>the</strong>se circumstances, <strong>the</strong> atmosphere most likely to induce self-renewal and essentially correct
this state <strong>of</strong> affairs is one that promotes openness to new ideas and <strong>the</strong> critical evaluation <strong>of</strong> signals
that are contrary to established beliefs and values (Akgün et al., 2007). Thus, we argue that it is
through <strong>the</strong> implementation <strong>of</strong> processes that result in <strong>the</strong> establishment <strong>of</strong> an ‘relational learning
process’ that members <strong>of</strong> an organisation are able to identify outdated procedures, rules and
processes. In addition, it becomes possible to identify values, behaviours, attitudes and habits that
have become inappropriate (Huber, 1991). Such questioning may also lead to new interpretations <strong>of</strong>
existing knowledge or <strong>the</strong> elimination <strong>of</strong> what was formerly considered to be knowledge or accepted
wisdom (Nonaka and Takeuchi, 1995). The hypo<strong>the</strong>sis we propose under this framework is:
H1: The relational learning process is positively associated with <strong>the</strong> WLB supporting culture
Relevant literature stresses that <strong>the</strong> existence <strong>of</strong> a WLB culture help both employees and
organisations obtain beneficial results. Among <strong>the</strong> benefits <strong>of</strong> implementing a WLB culture, academics
highlight improvement in public image as <strong>the</strong> company is seen to be socially responsible (Harrington
and Ladge, 2009). There are also o<strong>the</strong>r more tangible benefits, such as reduction <strong>of</strong> absenteeism,
lower stress levels, higher levels <strong>of</strong> productivity and performance and greater quality <strong>of</strong> life, satisfaction
and commitment among employees (Hughes and Bozionelos, 2007). In addition, as indicated above, a
WLB culture is considered key for retaining and attracting highly qualified pr<strong>of</strong>essionals (Harrington
and Ladge, 2009). Moreover, as O´Neill et al. (2009) argue, companies should take care <strong>of</strong> <strong>the</strong>ir
leaders’ WLB, implementing and negotiating WLB practices with <strong>the</strong>m, because <strong>of</strong> <strong>the</strong> crossover
effects <strong>of</strong> <strong>the</strong>ir work-to-family relationship spill over to <strong>the</strong> intentions <strong>of</strong> o<strong>the</strong>r employees in <strong>the</strong>
company with regard to commitment and turnover. O´Neill et al. (2009) call this phenomenon <strong>the</strong>
“contagion effect”. Under this framework, individuals that perceive that <strong>the</strong>ir companies are taking care
<strong>of</strong> <strong>the</strong>ir well-being (e.g. through WLB practices) might experience positive feeling towards <strong>the</strong> source <strong>of</strong>
that beneficial treatment and, thus, <strong>the</strong>ir satisfaction, commitment and performance in <strong>the</strong> organisation
is increased.
These considerations allow us to frame <strong>the</strong> second hypo<strong>the</strong>sis <strong>of</strong> <strong>the</strong> work:
H2: The existence <strong>of</strong> a WLB supporting culture will determine <strong>the</strong> extent to which <strong>the</strong>
company achieves better performance
3. Method
3.1 Data collection
The metal industry in south east Spain was <strong>the</strong> subject <strong>of</strong> our data collection. The metal sector is one
<strong>of</strong> <strong>the</strong> most important industrial sectors <strong>of</strong> <strong>the</strong> Spanish economy. According to <strong>the</strong> State Industry
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Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
Survey (INE, 2007a) <strong>the</strong> metal industry produced an output worth EUR 229,022 million, and it
employed 625,014 workers in 2007 (24.22% <strong>of</strong> Spanish industrial workers). Annual business turnover
represents 36.59% <strong>of</strong> total industrial turnover. Metal transformation activity is composed <strong>of</strong>
approximately 52,250 establishments, in which 96.8% have fewer than 50 workers and, among <strong>the</strong>m,
84.18% have even fewer than 10 employees (INE, 2007a). The metal industry usually absorbs
29.05% <strong>of</strong> <strong>the</strong> total employment in industry in <strong>the</strong> south-east <strong>of</strong> Spain (INE, 2007b). 91.38% <strong>of</strong> <strong>the</strong>
companies employ fewer than 50 workers and, among <strong>the</strong>m, 83.46% <strong>of</strong> <strong>the</strong> companies do not have
more than 10 workers (INE, 2007b). Fur<strong>the</strong>rmore, <strong>the</strong> metal industry in south-east Spain is an
appropriate setting for an investigation into a relational learning context and its impact on a work-life
balance supporting culture because <strong>the</strong> sector has suffered increased competition in <strong>the</strong> last year,
which creates a very dynamic environment with high levels <strong>of</strong> competition, requiring <strong>the</strong> intensive use
and updating <strong>of</strong> knowledge.
According to <strong>the</strong> Establishments Industrial Register (Carm, 2009) <strong>the</strong> metal industry is composed <strong>of</strong>
832 SMEs. We used a list <strong>of</strong> small companies (832 companies) provided by <strong>the</strong> Establishments
Industrial Register (http://www.carm.es) as an initial sampling frame. All companies were invited to
participate in <strong>the</strong> study, and 263 agreed. The information was collected by personal visits to all
companies that agreed. In a first visit, managers were given a questionnaire to be filled in and were
given an explanation <strong>of</strong> every part <strong>of</strong> <strong>the</strong> questionnaire. They were assured <strong>of</strong> its strictly scientific and
confidential character, as well as <strong>the</strong> global and anonymous treatment <strong>of</strong> <strong>the</strong> data. They were also
told that in case <strong>of</strong> doubt <strong>the</strong>y could contact <strong>the</strong> researchers who would answer <strong>the</strong>ir questions. In a
second visit, <strong>the</strong>y were asked to give <strong>the</strong> questionnaire back. In total, 610 visits were carried out and
230 surveys were collected.
Surveying took place over a period <strong>of</strong> eight months, from March to October 2009. Only those
companies which provided complete answers to <strong>the</strong> survey questions were included in our analysis.
As a result, one company was excluded for failing to provide complete answers. Consequently, we
had 229 complete surveys giving a response rate <strong>of</strong> 27.52% <strong>of</strong> <strong>the</strong> total number <strong>of</strong> companies invited
to participate (87.07% response rate from <strong>the</strong> companies who agreed), with a factor <strong>of</strong> error <strong>of</strong> 5.5%
for p=q=50% and a reliability level <strong>of</strong> 95.5%. All companies were classified according to <strong>the</strong> <strong>European</strong>
Union classification as SMEs 1 , with a total number <strong>of</strong> employees under 250. Most <strong>of</strong> <strong>the</strong> firms employ
between 10 and 49 individuals: 88.2% <strong>of</strong> <strong>the</strong>m have fewer than 50 workers and 23.58% fewer than
10.
3.2 Measures
Churchill’s (1979) approach to questionnaire development was used. Scales were combined from
several o<strong>the</strong>r relevant empirical studies with new items to make an initial list <strong>of</strong> 9 items (3 measuring
<strong>the</strong> range <strong>of</strong> relational learning, 3 measuring <strong>the</strong> range <strong>of</strong> <strong>the</strong> work-life balance supporting culture and
3 relating to organisational performance). The questionnaire constructs comprised:
Consistent with Cegarra & Rodrigo (2003), items that tapped <strong>the</strong> relational learning process were
interwoven with issues related to encouraging individuals in <strong>the</strong> organization to track changing
markets and share market intelligence with external agents.
Work-life balance supporting culture (WBC) was measured using a scale <strong>of</strong> five items taken from
previous studies (K<strong>of</strong>odimos, 1995; Burke, 2004; Burke et al., 2006). These items recognise
support <strong>of</strong> policies, rules, reporting structures and decision-making protocols that encourage <strong>the</strong>
introduction <strong>of</strong> work-family benefit measures and approaches to improve worker family lives.
The measures <strong>of</strong> perceived organisational performance (OP) are based on five measures <strong>of</strong>
perceptions <strong>of</strong> organisational performance used by Delanie and Huselid (1996). Therefore, we
measured perceived organisational performance by asking <strong>the</strong> managers to evaluate different
aspects <strong>of</strong> <strong>the</strong>ir business results in <strong>the</strong> last 3 years compared to competitors.
In order to obtain a more robust evaluation <strong>of</strong> <strong>the</strong> quality <strong>of</strong> <strong>the</strong> resulting 9 items, a confirmatory
analysis (CFA) was carried out using <strong>the</strong> covariance matrix as input via <strong>the</strong> EQS 6.1 robust maximum
likelihood method. The CFA produced a good fit with an incremental fit index (IFI) <strong>of</strong> 0.98 and a
comparative fit index (CFI) <strong>of</strong> 0.98 (also, Satorra-Bentler χ 2
(80)= 85.16; χ 2 /d.f= 1.06 and RMSEA=
1 According to <strong>the</strong> <strong>European</strong> Commission (2003), SMEs comprise <strong>of</strong> fewer than 250 employees, with an annual turnover not exceeding EUR
50 million, and an annual balance sheet total not exceeding EUR 43 million.
201

Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
0.033). From an examination <strong>of</strong> <strong>the</strong> results shown in Table 1, we can state that all <strong>of</strong> <strong>the</strong> constructs
are reliable. For all <strong>the</strong> measures, Bagozzi and Yi’s (1988) composite reliability index and Fornell and
Larker’s (1981) average variance extracted index are higher than <strong>the</strong> evaluation criteria <strong>of</strong> 0.7 for
composite reliability and 0.5 for <strong>the</strong> average variance extracted.
Table 1: Factor loadings <strong>of</strong> <strong>the</strong> resulting items and scale reliability
g g y
Construct Standardiz
ed loading
t-value Reliability
(SCR a ., AVE b The framework for relational learning (RL)
)
In this business unit, we meet with customers at least once a year to find
out what products or services <strong>the</strong>y will need in <strong>the</strong> future 0.90
17.942 AVE=0.51
We poll end users at least once a year to assess <strong>the</strong> quality <strong>of</strong> our product
and services 0.94
16.840 SCR=0.76
We periodically review <strong>the</strong> likely effect <strong>of</strong> changes in our business
environment (e.g., regulation) on customers
Work life balance culture (WBC)
0.93
10.274
Starting a family, becoming pregnant or adopting a child 0.744 10.986 AVE=0.657
Going home during <strong>the</strong> workday to attend personal responsibilities such
as a sick child 0.846
13.637 SCR=0.851
Setting limits on hours spent at work
Organizational performance (OP)
0.699 10.387
Quality <strong>of</strong> products, services and programmes 0.682 8.817 AVE=0.684
Ability to retain essential employees 0.664 9.254 SCR=0.866
Satisfaction <strong>of</strong> customers or clients
Notes:
The fit statistics for <strong>the</strong> measurement model were:
0.731 10.618
Satorra-Bentler χ 2 (80)= 85.16; χ 2 /d.f= 1.06; GFI=0.94; CFI=0.98; IFI=0.98; RMSEA= 0.033.
a 2 2
Scale Composite Reliability (SCR) <strong>of</strong> pc= (Σλi) var (ξ) / [(Σλi) var (ξ) +Σ θii] (Bagozzi and Yi, 1998).
b 2
Average variance extracted (AVE) <strong>of</strong> pc= (? λi2 var (ξ))/[? λi var (ξ) + ? θii] (Fornell and Larcker, 1981).
The asymptotic covariance matrices were generated to obtain <strong>the</strong> scaled chi-square (Satorra and Bentler, 1988) and robus
estimation <strong>of</strong> standard errors.
Discriminant validity was determined by comparing <strong>the</strong> square root <strong>of</strong> <strong>the</strong> AVE (i.e., <strong>the</strong> diagonals in
Table 2) with <strong>the</strong> correlations among constructs (i.e., <strong>the</strong> lower triangle <strong>of</strong> <strong>the</strong> matrix in Table 2). On
average, each construct related more strongly to its own measures than to o<strong>the</strong>rs (Fornell & Larcker,
1981). In <strong>the</strong> interest <strong>of</strong> thorough discriminant validity, an additional test was examined, which
supports this assumption since <strong>the</strong> confidence interval (± 2 standard errors) around <strong>the</strong> correlation
estimated between any two latent indicators never includes 1.0 (Anderson & Gerbing, 1988). The
constructs correlation matrix, shared variances, means and standard deviations are shown in Table 2.
Table 2: Construct correlation matrix
Correlation matrix
Mean S.D CA 1 2 3
1. Relational learning 4.93 1.29 0.92 0.71
2. Work life balance culture 4.44 1.29 0.79 0.38 0.80
3. Organizational performance
Notes:
5.37 0.86 0.72 0.46 0.33 0.82
Mean = <strong>the</strong> average score for all <strong>of</strong> <strong>the</strong> items included in this measure; S.D. = Standard Deviation; CA = Cronbach’s
Alpha; Intercorrelations are presented in <strong>the</strong> lower and shady triangle <strong>of</strong> <strong>the</strong> matrix. The bold numbers on <strong>the</strong>
diagonal are <strong>the</strong> square root <strong>of</strong> <strong>the</strong> Average Variance Extracted.
Once <strong>the</strong> psychometric properties <strong>of</strong> <strong>the</strong> measures had been checked, <strong>the</strong> next step was <strong>the</strong>
evaluation <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>sised relationships developed from consideration <strong>of</strong> <strong>the</strong> relevant literature
(see Figure 1), discussed in <strong>the</strong> text as H1–H2. The fit <strong>of</strong> <strong>the</strong> model is satisfactory (Satorra-Bentler
χ2(85)=110.342; χ2/d.f= 1.29; GFI=0.927; CFI=0.980; IFI=0.973; RMSEA= 0.037), <strong>the</strong>reby suggesting
that <strong>the</strong> nomological network <strong>of</strong> relationships fits our data – ano<strong>the</strong>r indicator <strong>of</strong> support for <strong>the</strong> validity
<strong>of</strong> <strong>the</strong>se scales (Churchill, 1979). Figure 1 shows that <strong>the</strong> relational learning process had a positive
influence on <strong>the</strong> existence <strong>of</strong> a work-life balance supporting culture at a level <strong>of</strong> (p

Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
Relational
learning
Unlearning
context
0. 459 *** 0. 447 ***
Notes:
The fit statistics for <strong>the</strong> measurement model were: GFI=0.927; CFI=0.980; IFI=0.973; RMSEA= 0.037
***p < 0.01
Figure 1: Model statisitcs
4. Discussion
R2 R =0. 20 2 =0. 20
Organizational
performance
A possible explanation for <strong>the</strong> numerous failures in <strong>the</strong> implementation <strong>of</strong> WLB supporting cultures
may relate to <strong>the</strong> fact that <strong>the</strong> majority <strong>of</strong> companies have only introduced WLB practices without
modifying <strong>the</strong> existing prevalent culture in <strong>the</strong> company. This paper analyses <strong>the</strong> relationships
between relational learning and WLB culture and tries to identify whe<strong>the</strong>r WLB culture impacts on <strong>the</strong>
business performance through an empirical study <strong>of</strong> 229 SMEs in <strong>the</strong> Spanish metal industry.
Therefore, <strong>the</strong> first contribution <strong>of</strong> this research is to question <strong>the</strong> existing models which relate to WLB
culture and business performance.
Our findings show that in order to support a positive aptitude toward WLB, managers need to provide
and support a relational learning. We think that this is an important finding, as potential for any small
enterprise to develop will depend substantially on its ability to maintain a balance between
responsibilities at work and at home, thus, managers may be trapped in a suboptimal stable
equilibrium. As many overloaded managers are paring <strong>the</strong>ir resources, <strong>the</strong>y are not actively listening
to <strong>the</strong>ir employees, and managers may be over-investing in <strong>the</strong> development <strong>of</strong> WLB practices
preserving and following old beliefs and traditions (i.e. <strong>the</strong> old culture).
Adapting to a new culture is not an easy task, particularly while <strong>the</strong>ir founders or owners are still
actively involved with <strong>the</strong> business. For example, not everyone has <strong>the</strong> time or inclination to build a
WLB supporting culture. Under this framework, relational learning could be a necessary task to adopt
a new WLB culture that is not compatible with <strong>the</strong> current culture, especially for those companies
urged to retain skills and knowledge for longer periods <strong>of</strong> time (Harrington and Ladge, 2009). As
Chirico (2008) noted, a bad knowledge creation network proves a cause <strong>of</strong> difficulties and
misalignments to <strong>the</strong> operational environment <strong>of</strong> family firms, mostly due to <strong>the</strong> peculiar nature <strong>of</strong> <strong>the</strong>
decision-making processes in SMEs, which are generally not very formalised and are very centralised
(Chirico and Salvato, 2008). In this regard, this paper provides a starting point to bring toge<strong>the</strong>r
managers and employees in <strong>the</strong> implementation <strong>of</strong> a WLB culture from which WLB practices for <strong>the</strong>
diverse needs <strong>of</strong> <strong>the</strong> workforce can derive. From our framework, we suggest that any SME wishing to
implement a new WLB culture should initially make efforts to update <strong>the</strong> relevant knowledge <strong>of</strong> its
workforce. This is vital to <strong>the</strong> processes that are needed to meet <strong>the</strong> changing needs <strong>of</strong> building a
new culture.
This research’s second contribution is provided by <strong>the</strong> results <strong>of</strong> empirically testing <strong>the</strong> proposed
hypo<strong>the</strong>ses. The managerial implications <strong>of</strong> <strong>the</strong> relationships observed between <strong>the</strong> factors that
constitute <strong>the</strong> conceptual framework shown in Figure 1 are discussed in more detail below.
With respect to <strong>the</strong> test <strong>of</strong> hypo<strong>the</strong>sis H1, <strong>the</strong> results support <strong>the</strong> position that, in order to implement a
WLB supporting culture and hence foster <strong>the</strong> adoption <strong>of</strong> WLB practices, companies need to provide
and support a relational learning process. One interpretation <strong>of</strong> this relationship is that through <strong>the</strong>
relational learning process, a company can allow individuals to adjust <strong>the</strong>ir mental models and <strong>the</strong>
nature <strong>of</strong> <strong>the</strong> assumptions shared to break current workplace culture. Without care, organisations can
fall into a ‘competence trap’ (Leonard-Barton, 1992), increasingly exploiting obsolete competences, or
<strong>the</strong>y can fall into a ‘failure trap’ (Levinthal and March, 1993), where a failure while exploring new
opportunities may lead to more research and change, and so to failure again, which leads to more
research and so on. This means that a relational learning process can encourage individuals to
question not only <strong>the</strong> information <strong>the</strong>y own but also whe<strong>the</strong>r <strong>the</strong>ir particular approach to adopt a new
WLB culture is applicable or not (Sinkula et al., 1997). Such questioning may also lead to new
203

Juan-Gabriel Cegarra-Navarro, Mª Eugenia Sánchez-Vidal and David Cegarra-Leiva
interpretations <strong>of</strong> existing knowledge or <strong>the</strong> elimination <strong>of</strong> what was formerly considered to be
knowledge or accepted wisdom.
With regard to H2, <strong>the</strong> results support <strong>the</strong> position that business performance is likely to suffer if an
SME does not have a WLB-supporting culture. This confirms <strong>the</strong> position adopted by Malik and
Wilson (1995) when <strong>the</strong>y emphasise <strong>the</strong> role <strong>of</strong> organisational climate in organisational performance
and effectiveness. Therefore, for an SME to grow and prosper in a dynamic environment, such as <strong>the</strong>
Spanish metal industry during <strong>the</strong> period we have examined, it is necessary for management to foster
a WLB-supporting culture (Allen, 2001). This is in accordance to <strong>the</strong> social exchange <strong>the</strong>ory that
emphasises <strong>the</strong> importance <strong>of</strong> employee perceptions <strong>of</strong> managerial support. Hence, this research
highlights <strong>the</strong> importance <strong>of</strong> support from <strong>the</strong> managerial team for WLB initiatives in organisations.
This also confirms <strong>the</strong> views <strong>of</strong> authors, such as Scandura and Lankau (1997) and Hughes and
Bozionelos (2007), who argue that WLB is much more than flexible working hours – it is about
employee fulfilment and engagement resulting in increased productivity and competitive advantage.
Therefore, a WLB-supporting culture does benefit <strong>the</strong> workforce but it also increases company
productivity (Harrington and Ladge, 2009). Considering this, we argue that executives and o<strong>the</strong>r
senior leaders must signal <strong>the</strong>ir level <strong>of</strong> support for addressing work life issues in order to increase
organisational results.
The study has some limitations. Firstly, <strong>the</strong> research is based on self-reports, which can bias findings.
In addition, <strong>the</strong> transverse nature <strong>of</strong> this research impedes analysis <strong>of</strong> <strong>the</strong> effect <strong>of</strong> time on
organisational results, <strong>the</strong> WLB-supporting culture and level <strong>of</strong> implementation <strong>of</strong> WLB practices.
Finally, this research was performed in a specific country and sector <strong>of</strong> activity, which might prevent
<strong>the</strong> generalisation <strong>of</strong> <strong>the</strong> results to o<strong>the</strong>r sectors or countries.
The limitations <strong>of</strong> <strong>the</strong> current research could be addressed in future studies. For example, one line <strong>of</strong>
research could be to examine employees’ views on <strong>the</strong> WLB-supporting culture in order to obtain <strong>the</strong>
workforce view point. Ano<strong>the</strong>r line <strong>of</strong> research could be to develop a longitudinal study that examines
<strong>the</strong> changes in <strong>the</strong> variables <strong>of</strong> <strong>the</strong> study over time. On <strong>the</strong> o<strong>the</strong>r hand, we encourage conducting
research in o<strong>the</strong>r sectors <strong>of</strong> activity and types <strong>of</strong> firms (large organisations) and examining <strong>the</strong> effect
<strong>of</strong> <strong>the</strong> existence <strong>of</strong> a relational learning process on <strong>the</strong> implementation <strong>of</strong> o<strong>the</strong>r organisational policies
or practices. Finally, we suggest future researchers to investigate <strong>the</strong> factors that dissuade managers
from adopting an relational learning process in <strong>the</strong>ir firms, in order to develop recommendations for
improvements in <strong>the</strong> future.
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205

The Influence <strong>of</strong> Repatriation Support and Social Climate
Perceptions on Repatriate Knowledge Sharing
Huei-Fang Chen and Yi-Wen Lin
Department <strong>of</strong> International Business, Soochow University, Taipei, Taiwan
hfchen@scu.edu.tw
yiwencmgsh@hotmail.com
Abstract: Research on knowledge management in multinational corporations has generally focused on
subsidiaries. Only a few studies have explored knowledge management in connection with international
assignees. Repatriate knowledge-sharing is an important channel for accumulating foreign experience and
knowledge within multinational corporations. Researchers recommend that human resource practitioners at
multinationals focus on repatriates to identify <strong>the</strong> knowledge assets <strong>the</strong>y hold. A better understanding <strong>of</strong> this
problem may help to lower resistance to knowledge-sharing by international assignees who have returned home.
Given that companies can influence <strong>the</strong> interactions, behaviors, and motivation <strong>of</strong> <strong>the</strong>ir employees using various
human resource practices, it stands to reason that multinationals can motivate individual repatriates to share <strong>the</strong>ir
knowledge by implementing appropriate repatriation practices. Social climate is one kind <strong>of</strong> organizational
environment that may affect employee attitudes. Researchers have defined social climate as a collective set <strong>of</strong>
norms, values, and beliefs that reflect employee views <strong>of</strong> how <strong>the</strong>y interact with one ano<strong>the</strong>r while carrying out
tasks for a firm. Previous research has confirmed that human resource practices help to create a social climate
that facilitates knowledge exchange. It is surprising that so few studies focus on <strong>the</strong> relationship between
repatriation practices, social climate and knowledge management issues in a multinational environment. To fill
this gap, this research considers repatriation practices and draws on social climate <strong>the</strong>ory, working to investigate
<strong>the</strong> influence <strong>of</strong> such practices on repatriates’ willingness to disseminate <strong>the</strong>ir knowledge and <strong>the</strong>ir knowledgesharing
behavior within <strong>the</strong>ir home country. We used a questionnaire survey to collect our empirical data. The
research subjects were repatriates from Taiwanese enterprises with operations abroad. Two hundred and
thirteen valid samples were included in <strong>the</strong> final analysis. Structural equation modeling was used to test <strong>the</strong> path
relationships among <strong>the</strong> variables in <strong>the</strong> research framework. The major conclusion <strong>of</strong> this study is that employee
perceptions <strong>of</strong> repatriation support have a positive and significant influence on perceptions regarding <strong>the</strong> social
climate. The results also demonstrate that <strong>the</strong> social climate as perceived by repatriates has a significant and
positive influence on <strong>the</strong>ir willingness to widely disseminate <strong>the</strong>ir acquired knowledge. Such willingness, in turn,
has a positive and significant impact on repatriate knowledge-sharing. This study extends <strong>the</strong> concept <strong>of</strong>
knowledge management from <strong>the</strong> organizational level to <strong>the</strong> individual repatriate level. As such, it adds academic
value to <strong>the</strong> study <strong>of</strong> international human resource management. All in all, <strong>the</strong> study results provide new insight
into repatriate management. International human resource managers might consider implementing appropriate
repatriation support measures (including repatriation training, repatriation assistance, and repatriation
compensation) to create a high-quality social climate as perceived by repatriates. This process could encourage
repatriates to disseminate knowledge and, in turn, enhance knowledge-sharing behavior in <strong>the</strong> home country. We
also acknowledge certain limitations and suggest potentially fruitful avenues for future research.
Keywords: Repatriation support, social climate, disseminative willingness, knowledge sharing
1. Introduction
Research on knowledge management in multinational corporations has generally focused on
subsidiaries (Minbaeva 2005; Bjorkman, Barner-Rasmussen and Li 2004; Buckley and Carter 2004;
Foss and Pedersen 2004; Gupta and Govindarajan 2000). Only a few studies have explored
knowledge management in connection with international assignees (Kwok and Gao 2005; Minbaeva
and Michailova 2004; Riusala and Suutari 2004). Blakeney, Oddou and Osland (2006) recommend
that human resource practitioners at multinationals focus on expatriates and repatriates to identify <strong>the</strong>
knowledge assets that <strong>the</strong>y hold. A better understanding <strong>of</strong> this question might lower resistance to
knowledge-sharing by international assignees when <strong>the</strong>y come home (Dowling, Festing and Engle
2008). Because knowledge-sharing at multinationals is mostly driven by <strong>the</strong> contributions <strong>of</strong>
international assignees, it may be valuable to shift <strong>the</strong> focus back to <strong>the</strong> individual level to discuss
knowledge management issues at multinational corporations (MNCs).
Huselid (1995) and Wright, Dunford and Snell (2001) have argued that companies can influence <strong>the</strong>
interactions, behaviors, and motivation <strong>of</strong> employees using various human resource practices. For
instance, MNCs might motivate individual repatriates to share knowledge widely by ensuring that
international assignees have appropriate repatriation support (Lazarova and Tarique 2005). Such
support could include repatriation training, assistance with <strong>the</strong> return home, and appropriate
compensation for repatriates. Furuya, Stevens, Bird, Oddou and Mendenhall (2009) have shown that
organization support and repatriate policies have a positive and significant influence on <strong>the</strong> transfer <strong>of</strong>
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Huei-Fang Chen and Yi-Wen Lin
global competencies. Therefore, it is reasonable to predict that perceptions <strong>of</strong> strong repatriation
support will affect <strong>the</strong> attitudes and behavior <strong>of</strong> repatriates with regard to knowledge-sharing.
Social climate is one kind <strong>of</strong> organizational environment that may affect employee attitudes. Smith,
Collins and Clark (2005) define social climate as a collective set <strong>of</strong> norms, values, and beliefs that
reflect employee views regarding how <strong>the</strong>y interact with one ano<strong>the</strong>r while carrying out tasks for <strong>the</strong>ir
firm. Collins and Smith (2006) argue that human resource practices help to create a social climate that
facilitates knowledge exchange and in turn positively influences firm performance. However, <strong>the</strong>re
have been no studies that have empirically applied this concept to an MNC. Inspired by <strong>the</strong> research
<strong>of</strong> Collins and Smith (2006), this study suggests that perceptions regarding repatriation support may
affect repatriates’ perceptions regarding <strong>the</strong>ir social climate and affect <strong>the</strong>ir attitudes and behavior
with regard to knowledge-sharing upon <strong>the</strong>ir return.
Researchers working on MNC knowledge management have argued that <strong>the</strong> disseminative capacity
<strong>of</strong> knowledge senders is one <strong>of</strong> <strong>the</strong> determinants <strong>of</strong> knowledge transfer (Simonin 1999; Minbaeva and
Michailova 2004). Minbaeva and Michailova (2004) have suggested that disseminative capacity
depends on <strong>the</strong> ability and willingness <strong>of</strong> employees to transfer knowledge. When expatriates return
home as repatriates, <strong>the</strong>y serve as vehicles for knowledge-sharing (Dowling et al. 2008). Therefore, it
is important to explore <strong>the</strong> willingness and ability <strong>of</strong> repatriates to share <strong>the</strong> knowledge that <strong>the</strong>y have
acquired abroad, as such sharing may increase <strong>the</strong> competitive advantage <strong>of</strong> a multinational
corporation. It is surprising that so few studies focus on <strong>the</strong> relationship between repatriation
practices, social climate, and disseminative capacity as facets <strong>of</strong> knowledge management in an MNC
environment. To fill this gap, this research examines repatriation practices and builds on social climate
<strong>the</strong>ory to explore <strong>the</strong>ir influence on repatriates’ willingness to share information and knowledgesharing
behavior in <strong>the</strong>ir home country.
2. Literature review and hypo<strong>the</strong>ses
2.1 Repatriation support and social climate
Researchers have argued that companies can effectively influence <strong>the</strong> interactions, behaviors and
attitudes <strong>of</strong> employees using different human resource practices (Huselid 1995; Wright et al. 2001;
Collins and Smith 2006). Furuya et al. (2009) note that <strong>the</strong>re is no guarantee that repatriates will
transfer global management competencies acquired on an international assignment. They identify two
<strong>of</strong> <strong>the</strong> four factors that may encourage successful transfer: strong organizational support and
constructive repatriation policies and practices on <strong>the</strong> part <strong>of</strong> <strong>the</strong> firm. Based on <strong>the</strong>ir study, we define
repatriation support as including repatriation training, repatriation assistance, and repatriation
compensation arrangements.
Many researchers have argued that human resource practices do not directly affect performance but
instead foster social climates that facilitate <strong>the</strong> development <strong>of</strong> employee-based capabilities, which in
turn create a competitive advantage (Bowen and Ostr<strong>of</strong>f 2004; Collins and Clark 2003; Collins and
Smith 2006). The social climate <strong>of</strong> a firm can affect employee abilities and motivation, and create or
inhibit opportunities for <strong>the</strong>m to exchange and combine <strong>the</strong>ir knowledge (Argote, McEvily and
Reagans 2003; Kogut and Zander 1992). In particular, <strong>the</strong> relational aspects <strong>of</strong> social climate,
including trust, cooperation, and a shared code and language, are key mechanisms that enable
employees to exchange and recombine knowledge (Nahapiet and Ghoshal 1998; Smith, Collins and
Clark 2005). In this study, trust, cooperation, and a shared code and language are regarded as <strong>the</strong>
three core dimensions <strong>of</strong> social climate.
First, Mayer, Davis and Schoorman (1995) define trust as “<strong>the</strong> willingness <strong>of</strong> a party to be vulnerable
to <strong>the</strong> actions <strong>of</strong> ano<strong>the</strong>r party based on <strong>the</strong> expectation that <strong>the</strong> o<strong>the</strong>r will perform a particular action
important to <strong>the</strong> truster, regardless <strong>of</strong> <strong>the</strong> ability to monitor or control <strong>the</strong> o<strong>the</strong>r party”. Trust, <strong>the</strong>
authors suggest, is rooted in three related components: ability (<strong>the</strong> belief that <strong>the</strong> o<strong>the</strong>r is capable and
skilled), benevolence (<strong>the</strong> desire to do good for <strong>the</strong> o<strong>the</strong>r), and integrity (<strong>the</strong> belief that <strong>the</strong> o<strong>the</strong>r is
motivated by principles <strong>of</strong> fairness). Following Collins and Smith (2006), we suggest in a home
country environment, a social climate <strong>of</strong> trust as perceived by repatriates is related to human resource
practices associated with repatriation. If repatriates receive training and support from <strong>the</strong>ir company
along with appropriate compensation, <strong>the</strong>y should perceive <strong>the</strong>ir colleagues as more trustworthy.
Secondly, Collins and Smith (2006) defined a social climate for cooperation as <strong>the</strong> organizational
norms that emphasize personal effort to achieving group outcomes as opposed to individual
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Huei-Fang Chen and Yi-Wen Lin
outcomes. If repatriates perceive <strong>the</strong>mselves as well-treated during <strong>the</strong> repatriation process, <strong>the</strong>y may
reciprocate by making an effort to work with <strong>the</strong>ir colleagues, thus enhancing <strong>the</strong> climate <strong>of</strong>
cooperation.
Thirdly, shared codes and language within <strong>the</strong> social climate create a common set <strong>of</strong> terms, symbols,
and perspectives that allow individuals to communicate effectively with one ano<strong>the</strong>r (Collins and Smith
2006). Repatriation-related practices can help repatriates to become familiar with <strong>the</strong> code and
language <strong>of</strong> <strong>the</strong> company and elicit greater commitment to <strong>the</strong>ir firm. These individuals will <strong>the</strong>n have
a greater interest in learning company norms and codes <strong>of</strong> action.
The empirical results presented by Collins and Smith (2006) show that human resource practices are
positively related to organizational social climate. By <strong>the</strong> same token, we predict that perceived
repatriation support will have a positive impact on <strong>the</strong> social climate that repatriates perceive as
greeting <strong>the</strong>m in <strong>the</strong>ir home country. Therefore, <strong>the</strong> following hypo<strong>the</strong>sis is proposed:
Hypo<strong>the</strong>sis 1: Repatriation support perception will have a positive influence on
repatriates’ perceptions regarding <strong>the</strong>ir social climate.
2.2 Disseminative willingness
Research on knowledge transfer in multinationals has consistently indicated that knowledge senders’
disseminative capacity is a determinant <strong>of</strong> knowledge transfer. Minbaeva and Michailova (2004) reoperationalize
disseminative capacity as dependent on <strong>the</strong> ability and <strong>the</strong> willingness to share MNC
knowledge. Many studies indicate that <strong>the</strong> behavior <strong>of</strong> knowledge senders depends on individual
characteristics, which include previous knowledge and experience and willingness to transfer
knowledge (Husted and Michailova 2002; Michailova and Husted 2003; Cabrera 2003). The decision
to share knowledge is largely individual and is driven by at least two behavioral factors: ability and
willingness, which are both essential to achieving high-performance knowledge transfer at any level
(Minbaeva and Michailova, 2004).
Bonache and Zarraga-Oberty (2008) suggest that human resource practices play a key role in
international knowledge transfer. When returning to <strong>the</strong>ir home country, repatriates may display more
or less willingness to share knowledge as a result <strong>of</strong> human resource practices such as repatriation
training. Repatriates will feel grateful for organizational support and compensation, which in turn will
increase <strong>the</strong>ir willingness to share <strong>the</strong>ir international knowledge with <strong>the</strong>ir colleagues. Therefore, <strong>the</strong>
following hypo<strong>the</strong>sis is presented:
Hypo<strong>the</strong>sis 2: The perception <strong>of</strong> repatriation support will have a positive influence on
repatriates’ willingness to disseminate <strong>the</strong> knowledge that <strong>the</strong>y possess.
Cabrera (2003) suggests that norms that encourage <strong>the</strong> open exchange <strong>of</strong> knowledge among
organization members will lead to greater knowledge-sharing. A social climate <strong>of</strong> trust is essential to
increasing interactions and <strong>the</strong> likelihood <strong>of</strong> information exchange among individuals (Nahapiet and
Ghoshal 1998). A cooperative climate limits competition among employees and increases <strong>the</strong>ir
willingness to share critical information (Szulanski 1996). Moreover, shared codes and language
facilitate both access to information and <strong>the</strong> integration <strong>of</strong> knowledge (Collins and Smith 2006).
Therefore, when repatriates return home, a social climate marked by trust, cooperation, and shared
codes and language will encourage <strong>the</strong>m to share <strong>the</strong>ir international knowledge with <strong>the</strong>ir colleagues
at home. The following hypo<strong>the</strong>sis is thus presented:
Hypo<strong>the</strong>sis 3: Perceived social climate will have a positive influence on repatriates’
willingness to share <strong>the</strong>ir knowledge.
2.3 Repatriate knowledge-sharing
Researchers have suggested that knowledge senders’ disseminative capacity is a determinant <strong>of</strong>
knowledge transfer (Minbaeva and Michailova, 2004). Disseminative capacity includes <strong>the</strong> ability and
<strong>the</strong> willingness <strong>of</strong> organizational actors to transfer knowledge. Repatriates with <strong>the</strong> willingness to
share <strong>the</strong>ir knowledge with home-country colleagues may exhibit a higher level <strong>of</strong> knowledge-sharing
behavior in <strong>the</strong>ir home country. Therefore, we propose <strong>the</strong> following hypo<strong>the</strong>sis:
Hypo<strong>the</strong>sis 4: Repatriates’ willingness to disseminate knowledge will have a positive
influence on <strong>the</strong>ir knowledge-sharing behavior at home.
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3. Methodology
3.1 Procedure and data collection
Huei-Fang Chen and Yi-Wen Lin
The research subjects were repatriates from Taiwanese firms that have operations abroad. Before
distributing <strong>the</strong> questionnaires to <strong>the</strong> entire sample, we asked 10 MBA students and 10 current
repatriates to review <strong>the</strong> questionnaire. Then, we incorporated <strong>the</strong>ir suggestions into <strong>the</strong>
questionnaire where appropriate. We formed a research team to implement this project. The team
used convenience sampling to collect data. The team collected <strong>the</strong> data during <strong>the</strong> first quarter <strong>of</strong>
2009 and processed <strong>the</strong> survey through multiple channels, including in-person visits, regular mail, email,
and web sites. A preliminary test showed that <strong>the</strong> method <strong>of</strong> data collection made no significant
difference in <strong>the</strong> background information for <strong>the</strong> survey.
The team collected 213 valid repatriate samples for analysis. Sixty percent had returned from China,
16 percent were back from o<strong>the</strong>r Asian countries, 10 percent had returned from North America, eight
percent were returning from <strong>European</strong> countries, and five percent had been living in o<strong>the</strong>r areas.
Eighty percent <strong>of</strong> <strong>the</strong> respondents were male and 20 percent female. Large companies employed 66
percent <strong>of</strong> <strong>the</strong> repatriates and small and medium-sized companies 34 percent. At <strong>the</strong> time <strong>of</strong> <strong>the</strong>
survey, half <strong>of</strong> <strong>the</strong> respondents (50 percent) held executive positions at home country. Of <strong>the</strong>se
repatriates, 25 percent worked in high-tech industries, 32 percent worked in traditional industries, and
28 percent were employed in financial and service industries. Most <strong>of</strong> <strong>the</strong> respondents (92 percent)
had graduated from college. Twenty-nine percent <strong>of</strong> <strong>the</strong> respondents had worked overseas for less
than one year, 38 percent had worked abroad for one to three years, 14 percent had worked outside
<strong>the</strong> country for three to five years, and 15 percent had worked abroad for more than five years.
3.2 Measures
In addition to a section used to record demographic data (as above), <strong>the</strong> repatriate questionnaire
included four parts, with one measuring each <strong>of</strong> <strong>the</strong> major research variables: perceptions regarding
repatriation support, perceptions regarding social climate, willingness to disseminate information and
knowledge-sharing.
3.2.1 Repatriation support perception
There are three categories <strong>of</strong> repatriation support practices: repatriation training, repatriation
assistance, and repatriate compensation. Self-reported responses to 10 questions based on Lazarova
and Caligiuri (2001) were provided using a 7-point Likert-type scale (1 = ‘completely disagree’ to 7 =
‘completely agree’). Sample items included ‘The company provided me with appropriate training
during repatriation’; ‘The company provided me with assistance during repatriation’; and ‘The
company has a clear compensation arrangement for repatriates’. The Cronbach’s α <strong>of</strong> <strong>the</strong> ten items is
0.93.
3.2.2 Social climate perception
A nine-item scale based on Collins and Smith (2006) was used to assess social climate as perceived
by repatriates. Self-reporting for three items was designed to indicate trust, cooperation, and shared
codes and language; again, a 7-point Likert-type scale was used (1 = ‘completely disagree’ to 7 =
‘completely agree’). Sample items included ‘I feel that my home country colleagues keep <strong>the</strong>ir
promises’; ‘I work cooperatively with home country employees’; and ‘The home country employees
and I are always on <strong>the</strong> same page when we talk about work’. Because <strong>of</strong> a failure to converge, one
trust-related item was deleted. The Cronbach’s α <strong>of</strong> <strong>the</strong> eight items is 0.93.
3.2.3 Disseminative willingness
This section included five items on disseminative willingness, modified from Minbaeva and Michailova
(2004) and Reagans and McEvily (2003). The values for <strong>the</strong>se items were measured using a 7-point
Likert-type scale (1 = completely disagree to 7 = completely agree). Sample items include ‘I am willing
to communicate related knowledge to home country employees.’ The Cronbach’s α is 0.95 for <strong>the</strong>se
five items.
209

3.2.4 Knowledge-sharing
Huei-Fang Chen and Yi-Wen Lin
This section included three items on knowledge-sharing modified from <strong>the</strong> ideas <strong>of</strong> Bock and Kim
(2002). The values for <strong>the</strong>se items were measured using a 7-point Likert-type scale (1 = completely
disagree to 7 = completely agree). One sample item for repatriate knowledge-sharing is ‘I <strong>of</strong>ten
actively tell my colleagues about <strong>the</strong> working knowledge <strong>of</strong> overseas.’ The Cronbach’s α is 0.88 for
<strong>the</strong> three items. Because <strong>the</strong> answers to this survey were self-reported, we used Harman’s one-factor
test and scale item trimming, as suggested by Podsak<strong>of</strong>f and Organ (1986), to address <strong>the</strong> issue <strong>of</strong>
common method variance. Confirmatory factor analysis was conducted to test whe<strong>the</strong>r <strong>the</strong> four-factor
model was superior to <strong>the</strong> one-factor model. The results show that <strong>the</strong> four-factor model (GFI = 0.97;
CFI = 0.99; NFI = 0.98; RMSEA = 0.049) better fits <strong>the</strong> data than <strong>the</strong> single-factor model (GFI = 0.72;
CFI = 0.77; NFI = 0.76; RMSEA = 0.27). Thus, common method variance was not a serious problem
in this study.
3.3 Reliability and validity
A measurement model was used to test <strong>the</strong> reliability and <strong>the</strong> convergent and discriminant validity <strong>of</strong>
<strong>the</strong> instrument items. The results show that all reliability measures (Cronbach’s α) are above <strong>the</strong>
recommended level <strong>of</strong> 0.70 (0.88~0.95), thus indicating adequate internal consistency (Nunnally,
1978). Convergent validity is demonstrated when items load high (factor loading > 0.50) on <strong>the</strong>ir
associated factors (Hair, Black, Babin, Anderson and Tatham 2006). The results <strong>of</strong> <strong>the</strong> confirmatory
factor analysis show that all measures have significant factor loadings (λ) (0.67~0.97) and in fact
loaded higher than <strong>the</strong> suggested threshold. Convergent validity was also adequate when <strong>the</strong>
constructs had an average variance extracted (AVE) <strong>of</strong> at least 0.50 (Hair et al. 2006). Table 1 lists
<strong>the</strong> correlation matrix, which indicates <strong>the</strong> correlations among <strong>the</strong> constructs and <strong>the</strong> square root <strong>of</strong>
AVE on <strong>the</strong> diagonal (0.76~0.94); all AVEs were above <strong>the</strong> recommended level <strong>of</strong> 0.50.
Discriminant validity is used to assess <strong>the</strong> extent to which constructs differ. Based on Anderson and
Gerbing’s (1988) recommendations, we assessed discriminant validity for two estimated constructs by
limiting <strong>the</strong> estimated correlation parameter (φi, j) to 1.0 and <strong>the</strong>n performing a chi-squared difference
test on <strong>the</strong> values obtained for <strong>the</strong> constrained and unconstrained models.
In accordance with Bagozzi, Yi and Phillips (1991) and Anderson and Gerbing (1988), we tested
discriminant validity by comparing six constrained models with <strong>the</strong> unconstrained model using a chisquared
difference test for each data set. The results showed that nei<strong>the</strong>r <strong>the</strong> chi-squared values nor
<strong>the</strong> fit indices <strong>of</strong> <strong>the</strong> six constrained models were significantly superior to those <strong>of</strong> <strong>the</strong> unconstrained
model. These four constructs thus exhibit adequate discriminant validity.
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4. Results
Huei-Fang Chen and Yi-Wen Lin
Structural equation modeling was used to test <strong>the</strong> path relationships among <strong>the</strong> variables. Figure 1
shows that <strong>the</strong> research model fits <strong>the</strong> data very well. The χ 2 /df <strong>of</strong> 30.90/18 (1.72) is below <strong>the</strong>
recommended value <strong>of</strong> 5.0 (Hair et al., 2006). The four major indices (NFI = 0.97, NNFI = 0.98, CFI =
0.99, GFI = 0.96) are all above <strong>the</strong> recommended level <strong>of</strong> 0.90 (Hair et al. 2006). RMSEA is 0.058.
The standardized path coefficients for <strong>the</strong> structural model are presented in Table 2.
It is apparent that repatriate perceptions <strong>of</strong> repatriation support have a positive and significant
influence on perceptions regarding social climate; γ11 = 0.54 (t = 7.12, p < 0.001). Hypo<strong>the</strong>sis 1 (which
proposed that perceptions regarding repatriation support will have a positive influence on repatriates’
perceptions regarding social climate) is thus supported. This finding is similar to those proposed in
Collins and Smith (2006) but is specific to repatriation.
However, repatriate perceptions <strong>of</strong> repatriation support have a negative and significant influence on
willingness to disseminate information; γ21 = -0.35 (t = -4.69, p

Huei-Fang Chen and Yi-Wen Lin
that perceptions regarding repatriation support will have a positive influence on repatriates’
willingness to disseminate <strong>the</strong>ir knowledge) is not supported. The results require fur<strong>the</strong>r discussion.
Perceptions regarding social climate have a positive and significant effect on disseminative
willingness, β21 = 0.83 (t = 10.17, p

5.3 Limitations and suggestions
Huei-Fang Chen and Yi-Wen Lin
As with all research, this study has certain limitations. These limitations suggest potentially fruitful
avenues for future research. First, although self-reported data can be somewhat problematic in an
empirical study, <strong>the</strong>y can also serve to simplify a complex international study. The validity test
conducted as part <strong>of</strong> this study has limited this disadvantage to an acceptable level.
Secondly, this research used convenience sampling, and <strong>the</strong> results may be somewhat limited in
explaining <strong>the</strong> general situation. Moreover, because <strong>the</strong> research subjects were all incumbent
Taiwanese business repatriates returning from mainly Asian countries, <strong>the</strong> conclusions presented
here may apply only or primarily to Asian repatriates. Broader samples <strong>of</strong> respondents (including
repatriates from o<strong>the</strong>r nations or localities) could be used to determine whe<strong>the</strong>r <strong>the</strong> findings <strong>of</strong> this
research framework can be generalized to o<strong>the</strong>r locations and populations.
Thirdly, this study focused on a few major constructs (repatriation support, social climate, willingness
to disseminate knowledge, and knowledge-sharing), and o<strong>the</strong>r variables may also be worth
considering. For example, knowledge application or social capital could be considered in future
studies.
Finally, this study used cross-sectional surveys to collect data on repatriates. We recommend that
future research employ a longitudinal design and follow <strong>the</strong> same group <strong>of</strong> international assignees
from expatriation to repatriation. It would be valuable to examine <strong>the</strong> relationships between <strong>the</strong>
variables from a long-term perspective.
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214

Social Capital, Knowledge Sharing and Intellectual Capital
in <strong>the</strong> Web 2 Enabled World
Marguerite Cronk
Harding University, Searcy, USA
rcronk@harding.edu
Abstract: Web 2 technologies have facilitated an unprecedented era <strong>of</strong> social knowledge sharing. Many
businesses are examining how <strong>the</strong>y can tap into this phenomenon to enhance knowledge sharing within <strong>the</strong>
organization. This study explores links between social capitol created through online social network knowledge
sharing and Intellectual capitol. It is suggested that within <strong>the</strong> organizational context, intellectual capitol can be
generated from social capitol through knowledge sharing, facilitated by web 2 technologies. The benefits <strong>of</strong> web 2
technologies in <strong>the</strong> area <strong>of</strong> knowledge sharing are well documented, however this study suggests that web 2
technologies not only provide <strong>the</strong> platform to share but also <strong>the</strong> motivation to share as participants gain and
benefit from increased social capitol, which may in turn overcome perceptions <strong>of</strong> ‘loss <strong>of</strong> personal competitive
advantage’ concerns associated with traditional knowledge sharing.
Keywords: Knowledge sharing, Intellectual capital, Web 2, Social Capital, Knowledge management
1. Introduction
It is a time <strong>of</strong> unprecedented internet access and connections between individuals. There is no
shortage <strong>of</strong> statistics on <strong>the</strong> exponential growth <strong>of</strong> web 2 sites. The rapid increase in <strong>the</strong> number and
type <strong>of</strong> web 2 sites, such as wikis, online social networks, blogs, Flickr, YouTube, discussion forums,
mashups, and so forth makes it almost pointless to quote usage statistics, as by <strong>the</strong> time this article is
published <strong>the</strong>se figures will have changed significantly. However quotes such as “The number <strong>of</strong> blog
sites doubles every 150days, approximately150,000new blogs are added every day, wikipedia has
217million unique visitors every month, social networking sites such as Facebook have 175 million
active users with 180 million photos uploaded each month” and so forth. (Casarez 2009) are sufficient
to make <strong>the</strong> point that this is no temporary fad and hence warrants investigation.
Nie (2001), argued that Internet use detracts from face-to-face time with o<strong>the</strong>rs, which might diminish
an individual’s social capital. However, this perspective has received strong criticism (Bargh &
McKenna, 2004). Some researchers have claimed that online interactions may supplement or replace
in-person interactions, mitigating any loss from time spent online (Wellman, Haase, Witte, & Hampton,
2001). This paper suggests that online interaction can increase most forms <strong>of</strong> social capital, including
those that facilitate intellectual capital building. The body <strong>of</strong> knowledge on both social and intellectual
capital is immense. The literature suggests a sound <strong>the</strong>oretical link between <strong>the</strong>se two constructs and
knowledge sharing. This paper intends to examine <strong>the</strong>se links highlighting aspects that are <strong>of</strong>
particular significance to <strong>the</strong> knowledge sharing environment provided by web 2 tools.
The basic proposition <strong>of</strong> this paper is that web 2 tools facilitate increased knowledge sharing by not
only providing an excellent platform for exchange, combination and creation <strong>of</strong> new knowledge, but
also by stimulating motivation for knowledge sharing through <strong>the</strong> development <strong>of</strong> bonding and
bridging social capital. The research method used in this paper is primarily literature syn<strong>the</strong>sis
involving inductive interpretation <strong>of</strong> qualitative research to establish associations not previously
known. The paper structure will be as follows. Firstly definitions and literature on social capital,
intellectual capital and web 2 technologies pertinent to <strong>the</strong> proposition will be introduced. Secondly
<strong>the</strong> literature will be summarized highlighting potential relationships between <strong>the</strong> constructs and a
model reflecting <strong>the</strong> nature <strong>of</strong> <strong>the</strong>se relationships constructed.
2. Literature review
2.1 Definitions
2.1.1 Web 2.
‘Web 2’ technologies refer to <strong>the</strong> range <strong>of</strong> interoperable technologies that facilitate communication in
multiple formats, information sharing and critique, social exchanges, community and collective
wisdom (Casarez 2009). These include social network sites such as Facebook and Myspace, wikis,
blogs, flickr, youtube, discussion forums, mashups, wikis, micro-blogging and so forth.
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2.1.2 Social capital
Marguerite Cronk
There are many definitions <strong>of</strong> Social Capital, however most revolve around <strong>the</strong> notion <strong>of</strong> shared
accumulated resources that exist across social networks. The social network can be seen as <strong>the</strong>
structure or system <strong>of</strong> connections between nodes. Bourdieu and Wacquant (1992) define social
capital as ‘‘<strong>the</strong> sum <strong>of</strong> <strong>the</strong> resources, actual or virtual, that accrue to an individual or a group by virtue
<strong>of</strong> possessing a durable network <strong>of</strong> more or less institutionalized relationships <strong>of</strong> mutual acquaintance
and recognition’’ (p. 14).
2.1.3 Bonding Social Capital
Bonding is horizontal, among equals within a community. Bonding capital is localized which is defined
as being found among people who live in <strong>the</strong> same or adjacent communities. It is also <strong>the</strong> form <strong>of</strong>
social capital associated with thick trust (Anheier and Kendall 2002). It is associated with strong ties,
or strong social bonds, and is called “sociological superglue” by Putman (2000).
2.1.4 Bridging Social Capital
Bridging is said to be vertical between communities (Dolfsma and Dannreu<strong>the</strong>r 2003; Narayan 2002;
Narayan and Pritchett 1999) Bridging capital extends to individuals and organizations that are more
removed, geographically socially or emotionally. Bridging social capital is associated with thin trust
and weak ties.
2.1.5 Linking Capital
Linking capital refers to relations between individuals or groups in different social starta (Cote and
Healy, 2001) or groups that have nothing in common.
2.1.6 Intellectual Capital
Nahapiet and Ghoshal (1998) define "intellectual capital" as <strong>the</strong> “knowledge and knowing capability <strong>of</strong>
a social collectivity, such as an organization, intellectual community, or pr<strong>of</strong>essional practice”. Jar-Der
(2005), refer to intellectual capital as <strong>the</strong> knowledge possessed by groups that is more than <strong>the</strong>
aggregation <strong>of</strong> individual groups.
Each <strong>of</strong> <strong>the</strong> above defined constructs has multiple definitions and sub-components. The intention
behind selecting broad definitions is to establish possible relationships between <strong>the</strong> constructs at <strong>the</strong>
macro level, and suggest areas for fur<strong>the</strong>r investigation.
2.2 Social capitol and Social networks
In order to establish a context, <strong>the</strong> following section briefly defines social capital, notes benefits
derived from social capital and examines social capital in <strong>the</strong> virtual space.
Social capital is a somewhat elastic term with a variety <strong>of</strong> definitions in multiple fields (Adler & Kwon,
2002), and is conceived <strong>of</strong> as both a cause and an effect (Resnick, 2001; Williams, 2006). As stated
above, Bourdieu and Wacquant (1992) define social capital as ‘‘<strong>the</strong> sum <strong>of</strong> <strong>the</strong> resources, actual or
virtual, that accrue to an individual or a group by virtue <strong>of</strong> possessing a durable network <strong>of</strong> more or
less institutionalized relationships <strong>of</strong> mutual acquaintance and recognition’’ (p. 14). The resources
from <strong>the</strong>se relationships can differ in form and function based on <strong>the</strong> relationships <strong>the</strong>mselves. Social
capital broadly refers to <strong>the</strong> resources accumulated through <strong>the</strong> relationships or connections among
people (Coleman, 1988). Some authors distinguish between various forms <strong>of</strong> social capital and o<strong>the</strong>rs
refer to it as collective term. Generally speaking social capital has been linked to a variety <strong>of</strong> positive
social outcomes, such as better public health, lower crime rates, and more efficient financial markets
(Adler & Kwon, 2002). Moreover, social capital researchers have found that various forms <strong>of</strong> social
capital, including ties with friends and neighbors, are related to indices <strong>of</strong> psychological well-being,
such as self esteem and satisfaction with life (Bargh & McKenna, 2004; Helliwell & Putnam, 2004).
Vertovec (2001) highlighted <strong>the</strong> benefits <strong>of</strong> using social networks, by explaining how interpersonal
relations cut across boundaries such as neighborhood, workplace, kinship or class and could be
abstracted on an individual basis (Vivian and Fay 2003)
Even though most <strong>of</strong> <strong>the</strong> research on social capital focuses on <strong>the</strong> collective benefits <strong>of</strong> social capital
as discussed above, Bourdieu and Coleman (1991), provide conceptualization at <strong>the</strong> individual level.
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They believe that social capital exists between individuals and can be studied at <strong>the</strong> individual level.
Social capital is said to reside in <strong>the</strong> relations (links) among <strong>the</strong> nodes (individuals) <strong>of</strong> a network and
‘just as physical and human capital facilitate productive activity, social capital does as well’ (Coleman,
1988, p 101). Social capital is said to exist between individuals and by extension can be accumulated
by <strong>the</strong> individuals. Such a view <strong>of</strong> social capital rests on <strong>the</strong> premise that ‘my connections can help
me’ (Cross and Cummings, 2004) and is all about establishing relationships purposefully and
employing <strong>the</strong>m to generate intangible and tangible benefits in short or long terms. Hence it is
suggested that <strong>the</strong> study <strong>of</strong> individual connections may provide insight into <strong>the</strong> development <strong>of</strong> <strong>the</strong>
social network and resulting social capital.
In a virtual setting, social capital is said to be a common social resource that facilitates information
exchange, knowledge sharing, and knowledge construction through continuous interaction, built on
trust and maintained through shared understanding (Daniel, Schwier & McCalls, 2003). It is assumed
this refers more to <strong>the</strong> bonding social capital as bonding has more to do with trust (Nahapiet and
Ghoshal (1998). Huysman and Wulf (2005), propose that <strong>the</strong> higher <strong>the</strong> level <strong>of</strong> social capital, <strong>the</strong>
more members are stimulated to connect and share knowledge. This sharing aspect challenges
individuals to draw upon and provide value for <strong>the</strong>mselves and <strong>the</strong> community, with obvious benefits
to both parties. This notion is in a way a demonstration <strong>of</strong> <strong>the</strong> purposeful establishment <strong>of</strong>
relationships for mutual benefit suggested by Cummings and Cross (2004) above.
As defined above, bonding social capital is associated with strong ties. It refers to <strong>the</strong> kinds <strong>of</strong> support
that originates in close knit relations such as intimate friends and family (Putnam, 2000). Bonding
social capital in an organizational implies that <strong>the</strong>re is trust and a sense <strong>of</strong> obligation that encourages
reciprocity (Steinfield et. al, 2009). Social networking site studies have reported <strong>the</strong> usage <strong>of</strong> such
sites association with increased bonding capital, for example, directed communication between
friends. This may take <strong>the</strong> form <strong>of</strong> wall posts between friends intended for and consumed by a close
friend (Burke et al 2010). It is suggested that <strong>the</strong> greater <strong>the</strong> degree <strong>of</strong> sharing, <strong>the</strong> greater <strong>the</strong> degree
<strong>of</strong> bonding social capital within <strong>the</strong> network. In addition to streng<strong>the</strong>ning already strong bonds, web
based social networking streng<strong>the</strong>ns weak ties as more is shared with those more distant (emotionally
socially or geographically). For example, a connection may be made with an acquaintance from <strong>the</strong>
distant past, country, or a friend <strong>of</strong> a friend. At <strong>the</strong> point <strong>the</strong> connection is made <strong>the</strong> tie is weak and
little is known <strong>of</strong> <strong>the</strong> ‘friend’. After <strong>the</strong> newly connected friend shared <strong>the</strong>ir thoughts, photos and
experiences, a stronger understanding and emotional connection is made transforming this
connection from a weak tie to stronger or possibly from bridging to bonding capital.
Both bridging and linking capital are said to be associated with notions <strong>of</strong> ‘getting ahead’ creating
opportunities and <strong>the</strong> ability to leverage resources. Studies <strong>of</strong> social networking sites list
advancement <strong>of</strong> career, support for campaigns or projects, and expectations from an extended
network. (DiMicco, 2008; Morris et al,2010)
Ellison et al (2007), investigated <strong>the</strong> linkages between Facebook usage and various types <strong>of</strong> social
capital and found a positive relationship between certain kinds <strong>of</strong> Facebook use and <strong>the</strong> maintenance
and creation <strong>of</strong> social capital including bridging (between communities) and to a lesser extent bonding
social capital (among individuals within a community). Recently, researchers have emphasized <strong>the</strong>
importance <strong>of</strong> Internet-based linkages for <strong>the</strong> formation <strong>of</strong> weak ties (temporary and contingent),
which serve as <strong>the</strong> foundation <strong>of</strong> bridging social capital and suggest that it is possible that new forms
<strong>of</strong> social capital and relationship building such as bridging social capital will occur in online social
network sites (Ellison et al., 2007).
In summary it can be said that participation in web2 social networking, increases social capital.(see
figure 1.)
SNS
Social capital
Figure 1: Relationship between Web 2 Social Networking and Social Capital.
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Marguerite Cronk
2.3 Social capital and knowledge sharing in <strong>the</strong> web 2 environment
Social capital is <strong>the</strong> shared accumulated resources that exist across social networks. Knowledge is
<strong>the</strong> primary resource shared and accumulated in <strong>the</strong> network. Social relations, <strong>of</strong>ten established for
o<strong>the</strong>r purposes, constitute information channels that reduce <strong>the</strong> amount <strong>of</strong> time and investment
required to ga<strong>the</strong>r information. (Nahapiet and Ghoshal 1998). As mentioned above <strong>the</strong> amount and
nature <strong>of</strong> what is being shared depends on <strong>the</strong> type <strong>of</strong> social capital. For example, <strong>the</strong>re is mounting
evidence demonstrating that where parties trust each o<strong>the</strong>r, <strong>the</strong>y are more willing to engage in
cooperative activity through which fur<strong>the</strong>r trust may be generated (Fukuyama, 1995; Putnam, 1993;
Tyler & Kramer, 1996). Morris et al (2010) suggests that web2 social network users actually use <strong>the</strong>
network to both gain and share knowledge (as information, knowledge, or experience) by asking
questions as <strong>the</strong>ir ‘status. In this study network users site speedier better quality information, as <strong>the</strong>
motivator as it comes from a trusted source (group <strong>of</strong> friends).
Studies <strong>of</strong> knowledge sharing in <strong>the</strong> web 2 era have ventured down some different paths than in <strong>the</strong>
‘pre’ web 2 studies. For example, in relation to social technologies ‘connectivists’ assert that
knowledge is distributed across a network <strong>of</strong> connections, and <strong>the</strong>refore that learning consists <strong>of</strong> <strong>the</strong>
ability to construct and traverse those networks. Some domains <strong>of</strong> knowledge contain vast numbers <strong>of</strong>
weak interrelations that, if properly exploited, can greatly amplify learning by a process <strong>of</strong> inference
(Siemens, 2004; Downes 2006). This sounds very similar to ‘accumulated shared resources’ or social
capital. These authors refer to learning specifically, but knowledge sharing/transfer is a precursor to
learning.
Hence <strong>the</strong> significance <strong>of</strong> web2 in knowledge sharing is not only to provide an excellent sharing
platform but to assist in <strong>the</strong> building <strong>of</strong> social capital which in turn fuels more knowledge sharing. (See
figure 2.)
Social Capital
Web 2 SNS
Knowledge Sharing
Figure 2: The relationship between Social Capital and Knowledge Sharing, facilitated by Web 2 SNS.
2.4 Social capital and intellectual capital
Nahapiet and Ghoshal 1998 define "intellectual capital" as <strong>the</strong> “knowledge and knowing capability <strong>of</strong> a
social collectivity, such as an organization, intellectual community, or pr<strong>of</strong>essional practice. They
defend <strong>the</strong>ir definition in terms <strong>of</strong> its clear parallel with <strong>the</strong> concept <strong>of</strong> human capital, which embraces
<strong>the</strong> acquired knowledge, skills, and capabilities that enable persons to act in new ways (Coleman,
1988). They affirm <strong>the</strong> notion that social capital facilitates <strong>the</strong> creation <strong>of</strong> new intellectual capital
(Nahapiet and Ghoshal 1998) through knowledge processes. (See figure 3)
Social Capital
Knowledge Sharing
Intellectual Capital
Figure 3: The relationship between Social Capital and Intellectual Capital, facilitated by Knowledge
Sharing
2.5 Intellectual capital and knowledge sharing
Knowledge sharing appears to be <strong>the</strong> intersection or pivotal point that both social capital and
intellectual capital revolve around.
Following Schumpeter (1934), Moran and Ghoshal (1996) have argued that all new resources,
including knowledge, are created through two generic processes: namely, combination and exchange.
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Marguerite Cronk
There appears to be a consensus that both types <strong>of</strong> knowledge creation involve making new
combinations ei<strong>the</strong>r by combining elements previously unconnected or by developing novel ways <strong>of</strong>
combining elements previously associated. This is exactly what <strong>the</strong> connectivists believe about <strong>the</strong>
potential <strong>of</strong> <strong>the</strong> web 2 social network. That through an extensive network <strong>of</strong> connections/ties
knowledge is distributed, remixed and added back to <strong>the</strong> network in a cyclical fashion as people from
diverse back grounds, cultures, disciplines and experiences recombine it with <strong>the</strong>ir existing knowledge
base and return it to <strong>the</strong> network. New knowledge creation is said to be created through this process
and empowered by social interaction and coactivity. The main <strong>the</strong>sis being presented here is that
social capital facilitates <strong>the</strong> development <strong>of</strong> intellectual capital by affecting <strong>the</strong> conditions necessary
for exchange and combination to occur.
3. Model building
From <strong>the</strong> literature above it is proposed that social capital is <strong>the</strong> potential that exists across a social
network. “In a similar fashion, Verhagen, (2006) when speaking <strong>of</strong> <strong>the</strong> connectivist view <strong>of</strong> <strong>the</strong> social
web, sees this potential “as using knowledge that you do not have at <strong>the</strong> ready, whose existence and
usefulness you are aware <strong>of</strong> and to which you also have access”. The amount <strong>of</strong> social capital is
dependent on <strong>the</strong> number <strong>of</strong> connections (connectivism) to deliver <strong>the</strong> potential and <strong>the</strong> quality or
nature <strong>of</strong> those connections. Strong connections will deliver bonding social capital and weak
connections tend to deliver bridging social capital which can <strong>the</strong>n in turn through more frequent
connection and value gained from those connections, become bonding social capital. Bonding social
capital is a higher level <strong>of</strong> social capital and delivers greater value because <strong>of</strong> <strong>the</strong> trust element
involved. Intellectual capital has elements <strong>of</strong> potential also in <strong>the</strong> knowledge capability aspect. It is
more focused on knowledge sharing and exchange and <strong>the</strong> outcome is new knowledge. Intellectual
capital may develop using <strong>the</strong> same pathways as social capital and to some extent <strong>the</strong> same
dynamics. Knowledge is shared through <strong>the</strong> social connections. Thus, <strong>the</strong> better <strong>the</strong> social
connections <strong>the</strong> greater <strong>the</strong> potential for knowledge sharing is. The connectivisits would add, <strong>the</strong> more
extensive and diverse <strong>the</strong> network <strong>the</strong> greater <strong>the</strong> possibility for new knowledge creation sharing and
ultimately intellectual capital.
Moran and Ghoshal (1996) propose a mechanism for <strong>the</strong> development <strong>of</strong> intellectual capital from
social capital via information benefits. These benefits can be amplified via web 2 technologies.
Information benefits occur in three forms: access, timing, and referrals.
"Timing" <strong>of</strong> information flows refers to <strong>the</strong> ability <strong>of</strong> personal contacts to provide information sooner
than it becomes available to people without such contacts. "Referrals" are those processes providing
information on available opportunities to people or actors in <strong>the</strong> network, hence influencing <strong>the</strong>
opportunity to combine and exchange knowledge frequently include reputational endorsement for <strong>the</strong>
actors involved (such as happens in web 2 technologies through rating systems)-<strong>the</strong>reby influencing
both <strong>the</strong> anticipated value <strong>of</strong> combination and exchange and <strong>the</strong> motivation for such exchange (see
Granovetter, 1973, and Putnam, 1993). Much <strong>of</strong> <strong>the</strong> evidence for <strong>the</strong> relationship between social
capital and intellectual capital highlights <strong>the</strong> significance <strong>of</strong> <strong>the</strong> relational dimension <strong>of</strong> social capital.
Figure 4 below shows knowledge sharing as <strong>the</strong> pivotal element that increases and creates social
capital (cause and effect ,see Resnick, 2001; Williams, 2006) It also plays a similar role in <strong>the</strong>
creation <strong>of</strong> intellectual capital. It is also <strong>the</strong> process that links social capital with intellectual capital
facilitating <strong>the</strong> creation <strong>of</strong> new knowledge through exchange and recombination, driven by <strong>the</strong> ties <strong>of</strong>
social capital. The entire process has been enabled by <strong>the</strong> web 2 technologies.
4. Conclusion and suggestions for fur<strong>the</strong>r research
It is generally accepted that <strong>the</strong> capacity <strong>of</strong> an organization to innovate lies in its capacity to generate
new knowledge (Nonaka and Takeuchi, 1995; Nonaka, Toyama and Byosière, 2003). For this to be
possible, knowledge sharing is considered a necessary condition (Nonaka and Takeuchi, 1995;
Nonaka, von Krogh and Voelpel, 2006). It is suggested that Web 2 tools facilitate <strong>the</strong> development <strong>of</strong>
social capital through knowledge sharing which in turn increases <strong>the</strong> potential to create intellectual
capital. Web 2 is an important piece in this equation because it reinforces <strong>the</strong> notion that ‘my
connections help me’ as it provides <strong>the</strong> means by which more connections can be made as well as
facilitating <strong>the</strong> development <strong>of</strong> <strong>the</strong> social capital which in turn stimulates more sharing. It is suggested
that organizations consider how <strong>the</strong>y might use web 2 technologies to tap into <strong>the</strong> power <strong>of</strong> social
capital driven knowledge sharing.
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Marguerite Cronk
Figure 4: The relationship between social capital, knowledge sharing and intellectual capital in <strong>the</strong>
‘web 2’ enabled environment.
As mentioned at <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> paper, <strong>the</strong> constructs discussed have many components and
this discussion has taken place at a high level, identifying some <strong>of</strong> <strong>the</strong> possible connections and
relationship that exist in <strong>the</strong> conversion <strong>of</strong> social capital into intellectual capital via <strong>the</strong> web 2 enabled
information and knowledge sharing. Questions that remain include; which types <strong>of</strong> social capital are
most important for <strong>the</strong> formation <strong>of</strong> intellectual capital, which types <strong>of</strong> social capital are best supported
by web 2 social networks, which types <strong>of</strong> web 2 technologies best engender <strong>the</strong> building <strong>of</strong> social
capital and knowledge sharing, and what types <strong>of</strong> knowledge is shared under what social
circumstances?
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An Exploratory Study <strong>of</strong> Knowledge Strategy in a
Knowledge-Intensive Firm using a Strategy-as-Practice
approach
Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
CRECIS Center for Research in Entrepreneurial Change and Innovative
Strategies, Louvain School <strong>of</strong> Management - Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
francoise.deviron@uclouvain.be
thomas.lederer@uclouvain.be
tanguy.dejaegere@uclouvain.be
alain.vas@uclouvain.be
Abstract: We analyse <strong>the</strong> knowledge strategy (Zack 1999, 2002) in a knowledge-intensive firm, using a Strategyas-Practice
approach (Whittington 1996, 2006), two major research fields in Strategic and Knowledge
Management. In order to provide a relevant research field to our investigation, we study <strong>the</strong> development <strong>of</strong> <strong>the</strong>
knowledge strategy at one <strong>of</strong> <strong>the</strong> leading consultant firms in <strong>the</strong> fields <strong>of</strong> strategy as well as knowledge
management. First, we explain <strong>the</strong> Knowledge Strategy framework proposed by Zack (2002). Then, we focus on
our research methodology, mainly <strong>the</strong> approach <strong>of</strong> Strategy-as-Practice. Thirdly, we present our main analysis
results, identifying <strong>the</strong> key actors <strong>of</strong> knowledge strategizing and how <strong>the</strong>y are organised to do it. A process view
<strong>of</strong> knowledge strategy is presented identifying key actors, practices and organizational structures. A conclusion <strong>of</strong>
our study is that <strong>the</strong> conceptualization <strong>of</strong> knowledge strategy and knowledge gaps does effectively exist in reality.
Finally, we discuss <strong>the</strong> limitations <strong>of</strong> <strong>the</strong> present research and propose fur<strong>the</strong>r research directions and
development. This paper explores a new research approach <strong>of</strong> knowledge management, and could also help
knowledge management practitioners.
Keywords: Knowledge Strategy, Knowledge Intensive Firm, Qualitative Research, Strategy-as-Practice
approach
1. What is Knowledge strategy?
As pointed by Argote, Mc Evily and Reagans (2003), knowledge management research spans <strong>the</strong>
disciplines <strong>of</strong> “economics, information systems, organizational behavior, organizational <strong>the</strong>ory,
psychology, strategic management, and sociology”. The number <strong>of</strong> disciplines involved in <strong>the</strong> area
gives a hint about <strong>the</strong> global awareness that knowledge management has become <strong>of</strong> <strong>the</strong> utmost
importance in today’s economy (Nonaka, 1991, 2009).
However, <strong>the</strong> link between Knowledge management and Business Strategy, while <strong>of</strong>ten discussed,
has rarely been put into practice (Zack, 2002).
The Knowledge Strategy framework developed by Zack (1999b) is a significant step over that gap. In
its research with more than 25 firms, Zack (2002) revealed that “while many managers intuitively
believe that strategic advantage can come from knowing more than competitors, <strong>the</strong>y are unable to
explicitly articulate <strong>the</strong> link between knowledge and strategy.” In o<strong>the</strong>r words, “many initiatives being
undertaken to develop and exploit organizational knowledge are not explicitly linked to or framed by
<strong>the</strong> organization’s business strategy.” Later, he observed that a vast majority <strong>of</strong> <strong>the</strong> knowledge
management initiatives do not help <strong>the</strong> organization “determine which efforts are appropriate, or
which knowledge should be managed and developed” (Zack, 2002).
In this research, Zack (2002) found <strong>the</strong> most important context for guiding knowledge management is
<strong>the</strong> firm's strategy. An organization's strategic context indeed “helps to identify knowledge
management initiatives that support its purpose or mission, streng<strong>the</strong>n its competitive position, and
create shareholder value” (Zack, 2002). Despite its <strong>the</strong>oretical value, <strong>the</strong> link between knowledge
management and business strategy is widely ignored in practice. Especially when it comes to
evaluate knowledge management efforts and choose between different knowledge management
projects (Davenport and al, 1998, Ruggles, 1998).
From this ascertainment, Zack (2002) advocates that firms need a “pragmatic, yet <strong>the</strong>oretically sound
model <strong>of</strong> what he calls knowledge strategy”. The knowledge strategy model was developed by Zack
to complete <strong>the</strong> traditional SWOT analysis. After performing <strong>the</strong>ir SWOT analysis and guiding <strong>the</strong>ir
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
strategic intent, firms would perform a knowledge-based SWOT analysis. It will help <strong>the</strong>m identify both
<strong>the</strong> current knowledge <strong>the</strong>y possess and <strong>the</strong> knowledge <strong>the</strong>y need to execute <strong>the</strong>ir strategy. The
difference between <strong>the</strong> actual knowledge base and <strong>the</strong> knowledge needs constitutes a set <strong>of</strong>
knowledge gaps (see Figure 1).
Once <strong>the</strong> firm’s knowledge gaps identified, <strong>the</strong> firm can use it as <strong>the</strong> building blocks to draw its
knowledge strategy. According to Zack (2002), knowledge strategy “describes <strong>the</strong> overall approach
an organization intends to take to align its knowledge resources and capabilities to <strong>the</strong> intellectual
requirements <strong>of</strong> its strategy.”
Figure 1: A Knowledge Strategy framework (Zack, 2002)
The role <strong>of</strong> <strong>the</strong> knowledge strategy is summarized by Zack (2002) as follows: “Knowledge is <strong>the</strong>
fundamental basis <strong>of</strong> competition. Competing successfully on knowledge requires ei<strong>the</strong>r aligning
strategy to what <strong>the</strong> organization knows, or developing <strong>the</strong> knowledge and capabilities needed to
support a desired strategy.”
Our objective in this study - and <strong>the</strong> originality <strong>of</strong> our contribution - is to understand how knowledge
strategy is developed and decided in knowledge intensive firms by using <strong>the</strong> ‘strategy as practice’
approach within one firm.
2. Research methodology
2.1 The strategy-as-practice approach
For this research, we choose to use <strong>the</strong> approach advocated by <strong>the</strong> Strategy-as-Practice school
(Whittington 1996, 2003, 2006, Jarzabkowski 2004, 2007, Régner 2003, 2008).
The Strategy-as-Practice approach followed a trend in social sciences that study, with a growing
interest, what practitioners are actually doing (Jarzabkowski, 2004). The Strategy-as-Practice school
has indeed grown based upon <strong>the</strong> concern over <strong>the</strong> gap between <strong>the</strong> <strong>the</strong>ory <strong>of</strong> what people do and
what people actually do (Jarzabkowski, 2004). It pleads for focusing research on <strong>the</strong> way that actors
interact with <strong>the</strong> social and physical features <strong>of</strong> context in <strong>the</strong> everyday activities, which constitute
practice (Jarzabkowski, 2004). It recommends focusing upon strategists engaged in <strong>the</strong> real work <strong>of</strong>
strategizing (Jarzabkowski 2004; Hendry 2000; Whittington 1996, 2002, 2003). Practically, this comes
down to examine strategy not as something an organization has, but as something people do
(Hambrick, 2004; Jarzabkowski, 2004; Whittington, 2006).From <strong>the</strong> Strategy-as-Practice perspective,
strategy is conceptualized as a situated, socially accomplished activity (Jarzabkowski, 2005). Authors
in <strong>the</strong> Strategy-as-Practice field also <strong>of</strong>ten use <strong>the</strong> concept <strong>of</strong> ‘Strategizing’ referring to <strong>the</strong> ‘doing <strong>of</strong>
strategy’. In o<strong>the</strong>r words, it means <strong>the</strong> construction <strong>of</strong> a flow <strong>of</strong> activity through <strong>the</strong> actions and
interactions <strong>of</strong> multiple actors and <strong>the</strong> practices that <strong>the</strong>y draw upon (Jarzabkowski, 2007).
In 2003, Whittington highlighted six key questions that should lead <strong>the</strong> agenda <strong>of</strong> Strategy-as-Practice
research. Our objective is to apply this approach in <strong>the</strong> field <strong>of</strong> Knowledge Management, i.e. to
address <strong>the</strong>se questions, not on strategy as a whole, but on Knowledge Strategy. We started our
research from three <strong>of</strong> <strong>the</strong>se questions to structure our exploratory study:
Who does <strong>the</strong> formal work <strong>of</strong> Strategizing in a knowledge context and how do <strong>the</strong>y get to do it?
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
How is <strong>the</strong> work <strong>of</strong> Knowledge Strategizing organized itself?
How and where is Knowledge Strategizing work actually done?
2.2 Empirical field: Consultancy firm as Knowledge Intensive Firm
According to Alvesson (2004), knowledge-intensive firms (KIFs) can be defined as “organizations that
<strong>of</strong>fer to <strong>the</strong> market <strong>the</strong> use <strong>of</strong> fairly sophisticated knowledge or knowledge-based products”. Two
important criteria added by <strong>the</strong> author help understand <strong>the</strong> definition: “The core <strong>of</strong> activities in <strong>the</strong>se
companies is based on <strong>the</strong> intellectual skills <strong>of</strong> a very large proportion <strong>of</strong> <strong>the</strong> labour force deployed
[…] in service work” and <strong>the</strong> use <strong>of</strong> “symbolic work-using ideas and concepts-is crucial”.
When reviewing <strong>the</strong> literature about consulting firm and <strong>the</strong>ir potential knowledge strategy this last ten
years, <strong>the</strong> main articles about <strong>the</strong> subject focused on knowledge management strategy. Regarding
this topic, it is important to note <strong>the</strong> existence <strong>of</strong> two main articles from 1999 directly related to
consulting firms. The first one, <strong>of</strong> Miklos Sarvary (1999), emphasizes <strong>the</strong> importance <strong>of</strong> <strong>the</strong> firm’s
culture and processes. The o<strong>the</strong>r one, <strong>of</strong> Hansen et al. (1999), describes two possible strategies for
knowledge management: codification and personalization. Those analyses have been extended later
by Alvesson (2004). About <strong>the</strong> work <strong>of</strong> Sarvary, Alvesson (2004) notes <strong>the</strong> impact it can have on ‘<strong>the</strong>
active sharing and <strong>of</strong>fering <strong>of</strong> experiences and insights’. He also points out that <strong>the</strong> personalization
strategy, in <strong>the</strong> consultancy industry, “is typically used by up-market companies that charge very high
fees for <strong>the</strong> delivering <strong>of</strong> highly customized solution to unique problems. It relies heavily on
socialization-that knowledge can be ‘managed’ by selecting and orchestrating a mix <strong>of</strong> situations and
individuals.” (Alvesson, 2004)
In <strong>the</strong> studies mentioned here above, <strong>the</strong>re is no clear distinction between knowledge strategy,
dedicated to <strong>the</strong> search <strong>of</strong> which knowledge and why, and knowledge management strategies,
dedicated to <strong>the</strong> ways to improve knowledge, as recommended by Zack (1999).
2.3 Single in-depth case study
As mentioned we would like to understand how knowledge strategy is being built in knowledgeintensive
firms by using <strong>the</strong> Strategy-as-Practice approach within one firm. As put forward by
Sigglekow (2007), “single-case studies can richly describe <strong>the</strong> existence <strong>of</strong> a phenomenon”.
Theoretical sampling <strong>of</strong> single cases is straightforward: <strong>the</strong>y are chosen because <strong>the</strong>y are unusually
revelatory, extreme exemplars, or opportunities for unusual research access (Yin, 1994, Eisenhardt,
2007). In this case we looked for a best-in-class example <strong>of</strong> an organization developing a knowledge
strategy. One <strong>of</strong> <strong>the</strong> firms at <strong>the</strong> forefront <strong>of</strong> Knowledge Strategy is McKinsey and Company.
McKinsey started a while ago to develop a dynamic strategy in order to turn knowledge into a
competitive advantage. Bartlett (2000) showed how McKinsey implemented over <strong>the</strong> years a number
<strong>of</strong> tools and processes in order to preserve and take advantage <strong>of</strong> its outstanding knowledge base.
He also illustrated how McKinsey worked on <strong>the</strong> development <strong>of</strong> a real knowledge sharing culture
among its employees. Besides, <strong>the</strong>y dedicated a large number <strong>of</strong> people, knowledge pr<strong>of</strong>essionals, to
<strong>the</strong> specific task <strong>of</strong> managing and developing <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> firm. What especially interested us
does not lie in <strong>the</strong>se initiatives. It was more <strong>the</strong> fact that <strong>the</strong>se efforts to develop <strong>the</strong> firm’s knowledge
seemed to be guided by a common and widely agreed-on effort. Therefore we are curious about
finding at McKinsey and Company <strong>the</strong> signs <strong>of</strong> what Zack (2002) calls a “knowledge strategy”.
According to <strong>the</strong> case study method principles highlighted by Eisenhardt (1989, 2007) and Yin (1994),
we did not start our exploratory study with any preconceived hypo<strong>the</strong>sis to test, but ra<strong>the</strong>r with two
conceptual guides: <strong>the</strong> knowledge strategy framework and <strong>the</strong> Strategy-as-Practice approach.
2.4 Primary and Secondary data
In order to carry on this research, we ga<strong>the</strong>red data based on a combination <strong>of</strong> primary and
secondary sources.
We collected primary sources through 14 semi-structured interviews at <strong>the</strong> McKinsey and Company
Knowledge Center in Belgium, between January and September 2010. We used a shared
questionnaire to conduct <strong>the</strong> interviews (see appendix I). However, we felt free to address o<strong>the</strong>r
questions related to our research objective. We selected <strong>the</strong> interviewees in order to get respondents
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
from different levels, functions and domains <strong>of</strong> expertise. We collected <strong>the</strong> secondary sources by
analyzing McKinsey internal documents and external communication during <strong>the</strong> same period.
3. The organizational structures <strong>of</strong> McKinsey and Company
McKinsey employs approximately 15.000 staff members, including 6.500 consultants, 1.500
knowledge pr<strong>of</strong>essionals and 7.000 support staff. They are spread over 94 <strong>of</strong>fices in 52 countries.
McKinsey describes itself as a management consulting firm advising leading companies on issues <strong>of</strong>
strategy, organization, technology, and operations (McKinsey, 2010). McKinsey advises clients in all
sectors, including administrations, governments and non-pr<strong>of</strong>it organizations.
3.1 Internal organization
Internally, McKinsey is organized in a dual structure: both along regional <strong>of</strong>fices and ‘<strong>the</strong>matic
practices’. This structure implies that employees can have a double affiliation: to an <strong>of</strong>fice and to one
or (exceptionally) two ‘practices’.
Offices are geography-based entities serving clients in a given country or region.
‘Practices’ work as cross-<strong>of</strong>fice networks grouping people around <strong>the</strong> globe with interest or
expertise in a specific field. Those fields can relate ei<strong>the</strong>r to an industry (such as Oil & Gas,
Pharmaceuticals…) or to a function (Marketing, Organization, Strategy…).
3.2 Decision making
The corporate model <strong>of</strong> McKinsey is a partnership. The firm belongs thus to a limited number <strong>of</strong> senior
members <strong>of</strong> <strong>the</strong> firm (<strong>the</strong> “partners”). Consequently <strong>the</strong> management mode is different from <strong>the</strong>
classical top-down “command and control” approach found in many firms, explains an executive. At
McKinsey, decision making is present at all levels in <strong>the</strong> firm: <strong>the</strong> partners, <strong>the</strong> <strong>of</strong>fices, <strong>the</strong> ‘practices’
and <strong>the</strong> individuals <strong>the</strong>mselves.
At each <strong>of</strong>fice level, <strong>the</strong> management decisions are taken by groups <strong>of</strong> partners representing <strong>the</strong>
different geographic entities forming <strong>the</strong> <strong>of</strong>fice.
Each <strong>of</strong>fice is chaired by an <strong>of</strong>fice manager -a “primus inter pares”-for a limited duration, following
a rotation mechanism.
At <strong>the</strong> highest level <strong>of</strong> <strong>the</strong> firm, <strong>the</strong> Managing Director himself does not have <strong>the</strong> power <strong>of</strong> a CEO.
He is also a “primus inter pares” among <strong>the</strong> <strong>of</strong>fice managers.
This structure reflects <strong>the</strong> willingness to preserve <strong>the</strong> independence <strong>of</strong> <strong>the</strong> partners. However, this
rake-shaped organization is difficult to maintain with a growing number <strong>of</strong> <strong>of</strong>fices. For that reason,
<strong>the</strong>y add ano<strong>the</strong>r regional level with <strong>the</strong> following three organizations, “EMEA” (Europe, Middle-East
and Africa), “Americas” and “Asia-Pacific”. Each one has an executive committee that is made up <strong>of</strong>
few <strong>of</strong>fice managers from that region. These executive committees report to <strong>the</strong> Managing Director.
4. Data analysis
As explained before, our research analysis is structured along <strong>the</strong> key questions provided by
Whittington (2003):
4.1 Who does <strong>the</strong> formal work <strong>of</strong> Knowledge Strategizing and how do <strong>the</strong>y get to do
it?
From our data, we observe that <strong>the</strong> development <strong>of</strong> <strong>the</strong> knowledge strategy at McKinsey and
Company involves five different actors at different hierarchical levels in <strong>the</strong> firm. We present <strong>the</strong>m with
some details about <strong>the</strong> identified rationales <strong>of</strong> <strong>the</strong>ir implication in <strong>the</strong> work <strong>of</strong> knowledge strategizing:
4.1.1 The consultants
They serve <strong>the</strong> clients directly, spend most <strong>of</strong> <strong>the</strong>ir time with <strong>the</strong>m and are responsible for delivering
recommendations as well as driving <strong>the</strong>ir implementation at <strong>the</strong> clients’ locations. One <strong>of</strong> <strong>the</strong>ir roles is
to report any information about new concerns faced by <strong>the</strong> clients, new market trends, ideas etc. They
bring thus a constant flow <strong>of</strong> information into <strong>the</strong> firm and, by doing so, fulfill <strong>the</strong> critical role <strong>of</strong>
nurturing <strong>the</strong> firm with <strong>the</strong> knowledge necessary to decide on future organizational moves.
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4.1.2 The researchers
Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
They serve as support for <strong>the</strong> consultants. “Researchers spend <strong>the</strong>ir time on both reactive and
proactive activities. Reactively, <strong>the</strong>y answer <strong>the</strong> questions from consultants in order to help <strong>the</strong>m in a
client assignment. Proactively, <strong>the</strong>y build <strong>the</strong>ir own expertise by reading and collecting information as
well as expertise within <strong>the</strong> firm by sharing <strong>the</strong>ir knowledge”, explains a senior researcher.
Researchers are usually not in direct contact with clients (for confidentiality reasons), although it can
happen occasionally. They are also referred to “Knowledge pr<strong>of</strong>essionals” or “experts”.
4.1.3 The partners
Partners are senior members <strong>of</strong> <strong>the</strong> firm that have <strong>the</strong> right to buy and own shares <strong>of</strong> <strong>the</strong> firm. We
distinguish two types <strong>of</strong> partners: <strong>the</strong> principals – junior partners without voting right - and <strong>the</strong>
directors – senior partners with <strong>the</strong> right to vote.
4.1.4 The Practice development manager and Practice Leader
Each ‘practice’ has a Practice Leader who is a senior consultant or partner that spends about one day
per week on <strong>the</strong> monitoring and <strong>the</strong> internal functioning <strong>of</strong> <strong>the</strong> ‘practice’. He acts as a ‘sponsor’ for <strong>the</strong>
‘practice’ within <strong>the</strong> company. The ‘practice’ also has a Practice Development Manager, whose role is
to manage <strong>the</strong> diffusion <strong>of</strong> knowledge within <strong>the</strong> network and to monitor <strong>the</strong> knowledge creation
efforts within <strong>the</strong> ‘practice’. The biggest ‘practices’ are divided in several service lines, sorts <strong>of</strong> ‘subpractices’
addressing a sub-field <strong>of</strong> <strong>the</strong> ‘practice’. Each service line has in turn its own service line
manager.
4.1.5 The members <strong>of</strong> <strong>the</strong> Knowledge committee
The Knowledge Committee is one <strong>of</strong> <strong>the</strong> four permanent committees at McKinsey. Only directors can
be members and <strong>the</strong>y are appointed for a period <strong>of</strong> three year, renewable once. The purpose is to
ensure a rotation <strong>of</strong> <strong>the</strong> members. The knowledge committee has a worldwide scope and overlooks all
knowledge-related investments, as we will detail fur<strong>the</strong>r below.
4.2 How is <strong>the</strong> work <strong>of</strong> Strategizing organized itself?
It appears from our primary and secondary data that <strong>the</strong> knowledge strategy at <strong>the</strong> firm level is <strong>the</strong>
result <strong>of</strong> a process divided into three clearly separated steps. We present here below <strong>the</strong> successive
steps <strong>of</strong> this process:
4.2.1 Step 1: Defining <strong>the</strong> knowledge gaps
The Knowledge strategy is based upon multiple continuous discussions between <strong>the</strong> consultants and
<strong>the</strong> people in charge <strong>of</strong> each ‘practice’. The consultants, who are in continuous contact with <strong>the</strong> client
and know its challenges, communicate to <strong>the</strong>ir ‘practice’ <strong>the</strong> knowledge gaps that have to be filled in
order to fit with <strong>the</strong> clients’ needs.
‘Practices’ have an infrastructure that enables <strong>the</strong>m to inventorize all <strong>the</strong>se gaps in <strong>the</strong>ir knowledge
domains and to aggregate and identify <strong>the</strong> most important ones. ‘Practices’ are indeed responsible for
developing <strong>the</strong> most distinctive and recent knowledge in <strong>the</strong>ir domain. Each ‘practice’ covers a wide
range <strong>of</strong> topics.
“The importance <strong>of</strong> some knowledge domains also varies in time: 12 years ago, e-commerce was
recognized as a major knowledge domain. It is now integrated within <strong>the</strong> existent traditional industry
‘sectors’ practices’ where it is used, for example ‘retail’ and ‘banking’. It is now perceived [as piece <strong>of</strong>
knowledge] as a specific marketing and sales channel”. “Today, <strong>the</strong> knowledge domain ‘Climate
Change’ is important. It has been identified as such five or six years ago [by <strong>the</strong> Knowledge
Committee] but as it is transversal to multiple sectors, an initiative - we could call it an “emerging
practice” - has been created. Today I don’t know if it will integrate ano<strong>the</strong>r practice, become a practice
or disappear.” explains a Senior Research Manager.
It is <strong>the</strong> role <strong>of</strong> <strong>the</strong> Practice Development Manager to collect and aggregate <strong>the</strong> knowledge gaps.
Then, <strong>the</strong>y regularly meet toge<strong>the</strong>r with <strong>the</strong> Practice Leaders to identify <strong>the</strong> topics in which knowledge
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
should be ga<strong>the</strong>red in priority. The arguments for developing knowledge in a particular direction may
be based on client’s feedback, but also on collective discussions or merely <strong>the</strong> experience <strong>of</strong> some
senior members <strong>of</strong> <strong>the</strong> practice. Many projects are proposed in <strong>the</strong>se meetings but only few get
through. “In our ‘practice’ we start about five to ten projects/year and reject many o<strong>the</strong>rs. This is <strong>of</strong>ten
a tough decision. But we need to focus our resources” explains a Practice Development Leader.
4.2.2 Step 2: Allocating resources to fill <strong>the</strong> gaps
Once <strong>the</strong> ‘practice’ agrees on a set <strong>of</strong> important knowledge gaps to fill, <strong>the</strong>y still need to ga<strong>the</strong>r
resources in order to address <strong>the</strong>m. The ‘practices’ are networks <strong>of</strong> people and do not have large
dedicated resources at <strong>the</strong>ir disposal. The main resources are in <strong>the</strong> hands <strong>of</strong> <strong>the</strong> Knowledge
Committee and <strong>the</strong> local <strong>of</strong>fices. The resources necessary to fill a gap are identified along two axis:
<strong>the</strong> “per diem” (cost <strong>of</strong> <strong>the</strong> people – consultants or knowledge pr<strong>of</strong>essionals – which are working on
<strong>the</strong> project) and <strong>the</strong> “out <strong>of</strong> pocket expenses” (all o<strong>the</strong>r expenses linked to <strong>the</strong> project such as trips,
development fees, cost <strong>of</strong> subcontracting, data, etc.).
The “out <strong>of</strong> pocket expenses” are covered by budgets that are in <strong>the</strong> hands <strong>of</strong> <strong>the</strong> Knowledge
Committee. All ‘practices’ regularly present to this Committee <strong>the</strong> gaps <strong>the</strong>y wish to fill in order to
obtain investment budgets. The Knowledge Committee has <strong>the</strong>refore a broader view on <strong>the</strong> gaps <strong>of</strong>
<strong>the</strong> whole firm and decides on prioritizing some gaps or ‘practices’ through a larger resource
allocation. On this basis, <strong>the</strong> Knowledge committee gives envelopes to ‘practices’, and within <strong>the</strong>
envelopes, ‘practices’ are able to allow <strong>the</strong> budgets to investments <strong>the</strong>y consider as priorities. The
investment policy is mainly concentrated at <strong>the</strong> level <strong>of</strong> <strong>the</strong> ‘practices’ in order to use <strong>the</strong> means on
<strong>the</strong> most important gaps and avoid a lack <strong>of</strong> cohesion in <strong>the</strong> efforts. As illustrated by a Senior
Research Manager, “more than three years ago, a ‘Water Management’ gap/initiative has been
identified because <strong>the</strong> ‘practices’ viewed it as a main issue. It was a prospective decision not based at
that time on client’s concerns.”
The second type <strong>of</strong> resource, <strong>the</strong> “per diem”, is an opportunity cost represented by <strong>the</strong> people that will
actually work on <strong>the</strong> project instead <strong>of</strong> serving a client or ano<strong>the</strong>r billable project. These opportunity
costs are supported by <strong>the</strong> local <strong>of</strong>fices which decide to allocate some people to a specific project. It
means that <strong>the</strong> people working on filling a knowledge gap are not paid by a specific budget. Finally,
<strong>the</strong> personal interests <strong>of</strong> <strong>the</strong> people who will work on <strong>the</strong> project is ano<strong>the</strong>r important factor taken into
account when <strong>the</strong>y decide whe<strong>the</strong>r to fill a gap or not. “If we don’t have a person that is really
passionate about working on <strong>the</strong> topic, a project champion, <strong>the</strong> project will <strong>of</strong>ten not be started.”
explains a Practice Development Leader.If all <strong>the</strong> conditions are ga<strong>the</strong>red, <strong>the</strong> ‘practices’ start filling
<strong>the</strong> gap by setting up a Knowledge Investment Project (KIP).
4.2.3 Step 3: Filling <strong>the</strong> gaps: Knowledge Investment Projects (KIPs)
Knowledge Investment Projects (KIPs) are <strong>the</strong> firm’s major vehicle to create knowledge. They are
structured as client engagements. It means that <strong>the</strong>re is a team <strong>of</strong> three to four knowledge experts
that work full-time on <strong>the</strong> project as well as a larger number <strong>of</strong> experts and consultants contributing
through ad-hoc interventions, typically for a few hours. The core team will look at what already exists
on <strong>the</strong> topic. They will interview knowledgeable people within and outside <strong>the</strong> firm, in order to develop
a corporate point <strong>of</strong> view on that topic. KIPs are sometimes realized in partnership with a client, when
<strong>the</strong> topic is directly linked to <strong>the</strong> challenge faced by that client. The KIP will be supervised by a
steering committee <strong>of</strong> senior consultants and experts that will review <strong>the</strong> progress <strong>of</strong> <strong>the</strong> project every
two/three months and allow it to continue. KIPs are sometimes stopped before <strong>the</strong>y reach <strong>the</strong>ir goal,
because ei<strong>the</strong>r <strong>the</strong>y do not make progress quickly enough, ei<strong>the</strong>r <strong>the</strong> question <strong>the</strong>y address has
become in <strong>the</strong> meantime less relevant.
4.3 How and where is Strategizing work actually done?
As shown in <strong>the</strong> previous section, <strong>the</strong> process <strong>of</strong> strategy definition does not happen at a single point
in time. It is ra<strong>the</strong>r spread over a period that can last several months, from <strong>the</strong> collect <strong>of</strong> information
on knowledge gaps until <strong>the</strong> setting up <strong>of</strong> Knowledge Investment Projects. Similarly, <strong>the</strong>re is no single
place <strong>of</strong> decision, but a multitude <strong>of</strong> places where successive decisions are made. The answer to <strong>the</strong>
“how and where is <strong>the</strong> work <strong>of</strong> strategizing and organizing actually done” is <strong>the</strong>refore more complex to
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identify. We will show that this answer is directly linked to <strong>the</strong> way decisions are made at McKinsey
and Company. Due to <strong>the</strong> particular structure <strong>of</strong> McKinsey, decision-making is present at different
levels in <strong>the</strong> firm: “If we had to point out a single place where <strong>the</strong> Knowledge strategy is decided, it
would be within <strong>the</strong> Knowledge Committee. But it does not all start <strong>the</strong>re” says a Knowledge
Pr<strong>of</strong>essional. Actually, it does nei<strong>the</strong>r start nor finish with <strong>the</strong> Knowledge committee. The process
ra<strong>the</strong>r looks like an up and down movement, beginning with individuals, coming gradually to <strong>the</strong> top -
represented by <strong>the</strong> Knowledge Committee - and falling back to <strong>the</strong> individuals for <strong>the</strong> very last
decisions.
The process begins indeed with <strong>the</strong> collect <strong>of</strong> information on knowledge gaps by consultants and
researchers. These gaps are <strong>the</strong>n aggregated and prioritized at <strong>the</strong> level <strong>of</strong> <strong>the</strong> ‘practices’. Afterwards
<strong>the</strong>y present <strong>the</strong>ir gaps to <strong>the</strong> Knowledge Committee where <strong>the</strong> decisions on budget allocations are
made. The budgets are in fact divided between <strong>the</strong> different ‘practices’. And <strong>the</strong>n begins <strong>the</strong> fall back:
based on <strong>the</strong> available budget, ‘practices’ make a prioritization <strong>of</strong> knowledge investment projects.
Local <strong>of</strong>fices participate in <strong>the</strong> decision process by deciding whe<strong>the</strong>r to dedicate some human
resources to <strong>the</strong> project. Finally, individuals (researchers and/or consultants) decide <strong>the</strong>mselves
whe<strong>the</strong>r <strong>the</strong>y want to contribute to a specific knowledge investment project. The decision depends on
<strong>the</strong>ir interests and <strong>the</strong>ir career plan. We see that at <strong>the</strong> end <strong>of</strong> <strong>the</strong> process, <strong>the</strong> ability to decide comes
back to <strong>the</strong> level where it all started. This also shows that <strong>the</strong> definition <strong>of</strong> <strong>the</strong> knowledge strategy is
actually not in <strong>the</strong> hands <strong>of</strong> a single person or group but involves four levels <strong>of</strong> decision. We present
in Table 1 <strong>the</strong> different levels <strong>of</strong> decision and detail <strong>the</strong> decisions being made at each level.
Level <strong>of</strong> decision People making <strong>the</strong>
decision
Knowledge Senior partners,
Committee directors
‘Practices’ Partners heading
<strong>the</strong> practice
Local <strong>of</strong>fices Partners heading
<strong>the</strong> <strong>of</strong>fice
Individuals Consultants and
researchers
Role Decision
Overlook <strong>the</strong> investments for
developing and disseminating
knowledge within <strong>the</strong> firm.
- Collect, aggregate and
identify <strong>the</strong> most important
knowledge gaps in <strong>the</strong>ir
knowledge domains.
- Present <strong>the</strong> knowledge gaps
to <strong>the</strong> Knowledge Committee
to obtain funding.
- Manage <strong>the</strong> knowledge
investment projects (KIPs)
Dedicate resources to KIPs in
<strong>the</strong> form <strong>of</strong> local staff
temporarily working on <strong>the</strong>
KIPs
Allocate budgets to <strong>the</strong>
‘practices’ to finance
<strong>the</strong>ir knowledge
investments.
Gives priority among
<strong>the</strong> many knowledge
gaps <strong>the</strong>y have
aggregated, which
gaps <strong>the</strong>y will present
to <strong>the</strong> Knowledge
Committee to obtain
funding.
Decide whe<strong>the</strong>r <strong>the</strong>y
allocate human
resources to KIPs
instead <strong>of</strong> serving
clients or o<strong>the</strong>r
projects.
Realize and achieve <strong>the</strong> KIP Decide to contribute to
projects according to
<strong>the</strong>ir own pr<strong>of</strong>essional
development priorities
and interests.
Table 1: Four levels <strong>of</strong> decision involved in <strong>the</strong> definition <strong>of</strong> <strong>the</strong> knowledge strategy
5. Conclusions
A first conclusion <strong>of</strong> our study is that <strong>the</strong> conceptualization <strong>of</strong> knowledge strategy and knowledge
gaps proposed by Zack (2002) does effectively exist in reality. We indeed find many signs <strong>of</strong> such
practices at McKinsey:
The identification <strong>of</strong> knowledge gaps is an existing practice, largely shared among consultants
and researchers. The gap between what <strong>the</strong> firm knows and what it must know receives a
constant attention thanks to <strong>the</strong> constant flow <strong>of</strong> information coming from consultants implicated
with several types <strong>of</strong> clients. However, our observation has been limited to <strong>the</strong> ‘selected’
knowledge gaps resulting from a successful selection process. We haven’t explored <strong>the</strong> ways
through which <strong>the</strong> identified but not selected gaps are managed: how “some gaps are ‘closed’ for
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
a period and eventually re-opened later” as explained by a Senior Research Manager for ‘Growth’
within <strong>the</strong> ‘Strategy Practice’.
The alignment <strong>of</strong> <strong>the</strong> corporate strategy and knowledge strategy is ensured at McKinsey through
<strong>the</strong> “upward and downward” flow <strong>of</strong> <strong>the</strong> knowledge strategy development. The gaps analyzed at
<strong>the</strong> researchers and consultants’ level are indeed ga<strong>the</strong>red, prioritized and presented to <strong>the</strong>
highest level <strong>of</strong> <strong>the</strong> company. The Knowledge committee, composed <strong>of</strong> senior directors and
partners <strong>of</strong> <strong>the</strong> firm, is <strong>the</strong>n largely implicated with <strong>the</strong> overall corporate strategy. They validate
and decide on which gaps <strong>the</strong>re will be a research. This decision-making at <strong>the</strong> highest level
ensures a consistency between <strong>the</strong> corporate strategy and <strong>the</strong> knowledge strategy.
Ano<strong>the</strong>r originality <strong>of</strong> our contribution is to analyze Knowledge Strategy by applying a Strategy-as-
Practice approach. In order to evaluate <strong>the</strong> contribution <strong>of</strong> this paper, we compare our findings to two
<strong>of</strong> <strong>the</strong> five key questions highlighted by Jarzabkowski et al. (2007). These two questions and our
contribution through this paper are:
5.1 Who is a Knowledge strategist?
We show in this paper that many types <strong>of</strong> actors can participate in <strong>the</strong> development <strong>of</strong> a strategy. We
indeed see a wide range <strong>of</strong> actors, at different levels in <strong>the</strong> firm’s organization, involved with <strong>the</strong>
definition <strong>of</strong> <strong>the</strong> Knowledge Strategy. Particularly, we highlight <strong>the</strong> fact that strategizing is not an
occupation exclusively in <strong>the</strong> hands <strong>of</strong> <strong>the</strong> boards <strong>of</strong> director or higher ranked members <strong>of</strong> <strong>the</strong> firm.
5.2 What do Knowledge strategists do?
We show that each type <strong>of</strong> actor has a different role, which results from its function in <strong>the</strong> firm and its
voluntary contribution. We also point out that each type <strong>of</strong> actor has <strong>the</strong> ability to decide at a certain
moment in <strong>the</strong> knowledge strategy definition process. We show that <strong>the</strong> process does not happen at a
single point in time but is made <strong>of</strong> successive and recursive steps, which we detail. Finally, we
observe that <strong>the</strong> successive decisions within a process follow an “up and down” movement along <strong>the</strong>
organizational pyramid. This movement begins and ends with <strong>the</strong> contribution <strong>of</strong> <strong>the</strong> researchers and
consultants.
6. Limitations and fur<strong>the</strong>r research directions
A first limitation, yet voluntary, <strong>of</strong> our research has to deal with our specific field <strong>of</strong> investigation. For
<strong>the</strong> sake <strong>of</strong> <strong>the</strong> illustration, we chose to analyse a best-in-class organization in terms <strong>of</strong> strategy and
knowledge management. Hence, this study must be considered as illustration <strong>of</strong> “exemplar practices”
and not as an example <strong>of</strong> widespread practices.
A second limitation is that we focus our understanding on <strong>the</strong> process ra<strong>the</strong>r than on <strong>the</strong> content <strong>of</strong>
<strong>the</strong> strategy development. We analyse <strong>the</strong> steps through which a strategy is developed in terms <strong>of</strong>
people and actions ra<strong>the</strong>r than looking into <strong>the</strong> changes in <strong>the</strong> content <strong>of</strong> <strong>the</strong> strategy. A future issue
<strong>of</strong> research could be to monitor more closely <strong>the</strong> strategy content and especially how it changes
throughout <strong>the</strong> process and under <strong>the</strong> interventions <strong>of</strong> <strong>the</strong> different actors.
A third limitation relates to <strong>the</strong> fact that our study only addresses explicit knowledge, or knowledge
made explicit. This leaves an important part <strong>of</strong> <strong>the</strong> firm’s knowledge beyond <strong>the</strong> borders <strong>of</strong> our study.
Tacit and explicit knowledge are indeed traditionally distinguished (Nonaka et al., 1994), or at least a
knowledge characterization on <strong>the</strong> continuum ‘tacit-explicit’ (Nonaka and von Krogh, 2009). The tacit
parts are here not considered as <strong>the</strong> mechanisms described in this paper require from all managers
involved an ‘explicitation’ <strong>of</strong> <strong>the</strong>ir ideas and intuitions, in order for <strong>the</strong> gaps to be proposed and
discussed. Therefore, <strong>the</strong> knowledge considered is mainly explicit, while tacit knowledge is mostly left
out <strong>of</strong> <strong>the</strong> process.
We think it would be interesting to understand <strong>the</strong> role <strong>of</strong> <strong>the</strong> tacit part <strong>of</strong> knowledge in <strong>the</strong> definition <strong>of</strong>
<strong>the</strong> knowledge strategy, in <strong>the</strong> continuum perspective. As pointed by Nonaka and Von Krogh (2009),
more longitudinal and empirical research should pursue to clarify this articulation and <strong>the</strong> link between
<strong>the</strong> social practice and <strong>the</strong> organizational knowledge creation, which could be one objective <strong>of</strong> a welldefined
knowledge strategy.
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Françoise de Viron, Thomas Lederer, Tanguy De Jaegere and Alain Vas
Potential fur<strong>the</strong>r research questions deriving from <strong>the</strong>se limitations include <strong>the</strong> following:
How is tacit knowledge made explicit in <strong>the</strong> process <strong>of</strong> knowledge strategy definition?
What part <strong>of</strong> knowledge is being left out <strong>of</strong> <strong>the</strong> process <strong>of</strong> definition because <strong>of</strong> its difficulty to be
explained?
How does <strong>the</strong> actor’s tacit knowledge influence <strong>the</strong>m when discussing and making choices about
which knowledge should be developed?
Acknowledgments
This article was developed thanks to <strong>the</strong> kind collaboration <strong>of</strong> McKinsey and Company, particularly in
<strong>the</strong> Knowledge Center in Belgium.
7. Appendix
7.1 Appendix I: Interview questions
7.1.1 Preliminary general interview guidelines
1. How is <strong>the</strong> Belgian Knowledge Centre (and o<strong>the</strong>rs) organised?
2. Who are <strong>the</strong> people (background, pr<strong>of</strong>ile) working at a Knowledge Centre?
3. What are <strong>the</strong> roles <strong>of</strong> <strong>the</strong> experts?
4. How do <strong>the</strong>y manage <strong>the</strong>ir roles?
7.1.2 Specific interviews guidelines
1. What’s your role?
2. How do you share your time between <strong>the</strong> different activities as knowledge
identification/acquisition/creation/delivery?
3. Are you involved in identification/selection <strong>of</strong> knowledge domains/blocks (Knowledge Intensive
Projects (KIP), Knowledge Initiatives …)? How?
4. Could you describe this identification? How and where does it take place? With whom?
5. Could you illustrate your ‘practices’ (examples <strong>of</strong> KIP topics, Knowledge day, Leadership call,
‘Practice Olympics’, Knowledge Initiatives …)?
6. How does <strong>the</strong> physical presence <strong>of</strong> ‘practices’ influence knowledge projects? (Examples <strong>of</strong>
projects that have been influenced by informal contacts)
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Intellectual Capital and Organizational Knowledge, Oxford: Oxford University Press, pp. 255-276.
231

The Application <strong>of</strong> <strong>the</strong> SECI Model in Cross-Cultural
Contexts
Nasser Easa 1,2 and Robin Fincham 2
1
Faculty <strong>of</strong> Commerce-Suez, Suez Canal University, Egypt,
2
Stirling Management School, University <strong>of</strong> Stirling, Stirling, UK
n.f.easa@stir.ac.uk
rf3@stir.ac.uk
Abstract: This study provides <strong>the</strong>oretical analysis for <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> SECI model in cross-cultural
contexts. The model was produced in 1995 by Nonaka and Takeuchi for creating knowledge in organisations
through four processes: socialisation, externalisation, combination and internalisation. The SECI model has
become widely accepted by scholars and widely quoted in approaches to classifying, creating, documenting,
sharing and transferring knowledge, from a knowledge management perspective. However, Glisby & Holden
(2003) criticised <strong>the</strong> universal applicability <strong>of</strong> this model and noted that each <strong>of</strong> <strong>the</strong> four modes <strong>of</strong> SECI is strongly
interpreted in reference to <strong>the</strong>ir embeddedness in traditional Japanese values and management practices in
comparison to Western culture. Weir and Hutchings (2005) concurred with Glisby and Holden that SECI does not
have universal application. However, Weir and Hutchings controversially suggested that some elements <strong>of</strong> <strong>the</strong>
SECI model have application in <strong>the</strong> Arab world and China. In contrast, Andreeva and Ikhilchik (2010) criticised
<strong>the</strong> claim that <strong>the</strong> SECI model is not universal and argued that <strong>the</strong> four modes <strong>of</strong> SECI model apply in Russian
context. However, <strong>the</strong>ir argument was also controversial when <strong>the</strong>y noted considerable differences between
Japanese and Russian cultures and management practices in comparison to <strong>the</strong> similarities. Recently, Haag et
al. (2010) considered <strong>the</strong> effects <strong>of</strong> two <strong>of</strong> H<strong>of</strong>stede’s culture dimensions on SECI processes and suggested also
that not all SECI processes reflect traditional Japanese values. The authors argued that Japanese companies
focus more on tacit knowledge which is related to <strong>the</strong> socialisation mode, while <strong>the</strong> western companies focus
more on explicit knowledge which is related to combination process. It was clear that not all SECI’ modes are
applicable in <strong>the</strong> same culture as some <strong>of</strong> <strong>the</strong>m were widely spread while some were not. Therefore, it is
concluded that not all SECI processes reflect traditional Japanese values, but <strong>the</strong>y should be adapted in order to
be successfully applied in different contexts. Being aware <strong>of</strong> culture and its impact on knowledge creation and <strong>the</strong>
application <strong>of</strong> SECI will enrich <strong>the</strong> insight <strong>of</strong> an organisation into <strong>the</strong>ir knowledge creation and <strong>the</strong> processes
involved in it.
Keywords: knowledge creation, SECI Model, national culture context
1. Introduction
Lots <strong>of</strong> Japanese companies are most probably quite successful and a world leader such as: Sony,
Toyota, Canon, Honda, Hitachi, and Olympus. Therefore, pro<strong>of</strong>ing <strong>the</strong> validity <strong>of</strong> SECI model <strong>of</strong>
creating knowledge in Japanese firms gave <strong>the</strong> model its great value. However, since <strong>the</strong> SECI model
was driven from Japanese culture, <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> model in different national cultures is
questionable. H<strong>of</strong>stede and H<strong>of</strong>stede (2005, p. 7) defined <strong>the</strong> national culture as "<strong>the</strong> collective
programming <strong>of</strong> <strong>the</strong> mind which distinguishes <strong>the</strong> members <strong>of</strong> one group or people from ano<strong>the</strong>r".
They suggested that people share a collective national character that represents <strong>the</strong>ir cultural mental
programming in order to share values, beliefs, assumptions, expectations, perceptions and behaviour.
This paper explains how <strong>the</strong> national culture affects <strong>the</strong> universal concept <strong>of</strong> SECI model by
discussing <strong>the</strong> literature conducted on this topic. The paper is constructed into four sections. Next to
<strong>the</strong> introduction, <strong>the</strong> <strong>the</strong>oretical concept <strong>of</strong> SECI model is provided. This will be followed by discussing
<strong>the</strong> applicability <strong>of</strong> each process <strong>of</strong> SECI in cross cultural contexts and finally <strong>the</strong> conclusion will be
suggested.
2. Nonaka’s model <strong>of</strong> knowledge creation process in organisations
Knowledge creation process in organisations is defined as <strong>the</strong> process <strong>of</strong> making available and
amplifying knowledge created by individuals as well as connecting it to an organisation's knowledge
system (Nonaka et al., 2006: p. 1180). According to Anna Lyude (2007), <strong>the</strong> key ideas <strong>of</strong> creating
knowledge within organisations, first published by Nonaka in 1991, drew on studies <strong>of</strong> information
creation in innovative Japanese Companies. In 1993, 105 questionnaires designed to test Nonaka’s
emerging <strong>the</strong>ory <strong>of</strong> knowledge creation were mailed to Japanese male middle managers. Hierarchical
confirmatory factor analysis <strong>of</strong> <strong>the</strong> data confirmed <strong>the</strong> suggestion that knowledge creation comprised
four modes <strong>of</strong> knowledge conversion (socialisation, externalisation, combination and internalisation)
which is known as SECI model. Subsequently, in 1994, Nonaka published a more extensive
<strong>the</strong>oretical paper and <strong>the</strong> results <strong>of</strong> a survey that validated <strong>the</strong> model. In 1995, <strong>the</strong> book length
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exposition <strong>of</strong> <strong>the</strong> <strong>the</strong>ory appeared where <strong>the</strong> SECI matrix is described as <strong>the</strong> engine <strong>of</strong> knowledge
creation (Nonaka and Takeuchi, 1995). They argued that <strong>the</strong> success <strong>of</strong> many Japanese firms
depends on <strong>the</strong>ir ability to create new organisational knowledge through a cyclic model <strong>of</strong> continuous
interactions and transformation <strong>of</strong> tacit and explicit knowledge on three levels: individuals, groups and
organisations. This occurs through <strong>the</strong> four processes <strong>of</strong> socialisation, externalisation, combination,
and internalisation, as depicted in <strong>the</strong>ir SECI model. This model has become widely accepted by
scholars and widely quoted in approaches to classifying, creating, documenting, sharing and
transferring and knowledge (Haggie and Kingston, 2003; Aurum et al., 2008). The following figure
describes <strong>the</strong> four processes <strong>of</strong> SECI model.
Figure 1: Nonaka and Takeuchi’s model <strong>of</strong> knowledge conversion modes
Socialisation process converts tacit knowledge into new tacit knowledge through shared experience
and this takes place through <strong>the</strong> every day social and cultural process linked to ongoing
organizational activities (Martin-de-Castro et al., 2008). Socialisation typically occurs in a traditional
apprenticeship ra<strong>the</strong>r than from written manuals or textbooks. Although it may occur in informal social
meetings outside <strong>of</strong> <strong>the</strong> workplace, where tacit knowledge such as world views, mental models and
mutual trust can be created and shared and beyond organisational boundaries through <strong>the</strong> interaction
with customers and suppliers (Nonaka et al., 2000).
Externalisation process articulates tacit knowledge into explicit knowledge, which happens when <strong>the</strong>
firm expresses formally its internal rules <strong>of</strong> functioning or when it explicitly sets organisational goals
and is captured and texted. When tacit knowledge is made explicit, knowledge is “crystallized”
allowing it to be shared by o<strong>the</strong>rs and to become <strong>the</strong> basis <strong>of</strong> new knowledge (Nonaka, et al., 2000).
Combination process converts already explicit knowledge into more systematic sets <strong>of</strong> explicit
knowledge. Explicit knowledge is collected from inside or outside <strong>the</strong> organization and <strong>the</strong>n combined
or edited. The new explicit knowledge is <strong>the</strong>n disseminated among <strong>the</strong> members <strong>of</strong> <strong>the</strong> organization.
Creative use <strong>of</strong> computerised communication networks and databases can facilitate this mode <strong>of</strong>
knowledge conversion. When, for example, <strong>the</strong> auditor <strong>of</strong> a company collects information from
throughout <strong>the</strong> organisation and puts it toge<strong>the</strong>r in a context to make a financial report, that report is
new knowledge in <strong>the</strong> sense that it syn<strong>the</strong>sises knowledge from many different sources in one context
(Nonaka et al., 2000).
Internalisation process embodies explicit knowledge into tacit knowledge, suggesting that we
internalise <strong>the</strong> explicit knowledge to our tacit knowledge. By internalising any creation <strong>of</strong> explicit
knowledge is shared throughout an organisation and converted into tacit knowledge by individuals.
Internalisation is closely related to `learning by doing', for example, training programmes can help
trainees to understand an organization and <strong>the</strong>mselves or reading documents or manuals about <strong>the</strong>ir
jobs and <strong>the</strong> organisation, trainees can internalise <strong>the</strong> explicit knowledge written in such documents to
enrich <strong>the</strong>ir tacit knowledge base. This tacit knowledge accumulated at <strong>the</strong> individual level can <strong>the</strong>n
set <strong>of</strong>f a new spiral <strong>of</strong> knowledge creation when it is shared with o<strong>the</strong>rs through socialisation (Nonaka
et al., 2000).
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3. The applicability <strong>of</strong> SECI model in cross-cultural contexts
SECI model has become widely accepted by scholars and widely quoted in approaches to classifying,
creating, documenting, sharing and transferring knowledge (Haggie and Kingston, 2003; Aurum et al.,
2008). However, little research has been done to test <strong>the</strong> applicability <strong>of</strong> this model in different cultural
contexts. Glisby and Holden (2003) highlighted <strong>the</strong> reflection <strong>of</strong> <strong>the</strong> Japanese cultural context in SECI
and stressed that this model is more distinctive to Japanese culture in comparison to <strong>the</strong> western
culture. Weir and Hutchings (2005) tested <strong>the</strong> applicability <strong>of</strong> this model in <strong>the</strong> Arab and Chinese
context and examined some <strong>of</strong> <strong>the</strong> salient characteristics <strong>of</strong> business culture in <strong>the</strong> Arab world and
China in terms <strong>of</strong> SECI model. Andreeva and Ikhilchik (2010) also tested <strong>the</strong> applicability <strong>of</strong> this model
in <strong>the</strong> Russian context. Both groups <strong>of</strong> authors confirmed that <strong>the</strong> model was applicable in o<strong>the</strong>r
contexts such as Arab, China and Russia, but <strong>the</strong>y stressed that <strong>the</strong> model should reflect <strong>the</strong> culture
dimensions <strong>of</strong> <strong>the</strong>se countries. Haag et al. (2010) argued that <strong>the</strong> SECI model is not universal but
culturally situated because it stems from a particular context (Japan) and its processes are strongly
influenced and shaped by culture and values. They argued that <strong>the</strong> SECI model is a contextualised
model, due to adapting <strong>the</strong> concept <strong>of</strong> ba, which is considered “to be a shared space that serves as a
foundation for knowledge creation” (Nonaka and Konno, 1998, p. 40). This shared space also points
to <strong>the</strong> cultural situatedness <strong>of</strong> <strong>the</strong> SECI model as it suggests that contexts have to be shared with
o<strong>the</strong>rs who have a similar understanding <strong>of</strong> <strong>the</strong> situation in order to be meaningful to <strong>the</strong>m–and
members <strong>of</strong> a different culture <strong>of</strong>ten have quite different understandings <strong>of</strong> <strong>the</strong> same situation. In
particular, Haag et al. considered two <strong>of</strong> H<strong>of</strong>stede’s cultural value dimensions to investigate SECI
model from <strong>the</strong> perspective <strong>of</strong> national culture dimensions. The first dimension is
individualism/collectivism which has been widely used and applied in research to date (Haag et al.,
2010). H<strong>of</strong>stede and H<strong>of</strong>stede (2005, p. 76) define individualism as “pertaining to societies in which
<strong>the</strong> ties between individuals are loose: everyone is expected to look after himself or herself and his or
her immediate family”. On <strong>the</strong> o<strong>the</strong>r hand, collectivism “pertains to societies in which people from birth
onwards are integrated into strong, cohesive in-groups, which throughout people’s lifetime continue to
protect <strong>the</strong>m in exchange for unquestioning loyalty” (H<strong>of</strong>stede and H<strong>of</strong>stede, 2005, p. 76). In addition
to individualism/collectivism, <strong>the</strong> power distance dimension is ano<strong>the</strong>r important aspect <strong>of</strong> crosscultural
differences at a national level. Power distance is defined as “<strong>the</strong> extent to which <strong>the</strong> less
powerful members <strong>of</strong> institutions and organizations within a country expect and accept that power is
distributed unequally” (H<strong>of</strong>stede and H<strong>of</strong>stede, 2005, p. 46). Therefore <strong>the</strong>y concluded that SECI
processes differ across cultures and that being conscious <strong>of</strong> <strong>the</strong> cultural situatedness <strong>of</strong> <strong>the</strong> SECI
model can lead to a more adequate use <strong>of</strong> <strong>the</strong> model for organizational knowledge creation. These
findings were also highlighted by Nonaka and his associates <strong>the</strong>mselves by mentioning that “In
knowledge creation, one cannot be free from context. Social, cultural and historical contexts are
important for individuals as such contexts provide <strong>the</strong> basis for one to interpret information to create
meanings” (Nonaka et al., 2000, p. 14). For more detail, <strong>the</strong> next section provides <strong>the</strong> features <strong>of</strong>
Japan, China, Arab and Russia contexts related to each process <strong>of</strong> SECI.
3.1 Socialisation
The first stage <strong>of</strong> <strong>the</strong> model, socialisation, implies sharing tacit knowledge, both internally and
externally. Nonaka and Takeuchi stressed that employees must be fundamentally willing to share
knowledge internally in <strong>the</strong> organisation as well as externally with suppliers, customers and o<strong>the</strong>r
stake-holders to make socialisation happen effectively (Nonaka and Takeuchi, 1995). Glisby and
Holden (2003) suggested that internal sharing <strong>of</strong> tacit knowledge is enabled by strong personal
affiliation with and commitment to organisation, cooperative attitudes between employees (ra<strong>the</strong>r than
competitive ones) and a focus on developing close relationships with those who share <strong>the</strong> same fate
(work for <strong>the</strong> same organisation). All <strong>of</strong> <strong>the</strong>se features are usually claimed to be distinctively part <strong>of</strong>
Japanese culture. As for external sharing <strong>of</strong> tacit knowledge, Glisby and Holden claimed that it is
facilitated by <strong>the</strong> existence <strong>of</strong> <strong>the</strong> networks <strong>of</strong> partners and close interrelationships between
companies in <strong>the</strong> industry that characterise Japanese cultural context but may not be present in o<strong>the</strong>r
societies. Thus, Glisby and Holden argued that <strong>the</strong> socialisation mode <strong>of</strong> knowledge conversion is
itself a deeply “Japanese” process. They argued that <strong>the</strong> key to <strong>the</strong> knowledge-sharing process is
personal commitment and <strong>the</strong> employees’ sense <strong>of</strong> identity with <strong>the</strong> enterprise and its mission and this
kind <strong>of</strong> commitment and loyalty is rooted in Japanese cultural values. Japanese employees also use
after-hours socialisation as a key means <strong>of</strong> sharing tacit knowledge, discussing all aspects <strong>of</strong> <strong>the</strong>ir
companies over meals and in protracted drinking sessions. Glisby and Holden added that <strong>the</strong>
Japanese are inclined to build strong interpersonal relationships with o<strong>the</strong>rs with whom <strong>the</strong>re is a
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strong sense <strong>of</strong> shared fate: people in one’s family, in <strong>the</strong> same company, from <strong>the</strong> same university,
etc.
In contrast, Weir and Hutchings (2005) in <strong>the</strong>ir analysis <strong>of</strong> <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> SECI model in
Chinese and Arab contexts suggest that networking is a traditional and wide-spread practice both in
China and <strong>the</strong> Arab world and thus conclude that socialisation works quite effectively in <strong>the</strong>se
contexts. Socilaisation occurs in <strong>the</strong> Arab world primarily in <strong>the</strong> family context, which typically
constitutes <strong>the</strong> fundamental matrix <strong>of</strong> social organisation. Thus, managers in Arab organisations are
already pre-socialised and members <strong>of</strong> networks, and potential members <strong>of</strong> o<strong>the</strong>rs in a virtual sense.
China is also a culture that demonstrates high level <strong>of</strong> socialisation and highly dependent upon <strong>the</strong>
existence <strong>of</strong> strong networks. Chinese are actually share knowledge freely but only within <strong>the</strong>ir trusted
networks in which an insider relationship exists between transmitter and receiver. Therefore, in China
<strong>the</strong> networks generally only operate on a departmental basis and consequently information is only
shared within departments. This means that Chinese networks are mostly “short-distance” and are
mostly concentrated inside departments, ra<strong>the</strong>r than between departments or between organisations.
It seems that this fact implies, contrary to <strong>the</strong> conclusion <strong>of</strong>, Weir and Hutchings that at least external
sharing <strong>of</strong> tacit knowledge is limited in Chinese cultural context as compared to Japanese one.
In <strong>the</strong> Russian context, Andreeva and Ikhilchik (2010) mentioned that <strong>the</strong>re are differences and
similarities between Russian and Japanese contexts, in terms <strong>of</strong> enabling SECI processes. Although
<strong>the</strong>y mentioned that <strong>the</strong> differences heavily outweigh <strong>the</strong> similarities <strong>the</strong>y concluded that SECI can be
applied in <strong>the</strong> Russian context. For socialisation, Andreeva and Ikhilchik argued that individuals’
willingness to share knowledge, both internally and externally, seems not to be common in Russia.
People in Russia consider knowledge as individual power that should not be shared unless necessary
and unless proper benefits are received. Regarding <strong>the</strong> intensity <strong>of</strong> networking with external partners,
this differs in Russia among different industries, but usually is not very strong. A competitive
atmosphere and obsession with privacy <strong>of</strong> information in many cases prevent Russian companies
from building relationships with external partners. A job design that allows sharing <strong>of</strong> experience also
does not seem to be very easy in Russian companies, as it needs a lack <strong>of</strong> fear <strong>of</strong> mistakes and
enough time for experimentation, that can be difficult to negotiate with managers due to <strong>the</strong>
dominating focus on short-term results. Andreeva and Ikhilchik (2010) claimed that <strong>the</strong> only similarity
between <strong>the</strong> Russia and Japan contexts to support socialisation is <strong>the</strong> mentoring which has been a
common practice during Soviet times and is still used nowadays. However, <strong>the</strong>y mentioned that <strong>the</strong>
use <strong>of</strong> mentoring in Russia usually is in much more limited sense compared to Japan. Most
companies limit mentoring to <strong>the</strong> probation period <strong>of</strong> new employees, and <strong>the</strong> mentor’s responsibilities
concern mainly explaining general organisational policies ra<strong>the</strong>r than intensive sharing experience
and learning through observation and limitation. This argument <strong>of</strong> Andreeva and Ikhilchik makes <strong>the</strong>ir
claim, that SECI processes have universal application, controversial.
Regarding <strong>the</strong> effect <strong>of</strong> H<strong>of</strong>stede’s cultural dimensions, Haag et al. (2010) suggested that <strong>the</strong> cultural
dimensions strongly influence <strong>the</strong> socialisation mode. They explained that cultures with high
collectivism are more socialised because <strong>the</strong> socialisation mode is closely connected with group
processes. In contrast, cultures that score low on power distance are more likely to support an open
and non-threatening environment for brainstorming than cultures that score high on power distance
(H<strong>of</strong>stede and H<strong>of</strong>stede, 2005; Haag, et al., 2010).
3.2 Externalisation
The next stage <strong>of</strong> <strong>the</strong> SECI model, externalisation, implies converting tacit knowledge into explicit.
Nonaka and Takeuchi (1995) and Nonaka and Konno (1998) emphasised <strong>the</strong> importance <strong>of</strong> group
commitment for its realisation. H<strong>of</strong>stede and H<strong>of</strong>stede (2005) suggested that group orientation is a
specific feature <strong>of</strong> <strong>the</strong> Japanese culture. In addition, Glisby and Holden (2003) mentioned that in
Japan group orientation must be seen in conjunction with <strong>the</strong> pre-eminent concern for <strong>the</strong> attainment
<strong>of</strong> harmony in human relationships and suggested that Japanese organisations experience much less
pressure from shareholders than <strong>the</strong>ir Western companies, and thus <strong>the</strong>y simply can spend <strong>the</strong>ir
resources (including time) more freely to do things <strong>the</strong> way <strong>the</strong>y want to, which includes externalising
knowledge. They argued that Japanese organisations can afford to devote much more attention to <strong>the</strong>
time-consuming and diffuse processes <strong>of</strong> explicating tacit knowledge. Japanese culture as manifested
in inter-firm relations/ networks, collegiality, corporate affiliation, <strong>the</strong> employment system, personal
relations and so forth provides a kind <strong>of</strong> natural backdrop for <strong>the</strong> willing and open sharing <strong>of</strong> tacit
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knowledge. It follows <strong>the</strong>n that <strong>the</strong> Japanese context well favours <strong>the</strong> explicating <strong>of</strong> knowledge which
is embedded.
Weir and Hutchings (2005) claimed that in <strong>the</strong> Arab culture externalisation works not exactly <strong>the</strong> way it
is supposed to according to <strong>the</strong> SECI model, however <strong>the</strong>y reported that Chinese organisations
externalise knowledge almost by <strong>the</strong> same way as Japanese companies do. They opined that it is not
clear that <strong>the</strong> concept <strong>of</strong> externalisation as used in <strong>the</strong> SECI framework works as well in <strong>the</strong> Arab
context as many <strong>of</strong> <strong>the</strong> networks already exist in virtual form. The process <strong>of</strong> externalisation is a
continuous one in which <strong>the</strong> knowledge base does not move inexorably from covert to overt status,
but can shift as <strong>the</strong> definitions <strong>of</strong> <strong>the</strong> social situations change according to events. As a result, some
knowledge can indeed remain tacit but available, while o<strong>the</strong>r knowledge can surface as explicit and
<strong>the</strong>n revert as situations develop. They claimed also that Arab culture is high in tolerance <strong>of</strong> ambiguity
and Arabs tend to prefer to keep <strong>the</strong>ir own knowledge until <strong>the</strong>re is an absolute need for disclosure.
But within relations <strong>of</strong> trust, all knowledge may be available to partners and colleagues. This means
that <strong>the</strong>se situations are governed by <strong>the</strong> nature <strong>of</strong> <strong>the</strong> relationships, ra<strong>the</strong>r than by legal rules or
judgments <strong>of</strong> business importance. Therefore, In Arab business people may prefer not to surface tacit
knowledge, but in case that happens it is usually <strong>of</strong> significance. China, however, demonstrates some
similarities with Nonaka’s characterisation <strong>of</strong> Japanese organisations in <strong>the</strong>ir approach to knowledge
management and <strong>the</strong> sharing <strong>of</strong> knowledge. In a similar fashion as in Japan, <strong>the</strong> willingness <strong>of</strong> <strong>the</strong>
Chinese to share knowledge is also underpinned by its group orientation and <strong>the</strong> Chinese also devote
considerable time to <strong>the</strong> diffuse processes <strong>of</strong> explicating tacit knowledge. Knowledge also in Japan
and China is created in “communities <strong>of</strong> practice” and exists in tacit forms and cannot be formalised
without loss <strong>of</strong> au<strong>the</strong>nticity.
Andreeva and Ikhilchik (2010) mentioned that <strong>the</strong> Russian culture is similar to Japan’s culture in terms
<strong>of</strong> usage <strong>of</strong> metaphors, analogies and models in explanations as mechanisms <strong>of</strong> externalisation.
However, <strong>the</strong>y are different in terms <strong>of</strong> <strong>the</strong> group orientation, as Russia is more individualistic than
Japan. Russians tend to be collectivistic <strong>of</strong> <strong>the</strong> level <strong>of</strong> small groups, while as soon as <strong>the</strong>y exit <strong>the</strong>ir
“inner circle”, <strong>the</strong>y can demonstrate more individualistic behaviour (H<strong>of</strong>stede and H<strong>of</strong>stede, 2005).
Concerning H<strong>of</strong>stede’s two key dimensions mentioned above, Haag et al. (2010) opined that <strong>the</strong>re
are not substantial differences in knowledge conversion from <strong>the</strong> perspective <strong>of</strong> individualismcollectivism
and power distance.
3.3 Combination
At this stage <strong>of</strong> SECI process, <strong>the</strong> tacit knowledge that has been explicated in <strong>the</strong> previous mode <strong>of</strong>
<strong>the</strong> model is now <strong>the</strong> subject <strong>of</strong> sorting, combination and categorization (Nonaka and Takeuchi,
1995). The whole process <strong>of</strong> continuously and holistically combining knowledge from all employees
throughout <strong>the</strong> organization has powerful roots in distinctive Japanese management practices.
Combination is supported by some typically Japanese practices, such as <strong>the</strong> lack <strong>of</strong> interdepartmental
rivalry, consultative decision-making, purposeful overlap <strong>of</strong> functional responsibilities and <strong>the</strong> more
secure context for free and open access to organisational information due to high personal
commitment and relatively permanent occupation (Nonaka and Takeuchi 1995; Glisby and Holden
2003). In comparison to western values, Glisby and Holden (2003) argued that <strong>the</strong> general absence
<strong>of</strong> inter-departmental rivalry and consequences <strong>of</strong> loss <strong>of</strong> face in Japan make combination more
straightforward than in Western organisations.
Weir and Hutchings (2005) concur with Glisby and Holden (2003) that combining knowledge from all
employees throughout <strong>the</strong> organisation has powerful roots in distinctive Japanese management
practices and this may not apply universally. They argued that both Arabic and Chinese worlds are
characterised by much more concentrated authority and decision-making in comparison to Japan.
Combination may play a ra<strong>the</strong>r different role in Arab business organisations for <strong>the</strong> evident reason
that <strong>the</strong> business organization as such is usually structured in terms <strong>of</strong> unfamiliar structures and <strong>the</strong>
discourse <strong>of</strong> <strong>the</strong> family and its internal and external relations is readily applied. Thus it is accepted
that power and authority are concentrated and that, none<strong>the</strong>less, <strong>the</strong> testimony <strong>of</strong> junior members is
likely to contain valuable elements. So <strong>the</strong> good leader has to be both authoritative and consultative.
This concludes that <strong>the</strong> Arab world is authoritarian but also consultative, so knowledge translated is
more complex than that described by Nonaka and Takeuchi. In China, in contrast to Japan’s
participative style <strong>of</strong> management, employees are certainly keen to save face and avoid conflict.
There, <strong>the</strong> authority and seniority are highly respected and managers do not feel comfortable in
receiving advice or information from people below <strong>the</strong>m in <strong>the</strong> hierarchy. So, employees are reluctant
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to share information with managers as that would cause loss <strong>of</strong> face to mangers. This situation
supports <strong>the</strong> top-down decision making and works against sharing <strong>of</strong> knowledge.
For Russia, Andreeva and Ikhilchik (2010) concluded that <strong>the</strong> combination process is applicable in
Russian context. However, this conclusion was also debatable. As mentioned above, Nonaka,
Takeuchi, (1995) consider consultative decision-making as a part <strong>of</strong> leadership style that is inherent to
<strong>the</strong> combination process. However, Andreeva and Ikhilchik suggested that such type <strong>of</strong> leadership is
not widely practiced by Russian managers due to <strong>the</strong> historical heritage <strong>of</strong> <strong>the</strong> leaders’ attitudes to <strong>the</strong>
followers. They also mentioned that <strong>the</strong>re is lack <strong>of</strong> free access <strong>of</strong> employees to corporate information
in Russian companies. This is because Russian companies are known for <strong>the</strong>ir obsession with
secrecy <strong>of</strong> any kind <strong>of</strong> information related to <strong>the</strong>ir business and reluctance to share it both internally
and externally. They commented also that Russian companies provide controversial Application <strong>of</strong>
purposeful overlap <strong>of</strong> functional responsibilities “redundancy”. Many Russian companies have such
an overlap but this typically has not been established intentionally, ra<strong>the</strong>r it developed with <strong>the</strong> growth
<strong>of</strong> <strong>the</strong> company due to <strong>the</strong> lack <strong>of</strong> attention <strong>of</strong> managers to organisational structuring. Accordingly <strong>the</strong>
unintentional nature <strong>of</strong> such redundancy toge<strong>the</strong>r with a highly competitive environment inside <strong>the</strong>
company usually leads to many conflicts both on individual and interdepartmental level. They
suggested that polychromic time orientation is <strong>the</strong> only combination supporter that existed in both
Japan and <strong>the</strong> Russian culture.
In general, Haag et al. (2010) criticised <strong>the</strong> views <strong>of</strong> Glisby and Holden (2003) and Weir and
Hutchings (2005) that <strong>the</strong> combination mode has powerful roots in a distinctive Japanese culture.
They argued that combination mode focuses on explicit knowledge only and Japanese companies
focus more on tacit knowledge, whereas organisations in Western cultures focus on explicit
knowledge.
3.4 Internalisation
The last stage <strong>of</strong> SECI, internalisation, assumes that employees broaden, extend and reframe <strong>the</strong>ir
own tacit knowledge as <strong>the</strong>y internalize <strong>the</strong> explicit knowledge that is shared throughout <strong>the</strong>
organization (Nonaka, 1991). Nonaka and Takeuchi (1995) stress <strong>the</strong> importance <strong>of</strong> learning-by doing
and <strong>the</strong> access <strong>of</strong> following up <strong>the</strong> training programmes documents and databases to support <strong>the</strong>
internalisation process. Glisby and Holden (2003) suggest that <strong>the</strong>re are typical Japanese practices
that create a context for efficient internalisation such as <strong>the</strong> focus on developing generalists ra<strong>the</strong>r
than specialists in one narrow domain, and wide acceptance <strong>of</strong> learning-by-doing. Weir and Hutchings
(2005) agree with Glisby and Holden (2003) that <strong>the</strong> depiction <strong>of</strong> internalisation proposed in <strong>the</strong> SECI
model does not have universal application. They reported that internalisation in <strong>the</strong> context <strong>of</strong> Arab
management is not widely spread and is limited to informal ways. The separation <strong>of</strong> life-spheres is
less distinct, so behaviours which work in one context can become models for o<strong>the</strong>r more formal
situations. Moreover, <strong>the</strong> Arab context is typically not aimed to change <strong>the</strong> employee’s competences
significantly, so <strong>the</strong> focus is still on specialists. Weir and Hutchings (2005) added that people in China
were conditioned no to admit mistakes during <strong>the</strong> communist years and this strong fear considerably
inhibits <strong>the</strong> learning-by-doing process within organisations. The fear <strong>of</strong> mistakes is also one aspect <strong>of</strong>
Russian employees and this makes <strong>of</strong> responsibilities does not work well in many Russian companies
(Andreeva and Ikhilchik, 2010). However, Andreeva and Ikhilchik <strong>the</strong>mselves suggested that <strong>the</strong>re is
a very similar situation between Japan and Russia with generalistic job descriptions – while many
Russian organisations use <strong>the</strong>m, <strong>the</strong>y are usually perceived by employees as a result <strong>of</strong>
mismanagement and potential source <strong>of</strong> conflicts. This argument is also controversial to <strong>the</strong>ir above
conclusion that SECI is applied in <strong>the</strong> Russian context and suggests that externalisation does not
work effectively in Russia relative to Japan.
4. Conclusion
Glisby and Holden (2003) noted that each <strong>of</strong> <strong>the</strong> four modes <strong>of</strong> SECI is strongly interpreted in
reference to <strong>the</strong>ir embeddedness in traditional Japanese values and management practices and <strong>the</strong>y
concluded that <strong>the</strong> applicability <strong>of</strong> this model is not universal. Weir and Hutchings (2005) concurred
with Glisby and Holden’s notes that SECI does not have universal application. However, Weir and
Hutchings controversially suggested that elements <strong>of</strong> <strong>the</strong> SECI model do have application in <strong>the</strong> Arab
world and China. They explained that people in <strong>the</strong> Arab world are naturally more socialised and
accordingly hold tacit knowledge and to this extent <strong>the</strong>y evidence <strong>the</strong> socialisation aspects <strong>of</strong> <strong>the</strong>
Nonaka and Takeuchi model. However, those people differ from <strong>the</strong> externalisation element in that
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Nasser Easa and Robin Fincham
<strong>the</strong>y may not convert tacit knowledge as much knowledge exists within <strong>the</strong> confines <strong>of</strong> virtual
networks. In respect to combination, <strong>the</strong> Arab world is authoritarian, so knowledge translated is more
complex than described by Nonaka and Takeuchi. Moreover, in <strong>the</strong> Arab world segments <strong>of</strong> work and
private life are not kept completely separate and, as such, internalisation is affected by confidence in
knowledge being directly related to confidence in individuals that hold that knowledge. In <strong>the</strong> case <strong>of</strong>
China, <strong>the</strong>y suggested <strong>the</strong> socialization and externalisation elements are remarkably similar to <strong>the</strong>
Japanese situation that Nonaka and Takeuchi describe, as <strong>the</strong> Chinese are also highly networked,
hold tacit knowledge within <strong>the</strong>se networks and are prepared to make this knowledge explicit only
within <strong>the</strong> context <strong>of</strong> <strong>the</strong>se pre-existing relationships. In respect to <strong>the</strong> combination and internalisation
elements, however, China differs from Nonaka and Takeuchi’s categorisations. The departmental
focus <strong>of</strong> <strong>the</strong> Chinese means that <strong>the</strong> combination <strong>of</strong> tacit knowledge is not straightforward, while
learning-by-doing that is a key to internalisation is also problematic because <strong>of</strong> individuals’ fear <strong>of</strong>
admitting mistakes. In contrast, Andreeva and Ikhilchik (2010) criticised <strong>the</strong> claim that SECI model is
not universal and argued that <strong>the</strong> four modes <strong>of</strong> SECI model apply in <strong>the</strong> Russian context. However,
<strong>the</strong>ir argument was also controversial when <strong>the</strong>y marked considerable differences between Japanese
and Russian cultures and management practices in comparison to <strong>the</strong> similarities. Therefore, by <strong>the</strong>
end <strong>the</strong>y concluded that Russian companies has to leverage <strong>the</strong> societal and organisational
conditions and managerial tools that are similar to Japan and compensate <strong>the</strong> differences in order to
support smooth functioning <strong>of</strong> <strong>the</strong>se modes <strong>of</strong> SECI. Haag et al. (2010) suggested also that not all
SECI modes are traditional Japanese values. They argued that Japanese companies focus more on
tacit knowledge which is related to socilaisation mode, whereas <strong>the</strong> western companies focus more
on explicit knowledge which is related to combination mode.
The first point to be highlighted from <strong>the</strong> above argument is that <strong>the</strong> SECI model should be adapted in
order to be successfully applied in a different context. It is clear that being aware <strong>of</strong> culture and its
impact on knowledge creation and <strong>the</strong> application <strong>of</strong> <strong>the</strong> SECI will enrich <strong>the</strong> insights <strong>of</strong> an
organisation into <strong>the</strong>ir knowledge creation and <strong>the</strong> processes involved in it. Therefore, adding a
consideration <strong>of</strong> “culturisation” in <strong>the</strong> four SECI processes, which would acts as a framework in which
companies can analyse what impact national culture could have on knowledge creation ( refer to
Figure 2), would make <strong>the</strong> model more appreciate for use in a multicultural context (Bryceson, 2007;
Haag et al., 2010). Second, it has been noted that <strong>the</strong> studies <strong>of</strong> Weir and Hutchings (2005) and
Andreeva and Ikhilchik (2010) regarding <strong>the</strong> applicability <strong>of</strong> SECI in Arab, Chinese and Russian
contexts are debatable. Moreover, <strong>the</strong> Arab culture should be reviewed after <strong>the</strong> political corruption
happened in <strong>the</strong> Arab region during spring 2011. Therefore, it is important to conduct fur<strong>the</strong>r studies
concerning <strong>the</strong>se particular contexts and o<strong>the</strong>r national contexts taking into consideration widely
acceptable culture dimensions such as H<strong>of</strong>stede’s dimensions. H<strong>of</strong>stede’s two cultural dimensions,
mentioned above, indicated that <strong>the</strong>re are different scores for Japan and Arab world. Japan has
higher score <strong>of</strong> Individualism Index (46) than Arab world has. In contrast, Japan has lower score <strong>of</strong>
Power Distance Index (54) than Arab world has (80). Japan has also higher score <strong>of</strong> Uncertainty
Avoidance Index (95) than Arab world has (68), even it is now necessary to investigate <strong>the</strong> effect <strong>of</strong>
current political corruption on <strong>the</strong> Arab culture and <strong>the</strong> management style. This means that <strong>the</strong>
particular culture context (Japan) that yielded Nonaka’s SECI model differs from <strong>the</strong> o<strong>the</strong>r cultures
such as <strong>the</strong> Arab culture. Therefore, it could be useful to conduct fur<strong>the</strong>r studies to investigate <strong>the</strong>
effect <strong>of</strong> <strong>the</strong>se cultural dimensions <strong>of</strong> <strong>the</strong> applicability <strong>of</strong> SECI model in different cultural contexts.
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Culturisation
Nasser Easa and Robin Fincham
Figure 2: Suggested universal concept <strong>of</strong> Nonaka’s SECI model
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239

Network Management as a way to Manage Intellectual
Capital
Eva Eckenh<strong>of</strong>er
Tomas Bata University, Zlín, Czech Republic
Eva_Eckenh<strong>of</strong>er@hotmail.com
Abstract: In order to meet <strong>the</strong> information and knowledge demands <strong>of</strong> today’s society, <strong>the</strong>re is a need for <strong>the</strong>
development <strong>of</strong> intellectual capital and most especially social capital. Intellectual assets are knowledge,
information, intellectual property and experience, while social capital is embedded in social structures and can be
accessed as well as mobilized in purposive actions. Social capital is based on shared norms, values and trust
which provokes transparency and improves <strong>the</strong> flow <strong>of</strong> information whiles reducing transaction costs. Social
capital eventually leads to rationalization; hence, it increases flexibility as well as performance and innovation. As
a result, <strong>the</strong>re are formal and informal social networks within firms which serve as leverage for sharing
information and knowledge. Factors <strong>of</strong> influence beside norms and values are <strong>the</strong> social skills <strong>of</strong> employees or
network members, which is called social maturity in holistic management. Social networks are a source <strong>of</strong>
intellectual and social capital, <strong>the</strong>refore, <strong>the</strong>re is a need for <strong>the</strong> development, fostering and controlling <strong>of</strong> <strong>the</strong>
networks within firms to achieve optimum results without losing control <strong>of</strong> roles that are played by employees in
<strong>the</strong> network. The aim <strong>of</strong> <strong>the</strong> study is to propose a network management model which can be used for managing
social networks within a firm with small additional expenses in order to achieve optimum effects from intellectual
capital. In this study, <strong>the</strong> author combines models <strong>of</strong> network management from literature review and results <strong>of</strong>
an exploratory survey <strong>of</strong> nine Austrian experts in <strong>the</strong> fields consulting, politics and management. This approach
was aimed at designing a suitable model for <strong>the</strong> management <strong>of</strong> both formal and informal networks within a
company. A suitable network model is that which fulfils <strong>the</strong> criteria <strong>of</strong> practicality in <strong>the</strong> development and fostering
<strong>of</strong> intellectual capital within a firm. This paper introduces intellectual capital, social capital and fur<strong>the</strong>r discusses
<strong>the</strong> principles for working with and within social networks. The paper proceeds fur<strong>the</strong>r by presenting common
network management and network governance models, after which <strong>the</strong> results <strong>of</strong> <strong>the</strong> expert survey are
explained. The paper concludes by presenting a practical model based on <strong>the</strong>oretical and empirical analysis. This
is followed by a proposal on how <strong>the</strong> model can be adapted in firms.
Keywords: intellectual capital, social capital, organisational networks, cluster, network management
1. Introduction
Studies have shown that social capital contributes to knowledge sharing (Chow and Chan, 2008),
which provides an opportunity for companies to gain value from <strong>the</strong>ir networks. Social Capital is
defined as <strong>the</strong> outcomes for individuals from networks with shared norms, values, and understandings
that facilitate co-operation within and among groups (OECD, 2000). Social capital can be used to
manage intellectual capital. A study by Castells (2000) indicates that, intellectual capital is a need in
this current dispensation <strong>of</strong> information and knowledge society and intellectual assets such as
knowledge, information, intellectual property and experience are a strategic factor in <strong>the</strong> value
creation <strong>of</strong> a firm (Stewart, 1997; OECD, 2007).
Moreover, this need lies in <strong>the</strong> human capital, which is considered as a part <strong>of</strong> <strong>the</strong> intellectual capital
<strong>the</strong>ory. Human capital is described as all <strong>the</strong> knowledge, skills, talents and abilities <strong>of</strong> <strong>the</strong> company’s
employee (Edvinsson and Malone, 1997; Kaplan and Norton, 2006). In <strong>the</strong> same context with
intellectual capital is <strong>the</strong> concept <strong>of</strong> knowledge management. Knowledge management is related to
activities like creation, coordination and usage <strong>of</strong> knowledge with <strong>the</strong> aim <strong>of</strong> turning individual
knowledge into organizational knowledge. This means that, <strong>the</strong> tacit as well as explicit knowledge is
important for <strong>the</strong> company. However, nei<strong>the</strong>r <strong>the</strong> initiatives on knowledge databases or network
systems nor initiatives to provoke knowledge sharing with incentive rewards have been really
satisfactory. Therefore, <strong>the</strong> author proposes a focus on <strong>the</strong> management <strong>of</strong> social networks for
initiating and pushing knowledge sharing in a company. This proposal emphasizes <strong>the</strong> role informal
networks play as a powerful source <strong>of</strong> collaboration within a company as well as with external
stakeholders. The ad-hoc networks build a huge gateway for information and knowledge, hence
should be considered by <strong>the</strong> company. However, <strong>the</strong> management <strong>of</strong> <strong>the</strong>se networks is problematic
because it is claimed that informal networks cannot be managed directly, but indirectly through <strong>the</strong>
establishment <strong>of</strong> formal networks (Bryan et al., 2007).
Clusters, a specific type <strong>of</strong> coherent network (Rosen, 2000) or groups <strong>of</strong> geographically concentrated
firms <strong>of</strong> different sizes, horizontally and/or vertically linked and operating in <strong>the</strong> same line <strong>of</strong> business
(OECD, 2000), occur or are established more <strong>of</strong>ten <strong>the</strong>se days. This is mainly because <strong>the</strong>y are a
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Eva Eckenh<strong>of</strong>er
source <strong>of</strong> innovation as <strong>the</strong>y are based on collaboration, proximity and networks that result in a
process <strong>of</strong> mutual learning, emulation <strong>of</strong> positive role models and personal contacts (Ionescu, 2002).
The requirements and tasks <strong>of</strong> managing social networks go far beyond <strong>the</strong> basic understanding <strong>of</strong>
management as a function as well as an institution within a company, whose tasks and duties is <strong>the</strong>
coordination, structuring, planning, organizing, leading and control (Staehle, 1992). Holistic
Management Theory takes a step fur<strong>the</strong>r by outlining <strong>the</strong> importance <strong>of</strong> <strong>the</strong> personality qualities <strong>of</strong> <strong>the</strong>
management <strong>of</strong> firms and <strong>the</strong>ir social maturity (Porvazník, 2008). This goes in line with <strong>the</strong> importance
<strong>of</strong> certain social skills necessary for a successful work in social networks. Such social skills includes,
social perception (<strong>the</strong> ability to gauge current moods <strong>of</strong> o<strong>the</strong>rs), impression management (<strong>the</strong> ability to
change one’s appearance and image), persuasion and influence, social adaptability (<strong>the</strong> ability to
adjust to a wide range <strong>of</strong> social situations) and emotional intelligence (<strong>the</strong> ability to regulate one’s own
emotions) (Beugelsdijk and Van Schaik, 2005). These qualities, toge<strong>the</strong>r with <strong>the</strong> unwritten networklaws
trust, tolerance, discretion, respect, fairness and cooperativeness (Friedschröder, 2005) are
important for successful communication and cooperation in an organizational network.
Moreover, <strong>the</strong> success <strong>of</strong> a network is not impacted by only <strong>the</strong> networking abilities <strong>of</strong> a single
management unit, but also, <strong>the</strong> networking ability <strong>of</strong> an organization as a whole makes a contribution.
The processes, information systems, organizational structure and culture have influence on <strong>the</strong> ability
<strong>of</strong> an organization to cooperate with o<strong>the</strong>r organizations in a way that added value emerges out <strong>of</strong> this
relation (Payer, 2002).
2. Objective and methodology
The aim <strong>of</strong> this paper is to analyze network management models proposed and described in scientific
literature, in order to develop a model which is suitable for <strong>the</strong> management <strong>of</strong> formal as well as
informal networks within a company. The model has to fulfil <strong>the</strong> criteria <strong>of</strong> being practical and suitable
for <strong>the</strong> development and fostering <strong>of</strong> intellectual capital within a firm.
The empirical part <strong>of</strong> this paper analysis <strong>the</strong> findings from an expert survey which was held in spring
2008. Due to time constraints, out <strong>of</strong> a total <strong>of</strong> ten Austrian experts contacted, only nine were able to
participate in <strong>the</strong> schedule interviews. The interviews were conducted with respondents from firms
who on a daily basis work with Strategic Networking and are <strong>the</strong>refore described as experts.
According to Gläser and Laudel (2006), experts are individuals who as a result <strong>of</strong> <strong>the</strong>ir experience
possess specific knowledge over a specific social issue. Among those interviewed were a head <strong>of</strong> a
human resource department, a head <strong>of</strong> a PR Department, two CEOs <strong>of</strong> public affairs agencies, two
consultants, two Secretary-Generals <strong>of</strong> organizations listed on <strong>the</strong> stock exchange and a lawyer. The
experts were selected to ensure a balanced sample from different sectors (consulting and industrial)
as well as sex (5 females and 4 males). As <strong>the</strong> interviews were held in German, <strong>the</strong> analysis was
done in German as well (native language <strong>of</strong> <strong>the</strong> author and <strong>the</strong> interview partners) and <strong>the</strong> results
were afterwards translated to English.
3. Managing networks
Formal and informal networks are strong factors that have tremendous influence on a company’s
success. Their usage is advantageous to firms because <strong>the</strong>y form <strong>the</strong> basis for innovation,
communication and collective support. Never<strong>the</strong>less, social networks can build a market entry barrier
for firms outside <strong>the</strong> network. Hence, <strong>the</strong> barrier to growth for firms with too strong informal networks
has to be taken into account and prevented by attentive network analysis and network management
(Fuller-Love, 2009).
In order to achieve <strong>the</strong> best results and prevent disadvantages, certain conditions have to be fulfilled
in a social network. The network members have to complement each o<strong>the</strong>r in <strong>the</strong>ir interests and
expectations, which will have to be coordinated. Also, <strong>the</strong> forms and methods <strong>of</strong> working as well as
<strong>the</strong> infrastructure <strong>of</strong> a network have to be suitable for <strong>the</strong> objective <strong>of</strong> <strong>the</strong> network. The most important
and underlying principles in networks are <strong>the</strong> norms, values, agreements and trust (Becker et al.,
2005). The emergence <strong>of</strong> trust, <strong>the</strong> expectations <strong>of</strong> regular, honest and cooperative behaviour
(Fukuyama, 1995), requires goodwill, competence and behavioural reliability. Trust influences <strong>the</strong>
ability to innovate and to adapt <strong>the</strong> quality <strong>of</strong> network flows for materials, information and human
communication. A high level <strong>of</strong> trust enables all process types to operate more efficiently in a selfreinforcing
manner (Dervitsiotis, 2006).
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Eva Eckenh<strong>of</strong>er
An important benefit from social networks for <strong>the</strong> creation <strong>of</strong> intellectual capital is knowledge sharing.
Knowledge sharing can be achieved and fostered by social networks. In order to boost this behaviour,
a clear mission and goal has to be developed by management. At <strong>the</strong> same time social networks that
share <strong>the</strong>se goals have to be established, as <strong>the</strong>y contribute significantly to attitudes toward
knowledge sharing and <strong>the</strong> intention to share knowledge within <strong>the</strong> organization (Chow and Chan,
2008). Problems in traditional strategies towards knowledge management <strong>of</strong>ten occur due to high
complexity and efforts, which make knowledge management too time-consuming for employees.
Group cohesion, trust, fault tolerance, open mindedness, responsibility and employee-orientation <strong>of</strong>
<strong>the</strong> management are factors which on one hand can facilitate <strong>the</strong> emergence <strong>of</strong> networks and on <strong>the</strong>
o<strong>the</strong>r hand supports knowledge sharing (Killich and Kopp, 2005).
According to <strong>the</strong> actor-network <strong>the</strong>ory, <strong>the</strong> emergence <strong>of</strong> social networks occurs in four steps, thus,
<strong>the</strong> four moments <strong>of</strong> translation. The first step is problematization, where certain problems are defined
and examined with <strong>the</strong> aim <strong>of</strong> resolving <strong>the</strong>m. This is followed by interessement, where <strong>the</strong> moment <strong>of</strong>
persuasion comes up; and in <strong>the</strong> moment <strong>of</strong> enrolment, human and non-human actors take specific
roles and responsibilities for <strong>the</strong> development <strong>of</strong> <strong>the</strong> solution. The last step is <strong>the</strong> moment <strong>of</strong>
mobilization where actors and resources are mobilized to ensure that <strong>the</strong> networks achieve its
objectives and sustain <strong>the</strong> commitment and energy <strong>of</strong> its members (Callon 1986, 1991 and Latour,
1987 in Alfer<strong>of</strong>f and Knights, 2009). The market drivers for network formation are technological
changes, new ways <strong>of</strong> communication and information processing technology, globalization in general
with <strong>the</strong> emergence <strong>of</strong> Internet, deregulation and opening <strong>of</strong> national markets. Ano<strong>the</strong>r reason for
network formation is <strong>the</strong> increasing information intensity and importance <strong>of</strong> knowledge in today’s
society. Typical industry patters, which favour <strong>the</strong> emergence <strong>of</strong> networks, are outsourcing,
standardization initiatives and co-operation (Riemer and Klein, 2006).
3.1 Network management models
‘Equal’, a program <strong>of</strong> <strong>the</strong> <strong>European</strong> social fund for fighting discrimination, realizes its projects within
networks. In <strong>the</strong>ir case networks emerge due to definition <strong>of</strong> <strong>the</strong> team, <strong>the</strong> operative and transnational
partners and <strong>the</strong> coordinative centre. The experience from <strong>the</strong>se programs was that, a competent
network management is necessary, though <strong>the</strong> classical hierarchical organizational principals are not
applicable. Therefore, <strong>the</strong>y established a guideline <strong>of</strong> tasks and questions for network management.
The objective <strong>of</strong> Network management is to choose <strong>the</strong> right network members, ensuring knowledge
transfer and target orientation and acting as a moderator and promoter within <strong>the</strong> team and as a
communicator outside <strong>of</strong> <strong>the</strong> network (Hellmann-Flocken and Unger, 2005).
Howaldt and Ellerkmann (2005) provide a guiding compendium that divides <strong>the</strong> development <strong>of</strong>
networks into seven phases: Idea and impulse (idea <strong>of</strong> one or more promoters), Design <strong>of</strong> <strong>the</strong>
partnership (Selection according to target and willingness), Constitution <strong>of</strong> <strong>the</strong> network (Establishment
<strong>of</strong> identity, organisational structure and form <strong>of</strong> business), Working phase (where work on <strong>the</strong> target <strong>of</strong>
<strong>the</strong> project is done), Evaluation <strong>of</strong> <strong>the</strong> project (continuously evaluation and monitoring),
Metamorphoses (Change or transformation into a legal form <strong>of</strong> enterprise), Conclusion
(documentation, formal ending).
3.1.1 From Sydow and Windeler to Strategic Networking
One <strong>of</strong> <strong>the</strong> most famous network management <strong>the</strong>ories is <strong>the</strong> one developed by Sydow and Windeler
(2001), which proposes four additional functions to traditional management: Selection, Regulation,
Allocation and Evaluation. In <strong>the</strong> first step <strong>of</strong> selection, <strong>the</strong> network members and organisations are
selected. During <strong>the</strong> step <strong>of</strong> allocation <strong>the</strong> tasks and resources in <strong>the</strong> network have to be coordinated
and <strong>the</strong> scope <strong>of</strong> alliance defined. Coordinating committees, controlling and conflict resolutions are
part <strong>of</strong> <strong>the</strong> regulation process, and this is also where formal and informal norms are established. In <strong>the</strong>
final step <strong>of</strong> evaluation, achievements <strong>of</strong> <strong>the</strong> network as well as its relations have to be coordinated.
The four network management function are recursive related to each o<strong>the</strong>r and <strong>the</strong>y are recurring and
not singular in order to fight controversies such as, trust and control, cooperation and concurrency,
formality and informality, flexibility and stability, market and hierarchy, that exist in <strong>the</strong> network
(Sydow, 2006; Payer, 2002). Strategic Networking adapts <strong>the</strong> model <strong>of</strong> Sydow and Windeler and puts
<strong>the</strong> target in <strong>the</strong> centre followed by network analysis as can be seen in Figure 1. Strategic Networking
is <strong>the</strong> strategic and target-oriented analysis, development, fostering and control <strong>of</strong> (inter- as well as
intra-organisational) networks on <strong>the</strong> basis <strong>of</strong> trust, with <strong>the</strong> intention to reach certain (organisational)
goals (Eckenh<strong>of</strong>er, 2008).
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Allocation
Eva Eckenh<strong>of</strong>er
Selection
target
Network Analysis
Regulation
Figure 1: Strategic Networking Source: Sydow, 2006, p.409, mod.
Evaluation
The core <strong>of</strong> Strategic Networking is a strategic target, which could be a new market, a new customer
or <strong>the</strong> introduction <strong>of</strong> a new product. On <strong>the</strong> basis <strong>of</strong> regular network analysis, <strong>the</strong> characteristics <strong>of</strong>
<strong>the</strong> network could be modified so that <strong>the</strong> stated goal could be reached. This is mainly because
network analysis helps to visualize <strong>the</strong> network and achieve knowledge about <strong>the</strong> company’s network
while being focused on <strong>the</strong> target all <strong>the</strong> time (Eckenh<strong>of</strong>er, 2008).
3.1.2 Network Management Framework
A ra<strong>the</strong>r holistic concept is <strong>the</strong> network management framework by Riemer and Klein (2006) that
combines <strong>the</strong> network view, <strong>the</strong> firm‘s view on network management, <strong>the</strong> view <strong>of</strong> <strong>the</strong> network
environment and <strong>the</strong> mode <strong>of</strong> network management.
Network View: The network life cycle emphasises <strong>the</strong> ongoing dynamics <strong>of</strong> <strong>the</strong> network
development and divides <strong>the</strong>m into <strong>the</strong> stages <strong>of</strong> initiation, configuration, implementation,
transformation and eventual dissolution. The network management areas within <strong>the</strong> network view
includes strategy finding, <strong>the</strong> organisation <strong>of</strong> tasks, roles, linkages and processes as well as <strong>the</strong>
network information management, which is <strong>the</strong> coordination <strong>of</strong> activities and resource sharing.
Firm View: As results and quality <strong>of</strong> network operations depend on <strong>the</strong> network and <strong>the</strong> individual
firm, <strong>the</strong> intra-firms structures have to be aligned to external network requirements in <strong>the</strong> three
domains <strong>of</strong> strategy, organisation and technology.
Network Environment View: Because networks affect <strong>the</strong> markets and industries around <strong>the</strong>m,
<strong>the</strong>y are also affected by <strong>the</strong>ir environment. General factors such as consumer behaviour, market
rules, technological changes, typical industry pattern and specific regional conditions such as
institutional policies have an impact on <strong>the</strong> emergence <strong>of</strong> a network.
Network management mode: The network management mode includes four interdependent
functions, which aims at ensuring <strong>the</strong> success <strong>of</strong> networks while facing <strong>the</strong> limits <strong>of</strong> managerial
control in boundary management, creating potentials and improvisation in networks. It also
includes <strong>the</strong> designing <strong>of</strong> governance structures, coordinating exchanges, fostering social
integration and <strong>the</strong> facilitation <strong>of</strong> shared visions and values.
3.2 Governance <strong>of</strong> networks
In many firms, governance is done by <strong>the</strong> board <strong>of</strong> directors, who have a legal obligation to represent
and protect <strong>the</strong> interests <strong>of</strong> shareholders (Provan and Kenis, 2005; Deutscher Corporate Governance
Kodex, 2008). The legal duty is not valid for social networks as <strong>the</strong>y are not legal entities though <strong>the</strong>re
are a number <strong>of</strong> factors, which necessitates governance in networks. A higher number <strong>of</strong> network
243

Eva Eckenh<strong>of</strong>er
participants create complexities. Therefore, governance is desirable because <strong>the</strong> needs and activities
must be accommodated and coordinated. Ano<strong>the</strong>r condition for <strong>the</strong> emergence <strong>of</strong> governance is <strong>the</strong>
shared competencies <strong>of</strong> <strong>the</strong> organizations or network members for coordinating tasks according to
<strong>the</strong>ir competencies. Provan and Kenis (2005, 2007) proposed four forms <strong>of</strong> network governance:
Participant-Governed Networks, Lead Organization Governed Networks, Network Administration
Organization (NAO) (separate administrative entity), Hybrid Forms <strong>of</strong> Network Governance. The forms
<strong>of</strong> governance partly evolve and partly are given depending on <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> network.
Fur<strong>the</strong>rmore, <strong>the</strong> form <strong>of</strong> governance may change as <strong>the</strong> size <strong>of</strong> network grows or network tasks are
becoming more complex (Provan and Kenis, 2007).
4. Network management in praxis
Using semi-structured interviews, nine Austrian experts were asked about network basics, <strong>the</strong>ir
personal opinion on network development, fostering and management and about network
management in <strong>the</strong>ir company. The average duration <strong>of</strong> every interview was 45 minutes and <strong>the</strong>
interviews were recorded and transliterated verbatim. In order to follow a structuring interpretation <strong>of</strong>
qualitative content analysis (Mayring, 2000), a criteria was defined to serve as a basis for <strong>the</strong> analysis.
For every variable different codes (flexible characteristics) were set and defined (see third and fourth
column in table 1). The qualitative content analysis summarizes <strong>the</strong> number <strong>of</strong> times a variable had a
specific characteristic (see <strong>the</strong> last column in table 1).
It can be observed in table 1 that <strong>the</strong> reason for developing a network (Variable 1) is never a problem;
but ra<strong>the</strong>r, it is triggered by a new project or <strong>the</strong> corporate goal. From <strong>the</strong> responses <strong>of</strong> <strong>the</strong> experts
interviewed, it was revealed that network development is never left totally to chance; but to some part,
experts totally act strategically and to ano<strong>the</strong>r part, both strategic and open to unexpected or future
eventualities (Variable 2).
As indicated by <strong>the</strong> experts, <strong>the</strong> main network development approach (Variable 3-6) is indirect - over
contacts and if this fails, <strong>the</strong> direct approach is used. A clear result is given for network management
which is never done formally, but from all those interviewed, it was found to be done in an informal
manner. This goes in line with network fostering, which is never done by just telephone and email, but
ei<strong>the</strong>r through personal contacts or by telephone, email and personal contacts. Even though five
interviewees answered that <strong>the</strong> position <strong>of</strong> a network manager should be created in <strong>the</strong> company, no
one actually could tell <strong>the</strong> successful implementation <strong>of</strong> such a position. One expert described an
experience from a project, which was aimed at introducing a network manager, but failed due to <strong>the</strong>
fear <strong>of</strong> losing power.
Networking is organized in <strong>the</strong> companies mainly by databases, address directories, CRM-Databases
and internal un<strong>of</strong>ficial meetings, which are used to clarify <strong>the</strong> existence <strong>of</strong> contacts (Variable 7).
Concerning network governance (Variable 8) <strong>the</strong> answers were not coherent - whe<strong>the</strong>r it is <strong>the</strong> duty <strong>of</strong>
<strong>the</strong> management, every employee or teams and project. This seems to be problematic, as it was
unclear as to who should manage <strong>the</strong> networks and who is responsible for it. The most important
protection method (Variable 9) against loss or drop out <strong>of</strong> network parts due to retirement or
fluctuation seems to be by internal networks. Protection by network illustration and lists was
mentioned three-times.
The main effects <strong>of</strong> networking (Variable 10) according to <strong>the</strong> experts were not only in <strong>the</strong> information
benefits and <strong>the</strong> fulfilment <strong>of</strong> <strong>the</strong> corporate goal, but also in <strong>the</strong> efficiency improvement and
contribution to market position and turnover.
5. Discussion
The network management models, which were presented in part 3, seem to be quite different at <strong>the</strong>
first glance, though <strong>the</strong>y show several similarities. Most <strong>of</strong> <strong>the</strong> models do not deal with <strong>the</strong> network
management in general - about <strong>the</strong> work within a network, but about how <strong>the</strong> management <strong>of</strong> <strong>the</strong>
network evolves or emerges. Figure 2 gives an overview <strong>of</strong> <strong>the</strong> above mentioned network
management models.
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Eva Eckenh<strong>of</strong>er
Table 1: Results <strong>of</strong> <strong>the</strong> qualitative content analysis. Source: Author’s own
No. Variable Code&Characteristic Definition Sum
1 NW-Start
2 NW-ABZ
3 NW-AB
4
NW-MMform
5 NW-PF
6 NW-MM
7 NW-Org
8 NWMM-V
9 NW-Abs
10 NW-Eff
Problem
Project Reasons for start and 7
Corporate Goal
Information
development <strong>of</strong> a social
network.
7
2
New Position 4
Targeted
Hazard How <strong>the</strong> development <strong>of</strong> 0
Both
networks is organized.
5
future-oriented 2
Direct
6
indirect over contacts Networking approach. 7
Events… 2
Formal
Network management
0
Informal 6
Personal
3
Email, telephone… Network fostering
0
Both 6
Yes Network manager
5
No
Network-responsible
necessary?
1
person
1
Database, CRM-DB,
contact-directories…
internal <strong>of</strong>ficial meetings
internal un<strong>of</strong>ficial
Organization <strong>of</strong> networking
within <strong>the</strong> company.
6
3
meetings 6
Management Responsible persons for 4
everybody
Teams/Projects
network management /
network governance.
4
3
no protection
internal networks Protection <strong>of</strong> <strong>the</strong> network 8
illustrations and tables
protection due to buddy-
against loss / drop out. 3
systems 1
Information
Corporate Goal Effects <strong>of</strong> networking for 4
Efficiency
<strong>the</strong> company.
2
Market position, turnover 2
245
0
4
1
4

Initiation
Configuration Interessement
Stabilization
Eva Eckenh<strong>of</strong>er
Problematization
Idea - Impuls
Design <strong>of</strong>
partnership
Implementation Enrolement
Constitution <strong>of</strong>
network
Transformation Evaluation
Dissolution
Selection
Allocation
Regulation
Mobilization Working phase Evaluation
Metamorphoses
Conclusion
Figure 2: Models describing <strong>the</strong> evolvement <strong>of</strong> networks. Source: Author’s own.
Three <strong>of</strong> <strong>the</strong> models presented in figure 2 start with a problem or an idea which is <strong>the</strong> trigger for <strong>the</strong>
emergence <strong>of</strong> <strong>the</strong> network, followed by <strong>the</strong> choice or selection <strong>of</strong> <strong>the</strong> network partners which is similar
in all models. In <strong>the</strong> following step(s) <strong>the</strong> network is built up, gets shaped and identifies where certain
norms and rules are valid. Just one model describes <strong>the</strong> actual phase <strong>of</strong> working in a network, while
all o<strong>the</strong>r models already describe <strong>the</strong> transformation, metamorphoses and evaluation <strong>of</strong> <strong>the</strong> network.
Therefore, <strong>the</strong> 7 phases network development model by Howaldt and Ellerkmann (2005) describes
accurately <strong>the</strong> evolution <strong>of</strong> networks. Never<strong>the</strong>less <strong>the</strong> <strong>the</strong>oretical models build a contrast to <strong>the</strong>
results <strong>of</strong> <strong>the</strong> expert survey. From <strong>the</strong> interviews conducted, it was revealed that on one hand,
networks are developed for new projects or business objectives and on <strong>the</strong> o<strong>the</strong>r hand for future
purposes without any specific reason. Moreover, it seems that <strong>the</strong> development <strong>of</strong> networks is done
mostly intuitively. Simple analysis and schedules indicating <strong>the</strong> persons to meet and <strong>the</strong> topic to talk
about are used for network development. Therefore it follows that an open guideline for network
development is needed, not a tight plan.
As <strong>the</strong> development <strong>of</strong> networks is just one <strong>of</strong> <strong>the</strong> tasks <strong>of</strong> network management, <strong>the</strong> focus on <strong>the</strong>
actual management <strong>of</strong> <strong>the</strong> work within <strong>the</strong> network should be highlighted in a greater extent.
Fur<strong>the</strong>rmore, <strong>the</strong> results <strong>of</strong> <strong>the</strong> interviews showed that, network management does not need a lot <strong>of</strong>
organisations, but clear rules and responsibilities. The network management framework suggests <strong>the</strong>
following tasks: The design <strong>of</strong> <strong>the</strong> government structure, <strong>the</strong> coordination <strong>of</strong> exchanges, <strong>the</strong> fostering
<strong>of</strong> social integration, facilitating shared visions and values. Consequently, <strong>the</strong> aligning <strong>of</strong> strategy,
organisation and technology (in <strong>the</strong> sense <strong>of</strong> communication and information technology) <strong>of</strong> <strong>the</strong>
network, <strong>the</strong> networks members and <strong>the</strong>ir organisations is proposed as ano<strong>the</strong>r important network
management task. All <strong>the</strong>se duties have to be arranged and executed in order to build a functioning
network, which shall allow <strong>the</strong> flow <strong>of</strong> information and knowledge sharing. Most importantly, <strong>the</strong>
fostering <strong>of</strong> social integration and <strong>the</strong> facilitation <strong>of</strong> shared visions and values are crucial in that
regard. This is because, from <strong>the</strong> interviews, it was revealed that, communication contributes to <strong>the</strong>
success <strong>of</strong> work within networks as well as <strong>the</strong> enhancement <strong>of</strong> social integration in networks. An
underlining criterion for knowledge sharing in network is <strong>the</strong> clarity <strong>of</strong> <strong>the</strong> responsibility for network
governance and <strong>the</strong> rules for coordination <strong>of</strong> exchange. Therefore, <strong>the</strong> coordination <strong>of</strong> exchanges has
to be supported by technology and facilitated to ensure <strong>the</strong> alignment <strong>of</strong> strategy and organisation.
Therefore <strong>the</strong> author suggests adapting <strong>the</strong> existing model <strong>of</strong> Strategic Networking as shown in
Figure 3 below:
246

Eva Eckenh<strong>of</strong>er
Figure 3: Enhancement <strong>of</strong> Strategic Networking. Source: Author’s own.
In <strong>the</strong> centre <strong>of</strong> <strong>the</strong> Strategic Networking-Model is <strong>the</strong> aim or target <strong>of</strong> <strong>the</strong> network, surrounded by
Sydows and Windelers model <strong>of</strong> network management. Selection, allocation, regulation and
evaluation are seen as <strong>the</strong> management <strong>of</strong> <strong>the</strong> network development and <strong>the</strong>refore an ongoing
process in a social network. Ano<strong>the</strong>r central tool is network analysis, which should be done regularly
in order to give an overview <strong>of</strong> <strong>the</strong> network, <strong>the</strong> roles and positions <strong>of</strong> <strong>the</strong> network members and to
indentify needs for network development. The most important tasks for managing <strong>the</strong> work within <strong>the</strong>
network are network governance, coordination <strong>of</strong> exchange, alignment <strong>of</strong> strategy, organisation and
technology, facilitating shared visions and values as well as fostering <strong>of</strong> social integration. These
tasks are circular, unsystematic and with reciprocal influence and are <strong>the</strong>refore located in <strong>the</strong> inner
circle <strong>of</strong> <strong>the</strong> model. The network development processes, namely, selection, allocation, regulation and
evaluation are also not a singular process but circular as long as <strong>the</strong> target <strong>of</strong> <strong>the</strong> network is valid for
all network members. As <strong>the</strong> expert interviews showed, an accurate model <strong>of</strong> network development is
not necessary, <strong>the</strong>se four steps describing <strong>the</strong> process <strong>of</strong> network development, build a loose
guideline, but do not prescribe every single step in <strong>the</strong> network development process. As <strong>the</strong> network
development provides <strong>the</strong> basic structures <strong>of</strong> <strong>the</strong> network, while <strong>the</strong> network management tasks
influence <strong>the</strong> processes in <strong>the</strong> networks, <strong>the</strong>y build <strong>the</strong> outside circle <strong>of</strong> <strong>the</strong> model, which symbolizes
<strong>the</strong> framework <strong>of</strong> <strong>the</strong> network.
6. Conclusion
From <strong>the</strong> introduction about holistic management and <strong>the</strong> definition <strong>of</strong> Intellectual Capital and social
networks, common models and guidelines for <strong>the</strong> management <strong>of</strong> social networks were introduced.
The results <strong>of</strong> <strong>the</strong> expert survey with nine Austrian managers were presented in order to compare with
<strong>the</strong> findings from literature research. The result <strong>of</strong> <strong>the</strong> comparison is <strong>the</strong> creation <strong>of</strong> a practical model
that can be used for <strong>the</strong> development and fostering <strong>of</strong> Intellectual Capital in a firm.
247

Eva Eckenh<strong>of</strong>er
Even though <strong>the</strong> literature about social networks and Intellectual Capital is vast, <strong>the</strong> literature about
network management is scanty. The presented models from <strong>the</strong> literature reviewed are in most part
highly <strong>the</strong>oretical and seem to be far away from being used in companies. Also, <strong>the</strong>y deal more with
<strong>the</strong> development <strong>of</strong> social networks than with <strong>the</strong> actual work within networks. Therefore <strong>the</strong> models
were adapted and reassembled to a more complex version <strong>of</strong> <strong>the</strong> Strategic Networking model, which
shall serve as a compendium for triggering and managing knowledge sharing in organizations.
It is important to note that, this model is a proposal that has to be tested and adapted toge<strong>the</strong>r with
managers who work in and with networks. Moreover, it will be useful to study <strong>the</strong> extent to which <strong>the</strong>
use <strong>of</strong> such a model influences <strong>the</strong> development <strong>of</strong> Intellectual Capital in a firm. The possibility <strong>of</strong>
developing Intellectual Capital as well as Social Capital with <strong>the</strong> help <strong>of</strong> social networks has been
stated in scientific literature, though empirical studies are still missing. Therefore, this paper
represents a starting point in network management in firms with a special focus on intra- as well as
inter-organisational networks. The findings <strong>of</strong> this paper s not conclusive hence, <strong>the</strong>re is a need for
fur<strong>the</strong>r research.
Acknowledgement:
The preparation <strong>of</strong> this paper was financially supported by <strong>the</strong> Internal Grant Agency <strong>of</strong> Faculty <strong>of</strong>
Management and Economics on Tomas Bata University, Project-No. IGA/49/FaME/11/D “Social
Network Analysis in Performance Measurement and Network Visualisation” and Project-No.
IGA/61/FaME/10/A “The Development and Evaluation <strong>of</strong> <strong>the</strong> Performance <strong>of</strong> Cluster Policies, <strong>of</strong>
Clusters and <strong>the</strong>ir Members with <strong>the</strong> Usage <strong>of</strong> <strong>the</strong> Principles <strong>of</strong> Benchmarking”.
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The Essence <strong>of</strong> Knowledge Management
Emmanuel Innocents Edoun and Valdenisa Norris
The University <strong>of</strong> <strong>the</strong> Witwatersrand Johannesburg South Africa
edounsen@hotmail.com
norrisnisa@hotmail.com
Abstract: Recent studies have shown that, over <strong>the</strong> past decade, Knowledge Management (KM) has been <strong>the</strong>
center <strong>of</strong> many debates. This study infers that knowledge management is an audit <strong>of</strong> human capital that
highlights unique sources, critical functions and potential bottlenecks which hinder knowledge flows to point <strong>of</strong>
use. This study argues that it protects human capital from decay, seeks opportunities to enhance decisions,
services and products through adding intelligence, increasing value and providing flexibility. Therefore a number
<strong>of</strong> organisations have been reminded that <strong>the</strong>y will not survive in <strong>the</strong> “Modern Knowledge Village” unless <strong>the</strong>y
have a strategy for managing and leveraging value from <strong>the</strong>ir human capital. However, it has become clear that
<strong>the</strong> term "Knowledge Management" has been used in a wide range <strong>of</strong> activities designed to manage, exchange
and create or enhance human capital within an organisation, and that <strong>the</strong>re is no widespread agreement on what
KM actually is. Information Technology (IT) applications that are termed "knowledge management applications"
range from <strong>the</strong> development <strong>of</strong> highly codified help desk systems to <strong>the</strong> provision <strong>of</strong> video conferencing to
facilitate <strong>the</strong> exchange <strong>of</strong> ideas between people. The objective <strong>of</strong> this paper is to survey a number <strong>of</strong> different
knowledge management strategies and a range <strong>of</strong> driving forces for knowledge management activities. These
are syn<strong>the</strong>sised using an extended version <strong>of</strong> an existing “KM spectrum”. The methodology used in this study is
qualitative in nature. For <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> current study it is applied to provide direction for <strong>the</strong> KM spectrum
and <strong>the</strong>n gives a description <strong>of</strong> a simple classification approach that relates to KM strategies. The methodology
fur<strong>the</strong>r uses a case study to apply <strong>the</strong> above approach and discuss its usefulness. The findings reveal that KM is
a powerful tool that stimulates success in any working environment if it is subjected to adequate leadership where
good governance is taken into consideration. In a school environment successful knowledge management
involves different aspects such as accessibility <strong>of</strong> information technology, strong leadership, cultural influences,
organisational structure and human characteristics. Leaders have to ensure that, <strong>the</strong> necessary measures are in
place and <strong>the</strong>y should lead by example to sustain <strong>the</strong> successful implementation <strong>of</strong> knowledge management
strategies in order to continuously empower human capital.
Keywords: Essence, knowledge management, modern knowledge village, human capital, information
technology, leadership
1. Introduction
Knowledge Management (KM) has been <strong>the</strong> center <strong>of</strong> many debates. Even though past studies are
still questioning <strong>the</strong> essence <strong>of</strong> knowledge management. Recent studies seem to be more relevant as
<strong>the</strong>y are always thriving to put forward <strong>the</strong> merits <strong>of</strong> knowledge management across multi-disciplinary,
inter-disciplinary and trans-disciplinary studies.
This paper has used a case study approach to explain how knowledge management could facilitate
knowledge sharing in <strong>the</strong> educational context. The paper has reviewed a number <strong>of</strong> concepts that will
have a positive impact on <strong>the</strong> research findings.
The findings reveal that knowledge management can only be sustainable if it is a moral purpose
which aims at sharing knowledge that is valued by social interactions.
Knowledge Management has been used in a wide range <strong>of</strong> activities designed to manage, exchange,
create or enhance intellectual assets within an organisation. IT applications as a knowledge
management application ranges from <strong>the</strong> development <strong>of</strong> highly codified help desk systems to <strong>the</strong>
provision <strong>of</strong> video conferencing to facilitate <strong>the</strong> exchange <strong>of</strong> ideas between people (Haggie &
Kingston 2003). O<strong>the</strong>r IT applications involve <strong>the</strong> designing <strong>of</strong> websites, internet portals and social
networking sites for <strong>the</strong> sustainability <strong>of</strong> <strong>the</strong> knowledge shared. In order to ensure that knowledge
shared is accessible by all regardless <strong>of</strong> socio-economic inequalities, cell phones as an IT application
will be used as a platform to sustain communication.
This paper is important as it surveys a number <strong>of</strong> different knowledge management strategies and a
range <strong>of</strong> driving forces for knowledge management activities. These are syn<strong>the</strong>sised using an
extended version <strong>of</strong> an existing “KM spectrum”. The KM spectrum has been developed to assist
organisations in understanding <strong>the</strong> range <strong>of</strong> KM options, applications and technologies available to
<strong>the</strong>m. It provides a view <strong>of</strong> <strong>the</strong> totality and complexity <strong>of</strong> <strong>the</strong> various KM <strong>the</strong>ories, tools and techniques
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Emmanuel Innocents Edoun and Valdenisa Norris
presented in <strong>the</strong> literature. It equally provides a framework within which management can balance its
KM focus and establish and communicate its strategic KM direction (D Binney 2009). In <strong>the</strong> South
African school context, KM direction is determined and communicated through a process/system
called Whole School Evaluation (WSE). During this process a SWOT (Strengths, Weakness,
Opportunity and Threats) Analysis is done to assist school in sustaining KM.
The paper also applies a knowledge management approach to provide direction for <strong>the</strong> KM spectrum
and <strong>the</strong>n gives a description <strong>of</strong> a simple classification approach that relates forces to KM strategies.
This paper is organised as follow: a problem statement , literature review, methodology, case study
and analysis, findings, recommendations and conclusion.
2. Problem Statement
The objective <strong>of</strong> this paper is <strong>the</strong>refore to look at knowledge management as a moral purpose that
thrives in improving global education through knowledge management strategies. With this aim in
mind this paper seeks to address <strong>the</strong> following questions.
How can a local solution have an impact into creating a concept <strong>of</strong> “Global Knowledge Village”?
How can school leaders/ managers ensure that <strong>the</strong> global socio-economic gaps are bridged
through <strong>the</strong> sharing <strong>of</strong> knowledge?
2.1 Review <strong>of</strong> Literature
This paper argues that knowledge management is dynamic and seeks to address data, process and
information as elements that constitute <strong>the</strong> core <strong>of</strong> managing knowledge. Data represents facts and it
becomes information when one understands <strong>the</strong> relationships between <strong>the</strong> pieces <strong>of</strong> data (Friehs
1995). Information is only knowledge when it takes on a social life. Meaning that sharing and
interaction amongst people makes information a source <strong>of</strong> knowledge. Knowledge is linked to people
and is seen as a social phenomenon that can be used to improve and change social issues/problems
for instance poverty or natural disasters that affects humans and <strong>the</strong> environment (Fullan
2002).Fur<strong>the</strong>rmore this study holds that in an organisational context, <strong>the</strong>re are patterns in relations <strong>of</strong>
data and information that have <strong>the</strong> capacity to represent knowledge. So <strong>the</strong> value <strong>of</strong> any information
is always connected to a system or a situation and cannot be evaluated in an isolated manner. By
including <strong>the</strong> information into a context <strong>of</strong> experience, knowledge can be created, as <strong>the</strong> contextuality
develops knowledge (Roehl 2000).
From <strong>the</strong> above analysis this study <strong>the</strong>refore seeks to understand what is knowledge? Knowledge is
defined as (i) expertise, and skills acquired by a person through experience or education; <strong>the</strong>
<strong>the</strong>oretical or practical understanding <strong>of</strong> a subject; (ii) what is known in a particular field or in total;
facts and information; or (iii) be absolutely certain or sure about something(Oxford Dictionary).
Denham Grey (1995) defines knowledge as <strong>the</strong> full utilisation <strong>of</strong> information and data, coupled with
<strong>the</strong> potential <strong>of</strong> people's skills, competencies, ideas, intuitions, commitments and motivations. He
argues that knowledge is <strong>the</strong> basis for, and <strong>the</strong> driver <strong>of</strong>, our post-industrial economy and <strong>the</strong>refore is
<strong>the</strong> result <strong>of</strong> learning which provides <strong>the</strong> only sustainable competitive advantage. Takeuchi & Nonaka
(1995) argue that knowledge is about beliefs, commitment and action to achieve some end. It is also
about meaning, being context-specific and relational. Their view shows that <strong>the</strong>re is no knowledge
without people who create, process, interpret and transfer it. Friehs (2003) argues that in this society,
knowledge is <strong>the</strong> primary resource for individuals and for <strong>the</strong> economy overall.
Knowledge is <strong>the</strong> entirety <strong>of</strong> abilities that people use for solving problems. It comprises <strong>the</strong>oretical
knowledge as well as general wisdom, attitudes, etc. Knowledge relies on data and information and in
relation to <strong>the</strong>m is always bound to a person. Individuals construct and represent <strong>the</strong>ir expectations
on <strong>the</strong> connection between cause and effect (Probst/Raub/Romhardt 1999).
For <strong>the</strong> processing <strong>of</strong> data from information to knowledge, this data needs to be managed efficiently
which eventually raises <strong>the</strong> question <strong>of</strong> what is management and its implication to knowledge?
Peter Drucker (1993) defined management as supplying knowledge to find out how existing
knowledge can best be applied to produce results, in effect, what we mean by management. But
knowledge is now also being applied systematically and purposefully to determine what new
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knowledge is needed, whe<strong>the</strong>r it is feasible, and what has to be done to make knowledge effective. It
is being applied, in o<strong>the</strong>r words, to systematic innovation (Drucker, 1993). He argues that
management is a creative, as well as a systematic flow <strong>of</strong> knowledge that can be applied to produce
results by using human, as well as o<strong>the</strong>r resources in an effective way.
From <strong>the</strong> above definitions this paper attempts to define knowledge management as an audit <strong>of</strong>
human capital in order to achieve greater results. Thomas Bertels (1996) on <strong>the</strong> o<strong>the</strong>r hand posits that
knowledge management is <strong>the</strong> management <strong>of</strong> <strong>the</strong> organisation towards <strong>the</strong> continuous renewal <strong>of</strong>
<strong>the</strong> organisational knowledge base. This involves <strong>the</strong> creation <strong>of</strong> supportive organisational structures,
facilitation <strong>of</strong> organisational members, putting IT-instruments with emphasis on teamwork and <strong>the</strong>
diffusion <strong>of</strong> knowledge. .
Brain Newman (1991) asserts that Knowledge Management is <strong>the</strong> collection <strong>of</strong> processes that govern
<strong>the</strong> creation, dissemination, and utilisation <strong>of</strong> knowledge. Donna Bible (1995) views knowledge
management as an attempt to secure <strong>the</strong> experience as well as <strong>the</strong> work product <strong>of</strong> <strong>the</strong> individuals
who comprise a corporation. Gregory Wenig (1995) views KM as a set <strong>of</strong> activities focused on <strong>the</strong>
organisation gaining knowledge from its own experience and from <strong>the</strong> experience <strong>of</strong> o<strong>the</strong>rs, and on
<strong>the</strong> judicious application <strong>of</strong> that knowledge to fulfill <strong>the</strong> mission <strong>of</strong> <strong>the</strong> organisation. He argues that
<strong>the</strong>se activities are executed by merging technology, organisational structures, and appropriate
strategies to enhance existing knowledge that can contribute to producing new knowledge.
One <strong>of</strong> <strong>the</strong> primary advantages <strong>of</strong> KM is that it addresses and seeks to facilitate both <strong>the</strong> technology
culture and information simultaneously at any given institution. Kidwell, Van Der Linde & Johnson
(2000) explain that colleges and universities have significant opportunities to apply KM practices to
support every part <strong>of</strong> <strong>the</strong>ir mission. Surely, nowadays new applications that integrate collaborative
tools, business intelligence are viewed as portals that allow users to customise <strong>the</strong>ir desktops to show
information from a variety sources within <strong>the</strong> organisation. Several schools and universities are
extensively using <strong>the</strong> concept <strong>of</strong> corporate concept. The rational <strong>of</strong> using portal at schools and
universities include, institutional marketing, creating brand identity, becoming <strong>the</strong> gateway for finding
information about schools and university resources and programmes and providing a rich information
environment for decision making. Fur<strong>the</strong>rmore Butcher (2002) infers that in effect, a portal can contain
any service available via <strong>the</strong> internet. It is not limited to <strong>the</strong> World Wide Web as it can be expanded to
include e-mail services, chat rooms, and o<strong>the</strong>r internet applications not dependent on <strong>the</strong> web.
However, Petrides and Nodine (2003) argue that technical systems can help to generate data and
information. To support <strong>the</strong>ir argument <strong>the</strong>y infer that, in order to facilitate <strong>the</strong> sharing <strong>of</strong> information
and knowledge, educational organisations have began to look beyond technical capacities. They
fur<strong>the</strong>r argue that KM brings three core organisational resources toge<strong>the</strong>r namely, people, processes,
and technologies to enable <strong>the</strong> organisation to use and share information effectively. These core
organisational resources brought <strong>the</strong> paper to consider <strong>the</strong> following diagram which is followed by a
linear equation to show <strong>the</strong> effectiveness <strong>of</strong> KM
Technology
People
Knowledge
Management
Processes
Figure1: The interrelation between people, processes and technology to promote KM.
From <strong>the</strong> above diagram this paper found that knowledge management depends on <strong>the</strong>
abovementioned element as indicated in <strong>the</strong> following equation.
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KM= f (people, processes, technologies)
The above equation holds that, KM is a function that includes people, processes and technologies.
The rationale behind this equation is that, people manage knowledge not a system as KM remains
dependent on people, processes and technologies. However organisations can promote policies and
practices that help people share and manage knowledge.
This paper also teams up with Petrides et al (2003) to support that, KM builds upon collegial and
pr<strong>of</strong>essional teamwork by actively engaging people at many organisational levels in sharing with
o<strong>the</strong>rs what <strong>the</strong>y are learning. On <strong>the</strong> o<strong>the</strong>r hand, knowledge assessment audits help to put in place
adequate processes that allow people to get information <strong>the</strong>y require. Technology is equally
necessary for <strong>the</strong> effectiveness <strong>of</strong> any given organisation. As mention earlier, a portal is very
important in sharing knowledge within an organisation as <strong>the</strong> goal <strong>of</strong> KM is to build and exploit
intellectual capital effectively and gainfully (Wiig, 1999). The essence <strong>of</strong> knowledge management is
<strong>the</strong>refore important as it enhances an organisation's ability and capacity to deal with, and develop in,
<strong>the</strong>se dimensions mentioned above. Ano<strong>the</strong>r way to explain <strong>the</strong> essence <strong>of</strong> knowledge management
is to promote <strong>the</strong> willingness to share experience for instance through numerous strategies such as
inter-visitation, peer networks, conference, association, public lectures, meetings forums etc. In <strong>the</strong>
school context leaders <strong>of</strong> schools and universities should use <strong>the</strong>ir leadership ability to manage
knowledge and ensure that this knowledge is able to advance <strong>the</strong> condition <strong>of</strong> learners and <strong>the</strong>ir
teachers (Fullan 2002). The essence <strong>of</strong> knowledge management is <strong>the</strong> awareness amongst
stakeholders in education to realise that, <strong>the</strong> norm <strong>of</strong> contributing one’s knowledge to o<strong>the</strong>rs within in
education is <strong>the</strong> key to continuous growth for all. Therefore <strong>the</strong> focus <strong>of</strong> knowledge management is
about people not information or knowledge. The entirety <strong>of</strong> relevant knowledge in schools is called <strong>the</strong>
organisational knowledge basis . This knowledge basis consists <strong>of</strong> individual and collective store <strong>of</strong>
knowledge that is available for an organisation to be successful. Knowledge is a combination <strong>of</strong>
structured experience, values, context information and expert knowledge. All <strong>of</strong> <strong>the</strong> above <strong>of</strong>fers a
structural framework for <strong>the</strong> judgment and integration <strong>of</strong> new experiences.So when using <strong>the</strong> term
KM, one refers to <strong>the</strong> managing <strong>of</strong> people for KM goals. For instance this study supports that in
applying KM techniques in schools and higher education in an adequate manner, this contribute in
improving decision-making abilities, academic and administrative services and will be cost –effective.
3. Methodology
This study adopted a qualitative research approach with a case study in order to explain <strong>the</strong> essence
<strong>of</strong> Knowledge Management in <strong>the</strong> educational setting in Gauteng, South Africa. A case study is a
research method commonly used in social science which can ei<strong>the</strong>r be explanatory or descriptive.
This study use explanatory case study as it explores causation in order to find underlying principles.
Ra<strong>the</strong>r than using samples and following a rigid protocol to examine limited number <strong>of</strong> variables this
case study methods involve an in-depth examination <strong>of</strong> a single process. The current case study
provides a systematic way <strong>of</strong> looking at collecting data, analyzing information and reporting <strong>the</strong>
results. As a result, this paper gains an adequate understanding <strong>of</strong> why knowledge management
strategy is important in improving <strong>the</strong> quality <strong>of</strong> education in Gauteng, South Africa.
The method used in this case study involves <strong>the</strong> Annual National Assessment (ANA) question paper
to benchmark <strong>the</strong> South African curriculum in regards to <strong>the</strong> Numeracy/Ma<strong>the</strong>matical and Literacy/
English competency levels <strong>of</strong> primary school learners in grade 1-6. It also looks at <strong>the</strong> vital role that
school principal’s plays in ensuring that <strong>the</strong>se results are interpret, analysed and reported.
From a school performance on this ANA, schools are classified as performing and non-performing
schools. The learner performance is measured at 3 levels district, provincial and national. It is in view
<strong>of</strong> this role that principals indicated that <strong>the</strong>y find it difficult to put strategies in place to ensure that
<strong>the</strong>y intervene and improve learner’s performance. As strategic planning is important to ensure that
appropriate knowledge is shared <strong>the</strong>re was need for a conference focusing on strategic leadership
and management. The aim was to equip school leaders with <strong>the</strong> necessary knowledge to bridge <strong>the</strong>
gap between <strong>the</strong> curriculum delivery within <strong>the</strong> school and <strong>the</strong> national requirements for learner’s
competencies.
It is based on internal and external sharing <strong>of</strong> knowledge amongst different role players that have an
influence on <strong>the</strong> strategic leadership for organisational effectiveness.
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Emmanuel Innocents Edoun and Valdenisa Norris
The essence <strong>of</strong> knowledge management at school level is to share knowledge through
exchanges/interactions between various groups <strong>of</strong> people at different levels within <strong>the</strong> school
environment. The aim <strong>of</strong> <strong>the</strong> case study is to address <strong>the</strong> close connection that knowledge
management has with schools as a learning organisation. This connection is based on <strong>the</strong> willingness
and ability <strong>of</strong> members <strong>of</strong> <strong>the</strong> organisation to learn and share in order to re-evaulate existing practices
and improve it if <strong>the</strong>re is a need. The purpose <strong>of</strong> knowledge management should not only focus on
gaining more knowledge, but to be able to create, transfer and apply knowledge with <strong>the</strong> aim to
improve and achieve better outcomes.
In a school context <strong>the</strong>re are different role players known as partners in education e.g. learners,
educators, parents and educational districts etc. It fur<strong>the</strong>r deals with knowledge management in <strong>the</strong>
internal and external location <strong>of</strong> schools. Internal context <strong>of</strong> knowledge management for school
consists <strong>of</strong> <strong>the</strong> continuous interaction with <strong>the</strong> school environment, <strong>the</strong> integration into <strong>the</strong> school’s
cultural network and all individual experiences within <strong>the</strong> school. External locations will involve
networking with o<strong>the</strong>r schools, districts and/or o<strong>the</strong>r national or international educational institutions in
order for schools to acquire knowledge from <strong>the</strong>se sources. (Friehs,2003).
Schools use SWOT analysis as a tool for assessment in order to provide adequate information to
<strong>the</strong> school governing bodies, school managers, teachers and staff involved in <strong>the</strong> analysis <strong>of</strong> what is
effective and less effective in <strong>the</strong> schools systems and procedures, in preparation for a plan. In fact a
SWOT can be used for any planning or analysis activity which could impact future finance, planning
and management decisions. It can enable <strong>the</strong> school governing bodies and school managers to carry
out a more comprehensive analysis with <strong>the</strong> focus on <strong>the</strong> strengths, weaknesses, opportunities and
threats <strong>of</strong> an organisation.
With <strong>the</strong> above aim in mind, strengths are related to factors that are likely to have a positive effect on
achieving <strong>the</strong> school’s objectives. Weaknesses on <strong>the</strong> o<strong>the</strong>r hand are factors that are likely to have a
negative effect on achieving <strong>the</strong> school’s objectives. Strengths and weaknesses are internal factors
that are easily monitored within <strong>the</strong> organisation. The information is usually shared amongst staff or
learners so that appropriate strategies can be considered to improve or advert <strong>the</strong> situation at hand.
Whereas opportunities as external factors have a positive effect on achieving or exceeding <strong>the</strong>
school’s objectives, or goals not previously considered. Threats are ano<strong>the</strong>r external factors and
conditions that have a negative effect on achieving <strong>the</strong> school’s objectives, or making <strong>the</strong> objective
redundant or un-achievable.
This SWOT analysis is a contributor that helps <strong>the</strong> leadership <strong>of</strong> <strong>the</strong> school to learn from
achievements and mistakes so that policies and processes are put in place to evaluate and guide
learners, educators and parents on performance. For instance in a case concerning a school in
Gauteng, South Africa <strong>the</strong> findings revealed after Grade 1-6 learners participated in an Annual
National Assessment(ANA) written in November 2009 will be explained. The findings fur<strong>the</strong>r revealed
that <strong>the</strong> Ma<strong>the</strong>matics and English levels <strong>of</strong> learner within <strong>the</strong> intermediate phase grade 4-6 was
extremely low. These scores are benchmarked at local, provincial and national levels. Although
learners in <strong>the</strong>se grades average score was 5% higher than <strong>the</strong>ir provincial average and 2% higher
than <strong>the</strong> national average it was felt that this a matter <strong>of</strong> concern. For literacy and numeracy in <strong>the</strong>
foundation phase, grade 1-3 learners, <strong>the</strong> average scores was 15% higher than <strong>the</strong> national average.
The school leader, school governing body and educators teaching Ma<strong>the</strong>matics and English decided
to put systems in place to look at <strong>the</strong> gap in performance between learners in grade 3 and learners in
grade 4. Educator intervention strategies included meetings with <strong>the</strong> Ma<strong>the</strong>matics and English
educators. During <strong>the</strong>se meetings educators looked at <strong>the</strong> ma<strong>the</strong>matical concepts and language
structures that learners are expected to achieve at a certain stage in <strong>the</strong> different grades. A diagnostic
analysis <strong>of</strong> every question for every grade ANA question papers was done and a report was compiled
<strong>of</strong> <strong>the</strong>ir findings. After compiling <strong>the</strong>ir report <strong>the</strong> group <strong>of</strong> educators requested a meeting with <strong>the</strong>
School Assessment Team (SAT).
At <strong>the</strong> first meeting with <strong>the</strong> SAT, teachers looked at <strong>the</strong> learning programmes, assessment
standards and <strong>the</strong> foundation <strong>of</strong> learning policy from <strong>the</strong> Department <strong>of</strong> Education. Numerous meeting
and discussions took place which involved sharing <strong>of</strong> ideas, good practices on certain aspects <strong>of</strong> <strong>the</strong>
specific learning areas. By tapping into everyone’s experience, an intervention plan was devised
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Emmanuel Innocents Edoun and Valdenisa Norris
which would place <strong>the</strong> focus on going back to basics in order to promote enrichment. The
ma<strong>the</strong>matics educators felt that, incorporating technology into <strong>the</strong>ir lessons would improve learner’s
performance.
The rationale behind this was that, we should promote holistic learning and show <strong>the</strong> connection
between <strong>the</strong>ory and practice. The intervention plan includes activities like mental maths; spelling bee;
design and making using patterns, shape, angles; entrepreneurial skills etc. All learners have been
registered with World Maths, World English and World Education websites. The educators have
fur<strong>the</strong>r created a chat line for learners on <strong>the</strong>ir cell phones to share Maths and English problems on a
daily basis. This intervention plans were included in order to integrate information technology into <strong>the</strong>
current teaching and learning practices.
The School Governing Body (SGB) played and oversight role to see into that, <strong>the</strong> above strategy is
working. Amongst its multiple task, <strong>the</strong> role <strong>of</strong> <strong>the</strong> (SGB) in view <strong>of</strong> <strong>the</strong> promotion <strong>of</strong> quality education
and producing global learners is to ensure that <strong>the</strong> educators and learners are <strong>the</strong> necessary
resources to facilitate <strong>the</strong> effective managing <strong>of</strong> knowledge. One <strong>of</strong> <strong>the</strong> functions <strong>of</strong> <strong>the</strong> School
Governing Body which represents <strong>the</strong> parents <strong>of</strong> <strong>the</strong> learners <strong>of</strong> <strong>the</strong> school is to draw up a 3 year
strategic plan. As governors <strong>the</strong> members <strong>of</strong> <strong>the</strong> SGB planned a teambuilding activity to investigate
<strong>the</strong> brand <strong>of</strong> <strong>the</strong> school. They aligned this activity with results <strong>of</strong> learner’s performance and teacher’s
intervention strategies. The focus points was based on what <strong>the</strong>ir role and function as parents are in
achieving <strong>the</strong> mission <strong>of</strong> <strong>the</strong> school and at <strong>the</strong> same time reach <strong>the</strong> vision <strong>of</strong> <strong>the</strong> Gauteng Department
<strong>of</strong> Education.
5. School leaders conference
As a school leader <strong>of</strong> a primary school in Gauteng (Tshwane Education District) and a member <strong>of</strong> <strong>the</strong>
South African Principal Association (SAPA) along with two o<strong>the</strong>r principals we have identified strategic
planning as a weakness amongst principals. The focus was on exploring <strong>the</strong> significance <strong>of</strong> strategic
leadership and management in schools. Delegates from all educational sectors were invited e.g.
principals, academics from universities, department <strong>of</strong> education <strong>of</strong>ficials etc. The aim was to share
knowledge on how school leaders can use <strong>the</strong> results from <strong>the</strong> conference and use <strong>the</strong>m effectively
so that <strong>the</strong>y can have a direct impact on learners, educators and <strong>the</strong> school environment. As a result
<strong>of</strong> this conference a portal was created to <strong>of</strong>fload <strong>the</strong> relevant information shared so that it can
become a model for fur<strong>the</strong>r use. This portal is also used to create video conferencing, social
networking education sites, smsing, instant messaging to address burning issues and best practices
within <strong>the</strong> education sector that can contribute to <strong>the</strong> smooth running <strong>of</strong> schools.
However in achieving <strong>the</strong> moral purpose <strong>of</strong> knowledge management, strategic leadership at school,
universities and related institutions levels should ensure that, knowledge management strategy is
adequately implemented so that learners can form <strong>the</strong> core intellectual assets. These assets with <strong>the</strong>
necessary knowledge acquired will be utilised at different layers <strong>of</strong> <strong>the</strong> society so to contribute to
socio-economic development, economic growth, poverty reduction etc.
6. Findings
This study found that KM is important and useful in order to understand <strong>the</strong> role <strong>of</strong> knowledge in <strong>the</strong>
performance <strong>of</strong> schools. KM equally set <strong>the</strong> corresponding mechanisms and practices to make
knowledge sharing become a cultural value through people, processes and technology. The findings
also prove that <strong>the</strong> essence <strong>of</strong> KM is dynamic and evolves over time to face new challenge at <strong>the</strong>
workplace. This is why IT applications became useful to sustain information that is permanently
shared amongst all stakeholders.
For instance schools are <strong>the</strong> learning organisations where information is developed into knowledge
through social interactions. At school level, individual teacher knowledge, skills dispositions,
pr<strong>of</strong>essional learning community, technical resources and principal leadership are <strong>the</strong> core <strong>of</strong>
sustainable KM. The principal as <strong>the</strong> leader takes on <strong>the</strong> key role <strong>of</strong> leading for educational changes.
Schools are expected to embark on creating a training module where KM values <strong>the</strong> exchange <strong>of</strong>
knowledge locally and abroad in order to improve <strong>the</strong> organisation effectiveness. As mentioned in <strong>the</strong>
Case Study <strong>the</strong> school used <strong>the</strong> ANA which is a national assessment in South Africa, <strong>the</strong> school
measured learner’s performance on a national level. The figure below shows <strong>the</strong> grade percentage
and <strong>the</strong> levels. The levels are from 1-4 with 1= poor; 2= partially achieved ; 3=achieved and 4=
outstanding. From <strong>the</strong> table below it is evident that scores in Numeracy/Ma<strong>the</strong>matics Grades 3-6 is
255

Emmanuel Innocents Edoun and Valdenisa Norris
very low and needs serious intervention. Scores for Literacy/ English are satisfactory but <strong>the</strong>re is room
for improvement.
Literacy
English
Numeracy
Maths
Grade
Level
Level
%
%
1 75 4 71 4
2 74 4 76 4
3 53 3 34 1
4 53 3 41 2
5 57 3 39 2
6 58 3 46 2
We fur<strong>the</strong>rmore found that our learners lack <strong>the</strong> ability to apply <strong>the</strong>ir knowledge when <strong>the</strong> layout <strong>of</strong> <strong>the</strong>
assessment differs from <strong>the</strong> assessment set by <strong>the</strong>ir educators. Our biggest challenge was to teach
application skills to our learners by using a system where <strong>the</strong> assessment was compiled externally
and educators invigilate across grades. The intervention programmes was designed to ensure that
learners are able to excel in any environment and under different circumstances. As a learning
organisation preparing learners to be able to compete locally, nationally and globally our staff used
<strong>the</strong>ir expertise to draft a reading programme that measure speed, fluency and comprehension mental
maths programmes focusing on basic maths concepts like basic operations, times tables and bonds
were implemented. To sustain <strong>the</strong> above practice <strong>the</strong> portal as an IT application is roped in to improve
<strong>the</strong> efficiency <strong>of</strong> knowledge exchange and ought to deliver a set <strong>of</strong> shared educational objectives that
include communications around best policies and practices, a gateway to research with <strong>the</strong> use <strong>of</strong>
technological applications. The strategies used by <strong>the</strong> school to use IT as a tool to improve teaching
and learning includes activities like:
Strategies/Activities Performance Indicators
Equip all educators with laptops and
train <strong>the</strong>m on <strong>the</strong> use <strong>of</strong> IT equipment to
enhance teaching and learning .
Equip class with a data projector and
have internet connection to every
classroom.
Utilise IT centre with s<strong>of</strong>tware that links
with teaching topics and create
opportunities to learners to connect with
o<strong>the</strong>r learners across <strong>the</strong> global to share
learning experience.
Incorporate it in lessons to stimulate
learners visual and audio learning
exposure.
Integrate technology as a teaching
method e.g. phonic programme, maths :
hear and see.
Research projects using google etc.
,email as a form to communicate, set
portal for maths etc. surf <strong>the</strong> net.
.
It was equally found that <strong>the</strong> portal provides access to different links that supports teaching and
learning through technology. Three types <strong>of</strong> portal were identified where services are merged in a
single portal. These include networking portals that provide managers, educators and learners a
particular point from where to access relevant information related to educational tools and facilities.
Organisational portals are ra<strong>the</strong>r designed specifically to <strong>of</strong>fer educational material. Whereas
resources-based portals facilitate access to a range <strong>of</strong> educational resources on line such as Maths,
English, Science etc..
The findings also reveal that in schools where principals encourage a culture <strong>of</strong> teamwork, provide <strong>the</strong>
necessary resource and access to information and communication technology (ICT) learners and
educators display a willingness to share and exchange information. They know that this obviously
means doing traditional activities in new and better ways or expanding <strong>the</strong> boundaries <strong>of</strong> what is
possible. The best leaders capitalise on above situation to create <strong>the</strong> right atmosphere for success,
recognise and reward achievement, technology and act incisively where performance is not good
enough because processes has been established, are readily available and user-friendly. If a school
succeed in linking knowledge to technology where people are called to exchange knowledge it will
become a model and organisations, institutions and <strong>the</strong> relevant school locally and abroad could be
inspired by that model to achieve greater results.
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7. Recommendations
Emmanuel Innocents Edoun and Valdenisa Norris
It is now widely recognised that knowledge assets and technological enhancements are essential
strategic resource for any organisation to achieve sustainability and competitive advantage.
Organisations are now, more than ever, aware <strong>of</strong> this unique resource; <strong>the</strong>refore, <strong>the</strong>y should plan to
manage it more effectively through various initiatives including knowledge management projects and
practices. In this way, <strong>the</strong>se organisations will survive and could improve <strong>the</strong>ir result-based
performance. The knowledge management practices should be monitored and evaluated in line with
organisational performances (Wagner et al 2005). As knowledge is created and disseminated within
<strong>the</strong> organisation, it should have a direct impact in <strong>the</strong> organisation’s performance and improve its
readiness to face future challenges and capitalise on new opportunities.
As <strong>the</strong> world is becoming a global village, many schools are becoming increasingly aware that <strong>the</strong>ir
main assets are <strong>the</strong>ir learners who need to enter <strong>the</strong> job market in <strong>the</strong> future. With this aim in mind,
<strong>the</strong> strength <strong>of</strong> a school as a learning organisation will depend on <strong>the</strong> successful managing <strong>of</strong>
knowledge by its members. This will require trained and committed teachers but <strong>the</strong>y, in turn, need
<strong>the</strong> leadership <strong>of</strong> highly effective principals and <strong>the</strong> support <strong>of</strong> o<strong>the</strong>r senior and middle managers.
Effective leadership and management by principals, inspectors and school staff are key factors in
bringing about high standards <strong>of</strong> pupils’ achievement and school improvement.
However <strong>the</strong> initial findings reveal that, “Leadership” is an important element that harnesses <strong>the</strong>
energies and commitment <strong>of</strong> staff, pupils and parents, and provides a clear direction for <strong>the</strong> work and
development <strong>of</strong> <strong>the</strong> school. This type <strong>of</strong> leadership should be instrumental in enhancing <strong>the</strong> essence
<strong>of</strong> Knowledge Management in order to ensure <strong>the</strong> efficient administration and organisation <strong>of</strong> <strong>the</strong>
school. Day-to-day management expertise without effective leadership can undermine knowledge
management strategies.
This can result in a school that runs efficiently but lacks a sense <strong>of</strong> vision and purpose. The best
leaders put pupils’ learning at <strong>the</strong> centre <strong>of</strong> <strong>the</strong>ir leadership and management activities. The best
principals are <strong>the</strong> driving force in taking a school forward and ensure a strong commitment to high
standards in all aspects <strong>of</strong> <strong>the</strong> work <strong>of</strong> <strong>the</strong> school. Successful leaders should continually look for ways
<strong>of</strong> doing things better by implementing knowledge management strategy so that <strong>the</strong> country can
benefit from <strong>the</strong> learners who become more knowledgeable to solve problems that can hinders <strong>the</strong>
communities at all levels <strong>of</strong> government. These learners are seen as <strong>the</strong> power <strong>of</strong> <strong>the</strong> nation as
slogans convincingly hold that ‘Knowledge is Power’.
8. Conclusions
Knowledge management is instrumental in shaping change within organisations, schools, colleges
or universities. The fact that knowledge is considered to be power means that, if citizens are
adequately equipped with knowledge, <strong>the</strong>y will form <strong>the</strong> core <strong>of</strong> what is termed as intellectual assets.
It is <strong>the</strong>refore evident that, good leadership with <strong>the</strong> support <strong>of</strong> good governance is a contributing
factor in <strong>the</strong> effective management <strong>of</strong> Knowledge.
The essence <strong>of</strong> knowledge management is <strong>the</strong>refore to use <strong>the</strong> intellectual assets for <strong>the</strong>
transformation <strong>of</strong> <strong>the</strong> society in a positive manner by using IT spectrum to sustain development as
digital technologies fuel exponential growth in society’s ability to generate, exchange, and consume
information. The knowledge society is one where growth, development and innovation are driven by
optimal use <strong>of</strong> information and information products (Bodo, 2006).
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Binney, D.(2001) The knowledge management spectrum : Understanding <strong>the</strong> KM landscape, Journal <strong>of</strong>
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Bertels, T. (1995) The Knowledge Management Forum 2002. www.km-forum.org
Bible, D (1995) The Knowledge Management Forum 2002. www.km-forum.org
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Friehs, B (2003) Knowledge Management in Educational Settings
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Haggie, K and Kingston, J.( 2003) Choosing Your Knowledge Management Strategy, Journal <strong>of</strong> Knowledge
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Kidwell ,JJ. ; Van Der Linde, KM. and Johnson, SL. (2000) Applying Corporate Knowledge Management
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Nonaka, I. and Takeuchi, H. (1995) The Knowledge-Creating Company: How Japanese Companies Create <strong>the</strong>
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Petrides, LA and Nodine, TR. (2003) Knowledge Management: Defining <strong>the</strong> Landscape. The institute for <strong>the</strong>
study <strong>of</strong> knowledge management in education.
Roehl, H. (2000) Instrumente der Wissensorganization. Wiesbaden: Gabler.
Wagner et al (2005). Monitoring and Evaluation <strong>of</strong> ICT in Education Projects: A Handbook for developing
countries info Dev
Wenig, G (1995) The Knowledge Management Forum 2002. www.km-forum.org
Wiig, K. (1996). Knowledge Management Methods: Practical Approaches to Managing Knowledge, Arlington,
TX:Schema Press.
Wiig, K. (1997). “Knowledge Management: Where did it come from and where will it go?”
258

Managing Uncertainty in Projects – A Means to Knowledge
Transfer, Learning and Organization Development
Anandasivakumar Ekambaram and Agnar Johansen
SINTEF – Technology and Society, Productivity and Project Management,
Trondheim, Norway
siva@sintef.no
Agnar.Johansen@sintef.no
Abstract: This paper is about a research project called “Practical uncertainty management in a project owner’s
perspective” (in short, <strong>the</strong> PUS-project). This paper describes efforts taken by <strong>the</strong> PUS-project to create better
understanding and knowledge within <strong>the</strong> field <strong>of</strong> managing uncertainty in projects. Traditional way <strong>of</strong> managing
uncertainty falls within <strong>the</strong> iron triangle <strong>of</strong> cost, time and quality that are associated with <strong>the</strong> project, and focuses
mainly on threats ra<strong>the</strong>r than opportunities. The PUS-project proposed <strong>the</strong> perspective <strong>of</strong> project owner in order
to deal with and manage uncertainty in projects effectively. This paper looks at how cooperation between <strong>the</strong>
PUS-project and one <strong>of</strong> its main industrial partners (Norwegian Directorate <strong>of</strong> Public Construction and Property
Management (Statsbygg)) took place. In this regard, this paper presents what was done and what was achieved
through this cooperation. Statsbygg organises, plans and executes around 160 projects – large and small – at all
times, and 20-30 large projects are completed every year. Managing uncertainty is an important part <strong>of</strong> project
management. This paper describes how improving uncertainty management in Statsbygg’s projects becomes a
means to knowledge transfer, learning and organization development. Efforts related to learning and knowledge
transfer played an important role in <strong>the</strong> process <strong>of</strong> organization development that Statsbygg experienced. These
kinds <strong>of</strong> efforts have <strong>the</strong> potential to promote a culture that positively influences <strong>the</strong> way uncertainty is dealt with
and managed in projects in Statsbygg. These efforts can contribute to create awareness and proper attitudes that
are needed to obtain <strong>the</strong> desired results. Cooperation between <strong>the</strong> PUS-project and Statsbygg, which is
presented in this paper, is an example that illustrates how knowledge and value can be created through
collaboration between practitioners from <strong>the</strong> industries and researchers.
Keywords: Managing uncertainty, project management, knowledge transfer, learning, organization development
1. Introduction
Projects are applied as a work-form in several organisations. Both public and private sectors widely
adopt <strong>the</strong> project concept to create <strong>the</strong> product or service that <strong>the</strong>y aim at. Projects are one-time
activities that have time and resource limitations. Each project is unique, at least to a certain extent.
The degree <strong>of</strong> uniqueness varies from project to project, and this variation suggests that <strong>the</strong>re can be
certain known elements in projects, such as known solutions, work methods, tool, etc. This kind <strong>of</strong> a
work setting provides a significant opportunity for project participants to learn new things; share <strong>the</strong>ir
knowledge as well as create new knowledge. Hence, projects can be seen as a vehicle for learning
and knowledge transfer.
This paper focuses on a research project. The research project is called “Practical uncertainty
management in a project owner’s perspective” – in short, <strong>the</strong> PUS-project. The PUS-project is aimed
at developing a new way to think, lead and manage projects, by focusing on leadership and culture, in
connection with practical management <strong>of</strong> uncertainty. The purpose <strong>of</strong> this paper is to describe how
this research project contributed to create value in <strong>the</strong> industry through knowledge transfer and
learning.
In order to materialize this purpose, this paper has <strong>the</strong> following structure: The paper starts with a
short description <strong>of</strong> <strong>the</strong> PUS-project. The following section includes <strong>the</strong>oretical aspects that are
relevant to <strong>the</strong> paper: knowledge management and <strong>the</strong> role <strong>of</strong> project owner. After that, <strong>the</strong> paper
presents a brief description <strong>of</strong> methodology associated with <strong>the</strong> paper as well as with <strong>the</strong> nature <strong>of</strong>
cooperation that <strong>the</strong> PUS-project had with one <strong>of</strong> its industrial partners to facilitate learning and value
creation. Followed by this presentation, one <strong>of</strong> <strong>the</strong> contributions <strong>of</strong> <strong>the</strong> PUS-project is described – an
example that shows <strong>the</strong> nature cooperation between <strong>the</strong> PUS-project and one <strong>of</strong> its industrial partner
and <strong>the</strong> results that <strong>the</strong> industrial partner has achieved through <strong>the</strong> cooperation. Finally, concluding
remarks wind up <strong>the</strong> whole discussion.
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2. The PUS-project
Anandasivakumar Ekambaram and Agnar Johansen
The PUS-project had an ambition <strong>of</strong> focusing on leadership and culture connected to practical
management <strong>of</strong> uncertainty in major public and private projects. Lot <strong>of</strong> work was done on <strong>the</strong> issue <strong>of</strong>
uncertainty analysis both in Norway and abroad, and much <strong>of</strong> this kind <strong>of</strong> work was carried out in <strong>the</strong>
early phase ("front end loading") <strong>of</strong> projects. But, <strong>the</strong>re was less research on <strong>the</strong> issue <strong>of</strong> how to
manage opportunities and threats in a project’s life cycle in a practical manner. Fur<strong>the</strong>r more, <strong>the</strong>re
was not much research on how and what <strong>the</strong> project owner role should be with respect to
management <strong>of</strong> uncertainty; from <strong>the</strong> very start <strong>of</strong> <strong>the</strong> project to <strong>the</strong> phase where <strong>the</strong> intended
benefits <strong>of</strong> <strong>the</strong> projects are realised. PUS had an ambition to shed light on <strong>the</strong> owner's role related to
uncertainty management throughout <strong>the</strong> project life cycle. The project had a keen interest in
influencing large organizations’ thinking patterns and actions associated with identification and
management <strong>of</strong> uncertainty elements in projects.
The idea to start up <strong>the</strong> PUS-project was appeared in 2005, and <strong>the</strong> project was started in January
2006. The project was collaborated with <strong>the</strong> Research Council <strong>of</strong> Norway and Norwegian Center <strong>of</strong>
Project Management (NSP). The main industrial partners <strong>of</strong> <strong>the</strong> project were:
Statoil (an international energy company with operations in 34 countries, headquartered in
Norway),
Norwegian Directorate <strong>of</strong> Public Construction and Property Management (Statsbygg),
Telenor (one <strong>of</strong> <strong>the</strong> world’s largest mobile operators with 33 200 employees worldwide,
headquartered in Norway)
Norwegian Armed Forces (Forsvaret)
Norwegian Public Roads Administration (Statens vegvesen)
Norwegian National Rail Administration (Jernbaneverket)
Apart from <strong>the</strong>se main industrial partners, o<strong>the</strong>r Norwegian organizations were also involved in <strong>the</strong>
PUS-project. The project’s cost frame was around 30 million Norwegian kroner (NOK) (ca 3.8 million
EURO). This frame included spin-<strong>of</strong>f projects and own efforts.
3. Theoretical background
In this section, we shall present <strong>the</strong>ories that are relevant to this paper. In this regard, <strong>the</strong> following
aspects are described:
• Knowledge management
• The role <strong>of</strong> project owner
3.1 Knowledge management
There are several definitions <strong>of</strong> knowledge management. Our intension here is not to present different
definitions. We shall present a major categorization <strong>of</strong> knowledge and a definition that we consider
with respect to this paper.
There are two major perspectives that can be associated with <strong>the</strong> topic <strong>of</strong> knowledge management
(Hislop, 2009). They are:
Objective perspective: This perspective considers knowledge as an object and incorporates a
positivistic philosophy. Application <strong>of</strong> information technology traditionally tends to approach
knowledge management from this perspective.
Practice-based perspective: This perspective considers that knowledge is embedded in practice
and is socially constructed. And hence, <strong>the</strong> approach suggests that it is not possible to consider
knowledge as objective “facts”, completely free from <strong>the</strong> context.
In this paper, we shall consider <strong>the</strong> following definition, given by Davenport et al. (1998):
Davenport et al. (1998, page 5) say
Knowledge is 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 organizations, it <strong>of</strong>ten
becomes embedded not only in documents or repositories but also in organizational
routines, processes, practices and norms.
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Anandasivakumar Ekambaram and Agnar Johansen
(According to Davenport et al. (1998, page 2), data is “a set <strong>of</strong> discrete, objective facts about events”,
and information is “a message, usually in <strong>the</strong> form <strong>of</strong> a document or an audible or visible
communication”.)
The definition <strong>of</strong> knowledge mentioned above seems to suggest <strong>the</strong> practice-based perspective <strong>of</strong>
knowledge. In our opinion, knowledge is practice-based and that it can be created through reflection,
interpretation, action, communication and cooperation. These mechanisms, possibly interacting with
one ano<strong>the</strong>r when knowledge is created, reflect <strong>the</strong> fluid mix <strong>of</strong> framed experience, values, contextual
information and expert insight that are mentioned in <strong>the</strong> definition given by Davenport et al. (1998).
3.2 The role <strong>of</strong> project owner
A project owner has rights to and is responsible for <strong>the</strong> project. The project owner takes <strong>the</strong> risk
connected to <strong>the</strong> project’s cost and its future (Eikeland, 2001) Olsson, N, et al. (2007) say:
The beauty behind <strong>the</strong> concept <strong>of</strong> a project owner lies in <strong>the</strong> fact that a project owner has
incentives for weighing costs against benefits for a project. Project owners are <strong>the</strong>refore
expected to strive for project governance aimed at maximising <strong>the</strong> value from <strong>the</strong> project.
The role <strong>of</strong> project owner can be explained by comparing it with <strong>the</strong> role <strong>of</strong> project manager. This
comparison is described by a <strong>the</strong>ory called principal-agent <strong>the</strong>ory. When describing agent <strong>the</strong>ory,
Andersen says (2008, page 13):
The base organisation is setting up a temporary organisation and giving it an assignment
to perform work on its behalf [...] The base organisation will usually appoint a project
owner, who <strong>the</strong>n takes on <strong>the</strong> role as <strong>the</strong> representative for <strong>the</strong> principal. The project will
be headed by <strong>the</strong> project manager, who will represent <strong>the</strong> agent. The <strong>the</strong>ory will
accordingly throw light on <strong>the</strong> relationship between <strong>the</strong> project owner and <strong>the</strong> project
manager.
Regarding <strong>the</strong> interaction between project owner and project manager, <strong>the</strong> principal–agent <strong>the</strong>ory
(Turner et al., 2004; Eisenhardt, 1989; Andersen 2008) pinpoints some factors that are useful for
understanding <strong>the</strong> relationship:
The project owner (regarded as <strong>the</strong> principal) does not have <strong>the</strong> same interests as <strong>the</strong> project
manager (regarded as <strong>the</strong> agent), provided that <strong>the</strong>y both want to maximize <strong>the</strong>ir utilities.
The project owner and project manager both have different information, due to <strong>the</strong> limited
resources <strong>the</strong>y can use to acquire such information.
The project owner and project manager both have different attitudes towards risk, due to <strong>the</strong>ir
different interests.
Cooperation between project owner and project manager can contribute to set appropriate arena not
only for <strong>the</strong> handling <strong>of</strong> uncertainty and <strong>the</strong> execution <strong>of</strong> <strong>the</strong> project, but also for learning and
knowledge transfer in <strong>the</strong> project organisation.
4. Methodology
During <strong>the</strong> cooperation with its main industrial partners, <strong>the</strong> PUS-project used both qualitative
research methods (for instance, interviews, document analysis and action research) and quantitative
methods (for instance, questionnaire study). In this paper, we shall look at <strong>the</strong> contribution <strong>of</strong> <strong>the</strong>
PUS-project to create value in one <strong>of</strong> its main industrial partners called Norwegian Directorate <strong>of</strong>
Public Construction and Property Management (Statsbygg). There were several methodological
approaches connected to this contribution: (1) Questionnaire studies conducted by <strong>the</strong> PUS-project
(2) Several interviews – both structured and semi-structured – through <strong>the</strong> course <strong>of</strong> <strong>the</strong> SUS-project
(3) Document analysis (4) Action research activities connected to 5 different cases, while 3 cases
were used as testing arenas (5) Two focus-seminars per year with <strong>the</strong> intention <strong>of</strong> anchoring plans,
developing new models, procedures, routines and transfer <strong>of</strong> experience between project managers
and project owners in <strong>the</strong> organization.
Among <strong>the</strong>se methodological approaches, we would like to mention <strong>the</strong> following approaches, since
<strong>the</strong>y played a prominent role in creating knowledge and value in Statsbygg.
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Anandasivakumar Ekambaram and Agnar Johansen
Action research: Several sessions <strong>of</strong> uncertainty analysis were carried out in various projects in
Statsbygg. Researchers from <strong>the</strong> PUS-project participated actively in Statsbygg’s effort to
improve <strong>the</strong>ir competence in managing uncertainty in projects. This participation includes, among
o<strong>the</strong>r things, uncertainty analysis sessions. For instance, one <strong>of</strong> <strong>the</strong> researchers (one <strong>of</strong> <strong>the</strong>
authors <strong>of</strong> this paper) participated actively over <strong>the</strong> years and indeed led <strong>the</strong> uncertainty analysis
sessions. This participation can be compared with action research. In this regard, it is relevant to
look at a definition <strong>of</strong> action research. Greenwood et al. (2007, page 3) define action research as
[…] social research carried out by a team that encompasses a pr<strong>of</strong>essional action
researcher and <strong>the</strong> members <strong>of</strong> an organization, community, or network (“stakeholders”)
who are seeking to improve <strong>the</strong> participants’ situation.
This definition suggests <strong>the</strong> relevance <strong>of</strong> action research to this research work.
Interviews: Interviews were carried out (both individual and group interviews with 17 interviewees
in total) in Statsbygg by researchers from <strong>the</strong> PUS-project in order to uncover and obtain
experiences connected to implementation and usage <strong>of</strong> <strong>the</strong> models, methods, routines and
procedures in managing uncertainty in projects (Langlo et al., 2009). Besides <strong>the</strong> interviews,
document study was also done.
Meetings and discussion: With respect to this paper, we specially consider a forum that
symbolises <strong>the</strong> formal completion <strong>of</strong> <strong>the</strong> PUS-project.
5. One <strong>of</strong> <strong>the</strong> major contributions – knowledge transfer, learning and
organization development in Statsbygg
Now, we shall describe how an organization (Statsbygg) can learn and develop itself in collaboration
with <strong>the</strong> PUS-project. Statsbygg has 830 employees. It organises, plans and executes around 160
projects – large and small – at all times, and 20-30 large projects are completed every year.
Two master degree <strong>the</strong>ses at Norwegian University <strong>of</strong> Science and Technology (NTNU), Trondheim,
Norway (Løken et al., 2006; Lund, 2007) were carried out in <strong>the</strong> very start <strong>of</strong> <strong>the</strong> PUS-project. The
<strong>the</strong>ses focused on describing how uncertainty management in Statsbygg was actually carried out.
Conclusion from this work pointed out that Statsbygg had an unstructured approach to uncertainty
management, and <strong>the</strong>re was lack <strong>of</strong> methodology and supportive culture connected to uncertainty
management in Statsbygg.
Statsbygg, in collaboration with <strong>the</strong> PUS-project, started its own development project called
“Uncertainty management in Statsbygg” – in short, <strong>the</strong> SUS-project. Statsbygg (with its SUS-project)
worked closely with <strong>the</strong> researchers connected to <strong>the</strong> PUS-project. Through <strong>the</strong> cooperation with <strong>the</strong>
PUS-project, Statsbygg developed itself in such a way to deal with uncertainty effectively and
efficiently.
The SUS-project has 3 phases. They are:
Studies in <strong>the</strong> case projects: The case projects were: Lapp Science Center, Norwegian Central
Bank, Domus Media (a part <strong>of</strong> University <strong>of</strong> Oslo), R6 (Government buildings), National Theatre,
Halden prison, Vestfold University College, and Department <strong>of</strong> Computer Science II (IFI2,
University <strong>of</strong> Oslo). Methods and tools were tested in this phase; for example, a matrix for
visualizing situations <strong>of</strong> uncertainty, risk register for monitoring uncertainty and monthly reporting
<strong>of</strong> uncertainty in <strong>the</strong> case projects. O<strong>the</strong>r activities associated with this phase include: work
related to establishing training courses, development <strong>of</strong> culture in accordance with <strong>the</strong> focus <strong>of</strong>
<strong>the</strong> SUS-project, experience reports from 4 <strong>of</strong> <strong>the</strong> case projects.
Developing <strong>the</strong> systems – methods and tools: Based on experiences from <strong>the</strong> case projects,
Statsbygg developed methods and tools. New governing documents were created, and a new
role called “uncertainty coordinator” was established.
Implementing <strong>the</strong> systems: As per October 2010, <strong>the</strong> tools were used by about 20 projects.
Procedures, guidelines, templates and training programs were in use. Statsbygg’s school <strong>of</strong>fers
courses and training for <strong>the</strong>ir employees. The courses are conducted according to Statsbygg’s
own direction and guidelines. Uncertainty analyses and uncertainty workshops are conducted,
and uncertainty analyses are seen as a basis for uncertainty management. Uncertainty analyses
focus mainly on quantitative aspects, while uncertainty workshops focus more on qualitative
aspects. Uncertainty workshops are conducted every o<strong>the</strong>r month. Five to 8 people participate in
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Anandasivakumar Ekambaram and Agnar Johansen
such workshops. People linked to o<strong>the</strong>r projects can participate in <strong>the</strong> workshops as observers;
this participation can be viewed as a means to transfer knowledge and experience.
Statsbygg acknowledges that it has become more mature in dealing with and managing uncertainty in
<strong>the</strong>ir projects, and will continue with <strong>the</strong> SUS-project (Forum-report, 2010).
Table 1 summarises how <strong>the</strong> case projects were used in <strong>the</strong> SUS-project.
Table 1: Overview <strong>of</strong> <strong>the</strong> case projects and what was developed / tested
Case project What was tested
R6 The role <strong>of</strong> uncertainty coordinator was developed and tested out. The first version <strong>of</strong> <strong>the</strong>
monthly report was developed, and it included <strong>the</strong> issue <strong>of</strong> uncertainty. Uncertainty matrix –
a tool for managing uncertainty was developed and tested out. A testing was made in order
to know how frequent large scale <strong>of</strong> uncertainty analysis should be carried out in <strong>the</strong>
planning and <strong>the</strong> execution phase. A plan for uncertainty management was developed and
tested out. A testing was conducted in order to understand what kinds <strong>of</strong> uncertainty
analysis were relevant in <strong>the</strong> planning and <strong>the</strong> execution phase – Time analysis and cost
analysis were applied here. A researcher from <strong>the</strong> PUS-project directly involved in <strong>the</strong>
development process as an action researcher.
IFI2 A routine for reporting uncertainty in <strong>the</strong> execution phase was tested out. This case was
used as a testing arena where <strong>the</strong> results <strong>of</strong> <strong>the</strong> test were obtained through interviews by
researchers from <strong>the</strong> PUS-project.
University A routine for reporting uncertainty in <strong>the</strong> execution phase was tested out. This case was
college in used as a testing arena where <strong>the</strong> results <strong>of</strong> <strong>the</strong> test were obtained through interviews by
Vestfold researchers from <strong>the</strong> PUS-project.
Halden A routine for reporting uncertainty in <strong>the</strong> execution phase was tested out. This case was
Prison used as a testing arena where <strong>the</strong> results <strong>of</strong> <strong>the</strong> test were obtained through interviews by
researchers from <strong>the</strong> PUS-project.
Lapp Establishing uncertainty register (a tool for registering uncertainty) in <strong>the</strong> execution phase
scientific was tested out. A new routine for reporting uncertainty was tested out. A researcher from
centre <strong>the</strong> PUS-project directly involved in <strong>the</strong> development process as an action researcher.
Norwegian Establishing uncertainty register (a tool for registering uncertainty) in <strong>the</strong> execution phase
central bank was tested out. A new routine for reporting uncertainty was tested out. A researcher from
<strong>the</strong> PUS-project directly involved in <strong>the</strong> development process as an action researcher.
Domus Establishing uncertainty register (a tool for registering uncertainty) in <strong>the</strong> planning phase
media was tested out. A routine for reporting uncertainty was tested out. A methodology for
auditing <strong>the</strong> uncertainty register every o<strong>the</strong>r month was tested out. The role <strong>of</strong> uncertainty
coordinator was tested out. A researcher from <strong>the</strong> PUS-project directly involved in <strong>the</strong>
development process as an action researcher.
National Establishing uncertainty register (a tool for registering uncertainty) in <strong>the</strong> planning phase
<strong>the</strong>atre was tested out. A routine for reporting uncertainty was tested out. A methodology for
auditing <strong>the</strong> uncertainty-register every o<strong>the</strong>r month was tested out. The role <strong>of</strong> uncertainty
coordinator was tested out. A researcher from <strong>the</strong> PUS-project directly involved in <strong>the</strong>
development process as an action researcher.
Kerzner (2009) describes a maturity model (cf. Figure 1) in connection with project management. This
model can be used to look at <strong>the</strong> levels through which an organization learns and develops itself.
Figure 1: The five levels <strong>of</strong> maturity (Kerzner, 2009)
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Anandasivakumar Ekambaram and Agnar Johansen
The learning and development and that happened in Statsbygg through its cooperation with <strong>the</strong> PUSproject
can also reflect <strong>the</strong> 5 levels <strong>of</strong> <strong>the</strong> maturity model. Statsbygg seems to lie between level 3 and
5 at present. Here, it is relevant to mention Kerzner’s description on feedback from Level 5 to Level 4
and Level 3.
The feedback form Level 5 to Level 4 and Level 3 [...] implies that <strong>the</strong>se three levels form
a continuous improvement cycle, and it may even be possible for all three <strong>of</strong> <strong>the</strong>se levels
to overlap.
The learning process that Statsbygg went through in collaboration with <strong>the</strong> PUS-project incorporates
reflect-on-action (reflecting on what has happened before) and reflect-in-action (reflecting on <strong>the</strong>
action while it is being experienced). These two concepts are mentioned by Schön (1991), when he
talks about <strong>the</strong> reflective practitioner – how pr<strong>of</strong>essionals think in action. Reflecting on practice can
also be seen as a sense making process. Weick (2001) describes a <strong>the</strong>ory <strong>of</strong> sensemaking. This
<strong>the</strong>ory can be considered in connection with knowledge transfer and learning. The <strong>the</strong>ory describes
“how chaos is transformed into sensible and orderly process in an organization through <strong>the</strong> shared
interpretation <strong>of</strong> individuals” (Dalkir, 2005, page 58).
Shared interpretation <strong>of</strong> individuals and sensemaking – which are significant aspects <strong>of</strong> learning,
development and improvement – can be facilitated by cooperative work, such as action research, that
<strong>the</strong> PUS-project and Statsbygg adopted. Cooperation between <strong>the</strong> researcher and <strong>the</strong> members <strong>of</strong> an
organization can enhance <strong>the</strong> sensemaking process by, for instance, sharing, reflecting upon and
acting on <strong>the</strong>ir understanding <strong>of</strong> <strong>the</strong> reality. Coghlan et al (2005) say that a major process in action
research is how individuals acquire “practical-knowing” and how <strong>the</strong>y learn in action. This description
suits well with our approach to defining knowledge and learning.
In addition to involving action researcher from <strong>the</strong> PUS-project, Statsbygg took initiatives to facilitate
knowledge transfer and learning in connection with managing uncertainty in projects. Some <strong>of</strong> <strong>the</strong>
initiatives are:
“The House”: This includes Statsbygg’s internal webpages, routines, procedures, project models,
guidelines, rules etc. that its projects have to refer to.
Statsbygg School – “E-modul”: Statsbygg conducts its own training course in uncertainty
management and uncertainty analysis at its school. This initiative reflects a formal learning
process.
Experience-transfer meetings: Projects take initiative to arrange ad hoc meetings. In <strong>the</strong>se
meetings, project experiences are presented and transferred directly between project participants.
Breakfast seminar, project forum, section-wise meeting, etc.: These arenas where individuals and
project representatives meet in order to discuss various topics and share experiences.
Discussions can be formal or informal.
Research activities: These initiatives are include several internal and external research activities
that focus on uncertainty management; for example, <strong>the</strong> SUS-project, <strong>the</strong> PUS-project, internal
project uncertainty analysis, and activities connected to Norwegian Center <strong>of</strong> Project
Management (NSP).
As we mentioned earlier, Statsbygg, in collaboration with <strong>the</strong> PUS-project, started its own
development project called “Uncertainty management in Statsbygg” – in short, <strong>the</strong> SUS-project. And,
Statsbygg (with its SUS-project) worked closely with <strong>the</strong> researchers connected to <strong>the</strong> PUS-project.
In <strong>the</strong> beginning <strong>of</strong> 2011, <strong>the</strong> SUS-project won Statsbygg’s innovation prize. A description that
accompanied <strong>the</strong> prize says that <strong>the</strong> project has provided documentation <strong>of</strong> both threats and
opportunities over time in projects, including effects and efforts related to <strong>the</strong>m. The overview <strong>of</strong>
uncertainty, provided by <strong>the</strong> documentation, gives both project managers and project owners more
confidence in executing <strong>the</strong>ir roles in managing uncertainty in projects.
6. Concluding remarks
Many literature use a narrow project perspective when analyzing and describing uncertainty
management in projects. We propose a project owner perspective that could bring <strong>the</strong> project owner
closer to a shared and continuous uncertainty management process. The PUS-project showed that
uncertainty management is not just about avoiding risks; it is also about evaluating and making <strong>the</strong>
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most <strong>of</strong> opportunities that will provide <strong>the</strong> best outcome – seen from a lifecycle perspective. In our
view, it is important that <strong>the</strong> project organization acknowledges and communicates <strong>the</strong> fact that <strong>the</strong>
world is uncertain and is not always predictable, and that <strong>the</strong> project owner acknowledges this
situation. Our approach to uncertainty with <strong>the</strong> focus on knowledge transfer and learning that is
presented in this paper can be compared to <strong>the</strong> following description given by Perminova et al. (2008,
page 78), when <strong>the</strong>y describe uncertainty management in projects:
[...] key elements in managing uncertainty are reflective learning and sensemaking as
enablers <strong>of</strong> flexibility and rapidness in decision-making regarding <strong>the</strong> choice <strong>of</strong> alternative
actions in response to <strong>the</strong> situation. At <strong>the</strong> same time, standardized and modularized
processes and procedures constitute a necessary basis for supporting reflective
processes. All <strong>of</strong> <strong>the</strong>se measures can be regarded as important tools for project
managers to recognize and establish <strong>the</strong> core competences, and thus perform ra<strong>the</strong>r
than simply conform to <strong>the</strong> plan. Continuous following <strong>of</strong> such procedures at different
stages <strong>of</strong> <strong>the</strong> project is an essential part <strong>of</strong> project success.
This description in way highlights and connects various aspects that we presented in this paper.
In this paper, we chose to present an industrial example (or a case) that describes how knowledge
transfer and learning with respect to managing uncertainty in projects could contribute to make and
facilitate organisation development. We have tried to include important issues in a coherent manner
within <strong>the</strong> scope <strong>of</strong> this paper. We could have discussed more on each <strong>of</strong> <strong>the</strong> research methods
associated with <strong>the</strong> cooperation between <strong>the</strong> PUS-project and Statsbygg. But, we could not do so
because <strong>of</strong> word limitation <strong>of</strong> this paper. However, we think that focusing on one <strong>of</strong> <strong>the</strong> case projects
that <strong>the</strong> PUS-project and Statsbygg considered in <strong>the</strong>ir cooperation and having a closer look at <strong>the</strong>
research methods <strong>of</strong> that particular case project can be a future study that can shed more light on <strong>the</strong>
topic <strong>of</strong> knowledge transfer and learning.
Our description <strong>of</strong> uncertainty management calls for a strong owner involvement throughout <strong>the</strong>
project phase. The PUS-project managed to create a positive culture that can promote effective and
efficient uncertainty management in projects. Organizations such as Statsbygg acknowledged <strong>the</strong>
development <strong>of</strong> <strong>the</strong> positive culture in <strong>the</strong>ir organizations through <strong>the</strong> collaboration with <strong>the</strong> PUSproject
and <strong>the</strong> need to involve project owner more in managing uncertainty in projects (Forum-report,
2010). Fur<strong>the</strong>r more, <strong>the</strong> cooperation between <strong>the</strong> PUS-project and <strong>the</strong> involved organizations, such
as Statsbygg, illustrates how a research project attempted to create knowledge and value in <strong>the</strong>
industry – an example <strong>of</strong> collaboration between researchers and practitioners.
Acknowledgement
We sincerely thank Ms. Ragnhild Aalstad (Project Manager, Statsbygg) for her support to <strong>the</strong> PUSproject
and thus to <strong>the</strong> work related to this paper.
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266

A KM Strategy for Improving <strong>the</strong> BPR Process
Jamal El-Den 1 and Xin Zhou 2
1
Charles Darwin University, School <strong>of</strong> International Education, Darwin,
Australia
2
Wuhan University <strong>of</strong> Technology, School <strong>of</strong> Engineering and IT, Wuhan, China
Jamal.el-den@cdu.edu.au
zxroyzx@yahoo.com.cn
Abstract: The paper argues that implementing a knowledge management (KM) strategy during <strong>the</strong>
implementation <strong>of</strong> Business Process Re-engineering (BPR) would improve, in terms <strong>of</strong> effectiveness, efficiency,
speed, and accuracy, <strong>the</strong> phases <strong>of</strong> <strong>the</strong> BPR. A specific BPR process from <strong>the</strong> literature was chosen. The paper
provides a strategy on how KM, in particular tacit knowledge management, can be introduced during <strong>the</strong>
execution <strong>of</strong> <strong>the</strong> first three phases <strong>of</strong> <strong>the</strong> identified BPR process. The paper identifies for each <strong>of</strong> <strong>the</strong>se phases
<strong>the</strong> type and kind <strong>of</strong> knowledge sources, what knowledge should be captured? What knowledge should be
nurtured and retained? In addition, <strong>the</strong> paper addresses tacit knowledge transformation among <strong>the</strong> phases as a
result <strong>of</strong> <strong>the</strong> capture and nurture <strong>of</strong> such knowledge among <strong>the</strong> members <strong>of</strong> <strong>the</strong> team involved in <strong>the</strong> BPR
process.
Keywords: Knowledge management, business process re-engineering, tacit knowledge capture, tacit knowledge
transfer, km strategy
1. Introduction
The aim <strong>of</strong> this paper is to identify which Knowledge Management (KM) factors could be used to
enhance <strong>the</strong> phases <strong>of</strong> <strong>the</strong> Business Process Reengineering (BPR) during its implementation.
Reengineering is <strong>the</strong> fundamental rethinking and radical redesign <strong>of</strong> business processes to achieve
dramatic improvements in critical, contemporary measures <strong>of</strong> performance such as cost, quality, and
service and speed (Hammer & Champy, 1993). Employees’ know-how and past experiences about
<strong>the</strong> existing (legacy) system is vital for <strong>the</strong> success <strong>of</strong> organizational future development and
reengineering. The BPR process’s phases successfulness highly depends on information/ knowledge
<strong>of</strong> employees as well as management’s past experiences with <strong>the</strong> legacy system and its
functionalities. The knowledge <strong>of</strong> <strong>the</strong>se personnel, as well as <strong>the</strong>ir past experience, should be
captured, nurtured, externalized, and retained in order to help <strong>the</strong> developers during <strong>the</strong> BPR
process. This will facilitate <strong>the</strong> development <strong>of</strong> <strong>the</strong> to-be system being developed. The research will
identify how and what in KM would be enablers for <strong>the</strong> BPR efficiency, effectiveness and success.
Globalization compelled businesses and organizations intend to realize that KM, which emerged as a
vital <strong>the</strong>oretical discipline and covers several categorizations <strong>of</strong> current information technologies,
plays a critical role for <strong>the</strong>ir future success, especially during systems development. The advantages
<strong>of</strong> KM is to bring to BPR <strong>the</strong> essential knowledge which will enable it to build on past experiences and
employees’ knowledge as this will certainly affect <strong>the</strong> BPR current problems which are <strong>the</strong> result <strong>of</strong> reengineering
from scratch.
Eventually, this report will list possible KM tools which aim to enhance <strong>the</strong> first three phases <strong>of</strong> a BPR
process. Given <strong>the</strong> time limitations, <strong>the</strong> KM application for <strong>the</strong> BPR process will focus on <strong>the</strong> initial
three stages <strong>of</strong> a literary existing BPR methodology.
The strategic importance <strong>of</strong> knowledge was recognized by <strong>the</strong> Committee <strong>of</strong> economic commission
for Africa <strong>of</strong> UN (2001) report which asserts that it is generally accepted that <strong>the</strong> modern economy <strong>of</strong>
a typical developed country is knowledge based, and with globalization, this will become <strong>the</strong> norm in
all sections <strong>of</strong> <strong>the</strong> world. The so-called knowledge can ei<strong>the</strong>r be tangible in <strong>the</strong> form <strong>of</strong> data saved on
databases, files, books etc or intangible such as employees’ ideas, opinions, thoughts, past
experiences etc accumulated from <strong>the</strong> daily business procedures and operation. As an example, a
call centre service becomes vital for businesses because <strong>of</strong> <strong>the</strong> expectations from customers to get
accurate answers fast. Knowledge management has <strong>the</strong> potential to deliver some (or all) <strong>of</strong> <strong>the</strong> listed
benefits for organizations. There is no doubt that knowledge improves productivity significantly and
increases customers’ satisfaction as well as <strong>the</strong> effectiveness <strong>of</strong> an organization’s system <strong>of</strong>
harnessing its knowledge base. Recently, <strong>the</strong> majority <strong>of</strong> researchers and scientists have shared <strong>the</strong>
view that KM should address both tacit and explicit knowledge (EL-DEN, 2009). Ideas and opinions
were identified as <strong>the</strong> types <strong>of</strong> tacit knowledge which are easier for <strong>the</strong> transformation than o<strong>the</strong>r
forms <strong>of</strong> tacit knowledge (El-Den, 2008).
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Jamal El-Den and Xin Zhou
This research will list some possible KM tools which aim to enhance <strong>the</strong> BPR process. Given <strong>the</strong> time
limitations, <strong>the</strong> KM application for <strong>the</strong> BPR process will focus on <strong>the</strong> initial three stages <strong>of</strong> (Bitzer &
Kamel, 1997) BPR life cycle.
Create project prospectus (define project)
Learn from o<strong>the</strong>rs (customers, users, associates, technology)
Create vision and design new business process model
Develop enabling technology architecture and organizational model
Perform a gap analysis and prepare a business case for change (benefits and costs)
Define process, systems and training requirements, and plan implementation
Develop and pilot solutions
Implement solution and measure performance
The following figure shows <strong>the</strong> interaction between <strong>the</strong> different components <strong>of</strong> <strong>the</strong> method proposed
in <strong>the</strong> paper:
KB
GSS
Figure 1: Interaction among <strong>the</strong> proposed components
Figure-1 shows that <strong>the</strong> GSS provides functionalities during all phases <strong>of</strong> <strong>the</strong> BPR. It maintains control
over <strong>the</strong> externalization <strong>of</strong> knowledge among <strong>the</strong> developers and users <strong>of</strong> <strong>the</strong> legacy system by
allowing articulation, capture, nurture, transformation and retention <strong>of</strong> individuals as well as groups’
knowledge. This knowledge is retained in <strong>the</strong> KB where it can be retrieved for future development.
The BPR also retrieves knowledge from <strong>the</strong> KB. This knowledge is basically <strong>the</strong> strategies, goals,
objectives, and/or any knowledge from/about external environment.
2. Research methodology
The paper proposes that KM provides <strong>the</strong> platform to improve <strong>the</strong> BPR process. The paper introduces
current BPR life cycles and identifies one as an example in <strong>the</strong> process <strong>of</strong> demonstrating that <strong>the</strong> KM
concept <strong>of</strong> externalization are integral to enhance/improve <strong>the</strong> BPR cycle’s steps. The research
demonstrates <strong>the</strong>se improvements on <strong>the</strong> first three phases/steps <strong>of</strong> <strong>the</strong> chosen methodology for
reasons <strong>of</strong> limitations in terms <strong>of</strong> space and time.
3. Create project prospectus (define project)
3.1 Identify a need for process improvement
Traditionally, organizations undergo BPR in <strong>the</strong>ir quest to maintain customer satisfaction, improve
financial performance, better operations, and agility. These factors are <strong>the</strong> initial drivers for <strong>the</strong> BPR
process and mark <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> preparation activity (problem definition, investigation and
analysis) during <strong>the</strong> process (Mayer & Dewitte, 1998). Customer satisfaction should be based on
knowledge about <strong>the</strong> customers, <strong>the</strong>ir needs, <strong>the</strong>ir buying power, <strong>the</strong>ir financial state etc. Better
performance and operations should be based on employees’ operational knowledge and experience
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P2
P3
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Jamal El-Den and Xin Zhou
with <strong>the</strong> existing system. Accordingly, <strong>the</strong> ease <strong>of</strong> accessibility to <strong>the</strong> different past-experiences and
knowledge about <strong>the</strong> existing system will highly influence <strong>the</strong> successfulness <strong>of</strong> <strong>the</strong> BPR process,
hence <strong>the</strong> to-be system. This knowledge is collected across functional departments from employees
and users within <strong>the</strong> organization, or external information <strong>of</strong> stakeholders such as customers,
suppliers and shareholders.
The current problem is how to make use <strong>of</strong> all <strong>the</strong>se needs which are in various data forms in order to
capture <strong>the</strong> valued knowledge required by <strong>the</strong> developers at each phase when needed. There are
some concepts and techniques in knowledge management which can highly assist in <strong>the</strong>se tasks,
namely, <strong>the</strong> externalization <strong>of</strong> tacit knowledge (articulation, capture, nurture, transformation and
retention). The knowledge within <strong>the</strong> organization is classified as tacit and explicit knowledge. Holding
<strong>the</strong> knowledge explicitly has a number <strong>of</strong> advantages (O’Hara & Shadbolt, 2001). As a result, it is
critical for organizations and businesses to turn <strong>the</strong>ir tacit wealth into explicit easily accessed realities.
The explicit knowledge is easily transferred from normal documents, database and even multimedia,
whereas, tacit knowledge is hard to articulate, capture transform, and retain unless it is transformed
into an explicit form.
Knowledge management is a suitable tool to capture and retain this tacit knowledge and improve,
enhance, and/or enable knowledge externalization contrary to <strong>the</strong> traditional fulfillment <strong>of</strong> activities in
o<strong>the</strong>r disciplines through <strong>the</strong> use <strong>of</strong> technology (EI-Den, 2008). Among <strong>the</strong> diversification <strong>of</strong> <strong>the</strong> use <strong>of</strong>
technology in knowledge management, <strong>the</strong> reliance on collaborative tools facilitates <strong>the</strong>
externalization <strong>of</strong> tacit knowledge. One <strong>of</strong> <strong>the</strong> core tenets <strong>of</strong> knowledge management is <strong>the</strong> ability to
collaborate, including collaboration among employees within an organization, collaboration between
different organizations, and even collaboration between different computer systems without human
interaction (Luan & Serban, 2002). This is only possible through <strong>the</strong> organizational self development
<strong>of</strong> a collaborative system (Group-Support System), or <strong>the</strong> organizational outsourcing <strong>of</strong> this need.
An integral part <strong>of</strong> knowledge capture is <strong>the</strong> emphasis on human interaction and more and more
today on remote interaction among employees as a result <strong>of</strong> globalization. Accordingly, <strong>the</strong> existence
<strong>of</strong> a organizational collaborative group-support system is vital for <strong>the</strong> externalization <strong>of</strong> employees and
users knowledge. The pioneer <strong>of</strong> collaboration applications has been Lotus with its application Lotus
Notes launched in 1998. Since <strong>the</strong>n many products have been developed which provide users with
<strong>the</strong> ability to work collaboratively, any time, regardless <strong>of</strong> physical location (Luan & Serban, 2002).
The problems with <strong>the</strong>se systems and o<strong>the</strong>r existing group support systems, such as Wiki’s, Micros<strong>of</strong>t
groups, Google groups etc, is <strong>the</strong>ir inabilities to support <strong>the</strong> externalization <strong>of</strong> knowledge (El-Den,
2008).
3.2 Describe current business processes
As stated earlier, <strong>the</strong> past experiences and <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> users about <strong>the</strong> legacy system are
essential for <strong>the</strong> effective and efficient development during <strong>the</strong> initial three phases <strong>of</strong> <strong>the</strong> BPR life
cycle. This is due <strong>the</strong> fact that <strong>the</strong>y equip <strong>the</strong> developer with <strong>the</strong> necessary knowledge and better
understanding <strong>of</strong> <strong>the</strong> system’ functionalities; who is doing what, how and how <strong>of</strong>ten.
The problem is <strong>the</strong> diversity <strong>of</strong> <strong>the</strong> knowledge ga<strong>the</strong>red, as its origin could be from diverse users with
diverse and sometimes contradictory knowledge, know-how, expertise, and interpretation <strong>of</strong> <strong>the</strong>
legacy system, especially at <strong>the</strong> operational level, and <strong>the</strong> decision <strong>of</strong> what <strong>of</strong> that knowledge is <strong>of</strong>
value to <strong>the</strong> developer is a real challenge. Accordingly, <strong>the</strong> process <strong>of</strong> externalization <strong>of</strong> such
knowledge will have considerable effect on <strong>the</strong> to-be processes. As a result, developing a Knowledge
Base (KB) is also integral to <strong>the</strong> overall process. This KB acts as a source <strong>of</strong> knowledge, know-how,
and expertise about functional processes within <strong>the</strong> organization. In many cases, it is essential that
<strong>the</strong> developer has <strong>the</strong> right piece <strong>of</strong> knowledge, functionality, and interaction <strong>of</strong> a particular process
with o<strong>the</strong>r processes within and outside <strong>the</strong> organization, accurately and on time.
During this phase <strong>the</strong> KB has to accommodate for:
1. Current processes’ execution.
2. Results <strong>of</strong> survey or interview with employees, managers, and senior management
3. Process in action. Faults, input, output, description, work-flow and procedures. In addition
external links <strong>of</strong> processes.
4. Lessons learned from competitors.
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5. After testing weaknesses and strengths <strong>of</strong> processes.
6. Customer data
Organizational explicit knowledge is easily acquired by processing information using different data
mining technologies. In contrast, organizational tacit knowledge which is based on communication,
collaboration, and cooperation is hard to be shared and externalized and requires a proper
collaborative working environment and system. The KM system toge<strong>the</strong>r with a group-support system
for collaboration and knowledge externalization facilitate <strong>the</strong> release <strong>of</strong> ideas, opinions, thoughts,
expertise, and experience which are essential for <strong>the</strong> phases <strong>of</strong> <strong>the</strong> BPR process. Consequently, this
leads to knowledge mapping <strong>of</strong> tacit into explicit knowledge which, combined with <strong>the</strong> results from
data mining form <strong>the</strong> basis for <strong>the</strong> to-be system being developed.
The Tiered Knowledge Management Model (TKMM) below demonstrates <strong>the</strong> above arguments.
Figure 2: Tiered Knowledge Management Model (TKMM)
3.3 Business/Project Environment analysis
BPR takes place not only based on “demands for change” within <strong>the</strong> organization but also as a result
<strong>of</strong> analysis <strong>of</strong> <strong>the</strong> environment, competitiveness, buyers power, suppliers power, and rivalry among
competitors. Today, strategic planners and developers recognize that process change initiatives are
also highly dependent and driven by environmental competitive with factors such as deregulation,
globalization, technical obsolescence, demands for better customer service and past failures, ra<strong>the</strong>r
than merely <strong>the</strong> desire for internal productivity gains (William & James,1998). In this situation, KM also
acts as an enabler for better execution <strong>of</strong> <strong>the</strong> processes/projects involved in <strong>the</strong> BPR phases.
Organization, under such constant pressures, cannot afford to change <strong>the</strong> way <strong>the</strong>y do business and
reengineer new systems, projects and processes constantly. They should quickly provide better
solutions and services for <strong>the</strong> processes based on existing knowledge/know-how/expertise <strong>of</strong> existing
processes and functionalities, hence <strong>the</strong> need for support from an effective and efficient KB which
provides <strong>the</strong> necessary knowledge about how, what, and who does what in <strong>the</strong> organization. These
act as an integral support mechanism for <strong>the</strong> developers or engineers.
3.4 Project environment analysis
The three driving forces behind this radical change are an extension <strong>of</strong> Porter’s (Porter, 1980, 1985,
1990) work on competitive advantage, and were summarized by (Hammer & Champy, 1993) as:
Customers who can now be very diverse, segmented, and are expectant <strong>of</strong> consultation,
Competition that has intensified to meet <strong>the</strong> needs <strong>of</strong> customers in every niche, and
Change that has become pervasive, persistent, faster and in some markets a pre-requisite.
Business intelligence applications are also used to ga<strong>the</strong>r and organize data and information existing
in internal sources (Luan & Serban, 2002). Some <strong>of</strong> <strong>the</strong>se applications, such as “MicroStrategy
Transactor”, can “collect data from back-end databases, Web servers, and enterprise resource
planning (ERP) systems, according to predefined criteria. It <strong>the</strong>n pushes that information out as
actionable content in eXtended Markup Language (XML) format, so a desktop computer with a Web
browser, a personal digital assistant, and even a telephone enabled with <strong>the</strong> Wireless Application
Protocol (WAP) subset <strong>of</strong> XML can receive it. A user who has received this information can send back
an action request which in turn prompts a back-end system to complete a transaction” (Gill, 2000).
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Jamal El-Den and Xin Zhou
This phase in <strong>the</strong> BPR process is extremely important as a source <strong>of</strong> knowledge for <strong>the</strong> following
phases <strong>of</strong> <strong>the</strong> methodology. The issue here is not what developers learn about <strong>the</strong> legacy system but
how and what knowledge is to be retained as a result <strong>of</strong> this learning process. Clearly, <strong>the</strong> existence
<strong>of</strong> a support system here which will assist <strong>the</strong> developers to fulfill <strong>the</strong> learning task is integral to
success. Relying on existing diverse technological support for communication is not, in our opinion, a
solution to developing a sound learning from o<strong>the</strong>rs but a group-support system which provides <strong>the</strong>
different functions in <strong>the</strong> KM process is necessary. The key ingredients in this step are:
How/what to do with <strong>the</strong> knowledge gained from customers’ perception <strong>of</strong> <strong>the</strong> business
How/what to do with <strong>the</strong> knowledge gained from users <strong>of</strong> <strong>the</strong> legacy system regarding <strong>the</strong>
processes and how-to do in <strong>the</strong> system
How/what to do with knowledge gained from associates in <strong>the</strong> organization in regards to
strategies and goals
How/what/which technology should be adopted with <strong>the</strong> introduction <strong>of</strong> <strong>the</strong> new system
The combined KM features and <strong>the</strong> developed group-support system provide answers in this phase <strong>of</strong>
<strong>the</strong> BPR.
5. Create vision and design new business process model
After successful completion <strong>of</strong> first two phases <strong>of</strong> problem definition and learning from o<strong>the</strong>rs, BPR
developers need to consider creating vision and designing <strong>the</strong> to-be processes. This phase is highly
depends on <strong>the</strong> developers’ creativity and imagination and based on <strong>the</strong>, now, available knowledge in
<strong>the</strong> KB. The vision statement is not about what <strong>the</strong> company currently is, but what <strong>the</strong> company hopes
to become. Teams need to develop a vision <strong>of</strong> what <strong>the</strong>y want <strong>the</strong> new process to be and how <strong>the</strong>y
will achieve it. The vision is developed taking into considerations aspects from explicit and tacit
knowledge. As an example, a vision statement may acknowledge that <strong>the</strong> company already meets
industry standards in customer support, while at <strong>the</strong> same time setting goals for moving customer
care to a higher level within a given time period (http://www.wisegeek.com/what-is-a-visionstatement.htm).
BPR is a strategic action and requires a clear understanding <strong>of</strong> customers, market,
industry and competitive directions. Fur<strong>the</strong>rmore, like any o<strong>the</strong>r strategic action, it requires
consistency between <strong>the</strong> company’s business strategy and vision. Defining business strategy and
developing a strategic vision requires understanding <strong>of</strong> <strong>the</strong> company’s strengths and weaknesses,
which can only be available if captured, nurtured, transformed and retained in a KB, and <strong>the</strong> market
structure and opportunities (Attaran, 2003) which also depends on <strong>the</strong> availability <strong>of</strong> such information
from DB’s and <strong>the</strong> knowledge base.
This phase consists <strong>of</strong> <strong>the</strong> two stages: understanding <strong>the</strong> selected business processes and creating
vision to redesign <strong>the</strong> new business processes. During <strong>the</strong> first two phases <strong>the</strong> BPR group members
engaged in <strong>the</strong> reengineering process form an understanding <strong>of</strong> <strong>the</strong> current processes and activities
through KB access, discussions, observations, and interviews. In o<strong>the</strong>r words, <strong>the</strong>re is a substantial
accumulation/capture <strong>of</strong> categorized types <strong>of</strong> knowledge (explicit and tacit knowledge) by
developer(s). This tacit knowledge cannot be limited to <strong>the</strong> employees’ know-how, but also <strong>the</strong>ir
thoughts, opinions and ideas. The use <strong>of</strong> information and communication technologies can only partly
help <strong>the</strong> externalization <strong>of</strong> tacit knowledge, as much <strong>of</strong> that knowledge is transformed by
observations, skills, body language, physical demonstration, doing, and repetition (EI-den, 2008). The
externalization and retention <strong>of</strong> <strong>the</strong> important tacit knowledge is integral and critical for <strong>the</strong> vision
creating phase. Current KM technologies intend to improve, enhance, and/or enable knowledge
externalization contrary to <strong>the</strong> traditional fulfillment <strong>of</strong> activities in o<strong>the</strong>r disciplines through <strong>the</strong> use <strong>of</strong>
technology. This is mainly due to <strong>the</strong> elusive nature <strong>of</strong> knowledge and <strong>the</strong> resulting diverse KM
processes and knowledge conversion activities (EI-den, 2008)
The diversification <strong>of</strong> KM technologies and products can be used to support this phase. Based on <strong>the</strong>
purposes <strong>the</strong>y serve, <strong>the</strong>y can be classified in one or more <strong>of</strong> <strong>the</strong> following categories: business
intelligence, collaboration, content and document management, e-learning, knowledge base, portals,
customer relationship management, data mining, workflow, and search (Luan & Serban, 2002).
Especially, <strong>the</strong> KM technologies such as Groupware Support System (GSS) can be used to capture a
huge tacit knowledge and retain this knowledge for developing a completed vision. The knowledge
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includes defining <strong>the</strong> scope and objectives <strong>of</strong> reengineering project and finding problematic
processes, <strong>the</strong>n going through a learning process (with your customers, your employees, your
competitors and non-competitors, and with new technology). What <strong>the</strong> BPR team needs within this
phase is make a fully clear understanding about <strong>the</strong> valued knowledge which is transferred from
accumulative tacit knowledge through a collaborative environment. The o<strong>the</strong>r purpose <strong>of</strong> <strong>the</strong> use <strong>of</strong>
collaborative tools within <strong>the</strong> BPR process is that will result in faster, better and cheaper solutions at
this stage. Additionally, in each <strong>of</strong> <strong>the</strong> phases, effective use <strong>of</strong> collaborative methods and tools can
shorten <strong>the</strong> project schedule and improve overall results (BPR online). Ga<strong>the</strong>ring and analyzing
information about <strong>the</strong> performance and structure <strong>of</strong> a process is an important step in identifying and
selecting process for redesign (Attaran, 2003). KM applications and associated functionality for
business intelligence provide a means for ga<strong>the</strong>ring and organizing data, information, and knowledge
in internal sources.
Collaborative tools (groupware) are computer-based tools that help people work toge<strong>the</strong>r and share
information. They allow for virtual on-line meetings and data sharing and support knowledge
externalization within KM lifecycle. A table clearly demonstrates some examples <strong>of</strong> collaborative tools:
Examples <strong>of</strong> Generic Collaborative Tools
Tool Description
Chat (Audio and text) Use this to conduct toll-free conversations. No need to
wait for something to arrive in your mailbox.
Whiteboard
Bulletin board
Video
Discussion groups (newsgroups)
File sharing tools
Presentation tools
Application sharing
Text tools
Permits real time display <strong>of</strong> drawings, pictures or
documents for group discussion and comment.
Participants can annotate in real time as well.
Used to post notices and facilitate discussions on any
topic.
Use video at a desktop computer or a video
teleconferencing center to see <strong>the</strong> person or group with
whom you are working virtually.
Topics are posted to a website for discussion and
comment where participants can follow a line <strong>of</strong>
discussion on a topic.
Virtual file cabinets allow information to be stored on web
servers, and are available to anyone having access to <strong>the</strong>
site and electronic permission to use <strong>the</strong> files.
These are used in a virtual auditorium and allow lectures
and briefings to be given to an audience.
Using this tool, an entire team can use an application
running on one computer to revise documents.
Allows live text input and editing by group members. Once
complete, <strong>the</strong> text document can be copied into word
processing s<strong>of</strong>tware.
Email The most popular way <strong>of</strong> outrunning "snail mail."
Persistent capability
Instant messaging
Table 1: Examples <strong>of</strong> Generic Collaborative Tools (BPR online)
This is <strong>the</strong> ability to preserve files, briefings or o<strong>the</strong>r team/
project material for future reference. Properly organized, it
becomes a knowledge management device and is
invaluable to a long term effort.
Most popularly known as "text chat," this allows real time
exchange <strong>of</strong> notes and messages.
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These collaborative tools bring substantial benefits for communication and information/knowledge
transfer among dispersed individuals within <strong>the</strong> organization which in today’s economy is in most
cases a global organization. During <strong>the</strong> BPR process, interaction is a key issue for <strong>the</strong> developers at
any pahse <strong>of</strong> <strong>the</strong> process. The collaborative environment makes knowledge externalization more
effectively and <strong>the</strong> BPR team members can obtain relatively complete knowledge to make decisions
for work/process redesign. Additionally, collaborative tools allow better change management by
permitting <strong>the</strong> team to continuously communicate with <strong>the</strong> organization at large. It is <strong>the</strong> opinion <strong>of</strong> <strong>the</strong>
authors that although current collaborative group support systems and o<strong>the</strong>r communication facilities
provide support for <strong>the</strong> developers in terms <strong>of</strong> exposure to organizational wealth <strong>of</strong> information and
knowledge, <strong>the</strong>y believe that organization “must” develop <strong>the</strong>ir own group-support systems which,
among o<strong>the</strong>rs, should address <strong>the</strong> following functionalities (El-Den 2008):
A work-space (El-Den, 2008) or a “Ba” (Nonaka & Takeouchi, 1996) where group members can
communicate on a same platform with high levels <strong>of</strong> awareness <strong>of</strong> work progress.
Document-based Knowledge articulation functionality where collaborating members can release
<strong>the</strong>ir knowledge/expertise in a written form on <strong>the</strong> work-space.
Knowledge capture functionality where articulated knowledge is captured and temporarly retained
in <strong>the</strong> system for fur<strong>the</strong>r discussions and nurture among <strong>the</strong> members.
Knowledge nurture functionality where discussions on articulated/captured knowledge, in <strong>the</strong> form
<strong>of</strong> opinions, ideas, thoughts, feelings, know-how etc, can be performed among <strong>the</strong> members to
decide on what <strong>of</strong> that “knowledge” is worth retention in <strong>the</strong> DB for future use.
Retention facilities in a form <strong>of</strong> an intelligent DB.
The primary purpose <strong>of</strong> knowledge base applications is to create such repositories in a structured
fashion that can facilitate searching and circulating knowledge throughout <strong>the</strong> organization (Luan &
Serban, 2002). Fur<strong>the</strong>rmore, collaborative tools should include knowledge management and o<strong>the</strong>r
"persistent" means that can decrease <strong>the</strong> time required to accomplish operational activities. Lessons
learned data banks, project management records and reports, corporate letter templates, procedures
and guidelines, and sales contracts are only a few examples where "reinventing <strong>the</strong> wheel" can be
avoided through <strong>the</strong> organization and maintenance <strong>of</strong> corporate knowledge and making it available to
those who will want it and need it (Boyce, 2009). In addition, (Willems, 2009), stated that this sort <strong>of</strong>
communication situation within <strong>the</strong> collaborative environment governs <strong>the</strong> level <strong>of</strong> tacit information that
may be transferred.
By using <strong>of</strong> <strong>the</strong> GSS, <strong>the</strong> team members’ thoughts, know-how, opinions and ideas are freely
articulated and captured. A new vision based on <strong>the</strong> knowledge <strong>of</strong> first two phases through a process
<strong>of</strong> knowledge capture and nurture would be created. Additionally, Collaborative tools enable
participants to better analyze and understand organizational business processes as well as roles in
<strong>the</strong>se processes. By understanding current problems, members may better comprehend <strong>the</strong> need for
change (Marjarnovic, 2000).
6. Conclusion
The paper argued that BPR process can be enhanced by introducing <strong>the</strong> features <strong>of</strong> knowledge
management related to <strong>the</strong> externalization <strong>of</strong> knowledge. It also argued that <strong>the</strong> success <strong>of</strong>
knowledge articulation, capture, nurture, transformation, and retention can be better achieved by <strong>the</strong>
development <strong>of</strong> a group-support system as relying on existing diverse technologies for communication
and externalization <strong>of</strong> knowledge proved to be difficult and leads to diverse learning within <strong>the</strong>
organization. The paper dealt only with <strong>the</strong> first three phases <strong>of</strong> an existing BPR methodology
because <strong>of</strong> length limitations. The paper addressed tacit knowledge transformation among <strong>the</strong> phases
as a result <strong>of</strong> <strong>the</strong> capture and nurture <strong>of</strong> such knowledge among <strong>the</strong> members <strong>of</strong> <strong>the</strong> team involved in
<strong>the</strong> BPR process.
References
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reengineering using workflow management technology”, 30th International <strong>Conference</strong> on System Sciences
(HICSS), Hawaii, 3 January to 6 January
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Boyce, C. 2009 ‘Knowledge management and collaboration tools in business process reengineering - Tutorial 1’.
BPR online learning center. BPR Tutorial Series, 2010. Viewed 6 September 2010,
http://www.prosci.com/mod1.htm
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Journal <strong>of</strong> Konwledge Management,Vol.12 No.1, pp92-105.
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management”, Knowledge Management Magazine, May 2001. Available from
http://www.destinationkm.com/articles/default.asp?ArticleID=231 [Accessed August 25th 2004]
Hammer, M., Champy, J. (1993) “Reengineering <strong>the</strong> Corporation: A Manifesto for Business Revolution”, Harper
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11-12, 1997
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online liabrary,Viewed 6 September 2010, http://onlinelibrary.wiley.com/doi/10.1002/ir.39/pdf
Marjarnovic, O. 2000 “Supporting <strong>the</strong> “s<strong>of</strong>t” side <strong>of</strong> business process reengineering”, Business process
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Nonaka, I. And Takeuchi, H. 1995 “The Knowledge-Creating Company: How Japanese Companies Create <strong>the</strong>
Dynamics <strong>of</strong> Innovation”, OUP, New York, 1995.
O’Hara, K. & Shadbolt, N. 2001 ‘Managing Knowledge Capture: Economic, Technological and Methodological
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Radboud University Nijmegen.
274

Perceptions <strong>of</strong> Knowledge Management Prerequisites and
Challenges <strong>of</strong> Organisational Learning
Tiit Elenurm
Estonian Business School, Tallinn, Estonia
tiit.elenurm@ebs.ee
Abstract: The paper reflects on <strong>the</strong> action learning processes <strong>of</strong> postgraduate students who had to assess
knowledge management prerequisites in <strong>the</strong>ir organisations and to implement teamwork for proposing both a
knowledge management development vision and tools for selected organisations. The time periods <strong>of</strong> 2001-2004
and 2006-2010 are compared. The action learning processes disclosed conflicts between basic IT-centred and
people-centred assumptions, which appeared as a challenge in <strong>the</strong> organisational learning for creating and
implementing knowledge management development visions. The comparison <strong>of</strong> <strong>the</strong> rankings <strong>of</strong> knowledge
management prerequisites <strong>of</strong> 2001-2004 and 2006-2010 demonstrates <strong>the</strong> important role <strong>of</strong> trust and free
circulation <strong>of</strong> information. The desire to clarify recognised fields for employees as experts gained higher priority in
2006-2010 whereas in 2001-2004 it was ranked lower. Young managers and o<strong>the</strong>r categories <strong>of</strong> employees both
in large and small organisations are looking for recognition <strong>of</strong> <strong>the</strong> value <strong>of</strong> <strong>the</strong>ir personal expertise as a
prerequisite for more active contribution to knowledge sharing. Underestimating such a need may inhibit
organisational learning efforts and can diminish <strong>the</strong> readiness to use new information processing and knowledge
sharing tools. That has been an especially important challenge in <strong>the</strong> situations <strong>of</strong> economic crises in
organisations experiencing downsizing. The IT-centred approach to knowledge management can be seen by
some employees as a way to “hijack” knowledge <strong>of</strong> individuals prior to <strong>the</strong>ir dismissal. Knowledge management
development efforts that ignore knowledge management prerequisite perceptions <strong>of</strong> team members face <strong>the</strong> risk
<strong>of</strong> conflict in <strong>the</strong> organisational learning process. The analysis <strong>of</strong> perceptions <strong>of</strong> knowledge management
prerequisites may help one to understand whe<strong>the</strong>r <strong>the</strong>re are contradictions between strategic goals, principles
and individual attitudes concerning <strong>the</strong> role and nature <strong>of</strong> knowledge management.
Keywords: knowledge management prerequisites, organisational learning, knowledge sharing, success factors,
action learning
1. Introduction
Knowledge management success factors are among <strong>the</strong> topical issues that have been studied widely
in recent years. Using <strong>the</strong> meta-research <strong>of</strong> 60 studies in this field that revealed a diverse
understanding <strong>of</strong> knowledge management and its success factors, Lehner and Haas (2010) have
proposed <strong>the</strong> structural model for fur<strong>the</strong>r research. This includes knowledge management system
content, system quality, system application, knowledge management process, performance ability,
social relations and personnel development. However, in order to develop personnel for knowledge
management initiatives, it is important to understand <strong>the</strong>ir perceptions <strong>of</strong> knowledge management
prerequisites and to analyse how different perceptions may influence knowledge management
development priorities in <strong>the</strong> team. Knowledge management prerequisites can be ranked differently
depending on <strong>the</strong> pr<strong>of</strong>essional background <strong>of</strong> team members, <strong>the</strong> nature and scope <strong>of</strong> <strong>the</strong> business,
<strong>the</strong> previous experience <strong>of</strong> knowledge management development in organisations and changes in <strong>the</strong>
business environment.
The present paper studies perceptions <strong>of</strong> knowledge management prerequisites and challenges <strong>of</strong>
organisational learning and knowledge management development reflected in <strong>the</strong>se perceptions. The
research question is: how have perceptions <strong>of</strong> knowledge management prerequisites changed in
Estonia as in a small open economy, and what are <strong>the</strong>ir implications for knowledge management
development priorities?
The discourse on <strong>the</strong> role <strong>of</strong> aligning perceptions <strong>of</strong> knowledge management prerequisites in a
development team for understanding knowledge management success factors and strategic
development priorities serves as <strong>the</strong> departure point for <strong>the</strong> empirical research. In section 2,
knowledge management prerequisites are discussed following <strong>the</strong> discourse on knowledge
management success factors and frameworks for knowledge management development and aligning
knowledge management efforts to different types <strong>of</strong> organisations. Section 3 focuses on <strong>the</strong> method
and process <strong>of</strong> studying knowledge management prerequisites in <strong>the</strong> action learning framework.
Section 4 reflects upon <strong>the</strong> results <strong>of</strong> studying perceptions <strong>of</strong> knowledge management prerequisites in
2001-2004 and 2006-2010. Section 5 explains <strong>the</strong> utilisation <strong>of</strong> <strong>the</strong> knowledge management
prerequisite analysis and <strong>the</strong> classification <strong>of</strong> organisations for teamwork on knowledge management
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development visions. Conclusions on <strong>the</strong> role <strong>of</strong> knowledge management prerequisites in training
programmes and practical knowledge management development initiatives are presented.
2. Knowledge management prerequisites as a departure point for knowledge
management development efforts
Identifying knowledge management best practices and factors that influence <strong>the</strong> success <strong>of</strong>
knowledge management projects has been a research task for many knowledge management
researchers and consultancies during <strong>the</strong> recent decade (Darroch 2003, Lucas and Ogilvie 2006,
KMPG 2003, Zheng et al. 2010). Cryywagen et al. (2008) have pointed out that “one size does not fit
all” and advocated <strong>the</strong> “best fit” approach that addresses <strong>the</strong> contextual differences <strong>of</strong> organisations.
Studying knowledge management prerequisites in organisations supports <strong>the</strong> best fit approach by
clarifying <strong>the</strong> situation that serves as a departure point for a new knowledge management
development initiative. Knowledge management does not start from creating <strong>the</strong> job <strong>of</strong> <strong>the</strong> chief
knowledge management <strong>of</strong>ficer or launching an initiative labelled as “knowledge management
development project”. It is important to understand how members <strong>of</strong> <strong>the</strong> organisation interpret existing
practices and tools that can be related to <strong>the</strong> knowledge management field even if <strong>the</strong> concept <strong>of</strong>
knowledge management has not been <strong>of</strong>ficially introduced. The analysis <strong>of</strong> <strong>the</strong> classical story <strong>of</strong>
Xerox photocopier repairmen by Cox (2007) demonstrates that while some interpretations <strong>of</strong> this story
stress <strong>the</strong> community <strong>of</strong> practice that emerged in <strong>the</strong> process <strong>of</strong> voluntary face-to-face communication
between repairmen at lunchtime or around a water cooler, o<strong>the</strong>r interpretations stress <strong>the</strong>
management initiative to create a computer system for sharing knowledge in a global corporation.
Comparing different interpretations <strong>of</strong> knowledge management prerequisites can be useful for
deconstructing stories about successful or unsuccessful knowledge management initiatives. In this
article we treat knowledge management prerequisites as principles and main tools that people
involved in knowledge creation and application practices consider crucial for successful knowledge
management.
Nonaka and Takeuchi (1995) have developed <strong>the</strong> model <strong>of</strong> four modes <strong>of</strong> knowledge conversion,
which explains conversions between tacit and explicit knowledge domains. This classical knowledge
spiral model allows one to assess whe<strong>the</strong>r <strong>the</strong> organisational environment should become more
supportive <strong>of</strong> socialization, knowledge externalization and a combination, or knowledge internalization
processes. Socialization and internalization are directly linked to personal trust, whereas knowledge
externalization is more connected to ei<strong>the</strong>r paper documents or electronic tools.
Nie et al. (2008) point out diversified interpretations <strong>of</strong> <strong>the</strong> knowledge management field. They use
200 most important keywords from knowledge management journals as an input for <strong>the</strong> domain
analysis and present <strong>the</strong> typology that differentiates such domains as external and internal changes,
s<strong>of</strong>t and hard technologies, different targets and applications. The differentiation between strategyoriented,
process-oriented, human-oriented and information-oriented functions and entities is most
relevant to <strong>the</strong> study <strong>of</strong> perceptions <strong>of</strong> knowledge management prerequisites. Information-centred and
knowledge-centred approaches have been competing for many decades in <strong>the</strong> field <strong>of</strong> developing
knowledge-based assets (Sveiby 1997). Face-to-face communication is <strong>the</strong> traditional tool <strong>of</strong>
socialization. At <strong>the</strong> same time, web technology facilities are used to implement many knowledge
management solutions, including virtual project teams and communities <strong>of</strong> practice supported by
virtual tools <strong>of</strong> socialization. Information technology is a key enabler <strong>of</strong> knowledge management
assuming that <strong>the</strong> balance between data ga<strong>the</strong>ring, information processing and knowledge sharing is
retained in knowledge management practices (Tiago et al. 2009). Hall et al. (2008) stress learning to
differentiate between hard and s<strong>of</strong>t knowledge. However, <strong>the</strong> harmonisation <strong>of</strong> codification and
socialization in knowledge management also assumes <strong>the</strong> integration between applications <strong>of</strong> <strong>the</strong>
information technology for <strong>the</strong> knowledge content creation, storage and reuse in computer-based
organisational memories and helping people to communicate knowledge, ra<strong>the</strong>r than storing it
(Apostolou et al. 2007).
The focus <strong>of</strong> knowledge management development efforts depends on <strong>the</strong> knowledge-intensity <strong>of</strong> <strong>the</strong>
competition and business strategies followed by competitors. Griffiths and Remenyi (2008) have
demonstrated how <strong>the</strong> framework for aligning knowledge management with a competitive strategy can
be applied for positioning consulting companies that have developed technology-based networking
versus companies that focus more on <strong>the</strong> personal networking <strong>of</strong> <strong>the</strong>ir staff. The priority given to one
<strong>of</strong> <strong>the</strong>se approaches can be revealed by discussing <strong>the</strong> prerequisites <strong>of</strong> knowledge management.
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The comparison <strong>of</strong> <strong>the</strong> mechanistic and organic organisation was introduced already 50 years ago
(Burns and Stalker 1961). The matrix that combines <strong>the</strong> dimension <strong>of</strong> mechanistic versus organic
organisations with <strong>the</strong> dimension <strong>of</strong> <strong>the</strong> product-focused versus service-focused organisation (Nurmi
1999) can reveal <strong>the</strong> strategic context <strong>of</strong> applying knowledge management and to discuss knowledge
management prerequisites. The matrix helps to differentiate knowledge management focus in
industrial organisations, bureaucratic organisations and organisations where <strong>the</strong> strategic focus is on
creating and commercializing new know-how or combining different types <strong>of</strong> knowledge to <strong>of</strong>fer a
tailor-made solution to specific clients. The industrial organisation processes material inputs and
optimises <strong>the</strong> use <strong>of</strong> different resources for producing products. Never<strong>the</strong>less, innovating products or
moving to new markets is not <strong>the</strong> main challenge <strong>of</strong> this organisation type. The core competence <strong>of</strong>
<strong>the</strong> know-why organisation is related to understanding changing markets and client needs. It is
different from <strong>the</strong> know-how organisation, where <strong>the</strong> core competence is built around developing new
products and technologies that can be commercialised ei<strong>the</strong>r by selling <strong>the</strong> intellectual property or by
implementing innovative products or technologies. The bureaucratic organisation can be efficient if it
serves <strong>the</strong> needs <strong>of</strong> stakeholders by following routines in a reliable and transparent way. The servicefocus
<strong>of</strong> such an organisation, however, is limited to avoiding mistakes and minimizing <strong>the</strong> turbulent
influence <strong>of</strong> <strong>the</strong> environment on relatively stable procedures.
Large international corporations have <strong>of</strong>fered many examples <strong>of</strong> advanced knowledge management
systems but <strong>the</strong> adoption <strong>of</strong> knowledge management in small firms and in networks <strong>of</strong> such firms has
in recent years also become a topical research issue (Valkokari and Helander 2007). Understanding
prospects <strong>of</strong> knowledge management in small and medium-sized enterprises is especially relevant to
a country such as Estonia, where <strong>the</strong> number <strong>of</strong> large corporations founded on local capital is limited,
but where many innovative start-up enterprises with internationalisation ambitions were created after
Estonia joined <strong>the</strong> <strong>European</strong> Union. At <strong>the</strong> same time, Estonia has become a location for subsidiaries
<strong>of</strong> Nordic and o<strong>the</strong>r international corporations, where relations between <strong>the</strong> headquarters and foreign
subsidiaries are shaping knowledge management practices. During <strong>the</strong> period <strong>of</strong> rapid societal and
economic changes an important factor influencing <strong>the</strong> knowledge management agenda is <strong>the</strong>
dilemma <strong>of</strong> exploiting <strong>the</strong> “windows <strong>of</strong> opportunity” for rapid business gains versus developing an
organisation directed by <strong>the</strong> vision <strong>of</strong> sustainable competitiveness. Studies <strong>of</strong> knowledge
management practices in emerging economies (Pillania 2008) suggest that more top management
support for aligning knowledge management with strategy is needed. One knowledge management
development challenge is how to avoid <strong>the</strong> premature and formal use <strong>of</strong> knowledge management
tools and how to develop clear priorities and a common understanding <strong>of</strong> <strong>the</strong> prerequisites for
implementing advanced knowledge management solutions if and when <strong>the</strong>se support <strong>the</strong> strategy <strong>of</strong>
<strong>the</strong> organisation. Studying and discussing perceptions <strong>of</strong> knowledge management prerequisites in <strong>the</strong>
action learning framework is not only a tool for training and understanding motivation <strong>of</strong> <strong>the</strong>
stakeholders involved, but also a way for shaping <strong>the</strong> participative culture and developing human
resources for inter-firm collaboration.
3. Studying knowledge management prerequisites in <strong>the</strong> action learning
framework
Social constructivism as a research approach allows researchers to obtain insights into complex
contexts <strong>of</strong> organisations (Saunders et al. 2003). We have used ratings <strong>of</strong> knowledge management
prerequisites as a way to understand how members <strong>of</strong> an organisation or a learning community
construct <strong>the</strong>ir understanding <strong>of</strong> knowledge management. Perceptions <strong>of</strong> knowledge management
prerequisites are analyzed with <strong>the</strong> help <strong>of</strong> a list <strong>of</strong> statements that reflect some essential knowledge
management practices and tools related to <strong>the</strong>se practices. The list reflects s<strong>of</strong>t and hard
prerequisites for socialisation and codification. The principles essential for personal knowledge
sharing and <strong>the</strong> tools for technology-based networking are included. Master’s students that have
participated in knowledge management courses have assessed <strong>the</strong> importance <strong>of</strong> each from 10
knowledge management prerequisites for <strong>the</strong>ir organisation. Our research also applied <strong>the</strong> action
learning for qualitative research by forming teams <strong>of</strong> students that had to use knowledge
management prerequisite assessments as an input for creating a knowledge management
development vision for an organisation represented in <strong>the</strong> team. Evert Gummesson (2000) treats
action research/action science as <strong>the</strong> most advanced step in <strong>the</strong> qualitative research compared to
interviews and observations.
In <strong>the</strong> process <strong>of</strong> running knowledge management training courses since 2001, we have asked
participants to rank knowledge management prerequisites according to <strong>the</strong>ir importance to <strong>the</strong>
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companies <strong>the</strong>y work for. Respondents were asked to provide supporting arguments for <strong>the</strong>ir
evaluation <strong>of</strong> <strong>the</strong> importance <strong>of</strong> <strong>the</strong> knowledge management prerequisites. Table 1 presents a list <strong>of</strong>
short formulations <strong>of</strong> <strong>the</strong> knowledge management prerequisites that has been used. Students were
also encouraged to add <strong>the</strong>ir own prerequisites to <strong>the</strong> original checklist or to propose amendments to
prerequisite specifications. During recent years additional prerequisite proposals <strong>of</strong> participants are
most <strong>of</strong>ten linked to using social s<strong>of</strong>tware tools to be involved in broader pr<strong>of</strong>essional networks.
Readiness to co-operate in <strong>the</strong> value chain has been suggested as a prerequisite for interorganisational
knowledge management. At <strong>the</strong> same time, inside <strong>the</strong> organisation <strong>the</strong> need for regular
personal contacts has been pointed out.
4. Changing perceptions <strong>of</strong> knowledge management prerequisites and <strong>the</strong>ir
implications for creating knowledge management development visions
In 2006-2010, <strong>the</strong> highest placed prerequisite in <strong>the</strong> sample on average was: employees have
recognised fields where <strong>the</strong>ir expert knowledge can support o<strong>the</strong>rs. It was followed by: virtual
information processing and knowledge sharing tools are used actively (Table 1). The difference found
was that this prerequisite ranked higher in larger companies and lower in smaller enterprises. In
2001–2003 <strong>the</strong> same prerequisite had received much lower ratings both in SMEs and large
enterprises.
Free circulation <strong>of</strong> information has received high rankings not only during <strong>the</strong> years 2001-2003, but
also during 2007-2010. However, during <strong>the</strong> economic boom in 2006, free circulation <strong>of</strong> information,
<strong>the</strong> ability to combine different sources <strong>of</strong> information and <strong>the</strong> promotion <strong>of</strong> information sharing
between colleagues by using bonus schemes received relatively low rankings. Trust between
employees was, on average, <strong>the</strong> third placed prerequisite in 2006-2009. However, it had topped <strong>the</strong>
scale in 2002 and 2003 before Estonia joined <strong>the</strong> <strong>European</strong> Unions, having climbed from fifth place in
2001. In 2010, after <strong>the</strong> Estonian GDP had decreased 13.9% and <strong>the</strong> average salary had dropped
5.0% in 2009 (Statistics Estonia 2011), this prerequisite was again ranked higher than all o<strong>the</strong>r
prerequisites. In some written comments by participants, trust is interpreted as <strong>the</strong> main prerequisite
that enables one to learn from past mistakes. Internal competition inside an organisation, especially in
<strong>the</strong> sales units, is seen as a factor that inhibits trust.
The prerequisite, such as <strong>the</strong> active use <strong>of</strong> virtual information processing and knowledge sharing
tools, has had volatile rankings. Group discussions have demonstrated divergent views about this
prerequisite. Some participants link <strong>the</strong> active use <strong>of</strong> virtual tools mainly to <strong>the</strong> human factor and see
changing attitudes and <strong>the</strong> readiness <strong>of</strong> personnel as a high priority, while o<strong>the</strong>rs believe that active
use is mainly enhanced by developing information technology s<strong>of</strong>tware and hardware. The active use
<strong>of</strong> such tools as a separate ranking item has received both high and low ratings, depending on
different perceptions <strong>of</strong> participants concerning <strong>the</strong> role <strong>of</strong> infrastructure for knowledge management
and motivation <strong>of</strong> employees to use <strong>the</strong> infrastructure. Master’s students from subsidiaries <strong>of</strong>
international corporations link <strong>the</strong> active use <strong>of</strong> virtual knowledge sharing tools to a strong corporate
culture and <strong>the</strong> promotion <strong>of</strong> younger employees to managerial positions.
Considering virtual databases/knowledge bases and colleagues more valuable information sources
than paper documents has received lower ratings during <strong>the</strong> recent three years. Written comments
have pointed out that <strong>the</strong> digitalization <strong>of</strong> documents is now a normal practice in many organisations
and for this reason it has lost its high ranking. In Estonia, <strong>the</strong> digital exchange <strong>of</strong> documents is
supported by <strong>the</strong> state authorized ID-card available to all Estonian residents, which can be used to
digitally sign all documents.
Virtual project workrooms and <strong>the</strong> provision <strong>of</strong> information about <strong>the</strong> competencies <strong>of</strong> all members or
organisations in electronic format have received low ranking during <strong>the</strong> whole observed period from
2001 to 2010. It can be explained by <strong>the</strong> domination <strong>of</strong> small and medium-sized enterprise
representatives among course participants as stated by students in <strong>the</strong>ir written comments. At <strong>the</strong>
same time, <strong>the</strong> principle that employees have recognized fields where <strong>the</strong>ir expert knowledge can
support o<strong>the</strong>rs has gained higher popularity. However, it is evident that <strong>the</strong> application <strong>of</strong> this principle
is not seen as a function <strong>of</strong> a knowledge base <strong>of</strong> personal competencies, but ra<strong>the</strong>r an issue <strong>of</strong>
leadership, division <strong>of</strong> labour and performance assessment. The need to have more regular feedback
from <strong>the</strong> boss was stressed by several participants in <strong>the</strong>ir written arguments.
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Table 1: Ranking <strong>of</strong> knowledge management prerequisites 2001-2004 and 2006-2010
Prerequisites <strong>of</strong> efficient
knowledge management
Free circulation <strong>of</strong> information
Employees are able to combine
different sources <strong>of</strong> information
(2001-2003 – integration
between databases <strong>of</strong>
headquarters and partners)
Virtual databases/knowledge
bases, Intranet and colleagues
are more valuable sources <strong>of</strong>
information than paper
documents
The information search and
retrieval system is used
efficiently
Information about <strong>the</strong>
competencies <strong>of</strong> all members <strong>of</strong>
<strong>the</strong> organisation is accessible
on electronic yellow pages (staff
accounting cards)
Employees have recognised
fields where <strong>the</strong>ir expert
knowledge can support o<strong>the</strong>rs
Special virtual project
workrooms have been created
for project teams
Virtual information processing
and knowledge sharing tools are
used actively
Promoting information sharing
between colleagues is linked to
bonus schemes
Trust between employees as a
basis for knowledge sharing
2001
(31)
2002
(529
Year
(number <strong>of</strong> organizations)
2003 2004 2006 2007 2008
(12) (20) (37) (43) (14)
2009
(22)
2010
(22)
II II VI X III III IV II
IV V IV IX VII -
VIII
III IV VII V III
VII-VIII VI V X V
X IX X VIII VII -
VIII
VIII VIII VIII IV
II X X
IV V -
VI
IX
VI VIII
X IX VII X
VII-VIII VII VIII IV I I I III III
VII-VIII
VIII IX VII VI VI -
VII
IX X VI II IV
II III III I IX
V I I III II
Remark: priority rankings from highest (I) to lowest (X)
IX V -
VI
VII IX VII
I VI
V IV II V
VI -
VII
II V I
5. A framework for creating knowledge management development vision
After giving individual ratings to knowledge management prerequisites <strong>the</strong> typology <strong>of</strong> organisations
by Nurmi (1999) was presented to participants, and <strong>the</strong>y had to select from <strong>the</strong> four quadrants <strong>of</strong>
Table 2 <strong>the</strong> one that best characterizes <strong>the</strong> present strategic position <strong>of</strong> <strong>the</strong>ir organisation.
Borderlines between <strong>the</strong>se four spaces <strong>of</strong> <strong>the</strong> matrix are indeed conditional. Some organisations are
engaged in producing know-how through new product development, but <strong>the</strong>y also anticipate or even
try to create new client needs or re-define client segments. Group discussions were conducted to
clarify features <strong>of</strong> organic versus mechanistic organisations, <strong>the</strong> four value creation paths in Table 2
and <strong>the</strong>ir possible links with strategic priorities and challenges <strong>of</strong> knowledge management
development. Each participant had to specify <strong>the</strong> present and future position <strong>of</strong> his/her organisation in
one <strong>of</strong> <strong>the</strong> four quadrants or to explain why <strong>the</strong> organisation had features <strong>of</strong> more than one quadrant.
The next step was forming teams <strong>of</strong> master’s students who had positioned <strong>the</strong>ir organisation in <strong>the</strong>
same way in table 2 or had, for o<strong>the</strong>r reasons, a similar knowledge management development
agenda. Each team was asked to choose one team member in order to create a knowledge
management development vision for his/her organisation. In this process team members had to align
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<strong>the</strong>ir interpretations <strong>of</strong> knowledge management prerequisites for <strong>the</strong>ir development case organisation.
Reflective discussions and reports <strong>of</strong> teams have given evidence about changing knowledge
management development priorities and highlighted challenges that could be interpreted in <strong>the</strong>
context <strong>of</strong> knowledge management prerequisites.
Table 2: Typology <strong>of</strong> organisations for discussing <strong>the</strong> contradiction between existing knowledge
management prerequisites and development priorities
Product-focused Service-focused
Mechanistic
Organic
Industrial organisation –
value creation based on internal
efficiency <strong>of</strong> relatively stable
production processes
KM challenge: linking quality
management routines and
access <strong>of</strong> workers to virtual
knowledge sharing tools,
incentives to share knowledge
Know-how organisation –
value creation based on knowhow
generated through new
product and technology
development
KM challenge: monitoring and
combining different sources <strong>of</strong>
information about technological
trends; recognizing expert
knowledge <strong>of</strong> employees
Bureaucratic organisation -
value creation through following
institutionally pre-determined
routines in a reliable and rational
way
KM challenge: limitations to free
circulation <strong>of</strong> information
Know-why organisation –
value creations driven by
monitoring and anticipating client
needs and compiling complex
solutions to problems <strong>of</strong> clients
KM challenge: linking CRM and
cross-functional knowledge
sharing in organisations; lack <strong>of</strong>
trust inside sales teams
During <strong>the</strong> crises years 2008-2010 master’s students employed at sales units that market imported
products were eager to link <strong>the</strong> future <strong>of</strong> knowledge management to <strong>the</strong> transition <strong>of</strong> <strong>the</strong>ir enterprise
from <strong>the</strong> quadrant <strong>of</strong> a know-why organisation towards <strong>the</strong> quadrant <strong>of</strong> a know-how organisation,
which is able to generate value through developing new services and products. The limited financial
and human resource base is <strong>the</strong> broader strategic challenge on this development path but <strong>the</strong> more
specific KM challenge involves combining information from different external sources in order to follow
new technological trends. Subsidiaries <strong>of</strong> international corporations have to gain <strong>the</strong> status <strong>of</strong>
competence centres while small enterprises founded by local entrepreneurs could use international
networking and related EU support schemes more actively for knowledge sharing.
Master’s students from know-why organisations that market a large variety <strong>of</strong> products and services
see synergy between customer relationship marketing and knowledge management ideas, but <strong>the</strong>ir
challenge lies in many cases in <strong>the</strong> lack <strong>of</strong> trust inside sales teams, where individual sales provision
schemes inhibit <strong>the</strong> use <strong>of</strong> customer relationship marketing tools for sharing information about <strong>the</strong>
sale person’s “own” client relations. Client feedback is collected in many large service firms, but its
efficient use for continuous improvement <strong>of</strong> services assumes more systematic dissemination <strong>of</strong> client
information in <strong>the</strong> value chain. This has been an issue when creating knowledge management
development vision. In <strong>the</strong> years 2006-2010 representatives <strong>of</strong> industrial organisations involved in
subcontracting have more frequently linked <strong>the</strong> knowledge management development vision in <strong>the</strong>ir
organisations to new product development than in 2001-2004, when implementing quality
management routines was an important context for knowledge management. However, during both
periods, limited access <strong>of</strong> workers to virtual knowledge sharing tools and promoting information and
knowledge sharing between “blue collar” workers has been perceived as a knowledge management
development challenge.
Only few representatives <strong>of</strong> organisations that have <strong>the</strong> clear features <strong>of</strong> bureaucratic organisations
have been involved in knowledge management courses, but <strong>the</strong>ir common challenge, both in some
state agencies and commercial banks, has been limitations to <strong>the</strong> free circulation <strong>of</strong> sensitive
information and <strong>the</strong> demarcation <strong>of</strong> <strong>the</strong> line between privacy and open knowledge sharing. Moving
from paper documents to virtual knowledge sources has been a priority in some <strong>of</strong> <strong>the</strong>se
organisations, especially before 2004. Virtual documentation handling is also considered a tool that
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makes it easier to disseminate a part <strong>of</strong> <strong>the</strong> document if <strong>the</strong> o<strong>the</strong>r part contains confidential
information. Developing a learner-friendly environment is seen by master’s students from all types <strong>of</strong>
organisations as a process that should link knowledge management to more advanced organisational
learning practices.
6. Discussion
A comparison <strong>of</strong> <strong>the</strong> rankings <strong>of</strong> knowledge management prerequisites from 2001 to 2010
demonstrates not only <strong>the</strong> increasing role <strong>of</strong> <strong>the</strong> virtual knowledge sharing tools, but also <strong>the</strong> desire to
clarify and value <strong>the</strong> expert knowledge <strong>of</strong> employees. Trust between employees as a knowledge
management prerequisite has received especially high ratings during <strong>the</strong> years <strong>of</strong> economic crises.
Evangelista et al. (2010) have studied a cluster <strong>of</strong> high-technology SMEs in Sou<strong>the</strong>rn Italy and
concluded that wider external knowledge management enabling inter-firm collaboration should be
given priority. Our research indicates that open innovation and related knowledge management
initiatives can succeed if virtual tools for knowledge sharing are used in <strong>the</strong> situation where such
prerequisites <strong>of</strong> knowledge management as understanding and recognizing <strong>the</strong> expertise <strong>of</strong> all
members <strong>of</strong> an organisation, free circulation <strong>of</strong> information and trust between employees are in place.
Although networking and team-based project work were seen as a priority when developing
knowledge management visions, virtual project workrooms were not treated as an essential
prerequisite for knowledge management. In recent economic crises years, MBA students tended to
give higher priority to receiving a clear answer to <strong>the</strong> question: “Where is <strong>the</strong> space in my organisation
for my expert knowledge?”
Wong and Aspinwall (2005) proposed a list <strong>of</strong> factors for successfully implementing knowledge
management in SMEs, including management support, culture, information technology, strategy and
purpose, measurement, organisational infrastructure, processes and activities, motivational aids,
resources, training and education and finally human resource management. Ranking <strong>of</strong> knowledge
management prerequisites helps to understand <strong>the</strong> role <strong>of</strong> cultural values, trust and motivation
compared to <strong>the</strong> role <strong>of</strong> information technology applications for developing knowledge management
processes in different situations.
We have found that <strong>the</strong> rankings <strong>of</strong> knowledge management prerequisites do vary depending on <strong>the</strong>
size <strong>of</strong> <strong>the</strong> MBA students’ companies and on <strong>the</strong> different business sectors <strong>the</strong>y operate in. The role
<strong>of</strong> cross-border integration is also crucial. During <strong>the</strong> years 2001-2003 insufficient integration between
databases <strong>of</strong> overseas headquarters and databases <strong>of</strong> <strong>the</strong>ir Estonian subsidiaries was regularly
pointed out by young Estonian managers who believed that managers in Finnish or Swedish
headquarters had more conservative attitudes towards knowledge management than <strong>the</strong> young
personnel <strong>of</strong> <strong>the</strong>ir foreign subsidiaries. That contradiction has not been a key discussion issue in
recent years.
The limitation <strong>of</strong> <strong>the</strong> present study results from <strong>the</strong> use <strong>of</strong> different samples <strong>of</strong> participants in different
years in <strong>the</strong> training context. The change in <strong>the</strong> composition <strong>of</strong> <strong>the</strong> organisations represented by
action learning participants means that although some conclusions <strong>of</strong> change trends in interpretations
<strong>of</strong> knowledge management prerequisites can be made, such comparisons do not create a longitudinal
description <strong>of</strong> knowledge management development cycles in specific organisations.
7. Conclusions
Individual assessments <strong>of</strong> knowledge management prerequisites can be combined with action
learning steps where teams for creating knowledge management development visions are formed,
taking into consideration mechanistic versus organic nature and service versus product focus <strong>of</strong>
organisations. Such teams can use knowledge management prerequisite assessments for revealing
knowledge management challenges in different types <strong>of</strong> organisations and for aligning <strong>the</strong>ir
knowledge management priorities. Business schools have to apply a customised approach to
empower postgraduate students to develop knowledge management practices in <strong>the</strong>ir organisations
depending on several contextual factors such as <strong>the</strong> role <strong>of</strong> knowledge as a source <strong>of</strong> competitive
advantage and <strong>the</strong> value <strong>of</strong> expert knowledge; <strong>the</strong> business sector and scale <strong>of</strong> activity; domestic or
international ownership and involvement in international value chains and networks. The common
interpretation <strong>of</strong> knowledge management prerequisites in <strong>the</strong> development team is, however, also a
key success factor.
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282

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)}
Isaac Enakimio and Abdallah Al-Shawabkeh
University <strong>of</strong> Greenwich, London, UK
isaac.enakimio@gmail.com
aa14@gre.ac.uk,
Abstract: In May 2010, following <strong>the</strong> election <strong>of</strong> <strong>the</strong> new coalition government <strong>the</strong>re were strong signals <strong>of</strong>
change. These changes in <strong>the</strong> form <strong>of</strong> job cuts, pay freezes and organisational restructuring are constantly
changing <strong>the</strong> shape <strong>of</strong> <strong>the</strong> economic sector <strong>of</strong> <strong>the</strong> UK. The most affected sector is <strong>the</strong> public sector and <strong>the</strong> top <strong>of</strong>
that list is <strong>the</strong> National Health Service (NHS). The first two radical changes have been <strong>the</strong> abolition <strong>of</strong> <strong>the</strong> Primary
Care Trusts (PCTs) and <strong>the</strong> introduction <strong>of</strong> <strong>the</strong> new General Practitioners (GP) consortia which is a Clinician led
group that will assume <strong>the</strong> responsibility for healthcare needs and should fully be in place by 2013. Despite <strong>the</strong>se
radical organisational changes, two issues have remained constant;
1. The objectives <strong>of</strong> <strong>the</strong> GP consortium and Community Healthcare services (i.e. Providers) this is to support
patients better.
2. The need for <strong>the</strong> implementation <strong>of</strong> KM in <strong>the</strong> National Health Service and even beyond.
In <strong>the</strong> Health Service Journal (HSJ), Ford (2010) quoted an unnamed chief executive pioneering <strong>the</strong> GP
consortium idea as saying “Handing commissioning control to clinicians but maintaining primary care trusts to
support <strong>the</strong>m would be <strong>the</strong> “dream ticket”, ra<strong>the</strong>r than abolition...” which is currently <strong>the</strong> case. This highlights <strong>the</strong>
issue <strong>of</strong> managing knowledge within <strong>the</strong> NHS because a knowledge retaining system would help maintain
stability in <strong>the</strong> provision <strong>of</strong> patient care through <strong>the</strong> transition and beyond. The earlier paper titled “The Status <strong>of</strong>
Knowledge Management (KM) in Medway Primary Care Trust <strong>of</strong> <strong>the</strong> National Health Service (NHS)” (Enakimio,
Al-Shawabeh, and Sharp, 2010) gives an inside knowledge <strong>of</strong> <strong>the</strong> organisation, how it fits into <strong>the</strong> overall plan for
KM in <strong>the</strong> NHS as well as <strong>the</strong> current status <strong>of</strong> KM in <strong>the</strong> organisation. The paper dealt with <strong>the</strong> capability <strong>of</strong>
Knowledge Management (KM) to assist <strong>the</strong> NHS in meeting its strategic objective <strong>of</strong> supporting patients better
within <strong>the</strong> Medway community. However, <strong>the</strong> focus for this paper is examining <strong>the</strong> strategic objectives <strong>of</strong>
implementing KM in NHS Medway (Commissioners) and Medway Community Healthcare (Providers) and
continues from <strong>the</strong> earlier paper. The data collected from <strong>the</strong> earlier survey which was conducted in <strong>the</strong> different
locations <strong>of</strong> <strong>the</strong> trust such as <strong>the</strong> hospices, hospitals, healthy living centres, <strong>of</strong>fices and clinics, was analysed
using SPSS 16 to explore <strong>the</strong> challenges <strong>of</strong> implementing KM in <strong>the</strong> NHS to meet its strategic objectives. The
aim <strong>of</strong> this paper is to examine <strong>the</strong> strategic objectives <strong>of</strong> implementing KM in <strong>the</strong> NHS and show <strong>the</strong> correlation
between KM and <strong>the</strong> strategic objectives <strong>of</strong> this organisation. This paper provides a real example <strong>of</strong> possible
strategies for implementing KM within Medway PCT, and evaluates how <strong>the</strong>se strategies could impact on <strong>the</strong>
organisation and improve its performance.
Keywords: Knowledge Management (KM), National Health Service (NHS) Medway Primary Care Trust (PCT),
Strategic Health Authority (SHA), General Practitioner (GP) and Information Technology (IT)
1. Introduction
The organisation had previously carried out a number <strong>of</strong> self-assessments, which recorded a general
state <strong>of</strong> KM underperformance across <strong>the</strong> whole organisation and <strong>the</strong> need to work on getting a KM
solution in place. Based on this evaluation, <strong>the</strong> challenge became <strong>the</strong> definition <strong>of</strong> a suitable approach
to implement KM in <strong>the</strong> organisation and <strong>the</strong> realisation <strong>of</strong> <strong>the</strong> Strategic Health Authority’s (SHA)
objectives for <strong>the</strong> implementation.
In order to achieve this, <strong>the</strong> researcher set out to analyse <strong>the</strong> impact <strong>of</strong> KM on <strong>the</strong> SHA given
objectives by understanding <strong>the</strong> contribution <strong>of</strong> <strong>the</strong> three CSF groups i.e. People and Culture,
Processes, Information Technology in supporting KM in Medway PCT.
The study attempted to answer <strong>the</strong> following questions:
Q1 - What is <strong>the</strong> overall correlation between KM and <strong>the</strong> Strategic Objectives for Medway PCT?
Q1.1 What is <strong>the</strong> correlation between ‘People & Culture’ factor and <strong>the</strong> Strategic
Objectives for Medway PCT?
Q1.2 What is <strong>the</strong> correlation between ‘Processes’ factor and <strong>the</strong> Strategic Objective for
Medway PCT?
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Isaac Enakimio and Abdallah Al-Shawabkeh
Q1.3 What is <strong>the</strong> correlation between ‘IT’ factor and <strong>the</strong> Strategic Objectives for Medway
PCT?
A questionnaire was distributed which was made up <strong>of</strong> 40 questions with answers that
were based on <strong>the</strong> 5-point Likert-scale with <strong>the</strong> values;
1 - strongly disagree, 2 - disagree, 3 - neutral, 4 - agree and 5 - strongly agree.
In comparison to most researches conducted in NHS organisations, <strong>the</strong> responses were very
impressive; largely because <strong>of</strong> <strong>the</strong> researchers close involvement with members <strong>of</strong> staff and
relationship with <strong>the</strong> different management teams within <strong>the</strong> organisation. A total number <strong>of</strong> 124
respondents returned <strong>the</strong>ir questionnaires but 22 <strong>of</strong> <strong>the</strong> questionnaires were not filled in properly
<strong>the</strong>reby were excluded for <strong>the</strong> purpose <strong>of</strong> this research.
2. Relationship between KM and <strong>the</strong> NHS
Prior to <strong>the</strong> recent NHS reform, which has led to <strong>the</strong> on-going changes in <strong>the</strong> NHS, <strong>the</strong> Strategic
Health Authority (SHA) had begun exploring <strong>the</strong> use <strong>of</strong> KM tools in order to achieve its strategic
objectives in <strong>the</strong> hope that, <strong>the</strong> quality <strong>of</strong> knowledge used within <strong>the</strong> organisation with <strong>the</strong> aid <strong>of</strong> KM
tools could help it to gain an improved edge in providing better patient care.
The recent changes mean that, a lot <strong>of</strong> employees are being lost due to job cuts, transfers,
relocations etc. which in turn means <strong>the</strong> loss <strong>of</strong> valuable knowledge about <strong>the</strong> organisation and
patient care in <strong>the</strong> Medway area. The irony <strong>of</strong> all <strong>of</strong> it is that, <strong>the</strong> mission <strong>of</strong> <strong>the</strong> NHS in <strong>the</strong> Medway
area irrespective <strong>of</strong> <strong>the</strong> transformations, have still remained <strong>the</strong> same.
Robertson, S (2005) states that:
“Knowledge management is based on <strong>the</strong> idea that an organisation’s most valuable
resource is <strong>the</strong> knowledge <strong>of</strong> its people. This is not a new idea but <strong>the</strong> focus on
knowledge is and this is driven by <strong>the</strong> accelerated rate <strong>of</strong> change in today’s organisations
and in society as a whole”.
The world has changed a lot over <strong>the</strong> years with technology, culture and processes adding a different
dimension to our jobs. This has led to more staff dependency on knowledge to carry out <strong>the</strong>ir jobs and
based on this, it is safe to say that to some degree or <strong>the</strong> o<strong>the</strong>r all workers can be classified as
“knowledge workers”
Drucker, (1993)“KM recognises that today nearly all jobs involve 'knowledge work' and
so all staff are 'knowledge workers' - meaning that <strong>the</strong>ir job depends more on <strong>the</strong>ir
knowledge than <strong>the</strong>ir manual skills. This means that creating, sharing and using
knowledge are among <strong>the</strong> most important activities <strong>of</strong> nearly every person in every
organisation.”
In <strong>the</strong> health sector, since all health personnel function based on how much <strong>the</strong>y know, <strong>the</strong>
importance <strong>of</strong> KM cannot be over emphasised. The age old question still remains: “how much do <strong>the</strong>y
really know?” An exercise that seems as simple to a doctor or nurse as giving an injection has
different effects on people <strong>of</strong> different races. Experienced nurses have discovered that injecting
patients from a black racial background requires more force at a different angle <strong>of</strong> penetration
compared to <strong>the</strong>ir racially white counterparts. This finding in most cases is not covered in textbooks
and even when <strong>the</strong>y are covered we know that <strong>the</strong>oretical knowledge and practical experience is a
whole different ball game.
With all <strong>the</strong> advances being made in medicine every day, <strong>the</strong>re is always a new knowledge to be
learned. There are also continuously changing governments and policies, as well as management
practices which are instantly evident with <strong>the</strong> on-going NHS reform.
Also, health practitioners usually gain experience and knowledge <strong>of</strong> <strong>the</strong>ir fields and communities over
time, this experiential knowledge can instantly be lost by <strong>the</strong> loss <strong>of</strong> <strong>the</strong>se kinds <strong>of</strong> staff and could
subsequently lead to poor patient care. Therefore, <strong>the</strong> need for KM in <strong>the</strong> health sector cannot be
over emphasised in aiding <strong>the</strong> provision <strong>of</strong> better patient care.
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Isaac Enakimio and Abdallah Al-Shawabkeh
3. Ma<strong>the</strong>matical Specification <strong>of</strong> Multi-criteria Analysis Model, structured by a
Criteria and Indicators’ matrix
The table below represents <strong>the</strong> principles used in <strong>the</strong> MCA-CSF Model, which include:
People and Culture
Processes
Information Technology (IT)
Table 1: SECI-CSF Performance model Factors and Criteria
Principle
1. People & Culture
Criteria
Employee training
Trustworthy teamwork and employee involvement
Top-management leadership support
Employee awareness about knowledge inside <strong>the</strong> organisation
2. Processes
Standard processes established for knowledge
contribution and content management
Organisation has ability to access, structure, and
categorise content <strong>of</strong> knowledge
3. IT
Robust and user-friendly technology available to employees put
in place
Tools established for managing knowledge cycles activities
Source: Al-Shawabkeh. A (2010)
The table also shows <strong>the</strong> eight criteria used in <strong>the</strong> model:
Employee training
Employee involvement and teamwork
Top management leadership & commitment
Employee awareness about knowledge inside <strong>the</strong> organisation
Standard processes for knowledge contribution and content management
Organisation ability to structure, categorise and access <strong>the</strong> content <strong>of</strong> knowledge
Robust and user friendly technology
Tools for managing knowledge cycles activities
4. CSF-MCA Implementation model
Based on <strong>the</strong> CSF – MCA implementation model, <strong>the</strong> variables represented in this model were
factored into <strong>the</strong> questionnaire. Pilot tests were run with a selected sample <strong>of</strong> staff and <strong>the</strong> results
from <strong>the</strong> pilot as well as <strong>the</strong> feedback from this test resulted in a few modifications to <strong>the</strong> survey
questionnaire after which it was distributed, in order to obtain scores for <strong>the</strong> listed KM practices.
5. Correlation between KM and <strong>the</strong> Strategic Objective for Medway PCT
Based on <strong>the</strong> objectives set by <strong>the</strong> SHA for o<strong>the</strong>r NHS organisations and Medway PCT, <strong>the</strong> results <strong>of</strong>
<strong>the</strong> tests carried out to evaluate <strong>the</strong> influences <strong>of</strong> KM on <strong>the</strong> key areas <strong>of</strong> <strong>the</strong> strategic objectives are
displayed below.
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Isaac Enakimio and Abdallah Al-Shawabkeh
Figure 1: Correlation Framework between KM and <strong>the</strong> Strategic Objective for Medway PCT
6. Data analysis
The scoring system in <strong>the</strong> questionnaire was based on a 5-point Likert-scale with <strong>the</strong> values (1)
strongly disagree, (2) disagree, (3) neutral, (4) agree and (5) strongly agree.
The Statistical Package for Social Sciences (SPSS) version 16 (2007) was used to analyse <strong>the</strong>
collected data because <strong>of</strong> its included features which were very important to achieve <strong>the</strong> main
objectives <strong>of</strong> this research. The following statistics were used:
Frequencies and percentages to describe <strong>the</strong> selected characteristics <strong>of</strong> <strong>the</strong> study participants.
One-way and Two-way ANOVA models to test <strong>the</strong> research hypo<strong>the</strong>ses due to <strong>the</strong>ir ability to test
multiple variables at <strong>the</strong> same time instead <strong>of</strong> running two separate tests, but <strong>the</strong> main reason
was <strong>the</strong>ir ability to determine whe<strong>the</strong>r one variable affects <strong>the</strong> o<strong>the</strong>rs.
6.1 The relationship between KM and Data-quality:
Table 2: The relationship between KM and Data-quality (Coefficients a )
Coefficients a
Model Unstandardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 1.877 .475 3.948 .000
KM .582 .152 .357 3.823 .000
a. Dependent Variable: Data_quality
From <strong>the</strong> results above, <strong>the</strong>re is a positive relationship between KM and Data quality in NHS (B=
.582). This goes to show that KM practices will improve <strong>the</strong> quality <strong>of</strong> data collected and enhance a
standard for <strong>the</strong> terminology used in data collection and recording. From <strong>the</strong> previous tables, <strong>the</strong>
results showed that <strong>the</strong> current criterion which needs emphasis is <strong>the</strong> IT criterion. An improvement in
this direction will bring about a subsequent improvement in <strong>the</strong> quality <strong>of</strong> data collected.
6.2 The relationship between KM and Data Sharing
Table: 3: The relationship between KM and Data Sharing (coefficients a )
Coefficients a
Model Unstandardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 1.660 .446 3.724 .000
KM .667 .143 .423 4.673 .000
a. Dependent Variable: Data_Sharing
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Isaac Enakimio and Abdallah Al-Shawabkeh
From <strong>the</strong> results above, <strong>the</strong>re is a positive relationship between KM and Data Sharing in NHS (B=
.667).
6.3 The relationship between KM and existing tools
Table 4: The relationship between KM and existing tools (Coefficients a )
Coefficients a
Model Unstandardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 1.425 .458 3.109 .002
KM .735 .147 .448 5.005 .000
a. Dependent Variable: Already_existing_tools
From <strong>the</strong> results above, <strong>the</strong>re is a positive relationship between KM and existing tools in NHS
(B=.735).
Table 5: Correlations between KM and <strong>the</strong> different criterion in <strong>the</strong> People and Culture Factor
Employee_T
raining
Team_W
ork
Top
management
Support
Employee
awareness
KM Pearson Correlation .460 ** .384 ** .536 ** .581 **
Sig. (2-tailed) .000 .000 .000 .000
N 102 102 102 102
7. What is <strong>the</strong> impact <strong>of</strong> <strong>the</strong> three factors (People and culture, Processes, and
IT) on data sharing?
Table 6: The impact <strong>of</strong> <strong>the</strong> three factors on data sharing (Coefficients a )
Coefficients a
Model Unstandardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 1.930 .477 4.043 .000
People_and_Culture -.028 .171 -.018 -.163 .871
Processes .187 .142 .159 1.316 .191
IT .423 .118 .374 3.593 .001
a. Dependent Variable: Data_Sharing
It’s clear that <strong>the</strong> only significant variable in sharing data in NHS is IT (positive, B=.423, Sig. =.001)
while <strong>the</strong> o<strong>the</strong>r two factors are not significant (p&c=.871 >.05, processes= .191>.05).
8. What is <strong>the</strong> impact <strong>of</strong> <strong>the</strong> three factors (People and culture, Processes, and
IT) on data quality?
Table 7: The impact <strong>of</strong> <strong>the</strong> three factors on data Quality (Coefficients a )
Coefficients a
Model Unstandardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 2.067 .504 4.100 .000
People_and_Culture .007 .181 .004 .041 .968
Processes .029 .150 .024 .196 .845
IT .490 .124 .419 3.938 .000
a. Dependent Variable: Data_quality
It’s clear that <strong>the</strong> only significant variable in data quality in NHS is IT (positive, B=.490, Sig. =.000)
while <strong>the</strong> o<strong>the</strong>r two factors are not significant.
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Isaac Enakimio and Abdallah Al-Shawabkeh
9. What is <strong>the</strong> impact <strong>of</strong> <strong>the</strong> three factors (People and culture, Processes, and
IT) on existing tools?
Table 8: The impact <strong>of</strong> <strong>the</strong> three factors on already exiting tools (Coefficients a )
Coefficients a
Model Un-standardised
Standardised t Sig.
Coefficients
Coefficients
B Std. Error Beta
1 (Constant) 1.441 .500 2.884 .005
People_and_Culture .221 .179 .134 1.233 .220
Processes .125 .149 .102 .841 .403
IT .387 .123 .328 3.139 .002
a. Dependent Variable: Already_existing_tools
It’s clear that <strong>the</strong> only significant variable in existing tools in NHS is IT (positive, B=.387, Sig. =.002)
while <strong>the</strong> o<strong>the</strong>r two factors (people and Culture, Processes) are not significant.
10. Conclusion
The results above, show a positive relationship between KM and Data quality (B= .582), KM and Data
Sharing (B= .667), and KM and existing tools in <strong>the</strong> NHS (B=.735).
These results show that KM practices will improve <strong>the</strong> quality <strong>of</strong> data collected and how <strong>the</strong>se data
are shared <strong>the</strong>reby enhancing a standard for <strong>the</strong> terminology used in data collection, recording as well
as how <strong>the</strong> data will be made available to o<strong>the</strong>r agencies. It also shows that KM will also enable <strong>the</strong>
exploration <strong>of</strong> currently existing tools and technologies.
The results derived from examining <strong>the</strong> impact <strong>of</strong> <strong>the</strong> three factors (People and culture, Processes,
and IT) on <strong>the</strong> different aspects <strong>of</strong> <strong>the</strong> SHA objective revealed that <strong>the</strong> current criterion which needs
<strong>the</strong> most emphasis is <strong>the</strong> IT criterion. {i.e. I.T’s significance on data sharing (positive, B=.423, Sig.
=.001), data quality (positive, B=.490, Sig. =.000) and existing tools (positive, B=.387, Sig. =.002)}
Based on <strong>the</strong> framework between KM and <strong>the</strong> strategic objectives, <strong>the</strong> results <strong>of</strong> <strong>the</strong> test show that
KM has a positive influence on <strong>the</strong> quality <strong>of</strong> data, data sharing and existing tools, however fur<strong>the</strong>r
analysis showed that <strong>the</strong> most influential criterion, is <strong>the</strong> IT criterion and that an improvement in <strong>the</strong> IT
criterion will bring about a subsequent improvement in <strong>the</strong> ability for <strong>the</strong> organisation to achieve <strong>the</strong>
objectives set for it by <strong>the</strong> SHA.
The existing IT systems in place in <strong>the</strong> organisation are top <strong>of</strong> <strong>the</strong> range, in line with today’s
technology. The use <strong>of</strong> Tablets, latest models <strong>of</strong> Dell PCs, blackberry’s SAN servers and <strong>the</strong> NHS
coin cloud go a long way to demonstrate heavy investments in IT tools and infrastructure but non <strong>the</strong>
less <strong>the</strong> results from <strong>the</strong> test reveal a great emphasis on <strong>the</strong> need for improvement in IT training. For
<strong>the</strong> employees to make <strong>the</strong> best use <strong>of</strong> <strong>the</strong> existing and future technologies, this area <strong>of</strong> emphasis will
have to be addressed and will in turn greatly enhance <strong>the</strong> objectives set out by <strong>the</strong> SHA for
implementation <strong>of</strong> KM.
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289

A proposed Framework for Discovering Key Knowledge
Areas in Supply Chain and Determining <strong>the</strong> Relationship
With Major Logistic Processes: A Case Study
Mohamad Ali Feyz 1 , Babak Akhgar 2 and Hamidreza Shahbaznezhad 1
1
Department <strong>of</strong> IT Management, Faculty <strong>of</strong> Management, University <strong>of</strong> Tehran,
Tehran, Iran
2
Information Research Group, Faculty <strong>of</strong> ACES, Sheffield Hallam University,
Sheffield, UK
ali.feyz@gmail.com
B.Akhgar@shu.ac.uk
hamidshahbaz@ut.ac.ir
Abstract: Nowadays, knowledge is <strong>the</strong> only resource that has <strong>the</strong> capability <strong>of</strong> <strong>of</strong>fering competitive advantage
and continuous improvement for supply chain partners. Thus, we should identify <strong>the</strong> enterprise key knowledge. In
order to create new knowledge, supply chain partners are involved in continuous processes that result in sharing
rich information and building IT infrastructure that permit supply chain members to process information coming
from o<strong>the</strong>r partners. In this paper, we try to identify <strong>the</strong> key knowledge areas in Iran automotive industry and
discovering <strong>the</strong> relationships between <strong>the</strong>se key knowledge areas and <strong>the</strong> main logistic processes. Creating
explicit and structured knowledge from tacit knowledge is <strong>the</strong> primary goal <strong>of</strong> several KM projects. Since
knowledge in <strong>the</strong> enterprise or supply chain flows through processes, we can identify <strong>the</strong> key knowledge in a
specific area <strong>of</strong> an organization through knowledge management tools or <strong>the</strong> ideas <strong>of</strong> related experts. Hence, in
order to acquire key knowledge, we should look for specific processes in that area. For this purpose, a framework
is proposed which enables organizations to identify key knowledge areas, main processes in <strong>the</strong> supply chain,
and <strong>the</strong>ir relationships more effectively. The proposed framework is designed by applying a blended methodology
and contains a vast look at <strong>the</strong> relationship between knowledge management and supply chain management
concepts. For achieving this goal, a sequential blended methodology was applied.
Keywords: Supply chain, Key knowledge areas, Knowledge Management, Logistic Processes, SCOR model
1. Introduction
To survive <strong>the</strong> competition, <strong>the</strong>re is a need for organizing <strong>the</strong> internal activities, providing <strong>the</strong>
necessary potential for creating competitive advantage, and developing a systematic and dynamic
connection with customers and suppliers. To this end, a strong rational relationship should first be
made with o<strong>the</strong>r members, in order to organize <strong>the</strong> internal activities based on it. This idea, which can
be considered as “systems thinking” in <strong>the</strong> area <strong>of</strong> goods and services supply, leads to what we call
supply chain management. As it is clear, members <strong>of</strong> supply chain are connected toge<strong>the</strong>r with a
forward physical flow which is called logistics. There are several aspects within <strong>the</strong> SCM domain that
need to be tackled for <strong>the</strong> purpose <strong>of</strong> practical applications; topics such as performance evaluation <strong>of</strong>
a supply chain and its members, inter-organizational coordination and management, how <strong>the</strong> supply
chain members share <strong>the</strong> outcome <strong>of</strong> operations (Lambert et al., 1998; Croxton et al., 2001),
management <strong>of</strong> knowledge among supply chain members, and human interaction in a supply chain.
A thorough review <strong>of</strong> each element will improve <strong>the</strong> supply chain’s total performance. Knowledge
management is <strong>the</strong> most important one among o<strong>the</strong>r elements. Creating explicit and structured
knowledge from tacit knowledge is <strong>the</strong> primary goal <strong>of</strong> several KM projects (Babak Akhgar, 2008).
Since knowledge is based on processes, key processes should be identified in order to manage
knowledge more effectively. For this reason, <strong>the</strong> management should be able to balance <strong>the</strong>
organization in <strong>the</strong> knowledge documentation and codification level and find areas in <strong>the</strong> organization
with less explicit knowledge. In this paper we use <strong>the</strong> term “key processes” for <strong>the</strong>se processes that
are critical for finding key knowledge.
2. Literature review
2.1 KM in SCM
SCM is ‘‘<strong>the</strong> integration <strong>of</strong> key business processes from end user through original suppliers that
provide products, services, and information that add value for customer and o<strong>the</strong>r stakeholders’’. In
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Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
SCM, <strong>the</strong> network <strong>of</strong> organizations is structured through upstream and downstream linkages among
<strong>the</strong> processes and activities that add value along <strong>the</strong> supply chains (Christopher, 1992). Successful
SCM requires a change from managing individual functions to integrating activities into key supply
chain business processes (Lambert and Cooper, 2000). For any supply chains, <strong>the</strong> general processes
and structure can be integrated into six core processes that are linked: supplier, inbound logistics,
manufacturing, outbound logistics, marketing and sales, and end customers (Felix T.S. Chan, H.J. Qi
). These core processes categorize <strong>the</strong> typical function areas in supply chains.
Knowledge-sharing and knowledge-evolving activities <strong>of</strong>fer several opportunities and challenges for
SMEs to contribute effectively to <strong>the</strong> supply chain business (Wadhwa et al., 2006). The management
<strong>of</strong> knowledge is generally accepted as being open to considerable improvement, both within
organizations and between partners in <strong>the</strong> supply chain (Siemieniuch and Sinclair, 1993; Siemieniuch
and Sinclair, 1999a). According to Markus (2001) knowledge generated within one project is usually
buried in unread reports and arcane filing systems or lost as people move on (Louise Fletcher,
Yiannis E. Polychronakis). Failure to transfer this knowledge leads to wasted activity and impaired
performance (Siemieniuch and Sinclair, 2004b).
2.2 SCOR
The Supply Chain Operations Reference-model (SCOR) is <strong>the</strong> product <strong>of</strong> <strong>the</strong> Supply-Chain Council
(SCC), an independent, not-for-pr<strong>of</strong>it, global corporation with membership open to all companies and
organizations interested in applying and advancing <strong>the</strong> state-<strong>of</strong>-<strong>the</strong>-art in supply-chain management
systems and practices. Figure 1 depicts The SCOR model-based supply chain infrastructure.
According to SCC (1999) SCOR model integrates <strong>the</strong> well-known concepts <strong>of</strong> business process reengineering,
benchmarking and process measurement into a cross-functional framework which
contains (Samuel H. Huan , et al. , 2004):
Standard description <strong>of</strong> management processes
A framework <strong>of</strong> relationships among <strong>the</strong> standard processes
Standard metrics to measure process performance
Management practices that produce best in class performance, and
Standard alignment to s<strong>of</strong>tware features and functionality
Figure1: The SCOR model-based supply chain infrastructure (Samuel H. Huan et al., 2004)
SCOR is designed to enable companies to communicate, compare and develop new or improved
supply-chain practices from companies both within and outside <strong>of</strong> <strong>the</strong>ir industry segment (Gordon
Stewart, 1997). The four distinct processes for <strong>the</strong> SCOR model are (Samuel H. Huan et al., 2004):
Source
Make
Deliver
Plan.
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2.3 Knowledge mapping
Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
A knowledge map is a presentation <strong>of</strong> one or more aspects <strong>of</strong> <strong>the</strong> knowledge available within an
organization that aims to fulfill a specific information need for one or more employee roles within <strong>the</strong>
organization (Samuel Driessen et al., 2007). Knowledge maps are techniques and tools for visualizing
knowledge and relationships in a clear form in such a way, that relevant features are clearly
highlighted (Vail, 1999). They can be used to elicit, hare, learn and create knowledge as well as to
enable better decision-making, assessing an organization’s knowledge culture and to overcome
conflicting or competing issues (Carla van den Berg et al., 2005). The purpose <strong>of</strong> knowledge mapping
is to identify what knowledge is essential and where this knowledge resides (Rumizen, 2002).
There are various tools and techniques for knowledge mapping such as STORM model. Figure 2
illustrates <strong>the</strong> model’s detail. To assist with <strong>the</strong> implementation <strong>of</strong> STORM <strong>the</strong>re needs to be
transparency about <strong>the</strong>ir goals, problems, and ways <strong>of</strong> working within <strong>the</strong>ir supply chain (Siemieniuch
and Sinclair, 1999b; Siemieniuch and Sinclair, 2000).
Figure 2: STORM model (Fletcher and Polychornakis, 2004)
The model is broken down in to three stages that are interlinked to create a continuous knowledge
cycle. The cycle is constantly ga<strong>the</strong>ring valuable knowledge from different sources (Louise Fletcher et
al., 2007).
2.4 Key knowledge areas
The concept <strong>of</strong> knowledge is different in diverse areas. Thus key knowledge is not similar to each
o<strong>the</strong>r in any area. Therefore identification <strong>of</strong> key knowledge in one specific area is so difficult, and
<strong>of</strong>ten is determined by related experts and specialists and sometimes through KM models and
frameworks. According to Jose Lois Galan Gonzalez, Gabriel Cependa research (2004), key
knowledge areas frequently is appeared as:
Training
Innovation environment
Contact networks
Project management
Employee know-how or expertise
Team work
Quality
3. Research methodology
The main purpose <strong>of</strong> this paper is presenting a comprehensive framework for identifying key
knowledge areas and modifying logistic processes, and discovering <strong>the</strong> relationship between <strong>the</strong>m.
The proposed framework is designed by applying a blended methodology and contains a vast look at
<strong>the</strong> relationship between knowledge management and supply chain management concepts. For
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Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
achieving this goal, a sequential blended methodology was applied. At <strong>the</strong> first step, <strong>the</strong> past literature
is reviewed and a primary framework generated. The primary framework considered two different
parts, first part is related to SCM and using SCOR model for finding and standardizing SCM related
processes and next one is for finding key knowledge areas in supply chain.
In <strong>the</strong> next step, <strong>the</strong> selected case was studied in order to complete <strong>the</strong> primary framework. “Iran
Khodro Co.“ as <strong>the</strong> selected case, has special characteristics for its size and extent. IKCO is <strong>the</strong>
leading company in car manufacturing in Iran and <strong>the</strong> Middle East and has many pr<strong>of</strong>essional experts
in <strong>the</strong> field <strong>of</strong> KM and SCM. Because <strong>of</strong> its size, IKCO has many suppliers and customers in Iran.
Having semi-structured interviews with experts in <strong>the</strong> field <strong>of</strong> this research in IKCO, <strong>the</strong> primary
framework was expanded fur<strong>the</strong>r. Seven experts were interviewed. The goal <strong>of</strong> <strong>the</strong>se interviews was
recognizing new steps and reorganizing <strong>the</strong> position <strong>of</strong> each step in <strong>the</strong> primary framework. Using
<strong>the</strong>matic analysis, text and voices <strong>of</strong> semi-structured interviews are reviewed carefully. Then <strong>the</strong>
meaningful sentences are inserted into a table. The rows <strong>of</strong> <strong>the</strong> mentioned table consist <strong>of</strong> different
concepts which are coded to reach specific classes. Finally, <strong>the</strong>se classes make some changes in <strong>the</strong>
steps in <strong>the</strong> primary framework. In <strong>the</strong> end, twelve university pr<strong>of</strong>essors were asked to evaluate <strong>the</strong>
framework using questionnaire in survey. All <strong>the</strong> steps and <strong>the</strong> priority <strong>of</strong> steps were accepted with
Content Validity Ratio (CVR) at least 0.6. After finalizing <strong>the</strong> framework, <strong>the</strong> selected case is surveyed
with <strong>the</strong> proposed steps and <strong>the</strong> results are presented.
4. Proposed framework
Before introducing <strong>the</strong> proposed framework, it is better to study <strong>the</strong> organization from a knowledge
mapping point <strong>of</strong> view. For managing knowledge in an organization more properly, it is advisable to
consider cultural, organizational, and technical infrastructures in <strong>the</strong> main processes. KM project
implementation requires cultural acceptance by employees. Thus, some efforts should be applied on
employees’ behavior for <strong>the</strong> acceptance <strong>of</strong> using KM. In addition <strong>the</strong>se projects require extensive
support and collaboration <strong>of</strong> senior managers within an organization in order to facilitate project
implementation. Intranet and information systems can also provide an effective approach for
transmitting information and knowledge. One <strong>of</strong> <strong>the</strong> knowledge mapping tools which can be used to
investigate <strong>the</strong> organization is <strong>the</strong> STORM model. From a strategic aspect, after identifying <strong>the</strong>
strategic gap, current state <strong>of</strong> organizational knowledge is identified in order to understand in which
area <strong>of</strong> <strong>the</strong> organization <strong>the</strong> knowledge gap exists.
The proposed framework is as follows:
First, identify <strong>the</strong> supply chain processes <strong>of</strong> specific department <strong>of</strong> an organization based on
SCOR model up to level n (for example up to level 3).
Equalize <strong>the</strong> tasks or activities <strong>of</strong> each processes with SCOR model and if required some
correction should be applied.
Identify key knowledge areas <strong>of</strong> <strong>the</strong> organization's specific department. Some tools such as
knowledge management models, frameworks, or related expert ideas can be applied.
Finally, discover <strong>the</strong> relationships between <strong>the</strong>se key knowledge areas and <strong>the</strong> supply chain
processes by utilizing <strong>the</strong> expert’s viewpoints, and prioritize <strong>the</strong> processes that include <strong>the</strong>se key
knowledge areas.
5. Case study findings
Nowadays, logistics play a critical role as a core competency in <strong>the</strong> competitive market for diverse
companies, especially for automotive companies which interact with several suppliers and distributors.
Iran Khodro Co. is <strong>the</strong> biggest car manufacturer in Iran and Middle East in designing and producing
automobiles, buses, minibuses, and trucks. This company is one <strong>of</strong> <strong>the</strong> top twenty car manufacturers
all around <strong>the</strong> world and organizes its operations so as to address <strong>the</strong> market requirements, enter in
<strong>the</strong> international markets, localize parts manufacturing, and finally, reaching <strong>the</strong> strategic goals <strong>of</strong> <strong>the</strong>
country. With regard to <strong>the</strong> volume and diversity <strong>of</strong> various products and <strong>the</strong> necessity <strong>of</strong> coordination
between all suppliers <strong>of</strong> parts and consumables, <strong>the</strong> supply chain topic has a specific importance and
priority to achieve optimum quality and objective cost management. Analyzing <strong>the</strong> semi-structured
interviews with experts in Iran Khodro Co. it was found out that <strong>the</strong> organizational supply chain suffers
some knowledge shortages in its logistics. Therefore, <strong>the</strong> processes in logistics area were studied and
identified. As figure 3 depicts, after identifying processes down to <strong>the</strong> third level <strong>of</strong> SCOR model, it
became apparent that <strong>the</strong> processes are in symmetry with SCOR model, and finally some
modifications were done in <strong>the</strong> processes . Some <strong>of</strong> <strong>the</strong>se processes were not related to <strong>the</strong> scope <strong>of</strong>
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Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
our study, which are highlighted in figure 3. After comparing with SCOR model and adjusting with
current processes, <strong>the</strong> reorganized key processes <strong>of</strong> logistics are generated, depicted in figure 4.
No. Activity description SCOR equivalent Corrective action
1 inventory checking P2.2
2 comparing production plan to inventory and producing <strong>the</strong> list <strong>of</strong> demand parts P2.3
3 ordering based on <strong>the</strong> list <strong>of</strong> demand parts P2.4
4 inspecting <strong>the</strong> items at <strong>the</strong> entrance to <strong>the</strong> firm It is not defined in this model and need to be revised
5 cheking <strong>the</strong> order list with bill <strong>of</strong> loading S1.3
6 obviating <strong>the</strong> inconsistency between <strong>the</strong> order list anf bill <strong>of</strong> loading S1.3
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
7 quality control operation S1.3
8
9
dicharging <strong>the</strong> load
control <strong>the</strong> quality
S1.2
S1.2
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
10 loading D1.5
11 routing D1.6
12 transmitting to destination D1.7
13 transmitting to repository salon D1.8
14 discharging <strong>the</strong> parts in repository D1.9
15 counting and comparing to <strong>the</strong> received list D1.13
16 storing S1.4
17 updating <strong>the</strong> inventory database It is not defined in this model and need to be revised
18 receiving <strong>the</strong> order <strong>of</strong> items from production lines M1.1
19 preparing <strong>the</strong> existing items <strong>of</strong> <strong>the</strong> order list M1.2
20
21
loading <strong>the</strong> items
transmiting
M1.2
M1.2
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
22 updating <strong>the</strong> inventory database It is not defined in this model and need to be revised
23 ordering based on <strong>the</strong> production plan and production line inventory M1.1
24 receiving items from repository M1.2
25 temporary storing M1.2
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
26 preparing and transmitting items to production line based on its demand M1.2
27
28
transmitting products to conveyor belt
transmitting products to delivery <strong>of</strong>fice
M1.3
M1.3
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
29 order allocating <strong>of</strong> parts M1.1
30 preparing and transmitting parts M1.2
31 reception M1.2
32 temprorary storing for transmitting to workstations M1.2
33 confirmation <strong>of</strong> automobile by checker M1.3
34 receiving automobile by contractor M1.3
35
36
putting automobile in temprorary parking
transmitting automobile to <strong>the</strong> main parking
M1.5
M1.5
<strong>the</strong>se activities could be combined and <strong>the</strong>ir sequences should be revised
37 presenting automobile report by computer
38 Maintenance <strong>of</strong> auto mobile in main parking
39 long staying <strong>of</strong> automobile in parking
40 presenting list <strong>of</strong> automobiles which stay in parking over a month
41 determining <strong>the</strong> situation <strong>of</strong> staying automobile
42 receiving produced automobile
43 automobile inspection
44 implementing corrective action
45 issuing script
46 delivering automobile to customer
47 collecting none‐conforming items beside <strong>the</strong> line R
48 temporary storing <strong>of</strong> waste R
49 transmitting to waste enclosure R
50 separating waste R
51 recycling waste R
52 delivering to waste repository R
Figure3: The logistic processes with <strong>the</strong>ir SCOR equivalent
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Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
Figure 4: Categorized processes <strong>of</strong> <strong>the</strong> logistic department
Subsequently as a result <strong>of</strong> <strong>the</strong> semi-structured interviews and sessions with logistic experts in Iran
Khodro Co. and after reviewing <strong>the</strong> KM models and frameworks, key knowledge areas in this zone are
recognized. These key knowledge areas used for this case study are <strong>the</strong> combination <strong>of</strong> some items
<strong>of</strong> Jose Lois Galan Gonzalez, Gabriel Cependa research (2004) and ideas <strong>of</strong> IKCO experts. These
key knowledge areas are illustrated in table 1.
Finally among current main processes and specified key knowledge areas, a matrix is designed to
identify and rank <strong>the</strong>se processes in <strong>the</strong> logistics area based on <strong>the</strong> experts’ ideas.
As it is shown in table 1, <strong>the</strong> columns <strong>of</strong> this matrix are representative
<strong>of</strong> key knowledge areas in
supply chain and <strong>the</strong> rows are illustrative <strong>of</strong> main processes in <strong>the</strong> logistics area.
Experts identified <strong>the</strong> processes related to <strong>the</strong> key knowledge areas and scored this relationship with
numbers from 1 to 10.
Table1: Implementation matrix
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Mohamad Ali Feyz, Babak Akhgar and Hamidreza Shahbaznezhad
The values that are shown in this table are <strong>the</strong> average <strong>of</strong> <strong>the</strong> scores obtained from expert opinion.
These scores illustrate <strong>the</strong> extent to which each scope <strong>of</strong> <strong>the</strong>se key knowledge areas has relationship
to each main process. For example innovation has <strong>the</strong> closest relationship with plan. After
understanding
<strong>the</strong>se types <strong>of</strong> relations, we realize each key knowledge area belongs to which main
processes in <strong>the</strong> supply chain. For reaching to more detail, it is also possible to find <strong>the</strong> relationship
between <strong>the</strong>se knowledge areas and <strong>the</strong> logistic sub-processes. Finally it is specified that for
acquiring <strong>the</strong>se key knowledge areas, which processes have precedence.
6. Discussion and conclusion
For this paper, <strong>the</strong> authors surveyed two different scopes and a framework was developed on this
basis.
Knowledge management and supply chain management are two important
issues in today’s
organizations. Therefore, <strong>the</strong> importance <strong>of</strong> a framework for finding key knowledge areas in <strong>the</strong>
supply chain processes is apparent.
In <strong>the</strong> literature, <strong>the</strong> use <strong>of</strong> knowledge management in supply chain management is reviewed and
SCOR model is described. Then for illustrating <strong>the</strong> application <strong>of</strong> knowledge management, knowledge
mapping and key knowledge areas are discussed. The mentioned topics were directly used in <strong>the</strong>
primary
framework. For validating <strong>the</strong> primary framework and reaching <strong>the</strong> goals <strong>of</strong> this paper, a
blended methodology was applied. Semi-structured interviews and questionnaires are <strong>the</strong> tools for
ga<strong>the</strong>ring data. After data ga<strong>the</strong>ring, <strong>the</strong> collected materials from IranKhodro experts and university
pr<strong>of</strong>essors were analyzed with <strong>the</strong>matic analysis and content validity method and <strong>the</strong> proposed
framework was shaped.
Before using this framework, it will be useful to analyze <strong>the</strong> organization from a strategic and
knowledge viewpoint. If <strong>the</strong> organization has knowledge gap <strong>the</strong>n this framework can improve <strong>the</strong>
current state <strong>of</strong> knowledge
through <strong>the</strong> logistics department.
At <strong>the</strong> first step <strong>of</strong> <strong>the</strong> proposed framework, identification and documentation should be done on <strong>the</strong>
logistic processes. Second, <strong>the</strong>se processes should be equalized with supply chain operations
reference (SCOR) model. The goal <strong>of</strong> this step is <strong>the</strong> standardization
and optimization <strong>of</strong> current
processes
in <strong>the</strong> logistic department. Third, key knowledge areas <strong>of</strong> <strong>the</strong> organization's specific
department should be discovered by means <strong>of</strong> knowledge management models, frameworks, and
related expert ideas. In <strong>the</strong> final step, <strong>the</strong> relationship between <strong>the</strong> key knowledge areas and logistic
processes is determined. For reaching more detail and discovering <strong>the</strong> relationships in <strong>the</strong> inner
levels, it is possible to find <strong>the</strong> connection between key knowledge areas and logistic sub-processes.
Identifying and prioritizing key processes <strong>of</strong> <strong>the</strong> supply chain in an organization can improve <strong>the</strong>
current state <strong>of</strong> knowledge throughout <strong>the</strong> supply chain. The more <strong>the</strong> extent <strong>of</strong> explicit knowledge is,
<strong>the</strong> better <strong>the</strong> performance <strong>of</strong> <strong>the</strong> organization will be. The efficiency <strong>of</strong> supply chain partners is also
improved
as a result.
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297

Knowledge Workers: A Typology Framework as a
Theoretical Basis for Knowledge Worker Identification
Instrument
Jiří Franek 1 and Eva Grublova 2
1
Faculty <strong>of</strong> economics/VSB – Technical University <strong>of</strong> Ostrava, Czech Republic
2
Moravska vysoka skola Olomouc, Czech Republic
jiri.franek.st2@vsb.cz
eva.grublova@mvso.cz
Abstract: As we consider that <strong>the</strong> concepts <strong>of</strong> knowledge, knowledge work, knowledge workers and knowledge
management have a strong relationship to organizational performance and competitiveness, <strong>the</strong> aim <strong>of</strong> this paper
is to present relations among mentioned concepts, find appropriate characteristics with regard to knowledge
workers and design a framework <strong>of</strong> knowledge worker typology identification instrument. The considerable lack <strong>of</strong>
focus <strong>of</strong> tacit knowledge in knowledge management applications is inappropriate to broadly accepted claim that
this type <strong>of</strong> knowledge is most important to knowledge work and knowledge management performance.
According to various sources tacit knowledge defines knowledge workers. Knowledge works are scattered
among all organizations but <strong>the</strong>y are not treated accordingly so <strong>the</strong>ir performance could be hindered lack <strong>of</strong>
attention to <strong>the</strong>ir special needs. Knowledge workers cannot be assessed or evaluated <strong>the</strong> same way as o<strong>the</strong>r
workers <strong>the</strong>refore <strong>the</strong> recognition <strong>of</strong> knowledge workers in an organization sees crucial. The first part <strong>of</strong> <strong>the</strong> paper
deals with fundamental definitions, description and basic assumptions that lead authors to <strong>the</strong> topic. Then
authors focus on concepts related to <strong>the</strong> problem especially knowledge workers. Knowledge workers are
characterized and categorized according to literature review <strong>of</strong> <strong>the</strong> topic. Authors suggest own knowledge
workers typology framework. This framework consists <strong>of</strong> three different types <strong>of</strong> knowledge worker: <strong>the</strong>
knowledge users (KU), <strong>the</strong> knowledge creators (KC) and knowledge facilitators (KF). The concept is illustrated
figures that show relationships among those types <strong>of</strong> knowledge workers. Based on previous suggestions a
design <strong>of</strong> identification instrument is drawn. The following part focuses on <strong>the</strong> knowledge worker typology
identification instrument concept based on cognitive processes and Bloom’s taxonomy <strong>of</strong> learning processes. It
distinguishes several domains and levels that determine each type. A probe is made to support <strong>of</strong> disprove
authors proposals and suggestions. Results <strong>of</strong> <strong>the</strong> probe will be presented at <strong>the</strong> conference. The purpose <strong>of</strong> this
paper is to promote author’s assumptions and implications <strong>of</strong> <strong>the</strong> knowledge worker typology and identification for
fur<strong>the</strong>r research.
Keywords: Knowledge, knowledge work, knowledge workers, knowledge management
1. Introduction
Organizational performance is strongly affected by appropriate knowledge availability and utilization.
Knowledge as well as information and data should be available to those who need <strong>the</strong>m at <strong>the</strong> right
time and on <strong>the</strong> right place and in <strong>the</strong> right form Barták (2008, p. 20). Knowledge is considered as <strong>the</strong>
essential part <strong>of</strong> organization’s performance. As one <strong>of</strong> <strong>the</strong> well known scholars P. Drucker (1993,
p. 112) claimed, innovations based on new knowledge are “superstars” <strong>of</strong> entrepreneurship.
Innovativeness is considered as one <strong>of</strong> <strong>the</strong> main factors <strong>of</strong> organization competitiveness.
Contributions <strong>of</strong> knowledge management include improved ability to innovate, improved coordination
<strong>of</strong> efforts, responsiveness to market change, and reduced redundancy <strong>of</strong> knowledge. Therefore
knowledge also contributes to competitive advantage <strong>of</strong> organizations. Knowledge is perceived
combination <strong>of</strong> data and information, to which is added expert opinion, skills, and experience,
resulting in a valuable asset that aids decision making. In organizational terms, knowledge is
generally thought <strong>of</strong> as being know-how, applied information, information with judgment, or <strong>the</strong>
capacity for effective action. Knowledge may be tacit, explicit, individual, and/or collective. Knowledge
is inherent to people (e.g. employees, workers). People in <strong>the</strong> organization are <strong>the</strong> main source
knowledge creation, utilization, retention and sharing. According to various references <strong>the</strong>re are two
basic dimensions (or types) <strong>of</strong> knowledge: tacit and explicit. Tacit knowledge embedded in <strong>the</strong>
expertise and experience <strong>of</strong> individuals and groups. It is difficult to codify or share, <strong>the</strong>refore hardly
manageable and retainable for organizations. Mládková (2008) claims, that tacit knowledge is
considered to be <strong>the</strong> most important factor <strong>of</strong> knowledge workers. According to Drucker (in Haas
Edersheim 2008) knowledge worker (KW) is a person who uses its mind ra<strong>the</strong>r <strong>the</strong>n muscles.
Because <strong>the</strong> knowledge is mostly perceived in its explicit form, <strong>the</strong> wide public mostly see KWs as
scientists, clerks, managers, white collars in general. But this is wrong understanding <strong>of</strong> <strong>the</strong> concept.
KWs are people who utilize <strong>the</strong>ir both explicit and tacit knowledge in better way and think about all
activities <strong>the</strong>y do. They try to add more value to every action. It must be said that KWs can be found
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Jiří Franek and Eva Grublova
among all workers in all industries and institutions. But an accurate identification <strong>of</strong> knowledge works
is <strong>the</strong> problem. The business world heavily favours <strong>the</strong> use <strong>of</strong> instruments such as <strong>the</strong> Myer-Briggs
Type Indicator (MBTI) personality style assessment (Myers et al. 1998, Mládková 2008), to assess
differences in personality styles or talent management (Buckingham, C<strong>of</strong>fman 2005). But such as
tools are helpful but not sufficient when it comes to knowledge work itself. Also o<strong>the</strong>r authors mention
<strong>the</strong> lack <strong>of</strong> tacit knowledge focus in knowledge management assessments and application (Fahey,
Prusak 1998, Albers 2009). According to claims above <strong>the</strong> tacit knowledge could be a key for
identification <strong>of</strong> knowledge workers, but comprehensive personality assessments are not properly
focused on it.
An instrument for identification <strong>of</strong> knowledge workers in organizational structure should help to
uncover knowledge workers among o<strong>the</strong>r members <strong>of</strong> an organization and by applying appropriate
support to those specific types <strong>of</strong> KWs, change <strong>the</strong> way, how are information and knowledge formally
distributed and shared. Knowledge managers can draw a better picture <strong>of</strong> knowledge workers
network and find best way how to support knowledge management activities more efficiently. It can
also contribute to human resources management in tasks <strong>of</strong> building cross organizational teams,
finding <strong>of</strong> right working positions and managers to delegate work to subordinates that are best for <strong>the</strong>
desired job.
Therefore, identification <strong>of</strong> KWs in any organization level seems to be crucial for fur<strong>the</strong>r organizational
knowledge development and thus for its competitiveness. There were various studies and works on
knowledge workers performance evaluation and measurement or utilization <strong>of</strong> tacit knowledge.
However, <strong>the</strong> aim <strong>of</strong> this paper is to point out on importance <strong>of</strong> <strong>the</strong> relationship <strong>of</strong> <strong>the</strong> tacit knowledge
and knowledge workers and design a concept <strong>of</strong> <strong>the</strong> instrument for knowledge worker typology
identification in organizations. The paper also summarizes <strong>the</strong>oretical background and major claims
and statements for fur<strong>the</strong>r research <strong>of</strong> this matter.
2. The relationship between knowledge, knowledge work, and knowledge
workers
2.1 Knowledge and its types
As it was mentioned above an organization (e.g. its employees, workers) possesses tacit and explicit
knowledge. Explicit knowledge is tangible mostly as a written text or in a form <strong>of</strong> audio and video files.
Explicit knowledge can be codified in organizational rules, routines, and procedures, or made tangible
o<strong>the</strong>rwise. Ano<strong>the</strong>r very important factor is that people are consciously aware <strong>of</strong> its usage and its
creation. Organizations use various information technology (IT) tools to support retention and sharing.
Explicit knowledge can be perceived as a knowledge that is adequately and properly represented by
facts, figures, symbols, and data. On <strong>the</strong> o<strong>the</strong>r hand tacit knowledge originally identified by Polanyi
and his conception <strong>of</strong> it has been consistently applied by researchers in a variety <strong>of</strong> fields. Tacit
knowledge represents almost 90% <strong>of</strong> all knowledge (Mládková 2008). It is a combination <strong>of</strong> explicit
knowledge, experiences, behaviour, mental models, feelings, emotional and physical dispositions,
etc. Tacit knowledge is not inherent only to individuals but also to groups. Critics mention classical
statement from Wittgenstein (in Ein-Dor 2006, p. 849-850): “The limits <strong>of</strong> my language are <strong>the</strong> limits <strong>of</strong>
my mind. All I know is what I have words for.” Polanyi (in Ein-Dor 2006, p. 850) claimed “...we can
know more than we can tell.” But knowledge that “we cannot tell” can sometimes crucial when words
cannot express all <strong>the</strong> detail <strong>of</strong> a physical entity. This is important when it comes to creativity and thus
innovativeness. The concept tacit knowledge can be regarded as cognitive sensations for which we
have not yet found words or we may regard to tacit knowledge as a form <strong>of</strong> sensory inputs and
gestalts. In o<strong>the</strong>r words tacit knowledge is kind <strong>of</strong> personal “know-how” that is difficult to articulate
because it is derived from individual experience and beliefs. Transfer <strong>of</strong> <strong>the</strong> tacit knowledge is a major
challenge for knowledge workers and managers. More knowledge dimensions were derived by Alavi
and Leindner (2001). The have added individual, social, declarative, procedural, causal, conditional,
regional and pragmatic levels.
2.2 Knowledge work
According to Heavin and Neville (2006) knowledge work means <strong>the</strong> ability to create an understanding
<strong>of</strong> nature, organizations, and processes, and to apply this understanding as a means <strong>of</strong> generating
wealth in <strong>the</strong> organization. That is, work that comprises to a large extent <strong>the</strong> tasks <strong>of</strong> retrieving,
evaluating, integrating, and creating knowledge. Knowledge work thus involves both application and
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Jiří Franek and Eva Grublova
creation <strong>of</strong> knowledge. Therefore it can be perceived as a creative work solving unstructured
problems that requires <strong>the</strong> creation, application, packaging, teaching, and acquisition <strong>of</strong> knowledge
claimed Kelloway and Barling (2000). Knowledge work is conducted by knowledge workers.
2.3 Knowledge workers
Knowledge worker (KW) is a concept firstly mentioned by P. Drucker (1992). KWs are described as
participants in an economy where information and its manipulation are <strong>the</strong> commodity and <strong>the</strong> activity.
Sveiby (1997) defines KWs as highly qualified and highly educated pr<strong>of</strong>essionals whose work
consists largely <strong>of</strong> using <strong>the</strong>ir own competencies to convert information into knowledge, sometimes
with <strong>the</strong> assistance <strong>of</strong> suppliers <strong>of</strong> information or specialized knowledge. Lindsey (in Schwartz, 2006,
p. 506) has more general view, when perceives KW as anyone whose work involves tasks that require
<strong>the</strong> processing <strong>of</strong> information. These tasks include collecting, analyzing, syn<strong>the</strong>sizing, structuring,
storing, retrieving, and using information. They are people who look to different repositories or wealth
<strong>of</strong> knowledge to find new solutions to challenges faced in a dynamic and changing work environment
and primarily focuses on <strong>the</strong> generation, processing, storage, and use <strong>of</strong> knowledge. Unlike data or
information workers, knowledge workers create and utilize <strong>the</strong> meaning and context inherent in
knowledge. KWs are pr<strong>of</strong>essionals who have gained knowledge through formal education (explicit)
and through learning on <strong>the</strong> job (tacit). The concept <strong>of</strong> knowledge management enables KWs to
flourish <strong>the</strong>ir abilities and positively influences <strong>the</strong>ir performance. Bergeron (2003) claimed that
employees and managers who contribute significantly to <strong>the</strong> intellectual capital <strong>of</strong> <strong>the</strong> company are in
fact knowledge workers. In practice, <strong>the</strong> distinction between ordinary employees and KWs is a matter
<strong>of</strong> degree, in that even manual labourers bring to <strong>the</strong>ir company <strong>the</strong>ir knowledge in terms <strong>of</strong> skill and
experience. Knowledge workers typically add to <strong>the</strong> value <strong>of</strong> <strong>the</strong> corporation by contributing to <strong>the</strong>
corporate knowledge assets, by documenting problems solving activities, by reporting best practices,
and by disseminating information in newsletters, online, and in o<strong>the</strong>r publications. According to
Jennex, et. al. (2008) KWs apply <strong>the</strong>ir experiences and skills in innovative ways as to handle
unstructured tasks, so knowledge management initiatives should support <strong>the</strong>m in order to leverage
<strong>the</strong>ir work. A pr<strong>of</strong>ound characteristic <strong>of</strong> knowledge workers gives also Vymětal et.al. (2005), when
emphasizes competence on a model <strong>of</strong> “ideal knowledge worker”.
2.4 Knowledge workers and knowledge work
In evaluating <strong>the</strong> contribution <strong>of</strong> KWs in <strong>the</strong> modern knowledge organization, <strong>the</strong>re is a significant
difference between knowing and doing. Successful companies actively manage <strong>the</strong>ir knowledge
workers in good times and more challenging times as if those workers were customers. Fur<strong>the</strong>rmore
Bergeron (2003) states that KWs are central to <strong>the</strong> operation <strong>of</strong> a knowledge organization. Not only
do <strong>the</strong>y represent <strong>the</strong> greatest potential for multiplying <strong>the</strong> value <strong>of</strong> a company, but <strong>the</strong>y also
represent <strong>the</strong> greatest risk to value loss.
Oshri (2006, p 492) mentioned special category <strong>of</strong> knowledge workers <strong>the</strong> knowledge facilitators who
ensure that knowledge is captured, indexed, and was made available for reuse. Markus (2001)
suggested that knowledge facilitators (also known as knowledge brokers and knowledge managers)
can improve <strong>the</strong> capture and filtering <strong>of</strong> knowledge for reuse.
Miller (in Montano 2008, p. 292) regards knowledge workers as knowledge creators <strong>the</strong>y are
individuals who have a role in seeking out knowledge sources. Knowledge creators recognize <strong>the</strong>
potential <strong>of</strong> information to be reused in novel ways and are able to create new or improved products
and services by working individually or in groups with o<strong>the</strong>r knowledge creators. Because
organizations cannot create knowledge without people, <strong>the</strong> knowledge worker has become an
organization’s single greatest asset. With reference to Maslow’s hierarchy <strong>of</strong> needs (e.g. Maslow
1987), knowledge workers are motivated by esteem and self-actualization needs.
Prusak and Davenport (1998) linked tacit knowledge to knowledge workers. Knowledge workers
possess tacit knowledge, which various knowledge methodologies and specialized knowledge
workers such as <strong>the</strong> “integrators, librarians, syn<strong>the</strong>sizers, reporters, and editors” (Prusak, Davenport
1998, p. 110) convert to explicit knowledge when <strong>the</strong>y “extract knowledge from those who have it, put
it in a structured form, and maintain it or refine it over time” (Prusak, Davenport 1998, p. 110). Thus a
model <strong>of</strong> knowledge worker and knowledge relationship is designed (see figure 1).
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text sources
visual sources
audio sources
Jiří Franek and Eva Grublova
accesses
Knowledge
worker shares
Explicit
knowledge
gains
acquires
transfer/
codification
SECI
Tacit
knowledge
mental models
experiences
physical,
emotional, spiritual
and mental
inteligence
Figure 1: Knowledge worker and knowledge relationship model, own elaboration.
In general knowledge worker need access to explicit knowledge that can be acquired. This explicit
knowledge can be compiled from <strong>the</strong> mix <strong>of</strong> text, visual or audio sources. In practice this is included in
enterprise information systems (ERPs) or easily accessible through World Wide Web. The tacit
knowledge is shared among people. Knowledge worker <strong>of</strong>ten gains this knowledge by learning on <strong>the</strong>
job. Tacit knowledge evolves form interaction <strong>of</strong> experience, intelligence and mental models (thought
processes). The process <strong>of</strong> transferring tacit knowledge to explicit and back was described by Nonaka
and Takeuchi (1995). It is called SECI model, i.e. a abbreviation <strong>of</strong> terms socialization, externalization,
combination and internalization.
2.5 Knowledge worker’s typology
To sum up previous characteristic a knowledge worker system is created. We suggest three basic
types <strong>of</strong> KWs (see figure 2.). Knowledge users (KU) are KWs that prefer to use explicit knowledge
and implement it into <strong>the</strong>ir routine operations. Their knowledge is mostly practical. On <strong>the</strong> o<strong>the</strong>r hand
junior employees also fall into this category. They have gain explicit as well as tacit knowledge. After
<strong>the</strong>y will proceed to level <strong>of</strong> knowledge creators or stay in position <strong>of</strong> knowledge users e.g. clerks,
<strong>of</strong>ficers, secretaries, accountants, labourers etc. Knowledge creators (KC) are KWs who use <strong>the</strong>ir
gained tacit and explicit knowledge extensively to create new knowledge. Besides <strong>the</strong>ir main job, <strong>the</strong>y
seek out new knowledge sources, recognize potential for knowledge reuse, make networks with o<strong>the</strong>r
KWs, and implement <strong>the</strong>ory into practice. These KWs are not always in executive positions or
research and development. Some <strong>of</strong> <strong>the</strong>m are hidden in <strong>the</strong> organization structure and <strong>the</strong>ir
capabilities are not formally recognized. However, <strong>the</strong>y are <strong>of</strong>ten known for <strong>the</strong>ir expertise informally.
Knowledge creators are KUs that evolved in to this position by pursuing <strong>the</strong>ir ambitions to know more,
have distinctive talent, education and experience. Some <strong>of</strong> those creators became knowledge
facilitators.
Knowledge
creators
Knowledge workers
Knowledge
facilitators
Figure 2: Knowledge worker’s typology, own elaboration.
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Knowledge
users

Jiří Franek and Eva Grublova
Knowledge facilitators (KF) are workers who facilitate capturing, indexing, availability <strong>of</strong> knowledge to
KUs and KCs. They are responsible for organizational knowledge base, e.g. an organized structure
that facilitates <strong>the</strong> storage <strong>of</strong> data, information, and knowledge to be retrieved in support <strong>of</strong> a
knowledge management process. KF’s job includes knowledge integrating (to find and close
knowledge gaps), retention (to store data, information and knowledge in e.g. databases), syn<strong>the</strong>sizing
(to network knowledge bases and workers), reporting (to make lists, abstracts <strong>of</strong> new knowledge and
information sources, to support knowledge sharing) and editing (o edit and administer knowledge
base). They can be called knowledge managers or knowledge brokers. KFs have to possess not only
all knowledge worker abilities but also have to have a managerial or leadership skills. Their main job
is to communicate and make connections between knowledge and people. With regard to <strong>the</strong> scale <strong>of</strong>
organization’s operations and size, organization should set up a formal position <strong>of</strong> knowledge
manager. It is a role with operational and developmental responsibility for implementing and
reinforcing knowledge management principles and practices. Knowledge manager work involves
promotion <strong>of</strong> access to information, intelligence support, expertise, and good practices. As a head KF
knowledge manager has to monitor knowledge flow between knowledge workers. On <strong>the</strong> figure 3 <strong>the</strong>
knowledge flow among mentioned type <strong>of</strong> knowledge workers is shown. KFs should capture, share,
store and disseminate knowledge between knowledge creators and users. Their actions are focused
on knowledge assessment and identification <strong>of</strong> gaps. KUs contextualize <strong>the</strong> desired knowledge and
KCs doing updates <strong>of</strong> knowledge to cover knowledge gaps.
Knowledge
retrieval and
creation
knowledge
assessment
knowledge
update
Knowledge
acquisition,
utilization and
application
Knowledge
capture, sharing,
storing and
dissemination
knowledge gap
identification
knowledge
contextualization
Figure 3: Model <strong>of</strong> <strong>the</strong> knowledge flow among knowledge workers, own elaboration.
3. Knowledge worker typology identification instrument concept
3.1 Basic assumptions
Based on <strong>the</strong>ories and concepts about tacit and explicit knowledge, knowledge workers and
knowledge work <strong>the</strong> aim <strong>of</strong> this framework is to find a method that can be used for identification <strong>of</strong>
knowledge workers in organizations. To support validity <strong>of</strong> presented methodology a pilot study will be
performed and analysed. The instrument is inspired by works <strong>of</strong> o<strong>the</strong>r scholars and authors. The
design follows <strong>the</strong> approach to knowledge worker as mentioned above.
3.2 Knowledge worker’s typology factor determination
The business world heavily favours <strong>the</strong> use <strong>of</strong> instruments such as <strong>the</strong> Myer-Briggs Type Indicator
(MBTI) personality style assessment (Myers et al., 1998, Mládková, 2008), to assess differences in
personality styles or talent management (Buckingham, C<strong>of</strong>fman, 2005). But such as tools are
inspirational not sufficient when it comes to knowledge work itself. Essential principles about
knowledge work (i.e. activity conducted by knowledge workers) can be more appropriately derived
from works <strong>of</strong> Bloom (1956) (in Dalkir, 2005, p. 152-155) and his Taxonomy <strong>of</strong> Learning Objectives.
Bloom divided knowledge into a hierarchical scheme that distinguishes between psychomotor skills
(manual or physical skills), <strong>the</strong> affective domain (e.g., attitudes, emotions), and <strong>the</strong> cognitive domain
(e.g., knowledge, mental skills). We can assume that psychomotor and cognitive domain can be
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Jiří Franek and Eva Grublova
learned and trained using mostly <strong>the</strong> explicit knowledge, and <strong>the</strong>n <strong>the</strong> affective domain is <strong>of</strong>ten a
question <strong>of</strong> tacit knowledge (learned by experience on <strong>the</strong> job). The main purpose <strong>of</strong> this taxonomy
was to help in learning activities. However some works on knowledge management suggests its
implication in this area.
3.2.1 Cognitive domain levels
If we assume that cognitive domain represents basic knowledge work mental skills, <strong>the</strong>n its levels can
be used as a framework for knowledge worker typology. The cognitive domain taxonomy levels from
low to high consist <strong>of</strong> knowledge, comprehension, application, analysis, syn<strong>the</strong>sis, and evaluation.
Reviewed Bloom’s Taxonomy by Pohl (2000) brought a few changes. So, <strong>the</strong> current levels are:
remembering (knowledge), understanding (comprehension), applying (application), analyzing
(analysis), evaluating (evaluation), and creating (syn<strong>the</strong>sis) (for fur<strong>the</strong>r details see appendix 1). The
cognitive domain levels could help to define <strong>the</strong> level <strong>of</strong> knowledge work conducted by KWs.
Comparing knowledge workers typology with given knowledge levels, we can assume that first three
levels (remembering, understanding, and applying) represent knowledge user’s approach to
knowledge work. Fur<strong>the</strong>r three levels (analysing, evaluating, and creating) regard to knowledge
creator’s knowledge activities. The third knowledge worker type, <strong>the</strong> knowledge facilitator, should be
able to evolve from all <strong>of</strong> previous levels but <strong>the</strong>re should be ano<strong>the</strong>r level that could differentiate KFs
from o<strong>the</strong>r knowledge workers. According to requirements that KFs should fulfil, based on previous
levels it can be assumed that a level <strong>of</strong> system perception (e.g. knowledge flow) has to be one <strong>of</strong>
<strong>the</strong>m (see appendix 1). We can describe it as <strong>the</strong> ability to see knowledge work in relations. Using
system thinking to make purposeful complexes with regard to o<strong>the</strong>rs (e.g. to combine, communicate,
compile, create, goal setting, initiate, lead, manage and organize, setting a vision, etc.). First three
levels should characterize utilization <strong>of</strong> explicit knowledge by knowledge users. Next three levels
define more creative approach to work with explicit knowledge and information. The last level focuses
on managerial skills necessary for KF’s work.
3.2.2 Affective domain levels
We can assume that affective domain represents <strong>the</strong> tacit knowledge and its cycle (see appendix 2).
Knowledge users should be able to receive and respond to given explicit knowledge. It can be
assume that evolving in to <strong>the</strong> role <strong>of</strong> knowledge creators, KWs have to be able to value, organize
and internalize explicit knowledge. According to claims above knowledge creators could become
knowledge facilitators. Based on given characteristics <strong>of</strong> KFs, we can assume that <strong>the</strong> tacit
communication skills make <strong>the</strong> difference. It means an ability to communicate, persuade and promote
knowledge to o<strong>the</strong>r KWs.
3.2.3 Psychomotor domain levels
Psychomotor domain level represents <strong>the</strong> manual knowledge. As discussed previously, knowledge
workers are to be found also among manual workers (e.g. Truneček 2004; Bureš 2007). Even
knowledge work includes some basic psychomotor aspects. As Truneček (2004) states <strong>the</strong> border
between manual wand knowledge workers is fuzzy. Knowledge users among manual workers can be
perceived as operation workers (operators) using only explicit manual knowledge. They should be
able to use cues to guide activities (level <strong>of</strong> perception), readiness to act (level <strong>of</strong> set), <strong>the</strong> adequate
performance is achieved by practicing (level <strong>of</strong> guided response), responses are habitual and <strong>the</strong>
movements can be performed with confidence and pr<strong>of</strong>iciency (level <strong>of</strong> mechanism and complex over
response). On <strong>the</strong> o<strong>the</strong>r hand knowledge creators have well developed skills and can modify
movement patterns to fit special requirements, <strong>the</strong>y are able to create new movement patterns to fit a
particular situation or specific problem. Results <strong>of</strong> <strong>the</strong>ir work can be used by o<strong>the</strong>r workers. The third
type <strong>of</strong> KW should also evolve through mentioned levels. The position <strong>of</strong> knowledge facilitator among
manual workers falls into collaboration (last psychomotor level). Their main purpose is to match right
workers toge<strong>the</strong>r to achieve a common goal. KFs promote cooperation among o<strong>the</strong>rs. They have to
be aware <strong>of</strong> worker’s knowledge. They build a network inside <strong>the</strong> operation and ensure <strong>the</strong>re are no
problems related to one’s manual knowledge gaps.
3.3 The instrument concept
In order to construct an instrument from assumptions discussed above (see table 1) a study should be
conducted. Given cognitive, affective and psychomotor domain levels have to be recognized as
characteristics that must be proved. Based on those characteristics a probe into organization
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members (e.g. employees) have to be performed. This probe should be a resource for analysis that
would find relevant characteristic and confirm or disapprove given characteristics.
Table 1: Knowledge workers typology, cognitive, affective and psychomotor domain levels, own
elaboration.
Knowledge worker type Cognitive domain levels Affective domain levels Psychomotor domain
levels
Knowledge user Remembering (knowledge) Receiving Perception
Set
Understanding (comprehension) Responding Guided response
Applying (application) Mechanism
Complex overt
response
Knowledge creator Analysing (analysis) Valuing Adaptation
Evaluating (evaluation) Organization
Creation (syn<strong>the</strong>sis) Internalizing Origination
Knowledge facilitator System perception Communication Collaboration
The methodology <strong>of</strong> <strong>the</strong> probe would have to combine qualitative and quantitative research methods.
In order to get representative set <strong>of</strong> data an enquiry using specifically designed questionnaire will be
used. Then <strong>the</strong> cluster analysis will be used as appropriate method for ga<strong>the</strong>red data analysis. Its
purpose will be to narrow <strong>the</strong> characteristics for each domain. In order to find relations between
domains that characterize each knowledge worker type <strong>the</strong> correlation analysis can be applied.
According to <strong>the</strong> result <strong>of</strong> <strong>the</strong> probe <strong>the</strong>re should be a room for improvement <strong>of</strong> <strong>the</strong> instrument and
refinement <strong>of</strong> stated allegations and assumptions. Those would be <strong>the</strong>n evaluated by research results
with appropriate reliability.
4. Conclusion
Knowledge belongs to key organization competitiveness factors. Organizations use <strong>the</strong>m to gain and
maintain <strong>the</strong> competitive advantage. This is due knowledge intensive work that can produce added
value. Knowledge work is performed by knowledge workers. The majority <strong>of</strong> <strong>the</strong> workforce in most
industries in developed economies is comprised <strong>of</strong> knowledge workers, who apply <strong>the</strong>ir brainpower
and skills to process information into applicable knowledge to make new products. Knowledge
workers need time to experiment, innovate, contemplate, and brainstorm. Even <strong>the</strong> best databases
with state-<strong>of</strong>-art knowledge (explicit resources) do not provide a strategic strength unless
complemented by relevant knowledge workers to apply and develop it (tacit resources). Similarly, <strong>the</strong>
best people with <strong>the</strong> best knowledge (tacit resources) can be frustrated by lack <strong>of</strong> explicit resources
that provide <strong>the</strong> information <strong>the</strong>y need.
Presented knowledge worker typology and identification framework consists <strong>of</strong> combination <strong>of</strong> tacit
and explicit knowledge <strong>the</strong>ory with regard to knowledge work and knowledge workers. It proposes
three main types <strong>of</strong> knowledge workers: <strong>the</strong> knowledge users, <strong>the</strong> knowledge creators and knowledge
facilitators. Designed framework also includes basic characteristics <strong>of</strong> those types. The paper
suggests relationships in-between knowledge workers types and possible evolution <strong>of</strong> knowledge
workers within <strong>the</strong>m.
The purpose <strong>of</strong> this paper was to promote complex approach to knowledge worker identification and
typology. It will be a foundation to more pr<strong>of</strong>ound research on differences knowledge flow among
knowledge workers. Fur<strong>the</strong>r research areas could imply <strong>the</strong> evolution <strong>of</strong> knowledge workers in time
with regard to <strong>the</strong>ir knowledge and information needs. Research in this area could enrich
contemporary knowledge management field. There are some constraints that should be identified by
verification <strong>of</strong> presented models and allegations. Results <strong>of</strong> <strong>the</strong> study will be published and presented
at <strong>the</strong> conference. They should reveal if <strong>the</strong>re are relationships between <strong>the</strong> type <strong>of</strong> an institution,
number <strong>of</strong> direct colleagues, length <strong>of</strong> employment and scale <strong>of</strong> <strong>the</strong> organization.
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Middle Managers’ Maturity <strong>of</strong> Knowledge Sharing:
Investigation <strong>of</strong> Middle Managers Working at Medium- and
Large-sized Enterprises
Zoltán Gaál, Lajos Szabó, Nóra Obermayer-Kovács and Anikó Csepregi
University <strong>of</strong> Pannonia, Department <strong>of</strong> Management, Veszprém, Hungary
gaal@gtk.uni-pannon.hu
szabola@gtk.uni-pannon.hu
obermayerkovacs@gtk.uni-pannon.hu
csepregia@gtk.uni-pannon.hu
Abstract: Knowledge is becoming is becoming an increasingly important factor <strong>of</strong> organizational
competitiveness. The way knowledge is shared within <strong>the</strong> organization is essential and central not only to <strong>the</strong>
success <strong>of</strong> <strong>the</strong> organization but also among those employee who share it, since those who take part in <strong>the</strong>
knowledge sharing process also benefit from it. Since middle managers have an important position <strong>of</strong> within <strong>the</strong>
organization and play a significant role in <strong>the</strong> process <strong>of</strong> knowledge sharing this paper has focused on <strong>the</strong>
knowledge sharing <strong>of</strong> <strong>the</strong>se middle managers in Hungary. A new method <strong>of</strong> how to measure middle managers’
maturity <strong>of</strong> knowledge sharing is presented in this paper. Between 2007 and 2010 an empirical survey was
conducted during which 400 middle managers working at medium- and large-sized enterprises in Hungary were
investigated by a questionnaire. The answers <strong>of</strong> this survey have been analysed using Principal Component
Analysis and four different components regarding <strong>the</strong> maturity <strong>of</strong> knowledge sharing have been identified. These
four components are <strong>the</strong> availability among middle managers, <strong>the</strong> availability among <strong>the</strong> middle manager and
subordinates, <strong>the</strong> usefulness <strong>of</strong> knowledge among middle managers and <strong>the</strong> usefulness <strong>of</strong> knowledge among <strong>the</strong>
middle manager and subordinates.
Keywords: knowledge sharing, maturity, middle managers
1. Introduction
Knowledge sharing is considered to be <strong>the</strong> fundamental means though which organizational
competitive advantage can be reached (Jackson et al. 2006). The way knowledge is shared within <strong>the</strong>
organization is essential and central not only to <strong>the</strong> success <strong>of</strong> <strong>the</strong> organization where it takes place
but also among those who share it, since those who take part in <strong>the</strong> knowledge sharing process also
benefit from it.
Middle managers play a key role in <strong>the</strong> knowledge sharing process. During <strong>the</strong> process <strong>of</strong> knowledge
sharing <strong>the</strong> role <strong>of</strong> <strong>the</strong>se middle managers has to change from control to mentor and facilitate o<strong>the</strong>rs.
However <strong>the</strong>y <strong>of</strong>ten resist <strong>the</strong> realization <strong>of</strong> such changes. After building <strong>the</strong>ir careers and lives
around <strong>the</strong> hierarchical pathways that exist within <strong>the</strong> organization, <strong>the</strong> appearance <strong>of</strong> a nonhierarchical
work flow which does not require management behaviours concerning command-andcontrol
may threaten <strong>the</strong>m (Pommier et al. 2000). The fact regarding <strong>the</strong> poor knowledge sharing and
<strong>the</strong> resistance towards knowledge sharing <strong>of</strong> middle managers should not be neglected since it may
cause serious damages within <strong>the</strong> organization.
2. Theoretical background
2.1 Knowledge sharing
Knowledge sharing represents <strong>the</strong> key knowledge management processes in organizations and is
fundamental for generating new ideas and developing new business opportunities (Lin 2007).
Huysman and de Wit (2002:23) also stress <strong>the</strong> significance <strong>of</strong> knowledge sharing while determining
knowledge management, which according to <strong>the</strong>m is „nothing o<strong>the</strong>r than managing knowledge
sharing”. Géró (2000) emphasises <strong>the</strong> significance <strong>of</strong> knowledge sharing besides o<strong>the</strong>r activities as
well by mentioning that nowadays one <strong>of</strong> <strong>the</strong> biggest challenges includes <strong>the</strong> mapping, using and also
<strong>the</strong> sharing <strong>of</strong> available knowledge. The reason why knowledge sharing within an organization is so
important is defined by Dunford (2000:296) as follows ‘‘much <strong>of</strong> <strong>the</strong> key knowledge is held by
individuals unless <strong>the</strong>re is some structure to retain it within <strong>the</strong> organizational memory’’. Finally, <strong>the</strong>
goal <strong>of</strong> knowledge sharing according to Christensen (2007:37) “can ei<strong>the</strong>r be to create new
knowledge by differently combining existing knowledge or to become better at exploiting existing
knowledge”.
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Regarding <strong>the</strong> definitions <strong>of</strong> knowledge sharing, it is mainly described as an activity during which
information or o<strong>the</strong>r important contents are shared (Bartol, Srivastava 2002; Möller, Svahn 2004;
Kocsis 2004; Li 2010). The approach <strong>of</strong> Bartol and Srivastava (2002) contains information as an
element <strong>of</strong> knowledge sharing and defines it as <strong>the</strong> action in which relevant information are diffused
by employees to o<strong>the</strong>rs across <strong>the</strong> organization. Möller and Svahn (2004:220) emphasize that
knowledge sharing is “sharing not only codified information, such as production and product
specifications, delivery and logistics information, but also management beliefs, images, experiences,
and contextualized practices such as business-process development”. Kocsis (2004:41) defines
knowledge sharing as “<strong>the</strong> activity <strong>of</strong> individuals following <strong>the</strong>ir self-interest”. The sharing <strong>of</strong>
knowledge is also defined by Li (2010:40) as an activity, but “in which participants are involved in <strong>the</strong>
joint process <strong>of</strong> contributing, negotiating and utilizing knowledge”.
After reviewing <strong>the</strong>se definitions it can be seen that none <strong>of</strong> <strong>the</strong>m deal with middle managers and <strong>the</strong>y
also lack in certain elements that are important regarding <strong>the</strong> knowledge sharing <strong>of</strong> middle managers.
This has inspired us to create our own definition <strong>of</strong> knowledge sharing from <strong>the</strong> combination <strong>of</strong> <strong>the</strong>
above mentioned ones. Thus this research defines knowledge sharing as a two-way process (giving
and receiving knowledge) between <strong>the</strong> knowledge giver(s) and <strong>the</strong> knowledge receiver(s) who as
participants <strong>of</strong> knowledge sharing exchange <strong>the</strong> knowledge found in <strong>the</strong>ir minds or <strong>the</strong> knowledge
found in electronic or paper documents fur<strong>the</strong>rmore knowledge sharing can occur at <strong>the</strong> same time
when <strong>the</strong> participants are present or at different times when <strong>the</strong>y make <strong>the</strong>ir knowledge explicit.
2.2 Measurement <strong>of</strong> knowledge management maturity and knowledge sharing
The existing knowledge management maturity models can be categorized into two groups, depending
on whe<strong>the</strong>r <strong>the</strong>y are based on S<strong>of</strong>tware Engineering Institute’s (SEI) Capability Maturity Model (CMM)
or not. In <strong>the</strong> CMM, five levels <strong>of</strong> maturity are defined and each level is described by a unique set <strong>of</strong>
characteristics. Examples <strong>of</strong> CMM-based knowledge management maturity models are Siemens’
KMMM, Paulzen and Perc’s (2002) Knowledge Process Quality Model. Non-CMM-based
knowledge management maturity models contain examples <strong>of</strong> KPMG’s (2000) Knowledge Journey,
Klimko’s (2001) KMMM, TATA Consultancy Services’ 5iKM3 KMMM (Mohanty, Chand 2004), and
WisdomSource’s (2004) K3M. These models mainly evaluate <strong>the</strong> maturity levels <strong>of</strong> organizations and
not individuals, thus <strong>the</strong>y cannot be used in our research. Fur<strong>the</strong>rmore <strong>the</strong>y describe steps <strong>of</strong> growth
and if <strong>the</strong>se are achieved by <strong>the</strong> organizations <strong>the</strong>n <strong>the</strong>y can reach <strong>the</strong>ir knowledge management
development (Khatibian et al. 2010).
The majority <strong>of</strong> studies have measured individual knowledge sharing from <strong>the</strong> point <strong>of</strong> view <strong>of</strong>
willingness (or intention) <strong>of</strong> employees towards knowledge sharing or investigated self-reported
knowledge sharing behaviours (Bock et al. 2005; Lin 2007; Jiacheng et al. 2010). From an o<strong>the</strong>r point
<strong>of</strong> view it can be seen that certain knowledge sharing studies have been conducted from
organizational perspective (Yang, Chen 2007; Bosua, Scheepers 2007; Lin 2008) while o<strong>the</strong>rs from
<strong>the</strong> behavioural perspective (Bock et al. 2005; Matzler et al. 2008; Chow, Chan 2008).
Since <strong>the</strong>se researches did not investigate middle managers and <strong>the</strong>ir knowledge sharing our
research focuses on this domain.
3. Empirical study
3.1 The purpose <strong>of</strong> <strong>the</strong> research and <strong>the</strong> research question
The purpose <strong>of</strong> this research has been to reveal <strong>the</strong> role <strong>of</strong> Hungarian middle managers in <strong>the</strong>
maturity <strong>of</strong> knowledge sharing and how <strong>the</strong>ir role can be characterised.
Regarding this purpose <strong>the</strong> following question needs to be answered:
Question: Which components describe Hungarian middle managers’ maturity <strong>of</strong>
knowledge sharing who work at medium-and large-sized enterprises?
Using prior interviews <strong>the</strong> elements defining middle managers’ maturity <strong>of</strong> knowledge sharing are
defined and examined in this research by categories called availability and <strong>the</strong> usefulness <strong>of</strong>
knowledge.
Availability to one ano<strong>the</strong>r is measured as follows:
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investigated middle manager’s availability to o<strong>the</strong>r middle managers working on <strong>the</strong> same
organizational level when <strong>the</strong> investigated middle manager is asked for help,
o<strong>the</strong>r middle managers’ availability working on <strong>the</strong> same organizational level to <strong>the</strong> investigated
middle manager when to <strong>the</strong> investigated middle manager asks for help,
<strong>the</strong> investigated middle manager’s availability to his/her subordinates when <strong>the</strong> investigated
middle manager is asked for help,
availability <strong>of</strong> <strong>the</strong> investigated middle managers’ subordinates to <strong>the</strong> investigated middle manager
when <strong>the</strong> investigated middle manager asks for help.
The usefulness <strong>of</strong> knowledge is measured as follows:
usefulness <strong>of</strong> <strong>the</strong> knowledge given by <strong>the</strong> investigated middle manager to o<strong>the</strong>r middle managers
working on <strong>the</strong> same organizational level,
usefulness <strong>of</strong> <strong>the</strong> knowledge given by o<strong>the</strong>r middle managers working on <strong>the</strong> same organizational
level to <strong>the</strong> investigated middle manager,
usefulness <strong>of</strong> <strong>the</strong> knowledge given by <strong>the</strong> investigated middle manager to his/her subordinates,
usefulness <strong>of</strong> <strong>the</strong> knowledge given by <strong>the</strong> investigated middle manager’s subordinates to <strong>the</strong>
investigated middle manager.
In order to answer <strong>the</strong> research question <strong>the</strong> following Hypo<strong>the</strong>sis has been stated:
Hypo<strong>the</strong>sis: Middle managers’ maturity <strong>of</strong> knowledge sharing who work at medium- and
large-sized enterprises can be characterised by <strong>the</strong> availability among middle managers,
<strong>the</strong> availability among <strong>the</strong> middle manager and his/her subordinates, <strong>the</strong> usefulness <strong>of</strong>
knowledge among <strong>the</strong> middle managers, and <strong>the</strong> usefulness <strong>of</strong> knowledge among <strong>the</strong>
middle manager and his/her subordinates.
The arrows in Figure 1 represent <strong>the</strong> elements that are examined regarding this Hypo<strong>the</strong>sis.
Figure 1: Elements <strong>of</strong> maturity <strong>of</strong> knowledge sharing under investigation
3.2 Method chosen for testing <strong>the</strong> hypo<strong>the</strong>sis
The initial assumption regarding <strong>the</strong> investigated middle managers’ maturity <strong>of</strong> knowledge sharing
was that it could be described by four elements. Fur<strong>the</strong>rmore <strong>the</strong>se elements could retain as much <strong>of</strong>
<strong>the</strong> information <strong>of</strong> <strong>the</strong> original variables as possible. Thus principal component analysis (PCA) has
been chosen, since <strong>the</strong> requirements <strong>of</strong> retaining large amount <strong>of</strong> information <strong>of</strong> <strong>the</strong> original variables
by four components can be tested and proved by <strong>the</strong> usage <strong>of</strong> PCA. Ano<strong>the</strong>r reason <strong>of</strong> choosing this
method is that <strong>the</strong> principal components are based upon <strong>the</strong> measured responses (DeCoster 1998).
Fur<strong>the</strong>rmore as a result <strong>of</strong> PCA <strong>the</strong> number <strong>of</strong> principal components is also less <strong>the</strong>n <strong>the</strong> number <strong>of</strong>
variables, and this method reduces <strong>the</strong> number <strong>of</strong> variables as well (Myatt, Johnson 2009).
3.3 Data collection
The data collection was supported by Department <strong>of</strong> Management, University <strong>of</strong> Pannonia between
2007 and 2010. 4000 medium- and large-sized enterprises in Hungary have been selected randomly
from <strong>the</strong> average number <strong>of</strong> 5780 and questionnaires have been sent to <strong>the</strong>m by post and via e-mail.
The enterprises have been asked to have <strong>the</strong> questionnaire filled in by at least one <strong>of</strong> <strong>the</strong>ir middle
managers. The questionnaire <strong>of</strong> <strong>the</strong> survey comprises seven categories. One <strong>of</strong> <strong>the</strong>m is Maturity <strong>of</strong>
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Zoltán Gaál et al
Knowledge Sharing which contains questions regarding <strong>the</strong> extent <strong>of</strong> availability and usefulness <strong>of</strong>
knowledge based on a 5-point Likert scale.
The participants <strong>of</strong> <strong>the</strong> research can be found in various working areas and industries <strong>the</strong> data <strong>of</strong>
which are presented in Figure 2.
Figure 2: Distribution <strong>of</strong> participant <strong>of</strong> <strong>the</strong> survey according to industries and working areas
3.4 Results
In this part <strong>of</strong> <strong>the</strong> Empirical study <strong>the</strong> results using PCA will be presented.
3.4.1 Results <strong>of</strong> KMO and Bartlett’s Tests
To determine <strong>the</strong> appropriateness <strong>of</strong> <strong>the</strong> data set for PCA Kaiser-Meyer-Olkin (KMO) measure <strong>of</strong>
sampling adequacy and Bartlett’s test <strong>of</strong> sphericity is used. By using correlations and partial
correlations for testing whe<strong>the</strong>r <strong>the</strong> variables used are adequate to correlate <strong>the</strong> KMO statistic is
calculated, while Bartlett’s test is used for revealing <strong>the</strong> relationship between <strong>the</strong> variables by testing
<strong>the</strong> null hypo<strong>the</strong>sis that <strong>the</strong> variables are uncorrelated in <strong>the</strong> population (Hinton et al 2004; Foster et al
2006; Székelyi, Barna 2002). Although <strong>the</strong> values <strong>of</strong> KMO statistic can vary from 0 to 1, Kaiser (1974)
recommended values greater than 0.5 to be accepted. If <strong>the</strong> significance value <strong>of</strong> Bartlett’s test is less
than 0.05, <strong>the</strong>n this test is significant, and thus <strong>the</strong> analysis is appropriate (Field 2005; Sajtos, Mitev
2007).
The results <strong>of</strong> both tests can be found in Table 1.
Table 1: The KMO and Bartlett values <strong>of</strong> maturity <strong>of</strong> knowledge sharing
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure <strong>of</strong> Sampling Adequacy .740
Bartlett's Test <strong>of</strong> Sphericity Approx. Chi-Square 1105.361
df 28
Sig .000
Table 1 shows that <strong>the</strong> KMO test with <strong>the</strong> value <strong>of</strong> 0.740 has been above <strong>the</strong> accepted limit <strong>of</strong> 0.5. In
addition, <strong>the</strong> Bartlett test yields a high Chi-square value <strong>of</strong> 1105.361, and a significance level <strong>of</strong> 0.000
which is also under <strong>the</strong> accepted limit <strong>of</strong> 0.05. Thus both tests have verified that <strong>the</strong> data are
appropriate for PCA.
3.4.2 Results <strong>of</strong> Total Variance Explained
The table <strong>of</strong> Total Variance Explained lists <strong>the</strong> eigenvalues associated with each component before
extraction, after extraction and after rotation. In social science <strong>the</strong> total cumulative variance explained
above 60 % is considered acceptable (Sajtos, Mitev 2007).
Table 2 shows <strong>the</strong> result <strong>of</strong> Total Variance Explained.
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Table 2: Total Variance Explained for maturity <strong>of</strong> knowledge sharing variables
Component
Total
Initial Eigenvalues
% <strong>of</strong>
Variance
Cumulative
%
Total Variance Explained
Extraction Sums <strong>of</strong> Squared
Loadings
Total
% <strong>of</strong>
Variance
Cumulative
%
Rotation Sums <strong>of</strong> Squared
Loadings
Total
% <strong>of</strong>
Variance
Cumulative
%
1 3.460 43.252 43.252 3.460 43.252 43.252 1.687 21.085 21.085
2 1.331 16.633 59.885 1.331 16.633 59.885 1.632 20.395 41.481
3 .987 12.341 72.226 .987 12.341 72.226 1.595 19.943 61.424
4 .671 8.387 80.614 .671 8.387 80.614 1.535 19.190 80.614
Extraction Method: Principal Component Analysis
The PCA shows that <strong>the</strong> eigenvalues <strong>of</strong> <strong>the</strong> first three principal components have represented up to
61.424 % <strong>of</strong> <strong>the</strong> total variance (PC1 21.085%; PC2 20.395%; PC3 19.943%) <strong>of</strong> <strong>the</strong> observations.
Thus three components would have fulfilled <strong>the</strong> requirements <strong>of</strong> exceeding <strong>the</strong> 60 % limit but it would
have been difficult to interpret <strong>the</strong> components. The percentage <strong>of</strong> <strong>the</strong> cumulative eigenvalues has
risen up to 80.614% when taking into account four components which thus on <strong>the</strong> one hand this
would have fulfilled <strong>the</strong> aim <strong>of</strong> my initial assumption regarding <strong>the</strong> number <strong>of</strong> components and on <strong>the</strong>
o<strong>the</strong>r hand would have helped <strong>the</strong> interpretation <strong>of</strong> <strong>the</strong> final component. Therefore four components
have been retained in <strong>the</strong> final analysis.
3.4.3 Results <strong>of</strong> rotated component matrix
Since <strong>the</strong> interpretation <strong>of</strong> <strong>the</strong> Component Matrix is ra<strong>the</strong>r difficult <strong>the</strong> rotation <strong>of</strong> <strong>the</strong> components has
been needed. By using rotation <strong>the</strong> output <strong>of</strong> <strong>the</strong> PCA is more understandable and <strong>the</strong> interpretation
<strong>of</strong> <strong>the</strong> component is much easier. Component loadings are correlation coefficients between <strong>the</strong>
variables and <strong>the</strong> components and inform about <strong>the</strong> relationship <strong>of</strong> <strong>the</strong> variable and <strong>the</strong> component. If
<strong>the</strong> variable has a loading value above 0.25 on <strong>the</strong> component and is loaded only on one component
<strong>the</strong>n that variable is considered to belong only to that component. Rotation has two major types:
orthogonal rotation (Varimax, Equimax and Quartimax) and oblique rotation (Direct Oblimin, Promax)
(Loehlin 1998; Székelyi, Barna 2002; Sajtos, Mitev 2007).
Regarding <strong>the</strong> analysis <strong>the</strong> use <strong>of</strong> Varimax rotation method has been chosen, because it finds <strong>the</strong>
angles that can maximize <strong>the</strong> variance <strong>of</strong> <strong>the</strong> squared loadings and it also splits <strong>the</strong> variables into
disjoint sets and thus each variable has been associated with one <strong>of</strong> <strong>the</strong> components and this has
simplified <strong>the</strong> interpretation.
The results <strong>of</strong> Rotated Component Matrix can be seen in Table 4.
Table 4: Rotated Component Matrix <strong>of</strong> maturity <strong>of</strong> knowledge sharing
Rotated Component Matrix a
Component
1 2 3 4
Usefulness <strong>of</strong> o<strong>the</strong>r middle managers’ knowledge to middle manager .899 .101 .168 .105
Usefulness <strong>of</strong> middle manager’s knowledge to o<strong>the</strong>r middle managers .823 .018 .156 .274
Availability <strong>of</strong> subordinates to middle manager .092 .858 .222 .127
Availability <strong>of</strong> middle manager to subordinates .033 .854 .175 .213
Availability <strong>of</strong> o<strong>the</strong>r middle managers to middle manager .238 .181 .858 .047
Availability <strong>of</strong> middle manager to o<strong>the</strong>r middle managers .104 .240 .833 .222
Usefulness <strong>of</strong> middle manager’s knowledge to subordinates .094 .209 .124 .874
Usefulness <strong>of</strong> subordinates’ knowledge to middle manager
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
.340 .146 .132 .756
310

Zoltán Gaál et al
The eight variables have been listed in Table 4 in <strong>the</strong> order <strong>of</strong> <strong>the</strong> size <strong>of</strong> <strong>the</strong>ir component loadings.
For each variable <strong>the</strong> strongest (above 0.25) loadings are highlighted indicating which variables load
most strongly on which component.
As <strong>the</strong> result <strong>of</strong> PCA four different components have been identified. Table 5 contains <strong>the</strong> principal
components <strong>of</strong> maturity <strong>of</strong> knowledge sharing and <strong>the</strong> variables that are loaded on <strong>the</strong>m.
Table 5: Components <strong>of</strong> maturity <strong>of</strong> knowledge sharing and <strong>the</strong> variables loaded on <strong>the</strong>m
1.
2.
3.
4.
Name <strong>of</strong> <strong>the</strong> Component Name <strong>of</strong> <strong>the</strong> Variable
Availability among
middle managers
Availability among
<strong>the</strong> middle manager and subordinates
Usefulness <strong>of</strong> knowledge among
middle managers
Usefulness <strong>of</strong> knowledge among
<strong>the</strong> middle manager and subordinates
o<strong>the</strong>r middle mangers’ availability towards <strong>the</strong> investigated
manager
<strong>the</strong> investigated manager’s availability towards o<strong>the</strong>r
middle mangers
availability <strong>of</strong> <strong>the</strong> investigated middle manger’s
subordinates towards <strong>the</strong> middle manager
<strong>the</strong> investigated manager’s availability towards his/her
subordinates
usefulness <strong>of</strong> o<strong>the</strong>r managers’ knowledge to <strong>the</strong>
investigated manager
usefulness <strong>of</strong> <strong>the</strong> investigated manager’s knowledge to
o<strong>the</strong>r managers
usefulness <strong>of</strong> knowledge <strong>of</strong> <strong>the</strong> investigated manager’s
subordinates to <strong>the</strong> investigated manager
usefulness <strong>of</strong> <strong>the</strong> investigated manager’s knowledge to
his/her subordinates
Based on <strong>the</strong>se results <strong>the</strong> following Thesis can be determined:
Thesis: Middle managers’ maturity <strong>of</strong> knowledge sharing who work at medium- and
large-sized enterprises is characterized by <strong>the</strong> availability among middle managers, <strong>the</strong>
availability among <strong>the</strong> middle manager and his/her subordinates, <strong>the</strong> usefulness <strong>of</strong>
knowledge among middle managers, and <strong>the</strong> usefulness <strong>of</strong> knowledge among <strong>the</strong> middle
manager and his/her subordinates.
3.4.4 . Interpretation <strong>of</strong> <strong>the</strong> Results
The maturity <strong>of</strong> knowledge sharing is examined by <strong>the</strong> indices <strong>of</strong> availability to one ano<strong>the</strong>r and <strong>the</strong>
usefulness <strong>of</strong> knowledge, which are presented in <strong>the</strong> followings.
Availability among Middle Managers: The higher <strong>the</strong> index <strong>of</strong> availability is <strong>the</strong> more <strong>the</strong> investigated
middle managers are available to each o<strong>the</strong>r, <strong>the</strong> lower this index is <strong>the</strong> less <strong>the</strong> middle managers try
to find time for each o<strong>the</strong>r. Accordingly <strong>the</strong> more time <strong>the</strong> middle manager is willing to find from his
work time to help o<strong>the</strong>r middle managers, <strong>the</strong> more <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong>se middle managers will
enlarge and <strong>the</strong> more it promotes <strong>the</strong> growth <strong>of</strong> <strong>the</strong> organizational knowledge base as well.
Influencing factor for being available originates from <strong>the</strong> appreciation, understanding and identification
with <strong>the</strong> organizational goals. Middle managers can be more open to be available to o<strong>the</strong>r middle
managers if <strong>the</strong>ir goals and tasks are mutual or if <strong>the</strong>ir career depends on <strong>the</strong> knowledge sharing
behaviour. The willingness to be available exposes <strong>the</strong> sign <strong>of</strong> cooperativeness within <strong>the</strong>
organization which plays a significant role in <strong>the</strong>se middle managers’ availability to each o<strong>the</strong>r. Thus
<strong>the</strong> more <strong>the</strong>y are willing to co-operate, <strong>the</strong> more <strong>the</strong>y will be available, <strong>the</strong> less <strong>the</strong>y are willing to cooperate,
<strong>the</strong> lower <strong>the</strong>ir availability will be. Those areas in <strong>the</strong> organization can also be revealed
where middle managers ra<strong>the</strong>r compete than cooperate that should lead to <strong>the</strong> revision <strong>of</strong> personal
differences and also <strong>the</strong> perception and understanding <strong>of</strong> goals <strong>of</strong> <strong>the</strong> given organization. The sign <strong>of</strong>
competition can also draw attention to <strong>the</strong> “knowledge is power” attitude that can exist within <strong>the</strong>
organization that is against <strong>the</strong> fulfilment <strong>of</strong> organizational goals or at least makes it harder to fulfil.
Availability among <strong>the</strong> Middle Manager and Subordinates: The higher <strong>the</strong> index <strong>of</strong> availability is <strong>the</strong>
more <strong>the</strong> investigated middle manager and his/her subordinates are available to each o<strong>the</strong>r, <strong>the</strong> lower
this index is <strong>the</strong> less <strong>the</strong>y are available for each o<strong>the</strong>r. Fur<strong>the</strong>rmore <strong>the</strong> more <strong>the</strong> middle manager is
311

Zoltán Gaál et al
characterised by having a participative leadership style (Tannenbaum, Schmidt 1958, Hersey,
Blanchard 1969) <strong>the</strong> more <strong>the</strong>y are available to each o<strong>the</strong>r, thus <strong>the</strong> extent <strong>of</strong> availability shows <strong>the</strong>
extent <strong>of</strong> participation as well. If <strong>the</strong> level <strong>of</strong> availability is higher between <strong>the</strong> middle manager and
his/her subordinates, it results in better communication and <strong>the</strong> goals for <strong>the</strong> manager and his/her
subordinates can be fulfilled toge<strong>the</strong>r. By being available to each o<strong>the</strong>r <strong>the</strong> participants can get into
win-win situation. O<strong>the</strong>r pairing (win-lose, lose-lose, lose-win) can only lead to low level <strong>of</strong> availability,
which raises <strong>the</strong> question whe<strong>the</strong>r <strong>the</strong> “knowledge is power” behaviour occurs again. This attitude can
cause damage in <strong>the</strong> communication, and can undermine <strong>the</strong> fulfilment <strong>of</strong> organizational and
operational goals. In addition problems may also appear if <strong>the</strong> organizational goals <strong>of</strong> <strong>the</strong> manager
and <strong>the</strong> subordinates differ. The higher <strong>the</strong> power distance <strong>of</strong> a country is, <strong>the</strong> more <strong>the</strong> authority,
power differences and status privileges are accepted in that country and <strong>the</strong> stronger <strong>the</strong> hierarchical
power practices are, and <strong>the</strong> higher <strong>the</strong> organizational power distance is, <strong>the</strong> more <strong>the</strong> self-interest is
dominant within <strong>the</strong> group (Carl et al. 2004). Thus <strong>the</strong>se features result in a low level <strong>of</strong> availability
among <strong>the</strong> middle manager and his/her subordinates. Hungary according to Bakacsi and Takács
(1998) is characterized by higher power distance and as a result it is understandable that <strong>the</strong>
availability between <strong>the</strong> middle manager and his/her subordinates is lower.
Usefulness <strong>of</strong> Knowledge among Middle Managers: The higher <strong>the</strong> index <strong>of</strong> usefulness <strong>of</strong> knowledge
is, <strong>the</strong> more valuable <strong>the</strong> shared knowledge is for <strong>the</strong> middle managers, <strong>the</strong> lower this index is <strong>the</strong>
less valuable this knowledge is for <strong>the</strong> middle managers. Co-operation also plays a significant role in
<strong>the</strong> usefulness <strong>of</strong> knowledge since <strong>the</strong> more <strong>the</strong>y are willing to co-operate, <strong>the</strong> more <strong>the</strong>y will know
what kind <strong>of</strong> knowledge is useful for <strong>the</strong> o<strong>the</strong>rs, <strong>the</strong> less <strong>the</strong>y are willing to co-operate <strong>the</strong> lower <strong>the</strong>
usefulness <strong>of</strong> <strong>the</strong>ir knowledge will be. If common organizational knowledge, language and jargon
emerge in <strong>the</strong> organization, it can foster <strong>the</strong> usefulness <strong>of</strong> knowledge. If middle managers are loyal to
<strong>the</strong>ir organization <strong>the</strong>y know what kind <strong>of</strong> knowledge is needed by o<strong>the</strong>r middle managers. However if
<strong>the</strong>y are not loyal, <strong>the</strong>y will not put effort in sharing useful knowledge with o<strong>the</strong>rs. When <strong>the</strong> level <strong>of</strong>
usefulness <strong>of</strong> knowledge is low, not only <strong>the</strong> time and effort for sharing but also <strong>the</strong> intention and <strong>the</strong>
knowledge <strong>of</strong> <strong>the</strong> transmitter are queried. The presence <strong>of</strong> competition leading to <strong>the</strong> failure <strong>of</strong>
communication can also appear in case <strong>of</strong> low usefulness. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> knowledge itself
that is shared by one middle manager can be misleading since it can be found useful for <strong>the</strong>
transmitter while it is less useful for <strong>the</strong> recipient(s). The difference in <strong>the</strong> knowledge base, <strong>the</strong>
existing jargon can result in knowledge that is less useful for o<strong>the</strong>rs. By sharing knowledge that is
useful for o<strong>the</strong>r middle managers <strong>the</strong> knowledge base <strong>of</strong> not only o<strong>the</strong>r middle managers but also <strong>the</strong>
knowledge base <strong>of</strong> <strong>the</strong> organization will grow.
Usefulness <strong>of</strong> Knowledge among <strong>the</strong> Middle Manager and Subordinates: The higher <strong>the</strong> index <strong>of</strong>
usefulness <strong>of</strong> knowledge is, <strong>the</strong> more valuable <strong>the</strong> knowledge shared by <strong>the</strong> parties is for each o<strong>the</strong>r,
<strong>the</strong> lower this index is, <strong>the</strong> less valuable <strong>the</strong> knowledge shared is. In this case <strong>the</strong> shared knowledge
is in connection with day-to-day work. The low level <strong>of</strong> usefulness queries not only <strong>the</strong> competence <strong>of</strong>
<strong>the</strong> person in that given scope <strong>of</strong> activities but also <strong>the</strong> intention <strong>of</strong> knowledge sharing. Besides <strong>the</strong>
characteristics <strong>of</strong> <strong>the</strong> person can also have an affect on how his/her colleagues perceive <strong>the</strong> quality <strong>of</strong>
<strong>the</strong> shared knowledge. Middle managers have mainly long term goals, while <strong>the</strong> subordinates have
short term goals which may lead to less usefulness <strong>of</strong> knowledge for each o<strong>the</strong>r. Sharing a part <strong>of</strong> <strong>the</strong>
needed knowledge can lead to <strong>the</strong> lack <strong>of</strong> fulfilment <strong>of</strong> <strong>the</strong> tasks. However <strong>the</strong> usefulness <strong>of</strong>
knowledge probably can be improved by <strong>the</strong> use <strong>of</strong> coaching, mentoring, reporting or feedback.
4. Conclusion
This paper presents <strong>the</strong> results <strong>of</strong> an empirical research conducted between <strong>the</strong> years <strong>of</strong> 2007 and
2010 among 400 Hungarian medium- and large-sized enterprises. The paper focuses on <strong>the</strong> research
methodology and <strong>the</strong> results <strong>of</strong> data analysis. Findings <strong>of</strong> <strong>the</strong> research indicate that four principal
components can be considered by middle managers during knowledge sharing. Two <strong>of</strong> <strong>the</strong>m relate to
availability. These are availability among middle managers and availability among <strong>the</strong> middle
manager and subordinates. The remaining two relate to usefulness <strong>of</strong> knowledge. These are
usefulness <strong>of</strong> knowledge among middle managers and usefulness <strong>of</strong> knowledge among <strong>the</strong> middle
manager and subordinates. Concerning availability it has been revealed by Kankanhalli et al (2005)
that knowledge sharing can appear as a result <strong>of</strong> reciprocation or simply as <strong>the</strong> enjoyment <strong>of</strong> helping
o<strong>the</strong>rs. Regarding usefulness <strong>of</strong> knowledge prior studies have shown that whose who are confident in
<strong>the</strong>ir ability regarding useful knowledge or have higher expertise are more likely to share <strong>the</strong>ir
knowledge and are engaged higher in knowledge sharing (Cabrera et al. 2006, Constant et al. 1996;
312

Zoltán Gaál et al
Lin 2007) thus it has been suggested that it is important to increase individuals’ confidence (Wang,
Noe 2010).
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314

Knowledge Management in <strong>the</strong> Quebec Mining Industry: A
Framework <strong>of</strong> Practice to Ensure Evidence-Based
Knowledge Translation
Charles Gagné 1 , Louis Lazure 1 , Élise Ledoux 1 , Sylvie Ouellet 1 , Pierre-Sébastien
Fournier 2
1
Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST),
Montreal, Canada
2
Management Department, Faculty <strong>of</strong> Business Administration, Université
Laval, Quebec, Canada
gagne.charles@irsst.qc.ca
lazure.louis@irsst.qc.ca
ledoux.elise@irsst.qc.ca
ouellet.sylvie@irsst.qc.ca
pierre-sebastien.fournier@fsa.ulaval.ca
Abstract: Demographic changes in recent decades in industrialized countries and changes in <strong>the</strong> working world
are introducing major challenges regarding <strong>the</strong> question <strong>of</strong> age management and knowledge management. To
better understand <strong>the</strong>se issues and <strong>the</strong>ir impacts on occupational health and safety, <strong>the</strong> Institut de recherche
Robert-Sauvé en santé et en sécurité du travail (IRSST) has developed a research program that focuses on
knowledge management in different industries. In this context, an IRSST research team was asked by Quebec
mining stakeholders to document <strong>the</strong> mining sector conditions that favour a safe and competent integration <strong>of</strong>
new workers (<strong>of</strong> varying ages and occupational paths), particularly <strong>the</strong> conditions that foster knowledge
transmission between experienced and new workers. To support <strong>the</strong> research team’s work and promote an
appropriation <strong>of</strong> evidence-based knowledge by <strong>the</strong> stakeholders/end-users, a Knowledge Transfer Advisor (KTA)
was integrated into <strong>the</strong> research team. This approach is integral to <strong>the</strong> framework <strong>of</strong> practice <strong>of</strong> <strong>the</strong> Research and
Knowledge Translation Cycle developed by <strong>the</strong> IRSST. The framework was developed through a rigorous
process led by knowledge transfer researchers and KTAs on <strong>the</strong> basis <strong>of</strong> an extensive scientific literature review.
This paper presents <strong>the</strong> IRSST-developed Research and Knowledge Translation Cycle and <strong>the</strong> conditions
implemented in <strong>the</strong> context <strong>of</strong> this research project to enhance stakeholder participation in <strong>the</strong> KT activities held.
Keywords: Experience and expertise transfer, Knowledge exchange, Knowledge utilization, Framework <strong>of</strong>
practice, Stakeholders
1. Introduction
Knowledge translation (KT) is an increasingly widespread practice in <strong>the</strong> research community
(Graham, 2006). In fact, while research-generated knowledge is recognized as improving
organizational performance (De long, 2000) and public policies, studies by Landry et al (2004) have
shown that scientific literature is not considered a primary source <strong>of</strong> information by organizations, and
it <strong>the</strong>refore appears that additional efforts are required to reach end-users. We have no choice but to
acknowledge that a good portion <strong>of</strong> research results are not necessarily transferred to <strong>the</strong> potential
users. The production <strong>of</strong> new scientific knowledge does not guarantee that it will be applied. To
promote knowledge translation, a process involving a series <strong>of</strong> activities must be put in place (Ward,
2010). An overview <strong>of</strong> <strong>the</strong> scientific literature reveals that <strong>the</strong> following conditions are essential to
achieving effective knowledge translation: personal contacts must be established between
researchers and stakeholders/end-users (Innvaer, 2002; Reardon, 2006); <strong>the</strong> stakeholders (SHs)
must be continuously involved throughout <strong>the</strong> research process (Lomas, 2000; Wilson et al, 2010);
<strong>the</strong> research results must be made available to <strong>the</strong> SHs at <strong>the</strong> appropriate time to ensure <strong>the</strong>ir use
(Innvaer, 2002); <strong>the</strong> results must be relevant and applicable to facilitate <strong>the</strong>ir actual appropriation by
workplaces (Zahra & George, 2002; Kramer & Cole, 2003); and <strong>the</strong> stakeholders/end-users must
collaborate with <strong>the</strong> researchers to adapt and implement <strong>the</strong> research results (Bourdouxhe 2003;
Henriksen et al., 2004). Knowledge translation in <strong>the</strong> field <strong>of</strong> occupational health and safety faces <strong>the</strong>
same challenges.
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This paper presents <strong>the</strong> Research and Knowledge Translation Cycle developed by <strong>the</strong> Institut de
recherche Robert-Sauvé en santé et sécurité au travail 1 (IRSST) and <strong>the</strong> conditions implemented to
enhance SH participation in <strong>the</strong> KT activities organized as part <strong>of</strong> this research project on knowledge
management in <strong>the</strong> Quebec mining sector.
2. The IRSST Research and Knowledge Translation Approach
Knowledge translation is a process which <strong>the</strong> IRSST, as a research and granting organization, uses
to promote <strong>the</strong> appropriation <strong>of</strong> research results in workplaces. Although <strong>the</strong>re is no consensus on <strong>the</strong>
definition <strong>of</strong> KT (Graham 2006), <strong>the</strong> IRSST gives it a comprehensive definition that includes <strong>the</strong>
concepts <strong>of</strong> both knowledge sharing and knowledge exchange.
In 2006, following a strategic organizational repositioning, <strong>the</strong> IRSST created a specific department to
support researchers in knowledge translation activities. Knowledge Transfer Advisors (KTA) were
assigned to all research field programs to help implement KT strategies and enhance SH involvement
in research activities. By explicitly including KT activities as a requirement for awarding research
grants and by providing specialized resources to guide researchers, <strong>the</strong> IRSST clearly demonstrated
its strong commitment to making research results available to end-users. It <strong>the</strong>refore undertook to
develop a sustainable approach that would support researchers in this regard, by identifying a broad
range <strong>of</strong> issues, proposing solutions and integrating <strong>the</strong>se solutions into a framework <strong>of</strong> practice.
The framework <strong>of</strong> practice was developed through a rigorous process led by knowledge transfer
researchers and KTAs. The process was based on an extensive scientific literature review and a
structured analysis <strong>of</strong> KT activities carried out at <strong>the</strong> IRSST in <strong>the</strong> past 15 years. The framework
incorporates <strong>the</strong> various phases <strong>of</strong> knowledge transformation required to promote <strong>the</strong> appropriation <strong>of</strong>
this knowledge: generation, transformation, diffusion, reception, adoption and utilisation (Roy et al,
2003).
The IRSST Research and Knowledge Translation Cycle (Figure 1 - www.irsst.qc.ca/en/organizationalpriority.html)
includes eight steps that can be grouped into four major phases (anchoring, research,
knowledge translation, and evaluation <strong>of</strong> impacts). The objectives sought in each phase and <strong>the</strong>
activities implemented to attain <strong>the</strong>se objectives in our case study in <strong>the</strong> mining sector will be
described in <strong>the</strong> following sections.
In <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong> cycle, each step is carried out interactively with <strong>the</strong> partners involved.
Consequently, when needs are expressed and a research project is created prior to submission <strong>of</strong> a
grant application, <strong>the</strong> researchers and partners interact in order to ensure that <strong>the</strong> goal <strong>of</strong> <strong>the</strong> project
meets <strong>the</strong> needs and corresponds to <strong>the</strong> realities <strong>of</strong> <strong>the</strong> potential users. This anchoring in reality right
from <strong>the</strong> start <strong>of</strong> a project also allows for <strong>the</strong> creation <strong>of</strong> a context that will be conducive to <strong>the</strong>
appropriation and use <strong>of</strong> <strong>the</strong> research results by <strong>the</strong> various end-users. The anchoring process
reflects <strong>the</strong> importance <strong>of</strong> integrating a knowledge translation strategy right from <strong>the</strong> start <strong>of</strong> a
research project (Ward et al, 2010).
The IRSST Research and Knowledge Translation Cycle is structured around <strong>the</strong> development and
maintenance <strong>of</strong> networks that represent all <strong>the</strong> actors (researchers, employer and worker
associations, and prevention associations). The dynamics <strong>of</strong> this approach respect <strong>the</strong> principle <strong>of</strong>
union-management cooperation that characterizes occupational health and safety activities in <strong>the</strong>
province <strong>of</strong> Quebec. Recognizing that KT cannot be limited to <strong>the</strong> simple dissemination <strong>of</strong> results after
a research project has been completed, it is essential that SHs be continuously involved in all phases
<strong>of</strong> <strong>the</strong> project. For <strong>the</strong> IRSST, research becomes most relevant when workplaces appropriate results.
1 Established in Quebec (Canada) since 1980, <strong>the</strong> IRSST is a non-pr<strong>of</strong>it scientific research organization that contributes,
through research, to <strong>the</strong> prevention <strong>of</strong> industrial accidents and occupational diseases, and to <strong>the</strong> rehabilitation <strong>of</strong> affected
workers.
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Figure 1: The IRSST Research and Knowledge Translation Cycle
The active involvement <strong>of</strong> <strong>the</strong> KTA is instrumental in all research team activities (including <strong>the</strong> coconstruction
<strong>of</strong> <strong>the</strong> research agenda, <strong>the</strong> scientific/non-scientific activities and <strong>the</strong> KT activities related
to <strong>the</strong> dissemination <strong>of</strong> results). The KTA’s role must be tailored to <strong>the</strong> research agenda to maximize
his or her contribution to each research project. In addition to participating in KT strategies in
collaboration with <strong>the</strong> research team, <strong>the</strong> KTA interfaces with <strong>the</strong> SHs, mainly to form a follow-up
committee and to develop and facilitate <strong>the</strong> committee’s activities. The committee’s role is to provide
its field expertise to <strong>the</strong> researchers during <strong>the</strong> definition, development and realization <strong>of</strong> <strong>the</strong> research
project. These SHs act as intermediaries with <strong>the</strong> workplaces. We will illustrate <strong>the</strong> implementation <strong>of</strong>
this framework <strong>of</strong> practice in <strong>the</strong> context <strong>of</strong> a research project on knowledge management conducted
in <strong>the</strong> mining sector.
3. Translating <strong>the</strong> knowledge generated by a research project on knowledge
management: <strong>the</strong> case <strong>of</strong> <strong>the</strong> mining industry
A number <strong>of</strong> studies have investigated intergenerational knowledge transfer in workplaces from <strong>the</strong>
perspective <strong>of</strong> organizational performance, but also that <strong>of</strong> employment injury prevention. In fact, a
greater risk <strong>of</strong> employment injuries is noted among young worker cohorts. Several studies <strong>of</strong> various
pr<strong>of</strong>essions in diverse economic activity sectors have shown that older, experienced personnel
develop numerous work strategies that allow <strong>the</strong>m to deal with <strong>the</strong> many constraints encountered in
<strong>the</strong>ir work and <strong>the</strong>reby protect <strong>the</strong>mselves from risks (Cloutier, 1994; Pueyo, 1998). Thus, <strong>the</strong>
transmission <strong>of</strong> this “prudent knowledge” (knowledge <strong>of</strong> safe work behaviours) also becomes an issue
for companies, particularly those working in high-risk activity sectors.
In this context, <strong>the</strong> IRSST developed a research program on <strong>the</strong> transmission <strong>of</strong> job-related and
prudent knowledge in workplaces. Above all, <strong>the</strong> projects in this program seek to identify <strong>the</strong>
conditions that may support or hinder knowledge transmission between workers <strong>of</strong> varying ages and
experience. The goal is to ensure <strong>the</strong> safe, competent integration <strong>of</strong> new workers as well as <strong>the</strong>
retention <strong>of</strong> aging workers. One <strong>of</strong> <strong>the</strong> research projects in this program is being carried out in <strong>the</strong>
mining sector. The Quebec mining industry is experiencing a growing need for manpower. In addition,
nearly 30% <strong>of</strong> its workforce is expected to retire over <strong>the</strong> next five years. The issues <strong>of</strong> recruiting and
integrating a new workforce are <strong>the</strong>refore paramount concerns for mining companies, which are also
faced with recurrent problems involving worker absences due to illnesses or injuries that are partly
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attributable to work accidents. To grapple with <strong>the</strong>se many challenges, an occupational health and
safety (OHS) prevention agency in <strong>the</strong> mining sector approached a team <strong>of</strong> IRSST researchers and
asked <strong>the</strong>m to document <strong>the</strong> conditions that foster <strong>the</strong> safe and competent integration <strong>of</strong> new workers,
particularly conditions conducive to <strong>the</strong> transmission <strong>of</strong> knowledge between experienced and new
workers.
4. Ensuring <strong>the</strong> translation <strong>of</strong> evidence-based knowledge on knowledge
management: using a framework <strong>of</strong> practice
In order to maximize <strong>the</strong> translation <strong>of</strong> <strong>the</strong> knowledge emerging from this research project, <strong>the</strong>
research team sought <strong>the</strong> assistance <strong>of</strong> a KTA. The KTA was instrumental in supporting <strong>the</strong> team’s
work, particularly before <strong>the</strong> start <strong>of</strong> <strong>the</strong> actual research phase, by helping to implement <strong>the</strong> conditions
that would ensure effective knowledge translation.
4.1 The anchoring phase
The purpose <strong>of</strong> this first phase was to provide <strong>the</strong> researchers with practical information, increase
<strong>the</strong>ir knowledge <strong>of</strong> <strong>the</strong> workplaces concerned, and ensure a correspondence between <strong>the</strong> research
aim and <strong>the</strong> needs expressed. In order to establish interactive dialogue between <strong>the</strong> researchers and
<strong>the</strong> main SHs right from <strong>the</strong> outset, <strong>the</strong> KTA first undertook to build a partnership. The partnership
building phase took two years. An exploratory study carried out by <strong>the</strong> research team helped pinpoint
<strong>the</strong> context in which a study on KM should be carried out in <strong>the</strong> mining sector. This study also served
to build relationships with certain SHs and companies in <strong>the</strong> sector. Concurrently with this exploratory
study, <strong>the</strong> KTA’s work helped to concretely tie <strong>the</strong> research project to <strong>the</strong> stakeholders’ needs by
using a number <strong>of</strong> strategies. An in-depth interview was conducted with <strong>the</strong> individual who requested
<strong>the</strong> research in order to identify <strong>the</strong> essential SHs in <strong>the</strong> mining sector. This individual turned out to be
a knowledge champion (KC) (Jones, 2003) in <strong>the</strong> sector. The KTA <strong>the</strong>n prepared a map identifying
<strong>the</strong> organizations and individuals involved in <strong>the</strong> sector; this tool brought to light <strong>the</strong> linkages among
<strong>the</strong> different SHs. To enlist <strong>the</strong> commitment <strong>of</strong> <strong>the</strong> organizations identified and to document issues
pertaining to <strong>the</strong> recruitment <strong>of</strong> new workers and <strong>the</strong> training tools/mechanisms <strong>the</strong>y generally used,
meetings were held with <strong>the</strong>ir representatives (senior executives, OHS and training managers),
members <strong>of</strong> <strong>the</strong> research team, and <strong>the</strong> KTA. The SHs’ comments informed <strong>the</strong> researchers during
<strong>the</strong> meetings, and <strong>the</strong> participating organizations’ involvement was confirmed through follow-up
support letters and by designating some <strong>of</strong> <strong>the</strong>m to sit on <strong>the</strong> follow-up committee.
Conducted prior to <strong>the</strong> research phase, this work led to <strong>the</strong> setting up <strong>of</strong> a follow-up committee
comprising all <strong>the</strong> SHs from <strong>the</strong> sector concerned by this type <strong>of</strong> research. In addition to OHS
representatives (from <strong>the</strong> mining sector’s OHS association and <strong>the</strong> Workers’ Compensation Board),
<strong>the</strong> committee included representatives <strong>of</strong> two principal trade unions, <strong>the</strong> employers’ association, a
government employment and skill development agency, and a human resources organization from
<strong>the</strong> mining industry.
4.2 The research phase
According to <strong>the</strong> network-based framework <strong>of</strong> practice, new knowledge generated should be <strong>the</strong>
subject <strong>of</strong> exchanges throughout <strong>the</strong> research process. To foster and maintain this dynamic, it was
decided that <strong>the</strong> follow-up committee would meet twice a year. Regular and personalized contacts
were also developed between <strong>the</strong> SHs and <strong>the</strong> KTA to allow for ongoing exchanges among all <strong>the</strong>
actors, both social and scientific. Thanks to <strong>the</strong> dynamics and <strong>the</strong> relationship created, <strong>the</strong> research
team members were invited on numerous occasions to participate as observers in committees
reserved exclusively for certain SHs/end-users. These formal and informal exchanges fuelled <strong>the</strong>
research work by providing <strong>the</strong> researchers with a clearer grasp <strong>of</strong> <strong>the</strong> companies’ current KM
practices and enabling <strong>the</strong>m to document <strong>the</strong> conditions that would have to be implemented to
facilitate <strong>the</strong> eventual use <strong>of</strong> <strong>the</strong> results <strong>of</strong> <strong>the</strong> research.
Moreover, <strong>the</strong> field research context also promoted exchanges in <strong>the</strong> mining sector itself. The
presence <strong>of</strong> <strong>the</strong> research team at five mining sites prompted direct exchanges <strong>of</strong> knowledge with
managers and workers at <strong>the</strong> participating mining sites.
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The knowledge translation process has <strong>of</strong>ten been associated with <strong>the</strong> KT phase alone. While our
research project is still under way, we are working in close collaboration with <strong>the</strong> SHs to strategically
prepare for <strong>the</strong> KT phase in order to optimize <strong>the</strong> KT activities. We are currently analyzing <strong>the</strong> SHs’
contexts and potential obstacles to results appropriation, and developing strategies for disseminating
our results. A communication strategy had previously been developed to raise awareness among all
<strong>the</strong> actors (both SHs and end-users) <strong>of</strong> <strong>the</strong> importance <strong>of</strong> providing conditions conducive to new
worker integration. The newsletters <strong>of</strong> <strong>the</strong> various organizations and sector-based conferences were
<strong>the</strong> priority information dissemination mechanisms used. Exchanges with <strong>the</strong> SHs revealed that <strong>the</strong>y
had developed <strong>the</strong>ir own KM training programs and strategies. Instead <strong>of</strong> developing new tools, we
decided to integrate <strong>the</strong> knowledge generated by our research into <strong>the</strong> tools/training programs already
developed by <strong>the</strong> SHs.
4.4 The evaluation <strong>of</strong> impacts
The impacts and outcomes <strong>of</strong> research results in workplaces must be analyzed in order to make an
informed judgment about <strong>the</strong>ir usefulness. While this phase is usually carried out after <strong>the</strong> research
project has been completed, in our case study, a number <strong>of</strong> intermediary indicators (mainly <strong>the</strong> SHs’
active involvement, <strong>the</strong> companies’ active participation, and <strong>the</strong>ir generous collaboration in
disseminating <strong>the</strong> knowledge produced) suggest that <strong>the</strong> conditions needed to foster results
appropriation are already in place.
5. Discussion
A number <strong>of</strong> challenges had to be met in order for <strong>the</strong> research-generated knowledge on <strong>the</strong> requisite
conditions for <strong>the</strong> safe and competent integration <strong>of</strong> new workers in <strong>the</strong> mining sector to be applied in
<strong>the</strong> workplace, and to ensure effective KT. To overcome <strong>the</strong>m, a KTA was added to <strong>the</strong> research
team, serving again as a reminder that KT must not be limited to <strong>the</strong> dissemination <strong>of</strong> knowledge only
after <strong>the</strong> research project is completed, but ra<strong>the</strong>r that it must consist <strong>of</strong> a multi-phase process aimed
at continuously involving <strong>the</strong> SHs concerned. In accordance with <strong>the</strong> scientific literature on KT and on
<strong>the</strong> uptake and application <strong>of</strong> knowledge, <strong>the</strong> use <strong>of</strong> a framework <strong>of</strong> practice (Parent & al, 2007;
Straus & al, 2009) like that developed by <strong>the</strong> IRSST proved to be a productive strategy in our case
study. Its application by <strong>the</strong> KTA helped identify and implement, in collaboration with <strong>the</strong> researchers,
<strong>the</strong> conditions likely to facilitate KT before <strong>the</strong> actual research project even began.
Many authors have shown <strong>the</strong> importance <strong>of</strong> building partnerships between end-user representatives
and researchers as early as possible in <strong>the</strong> research process. In order to identify <strong>the</strong> essential
actors/organizations in <strong>the</strong> activity sector involved, some pre-project work was accomplished by <strong>the</strong>
KTA. Not only did this preliminary work identified <strong>the</strong> essential organizations, but it also provided us
with information about <strong>the</strong> relations between <strong>the</strong>se organizations and <strong>the</strong> issues <strong>the</strong>y face. By
adopting a holistic approach to <strong>the</strong> research problem (Pan & Scarbrough, 1999), we were able to
understand <strong>the</strong> organizational context and decode <strong>the</strong> interweaving <strong>of</strong> social and political relations
involved, thus highlighting <strong>the</strong> importance <strong>of</strong> taking <strong>the</strong> organization’s entire structural system into
account. To guarantee successful KT in this research project, we ensured that several SHs would be
present so that <strong>the</strong> interests, points <strong>of</strong> view and different issues experienced in <strong>the</strong> respective
workplaces would be truly represented. The identification <strong>of</strong> <strong>the</strong> relevant SHs in turn made it possible
to pinpoint <strong>the</strong> key actors likely to foster <strong>the</strong> eventual uptake <strong>of</strong> <strong>the</strong> results, and accordingly, <strong>the</strong>ir
appropriation in organizations.
This case study confirms <strong>the</strong> key role played by a recognized KC (knowledge champion) in a given
activity sector. In our project, it turned out that <strong>the</strong> principal person who requested <strong>the</strong> research
implicitly played this role. By endorsing <strong>the</strong> project through his involvement, <strong>the</strong> KC thus guaranteed
<strong>the</strong> research team and project’s credibility. In addition to legitimizing our approaches to <strong>the</strong> SHs, <strong>the</strong>
KC’s involvement facilitated our meetings with <strong>the</strong> top management <strong>of</strong> <strong>the</strong> participating organizations.
In our view, <strong>the</strong> relations with all <strong>the</strong> SHs were greatly enhanced by <strong>the</strong> presence <strong>of</strong> this actor, thanks
to his expertise, knowledge <strong>of</strong> <strong>the</strong> sector, <strong>the</strong> fact that he was recognized by his peers, his neutral
role, and his influence on opinion leaders in <strong>the</strong> sector. The establishment <strong>of</strong> an ongoing dialogue
between <strong>the</strong> KC and <strong>the</strong> KTA provided <strong>the</strong> research team with information and kept <strong>the</strong>m up to date
on <strong>the</strong> latest input from <strong>the</strong> sector. It was also possible to verify certain information throughout <strong>the</strong>
project via this special contact. The follow-up committee was created using <strong>the</strong> following criteria:
respect for worker-manager representation, representativity <strong>of</strong> people who could play <strong>the</strong> role <strong>of</strong>
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intermediary during <strong>the</strong> knowledge transfer and application phase, and diversity <strong>of</strong> viewpoints. This
case study suggests that <strong>the</strong> application <strong>of</strong> <strong>the</strong>se criteria contributed to <strong>the</strong> smooth operation <strong>of</strong> <strong>the</strong>
committee while also ensuring inter-participant exchanges based on a relationship <strong>of</strong> trust (Evans &
Wensley, 2009). The fact <strong>of</strong> seeking an <strong>of</strong>ficial organizational commitment from <strong>the</strong> SHs ensured <strong>the</strong>ir
genuine, concrete involvement. This gave us <strong>the</strong> assurance that <strong>the</strong> individuals representing <strong>the</strong>ir
organizations had a firm mandate and <strong>the</strong> decision-making leeway needed to sit on <strong>the</strong> committee.
The role and importance <strong>of</strong> this type <strong>of</strong> networking proved its worth (Robeson, 2009; Kramer & Wells,
2005). While beneficial for <strong>the</strong> stakeholders, <strong>the</strong> ongoing exchanges were essential for <strong>the</strong> research
team, mainly due to <strong>the</strong> fact that <strong>the</strong> team lacked a detailed knowledge <strong>of</strong> <strong>the</strong> activity sector
concerned. These exchanges helped <strong>the</strong>m better understand <strong>the</strong> realities and issues faced in <strong>the</strong>
sector. Solid, frequent and ongoing interactions based on mutual trust were thus created among all
<strong>the</strong> actors concerned. The SHs already showed <strong>the</strong>ir support for <strong>the</strong> research results by passing <strong>the</strong>m
on to <strong>the</strong>ir members. Also, <strong>the</strong> SHs helped convince <strong>the</strong> mining companies to participate in this
research project through <strong>the</strong>ir own support for <strong>the</strong> project.
In addition, <strong>the</strong> SHs’ involvement throughout <strong>the</strong> project made it possible to document <strong>the</strong> different
mechanisms and tools (Roy et al, 2003) that will be useable in <strong>the</strong> subsequent phases to effect KT.
The challenge is not <strong>the</strong>refore to disseminate knowledge or develop new KT mechanisms that fail to
take <strong>the</strong> different organizational contexts into account (Cruywagen, Swart & Gevers, 2008). All <strong>the</strong>
actors are convinced <strong>of</strong> <strong>the</strong> importance <strong>of</strong> <strong>the</strong> role <strong>of</strong> KM in <strong>the</strong> training <strong>of</strong> new workers; <strong>the</strong> challenge
lies ra<strong>the</strong>r in <strong>the</strong> application <strong>of</strong> <strong>the</strong> knowledge generated. What this means is that we must draw on
<strong>the</strong> SH network’s expertise while at <strong>the</strong> same time providing <strong>the</strong>m with information throughout <strong>the</strong>
study so that <strong>the</strong>y are better able to collaborate in <strong>the</strong> adaptation, dissemination and possible
application <strong>of</strong> <strong>the</strong> results. The fact that we have identified <strong>the</strong> tools/mechanisms developed and used
by <strong>the</strong> different SHs <strong>the</strong>refore <strong>of</strong>fers a real potential for facilitating application <strong>of</strong> <strong>the</strong> knowledge
generated by <strong>the</strong> research project. In o<strong>the</strong>r words, ra<strong>the</strong>r than <strong>the</strong> research team developing new KT
mechanisms (such as a good practices guide for <strong>the</strong> industry) to foster <strong>the</strong> SHs’ appropriation <strong>of</strong>
evidence-based knowledge, we are planning on using <strong>the</strong> mechanisms <strong>the</strong>y have already produced,
recognized and used in <strong>the</strong>ir sector. We hypo<strong>the</strong>size that this strategy will, in <strong>the</strong> medium and long
term, enhance <strong>the</strong> new worker integration strategies that already exist in <strong>the</strong> mining companies. Not
only does this strategy avoid a “knowledge push” approach, but it enables us to act at different levels
in <strong>the</strong> activity sector as a whole and in <strong>the</strong> companies <strong>the</strong>mselves. This strategy is again based on <strong>the</strong>
observation that simply disseminating research results is insufficient to change practices in <strong>the</strong>
workplace (Jacobson, 2003) and to have a real impact on workers’ health and safety.
While for now it remains difficult to identify impact indicators (Duryea et al, 2007) that can be used to
evaluate <strong>the</strong> degree <strong>of</strong> results penetration in <strong>the</strong> workplaces, a number <strong>of</strong> factors suggest that <strong>the</strong>
strategic KT approach established will enable <strong>the</strong> users to appropriate <strong>the</strong> results. The ongoing
involvement <strong>of</strong> <strong>the</strong> SHs in <strong>the</strong> committee, despite <strong>the</strong> major travel costs that have had to be assumed
by <strong>the</strong> participating organizations, attests to <strong>the</strong>ir real interest in participating in <strong>the</strong> research project.
In addition, <strong>the</strong> invitations received by <strong>the</strong> research team to disseminate knowledge through <strong>the</strong>
organizations’ communication tools (e.g. newsletter, website, symposia, and press conferences)
fur<strong>the</strong>r attest to <strong>the</strong> SHs’ recognition <strong>of</strong> <strong>the</strong> importance <strong>of</strong> <strong>the</strong> knowledge acquired, and to <strong>the</strong>ir interest
in being associated with it. Moreover, <strong>the</strong> SHs have already invited us to improve certain KT tools,
including training programs (occupational, trade union or sector-based) and to participate in <strong>the</strong>
review <strong>of</strong> an occupational standard.
6. Conclusion
Recognizing that KT cannot be limited to <strong>the</strong> simple dissemination <strong>of</strong> results after a research project
has been completed, it is essential that stakeholders be continuously involved in all phases <strong>of</strong> <strong>the</strong>
project. For <strong>the</strong> IRSST, research becomes most relevant when workplaces appropriate results. In this
case study, it was found that several conditions were essential to ensuring results appropriation: a
framework <strong>of</strong> practice for Knowledge Management that <strong>of</strong>fered a certain leeway in terms <strong>of</strong> its use,
<strong>the</strong> presence <strong>of</strong> a Knowledge Transfer Advisor on <strong>the</strong> research team, and pre-project work to ensure
that <strong>the</strong> project would meet <strong>the</strong> workplaces’ real needs and secure a firm commitment from <strong>the</strong> SHs.
Finally, <strong>the</strong> involvement <strong>of</strong> a credible “champion” in <strong>the</strong> sector facilitated interactions between <strong>the</strong>
research team and <strong>the</strong> SHs, in turn creating a relationship <strong>of</strong> trust and legitimizing <strong>the</strong> KT action plan.
320

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321

Adoption <strong>of</strong> Knowledge Management Systems in SMEs;
Realities and Challenges from Ethiopia
Tendayi Gondo 1 and Edmore Kori 2
1
Department <strong>of</strong> Urban and Regional Planning, University <strong>of</strong> Venda,
Thohoyandou, South Africa
2
Department <strong>of</strong> Geography and Geo-Information Sciences, University <strong>of</strong> Venda,
Thohoyandou, South Africa
gondotee@gmail.com
edmore.kori@univen.ac.za
Abstract: Most Small-scale Medium Enterprises (SME’s) are becoming relentless in managing and developing
knowledge management systems. This is a view however that is based on anecdote ra<strong>the</strong>r than reliable empirical
evidence. Using data from 300 SME’s in Ethiopia, this paper helps redress this deficit. Specifically <strong>the</strong> paper
explores <strong>the</strong> extent to which SMEs in Ethiopia engage in knowledge management programmes and deciphers
some <strong>of</strong> <strong>the</strong> factors which may explain <strong>the</strong> use and non – use <strong>of</strong> knowledge management practices. The analysis
reveals that although a significant number <strong>of</strong> SME’s are knowledge conscious, many more seemingly adopt an
ad hoc or haphazard approach. For example less than tenth have credible knowledge management systems.
Consequently it seems that <strong>the</strong>re is considerable distance to be travelled to arrive at a universal appreciation <strong>of</strong>
<strong>the</strong> need to incorporate knowledge portals in SME operations. The analysis also reveals that larger SMEs are
more likely to adopt knowledge management systems where <strong>the</strong> employed labourforce is high and where
government assistance is readily available.
Keywords: knowledge management, organizational learning, capacity, performance
1. Introduction
Today’s global economy has created a more complex and turbulent environment in which most
enterprises must learn to compete effectively to achieve sustainable growth (Caligiuri 2009). The
resulting changing economic environment has led to an increasing interest in improving organizational
business processes to enhance performance (Ranganathan and Dhaliwal 2001; McCormack et al
2010; Trkman 2010). To this end, Leaders who can effectively manage through this complex,
changing, and <strong>of</strong>ten ambiguous global environment have in recent times become critical for <strong>the</strong> future
survival <strong>of</strong> enterprises (Caligiuri 2006; Mendenhall 2006; Caligiuri 2009). One <strong>of</strong> <strong>the</strong> fields dealing with
<strong>the</strong>se challenges is Knowledge Management (KM) and <strong>the</strong>re has been a surge <strong>of</strong> research
scholarship and practitioner’s interest in this area for more than a decade. Such a development does
not come as a surprise given that <strong>the</strong> core <strong>of</strong> enterprises has over <strong>the</strong> recent past shifted from being
labour or capital intensive to being knowledge intensive.
The advent <strong>of</strong> <strong>the</strong> knowledge economy has seen most enterprises becoming relentless in managing
and developing Knowledge Management Systems (KMS) for sustained competitive advantage (Zhen
et al 2010). Both academics and industry concur that KMS has turned out to be <strong>of</strong> great interest and
potential value to business managers (King et al 2002; Rouse 2002; Zhen et al 2010). Such a view is
premised on <strong>the</strong> <strong>the</strong>sis that knowledge which is related to company’s customers, products and
services, operational procedures, competitors, job associates and previous successes and failures
can produce long term and sustained competitive predominance for enterprises (Rouse 2002; Yang
and Wan 2004; Zhen et al 2010). For <strong>the</strong> SME sector, this is a view however that is based on
anecdote ra<strong>the</strong>r than reliable empirical evidence. Using data from 300 SME’s in Ethiopia, this paper
helps redress this deficit. Specifically <strong>the</strong> paper explores <strong>the</strong> extent to which SMEs in Ethiopia engage
in knowledge management practices and deciphers some <strong>of</strong> <strong>the</strong> factors which may explain <strong>the</strong> use
and non – use <strong>of</strong> knowledge management practices.
Following this introduction, we define KM. This is <strong>the</strong>n followed by a brief discussion on <strong>the</strong> KM
development process. The status <strong>of</strong> KMS in <strong>the</strong> SME sector is <strong>the</strong>n given in <strong>the</strong> next section. We also
give an overview <strong>of</strong> materials and methods employed in <strong>the</strong> following section, before we discuss <strong>the</strong>
results and give some concluding remarks.
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Tendayi Gondo and Edmore Kori
1.1 What are knowledge management systems?
Existing literature on KM is pregnant with a taxonomy <strong>of</strong> explanations about what <strong>the</strong> term KM refers
to (see Nonaka 1991; Nonaka and Takeuchi 1995; Bassi 1997; Mayo 1998; Nonaka and Konno 1998;
Parlby 1998; Beckman 1999; Gordon 2000; Martin 2000; Allee 2001 to name a few). We adopt a
syn<strong>the</strong>sis <strong>of</strong> such definitions given by Yang and Wan (2004: 595) who describes KM as <strong>the</strong> process
<strong>of</strong> collecting and identifying useful information (i.e. knowledge acquisition), transferring tacit
knowledge to explicit knowledge (i.e. knowledge creation or transfer), storing <strong>the</strong> knowledge in <strong>the</strong>
repository (i.e. organizational memory), disseminating it through <strong>the</strong> whole organization (i.e.
knowledge sharing), enabling employees to easily retrieve it (i.e. knowledge retrieval) and exploiting
and usefully applying knowledge (i.e. knowledge leverage). “Knowledge in SME operations can
<strong>the</strong>refore be defined as that knowledge which is related to company’s customers, products and
services, operational procedures, competitors and job associates. Such knowledge could be found in
people’s heads, stored in documents or papers, or located in types <strong>of</strong> electronic devices (Yang and
Wan 2004).
1.1.1 KM development
The development <strong>of</strong> a KM system requires that three important threads are addressed (Davenport &
Prusak 2000; Lesser and Prusak 2000; O’Dell and Grayson 1999; Nonaka and Takeuchi 1995). First,
social interactions and networks are deemed to be crucial in accelerating knowledge sharing, in
assembling divergent resources from dispersed locations within an organization, and in enhancing <strong>the</strong>
effectiveness <strong>of</strong> storing individual and organizational knowledge. Second, technologies can be
employed for nurturing knowledge sharing and storing practices. The third thread is that top managers
need to remove all obstacles that impede <strong>the</strong> development <strong>of</strong> <strong>the</strong> KM best practice.
In <strong>the</strong> process <strong>of</strong> knowledge flow, individuals could more easily acquire knowledge and retrieve
special resources if trust and trustworthiness are embedded among people through personal contact,
inter-relationship and networking (Lesser and Prusak 2000). A number <strong>of</strong> technologies can be utilized
for knowledge repositories, including among o<strong>the</strong>rs. In addition, <strong>the</strong> repositories in any forms should
be accessible, traceable, retrievable, and useable, regardless <strong>of</strong> <strong>the</strong> spatial–temporal discrepancy
(Johnson and Paper 1998; Martin 2000). KM has a emerged as a multidisciplinary discipline –
borrowing concepts and principles from such fields as systems engineering, organizational
development, systems management and organizational behavior. Following literature on KM, we can
distinguish between 4 pillars <strong>of</strong> any knowledge enterprising institution – including leadership,
organization, technology and learning (Figure 1). The leadership component questions <strong>the</strong>
organization’s business culture, strategic goals and its communications among o<strong>the</strong>r issues.
Organizational processes and procedures are also critical to any effective KMS. The role <strong>of</strong>
Technology, particularly IT has been underscored by many (Melville et al 2004; Ray et al 2007). Only
learning organizations are open to KM driven change process.
Figure 1: Major pillars <strong>of</strong> Knowledge Management
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1.1.2 SMEs and KMS
Tendayi Gondo and Edmore Kori
The need for, and usefulness <strong>of</strong>, KMS in enterprise development has been identified. The use <strong>of</strong> such
systems is however under-developed for, and under-researched in SMEs (Blili and Raymond 1993;
Levy and Powell 2000; de Haan 2007). Many scholars recognize that SMEs have characteristics that
impact on <strong>the</strong>ir ability to adopt and develop KMSs. These factors are however not pursued (Blili and
Raymond 1993; McCahon 1993; Levy and Powell 2000; Haan 2007). Most studies have tended to
focus more on one thread <strong>of</strong> KMS – that is <strong>the</strong> technology component. There is a widespread
recognition that planning for Information Systems (IS) in SMEs has become more critical as
technology has become more central to <strong>the</strong> SME’s products and processes (Levy and Powell 2000)
yet planning for IS has always lagged behind for many SMEs. The limited planning that is <strong>of</strong>ten
undertaken has focused on operational systems to improve efficiency and effectiveness. Leveraging
such IS for competitive advantage has rarely been an objective. Several reasons to this view have
been given. Hashmi and Cuddy (1990) for example observe that most SMEs invest in IS
incrementally. They fur<strong>the</strong>r observe that such investments in IS are usually limited to administration
and transaction processing. The strategic decision making process has not benefited much from <strong>the</strong>
deployment <strong>of</strong> IS in SMEs. Hann et al (2007) on <strong>the</strong> o<strong>the</strong>r hand identifies lack <strong>of</strong> financial and material
assets as o<strong>the</strong>r limiting factors. Evidence <strong>of</strong> usage <strong>of</strong> information, information systems and
computers in SMEs has been presented by Hann et al (2007). Like o<strong>the</strong>rs, <strong>the</strong>se authors observe that
SMEs lag behind not only quantitative but also qualitative standards, largely restricting use <strong>of</strong>
information technology (IT) to operational and administrative tasks. Despite <strong>the</strong>ir limited use, most
scholars concur that IS are critical to <strong>the</strong> survival rates <strong>of</strong> SMEs in a fast changing business
environment (Churchill and Lewis 1983; Agarwal 1998). Focusing on a single thread <strong>of</strong> KMS has
however impoverished our own understanding <strong>of</strong> <strong>the</strong> status <strong>of</strong> KMS in SMEs. It has also stifled any
efforts targeted at determining how SMEs can best adopt and develop, KMSs for improved
competitive advantage.
1.2 SMEs in Ethiopia
It is generally acknowledged that data on SME operations in Ethiopia is very limited (Zewde and
Associates 2002). Available statistics reveal that <strong>the</strong>re were 584,913 informal sector activity operators
and 2731 small scale manufacturing industries that gave employment to 739,898 persons. Ethiopian
women operated about 65% (<strong>of</strong> <strong>the</strong> total 584,913) <strong>of</strong> urban micro enterprises. Women-owned
businesses account for 26% (<strong>of</strong> <strong>the</strong> total 584,913) <strong>of</strong> small-scale enterprises (Stevenson and St-Onge
2005). Major barriers faced by Ethiopian SME’s include lack <strong>of</strong> sufficient financial resources, limited
access to markets and information, lack <strong>of</strong> affordable technology (including training in business skills),
lack <strong>of</strong> managerial skills and limited space for working premises (ILO 2003; Stevenson and St-onge
2005). In 1997, <strong>the</strong> Ethiopian government adopted an SME development and promotion strategy that
sought to provide a systematic approach to alleviating problems faced by SMEs and to promote <strong>the</strong>ir
growth (MOTI, 1997). Government support is mostly channeled through <strong>the</strong> federal Micro and Small
Enterprise Development Agency (FeMSEDA). Anumber <strong>of</strong> activities have been promoted since <strong>the</strong>n
through <strong>the</strong> Business Development Service (BDS) scheme. Such activities include basic training in
technologies and business skills, development <strong>of</strong> low-level serviced working premises, <strong>the</strong> provision
<strong>of</strong> micro credit and information on markets and techniques, and working with producers to identify
constraints among o<strong>the</strong>rs (MWUD 2006).
1.3 Materials and methods
The study draws empirical evidence from a random sample <strong>of</strong> 300 Ethiopian SMEs. The survey was
conducted in July, 2009 in Addis Ababa <strong>the</strong> capital city <strong>of</strong> Ethiopia. The questionnaire method was
used to elicit <strong>the</strong> views <strong>of</strong> SME operators in three out <strong>of</strong> ten purposively selected sub cities including
Kirkos, Bole and Yeka sub cities.
The analysis measured <strong>the</strong> extent to which SMEs in Ethiopia are adopting KMS by making reference
to some <strong>of</strong> <strong>the</strong> widely mentioned attributes <strong>of</strong> an effective KMS. Such attributes include among o<strong>the</strong>rs
knowledge demand (Zhen et al 2008), knowledge seeking and / or acquisition (Gunn et al 1999; King
and Lekse 2006; Miah et al 2008; Zhen and Jiang 2008), existence <strong>of</strong> internet / intranet facilities (Li et
al 2006; Nevo and Chan 2007), knowledge flow, transfer or sharing (Zhuge 2003; Ko etal 2005; Li et
al 2005; 2006; Artail 2006; Kotlarsky et al 2008; Zhen et al 2008), benchmarking <strong>of</strong> operations
(Reijers and Mansar 2005; Trkman 2010), management process (Barjis 2008), fit between
technology and business processes (Bendoly and Cotteleer 2008; Mutschler 2008), Information
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Tendayi Gondo and Edmore Kori
storage and retrieval (Haa et al, 2007), organizational learning, communications, knowledge flow or
transfer (Reijers and Mansar, 2005; Nevo and Chan, 2007).
A series <strong>of</strong> questions directed to SME operators were asked. The difference between categorical
responses was measured by computing <strong>the</strong> standard deviation. All responses were subjected to a
Kolmogov Smirnov Z test – a goodness <strong>of</strong> fit test that was used to measure <strong>the</strong> extent to which <strong>the</strong>
portrayed distribution <strong>of</strong> responses conformed to a normal distribution. Such a test boosted
confidence in <strong>the</strong> use <strong>of</strong> <strong>the</strong> standard deviation (as a measure <strong>of</strong> differences on proportions) since all
test result pointed to a normal distribution at 5 % level <strong>of</strong> significance. The analysis was performed
using <strong>the</strong> Statistical Package for Social Scientists (SPSS).
2. Results and discussion
The analysis used <strong>the</strong> classification employed by Haan et al, (2007) to distinguish between 3 types <strong>of</strong>
sampled SMEs. Such a classification compares well with that proposed by Levy and Powell, (2000)
who identified four classes including Manufacturing, wholesale and retail trade, Business serves, and
social services and public administration. Our analysis reveals that <strong>the</strong> majority (41.67%) <strong>of</strong> <strong>the</strong>
sampled SMEs fall within <strong>the</strong> service industry sector followed by manufacturing (37.33%). Traders
constitute 21% <strong>of</strong> <strong>the</strong> sample (refer to figure 2). The service industry is largely dominated by
restaurants, internet services, informal solid waste collection and <strong>the</strong> shoe cleaning industry, while <strong>the</strong>
bulk <strong>of</strong> <strong>the</strong> manufacturing is limited to textiles, carpentry, brick making, shoe making and steel works.
The trading sector largely relates to wholesaling and retail trade.
Figure 2: Sample characteristics
The analysis reveals that most SMEs in Ethiopia have not implemented credible KMSs. Using <strong>the</strong>
commonly mentioned building blocks <strong>of</strong> an effective KMS we reach <strong>the</strong> conclusion that certain SME
characteristics in Ethiopia limit <strong>the</strong> adoption <strong>of</strong> credible KMSs. Table 1 gives an overview <strong>of</strong>
responses to some KM variables that need to be present in any credible KMS. Despite <strong>the</strong> invaluable
role played by social interactions and networks in accelerating knowledge sharing in assembling
divergent resources from dispersed locations within an organization, and in enhancing <strong>the</strong>
effectiveness <strong>of</strong> storing individual and organizational knowledge, our analysis reveals that networking
practices within SMEs are still weak. Approximately 59% <strong>of</strong> <strong>the</strong> sampled SMEs reported that <strong>the</strong>ir
networking practices were less that good. SMEs are also constrained in terms <strong>of</strong> trust – an essential
ingredient in facilitating <strong>the</strong> much needed transfer <strong>of</strong> knowledge among individuals. Subsequently <strong>the</strong>
free acquisition knowledge and retrieval <strong>of</strong> o<strong>the</strong>r special resources has been severely limited by lack
<strong>of</strong> trust. It is however important to note that a significant proportion (41.7%) reported fair to good
levels <strong>of</strong> trust in <strong>the</strong>ir operations. Fur<strong>the</strong>r analysis however revealed that <strong>the</strong> bulk <strong>of</strong> such SMEs
belong to what McMahon (2001) refers to as traditional SMEs whose primary existence is to provide
<strong>the</strong>ir owners with a source <strong>of</strong> employment. In such enterprises trust is usually based on family ties as
most employers seek to assist family members in securing employment. Innovation is not part <strong>of</strong> <strong>the</strong>
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Tendayi Gondo and Edmore Kori
ambitions <strong>of</strong> <strong>the</strong> owners. It is <strong>the</strong>refore not surprising to note that at least 83% <strong>of</strong> SME operators
reported more that fair level <strong>of</strong> knowledge flow among <strong>the</strong>ir employees. The o<strong>the</strong>r explanatory
variable is <strong>the</strong> size <strong>of</strong> <strong>the</strong> workforce. Knowledge flow is better facilitated in smaller organizations that
bigger ones. In our sample <strong>the</strong> number <strong>of</strong> employees ranged from 2 to 32 employees with an average
<strong>of</strong> 6.52 (standard dev. = 8.06). The majority <strong>of</strong> SME workforce is between 1 – 14 employees (refer to
figure 3). The o<strong>the</strong>r constraining factor is communications. Most SMEs are limited in terms <strong>of</strong> <strong>the</strong>ir
communications.
Table 1: Perceived performance <strong>of</strong> SMEs on sample KMS attributes.
Most (53.7%) SMEs however reported that <strong>the</strong>y are engaged in a continuous search <strong>of</strong> information
related to <strong>the</strong>ir operations. A fur<strong>the</strong>r 29 % indicated that <strong>the</strong>y occasionally seeking for business
related information. It is <strong>the</strong> record keeping part <strong>of</strong> such information that is <strong>of</strong>ten problematic. Sample
statistics reveal that 88% <strong>of</strong> SMEs are facing serious challenges in keeping and maintain records <strong>of</strong>
information solicited. It is also not surprising to note that about 87% <strong>of</strong> SMS reported that <strong>the</strong>y are
seriously constrained in terms <strong>of</strong> <strong>the</strong>ir information retrieving capabilities (refer to table 1). One <strong>of</strong> <strong>the</strong>
several reasons to this is that most SMEs have not taken advantage <strong>of</strong> <strong>the</strong> existing state <strong>of</strong> <strong>the</strong> art
technology architectures in information storage and retrieval. Table 2 portrays computer ownership
status among <strong>the</strong> SMEs. Only 24.3% <strong>of</strong> <strong>the</strong> SMEs reported that <strong>the</strong>y owned computer architectures.
The bulk <strong>of</strong> such SMEs belong to <strong>the</strong> service and manufacturing sectors. The traders had virtually no
computers. The analysis revealed a significant relationship between industry type and computer
ownership (Pearson chi-square = 135.539; df = 2, p < 0.05). Computer technology is critical in any
transformation process that seeks to turn organization into a knowledge enterprising one.
Figure 3: Size <strong>of</strong> SMEs
326

Does your
organization own a
computer?
Tendayi Gondo and Edmore Kori
Manufacturi
Industry type
ng Traders
Service
industry
Total
Yes Count 24 0 49 73
No, not at
all
% within Industry
type
19.2% .0% 77.8% 24.3%
Count 101 112 14 227
% within Industry
type
80.8% 100.0% 22.2% 75.7%
Total Count 125 112 63 300
Table 2: Computer ownership
% within Industry
type
100.0% 100.0% 100.0% 100.0%
Such transformation is however not spontaneous, it requires that computer architecture be utilized
properly. The KM infrastructure utilized by most SMEs (75.6%) is primarily manual as opposed to
computerized (refer to table 3). O<strong>the</strong>rs however reported that <strong>the</strong>ir KM architecture was ei<strong>the</strong>r
computerized (4%) or a combination <strong>of</strong> both manual and computerized (20.3%). Fur<strong>the</strong>r statistical
analysis revealed a significant association between computer ownership and <strong>the</strong> size <strong>of</strong> <strong>the</strong> workforce
(Pearson Chi-Square = 311.122, df = 4, p

Tendayi Gondo and Edmore Kori
<strong>the</strong> enterprise’s strategy. Unfortunately, many SMEs reported that <strong>the</strong>y do not have a clear
organizational vision or goal (refer to figure 4). In addition, many SMES are also constrained in terms
<strong>of</strong> <strong>the</strong>ir management capabilities. Only a small proportion (24,3%) reported that <strong>the</strong>ir management
capabilities were good or very good (refer to table 1). Such operators benefited from government
funded Technical Vocational Education Training (TVET) programmes. Gondo and Dafuleya (2010)
have however revealed that such programmes are limited in <strong>the</strong>ir coverage.
Figure 4: Existence / no existence <strong>of</strong> SME strategic direction
Despite <strong>the</strong> obvious advantages brought about benchmarking, a significantly large proportion (33.7%)
<strong>of</strong> SMEs has never compared <strong>the</strong>ir operations with those <strong>of</strong> o<strong>the</strong>r players (refer to table 1).
It was possible to determine whe<strong>the</strong>r <strong>the</strong> SMEs had a (in relative terms) a credible KMS in place. A 3
point rating scale was developed for this purpose in which a score <strong>of</strong> 3 was given to those SMEs
which performed better on all three threads <strong>of</strong> <strong>the</strong> KM development process. A score <strong>of</strong> 2 was given to
those which perfumed better on at least two threads, while a score <strong>of</strong> 1 was given to those performing
poorly on all <strong>the</strong> three threads. Only 8% <strong>of</strong> <strong>the</strong> SMEs had in relative terms a better KMS system. The
majority (71%) <strong>of</strong> <strong>the</strong>m were rated as having no credible KMS. Twenty one percent performed
relatively better in at least two threads <strong>of</strong> <strong>the</strong> KM development process. A regression analysis
revealed a significant positive correlation between KMS rating and <strong>the</strong> number <strong>of</strong> employees in each
operation (R square = 0.742, p = 0.31969 F=855.861). A summary <strong>of</strong> such regression coefficients is
given in Table 4. These statistics alone suggest that size matters in SMEs ability to adopt KMS.
Larger SMEs are more likely to adopt KMS as opposed to smaller ones. Fur<strong>the</strong>r statistical analysis
however reveals that this is more likely in <strong>the</strong> service and manufacturing sectors than in <strong>the</strong> trading
sectors.
Table 4: Regression coefficients a (KMS rating versus number <strong>of</strong> employees)
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta
t Sig.
1 (Constant) .932 .024 39.219 .000
Number <strong>of</strong> your
employees
.067 .002 .861 29.255 .000
a. Dependent Variable: KMS rating
This is because, larger SMES have different specialized functions / divisions and possible a high
number <strong>of</strong> business units that would make KMS attractive. Our analysis revealed a fur<strong>the</strong>r positive
association between <strong>the</strong> number <strong>of</strong> business functions and two independent variables which included
number <strong>of</strong> employees and number <strong>of</strong> business units (R square = 0.708, df = 2, p = 0.66646 F =
328

Tendayi Gondo and Edmore Kori
360.595). Summary coefficients are given in table 5. It is disturbing to note that many SMEs do not
have notable or ra<strong>the</strong>r distinctive business functions (refer to figure 5) and hence are severely
constrained in <strong>the</strong>ir ability to adopt KMS.
Table 5. Multiple regression coefficients a
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta
t Sig.
1 (Constant) .033 .100 .326 .744
Number <strong>of</strong> your
employees
Howmany business
units do you have?
.118 .012 .774 9.690 .000
.098 .106 .073 .920 .358
a. Dependent Variable: How many business functions / divisions do you have?
Figure 5: Number <strong>of</strong> distinctive business functions
3. Concluding remarks
50%
21%
21%
4 4%
No distinctive / specialized business functions (50%), one (21%) two
There is a general consensus among researchers that KMSs can produce long term and sustained
competitive predominance for enterprises (Rouse 2002; Yang and Wan 2004; Zhen et al 2010) such a
view however is more <strong>of</strong> a lip service that reality in <strong>the</strong> SME sector. Using empirical evidence from
300 SMEs in Ethiopia, our analysis reveals that KMSs are not (yet) well developed in <strong>the</strong> majority <strong>of</strong>
Ethiopian SMEs. While evidence <strong>of</strong> being knowledge conscious exist we found out that only 8% <strong>of</strong> <strong>the</strong>
sampled SMES were performing relatively better on <strong>the</strong> three major threads <strong>of</strong> <strong>the</strong> KM development
process. Such enterprises were found to be what McMahon (2001) referred to as entrepreneurial
SMEs gifted with entrepreneurial aptitude and technical and commercial innovation. The remaining
SMEs can be dismissed as those that do not show desire to make progress toward being knowledge
enterprising companies. Such an observation coincides with that <strong>of</strong> Sorey (1994) who observed that
many firms show no desire to make progress. Such firms have been dismissed by McMahon (2001)
as traditional SMEs whose primary purpose is to provide owners with a source <strong>of</strong> employment or
livelihood (Haan et al, 2007).
While many academics and business practitioners advocate for embedding KMSs in business
operations, <strong>the</strong> prevailing logic has up this point, largely assumed that everyone benefits equally from
KMS. The findings in this study begin to refine <strong>the</strong> practice <strong>of</strong> KM by suggesting that certain KM
practices and certain enterprises do. Our analysis revealed that on SMEs whose operations
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Tendayi Gondo and Edmore Kori
demonstrated an element <strong>of</strong> specialization <strong>of</strong> functions were performing relatively better on <strong>the</strong> three
major threads <strong>of</strong> <strong>the</strong> KM development process. Such specialization was also found to be consistently
related to <strong>the</strong> size <strong>of</strong> <strong>the</strong> SMS. Larger SMEs rated well on our generated KMS scale than smaller
ones. These findings compare well with those by Haan et al (2007), who observed that larger SMEs
were more likely to adopt IT resources to manage <strong>the</strong>ir logistics. In our analysis one major limiting
factor for most SMEs is <strong>the</strong> fact that specialization <strong>of</strong> functions in most SMEs is not too deep (Mentzer
et al 2001).
Several o<strong>the</strong>r limiting factors were also discerned, including <strong>the</strong> limited role <strong>of</strong> computer technology,
lack <strong>of</strong> credible leadership, absence <strong>of</strong> a business strategy and <strong>the</strong> relative lack <strong>of</strong> government
assistance. Government assistance has always played an instrumental role in SME development in
Ethiopia, through training and o<strong>the</strong>r Business Development Servises (BDS) – including technical
advise and <strong>the</strong> much needed financial assistance. Such assistance was however found to be limited
to a few SME who had been exposed to TVET training programmes. Gondo and Dafuleya (2010)
have however shown that such government extension services are always restricted in <strong>the</strong>ir
coverage. The role <strong>of</strong> IT as both an enabler and facilitator <strong>of</strong> <strong>the</strong> change process (Attaran 2004; Hung
2006; Trkman et al 2007; Groznik et al 2008) has not been harnessed. Several o<strong>the</strong>r studies have
underscored <strong>the</strong> important role played by IT more specifically at <strong>the</strong> business activity / process level
(Melville et al 2004; Ray et al 2007). Despite significant investments in computer technologies, a
considerable number <strong>of</strong> enterprises have not been able to derive full benefits due to <strong>the</strong>ir inability to
effectively deploy IT in <strong>the</strong>ir business strategies (Karim et al 2007). The likelihood <strong>of</strong> innovation in
SME operations have also been severe hampered by lack <strong>of</strong> benchmarking. Benchmarking usually
result in <strong>the</strong> adoption <strong>of</strong> best practices (Reijers and Mansar, 2005). Organizational learning <strong>the</strong>refore
becomes critical in any enterprise that seeks to be a knowledge driven enterprise. Our analysis also
reveals that some benchmarking tendencies in some SMEs are retrogressive in that <strong>the</strong>y do not
encourage effective learning. Many more SMEs seemingly compare <strong>the</strong>ir operations with o<strong>the</strong>r similar
and <strong>of</strong>ten struggling SMEs. We did not find any evidence <strong>of</strong> vertical benchmarking. Given a host <strong>of</strong>
<strong>the</strong>se and o<strong>the</strong>r limiting factors, it is not surprising to find out that many more seemingly adopt an ad
hoc or haphazard approach to KM. Such ad hoc approaches to KM are characterized by good
performance in some KM attributes and non performance in o<strong>the</strong>rs. Such performance and / non
performance is usually not consistent over time as some KM practices are not performed regularly.
Consequently it seems that <strong>the</strong>re is considerable distance to be travelled to arrive at a universal
appreciation <strong>of</strong> <strong>the</strong> need to incorporate knowledge portals in SME operations <strong>of</strong> Ethiopia.
Acknowledgements
The author would like to thank <strong>the</strong> institutional support rendered by <strong>the</strong> Ethiopian Civil Service College
under its World Bank / UNDP funded Urban Management Masters Programme (UMMP) and <strong>the</strong>
Department <strong>of</strong> Urban and Regional Planning at <strong>the</strong> University <strong>of</strong> Venda in South Africa. The views
expressed here are however <strong>the</strong> sole responsibility <strong>of</strong> <strong>the</strong> authors and not <strong>of</strong> <strong>the</strong> supporting
institutions.
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331

The Chain Value Process and Knowledge Transfer in a
Bioengineering Case
Manel González-Piñero 1 , ElenaCano López 2 , Miguel Ángel Mañanas
Villanueva 1 , Juan Ramos Castro 1 and Pere Caminal Magrans 1
1
Biomedical Engineering Research Centre (CREB) <strong>of</strong> <strong>the</strong> Technical University
<strong>of</strong> Catalonia. Barcelona, Spain.
2
Technology Transfer Office, IPR Department <strong>of</strong> <strong>the</strong> Technical University <strong>of</strong>
Catalonia, Barcelona, Spain
manel.gonzalez@upc.edu
elena.lopez-cano@upc.edu
miguel.angel.mananas@upc.edu
jramos@eel.upc.edu
pere.caminal@upc.edu
Abstract: This study highlights <strong>the</strong> complete management process <strong>of</strong> a bioengineering knowledge,
and how it follows all <strong>the</strong> steps <strong>of</strong> <strong>the</strong> Valorisation chain that contains <strong>the</strong> following five steps:
detection, selection, evaluation, protection and commercialization. In this process we show how
researchers, IP managers and business development managers work toge<strong>the</strong>r to achieve a common aim. The
knowledge selected to advance in <strong>the</strong> value chain before being transferred to <strong>the</strong> market is a
technology <strong>of</strong> a Bi<strong>of</strong>eedback equipment specifically for urinary incontinence. Bi<strong>of</strong>eedback instruments for <strong>the</strong>
treatment <strong>of</strong> urinary incontinence have <strong>the</strong> purpose <strong>of</strong> generating (and also recording for fur<strong>the</strong>r analysis) signals
directly related to <strong>the</strong> activity <strong>of</strong> <strong>the</strong> pelvic floor muscles. Although a few instruments use <strong>the</strong> exerted pressure as
<strong>the</strong> measured signal, <strong>the</strong> most frequently employed signal is <strong>the</strong> electrical activity associated to <strong>the</strong> muscle
contractions or electromyogram (EMG). This Bi<strong>of</strong>eedback case developed in our University has followed a
methodology <strong>of</strong> Valorization in which <strong>the</strong> interaction between researchers, IP managers and business
development managers is significant to know <strong>the</strong> market requirements and to direct <strong>the</strong> research efforts towards
<strong>the</strong> market necessities; that is why <strong>the</strong> last step <strong>of</strong> <strong>the</strong> design is a commercialization planning to explode <strong>the</strong>
technology. In this case, <strong>the</strong> goal is to find an industrialized partner interested in producing <strong>the</strong> clinical validated
prototype following all <strong>the</strong> EC regulations.
Keywords: Bioengineering, bi<strong>of</strong>eedback, marketing in higher education, technology transfer, knowledge transfer,
valorisation process
1. Introduction
Universities are involved in <strong>the</strong> creation <strong>of</strong> innovation and knowledge through <strong>the</strong> participation in
research projects funded by <strong>the</strong> public sector. They have been involved in <strong>the</strong> creation <strong>of</strong> innovation
through collaborative agreements with companies transferring, in most <strong>of</strong> <strong>the</strong> cases, <strong>the</strong> intellectual
property rights to <strong>the</strong> companies.
That is why <strong>the</strong> University is an agent at <strong>the</strong> Triple Helix system, a model designed by Etzkowitz,
(2002) and Leydesdorff (2003), in which <strong>the</strong> knowledge management activities are basics and have to
be integrated into <strong>the</strong> business processes, due to <strong>the</strong> fact that <strong>the</strong> knowledge created in <strong>the</strong> University
has <strong>the</strong> ambition to be pr<strong>of</strong>itable at <strong>the</strong> society. This <strong>the</strong>ory about innovation systems is based on
university-industry-government relations, and <strong>the</strong> generation <strong>of</strong> knowledge and its effective
application. So, how it can be possible <strong>the</strong> technology transfer <strong>of</strong> <strong>the</strong> technological opportunities
identified in <strong>the</strong> university? It could be possible through an effective commercial model defined in a
Valorisation Process.
In this case study, we would like to highlight all <strong>the</strong> management process <strong>of</strong> a bioengineering
knowledge, and how it has passed all <strong>the</strong> steps in <strong>the</strong> chain <strong>of</strong> <strong>the</strong> Valorisation Process that contains
<strong>the</strong> following five steps: detection, selection, evaluation, protection and commercialization.
The knowledge selected to advance in <strong>the</strong> value chain before being transferred to <strong>the</strong> market is a
technology <strong>of</strong> a Bi<strong>of</strong>eedback equipment specifically for urinary incontinence. It is placed in a female's
vagina or <strong>the</strong> anus <strong>of</strong> a male and connected to a computer or a portable home <strong>the</strong>rapy unit to give <strong>the</strong>
person a light signal, bar graph, or audible tone to show how <strong>the</strong> muscles <strong>of</strong> <strong>the</strong> pelvic floor are
contracting and relaxing. Surface electrodes can also be placed on <strong>the</strong> abdomen or <strong>the</strong> buttocks and
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Manel González-Piñero et al
used in conjunction with internal sensors. The instrument accurately measures, processes and
'feedback' to <strong>the</strong> patient and clinician <strong>the</strong> data in <strong>the</strong> form <strong>of</strong> visual and/or auditory signals. This
information has educational and reinforcing characteristics about <strong>the</strong> muscle activity. The knowledge
<strong>of</strong> muscle activity allows <strong>the</strong> patient and clinician to adjust existing motor patterns by re-educating <strong>the</strong>
muscle or muscle groups for efficient muscle function and voluntary control.
Bi<strong>of</strong>eedback instruments for <strong>the</strong> treatment <strong>of</strong> urinary incontinence have <strong>the</strong> purpose <strong>of</strong> generating
(and also recording for fur<strong>the</strong>r analysis) signals directly related to <strong>the</strong> activity <strong>of</strong> <strong>the</strong> pelvic floor
muscles. Although a few instruments use <strong>the</strong> exerted pressure as <strong>the</strong> measured signal, <strong>the</strong> most
frequently employed signal is <strong>the</strong> electrical activity associated to <strong>the</strong> muscle contractions or
electromyogram (EMG).
The study wants to put forward how a real biomedical technology developed at <strong>the</strong> Biomedical
Engineering Research Centre <strong>of</strong> <strong>the</strong> Technical University <strong>of</strong> Catalonia is finally successfully
transferred to <strong>the</strong> market, showing how researchers, IP managers and business development
managers work toge<strong>the</strong>r to achieve a common aim.
2. The value chain
This concept from business management was first described and popularized by Porter (1985: 36)
“Every firm is a collection <strong>of</strong> activities that are performed to design, produce, market, deliver, and
support its product All <strong>of</strong> <strong>the</strong>se activities can be represented using a value chain...”. Porter termed <strong>the</strong>
larger interconnected system <strong>of</strong> value chains <strong>the</strong> "value system" (1985: 34) when states that “A firm’s
value chain is embedded in a larger stream <strong>of</strong> activities that I term <strong>the</strong> value system...”. A value
system includes <strong>the</strong> value chains <strong>of</strong> a firm's supplier (and <strong>the</strong>ir suppliers all <strong>the</strong> way back), <strong>the</strong> firm
itself, <strong>the</strong> firm distribution channels, and <strong>the</strong> firm's buyers (and presumably extends to <strong>the</strong> buyers <strong>of</strong>
<strong>the</strong>ir products, and so on).
Figure 1: Porter’s Value Chain
Value Streams were introduced in Porter’s work but were explained more clearly by Martin (1995: 66),
which pulls toge<strong>the</strong>r <strong>the</strong> many issues, models, and methods for transforming <strong>the</strong> traditional old-world
organization into a value-creating enterprise. Martin uses value stream, ra<strong>the</strong>r than process, to define
<strong>the</strong> end-to-end stream <strong>of</strong> activities that deliver particular results for a given customer (external or
internal).
Value chain is a high-level model <strong>of</strong> how businesses receive raw materials as input, add value to <strong>the</strong>
raw materials through various processes, and sell finished products to customers. The value chain
categorizes <strong>the</strong> generic value-adding activities <strong>of</strong> an organization.
A value chain is <strong>the</strong> disaggregating <strong>of</strong> a firm into its strategically relevant activities for <strong>the</strong> purpose <strong>of</strong>
understanding <strong>the</strong> behaviour <strong>of</strong> costs as well as <strong>the</strong> existing and potential sources <strong>of</strong> differentiation.
The concept has been extended beyond individual organizations. The industry wide synchronized
interactions <strong>of</strong> those local value chains create an extended value chain, sometimes global in extent.
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Manel González-Piñero et al
Capturing <strong>the</strong> value generated along <strong>the</strong> chain is <strong>the</strong> new approach taken by many management
strategists. By exploiting <strong>the</strong> upstream and downstream information flowing along <strong>the</strong> value chain, <strong>the</strong>
firms may try to bypass <strong>the</strong> intermediaries creating new business models.
The chain <strong>of</strong> <strong>the</strong> Technology Valorisation Process contains <strong>the</strong> following five steps: detection,
selection, evaluation, protection and commercialization.
3. Detecting a problem: a R&D and business opportunity
Urinary incontinence is defined by <strong>the</strong> International Continence Society (ICS) as a 'condition in which
involuntary loss <strong>of</strong> urine is a social or hygienic problem and is objectively demonstrable' (Abrams et al.
1988). It is <strong>of</strong>ten a devastating condition severely affecting <strong>the</strong> quality <strong>of</strong> life for sufferers.
The most common form <strong>of</strong> urinary incontinence (affecting approximately 45% <strong>of</strong> incontinent people),
is stress incontinence, described as a leakage <strong>of</strong> urine associated with an increase in intra-abdominal
pressure for example during a cough sneeze, laugh or physical exertion. If <strong>the</strong> client is investigated
using subtracted cystometry (urodynamics) where <strong>the</strong> behaviour <strong>of</strong> <strong>the</strong> Detrusor muscle can be
observed, <strong>the</strong>n a diagnosis <strong>of</strong> genuine stress incontinence (GSI), may be made. This is in accordance
with <strong>the</strong> ICS definition <strong>of</strong> genuine stress incontinence (Abrams, 1988): “ GSI is <strong>the</strong> involuntary loss <strong>of</strong>
urine occurring when in <strong>the</strong> absence <strong>of</strong> detrusor contraction, <strong>the</strong> intravesical pressure exceeds <strong>the</strong>
intraurethal pressure.'. Acknowledging <strong>the</strong> role <strong>of</strong> <strong>the</strong> change in intra-abdominal pressure, (Abrams,
1999) suggests that this definition should be modified to read: ¨The involuntary loss <strong>of</strong> urine occurring
when due to raised intra-abdominal pressure, <strong>the</strong> intravesical pressure exceeds <strong>the</strong> intraurethral
pressure.¨
Urinary incontinence is a common problem, estimated to affect more than 13 million people in <strong>the</strong>
United States -male and female, young and old- and a condition more prevalent than diabetes
mellitus (National Kidney and Urologic Diseases Information Clearinghouse, 2002). Overactive
bladder alone, which includes patients who do not leak urine, is reported to affect 17 million
Americans. Because <strong>of</strong> <strong>the</strong> recognized phenomenon <strong>of</strong> underreporting, <strong>the</strong>se numbers may be low.
Millions more people are not incontinent but would be if <strong>the</strong>y did not continually void to prevent
leakage. This reality is mirrored in countries worldwide (see Fig. 2 from Hampel et al, 1999). Patients
with this problem <strong>of</strong>ten lead lives <strong>of</strong> quiet desperation and social isolation.
The National Association for Continence (NAFC) states “that while only one out <strong>of</strong> twelve incontinent
patients in <strong>the</strong> United States actually reports <strong>the</strong>ir symptoms to <strong>the</strong>ir doctors, approximately 80% can
be cured or improved.” Unfortunately, women wait an average <strong>of</strong> 3 years before admitting <strong>the</strong>ir
incontinence to a health care provider (Woolner et al, 2001). A persistent myth is that incontinence is
a natural part <strong>of</strong> aging.
Country Average Prevalence <strong>of</strong> female UI
USA 37,0%
EU 26,0%
UK 28,7%
O<strong>the</strong>rs 20,5%
Figure 2: Average prevalence in female Urinary Incontinence (from Hampel et al, 1999])
Urinary incontinence not only causes considerable medical and psychosocial morbidity but it also
engenders enormous costs (Resnick, 1998) (Urinary Incontinence Guideline Panel 1992). In 1994, it
was estimated that $11.2 million was spent on <strong>the</strong> direct treatment <strong>of</strong> incontinence, and $5.2 million
on associated nursing home costs. Wagner and Hu at <strong>the</strong> University <strong>of</strong> California at Berkeley
documented <strong>the</strong> direct cost <strong>of</strong> incontinence in <strong>the</strong> Medicare-age population to be more than $16 billion
per year [Wagner and Hu, 1998]. When indirect costs (eg, time lost from work) were added, <strong>the</strong> figure
rose to more than $26 billion per year, and that only included senior citizens. It is more than is
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Manel González-Piñero et al
expended by Medicare on dialysis, and coronary bypass grafting combined (Health Care Financing
Review, 1997). Moreover, <strong>the</strong>se costs apply only to individuals older than 65 years, who constitute
less than half <strong>of</strong> those with urinary incontinence.
For older adults living in <strong>the</strong> community, <strong>the</strong> prevalence <strong>of</strong> urinary incontinence is between 15% and
35%, with women affected twice as <strong>of</strong>ten as men (Lefevre, 2000). The condition is even more
common among residents <strong>of</strong> nursing homes, where more than half <strong>of</strong> <strong>the</strong> residents experience urinary
incontinence. The prevalence <strong>of</strong> Urinary Incontinence depends on its definition (Hampel et al., 1999)
(see Fig. 3). In addition, urinary incontinence has been cited as one <strong>of</strong> <strong>the</strong> major precipitants for
placement in a nursing home (Ouslander et al. 1982). Thus, among <strong>the</strong> elderly Medicare population,
this condition is associated with a high burden <strong>of</strong> illness, high costs, and has a substantial effect on
quality <strong>of</strong> life.
45
40
35
30
25
20
15
10
5
0
40,5
14
23,5
28,3
Def. 1 Any incontrolled loss <strong>of</strong> urine
during <strong>the</strong> previous 12 months
Def. 2 More than 2 times <strong>of</strong>
incontrolled loss <strong>of</strong> urine
Def. 3 Condition that <strong>the</strong>re is an
involuntary loss <strong>of</strong> urine as a
social and higienic problem
Average <strong>of</strong> <strong>the</strong> o<strong>the</strong>r definitions
Figure 3: Prevalence or urinary incontinence (from [Hampel et al., 2009])
Urinary incontinence (UI) affects up to one-third <strong>of</strong> <strong>the</strong> non institutionalized population over age 60,
with 25 percent to 30 percent <strong>of</strong> <strong>the</strong>se individuals having frequent urinary incontinence episodes (US
Dept. <strong>of</strong> Health and Human Services, 1996). UI may vary from non-significant to severe, causing
extreme activity limitation and social isolation.
Because urinary incontinence impacts function, it is a primary predictor <strong>of</strong> decline in elderly people
(Tinetti et al, 1995). Beyond <strong>the</strong> patient, urinary incontinence may also cause significant psychosocial
distress to family, friends, and caregivers. National data show <strong>the</strong> annual direct cost <strong>of</strong> UI exceeds
$11 billion dollars for community dwelling persons alone (US Dept. <strong>of</strong> Health and Human Services,
1996).
While it is estimated that <strong>the</strong> number <strong>of</strong> incontinent geriatric patients can be as high as 80% (Portnoi,
1981), it is more difficult to estimate <strong>the</strong> incidence in younger populations, though studies by Nygaard
show incontinence to be common in young nulliparous women, particularly during physical activities.
One Danish study (Hording, 1986), conducted with a group <strong>of</strong> 45-year-old women, found that 22%
experienced stress incontinence. It was also noted that only three percent <strong>of</strong> <strong>the</strong>se women sought
medical attention for <strong>the</strong>ir problem.
Unfortunately, most physicians have received little education about incontinence, fail to screen for it,
and view <strong>the</strong> likelihood <strong>of</strong> successful treatment as low (Fantl et al, 1996). Thus, it is fitting that <strong>the</strong>
study by Burgio et al, which examines <strong>the</strong> efficacy <strong>of</strong> bi<strong>of</strong>eedback for urge incontinence, should
appear in (Burgio et al, 1998).
Older women, more <strong>of</strong>ten than younger women, experience incontinence. But incontinence is not
inevitable with age. Incontinence is treatable and <strong>of</strong>ten curable at all ages. An understanding <strong>of</strong> <strong>the</strong>
physiologic forces that leave <strong>the</strong> patient predisposed to incontinence is necessary for proper
treatment selection.
335

4. What a bi<strong>of</strong>eedback is
Manel González-Piñero et al
Bi<strong>of</strong>eedback, is in simple terms, a means <strong>of</strong> attaching a device whe<strong>the</strong>r it have sound, lights or a
picture, to show <strong>the</strong> patient how <strong>the</strong>y are performing. It can be as simple as touching a muscle with
ones fingers to feel when it contracts. It has been defined as: '… <strong>the</strong> detection <strong>of</strong> a physiological
process (ei<strong>the</strong>r directly or indirectly) and <strong>the</strong> presentation <strong>of</strong> this information to a patient who is <strong>the</strong>n
able, with training, to use <strong>the</strong> information to gain control over <strong>the</strong> process.'
It is a technique developed over <strong>the</strong> last 3 decades, which is intended to teach subjects to bring
certain physiologic processes under voluntary control. Application <strong>of</strong> this technique to medical
conditions was popularized during <strong>the</strong> 1970s, along with a variety <strong>of</strong> o<strong>the</strong>r behavioural <strong>the</strong>rapies
(Subcommittee on Nonpharmacologic Therapy Report, 1986). These <strong>the</strong>rapies were primarily directed
toward disorders that were thought to include a component <strong>of</strong> stress, psychosomatic, or
psychophysiologic features.
5. Benefits <strong>of</strong> bi<strong>of</strong>eedback vs. medication
There are multiple behavioural techniques and protocols, but <strong>the</strong>ir comparative efficacy is unknown.
Because technical aspects cannot be detailed sufficiently in reports <strong>of</strong> clinical trials, behavioural
interventions also are difficult to replicate in practice. One behavioural technique, bi<strong>of</strong>eedback, has
been even less widely used for urge incontinence because it <strong>of</strong>ten has required repeated
instrumentation <strong>of</strong> <strong>the</strong> bladder and urinary sphincter (Resnick, 1998). Moreover, despite <strong>the</strong> expertise
and time entailed, behavioural techniques are poorly reimbursed. By contrast, pharmaco<strong>the</strong>rapy
works more quickly and also requires no behavioural expertise, less physician time, and less patient
participation. None<strong>the</strong>less, although drugs help most patients, non drug restores continence to <strong>the</strong>
majority. Fur<strong>the</strong>rmore, all <strong>of</strong> <strong>the</strong> agents currently used engender adverse effects, expense, and
inconvenience (Fantl et al., 1996), and most must be taken several times daily and indefinitely. Thus,
an equally or more effective one-time intervention would be welcome.
The study by Burgio (Burgio et al., 1998) demonstrates that a less-invasive bi<strong>of</strong>eedback approach can
achieve <strong>the</strong>se goals. The study also serves as a rich source <strong>of</strong> information and provides valuable
lessons for clinicians. The investigators’ decision to begin <strong>the</strong>rapy with oxybutynin at 2.5mg3 times
daily was wise. The effect <strong>of</strong> oxybutynin was equivalent to that achieved in trials using higher
dosages, but it caused far fewer adverse effects and a lower rate <strong>of</strong> subject attrition (Fantl et al.
1996). Equally important, efficacy continued to increase beyond 2 weeks, longer than previously
reported, but consistent with o<strong>the</strong>r recent data (Appell, 1997) (Abrams et al., 1998). Thus, clinicians
should avoid escalating dosages <strong>of</strong> oxybutynin too quickly or abandoning pharmaco<strong>the</strong>rapy too soon.
6. The open innovation process followed in <strong>the</strong> R&D process <strong>of</strong> a
Bi<strong>of</strong>eedback for urinary incontinence
Technology
Detection
Selection Evaluation Protection Exploitation
License
Transfer
Spin‐<strong>of</strong>f
Agreement
Figure 4: Technical University <strong>of</strong> Catalonia’s Value Chain
“Open innovation is a paradigm that assumes that firms can and should use external ideas as well as
internal ideas, and internal and external paths to market, as <strong>the</strong> firms look to advance <strong>the</strong>ir
technology” , as Chesbrough (2003: Introduction), defined in his work. The boundaries between a firm
and its environment have become more permeable; innovations can easily transfer inward and
outward. The central idea behind open innovation is that in a world <strong>of</strong> widely distributed knowledge,
companies cannot afford to rely entirely on <strong>the</strong>ir own research but should instead buy or license
processes or inventions (i.e. patents) from o<strong>the</strong>r companies.
336

6.1 A) Detection
Manel González-Piñero et al
In a first phase, we would need to know which technologies are mostly thought out, or in a developed
state <strong>of</strong> research. Those developed technology would be in optimal conditions to be transfer to <strong>the</strong>
market, because <strong>the</strong>y have been got over <strong>the</strong> <strong>the</strong>oretical phase to arrive to <strong>the</strong> industrial
implementation.
University has a huge technology stock most <strong>of</strong> <strong>the</strong> time not pr<strong>of</strong>itable for <strong>the</strong> market. Traditionally, <strong>the</strong>
technology transfer <strong>of</strong> <strong>the</strong> university to <strong>the</strong> market has been concentrated basically in collaborative
agreements with industries to solve ad hoc necessities.
Nowadays in our universities remain a lot <strong>of</strong> developed technologies ready to be exploited
commercially. Our university has lately detected a large number <strong>of</strong> technologies thanks to <strong>the</strong> new
grants <strong>of</strong>fered by our regional Government <strong>of</strong> Catalonia, which goal is <strong>the</strong> valorization <strong>of</strong> technologies
created in <strong>the</strong> core <strong>of</strong> research centres, like universities, hospitals or technology centres in order to
transfer <strong>the</strong>m to <strong>the</strong> Catalan industry to make <strong>the</strong>m internationally more competitive (González-
Piñero, 2009).
A first development <strong>of</strong> <strong>the</strong> bi<strong>of</strong>eedback was developed under <strong>the</strong> petition <strong>of</strong> a Catalan SME that
detected <strong>the</strong> market opportunities <strong>of</strong> this new medical device. Two divisions <strong>of</strong> <strong>the</strong> Biomedical
Engineering Research Centre (CREB) <strong>of</strong> <strong>the</strong> Technical University <strong>of</strong> Catalonia (<strong>the</strong> Biomedical
Signals and Systems Division and <strong>the</strong> Instrumentation and Bioengineering Division) are <strong>the</strong> leaders <strong>of</strong>
this project in which also <strong>the</strong> Hospital Clínic de Barcelona would be part <strong>of</strong> <strong>the</strong> project as a
responsible <strong>of</strong> <strong>the</strong> validation process at <strong>the</strong> hospital.
6.2 B) Selection
We must consider that not all <strong>the</strong> technologies satisfy <strong>the</strong> optimal commercial parameters to be
exploited. That is <strong>the</strong> reason to evaluate <strong>the</strong> technology detected and to do a “Pro<strong>of</strong> <strong>of</strong> concept” if we
want to continue in <strong>the</strong> value chain. This evaluation consist in two aspects: one related with marketing
and science-business, and ano<strong>the</strong>r that evaluate <strong>the</strong> technology quality. The first evaluation is done
for experts in marketing and business in <strong>the</strong> university, and <strong>the</strong> second evaluation for a Scientific
Committee.
In our case, we have studied all <strong>the</strong> possibilities that currently are implanted in <strong>the</strong> market. It will be
helpful to define our strategy to achieve an innovative methodology and final prototype.
There are lot devices in <strong>the</strong> market for bi<strong>of</strong>eedback treatment, <strong>the</strong> most part <strong>of</strong> <strong>the</strong>m are from USA,
where exist a great demand. We have included <strong>the</strong> devices than can be applied, as specified by <strong>the</strong>
manufacturer, in <strong>the</strong> treatment <strong>of</strong> urinary incontinence.
The instruments have been divided into three categories according to <strong>the</strong>ir use:
Computer based: They are not intended for home use. Its use is restricted to practitioner consult.
Normally <strong>the</strong>y are bulk devices. They can record different signals (EMG, pressure, temperature,
skin conductivity, etc.) and <strong>the</strong> user interface is build around a PC. Table 17 summarizes <strong>the</strong> main
characteristics for several manufacturers. Prices range from 3000 $ to 8000 $ and <strong>the</strong>ir use is not
oriented specifically for urinary incontinence treatment.
Portable: They are intended for home use and <strong>the</strong>ir main characteristics are easy operation,
simple user interface (normally a bar led and 1 or 2 buttons) 1 or 2 EMG channels. Table 18
summarizes <strong>the</strong> main characteristics for several manufacturers. Due to <strong>the</strong> differences in
specifications between manufacturers prices range from 300 $ to 1300 $.
Portable with computer interface: They are intended for home and ambulatory use and <strong>the</strong>ir main
characteristics are easy operation, user graphical interface (normally a bar led and a LCD display)
2 EMG channels and an interface link to connect to a PC where a specific s<strong>of</strong>tware can be used
for monitoring online, download recorded data or program different protocols. Table 19
summarizes <strong>the</strong> main characteristics for several manufacturers.
After this process, <strong>the</strong> final specifications <strong>of</strong> a bi<strong>of</strong>eedback system for urinary incontinence have been
defined and we have started <strong>the</strong> R&D process under this assumption. The validation stage has been
developed at <strong>the</strong> Hospital Clinic, under <strong>the</strong> supervision <strong>of</strong> doctors and <strong>the</strong> researchers <strong>of</strong> <strong>the</strong> CREB.
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Manel González-Piñero et al
The project is funded by <strong>the</strong> Catalan Government to foster transfer technology strategies in research
applied projects.
6.3 C) Evaluation
This business case <strong>of</strong> this new technology requires to pay attention on three areas that will define <strong>the</strong>
impact <strong>of</strong> <strong>the</strong> technology once launched into <strong>the</strong> market:
Industrial Property: Analysis <strong>of</strong> <strong>the</strong> state-<strong>of</strong>-<strong>the</strong>-art, ano<strong>the</strong>r existing patents concerning to <strong>the</strong>
technology ,family patents ...
At <strong>the</strong> moment, we are studying with a consulting firm <strong>the</strong> patentability <strong>of</strong> <strong>the</strong> final device. It has
many possibilities and <strong>the</strong> results <strong>of</strong> <strong>the</strong> R&D can be protected and <strong>the</strong>re is <strong>the</strong> freedom to
operate. A national patent has been requested.
Market prospection: interesting sectors, advantages <strong>of</strong> our technology respect ano<strong>the</strong>r solutions
in <strong>the</strong> market, industrial applications, time-to-market, competitors, risks, potential clients, ...
This stage has been developed by a consulting firm (KIMBCN) in order to detect which
companies could be interested in <strong>the</strong> acquisition <strong>of</strong> this device. This market study has been
opened to firms around <strong>the</strong> world and some initial contacts have been established with some <strong>of</strong>
<strong>the</strong>m.
Legal Aspects: rules and regulations. The final prototype has to be validated and follow <strong>the</strong> EU
regulations.
6.4 D) Protection
Considering <strong>the</strong> protection aspects <strong>of</strong> <strong>the</strong> technology is a key aspect. Firstly, we must know if <strong>the</strong>
technology is developed in collaboration with o<strong>the</strong>r partners in order to negotiate <strong>the</strong> co-property
percentages. Secondly, it is necessary to consider <strong>the</strong> different ways <strong>of</strong> protection (patent, design,
s<strong>of</strong>tware ...), or to take into account <strong>the</strong> possibility <strong>of</strong> non protection (industrial secret). Finally, we
have to evaluate if <strong>the</strong> technology fulfils all <strong>the</strong> requirements to be patented (invention must be novel,
invention must not be obvious to someone with a good knowledge <strong>of</strong> <strong>the</strong> subject, invention must have
an industrial use).
In our case, we are considering <strong>the</strong> possibility <strong>of</strong> licensing <strong>the</strong> patent. Licensing occurs when a
licensor grants exploitation rights over a patent to a licensee. This is also a legal contract, and so it
will set out <strong>the</strong> terms upon which <strong>the</strong> exploitation rights are granted, including performance obligations
that a licensee must comply with. Therefore, <strong>the</strong> failure to comply with those obligations may lead to
<strong>the</strong> termination <strong>of</strong> <strong>the</strong> license, and <strong>the</strong> reversion <strong>of</strong> exploitation rights back to <strong>the</strong> licensor.
6.5 E) Exploitation
The final step in <strong>the</strong> chain <strong>of</strong> value is to consider <strong>the</strong> exploitation <strong>of</strong> <strong>the</strong> technology. Not all <strong>the</strong>
technologies can be exploited in <strong>the</strong> same way, we have different formulas:
License (in all its forms)
Transfer to a customer (sale) keeping a free license use
Spin-<strong>of</strong>f
Collaborative agreements with companies to industrialize a final product. The industry participates
in <strong>the</strong> final development in order to exploit later toge<strong>the</strong>r.
In our case, we want to transfer <strong>the</strong> technology to a company well positioned at <strong>the</strong> market and with a
good knowledge <strong>of</strong> <strong>the</strong> specifications <strong>of</strong> <strong>the</strong> product, demands <strong>of</strong> <strong>the</strong> market and business
opportunities.
338

7. Regulations and certifications:
Manel González-Piñero et al
The <strong>European</strong> Directive 93/42/CEE <strong>of</strong> 14 June 1993 concerning to medical devices makes it
mandatory to fulfill CE certification requirements in order to product medical devices, <strong>of</strong> Class I, IIa,
IIb, and III to export <strong>the</strong>m to any country within <strong>the</strong> <strong>European</strong> community.
The Medical Device Directive, MDD 93/42/EEC, becomes mandatory on March 21, 2010. This means
that all medical device manufacturers have to review <strong>the</strong>ir compliance to this directive and be in
compliance. O<strong>the</strong>rwise <strong>the</strong>y have to stop selling <strong>the</strong>ir devices in <strong>the</strong> <strong>European</strong> community. This is
especially critical for <strong>the</strong> new classification rules, if a Class I device is now classified as Class II a or b,
or from Class II to Class III, a new conformity assessment is required.
According to Directive 2007/47/EC which has amended <strong>the</strong> Directive 93/42/EEC, Medical Device
means: any instrument, apparatus, appliance, s<strong>of</strong>tware, material or o<strong>the</strong>r article, whe<strong>the</strong>r used alone
or in combination, including <strong>the</strong> s<strong>of</strong>tware intended by its manufacturer to be used specifically for
diagnostic and/or <strong>the</strong>rapeutic purposes and necessary for its proper application, intended by <strong>the</strong>
manufacturer to be used for human beings for <strong>the</strong> purpose <strong>of</strong>:
diagnosis, prevention, monitoring, treatment or alleviation <strong>of</strong> disease,
diagnosis, monitoring, treatment, alleviation <strong>of</strong> or compensation for an injury or handicap,
investigation, replacement or modification <strong>of</strong> <strong>the</strong> anatomy or <strong>of</strong> a physiological process,
control <strong>of</strong> conception,
And which does not achieve its principal intended action in or on <strong>the</strong> human body by pharmacological,
immunological or metabolic means, but which may be assisted by such means.
Medical Devices shall be divided into Classes I, IIa, IIb and III (see 93/42/EEC), which shall be carried
out in accordance with Annex IX. Examples <strong>of</strong> devices in each type:
Class I:
Wheel Chairs Patient electrodes. Scalpels. Dental Drills. Wound Management systems. Hearing
Aid Tester.
Class IIa:
All patient monitoring equipment, Syringes. Needles. Ultra Sound devices. External ECGs.
Diagnosis devices mainly.
Class IIb:
Lasers Devices for application. Internal ECGs. RF Generators. Non-energized implants.
Treatment devices mainly.
Class III:
Energized implants All Intracardiac applications. Heart valves. Cauterters. Non-energized
implants. All devices in contact with <strong>the</strong> central nervous system.
Member States shall take all necessary steps to ensure that devices may be placed on <strong>the</strong>
market and put into service only if <strong>the</strong>y do not compromise <strong>the</strong> safety and health <strong>of</strong> patients,
users and, where applicable, o<strong>the</strong>r persons when properly installed, maintained and used in
accordance with <strong>the</strong>ir intended purpose, following <strong>the</strong> requirements set out in <strong>the</strong> Annexes <strong>of</strong>
<strong>the</strong> Directive 93/42/CEE.
8. Conclusions
The technologies developed in <strong>the</strong> University must follow a methodology <strong>of</strong> Valorization if <strong>the</strong>y want to
be successfully exploited. That is why a good interaction between all <strong>the</strong> actors involved in <strong>the</strong>
process like researchers, IP managers and business development managers is crucial. It is also
significant to know <strong>the</strong> requirements <strong>of</strong> <strong>the</strong> market to direct <strong>the</strong> research efforts towards <strong>the</strong> market
necessities because demands from industry, hospital and final users are <strong>the</strong> key to be successful in
applied research and <strong>the</strong> subsequent transfer.
Currently, <strong>the</strong> Bi<strong>of</strong>eedback is under prototyping and clinical trial has been successful. At <strong>the</strong> moment,
<strong>the</strong> process is being finished to apply <strong>the</strong> National patent and a final planning <strong>of</strong> commercial activities
has been developed. Now, we are in contact with industry partners to negotiate <strong>the</strong> production <strong>of</strong> <strong>the</strong>
prototype and <strong>the</strong> license <strong>of</strong> <strong>the</strong> patent.
339

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340

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
Nebojsa Graca 1 and Ana Lucija Gojakovic 2
1
Independent scientific researcher, Belgrade, Serbia
2
Independent economic researcher, Belgrade, Serbia
graca@svestizdravlje.com
ana.lucija@svestizdravlje.com
Abstract: Previous Knowledge Management research has shown that knowledge, <strong>the</strong> way available knowledge
is used, as well as speed <strong>of</strong> acquiring new knowledge, have a considerable influence on competitive advantage
<strong>of</strong> contemporary companies. A deficiency in <strong>the</strong> field <strong>of</strong> Knowledge Management was dicovered in Knowledgeintensive
companies within domains <strong>of</strong> research and development acitivities, and application <strong>of</strong> acquired results
during production <strong>of</strong> new products and provision <strong>of</strong> new services based on absolutely new knowledge (scientific
discoveries). In o<strong>the</strong>r words, <strong>the</strong> above mentioned deficiency manifests itself during transformation <strong>of</strong> absolutely
new knowledge into a newly developed value resulting from scientific and research work - <strong>the</strong> intellectual capital.
Considering that knowledge - a base model <strong>of</strong> economic development <strong>of</strong> <strong>the</strong> 21st century, is based on intellectual
capital, a question how to remove <strong>the</strong> essential deficiencies <strong>of</strong> Knowledge Management in Knowledge-intensive
companies which can be viewed as unique factories for „production“ <strong>of</strong> absolutely new knowledge, needs to be
asked? In o<strong>the</strong>r words, how can we connect <strong>the</strong> „production“ <strong>of</strong> knowledge and market demand in order to create
a value which can be valorized on <strong>the</strong> market? If coefficient <strong>of</strong> intellectual value added is <strong>the</strong> most significant
indicator <strong>of</strong> business performance <strong>of</strong> Knowledge-intensive companies, ano<strong>the</strong>r question needs to be asked. How
can we manage <strong>the</strong> intellectual component <strong>of</strong> a business <strong>of</strong>fer (intellectual material) efficiently and effectively,
considering that this intellectual material represents knowledge in <strong>the</strong> process <strong>of</strong> its „production“ from a phase <strong>of</strong>
elementary, applied and developmental research and market placement in <strong>the</strong> form <strong>of</strong> apsolutely new products
and services while providing total quality which surpasses clients’ and competitors’ expectations - Total Quality
Management? In our work we also show that knowledge based on scientific research, represents <strong>the</strong> most
pr<strong>of</strong>itable investment, covert competitive advantage to its owner and unlimited resource for continuous positive
economic results. This is how we confirm, that intellectual capital based on knowledge, justifies its dominant
participation <strong>of</strong> about 77% <strong>of</strong> society’s total capital according to <strong>of</strong>ficial reports <strong>of</strong> <strong>the</strong> World Bank.
Keywords: knowledge;research;development;knowledge-intensive;management
1. Introduction
At <strong>the</strong> transition from one century into ano<strong>the</strong>r, a certain number <strong>of</strong> companies showed an interest
into transformation <strong>of</strong> <strong>the</strong> existing way <strong>of</strong> business operations. Numerous economic researchers and
analysts in <strong>the</strong> field <strong>of</strong> management and organizational studying agree with this statement. Therefore,
Morgan believes changes represent natural condition <strong>of</strong> a company. The first change refers to <strong>the</strong>
transition <strong>of</strong> conventional economy into knowledge economy. Knowledge economy has induced
change <strong>of</strong> development concept. Concept <strong>of</strong> <strong>the</strong> sustainable development is replaced with absolutely
new concept <strong>of</strong> development based on intensive knowledge. Optimal control <strong>of</strong> exhaustable
resources (declarative knowledge as a synonim for exhaustable resources) has shown its
incorrespondence in comparison to an authorized concept <strong>of</strong> <strong>the</strong> unlimited development based on
absolutely new knowledge (Graca, 2010). Observed from <strong>the</strong> angle <strong>of</strong> knowledge economy, company
<strong>of</strong> <strong>the</strong> 21st century represents syn<strong>the</strong>sis <strong>of</strong> <strong>the</strong> three types <strong>of</strong> resources: material, human and
intangible resources (Stewart, 1997). Appearance <strong>of</strong> <strong>the</strong> intangible resources have also conditioned
appearance <strong>of</strong> so called s<strong>of</strong>t organizations. S<strong>of</strong>t organizations gradually replaced conventional
organizations, changing <strong>the</strong> business operations paradigm. Changing <strong>the</strong> business operations
paradigm has influenced change <strong>of</strong> managing and developing functions in companies. According to
<strong>the</strong> traditional concept, management is defined as an ability <strong>of</strong> individuals to do business with and by
means <strong>of</strong> people within four stages: planning, organizing, leading and controlling. According to <strong>the</strong>
knowledge management concept, manager is a person responsible for his knowledge and knowledge
<strong>of</strong> his subordinates.
In <strong>the</strong> new business ambience, term knowledge-intensive becomes a synonim for companies which
have tendency towards creation, maintaining and enlarging collective knowledge balance.
Knowledge-intensive companies
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1.1 ▪Basic characteristics <strong>of</strong> knowledge-intensive companies
Term knowledge-intensive was used in 1995. for <strong>the</strong> first time in <strong>the</strong> report <strong>of</strong> <strong>the</strong> <strong>European</strong>
Commission „Knowledge-Intensive Business Services: Users, Carriers and Sources <strong>of</strong> Innovation“. It
attracted considerable attention <strong>of</strong> <strong>the</strong> scientific and expert public. In <strong>the</strong> last years <strong>European</strong> Union
give a great significance to business operations <strong>of</strong> <strong>the</strong>se companies.
Alvesson (2001) defines knowledge-intensive companies as companies in which majority <strong>of</strong> <strong>the</strong>
employees are highly qualified. Bontis (1998) believes <strong>the</strong> quality <strong>of</strong> human capital is a source <strong>of</strong>
innovations and strategic renovation <strong>of</strong> knowledge-intensive companies.
In <strong>the</strong> economic <strong>the</strong>ory a difference is made between <strong>the</strong> two types <strong>of</strong> <strong>the</strong> knowledge-intensive
companies, as follows:
P-KIBS (Knowledge-intensive business services), knowledge-intensive companies in which <strong>the</strong>ir
business <strong>of</strong>fer is based on traditional pr<strong>of</strong>essional services: law, accounting, consulting, marketing
...
T-KIBS with a high participation <strong>of</strong> scientific and technological knowledge, as well as services
based on research and development.
The authors <strong>of</strong> <strong>the</strong> paper will direct <strong>the</strong> focus <strong>of</strong> <strong>the</strong>ir lecture towards <strong>the</strong> o<strong>the</strong>r type <strong>of</strong> <strong>the</strong> knowledgeintensive
companies. Leading researchers think T-KIBS have considerable role in popularization <strong>of</strong>
research work and innovations. <strong>European</strong> Union divert business operations <strong>of</strong> knowledge-intensive
company from conventional business services to <strong>the</strong> services based on <strong>the</strong> intensive knowledge.
Knowledge-intensive companies are different from <strong>the</strong> conventional ones in <strong>the</strong> following:
structure <strong>of</strong> <strong>the</strong> employees; in <strong>the</strong> knowledge-intensive companies pr<strong>of</strong>essionals are dominantly
employed: scientists, technologists, operaters in informatics, researchers (Alvesson, 1995;
Robertson and Swan, 1998; Starbuck, 1992);
value creating; value in knowledge-intensive companies is mainly based on <strong>the</strong> knowledge as an
incoming and outcoming component;
business <strong>of</strong>fer, which is in knowledge-intensive companies based on knowledge (Alvesson, 2000,
2001).
In knowledge-intensive companies, clients also have a significant component in <strong>the</strong> intellectual capital
portfolio (ICP) (Harvard Business Online). Constant co-operation <strong>of</strong> knowledge-intensive companies
and users favourably influences creation <strong>of</strong> sustainable competitive advantage.
The most important business activity <strong>of</strong> <strong>the</strong> T-KIBS company is maintaining, intensive increasing and
transfer <strong>of</strong> knowledge with <strong>the</strong> aim <strong>of</strong> acquiring and maintaining leading position in <strong>the</strong> market.
Success <strong>of</strong> a knowledge-intensive company depends on <strong>the</strong> unique <strong>of</strong>fes <strong>of</strong> services or products
based on Explicit and Tacit Knowledge. Commercial exchange between knowledge-intensive
companies and clients represents a main value for market performances <strong>of</strong> a company.
Knowledge-intensive companies, as far as business is concerned, are oriented towards continual
improving <strong>of</strong> quality which will satisfy and surpass clients’ expectations - Total Quality Management
(TQM). TQM is actually efficient and effective quality managing which also represents pr<strong>of</strong>essional
style <strong>of</strong> each individual in a knowledge-intensive company. The essence <strong>of</strong> TQM is reflected in
permanent development <strong>of</strong> <strong>the</strong> imperative knowledge portfolio. In fact, <strong>the</strong> task <strong>of</strong> TQM is an intensive
quality development beyond expectations <strong>of</strong> clients and competition.
Therefore it can be concluded that intellectual material (knowledge, information, intellectual property,
experience) (Stewart, 1997), has <strong>the</strong> greatest importance (app. 90%) compared to o<strong>the</strong>r inputs
(physical and financial capital), different to conventional organizations where physical and financial
capital are dominant (Starbuck, 1992).
For T-KIBS it is ordinary that intensive knowledge represents essential input and output in business
process. Intensive knowledge in modern conditions <strong>of</strong> business operations represents „par
excellence“ commodity in national and especially in international trade. Different to o<strong>the</strong>r commodities
and services intensive knowledge records exponential growth.
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1.2 Knowlede protecton in <strong>the</strong> form <strong>of</strong> intellectual property
Intensive knowledge is created by research work. Therefore it is necessary to protect research results
in <strong>the</strong> form <strong>of</strong> intellectual property. By comong into power <strong>of</strong> <strong>the</strong> „Convention Establishing World
Organization for Intellectual Property“, adopted on 14th Jully 1967. in Stockholm, <strong>the</strong> conditions have
been made for generation <strong>of</strong> <strong>the</strong> concept <strong>of</strong> <strong>the</strong> intellectual capital and knowledge-intensive
entrepreneurship.
In that way knowledge is also protected in <strong>the</strong> form <strong>of</strong> intellectual property and it has become a
commodity which in time gets in its significance.
Intellectual property is a synonym for nonmaterial goods. Owners have protection <strong>of</strong> <strong>the</strong>ir intellectual
creations on <strong>the</strong> basis <strong>of</strong> different levels <strong>of</strong> exclusive rights. Since <strong>the</strong> intellectual property rights are
similar to ownershp rights, it means that intellectual property can be commercialized in <strong>the</strong> market.
Intellectual property essentially always refers to certain knowledge. Does each knowledge can be a
subject <strong>of</strong> protection?
Intellectual property as a private right is available only under certain conditions “…for new, original,
unobvious or distinct intellectual creations, which surpass public domain and intellectual property <strong>of</strong>
o<strong>the</strong>rs...“ (Convention on Founding WIPO). It means that knowledge must have an epi<strong>the</strong>t <strong>of</strong>
innovaton or scientific discovery.
In companies which aspire to be successful, majority <strong>of</strong> financial funds are invested into research
work and development. Why? During research and developing activities a certain fund <strong>of</strong> knowledge
is formed. Depending on <strong>the</strong> quality and applicability, knowledge becomes a subject <strong>of</strong> elementary
and/or complementary protection. At <strong>the</strong> stage <strong>of</strong> fundamental researches creative teams are directed
towards acquiring new knowledge. New knowledge is based on phenomena and facts without
concrete application and usage. Applied researches, different to fundamental ones, are undertaken
with <strong>the</strong> aim <strong>of</strong> acquiring absolutely new knowledge.The goal <strong>of</strong> achieving this type <strong>of</strong> knowledge is a
practical application in production <strong>of</strong> new materials, products and devices, as well as implementation
<strong>of</strong> new processes, systems and services in developing activities.
Intellectual property represents a central value in <strong>the</strong> knowledge economy and strategic nonmaterial
property <strong>of</strong> a company. Although we talk about a set <strong>of</strong> rights and privileges, intellectual property
represents property <strong>of</strong> a company as well. Also, it shows a need for managing and nominating new
function <strong>of</strong> a manager (brand management). Intellectual property portfolio is becoming a synonym for
research and developing functions. Dynamism <strong>of</strong> <strong>the</strong> protected knowledge in <strong>the</strong> form <strong>of</strong> intellectual
property has enabled <strong>of</strong>fer differentiating <strong>of</strong> knowledge-intensive companies compared to <strong>the</strong> <strong>of</strong>fer <strong>of</strong>
conventional companies.
In conventional companies <strong>the</strong> quality <strong>of</strong> <strong>of</strong>fer is directly connected with <strong>the</strong> price which is determined
by expenses <strong>of</strong> business operations. The quality in <strong>the</strong> knowledge-intensive companies, depending
on <strong>the</strong> type <strong>of</strong> <strong>the</strong> intellectual property is divided into three sub-categories as follows:
new (added, essential) quality <strong>of</strong> new products and services based on imperative knowledge (S<strong>of</strong>t
Intellectual Property-S<strong>of</strong>t IP),
formal quality, determined by new visual and aes<strong>the</strong>tic look <strong>of</strong> products (industrial design),
market (marketing) quality <strong>of</strong> products and services based on commercial symbols, trade names
and market reputation.
We conclude that, existance <strong>of</strong> absolutely new knowledge protected in <strong>the</strong> form <strong>of</strong> intellectual property
is, for a knowledge-intensive company, a basic condition for existance <strong>of</strong> intellectual capital.
1.3 Role <strong>of</strong> intellectual capital in knowledge-intensive companies
Intellectual capital represents <strong>the</strong> most significant development factor <strong>of</strong> economy <strong>of</strong> <strong>the</strong> 21st century.
Economic analysts observe <strong>the</strong> role and significance <strong>of</strong> intellectual capital through three great
developing social epohs. In <strong>the</strong> knowledge economy developing factor is knowledge (intellectual
capital). In agrarian epoch developing factor was land, and in industrial, it was financial and manual
work.
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Concept <strong>of</strong> <strong>the</strong> intellectual capital appeared two decades upon bringing <strong>the</strong> Convention and
establishing <strong>the</strong> World Organzation for Intellectual Property (WIPO). Intellectual capital is in
connection with <strong>the</strong> market value <strong>of</strong> <strong>the</strong> company which many times surpasses <strong>the</strong> book value <strong>of</strong> <strong>the</strong>
overall material and financial property. Although certan researches had been done earlier as well,
revolution in <strong>the</strong> field <strong>of</strong> studying <strong>the</strong> phenomenon <strong>of</strong> <strong>the</strong> intellectual capital was provoked by Thomas
Stewart.
Expression „intellectual capital “ is interpreted and defined in different ways. Stewart (1997) defines it
as „packaged useful knowledge, which includes organizational processes, technologies, patents,
abilities <strong>of</strong> <strong>the</strong> emloyees, and information on customers, suppliers and stakeholders“. According to
Sullivan (1998) intellectual capital is„ knowledge which can be transformed into pr<strong>of</strong>it“. According to
W. Bukowitz it is a „capital <strong>of</strong> knowledge” or „intellectual capital means everything „intangible“ by
means <strong>of</strong> which an organization creates value“. Roos (1997) defines intellectual capital as: „sum <strong>of</strong>
knowledge <strong>of</strong> members <strong>of</strong> an organization and tansformation <strong>of</strong> this knowledge into brands, trade
marks and processes“. Edvinsson and Malone (1997) give a broader definition <strong>of</strong> <strong>the</strong> intellectual
capital as „property <strong>of</strong> knowledge, applied experience, organizational technologies, relations with
consumers and pr<strong>of</strong>essional skills which give a company a competitive advantage in <strong>the</strong> market“.
Andriessen (2001) fur<strong>the</strong>r widens <strong>the</strong> existing definitions including also those abilities which are not
based only on brain. „Intellectual capital is a combination <strong>of</strong> <strong>the</strong> intangible means , which enable
companies to funcion.“
Knowledge becomes capital in <strong>the</strong> moment when it brings new essential quality or new value. It
means that as intellectual capital can be named only <strong>the</strong> knowledge as follows:
which is applicable,
has a practical benefit,
brings value.
In literature, we encounter two appearing forms <strong>of</strong> knowledge:
Explicit Knowledge - intellectual capital in <strong>the</strong> form <strong>of</strong> plans, drafts, patents, licences, data bases,
manuals, standards <strong>of</strong> business operations, computer programs...
Tacit Knowledge - intellectual capital „in heads“ <strong>of</strong> <strong>the</strong> owner and his associates, know-how, data
exclusivity, methods <strong>of</strong> solving problems, business culture, entrepreneurship system, leadership
and management... (Polanyi, 1966).
Except for knowledge <strong>of</strong> <strong>the</strong> owner and his associates which represent human capital, <strong>the</strong> <strong>the</strong>ory <strong>of</strong>
intellectual capital is founded on structural capital and capital <strong>of</strong> clients (Bontis; Edvinsson and
Malone; Roos; Stewart).
The first category <strong>of</strong> <strong>the</strong> intellectual capital - human capital. It is defined as knowledge,
experience, ability, skill, creativity <strong>of</strong> an individual. Knowledge workers, as <strong>the</strong>mselves only, are
not a condition for business success <strong>of</strong> <strong>the</strong> organization in which <strong>the</strong>y are employed. In order to
use intellectual potential <strong>of</strong> <strong>the</strong> employees, an equally intelligent organization is necessary as well
as a team <strong>of</strong> pr<strong>of</strong>essional managers capable <strong>of</strong> managing static and dynamic knowledge fund;
Structural capital represents all that „remains“ in an organization when <strong>the</strong> employees go home.
Structural capital is created by work <strong>of</strong> <strong>the</strong> human capital in past, and it is made <strong>of</strong> Explicit
Knowledge;
Clients’ capital comprises relations with external stakeholders, brands’ names, trade marks,
goodwill.
„Packaged useful knowledge“ (Stewart, 1997) has a main importance in forming competitive positions
<strong>of</strong> <strong>the</strong> owner. It requires a need for a constant managing with <strong>the</strong> aim <strong>of</strong> permanent increasing <strong>of</strong> its
performances. Intellectual capital represents a base <strong>of</strong> present and future market growth.
Holders <strong>of</strong> development research activites in knowledge-intensive companies are expected to
permanently increase <strong>the</strong> value <strong>of</strong> knowledge in <strong>the</strong> market. Knowledge Managers and Intellectual
Capital Managers (IC Managers) are expected to manage knowledge responsibly as an essential
component <strong>of</strong> pr<strong>of</strong>itability <strong>of</strong> <strong>the</strong> knowledge-intensive companies (Graca,2010).
Scientific-research work - basic activity <strong>of</strong> <strong>the</strong> T-KIBS knowledge-intensive companies
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Scientific-research work in great number <strong>of</strong> T-KIBS knowledge-intensive companies ends up with a
failure. It comes to a failure when research and development do not coincide with market needs. In
economic literature it is called “death valley”. In o<strong>the</strong>r group <strong>of</strong> successful companies <strong>the</strong> results
obtained in research are protected in a certain form <strong>of</strong> intellectual property. In T-KIBS companies <strong>the</strong>
greatest part <strong>of</strong> funds are invested into research work and development. Financial investments are
aimed at cumulative increase <strong>of</strong> <strong>the</strong> fund <strong>of</strong> absolutely new knowledge. Mentioned investments
measured by working hours <strong>of</strong> <strong>the</strong> pr<strong>of</strong>essionals represent <strong>the</strong> highest value <strong>of</strong> expenses, while
production costs are considerably lower. Economic value <strong>of</strong> intellectual property rights <strong>of</strong> <strong>the</strong> new
products is a basic element for calculating cost price. The most significant economic reasons which
stimulate intellectual property using in modern business operations are as follows:
competitive advantage in <strong>the</strong> form <strong>of</strong> leading position and de facto monopoly,
market expansion, without additional financial investments, by cession <strong>of</strong> rights for using
intellectual property through selling rights ,
possibility <strong>of</strong> providing capital for own development.
Intellectual capital portfolio <strong>of</strong> <strong>the</strong> T-KIBS company is made <strong>of</strong> <strong>the</strong> following:
absolutely new, genuine unique, au<strong>the</strong>ntic and exclusive scientific discoveries which create
dynamic knowledge fund (absolutely new, imperative),
sensitive information,
intellectual property in <strong>the</strong> narrow and broad sense,
practically applicable experience used in creating market value (Graca, 2010).
Predominating part <strong>of</strong> <strong>the</strong> portfolio is based on Tacit Knowledge. Ano<strong>the</strong>r, less represented fund is
Explicit Knowledge which can be said without limits to <strong>the</strong> interested scientific, expert and business
public. Contracted amounts depend on <strong>the</strong> exclusivity <strong>of</strong> Tacit Knowledge which can be achieved
when concluding franchising contracts. Therefore, <strong>the</strong> pr<strong>of</strong>itability <strong>of</strong> knowledge-intensive companies
directly depend on <strong>the</strong> quality and possibility <strong>of</strong> <strong>the</strong> Tacit Knowledge transfer towards <strong>the</strong> interested
clients.
Absolutely new knowledge (Tacit Knowledge) is mainly hushed up knowledge. It can not be easily
acquired and used. It is in <strong>the</strong> property <strong>of</strong> individuals or companies who and which have information
obtained from fundamental, applied and developing researches. Relative knowledge (Explicit
Knowledge) is easily accessable. It is acquired by <strong>of</strong>ficial academic education and can be directly
applied to a certain activity. Absolutely new knowledge consists <strong>of</strong>: trade secrets, know-how, data
exclusivity, scientific discoveries and it represents a posteriori or empiric knowledge. If it is freed from
ma<strong>the</strong>matical and technical interpretation, and is applicable in modern knowledge-intensive business
operations, absolutely new knowledge becomes in time universal and repeatable. It becomes clearer
and more obvious, since almost every business activity requires new knowledge based on intensive
development.
The first stage <strong>of</strong> <strong>the</strong> research work - stage <strong>of</strong> fundamental researches
In <strong>the</strong> stage <strong>of</strong> fundamental research companies confront with two parallel processes:
process <strong>of</strong> knowledge acquiring (from internal and/or external sources), and
process <strong>of</strong> value creating verified by <strong>the</strong> coefficient <strong>of</strong> <strong>the</strong> added knowledge value.
Inputs in <strong>the</strong> process <strong>of</strong> value creating are not different from inputs in conventional economic subjects.
They are consisted from material, financial and human resources. The realized value <strong>of</strong> <strong>the</strong> market is
directly conditioned by <strong>the</strong> quality <strong>of</strong> <strong>the</strong> used knowledge and by using knowledge in <strong>the</strong> existing
business activities. The process <strong>of</strong> improving <strong>the</strong> exisitng relative knowledge starts from <strong>the</strong> working
collective as a whole. Through <strong>the</strong> transfer <strong>of</strong> elementary relative knowledge <strong>the</strong> working collective
gets ready for transition onto a higher level <strong>of</strong> education. During <strong>the</strong> transition period, <strong>the</strong> company
will gradually, by increasing quality <strong>of</strong> business operations and with decreasing risks provide creative
vitality in comparison with <strong>the</strong> competition. Managers at all organizational levels are expected to
provide conditions for building-up <strong>the</strong> acquired relative knowledge. If relations managers-employees
are impaired, or if <strong>the</strong> working collective does not recognize <strong>the</strong> intellectual impulse <strong>of</strong> <strong>the</strong>
management <strong>of</strong> <strong>the</strong> company, organizational vitality is impaired which negatively effects <strong>the</strong> total
research project. At <strong>the</strong> same time this is <strong>the</strong> first and most important step in <strong>the</strong> process <strong>of</strong> creating
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absolutely new knowledge. If it is successfully overcome, <strong>the</strong>re is a new challenge in front <strong>of</strong> <strong>the</strong>
leaders-creating innovative knowledge. Leaders are expected to have a long term orientation towards
successful business operations. The quality <strong>of</strong> innovative knowledge is conditioned with <strong>the</strong> quality <strong>of</strong>
relative knowledge as a base and with <strong>the</strong> buld-up <strong>of</strong> <strong>the</strong> existing knowledge. Success <strong>of</strong> <strong>the</strong> creative
expansion with <strong>the</strong> market domination will be enabled only by innovative knowledge <strong>of</strong> high quality.
Joint work <strong>of</strong> <strong>the</strong> managers and leaders in <strong>the</strong> first stage <strong>of</strong> <strong>the</strong> research work, is based on <strong>the</strong>
education <strong>of</strong> <strong>the</strong> employees at all levels, will have as for its outcome, forming <strong>of</strong> <strong>the</strong> advanced relative
knowledge fund. Success <strong>of</strong> <strong>the</strong> first stage <strong>of</strong> <strong>the</strong> research work means for a company acquiring a
leading position in <strong>the</strong> existing market ambience. (Graca, 2010).
The second stage <strong>of</strong> <strong>the</strong> research work - stage <strong>of</strong> <strong>the</strong> applied reseasrch
Applied researches are conducted with <strong>the</strong> aim <strong>of</strong> acquiring new knowledge. In order to build and
develop new knowledge, it is necessary to manage knowledge in four separate stages. Management
is expected to use advanced relative knowledge as a scientific base for building-up or development <strong>of</strong>
<strong>the</strong> advanced knowledge.
Generic strategy <strong>of</strong> knowledge managing commences with streng<strong>the</strong>ning knowledge acquired in <strong>the</strong>
stage <strong>of</strong> fundamental research. The aim <strong>of</strong> <strong>the</strong> mentioned strategy is streng<strong>the</strong>ning <strong>the</strong> Explicit
Knowledge <strong>of</strong> <strong>the</strong> employees and increasing knowledge fund. Explicit Knowledge streng<strong>the</strong>ning will
influence increasing <strong>the</strong> scope <strong>of</strong> <strong>the</strong> existing activites and expanding <strong>the</strong> diapason <strong>of</strong> new jobs. At
this stage <strong>the</strong> company starts manifesting its interdisciplinarity in scientific as well as in business
sense.
At <strong>the</strong> second stage, by <strong>the</strong> strategy <strong>of</strong> creative expansion, management create conditions for
creative bulding-up <strong>of</strong> <strong>the</strong> acquired knowledge. Management is expected to recognize creative
potentials <strong>of</strong> each individual and to use <strong>the</strong>m with <strong>the</strong> aim <strong>of</strong> increasing knowledge fund. If this stage
finishes successfully, at <strong>the</strong> third stage, by <strong>the</strong> strategy <strong>of</strong> creative initiative, independent creative
work is expected from <strong>the</strong> employees in accordance with <strong>the</strong>ir personal potential to enable <strong>the</strong>
company to start a long term process <strong>of</strong> creating advanced knowledge. In <strong>the</strong> process <strong>of</strong> creating
advanced knowledge, <strong>the</strong> working collective, management, leaders and owners will confront
numerous challenges. The process <strong>of</strong> creating absolutely new knowledge will depend on <strong>the</strong>
capability, knowledge and experience, as well as on <strong>the</strong> following:
forming <strong>of</strong> <strong>the</strong> intellectual property portfolio,
share <strong>of</strong> Tacit Knowledge in <strong>the</strong> intellectual capital portfolio,
basic activity <strong>of</strong> <strong>the</strong> knowledge-intensive company, and
market positioning.
If <strong>the</strong> interested state organs, institutions and o<strong>the</strong>r economic subjects recognize a need for
absolutely new knowledge in <strong>the</strong> stage <strong>of</strong> advanced knowledge creating, and <strong>the</strong>y invest certain funds
for financing <strong>the</strong>m, new knowledge will be created in accordance with <strong>the</strong>ir needs. Protection <strong>of</strong> <strong>the</strong>
obtained results will be enabled only in <strong>the</strong> form <strong>of</strong> copyrights. Protection in <strong>the</strong> form <strong>of</strong> patents is not
possible to be achieved, since <strong>the</strong> obtained results must be available to <strong>the</strong> public and <strong>the</strong>y become a
public commodity, which is not in accordance with <strong>the</strong> legal regulative as far as protection <strong>of</strong> patents
is concerned. Also, protection by informal forms <strong>of</strong> intellectual property (S<strong>of</strong>t IP) can be useful.
In <strong>the</strong> second case, when <strong>the</strong>re is no interest <strong>of</strong> <strong>the</strong> external financiers, <strong>the</strong> company continues <strong>the</strong>
strategy <strong>of</strong> new knowledge creating by its own funds. In that way a possibility <strong>of</strong> protecting research
results in accordance with a protection plan is given. Basic protection is recommended in <strong>the</strong> form <strong>of</strong>
copyrights. Also, it is possible to realize protection in <strong>the</strong> form <strong>of</strong> patents or through informal forms
which are considered obligatory.
The third stage <strong>of</strong> <strong>the</strong> research work - formation <strong>of</strong> <strong>the</strong> intellectual property portfolio
Essential role and significance <strong>of</strong> <strong>the</strong> knowledge management is fully expressed exactly in forming <strong>the</strong>
intellectual property portfolio. It is not an easy assignment to distribute regularily obtained exclusive
information into two aspects <strong>of</strong> protection:
basic protection <strong>of</strong> research results in <strong>the</strong> form <strong>of</strong> intellectual property in <strong>the</strong> narrow sense
(Explicit Knowledge), and
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additional (complementary, exclusive) protection in <strong>the</strong> form <strong>of</strong> <strong>the</strong> intellectual property in <strong>the</strong>
broad sense (Tacit Knowledge).
During formulating <strong>the</strong> protection plan, care must be taken that S<strong>of</strong>t IP is an imperative resource, a
property <strong>of</strong> <strong>the</strong> invaluable value. It gives <strong>the</strong> owner <strong>the</strong> most efficient aspect <strong>of</strong> protection from unfair
competition, market function <strong>of</strong> leader in <strong>the</strong> form <strong>of</strong> de facto monopoly and protection from revealing
sensitive information in <strong>the</strong> form <strong>of</strong> particular licence contracts. S<strong>of</strong>t IP represents a set <strong>of</strong> relative
rights which refer to protection from <strong>the</strong> unfair competition, trade secrets, know-how and data
exclusivsity. Protection <strong>of</strong> trade secrets represents an atypical form <strong>of</strong> <strong>the</strong> intellectual property
protection. Trade secrets represent private intellectual property <strong>of</strong> <strong>the</strong> owner. Inherent owner’s value
<strong>of</strong> <strong>the</strong> knowledge, given by trade secrets vary from country to country, different from <strong>the</strong> legal
regulative rights <strong>of</strong> <strong>the</strong> intellectual property in <strong>the</strong> narrow sense, which is incorporated into formal
conventions, providing <strong>the</strong> same protection <strong>of</strong> this type <strong>of</strong> property to <strong>the</strong> owners. Examples are Paris
Convention for <strong>the</strong> Protection <strong>of</strong> Industrial Property and Convention <strong>of</strong> WIPO, which under <strong>the</strong>
administartion <strong>of</strong> <strong>the</strong> United Nations support an organization to „induce creative activities and promote
protection <strong>of</strong> <strong>the</strong> intellectual property in <strong>the</strong> world“. Therefore with regular balance at <strong>the</strong> relation
protectable-unprotectable ownership, knowledge management must secure a safe position to <strong>the</strong>
company if <strong>the</strong> employees with Tacit Knowledge and experience leave <strong>the</strong> company. How <strong>the</strong>
knowledge managers will manage knowledge in a knowledge-intensive companies will depend on:
relation <strong>of</strong> Explicit and Tacit Knowledge in <strong>the</strong> ICP,
erudition and responsibility <strong>of</strong> managers, and
feeling <strong>of</strong> responsibility and belonging <strong>of</strong> each individual to <strong>the</strong> collective (Graca,2010).
The fourth stage <strong>of</strong> <strong>the</strong> research work - intensive development <strong>of</strong> <strong>the</strong> knowledge-intensive companies
At <strong>the</strong> last stage <strong>of</strong> <strong>the</strong> research work <strong>the</strong>re is a strategy <strong>of</strong> managing intellectual property portfolio
within which attention is directed towards <strong>the</strong> following priorities:
<strong>the</strong> first step - forming <strong>of</strong> <strong>the</strong> ICP is in <strong>the</strong> competence <strong>of</strong> <strong>the</strong> IC Managers and Knowlege
Managers. At this stage a number <strong>of</strong> Knowledge Managers are educated for managing <strong>the</strong> brand;
<strong>the</strong> second step - forming conditions for transfer from a conventional to a s<strong>of</strong>t organization;
<strong>the</strong> third step - acquiring and maintaining <strong>of</strong> <strong>the</strong> leading domination achieved by permanent
development within a knowledge-intensive company founded on <strong>the</strong> intellectual capital.
Based on <strong>the</strong> previously stated, a conclusion can be made that permanetly intensive development <strong>of</strong>
<strong>the</strong> knowledge-intensive compnies represents at <strong>the</strong> same time personal and pr<strong>of</strong>essional philosophy
and orientation <strong>of</strong> all <strong>the</strong> employees, by which <strong>the</strong> feedback process <strong>of</strong> creating added value <strong>of</strong>
knowledge as a whole is achieved.
2. Concluson
Key economic and developing resource <strong>of</strong> <strong>the</strong> modern economy represents knowledge. New
paradigm <strong>of</strong> economy based on knowledge brings creative individuals (Knowledge Managers) into <strong>the</strong>
centre <strong>of</strong> a company’s interest, considering that in contemporary conditions <strong>of</strong> business operating,
knowledge, innovations and scientific discoveries are „condicio sine qua non“ <strong>of</strong> a knowledgeintensive
company. A new researches <strong>of</strong> <strong>the</strong> leading economic analysts in <strong>the</strong> field <strong>of</strong> <strong>the</strong> intellectual
capital indicate that intellectual capital represents 80-90% <strong>of</strong> <strong>the</strong> total value and it is a typical
ownership form <strong>of</strong> consulting companies. Knowledge-intensive companies and holders <strong>of</strong> managing
functions have become a central subject <strong>of</strong> economic researches. Why? First, it is interesting to
explore companies which for its basic activity have businss operations on <strong>the</strong> basis <strong>of</strong> permanent
research and development. Second, in <strong>the</strong>se organizations basic determinant <strong>of</strong> capital is not money
any more and securities, but intensive knowledge. And third, knowledge as a predominant part <strong>of</strong>
capital manifests permanently-intensive form <strong>of</strong> quality and uniqueness. Competitive advantage <strong>of</strong> <strong>the</strong>
knowledge-intensive companies depends on great number <strong>of</strong> factors which are different from factors
<strong>of</strong> successfulness <strong>of</strong> conventional companies. These are <strong>the</strong> following developing factors:
transfer <strong>of</strong> imperative knowledge and intellectual performances,
orientation towards pr<strong>of</strong>essional development <strong>of</strong> clients,
high level <strong>of</strong> corporative culture,
development <strong>of</strong> new products and services,
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creating new, added, essential quality,
Nebojsa Graca and Ana Lucija Gojakovic
exclusivity <strong>of</strong> knowledge contained in <strong>the</strong> business <strong>of</strong>fer,
competence <strong>of</strong> Knowledge Managers and IC Managers,
capability <strong>of</strong> building capital <strong>of</strong> clienats...
Above mentioned developing factors mainly refer to <strong>the</strong> quality <strong>of</strong> intellectual capital portfolio (ICP).
ICP is built gradually along with <strong>the</strong> existance <strong>of</strong> proper strategy <strong>of</strong> development and research.
Portfolio forming directly depends on <strong>the</strong> results <strong>of</strong> fundamental, applied research and development.
The expert team <strong>of</strong> managers will choose <strong>the</strong> most effective means <strong>of</strong> protection and in accordance
with <strong>the</strong>m build ICP, depending on:
quality and all-inclusiveness <strong>of</strong> <strong>the</strong> obtained results,
possibility <strong>of</strong> <strong>the</strong>ir application,
market needs for new au<strong>the</strong>ntic services and products,
available financial means,
funding sources.
Post-industrial society in <strong>the</strong> globalization creates a knowledge economy in which pr<strong>of</strong>itabile system <strong>of</strong>
knowledge-intensive companies take a central position. In order to provide fur<strong>the</strong>r development <strong>of</strong>
knowledge economy and knowledge-intenisve entrepreneurship, it is necessary to pay tribute to
successful Knowledge Managers who have an exceptionally serious assignment in future. Complexity
<strong>of</strong> <strong>the</strong>ir task is even greater because manager functions in companies exist, while <strong>the</strong> <strong>the</strong>ory <strong>of</strong>
efficient knowledge managing in <strong>the</strong> knowledge-intenisve companies is just developing.
References
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Alvesson, M. (2000) 'Social identity and <strong>the</strong> problem <strong>of</strong> loyalty in knowledge-intensive companies', Journal <strong>of</strong>
Management Studies , vol. 37, no. 8, pp.1101-1123.
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Capital, vol. 2, no. 3, pp. 204-214.
Bettencourt, L.A. et al (2002) ‘Client Co-Production in Knowledge-Intensive Business Services’, California
Management Review, vol. 44, no. 4, July, pp. 100-115.
Bontis, N. (1998) ‘Intellectual capital: an exploratory study that develops measures and models’, Management
Decision, vol. 36, no. 2, pp. 63-76.
Bukowitz, W.R. and Williams, R.L. (1995) The Knowledge Management Fieldbook, London: Financial Times
Prentice Hall.
Graca, N. (2010) Consciousness and Knowledge Economy, Portfolio <strong>of</strong> <strong>the</strong> absolutely new knowledge - <strong>the</strong>
scientific discoveries <strong>of</strong> nature <strong>of</strong> <strong>the</strong> consciousness and <strong>the</strong>ir application in <strong>the</strong> knowledge economy.
Edvinsson, L. and Malone, M.S. (1997) Intellectual Capital, London: Piatkus.
Miles, I., Kastrinos, N. et al (1995) EIMS Publication N o 15, [Online],
Available: http://cordis.europa.eu/eims/src/eims-15.htm [19 Mart 2011].
Morgan, G. (1989) Creative Organization Theory, California: SAGE Publications Inc.
Polanyi, M. (1966) The Tacit Dimension , London: Routledge and Kegan Paul.
Robertson, M. and Swan, J. (1998), ’Modes <strong>of</strong> organizing in an expert consultancy: a case <strong>of</strong> knowledge power
and egos’, Organization, vol. 5, no.4, pp. 543-564.
Roos, J. et al (1997) Intellectual capital: Navigating in <strong>the</strong> new business landscape, New York: New York
University Press.
Starbuck, W.H. (1992) 'Learning by knowledge-intensive firms', Journal <strong>of</strong> Management Studies, vol. 3, no. 4, pp.
262-275.
Stewart, T.A. (1997) Intellectual Capital - The new wealth <strong>of</strong> organizations, New York: Doubleday.
Sullivan, P.H. (1998) Pr<strong>of</strong>iting from Intellectual Capital - Extracting Value from Innovation, New York: John Wiley
& Sons.
World Intellectual Property Organization (2011) Convention Establishing <strong>the</strong> World Intellectual Property
Organization, [Online],
Available: http://www.wipo.int/treaties/en/convention/trtdocs_wo029.html [19 Mart 2011].
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Available: http://www.wipo.org/about-ip/en/ [19 Mart 2011].
348

Cyclic Process Model Transformation
Norbert Gronau, Edzard Weber, Christ<strong>of</strong> Thim, Priscilla Heinze and Gergana
Vladova
University <strong>of</strong> Potsdam, Germany
edzard.weber@wi.uni-potsdam.de
christ<strong>of</strong>.thim@wi.uni-potsdam.de
priscilla.heinze@wi.uni-potsdam.de
gergana.vladova@wi.uni-potsdam.de
Abstract: Process analysis usually focuses only on single and selected processes. It is ei<strong>the</strong>r existent
processes that are recorded and analysed or reference processes that are implemented. So far no evident effort
has been put into generalising specific process aspects into patterns and comparing those patterns with regard to
<strong>the</strong>ir efficiency and effectiveness. This article focuses on <strong>the</strong> combination <strong>of</strong> dynamic and holistic analytical
elements in enterprise architectures. Our goal is to outline an approach to analyse <strong>the</strong> development <strong>of</strong> business
processes in a cyclical matter and demonstrate this approach based on an existent modelling language. We want
to show that organisational learning can derive from <strong>the</strong> systematic analysis <strong>of</strong> past and existent processes from
which patterns <strong>of</strong> successful problem solving can be deducted.
Keywords: knowledge management, process model, process-oriented knowledge, cyclic process
transformation
1. Introduction
Since <strong>the</strong> rise <strong>of</strong> business process orientation, a plethora <strong>of</strong> modelling techniques and methods have
been developed that can be used to visualise, optimise and simulate business processes. A lot <strong>of</strong>
knowledge has been cumulated in those modelling and analysis sessions. Yet <strong>the</strong>re is no structured
way to facilitate this knowledge for learning. Models are generated, analysed, applied and <strong>the</strong>n <strong>of</strong>ten
forgotten. A broad knowledge base to design better processes lies fallow. There is <strong>of</strong>ten only a
particularistic approach on analysing business processes, even though steps have been made to
integrate different processes into an overarching model (Scheer and Nüttgens 2000, Mertins and
Jochem 2000).
Processes are <strong>of</strong>ten seen as static snapshots <strong>of</strong> activities within an organisation. There is no
approach that focuses on <strong>the</strong> emergent process development and regards business processes as
fluent objects that adapt to necessities with <strong>the</strong>ir own logic. According to <strong>the</strong> dimensions laid out
above one can identify four different process analysis approaches (Table 1):
Particularistic Holistic
Static One process at a moment Consolidated organisation model
Dynamic Different instances <strong>of</strong> <strong>the</strong> same
process observed over time
Different process classes
observed over time
Table 1: Process analysis approaches
This paper focuses on <strong>the</strong> fourth approach, combining dynamic and holistic analytical elements. Our
goal is to outline an approach to analyse <strong>the</strong> development <strong>of</strong> business processes in a cyclical manner
and demonstrate this approach based on an existing modelling language. In that way we want to
show that organisational learning can derive from <strong>the</strong> systematic analysis <strong>of</strong> past and existent
processes from which patterns <strong>of</strong> successful problem solving can be deducted.
2. Processes, models and organisational learning
This section deals with <strong>the</strong> link between process models and organisational learning. A knowledge
based approach will serve as a <strong>the</strong>oretical umbrella and prepare <strong>the</strong> ground for <strong>the</strong> description <strong>of</strong> <strong>the</strong>
cyclic transformation model.
2.1 Processes and models as knowledge sources
Organizations store <strong>the</strong>ir experience with <strong>the</strong> efficient solution <strong>of</strong> problems in standard operating
procedures and routines (Argyris and Schön 1978; Levitt and March 1988; Walsh and Ungson 1991).
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Those processes can arise from two different sources. First <strong>the</strong>y can be introduced into <strong>the</strong>
organization with an external focus. Such approaches can be based on reference process adoption
(Becker et al. 2003) or on <strong>the</strong> influence <strong>of</strong> business process re-engineering, i.e through consulting
companies. Those top-down approaches follow a well-defined design aim. The o<strong>the</strong>r source <strong>of</strong>
business processes is emergence which is not led by an overall design but results from <strong>the</strong> actions
and interaction <strong>of</strong> members in <strong>the</strong> organization (Davenport 1993; Bititci and Muir 1997). They tend to
be ad hoc solutions, which are manifested and institutionalized into “quasi” formal processes.
According to <strong>the</strong> knowledge based approach (Duncan and Weiss 1979), <strong>the</strong> aforementioned
processes can be considered as memory resources. They are subject to change imposed from
different immanent and exogenous influences (Lyles and Schwenk 1992). While exogenous effects
such as changing environmental inputs and conditions force organizations to adapt <strong>the</strong>ir decision and
resource allocation routines, immanent effects come from ad hoc adaption <strong>of</strong> <strong>the</strong> process due to
changed internal conditions, i.e. a modified power constellation, new cooperative agreements, etc.
(Weick 1979) We can <strong>the</strong>refore conclude that active processes contain both, components <strong>of</strong> design
and components <strong>of</strong> emergence.
Modelling <strong>of</strong> business processes should lead to an idealistic, simplified and similar mapping <strong>of</strong> a
subject, system or o<strong>the</strong>r part <strong>of</strong> <strong>the</strong> world and hence reduce <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> organisation
system, concealing <strong>the</strong> internal dynamics. Here a process flow (or business process flow in <strong>the</strong>
managerial context) includes rules as regulation for a certain proceeding (Knolmayer et al. 2000).
These rules determine <strong>the</strong> sub-processes and tasks/functions within a process and allow <strong>the</strong>
existence <strong>of</strong> a logically coherent chain. The processes include a combination <strong>of</strong> material or immaterial
input objects, which are transformed into an output object according to <strong>the</strong> given process rules
(Schwickert and Fischer 1996). The procurement <strong>of</strong> <strong>the</strong> needed material input or information objects
<strong>of</strong>ten correspondents with o<strong>the</strong>r well defined processes. Thus, this input can be easily specified
because <strong>the</strong> characteristic <strong>of</strong> <strong>the</strong> required objects remain nearly unchanged for different process
flows. Process models do not show a large variance from <strong>the</strong> reference process.
In contrast, knowledge as process input cannot be predetermined and described easily. It is bound to
<strong>the</strong> person – <strong>the</strong> acting employee – and to <strong>the</strong> environmental situation <strong>the</strong> process is embedded in.
Nei<strong>the</strong>r <strong>the</strong> quality and quantity <strong>of</strong> a knowledge demand nor <strong>the</strong> time <strong>of</strong> its use can be forecasted
(Gronau and Weber 2004). This knowledge feature leads to variation in <strong>the</strong> resulting models from
used reference business processes or to missing sufficiently structured process models.
Business process models can also be used to analyse <strong>the</strong> given situation and to develop a
generalised reference concept based on <strong>the</strong> analysed models. One important feature <strong>of</strong> <strong>the</strong>se
reference concepts and models is <strong>the</strong>ir reusability. Reference models are used as generic conceptual
models to formalize recommended practices in <strong>the</strong> enterprise. Thus, <strong>the</strong>y are <strong>of</strong>ten labeled with <strong>the</strong>
term “best practice" models, which claim to capture reusable state-<strong>of</strong>-<strong>the</strong>-art practices. The main
objective <strong>of</strong> reference models is to streamline <strong>the</strong> design <strong>of</strong> particular models by providing a generic
solution (Rosemann 2003). However, <strong>the</strong> dynamic and context sensitivity <strong>of</strong> <strong>the</strong> resource knowledge
has to challenge <strong>the</strong> reusability <strong>of</strong> <strong>the</strong>se models. Hence o<strong>the</strong>r aspects such as emergent process
adoption have to be considered.
Those models, being explicit knowledge, can be <strong>the</strong> source <strong>of</strong> organisational learning. They were
recorded or designed top down and stored in documents and diagrams. They are abstract to a large
extent and need to be internalised by <strong>the</strong> members in order to take effect. Tacit organisational
knowledge, on <strong>the</strong> contrary, is found in <strong>the</strong> emergent processes. They are inscribed into <strong>the</strong> direct
actions and behaviour <strong>of</strong> <strong>the</strong> members <strong>of</strong> <strong>the</strong> organisation. The accumulated tacit knowledge cannot
be directly gasped and is stored, sometimes unconsciously, in <strong>the</strong> minds <strong>of</strong> <strong>the</strong> members.
2.2 Organisational Learning from process models
Applying <strong>the</strong> knowledge perspective back on organisational learning leads to <strong>the</strong> definition <strong>of</strong> a
mechanism by which <strong>the</strong> tacit and <strong>the</strong> manifest dimension can interact. Hence we refer to <strong>the</strong> works <strong>of</strong>
Nonaka and Takeuchi (1995) with <strong>the</strong>ir model <strong>of</strong> socialization, externalisation, combination and
internalisation. They regard learning as a cycle <strong>of</strong> transformation <strong>of</strong> knowledge between <strong>the</strong> tacit and
<strong>the</strong> explicit (or manifest) dimension. The four transformations stated above have been applied to our
model <strong>of</strong> process model learning. When tacit process models are spread throughout <strong>the</strong> organisations
by direct interaction or by mere observation <strong>of</strong> o<strong>the</strong>r persons, socialisation takes place. This
transformation is needed but it cannot be controlled or observed by <strong>the</strong> organisation. It can <strong>the</strong>refore
only be regarded as unstructured learning.
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On <strong>the</strong> o<strong>the</strong>r extreme, an organisation can also learn from o<strong>the</strong>r external or internal information
sources. In our case those sources would be o<strong>the</strong>r reference processes or o<strong>the</strong>r abstract process
models. It would mean that learning only takes place within <strong>the</strong> manifest realm. The drawback on this
perspective is its abstract nature. The models are only refined, but its link to <strong>the</strong> individual action and
<strong>the</strong> connection to organisational habits is lost. Hence it is necessary to include two o<strong>the</strong>r directions <strong>of</strong>
learning into <strong>the</strong> framework. The explication is <strong>the</strong> transformation <strong>of</strong> tacit elements into a manifest
form. Actors document <strong>the</strong>ir knowledge about processes and routines and make it shareable and
directly accessible for <strong>the</strong> organisation. The o<strong>the</strong>r course <strong>of</strong> knowledge transfer is internalisation.
Information, such as formalised process models, is included into <strong>the</strong> behaviour <strong>of</strong> organisation
members through individual learning.
If those transformations were cyclically linked, processes such as incremental learning (through
socialisation and internalisation) and <strong>the</strong> abstraction <strong>of</strong> <strong>the</strong> incrementally learned process models
(through externalisation and combination) could be taken into account. Yet, <strong>the</strong>re is no way to identify
efficient processes that emerged from day-to-day business and transfer <strong>the</strong>m to new units.
Anderson’s (1996) <strong>the</strong>ory <strong>of</strong> <strong>the</strong> Adaptive Character <strong>of</strong> Thought is an approach to bridge this gap
based on cognition. He distinguishes knowledge into three types <strong>of</strong> memory structures:
Declarative memory consists <strong>of</strong> a semantic net <strong>of</strong> propositions, images and sequences. Applied to
<strong>the</strong> organisational context <strong>the</strong>se are all process elements (roles, resources, tasks and decisions)
form a repository which <strong>the</strong> actual process draws from. In our investigation <strong>the</strong> can be identified
as process patterns.
Procedural memory is <strong>the</strong> concrete linking <strong>of</strong> those objects. It constitutes <strong>the</strong> map <strong>of</strong> all processes
available in <strong>the</strong> organisation. Again those processes can be distinguished into manifest and tacit
processes. In <strong>the</strong> cyclic transformation model this memory resource serves as a case base.
The working memory consists <strong>of</strong> all processes currently in action in an organisation. They are
process instances in our model.
The aforementioned memory sources can be transformed in different ways: generalisation,
discrimination and streng<strong>the</strong>ning.
The transformation (learning) process can be described with <strong>the</strong> OADI-SMM (shared mental models)
cycle (Kim 1993). Based on <strong>the</strong> OADI (Observe, Assess, Design, Implement) cycle two different
interlinking cycles can be constructed. The foundation is <strong>the</strong> OADI-IMM (individual mental models)
cycle in <strong>the</strong> individual actor modifies his mental model through <strong>the</strong> four mentioned stages. The SMM
cycle enhances this procedure with commonly shared models such as beliefs and SOPs. Learning in
this SMM cycle employs <strong>the</strong> same OADI procedure as in <strong>the</strong> IMM cycle, but as its result, processes
change. This change can occur on two levels. On completion <strong>of</strong> <strong>the</strong> cycle, <strong>the</strong> modified process is
implemented again. This learning is parallel to <strong>the</strong> emergent modification process and represents
incremental learning activities. At <strong>the</strong> same time we find abstract process models in <strong>the</strong> cycle, which
can be considered as a part <strong>of</strong> an organisational repository for possible solutions. This means that not
only <strong>the</strong> concrete processes are a part <strong>of</strong> <strong>the</strong> organisational memory, but also potential processes.
During different loops <strong>of</strong> <strong>the</strong> cycle many potential and concrete processes emerge, which are not
accounted for properly in current modelling methodologies. The dynamic development and learning
from <strong>the</strong> previous development steps has to be taken into consideration to increase <strong>the</strong> quality <strong>of</strong>
process models and <strong>the</strong>ir design implications. The following section will outline an approach to
achieve this with cyclic model transformation.
3. Proposed model cycle approach
After we learned about <strong>the</strong> potentials <strong>of</strong>fered by business process modelling in enhancing <strong>the</strong>
organisational learning, <strong>the</strong> next step would be to define a classification system for process models, in
order to be able to compare and limit process models <strong>of</strong> different phases and role memberships.
1.1. Classification characteristics for process models
A classification system contains rules for transferring a set <strong>of</strong> unsorted elements into a new overall
system in order to achieve an ordered representation <strong>of</strong> <strong>the</strong>se elements. Therefore, one needs to
define classification parameters such as quanta, characteristics, dimensions, value and principles <strong>of</strong>
classification (Laisiepen et al. 1972, Bailey 1994).
The classification goal and <strong>the</strong> classification set have already been discussed above. The amount <strong>of</strong>
partial and entire process models have to be prepared by <strong>the</strong> classification system in order to make a
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systematic model reuse possible. We apply <strong>the</strong> term model reuse in different ways. A model may be
<strong>the</strong> basis for new process occurrences or models are <strong>the</strong> basis for process analysis. In <strong>the</strong> context <strong>of</strong>
this work, two characteristics <strong>of</strong> process models are important: abstraction and institutionalisation.
The level <strong>of</strong> abstraction describes whe<strong>the</strong>r <strong>the</strong> model represents a process schema or a process
instance. A (cognitive) schema is an internal data structure, which generalise experiences. It
represents typically expected facts resp. relationships in a particular domain <strong>of</strong> <strong>the</strong> reality. A schema
with empty element fields is a conceptual structure that describes entities <strong>of</strong> a particular category in
an abstract way (Schnotz 1994). A process instance is a specific occurrence <strong>of</strong> a process (at a
particular point <strong>of</strong> time). All elements in this model are well-defined. Process schema and process
instances can be differentiated although <strong>the</strong>re could be a smooth transition, that is when only
particular elements or element types are kept abstract. For a precise classification we define several
abstraction levels. These may be based on <strong>the</strong> relative part <strong>of</strong> an abstract model elements or on <strong>the</strong>
set <strong>of</strong> abstract element types (see Figure 1).
As-Is / Reference
Instance
Level <strong>of</strong> Abstraction
Schema
Figure 1: Level <strong>of</strong> Abstraction
The institutionalisation describes <strong>the</strong> <strong>of</strong>ficial justification <strong>of</strong> a process model within an organisation.
One extreme is formed by reference models, which are provided by <strong>the</strong> process manager that in turn
suggests and schedule <strong>the</strong> required resources for a process execution. The o<strong>the</strong>r extreme is formed
by as-is process models. These may include unpredicted and unplanned process changes as
compared with <strong>the</strong> initial reference process. These changes are <strong>of</strong>ten caused by context-specific
influencing factors. Therefore, <strong>the</strong>se as-is process instance models have no absolute reference
character any longer.
To make <strong>the</strong> distinction <strong>of</strong> as-is and reference models measurable, <strong>the</strong>re are two approaches. In case
<strong>of</strong> two comparable process instances, <strong>the</strong> relative degree <strong>of</strong> <strong>the</strong> change can be calculated. Process
patterns can use <strong>the</strong> representativeness. This depends on <strong>the</strong> size <strong>of</strong> <strong>the</strong> case base. The more
frequent a certain process pattern can be identified in <strong>the</strong> case base, <strong>the</strong> larger is <strong>the</strong>
representativeness <strong>of</strong> this pattern and <strong>the</strong> usability as a draft for future process runs. Whe<strong>the</strong>r this
representativeness is valid for e.g. individual work procedures, departments or <strong>the</strong> entire organization
can also be differentiated (see Figure 2).
Instance
As-Is Reference
Degree <strong>of</strong> Change
Schema
As-Is Reference
Level <strong>of</strong> Representativity
Figure 2: Degree <strong>of</strong> change and level <strong>of</strong> representativeness
These two process model characteristics allow us to differentiate four extreme types <strong>of</strong> process
models: as-is process instances, as-is process schemata, reference process schemata and reference
process instances (see Figure 3).
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Figure 3: Model types
Instance
Schema
3.1 Model transformation
Norbert Gronau et al
Instance
(As-Is Situation)
Abstracted
As-Is Situation
Instance
(Initial Situation)
Reference
Schema
As-Is Model Reference Model
The four introduced model types are related to each o<strong>the</strong>r. Each model type has a particular
role/function within <strong>the</strong> organisation process management. By working with <strong>the</strong>se model, models <strong>of</strong> a
different type are used as pre-condition or created as result.
Reference process schemata are <strong>the</strong> base for new process instances. These are created by
instantiation. Abstract objects are replaced by specific objects. This complies an interpretative
usage <strong>of</strong> declarative memory (see section 2.2).
Reference process instances are <strong>the</strong> basis for <strong>the</strong> documentation <strong>of</strong> process changes. New
models are created by comparing <strong>the</strong> reference model to <strong>the</strong> current reality. These models
describe <strong>the</strong> current situation <strong>of</strong> particular process runs. From a process manager’s point <strong>of</strong> view
it is a combination <strong>of</strong> production rules (see section 2.2). Different models or partial models are (re-
)combined.
As-is process instances are <strong>the</strong> base for creating new process schemata. Elements, element
types or partial process models are abstracted. From a process manager’s point <strong>of</strong> view it is a
proceduralisation <strong>of</strong> knowledge. Knowledge about a specific procedure is stored context-nonspecific
(depending on <strong>the</strong> abstraction level) in <strong>the</strong> case base. Declarative knowledge is encoded
into rules (see section 2.2) and creates process schemata as well as schemata without a
noticeable process structure but with a description <strong>of</strong> o<strong>the</strong>r kinds <strong>of</strong> patterns (e.g. relationship
between person and knowledge). This schematisation can be realised computer-based. Any kind
<strong>of</strong> abstraction level and decomposition <strong>of</strong> process models is possible.
As-is process schemata are <strong>the</strong> base for model refinement. Process schemata are analysed and
optimised. Re-occurring patterns are searched for and checked, whe<strong>the</strong>r this pattern is a success
factor or a barrier. From a process manager’s point <strong>of</strong> view it is <strong>the</strong> generalisation, discrimination,
amplification <strong>of</strong> process knowledge (see section 2.2). It is a knowledge optimisation resp. model
optimisation. The command variable is <strong>the</strong> representativeness a process schema could get.
After four transformation activities, <strong>the</strong> cycle is run through (see Figure 4). Of special importance are
<strong>the</strong> entrance points to this cycle. One entrance point is <strong>the</strong> adoption <strong>of</strong> external reference models,
which are already abstracted. They claim a high degree <strong>of</strong> representativeness resp. generality and
become reference process schemata. Ano<strong>the</strong>r point <strong>of</strong> entrance is <strong>the</strong> process capture in <strong>the</strong> context
<strong>of</strong> a system analysis. A specific system situation is captured and <strong>the</strong> documentation becomes a as-is
process instance.
Reference process instances and as-is process schemata are no adequate entrance points. Their
usage always implies <strong>the</strong> hidden creation <strong>of</strong> a reference process schema or a as-is process instance
and <strong>the</strong>ir schematisation as well as instantiation.
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Instance
Schema
Figure 4: Cyclic transformation
Process
Capture
Norbert Gronau et al
Instance
(As-Is Situation)
3.2 The Knowledge Cycle (K-Cycle)
Schematisation
Reality
Check
Abstracted
As-Is Situation Refinement
Instance
(Initial Situation)
Instantiation
Reference
Schema
Schema
Adoption
As-Is Model Reference Model
The K-cycle denotes <strong>the</strong> cycling run-through <strong>of</strong> model transformations. Schemata are continuously
tested and optimised by <strong>the</strong> use <strong>of</strong> <strong>the</strong> case base <strong>of</strong> past process runs (see Figure 5)
Process Instance
Process Schema
Figure 5: Model Transformation Cycle
Real.
Inst.
Schem.
Opt.
Knowing and understanding <strong>the</strong> level <strong>of</strong> abstraction, change and representativeness is important for
<strong>the</strong> selection <strong>of</strong> process model as base for model optimisation. Thus, <strong>the</strong> model is also qualified for a
particular process management task. As-is process instance models with only few changes to <strong>the</strong>
reference model will cause a higher representativeness after schematisation. Just as models used for
<strong>the</strong> analysis should have a comparable level <strong>of</strong> abstraction. The usage history <strong>of</strong> a model or partial
model can be observed in <strong>the</strong> classification system. The cycle will become visible but <strong>the</strong>re are also
branches going outside <strong>of</strong> <strong>the</strong> cycle when <strong>the</strong> model is <strong>the</strong> base for more than one reference process
instances or when multiple schemata <strong>of</strong> different abstraction levels are created. And <strong>the</strong>re will be
branches going inside when <strong>the</strong> analysis is based on many schemata or when a process instance is
updated by fur<strong>the</strong>r partial process models (see Figure 6).
Figure 6: Continuous cycle
4. Application <strong>of</strong> method
As-Is Reference
Instance
Instance
Schema Schema
As-Is
Reference
The following section gives an overview <strong>of</strong> how <strong>the</strong> above explained concept can be applied into a
reality-based scenario. First we will introduce <strong>the</strong> Knowledge Modeling Description Language (KMDL),
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a semi-formal knowledge activities modelling method, which was selected over o<strong>the</strong>r methods
because <strong>of</strong> its capability to visualize informal relationships between process participants along <strong>the</strong>
chain <strong>of</strong> tasks (Gronau and Weber 2004). After a brief <strong>the</strong>oretical background we will show some
excerpts <strong>of</strong> KMDL models taken from a reality-based scenario. These models will be transformed into
a conversion map by running through <strong>the</strong> proposed K-Cycle phases.
4.1 Knowledge modeling and description language (KMDL)
The development <strong>of</strong> KMDL was triggered by <strong>the</strong> limitations <strong>of</strong> conventional modelling methods in
visualizing knowledge processes and activities, especially in informal relationships. Aiming to address
this problem, KMDL builds its core elements based on <strong>the</strong> differentiation <strong>of</strong> knowledge by Polanyi
(1966) into tacit and explicit knowledge as well as <strong>the</strong> SECI-Model (see section 2.2) by Nonaka and
Takeuchi (1995). Modelling with KMDL is primarily intended as an instrument to visualize knowledge
intensive business processes (see section 2.1). The course <strong>of</strong> tasks within <strong>the</strong> processes is
represented in <strong>the</strong> process view. Each tasks is assigned to certain departments or organisational
units, called Role. This view also allows alternative routes in cases <strong>of</strong> exceptions through <strong>the</strong> OR,
XOR and AND operators. In case an information system is utilized to perform a task, <strong>the</strong> type <strong>of</strong> <strong>the</strong> IS
should also be mentioned. Figure 7 shows an example <strong>of</strong> a process view.
Figure 7: Exemplary process view
Each task within <strong>the</strong> process view can be elaborated into specific activities, which are to be found in
<strong>the</strong> activity view. This view shows <strong>the</strong> types <strong>of</strong> knowledge conversion occurring within each task and
thus, <strong>the</strong> knowledge flow. Since knowledge is person-bound and context dependent, <strong>the</strong> performers
<strong>of</strong> <strong>the</strong> knowledge activities are individuals. Figure 8 shows <strong>the</strong> objects <strong>of</strong> <strong>the</strong> process and activity
view.
Figure 8: Objects <strong>of</strong> <strong>the</strong> process and activity view
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4.2 Feasibility analysis: a reality-based Scenario
The following KMDL models are excerpts from an innovation process captured in a small enterprise
that builds video solutions for mobile applications. The following models describe how products go
through a feasibility analysis within an innovation process in <strong>the</strong> company. Using <strong>the</strong> KMDL
procedural model we ga<strong>the</strong>red information about <strong>the</strong> course <strong>of</strong> action within this event by interviewing
<strong>the</strong> process participants bearing various roles and responsibilities. For <strong>the</strong> publication purpose <strong>the</strong>
names <strong>of</strong> <strong>the</strong> participants are anonymised. The integration <strong>of</strong> K-Cycle occurs within <strong>the</strong> activity view,
since it enables <strong>the</strong> visualization <strong>of</strong> knowledge exchange processes from a knowledge bearer to
ano<strong>the</strong>r.
4.2.1 As-is process instance models
As explained in section 3 <strong>the</strong> as-is process instance model serves as a means to describe <strong>the</strong>
detailed possibilities <strong>of</strong> occurrence within a process. A process instance is a concrete specification <strong>of</strong>
a process class, which builds a real ongoing process, in this case <strong>of</strong> a feasibility analysis process.
During <strong>the</strong> first interview rounds we captured some scenarios <strong>of</strong> how <strong>the</strong> feasibility analysis for new
products and features is being generally performed. Its varying implementations depend on various
factors, for instance <strong>the</strong> provider <strong>of</strong> <strong>the</strong> suggestions, <strong>the</strong> type <strong>of</strong> suggested product or feature, <strong>the</strong>
criticality <strong>of</strong> implementation, etc. Figure 9 shows <strong>the</strong> first process instance alternative model.
Suggestion as
E-Mail
Product
features
John
Compare
suggestions with
own features
Linda
Nigel
John
Customizables
Market
knowledge
Technical
experiences
Estimate cost and
pr<strong>of</strong>it
Truxton
John
Operational
cost and pr<strong>of</strong>it
estimation
Authority and
expertise
Decide execution
Truxton
Decision to
execute
Figure 9: As-is process instance model 1
Within this process instance model suggestions come from one <strong>of</strong> <strong>the</strong> employee <strong>of</strong> <strong>the</strong> company as
an E-Mail. This suggestion is processed by John Brown, who is <strong>the</strong> product manager, by comparing it
with already existing products and features. In order to do this, Brown needs to be familiar with <strong>the</strong>
company's product features. The comparison yields <strong>the</strong> result <strong>of</strong> which customizing possibilities might
be undertaken. Since <strong>the</strong> suggestion came from someone who is ra<strong>the</strong>r familiar with <strong>the</strong> situation and
competence <strong>of</strong> <strong>the</strong> company, Brown only talks to Linda Bray from Marketing and Nigel White from
Product Development informally during lunch and asks <strong>the</strong>ir opinion based on <strong>the</strong>ir knowledge and
experiences on market and technique. The discussion results in <strong>the</strong> operational cost and pr<strong>of</strong>it
estimation, which Brown shares with Truxton Spangler, one <strong>of</strong> <strong>the</strong> executives. With his authority and
expertise in running <strong>the</strong> company Spangler decides on whe<strong>the</strong>r to execute production <strong>of</strong> <strong>the</strong>
suggested product or to save it for later.
Will John
Suggestion
Receive
suggestions
Suggestion
Product
features
John
Compare
suggestions with
own features
John
Customizables
Technical
experiences
Nigel
Figure 10: As-is process instance model 2
Analyse market
Analyse feasibility
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John
Linda
Market
knowledge
Market analysis
report
Feasibility
analysis report
Customizables
Truxton
Authority and
expertise
Decide execution
Estimate cost and
pr<strong>of</strong>it
John
Customizables
Decision to
execute
Truxton John

Norbert Gronau et al
In ano<strong>the</strong>r interview we captured <strong>the</strong> second scenario <strong>of</strong> <strong>the</strong> process (see Figure 10). A suggestion
comes from Will Andrews, a customer through an informal discussion with John Brown on <strong>the</strong>
telephone. Brown writes this down and compares this suggestion with <strong>the</strong> existing products and
features and arrives at some possible customizing activities. Since this product suggested is
something new, Brown tells Bray and White to do some market and technical feasibility analysis on
this suggestion. The results <strong>of</strong> <strong>the</strong> analysis were written down as reports. Brown estimates <strong>the</strong> cost
and pr<strong>of</strong>it based on <strong>the</strong> technical feasibility while Spangler takes a look at <strong>the</strong> market analysis report.
Brown and Spangler agree upon whe<strong>the</strong>r or not <strong>the</strong> suggestion should be followed up.
Truxton
Customizables
Receive
suggestions
John
Market
knowledge
Linda
Analyse market
Market analysis
report
Truxton
Authority and
expertise
Decide execution
Estimate cost and
pr<strong>of</strong>it
John
Customizables
Decision to
execute
Truxton John
Figure 11: As-is process instance model 3
The third scenario is captured in Figure 11. Spangler shares his idea <strong>of</strong> a new product with Brown,
who discusses <strong>the</strong> market chances for this suggestion with Bray. She writes down <strong>the</strong> analysis into a
report, which becomes <strong>the</strong> basis for <strong>the</strong> cost and pr<strong>of</strong>it estimation and <strong>the</strong> decision to execute, which
is met by Spangler according to a prior discussion with Brown.
4.2.2 As-is process schema model
The next step is to build a generalised model based on <strong>the</strong> three different specific scenarios. Since
<strong>the</strong> process schema model does not include specific input and output objects we represent <strong>the</strong>
knowledge and information objects within this process schema model as exchangeable objects.
Within this model (see Figure 12) we conclude that all suggestions are received and documented
before <strong>the</strong>y are compared with <strong>the</strong> products and features already existing in <strong>the</strong> company. This
comparison serves to decide whe<strong>the</strong>r a radical innovation (developing from scratch) or incremental
innovation (building on existing structures or customizing) should be performed. The decision provides
<strong>the</strong> foundation for <strong>the</strong> market and technical feasibility analysis in order to estimate <strong>the</strong> cost and pr<strong>of</strong>it
obtained when executing <strong>the</strong> innovation process.
Receive
suggestions
Document all
suggestions
Compare
suggestions with
own features
Figure 12: As-is process schema model
4.2.3 Reference process schema model
Analyse market
Analyse feasibility
Estimate cost and
pr<strong>of</strong>it
Decide execution
We analysed <strong>the</strong> as-is process schema model and identified some needs for adjustments (see Figure
13). The activity “document all suggestions” has not been consequently performed since some
suggestions come only as informal discussion point or already in a written form in an E-Mail. Based
on <strong>the</strong> captured process object models this externalisation <strong>of</strong> knowledge is not significant. The
technical feasibility analysis has also been left out in some cases, since some suggestions come from
actors with product knowledge. This means that <strong>the</strong> suggestions have undergone a technical
feasibility at a certain extent. In order to estimate cost and pr<strong>of</strong>it, however, a documentation <strong>of</strong> <strong>the</strong>
market analysis is necessary, which in turn, affects <strong>the</strong> decision <strong>of</strong> execution.
357

Receive
suggestions
Compare
suggestions with
own features
Figure 13: Reference process schema model
4.2.4 Reference process instance model
Norbert Gronau et al
Analyse market
and feasibility
Estimate cost and
pr<strong>of</strong>it
Document market
and feasibility
analysis
Decide execution
The above model was built based on <strong>the</strong> success factors identified in single process objects. It serves
as a reference model that shows how <strong>the</strong> process “should” be implemented. Companies can use this
reference process class model as a “blueprint” for strategically planning and optimizing <strong>the</strong> company
infrastructure and objectives. Never<strong>the</strong>less, it is not sufficient for <strong>the</strong> day-to-day operation within <strong>the</strong>
process. The process schema model has to be specified into concrete activities with real actors and
real environments. Figure 14 shows some excerpts <strong>of</strong> some possible reference process instance
models derived from <strong>the</strong> reference process schema model.
Truxton
Authority and
expertise
Decide execution Linda
Analyse market
Market
knowledge
Market analysis
report
John Customizables
Estimate cost and
pr<strong>of</strong>it
Figure 14: Reference process instance models
In receiving suggestions, <strong>the</strong> role <strong>of</strong> John Brown as a product manager is indispensable due to his
knowledge about product features and his capability to derive on <strong>the</strong> customizing potentials, as we
can see from all <strong>the</strong> scenarios. Truxton Spangler as decision maker can also be found repeatedly.
Linda Bray is ano<strong>the</strong>r key person for <strong>the</strong> market feasibility analysis. Based on <strong>the</strong>se scenarios <strong>the</strong>
reference process schema model can be broken down into elements that consist <strong>of</strong> parts <strong>of</strong> <strong>the</strong>
process that appear frequently. Reference process instance models can also be derived from o<strong>the</strong>r
points <strong>of</strong> view, for example: <strong>the</strong> commonly used methods, <strong>the</strong> type <strong>of</strong> knowledge conversions, <strong>the</strong>
frequency <strong>of</strong> activities, etc. This concept might serve as a foundation for a computer based best
practice pattern recognition system.
5. Conclusion
In <strong>the</strong> practices <strong>of</strong> business process modelling and organizational design, <strong>of</strong>ten only certain and
obvious aspects <strong>of</strong> <strong>the</strong> process are being captured and used for fur<strong>the</strong>r strategic improvement
planning. The emerging models cannot be sustained for future usage since a systematic learning
using <strong>the</strong>se process models is not imposable.
Knowledge Cycle aims to enable <strong>the</strong> learning process within an organisation based on its own
process models. Successful process models can be recognized through <strong>the</strong> identification <strong>of</strong> particular
patterns. These patterns account for <strong>the</strong> specific elements <strong>of</strong> <strong>the</strong> process model so that by
consequently undergoing <strong>the</strong> model transformation companies can learn from <strong>the</strong>ir own “mistakes”
and “successes”.
Elements that are hidden or cannot be easily captured by using conventional modelling approaches
but are significant for <strong>the</strong> process can also be identified. Companies can decide on fur<strong>the</strong>r measures
to be taken in order to institutionalise and enforce <strong>the</strong>ir implementation as well as to provide <strong>the</strong>
necessary infrastructures and resources. Important process building blocks also imply <strong>the</strong>
supplementary competence <strong>of</strong> <strong>the</strong> company. These competences can be integrated into <strong>the</strong> success
patterns in order to produce an effective reference process.
The ongoing cycle <strong>of</strong> <strong>the</strong> model transformation provides a basis for a Continuous Improvement
Process (CIP) and thus, a dynamically functioning quality management. The company will be able to
organize itself according to its own proven measures, on which <strong>the</strong> top management can base <strong>the</strong>ir
decision regarding <strong>the</strong> vision and mission <strong>of</strong> <strong>the</strong> company.
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One <strong>of</strong> <strong>the</strong> fur<strong>the</strong>r research needs for this concept is <strong>the</strong> integration <strong>of</strong> <strong>the</strong> business process reengineering
concept into <strong>the</strong> classification system. Within this paper we assume that changes happen
gradually and during <strong>the</strong> transformation process itself, which means that improvement measures can
be taken progressively. In some cases this is not worth <strong>the</strong> effort, for example when <strong>the</strong> entire
personal management has to be replaced or when <strong>the</strong> company decides to merge or change its
direction <strong>of</strong> growth. A radical business process re-engineering can provide a better solution, which is
why <strong>the</strong> aspect <strong>of</strong> a total re-engineering should be integrated in <strong>the</strong> future.
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359

The Not-Invented-Here Syndrome in Academia – How to
Measure and Manage it
David Grosse Kathoefer and Jens Leker
Institute for Business Administration at <strong>the</strong> Department <strong>of</strong> Chemistry and
Pharmacy, University <strong>of</strong> Muenster, Germany
grossekathoefer@uni-muenster.de
leker@uni-muenster.de
Abstract:Today’s society is <strong>of</strong>ten described as a “knowledge society”. Accordingly, knowledge transfer plays a
pivotal role for innovation and economic development. In this light, universities have <strong>of</strong>ten been <strong>the</strong> focus <strong>of</strong>
interest for research and practice in recent years. However, <strong>the</strong> perspective <strong>of</strong> <strong>the</strong> knowledge recipient in this
process is very much neglected. Thus, this paper concentrates on <strong>the</strong> Not-Invented-Here syndrome in academia.
We develop a robust measurement model and test this construct and 8 potential antecedents in a structural
equation model. The sample consists <strong>of</strong> 477 German university pr<strong>of</strong>essors from <strong>the</strong> natural sciences. We figure
out that 4 <strong>of</strong> <strong>the</strong> hypo<strong>the</strong>ses find support in <strong>the</strong> data. Especially, experience and a knowledge sharing-friendly
environment help to decrease a potential NIH infection <strong>of</strong> academic scientists. At <strong>the</strong> end <strong>of</strong> <strong>the</strong> paper, practical
implications for industrial managers and university policy-makers are derived.
Keywords: Not-Invented-Here syndrome, knowledge transfer, academia, recipient, barrier
1. Introduction
In industrialized countries lacking natural resources, knowledge plays a pivotal role for economic
development. Accordingly, innovations are indispensable ingredients for growth <strong>of</strong> both, economy in
general and companies in particular (David and Foray, 2002). For this reason, today’s society is <strong>of</strong>ten
described as a “knowledge society” (Lytras and Sicilia, 2005).
In recent years, product-life cycles have become shorter and shorter (Wang and Benaroch, 2004). To
satisfy that demand <strong>of</strong> innovations, corporations are not only dependent on collaborations with o<strong>the</strong>r
companies but also with public research institutions like universities. Accordingly, <strong>the</strong> network
between industrial and academic research and development (R&D) has been streng<strong>the</strong>ned and
knotted more tightly (Baba, Shichijo and Sedita, 2009). To succeed in such collaborations,
bidirectional knowledge flows are necessary. Cockburn and Henderson (1998) argue that basic
science can also be influenced by product development, questioning <strong>the</strong> long accepted linear model
<strong>of</strong> R&D process. Thus, <strong>the</strong> importance <strong>of</strong> multidirectional knowledge transfer has increased
significantly.
So far, several papers have identified barriers <strong>of</strong> knowledge transfer (e.g. Husted and Michailova,
2002, Riege, 2005, Szulanski, 1996). In this context, most empirical studies have focused on <strong>the</strong>
sender’s perspective, evaluating <strong>the</strong> willingness to share knowledge (e.g. Bock et al., 2005,
Haeussler, 2011, Siemsen, Roth and Balasubramanian, 2008). However, <strong>the</strong> recipient’s side is still
very much neglected. In this context, one <strong>of</strong> <strong>the</strong> most important barriers is <strong>the</strong> so-called Not-Invented-
Here (NIH) Syndrome (Husted and Michailova, 2002, Szulanski, 1996). It describes a negative
attitude towards knowledge from external sources (Lichtenthaler and Ernst, 2006). Although this
phenomenon is well accepted in <strong>the</strong>ory and practice, only very few studies have actually empirically
analyzed it. Additionally, most <strong>of</strong> <strong>the</strong>se studies have been conducted in an industry setting, whereas
<strong>the</strong> academic perspective is still a largely white spot on <strong>the</strong> research map (Grosse Kathoefer and
Leker, 2010). Fur<strong>the</strong>r, it seems as if <strong>the</strong> construct has been reified so far. This means that <strong>the</strong>
<strong>the</strong>oretical concept has been detached from its original context and important aspects and
requirements have been neglected, leading to a frequent use without scrutinizing its applicability. A
similar development has been identified for <strong>the</strong> concept <strong>of</strong> “absorptive capacity” (Lane, Koka and
Pathak, 2006). In <strong>the</strong> case <strong>of</strong> NIH, <strong>the</strong> numerous utilizations contradict reality as <strong>the</strong> number <strong>of</strong>
operationalizations can still be counted on <strong>the</strong> fingers <strong>of</strong> one hand. Moreover, causal relationships
between antecedents, consequences and NIH have not been tested empirically, yet. Our recent paper
(2010) is one exception here as we made a first attempt to operationalize NIH and provide first
insights into potential influencing factors. Therefore, <strong>the</strong> paper at hand fur<strong>the</strong>r aims to address <strong>the</strong>
abovementioned shortcomings and investigates <strong>the</strong> NIH syndrome in an academic setting.
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David Grosse Kathoefer and Jens Leker
In a first step, we deduce potential influencing factors from <strong>the</strong>ory. In a second step, we take up <strong>the</strong>
insights from <strong>the</strong> corporate context and an exploratory study in a university setting to develop a robust
measurement model for <strong>the</strong> academic NIH Syndrome. To test this model, we conducted an online
survey among German pr<strong>of</strong>essors from <strong>the</strong> natural sciences in 2010. In total, 477 completed
questionnaires build <strong>the</strong> basis for <strong>the</strong> structural equation model used for hypo<strong>the</strong>ses testing. Thus, <strong>the</strong>
remainder <strong>of</strong> this paper is structured as follows. In <strong>the</strong> next sections, <strong>the</strong> hypo<strong>the</strong>ses are deduced
from existing studies and NIH is operationalized. Based on insights from prior papers, constructs are
transferred and adapted to <strong>the</strong> new context. Finally, <strong>the</strong> presentation <strong>of</strong> <strong>the</strong> research design and <strong>the</strong>
results follows before <strong>the</strong> paper closes with a critical discussion and a conclusion.
2. Hypo<strong>the</strong>ses
The Not-Invented-Here Syndrome was introduced by Clagett (1967). He described NIH as “<strong>the</strong>
attitude (<strong>of</strong>ten spoken <strong>of</strong> it as if it were a disease) <strong>of</strong> technical organizations who resist adoption <strong>of</strong> an
innovation proposed from a source outside <strong>the</strong> organization” (Clagett, 1967, p. ii). Mehrwald (1999)
embedded <strong>the</strong> construct into a social psychological framework, claiming it to be a prejudice against
external technology. Although definitions varied in <strong>the</strong> following works, <strong>the</strong> common denominator is
<strong>the</strong> negative attitude towards external knowledge. As this attitude is only hardly observable a closer
look at people’s characteristics potentially shaped by NIH sheds more light on this phenomenon. Katz
and Allen (1982), who introduced NIH to a broader scientific audience, stated that member <strong>of</strong> project
teams infected with NIH believe to possess a monopoly <strong>of</strong> knowledge. Fur<strong>the</strong>r, Mehrwald (1999)
argues that people underestimate <strong>the</strong> value <strong>of</strong> external knowledge. All in all, it can be concluded that
NIH is expressed in a focus on internal knowledge and in a high skepticism against external sources.
To get more insights into <strong>the</strong> antecedents <strong>of</strong> NIH, we deduce 8 hypo<strong>the</strong>ses from <strong>the</strong>ory and prior
studies. First <strong>of</strong> all, we take a look at <strong>the</strong> direct research surrounding <strong>of</strong> <strong>the</strong> respective academics.
Research colleagues are an important mean to foster or cure <strong>the</strong> NIH syndrome (Mehrwald, 1999).
Being infected with NIH, discussing with scientists who are in favor <strong>of</strong> external knowledge may help to
reduce <strong>the</strong> negative attitude <strong>of</strong> <strong>the</strong> respective person (Mehrwald, 1999). Moreover, Venkatesh and
Davies (2000) extended <strong>the</strong> Technology Acceptance Model and introduced social influence, showing
a significant influence on <strong>the</strong> decision to adopt a respective technology. Thus, we can deduce <strong>the</strong>
following hypo<strong>the</strong>sis:
Hypo<strong>the</strong>sis 1: The more positive <strong>the</strong> opinion <strong>of</strong> colleagues on external knowledge is,
<strong>the</strong> lower <strong>the</strong> NIH infection <strong>of</strong> <strong>the</strong> academic researchers.
A second aspect deals with <strong>the</strong> communication pattern. Communication in <strong>the</strong> organizational group is
regarded as a primary rationale for NIH development. Clagett (1967) argues that <strong>the</strong> main reason for
<strong>the</strong> occurrence <strong>of</strong> NIH is that supervisors implement external technology without communicating <strong>the</strong>
decision properly. Moreover, Katz and Allen (1982) explain that a low degree <strong>of</strong> interaction with <strong>the</strong>
surrounding increased <strong>the</strong> walling-<strong>of</strong>f external partners. De Pay (1995) agrees with <strong>the</strong> previous
authors, indicating that communication problems are a main driver <strong>of</strong> NIH emergence. Therefore, we
hypo<strong>the</strong>size:
Hypo<strong>the</strong>sis 2: The better <strong>the</strong> communication inside <strong>the</strong> organization, <strong>the</strong> lower <strong>the</strong>
NIH infection <strong>of</strong> <strong>the</strong> academic researchers.
Third, human beings generally strive for security (Thomas, 1991). This behavior has been observed in
a social psychological context as well as in an economic one. In <strong>the</strong> former framework, people try to
avoid uncertainty by e.g. generating stereotypes or identifying with one group (Hogg and Vaughan,
2011). Referring to <strong>the</strong> economic perspective, Samuelson and Zeckhauser (1988) identified <strong>the</strong> status
quo bias. Here, individuals choose <strong>the</strong> status quo alternative more <strong>of</strong>ten in decision making situation
as it could be explained with economic rationalism. The confrontation with external knowledge can be
such a situation in which <strong>the</strong> status quo is at stake. One mean to reduce uncertainty in work situation
is to introduce work routines. Various authors proposed that threatening <strong>the</strong>se routines may lead to
NIH development as a kind <strong>of</strong> protective mechanism (Boyens, 1998, Clagett, 1967, Katz and Allen,
1982, Mehrwald, 1999). Thus, we hypo<strong>the</strong>size a similar relationship in an academic setting:
Hypo<strong>the</strong>sis 3: The more routinized work situations are, <strong>the</strong> higher <strong>the</strong> NIH infection <strong>of</strong>
academic researchers.
Fourth, Sumner (1906) already observed a behavior among people that is called in-group favoritism.
Here, members <strong>of</strong> one group agree with <strong>the</strong> norms and activities <strong>of</strong> <strong>the</strong>ir own group. Simultaneously,
<strong>the</strong>y disrespect o<strong>the</strong>r groups. Belonging to a certain group can lead to feelings <strong>of</strong> pride for <strong>the</strong>
respective person. This sentiment can be threatened by external knowledge. People may feel
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David Grosse Kathoefer and Jens Leker
affronted when external ideas may be regarded as superior to <strong>the</strong>ir own ones (Herzog, 2011, Katz
and Allen, 1982, Mehrwald, 1999). Therefore, we can deduce <strong>the</strong> following hypo<strong>the</strong>sis:
Hypo<strong>the</strong>sis 4: The prouder academic researchers are <strong>of</strong> <strong>the</strong>ir university, <strong>the</strong> higher <strong>the</strong> NIH infection.
The fifth hypo<strong>the</strong>sis is tied to <strong>the</strong> findings <strong>of</strong> <strong>the</strong> most influential paper in NIH research. Katz and Allen
(1982) analyzed project teams and find a curvilinear relationship between project team performance
and average project team tenure. Moreover, <strong>the</strong>y show that longer membership leads to weaknesses
in communication patterns. Combining both streams <strong>of</strong> argumentation, <strong>the</strong> authors argue that longer
tenure causes NIH development and, thus, isolation from outside partners. This finding is again
justified by <strong>the</strong> striving for security. The more stable people’s positions are, <strong>the</strong> more people tend to
close <strong>the</strong>ir minds to new ideas. Subsequently, we hypo<strong>the</strong>size:
Hypo<strong>the</strong>sis 5: The longer academic researchers are affiliated to one university, <strong>the</strong>
higher <strong>the</strong> NIH infection.
Research projects in collaboration with external partners need a different way <strong>of</strong> communication than
internal working processes (Katz and Tushman, 1979, Katz and Tushman, 1981). Accordingly, such
projects are extraordinary situations in which people might feel uncomfortable. In our recent study, we
showed empirically that a higher number <strong>of</strong> collaboration research projects diminishes NIH infection.
Mehrwald (1999) and Herzog (2011) argue on a conceptual basis that it is <strong>the</strong> quality <strong>of</strong> <strong>the</strong> project
experience that facilitates NIH development. Consequently, we test this assumption and deduce <strong>the</strong>
following hypo<strong>the</strong>sis:
Hypo<strong>the</strong>sis 6: The more satisfied academic researchers are with past projects, <strong>the</strong>
lower <strong>the</strong> NIH value.
As already mentioned above, <strong>the</strong> experience in collaboration projects decreases <strong>the</strong> value <strong>of</strong> NIH.
Taking <strong>the</strong> point <strong>of</strong> view <strong>of</strong> an academic scientist, <strong>the</strong> most important research partners are o<strong>the</strong>r
universities and industrial organizations. Both research environments differ significantly in aspects like
culture, motivation or R&D process (Cyert and Goodman, 1997). Academia is still heavily influenced
by <strong>the</strong> Mertonian norms, building on <strong>the</strong> four pillars <strong>of</strong> universalism, communism, disinterestedness
and organized skepticism (Merton, 1968). In contrast to this, industry R&D is more interested in
proprietary knowledge, looking for individualized problem-oriented solutions (Jain, George and
Maltarich, 2009, Ziman, 2000). Due to <strong>the</strong> enormous differences in R&D between <strong>the</strong> two
communities, we argue that academic are more skeptical against corporate R&D than <strong>the</strong>y are
against intra-academic colleagues. This can again be explained by <strong>the</strong> above-mentioned in-group
favoritism. Thus, we hypo<strong>the</strong>size:
Hypo<strong>the</strong>sis 7a: The more experienced academic researchers are with industry
collaboration, <strong>the</strong> higher <strong>the</strong> NIH value.
Hypo<strong>the</strong>sis 7b: The more experienced academic researchers are with university
collaboration, <strong>the</strong> lower <strong>the</strong> NIH value.
Figure 1 provides an overview <strong>of</strong> <strong>the</strong> respective hypo<strong>the</strong>ses which are tested in a structural equation
model. The research design, <strong>the</strong> operationalizations and <strong>the</strong> sample are described in <strong>the</strong> subsequent
chapter.
3. Research design
3.1 Data
As <strong>the</strong> variety <strong>of</strong> knowledge in universities is enormous and hard to cover in one single study, we take
<strong>the</strong> classical natural sciences, biology, chemistry and physics, as examples for our analysis (Stokes,
1997). Several <strong>of</strong> <strong>the</strong> existing studies on NIH choose organizations like companies (Boyens, 1998),
business units (Herzog, 2011) or project teams (Katz and Allen, 1982) as <strong>the</strong> level <strong>of</strong> analysis. These
macro perspectives do not fit <strong>the</strong> academic context. Universities are too diverse to serve as a
homogenous level <strong>of</strong> analysis. Thus, we concentrate on <strong>the</strong> single tenured senior scientist who is
responsible for <strong>the</strong> own direction <strong>of</strong> research.
Previously to our study, extensive interviews with six post-doctoral researchers from <strong>the</strong> respective
research fields were conducted. In <strong>the</strong>se talks, a preliminary version <strong>of</strong> <strong>the</strong> questionnaire was
discussed. Subsequently, a fur<strong>the</strong>r pre-test was carried out using a sample <strong>of</strong> 152 Austrian pr<strong>of</strong>essors
<strong>of</strong> <strong>the</strong> same disciplines. Based on <strong>the</strong> suggestions <strong>of</strong> <strong>the</strong>se two groups, some questions were
changed, eliminated or added. Finally, <strong>the</strong> data for this study was ga<strong>the</strong>red in a national online survey
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David Grosse Kathoefer and Jens Leker
among German university pr<strong>of</strong>essors. In total, 3,023 scientists were addressed with a personal email
invitation in fall 2010. 233 <strong>of</strong> <strong>the</strong> addressees were ei<strong>the</strong>r not approachable due to invalid email
addresses or refused to take part in <strong>the</strong> survey for various reasons like time constraints or general
denial. The survey was open for three weeks with a reminder sent after two weeks. Finally, 675
pr<strong>of</strong>essors participated in <strong>the</strong> study by opening <strong>the</strong> link to <strong>the</strong> standardized online questionnaire. This
represents a response rate <strong>of</strong> 24.16%. Subsequently, we eliminated all questionnaires that were not
completed to avoid severe missing data issues resulting in a sample <strong>of</strong> 494 people. Fur<strong>the</strong>rmore, 11
questionnaires were deleted as participants did not fit <strong>the</strong> sample criteria <strong>of</strong> tenured researchers in <strong>the</strong>
according disciplines. To fur<strong>the</strong>r prepare <strong>the</strong> data for multivariate analysis, we checked for univariate
and multivariate outliers. In total, 24 potential univariate outliers were detected. Evaluating <strong>the</strong>se in
detail, no case showed unrealistic values. To check for multivariate outliers, we applied Mahalanobis
distances divided by <strong>the</strong> number <strong>of</strong> variables (Hair, 2006) and <strong>the</strong> Mahalanobis measurement function
in AMOS s<strong>of</strong>tware. In this way, 6 outliers were identified. These cases were also responsible for 13 <strong>of</strong>
<strong>the</strong> univariate outliers. Thus, all 6 cases were deleted. Finally, our basic sample consists <strong>of</strong> 477
participants.
Opinion <strong>of</strong>
colleagues
Internal
communication
Work routine
Group pride
Years at
university
Quality <strong>of</strong> past
projects
Industry
collaboration
University
collaboration
H1 (-)
H2 (-)
H3 (+)
H4 (+)
H5 (+)
H6 (-)
H7a (+)
H7b (-)
Not-Invented-
Here Syndrome
Figure 1: Summary <strong>of</strong> hypo<strong>the</strong>ses
A possible non-response bias was tested for by splitting <strong>the</strong> sample into two groups: early
respondents (first 75%) and late respondents (last 25%) (Li and Calantone, 1998). The rationale
underlying this procedure is that late respondents are similar to non-respondents (Armstrong and
Overton, 1977). Using a Mann–Whitney-U-test, no item showed significant differences (p

David Grosse Kathoefer and Jens Leker
Never<strong>the</strong>less, our study tries to improve <strong>the</strong> measurement model for academic NIH making it more
robust. Therefore, we rely on existing measures from literature and adapt <strong>the</strong> wording to <strong>the</strong> university
context. We propose a reflective four-factor model:
NIH 1: Knowledge <strong>of</strong> external partners
NIH 2: Performance <strong>of</strong> external knowledge
NIH 3: Trust in own competence
NIH 4: Sourcing <strong>of</strong> knowledge
The first construct was taken from <strong>the</strong> study <strong>of</strong> Bock, Zmud, Kim and Lee (2005). Their factor “Sence
<strong>of</strong> Self-Worth” evaluated <strong>the</strong> helpfulness <strong>of</strong> <strong>the</strong> participants’ knowledge sharing for o<strong>the</strong>rs. This
helpfulness was replaced by <strong>the</strong> knowledge <strong>of</strong> external partners and <strong>the</strong> point <strong>of</strong> view was changed to
<strong>the</strong> participant. Additionally, <strong>the</strong> wording was adapted to <strong>the</strong> university setting. The second factor was
developed by Mehrwald (1999). The item asking for financial expenses for <strong>the</strong> projects was eliminated
in advance as it did not fitted <strong>the</strong> respective context. The third construct was also taken from
Mehrwald’s study. In both cases, <strong>the</strong> wording <strong>of</strong> all items was changed from technology orientation to
<strong>the</strong> broader scope <strong>of</strong> knowledge. The last factor was initiated by an item <strong>of</strong> McDonough and Barczak
(1991) asking for <strong>the</strong> extent to which technological developments for a product have come from
internal or external sources, respectively. In <strong>the</strong> course <strong>of</strong> <strong>the</strong> pre-tests, this item was enlarged to a
factor, differentiating between several stages <strong>of</strong> a typical academic experimental research process.
These four constructs are fur<strong>the</strong>r merged to <strong>the</strong> reflective second-order factor coined NIH.
To secure unidimensionality, all single factors were assessed in principal component analyses. All
items <strong>of</strong> a single construct should show higher factor loadings than 0.5 and in combination form one
single factor (Hair, 2006). The respective items should fur<strong>the</strong>r explain more than 50% <strong>of</strong> <strong>the</strong> total
variance (Hair, 2006). As a reliability measure, Cronbach’s alpha was used (Cronbach, 1951). In this
context, early stage studies and mature scales can be distinguished. For <strong>the</strong> former, a cut<strong>of</strong>f value <strong>of</strong>
0.6 can be regarded as sufficient, whereas for <strong>the</strong> latter a value <strong>of</strong> 0.7 is <strong>of</strong>ten required (Hair, 2006).
Although <strong>the</strong> scales <strong>of</strong> this study are taken from existing literature, <strong>the</strong> transfer to <strong>the</strong> new context
shows an exploratory nature. Thus, we accept a threshold value <strong>of</strong> 0.6 regarding Cronbach’s alpha. In
total, one item <strong>of</strong> each <strong>of</strong> <strong>the</strong> factors NIH 3 and NIH 4 had to be eliminated, ending in α = 0.677 (for
fur<strong>the</strong>r details concerning factor loadings and quality criteria see Appendix).
In a second step, a confirmatory analysis was applied to validate our measurement model, including
predictor and control variables. Although multivariate normal distribution was not given, we used
maximum likelihood as <strong>the</strong> procedure <strong>of</strong> choice. West, Finch and Curran (1995) demonstrate that, as
long as skewness and kurtosis do not exceed values <strong>of</strong> two and seven, respectively, maximum
likelihood can be regarded as a robust technique providing reasonable results. During <strong>the</strong> analysis,
modification indices gave hints that five error variances should correlate. Finally, <strong>the</strong> global criteria are
very well fulfilled (Comparative Fit Index (CFI) = 0.953; Root Mean Square Error <strong>of</strong> Approximation
(RMSEA) = 0.039; Standardized Root Mean Residual (SRMR) = 0.046) (Hu and Bentler, 1999). To
assess <strong>the</strong> single construct measurements, different quality criteria, like factor and item reliability and
average variance extracted, were employed in this step (Herzog, 2011). Fur<strong>the</strong>r, we used <strong>the</strong> Fornell-
Larcker criterion to test for discriminant validity (Fornell and Larcker, 1981). Overall, we could show
that <strong>the</strong> measurement model <strong>of</strong> NIH fulfills <strong>the</strong> statistical requirements to use it in <strong>the</strong> fur<strong>the</strong>r analysis.
3.2.2 Independent variables
The operationalizations <strong>of</strong> <strong>the</strong> independent constructs were tested in <strong>the</strong> same way as <strong>the</strong> dependent
one. The first predictor variable, “Opinion <strong>of</strong> colleagues”, is based on <strong>the</strong> construct “Attitude towards
<strong>the</strong> act” by Ajzen and Fishbein (1980). Using a semantic differential, <strong>the</strong> construct was confirmed in
several studies (e.g. Chang, 1998, O'Hara, Netemeyer and Burton, 1991). The introducing sentence
was adapted to <strong>the</strong> respective context (α = 0.969). Fur<strong>the</strong>r, “Internal communication” was taken from
Holtorf (2011). Here, it was used to measure communication inside teams. Therefore, we transferred
it to <strong>the</strong> organizational level <strong>of</strong> <strong>the</strong> university (α = 0.933). “Work routine” was based on “routine
discretion” in <strong>the</strong> study <strong>of</strong> Kelley, Longfellow and Malehorn (1996). In <strong>the</strong> run <strong>of</strong> <strong>the</strong> analysis, two
items had to be deleted due to missing cut-<strong>of</strong>f criteria. Hence, <strong>the</strong> construct consisted only <strong>of</strong> three
items (α = 0.747). Finally, “Group pride” was adapted from Tyler, Degoey and Smith (1996). Here, we
chose <strong>the</strong> construct analyzed in <strong>the</strong> university setting as it best fits our context as well. During
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David Grosse Kathoefer and Jens Leker
analysis, again one item had to be eliminated (α = 0.814). All o<strong>the</strong>r exogenous variables were
measured as single-item scales due to <strong>the</strong>ir unambiguous nature.
Additionally, we employed <strong>the</strong> age <strong>of</strong> <strong>the</strong> scientist, <strong>the</strong> type <strong>of</strong> research (basic or applied) and his
research discipline as controls in <strong>the</strong> structural equation model. All three may distort <strong>the</strong> influencing
factors on NIH significantly. Table 1 contains <strong>the</strong> correlation matrix <strong>of</strong> all employed variables and
constructs.
3.3 Methodology
To test our hypo<strong>the</strong>ses, we employ structural equation modelling (SEM) disclosing causal
relationships with multiple independent and dependent variables (Bentler, 1988). A SEM consists <strong>of</strong> a
measurement model and a structural model (Hair, 2006). As we already assessed <strong>the</strong> quality <strong>of</strong> <strong>the</strong>
measurement model in a confirmatory factor analysis (CFA), <strong>the</strong> structural model has to be tested in a
second-step (Hair, 2006). One <strong>of</strong> <strong>the</strong> most wide-spread estimation methods in SEM is <strong>the</strong> maximumlikelihood
(ML) procedure. Similar to <strong>the</strong> CFA, we also employ this estimation technique for <strong>the</strong> SEM.
To evaluate <strong>the</strong> overall fit <strong>of</strong> <strong>the</strong> model, same quality criteria as in <strong>the</strong> CFA are used.
Table 1: Correlation matrix
Variables Scale Mean St.
dev.
NIH a 1-7 3.95 0.78 1.00
Opinion <strong>of</strong>
colleagues a
Internal
communication a
1-7 2.17 1.09 .54*** 1.00
1-7 4.21 1.33 -.08 -.11* 1.00
Work routine a 1-7 3.87 1.37 -.04 -.02 -.09 1.00
Group pride a 1-7 4.74 1.40 -.11* -.10* .41*** .05 1.00
Industry
collaboration a
University
collaboration a
Years at
university a
Quality <strong>of</strong> past
projects a
1 2 3 4 5 6 7 8 9 10 11 12 13
Open 0.77 0.78 .10* .05 -.06 .03 .11* 1.00
Open 1.96 0.67 -.14* -.12* .14** -.06 .15** .22*** 1.00
Open 9.68 8.18 .04 .04 -.12** -.04 -.07 .08 -.02 1.00
1-7 5.05 1.16 -.42*** -.27*** .11* -.10* .02 .07 .10* .15** 1.00
Age a Open 49.52 8.39 -.02 -.00 -.14** -.01 -.09 .10* -.04 .81*** .09 1.00
Type <strong>of</strong> research a 1-20 5.51 4.52 .01 -.01 -.15** .01 .01 .56*** .03 .11** .03 .13** 1.00
Dummy chemistry b 0-1 0.30 0.46 .13** .11** -.04 -.01 .06 .37*** .09* -.01 .01 -.06 .20*** 1.00
Dummy physics b 0-1 0.34 0.47 -.06 -.08 .07 -.16*** .02 -.15** -.03 .01 .08* -.01 -.07 -.47*** 1.00
Notes: N = 477; factor scores were calculated using summated scales; Industry Collaboration and University Collaboration are
transformed values using <strong>the</strong> square root function, a correlations are based on Pearson’s R; b correlations are based on
Kendall’s Tau-b; * p < 0.05; ** p < 0.01; *** p < 0.001.
4. Results
Taking <strong>the</strong> measurement model from our CFA and implementing it into our hypo<strong>the</strong>ses framework, we
construct our structural model (Byrne, 2001). The structural model represents <strong>the</strong> suggested
regression paths and completes <strong>the</strong> SEM. According to <strong>the</strong> proposed relationships, we set up a model
representing all hypo<strong>the</strong>ses as regression paths between <strong>the</strong> respective variables and latent
constructs. Testing this model shows a ra<strong>the</strong>r bad fit as CFI only amounts to 0.839, RMSEA to 0.068
and SRMR to 0.095. Therefore, we respecify our model to achieve a better fit to <strong>the</strong> covariance matrix
(Hair, 2006). The final model is presented in figure 2 and demonstrates an adequate fit, keeping in
mind that a structural model can never achieve a better fit than its underlying measurement model
(Hair, 2006). CFI is at 0.935, RMSEA at 0.043 and SRMR is at 0.070. Taking <strong>the</strong>se values as
acceptable, we use <strong>the</strong> final model for evaluating <strong>the</strong> hypo<strong>the</strong>ses.
365

Opinion <strong>of</strong>
colleagues
Internal
communication
Work routine
Group pride
Years at
university 1
Quality <strong>of</strong> past
projects
Industry
collaboration 2
University
collaboration
Figure 2: Final structural equation model
David Grosse Kathoefer and Jens Leker
-0.090 †
R 2 = 0.375
NIH
Knowledge <strong>of</strong>
external
partners
Performance <strong>of</strong>
external
knowledge
Trust in own
competence
Sourcing <strong>of</strong>
knowledge
Notes: The error variance terms, items <strong>of</strong> constructs and controls are omitted from <strong>the</strong> diagram for simplicity. Only significant
paths are depicted. 1 Significant correlation with “Age”. 2 Significant correlations with “Type <strong>of</strong> research” and “Dummy
chemistry”. Fur<strong>the</strong>r “Dummy chemistry” and “Dummy physics” are intercorrelated. Estimations are run with AMOS 18.0
and standardized data. N = 477. † p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001; circle = 2 nd -order factor; ellipse = 1 st -
order factor; rectangles = single items.
It can be observed that H1 finds support in our data. “Opinion <strong>of</strong> colleagues” shows <strong>the</strong> second
highest regression weight. In contrast, hypo<strong>the</strong>ses 2 and 3 have to be rejected. “Group pride”
influences NIH in a negative way. This is contrary to our assumed relationship. Thus, H4 does not find
support in <strong>the</strong> data as well. The same holds true for H5 as “Years at university” does not show any
impact on <strong>the</strong> dependent variable. The highest regression weight is <strong>the</strong> one that represents <strong>the</strong>
relationship between “Quality <strong>of</strong> past projects” and “NIH”, leading to a confirmation <strong>of</strong> H6. Hypo<strong>the</strong>ses
7a and 7b are both verified in our sample. The controls do not seem to influence <strong>the</strong> dependent
variable. Moreover, <strong>the</strong> structural has to be expanded by 6 correlation paths between various
variables. A summary <strong>of</strong> <strong>the</strong> results <strong>of</strong> our hypo<strong>the</strong>ses testing can be found in figure 3. The results are
discussed in detail in <strong>the</strong> next section.
Opinion <strong>of</strong>
colleagues
Internal
communication
Work routine
Group pride
Years at
university
Quality <strong>of</strong> past
projects
Industry
collaboration
University
collaboration
Figure 3: Summary <strong>of</strong> hypo<strong>the</strong>ses after testing
H1
H2
H3
H4
H5
H6
H7a
H7b
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Not-Invented-
Here Syndrome

5. Discussion and conclusion
David Grosse Kathoefer and Jens Leker
In this paper, we developed a robust measurement model for <strong>the</strong> Not-Invented-Here Syndrome in an
academic context. We adapted existing scales from literature and transferred <strong>the</strong>m to <strong>the</strong> university
research setting. Fur<strong>the</strong>r, we deduced 8 hypo<strong>the</strong>ses from <strong>the</strong>ory and existing studies on NIH and
tested <strong>the</strong>m in a SEM, using a sample <strong>of</strong> 477 German pr<strong>of</strong>essors from <strong>the</strong> natural sciences.
The antecedents prior studies proposed to influence NIH do not fit in an academic context totally.
Thus, we discuss <strong>the</strong> falsified hypo<strong>the</strong>ses first. A good internal communication seems to be no
possible means to reduce NIH infection. Although most <strong>of</strong> NIH studies mention that as a critical
aspect (e.g. Clagett, 1967, Katz and Allen, 1982), academia is different. One reason might be that <strong>the</strong>
organizational structures are not comparable. In a university setting, <strong>the</strong> researchers are more selffocused
and more or less independent in <strong>the</strong>ir research directions. Moreover, academics are <strong>of</strong>ten
more interconnected with scientists in <strong>the</strong> scientific community than with colleagues inside <strong>the</strong> own
university (Cyert and Goodman, 1997). Therefore, internal communication does not play such an
important role in academics’ all-day work at all.
A similar stream <strong>of</strong> arguments can be presented for <strong>the</strong> non-significant relationship between “Years at
university” and NIH. As <strong>the</strong> ties in universities seem to be ra<strong>the</strong>r weak <strong>the</strong> danger <strong>of</strong> forming too
stable groups hardly exists. Katz and Allen (1982) argue that <strong>the</strong> length <strong>of</strong> tenure in a project group
foster <strong>the</strong> feeling <strong>of</strong> possessing a monopoly <strong>of</strong> knowledge in <strong>the</strong> respective research field. Due to <strong>the</strong>
orientation towards <strong>the</strong> scientific community, potential strong group can ra<strong>the</strong>r be found in external
networks. Thus, <strong>the</strong> period <strong>of</strong> time a researcher is affiliated with a university does not impact NIH.
The third discussable indicator is “Work routine”. Again, <strong>the</strong> proposed relationship from industryrelated
papers does not hold true in our academic setting. A reason might be that industrial R&D <strong>of</strong>ten
searches for problem-oriented solutions (Ziman, 2000). Here, this finding process may be more
structured and optimized with regards to efficiency. In contrast to this, university R&D is more
interested in creating a deeper understanding <strong>of</strong> <strong>the</strong> problem itself (Ziman, 2000). Thus, thirst for
knowledge plays a more important role in academic R&D than efficiency.
Although <strong>the</strong> relationship between “Group pride” and NIH is significant, H4 was rejected as <strong>the</strong>
direction is reverse. In this light, we can assume that scientists being proud <strong>of</strong> being part <strong>of</strong> <strong>the</strong>ir
university tend to be affiliated with institutions <strong>of</strong> high reputation. Regularly, equivalent status plays an
important role in partner choice (Podolny, 1994). Thus, <strong>the</strong> respective scientists may experience
external knowledge <strong>of</strong> higher quality and, <strong>the</strong>refore, may be less reluctant to it.
All o<strong>the</strong>r hypo<strong>the</strong>ses are supported. Therefore, we can draw three main conclusions. First <strong>of</strong> all,
experience is an important factor. If scientists are satisfied with collaboration activities with external
partners <strong>the</strong>y will show less prejudices against external knowledge. This is in line with psychological
literature as human beings try to avoid depressing situations but are in favor <strong>of</strong> pleasant situations
(Smith and Mackie, 2007). Secondly, collaboration is a double-edged sword. Industry partnership
increases NIH infection whereas academic partnership decreases it. Due to <strong>the</strong> different research
environments, collaboration between university and industry may inherit a higher risk <strong>of</strong> failure (Cyert
and Goodman, 1997). In combination with <strong>the</strong> first conclusion, this may be an explanation for <strong>the</strong>
respective finding. However, future research should clarify this aspect and dig deeper into <strong>the</strong>
interrelations <strong>of</strong> research setting and quality <strong>of</strong> project experience. Thirdly, <strong>the</strong> social influence seems
to be important. Thus, creating an atmosphere <strong>of</strong> sharing in <strong>the</strong> university plays a pivotal role and
enhances <strong>the</strong> fruitful and multidirectional discussions among researchers.
All in all, we can derive two main implications. Firstly, academic policy-makers should create a
knowledge-sharing friendly environment. On <strong>the</strong> one hand, this includes <strong>the</strong> easy initiation <strong>of</strong>
collaboration activities. On <strong>the</strong> o<strong>the</strong>r hand, especially collaborations with industrial partners should be
supported to increase <strong>the</strong> quality <strong>of</strong> such relationships. Secondly, industrial managers should be
aware <strong>of</strong> <strong>the</strong> potential existence <strong>of</strong> NIH in an academic context. Besides, <strong>the</strong>y should also improve
<strong>the</strong>ir collaboration activities with university partners to secure a better quality <strong>of</strong> research. Moreover,
<strong>the</strong>y should consider collaboration-experienced partners as <strong>the</strong>y tend to be more open to bidirectional
discussion. Finally, we can state that this paper contributes to <strong>the</strong> existing body <strong>of</strong> knowledge
literature. It establishes a robust measurement model for <strong>the</strong> well-known knowledge transfer obstacle,
367

David Grosse Kathoefer and Jens Leker
<strong>the</strong> NIH Syndrome. Additionally, it analyzes potential drivers <strong>of</strong> NIH and provides practical implications
to improve knowledge transfer.
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Appendix
David Grosse Kathoefer and Jens Leker
Constructs, items, factor loadings, Cronbach’s Alphas
Questionnaire items Factor loadings
NIH 1 (Cronbach’s Alpha = 0.895; explained variance = 71.00%; construct reliability = 0.887)
1. The knowledge <strong>of</strong> external partners helps our research group to solve problems. (R) .878
2. The knowledge <strong>of</strong> external partners provides us new research opportunities. (R) .834
3. The knowledge <strong>of</strong> external partners helps us to improve internal working processes. (R) .750
4. The knowledge <strong>of</strong> external partners helps us to increase our research productivity. (R) .868
5. The knowledge <strong>of</strong> external partners helps us to achieve our research goals. (R) .875
NIH 2 (Cronbach’s Alpha = 0.841; explained variance = 61.49%; construct reliability = 0.855)
How well did research projects, in which predominantly external knowledge was used, in comparison to those projects mainly using
internal knowledge with regards to <strong>the</strong> following dimension?
1. Time to finish <strong>of</strong> project. (R) .746
2. Realization <strong>of</strong> project goals. (R) .803
3. Risk <strong>of</strong> failure. (R) .859
4. Appearance <strong>of</strong> unforeseen problems. (R) .799
5. Generation <strong>of</strong> follow-up projects. (R) .704
NIH 3 (Cronbach’s Alpha = 0.644; explained variance = 48.58%; construct reliability = 0.652)
1. We ra<strong>the</strong>r develop knowledge on our own than being dependent on <strong>the</strong> knowledge provider’s collaboration in order to understand .796
<strong>the</strong> external knowledge.
2. Even without using external technology, we can achieve research success. .760
3. External knowledge sourcing is less attractive to our research unit, because we would run <strong>the</strong> risk <strong>of</strong> disclosing our technological .590
knowledge during <strong>the</strong> collaboration with a knowledge provider.
4. Using external knowledge is an important alternative for knowledge sourcing within this research unit. (R) .619
NIH 4 (Cronbach’s Alpha = 0.620; explained variance = 56.86%; construct reliability = 0.635)
To what extent do ideas for <strong>the</strong> following knowledge types come from internal sources (own ideas, staff etc.) in contrast to
external sources (industry partners, research colleagues, requests for project proposals etc.)?
1. Experimental methodological skills. (R) .721
2. Theoretical methodological skills. (R) .832
3. Knowledge for analysis and interpretation <strong>of</strong> results. (R) .703
NIH (Cronbach’s Alpha = 0.677; explained variance = 51.26%; construct reliability = 0.761)
• NIH 1 .813
• NIH 2 .698
• NIH 3 .751
• NIH 4 .582
Opinion <strong>of</strong> colleagues (Cronbach’s Alpha = 0.969; explained variance = 94.22%; construct reliability = 0.969)
Independent <strong>of</strong> your personal opinion, what do your colleagues think about external knowledge sourcing?
• Bad vs. good .949
• Foolish vs. wise .953
• Harmful vs. beneficial .924
370

David Grosse Kathoefer and Jens Leker
Internal Communication (Cronbach’s Alpha = 0.933; explained variance = 75.00%; construct reliability = 0.927)
• The pr<strong>of</strong>essors at my university communicate frequently with each o<strong>the</strong>r. .888
• The pr<strong>of</strong>essors at my university are satisfied with <strong>the</strong> timeliness <strong>of</strong> receiving information from colleagues. .806
• Information relevant for research is frequently exchanged between <strong>the</strong> pr<strong>of</strong>essors. .854
• There is an intensive communication between <strong>the</strong> pr<strong>of</strong>essors at my university. .928
• The pr<strong>of</strong>essors at my university are satisfied with <strong>the</strong> accurateness <strong>of</strong> information <strong>the</strong>y receive from colleagues. .808
• The pr<strong>of</strong>essors at my university communicate directly and personally with each o<strong>the</strong>r. .905
Work routine (Cronbach’s Alpha = 0.747; explained variance = 66.40%; construct reliability = 0.748)
• I use routine procedures to complete my job tasks when possible. .836
• My co-workers consult standardized manuals for <strong>the</strong>ir job task when necessary. .811
• I try to develop a routine for each <strong>of</strong> <strong>the</strong> typical duties involved in my job. .797
Group pride (Cronbach’s Alpha = 0.814; explained variance = 73.24%; construct reliability = 0.815)
• I am proud to think <strong>of</strong> myself as a member <strong>of</strong> this university. .856
• When someone praises my university’s members, I feel it as a personal compliment to me. .832
• I talk up my university to friends as a great university to be part <strong>of</strong>. .879
Notes: N = 477; (R) = reverse coded item; SPSS 18.0 and AMOS 18.0 were used for calculations; item values were
standardized to run analysis.
371

Individual Level Influencers on Tacit Knowledge Sharing
Processes
Claire Gubbins 1 , Grainne Kelly 1 , Lawrence Dooley 2 , Cécile Emery 3 and Eamonn
Murphy 4
1
HRM & Organizational Psychology Group, Dublin City University, Dublin,
Ireland
2
Department <strong>of</strong> Management and Marketing, University College Cork, Ireland
3
Employment Relations & Organizational Behaviour Group, London School <strong>of</strong>
Economics & Political Science, London, UK
4
Enterprise Research Centre, University <strong>of</strong> Limerick, Limerick, Ireland
claire.gubbins@dcu.ie
grainne.kelly@dcu.ie
L.Dooley@ucc.ie
c.emery@lse.ac.uk
eamonn.murphy@ul.ie
Abstract: Smith (2001) notes that "much <strong>of</strong> <strong>the</strong> knowledge on which performance in real-world settings is based
is tacit knowledge" and 90 percent <strong>of</strong> knowledge in any organisation is embedded in people's heads. It is through
converting tacit knowledge to value that <strong>the</strong> organisation obtains <strong>the</strong> competitive advantages achievable through
knowledge management (KM) (KPMG, 2000). Much research explores <strong>the</strong> management <strong>of</strong> explicit knowledge
but limited progress is made on that <strong>of</strong> tacit knowledge (Insch et al., 2008; Perez & Mitra, 2007). Employees are
<strong>the</strong> fundamental repositories <strong>of</strong> tacit knowledge and are <strong>the</strong> key players in knowledge management initiatives.
Tacit knowledge is personal, hard to communicate and exists in individuals mental models (Cho, Li, Su, 2007). It
is <strong>the</strong>refore <strong>the</strong> individual whom decides if <strong>the</strong>y will share <strong>the</strong>ir knowledge (Constant et al., 1994). Thus, an
understanding <strong>of</strong> individual factors and <strong>the</strong>ir influence on knowledge management processes is key to <strong>the</strong>
success <strong>of</strong> any knowledge management initiative (Tohidinia & Mosakhani, 2010). Wang and Noe (2010) and
Cho, Li and Su (2007) argue for fur<strong>the</strong>r research on individual level variables. Additionally, <strong>the</strong> number <strong>of</strong> studies
on <strong>the</strong>se variables is limited and many define knowledge broadly to include explicit knowledge. This study aims to
advance on this research gap and explore <strong>the</strong> influence <strong>of</strong> individual level factors on tacit knowledge
management processes. The case study is based on an American multinational manufacturing site and <strong>the</strong>
participants are engineers whom engage in routine and non-routine tasks which demand use <strong>of</strong> both explicit and
tacit knowledge. A qualitative methodology is utilised. This paper presents <strong>the</strong> preliminary results <strong>of</strong> this
investigation.
Keywords: Tacit Knowledge, Knowledge Management, Individual Factors
1. Introduction
There is broad agreement in <strong>the</strong> literature as to <strong>the</strong> processes involved in knowledge management.
Alavi and Leidner (2001) succinctly identify four basic steps involved in <strong>the</strong> knowledge management
process: creation, storage/retrieval, transfer, and application <strong>of</strong> knowledge. Kakabadse, Kouzmin, and
Kakabadse (2001) and Yahya and Goh (2002) both refer to an additional step; knowledge acquisition
or capture. Nonaka and Takeuchi (1995), in specific consideration <strong>of</strong> <strong>the</strong> tacit-explicit dimension <strong>of</strong>
knowledge, refer to <strong>the</strong> steps <strong>of</strong> socialisation, externalisation, combination and internalisation. The
review suggests that <strong>the</strong> sub-processes in <strong>the</strong> knowledge management process are knowledge
seeking, access, sharing (socialisation), capture (externalisation) and transfer (internalisation and
application). These sub-processes are not necessarily sequential but ra<strong>the</strong>r interactive. While such
sub-processes are evident in <strong>the</strong> <strong>the</strong>oretical literature, <strong>the</strong> majority <strong>of</strong> empirical studies reviewed are
concerned with <strong>the</strong> step <strong>of</strong> knowledge sharing or fail to disentangle <strong>the</strong> steps thus potentially
neglecting key precursor and post cursor steps and <strong>the</strong> associated influential nuances which may
better inform understanding <strong>of</strong> how to manage knowledge, in particular tacit knowledge. In this regard,
this paper focuses on individual level influencers on each <strong>of</strong> <strong>the</strong>se sub-processes. The literature
emphasises that an important reason for failure in knowledge sharing is <strong>the</strong> lack <strong>of</strong> consideration <strong>of</strong>
<strong>the</strong> influence <strong>of</strong> organizational and interpersonal characteristics (Carter & Scarbrough, 2001; Voelpel,
Dous & Davenport, 2005). Wang & Noe (2010) focus on <strong>the</strong> individual level factors that influence
knowledge sharing between employees as <strong>the</strong>y argue that team and organizational level knowledge
is influenced by <strong>the</strong> extent to which knowledge sharing occurs between employees (Cabrera &
Cabrera, 2005; Gupta & Govindarajan, 2000; Nonaka, 1994; Polanyi, 1966; Tsoukas & Vladimirous,
2001). In a recent review <strong>of</strong> empirical studies focused at <strong>the</strong> individual level <strong>of</strong> knowledge sharing,
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Claire Gubbins et al
Wang and Noe (2010) identifies a number <strong>of</strong> individual level characteristics as key influencers but
<strong>the</strong>y and Cho, Li and Su (2007) recognise a number <strong>of</strong> gaps and argue for fur<strong>the</strong>r research on
individual level variables. Additionally, Wang and Noe’s (2010) review only focused on <strong>the</strong> knowledge
sharing process <strong>of</strong> knowledge management. In this regard, this paper focuses on exploring individual
level influencers on each <strong>of</strong> <strong>the</strong> sub-processes <strong>of</strong> knowledge management. Also, many empirical
studies concerned with knowledge management fail to differentiate between tacit and explicit
knowledge and <strong>the</strong> inherent differences in <strong>the</strong> challenges presented in managing <strong>the</strong>se different forms
<strong>of</strong> knowledge. Therefore, this study aims to explore <strong>the</strong> influence <strong>of</strong> individual level factors on tacit
knowledge management processes.
2. Research Methodology
This research is part <strong>of</strong> a wider study <strong>of</strong> tacit knowledge management in seven manufacturing MNC’s
that is being undertaken as part <strong>of</strong> an academic-industry collaborative research project. The aim <strong>of</strong>
<strong>the</strong> study is to design, develop and implement a methodology to enable <strong>the</strong> description and evaluation
<strong>of</strong> tacit knowledge practices and preferences among engineers in manufacturing MNC’s in Ireland.
This paper reports <strong>the</strong> findings from interviews with a sample <strong>of</strong> engineers and managers from one
case study site. The participants are engineers whom engage in routine and non-routine tasks which
demand use <strong>of</strong> both explicit and tacit knowledge. The participants were selected by <strong>the</strong> lead
researcher to meet <strong>the</strong> following criteria: ensure a cross-section <strong>of</strong> views were obtained and ensure
participants work included routine and non-routine tasks which demand <strong>the</strong> use <strong>of</strong> both explicit and
tacit knowledge. Then, a snowball sampling approach (Noy, 2008) was utilised, whereby <strong>the</strong> initial
interviews were used to identify fur<strong>the</strong>r populations for study whom were important to <strong>the</strong> focal
individuals efforts to manage <strong>the</strong> knowledge required to do <strong>the</strong>ir work. The qualitative methodology
consisted <strong>of</strong> structured interviews (Miles & Huberman, 1994; Streb, 2009) ranging in duration from 75
to 120 minutes. A total <strong>of</strong> 33 interviews were conducted. This paper focuses predominately on <strong>the</strong>
views <strong>of</strong> <strong>the</strong> maintenance engineers with <strong>the</strong> views from <strong>the</strong> o<strong>the</strong>r interviewees used to validate,
cross-check, understand or elaborate on <strong>the</strong> views <strong>of</strong> <strong>the</strong> engineers. A qualitative exploratory and
explanatory methodology is utilised which adopts a funnelling approach (see Minichiello et al, 1992) to
semi-structured interviews. The interviews were audio recorded, transcribed verbatim and content
analysed using NVivo.
3. Findings
The results <strong>of</strong> this study reveal that, in considering <strong>the</strong> knowledge circulation sub-processes with a
focus explicitly on tacit knowledge or ‘know-how’, it is <strong>the</strong> impact <strong>of</strong> individual level factors on <strong>the</strong> subprocesses
<strong>of</strong> seeking and capture that most strongly impact <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> tacit knowledge
management process. The importance <strong>of</strong> individual level factors on intention or willingness to share
knowledge also emerges. Therefore, <strong>the</strong> findings are organised under <strong>the</strong>se headings <strong>of</strong>: seeking
tacit knowledge, capturing tacit knowledge and willingness to share. The results reveal that this
knowledge circulation is mediated by a number <strong>of</strong> individual level processes. These processes can be
categorised into two clusters; attributes <strong>of</strong> <strong>the</strong> tacit knowledge seeker as perceived by <strong>the</strong> seeker and
<strong>the</strong>ir influence on <strong>the</strong> knowledge management process steps <strong>of</strong> seeking and capture; and attributes <strong>of</strong>
<strong>the</strong> knowledge source as perceived by both <strong>the</strong> seeker during <strong>the</strong> knowledge seeking process and as
perceived by both <strong>the</strong> seeker and <strong>the</strong> source during <strong>the</strong> collaborative knowledge capture process. A
third cluster <strong>of</strong> influencers moderating <strong>the</strong> seeking and capture processes pertain to <strong>the</strong> tacit
knowledge source. The source whom is approached by a knowledge seeker engages in a decisionmaking
and analysis process, influenced by his/her personal attributes, which determines his/her
willingness to share knowledge and what or how much to share before <strong>the</strong> capture process can
commence. Figure 1 presents a tentative, descriptive model to illustrate <strong>the</strong>se observations and <strong>the</strong>
results leading to this model will be discussed in <strong>the</strong> following sections.
3.1 Individual Influencers on Efforts to Seek Tacit ‘Know-How’
The results <strong>of</strong> this case site illustrate that <strong>the</strong>re is a strong awareness among respondents <strong>of</strong> where to
seek required ‘know-how’ and whom <strong>the</strong> experts are in any given functional area or with respect to
any given machine. This awareness is facilitated informally through <strong>the</strong> social network. However
decisions on whe<strong>the</strong>r to seek knowledge, when to seek knowledge and whom to seek knowledge
from are influenced by a number <strong>of</strong> factors discussed below. Seeker Attributes; The first <strong>of</strong> <strong>the</strong> most
frequently cited seeker attribute influencers included <strong>the</strong> individual’s social confidence in seeking
knowledge from o<strong>the</strong>rs. There is a general belief that engineers must be willing to use <strong>the</strong>ir initiative
and actively go and seek and ask for <strong>the</strong> knowledge <strong>the</strong>y require. This extends to some perceptions
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that such knowledge will not be provided unless one is prepared to ask for it. In addition to initiative
and a personal drive to ask, it is perceived that possessing an ability to ask questions and get <strong>the</strong>
knowledge source to talk is required.
“you just have to go… you have to go down, find out what’s wrong. The biggest thing is
find out what’s wrong, talk to everybody, get as much information as possible” (Engineer
1)
“well what was instilled in me when I started engineering was if in doubt ask and that
was it and if you don’t know something don’t be afraid to say you don’t know. ……Now I
have a way <strong>of</strong> getting people to talk and I put people at <strong>the</strong>ir ease and I can talk easily to
people” (Engineer 5)
The second set <strong>of</strong> seeker attributes relates to his/her personal motivation for seeking knowledge.
These motivators can be categorised into four <strong>the</strong>mes; beliefs about <strong>the</strong> learning process, concerns
over how o<strong>the</strong>rs perceive <strong>the</strong> seeker in terms <strong>of</strong> competence, beliefs about what is priority in <strong>the</strong>ir job
and confidence levels in own knowledge before applying it to a problem. Mirroring <strong>the</strong> previous
discussion about respondents beliefs about how individuals learn best and <strong>the</strong>ir associated
willingness to share <strong>the</strong>ir knowledge is <strong>the</strong> finding that some respondents do not wish to overly rely on
seeking o<strong>the</strong>rs knowledge but ra<strong>the</strong>r learn that knowledge <strong>the</strong>mselves through on <strong>the</strong> job
experiences, self-directed formal training and trial and error. In <strong>the</strong> same vein, when <strong>the</strong>se
respondents do decide to seek o<strong>the</strong>rs’ knowledge <strong>the</strong>y focus on obtaining specialised knowledge
about <strong>the</strong> problem or knowledge pertaining to an individuals’ problem solving process as opposed to
<strong>the</strong> actual solution.
“Its good to let <strong>the</strong>m become independent too. It’s a better way for <strong>the</strong>m to learn. A bit <strong>of</strong>
both really, is that you let <strong>the</strong>m find a lot <strong>the</strong>mselves and <strong>the</strong>y do generally, if you have a
guy he will learn a lot by himself if he’s allowed to or asked to and he’ll work up to that
level and if a guy has a certain basic knowledge, he’d pick up <strong>the</strong> more specialised stuff
a lot more quickly whereas if a guy comes in <strong>of</strong>f <strong>the</strong> street and you just tell him everything
<strong>the</strong>n he still has no understanding <strong>of</strong> material” (Engineer 5)
Figure 1: Individual Level Influencers on Tacit Knowledge Management Process Success
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“sometimes guys will electrically obviously know exactly why this happened and have a
very hard time explaining why this is happening because I might not understand what this
component does and in order to find out what that component does it might need to read
a full manual on it….if I had time I might look into what that component does in my own
time” (Engineer 4)
Respondents also showed concern for how o<strong>the</strong>rs would perceive <strong>the</strong>m in terms <strong>of</strong> <strong>the</strong>ir competence
if <strong>the</strong>y engaged in knowledge seeking activities.
“some fixes could be very easy to fix…it might be embarrassingly easy. What would
happen to us many times is that we could be looking at something for an hour and all <strong>of</strong> a
sudden <strong>the</strong>n, you’d call some guy and he’d say ‘look its that <strong>the</strong>re’ and you’d say ‘how
could I miss that’, so you try and avoid that as well by making a good effort to find out
what it could be” (Engineer 5)
The attitude <strong>of</strong> trying to figure out <strong>the</strong> problem and learn <strong>the</strong> knowledge to fix it yourself before
seeking o<strong>the</strong>rs knowledge is also partially explained by individuals’ view <strong>of</strong> <strong>the</strong>ir role and <strong>the</strong> priorities
<strong>of</strong> that role. For some <strong>the</strong>re is a reluctance to seek knowledge from o<strong>the</strong>rs too quickly due to a belief
that it is your job to possess sufficient knowledge to be able to fix machines without relying on o<strong>the</strong>rs.
As one respondent stated:
“well obviously you try and fix as many machines as you can on your own, because that’s
what I think you’re kind <strong>of</strong> expected. O<strong>the</strong>rwise if you ring everyone and you ask
everyone <strong>the</strong>n in <strong>the</strong>ory ….. you’re not doing your job” (Engineer 4)
However for some <strong>the</strong> priorities are to get <strong>the</strong> machine fixed, get <strong>the</strong> job done, get <strong>the</strong> machine fixed
as quickly as possible and/or ensure <strong>the</strong> machine is fixed for <strong>the</strong> long term and for one respondent it
was about recognising that you can’t know everything. Consequently <strong>the</strong>se respondents believed in
seeking o<strong>the</strong>rs knowledge in order to achieve <strong>the</strong>se priorities.
The final influencer which drives individuals to seek knowledge is confidence in <strong>the</strong>ir own knowledge
combined with concern over <strong>the</strong> implications <strong>of</strong> applying that knowledge to a problem if <strong>the</strong>y are not
correct. In <strong>the</strong>se instances <strong>the</strong>re is an “if in doubt ask” (Engineer 5) attitude.
“you’re saying [to yourself] will I do this, see if this happens…sometimes you can leave
yourself open as well. You’d take it so far, <strong>the</strong>n I’d be asking questions <strong>of</strong> myself if I take
it any fur<strong>the</strong>r would this happen,…. my head on <strong>the</strong> line? I might <strong>the</strong>n go to [team leader
1] or [team leader 2] and say if I do this I may get this going but this may be <strong>the</strong>
consequence” (Engineer 6)
This confidence issue is also noted by one team manager whom was partly frustrated that two
engineers were required for one job but could also understand that one engineer required <strong>the</strong> o<strong>the</strong>r
for support and/or knowledge.
“I experienced myself that I might send an [engineer] to a particular problem and I could
be guaranteed when I arrive <strong>the</strong>re would be 2 <strong>of</strong> <strong>the</strong>m <strong>the</strong>re. ….What it was at <strong>the</strong> time
was it was maybe lack <strong>of</strong> confidence in <strong>the</strong> first individual that is sent <strong>the</strong>re and he’d look
for support from <strong>the</strong> o<strong>the</strong>r individual. ……….But one <strong>of</strong> <strong>the</strong>m was picking up from <strong>the</strong>
o<strong>the</strong>r. So <strong>the</strong>re was no harm in it but that’s how information was transferred.” (Team
Manager 1)
Source Attributes: Respondents described <strong>the</strong> people <strong>the</strong>y seek knowledge from using terminology
that can be categorised under three headings; knowledge competence, communication skills and
personality attributes. Under <strong>the</strong> category <strong>of</strong> knowledge competence respondents are likely to seek
knowledge from those <strong>the</strong>y describe as knowledgable, good capabilities, good interpretation skills and
practical. In terms <strong>of</strong> communication skills respondents described knowledge sources sought as
possessing an ability to explain. In terms <strong>of</strong> personality attributes those sought are described as
willing to take time to explain, approachable, obliging, not ‘cocky’, won’t make jokes at requesters
expense about <strong>the</strong>ir lack <strong>of</strong> knowledge or ability and are likely to reciprocate by also asking <strong>the</strong> seeker
for his/her knowledge when required. Under this category respondents described characteristics <strong>of</strong>
those <strong>the</strong>y would not seek knowledge from as ‘know it all’s’, bluffers, a horrible person and overly
strong-willed in not willing to see anyone else’s point <strong>of</strong> view.
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3.2 Individual Influencers on Efforts to Capture and Externalise Tacit ‘Know-How’
While decisions to seek tacit knowledge and willingness to share tacit knowledge are largely based on
individual personal attributes and motivators, <strong>the</strong> process <strong>of</strong> capturing tacit knowledge is complex in
that it is based on mental processes and individual cognitive competences. This process is necessary
for ei<strong>the</strong>r/or capture <strong>of</strong> tacit knowledge in an external (explicit) or internal (tacit) form and thus a
necessary component <strong>of</strong> effective management <strong>of</strong> tacit knowledge. Four key influencers on
individuals’ efforts to capture ano<strong>the</strong>r’s knowledge were among <strong>the</strong> most frequently discussed or cited
by respondents. These influencers were common attributes required by both knowledge seekers and
<strong>the</strong> knowledge source due to <strong>the</strong> collaborative nature <strong>of</strong> tacit knowledge capture processes.
The first related to <strong>the</strong> recipients’ absorptive capacity. In this case site engineers typically possessed
backgrounds in mechanics or electrics but as part <strong>of</strong> <strong>the</strong>ir role were frequently involved in machine
maintenance that required knowledge <strong>of</strong> both mechanics or electrics. This required that <strong>the</strong>y obtain
<strong>the</strong> component knowledge which <strong>the</strong>y lacked from training, manuals or o<strong>the</strong>r engineers. Respondents
spoke <strong>of</strong> <strong>the</strong> difficulties <strong>of</strong> understanding ano<strong>the</strong>r persons’ knowledge, explanations and ‘know-how’ to
fix a machine when <strong>the</strong> problem combined both electrics and mechanics. Respondents disagreed
about whe<strong>the</strong>r those with deeper contextual knowledge were better able to explain or more difficult to
understand when <strong>the</strong> recipient had a different knowledge base. One respondent also felt that those
whom were educated through <strong>the</strong> college system had a “different way <strong>of</strong> thinking” (Engineer 4) than
those whom developed <strong>the</strong>ir skill through <strong>the</strong> apprenticeship system and that this made is difficult to
understand each o<strong>the</strong>r.
“I think <strong>the</strong> guy that’s able to explain it better has more knowledge, he has a deeper
knowledge <strong>of</strong> it than <strong>the</strong> guy who is not able to explain it well” (Engineer 6)
“I could probably explain to someone over <strong>the</strong> phone or bring <strong>the</strong>m down and show <strong>the</strong>m
but I don’t think <strong>the</strong>y’d know specifically- <strong>the</strong>y’d need to know a little bit more about <strong>the</strong>
inside <strong>of</strong> a computer. Computers is what I do…so it would be hard to explain to
somebody who wouldn’t understand <strong>the</strong> computer end <strong>of</strong> it” (Engineer 1)
This <strong>the</strong>me also emerged in terms <strong>of</strong> having a common language through which understanding with
minimal explanation is enabled.
“a lot <strong>of</strong> <strong>the</strong> gang now are here 8,9 or 10 years so we know <strong>the</strong> scene here, we know we
can say one word to a person and <strong>the</strong>y’ll know exactly what we’re talking about so it’s
almost no need to go [to a system to help <strong>the</strong>m understand it]” (Engineer 5)
A second influencer on capture related to individuals communication skills in efforts to capture
ano<strong>the</strong>r’s knowledge. Respondents described those from whom <strong>the</strong>y were able to capture knowledge
as possessing <strong>the</strong> ability to explain, can make <strong>the</strong> knowledge easy to understand, easy to follow and
explain at a speed appropriate to <strong>the</strong> recipient. There was a belief that some people are easier to
understand than o<strong>the</strong>rs and are better at explaining things, possibly in ‘layman’s terms’.
Respondents also spoke about how requesters <strong>of</strong> knowledge need to be able to ask <strong>the</strong> source to
slow down, request step-by-step knowledge, be willing to ask questions <strong>of</strong> <strong>the</strong> source to ensure <strong>the</strong>y
understand, force <strong>the</strong> source to explain and be in a position to take notes to help <strong>the</strong>m recall <strong>the</strong>
knowledge or decipher it fur<strong>the</strong>r. One respondent spoke about an acknowledged in-house machine
expert and how you had to engage with him to try and understand and capture his knowledge.
“[Engineer X] is really good at electronics but he’s running at twice <strong>the</strong> speed <strong>of</strong> <strong>the</strong> rest
<strong>of</strong> use, so we have to slow him down to say ‘how do you do that? How do you get to that
step from that step ….that I understand?’” (Engineer 5)
A number <strong>of</strong> respondents discussed factors pertaining to self-efficacy in engaging in tacit knowledge
sharing and capture processes. They discussed how <strong>the</strong>y felt about explaining <strong>the</strong>ir ‘know-how’ to
o<strong>the</strong>rs and <strong>the</strong>se discussions illuminated some possible explanations for knowledge sources
experiencing difficulties in or failing to impart <strong>the</strong>ir knowledge. Essentially, respondents possessed
high confidence in terms <strong>of</strong> <strong>the</strong>ir expertise in electronics or mechanics but were less confident about
<strong>the</strong>ir abilities to ei<strong>the</strong>r/or verbalise or write down <strong>the</strong>ir knowledge. This was expressed as personal
beliefs about lacking skills to explain or write and was apparent in some respondent’s efforts to write
down some <strong>of</strong> <strong>the</strong>ir knowledge but <strong>the</strong>n have o<strong>the</strong>rs check it for clarity.
“some things are very hard to write up. I suppose its people that are good at writing, it’s
like telling a story- I wouldn’t be very good at that, it takes me forever to explain how
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something came about and got to here. ….Normally it is very simple and very basic [<strong>the</strong>
machine fix] and that’s it, …. I might even end up going go asking o<strong>the</strong>r people ‘what do
you think <strong>of</strong> this’ because <strong>the</strong>re may be quality implications and all sorts….<strong>the</strong>y’ll all have
to understand it so it has to be written in reasonable English that is understandable”
(Engineer 1)
The final individual level factor impacting <strong>the</strong> success <strong>of</strong> tacit knowledge capture efforts related to
individuals information processing preferences. For <strong>the</strong> most part respondents from this case site had
a preference for visual forms <strong>of</strong> information. They referred to use <strong>of</strong> diagrams, drawings, sketches and
taking or referring to photographs in both capturing <strong>the</strong>ir knowledge for personal use and in efforts to
explain <strong>the</strong>ir knowledge to o<strong>the</strong>rs. They also expressed a strong preference for capturing ano<strong>the</strong>r
person’s knowledge using a ‘show and tell approach’ by being shown parts and <strong>the</strong> process on a live
machine with a live problem. There was also significant evidence <strong>of</strong> textual knowledge capture efforts
in <strong>the</strong> form <strong>of</strong> personal diaries, personal portfolio’s and folders and notes on training programmes.
However, this form <strong>of</strong> capture was reserved for personal use. An in-house knowledge management
system was also available but textual inputs varied from scant to ‘essay style. In general respondents
expressed a preference for textual knowledge to be explained in a step-by-step format.
3.3 Individual Influencers on Willingness to Share Tacit ‘Know-How’
A number <strong>of</strong> individual level factors emerged as influencers on individuals willingness to share ‘knowhow’.
The most frequently cited influencers are discussed here.
Source Processes: A key process discussed by respondents which influenced <strong>the</strong>ir willingness to
share or was perceived to determine o<strong>the</strong>rs willingness to share related to a process <strong>of</strong> engaging in a
cognitive cost-benefit analysis on sharing knowledge. A large number <strong>of</strong> respondents pr<strong>of</strong>essed that
sharing benefitted everyone more than not sharing. Specifics reasons for this included that sharing
knowledge between engineers was better than “being stuck with a problem you could not fix”
(Engineer).
“from my point <strong>of</strong> view if you want to get ahead ... if a guy is protective <strong>of</strong> his knowledge it
is himself he is hurting and that is <strong>the</strong> way I look at it” (Engineer 2)
“<strong>the</strong> goal is to get <strong>the</strong> machine going and up and running, o<strong>the</strong>r than that, that is what
your job is and however you can do that” (Engineer)
However, contrary to <strong>the</strong>se findings, respondents perceived that o<strong>the</strong>rs viewed <strong>the</strong>ir personal
knowledge as personal power. Specific examples <strong>of</strong> incidences <strong>of</strong> this included retaining knowledge
about fixing certain machines so that <strong>the</strong>y would only be asked by management to fix that machine
and not be asked to work on o<strong>the</strong>rs for which <strong>the</strong>y had lesser knowledge, retaining knowledge on how
to fix a machine so that <strong>the</strong>y would continually be needed by <strong>the</strong> organisation as a contractor to fix
that machine, a fear that to share knowledge would result in loss <strong>of</strong> <strong>the</strong>ir differentiating factor in terms
<strong>of</strong> skills and keeping back knowledge so that <strong>the</strong>ir work ‘appears’ complex to o<strong>the</strong>rs and takes more
time and results in greater recognition when success is obtained. The most frequently cited examples
were engineers wanting to be perceived as “<strong>the</strong>” expert <strong>of</strong> a machine and wanting to keep <strong>the</strong>
‘brownie points’, from management for successfully fixing machines or being quicker at fixing
machines, to <strong>the</strong>mselves by retaining knowledge on how <strong>the</strong>y worked.
“I feel that if somebody wants to hang onto it and won’t tell anybody <strong>the</strong>y can fix <strong>the</strong>
machine <strong>the</strong>n <strong>the</strong>ir looking for…I feel brownie points…” (Engineer 4)
“If you had a problem and you couldn’t fix it and someone else kind <strong>of</strong> went down to it, if
you were away and <strong>the</strong>y got it fixed straight away and you asked <strong>the</strong>m could you show
me please how you done that..’…that will be handy to know’ and <strong>the</strong>y would show you
but <strong>the</strong>y wouldn’t show you exactly what <strong>the</strong>y done…That to me is a kind <strong>of</strong> brownie
points to your boss is <strong>the</strong> way I see it….its like…<strong>the</strong> quicker you get a machine up and
running <strong>the</strong> more efficient you are and <strong>the</strong> more good you are at your job. So if it takes
me 10 hours and one guy 10 minutes it looks like he’s better than me but if he showed
me <strong>the</strong>n in <strong>the</strong>ory I’d be <strong>the</strong> same as him. Some guys like to be kind <strong>of</strong> know more”
(Engineer 3)
This knowledge as power perception was also felt by <strong>the</strong> engineer’s immediate team leader who
noted:
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“my thing with [engineers] is <strong>the</strong>y keep things close to <strong>the</strong>ir hearts, like ‘this is my thing
and I’m not going to show anyone how to fix this because I won’t have a job tomorrow if I
show someone else how to fix this’.. I always get that impression from <strong>the</strong>m ‘I won’t tell
you what I’m doing because <strong>the</strong>n you’ll be able to do it tomorrow’. They’ll give <strong>the</strong>
information to each o<strong>the</strong>r but when I’m doing that overview meeting <strong>the</strong>y keep
<strong>the</strong>mselves to <strong>the</strong>mselves. I get that impression that <strong>the</strong>y don’t want to pass on that
knowledge. I think it’s because <strong>the</strong>y think <strong>the</strong>y might not have a job tomorrow if <strong>the</strong>y
hand it over. I get that impression, although it’s not that anybody has ever said it.”
(Engineer Team Leader 1)
Source Attributes: Potential explanations for <strong>the</strong> expressed perceptions that knowledge is retained for
benefit suggest that in some cases it may be more to do with individual attributes than hoarding for
personal power benefits. These attributes include <strong>the</strong> individual’s beliefs about <strong>the</strong> learning process
and willingness to engage with those outside <strong>of</strong> personal friendship networks. Some respondents are
<strong>of</strong> <strong>the</strong> opinion that <strong>the</strong> best way to learn is by figuring it out yourself ra<strong>the</strong>r than being told. O<strong>the</strong>r
respondents believe that some are <strong>of</strong> <strong>the</strong> opinion that as <strong>the</strong>y had to learn <strong>the</strong> hard way <strong>the</strong>n so
should all o<strong>the</strong>rs.
“I find myself that if you learn <strong>the</strong> hard route, <strong>the</strong> more pain you go through in learning
something, you’re never going to forget it…..Its a lot better to learn it yourself by doing it
yourself in a way, its more real. It’s like a person can talk to you forever but you won’t
take it in until you experience it. …. <strong>the</strong> fatal mistake is to tell him everything. He must
work it out himself and must learn it himself, no point in just telling a fella. That’s <strong>the</strong>
problem here, is that it’s a lot better to teach a person how to fish than to give <strong>the</strong>m a
fish” (Engineer 5)
The second explanation related to personal friendship networks and perceptions that knowledge is
shared only between those whom were/are already friends.
“I think you have shift favourites in that you have some guy that would have worked with
some o<strong>the</strong>r guy on a particular shift so <strong>the</strong> next time he comes in on shift he will work
with him again. (Engineer 5)
The fifth key influencer is also a potential explanation for that on knowledge as personal power,
namely recognition. This is driven by individual’s motivations as well as by <strong>the</strong> organisations reward
mechanism, <strong>the</strong> latter <strong>of</strong> which is outside <strong>the</strong> scope <strong>of</strong> this paper. Engineers seek recognition or
‘brownie points’ from management for being <strong>the</strong> ‘expert’, for applying <strong>the</strong>ir knowledge to a machine
breakdown in an efficient manner or in <strong>the</strong>ir ability to deal with complex problems. In some cases
<strong>the</strong>se experts are also asked to design and/or deliver training programmes for o<strong>the</strong>rs which adds a
new dimension to <strong>the</strong>ir role, potentially allows for overtime and again is a form <strong>of</strong> recognition for <strong>the</strong>ir
knowledge.
4. Conclusion
Carbera et al., (2006) found that KM system level variables such as perceived availability <strong>of</strong> <strong>the</strong>
system and perceived quality were <strong>the</strong> least predictive variables <strong>of</strong> knowledge sharing (lesser than
individual and organisational level variables). Knowledge sharing requires dissemination mediums
however this is not <strong>the</strong> only requirement; psychological and organisational factors need to be
appropriately managed.
In summary, a number <strong>of</strong> <strong>the</strong> findings outlined are worthy <strong>of</strong> discussion. The attributes <strong>of</strong> <strong>the</strong> seeker
and <strong>the</strong> source and specifically his/her personal, interpersonal and meta-competencies that facilitate
<strong>the</strong> process <strong>of</strong> knowledge seeking such as personal drive and confidence in his/her social skills,
knowledge <strong>of</strong> task and in how o<strong>the</strong>rs perceive his/her knowledge are found to be important. Individual
level factors <strong>of</strong> this nature are important for both <strong>the</strong> knowledge seeker and <strong>the</strong> knowledge source
during <strong>the</strong> knowledge capture process. Endres et al.,(2007) discuss <strong>the</strong> role <strong>of</strong> self-efficacy in tacit
knowledge sharing which is a concept which represents <strong>the</strong> results <strong>of</strong> this study well. Endres et al.,
(2007) suggest that individuals require self-efficacy or a personal belief in <strong>the</strong>ir ability to perform a
task in order to be motivated to do it. Self-efficacy is argued to increase under four conditions;
enactive mastery, vicarious experience, verbal persuasion and physiological arousal (Gist & Mitchell,
1992). In <strong>the</strong> case <strong>of</strong> tacit knowledge Endres et al., (2007) propose that <strong>the</strong>se conditions should be
enabled respectively through; individuals having <strong>the</strong> opportunity to successful engage in tacit
knowledge seeking and capture, viewing/modelling o<strong>the</strong>rs engage successfully in seeking and
capture and receiving praise/encouragement from o<strong>the</strong>rs in efforts to do this task. Higher self-efficacy
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Claire Gubbins et al
in one’s ability to engage in tacit knowledge management processes may <strong>the</strong>n result in an individual
challenging his/her personal goals such as effort and persistence (Bandura, 1997), or in <strong>the</strong> case <strong>of</strong>
this study personal drive.
The self-efficacy model also discusses how an individual positively or negatively considers his/her
capability based on <strong>the</strong> environment and task (Gist & Mitchell, 1992). The results <strong>of</strong> this study
highlight how a knowledge source is more willing to share his/her knowledge with those whom s/he
has a stronger relationship. This finding is consistent with social network <strong>the</strong>ory and related empirical
research (Granovetter, 1973; Wasko & Faraj, 2005); individuals view this environment as supportive
and receive encouragement from it which <strong>the</strong>refore enhances <strong>the</strong> likelihood <strong>of</strong> knowledge sharing.
While strong social networks facilitate willingness to share, extrinsic rewards are found to negatively
impact willingness to share knowledge (Bock et al., 2005). The results <strong>of</strong> this case study discuss how
<strong>the</strong> knowledge source engages in a decision making process regarding sharing his/her knowledge,
which is perceived by respondents to be caused by perceptions that knowledge is power in this
organisation. This perception is reinforced by an organisational environment which both praises those
whom are more knowledgable and also rewards <strong>the</strong>m with additional assignments or overtime
opportunities and payments. Therefore, at one level while praise develops self-efficacy it would
appear in this case that it also dis-encourages some individuals from sharing too much for fear <strong>of</strong>
losing <strong>the</strong>ir power and <strong>the</strong> associated praise. Secondly, <strong>the</strong> implications <strong>of</strong> <strong>the</strong> extrinsic reward
system, reflects <strong>the</strong> propositions <strong>of</strong> social exchange <strong>the</strong>ory whereby individuals, in this case, are
evaluating <strong>the</strong> perceived ratio <strong>of</strong> benefits to costs and basing <strong>the</strong>ir decisions on whe<strong>the</strong>r to share
knowledge or not on <strong>the</strong> expectation that it will deliver benefits such as respect, enhanced reputation
or tangible incentives (Blau, 1964; Emerson, 1981).
Ano<strong>the</strong>r set <strong>of</strong> individual factors influencing <strong>the</strong> knowledge seeking process and <strong>the</strong> sources
willingness to share knowledge pertain to <strong>the</strong>ir learning or performance orientation, that is whe<strong>the</strong>r
s/he is driven to seek knowledge by a desire to learn or driven not to seek knowledge by a desire to
be seen to be performing his/her role without assistance from o<strong>the</strong>rs. Matzler & Mueller (2010)
conducted a study <strong>of</strong> engineers and reported a direct positive influence <strong>of</strong> learning orientation on
knowledge sharing and a negative impact <strong>of</strong> performance orientation on knowledge sharing. The
results <strong>of</strong> this case study illuminate what may be a conflict between an individual’s learning orientation
and <strong>the</strong> performance orientation commonly emphasised in large manufacturing MNC’s, where
production and output is key. The results highlight cases <strong>of</strong> engineers seeking knowledge due to a
desire to learn but being hesitant to do so due to a fear <strong>of</strong> being perceived as not able to do <strong>the</strong>ir jobs.
Concomitantly, <strong>the</strong> case company praises those with most knowledge (practice which may serve to
encourage a performance orientation), encourages those with less knowledge to gain it (which may
serve to encourage a learning orientation) but managers can sometimes get frustrated in observing
one engineer needing ano<strong>the</strong>r engineer to do a task (which may serve to push those with a learning
orientation to hide <strong>the</strong>ir learning efforts).
This study presents a tentative model <strong>of</strong> <strong>the</strong> influence <strong>of</strong> individual level factors on what emerged as
two key steps <strong>of</strong> <strong>the</strong> tacit knowledge management process, namely seeking and capture. It illustrates
how <strong>the</strong>se steps are key pre and post-cursor steps to any actual knowledge sharing effort but are
under-researched and <strong>the</strong>refore less understood. Also emerging from <strong>the</strong> results are <strong>the</strong> individual
level factors which are key to <strong>the</strong> success <strong>of</strong> <strong>the</strong> seeking and capturing processes and also to
determining an individual’s willingness to share.
Acknowledgements
This research is part <strong>of</strong> a wider project conducted on behalf <strong>of</strong> <strong>the</strong> Irish Centre for Manufacturing
Research and funded by Enterprise Ireland and <strong>the</strong> IDA.
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380

Knowledge Identification, Capture and Dissemination:
ECOPETROLS.A. Real Example <strong>of</strong> Implementation
Oscar Guerra and Janeth Rojas
ECOPETROL S.A., Colombia
oscar.guerra@ecopetrol.com.co
Janeth.Rojas@ecopetrol.com.co
Abstract: ECOPETROL S.A. is <strong>the</strong> National Oil Company (NOC) from Colombia, currently is located at number
13 in <strong>the</strong> Annual Ranking <strong>of</strong> <strong>the</strong> World’s Largest Listed Energy Firms, according to PFC Energy 50 - January
2011-. In July 2010, approximately <strong>of</strong> 30% <strong>of</strong> direct employees from ECOPETROL S.A. could qualify for <strong>the</strong>
retirement benefit according to <strong>the</strong> Colombian law. Given that <strong>the</strong> percentage <strong>of</strong> people who could leave <strong>the</strong>
company is a representative <strong>of</strong> <strong>the</strong> total number <strong>of</strong> employees and that this population is <strong>the</strong> greatest experience
has within <strong>the</strong> company, between 17 and 25 years <strong>of</strong> expertise, <strong>the</strong> company started from 2005 an initiative
aimed at mitigating <strong>the</strong> impact <strong>of</strong> loss <strong>of</strong> knowledge due to decoupling <strong>of</strong> staff, this initiative is created,
consolidated and routed within <strong>the</strong> Knowledge Management Corporate Program <strong>of</strong> ECOPETROL and is
implemented in all departments <strong>of</strong> <strong>the</strong> company. The initiative in question has several stages from <strong>the</strong> selection
<strong>of</strong> personnel who can retire and has key knowledge for <strong>the</strong> operation <strong>of</strong> <strong>the</strong> Company, <strong>the</strong> identification <strong>of</strong> key
knowledge <strong>of</strong> this persons, <strong>the</strong> training <strong>of</strong> those who undertake <strong>the</strong> transfer <strong>of</strong> knowledge as trainers or coaches,
building plans or transfer programs, monitoring <strong>the</strong> implementation <strong>of</strong> <strong>the</strong>se plans and <strong>the</strong> consolidation and
evaluation <strong>of</strong> <strong>the</strong> results <strong>of</strong> <strong>the</strong> transfer made, all framed within <strong>of</strong> <strong>the</strong> Deming cycle: Plan, Do, Check and act. In
<strong>the</strong> same way this process is part <strong>of</strong> a job <strong>of</strong> sensitization <strong>of</strong> <strong>the</strong> people selected as tutors and apprentices who
would be supported by <strong>the</strong> top management <strong>of</strong> Company. The work done by ECOPETROL allowed <strong>the</strong> tacit
knowledge identification, capture and dissemination within <strong>the</strong> company through a structured method, with
specific deliverables and results that have allowed <strong>the</strong> continued performance and safe in <strong>the</strong> operation, even
though <strong>the</strong> retirement <strong>of</strong> many <strong>of</strong> <strong>the</strong> people involved in this process it is a fact. This paper will be <strong>of</strong> interest to
<strong>the</strong> participants because it shows a real and practical case in <strong>the</strong> identification, capture and dissemination <strong>of</strong> tacit
knowledge within a company that is exposed to <strong>the</strong> imminent departure <strong>of</strong> a large number <strong>of</strong> employees.
Keywords: Knowledge management, tacit knowledge capture and dissemination, transfer knowledge strategy
1. Introduction
Since 2003, when ECOPETROL was restructured as a company integrated to <strong>the</strong> chain <strong>of</strong> petroleum
value, <strong>the</strong> knowledge management (KM) began to consolidate as an important element to achieve <strong>the</strong>
objectives and goals <strong>of</strong> <strong>the</strong> company. Since <strong>the</strong>n, <strong>the</strong> responsibility to lead <strong>the</strong> knowledge
management strategy for <strong>the</strong> whole organization was designated to its research center, Instituto
Colombiano de Petróleo (ICP).
One <strong>of</strong> <strong>the</strong> great challenges <strong>of</strong> ECOPETROL was imminent departure <strong>of</strong> nearly 30% <strong>of</strong> direct
employees in 2010, because this population may leave retirement. Since 2005 ECOPETROL begins
an initiative within <strong>the</strong> Knowledge Management Corporate Program aimed at mitigating <strong>the</strong> impact <strong>of</strong>
loss <strong>of</strong> knowledge due to decoupling <strong>of</strong> staff established across all ECOPETROL.
The above initiative is a process which identifies people at high risk <strong>of</strong> leaving <strong>the</strong> company,
identification <strong>of</strong> key knowledge <strong>of</strong> each person and <strong>the</strong>ir main functions and processes in charge,
assess <strong>the</strong> criticality <strong>of</strong> this knowledge, awareness <strong>of</strong> <strong>the</strong>se people, training people as a trainer <strong>of</strong>
trainers, design and construction <strong>of</strong> transfer plans, monitoring <strong>the</strong> implementation <strong>of</strong> <strong>the</strong>se plans and
<strong>the</strong> evaluation <strong>of</strong> <strong>the</strong> results <strong>of</strong> <strong>the</strong> transfer.
This article describes how ECOPETROL implemented this process for <strong>the</strong> development <strong>of</strong> <strong>the</strong>
initiative which has been discussed, some lessons learned in <strong>the</strong> process and main findings.
2. Theoretical framework
The knowledge management (KM) strategy in <strong>the</strong> companies has had many kinds <strong>of</strong> approaches, all
<strong>of</strong> <strong>the</strong>m valid assuming it contributes to improvement and competitiveness <strong>of</strong> an organization.
Adapting <strong>the</strong> approach <strong>of</strong> Krogh et al, we see many organizational processes can be impacted by KM
strategy. Hiring, training, new technology acquisition, research and development, learned lessons
cycles and technology marketing are examples <strong>of</strong> <strong>the</strong>m.
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Oscar Guerra and Janeth Rojas
According to Sonia Castro y Oscar Guerra´s in 2010, when ECOPETROL was restructured and
converted in an integrated company in <strong>the</strong> petroleum value chain, adopted <strong>the</strong> APQC (American
Productivity and Quality Center) model to implement its new KM strategy, which was designed as a
mix <strong>of</strong> <strong>the</strong> best practices selected among approaches <strong>of</strong> its different former business units. In that
way, it was established at first time <strong>the</strong> “General Policy for Knowledge and Technology Management
at ECOPETROL” as a unique vision this vision contain: <strong>the</strong> statement, a basic principles, roles and
responsibilities <strong>of</strong> key figures and means <strong>of</strong> communication and coordination.
After that, a corporative program was structured to implement that unique vision, including initiatives
for:
Converting principles to operating elements.
Institutionalize roles and responsibilities.
Install and operate <strong>the</strong> means <strong>of</strong> communication and coordination for <strong>the</strong> purpose to facilitate <strong>the</strong>
implementation and operation <strong>of</strong> KM Strategy.
Tacit knowledge identification, capture and dissemination within <strong>the</strong> company through a
structured method, from people who could leave <strong>the</strong> company for <strong>the</strong> retirement benefit according
to <strong>the</strong> Colombian law.
Develop KM pilots on critical knowledge areas (key practices) defined by <strong>the</strong> top and medium
managers, with a methodological measurement model as a guide.
Install complementary strategies in human resources, information technology aspects and KM
methods and tools, in order to leverage <strong>the</strong> cultural change, <strong>the</strong> development <strong>of</strong> KM pilots and <strong>the</strong>
institutionalization <strong>of</strong> <strong>the</strong> model.
3. Used process description
This article describes <strong>the</strong> six steps used in ECOPETROL, and helped implement <strong>the</strong> actions to
mitigate <strong>the</strong> risk <strong>of</strong> loss <strong>of</strong> consciousness in most <strong>of</strong> <strong>the</strong> selected cases.
Identify <strong>the</strong> population with high risk <strong>of</strong> decoupling
Identify <strong>the</strong> key knowledge <strong>of</strong> each person and <strong>the</strong>ir main functions and processes
Assess <strong>the</strong> criticality <strong>of</strong> knowledge
Awareness <strong>of</strong> <strong>the</strong> people involved
Trainer <strong>of</strong> Trainers Workshop
Design Knowledge Transfer Plan
3.1 Identification <strong>of</strong> population with risk <strong>of</strong> decoupling
Although <strong>the</strong> loss mitigation knowledge initiative comes at ECOPETROL about <strong>the</strong> high number <strong>of</strong>
people who can retire, this article suggests including people with <strong>the</strong> following characteristics:
People with retirement expectations in a time less than or equal to three years
People with temporary contracts
People with permanent contracts with less than five years old
Persons applying for succession planning or area change request
People with key knowledge for development and sustainability <strong>of</strong> <strong>the</strong> processes
3.2 Identification <strong>of</strong> key knowledge
To identify <strong>the</strong> key knowledge <strong>of</strong> <strong>the</strong> people selected in <strong>the</strong> previous item, its main activities, functions
and processes in charge, we suggests a survey <strong>of</strong> 360 ° to be evaluated by <strong>the</strong> immediate supervisor,
an employee and a customer, people selected, and likewise <strong>the</strong> survey must be evaluated by <strong>the</strong>
people selected. In this way it has to have at least three different views about <strong>the</strong> key knowledge <strong>of</strong>
<strong>the</strong> people selected.
Figures No. 4 and No. 5 show <strong>the</strong> documents suggested to conduct <strong>the</strong> survey <strong>of</strong> 360 ° as an
evaluator and evaluated version, respectively.
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3.3 Assess <strong>the</strong> criticality <strong>of</strong> knowledge
Oscar Guerra and Janeth Rojas
With <strong>the</strong> results <strong>of</strong> <strong>the</strong> survey <strong>of</strong> 360 °, we proceed to evaluate <strong>the</strong> criticality <strong>of</strong> knowledge and <strong>the</strong>
relevance <strong>of</strong> <strong>the</strong> main activities or functions <strong>of</strong> each worker identified, taking into account <strong>the</strong> strategic
implications <strong>of</strong> <strong>the</strong> loss <strong>of</strong> knowledge in reliability operation, <strong>the</strong> integrity <strong>of</strong> persons and facilities.
ECOPETROL used <strong>the</strong> following information used to assess <strong>the</strong> criticality <strong>of</strong> knowledge
Figure 1: ECOPETROL used <strong>the</strong> following information used to assess <strong>the</strong> criticality <strong>of</strong> knowledge
It should assess <strong>the</strong> degree <strong>of</strong> difficulty <strong>of</strong> replacement, considering <strong>the</strong> existence <strong>of</strong> o<strong>the</strong>r employees
with an equal or similar responsiveness, existing burdens <strong>of</strong> work in <strong>the</strong>se staff (availability), labor
market conditions to acquire <strong>the</strong> personal skills required, and <strong>the</strong> time necessary to fulfill <strong>the</strong> "learning
curve" required to perform key activities.
Must be assigned a rating <strong>of</strong> "high or low" to each employee, according to <strong>the</strong> criteria <strong>of</strong> relevance
and difficulty <strong>of</strong> replacement mentioned above and define <strong>the</strong> target population on which to implement
mitigation actions as a group <strong>of</strong> people whose activities or functions are considered high importance
and high difficulty <strong>of</strong> replacement.
You can use <strong>the</strong> following information to identify <strong>the</strong> difficulty <strong>of</strong> replacing
Figure 2: information to identify <strong>the</strong> difficulty <strong>of</strong> replacing
For staff located within <strong>the</strong> squares <strong>of</strong> high or low difficulty <strong>of</strong> replacement and low relevance <strong>of</strong> <strong>the</strong>ir
activity, <strong>the</strong>se activities could be considered as an object <strong>of</strong> study for <strong>the</strong> development <strong>of</strong> strategies
for outsourcing, process optimization, or <strong>the</strong> relocation <strong>of</strong> staff runs.
Staff located in <strong>the</strong> lower quadrant replacement difficulty and high importance <strong>of</strong> <strong>the</strong>ir activity, is not
more demanding in terms <strong>of</strong> mitigation actions are concerned.
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Figure No. 3 is a summary <strong>of</strong> recommended strategies as a result <strong>of</strong> <strong>the</strong> evaluation purpose <strong>of</strong> this
step:
Figure 3: Transfer Strategies
Finally, it should make a comparison <strong>of</strong> <strong>the</strong> topics identified as high importance and high difficulty <strong>of</strong>
replacement and training programs <strong>of</strong> each Unit, this helps streng<strong>the</strong>n and establish <strong>the</strong> key issues in
training.
Figure 4. EVALUATION OF 360 DEGREES - Version Evaluators.
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Oscar Guerra and Janeth Rojas
Figura 5. Evaluation <strong>of</strong> 360 degrees - Evaluated Version
3.4 Awareness <strong>of</strong> <strong>the</strong> people involved
It is important to carry out a process <strong>of</strong> sensitization <strong>of</strong> <strong>the</strong> people identified and selected in <strong>the</strong>
previous steps, one that emphasizes <strong>the</strong> importance <strong>of</strong> transferring knowledge as a way to find
meaning beyond within <strong>the</strong> organization, leaving a legacy in <strong>the</strong>ir peers and Business and help for
o<strong>the</strong>rs to do likewise.
These are some powerful questions that guide <strong>the</strong> process to develop awareness:
What is assurance <strong>of</strong> knowledge?
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Why does knowledge transfer process?
What is my responsibility in <strong>the</strong> matter?
What is leaved a personal touch?
Oscar Guerra and Janeth Rojas
Development <strong>of</strong> <strong>the</strong> awareness session should be <strong>the</strong> responsibility <strong>of</strong> a leader that <strong>the</strong> group <strong>of</strong>
people who will transfer <strong>the</strong> knowledge to recognize as such, ei<strong>the</strong>r Vice President, Manager or Chief.
3.5 Trainer <strong>of</strong> Trainers Workshop
This activity aims to workshop participants; in which people are identified for transfer perform <strong>the</strong>
following activities:
Apply basic, intermediate and specialized knowledge in adult education for <strong>the</strong> design assurance
processes and knowledge transfer.
Identify <strong>the</strong>ir role as trainers in <strong>the</strong> Organization given <strong>the</strong> knowledge and experience gained in
<strong>the</strong>ir working lives.
Recognize <strong>the</strong>ir potential and <strong>the</strong> mechanisms available to transfer key knowledge effectively
among <strong>the</strong>ir peers and colleagues.
The methodology should be based on "learning by doing” through dynamic, group activities,
workshops and practical exercises, we recommend you seek <strong>the</strong> support <strong>of</strong> a training center or
training for that role. In <strong>the</strong> case <strong>of</strong> ECOPETROL this activity was carried out under <strong>the</strong> support <strong>of</strong> <strong>the</strong>
training center <strong>of</strong> <strong>the</strong> company.
3.6 Design knowledge transfer plans
Identified <strong>the</strong> key knowledge <strong>of</strong> each person through <strong>the</strong> survey <strong>of</strong> 360 degrees are structured
through a workshop, plans <strong>of</strong> transferring knowledge <strong>of</strong> <strong>the</strong> people selected for this purpose it is
important to make a short leveling conceptual definitions <strong>of</strong> tacit knowledge explicit and implicit.
The work program shall include at least <strong>the</strong> following key points for each item:
Figure 6: Steps to structure knowledge transfer plan
Below is a brief explanation <strong>of</strong> <strong>the</strong> different phases associated with Figure No.6.
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Oscar Guerra and Janeth Rojas
Objective: What to obtain <strong>the</strong> final result <strong>of</strong> <strong>the</strong> transfer process described with what I transfer? (<strong>the</strong>
subject) and why I'm going to transfer? (purpose).
Scope: determines <strong>the</strong> depth reached by <strong>the</strong> transfer process, to identify you should locate <strong>the</strong> type
<strong>of</strong> knowledge or agreement subject to <strong>the</strong> following definitions:
Theoretical knowledge: generally classified as ei<strong>the</strong>r <strong>the</strong>oretical or conceptual minimum
knowledge needed to run a practice, this is easily transferred and can be encoded on a physical
medium such as a manual, a standard document or presentation, <strong>the</strong>refore classified as
knowledge explicit when it is moved can be reached at induction and training.
Ability: <strong>the</strong> ability to perform an activity or a practice run efficiently, it is largely tacit but a portion
<strong>of</strong> this is likely to be transferred through training, so it is classified as implicit knowledge, how to
develop it is through learning by doing and <strong>the</strong> level <strong>of</strong> coverage is training.
Experience: is <strong>the</strong> knowledge that has been developed by <strong>the</strong> implementation <strong>of</strong> a practice over
time, this knowledge is not easily transferred to what is classified as tacit knowledge and how to
develop it is through learning by doing and a partner process. To get to transfer an experience
may be required prior induction and training.
The Figure No.7 is an aid in identifying <strong>the</strong> scope given <strong>the</strong> type <strong>of</strong> knowledge transfer, place it in <strong>the</strong>
appropriate column and will determine <strong>the</strong> classification <strong>of</strong> knowledge and <strong>the</strong> minimum scope <strong>of</strong> <strong>the</strong>
work program. Note that <strong>the</strong> skill development implicates induction and training, and experience
development implicates induction, training and support.
The minimum scope involves <strong>the</strong> left before
Figure 7: Guidelines for developing a transfer plan for <strong>the</strong> type <strong>of</strong> knowledge
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3.7 Monitoring and control
Oscar Guerra and Janeth Rojas
Once established transfer schemes and scenarios is essential to establish monitoring mechanisms at
<strong>the</strong> strategic, tactical and operational levels to verify compliance with <strong>the</strong> plans. Similarly should be
clear to those involved in <strong>the</strong> process <strong>of</strong> transferring <strong>the</strong> way <strong>the</strong>y conduct <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong>
transfer process, <strong>the</strong> evaluation <strong>of</strong> <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> transfer is always performed on <strong>the</strong>
receiver and <strong>the</strong> best way is in <strong>the</strong> doing, observing and measuring <strong>the</strong> outcome <strong>of</strong> <strong>the</strong> work <strong>of</strong> <strong>the</strong>
receiver after fulfilling all <strong>the</strong> induction process, training and / or accompaniment. It is also
recommended to give feedback to <strong>the</strong> tutor to find opportunities for improvement during and after <strong>the</strong>
transfer process.
3.8 Barriers and results
To obtain <strong>the</strong> objectives in <strong>the</strong>se activities must overcome some obstacles that arise during <strong>the</strong>
development <strong>of</strong> this process, some <strong>of</strong> <strong>the</strong> most important barriers are:
• The top management time to participate in <strong>the</strong> workshops to structure <strong>the</strong> transfer plans.
• The assignment and respect for time to run <strong>the</strong> activities and plans for transfer <strong>of</strong> both people who
transfer as people who receive knowledge.
• The assignment <strong>of</strong> financial resources for implementation <strong>of</strong> activities that do not generate return
on investment in an explicit way.
• The generations <strong>of</strong> scenarios to follow up and control at <strong>the</strong> operational, tactical and strategic
levels within <strong>the</strong> organization.
• Create new paradigms to share and transfer knowledge and collaborative work are new ways <strong>of</strong>
working within <strong>the</strong> organization.
However, <strong>the</strong>se barriers were overcome and <strong>the</strong> activities in <strong>the</strong> transfer plans achieved <strong>the</strong>ir goals so
that <strong>the</strong> final <strong>of</strong> <strong>the</strong>se transfers’ plans have been obtained <strong>the</strong> following results:
Continuity in business processes without affecting <strong>the</strong> operating results for <strong>the</strong> retirement <strong>of</strong> <strong>the</strong>
people.
Continuity in customer satisfaction levels and users <strong>of</strong> products and services <strong>of</strong>fered even if <strong>the</strong>
individuals providing services are not <strong>the</strong> same
Documentation <strong>of</strong> processes and operational activities within <strong>the</strong> quality system <strong>of</strong> <strong>the</strong> Company.
Documentation in video <strong>of</strong> operational activities.
New programs and training methods.
Results above 80% in surveys <strong>of</strong> <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong> transfer made to <strong>the</strong> people who receive
<strong>the</strong> knowledge.
4. Conclusions
Although it represents a challenge is possible to do a processes <strong>of</strong> identification, collection and
distribution <strong>of</strong> tacit knowledge in organizations under a structured and framed within a cycle <strong>of</strong>
Deming.
The results to establish methods and mechanisms for capturing and dissemination <strong>of</strong> tacit
knowledge in an organization depends on <strong>the</strong> motivation, culture and sense <strong>of</strong> belonging for those
involved in this process because it's ultimately a human process performing a proper transfer <strong>of</strong>
something that is not palpable: knowledge.
It must secure <strong>the</strong> support <strong>of</strong> senior management and should be given <strong>the</strong> strategic importance to
<strong>the</strong> knowledge, especially to <strong>the</strong> tacit knowledge to ensure time and resources required for
implementation <strong>of</strong> transfer schemes established.
The transfer <strong>of</strong> knowledge should be a systematic and ongoing process to ensure that this
extends to individual organizational, must become an integral part <strong>of</strong> <strong>the</strong> role <strong>of</strong> all employees in
<strong>the</strong>ir day to day.
The quality and outcomes <strong>of</strong> knowledge transfer are proportional to <strong>the</strong> time and resources that
<strong>the</strong> organization has for <strong>the</strong> process, especially when it comes to training experience or expertise
in issues <strong>of</strong> great complexity, it is possible to make a successful transfer as a special initiative at
any given time, must be a process that is part and be accompanied by various elements <strong>of</strong>
organizational culture.
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Oscar Guerra and Janeth Rojas
The awareness and recognition (not money) are main pillars for staff motivation towards a
process <strong>of</strong> transferring knowledge, making sense <strong>of</strong> transcendence and inspiring legacy
construction workers to work with excellence in <strong>the</strong> education <strong>of</strong> o<strong>the</strong>rs and design appropriate
tools for training.
Acknowledgements
The authors acknowledgments to <strong>the</strong> Group <strong>of</strong> Technology and Knowledge Management <strong>of</strong> <strong>the</strong> Vice
President <strong>of</strong> Transportation in ECOPETROL S.A., for <strong>the</strong>ir contributions and support in <strong>the</strong>
development <strong>of</strong> <strong>the</strong> above initiative. Additionally recognize <strong>the</strong> special contribution <strong>of</strong> <strong>the</strong> following
persons in <strong>the</strong> implementation and positioning <strong>of</strong> this activity: Javier Gutierrez (current CEO) and
Alvaro Castañeda (VP <strong>of</strong> Transportation).
References
Castro, S., Serrano, M., Guerra, O. (2010) Technological Environment Forums: a Strategic Space <strong>of</strong> Dialogue in
ECOPETROL S.A. ECKM 2010.
Dixon, N. (2000) Common Knowledge: How Companies Thrive by Sharing What They Know. Harvard Bussines
School Press 17.
O´Dell, C. (2000) APQC´s Road Map Knowledge Management Results: Stages <strong>of</strong> Implementation. APQC´s
Knowledge Base.
Von Krogh, G., Nonaka I. and Aben, M. (2001) Making <strong>the</strong> Most <strong>of</strong> your Company´s Knowledge: A Strategic
Framework. Long Range Planning 34, 421-439.
Walton, Mary (1986). The Deming Management Method. Penguin Group. New York.
389

Understanding Personal Knowledge Development in
Online Learning Environments: An Instrument for
Measuring Externalisation, Combination and
Internalisation
Markus Haag and Yanqing Duan
University <strong>of</strong> Bedfordshire, Luton, UK
markus.haag@web.de
yanqing.duan@beds.ac.uk
Abstract: This paper investigates personal knowledge development in online learning environments using <strong>the</strong>
perspective <strong>of</strong> a model adapted from Nonaka and colleagues’ SECI model. To this end, <strong>the</strong> SECI model, which
was originally designed to describe organisational knowledge creation and conversion, was adapted to
conceptualise personal knowledge development in online learning at <strong>the</strong> individual level. As <strong>the</strong> SECI model was
originally conceived at <strong>the</strong> organisational level, in order to measure personal knowledge development at <strong>the</strong>
individual level in <strong>the</strong> context <strong>of</strong> online learning, a measurement instrument was created in order to measure <strong>the</strong>
scores <strong>of</strong> individual online learners on Externalisation, Combination and Internalisation. It is argued that
Socialisation is not a relevant mode in <strong>the</strong> context <strong>of</strong> online learning and is <strong>the</strong>refore not covered in <strong>the</strong>
measurement instrument; this is explained fur<strong>the</strong>r in <strong>the</strong> paper. This measurement instrument also examines <strong>the</strong>
interrelationships between <strong>the</strong> three modes and a new model – <strong>the</strong> so-called EC-I model – is proposed to depict
<strong>the</strong>se interrelationships. The measurement instrument is based on data collected through an online survey, in
which online learners report on <strong>the</strong>ir experiences <strong>of</strong> personal knowledge development in online learning
environments. In o<strong>the</strong>r words, <strong>the</strong> instrument measures <strong>the</strong> magnitude <strong>of</strong> online learners’ Externalisation and
Combination activities as well as <strong>the</strong>ir level <strong>of</strong> Internalisation, i.e. <strong>the</strong> outcomes <strong>of</strong> <strong>the</strong>ir personal knowledge
development in online learning. For Externalisation and Combination, formative indicators were used, whereas
for Internalisation reflective indicators were used. The measurement instrument is one <strong>of</strong> <strong>the</strong> main foci <strong>of</strong> this
paper and is <strong>the</strong>refore discussed in-depth. In sum, <strong>the</strong> paper proposes a modified version <strong>of</strong> <strong>the</strong> SECI model,
extending <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> original SECI model from <strong>the</strong> organisational to <strong>the</strong> individual level. It outlines a
new measurement instrument which can be used to measure Externalisation and Combination, i.e. personal
knowledge development processes, and Internalisation, i.e. personal knowledge development outcomes.
Keywords: personal knowledge development, SECI model, EC-I model, measurement instrument, measurement
indicators, online learning
1. Introduction
The objective <strong>of</strong> this paper is to examine and understand personal knowledge development (PKD) in
<strong>the</strong> context <strong>of</strong> online learning environments (OLEs). It is suggested that one should distinguish
between PKD processes and PKD outcomes. For this context, no measurement instrument yet exists
that could measure both <strong>the</strong> processes and <strong>the</strong> outcomes – <strong>the</strong> research presented here proposes
such an instrument. The research also examines <strong>the</strong> relationships between PKD processes and PKD
outcomes by proposing a new model called EC-I.
Knowledge creation at <strong>the</strong> organisational level has been researched and described intensively by
using Nonaka and colleagues’ SECI model (e.g. Nonaka and Takeuchi 1995). Gourlay (2006) claims
that SECI has even achieved a paradigmatic status in <strong>the</strong> field <strong>of</strong> knowledge management. The model
was first proposed in <strong>the</strong> early 1990s (Nonaka 1991) and has since been modified and extended by,
for example, Nonaka (1994), Nonaka and Takeuchi (1995), Nonaka and Konno (1998), Nonaka,
Toyama and Konno (2000), Nonaka, Toyama and Byosière (2001), Nonaka and Toyama (2003),
Takeuchi and Nonaka (2004), Nonaka, von Krogh and Voelpel (2006), and Nonaka and von Krogh
(2009).
However, actual measurement instruments and measurement indicators <strong>of</strong> <strong>the</strong> SECI model and/or its
four modes are extremely rare. Therefore, this paper – which is based on a doctoral research project
described in detail in Haag (2010) and in publications related to <strong>the</strong> research (Haag, Duan and
Ma<strong>the</strong>ws 2007, 2008, 2009) – presents a measurement instrument for three <strong>of</strong> <strong>the</strong> four SECI modes,
namely Externalisation, Combination and Internalisation – in <strong>the</strong> context <strong>of</strong> online learning. That
means that <strong>the</strong> SECI model will be used as <strong>the</strong> basis for a new PKD model at <strong>the</strong> individual level <strong>of</strong>
ra<strong>the</strong>r than at <strong>the</strong> level <strong>of</strong> organisational knowledge creation for which it has originally been
conceived. This new model, called <strong>the</strong> EC-I model, describes <strong>the</strong> PKD <strong>of</strong> an individual learner in
390

Markus Haag and Yanqing Duan
OLEs and extends <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> original SECI model from a focus on <strong>the</strong> organisational level
to a focus on <strong>the</strong> individual level.
First, this paper gives a brief overview <strong>of</strong> <strong>the</strong> SECI model and its four modes. Then, <strong>the</strong> methodology
<strong>of</strong> designing and validating <strong>the</strong> measurement instrument will be presented. The paper <strong>the</strong>n outlines
newly proposed measurement indicators for three <strong>of</strong> <strong>the</strong> SECI modes, namely Externalisation,
Combination and Internalisation. Finally, <strong>the</strong> EC-I model, a model <strong>of</strong> PKD in OLEs, is introduced.
2. The SECI model and its modes
The SECI model describes four modes <strong>of</strong> knowledge creation through a continuous interaction
between explicit and tacit knowledge. The four modes are now explained one by one:
Socialisation is defined as <strong>the</strong> “process <strong>of</strong> sharing experiences and <strong>the</strong>reby creating tacit knowledge
such as shared mental models and technical skills” (Nonaka and Takeuchi 1995, p 62). In this mode,
knowledge is acquired mainly by observation, imitation and learning by doing, similar to an
apprenticeship (Nickols 2000). Here, tacit knowledge is converted into tacit knowledge.
Externalisation is “typically seen in <strong>the</strong> process <strong>of</strong> concept creation and is triggered by dialogue or
collective reflection” (Nonaka and Takeuchi 1995, p 64). Here, tacit knowledge is converted into
explicit knowledge.
Combination “involves combining different bodies <strong>of</strong> explicit knowledge” (Nonaka & Takeuchi 1995, p
67). This is done by individuals exchanging and combining this knowledge in <strong>the</strong> form <strong>of</strong> documents,
etc. Here, explicit knowledge is converted into explicit knowledge. This combining and processing <strong>of</strong>
explicit knowledge is likely to lead to more complex and systematic knowledge (Nonaka and Toyama
2003).
Finally, Internalisation is <strong>the</strong> process by which knowledge becomes valuable when it “[knowledge] is
internalized in individuals’ tacit knowledge bases through shared mental models or technical knowhow”
(Nonaka, Toyama and Byosière 2001, p 497), and it is closely related to learning by doing
(Nonaka and Takeuchi 1995). Here, explicit knowledge is converted into tacit knowledge. Figure 1
(based on Nonaka and Konno 1998, p 46) depicts <strong>the</strong> SECI model and its four modes.
Figure 1: The SECI model and its four modes (based on Nonaka and Konno 1998, p 46)
3. Methodology <strong>of</strong> designing and validating <strong>the</strong> measurement instrument
The aim <strong>of</strong> <strong>the</strong> research presented here was to design measurement indicators that are valid in <strong>the</strong>
context <strong>of</strong> high-level, more generic online learning. To this end, a maximally diverse and
heterogeneous sample must be recruited; this results in a broad geographical dispersion <strong>of</strong> <strong>the</strong>
sample.
First, <strong>the</strong> survey was piloted with students and academics at <strong>the</strong> University <strong>of</strong> Bedfordshire, UK. Both
<strong>the</strong> content and <strong>the</strong> wording <strong>of</strong> <strong>the</strong> questions were checked and <strong>the</strong> questionnaire was modified
accordingly. Thus, validity issues were addressed (Moser and Kalton 1971), and face validity could be
established.
391

Markus Haag and Yanqing Duan
In order to get a highly diverse sample, three different ways <strong>of</strong> accessing participants were followed.
This allows for triangulation <strong>of</strong> data (Denzin and Lincoln 2005) by different types <strong>of</strong> students (e.g.
undergraduates versus postgraduates) and by different modes <strong>of</strong> instruction (e.g. fully online versus
blended learning). It was decided to target a) <strong>the</strong> students <strong>of</strong> <strong>the</strong> eMBA course at <strong>the</strong> University <strong>of</strong>
Bedfordshire, b) <strong>the</strong> members <strong>of</strong> three different Yahoo! Groups, called com-prac, interculturalinsights,
and onlinefacilitation, respectively, and c) <strong>the</strong> members <strong>of</strong> dialogin The Delta Intercultural Academy, a
knowledge community on culture and communication in international business. SurveyMonkey
(www.surveymonkey.com) was used to host <strong>the</strong> survey. The data was <strong>the</strong>n exported into SPSS (Field
2009) and analysed using this statistical analysis s<strong>of</strong>tware. It is important to note that only some <strong>of</strong> <strong>the</strong>
members <strong>of</strong> <strong>the</strong> three data sources are actual online learners; this means that <strong>the</strong> response rate could
not be calculated. In total, 171 answers could be used in subsequent data analysis.
Table 1 shows <strong>the</strong> shorthand name <strong>of</strong> <strong>the</strong> measurement items, <strong>the</strong> SECI mode <strong>the</strong>y refer to, and <strong>the</strong>
respective questions asked in <strong>the</strong> survey. The two items for Internalisation shown in square brackets
were not used in <strong>the</strong> final measurement instrument. The reasons for this are discussed in <strong>the</strong> analysis
section below.
Table 1: Measurement items and respective SECI mode and question
Measurement items SECI mode Survey question
Discussion forums
How <strong>of</strong>ten do you post in discussion forums?
Blog How <strong>of</strong>ten do you contribute to a blog (e.g. adding,
Wiki
Externalisation
changing or deleting parts <strong>of</strong> it)?
How <strong>of</strong>ten do you contribute to a wiki (e.g. adding,
Instant Messaging (IM)
(PKD processes)
changing or deleting parts <strong>of</strong> it)?
How <strong>of</strong>ten do you take part in Instant Messaging
(IM) with o<strong>the</strong>r learners or tutors?
Online chats How <strong>of</strong>ten do you take part in online chats with o<strong>the</strong>r
learners or tutors?
Search engines
How <strong>of</strong>ten do you use search engines to find
materials in addition to those provided by <strong>the</strong> online
learning environment?
Different types <strong>of</strong> functions How many different types <strong>of</strong> functions do you
usually access when learning about one particular
topic? Examples <strong>of</strong> <strong>the</strong>se functions, among o<strong>the</strong>rs,
are: discussion forums, blogs, wikis, instant
messaging, chats, listening to audio files, watching
Combination video files, self-assessment quizzes, downloading
course documents, etc.
Getting to know o<strong>the</strong>r (PKD processes) How interested are you in getting to know o<strong>the</strong>r
learners' opinions
learners' opinions through reading <strong>the</strong>ir postings in
discussion forums?
Sharing information How <strong>of</strong>ten do you share information with o<strong>the</strong>r
learners (e.g. posting links or o<strong>the</strong>r documents for
<strong>the</strong>m to read, using online communication tools to
let <strong>the</strong>m know about something, etc.)?
Working toge<strong>the</strong>r with
How <strong>of</strong>ten do you work toge<strong>the</strong>r with o<strong>the</strong>r learners
o<strong>the</strong>r learners
to create new materials (e.g. wikis, blogs, etc.)?
Measurement items SECI mode Survey question
[Application <strong>of</strong> knowledge]
How strongly do you agree or disagree with <strong>the</strong>
following statements? (same for all five
Internalisation items):
[Functions for self-
Internalisation
[I can apply <strong>the</strong> knowledge that I have acquired in
<strong>the</strong> online learning environment in o<strong>the</strong>r contexts.]
[The functions for self-assessment (e.g. quizzes,
assessment]
Acquiring new knowledge
(PKD outcomes)
tests, simulations) help me to learn.]
The functions <strong>of</strong> <strong>the</strong> online learning environment
contribute to me acquiring new knowledge.
Improving my skills The functions <strong>of</strong> <strong>the</strong> online learning environment
contribute to improving my skills.
I have learned a lot Overall, I have learned a lot through <strong>the</strong> online
learning environment.
392

Markus Haag and Yanqing Duan
In terms <strong>of</strong> <strong>the</strong> Socialisation mode, it is suggested here that <strong>the</strong> direct physical proximity necessary in
this mode is, by definition, not possible in an OLE. Nonaka and Toyama (2003) also stress that
successful Socialisation is fostered by ‘indwelling’ and ‘living in’ <strong>the</strong> world, which in turn suggests that
<strong>the</strong> context in which knowledge creation and PKD occurs has to be actively experienced and made
sense <strong>of</strong>. However, in <strong>the</strong> vast majority <strong>of</strong> today’s OLEs, particularly at <strong>the</strong> generic level <strong>of</strong> online
learning, this in-dwelling is normally not possible. Therefore, in <strong>the</strong> context <strong>of</strong> this study, Socialisation
was considered to be not relevant and was <strong>the</strong>refore not examined. Moreover, one could argue that
some elements <strong>of</strong> Socialisation are also covered by ei<strong>the</strong>r Externalisation and/or Combination. The
role <strong>of</strong> Socialisation in online learning requires fur<strong>the</strong>r clarification and fur<strong>the</strong>r research.
The answer options were identical for all five items representing Externalisation and were based on a
Likert-type ordinal scale: ‘Never’ was coded as 1, ‘once or twice a month’ as 2, ‘once or twice a week’
as 3, ‘3-5 times a week’ as 4, and ‘more than 5 times a week’ as 5. The cases that answered ‘Not
applicable’ for a particular item were not included in <strong>the</strong> calculations.
The coding for <strong>the</strong> five Combination items was similar to <strong>the</strong> Externalisation items, with a coding <strong>of</strong> 1
to 5 starting from <strong>the</strong> lowest intensity to <strong>the</strong> highest intensity. The wording <strong>of</strong> <strong>the</strong> five Combination
items for <strong>the</strong> codes <strong>of</strong> 1 to 5 differs; <strong>the</strong> respective wordings are:
‘Search engines’: never, rarely, sometimes, <strong>of</strong>ten, very <strong>of</strong>ten, not applicable
‘Different types <strong>of</strong> functions’: only one, two, three, four, five or more, not applicable
‘Getting to know o<strong>the</strong>r learners’ opinions’: very much interested, somewhat interested, nei<strong>the</strong>r
interested nor disinterested, somewhat disinterested, not interested at all, not applicable
‘Sharing information with o<strong>the</strong>r learners’ and ‘working toge<strong>the</strong>r with o<strong>the</strong>r learners’: never, once or
twice a month, once or twice a week, 3-5 times a week, more than 5 times a week, not applicable
The answer options for all five Internalisation items were identical, namely: strongly agree, agree,
nei<strong>the</strong>r agree nor disagree, disagree, strongly disagree, not applicable.
4. Analysis <strong>of</strong> measurement indicators for a model depicting personal
knowledge development in online learning environments
4.1 Scale development: Formative versus reflective indicators
The nature <strong>of</strong> <strong>the</strong> ECI modes in <strong>the</strong> context <strong>of</strong> online learning will now be discussed. It is suggested
here that constructs <strong>of</strong> Externalisation and Combination differ from <strong>the</strong> Internalisation construct in
terms <strong>of</strong> <strong>the</strong>ir characteristics <strong>of</strong> measurement and that one should distinguish between formative and
reflective indicators. This distinction will now be discussed.
The main approach to <strong>the</strong> development <strong>of</strong> measures centres on “scale development, whereby items
(i.e., observed variables) composing a scale are perceived as reflective (effect) indicators <strong>of</strong> an
underlying construct (i.e., latent variable)” (Diamantopoulos and Winklh<strong>of</strong>er 2001, p 269). An
alternative to scale development (Hinkin 1995) is <strong>the</strong> creation <strong>of</strong> formative or causal indicators and
requires <strong>the</strong> creation <strong>of</strong> an index ra<strong>the</strong>r than a scale (Bollen and Lennox 1991). Formative indicators
are observed variables, i.e. items that make up an index, and that cause a latent variable. Contrary to
that, reflective indicators, i.e. effect indicators, are observed variables or indicators that are caused by
a latent variable (Diamantopoulos and Winklh<strong>of</strong>er 2001).
It is argued here that Externalisation and Combination are latent variables that can be measured by
measurement items which are <strong>the</strong> cause <strong>of</strong> ei<strong>the</strong>r <strong>the</strong> Externalisation or Combination construct. One
can say, <strong>the</strong>refore, that Externalisation and Combination are <strong>the</strong> dependent variables that are
determined by a linear combination <strong>of</strong> measures <strong>of</strong> independent variables, namely <strong>the</strong>ir respective
formative indicators (Bagozzi 1994). In o<strong>the</strong>r words, it is not <strong>the</strong> objective <strong>of</strong> <strong>the</strong> items to represent <strong>the</strong>
same uni-dimensional construct but to give a relevant representation <strong>of</strong> a range <strong>of</strong> Externalisation
processes or Combination processes, respectively. Contrary to that, it is suggested here that
Internalisation should be measured by reflective indicators, because <strong>the</strong> scale for Internalisation
consists <strong>of</strong> reflective scale items. A more detailed discussion <strong>of</strong> formative and reflective indicators and
<strong>the</strong>ir role in this study can be found in Haag (2010).
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Markus Haag and Yanqing Duan
4.2 Intercorrelations between <strong>the</strong> measurement items
The intercorrelations between <strong>the</strong> measurement items for Externalisation, Combination, and
Internalisation (ECI items) and <strong>the</strong>ir respective aggregates will now be examined. Ra<strong>the</strong>r than using
<strong>the</strong> widespread Pearson’s correlation coefficient, Kendall’s tau (τ) was used instead. This is because
<strong>the</strong> distribution <strong>of</strong> <strong>the</strong> data in this study was considerably non-normal. In such a case, it is <strong>of</strong>ten
suggested that non-parametric statistics should be used as <strong>the</strong>y do not require normality (Field 2009).
In addition to that, it has been suggested that Kendall’s tau is a better estimate <strong>of</strong> <strong>the</strong> correlation in
<strong>the</strong> population (Howell 2009).
4.2.1 Intercorrelations between Externalisation items
Table 2 shows <strong>the</strong> interrelationships, i.e. <strong>the</strong> correlation coefficients, between <strong>the</strong> items for
Externalisation and <strong>the</strong> aggregate value for <strong>the</strong> Externalisation index. All correlations are positive and
significant at <strong>the</strong> p

Markus Haag and Yanqing Duan
Search Types <strong>of</strong>
engines functions
Interest in
o<strong>the</strong>r
learners'
opinions
Sharing
information
with o<strong>the</strong>r
learners
Working
toge<strong>the</strong>r with
o<strong>the</strong>r learners Combination
Search engines Corr.
Coeff.
– .168* -.019 .041 .057 .309**
Sig. . .011 .773 .537 .414 .000
Types <strong>of</strong> functions Corr.
Coeff.
.168* – .230** .321** .277** .624**
Interest in o<strong>the</strong>r
learners' opinions
Sharing information
with o<strong>the</strong>r learners
Working toge<strong>the</strong>r
with o<strong>the</strong>r learners
Sig. .011 . .000 .000 .000 .000
Corr.
Coeff.
-.019 .230** – .149* .215** .429**
Sig. .773 .000 . .022 .001 .000
Corr.
Coeff.
.041 .321** .149* – .414** .550**
Sig. .537 .000 .022 . .000 .000
Corr.
Coeff.
.057 .277** .215** .414** – .560**
Sig. .414 .000 .001 .000 . .000
Table 4: Interrelationships between Internalisation items and <strong>the</strong>ir aggregate
Applying
knowledge
Functions for selfassessment
Acquiring new
knowledge
Applying
knowledge
Functions for selfassessment
Acquiring new
knowledge
Improving
skills
Having
learned a
lot Internalisation
– .278** .425** .409** .469** .458**
.278** – .352** .304** .298** .329**
.425** .352** – .710** .599** .782**
Improving skills .409** .304** .710** – .662** .828**
Having learned a
lot
.469** .298** .599** .662** – .824**
For Internalisation, <strong>the</strong> inter-item correlations range from τ=.278 for ‘applying knowledge’ and
‘functions for self-assessment’ to τ=.710 for ‘improving skills’ and ‘acquiring new knowledge’. The
item-to-aggregate correlations were also high, ranging from τ=.329 to τ=.828. It has to be noted that
<strong>the</strong> aggregate for Internalisation is calculated on <strong>the</strong> basis <strong>of</strong> taking into account only <strong>the</strong> following
three items: ‘acquiring new knowledge’, ‘improving skills’, and ‘having learned a lot’. This is because
<strong>the</strong> Internalisation scale is regarded as <strong>the</strong> dependent variable <strong>of</strong> Externalisation and Combination
and a mean scale was used for Internalisation with <strong>the</strong> aim <strong>of</strong> improving Cronbach alpha, something
which was achieved by deleting two <strong>of</strong> <strong>the</strong> items, namely ‘applying knowledge’ and ‘functions for selfassessment’.
Thus, Cronbach alpha for Internalisation rose from .823 to .878. On <strong>the</strong> o<strong>the</strong>r hand, as
Externalisation and Combination are multidimensional constructs representing conceptually broad
definitions ra<strong>the</strong>r than overlapping constructs, Cronbach alpha is not a particularly relevant concept
(cf. Rojas-Méndez, Davies, Omer, Chetthamrongchai and Madran 2002).
5. The EC-I model: personal knowledge development in online learning
environments
It was argued before that Socialisation in its definition by Nonaka and colleagues is not relevant in <strong>the</strong>
context examined in this paper. The proposed model <strong>of</strong> PKD in online learning <strong>the</strong>refore includes only
three <strong>of</strong> <strong>the</strong> SECI modes: Externalisation/Combination – Internalisation (EC-I model). The structural
relationships <strong>of</strong> <strong>the</strong> model are based on <strong>the</strong> interrelationships <strong>of</strong> <strong>the</strong> ECI modes as shown in Table 5.
All correlations are highly significant at <strong>the</strong> p

Markus Haag and Yanqing Duan
have a stronger impact on Internalisation, i.e. PKD outcomes, than Externalisation processes have on
Internalisation. However, <strong>the</strong> difference in effect size is not substantial.
Table 5: Interrelationships <strong>of</strong> <strong>the</strong> ECI modes: Correlation coefficients
Externalisation Combination Internalisation
Externalisation – .533** .226**
Combination .533** – .309**
Internalisation .226** .309** –
Moreover, <strong>the</strong> strong correlation between Externalisation and Combination (τ=.533) suggests that
Externalisation and Combination could be interpreted as <strong>the</strong> two constituents <strong>of</strong> one latent factor that
shares some characteristics with both Externalisation and Combination. It is argued here that <strong>the</strong>
main shared characteristic is that both modes deal with ‘PKD processes’ as opposed to ‘PKD
outcomes’ which are represented by Internalisation. Figure 2 depicts <strong>the</strong> EC-I model. It has to be
pointed out that <strong>the</strong> EC-I model is only applicable in <strong>the</strong> context <strong>of</strong> PKD in online learning and not in
o<strong>the</strong>r contexts. The model contains <strong>the</strong> following two main elements: Externalisation and Combination
(i.e. PKD processes), and Internalisation (i.e. PKD outcomes). A more detailed discussion <strong>of</strong> EC-I can
be found in Haag (2010).
Figure 2: The EC-I model: A model <strong>of</strong> PKD in online learning
6. Conclusion
In this paper, a new measurement instrument was discussed which measures <strong>the</strong> scores <strong>of</strong> a learner
on Externalisation and Combination, representing PKD processes in OLEs, and on Internalisation,
representing PKD outcomes in OLEs. This instrument can only be applied in <strong>the</strong> context <strong>of</strong> online
learning and must be modified to make it suitable and relevant to a different context. Therefore, <strong>the</strong>
items dealing with Externalisation and Combination must be revised in such a way so that <strong>the</strong>y
adequately represent <strong>the</strong> PKD processes <strong>of</strong> <strong>the</strong> PKD context under investigation. The measurement
items for Internalisation do not necessarily need to be modified because <strong>the</strong>y measure PKD
outcomes, a concept that does not differ across PKD contexts.
It was also shown that <strong>the</strong> SECI model can act as a useful starting point to investigate PKD in online
learning. A new model, named <strong>the</strong> EC-I model, was presented in this paper. EC-I is based on <strong>the</strong>
original SECI model and modified in such a way so that it is relevant in <strong>the</strong> context <strong>of</strong> PKD in OLEs at
<strong>the</strong> individual level. In order to create fur<strong>the</strong>r models <strong>of</strong> PKD in contexts o<strong>the</strong>r than online learning,
more research is needed to address this shortage <strong>of</strong> empirical measurement instruments that can
measure <strong>the</strong> magnitude <strong>of</strong> Socialisation, Externalisation and Combination activities as well as <strong>the</strong>
level <strong>of</strong> Internalisation, i.e. <strong>the</strong> end-results <strong>of</strong> such activities. This will make <strong>the</strong> SECI model or models
based on SECI more useful for both researchers and practitioners in <strong>the</strong> field <strong>of</strong> knowledge
management.
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397

Chaordic Knowledge Management – Shifting Paradigms for
Corporate Knowledge Networks
Frank Habermann 1 , Jörg Fehlinger 2 and Karen Schmidt 2
1
Berlin School <strong>of</strong> Economics and Law, Berlin, Germany
2
Becota – The Berlin Talent & Consulting Association, Berlin, Germany
frank.habermann@fu-berlin.de
Fehlinger@becota.com
Schmidt@becota.com
Abstract: Saying that corporate knowledge networks are important is stating <strong>the</strong> obvious. In general, but <strong>the</strong>
most in service industries and “knowledge intensive firms” work has become a collaborative endeavor carried out
to a significant extent through informal networks <strong>of</strong> individual relationships. This article deals with <strong>the</strong> <strong>the</strong>sis that
understanding, accepting and actively handling informal (knowledge) networks can help managers to deploy <strong>the</strong>
real intellectual capital in <strong>the</strong>ir companies. Nowadays, in <strong>the</strong> age <strong>of</strong> Web 2.0, many companies put <strong>the</strong>ir hope on
social s<strong>of</strong>tware as an effective tool for corporate networking, discussion and knowledge sharing. However, after
<strong>the</strong> first wave <strong>of</strong> euphoria, enterprises faced serious problems <strong>of</strong> realization. The reason for this is that online
social networks are determined by <strong>the</strong> personal goals <strong>of</strong> <strong>the</strong>ir members, self-organized structures as well as
emergent processes and contents. From an enterprise perspective this means <strong>the</strong> rise <strong>of</strong> a non-transparent
construct which is nei<strong>the</strong>r controllable nor predictable. The key management task is to find appropriate
approaches which are not suppressing <strong>the</strong> dynamics <strong>of</strong> a social network but utilizing its value for corporate
purposes. Following an initial analysis <strong>of</strong> <strong>the</strong> challenges which result from <strong>the</strong> goal <strong>of</strong> implementing online
knowledge networks, <strong>the</strong> concept <strong>of</strong> “chaordic knowledge management” is introduced in <strong>the</strong> second part <strong>of</strong> <strong>the</strong>
article. This management approach is based on <strong>the</strong> assumption that a substantial part <strong>of</strong> knowledge sharing and
innovation happens at <strong>the</strong> crossroad between chaos and order. In o<strong>the</strong>r words, valuable knowledge solutions
<strong>of</strong>ten result from a dynamic, multi-dimensional process that can nei<strong>the</strong>r planned nor controlled step-by-step.
Instead, in order to cope with complex situations, enterprises need to prepare for exploration, openness and
observation.
Keywords: Knowledge Networks, Knowledge Sharing, Enterprise 2.0, Social Communities, Complex Adaptive
Systems, Organizational Behavior
1. Introduction
Today, in our highly globalized world, one can assume that most knowledge networks, also referred to
as “knowledge communities” or “communities <strong>of</strong> practice” (Beinhauer 2004), exist “in real life” as well
as “on <strong>the</strong> web”. Some network members might be colleagues, seeing each o<strong>the</strong>r regularly in <strong>the</strong>ir
<strong>of</strong>fice; o<strong>the</strong>rs might maintain a pure web relationship. Additionally one can presume that a corporate
social network displays both dimensions <strong>of</strong> an enterprise, <strong>the</strong> formal organization (as published by an
org chart) as well as <strong>the</strong> informal organization, <strong>the</strong> partly hidden and not <strong>of</strong>ficially managed
relationships that employees establish across functions and divisions (Krackhardt/Hanson 1993).
When this article refers to “knowledge networks” in <strong>the</strong> following, it refers to hybrid communities that
include all <strong>the</strong>se facets, face-to-face and computer-mediated communication as well as formal and
informal relationships.
This paper deals with <strong>the</strong> fundamental aspects <strong>of</strong> “knowledge sharing” (see. e.g. Boisots 2002), <strong>the</strong>
respective impacts on online networks as well as <strong>the</strong> management challenges which derive from <strong>the</strong>
intention <strong>of</strong> realizing such a system. In order to find satisfactory solutions, not only organizational,
managerial and technological aspects need to be considered, but also human interests and
motivation. Regarding <strong>the</strong> latter, a multi-dimensional system <strong>of</strong> partly conflicting roles and
perspectives needs to be taken into account.
For example, it is a well-known fact that knowledge owners are only partially willing and capable to
pass <strong>the</strong>ir intellectual capital (Nonaka 1991, Blackler 1995). This was a major issue in <strong>the</strong> early years
<strong>of</strong> knowledge management, when enterprises made enormous efforts to create content taxonomies,
tried to extract intelligence from artifacts and experts, produced yellow pages and o<strong>the</strong>r pieces <strong>of</strong>
information, and collated <strong>the</strong>se into central databases (see e.g. EFQM et al. 1997, Erickson/Kellogg
1999, O’Leary 1997). Technically speaking, at this stage <strong>of</strong> knowledge management, companies were
aiming at a common meta-model and unification.
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
Already ten years ago an analysis <strong>of</strong> corporate knowledge management projects has indicated severe
practical problems <strong>of</strong> implementing <strong>the</strong>se <strong>the</strong>oretical concepts (Lai/Chu 2000, Storey/Barnett 2000).
Today, most researchers as well as practitioners would agree that this highly centralized and mainly
technology-driven approach was almost doomed to fail (see e.g. Riege 2005, Nevo/Chan 2007, Chua
2007). But <strong>the</strong> issue is not a lack <strong>of</strong> appropriate technologies. Quite in contrast, numerous studies
have demonstrated that powerful s<strong>of</strong>tware for storing, searching and sense-making <strong>of</strong> knowledge
exist (see e.g. Jacobson/ Prusak 2006). Thus, <strong>the</strong> major cause for knowledge management failure is
not availability or applicability <strong>of</strong> technology, it ra<strong>the</strong>r is organizational misconception. The actual
reason is that each unification project runs against individual, organizational and cultural barriers and
thus takes an enormous effort to implement and leads to relatively inflexible architectures (Handy
1992).
Having learned this lesson, many people nowadays stress <strong>the</strong> advantages <strong>of</strong> social media and
“Enterprise 2.0” (McAfee 2009). And indeed, with its decentralized concept, user-generated content,
and emergent structures, it seems to provide <strong>the</strong> right instruments to address employees’ needs. But
does <strong>the</strong> new set <strong>of</strong> technologies automatically lead to better knowledge-sharing practice? Empirical
studies state that this has not yet been, for most companies, <strong>the</strong> case (see e.g. Osimo et al. 2010,
BITKOM 2008).
The entire discussion illustrates a major dilemma <strong>of</strong> knowledge management and organizational
change. It can be brought back to <strong>the</strong> question <strong>of</strong> “control” and <strong>the</strong> organizational role a person
possesses (Habermann 2011). If one asks anyone whe<strong>the</strong>r (s)he enjoys being controlled, <strong>the</strong> most
probable answer one will get is a clear “no!”. However, if <strong>the</strong> person were a manager, and one asked
whe<strong>the</strong>r (s)he wanted to abandon control, you might also receive a negative reply. Therefore, in <strong>the</strong>
first case, in “Knowledge Management 1.0”, knowledge sharing is prevented because <strong>the</strong> majority <strong>of</strong>
<strong>the</strong> knowledge owners, <strong>the</strong> employees within an organization, fear loss <strong>of</strong> power and being controlled.
In <strong>the</strong> second case, in “Enterprise 2.0”, <strong>the</strong> employees appreciate <strong>the</strong> underlying concept but in many
corporate cultures <strong>the</strong> authorities fear loss <strong>of</strong> transparency and managerial influence.
Consequently, this article concludes that corporate knowledge networks require a well-defined mixture
<strong>of</strong> structure and emergence as well as a clear balance <strong>of</strong> interests! In <strong>the</strong> following, <strong>the</strong> corresponding
challenges and management approaches are discussed.
2. The key challenges
In this chapter, we shall briefly analyze (a) complex living systems (organization view), (b) individual
learning relationships (people view), and (c) online social communities (technology view), as <strong>the</strong> three
systemic pillars <strong>of</strong> corporate knowledge networks (see <strong>the</strong> following figure). The main objective <strong>of</strong> this
approach is to clarify key assumptions, agree on core patterns and collect <strong>the</strong> resulting challenges
from each systemic dimension as an accepted set <strong>of</strong> managerial requirements.
Living
Systems
Online
Social Communities
Corporate
Knowledge
Networks
Figure 1: Systemic Pillars <strong>of</strong> Corporate Knowledge Networks
2.1 Complex living systems
Individual
Relationships
As online knowledge networks involve a technical factor as well as a people factor, we consider <strong>the</strong>m
as “socio-technical systems”. In academia, socio-technical systems design has gained a longstanding
reputation as an integrated approach to organizational development (Eijnatten 2001). In
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
corporate practice however, one will be surprised at <strong>the</strong> number <strong>of</strong> high-ranked managers who are
ei<strong>the</strong>r completely ignorant <strong>of</strong> <strong>the</strong> fact or at least underestimating its impacts (Habermann 2011).
The fact namely is <strong>the</strong> pervasiveness and power <strong>of</strong> informal relationships (Jahnke 2009), something
which makes an organization much more a “living system” (Miller 1978) ra<strong>the</strong>r than a technical
instrument or machine! This is a radical differentiation and <strong>the</strong> main differentiator is called
“complexity”.
Complexity must not be mistaken with “complicatedness”. A pure technical system, a machine, might
be complicated, but it never is complex. The distinction between “complex” and “complicated” lies in
<strong>the</strong> answer to <strong>the</strong> question as to whe<strong>the</strong>r we can decompose a system in its parts and entirely predict
its behavior, or not (Joslyn/Rocha 2000, Holland 2006).
For example, a Swiss watch is a complicated system, but not a complex one. We probably need <strong>the</strong>
highly specialized skills <strong>of</strong> a watchmaker and years <strong>of</strong> pr<strong>of</strong>essional experience in order to fully
understand such a technical masterpiece. But once we have achieved <strong>the</strong>se skills, we will be able to
disassemble <strong>the</strong> watch and recreate <strong>the</strong> system’s original behavior, as <strong>of</strong>ten as we like.
A complex system in contradistinction is “greater than <strong>the</strong> sum <strong>of</strong> its parts” (Miller/Miller 1992). It is
strongly influenced by time and space (dynamic complexity), as well as <strong>the</strong> actors who collectively
define <strong>the</strong> purpose <strong>of</strong> its existence (behavioral complexity) (Senge et al. 1999). Consequently, a
complex system cannot be decomposed and reconstructed without destroying connections between
<strong>the</strong> system’s elements, losing significant information, and thus irreversibly modifying <strong>the</strong> original
system behavior.
Market mechanisms are a good example for a complex system. If a company like Apple or BMW
releases a new product, a chain <strong>of</strong> cause and effect is initiated. This chain, its phases and all <strong>of</strong> its
cycles (time!) involve various perspectives <strong>of</strong> competitors, customers, suppliers, and o<strong>the</strong>r
stakeholders (people!) from various markets around <strong>the</strong> world (place!). Such a process typically
becomes far too dynamic and multi-dimensional to be handled step-by-step (see. e.g. Joslyn/Rocha
2000, Smith/Page 2007, Fioretti/Visser 2010). Instead, in order to cope with complex systems, we
need a holistic understanding <strong>of</strong> <strong>the</strong> system. The answer to <strong>the</strong> unpredictable momentum <strong>of</strong>
complexity is openness and sensing, ra<strong>the</strong>r than strict structuring and control (Scharmer 2009).
In sum, managing a living system is entirely different from operating a machine! Organizations are
complex living systems, and <strong>the</strong>re is no way <strong>of</strong> “reducing complexity”, nei<strong>the</strong>r methodically nor
technically. People who maintain <strong>the</strong> opposite typically mistake “complexity” with “complicatedness”
and follow <strong>the</strong> illusions <strong>of</strong> comprehensive control. This misconception will lead to mechanistic,
bureaucratic behavior and thus will be a waste <strong>of</strong> money and energy. Since we assume a corporate
knowledge network as part <strong>of</strong> one or multiple organizations and thus a complex sub-system, <strong>the</strong>
identical patterns apply. This leads to <strong>the</strong> first two major challenges for <strong>the</strong> development <strong>of</strong> effective
knowledge networks:
Abandon <strong>the</strong> myth <strong>of</strong> comprehensive control (in order to save time and money)
Enabling emerging processes (in order to utilize <strong>the</strong> positive power <strong>of</strong> complexity)
2.2 Individual learning relationships
If a public or private institution decides to invest money in implementing an online knowledge network,
it aims at improving “organizational learning” to achieve a return <strong>of</strong> this investment. In this article, we
shall stress <strong>the</strong>se organizational and economic aspects <strong>of</strong> “learning”. Since <strong>the</strong> value <strong>of</strong> corporate
knowledge sharing as well as <strong>the</strong> interdependencies between individual and organizational learning
have been intensively researched (see e.g. Argyris/Schön 1978, Wiig 1989, Senge 1990, Birkenkrahe
2002), we shall not go into <strong>the</strong>se details here. However, since a knowledge network is a system <strong>of</strong>
relationships between single entities, we focus on “learning” as a communication process between a
single (knowledge) sender and a single receiver. The communication can be verbal, non-verbal, and
visual as well as a combination <strong>of</strong> <strong>the</strong>se. The sender can be ei<strong>the</strong>r human or not. In <strong>the</strong> latter case,
we talk about mediated communication. However, even when using media like reading a book, a
Twitter post, a Blog article, or participating in a virtual classroom session, we can track <strong>the</strong> author as
<strong>the</strong> original source <strong>of</strong> knowledge. The following figure illustrates <strong>the</strong> described relationship.
400

sense-making
(context A)
activation
Frank Habermann, Jörg Fehlinger and Karen Schmidt
codification
KNOWLEDGE
(content)
media
interpretation
SENDER RECEIVER
CONNECTION
sense-making
(context B)
Figure 2: The Communication Process as a Learning Relationship (inspired by Cummings 2003)
The fundamental communication problems <strong>of</strong> knowledge sharing and learning are well known and
widely researched in literature and via case studies (see e.g. Blackler 1995). They can be
summarized in three groups:
Activation problem: deals with <strong>the</strong> fundamental difficulty <strong>of</strong> convincing a knowledge owner, an
expert, to share his/her intellectual capital. Since <strong>the</strong> idea <strong>of</strong> extracting knowledge from people
works against <strong>the</strong>ir natural interests (“knowledge is power”), companies mainly have two options.
The first is to change <strong>the</strong> organizational culture in order to create intrinsic motivation; <strong>the</strong> second
is to invest in financial compensation and incentives. While <strong>the</strong> first way is long-term, some
skeptics even say unrealistic, <strong>the</strong> second is expensive and has not many proved success stories,
too (Gurteen 2010).
Codification problem: addresses <strong>the</strong> idea <strong>of</strong> documenting knowledge and storing it in a retrievable
form. Even if people are fearless and motivated to pass on <strong>the</strong>ir knowledge, <strong>the</strong>y might not be
able to do so. This relates to <strong>the</strong> popular problem <strong>of</strong> embedded and tacit knowledge (Bohn 1994).
Not every piece <strong>of</strong> knowledge can be broken down into data and not each individual is able to
realize what (s)he knows and to express it in a replicable way. Thus, as an extreme, <strong>the</strong> most
valuable knowledge owner could be <strong>the</strong> least valuable knowledge sender (because (s)he is not
able to share anything at all).
Effectiveness problem: discusses <strong>the</strong> applicability and utilization <strong>of</strong> “received” knowledge within a
new domain or context. From Bloom’s taxonomy we know that gaining scenario-related
knowledge as well as self-reflective “know why” are <strong>the</strong> most valuable stages <strong>of</strong> learning (Bloom
1984). Unfortunately, <strong>the</strong>y are also <strong>the</strong> most complex forms that demand significant time and
effort, as well as many interactions between knowledge sender and receiver to be fully reached.
Therefore, “real value learning” predominantly takes place via face-to-face events such as
meetings, workshops and “on <strong>the</strong> job”, while even <strong>the</strong> most sophisticated knowledge
management tools (such as Artificial Intelligence Systems) are limited to providing support, e.g.
by collecting and analyzing vast quantities <strong>of</strong> data (Hey 2010).These technical systems are
certainly important and in many cases even indispensable, but for <strong>the</strong> particular area <strong>of</strong>
“knowledge sharing” <strong>the</strong>y are bound to less effective tasks (Jacobson/Prusak 2006).
In brief, <strong>the</strong> organizational and in particular <strong>the</strong> technical instruments to foster individual learning
relationships are limited. Employees are only partly capable and willing to share <strong>the</strong>ir knowledge and
even if <strong>the</strong>y do, it is hard to gain measurable value that justifies <strong>the</strong> costs invested in a “knowledge
management system”. If we agree on <strong>the</strong>se core patterns, we can define <strong>the</strong> following three
challenges for designing cost-efficient knowledge networks.
Do not try to extract knowledge
Do not rely on codified knowledge
Focus on <strong>the</strong> best knowledge sharers (ra<strong>the</strong>r than on <strong>the</strong> best experts)
2.3 Online social communities
Nowadays, information technology for building online social networks, so called “social s<strong>of</strong>tware” like
wikis, mashup tools, or blogs is already a commodity (Forrester 2009). Fur<strong>the</strong>rmore, everyone is
familiar with <strong>the</strong> features <strong>of</strong> online communities like Facebook, or LinkedIn. But has <strong>the</strong> reader <strong>of</strong> this
article ever asked a colleague or friend why (s)he has joined a Web 2.0 community? And if so, did <strong>the</strong>
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
reader ever receive <strong>the</strong> answer, “Well, I am going to showcase myself, telling <strong>the</strong> world how great I
am”? Probably not! But this should have been <strong>the</strong> case – because “personal identity” is considered to
be <strong>the</strong> most important reason for people to join an online social network like Myspace, Facebook,
LinkedIn, Gowalla, Flickr, and Twitter (Kabani 2010).
We know this behavioral pattern from social network analysis (Wasserman/Faust 1997). The need for
recognition is entirely human, a natural source <strong>of</strong> intrinsic motivation. The impulse to express our
individuality is a positive source <strong>of</strong> energy, probably <strong>the</strong> ultimate driver <strong>of</strong> creativity and innovation
(Gratton et al. 1999). And this is not limited to online communities, <strong>of</strong> course. Being proud <strong>of</strong> a large
number <strong>of</strong> attendees <strong>of</strong> your “real world” performances, and enjoying a high count <strong>of</strong> followers in <strong>the</strong>
“virtual world”, basically means <strong>the</strong> same thing. Web communities are just an additional channel for
creating social contacts and receiving appreciation. And due to <strong>the</strong> fact that online communities are
available at low cost and with no special requirements, <strong>the</strong>y are heavily used. People have an ego,
and <strong>the</strong>re is nothing negative about it. It is just a remarkable fact that clearly admitting this does not
appear to be socially acceptable in most <strong>of</strong> our cultures.
Therefore, <strong>the</strong> interviewed person most likely would not reveal his/her eagerness to gain recognition.
Instead, (s)he would probably tell something about <strong>the</strong> intention to meet people and share
information. Both, in line with expectations, express a certain level <strong>of</strong> activity and involvement.
But even in this case, Web reality might look different. According to empirical studies, almost two
thirds <strong>of</strong> online community users are passive spectators, readers ra<strong>the</strong>r than writers <strong>of</strong> micro
messages, product ratings, and blog posts (see e.g. Forrester 2009).
In summary, <strong>the</strong> majority <strong>of</strong> online social network members are ei<strong>the</strong>r passive content consumers or
active producers that are clearly driven by individual goals. Since online knowledge networks are
based on <strong>the</strong> same set <strong>of</strong> technologies and mechanisms, we assume that <strong>the</strong> very same core
patterns apply. If we agree on this conclusion, we can add two more challenges for <strong>the</strong> development
<strong>of</strong> valuable knowledge networks:
Use <strong>the</strong> power <strong>of</strong> individual goals
Activate <strong>the</strong> waiting assets <strong>of</strong> passive members
3. The management approach
The following figure summarizes <strong>the</strong> above-mentioned challenges. In order to cope with <strong>the</strong>se, it
seems reasonable to look for governance models that mediate between an enterprise’s need for
effective management <strong>of</strong> its human capital and employees’ willingness to support this strategy.
How to succeed in corporate knowledge networks
1. Use <strong>the</strong> power <strong>of</strong> individual goals
2. Do not try to extract knowledge
3. Do not rely on codified knowledge
4. Focus on <strong>the</strong> best knowledge sharers
5. Activate <strong>the</strong> waiting assets <strong>of</strong> passive members
6. Abandon <strong>the</strong> myth <strong>of</strong> comprehensive control
7. Enable emerging processes
Figure 3: Seven Key Challenges for Developing Knowledge Networks
The desired management model should include <strong>the</strong> fact that most relevant learning as well as
significant innovation cannot be planned step-by-step. Instead, creative solutions as well as valuable
improvements typically develop from a dynamic, multi-dimensional process that happens at <strong>the</strong>
crossroads <strong>of</strong> chaos and order (see e.g. Taylor 1994, Madanmohan 2005, Govindarajan 2010). To
describe this situation, i.e. “any non-linear, (…) complex system, which shows characteristics <strong>of</strong> both,
order and chaos”, Dee Hock, founder and former CEO <strong>of</strong> VISA, introduced <strong>the</strong> term “chaordic” (Hock
1999). Building up on Hock’s understanding <strong>of</strong> a “chaordic organization”, chaordic knowledge
management deliberately abandons <strong>the</strong> illusions <strong>of</strong> comprehensive control. Additionally, in order to
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
initiate and master effective knowledge sharing, chaordic knowledge management tries to arrange a
well-defined field for communication, exploration, and observation – a discipline which we could
describe as “framing and farming <strong>the</strong> chaos”.
Roughly, chaordic knowledge management can be characterized by its attitudes regarding:
Corporate Members: Many organizations, particularly large multinational firms, established a finetuned
scheme <strong>of</strong> job roles, titles, ranks, and statuses. Employees are determined by <strong>the</strong>ir
business cards. Personal privileges grow by management level and thus people are strongly
categorized and treated by <strong>the</strong>ir level.
The implications <strong>of</strong> this corporate behavior are non-constructive to productive knowledge
networks. The reason for this is that individuals are more interested in o<strong>the</strong>rs’ titles instead <strong>of</strong> <strong>the</strong>ir
person. In this way, a potential knowledge exchange is at risk <strong>of</strong> becoming biased, superficial and
replaceable, and thus not very likely to create veritable value.
Chaordic knowledge management appreciates <strong>the</strong> capabilities <strong>of</strong> each corporate member,
independent <strong>of</strong> <strong>the</strong>ir job role or title. Particularly, it identifies and recognizes <strong>the</strong> best knowledge
sharers, ra<strong>the</strong>r than <strong>the</strong> best (and highest paid) experts or managers. Fur<strong>the</strong>rmore, it fosters high
individual autonomy, where each corporate member is encouraged to share any kind <strong>of</strong>
knowledge content, which can also go beyond his/her current corporate role and assignment.
Working Relationships: Traditional instruments used to describe working relationships are
organizational handbooks and charts. Chaordic knowledge management does nei<strong>the</strong>r believe in
<strong>the</strong> one nor <strong>the</strong> o<strong>the</strong>r. The reason for this is that an organizational chart/handbook is static but
business life is dynamic. Therefore, an organizational chart/handbook will never capture <strong>the</strong> broad
spectrum <strong>of</strong> actual working relationships. Not to mention <strong>the</strong> informal and hidden mechanisms
within (Krackhardt/Hanson 1993).
In a corporate world where career steps happen every two or three years, formal working
relationships change quickly but personal connections, e.g. from a former assignment, endure.
Besides <strong>the</strong> business line, working relationships are established within internal projects, which
typically run across <strong>the</strong> boundaries <strong>of</strong> units, departments and firms. Last but not least, employees
have links to external partners, such as suppliers, distributors or customers.
In this way, each corporate member develops a widespread network <strong>of</strong> individual business
relationships that grow over time. Thus, with hundreds, thousands or tens <strong>of</strong> thousands <strong>of</strong>
employees, an enormous system <strong>of</strong> interrelated individual networks exists. And with all its
informal as well as formal, current as well as historic relationships, it comprises many millions <strong>of</strong>
dots and connections. From a corporate perspective, this massive “network <strong>of</strong> networks” is nei<strong>the</strong>r
transparent nor controllable.
Chaordic knowledge management accepts this complexity and withstands <strong>the</strong> desire to
harmonize structures or limit dimensions. Instead, chaordic knowledge management encourages
each individual to intensively create and maintain knowledge relationships, communicate with
internal as well as external individuals, and develop <strong>the</strong> personal knowledge network in all
directions, even beyond vertical and horizontal organizational borders.
Office Space: Many <strong>of</strong>fice buildings (and <strong>the</strong> way companies are using <strong>the</strong>m) provide a similar
picture: an assembly <strong>of</strong> concrete walls, where spacious rooms on higher floors are subject <strong>of</strong>
personal status, doors remain closed, and even middle managers are shielded by secretaries.
Additionally, meeting rooms are limited and carefully administered by facility management. In
such a rigid structure knowledge sharing is not fostered, it is actively prevented!
Fortunately, some companies have understood <strong>the</strong> enormous impacts <strong>of</strong> this aspect. If one takes
a look at Google’s Swiss <strong>of</strong>fice in Zurich for example, one will find a normal <strong>of</strong>fice building from
<strong>the</strong> outside but once one goes inside, one quickly understands <strong>the</strong> difference to many o<strong>the</strong>r
corporations. The huge size <strong>of</strong> <strong>the</strong> building would allow every employee an own spacious and
private <strong>of</strong>fice room. However, this is not <strong>the</strong> case. Instead, staff’s working places are arranged in
cubicles, giving each <strong>of</strong> <strong>the</strong>m just a small place <strong>of</strong> privacy. The remaining space <strong>of</strong> <strong>the</strong> building is
used as a communal area, where meetings <strong>of</strong> all types and sizes can be instantly arranged. The
same also applies to communication among <strong>the</strong>ir <strong>of</strong>fices. Being one <strong>of</strong> <strong>the</strong> biggest buyers <strong>of</strong> video
conferencing systems, connections are established at a fingertip, making Google literally a
company without walls.
Chaordic knowledge management promotes exactly this philosophy! Effectively sharing
knowledge needs room! Two (or more) people have to come toge<strong>the</strong>r, find a basis <strong>of</strong>
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
understanding, exchange ideas and assess information. For such a meeting, <strong>the</strong>y need a space,
ei<strong>the</strong>r physical or virtual. The easier communal rooms can be accessed, <strong>the</strong> better knowledge can
flow. Therefore, <strong>the</strong> ratio between individually occupied (fixed) rooms and communal (flexible)
<strong>of</strong>fice space should be 30/70 ra<strong>the</strong>r than <strong>the</strong> opposite, which to <strong>the</strong> authors’ experience still is <strong>the</strong>
predominant business reality.
Information Technology: “Corporate knowledge maps”, “shared discussion forums”, “joint learning
platforms”; all <strong>the</strong>se terms are indicating <strong>the</strong> same: <strong>the</strong> classical raison d’être for knowledge
management is to provide a high level <strong>of</strong> commonality, very <strong>of</strong>ten manifested as so called
integrated s<strong>of</strong>tware solutions. In this regard, integration in <strong>the</strong> past almost became a killer
argument. If information technology (IT) was integrated, it automatically was “seamless”,
“efficient”, and “lean”. But this is a myth, because it tends to simplify <strong>the</strong> very disadvantages <strong>of</strong>
technical integration. Although creating an integrated user perspective is always a crucial
objective, integration <strong>of</strong> data and functions is not necessarily <strong>the</strong> right way <strong>of</strong> reaching this goal
(Habermann 2011).
Not only Enterprise Application Integration (EAI) has demonstrated that aiming for deep
integration increases complexity significantly. The reason for this is that integration requires a
common meta-model and changes to all involved sub-systems. Business projects that try to
overcome <strong>the</strong>se technical and organizational hurdles usually end up with massive implementation
costs. And <strong>the</strong> resulting IT architectures are characterized by disproportionate maintenance
efforts and exorbitant costs <strong>of</strong> ownership. (Themistocleous 2004, Boisots 2006)
Chaordic knowledge management does not favor integrated s<strong>of</strong>tware solutions. Instead, it is
looking for an alternative IT architecture that helps to overcome isolated legacy systems, without
endangering <strong>the</strong>ir existence. For supporting cross-organizational knowledge sharing <strong>the</strong> desired
technical model should be able to cope with various system instances and combine local
customization needs with <strong>the</strong> corporate demand for aggregated information. What chaordic
knowledge management is looking for is orchestration instead <strong>of</strong> integration! A demand which
goes along perfectly with service oriented architectures, cloud computing and s<strong>of</strong>tware as a
service.
4. Conclusions and outlook
“Knowing <strong>the</strong> owners <strong>of</strong> intellectual capital enables learning, saves time and saves money!” It is very
easy to see and understand <strong>the</strong>se benefits <strong>of</strong> corporate knowledge networks. And, in contrast to
earlier knowledge management philosophies and technologies, social s<strong>of</strong>tware seems to provide <strong>the</strong>
right instruments to utilize <strong>the</strong> full potential <strong>of</strong> corporate knowledge communities.
However, in <strong>the</strong> past, after <strong>the</strong> first wave <strong>of</strong> euphoria, enterprises faced serious problems in
implementing <strong>the</strong>se benefits. The two most significant obstacles are as follows:
Adapting management and leadership approaches: It is true that effectively managing talent,
knowledge and intellectual capital are crucial business issues. But this is a bird’s eye perspective.
The employees’ view is different. The first priority <strong>of</strong> an employee is his or her personal career
and this egoistic agenda is typically fostered by local structures and informal relationships, but
very rarely by enterprise-wide, top-down driven initiatives. This is <strong>the</strong> reason why <strong>the</strong> success <strong>of</strong>
many centralized knowledge management approaches has been prevented. And it is <strong>the</strong> reason
why employees like <strong>the</strong> decentralized concepts <strong>of</strong> Enterprise 2.0 much better. On <strong>the</strong> o<strong>the</strong>r hand,
we have managers and <strong>the</strong>ir self-image as controllers and leaders. Traditionally <strong>the</strong>ir positions
and roles (as well as <strong>the</strong> bonus system) are based on <strong>the</strong>ir capabilities to provide high level
commonality, methodologically and technically realized by means <strong>of</strong> standardization, classification
and unification. Unfortunately, this runs against <strong>the</strong> paradigms <strong>of</strong> social s<strong>of</strong>tware and dynamic
knowledge networks. This essential conflict has stood <strong>the</strong> test <strong>of</strong> time. It frustrated knowledge
management enthusiasts in <strong>the</strong> nineties and is still interfering implementation <strong>of</strong> effective
corporate knowledge networks. Thus, enterprises have to face <strong>the</strong> truth. If <strong>the</strong>y want to eventually
succeed in utilizing <strong>the</strong> full potential <strong>of</strong> knowledge networks, <strong>the</strong>y have to find a well-defined
balance <strong>of</strong> interests and adapt <strong>the</strong>ir incentive systems accordingly. This article has outlined seven
major challenges in this regard. Consequently meeting <strong>the</strong>se challenges means a fundamental
shift <strong>of</strong> <strong>the</strong> existing management practice for many companies.
Handling individual and informal relationships: Knowledge sharing is a very personal relationship
(see e.g. Boone, 2001, Leistner 2010). If person A might be able to easily share knowledge with
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
person B, it might not work with person C, or less efficiently with person D. And those personal
preferences might interfere with planned communication structures as described in corporate
handbooks or with <strong>of</strong>ficial processes as implemented by workflow systems. Additionally, a study
<strong>of</strong> 200 knowledge workers has shown that it does not make too much sense to highlight <strong>the</strong> best
knowledge senders (e.g. busiest authors <strong>of</strong> articles, writers <strong>of</strong> Blog posts, etc.), or <strong>the</strong> “objectively”
acknowledged experts (e.g. people with certificates or o<strong>the</strong>r formal approvals <strong>of</strong> expertise). If a
company follows this path, it runs at risk <strong>of</strong> spending too much time with knowledge owners who
can hardly share <strong>the</strong>ir expertise (Jacobson/Prusak 2006). This might ei<strong>the</strong>r be due to a general
lack <strong>of</strong> communication skills or simply because <strong>the</strong> chemistry between a particular knowledge
sender and receiver is not right. It is a simple but no less powerful truth: management practice as
well as s<strong>of</strong>tware tools for effectively supporting knowledge networks should concentrate on
individual knowledge connections that actually work! The best way <strong>of</strong> doing this is capturing those
personal relationships that have already been proved to be valuable (in <strong>the</strong> sense <strong>of</strong> knowledge
sharing).
These considerations as well as <strong>the</strong> previously defined managerial challenges led to <strong>the</strong> development
<strong>of</strong> a s<strong>of</strong>tware system that is currently prototyped. The system, which is called “Complido”, is based on
Web 2.0 technology and visualizes all <strong>the</strong> people a user has learned from as well as all <strong>the</strong> individuals
who have learned from <strong>the</strong> user. By creating a new knowledge connection (an instance, not a type), a
person records a situation <strong>of</strong> successful knowledge reception and appreciates both ano<strong>the</strong>r individual
(who has shared knowledge), as well as <strong>the</strong> content (which has been shared).
Figure 4: Using Compliments for Creating Knowledge Networks (Source: http:/complido.net)
The figure above shows a user’s homepage on Complido. In our example, <strong>the</strong> user has a personal
network (a learning tribe) <strong>of</strong> 265 persons. The user can e.g. investigate his tribe in greater detail (and
explore its topics and/or international distribution), and navigate to o<strong>the</strong>r people’s individual
knowledge networks (<strong>the</strong>ir learning tribes). In this way, <strong>the</strong> user can find out “from whom people have
learned, whom I have learned from” or he could explore certain fields <strong>of</strong> knowledge (knowledge
domains).
The genuinely novel aspect <strong>of</strong> Complido however is that <strong>the</strong> networking process is initiated from <strong>the</strong>
knowledge receiver’s side. That means, contrary to o<strong>the</strong>r pr<strong>of</strong>essional networks people cannot state
<strong>the</strong>ir “Haves” (e.g. <strong>the</strong>ir “competencies”, “lessons learned”, etc.), without mentioning <strong>the</strong> original
source <strong>of</strong> knowledge. In o<strong>the</strong>r words, <strong>the</strong> user (a learner, a receiver <strong>of</strong> knowledge) cannot praise
himself without praising <strong>the</strong> original source <strong>of</strong> knowledge (<strong>the</strong> “teacher”, <strong>the</strong> sender <strong>of</strong> knowledge).
This, what at first sight looks like a small thing bears a bunch <strong>of</strong> advantages. For example, it uses <strong>the</strong>
power <strong>of</strong> individual goals and at <strong>the</strong> same time activates <strong>the</strong> waiting assets <strong>of</strong> passive members. A
knowledge sender who becomes aware that o<strong>the</strong>rs are recognizing his/her person will feel motivated
and committed (Gratton 1999).
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Frank Habermann, Jörg Fehlinger and Karen Schmidt
Additionally, <strong>the</strong> system does not try to extract knowledge from people. Quite <strong>the</strong> opposite, by making
a compliment (“Thank you, I have learned from you!”), it identifies and highlights experts as being
most valuable and lets <strong>the</strong>m remain <strong>the</strong> knowledge host. Since <strong>the</strong> sender <strong>of</strong> knowledge does not
even have to know about a successful knowledge transfer, it can also happen implicitly (e.g. by
observation). Thus Complido fulfills even more <strong>of</strong> <strong>the</strong> outlined challenges (see figure 3), because it
goes beyond explicit and codified knowledge.
To fur<strong>the</strong>r test <strong>the</strong> mentioned <strong>the</strong>ses and improve <strong>the</strong> s<strong>of</strong>tware, <strong>the</strong> existing prototype is being piloted
with various targets groups, such as university students during <strong>the</strong>ir summer term as well as business
people from various industries.
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407

Intellectual Capital in Universities: Faculty and Student
Perceptions
Meliha Handzic and Kursad Ozlen
International Burch University, Sarajevo, Bosnia-Herzegovina
mhandzic@ibu.edu.ba
kozlen@ibu.edu.ba
Abstract: This paper proposes a specific IC measurement model to assist universities to obtain an accurate
picture <strong>of</strong> <strong>the</strong>ir knowledge-based assets and help <strong>the</strong>m to develop better strategies to manage that knowledge
across <strong>the</strong> institution. The model also serves as a means for assessing how well <strong>the</strong> identified assets meet
institutional business goals and strategies. The practical application <strong>of</strong> <strong>the</strong> model is illustrated in <strong>the</strong> context <strong>of</strong> a
new Eastern <strong>European</strong> university. The findings reveal some interesting IC dynamics and identify current
shortcomings. Practical implications and <strong>the</strong> agenda for future research were also discussed.
Keywords: Intellectual capital, measurement model, <strong>European</strong> higher education, survey
1. Introduction
<strong>European</strong> higher education is going through an important transformation process with <strong>the</strong> aim <strong>of</strong>
making it more flexible, transparent and competitive. Consequently, <strong>the</strong>re is <strong>the</strong> growing interest
regarding measuring and managing intellectual capital (IC) in universities. It is believed that
universities need to use <strong>the</strong> IC model as a tool to aid <strong>the</strong>m in meeting new management challenges
and diffusing <strong>the</strong>ir intangible resources and activities to <strong>the</strong>ir stakeholders and society at large.
Intellectual capital has gained <strong>the</strong> increased prominence as a business and research topic principally
due to <strong>the</strong> rise <strong>of</strong> <strong>the</strong> knowledge economy (Bullen et al. 2006, Farrell 2001, Roberts 2001, 2009,
Rooney and Mckenna 2005). Knowledge economy is described by OECD as <strong>the</strong> economic activities
and systems which are directly established by creation, circulation and application <strong>of</strong> knowledge and
information (Chen 2008). Intellectual capital (IC) has been widely recognised as a major resource <strong>of</strong>
organisations in <strong>the</strong> knowledge economy (KE).
The concept <strong>of</strong> IC combines <strong>the</strong> idea <strong>of</strong> <strong>the</strong> intellect power with <strong>the</strong> economic concept <strong>of</strong> capital for
<strong>the</strong> purpose <strong>of</strong> producing innovative or more efficient and effective goods and services (Stewart
1997). Typically, <strong>the</strong> term IC refers to all knowledge resources that determine <strong>the</strong> competitiveness <strong>of</strong>
an organisation. It includes intellectual property such as patents and licences, as well as <strong>the</strong> skills and
know-how <strong>of</strong> people, documents and information systems (Robinson and Kleiner 1996). Intellectual
capital is synonymous with knowledge assets, intellectual assets or intangible assets (Guthrie 2001).
According to Sveiby (1997) it is <strong>the</strong> difference between <strong>the</strong> market and book value <strong>of</strong> <strong>the</strong> company.
In recent economic and social developments, intellectual capital is implicated as <strong>the</strong> instrumental in
<strong>the</strong> determination <strong>of</strong> enterprise value and economic performance. From <strong>the</strong> IC perspective, <strong>the</strong> aim <strong>of</strong>
an organisation is to create and/or extract value from knowledge assets by maximizing <strong>the</strong>
interrelationship between different types <strong>of</strong> its intellectual capital (Handzic and Zhou 2005). It has also
been widely recognised that <strong>the</strong> success <strong>of</strong> <strong>the</strong> knowledge economy or society depends on <strong>the</strong>
effective utilisation <strong>of</strong> its intangible assets such as knowledge, skills and innovative potential
(Economic and Social Research Council, 2007).
In research, <strong>the</strong> current emphasis is on important <strong>the</strong>oretical and empirical contributions relating to <strong>the</strong>
measurement and reporting <strong>of</strong> intellectual capital. Therefore, in this study, we address <strong>the</strong> issue <strong>of</strong> IC
measurement by introducing and applying a specific IC measurement model for universities.
The paper is structured as follows. It starts with this introductory section. In <strong>the</strong> following section, we
review relevant literature on universities and <strong>the</strong>ir IC models <strong>of</strong> interest to this study. Next, we
describe <strong>the</strong> survey research method applied in <strong>the</strong> current investigation. Then, we present results <strong>of</strong>
our quantitative and qualitative data analyses. A discussion <strong>of</strong> our findings in <strong>the</strong> light <strong>of</strong> past literature
follows. Finally, conclusions and contributions to research and practice are <strong>of</strong>fered in <strong>the</strong> last section.
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2. Literature review
Meliha Handzic and Kursad Ozlen
This section draws from past research and practice in order to develop a more comprehensive and
holistic understanding <strong>of</strong> IC frameworks as a common ground for developing a specific IC
measurement model for universities. First, <strong>the</strong> section looks into <strong>the</strong> place <strong>of</strong> universities in knowledge
economy. Then it presents some recent efforts at developing IC models and examines <strong>the</strong>ir use in
measuring and reporting human, structural and relational capital categories. In <strong>the</strong> end, it states <strong>the</strong>
objectives <strong>of</strong> <strong>the</strong> present study.
2.1 University and knowledge economy
In his recent essay, Ozlen (2010) states that <strong>the</strong> central task <strong>of</strong> universities is to create new
knowledge and <strong>the</strong>reby engender innovations. Innovations are based on information and knowledge
processes <strong>of</strong> various kinds. Such processes cannot take place without accurate, up-to-date
knowledge in <strong>the</strong> focal areas <strong>of</strong> <strong>the</strong> universities’ research interests and efforts. The significance <strong>of</strong>
education and training is also stressed as keys to participation in <strong>the</strong> new global knowledge economy.
The role <strong>of</strong> universities is crucial for <strong>the</strong> development <strong>of</strong> knowledge workers, for up-skilling and
increasing <strong>the</strong>ir competencies. By producing new knowledge and developing new workforce,
universities make a significant contribution to economic development, social progress and political
democracy <strong>of</strong> a nation. The recognition <strong>of</strong> <strong>the</strong> importance <strong>of</strong> universities in <strong>the</strong> new knowledge
economy has been <strong>the</strong> fundamental to raising <strong>the</strong> concerns about <strong>the</strong> quality <strong>of</strong> higher education in
Europe. Intellectual capital <strong>of</strong> universities is directly or indirectly associated with <strong>the</strong> quality <strong>of</strong> higher
education. It is argued that IC is <strong>the</strong> most vital and strategic resource for universities (Williams 2007,
Warhurst 2008). Actually, IC is at <strong>the</strong> core <strong>of</strong> <strong>the</strong> University’s mission and reason <strong>of</strong> existence.
Attracting and retaining qualified intellectual capital plays a vital role in reinforcing <strong>the</strong> University’s
educational standards. IC is not only an input resource for a University, but it is also a product in <strong>the</strong>
sense that faculty members’ IC is used to nurture that <strong>of</strong> its students. As an institution earns a
reputation as a superior quality provider <strong>of</strong> education, it will be able to attract more local and foreign
students which will contribute in turn to <strong>the</strong> institution’s prosperous continuity. IC reports recognise
that “<strong>the</strong> efficient use <strong>of</strong> IC is essential for a university’s performance”.
2.2 University knowledge assets portfolio
There are many classification schemes which attempt to categorise knowledge assets or IC in
organisations (Dumay 2009, Johanson et al 2009, Rogers and Housel 2009, Zeghal and Maaloula
2010). Handzic and Zhou (2005) adopted a widely-accepted Edvinsson and Malone (1997) scheme <strong>of</strong>
three sub-categories: human capital, organisational (internal) capital and customer (external) capital.
In addition to this, knowledge assets can be divided into core and supporting assets Core knowledge
assets comprise a firm’s core skills and competencies. They lie in <strong>the</strong> areas in which <strong>the</strong> firm has
competitive strengths. In contrast, supporting knowledge assets are complementary generic and
operational assets that support or enable <strong>the</strong> delivery, storage and acquisition <strong>of</strong> core knowledge
assets. Every organisation possesses valuable intellectual materials in <strong>the</strong> form <strong>of</strong> data, documents,
procedures, capabilities, etc. These can be found in people, organisational structures and processes,
and customer relationships. To succeed, organisations need to have a clear understanding <strong>of</strong> which
knowledge assets are important to <strong>the</strong>ir success and how <strong>the</strong>se assets are distributed over different
parts <strong>of</strong> <strong>the</strong> company and among different functions and workers.
The portfolio <strong>of</strong> knowledge assets is typically determined by an organisation’s strategic plan. The
following sections present some examples <strong>of</strong> knowledge assets <strong>of</strong> universities under human,
structural and relational subtitles. Typically, key indicators <strong>of</strong> intellectual capital <strong>of</strong> universities include
<strong>the</strong> ability to attract and retain staff <strong>of</strong> good caliber, dedicated staff, implementation <strong>of</strong> effective staff
and student equity measures. They also involve projecting a highly visible positive image, ability to
attract good students, technological support, quality research, relevant tuition programmes,
internationalization, visionary participative strategic management, adherence to mission, financial
health <strong>of</strong> <strong>the</strong> institution.
2.3 Study objectives
There is growing evidence in support <strong>of</strong> <strong>the</strong> application <strong>of</strong> IC measurement tools in universities and
<strong>the</strong> potential benefit this would bring (Hellstrom and Husted 2004, Sanchez and Elena 2006, Sanchez
et al. 2009). Up to now, only a few universities have taken <strong>the</strong> challenge <strong>of</strong> trying to measure and
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Meliha Handzic and Kursad Ozlen
report on intangible assets (Ramirez et al. 2007). However, <strong>the</strong>re is <strong>the</strong> growing interest regarding
measuring and managing intellectual capital (IC) in universities. Following <strong>the</strong> current trend, this paper
has two objectives: (i) to highlight some conceptual and methodological issues in relation to IC
measurement by presenting an IC reporting model specifically adapted for universities; and (ii) to
contribute to <strong>the</strong> analysis <strong>of</strong> <strong>European</strong> Universities by examining faculty and student perceptions <strong>of</strong> IC
in a specific Eastern <strong>European</strong> university. The current study is a part <strong>of</strong> a larger research project
aimed at comparative analysis <strong>of</strong> <strong>European</strong> Universities.
3. Research method
This section describes <strong>the</strong> design <strong>of</strong> <strong>the</strong> study, <strong>the</strong> population and sample, <strong>the</strong> operationalisation <strong>of</strong>
examined variables, and <strong>the</strong> instrumentation development, procedures, and data analyses employed.
3.1 Research design and instrument
Our intention in this study was to produce a broad picture <strong>of</strong> intellectual capital in <strong>the</strong> higher education
institution. One <strong>of</strong> many new international universities established in East Europe was chosen as a
study case for convenience reasons and a descriptive survey was adopted as a preferred method for
this research (Judd et al. 1991). The name <strong>of</strong> <strong>the</strong> case university has been concealed for <strong>the</strong> purpose
<strong>of</strong> privacy protection. The survey design was based on a range <strong>of</strong> insights from <strong>the</strong> <strong>the</strong>oretical IC
literature and <strong>the</strong> review <strong>of</strong> prior related survey research.
The university IC was evaluated by assessing its human, structural and relational facets. Human
capital was evaluated in terms <strong>of</strong> explicit and tacit knowledge <strong>of</strong> <strong>the</strong> universities’ personnel acquired
through formal and informal educational and actualization processes embodied in <strong>the</strong>ir activities;
Structural capital captured <strong>the</strong> explicit knowledge related to <strong>the</strong> internal process <strong>of</strong> dissemination,
communication and management <strong>of</strong> scientific and technical knowledge in <strong>the</strong> organization; and
relational capital ga<strong>the</strong>red <strong>the</strong> wide set <strong>of</strong> economical, political and institutional relationships
developed and maintained by <strong>the</strong> university (Ramirez at al. 2007). A mix <strong>of</strong> objective and subjective,
financial and nonfinancial measures can be used to assess <strong>the</strong>se IC aspects. For <strong>the</strong> purpose <strong>of</strong> this
study, we adopted <strong>the</strong> subjective nonfinancial measurement approach.
The design <strong>of</strong> <strong>the</strong> survey took into account time requirements, emotional cost, trust, nature <strong>of</strong> <strong>the</strong>
relationship between respondents and researchers. Thus <strong>the</strong> survey form was compact and no
personal information was required. The questionnaire was written on a single page with 18 questions
written in 10-point font. This layout was selected based on <strong>the</strong> time cost to <strong>the</strong> respondents. It was
tested to take approximately 15 minutes to complete in <strong>the</strong> participants’ own chosen time. The survey
was also formatted for clarity. It was divided into 5 major parts, and included short starting
instructions. The 5 sections <strong>of</strong> <strong>the</strong> survey comprised: (i) Demographic information (role, gender); (ii)
Human capital (importance and actual status); (iii) Structural capital (importance and actual status);
(iv) Relational capital (importance and actual status); and (v) Comments.
3.2 Subjects and procedure
Participants in this survey included 32 full time faculty staff and 34 graduate students from three case
university faculties (economics, engineering, education). Respondent demographics sought included
role (faculty, student) and gender (male or female). The names <strong>of</strong> <strong>the</strong> respondents were unknown and
not required for <strong>the</strong> purpose <strong>of</strong> collecting data. The questionnaires were distributed to <strong>the</strong> recipients
by one <strong>of</strong> <strong>the</strong> authors. Given that surveys <strong>of</strong>ten receive <strong>the</strong> low fraction <strong>of</strong> responses, <strong>the</strong> authors tried
to lift <strong>the</strong> response rate by personally contacting colleagues and students at <strong>the</strong> case university,
handing out <strong>the</strong> questionnaires, reminding <strong>the</strong> recipients that <strong>the</strong> survey should be completed and
collecting <strong>the</strong>m when done. Data collection was carried out over a period <strong>of</strong> one week. A total <strong>of</strong> 66
responses were received. The maximum response rate was achieved.
In replying to <strong>the</strong> questions and statements <strong>the</strong> respondents were required in most questions to tick
appropriate responses using attitude questions in <strong>the</strong> questionnaire. This allowed respondents to rank
<strong>the</strong>ir agreement to a statement relative to positive and negative end-points <strong>of</strong> a seven-point Likert
scale. Some questions were multiple choice or required textual responses.
The questionnaire responses were encoded, entered into a computer and <strong>the</strong>n analysed using
Micros<strong>of</strong>t Excel spreadsheet program. The survey responses were combined into one file and <strong>the</strong>
descriptive statistics were calculated for each variable from <strong>the</strong> responses to each <strong>of</strong> <strong>the</strong> items.
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Meliha Handzic and Kursad Ozlen
Relevant descriptive statistics and any uncharacteristic results are described in <strong>the</strong> following results
section <strong>of</strong> this paper.
4. Results
This part presents <strong>the</strong> results <strong>of</strong> <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> collected survey data. The first section examines
<strong>the</strong> respondents’ demographic information. The next three sections present respondents’ views about
<strong>the</strong> case university’s current human, structural and relational capital. The final section presents <strong>the</strong>
most notable comments on all three IC facets. Additional information about respondents’ perceived IC
importance was collected for control purposes. The analysis <strong>of</strong> obtained answers to <strong>the</strong>se questions
revealed an overwhelming agreement among faculty and students about <strong>the</strong> importance <strong>of</strong> human
capital (92 vs. 97%), structural capital (94 vs. 87%) and relational capital (96 vs. 91%).
4.1 Respondent demographics
The pr<strong>of</strong>ile <strong>of</strong> <strong>the</strong> sample is examined in terms <strong>of</strong> <strong>the</strong> respondents’ role at <strong>the</strong> case university (faculty
staff, graduate student) and gender (male, female). Of <strong>the</strong> total <strong>of</strong> 66 survey respondents, 52% were
faculty staff and 48% were graduate students. Fur<strong>the</strong>rmore, 67% <strong>of</strong> <strong>the</strong> respondents were male and
33% female. This distribution suggests that <strong>the</strong> survey responses generated may reflect more closely
<strong>the</strong> views <strong>of</strong> male population (Table 1).
Table 1: Respondent Demographics
Role Male Female
Faculty staff 81% 19%
Graduate students 53% 47%
4.2 Human capital
This section examines <strong>the</strong> respondents’ opinions about human capital at <strong>the</strong> case university. The next
table (Table 2) shows <strong>the</strong> distribution <strong>of</strong> faculty and student opinions about existent human capital at
<strong>the</strong> case university. Opinions were separated into those that agree (rates > 4) and those that disagree
(rates < 4) with given statements. Thus, it was possible to identify more clearly positive and negative
feelings about various evaluated aspects. Each statement was rated separately by <strong>the</strong> respondents.
Overall, <strong>the</strong> tone is positive with more agreements than disagreements about all six aspects <strong>of</strong> human
capital in both respondent groups. There was a shared view by 65% faculty and 62% student
respondents that <strong>the</strong> case university provides high quality teaching in small groups (statement 5).
Fur<strong>the</strong>rmore, 50% faculty and 58% students held <strong>the</strong> view that <strong>the</strong> case university <strong>of</strong>fered full
administrative support for teaching and research (statement 4).
In addition, 47% faculty and 65% students recognised that <strong>the</strong> university had strong and visionary
leadership (statement 1) and that academic staff were motivated to do research (statement 6). Finally,
45% faculty and 55% students felt that <strong>the</strong> university hired high quality academic staff (statement 2)
and 44% faculty and 55% students believed that academic staff members were dedicated
researchers and instructors (statement 3).
Table 2: Human Capital
Statements
Faculty
Agree/Disagree
Students
Agree/Disagree
1.The university has strong and visionary leadership 47% 26% 65% 21%
2.The university hires high quality academic staff 45% 23% 55% 18%
3.Academic staff are dedicated full time researchers/instructors 44% 19% 55% 18%
4.The university provides full admin support for academics 50% 28% 58% 24%
5.Academic staff work with small groups <strong>of</strong> students 65% 26% 62% 21%
6.Academic staff are motivated to do research 47% 34% 65% 26%
To examine possible differences in <strong>the</strong> magnitude <strong>of</strong> ratings between two groups, <strong>the</strong> average
responses for human capital were fur<strong>the</strong>r analysed by t-test. Indeed, mean rates <strong>of</strong> students were
found to be significantly higher than those <strong>of</strong> faculty (Table 3).
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Table 3: Opinions about Human Capital by Role
4.3 Structural capital
Meliha Handzic and Kursad Ozlen
Role Mean Std t-test
Faculty staff 4.28 1.03 p=0.04
Graduate students 4.85 0.98 (sig)
This section examines <strong>the</strong> respondents’ opinions about structural capital <strong>of</strong> <strong>the</strong> case university. With
respect to various aspects <strong>of</strong> <strong>the</strong> existing structural capital at <strong>the</strong> case university, <strong>the</strong> respondents’
opinions were divided, and included more negative than positive tendencies (Table 4). On <strong>the</strong> positive
side, <strong>the</strong>re was appreciation by 53% faculty and 45% students that <strong>the</strong> university started own
research outlets (statement 4) and agreement by 38% faculty and students with <strong>the</strong> observation that a
wide variety <strong>of</strong> disciplines were taught at <strong>the</strong> university (statement 1).
Table 4: Structural Capital
Statements
Faculty
Agree/Disagree
Students
Agree/Disagree
1.Wide variety <strong>of</strong> disciplines are taught at <strong>the</strong> university 38% 28% 38% 26%
2.The university has several specialty domains 34% 44% 24% 36%
3.The university has contributed to many scholarly
outlets
16% 56% 32% 44%
4.Own research outlets have been started by <strong>the</strong>
university
53% 19% 45% 33%
5.The university <strong>of</strong>fers necessary library and infoservices
38% 47% 42% 30%
6.IT provides rel.infrastructural support for
teaching/research
25% 38% 29% 53%
However, 56% faculty and 44% students disagreed with <strong>the</strong> assertion that <strong>the</strong> university contributed
to many scholarly outlets (statement 3). Similarly, 44% faculty and 36% students disagreed with <strong>the</strong>
proposition that <strong>the</strong> university had specialty domains (statement 2). Fur<strong>the</strong>rmore, 38% faculty and
53% students opposed <strong>the</strong> claim that IT support for teaching and research (statement 6).
On one aspect, regarding <strong>the</strong> claim that <strong>the</strong> university <strong>of</strong>fered necessary library/information services
(statement 5), <strong>the</strong> message was mixed with more negative opinions expressed by 47% faculty and
more positive ones by 42% students. Fur<strong>the</strong>r analysis by t-test found no difference between mean
scores <strong>of</strong> faculty and students (3.83 vs. 3.97, ns)
4.4 Relational capital
This section examines <strong>the</strong> respondents’ opinions about <strong>the</strong> actual status <strong>of</strong> relational capital at <strong>the</strong>
case university.
Table 5: Relational Capital
Statements
Faculty
Agree/Disagree
Students
Agree/Disagree
1.High quality students are being attracted to <strong>the</strong> university 56% 31% 53% 32%
2.University <strong>of</strong>fers expertise to external stakeholders 16% 41% 24% 42%
3.There is close partnership established with o<strong>the</strong>r universities 25% 28% 36% 39%
4.The university is a member <strong>of</strong> scientific/pr<strong>of</strong>essional assoc. 19% 44% 30% 42%
5.The university encourages academic networking 56% 28% 36% 39%
6.The university promotes positive public image 41% 19% 56% 24%
Overall, respondents’ opinions concerning <strong>the</strong> existent relational capital at <strong>the</strong> case university were
divided, but show no dominant positive or negative direction (Table 5). There was agreement by 56%
faculty and 53% students that <strong>the</strong> university attracted high quality students (statement 1). There was
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Meliha Handzic and Kursad Ozlen
also agreement by 41% faculty and 56% students with <strong>the</strong> view that <strong>the</strong> university promoted positive
public image (statement 6).
However, 44% (or 41%) faculty and 42% students opposed <strong>the</strong> claim that <strong>the</strong> university was a
member <strong>of</strong> scientific and pr<strong>of</strong>essional associations (statement 4) or established close partnership with
o<strong>the</strong>r universities (statement 3). Finally, 28% faculty and 39% students rejected <strong>the</strong> proposition that it
<strong>of</strong>fered expertise to external stakeholders (statement 2).
On one aspect <strong>of</strong> relational capital only, <strong>the</strong>re was a difference <strong>of</strong> opinions between two groups with
more agreements by 56% faculty and more disagreements by 39% students that <strong>the</strong> university
encouraged networking (statement 5). With respect to possible differences in <strong>the</strong> magnitude <strong>of</strong>
ratings, t-test found no difference between mean scores <strong>of</strong> faculty and students (4.03 vs. 4.09, ns).
4.5 Textual comments
Respondents were asked to give textual comments about <strong>the</strong> three facets <strong>of</strong> intellectual capital. From
<strong>the</strong> content analysis <strong>of</strong> <strong>the</strong> responses obtained, <strong>the</strong> most notable negative comments were: (1)
barriers to research created due to teaching overload and lack <strong>of</strong> time and opportunity to attend
conferences; and (2) inadequate study space provided in <strong>the</strong> library; One <strong>the</strong> positive side <strong>the</strong>re was
(3) deeply felt belief that <strong>the</strong> case university would attain a position among top 100 universities in
Europe.
5. Discussion
This section analyses and interprets empirical data from <strong>the</strong> survey <strong>of</strong> intellectual capital (IC) status at
<strong>the</strong> case university. It seeks to establish whe<strong>the</strong>r IC has developed adequately and whe<strong>the</strong>r and how
it differs in <strong>the</strong> eyes <strong>of</strong> faculty staff and graduate students. Responses to survey questions and
comments about human, structural and relational assets expose <strong>the</strong> following current IC status at <strong>the</strong>
case university.
5.1 Analysis <strong>of</strong> key findings
In summary, <strong>the</strong> study findings for human capital indicate a positive overall picture. These findings
reveal that <strong>the</strong>re is <strong>the</strong> universal recognition <strong>of</strong> <strong>the</strong> importance, as well as <strong>the</strong> positive collective view
<strong>of</strong> <strong>the</strong> existent human capital at <strong>the</strong> case university. This implies that human capital is <strong>the</strong> major
strength <strong>of</strong> <strong>the</strong> case university. Such contention is reinforced fur<strong>the</strong>r by deeply felt belief that <strong>the</strong> case
university has <strong>the</strong> potential to attain a position among <strong>the</strong> 100 best universities in Europe. This is an
important and encouraging finding.
In contrast, <strong>the</strong> findings for structural capital are divided. While <strong>the</strong>y show appreciation for its
disciplinary and research endeavours, <strong>the</strong> distribution <strong>of</strong> current results implies a ra<strong>the</strong>r negative
overall judgement <strong>of</strong> <strong>the</strong> case university’s structural capital at present. Major shortcomings have been
identified in <strong>the</strong> areas <strong>of</strong> (i) specialty research domains and scholarly contributions; and (ii) supporting
information technology and library infrastructure and services. The first problem is reinforced by
comments <strong>of</strong> teaching overload and <strong>the</strong> lack <strong>of</strong> necessary time and opportunity to do and present
research. This suggests <strong>the</strong> need for <strong>the</strong> case university’s leadership to find a way to eliminate current
barriers to research by rebalancing teaching and research activities at <strong>the</strong> case university. The
second (infrastructural) problem is currently being addressed through major construction work. With a
new building construction well underway, it is expected that <strong>the</strong> sophisticated teaching and research
infrastructure will be available to students and employees at <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> next academic year.
The findings with respect to relational capital are similarly divided. While <strong>the</strong>y show common
awareness <strong>of</strong> high quality image and students at <strong>the</strong> case university, <strong>the</strong> findings also suggest <strong>the</strong>
need for better networking and building closer institutional relationships with educational, pr<strong>of</strong>essional
and industrial segments. It appears that <strong>the</strong> case university’s promotion <strong>of</strong> positive public image
enables it to attract high quality students. However, its institutional presence and involvement in
academic and pr<strong>of</strong>essional associations, government and industry sectors, as well as <strong>the</strong> society at
large is lacking. The case university’s leadership should take a closer look at <strong>the</strong> issue and find a way
to contribute to national and regional development.
In summary, <strong>the</strong> visualisation and analysis <strong>of</strong> key elements indicate some aspects <strong>of</strong> IC which are
fairly well aligned with educational strategy and successful in <strong>the</strong> context <strong>of</strong> <strong>the</strong> case university.
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However, <strong>the</strong>re is room for fur<strong>the</strong>r improvement in a number <strong>of</strong> IC issues. There is also an opportunity
for fur<strong>the</strong>r development. The greatest challenge for <strong>the</strong> case university is to move in <strong>the</strong> direction <strong>of</strong> its
mission <strong>of</strong> advancing learning and transforming life by consciously and deliberately addressing its
strategic IC.
5.2 Implications and limitations
The results <strong>of</strong> this IC model development and application have a number <strong>of</strong> important implications for
university in continuing its education and research journey. The high number <strong>of</strong> unsure and no
responses indicate <strong>the</strong> need for raising awareness <strong>of</strong> IC throughout <strong>the</strong> university. The proposed IC
model may be helpful in this regard by creating a common understanding <strong>of</strong> <strong>the</strong> IC phenomena
including basic components and indicators. It should also help to avoid any danger <strong>of</strong> misconception
and misunderstanding by promoting <strong>the</strong> use <strong>of</strong> a common terminology.
It is obvious that <strong>the</strong> results <strong>of</strong> this research are limited by a specific higher education context and a
group <strong>of</strong> academic and support staff participants in which <strong>the</strong> evaluation <strong>of</strong> intellectual capital was
investigated. In order to generalise <strong>the</strong> findings, fur<strong>the</strong>r research is required involving o<strong>the</strong>r contexts
and subject groups. Consideration <strong>of</strong> some kind <strong>of</strong> maturity model or method to support <strong>the</strong>
development <strong>of</strong> IC at universities would be useful. Future research is also required to study <strong>the</strong> impact
<strong>of</strong> various tasks, environment and people related contingencies on IC, and how <strong>the</strong>y relate to
performance outcomes. Only by systematically investigating fundamental aspects <strong>of</strong> IC, by cultivating
better measures and by critically examining alternative <strong>the</strong>oretical models, can <strong>the</strong> IC field continue to
progress.
6. Conclusions
This paper made two contributions: (i) highlighted some conceptual and methodological issues in
relation to IC measurement by presenting an IC reporting model specifically adapted for universities;
and (ii) provided some insights into IC at a specific case organisation within <strong>the</strong> higher education
sector: a new international university from an Eastern <strong>European</strong> country. The study applied a
subjective IC measurement approach to explore <strong>the</strong> importance and actual state <strong>of</strong> IC at <strong>the</strong> case
university. From <strong>the</strong> conceptual point <strong>of</strong> view, <strong>the</strong> applied measurement model and terminology were
adapted from a general IC framework. The assessment was performed subjectively, taking into
account <strong>the</strong> faculty and student perspectives on importance and actual state <strong>of</strong> human, structural and
relational capital at <strong>the</strong> case university. The findings point to human capital as <strong>the</strong> case university’s
major strength. They also identify several weaknesses in structural and relational capital. These
points may define <strong>the</strong> agenda for future research in <strong>the</strong> field for <strong>the</strong> benefit <strong>of</strong> both <strong>the</strong>ory and practice
<strong>of</strong> IC reporting and management in universities.
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415

Developing a Knowledge Strategy Using Tacit Knowledge
Measurement: Implications for <strong>the</strong> Balanced Scorecard
Innovation and Learning Perspective
Harold Harlow
Wingate University, Wingate, North Carolina, USA
h.harlow@wingate.edu
Abstract: This research paper develops practical measures that can be used by managers to develop a
knowledge strategy for <strong>the</strong> balanced scorecard innovation and learning perspective. My research proposes <strong>the</strong>
use <strong>of</strong> <strong>the</strong> (1) tacit knowledge index (TKI) to assess <strong>the</strong> level <strong>of</strong> tacit knowledge within firms and that effect on
firm performance and (2) <strong>the</strong> development <strong>of</strong> a knowledge strategy for <strong>the</strong> BSC innovation and learning
perspective based on <strong>the</strong> TKI. My prior research surveyed 108 United States, <strong>European</strong> and Canadian firms that
are using knowledge management to determine each firm’s Tacit Knowledge Index which was <strong>the</strong>n related to <strong>the</strong>
innovation and financial results at each firm as well as <strong>the</strong> knowledge strategy employed. This research revealed
significant relationships between a firm’s knowledge strategy, level <strong>of</strong> TKI and <strong>the</strong> firm’s innovation performance.
The relationship to <strong>the</strong> financial outcomes was less strong.
Keywords: tacit knowledge, balanced scorecard, knowledge strategy
1. Introduction
The late Peter Drucker (1999) said that that “knowledge has become <strong>the</strong> key economic resource and
<strong>the</strong> dominant-and perhaps even <strong>the</strong> only-source <strong>of</strong> competitive advantage.” According to a survey
conducted by <strong>the</strong> International Center for Business Information, 97% <strong>of</strong> executives in eleven countries
considered knowledge an essential part <strong>of</strong> value creation (Halton 1997). According to Krogh, Ichigo
and Nonaka (2000), “<strong>the</strong> first responsibility <strong>of</strong> managers is to unleash <strong>the</strong> potential <strong>of</strong> an
organization’s knowledge into value creating activities”. The firm specific concept <strong>of</strong> intellectual capital
was introduced in <strong>the</strong> early 1990s which connected <strong>the</strong> idea <strong>of</strong> a firm’s knowledge to <strong>the</strong> concept <strong>of</strong>
firm intellectual capital to address valuation <strong>of</strong> intangibles and to fur<strong>the</strong>r explain <strong>the</strong> idea <strong>of</strong> value
creation and its relationship to firm performance (Edvinsson and Malone 1997; Roos and Roos 1997;
Stewart 1997; Sveiby 1997). Fur<strong>the</strong>r, a firm’s knowledge and intellectual capital can be dynamically
deployed and redeployed to form a basis for competitive advantage (Teece 2000).
Knowledge is a resource that can be employed for strategic purposes but not all knowledge has
strategic value nor can all knowledge be converted into value-creating activities. Strategic
frameworks have been proposed to relate <strong>the</strong> role <strong>of</strong> firm knowledge to strategy (Von Krogh, Ichijo,
and Nonaka 2000) with astute management <strong>of</strong> <strong>the</strong> value in a firm’s competence/knowledge base as a
central issue in developing firm strategies (Teece 1986).
Business has recognized that not all knowledge yields competitive advantage (Von Krogh et al.,
2000). Of <strong>the</strong> two types <strong>of</strong> knowledge (tacit and explicit), tacit knowledge is <strong>the</strong> more strategically
valuable since it is hard to duplicate, cannot be easily transported to ano<strong>the</strong>r firm and is embedded in
<strong>the</strong> group culture and social understandings.
There is a constant interaction between corporate strategy and knowledge strategy. The corporate
strategy guides <strong>the</strong> strategic intent <strong>of</strong> <strong>the</strong> organization and this intent must be used to create <strong>the</strong>
knowledge management strategy. Development <strong>of</strong> <strong>the</strong> knowledge strategy creates new organizational
capabilities that may form <strong>the</strong> basis for new corporate strategies as knowledge is spread through <strong>the</strong>
organization.
A knowledge strategy must be one that is based on both overall business goals and measurable
outcomes which should be measureable as to <strong>the</strong>ir effectiveness. One way to measure <strong>the</strong>
effectiveness <strong>of</strong> a knowledge strategy is to develop key performance indicators (KPI) using <strong>the</strong>
balanced scorecard topology, specifically in <strong>the</strong> research <strong>the</strong> learning and growth perspective and <strong>the</strong>
financial perspective. This research connects <strong>the</strong> BSC financial outcomes and <strong>the</strong> learning and
growth perspective KPIs as measured outcomes.
Kaplan and Norton (1996) have provided three outcomes areas in <strong>the</strong> learning and growth
perspective that contain what are both tacit and explicit measures <strong>of</strong> effectiveness as well as potential
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Harold Harlow
KPIs. Those areas are: (1) employee capabilities and skills in sharing knowledge and new skill
development, (2) information systems capabilities as a strategic advantage and (3) motivation,
empowerment and alignment with strategy (degree <strong>of</strong> outcome rewards in place).
This research develops <strong>the</strong> concept <strong>of</strong> a knowledge strategy that in turn produces (1) a culture that
rewards and encourages knowledge sharing and (2) selection <strong>of</strong> KM methods to produce firm results.
Fur<strong>the</strong>r, this exploratory research uses an operational definition <strong>of</strong> a tacit knowledge index (TKI)
(Harlow 2008) that provides a measurement tool for managers to use in determining if a knowledge
strategy and appropriate knowledge management methods produce <strong>the</strong> desired outcomes (innovation
and financial). The development <strong>of</strong> this scale is significant because no current research exists that
develops an operational definition <strong>of</strong> tacit knowledge that combines <strong>the</strong> degree <strong>of</strong> tacit knowledge
content <strong>of</strong> various KM methods with usage <strong>of</strong> <strong>the</strong>se methods at <strong>the</strong> firm to measure firm performance.
This measure will be useful as firms try to (a) measure <strong>the</strong>ir ability to understand if <strong>the</strong>ir knowledge
strategy is successful, (b) create a core competence in KM and (c) maintain or improve <strong>the</strong>ir capability
to create and disseminate tacit knowledge.
2. Literature review
All <strong>of</strong> <strong>the</strong> definitions <strong>of</strong> intellectual capital imply that knowledge is both known to <strong>the</strong> management and
can be converted into value (Edvinsson and Sullivan 1996) and is about knowledge and knowing
capability <strong>of</strong> a social collectivity (Nahapiet and Ghoshal 1998), packaged useful knowledge (Stewart,
1997), " and Intellectual capital= competence × commitment” (Ulrich 1998). This research aims to
close <strong>the</strong> current gaps that prescribe ways <strong>of</strong> converting knowledge into intellectual capital without
including a metric than can determine for managers what is being converted and at what rate. My
research includes specific inclusion <strong>of</strong> <strong>the</strong> variable within <strong>the</strong> TKI that includes <strong>the</strong> use <strong>of</strong> knowledge
by <strong>the</strong> firm to create customer value which in turn drives firm outcomes.
The definition <strong>of</strong> knowledge management (KM) is that it is <strong>the</strong> formal process <strong>of</strong> determining what
internally held information could be used to benefit a company and ensuring that this information is
easily and systematically (Love 2000) made available to those who need it (Roy 2002). A firm’s
overall economic, strategic, and innovation performance is dependent on <strong>the</strong> degree to which <strong>the</strong> firm
can use all <strong>of</strong> <strong>the</strong> knowledge created by <strong>the</strong> firm and turn this knowledge into value-creating activities
(Krogh 1998). KM is a strategic process, <strong>the</strong> desired goal <strong>of</strong> which is to harness <strong>the</strong> value <strong>of</strong>
information by integrating it with processes that govern <strong>the</strong> manipulation <strong>of</strong> intellectual assets
(Lashing 2001). The use <strong>of</strong> KM enables firms to have more effective decision-making processes and
enables firms both to create new knowledge and to apply this knowledge to generate more innovation
in products, strategy, and processes (Probir 2002). Greater levels <strong>of</strong> innovation and improved
processes in turn lead to enhanced market and financial performance.
Measuring <strong>the</strong> value <strong>of</strong> knowledge assets and/or impact <strong>of</strong> knowledge management processes is <strong>the</strong>
main thrust <strong>of</strong> this research and is <strong>the</strong> major gap in <strong>the</strong> current field <strong>of</strong> study. My aim is to enable
knowledge creation that “unlocks <strong>the</strong> mystery <strong>of</strong> tacit knowledge toward greater innovation within <strong>the</strong>
firm (Von Krogh et al 2000)”.
A lack <strong>of</strong> empirical information on <strong>the</strong> impact <strong>of</strong> a Knowledge Management Strategy that includes both
tacit and explicit methods has meant that firms <strong>of</strong>ten choose technology solutions that are designed to
capture and disseminate mostly explicit knowledge (Almeida and Kogut 1999). While <strong>the</strong>se systems
<strong>of</strong>fer <strong>the</strong> advantage <strong>of</strong> ready usage metrics, <strong>the</strong>ir actual contribution to effective KM within <strong>the</strong> firm is
less clear (Berman, Down and Hill 2002). The degree <strong>of</strong> explicit codification- more manuals or product
plans do not presage success at firms-does not indicate that <strong>the</strong> knowledge encoded is valuable or
unique. Firms may have extensive libraries <strong>of</strong> codified knowledge that is rarely accessed or is
bypassed by unmapped tacit processes.
Gaps in <strong>the</strong> current research reveal that <strong>the</strong>re is no validation <strong>of</strong> which KM methods (ei<strong>the</strong>r explicit or
tacit or a combination <strong>of</strong> both) are more or less effective, and <strong>the</strong>re has been little research that looks
at <strong>the</strong> relationship <strong>of</strong> KMS to <strong>the</strong> firm’s outcomes (Grant 1996).
A firm’s overall economic, strategic, and innovation performance is dependent on <strong>the</strong> degree to which
<strong>the</strong> firm can use all <strong>of</strong> <strong>the</strong> knowledge created by <strong>the</strong> firm and turn this knowledge into value-creating
activities (Krogh 1998). These firms are able to use <strong>the</strong> tacit knowledge component <strong>of</strong> KM to create
hard-to-duplicate core competence in managing, identifying, capturing, systemizing, and applying tacit
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Harold Harlow
knowledge to create customer value as measured by innovation and economic outcomes. In order to
measure <strong>the</strong> innovation and economic outcomes <strong>of</strong> <strong>the</strong> firm, it is important to understand how and
why tacit KM is both crucial and necessary in today’s firms.
It is not sufficient to have knowledge assets, patents, or o<strong>the</strong>r marketable intellectual property. In a
knowledge creating company, managers have <strong>the</strong> responsibility to unleash that knowledge into valuecreating
actions aimed at customers and to generate and exploit that knowledge-ei<strong>the</strong>r public or
proprietary-more effectively than <strong>the</strong>ir competitors. In addition, managers are also responsible to
generate and exploit current firm knowledge better than <strong>the</strong>ir competitors and to use public knowledge
better than <strong>the</strong>ir rivals (Von Grogh Ichigo et al 2000). Von Krogh, Roos and Slocum (1994) suggest
that <strong>the</strong>re are essentially only two strategies used and that those are advancement and survival.
Ans<strong>of</strong>f (1990) also uses <strong>the</strong>se two distinctions as operational effectiveness strategy and developing
future pr<strong>of</strong>it potential strategies.
.
Miles and Snow (1978) identified three primary strategic types—defenders, prospectors, and
analyzers—and one lesser strategic type: reactors. Each <strong>of</strong> <strong>the</strong>se strategic types has different
performance characteristics based on environmental conditions such as stage in <strong>the</strong> product life
cycle, market growth, and industry product innovation. Each <strong>of</strong> <strong>the</strong>se strategies also has its own set
<strong>of</strong> unique knowledge competencies that enable <strong>the</strong> firm to compete. Strategic fit is an important
consideration as firms employ KM systems and determine whe<strong>the</strong>r to use systems that are tacit,
explicit, or a mix <strong>of</strong> both (Miles and Snow 1984). Porter also identified cost and differentiation as two
primary strategies <strong>of</strong> <strong>the</strong> firm (Porter 1980). While o<strong>the</strong>r strategies have been proposed as guiding <strong>the</strong>
operation <strong>of</strong> <strong>the</strong> firm, a manager who is interested in any <strong>of</strong> <strong>the</strong>se strategies has to choose a
knowledge strategy that fits <strong>the</strong>ir firm and that knowledge strategy is driven by <strong>the</strong> overall strategy <strong>of</strong>
<strong>the</strong> firm.
My earlier research has developed a tacit knowledge index used in this paper (Harlow 2008). My prior
research also included <strong>the</strong> effect <strong>of</strong> tacit knowledge and knowledge management methods both in <strong>the</strong>
aggregate and individually on firm performance. This research forms <strong>the</strong> background and structure for
this follow-on paper on knowledge strategy development and its relationship to <strong>the</strong> innovation and
learning perspective <strong>of</strong> <strong>the</strong> balanced scorecard.
This research uses analysis <strong>of</strong> <strong>the</strong> performance outcomes where <strong>the</strong> results <strong>of</strong> <strong>the</strong> firm, in both
innovation and financial outcomes, are equated to customer value. That value-creating capability
resides in <strong>the</strong> know-how or tacit knowledge <strong>of</strong> <strong>the</strong> engineers, managers and marketing staff and this
dynamic tacit knowledge capability creates sustainable competitive advantage (Teece, 1998). These
subject matter experts must be able to fit into an automated system that allows tacit knowledge
dispersal and tacit knowledge use by both <strong>the</strong> experts and <strong>the</strong> rest <strong>of</strong> <strong>the</strong> firm’s staff and depends to a
large degree on <strong>the</strong> KM systems that are employed (Maybury D’Amore and House, 2000). This is
needed for both <strong>the</strong> firm’s survival and advancement strategy (Von Grogh Ichigo et al, 2000).
3. Research Hypo<strong>the</strong>sis and Results
Dependent performance variables for innovation and financial were operationalized in <strong>the</strong> prior
research survey (Harlow 2008) based on <strong>the</strong> research performance variables from <strong>the</strong> Pr<strong>of</strong>it Impact <strong>of</strong>
Market Strategies (PIMS) database (Anderson and Paine 1978). Scores from <strong>the</strong> 1-5 Likert scale
factor questions were summed to create an overall innovation or financial score. Higher additive
scores indicated higher performance on each measure. Table 1 below indicates <strong>the</strong> factors used to
sum <strong>the</strong>se variables.
Table 1: Innovation and Financial Performance Variables
Innovation Financial
Product turns (as related to competitors) Return on sales
Number <strong>of</strong> new products (as related to competitors) Cost <strong>of</strong> goods sold
Patents (as related to competitors) Earnings per share
New products compared to <strong>the</strong> competition Share price over past 3 years
Market share compared to <strong>the</strong> competition Return on equity
Age <strong>of</strong> product lines Revenues
Marketing effectiveness Pr<strong>of</strong>its
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Harold Harlow
The independent variable, knowledge strategy, was operationalized and a summation developed
based on <strong>the</strong> following four main areas: (1) Capability <strong>of</strong> sharing both internal and external
knowledge; (2) Ability to measure <strong>the</strong> sharing <strong>of</strong> that knowledge electronically; (3) Rewards for
sharing knowledge; (4) Use <strong>of</strong> information systems that provide a competitive advantage in sharing
knowledge.
The research was based on <strong>the</strong> assumptions that <strong>the</strong> firm knowledge management strategy deployed
a system <strong>of</strong> both explicit and tacit knowledge management methods to positively affect firm innovation
and financial performance. This includes various methods <strong>of</strong> KM that are tacit and not technology
based, such as teaming, master craftsman, communities <strong>of</strong> practice, experting, and collaboration.
The explicit methods in <strong>the</strong> research includes <strong>the</strong> use <strong>of</strong> technology-based methods such as e-mail
data-mining, digital databases, capturing lessons learned digitally, use <strong>of</strong> groupware, technology for
Computer Aided Design/Computer Aided Manufacturing/Computer Aided Engineering, content
capture, expertise capture and written processes and procedures. The main hypo<strong>the</strong>sis <strong>of</strong> this
research is as follows:
Hypo<strong>the</strong>sis 1-There is a positive relationship between firms that have a knowledge
strategy and innovation and financial outcomes.
Hypo<strong>the</strong>sis 2-There is a positive association between <strong>the</strong> Tacit Knowledge Index (TKI)
and Firm Outcomes.
Hypo<strong>the</strong>sis 3-There is a positive relationship between firms that have a knowledge
strategy and innovation and financial outcomes.
The following Table 2 presents <strong>the</strong> results <strong>of</strong> prior research on <strong>the</strong> tacitness <strong>of</strong> various knowledge
management methods based on an experts test (Harlow 2008):
Table 2: Industry Experts Tacit Scoring <strong>of</strong> KM Methods-Group 1 vs. Group 2
Group Group Mean Means Std Dev Std Dev Std Dev
KM Method 1 2 1&2 Difference Difference Group 1 Group 2
Collaboration 4.83 4.67 4.75 -0.17 -0.07 0.52 0.44
Communities 4.83 4.67 4.75 -0.17 -0.07 0.52 0.44
Experting 4.67 4.33 4.50 -0.33 -0.15 0.82 0.67
Teaming
Master
4.17 3.83 4.00 -0.33 -0.20 0.75 0.56
Craftsman 4.33 4.67 4.50 0.33 -0.07 0.52 0.44
CRM 4.00 4.00 4.00 0.00 -0.23 0.89 0.67
Lessons 3.83 4.00 3.92 0.17 -0.23 0.89 0.67
Data-mining
Digital
2.00 2.00 2.00 0.00 -0.23 0.89 0.67
Databases 1.50 1.50 1.50 0.00 -0.05 0.55 0.50
Groupware
Written
1.33 1.50 1.42 0.17 -0.05 0.55 0.50
Procedures 1.83 1.83 1.83 0.00 -0.20 0.75 0.56
Technology Use 1.67 1.67 1.67 0.00 -0.15 0.82 0.67
Content Capture
Written
1.50 2.17 1.83 0.67 -0.15 0.98 0.83
Processes
Capture
2.83 3.17 3.00 0.33 -0.20 0.75 0.56
Expertise 4.00 4.17 4.08 0.17 -0.20 0.75 0.56
These tacit ratings were used in <strong>the</strong> prior research (Harlow 2008) to develop a tacit knowledge index
score based on use. The highest tacit rated methods were collaboration (4.8), master craftsman (4.8)
and communities <strong>of</strong> practice (4.7). Teaming was rated lower (4.3) which was unexpected. The
resultant TKI variable was one that combined <strong>the</strong> expert’s tacitness (TK) measurement with <strong>the</strong> usage
at each firm to arrive at <strong>the</strong> TKI for <strong>the</strong> firm.
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3.1 TKI Regression results
Harold Harlow
My prior published research consisted <strong>of</strong> <strong>the</strong> entire sample <strong>of</strong> 108 firms. Table 3 below indicates that
predictor TKI variable had a R 2 value <strong>of</strong> .389 and .237 when regressed against <strong>the</strong> firm performance
variables, innovation and financial performance, respectively. The higher R 2 effect on I (innovation <strong>of</strong>
<strong>the</strong> firm) indicates that TKI has <strong>the</strong> greatest influence on innovation with a more moderate relationship
to F (financial performance)
Table 3: Summary Results <strong>of</strong> Regression Analysis
TKI (I) Innovation (D) Financial (D)
R
R2
F
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Harold Harlow
methods should be included and to <strong>the</strong> use <strong>of</strong> an overall scale to capture what is a tacit attribute <strong>of</strong>
each method. The five point Likert scale used in <strong>the</strong> study provides a limited measurement scale and
may miss significant resolutions in <strong>the</strong> data set. Fur<strong>the</strong>r research using a seven point scale might
produce results that would be more clearly defined.
The results <strong>of</strong> this study provide empirical results that appear to indicate that use <strong>of</strong> tacit methods has
a greater effect on innovation than on financial measures, especially in those firms with an explicit
knowledge strategy. The implication is that firms that want to innovate should both have a strategy
and employ a higher degree <strong>of</strong> tacit method usage than firms whose goals are more financial.
Fur<strong>the</strong>r, firms whose environments require more innovation would be advised to develop a knowledge
strategy and use <strong>the</strong> methods that are highest in TK throughput such as experting, collaboration and
master craftsman.
The ability to measure TK and <strong>the</strong> development <strong>of</strong> <strong>the</strong> TKI is <strong>the</strong> most important contribution <strong>of</strong> this
study to <strong>the</strong> field <strong>of</strong> strategic KM. In addition, <strong>the</strong> research results also showed a strong relationship
between firms that have a knowledge strategy (KS). Armed with <strong>the</strong>se recommendations, managers
are now able to measure <strong>the</strong> KS and TKI <strong>of</strong> <strong>the</strong>ir firm by determining <strong>the</strong> usage <strong>of</strong> <strong>the</strong> various KM
methods and using our expert rating <strong>of</strong> <strong>the</strong> underlying tacit-ness <strong>of</strong> each method. This is a significant
step because it sets <strong>the</strong> stage for more detailed studies with more definition <strong>of</strong> each <strong>of</strong> <strong>the</strong> variables
and perhaps a look at how this tacit measure affects firm strategic choices.
TKI measure should allow firms to make better strategic decisions, since firms that identify TK as
important make better decisions during <strong>the</strong> strategic decision-making process (Brock and Anthony,
2002). The study by Brock and Anthony provided an integration <strong>of</strong> <strong>the</strong> cognitive and strategic
literatures to show that TK is accessible and how it plays an integral role in <strong>the</strong> context <strong>of</strong> strategic
decision-making. Brock and Anthony proposed that better decisions would occur when TK is
employed overtly during strategy sessions. This lends itself to <strong>the</strong> creation <strong>of</strong> a knowledge strategy
and development <strong>of</strong> <strong>the</strong> KS to drive <strong>the</strong> business performance.
5. Balanced scorecard innovation and learning perspective TKI Metrics
Many executives in <strong>the</strong> 1980’s argued that financial measures alone did not allow <strong>the</strong>m to manage
effectively and wanted to replace those measures with operational measures. David Norton and
Robert Kaplan (1992) proposed and championed <strong>the</strong> Balanced Scorecard (BSC) that included both
operational and financial measures that would lead to a better understanding <strong>of</strong> whe<strong>the</strong>r or not <strong>the</strong>
company was effectively achieving its long-term strategy (Kaplan and Norton 1996a). The BSC uses
four parameters (financial, customer, internal processes and learning and innovation) to develop
metrics that directly link to <strong>the</strong> strategy <strong>of</strong> <strong>the</strong> firm (Kaplan and Norton 1996b). By tying each <strong>of</strong> <strong>the</strong>se
parameters to <strong>the</strong> overall firm strategy (Kaplan and Norton 2001), firms link <strong>the</strong> strategy to programs
and actions(Kaplan and Norton 1996c) and gain from <strong>the</strong> inter-relational effect <strong>of</strong> each parameter’s
metrics resulting in even better firm performance.
Straightforward measures for <strong>the</strong> financial, customer perspective and internal processes parameters
have been developed. However, many companies implementing BSC have struggled to devise
scorecard metrics for <strong>the</strong> learning and innovation parameter. Kaplan and Norton (1994) have found
that companies draw from a common core <strong>of</strong> three outcome measurements-employee satisfaction,
retention and productivity. Within this core, <strong>the</strong> employee satisfaction objective is considered <strong>the</strong>
driver <strong>of</strong> <strong>the</strong> o<strong>the</strong>r two dimensions. They explain that staff competencies, technology infrastructure
and a climate for action enable <strong>the</strong> core measurements. The drivers for staff competency are strategic
skills, training levels and skill leverage. Drivers for technology infrastructure include strategic
technologies, strategic databases, experience capture, proprietary s<strong>of</strong>tware and intellectual property.
The climate-for-action driver includes key decision cycle, strategic focus, staff empowerment,
personal alignment, morale and teaming. These drivers are <strong>the</strong> same survey measures that determine
<strong>the</strong> extent <strong>of</strong> <strong>the</strong> firm’s Knowledge Strategy (KS) used in this research and form <strong>the</strong> basis for <strong>the</strong> use
<strong>of</strong> <strong>the</strong> TKI to implement <strong>the</strong>se drivers.
The TKI fits into <strong>the</strong> BSC innovation and learning framework by addressing all <strong>of</strong> three <strong>of</strong> <strong>the</strong>se drivers
that enable core measurements (Norton and Kaplan, 2005). Use <strong>of</strong> <strong>the</strong> TKI develops staff
competencies by increasing <strong>the</strong> knowledge flows and knowledge networking within <strong>the</strong> firm between
<strong>the</strong> knowledge holders and increasing strategic skills by developing collaborative skills, training <strong>the</strong>
staff through experting and master craftsman approaches and leveraging <strong>the</strong> skill <strong>of</strong> experts. The use
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<strong>of</strong> <strong>the</strong> 15 KM methods coupled with <strong>the</strong> TK methods enables <strong>the</strong> development and measurement
(using TKI) <strong>of</strong> strategic database progression and use, degree <strong>of</strong> experience capture and <strong>the</strong>
identification <strong>of</strong> proprietary s<strong>of</strong>tware and intellectual property. Key decision cycles are measured
indirectly using TKI in that innovation and financial results improve as a direct result <strong>of</strong> an increase in
TKI capability. Use <strong>of</strong> methods to increase TKI has improves strategic focus by developing shared
ideas and cooperation toward common goals. Since many <strong>of</strong> <strong>the</strong> KM methods are people-centric, <strong>the</strong>
staff is fur<strong>the</strong>r empowered which creates better personal alignment, morale and atmosphere for
teaming. Again, <strong>the</strong> TKI metric allows managers to set targets and allocate resources toward <strong>the</strong>se
measures by increasing <strong>the</strong> usage and quality <strong>of</strong> both tacit and explicit KM methods.
The development <strong>of</strong> a KM strategy within <strong>the</strong> BSC framework enables firms to address survival and
advancement strategies with appropriate measures and resources. Tacit knowledge plays a key role
in both <strong>of</strong> <strong>the</strong>se strategies on <strong>the</strong> one hand in survival <strong>of</strong> ensuring that firms maintain a competitive
advantage by easily transferring hard to imitate knowledge and in <strong>the</strong> case <strong>of</strong> advancement creating
new knowledge through radical or disruptive innovation. The use <strong>of</strong> a TKI based metric within <strong>the</strong>
BSC framework could help assure that both operational and strategic knowledge use and creation
was being supported and accomplished.
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423

Understanding <strong>the</strong> Fit between KAs and <strong>the</strong> Firm in Five
S<strong>of</strong>tware SMEs
Ciara Heavin and Frederic Adam
Business Information Systems, University College Cork, Ireland
c.heavin@ucc.ie
fadam@afis.ucc.ie
Abstract: There remains a lack <strong>of</strong> empirical evidence exploring how s<strong>of</strong>tware SMEs operationalise <strong>the</strong>ir
approach to knowledge management (KM). This study endeavours to <strong>of</strong>fer a tangible mechanism for
understanding and diagnosing how this type <strong>of</strong> firm manages knowledge. The objective <strong>of</strong> this study was to
devise a classification <strong>of</strong> knowledge activities (KAs) which facilitates <strong>the</strong> exploration <strong>of</strong> a Small to Medium Sized
Enterprises (SMEs) in terms <strong>of</strong> <strong>the</strong> type and extent to which knowledge is managed. Fur<strong>the</strong>r to this, analysis <strong>of</strong>
KAs provided a greater understanding <strong>of</strong> <strong>the</strong> fit between <strong>the</strong> firm’s objectives and <strong>the</strong> KM approach pursued. In
order to achieve this, five case studies were conducted and subsequently. Based on <strong>the</strong> classification <strong>of</strong> KAs
identified, a qualitative analysis approach was used to code each <strong>of</strong> <strong>the</strong> twenty eight interviews conducted. Both
quantitative and qualitative content analysis methods were applied to facilitate data reduction and generate
meaning from <strong>the</strong> significant volume <strong>of</strong> data collected. The output from this study includes a classification <strong>of</strong> KAs
which provide a rich insight into how SMEs are motivated to deal with knowledge as a means <strong>of</strong> achieving <strong>the</strong>ir
organisational objectives. From a practitioner viewpoint, this study seeks to <strong>of</strong>fer an improved understanding <strong>of</strong>
an SMEs’ approach to KM, particularly in <strong>the</strong> current economic climate where SMEs with significantly curtailed
resources endeavour to seek new ways <strong>of</strong> leveraging knowledge in order to deal with unanticipated events.
Keywords: Knowledge Management (KM), Knowledge Activity (KA), knowledge, Small to Medium Sized
S<strong>of</strong>tware Enterprises (SMES) and s<strong>of</strong>tware
1. Introduction
Recently, Crawford (2009) has argued that <strong>the</strong> knowledge economy has done to white collar jobs,
what assembly line workers feared during <strong>the</strong> automation <strong>of</strong> <strong>the</strong> Piney Wood paper mills (Zub<strong>of</strong>f,
1991), turned work into repetitive, tedious and frustrating tasks, creating what Leavitt and Whistler
(1958, p46) referred to as “programmed personnel”. While computerisation eliminated <strong>the</strong> need to
‘smell <strong>the</strong> rubber’ during <strong>the</strong> paper production process, <strong>the</strong>se changes enabled <strong>the</strong> development <strong>of</strong>
new skill sets, particularly in terms <strong>of</strong> leveraging new technology to ensure <strong>the</strong> mill was operating at
optimum production capacity (Zub<strong>of</strong>f, 1991). Although, for some organisations <strong>the</strong> pursuit <strong>of</strong> a KM
approach has meant more routinised tasks, this change has created an availability <strong>of</strong> resources to
facilitate heightened levels <strong>of</strong> creativity. Nonaka and Takeuchi (1995) observed this plane <strong>of</strong>
knowledge creativity in companies such as Honda, Canon, Matsushita, Kao and Sharp. These
organisations have realised Leavitt and Whisler’s (1958) vision, a time where information would be
managed and categorised more effectively, facilitating <strong>the</strong> extended thinking range <strong>of</strong> managers,
enabling <strong>the</strong>m to categorise, digest and act on a wider range <strong>of</strong> issues. For o<strong>the</strong>rs this level <strong>of</strong> KM has
not been achieved. Recently, Delen and Al-Hawamdeh (2009, p141) argue that with “<strong>the</strong> massive
amounts <strong>of</strong> information being added to corporate databases and <strong>the</strong> Internet every day, effective and
efficient knowledge discovery has become an imminent problem”. It seems that with more
sophisticated technology and <strong>the</strong> heightened availability <strong>of</strong> knowledge, KM has become a more
pressing issue for those organisations who have not achieved this level <strong>of</strong> routinisation presented in
extant research (Leavitt and Whisler, 1958; Huber, 1984). Moreover, Leavitt and Whisler’s (1958, p41)
postulations applied to “<strong>the</strong> medium and large business firms <strong>of</strong> <strong>the</strong> future” consequently overlooking
<strong>the</strong> strategic future <strong>of</strong> smaller organisations.
In examining <strong>the</strong> KM related literature, it may be observed that much <strong>of</strong> <strong>the</strong> current literature reflects
<strong>the</strong> discourse proliferated by strategies and technologies implemented in larger organisations. This
study seeks to overcome this problem by pursuing a study <strong>of</strong> KM in SMEs.
2. Defining knowledge
The concept <strong>of</strong> a continuum in IS is widely considered (Davis and We<strong>the</strong>rbe, 1979; Mason and Mitr<strong>of</strong>f,
1973; Davenport and Prusak, 1998; Wurman, 2001). A continuum is defined as a “continuous
sequence in which adjacent elements are not perceptibly different from each o<strong>the</strong>r, but <strong>the</strong> extremes
are quite distinct.” (Compact Oxford English Dictionary, 2005). Defining data, information and
knowledge as distinct and independent phenomena is an arduous endeavour. In particular it is noted
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that many authors use <strong>the</strong> terms information and knowledge interchangeably, those (Dennis, Earl, El
Sawy, Huber) that considered organisational information processing in <strong>the</strong> 1970s, 1980s and early
1990s now focus <strong>the</strong>ir attentions on KM as an organisational strategy. In essence information
processing/management has become, at least in part, knowledge management. Figure 1 represents
data, information and knowledge as a continuum.
Figure 1: Knowledge Continuum (after Davenport and Prusak, 1998; Wurman, 2001)
Figure 1 illustrates that <strong>the</strong> extremes <strong>of</strong> each phenomenon are distinct however <strong>the</strong>re is significant
overlap between data/information and information/knowledge. According to Davenport and Prusak
(1998, p147) “<strong>the</strong> distinction between knowledge and information is seen as more <strong>of</strong> a continuum than
a sharp dichotomy. Most projects that focus on internal knowledge [repository] deal with <strong>the</strong> middle <strong>of</strong>
<strong>the</strong> continuum-information that represents knowledge to certain users”. The point where information
becomes knowledge and vice versa is difficult to pinpoint with complete accuracy, however <strong>the</strong>re is no
doubt that <strong>the</strong>se phenomena are closely linked. For <strong>the</strong> purpose <strong>of</strong> this study, it is at this point
(illustrated in Figure 1) in <strong>the</strong> continuum where <strong>the</strong> knowledge focus <strong>of</strong> this study lies in order to
identify and explore <strong>the</strong> nature <strong>of</strong> knowledge within <strong>the</strong> context <strong>of</strong> an SME.
3. Establishing a classification <strong>of</strong> KAs
Researchers (Choo, 1996; Holsapple and Whinston, 1987; Huber, 1990; Kraaijenbrink et al., 2006;
Pentland, 1995; Szulanski, 1994; Holsapple and Joshi, 2004) use different terms to describe similar
KAs. For <strong>the</strong> purpose <strong>of</strong> this research a KA is defined as “transactions or manipulations <strong>of</strong> knowledge
where <strong>the</strong> knowledge is <strong>the</strong> object not <strong>the</strong> result” (Kraaijenbrink et al., 2006, p23). Based on extant
research, a refined set <strong>of</strong> six KA is presented in Table 1. These encompass <strong>the</strong> key activities
experienced when contemplating an organisation’s KM approach.
Table 1: An Organisation’s KAs
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3.1 Acquire
Ciara Heavin and Frederic Adam
From Huber’s (1991, p90) perspective “Knowledge acquisition is <strong>the</strong> process by which knowledge is
obtained”. Kraaijenbrink et al. (2006) define knowledge acquisition as knowledge transferred from a
source to a company through sub processes: written form, physical objects, people; cooperation
between source and recipient; courses; and outsourcing.
3.2 Codify
Knowledge codification converts <strong>the</strong> generated knowledge into accessible and applicable formats
(Davenport and Prusak, 1998). Combine, internalise or absorb are verbs that may be used to describe
this activity. Knowledge codification is concerned with <strong>the</strong> capture, representation and storage <strong>of</strong>
knowledge in computerised knowledge bases (Nevo et al., 2007). Hansen et al.’s (1999) codification
strategy supports <strong>the</strong> use <strong>of</strong> knowledge repositories e.g. documentation and more specifically
technology i.e. databases to store organisational knowledge.
3.3 Store
According to Alavi and Leidner (2001, p127) knowledge storage “involves obtaining <strong>the</strong> knowledge
from organisational members and/or external sources, coding and indexing <strong>the</strong> knowledge (for later
retrieval) and capturing it”. The key to storing organizational knowledge is that <strong>the</strong> members must be
able to anticipate <strong>the</strong> future needs <strong>of</strong> <strong>the</strong> organisation in order for <strong>the</strong> most valuable knowledge to be
stored (Huber, 1991).
3.4 Maintain
The maintenance <strong>of</strong> knowledge stores is essential to <strong>the</strong> continued progression <strong>of</strong> an organisation’s
ability to learn. Holsapple and Singh (2004) refer to knowledge control to describe <strong>the</strong> provision <strong>of</strong>
quantity and quality knowledge as a significant KA. They advocate that knowledge should be accurate,
consistent (have validity), relevant and important (have utility). Accordingly, Anderson Consulting
(1996) acknowledged <strong>the</strong> need for control over <strong>the</strong>ir knowledge repository to ensure useful, fresh and
high quality knowledge, “it would have be more than a dumping ground <strong>of</strong> documents” (Holsapple and
Singh, 2004, p239).
3.5 Transfer
Knowledge transfer is established through person-to-person or system-to-person interaction (Joe and
Yoong, 2004). This supports Hansen et al.’s (1999) personalization strategy. From Table 1, it is
evident that knowledge transfer occurs inside and outside <strong>the</strong> organisation. Thus, an organisation may
transfer knowledge or receive it from outside <strong>the</strong> organisation, which is knowledge acquisition.
3.6 Create
Table 1 shows that knowledge creation involves developing new content or replacing existing content
within <strong>the</strong> organization’s tacit and explicit knowledge (Pentland, 1995). While it may be argued that
new knowledge may be created through formalized mechanisms e.g. surveys and research and
development (Kayworth and Leidner, 2004) o<strong>the</strong>rs propose that <strong>the</strong> creation <strong>of</strong> new knowledge should
not be a formalised process but one which is socially constructed and occurs over time through human
networks (Brown and Duguid, 2000; Fahey and Prusak, 1998).
4. Research approach
The complex nature <strong>of</strong> KM coupled with <strong>the</strong> multifaceted characteristics <strong>of</strong> small s<strong>of</strong>tware firms
demanded an approach that facilitated <strong>the</strong> identification and exploration <strong>of</strong> <strong>the</strong>se phenomena. The
researcher pursued <strong>the</strong> strategy <strong>of</strong> purposeful sampling as a means <strong>of</strong> selecting information rich
cases for this study (Miles and Huberman, 1994; Patton, 1990); <strong>the</strong> nature <strong>of</strong> innovative s<strong>of</strong>tware
products developed by <strong>the</strong>se firms meant that this case displayed a wealth <strong>of</strong> KAs from <strong>the</strong> outset. As
<strong>the</strong> objective <strong>of</strong> this study was to explore <strong>the</strong> KAs <strong>of</strong> small s<strong>of</strong>tware firms, <strong>the</strong> focus <strong>of</strong> <strong>the</strong> study was
on <strong>the</strong> two core business processes <strong>of</strong> sales and s<strong>of</strong>tware development. Based on Knoke’s (1994)
selection strategy, <strong>the</strong> positional method was utilised to uncover those key players in <strong>the</strong> case study.
The sales and s<strong>of</strong>tware development managers were identified; o<strong>the</strong>r respondents were selected
based on <strong>the</strong>ir reputations. In some cases <strong>the</strong> key technical roles were heavily involved in <strong>the</strong> sales
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Ciara Heavin and Frederic Adam
function and were able to provide an in-depth insight into sales processes at <strong>the</strong> organisation. Twenty
eight respondents in total were interviewed for this study.
The “thick transcripts” (Miles and Huberman, 1994) derived from <strong>the</strong> interview process coupled with
<strong>the</strong> complexity <strong>of</strong> KM as an area <strong>of</strong> research provided <strong>the</strong> rationale for pursuing qualitative analysis
through <strong>the</strong> use <strong>of</strong> coding techniques (Ägerfalk and Fitzgerald, 2008). For <strong>the</strong> purpose <strong>of</strong> this study
six “seed categories” (Miles and Huberman, 1994) were proposed, <strong>the</strong>se categories were initially
derived from <strong>the</strong> literature. The many classifications <strong>of</strong> KAs have been assessed and evaluated to
develop a complete classification <strong>of</strong> KAs for <strong>the</strong> purpose <strong>of</strong> data analysis in this study. Each KA was
assigned a code and this code was utilised to classify <strong>the</strong> nature <strong>of</strong> KAs that occur, <strong>the</strong>se categories
were <strong>the</strong>n assigned chunks <strong>of</strong> data derived from <strong>the</strong> interview transcripts. This iterative process
derived a set <strong>of</strong> categories which capture <strong>the</strong> occurrences <strong>of</strong> KAs in <strong>the</strong> organisation as informed by
<strong>the</strong> interviews. The KAs for each <strong>of</strong> <strong>the</strong> firms were collated based on each individual memo generated
at <strong>the</strong> level <strong>of</strong> <strong>the</strong> interview. A counting analysis technique was used to derive meaning from <strong>the</strong> large
volume <strong>of</strong> data ga<strong>the</strong>red. Miles and Huberman (1994) advocate <strong>the</strong> use <strong>of</strong> counting in qualitative
analysis “to rapidly see what you have in large batches <strong>of</strong> data” (p253). The knowledge types were
counted, <strong>the</strong> KAs were counted and <strong>the</strong> distribution across <strong>the</strong> six KAs was calculated for each case.
The counts were derived from a populated table <strong>of</strong> KAs for each case (not included); aliases are used
to protect <strong>the</strong> privacy <strong>of</strong> <strong>the</strong> companies under consideration.
5. Findings
Table 2: Case Background
5.1 Knowledge Activity at HelpRead Ltd
Table 3 identifies 82 instance <strong>of</strong> KA at HelpRead; <strong>the</strong> majority <strong>of</strong> activities presented <strong>the</strong>mselves
through knowledge acquisition, storage and transfer.
Table 3 identifies <strong>the</strong> distribution <strong>of</strong> KAs and <strong>the</strong> type <strong>of</strong> knowledge focus for each <strong>of</strong> <strong>the</strong>se activities -
<strong>the</strong>se statistics are derived from <strong>the</strong> individual tables <strong>of</strong> KAs. The study identified 82 KAs; however we
uncovered 113 instances <strong>of</strong> knowledge types. The difference in <strong>the</strong>se figures can be explained by
single KAs leveraging multiple knowledge types in some instances, thus increasing <strong>the</strong> instances <strong>of</strong>
knowledge types identified in <strong>the</strong> analytic memos. It is also important to note that, at <strong>the</strong> time,
HelpRead Ltd. was not in a new product development phase - at a time <strong>of</strong> new product development,
higher number <strong>of</strong> KAs would be anticipated.
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Table 3: Distribution <strong>of</strong> KAs at HelpRead Ltd.
Ciara Heavin and Frederic Adam
Using Table 3 <strong>the</strong> difference in intensity between <strong>the</strong>se types <strong>of</strong> activities is indicative <strong>of</strong> HelpRead’s
position as a growing organisation. Knowledge acquisition intensity at 21 percent (n=82) showed that
HelpRead were focused on building a collective organisational memory that facilitates continued
growth through <strong>the</strong> introduction <strong>of</strong> new hires and new products. This is particularly important to <strong>the</strong>m
in terms <strong>of</strong> acquiring external knowledge to inform new product development. Fifty three percent <strong>of</strong> all
knowledge acquisition activity was focused on ga<strong>the</strong>ring product knowledge supporting Groen’s
(2006, p124) view that in high-technology SMEs “intensive interaction is needed to enhance <strong>the</strong>
product creation process with knowledge from external sources”.
At 13 percent (n=82) codification activity was relatively low intensity - this was reflective <strong>of</strong> <strong>the</strong>
uncertainty around what <strong>the</strong> company needs to know in <strong>the</strong> future. This is predominantly evident with
<strong>the</strong> Technical FAQ, which lacked buy-in from <strong>the</strong> entire development team. The Development
Manager admitted that as a team “<strong>the</strong>y didn’t know what <strong>the</strong>y should know”. Most codification activity
was directly related to refining <strong>the</strong> discussions at group meetings into documents which are made
available over <strong>the</strong> Intranet. Over 90 percent (n=11) <strong>of</strong> all codification activity identified in Table 3 was
related to product development knowledge. Codification is largely not a sales related activity. The well
defined scope <strong>of</strong> <strong>the</strong> Goldmine TM sales system meant that no KA was required to support <strong>the</strong>
refinement and distillation <strong>of</strong> sales related knowledge. In addition, <strong>the</strong> experience <strong>of</strong> <strong>the</strong> sales team
meant <strong>the</strong>y know what important customer and sales related knowledge should be stored for future
use.
The high occurrence <strong>of</strong> storage activities at 29 percent was indicative <strong>of</strong> <strong>the</strong> importance placed on
storing knowledge in <strong>the</strong> new Intranet-based quality system - approx 74 percent (17 <strong>of</strong> n=24 storage
activities) <strong>of</strong> storage activity involved <strong>the</strong> Intranet. These activities primarily included storing s<strong>of</strong>tware
project documents and employee skills documents, in line with <strong>the</strong> compliance requirements outlined
by IS9001:2000. The codification intensity also included <strong>the</strong> level <strong>of</strong> customer information captured
and stored by <strong>the</strong> sales team. This 29 percent reflected <strong>the</strong> move to store <strong>the</strong> knowledge ga<strong>the</strong>red
from acquisition, codification and transfer activities. Hoch et al. (1998) purport that s<strong>of</strong>tware
companies with well established structure and processes reduce low level s<strong>of</strong>tware rework and
facilitates s<strong>of</strong>tware reuse, it appears that HelpRead were moving in this direction. While <strong>the</strong> company
were not focused on very sophisticated process certification, i.e. CMM, leveraging KM approaches
<strong>of</strong>fer an effective alternative (Baskerville and Pries-Heje, 1999).
Maintenance activities at 10 percent highlighted <strong>the</strong> company’s focus on maintaining s<strong>of</strong>tware and
product development knowledge. Surprisingly, transfer activity was high intensity at 19 percent - with
closer inspection; <strong>the</strong> role <strong>of</strong> <strong>the</strong> Technical Director was integral to this. At 6 percent, knowledge
creation was very low. While Table 3 shows that 80 percent <strong>of</strong> knowledge creation activity was
focused on product knowledge, in line with company strategy, <strong>the</strong> lack <strong>of</strong> o<strong>the</strong>r types <strong>of</strong> knowledge
creation may be attributed to <strong>the</strong> pressures associated with <strong>the</strong> rapid growth in employee headcount
and <strong>the</strong> increased product portfolio.
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5.2 Knowledge Activity at TravelS<strong>of</strong>t Ltd
Ciara Heavin and Frederic Adam
Table 4 shows that 147 instances <strong>of</strong> KAs were identified at TravelS<strong>of</strong>t Ltd.
Table 4: Distribution <strong>of</strong> KAs at TravelS<strong>of</strong>t Ltd.
The KAs in Table 4 leveraged more than one type <strong>of</strong> knowledge during a single activity, thus
providing <strong>the</strong> rationale for <strong>the</strong> 211 instances <strong>of</strong> knowledge types identified for TravelS<strong>of</strong>t. The
knowledge focus at TravelS<strong>of</strong>t was quite consistent and reflected <strong>the</strong> company’s strategic objectives.
The emphasis on s<strong>of</strong>tware development, project, process and product knowledge was clear.
Knowledge <strong>of</strong> <strong>the</strong> travel industry made up a quarter <strong>of</strong> <strong>the</strong> knowledge acquisition activity.
At HelpRead Ltd. 82 instances <strong>of</strong> KAs were observed, KA at TravelS<strong>of</strong>t was considerably higher at
147 instances. This intensity may be explained by a number <strong>of</strong> factors. At <strong>the</strong> time <strong>of</strong> interview a new
Application Solutions Manager had been in place for approximately eight months. From a Telecoms
background, he implemented a number <strong>of</strong> organisational strategies to develop embedded processes
and most importantly to bring a new product to <strong>the</strong> travel s<strong>of</strong>tware marketplace. It is primarily <strong>the</strong>se
management initiatives that contributed to <strong>the</strong> high density <strong>of</strong> KAs.
Table 4 shows knowledge acquisition activity at 11 percent (16 <strong>of</strong> n=147), this is due to <strong>the</strong> acquisition
<strong>of</strong> consultant knowledge on new product development, employee training, relevant books, journals
and travel conferences. In terms <strong>of</strong> codification at 20 percent activity, project related knowledge was
refined and stored. At 21 percent activity, storage activity was almost in line with <strong>the</strong> volume <strong>of</strong>
codification activity. This indicates that TravelS<strong>of</strong>t were good at following through on this type <strong>of</strong>
activity. For example <strong>the</strong> steering committee refine and store <strong>the</strong> new Adept framework templates in
<strong>the</strong> relevant artefacts. R&D acquisition, codification, storage and maintenance <strong>of</strong> knowledge
contribute to <strong>the</strong> dense volumes <strong>of</strong> KA. Activities such as Internet research in <strong>the</strong> travel area add to
<strong>the</strong> level <strong>of</strong> knowledge acquisition activities, while refining and storing this knowledge contributes to
<strong>the</strong> volume <strong>of</strong> codification and storage activity. At 14 percent, maintenance activity was lower than
knowledge codification and storage activity. This could be due to <strong>the</strong> fact that some <strong>of</strong> <strong>the</strong> knowledge
stored does not require updating, for example conference and journal papers on <strong>the</strong> travel industry
will not be changed although new papers may be added over time resulting in increased storage
activity.
Knowledge transfer at 28 percent (41 where n=147) represented <strong>the</strong> highest volume <strong>of</strong> KA.
Leveraging a variety <strong>of</strong> routine and non-routine modes (<strong>the</strong>se are outlined in next section). This
organisation encourages knowledge transfer at all levels <strong>of</strong> <strong>the</strong> organisation. Knowledge creation is
considerably lower at 9 instances (6 percent where n=147). These activities were all generated
around new product and process development placing <strong>the</strong>se initiatives at <strong>the</strong> core <strong>of</strong> all KAs <strong>of</strong>
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Ciara Heavin and Frederic Adam
TravelS<strong>of</strong>t at that time. Table 4 shows a spread <strong>of</strong> 66 percent <strong>of</strong> KA at TravelS<strong>of</strong>t across knowledge
acquisition, codification, storage and maintenance activity, while transfer and creation activity account
for 34 percent <strong>of</strong> all KA. By comparison, <strong>the</strong> distribution at HelpRead for <strong>the</strong> same activities was 73
percent and 27 percent respectively. This shows that through <strong>the</strong>ir change process, TravelS<strong>of</strong>t are
are good at leveraging <strong>the</strong> more valuable types <strong>of</strong> KA.
5.3 Knowledge Activity at Systems Solutions Ltd
Table 5 illustrates a total <strong>of</strong> 105 KAs identified at Systems Solutions.
Table 5: Distribution <strong>of</strong> KAs at Systems Solutions
We identified 131 instances <strong>of</strong> knowledge type across <strong>the</strong> KAs; this indicates that some KAs
leveraged multiple knowledge types. From examining Table 5, it is apparent that knowledge
acquisition and maintenance were exceptions in terms <strong>of</strong> <strong>the</strong>ir knowledge focus. Knowledge
acquisition was focused on product and customer knowledge, <strong>the</strong>se knowledge types are largely
pertinent to <strong>the</strong> Business Service Management and SAP Solutions divisions focused on s<strong>of</strong>tware
resale. Knowledge maintenance activity was focused on sales knowledge at 38 percent <strong>of</strong> knowledge
focus for that activity. The emphasis on sales primarily reflects <strong>the</strong> knowledge requirements <strong>of</strong> <strong>the</strong>se
two divisions. From Table 5, <strong>the</strong> o<strong>the</strong>r KAs were focused on s<strong>of</strong>tware development and project
knowledge serving <strong>the</strong> knowledge needs <strong>of</strong> <strong>the</strong> Business Intelligence (data warehousing) and
Application Development divisions.
At 105 instances, KA is mostly characterised by its informal nature. The Managing Director admitted
that when he is involved with requirements analysis for <strong>the</strong> business intelligence division <strong>the</strong>
knowledge is documented and stored in an A4 pad. In addition, one Project Manager from <strong>the</strong>
Application Division admitted that it was not uncommon to calculate a project price on <strong>the</strong> back <strong>of</strong> a
piece <strong>of</strong> paper in <strong>the</strong> car park before attending a meeting with a prospective customer.
Project related knowledge was codified, stored and maintained in order to meet <strong>the</strong> requirements <strong>of</strong>
pharmaceutical customers who must abide by Food and Drugs Authority (FDA) regulations. From
Table 5, it is evident that at 26 percent, storage activity was higher than both codification and
maintenance activity toge<strong>the</strong>r at 21 percent. This may mean that Systems Solutions store large
volumes <strong>of</strong> documentation without refining and formatting it, and in <strong>the</strong> longer term, without updating
it. As a result, it seems <strong>the</strong>y hold large <strong>the</strong>se volumes for <strong>the</strong> sole purpose <strong>of</strong> protecting <strong>the</strong>mselves
from external threats such as possible audits. At 15 percent, knowledge acquisition appeared
important, however more than half <strong>of</strong> this activity is attributed to sales and customer interaction.
At 5 percent, knowledge creation activity was very low. The Managing Director was <strong>the</strong> main source
<strong>of</strong> <strong>the</strong> knowledge creation activity at Systems Solutions. It seems that <strong>the</strong> time pressures associated
with meeting project deadlines means that <strong>the</strong>re was little time for knowledge creation activity
amongst <strong>the</strong> divisions. In <strong>the</strong> case <strong>of</strong> Systems Solutions knowledge creation was not <strong>the</strong> responsibility
<strong>of</strong> those at an operational level.
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Ciara Heavin and Frederic Adam
Knowledge acquisition, codification, storage and maintenance account for 66 percent <strong>of</strong> all KAs while
knowledge transfer and creation amount to 34 percent. This is consistent with TravelS<strong>of</strong>t however, it
differs in <strong>the</strong> case <strong>of</strong> HelpRead whose focus on knowledge storage activity through <strong>the</strong> new company
Intranet tips <strong>the</strong> balance <strong>of</strong> KA distribution towards <strong>the</strong> earlier activities.
5.4 Knowledge Activity at FinS<strong>of</strong>t Ltd
Table 6 illustrates 78 instances <strong>of</strong> KAs identified at FinS<strong>of</strong>t Ltd..
Table 6: Distribution <strong>of</strong> KAs at FinS<strong>of</strong>t Ltd.
We identified 111 instances <strong>of</strong> knowledge types across <strong>the</strong> KAs; this indicates that KAs at FinS<strong>of</strong>t Ltd.
leverage more than one type <strong>of</strong> knowledge during KAs. Table 6 shows a consistent knowledge focus
on s<strong>of</strong>tware development and project knowledge across knowledge codification, storage,
maintenance and transfer activity. Knowledge acquisition activity focused on customer knowledge,
while creation activity leveraged product knowledge.
Knowledge acquisition activity is primarily related to <strong>the</strong> sales and customer knowledge at FinS<strong>of</strong>t
Ltd.. While 11 percent (9 where n=78) <strong>of</strong> KA was external knowledge acquisition, a large portion <strong>of</strong>
this activity was impromptu in nature although it was driven by <strong>the</strong> Head <strong>of</strong> Sales and <strong>the</strong> Chief
Technology Officer (CTO). Knowledge codification, storage and maintenance activity at 46 percent
(43 where n=78) was primarily related to project knowledge and s<strong>of</strong>tware development activity.
However as FinS<strong>of</strong>t Ltd. is a supplier <strong>of</strong> investment fund data feeds and bespoke fund management
s<strong>of</strong>tware, this activity was conducted to protect <strong>the</strong> organisation from issues arising from regular
internal audits.
Knowledge transfer at 29 percent (23 where n=78) intensity occurred amongst <strong>the</strong> teams, s<strong>of</strong>tware
developers, quality assurance (QA), data, sales and senior management. While it was admitted that
knowledge transfer between <strong>the</strong> s<strong>of</strong>tware development team and Quality Assurance was effective, <strong>the</strong>
Head <strong>of</strong> <strong>the</strong> Data team admitted that knowledge transfer amongst <strong>the</strong> o<strong>the</strong>r division needed
improvement. Knowledge creation activity is extremely low at 3 instances (4 percent where n=78),
management are typically involved in <strong>the</strong>se activities focused around attracting new customers and
creating potential new product ideas. However, <strong>the</strong>y did not seem to expend significant resources in
creating new ideas in-house.
At FinS<strong>of</strong>t Ltd. <strong>the</strong> distribution <strong>of</strong> acquire, codify, store and maintain activities to knowledge transfer
and creation activities were at 66 percent and 34 percent respectively. While at HelpRead this
distribution was at 74 percent and 26 percent, however <strong>the</strong> focus <strong>the</strong>re was on <strong>the</strong> development <strong>of</strong> <strong>the</strong>
Intranet and collaborative wiki technologies, with knowledge storage activity accounting for a
significant proportion <strong>of</strong> <strong>the</strong> 74 percent (29 percent storage activity).
5.5 Knowledge Activity at DocMan (Ireland) Ltd
The breakdown <strong>of</strong> KAs for DocMan (Ireland) Ltd. is presented in Table 7.
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Ciara Heavin and Frederic Adam
Table 7: Distribution <strong>of</strong> KAs at DocMan (Ireland) Ltd.
From Table 7 at 60 activities, <strong>the</strong> total volume <strong>of</strong> KA was low in comparison to <strong>the</strong> o<strong>the</strong>r cases
considered in this study. This may be attributed to <strong>the</strong> nature <strong>of</strong> <strong>the</strong> well defined work on s<strong>of</strong>tware
development components at <strong>the</strong> DocMan site in Ireland. The operations at <strong>the</strong> Irish site are part <strong>of</strong> a
larger document management s<strong>of</strong>tware component and <strong>the</strong> output from DocMan (Ireland) is
integrated by <strong>the</strong> s<strong>of</strong>tware integrator at <strong>the</strong> Swiss headquarters. As a result <strong>of</strong> this task specificity,
<strong>the</strong>re appeared to be a set <strong>of</strong> core KAs from which <strong>the</strong>re was minimal diversification at <strong>the</strong> Irish site.
Table 7 illustrates a significant level <strong>of</strong> knowledge consistency across all <strong>of</strong> <strong>the</strong> KAs. S<strong>of</strong>tware
development and project knowledge represented at least 57 percent <strong>of</strong> <strong>the</strong> knowledge focus for all six
KAs. This uniformity across activities also supports <strong>the</strong> task specialisation activity at <strong>the</strong> DocMan
(Ireland) site.
Table 7 indicates that DocMan (Ireland) leveraged some external knowledge resources at 12 percent
(7 where n=60) knowledge acquisition, however <strong>the</strong> main source <strong>of</strong> knowledge is <strong>the</strong> headquarters in
Switzerland and this was achieved through knowledge transfer activity which is very high at 40
percent (24 where n=60) <strong>of</strong> total activity. It is from here that <strong>the</strong> majority <strong>of</strong> customer requirements are
received as well as any new product knowledge.
In addition, knowledge creation activity was very low at 3 percent (2 where n=60). This may be <strong>the</strong>
result <strong>of</strong> <strong>the</strong> location <strong>of</strong> this development team with most new ideas being generated at a higher level
at company headquarters.
Although <strong>the</strong> total volume <strong>of</strong> KA is low, <strong>the</strong> split between <strong>the</strong> acquire, codify, store and maintain KA at
57 percent and <strong>the</strong> knowledge transfer and creation activity at 43 percent is more evenly balanced
than that observed at HelpRead Ltd., at 73 percent to 27 percent respectively. The geographic
location, <strong>the</strong> task specialisation and <strong>the</strong> maturity <strong>of</strong> <strong>the</strong> parent organisation may be attributed to <strong>the</strong>
knowledge transfer capabilities identified at DocMan Ireland.
6. Discussion
Table 8 syn<strong>the</strong>sizes KAs across <strong>the</strong> 5 cases. The highest and <strong>the</strong> lowest occurrences <strong>of</strong> KA for each
company are coloured green and red respectively. The real number and <strong>the</strong> percentage <strong>of</strong> KA
instances are presented along with <strong>the</strong> total KA and percentage <strong>of</strong> KAs.
Table 8 identifies <strong>the</strong> difference in <strong>the</strong> number <strong>of</strong> occurrences <strong>of</strong> KAs in each company. TravelS<strong>of</strong>t
represents 31 percent (147 where n=472) <strong>of</strong> all KA identified. At <strong>the</strong> time <strong>of</strong> this study, TravelS<strong>of</strong>t was
very focused on establishing and implementing a project management framework for all new projects
as well as an innovation management process for new product development. As a result, it may be
that TravelS<strong>of</strong>t was experiencing exceptionally high levels <strong>of</strong> KAs at that time. In sharp contrast,
DocMan provided <strong>the</strong> lowest number <strong>of</strong> instances <strong>of</strong> KAs with 13 percent (60 where n=472) <strong>of</strong> all
occurrences. While one could attribute this to <strong>the</strong> number <strong>of</strong> respondents interviewed for <strong>the</strong> study (4),
it is more likely that this low density <strong>of</strong> KAs may be explained by <strong>the</strong> very well defined nature <strong>of</strong> <strong>the</strong>
work carried out at this small company.
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Table 8: KAs in five S<strong>of</strong>tware SMEs
Ciara Heavin and Frederic Adam
In Table 8, it is evident that knowledge transfer at 28 percent (134 n=472) represents <strong>the</strong> highest
intensity <strong>of</strong> all KAs. For all companies apart from HelpRead Ltd., knowledge transfer was <strong>the</strong> most
intense KA with DocMan displaying <strong>the</strong> greatest intensity <strong>of</strong> transfer activity at 40 percent (n=60). This
may be due to DocMan’s constant interaction and exchange with <strong>the</strong>ir Swiss headquarters, <strong>the</strong>y are
particularly focused on ensuring that s<strong>of</strong>tware requirements are well understood by all parties coupled
with <strong>the</strong> need to maintain contact with <strong>the</strong> s<strong>of</strong>tware integrator, who is responsible for integrating <strong>the</strong>
components developed in <strong>the</strong> Irish <strong>of</strong>fice into <strong>the</strong> complete s<strong>of</strong>tware product. Knowledge transfer is
undoubtedly high across all companies this may be attributed to <strong>the</strong> flat structure <strong>of</strong> SMEs where few
employees operate in flexible working environments (Younger, 1990).
From Table 8, unlike <strong>the</strong> o<strong>the</strong>r companies <strong>the</strong> highest incidence <strong>of</strong> activity occurrence for HelpRead
Ltd. is in knowledge codification at 30 percent (24 where n=82). HelpRead’s focus on codification
activity was highlighted in this study; it may be explained by <strong>the</strong>ir focus on establishing a quality
system to meet ISO regulations.
Table 8 highlights that knowledge creation activity across all cases was low at 5 percent (234 where
n=472). This activity may be low intensity as it is a more difficult phenomenon to observe. However,
ano<strong>the</strong>r reason for <strong>the</strong> low count <strong>of</strong> knowledge creation activities could be attributed to <strong>the</strong> nature <strong>of</strong>
SMEs where it is typical for <strong>the</strong> manager to be <strong>the</strong> “driving force” behind <strong>the</strong> organisation (Nunes et al.,
2005) and where <strong>the</strong> founder has <strong>the</strong> initial idea and continues to have significant hands on
involvement in <strong>the</strong> running <strong>of</strong> <strong>the</strong> organisation (Schein, 1993). In o<strong>the</strong>r words, knowledge creation may
be <strong>the</strong> responsibility <strong>of</strong> a few people in a SME, namely <strong>the</strong> founder and senior management. Chan and
Chao (2008) contend that knowledge generation in SMEs can be improved if management provide
more opportunities to s<strong>of</strong>tware developers in terms <strong>of</strong> providing opinions and ideas based on <strong>the</strong>ir
expertise.
The justification for each company’s KA is closely matched to <strong>the</strong> organisational objectives pursued by
that firm. Across <strong>the</strong> cases <strong>the</strong>re is a common focus on s<strong>of</strong>tware development, project and product
knowledge, although niche knowledge is important in some cases. While <strong>the</strong> number <strong>of</strong> KAs varied,
<strong>the</strong> case data showed that across <strong>the</strong> board, <strong>the</strong>se companies are good at knowledge transfer and
evidently, weak at knowledge creation this is supported by extant Irish research (CSO, 2007).
7. Conclusion
Based on considerable extant research a working definition for knowledge and a classification <strong>of</strong> KAs
was established in order to enable <strong>the</strong> identification <strong>of</strong> occurrences <strong>of</strong> KA across <strong>the</strong> five cases thus
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Ciara Heavin and Frederic Adam
providing an account <strong>of</strong> how knowledge is managed in each organisation. This classification <strong>of</strong> KAs
provides a common vocabulary which is a useful and practical method <strong>of</strong> assessing <strong>the</strong> KM approach
<strong>of</strong> s<strong>of</strong>tware SMEs in both a tangible and practical manner. This classification could be used by
s<strong>of</strong>tware and o<strong>the</strong>r types <strong>of</strong> SMEs to understand and develop <strong>the</strong>ir KAs in order to best serve <strong>the</strong>
needs <strong>of</strong> <strong>the</strong> firm.
This study identifies that KM is adapted based on organisational context e.g. to serve <strong>the</strong>
organisation’s business needs as required. In order to leverage KM, an organisation’s KM strategy
should support <strong>the</strong> organisation’s business strategy and objectives. This study shows that, while <strong>the</strong>
organisational objectives and priorities may be different, KM should be implemented to meet <strong>the</strong>
organisations strategy at a particular time, in essence developing <strong>the</strong> appropriate KAs to fit <strong>the</strong>
knowledge needs <strong>of</strong> <strong>the</strong> firm, for example, to support new product development activity or to support a
focused sales strategy when required. Essentially, organisations consider <strong>the</strong> economy <strong>of</strong> knowledge
when <strong>the</strong>y expend resources to manipulate knowledge in a way that is commensurate to <strong>the</strong> benefits
that stand to be obtained from this effort. This principle has clear implications for fur<strong>the</strong>r research and
practice, as <strong>the</strong> benefits obtained from a firm’s KM approach must be in keeping with <strong>the</strong> efforts <strong>of</strong> its
implementation and daily use.
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435

Key Knowledge Sharing Points: Exploring a new Concept
for Studying Crossroads in Global Innovation Projects
Tore Hoel 1 and Jan Pawlowski 2
1
Oslo University College, Oslo, Norway
2
University <strong>of</strong> Jyväskylä, Jyväskylä, Finland
tore.hoel@hio.no
jan.pawlowski@jyu.fi
Abstract: This paper introduces <strong>the</strong> concept <strong>of</strong> Key Knowledge Sharing Point (KKSP) to describe potential
crossroads in global innovation projects. As Knowledge Management becomes more and more important in
global settings it is essential to identify when and where key knowledge is or must be shared within specific
process cycles in order to run innovation projects successfully. The goal <strong>of</strong> <strong>the</strong> paper is to contribute to <strong>the</strong>
methods development <strong>of</strong> standardisation as a design activity in <strong>the</strong> domain <strong>of</strong> learning technologies. This activity
is reported suffering from a legitimacy crisis and needs both improved process and products. As a Key
Knowledge Sharing Point <strong>the</strong> authors understand situations or events in which knowledge sharing is <strong>of</strong> essential
importance for <strong>the</strong> success <strong>of</strong> an activity. The KKSP construct is explored in in two cases studies in order to test
its abilities as a descriptive lens. The first case study analysed a case <strong>of</strong> legitimacy breakdown in stakeholder
engagement, focussing on Key Knowledge, Key Sharing Points and Key Timing, <strong>the</strong> three intersecting aspects <strong>of</strong>
<strong>the</strong> KKSP constructs. The second case study was chosen for its critical timing (scope definition <strong>of</strong> new
standardisation work items), and demonstrated <strong>the</strong> analytical strength <strong>of</strong> <strong>the</strong> KKSP construct highlighting <strong>the</strong>
importance <strong>of</strong> what knowledge to be exchanged through which channels. This paper describes a first step
towards a prescriptive model based on a iterative development with several cycles <strong>of</strong> descriptive studies. The
authors use Actor Network Theory to analyse standardisation activities understood as a recursive process <strong>of</strong>
design, sense-making and negotiations. For fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> KKSP construct in a prescriptive direction
<strong>the</strong> authors point to <strong>the</strong> problematisation and perspective primitive Why as a starting point.
Keywords– Organisational learning; Key Knowledge Management Point; Descriptive and Prescriptive <strong>the</strong>ory
building; Global Innovation; Anticipatory Standardisation
1. Introduction
Global innovation projects always run <strong>the</strong> risk <strong>of</strong> breakdown. Global cooperation has its own risks,
prone to a mixed set <strong>of</strong> influence factors given by <strong>the</strong> context (Pawlowski & Bick, 2011). Add an
innovation focus to <strong>the</strong> activity and <strong>the</strong> risk multiplies, as <strong>the</strong> object <strong>of</strong> <strong>the</strong> activity is not yet defined.
Breakthrough innovations seem to have a local point <strong>of</strong> origin. Even if <strong>the</strong> latest gadget from Apple is
assembled in China <strong>of</strong> parts produced all over <strong>the</strong> globe, <strong>the</strong> company makes a point <strong>of</strong> it being
“Designed in California”. In this case <strong>the</strong> locus <strong>of</strong> control over <strong>the</strong> design process is well defined,
grounded in an organisational culture that is refined over years. In this paper we will focus on
knowledge management challenges related to globally distributed design <strong>of</strong> future artefacts (Tuikka
2002). The domain we have chosen is international standardisation <strong>of</strong> learning technologies.
The very idea <strong>of</strong> “innovative design by committee” sounds like an oxymoron. In <strong>the</strong> innovation life
cycle described by Abernathy and Utterback (1975) standardisation is <strong>the</strong> last third stage, after a first
fluid explorative stage leading to a second transitional stage with a dominant design. When
international standards groups engage in creating anticipatory standards, i.e., standards that are
designed to meet future needs <strong>of</strong> users <strong>of</strong> technologies and services, <strong>the</strong>y run huge risks <strong>of</strong> failure.
However, <strong>the</strong> potential upside seems to be interesting enough to keep <strong>the</strong> activity going: If standards
were successful <strong>the</strong> market could benefit greatly given <strong>the</strong> global scope <strong>of</strong> information technologies.
Our goal with this paper is to give a small contribution to <strong>the</strong> methods development <strong>of</strong> learning
technology standardisation by constructing a new concept <strong>of</strong> a Key Knowledge Sharing Point (KKSP).
We will explore this concept within <strong>the</strong> context <strong>of</strong> global knowledge management, shaping <strong>of</strong>
technology and international standardisation. In <strong>the</strong> following we will give a short overview <strong>of</strong> related
research before we look into <strong>the</strong> dimensions <strong>of</strong> <strong>the</strong> KKSP concept and apply it in two case studies.
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2. Related research
2.1 Learning Technology Standardization
Tore Hoel and Jan Pawlowski
Standardisation as an activity is not well understood (Cargill 1989, Mitra et al. 2005). Cargill found
more than 20 years ago that “<strong>the</strong> philosophical and conceptual bond that ties <strong>the</strong> activity toge<strong>the</strong>r is,
at best, very weak. (..) Standards represent different things to groups and disciplines, to
organizations, and to individuals, and <strong>the</strong>se meanings vary with time and context. The disagreement
between <strong>the</strong> organizational and individual rationales and goals for participating in standardization is a
major cause <strong>of</strong> confusion about standards and standardisation in <strong>the</strong> IT industry” (Cargill 1989, p. 10
ff.). Based on participation in LET standardisation <strong>the</strong> last decade, we conclude that <strong>the</strong> situation has
not changed.
The field <strong>of</strong> learning technology standardisation has produced very few successful standards in <strong>the</strong>
past decade (Hoel, Hollins & Pawlowski 2010). However, anticipatory standards are urgently needed
due to <strong>the</strong> fragmentation <strong>of</strong> <strong>the</strong> market, lack <strong>of</strong> agreements on common base concepts and
technologies as well as lack <strong>of</strong> large industry interests. Learning technology standardisation is a
typical example showing <strong>the</strong> issues, problems and contradictions we find in <strong>the</strong> field <strong>of</strong>
standardisation. We are dealing with consensus standards that mainly fall in <strong>the</strong> category <strong>of</strong> voluntary
standards (Cargill 1989). Never<strong>the</strong>less, when published by formal standards bodies as ISO or CEN
<strong>the</strong>y are attributed mandatory qualities. Cargill has identified three aspects that produce challenges to
consensus standards, LET standards included: The importance to <strong>the</strong> marked is contested; <strong>the</strong>re is
an intermix <strong>of</strong> standards to be implemented in products and standards <strong>of</strong> conceptual nature; and
some standards are process standards, while o<strong>the</strong>rs are product standards (ibid., p. 26). Besides,
rapid changes in <strong>the</strong> market make <strong>the</strong> LET standards community more involved in shaping <strong>the</strong>
technology <strong>of</strong> <strong>the</strong> future (Egyedi 1996) than regulating or stabilising what is already in <strong>the</strong> market.
Ra<strong>the</strong>r than creating stable points <strong>of</strong> reference, supporting interoperability and minimising
informational transaction costs (Egyedi & Sherif 2008) in systems already adopted <strong>the</strong> focus <strong>of</strong> this
community is on anticipatory standardisation (Jakobs 2001), which is part <strong>of</strong> a global innovation
enterprise with a host <strong>of</strong> complex knowledge management challenges (Tidd and Bessant 2009).
When results are questioned <strong>the</strong> legitimacy <strong>of</strong> <strong>the</strong> activity comes under attack. Hoel and Hollins
(2008) have developed a model describing input and output factors that contribute to <strong>the</strong> overall
legitimacy <strong>of</strong> a LET standard. The input legitimacy factors relate mainly to <strong>the</strong> process, while output
legitimacy factors relate to <strong>the</strong> product (i.e., <strong>the</strong> technical standard), hence <strong>the</strong> name Process and
Product Legitimacy model (PPL).
Table 1:Process and Product Legitimacy model <strong>of</strong> standardisation (Hoel & Hollins 2008)
Input legitimacy Output legitimacy
All 'interests' considered and ideally represented Inscription <strong>of</strong> stakeholders' interests
Open process Enactment status (is <strong>the</strong> specification
implemented and used in services?)
Balanced choice <strong>of</strong> Standard Setting Body Technical maturity <strong>of</strong> <strong>the</strong> specification
We will use this model to structure our empirical study and to identify where we may find breakdowns
in <strong>the</strong> standardisation process.
2.2 Knowledge Management & Knowledge Sharing
The global knowledge sharing challenges related to international standardisation are obvious.
However, it is highly necessary to identify at which points <strong>of</strong> <strong>the</strong> standardisation process knowledge
sharing breakdowns occur, leading to failure <strong>of</strong> <strong>the</strong> standardisation process.
A variety <strong>of</strong> studies have been done to identify critical success factors, e.g., knowledge-sharing
barriers that have to be crossed (Riege 2005) and critical information that must be considered
(Soliman & Youssef 2003, Wong 2005). The framework set up by Pawlowski and Bick (2011)
identifies <strong>the</strong> interrelationship between stakeholder contexts, knowledge, processes, instruments and
results. Critical success factors and barriers are strongly connected to certain activities (processes) or
interventions (e.g. introduction <strong>of</strong> new knowledge-related technologies). This group <strong>of</strong> studies
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Tore Hoel and Jan Pawlowski
identifies how knowledge sharing can be supported and how it can fail. However, we need a construct
that produces more than flat lists <strong>of</strong> hurdles managers must consider, as we need to show in which
part <strong>of</strong> <strong>the</strong> process things can go wrong. The time aspects are in most cases not considered.
With <strong>the</strong> advancement <strong>of</strong> ICT systems support to almost all sectors <strong>of</strong> society we see introduced
embedding <strong>of</strong> time critical knowledge in ordinary work procedures (Davenport & Glaser 2002). We
would argue that a phase- or time-based concept is necessary. This has <strong>the</strong> potential to extend justin-time
knowledge management by conceptualising <strong>the</strong> more subtle processes where conflicts occur
because knowledge was not shared, or <strong>the</strong> mix <strong>of</strong> knowledge, timing and delivering context was not
right.
3. Construction <strong>of</strong> KKSP – <strong>the</strong>orising about methods improvement
In <strong>the</strong> following, we describe Key Knowledge Sharing Points as well as <strong>the</strong> <strong>the</strong>oretical background.
3.1 Defining Key Knowledge Sharing Point (KKSP)
Sharing <strong>of</strong> key knowledge is critical for global projects (Holden 2002, Kalkan 2008, Riege 2005).
However, at certain points knowledge sharing is essential and more crucial than in o<strong>the</strong>r project
phases. Thus timing is an important research aspect (Ancona et al. 2001). A Key Knowledge Sharing Point
(KKSP) is <strong>the</strong>refore a stage in a sequence <strong>of</strong> processes that is critical for <strong>the</strong> overall project success
due to certain barriers or influence factors. The global knowledge management framework developed
by Pawlowski and Bick (2011) can help to identify in which contexts and situations KKSP occur and
how <strong>the</strong>y can be predicted. The overall aim with our research is to contribute to an improved method
for standards development. We see learning technology standardisation as a design process
consisting <strong>of</strong> a number <strong>of</strong> development cycles punctuated by KKSPs. What is presented in this paper
is just a small step towards such an improved method, which may need additional constructs to
KKSP.
3.2 Theoretical Foundation & Methodology
Tsang (1997) has introduced a simple model for how to integrate descriptive and prescriptive
research so that <strong>the</strong> proposed methods improvement rest on a rigorous scientific approach. The first
step is through a descriptive study to get an understanding <strong>of</strong> <strong>the</strong> domain, its major variables and <strong>the</strong>ir
relationships. Based on this study we would be able to formulate prescriptive implications. The third
step should be to study <strong>the</strong> outcome <strong>of</strong> implementing <strong>the</strong> prescriptions, and so feed <strong>the</strong>se results
back to revising <strong>the</strong> prescriptions and to continue a methods development in a number <strong>of</strong> iterations.
Figure 1: Integrating descriptive and prescriptive <strong>the</strong>orising, from Tsang (1997)
The KKSP concept is well suited as a descriptive lens to get an understanding <strong>of</strong> <strong>the</strong> system <strong>of</strong>
activities we study. The assumption is that by identifying KKSPs we will get a handle on <strong>the</strong> aspects
that will inform prescriptions for an improved method.
Actor Network Theory (ANT) in <strong>the</strong> tradition <strong>of</strong> Bruno Latour (1986), Michel Callon (1999) and John
Law (1999) provides a small set <strong>of</strong> concepts that are proven powerful in understanding
standardisation activities (Egyedi 1996). We will make use <strong>of</strong> ANT perspectives and methodologies in
developing <strong>the</strong> KKSP construct, which is very much related to translation, described by Crawford
(2005) to be <strong>the</strong> core concept <strong>of</strong> ANT. “Translation (transport with deformation), as distinguishable
from diffusion (transfer without distortion), is both a process and effect” (ibid.).
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Our research is aimed at locating <strong>the</strong> centres <strong>of</strong> translation “where network elements are defined and
controlled, and strategies for translation are developed and considered” (ibid.) in order to identify <strong>the</strong>
Key Knowledge Sharing Points and open <strong>the</strong>m up for discussion (refer <strong>the</strong> ANT concept <strong>of</strong> blackboxing
and what it takes to see what is in <strong>the</strong> “black box”).
Focussing on <strong>the</strong> standardisation activities <strong>the</strong>mselves, Fomin et al. (2003) have provided a generic
framework, <strong>the</strong> D-S-N model. The model (Figure 2) understands <strong>the</strong> standardisation activities as a
recursively organised process that combine Design, Sense-making and Negotiations processes. In all
<strong>the</strong>se processes we would expect to find KKSPs. According to this model design involves intelligence
ga<strong>the</strong>ring; inventing, developing and analysing possible courses <strong>of</strong> action; and selecting a particular
course <strong>of</strong> action. This will involve sense-making processes, <strong>of</strong>ten proactive and related to not-yetinvented
technologies. In order to produce an agreed standard we need <strong>the</strong> negotiation processes,
enrolling actors and artefacts in <strong>the</strong> network that constitutes <strong>the</strong> ongoing standardisation activity.
Figure 2 The D-S-N model describing <strong>the</strong> standardisation process as a recursive process <strong>of</strong>
combining design, sense-making and negotiations, from Fomin et al. (2003).
In <strong>the</strong> first case study we focused on <strong>the</strong> contextual aspects <strong>of</strong> stakeholder mobilisation. We look into
<strong>the</strong> stakeholder motivation for taking part in international standardisation, and how this activity is
perceived and fed into national policies and processes. How is knowledge defined and managed? Are
we able to identify any knowledge that plays or could play a key role in legitimacy processes?
The results <strong>of</strong> <strong>the</strong> first case study led us to focus on a particular processes, zooming in on <strong>the</strong> actual
work being done in standards group meetings. The question we ask is if <strong>the</strong>re are crossroads towards
a final standard that are more critical than o<strong>the</strong>rs where intervention actions may be taken, guided by
analysis where <strong>the</strong> new KKSP concept gives added value. If so, could <strong>the</strong> understanding <strong>of</strong> <strong>the</strong> nature
<strong>of</strong> <strong>the</strong>se crossroads help us better define <strong>the</strong> KKSP concept itself? .For <strong>the</strong> case studies we used a
qualitative approach combining an archival document study with a discourse analysis <strong>of</strong> a three hours
session with key persons working in <strong>the</strong> domain. The data for <strong>the</strong> first case were strategy outlines,
budget briefs, reports from support actions, guidelines, project reports etc. relating to <strong>the</strong> national
organisations since 2003 involved in LET standardisation in Norway. Key phrases in <strong>the</strong> documents
were tagged and analysed according to a number <strong>of</strong> indicators developed through an iterative
process <strong>of</strong> repeated data analysis. The resulting “stop words” were <strong>the</strong>n used as prompts in <strong>the</strong> group
discussion, which was <strong>the</strong>n transcribed and analysed.
In <strong>the</strong> second case study <strong>the</strong> same qualitative approach was used to analyse 16 new work item
proposals presented to <strong>the</strong> CEN Workshop on Learning Technologies.
4. Two case studies from LET standardisation
4.1 Legitimacy breakdown in stakeholder engagement - a national case
What prompted <strong>the</strong> first case study was a breakdown <strong>of</strong> legitimacy <strong>of</strong> international LET
standardisation seen from <strong>the</strong> perspective <strong>of</strong> a national governmental stakeholder. Since 2003 <strong>the</strong>
Norwegian Ministry <strong>of</strong> Education, or agencies acting on behalf <strong>of</strong> <strong>the</strong> ministry supported
standardisation activities in CEN WS-LT and ISO/IEC JTC1 SC36 covering <strong>the</strong> travel expenses <strong>of</strong>
selected experts attending meetings in <strong>the</strong>se committees. From 2011 this semi-formal arrangement
was put on halt and <strong>the</strong> experts who were committed to different ongoing tasks in <strong>the</strong> standard groups
were told that no travel support was guaranteed for future meetings due to questions as to <strong>the</strong>
benefits <strong>of</strong> international standards development to <strong>the</strong> Norwegian educational sector.
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Tore Hoel and Jan Pawlowski
The archival analysis shows how international standardisation over <strong>the</strong> years had been written into
<strong>the</strong> Norwegian policy related to ICT in education as a concept on par with “ICT architecture”. The
Norwegian eStandard project (2003 - 2006) was set up to explore how standards could be promoted
and to coordinate <strong>European</strong> and international participation. In 2007 <strong>the</strong> portfolio was transferred to <strong>the</strong>
new National Secretariat for Standardisation <strong>of</strong> Learning Technologies. Since <strong>the</strong>n <strong>the</strong> discussion on
<strong>the</strong> nature <strong>of</strong> standards, where to participate, how to engage in <strong>the</strong> process with which organisation
gradually disappeared. The discussion on why standards and standardisation appears as frozen or
black-boxed, and <strong>the</strong> focus is more on how to apply a subset <strong>of</strong> <strong>the</strong> standards that comes out <strong>of</strong> <strong>the</strong>
process. When <strong>the</strong> why questions fade out we see from our material that <strong>the</strong> discussion on <strong>the</strong> state
<strong>of</strong> art and recommendations <strong>of</strong> particular standards become more a matter <strong>of</strong> fact: “In <strong>the</strong> Norwegian
educational sector <strong>the</strong> specification NORLOM is used for describing digital learning resources”. This
recommendation is copied and pasted between documents without any fur<strong>the</strong>r justification.
For an uninitiated observer standardisation is difficult to understand, a reason why <strong>the</strong> Ministry <strong>of</strong>
Education at an early stage asked for a “eStandards for Dummies” report from eStandards project
(2005). When LET standardisation as a national activity was handed over from a project to a national
secretariat, and <strong>the</strong>n to a government agency <strong>the</strong> meaning <strong>of</strong> that activity became more and more
defined by <strong>the</strong> organisational context, than by <strong>the</strong> activity itself. As an example, <strong>the</strong> document study
shows quality as a strong interest <strong>of</strong> <strong>the</strong> Ministry <strong>of</strong> Education, and brought into <strong>the</strong> context <strong>of</strong>
standardisation quality and standards are given an almost similar meaning. When <strong>the</strong> Ministry asks
for quality guidelines for learning resources, we see <strong>the</strong> recommendations related to international
standardisation is limited to <strong>the</strong> subset <strong>of</strong> interoperability standards that <strong>the</strong> agency defines a “used in
<strong>the</strong> Norwegian educational sector”. We find no reference to international quality standards, developed
by committees with Norwegian participation. And we find no discussion on alternative standards that
are being developed over years with heavy involvement <strong>of</strong> Norwegian experts.
It is clear from <strong>the</strong> document study that <strong>the</strong> network spun by <strong>the</strong> national stakeholder to international
standardisation was developing in ano<strong>the</strong>r direction than <strong>the</strong> network made out <strong>of</strong> <strong>the</strong> textual,
conceptual, social and technical actors taking part in <strong>the</strong> CEN and ISO groups. The breakdown <strong>of</strong>
legitimacy may be seen as a result <strong>of</strong> lack <strong>of</strong> alignment, centred around a concept <strong>of</strong> uptake <strong>of</strong> results
emerging in <strong>the</strong> discussion around <strong>the</strong> “stop words” resulting from <strong>the</strong> document study. However, if
uptake <strong>of</strong> results is <strong>the</strong> success criterion <strong>the</strong>re is a need for a common understanding <strong>of</strong> results. The
analysis <strong>of</strong> <strong>the</strong> discussion session showed that <strong>the</strong>re is no consensus about pivotal concepts like
standards and standardisation.
actually, when we get things from o<strong>the</strong>rs (.) maybe particularly from KD (<strong>the</strong> Ministry <strong>of</strong>
Education and Research), which we have a lot to do with (1.5) when <strong>the</strong>y talk about
standards <strong>the</strong>n <strong>the</strong>y talk about _a lot more_ than ah (.) formal standards and it is not at
all given that it is open standards (.) what is <strong>the</strong> sort <strong>of</strong> definition for that
what do KD mean?
sh: when <strong>the</strong>y say standards <strong>the</strong>y may really think about some kind <strong>of</strong> rules
If standards are just “some kind <strong>of</strong> rules”, from a ministry’s point <strong>of</strong> view <strong>the</strong>re is no need for
international standardisation. Ministries and government agencies deal with rules all <strong>the</strong> time, and
<strong>the</strong>y are based on national legislation and political processes, not in consensus processes in
international standards setting bodies. However, <strong>the</strong> 2011 goal for <strong>the</strong> Norwegian agency remains:
“The Centre has contributed to development <strong>of</strong> international standards and specifications for learning
technology (..) in cooperation with <strong>the</strong> sector and Standards Norway”.
In Table 2 we have summarised this case study in terms <strong>of</strong> Key Knowledge Sharing Points.
4.1.1 Discussion – Case 1
As one <strong>of</strong> <strong>the</strong> discussants remarked, it would be nice if <strong>the</strong> reasoning for why standardisation is a
sensible thing to do had been done once and for all as a high level clarification. He found it arduous to
argue in every project in favour <strong>of</strong> standardisation. The case study showed that lines <strong>of</strong> argument for
<strong>the</strong> benefits <strong>of</strong> standardisation, held in a popularised form, had been a resource in <strong>the</strong> actor network
from <strong>the</strong> very beginning, even commissioned by <strong>the</strong> Ministry itself. This illustrates that availability <strong>of</strong>
key knowledge is not in itself enough to make a difference. Two more conditions must be present in
order to open up a node for translation. First, <strong>the</strong> key knowledge must be found in a strategic sharing
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point. In <strong>the</strong> case we are discussing, this sharing point may have been a budget or planning
document. Even this is not enough, if not <strong>the</strong> last condition is fulfilled: timing. We see that common
understandings are <strong>of</strong> little value if new actors enter <strong>the</strong> network. It might be new persons or
organisations picking up <strong>the</strong> tasks, or it might be new adoption projects or new technologies entering
<strong>the</strong> scene. In <strong>the</strong> case we have studied we see that <strong>the</strong> planning cycle <strong>of</strong> a governmental organisation
is important for timing. However, if <strong>the</strong> timing and channels are right but <strong>the</strong> key knowledge is missing,
we still have not got a Key Knowledge Sharing Point. International standardisation as a prioritised
work item <strong>of</strong> <strong>the</strong> Norwegian agency is a case in point. The work item is acknowledged but lacks
legitimacy because <strong>the</strong> key knowledge is not translated to <strong>the</strong> primary actors in <strong>the</strong> network.
Table 2 Key Knowledge Sharing Point summary <strong>of</strong> Case study 1
Key Knowledge
Sharing Points (channels / translation
nodes)
Timing
Typology <strong>of</strong> standards; Affordances <strong>of</strong>
International Standardisation; Standardisation
as a expertise, e.g., how to deal with
impenetrable expert lingo
Budget documents; National implementation
projects, e.g., digital exams, Learning
Management Systems, basic infrastructure
(Identity Management), Open Educational
Resources & o<strong>the</strong>r technology innovation;
Guidelines
Budget work/planning deadline; Organisational
reshuffles; Technology ruptures
The first case study demonstrated that timing is critical to be able to open network nodes that are
black-boxed and not open for translation. What is <strong>the</strong> critical time in <strong>the</strong> process that relates to <strong>the</strong>
technical qualities <strong>of</strong> a standard and influence what we have called product legitimacy? We would
suggest that <strong>the</strong> stage when new work items are scoped would be critical. Therefore we have for <strong>the</strong>
second case study chosen a dataset that consists <strong>of</strong> New Work Item proposals. Analysing data
originating from <strong>the</strong> same process stage we should be able to gain more insights in <strong>the</strong> key
knowledge part <strong>of</strong> <strong>the</strong> KKSP construct.
4.2 Case 2: In search for key knowledge in a proposals for new work items for
standardisation
How do <strong>the</strong> new work item proposals address <strong>the</strong> purpose <strong>of</strong> <strong>the</strong> technical artefact that should be <strong>the</strong>
outcome <strong>of</strong> <strong>the</strong> standardisation process? In <strong>the</strong> second case study we looked for exchange about how
standards should be done. The analysis show how guidelines and description templates guide <strong>the</strong>
project description in <strong>the</strong> direction <strong>of</strong> <strong>the</strong> economic-political context, leaving out aspects more related
to <strong>the</strong> domain, e.g., <strong>the</strong> pedagogical need for <strong>the</strong> standard.
Overall, methods for doing standards are understood by <strong>the</strong> authors <strong>of</strong> new work item proposals as
execution <strong>of</strong> <strong>the</strong> consensus developing script that comes with <strong>the</strong> Workshop on Learning
Technologies: formation <strong>of</strong> project team, presentation <strong>of</strong> drafts to <strong>the</strong> meeting, submission <strong>of</strong> interim
reports, and finally publication <strong>of</strong> <strong>the</strong> Workshop Agreement. However, <strong>the</strong> approach towards both <strong>the</strong>
design object and method is discussed indirectly in <strong>the</strong> proposals by relating <strong>the</strong> work to alternative
standards or candidate standards for harmonisation, extension or pr<strong>of</strong>iling.
Table 3: Summarises this case study in terms <strong>of</strong> Key Knowledge Sharing Points. Key Knowledge
Sharing Point summary <strong>of</strong> Case study 2
Key Knowledge
Sharing Points (channels / translation
nodes)
Timing
441
Methodological approaches to standards
development related to <strong>the</strong> domain and task
New Work Item description; EU funding
procedures
Scoping stage in standards development life
cycle

4.2.1 Discussion – Case 2
Tore Hoel and Jan Pawlowski
By design <strong>the</strong> scoping phase <strong>of</strong> a new standardisation project is an important Key Knowledge Sharing
Point. This is <strong>the</strong> time where all critical dimensions <strong>of</strong> a project are defined: aims, scope, project team,
resources, time frame, etc. If <strong>the</strong>se aspects are not sufficiently detailed and resolved <strong>the</strong> design,
sense-making and negotiation processes will be strained with tasks not contributing to an efficient
standardisation process. In <strong>the</strong> design <strong>of</strong> anticipatory standards this is especially important as this
category <strong>of</strong> standards is <strong>of</strong>ten subject to scope creep (uncontrolled change <strong>of</strong> scope) as nobody is
able to foresee what is needed in <strong>the</strong> future. The focus on funding instead <strong>of</strong> on <strong>the</strong> technical needs
increases <strong>the</strong> danger <strong>of</strong> scope creeps. The case study showed that <strong>the</strong> point <strong>of</strong> time was right, but <strong>the</strong>
sharing context did not allow for <strong>the</strong> right key knowledge to emerge. Knowledge related to alternative
methods <strong>of</strong> development and design was transferred as fixed or frozen artefacts, encapsulated in <strong>the</strong>
standards <strong>the</strong> authors suggested as a starting point for <strong>the</strong> work. It is expected that <strong>the</strong> proposal
clarifies what technologies on which <strong>the</strong> new solution will be built. However, if <strong>the</strong> base standards are
not opened up for translation, <strong>the</strong> implicit methods will not be exposed. In <strong>the</strong> document analysis <strong>the</strong>
base standards act as immutable mobiles ra<strong>the</strong>r than key knowledge objects under transformation.
5. Towards a prescriptive <strong>the</strong>ory
We have seen that <strong>the</strong> multifaceted KKSP concept is useful studying crossroads in global innovation
projects. The notion <strong>of</strong> a crossroad comes with an understanding <strong>of</strong> a choice between different
directions. In <strong>the</strong> simplified model <strong>of</strong> <strong>the</strong> innovation process described by Tidd and Bessant (2009) <strong>the</strong>
concept is particularly useful in <strong>the</strong> second stage <strong>of</strong> <strong>the</strong> process: Select – what are we going to do –
and why?, <strong>the</strong> step that comes after Search – how can we find opportunities for innovation?
However, a fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> KKSP concept towards a prescriptive <strong>the</strong>ory should give
guidance on how to make a rational choice <strong>of</strong> direction. In <strong>the</strong> case <strong>of</strong> LET standardisation this should
contribute to enhanced legitimacy for <strong>the</strong> activity.
Going back to <strong>the</strong> overall objective <strong>of</strong> standards <strong>the</strong> authors <strong>of</strong> this paper acknowledge that
implementors and end users alike want to push into <strong>the</strong> background routinised actions that could be
called by reference. We want systems “just to work”, not knowing what goes on behind <strong>the</strong> scene.
However, sometimes when a system is questioned (e.g., <strong>the</strong> legitimacy breaks down due to errors or
accusations that one particular interest is favoured by <strong>the</strong> standard, s<strong>of</strong>tware, system or service) we
want to check. And we want to make sure that new systems are based on sound principles.
Therefore, on a regular basis, we need to be able to foreground <strong>the</strong> punctualised actants. The
prescriptive steps we are searching for is <strong>the</strong> ones that let us identify and reopen <strong>the</strong> black boxes.
The actor network <strong>the</strong>ory sees order always as a provisional state. Networks needs continual
maintenance. This was demonstrated by both case studies. The first case study showed that Key
Knowledge needs to be revisited again and again in order to mobilise <strong>the</strong> network. In <strong>the</strong> second case
we observed that even if <strong>the</strong> network was mobilised (a team was making a proposal) translation <strong>of</strong>
key knowledge was missing.
Callon (1986) have described <strong>the</strong> translation process in three steps that broadly correspond to <strong>the</strong>
Design - Sense-making - Negotiation process Fomin et al. (2003) used to <strong>the</strong>orise about <strong>the</strong>
standardisation process. These steps include Problematisation (setting up <strong>the</strong> objectives, identifying
actants, etc.); Interessement and Enrollment; and Mobilisation. Problematisation includes an
identification <strong>of</strong> one or more Obligatory Point <strong>of</strong> Passage. In an actor network concerned with LET
standardisation some actors may have a position that gives <strong>the</strong>m a disproportionate influence on <strong>the</strong>
problem definition, e.g., certain stakeholder groups or in <strong>the</strong> case <strong>of</strong> <strong>the</strong> new work items in <strong>the</strong> CEN
group, <strong>the</strong> <strong>European</strong> Commission. The KKSPs are definitely found in Obligatory Points <strong>of</strong> Passage.
However, <strong>the</strong>se are concepts <strong>of</strong> different granularity. We would assume that KKSPs could be found
all over <strong>the</strong> network, depending a network node is opened for translation. The interesting perspective
arising from analysing <strong>the</strong> KKSP concept in relation to <strong>the</strong> process stage <strong>of</strong> Problematisation is that it
gives us an idea how we could fur<strong>the</strong>r develop <strong>the</strong> concept in a prescriptive setting.
6. Conclusion
In this paper we have explored a new concept <strong>of</strong> Key Knowledge Sharing Point as a contribution to
<strong>the</strong> methods development within a subdomain <strong>of</strong> global technology innovation. The concept is
constructed as <strong>the</strong> intersection between Key Knowledge - Key Sharing Point - Key Timing. We have
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Tore Hoel and Jan Pawlowski
done a first validation <strong>of</strong> <strong>the</strong> construct in two steps, applying <strong>the</strong> concept in <strong>the</strong> context <strong>of</strong> setting up a
process, and in <strong>the</strong> context <strong>of</strong> defining how <strong>the</strong> process should be designed.
We have used <strong>the</strong> KKSP concept to describe key characteristics <strong>of</strong> processes. However, our aim is to
come up with a concept that has prescriptive qualities, i.e., a concept that contributes to steer <strong>the</strong>
activity in a more optimal direction. To achieve this we need fur<strong>the</strong>r studies where <strong>the</strong> KKSP concept
is used by actors to inform actions. This transformation from using a construct as an analytical lens to
applying it as an heuristic is ripe with methodological challenges. In this paper we have just indicated
where to start this fur<strong>the</strong>r development, using <strong>the</strong> problematisation and perspective primitive why as
<strong>the</strong> entry point. The why question relates to all aspects <strong>of</strong> <strong>the</strong> KKSP concept, and could also be
applied as negation, why not. Why could be asked to extract key knowledge. Why not could be used
to identify KKSPs where key knowledge is missing.
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443

Using Web 2.0 Technologies to Support Technology
Surveillance in a University Context
Marta Infante Abreu 1 , Florian Mat<strong>the</strong>s² and Alexander Steinh<strong>of</strong>f²
1 Polytechnic University <strong>of</strong> Habana (CUJAE), Havana, Cuba
²Technische Universität München, Munich, Germany
miabreu@ind.cujae.edu.cu
mat<strong>the</strong>s@in.tum.de
steinh<strong>of</strong>@in.tum.de
Abstract: Technology surveillance (TS) can be seen as a particular branch <strong>of</strong> knowledge management (KM)
primarily focusing on technology related information. It constitutes a systematic process including <strong>the</strong> activities <strong>of</strong>
ga<strong>the</strong>ring, organizing, analyzing, and disseminating information from internal and external sources to assist
organizations in managing risks in <strong>the</strong>ir operational and strategic environment. In this paper we focus on TS in a
university environment where different departments, research groups and individual researchers critically depend
on up-to-date technology-related information in <strong>the</strong>ir field. Today, <strong>the</strong> actors in <strong>the</strong> TS process are challenged
with an exponential growth <strong>of</strong> scientific knowledge and technology related information. However, <strong>the</strong> fact that all
<strong>of</strong> this information is available in digital formats and <strong>the</strong> trend that it is increasingly available free <strong>of</strong> charge or at
low prices <strong>of</strong>fers many new opportunities. We suggest an approach for TS that helps universities to seize <strong>the</strong>se
opportunities and to master <strong>the</strong> challenges using proven Web 2.0 technologies. We first describe <strong>the</strong> goals,
activities and roles <strong>of</strong> traditional TS approaches toge<strong>the</strong>r with typical problems that are encountered when <strong>the</strong>
activities are performed in practice, particularly in a research context. Then, after shortly introducing <strong>the</strong> most
important Web 2.0 technologies and <strong>the</strong>ir application in enterprises and organizations, a <strong>the</strong>oretical model <strong>of</strong> a
Web 2.0 supported TS process is developed. Subsequently, we demonstrate how this approach can be applied
in practice using an existing commercial web based collaboration platform. We show by an example scenario
how <strong>the</strong> <strong>of</strong>fered integrated structuring capabilities, access rights management, linking and tagging functionality
can support <strong>the</strong> activities <strong>of</strong> <strong>the</strong> proposed model.
Keywords: Technology surveillance, collaboration, web 2.0, technology integration, knowledge management
1. Introduction
The amount <strong>of</strong> scientific literature is growing every day by thousands <strong>of</strong> publications. Fur<strong>the</strong>rmore,
more scientists live on earth than ever before, so even <strong>the</strong> growth <strong>of</strong> available information still
increases and this does not seem to change in <strong>the</strong> near future (Savioz 2004; Marcum and George
2009). This poses significant challenges to organizations concerned with research and development
(R&D). Like knowledge management (KM) in general, Technology Surveillance (TS) aims to apply a
systematic process for <strong>the</strong> collection, organization and dissemination <strong>of</strong> this particular information in
order to enable organizations to systematically monitor <strong>the</strong> technological advances resulting from
research to identify upcoming trends and new opportunities.
However, <strong>the</strong> problem <strong>of</strong> an ever growing amount <strong>of</strong> information that is created by millions <strong>of</strong> people is
not a particular research related. The ra<strong>the</strong>r vague term Web 2.0 refers to this development on <strong>the</strong>
web in general and comprises tools that allow many people to contribute content, as individuals or in
a group effort, toge<strong>the</strong>r with new solutions to organize this information. Applying <strong>the</strong>se tools and
solutions on a smaller scale, like in enterprises or o<strong>the</strong>r organizations, is sometimes referred to as
Enterprise 2.0 (McAfee 2006, Stocker & Tochtermann 2009). Fur<strong>the</strong>rmore, <strong>the</strong>re are already efforts to
use <strong>the</strong>m in a research context (Procter, Williams et al. 2010).
In this article, we investigate how a TS process can be implemented in a university context based on
Web 2.0 principles and solutions. TS is special in a university context because <strong>the</strong> university
management does not depend on <strong>the</strong> expertise <strong>of</strong> external experts but on <strong>the</strong> knowledge <strong>of</strong> its own
researchers. Our primary goal is to tap this source <strong>of</strong> knowledge for <strong>the</strong> management that has to
make strategic decisions. Additionally, we aim to support researchers in <strong>the</strong>ir daily work by making <strong>the</strong>
knowledge base available for <strong>the</strong>m, too.
The document is structured as follows: Section 2 outlines <strong>the</strong> activities involved in <strong>the</strong> Technology
Surveillance process with an emphasis on <strong>the</strong> common problems that are encountered in <strong>the</strong>
traditional approaches. Section 3 presents an overview <strong>of</strong> Web 2.0 technologies and trends. Section 4
provides <strong>the</strong> core contribution <strong>of</strong> <strong>the</strong> article. It is described how <strong>the</strong> Web 2.0 Technologies can be
444

Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
applied for TS in a university context. Our implementation <strong>of</strong> this model as a prototype is covered in
Section 5. Section 6 concludes <strong>the</strong> article and identifies opportunities for fur<strong>the</strong>r research.
2. Technology surveillance
There are two <strong>European</strong> Standards, French (AFNOR 1998) and Spanish (Malvido 2008), that provide
a basic terminology for <strong>the</strong> subject <strong>of</strong> TS. Additionally, among <strong>the</strong> literature, <strong>the</strong> authors have
identified a variety <strong>of</strong> TS definitions, processes and actors involved in it. TS has several functions:
scanning <strong>the</strong> environment (Porter and Cunningham 2005; Porter, Alencar et al. 2006), identification
and assessment <strong>of</strong> critical technology advances (Nosella, Petroni et al. 2008), continuous monitoring
<strong>of</strong> leading technological developments, to absorb information <strong>of</strong> science and technology from <strong>the</strong>
outside, and finally, <strong>the</strong> organization <strong>of</strong> this information to select, analyze, disseminate and
communicate it to turn it into knowledge (Rey Vázquez 2009) for <strong>the</strong> actors involved.
We will describe TS as a process, which generally involves 5 steps that are described in <strong>the</strong> following.
To some extent <strong>the</strong>se steps resemble <strong>the</strong> activities in <strong>the</strong> implementation part <strong>of</strong> <strong>the</strong> KM cycle <strong>of</strong>
Probst (Probst 1998). However, <strong>the</strong>re is no correspondent for preservation since this only plays a
minor role in our approach. Fur<strong>the</strong>rmore, we name common problems that are related to <strong>the</strong>
traditional approaches (Lichtenthaler 2003; Savioz 2004; Porter and Cunningham 2005; Nosella,
Petroni et al. 2008) <strong>of</strong> TS:
Formulation <strong>of</strong> information needs: Information about technology and trends is crucial for an
organization’s success. However, companies rarely explicitly formulate <strong>the</strong>ir information needs.
They have difficulties in seizing <strong>the</strong> innovation potential <strong>of</strong> trends. The mission and specific
characteristics <strong>of</strong> an organization have to be taken into account when identifying information
needs.
Information collection: Formal and informal information, such as journal articles, conference
proceedings and scientific seminars, are collected for <strong>the</strong> organization. In (Savioz 2004) a survey
is conducted to analyze <strong>the</strong> TS capacities in a group <strong>of</strong> companies that reveals interesting
problems regarding this activity: <strong>the</strong> use <strong>of</strong> informal network <strong>of</strong> experts, lack <strong>of</strong> time for information
collection and no formal knowledge flows between researchers and managers who avoid an
organizational strategy. There is no systematic matching <strong>of</strong> <strong>the</strong> internal information structure with
<strong>the</strong> external environment.
Information analysis: There are a variety <strong>of</strong> tools proposed for TS. However, an analysis <strong>of</strong> <strong>the</strong>ir
integration and <strong>the</strong> influence on <strong>the</strong> final results are not explicitly demonstrated in many
approaches. The reviewed approaches have a consensus about that this step requires expertise
in many disciplines. The methods applied include bibliometric analysis, collaboration network
analysis, competitors monitoring, and o<strong>the</strong>rs. Here, <strong>the</strong> company basically depends on <strong>the</strong>
abilities <strong>of</strong> external experts.
Information dissemination: Oral presentations, meetings, reports for <strong>the</strong> high managers or
technology boards, are some <strong>of</strong> <strong>the</strong> methods used for this activity. Time intervals vary from
weekly to annual. The objective is to inform <strong>the</strong> persons involved in <strong>the</strong> decision making process
about technology related trends and opportunities. However, <strong>the</strong> methods are not sufficiently
effective because <strong>of</strong> <strong>the</strong> sometimes long time intervals and <strong>the</strong> fact that informal information
exchange is not adequately considered.
Information application: The results are presented through reports that tend to outdate very fast
and thus <strong>the</strong>re is a lack <strong>of</strong> influence on future decisions. The application <strong>of</strong> information should
begin with <strong>the</strong> first steps <strong>of</strong> this process, when <strong>the</strong> internal and external information needs are
clear and information is collected.
The specification <strong>of</strong> <strong>the</strong> roles involved in <strong>the</strong> process varies widely among <strong>the</strong> consulted literature. In
(Savioz 2004), for example many roles are identified: Facilitator, process coordinator, idea medium,
expert, information specialist, process promoter, scanner, analyst, external specialist and method
specialist. Roles covered in <strong>the</strong> literature go from individual responsibilities to contractor companies
providing TS as a service. It is common to all approaches that <strong>the</strong> TS process is not <strong>the</strong> work <strong>of</strong> one
person, but a team and it depends on external experts.
In <strong>the</strong> literature, <strong>the</strong> typical TS approach is not described in a systematic form. In o<strong>the</strong>rs words, <strong>the</strong>
relations between roles, processes and <strong>the</strong> analytical information that decisions are based on are not
specified explicitly.
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
3. Web 2.0 Technologies and Trends
In <strong>the</strong> following, we briefly describe technologies and applications that – after becoming popular on
<strong>the</strong> web – are increasingly used in <strong>the</strong> intranets <strong>of</strong> modern enterprises to foster knowledge sharing
and collaboration. In section 4 we show how <strong>the</strong>y can be used to support technology surveillance.
3.1 Wikis
A wiki is basically a collection <strong>of</strong> web pages that can be easily edited by a group <strong>of</strong> people pursuing a
common goal. Typically anybody who is allowed to view a page may also edit its contents. In this way,
contributions are encouraged and <strong>the</strong> quality <strong>of</strong> <strong>the</strong> information in <strong>the</strong> wiki can be improved. Wikis can
be very flexibly used, and <strong>the</strong>y are widely applied in enterprise intranets and even for personal
information management.
Since Ward Cunningham developed <strong>the</strong> first wiki to support <strong>the</strong> collaborative documentation <strong>of</strong>
s<strong>of</strong>tware design patterns, wikis have evolved significantly. Versioning <strong>of</strong> pages, notifications on
changes, and <strong>the</strong> ability to configure access rights are only basic functionalities that all modern wikis
<strong>of</strong>fer. While wiki pages traditionally contain only pure text, <strong>the</strong>re are many attempts to allow <strong>the</strong> user
to enter more structured information to pages. This does not only <strong>of</strong>fer new opportunities like making
<strong>the</strong> information more accessible through new forms <strong>of</strong> representation and exploration but it also
allows o<strong>the</strong>r applications to use <strong>the</strong> structured information.
3.2 Blogs
As an easy way for individuals to publish information on <strong>the</strong> web to a potentially very large audience,
blogs became very popular in <strong>the</strong> last decade. Characteristic for blogs is that entries are shown in
reverse-chronological order and <strong>the</strong>y are authored ei<strong>the</strong>r by individuals or a limited group <strong>of</strong> people
with a common interest. Once published, a blog post is usually not substantially modified and readers
are able to leave comments for each post.
From a technical point <strong>of</strong> view, blogs are a kind <strong>of</strong> content management system and modern blogging
s<strong>of</strong>tware <strong>of</strong>fers capabilities that allow to use it as such a system in a more general way. This includes
<strong>the</strong> creation <strong>of</strong> pages separate from <strong>the</strong> sequence <strong>of</strong> blog posts, management <strong>of</strong> access rights for
different users and <strong>the</strong> creation <strong>of</strong> menu structures
3.3 Social Networking
Social Networking is ano<strong>the</strong>r trend that first became popular on <strong>the</strong> web and was <strong>the</strong>n adapted by
enterprises to improve internal communication and knowledge transfer. Social Networks allow users
to create a personal pr<strong>of</strong>ile containing information about <strong>the</strong>mselves and to connect to people <strong>the</strong>y
know. Users can write each o<strong>the</strong>r messages, share photos or links to web sites <strong>the</strong>y find interesting or
share <strong>the</strong>ir thoughts using an integrated blogging or microblogging functionality. The currently most
popular social network, facebook, fur<strong>the</strong>r <strong>of</strong>fers capabilities for <strong>the</strong> deep integration with o<strong>the</strong>r
applications by providing access to <strong>the</strong>ir user's data by an API.
3.4 Tagging
In recent years, tags – i.e., freely-chosen text labels assigned to information items by users –
appeared on <strong>the</strong> web as a means to flexibly classify arbitrary information items. They have proven
successful in contexts were <strong>the</strong> dynamics <strong>of</strong> (<strong>of</strong>ten user-generated) contents make it impossible or too
expensive to provide a controlled classification scheme. Popular examples are web sites like flickr or
delicious for <strong>the</strong> sharing <strong>of</strong> photos or bookmarks respectively. The main advantages are that users
can use <strong>the</strong>ir own vocabulary and that new tags can be added whenever <strong>the</strong> demand arises. On <strong>the</strong>
o<strong>the</strong>r hand, with <strong>the</strong> flexibility come new problems: It is hard to achieve consistency regarding <strong>the</strong>
usage <strong>of</strong> terms among different users. The same tags are used for different concepts and for <strong>the</strong>
same concept <strong>the</strong>re exist several terms. The problem is worsened by spelling variants and
abbreviations.
However, <strong>the</strong> advocates <strong>of</strong> tagging argue that despite <strong>the</strong>ir problems "tags are better than nothing" in
many situations since it is not always possible to create controlled vocabularies upfront
(http://many.corante.com/archives/2005/01/07/folksonomies_controlled_vocabularies.php). This
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
makes <strong>the</strong>m a valuable tool, particularly for TS where it cannot be relied on an established list <strong>of</strong>
categories since it is part <strong>of</strong> <strong>the</strong> problem to find <strong>the</strong>m.
3.5 Content Syndication with News Feeds
In addition to making <strong>the</strong>ir contents available on a website, news portals and blogs <strong>of</strong>ten publish new
entries in so-called web feeds. These feeds can be subscribed to by users using <strong>the</strong> most web
browsers, email programs or special feed readers. Subscribers <strong>of</strong> a feed do not have to register at <strong>the</strong>
publisher but add a certain web address to <strong>the</strong>ir feed reader that is <strong>the</strong>n periodically polled for new
updates. The most common protocols for data exchange are ATOM and RSS.
For users this means that <strong>the</strong>y do not have to visit <strong>the</strong> websites regularly to be informed <strong>of</strong> updates.
Additionally this technology allows applications to aggregate and combine <strong>the</strong> contents <strong>of</strong>fered by
o<strong>the</strong>r information sources. In <strong>the</strong> context <strong>of</strong> TS, this technology is particularly interesting, since
catalogues <strong>of</strong> scientific publications can be monitored this way.
3.6 Open Web Interfaces
The possibility <strong>of</strong> easily sharing resources with o<strong>the</strong>r researchers makes <strong>the</strong> Web 2.0 a key driver for
research collaboration, research productivity and knowledge transfer between disciplinary
communities and with external stakeholders (Procter, Williams et al. 2010). Special web platforms like
for example Mendeley (http://www.mendeley.com), that allows to connect to o<strong>the</strong>r researches and
share documents, follow <strong>the</strong> general Web 2.0 trend and allow to access <strong>the</strong>ir contents via open web
based programming interfaces (web APIs).
4. Applying Web 2.0 Technologies for TS in a university context
The following section presents our approach to technology surveillance based on Web 2.0
technologies. It is illustrated as a model <strong>of</strong> different kinds <strong>of</strong> information which are stored and
organized using <strong>the</strong> web 2.0 technologies described in Section 3. The information is acquired,
disseminated, processed and consumed in parallel by <strong>the</strong> staff in a continuous process.
Subsequently, we give an overview <strong>of</strong> <strong>the</strong> information needs and operational as well as strategic
decisions that are relevant in this new kind <strong>of</strong> technology surveillance process. We give examples <strong>of</strong>
how <strong>the</strong> available data can be exploited in this context.
5. Classification <strong>of</strong> information
As it is visible in Figure 1 we classify information along two dimensions: We distinguish whe<strong>the</strong>r
information comes from internal or external sources and whe<strong>the</strong>r it is structured or unstructured.
Information resources may be classified somewhere between <strong>the</strong> two extremes <strong>of</strong> both dimensions:
Information from external sources may be augmented, connected and categorized by <strong>the</strong> university
staff and thus considered internal to some extent. Similarly, unstructured text resources may carry
structured metadata or links to o<strong>the</strong>r resources so one may regard <strong>the</strong>m as partially structured.
We will now systematically describe <strong>the</strong> four quadrants shown in <strong>the</strong> figure, toge<strong>the</strong>r with <strong>the</strong>
respective processing <strong>of</strong> information and <strong>the</strong> application <strong>of</strong> Web 2.0 technologies.
5.1 Unstructured external information
The information items in this quadrant are mainly blog posts, short notifications or announcements
that are published by external sources via news feeds. These sources range from blogs <strong>of</strong> individual
researchers to large catalogs <strong>of</strong> scientific literature. The university subscribes to a selection <strong>of</strong> <strong>the</strong>se
sources and imports <strong>the</strong>m into its knowledge base in <strong>the</strong> form <strong>of</strong> blog posts. There exists blogging
s<strong>of</strong>tware that allows <strong>the</strong> feed based import <strong>of</strong> external posts. This import may be filtered by certain
tags or categories. Once <strong>the</strong> information is imported, tags can be added by <strong>the</strong> users to classify <strong>the</strong>
information and group related documents. It can be linked to from o<strong>the</strong>r documents to make important
pieces <strong>of</strong> information better accessible and it can be copied into o<strong>the</strong>r documents or collections. In <strong>the</strong>
figure, this is represented as a shift towards <strong>the</strong> lower right quadrant.
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
Figure 1: Organization and transformation <strong>of</strong> information
5.1.1 Structured external information
Sources <strong>of</strong> structured data usually require a bit <strong>of</strong> programming effort to access. However, for many
interfaces and formats s<strong>of</strong>tware libraries exist that make it easy to develop an importer that regularly
pulls structured data -- for example from a catalog <strong>of</strong> publications -- and saves it in an internal
structured wiki.
It is essential that <strong>the</strong> information is imported into a system that allows fur<strong>the</strong>r editing and refinement:
The imported contents can <strong>the</strong>n be fur<strong>the</strong>r integrated in <strong>the</strong> existing organization structure by tagging
and linking <strong>the</strong>m appropriately. Additionally, <strong>the</strong> content <strong>of</strong> <strong>the</strong> wiki pages may be extended by <strong>the</strong>
users by adding text or structured attributes. This blurs <strong>the</strong> border between external and internal
information.
5.1.2 Unstructured internal information
In this category falls all unstructured content that is produced by <strong>the</strong> university staff, for example
personal blog posts <strong>of</strong> <strong>the</strong> researchers. Additionally, messages sent by users via microblogging
services or <strong>the</strong> web pages researchers bookmark can be included here (see Section 5 for an example
<strong>of</strong> <strong>the</strong> latter).
As for <strong>the</strong> unstructured external information, it is desirable that <strong>the</strong> information in this quadrant is
integrated with <strong>the</strong> more structured documents. Linking and tagging are appropriate means here as
well. This is facilitated if updates to <strong>the</strong> contents can be subscribed to by news feeds.
5.1.3 Structured internal information
It is <strong>the</strong> goal <strong>of</strong> <strong>the</strong> university to have as much as possible <strong>of</strong> <strong>the</strong> internal information in a structured
form because in this form it is better suited to support <strong>the</strong> TS decisions. The information in this
quadrant comprises:
The structure <strong>of</strong> fields <strong>of</strong> research, important publications in <strong>the</strong>se fields and a glossary <strong>of</strong>
important terms
The organizational structure <strong>of</strong> <strong>the</strong> university
Personal pr<strong>of</strong>iles <strong>of</strong> <strong>the</strong> researchers including research interests and a list <strong>of</strong> publications
Information about research communities and events
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
As it was mentioned earlier, in addition to <strong>the</strong> structured contents in this quadrant, that directly
originate from staff <strong>of</strong> <strong>the</strong> university, <strong>the</strong>re is a steady flow <strong>of</strong> information from <strong>the</strong> o<strong>the</strong>r quadrants.
These contents were originally external or unstructured and are being structured and internalized by
adding attributes and tags. In this way, <strong>the</strong> contents can be manually transformed in a structured
format and <strong>the</strong>y are additionally internalized by adding meta-data – like tags – to align <strong>the</strong>m to <strong>the</strong>
internal classification scheme.
We suggest using a structured wiki to collaboratively ga<strong>the</strong>r and manage this information. However, if
<strong>the</strong>re are specialized tools for certain kinds <strong>of</strong> information it can be advantageous to use <strong>the</strong>m. For
example <strong>the</strong> individual pr<strong>of</strong>iles <strong>of</strong> researchers might be better managed with social networking
s<strong>of</strong>tware.
5.2 Exploiting <strong>the</strong> available data
In contrast to traditional models <strong>of</strong> technology surveillance, in our approach we see it as a continuous
process carried out by many people in parallel, and not as a sequence <strong>of</strong> consecutive steps. It is
characterized by <strong>the</strong> type <strong>of</strong> information that is processed and by <strong>the</strong> questions that have to be
answered using this data. In <strong>the</strong> following we describe <strong>the</strong> information needs <strong>of</strong> individual researchers
and <strong>the</strong> university management and how <strong>the</strong>y relate to <strong>the</strong> research information managed in web
based systems.
5.2.1 Information needs <strong>of</strong> individual researchers
Very beneficial for <strong>the</strong> work <strong>of</strong> individual researchers is <strong>the</strong> contact to o<strong>the</strong>r researchers working on a
similar or related topic. Even inside one university it is hard to maintain an overview <strong>of</strong> <strong>the</strong> people
working in a specific area. Personal Pr<strong>of</strong>iles in social networking s<strong>of</strong>tware are a significant advance in
this problem context. Tags can be used to make a statement about research interests to facilitate
finding and grouping <strong>the</strong> right persons. The connection to o<strong>the</strong>r information resources like publications
and personal blog posts makes it easy for researchers to assess <strong>the</strong>ir colleagues’ experience and
whe<strong>the</strong>r <strong>the</strong>ir work is relevant for <strong>the</strong>ir own.
Ano<strong>the</strong>r important information source is <strong>the</strong> shared structured wiki. It contains information about
important research groups, journals and events which is particularly valuable for researchers starting
to work on a new topic. Experts on a topic can be identified by <strong>the</strong>ir contributions to <strong>the</strong> respective
pages.
Being visible in <strong>the</strong> university is also an important incentive for researchers to contribute content. It is
an important aspect <strong>of</strong> our approach that <strong>the</strong> creation and management <strong>of</strong> information by <strong>the</strong>
university's researchers is not carried out to serve a small group <strong>of</strong> people that is concerned with
technology surveillance but that everybody has an immediate benefit.
If researchers contribute information being valuable for o<strong>the</strong>rs, <strong>the</strong>y can in turn pr<strong>of</strong>it from
contributions by <strong>the</strong>se people. For example if someone creates a list <strong>of</strong> important conferences in a
certain field <strong>of</strong> research, that he collected personally, o<strong>the</strong>r people may add conferences to <strong>the</strong> list
that <strong>the</strong> original author <strong>of</strong> <strong>the</strong> list was not aware <strong>of</strong>.
Finally, provided that <strong>the</strong>re is a large amount <strong>of</strong> content in <strong>the</strong> system, it becomes possible to
automatically generate recommendations for individual users. Based on <strong>the</strong> contents <strong>the</strong>y read or
contributed in <strong>the</strong> past, <strong>the</strong>ir personal interests and similarity to o<strong>the</strong>r researchers, suggestions can be
made regarding technological opportunities (Porter 2007; Yoon 2008), possible collaboration with
o<strong>the</strong>r researchers or relevant publications.
5.2.2 Information needs <strong>of</strong> <strong>the</strong> university management
For <strong>the</strong> university management, a major concern is, whe<strong>the</strong>r <strong>the</strong> research departments are
reasonably structured and all relevant fields <strong>of</strong> research are covered. Decisions have to be taken if
lines <strong>of</strong> research are split or a new line <strong>of</strong> research is to be created, if new employees have to be
hired or if <strong>the</strong>re are redundancies.
For <strong>the</strong>se questions, <strong>the</strong> data <strong>the</strong> collaboratively generated data is an invaluable information source.
In contrast to individual researchers, <strong>the</strong> management is ra<strong>the</strong>r interested in aggregated. Different
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
measures could for example be calculated for a particular department: <strong>the</strong> number <strong>of</strong> publications per
year, activity measures regarding certain keywords, cross-links to o<strong>the</strong>r departments, and many more.
Especially <strong>the</strong> observation <strong>of</strong> <strong>the</strong> usage <strong>of</strong> keywords and tags can give important insights into <strong>the</strong>
development <strong>of</strong> a certain technology or <strong>the</strong> emergence <strong>of</strong> new technologies.
6. prototype system
In this section we describe how we implemented our approach in a prototype system. We used <strong>the</strong>
commercial web-based collaboration platform Tricia that is developed by <strong>the</strong> infoasset AG
(http://www.infoasset.de). The platform features a structured wiki, blogs, a file share and it <strong>of</strong>fers
tagging functionality and a powerful text search. Additionally, it is possible to import news feeds (in <strong>the</strong>
RSS format). Connectors for third-party repositories for structured data can be added as plug-ins. We
chose to integrate everything in one system because in this way it is possible to use a global full-text
search, a shared tag vocabulary and common roles and access rights. In this section, we will cover
<strong>the</strong> types <strong>of</strong> information artifacts that are managed and how <strong>the</strong>y are organized using <strong>the</strong> capabilities
<strong>of</strong> <strong>the</strong> system.
This prototype will be actually used and evaluated in <strong>the</strong> Polytechnic University <strong>of</strong> Havana, Cuba. The
roll-out <strong>of</strong> <strong>the</strong> system is supported by <strong>the</strong> university management, so we are confident that it will be
adopted by <strong>the</strong> researchers. This assumption is also supported in <strong>the</strong> literature. Procter, Williams et
al. found, that <strong>the</strong> selection <strong>of</strong> appropriate tools is an important barrier when applying Web 2.0
technologies for research, not so much <strong>the</strong> technical skills required (Procter, Williams et al. 2010).
6.1 Structured contents
The structuring capabilities <strong>of</strong> <strong>the</strong> Tricia platform enable <strong>the</strong> users to add attributes and types to all
wiki pages. Attributes are simple key-value pairs, with values being ei<strong>the</strong>r text literals or links to o<strong>the</strong>r
wiki pages. There can be multiple values for one attribute and new attributes can by dynamically
added by users whenever <strong>the</strong>y are needed. The attributes <strong>of</strong> several pages <strong>of</strong> <strong>the</strong> same type can be
displayed in editable tabular views. Wikis using <strong>the</strong>se features are called hybrid wikis in Tricia
because <strong>the</strong>y mix data records with text content. These wikis are used in our prototype to capture a
large variety <strong>of</strong> information. This includes but is not limited to
Personal pr<strong>of</strong>iles <strong>of</strong> all researchers (See Figure 2 for an example)
Data about scientific papers and books that were written by researchers <strong>of</strong> <strong>the</strong> university as well
as those <strong>of</strong> external researchers. The data is imported using a connector to <strong>the</strong> Mendeley
platform (see Section 3.6)
Lists <strong>of</strong> important research communities, events and individual researchers that are considered
leaders in <strong>the</strong>ir topic
The organizational structure <strong>of</strong> <strong>the</strong> university including <strong>the</strong> respective research topics and <strong>the</strong>ir
relationships
All those elements are visible for all members <strong>of</strong> <strong>the</strong> university.
6.2 Unstructured contents
Since all wiki pages can be enriched with structured attributes, <strong>the</strong> only unstructured contents are
blog posts and files (which can be attached to wiki pages). Part <strong>of</strong> <strong>the</strong> blog posts are created by <strong>the</strong>
researchers <strong>the</strong>mselves in personal blogs about <strong>the</strong>ir research. Fur<strong>the</strong>rmore, <strong>the</strong> researchers can
configure a blog to import <strong>the</strong>ir browser bookmarks from <strong>the</strong> social bookmarking service delicious
(http://www.delicious.com) via a news feed. Since <strong>the</strong> tags are imported as well, it is possible to share
news and interesting web sites about particular topics with <strong>the</strong> o<strong>the</strong>r researchers.
Additionally, news feed subscriptions to literature databases that do not <strong>of</strong>fer structured updates but
only RSS notifications are configured by experienced researchers. They import news about
publications relevant to <strong>the</strong> particular fields <strong>of</strong> research relevant for <strong>the</strong> university.
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
Figure 2: Researcher pr<strong>of</strong>ile as a structured wiki page
6.3 Navigational structure and access rights
In Tricia, documents are organized in spaces (blogs and wikis) functioning as containers that have
default access rights settings. Within wikis, it is possible to configure a hierarchical menu. All contents
can be tagged and tag clouds are a central navigation mechanism. The menu in <strong>the</strong> primary public
wiki resembles <strong>the</strong> organizational structure <strong>of</strong> <strong>the</strong> university (faculties, departments, lines <strong>of</strong> research).
In addition, new wikis (or blogs) with specific access rights settings can be created on demand, e.g.
for a new research project or a cooperation with ano<strong>the</strong>r university. Access rights can be specified
with regard to groups that bundle several individual user accounts or o<strong>the</strong>r groups. In this way a role
concept can be implemented. Among o<strong>the</strong>rs, we distinguish <strong>the</strong> following roles:
Researcher: Someone who participates in research projects.
Lead researcher: Researcher who is qualified to lead a research project, has several years <strong>of</strong>
experience and who is responsible for <strong>the</strong> direction and management <strong>of</strong> <strong>the</strong> research project.
Research Manager: Person who manages <strong>the</strong> research at <strong>the</strong> university.
Complementary to <strong>the</strong> organization <strong>of</strong> contents spaces and menus, <strong>the</strong> tagging functionality <strong>of</strong> Tricia
is used to group related information resources across <strong>the</strong> boundaries <strong>of</strong> separate containers. New
tags can be easily added where appropriate and tag suggestions are presented to <strong>the</strong> users as <strong>the</strong>y
type to maintain consistency. Tags are primarily used to indicate which research topic a document
belongs to but also for events and projects and <strong>the</strong> organization <strong>of</strong> contents <strong>of</strong> individual users.
7. Conclusion and outlook
Starting with a short introduction <strong>of</strong> <strong>the</strong> traditional view <strong>of</strong> technology surveillance and technologies
and trends on <strong>the</strong> web, we developed a new approach to technology surveillance in <strong>the</strong> university
context based on typical Web 2.0 solutions. We described how different kinds <strong>of</strong> information can be
managed using web based tools and how <strong>the</strong> resulting knowledge base can be exploited by
researchers and <strong>the</strong> university management. Subsequently we described a prototype system that was
realized according to this approach using a web based collaboration platform.
In <strong>the</strong> next months, <strong>the</strong> prototype will be evaluated in practice and we are planning to systematically
improve it based on <strong>the</strong> experiences with <strong>the</strong> system. Fur<strong>the</strong>rmore we will use <strong>the</strong> plug-in mechanism
<strong>of</strong> <strong>the</strong> platform to add more connectors to o<strong>the</strong>r data sources and provide more ways to analyze <strong>the</strong>
content.
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Marta Infante Abreu, Florian Mat<strong>the</strong>s and Alexander Steinh<strong>of</strong>f
Fur<strong>the</strong>r, it would be interesting to transfer our approach to technology in general. However, this is a
challenging task because a major advantage <strong>of</strong> <strong>the</strong> university context is that no external experts are
required and all <strong>the</strong> necessary data is generated by <strong>the</strong> staff <strong>of</strong> <strong>the</strong> university, <strong>the</strong> researchers. In a
next step, it could be systematically examined in how far our approach is generally applicable to
knowledge management problems, precisely how it can support <strong>the</strong> conversions <strong>of</strong> knowledge
identified by Takeuchi & Nonaka (Takeuchi & Nonaka 1995), particularly <strong>the</strong> conversion <strong>of</strong> individual
to collective knowledge.
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452

Knowledge Creation in Multinational Corporations: The
Role <strong>of</strong> Organizational Structure
Pamela Chidiogo Izunwanne
University <strong>of</strong> Agder, Kristiansand, Norway
pamelaci@uia.no
Abstract: Organizational structure has been linked to several strategy constructs: organizational learning; firm
internationalization; innovation;firm performance;organizational capabilities; product development etc. However,
until now, studies have not classified <strong>the</strong> role that organizational structure plays in facilitating or inhibiting
knowledge creation. This paper attempts a classification using multinational corporations as context. The paper’s
aim is to show how <strong>the</strong> formal organizational structure could influence knowledge creation activities.
Organizational structure components include: reporting relationships; organization routines; standard operating
procedures; reward systems. This list is by no means exhaustive and could vary from one organization to <strong>the</strong>
o<strong>the</strong>r but this paper focuses on reporting relationships and routines.
Knowledge creation is a process that adds value for multinational corporations. It has been identified by <strong>the</strong>
knowledge based view as a process that leads to competitive advantage for firms if well coordinated (Grant,
1996b, Nonaka, 1994b). This paper adopts ideas <strong>of</strong> <strong>the</strong> emerging knowledge governance approach which
hypo<strong>the</strong>sizes that organizations can influence knowledge creation through <strong>the</strong> use <strong>of</strong> formal mechanisms(Foss,
2007). The overall proposition is that formal organizational structure components particularly reporting
relationships and routines influence knowledge creation through particular organizational controls.
Keywords: Knowledge creation; organizational structure; organizational controls; knowledge governance;
knowledge-based view; multinational corporations
1. Introduction
Knowledge creation has generated a lot <strong>of</strong> research in recent times. Scholars have proposed different
mechanisms and methods through which knowledge can be created in an organization. The
mechanisms and methods proposed are mostly based on different <strong>the</strong>ories <strong>of</strong> knowledge creation; <strong>the</strong>
emerging knowledge governance approach (Foss, 2007, Foss et al., 2010); <strong>the</strong> organic approach
(adhoc freelance knowledge creation)(Bartunek et al., 2001, Ba<strong>the</strong>lt et al., 2004, Choo, 1996) and a
combination <strong>of</strong> both approaches (Nonaka et al., 2000, Nonaka, 1994a).
New approaches are however emerging and gaining ground fast, proposing <strong>the</strong> role <strong>of</strong> outsiders;
personal social networks; virtual teams and customers in <strong>the</strong> knowledge creation activities <strong>of</strong>
organizations. The emerging knowledge governance approach is backed by research on <strong>the</strong> use <strong>of</strong>
organizational controls in knowledge creation(Turner and Makhija, 2006). Henceforth referred to as
KGA, <strong>the</strong> emerging knowledge governance approach highlights <strong>the</strong> role that formal mechanisms like
organizational structure play in knowledge creation in firms(Foss, 2007). It is <strong>the</strong> aim <strong>of</strong> this paper to
specifically highlight <strong>the</strong> role that formal structural components like reporting relationships and
routines play in facilitating or inhibiting knowledge creation in multinational corporations henceforth
MNCs.
The application <strong>of</strong> knowledge in organizations known as knowledge management (Walczak,
2005)does not only involve <strong>the</strong> storing or codification <strong>of</strong> knowledge as many IT pr<strong>of</strong>essionals like to
think, but also involves <strong>the</strong> creation and management <strong>of</strong> knowledge intensive work. It can be broken
down into knowledge creation, knowledge sharing and knowledge transfer (Alavi and Leidner, 2001,
Alavi and Leidner, 1999, Nonaka, 1994a). These three elements are difficult to separate in
practice(Argote et al., 2003), and this paper would not attempt to do so because knowledge creation
may be impossible without knowledge transfer.
The paper is organized as follows.First, using MNCs as context, I present a review <strong>of</strong> knowledge
creation that includes a brief overview <strong>of</strong> existing knowledge creation <strong>the</strong>ories discovered in literature.
Secondly, I present propositions highlighting <strong>the</strong> role that formal organizational structure components
like reporting relationships and routines play in inhibiting or enhancing knowledge creation through
specific control mechanisms. Finally, based on <strong>the</strong> identified knowledge creation <strong>the</strong>ories and
approaches, I present a conceptual model and propose possible classifications.
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Pamela Chidiogo Izunwanne
2. Knowledge creation in multinational corporations
knowledge creation is endowed with many definitions as observed in literature reviews: defined as a
major factor that allows an increase in <strong>the</strong> value/cost ratio <strong>of</strong> a cooperative process; building new
knowledge (Matusik and Hill, 1998); bringing innovative products and services into <strong>the</strong> market
place(Ganesh, 2000); a process that allows insights <strong>of</strong> individuals to be converted into knowledge that
can <strong>the</strong>n be used to develop new products and improve performance (Choo, 1996); <strong>the</strong> difference
between what is known and what must be known for success <strong>of</strong> a project (Johnson, 2002); use <strong>of</strong>
complex and discontinuing phenomena and a set <strong>of</strong> components emerging from communication
across interpersonal relationships(Styhre et al., 2002).
Many authors however discuss this subject without specifically pointing out <strong>the</strong> aspect <strong>of</strong> knowledge
creation that is being studied. According to Mitchell and Boyle in <strong>the</strong>ir recently developed taxonomy <strong>of</strong>
knowledge creation, <strong>the</strong>re is an input, output and outcome dimension to take into consideration
instudying knowledge creation (Mitchell and Boyle, 2010). Figure 1 below presents a good illustration
<strong>of</strong> this idea.
Knowledge
Creation input
Knowledge
Creation output
Knowledge
Creation outcome
Figure 1: The knowledge creating activity chain
The knowledge creation input refers to <strong>the</strong> method or process used in knowledge creation, <strong>the</strong> output
refers to <strong>the</strong> initial result <strong>of</strong> <strong>the</strong> knowledge creation activity (example; new product idea, prototypes,
new suggestions etc.) while <strong>the</strong> outcome refers to <strong>the</strong> final result <strong>of</strong> <strong>the</strong> knowledge creation activity
(new product in <strong>the</strong> market, new service, new procedures) (Mitchell and Boyle, 2010). The fact that
<strong>the</strong>re is new knowledge (output) adds a certain level <strong>of</strong> value in a multinational corporation but <strong>the</strong>
highest value would be realized when <strong>the</strong> new knowledge is translated into routine knowledge
(outcome) (Gooderham, 2007, Alavi and Leidner, 2001, Roth, 2003). A clear specification <strong>of</strong> <strong>the</strong>
aspect <strong>of</strong> knowledge creation under study is highly important to facilitate comparisons between
different models <strong>of</strong> knowledge creation(Mitchell and Boyle, 2010).
According to <strong>the</strong> knowledge based view <strong>of</strong> <strong>the</strong> firm, it is not enough that a firm’s knowledge base is
dynamic or difficult to imitate(Walczak, 2005, Nonaka et al., 2000), it must also be efficiently and
quickly transferable within <strong>the</strong> organization to all <strong>the</strong> areas where it will be useful (Grant, 1996b, Roth,
2003, Nonaka et al., 2000); be <strong>of</strong> high quality(Walczak, 2005) (only highest quality options are to be
selected) and it must be productive i.e. aligned with company policy, objectives and business
model(Walczak, 2005, Claver-Cortés et al., 2007, Hong, 1999). Thus, emphasis is placed on allowing
as many inputs as possible for <strong>the</strong> knowledge creation process, selecting high quality outputs and
ensuring fast implementation and adaptation in order to obtain desired outcomes.
MNCs are firms with business operations in more than one country. Some MNCs are extremely
influential in <strong>the</strong> countries where <strong>the</strong>y operate, with budgets exceeding <strong>the</strong> gross domestic product <strong>of</strong>
entire nations in some cases. The continuous creation <strong>of</strong> knowledge is a major source <strong>of</strong> global
competitive advantage for firms in this category (Choi and Lee, 2002). The focus on MNCs in this
study is interesting because knowledge creation for an MNC as a whole becomes more complex as
<strong>the</strong> number <strong>of</strong> its subsidiaries increase. Knowledge creation does not only involve problem solving
and knowledge acquisition in one or two subsidiaries, it involves ensuring that new knowledge
realized benefits <strong>the</strong> whole firm(Turner and Makhija, 2006, Hong, 1999).
The complexity <strong>of</strong> <strong>the</strong> knowledge creation activity is somewhat dependent on size (Walczak,
2005).And as I propose in this paper: organizational structure. Knowledge creation in structures that
require high integration (formalized reporting relationships between subsidiaries and headquarters) <strong>of</strong>
subsidiaries with <strong>the</strong> corporate headquarters and low responsiveness to local needs (also known as
global structure (Harzing, 2000)) may be more complex than knowledge creation in structures with
directly opposite characteristics (multidomestic (Harzing, 2000)).
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3. Theories <strong>of</strong> knowledge creation
Pamela Chidiogo Izunwanne
The promotion <strong>of</strong> knowledge creation as a source <strong>of</strong> competitive advantage(Choi and Lee, 2002) has
led to <strong>the</strong> generation <strong>of</strong> several knowledge creation <strong>the</strong>ories. A look at <strong>the</strong> <strong>the</strong>ories most frequently
proposed by researchers reveals that most authors adopt an organic approach to studying knowledge
creation: adhoc freelance knowledge creation (Bartunek et al., 2001, Ba<strong>the</strong>lt et al., 2004, Choo,
1996). The idea promoted here is that flexibility encourages creativity which in turn, facilitates
knowledge creation (Lee and Cole, 2003). The emerging knowledge governance approach (KGA)
argues <strong>the</strong> opposite. According to this approach, formalization is beneficial in facilitating <strong>the</strong>
knowledge creation process and <strong>the</strong> knowledge creation process can be coordinated and better
organized using <strong>the</strong> formal aspects <strong>of</strong> organizational structure(Miner, 1990, Foss, 2007, Foss et al.,
2010). Some Authors present arguments based on a combination <strong>of</strong> both approaches in <strong>the</strong>ir study
i.e. flexibility and structure(Chua, 2001, Miner, 1990). While <strong>the</strong>se <strong>the</strong>ories are important in studying
knowledge creation in MNCs (Nonaka et al., 2000, Spender, 1996), past and recent studies have
shown that <strong>the</strong>re is much more to knowledge creation.
The concept <strong>of</strong> lead users by Hippel and utilization <strong>of</strong> contingent work by Hill and Matusik illustrate
how consumers directly and indirectly participate in <strong>the</strong> knowledge creation process (Hippel, 1986,
Matusik and Hill, 1998). Fur<strong>the</strong>rmore, communities <strong>of</strong> practice, virtual networks, clusters as well as
personal social networks have been found to play huge roles in facilitating knowledge creation by
enhancing problem solving for firms. Nowadays, <strong>the</strong> impact <strong>of</strong> external influence in <strong>the</strong> form <strong>of</strong>
personal social networks, social media etc. can not be overlooked. In addition, top management,
middle manager and employee perspective have become equally important.
The total sum <strong>of</strong> individual knowledge in an organization is not equal to <strong>the</strong> organizational knowledge
<strong>of</strong> <strong>the</strong> firm (Grant, 1996a, Roth, 2003). Grant suggested that firms engage in knowledge application
ra<strong>the</strong>r than knowledge creation (Grant, 1996a, Huang and Newell, 2003, Grant, 1996b). Expatriation
indicates that what an individual knows is <strong>of</strong>ten different from what <strong>the</strong> organization knows (Minbaeva
and Michailova, 2004, Bonache et al., 2001). It is <strong>the</strong>refore important for organizations to be
structured in ways that enhance knowledge creation (Dixon, 2000, Swee, 2002).
First conceptualized by Stene in 1940, organizational routines is how an organization chooses to run
its affairs (Feldman and Pentland, 2003). As far as knowledge creation is concerned, routines involve:
techniques (macro or micro) and frequency <strong>of</strong> knowledge creation projects. An MNC that allows
flexibility and diversity <strong>of</strong> knowledge creation techniques will benefit more in terms novel ideas than
MNCs that limitmethods or frequency <strong>of</strong> knowledge creation projects (Turner and Makhija, 2006).
3.1 The knowledge creation activity chain and processes
The Knowledge
creating input
The creation process
Output <strong>of</strong> <strong>the</strong>
knowledge
creation activity
Implementation,
commercialization
adaptation
Outcome <strong>of</strong> <strong>the</strong>
knowledge
creation activity
Figure 2: Knowledge creating activity chain
In practice, <strong>the</strong>re are several processes involved in <strong>the</strong> knowledge creation activity chain and <strong>the</strong>se
processes run continuously and interactively (Nonaka and Toyama, 2003). The illustration above
shows <strong>the</strong> stages and phases involved in knowledge creation. Input, output and outcome are referred
to as stages while translating from one stage to <strong>the</strong> o<strong>the</strong>r is termed a phase (input to output and
output to outcome) and involves processes.
3.1.1 Input to output
This phase involves actual knowledge creation processeslike: research and development (Roth,
2003); socialization (Nonaka et al., 2000, Nonaka, 1994a); open dialogue and discussions coupled
with <strong>the</strong> use <strong>of</strong> facilitators skilled at extracting useful knowledge (Fong et al., 2007); care (Krogh,
1998); cooperation (Choo, 1996); mentorship, openness and use <strong>of</strong> teams (Mitchell et al., 2009); and
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Pamela Chidiogo Izunwanne
use <strong>of</strong> clusters (Ba<strong>the</strong>lt et al., 2004). By directly or indirectly specifying particular inputs (controls) for
subsidiaries, MNCs can limit or enhance <strong>the</strong> effectiveness and productivity <strong>of</strong> <strong>the</strong>ir knowledge creation
activities. They can directly specify inputs by explicitly stating <strong>the</strong> methods to be used by subsidiaries
and headquarters to generate knowledge (for example R&D projects, special meeting sessions,
virtual teams etc) and <strong>the</strong>y can indirectly specify inputs by taking ideas generated through particular
methods lightly.
3.1.2 Output to Outcome
Processes that could occur in this phase include commercialization, implementation and adaptation.
The outputs (ideas, prototypes etc) chosen by <strong>the</strong> organization as most in sync with business policy
and objectives are <strong>the</strong>n commercialized, implemented within <strong>the</strong> firm and adapted for maximum
benefit. Adaptation is <strong>the</strong> organization’s ability to obtain successful implementation and
commercialization (Miner, 1990). It <strong>of</strong>ten starts within <strong>the</strong> employees who will ultimately be in charge
<strong>of</strong> <strong>the</strong> new product or technique as may be <strong>the</strong> case and involves <strong>the</strong> Knowledge management
system in <strong>the</strong> MNC. This system supports knowledge management practices in an organization
(Gallupe, 2001, Skuce, 1993). It normally includes but is not limited to expatriation
strategy/mechanism, management information systems and transmission channels (Bonache et al.,
2001, Minbaeva and Michailova, 2004) that ensure that all subsidiaries benefit from new knowledge
realized. Structural changes within <strong>the</strong> knowledge management system could become necessary in
order to facilitate <strong>the</strong> implementation and commercialization processes (Davenport and Prusak, 1997).
The ultimate goal <strong>of</strong> organizations is or should be to attain <strong>the</strong> outcome stage as fast as possible. It is
at this stage that innovation is said to occur(Popadiuk and Choo, 2006), i.e., when <strong>the</strong> new knowledge
begins to yield benefit for <strong>the</strong> MNC. Sometimes, attaining this stage is relatively easy in <strong>the</strong> case <strong>of</strong> an
incremental innovation but is more challenging when <strong>the</strong> innovation or new knowledge is entirely new
to <strong>the</strong> organization (Christensen, 1997). Depending on <strong>the</strong> structure <strong>of</strong> <strong>the</strong> MNC, attaining <strong>the</strong>
outcome stage may take a long time as a result <strong>of</strong> existing output to outcome controls (how to
implement, how to commercialize and adaptation decisions). Reporting relationships could affect <strong>the</strong>
speed and efficiency <strong>of</strong> knowledge creation in cases where commercialization, implementation and
adaptation decisions are to be agreed upon by corporate headquarters.
4. Knowledge creation and organizational structure
Having shown <strong>the</strong> relevance <strong>of</strong> knowledge creation in MNCs, <strong>the</strong> remainder <strong>of</strong> this paper first
sketches <strong>the</strong> contours <strong>of</strong> what an appropriate knowledge creation <strong>the</strong>ory should be and proceeds to
show how <strong>the</strong> speed; efficiency; quality and productivity <strong>of</strong> knowledge creation in MNCs is influenced
by reporting relationships and routines when organizational controls exist and influence <strong>the</strong> phases <strong>of</strong>
<strong>the</strong> knowledge creation activity chain. Organization structure can be considered <strong>the</strong> skeletal system <strong>of</strong>
an organization (Dalton et al., 1980) that provides <strong>the</strong> platform within which an organization performs
its operations. Scholars have presented arguments about whe<strong>the</strong>r organizational structures shapes or
is being shaped by individuals (Sewell, 1992, Pugh et al., 1968). Organizational structure is said to
have many dimensions: concentration <strong>of</strong> authority; line <strong>of</strong> workflow and size <strong>of</strong> supportive component
(Pugh et al., 1968). Its dimensions also vary from one organization to <strong>the</strong> o<strong>the</strong>r.
4.1 Theory <strong>of</strong> knowledge creation: a general perspective
It can be said that <strong>the</strong> organic approach has been <strong>the</strong> most common approach in studying knowledge
creation in firms. However this approach alone does not adequately accommodate <strong>the</strong> evolving
knowledge creation methods mentioned in preceding paragraphs. The KGA on <strong>the</strong> o<strong>the</strong>r hand does
not totally account for <strong>the</strong> informal knowledge creation activities that go on inside and outside <strong>the</strong>
organization. According to <strong>the</strong> structural evolution <strong>the</strong>ory, some degree <strong>of</strong> formalization in conjunction
with variation i.e., flexibility and structure is needed to improve knowledge creation and learning within
an organization (Miner, 1990). But even <strong>the</strong> combined approach does not account for <strong>the</strong> role <strong>of</strong>
customers and <strong>the</strong> external influence <strong>of</strong> social media on <strong>the</strong> knowledge creation activities <strong>of</strong> MNCs.
Perhaps <strong>the</strong> only way to accommodate <strong>the</strong> new useful <strong>the</strong>ories and approaches; incorporate macro
and micro perspectives; monitor <strong>the</strong> knowledge creating processes and tackle <strong>the</strong> related challenges
is by a customized organizational structure and this can only become possible when organizations
realize <strong>the</strong> exact impacts <strong>the</strong>ir structure has on <strong>the</strong>ir knowledge creation efforts.
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4.2 Control in organizational structure
Pamela Chidiogo Izunwanne
Organizational structure enforces coordination through <strong>the</strong> use <strong>of</strong> control mechanisms (Turner and
Makhija, 2006, Pugh et al., 1968). Control involves <strong>the</strong> cognitive dimensions as well as norms that
influence how organizations carry out <strong>the</strong>ir operations (Turner and Makhija, 2006). It could also be
referred to as <strong>the</strong> invisible mechanism that coordinates processes within an organization. Bringing <strong>the</strong>
perspectives toge<strong>the</strong>r, <strong>the</strong> role <strong>of</strong> organizational control is seen in its ability to streng<strong>the</strong>n
organizational structure components and <strong>the</strong>reby influence employees in organizations (Sewell, 1992,
Turner and Makhija, 2006).
5. Conceptual model
Through organizational controls: organizational norms(Turner and Makhija, 2006) and what is
permissible in an organization, organizational structure is able to affect knowledge creation activity by
influencing <strong>the</strong> input/output phase and <strong>the</strong> output/outcome phase. The model below illustrates this
idea. The taxonomy by Mitchell and Boyle is used again to show distinctions between phases and <strong>the</strong>
entire knowledge creation activity chain (from input until outcome).
3
Figure 3: Conceptual Model.
5.1 Routines; controls; input/output; quality and productivity
The input/output phase refers to <strong>the</strong> knowledge creation process itself i.e., how an organization
decides to create knowledge and <strong>the</strong> process itself. This is <strong>of</strong>ten embedded in organizational
routines(Feldman and Pentland, 2003). An MNC can impose input/output controls directly or indirectly
by having specific routines (Claver-Cortés et al., 2007) and giving little opportunities for subsidiaries to
determine how <strong>the</strong> process is to be conducted. MNCs may also decide not to allow specific routines
as far as <strong>the</strong> knowledge creation process is concerned or to allow subsidiaries <strong>the</strong> opportunity to
conduct <strong>the</strong> process as <strong>the</strong>y deem fit. However, by determining <strong>the</strong> approach <strong>of</strong> subsidiaries to <strong>the</strong>
input/output phase (knowledge creation process) an MNC may decrease or improve <strong>the</strong> quality and
productivity <strong>of</strong> its entire knowledge creation activity chain. When an MNC has a structure that allows
its subsidiaries to use several kinds <strong>of</strong> inputs or knowledge creation methods, <strong>the</strong>re will be an
increase in its knowledge creating potential while an MNC that allows only limited knowledge creation
methods will have a relatively low knowledge creating potential(Turner and Makhija, 2006). This is
because, more diverse approaches increase <strong>the</strong> chance <strong>of</strong> novel discoveries(Turner and Makhija,
2006) hence improving <strong>the</strong> overall quality and productivity <strong>of</strong> <strong>the</strong> knowledge realized.
5.2 Reporting relationships; controls; Output/outcome; speed and efficiency
Organizational structure can determine to a great extent, <strong>the</strong> speed and efficiency <strong>of</strong>
commercialization, implementation and adaptation processes that characterize <strong>the</strong> output to outcome
phase <strong>of</strong> knowledge creation activities. Firms that are characterized by hierarchy and bureaucracy (a
lot <strong>of</strong> reporting relationships) undergo this stage slowly. The outcome <strong>of</strong> knowledge creating activity is
<strong>the</strong> final result <strong>of</strong> a knowledge creating activity(Mitchell and Boyle, 2010). Outcomes can be new
products, techniques and procedures that have successfully undergone implementation,
commercialization and adaptation processes.
In MNCs with several reporting relationships, <strong>the</strong> three aforementioned processes take more time
because <strong>the</strong>re has to be solid agreement between corporate headquarters and subsidiaries on each
<strong>of</strong> <strong>the</strong> separate processes. Although this enables <strong>the</strong> MNC achieve integration (Harzing, 2000), it
reduces <strong>the</strong> speed and efficiency <strong>of</strong> <strong>the</strong> entire knowledge creation activity chain.
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Pamela Chidiogo Izunwanne
5.3 Organizational Structure <strong>of</strong> MNCs and knowledge creation
Several classifications <strong>of</strong> MNCs exist. I chose to adopt <strong>the</strong> classification by Harzing used by many
scholars perhaps due to clarity and simplicity. The classification is based on strategies followed by
corporate headquarters and it groups MNCs into transnational, global and multidomestic (Harzing,
2000).
Multidomestic MNCs are characterized by a decentralized structure and are designed to adapt to
national differences. MNC routines in this category <strong>of</strong> firms are relatively flexible and allow for
autonomy. However, flexibility lowers integration which is supposed to ensure that <strong>the</strong> process is in
sync with company policies, goals and objectives. Reporting relationships (exchanges between
headquarters and subsidiaries) are also low resulting in faster knowledge creation. Reporting
relationships coupled with controls reduce knowledge creation outcomes in <strong>the</strong> MNC as a whole and
streng<strong>the</strong>n <strong>the</strong> bureaucracies involved in <strong>the</strong> output to outcome phase. The absence <strong>of</strong> this in
multidomestic MNCs improves <strong>the</strong> speed and efficiency <strong>of</strong> <strong>the</strong> knowledge creation activity chain but
lowers its quality and productivity because knowledge created at <strong>the</strong> subsidiary is not necessarily
available at corporate headquarters. So in <strong>the</strong> knowledge creation activity chain <strong>of</strong> <strong>the</strong>se MNCs,
productivity and quality is low.
Proposition 1: Knowledge creation in multidomestic firms is fast, efficient but has low
quality and productivity.
In global MNCs, emphasis is laid on integration between <strong>the</strong> subsidiaries and corporate headquarters.
Subsidiaries are seen as custodians <strong>of</strong> headquarter policies. Reporting relationships coupled with
controls enforce <strong>the</strong> bureaucracies involved in both input to output (creation process) and output and
outcome phases (implementation, commercialization and adaptation processes) phases. Although <strong>the</strong>
resulting knowledge creation activity is <strong>of</strong> high quality and productivity due to alignment with company
goals and policies MNC wide, <strong>the</strong> speed and efficiency <strong>of</strong> <strong>the</strong> entire process is significantly reduced.
Proposition 2: Knowledge creation in global firms is slow and inefficient but productive
and <strong>of</strong> high quality.
Transnational MNCs are able to combine <strong>the</strong> advantages and disadvantages <strong>of</strong> <strong>the</strong> two above
categories <strong>of</strong> MNCs. The knowledge creation performance <strong>of</strong> this class <strong>of</strong> firms depends on how <strong>the</strong>y
are able to emphasize and de-emphasize <strong>the</strong>ir advantages and disadvantages respectively. With a
relatively high level <strong>of</strong> autonomy; low reporting relationships compared to global MNCs, <strong>the</strong>y are able
to go through <strong>the</strong> output to outcome phase faster but <strong>the</strong> still significant emphasis on conforming to
headquarter standards places a restriction on routines which reduces <strong>the</strong> flexibility <strong>of</strong> <strong>the</strong> knowledge
creating process (input to output phase) in comparison to multidomestic MNCs. Conforming to
headquarter standards <strong>the</strong>reby ensures that <strong>the</strong>se categories <strong>of</strong> firms are able to achieve a higher
level <strong>of</strong> productivity and quality in <strong>the</strong>ir knowledge creation activities in comparison to multidomestic
MNCs.
Proposition 3: Knowledge creation in transnational firms in terms <strong>of</strong> speed, efficiency
productivity and quality is medium compared to global and multidomestic firms.
The proposed classification is shown in table 2 below.
6. Conclusion, limitations and suggestions for fur<strong>the</strong>r research
The above propositions are based on <strong>the</strong> characteristics or classification <strong>of</strong> MNCs and <strong>the</strong> emerging
knowledge governance approach. It would be useful to empirically investigate <strong>the</strong>se proposiitons. By
using <strong>the</strong> gaps in existing literature to construct a conceptual model, this paper hopes to trigger new
insight and generate a spiral <strong>of</strong> new ideas along its path. It is interesting to see that <strong>the</strong> entire
knowledge creation activity or chain (input-output-outcome) can be separable both <strong>the</strong>oretically and
practically. It must however be mentioned that <strong>the</strong> impact <strong>of</strong> reporting relationships and routines may
not be easily separable in practice as I have done <strong>the</strong>oretically in this paper. In practice, reporting
relationships may be classified as part <strong>of</strong> organizational routines. It is also worthy <strong>of</strong> note that
organizational controls can sometimes be indirect or unobvious. There may be invisible controls that
determine how subsidiaries or individuals in a firm carry out <strong>the</strong>ir operations. An implicit objective <strong>of</strong>
this paper has been to highlight some <strong>of</strong> <strong>the</strong> critical structural components that organizations must be
concerned with in o<strong>the</strong>r to achieve knowledge creation success.
Although <strong>the</strong> informal organizational structure is believed to foster knowledge creation, this study has
attempted to show how<strong>the</strong> formal organizationalstructure could perhaps be equally
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Pamela Chidiogo Izunwanne
significant.However, a more interesting insight might be gained by studying <strong>the</strong> interaction between
<strong>the</strong> formal and <strong>the</strong> informal aspects <strong>of</strong> organizational structure as far as knowledge creation is
concerned. It would also be insightful to investigate <strong>the</strong> difference in knowledge creation performance
between MNCs that do not formalize <strong>the</strong>ir knowledge creation activities and those thatformally
structure <strong>the</strong>ir knowledge organization taking into account, individual and aggregate level objectives
(macro and micro level perspectives); <strong>the</strong> challenges associated with knowledge creation (market,
institutional and employee factors) and <strong>the</strong> different phases in <strong>the</strong> knowledge creation activity or chain
shown in figure 2.
Table 2: Proposed relationships
MNC Organizational
Structure
Multidomestic
Reporting relationships:
subsidiaries/ Headquarters
Low integration
and Low exchanges
Global High Integration and High
exchanges
Transnational Medium integration and
medium exchanges
Routines/processes Knowledge Creation
Decentralized
systems
Fast
Efficient
Low quality
Low productivity
Integrated routines Slow
Inefficient
High quality
Semi-integrated
routines
High productivity
Medium speed
Medium efficiency
Medium quality
Medium productivity
This study would like to suggest that for future research involving <strong>the</strong> organizational structure <strong>of</strong>
MNCs, <strong>the</strong> unique attributes <strong>of</strong> transnational firms should be studied seperately not just in comparison
with global and multidomestic firms. It is also possible to develop original classifications based on
different combinations <strong>of</strong> diverse categories. It would also be interesting to link <strong>the</strong> different stages <strong>of</strong>
<strong>the</strong> knowledge creation activity or chain to o<strong>the</strong>r constructs in order to fur<strong>the</strong>r illuminate <strong>the</strong> issues
affecting or being affected by knowledge creation in organizations.
Acknowledgements
The Author would like to thank Pr<strong>of</strong>essor Joyce Ellen Falkenberg for her contributions. Her comments
were useful in <strong>the</strong> development <strong>of</strong> this paper.
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460

A Pattern-Based Ontology Engineering Platform
Thomas Janke
SAP Research, Dresden, Germany
thomas.janke@sap.com
Abstract: Building knowledge management systems based on semantic technologies can pr<strong>of</strong>it from <strong>the</strong> use <strong>of</strong>
ontologies. The latter provide a shared vocabulary, which helps to shape common understanding, fosters reuse
and data integration as well as facilitates reasoning. The problem companies are facing, though, is that, despite
all advantages, ontology engineering is very hard to master. First <strong>of</strong> all, domain engineers need to select <strong>the</strong>
most suitable ontology language from <strong>the</strong> pool <strong>of</strong> available languages ensuring that <strong>the</strong> chosen language reflects
<strong>the</strong> requirements <strong>of</strong> <strong>the</strong> given domain by means <strong>of</strong> expressivity, reasoning performance and tool support.
Moreover, in order to model high-quality ontologies, a good knowledge about <strong>the</strong> various logical formalisms, <strong>the</strong>
<strong>of</strong>fered ontology languages are based on, <strong>the</strong>ir characteristics and <strong>the</strong>ir differences is required. Published best
practices for ontology engineering, so called Ontology Design Patterns (ODPs) (Gangemi and Presutti, 2009) can
help here. They describe how to solve common modeling problems. The problem, though, is that in order to apply
such a pattern, ontology engineers not only have to find a suitable pattern but also need to understand its
semantics. The platform presented in this paper tackles those problems from two directions. First <strong>of</strong> all, domain
engineers are enabled to model <strong>the</strong>ir domain utilizing domain-specific languages reflecting <strong>the</strong> specific
requirements <strong>of</strong> <strong>the</strong> particular domain. As a result, <strong>the</strong> modeling task will be significantly simplified. Moreover,
based on <strong>the</strong> introduced level <strong>of</strong> abstraction, <strong>the</strong> platform will enable <strong>the</strong> generation <strong>of</strong> concrete ontologies in
various target languages and in doing so, mitigates <strong>the</strong> problem <strong>of</strong> language selection. Fur<strong>the</strong>rmore, <strong>the</strong> ontology
generation component introduces room for <strong>the</strong> second main principle which is <strong>the</strong> automatic and transparent
application <strong>of</strong> ODPs. This means that every transformation <strong>of</strong> domain-specific models into ontologies is based on
executable ontology patterns which can be contributed and registered to <strong>the</strong> platform by ontology experts.
Keywords: Ontology engineering, Model driven development, Ontology design patterns, Domain-specific
languages
1. Background
Knowledge management can be facilitated by utilizing semantic technologies. The reasons for that
are manifold. First <strong>of</strong> all, ontologies provide a vocabulary <strong>of</strong> domain relevant terms which helps to
build a common understanding. Moreover, <strong>the</strong>y can be designed in a modular way which fosters
reuse, helps to build standard vocabularies for certain domains (e.g. Friend <strong>of</strong> a Friend, Dublin Core)
and provides a basis for data integration. In addition, ontologies allow for inferring implicit knowledge
by means <strong>of</strong> reasoning. As a result, relevant parts <strong>of</strong> <strong>the</strong> knowledge in a specific domain can be
explicitly modelled in <strong>the</strong> ontology, in contrast to implementing <strong>the</strong> required logic in <strong>the</strong> business logic
layer <strong>of</strong> a target application.
The problem, however, is that ontology engineering is challenging, especially for non-experts. This
problem has many dimensions. First <strong>of</strong> all, <strong>the</strong>re is <strong>the</strong> aspect <strong>of</strong> language selection. In order to
choose <strong>the</strong> ontology language best suited for a given domain, available languages such as RDF(S),
OWL 2 with all its flavours and pr<strong>of</strong>iles, Topic Maps, F-Logic, etc., need to be compared with respect
to <strong>the</strong>ir expressivity, <strong>the</strong>ir reasoning performance, available frameworks, editing support and so on.
Moreover, if <strong>the</strong> requirements for <strong>the</strong> domain model change, <strong>the</strong> process <strong>of</strong> evaluating all <strong>the</strong>
languages needs to be repeated. This is not only time consuming but requires deep knowledge about
all <strong>the</strong> languages. After selecting a language, <strong>the</strong> next problem emerges and this is <strong>the</strong> lack <strong>of</strong>
modeling expertise. The various languages are based on different logical formalisms, starting from
description logics and semantic networks to rule based languages. Each formalism has its specific
characteristics, for example <strong>the</strong> existence <strong>of</strong> <strong>the</strong> unique name assumption or <strong>the</strong> open world
assumption. Knowing about those differences and <strong>the</strong>ir impact is a key requirement to model good
ontologies. It addition to that, it is favourable to follow published and widely accepted modeling best
practices, in order to guarantee high modeling quality. Referring to <strong>the</strong> related work in this area, those
best practices are called ontology design patterns. They describe how to solve common modeling
problems and cover experiences gained by ontology engineering experts. The problem, though, is
that in order to apply those pattern, domain engineers have to be aware <strong>of</strong> <strong>the</strong>ir existence, <strong>the</strong>y need
to find a matching pattern for <strong>the</strong>ir problem, need to understand <strong>the</strong> provided solution and finally have
to adopt <strong>the</strong> solution to <strong>the</strong>ir own domain models. As a non-ontology export, each <strong>of</strong> those steps
represents a tough challenge. At <strong>the</strong> moment, <strong>the</strong> gap between domain engineers, knowing about <strong>the</strong>
particular domain, and ontology engineers, knowing how to transfer <strong>the</strong> modeling requirements into
concrete ontologies, can only be filled by an intense collaboration between both roles. This introduces
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room for communication errors as wells as produces high costs in terms <strong>of</strong> development time and
money to buy <strong>the</strong> expertise.
In order to mitigate those problems, this paper proposes an ontology engineering platform based on
two main principles. The first one is that domain experts model <strong>the</strong>ir domain using a domain-specific
language reflecting <strong>the</strong> special needs <strong>of</strong> that domain. This can be in terms <strong>of</strong> <strong>the</strong> vocabulary provided,
<strong>of</strong> <strong>the</strong> level <strong>of</strong> abstraction as well as <strong>of</strong> expressivity <strong>of</strong>fered. In doing so, <strong>the</strong> process <strong>of</strong> modeling <strong>the</strong>
domain <strong>of</strong> interest is supposed to be much easier. The resulting model, in turn, is <strong>the</strong> basis for
generating concrete ontologies in various ontology languages. The second principle is that ontology
design patterns are applied automatically and transparently during ontology generation. As a result,
<strong>the</strong> domain engineer can directly pr<strong>of</strong>it from <strong>the</strong> knowledge <strong>of</strong> ontology experts.
The paper is structured in <strong>the</strong> following way: First, <strong>the</strong> most relevant research fields addressed by <strong>the</strong>
proposed platform are introduced. Thereafter, <strong>the</strong> architecture <strong>of</strong> <strong>the</strong> platform is outlined. Based on
this, a reference implementation <strong>of</strong> <strong>the</strong> most important components is presented. The paper
concludes by summarizing <strong>the</strong> approach and our contributions as well as by discussing future work.
2. Related work
This section provides an overview <strong>of</strong> <strong>the</strong> most relevant research fields affected by <strong>the</strong> proposed
platform. Preliminary contributions and how <strong>the</strong> proposed approach can be refined from <strong>the</strong> latter are
discussed in particular.
2.1 Ontology design patterns
In analogy to s<strong>of</strong>tware design patterns, which are well established in <strong>the</strong> field <strong>of</strong> s<strong>of</strong>tware engineering
to describe best practices, <strong>the</strong> concept <strong>of</strong> ontology design patterns (ODP) has been introduced in <strong>the</strong>
domain <strong>of</strong> ontology engineering (Staab, Erdmann and Maedche, 2001), (Svátek, 2004), (Presutti and
Gangemi, 2008). In (Blomqvist and Sandkuhl, 2005) ODPs are defined as reusable, successful
solution to recurrent modeling problems. ODPs are also addressed by <strong>the</strong> W3C (W3C, n.d.).
Moreover, <strong>the</strong>re is an active community around ODPs which collects and categorizes patterns (ODP,
n.d.). Following <strong>the</strong> terminology used <strong>the</strong>re, this paper focuses on Transformational Patterns which
are used to translate expressions from one language into ano<strong>the</strong>r one. Examples <strong>of</strong> such modeling
problems are n-ary relations, inverse relations, and cardinality constraints. Those are introduced in
more detail in Section 4.2. For more patterns, <strong>the</strong> reader is referred to <strong>the</strong> cited sources. Apparently,
ontology patterns are a good way to help engineers to model better and more reusable ontologies.
Unfortunately, currently <strong>the</strong>y cannot be applied in a way that is transparent for domain engineers.
Some ontology editors, like Protégé 1 , try to tackle this problem by exposing patterns by means <strong>of</strong>
wizards, e.g. for creating value partitions in OWL. A similar approach is used by <strong>the</strong> XD Tools
project 2 . Although those efforts and tools simplify <strong>the</strong> application <strong>of</strong> patterns, <strong>the</strong>y do not allow for
automatic and transparent application <strong>of</strong> patterns.
The platform presented in this paper <strong>of</strong>fers authors <strong>of</strong> ODPs <strong>the</strong> possibility to provide <strong>the</strong>ir patterns
not only in text form but also in an executable way. A pattern can <strong>the</strong>n be plugged into <strong>the</strong> framework
and will be applied automatically during ontology generation. By providing patterns in such a way,
domain experts are empowered to apply <strong>the</strong>m without even realizing it. Given this, <strong>the</strong> preliminary
identified textual patterns, published by various contributors, can be implemented as executable
patterns in order to evaluate <strong>the</strong> platform. In reverse, <strong>the</strong> platform can foster pattern development and
in doing so contribute back to <strong>the</strong> ODP community.
2.2 Model driven ontology engineering
Model driven development (MDD) is a s<strong>of</strong>tware engineering methodology based on describing
applications in <strong>the</strong> form <strong>of</strong> abstract models which <strong>the</strong>n are iteratively transformed into more specific
models. There has been extensive research in <strong>the</strong> field <strong>of</strong> MDD and ontologies. Contributions in this
field mainly are focused on <strong>the</strong> relationship between UML and ontology languages. The main goal
was to understand <strong>the</strong> similarities and differences between <strong>the</strong> standard modeling language in <strong>the</strong>
field <strong>of</strong> s<strong>of</strong>tware engineering and ontology languages in order to bridge <strong>the</strong> gap between those two
worlds. Therefore, transformations from UML into RDF(S) has been discussed (Cranefield, Haustein
1 http://protege.stanford.edu/
2 http://extremedesign.sourceforge.net/
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Thomas Janke
and Purvis, 2001) as well as UML extensions and transformations in order to express DAML+OIL and
OWL ontologies (Baclawski et al., 2002) (Falkovych, Sabou and Stuckenschmidt, 2003), respectively.
Apart from that, <strong>the</strong>re are also significant preliminary contributions in <strong>the</strong> field <strong>of</strong> ontology meta
models. There are various proposals for OWL Full (Djuric, Gasevic and Devedzic, 2005), OWL DL
with and without rule extensions (Brockmans et al., 2006) and many more. Those contributions led to
<strong>the</strong> specification <strong>of</strong> <strong>the</strong> Ontology Definition Metamodel (ODM) (OMG, 2009) which contains a set <strong>of</strong>
meta models for Description Logics, RDF(S), OWL and Topic Maps as well as a number <strong>of</strong> UML
pr<strong>of</strong>iles. What <strong>the</strong> ODM does not <strong>of</strong>fer is an abstraction on top <strong>of</strong> <strong>the</strong> different ontology meta models.
This means that domain engineers still have to model <strong>the</strong>ir domain by using ontology languagespecific
concepts and thus have to understand <strong>the</strong> underlying logical formalisms and have to use <strong>the</strong>
very same language constructs <strong>the</strong>y would need to use with pure OWL or F-Logic. The only difference
is that <strong>the</strong>y describe <strong>the</strong>ir domain using Ecore.
2.3 Domain-specific languages and ontologies
There are various approaches in <strong>the</strong> literature discussing aspects <strong>of</strong> bringing toge<strong>the</strong>r DSLs and
ontologies. Many <strong>of</strong> <strong>the</strong>m, e.g. (Walter, Silva Parreiras and Staab, 2009) and (Tairas, Mernik and
Gray, 2008), aim at using semantic technologies in order to support <strong>the</strong> process <strong>of</strong> DSL engineering.
Apart from that, in (Walter and Jürgen, 2009) an approach is described which integrates existing
domain-specific languages with OWL ontologies. As a result, <strong>the</strong> domain expert is able to extend <strong>the</strong>
domain model with ontology features like class constructors and property constraints. This work is
similar to DSL-approach described here. Never<strong>the</strong>less, it does not aim at abstracting ontology
concepts in order to simplify ontology engineering by encapsulating knowledge about logical
formalisms and modeling best practices.
3. Architecture <strong>of</strong> <strong>the</strong> ontology engineering platform
This section will introduce an ontology engineering platform based on domain-specific languages and
ontology generation on top <strong>of</strong> ontology design patterns. The main components are depicted in Figure
1. A core part <strong>of</strong> <strong>the</strong> proposed platform is <strong>the</strong> layer <strong>of</strong> domain-specific languages. They are used by
domain experts for building and maintaining domain models and reflect <strong>the</strong> specific needs <strong>of</strong> <strong>the</strong>
target domain. The latter is achieved by utilizing well known terms in that domain as language
constructs as wells as by providing exactly <strong>the</strong> expressivity needed to model <strong>the</strong> domain. Every DSL
<strong>of</strong>fered in this layer is mapped to a common ontology meta model. This meta model, in turn, provides
an abstract interface for concrete ontology meta models. It knows, for example, <strong>the</strong> notion <strong>of</strong> relations
being n-ary or posing cardinality constraints. The translation between those concepts and language
constructs <strong>of</strong> concrete ontology languages, like RDF(S) or OWL 2, is realized by applying ontology
design patterns. They describe best practices <strong>of</strong> how to model those common concepts with language
constructs provided by concrete ontology languages. In that sense, <strong>the</strong>y can be seen as modular
transformation patterns, describing <strong>the</strong> transformation <strong>of</strong> one self-contained language feature into a
concrete ontology language.
Figure 1: A model driven ontology engineering platform
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Thomas Janke
4. Reference implementation <strong>of</strong> <strong>the</strong> platform
In order to illustrate <strong>the</strong> feasibility <strong>of</strong> <strong>the</strong> approach as well as to demonstrate its capabilities, a
prototypical implementation <strong>of</strong> <strong>the</strong> architecture has been implemented. The implementation applies
<strong>the</strong> frameworks and tools for model driven development provided by <strong>the</strong> Eclipse Modeling Project
(EMP) and its sub-projects. All meta models used and developed in <strong>the</strong> context <strong>of</strong> <strong>the</strong> implemented
platform are described in Ecore, a meta model description language provided by <strong>the</strong> Eclipse Modeling
Framework (EMF). This does not only allow <strong>the</strong> reuse <strong>of</strong> existing Ecore models, e.g. for OWL, RDF(S)
and XML, but also enables access to a huge number <strong>of</strong> o<strong>the</strong>r compatible components and
frameworks, for various purposes, ranging from model transformation and code generation to model
persistence. EMF has also been used to implement <strong>the</strong> ontology generation layer. Fur<strong>the</strong>rmore, <strong>the</strong>
Xtext 3 framework has been applied to develop domain-specific languages. The implementation <strong>of</strong> <strong>the</strong>
most important components <strong>of</strong> <strong>the</strong> platform is discussed in more detail in <strong>the</strong> following sections.
4.1 The common ontology meta model and o<strong>the</strong>r meta models
The common ontology meta model introduced in Section 3 aims at providing an abstract interface
suitable for describing arbitrary domain models. Therefore, it <strong>of</strong>fers constructs to address common
modeling requirements. Part <strong>of</strong> this is to describe concept and property hierarchies. Moreover,
properties can be transitive, inverse or n-ary. Fur<strong>the</strong>rmore, <strong>the</strong>ir cardinality can be constrained. The
meta model created in order to evaluate <strong>the</strong> approach is illustrated in Figure 2. It provides
expressions for <strong>the</strong> following modeling primitives: entities and entity hierarchies, attributes, relations
and relation hierarchies, inverse relations, transitive relations, functional relations, n-ary relations and
relation cardinalities. In addition imports <strong>of</strong> o<strong>the</strong>r models as well as <strong>the</strong> notion <strong>of</strong> namespaces are
supported.
Figure 2: Common ontology meta model
Based on this meta model, patterns can be described that transform individual modeling primitives
into language constructs provided by <strong>the</strong> target ontology language. It is important to note, though, that
this transformation can only approximate <strong>the</strong> semantics <strong>of</strong> <strong>the</strong> common ontology model (Gangemi and
Presutti, 2009). However, <strong>the</strong> existence and <strong>the</strong> spread <strong>of</strong> such patterns show that this fact is
acceptable in many real world scenarios and applications. The selection <strong>of</strong> primitives for <strong>the</strong> initial
version <strong>of</strong> this meta model was motivated by two requirements. First, it had to be sufficient to apply
various patterns for different ontology languages. This was achieved by introducing n-ary relations,
inverse relations, imports and so on which all have different representations in RDF(S), OWL and F-
Logic. The second demand was to provide an expressivity suitable to describe widely accepted
ontologies like Friend <strong>of</strong> a Friend (FOAF) or Dublin Core (DC).
3 http://www.eclipse.org/Xtext/
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Apart from <strong>the</strong> developed meta model, <strong>the</strong>re are 3 o<strong>the</strong>r meta models involved in <strong>the</strong> reference
implementation, namely a RDF(S) as well as an OWL and a XML meta model. The two ontology meta
models are used as target models for <strong>the</strong> ontology generation. They are part <strong>of</strong> a use case 4 published
by <strong>the</strong> SIDo Group <strong>of</strong> <strong>the</strong> L3I 5 in order to evaluate how to apply <strong>the</strong> Atlas Transformation Language
framework (ATL). The XML meta model is provided by <strong>the</strong> AMMA (AtlanMod Model Management
Architecture) project and is used to serialize <strong>the</strong> ontology models into XML syntax for fur<strong>the</strong>r
processing.
4.2 Applying ontology design patterns
Every ontology design pattern describes a best practice on how to implement one self-contained
language feature in a concrete ontology language. With respect to <strong>the</strong> proposed platform this means
that every modeling primitive supported by <strong>the</strong> common ontology model needs to be covered by a
corresponding pattern for all target languages. As a result, <strong>the</strong>re need to be patterns on how to model
cardinality constraints in OWL, how to implement inverse relations in OWL and in F-Logic, how to
express n-ary relations in RDF(S) and so on. In order to make <strong>the</strong> idea <strong>of</strong> ontology design pattern
even more tangible, two examples are presented.
4.2.1 Example 1: N-ary relations
If a property in <strong>the</strong> common meta model has more than one property types (see Figure 2), it is called
n-ary. In o<strong>the</strong>r words, <strong>the</strong> property references instances <strong>of</strong> more than one type. As an example, take
<strong>the</strong> relation isGeneratedFrom defined on <strong>the</strong> concept HtmlDocument. This relation would point to two
different types, namely XsltDocument and XmlDocument. The semantics here is that <strong>the</strong> HTML
document was generated from <strong>the</strong> combination <strong>of</strong> both documents, as a result <strong>of</strong> applying <strong>the</strong> XSLT
script
on <strong>the</strong> XML data file. This makes isGeneratedFrom a 1:1:1 relation. In OWL, this transforms to
<strong>the</strong> classes and properties depicted in Figure 3.
Figure 3: An example <strong>of</strong> an n-ary relation in OWL (W3C, 2006)
OWL only supports binary relations. Therefore, <strong>the</strong> relation isGeneratedFrom is transformed into a
class, in this case called TransformationRelation. In addition, <strong>the</strong> latter constitutes <strong>the</strong> domain <strong>of</strong> two
object properties called uses_XML and uses_XSLT with <strong>the</strong>ir corresponding ranges. Moreover, a
isGeneratedFrom object property is introduced which “points” to <strong>the</strong> new relation class instead <strong>of</strong>
directly referencing
<strong>the</strong> XML and XSLT documents. A similar pattern can be applied for RDF(S)
ontologies. The only difference, though, is that RDF(S) does not distinguish between data and object
properties.
4.2.2 Example 2: Inverse relations
Inverse relations are supported in OWL directly via <strong>the</strong> owl:ObjectInverseOf property. In RDF(S),
though, <strong>the</strong>y are not supported
at all. In contrast to that, in F-Logic, inverse relations are not reflected
as
a language keyword but <strong>the</strong> semantics can be expressed by means <strong>of</strong> additional rules. This is
illustrated in Listing 1.
XsltDocument[transforms=>XmlDocument].
XmlDocument[isTransformedBy=>XsltDocument].
/ / inverse relations are populated via rules
FORALL X,Y X[transforms->Y] < -> Y[isTransformedBy->X].
Listing 1: Inverse relations in F-Logic
4
http://www.eclipse.org/m2m/atl/usecases/ODMImplementation/#download
5
http://l3i.univ-larochelle.fr/
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The rule states that whenever an instance X is related to an instance Y via <strong>the</strong> relation transforms,
<strong>the</strong>n Y is related to X via isTransformedBy. The implication also holds in <strong>the</strong> o<strong>the</strong>r direction.
Both examples <strong>of</strong> ontology patterns show that <strong>the</strong> very same modeling primitives can have very
different representations in various ontology languages.
4.3 Ontology generation based on patterns
The generation <strong>of</strong> concrete ontologies is implemented as a model to model transformation based on
modular patterns. Modularity is a key requirement in this layer. The reasons for that are manifold. The
proposed approach is based on <strong>the</strong> idea that modeling best practices can be provided in an
executable form ready to be applied transparently. This is only possible if <strong>the</strong> platform provides a
framework which allows <strong>the</strong> modular description <strong>of</strong> patterns and <strong>the</strong>refore <strong>the</strong> contribution <strong>of</strong> selfcontained
patterns instead <strong>of</strong> complete transformations. In o<strong>the</strong>r words, various ontology experts
should be enabled by <strong>the</strong> framework to contribute single parts <strong>of</strong> <strong>the</strong> whole transformation. This does
not only lower <strong>the</strong> initial effort necessary to contribute knowledge in form <strong>of</strong> patterns but is also well
aligned to <strong>the</strong> approach followed by <strong>the</strong> ODP community in <strong>the</strong>ir pattern collection wiki (ODP, n.d.).
Moreover, modular patterns introduce room for configurable variability. Certain modeling problems
can be solved by more than one pattern. The W3C, for example, describes two different patterns
(W3C, 2006) for n-ary relations with <strong>the</strong>ir corresponding advantages and disadvantages. In a modular
transformation framework, patterns can easily be exchanged according to specific requirements
without <strong>the</strong> need to edit one monolithic transformation. The Eclipse Modeling Project <strong>of</strong>fers various
model transformation frameworks as candidates to be used to implement <strong>the</strong> ontology generation
layer. The most prominent and widely used frameworks are ATL and Epsilon. Unfortunately, both <strong>of</strong>
<strong>the</strong>m only <strong>of</strong>fer limited support
for pattern modularization and extensibility. Therefore, ano<strong>the</strong>r model
to model transformation framework has been designed and implemented. Its architecture is
introduced in Figure 4.
Figure 4: Model transformation framework based on patterns
The framework consists <strong>of</strong> a number <strong>of</strong> transformations which can be executed by <strong>the</strong> runtime
component. Every transformation contains <strong>of</strong> a number <strong>of</strong> patterns which are applied during
execution. Moreover, every transformation provides helpers that encapsulate functionality used by
multiple patterns and is bound to a number <strong>of</strong> input and <strong>the</strong>ir corresponding output models. Every
pattern is implemented using plain Java. As a result, all IDE features, namely code completion,
debugging, logging, syntax checking etc., are available for pattern development. In order to describe
concrete transformation steps, every pattern defines an arbitrary
number <strong>of</strong> rules whereat every rule is
a plain method with custom annotations provided by <strong>the</strong> framework. Listing 2 illustrates <strong>the</strong> method
signature for <strong>the</strong> n-ary relation pattern discussed above.
@Rule(label="Property2NaryObjectProperty",extend="Property2Property",
priority=1)
public void match(@In Property property,
@Out OWLObjectProperty objectProperty,
@Out OWLClass relationClass)
{ … }
Listing 2: Transformation rule contained in an n-ary relation pattern
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Thomas Janke
A rule specifies a source model element, annotated with @In, and a number <strong>of</strong> target elements,
marked with @Out. In order to foster code reuse and modularization, rules can extend each o<strong>the</strong>r,
inheriting all <strong>the</strong> instructions defined in <strong>the</strong> base rule. Beyond that, rule priorities can be expressed in
order to describe complex dependencies between rules <strong>of</strong> one single pattern as well as between
different patterns. Patterns can easily be registered or unregistered as needed for a given
transformation using <strong>the</strong> frameworks API. Even though <strong>the</strong> framework has been developed and will
be evaluated in <strong>the</strong> context <strong>of</strong> <strong>the</strong> ontology engineering platform, it is designed as a general purpose
model to model transformation framework. This means, it can be applied for every model
transformation based on Ecore models.
4.4 Developed domain-specific languages
Domain-specific languages are used in order to provide suitable and easy to use modeling languages
for domain engineers. They can be seen as interfaces for populating <strong>the</strong> common ontology model. As
a result, <strong>the</strong>y inherit <strong>the</strong> abstraction level, introduced by <strong>the</strong> common ontology meta model.
Additionally, <strong>the</strong>y provide a syntax which reflects requirements <strong>of</strong> a specific domain. This can be
achieved by means <strong>of</strong> <strong>the</strong> provided vocabulary in forms <strong>of</strong> <strong>the</strong> collection <strong>of</strong> supported keywords as
well as <strong>the</strong> level <strong>of</strong> expressivity. This enables domain engineers to model <strong>the</strong>ir domain <strong>of</strong> expertise in
an easy and intuitive way without <strong>the</strong> need to understand <strong>the</strong> logical formalisms and best practices
behind. The reference implementation <strong>of</strong> <strong>the</strong> platform uses <strong>the</strong> Xtext framework to implement two
textual DSLs.
This design decision was tailored by <strong>the</strong> fact that Xtext provides rich tool support for
deriving
parser implementation from <strong>the</strong> DSL grammar as well as for automatically generating a basic
Eclipse-based editor. The latter has been extended to <strong>the</strong> engineering workbench introduced in
Section 5.
The reference implementation contains two DSLs mainly designed to evaluate <strong>the</strong> concepts <strong>of</strong> <strong>the</strong>
platform. The first modeling language is not specifically bound to a certain domain but <strong>of</strong>fers keywords
that are very close to <strong>the</strong> terms used in <strong>the</strong> common ontology meta model. As a result, <strong>the</strong> provided
vocabulary contains terms like ontology, concept and property.
Never<strong>the</strong>less, it also provides
examples on how to provide sufficient abstraction from complex
modeling primitives. Listing 3
introduces
an example on how to describe n-ary relations in conjunction with cardinality constraints.
ontology 'http://s<strong>of</strong>tware/traceability/v1.0'
{
concept HtmlDocument
{
property
isGeneratedFrom as 'TransformationRelation' :
XmlDocument[1,1] as 'has_XML',
XsltDocument[1,1] as 'has_XSLT'
}
}
Listing 3: DSL describing n-ary relation and cardinality constraints
The given example defines <strong>the</strong> same n-ary relation as introduced in Section 4.2. Additionally, it poses
cardinality constraints on <strong>the</strong> XML and <strong>the</strong> XSLT document. The optional as keyword can be used to
explicitly label properties or concepts instead <strong>of</strong> using auto-generated names. This fairly simple
example shows, though, how abstraction can be used to simplify ontology modeling. Although <strong>the</strong>
domain engineer does not know about how
to model n-ary relations or cardinality constraints in OWL,
an
according ontology can be generated automatically. Figure 5 illustrates <strong>the</strong> workbench
implemented to foster domain model editing and ontology generation. In addition, a small excerpt <strong>of</strong>
<strong>the</strong> resulting OWL ontology is depicted.
The second DSL has been designed specifically for modeling s<strong>of</strong>tware quality aspect <strong>of</strong> Ambient
Assisted Living (AAL) systems. As a requirement, <strong>the</strong> provided language should reuse an existing
meta model (Kläs et al., 2010) developed in <strong>the</strong> Quamoco project
pts like QualityAspect,
ualityRequirement, Impact, Factor and EntityType, toge<strong>the</strong>r with <strong>the</strong>ir relations to each o<strong>the</strong>r, are
ed DSL by means <strong>of</strong> keywords (for an example
e List
6 . This meta model provides a
vocabulary for specifying and evaluating s<strong>of</strong>tware quality. Therefore, conce
Q
described. The meta model is reflected in <strong>the</strong> provid
se ing 4) and <strong>the</strong>refore is seamlessly integrated in <strong>the</strong> modeling process.
6 https://quamoco.in.tum.de/
467

Thomas Janke
S<strong>of</strong>twareQualityModel domain=”AAL” {
QualityAspect : Maintainability
QualityAspect : Expressivity isPartOf Maintainability Factor
: TestCoverage positivelyImpacts Maintainability EntityType : SourceCode {
influences TestCoverage by linesOfCode
}
EntityType
: TestCase {
influences TestCoverage by coveredSourceCode
}
...
}
Listing 4: DSL for describing s<strong>of</strong>tware quality in AAL systems
For domain engineers, <strong>the</strong> domain description is very cohesive because <strong>the</strong>re is just one level <strong>of</strong>
abstraction. They do not have to import o<strong>the</strong>r models, in this case <strong>the</strong> Quamoco meta model, but can
rely on <strong>the</strong> provided vocabulary and
syntax. During ontology generation, though, this single
description
can be modularized into an ontology specifically describing s<strong>of</strong>tware quality aspects in <strong>the</strong>
AAL domain and ano<strong>the</strong>r one describing s<strong>of</strong>tware quality in general. The latter is <strong>of</strong> course derived
from <strong>the</strong> given Quamoco meta model.
Both example languages, provided as part <strong>of</strong> <strong>the</strong> reference implementation,
illustrate that, depending
on <strong>the</strong> requirements <strong>of</strong> a given domain, various levels <strong>of</strong> abstraction may
be applied in order to foster
ontology modeling. Fur<strong>the</strong>rmore, those DSLs are used to fur<strong>the</strong>r evaluate <strong>the</strong> presented approach.
5. A domain-specific ontology engineering workbench
In order to facilitate <strong>the</strong> domain modeling task for domain experts and to evaluate <strong>the</strong> applicability <strong>of</strong>
<strong>the</strong> proposed platform, we implemented a textual editor based on Eclipse and Xtext. The editor <strong>of</strong>fers
syntax checking, code completion and highlighting for <strong>the</strong> domain-specific language introduced in
Section 4.4. Moreover, <strong>the</strong> editor allows <strong>the</strong> import and reuse <strong>of</strong> o<strong>the</strong>r domain descriptions, ei<strong>the</strong>r
created by means <strong>of</strong> <strong>the</strong> same DSL or in form <strong>of</strong> OWL or RDF(S) ontologies. In addition, we
seamlessly integrated <strong>the</strong> ontology generation process. Every time <strong>the</strong> domain model is edited, <strong>the</strong>
workbench automatically generates <strong>the</strong> corresponding target ontologies, applying all registered
ontology design patterns. As a result, <strong>the</strong> current domain model is reflected by concrete ontologies at
any time. The latter also introduces <strong>the</strong>
possibility to review <strong>the</strong> changes applied to <strong>the</strong> ontology with
regard to every modeling decision. Figure
5 illustrates <strong>the</strong> implemented workbench by means <strong>of</strong> <strong>the</strong>
introduced modeling example.
6. Summary and future work
In order to mitigate <strong>the</strong> problems <strong>of</strong> language selection and lack <strong>of</strong> modeling expertise by domain
engineers in <strong>the</strong> field <strong>of</strong> ontology engineering, a pattern-based ontology engineering platform has
been proposed. The latter enables <strong>the</strong> use <strong>of</strong> domain-specific languages, reflecting <strong>the</strong> requirements
<strong>of</strong> <strong>the</strong> given domain, on top <strong>of</strong> a common ontology model. This does not only simplifies <strong>the</strong> modeling
process but also introduces an additional layer <strong>of</strong> abstraction. Based on that, concrete ontologies in
RDF(S) and OWL are generated. During ontology generation, published modeling best practices, so
called ontology design patterns, contributed by ontology experts are automatically and transparently
applied. As a result, domain engineers can pr<strong>of</strong>it from <strong>the</strong> expertise <strong>of</strong> ontology experts without <strong>the</strong>
need to know about <strong>the</strong> underlying logical formalisms. Apart from <strong>the</strong> architecture <strong>of</strong> <strong>the</strong> platform, we
introduced a reference
implementation, covering <strong>the</strong> most relevant components. This includes an
implementation
<strong>of</strong> a common ontology model, <strong>the</strong> implementation <strong>of</strong> a modular ontology generation
component as well as <strong>the</strong> design <strong>of</strong> two domain-specific languages used to evaluate <strong>the</strong> concepts <strong>of</strong>
<strong>the</strong> platform.
The main item for future work is to evaluate <strong>the</strong> ideas <strong>of</strong> <strong>the</strong> platform in a bigger real world scenario. A
corresponding study needs to reflect estimates about efforts for designing and maintaining <strong>the</strong> DSL,
<strong>the</strong> ease <strong>of</strong> use for domain experts as well as <strong>the</strong> quality <strong>of</strong> <strong>the</strong> generated ontologies.
In this context, it
might also be necessary to adjust <strong>the</strong> expressivity <strong>of</strong> <strong>the</strong> common ontology meta model. Potential
candidates for extensions are <strong>the</strong> ability to formulate rules and <strong>the</strong> annotation triples (e.g. via
reification). Fur<strong>the</strong>rmore, we tend to implement patterns for F-Logic in <strong>the</strong> future.
468

Thomas Janke
Figure 5: Domain-specific ontology engineering workbench
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469

Towards a Detailed View on <strong>the</strong> Influence <strong>of</strong> Organizational
Culture on Knowledge Sharing
Vincent de Jong and Remko Helms
Utrecht University, Department <strong>of</strong> Information and Computing Science,
Utrecht, The Ne<strong>the</strong>rlands
veedeejee@gmail.com
r.w.helms@cs.uu.nl
Abstract: It is <strong>of</strong>ten mentioned in <strong>the</strong> literature, ei<strong>the</strong>r academic or practitioner, that for knowledge sharing to
flourish in an organization it is important that <strong>the</strong> organizational culture allows such sharing to take place. Despite
<strong>the</strong> acknowledgement <strong>of</strong> organizational culture as a major influence on knowledge sharing, <strong>the</strong> research on this
topic still lacks some depth. Research that has been done, is ei<strong>the</strong>r focusing on organizational culture as an
abstract concept, or is trying to test <strong>the</strong> hypo<strong>the</strong>sis that certain aspects <strong>of</strong> organizational culture lead to more and
better knowledge sharing. A good understanding <strong>of</strong> how aspects <strong>of</strong> culture influence knowledge sharing behavior
is however lacking. This research is intending to fill this gap by studying how organizational culture affects
knowledge sharing. A comparative case study has been conducted at different departments from subsidiaries <strong>of</strong>
a large Dutch construction company. Based on observations and interviews with employees <strong>of</strong> <strong>the</strong> selected
departments, <strong>the</strong> organizational culture as well as <strong>the</strong> knowledge sharing behavior could be assessed. After
processing and tabulating <strong>the</strong> qualitative data it has been analyzed to detect patterns in knowledge sharing
behavior that are related to <strong>the</strong> dimensions <strong>of</strong> organizational culture. In this analysis step, several influences <strong>of</strong>
organizational culture on knowledge sharing behavior were found. In terms <strong>of</strong> practical benefits, four out <strong>of</strong> eight
dimensions can be regarded as most influencing. This could be interpreted as a focus for cultural interventions in
organizations to stimulate knowledge sharing. The contribution to <strong>the</strong> KM research field is that propositions are
formulated that could eventually lead to a complete <strong>the</strong>ory on <strong>the</strong> way how organizational culture is influencing
knowledge sharing. So far, <strong>the</strong> propositions are plausible to be valid for <strong>the</strong> researched company and at best for
comparable contractors in <strong>the</strong> construction industry. Future research is needed to show a broader generalization
<strong>of</strong> <strong>the</strong> results.
Keywords: organizational culture, knowledge sharing, knowledge sharing behavior, multiple case studies
1. Introduction
It is widely acknowledged that applying knowledge management (KM) in an organizational context
can be a source <strong>of</strong> competitive advantage (Grant 1996; Holsapple & Singh 2001; Argote & Ingram
2000; Kim & Lee 2001; Osterloh & Frey 2000). However, simply implementing a knowledge
management program is not enough to gain <strong>the</strong>se competitive advantages. Davenport, De Long &
Beers (1999), accompanied by Alavi & Leidner (2001) see a culture in which knowledge sharing is
promoted as a critical condition for successful knowledge management projects. The challenge is to
take care that every entity within <strong>the</strong> organization knows what o<strong>the</strong>r entities know and are capable <strong>of</strong>.
An important reason for failure <strong>of</strong> knowledge management initiatives is a lack <strong>of</strong> knowledge sharing
(Davenport & Prusak 1998; Hendriks 1999). In order to focus on getting knowledge actively shared, it
is needed to know what influences <strong>the</strong> process <strong>of</strong> sharing knowledge.
The research on <strong>the</strong> influence <strong>of</strong> organizational culture on knowledge sharing has received a lot <strong>of</strong>
attention. However, in most cases <strong>the</strong>se researches are lacking in <strong>the</strong> way <strong>the</strong>y approach both
concepts. The current research on knowledge management is approaching organizational culture as
an abstract concept. From this point <strong>of</strong> view some general recommendations are formulated to enable
knowledge sharing. De Long & Fahey (2000) and McDermott & O’Dell (2001) both have this ‘global’
approach to culture. De Long & Fahey (2000) describe ways how an organizational culture can
influence knowledge creation and sharing behavior <strong>of</strong> employees, however <strong>the</strong>y do not go in depth,
trying to specify this influence. McDermott & O’Dell (2001) only give some general recommendations
with regard to <strong>the</strong> relationship between organizational culture and knowledge sharing. Many <strong>of</strong> <strong>the</strong>
recommendations within this context involve collaboration. However, an organizational culture has
more dimensions than <strong>the</strong> role <strong>of</strong> collaboration within work practices. The complex character <strong>of</strong>
organizational cultures complemented with <strong>the</strong> fact that every organizational culture is different, needs
a more specific view on its impact on knowledge sharing.
This leads to <strong>the</strong> following research question for this research: How can knowledge sharing behavior
be explained by influences <strong>of</strong> individual dimensions <strong>of</strong> an organizational culture?
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Vincent de Jong and Remko Helms
The remainder <strong>of</strong> this paper is organized as follows. Section 2 presents a literature review and is
followed by section 3 presenting <strong>the</strong> research framework. The set-up <strong>of</strong> <strong>the</strong> comparative case study
and <strong>the</strong> analysis <strong>of</strong> <strong>the</strong> case study is presented in section 4 and finally <strong>the</strong> conclusions are presented
in section 5.
2. Literature review
2.1 Organizational culture
Within <strong>the</strong> organizational culture literature, <strong>the</strong>re is no consensus about a definition (Detert, Mauriel &
Schroeder 2000; Van Muijen 1994). A confirmative example is a list made by Schein (2004, p.11) <strong>of</strong>
many different terms that are used to describe culture and <strong>the</strong> sources <strong>of</strong> publication. Despite <strong>of</strong> <strong>the</strong>
lack <strong>of</strong> consensus on a general definition <strong>of</strong> organizational culture, <strong>the</strong> definition <strong>of</strong> Schein (2004) will
be used: “A pattern <strong>of</strong> shared basic assumptions that was learned by a group as it solved its problems
<strong>of</strong> external adaptation and internal integration, that has worked well enough to be considered valid
and, <strong>the</strong>refore, to be taught to new members as <strong>the</strong> correct way to perceive, think and feel in relation
to those problems.” A reason for this choice is that this definition reflects that organizational culture is
about an implicit group view <strong>of</strong> a ‘truth’, which sets <strong>the</strong> boundaries for behavior.
The concept organizational culture is studied from a variety <strong>of</strong> research perspectives (Smircich 1983).
In this research <strong>the</strong> corporate culture perspective is applied, as <strong>the</strong> relation between variables is
researched and <strong>the</strong> emphasis is on measuring <strong>the</strong> concepts. Within this corporate culture approach,
<strong>the</strong> concept is studied in two ways: by using typologies and by using dimensions (Mannion, Konteh,
McMurray, Davies, Scott, Jung, et al. 2008; Liu, Sheibo & Meiyung 2006).
A typology is focusing on a few characteristics <strong>of</strong> <strong>the</strong> organizational culture in order to study <strong>the</strong>
concept en compare different organizational cultures. An example <strong>of</strong> a widely used typology is
created by Deal & Kennedy (1982). This typology is measuring two dimensions with binary values :
degree <strong>of</strong> risk and speed <strong>of</strong> feedback. By doing this, four cultures are characterized, based on a ‘high’
and ‘low’ score on <strong>the</strong> dimensions.
A dimensional model is focusing on making a complete description <strong>of</strong> an organizational culture. In
most <strong>of</strong> <strong>the</strong>se models at least 5 dimensions are distinguished. An example <strong>of</strong> a well known
dimensional model for organizational culture are <strong>the</strong> dimensions <strong>of</strong> H<strong>of</strong>stede, Neuijen, Ohayv, &
Sanders (1990). However over time, also some dimensional models were proposed that were based
on a literature study on <strong>the</strong> extensively growing list <strong>of</strong> dimensional models for organizational culture.
The eight dimensions <strong>of</strong> Detert, et al. (2000) are an example <strong>of</strong> such a literature based dimensional
model, which covers and combines multiple models <strong>of</strong> organizational culture.
Ano<strong>the</strong>r aspect <strong>of</strong> researching organizational culture concerns <strong>the</strong> measurement. Schein (2004)
distinguishes between three different approaches. Organizational culture can be studied ei<strong>the</strong>r
quantitative, qualitative or in an intermediate form where data collection is done both qualitatively as
well as quantitatively. Fur<strong>the</strong>rmore, with regard to quantitative measurement, several questionnaires
are published. An example is <strong>the</strong> Denison Organizational Culture Survey (Denison, Jovanivics, Cho &
Young; 2005).
2.2 Knowledge sharing
The goal <strong>of</strong> this research was to come up with a way to study <strong>the</strong> complex phenomenon <strong>of</strong> knowledge
sharing in a structured way, where all aspects <strong>of</strong> knowledge sharing are considered. Several attempts
are described in <strong>the</strong> literature to come up with a model to describe knowledge sharing and all its
influences. However, <strong>the</strong> problem is that <strong>the</strong>se researches have <strong>the</strong>ir own specific research aim,
which disregards <strong>the</strong> aspects (and terminology) <strong>of</strong> earlier work proposed by o<strong>the</strong>r researchers.
Therefore, in our research, we combined aspects from different models. Our model for knowledge
sharing takes on a process-based view, which has combined aspects <strong>of</strong> Bosua & Scheepers (2007),
Goh (2002), Gupta & Govindarajan (2000), Ipe (2003), Lichtenstein & Hunter (2006). The knowledge
sharing process is depicted below.
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Vincent de Jong and Remko Helms
Figure 1: The knowledge sharing process. Based on different sources from <strong>the</strong> literature on
knowledge sharing
The knowledge sharing process involves <strong>the</strong> following five aspects: knowledge type, knowledge
contributor, knowledge sharing medium, knowledge consumer, social network. The knowledge
typology that is being used is that <strong>of</strong> Christensen (2007), discriminating between pr<strong>of</strong>essional,
coordinating, object-based and know-who knowledge. Three important aspects can be identified for
<strong>the</strong> role <strong>of</strong> a knowledge contributor, <strong>the</strong> person who is sharing his knowledge. These are motivation,
trust and <strong>the</strong> frequency <strong>of</strong> sharing. The knowledge sharing medium concerns <strong>the</strong> way how knowledge
is transferred from contributor to consumer. This can be done in a variety <strong>of</strong> ways, discriminating
between traditional and technology-aided knowledge sharing (Bordia, Irmer & Abusah 2006; Chua
2001; Wah, Menkh<strong>of</strong>f, Loh & Evers 2005). Traditional knowledge sharing is <strong>the</strong> act <strong>of</strong> sharing
knowledge that is done through direct personal contact, closely related to a face-to-face setting.
Technology-aided knowledge sharing is knowledge sharing that is realized through one or more
technologies. Also, a degree <strong>of</strong> formality can be associated to <strong>the</strong> knowledge sharing media that are
being used (Bartol & Srivastava 2002). With regard to <strong>the</strong> knowledge consumer, three concepts play
a role: status <strong>of</strong> a knowledge contributor (Argote, McEavily & Reagans 2003), which is used as a
synonym for competence-based trust. Frequency <strong>of</strong> consuming knowledge and timeliness <strong>of</strong> <strong>the</strong>
response are <strong>the</strong> o<strong>the</strong>r two important aspects. Finally, <strong>the</strong> last aspect concerns <strong>the</strong> social network.
This aspect focuses on <strong>the</strong> knowledge being shared in a community and <strong>the</strong> default mode <strong>of</strong>
knowledge sharing, e.g. push and pull mechanisms (as described by Dixon (2000) and Helms &
Buijsrogge (2006)).
When researching knowledge sharing, it is important to operationalize what exactly will be measured.
From this point <strong>of</strong> view, it is clear that measuring <strong>the</strong> actual knowledge sharing is very hard, as is also
endorsed by Liebowitz (1999), who claims that knowledge work is most <strong>of</strong> <strong>the</strong> time intangible, and
thus hard to measure. Therefore <strong>the</strong> focus in this research is on knowledge sharing behavior. The
definition that is used is: <strong>the</strong> choices employees make during <strong>the</strong> process <strong>of</strong> knowledge sharing.
3. Research framework
In order to measure organizational culture in a thorough way, we use a dimensional model, since this
has a wider coverage than when a typology would have been used. From <strong>the</strong> four models that were
found, <strong>the</strong> model <strong>of</strong> Detert et al. (2000) has <strong>the</strong> best coverage and is used in this research. The
dimensions <strong>of</strong> Detert et al. are shown on <strong>the</strong> left side <strong>of</strong> figure 2. First, a brief explanation for <strong>the</strong>
dimensions is given below in table 1.
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Vincent de Jong and Remko Helms
Dimension Explanation
Basis <strong>of</strong> truth and
rationality
Prescribes <strong>the</strong> decision making process. What is seen as <strong>the</strong> truth?
The nature <strong>of</strong> time and time The way time is being perceived; how does <strong>the</strong> organization plan
horizon
ahead?
Motivation The way employees are motivated. Do <strong>the</strong>y work to earn money or to
develop <strong>the</strong>mselves.
Stability versus change Does <strong>the</strong> organization strive towards stability or are <strong>the</strong>y continuously
reinventing <strong>the</strong>ir processes and products.
Orientation to work, task Describes how work is done, ei<strong>the</strong>r in a process oriented or goal
and coworkers
oriented way.
Isolation versus
collaboration
How is <strong>the</strong> most elementary work done? Collaborative or individual.
Control, coordination and
responsibility
Concerns <strong>the</strong> autonomy <strong>of</strong> employees within <strong>the</strong> organization.
Orientation and focus Describes <strong>the</strong> relationship <strong>of</strong> an organization with <strong>the</strong> environment.
Table 1: Short description <strong>of</strong> <strong>the</strong> dimensions <strong>of</strong> Detert et al. (2000)
With regard to knowledge sharing, we are focusing on behavior occurring in a knowledge sharing
process, as measuring actual knowledge sharing will be a too comprehensive task. While combining
aspects <strong>of</strong> knowledge sharing, identified from <strong>the</strong> literature, <strong>the</strong> knowledge sharing process was
developed in order to structure <strong>the</strong> data collection on <strong>the</strong> concept <strong>of</strong> knowledge sharing behavior.
After defining <strong>the</strong> concepts to be used within this research, <strong>the</strong> research framework can be depicted.
This
is done in figure 2. The research framework reflects <strong>the</strong> research question and shows that we are
interested
in how different organizational culture dimensions influence knowledge sharing behavior.
Figure 2: Visualization <strong>of</strong> <strong>the</strong> research framework.
4. Case study
For this research we choose to do a comparative case study. Case studies are <strong>of</strong>ten used for <strong>the</strong>ory
building (Dul & Hak 2008), which is also <strong>the</strong> main aim <strong>of</strong> this research.
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4.1 Case study setup
Vincent de Jong and Remko Helms
The case study was carried out at a large construction company in The Ne<strong>the</strong>rlands. The reason to
study multiple cases from one company above several companies, is that <strong>the</strong> availability and usage <strong>of</strong>
knowledge sharing tools would vary very much between cases. These differences in usage can <strong>the</strong>n
not only be directed to <strong>the</strong> organizational culture as <strong>the</strong> dependent factor, as more external influences
would be present. In order to overcome this, a large company was selected where departments would
be studied <strong>of</strong> subsidiaries. Being subsidiaries <strong>of</strong> <strong>the</strong> same company does not imply that <strong>the</strong>y have
identifical organizational culture however. G<strong>of</strong>fee & Jones (1996) and Schein (2004) already showed
that organizational culture can differ between departments.
The departments that were studied included: (1) a project execution department at a regional
construction and development company (BNBW), (2) <strong>the</strong> tendering department <strong>of</strong> a regional
infrastructural company (BNIZW), (3) <strong>the</strong> constructions department <strong>of</strong> <strong>the</strong> engineering subsidiary
(BNE) and (4) <strong>the</strong> system administrators department <strong>of</strong> <strong>the</strong> ICT subsidiary (BNICT). The size <strong>of</strong> <strong>the</strong>
departments were between 6 and 30 employees.
With regard to <strong>the</strong> data collection methods, <strong>the</strong> focus was to use qualitative research methods. For
qualitative data collection, interviews and observations were used. During a period <strong>of</strong> two weeks at all
departments, five or six interviews <strong>of</strong> approximately 1-1.5 hours were conducted. Between <strong>the</strong>
interviews, <strong>the</strong> researcher was located at a central point within <strong>the</strong> department, which <strong>of</strong>fered <strong>the</strong>
opportunity <strong>of</strong> observing how work was done, observing <strong>the</strong> atmosphere and asking specific
questions to subjects. The interviews had a strong emphasis on measure knowledge sharing, <strong>the</strong>
knowledge sharing practices and creating an impression about <strong>the</strong> knowledge sharing behavior <strong>of</strong>
employees. Also some aspects <strong>of</strong> dimensions <strong>of</strong> organizational culture were addressed in <strong>the</strong>se
interviews. The observations were both used to validate findings from interviews as well as ga<strong>the</strong>ring
additional data about knowledge sharing and <strong>the</strong> organizational culture <strong>of</strong> <strong>the</strong> department.
4.1.1 Validity
In order to improve <strong>the</strong> validity <strong>of</strong> <strong>the</strong> research, a number <strong>of</strong> precautions were made. Because our
research is exploratory research, according to Yin (2008) <strong>the</strong> internal and external validity are outside
<strong>the</strong> scope <strong>of</strong> <strong>the</strong> research. So <strong>the</strong> precautions were mainly directed to increase reliability and
construct validity.
Combining at least two different data collection methods for each concept is one measure used to
increase <strong>the</strong> construct validity. Organizational culture was measured using observations and
interviews, where knowledge sharing behavior was measured using interviews and observations.
Ano<strong>the</strong>r important decision that was made to increase construct validity, was to maintain <strong>the</strong> chain <strong>of</strong>
evidence, as described by Yin (2008). This resulted in explicitly formulating different levels <strong>of</strong>
questions, where <strong>the</strong> research questions are directed towards <strong>the</strong> individual questions asked to <strong>the</strong>
subjects. Fur<strong>the</strong>rmore, <strong>the</strong> case study protocol was checked beforehand by experts within <strong>the</strong>
company as well as experts in this research topic.
The reliability <strong>of</strong> <strong>the</strong> case study was also important, as <strong>the</strong> case study protocol had to be reproduced
for all cases. The first important measure that was taken was to use a case study protocol. One part
<strong>of</strong> <strong>the</strong> case study protocol was an interview protocol that was used to streamline <strong>the</strong> data collection <strong>of</strong>
<strong>the</strong> interviews. Replicating <strong>the</strong> interviews was ano<strong>the</strong>r way <strong>of</strong> improving <strong>the</strong> reliability, because a more
general view will evolve over time as more interviews would be conducted, ignoring <strong>the</strong> more
individual points <strong>of</strong> view. Also, in <strong>the</strong> end, case study reports were created in order to see whe<strong>the</strong>r <strong>the</strong>
interpretations that were done are in line with <strong>the</strong> subjects’ view.
4.2 Case study analysis
Based on <strong>the</strong> interviews and observations a detailed view was gained both on <strong>the</strong> organizational
culture and <strong>the</strong> knowledge sharing behavior within <strong>the</strong> departments. In order to compare <strong>the</strong> findings
and to see patterns, two comparison tables were made. In <strong>the</strong> first table <strong>the</strong> dimensions <strong>of</strong> culture are
described per department. In <strong>the</strong> second table <strong>the</strong> knowledge sharing is characterized per
department. After describing <strong>the</strong> organizational culture and knowledge sharing behavior <strong>of</strong> <strong>the</strong>
departments, <strong>the</strong> differences found in knowledge sharing behavior were input to find relationships
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Vincent de Jong and Remko Helms
between organizational culture and knowledge sharing behavior. In <strong>the</strong> last table <strong>of</strong> this paragraph,
<strong>the</strong> relationships that are found are presented in table 3.
Table 2: Generalized observations <strong>of</strong> organizational culture, arranged per dimension.
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Vincent de Jong and Remko Helms
The purpose <strong>of</strong> table 1 is to identify similarities in organizational culture between departments, which
could suggest influences on knowledge sharing behavior. The observations are generalized to
compare organizational culture between <strong>the</strong> departments, where <strong>the</strong>se generalized observations
originate from <strong>the</strong> descriptions <strong>of</strong> organizational culture per dimension per department.
Table 3: Summary <strong>of</strong> differences between departments regarding <strong>the</strong> knowledge sharing process
Table 2 was created to identify contrasting behaviors in knowledge sharing, distinguishing between
<strong>the</strong> five aspects <strong>of</strong> <strong>the</strong> knowledge sharing process. Examples <strong>of</strong> differences in knowledge sharing
behavior that were found are <strong>the</strong> importance <strong>of</strong> coordinating knowledge, <strong>the</strong> importance <strong>of</strong> reciprocal
knowledge sharing and <strong>the</strong> approach <strong>of</strong> locating knowledge. After identifying both concepts, attempts
were made in order to relate <strong>the</strong> differences in knowledge sharing behavior to corresponding
differences found in <strong>the</strong> organizational culture <strong>of</strong> departments. The results <strong>of</strong> this process are shown
in table 3.
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Vincent de Jong and Remko Helms
Table 4: Relationships between organizational culture and knowledge sharing behavior specified by
organizational culture dimension and knowledge sharing process aspect
Besides identifying influences on knowledge sharing, not finding or falsifying influences can also
enrich <strong>the</strong> insight on a specific dimension that is being obtained. For example, an influence <strong>of</strong>
‘Isolation and collaboration’ on knowledge sharing behavior that could be argued is that <strong>the</strong> more
individual employees are working, <strong>the</strong> more advanced knowledge sharing media will be used
(focusing on higher reach) in order to fulfill <strong>the</strong>ir always present need for knowledge. This influence is
not confirmed, since o<strong>the</strong>r dimensions are more accurately explaining <strong>the</strong> usage <strong>of</strong> knowledge
sharing media.
Fur<strong>the</strong>rmore, it is striking that <strong>the</strong> dimension ‘Isolation versus collaboration’ is not indicated as an
influence on knowledge sharing behavior. This is even more remarkable since this is one <strong>of</strong> <strong>the</strong>
characteristics <strong>of</strong> an organizational culture that is, in <strong>the</strong> current literature, seen as a major influence
on knowledge sharing. However, an explanation can be given for this. It can be argued that this
dimension is more influencing <strong>the</strong> (amount <strong>of</strong>) knowledge sharing occasions than <strong>the</strong> behavior
surrounding knowledge sharing. And because <strong>of</strong> <strong>the</strong> difficulties <strong>of</strong> measuring <strong>the</strong> occasions <strong>of</strong>
knowledge sharing this is left outside <strong>the</strong> scope <strong>of</strong> this research. Looking at <strong>the</strong> ga<strong>the</strong>red data and
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Vincent de Jong and Remko Helms
making a rough estimation about <strong>the</strong> amount <strong>of</strong> knowledge that is being shared, this reformulated
influence can be confirmed. BNE and BNICT are having <strong>the</strong> most open work floor layout as well as
more collaborative aspects integrated in <strong>the</strong>ir work. What can be estimated about <strong>the</strong> amount <strong>of</strong>
knowledge that is being shared, is that BNE is sharing <strong>the</strong> most knowledge, where at BNICT
knowledge is also continuously being shared. BNBW and BNIZW are sharing less knowledge, and
are also working more individually.
5. Conclusion
The findings presented in last paragraph, are all very specific and reflect a high level <strong>of</strong> detail
corresponding to <strong>the</strong> research goal. However, also some higher level findings can be formulated. The
first finding regards <strong>the</strong> personalization or codification strategy for knowledge management, as
discussed by Hansen et al. (1999). It is hard to relate <strong>the</strong> usage <strong>of</strong> a personalization/codification
strategy to <strong>the</strong> organizational culture <strong>of</strong> an organization. The usage <strong>of</strong> such a knowledge
management strategy seems to be directed by contextual factors: it is related to <strong>the</strong> type <strong>of</strong> work that
has to be done. Examples <strong>of</strong> <strong>the</strong>se factors are <strong>the</strong> type <strong>of</strong> department, <strong>the</strong> industry or <strong>the</strong> environment
<strong>of</strong> an organization. The influence <strong>of</strong> organizational culture on knowledge sharing manifests at a lower
level than this ‘high level’ decision to pursue a personalization or codification strategy.
Fur<strong>the</strong>rmore, what also can be said is that this research provides more insight on <strong>the</strong> way knowledge
sharing behavior is influenced by organization culture. Influences <strong>of</strong> dimensions <strong>of</strong> organizational
culture are found on specific (behavioral) parts <strong>of</strong> <strong>the</strong> knowledge sharing process. This contradicts <strong>the</strong>
study <strong>of</strong> for instance Al-Alawi et al. (2009), where <strong>the</strong> influence <strong>of</strong> organizational culture is related to
<strong>the</strong> amount <strong>of</strong> knowledge sharing (i.e. better relationships, more trust, which will lead to better/more
knowledge sharing). The specific relations that were found are given in previous paragraph. By
visualizing <strong>the</strong>se influences in figure 3, also some more high level findings can be done. These
findings will be discussed below.
Figure 3: Mapping <strong>of</strong> dimensions <strong>of</strong> organizational culture to <strong>the</strong> parts <strong>of</strong> <strong>the</strong> knowledge sharing
process <strong>the</strong>y are influencing.
The parts knowledge contributor and knowledge consumer are most influenced by dimensions <strong>of</strong>
organizational culture. These influences also show some relatedness between <strong>the</strong> role <strong>of</strong> knowledge
contributor and knowledge consumer within <strong>the</strong> knowledge sharing process. The result <strong>of</strong> this finding
is that <strong>the</strong>se parts <strong>of</strong> <strong>the</strong> knowledge sharing process should get <strong>the</strong> most attention, when <strong>the</strong>
organizational culture is taken into account. Fur<strong>the</strong>rmore, <strong>the</strong> dimensions ‘basis <strong>of</strong> truth’, ‘control,
coordination & responsibility’, ‘orientation to work’ and ‘orientation & focus’ are <strong>the</strong> most influencing
dimensions. When looking at <strong>the</strong> organizational culture <strong>of</strong> an organization, in order to change or
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understand <strong>the</strong> knowledge sharing behavior, focusing on <strong>the</strong>se four dimensions will be <strong>the</strong> most
effective way to get a good insight in <strong>the</strong> way knowledge sharing behavior is influenced. These
insights can be used to focus on <strong>the</strong> most important relationships between both concepts, what
eventually should result in selecting <strong>the</strong> best measures to stimulate knowledge sharing within <strong>the</strong>
organization.
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480

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”
Claudia Jooß, Florian Welter, Anja Richert, and 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, Aachen, Germany
claudia.jooss@ima-zlw-ifu.rwth-aachen.de
florian.welter@ima-zlw-ifu.rwth-aachen.de
anja.richert@ima-zlw-ifu.rwth-aachen.de
sabina.jeschke@ima-zlw-ifu.rwth-aachen.de
Abstract: The initiation <strong>of</strong> so called Clusters <strong>of</strong> Excellence constitutes a milestone with regard to Germany’s
efforts to foster integrative and interdisciplinary research. Clusters <strong>of</strong> Excellence constitute spatially concentrated
research networks including about 20 university institutes, non-university research institutes as well as fur<strong>the</strong>r
selected partners and advisors from industry and science. The heterogeneity <strong>of</strong> <strong>the</strong> included actors can lead to
structural, cognitive and cultural challenges in coordination and cooperation, because diverse disciplines (e. g.
production and material engineering, informatics as well as business sciences) are integrated using different
methodologies and technical terms. Therefore, <strong>the</strong> question arises in how far cross linked, knowledge-intensive
and highly complex scientific cooperations can be organized to ensure a high quality <strong>of</strong> <strong>the</strong> research cluster
output. To cope with <strong>the</strong> challenge <strong>of</strong> scientific cooperation, <strong>the</strong> conceptual framework <strong>of</strong> <strong>the</strong> Cross Sectional
Processes in <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-Wage Countries” at RWTH
Aachen University supports effective networking processes and strategic cluster development by means <strong>of</strong>
learning and knowledge management. In order to contribute to <strong>the</strong> aforementioned research question, a model
for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes is developed. The model will define specific measures for a
promotion <strong>of</strong> interdisciplinary cooperation and cluster development at respective phases <strong>of</strong> network development
– e. g. from <strong>the</strong> initiation phase to <strong>the</strong> steady phase. Following <strong>the</strong> research approach <strong>of</strong> <strong>the</strong> Grounded Theory,
<strong>the</strong> generation <strong>of</strong> <strong>the</strong> model bases on a triangulation <strong>of</strong> quantitative as well as qualitative data, captured through
a Balanced-Scorecard-based performance measurement tool, direct evaluations and structured interviews with
cluster members. First results <strong>of</strong> <strong>the</strong> data triangulation make obvious that e. g. <strong>the</strong> role <strong>of</strong> project leaders as
cluster-internal knowledge agents is crucial for a successful knowledge transfer in a hierarchical Cluster <strong>of</strong>
Excellence. Moreover, a high rate <strong>of</strong> staff turnover in <strong>the</strong> research environment strongly influences <strong>the</strong>
implementation <strong>of</strong> cross sectional activities. A fur<strong>the</strong>r transfer <strong>of</strong> cross sectional measures to o<strong>the</strong>r forms <strong>of</strong>
scientific oriented clusters and comparable networks is aspired by <strong>the</strong> Cross Sectional Processes to support
scientific oriented interdisciplinary cooperation in future.
Keywords: Challenge <strong>of</strong> Interdisciplinary Cooperation, Cross Sectional Processes, Learning and Knowledge
Management in Clusters <strong>of</strong> Excellence, Model for Cluster Development
1. Background and Introduction
In order to overcome <strong>the</strong> pillarization <strong>of</strong> basic research, applied research and operative research and
development (R&D) towards a more dynamic-interactive process <strong>of</strong> interdisciplinary knowledge
generation, <strong>the</strong> German federal government initiated <strong>the</strong> Excellence Initiative to streng<strong>the</strong>n <strong>the</strong>
research location Germany in a sustainable way.
In light <strong>of</strong> <strong>the</strong> German Excellence Initiative, one <strong>of</strong> <strong>the</strong> first scientific approaches for promoting
interdisciplinary knowledge generation constitutes <strong>the</strong> Cross Sectional Processes in <strong>the</strong> Cluster <strong>of</strong>
Excellence “Integrative Production Technology for High-Wage Countries”. The approach aims at
providing adequate measures <strong>of</strong> learning and knowledge management to support scientists working
in interdisciplinary research areas. The main research question <strong>of</strong> <strong>the</strong> Cross Sectional Processes can
be described with:
How should cross-linked, knowledge-intensive and highly complex scientific cooperations
within <strong>the</strong> Cluster <strong>of</strong> Excellence be organized and simplified to ensure <strong>the</strong>
high quality <strong>of</strong> <strong>the</strong> cluster output?
As one <strong>of</strong> 37 funded Clusters <strong>of</strong> Excellence, initiated through <strong>the</strong> German Excellence Initiative, <strong>the</strong>
Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-Wage Countries” was established
481

Claudia Jooß et al
at RWTH Aachen University in 2006 bringing toge<strong>the</strong>r interdisciplinary scientists – mainly from
production and material sciences – into a common network to investigate integrative production
<strong>the</strong>ories. It integrates a highly complex and heterogeneous network <strong>of</strong> 19 university institutes, 7
associated institutes and fur<strong>the</strong>r non-university research institutions (e. g. from <strong>the</strong> Fraunh<strong>of</strong>er
Society) as well as different industrial advisors. Because <strong>of</strong> this, <strong>the</strong> organizational structure <strong>of</strong> <strong>the</strong>
cluster contains a cross sectional research field to cope with <strong>the</strong> challenge <strong>of</strong> interdisciplinarity in <strong>the</strong>
context <strong>of</strong> production engineering. With reference to <strong>the</strong> introduced cluster, contemporary tendencies
such as <strong>the</strong> integration <strong>of</strong> cognitive elements or <strong>the</strong> contribution <strong>of</strong> information technologies to
production engineering extend <strong>the</strong> scope <strong>of</strong> classical mechanical engineering. Fur<strong>the</strong>rmore, <strong>the</strong>
challenge can be characterized by external requirements to interdisciplinary research following <strong>the</strong>
strategies <strong>of</strong> <strong>the</strong> German Research Foundation, <strong>the</strong> German Science Council or <strong>the</strong> <strong>European</strong> Union.
According to that, after an overview <strong>of</strong> actual challenges in interdisciplinary research and network
management, <strong>the</strong> cross sectional approach will be outlined. Moreover, <strong>the</strong> development <strong>of</strong> a model for
<strong>the</strong> management <strong>of</strong> Cross Sectional Processes and <strong>the</strong> respective methodological approach are
discussed. The paper concludes with preliminary findings in cluster-specific interdisciplinary research
and recommendations for successful cooperations in <strong>the</strong> context <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence and its
learning and knowledge management activities. Fur<strong>the</strong>r possible research questions in <strong>the</strong> scope <strong>of</strong>
Cross Sectional Processes will be introduced in a final outlook <strong>of</strong> <strong>the</strong> paper.
2. Interdisciplinary Network Management as a Contemporary Challenge in
Research Environments
Processes <strong>of</strong> cross linking and networking reveal chances as well as challenges for involved
organizations and networks such as a production engineering oriented Cluster <strong>of</strong> Excellence. Thus
interdisciplinary work depicts a research field exploring phenomena like an augmenting complexity
through heterogeneous partners, a growing application <strong>of</strong> hybrid methodologies or an augmenting
dynamic <strong>of</strong> innovation cycles (Bryson 2009). Because <strong>of</strong> this <strong>the</strong> question <strong>of</strong> managing
interdisciplinary cooperation needs to be fur<strong>the</strong>r discussed (Öberg 2011). Primarily, two conditions
have to be fulfilled to guarantee network coordination (Siebert 2006): On <strong>the</strong> one hand, <strong>the</strong> condition
<strong>of</strong> effectiveness demands a higher performance <strong>of</strong> networks than in o<strong>the</strong>r forms <strong>of</strong> coordination like
market or hierarchy. Concerning research networks, <strong>the</strong> output through cooperation is supposed to be
higher than in conventional forms <strong>of</strong> non-integrative research. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> condition <strong>of</strong>
efficiency aims at <strong>the</strong> satisfaction <strong>of</strong> all actors. In <strong>the</strong> context <strong>of</strong> research networks this means that <strong>the</strong>
benefit <strong>of</strong> cooperation has to be greater than communication and cooperation costs to be executed by
each actor (Siebert 2006).
For <strong>the</strong> development <strong>of</strong> a structure and organization <strong>of</strong> Clusters <strong>of</strong> Excellence and adequate Cross
Sectional Processes <strong>the</strong> following aspects have to be considered in order to ensure <strong>the</strong> high quality <strong>of</strong>
<strong>the</strong> cluster output:
“One <strong>of</strong> <strong>the</strong> main attractions <strong>of</strong> interdisciplinary research is that it allows researchers freedom
from disciplinary constraints” (Repko et al. 2010). But <strong>the</strong> heterogeneity <strong>of</strong> actors can lead to
structural, cognitive and cultural challenges in coordination and cooperation, because diverse
disciplines (e. g. production and material engineering, informatics as well as business sciences)
are integrated in a network applying different or partly shared methodologies, <strong>the</strong>ories and
technical terms (cf. ibid.). This gives space to new scientific possibilities as well as to new
scientific challenges regarding communication (like <strong>the</strong> lack <strong>of</strong> a common language) and
cooperation among single disciplines (Röbbecke et al. 2004, Sauer 2005). Hence, <strong>the</strong> cultural
differences within <strong>the</strong> cluster afford chances as well as risks concerning <strong>the</strong> performance <strong>of</strong> <strong>the</strong>
entire cluster.
Especially project oriented networks distinguished by a limited project period are characterized by
a high staff turnover (Sydow 2010). Because <strong>of</strong> this, an on-going fluctuation <strong>of</strong> actors may have
chances and risks regarding <strong>the</strong> coherence <strong>of</strong> <strong>the</strong> network which may lead to a delay in <strong>the</strong>
process <strong>of</strong> network development.
The highly-complex research processes in networks such as a Cluster <strong>of</strong> Excellence demand <strong>the</strong>
identification and continuous adaption <strong>of</strong> knowledge interfaces in terms <strong>of</strong> human, organizational
and technological aspects. Following Sydow (2010) this implicates that methods <strong>of</strong> evaluation and
reflection have to be included into a network management approach. Thus, a model for <strong>the</strong>
management <strong>of</strong> Cross Sectional Processes has to contain elements <strong>of</strong> evaluation and reflection.
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With reference to <strong>the</strong> challenges <strong>of</strong> interdisciplinary management in scientific oriented networks, <strong>the</strong>
Cross Sectional Processes comprise two approaches:
The provision <strong>of</strong> special services cross-linking <strong>the</strong> cluster actors, so called “Network Services”
(Sydow & Zeichhardt 2009, Buhl & Meier zu Köcker 2009, Jooß et al. 2010) (e. g. organizational
measures like colloquia for employees or digital knowledge management tools) aims at promoting
<strong>the</strong> cluster-internal cooperation and <strong>the</strong> scientific output.
Cross Sectional Processes also comprise an own scientific layer in <strong>the</strong> Cluster <strong>of</strong> Excellence
“Integrative Production Technology for High-Wage Countries” researching on <strong>the</strong> continuous
optimization <strong>of</strong> cross linking interdisciplinary scientific actors. The results <strong>of</strong> a performance
measurement system (cf. chapter 3.1) are used for a fur<strong>the</strong>r development <strong>of</strong> <strong>the</strong> entire cluster
strategy and single “Network Services” (ibid.).
A strong dependency exists between both approaches. On <strong>the</strong> one hand, results <strong>of</strong> <strong>the</strong> scientific layer
continuously influence a modification <strong>of</strong> <strong>the</strong> “Network Services” (ibid.). On <strong>the</strong> o<strong>the</strong>r hand, new
research questions arise with <strong>the</strong> implementation <strong>of</strong> different “Network Services” (ibid.).
3. Cross Sectional Processes and Methodology Used
With reference to contemporary challenges in interdisciplinary network management, fur<strong>the</strong>r research
on an overall model for <strong>the</strong> management <strong>of</strong> Cross Sectional Processes in <strong>the</strong> context <strong>of</strong> scientific
research networks like <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-Wage
Countries” is necessary. Although e. g. Defila et al. (2006) define in <strong>the</strong>ir work relevant steps to
manage interdisciplinary projects, fur<strong>the</strong>r specific aspects in <strong>the</strong> context <strong>of</strong> highly-complex
interdisciplinary research networks, like different hierarchical levels or staff-turnover, have to be
considered in this research field. These aspects directly influence <strong>the</strong> model for <strong>the</strong> management <strong>of</strong>
Cross Sectional Processes which simultaneously includes <strong>the</strong> levels: human, organization and
technology.
Hence, to face this challenge and to answer <strong>the</strong> research question (cf. chapter 1), Cross Sectional
Processes are developing a model to point out specific measures for a promotion <strong>of</strong> interdisciplinary
cooperation and cluster development at respective phases <strong>of</strong> network development. Ahrens et al.
(2004) classify phases <strong>of</strong> network development in initial phase, stable phase and steady phase
(Ahrens et al. 2004). According to this, a toolbox <strong>of</strong> cross sectional measures for different levels <strong>of</strong>
cluster-internal scientific cooperation (such as Knowledge Organisation, Research Organisation,
Communication, and Knowledge Output) is implemented. The field-tested measures originating from
<strong>the</strong> Federal Ministry <strong>of</strong> Education and Research (BMBF)-project SENEKA were adapted to <strong>the</strong> needs
<strong>of</strong> a production engineering cluster. On basis <strong>of</strong> <strong>the</strong> experience in cross linking heterogeneous actors,
first measures <strong>of</strong> <strong>the</strong> toolbox were advised to be implemented in <strong>the</strong> cluster in accordance with <strong>the</strong>
steering committee and <strong>the</strong> cluster management board (cf. Fig. 1).
Figure 1: Toolbox <strong>of</strong> Cross Sectional Measures
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Regarding <strong>the</strong> actually implemented cross sectional measures in Fig. 1 <strong>the</strong> focus is set on <strong>the</strong>
segment <strong>of</strong> Scientific Co-operation underlining a major challenge <strong>of</strong> cluster coordination (cf. chapter
2). In order to examine <strong>the</strong>se measures, a set <strong>of</strong> quantitative and qualitative methodologies is crucial
to manage <strong>the</strong> highly cross linked scientific cooperations. Therefore, three different methodologies
(used to generate three different data sets) are explained in <strong>the</strong> following lines.
3.1 Performance Measurement
The implementation <strong>of</strong> a research-cluster-specific Balanced-Scorecard-Approach – as a first
methodology used – refers to <strong>the</strong> model <strong>of</strong> Kaplan and Norton (1992) and constitutes a “performance
measurement system” originally designed as a communication, information and learning system for
enterprises (Kaplan & Norton 1992). The approach is re-designed to <strong>the</strong> needs <strong>of</strong> scientific research
clusters and apart from an implementation in <strong>the</strong> production engineering cluster it is used in <strong>the</strong>
Cluster <strong>of</strong> Excellence “Tailor-Made Fuels from Biomass” at RWTH Aachen University (cf. Welter et al.
2010). The approach is characterized by <strong>the</strong> measurement and comparison <strong>of</strong> performance indicators
on different hierarchical layers (e. g. <strong>the</strong> layer <strong>of</strong> sub-project leaders or <strong>the</strong> layer <strong>of</strong> <strong>the</strong> management
board) and bases on a survey among all cluster actors to evaluate for instance <strong>the</strong> quality <strong>of</strong> scientific
cooperation or <strong>the</strong> scientific output. With reference to <strong>the</strong> initial Balanced Scorecard, <strong>the</strong> clusterspecific
tool contains four adapted perspectives which strictly refer to <strong>the</strong> overall vision <strong>of</strong> <strong>the</strong> cluster –
<strong>the</strong> solution <strong>of</strong> <strong>the</strong> Polylemma <strong>of</strong> Production Technology:
1 st The Internal Perspective/Research Cooperation,
2 nd The Learning and Development Perspective,
3 rd The Output/Customer Perspective,
4 th The Financial Perspective.
This enables <strong>the</strong> measurement <strong>of</strong> scientific oriented performance indicators, e. g. <strong>the</strong> generation <strong>of</strong>
excellent publications or <strong>the</strong> education <strong>of</strong> excellent researchers in production engineering. Concerning
this, <strong>the</strong> development <strong>of</strong> <strong>the</strong> different sub-targets can be measured yearly by means <strong>of</strong> cluster-internal
evaluations. Through yearly iterations, data is collected and analysed in order to work out
recommendations toge<strong>the</strong>r with <strong>the</strong> management board <strong>of</strong> <strong>the</strong> cluster. Thus, <strong>the</strong> performance <strong>of</strong> <strong>the</strong>
cluster can be reflected to enable a steering <strong>of</strong> <strong>the</strong> entire network. Fur<strong>the</strong>rmore, this supports <strong>the</strong>
adaption <strong>of</strong> cluster strategies through <strong>the</strong> consideration <strong>of</strong> relevant external influences (single-looplearning)
and <strong>the</strong> adaption <strong>of</strong> fundamental management ideologies and values (double-loop-learning)
(cf. Brall 2010; Kaplan & Norton 1997).
Until today, three Balanced-Scorecard-based evaluations were implemented in <strong>the</strong> Cluster <strong>of</strong>
Excellence with 117 respondents in average. In contrast to Kaplan and Norton, who do not suggest a
certain method to generate and to collect key performance indicators, a standardized survey has
been developed by <strong>the</strong> Cross Sectional Processes toge<strong>the</strong>r with <strong>the</strong> management board <strong>of</strong> <strong>the</strong>
cluster. By this means performance indicators <strong>of</strong> a scientific network become measurable (e. g. <strong>the</strong>
quality <strong>of</strong> cooperation). Analyzed by use <strong>of</strong> descriptive statistics, <strong>the</strong> performance indicators become
measurable and comparable through arithmetic averages or median.
3.2 Direct Evaluations
In addition to <strong>the</strong> yearly Balanced-Scorecard-based evaluation (which constitutes an indirect form <strong>of</strong>
evaluation), cluster-specific meetings and events (like colloquia for employees) are evaluated with
additional surveys by <strong>the</strong> participants subsequently to each event. This second methodology is
implemented to gain fur<strong>the</strong>r feedback <strong>of</strong> <strong>the</strong> use <strong>of</strong> single cross sectional measures at a respective
phase <strong>of</strong> network development.
3.3 Structured Interviews
In contrast to <strong>the</strong> performance measurement approach and direct evaluations, a qualitative method <strong>of</strong>
generating data is given by <strong>the</strong> method <strong>of</strong> structured interviews with participants <strong>of</strong> <strong>the</strong> cluster. Hence,
24 interviews (representing all sub-projects <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence) were carried out on different
hierarchical levels by representatives <strong>of</strong> <strong>the</strong> Cross Sectional Processes – including pr<strong>of</strong>essors,
leading researchers, members <strong>of</strong> <strong>the</strong> management board and sub-project leaders – to enable a broad
review <strong>of</strong> already introduced cross sectional measures.
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Claudia Jooß et al
With <strong>the</strong> approach <strong>of</strong> <strong>the</strong> Grounded Theory (Glaser & Strauss 2008, Strauss & Corbin 1996, Strübing
2008), which depicts a widespread method for <strong>the</strong> development <strong>of</strong> organizational management
<strong>the</strong>ories and models, a data based <strong>the</strong>ory can be developed to illustrate <strong>the</strong> complex social reality by
data. Because <strong>of</strong> this, a data triangulation <strong>of</strong> <strong>the</strong> three introduced methodologies is necessary. During
<strong>the</strong> process <strong>of</strong> triangulation, hypo<strong>the</strong>ses are formulated defining cross sectional measures that aim at
ensuring <strong>the</strong> high quality <strong>of</strong> scientific output at a certain phase <strong>of</strong> network development (initial phase,
stable phase and steady phase) (Ahrens et al. 2004).
4. Discussion <strong>of</strong> preliminary results
Cross Sectional Processes in <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative Production Technology for High-
Wage Countries” at RWTH Aachen University provide cluster-specific means <strong>of</strong> learning and
knowledge management to support <strong>the</strong> high quality <strong>of</strong> scientific output in <strong>the</strong> cluster. The following
preliminary results <strong>of</strong> <strong>the</strong> yearly performance measurement, <strong>the</strong> direct evaluations and <strong>the</strong> structured
interviews outlined several aspects that have to be considered:
Through <strong>the</strong> triangulated data analysis <strong>the</strong> importance <strong>of</strong> more intensive cross sectional activities
on <strong>the</strong> level <strong>of</strong> <strong>the</strong> four core Integrated Cluster Domains (ICDs) was underlined for a successful
cluster development. Following <strong>the</strong> results <strong>of</strong> <strong>the</strong> data, especially in <strong>the</strong> initial phase <strong>of</strong> <strong>the</strong> cluster,
<strong>the</strong> work on <strong>the</strong> level <strong>of</strong> <strong>the</strong> single ICDs has to be initialized and supported e. g. through regular
ICD-meetings in order to foster <strong>the</strong> exchange <strong>of</strong> sub-project leaders and researchers.
As a reaction to <strong>the</strong> results <strong>of</strong> <strong>the</strong> Balanced-Scorecard-based evaluations from 2008 to 2010, an
optimization <strong>of</strong> interlinking <strong>the</strong> different ICDs was advised. This was realized through so called
‘demonstrators’ by <strong>the</strong> cluster management board. Demonstrators can be described as highly
cross linked sub-projects integrating researchers from different ICDs and/or single partners from
industry to foster cross sectional scientific exchange on <strong>the</strong> one hand and <strong>the</strong> integration <strong>of</strong>
applied research on <strong>the</strong> o<strong>the</strong>r hand.
Fur<strong>the</strong>r figures from <strong>the</strong> analysed data made obvious that <strong>the</strong> personal benefit for researchers
participating in meetings decreases with an augmenting number <strong>of</strong> participants in meetings.
Considering that, a stronger implementation <strong>of</strong> regular meetings with smaller groups <strong>of</strong>
participants (e. g. sub-projects and ICDs) has to be favoured to foster <strong>the</strong> interdisciplinary
cooperation instead <strong>of</strong> too many meetings on <strong>the</strong> level <strong>of</strong> a general assembly.
Especially following <strong>the</strong> interview statements <strong>of</strong> project leaders, a new definition <strong>of</strong> <strong>the</strong> role <strong>of</strong>
project leaders as knowledge agents has to be aspired, because it is mainly this group that
integrates and transfers project-specific knowledge into <strong>the</strong> ICD and o<strong>the</strong>r parts <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong>
Excellence.
Moreover, with regard to o<strong>the</strong>r forms <strong>of</strong> cross linking actors within <strong>the</strong> cluster, <strong>the</strong> data analyses <strong>of</strong>
<strong>the</strong> Cross Sectional Processes revealed that although <strong>the</strong>re is a high awareness among <strong>the</strong>
researchers about <strong>the</strong> need <strong>of</strong> digital tools to network different actors, <strong>the</strong> current networking
devices are not used regularly (e. g. <strong>the</strong> common data platform). Possible solutions thus comprise
e. g. a stronger integration <strong>of</strong> <strong>the</strong> data platform into <strong>the</strong> <strong>of</strong>ficial cluster homepage, <strong>the</strong> integration
<strong>of</strong> a semantic web into <strong>the</strong> data platform to connect contents with single scientists and projects or
<strong>the</strong> integration <strong>of</strong> calendar functions for common appointments and seminars with regard to
advanced training activities.
In general a more interactive form <strong>of</strong> digital networking is desired following <strong>the</strong> data analyses and
seems to be promising considering an increase <strong>of</strong> interdisciplinary cooperation as well as <strong>the</strong>
scientific output.
As an overall cross sectional measure, <strong>the</strong> regular implementation <strong>of</strong> <strong>the</strong> Balanced-Scorecard-
Approach has been shown as important to control <strong>the</strong> research paths <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence
in all development phases <strong>of</strong> <strong>the</strong> network by means <strong>of</strong> a comparable common data set. The
performance measurement tool enables a reflection and redesign <strong>of</strong> already introduced
operational activities on <strong>the</strong> one hand and <strong>the</strong> adjustment <strong>of</strong> long-term management strategies on
<strong>the</strong> o<strong>the</strong>r hand. Referring to <strong>the</strong> process <strong>of</strong> critical examining long-term strategies, Kaplan and
Norton speak <strong>of</strong> “Double-Loop-Learning” (Kaplan & Norton 1997).
With regard to <strong>the</strong> production engineering focus <strong>of</strong> <strong>the</strong> cluster, <strong>the</strong> Balanced-Scorecard-Approach
thus enables <strong>the</strong> regular measurement <strong>of</strong> <strong>the</strong> status quo to support <strong>the</strong> high quality <strong>of</strong> <strong>the</strong>
scientific output or <strong>the</strong> training <strong>of</strong> excellent researchers. Although <strong>the</strong> process <strong>of</strong> double-loop-
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learning (cf. Brall 2010; Kaplan & Norton 1997) has started on <strong>the</strong> level <strong>of</strong> <strong>the</strong> management
board, a fur<strong>the</strong>r adaptation <strong>of</strong> <strong>the</strong> tool to actual needs <strong>of</strong> <strong>the</strong> production engineering oriented
network is aspired sharpening <strong>the</strong> outcome <strong>of</strong> <strong>the</strong> measurement approach for all groups <strong>of</strong> actors.
In addition to that, a stronger bottom-up approach during <strong>the</strong> definition <strong>of</strong> cluster-specific key
performance indicators (for instance by means <strong>of</strong> a kick-<strong>of</strong>f workshop) is recommendable to
augment <strong>the</strong> willingness <strong>of</strong> all actors <strong>of</strong> <strong>the</strong> cluster to participate in <strong>the</strong> evaluations.
The preliminary results <strong>of</strong> <strong>the</strong> Cross Sectional Processes underlined <strong>the</strong> importance <strong>of</strong> a holistic view
– integrating human, organizational and technological levels with regard to learning and knowledge
management activities. Referring to <strong>the</strong> condition <strong>of</strong> effectiveness and <strong>the</strong> condition <strong>of</strong> efficiency (cf.
chapter 2) this suggests for <strong>the</strong> human level that e. g. a cluster-specific advanced training for project
leaders has to be designed. On <strong>the</strong> organizational level, e. g. formats like ‘demonstrators’ streng<strong>the</strong>n
<strong>the</strong> exchange beyond scientific disciplines. In doing so, a value added for <strong>the</strong> scientific working
process is generated and synergies among scientists are supported. Concerning <strong>the</strong> technological
level <strong>of</strong> learning and knowledge management, methods and concepts like knowledge retrieval are
increasingly important to support a digital form <strong>of</strong> interlinking actors and to reduce time for searching
information. Through <strong>the</strong> implementation <strong>of</strong> regular cluster-specific evaluations and performance
measurement activities, not only tailor-made measures can be adapted continuously but also a
reflection <strong>of</strong> <strong>the</strong> entire structure and organization <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence becomes possible.
5. Outlook and Fur<strong>the</strong>r Research Questions
Due to <strong>the</strong> fact that <strong>the</strong> empirical study in <strong>the</strong> context <strong>of</strong> <strong>the</strong> model generation included <strong>the</strong> evaluation
<strong>of</strong> already implemented measures <strong>of</strong> learning and knowledge management, <strong>the</strong> project results can be
used for two targets. On <strong>the</strong> one hand <strong>the</strong>y constitute <strong>the</strong> empirical pillar <strong>of</strong> <strong>the</strong> ASPO (a model <strong>of</strong> use
for Arrangements and Instruments <strong>of</strong> <strong>the</strong> Cross Sectional Processes in <strong>the</strong> knowledge-intensive
Organization Cluster <strong>of</strong> Excellence), besides <strong>the</strong> <strong>the</strong>oretical pillar <strong>of</strong> interdisciplinary network
management. On <strong>the</strong> o<strong>the</strong>r hand, <strong>the</strong> results are used for a strategic re-alignment <strong>of</strong> <strong>the</strong> Cross
Sectional Processes concerning a second funding period <strong>of</strong> <strong>the</strong> Cluster <strong>of</strong> Excellence “Integrative
Production Technology for High-Wage Countries”. Referring to <strong>the</strong> latter, cross sectional research
areas like performance measurement (e. g. by means <strong>of</strong> <strong>the</strong> Balanced-Scorecard-Approach as well as
intellectual capital reporting) and diversity management (in terms <strong>of</strong> culture, age and gender) will be
extended. The new cross sectional research area <strong>of</strong> interdisciplinary innovation management will be
initiated to raise <strong>the</strong> researchers’ capability <strong>of</strong> learning and applying interdisciplinary methodologies
and <strong>the</strong>ories. Moreover, <strong>the</strong> area <strong>of</strong> knowledge and cooperation engineering will be integrated
underlining <strong>the</strong> importance <strong>of</strong> cross sectional aspects like modelling and managing interdisciplinary
knowledge flows.
During <strong>the</strong> next months ASPO will deliver fur<strong>the</strong>r answers to <strong>the</strong> question:
Which Cross Sectional Processes are crucial to foster <strong>the</strong> development <strong>of</strong> <strong>the</strong>
knowledge-intensive network into phases <strong>of</strong> higher scientific productivity ensuring a high
quality <strong>of</strong> <strong>the</strong> output?
Since models <strong>of</strong> scientific network development (cf. Ahrens et al. 2004; Morath 1996; Defila et al.
2006) have to cope with a highly complex and dynamic environment, <strong>the</strong> ASPO aims at creating a
concept <strong>of</strong> cross sectional management providing <strong>the</strong> right cross sectional measure for a certain
phase <strong>of</strong> network development. Dynamic in terms <strong>of</strong> a high fluctuation among <strong>the</strong> employed scientists
in <strong>the</strong> cluster thus depicts a major challenge for learning and knowledge management. Due to <strong>the</strong> fact
that <strong>the</strong> cluster is mainly embedded in a German university environment, which is characterized by
relative instable and discontinuous working conditions, <strong>the</strong> number <strong>of</strong> fixed term contracts is relatively
high. Because <strong>of</strong> this, ano<strong>the</strong>r research question has to be answered by Cross Sectional Processes in
future:
Which Cross Sectional Measures are exceptionally important to deal with an increased
staff turnover in <strong>the</strong> context <strong>of</strong> Clusters <strong>of</strong> Excellence?
A fur<strong>the</strong>r transfer <strong>of</strong> cross sectional measures and tools to o<strong>the</strong>r forms <strong>of</strong> scientific oriented clusters
and comparable networks is aspired by <strong>the</strong> Cross Sectional Processes to support scientific oriented
interdisciplinary cooperation in future.
The depicted research has been funded by <strong>the</strong> German Research Foundation DFG as part <strong>of</strong>
<strong>the</strong> Cluster <strong>of</strong> Excellence ‘Integrative Production Technology for High-Wage Countries’.
486

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487

Expert Knowledge: Does it Help or Hinder Idea Generation
and Creativity for Innovation?
Selvi Kannan
Victoria University, Melbourne, Australia
selvi.kannan@vu.edu.au
Abstract: Managing a diverse multigenerational workforce <strong>of</strong> ageing experts and young talented entrants is
becoming a challenge for organisations. Current research acknowledges that experience and expertise will walk
out <strong>the</strong> door with <strong>the</strong> retiring workforce and little replaced with new entrants coming into <strong>the</strong> organisations who
have little or no experience. This leaves organisations in a susceptible position. One key challenge facing
organisations today is to manage <strong>the</strong> knowledge and talent for sustained competitive performance and innovation
in this workforce shift. Innovation is crucial for economic performance globally. Drucker (1985) as quoted by
Tidd et al (2005) identified demographic change as one <strong>of</strong> <strong>the</strong> common sources <strong>of</strong> opportunities in managing
innovation. Was Drucker (1985) really seeding waves <strong>of</strong> thinking that <strong>the</strong> new organisational order to continue to
sustain and innovate, an organisation has to manage knowledge towards innovation <strong>of</strong> a diverse workforce? Can
experts really help? Or do <strong>the</strong> novices consider <strong>the</strong>m to be a hindrance?. This paper focuses by questioning: Is
an expert's knowledge and expertise, especially those veteran experts, critical in idea generation and creativity
towards innovation or more <strong>of</strong> a hindrance? There is clearly a lack <strong>of</strong> scholarly and empirical work in linking
knowledge management and innovation. Fur<strong>the</strong>rmore <strong>the</strong>re is definitely lack <strong>of</strong> studies focussing on experts,
talents and experience contribution in <strong>the</strong> innovation process. This conceptual paper attempts to conceptualize a
knowledge exchange and barrier model within a typified innovation process. It takes <strong>the</strong> view that knowledge is
valued resource and <strong>the</strong> perspectives and experiences <strong>of</strong> young talents versus veteran experts on working
toge<strong>the</strong>r to generate creative ideas and ultimately start up innovative projects in an organisation can be
challenging. The expert's knowledge and experience in applying to an organisation's innovation process looks at
tacit exchange and barriers <strong>of</strong> a case organisation in Australia. This paper may benefit two primary groups -
researchers and academics who are interested in <strong>the</strong> link between innovation process and knowledge
management. Secondly it may benefit managers in an organisation to understand better as to how to manage
knowledge for idea generation and creativity and sustain innovation with a multigenerational workforce.
Keywords: Multigenerational workforce, creativity, knowledge management, innovation process, barriers
1. Introduction
Managing organisational knowledge <strong>of</strong> a multigenerational workforce comprising <strong>of</strong> veteran experts
and inexperienced young enterprising talents, is a balance that many organisations face and learn to
master in order to sustain performance and be competitive. Capturing <strong>the</strong> "know-what" <strong>of</strong> individuals
is seemingly becoming critical especially for organisations to compete with speed.
Evidence from this research has shown that it is seemingly difficult to have different generational
pr<strong>of</strong>essional groups to work toge<strong>the</strong>r on creative ideas and overcome resistance. Adding fur<strong>the</strong>r mix
to <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> generational make up, <strong>the</strong> innovation process in an organisation, to be
successful, must have its workers contend to <strong>the</strong> organisational vision and goals and sustainable
performance.
The key question this paper raises is whe<strong>the</strong>r an expert's knowledge and expertise helps or hinders
idea generation and creativity towards innovation. This question could pose fur<strong>the</strong>r discussions
amongst scholars as to how <strong>the</strong> expertise <strong>of</strong> <strong>the</strong> retiring workforce be best tapped into for creative use
before organisational-knowledge loss situation arises. It may also raise questions <strong>of</strong> effective tacit
exchange between different generational groups and managing <strong>the</strong> multigenerational friction.
Innovation is birth <strong>of</strong> an idea and it can be a combination <strong>of</strong> new and old ideas. Old ideas can
challenge organisations where unique approaches and past knowledge where frames <strong>of</strong> reference
drawn from historical data, experiences and activities needs validation (Van de Ven, 1986), especially
in mature organisations. New ideas may face barriers.
Idea generation and creativity are critical processes in innovation. Studies in innovation have given
very little attention to <strong>the</strong> issues <strong>of</strong> experience and expertise knowledge within <strong>the</strong> innovation process.
Authors Drucker (1985); Griffiths and Biosot (2000); and Tidd, Bessant and Pavitt (2005) have
identified <strong>the</strong> routines, spaces and importance <strong>of</strong> knowledge and knowledge management for
innovation in <strong>the</strong>ir studies. There is however little explored on <strong>the</strong> assistance or hindrance <strong>of</strong> experts
knowledge contribution towards idea generation and creativity.
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Selvi Kannan
This paper will discuss critical areas concerning <strong>the</strong> experts’ expertise in knowledge sharing in relation
to creativity and innovation. Changing workforce dynamics that pose unique set <strong>of</strong> challenges to
organizations will be initially discussed. I will <strong>the</strong>n go on to introduce a debate on knowledge
exchange between experts and novice talents towards innovation. The discussion will lead onto
introduce a diagrammatic illustration <strong>of</strong> <strong>the</strong> exchange-barrier innovation process model. This
conceptual paper is based on findings from a case organization.
2. A Multigenerational Workforce
The current challenges facing organizations are workforce retirements, recruitments and retentions.
The ageing workforce, many with great number <strong>of</strong> years <strong>of</strong> experience and expertise are retiring.
Industrialised countries such as Japan, USA, Europe, Italy and Australia have been experiencing this
since 2005 and will face this with more intensity towards 2020. In relation to innovation McCrindle
(2010) said, “Every organisation, every product and every brand is just one generation away from
extinction. Unless companies can recruit and retain <strong>the</strong> new generation <strong>of</strong> staff, and engage with <strong>the</strong>
emerging customers, <strong>the</strong>y will struggle to survive". The McCrindle report (2010) however fails to
discuss <strong>the</strong> retiring experts’ and <strong>the</strong> loss <strong>the</strong>ir knowledge. On <strong>the</strong> contrary, <strong>the</strong> Committee for
Economic Development, New Opportunities report that: “<strong>the</strong> value <strong>of</strong> experience may be difficult to
measure in individual firms, but it provides concrete and quantifiable benefits to <strong>the</strong> economy as a
whole” and identifying that older workers demonstrate a higher degree <strong>of</strong> loyalty and are “more stable”
than younger hires (American Taskforce Report, 2008). The critical issue for organisations is to
continue engagement with customers. Threatened are just not companies who may potentially lose
significant number <strong>of</strong> employees through <strong>the</strong> retirement process but employees who may wish to
continue to be in <strong>the</strong> workforce face being threatened with more qualified, even though less skilled
and experienced younger challenging workers who stand to make <strong>the</strong> long-standing expert's
knowledge and skills redundant. The diverse workforce challenges make organisations struggle in
active management <strong>of</strong> <strong>the</strong>ir knowledge which not only is critical to <strong>the</strong>ir commercialisation but from <strong>the</strong>
inception <strong>of</strong> an idea.
This paper's focus given <strong>the</strong> workforce dilemmas is to understand if experts' knowledge can act as a
hindrance or assistance in <strong>the</strong> innovation.
3. Research methodology
In order to investigate <strong>the</strong> intricate details <strong>of</strong> exchange and barriers <strong>of</strong> experts within <strong>the</strong> innovation
process, <strong>the</strong> qualitative method was used for understanding <strong>the</strong> complexity <strong>of</strong> this knowledge
exchange-barrier process. A case organisation was sourced and 30 employees were interviewed
who included experts, specialists, trainees and cadets. This conceptual paper aims to introduce a
framework based on <strong>the</strong> research findings and literature review. It is realized that <strong>the</strong>re is scope to
fur<strong>the</strong>r expand on this research. This paper may also raise <strong>the</strong> challenges in designing an
appropriate methodology for knowledge management-innovation type research. The weakness <strong>of</strong>
this paper is that <strong>the</strong> findings are on a case organisation and an extended research will help test <strong>the</strong>
Expert Knowledge Exchange-Barrier Model and provide more evidence.
4. Literature review and conceptual findings
4.1 Experts Vs novices - tacit exchange and barriers towards innovation
In today’s competitive business environment organisations aim to obtain as much knowledge as
possible to ensure <strong>the</strong>ir continued sustainability. In particular tacit knowledge has become extremely
important due to <strong>the</strong> aging workforce where managers are needing to find ways to extract, impart and
transfer as much information as possible before workers retire (Ebrahimi, Saives & Holford, 2008;
Alfeis, 2008). The two diverse groups at work that managers facilitate on managing knowledge are
experts, belonging to <strong>the</strong> retiring group and novices, belonging to <strong>the</strong> younger cohort mainly from
Generation Y.
Experts are people who perform routine and non-routine jobs and generate new knowledge as
necessary (Jacobs, 2001, 2003). Expert knowledge is a fusion <strong>of</strong> knowing, know-how and reflection
constructed from social interaction within a specific socio-cultural setting (Gherardi, Nicolini & Odella
(1998). Sassower (1993) defines expertise as one who has "gained skill from experience and ability to
replicate past successes and modify past failures" (Sassower, 1993 : 55) where <strong>the</strong>re is boundless
acquisition <strong>of</strong> tacit knowledge in <strong>the</strong> domain <strong>of</strong> specialization (Polanyi, 1966). It is evident that experts
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Selvi Kannan
are people who have extensive knowledge, experience and <strong>the</strong>ir traits include commitment, reliability,
performance and skills that correspond well and help on essential decision making and actions by
focussing and filtering out any "noise" (Dychtwald et al, 2006). Sassower (1993) drawing on Michael
Polanyi and Thomas Kuhn's consensual agreement that experts share well with experts due to <strong>the</strong>ir
shared rationality and logical procedures, raises a question whe<strong>the</strong>r it is <strong>the</strong> same when experts share
with novices.
On <strong>the</strong> o<strong>the</strong>r hand, novices or <strong>the</strong> younger generations with limited experience are claimed to be
spirited, highly energized and ambitious and dislike being treated like a 'child' by <strong>the</strong> mature workers.
They have high expectations <strong>of</strong> <strong>the</strong>mselves and seek out creative challenges with a keenness to build
up ownership <strong>of</strong> tasks. Fur<strong>the</strong>rmore, <strong>the</strong>y like experimenting with work and career and are less loyal
or following on company ways compared to <strong>the</strong> mature generations (Dychtwald et al, 2006). Dwyer
(2008) suggests that Generation Y managers want to prove that <strong>the</strong>y are capable <strong>of</strong> performing <strong>the</strong>
functions normally associated with such authoritative positions. However, quite frequently <strong>the</strong> older
subordinate workers are alienated, and thus create a larger generation gap.
There are clear differences in work attitudes and interests in work between <strong>the</strong> two generations in
discussion. This is already a start <strong>of</strong> conflict towards work processes. Given <strong>the</strong> high conflict and
barrier between generations, it can be seen that <strong>the</strong> very critical initial stage <strong>of</strong> ideas for innovation
can be a difficult process, especially in a mature organisations.
Organisational knowledge is a key differentiator and investor in innovation. The knowledge domain
consists <strong>of</strong> data, information and articulated knowledge and residing within <strong>the</strong> experts who have
been with <strong>the</strong> organisation for a period <strong>of</strong> time is <strong>the</strong> valuable tacit knowledge.
Tacit knowledge is difficult to tap into because it is arcane, complex and elusive (Krogh, Ichijo &
Nonaka (2000); Florida (2002). Tacit knowledge is obtained by processes such as individual
experience and reflection, meaning it cannot be managed or transferred easily like explicit knowledge
(Haldin-Herrgard, 2000). Schon (1983: 54) defines it is "knowing in action" which has <strong>the</strong> properties <strong>of</strong>
<strong>the</strong> individual's actions, recognitions, and judgments, which have been acquired through experience
and internalized. Tacit knowledge can be a powerful tool for organisations to create competitive
advantage when utilized (Alwis & Hartmann, 2008; Mayfield, 2010; Sigala & Chalkiti, 2007) towards
innovation, as tacit knowledge is unique. Authors Cohen and Bacdayan (1994) state that tacit
knowledge resides in procedural memory: "Procedural memory has close links to notions relatively
automatic and inarticulate, and it encompasses cognitive as well as motor activities" (Cohen &
Bacdayan, 1994: 554). Given <strong>the</strong> complexity <strong>of</strong> tacit knowledge, sharing and transferring being
challenging and acts as a barrier however it can occur in a socially cohesive environment.
4.2 Socially cohesive environment
Paquet (2000) describes tacit knowledge <strong>of</strong> 'know-how and know-who' to be socially embedded,
difficult to transfer and resist codification. In order to surface tacit knowledge and to build new
cognitive associations, collaboration based on trust, openness and reciprocity between workers is
required. Tacit knowledge can only be shared through cooperation and interaction (<strong>European</strong>
Foundation (1996); Lundvall & Borrás (1997); Krogh, Ichijo & Nonaka (2000) which highly involves
desire to understand or learn and respect. Individuals however, can only communicate and cooperate
effectively in a socially cohesive environment (Lundvall & Borrás (1997).
The innovation process may not necessarily start as a high social cohesive juncture due to its high
conflict possibility with idea exchanges. In relating to <strong>the</strong> innovation process Hayek's (1945) definition
<strong>of</strong> tacit knowledge is appropriate: "knowledge <strong>of</strong> particular circumstances <strong>of</strong> time and place". The
individual's tacit knowledge is <strong>the</strong> capacity to take actions where <strong>the</strong>y are "situated in a particular
social and physical circumstance" (Hayek, 1945: 521), when <strong>the</strong> individual is totally interacting with
<strong>the</strong> environment and creating a dimension <strong>of</strong> experience, which can potentially become part <strong>of</strong> <strong>the</strong>
pattern <strong>of</strong> task approach or <strong>the</strong> practice itself (Chun, 1998: 117).
Managers successful in facilitating exchange <strong>of</strong> ideas between <strong>the</strong> experts and novices will set a
powerful trail that helps understand an "organisation's innovation projectory" says Von Stamm (2008:
255). The creative process assists in <strong>the</strong> sharing and exchange <strong>of</strong> perennial wisdom that can be
liberating to a learning workforce that believes in innovation.
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4.3 Managing knowledge and creativity
Selvi Kannan
Zack (2003) states <strong>the</strong> end product <strong>of</strong> creative thought as: “Products and services are only what are
visible or tangible to customers - <strong>the</strong>y’re <strong>the</strong> tip <strong>of</strong> <strong>the</strong> iceberg. But like <strong>the</strong> iceberg, most <strong>of</strong> what
enables a company to produce anything lies below <strong>the</strong> surface, hidden within <strong>the</strong> so-called invisible
assets <strong>of</strong> <strong>the</strong> organization - its knowledge about what it does, how it does it, and why.” (Zack, 2003,
p.67)
Creativity as Amabile (1998) describes is a way people think, inventively and imaginatively and draw
on <strong>the</strong>ir expertise and motivation. Bruner (1962) defined creativity as "effective surprise"; "shock <strong>of</strong>
recognition", "product <strong>of</strong> response". Some contemporary <strong>the</strong>orists define creativity as a process that
is evidenced in persons (Stein, 1974-1975). Creativity does tap into <strong>the</strong> tacit knowledge <strong>of</strong> an
individual, because even though creativity requires originality, <strong>the</strong> idea must be valuable and
appropriate for widespread acceptance (Sternberg and Lubart, 1991:3). For it to be valuable and to
create a leap difference, creativity requires <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> past; such as best practices and
routinized rules <strong>of</strong> <strong>the</strong>m (Malhotra, 1999).
How experts can help with creativity? Expertise encompasses an individual's knowledge and what
he/she can do in <strong>the</strong> broad domain <strong>of</strong> work and <strong>the</strong> ability to think to solve problems (Amabile, 1998),
especially at <strong>the</strong> initial idea conceptualization stage. Amabile (1998) substantiates fur<strong>the</strong>r that
expertise and creative thinking is an individual's natural resources. This is where <strong>the</strong> expert's tacit
knowledge embroiled with <strong>the</strong> organisational culture and process such as <strong>the</strong> "know-how", "knowwhat"
becomes important. Scholars Amabile (1983),
Sternberg et al, (1997) and Hargadon & Sutton (2000) agree how ideas generated by domainspecialists
and <strong>the</strong>ir past ideas are important in <strong>the</strong> generation <strong>of</strong> new ideas. Figure 1 illustrates how
individuals work within <strong>the</strong> innovative creative space creating organisational knowledge. The
organisational knowing enhances when knowledge is captured and sustained through knowledge
management systems in organisations. Regeneration and experiment can <strong>the</strong>n occur with fewer
barriers as <strong>the</strong> organisational knowing gets stored and continuously used and sensemaking occurs. It
is at stage 3 <strong>of</strong> regeneration where brainstorming and lateral thinking that normally involves a creative
session for experts and novices to engage in a relatively high tacit exchange. This stage heightens
conflict levels, where barriers are faced.
Figure 1: Organisational Knowledge And Creativity Cycle (Adapted from Hargadon & Sutton (2000)
Knowledge Brokering Cycle)
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Selvi Kannan
Amabile (1983) states that domain specific knowledge influences creativity and syn<strong>the</strong>sizes new basis
and argues that an expert's technical, procedural and intellectual knowledge toge<strong>the</strong>r with <strong>the</strong> ability
to imaginatively approach problems is a critical component to creativity within organisations. On <strong>the</strong>
contrary, Andriopoulos (2003); Stein, (1989); Sternberg and Lubart (1995); Gordan (1961) and
Bengston (1982) claim that an expert's knowledge can cause preconditioning or previous patterned
thoughts that may interfere and impede <strong>the</strong> creative process and any idea generation. In fact,
Bengston (1982) refers this problem as "expertise" where <strong>the</strong> experts become enslaved by <strong>the</strong>ir own
pattern thoughts and refuse change. Being able to manage this knowledge and creativity cycle is part
<strong>of</strong> <strong>the</strong> innovation process.
4.4 Innovation process and managing organizational knowledge
Amidon (1997) coined 'Knowledge Innovation' as "<strong>the</strong> creation, evolution, exchange and application <strong>of</strong>
new ideas into marketable goods and services leading to <strong>the</strong> success <strong>of</strong> an enterprise, <strong>the</strong> vitality <strong>of</strong> a
nation's economy and <strong>the</strong> advancement <strong>of</strong> society. Innovation is a complex issue in businesses, as it
has today become a way <strong>of</strong> not only increasing functionality and pr<strong>of</strong>itability <strong>of</strong> organisations, where it
is vital for sustainability and continued competitive advantage. It is also a fuel for growth. A process if
knowledge is managed well is used to increase strategic intangible people flexibility (Strambach,
2001).
H<strong>of</strong>fman (1999) states that <strong>the</strong> only way a firm can beat competition and continually innovate is when
organisations create new markets, products, services and systems as contingent to one factor -
'knowledge'. Managing knowledge permits organisations to innovate by harnessing and leveraging
knowledge to create value and contribute creatively (Woodman et al, 1993) to <strong>the</strong> organisational
goals. It is, at times, this complexity and value laden process that places barriers and power struggle
amongst experts and novices.
The complexity created by <strong>the</strong> richness and <strong>the</strong> fusion <strong>of</strong> knowledge has to be managed for
successful innovation (Cardinal et al, 2001). As Parlby and Taylor (2000) advocate that knowledge
management is about supporting innovation, generating new ideas and exploiting <strong>the</strong> organisation's
thinking power. Plessis (2007) clarifies that <strong>the</strong> role <strong>of</strong> knowledge management is to aid <strong>the</strong>
complexity <strong>of</strong> innovation. Knowledge management <strong>the</strong>refore is not solely focused on knowledge but
in creating an environment for innovation to take place. It is in fact a space that a manager needs to
manage <strong>the</strong> talent, expertise and motivation for creativity in <strong>the</strong> organisation. Managers can nurture
and align talent and motivation by creating <strong>the</strong> right context and space for knowledge creation for a
complex challenging competitive environment through synergistic creation and knowledge
management (Salmador, 2007; Cavusgil et al, 2003 and Pyka, 2002)
Illustrated in Figure 2 is a conceptual model <strong>of</strong> <strong>the</strong> three major stages within a typical innovation
process. The three stages are: (1) Search (2) Select (3) Implement. It is believed that <strong>the</strong> first stage
which is essentially to generate ideas draws barriers in <strong>the</strong> form <strong>of</strong> power. The argument posed here
is that experts tend to make judgments about 'truth' <strong>of</strong> <strong>the</strong>ir domain specialization and poses <strong>the</strong>
discretionary decision to stop any new ideas saying it had been tried before. "Being <strong>the</strong>re done it
before" syndrome is a common barrier at this stage. Also at this stage, <strong>the</strong> organisational structure
plays a role, where an expert uses it as a reason for delaying or discarding an idea.
At <strong>the</strong> second stage – Select, <strong>the</strong> barriers faced are <strong>the</strong> controls placed by <strong>the</strong> experts on idea
selection fearing <strong>the</strong> changes that it might possibly bring. Experts who tend to believe <strong>the</strong>ir
knowledge far exceeds <strong>the</strong> novices are <strong>the</strong> ones more likely to create <strong>the</strong>se barriers. Experts once
again tend to exercise power and control based on past practices on <strong>the</strong> reasons for selecting an
idea. This alludes to experts exercising authority that has been institutionalized legitimating
underlying power (Parsons, 1967, Giddens, 1968) supported by <strong>the</strong> organisation's value system. The
longer an expert serves in an organisation, <strong>the</strong> expertise naturally increases causing an effect <strong>of</strong>
'complacent knowing' <strong>of</strong> an organisation. Sternberg (1997) argues that <strong>the</strong> experts’ mental
representations become more abstract and simplified and tacit knowledge becoming harder to be
expressed or shared, once again giving rise to <strong>the</strong> barriers. On <strong>the</strong> o<strong>the</strong>r hand novices may also use
<strong>the</strong>se barriers to impose political challenges and o<strong>the</strong>r forms <strong>of</strong> control.
This can also be viewed <strong>of</strong> experts who invest <strong>the</strong>ir effort, energy, time through <strong>the</strong>ir knowledge and
skills in convening when it's a way to express <strong>the</strong>ir own passions and visions but paradoxically <strong>the</strong>y
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Selvi Kannan
also hang on to <strong>the</strong>ir cherish views and beliefs. No doubt bringing in different talent and generational
groups to work on ideas for an innovation is a strategic convening <strong>of</strong> an organisation's direction; it can
also be a challenging process unless managed. As Senge et al (2008) frames it that creativity builds
some tension between <strong>the</strong> visions for <strong>the</strong> future and reckoning <strong>of</strong> <strong>the</strong> current existence.
At <strong>the</strong> Implementation Stage, <strong>the</strong>re is very little barrier, in fact this stage sees more exchange <strong>of</strong> tacit
knowledge <strong>the</strong>n at any o<strong>the</strong>r stage. At this stage both experts and novices tend to be more aligned
with <strong>the</strong> organisation's vision and goals. The novices assess <strong>the</strong> value <strong>of</strong> knowledge for <strong>the</strong>ir use and
future adaptation.
Stage 1
Search
Idea Generation
Benchmarking
SWOT Analysis
Barriers
Power
Hierarchy
Stage 2
Select
Comprehensive
Pr<strong>of</strong>iling
Forecasting
Innovation Audit
Barriers
Change Factors
Control
Cross-Pollination
History, Patterns and
Institutional Knowledge
Stage 3
Implement
Selling <strong>the</strong> Idea
Commercialisation
Contribution to Base Knowledge and Wisdom
Figure 2: Expert Knowledge Exchange and Barrier Model Towards Innovation
Christiansen (2000) explains <strong>the</strong> complexity <strong>of</strong> idea generation within a diverse workforce group
potentially identifies organisations as having a base knowledge and base wisdom. He ascribes base
knowledge as technical concepts and market behaviour and base wisdom as ways <strong>of</strong> making
interpretation <strong>of</strong> <strong>the</strong> phenomenon and <strong>the</strong> future. The differences arise within <strong>the</strong>se two base factors
where if novices do not acknowledge or accept it as truth than conflict arises with <strong>the</strong> experts. An
interesting issue Christiansen (2000) raises is that experience and education limits idea generation
where with both <strong>the</strong>se elements once tends to search for right answers ra<strong>the</strong>r than allowing creativity
to take charge.
5. Conclusion
Researchers suggest that individual creativity is essential to organisational innovation (Amabile, 1988;
Woodman et al., 1993), which in turn is imperative to long-term organisational survival and success
(Kanter, 1983; Tushman and O’Reilly, 1997; Utterback, 1994). In order to enhance <strong>the</strong> chances <strong>of</strong>
long-term sustainability and growth organisations should focus on supporting individual creativity in
<strong>the</strong> workplace (Amabile, 1988; Woodman et al., 1993) through culture and <strong>the</strong> transfer <strong>of</strong> tacit
knowledge from experts without it becoming a barrier. A level <strong>of</strong> trust, understanding and listening
needs to be in place.
Innovation indeed is <strong>the</strong> application <strong>of</strong> organisational knowledge. Managing <strong>the</strong> knowledge can
create new knowledge in <strong>the</strong> process. Innovation is a social, spatially embedded, interactive and
dynamic knowledge transfer and sharing space within <strong>the</strong> organisation that is drawn in context <strong>of</strong> <strong>the</strong>
culture (Braczyk, et al, 1998; Lundvall, 1992; Freeman, 1998). This case attempts to shed some light
on <strong>the</strong> close relationship between knowledge and innovation.
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495

Integration <strong>of</strong> Knowledge into <strong>the</strong> Value Stream Mapping
Method – Benefits, Challenges, Solution Statement
Eva-Maria Kern 1 , Wendelin Schmid 1 , Julia Boppert 2 and Carolin Fiechter 1
1
Universität der Bundeswehr München, Chair for knowledge management and
business process design, Neubiberg, Germany
2
trilogIQa, München, Germany
eva-maria.kern@unibw.de
wendelin.schmid@unibw.de
julia.boppert@trilogiqa.de
carolin.fiechter@gmx.de
Abstract: The purpose <strong>of</strong> this conceptual paper is to present a practicable method for conducting knowledge
management in a value-oriented way. Therefore, a concept is deduced for integrating <strong>the</strong> resource knowledge
into value stream mapping, an established method for business process optimization. The concept is discussed
by examining its expected benefits as well as <strong>the</strong> challenges identified. In order to allow for <strong>the</strong> implementation <strong>of</strong>
this concept, a three-layered approach for a knowledge-integrating value stream mapping method is presented:
The first layer includes components for data collection, knowledge-specific preparation and visualization. The
second layer contains <strong>the</strong> selection mechanisms for interlinking layer 1 and layer 3, which finally comprises
knowledge management processes as well as a wide set <strong>of</strong> according methods and tools. The practical
application <strong>of</strong> <strong>the</strong> approach is exemplarily illustrated on <strong>the</strong> basis <strong>of</strong> experiences from a case study. The paper
concludes by identifying <strong>the</strong> specific need for fur<strong>the</strong>r research on this topic.
Keywords: Business process, efficiency, knowledge management, lean thinking, value creation, value stream
mapping
1. Introduction
It is widely agreed that knowledge is a key factor for value creation in <strong>the</strong> actual business environment
(North 2011, Willke 1999). However, little research or practical solutions can be found addressing <strong>the</strong>
issue <strong>of</strong> how to apply knowledge as a resource in business processes in a value-driven manner.
Thus, <strong>the</strong> purpose <strong>of</strong> this paper is to present an approach for integrating knowledge management and
value-orientation in existing business processes in an applicable way, supported by appropriate
methods and instruments.
An established method for <strong>the</strong> value oriented analysis and design <strong>of</strong> business processes is value
stream mapping, which primarily focuses on material and information flows. Up to now, <strong>the</strong>re is no
adaption <strong>of</strong> this method explicitly integrating knowledge as a resource. In <strong>the</strong> following, such an
integrated approach is to be developed.
In order to evaluate potential benefits <strong>of</strong> a knowledge-integrating value stream mapping, <strong>the</strong> role <strong>of</strong>
knowledge within value creation is discussed in detail and potential benefits <strong>of</strong> a value-oriented
knowledge management design are discussed. Based on <strong>the</strong> specific characteristics <strong>of</strong> knowledge,
<strong>the</strong> challenges <strong>of</strong> this integration are examined and specific requirements for <strong>the</strong> method are
deduced. In order to assure <strong>the</strong> applicability <strong>of</strong> <strong>the</strong> adapted method, a practical approach for
integrating knowledge into <strong>the</strong> value stream mapping method is developed and first practical
experiences in using it are depicted. Finally, <strong>the</strong> need for fur<strong>the</strong>r research is deduced.
2. Knowledge and value creation
The contribution <strong>of</strong> <strong>the</strong> resource knowledge to value creation is undisputed in practice as well as in
research (H<strong>of</strong>mann 2007, Stein 2003). Yet <strong>the</strong>re is a significant lack <strong>of</strong> transparency about its degree
<strong>of</strong> contribution and its mechanism <strong>of</strong> action.
Value creation can be defined as a series <strong>of</strong> processes combining and / or converting resources into
goods or services sold at prices exceeding <strong>the</strong> costs incurred (Kalanidhi 2001, Ndou/Passiante 2005).
The principle <strong>of</strong> value orientation, whose origin can be traced back to <strong>the</strong> development <strong>of</strong> <strong>the</strong> Toyota
Production System in <strong>the</strong> post-World-War II era, has become generally known in Europe under <strong>the</strong>
terms “lean management”, “lean production” or “lean thinking”. According to this concept, all activities
(including <strong>the</strong> resources required) which do not contribute to <strong>the</strong> generation <strong>of</strong> a value perceived by
<strong>the</strong> customer are considered as waste and have to be eliminated or minimized in order to achieve
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efficiency (Womack/Jones 1997). In addition to labor, capital and land, <strong>the</strong> consideration <strong>of</strong>
knowledge as <strong>the</strong> fourth resource contributing to value creation is decisive for a lean design <strong>of</strong>
business processes.
Knowledge is very special in <strong>the</strong> way it may occur in business processes: Regarding knowledge as a
resource, it is an important input for process execution (North 2011) as e.g. skills required for <strong>the</strong>
execution <strong>of</strong> process steps. Alternatively, it can be <strong>the</strong> intended product <strong>of</strong> a process, as <strong>the</strong>
knowledge developed within an innovation process. In most processes, knowledge is also generated
as a by-product, e.g. as an increase in experience through <strong>the</strong> repeated execution <strong>of</strong> a process step.
In each <strong>of</strong> <strong>the</strong>se cases, knowledge is anchored in business processes, which thus can be considered
<strong>the</strong> companies’ knowledge platform (Abecker et al. 2002). Hence, knowledge management is in <strong>the</strong>
following understood as a means <strong>of</strong> achieving business objectives by designing, controlling and
developing <strong>the</strong> organizational knowledge base (North 2011). Applying <strong>the</strong> principle <strong>of</strong> value
orientation to knowledge management means to concentrate on process related knowledge which
creates value and to disburden processes from inherent waste in this regard.
Accordingly, value orientation in knowledge management can be divided into two consecutive fields <strong>of</strong>
action:
Firstly, it is necessary to analyze business processes in order to identify <strong>the</strong> particular knowledge
demands for execution <strong>of</strong> <strong>the</strong> different process steps as well as <strong>the</strong> knowledge created within <strong>the</strong>
different stages <strong>of</strong> <strong>the</strong> process. This analysis is a precondition for designing knowledge
management in a demand-driven way.
Secondly, <strong>the</strong> handling <strong>of</strong> knowledge at <strong>the</strong> different process steps has to be particularly aligned
to <strong>the</strong> characteristics and requirements <strong>of</strong> <strong>the</strong> knowledge identified. Knowledge management
methods and tools need to be specifically selected and customized in order to assure <strong>the</strong>
achievement <strong>of</strong> <strong>the</strong> processes goals while avoiding any kind <strong>of</strong> waste.
As it is widely agreed upon in <strong>the</strong> scientific community, <strong>the</strong>re are no processes in which knowledge is
totally irrelevant (Amelingmeyer 2004, Davenport 2005, Remus 2002, Stewart 1998).Yet <strong>the</strong> demand
for knowledge and thus knowledge management can differ significantly between processes and even
between process steps (Kern/Boppert 2010). In order to avoid waste in <strong>the</strong> sense <strong>of</strong> value orientation,
<strong>the</strong> extent <strong>of</strong> knowledge management supply for business processes should be directly derived from
<strong>the</strong>ir knowledge demand.
A class <strong>of</strong> business processes typically bearing substantial knowledge demands are so-called
knowledge-intensive business processes. These processes or more precisely <strong>the</strong>ir individual steps
typically (Davenport 2005, Kern/Boppert 2010, Riedl/Schauer 2010)
require a high level <strong>of</strong> qualification,
show a high degree <strong>of</strong> interaction,
generate a lot <strong>of</strong> knowledge,
are highly innovative and
possess a low degree <strong>of</strong> standardization.
Although <strong>the</strong>se characteristics are difficult to measure, knowledge intensity seems to be a suitable
criterion for deciding upon <strong>the</strong> extent <strong>of</strong> and efforts justified for implementing knowledge management:
It allows for narrowing down <strong>the</strong> range <strong>of</strong> generally suitable knowledge management processes,
methods and tools to those expected to facilitate <strong>the</strong> goals <strong>of</strong> value orientation.
For <strong>the</strong> knowledge management processes mentioned above, different classifications and notations
are used in <strong>the</strong> scientific community. This paper draws upon <strong>the</strong> knowledge management processes
defined by Probst et al. (2010), namely knowledge identification, acquisition, development, transfer,
preservation and utilization.
As illustrated in figure 1, business processes and knowledge management processes are strongly
interconnected:
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Figure 1: Relationship between business processes and knowledge management processes
Business processes provide input for and absorb output from knowledge management processes and
vice versa. The superordinate goal <strong>of</strong> knowledge management processes is to provide business
processes with <strong>the</strong> knowledge required for <strong>the</strong>ir execution (H<strong>of</strong>fmann et al. 2002). Thus, knowledge
management processes shape <strong>the</strong> organizational knowledge base in terms <strong>of</strong> <strong>the</strong> quality, <strong>the</strong> nature,
<strong>the</strong> domains and <strong>the</strong> disposability <strong>of</strong> its contents while being strongly determined by <strong>the</strong> contents and
to some degree <strong>the</strong> needs <strong>of</strong> business processes. This is ano<strong>the</strong>r aspect why knowledge
management processes should be designed and managed in a value oriented way (Abecker et al.
2002).
As knowledge management is at <strong>the</strong> same time a facilitator <strong>of</strong> business processes and a cost factor
itself, it should be designed and applied well reflected in terms <strong>of</strong> <strong>the</strong> achievable value. As shown
above, <strong>the</strong> knowledge intensity <strong>of</strong> a process step can be a suitable criterion to decide on <strong>the</strong> effort for
and design <strong>of</strong> knowledge management. In order to deduce a consistent toolbox meeting <strong>the</strong>
requirements <strong>of</strong> value orientation, <strong>the</strong> next section <strong>of</strong> this paper is to examine <strong>the</strong> established method
<strong>of</strong> value stream mapping for its suitability as a basis for a value-integrating analysis and design <strong>of</strong>
business processes.
3. Traditional value stream mapping
Value stream mapping is an established method for business process optimization, originated in <strong>the</strong>
production environment. In times <strong>of</strong> its development, labor was considered as <strong>the</strong> decisive factor for
value creation. Hence, value stream mapping in its original form focuses especially on this production
factor. The o<strong>the</strong>r traditional production factors capital and land are taken into account ei<strong>the</strong>r allusively
or by means <strong>of</strong> an adaptation <strong>of</strong> <strong>the</strong> original method. Until now, only knowledge did not get any
systematic consideration in it.
The idea behind value stream mapping is to direct a company’s attention to its value stream. For
products as well as for services <strong>the</strong> value stream covers all activities – both value-creating ones and
non-value-creating ones – which are part <strong>of</strong> <strong>the</strong> production process beginning with <strong>the</strong> delivery <strong>of</strong> raw
material and ending with <strong>the</strong> shipment <strong>of</strong> finished goods. This supports <strong>the</strong> orientation to <strong>the</strong>
customer´s value regarding <strong>the</strong> principles <strong>of</strong> flow, pull and perfection to systematically avoid waste
and to preventively eliminate <strong>the</strong> causes <strong>of</strong> waste.
For mapping <strong>the</strong> current state <strong>of</strong> <strong>the</strong> predefined value stream <strong>the</strong> material and information flows are
represented by arrows. In combination with <strong>the</strong> boxes representing individual process steps and
containing typical process variables (e.g. cycle time), and inventories, <strong>the</strong>se arrows depict <strong>the</strong> entire
value stream (figure 2).
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Figure 2: Traditional value stream mapping (Ro<strong>the</strong>r/Shook 2000)
The result <strong>of</strong> <strong>the</strong> mapping is transparency about <strong>the</strong> value stream, about significant points containing
waste and about <strong>the</strong> corresponding amount <strong>of</strong> waste. This is <strong>the</strong> basis <strong>of</strong> optimization. As soon as
initial process improvements have been realized, <strong>the</strong> cycle starts anew: upon mapping <strong>the</strong> changed
current state, a new target state is determined and subsequently implemented in order to get closer to
perfection (Ro<strong>the</strong>r/Shook 2000).
The traditional production factor labor is considered in a direct way, whereas land is not taken into
account directly. It is implicitly included by mapping inventories between individual process steps, thus
considering <strong>the</strong> respective space requirement. As a result, conclusions can be drawn about a possible
waste <strong>of</strong> land.
The production factor capital can also be taken into account with <strong>the</strong> help <strong>of</strong> an adapted form <strong>of</strong> value
stream mapping, referred to as cost-oriented value stream planning (Balsliemke 2008).
Summing up, it can be concluded that up to this day <strong>the</strong>re are relatively fully developed approaches to
take all production factors into account in value stream mapping, apart from knowledge. Only for
knowledge <strong>the</strong> integration is still in its early stages (e.g. Kern/Boppert 2010, Fiechter et al. 2011).
A suitable basis for a value-oriented knowledge-integrating analysis and design <strong>of</strong> business
processes should meet two elementary criteria: It has to facilitate a value-oriented analysis and
design <strong>of</strong> business processes and it has to <strong>of</strong>fer <strong>the</strong> possibility <strong>of</strong> explicitly integrating and depicting
knowledge elements. Being an established lean management tool visualizing resource consumption
during value creation from <strong>the</strong> process perspective, value stream mapping clearly is suitable for
analyzing and designing processes in a value-oriented way with respect to <strong>the</strong> traditional production
factors. Knowledge aspects can be examined and documented separately for each process step,
while <strong>the</strong> flexible graphical representation provides <strong>the</strong> possibility for integrating and depicting
knowledge elements. Thus, value stream mapping can be considered a suitable basis for a valueoriented
knowledge-integrating analysis and design <strong>of</strong> business processes.
4. Potential benefits and challenges <strong>of</strong> <strong>the</strong> integration <strong>of</strong> knowledge into
value stream mapping
The benefits <strong>of</strong> integrating <strong>the</strong> factor knowledge into value stream mapping would be expected in <strong>the</strong>
field <strong>of</strong> knowledge identification and visualization:
By integrating <strong>the</strong> factor knowledge into a value stream map, actual knowledge processes for each
process step can be distinctly identified. The detailed process representation facilitates <strong>the</strong> detailed
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identification <strong>of</strong> knowledge demands in terms <strong>of</strong> knowledge content, knowledge type and knowledge
intensity. At <strong>the</strong> same time, knowledge creation can be ga<strong>the</strong>red in detail and be documented in terms
<strong>of</strong> knowledge content and knowledge type. Additionally, actual knowledge management processes
can be identified, i.e. <strong>the</strong> means actually used for ga<strong>the</strong>ring, transferring, preserving and utilizing
knowledge. By graphically representing <strong>the</strong> findings and indicating central parameters as knowledge
intensity and actual extent <strong>of</strong> knowledge management activities, knowledge-integrating value stream
mapping discloses starting points for designing process-related knowledge management in a valueoriented
way. Thus, it provides reference points where and which knowledge management processes
should be installed and how <strong>the</strong>y should be managed in order to purposefully and systematically
develop <strong>the</strong> organizational knowledge base while preventing <strong>the</strong> waste <strong>of</strong> resources as postulated by
<strong>the</strong> concept <strong>of</strong> value orientation.
To sum it up, <strong>the</strong> transparency generated concerning <strong>the</strong> knowledge required within <strong>the</strong> single
process steps <strong>of</strong>fers <strong>the</strong> possibility to efficiently tailor knowledge processes to <strong>the</strong> needs <strong>of</strong> <strong>the</strong>
business processes examined.
In order to realize <strong>the</strong> potential benefits stated above, knowledge-integrating value stream mapping
has to support and enable several central activities: The first ones are <strong>the</strong> identification and
assessment <strong>of</strong> knowledge demands in terms <strong>of</strong> knowledge content, knowledge type and knowledge
intensity, <strong>the</strong> identification and assessment <strong>of</strong> knowledge creation in terms <strong>of</strong> knowledge content and
knowledge type and <strong>the</strong> identification and assessment <strong>of</strong> knowledge management processes in terms
<strong>of</strong> <strong>the</strong>ir functions and <strong>the</strong> tools and methods used. The second bunch <strong>of</strong> activities to be supported is
<strong>the</strong> deduction <strong>of</strong> need for knowledge management activities for <strong>the</strong> single process steps, <strong>the</strong>
deduction <strong>of</strong> requirements for methods and tools for implementing <strong>the</strong>se activities and thus <strong>the</strong>
deduction <strong>of</strong> <strong>the</strong> actual need for action concerning each process step.
The main challenges in adapting and applying knowledge-integrating value stream mapping this way
result from <strong>the</strong> specificity <strong>of</strong> <strong>the</strong> resource knowledge, which differs significantly from <strong>the</strong> traditional
production factors:
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”. Knowledge “originates and is applied in <strong>the</strong> minds <strong>of</strong> knowers. In organizations, it <strong>of</strong>ten
becomes embedded not only in documents or repositories but also in organizational routines,
process, practices, and norms.” (Davenport/Prusak 1998) This definition illustrates that knowledge as
a resource is very heterogeneous and can occur in different forms. Depending on <strong>the</strong> intended
purpose, literature review <strong>of</strong>fers various classification schemes for knowledge. Examples include <strong>the</strong>
differentiation between tacit and explicit knowledge (Polanyi 1985) or <strong>the</strong> classification in fundamental
knowledge – subdivided in know-what and know-why (facts and explication), and know-how and
know-when (methods and processes) – and additional knowledge – subdivided into know-where
(sources for fur<strong>the</strong>r information) and <strong>the</strong> so called meta-cognitive knowledge as self-reflecting
evaluative knowledge <strong>of</strong> one person about <strong>the</strong> own cognizance (Boppert 2008). The practical
relevance <strong>of</strong> <strong>the</strong>se differentiations results from <strong>the</strong>se knowledge types’ different requirements
concerning <strong>the</strong>ir handling and thus on <strong>the</strong> actual design <strong>of</strong> knowledge management: The suitability <strong>of</strong>
knowledge management tools and methods is determined by <strong>the</strong> function to be fulfilled as well as by
<strong>the</strong> specific characteristics <strong>of</strong> <strong>the</strong> targeted knowledge. For example, <strong>the</strong> transfer <strong>of</strong> tacit knowledge is
mostly associated with personal interaction, so specific methods associating knowledge carriers and
knowledge demand carriers as e.g. mentoring should be applied here.
The explanations above already indicate <strong>the</strong> main challenges for integrating knowledge into value
stream mapping: In order to create transparency by capturing knowledge demands, knowledge
creation and knowledge processes, criteria have to be defined for determining knowledge
characteristics and suitable capturing methods have to be provided. Additionally, <strong>the</strong>re is a need for
defining criteria to classify process steps according to <strong>the</strong>ir knowledge intensity in order to decide on
<strong>the</strong> justifiable extent <strong>of</strong> knowledge management to support each step (Fiechter et al. 2011). Finally,
mechanisms have to be defined for selecting appropriate knowledge management functions and
knowledge management tools enabling <strong>the</strong>se functions. All <strong>of</strong> <strong>the</strong>se requirements are specifically
challenging because <strong>of</strong> <strong>the</strong> process specificity and <strong>the</strong> heterogeneous manifestations <strong>of</strong> knowledge.
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In <strong>the</strong> following, a comprehensive approach for implementing knowledge-integrating value stream
analysis is presented and <strong>the</strong> challenges mentioned above are addressed.
5. The knowledge-integrating value stream analysis – an approach
In order to ensure viable and purposeful outcomes, a comprehensive yet straightforward model for
executing a knowledge-oriented value stream analysis has to be defined. This approach has to
contain components for data collection, analysis and visualization on <strong>the</strong> one hand and components
for selection and design <strong>of</strong> knowledge management tools on <strong>the</strong> o<strong>the</strong>r hand.
5.1 General description <strong>of</strong> <strong>the</strong> approach
The approach proposed for integrating knowledge into <strong>the</strong> value stream analysis consists <strong>of</strong> three
layers as depicted in figure 3.
Component (I) – Data collection:
• Structured pre-interviews with
process owners
• General observation
• Detailed observation and questioning
• Semi-structured interviews
Layer 2
Selection mechanism:
• Selection by experts
• Automated assignment
• Implementation <strong>of</strong> best practices
Component (IV) – Knowledge
management processes:
Layer 1
Component (II) – knowledge-specific
preparation:
• Knowledge typology
• Knowledge intensity
Layer 3
Component (III) – visualization:
Figure 3: General approach for conducting a knowledge-integrating value stream analysis
Layer 1 is used for structuredly investigating and representing <strong>the</strong> actual condition <strong>of</strong> a selected
business process based on a knowledge-integrating value stream analysis. This layer contains
components for data collection (I), knowledge-specific preparation (II) and visualization (III).
Component (I) – Data collection: At <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> analysis, data and information about <strong>the</strong>
considered processes and <strong>the</strong> inherent knowledge have to be collected. While input for a
traditional value stream map typically is created by observing <strong>the</strong> relevant process steps and
interviewing <strong>the</strong> employees concerned in an unstructured manner, this is not sufficient to get
transparency about <strong>the</strong> process knowledge. Therefore a structured and comprehensive collection
<strong>of</strong> evidence is necessary to ensure a holistic view on <strong>the</strong> process:
Structured pre-interviews with process owners: Initial interviews have to be conducted
with <strong>the</strong> process owners to roughly map <strong>the</strong> processes, taking into account relevant inand
outputs, material flows, key actions, important decisions and involved persons.
General observation: The processes have to be observed in order to verify <strong>the</strong> general
course <strong>of</strong> <strong>the</strong> process and in order to deepen <strong>the</strong> awareness for process and knowledge
streams.
Detailed observation and questioning: By focused observation <strong>of</strong> <strong>the</strong> employees’ work
and by questioning all activities, <strong>the</strong> process can be detailed in-depths, and <strong>the</strong> patterns
and preconditions <strong>of</strong> process execution can be revealed.
Semi-structured interviews: Subsequent detailed interviews allow for ga<strong>the</strong>ring additional
information and creating meta-data for <strong>the</strong> process, e.g. training time or available media.
The interviews should be conducted with <strong>the</strong> operating personnel as well as <strong>the</strong> process
owners and some selected executives.
Component (II) – Knowledge-specific preparation: Additionally to <strong>the</strong> data and information
prepared for traditional value stream mapping, specific data on <strong>the</strong> resource knowledge have to
experts
required
based on
experience
Component (V) – Knowledge
management methods and tools:
lessons learned
space management
expert circles
learing album
people expert circles
knowledge broker
story telling
wikis visualization
debriefing mentoring
yellow pages
data bases
training on <strong>the</strong> job
folder structure
assistence by key persons
digital planning tools
standardization
technology organization advanced training
ontologies
data bases knowledge maps working instructions
knowledge market places
simplifying processes
…
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knowledge
management
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Eva-Maria Kern et al
be ga<strong>the</strong>red and prepared. On <strong>the</strong> one hand, this includes establishing a practicable knowledge
typology relevant in respect <strong>of</strong> <strong>the</strong> process requirements. On <strong>the</strong> o<strong>the</strong>r hand, suitable indicators
for operationalizing <strong>the</strong> knowledge intensity <strong>of</strong> single process steps have to be selected and
collected.
Component (III) – Visualization: The third component <strong>of</strong> layer 1 comprises <strong>the</strong> visualization <strong>of</strong>
process-related knowledge as well as <strong>the</strong> visualization <strong>of</strong> <strong>the</strong> insights concerning <strong>the</strong> relevant
knowledge types and <strong>the</strong> specific knowledge intensity, each on a detailed process step level. The
main requirement <strong>of</strong> this component is to present <strong>the</strong> findings <strong>of</strong> <strong>the</strong> first two components in a way
that is straightforward and meaningful at <strong>the</strong> same time.
The overall objective <strong>of</strong> layer 1 is to create transparency about <strong>the</strong> knowledge types required for and
created within <strong>the</strong> single process steps as well as about <strong>the</strong> knowledge intensity <strong>of</strong> each process step.
Additionally, <strong>the</strong> status quo regarding <strong>the</strong> existence and configuration <strong>of</strong> knowledge management
processes is revealed. The outcomes <strong>of</strong> layer 1 serve as basis for a targeted and thus lean selection
and design <strong>of</strong> knowledge management processes, methods and tools.
Layer 3 contains <strong>the</strong> whole set <strong>of</strong> knowledge management processes, methods and tools. It provides
all <strong>of</strong> <strong>the</strong>m as a generic knowledge management toolbox, i.e. well prepared and presented in a
structured way, so that adequate functions and tools can be selected according to <strong>the</strong> requirements
determined in layer 1. Layer 3 consists <strong>of</strong> two components: Knowledge management processes (IV)
and methods and tools (V). Component (IV) contains all functions possibly needed for handling
knowledge. The implementation options for <strong>the</strong>se functions are located in component (V): It contains
a wide set <strong>of</strong> knowledge management tools and methods that are structured and tagged in a way that
allows for <strong>the</strong>ir uncomplicated assignment to <strong>the</strong> functions provided in component (IV) and <strong>the</strong>
knowledge types identified in component (II) <strong>of</strong> layer 1: If <strong>the</strong> type <strong>of</strong> knowledge utilized within a
process step is tacit knowledge (II) and a need for transferring (IV) this knowledge has been
identified, component (V) would provide adequate tools and methods like mentoring or storytelling.
Layer 2 serves as interconnection between <strong>the</strong> layers 1 and 3: In layer 2, <strong>the</strong> actual condition
concerning knowledge handling within <strong>the</strong> process is evaluated, a decision is made on whe<strong>the</strong>r <strong>the</strong>re
is a need for formalized knowledge management and, if necessary, <strong>the</strong> requirements on <strong>the</strong> valueoriented
design <strong>of</strong> knowledge management are deduced. According to <strong>the</strong>se requirements, <strong>the</strong>
functions as well as <strong>the</strong> tools and methods are firstly filtered for general suitability. From this process
specific pool, <strong>the</strong> functions to be implemented are selected in <strong>the</strong> second step. Thirdly, <strong>the</strong> pool is
scanned for tools and methods for implementing <strong>the</strong>se special functions and a decision is made on
<strong>the</strong> specific actions to be taken.
There are different selection mechanisms applicable for executing layer 2:
Selection by experts on a case-by-case basis
Automated assignment <strong>of</strong> functions and methods or tools to predefined specific outcomes <strong>of</strong> <strong>the</strong>
analysis
Taking recourse to applicable best practices for knowledge management design and
implementation.
Critical success factors are <strong>the</strong> appropriate selection <strong>of</strong> <strong>the</strong> criteria in component (II) as well as <strong>the</strong>
structuring and tagging <strong>of</strong> <strong>the</strong> methods and tools in component (V) and <strong>the</strong> explicit definition <strong>of</strong> <strong>the</strong>
mechanisms connecting layer 1 and 3. The mechanisms for <strong>the</strong>se functions may at least partially be
settled based on <strong>the</strong>oretical deduction, but successful mechanisms typically are substantially derived
from practical experience. Claiming to be a lean approach, <strong>the</strong> conduction <strong>of</strong> knowledge-integrating
value stream analysis itself has to be lean, i.e. pragmatic in defining <strong>the</strong> criteria and assessing
knowledge and process properties.
The following section illustrates <strong>the</strong> employment <strong>of</strong> <strong>the</strong> approach with <strong>the</strong> help <strong>of</strong> a practical example.
5.2 First practical experiences in using <strong>the</strong> knowledge-integrating value stream
analysis
Using an action research design, <strong>the</strong> approach described above was developed by conducting a
multiple case study at a logistics service provider, examining 14 processes in seven different
locations. Details on <strong>the</strong> case study can be found in (Kern/Boppert 2010).
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The following example depicts <strong>the</strong> practical adoption <strong>of</strong> selected critical components by illustrating a
part <strong>of</strong> a process chain containing process steps and knowledge components. In <strong>the</strong> process called
“reverse logistics” <strong>the</strong> logistics service provider takes delivery <strong>of</strong> diverse goods (goods receipt) from
different <strong>European</strong> locations, inspects and returns <strong>the</strong>m to his customer’s warehouse. Therefore, <strong>the</strong>
parts are unpacked, checked basically as well as particularly based on specific guidelines and finally
are ei<strong>the</strong>r sorted out or repacked with new packaging materials (special processes preparation,
processing, packing). In figure 4, <strong>the</strong> first three process steps are presented as a value stream map,
with <strong>the</strong> process relevant knowledge (detailed by <strong>the</strong> dimensions knowledge form, type and source)
depicted as an anchor, symbolizing <strong>the</strong> way <strong>the</strong> process step is anchored in <strong>the</strong> organizational
knowledge base.
Figure 4: Three steps <strong>of</strong> a logistic process as knowledge-integrating value-stream map
On <strong>the</strong> basis <strong>of</strong> this excerpt from a process that beforehand had already been optimized using
traditional value stream design, <strong>the</strong> components (II) and (III) <strong>of</strong> layer 1 as well as <strong>the</strong> mechanisms
used in layer 2 are delineated.
In order to enable knowledge-specific preparation (component II), <strong>the</strong> knowledge typology chosen for
this process was <strong>the</strong> distinction between fundamental knowledge (know-what, know-why, know-how
and know-when) and additional knowledge (know-where and meta-cognitive knowledge), combined
with <strong>the</strong> distinction between tacit and explicit knowledge and a differentiation between internal and
external knowledge sources. Based on expert interviews, experience required, training time and
media diversity were selected as meaningful yet practicable criteria for determining knowledge
intensity.
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Eva-Maria Kern et al
For visualizing <strong>the</strong> findings (component III), <strong>the</strong> knowledge sources accessed in <strong>the</strong> single process
steps were depicted as anchors labeled with a short description <strong>of</strong> <strong>the</strong> source and <strong>the</strong> corresponding
knowledge typology. Next to <strong>the</strong> anchors, a separate legend depicts <strong>the</strong> knowledge intensity <strong>of</strong> each
process step.
This graphical representation <strong>of</strong> <strong>the</strong> value stream and <strong>the</strong> associated knowledge utilization directs
attention to a first potential need for action (layer 2): While <strong>the</strong> first process step, goods receipt, can
be considered little knowledge intensive, it is yet strongly supplied with knowledge as <strong>the</strong> twin
knowledge anchor points out. This is a clear indication that <strong>the</strong>re might be room for improvement:
From a lean perspective, knowledge management efforts for process steps with little knowledge
intensity should be minimized. In practical terms, this means <strong>the</strong> optimization by standardisation <strong>of</strong> <strong>the</strong>
process steps and codification and visualization <strong>of</strong> <strong>the</strong> specific knowledge relevant for each step. This
also included elimination <strong>of</strong> unnecessary external information not relevant for decision making. Based
on experts’ advice, knowledge transfer and provision were implemented by simply developing and
installing explicit pictured decision guidance for <strong>the</strong> acceptance <strong>of</strong> trucks. Thus <strong>the</strong> need for going
back on external knowledge sources was avoided and <strong>the</strong> twin knowledge anchor was eliminated.
Additionally, this simple measure reduced cycle times and enabled improved decision making and
higher process quality in terms <strong>of</strong> decrease in wrong decisions.
6. Conclusion and perspective
The suitability <strong>of</strong> an extended value stream mapping as a basis for value-oriented knowledge
management design on <strong>the</strong> business process level has been deduced <strong>the</strong>oretically and demonstrated
on an exemplary business case. As <strong>the</strong> main intention <strong>of</strong> analyzing and presenting <strong>the</strong> case was to
give a pro<strong>of</strong>-<strong>of</strong>-concept, a linear process with only few knowledge anchors and a limited set <strong>of</strong>
available tools was chosen. The next necessary step to enhance and refine <strong>the</strong> approach would be
<strong>the</strong> examination and visualization <strong>of</strong> more complex processes with a higher amount <strong>of</strong> knowledge
anchors. A particular challenge is <strong>the</strong> preparation <strong>of</strong> an in-depth process mapping as an adequate
basis for knowledge oriented optimization. This also requires sensitizing <strong>the</strong> employees to <strong>the</strong>
relevance <strong>of</strong> <strong>the</strong>ir tacit knowledge in order to create <strong>the</strong> necessary transparency.
The method holds two major limitations: It is suitable only for structured processes with a certain
repetitiveness, and it primarily focuses on <strong>the</strong> examined processes’ knowledge requirements, thus
only marginally considering <strong>the</strong> corporate knowledge base.
In general, fur<strong>the</strong>r research is needed on how to purposefully classify knowledge and determine <strong>the</strong>
knowledge intensity (component II) on <strong>the</strong> one hand and on a generalizable scheme for <strong>the</strong>
structuring and tagging <strong>of</strong> tools and methods (component V) on <strong>the</strong> o<strong>the</strong>r hand. Lastly, based on <strong>the</strong>
results <strong>of</strong> <strong>the</strong> previous, algorithms for automatically assigning (layer 2) tools (component V) to defined
knowledge management processes according to process and knowledge specifics and requirements
(component II) have to be developed. Thus, <strong>the</strong> main open issues arise in detailing <strong>the</strong> configuration
<strong>of</strong> <strong>the</strong> components (II) and (V) and defining viable mechanisms for interconnecting both <strong>of</strong> <strong>the</strong>se
through layer 2.
Yet, <strong>the</strong> findings <strong>of</strong> <strong>the</strong> present research already reveal a significant potential for designing knowledge
management in a value-oriented way by employing knowledge-integrating value stream analysis.
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505

Knowledge Management Practices (KMP) and its Impact on
Organizational Performance in Pharmaceuticals Firms
Radwan Kharabsheh 1 , Ihab Magableh 2 and Sukina Sawadha 1
1 Department <strong>of</strong> Business Administration, Faculty <strong>of</strong> Economics and
Administrative Studies, Hashemite University, Zarqa, Jordan
2 Department <strong>of</strong> Managerial Sciences, Talal Abu-Gazaleh College <strong>of</strong> Business,
The German-Jordan University, Amman, Jordan
r.kharab@hu.edu.jo
ihabmagableh@yahoo.co.uk
sukinahu@yahoo.com
Abstract: An extensive amount <strong>of</strong> studies found a positive relationship between knowledge management (KM)
and organizational performance (OP). However, <strong>the</strong>re remains a lack <strong>of</strong> clarity regarding <strong>the</strong> relationship between
KMPs and OP. For example, Davenport (1999) argued that although <strong>the</strong> relationship between KM and
performance indicators has been discussed at length, few firms have been able to establish a casual relationship
between KM activities and firm performance. Likewise, <strong>the</strong>re are no straightforward links between KM and
business performance but, instead, a complex relationship (Carlucci and Schiuma, 2006). Wang et. al. (2009)
argued that a growing belief has emerged that effectively managing knowledge can enhance performance, to
date; however, <strong>the</strong>re is limited empirical evidence. More importantly, a careful survey <strong>of</strong> literature shows that
even fewer studies found a direct link between KMPs and financial performance and KMPs and new product
success. New product success is especially important because it relates to a firm’s innovative performance,
which captures <strong>the</strong> critical domains <strong>of</strong> firms’ competitive advantage, which can be defined as <strong>the</strong> contribution <strong>of</strong>
product and process innovations to firm performance (Jiang and Li, 2009). While innovative performance is very
important in numerous industries it is far more important in <strong>the</strong> pharmaceutical industry. Ingelgard (2002) argued
that <strong>the</strong> competitive advantage in <strong>the</strong> pharmaceutical industry is entangled with <strong>the</strong> company’s ability to generate
new knowledge that can produce patents and new medicines that are turned into marketable products.
Therefore, this study aims to explore <strong>the</strong> extent <strong>of</strong> adoption <strong>of</strong> KMPs in pharmaceutical companies in Jordan.
Fur<strong>the</strong>r, <strong>the</strong> study aims to examine <strong>the</strong> relationship between KMPs and OP. Using questionnaire survey data was
ga<strong>the</strong>red from 13 pharmaceutical companies in Jordan. The study found a positive and direct relationship
between KMPs and OP. Specifically, <strong>the</strong> results showed that KMPs had <strong>the</strong> highest impact on new product
success followed by financial performance.
Keywords: Knowledge management practices, organizational performance, communication, training, knowledge
management strategies, pharmaceutical and medical industries firms
1. Introduction
It has been noted that KM is becoming <strong>the</strong> basic building block <strong>of</strong> organization. Organizations are
realizing that KM is a valuable instrument in improving performance. Liang et. al. (2007) stated that
managers implement KM programs to gain advantage, increase productivity, and remain competitive.
Within this context, an organization’s ability to effectively implement knowledge-based activities
becomes increasingly important for <strong>the</strong> development and sustenance <strong>of</strong> a competitive advantage (De
Carolis, 2003, Grant, 1996). Fundamentally, knowledge activities include <strong>the</strong> creation and integration,
<strong>of</strong> knowledge, <strong>the</strong> accumulation and utilization <strong>of</strong> knowledge, and <strong>the</strong> learning and sharing <strong>of</strong>
knowledge (Shieh-Chieh et. al., 2005). Zack, McKeen and Singh (2009) defined KMPs as “observable
organizational activities that are related to knowledge management”.
An extensive amount <strong>of</strong> studies found a positive relationship between KM and organizational
performance (OP) (Chadha et. al., 2010; Fugate et. al., 2009; Wang et. al., 2009; Ibrahim et. al.,
2009; Zack et. al., 2009; Kasim, 2008; Boumarafi et. al., 2008; Marque´s and Simo´n, 2006).
However, <strong>the</strong>re remains a lack <strong>of</strong> clarity regarding <strong>the</strong> relationship between KMPs and OP. For
example, Davenport (1999) argued that although <strong>the</strong> relationship between KM and performance
indicators has been discussed at length, few companies have been able to establish a casual
relationship between KM activities and firm performance. Likewise, <strong>the</strong>re are no straightforward links
between KM and business performance but, instead, a complex relationship (Carlucci and Schiuma,
2006). Marque´s and Simo´n (2006, p.3) stated that “<strong>the</strong>re are no models that measure <strong>the</strong>
relationship between <strong>the</strong> KMPs and firm performance”'. In a recent study Zack et. al. (2009a, p.393)
pointed out that “perhaps <strong>the</strong> most significant gap in <strong>the</strong> literature is <strong>the</strong> lack <strong>of</strong> large-scale empirical
evidence that KM makes a difference to OP”. Wang et. al. (2009) argued that a growing belief has
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Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
emerged that effectively managing knowledge can enhance performance, to date; however, <strong>the</strong>re is
limited empirical evidence.
More importantly, a careful survey <strong>of</strong> <strong>the</strong>se studies shows that even fewer studies investigated a
direct link between KMPs and financial performance and new product success. New product success
is especially important because it relates to a firm’s innovative performance, which captures <strong>the</strong>
critical domains <strong>of</strong> companies’ competitive advantage, which can be defined as <strong>the</strong> contribution <strong>of</strong>
product and process innovations to firm performance (Jiang and Li, 2009). A firm’s innovative
performance is determined by its innovation activities, such as R&D expenditure, patents, and new
products. Also while this direct link is important in numerous industries because it draws attention to
<strong>the</strong> importance <strong>of</strong> KMPs in organizations it is far more important in <strong>the</strong> pharmaceutical industry.
Ingelgard (2002) argued that <strong>the</strong> competitive advantage in <strong>the</strong> pharmaceutical industry is entangled
with <strong>the</strong> company’s ability to generate new knowledge that can produce patents and new medicines
that are turned into marketable products. Fur<strong>the</strong>rmore, most <strong>of</strong> <strong>the</strong> studies showing a positive
relationship between KMPs and OP were conducted in developed countries’ context. Few studies
were conducted in developing countries such as Jordan.
Therefore, this study aims to explore <strong>the</strong> extent <strong>of</strong> adoption <strong>of</strong> KMPs in pharmaceutical companies in
Jordan. Fur<strong>the</strong>r, <strong>the</strong> study aims to examine <strong>the</strong> relationship between KMPs and OP. While OP in itself
is a useful metric <strong>the</strong> ultimate measures revolve around financial performance and new product
success. The paper also examines <strong>the</strong> impact <strong>of</strong> control variables: companies’ size and companies’
age on <strong>the</strong> relationship between <strong>the</strong> KMPs and OP.
2. Conceptual framework <strong>of</strong> <strong>the</strong> study
It is essential to understand <strong>the</strong> definition <strong>of</strong> knowledge before having a better understanding <strong>of</strong> KM
(Ooi, 2009; Alrawi and Elkhatib, 2009). Knowledge is a multifaceted concept with multilayered
meanings. As <strong>the</strong>re are different dimensions <strong>of</strong> knowledge, it is difficult to identify precisely what
knowledge is. Liang et. al. (2007) defined knowledge as information that is relevant, actionable, and
linked to meaningful behavior and information that is especially characterized by its tacit elements that
are derived from firsthand experience. Walczak (2005) defined knowledge as any data, skill, context,
or information that enables high quality decision making and problem solving to occur.
Ooi (2009) defined KM as a methodological method that enhances <strong>the</strong> capability <strong>of</strong> a company to
assemble and organize <strong>the</strong> knowledge in order to improve <strong>the</strong> decision making ability and business
strategy formulation process. The knowledge to be managed includes both explicit, documented
knowledge, and tacit, subjective knowledge. Chesebrough (2006) pointed out that KM is
fundamentally <strong>the</strong> efforts <strong>of</strong> an organization to generate communicate and leverage intellectual
assets. Rowley (1999) stated that KM is concerned with <strong>the</strong> exploitation and development <strong>of</strong> <strong>the</strong>
knowledge assets <strong>of</strong> an organization with a view to fur<strong>the</strong>ring <strong>the</strong> organization’s objectives. Singh and
Soltani (2010) argued that “most <strong>of</strong>ten, generating value from such assets involves sharing <strong>the</strong>m
among employees, departments and even with o<strong>the</strong>r companies in an effort to devise best practices”.
This definition, Singh and Soltani (2010) argue says nothing about technology; while knowledge
management is <strong>of</strong>ten facilitated by IT, technology by itself is not KM.
As knowledge is created and disseminated throughout <strong>the</strong> firm, it has <strong>the</strong> potential to contribute to <strong>the</strong>
firm’s value by enhancing its capability to respond to new and unusual situations (Percin, 2010).
There is growing evidence that companies are increasingly investing in KM initiatives and establishing
KM systems in order to acquire and better exploit this resource (Sarvary, 1999). The growing
importance <strong>of</strong> knowledge as a critical resource has encouraged managers to pay greater attention to
companies’ KM strategies and practices. Appropriate KM strategies and practices are important to
ensure that <strong>the</strong> alignment <strong>of</strong> organizational processes, culture and KN-related in-formation technology
(IT) deployment produce effective knowledge creation, sharing, and utilization (Zack, 1999).
While researchers have sought to enhance OP by providing guidelines for appropriate KM strategies
and practices, how different KM strategies and practices affect OP is not well understood (Choi, Poon
and Davis, 2008). The assumption underlying <strong>the</strong> practice <strong>of</strong> KM is that by locating and sharing useful
knowledge, organizational performance will improve (Davenport and Prusak, 1998). Marque´s and
Simo´n (2006) found that that companies that adopt KMPs obtain better results than <strong>the</strong>ir competitor
and that KMPs have a positive incidence on firm performance. Kasim (2008) concluded that KMPs at
<strong>the</strong> organizational level are a prerequisite for successful OP. A recent study by Zack et. al. (2009)
507

Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
investigated <strong>the</strong> organizational impact <strong>of</strong> KM. Zack et. al. (2009) argued that it is expected that KM
influences many different aspects <strong>of</strong> OP. Their study revealed 12 KMPs whose performance impact
was assessed via a survey <strong>of</strong> business organizations. Their study found that KMPs was directly
related to OP which, in turn, was directly related to financial performance. Their study did not find a
direct relationship between KMPs and financial performance.
Figure 1 depicts <strong>the</strong> <strong>the</strong>oretical framework <strong>of</strong> <strong>the</strong> study. The left side depicts <strong>the</strong> independent variable
<strong>of</strong> <strong>the</strong> study (KMPs) which is reflected in numerous knowledge management practices and strategies.
The right side <strong>of</strong> figure 1 depicts <strong>the</strong> dependent variable <strong>of</strong> <strong>the</strong> study (OP). The figure also shows <strong>the</strong>
mediating variables: firm’s age and firm’s size.
Independent Variables
- Communication
KMPs
- The ability to experiment and create new
knowledge
- Knowledge capturing and acquisition
- Training and competences development
- Policies and strategies <strong>of</strong> KM
Firm's
Figure 1: Research Model
Marque´s and Simo´n (2006a, p.144) stated that KMPs refer to more practical and perceptible level <strong>of</strong>
research. There are numerous conceptualizations <strong>of</strong> KMPs in <strong>the</strong> literature. OECD (2002)
emphasized that KM is a matter <strong>of</strong> using a category <strong>of</strong> practices which are difficult to observe and
manipulate and sometimes are even unknown to those who possess <strong>the</strong>m. Zack et. al. (2009) found
that KMPs was found to be directly related to OP which, in turn, was directly related to financial
performance. In <strong>the</strong> same context, Kasim (2008) study supported <strong>the</strong> notion that <strong>the</strong> practices <strong>of</strong> KM
at <strong>the</strong> organizational level are a prerequisite for successful OP and seeks to explore <strong>the</strong> role <strong>of</strong> KMPs
and competencies in achieving superior performance among organizations in Malaysia in <strong>the</strong> broader
perspective. Marque´s and Simo´n (2006) studied <strong>the</strong> connection between KMPs and firm
performance. Their study showed that companies that adopt KMPs obtain better results than <strong>the</strong>ir
competitors. Fur<strong>the</strong>rmore, KMPs has a positive incidence on firm performance. Therefore, this paper
makes <strong>the</strong> following hypo<strong>the</strong>sis:
H1: <strong>the</strong>re is a positive relationship between KMPs and OP.
Among <strong>the</strong> KMPs communication to share knowledge plays a major role in knowledge sharing within
organizations. Communication in this context refers to <strong>the</strong> ability to transfer knowledge and make <strong>the</strong>
implicit knowledge <strong>of</strong> <strong>the</strong> employee an explicit knowledge for all organizational levels by using best
practices such as: get face to face advice from experts in organization, preparing written
documentation, or codified forms like manuals (Zack et. al., 2009). Fugate et. al. (2009) pointed out
that a shared interpretation <strong>of</strong> knowledge among operational personnel mediates how knowledge is
disseminated and used to design and implement a unified operational response to that knowledge.
Fur<strong>the</strong>rmore, Kumarawadu (2008) stated that KM transformation, can give business opportunities <strong>of</strong>
small- and medium-scale s<strong>of</strong>tware companies. Similarly, Choi and Lee (2000) found that knowledge
sharing are significant predictors <strong>of</strong> organizational performance. Therefore, this paper makes <strong>the</strong>
following hypo<strong>the</strong>sis:
H2: There is a positive relationship between communication to share knowledge and OP.
508
Size
Dependant Variables
OP
- Financial performance
- New product success
- Customer satisfaction,
- Market share,
- Competitive intensity.

Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
Ho (2009) found that knowledge creation, knowledge internalization on <strong>the</strong> operational side and
knowledge internalization on <strong>the</strong> customer side, all are all positively related to <strong>the</strong> importance <strong>of</strong> <strong>the</strong>
financial performance index. KM creation, can give business opportunities <strong>of</strong> small- and medium-scale
s<strong>of</strong>tware companies (Kumarawadu, 2008). Uhlaner et. al. (2007) emphasized <strong>the</strong> importance <strong>of</strong> both
knowledge absorption and knowledge creation to <strong>the</strong> success <strong>of</strong> innovative efforts in small
companies. In <strong>the</strong> same context, Choi and Lee (2000) stated that organization can achieve strategic,
economic benefits <strong>of</strong> KM from effective KM processes such as <strong>the</strong> creation and sharing. Therefore,
this paper makes <strong>the</strong> following hypo<strong>the</strong>sis:
H3: There is a positive relationship between <strong>the</strong> ability to experiment and create new
knowledge and OP.
Kasim (2008) stated that knowledge acquisition, at <strong>the</strong> organizational level are a prerequisite for
successful OP and seeks to explore <strong>the</strong> role <strong>of</strong> KMPs and competencies in achieving superior
performance among organizations. Therefore, this paper makes <strong>the</strong> following hypo<strong>the</strong>sis:
H4: There is a positive relationship between knowledge capturing and acquisition and
OP.
Kumarawadu (2008) argued that KM education and training can give business opportunities <strong>of</strong> smalland
medium-scale s<strong>of</strong>tware companies (SMSCs) to improve productivity, product quality, flexibility,
inter-employee relationships, effective knowledge creation and knowledge utilization while achieving
<strong>the</strong>ir cost, quality and time targets and thus, SMSCs can gain competitive advantage to sustain <strong>the</strong>ir
business. His study found that organization preparedness, KM education and training and knowledge
creation and transformation play significant roles in enabling KM initiative in <strong>the</strong> organization and will
increase <strong>the</strong> potential <strong>of</strong> gaining competitive advantage for <strong>the</strong> organization. Therefore, this paper
makes <strong>the</strong> following hypo<strong>the</strong>sis:
H5: There is a positive relationship between training and competences development and
OP.
Uhlaner et. al. (2007) found that KM input strategies related to knowledge acquisition are positively
related to sales turnover growth also result showed <strong>the</strong> variations in sales turnover growth from
various measures <strong>of</strong> KM strategies. Similarly, Ho (2009) found that strategy and leadership shows <strong>the</strong>
most significant, positive relationship with <strong>the</strong> KM process performance index among all <strong>the</strong> o<strong>the</strong>r KM
enablers. A recent study by Yang (2010) found that strategic performance is significantly related to
KM strategy. (Choi et. al., 2008) stated that <strong>the</strong>re is a synergistic effects <strong>of</strong> KM strategies on OP.
Therefore, this paper makes <strong>the</strong> following hypo<strong>the</strong>sis:
H6: There is a positive relationship between policies and strategies <strong>of</strong> knowledge
management and OP.
3. Organizational performance
KM has been linked positively to financial performance measures such as pr<strong>of</strong>itability (Marque´s and
Simo´n, 2006; Choi and Lee, 2003; Choi et. al., 2008 ; Fugate et. al.., 2009), and growth (Marque´s
and Simo´n, 2006), and non financial performance measures such as innovation (Kiessling, 2009;
Lundvall and Nielsen, 2007; Gloet and Terziovski, 2004; Kremp et. al. 2003), competitive advantage
(Massa et. al.. 2009; Kumarawadu, 2008; Gupta, 2002), and stakeholder satisfaction (Marque´s and
Simo´n, 2006). This study adopts <strong>the</strong> measure by Farrell et. al. (2008) who measured OP using <strong>the</strong>
following five dimensions: financial performance, new product success, customer satisfaction,
competitive intensity and market share. Accordingly, <strong>the</strong> study makes <strong>the</strong> following hypo<strong>the</strong>ses:
H7: <strong>the</strong>re is a positive relationship between KMPs and financial performance.
H8: <strong>the</strong>re is a positive relationship between KMPs and new product success.
H9: <strong>the</strong>re is a positive relationship between KMPs and customer satisfaction
H10: <strong>the</strong>re is a positive relationship between KMPs and competitive intensity.
H11: <strong>the</strong>re is a positive relationship between KMPs and market share.
4. Research methodology
There are 16 pharmaceutical companies in Jordan (JAPM, 2011) <strong>of</strong> which 13 companies only agreed
to participate in this study. The study used a self-administered questionnaire survey. A brief
509

Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
description <strong>of</strong> <strong>the</strong> study was provided and <strong>the</strong>n an appointment was sought with <strong>the</strong> managers in <strong>the</strong>
listed companies. A total <strong>of</strong> 116 useable questionnaires were collected.
The data was codified and entered into SPSS. The Cronbach Alphas were acceptable exceeding
<strong>the</strong> recommended value <strong>of</strong> 0.6 (Sekaran, 2003) as shown in table 1. With regards to <strong>the</strong> data
distribution, it was found that <strong>the</strong> Asymp. Sig. (2-tailed) values for each variable were above 0.05
indicating that <strong>the</strong> data are normally distributed.
KMPs was measured by 5 items developed by (Zack et. al. 2009; Islam et. al. 2008; Maponya, 2004;
Earl, 2003; OECD, 2002; Aurum et. al., 2007; Maponya, 2004; Kasim et. al., 2008; Bozbura, 2007).
OP was measured by 5 items developed by (Fugate et. al., 2009; Choi et. al., 2008; Choi and Lee,
2003; Barker and Sinkula, 1999; Canlantone et. al., 2002). The size <strong>of</strong> <strong>the</strong> company was measured by
number <strong>of</strong> employees (Ling et. al., 2007) and <strong>the</strong> firm's age was measured by <strong>the</strong> number <strong>of</strong> years <strong>of</strong>
operation (Yang, 2010).
Table 1: Descriptive and goodness <strong>of</strong> fit statistics
Measure Mean
Standard
deviation
Asymp.
Sig. (2tailed)
Crombach’s
alpha
KMPs (five dimensions) 3.36786 0.9459
Communication (six items) 3.4756 0.78858 0.253 0.8043
The ability to experiment and create new
knowledge (four items)
3.4009 0.71161 0.428 0.7201
Knowledge capturing and acquisition (five
items)
3.4207 0.8392 0.169 0.8049
Training and competences development
3.2303
(seven items)
0.83316 0.305 0.8487
Policies and strategies <strong>of</strong> knowledge
management (six items)
3.3118 0.82665 0.33 0.8349
Organizational
dimensions)
performance (five
3.3758 0.4617 0.158 0.8066
Financial performance (two items) 3.2931
New product success (six items) 3.3836
Customer satisfaction (three items) 3.1724
Competitive intensity (four items) 3.5884
Market share (two items) 3.3147
5. Multiple Regressions Analysis (MRA)
The estimation process consists <strong>of</strong> three stages. In <strong>the</strong> first stage, <strong>the</strong> impact <strong>of</strong> KMPs on OP was
estimated (H1). Also, <strong>the</strong> effect <strong>of</strong> <strong>the</strong> control variables: firm's size and firm's age on <strong>the</strong> relationship
between KMPs and OP was estimated. In order to get a better understanding <strong>of</strong> <strong>the</strong> impact <strong>of</strong> KMPs
on OP, in <strong>the</strong> second stage, <strong>the</strong> impact <strong>of</strong> each dimension <strong>of</strong> KMPs on OP was estimated (H2 to H6).
This will allow us to find practice has <strong>the</strong> highest impact on OP. Finally, <strong>the</strong> impact <strong>of</strong> KMPs on each
dimension <strong>of</strong> OP was estimated (H7 to H11).
5.1 Stage 1: hypo<strong>the</strong>sis H1
The dependent variable is OP and <strong>the</strong> independent variable is KMPs. The estimation results show a
positive and significant impact <strong>of</strong> KMPs on OP at 1% significance level (refer to table 2). Fur<strong>the</strong>r, <strong>the</strong>
two control variable: firm's age and firm's size were found to have significant effect on OP and
<strong>the</strong>refore were omitted.
Table .2: estimation results <strong>of</strong> <strong>the</strong> impact <strong>of</strong> KMPs on OP
Hypo<strong>the</strong>sis Structural Path R- square B- value Prob
H KMPs (+) OP 0.54 .298 0.000
510

Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
5.2 Stage 2: hypo<strong>the</strong>ses H2 to H6
The dependent variable is OP and <strong>the</strong> independent variables <strong>the</strong> dimensions <strong>of</strong> KMPs:
communication, <strong>the</strong> ability to experiment and create new knowledge, knowledge capturing and
acquisition, training and competences development, and policies and strategies <strong>of</strong> knowledge
management. The estimation results show that all dimensions <strong>of</strong> KMPs have a positive and a
significant impact on OP at 1% significance level (refer to table .3). The B-values in table 3 show that
training and competences development has <strong>the</strong> highest impact on OP (0.522), followed by policies
and strategies <strong>of</strong> KM (0.489), while communication has <strong>the</strong> lowest impact (0.292). Accordingly,
hypo<strong>the</strong>ses H2 to H6 are accepted.
Table .3: estimation results <strong>of</strong> <strong>the</strong> impact <strong>of</strong> each <strong>of</strong> <strong>the</strong> KMPs dimensions on OP
Hypo<strong>the</strong>sis Structural Path R-
square
B- value Prob
H2 Communication (+) OP .292 .896 0.000
H3 <strong>the</strong> ability to experiment and create
new knowledge (+) OP
.339 1.605
0.000
H4 knowledge capturing
acquisition (+) OP
and
.466 1.276
0.000
H5 training and competences
development (+) OP
.522 .972
0.000
H6 Policies and strategies <strong>of</strong> KM (+)
OP
.489 1.107
0.000
5.3 Stage 3: hypo<strong>the</strong>ses H7 to H11
In this stage <strong>the</strong> impact <strong>of</strong> KMPs on each dimension <strong>of</strong> OP: financial performance, new product
success, customer intensity, competitive intensity, and market share was estimated. The results in
table .4 show that KMPs has a positive and significant impact on each <strong>of</strong> <strong>the</strong> OP dimensions. The
KMPs was found to have <strong>the</strong> highest impact on new product success (0.156) followed by financial
performance (0.046), while <strong>the</strong> lowest impact <strong>of</strong> KMPs was on competitive intensity (0.018). The
values are R-squired confirm <strong>the</strong> previous results.
Table .4: estimation results <strong>of</strong> <strong>the</strong> impact <strong>of</strong> KMPs on each dimension <strong>of</strong> OP
Hypo<strong>the</strong>sis Structural Path R- square B- value Prob
H7 KMPs (+) financial performance .368 .046 0.000
H8 KMPs (+) new product success .513 .156 0.000
H9 KMPs (+) customer intensity .163 .035 0.000
H10 KMPs (+) competitive intensity .026 .018 0.000
H11 KMPs (+) market share .335 .043 0.000
6. Discussion
This study aimed to explore <strong>the</strong> extent <strong>of</strong> adoption <strong>of</strong> KMPs in pharmaceutical companies in Jordan.
More importantly, <strong>the</strong> study aimed to examine <strong>the</strong> impact <strong>of</strong> KMPs on OP. The study found relatively
high levels <strong>of</strong> KMPs implementation in <strong>the</strong>se companies (mean = 3.37 out <strong>of</strong> 5). Among KMPs
dimensions communication had <strong>the</strong> highest mean (3.48), while <strong>the</strong> lowest mean was for training and
competences development.
The study found a positive and significant impact <strong>of</strong> KMPs on OP. The study did not find a significant
impact <strong>of</strong> company's size and company's age on OP. In order to achieve a better understanding <strong>of</strong> <strong>the</strong>
impact <strong>of</strong> KMPs on OP; all dimensions <strong>of</strong> KMPs were examined for <strong>the</strong>ir impact on OP. The study
found that each <strong>of</strong> KMPs <strong>of</strong> had a positive and significant on OP. However, <strong>the</strong>re was a variation in
<strong>the</strong> strength <strong>of</strong> <strong>the</strong> impacts <strong>of</strong> <strong>the</strong>se dimensions. For example, training and competences development
has <strong>the</strong> highest impact on OP (0.522), followed by policies and strategies <strong>of</strong> KM (0.489), while
communication has <strong>the</strong> lowest impact (0.292). The study’s findings are consistent with <strong>the</strong> findings <strong>of</strong>
(Chadha et al., 2010; Fugate et al., 2009; Wang et al., 2009; Ibrahim et al., 2009; Zack et. al., 2009;
Kasim, 2008; Boumarafi et al., 2008; Marque´s and Simo´n, 2006) who found a positive relationship
between KM and OP.
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Radwan Kharabsheh, Ihab Magableh and Sukina Sawadha
Because <strong>of</strong> <strong>the</strong> importance <strong>of</strong> <strong>the</strong> different dimensions <strong>of</strong> OP, <strong>the</strong> study examined <strong>the</strong> impact <strong>of</strong> KMPs
on each <strong>of</strong> <strong>the</strong> different dimensions <strong>of</strong> OP. While KMPs had a positive and significant on each
dimension <strong>of</strong> OP, <strong>the</strong> highest impact was on new product success followed by financial performance.
This is consistent with findings <strong>of</strong> Almashari’s et. al. (2002) and Kiessling et. al. (2009) who found that
KMPs have a positive impact on new product success. This is also consistent with <strong>the</strong> findings <strong>of</strong> Ho
(2009) who found a positive and direct relationship between KM and financial performance and Zack
et. al. (2009) who found a direct relationship between KMPs and OP but not a direct relationship with
between KMPs and financial performance.
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513

Knowledge Management Across <strong>the</strong> Globe – An
International Survey <strong>of</strong> KM Awareness, Spending,
Practices and Performance
Aino Kianto 1 , Tatiana Andreeva 2 and Xing Shi 1
1 School <strong>of</strong> Business, Lappeenranta University <strong>of</strong> Technology, Finland
2 Graduate School <strong>of</strong> Management, St. Petersburg State University, Russia
aino.kianto@lut.fi
andreeva@gsom.pu.ru
xing.shi@lut.fi
Abstract: Several decades since its uprising, knowledge management (KM) has now filtered companies all
across <strong>the</strong> globe. Issues like knowledge economy, intellectual capital, social capital and renewal have become
part <strong>of</strong> <strong>the</strong> standard language <strong>of</strong> strategy processes and business magazines. However, even though main ideas
<strong>of</strong> KM have spread, <strong>the</strong>re are surprisingly few studies exploring what actually really is done in firms related with
KM outside few countries where KM originated. Therefore, we still lack a bigger picture that could reveal to which
extent KM is actually applied in various companies, especially operating in various cultural and economic
contexts. Also, as KM has spread around <strong>the</strong> world, it would be interesting to know what kind <strong>of</strong> national
differences <strong>the</strong>re are in KM practices between firms operating in different countries. This paper aims to address
<strong>the</strong> aforementioned gaps. It examines and compares <strong>the</strong> current state <strong>of</strong> KM in 3 very different countries: Finland,
Russia and China. The data collected with a web-based survey consists <strong>of</strong> 74 Chinese, 84 Finnish and 64
Russian companies. We analyze <strong>the</strong> data statistically to examine <strong>the</strong> state <strong>of</strong> KM and to explore country
differences in KM practices and results. We start by examining general awareness about KM, <strong>the</strong> terminology
used for it, as well as resources that companies spend on it. Next, we address a number <strong>of</strong> KM practices that
include strategic management <strong>of</strong> knowledge, organizational culture, HRM practices, organizational structure and
ICT tools that are aimed to support efficient usage <strong>of</strong> knowledge in organizations. Finally, we discuss perceptions
<strong>of</strong> KM outcomes. This paper contributes to <strong>the</strong> deeper understanding <strong>of</strong> KM practice in real organizations
functioning in different cultural, social and economic environments and thus is be <strong>of</strong> interest for a wide audience
<strong>of</strong> KM experts.
Keywords: knowledge management, survey, Finland, China, Russia
1. . Introduction
The role <strong>of</strong> knowledge as <strong>the</strong> key productive factor has become one <strong>of</strong> <strong>the</strong> truisms <strong>of</strong> current
managerial discourse. The literature contains many normative but ra<strong>the</strong>r abstract prescriptions for
knowledge management (KM) as well as anecdotal pieces <strong>of</strong> evidence from world-class leading firms.
Some recent surveys, e.g., Canada KM practice survey 2001(Statistics Canada 2002), OECD 2002
Danish KM Survey (OECD 2002), and New Zealand KM practice survey (Gerry et al.2004)
demonstrate that <strong>the</strong> usage <strong>of</strong> KM practices grows. However, <strong>the</strong>se surveys still leave some blank
spaces.
First, <strong>the</strong>y are predominantly based on data from one country. As <strong>the</strong>ir methodologies differ, <strong>the</strong>y do
not allow for direct comparison and deeper analysis <strong>of</strong> <strong>the</strong> factors that influence KM practice in <strong>the</strong>
particular country. To address this issue, several comparative international surveys have been
implemented, e.g. KPMG survey that examined KM in UK, France, Germany and <strong>the</strong> Ne<strong>the</strong>rlands
(KMPG 2003) and a survey on law firms in U.S, U.K. and Australia (Curve 2002).
Second, all <strong>of</strong> <strong>the</strong> abovementioned surveys are focused on developed countries. Few studies tried to
address this gap recently, e.g., Turkish KM survey (Bozbura 2007), Malaysia KM survey (Wong
2008), and a survey on KM in India, Singapore, Indonesia, Taiwan, Korea, Thailand, Malaysia,
Vietnam and Philippines .(Serafin and Talisayon 2007). Yet we still know very little about KM practices
in developing and emerging economies.
Third, most <strong>of</strong> <strong>the</strong>se surveys focused on some particular aspects <strong>of</strong> KM, and did not provide <strong>the</strong> full
and comprehensive picture <strong>of</strong> <strong>the</strong> state-<strong>of</strong>-<strong>the</strong>-art KM across various economic and cultural contexts
(e.g. Peng et al. 2007). For example, <strong>the</strong> above mentioned survey in Asian Pacific countries (Serafin
and Talisayon 2007) focused on examining KM indicators.
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Aino Kianto, Tatiana Andreeva and Xing Shi
To bridge <strong>the</strong>se gaps in <strong>the</strong> literature, our study examines and compares <strong>the</strong> current state <strong>of</strong> KM in
three very different countries: Finland, China and Russia. We propose that <strong>the</strong>se three countries are
especially informative and interesting for this kind <strong>of</strong> a comparison because <strong>of</strong> several noteworthy
differences between <strong>the</strong> national characteristics impacting attitudes and behaviours concerning KM.
Finland has been heralded as one <strong>of</strong> <strong>the</strong> forerunners in building a sustainable knowledge-based
economy and knowledge society, and has recently been ei<strong>the</strong>r <strong>the</strong> first or at least in <strong>the</strong> top three <strong>of</strong>
international competitiveness and educational comparisons. China and Russia are <strong>the</strong> biggest and
growing emerging economies and both have recently put innovation to <strong>the</strong> forefront <strong>of</strong> <strong>the</strong>ir national
development strategy. Therefore, <strong>the</strong> KM practices become very relevant as <strong>the</strong>y have a potential to
support such strategy.
The paper is organized as follows. First, we summarize <strong>the</strong> existing literature on KM in 3 countries <strong>of</strong>
our focus. We <strong>the</strong>n explain our research methods and present <strong>the</strong> results concerning Finnish,
Chinese and Russian companies’ KM awareness, spending on KM, KM practices and perceived
benefits <strong>of</strong> KM. We conclude by discussing <strong>the</strong> nature and relevance <strong>of</strong> our findings.
2. KM in China, Finland and Russia: what we know so far
2.1 China
China is one <strong>of</strong> <strong>the</strong> fastest developing countries, and has become <strong>the</strong> second largest economy in <strong>the</strong>
world (NBS, 2011), however, sustaining this growth is presenting China with quite a challenge (Fulin,
2000). Some experts highlight that in order to do so, China needs to manage efficiently <strong>the</strong> shift
towards knowledge economy (Dahlman and Aubert, 2001). Chinese businesses are well-known for
<strong>the</strong>ir capabilities for imitation or replication, however, to compete in a long run <strong>the</strong>y will need to
develop knowledge creation capabilities as well. Consequently, Chinese policymakers have been
implementing a number <strong>of</strong> measures in different sectors aimed at shaping knowledge economy and
helping to transform China from a technology market taker to a market maker (Wilson et al., 2005). In
this context, evidently, KM has become very relevant for Chinese companies.
The term “KM” was formally introduced into China in 1997 (Song, 2003). However, Peng et al (2007)
suggested that KM practice in Chinese companies was still at a very early stage before 2005. After
2005, discussions about how to implement KM in <strong>the</strong> Chinese context and critical issues <strong>of</strong> KM in
Chinese culture began to draw KM researchers’ attention (Yeh et al, 2006; Chang and Lee, 2007; Lin,
2010). Guanxi, collective thinking and Confucianism are discussed as important cultural factors that
shape KM in China. Guanxi is crucial for building trust and <strong>the</strong>refore is claimed to enhance knowledge
sharing and knowledge acquisition (Hutchings and Weir 2006; Michailova and Hutchings, 2006).
Collective thinking is proposed to influence positively <strong>the</strong> willingness <strong>of</strong> Chinese people to share
knowledge, especially when <strong>the</strong>re is a conflict between self and collective interests.. Similarly,
Confucian dynamism, with its focus on harmony, is claimed to influence positively knowledge sharing
behaviors (Zhang et al, 2006).
To summarize, Chinese cultural specifics seem to provide highly supportive context for knowledge
management implementation. However, most <strong>of</strong> <strong>the</strong> discussed above papers are conceptual, and
limited empirical studies are based on few cases. Therefore, <strong>the</strong> current literature lacks
comprehensive and realistic picture <strong>of</strong> what is actually done in terms <strong>of</strong> KM in Chinese organizations.
2.2 Finland
The small country that cannot boast <strong>of</strong> enormous natural resources, Finland in 1990s chose to focus
on knowledge and innovation. In 1998, <strong>the</strong> Finnish government developed <strong>the</strong> national action plan to
develop towards <strong>the</strong> knowledge society (Suurla et al., 2002). After that KM started to play an
important role both in government and in business organizations (Dahlman et al, 2001; Jussilainen,
2001; Suurla et al., 2002; Salojarvi et al, 2005). Finnish government pioneered in implementing
knowledge management programs in its own organizations (Maija, 2001; Suurla et al., 2002;
Markkula, 2006) and a clear knowledge strategy from Finnish government pushed <strong>the</strong> KM
implementation in Finnish business organizations. Finnish mass media has demonstrated a high level
<strong>of</strong> interest in knowledge management and thus also helped to promote <strong>the</strong> discipline (Lelic, 2002). All
<strong>the</strong>se efforts seem to pay <strong>of</strong>f, as Finland nowadays is considered as one <strong>of</strong> <strong>the</strong> most innovative and
competitive economies in <strong>the</strong> world (Dahlman et al., 2007; Schienstock, 2004).
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Aino Kianto, Tatiana Andreeva and Xing Shi
As early as in mid-1990s, KM has been already established in Finland as a distinct management
discipline (Ståhle, Grönroos, 2000; Castells, Himanen, 2002). Several cultural traits are claimed to
support <strong>the</strong> development <strong>of</strong> KM in Finland. The country has a centuries-old custom <strong>of</strong> oral storytelling,
and openness in communications is valued in <strong>the</strong> society and additionally promoted by <strong>the</strong> education
system (Lelic, 2002). Toge<strong>the</strong>r, <strong>the</strong>se two factors may naturally support knowledge sharing behaviors.
Finland also <strong>of</strong>fers much wider empirical evidence <strong>of</strong> <strong>the</strong> current state <strong>of</strong> KM in <strong>the</strong> country (e.g.
Jussilainen, 2001; Suurla et al., 2002; Kukko et al., 2003; Salojarvi et al, 2005; Ståhle, 2007; Hyrkäs,
2009; Hautala, 2011). According to a KM survey conducted by Salojarvi et al. (2005), Finnish SMEs
displayed a surprisingly high awareness about KM - a total <strong>of</strong> 53 % knew <strong>the</strong> concept <strong>of</strong> KM
personally, 35 % <strong>of</strong> <strong>the</strong> respondents reported <strong>the</strong>ir enterprise to actively discuss KM or to deal
systematically with <strong>the</strong> KM, and about 11 % <strong>of</strong> <strong>the</strong> companies reported that <strong>the</strong>y have constructed
<strong>the</strong>ir own knowledge management system.
2.3 Russia
Russia is ano<strong>the</strong>r promising emerging economy (The World Bank, 2011) that tries to re-gain its
position in <strong>the</strong> international economy after <strong>the</strong> collapse <strong>of</strong> <strong>the</strong> USSR and turmoil <strong>of</strong> 1990s. However,
<strong>the</strong> recent rise <strong>of</strong> Russia has been highly associated with natural resources (The World Bank, 2011).
For this reason, Russian authorities recently called for <strong>the</strong> diversification <strong>of</strong> <strong>the</strong> economy and
establishing knowledge economy in <strong>the</strong> country (Medvedev, 2009). The country has a rich Soviet
heritage <strong>of</strong> knowledge capital, however, it needs to be significantly modernized and renewed (Goltz et
al., 2010). In addition, accession to <strong>the</strong> WTO increases <strong>the</strong> pressure <strong>of</strong> international competition for
Russian companies (Ru<strong>the</strong>rford, Tarr, 2005). In this context, knowledge management has become
recently one <strong>of</strong> <strong>the</strong> priorities for both government and business organizations in Russia.
KM in Russia became a very popular <strong>the</strong>me among top-managers and management consultants
during <strong>the</strong> last decade. Both governmental and mass media discourses on knowledge economy and
KM in Russia seem to be dominated by <strong>the</strong> technological aspects <strong>of</strong> KM (e.g., Medvedev, 2009). In
particular, knowledge management and innovations are highly associated with <strong>the</strong> ICT sector.
In general, <strong>the</strong> Russian context has been portrayed as a challenging one for <strong>the</strong> implementation <strong>of</strong>
KM practices (Michailova, Husted 2003; Michailova, Hutchings, 2004), because <strong>of</strong> <strong>the</strong> strong in-group
orientation that makes Russians hostile to share knowledge out <strong>of</strong> <strong>the</strong> group, and low level <strong>of</strong> trust
between different social and organizational groups. It even has been claimed that Russian
organizations are inherently hostile to knowledge sharing, arguing that one <strong>of</strong> <strong>the</strong> key reasons for this
relates to a deeply held belief that knowledge is (individual) power and thus should not be shared
unless necessary, and/or unless appropriate benefits are received (Michailova, Husted, 2003;
Michailova, Hutchings, 2006).
Most <strong>of</strong> <strong>the</strong> literature on KM in Russian context is ei<strong>the</strong>r conceptual or based on a limited number <strong>of</strong>
case studies. Consequently, even though KM discourse has gained popularity in Russia during <strong>the</strong>
last decade, empirical evidence <strong>of</strong> what Russian companies are actually doing in terms <strong>of</strong> KM is still
lacking.
To summarize, KM has received significant attention recently in all three countries, and recent
governmental strategies seem to promote KM as well. However, Finland appears to be much more
mature from KM perspective, as surveys show that KM is getting wide acceptance in Finnish
organizations. In contrast, both China and Russia do not have yet empirical evidence (beyond some
anecdotal cases) <strong>of</strong> whe<strong>the</strong>r Chinese and Russian companies use KM and how <strong>the</strong>y do it. Yet <strong>the</strong>
conceptual literature suggests that Chinese cultural context is quite conducive to KM efforts, while
Russian cultural context might be ra<strong>the</strong>r unfavorable for KM. From this perspective, <strong>the</strong> exploratory
empirical study that could provide empirical evidence on <strong>the</strong> level <strong>of</strong> <strong>the</strong> acceptance <strong>of</strong> KM practices
in China and Russia and on <strong>the</strong> contents <strong>of</strong> <strong>the</strong>se practices, as well as contrast it with data from a
“more KM mature” Finland, appears to be both relevant and interesting.
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3. Methods
3.1 Scales
Aino Kianto, Tatiana Andreeva and Xing Shi
To examine <strong>the</strong> state <strong>of</strong> KM in China, Finland and Russia, we adopted <strong>the</strong> survey methodology and
collected data with structured questionnaires.
The first issue we were interested in was <strong>the</strong> general awareness <strong>of</strong> KM and <strong>the</strong> terminology used for it
in <strong>the</strong> surveyed organizations. To explore <strong>the</strong>se issues, we simply posed a number <strong>of</strong> questions
(presented fur<strong>the</strong>r in <strong>the</strong> table 3) with dichotomous response options (yes/no)..
Next, we wanted to find out whe<strong>the</strong>r KM receives any dedicated budgets in <strong>the</strong> companies currently
and in <strong>the</strong> next 24 months, as well as how <strong>the</strong> spending is estimated to change. This can be taken as
a measure <strong>of</strong> whe<strong>the</strong>r <strong>the</strong> companies really are doing things in terms <strong>of</strong> KM; while it is very easy to
say that knowledge is a crucial resource, dedicated budgets indicate actual enacted KM activities. For
<strong>the</strong>se items <strong>the</strong> response format was a simple dichotomy (yes/no) or multiple choice
(increase/decrease/stay <strong>the</strong> same).
The following issue we explored was <strong>the</strong> KM practices employed by <strong>the</strong> companies. Drawing from
previous literature discussing <strong>the</strong> conscious and systematic management practices used for
facilitating knowledge processes in organizations, we examined KM practices from five perspectives:
strategic management <strong>of</strong> knowledge, organizational culture, HRM practices, organizational structures
and information and communication technologies (ICT). However, as knowledge management
discipline is still in <strong>the</strong> development phase, widely utilized operationalizations <strong>of</strong> <strong>the</strong>se concepts do not
exist. Therefore, for <strong>the</strong> purposes <strong>of</strong> this research, <strong>the</strong> scales for KM practices were combined by <strong>the</strong>
authors based on <strong>the</strong> existing literature, constructing new items where needed. For all <strong>the</strong> KM practice
items, <strong>the</strong> respondent was asked to indicate his/her agreement to a particular statement on a six-point
Likert scale (1= strongly disagree, 6 = strongly agree).
Strategic management <strong>of</strong> knowledge scale aimed to measure <strong>the</strong> organization’s ability to establish <strong>the</strong>
knowledge-strategy link and existence <strong>of</strong> a clear knowledge management strategy (Zack, 1999;
Skyrme & Amidon 1997; Dalkir 2005). The scale consists <strong>of</strong> six items which were drawn from Kianto
(2008) and McKeen et al. (2005)..
Organizational culture is <strong>the</strong> combination <strong>of</strong> shared history, expectations, unwritten rules, and social
customs that are rarely articulated but can influence people‘s communicational behaviours (Jarkko,
2004). A lot <strong>of</strong> empirical studies revealed <strong>the</strong> importance <strong>of</strong> building an organizational culture <strong>of</strong> KM in
facilitating knowledge processes (Davenport et al. 1998, Dorothy et al.2006, Kristen et al. 2004 ). The
scale for organizational culture consists <strong>of</strong> six items, drawn from KMAT survey (2001), Kulkarni (2003)
and Steyn et al. (2008).
Human resource management (HRM) policy and practice play a significant role in KM (Hislop, 2003;
Scarbrough, 2003; Wong, 2005). Human resource management practices scale includes five items,
four <strong>of</strong> which are drawn from <strong>the</strong> Canada KMP Survey (2001) and one generated by <strong>the</strong> research
team.
Organizational structure is ano<strong>the</strong>r central aspect in implementing KM (Gold et al, 2001; Quintas et al,
1997, Wong, 2005). Most items in this scale were generated by <strong>the</strong> research teams and one item was
adapted from Kianto (2008).
Finally, Information and communication technologies scale aimed to identify <strong>the</strong> extent to which <strong>the</strong>
organization is able to use technological tools and infrastructures for facilitating knowledge work
processes (Alavi et al., 2001; Davenport et al., 1998). Items from Liu (2006), Kulkarni et al. (2003),
Kruger (2007) and Kianto (2008) were adopted to build this scale.
The principal component analysis yielded a five-factor solution, representing <strong>the</strong> five knowledge
management practices. Composite measures were calculated from <strong>the</strong> averaged item responses <strong>of</strong>
each construct. Table 1 presents <strong>the</strong> items, factor loadings and internal consistencies <strong>of</strong> <strong>the</strong> KM
practice variables. The coefficient alphas range from .854 to 0.942, exhibiting a good internal
consistency <strong>of</strong> all <strong>the</strong> composites.
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Aino Kianto, Tatiana Andreeva and Xing Shi
Table 1: Factor loadings and coefficient alphas <strong>of</strong> KM practices scales
Items Component
1 2 3 4 5
Strategic management <strong>of</strong> knowledge Cronbach α = .892
Our organisation has a clear
understanding <strong>of</strong> our current core
.748
knowledge
Our organisation has a clear view <strong>of</strong> what
knowledge and competences are <strong>the</strong> most
relevant for <strong>the</strong> objectives
Our organisation's knowledge and
competences are evaluated systematically
Our organisation benchmarks our strategic
knowledge against that <strong>of</strong> our competitors
Our organisation explicitly recognizes
knowledge as a key element in <strong>the</strong>
strategic planning exercises
Our organisation has a clear strategy for
developing knowledge and competences
Organizational culture Cronbach α = .942
Openness and trust are valued in our .764
organisation
Flexibility and a desire to innovate are .799
valued in our organisation
Employees who take initiative <strong>of</strong> <strong>the</strong>ir own
learning are highly valued in our
organisation
Willingness to share lessons learned is
valued in our organisation
In our organisation lessons learned both
successful and unsuccessful are
considered valuable
In our organisation various units are
encouraged to collaborate with each o<strong>the</strong>r
.835
.824
.805
Human resource management Cronbach α = .877
Our organisation specifically rewards
knowledge sharing with monetary
incentives
Our organisation specifically rewards
knowledge sharing with non-monetary
incentives
Our organisation specifically rewards
knowledge creation with monetary
incentives
Our organisation specifically rewards
knowledge creation with non-monetary
incentives
In our organisation knowledge sharing is a
component in employees’ performance
evaluation
Organizational structure Cronbach α = .853
People from different parts <strong>of</strong> our
organisation interact informally with each
o<strong>the</strong>r in a frequent manner
In our organisation open dialogues are
common among/between employees and
manager
.755
.724
.531
.747
.713 .306 .308
.592 .312
.821
.751
.743
.666
.708
.739
.389 .680
In our projects our organisation uses
teams consisting <strong>of</strong> people with skills and
expertise from diverse fields
.375 .674
In our organisation we frequently use
cross-functional teams and projects
.672
In our organisation we have purposeful
overlap <strong>of</strong> functional responsibilities
.675
Information and communication technologies Cronbach α = .884
518

Our organisation uses technologies (e.g.
Intranet. Internet. e-mail and e-learning) to
facilitate employees sharing new
ideas/knowledge with each o<strong>the</strong>r
KM systems and tools in our organization
are widely accepted. monitored. and
updated
Our organisation's ICT is capable <strong>of</strong>
supporting management decisions and
knowledge work
Our organisation's ICT architecture is
capable <strong>of</strong> sharing data and information.
knowledge and expertise with all
stakeholders in <strong>the</strong> organisation's
extended value chain
Our organisation's current ICT systems are
sufficient to support <strong>the</strong> daily work
Aino Kianto, Tatiana Andreeva and Xing Shi
Finally, we wanted to find out whe<strong>the</strong>r and what kind <strong>of</strong> benefits KM had yielded to <strong>the</strong> respondents’
companies. We <strong>the</strong>refore asked <strong>the</strong> respondents to evaluate <strong>the</strong> performance <strong>of</strong> KM in <strong>the</strong>ir
organization. The scale examined performance improvement from KM through four perspectives: <strong>the</strong>
money saved, <strong>the</strong> time saved, and <strong>the</strong> increases in revenue and innovativeness (Zhu, 2004). The
response format was a six-point Likert-scale, anchored by “strongly disagree” and “strongly agree”.
The initial measures were built in English, based on <strong>the</strong> relevant international literature, as we have
demonstrated above. However, as English is not <strong>the</strong> native language for any <strong>of</strong> <strong>the</strong> 3 target countries
<strong>of</strong> our survey, we wanted to ensure that respondents fully understand <strong>the</strong> questions and to raise <strong>the</strong>
response rate by reaching non-English speaking respondents and <strong>the</strong>refore <strong>the</strong> survey items were
translated into respective languages <strong>of</strong> <strong>the</strong> countries in our sample (Harzing, 2000). To secure
measurement equivalence, translation procedure followed several iterations, as recommended in <strong>the</strong>
literature on cross-national research (Singh, 1995).
3.2 Data collection and sample
The data were collected in 3 countries – Finland, Russia and China during February-April 2010 In
order to obtain reliable, diverse and comparable data, it was decided to select companies with 50 or
more employees that represent both production and service sectors, and industries with different
growth rates.
The survey was run with <strong>the</strong> usage <strong>of</strong> <strong>the</strong> web-based survey s<strong>of</strong>tware. Therefore, ano<strong>the</strong>r criterion for
selection <strong>of</strong> <strong>the</strong> companies into <strong>the</strong> research pool was added – <strong>the</strong> company should have a publicly
available email address so that <strong>the</strong> link to <strong>the</strong> survey could be sent <strong>the</strong>re. The survey has been
formulated in a way that any employee <strong>of</strong> <strong>the</strong> organization can respond to it, in order to enlarge <strong>the</strong>
potential sample. The administration <strong>of</strong> <strong>the</strong> survey proceeded in several stages and differed slightly
among three countries due to differences in business culture and attitudes to surveys.
As a result <strong>of</strong> data collection efforts, 260 responses in 3 countries were collected. 38 responses were
excluded from fur<strong>the</strong>r analyses as <strong>the</strong>y belonged to companies with less than 50 employees or had
failed to provide a response on <strong>the</strong> number <strong>of</strong> employees in <strong>the</strong> organization. Therefore, <strong>the</strong> usable
sample consisted <strong>of</strong> 222 responses, quite evenly representing 3 countries <strong>of</strong> our survey with 84
Finnish (37,8%), 64 Russian (28,8%) and 74 (33,3%) Chinese responses.
3.3 Analyses
To analyze aspects <strong>of</strong> KM in <strong>the</strong> three countries under study, we tested <strong>the</strong> statistical difference in <strong>the</strong>
amount <strong>of</strong> responses for <strong>the</strong> nominal variables by using <strong>the</strong> Pearson Chi-Square test. To examine <strong>the</strong>
difference in means for continuous variables we employed one-way analysis <strong>of</strong> variance test. The
overall results are reported in <strong>the</strong> following.
4. Results
Tables 3-6 report <strong>the</strong> results <strong>of</strong> <strong>the</strong> empirical investigation. Table 3 distinguishes between <strong>the</strong> aspects
<strong>of</strong> KM awareness in Finnish, Russian and Chinese companies. Over 90% <strong>of</strong> <strong>the</strong> surveyed companies
across all three countries recognized <strong>the</strong> importance <strong>of</strong> knowledge as a resource. Two thirds <strong>of</strong>
519
.708
.746
.791
.746
.658

Aino Kianto, Tatiana Andreeva and Xing Shi
companies also took knowledge into account in <strong>the</strong>ir strategies and strategic planning. However
Chinese companies did this significantly more rarely than Finnish and Russian companies. Somewhat
half <strong>of</strong> <strong>the</strong> surveyed companies across all three countries used <strong>the</strong> concept “knowledge management”
for <strong>the</strong>ir activities related to more efficient usage <strong>of</strong> knowledge as a resource. The utilization <strong>of</strong> <strong>the</strong>
term was significantly more common in Finland as opposed to China and Russia, which might be
explained by <strong>the</strong> fact that Finnish national and business press have actively discussed issues such as
knowledge society and knowledge economy as well as management in <strong>the</strong>m since <strong>the</strong> 90’s, and this
discourse seems to have permeated Finnish companies ra<strong>the</strong>r well. About two thirds <strong>of</strong> all companies
had drawn concrete benefits from KM practices. Chinese companies were <strong>the</strong> most likely to have
concretely benefited from engaging in KM, while Finnish companies were <strong>the</strong> least likely to have done
so.
Table 3: KM awareness in China, Finland and Russia
China Finland Russia Total
Consideration <strong>of</strong> knowledge as an important resource in <strong>the</strong> organization
No 9 (12,2%) 8 (9,5%) 2 (3,2%) 19 (8,6%)
Yes 65 (87,8%) 76 (90,5%) 60 (96,8%) 201 (91,4%)
Chi-Square = 3,550, df = 2, p = .169
Organization takes knowledge into account in its strategy and strategic planning
No 32 (44,4%) 24 (29,6%) 15 (25,4%) 71 (33,5%)
Yes 40 (55,6%) 57 (70,4%) 44 (74,6%) 141 (66,5%)
Chi-Square = 6.144, df = 2, p = .046
Usage <strong>of</strong> <strong>the</strong> term “knowledge management” to label <strong>the</strong> activities related to more efficient usage
<strong>of</strong> knowledge as a resource in <strong>the</strong> organization
No 38 (52,1%) 30 (36,1%) 40 (63,5%) 108 (49,3%)
Yes 35 (47,9%) 53 (63,9%) 23 (36,5%) 111 (50,7%)
Chi-Square = 11.045, df = 2, p = .004
Organization has had concrete benefits from KM practices
No 18 (24,7%) 36 (43,9%) 22 (36,7%) 76 (35,3%)
Yes 55 (75,3%) 46 (56,1%) 38 (63,3%) 139 (64,7%)
Chi-Square = 6.322, df = 2, p = .042
(Percentages indicate % <strong>of</strong> responses within country)
Table 4 reports <strong>the</strong> observations concerning budgeting for KM and its estimated development in <strong>the</strong>
next 24 months. The results demonstrate that currently it is less common for Finnish companies to
invest in KM than for Chinese and Russian firms. The spending on KM is seen to be on <strong>the</strong> rise: while
currently some 40% <strong>of</strong> companies across all three countries have dedicated budgets for KM, nearly
70% expect to have <strong>the</strong>m in <strong>the</strong> next 2 years. Chinese companies seem to be <strong>the</strong> most eager to
invest in KM and to increase <strong>the</strong>ir investments from <strong>the</strong> current levels. Very few companies expect <strong>the</strong>
KM spending to decrease in <strong>the</strong> next 2 years and a little bit more than half estimate it will increase.
Based on <strong>the</strong>se results, it seem that Chinese companies are taking KM seriously and are willing to
really engage in it by allocating dedicated budgets for improving KM practices and by increasing <strong>the</strong>m
in <strong>the</strong> forthcoming years. Half <strong>of</strong> Russian companies is currently investing in KM and a third <strong>of</strong> <strong>the</strong>m
estimate <strong>the</strong> budgets will increase in <strong>the</strong> next 2 years. Finnish companies are <strong>the</strong> most reluctant to
invest in KM, as currently only 28% have a budget for KM, but <strong>the</strong> trend seems to be on <strong>the</strong> rise as a
double <strong>of</strong> <strong>the</strong> companies (61%) view that <strong>the</strong>re will be a dedicated budget in two years.
Table 4: KM spending in China, Finland and Russia
China Finland Russia Total
Organization currently has dedicated budgets or spending in use for KM practices
No 19 (41,3%) 54 (72,0%) 31 (50,8%) 104 (57,1%)
Yes 27 (58,7%) 21 (28,0%) 30 (49,2%) 78 (42,9%)
Chi-Square = 12.468, df = 2, p = .002
Existence <strong>of</strong> dedicated budgets for KM practices in <strong>the</strong> next 24 months
No 7 (14,3%) 23 (39,0%) 25 (41,7%) 55 (32,7%)
Yes 42 (85,7%) 36 (61,0%) 35 (58,3%) 113 (67,3%)
Chi-Square = 10.794, df = 2, p = .005
Development <strong>of</strong> KM practices budgets in <strong>the</strong> next 24 months
Decrease 1 (2,1%) 1 (1,7%) 5 (8,2%) 7 (4,2%)
Stay <strong>the</strong> same 5 (10,6%) 32 (53,3%) 38 (62,3%) 75 (44,6%)
Increase 41 (87,2%) 27 (45,0%) 18 (29,5%) 86 (51,2%)
Chi-Square = 39.172, df = 4, p = .000
(Percentages indicate % <strong>of</strong> responses within country)
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Aino Kianto, Tatiana Andreeva and Xing Shi
Table 5 distinguishes between five KM practices, namely strategic management <strong>of</strong> knowledge,
organizational culture, HRM practices, organizational structure and ICTs, across <strong>the</strong> three countries
under study. As a whole organizational culture seems to be <strong>the</strong> most extensively applied KM practice,
followed by strategic KM and ICTs. There are no significant differences between <strong>the</strong> three countries in
terms <strong>of</strong> strategic management <strong>of</strong> knowledge and organizational culture. In contrast, Chinese
companies employ KM-supporting HRM practices significantly more than Finnish and Russian firms.
Finnish companies employ KM-supporting structural arrangements to a significantly lesser extent than
Chinese and Russian firms. Finally, Russian firms employ KM-supporting ICTs more than Chinese
and Finnish firms, <strong>the</strong> difference being statistically significant between Russia and Finland.
Mean Std. Deviation
F Sig
Strategic management <strong>of</strong> China 4.1689 1.19525
knowledge
Finland 4.0705 1.02664
Russia 4.1831 1.10298
Total 4.1357 1.10314 ,235 ,791
Organizational culture China 4.2842 1.33751
Finland 4.2990 1.21387
Russia 4.5053 1.13985
Total 4.3529 1.23491 ,672 ,512
HRM China 3.6110 1.30847
Finland 2.9337 1.25351
Russia 2.9161 1.21873
Total 3.1555 1.29782 7,145 ,001
Organizational structure China 4.1329 1.11854
Finland 3.6940 1.10261
Russia 4.2675 1.10589
Total 4.0039 1.13167 5,553 ,004
ICT China 4.2167 1.22056
Finland 3.8711 1.15909
Russia 4.3701 1.37794
Total 4.1298 1.25781 3,140 ,045
Table 5: KM practices in China, Finland and Russia
Finally, Table 6 demonstrates <strong>the</strong> situation concerning perceived results acquired through KM
practices. Chinese firms have been <strong>the</strong> most able to save significantly more money and time by KM
than Russian firms, and to increase innovativeness significantly more than Finnish firms. In sum, it
seems that Chinese companies are <strong>the</strong> best at gaining benefits from KM.
Mean Std. Deviation
F Sig
Money saving China 3.88 1.377
Finland 3.42 1.308
Russia 3.02 1.459
Total 3.44 1.409 5,473 ,005
Time saving China 4.18 1.349
Finland 3.62 1.319
Russia 3.47 1.743
Total 3.76 1.489 3,946 ,021
Increased revenue China 3.88 1.428
Finland 3.44 1.423
Russia 3.67 1.667
Total 3.65 1.504 1,358 ,260
Innovativeness China 4.29 1.403
Finland 3.62 1.372
Russia 4.02 1.493
Total 3.95 1.439 3,722 ,026
Table 6: KM benefits in China, Finland and Russia
521

5. Conclusions
Aino Kianto, Tatiana Andreeva and Xing Shi
This paper examined <strong>the</strong> current state <strong>of</strong> KM in Finland, Russia and China. Despite significant
differences among <strong>the</strong>se countries, based on our results it seems that <strong>the</strong> difference between KM
awareness, budgets, practices and results is not as big as one could expect.
While Finland has been heralded as an archetype <strong>of</strong> a knowledge-based economy, and based on this
it could be assumed to be a forerunner in organizational KM as well, based on our results it seems
that <strong>the</strong> case is not that simple. Indeed, we found that KM terminology is more spread in Finland than
in o<strong>the</strong>r countries in our sample, but thus could be explained by <strong>the</strong> governmental strategy and
related campaign in <strong>the</strong> mass media.
However, our results suggest that awareness about KM terms does not automatically lead to wider
application <strong>of</strong> KM practices or to higher <strong>the</strong> perceived efficiency <strong>of</strong> such efforts. In our sample Finland
appears to be behind <strong>the</strong> two emerging economies along some indexes, and Chinese companies
appear to be most satisfied with <strong>the</strong>ir KM.
Ano<strong>the</strong>r interesting finding relates to <strong>the</strong> different focus <strong>of</strong> KM efforts among 3 countries. In order to
support knowledge processes, Chinese companies employ widely HRM practices, while Russian
companies rely more on ICT, and Finnish companies use much less organizational design solutions.
One <strong>of</strong> <strong>the</strong> explanations for this finding may be linked to cultural and historical contingencies.
Technical disciplines were in favour during Soviet times, and many Russian managers have technical
education as <strong>the</strong>ir first degree education. Therefore, a number <strong>of</strong> studies report that <strong>the</strong>y feel more
comfortable with technical solutions ra<strong>the</strong>r than with any “s<strong>of</strong>t” ones (Andreeva, 2008). Chinese focus
on “s<strong>of</strong>t”, human-related practices, can be explained by <strong>the</strong> culture <strong>of</strong> collectivism and networking. Yet
we find Finnish lack <strong>of</strong> attention to structural solutions surprising, as such solutions seem to fit very
well with Finnish culture.
We suggest that our findings contribute to existing KM discourse in several ways. First, <strong>the</strong>y provide
evidence that companies from emerging economies are also actively engaged in KM, and in some
issues <strong>the</strong>y actually outperform companies from developed economy. Second, <strong>the</strong>y suggest that
different national KM models may exist, linked to <strong>the</strong> different cultural and economic environments.
Yet our research should be considered only as a first empirical step towards understanding <strong>of</strong> such
national models and <strong>the</strong> factors that form <strong>the</strong>m. The main limitation <strong>of</strong> this study lies in <strong>the</strong> data, as
our country sub-samples are not very big, and were collected using a bit different data collection
procedures, that may have yielded in <strong>the</strong> respondents who are already aware about KM and <strong>the</strong>refore
are not fully representative <strong>of</strong> <strong>the</strong> total population <strong>of</strong> national companies. Therefore, <strong>the</strong> topic may
benefit from fur<strong>the</strong>r studies based on bigger samples.
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523

Facilitating Knowledge Sharing in Virtual Networks
Andrea Kő, Péter Fehér and Krisztián Varga
Corvinus University <strong>of</strong> Budapest, 1093 Budapest, Fővám tér 8
ko@informatika.uni-corvinus.hu
pfeher@informatika.uni-corvinus.hu,
kvarga@informatika.uni-corvinus.hu
Abstract: The exploitation <strong>of</strong> knowledge networks’ intellectual capital is one <strong>of</strong> <strong>the</strong> key factors <strong>of</strong> <strong>the</strong>
competitiveness in SMEs, which is strongly connected to <strong>the</strong> application <strong>of</strong> ICT. Our team aimed to deal with <strong>the</strong>
innovation activities <strong>of</strong> SMEs, through <strong>the</strong> analyses <strong>of</strong> <strong>the</strong>ir knowledge management related activities from ICT
perspectives. We pay special attention to <strong>the</strong> ICT-based analyses <strong>of</strong> knowledge transfer, sharing and knowledge
codification fields. The empirical evidence was collected from five EU countries (Italy, Spain, Greece, Hungary
and Slovakia), that were analysed through an online questionnaire, and selected companies were explored
through case study methodology.
Keywords: knowledge sharing, virtual networks, associations, e-business, collaboration
1. Introduction
The exploitation <strong>of</strong> knowledge networks’ intellectual capital is one <strong>of</strong> <strong>the</strong> key factors <strong>of</strong> <strong>the</strong>
competitiveness in SMEs, which is strongly connected to <strong>the</strong> application <strong>of</strong> ICT. In <strong>the</strong> globalised and
highly competitive market SMEs can build advantage on strong collaboration and knowledge sharing
with both <strong>the</strong>ir suppliers and customers that enables stabile market presence not only for a single
SME but even for <strong>the</strong> whole network. ICT solutions are vital to establish this network <strong>of</strong> organisations
with supporting communication and collaboration tools. Our team aimed to deal with <strong>the</strong> innovation
activities <strong>of</strong> SMEs, through <strong>the</strong> analyses <strong>of</strong> <strong>the</strong>ir knowledge management related activities from ICT
perspectives. We conducted several <strong>European</strong> and national projects in <strong>the</strong> field <strong>of</strong> ICT and knowledge
management as well; amongst o<strong>the</strong>r we are active in FP7 projects.
The research objective is <strong>the</strong> investigation <strong>the</strong> role <strong>of</strong> ICT in knowledge networks. The following
questions, problems will be analysed:
what are <strong>the</strong> key ICT solutions applied in knowledge networks
how can ICT solutions help <strong>the</strong> elimination <strong>of</strong> <strong>the</strong> barriers <strong>of</strong> knowledge networks formulation
how can ICT support building, operation and improvement <strong>of</strong> knowledge networks.
We pay special attention to <strong>the</strong> ICT-based analyses <strong>of</strong> knowledge transfer, sharing and knowledge
codification fields. Beyond <strong>the</strong> investigation <strong>of</strong> current situation we plan to figure out best practices in
ICT application. The empirical evidence is collected at Hungarian SMEs; through e-surveys. Data will
be processed by statistical analyses and data mining.
1.1 Knowledge networks
Collaboration becomes more and more important in business practice. Companies can work toge<strong>the</strong>r
in several forms and ways, but all <strong>of</strong> <strong>the</strong>m have <strong>the</strong> same benefits like sharing operational risks,
assigning task based on <strong>the</strong> competences <strong>of</strong> <strong>the</strong> participants, faster innovation, and usage <strong>of</strong> <strong>the</strong>
separately owned knowledge elements in order to achieve <strong>the</strong> common goals. Alliances can be
streng<strong>the</strong>n in <strong>the</strong>ir own markets, and can act more effectively against <strong>the</strong>ir competitors with <strong>the</strong> help <strong>of</strong>
<strong>the</strong>ir alliesThree main types <strong>of</strong> strategic alliances can be seen in Figure 1, which evaluates <strong>the</strong>se
types based on <strong>the</strong> risks, <strong>the</strong> intensity <strong>of</strong> cooperation, <strong>the</strong> control, and <strong>the</strong> periodicity.
Buyer-seller Alliance Merger, acquisition
Risk One time Low High
Cooperation One time Reversible Irreversible
Control None Low High
Periodicity One time Limited Permanently
Figure 1: Types <strong>of</strong> strategic alliances (Fehér, 2010)
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Andrea Kő, Péter Fehér and Krisztián Varga
Sometimes <strong>the</strong> main goal <strong>of</strong> strategic alliances is to transfer knowledge, or to support <strong>the</strong> learning
process. There are techniques available in all kinds <strong>of</strong> strategic alliances that can support learning
and knowledge transfer:
Sharing <strong>of</strong> technology: guided tour in plants, exchange programmes
Joint ventures – parent company: tour in joint venture, „communities <strong>of</strong> practice”
Transfer <strong>of</strong> work force: rotation, career planning via various jobs in several companies
Harmonization <strong>of</strong> <strong>the</strong> strategy <strong>of</strong> <strong>the</strong> joint ventures and <strong>the</strong> parent company: handling <strong>the</strong> whole
joint venture as one entity
Forming regional clusters can be ano<strong>the</strong>r way to companies to work toge<strong>the</strong>r, collaborate and
cooperate. Porter (1999) defines a regional cluster as a network <strong>of</strong> companies, that is built in a
supply chain, have exact geographical boarders, and concentrates in that specific geographical area.
The companies in <strong>the</strong> cluster are connected tightly (productive, service, R&D), <strong>the</strong>y work toge<strong>the</strong>r in
long term.
The “cluster” definition mentioned above has been changed, and it has transformed to a dynamic,
changeable concept <strong>of</strong> clusters, or networks. In a dynamic, changeable cluster, <strong>the</strong> connections
between <strong>the</strong> participants are weaker, for example, it can be a consortium for only one project. The
cluster is not organized because <strong>of</strong> geographical aspects, <strong>the</strong> knowledge and adoptability <strong>of</strong> <strong>the</strong>
participants becomes more important. The cooperation can be organised both vertically and
horizontally (Imreh – Lengyel, 2002).
Figure 2: Dynamic network (Imreh - Lengyel, 2002)
In networks emphasizing <strong>the</strong> importance <strong>of</strong> knowledge, <strong>the</strong> project-based connections are accepted;
<strong>the</strong>re is no need to be geographically close. In <strong>the</strong>se networks, <strong>the</strong> main value cannot be found only in
<strong>the</strong> products, ra<strong>the</strong>r in <strong>the</strong> information and knowledge.
Gelsing (1992) gave this definition to knowledge networks: A network, in which <strong>the</strong> flow <strong>of</strong> information
and <strong>the</strong> exchange <strong>of</strong> knowledge is emphasized, and not <strong>the</strong> flow <strong>of</strong> product (but in most cases both <strong>of</strong>
<strong>the</strong>m can happen in <strong>the</strong> same time). The definition points it out, that a knowledge network can be a
classic network with that little plus in it, that <strong>the</strong> exchange <strong>of</strong> goods is combined with <strong>the</strong> exchange <strong>of</strong>
information and knowledge.
In ano<strong>the</strong>r definition, <strong>the</strong> knowledge network is considered as an internal network in an organization “
Network for transmitting information within an organization that is based on informal contacts between
managers within an enterprise and on distributed information systems.”
In a knowledge network, because <strong>of</strong> <strong>the</strong> bigger importance <strong>of</strong> information, it is easier to learn and
faster to gain advantage <strong>of</strong> <strong>the</strong> information, and to innovate. That is why knowledge networks even
have <strong>the</strong> name “innovative networks” or “learning networks”.
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Andrea Kő, Péter Fehér and Krisztián Varga
Figure 3: Graphical representation <strong>of</strong> a knowledge network (Anklam, 2009)
Anklam (2009) a gives <strong>the</strong> following representational rules and interpretational framework to <strong>the</strong>
knowledge networks (or communities <strong>of</strong> practice) (Figure 3):
Each circle (node) indicates one participant <strong>of</strong> <strong>the</strong> knowledge network.
A participant can be one person or an organization ei<strong>the</strong>r.
There is a line drawn between two nodes, if <strong>the</strong>y have any kind <strong>of</strong> a connection (<strong>the</strong>y know eacho<strong>the</strong>r,
<strong>the</strong>y communicate, <strong>the</strong>y are partners, etc.).
The size <strong>of</strong> a node is directly proportional to <strong>the</strong> number <strong>of</strong> connections it has. So <strong>the</strong> size is a
quantitative measurement unit, it depends on <strong>the</strong> number <strong>of</strong> connections only.
The thickness <strong>of</strong> a connection line indicates <strong>the</strong> quality <strong>of</strong> <strong>the</strong> connection. The stronger <strong>the</strong>
connection is (frequent communication, long-term partnership), <strong>the</strong> thicker <strong>the</strong> line is.
1.2 ICT Solutions in Knowledge Management and Knowledge Networks
Different approaches to knowledge management can be distinguished on <strong>the</strong> basis <strong>of</strong> <strong>the</strong> investigated
research questions, as <strong>the</strong> learning focused approach; <strong>the</strong> process focused approach; <strong>the</strong> technology
focused approach; <strong>the</strong> environment focused (ecological) approach and <strong>the</strong> purpose focused approach
(Klimkó, 2001). Researchers who follow <strong>the</strong> technology focused approach consider knowledge as a
transferable object. For representatives <strong>of</strong> this <strong>the</strong>ory <strong>the</strong> ultimate task <strong>of</strong> knowledge management is
to ensure accessibility and/or transferability <strong>of</strong> knowledge with <strong>the</strong> help <strong>of</strong> an information technology
tools, <strong>the</strong>refore <strong>the</strong>se researchers <strong>of</strong>ten come from <strong>the</strong> field <strong>of</strong> artificial intelligence.
There are several unanswered questions about <strong>the</strong> role <strong>of</strong> ICT played in <strong>the</strong> area <strong>of</strong> <strong>the</strong> management
<strong>of</strong> <strong>the</strong> organizational knowledge. Zack divides <strong>the</strong>se problems into four bigger categories (Zack 1999):
Uncertainty: being not enough knowledge available
Complexity: being too much knowledge available, which is hard to manage
Ambiguity: <strong>the</strong>re is not available any conceptual framework accepted uniformly for <strong>the</strong>
interpretation <strong>of</strong> knowledge
There are too many conceptual frameworks for <strong>the</strong> interpretation <strong>of</strong> framework.
The main role <strong>of</strong> ICT in knowledge management is to lower obstructive elements in knowledge
sharing and delivery radically. Knowledge elements can be accessed easier by electronic storage
capacity, and <strong>the</strong> development <strong>of</strong> <strong>the</strong> knowledge sharing process may become more effective
(Hendriks, 1999).
The information technology supports <strong>the</strong> organizations substantially in <strong>the</strong> treatment <strong>of</strong> problems
which are related to complexity e.g. with augmentation <strong>of</strong> information processing capacity. The ICT
still helps <strong>the</strong>m in treating <strong>the</strong> problems <strong>of</strong> uncertainty, because it provides localization and collection
<strong>of</strong> information and recognition <strong>of</strong> fields having not enough information available. For map, evaluate,
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Andrea Kő, Péter Fehér and Krisztián Varga
and discover knowledge elements, ICT solutions can be used, which focus on not <strong>the</strong> storage <strong>of</strong>
knowledge elements, ra<strong>the</strong>r on creating a map <strong>of</strong> <strong>the</strong> intellectual capital and competences in an
organization (Davenport and Prusak, 1999). It is possible to reach <strong>the</strong> experts, “localize” <strong>the</strong>m (e.g.
with knowledge maps). In this way it facilitates <strong>the</strong> distribution <strong>of</strong> knowledge. Intelligent agents
automate <strong>the</strong> identification <strong>of</strong> important and valuable knowledge elements (Malone et al, 1997), while
discussion forums and chat rooms can be used to collect knowledge from its origin: <strong>the</strong> people
<strong>the</strong>mselves (Pan and Scarbrough, 1998).
To support creation <strong>of</strong> new knowledge, data mining, OLAP-based techniques, statistical or o<strong>the</strong>r
analytical tools are useful (Shaw et al, 2001). Organizations can use decision support systems, data
warehouses, expert systems, data mining tools to treat and analyse input and output variables,
processes that are related to each o<strong>the</strong>r in complex way; to process and use huge amount <strong>of</strong>
information in decision-making and statistic analysis. As part <strong>of</strong> <strong>the</strong> data mining process, neural
networks can play an important role. Neural networks model <strong>the</strong> neurons <strong>of</strong> <strong>the</strong> human brain, and use
ma<strong>the</strong>matical rules and formulas in order to make decisions or find new knowledge elements (Lin and
Shaw, 1997). Ano<strong>the</strong>r popular way to discover new pieces <strong>of</strong> knowledge in an organization is text
mining, when information and knowledge are searched beneath <strong>the</strong> meaning, “between <strong>the</strong> lines” <strong>of</strong>
documents, e-mails, etc. (Lang and Burnett, 2000; Abecker et al, 2000; Ahmed and Ward, 2000, Rao
and Sprague, 1998).
To share and deliver knowledge IT <strong>of</strong>fers <strong>the</strong> usage <strong>of</strong> search engines, enterprise portals or document
management systems (Simon, 2001). In case <strong>of</strong> explicit knowledge an online, central repository can
be developed supporting <strong>the</strong> search and access <strong>of</strong> information for anybody who requires it.
Employees not only can read <strong>the</strong> information stored in <strong>the</strong> repositories, <strong>the</strong>y can share <strong>the</strong>ir own
experiences and knowledge as well.
The information technology has an important role in <strong>the</strong> uncertainty management (e.g. fuzzy
systems). The development <strong>of</strong> organizational ontology can solve <strong>the</strong> problems related to <strong>the</strong>
ambiguity mentioned above (Abecker et al. 1998; O’Leary, 1998). Primarily specifying <strong>the</strong> meaning <strong>of</strong>
<strong>the</strong> occurring expressions more accurately, than <strong>the</strong> natural language provides it. It has <strong>the</strong>
advantage <strong>of</strong> supporting <strong>the</strong> realization <strong>of</strong> consistency and completeness.
More areas <strong>of</strong> KM can be supported with groupware systems. These systems helps <strong>the</strong>ir users to
collaborate, and in this way, to create, share and use new, common knowledge elements (Gábor,
2002, Hayes and Walsham, 2001). Solutions supporting collaborative work assist <strong>the</strong> learning
process <strong>of</strong> <strong>the</strong> organization (e.g. Lotus Notes).
Expert systems are useful in <strong>the</strong> phase <strong>of</strong> using <strong>the</strong> existing (or discovered, newly created) knowledge
(Liebowitz and Beckman, 1998).
Workflows or o<strong>the</strong>r automated processes may pr<strong>of</strong>it from <strong>the</strong> usage <strong>of</strong> a knowledge-based system,
like a repository, ontology or expert system. In an activity, a query can be defined, <strong>the</strong> knowledgebased
system may support explicit knowledge as answer, and this, combined with <strong>the</strong> knowledge <strong>of</strong>
<strong>the</strong> user will form a new solution to <strong>the</strong> main question (Salisbury, 2003).
The expression “Knowledge management system” also occurs in <strong>the</strong> literature. Schreiber has <strong>the</strong>
following definition (Schreiber et al, 1998): <strong>the</strong> knowledge management system is a tool for managing
<strong>the</strong> organizational knowledge, supporting organizations in problem-solving activities and facilitating
<strong>the</strong> decision-making.
The application <strong>of</strong> technology is not enough for <strong>the</strong> development <strong>of</strong> <strong>the</strong> knowledge intensive
environment. It has an important role in <strong>the</strong> knowledge management life cycle, but it cannot guarantee
<strong>the</strong> organizational success in <strong>the</strong> area <strong>of</strong> knowledge management. The technology is a vital element
<strong>of</strong> knowledge management, but it does not equal to it.
Knowledge management projects <strong>of</strong>ten rely on <strong>the</strong> latest technologies and in some cases <strong>the</strong>y act as
a catalyst in knowledge management initiatives. Information and communication technologies have
mainly supporting role in knowledge codification; transfer, sharing and utilization, but sometimes <strong>the</strong>y
get a decisive role in knowledge management processes. Several frameworks are available in <strong>the</strong>
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Andrea Kő, Péter Fehér and Krisztián Varga
literature (Kő, Klimkó, 2010), (Fehér, 2004) which describes and categorizes technologies by
grouping <strong>the</strong> enabling technologies.
Figure 4: Technologies supporting knowledge management
The layers <strong>of</strong> <strong>the</strong> framework in Figure 1 are built on <strong>the</strong> following rationale: to get technical support for
a certain knowledge management problem, we need facts, knowledge sources, which are used to get
codified knowledge about <strong>the</strong> problem domain with a certain structure. This aspect is described in <strong>the</strong>
codification layer. We can analyze <strong>the</strong> codified knowledge afterwards for getting <strong>the</strong> proper input to
find a solution for our knowledge management problem. Finally, if we have a solution for a certain
knowledge management problem, we have to leverage it in <strong>the</strong> organization capitalizing <strong>the</strong>
technological support; in order to increase <strong>the</strong> knowledge assets <strong>of</strong> <strong>the</strong> company. We look through <strong>the</strong>
layers <strong>of</strong> <strong>the</strong> framework and we give hints for <strong>the</strong> selection <strong>of</strong> <strong>the</strong> proper technology in a certain
knowledge management situation.
In knowledge networks, <strong>the</strong> requirements in ICT are to support collaboration, to support working and
thinking toge<strong>the</strong>r, and to give <strong>the</strong> social experience to <strong>the</strong> participants <strong>the</strong>y are familiar with from <strong>the</strong>
web2.0 techniques and sites. That is why services and techniques from web2.0 platforms and sites
are introduced in <strong>the</strong> enterprise portfolio supporting knowledge networks.
Intranet
Web2.0 tools
Discussion forums
Chat
Wikis
Blogs
Social networks
E-learning services
Scorecarding
Virtual solutions
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2. Research methodology
2.1 Qualitative and explorative research
Andrea Kő, Péter Fehér and Krisztián Varga
Qualitative researches should be conducted in research areas, in which <strong>the</strong>re are no mature,
commonly accepted concepts, methods and methodologies (Elenurm, 2003). In <strong>the</strong> field <strong>of</strong> knowledge
management, even <strong>the</strong> basic concept <strong>of</strong> knowledge has no shared definition. Because knowledge
management is at <strong>the</strong> frontier <strong>of</strong> different scientific areas, approaches and interpretations can be
radically different.
In qualitative researches, <strong>the</strong>re is <strong>the</strong> possibility to explore thoughts, definitions and assumptions <strong>of</strong>
researched persons and organisation, namely <strong>the</strong> context <strong>of</strong> research. In <strong>the</strong> complex field <strong>of</strong>
knowledge management, researchers have more possibilities to explore new, not expected results
that are relevant to research (Oakley, 1999). In qualitative research, analysis <strong>of</strong> numerical data is also
possible, but <strong>the</strong> main emphasis is on deep exploration and understanding <strong>of</strong> relationships <strong>of</strong> <strong>the</strong>
research area (Blaxter et al, 2001).
A special characteristic <strong>of</strong> knowledge management is that researches should be explorative, instead
<strong>of</strong> hypo<strong>the</strong>sis-testing, because <strong>of</strong> <strong>the</strong> immature nature <strong>of</strong> <strong>the</strong> field. The goal <strong>of</strong> this research is <strong>the</strong>
deep examination <strong>of</strong> a quite unexplored area.
2.2 Case study based research
Among <strong>the</strong> tools <strong>of</strong> qualitative research approaches, <strong>the</strong> case study based research method is <strong>the</strong>
most suitable, because it provides <strong>the</strong> possibility <strong>of</strong> deep understanding. Based on Yin (1993), case
study based research should be used, when <strong>the</strong> field <strong>of</strong> research is wide and complex. The research
can answer <strong>the</strong> questions <strong>of</strong> why…? and how…?, but <strong>the</strong> questions should be posed by <strong>the</strong>
researcher (Yin, 1994). Case study based researches are suitable for testing, developing and
competing <strong>the</strong>ories (Eisenhardt, 1989). This case study based research is complemented by
statistical analysis.
2.3 Scope and method <strong>of</strong> research
The research sample consists <strong>of</strong> 36 companies from 5 EU countries (Italy, Spain, Greece, Hungary
and Slovakia), that were analysed through an online questionnaire, and selected companies were
explored through case study methodology. Although <strong>the</strong> limitations <strong>of</strong> <strong>the</strong> sample, <strong>the</strong> questionnaire
based results provide an overview, while <strong>the</strong> case studies provide better understanding. All <strong>of</strong> <strong>the</strong>
analysed company belongs to <strong>the</strong> SME category. Despite <strong>of</strong> <strong>the</strong> relatively small size <strong>of</strong> <strong>the</strong> sample,
<strong>the</strong> analysed companies covers several industrial areas.
Services
23%
N/A Healthcare industry
Textile Industry
3%
Energy Industry
11%
3%
3%
Agriculture
9%
Research & Development
3%
Commercial
Figure 5: The sample is dominated by <strong>the</strong> IT and Services Industries.
11%
529
34% IT

Andrea Kő, Péter Fehér and Krisztián Varga
Because <strong>of</strong> <strong>the</strong> combined approach (questionnaire and case studies), <strong>the</strong> research was able to
explore <strong>the</strong> properties and current challenges <strong>of</strong> <strong>the</strong> EU SMEs. Results <strong>of</strong> this research are expected
to be <strong>the</strong> basis <strong>of</strong> fur<strong>the</strong>r EU-wide questionnaire based researches. This paper is aimed to present <strong>the</strong>
first results, and identified areas for fur<strong>the</strong>r research.
<strong>European</strong> perspective <strong>of</strong> this research are partly acquired from research project on “Empowering
Business Ecosystems <strong>of</strong> Small Service Enterprises to Face <strong>the</strong> Economic Crisis”, with <strong>the</strong> thankful
donation <strong>of</strong> <strong>the</strong> EU FP7 Framework Programme, and this actual research project in financed from <strong>the</strong>
TÁMOP-4.2.1/B-09/1/KMR-2010-0005 project on ICT-based analyses <strong>of</strong> knowledge transfer, sharing
and knowledge codification fields.
3. Research results
3.1 Collaboration Habits and Needs
The results show that SMEs are more protective on <strong>the</strong>ir existing activities, like procurement,
production, or collaborating on <strong>of</strong>fering existing products and services, instead concentrating on future
developments.
In collaboration needs and habits, SMEs value <strong>the</strong> most <strong>of</strong> <strong>the</strong> development <strong>of</strong> <strong>the</strong>ir internal capability
basis, in sharing existing technology and practical expertise. Although <strong>the</strong>re are differences between
organisational practices, generally organisations value more <strong>of</strong> creating a good basis for product and
services, opposite to just selling new solutions. In <strong>the</strong>se topics SMEs collaboration with shared ecommerce
portals, web pages, and even in strategic partnerships.
As a good example, in Spain <strong>the</strong> research observed <strong>the</strong> following strong collaboration:
The partners involved in a collaborative agreement, will be benefited with products or
services <strong>the</strong> o<strong>the</strong>r partner have, at <strong>the</strong> same time, <strong>the</strong>y have to provide <strong>the</strong>ir own
products or services, to make an integrated <strong>of</strong>fer, and toge<strong>the</strong>r get specific advantages.
The most important issue, is to have clear <strong>the</strong> responsibilities, benefits, risks, solutions in
case <strong>of</strong> something goes wrong, etc, <strong>the</strong> partners have to fulfill, to obtain successful
results. Every organization is specialized in a specific area, and organizations considered
<strong>the</strong> summary <strong>of</strong> different knowledge and expertise, as a capability to create innovative
products and services. Although <strong>the</strong>se collaboration forms, <strong>the</strong> organizations considered
to be important to define very well topics like IPR at <strong>the</strong> moment <strong>of</strong> making collaborative
agreements.
Sharing technology expertise
Share practical expertise
Collaborative <strong>of</strong>fers <strong>of</strong> new products/services (inovation)
Shared product development
Common commercial activity towards current customers
Developing common R+D projects
Common commercial activity towards new customers
Integrated <strong>of</strong>fer <strong>of</strong> existing products/services
Shared trainings
Collaboration in negotiation with financing organisations
Shared production location
Shared procurement <strong>of</strong> goods and services
Figure 6: Which type <strong>of</strong> collaboration in regular in your company (average on scoring 1-5)
530
2,08
2,33
2,25
2,25
2,40
2,38
2,61
2,53
2,71
2,67
2,83
3,42

3.2 Communication between Partners
Andrea Kő, Péter Fehér and Krisztián Varga
The research analysed <strong>the</strong> used communication forms between business partners. Although <strong>the</strong>re is
a strong variance in <strong>the</strong> practice <strong>of</strong> each organisation, <strong>the</strong> tendencies are clear: <strong>the</strong> role <strong>of</strong> <strong>the</strong>
traditional personal communication is decreasing, while new communication forms are gaining space.
Surprisingly, <strong>the</strong> most popular communication form was <strong>the</strong> use <strong>of</strong> different instant messaging
solutions, like Skype <strong>of</strong> MSN Messenger. In <strong>the</strong> case studies, <strong>the</strong> companies valued <strong>the</strong>se solutions,
because <strong>the</strong>se solutions are cheap, easy to use, and combining several communication forms (like
messaging, voice calls, video calls and even screen sharing). This flexibility <strong>of</strong> <strong>the</strong>se tools made
available a wide range <strong>of</strong> applications, starting from <strong>the</strong> change <strong>of</strong> only a few sentences in written
(e.g. confirming arrival <strong>of</strong> shipment or e-mail, or discussing meeting time), while screen sharing makes
available even deeper collaboration.
Beside <strong>the</strong> importance <strong>of</strong> <strong>the</strong>se new technological solutions, communication via phone and e-mail is
still popular, although <strong>the</strong>se forms are not used with every business partner regularly (please note,
that <strong>the</strong> results are not mirroring <strong>the</strong> availability <strong>of</strong> <strong>the</strong>se tools). Moreover in case <strong>of</strong> SMEs, it is not
surprise, that a company neglect totally a communication for, event personal meeting, or phone call, if
<strong>the</strong>re is something that is more suitable for achieving goals.
The survey shows that even more advanced technical solutions are getting into <strong>the</strong> daily life <strong>of</strong> SMEs.
Despite SMEs are usually considered as technology followers, <strong>the</strong> share <strong>of</strong> using videoconference
and social site (e.g. Facebook) is promising for <strong>the</strong> future. Based on <strong>the</strong> case study analysis, <strong>the</strong>
research concluded, that o<strong>the</strong>r specialised solutions like extranet and e-commerce solutions are used
only in special circumstances for special purposes. Especially <strong>the</strong> ICT and services sector is <strong>the</strong>
pioneer <strong>of</strong> using <strong>the</strong>se solutions.
What <strong>the</strong> survey results, but especially <strong>the</strong> case studies presented, that <strong>the</strong> companies use <strong>the</strong>
combination <strong>of</strong> <strong>the</strong> available communication tools, and do not use every tool with every partner.
Based on <strong>the</strong> case studies, it is clear, that this combination is more an result <strong>of</strong> an organic
development, and less as a conscious communication strategy.
Instant Messaging
Phone
E-mail
Personal
Fax
Groupwork Solution
Videoconference
Social Site (e.g. Facebook)
Extranet
E-commerce solution
Figure 7: Channels <strong>of</strong> collaboration with business partners (as percentage <strong>of</strong> respondents)
3.3 E-business service valuation
7,69%
7,69%
15,38%
30,77%
41,01%
38,46%
38,46%
61,54%
60,72%
57,88%
In case <strong>of</strong> e-business collaboration, document related activities gained over average evaluation from
<strong>the</strong> companies. Although <strong>the</strong> importance <strong>of</strong> document sharing and collaboration is important in <strong>the</strong>
relationship with business partners, it has it limitations. Organisation value not only document
531

Andrea Kő, Péter Fehér and Krisztián Varga
management but also shared resource planning tools and platforms, as well. Organisations usually
quote its importance in collaborative research or development projects, or in international
collaborations.
The survey showed, and <strong>the</strong> case studies reflected, that organisations are waiting for <strong>the</strong> maturity <strong>of</strong>
tendering and procurement solutions, although some pioneers already started <strong>the</strong>se activities.
Figure 8: Evaluation <strong>of</strong> possible e-business solutions for SMEs (average on scale 1-5)
3.4 Role <strong>of</strong> associations
Plan resources
Share edition <strong>of</strong> document
Document management
Document import/export
Integrated electronic <strong>of</strong>fer
Common tender preparation
The work <strong>of</strong> <strong>the</strong> SMEs is helped through pr<strong>of</strong>essional organisations. The research aimed to explore
<strong>the</strong> requirements for <strong>the</strong>se associations, and <strong>the</strong> <strong>of</strong>fered services. Most <strong>of</strong> <strong>the</strong> possible <strong>of</strong>fered
services are assessed slightly above average, generally supporting networking and knowledge
sharing, but <strong>the</strong>re is two significantly different factors:
SMEs require strong support to appear in international markets. These companies lack both <strong>the</strong>
knowledge and competence to lead a successful internalisation strategy, <strong>the</strong>refore require help
from business associations
Despite financing is as valid and continuous problem <strong>of</strong> SMEs, <strong>the</strong> requirement for support on
achieving public funds is below average. As it was seen in <strong>the</strong> case studies, organisations are
participating in public programs, but without <strong>the</strong> help <strong>of</strong> business associations. Instead relying on
<strong>the</strong>ir own expertise, or collaborating with specialised consultants to gain funds.
Support to internationalization for a
sector/district
Promotion <strong>of</strong> innovative technologies
Promotion <strong>of</strong> a service sector to o<strong>the</strong>r
sectors
Promotion <strong>of</strong> horizontal aggregation for
purchasing and resource sharing
Sectoral events to promote networking
and knowledge update
Promotion <strong>of</strong> vertical aggregation for new
products
Support to access public funding for
enterprise networks
Figure 9: Evaluation <strong>of</strong> <strong>the</strong> role <strong>of</strong> sectorial and o<strong>the</strong>r pr<strong>of</strong>essional associations (average on scale 1-
5)
532
2,96
2,84
3,37
2,36
3,61
3,81
3,79
3,28
3,16
3,16
3,14
4,28
3,44

4. Conclusion
Andrea Kő, Péter Fehér and Krisztián Varga
The case study points out that <strong>the</strong>re is demand in <strong>the</strong> organisations to knowledge management,
Based on <strong>the</strong> survey and case study results, <strong>the</strong> research was formalised <strong>the</strong> following main
experiences:
Collaboration must be based in a strategy win-win.
To foster cooperation we need to prompt first move
The members need to talk about <strong>the</strong> future
The members need to know that <strong>the</strong> group will meet again and again
We should call individuals for cooperation not firms. Them <strong>the</strong>y can form groups for collaboration
and <strong>the</strong>m companies can cooperate
Resources and time are needed for contextualization
Cooperation is <strong>the</strong> objective but is not affordable directly. The entire process should be split on
three parts: 1) Interaction 2) Collaboration 3) Cooperation.
Collaboration is easier if it is based on complementarities between participants and <strong>the</strong> goal is
exciting for all <strong>of</strong> <strong>the</strong>m.
Interaction between individuals must precede collaboration between firms.
It is important to start interaction with some basic common objectives, but <strong>the</strong> participants should
be open to any initiative appearing during <strong>the</strong> dynamics <strong>of</strong> interactions.
Taking into account <strong>the</strong>se lessons, in our experience working with SMEs a fruitful way to achieve <strong>the</strong>
objectives, is to start <strong>the</strong> initial interaction between <strong>the</strong> managers <strong>of</strong> <strong>the</strong> firms with <strong>the</strong> discussion <strong>of</strong>
new (potential) business opportunities based on <strong>the</strong> core competences <strong>of</strong> each company. Identifying
<strong>the</strong>se core competences, it is possible to discover complementarities between <strong>the</strong> companies that can
open for <strong>the</strong>m new business opportunities, out <strong>of</strong> <strong>the</strong> reach <strong>of</strong> each one <strong>of</strong> <strong>the</strong>m individually. These
potential market opportunities should allow obtaining balanced benefits from its exploitation to each
partner, and can be <strong>the</strong> starting point towards deeper levels <strong>of</strong> collaboration between <strong>the</strong> firms,
promoting a real collaborative culture.
5. Acknowledgements
The research is part <strong>of</strong> <strong>the</strong> TÁMOP-4.2.1/B-09/1/KMR-2010-0005 project, which is a research and
innovation program <strong>of</strong> <strong>the</strong> Corvinus University <strong>of</strong> Budapest aiming ICT-based analyses <strong>of</strong> knowledge
transfer, sharing and knowledge codification fields. The financial support from TÁMOP 4.2.1/B-
09/1/KMR-2010-0005 is gratefully acknowledged.
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534

Practices to Promote Organizational Knowledge: a Case
Study in a Mining Company
Es<strong>the</strong>r Lage¹, Antônio Luiz Marques², Alexandre Carrieri², Bráulio Alturas¹
¹Information Sciences and Technology Department, Lisbon University
Institute, Lisbon, Portugal
²Economic Sciences Department, Federal University <strong>of</strong> Minas Gerais, Belo
Horizonte, Brazil
Elleo@iscte.pt
Marques@face.ufmg.br
Carrieri@face.ufmg.br
Braulio.Alturas@iscte.pt
Abstract: During <strong>the</strong> last two decades <strong>of</strong> <strong>the</strong> last century, new conceptual approaches in <strong>the</strong> field <strong>of</strong> Strategic
Management have highlighted intangible assets, especially information and knowledge, as <strong>the</strong> basis for
generating and sustaining competitive advantage. This paper aims to describe routines that foster organizational
knowledge in a mining company located in Brazil. Nine managers from <strong>the</strong> following departments were
interviewed: Technological Development, in charge <strong>of</strong> knowledge management in <strong>the</strong> organization; Human
Resources, Communications and Marketing, directly related to employees and stakeholders´ knowledge;
Industrial Engineering and Industrial Automation for <strong>the</strong>ir relation to <strong>the</strong> innovation in <strong>the</strong> manufacturing process;
and Mining and Processing departments, since <strong>the</strong>y represent <strong>the</strong> core business <strong>of</strong> <strong>the</strong> company. A qualitativedescriptive
approach was applied in <strong>the</strong> research. Semi-structured interviews were used in order to collect
primary data. The company’s managers were asked to identify routines that promote <strong>the</strong> creation, acquisition,
codification, sharing, usage and protection <strong>of</strong> knowledge. Those interviews were recorded and transcribed.
Internal documents were also used to describe <strong>the</strong> routines. Routines that promote organizational knowledge in
<strong>the</strong> researched company are strongly aligned to <strong>the</strong> strategic outcomes and to <strong>the</strong> consequent critical knowledge
necessary to achieve <strong>the</strong>m. Considerable efforts have been made by <strong>the</strong> company regarding technology,
compensation and structure in order to support <strong>the</strong> routines. Those investments reveal <strong>the</strong> importance <strong>of</strong> a strong
commitment <strong>of</strong> <strong>the</strong> top managers to <strong>the</strong> knowledge management. The company recognizes <strong>the</strong> competitive
advantage it gains by utilizing its employees as a source <strong>of</strong> information and knowledge. Despite this awareness
and all measures connected to effectively implement routines which maximize <strong>the</strong> creation <strong>of</strong> internal information,
<strong>the</strong> information sharing process lacks efficiency. The resistance <strong>of</strong> employees in sharing information brings up
<strong>the</strong> relationship between knowledge and power. Among <strong>the</strong> organizational aspects that permeate all routines it is
possible to highlight interdisciplinary work, financial and material support and financial and symbolic rewards.
Keywords: information; organizational knowledge; routines; organizational learning
1. Introduction
During <strong>the</strong> last two decades <strong>of</strong> <strong>the</strong> last century, new conceptual approaches in <strong>the</strong> field <strong>of</strong> Strategic
Management, such as <strong>the</strong> Resource Based View and <strong>the</strong> Dynamic Capabilities Approach, have
highlighted intangible assets, especially information and knowledge, as <strong>the</strong> basis for generating and
sustaining competitive advantage (Prahalad, Hamel 2000; Stalk Jr., Evans, Shulman 2000).
In seeking to understand <strong>the</strong> role <strong>of</strong> information and knowledge in developing innovative products,
improving processes and making <strong>the</strong> decision-making process more effective, new <strong>the</strong>ories began to
characterize organizations as information based organizations (Drucker 2000), knowledge
organizations (Nonaka, Takeuchi 1997) and learning organizations (Senge 1990).
In order to maintain and increase competitive advantage, companies’ attention turned to issues as <strong>the</strong>
integration <strong>of</strong> knowledge disperse among people and departments (Grant 1996) and knowledge
protection (Liebeskind 1996). In this context, knowledge management, previously a subject restricted
to scholars and R&D pr<strong>of</strong>essionals became a familiar <strong>the</strong>me in <strong>the</strong> organizational contexts (Oliveira,
Fleury 2001).
As pointed out by Barbosa and Paim (2003), knowledge management is a controversial topic that
arouses three types <strong>of</strong> reactions: adhesion, skepticism or questioning. For <strong>the</strong> first group, knowledge
management should be <strong>the</strong> strategy used by companies to cope with <strong>the</strong> current environmental
challenges. The second group is divided into two: those who think knowledge management is just
ano<strong>the</strong>r fad in <strong>the</strong> administration field and those who believe, for conceptual reasons, that knowledge
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can not be managed. For <strong>the</strong> third group knowledge management is a more modern technique to
exploit <strong>the</strong> mind <strong>of</strong> workers and make <strong>the</strong>m disposable.
Despite <strong>the</strong> controversies, information and knowledge have always been regarded relevant for
organizations. What may be considered different is <strong>the</strong> recent convergence <strong>of</strong> interests around <strong>the</strong>m,
which, as emphasized by Barbosa and Paim (2003), can be seen as a reflection <strong>of</strong> <strong>the</strong> increasing
importance <strong>of</strong> information and knowledge in <strong>the</strong> social and economic spheres.
Companies that adhere to knowledge management seek to discover how to achieve results through
<strong>the</strong> intelligence, skills and education dispersed among its members. This challenge becomes greater
as <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> organization increases.
This paper aims to describe <strong>the</strong> routines adopted by a large mining company located in Brazil to
enhance <strong>the</strong> processes <strong>of</strong> creation, acquisition, codification, sharing, use and protection <strong>of</strong>
organizational knowledge.
2. Knowledge management
Knowledge management can be understood as efforts undertaken by organizations with <strong>the</strong> objective
to create, acquire, convert, protect and use <strong>the</strong>ir knowledge in order to build <strong>the</strong>ir competitive
advantage (Gold, Malhotra, Segars 2001).
The starting point for knowledge management is <strong>the</strong> understanding <strong>of</strong> what was called by Leonard-
Barton (1998) <strong>the</strong> company's strategic skills. Companies must identify <strong>the</strong> competences that were
established over time and that can not be easily imitated (Hamel, Prahalad 2000). The role <strong>of</strong>
knowledge management is to ensure that <strong>the</strong> company has <strong>the</strong> necessary knowledge to support and
develop those competences.
The internal creation <strong>of</strong> organizational knowledge involves individual and group learning through
continuous recasting or restructuring <strong>of</strong> cognitive models. The cognitive structure <strong>of</strong> individuals is
developed through interaction with social contexts, via communication functions, imitation and
comparison. These social interactions encourage individuals to analyze <strong>the</strong> strengths and
weaknesses <strong>of</strong> <strong>the</strong>ir mental models, favoring <strong>the</strong> development <strong>of</strong> new patterns <strong>of</strong> interpretation
regarding reality (Turvan 2001). When evaluated, <strong>the</strong> goal is not <strong>the</strong> group consensus, but <strong>the</strong> search
for <strong>the</strong> best model (Sartor 2002).
In addition to internally developing <strong>the</strong>ir own knowledge, it is also possible and intended that
companies learn from outside <strong>the</strong>ir own borders. Organizations seek to acquire knowledge when <strong>the</strong>y
identify an internal deficiency <strong>of</strong> skill. A huge need for external knowledge acquisition can be found
when <strong>the</strong> insufficient or outdated knowledge affects <strong>the</strong> company’s long term competitiveness
(Leonard-Barton, 1998).
Knowledge codification aims to register and organize <strong>the</strong> information spread internally so that this
information is not lost and can become accessible to those who need it. Besides being encoded,
information should be shared among members <strong>of</strong> <strong>the</strong> organization. It is known that companies have
certain skills that are shared by all, but also stocks <strong>of</strong> knowledge belonging to specific groups or
individuals. Companies should identify <strong>the</strong> type <strong>of</strong> knowledge which is intended to be shared, with
whom and what are <strong>the</strong> advantages for <strong>the</strong> business strategy. Oliveira Jr. (2001, p. 295) defines
knowledge sharing as "<strong>the</strong> process <strong>of</strong> dissemination <strong>of</strong> knowledge <strong>of</strong> a company under <strong>the</strong> control <strong>of</strong>
<strong>the</strong> owner <strong>of</strong> that knowledge."
As pointed out by Davenport and Prusak (1998), none <strong>of</strong> <strong>the</strong> following routines are relevant if <strong>the</strong>
knowledge is not put into use, leading to changes in behavior, strategies, products and competences.
Gold, Malhotra and Segars (2001) identified that little attention has been given to this aspect in
literature. Many factors can influence people not to use knowledge such as: not respecting <strong>the</strong>
source, missing time, missing opportunities, being afraid to take risks and stubbornness (Davenport,
Prusak 1998).
In <strong>the</strong> end, a factor that can not be neglected in <strong>the</strong> process <strong>of</strong> knowledge management is <strong>the</strong> need to
strategically protect it, ei<strong>the</strong>r against <strong>the</strong> competition or against <strong>the</strong> possibility that it leaves <strong>the</strong>
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company with <strong>the</strong> people who possess it. It is important that companies decide first which knowledge
<strong>the</strong>y want to protect and what mechanisms should be used for that purpose.
In order to make <strong>the</strong>se processes work organizations must count on various organizational
components, such as information technology, organizational structure, <strong>the</strong>ir vision and values and,
above all, on <strong>the</strong> individual and group behavior <strong>of</strong> <strong>the</strong>ir members (Richter 2002).
3. Methodology
This paper applies a qualitative-descriptive research method aiming to identify and describe routines
related to <strong>the</strong> leveraging <strong>of</strong> organizational knowledge from <strong>the</strong> viewpoint <strong>of</strong> <strong>the</strong> managers. A case
study was chosen since it is considered to be <strong>the</strong> research strategy most appropriate to answer
questions such as why and when; to understand a phenomenon upon which <strong>the</strong>re is little control; and
when <strong>the</strong> objective is to focus on a real situation (Yin 2001). The main limitation <strong>of</strong> <strong>the</strong> method, as
outlined by Gil (1991), refers to <strong>the</strong> difficulty <strong>of</strong> generalization <strong>of</strong> its results. Due to being an
outstanding company in its industry which has been making investments in knowledge management,
a Mining Company, located in Minas Gerais, Brazil, was selected to be analyzed in this paper. Nine
managers <strong>of</strong> areas related to knowledge management, both administrative and operational, were
interviewed during two visits to <strong>the</strong> company. The following departments were represented:
Technological Development, in charge <strong>of</strong> knowledge management in <strong>the</strong> organization; Human
Resources, Communications and Marketing, directly related to employees and stakeholders´
knowledge; Industrial Engineering and Industrial Automation for <strong>the</strong>ir relation to <strong>the</strong> innovation in <strong>the</strong>
manufacturing process; and Mining and Processing departments, since <strong>the</strong>y represent <strong>the</strong> core
business <strong>of</strong> <strong>the</strong> company.
Semi-structured interviews were used in order to collect primary data. The managers were asked to
identify routines that promote <strong>the</strong> creation, acquisition, codification, sharing, usage and protection <strong>of</strong>
knowledge. Those interviews were recorded and transcribed. Secondary data were also used such as
<strong>the</strong> company’s website, <strong>the</strong> Code <strong>of</strong> Ethics, a brochure about <strong>the</strong> Patent Process, a model <strong>of</strong> <strong>the</strong>
script used for <strong>the</strong> codification <strong>of</strong> critical technologies and a CD Rom entitled “The Field <strong>of</strong> Ideas -
Reward and Recognition Program”. Despite those data were collected in 2003, <strong>the</strong>y have not been
published so far. The data were analyzed through <strong>the</strong>matic analysis in a qualitative way.
4. Knowledge management routines
4.1 Knowledge creation
Three routines related to <strong>the</strong> creation <strong>of</strong> knowledge stand out: <strong>the</strong> Field <strong>of</strong> Ideas Program, Learning
Groups and Master and Doctoral Programs. The Field <strong>of</strong> Ideas Program was created in September
1999. All employees can participate, including contractors. It aims to stimulate <strong>the</strong> creativity <strong>of</strong>
employees, especially on <strong>the</strong> operational level, through group work. The table below shows <strong>the</strong> seven
phases <strong>of</strong> <strong>the</strong> program:
Table 1: Steps <strong>of</strong> <strong>the</strong> Field <strong>of</strong> Ideas Program
Steps Responsible Description
1 Identification <strong>of</strong>
Opportunities
Employee Identification <strong>of</strong> a possible improvement, considering <strong>the</strong>
company´s goals and <strong>the</strong> experience <strong>of</strong> <strong>the</strong> employee.
The employee should fill out a form that is available on <strong>the</strong>
program´s website in order to participate in <strong>the</strong> program.
2 Proposal Analysis Managers The employee´s manager and <strong>the</strong> Field <strong>of</strong> Idea’s manager
have to assess <strong>the</strong> suggestion regarding <strong>the</strong> technical and
financial viability and <strong>the</strong> company’s goals. This process
can take up to 10 days.
3 Action Plan Employees The employees fill out <strong>the</strong> form “Action Plan” and negotiate
with <strong>the</strong> manager all <strong>the</strong> support needed to implement <strong>the</strong>
idea.
4 Implementation Employees Implementation <strong>of</strong> planned ideas.
5 Assessment <strong>of</strong>
Results
Employee´s manager and
<strong>the</strong> Field <strong>of</strong> Idea’s manager
The form "Verification Results” should be filled out by <strong>the</strong>
managers. If <strong>the</strong> results are not as expected, <strong>the</strong> process
returns to stage 3.
6 Standardization Employees´ manager and A standard is defined. If necessary, goals are reviewed and
<strong>the</strong> Field <strong>of</strong> Idea’s manager
employees are trained accordingly.
7 Reward Quality System Department Employees are rewarded according to rules previously
defined.
Source: CD-Rom: The Field <strong>of</strong> Ideas Program
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Two aspects <strong>of</strong> <strong>the</strong> program contribute most to stimulate <strong>the</strong> generation <strong>of</strong> new ideas: <strong>the</strong> openness
<strong>of</strong> <strong>the</strong> management team to analyze all ideas <strong>of</strong> <strong>the</strong> employees and <strong>the</strong> successive rounds <strong>of</strong>
negotiation and dialogue among <strong>the</strong> employees. This type <strong>of</strong> employees meeting in which <strong>the</strong>y seek
to develop improvements can be classified into what Davenport and Prusak (1998) call “<strong>the</strong>
networks”, i.e. communities <strong>of</strong> knowledge holders which are assembled by <strong>the</strong> same interests and
goals and seek to resolve problems toge<strong>the</strong>r.
The employees are rewarded with a financial prize for all <strong>the</strong> implemented ideas. The best five ideas
selected by criteria previously defined and published are presented and awarded at an annual
exhibition which is open to <strong>the</strong> community. The purpose is to give an opportunity for <strong>the</strong> employees to
have <strong>the</strong>ir ideas recognized also by friends and families. The award is divided into two categories:
ideas regarding quality, cost and service and ideas relating to environment, safety and morals. This
division was set due to difficulties in comparing proposals related to <strong>the</strong> first group, whose payback
can be much more easily calculated, to <strong>the</strong> proposals <strong>of</strong> <strong>the</strong> second group.
According to <strong>the</strong> manager responsible for this program, two points distinguish it from most
“Suggestion Box Programs” that do not work in practice: <strong>the</strong> fact that <strong>the</strong> group who suggests <strong>the</strong> idea
is responsible for its implementation, which prevents <strong>the</strong> idea from dying; and <strong>the</strong> support <strong>of</strong> <strong>the</strong> board
and <strong>of</strong> <strong>the</strong> management team by ensuring <strong>the</strong> necessary conditions to implement <strong>the</strong> idea.
Fur<strong>the</strong>rmore, all those steps are formalized in a s<strong>of</strong>tware, which allows employees to monitor <strong>the</strong>
progress through <strong>the</strong> seven steps.
The learning groups are created according to <strong>the</strong> company's goals. The general goals are split into
more specific ones and are assigned to a manager, who will be responsible to form a learning group
that encourages <strong>the</strong> development <strong>of</strong> knowledge related to <strong>the</strong>ir goal. The group is supposed to work
on <strong>the</strong> project full time. After <strong>the</strong> creation <strong>of</strong> <strong>the</strong> group, <strong>the</strong> next step is <strong>the</strong> identification <strong>of</strong> <strong>the</strong>
knowledge that will be needed to achieve <strong>the</strong> goal. The group should identify what <strong>the</strong> organization
already knows and what it will have to learn. The final step is <strong>the</strong> search for external information,
through formal training, technical visits, discussions, exchange <strong>of</strong> experiences and, if necessary,
hiring <strong>of</strong> specialized pr<strong>of</strong>essionals. In <strong>the</strong> viewpoint <strong>of</strong> one <strong>of</strong> <strong>the</strong> managers, <strong>the</strong> greatest benefits <strong>of</strong>
those groups are <strong>the</strong> multidisciplinarity and <strong>the</strong> comparison <strong>of</strong> mental models, as shown by <strong>the</strong>
statement below:
When you start to align people with different visions, <strong>the</strong> more multidisciplinary this group
is, <strong>the</strong> richer <strong>the</strong> discussion ... We discuss <strong>the</strong> strengths, <strong>the</strong> biases and it makes us
review some ideas, some absolute truths are no longer … (Interviewee 03).
In order to develop <strong>the</strong> ability <strong>of</strong> systemic thinking, <strong>the</strong> company provides a s<strong>of</strong>tware and uses
simulations, seeking to reproduce <strong>the</strong> complex and dynamic behavior <strong>of</strong> real systems.
The third routine related to knowledge creation is <strong>the</strong> sponsorship <strong>of</strong> master and doctoral programs.
To this end, <strong>the</strong> company creates a survey <strong>of</strong> <strong>the</strong> topics in each field <strong>of</strong> knowledge that need to be
developed and establishes agreements with <strong>the</strong> best Universities in <strong>the</strong> respective field. The
company makes sure, by using contracts with <strong>the</strong> researches, that <strong>the</strong> results that might arise are
property <strong>of</strong> <strong>the</strong> company.
4.2 Knowledge acquisition
Two practices related to <strong>the</strong> acquisition <strong>of</strong> knowledge stand out: <strong>the</strong> Reference Search Program and
<strong>the</strong> Relationship with Stakeholders.
The first aims to search for knowledge, be it administrative or operational, all over <strong>the</strong> word. Usually,
this search is done through technical missions. The employees who participate have <strong>the</strong> obligation to
pass on <strong>the</strong> knowledge gained through technical reports. Leonard-Barton (1998) calls this practice
“technological tracking”. According to <strong>the</strong> statement below, <strong>the</strong> company provides all <strong>the</strong> resources
needed to find <strong>the</strong> knowledge:
Typically, <strong>the</strong> resources we need to achieve <strong>the</strong>se goals are provided, whe<strong>the</strong>r financial,
training, physical ... (Interviewee 3).
Regarding <strong>the</strong> second aspect, <strong>the</strong> company maintains institutional relationships with universities,
industry bodies and research centers and monitors basic research in order to keep itself strategically
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positioned. The criterion for choosing <strong>the</strong> partner is always <strong>the</strong> best worldwide reference on <strong>the</strong>
respective subject.
The company is fur<strong>the</strong>rmore concerned about knowing its customer. This knowledge is generated
within <strong>the</strong> company's marketing department, through <strong>the</strong> use <strong>of</strong> <strong>the</strong> following four practices:
Quality Function Deployment Methodology - QFD: seeks to measure customer satisfaction by
comparing what is <strong>of</strong>fered to <strong>the</strong>m by <strong>the</strong> company and by <strong>the</strong> competitors;
Scenario planning: aims to ascertain <strong>the</strong> possible inter-relationships among <strong>the</strong> variables that
make up <strong>the</strong> environment and <strong>the</strong> market;
Use Value: s<strong>of</strong>tware to simulate <strong>the</strong> use <strong>of</strong> different specifications <strong>of</strong> iron ores and metallic
charges compositions for a particular client. This simulation helps <strong>the</strong> company to evaluate <strong>the</strong>
productivity, <strong>the</strong> amount <strong>of</strong> slag generated, <strong>the</strong> consumption <strong>of</strong> coke required, among o<strong>the</strong>r
important information. Based on that information, <strong>the</strong> company can identify <strong>the</strong> products’
properties that bring real gains to <strong>the</strong> client and price <strong>the</strong> product more effectively.
Headlights Quality Program: this program aims to compare each property <strong>of</strong> <strong>the</strong> iron ore or <strong>of</strong> <strong>the</strong>
pellets to <strong>the</strong> satisfaction <strong>of</strong> <strong>the</strong> customers.
4.3 Knowledge codification
The codification <strong>of</strong> knowledge aims to develop <strong>the</strong> organizational memory and to minimize <strong>the</strong>
company's losses. The program designed for this purpose is called Identification <strong>of</strong> Critical
Technologies and its steps can be seen in <strong>the</strong> table below:
Table2: Steps <strong>of</strong> <strong>the</strong> program: Identification <strong>of</strong> Critical Technologies
Steps Responsible
1 Instruction on how to identify critical Technological Development Department
technologies
2 Identification <strong>of</strong> critical technologies All departments
3 Codification process Technological Development Department
with <strong>the</strong> help <strong>of</strong> all departments
4 Information sharing Technological Development Department
5 Follow up <strong>of</strong> <strong>the</strong> critical technologies Technological Development Department
Source: interviews
The first step <strong>of</strong> <strong>the</strong> program is <strong>the</strong> organization <strong>of</strong> a workshop in which <strong>the</strong> departments are
instructed on how to identify critical technologies. The critical technologies are identified through an
evaluation based on predefined criteria such as urgency and impact on <strong>the</strong> organization.
Once identified, <strong>the</strong> Technological Development Department is responsible for interviewing <strong>the</strong>
employees who possess <strong>the</strong> knowledge about <strong>the</strong> critical technologies, by applying a script consisting
<strong>of</strong> <strong>the</strong> following items: a brief history, difficulties in applying <strong>the</strong> technology, existence <strong>of</strong> standard
procedures, visits carried out to search for references, standards associated with <strong>the</strong> technology,
related procedures, existence <strong>of</strong> recorded information about <strong>the</strong> technology such as a film or
photographs, possibility <strong>of</strong> patenting, people who know <strong>the</strong> technology, related documents and
additional comments.
These interviews are transcribed and made available on <strong>the</strong> intranet, so that, all employees have
access not only to <strong>the</strong> information but also to <strong>the</strong> employee who was interviewed. Each quarter, <strong>the</strong>
Technological Development Department should check <strong>the</strong> emergence <strong>of</strong> new technologies or <strong>the</strong> loss
<strong>of</strong> importance <strong>of</strong> technologies once considered critical.
4.4 Knowledge sharing
Five practices are highlighted regarding <strong>the</strong> sharing <strong>of</strong> knowledge within <strong>the</strong> company. First,
employees are in charge <strong>of</strong> passing on <strong>the</strong> information obtained at conferences and seminars.
Information technology supports this practice through a form available on <strong>the</strong> intranet.
The second practice is <strong>the</strong> company's management model, which includes a series <strong>of</strong> meetings that
contribute to <strong>the</strong> exchange <strong>of</strong> information among employees. There are annual lectures in which <strong>the</strong>
Board informs about <strong>the</strong> company’s vision and <strong>the</strong> actions being taken to achieve it. There are also
lectures led by <strong>the</strong> general managers in which <strong>the</strong> company's results regarding production, quality,
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environment and human resources are presented for all employees. Monthly, during meetings related
to routine management, all <strong>the</strong> employees have <strong>the</strong> opportunity to know <strong>the</strong> role, goal and processes
<strong>of</strong> <strong>the</strong>ir department. Also, <strong>the</strong>re are seminars that are held quarterly in order to discuss and share
research and development results related to <strong>the</strong> long-term strategy.
The third practice identified is <strong>the</strong> use <strong>of</strong> stories. According to Swap et al. (2001), stories are an ideal
mechanism for transmission <strong>of</strong> values. The most commented story among employees refers to <strong>the</strong>
innovative feature <strong>of</strong> <strong>the</strong> company since its founding, as shown below:
The company was born revolutionary in all its processes, it created an attitude…that
remains. It was passed down from generation to generation and I believe that if we lose
this, in essence, <strong>the</strong> company ends, <strong>the</strong> company survives because <strong>of</strong> this culture
(Interviewee 7).
The fourth practice is a monthly magazine published by <strong>the</strong> company. This magazine contains
sections related to technology and innovation.
The last routine are <strong>the</strong> standardization and certification processes achieved by <strong>the</strong> company such as
<strong>the</strong> ISO 9000 and ISO 14000. In order to obtain <strong>the</strong>se certifications, <strong>the</strong> company had to explicit all <strong>the</strong>
knowledge that was disorganized or implied. In terms <strong>of</strong> safety, for instance, <strong>the</strong> knowledge came from
<strong>the</strong> operational level employees. Periodic meetings were held, in which groups <strong>of</strong> 40 people per shift
participated. Diverse topics were discussed such as production, equipment, mail systems, etc. Each
group was led by an engineer whose function was to ga<strong>the</strong>r all <strong>the</strong> information about <strong>the</strong> topic. At <strong>the</strong>
end <strong>of</strong> <strong>the</strong> meeting with all groups, an agreement about which procedures <strong>the</strong> company should apply
was expected.
4.5 Knowledge use
Aiming to prevent knowledge from not being transformed into best practices and decisions within <strong>the</strong>
company, <strong>the</strong>re is, according to one interviewee from <strong>the</strong> Technological Development Department, an
informal routine, named Rolling Load. Effective practices that were identified but were not
implemented in o<strong>the</strong>r departments are checked by <strong>the</strong> Technological Development Department,
which tries to identify <strong>the</strong> causes for <strong>the</strong>ir no implementation. The goal, according to <strong>the</strong> interviewee is
not to penalize people who do not use knowledge, but make sure <strong>the</strong>y know it. The mandatory use
will depend on <strong>the</strong> result brought by <strong>the</strong> implementation <strong>of</strong> <strong>the</strong> knowledge in o<strong>the</strong>r departments.
There is also a strong association between <strong>the</strong> courses <strong>of</strong>fered or sponsored by <strong>the</strong> company and <strong>the</strong>
application <strong>of</strong> knowledge. All training programs are aligned with <strong>the</strong> long-term strategy and emerge
from <strong>the</strong> needs identified to meet <strong>the</strong> goals. They have an application module by which <strong>the</strong>
employees, knowing <strong>the</strong> deficiencies and improvement opportunities in <strong>the</strong>ir areas, seek to apply <strong>the</strong>
content learned during <strong>the</strong> training. The Field <strong>of</strong> Ideas Program itself was created from ideas <strong>of</strong> an
applied project.
4.6 Knowledge protection
The company understands <strong>the</strong> strategic role that knowledge plays in ensuring its competitive
advantage and defined practices that seek to prevent that relevant knowledge is lost by naivety or
carelessness.
To this end, <strong>the</strong> policy <strong>of</strong> intellectual property was created to recognize <strong>the</strong> creativity <strong>of</strong> employees,
enable <strong>the</strong> economic exploitation <strong>of</strong> patents and develop <strong>the</strong> organization's memory. By means <strong>of</strong> its
Program <strong>of</strong> Patents, <strong>the</strong> company intends to inform employees about <strong>the</strong> patenting process and
encourage <strong>the</strong>ir participation. All employees can access information on patents and insert a request
for evaluation online. The application is evaluated by a collegiate, which will examine <strong>the</strong>
technological state <strong>of</strong> <strong>the</strong> art <strong>of</strong> <strong>the</strong> proposals. If <strong>the</strong> ideas are found patentable, <strong>the</strong>y are forwarded to
<strong>the</strong> Industrial Property National Institute and <strong>the</strong> inventor receives an award.
Through its Code <strong>of</strong> Ethical Conduct <strong>the</strong> company seeks to clarify <strong>the</strong> acceptable behavior with
respect to <strong>the</strong> information entering and leaving <strong>the</strong> organization. Ano<strong>the</strong>r form <strong>of</strong> protection used is
<strong>the</strong> filtering <strong>of</strong> strategic information that can not leave <strong>the</strong> company, ei<strong>the</strong>r at conferences, papers or
presentations. An exception is made for matters relating to safety. The company recognizes its
responsibility to share such information, no matter how innovative, with o<strong>the</strong>r companies.
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Finally, <strong>the</strong> company uses <strong>the</strong> contract <strong>of</strong> service as a means <strong>of</strong> securing its knowledge with relation
to contractors, be <strong>the</strong>y partners, suppliers or consultants. Through it all, <strong>the</strong> partners assume
responsibility for not passing on information and become aware that any development <strong>of</strong> knowledge
gained from that contract will belong to <strong>the</strong> company. Any situation outside <strong>the</strong>se rules must be
negotiated with <strong>the</strong> company. The company also does what Winter (1998) called “observation
restriction” by controlling visitor activities and tracking what may or may not be filmed, photographed
or recorded.
5. Routines: supportive and challenging aspects
It is possible to highlight three main factors which support <strong>the</strong> routines in <strong>the</strong>ir role <strong>of</strong> promoting
organizational knowledge. The first one is <strong>the</strong> existence <strong>of</strong> a specific department, <strong>the</strong> Technological
Development, in charge <strong>of</strong> knowledge management in <strong>the</strong> organization. The second one is <strong>the</strong>
alignment <strong>of</strong> <strong>the</strong> routines to <strong>the</strong> strategic objectives <strong>of</strong> <strong>the</strong> company and to <strong>the</strong> compensation model.
The third one is <strong>the</strong> use <strong>of</strong> information and communication technology, mainly for <strong>the</strong> processes <strong>of</strong>
codification and information sharing.
Regarding <strong>the</strong> challenges, despite all those practices mentioned, some respondents stated that
knowledge sharing is <strong>the</strong> main problem in <strong>the</strong> company, as shown by <strong>the</strong> statement below:
People travel, visit, get information, but do not share it, and this difficulty, this change <strong>of</strong>
mentality has to occur within <strong>the</strong> company (interviewee 1).
Some reasons are cited as causes for that such as <strong>the</strong> lack <strong>of</strong> personnel and <strong>of</strong> a well designed
methodology; <strong>the</strong> perception that employees are giving up something important when <strong>the</strong>y volunteer
<strong>the</strong>ir expertise to <strong>the</strong> company; and time pressure.
It was also possible to identify some resistance shown by <strong>the</strong> engineers when confronted with
situations in which <strong>the</strong>y should share <strong>the</strong>ir knowledge with o<strong>the</strong>r colleagues, as shown by <strong>the</strong>
statement below:
Sometimes, at <strong>the</strong> level <strong>of</strong> <strong>the</strong> engineers, <strong>the</strong>y can show a little more difficulty to write
down <strong>the</strong>ir knowledge, ei<strong>the</strong>r because <strong>of</strong> <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> knowledge…or even
because <strong>of</strong> fear <strong>of</strong> loosing <strong>the</strong> knowledge (interviewee 8).
The fear <strong>of</strong> <strong>the</strong> engineers also brings out two views about knowledge management raised by Spender
(2001): <strong>the</strong> one that considers knowledge as an object to be possessed and <strong>the</strong> o<strong>the</strong>r which sees
knowledge as a process to be developed. The two complement each o<strong>the</strong>r, but <strong>the</strong>y give different
messages to managers and employees. The focus on control can also be seen below:
We have to be able to get <strong>the</strong> knowledge at <strong>the</strong> right time…because, sometimes, if you
do not get <strong>the</strong> knowledge at that time, you will take a while to get it or will not get it
anymore (interviewee 8).
Concentrating on control can make employees more defensive and less likely to spend time on
activities <strong>of</strong> reflection, dialogue and debate which are fundamental during <strong>the</strong> process <strong>of</strong> knowledge
creation. Routine pressure was also identified as an aspect that inhibits <strong>the</strong> process <strong>of</strong> creation,
sharing and using knowledge:
Our big problem is that…routine that does not allow us to devote time to things that
would be strategic. People are too absorbed by routine. And I realize this claim in all
areas (Interviewee 2),
Focusing on routine may inhibit openness to new ideas and development <strong>of</strong> systemic thinking,
whereby <strong>the</strong> employee can see <strong>the</strong> organization as an interdependent whole.
6. Conclusion
Routines that promote organizational knowledge in <strong>the</strong> researched company are strongly aligned to
<strong>the</strong> strategic outcomes and to <strong>the</strong> consequent critical knowledge necessary to achieve <strong>the</strong>m.
Considerable efforts have been made by <strong>the</strong> company regarding technology, compensation and
structure in order to support <strong>the</strong> routines. Those investments reveal <strong>the</strong> importance <strong>of</strong> a strong
commitment <strong>of</strong> <strong>the</strong> top managers with <strong>the</strong> knowledge management. Among <strong>the</strong> organizational
aspects that permeate all routines it is possible to highlight interdisciplinary work, financial and
material support and financial and symbolic rewards.
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The company recognizes <strong>the</strong> competitive advantage it gains by utilizing its employees as a source <strong>of</strong>
information and knowledge. Despite this awareness and all measures connected to effectively
implement routines which maximize <strong>the</strong> creation <strong>of</strong> internal information, <strong>the</strong> information sharing
process lacks efficiency. The resistance <strong>of</strong> employees in sharing information brings up <strong>the</strong>
relationship between knowledge and power. The pressure arising from employees’ routines questions
<strong>the</strong> company’s classification <strong>of</strong> knowledge as a strategic asset and might prevent fur<strong>the</strong>r innovation.
More qualitative and quantitative researches are recommended in order to bring insights about <strong>the</strong>
perception <strong>of</strong> workers considering <strong>the</strong> routines applied by <strong>the</strong> company in order to foster
organizational knowledge.
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542

Knowledge Translation and Transfer Research Across
Québec’s Occupational Health and Safety Research
Network
Monique Lortie 1 and Lise Desmarais 2
Biological Sciences, University <strong>of</strong> Québec at Montréal, Montréal, Québec,
Canada
Faculty <strong>of</strong> Administration Sciences, University <strong>of</strong> Sherbrooke, Sherbrooke,
Québec, Canada
lortie.monique@uqam.ca
lise.desmarais@usherbrooke.ca
Abstract. Knowledge transfer has been <strong>the</strong> object <strong>of</strong> abundant literature in recent decades. Various models and
<strong>the</strong>oretical frameworks have been proposed, and numerous studies have been conducted on factors which
facilitate or represent obstacles to knowledge transfer. In Québec, researchers in Occupational Health and Safety
(OHS) are linked in a network which groups toge<strong>the</strong>r some 150 researchers. In <strong>the</strong> past five years, <strong>the</strong> strategic
knowledge transfer arm <strong>of</strong> this network has organized a number symposia and events on this topic. The last such
event specifically targeted translation tools, namely guides and training programs, with a book to follow. One <strong>of</strong>
<strong>the</strong> goals <strong>of</strong> <strong>the</strong> knowledge transfer community in organizing activities <strong>of</strong> <strong>the</strong> like is to better formalize <strong>the</strong>
expertise developed by OHS researchers. The object is to map points <strong>of</strong> views and perspectives specific to <strong>the</strong>
OHS field, in particular user status. Albeit OHS researchers usually write little on this topic, most have been
involved in transfer activities at some point in <strong>the</strong>ir career. We firstly present <strong>the</strong> particularities <strong>of</strong> <strong>the</strong> network, <strong>the</strong>
socio-cultural context in which OHS evolves: network highly transdiciplinary, powerful field research thrust, multilevel
partnerships–between users and organizations–background, importance <strong>of</strong> unionization, framework <strong>of</strong>
paritarism underpinning OHS institutions and organizations. The subsequent sections provide an overview <strong>of</strong> <strong>the</strong>
outlook respecting knowledge transfer and <strong>the</strong> concept <strong>of</strong> knowledge developed by <strong>the</strong> various component
disciplines: health sciences, natural and engineering sciences, human sciences, management sciences,
education sciences. This paper exposes <strong>the</strong> community point <strong>of</strong> view on three issues, namely: users as
knowledge end-users, as knowledge co-generators, and as brokers or transfer agents. Each <strong>the</strong>me is
substantiated with three examples <strong>of</strong> network-based research designed to illustrate <strong>the</strong> manner in which user
<strong>the</strong>mes were explored or integrated into <strong>the</strong> different projects.
Keywords: Occupational health and safety, knowledge end-user, knowledge generator, transfer agent, guides
and training
1. Background
In Québec, <strong>the</strong>re are presently 16 <strong>the</strong>me-based research networks in health, including <strong>the</strong>
Occupational Health and Safety (OHS) research network (Réseau de Recherche en Santé et Sécurité
au Travail du Québec, RRSSTQ), which group toge<strong>the</strong>r some 150 researchers. This research network
aims to enhance <strong>the</strong> capacity to compete in <strong>the</strong> development <strong>of</strong> new knowledge and to create a
leverage effect fostering access to broader funding programs (e.g. at <strong>the</strong> federal level where <strong>the</strong>
general framework is similar). The goal is to mobilize research forces to create synergy. RRSSTQ is
funded by <strong>the</strong> three major Québec research funding organizations (nature and technology; health
sciences; society and culture), in partnership with <strong>the</strong> OHS research institute. This highly
transdisciplinary network groups toge<strong>the</strong>r researchers in natural and engineering sciences, human
sciences, health sciences, managerial sciences, as well as technical and artistic disciplines (e.g.
designers). Decidedly quantitative research shares <strong>the</strong> limelight with qualitative methodologies, with
field and action research dominating. Ano<strong>the</strong>r important network feature relates to <strong>the</strong> breadth <strong>of</strong>
interlocutor and partner involvement in projects, whe<strong>the</strong>r individuals or organizations.The OHS
framework in Québec has been developed around a number <strong>of</strong> public organizations : OHS Research
Institute, 12 joint sectoral associations (e.g. mining, transport industry), OHS board, departments <strong>of</strong>
health and community services, unions, mutuals, etc. This working order is based on joint union and
management input (some 42% <strong>of</strong> Québecers are unionized vs 23% <strong>of</strong> workers elsewhere in Canada
and 12% in <strong>the</strong> USA).
This transdisciplinary culture is also favoured by o<strong>the</strong>r contextual factors such as <strong>the</strong> yearly staging <strong>of</strong>
a major convention (ACFAS: French-language association for <strong>the</strong> knowledge sector) which, for over
90 years, has attracted players active in all fields <strong>of</strong> knowledge-based research. The latter is currently
grouped toge<strong>the</strong>r into six sections: 1) life and health sciences; 2) physics, ma<strong>the</strong>matics and
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Monique Lortie and Lise Desmarais
engineering; 3) literature, arts and human sciences; 4) social sciences; 5) education, letters and
human sciences; and, 6) multidisciplinary domains into which OHS falls. For a long time,
communications were essentially discipline-based. In recent years, however, <strong>the</strong>re has been a shift
towards <strong>the</strong> organization <strong>of</strong> <strong>the</strong>me-based symposia over a period <strong>of</strong> one week with sectoral input
scheduled over a half day to two full days. The issue <strong>of</strong> knowledge transfer, doubtless one <strong>of</strong> <strong>the</strong> most
transdiciplinary by nature, is broached by a combination <strong>of</strong> historians, philosophers, educators,
management experts, sociologists and health specialists.
One <strong>of</strong> <strong>the</strong> first activities <strong>of</strong> <strong>the</strong> knowledge transfer community entailed <strong>the</strong> preparation <strong>of</strong> a document
detailing knowledge transfer benchmarks (concepts, terminology, models, research issues, etc.) with
a view to assisting OSH network researchers in navigating <strong>the</strong> various funding organizations and
developing a commun background (Faye et al., 2007, 2008). Various symposia focusing on <strong>the</strong>
issue <strong>of</strong> exchange have been organized. The last such event specifically targeted translation tools,
namely guides and training programs, with a book to follow (Desmarais and Lortie, 2011). One <strong>of</strong> <strong>the</strong>
goals <strong>of</strong> <strong>the</strong> knowledge transfer community in organizing activities <strong>of</strong> <strong>the</strong> like is to better formalize <strong>the</strong>
expertise developed by OHS researchers in <strong>the</strong> field <strong>of</strong> knowledge transfer and to situate <strong>the</strong> latter
with respect to o<strong>the</strong>r fields. Albeit OHS researchers usually write little on this topic, most have been
involved in knowledge transfer activities at some point in <strong>the</strong>ir career. For example, recent research
conducted by Laroche (2009) showed that 32% <strong>of</strong> OSH researchers had been involved in <strong>the</strong>
development <strong>of</strong> a guide over <strong>the</strong> course <strong>of</strong> <strong>the</strong> last five years.
The object <strong>of</strong> this paper is to map knowledge transfer points <strong>of</strong> view, perspectives and expertise
specifically developed by <strong>the</strong> OHS network. The latter’s multipartnership and transdisciplinary makeup
has favoured a point <strong>of</strong> view which largely centres on end-users, with methods employed inspired
by socio-constructivist approaches. Our aim is to present this perspective and to illustrate how <strong>the</strong>
issue <strong>of</strong> knowledge transfer is approached in various situations.
In Section 2, we outline how knowledge transfer in OHS is distinguishable from knowledge transfer in
o<strong>the</strong>r fields as well as what <strong>the</strong> different fields borrow from one ano<strong>the</strong>r. OHS players are indeed
effective end-users <strong>of</strong> techniques and perspectives developed by o<strong>the</strong>r disciplines. We have naturally
chosen to focus on differences within <strong>the</strong> health sector, since health is our common, shared point <strong>of</strong>
interest. Section 3 focuses on <strong>the</strong> issue <strong>of</strong> knowledge itself and <strong>the</strong> various points <strong>of</strong> view implicitly
expressed in various domains. Section 4 exposes, through specific examples <strong>of</strong> network-based
research, <strong>the</strong> manner in which user <strong>the</strong>mes were explored or integrated into <strong>the</strong> different projects:
users as knowledge end-users, generators <strong>of</strong> knowledge or transfer agents.
2. Knowledge transfer in OHS as well as health, social, managerial and
educational sciences
The overview which follows reflects our perception <strong>of</strong> sectoral benchmarks and has been fed by
literature published, follow-ups <strong>of</strong> various monitoring networks, as well as seminars, workshops,
exchanges and symposia organized by o<strong>the</strong>r research networks and organizations.
The topic has been <strong>the</strong> object <strong>of</strong> abundant, if not pr<strong>of</strong>use literature in <strong>the</strong> last decade, especially in <strong>the</strong>
field <strong>of</strong> health: various models, conceptual notions and <strong>the</strong>oretical frameworks have been proposed
and numerous studies have been conducted on factors which facilitate or represent obstacles to
transfers respecting implementation tools, translation processes, etc. This section summarizes our
interpretation <strong>of</strong> what may be viewed as specific to <strong>the</strong> different scientific disciplines and what is
shared or not shared with OHS. We have attempted to capture <strong>the</strong> primary differences.
The health sector has undoubtedly been <strong>the</strong> most openly active respecting knowledge transfer. In no
o<strong>the</strong>r area has knowledge management represented a more central issue. The number <strong>of</strong> articles
indexed in Medline has grown from 413 375 to 727 546 over a period <strong>of</strong> 13 years (Turner et al.,
2008). Yet in <strong>the</strong> mid-1900s, Mulrow (1994) estimated at two million <strong>the</strong> number <strong>of</strong> medical papers
published across 20 000 journals. Unsurprisingly, to manage this explosion <strong>of</strong> data, systematic
procedures have been developed (e.g. Audet et al., 1990; Greenhalgh, 2010) to identify, sort,
classify, assess and combine data such as by dint <strong>of</strong> meta-analyses. By comparison, <strong>the</strong> OHS
research and journal publication network appears ra<strong>the</strong>r microscopic. A considerable quantity <strong>of</strong>
knowledge is disseminated through reports, in particular field research, which in <strong>the</strong> health field is
referred to as grey literature. This is <strong>the</strong> format <strong>of</strong> choice when funds derive from an Institute <strong>of</strong>
research as it is deemed a more efficient manner in which to reach potential users. As a result,
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Monique Lortie and Lise Desmarais
funding organizations and <strong>the</strong>ir boards retain control over both quality and content. This same
structure is found in several o<strong>the</strong>r countries (e.g. Nordic nations, France). Also, in OHS, a notable
portion <strong>of</strong> knowledge aimed at researchers is knowledge developed by partners, practitioners,
organizations, workers, etc., with formalization and organization coming afterwards. Hence it is more
a matter <strong>of</strong> how one manages <strong>the</strong> field to bring to light implicit or latent knowledge than a problem <strong>of</strong>
knowledge management per se.
Literature on guides, which underpin <strong>the</strong> translation process, is equally pr<strong>of</strong>use in <strong>the</strong> health sector
where <strong>the</strong> number <strong>of</strong> papers published has risen from 444 in 1993 to 4975 in 2006 (Turner et al.,
2008). Given <strong>the</strong> explosion <strong>of</strong> writing, which is not <strong>the</strong> case in OHS, national and international task
forces have been put into place to draw up standards and procedures governing <strong>the</strong> development <strong>of</strong>
guides, especially in Anglo-Saxon countries (e.g. Field and Lohr, 1990: Institute <strong>of</strong> Medicine in US,
National Institute for Health and Clinical Excellence (NICE) in UK, Scottish Intercollegiate Guideline
Network (SIGN) in Scotland, New Zealand Guidelines Group in New Zealand), as well as tools for
assessing <strong>the</strong>se guides (e.g. Appraisal <strong>of</strong> Guidelines Research and Evaluation in Europe – AGREE,
2003). All <strong>of</strong> <strong>the</strong>se guides on guides outline procedures providing for <strong>the</strong> systematic review <strong>of</strong>
literature based on carefully detailed criteria relating to search, data inclusion, exclusion and
assessment. Compliance with strict methodology and procedures in relation to reporting is viewed as
a basis for valid development. This process reflects <strong>the</strong> two-step process described by Graham et al.
(2006): The production <strong>of</strong> knowledge and <strong>the</strong> application. The guides are expected to take <strong>the</strong> form <strong>of</strong>
statements. In OHS, both steps are closely intertwined. As a consequence, <strong>the</strong> processing <strong>of</strong> a
systematic literature review is not seen as a mandatory first step and <strong>the</strong> evidence-based concept is
not as broadly employed. OHS researchers are sensitive to <strong>the</strong> gap which exists between <strong>the</strong> format
<strong>of</strong> published scientific data and translation <strong>of</strong> <strong>the</strong> latter into practical guides or tools. The focus is more
on what is needed than on what is known.
In <strong>the</strong> instance <strong>of</strong> OHS, issues <strong>of</strong> knowledge transfer are <strong>of</strong>ten regarded as being closer to social
science perspectives where knowledge is normally viewed from a socio-constructivist standpoint and
where qualitative approaches are <strong>the</strong>refore dominant (Dagenais and Janosz, 2008; Gervais and
Chagnon, 2010). Verbal procedures are at <strong>the</strong> heart <strong>of</strong> OHS methodology and unsurprisingly, <strong>the</strong>
matter <strong>of</strong> exchanges falls at <strong>the</strong> centre <strong>of</strong> topical knowledge transfer literature. In social sciences, <strong>the</strong>
extraction <strong>of</strong> user points <strong>of</strong> view, to allow citizens to express <strong>the</strong>ir opinions, is not so much regarded
as a means <strong>of</strong> improving <strong>the</strong> implementation <strong>of</strong> knowledge in <strong>the</strong> health sector but ra<strong>the</strong>r as a valuebased
or ethical issue (Christie, 2003), a perspective shared in OHS where workers are deemed
important knowledge partners. Also, guides published in social sciences <strong>of</strong>ten target a specific
population or context, <strong>the</strong>reby tending to be less universal in scope (Boisvert, 2008).
Contextualization is also <strong>of</strong>ten a major issue in OHS.
In management, more than in any o<strong>the</strong>r domain, transfer issues are formulated in terms <strong>of</strong> systems
and organizations ra<strong>the</strong>r than in terms <strong>of</strong> individuals (Roy et al., 1995, Haandzic et al., 2008). Transfer
issues are posed in terms <strong>of</strong> <strong>the</strong> capability <strong>of</strong> organizations to circulate, absorb, generate and adapt
knowledge. In OHS, <strong>the</strong> technical or organizational systems are seen as inescapable elements to be
used in problem resolution. Organizations are also partners and <strong>of</strong>ten major interlocutors.
In natural sciences and engineering, <strong>the</strong> issue <strong>of</strong> knowledge transfer is mostly viewed as a matter
pertaining to application, <strong>the</strong> dominant question being: “Could that knowledge be translated into
tangible, useful products?” The validity <strong>of</strong> knowledge is checked by putting it into action. The concept
<strong>of</strong> valorization is central (Beaudry et al., 2006). This perspective is at <strong>the</strong> centre <strong>of</strong> different domains in
OHS, in particular toxicology and safety. Quantitative approaches outstrip qualitative approaches.
Normative approaches and standardization processes are well accepted, and <strong>the</strong> formulation <strong>of</strong>
standards is regarded as necessary.
In education, knowledge transfer or transmission is central to <strong>the</strong> sectoral mission. Major issues
include as follows: “How can an individual appropriate knowledge? What resources does this
individual need to mobilize to acquire this knowledge? How does knowledge allow him or her to
evolve and to shift into self-apprenticeship mode?”(Tardif, 1999; Presseau and Frenay, 2000)
In OHS, training is an important issue and many researchers are familiar with issues pertaining to
education. The point specific to OHS is that trainers targeted are <strong>of</strong>ten workers and not pr<strong>of</strong>essionals.
As a result, workers are more <strong>of</strong>ten than not viewed as essential brokers <strong>of</strong> knowledge.
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3. OHS point <strong>of</strong> view respecting knowledge
English poet T.S. Eliot cleverly mused as follows: “Where is <strong>the</strong> wisdom we lost in knowledge, where
is <strong>the</strong> knowledge we lost in information?” (cited by E. Morin, 2005, p 144,).
In OHS, knowledge management can be understood only by reverting back to <strong>the</strong> concept <strong>of</strong>
knowledge itself. As previously indicated, researchers have developed a socio-constructivist
approach. A portion <strong>of</strong> researcher know-how <strong>the</strong>refore resides in researcher ability to capture,
decode, organize and structure worker knowledge with a view to activating a process <strong>of</strong> exchange
that will make it possible to develop more complex or better appropriated knowledge in a given
context. From one field to <strong>the</strong> next, research flows from application towards a more generic
understanding. In some ways, it is close to anthropological and ethnographic approaches. In most
areas <strong>of</strong> scientific endeavour, researchers proceed in <strong>the</strong> opposite direction, which is to say from
generic to applied knowledge (Paquette, 2002; Sankaran, 2006). In OHS, a generous share <strong>of</strong>
knowledge is derived through action, <strong>the</strong> object being to generate action-relevant knowledge. The
terminology used to refer to knowledge is sophisticated: declarative, procedural, circumstantial,
conceptual, strategic, tacit, implicit, codified, explicit, <strong>of</strong>ficial, and so on.
In science, knowledge is developed mainly through deductive or inductive approaches. In OHS, an
abductive approach is <strong>of</strong>ten employed. Developed by philosopher Charles S. Peirce (Deledalle,
1990), this approach is an iterative process based on successions <strong>of</strong> observations, interpretations and
cross-checking (Please see: http://en.wikipedia.org/wiki/Abductive_reasoning)
Lastly, in French, two terms are used to refer to knowledge: connaissance and savoir. The word
savoir is less neutral than connaissance and includes notions <strong>of</strong> understanding, perspicacity,
sapiency. OHS reseachers (in Québec and o<strong>the</strong>r French-speaking regions) resonate more
spontaneously with <strong>the</strong> concept <strong>of</strong> savoir than with connaissance to convey <strong>the</strong> notion <strong>of</strong> knowledge.
This point <strong>of</strong> view naturally tends to place users at <strong>the</strong> centre <strong>of</strong> our topical reflections.
4. Knowledge users in OHS
Three types <strong>of</strong> users are <strong>of</strong> interest to us: users as knowledge end-users, as knowledge cogenerators
and as knowledge brokers or transfer agents. In <strong>the</strong> following section, each will be
illustrated through cases extracted from <strong>the</strong> book prepared by <strong>the</strong> community. These project
examples are typical <strong>of</strong> <strong>the</strong> manner in which way OHS handles knowledge transfer and knowledge
management. The subjects <strong>of</strong> <strong>the</strong> nine cases are summarized in Table 1.
4.1 Users as end-users
Here, ‘users’ refer to <strong>the</strong> targeted group <strong>of</strong> ultimate ‘end-users’. Thus users are active end-users. This
is <strong>of</strong> particular interest in OHS and relates to <strong>the</strong> use <strong>of</strong> guides.
In <strong>the</strong> first instance, researchers sought to ascertain how organizations absorbed, adapted, circulated
and used guidelines. Follow-up showed that guidelines were effectively used to detect and remedy
non-compliant elements, albeit in part by means o<strong>the</strong>r than those foreseen. Final end-users, for
example, were o<strong>the</strong>r than <strong>the</strong> ones prospected (e.g. engineers appropriated guidelines). Many endusers
learned <strong>of</strong>/assimilated <strong>the</strong> guidelines in a manner o<strong>the</strong>r than through <strong>the</strong> OHS board and <strong>the</strong>
latter’s capacity <strong>of</strong> <strong>of</strong>ficial purveyor <strong>of</strong> <strong>the</strong> guidelines in question. Indeed, <strong>the</strong> guide was unexpectedly
used to train operators and was adapted for different uses. Researchers viewed this as an interesting
finding since it contributed to generating an internal flow <strong>of</strong> exchanges on something quite concrete,
namely <strong>the</strong> conveyor belts.
In <strong>the</strong> second instance, eighty-four Occupational Lesion Board (an appeals board) files where carpal
tunnel syndrome was challenged as being or not being work-related were analysed to understand
actual knowledge use. As pointed up in o<strong>the</strong>r studies, <strong>the</strong> most used sources were local documents,
apparently because end-users had more faith in local sources which were seen as better adapted to
<strong>the</strong>ir context (Hayward et al., 1997). Overall, 78% <strong>of</strong> <strong>the</strong> 226 references were quoted only one or two
times. A similar trend was found by Christiaens et al. (2004) where, <strong>of</strong> <strong>the</strong> 205 references appearing
in four guides, only eight were shared by three or four guides. Also, as in <strong>the</strong> previous instance, endusers
were found not to be <strong>the</strong> individuals one might have expected. Workplace specialists such as
ergonomists and occupational physicians were found to be in <strong>the</strong> minority, whereas general and
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Monique Lortie and Lise Desmarais
specialist practitioners dominated. The latter cited mainly medical journal sources as opposed to OHS
journals. The issues raised by <strong>the</strong> occupational specialists evidenced that <strong>the</strong> knowledge conveyed
did not provide clear answers to <strong>the</strong> issues at hand. Knowledge produced was based on <strong>the</strong>
identification <strong>of</strong> a single factor or simple combination <strong>of</strong> factors. Conversely, exposure related to a
complex mix <strong>of</strong> factors and situations. Accordingly, knowledge generated in <strong>the</strong>se cases did not
appear to be well adapted.
The third experience relates to a tool developed to assess risks faced by individuals working in <strong>the</strong>
garbage sorting industry. The guide was written in <strong>the</strong> first person to foster enhanced appropriation. It
was developed by a partnership involving sectoral pr<strong>of</strong>essionals and researchers. Choices were
reported as partly intuitive, based on <strong>the</strong>ir relational experiences with workers in <strong>the</strong> sector. The guide
was not prescriptive. Ra<strong>the</strong>r it provided information deemed helpful for remedying action objectives.
One <strong>of</strong> <strong>the</strong> goals was, in fact, to foster internal dialogue and enhance life in <strong>the</strong> workplace.
Table 1: Examples <strong>of</strong> projects conducted involving users (Desmarais and Lortie, 2011)
1. Users as end-users
1.1 Assessment <strong>of</strong> impact <strong>of</strong> conveyor belt safety guide: An overview <strong>of</strong> use
by sectoral players
1.2 Interventions at Occupational Lesion Board: Use <strong>of</strong> knowledge in relation
to musculoskeletal issues raised by interveners
1.3 Self-assessment chart <strong>of</strong> workplace health and safety risks at sorting
centres for recyclable waste
2. Users as generators <strong>of</strong> knowledge
2.1 Formulation <strong>of</strong> discourse for pr<strong>of</strong>essional jockeys incorporating notions <strong>of</strong>
health and safety in <strong>the</strong> workplace
2.2 Sharing <strong>of</strong> knowledge and organization <strong>of</strong> dialogue: Incorporation <strong>of</strong>
knowledge and points <strong>of</strong> view in an approach relating to situations giving rise
to musculoskeletal problems
2.3 Lexicological analysis <strong>of</strong> a description <strong>of</strong> events and onset <strong>of</strong> symptoms
<strong>of</strong> occupational lesions
3. Users as transfer agents
3.1 Learning history as a learning tool: Case involving training program
transfer between two research teams in two geographical areas relatively far
removed from one ano<strong>the</strong>r (Québec vs Newfoundland and Labrador )
3.2 Training to prevent back injuries in <strong>the</strong> workplace: Issues relating to <strong>the</strong>
transfer <strong>of</strong> research results and knowledge
3.3 Skills transfer in a unionized work environment: Supporting unions in this
regard
4.2 Users as generators <strong>of</strong> knowledge
Trépanier, J.,
Desmarais, L.,
Giraud, L. and
Bélanger, J.
Tcaciuc, R., Lortie, M.
and Patry, L.
Lavoie, J., Moquin,
S., Lajoie, A.,
Lambert, N., Trudel,
M., et al.
Brunet, R.
Lortie, M., Hastey, P.
and Mo, K.
Gravel, S.
Desmarais, L;.Parent,
R., Vézina, N. and
leclerrc, L.
Faye, C., Ber<strong>the</strong>lette,
D. and Leduc, D.
de Bruycker, J.
Exchanges are viewed as a way <strong>of</strong> evidencing knowledge and related reasoning. Dialogue was
regarded by prominent Greek philosophers as “a means <strong>of</strong> investigation allowing one to reach what is
closer to <strong>the</strong> truth” (cited by El Murr 2010, p. 21). Socrates, one <strong>of</strong> <strong>the</strong> major contributors to <strong>the</strong>
development <strong>of</strong> inductive reasoning, was <strong>the</strong> first attempt to define a set <strong>of</strong> rules. Aristotle, on <strong>the</strong>
o<strong>the</strong>r hand, attempted to define <strong>the</strong> rhetorical and argumentation process. Basically, dialogue is a
process <strong>of</strong> exchanges which may be used to help explicit knowledge by organizing it (Martel, 2000;
Auchlin et al., 2004). Dialogue can be short, be derived from a questionnaire or be elaborated through
interviews. Each is not only a way <strong>of</strong> accessing <strong>the</strong> knowledge worker but also returning knowledge to
<strong>the</strong> worker.
In <strong>the</strong> first instance, jockey speech was targeted and analysed from an ethnographic and ethnological
point <strong>of</strong> view. Jockeys are highly reluctant to adopt OHS preventive rules as <strong>the</strong>y tend to interfere with
performance objectives. Their speech was analysed to accurately identify <strong>the</strong> area <strong>of</strong> resistance and
to be able to articulate a preventive stance which would be acceptable to jockeys. Speech was used
here as a means <strong>of</strong> intervention. A video was produced featuring pr<strong>of</strong>essional jockeys and intended
for training masters. As we can see, this approach is completely different from approaches developed
in <strong>the</strong> health sector and based on an extensive literature review.
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Monique Lortie and Lise Desmarais
In <strong>the</strong> second text, <strong>the</strong> authors propose a structured method to help workers exposed to repetitive
tasks to talk about <strong>the</strong> difficulties encountered in <strong>the</strong>ir work. Workers exposed to physical tasks are
rarely used to translate into words what <strong>the</strong>y experience. They think through <strong>the</strong>ir body. One well
known clarification procedure involves anchoring questioning in tangible, specific actions or
operations. (e.g. “Is this operation demanding in terms <strong>of</strong> skill, attention, accuracy, posture, strength,
etc?”). The method combines three standard questionnaires well circulated in literature with three
contextual questionnaires based on <strong>the</strong> operations specific to each work situation. The latter were
developed to allow body memory to emerge by engaging in a systematic review <strong>of</strong> <strong>the</strong> operations
executed in various contexts. In return, <strong>the</strong> memory narration made it possible to evidence potential
transformation tracks. One <strong>of</strong> <strong>the</strong> goals pursued through this project was to connect generic data
obtained through standard questionnaires with contextual data to achieve a better understanding <strong>of</strong>
<strong>the</strong> issues at hand. The general purpose was, in fact, to translate worker knowledge intopotential
transformations which could be understand by management.
In <strong>the</strong> third case, <strong>the</strong> object <strong>of</strong> <strong>the</strong> study was to better understand <strong>the</strong> experience <strong>of</strong> immigrants with
<strong>the</strong> compensation system to facilitate <strong>the</strong>ir access <strong>the</strong>reto. Two sources <strong>of</strong> material were used: claims
and interviews. The analysis technique employed drew inspiration from a police investigation
technique. The study showed that two-thirds <strong>of</strong> workers – immigrants or not – did not understand <strong>the</strong>
administrative procedures. Claims tended to be rejected whenever <strong>the</strong> workers’ initial description <strong>of</strong>
<strong>the</strong> event was ‘copied’ on <strong>the</strong> claim transmitted by <strong>the</strong> organization. The latter was perceived as
lacking credibility given its identical nature.
4.3 Users as transfer agents
In OHS, this issue is closely tied to training-related issues. It is usual to involve workers in training,
whe<strong>the</strong>r through a formal training program or more informal journeyman system. Users are <strong>the</strong>refore
naturally seen as transfer agents and important knowledge brokers. However, <strong>the</strong> nature <strong>of</strong>
knowledge to be transferred and <strong>the</strong> means <strong>of</strong> transferring it can vary broadly from one situation to
ano<strong>the</strong>r. In <strong>the</strong> case <strong>of</strong> this paper, <strong>the</strong> three cases cited describe three typical situations.
In <strong>the</strong> first case, <strong>the</strong> knowledge to be transferred – how to sharpen and grind a knife – was based on
previous research work conducted with skilled sharpeners with a view to clarifying or explaining <strong>the</strong>ir
knowl-how. The authors described implementation experiences in two two provinces. The method
used, a learning history technique developed at <strong>the</strong> Massachusetts Institute <strong>of</strong> Technology, is a
collective apprenticeship method entailing interviews during which participants can verbalize <strong>the</strong>ir
experiences. Interview content is collected, structured and returned to participants to garner
subsequent feedback. In this case, a better understanding <strong>of</strong> <strong>the</strong> impact <strong>of</strong> <strong>the</strong> socio-cultural and
organizational context on <strong>the</strong> transfer was achieved (e.g. context to take into account when translating
<strong>the</strong> material, as well as <strong>the</strong> regulatory differences).
In <strong>the</strong> second situation, <strong>the</strong> authors addressed <strong>the</strong> implementation <strong>of</strong> a standard program providing
for manual handling in a hospital context. The knowledge transfer was seen as a knowledge shift,
since <strong>the</strong> latter had to be adapted to various contexts, including <strong>the</strong> possibility – real or not – <strong>of</strong> <strong>the</strong>
knowledge being applicable. Training was regarded as a way <strong>of</strong> developing <strong>the</strong> capability <strong>of</strong> using
knowledge to solve a problem or take a decision. In a sense, <strong>the</strong>re is considerable similarity to <strong>the</strong>
development prospects suggested for <strong>the</strong> clinical practice guidelines (CPG). In fact, many authors
suggest developing CPGs which are less normative and more closely aligned with <strong>the</strong> decisionmaking
process (Browman, 2001). Physicians want CPGs which provide information that can be
adjusted based on <strong>the</strong> context. Thus, whatever <strong>the</strong> level <strong>of</strong> expertise – worker or health pr<strong>of</strong>essional –
demands are similar. Ano<strong>the</strong>r common point raised in both domains related to <strong>the</strong> impact <strong>of</strong> <strong>the</strong>
organizational context on implementation and <strong>the</strong> knowledge transfer process: precarious work, parttime
work, occasional work, outside employer, turnover, workload, etc. Indeed, despite efforts devoted
to seek out <strong>the</strong> best in terms <strong>of</strong> knowledge, if prevailing conditions are not rite for implementation, little
will come <strong>of</strong> action taken in this regard.
The third paper presented <strong>the</strong> findings <strong>of</strong> a union-based initiative on <strong>the</strong> intergenerational transfer <strong>of</strong>
knowledge and know-how. Increasing numbers <strong>of</strong> individuals were indeed concerned over losses in
terms <strong>of</strong> knowledge and skills certain to follow in <strong>the</strong> wake <strong>of</strong> large numbers <strong>of</strong> individuals becoming
eligible for retirement. For various reasons, <strong>the</strong> current status <strong>of</strong> knowledge transfer was perceived as
dysfunctional. In proceeding with this initiative, <strong>the</strong> union sought to draw up an intervention guide
detailing action plans designed to limit apprehended losses. The project could thus be viewed as an
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Monique Lortie and Lise Desmarais
attempt to manage <strong>the</strong> apprehended loss <strong>of</strong> knowledge. Twenty transfer experiences were
documented and 200 workers were polled. Skills transfer was shown to be complex and reliant upon
a multitude <strong>of</strong> factors (e.g. noise, space, attitudes, stereotypes, work load, pedagogical skills, etc.).
One interesting proposal put forward by <strong>the</strong> union was to provide for <strong>the</strong> meshing <strong>of</strong> new and more
established know-how so that younger and older workers could gain a mutual appreciation <strong>of</strong> <strong>the</strong>
importance <strong>of</strong> <strong>the</strong> exchange process.
5. In conclusion
The different projects detailed herein demonstrate that knowledge is dynamic and continually
transforms based on how situations evolve. One challenge is to allow apprenticeship models
developed in <strong>the</strong> past to be used to build for <strong>the</strong> future. In OHS, this could be achieved focusing on
users and ensuring that <strong>the</strong>y are part and parcel <strong>of</strong> <strong>the</strong> generation, dissemination and application <strong>of</strong>
knowledge. Users are <strong>the</strong>refore less viewed as receptors and more as partners questioned on <strong>the</strong>
utilization and organization <strong>of</strong> knowledge generated. The intent is not so much to understand
obstacles – a typical focus in literature on <strong>the</strong> implementation <strong>of</strong> knowledge – but to attempt to better
situate <strong>the</strong> limits <strong>of</strong> <strong>the</strong> knowledge developed, to better apprehend user needs and <strong>the</strong> manner in
which <strong>the</strong>y appropriate both knowledge and <strong>the</strong> tools made available to <strong>the</strong>m. Thus it is less a matter
<strong>of</strong> identifying how to facilitate knowledge transfer and more about enhancing our understanding <strong>of</strong> <strong>the</strong>
interaction between scientifically generated knowledge and knowledge generated by <strong>the</strong> combination
<strong>of</strong> users and user experiences. OHS researchers are <strong>of</strong> <strong>the</strong> opinion that this thrust will ultimately
cause knowledge to evolve and increase <strong>the</strong> potential for implementation. One important point
stressed by knowledge transfer community researchers relates to <strong>the</strong> need to enrich <strong>the</strong> scientific
corpus <strong>of</strong> knowledge with user knowledge. Ano<strong>the</strong>r issue raised by <strong>the</strong> cases summarized herein
concerns how one accesses this knowledge and spawns a dynamic flow <strong>of</strong> knowledge designed to
generate action. Lastly, <strong>the</strong> different cases exposed show that <strong>the</strong>re is no single recipe or method.
The methodological approach developped for <strong>the</strong> elaboration <strong>of</strong> clinical practice guidelines, which
provides for fixed rules and procedures and required that each step be documented, is seen as not
well adapted to <strong>the</strong> OHS context. Ra<strong>the</strong>r, we would advocate improving <strong>the</strong> transfer and exchange
mechanisms for both researchers and <strong>the</strong>ir partners involved in <strong>the</strong> transfer experiences conducted.
The concepts <strong>of</strong> demonstrated evidence and good practices as developed in <strong>the</strong> medical sciences are
also <strong>of</strong> lesser import in OHS, partly because contexts and situations evolve so quickly, and partly
because evidence must <strong>of</strong>ten be contextualized prior to attempting application. The best manner in
which to ensure appropriate application and successful transfer <strong>of</strong> knowledge is to gain a sound
understanding <strong>of</strong> <strong>the</strong> situation and <strong>the</strong> links which exist between each <strong>of</strong> <strong>the</strong> component factors.
Acknowledgments
The network isfunded by: Fonds de la recherche en santé du Québec, Institut de recherche Robert-
Sauvé en santé et sécurité du travail, Fonds de recherche sur la société et la culture du Québec,
Fonds québécois de la recherche sur la nature et les technologies.
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550

Knowledge Sharing Practices, Managerial Tacit
Knowledge, and Individual Performance: Their
Interrelationships and <strong>the</strong> Moderating Role <strong>of</strong> Employee
Personality
Halimah Abdul Manaf 1 , Steven Armstrong 1 and Alan Lawton 2
1 Business School, The University <strong>of</strong> Hull, UK
2 School <strong>of</strong> Business and Economics, Monash University Gippsland, Australia
H.Abdul-Manaf@2008.hull.ac.uk
S.J.Armstrong@hull.ac.uk
alan.lawton@monash.edu
Abstract: Tacit knowledge is believed to be a significant factor distinguishing successful managers from o<strong>the</strong>rs.
The ability <strong>of</strong> an organisation to effectively share managerial tacit knowledge is one <strong>of</strong> <strong>the</strong> key sources <strong>of</strong>
competitive advantage for many <strong>of</strong> today’s organisations. Effective knowledge transfer not only provides access
to rich and timely information but can also lead to <strong>the</strong> development <strong>of</strong> knowledge-sharing routines that result in
new knowledge and improved organisational learning – a pre-requisite for survival in today’s complex and
turbulent environment. This study investigates <strong>the</strong> relationship between knowledge sharing practices, managerial
tacit knowledge, and individual performance in <strong>the</strong> Malaysian public sector. Moderation effects <strong>of</strong> employee
personality on <strong>the</strong>se variables were also examined. Instruments used in <strong>the</strong> study, involving 310 Malaysian public
sector employees, included Sternberg et al.’s (2000) Tacit Knowledge Inventory for Managers (TKIM), John et al.
’s (2008) Big Five Inventory (BFI) <strong>of</strong> personality, Bryant’s (2005) peer mentoring instrument, and Boh’s (2007)
instrument for measuring <strong>the</strong> effectiveness <strong>of</strong> knowledge sharing mechanisms. Findings suggest that individual
performance is influenced by both <strong>the</strong> effectiveness <strong>of</strong> knowledge sharing practices, and levels <strong>of</strong> accumulated
managerial tacit knowledge. Both <strong>of</strong> <strong>the</strong>se relationships were moderated to some degree by employee
personality traits.
Keywords: Knowledge sharing practices, tacit knowledge, personality traits, individual performance
1. Introduction
The ability <strong>of</strong> an organisation to create and share knowledge is one <strong>of</strong> <strong>the</strong> key sources <strong>of</strong> competitive
advantage for today’s organisations. It has been argued that building networks and forming alliances
(both internal and external) have <strong>the</strong> potential for enhancing an organisation’s core competencies and
can significantly improve organisational performance (Dyer & Singh 1998). Creating and organising
knowledge, however, remains a major challenge to organisations. New knowledge is created through
interactions between explicit and tacit knowledge (Nonaka & Takeuchi 1995). Drawing on Polanyi’s
(1966) distinction between explicit and tacit knowledge, it is clear that <strong>the</strong> former can be transferred
with relative ease, especially using advances in information technology. Sharing tacit knowledge on
<strong>the</strong> o<strong>the</strong>r hand, requires a relationship that involves dense, long-term social interactions. Despite its
strategic importance in developing distinctive competitive advantage, significant difficulties are known
to be associated with this type <strong>of</strong> knowledge transfer (Brown & Duguid 2001). According to Nonaka
& Konno (1998) <strong>the</strong> most effective way <strong>of</strong> transferring tacit knowledge is through interaction
processes: interacting with <strong>the</strong> task and <strong>the</strong>refore learning by doing (situated learning); or interacting
with a community and <strong>the</strong>refore learning from people (communities <strong>of</strong> practice). This article is
concerned with <strong>the</strong> latter: <strong>the</strong> exchange <strong>of</strong> knowledge through interaction among individuals in
specific relation to public sector organisations.
2. Context <strong>of</strong> <strong>the</strong> study
In <strong>the</strong> public sector, knowledge sharing plays an important role in preventing knowledge loss (Cong &
Pandya 2003). To enhance effectiveness in <strong>the</strong> public sector, government agencies need to find new
ways <strong>of</strong> developing <strong>the</strong>ir knowledge sharing practices. Governments have responded to this agenda
in different ways. The focus <strong>of</strong> our study is Malaysia whose government realized that increased levels
<strong>of</strong> performance, effectiveness and efficiency can be supported by knowledge management practices
(Raja Kasim 2008). The Malaysian government’s efforts to improve public sector performance and
efficiency began with <strong>the</strong> introduction <strong>of</strong> ‘Vision 2020’, published in <strong>the</strong> 9th Malaysia Plan which
became part <strong>of</strong> a national agenda. This Malaysia plan focuses on frontline government agencies such
as local authorities.
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Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Yet, in <strong>the</strong> Malaysian public sector, <strong>the</strong> field <strong>of</strong> knowledge management is still at an early stage and
<strong>the</strong>re are debates about <strong>the</strong> effectiveness <strong>of</strong> knowledge management practices (Zahidul et al. 2007)
and little empirical evidence available concerning <strong>the</strong> effectiveness <strong>of</strong> <strong>the</strong>se initiatives (Zhou 2004).
This paper proposes a framework <strong>of</strong> organizational knowledge sharing in which knowledge sharing
practices and tacit knowledge contribute to individual performance. This is developed through a
detailed consideration <strong>of</strong> knowledge sharing practices and important determinants <strong>of</strong> individual
engagement in knowledge sharing such as individual differences in personality traits.
3. Knowledge sharing
Knowledge sharing is defined as <strong>the</strong> transfer and exchange <strong>of</strong> knowledge between and among
individuals, teams, departments and organizations. Knowledge sharing is, <strong>of</strong> itself, a learning activity
that involves individuals in a mutual exchange <strong>of</strong> both tacit and explicit knowledge (Holtshouse 1998).
Knowledge sharing processes involve observation, listening, asking questions, sharing ideas, and
adopting new patterns <strong>of</strong> behaviour (Bosua & Scheepers 2007). These activities enable organisations
to capture, organise, re-use and transfer experience based knowledge. Collective learning <strong>of</strong> this
kind, however, cannot occur effectively in <strong>the</strong> absence <strong>of</strong> organisational systems designed to facilitate
sharing <strong>of</strong> information.
3.1 Knowledge sharing mechanisms
Knowledge sharing mechanisms within organisations can focus on ei<strong>the</strong>r information technology (IT)
based approaches for <strong>the</strong> transfer <strong>of</strong> explicit knowledge or more personalised approaches for <strong>the</strong>
transfer <strong>of</strong> tacit knowledge (Sternberg et al. 2000). This leads to different categories <strong>of</strong> knowledge
sharing mechanisms referred to as individualization personalization, institutionalization
personalization, individualization codification and institutionalization codification (Boh 2007) although
<strong>the</strong>se are not always mutually exclusive.
Individual-personalization mechanism: At <strong>the</strong> individual level, mechanisms can be
adopted to encourage employees to share knowledge and information through ad-hoc
and informal sessions such as social networking.
Institutionalized-personalization mechanisms: These sorts <strong>of</strong> mechanisms are created by
institutionalizing an organizations structure in such a way that individuals in receipt <strong>of</strong>
important knowledge and experience are encouraged to provide guidance to less
experienced pr<strong>of</strong>essionals.
Individual-codification mechanism: Individual codification mechanisms refer to <strong>the</strong> ways
documents or o<strong>the</strong>r artefacts are shared at an individual level, ei<strong>the</strong>r informally or
formally.
Institutional codification mechanisms: Using <strong>the</strong>se sorts <strong>of</strong> mechanisms, knowledge held
by individuals or groups is captured and made <strong>the</strong> property <strong>of</strong> <strong>the</strong> organization.
H1: There is a positive relationship between knowledge sharing mechanisms and levels
<strong>of</strong> accumulated managerial tacit knowledge.
3.2 Mentoring programs
Ragins and Scandura (1999) define a mentor as an influential individual in <strong>the</strong> working environment
who has advanced experience and knowledge and who is committed to providing upward mobility and
support to an individual’s (protégé) career. Nonaka (1991) emphasised that mentoring programs are
effective mechanism for sharing tacit knowledge and creating new knowledge.
H2: There is a positive relationship between effective mentoring programs and levels <strong>of</strong>
accumulated managerial tacit knowledge.
3.3 Tacit knowledge
Wagner (1987) refers to tacit knowledge as practical ‘know how’ that in general is not openly
expressed or stated and must be captured in <strong>the</strong> absence <strong>of</strong> direct instruction. He identified three
components <strong>of</strong> tacit knowledge: managing oneself; managing tasks; and managing o<strong>the</strong>rs. Selfmanagement
encompasses <strong>the</strong> skills required for self-motivation and self-organization; managing
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Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
tasks includes how to perform well in specific tasks. Managing o<strong>the</strong>rs involves <strong>the</strong> required skills and
knowledge for managing subordinates and interacting with peers.
Tacit knowledge related to self is concerned with knowledge about <strong>the</strong> relative importance <strong>of</strong> <strong>the</strong>
tasks one faces, efficient ways <strong>of</strong> approaching work and knowledge about <strong>the</strong> motivation skills
required in order to maximize one’s accomplishments (Wagner & Stenberg 1985).
H3: There is a positive relationship between managing self and effective knowledge
sharing practices.
Tacit knowledge related to managing o<strong>the</strong>rs refers to knowledge about managing subordinates and
social relationships (Wagner & Stenberg 1985).
H4:There is a positive relationship between managing o<strong>the</strong>rs and effective knowledge
sharing practices.
Tacit knowledge related to managing tasks refers to knowledge about how to establish careers, how
to enhance reputations, how to reflect organizational values and how to convince superiors about
ideas or products (Wagner & Stenberg 1985).
H5:There is a positive relationship between managing task and effective knowledge
sharing practices.
3.4 Personality Traits
Previous research has demonstrated that personality traits are linked to important organizational
outcomes, particularly job performance, training success, self assessment, rates <strong>of</strong> pay, career
development and leadership efficacy (Barrick et al. 2005). However, not all employees are willing to
share personal knowledge due to individual differences (Cabrera 2002). Research suggests that
psychological variables such as self efficacy, organizational commitment, cognitive style (Armstrong
et al. 2002) and personality have a significant impact on people’s tendency to participate in voluntary
knowledge sharing.
Personality can be defined as a combination <strong>of</strong> different traits or personal characteristics that cause
people to behave in different ways (Tokar et al. 1998). The traits most frequently raised for discussion
are labelled extraversion, agreeableness, conscientiousness, neuroticism and openness to
experiences (John et al. 2008). The person labelled as an extravert will typically be talkative,
energetic and assertive. Whilst <strong>the</strong> person labelled as being agreeable will typically be sympa<strong>the</strong>tic,
affectionate and kind. The term conscientiousness is used to describe a person who is organized,
truthful and has <strong>the</strong> capacity to plan ahead. Neuroticism/emotional instability results when a person
is tense, moody and anxious. Finally, <strong>the</strong> capacity to be open to experience involves traits such as
having wide interests, and <strong>the</strong> ability to be imaginative and insightful.
Personality traits are also believed to be a determinant <strong>of</strong> an individual’s propensity to engage in
knowledge sharing (Martzler et al. 2008).
H6: Personality traits moderate <strong>the</strong> effects <strong>of</strong> knowledge sharing practices on individual
performance.
Successful managers are known to have higher levels <strong>of</strong> tacit knowledge and <strong>the</strong>re is some support
for <strong>the</strong> idea that this may be due to personality traits (Tan & Libby 1997).
H7: Personality traits moderate <strong>the</strong> relationship between managerial tacit knowledge and
individual performance.
Collectively, <strong>the</strong>se seven hypo<strong>the</strong>ses lead us to present our research framework for <strong>the</strong> study shown
in Figure 1.
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Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Knowledge Sharing
Practices (KSP)
1.Mentoring programs
2.Knowledge Sharing
Mechanism (KSM)
H2 (+)
H3 (+)
H1(+) H4(+)
Managerial Tacit
Knowledge (LAMTK)
1. Managing One self
2. Managing O<strong>the</strong>rs
3. Managing Task
Figure 1: Research framework
4. Method
4.1 Population
H7(+)
H5(+)
H6(+)
Personality Traits
1. Agreeableness
2. Conscientiousness
3. Openness
Individual Performance
Individual Performance
Appraisal
Independent variables Moderating variables Dependent variables
The population <strong>of</strong> interest were 1364 employees from <strong>the</strong> Management and Pr<strong>of</strong>essional Group
(MPG) <strong>of</strong> 98 local authorities in <strong>the</strong> Malaysian Local Government. MPG in <strong>the</strong> context <strong>of</strong> <strong>the</strong>
Malaysian public sector refers to middle level managers involved in policy making. Middle level
managers <strong>of</strong> this kind have been identified as playing a key role in <strong>the</strong> organisational knowledge
creation process enabling <strong>the</strong>m to become future leaders (Nonaka & Takeuchi 1995).
4.2 Sample
Stratified random sampling techniques were used across <strong>the</strong>se 98 local authorities in <strong>the</strong> Malaysian
Local Government and <strong>the</strong> sampling frame comprised 1000 staff using postal surveys between March
and August 2010.
Completed questionnaires were returned by 310 subjects, representing an overall response rate <strong>of</strong>
31%. Of <strong>the</strong>se, 255 (82%) were classified as a typical group, 19 (6%) were classified as novice
group, and 36 (12%) were classified as experts. The novice group <strong>of</strong> managers comprised employees
with an average <strong>of</strong> 4 years <strong>of</strong> working experience, whereas <strong>the</strong> typical group <strong>of</strong> managers comprised
employees whose experience ranged from 11 – 34 years, and averaged 19 years.
4.3 Measures
Tacit knowledge – Wagner and Sternberg’s (1985) Tacit Knowledge Inventory for Managers (TKIM)
was administered to all subjects in order to determine <strong>the</strong>ir levels <strong>of</strong> managerial tacit knowledge.
554

Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Sample items for <strong>the</strong> instrument and <strong>the</strong> scoring regime can be found in Armstrong & Mahmud (2008).
Theoretically, expert managers are expected to respond differently to lower level managers on each
test item due to <strong>the</strong> content and organization <strong>of</strong> <strong>the</strong>ir tacit knowledge (Wagner 1987). A group <strong>of</strong>
expert managers were needed to create a pr<strong>of</strong>ile against which o<strong>the</strong>r subjects could be compared.
This is a fundamental requirement <strong>of</strong> <strong>the</strong> Tacit Knowledge Inventory for Managers (TKIM) used in <strong>the</strong>
study. The scoring system <strong>of</strong> <strong>the</strong> TKIM requires scores from <strong>the</strong> subjects to be compared against <strong>the</strong>
scores <strong>of</strong> <strong>the</strong> expert managers’ pr<strong>of</strong>ile. Subjects with TKIM scores close to <strong>the</strong> scores <strong>of</strong> <strong>the</strong> expert
pr<strong>of</strong>ile are deemed to have a higher level <strong>of</strong> managerial tacit knowledge.
The selection criteria adopted in <strong>the</strong> present study for <strong>the</strong> expert management group builds on those
adopted in previous studies. It does this by considering only those who stand out as being successful
within <strong>the</strong> same work context as <strong>the</strong> subjects being studied (i.e. within <strong>the</strong> Malaysian Local
Government).
Mentoring - Mentoring effectiveness was assessed using an instrument used in previous studies <strong>of</strong>
<strong>the</strong> influence <strong>of</strong> peer mentoring on knowledge sharing/creation (Bryant 2005). The peer mentoring
knowledge and skills instruments consist <strong>of</strong> a 14-item scale designed on <strong>the</strong> basis <strong>of</strong> previous
research on mentoring conducted by Scandura (1992). A 5-point likert scale was used for reporting
agreement/disagreement for each item. This instrument measures <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> peer
mentoring skill as well as <strong>the</strong>ir actual behaviour in terms <strong>of</strong> using skills.
Knowledge sharing mechanisms – Defined as <strong>the</strong> method, procedure, or process <strong>of</strong> sharing,
integrating and interpreting and applying know-what, know-how, and know-why in groups that directly
influence task performance. Items are divided into four groups: individual codification; individual
personalisation; organisational codification; organisational personalisation. A 5-point likert scale was
used for reporting agreement/disagreement for each item. The instrument was originally developed by
Boh (2007).
Personality – The big five (BFI) was used to measure <strong>the</strong> five broad dimensions <strong>of</strong> personality (John,
et al. 2008). In our study we focus on three specific personality measurements ra<strong>the</strong>r than five: <strong>the</strong>y
are agreeableness, conscientiousness and openness. The reason for choosing <strong>the</strong>se personality
traits because <strong>of</strong> <strong>the</strong>se three personality traits are thought to be determinants <strong>of</strong> knowledge sharing
behaviour amongst employees (Martzler et al. 2008).
Performance – Individual performance was measured using previous performance appraisals that
have been evaluated. To measure individual performance, <strong>the</strong> methods <strong>of</strong> Brewer and Selden (2000),
were followed. Respondents were asked to rate <strong>the</strong>ir last year performance using five point rating
scale as follows: (1) Poor, (2) Less Satisfactory, (3) Satisfactory, (4) Good, (5) Excellent.
5. Analyses and results
Tacit knowledge scores for novice and typical groups <strong>of</strong> managers were calculated using <strong>the</strong> method
<strong>of</strong> differences scoring between novice and expert. This procedure gives rise to a score for <strong>the</strong> level <strong>of</strong>
managerial tacit knowledge for every respondent compared with <strong>the</strong> expert managers’ pr<strong>of</strong>ile. It
should be noted that scores are expected to decrease ra<strong>the</strong>r than increase with advancing levels <strong>of</strong>
tacit knowledge because <strong>the</strong>se scores represent deviations from <strong>the</strong> expert. The closer <strong>the</strong> pattern <strong>of</strong>
responses to <strong>the</strong> successful group, <strong>the</strong> lower <strong>the</strong> score (Wagner 1987).
Internal consistency reliability estimates for <strong>the</strong> inventories used in <strong>the</strong> research are shown in Table 1.
Internal consistency reliabilities for <strong>the</strong> Knowledge Sharing Practices (KSP) Inventory, <strong>the</strong> Tacit
Knowledge Inventory for Managers (TKIM), and <strong>the</strong> Big Five Inventory <strong>of</strong> Personality (BFI) were all
acceptable for <strong>the</strong> total overall scores with reliabilities <strong>of</strong> .89, .78 and .86 respectively. The reliability <strong>of</strong>
<strong>the</strong> individual sub-scales ranged from .65-.89 for <strong>the</strong> KSP, .66-.85 for <strong>the</strong> TKIM and .67-.89 for <strong>the</strong>
BFI.
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Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Table 1: Reliability analysis for <strong>the</strong> KSP, TKIM and BFI
Variables n Mean SD α
KSP:
1. Mentoring Programme1 310 4 .48 0.867 (8)
(Competence)
2. Mentoring Programme2 310 4 .59 0.770 (4)
(Behaviour)
3. Individual Codification 310 3.7 .69 0.670 (2)
4. Individual Personalization 310 4.1 .58 0.646 (3)
5. Institutional Codification 310 3.9 .57 0.820 (5)
6. Institutional Personalization 310 4 .52 0.732 (4)
7. Knowledge Sharing Practices 310 3.9 .38 0.893 (26)
TKIM:
1. Managing Tasks 310 1.27 0.544 0.749 (11)
2. Managing O<strong>the</strong>rs 310 1.26 0.534 0.848 (9)
3. Managing Self 310 0.83 0.468 0.663 (12)
4. Total Tacit Knowledge 310 1.02 0.334 0.777 (32)
BFI:
1. Agreeableness 310 4.35 .59 0.890 (9)
2. Openness 310 4.00 .53 0.850 (8)
3. Conscientiousness 310 3.58 .73 0.671 (4)
4. Personality Traits 310 4.08 .42 0.856 (21)
A correlation matrix <strong>of</strong> measures used in <strong>the</strong> study showing <strong>the</strong> correlation coefficient can be found in
Table 2.
Table 2: Correlation matrix <strong>of</strong> variables (n = 310)
MEN MEN COD PER PER COD
O<strong>the</strong>
AGRE PER
CON
Variables IP 1 2 1 1 2 2 KS TK rs Task
-
Self E S OPEN S
Individual
.208* .216* 0.09 .198* .241* .236* - - .161*
.322*
.227
Performance (IP) 1 * * 9 * * .123* * .141*
-
.113* * .138* .286** * .185** **
Mentoring 1 (MEN 1)-
.323* .195* .256* .250* .328* .382* .151* .148* 0.04 0.08
.254*
.119
(competence) 1 * * * * * * *
-
* 3 2 .188** * .235** *
Mentoring 2 (MEN 2)-
.356* .352* .365* .334* .378* .185* 0.09 .179* .272*
.217*
0.02
(behaviour) 1 * * * * * * 4 * * .239** * .157** 8
Individual
.356* .273* .584* .363* .200* 0.06 0.07 0.08
.225*
Codification (COD 1) 1 * * * * * 1 2 4 .139* * .247** 0.1
Individual
-
Personalization (PER
.367* .362* .268* 0.02 0.04 0.00 0.08
.273*
0.08
1)
Institutional
1 * * * 6 1 7 2 .191** * .296** 8
Personalization (PER
.500* .382* 0.03
0.05 0.09
.217*
0.08
2) 1 * * 8 0.1 5 7 .161** * .209** 7
Institutional
.415* 0.02 0.03 0.08
.256*
.142
Codification (COD 2) 1 * .111* 8 1 8 .191** * .220** *
-
-
Knowledge Sharing
0.10 0.05 - .183*
.227*
.166
(KS) 1 2 4 .144* * .129* * .214** **
-
Tacit Knowledge (TK-
.467* .507* .227*
0.02
LAMTK) 1 * * * 0.031 0.06 0.107 1
.487* .165*
0.07
0.01
O<strong>the</strong>rs 1 * * 0.054 5 0.086 4
0.03
0.02
Task 1 .121* 0.011 8 0.05 5
.150*
0.07
Self 1 0.07 * .186** 5
Agreeableness
.814*
.292
(AGREE) 1 * .331** **
.612
Personality (PERS) 1 .733** **
.256
Openness (OPEN)
Conscientiousness
1 **
(CONS) 1
*p< 0.05, ** p< 0.01, *** p< 0.001
556

Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Hypo<strong>the</strong>ses 1: There is a positive relationship between effective knowledge sharing
mechanisms (KSM) and levels <strong>of</strong> accumulated managerial tacit knowledge (LAMTK).
A correlation analysis revealed no significant relationship between LAMTK and institutional
personalisation (r = -.038, p = .51), nor between LAMTK and individual personalisation (r = -
.026, p = .643) sub-scales <strong>of</strong> KSM. There were, however, significant though weak correlations
between LAMTK and both institutional codification (r = -.11, p = .025) and individual
codification (r = -.2, p = .00) sub-scales. These results are similar to those <strong>of</strong> a previous study
(Syed-Ikhsan & Rowland 2004) where some support was found for <strong>the</strong> claim that <strong>the</strong> better
<strong>the</strong> sharing <strong>of</strong> <strong>the</strong> tacit knowledge from individuals through formal and informal
discussions/meetings, <strong>the</strong> better <strong>the</strong> performance <strong>of</strong> knowledge transfer (r=0.279, p

Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Table 3: The result <strong>of</strong> moderator regression analysis for hypo<strong>the</strong>sis 6
Variables
Step 1
Standardized Coefficients
Beta
t Sig.
(Constant) 18.397 .000
Mentoring 1 .105* 1.714 .050
Institutional Personalization .153* 2.281 .023
Knowledge Sharing .126* 1.931 .050
Step 2
(Constant) 15.022 .000
Agreeableness .183* 3.121 .002
Openness .016 .266 .790
Conscientiousness .140* 2.472 .014
Step 3
(Constant) -3.229 .001
Agreeableness x Mentoring 2 2.568* 3.334 .001
Openness x Institutional Personalization -2.111* -2.142 .033
Conscientiousness x Mentoring 1 -1.197* -1.968 .050
Conscientiousness x Mentoring 2 -.989* -1.917 .046
Step 1 Step 2 Step 3
R 0.334 0.42 0.557
R² 0.111 0.177 0.31
Adj R² 0.091 0.149 0.236
R² Change 0.111 0.065 0.134
F Change 5.413 7.901 2.703
Sig F Change
* p< 0.05
0.000 0.000 0.000
Step 1 represents independent variables constructs and statistically significant regression coefficients.
The set <strong>of</strong> knowledge sharing practices variables entered at step 1 accounted for approximately 11%
<strong>of</strong> <strong>the</strong> variance in individual performance. Table 3 reveals that <strong>the</strong>re are only 3 variables that make a
statistically significant contribution (p

Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
Hypo<strong>the</strong>ses 7: Personality traits moderate <strong>the</strong> relationship between managerial tacit knowledge and
individual performance.
Table 4 shows <strong>the</strong> results <strong>of</strong> <strong>the</strong> hierarchical regression analysis related to H7. Findings indicate that
personality traits moderate <strong>the</strong> effect <strong>of</strong> managerial tacit knowledge on individual performance in <strong>the</strong>
typology depicted in Figure 1 as pure moderator and quasi moderator.
Table 4: The result <strong>of</strong> moderator regression analysis for hypo<strong>the</strong>sis 7
Variables
Step 1
Standardized Coefficients
Beta
t Sig.
(Constant) 79.902 .000
Self
Step 2
.183* 3.199 .002
(Constant) 24.712 .000
Agreeableness .220* 3.845 .000
Openness .072 1.253 .211
Conscientiousness
Step 3
.135* 2.405 .017
(Constant) 8.654 .000
Agreeableness x Self -1.157* -2.436 .015
Openness x Self .408* 2.919 .004
Conscientiousness x Task -.731* -2.061 .040
Conscientiousness x Self -.285* -2.034 .043
Step 1 Step 2 Step 3
R 0.25 0.407 0.491
R² 0.063 0.165 0.241
Adj R² 0.05 0.146 0.191
R² Change 0.063 0.103 0.075
F Change 5.085 12.414 2.399
Sig F Change
*p< 0.05
0.001 0.000 0.006
Overall managerial tacit knowledge and associated sub-scales (managerial o<strong>the</strong>rs, task and self)
were entered into step 1 <strong>of</strong> <strong>the</strong> regression analysis. In step 2 <strong>the</strong> moderator variables, agreeableness,
openness, and conscientiousness were entered. Interaction terms were entered into step 3 <strong>of</strong> <strong>the</strong>
regression model. Managerial tacit knowledge variables entered in step 1 account for 6.3% <strong>of</strong> <strong>the</strong>
variance in individual performance. Of <strong>the</strong> three sub-scales associated with <strong>the</strong> managerial tacit
knowledge variable, only ‘managing self’ was found to influence individual performance (β = -0.183, t
=-3.199, p =.002).
In <strong>the</strong> second step, <strong>the</strong> moderator factors, agreeableness, openness, and conscientiousness were
entered into <strong>the</strong> equation to examine <strong>the</strong>ir effects as predictors <strong>of</strong> individual performance. When,
<strong>the</strong>se moderator factors were entered in <strong>the</strong> regression model, <strong>the</strong> R² increased from 6.3% to 16.5%
accounting for a change <strong>of</strong> approximately 10% in individual performance.
In <strong>the</strong> third step, <strong>the</strong> 12 interaction terms were entered into <strong>the</strong> model. It can be seen from Table 4
that <strong>of</strong> <strong>the</strong> variance explained by interaction terms, 24.1% was significant (p

Halimah Abdul Manaf, Steven Armstrong and Alan Lawton
interactions appear have no effects on individual performance. Thus, <strong>the</strong>se findings again provide
partial support for <strong>the</strong> hypo<strong>the</strong>sis H7.
6. Discussion and conclusions
The results <strong>of</strong> this study are important in demonstrating <strong>the</strong> interrelationship between knowledge
sharing practices, managerial tacit knowledge, personality traits and individual performance. The
findings are generally supportive <strong>of</strong> previous studies.
Hypo<strong>the</strong>sis 1 was only partially supported suggesting that institutional personalization and individual
personalization are probably not appropriate for sharing tacit knowledge. One explanation is that
individuals need to know <strong>the</strong> particular person closely before <strong>the</strong>y are willing to share <strong>the</strong>ir tacit
knowledge. Sharing tacit knowledge using <strong>the</strong>se two mechanisms requires <strong>the</strong> individual to have
close personal relationships and trust in <strong>the</strong> formal mechanisms such as codification. On <strong>the</strong> o<strong>the</strong>r
hand, managers in this present study believe that <strong>the</strong>ir tacit knowledge was increased by involvement
with individual and institutional codification. The results are consistent with <strong>the</strong> expectation that tacit
knowledge has to be codified before it can be shared with o<strong>the</strong>rs (Nonaka & Takeuchi 1995).
Consistent with <strong>the</strong> first hypo<strong>the</strong>sis, outcomes from <strong>the</strong> analysis showed that <strong>the</strong> level <strong>of</strong> managerial
tacit knowledge was related to <strong>the</strong> mentoring programme 1 (competence) and mentoring programme
2 (behaviour). Mentoring provides a valuable method for sharing knowledge in many different forms
from boss-subordinate to informal lateral and/or vertical mentoring relationships (Kaser & Miles 2002).
Results from hypo<strong>the</strong>sis 3 imply that managers who are involved with knowledge sharing practices
were able to manage oneself effectively. In an organisation that has a knowledge sharing culture, this
would lead people to share ideas and insights because <strong>the</strong>y see it as natural, ra<strong>the</strong>r than something
<strong>the</strong>y are forced to do (McDermott & O’Dell 2001). As predicted (H5), managers involved in successful
knowledge sharing practices tend to also be effective in terms <strong>of</strong> managing oneself in relation to work
tasks such as prioritising work, reflecting on organizational values and convincing o<strong>the</strong>rs <strong>of</strong> <strong>the</strong> value
<strong>of</strong> work (Wagner & Stenberg 1985). Surprisingly, <strong>the</strong> hypo<strong>the</strong>sis that managing o<strong>the</strong>rs (H4) would be
related to effective knowledge sharing practices was refuted, despite previous evidence that
suggesting people who highly knowledgeable about managing self (H3) and task (H4) tend to be also
knowledgeable about managing o<strong>the</strong>rs and vice versa (Wagner 1987).
Although, <strong>the</strong> results <strong>of</strong> Pearson correlation demonstrates <strong>the</strong> low value <strong>of</strong> tacit knowledge with some
constructs <strong>of</strong> knowledge sharing practices, <strong>the</strong>se findings are acceptable and consistent with o<strong>the</strong>r
studies that using similar measures <strong>of</strong> tacit knowledge. This was not surprising, since o<strong>the</strong>r studies <strong>of</strong>
tacit knowledge have been correlated at <strong>the</strong> same level (see Wagner & Sternberg 1987).
The moderating effects <strong>of</strong> personality traits on individual performance, Hypo<strong>the</strong>sis 6, were partly
supported. The findings are supported by previous studies, for instance an agreeable person may
create willingness to exchange knowledge in recipients that has been linked to knowledge sharing
and job performance (Barrick & Mount 1991).
In <strong>the</strong> last hypo<strong>the</strong>ses, personality traits (openness, agreeableness and conscientiousness) were
assumed have implications for <strong>the</strong> interaction <strong>of</strong> managerial tacit knowledge and individual
performance. Individuals who are agreeable tend to be favoured by o<strong>the</strong>rs and this could encourage
learning interactions that involve tacit knowledge. The finding <strong>of</strong> Judge et al., (1999) demonstrate a
positive relationship between managerial success and conscientiousness.
In conclusion, <strong>the</strong> results <strong>of</strong> this study demonstrate meaningful associations between mentoring
(behaviour), mentoring (competence), institutional and individual codification with tacit knowledge. An
interesting association is also found between managing self and managing task with knowledge
sharing practices. The current study helps to bridge understanding and support previous <strong>the</strong>ories that
some tacit knowledge can be shared in different mechanism <strong>of</strong> knowledge sharing in <strong>the</strong> context <strong>of</strong>
public servant working in local authorities. Meanwhile, <strong>the</strong> results reported agreeableness and
conscientiousness influenced stability between mentoring programme (behaviour), mentoring
programme (competence) and institutional personalization with individual performance. Fur<strong>the</strong>r, <strong>the</strong>
combination <strong>of</strong> agreeableness, openness, conscientiousness facilitates managing self and managing
o<strong>the</strong>rs have impact on <strong>the</strong> individual performance.
560

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562

Knowledge sharing in Romanian companies
Anca Mândruleanu
Academy <strong>of</strong> Economic Studies, Bucharest, Romania
anca.mandruleanu@fabiz.ase.ro
Abstract: The cultural differences influence <strong>the</strong> activities, <strong>the</strong> processes, <strong>the</strong> relationships and <strong>the</strong> culture within
an organization. Embodied knowledge is not easy to be seen and it is even more difficult to formalize and to
share it. Research have shown that Western firms <strong>of</strong>ten set out to exchange explicit knowledge, whereas Eastern
firms are <strong>of</strong>ten more interested in tacit knowledge. The more embodied <strong>the</strong> knowledge, <strong>the</strong> more difficult is to
acquire <strong>the</strong>m, but at <strong>the</strong> same time, <strong>the</strong> greater <strong>the</strong> extent that <strong>the</strong> knowledge is valuable. This paper analyses
<strong>the</strong> perception <strong>of</strong> some Romanian managers over <strong>the</strong> knowledge sharing in <strong>the</strong> companies <strong>the</strong>y run. Most <strong>of</strong> <strong>the</strong>
Romanian companies are already involved in knowledge management, but <strong>of</strong>ten this is done informally or
unconsciously. The goal <strong>of</strong> a formal knowledge management programme is to make knowledge marketplace
operate more efficiently. In this way, organizations recognize that ownership <strong>of</strong> knowledge can bring competitive
advantage and <strong>the</strong> lack <strong>of</strong> knowledge management means that <strong>the</strong>y are losing money or opportunities ei<strong>the</strong>r
because <strong>the</strong>y are unable to find <strong>the</strong> knowledge that <strong>the</strong>y need by reinventing <strong>the</strong> wheel or because <strong>the</strong>y are
making decisions based on inadequate information. Quantifying <strong>the</strong>se losses is difficult and this makes it hard to
design and justify knowledge projects. Knowledge can be contextual, <strong>the</strong> person who holds <strong>the</strong> knowledge also
knows what it means, what its limits are and how it can be used. The surrounding context <strong>of</strong> tacit knowledge is
sometimes lost when information is saved somewhere else. Even transmitting <strong>the</strong> information to someone else is
not <strong>the</strong> same as knowledge transfer, because <strong>the</strong> receiver must understand <strong>the</strong> information and learn how to use
it in appropriate ways. Knowledge is most effectively transferred through interaction, especially face-to-face
interaction. This is one <strong>of</strong> <strong>the</strong> main reasons for which <strong>the</strong> management’ perception and vision regarding <strong>the</strong>
knowledge transfer is very important. A manger and <strong>the</strong> organizational culture <strong>of</strong> <strong>the</strong> run company can essentially
influence <strong>the</strong> process <strong>of</strong> sharing knowledge, by encouraging or discouraging <strong>the</strong> interactions and <strong>the</strong> teamwork
within <strong>the</strong> company. The vision and <strong>the</strong> attitude <strong>of</strong> <strong>the</strong> management with regard to this issue differ from one
country to ano<strong>the</strong>r, taking into account <strong>the</strong> cultural differences. This paper presents some research about <strong>the</strong>
Romanian managerial perception concerning <strong>the</strong> knowledge transfer within Romanian companies.
Keywords: cultural differences, management perception, knowledge sharing.
1. Introduction
Knowledge management is a general term applied to almost any activity that an organization
undertakes which is designed to transfer and use knowledge from one part <strong>of</strong> <strong>the</strong> organization to
ano<strong>the</strong>r one or from one level to ano<strong>the</strong>r. Many companies are already involved in knowledge
management, but <strong>of</strong>ten this is done informally and implicitly. The job <strong>of</strong> a formal knowledge
management programme is simply to make knowledge marketplace operate more efficiently.
Companies are recognizing that ownership <strong>of</strong> knowledge is a key competitive differentiator and <strong>the</strong>
lack <strong>of</strong> knowledge management means loosing money and opportunities.
To take advantage <strong>of</strong> <strong>the</strong> organizational knowledge, efforts need to be linked to <strong>the</strong> organization’s
business strategy. Formal knowledge management projects should extend to <strong>the</strong> reach <strong>of</strong> <strong>the</strong>
knowledge markets and make <strong>the</strong>m more efficient, but knowledge is socially constructed and may be
best transferred through stories and interactive dialogue. Successful knowledge management projects
are possible but <strong>the</strong>y require people to focus on <strong>the</strong> transfer and on <strong>the</strong> quality <strong>of</strong> <strong>the</strong> transferred
knowledge. A knowledge management philosophy may require a change in <strong>the</strong> culture <strong>of</strong> <strong>the</strong>
organization and depends on each member’s willingness to participate and <strong>the</strong> extent to which <strong>the</strong>y
trust <strong>the</strong> management (Davenport and Prusak, 2000).
The paper presents and analyses <strong>the</strong> perception <strong>of</strong> some Romanian managers over <strong>the</strong> knowledge
sharing process within <strong>the</strong> companies <strong>the</strong>y run. Knowledge management is a means ra<strong>the</strong>r than an
end. The focus must be on core business process because <strong>the</strong> intention is to improve <strong>the</strong> company’s
ability to perform <strong>the</strong>se core processes and add value more effectively.
2. Theoretical background
The transfer <strong>of</strong> knowledge is intended to improve operations and embed knowledge in products and
services. The idea is to enable <strong>the</strong> company to reduce cycle times, increase customer
responsiveness, increase sales and, if necessary, cut costs. The intention is to bring <strong>the</strong> knowledge
base <strong>of</strong> <strong>the</strong> company to bear on customers needs. Such companies emphasize <strong>the</strong> importance <strong>of</strong>
563

Anca Mândruleanu
teams, networks and relationships as a basis for effective transfer. They have usually taken a variety
<strong>of</strong> approaches to encourage collaborative knowledge transfer (Gamble and Blackwell, 2001).
It is quite easy to create knowledge sharing systems that are out <strong>of</strong> <strong>the</strong> context, meaning on ano<strong>the</strong>r
side <strong>of</strong> what <strong>the</strong> members actually do. Foe example, a handbook containing all <strong>the</strong> rules, procedures
and policies <strong>of</strong> <strong>the</strong> company that is given to every new comer, but is seldom read. What most people
do when <strong>the</strong>y want to know how about something is done in <strong>the</strong> company is ask a co-worker or <strong>the</strong>
manager. Ano<strong>the</strong>r important issue is <strong>the</strong> quality <strong>of</strong> <strong>the</strong> knowledge. If knowledge management
processes are not aligned with <strong>the</strong> main business process or if <strong>the</strong> capture <strong>of</strong> <strong>the</strong> high knowledge
requires significant additional effort over <strong>the</strong> regular work, <strong>the</strong>n most people will not bo<strong>the</strong>r to capture
it. The relative difficulty <strong>of</strong> capturing and transferring knowledge depends on <strong>the</strong> kind <strong>of</strong> knowledge
involved. Knowledge that is more or less explicit can be embedded in procedures or represented in
documents and databases and rapidly transferred. Tacit knowledge transfer generally requires
extensive personal contact or some kind <strong>of</strong> working relationship. Such relationships are likely to
involve transferring different kinds <strong>of</strong> knowledge, from explicit to tacit. Not <strong>of</strong> <strong>the</strong> learning
communicated will be complex and intuitive, but it is <strong>the</strong> tacit knowledge that it is difficult to be
transferred in any o<strong>the</strong>r way.
3. 2BMethodology
In order to study <strong>the</strong> actual stage and <strong>the</strong> development <strong>of</strong> knowledge sharing in <strong>the</strong> Romanian
companies and to discover <strong>the</strong> perception and <strong>the</strong> influence <strong>of</strong> <strong>the</strong> Romanian managers over this
process, a research was conducted using both qualitative and quantitative methods.
The study is based on two hypo<strong>the</strong>ses:
Hypo<strong>the</strong>sis 1a: The Romanian managers encourage <strong>the</strong> knowledge transfer in <strong>the</strong>
companies <strong>the</strong>y run.
Hypo<strong>the</strong>sis 1b: The Romanian managers do not encourage <strong>the</strong> knowledge transfer in <strong>the</strong>
companies <strong>the</strong>y run.
Hypo<strong>the</strong>sis 2a: The Romanian managers consider <strong>the</strong> tacit knowledge more important
than <strong>the</strong> explicit knowledge.
Hypo<strong>the</strong>sis 2b: The Romanian managers do not consider <strong>the</strong> tacit knowledge more
important than <strong>the</strong> explicit knowledge
The population is formed by <strong>the</strong> total number <strong>of</strong> Romanian Small and Medium Enterprises (SMEs). In
<strong>the</strong> last years, <strong>the</strong> SMEs have been <strong>the</strong> core <strong>of</strong> <strong>the</strong> economies <strong>of</strong> <strong>the</strong> most important countries from
<strong>the</strong> <strong>European</strong> Union. The report <strong>of</strong> <strong>the</strong> <strong>European</strong> Union regarding <strong>the</strong> SMEs sector, 2003 edition,
emphasizes <strong>the</strong> remarkable results obtained by <strong>the</strong> Romanian SMEs and analyses <strong>the</strong>ir
characteristics to see if <strong>the</strong>y are still capable <strong>of</strong> playing <strong>the</strong> same important role in future. This is <strong>the</strong>
most important reason for which I have decided to analyze <strong>the</strong> Romanian SMEs. Thus, I used a
sample <strong>of</strong> 120 Romanian companies, with a number <strong>of</strong> managers ranging from 1 and 7 and a number
<strong>of</strong> employees ranging from 10 to 250. Several criteria were settled from <strong>the</strong> beginning, when choosing
<strong>the</strong> companies: <strong>the</strong> organization should have at least one Romanian shareholder and <strong>the</strong> company
should be a small or medium one (<strong>the</strong> number <strong>of</strong> employees should range between 10 and 250). In
order for <strong>the</strong> research to be significant, I also took into account <strong>the</strong> geographical distribution and <strong>the</strong>
industry <strong>of</strong> <strong>the</strong> companies, so I chose companies from every region <strong>of</strong> <strong>the</strong> country and from many
fields <strong>of</strong> activity. From <strong>the</strong> total population <strong>of</strong> almost 450, I used a sample <strong>of</strong> almost 120 managers
and <strong>the</strong> main criteria were age, <strong>the</strong> period <strong>of</strong> time worked within <strong>the</strong> company, <strong>the</strong> seniority in that
industry, <strong>the</strong> studies. Average age was 38.5. Most <strong>of</strong> <strong>the</strong>m (74.5%) had higher education. They were
predominately seniors in <strong>the</strong> field <strong>of</strong> work (more than 10 years) and <strong>the</strong>y had been with <strong>the</strong>ir current
organization on an average <strong>of</strong> 7.85 years (Bratianu et al., 2009).
The quantitative research was part <strong>of</strong> a larger survey on <strong>the</strong> role <strong>of</strong> <strong>the</strong> organizational knowledge as
an integrator over <strong>the</strong> organizational Intellectual Capital. Participants were asked to participate in a
twenty minutes, online (web-based) survey. They were informed that results would be used for
research purposes.
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The scalar questionnaire applied had several questions concerning <strong>the</strong> main macro-variable
Organizational Knowledge and contained statements similar to <strong>the</strong> followings with regard to <strong>the</strong>
benefits <strong>of</strong> <strong>the</strong> Knowledge transfer:
It speeds us <strong>the</strong> decision-making process.
It provides an effective way <strong>of</strong> inducting new staff.
It encourages <strong>the</strong> use <strong>of</strong> knowledge and promotes collaboration.
It stops reinventing <strong>the</strong> wheel and, as a result, saves time and reduces effort.
It encourages <strong>the</strong> transfer <strong>of</strong> best practices.
It helps trust become gradually institutionalized by collaborating and sharing.
The questions used to investigate <strong>the</strong> managers’ perception regarding <strong>the</strong> organizational knowledge,
<strong>the</strong> role and <strong>the</strong> importance <strong>of</strong> <strong>the</strong> sharing process were created and adjusted during a focus group
with specialists in <strong>the</strong> management <strong>the</strong>ory and representatives <strong>of</strong> <strong>the</strong> business environment. The final
addressed questionnaire is considered to contain items that lead to relevant results in determining <strong>the</strong>
management’s perception <strong>of</strong> <strong>the</strong> knowledge transfer for <strong>the</strong> company’s efficiency.
In <strong>the</strong> conducted qualitative research, <strong>the</strong> focus was on interviews. The research consists on an
exploratory component, detailed 20 minutes interviews with six managers chosen from <strong>the</strong> selected
organizations. They have a relevant experience in managing a company in <strong>the</strong> Romanian business
environment. They were interviewed individually, with <strong>the</strong> clear goal <strong>of</strong> identifying <strong>the</strong>ir perception
concerning <strong>the</strong> process <strong>of</strong> knowledge transfer. The interviews were informal and conversational. After
presenting <strong>the</strong> field <strong>of</strong> interest, <strong>the</strong>y were asked to identify <strong>the</strong> first five characteristics that <strong>the</strong>y
consider to be relevant for <strong>the</strong> knowledge sharing process.
4. 3BAnalysis
Below <strong>the</strong>re are <strong>the</strong> answers <strong>of</strong> <strong>the</strong> interviewed managers (Figure 1):
Manager 1 Manager 2 Manager 3 Manager 4 Manager 5 Manager 6
Open minded Intervention <strong>of</strong> Qualified Leadership Procedures Good
employees management employees
organizational
relationships
Clear
Well defined Clear chain <strong>of</strong> Centralized Managers’ Leadership
organizational strategy command decision attitude
goals
system
Strong
Financial Clear
Efficient Employees’ Good strategy
organizational rewards strategic Manager awareness
culture
options
Management’s Goals
Cooperation Practices Incentives Social
Encouragement
employees
Innovative
approach
Collaboration Friendly
atmosphere
Well known
objectives
Organizational
culture
Strong
competition <strong>of</strong>
<strong>the</strong> industry
Figure 1: Managers’ answers
Analyzing <strong>the</strong> managers’ answers, we can notice that four out <strong>of</strong> six managers related <strong>the</strong> company’s
knowledge transfer process to that <strong>of</strong> <strong>the</strong> Management’s attitude. More than that, four <strong>of</strong> <strong>the</strong>m
mention precisely that knowledge sharing involves <strong>the</strong> employees’ abilities, because in order to
transfer <strong>the</strong> knowledge, it is very important for all <strong>the</strong> workers to recognize and accept <strong>the</strong> need <strong>of</strong>
transferring knowledge and to understand <strong>the</strong> benefits <strong>of</strong> this process. The Romanian managers state
that sharing knowledge involves goals, objectives or strategy’ awareness and some <strong>of</strong> <strong>the</strong>m consider
that a strong competition within <strong>the</strong> industry, <strong>the</strong> incentives or a strong organizational culture based
on cooperation and collaboration or on rules and procedures can determine <strong>the</strong> employees to easily
share knowledge.
The Romanian managers are aware <strong>of</strong> <strong>the</strong> importance <strong>of</strong> knowledge creation and transfer. The
culture <strong>of</strong> a knowledge-driven company has to be collaborative in order to promote free flows <strong>of</strong>
information and facilitate knowledge creation. An effective manager has to focus attention on
organizational culture, in relation to <strong>the</strong> shared beliefs, values and expectations <strong>of</strong> <strong>the</strong> people from <strong>the</strong>
organization (Kermally, 2002). To make a start on becoming knowledge driven, organizations have to
investigate and study <strong>the</strong> best practice. They need to analyze <strong>the</strong> things that can work well in <strong>the</strong>ir
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organization and refine and change things that may have worked well elsewhere, but for a variety <strong>of</strong>
reasons will not be able to work well in <strong>the</strong>ir companies.
Concerning <strong>the</strong> quantitative research, data presented below were analyzed in SPSS program, with
identifying information removed. The survey consisted <strong>of</strong> a series <strong>of</strong> questions presented above on
<strong>the</strong> knowledge sharing influence in increasing <strong>the</strong> efficiency <strong>of</strong> <strong>the</strong> company (save time and reduce
effort). Measures used in this study included <strong>the</strong> geographical distribution and <strong>the</strong> demographic items
(year <strong>of</strong> birth). The descriptive statistics for <strong>the</strong> variable that describe <strong>the</strong> macro-variable
Organizational Knowledge is presented in Table 1. In Table 1, N represents <strong>the</strong> total number <strong>of</strong> <strong>the</strong>
participating managers (50), Min equals 1 and Max equals 5 represent <strong>the</strong> minimum and <strong>the</strong>
maximum values that <strong>the</strong>ir answers range between (1 represents totally disagree and 5, totally agree).
The fourth column (Mean) represents <strong>the</strong> mean <strong>of</strong> <strong>the</strong> answers and varies between 3,97 and 4,45.
The majority <strong>of</strong> <strong>the</strong> answers’ means is close to <strong>the</strong> maximum value (5), except for one variable that
have a value less than 4. The Standard Deviation (column 4) has values less than 1 that means that
<strong>the</strong> managers have similar opinions concerning <strong>the</strong> process <strong>of</strong> knowledge transfer.
Descriptive Statistics
N Min Max Mean Standard
deviation
Q1. Promote collaboration 50 1 5 4,25 ,834
Q2. Implement <strong>the</strong> knowledge tranfer
process
Q3. Promote innovation in products and
services
Q4. Capture tacit knowledge for
organizational use.
Q5. Create a strong organizational
culture
50 1 5 4,27 ,880
50 1 5 4,36 ,851
50 1 5 4,45 ,896
50 1 5 3,97 ,725
Table 1: Descriptive Statistics for managers
Few managers feel that knowledge is not for business. It is intangible and it cannot be measured.
Since it is not measurable, it cannot be managed, <strong>the</strong>y claim. The modern economic system is
increasingly based on knowledge. Organizations have to innovate and to improve <strong>the</strong>ir performance
and to compete effectively. To do so is to take advantage <strong>of</strong> <strong>the</strong> wealth <strong>of</strong> experience, talent and skills
in <strong>the</strong> company, because <strong>the</strong>y represent organizational knowledge. Knowledge embedded in products
and processes can be protected by law through copyright and patents, and <strong>the</strong>se can be managed
and measured. To what extent employees create and transfer knowledge can be managed and
measured as well (Kermally, 2002).
Analyzing <strong>the</strong> Reliability Statistics, <strong>the</strong> value <strong>of</strong> Cronbach’s Alpha is 0.829, more than 0.7, <strong>the</strong>
minimum value that shows <strong>the</strong> relevance <strong>of</strong> <strong>the</strong> obtained answers. We can conclude that <strong>the</strong> answers
are relevant and correctly correlated.
Items Statistics
Scale Mean if Item
Deleted
Scale
Variance if
Item Deleted
Corrected
Item
Q1. Promote collaboration 17,71 6,524 ,683 ,788
Q2. Implement <strong>the</strong> knowledge
tranfer process
16,14 5,879 ,584 ,761
Q3. Promote innovation in products
and services
16,48 6,129 ,586 ,818
Q4. Capture tacit knowledge for
organizational use.
17,96 6,326 ,606 ,891
Q5. Create a strong organizational
culture
16,77 6,503 ,517 ,782
Table 2: Items Statistics for managers
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Anca Mândruleanu
In Table 2, <strong>the</strong>re are two main aspects for this research: <strong>the</strong> values <strong>of</strong> <strong>the</strong> first column (Scale Mean <strong>of</strong>
Item deleted) and <strong>the</strong> values <strong>of</strong> <strong>the</strong> last column (Cronbach’s Alpha if Item deleted). Taking into
account <strong>the</strong> values obtained, we notice that item Q4 (17,96) has <strong>the</strong> strongest correlation with <strong>the</strong>
scale, meaning it has a major role in this analysis. The weakest correlation with <strong>the</strong> scale belongs to
<strong>the</strong> item Q2 (16,14). Thus, we can conclude that <strong>the</strong> management encourages <strong>the</strong> transfer <strong>of</strong> tacit
knowledge, by capturing tacit knowledge for organizational use. As <strong>the</strong> figures show, <strong>the</strong> managers
promote collaboration among employees, tries to build a strong organizational culture and promotes
innovation in products and services. Still, taking into account <strong>the</strong> value 16,14 <strong>of</strong> Q2, we can notice
that not all <strong>the</strong> managers are convinced that <strong>the</strong>y implement correctly <strong>the</strong> process <strong>of</strong> knowledge
transferring. In Table 2, we can also notice <strong>the</strong> value <strong>of</strong> <strong>the</strong> last column Cronbach’s Alpha if Item is
deleted. The values are less than <strong>the</strong> value <strong>of</strong> 0.829, previously obtained. Thus, we can conclude that
all <strong>the</strong> items that were initially selected are relevant for <strong>the</strong> present study. This value shows us that
removing any <strong>of</strong> <strong>the</strong> selected items means a loss <strong>of</strong> information in analyzing <strong>the</strong> results. In addition,
<strong>the</strong> Significance from <strong>the</strong> ANOVA table equals zero, which means that <strong>the</strong> analysis is significant at
any level.
Going back to <strong>the</strong> initial hypo<strong>the</strong>ses, after analyzing <strong>the</strong> data, we can conclude that both hypo<strong>the</strong>ses
are valid and we can state that in <strong>the</strong> analyzed Romanian companies, <strong>the</strong> managers consider that
managers encourage <strong>the</strong> knowledge transfer in <strong>the</strong> companies <strong>the</strong>y run and consider <strong>the</strong> tacit
knowledge more important than <strong>the</strong> explicit knowledge. In addition, <strong>the</strong>y promote innovation in
products and services; still, <strong>the</strong> encouraging <strong>the</strong> knowledge transfer is just <strong>the</strong> first step <strong>of</strong> <strong>the</strong> process
implementation itself, so more efforts in identifying <strong>the</strong> best way and approach in order to do this
effectively are needed. This will lead to a better cooperation a will stronger <strong>the</strong> organizational culture.
5. 4BConclusions
Romanian managers do believe that encouraging <strong>the</strong> knowledge transfer is essential, because this
will lead to an increase <strong>of</strong> <strong>the</strong> company’s effectiveness. Although <strong>the</strong> term “knowledge management”
implies formalized transfer, one <strong>of</strong> <strong>the</strong> most important elements is to develop specific strategies to
encourages spontaneous exchanges. When a business is struggling, people naturally ga<strong>the</strong>r to talk
through problems and share ideas about how to solve <strong>the</strong>m. These conversations are more likely to
generate creative solutions than keeping <strong>the</strong> employees at <strong>the</strong>ir desk and dealing with individual
tasks. Transferring knowledge through personal conversation is being threatened not only by
industrial age managers but also by <strong>the</strong> move in virtual <strong>of</strong>fices. Because <strong>of</strong> <strong>the</strong> financial crisis, many
Romanian SMEs are adopting work arrangements in which workers, especially those in <strong>the</strong> customer
oriented functions as ales and service, are encouraged to work at home or at a customer site.
Although <strong>the</strong>se arrangements <strong>of</strong>fer benefits for <strong>the</strong> employee in terms <strong>of</strong> flexibility and more time
spent with <strong>the</strong> client, it also lower <strong>the</strong> frequency <strong>of</strong> informal knowledge transfer.
Romanian managers understand <strong>the</strong> importance <strong>of</strong> knowledge transfer and <strong>the</strong>y are willing to find <strong>the</strong>
best ways for implementing it, even during national financial difficulties. To make a start on becoming
knowledge driven, managers have to investigate and study <strong>the</strong> best practice. They need to analyze
<strong>the</strong> things that can work well in <strong>the</strong>ir organization and refine and change things that may have worked
well elsewhere, but for a variety <strong>of</strong> reasons will not be able to work well in <strong>the</strong>ir companies
Acknowledgment
We would like to acknowledge <strong>the</strong> support received from UEFISCSU ROMANIA through PNII
research project ID_1812/2008
5BReferences
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Brătianu C., Vasilache S., Mândruleanu A. (2009) The dynamic <strong>of</strong> organisational integrators, Curtea Veche
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Davenport, T., Prusak, L. (2000) Working Knowledge, Harvard Business Press, pp. 88-102.
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Griffin, R.W., Moorhead, G. (2006) Fundamentals <strong>of</strong> organizational behavior. Houghton Mifflin Company: Boston,
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568

The Relational Capital as a key Factor for a Company’s
Success: General Insights
Simone Manfredi, Domenico Celenza and Rosa Lombardi
University <strong>of</strong> Cassino, Italy
s.manfredi@unicas.it
domenico.celenza@hotmail.it
rosa.lombardi@unicas.it
Abstract: Business system, particularly its basic components, has been widely reconsidered. It used to be a
whole <strong>of</strong> material elements, it is now a system <strong>of</strong> visible and invisible elements. Thanks to <strong>the</strong> rise <strong>of</strong> knowledge
economy, competitive modern businesses create value by stressing specific strategic invisible assets. More
specifically, among all <strong>the</strong> business immaterial components, <strong>the</strong> one that has gained more popularity is <strong>the</strong>
intellectual capital. It is based on three extremely important concepts: human capital, structural capital and
relational capital. The importance <strong>of</strong> each and every <strong>of</strong> <strong>the</strong>se concepts multiplies when you consider <strong>the</strong>m all
toge<strong>the</strong>r. This essay, though, aims to analyze one <strong>of</strong> <strong>the</strong>se three components as <strong>the</strong> success component <strong>of</strong> a
business: <strong>the</strong> relational capital. It represents <strong>the</strong> interaction and <strong>the</strong> integration between businesses and <strong>the</strong>ir
referential environment when specific exchanges – even financial ones - take place. These exchanges help keep
businesses alive. The relational capital originates from <strong>the</strong> value <strong>of</strong> <strong>the</strong> relationships that exist between
companies and <strong>the</strong>ir stakeholders. This essay specifically analyses <strong>the</strong> relational capital by identifying three
different areas: <strong>the</strong> kind <strong>of</strong> contractual relationship between companies and <strong>the</strong>ir stakeholders; <strong>the</strong> synergies,
which derive from collaborations between companies; <strong>the</strong> sharing <strong>of</strong> company values, such as reputation, image,
satisfaction, trust and loyalty marketing. Relational capital is examined through <strong>the</strong> analyses <strong>of</strong>:
relationships settled in time with defined roles, such as <strong>the</strong> relationships between companies and <strong>the</strong>ir
clients, providers, stakeholders and referents;
collaborations with o<strong>the</strong>r companies, which <strong>of</strong>ten become participation relations or proper contracts;
<strong>the</strong> companies <strong>the</strong>mselves, which share specific value with <strong>the</strong> external environment, thus help create a
connection between personal values and company values. For instance, reputation, image, trust, satisfaction
and loyalty marketing.
Therefore, it is necessary to highlight that rich and qualified relational resources not only help businesses gain a
strong competitive differential on <strong>the</strong> market, but also generate wealth in <strong>the</strong> long term.
As a result, research is as follows: what is <strong>the</strong> relational capital? What elements are necessary in order to
consider <strong>the</strong> relational capital a strategic business factor?
Keywords: intellectual capital, relational capital, stakeholders, values, trust, success factor.
1. Introduction
Since <strong>the</strong> rise <strong>of</strong> knowledge economy, many authors have shown deep interest in intellectual capital
(Rullani 2004; Foray 2006; Trequattrini 2008), defined as a whole <strong>of</strong> all <strong>the</strong> different kinds <strong>of</strong>
knowledge that <strong>the</strong> people who work for a company have and which make <strong>the</strong> company itself
competitive on <strong>the</strong> market (Stewart 1997).
The business system’s intellectual capital represents all <strong>the</strong> immaterial activities - such as capacities,
ideas, competencies - that <strong>the</strong> people who are part <strong>of</strong> a specific economic entity own (Zanda 1974;
Bertini 1990; Sciarelli 1985).
The intellectual capital (Bosworth, Webster 2006) coincides with three extremely important concepts:
human capital;
structural capital;
relational capital.
The human capital (Zanda, Lacchini, Onesti 1993) is represented by <strong>the</strong> people who form <strong>the</strong>
organization. These people help <strong>the</strong> organization be successful by providing <strong>the</strong>ir competencies, <strong>the</strong>ir
motivation and <strong>the</strong>ir intellectual capacities
(Roos, Roos, Dragonetti, Edvinsson 1997).
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Simone Manfredi, Domenico Celenza and Rosa Lombardi
The structural capital is represented by all <strong>the</strong> structures that form <strong>the</strong> organization and which support
<strong>the</strong> individuals while carrying out <strong>the</strong>ir work (Bonani 2002); it corresponds to all <strong>the</strong> structures, <strong>the</strong>
mechanisms, <strong>the</strong> procedures, <strong>the</strong> formal and non formal processes that make knowledge move from
<strong>the</strong> individual sphere to <strong>the</strong> organizational one.
The relational capital identifies with <strong>the</strong> values <strong>of</strong> <strong>the</strong> relationships which companies have with <strong>the</strong>ir
clients, suppliers and o<strong>the</strong>r third parties. Therefore, <strong>the</strong> relational capital is represented by <strong>the</strong> image,
<strong>the</strong> reputation, <strong>the</strong> satisfaction and loyalty marketing companies have in <strong>the</strong>ir relationship with
stakeholders (Donaldson, Preston 1995).
As a result, <strong>the</strong> interaction between <strong>the</strong> human, <strong>the</strong> structural and <strong>the</strong> relational capital fosters <strong>the</strong>
valorisation <strong>of</strong> each and every component <strong>of</strong> <strong>the</strong> intellectual capital and help businesses gain more
value.
This essay aims to analyze <strong>the</strong> relational capital as a key factor for a company’s success, which lets
companies get competitive differential and create value in <strong>the</strong> long term.
The essay will examine all <strong>the</strong> relationships companies can have, as well as <strong>the</strong> elements that
characterise <strong>the</strong>se relationships, in order to highlight <strong>the</strong> key role <strong>the</strong> relational strategic asset plays.
2. The relational capital: a possible definition
The relational capital (Zanda, 2006; Pirovano, Gilodi 2003) is composed by <strong>the</strong> amount <strong>of</strong> relations
that businesses have with <strong>the</strong> external environment and with all <strong>the</strong> people who get in touch with <strong>the</strong>
companies <strong>the</strong>mselves. In particular, <strong>the</strong> interlocutors who interact with companies are <strong>the</strong>
stakeholders (Freeman 1984; Mitchell, Agle, Wood 1997), e.g. clients, suppliers, business partners,
research centres, universities, local authorities.
The relationship between a company and its interlocutors must be examined from both a quantitative
and a qualitative point <strong>of</strong> view (Trequattrini 2008). The first aspect analyses how capable businesses
are <strong>of</strong> creating a certain number <strong>of</strong> relations with <strong>the</strong> external environment. The second aspect shows
<strong>the</strong> way <strong>the</strong> relationships between a business and its stakeholders must be managed.
Consequently, it is necessary to examine <strong>the</strong> relational capital through three areas <strong>of</strong> analysis:
contractual relations. They represent consolidated relationships with defined roles, such as
companies’ relations with clients, suppliers and stakeholders;
synergies. Through <strong>the</strong>m you can identify <strong>the</strong> collaborations that a business has with o<strong>the</strong>r
businesses. These collaborations may transform in participations or contracts;
shared values. Businesses share some values with <strong>the</strong> external environment. This helps personal
values and company values, such as reputation, image, trust, satisfaction and loyalty marketing,
merge.
By creating a relational capital, businesses can create value (Bang, Cleemann, Bramming, 2010).
Relationships with <strong>the</strong> external environment are to companies both a way <strong>of</strong> growing up and a chance
to create added value.
Relationships are one <strong>of</strong> <strong>the</strong> main source for companies, since <strong>the</strong> value <strong>of</strong> cash flow and stock <strong>of</strong>
resources depends on <strong>the</strong>m. The last makes companies develop and enhance cash flows in <strong>the</strong> long
term.
There is a national and international <strong>the</strong>ory that supports all this. It has focused on <strong>the</strong> concept <strong>of</strong>
relational capital, considered a key factor in order to gain and maintain competitive advantage in <strong>the</strong>
market.
The relational capital, which is a source for businesses (Costabile 2001), coincides with <strong>the</strong> capital
that supports <strong>the</strong> training and <strong>the</strong> transfer <strong>of</strong> a collective knowledge, in a collective knowledge system.
The relational capital is <strong>of</strong> paramount importance for <strong>the</strong> acquisition and reduction <strong>of</strong> knowledge
creation related costs.
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Simone Manfredi, Domenico Celenza and Rosa Lombardi
Fast changes in <strong>the</strong> relative environment (Zanda 2009) push companies to adopt a dynamic
organizational system which involves all <strong>the</strong> clients, providers and strategic partners, in order to
create links which generate high level creativity and satisfaction.
As a consequence, it figures how important <strong>the</strong> relational asset, characterized by stable relations, is.
All <strong>the</strong> relationships that take place are long term and stable. This point <strong>of</strong> view helps companies
create a relationship that last longer than just <strong>the</strong> duration <strong>of</strong> buying and selling.
The structural capital supports <strong>the</strong> relational capital management by using computer nets which
assure a higher relational efficiency. Relational technologies (Lipparini 2002) make <strong>the</strong> most <strong>of</strong> <strong>the</strong>
relationships that businesses have with <strong>the</strong>ir stakeholders. They also enhance <strong>the</strong> benefits <strong>of</strong> <strong>the</strong>
interaction between <strong>the</strong> components <strong>of</strong> <strong>the</strong> intellectual capital.
3. The management <strong>of</strong> <strong>the</strong> relationships with <strong>the</strong> main company stakeholders
The concept <strong>of</strong> stakeholder (Freeman, Rusconi, Dorigatti 2007) includes all <strong>the</strong> subjects interested in
companies’ policy and results. This term, which was long debated by <strong>the</strong> <strong>the</strong>ory, was widened by
Freeman in 1984 as a consequence <strong>of</strong> <strong>the</strong> studied carried out by <strong>the</strong> Stanford Research Institute and
<strong>the</strong> questionnaire set by <strong>the</strong> <strong>European</strong> Societal Strategy Project. The author described stakeholders
as a group or an individual who can influence or can be influenced by <strong>the</strong> targets reached by
companies (Freeman 1984). Consequently, clients, suppliers, shareholders, employers, creditors,
pr<strong>of</strong>essional associations, universities, <strong>the</strong> community, trade unions and <strong>the</strong>ir company connection,
judiciary power, submersed groups and so on can be all considered stakeholders.
The process through which you can map stakeholders consists <strong>of</strong> three stages:
<strong>the</strong> first stage is <strong>the</strong> “rational” one. It is necessary to map all <strong>the</strong> stakeholders related to a specific
organization;
<strong>the</strong> second is <strong>the</strong> “process” stage, which is necessary to analyze <strong>the</strong> organizational processes on
which lays <strong>the</strong> management <strong>of</strong> <strong>the</strong> relationships with <strong>the</strong> stakeholders and <strong>the</strong> checking <strong>of</strong> <strong>the</strong>
abovementioned map;
<strong>the</strong> third is <strong>the</strong> “transactive” stage. It is necessary to identify <strong>the</strong> abovementioned relationships in
relation to likely negotiations, mediations and agreements with o<strong>the</strong>r stakeholders.
According to <strong>the</strong> author, businesses must be capable <strong>of</strong> designing and realizing communicative
processes with many different interlocutors, <strong>of</strong> reaching agreement with <strong>the</strong>m on crucial issues <strong>of</strong> a
company’s life, and <strong>of</strong> choosing generalized marketing systems in order to match different needs.
Never<strong>the</strong>less, not all <strong>the</strong> relations with stakeholders are important (Lattanzi 2000). Relationships are
important if <strong>the</strong>re is a strong interdependence between <strong>the</strong> two parts. When a new relationship
between a company and an interlocutor takes place, several different factors must be taken into
account (Trequattrini 2008), such as <strong>the</strong> interlocutor’s level <strong>of</strong> mobility, <strong>the</strong> possible available
information on interlocutors, <strong>the</strong> frequency <strong>of</strong> exchanges.
The relation has different aspects during its length (Lane, Bachman 1998). There are an initial, an
intermediate and a final stage. In <strong>the</strong> first stage, <strong>the</strong> partners with whom to have a relation are
chosen and <strong>the</strong>re are no detailed nor complete information on <strong>the</strong>m, since no relationship existed
before. The relationship is linked to <strong>the</strong> contract and to a kind <strong>of</strong> trust that derives only from
previsions. In <strong>the</strong> intermediate stage, <strong>the</strong> knowledge <strong>of</strong> <strong>the</strong> partner deepens, since <strong>the</strong> relationship
lasts. The contract becomes less important and mutual trust grows, as well as less formal ways <strong>of</strong>
checking. Thanks to <strong>the</strong> mutual trust, in <strong>the</strong> final stage <strong>the</strong> two parts crate a strong link which is
characterised by affective and emotional exchanges.
Trust is a relational quality, which aims to minimise <strong>the</strong> complexity <strong>of</strong> <strong>the</strong> external environment (Mutti
1994) and refers to prevision <strong>of</strong> cooperative but not opportunistic behaviour (Six 2007). The process
through which trust generates is long and complex (Fukuyama 1995). It is based on positive reaction
which streng<strong>the</strong>n <strong>the</strong> psychological state <strong>of</strong> <strong>the</strong> parts involved, taking into account that <strong>the</strong>re is
absolutely no certainty that trust will be matched by a loyal behaviour.
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The relationships between companies and interlocutors base on interpersonal trust. They help create
value by exchanging resources (Ferrero 1992; Groonroos 1990; Hakansson, Snehota 1989),
information and knowledge.
Important relationships are to businesses a way <strong>of</strong> creating (Rullani 2004) and spreading knowledge.
For instance, <strong>the</strong> relations with clients let companies get more information on <strong>the</strong> referring market as
well as on <strong>the</strong> companies (Pirovano, Gilodi 2003; Sicca 2001).
Knowledge transforms into relational capital (Stewart 1997) in relation to how and how much
companies are able to react to <strong>the</strong>ir clients’ needs and to <strong>the</strong> o<strong>the</strong>r two intellectual capital’s
components. As a result, companies create products that match each and every client’s needs.
Depending on what stage is <strong>the</strong> relation at, it is possible to generate a gradual growth <strong>of</strong> <strong>the</strong> produced
surplus and <strong>of</strong> <strong>the</strong> competitive advantage gained in spite <strong>of</strong> <strong>the</strong> costs <strong>of</strong> new knowledge, which will
reduce in <strong>the</strong> future.
You must also take into account that <strong>the</strong> relationships between businesses generates as network
(D’Alessio 2008; Jhonson 2009; Lorenzoni 1992) or partnership agreements (Rullani 1989; Lai 1990;
Bastia 1989; Broglia Giuggi 2001).
A company reaches an agreement with o<strong>the</strong>r companies through some cooperative strategies and by
creating relations that can be included in a wider network. Therefore, businesses gain knowledge
transfer not through research and development, but through <strong>the</strong> process <strong>of</strong> innovation created by <strong>the</strong>
inter-relation with <strong>the</strong> external environment.
3.1 Relationships with clients
Among all stakeholders, clients are <strong>the</strong> most important ones and represent one <strong>of</strong> <strong>the</strong> main factor <strong>of</strong>
company income.
Success businesses aim to satisfy clients and make <strong>the</strong>m loyal (Vicari 1995). This helps <strong>the</strong>m protect
<strong>the</strong>ir market share. This process – based on a growing attention to clients’ needs – takes place even
in full and mature markets.
Customer satisfaction is <strong>the</strong> tool which is used to measure customer satisfaction and loyalty.
There is a sort <strong>of</strong> chart <strong>of</strong> indicators used to measure <strong>the</strong> immaterial activities linked to clients, which
are identified by input and output indicators. The first indicators do not completely represent <strong>the</strong>
abovementioned immaterial activities, since <strong>the</strong>y do not include all <strong>the</strong> efforts companies make to get
a critical group <strong>of</strong> clients. The second indicators point out <strong>the</strong> value <strong>of</strong> <strong>the</strong> abovementioned immaterial
activities at advanced stages <strong>of</strong> <strong>the</strong> value chain. For instance companies’ brand and reputation, which
certify that
a company has a wide competitive space;
a company has distinctive capacities;
<strong>the</strong> two components let a company grow and create value.
Brand value includes clients expectations, which is to say <strong>the</strong> maintenance <strong>of</strong> a constant level <strong>of</strong>
performance quality. Brand is important to clients in order to not only identify companies’ goods and
services but also to choose, evaluate and buy. It is also significant during <strong>the</strong> process <strong>of</strong> postpurchase
assistance.
Creating a relation with <strong>the</strong> company allows to generate a tight and permanent link with clients and to
get useful information to match <strong>the</strong>ir needs immediately. Businesses customize <strong>the</strong>ir products in order
to enhance clients’ satisfaction and loyalty (Rifkin 2001).
You can analyze relationships with clients through two logical processes <strong>of</strong> <strong>the</strong> relation companyclient.
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The first examines <strong>the</strong> income a client has created, by relating <strong>the</strong> contributing margin related to <strong>the</strong>
client itself and <strong>the</strong> length <strong>of</strong> <strong>the</strong> relationship (in years). Companies will bet on <strong>the</strong> ones who have or
will have wider margins.
This system lets you classify clients as follows:
<strong>the</strong> historical and unpr<strong>of</strong>itable clients. They have had a long term relationship with a company and
generate very little income;
<strong>the</strong> clients defined as partners. They generate more income compared to <strong>the</strong> previous ones.
<strong>the</strong> young and not yet pr<strong>of</strong>itable clients. They generate very little value.
Young and interesting clients. They guarantee good incomes. They are likely to contribute
significantly and constantly to create value.
The second process examines <strong>the</strong> relationship between duration and management cost and identifies
<strong>the</strong> historical and economical clients, <strong>the</strong> historical and demanding ones, <strong>the</strong> new and economical
ones, one shot.
The key indicators that evaluate <strong>the</strong> relationship with <strong>the</strong> clients (Bonani 2001) are as follows:
kind <strong>of</strong> client;
duration <strong>of</strong> supply;
client role;
support that clients have had;
client success.
The position <strong>of</strong> <strong>the</strong> client <strong>of</strong>ten becomes more important than o<strong>the</strong>r factors. In fact, <strong>the</strong> contribution <strong>of</strong>
each and every client is very important to <strong>the</strong> development <strong>of</strong> <strong>the</strong> relational capital.
3.2 Relationships with providers
Suppliers are ano<strong>the</strong>r key category <strong>of</strong> stakeholders (Mitchell, Agle, Wood, 1997). Over <strong>the</strong> last years,
companies have had <strong>the</strong> tendency to involve suppliers in <strong>the</strong> production process in order to create
successful, strong and long lasting relationships. These agreements <strong>of</strong>ten become inter-companies
relations which coincide with businesses nets (Cafaggi 2004) that allow mutual exchange <strong>of</strong>
knowledge. Never<strong>the</strong>less, <strong>the</strong> net must be optimized through two key elements:
common interests;
trust relationship (Luhmann 1979; Scott 1980).
If <strong>the</strong>se two elements do not exist, <strong>the</strong> relations which will take place will not create any value.
The relationship with <strong>the</strong> suppliers must not end with <strong>the</strong> exchange <strong>of</strong> productive factors. Companies
make <strong>the</strong> relationship long-lasting through common initiatives that highlight <strong>the</strong>ir resources.
The partnership relations has mutual advantages for both parties in term <strong>of</strong> lower costs, higher
incomes, more innovative capacities, knowledge sharing.
Generally speaking, <strong>the</strong> collaborations that usually take place are <strong>the</strong> just in time and <strong>the</strong>
comarkership (Lipparini 2002).
The management <strong>of</strong> <strong>the</strong>se relationships should be based on some key factors, such as mutual
collaboration, know how transfer, mutual assistance, cooperation.
3.3 Relationship with shareholders
Among <strong>the</strong> main stakeholders <strong>of</strong> a company (Freeman 1984) <strong>the</strong>re are <strong>the</strong> shareholders.
Shareholders can have short term or medium/long term objectives (Lacchini 1992). Never<strong>the</strong>less, <strong>the</strong>
longer <strong>the</strong> term <strong>the</strong> smaller <strong>the</strong> risk and <strong>the</strong> bigger <strong>the</strong> attention to <strong>the</strong> sustainability <strong>of</strong> <strong>the</strong> investment.
Long term can, usually, guarantee a satisfying income deriving from <strong>the</strong> investment.
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Stakeholders’ ways <strong>of</strong> getting information can be described as business budget and/or significant
information on companies, monthly published in respect <strong>of</strong> transparency, openness and neutrality
regulations (Lizza 1993).
Stakeholders can be classified as follows - depending on <strong>the</strong> target you want to reach:
arbitrator stakeholders. They try and get earnings by speculating on daily evaluating differences
given to companies;
liquidity stakeholders. They collect shares in order to get dividends or to arbitrage in <strong>the</strong> short
term;
institutional stakeholders. They keep share value in time, since <strong>the</strong>y aim to create business
value;
active stakeholders. They intend to manage a company.
Business management takes into account <strong>the</strong> relations with <strong>the</strong> stakeholders. Companies try and
highlight <strong>the</strong> role played by each and every stakeholders by stimulating - through an integrated
communication system – <strong>the</strong>ir participation in a company’s activity, from both a financial and
management point <strong>of</strong> view.
4. Relational synergies
Companies which create synergic relationships have wider access to information sharing, since <strong>the</strong>y
are based on relations that are <strong>of</strong>ten like network or business network (Cafaggi 2004).
The reasons why businesses ga<strong>the</strong>r are economic-financial, but <strong>the</strong>y are also linked to <strong>the</strong> structural
characteristics <strong>of</strong> <strong>the</strong> economic system. The first reason identifies <strong>the</strong> synergies that derive from <strong>the</strong>
birth <strong>of</strong> a group <strong>of</strong> companies. Business nets represent an organizational model that relates to two
phenomena:
<strong>the</strong> tendency <strong>of</strong> lots <strong>of</strong> small companies to ga<strong>the</strong>r without a leader – at least at this stage;
<strong>the</strong> function <strong>of</strong> <strong>the</strong> net – which aims to <strong>the</strong> mutual interest <strong>of</strong> all <strong>the</strong> participants – is to produce
and share technological knowledge to be used in productive processes and to be accumulated
only by generating a critic mass through <strong>the</strong> creation <strong>of</strong> equal forces (D’Alessio 2008; Rullani
1989; Pilotti, Pozzana 1990).
Nets foster <strong>the</strong> creation <strong>of</strong> a collective capital which supports trust, cooperation between members
and behaviour rules that must be shared. Nets let <strong>the</strong> people who are part <strong>of</strong> <strong>the</strong>m share knowledge.
Each and every relation that companies create is set in a referring network: depending on relations
between net nods, organizations develop <strong>the</strong>ir effectiveness (Lorenzoni 1992) in terms <strong>of</strong> collective
strategy.
Collaboration with o<strong>the</strong>r companies shows how much businesses are open minded and eager to
conquer <strong>the</strong> market. Now knowledge sharing intervenes.
Synergies in companies alliances (D’Amico 1996) can be interpreted as lever effect (Giaccari 2003):
<strong>the</strong> share lever effect represents <strong>the</strong> possibility to keep risk capital stable and enhance share
control;
<strong>the</strong> financial lever effect allows to multiply credit capacity by considering <strong>the</strong> companies that form
a group single entities;
<strong>the</strong> technical-productive lever effect lays in <strong>the</strong> productive organization <strong>of</strong> <strong>the</strong> productive system;
<strong>the</strong> technological lever effect is based on <strong>the</strong> possibility to improve <strong>the</strong> sharing <strong>of</strong> specific
technologies;
<strong>the</strong> managerial lever effect is represented by a whole <strong>of</strong> technologies, which can be activated in
<strong>the</strong> human resources management;
<strong>the</strong> lever effect <strong>of</strong> <strong>the</strong> distribution chain is represented by <strong>the</strong> use <strong>of</strong> mutual distribution chains in
order to put different products on <strong>the</strong> market.
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The potential knowledge <strong>of</strong> an alliance (Lizza 2002) derives from factors such as business learning,
knowledge creation, acquisition and transfer methods, different thinking <strong>of</strong> people who belong to <strong>the</strong>
organization, management involvement.
Business nets or networks, such as partnership agreements and global nets, represent collaboration
which are based on inter-company relationships (Canestrino 2008).
The acquisition <strong>of</strong> knowledge from <strong>the</strong> external environment fosters <strong>the</strong> investment on research for
internal resources, since only companies that have high level intellectual capital can be advantaged
by knowledge coming from <strong>the</strong> external environment (Cohen, Levinthal 1989).
5. Value sharing
Businesses and <strong>the</strong>ir environment share some values that express in <strong>the</strong> fusion <strong>of</strong> personal and
business values. Relational capital can be represented by factors like reputation, image, trust (Pelligra
2007), satisfaction, loyalty marketing. These factors are more or less important depending on <strong>the</strong> kind
<strong>of</strong> relationship that takes place.
Reputation and trust derive from <strong>the</strong> interlocutors’ level <strong>of</strong> satisfaction and loyalty marketing and on
how external interlocutors react to companies.
Business policy can widen or destroy reputation and trust. Companies try and optimize relational flows
by improving reputation, trust and image.
Reputation (Gambetta 1989) lays in what interlocutors think <strong>of</strong> companies. Good reputation, in
addition to fostering and helping find human resources, lets create relations with financial supporters
and suppliers and streng<strong>the</strong>n <strong>the</strong> existing ones with <strong>the</strong> interlocutors. A company which has good
reputation is represented by a management which can provide complete information to stakeholders
(Freeman 1984).
Reputation is a resource that forms in time and identifies business system as well as its interlocutors,
which can interpreter and judge incentives, spurs, behaviours coming from companies.
Therefore, reputation can be considered <strong>the</strong> sum <strong>of</strong> all <strong>the</strong> characteristics <strong>of</strong> a company: products,
brand, personnel, management, technical knowledge, etc. Companies convey <strong>the</strong>ir image and build
<strong>the</strong>ir own reputation.
Fiduciary assets are relational links that make businesses’ relationships more or less stable.
Trust is one <strong>of</strong> <strong>the</strong> strategic factor <strong>of</strong> relational capital success. It guarantees long term relations with
all <strong>the</strong> interlocutors that companies have.
Stable, long term relations make trust a company’s resource. It is based on two important elements:
communicative processes
business knowledge (Trequattrini 2008, Vicari 1991).
Trust improvement is, from companies’ point <strong>of</strong> view, <strong>the</strong> accumulation <strong>of</strong> immaterial resources
(Castaldo 2002), even though - referring to <strong>the</strong> relationships with <strong>the</strong> external environment - it
represents creation <strong>of</strong> value.
6. Conclusions
In <strong>the</strong> current stage <strong>of</strong> knowledge economy, this essay tries to emphasise <strong>the</strong> role relational capital
plays, from its definition to <strong>the</strong> identification <strong>of</strong> strategic success elements that support and streng<strong>the</strong>n
it in <strong>the</strong> long term.
The whole <strong>of</strong> relationships that companies have with <strong>the</strong>ir stakeholders – <strong>the</strong>ir partner companies – is
a key resource, on <strong>the</strong> one hand for business survival, on <strong>the</strong> o<strong>the</strong>r hand when you have to design,
share, develop and transfer new knowledge, which creates significant competitive differential.
The synergies which support business relationships, make trust, image and reputation derive from <strong>the</strong>
sharing <strong>of</strong> strategic relational values.
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The relations reinforced by <strong>the</strong> fiduciary asset and <strong>the</strong> sharing <strong>of</strong> values are characterised by a high
stability and represent an excellent source for brand new relationships.
Therefore, in <strong>the</strong> society <strong>of</strong> knowledge and information, <strong>the</strong> role <strong>of</strong> <strong>the</strong> relational capital spreads. It is
an immaterial resource, a strategic success factor for modern companies, which derive from <strong>the</strong>
merging <strong>of</strong> material and immaterial elements.
Significant business relationships, based on systemic cooperation and integration, allow companies
earn more by sharing new knowledge.
A step forward in analyzing relational capital is a financial quantification <strong>of</strong> business relationships.
These, made even more valuable, let companies have a referring <strong>the</strong>oretical value, in case
companies’ system is transferred.
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577

Modelling Knowledge Sharing Into a Medical Facility Using
Human and Virtual Agents (Knowbots)
Virginia Maracine 1 , Luca Iandoli 2 , Emil Scarlat 1 and Adriana Sarah Nica 3
1
Department <strong>of</strong> Economic Cybernetics, Faculty <strong>of</strong> Economic Cybernetics,
Statistics and Informatics, Bucharest University <strong>of</strong> Economics, Romania
2
Department <strong>of</strong> Business and Managerial Engineering, Faculty <strong>of</strong> Engineering,
University Federico II, Naples, Italy
3
Clinical Rehabilitation Department <strong>of</strong> National Institute <strong>of</strong> Rehabilitation and
Physical Medicine, University <strong>of</strong> Medicine and Pharmacology “Carol Davila”
Bucharest, Romania
virginia.maracine@csie.ase.ro
iandoli@unina.it
emil.scarlat@csie.ase.ro
adisarahnica@yahoo.com
Abstract: Knowledge-oriented organizations are bricks for <strong>the</strong> knowledge-based society construction. Building
knowledge-based society and economy suppose challenging transition processes from <strong>the</strong> classical structure <strong>of</strong>
an organization to new organizational forms that help to fill <strong>the</strong> gap between actual society and <strong>the</strong> future
knowledge-based society and economy. This transition generates new issues in knowledge creation and sharing
processes, related to <strong>the</strong> particularities <strong>of</strong> <strong>the</strong> new organizational forms. Therefore, in <strong>the</strong> last few years, our
researches are oriented to developing and testing a number <strong>of</strong> forms <strong>of</strong> organization designed to facilitate an
efficient and effective transition toward <strong>the</strong> knowledge-based society, like communities <strong>of</strong> practice, (virtual)
networks <strong>of</strong> pr<strong>of</strong>essionals or knowledge ecosystems (KE). Under this general frame, this paper presents <strong>the</strong>
results <strong>of</strong> our research aiming to capture <strong>the</strong> necessary changes that a medical organization specialized in
rehabilitation (<strong>the</strong> National Institute <strong>of</strong> Rehabilitation and Physical Medicine from Bucharest, Romania - INRMFB)
has to undertake for converting its classical structure into a new knowledge-oriented one, possible and easily to
being integrated into a Virtual Network for Home Health Rehabilitation <strong>of</strong> <strong>the</strong> impaired people – <strong>the</strong> meta goal <strong>of</strong>
our research in recent years.
Specifically, within its five sections, <strong>the</strong> paper outlines:
1. An introduction in <strong>the</strong> macro and micro-level empirical setting in which <strong>the</strong> study is carried out;
2. The methodological approach based on Social Network Analysis (SNA). Although quit <strong>of</strong>ten used in <strong>the</strong>
medical field, as we will see in <strong>the</strong> second section <strong>of</strong> <strong>the</strong> paper, <strong>the</strong> SNA methods and models aren’t used yet
in <strong>the</strong> particular area <strong>of</strong> health rehabilitation;
3. The objectives <strong>of</strong> <strong>the</strong> empirical study that can be summarized as follows:
Mapping <strong>of</strong> <strong>the</strong> knowledge flows & needs in <strong>the</strong> target community <strong>of</strong> practice. The aim <strong>of</strong> this step is to
produce an accurate picture <strong>of</strong> <strong>the</strong> knowledge flows that <strong>the</strong> target community identified at <strong>the</strong> INRMFB
actually enacts in <strong>the</strong> accomplishment <strong>of</strong> its organizational objectives.
Analysis & Diagnosis: Identification <strong>of</strong> critical aspects and areas <strong>of</strong> improvements (e.g. knowledge
needs, knowledge bottlenecks, structural determinants <strong>of</strong> inefficiency or <strong>of</strong> poor performance).
Design: definition <strong>of</strong> <strong>the</strong> functional specifications for redesigning <strong>the</strong> agents, network and <strong>of</strong> <strong>the</strong>
functionalities <strong>of</strong> Knowbots.
4. The survey we have designed for data collection. According with <strong>the</strong> particularities <strong>of</strong> <strong>the</strong> macro and microlevel
in which our study is carried out, we have designed a survey that will help us both for diagnosing <strong>the</strong>
knowledge-sharing-structure <strong>of</strong> INRMFB, and for finding adequate solutions for potential critical aspects
identified in this medical facility.;
5. A set <strong>of</strong> conclusions and recommendations for <strong>the</strong> new knowledge-oriented organizational structure to be
created within <strong>the</strong> INRMFB.
Alongside with performing SNA in <strong>the</strong> health rehabilitation field, an important output <strong>of</strong> our study is to find answer
to <strong>the</strong> following questions:
Cans <strong>the</strong> classical organizational structure <strong>of</strong> <strong>the</strong> INRMFB be transformed into a knowledge-based one,
by reengineering <strong>the</strong> knowledge flows and agent’s roles?
If and where within <strong>the</strong> actual structure a virtual knowledge agent (knowbot) can and should be
integrated?
Our paper is a consequent continuation <strong>of</strong> our work in <strong>the</strong> KE area, contributing to <strong>the</strong> completion <strong>of</strong> an integrate
vision over <strong>the</strong> role <strong>of</strong> <strong>the</strong> KM techniques, human and virtual agents in <strong>the</strong> emerging <strong>of</strong> knowledge-based society.
It presents a work still in progress, <strong>the</strong> final results <strong>of</strong> our study going to be presented within <strong>the</strong> ECKM2011
conference.
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Keywords: Community <strong>of</strong> Practice, Healthcare Knowledge Ecosystems, Social Network Analysis, Knowledge
Agent (Knowbot), Collective Learning, Knowledge-based organization.
1. Macro and micro environment <strong>of</strong> our study
For achieving our research goals – discovering, developing and testing new forms <strong>of</strong> organizations
that help to fill <strong>the</strong> gap between actual society and <strong>the</strong> future knowledge-based society and economy
(communities <strong>of</strong> practice (CoP), networks <strong>of</strong> pr<strong>of</strong>essionals or KEs.), by collaborating with our
colleagues from INRMFB (medical staff, pr<strong>of</strong>essors, and researchers) we have designed a study that
consists in:
• Analyzing <strong>the</strong> macro and micro-level empirical setting in which <strong>the</strong> study is carried out;
• Designing <strong>the</strong> main objectives <strong>of</strong> <strong>the</strong> empirical study and <strong>the</strong> methodological approach;
• Creating a personalized survey for data collection;
• Collect and process <strong>the</strong> necessary data;
• Drawing conclusions and recommendations both for <strong>the</strong> old and <strong>the</strong> new knowledge-oriented
organizational structure to be created within <strong>the</strong> INRMFB.
Some <strong>of</strong> <strong>the</strong>se phases were already accomplished, while o<strong>the</strong>rs are running as we speak. Both <strong>of</strong>
<strong>the</strong>m are described in our paperwork.
1.1 INRMFB macro-level empirical settings
For having a complete image over <strong>the</strong> extent, links, interdependencies and influences, we start our
paper by analyzing both <strong>the</strong> macro and micro environment that define <strong>the</strong> activity <strong>of</strong> INRMFB. Tacking
account that <strong>the</strong> Healthcare Ecosystems are among <strong>the</strong> most complex networks (Mărăcine, Scarlat,
2008), identifying all <strong>the</strong> “agents” and <strong>the</strong> interdependencies within such a structure is crucial for a
good diagnostic <strong>of</strong> <strong>the</strong> present knowledge transfer network, and especially for finding <strong>the</strong> best
solutions for increasing <strong>the</strong> fluidity <strong>of</strong> <strong>the</strong> knowledge creation and sharing processes. A graphic
overview <strong>of</strong> <strong>the</strong> macro-environment that includes <strong>the</strong> INRMFB is comprised by figure 1.
As we can see in figure 1, <strong>the</strong> INRMFB is part <strong>of</strong> a true Healthcare Ecosystem (Mărăcine, Scarlat,
2010). The typology <strong>of</strong> agents involved in <strong>the</strong> current process at macro level is quite wide, including
hospitals (especially those having emergency rooms), medical products suppliers, Laboratories,
Pharmacies, Universities, national authorities for scientific research, Romanian Healthcare Ministry,
and also links with knowledge repositories accessible through <strong>the</strong> Internet. The existence <strong>of</strong> such a
diversity <strong>of</strong> agents, links and knowledge flows brings many particularities in <strong>the</strong> process <strong>of</strong> knowledge
creation and sharing. Also, an increasing variety <strong>of</strong> competencies – o<strong>the</strong>r than <strong>the</strong> medical ones – are
more and more a must for <strong>the</strong> members <strong>of</strong> a medical community <strong>of</strong> practice:
extensive usage <strong>of</strong> <strong>the</strong> computers and dedicate s<strong>of</strong>tware;
virtual communications with colleagues and medical authorities;
continue learning on how to use <strong>the</strong> advanced technologies embedded with <strong>the</strong> new equipments;
facing <strong>the</strong> new attitude <strong>of</strong> <strong>the</strong> patients that are more and more informed and educated (also by <strong>the</strong>
Internet) on <strong>the</strong>ir medical conditions.
1.2 INRMFB micro-level empirical settings
Going deeper and looking at <strong>the</strong> micro-level, <strong>the</strong> structure <strong>of</strong> <strong>the</strong> INRMFB also involves different
typologies <strong>of</strong> agents (figure 2).
The main clusters we have found here are:
a) The Medical Community <strong>of</strong> Practice – Medical staff. For <strong>the</strong> Rehabilitation Clinic no. 3 (<strong>the</strong> one in
which we have started our study), this CoP includes 36 employees (out <strong>of</strong> all <strong>the</strong> 38 existent positions
within this clinic):
4 doctors specialists in medical recovery, one <strong>of</strong> <strong>the</strong>m is <strong>the</strong> chief <strong>of</strong> <strong>the</strong> clinic;
9 pr<strong>of</strong>essional nurses – 6 <strong>of</strong> <strong>the</strong>m were graduated post high school medical courses (2 years <strong>of</strong>
study) and <strong>the</strong> o<strong>the</strong>r 3 were graduated <strong>the</strong> University <strong>of</strong> Medicine on <strong>the</strong> Bologna system;
10 physio<strong>the</strong>rapists – 6 <strong>of</strong> <strong>the</strong>m are physio<strong>the</strong>rapist nurses and <strong>the</strong>y were graduated post high
school medical courses (3 years <strong>of</strong> study) and <strong>the</strong> o<strong>the</strong>r 4 were graduated <strong>the</strong> University <strong>of</strong>
Medicine (4 or 5 years <strong>of</strong> study) and <strong>the</strong>y are pr<strong>of</strong>essional physio<strong>the</strong>rapists and kineto-<strong>the</strong>rapists;
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1 psychologist also specialist in speaking deficiencies;
1 instructor <strong>of</strong> medical gym;
1 social worker – this position is vacant, in <strong>the</strong> Romanian medical educational system <strong>the</strong>re are
no programs that could train specialists on this domain;
1 occupational <strong>the</strong>rapist – this position is also vacant, for <strong>the</strong> same reason as for <strong>the</strong> social
worker;
8 nursing aids;
1 statistician and 1 department secretary.
Figure 1: Health rehabilitation within <strong>the</strong> INRMFB – macro level
b) The “Patient” CoP – i.e. <strong>the</strong> disabled patient, his/her family, friends, and colleagues. Within <strong>the</strong>
Rehabilitation Clinic no 3, in average <strong>the</strong>re are 60 patients, usually each <strong>of</strong> <strong>the</strong>m being hospitalized
for 2 or 3 weeks according with <strong>the</strong> severity <strong>of</strong> <strong>the</strong> impairment.
In <strong>the</strong> present, in Romania, <strong>the</strong> demand for medical services in <strong>the</strong> rehabilitation area far exceeds <strong>the</strong>
hospitals’ <strong>of</strong>fer. Therefore, in order to increase <strong>the</strong> ratio <strong>of</strong>fer/demand for rehabilitation services, <strong>the</strong>
hospital’s management team succeeded to find some alternative solutions for <strong>the</strong> people that need
specialized medical assistance, but who can be hospitalized only if <strong>the</strong>y are willing (or if <strong>the</strong>y can) to
wait between 2.5 and 3 months. Such alternative solutions for <strong>the</strong> INRMFB consist in programs such
as:
The “Outpatient” program – <strong>the</strong> patient come into <strong>the</strong> hospital just to receive <strong>the</strong> treatment,
without being hospitalized; and
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The “Hospital by day patient” program – <strong>the</strong> patient is hospitalized only during <strong>the</strong> day and is
supervised to accomplish his rehabilitation program, without receiving any medication or
food from <strong>the</strong> hospital.
In this way <strong>the</strong> number <strong>of</strong> assisted patients within <strong>the</strong> hospital was increased with around 30% per
year.
In this moment, from <strong>the</strong> structure pictured in figure 2, within all four Rehabilitation Clinics from <strong>the</strong>
INRMFB, <strong>the</strong> Occupational <strong>the</strong>rapist and <strong>the</strong> Social Worker are missing agents. This is in fact one <strong>of</strong>
<strong>the</strong> major lacks <strong>of</strong> <strong>the</strong> Romanian healthcare system – <strong>the</strong> pr<strong>of</strong>essionals like social workers,
occupational <strong>the</strong>rapists, and psychologists.
In <strong>the</strong> future, by extending <strong>the</strong> study to <strong>the</strong> “Patient” CoP we intent to find out how <strong>the</strong>ir recovery
process is affected by <strong>the</strong> missing <strong>of</strong> <strong>the</strong> Occupational <strong>the</strong>rapists, Social Workers and Knowbots.
2. Objectives <strong>of</strong> <strong>the</strong> study and <strong>the</strong> methodological approach
The objectives we intent to achieve through our study can be summarized as follows:
Mapping <strong>of</strong> <strong>the</strong> knowledge flows & needs in <strong>the</strong> target medical community <strong>of</strong> practice at <strong>the</strong>
INRMFB;
Analysis & Diagnosis: Identification <strong>of</strong> critical aspects and areas <strong>of</strong> improvements (e.g. knowledge
needs, knowledge bottlenecks, structural determinants <strong>of</strong> inefficiency or <strong>of</strong> poor performance);
Design: definition <strong>of</strong> <strong>the</strong> functional specifications for redesigning <strong>the</strong> agents, network and <strong>of</strong> <strong>the</strong>
functionalities <strong>of</strong> Knowbots.
These objectives will be achieved using <strong>the</strong> Social Network Analysis (SNA) specific methodology.
That is way we start this section <strong>of</strong> <strong>the</strong> paper with a brief <strong>of</strong> <strong>the</strong> SNA methods and tools, and we
continue it with <strong>the</strong> detailed analysis <strong>of</strong> each <strong>of</strong> <strong>the</strong> objectives <strong>of</strong> our study above outlined.
2.1 SNA – specific methods, tools, s<strong>of</strong>tware and applications
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As we all know, a social network is a very complex social structure made <strong>of</strong> nodes (individuals –
human or virtual – or organizations) that are tied by one or more specific types <strong>of</strong> interdependency
(values, ideas, financial exchange, information and knowledge, friendship, kinship, conflict or trade).
SNA views social reality in terms <strong>of</strong> nodes – individual agents within <strong>the</strong> networks –, and ties – <strong>the</strong>
relationships between <strong>the</strong> agents. The social networks operate on many levels, from families up to <strong>the</strong>
level <strong>of</strong> nations or group <strong>of</strong> nations, and play a critical role in determining <strong>the</strong> way problems are
solved, organizations are run, and <strong>the</strong> degree to which individuals succeed in achieving <strong>the</strong>ir goals.
SNA is an analytic approach, with its own <strong>the</strong>oretical statements, methods, models, s<strong>of</strong>tware,
researchers and applications. SNA produces a lot <strong>of</strong> models and methods, where <strong>the</strong> attributes <strong>of</strong>
individuals are less important than <strong>the</strong>ir relationships and ties with o<strong>the</strong>r actors within <strong>the</strong> network.
Social networks models (especially <strong>the</strong> dynamic models <strong>of</strong> networks) have also been used to examine
how organizations interact with each o<strong>the</strong>r, characterizing <strong>the</strong> many informal connections that link
executives toge<strong>the</strong>r, as well as <strong>the</strong> evolution <strong>of</strong> associations and connections between individual
employees at different organizations. SNA methods also play a key role in hiring, in business success,
and in job performance.
The models and methods used in SNA are based on a set <strong>of</strong> networks metrics such as:
Betweenness – degree an individual lies between o<strong>the</strong>r individuals in <strong>the</strong> network; it's <strong>the</strong> number
<strong>of</strong> people who a person is connecting indirectly through <strong>the</strong>ir direct links;
Closeness – <strong>the</strong> degree an individual is near all o<strong>the</strong>r individuals in a network (directly or
indirectly). It is <strong>the</strong> inverse <strong>of</strong> <strong>the</strong> sum <strong>of</strong> <strong>the</strong> shortest distances between each individual and every
o<strong>the</strong>r person in <strong>the</strong> network;
Centrality – <strong>the</strong> count <strong>of</strong> <strong>the</strong> number <strong>of</strong> ties to o<strong>the</strong>r actors in <strong>the</strong> network;
Flow betweenness centrality – <strong>the</strong> degree that a node contributes to sum <strong>of</strong> maximum flow
between all pairs <strong>of</strong> nodes (not that node);
Eigenvector centrality – a measure <strong>of</strong> <strong>the</strong> importance <strong>of</strong> a node in a network. It assigns relative
scores to all nodes in <strong>the</strong> network based on <strong>the</strong> principle that connections to nodes having a high
score contribute more to <strong>the</strong> score <strong>of</strong> <strong>the</strong> node in question.
Centralization – <strong>the</strong> difference between <strong>the</strong> number <strong>of</strong> links for each node divided by maximum
possible sum <strong>of</strong> differences;
Clustering coefficient – a measure <strong>of</strong> <strong>the</strong> likelihood that two associates <strong>of</strong> a node are also
associates <strong>the</strong>mselves. A higher clustering coefficient indicates a greater “cliquishness”;
Cohesion – <strong>the</strong> degree to which actors are connected directly to each o<strong>the</strong>r by cohesive bonds;
Radiality – degree an individual’s network reaches out into <strong>the</strong> network and provides novel
information and influence;
Reach – <strong>the</strong> degree any member <strong>of</strong> a network can reach o<strong>the</strong>r members <strong>of</strong> <strong>the</strong> network.
Structural cohesion – <strong>the</strong> minimum number <strong>of</strong> members who, if removed from a group, would
disconnect <strong>the</strong> group;
Structural equivalence – <strong>the</strong> extent to which actors have a common set <strong>of</strong> linkages to o<strong>the</strong>r actors
in <strong>the</strong> system. The actors don’t need to have any ties to each o<strong>the</strong>r to be structurally equivalent.
Most <strong>of</strong> <strong>the</strong>se metrics are <strong>of</strong> our interest in analyzing <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong> CoPs within <strong>the</strong> INRMFB’s
rehabilitation clinics.
2.1.2 SNA s<strong>of</strong>tware
Due to <strong>the</strong> extraordinary dynamics <strong>of</strong> <strong>the</strong> economic and social areas where SNA methods are used
today, a wide range <strong>of</strong> SNA s<strong>of</strong>tware was developed, like for example:
Detica NetReveal – Social Network Analysis for insurance or banking fraud, crime detection,
intelligence, tax evasion, border control and network risk based targeting
(http://www.deticanetreveal.com/);
Indiro SNA Plus – Highly scalable Social Network Analysis for Telecoms (http://www.idiro.com/);
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Keyplayer - a very good program for identifying nodes whose elimination can disrupt a network
(http://www.analytictech.com/keyplayer.htm);
MetaSight – Email/communication network visualization and analysis
(http://www.morphix.com/index.htm);
NodeXL – Network Overview, Discovery and Exploration for Excel (http://nodexl.codeplex.com/);
ORA – Social Network Analysis, Network Visualization, Meta-Network Analysis, Trail Analysis,
Geospatial Network Analysis, Network Generation
(http://www.casos.cs.cmu.edu/projects/ora/index.html);
StOCNET – open s<strong>of</strong>tware system for <strong>the</strong> statistical analysis <strong>of</strong> social networks using advanced
statistical methods based on explicit probability models for dynamic networks
(http://stat.gamma.rug.nl/stocnet/);
UCINET 6 – excellent for general SNA, having good help menus
(http://www.analytictech.com/ucinet.htm).
Of interest for our study is Micros<strong>of</strong>t’s NodeXL – an open-source template for using with Excel that
goes fur<strong>the</strong>r than o<strong>the</strong>r SNA tools and provides instant graphical representation <strong>of</strong> relationships <strong>of</strong>
complex networked data. NodeXL was developed by a multidisciplinary team <strong>of</strong> experts and it is <strong>of</strong>
interest also to researchers and students studying visual and network analytics and <strong>the</strong>ir application in
<strong>the</strong> real world. Networks can be imported from and exported to a variety <strong>of</strong> file formats, and built-in
connections for getting networks from Twitter, Facebook, YouTube, and a local email are provided.
2.1.3 SNA medical applications
As applications, until now, SNA and network modelling approaches have been used in epidemiology
to help understand how patterns <strong>of</strong> human contact aid or inhibit <strong>the</strong> spread <strong>of</strong> diseases such as HIV in
a population. The evolution <strong>of</strong> health care networks can sometimes be modelled by <strong>the</strong> use <strong>of</strong> agent
based models, providing insight into <strong>the</strong> interplay between communication rules, physicians’ opinions
and medical infra-structure.
Diffusion <strong>of</strong> innovations <strong>the</strong>ory (innovative networks) explores health care networks and <strong>the</strong>ir role in
influencing <strong>the</strong> spread <strong>of</strong> new medical ideas and practices. Change agents and opinion leaders <strong>of</strong>ten
play major roles in spurring <strong>the</strong> adoption <strong>of</strong> innovations, although factors inherent to <strong>the</strong> innovations
also play a role.
SNA has enjoyed extensive application in public health, particularly adolescent risk behaviours
oriented toward substance use. According with Valente (2010), applications <strong>of</strong> SNA to public health
and medical issues can be divided today into following areas:
Social support and its influence on mortality and morbidity - represent <strong>the</strong> largest area <strong>of</strong>
application (Knowlton, 2003);
AIDS/STDs and family planning research - have benefited from network <strong>the</strong>ory and modelling
(Aral et al., 1999);
Community health projects - have used network analysis to improve message dissemination and
program implementation (Stoebenau and Valente, 2003);
Interorganizational collaboration, cooperation, and exchange studies - have been conducted to
improve understanding <strong>of</strong> health service provision (Harris et al., 2008);
Understanding and improving health care provider performance (Soumerai et al., 1998).
Until now, none <strong>of</strong> <strong>the</strong> studies were dedicated to <strong>the</strong> particular field <strong>of</strong> health rehabilitation, leaving an
important research gap that could be filled by researches like <strong>the</strong> one we conduct at <strong>the</strong> INRMFB.
2.2 Mapping <strong>of</strong> <strong>the</strong> knowledge flows in <strong>the</strong> target community <strong>of</strong> practice
This step <strong>of</strong> our study aims to produce an accurate picture <strong>of</strong> <strong>the</strong> knowledge flows that <strong>the</strong> target
community identified in <strong>the</strong> medical area <strong>of</strong> rehabilitation process actually enacts in <strong>the</strong>
accomplishment <strong>of</strong> its organizational objectives. The output <strong>of</strong> <strong>the</strong> mapping step will be: (a) <strong>the</strong>
identifications <strong>of</strong> all <strong>the</strong> typologies and number <strong>of</strong> agents involved in <strong>the</strong> current process; (b) <strong>the</strong>
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mapping <strong>of</strong> <strong>the</strong> relationships among <strong>the</strong> subjects with a focus on <strong>the</strong> exchanges <strong>of</strong> information and
knowledge taking place in <strong>the</strong> network.
The above actions are performed by SNA through <strong>the</strong> following steps (Cross et al., 2002):
Identification <strong>of</strong> <strong>the</strong> target group: a number <strong>of</strong> four rehabilitation teams (clinics) with <strong>the</strong>ir patients
were identified at <strong>the</strong> INRMFB – a unique rehabilitation hospital in Romania. In general it is
recommended to pick up groups that cross physical, functional and even organizational
boundaries; from this perspective, <strong>the</strong> INRMFB meet all <strong>the</strong>se requirements.
Design <strong>the</strong> survey: in general different surveys may be designed to identify different kind <strong>of</strong>
networks, such as (i) Information network, (ii) Advice network, (iii) Learning networks, etc. The
survey that we have designed particularly for <strong>the</strong> INRMFB is used to reconstruct <strong>the</strong> existing
relational systems (see section 3 for <strong>of</strong> <strong>the</strong> paper).
Collect <strong>the</strong> data: <strong>the</strong> survey was distributed through <strong>the</strong> members <strong>of</strong> <strong>the</strong> CoP from <strong>the</strong>
Rehabilitation Clinic no. 3 and 4 within <strong>the</strong> INRMFB. It is important to secure a very high response
rate in <strong>the</strong> unit (between 80% and 100%) to not miss key individuals in <strong>the</strong> mapping.
The following set <strong>of</strong> attributes is considered in <strong>the</strong> survey to characterize a node to node link (N2N):
The object <strong>of</strong> exchange: which is <strong>the</strong> nature <strong>of</strong> exchanged information;
The frequency <strong>of</strong> exchange: how <strong>of</strong>ten interaction happens;
The medium <strong>of</strong> exchange: how exchange takes place? F2F conversations, telephone, email,
formal communication (e.g. standard letter, forms, bulletins …). A medium continuum has been
defined by Daft and Langel (1986) in which medium changes according to equivocality <strong>of</strong>
information. The less equivocal is <strong>the</strong> nature <strong>of</strong> information that has to be exchanged, <strong>the</strong> more
standardize and impersonal <strong>the</strong> communication medium is.
The development <strong>of</strong> <strong>the</strong> survey has taken into consideration variables operationalization and survey
structures already developed in <strong>the</strong> SNA literature.
2.3 Analysis: Identification <strong>of</strong> critical aspects and areas <strong>of</strong> improvements
The surveys content will be used to produce sociograms that will be analyzed by using SNA s<strong>of</strong>tware
(NodeXL and UCINET). The s<strong>of</strong>tware will produce graphical representations <strong>of</strong> <strong>the</strong> network structures
and compute relevant structural indicators. These outputs will allow us to identify network roles and
possible problems.
Roles are identified in <strong>the</strong> literature in various ways. For example:
a) Cross et al. (2002) base <strong>the</strong>ir definitions on <strong>the</strong> structural properties and <strong>the</strong>y have found:
connectors;
boundary spanners;
information brokers;
peripheral specialists.
b) Gould et al. (1989), in terms <strong>of</strong> brokerage capability and function, identify:
coordinator;
gatekeeper;
representative;
itinerant brokers.
c) Rogers (2003) in terms <strong>of</strong> <strong>the</strong> function played by an individual in knowledge diffusion processes:
innovators;
opinion leaders.
d) While Gladwell (2000) reports about:
connectors;
salesmen;
mavens.
Usually, typical organizations’ structural problems regard:
presence <strong>of</strong> bottlenecks in <strong>the</strong> information/knowledge flows (e.g. individuals that are too
central and suffer from work or information overload);
lack <strong>of</strong> boundary spanners, i.e. individuals providing effective connection among diverse
groups;
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presence <strong>of</strong> marginalized individuals whose more intense involvement in <strong>the</strong> process could
be instead desirable;
lower than needed connection density;
lack <strong>of</strong> knowledge hubs;
network fragmentation (presence <strong>of</strong> disconnected clusters).
By analyzing <strong>the</strong> actual structure <strong>of</strong> <strong>the</strong> four clinics from <strong>the</strong> INRMFB, one or more <strong>of</strong> <strong>the</strong>se problems
we expect to face. Given <strong>the</strong> particularities <strong>of</strong> this hospital, o<strong>the</strong>r structural issues also could be found.
2.4 Definition <strong>of</strong> <strong>the</strong> functional specifications for redesigning <strong>the</strong> agents, network and
<strong>the</strong> functionalities <strong>of</strong> Knowbots
Once <strong>the</strong> critical aspects have been identified we will start <strong>the</strong> redesign <strong>of</strong> <strong>the</strong> network with <strong>the</strong> aim <strong>of</strong>
developing an augmented socio-technical system both by increasing / reorienting / intensifying /
introducing new knowledge flows, and through <strong>the</strong> introduction <strong>of</strong> intelligent agents as nodes whose
role will be to provide knowledge and/or to solve structural problems in <strong>the</strong> existing organizational
networks. For instance, we may find out that we need better communication among specialists in <strong>the</strong>
different phases <strong>of</strong> <strong>the</strong> process for <strong>the</strong> purposes <strong>of</strong> a better traceability <strong>of</strong> <strong>the</strong> <strong>the</strong>rapeutic process. Or
we may discover that doctors need helps in some step <strong>of</strong> <strong>the</strong> diagnosis. Or that better patient-doctors
communication has to be provided to support home care. Given <strong>the</strong> set <strong>of</strong> needs, we will define a set
<strong>of</strong> priorities and focus. Then we will identify functional requirements for <strong>the</strong> design <strong>of</strong> intelligent agents
(knowbots) starting from <strong>the</strong> general description and architecture developed in our previous
researches. In fact, <strong>the</strong> Knowbot design will be an instantiation <strong>of</strong> <strong>the</strong> general knowbot model
introduced in Mărăcine and Scarlat (2010).
The augmented network will be represented in an agent-based model to simulate its behaviour and
perform what-if analysis to check if <strong>the</strong> proposed technological solutions and <strong>the</strong> redesign <strong>of</strong> <strong>the</strong>
overall network will achieve <strong>the</strong> expected performances, as specified by <strong>the</strong> beneficiary – <strong>the</strong>
management team <strong>of</strong> <strong>the</strong> INRMFB. The output <strong>of</strong> <strong>the</strong> simulation will be used to support <strong>the</strong> design <strong>of</strong>
<strong>the</strong> real knowbots.
3. Designing <strong>the</strong> survey for data collection
In order to give an answer to <strong>the</strong> first objective defined in section 2 we have created a survey that is
used to reconstruct <strong>the</strong> existing relational systems from <strong>the</strong> INRMFB, including questions that collects
some demographics, additional details to characterize <strong>the</strong> existing links within <strong>the</strong> network, and
quantitative data. In addition to <strong>the</strong> traditional SNA data collection, we collect more qualitative
information through interviews to each subject to elicit <strong>the</strong>ir perceptions about <strong>the</strong> quantity and <strong>the</strong>
quality <strong>of</strong> <strong>the</strong> knowledge flows by implementing a knowledge audit (Liebowitz, 2010).
The main sections <strong>of</strong> <strong>the</strong> survey are:
Section 1: Demographics: name, department, pr<strong>of</strong>ession;
Section 2: Knowledge resources: knowledge resources types (databases, magazines, books,
people, Internet), and frequency <strong>of</strong> use;
Section 3: Knowledge use: directly retrieving answers to specific questions, analyze and/or
syn<strong>the</strong>size to answer a specific question, routine and/or variable procedures, designing reports,
educational or promotional materials;
Section 4: Knowledge flows: connections between <strong>the</strong> Knowledge categories and <strong>the</strong> Staff
categories within or outside <strong>the</strong> INRMFB.
Section 5: Knowledge needs: main constrains in accessing different pieces <strong>of</strong> knowledge, risks
faced by <strong>the</strong> critical knowledge within <strong>the</strong> INRMFB departments due to <strong>the</strong> turnovers in
organization or lack <strong>of</strong> backup expertise in knowledge storage.
Within <strong>the</strong>se sections, a set <strong>of</strong> questions were designed to capture information about <strong>the</strong> following
issues:
Do you have enough information to do you job?
Which kind <strong>of</strong> additional knowledge and information would be helpful for you to have?
Through which channels and knowledge sources you fill <strong>the</strong>se knowledge gaps?
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Which are <strong>the</strong> criteria guiding knowledge search and evaluation?
As a node <strong>of</strong> <strong>the</strong> network we have also included <strong>the</strong> Internet, information systems and o<strong>the</strong>r IT-based
devices or channels that members <strong>of</strong> <strong>the</strong> community may use to access information inside or outside
<strong>the</strong>ir organization. For instance, it is reasonable to expect that members already access external
knowledge sources through <strong>the</strong> Internet. That’s way we also expect to find that some <strong>of</strong> <strong>the</strong> tasks <strong>the</strong>y
perform manually could be delivered in an automated way or facilitated by <strong>the</strong> knowbots.
Until now, <strong>the</strong> questionnaires were distributed through participants in <strong>the</strong> Rehabilitation Clinic no. 3
and no. 4. At clinic no. 3 data collecting is now complete; we succeed to secure an 83.34% response
rate in this unit (30 pr<strong>of</strong>essionals out <strong>of</strong> 36), and we have already started <strong>the</strong> data processing. In <strong>the</strong>
Clinic no. 4 we have so far a 59% response rate and we still have one month time until <strong>the</strong> end <strong>of</strong> <strong>the</strong>
data collecting stage. Data collecting starts also for <strong>the</strong> clinic no. 1 and it will start at <strong>the</strong> end <strong>of</strong> June
for <strong>the</strong> clinic no. 2.
4. Data analysis for <strong>the</strong> Rehabilitation Clinic no. 3 and 4 – first findings
A preliminary analyze <strong>of</strong> <strong>the</strong> actual structure <strong>of</strong> <strong>the</strong> INRMFB and especially <strong>of</strong> <strong>the</strong> Rehabilitation Clinic
no. 3 and 4, based on <strong>the</strong> section 4 and 5 <strong>of</strong> <strong>the</strong> designed survey, has conducted us, so far, to <strong>the</strong>
following findings / conclusions:
1. The Rehabilitation Clinics no. 1 and 2 are placed into a different location than <strong>the</strong> Clinics no. 3
and 4. This makes <strong>the</strong> communications among <strong>the</strong> four medical CoPs an exception instead <strong>of</strong> a
frequent habit;
2. Among <strong>the</strong> four clinics and also among <strong>the</strong>m and <strong>the</strong> management <strong>of</strong> <strong>the</strong> INRMFB <strong>the</strong>
communication is based on <strong>the</strong> old classical methods - printed documents transfer;
3. The INRMFB has no Intranet. Due to <strong>the</strong> distances among <strong>the</strong> fourth clinics, <strong>the</strong> absence <strong>of</strong> <strong>the</strong>
Intranet makes <strong>the</strong> information/knowledge transfer very slow, and <strong>of</strong>ten produces losses <strong>of</strong>
valuable information/knowledge for some <strong>of</strong> (or all) <strong>the</strong> clinics, and also for <strong>the</strong> managerial team
<strong>of</strong> <strong>the</strong> hospital;
4. The spatial separation <strong>of</strong> <strong>the</strong> INRMFB’s departments has also effects over <strong>the</strong> results from <strong>the</strong>
research activity <strong>of</strong> <strong>the</strong> Institute. Within this particular hospital <strong>the</strong>re are several research team: (1)
Balneo-climatology; (2) Cellular breeding; (3) Mineral waters and <strong>the</strong>rapeutic mud testing; (4)
Immunology lab; (5) Bacteriology lab for mineral waters. In <strong>the</strong> actual organizational structure,
<strong>the</strong>se labs and scientific teams rarely communicate among <strong>the</strong>m and share <strong>the</strong>ir activity results;
5. There are no virtual libraries or repositories within <strong>the</strong> INRMFB. So, <strong>the</strong> only categories <strong>of</strong> medical
staff that can access <strong>the</strong>se knowledge resources are those doctors who are also pr<strong>of</strong>essors at <strong>the</strong>
Bucharest “Carol Davila” Medical University which has its own such library. The absence <strong>of</strong>
virtual repositories results sometime in loosing important documents, patients’ medical files, tests
results or managerial decisions transcripts.
6. There are no policy/procedures manual or guidelines nei<strong>the</strong>r within <strong>the</strong> INRMFB or within <strong>the</strong>
clinics / departments;
7. There are no real boundary spanners, <strong>the</strong> effective connection among diverse groups within <strong>the</strong>
Clinic no 3, for example, this task being (informally) on charge <strong>of</strong> <strong>the</strong> chief <strong>of</strong> <strong>the</strong> clinic. Because
<strong>of</strong> this, <strong>the</strong> chief <strong>of</strong> <strong>the</strong> clinic is overloaded with her work and with administrative and information
sharing tasks;
8. In order to benefit for a second opinion (when need it), doctors within Clinic no. 3 usually contact
<strong>the</strong>y own personal connections via Internet (<strong>of</strong>ten foreign specialists) ra<strong>the</strong>r than call a fellow
colleague who works into ano<strong>the</strong>r clinic within <strong>the</strong> INRMFB;
9. One interesting aspect for <strong>the</strong> Rehabilitation Clinic no 3: each month <strong>the</strong> chief <strong>of</strong> <strong>the</strong> clinic
organizes a so-called “essay day” when all <strong>the</strong> doctors, <strong>the</strong> residents and <strong>the</strong> <strong>the</strong>rapists are
involved in presenting a paperwork describing <strong>the</strong> most recent events / aspects / achievements <strong>of</strong>
<strong>the</strong>ir activity in <strong>the</strong> hospital. This method is used in order to exchange knowledge, but also to
improve <strong>the</strong> communication techniques <strong>of</strong> <strong>the</strong> Medical Staff.
10. The absence <strong>of</strong> <strong>the</strong> two categories <strong>of</strong> specialists – <strong>the</strong> social workers and <strong>the</strong> occupational
<strong>the</strong>rapists – hardeners <strong>the</strong> process <strong>of</strong> medical rehabilitation for both CoPs – Medical CoP and
Patient CoP. Doctors are forced by this circumstance to involve <strong>the</strong>m selves in <strong>the</strong> process <strong>of</strong>
social reintegration <strong>of</strong> <strong>the</strong> impaired people, both by psychologically assisting <strong>the</strong> patients, and by
dedicating a lot <strong>of</strong> time in educating and training <strong>the</strong> patients’ families for coping with <strong>the</strong>ir
disabled family member condition.
In this period, <strong>the</strong> Clinic no.3 and 4 networks are defined and prepared to be analyzed with NodeXL
based on <strong>the</strong> data collected through <strong>the</strong> first two sections <strong>of</strong> <strong>the</strong> survey.
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Virginia Maracine et al
5. Discussions and future developments
From <strong>the</strong> analysis we have conducted so far, we can assert that <strong>the</strong> classical organizational structure
<strong>of</strong> <strong>the</strong> Rehabilitation Clinic no. 3 (as well as <strong>of</strong> <strong>the</strong> entire INRMFB) needs to be improved both
physically and virtually:
The real structure has to be improved by hiring pr<strong>of</strong>essionals for social workers and occupational
<strong>the</strong>rapists positions. In this moment, this could be done immediately by launching a call for
specialists on <strong>the</strong> <strong>European</strong> and/or international labour market. A better solution which
never<strong>the</strong>less requires more time consists in enrol Romanian students within <strong>the</strong> EU universities
that provide academic programs on <strong>the</strong>se particular pr<strong>of</strong>essions;
Virtually speaking, <strong>the</strong> organizational structure <strong>of</strong> <strong>the</strong> Rehabilitation Clinic no. 3 has to be
augmented with Intranet that needs to be populated with virtual species like Knowbots. These
virtual agents can facilitate <strong>the</strong> knowledge creation, retrieval and sharing inside <strong>the</strong> Medical CoPs,
among <strong>the</strong>se and <strong>the</strong> managerial team <strong>of</strong> <strong>the</strong> INRMFB, and also among <strong>the</strong> human resources <strong>of</strong>
<strong>the</strong> hospital and <strong>the</strong> (social, medical, and economical) macro environment. Knowbots should have
a particular role in create for <strong>the</strong> Patient CoP useful knowledge that can help <strong>the</strong>m in <strong>the</strong>
rehabilitation processes.
A generic image <strong>of</strong> <strong>the</strong> augmented organizational structure that, in this way, will be obtained for <strong>the</strong>
Clinic no. 3 is presented by figure 3.
Figure 3: The new Knowledge-oriented (augmented) structure <strong>of</strong> <strong>the</strong> Rehabilitation Clinic no. 3 <strong>of</strong> <strong>the</strong>
INRMFB – micro level
This representation is <strong>the</strong> general frame under which we will fur<strong>the</strong>r develop and finalize our study
following <strong>the</strong> three main aimed objectives:
587

Objective 1 –
Mapping <strong>of</strong> <strong>the</strong>
knowledge
flows & needs
in <strong>the</strong> target
community <strong>of</strong>
practice
Objective 2 –
Analysis &
Diagnosis
Objective 3 –
Design:
definition <strong>of</strong> <strong>the</strong>
functional
specifications
for redesigning
<strong>the</strong> agents,
network and <strong>of</strong>
<strong>the</strong>
functionalities <strong>of</strong>
Knowbots
Virginia Maracine et al
Accomplished so far Future developments Interesting
ideas to be
We have identified all <strong>the</strong>
typologies and number <strong>of</strong>
agents involved in <strong>the</strong>
current medical rehabilitation
process within <strong>the</strong> Clinic no.
3 and 4 at <strong>the</strong> INRMFB.
The primary analysis <strong>of</strong> <strong>the</strong>
answers we have obtained
for our survey highlighted a
set <strong>of</strong> issues faced by <strong>the</strong>
Departments, Clinics and <strong>the</strong>
entire studied hospital –
INRMFB (see section 4 <strong>of</strong>
<strong>the</strong> paper).
A first result in redesign <strong>the</strong>
knowledge network within
<strong>the</strong> INRMFB is presented in
figure 3.
In this period we map <strong>the</strong>
relationships among <strong>the</strong> subjects
with a focus on <strong>the</strong> exchanges <strong>of</strong>
knowledge within <strong>the</strong> network at
micro and macro level. The Excel
input matrixes for NodeXL were
designed and are now filled with
data.
The surveys content will be used in
<strong>the</strong> following period to produce
sociograms that will be analyzed by
using SNA s<strong>of</strong>tware NodeXL and
UCINET 6 for Windows. The
s<strong>of</strong>tware will produce graphical
representations <strong>of</strong> <strong>the</strong> network
structures and compute relevant
structural indicators. These outputs
will allow us to identify network roles
and possible problems, and it will be
presented within <strong>the</strong> conference.
As o<strong>the</strong>r critical aspects will emerge
from a detailed analysis <strong>of</strong> <strong>the</strong> first
two sections <strong>of</strong> <strong>the</strong> surveys, we will
define <strong>the</strong> roles that both <strong>the</strong> human
and virtual agents have to fulfil so
that <strong>the</strong> new organizational structure
<strong>of</strong> <strong>the</strong> INRMFB become a modern
knowledge-oriented one, <strong>of</strong>fering
<strong>the</strong> appropriate flexible microenvironment
capable to adapt itself
in real time to <strong>the</strong> dynamics <strong>of</strong> <strong>the</strong>
healthcare system as a whole.
discussed
Human nature
barriers in
Knowledge
sharing process:
What are <strong>the</strong>
most important
human nature
sources <strong>of</strong> <strong>the</strong>
knowledge-flows
bottlenecks
within <strong>the</strong>
INRMFB?
Nodes versus
Links:
Within <strong>the</strong> new
knowledgebased
organizational
structure, which
are more
important to
focus on, nodes
(human and
virtual agents),
or links between
<strong>the</strong>m (direct /
indirect
connections
and/or
relationships)?
As we have already mentioned in <strong>the</strong> abstract, our paperwork is still a work in progress. Until it will be
presented to <strong>the</strong> ECKM2011 conference, we will have <strong>the</strong> results <strong>of</strong> <strong>the</strong> collected data analysis, so we
will be able to inform <strong>the</strong> attendees about <strong>the</strong> current status <strong>of</strong> <strong>the</strong> Knowledge transfer process and
dynamics within <strong>the</strong> INRMFB. Also, a first draft <strong>of</strong> <strong>the</strong> redesigned and augmented network – by using
knowbots – will be discussed <strong>the</strong>n, as well as <strong>the</strong> particular role, place and functions <strong>of</strong> <strong>the</strong> knowbots
within <strong>the</strong> medical CoPs at <strong>the</strong> INRMFB.
Acknowledgement
This paperwork was supported by CNCSIS - UEFISCDI, project number PNII - IDEI 810/2008.
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589

From Knowledge Acquisition to Knowledge Elicitation - A
Roadmap
Peter Marshall and Damian Gordon
Department <strong>of</strong> Computing, Dublin Institute <strong>of</strong> Technology, Ireland
pjmarshall@hotmail.com
damian.gordon@dit.ie
Abstract: One <strong>of</strong> <strong>the</strong> fundamental challenges indentified in Knowledge Management is <strong>the</strong> process <strong>of</strong> capturing
knowledge. To successfully capture knowledge, it must first be acquired. Knowledge Acquisition is concerned
with <strong>the</strong> acquisition <strong>of</strong> knowledge from knowledge sources. This can occur in one <strong>of</strong> two ways, by using nonhuman
sources (e.g. electronic documents, organisational databases, etc.) or human sources. Knowledge
Elicitation is <strong>the</strong> field concerned with <strong>the</strong> acquisition <strong>of</strong> knowledge from human sources. In <strong>the</strong> literature, <strong>the</strong>
terms Knowledge Acquisition and Knowledge Elicitation are <strong>of</strong>ten used interchangeably; however <strong>the</strong>re is a clear
distinction between <strong>the</strong> two. In a new and emerging field such as Knowledge Management, it is important that
this delineation is made. This paper explores <strong>the</strong> relationship between <strong>the</strong> two concepts and provides a roadmap
from Knowledge Acquisition to Knowledge Elicitation. The research begins with an overview <strong>of</strong> <strong>the</strong> field <strong>of</strong>
Knowledge Acquisition, focusing on three different approaches, Automated, Semi-automated and Manual
Knowledge Acquisition techniques. The issues that impact <strong>the</strong> selection <strong>of</strong> each are discussed (known as <strong>the</strong>
Knowledge Acquisition Bottleneck). From this, <strong>the</strong> paper proceeds with an overview <strong>of</strong> <strong>the</strong> Knowledge Elicitation,
looking at <strong>the</strong> various influences that have directed research as well as <strong>the</strong> issues and requirements for
successful elicitation. The paper concludes with an examination <strong>of</strong> <strong>the</strong> various classifications <strong>of</strong> techniques that
exists with <strong>the</strong> field.
Keywords: Knowledge acquisition, knowledge elicitation, knowledge acquisition bottleneck
1. Knowledge Acquisition
Knowledge Capture is defined as “<strong>the</strong> process <strong>of</strong> retrieving ei<strong>the</strong>r explicit or tacit knowledge that
resides within people, artefacts or organisational entities” (Becerra-Fernandez et al., 2004). A subset
<strong>of</strong> this process is <strong>the</strong> Knowledge Acquisition (KA). KA comprises <strong>of</strong> a set <strong>of</strong> techniques that allow
knowledge to be extracted from different knowledge sources, which in turn allow it to be validated and
maintained (Cooke, 2003). The task <strong>of</strong> extracting knowledge is itself non trivial. The complexity
associated with <strong>the</strong> field is known as <strong>the</strong> Knowledge Acquisition Bottleneck.
1.1 Knowledge Acquisition Bottleneck
Wagner (2003) classifies <strong>the</strong>se issues into three broad categories, Narrow Bandwidth, Acquisition
Latency and Knowledge Inaccuracies. The first category, Narrow Bandwidth, is concerned with <strong>the</strong>
relatively limited number <strong>of</strong> channels that exist from which knowledge can be acquired. Knowledge
can exist both tacitly and explicitly in individuals, groups, organisational entities as well as extraorganisational
entities but it <strong>of</strong>ten resides in only a few <strong>of</strong> <strong>the</strong>se entities. Once <strong>the</strong>se sources have
been identified, ano<strong>the</strong>r factor that influences bandwidth is <strong>the</strong> availability <strong>of</strong> knowledge sources. For
example, organisations are <strong>of</strong>ten reluctant to move Subject Matter Experts (SME) from <strong>the</strong>ir day-today
functions in order to participate in knowledge initiatives. The next type <strong>of</strong> problem associated
within <strong>the</strong> Knowledge Acquisition Bottleneck is Acquisition Latency. This is <strong>the</strong> time taken from <strong>the</strong>
creation <strong>of</strong> knowledge to when knowledge is available and ready to be shared. Depending on <strong>the</strong>
ability <strong>of</strong> an SME to articulate <strong>the</strong>ir knowledge, it can be time consuming and an arduous process to
capture <strong>the</strong> full extent <strong>of</strong> it. The final component is Knowledge Inaccuracy. This refers to <strong>the</strong> mistakes
made in extraction <strong>of</strong> knowledge from knowledge sources, both in <strong>the</strong> initial knowledge capture phase
as well as <strong>the</strong> maintenance phase, required in <strong>the</strong> growth <strong>of</strong> <strong>the</strong> knowledge bases (Land, 2002).
1.2 Knowledge Acquisition Techniques
Given <strong>the</strong>se challenges, Knowledge Acquisition requires a set <strong>of</strong> tools that address each <strong>of</strong> <strong>the</strong>
bottlenecks. Knowledge Acquisition Techniques can be classified into three types according to <strong>the</strong>
way <strong>the</strong>y can deal with knowledge collected (Turban and Aronson, 1998). These types are Automated
Knowledge Acquisition Techniques, Semi-Automatic Knowledge Acquisition Techniques and Manual
Knowledge Acquisition Techniques.
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Peter Marshall and Damian Gordon
1.3 Automated Knowledge Acquisition Techniques
Automated Knowledge Acquisition techniques are defined as a set <strong>of</strong> techniques designed to
overcome <strong>the</strong> limitations <strong>of</strong> acquiring knowledge from human sources (Wagner, 2000). Typically <strong>the</strong>y
lie in <strong>the</strong> realm <strong>of</strong> machine learning (e.g. data mining, neural networks, fuzzy logic, genetic algorithms,
etc.). Figure 3.1 illustrates <strong>the</strong> automated knowledge acquisition process.
Figure 1 Automated Knowledge Acquisition
These tools aim to address <strong>the</strong> problem <strong>of</strong> latency by reducing <strong>the</strong> time taken and expense incurred in
<strong>the</strong> knowledge acquisition phase. Techniques in this area use non-human sources (such as
organisational corpora, databases, etc.) to acquire knowledge thus removing <strong>the</strong> time constraints
placed on human sources. Automated KA techniques can resolve issues associated with knowledge
inaccuracies. The systematic approach can expose naturally existing correlations in datasets that are
not easily obtained from o<strong>the</strong>r types <strong>of</strong> KA tools; making its use in complex domains significant. Its
application to group, organisational, and extra-organisational knowledge artefacts allows knowledge
to be collated from multiple sources addressing issues associated with narrow bandwidth issues by
creating a syn<strong>the</strong>sis <strong>of</strong> expertise that is less prone to error.
The key problem with <strong>the</strong>se techniques is <strong>the</strong> omission <strong>of</strong> human expertise. A survey conducted in
2000 estimated that over 90 percent <strong>of</strong> organisational knowledge is not codified but resides tacitly an
organisations entities (Bonner, 2000). By eliminating <strong>the</strong>se sources from <strong>the</strong> knowledge acquisition
process, <strong>the</strong>y are decreasing <strong>the</strong> bandwidth significantly, impacting on <strong>the</strong> accuracy <strong>of</strong> KM initiatives.
Among o<strong>the</strong>r limitations <strong>of</strong> automated KA techniques (e.g. neural networks) is <strong>the</strong> lack <strong>of</strong> an adequate
explanation system that detail <strong>the</strong> reasoning behind decisions generated. The difficultly associated
with human comprehension <strong>of</strong> rules generated discourages <strong>the</strong> widespread use <strong>of</strong> automated KA
techniques in mission critical systems where answers are required when things go wrong.
1.4 Semi-Automated Knowledge Acquisition Techniques
The next set <strong>of</strong> KA techniques, identified by Wagner (2003), is <strong>the</strong> semi-automated KA techniques.
They support <strong>the</strong> KA process in one <strong>of</strong> two ways. The first is to provide SMEs with tools that facilitate
<strong>the</strong> creation <strong>of</strong> knowledge bases with minimal assistance from knowledge engineers; helping to
reduce <strong>the</strong> effects <strong>of</strong> latency. An example is found in Marshall and Bandar’s (1999) work whereby a
tool was created in order to assist <strong>the</strong> acquisition <strong>of</strong> medical terms from medical corpora. These types
<strong>of</strong> tools improve <strong>the</strong> quality <strong>of</strong> acquired knowledge, through explicit sources, by using an expert in <strong>the</strong>
field to validate results.
There are also semi-automated KA techniques that assist <strong>the</strong> knowledge engineer to execute <strong>the</strong> KA
activities more efficiently by minimising time spent with <strong>the</strong> SME (again addressing issues <strong>of</strong> latency).
Examples <strong>of</strong> <strong>the</strong>se types <strong>of</strong> tools include techniques such as repository grid analysis whereby an SME
is presented with a set <strong>of</strong> parameters and asked to weight each attributes in terms <strong>of</strong> its significance
in <strong>the</strong> decision making process. These techniques benefit from <strong>the</strong> use <strong>of</strong> SME’s in <strong>the</strong> knowledge
acquisition process <strong>the</strong>refore increasing <strong>the</strong> bandwidth <strong>of</strong> knowledge sources. However Turban and
Aronson (1998) see <strong>the</strong> adoption <strong>of</strong> <strong>the</strong>se tools as best suited to well-structured problems, but warns
that such techniques do not provide a deep understanding <strong>of</strong> <strong>the</strong> decision making process.
1.5 Manual Knowledge Acquisition Techniques
In order to access <strong>the</strong> deep knowledge in organisations, manual methods are required. Knowledge
engineers use <strong>the</strong>se techniques in order capture knowledge from both knowledge artefacts and
human sources in order to populate knowledge bases (Figure 3.3).
An example from academia is <strong>the</strong> literature review where by a body <strong>of</strong> literature is summarised by
identifying, appraising, selecting and syn<strong>the</strong>sising research evidence. From this, conclusions can be
reached that are relevant to <strong>the</strong> questions being asked. In fields such as evidence-based medicine,
<strong>the</strong>se types <strong>of</strong> reviews are essential (CEBM, 2009). In terms <strong>of</strong> <strong>the</strong> KA bottleneck, dealing with only
explicit artefacts greatly reduces <strong>the</strong> bandwidth <strong>of</strong> sources in which <strong>the</strong>se techniques operate. The
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elimination <strong>of</strong> human resources is problematic. In order to address this, manual methods that employ
<strong>the</strong> use <strong>of</strong> human sources are required. Using manual methods, techniques are employed that
provide a richness <strong>of</strong> detail which cannot be achieved using automated or semi-automated methods.
However manual methods are slow, expensive and are <strong>of</strong>ten subject to bias especially when dealing
with a single expert. Multiple sources can be employed to increase <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong> knowledge
acquired by eliminating bias.
Figure.2 Semi Automated Knowledge Acquisition
Figure 3: Manual Knowledge Acquisition
2. Knowledge elicitation
From <strong>the</strong> roadmap in section 3, KE can be defined as a form <strong>of</strong> KA that is concerned with <strong>the</strong> manual
acquisition <strong>of</strong> knowledge from human sources. Whilst KE is a relatively new field <strong>of</strong> endeavour, work
in <strong>the</strong> area can be traced back in history to eras such as <strong>the</strong> enlightenment period in which <strong>the</strong>
eighteenth century. This period is characterised by its great works in which reason was advocated as
<strong>the</strong> primary source and legitimacy for authority. Early works include <strong>the</strong> Encylopedie by Diderot (1751-
1772), in which he documented <strong>the</strong> skills involved in trade’s craft. The field <strong>of</strong> knowledge elicitation,
as we know it today, finds its roots in <strong>the</strong> era <strong>of</strong> Expert System development and <strong>the</strong> work <strong>of</strong> Edward
Feigenbaum (1971). Since <strong>the</strong>n <strong>the</strong> field has benefitted from fur<strong>the</strong>r research into fields such as
Cognitive Psychology (Lesgold, 1984; Means & Gott, 1988), Instructional Design, (Gagne & Smith,
1994) and Psychology <strong>of</strong> Expertise (Glaser, 1987). Where once <strong>the</strong> field was purely an academic
endeavour, <strong>the</strong> delineation between academia and industry has almost completely been dissolved. In
organisations, such as Skandia, <strong>the</strong> systematic use <strong>of</strong> KE techniques is engrained in its KM model
(Edvinsson,1997).
A criticism <strong>of</strong> <strong>the</strong> field lies in <strong>the</strong> hangover from <strong>the</strong> behaviourist movement where <strong>the</strong> idea that
routines and task, located tacitly in <strong>the</strong> minds <strong>of</strong> an expert, are inexpressible via introspection. This
notion has never been formally demonstrated (H<strong>of</strong>fman & Lintern, 2006). In principle, all tacit
knowledge can be elicited. It is <strong>the</strong> collaborative process between a knowledge engineer and an
expert as well as <strong>the</strong> quality <strong>of</strong> <strong>the</strong> techniques being used which provides a mechanism for its
discovery. Once conducted, <strong>the</strong> knowledge gained can be used in <strong>the</strong> creation <strong>of</strong> models pertaining to
a practitioner’s knowledge and reasoning within a domain <strong>of</strong> knowledge. The integration <strong>of</strong> individual
models, in <strong>the</strong> same domain, can help synergise ideas and present a collective model <strong>of</strong> <strong>the</strong> work
domain as a whole. From this, we can identify various components <strong>of</strong> knowledge (e.g. domain
concepts, best practices, etc.), and knowledge used in <strong>the</strong> creation <strong>of</strong> knowledge based products. In
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tackling KE, <strong>the</strong> underlying question is succinct and simple; “how can we effectively elicit knowledge
from an expert?” (Cooke, 1994).
2.1 Issues in Knowledge Elicitation
In adopting KE as <strong>the</strong> basis for <strong>the</strong> KA process, not only are <strong>the</strong> difficulties associated with KA
(knowledge acquisition bottleneck) inherited but also additional issues specific to KE need to be
addressed. The development <strong>of</strong> a knowledge base is problematic and requires considerable more
time and effort than required in o<strong>the</strong>r forms <strong>of</strong> KA. In this sense, <strong>the</strong> KE process is an important costdetermining
factor in <strong>the</strong> whole Knowledge Creation process (Martinez-Bejar et al. 1996).
The selection and availability <strong>of</strong> expertise is one such issue. Whilst <strong>the</strong> use <strong>of</strong> a single expert might be
sufficient in some domains, in o<strong>the</strong>rs it is not. Martinez-Bejar et al. (1996) use an example from <strong>the</strong>
field <strong>of</strong> environmental planning task to illustrate this, whereby expertise, in areas such as botanical,
zoology, ecology, economy, and forestry engineering, are essential in fully understanding <strong>the</strong>
problem. Using a single domain expert with expertise in all areas seems highly unlikely. In dealing
with human sources, ano<strong>the</strong>r concern is <strong>the</strong> difficulties in encouraging experts to participate in <strong>the</strong> KE
process (Okafor and Osuagwu, 2006). Intrinsic fears associated with <strong>the</strong> loss <strong>of</strong> unique knowledge
previously used to guarantee relevance, status or job security. The cost / high demand for expertise
<strong>of</strong>ten prohibits <strong>the</strong>ir inclusion in KM related activities. Complexity is ano<strong>the</strong>r issue; <strong>the</strong> effort to elicit
knowledge is high in terms <strong>of</strong> time and expertise (Martinez-Bejar et al. 1996).
Once a Knowledge Elicitation activity is up and running, <strong>the</strong> task can be slow, inefficient, and
frustrating for all participants. Stefik and Conway (1982) described <strong>the</strong> knowledge elicitation as a
“necessary burden, carried out under protest so that one can get on with <strong>the</strong> study <strong>of</strong> cognitive
processes in problem solving.” Okafor and Osuagwu (2006) identify problems in conducting KE
sessions. Experts are inclined to present an unbalanced picture <strong>of</strong> <strong>the</strong>ir knowledge, particularly in
explaining <strong>the</strong>ir own knowledge. They tend only to present <strong>the</strong> procedural steps taken to achieve a
task as opposed to <strong>the</strong> actual problem solving strategies employed in decision making. In respect to
working with a knowledge engineer, Wilson and Holloway (2000) raised several important issues.
Experts can feel <strong>of</strong>ten nervous or intolerant <strong>of</strong> <strong>the</strong> process. This usually happens when <strong>the</strong>y feel
handicapped or insecure in giving <strong>the</strong> knowledge. Problems in <strong>the</strong> use <strong>of</strong> terminology can occur
between an expert and a knowledge engineer occurs; that leads to confusion. A problem with most
elicitation techniques is that in addition to capturing valuable knowledge <strong>the</strong>re is also a certain amount
which is irrelevant with respect to <strong>the</strong> goals <strong>of</strong> <strong>the</strong> KE session.
The development <strong>of</strong> multiple techniques and methodologies in <strong>the</strong> KE field has provided a basis for
confusion. In selecting appropriate techniques for KE, Cooke (1994) stated “no technique is
guaranteed to result in a complete and accurate representation <strong>of</strong> an expert's knowledge”. Selecting
<strong>the</strong> right techniques is an empirical and <strong>of</strong>ten iterative process. Techniques differ in terms <strong>of</strong> <strong>the</strong>ir
procedures, as well as <strong>the</strong>ir emphasis on one type <strong>of</strong> knowledge over ano<strong>the</strong>r. In selecting
appropriate techniques, a knowledge engineer must be aware <strong>of</strong> techniques used in similar or related
fields. In new domains, whereby previous case studies do not exist, <strong>the</strong> selection process is
complicated. From <strong>the</strong> literature, we see that techniques are almost always used in collaboration with
o<strong>the</strong>rs, thus adding to complexity <strong>of</strong> technique selection. Ano<strong>the</strong>r consideration when choosing a KE
technique is <strong>the</strong> use <strong>of</strong> a single expert or multiple experts. Using techniques such as interviewing in
<strong>the</strong> knowledge elicitation <strong>of</strong> multiple experts can exponentially increase <strong>the</strong> cost to perform <strong>the</strong> KE
activity. Ano<strong>the</strong>r factor that will impact <strong>the</strong> process is <strong>the</strong> ability <strong>of</strong> an expert to articulate <strong>the</strong>ir
knowledge as a clear set <strong>of</strong> modifiable facts, objects and rules, and give reasons for each decision.
2.2 Requirements for Knowledge Elicitation
From <strong>the</strong> outset <strong>of</strong> a KE program, identifying relevant knowledge sources is key its success. Hanes
and Gross (2002) noted that experts used in <strong>the</strong> process should ei<strong>the</strong>r be recognised as an expert in
<strong>the</strong> field and possess expertise in handling rare or infrequent events. As a means to encourage expert
participation, Slagle and Wick (1988) identified several key factors. Management participation is
essential as <strong>the</strong>y are responsible for creating a cultural change management program required to aid
all KM initiatives. Management must make <strong>the</strong> experts aware <strong>of</strong> <strong>the</strong> importance <strong>the</strong>ir contribution to
<strong>the</strong> KE process. Organisations can also encourage experts to participate through financial and
statutory pay<strong>of</strong>fs. In terms <strong>of</strong> <strong>the</strong>ir usage in <strong>the</strong> process, <strong>the</strong>y must provide assurances that <strong>the</strong> time
allocated to each elicitation session is kept to a minimum. In order to reduce <strong>the</strong> complexity <strong>of</strong> <strong>the</strong>
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knowledge management process, <strong>the</strong> selection <strong>of</strong> knowledge engineers is critical in minimising <strong>the</strong>
problems <strong>of</strong> knowledge inaccuracies. Welbank (1983) identified a set <strong>of</strong> interpersonal skills required
by a knowledge engineer. These include self-confidence, tact and diplomacy, versatility, empathy,
patience and persistence. Not only are <strong>the</strong>se ‘s<strong>of</strong>t’ skills critical to <strong>the</strong> success <strong>of</strong> a KE initiative, <strong>the</strong>
knowledge engineer should possess a working knowledge about <strong>the</strong> domain. It is vital to have an
awareness <strong>of</strong> recent developments and an ability to be critical <strong>of</strong> <strong>the</strong> knowledge transferred in order to
elicit deeper understanding <strong>of</strong> concepts being exposed. As a means <strong>of</strong> reducing costs, <strong>the</strong> KE
engineer must also be aware <strong>of</strong> <strong>the</strong> most appropriate KE techniques as well as ensuring that a
common set <strong>of</strong> terms is put in place in order to facilitate <strong>the</strong> use <strong>of</strong> multiple experts from differing
fields. They are essential in supporting <strong>the</strong> KE process, in terms <strong>of</strong> resolving issues concerned with
<strong>the</strong> unbalanced picture presented by <strong>the</strong> experts. It is <strong>the</strong> knowledge engineer who must keep <strong>the</strong> KE
process focused on <strong>the</strong> goals established at <strong>the</strong> start <strong>of</strong> <strong>the</strong> activity.
Whilst an expert’s knowledge cannot be extracted in its entirety, KE tools are capable <strong>of</strong> providing rich
information relating to concepts, relations, facts, rules, and strategies relevant to <strong>the</strong> domain in
question. The following section will provide an overview <strong>of</strong> <strong>the</strong> various methods and categorisations in
literature.
2.3 Knowledge Elicitation Techniques
Adding to <strong>the</strong> complexity <strong>of</strong> KE is <strong>the</strong> abundance <strong>of</strong> techniques that have been developed to assist in
<strong>the</strong> elicitation <strong>of</strong> knowledge from experts. In an attempt to aid <strong>the</strong> selection process, efforts have been
made to classify KE techniques. These categorisations provide an overview as to when and where
techniques are most appropriate.
2.3.1 Direct / Indirect Methods
The first categorisation is <strong>the</strong> distinction used by Olson and Biolsi (1991). They divided techniques
into two classifications, Direct and Indirect methods. Direct methods refer to those that expose
knowledge directly from <strong>the</strong> expert during a KE session. These range from interviews, whereby <strong>the</strong>
expert articulates knowledge, through to observations, in which expertise is demonstrated in real-time.
Table 4.1 contains a list <strong>of</strong> methods associated with this categorisation. Direct KA techniques allow
direct access to various types <strong>of</strong> knowledge. Whilst <strong>the</strong>se techniques are useful in ga<strong>the</strong>ring
knowledge at <strong>the</strong> onset <strong>of</strong> KM activities, <strong>the</strong>y <strong>of</strong>ten fail to elicit deep knowledge (Sanchez et al., 2010).
Typically <strong>the</strong> knowledge uncovered is declarative (i.e. lacking context) or surface knowledge (Olson &
Rueter, 1987). Ano<strong>the</strong>r limitation is that <strong>the</strong>se methods are limited to what an expert can articulate. In
conducting <strong>the</strong>se sessions, a common terminology must exist between knowledge engineer and
expert.
Table 1: Examples <strong>of</strong> Direct KE Methods
Category Examples
Direct Techniques Interviews
Think-aloud
Observation
Interruption Analysis
By contrast, Indirect Methods are defined as techniques whereby <strong>the</strong> knowledge engineer infers
knowledge from <strong>the</strong> expert’s behaviour. KE sessions are performed and retrospectively analysed in
order to elicit knowledge. Indirect techniques (Table 4.2) do not rely on an expert's ability to articulate
<strong>the</strong> knowledge. Actions and behaviours are collected from which inferences about what <strong>the</strong> expert’s
decision making processes can be made. These techniques are used to uncover a deeper
(procedural or semantic) knowledge, but <strong>of</strong>ten require interpretation that makes <strong>the</strong>m subject to bias if
misused (Olson and Rueter, 1987). In selecting KE techniques, a combination <strong>of</strong> indirect and direct is
seen as being desirable. Overreliance on one set would exclude o<strong>the</strong>r forms <strong>of</strong> knowledge from <strong>the</strong>
elicitation process.
Table 2: Examples <strong>of</strong> Indirect KE Methods
Category Examples
Indirect Techniques Multidimensional Scaling
Repertory Grid Analysis
General Weighted Networks
Ordered Trees from Recall
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2.3.2 Natural and Contrived KE techniques
Peter Marshall and Damian Gordon
The next categorisation is <strong>the</strong> difference between natural and contrived techniques (Shabolt & Burton,
1989). Natural Techniques are defined as those that involve <strong>the</strong> expert doing tasks normally
associated with <strong>the</strong>ir job allowing insights to be gained naturally from <strong>the</strong> expert in <strong>the</strong> form <strong>of</strong>
concurrent articulation, demonstrations or retrospectively analysis. These techniques are commonly
used in eliciting procedural knowledge about tasks. An advantage <strong>of</strong> natural techniques is that <strong>the</strong>y
are suited to a work environment, <strong>the</strong>refore minimising time taken out <strong>of</strong> a work schedule. However
<strong>the</strong>y are limited in acquiring tacit knowledge and atypical knowledge.
Table 3: Examples <strong>of</strong> Natural KE Methods
Category Examples
Natural Techniques Interviews
Observation
Group Meetings
Questionnaires
To facilitate <strong>the</strong> acquisition <strong>of</strong> such knowledge, Contrived Techniques can be employed. These
techniques put experts in artificial scenarios, far removed from <strong>the</strong>ir everyday work. Techniques, such
as card sorting and laddering, can be used to challenge an expert; exposing <strong>the</strong>ir problem solving
behaviours as well as revealing facets <strong>of</strong> knowledge (e.g. rules <strong>of</strong> thumb) developed through
experience. In performing a KE exercise, Shabolt and Burton (1989) recommends a combination <strong>of</strong>
both natural (table 4.3) and contrived (table 4.4) techniques.
Table 4: Examples <strong>of</strong> Contrived KE Methods
Category Examples
Contrived Techniques Card Sorting
Triadic Elicitation
Repository Grid Technique
Constraint Tasks
2.3.3 Individual and Group KE techniques
The majority <strong>of</strong> KE research focuses on <strong>the</strong> elicitation <strong>of</strong> knowledge from a single human expert.
Moore & Miles (1991) noted that <strong>the</strong> use <strong>of</strong> single expert normally occurs by default, as opposed to a
conscious selection choice. There are limitations when used in this mode e.g. scope restricted to <strong>the</strong>
size and complexity <strong>of</strong> an expert’s knowledge (McGraw & Seale, 1988). The absence <strong>of</strong> multiple
experts in <strong>the</strong> KE process also impacts <strong>the</strong> ability <strong>of</strong> KE process to fill gaps that exist in an expert's
knowledge (Mittal & Dym, 1995). As seen in section 3.2, access to even a single expert, particularly
those critical to an organisations primary function, can be difficult. Ano<strong>the</strong>r limitation <strong>of</strong> single expert
KE is that <strong>the</strong> reliance on <strong>the</strong> expertise <strong>of</strong> an individual as it might not accurately reflect <strong>the</strong> actual
decision making process; particularly when organisational decisions are made through collaboration
with multiple stakeholders (McGraw & Harbison-Briggs, 1989).
In response, KE techniques have been developed to foster <strong>the</strong> opinions <strong>of</strong> multiple experts (Table
4.5). There are several difficulties associated in dealing with groups <strong>of</strong> experts. In section 3.2, we
made reference to <strong>the</strong> issues <strong>of</strong> identifying and selecting <strong>of</strong> expertise for <strong>the</strong> KE process. Techniques
can also be limited in terms <strong>of</strong> <strong>the</strong> number <strong>of</strong> participants who can be involved in <strong>the</strong> KE process. The
number <strong>of</strong> experts must be large enough to ensure coverage <strong>of</strong> <strong>the</strong> domain, whilst small enough so
that <strong>the</strong> process can remain manageable (McGraw & Seale, 1988). Dealing with groups <strong>of</strong> individuals
will, almost always, result in some form <strong>of</strong> conflict. In order to facilitate group techniques some form <strong>of</strong>
conflict resolution and consensus mechanism needs to incorporate (Turban, 1998).
Table 5: Examples <strong>of</strong> Multiple Domain KE Methods
Category Examples
Multiple Domain Experts Focus Groups
DeBono’s 6 Hats <strong>of</strong> Thinking
Concept Maps
PMI
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2.3.4 Types <strong>of</strong> Knowledge Obtained
Peter Marshall and Damian Gordon
Ano<strong>the</strong>r grouping <strong>of</strong> KE techniques is by <strong>the</strong> type <strong>of</strong> knowledge elicited (Burge, 2008). In this
categorisation, Burge identifies six categories: procedures, problem solving strategy, goals and subgoals,
classification, relationships and evaluation. As with individual and group knowledge elicitation
techniques, KE techniques <strong>of</strong>ten fit into more than one category. From this it can be seen that KE
techniques are not limited to one type <strong>of</strong> knowledge. This categorisation is a powerful one in selecting
<strong>the</strong> appropriate techniques for KE sessions. The categorisation can aid in focusing elicitation to goals
set at <strong>the</strong> onset <strong>of</strong> KE activities (Marshall & Gordon, 2011). The first categorisation is procedures.
Techniques in this category can be used to elicit procedural knowledge. Burge (ibid) defines <strong>the</strong>se
types <strong>of</strong> tools as those “used to determine <strong>the</strong> steps followed to complete a task and <strong>the</strong> order in
which <strong>the</strong>y are taken”. Table 4.6 contains a list <strong>of</strong> examples <strong>of</strong> techniques used in this categorisation
<strong>of</strong> KE techniques.
Table 6: Examples <strong>of</strong> KE techniques for Procedural Knowledge
Category Examples
Procedural Techniques Interviewing
Concept Mapping
Interruption Analysis
Questionnaire
The next category is <strong>the</strong> set <strong>of</strong> KE tools that are used to elicit a problem solving strategy. Techniques
in this category can be used to elicit semantic and episodic knowledge (discussed in section 2.2.2.3).
These methods aim to elicit knowledge pertaining to <strong>the</strong> strategies employed by an expert in <strong>the</strong>ir
decision-making processes. Table 4.7 contains a list <strong>of</strong> examples <strong>of</strong> techniques used in this
categorisation <strong>of</strong> KE techniques.
Table 7: Examples <strong>of</strong> KE techniques for Problem Solving Strategic Knowledge
Category Examples
Problem Solving Strategy Techniques Interviewing
Protocol analysis
Commentary
20 questions
The following category refers to techniques that elicit knowledge pertaining to goals and sub-goals.
These techniques are used to decompose tasks being performed by an SME during a KE session.
Techniques in this category are used to elicit procedural knowledge. However Burge (ibid)
distinguishes <strong>the</strong>se methods from <strong>the</strong> ones found in <strong>the</strong> procedural section since ordering <strong>of</strong> goals /
sub-goals are not necessarily provided. Table 4.8 contains a list <strong>of</strong> examples <strong>of</strong> techniques used in
this categorisation <strong>of</strong> KE techniques.
Table 8: Examples <strong>of</strong> KE techniques for Goals / Sub-Goals Knowledge
Category Examples
Goals / Sub-goals Techniques Critical Decision Method
Task action mapping
Re-classification
Goal Decomposition
In Classification, <strong>the</strong> KE techniques are used to classify entities within <strong>the</strong> domain. Techniques in this
category are primarily used to elicit declarative knowledge. Table 4.9 contains a list <strong>of</strong> examples <strong>of</strong>
techniques used in this categorisation <strong>of</strong> KE techniques.
Table 9: Examples <strong>of</strong> KE techniques for Classification Knowledge
Category Examples
Classification Techniques Distinguishing goals
Decision analysis
Repertory grid
Triadic elicitation
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The next category relates to <strong>the</strong> knowledge found in dependencies / relationships. In this category
techniques are used to obtain relationships between domain entities. Techniques in this category are
used to elicit semantic knowledge. Table 4.10 contains a list <strong>of</strong> examples <strong>of</strong> techniques used in this
categorisation <strong>of</strong> KE techniques.
Table 10: Examples <strong>of</strong> KE techniques for Dependency / Relationship Knowledge
Category Examples
Dependencies / Relationships Techniques Card sorting
Triadic Elicitation
Discourse analysis (observation)
Semantic nets
The final set <strong>of</strong> KE techniques are used to evaluate systems, usually prototype systems, or o<strong>the</strong>r
types <strong>of</strong> KE session results. The final set deal with qualitative metrics. Table 4.11 contains a list <strong>of</strong>
examples <strong>of</strong> techniques used in this categorisation <strong>of</strong> KE techniques.
Table 11: Examples <strong>of</strong> KE techniques for Evaluation Knowledge
Category Examples
Evaluation Techniques Teach Back
Critiquing
Decision analysis
Rapid prototyping
3. Conclusions
Although not always evident in existing literature, <strong>the</strong> review serves as a means to illustrate <strong>the</strong>
relationship between KA and KE. Section 3 provided an overview <strong>of</strong> <strong>the</strong> different types <strong>of</strong> KA
techniques and identified <strong>the</strong> subset to which KE was classified as being part <strong>of</strong> i.e. <strong>the</strong> acquisition <strong>of</strong>
knowledge from human sources. Whilst <strong>the</strong> issues faced by KA (identified in section 2) are inherent in
KE, KE has its own set <strong>of</strong> KE specific issues as illustrated in section 4.1. These issues must be
addressed in order to minimise <strong>the</strong> risks involved in <strong>the</strong> KE (Outlined in section 4.2). Finally in <strong>the</strong> KE
techniques, section 4.3 examined <strong>the</strong> various categorisations <strong>of</strong> KE techniques. No single technique
can be used to fully elicit knowledge in its entirety. Understanding <strong>of</strong> <strong>the</strong> categorisations and <strong>the</strong>
different types <strong>of</strong> techniques available are <strong>of</strong> great importance in aiding <strong>the</strong> selecting process and
ensuring that appropriate techniques are selected relative to <strong>the</strong> goals <strong>of</strong> <strong>the</strong> KE activity. .
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