An Empirical Study of the Impact of Intellectual Capital Performance on Business Performance

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1 Cited as: Yu, K.Y., Ng, H.T., Wong, W.K., Chu, S.K.W. & Chan, K.H. (2010). An empirical study of the impact of intellectual capital performance on business performance. The 7 th International Conference on Intellectual Capital, Knowledge Management & Organisational Learning, The Hong Kong Polytechnic University, Hong Kong. An Empirical Study of the Impact of Intellectual Capital Performance on Business Performance Ka Yin Yu, Hing Tai Ng, Wai Kwan Wong, Kai Wah Samuel Chu and Kin Hang Chan The University of Hong Kong, HKSAR yukayin1212@yahoo.com.hk tai0119@hotmail.com kwan_118@hotmail.com samchu@hkucc.hku.hk hkukin@hkucc.hku.hk Abstract: This study attempts to shed light on intellectual capital (IC) performance of Hong Kong companies and its possible associations with business performance. Audited accounting data were collected from the constituent companies of Hang Seng Index in Hong Kong Stock Exchange between in order to compute a set of IC efficiency indexes based on VAIC. Value Added Intellectual Coefficient (VAIC ) is a quantitative IC measurement method which was pioneered by Pulic (2000) of the Austrian IC Research Centre. Four accounting ratios: market-to-book value (MB), return on assets (ROA), asset turnover (ATO) and return on equity (ROE) were used as proxies for measuring business performance. VAIC and its associated indexes, and the accounting ratios of sample companies are submitted to regression analysis for the detection of their associations. No conclusive evidence was found to support the associations between VAIC and the four financial indicators. However, components of VAIC were found to be able to predict a substantial variance in business performance. For example, Capital Employed Efficiency (CEE) has been found to be the key factor in predicting business financial performance. In addition, Structural Capital Efficiency (SCE) has an effect on market valuation as measured by MB as well as profitability as measured by ROE. Interestingly, negative correlations were observed between Human Capital Efficiency (HCE) and the financial indicators which, perhaps, were due to the existence of a gap between the traditional accounting perspective and value creation perspective which is central to the VAIC methodology in measuring IC. It is believed that the empirical results of this research have provided some new insights to the stakeholders of Hong Kong companies in utilizing IC, particularly the noted impact of structural capital. While physical and financial assets, still, appears to be taking on an important role as the key resources in delivering business success, IC, or to be exact, structural capital may be silently making headway into the fabric of Hong Kong s economy as illustrated by its impact in delivering return on equity (ROE). Keywords: intellectual capital, VAIC, financial performance, value creation, Hang Seng Index, Hong Kong 1. Introduction Knowledge-based economy is a term which has been widely used to describe the situation in today s world. Knowledge-based resources are described as the main source in sustaining competitive advantage of a company (Ting & Lean, 2009), and knowledge production has been regarded as part of the value creation process (Pulic, 2008). This transformation created a new perspective in viewing company resources. In the knowledge based economy, Intellectual Capital (IC), which affects business I:\Sam-research\IC_KM\Publications\Conf-HK-IC 05-08\conference paper_ revised-v5.doc 6/22/2012 1

2 2 performance, is critical in value creation (Edvinsson, 1997; Sveiby, 1997; Lynn, 1998; Pulic, 1998). As a result of a higher recognition of IC, researchers are keen to assess its impact on the business performance in companies. In this research, the association between IC and business performance of the companies was investigated. Value Added Intellectual Coefficient (VAIC ) was adopted as a quantifiable measure to assess the efficiency of a company in utilizing its IC. Accounting-based indicators, i.e. market-to-book value (MB), return on assets (ROA), asset turnover (ATO) and return on equity (ROE), were used to measure business performance. The scope of this research is to analyze the recent IC performance of Hong Kong companies within the context of VAIC. Data was collected from the constituent companies of Hang Seng Index (HSI) listed in the Hong Kong Stock Exchange from 2005 to 2008 for conducting regression analysis and hypothesis testing. 1.1 Intellectual Capital (IC) Physical capital refers to the traditional inputs of land, labor and capital (Goh, 2005, p. 386). Although it has been regarded as critical to the company s operations, its ability in reflecting the changes and conditions of contemporary business is questionable (Mohiuddin et al., 2006). Conventional accounting indicators may not have adequately considered IC elements, resulting in an unexplained market premium (Edvinsson & Sullivan, 1996). A noticeable difference then exists between knowledge based value added and physical value added (Pulic, 2008). As explained by Stewart (1997), IC is the aggregation of knowledge within a company which generates competitive advantages. Abdul and Fauziah (2007) described IC as the aggregation of human knowledge, structural and relational resources. Ting and Lean (2009) viewed IC as the result or the intellectual property generated from the process of knowledge transformation. Zéghal and Maaloul (2010) defined IC as a value added for the company, and it is the aggregation of knowledge which is used in the business value creation process. To summarise, IC refers not only the sum of knowledge within an organization, but also the factor in creating value and competitive advantage. One of the common classifications of IC components would be human capital and structural capital (Edvinsson, 1997; Edvinsson & Malone, 1997; Bontis, 2004; Chu, Chan & Wu, 2011). Human capital is in the heads of employees, while structural capital is what is left in the organization when people go home in the evening (Roos & Roos, 1997, p. 415). The examples that Ting and Lean (2009, p. 590) used to identify human capital include innovation capacity, creativity, know-how and previous experience, teamwork capacity, employee flexibility, tolerance for ambiguity, motivation, satisfaction, learning capacity, loyalty, formal training and education. For structural capital, Bontis et al. (2000) gave examples such as databases, organizational charts, process manuals, strategies and routines. Properly managed IC has been regarded as the key driving factor for sustainable corporate success (Yalama & Coskun, 2007; Ting & Lean, 2009; Chu, et al., 2011). 1.2 Value Added Intellectual Coefficient (VAIC ) Value Added Intellectual Coefficient (VAIC ) is a method used to measure the value creation efficiency of a company by using its accounting based figures (Pulic, 2000). The concept was developed by Ante Pulic and it was officially presented in Companies with higher VAIC represent higher value creation in using the available resources, e.g. IC and physical capital. VAIC is considered as a universal indicator showing abilities of a company in value creation and representing a measure for business efficiency in a knowledge-based economy (Pulic, 1998, p. 9). This indicator has been widely applied to other research studies (see Table 1.1) as a means of measuring IC (Zéghal & Maaloul, 2010; Chan, 2009a; Yalama & Coskun, 2007; Shiu, 2006). The concept of value added is central to the formulation of VAIC. Value added per individual s contribution may be regarded as the purest measure to produce economic value in a knowledge-based company (Sveiby, 2001). Furthermore, we are seeing more emphasis being placed on the skills and knowledge of the employees rather than on the physical assets of a company (Muhammad, 2009). Employees expenses, according to Pulic (2000), should be seen as investments rather than costs, as the knowledge held by these employees are the primary source of value creation. This is one of the basic assumptions made in the computation of VAIC indexes. Furthermore, without physical capital, IC alone is incapable of value creation. Therefore, Physical Capital Efficiency is an important consideration in VAIC (Pulic, 1998; Chu, Chan & Wu, 2011). As suggested by Pulic (2004), this

3 3 monetary measuring system could be useful in providing objective information to stakeholders about company s real value and performance. In addition, it allows comparison and future predictability in respect of the companies IC performance (Chu, et al., 2011). Table 1.1 Examples of research studies adopting VAIC Author(s) Zéghal & Maaloul (2010) Chan (2009) Yalama & Coskun (2007) Shiu (2006) Purpose of study To analyze the role of value added as an indicator of IC and its impact on the firm's economic, financial and stock market performance of 300 UK companies To investigate if IC has an impact on the financial aspects of organizational performance and to identify whether IC components are the drivers for the leading financial indicators of listed companies in Hong Kong. To test the effect of IC on the profitability of quoted banks on the Istanbul Stock Exchange Market (ISE) in Turkey using VAIC and DEA. To investigate the co-relation of VAIC and corporate performance in technological firms in Taiwan. 2. Methodology Regression analysis was conducted to investigate the association between IC performance and business performance. VAIC and its components were set as the independent variables while the four financial performance indicators were set as the dependent variables. 2.1 VAIC By using accounting based figures, the calculation of VAIC involved five steps (Pulic, 2000; Chan, 2009a), which have already been illustrated in detail by other researchers. For simplicity, we will demonstrate the practical procedures of VAIC calculation. First of all, the Value Added (VA) of the company had to be extracted. VA = OP + EC + D + A where OP = operating profits; EC = total employee expenses, which was viewed as investment; D = depreciation; A = amortization. Secondly, the Human Capital Efficiency (HCE) and Structural Capital Efficiency (SCE) were calculated. HCE = VA / HC SCE = ( VA HC ) / VA where HC = human capital, measured by total employee expenses. Since IC can only be operable under the support by financial and physical capital, Capital Employed Efficiency (CEE) was added to the formula. CEE = VA / CE VAIC = HCE + SCE + CEE where CE = capital employed, which was the book value of tangible assets. Finally, VAIC, which acted as an independent variable affecting the traditional financial performance of companies, was obtained by summing up HCE, SCE and CEE.

4 4 2.2 Dependent Variables The four traditional financial indicators used as dependent variables are market-to-book value (MB), return on assets (ROA), asset turnover (ATO) and return on equity (ROE), which is referred to the three dimensions, i.e. market valuation, profitability and productivity used by other studies (Firer & Williams, 2003; Chan, 2009a). 2.3 Independent Variables VAIC and it components, HCE, SCE and CEE, were used separately as independent variables in the regression models. Firm size (FSIZE) and firm leverage (DEBT) were added to the models as two control variables (Firer & Williams, 2003; Chan, 2009a), which helped reduce chances that the dependent variables being affected by other unknown variables (Shuttleworth, 2008). The dependent and independent variables were calculated using the published financial data of the companies with the following formulas. Table 2.1 Formulas to obtain the dependent and independent variables Variable MB ROA ATO ROE Formula = Market Capitalization / Shareholders Equity = Operating Profit / Total Assets = Total Revenue / Total Assets = Net Income / Shareholders Equity FSIZE = log (Market Capitalization) DEBT = Total Debt / Total Assets 2.4 Research Hypotheses A total of 16 hypotheses were proposed to examine the association between VAIC and the traditional financial performance indicators of the sample companies Hypotheses testing the association between VAIC as an aggregate measure and the financial performance indicators H1a. VAIC is positively associated with market valuation as measured by market to book value. H1b. VAIC is positively associated with profitability as measured by return on assets. H1c. VAIC is positively associated with productivity as measured by asset turnover. H1d. VAIC is positively associated with return on equity as measured by return on equity Hypotheses testing the association between VAIC components and the financial performance indicators H2a. HCE is positively associated with market valuation as measured by market-to-book value. H2b. HCE is positively associated with profitability as measured by return on assets. H2c. HCE is positively associated with productivity as measured by asset turnover. H2d. HCE is positively associated with return on equity as measured by return on equity. H3a. SCE is positively associated with market valuation as measured by market-to-book value. H3b. SCE is positively associated with profitability as measured by return on assets. H3c. SCE is positively associated with productivity as measured by asset turnover. H3d. SCE is positively associated with return on equity as measured by return on equity. H4a. CEE is positively associated with market valuation as measured by market-to-book value. H4b. CEE is positively associated with profitability as measured by return on assets. H4c. CEE is positively associated with productivity as measured by asset turnover. H4d. CEE is positively associated with return on equity as measured by return on equity.

5 5 2.5 Regression Models Eight regression models were used to investigate the 16 hypotheses. The first four models will investigate the association between VAIC and the four dependent variables. The other four models used the three components of VAIC as the independent variables to investigate on the association between VAIC components and dependent variables. Table 2.2 illustrates the equations of the regression models. Table 2.2 Regression models Model Regression equation 1 MBi = β1vaic +β2fsize+β3debt 2 ROAi = β1vaic +β2fsize+β3debt 3 ATOi = β1vaic +β2fsize+β3debt 4 ROEi = β1vaic +β2fsize+β3debt 5 MBi = β1hce+β2sce+β3cee+β4fsize+β5debt 6 ROAi = β1hce+β2sce+β3cee+β4fsize+β5debt 7 ATOi = β1hce+β2sce+β3cee+β4fsize+β5debt 8 ROEi = β1hce+β2sce+β3cee+β4fsize+β5debt 2.6 Data Collection Published financial data from sample companies had been extracted for regression analysis. The constituent companies of the Hang Seng Index (HSI) in the Hong Kong Stock Exchange (HKSE) are selected to be the samples of this research. It is one of the stock market indexes in Hong Kong which indicates the overall market performance in the HKSE. According to the figures from the Index Review Results (Hang Seng Indexes, 2008), an average aggregate market capitalization of approximately 67.3% of the total market capitalization in Hong Kong between 2005 and 2008 was calculated. The constituent companies are selected because of their market representativeness and the practicality in data collection. 2.7 Data Processing Since the composition of HSI may vary year from year, this research viewed and analyzed the sampling data by company-year. Company data collected for 4-year period was regarded as 4 discrete cases. In total, there were 154 discrete cases collected. Three of the cases, which either obtained a negative book value or a negative VAIC, were considered as problematic and hence were removed from the analysis to avoid the results being affected by outliers. Table 2.3 shows the distribution by sector of the 151 discrete cases. Table 2.3 Sample distribution by sector and frequency Sectors Frequency/company-year % Commerce and industry Finance Properties Utilities Total 151* 100 Notes: * company-year after removing three sets of problematic data

6 6 3. Findings Tables 3.11 to 3.18 reveal the correlations between the dependent and independent variables by conducting the Pearson product-moment correlation analysis. The significance of correlation (p value), indicating the level of statistical significance of the relationship, is described as follows: *** indicates a very high significant level of p <.001 ** indicates a high significant level of p <.01 * indicates a significant level of p <.05 Statistical values such as standardized and coefficient of determinations (R-square) are used in the following discussion to pinpoint the capability in predicting the variables and the explanatory power of the models. 3.1 The Associations Between VAIC and the Financial Indicators In this section, results testing hypotheses H1a to H1d is analyzed to find out if any statistical association exists between VAIC and the four financial performance measurements. Table 3.11: Multiple regression results of Model 1: MBi=β1VAIC +β2fsize+β3debt VAIC Firm Size Debt R-square = Notes: H1a. VAIC is positively associated with market valuation as measured by Market to Book Value Table 3.12: Multiple regression results of Model 2: ROAi=β1VAIC +β2fsize+β3debt VAIC * Firm Size Debt ** R-square = Notes: H1b. VAIC is positively associated with profitability as measured by Return on Assets Table 3.13: Multiple regression results of Model 3: ATOi=β1VAIC +β2fsize+β3debt VAIC * Firm Size Debt R-square = Notes: H1c. VAIC is positively associated with productivity as measured by Asset Turnover

7 7 Table 3.14: Multiple regression results of Model 4: ROE i=β1vaic +β2fsize+β3debt VAIC Firm Size ** Debt R-square = Notes: H1d. VAIC is positively associated with profitability as measured by Return on Equity As noted in Tables 3.11 to 3.14, the coefficients of determination (R-square) of the above four models indicate limited explanatory power for the variances in the dependent variables. Even Model 2 (Table 3.12) which has the highest value of R-square, (12%), was unable to meet the threshold of Cohen s minimum standard for large effect size of 14 %. (Grissom & Kim, 2005). Statistically, VAIC obtains the lowest significance level on ROA and ATO as shown by the p value (for ROA, Model 2, p=0.020*; for ATO, Model 3, p=0.044*). However, after taking R-square into account, VAIC is inadequate to support, firstly, the association between VAIC and ROA, and secondly, the association between VAIC and ATO. Viewing R-square in a reverse manner, that is the coefficient of non-determination, there is a high amount of unexplained variance of the dependent variable by the independent variables in Model 2 (88%) and Model 3 (96%). In our opinion, the explanatory powers of Models 2 and 3 are too weak to suggest that the association exists. Hypotheses H1a, H1b, H1c and H1d are thus not substantiated. The limited association between VAIC and the conventional financial indicators may be explained by the dominance of traditional industrial concepts in the minds of many stakeholders. They may still place high emphasis on the quantity of production, revenues and profits when attempting to evaluate business successes, instead of on knowledge content and value creation efficiency, which is what VAIC, seeks to measure (Pulic, 2008). 3.2 The Associations Between The Components of VAIC and Financial Indicators This section shows the results of testing hypotheses on the association between VAIC components, HCE, SCE, CEE, and the four financial performance indicators. Table 3.15: Multiple regression results of Model 5: MBi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT HCE *** SCE ** CEE *** Firm Size Debt *** R-square = Notes: H2a. HCE; H3a. SCE; H4a. CEE is positively associated with market valuation as measured by Market-to-Book Value

8 8 Table 3.16: Multiple regression results of Model 6: ROAi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT HCE SCE * CEE *** Firm Size Debt R-square = Notes: H2b. HCE; H3b. SCE; H4b. CEE is positively associated with profitability as measured by Return on Assets Table 3.17: Multiple regression results of Model 7: ATOi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT HCE * SCE ** CEE *** Firm Size Debt R-square = Notes: H2c. HCE; H3c. SCE; H4c. CEE is positively associated with productivity as measured by Asset Turnover Table 3.18: Multiple regression results of Model 8: ROEi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT HCE * SCE *** CEE *** Firm Size *** Debt *** R-square = Notes: H2d. HCE; H3d. SCE; H4d. CEE is positively associated with profitability as measured by Return on Equity HCE, SCE and CEE, were found to perform better in predicting the financial indicators compared with VAIC as shown in Models 5 to 8, which were able to obtain more than 30% of explained variances as shown by the R-square. According to the results, HCE is found to be a negative and a very highly significant predictor for MB (Model 5; β=-0.303; p=0.000***). MB may be viewed as an indicator to show how investors value companies. It seems that investors long held belief of treating employee expenses as costs is very much main stream orientation. This is reflected in the results that with the higher the employee expenses, the lower the market valuation of the company. Sample companies productivity as measured by ATO is statistically significant with negative effect on HCE (Model 7; β=-0.159; p=0.031*), while having a very highly significant with positive effect on CEE

9 9 (Model 7; β=0.671; p=0.000***). This result appears to suggest that when enhancing productivity, local companies seek to employ physical and financial assets rather than human asset. In the traditional view on productivity, given the same amount of input, a greater number of employees may result in less average output, which translates to a decrease in productivity. We would argue that this type of assumption may need to be reexamined in a knowledge economy. In the value creation perspective, human capital can be measured as a pool of knowledge. This pool of knowledge can become IC if it is transformed to value creation action (Pulic, 2008) and can thus contribute to the enhancement of the company s productivity. The gap between traditional accounting perspective and the value creation perspective when assessing human capital is perhaps, the fundamental question that we need to explore, study, discuss and research on. Finally, the empirical results showed that apart from the ROA, HCE was also capable of predicting MB, ATO and ROE in different significance levels, however, in negative correlations. Hence, the hypotheses H2a, H2b, H2c and H2d were not substantiated. The statistical association between SCE and financial indicators is a very interesting finding of this study. The empirical results showed that SCE has an influence on MB and ROE that SCE is highly associated with MB (Model 5; β=0.272; p=0.002**). The implication from this finding is that investors seem to consider structural capital as important when making investment decisions. This result is understandable as it is further supported by the finding that SCE is statistically and very highly significant to ROE (Model 8; β=0.575; p=0.000***), because ROE acts as one of the important indicators for investors to measure the financial condition of business. The association between SCE and ROE is of great interest because it appears to imply that the sample companies were able to utilize their structural capital, i.e. strategies, proprietary computer systems, routines and procedures, to yield higher profits from the shareholders equity. Among the independent variables and control variables, SCE was found to explain the greatest amount of ROE. This indicates that SCE has taken on a more important role in income generation and hence, shareholders equity, for companies in Hong Kong when compared with other VAIC components. This is a result which is different from a prior and similar study when physical capital efficiency (CEE) was found to be a better predictor for ROE (Chan, 2009b). As SCE has a positive and significant effect on MB, ROA and ROE, hypotheses H3a, H3c and H3d were substantiated Overall, CEE was found to be the best predictor for the four financial measurements when examining their associations with the three VAIC components. The associations are very highly significant and these findings support the traditional accounting point of view that physical and financial assets are critical when evaluating a company s business performance. The results agree with Ting and Lean (2009) that capital employed has been importantly utilized in generating high value returns. It is also consistent with Zéghal and Maaloul s findings (2010) that physical and financial assets are important for stockholders and the business management. The hypotheses H4a, H4b, H4c and H4d were supported by the regression results. The control variables, i.e. firm size and firm leverage, were found to be in association with business performance. Firm size appears to be very highly significant with ROE (Model 8; p=0.000***), and firm leverage is a very highly significant with MB (Model 5; p=0.000***) and ROE (Model 8; p=0.000***). Among the sample companies, 21% are financial institutions. This may partially contribute to the result of positive associations with MB. Since monetary deposit is regarded as debt and the major source to generate income in financial institutions, investors may evaluate these companies, among other indicators, with their sizes of deposit base. Moreover, the positive correlation between firm leverage and ROE shows that high-gear companies tend to have higher profitability. 4. Conclusion and Further Studies The research results provide some interesting insights for IC practitioners and business stakeholders about the utilization of IC by businesses in Hong Kong. The associations found between SCE and financial indicators showed that one of the IC components: structural capital (SC) may be an important driver in business performance in Hong Kong companies as illustrated by the positive association between SC and profitability. Physical and financial capital may no longer be the only and the most important resources in determining business success for these companies. The better management of SC may be taking on new meaning and new importance for Hong Kong companies in enhancing their performance: measuring them may be the first thing that they need to be able to do! Indeed, further

10 10 in-depth studies are needed, for example, to investigate the relationship between SC and corporate profitability, or others. There are many questions that are well worth exploring: What is the major structural capital in local companies that can most affect the corporate profitability? ; Would it be technologies or routines and procedures? ; Are these companies consciously aware of the importance of structural capital towards their financial performance? ; How do local businesses cultivate their structural capital in order to ensure a higher return? ; Would there be any differences in structural capital utilization in different industries or sectors? ; Does company size matter? ; Is information technology a determining factor? ; Would knowledge management practices further improve Structural Capital usage? ; Does the situation of the higher level of usage of structural capital apply only to Hong Kong? ; How does Hong Kong compare with her neighboring countries?. It is believed that there remains a vast body of potential researches, and the findings would be of utmost importance and interest not only to scholars and IC practitioners, but also to the business management, investors and other stakeholders as well. A coordinated research effort in IC in Hong Kong may help answer at least some of these questions. References Abdul, L.S. and Fauziah, S. (2007). "Intellectual Capital Management in Malaysian Public Listed Companies", International Review of Business Research Paper, Vol 3, No. 1, pp Bontis, N. (2004). "National Intellectual Capital Index: A United Nations initiative for the Arab region", Journal of Intellectual Capital, Vol 5, No. 1, pp Bontis, N., Keow, W.C.C. and Richardson, S. (2000). "Intellectual capital and business performance in Malaysian industries", Journal of Intellectual Capital, Vol 1, No. 1, pp Chan, K.H. (2009a). "Impact of intellectual capital on organisational performance :An empirical study of companies in the Hang Seng Index (Part 1) ", The Learning Organization, Vol 16, No. 1, pp Chan, K.H. (2009b). "Impact of intellectual capital on organisational performance :An empirical study of companies in the Hang Seng Index (Part 2) ", The Learning Organization, Vol 16, No. 1, pp Chu, S.K.W., Chan, K.H. & Wu, W.W.Y. (2011). Charting Intellectual Capital performance of The Gateway to China. Journal of Intellectual Capital, Vol. 12 No. 2, pp Chu, S.K.W., Chan, K.H., Yu, K.Y., Ng, H.T. & Wong, W.K. (2011). An Empirical Study of the Impact of Intellectual Capital on Business Performance. Journal of Information & Knowledge Management, Vol 10, No. 1, pp Edvinsson, L. (1997). "Developing capital at Skandia", Long Range Planning, Vol 30, No. 3, pp Edvinsson, L. and Malone, M. (1997). Intellectual Capital: Realizing Your Company's True Value by Finding its Hidden Brainpower, Harper Collins, New York. Edvinsson, L. and Sullivan, P. (1996). "Developing a model for managing intellectual capital", European Management Journal, Vol 14, No. 4, pp Firer, S. and Williams, S. M. (2003). Intellectual capital and traditional measures of corporate performance, Journal of Intellectual Capital, Vol 4, No. 3, pp Goh, P.C. (2005). "Intellectual Capital Performance of the Commercial Banks in Malaysia", Journal of Intellectual Capital, Vol 6, No. 3, pp Grissom, R.J. and Kim, J.J. (2005). Effect sizes for research: A broad practical approach, Lawrence Erlbaum Associates, Mahwah, N.J. Hang Seng Indexes. (2008). Press Releases, [online],

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