Intellectual capital impact on financial performance: industry effects Molodchik Mariya, Abramova Olga, Nursubina Jana. National Research University Higher School of Economics, Perm, Russia.
[email protected], Abstract The role of intellectual capital is recognized on theoretical and practical level. This study examines whether intellectual capital produces any industry effects on short-term and long-term financial performance. The importance of intellectual capital for the whole investigated sample is revealed. The explanatory power of models is higher when we include the industry variable as predictor for both short-term and long-term indicators. The regressions based on subsamples for particular industries show the difference in intellectual capital significance and its configuration. Keywords: Intellectual capital, industry effects, financial performance. Introduction Intellectual capital is gaining recognition as a topic worthy of academic investigation and practical application (Edvinson, 1997). According to empirical findings, intellectual capital has positive impact on financial performance (Chen et al., 2005). Theoretically, the role of intellectual capital is supported by the resource-based and value-based approaches. The resourcebased view outlines the importance of firm-specific endowments and difficult-to-imitate resource in value creation (Roos et al., 2005). Consequently, it downplays the industry level analysis and focuses on inter-industry differences and the role of firm‟s intellectual capital. According to this concept, product reputation, team learning, a variety of first-mover advantages, causal ambiguity that limits effective imitation and other specific conditions allow the firms earning “dramatically different rates of return” (Rumelt, 2003). There is empirical evidence that business-unit effects strongly outweigh industry membership (Rumelt, 2003). Nevertheless, the study by Schmalensee points out that “industry effects accounted for 20 percent of the variance in business-unit returns and at least 75 percent of the variance in industry returns” (Schmalensee, 1985). In this paper we don‟t compare business-unit effects and industry effects, which dominate in the value creation process. This study investigates the industry effects in short-term and long-term period by transformation of intellectual capital in financial performance. The previous empirical investigation showed that different industries have different configuration of intellectual capital, and the impact on organizational performance also varies from industry to industry. The experiment extends the understanding of intellectual capital as a hard-to measure category and throws light upon industry effects - the factor that should be considered carefully when valuing the role of intangible assets in company's performance. Literature review Economic evidence shows that industry effects need to be taken into account for the purposes of an overall study of intellectual capital impact on corporate performance and benchmarking. Therefore, human capital can be more important in an industry requiring special employee skills and competencies. Structural capital plays the major role in the industries demanding extensive IT infrastructure or strong corporate culture. (Bramhandkar et al., 2007) Relational capital can be more critical in the sectors with a focus on close supplier and customer contacts or strong brands. The importance of a particular class of knowledge assets can vary widely by industry (Rothberg & Erickson, 2005), and this render the comparison of random firms impossible. Theoretical and empirical studies of intellectual capital impact on financial performance in different sectors depend on the fact whether the industry is traditionally considered to be capitalbased or a knowledge-based one. Consequently, the major part of works concerning the
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peculiarities of industry effects can be found in such sectors as chemistry, financial services, and telecommunications. As we have already mentioned, few works include industry-level analysis, while investigating intellectual capital impact on financial performance. Most of the researchers stop their studies at the level of the specific sector descriptive analysis and make conclusions about the industry in general on the basis of a single firm case study (Lopes and Rodrigues, 2007, Kayakutlu, 2007). Some works are confined to the trend analysis of intellectual capital components and their contribution to the company‟s value added (Muhammad et al., 2006). Only few researchers extend theoretical surveys by adding quantitative and regression analysis of the impact produced by intellectual capital components on the financial performance of companies (Chen et al., 2005). Frequently, such studies are based on the Value Added Intellectual Coefficient (VAIC) model, developed by Pulic in 1997, and are conducted in the most knowledge-based and R&D-intensive industries. However, almost none of them include the industry variable as a predictor of both short-term and long-term indicators in the model that can make the analysis of intellectual capital impact on financial results more precise. The reason of such a small amount of empirical works can be found in poor disclosure of information in annual reports, and, consequently, the impossibility to make up a sufficient sample group. To some extent, it is easier to study the correlation of intellectual capital components and profitability for public listed companies in specific industries, as they usually disclose non-financial indicators, as well as financial ones. Finding this kind of information for unlisted companies is a very hard process, and, consequently, researchers are challenged in conducting an overall industry level analysis. The results of previous studies conducted in different sectors are presented in the table below: Table 1 Impact of intellectual capital on financial performance in different sectors Authors Industry Methodology Results Kamath
Bramhand kar et al. Muhamm ad et al. Goh Ting and Lean WICI Europe, 2010
Chemistry
Empirical study (VAIC model, regression analysis)
Chemistry
Quantitative analysis
Financial sector Financial sector
Empirical study (VAIC model) Empirical study (VAIC model)
Financial sector
Empirical study (VAIC model)
Telecommu nication sector
Case study
Lopes and Rodrigues
Transport
Case study
Geppert et al.
Transport
Quantitative analysis
Kayakutlu
Petrochemi cal
Case study
Human capital was the one which was seen to have the major impact on the profitability and productivity of the firms over the period of study. Positive influence of intellectual capital on stock price, returns and other organizational performance indicators. Structural capital produced highest contribution to the efficiency level of VAIC. More than 80 percent of value created in all domestic banks was attributed to human capital. Human and physical capital had significant positive effect on profitability, while structural capital had negative effect. Structural capital accounted for 70% of the analysis model and showed high explanatory power. The study revealed such components of intellectual capital as: preventive maintenance programs, specific learning programs, strategic alliances, brands, frequent passenger programs, R&D investments, software and databases. The dominant components of intellectual capital in the sector are R&D and ICT. Relational capital, represented by R&D infrastructure, construction of knowledge centres, alliances with academic institutions played a significant role in the successful performance of the firms.
Electronic copy available at: http://ssrn.com/abstract=2001472
Research hypotheses The objective of the study is to examine the question: “Does industry matter by the transformation of intellectual capital in financial performance?” This experiment rests upon the synthesis of resource-based and value-based approaches. Financial performance of the company was measured through economic value added (EVA), earnings before interests and taxes (EBIT) and future growth value (FGV). The reason for choosing several indicators is the fact that according to M. Miller and F. Modigliani, arguably the fathers of corporate finance, firm value is divided into (Burgman and Roos, 2004): 1. The present value of the uniform perpetual earnings on assets currently held, and 2. The present value of the opportunities the firm offers for making additional investments in real assets that will yield more than the “normal” market rate of return. None of contemporary financial indicators can be a universal instrument giving opportunity to analyze current activity and future growth of a company simultaneously. EVA and EBIT are short-term indicators as they provide information about company‟s results over a particular period of time and thus represent the first component of firm value. On the contrary, FGV is a long-term orientated characteristic that evaluates expectations about the future and comprises the second summand in the Miller–Modigliani formula. Intellectual capital is defined within the bounds of resource-based theory and is split into three components: human (HС), structural (SC) and relational (RC) capital (Roos et al., 2005). Figure 1 presents the theoretical framework for developing research hypotheses of this study. As mentioned above only few of the previous researches includes the industry variable as a predictor, we imply the significance of specific industrial characteristics for the transformation of intellectual capital in company‟s financial performance.
I N D U S T R Y
HC
SC
Resources
Performance Value creation process
EVA, EBIT or FGV
RC
Figure 1. Framework of the study of industry effects in the context of intellectual capital impact on financial performance Therefore, we expect that economic environment and main activity of the company make managers emphasize a particular component of intellectual capital which is more likely to become the competitive advantage giving a chance to enhance the performance. Proposed hypotheses of the experiment are as follows.
Hypothesis 1. There are positive industry effects by the transformation of intellectual capital in company’s financial performance in the long-term and short-term period. Hypothesis 2. Intellectual capital configuration varies with industry. Regression models The study uses the multiple linear regression models to identify whether belonging to an industry explains at least partially the difference in financial results through intellectual capital configuration. Our core econometric specification for the first hypothesis is as follows: Fin_Perf = α + (β1, ..., βn) HC + (δ1, ..., δn) SC + (γ1, ..., γn) RC + (λ1, ...,λn) Dummy + ε, where Fin_Perf is an indicator of company‟s performance (EBIT, EVA, FGV as independent variables); HC is a vector of variables responsible for human capital component; SC is a vector of variables responsible for structural capital component; RC is a vector of variables responsible for relational capital component; Dummy is a vector of dummy variables of industry appurtenance. For the second hypothesis (for each industry) it can be formulated as follows: Fin_Perf = α + (β1, ..., βn) HC + (δ1, ..., δn) SC + (γ 1, ..., γ n) RC + ε, where: Fin_Perf is an indicator of company‟s performance (EBIT, EVA, FGV as independent variables); HC is a vector of variables responsible for human capital component; SC is a vector of variables responsible for structural capital component; RC is a vector of variables responsible for relational capital component. Variable definition Dependent variables in our study are EVA, EBIT and FGV. 1. Economic value added (EVA). EVA is measured by formula suggested by the developer from Stern Stewart & Co:
EVAi NOPATi ICi 1 WACC , where: NOPAT is the net operating profit after taxation; IC is capital invested; WACC is the weighted average cost of capital; i is the number of period (year). 2.
Earnings before interest and taxes (EBIT) is the indicator of the company's profitability, calculated as revenue minus expenses, excluding tax and interest.
EBIT = Revenue - Operating Expenses. 3.
Future growth value (FGV) is calculated as: FGVi
EVAi ICi 1 MVi WACC ,
where: MVA is market value added of the company; FGV is future growth value; IC is capital invested; WACC is the weighted average cost of capital; i is number of period (year). The independent variables include the indicators of intellectual capital. As intellectual capital cannot be measured directly on the basis of financial reports, information about the company was grouped into three categories: (i) human capital linked to employees, (ii) relational capital linked to customers, and (iii) structural capital linked to processes, as they are shown in the Table 2. Table 2 Variables linked to the intellectual capital categories Intellectual capital category Available indicator Board of directors qualification Human capital (НС) Cost of employees Intangible assets Patents, licenses, trademarks Structural capital (SС) Enterprise Resource Planning System implementation Corporate strategy implementation Commercial expenses share Well-known brand Relational capital (RС) Citation in the Internet Site quality Data The data used for the study are drawn from a panel containing 351 public European companies from different countries such as Germany, Serbia, Great Britain, Finland, Denmark, Italy and Spain. The sample enterprises of the following industries: chemistry, petrochemistry, telecommunications, wholesale and retail trade, transport, financial services and mechanical engineering. Belonging to an industry was assigned according to the main activity of a firm. Financial reports are taken from the database Bureau Van Dijk (Amadeus) over a five year term from 2005 to 2009. Empirical results Table 3 presents the results of testing three regression models against dependent variables EVA, FGV and EBIT on the whole sample.
Table 3. Regression results of firm value model: dependent variables – EVA, EBIT, FGV. Dependent variable → Independent variables↓ (Constant) Board of directors qualification Cost of employees Patents, licenses, Trademarks ERP Corporate strategy Intangible assets Citations high Site quality high Well-known brand Commercial expenses share Ind_Chem Ind_Trade Ind_Machinery Ind_Finance Ind_Transport R adj. p (F-value) number of valid observations
EVA Beta coef. Prob. Stand. 0,008 0,015 0,068** 0,003 -0,089** -0,027 0,310
EBIT Beta coef. Prob. Stand. 0,009 0,000 0,095*** 0,074 -0,039* -0,005 0,826
-0,041 0,006 0,059** -0,001 -0,017 0,092** 0,596*** 0,107** 0,094 0,070 0,121* 0,076
-0,035* 0,003 0,296*** 0,065** -0,004 0,077*** 0,572*** 0,079** 0,055 0,054 0,094** 0,066*
0,132 0,840 0,050 0,957 0,537 0,001 0,000 0,024 0,164 0,249 0,051 0,133
0,39 0,000 1004
0,089 0,883 0,000 0,002 0,83833 0,000 0,000 0,031 0,282 0,249 0,050 0,090
0,62 0,000 1083
FGV Beta coef. Prob. Stand. 0,491 0,000 0,110*** -0,022 0,410 0,089 0,042* -0,025 -0,031 0,413*** 0,124*** 0,012 0,074** 0,349*** -0,015 0,010 0,048 0,013 0,006
0,329 0,266 0,000 0,000 0,652 0,004 0,000 0,733 0,873 0,390 0,810 0,885
0,519 0,000 909
Notes: * Significant at p
