Proceedings of the 3rd European Conference on Intellectual Capital

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3.2 Control variables Deborah Branswijck and Patricia Everaert To formally test whe<strong>the</strong>r <strong>the</strong>re exists a disclosure commitment, a number <strong>of</strong> control variables were included into <strong>the</strong> analysis. Vergauwen & Van Alem (2005) found that companies in The Ne<strong>the</strong>rlands disclose relatively low on IC in comparison with French and German companies. Ano<strong>the</strong>r research (Vandemaele et al., 2005) revealed that Swedish companies disclose on average more about IC than companies in The Ne<strong>the</strong>rlands and <strong>the</strong> UK. Based on a previous research (Orens & Derboven, 2008) <strong>of</strong> IC disclosure in <strong>the</strong> prospectus <strong>of</strong> Belgian companies, which indicated that <strong>the</strong>y do not disclose an elaborated level <strong>of</strong> IC disclosure, we assume that <strong>the</strong>re are no significant differences between <strong>the</strong>se two countries. Previous research has <strong>of</strong>ten seen company age as a proxy for risk since more established companies are less risky. From this perspective, a company’s amount <strong>of</strong> disclosure is expected to be related to <strong>the</strong> company age. Amir and Lev (1996) found that non-financial information is <strong>of</strong> greater importance for <strong>the</strong> valuation <strong>of</strong> younger companies. Fur<strong>the</strong>rmore, Jaggi (1996) found that <strong>the</strong> amount <strong>of</strong> inaccurate information in <strong>the</strong> prospectus is larger for younger companies. These previous studies might be an indication for a negative relationship between <strong>the</strong> level <strong>of</strong> disclosure on IC and company age. Company age is measured as <strong>the</strong> amount <strong>of</strong> years <strong>the</strong> company exists since its foundation. Next to company age, researchers also found a relationship between <strong>the</strong> level disclosure and company size. Watson et al. (2002) indicate that <strong>the</strong> cost <strong>of</strong> voluntary reporting is relatively high for smaller companies compared to <strong>the</strong> cost for larger ones. Fur<strong>the</strong>rmore, larger companies have more to win with an increased level <strong>of</strong> information since <strong>the</strong> trading <strong>of</strong> effects increases due to lower uncertainty. These firms are under <strong>the</strong> control <strong>of</strong> <strong>the</strong> government and an increased level <strong>of</strong> disclosure can reduce <strong>the</strong> governmental pressure (Buzby, 1975). Moreover, <strong>the</strong> need for capital for large companies is higher than for small firms. A lower cost <strong>of</strong> capital is obtained since <strong>the</strong> information asymmetry reduces. Based on a directive <strong>of</strong> Europe (2003/361/EG <strong>of</strong> <strong>the</strong> Commission <strong>of</strong> 6 may 2003) turnover, total assets and amount <strong>of</strong> employees can be used to determine <strong>the</strong> size <strong>of</strong> a company. All <strong>of</strong> <strong>the</strong>se indicators for size were obtained for each company. Previous research tried to find a link between <strong>the</strong> amount <strong>of</strong> disclosure on IC and <strong>the</strong> industry to which <strong>the</strong> company is affiliated. Due to historical reasons some industries disclose more information than o<strong>the</strong>rs. It is expected that if one company in a certain industry reports voluntarily on a specific topic, o<strong>the</strong>r players in <strong>the</strong> market will also report on that topic. Cooke (1991) found in his study that Japanese production companies disclosed significantly more in <strong>the</strong>ir reports compared to o<strong>the</strong>r industries. Also Wallace et al. (1994) and Dye & Sridhar (1995) suggest that <strong>the</strong> industry in which a company is active will have an influence on <strong>the</strong> voluntary disclosure behavior. A company that chooses not to disclose on a topic, is seen as a company that wants to hide bad news. The public attention that companies receive will influence <strong>the</strong> amount <strong>of</strong> disclosure. Different research has been performed in order to find evidence for this hypo<strong>the</strong>sis. Cooke (1989) used a detailed index for <strong>the</strong> variable industry that exists out <strong>of</strong> production, commercial, services and conglomerates in his investigation for Swedish companies. The results indicated that commercial companies report less voluntary information compared to o<strong>the</strong>rs. The research <strong>of</strong> Abdolmohammadi (2005) shows that certain industries <strong>of</strong> <strong>the</strong> new economy disclose more on intellectual capital and information systems compared to mature industries which disclose more on <strong>the</strong>ir brands and strategic alliances. The importance <strong>of</strong> intellectual capital disclosure is found in high-tech industries. Comparing <strong>the</strong> book value and <strong>the</strong> market to book ratio <strong>of</strong> <strong>the</strong>se companies, it could be noticed that <strong>the</strong> values for IT and biotech companies are generally higher. Bukh et al. (2005) indicate that <strong>the</strong>se companies will report more voluntary information next to <strong>the</strong> traditional financial reporting. In our study, <strong>the</strong> construct industry is measured by different dummy variables such as “Chemicals”, “Consumer goods”, “Industrial goods and utilities”, “Financial services” and “IT”. These categories are based on <strong>the</strong> Industry Classification Benchmark (ICB) used by Euronext. Allocation to <strong>the</strong> appropriate industries in ICB classification system is made in collaboration with <strong>the</strong> management <strong>of</strong> <strong>the</strong> companies. Some industry classes <strong>of</strong> <strong>the</strong> ICB system were grouped toge<strong>the</strong>r to increase <strong>the</strong> amount <strong>of</strong> observations for each industry group. Ano<strong>the</strong>r variable <strong>of</strong> interest is audit firm. An audit firm uses its reputation as a competitive advantage. This reputation capital is strongly influenced by accounting scandals. The Enron debacle, for example, showed what can happen to an audit firm involved in such a scandal. Andersen, <strong>the</strong> audit firm <strong>of</strong> Enron, went out <strong>of</strong> business even before a single lawsuit was filed against it. In order to avoid this risk <strong>of</strong> litigation, auditors prefer financial statements that are stated and audited strictly according to accounting regulations. Because <strong>of</strong> <strong>the</strong> vague and contradicting regulations on IC, it could be argued that auditors omit IC-related items out <strong>of</strong> <strong>the</strong> financial statements. Therefore, auditor 504
Deborah Branswijck and Patricia Everaert conservatism can be considered to have a prominent influence on <strong>the</strong> disclosure <strong>of</strong> IC-related information. Clarskon et al. (2003) found that international accounting firms in particular show conservative auditor behavior. Consequently, <strong>the</strong> dummy variable “Big4Auditor” was added to <strong>the</strong> model. Ownership retention during <strong>the</strong> IPO could be seen as an indication <strong>of</strong> <strong>the</strong> quality <strong>of</strong> <strong>the</strong> firm. Since it is costly to retain a significant stake in <strong>the</strong> firm, a large fractional ownership would preclude <strong>the</strong> entrepreneur from diversifying his personal investment. Leland and Pyle (1977) constructed a model to predict <strong>the</strong> behavior <strong>of</strong> <strong>the</strong> owner <strong>of</strong> a company when information asymmetry exists. The model implies that <strong>the</strong> ownership retention will be high if <strong>the</strong> owners expect that <strong>the</strong> firm value is higher than reflected in <strong>the</strong> financial statements. Consequently, rational investors will perceive ownership retention as a signal <strong>of</strong> firm value. Fur<strong>the</strong>rmore, according to O’Sullivan (2000), we can expect less disclosure from <strong>the</strong> company if <strong>the</strong> degree <strong>of</strong> ownership retention is high. If <strong>the</strong> directors do not own a substantial portion <strong>of</strong> <strong>the</strong> company, it can be expected that <strong>the</strong>y encourage more intensive disclosure in order to fulfill <strong>the</strong>ir monitoring role. Therefore, <strong>the</strong> variable “Ownership” was added to <strong>the</strong> model. 3.3 Model In this section <strong>the</strong> defined hypo<strong>the</strong>ses will be tested by means <strong>of</strong> a linear regression. The independent variable industry will be entered in <strong>the</strong> equation as several dummy variables. In order to avoid <strong>the</strong> dummy variable trap, one <strong>of</strong> <strong>the</strong> industries, namely “IT” was not included in <strong>the</strong> regression. Admission <strong>of</strong> this industry would lead to multicollinearity since <strong>the</strong>re is a linear relationship between <strong>the</strong> industryvariables (Baker, 2006) Score_ARj = λ0 + λ1 Score_IPOj + λ2 Crisisj + λ3 Sizej + λ4 Chemicalsj + λ5 Cons_Goodsj + λ6 IndGoods_Utulitiesj + λ7 Financial_servicesj + λ8 Agej + λ9 Auditor + λ10 Ownership + µj where: Score_ARj = <strong>the</strong> result on <strong>the</strong> index for <strong>the</strong> subsequent annual report after initial public <strong>of</strong>fering Score_IPOj = <strong>the</strong> result on <strong>the</strong> index for <strong>the</strong> prospectus on <strong>the</strong> year <strong>of</strong> initial public <strong>of</strong>fering Crisisj = a dummy variable equal to 0 for an initial public <strong>of</strong>fering in 2005, 2006 or 2007 and 1 o<strong>the</strong>rwise Sizej = <strong>the</strong> total assets <strong>of</strong> <strong>the</strong> company Chemicalsj = a dummy variable equal to 1 if firmj belongs to that industry and 0 o<strong>the</strong>rwise with IT as a reference category Cons_Goodsj = a dummy variable equal to 1 if firmj belongs to that industry and 0 o<strong>the</strong>rwise with IT as a reference category IndGoods_Utulitiesj = a dummy variable equal to 1 if firmj belongs to that industry and 0 o<strong>the</strong>rwise with IT as a reference category Financial_servicesj = a dummy variable equal to 1 if firmj belongs to that industry and 0 o<strong>the</strong>rwise with IT as a reference category Agej = <strong>the</strong> difference between 2010 and <strong>the</strong> year <strong>of</strong> establishment <strong>of</strong> firmj Auditorj = a dummy variable equal to 1 if firmj is audited by a big 4 company and 0 o<strong>the</strong>rwhise Ownershipj = <strong>the</strong> amount <strong>of</strong> shares retained by <strong>the</strong> owners during <strong>the</strong> IPO, expressed in percent 4. Research findings 4.1 Descriptive statistics Table 1 gives an overview <strong>of</strong> <strong>the</strong> different industries and <strong>the</strong> areas <strong>of</strong> IC disclosure. A percentage <strong>of</strong> 33% in <strong>the</strong> area costumers (CU) indicates that 33% <strong>of</strong> <strong>the</strong> companies in <strong>the</strong> chemical industry report 505
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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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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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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. The qu
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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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Page 500 and 501: 9. Discussion and conclusions Angel
Page 502 and 503: The Global Position of</str
Page 504 and 505: Piotr Wisniewski safeguarding IC vi
Page 506 and 507: Piotr Wisniewski Figure 2: Ten larg
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Page 530 and 531: Measure the Unmeas
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Page 540 and 541: Table 5: Sustainability checklist E
Page 542 and 543: Emmanouil Gkinoglou The next challe
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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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562
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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. Pöyhönen, A. an
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Proceedings of the 3rd European Conference on Intellectual Capital

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