Application of the Value Added Intellectual Coefficient to Measure Corporate Performance: Evidence from Technological Firms, The
This research applies a new accounting tool for measuring the ‘value creation’ efficiency of a company, the Value Added Intellectual Coefficient (VAIC TM) of Pulic (1998). It also examines its correlation with corporate performance, based on the 2003 annual report from 80 Taiwan listed technologies firms. After modifying the model, applications show that the index of VAIC had a significantly positive correlation with profitability (ROA) and market valuation (MB), and a negative correlation with productivity (ATO), three aspects of a firm’s performance. The findings suggest that technological industry in Taiwan is capable of transforming intangible assets such as intellectual capital to high value added products or services, as claimed by Pulic (2004). Tests of VAIC and measures of corporate performance suggest that there are certain represented the time lag relationships between the two.
In a knowledge economy, there is a difference between the modern approach of value creation and the traditional way of monitoring operations. This difference in business activities is due mainly to; the introduction of knowledge, an entirely different position of labor and changes in structural expenditures. In this respect, labour and capital are the primary factors in determining corporate wellbeing. (Bornemann 1999; Pulic 2000; Firer & Williams 2003; Mavridis 2004). Practically, three types of capital are found in a company: financial, physical and intelligent capital (Goh & Lim 2004), whose composition determines the production of low and high value added products or services. Since the traditional underlying factors of production have changed, there is a need to develop alternative economic theories about the information necessary for intelligent capital performance and perceptions of corporate performance.
Conventional accounting systems have been developed for manufacturing economies and for measuring the value of financial and physical assets, but with intangibles they have found it difficult to account for the rate of change. Except for accounting systems, there are several internal and external measures of intelligent capital. The Skandia Navigator was one of the first internal measures to calculate and visualize the value of intangible capital, which intelligent capital (IC) represents as the difference between market and book value (Leif 1997). Others are the human resource accounting method, the intangible assets monitoring method, and the balanced scorecard method. External measures include market-to-book value, Tobin’s Q and Real Option theory (Shaikh 2004).
Mainly because of the lack of a commonly accepted measuring system, an important empirical question remains: Do traditional measures of corporate performance effectively capture the new emerging intelligent-based measures of the same constructs? This empirical study applies a new accounting tool of VAICTM, or the Value Added Intellectual Coefficient, developed by Ante Pulic (1998) as his trade mark- and his colleagues at the Austrian IC Research Centre (Pulic 2000; Borhemann 1999) which is designed to help managers leverage their company’s potential. The key contribution of VAIC is to provide a standardized and consistent measure that can be used to conduct comparative analyses across various sectors locally and internationally.
This potential of VAIC is motivated by growing evidence in the literature, much of the research stemming from the work of Pulic ( 1998). Bornemann ( 1999) found a correlation between intelligent potential and economic performance. Williams (2001) discovered that a firm with a high level of VAIC it appears to reduce its ‘intelligent disclosures’ when performance reaches a threshold level for fear of competitive advantage being lost. Moreover, Firer and Williams (2004) found that the associations between the efficiency of value added (VA) and profitability, productivity and market valuation are generally limited and mixed. Overall, physical capital remains the most significant resource of corporate performance in this study from South Africa. In addition, research done by Mavridis (2004) confirmed the existence of significant performance differences among various groups of Japanese. A study by Pulic (2004) showed that in today’s conditions of value creation, quantity is not relevant. Literature about the use of VAIC in Taiwan is limited. The latest research by Wang and Cheung (2004) suggested an integrated theoretical model to investigate the impact of intelligent capital on business performance.
To investigate measures of corporate performance, this study mainly concentrates on Taiwan listed technological firms, as their special attributes are being intelligent-intensive and striving for innovative products or services to enhance the comparative advantages. Using the index of VAIC, it aims to examine its association with three measures of corporate performance, profitability, productivity and market valuation (Firer & Williams 2003). Before employing multiple regression analysis and to increase for the accuracy of the results, the research analyzes whether there is multicollinearity among the variables. Finally, it aims to find out whether VAIC and measures of corporate performance have time lag relationships.
The study has two major aims. The first is to introduce the VAIC method as a tool for assessing the efficiency of current business. The second is to modify the method of VAIC so that it can be used to assess the correlation of VAIC with measures of corporate performance.
The next section of this paper describes the data and methodology. Section 3 discusses the empirical results, while section 4 presents the conclusion.
2. Data and Methodology
In this study, data were collected from a sample set reported in the 2003 annual reports of 80 listed technological firms in Taiwan. The technology sector plays a crucial role in the economy of Taiwan, its innovation for products or service, and drive for competitive advantage mainly accounted for by intellectual capital. The data employed in this study were obtained from the Taiwan Economic Journal Database.
The VAIC Method
The method of Value Added Intellectual Coefficient (VAICTM) was first made public by Pulic (1998) and further developed by Manfred Boremann (1999). It gives a new insight to measures of value creation and monitors the value creation efficiency in companies using basic accounting figures. VAIC is designed to effectively monitor and evaluate the ‘efficiency’ in adding value (VA) to a firm’s total resources and each major resource component, focusing on value addition in an organization and not on cost control (Pulic 2000, Boremann 1999).
The VAIC approach is based on five assumptions.
Firstly, to find out the competence of a company in ‘creating’ or value added (VA) the difference between output and input should first be calculated.
OUT – IN = VA
where OUT (output) includes the overall income from all products and services sold on market, IN (input) contains all expenses for operating the company, exclusive of labour expenses, which is not regarded as a cost. VA (value added) results from how current business and related resources, capital employed, human and structural, are used or employed
Then, it is necessary to determine how much new value has been created by one unit of investment capital employed, with the second step being the calculation of the relation of value added and capital employed (including physical and financial capital)
VA/CA = VACA
where VACA is the Value Added Capital Coefficient.
The third step is to assess the relation between value added and human capital employed, to indicate how much value added has been created by one financial unit invested in employees.
VA/HC = VAHC
where VAHC is the Value Added Human Capital Coefficient.
In Pulic’s (1998) paper, structural capital (SC) is obtained when human capital (HC) is deducted from value added; with HC and SC being in reverse proportion. The fourth step is to find the relation between VA and SC, indicating the share of SC in created value.
SC/VA = STVA
where STVA is the Value Added Structural Capital Coefficient
The fifth step is to assess each resource that helps to create or produce VA.
VAIC(TM) = VACA + VAHC + STVA
where VAIC, the Value Added Intelligent Coefficient, indicates corporate value creation efficiency.
In a later research from Firer and William (2003), they define VAIC as a composite sum of three separate indicators:
(1) Capital employed efficiency (CEE): indicator of the VA efficiency of capital employed.
(2) Human capital efficiency (HCE): indicator of the VA efficiency of human capital.
(3) Structural capital efficiency (SCE): indicator of the VA efficiency of structural capital.
VAIC= CEE + HCE + SCE
In accordance with the VAIC equation of Firer and Williams (2003), the study uses the natural log of CEE so as to achieve equivalence in value with HCE and SCE and sets the minimum value of VAIC as zero. In practice, efficiency is not will not be given treated negative values, to make it possible to investigate the correlation between VAIC and measures of corporate performance.
As mentioned beforehand, the survey uses the VAIC method as modified by Firer and Williams (2003) and the measure of independent variables as follows:
VAICi = CEEi + HCEi + SCEi
where VAICi = VA intellectual coefficient for firm i;
CEEi = VAi / CEi; VA capital employed coefficient for firm i;
HCEi = VAi / HCl; human capital coefficient for firm i; and
SCEi = SCi /VAi; structural capital VA for firm i;
VAi = Ii (sum of interest expenses) + DPi (depreciation expenses) + Di (dividends) + Ti (corporate taxes) + Ri (profits retains for the year)
CEi = book value of the net assets for firm i;
HCi = total investment salary and wages for firm i;
SCi = VAi – HCi; structural capital for firm I;
To conduct the analysis, three dependent variables of ROA, ATO and MB were used as proxy measures respectively for profitability, productivity, and market valuation (Firer & Williams, 2003). Their definitions are:
(1) ROA: ratio of the net income divided by book value of total assets;
(2) ATO: ratio of the total revenue to total book value of assets;
(3) MB: ratio of the total market capitalization (share price times number of outstanding common shares) to book value of net assets.
This study uses correlation and linear multiple regression to analyze the data. The three control variables were, size of firm (Size), leverage and return on equity (ROE) (Firer and Williams, 2003).They were given by:
(1) Size of the firm (Size): natural log of total market capitalization.
(2) Leverage: total debt divided by book value of total assets.
(4) Return on Equity (ROE): ratio of the net income divided by book value of total shareholders’ equity.
The research framework of this research can be depicted as:
3. Empirical Results
Data was collected from the 2003 fiscal year annual reports of 80 Taiwan listed technological companies. The companies were limited to one sector so as to obtain a homogeneous sample. As indicated earlier, the research set the minimum value of VAIC as zero. According to the efficiency model, it is not practical to have negative values for this construct treat assign it will practically not be regarded as the value of negative. Due to special features of the Taiwanese technology sector, it is necessary to use treat the natural log of CEE in order to make it equivalent in value with other variables for comparison. Using this method for calculating VAIC, the descriptive statistics are as reported in Table 1. In Table 2 and Table 3, the results of testing for multicollinearity among the variables are given. The results of the linear multiple regression analyses used to measure the correlations between the variables are represented in Table 2-3. The results of the tests of the lag relationships of VAIC and the indicators of corporate performance are also given.
Table 1 presents the means, standard deviations, and minimum and maximum values of all the variables. The mean of VAIC is 1.2579 with a range from O to 3.0943, which suggests that the Taiwanese listed technology firms created $1.2579 for every $ employed. The mean of the measure of intelligent capital is 1.5004.
Linear Multiple Regression Results for each component of VAIC
The results for the linear multiple regression analysis of the correlations of CEE, HCE and SCE of VAIC with ROA, ATO and MB are reported in Table 2-1, 2-2 and 2-3. The ‘explanatory powers’ of the three regressions were 79.46%, 40.20% and 56.35% respectively. Using a cut-off value of VIF less than 5, no multicollinearity among the variables was found. Table 2-1 shows that CEE had a significantly positive correlation with ROA (p
Linear Multiple Regression Results for VAIC
The results of the linear multiple regression analysis of the correlations of VAIC with ROA, ATO and MB is reported in Table 3-1,3-2 and 3-3. The explanatory power of the three regression equations is 77.63%, 38.59% and 40.62% respectively. As VIF is less than 5, there is no multicollinearity within variables. Table 3-1 shows that VAIC has a significant positive is no multicollinearity among the variables. Table 3-1 shows that VAIC has a significant positive correlation with ROA (p
This research made a further investigation of the data from of 2003. This revealed that the one-year lagged data (the year of 2002) for VAIC had a significantly positive effect on MB and ROA. The two-year lagged data (the year of 2001) for VAIC had a significantly positive effect on MB, a positive effect on ROA and a negative effect on ATO.
In terms of the predicted hypotheses, the results from each component of VAIC, the correlation between the three resources bases and profitability, productivity and market valuation are mixed, a similar finding to Firer and Williams (2003). To make a further comparison, the explanatory power of 79.46% and the directional signs for CEE (+), HCE (+) and SCE (-) associated with profitability in this study were far ‘better’ than the explanatory power of 4.8% and the opposite directions found by Firer and William (2003). The study shows that CEE has a significantly positive effect on profitability, while HCE has a positive effect and SCE a negative effect. In our opinion, a major a contribution of this study is to be able to raise the explanatory power of the proposed model.
VAIC indicates efficiency in creating corporate value or the extent of corporate intellectual ability. In the light of the high degree of correspondence with MB and ROA, the results for VAIC demonstrate that increases in value creation efficiency influence profitability and market valuation. In addition to examining lag relationships, the results show that VAIC has a positive effect on MB and on ROA in the current year and the previous year. Although the negative correlation with ATO may be due to special features of the Taiwan technological firms, the fact that they seem to be able to ‘transform’ intangible assets such as intellectual capital into high value added products or services confirms the ideas of about value creation advanced by Pulic (2004).
In a new economic era, when knowledge-intensive companies tend to dominate as in the Taiwan technology sector, it is necessary to maximize the utilization of resources, in particular intelligent capital. However, few studies in Taiwan have examined issues about VAIC. This paper develops seeks to apply the new indicator of VAIC to the efficient creation of value among businesses in Taiwan. As indicted earlier, in this study we have ‘adjusted’ negative value of VAIC to be zero so that it can reflect differences in efficiency, in practice. For future research, it may be worthwhile concentrating on those technological firms in which VAIC has negative values.
Abeysekera, I., 2003. Intellectual Accounting Scorecard – Measuring and Reporting Intellectual Capital, Journal of American Academy of Business, 3(1/2): 422-427.
Bornemann, M., 1999. Potential of Value Systems According to the VAICTM Method, International Journal Technology Management, 18(5/6/7/8): 463-475.
Firer, S. and Williams S. M., 2003. Intellectual Capital and Traditional Measures of Corporate Performance, Journal of Intellectual Capital, 4(3): 348-360.
Goh P. C. and Lim K. P., 2004. Disclosing Intelligent Capital in Company Annual Reports: Evidence from Malaysia, Journal of Intellectual Capital, 5(3): 500-510.
Lwif E., 1997. Developing Intelligent Capital at Skandia, Long Range Planning, 30(3): 366
Marr, B., Gary, D. and Neely A., 2003. Why Do Firms Measure Their Intellectual Capital, Journal of Intellectual Capital, 4(4): 441-464.
Mavridis, D.G., 2004. The Intellectual Capital Performance of the Japanese Banking Sector, Journal of Intellectual Capital, 5(1): 92-115.
Pulie, A., 2000. VAICTM – an Accounting Tool for IC Management, International Journal Technology Management, 20(5/6/7/8): 702-714.
Pulic, A, 2004. Intelligent Capital – Does it Create or Destroy Value?, Measuring Business Excellence, 8(1): 62-68.
Shaikh J. M., 2004. Measuring and Reporting of Intellectual Capital Performance Analysis, Journal of American Academy of Business, 4(1/2): 439-448.
Wang, W. I., and Cheung C. F., 2004. The Impacts of Intellectual Capital on Performance: An Empirical Investigation of the Taiwan Semiconductor Industry, The International Journal of Accounting Studies, 39: 89-117 (in Chinese).
National Changhua University, Taiwan
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