For the purposes of this study, Intellectual Capital is defined as the sum of all intellectual material, knowledge, information, intellectual property, and experience that can be put in by a company to create wealth (Pulic). As such, this definition attempts to encapsulate all aspects of the term ‘intangible assets’, a fact that would be made further clear when the individual components of VAIC (Value Added Intellectual Coefficient) are analysed. Developed by Pulic in 1998, VAIC is a developing tool used as a performance measure for comparison of companies and also as a predictor of company performance. According to Pulic’s method, VAIC is modelled as the sum of the following three efficiency terms: Human Capital Efficiency (HCE) Structural Capital Efficiency (SCE) Capital Employed Efficiency (CEE) Each company’s own knowledge, skills, values, and solutions can be tangibalized into value in the market, which in turn affects the competitive advantage, and increases the productivity and market value (Pulic, 2002) These intangible assets together form the intellectual capital (Yalama and Coskun, 2007)
Intellectual capital is an “intellectual material, knowledge, information, intellectual property, and experience that can be put to create wealth”(Stewart, 1997). Leadbeater (1999) mentioned that only about 7 percent of Microsoft’s stock market Value was accounted by tangible assets, whereas, the remaining 93% of the company’s value was created by intangible assets. Kamath(2007) has analyzed the Intellectual and Physical capital value creating ability of the Indian banking sector by using VAIC for the 5-year period, and has then discussed the effect of intellectual and physical capital performance on value-based performance Kamath(2008) has studied the relationship between intellectual capital components and traditional performance measures, such as, protability, productivity, and market valuation between 1996 and 2006, in the drug and pharmaceutical industry in India.
Several methods have been developed to measure Intellectual capital, such as, Market Capitalization Approach, Direct Intellectual Capital Measurement Approach, Scorecard Approach, Economic Value Added Approach VAIC Approach
A 2 step process was followed in this project Calculation of the intellectual capital performances of the IT companies using VAIC Effects of VAIC and its components on the organizational performance were analyzed using multiple regression analyses
Prior to computing the three efficiencies, it is necessary to calculate the value addition capability of a company ith a given amount of financial and intellectual capital. Chang (2007) gives the formula for VA as follows:
The dependent variables in the regression analysis are the tradition measures of company competence. A list of these measures and their associated definitions is provided below: Market valuation – Market Valuation is the ratio of market capitalization to book value of common stocks”(Chan,2009) Protability – Protability is the “ratio of operating income-to-book value of total assets” (Chan, 2009) Productivity – Productivity is “the ratio of total revenue to book value of total assets” (Chan, 2009) Return on equity – Return on equity is “the ratio of net income to total shareholders’ equity” (Chan, 2009)
Firm leverage and Arm size were used as control variables in this project, to remove the effects they might produce on the dependent variables in the regression models Firm Leverage – It is calculated as the ratio of total debt to book value of total assets Firm Size – It is calculated as the natural logarithm of market capitalization, are designated as control variables in order to remove their effects on the dependent variables in the regression models. A composite view of the model is presented in the diagram: Data Collected (collated view presented in attached excel file) The following companies were chosen from the IT sector. The financial data from Capitaline database for the last 5 years for each of these companies was used to construct a panel data-set: Wipro HCL Mphasis Tech Mahindra Patni Satyam Infosys Polaris TCS Oracle
The linear OLS multiple regression was conducted on the software “R”. The dependent variables were Profitability, Productivity, Market Value and Return on Equity. The independent variables were VAIC, HCE, SCE, CEE with Firm Size and Firm Leverage as Control Variables. Two models were evaluated for each dependent variable, one in which the independent variables (apart from the control variables) were HCE, SCE and CEE and another in which the independent variable was VAIC. Below we present the results of the regression.
CEE is the only significant parameter while HCE, SCE, size and leverage have p values greater than 0.05 and hence insignificant. The coefficient of determination is 45% and CEE explains 34% of the variability in productivity. When we use VAIC as the independent variable, the percentage explanation is just 23%.
CEE, HCE and SCE are all significant in the regression of profitability. The model explains 91.1% of the variability in profitability with CEE explaining 61.4% and SCE explaining 14.2% of the variability. . When we use VAIC as the independent variable, the percentage explanation is just 80%.
Firm size is the only significant predictor of market value. The coefficient of determination is 38.8% of which firm size determines 26% of the variability. When using VAIC, the model has an explanation rate of 37.3%
The coefficient of determination for the regression of Return on Equity is 91.4%. CEE, HCE, SCE and firm leverage are all significant estimators. CEE explains 61.2% of the variability while HCE explains 5.5% and SCE explains 15.6% of Return on Equity. When using VAIC, the coefficient of determination is 75%.
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