Proceedings of the 3rd European Conference on Intellectual Capital

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Maria Molodchik and Anna Bykova 2. The Model is directed on <strong>the</strong> performance and VAIC components relationship search. It assumes that <strong>the</strong> positive link between <strong>the</strong> intellectual capital and capital employed efficiency and company performance indicators exists for <strong>the</strong> Russian industrial companies [Hypo<strong>the</strong>sis 2]. We allowed for companies’ size and industries’ diversification. We also checked for our results robustness to <strong>the</strong> omission <strong>of</strong> all control variables. With regard to <strong>the</strong>se assumptions and literature background we used <strong>the</strong> following research framework: Figure 1: The research scheme (source: modified from Laing, Dunn, Hughes-Lucas (2010)) The review <strong>of</strong> <strong>the</strong> empirical literature (Pulic, 2004; Firer, Williams, 2003; Chen et al., 2005) reveals <strong>the</strong> use <strong>of</strong> VAIC as both a dependent and an independent variable. It will be included in this study as a dependent variable, in order to test <strong>the</strong> extent to which it may be related to a company performance. Table 1 provides a brief description <strong>of</strong> variables used in our study that were selected based on earlier studies and <strong>the</strong>oretical models. In order to capture possible effects, we also consider natural logarithms <strong>of</strong> <strong>the</strong> measures. Table 1: Variable list Variable Variable description Dependent variables TLP_vad Total labour productivity (is measured by value added ) per 1 employee Sales_growth Total net sales figures (t) – total net sales figures (t-1)/total net sales figures (t-1); measures changes in <strong>the</strong> total sales <strong>of</strong> <strong>the</strong> firm. 272
Maria Molodchik and Anna Bykova Variable Variable description ROE Return on Equity Pr<strong>of</strong>itability Net pr<strong>of</strong>it figures / total net sales figures Intellectual capital variables VAIC Value added intellectual coefficient ICE Intellectual capital efficiency CEE Capital employed efficiency CAPEX Capital investments R&D Research and development expenditures (dummy variable) Before <strong>the</strong> empirical study results in <strong>the</strong> next section we will present <strong>the</strong> employed data and <strong>the</strong> research scheme. 4. Data and methodology The data for <strong>the</strong> survey was provided by Russian Federal Agency <strong>of</strong> Government Statistics (Rosstat) and its regional department (Permstat) as well as by SPARK-INTERFAX and FIRA Pro Russian databases based on <strong>the</strong> companies’ annual statistical and financial reports. The data includes information on 401 small, medium and large companies from different industries, classified according to <strong>the</strong> 3-digit OKVED codes (classification system <strong>of</strong> economic activity) over 2005-2007 years. The dataset compiled by <strong>the</strong> authors reports <strong>the</strong> gross value-added (GVA) <strong>of</strong> 23 sectors across <strong>the</strong> Perm region and includes <strong>the</strong> following information: Common indicators – form and structure <strong>of</strong> ownership, company age, industry and enterprise code. Economic indicators – total revenue, costs, export, company pr<strong>of</strong>itability and unit labour costs. Specific indicators – total labour productivity, R&D expenditures. The representativeness study shows <strong>the</strong> sample equivalence to Perm region economic structure at <strong>the</strong> sector level (with probability <strong>of</strong> 0,683). This fact alone provides motivation for studying <strong>the</strong> intellectual capital for manufacturing industries only. It should be mentioned that a preliminary segregation <strong>of</strong> <strong>the</strong> sample was made in this analysis to filter out those companies that did not fall into manufacturing sectors, leaving a total <strong>of</strong> 101 companies, using which <strong>the</strong> study was carried out. Table 2 helped us to characterize <strong>the</strong> type <strong>of</strong> a company that agreed to collaborate in our study. It presents several descriptive objectives <strong>of</strong> <strong>the</strong> sample, where <strong>the</strong> median and <strong>the</strong> standard deviation <strong>of</strong> <strong>the</strong> variables are detailed. Table 2: The sample descriptive adjectives (contains data <strong>of</strong> 2007) Indicator Objects observation numbers Median St. deviation Total labour productivity (calculated with value added), thous. $ 279 9,3 45,3 ROE, % 293 21,5 459,7 Pr<strong>of</strong>itability, % 289 3,9 42,8 Capex, thous. $ 300 12,7 19668,3 Sales growth, % 289 0,15 3,06 Employees, numbers 297 190 1705 SCE 248 1,0 2,2 HCE 248 1,9 2,6 CEE 248 0,5 0,4 ICE 248 2,3 2,8 VAIC 245 4,0 3,5 273
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Proceedings <stron
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. Hier
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Preface These proceedings represent
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educating through ‘hands on’ co
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Ibrahim Elbeltagi is a Senior lectu
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Theodora Ngosi has a PhD in Compute
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José Vale is an invited assistant
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Social Knowledge: Are you Ready? 1
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John Girard don’t know.” (Benne
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John Girard The real question becom
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John Girard our belief that many le
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John Girard organizations to gain k
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Intellectual Capital Accounting - H
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Academic Research Papers 15
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Khodayar Abili and Mahyar Abili The
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Khodayar Abili and Mahyar Abili Nah
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Khodayar Abili and Mahyar Abili Tim
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Knowledge Transfer in Romanian Univ
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Simona Agoston et al. Table 1: Know
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Simona Agoston et al. homoscedastic
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Simona Agoston et al. included in <
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A Modeling Approach to Intellectual
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Eckhard Ammann (referring to <stron
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Eckhard Ammann Figure 2: The IC spa
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Eckhard Ammann section 2. They are
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Eckhard Ammann From an overall pers
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Sorin Anagnoste and Gabriela Dumitr
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Gabriela Atanasiu and Florin Leon T
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Gabriela Atanasiu and Florin Leon
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Gabriela Atanasiu and Florin Leon B
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Gabriela Atanasiu and Florin Leon a
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Developing and Implementing Strateg
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2. Technology, faculty, and staff B
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Learning programs Individual poten
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Bob Barrett facts with the<
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Intellectual Capital Dynamics withi
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Ruxandra Bejinaru and Stefan Iordac
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Education and Training Practice Str
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Andrea Bencsik et al. We wanted to
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Andrea Bencsik et al. In this sampl
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Table 4: Types of
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References Andrea Bencsik et al. An
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Ayşen Berberoğlu and Emine Ünar
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Ayşen Berberoğlu and Emine Ünar
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Table 7: Descriptive statistics Ay
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Knowledge Dynamics Modeling Using A
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Constantin Bratianu et al. will hav
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Constantin Bratianu et al. 5. a) Gi
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Constantin Bratianu et al. 5. Data
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Constantin Bratianu et al. Nissen,
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2. Technology and infrastructure Sh
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Sheryl Buckley and Apostolos Gianna
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Donley Carrington and Mike Tayles t
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Donley Carrington and Mike Tayles f
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Donley Carrington and Mike Tayles i
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Donley Carrington and Mike Tayles G
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John Dumay and Jim Rooney on a more
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John Dumay and Jim Rooney Taking <s
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John Dumay and Jim Rooney “For ex
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John Dumay and Jim Rooney Mouritsen
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Marziye Ehrami et al. 1. Creation
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Marziye Ehrami et al. Note: In 2005
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Earnings Quality and Othe</
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Magdi El-Bannany resource-based per
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Magdi El-Bannany Based on this argu
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Magdi El-Bannany Table 2: Descripti
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Magdi El-Bannany variable equal to
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Magdi El-Bannany Edvinsson, L. & Ma
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Ahmed Elsetouhi and Ibrahim Elbelta
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Albrecht Fritzsche and Rebecca Geig
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Table 4: Applicability of</
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S&P 500 Market Cap ($ billions) 14,
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Tatiana Garanina and Yana Pavlova T
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Tatiana Garanina and Yana Pavlova I
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Impact of Investme
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Lidia García-Zambrano et al. 2003;
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Lidia García-Zambrano et al. 4.2 P
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Table 7: Goodness of</stron
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Lidia García-Zambrano et al. reaso
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Lidia García-Zambrano et al. Rodri
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Víctor Raúl López Ruiz et al. co
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Víctor Raúl López Ruiz et al. NI
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EI c k i 1 w PC i ic Víctor Raúl
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Víctor Raúl López Ruiz et al. Ta
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Víctor Raúl López Ruiz et al. Co
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Víctor Raúl López Ruiz et al. At
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Maria de Lourdes Machado and Odíli
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Intellectual Capital and Corporate
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Agnes Maciocha when objects in a da
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Agnes Maciocha 4. Presentation <str
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Agnes Maciocha while in the
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Agnes Maciocha Choong K., “Intell
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The Influence of H
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Anca Mândruleanu From the<
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Anca Mândruleanu In Table 2, <stro
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Page 278 and 279: 3. Our research Ludmila Mládková
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Page 284 and 285: Applying the VAIC
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Page 318 and 319: Increasing Knowledge Management Mat
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The Quality of Kno
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5.2 5 4.8 4.6 4.4 4.2 4 3.8 Compete
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Corina Pelau et al. In order to imp
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Hierarchy and Tacit Knowledge in <s
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Ulrica Pettersson and James Nyce in
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Ulrica Pettersson and James Nyce mi
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Katja Pook and Campbell Warden time
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Katja Pook and Campbell Warden futu
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5. Commonalities of</strong
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Katja Pook and Campbell Warden <str
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Katja Pook and Campbell Warden Lazl
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Nicolae Al. Pop et al. satisfaction
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Nicolae Al. Pop et al. the<
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Nicolae Al. Pop et al. satisfaction
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Knowledge Cities: A Portuguese Case
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Katia Rodrigues and Eduardo Tomé
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3. The empirical study 3.1 Methodol
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Table 3: Smart cities - application
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4.3 Suggestions to future work Kati
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Anna Romiti and Daria Sarti 2. Orga
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Anna Romiti and Daria Sarti based o
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Anna Romiti and Daria Sarti Governa
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Anna Romiti and Daria Sarti Therefo
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Anna Romiti and Daria Sarti Valkoka
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Kent Rondeau and Terry Wagar planne
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2.1 Study measures Kent Rondeau and
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Kent Rondeau and Terry Wagar contri
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Kent Rondeau and Terry Wagar Our re
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Intellectual Capital and a Firm’s
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Figure 1: Hypothes
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Helena Santos-Rodrigues et al. Norm
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Helena Santos-Rodrigues et al. To s
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Helena Santos-Rodrigues et al. Our
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Factors Influencing the</st
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Thanaletchumi Sathasivam et al. 2.3
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Thanaletchumi Sathasivam et al. The
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Thanaletchumi Sathasivam et al. gro
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Thanaletchumi Sathasivam et al. The
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Thanaletchumi Sathasivam et al. 11.
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Enhancing IC Formation by Evoking H
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Klaus Bruno Schebesch to portray <s
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Klaus Bruno Schebesch 4. Leaning an
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Klaus Bruno Schebesch We argue in f
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Klaus Bruno Schebesch come
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In Search of key F
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Karen Smits et al. contribution, wi
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Karen Smits et al. Table 1: Correla
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Karen Smits et al. staff feel that
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Marta-Christina Suciu et al. This p
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Marta-Christina Suciu et al. <stron
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Marta-Christina Suciu et al. The qu
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Marta-Christina Suciu et al. This t
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Information and Communication Techn
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Jukka Surakka et al. The participan
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Jukka Surakka et al. people. Commun
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Personal Knowledge Management (PKM)
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Marzena Świgoń provide th
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Marzena Świgoń (number and type <
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Marzena Świgoń measure self-perce
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Eduardo Tomé what we consider to b
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Eduardo Tomé IC. The intervention
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Eduardo Tomé In the</stro
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Eduardo Tomé to stress that <stron
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Eleni Magdalini Vasileiadou et al.
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José María Viedma Marti products
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Sustainable competitive advantage T
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José María Viedma Marti knowledge
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José María Viedma Marti pr<strong
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José María Viedma Marti All <stro
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ICBS I CBS José María Viedma Mart
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José María Viedma Marti Sullivan,
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2. Balanced scorecard framework Ang
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Angelos Vouldis and Angelica Kokkin
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Unclear benefits of</strong
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Angelos Vouldis and Angelica Kokkin
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9. Discussion and conclusions Angel
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The Global Position of</str
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Piotr Wisniewski safeguarding IC vi
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Piotr Wisniewski Figure 2: Ten larg
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Piotr Wisniewski suspected
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Piotr Wisniewski 2) Slow growth in
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Piotr Wisniewski US 3.8 3.8 3.9 4.3
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498
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2. Hypothesis deve
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3.2 Control variables Deborah Brans
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Deborah Branswijck and Patricia Eve
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Deborah Branswijck and Patricia Eve
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Korosh Gholami et al. Durkheim beli
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Korosh Gholami et al. The effect <s
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Measure the Unmeas
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Knowledge Capital 5. Efforts for de
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Exclusive use of L
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Emmanouil Gkinoglou from th
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Project Assets Need for synergies Q
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Table 5: Sustainability checklist E
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Emmanouil Gkinoglou The next challe
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Kai Mertins and Ronald Orth generat
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Kai Mertins and Ronald Orth Never<s
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Kai Mertins and Ronald Orth <strong
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Ad 2 Structure of
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Kai Mertins and Ronald Orth Hauff,
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Maria Simonova “Concept o
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Maria Simonova The concept regardin
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Maria Simonova Becker, G. S. (1983)
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2. The aim of appl
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Nellija Titova W+I+DP+D+T+M+R (equa
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5. Conclusions and furthe</
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Meaning in the tab
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Krishanthi Ganga Vithana et al. sti
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5. Conclusion Krishanthi Ganga Vith
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The Tobing Knowledge Management Arc
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Paul Lumbantobing et al. user, know
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Paul Lumbantobing et al. influence
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Develop KM Strategy Description Dev
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Yuli Purwanti et al. Figure 1: Imag
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Yuli Purwanti et al. Table below is
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Andreea Feraru and Nicoleta Ciucesc
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Roxana Mironescu This could be <str
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Roxana Mironescu Daft, R.L. (2002)
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Paloma Sánchez and Oihana Basilio
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Evaluating Intellectual Capital Usi
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Table 1. CRS input efficiency score
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Stelian Stancu and Anca Domnica Lup
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José Vale et al. However, it is im
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José Vale et al. Pöyhönen, A. an
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