Proceedings of the 3rd European Conference on Intellectual Capital

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Tatiana Garanina and Yana Pavlova The results <strong>of</strong> <strong>the</strong> analysis concerning <strong>the</strong> UK market are represented in Table 4. Table 4: The results <strong>of</strong> testing <strong>the</strong> model (M1) for <strong>the</strong> UK sample № Characteristic The sample as a whole 1 2 3 4 5 6 7 8 Model (М1)/VT; VI * 1 Intercept 9 815 Mechanical engineering 137,79 Extractive industry 818,06 Power engineering 10 789 Telecommunications 324 Metallurgy 154,8 2 Coefficient before <strong>the</strong> first independent variable ** 0,44 (5,25) 1,49 (7,20) 0,56 (21,87) 0,14 (1,52) 5,53 (15,84) 0,85 (55,03) 3 Coefficient before <strong>the</strong> second independent variable ** 0,21 (4,68) 1,38 (5,6) 0,43 (6,68) 0,03 (0,88) 0,3 (9,7) 0,83 (62,3) 4 t-critical 3,9 1,86 1,86 1,86 1,86 1,86 5 F-statistics 17,98 31,63 287 2,46 181,14 626 6 Coefficients <strong>of</strong> determination R 2 –R 2 –R 2 adj 0,23 0,19 0,74 0,67 The results <strong>of</strong> <strong>the</strong> test show that <strong>the</strong> model is significant, as both <strong>of</strong> its factors are. Null hypo<strong>the</strong>sis is rejected in all <strong>the</strong> industries. That means that <strong>the</strong> market value <strong>of</strong> assets depends on <strong>the</strong> fundamental value <strong>of</strong> tangible and intangible assets in all <strong>the</strong> researched branches. In <strong>the</strong> metallurgy industry market value <strong>of</strong> assets is explained by fundamental values <strong>of</strong> both types <strong>of</strong> assets by almost 100%. Extractive industry and telecommunications are characterized by a high influence <strong>of</strong> fundamental value <strong>of</strong> tangible and intangible assets on companies’ value. The model, which was tested for power engineering proved to be insignificant in <strong>the</strong> UK market. In this case we can not make any conclusions. Probably, all <strong>the</strong> results could be better for explanation if <strong>the</strong> sample were bigger. All in all, based on <strong>the</strong> data obtained, changes in market value <strong>of</strong> assets <strong>of</strong> companies are explained in <strong>the</strong> Russian market by 75%, while <strong>the</strong> UK market only by 20% by <strong>the</strong> fundamental value <strong>of</strong> tangible and intangible assets. But it should be mentioned that <strong>the</strong>se figures are based on a sample <strong>of</strong> 5 branches. The main problem in realization <strong>of</strong> this kind <strong>of</strong> research on <strong>the</strong> Russian market is <strong>the</strong> shortage <strong>of</strong> statistical information. A bigger number <strong>of</strong> companies-emitters match <strong>the</strong> necessary parameters <strong>of</strong> sample in o<strong>the</strong>r industries, but <strong>the</strong>ir reporting is not publicly available. If to speak about <strong>the</strong> results obtained in different industries in general, <strong>the</strong> UK market has appeared to be less predictable than <strong>the</strong> Russian one. We should not forget about some limitations <strong>of</strong> <strong>the</strong> study. We have a bit different samples – different amount <strong>of</strong> companies and also <strong>the</strong>re are differences in <strong>the</strong> periods <strong>of</strong> research, but according to <strong>the</strong> results obtained all <strong>the</strong> models and variables are valuable what allows us to make <strong>the</strong> following conclusions. The implicit connection was observed in <strong>the</strong> mechanical engineering industry in <strong>the</strong> Russian market between <strong>the</strong> explained and explanatory variables, while in <strong>the</strong> UK it was power engineering. A very obvious correlation between market value <strong>of</strong> assets and fundamental value <strong>of</strong> tangible and intangible * Tested at 5% significance level ** t-statistics is shown in paren<strong>the</strong>ses ** t-statistics is shown in paren<strong>the</strong>ses 172 0,95 0,95 0,15 0,09 0,93 0,92 0,98 0,97
Tatiana Garanina and Yana Pavlova assets was identified in <strong>the</strong> metallurgy industry on <strong>the</strong> data <strong>of</strong> <strong>the</strong> Russian market, which was also confirmed by <strong>the</strong> data <strong>of</strong> <strong>the</strong> UK market. 4. Conclusion Intangible Assets are a company’s “weightless wealth” that helps it to obtain real pr<strong>of</strong>it. Every company should understand that nowadays paying much attention to Value-based Management in general and to Intangible Assets especially (as <strong>the</strong>y are <strong>the</strong> key drivers <strong>of</strong> value creation in <strong>the</strong> economy <strong>of</strong> <strong>the</strong> XXI century) may help to create and develop its core competences and thus yield competitive advantage on <strong>the</strong> market. This question is discussed in different publications, where it is said about <strong>the</strong> problem that <strong>the</strong> most important resources <strong>of</strong> a company, which are Intangible Assets, are <strong>of</strong>ten not reflected in companies’ balance-sheets [Edvinsson, Mallone, 1997; Sveiby, 1997]. That is why a new approach for obtaining a fundamental value <strong>of</strong> intangible assets on <strong>the</strong> base <strong>of</strong> residual operating income is developed in <strong>the</strong> paper. It helps to get <strong>the</strong> value <strong>of</strong> those intangibles which are not shown on <strong>the</strong> balance sheets <strong>of</strong> companies. It was decided to test a hypo<strong>the</strong>sis and to compare results <strong>of</strong> influence <strong>of</strong> fundamental value <strong>of</strong> tangible and intangible assets on market value <strong>of</strong> assets on two markets: <strong>the</strong> emerging Russian market and <strong>the</strong> well-developed UK market. For <strong>the</strong> research a two-factor model has been developed. The research helped to find out that, indeed, <strong>the</strong>re is a connection between <strong>the</strong> selected indicators, but if to look at all <strong>the</strong> industries, <strong>the</strong> connection is not strong enough. The strongest relationship was observed in <strong>the</strong> metallurgy and it was very poor in <strong>the</strong> mechanical engineering for both markets. The tested econometric model shows that on both markets (<strong>the</strong> UK, and <strong>the</strong> Russian one) tangible assets still play a more important role in value creation <strong>of</strong> companies if to use <strong>the</strong> chosen method <strong>of</strong> evaluation. Of course <strong>the</strong> method has some limitations due to <strong>the</strong> assumptions represented in <strong>the</strong> Technical annex, but <strong>the</strong> method is very useful for obtaining $-valuation <strong>of</strong> Intangible assets for comparing different companies and industries between each o<strong>the</strong>r. The approach highlighted in <strong>the</strong> paper may be used by managers in order to understand what role Intangible Assets play in company’s value creation and also to understand whe<strong>the</strong>r <strong>the</strong>ir company overplays rivals or not. The method can also be used for external investors who may compare companies <strong>the</strong>y want to invest in from <strong>the</strong> point <strong>of</strong> view <strong>of</strong> Intangible assets. In this case investors need only external financial reports what makes this approach very easy to use. The fur<strong>the</strong>r research in this field will develop not only in <strong>the</strong> direction <strong>of</strong> testing <strong>the</strong> researched models for sustainability as statistical information accumulated, but also in <strong>the</strong> direction <strong>of</strong> developing and testing o<strong>the</strong>r models <strong>of</strong> Intangible Assets valuation. Moreover, <strong>the</strong> question <strong>of</strong> Intangible Assets structure demands <strong>the</strong> fur<strong>the</strong>r specification, and also <strong>the</strong> problem <strong>of</strong> extracting separate elements <strong>of</strong> Intangible Assets from <strong>the</strong>ir aggregate value needs to be solved. 5. Technical annex In this annex all <strong>the</strong> assumptions and formulas transformation are presented for obtaining fundamental values <strong>of</strong> tangible and intangible assets. Here, <strong>the</strong> residual operating income equals <strong>the</strong> residual earnings after deducting <strong>the</strong> cost <strong>of</strong> invested capital: BV REOI j = NOPAT j − kW × NA j−1 ( t) , (1a) where NOPAT — net operating pr<strong>of</strong>it after taxes (also EBI – earnings before interest), calculated according to <strong>the</strong> formula: NOPAT = NI + i × 1− , (2a) where NI — net income i t — interest — income tax rate according to <strong>the</strong> income statement 173
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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 l
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Donley Carrington and Mike Tayles i
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Page 148 and 149: Marziye Ehrami et al. 1. Creation
Page 150 and 151: Marziye Ehrami et al. Note: In 2005
Page 152 and 153: Earnings Quality and Othe</
Page 154 and 155: Magdi El-Bannany resource-based per
Page 156 and 157: Magdi El-Bannany Based on this argu
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Page 162 and 163: Magdi El-Bannany Edvinsson, L. & Ma
Page 164 and 165: Ahmed Elsetouhi and Ibrahim Elbelta
Page 174 and 175: Albrecht Fritzsche and Rebecca Geig
Page 178 and 179: Table 4: Applicability of</
Page 182 and 183: S&P 500 Market Cap ($ billions) 14,
Page 184 and 185: Tatiana Garanina and Yana Pavlova T
Page 186 and 187: Tatiana Garanina and Yana Pavlova T
Page 190 and 191: Tatiana Garanina and Yana Pavlova I
Page 192 and 193: Impact of Investme
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Page 222 and 223: Intellectual Capital and Corporate
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Eliciting Tacit Knowledge From a Do
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Peter Marshall and Damian Gordon MM
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Peter Marshall and Damian Gordon re
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Peter Marshall and Damian Gordon ar
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Peter Marshall and Damian Gordon Th
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Maurizio Massaro et al. crucial fac
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Maurizio Massaro et al. exclusive (
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Maurizio Massaro et al. autonomy <s
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Maurizio Massaro et al. Chennal (20
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Intellectual Capital Management: Ca
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Florinda Matos et al. These models
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Complaints System dos clients New
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Florinda Matos et al. Finally, <str
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Florinda Matos et al. The training
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Florinda Matos et al. 47. Opinion
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Florinda Matos et al. In th
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Florinda Matos et al. The Quadrant
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Florinda Matos et al. Integrated te
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Florinda Matos et al. The networks
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Knowledge Management for Knowledge
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3. Our research Ludmila Mládková
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Ludmila Mládková organizational s
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Ludmila Mládková mentioned only b
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Applying the VAIC
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Maria Molodchik and Anna Bykova pro
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Maria Molodchik and Anna Bykova 2.
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Maria Molodchik and Anna Bykova Let
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Maria Molodchik and Anna Bykova The
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Perspectives on the</strong
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Maria Cristina Morariu as we consid
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Maria Cristina Morariu Tobin’s q
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Maria Cristina Morariu for determin
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The Structural Model of</st
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Fattah Nazem Ackerley (2006) reveal
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Fattah Nazem The research instrumen
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Fattah Nazem intellectual capital m
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Fattah Nazem Johnson, M.J. (1986) T
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Andrei Stefan Nestian organization.
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Andrei Stefan Nestian Chaordic syst
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Andrei Stefan Nestian environment,
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Increasing Knowledge Management Mat
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Theodora Ngosi et al. 2.2 Dominatin
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Theodora Ngosi et al. Figure 1: Del
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Theodora Ngosi et al. IT systems, a
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5. Discussion and summary Theodora
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Theodora Ngosi et al. Weill, P., an
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Bongani Ngwenya late adopters respo
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Bongani Ngwenya This paper extends
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Bongani Ngwenya argument, t
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Bongani Ngwenya DiMaggio, P. J., an
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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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500
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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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Nellija Titova performing better. I
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5. Conclusions and furthe</
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Nellija Titova Annex 2: Summary on
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Meaning in the tab
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Krishanthi Ganga Vithana et al. for
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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. people con
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Paul Lumbantobing et al. influence
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Develop KM Strategy Description Dev
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Paul Lumbantobing et al. In <strong
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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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582
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584
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Nargiza Abdullaeva and Campbell War
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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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