Purpose – Intellectual capital (IC) shows a signi? cant growing acceptance as a worthy topic of academic investigation and practical implication. The purpose of this study is to examine the impact of IC on ? rms’ market value and ? nancial performance. Design/methodology/approach – The empirical data were drawn from a panel consisting of 96 Greek companies listed in the Athens Stock Exchange (ASE), from four different economic sectors, observed over the three-year period of 2006 to 2008.
Various regression models were examined in order to test the hypotheses included in the proposed conceptual framework. Findings – Results failed to support most of the hypotheses; only concluding that there is a statistically signi? cant relationship between human capital ef? ciency and ? nancial performance. Despite the fact that IC is increasingly recognised as an important strategic asset for sustainable corporate competitive advantage, the results of the present study give rise to various arguments, criticism and further research on the subject.
Research limitations/implications – The lack of available data for the appropriate analysis, the investigation of four sectors of economic activity and the relatively narrow three-year period for data collection are the main limitations of the present study. Practical implications – Results proved that, in the Greek business context, the development of human resources seems to be one of the most signi? cant factors of economic success. Focusing on human capital should, therefore, be at the centre of the companies’ attention.
Originality/value – The present study combines previous methodologies in order to investigate certain causal relationships considering the IC of Greek listed companies. The value of the paper is the empirical investigation of these relationships in the context of the Greek economy and the enrichment of the literature with another paper that follows the value-added intellectual coef? cient methodology for the measurement of IC. Keywords Intellectual capital, Market value, Financial performance, Organizations, Greece Paper type Research paper 1.
Introduction Intellectual capital (IC) can be brie?y de? ned as the knowledge-based equity of organizations and has attracted, during the last decade, a signi? cant amount of practical interest (Campisi and Costa, 2008; Petty and Guthrie, 2000). Although the importance of IC is constantly increasing, many organizations face problems with its management, mostly due to measurement dif? culties (Andrikopoulos, 2005; Kim et al. 2009, Nazari and Herremans, 2007). The increasing gap observed between market value and book value of many companies has drawn attention towards investigating the value missing from ? nancial statements.
According to various scholars, IC is considered to be the hidden value that escapes ? nancial statements and the one that leads organizations to obtain a competitive advantage (Chen et al. , 2005; Edvinsson and Malone, 1997; Lev and Radhakrishnan, 2003; Lev and Zarowin, 1999; Lev, 2001; Ruta, 2009; Yang and Lin, 2009). Additionally, it is believed that the limitations of ? nancial statements in precisely explaining ? rm value reveal the fact that, nowadays, the source of economic value is the creation of IC and no longer the production of material goods (Chen et al. , 2005).
The widespread acceptance of IC as a source of competitive advantage led to the development of appropriate methods of measurement, since traditional ? nancial tools are not able to capture all of its aspects (Campisi and Costa, 2008; Nazari and Herremans, 2007). Pulic (2000a, b) developed the most popular method that measures the ef? ciency of value added by corporate intellectual ability (value added intellectual coef? cient (VAIC)). VAIC measures the ef? ciency of three types of inputs: physical and ? nancial capital, human capital, and structural capital (Firer and Williams, 2003; Montequin et al.
2006; Public, 2000a, b). The main objective of the present study is to examine the relationship between IC, market value and ? nancial performance. The methodology for the measurement of IC was based on the studies of Firer and Williams (2003) and Chen et al. (2005). The empirical investigation was conducted using data drawn from a panel consisting of 96 Greek companies listed in the Athens Stock Exchange (ASE), from four different economic sectors (period 2006 to 2008).
Moreover, based on the aforementioned VAIC methodology, the study, analytically examines the separate effects of capital employed ef?ciency, human capital ef? ciency, and structural capital ef? ciency on market value and ? nancial performance. The following section includes a short literature review concerning the main variables of the study. In the third and fourth section, the proposed conceptual framework and the research methodology are being presented. The results, conclusions, study limitations and future research are discussed in the sections 5, 6 and 7 respectively.
2. Literature review Various attempts have been made towards developing a widely accepted de? nition of IC, until most authors ?nally agreed on its basic parameters. Klein and Prusak (1994) contributed to the creation of a universal de? nition by de? ning IC as the intellectual material that can be formalised, captured and leveraged to produce a higher value asset. In the same vain, Edvinsson and Malone (1997) de? ned IC as the knowledge that can be converted into value. Stewart (1997) argued that intellectual resources such as knowledge, information and experience, are the tools for creating wealth and de? ned IC The impact of intellectual capital 133 JIC 12,1 134 as the new wealth of organizations.
Sullivan (2000, p. 17) de? ned IC as “knowledge that can be converted into pro? ts”. According to Edvinsson and Malone (1997) IC can be also de? ned as the gap that is observed between a ? rm’s book and market value. Also, Kok (2007) argued that a method for determining the intellectual (intangible) assets of a company is to compare market to book value. These arguments are based on the nature of IC. The intellectual assets of a company are intangible in nature and, thus, do not have a certain shape or an appropriate ? nancial value.
They are characterised as “hidden assets”, since it is dif?cult to identify their contribution to a ? rm and quantify them in a ? nancial statement (Edvinsson, 1997; Fincham and Roslender, 2003). The observed gap between market and book value that has been highlighted in the bibliography (Andrikopoulos, 2005; Chaminade and Roberts, 2003; Fincham and Roslender, 2003; Lev and Radhakrishnan, 2003; Lev and Zarowin, 1999; Lev, 2001; Tseng and Goo, 2005; Zerenler and Gozlu, 2008) can be, therefore, attributed to the IC assets that are not recognised in balance sheets (Chaharbaghi and Cripps, 2006; Brennan and Connell, 2000).
The role of IC in ?lling the gap between book and market value has brought even wider research attention towards the investigation of its nature (Chen et al. , 2005). Although there is a variety of IC de? nitions, mostly due to the fact that both knowledge-based and economic-based approaches exist (Burr and Girardi, 2002; Walsh et al. , 2008), a considerable number of scholars and practitioners identify three basic components of IC; human capital, structural capital and customer (relational) capital (Bontis, 1998; Edvinsson, 1997; Holton and Yamkovenko, 2008; Mavridis and Kyrmizoglou, 2005; Ruta, 2009;
Tayles et al., 2007; Yang and Lin, 2009; Zerenler and Gozlu, 2008; Wall, 2007; Walsh et al. , 2008). The above categorisation, early manifested itself into the IC literature, led to the development of a method of indirect IC measurement. More speci? cally, Bornemann et al. (1999) argued that IC can be measured by the accumulate value of three categories of indicators; human capital (knowledge, skills), structural capital (databases and organizational structure) and customer capital (supplier and customer relations). The usefulness and importance of IC indicators was, moreover, highlighted by Brennan and Connell (2000).
Moreover, Sullivan (2000) supported that the various dif? culties that are inherent to the direct measurement of IC can be resolved by using individual indicators. The same approach has been supported and utilised by various researchers (Andriessen, 2007; Andrikopoulos, 2005; Chaminade and Roberts, 2003; Montequin et al. , 2006; Tseng and Goo, 2005; Wall, 2007). Pulic (2000a, b) developed a convenient method of measuring IC. He argued that the market value of organizations is created by capital employed and IC, the latter consisting of human and structural capital.
The method Pulic (2000a, b) proposed aims to provide information about the value creation ef? ciency of both tangible (capital employed) and intangible (human and structural capital) assets of an organization. This method is named VAIC and is distinguishable because it indirectly measures IC via the measurement of capital employed ef? ciency (VACA), human capital ef? ciency (VAHU), and structural capital ef? ciency (STVA). The higher the VAIC, the better the utilisation of the value creation potential of a ? rm.
The VAIC approach is being adopted in the present study, following the methodological framework of Firer and Williams (2003) and Chen et al. (2005). Despite the inherent limitations of VAIC as a method of measuring IC (discussed in a following section of the paper), its simplicity, subjectivity, reliability and comparability make it an ideal measure for the context of the present study. More speci? cally, according to Andriessen (2004), the use of VAIC as an indicator of IC is justi? ed by the suf? cient availability of the ? nancial data that the model uses as inputs.
Additionally, according to Schneider (1998), the danger of losing track of the main objective of a study arises when the procedures to collect and process the appropriate data become exceedingly sophisticated. Taking that into consideration, the simplicity of VAIC offers good services to researchers and, furthermore, enables cross-sectional comparisons (Schneider, 1998). Firer and Williams (2003), moreover, support the use of VAIC, mentioning that other developed models of IC measurement are, mostly, customised to ? t the pro? le of a speci? c company and, therefore, lack generalisation opportunities and have limited comparability.
Finally, according to Firer and Williams (2003), VAIC is argued to be an appropriate IC measurement tool due to the fact that all data applied in its calculation are based on audited information, which is objective and veri? able. On the ? eld of empirical research, many studies have empirically utilised VAIC as a measure of IC. Firer and Williams (2003) utilised the VAIC approach to measure the relationship between IC and traditional measures of corporate performance. They used a sample of 75 South African public traded companies, but the empirical results failed to support any relationship between the three value added ef?
ciency components and the three dependent variables (pro? tability, productivity and market value). Their ? ndings revealed that South African companies depend mostly on their tangible resources, pay the least importance to structural capital, while on the other hand, the market seems to react negatively to ? rms that concentrate solely on the enhancement of human assets. Overall, the ? ndings of Firer and Williams (2003) suggest that physical capital in South Africa remains the most signi? cant underlying resource of corporate performance, despite efforts to increase the IC base of the country. Chen et al.
(2005) conducted an empirical investigation on the relationship between IC, market value and ? nancial performance. They used a large sample of Taiwanese listed companies and utilised Pulic’s (2000a, b) VAIC. Their study underlined the importance of IC in the enhancement of ? rm pro? tability and revenue growth. The empirical results proved that: . investors valuate higher companies with better IC ef? ciency; and . companies with better IC ef? ciency obtain a higher degree of pro? tability and revenue growth in the current and following years.
Chen et al. (2005) concluded that IC is indeed a signi?cant strategic asset, since it is positively related to the ? rm’s market value and ? nancial performance. The VAIC approach, developed by Pulic (2000a, b), has been, moreover, adopted in various other studies, mostly in those conducted in emerging and developing countries. Muhammad and Ismail (2009) tried to investigate the ef? ciency of IC and its performance in Malaysian ? nancial sectors, based on data from 18 companies for the year 2007. It was found that the banking sector was the one relying the most on IC, followed by companies of the insurance sector and the brokerage sector.
It was also found that IC has a positive relationship with company performance (measured by pro? tability and ROA), but, on the other hand, it was discovered that in Malaysian The impact of intellectual capital 135 JIC 12,1 136 ?nancial sectors, market value is created more by capital employed (physical and ? nancial) rather than IC. This last ? nding of Muhammad and Ismail (2009) was consistent with a previous study conducted in the same country over the period 2001 to 2003 (Goh, 2005), where it was found that Malaysian banks with satisfactory ? nancial performance (measured by traditional economic measures) had low IC coef?cients.
On another study conducted in the banking sector of Turkey, Samiloglu (2006) tried to determine whether a signi? cant relationship between VAIC and market to book value ratio really exists. The author used data from the ? nancial statements of banks listed in the Istanbul Stock Market over the years 1998 to 2001. The results demonstrated that there was no signi? cant relationship between the dependent variable (MV/BV) and the independent variables (VAIC and its three components).
Gan and Saleh (2008), moreover, examined the relationship between IC and corporate performance of technology-intensive ?rms listed on Bursa (Malaysia), by investigating whether value creation ef? ciency (measured by VAIC), can be explained by market valuation, pro? tability, and productivity.
Overall, the study of Gan and Saleh (2008) concluded that VAIC can explain pro? tability and productivity, but fails to explain market valuation. On a similar study in Taiwan, Shiu (2006) found a signi? cant positive correlation between VAIC, pro? tability and market valuation and a negative correlation with productivity. Tseng and Goo (2005), in an empirical study of Taiwanese manufacturers, found a positive relationship between IC and corporate value.
Tan et al. (2007) used the VAIC methodology to examine data from 150 listed companies on the Singapore Stock Exchange, and conclude that: . IC and company performance are positively related; . IC is correlated to future company performance; . the rate of growth of a company’s IC is positively related to the company’s performance; and . the contribution of IC to company performance differs by industry. Appuhami (2007) investigated the impact of the value creation ef? ciency on investors’ capital gains on shares. The author used data collected from listed companies in Thailand’s stock market and utilised the VAIC approach.
The empirical research found that ? rms’ IC has a signi? cant positive relationship with its investors’ capital gains on shares. In a VAIC study that was conducted in a traditional Western economy, Puntillo (2009) examined the relationship between value creation ef? ciency, ? rms’ market valuation and ? nancial performance, by using data drawn from 21 banks enlisted in the Milan Stock Exchange, Italy. Results failed to show any positive signi? cant association between the studied variables, except from the relation between capital employed ef? ciency (a component of VAIC) and different measures of ?rm’s performance.
Finally, in an exploratory study, Mohiuddin et al. (2006) used VAIC to measure the IC performance of 17 commercial banks in Bangladesh for the period 2002 to 2004. According to their ? ndings, all 17 banks of the sample had relatively higher human capital ef? ciency than other capital ef? ciencies. In one of the very few IC studies that have been conducted in Greece, Mavridis and Kyrmizoglou (2005) used data from the banking sector for the period 1996-1999 and concluded that there is a positive correlation between value added and physical capital, but especially between value added and human or IC.
Authors make a note implying that results may be over over-positive, due to the fact that the Greek banking sector was on a signi? cant upward trend for the period under investigation. 3. The conceptual framework The present study introduces a conceptual framework that expands on previews methodologies (Bontis 1998; Bontis et al. , 2000; Chen et al. , 2005; Firer and Williams, 2003; Mavridis, 2004; Pulic 2000a, b) and investigates the relationship between IC, market value and ? nancial performance. The hypotheses of the study are presented below. 3. 1 IC and market value.
According to the traditional accounting practices the book value of an organization is solely calculated from its ? nancial statements. The simplistic method of such a calculation includes subtracting liabilities from the ? rm’s total assets. As a result, conservative accounting practices failed to account one the most important intangible assets of every organization: IC (Sveiby, 2000, 2001).
The gradual introduction of the International Accounting Standards (IAS) in nearly every developed and developing country (except from the USA which is expected to implement the IAS in the next ?ve years) forced companies to calculate assets at their real market value, while giving full de? nition and credit to all intangibles (International Financial Reporting Standards, 2008). Despite that, the inability of most companies to comply with the IAS and the signi? cant cost of such an implementation, still deteriorate the recognition of the intangible assets of every organization ( Judge et al. , 2010). The result of such a short seeing is a growing divergence between the market and book value of organizations.
In other words, the market estimates the value of companies with high intangible assets (IC) to be signi?cant higher that the calculated book value (Chen et al. , 2005; Firer and Williams, 2003; Riahi-Belkaoui, 2003). Therefore, it is hypothesised that the greater the IC, the higher the ratio of market-to-book value: H1. Companies with greater IC have higher ratios of market-to-book value. The above hypothesis uses VAIC as an aggregate measure for corporate intellectual ability (IC). As stated earlier in the paper, VAIC includes three component measures: capital employed ef? ciency (VACA), human capital ef? ciency (VAHU) and structural capital ef? ciency (STVA).
Since different signi? cance may be put on each of the three components of VAIC, it would be interesting to examine the separate effect of each on market-to-book value ratio. Such an investigation would increase the explanatory power of the conceptual framework and give raise to interesting observations. Thus, it is hypothesised: H1a. Companies with greater capital employed ef? ciency have higher ratios of market-to-book value. H1b. Companies with greater human capital ef? ciency have higher ratios of market-to-book value. H1c.
Companies with greater structural capital ef?ciency have higher ratios of market-to-book value. The impact of intellectual capital 137 JIC 12,1 138 3. 2 IC and ? nancial performance The impact of IC on ? nancial performance has not been investigated thoroughly on an empirical level, either it has led researchers to sold and unanimous conclusions. On a theoretical level, distinguished authors argue that IC is the value driver of all companies (Stewart, 1997), that knowledge management is a core organizational issue (Nonaka and Takeuchi, 1995) and that organizational knowledge is at the crux of every sustainable competitive advantage (Bontis, 1999).
On the other hand, empirical evidence are inconclusive and far from achieving a solid scienti? c consensus. The study of Riahi-Belkaoui (2003) found a positive relationship between IC and ?nancial performance, while Bontis et al. (2000) concluded that, regardless of industry, the development of structural capital has a positive impact on business performance.
On the other hand Firer and Williams (2003) examined the relationship between IC and traditional measures of ? rm performance (ROA, ROE) and failed to ? nd any relationship, while Chen et al. (2005), using the same methodology, concluded that IC has an signi? cant impact on pro? tability. The present paper makes an attempt to enrich the IC literature, thus, hypothesising:
H2. Companies with greater IC have better ? nancial performance. H2a. Companies with greater capital employed ef? ciency have better ? nancial performance. H2b. Companies with greater human capital ef? ciency have better ? nancial performance. H2c. Companies with greater structural capital ef? ciency have better ? nancial performance. Figure 1 summarises all the above hypotheses, thus, presenting the proposed conceptual framework of the study.
Figure 1. The conceptual framework of the study 4. Research methodology 4. 1 Sample and data selection The ? nal sample of the present study consists of 96 Greek companies listed in the ASE. These companies belong to four economic sectors (according to of? cial sector classi? cation): Construction and Materials (20 companies), Industrial Goods and Services (23), Food and Beverage (19) and Personal and Household Goods (34 companies). The selected data cover a period of three years, from 2006 to 2008. All four sectors are knowledge based and have a signi? cant importance to the Greek economy.
The initial target of the study was to draw data from all companies listed in the Athens Stock Exchange (approximately 280 companies with constant participation in the ASE for the three-year examination period). However, the ? rst screening of data availability demonstrated that such an endeavour was too ambitious. The second data screening led in the exclusion of many companies, leaving the sample with only 119 companies with suf? cient available data. Finally, 23 more companies were excluded from the sample after the third and most detail data screening.
The high degree of excluded companies re?ects the poor level of reporting of Greek listed companies. More precisely, the majority of the excluded companies provided insuf? cient data in more that two variables. Overall, the ? nal sample (96 companies) represents the 34. 2 per cent of the total number of listed companies in the ASE for the year 2010. 4. 2 Variable de? nition 4. 2. 1 Independent variables. The present study includes four independent variables (Pulic 2000a, b): (1) VACA, indicator of value added ef? ciency of capital employed. (2) VAHU, indicator of value added ef? ciency of human capital. (3) STVA, indicator of value added ef? ciency of structural capital.
(4) VAIC, the composite sum of the three separate indicators. The ? rst step towards the calculation of the above variables is to calculate value added (VA). VA is calculated according to the methodology proposed by Riahi-Belkaoui (2003). Second, capital employed (CE), human capital (HU) and structural capital (SC) are being calculated: CE ? Total assets* 2 intangible assets HU ? Total investment on employees ? salary; wages; etc:? SC ? VA 2 HU: ( * In Greece, salaries are calculated in the pro? t and loss (P&L) statement, therefore, are already included in total assets. ) Finally, VAIC and its three components are being calculated: VACA ?
VA=CE The impact of intellectual capital 139 JIC 12,1 VAHU ? VA=HU STVA ? SC=VA 140 VAIC ? VACA ? VAHU ? STVA: The use of the above measurement methodology is argued to provide certain advantages (Bontis, 1999; Chen et al. 2005; Firer and Williams, 2003; Pulic and Bornemann, 1999; Roos et al. , 1997; Sullivan, 2000): . It is easy to calculate. . It is consistent. . It provides standardised measures, thus, allowing comparison between industries and countries. . Data are provided by ? nancial statements that are more reliable than questionnaires, since they are usually audited by professional public accountants. 4. 2.
2 Dependent variables. The present study includes two dependent variables: (1) Market-to-book value ratios. (2) Financial performance. The market-to-book value ratio is simply calculated by dividing the market value (MV) with the book value (BV) of common stocks: MV ? Number of shares ? Stock price at the end of the year: BV* ? Stockholders’ equity 2 Paid in capital of preferred stocks: ( *In all cases that goodwill was included in the book value of a company of the sample, the required subtraction was conducted. ) The ? nancial performance is measured with the use of three indicators: (1) Return on equity (ROE):
ROE ? Net Income=Shareholder’s Equity: ROE measures an organizations pro? tability by revealing how much pro? t a company generates with the money shareholders have invested. (2) Return on assets (ROA): ROA ? Net Income=Total Assets: ROA is an indicator of how pro? table a company is in relation to its total assets. It gives an idea as to how ef? cient the management uses assets to generate earnings. The impact of intellectual capital (3) Growth revenues (GR): AA A A GR ? Currentyear’ srevenues=Lastyear’ srevenues 2 1 ? 100%: GR is the most traditional measure that indicates the growth of an organization.
4. 3 Regression models In order to examine the hypotheses of the study, various regression models have been evaluated. Models 1 and 2 examine the relationship between VAIC and market-to-book value ratio, and VACA, VAHU and STVA and market-to-book value ratio: H 1 : M=B ? a0 ? a1 VAIC ? e ?1? H 1a; H 1b and H 1c : M=B ? a0 ? a1 VACA ? a2 VAHU ? a3 STVA ? e: ?2? Regression models 3a to 4c examine the relationship between VAIC and ? nancial performance (ROE, ROA, GR), and VACA, VAHU and STVA and ? nancial performance (ROE, ROA, GR): H 2 : ROE ? a0 ? a1 VAIC ? e ?3a? H 2 : ROA ? b0 ? b1 VAIC ?e ?3b? H 2 : GR ? c0 ? c1 VAIC ? e ?3c? H 2a;
H 2b and H 2c : ROE ? a0 ? a1 VACA ? a2 VAHU ? a3 STVA ? e ?4a? H 2a; H 2b and H 2c : ROA ? b0 ? b1 VACA ? b2 VAHU ? b3 STVA ? e ?4b? H 2a; H 2b and H 2c : GR ? c0 ? c1 VACA ? c2 VAHU ? c3 STVA ? e: ?4c? 5. Results 5. 1 Descriptive statistics and correlation analysis Table I presents the descriptive statistics for all study variables. The market-to-book value ratio (1. 694) indicates that 40. 96 per cent of the ? rms’ market value is not re? ected on ? nancial statements: A AA A Hidden Value ? 1:694 2 1:000? =1:694 *100 ? 40:96 per cent: 141.
JIC 12,1 142 This ? nding supports previews empirical research that has underlined the existence of an increasing gap between market and book value of organizations (Lev and Radhakrishnan, 2003; Lev and Zarowin, 1999; Lev, 2001). More speci? cally, Lev (2001) conducted a longitudinal research in the US market (1977-2001) and concluded that about 80 per cent of corporate market value is omitted from ? nancial statements, while this percentage seems to be on an upward trend.
The correlation analysis provides an initial preview of the results, concluding that market-to-book value is signi?cantly related only with one of the three components of VAIC; human capital ef? ciency. All other correlation indexes (M/B correlated with VAIC, VACA STVA) were not found to be statistically signi? cant (Table II). 5. 2 Hypotheses veri? cation Table III presents the results considering H1 (Model 1) and Table IV the results considering H1a-H1c (Model 2).
As seen in Table III, the explanatory power of Model 1 is minimal and, moreover, all statistical indexes fail to comply with the usual standards. Therefore, the empirical results fail to support H1. Moreover, results depicted on Table IV give only support to H1b, since the signi?cance indices for the other two independent variables are also inadequate (p . 0:05).
Variable Table II. Correlation analysis for selected study variables Standard deviation Minimum Maximum M/B VAIC VACA VAHU STVA ROE ROA GR 1. 694 4. 052 0. 069 3. 364 0. 619 1. 211 1. 123 8. 311 1. 862 2. 555 0. 042 2. 364 0. 341 3. 148 2. 333 37. 318 0. 123 2 15. 631 2 0. 092 2 16. 369 2 0. 837 2 15. 689 2 4. 361 2 36. 145 7. 365 25. 148 0. 236 24. 342 2. 496 9. 361 5. 314 269. 329 Variable Table I. Descriptive statistics for all study variables Mean M/B VAIC VACA VAHU STVA 1. 000 0. 136 0. 369 0. 269 * 0. 029 1. 000 0. 514 * 0. 789 * 2 0. 013 *.
1. 000 0. 369 * 2 0. 129 1. 000 20. 236 1. 000 M/B VAIC VACA VAHU STVA Note: *Correlation signi? cant at the 0. 01 level (two-tailed) Independent variable Table III. Regression results – Model 1: M/B and VAIC Coef? cient t-statistic Signi? cance Constant VAIC 2 1,971. 535 20. 021 20. 495 20. 164 0. 622 0. 870 Notes: Adjusted R 2 ? 0. 000; F-value ? 99. 36 ( p-value . 0. 05) The empirical investigation failed to support the hypothesis that investors place higher value on ? rms with greater IC (VAIC). Nevertheless, it seems that investors take only the human capital of a company into consideration when they estimate its real value.
Therefore, results clearly indicate that investors place different value on each of the three components of VAIC, since human capital ef? ciency is treated differently that the other two components (capital employed ef? ciency and structural capital ef? ciency).
Finally, it should be pointed out that the statistical analysis produced the same results, even when each of the four sectors was separately analysed. Table V presents the results considering H2 (Model 3) and Table VI the results considering H2a-H2c (Model 4). Results in Table V demonstrate that there is no signi?cant relationship between IC (measured with VAIC) and the three ? nancial performance measures (ROE, ROA, GR), since all coef? cients or model solutions are statistically insigni? cant. Therefore, H2 is not supported by the empirical data.
Moreover, results depicted in Table VI indicate that the only statistically signi? cant Independent variables Coef? cient t-statistic 23,457. 817 0. 003 0. 126 20. 022 2 0. 706 0. 025 0. 325 2 0. 165 0. 483 0. 369 0. 032 0. 645 143 Signi? cance Constant VACA VAHU STVA The impact of intellectual capital Notes: Adjusted R 2 ? 0. 114; F-value ? 63. 14 ( p-value . 0. 05).
Independent variable ROE Coef? cient t-statistic Constant VAIC Adjusted R 2 F-value 1,907. 369 0. 095 0. 095 2. 653 2,948 * 0. 743 Dependent variables ROA Coef? cient t-statistic 2,253. 304 0. 062 0. 004 3. 698 2. 423 * 0. 498 GR Coef? cient t-statistic 7,124. 459 0. 019 0. 000 34. 652 1. 005 0. 153 Note: *Signi? cant at the 0. 05 level Independent variables ROE Coef? cient t-statistic Constant VACA VAHU STVA Adjusted R 2 F-value 3,392. 369 0. 009 0. 432 0. 085 0. 189 4. 698 * Note: *Signi? cant at the 0. 05 level 4. 689 * 0. 077 3. 627 * 0. 726 Dependent variables ROA Coef? cient t-statistic 2,555. 276 0. 056 0. 054.
0. 041 0. 009 21. 448 2. 276 * 0. 439 0. 416 0. 322 Table IV. Regression results – Model 2: M/B and VAICs components Table V. Regression results – Model 3: ? nancial performance and VAIC GR Coef? cient t-statistic 6,881. 598 0. 021 20. 025 0. 022 0. 002 9. 367 0. 890 0. 161 2 0. 190 0. 171 Table VI. Regression results – Model 4: Financial Performance and VAICs components JIC 12,1 144 relationship is the one between human capital ef? ciency (VAHU) and ROE. All other investigated models are statistically insigni? cant. Therefore, H2b and H2c are not supported by the empirical data, while H2a is partially supported.
The above results did not dramatically changed, even after each of the four sectors included in the study was separately analysed. The results of the present study offer the bibliography another paper that fails to fully support the importance of IC (measured under the VAIC methodology). In general, the empirical studies that have used the VAIC approach in order to investigate the impact of IC on various business variables have concluded on contradictory results. For example, Firer and Williams (2003), in a study conducted on South Africa, failed to identify a relationship between.
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