wen ying wang - intelectual capital and performance in causal models

8/10/2019 Wen Ying Wang - Intelectual Capital and Performance in Causal Models

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Intellectual capital andperformance in causal models

Evidence from the information technologyindustry in Taiwan

Wen-Ying Wang and Chingfu ChangDepartment of Accounting, National Chengchi University, Taipei, Taiwan

Abstract

Purpose This paper seeks to investigate the impact of intellectual capital elements on businessperformance, as well as the relationship among intellectual capital elements from a cause-effectperspective.

Design/methodology/approach The partial least squares approach is used to examine theinformation technology (IT) industry in Taiwan.

Findings Results show that intellectual capital elements directly affect business performance, withthe exception of human capital. Human capital indirectly affects performance through the other threeelements: innovation capital, process capital, and customer capital. There also exists a cause-effectrelationship among four elements of intellectual capital. Human capital affects innovation capital andprocess capital. Innovation capital affects process capital, which in turn influences customer capital.Finally, customer capital contributes to performance. The cause-effect relationship between leadingelements and lagged elements provides implications for the management of firms in the IT industry.

Research limitations/implications The model proposed in this study is applicable to thehigh-tech IT industry. Modification of the proposed model may be needed in applying this model toother industries.

Practical implications This study helps management identify relevant intellectual capital

elements and their indicators to enhance business performance.Originality/value This paper is a seminal work to propose an integrated cause-effect model toinvestigate the relationship among elements of intellectual capital for IT in Taiwan.

KeywordsIntellectual capital, Performance appraisal, Taiwan, Communication technologies

Paper typeResearch paper

IntroductionThe information technology (IT) industry in Taiwan has gained a reputation of rapidgrowth and global competitive capabilities. The literature contends that a firmscompetitive power and performance are largely influenced by its intellectual capital.Many researchers recognize that intellectual capital, which contains non-financialmeasures and other related information, is the value driver of an enterprise (Amir andLev, 1996; Edvinsson and Malone, 1997; Ittneret al., 1997; Stewart, 1997; Bontis, 1999,2001). They claim that intellectual capital assists enterprises in promoting competitiveadvantage and value. Therefore, intellectual capital can be viewed as the most valuableasset and the most powerful competitive weapon in business. This is especially so inthe IT industry, as its intangible assets are much more important than tangible assets.

The theoretical impact of intellectual capital on firm performance has never beenover emphasized in the literature. However, there is far from enough empirical researchinvestigating this issue. In particular, studies examining this issue in Taiwans IT

The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at

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industry are rare, even though Taiwan is one of the IT kingdoms of the world.Moreover, most of the research focuses on the impact of individual intellectual capitalon performance without looking into an integrated framework that describes therelationship among individual intellectual capital elements. However, as the

cause-effect relations among perspectives is emphasized, rather than merely lookinginto the relationship between measurable proxies of perspectives, in the BalancedScorecard system (Kaplan and Norton, 1996, 2001), the relationship among intellectualcapital elements should also be of interest to our research.

Many factors, such as corporate strategy and industrial characteristics, may affect afirms value drivers. Thus, it would be appropriate to put emphasis on theinterrelationship between intellectual capital elements from a more macroscopicperspective, rather than paying attention only to certain measures of intellectualcapital and performance when we examine the effect of intellectual capital onperformance. If the cause-and-effect relationship among elements could be understood,the improvement of business performance may be facilitated through the appropriatemanagement of leading elements of intellectual capital in advance and input more

resources in leading elements. Furthermore, for interested outside parties such asinvestors and creditors, understanding the items that affect business performance andbringing the related information into consideration is helpful for evaluating thecompanys true value and developmental potential. Although some researchers pointout that there exists a relationship among elements of intellectual capital, there is a lackof empirical studies on this issue. Following this line of argument, we attempt to forman integrated framework on which we investigate the relationship among intellectualcapital elements and the impact of these elements on firms performance.

Hypothesis developmentA consensus on the classification of intellectual capital elements has not yet been

reached in the literature, but there emerges a converged view that intellectual capital iscomposed of three forms of intellectual capital human capital, customer capital (orrelation capital), and structure capital which can be divided into innovation capital andprocess capital (Edvinsson and Malone, 1997; Bontis et al., 1999; Buren, 1999; Joia,2000; Bontis, 2002; Choo and Bontis, 2002).

Our study follows this view and classifies intellectual capital into four elements human capital, customer capital, innovation capital, and process capital. Each elementcould directly influence performance, as Figure 1 shows. Moreover, there may exist acause-effect relationship among human capital and other elements of intellectualcapital (Bontis and Fitz-enz, 2002). That is, human capital, the most fundamentalintellectual capital element, may affect other the three elements first, and then thesethree elements, in turn, affect performance, as indicated in Figure 2. Besides, as shown

in Figure 3, there may also exist a cause-and-effect relationship among innovationcapital, process capital and customer capital. These elements of intellectual capitalultimately affect performance indirectly through their interrelationship. Based on thecause-effect framework, this study forms the hypotheses as follows.

Direct influence of intellectual capital elements on performanceMany researchers find that there is a significant and positive correlation betweenresearch and development (R&D) expenditures and business performance as well as

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market value (Cockburn and Griliches, 1988; Hall, 1993; Chauvin and Hirschey, 1993).Sougiannis (1994) shows that when the company has a $1 increase in R&Dexpenditure, it has a $2 increase in earnings and $5 increase in market value over thenext seven years. According to Lev and Sougiannis (1996), $2.328 operating income

will be brought in the future if a company increases $1 R&D expenditure. Deeds (2001)finds that R&D intensity, the late period technology development capability, andtechnology absorption capability have a positive correlation with market value added.From the above studies, it could be inferred that companies R&D expenditure not onlyinfluences current performance and market value, but also future performance.

Canibanoet al.(2000) review a few of the research projects conducted relating to thevalue relevance of intellectual capital items which include R&D expenditure,advertisement expenses, patents, brand name, customer satisfaction, and humanresources. It implies that there are intellectual capital items other than R&Dexpenditure that could affect performance. Besides, many researchers emphasize that

Figure 1.Conceptual framework ofthe direct impact ofintellectual capitalelements on performance

Figure 2.Conceptual framework ofthe indirect impact ofhuman capital onperformance

Figure 3.Conceptual framework ofthe interrelationshipbetween intellectualcapital elements and theirimpacts on performance

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human capital and customer capital play a very important role in businessperformance or even in businesses survival (Pfeffer, 1994; Uzzi, 1996). According to Leeand Witteloostuijn (1998), companies that have existed longer, have accumulated moreabundant experience, use more highly-educated employees, or have higher connective

intensity with potential customers go bankrupt much more rarely. Although somestudies address the relationship between customer satisfaction and financialperformance, only very few studies actually provide empirical results. However,their conclusions are inconsistent. Some researchers find a significantly positiverelationship between customer satisfaction and financial performance (Ittner andLarcker, 1998a; Banker et al., 2000), but others do not (Ittner and Larcker, 1998b;Arthur Andersen & Co., 1994; Andersonet al., 1994). We regard customer satisfactionas merely an indicator of customer capital elements while investigating therelationship between customer capital and performance from a more integratedperspective. Based on the cause-effect relation between customer perspectives andfinancial perspectives in the Balanced Scorecard framework, we infer that customercapital would have a positive impact on performance.

In the era of the knowledge-based economy, the key factor that drives and creates afirms value is intellectual capital. Consequently, we infer that the creation andaccumulation of intellectual capital should be reflected in a firms performance.Whichever an indicator belongs to human capital, customer capital, innovation capital,or process capital, there is research supporting its relation with performance. However,most of the prior research focuses on the relation between an individual indicator ofsome intellectual capital element and performance, neglecting that between elements ofintellectual capital and performance. In our first hypothesis, the primary focus is theinfluence of each intellectual capital element, measured by several indicators, onperformance. As shown in Figure 1, H1 and its four sub-hypotheses are developed asfollows:

H1. Each intellectual capital element directly and positively affects performance.

H1a. Human capital directly and positively affects performance.

H1b. Innovation capital directly and positively affects performance.

H1c. Process capital directly and positively affects performance.

H1d. Customer capital directly and positively affects performance.

Indirect influence of intellectual capital elements on performanceAlthough elements of intellectual capital have a direct impact on performance, acompanys performance cannot rely solely on any single element. There is such a close

linkage between elements that a certain element may be improved or expanded throughimprovement on other elements. Consequently, if a company wants to create value,adequate combination of all elements is necessary (Edvinsson and Malone, 1997; vander Meer-Kooistra and Zijlstra, 2001; Hussi and Ahonen, 2002; Bukh, 2003). Edvinssonand Malone (1997) emphasize the importance of interaction between intellectual capitalelements, and point out that firm value is created through the combination of eachelement. The Balanced Scorecard system indicates that there exist linkages among thefinancial perspective, customer perspective, internal process perspective, and learning


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and growth perspective, as well as cause-effect relations among these four perspectives.Except for the financial perspective, three of these four perspectives almost belong tointellectual capital categories; they correspond to intellectual capital elements. Based onthe Balanced Scorecard concept, we could infer that relationships exist among our four

elements of intellectual capital, and that business performance would be influenced bythese elements in a cause-effect relationship.

Indirect impact of human capital on performanceIn this section we describe how human capital affects business performance throughother elements of intellectual capital including innovation capital, process capital, andcustomer capital.

Among the elements of intellectual capital, human capital is the most fundamental.van der Meer-Kooistra and Zijlstra (2001) point out that human knowledge andexperience is the main element, which is the base of other elements and which willimpact a companys value through affecting other elements. Employees knowledgeand capabilities are the source of innovation. In order to accumulate innovation capital,besides relying on the firms encouragement and active input, raising employeescapabilities is also an important impact factor. Consequently, it would be appropriateto infer that human capital is closely linked to innovation capital. Employees must berelied on to carry out the internal processes of a company, while employees alsoperform all customer services. Because employees provide the quality of service whileimplementing internal processes, the capability of employees would affect processefficiency, quality, and customer satisfaction. According to the cause-effectrelationship between the learning and growth perspective and internal processperspective, a similar result, stating that human capital possibly affects processcapital, could be obtained. Stewart (1997) emphasizes the relationship betweenemployee capabilities and customers. It indicates that employees should possess

suitable knowledge or skills to serve customer needs, which must also be the emphasisof the company. Therefore, in the model of the relationship between intellectual capitaland performance, accumulating human capital would be helpful to improve the otherforms of intellectual capital. As shown in Figure 2, we build the hypotheses as follows:

H2. Human capital directly and positively affects the other three intellectualcapital elements, which, in turn, affect performance.

H2a. Human capital positively affects innovation capital.

H2b. Human capital positively affects process capital.

H2c. Human capital positively affects customer capital.

H2d. Innovation capital positively affects performance.H2e. Process capital positively affects performance.

H2f. Customer capital positively affects performance.

Interrelationship of intellectual capital elements and their impacts on performanceFornell et al. (1996) propose the American Customer Satisfaction Index and contendthat customer expectations, perceived quality, and perceived value could influence

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customer satisfaction. Since customer expectations and perceived value are notcontrollable, a firms primary task to enhance customer satisfaction is to increaseperceived quality. Zeithamlet al.(1988) and Zeithaml and Bitner (1996) also point outthat the quality perceived by customers is the key factor of customer satisfaction. The

higher the perceived quality, the more they are satisfied. Through its internal process acompany provides service quality to its customers. The improvement in process capitalleads to customer satisfaction and enhancement of customer relations. Therefore,process capital is somewhat of a leading intellectual capital element, which affectscustomer capital element, while the customer capital element in turn influencesfinancial performance.

The implementation of process capital relies on employees, which belong to humancapital element. The quality of employees determines the internal process quality andservice quality. The Balanced Scorecard system emphasizes the cause-effect relation ofthose four perspectives in the model in which learning and growth perspective, internalprocess perspective, customer perspective, and financial perspective are interrelated.Correspondingly, in our model human capital affects process capital, which in turn

influences customer capital, and then financial performance. However, innovationcapital could not be mapped into the Balanced Scorecard concept. Customers maycherish a firms excellent innovative capabilities. In such a circumstance, innovationcapital affects customer capital. On the one hand, innovative ideas may be turned intoproducts and services through the transformation of the internal process. Theenhancement of innovation capabilities helps to increase the quality of the internalprocess. We therefore infer that innovation capital may affect process capital. Asshown in Figure 3, we propose the following hypotheses:

H3. Human capital directly and positively affects innovation capital and processcapital, and process capital affects customer capital, which then affectsperformance.

H3a. Human capital positively affects innovation capital.

H3b. Human capital positively affects process capital.

H3c. Innovation capital positively affects process capital.

H3d. Innovation capital positively affects customer capital.

H3e. Process capital positively affects customer capital.

H3f. Customer capital positively affects performance.

Research designIn literature, studies on the relationship between intellectual capital and businessperformance usually use regression analysis or the principal component method.These methods deal with only one dependent variable or component and cannotexamine the cause-effect relation between them. In regression analysis, we mayfrequently encounter a multi-collinearity problem if we include related variables asindependent variable in order to lessen the error-in-variable problem. To avoid themulti-collinearity and measurement errors, while addressing the cause-effect


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relationship between intellectual capital and business performance, we adopt thepartial least squares (PLS) method, which is a variance-based structural equationmodeling approach used in previous intellectual capital research studies (see Bontis,1998, 2004; Bontis et al., 2000; Bontis and Fitz-enz, 2002).

In this study we have three samples for three corresponding models. Each of thesesamples consists of all listed firms in the IT industry during the period 1997-2001. Wedelete observations with any missing values. Data have been retrieved from thedatabase of the Taiwan Economic Journal (TEJ), annual report, and prospectus.Although the samples are not too small, we, for the sake of conservativeconsiderations, still use bootstrap re-sampling methods to conduct inference.

In an attempt to maximize stockholders wealth, management usually tries toincrease operating earnings and stock price. Accounting earnings reflect pastperformance, while the stock price is an index of market expectation of a firmsperformance in the future. Both accounting earnings and stock price are included asperformance measures. The indicators of each intellectual capital element, as shown inTable I, are adopted from the literature on the measures of intellectual capital or

determinants of business performance (Sougiannis, 1994; Kaplan and Norton, 1996;Lev and Sougiannis, 1996; Edvinsson and Malone, 1997; Stewart, 1997; Sveiby, 1997;Bontis, 1998; Financial Management Accounting Committee, 1998; Lee andWitteloostuijn, 1998; Bukh et al., 2001; Deeds, 2001; Mouritsenet al., 2001).

Empirical resultsThis section presents the empirical results regarding the direct influence of intellectualelements on performance, the indirect impact of human capital on performance, and theinterrelationship of intellectual capital elements as well as their impacts onperformance.

Direct influence of intellectual capital elements on performanceIn our first hypothesis, we conjecture that each element of intellectual capital directlyand positively affects performance. Figure 4 shows the empirical results. All thecoefficients, with the exception of human capital, are shown to affect performancesignificantly (p-value , 0:01). It supports our H1b,H1c, andH1d. Innovation capital,process capital, and customer capital have significant and positive influences onperformance. Creating and accumulating these three elements of intellectual capitalcontributes to performance. Process capital has the strongest direct impact onperformance because Taiwans IT firms are mostly original equipment manufacturing-(OEM-) or original design manufacturing- (ODM-) oriented businesses and theirproduction efficiency relies on the improvement of the production process. Therefore,production efficiency is the most important factor for the industry.

Table II summarizes the loadings of the indicators for each element of intellectualcapital and performance for the model in Figure 4. The significant loadings forinnovation capital are R&D intensity, current R&D density, last R&D density, andR&D employee ratio. The results are consistent with those in the relevant literature inthat it suggests that the higher the R&D ratio, the better performance will be in thenext period. The significant loadings in order of magnitude for process capital arevalue added per employee, administrative expense per employee, plant assets turnover,productivity per employee, and firm age. One noteworthy thing is the negative loading

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Variable Definition

PerformanceReturn on assets (Per1) Net income after tax/average total assets

Adjusted return on assets (Per2) Operating income/average total assetsReturn on stockholders equity (Per3) Operating income/average common stockholders

equityAdjusted return on stockholders equity (Per4) (Operating income 2 interest expense)/average

common stockholders equityOperating income ratio (Per5) Operating income/net salesStock price (Per6) Year-end the closing priceMarket value (Per7) Year-end market valueMarket value book value (Per8) Year-end market value year-end book valueMarket-to-book (Per9) Year-end market value/year-end book value

Human capitalNumber of employees (H1) Total number of employeesNumber of advanced educational background (H2) Total number of college graduates

Average education degree (H3) Employees are divided into Masters, college, andhigh school or below, with the weight of 3, 2, 1,and 0 for each category to compute averageeducation degree of total employees

Ratio of advanced educational background (H4) College graduates/total number of employeesAverage years of service with company (H5) Average employees work years in the companyAverage age (H6) Average age of employeesRatio of change in number of employees (H7) (Number of employees at the end of the period 2

number of employees at the beginning of theperiod)/number of employees at the beginning ofthe period

Payroll expense ratio (H8) (Salary expense direct labor cost indirectlabor cost)/net sales

Innovation capital

Current R&D density (I1) R&D expense this year/net sales this yearLast R&D density (I2) R&D expense last year/net sales last yearCurrent R&D expense (I3)Last R&D expense (I4)Income per R&D expense (I5) Net income after tax/R&D expenseNumber of R&D employees (I6)R&D employee ratio (I7) Number of R&D employees/number of total

employeesR&D per employee (I8) R&D expense this year/number of R&D

employeesR&D intensity (I9) R&D expense this year/average total assetsPatent fee (I10)

Process capital

Productivity per employee (P1) Net sales/total number of employeeValue added per employee (P2) Net income after tax/total number of employeeFirm age (P3) Years of establishing the company to year-end of

the yearOrganizational stability (P4) Employees average work years/corporation ageCurrent capital turnover (P5) Net sales/average current assetsAdministrative expense ratio (P6) Administrative expense/net sale

(continued)Table I.

Variable definition


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Variable Definition

Administrative expense per employee (P7) Administrative expense/number of total employeeInventory turnover (P8) Cost of goods sold/average inventory

Plant assets turnover (P9) Net sales/average plant assetsCustomer capitalNumber of main customers (C1) Number of customers whose share in sales above

10 percentGrowth rate (C2) Growth rate in salesAdvertising expense (C3) Advertising expenseMarketing expense (C4) Marketing expenseMarketing expense ratio (C5) Marketing expense/net salesAcceptance rate (C6) 1 2 (sales returns and allowances/net sales)Concentration (C7) Net sales from the first big three customers/net

salesTable I.

Construct Significan tindicator

Innovation capital I9 I1 I2 I70.906* 0.890* 0.870* 0.775*

(38.549) (16.515) (15.235) (13.142)Process capital P2 P7 P9 P1 P3

0.909* 0.717* 0.669* 0.485* 20.437*(33.158) (2.418) (6.025) (4.786) ( 24.015)

Customer capital C6 C20.806* 0.764*

(7.778) (7.022)Performance Per2 Per1 Per4 Per3 Per5 Per6 Per9

0.950* 0.921* 0.890* 0.881* 0.743* 0.684* 0.645*(102.122) (70.882) (40.201) (38.673) (18.663) (8.770) (6.170)

Notes: * Significant at p-value , 0:01; Sample size is 131; t-value in brackets; For notation ofvariables, I1 denotes current R&D density, I2 last R&D density, I7 R&D employee ratio, I9 R&Dintensity, P1 productivity, P2 value added per employee, P3 firm age, P7 administrative expense peremployee, P9 plant assets turnover, C2 growth rate, C6 acceptance rate, Per1 ROA, Per2 adjusted ROA,Per3 ROE, Per4 adjusted ROE, Per5 operating income ratio, Per6 stock price, and Per9 market-to-book

Table II.Significant indicators fordirect impact ofintellectual capitalelements on performance

Figure 4.Empirical results for directimpact of intellectualcapital elements onperformance

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for firm age that implies that firm age has a negative impact on performance. Sveiby(1997) and Lee and Witteloostuijn (1998) argue that the older a firm is, the more stableand reliable the firm is. Our result shows a different story. We conjecture that dramaticchanges in the IT industry favor new comers for their adaptive capability to the

changing environment.Figures 4 shows the insignificance of the coefficient of human capital. This does not

imply human capital is not important to performance. Instead, human capital andperformance may not have a direct relation, but rather an indirect relation. Next, wewill conduct further investigation into this issue.

Indirect impact of human capital on performanceAs discussed above, human capital does not directly influence performance. Wetherefore conjuncture the two have an indirect relationship. Figures 5 and 6 show howhuman capital indirectly impacts performance through the other three elements. First,we assume Figure 5 describes the story, but find the path to performance from

innovation capital is insignificant. We therefore look into the model in Figure 6 whichexplains how human capital indirectly affects performance through innovation capitaland process capital, and how innovation capital has an indirect impact on performance

Figure 5.Empirical results for

indirect impact of humancapital on performance

Figure 6.Empirical results for

indirect impact of humancapital on performance


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through process capital. Figure 6 shows that the coefficient from innovation capital toprocess capital and the t-value are 0.341 and 2.435, respectively, and the coefficientfrom process to performance and its t-value are 0.606 and 12.645, respectively. Thisresult explains the insignificance in the coefficient to performance from human capital

in Figure 4. Although process capital is a very important factor affecting performance,it needs support from innovation capital and human capital, and human capital is mostfundamental to a firms overall performance. Figures 5 and 6 supportH2a,H2b,H2c,H2e, andH2f.

Table III summarizes the loadings of the indicators for each element of intellectualcapital and performance for the model in Figure 6. The significant loadings in order ofmagnitude for human capital are the number of advanced educational background,number of employees, and average age. The first two indicators have positive loadings,but the last item has negative loading. The negative loading of average age ofemployees implies that the younger the employees, the higher the possibility that theyhave creative thinking and learning capabilities to adapt to the rapidly changing ITenvironment.

The significant loadings in order of magnitude for process capital are value addedper employee, administrative expense per employee, plant assets turnover,productivity per employee, firm age, and current capital turnover. All of theloadings are positive, except for firm age. The significant loadings in order ofmagnitude for innovation capital are current R&D expenses, last R&D expense, andthe number of R&D employees. The significant loadings in order of magnitude forcustomer capital are marketing expenses, advertising expenses, and productacceptance rate. The first two indicators have negative loadings while the last one

Construct Significant indicator

Human capital H2 H1 H60.981* 0.972* 20.378*

(209.732) (128.795) (23.481)Innovation capital I3 I4 I6

0.980* 0.970* 0.913*(120.393) (83.761) (17.499)

Process capital P2 P7 P9 P1 P3 P50.880* 0.744* 0.743* 0.569* 20.385* 0.263*

(26.942) (3.224) (9.875) (6.598) ( 23.187) (2.067)Customer capital C4 C3 C6

20.841* 20.772* 0.390*(218.510) ( 26.383) (2.740)

Performance Per2 Per1 Per4 Per3 Per5 Per6 Per90.951* 0.921* 0.895* 0.887* 0.736* 0.686* 0.644*

(91.546) (68.431) (36.040) (36.785) (16.743) (7.794) (6.148)

Notes: * Significant at p-value , 0:01; Sample size is 131; t-value in parentheses; For notation ofvariables, H1 denotes number of employees, H2 number of advanced educational background, H6average age, I3 current R&D expense, I4 last R&D expense, I6 number of R&D employees, P1productivity, P2 value added per employee, P3 firm age, P5 current capital turnover, P7 administrativeexpense per employee, P9 plant assets turnover, C3 advertising expense, C4 marketing expense ratio,C6 acceptance rate, Per1 ROA, Per2 adjusted ROA, Per3 ROE, Per4 adjusted ROE, Per5 operatingincome ratio, Per6 stock price, and Per9 market-to-book

Table III.Significant indicators forindirect impact of humancapital on performance

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has a positive loading. Most IT firms in Taiwan are ODM or OEM manufacturers andtheir customers are not consumers. Marketing and advertisement may not promotetheir products. However, product acceptance rate is a positive indicator of customercapital and has positive contributions to performance.

Interrelationship of intellectual capital elements and their impacts on performanceAs previously discussed, human capital has a direct impact on the other three elementsand an indirect impact on performance. There may also exist an interrelationshipbetween innovation capital, process capital, and customer capital. Therefore, wepropose a model that presents the interrelationship between intellectual capitalelements and their impacts on performance, as shown in Figure 7. The significance ofthe coefficients in the figure supports H3a, H3b, H3c, H3e, and H3f, except H3d. Itimplies that human capital has a direct impact on innovation capital and processcapital, and that innovation capital directly influences process capital, which in turnaffects customer capital, while finally the customer capital affects performance.

Although innovation capital has a direct impact on process capital, it does not have aninfluence on customer capital. Customer satisfaction comes from the perceived qualityof products or services, which relies on process capital, but not innovation. Innovationhas only an indirect effect on customer capital through process capital.

Figure 7 shows human capital is the most important element of intellectual capital,for it has a direct influence on process capital (direct coefficient at 0.538) and an indirectinfluence on process capital through innovation capital (indirect coefficient at0:738*0:339 0:250). As such, the total coefficient of human capital to process capitalis 0:7880:5380:250. In addition, process capital has the most direct influence onperformance in all of these models, as shown in Figures 4-6. If we want to improveprocess capital, we need to improve innovation capital and human capital. If we wantto improve innovation capital, we need to improve human capital, too. Therefore,human capital is the most important element of intellectual capital for an enterprise.

For each element of intellectual capital and performance for the model in Figure 7,Table IV summarizes the loadings of the indicators. One thing worth noting is that thesignificant indicators in Table IV are very similar to those in Table III, although themodels are different. This may show evidence that the variables adopted in the modelsare robust.

Figure 7.Empirical results for the

interrelationship betweenintellectual capital

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ConclusionWith a sample of listed firms in the IT industry in Taiwan, this study investigates thecause-effect relationship between elements of intellectual capital and businessperformance. We try to find out, step by step, the interrelationship among four

elements of intellectual capital and how they influence performance. The results showthat, with the exception of human capital, innovation capital, process capital, andcustomer capital all have a direct effect on performance. Although human capital doesnot have a direct impact on performance, it has a direct impact on the other capitalelements, which in turn affect performance. There also exists an interrelationshipbetween intellectual capital elements. Human capital directly affects innovation capitaland process capital. Innovation capital and process capital further influenceperformance. Process capital has something to do with innovation capital in thatinnovation capital directly influences process capital. In sum, human capital is theprimary leading factor in which management should put the most effort. This study,showing how intellectual capital elements affect business performance in Taiwans ITindustry, provides some implications for management in the IT industry.

References

Amir, E. and Lev, B. (1996), Value-relevance of non-financial information: the wirelesscommunications industry, Journal of Accounting and Economics, August-December,pp. 3-30.

Anderson, E.W., Fornell, C. and Lehmann, D.R. (1994), Customer satisfaction, market share, andprofitability: findings from Sweden, Journal of Marketing, Vol. 58, July, pp. 53-66.

Construct Significant indicator

Human capital H2 H1 H6 H80.967* 0.966* 20.395* 0.248*

(39.232) (45.974) ( 23.668) (3.021)Innovation capital I3 I4 I6

0.980* 0.969* 0.914*(98.723) (77.270) (15.932)

Process capital P9 P1 P2 P7 P50.900* 0.794* 0.701* 0.652* 0.548*

(19.456) (11.646) (7.704) (4.261) (3.822)Customer capital C6 C2 C3

0.777* 0.643* 20.407*(6.420) (2.671) ( 21.815)

Performance Per2 Per1 Per4 Per3 Per5 Per90.973* 0.940* 0.936* 0.926* 0.795* 0.527*

(165.424) (79.281) (66.323) (55.067) (23.565) (5.817)

Notes: * Significant at p-value , 0:

01; Sample size is 131; t-value in parentheses; For notation ofvariables, H1 denotes number of employees, H2 number of advanced educational background, H6average age, H8 payroll expense ratio, I3 current R&D expense, I4 last R&D expense, I6 number ofR&D employees, P1 productivity, P2 value added per employee, P5 current capital turnover, P7administrative expense per employee, P9 plant assets turnover, C2 growth, C3 advertising expense, C6acceptance rate, Per1 ROA, Per2 adjusted ROA, Per3 ROE, Per4 adjusted ROE, Per5 operating incomeratio, and Per9 market-to-book

Table IV.Significant indicators forinterrelationship betweenintellectual capitalelements and theirimpacts on performance

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Arthur Andersen & Co. (1994),Customer Satisfaction Strategies and Tactics, Arthur Andersen &Co., Chicago, IL.

Banker, R.D., Potter, G. and Srinivasan, D. (2000), An empirical investigation of an incentiveplan that includes non-financial performance measures, The Accounting Review, Vol. 75

No. 1, pp. 65-92.Bontis, N. (1998), Intellectual capital: an exploratory study that develops measures and models,

Management Decision, Vol. 36 No. 2, pp. 63-76.

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