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Testing the relationship between intellectual capital and a company’s performance: Evidence from South Africa S Firer Department of Accounting and Finance University of Witwatersrand

L Stainbank Department of Accounting and Finance University of Natal Durban

Abstract The aim of this study was to investigate whether the performance of a company’s intellectual capital can explain organisational performance. The dimensions of a company’s performance are (1) profitability, (2) productivity and (3) market valuation. Data were obtained from a sample of 65 companies that are listed on the JSE Securities Exchange (high knowledge-base sectors). Findings from the empirical analysis indicate that the relationships between the performance of a company’s intellectual capital and (1) profitability, (2) productivity and (3) market valuation are informative but varied. The empirical findings suggest that the performance of a company’s intellectual capital can explain profitability and productivity, but not market valuation. Key words Intellectual capital Human capital Structural capital VAIC

1 Introduction “Research in intellectual capital involves the quest for understanding the roots of a company’s value, the measurement of the hidden dynamic factors that underlie the visible company” (Edvinsson and Malone 1997:11). This study aimed to explore whether intellectual capital – a primary root of a company’s value – is a primary business resource that can explain a company’s performance. Meditari Accountancy Research Vol. 11 2003 : 25–44

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Testing the relationship between intellectual capital and a company’s performance

The problem that confronts businesses, users of accounting information, standard setters and regulators is how to best understand and communicate the difference between the value of a company (usually expressed as market capitalisation) and the accounting book value of that company. It is possible to simply attribute the entire difference to some ill-defined notion of intangibles. The rise of the new economy is principally driven by information and knowledge and it is attributed to the increased prominence of intellectual capital. Intellectual capital appears to be a key construct in explaining this difference. Since returning from virtual obscurity after the demise of apartheid in 1994, South Africa has endured eight years of aggressive transition across nearly all aspects of its social, political and economic infrastructure. Although South Africa has been recognised historically for its underlying wealth of natural resources, recent efforts in the country have sought to develop the country’s intellectual capabilities and productivity. To establish South Africa’s progress in respect of its transition, the primary objective of this research study was to investigate empirically whether a company’s intellectual capital can explain its organisational performance. With the above discussion in mind, the following research question was formulated: l Does intellectual capital have significant explanatory power in determining a company’s performance? This study contributes to the literature on the topic by focusing on South Africa rather than on a developed Western economy as investigated in related research studies. The understanding and development of concepts of intellectual capital in emerging economies are still very much within their infancy. With global prosperity and balance being increasingly dependent on emerging economies, there is a need to establish a body of evidence on the development of intellectual capital in various socio-political and economic settings. In addition, since emerging from apartheid, South Africa has been a country in transition. Part of the transition involves efforts to alter the country’s economic base from a traditional reliance on natural resources to a base that encompasses intellectual capital. The analysis presented in this paper is based on a sample of 65 publicly listed South African companies in the high knowledge-base business sectors, comprising business service; chemical and pharmaceutical products; communications; electronic and electrical products; finance; insurance; real estate; and health and social services. The companies in the high knowledge-base business sectors derive their value exclusively from the efforts of people (human capital) and the collective routine systems, processes and information within the organisation (structural capital). The remainder of this paper is arranged as follows. The next section contains a brief summary of the relevant literature. It is followed by the development of the hypotheses that underlies this study. The research method, empirical results, recommendations, ideas for future research and conclusions are presented thereafter. 26

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2 Review of relevant literature Huselid (1995) evaluated the links that exist between systems of highperformance work practices and a company’s performance. The study concluded that these practices have a significant impact on a company’s performance. Youndt et al (1996) examined the relationship between human resource management, manufacturing strategy and a company’s performance. The study concluded that human resource systems are directly related to a company’s performance. Miller et al (1999) examined manager’s perceptions of the usefulness and potential use of intellectual capital. The study concluded that managers place great emphasis on intellectual capital regardless of the type of industry. Van Buren (1999) examined the relationship between a core set of intellectual capital indicators and a company’s performance. The study – the first to link intellectual capital to a company’s performance – concluded that intellectual capital is associated with a company’s performance. Low (2000) identified the importance of non-financial intangibles and examined their role in a company’s performance. The findings suggest that improvements in critical intangible resources result in increased market value. Bontis et al (2000) investigated three elements of intellectual capital, namely the human, structural and customer elements, as well as their interrelationships. The main conclusions that could be drawn from the study are that human and customer capital are significant factors in the way in which businesses are run and that structural capital has a positive influence on business performance. Reed (2000) tested the association between intellectual capital and companies’ performance in the banking industry. The results suggest that intellectual capital is a strong predictor of a company’s performance. Riahi-Belkaoui (2003) examined the relationship between intellectual capital and the performance of multi-national companies in the United States of America. The results of the study support the hypothesis that intellectual capital is positively associated with financial performance. The review of relevant literature on empirical studies clearly indicates the usefulness of intellectual capital and that there is a need to undertake an empirical investigation into intellectual capital within the context of the South African economy.

3 Definition of constructs and the rationale for the hypotheses In the resource-based theory of the company, a company is perceived to be a collection of physical and intangible assets and capabilities. Advocates of the resource-based theory suggest that a company’s performance is a function of the effective and efficient use of its respective tangible and intangible assets. Furthermore, value added (also called wealth creation), rather than the mere financial returns to a company’s owners, is considered to be the appropriate means for conceptualising a company’s performance. While illustrating the shortfalls Meditari Accountancy Research Vol. 11 2003 : 25–44

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Testing the relationship between intellectual capital and a company’s performance

of traditional financial measures, Sveiby (2000) suggests that value added epitomises an effective measure of an economy’s productive ability in the new knowledge economy. The question regarding whose income accountants strive to measure has mainly been ignored (Mitchell Williams 2001). A commonly accepted view is that income is the reward that is due to shareholders from their investment (Morley 1979). A significant factor that underlies the acceptance of this view is the dominance of “contractual theories of the company” within the accounting discipline (Mitchell Williams 2001). However, as dissatisfaction with the traditional model of the company has grown, alternative theories of the company have emerged. This has also led to various perceptions of the income that accountants attempt to measure. The enterprise theory of the company is one alternative theoretical perspective that provides an alternative notion of income (Van Staden 1998). Suojanen (1954) states that the an enterprise is perceived as being a decisionmaking centre for the people who are the participants, however fleeting or intimate their contacts with the organisation may be. Participants or stakeholders include shareholders, employees, customers, creditors and the government. In terms of enterprise theory, income is the reward that stakeholders get for their participation in the company (Morley 1979). This alternative interpretation of income is termed value added and is specifically defined as the wealth created or distributed by the company through the utilisation of its essential productive resources. In the opinion of the authors, which is supported by the statements of other researchers (Edvinsson and Malone 1997; Stewart 1997; Pulic 1998, Pulic 1999; Sveiby 2000), traditional measures of a company’s performance, which are based on conventional accounting principles of determining income, may be unsuitable in the new economic world in which competitive advantage is driven by intellectual capital. The use of traditional measures may lead investors and other relevant stakeholders to make inappropriate decisions when allocating scarce resources. These views can be expressed in the following questions: Assuming that knowledge is the key to future success, but is not adequately reflected in the traditional financial measures of accounting, and that financial measures are the main drivers of top management’s decision making, what measuring system fulfils the requirements of the new economy and the needs of modern companies? Given that traditional measures continue to dominate, it is important to determine the extent to which such measures may intrinsically capture the contribution of intellectual capital resources such as human resources; customer reputation; and research and development. The answer to this question is of particular importance in emerging economies that have borrowed long-held financial models from developed economies, but are striving to strengthen their intellectual capital base in an effort to increase economic development. The present study explores this issue empirically by analysing the relationship between a relevant measure of intellectual capital and three commonly used 28

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measures of various traditional subconstructs of a company’s performance, namely (1) productivity, (2) profitability and (3) market evaluation.I Although the focus of this research is on the relationship between intellectual capital and a company’s performance, many other factors (at the organisational level) contribute to a company’s performance. A company’s performance is not a direct result of intellectual capital, and in the theoretical model the latter should therefore be differentiated from other factors that contribute to a company’s performance. Given the importance of intellectual capital in creating and sustaining an organisation’s competitive advantage and capabilities, which will in turn influence its performance, the following three hypotheses are stated: Within a given industry, and controlling for the differences in factors at the organisational level, the greater the value (performance) of a company’s intellectual capital, l The greater the company’s productivity; l The greater the company’s profitability; and l The greater the market’s valuation of the company relative to the value of its financial and physical assets. These hypothesised relationships will be estimated as follows: Equation 1

(

TM

)+ β 2 (PC )+ β3 (OS ) + β 4 (DER)+ β5 (ATO ) + β 6 (IT )+ ε

(

TM

)+ β 2 (PC ) + β3 (OS )+ β 4 (DER) + β5 (ATO )+ β 6 (IT ) + ε

ATO = α + β1 VAIC

Equation 2

ROA = α + β1 VAIC

Equation 3 MB = α + β1 VAICTM  + β 2 (PC) + β3(OS) + β 4 (DER) + β5 (ATO) + β6 (IT) + β7 (ROA) + ε Whe 



re: VAIC

Intellectual capital performance as measured by the Value Added Intellectual Coefficient™

PC

Physical capital intensity as measured by fixed assets divided by total assets

OS

Company size as measured by the market capitalisation of the company

continued ________________________

I

Productivity (or efficiency) described with which inputs are converted to outputs. Conversely, profitability described corporate performance as the degree to which a firm’s revenues exceed costs. Finally, market evaluation concentrates on the degree to which a firm’s market value exceeds its book value. This last dimension is related to firm performance because if the firm was not operating well (not performing), then its market value would probably be limited to the net book value of its assets.

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Testing the relationship between intellectual capital and a company’s performance Risk

The risk profile of the company as measured by the debt-equity ratio

IT

Industry type as measured with the use of dummy variables

ROA

Company profitability as measured by the company’s return on assets

ATO

Company productivity as measured by the asset-turnover ratio

MB

Market valuation of the company as measured by the ratio of market capitalisation to book value of net assets

TM For each hypothesis: β1 (VAIC ) > 0

H 0 is that β1 (VAIC TM ) ≤ 0 TM H The alternative hypothesis 1 is that β1 (VAIC ) > 0 (i.e. a right-tailed test that TM there is a positive relationship between β1 (VAIC ) and company performance) The null hypothesis

4 Research method The hypotheses in this study propose functional relationships among multiple constructs. Consequently, correlation and multiple regression analyses were used to test the hypotheses that were derived from the research question. Any transformation that was required to meet the assumptions of normality were made after the data had been examined. Any evidence of multicollinearity among independent variables are reported with the statistical results. Statistical significance was assessed at the ρ =0.05 level.

Measure of dependent variables For the purpose of conducting the relevant analysis in the present study, three dependent variables were taken into account, namely profitability, productivity and market valuation (henceforth denoted as (1) ROA, (2) ATO and (3) MB respectively). The relevant literature documents various accounting and marketbased measures that may be utilised as a proxy measure that is designed to capture the respective properties of the three dependent variables. At present there is no specific theoretical perspective or empirical evidence that supports any specific proxy measure above another. For the purposes of the present study it was therefore decided to use proxy measures that are deemed to have been used widely in related research studies. Consequently, the proxy measures for each dependent variable were defined as follows: 1 ROA: The ratio of net income (less preference dividends) divided by the book value of total assets as reported in the company’s annual report for 2001. 2 ATO: The ratio of total revenue to the total book value of assets as reported in the company’s annual report for 2001. 30

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3 MB: The ratio of total market capitalisation (share price multiplied by the number of outstanding common shares) to the book value of net assets as reported in the company’s annual report for 2001.

Measure of independent variables The Value Added Intellectual Coefficient™ (VAIC™) (Pulic 1998) forms the underlying basis of measurement for the major explanatory independent variable (performance of intellectual capital) in the present study. VAIC™ is an analytical procedure that is designed to enable management, shareholders and other relevant stakeholders to effectively monitor and evaluate the efficiency of value added (VA) by a company’s total resources and each major resource component. Formally, VAIC™ is a composite sum of three separate indicators: (1) Capital Employed Efficiency (CEE) – an indicator of VA efficiency of capital employed; (2) Human Capital Efficiency (HCE) – an indicator of VA efficiency of human capital; and (3) Structural Capital Efficiency (SCE) – an indicator of VA efficiency of structural capital. The following equation formalises the relationship algebraically: VAIC™i = CEEi + HCEi + SCEi Where: VAIC™i = VA intellectual coefficient for company i; CEEi = VAi/CEi; VA capital employed coefficient for company i; HCEi = VAi/HCi; human capital coefficient for company i; and SCEi = SCi / VAi; structural capital VA for company i; VAi = Ii + DPi + Di + Ti + Mi + Ri;II VA for company i computed as the sum of interest expenses (Ii); depreciation expenses (DPi); dividends (Di); corporate taxes (Ti); equity of minority shareholders in the net income of subsidiaries (Mi); profits retained for the year (Ri); CEi = book value of the net assets for company i; HCi = total investment in salaries and wages for company i; SCi = VAi – HCi; structural capital for company i; Several key reasons support the use of VAIC™. ________________________

II

Prior research has defined VA by the following algebraic equation: Rev – B + Inv = W + I + DP + D + T + M + R [Equation (1a)] or S – B + Inv – DP = W + I + DP + D + T + M + R [Equation (1b)]. Equation (1a) is commonly referred to as the gross VA and Equation (1b) is termed the net VA. Theoretical arguments have been forwarded that support both approaches. Empirical research indicates both methods have been used in practice. Pulic (1998) argues that because of the central active role that human resources play in the value creation process, labour costs (wages expense) should not be included in VA computations. This view is consistent with the opinions of other IC experts (Edvinsson and Malone 1997; Sveiby 2000).

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Testing the relationship between intellectual capital and a company’s performance

l The value-creation efficiency analysis is unique in respect of its flexibility for application to various economic levels (macro and micro). This methodology can therefore be applied in a broader scope to enable stakeholders to develop an understanding of the intellectual capital performance of a single company, group of companies, specific business sector or capital market. l This methodology provides a standardised and consistent basis of measurement, thereby enabling to a greater extent the effective conduct of an international comparative analysis of companies within a country’s economic structure and across international boundaries. Alternative intellectual capital measures are limited in that they (1) utilise information that is associated with a select group of companies (for example stock data); (2) involve unique financial and non-financial indicators that can be readily combined into a single comprehensive measure; and (3) are customised to fit the profile of an individual company. l All data that are used in the value-creation efficiency analysis are based on audited information. Therefore calculations can be considered to be objective and verifiable. Other measures of intellectual capital have been criticised on the grounds of the subjectivity associated with their underlying indicators. As many other measures of intellectual capital are highly internalised and company specific, there is some difficulty in verifying the information that is used in the calculation of the indicators that are formed by these measures.

Factors at the organisational level (control variables) For the purposes of the present study it was decided to use the following control variables that are deemed to have been widely used in relevant literature: 1 Size of the company (LCAP): The natural log of the total market capitalisation (number of issued shares multiplied by market price per share). 2 Risk (LDER): The natural log of the total debt divided by the book value of the total assets as reported in the company’s annual report for 2001. 3 Industry type (IT): Dummy variables that represent six major industries within the high knowledge-base business sectors. 4 Physical capital intensity (LPC): The natural log of the ratio of a company’s fixed assets (the physical assets owned by the company) to its total assets (comprising the company’s physical assets and financial assets) (both in Rands) as reported in the company’s annual report for 2001.

Sample selection and descriptive statistics As a result of the difficulty experienced in obtaining information for private companies, it was decided to limit this study to public companies that are listed on the JSE Securities Exchange. For the purposes of this study, the extent of a company’s performance was measured with the use of its annual report. 32

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Data were collected from the annual reports for the 2001 fiscal year (McGregor BFA database) of 65 South African publicly traded companies (listed on the Johannesburg Securities Exchange) in industries that are extensively reliant on intellectual capital (namely business service; chemical and pharmaceutical products; communications; electronic and electrical products; finance; insurance and real estate; and health and social services). The sample was limited to these industries in view of the exploratory nature of the study and the desire to investigate a homogenous sample (high knowledge base). The population in this research study can be defined as all the companies that display the key variable of staff costs. Of the total number of 465 companies identified in the MCGREGOR BFA database, 224 companies displayed the key variable of staff costs. As a result of data-screening and transformation procedures, 130 companies were included in the final sample, of which 65 companies satisfied the criteria for inclusion in the high knowledge-base group. The final data set contained a total of six industries for the high knowledge-base group. Table 1 High knowledge-base industries and frequency Industry Business service

Frequency

Cumulative Percentage – percentage – % %

30

46

46

Chemical

4

6

52

Communication

7

11

63

Electronic and electrical Finance, insurance and real estate Health and social services Total

8

12

75

13

20

95

3

5

100

65

100

100

Before undertaking a statistical analysis, it is important to ensure that the underlying assumptions of correlation and regression analysis are in order. A critical assumption of correlation and regression analysis is the existence of normality, i.e. the scores for each variable in the analysis should be normally distributed. For each variable in the analysis that was not normally distributed, a natural logarithmic transformation was carried out. In order to ensure that the data was suitable for estimation purposes, the following restrictions were placed on the sample. Firstly, companies in respect of which key variables were omitted or data values were misreported (such as undisclosed or zero staff costs) were excluded from the data set. A second necessary restriction is the requirement that all variables should be positive. The removal of variables that are negative is justified by the natural logarithmic transformation of the data (Loof and Heshmati 2002). Meditari Accountancy Research Vol. 11 2003 : 25–44

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Testing the relationship between intellectual capital and a company’s performance

Table 2 presents the mean and standard deviation of the untransformed dependent variables, independent variables and control variables for the final useable sample. Profitability (ROA) and productivity (ATO) have means of 16.6052% and 1.2240 respectively. The overall financial performance of the sample companies is sound as indicated by the reasonably high ROA. Productivity is at low levels as indicated by the fact that for R1 spent on assets only R1.22 is generated in turnover. The mean for market valuation (market price per share divided by book value per share) of 2.0385 indicates that investors generally value the sample companies in excess of the book value of net assets as reported in the financial statements. The mean of 4.2657 for intellectual capital performance suggests that during 2001 the sample companies were generally effective in generating value from their intellectual capital base. Table 2 Descriptive statistics of untransformed variables Variable description

Variable name

Mean

Std deviation

Profitability: Ratio of net income to total assets

ROA

16.6052

9.71303

Productivity: Ratio of total turnover to total assets

ATO

1.2240

0.92978

Market Valuation: Ratio of the company’s market capitalisation to the company’s book value of net assets

MB

2.0385

1.74789

IC Performance: Value Added Intellectual Capital Coefficient

VAIC

4.2657

6.97203

5 Results Linear multiple regression results Table 3 presents the results of three linear multiple regressions. In panel A, the independent variables collectively explain 32.6% of the variance in return on assets, which is highly significant as indicated by the F value of 2.562. An examination of the t values (2.899) indicates that VAIC contributes to the prediction of profitability. In the profitability model for high knowledge-base companies with return on assets being the dependent variable, intellectual capital performance as measured by VAIC is statistically significant at ρ