Chu 2011 Empirical Study of Impact of Intellectual Capital on Business Performance

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Performance.doc 11/5/2010 1

Cited as: Chu, S.K.W. & Chan, K.H., Yu, K.Y., Ng, H.T. & Wong, W.K. An Empirical

Study of the Impact of Intellectual Capital on Business Performance. Journal of

Information & Knowledge Management. Forthcoming 2011.

Title: An Empirical Study of the Impact of Intellectual Capital on Business Performance

Authors: Samuel Kai Wah Chu* [email protected]

Kin Hang Chan† [email protected]

Ka Yin Yu* [email protected]

Hing Tai Ng, [email protected]

Wai Kwan Wong* [email protected]

* Faculty of Education, the University of Hong Kong, HKSAR

† Institute for China Business, School of Professional and Continuing Education, the

University of Hong Kong, HKSAR

mailto:[email protected]







Abstract

This empirical study examines the intellectual capital (IC) performance of Hong Kong

companies and its association with business performance. Data were collected

from constituent companies of the Hang Seng Index listed on the Hong Kong Stock

Exchange (2005 – 2008). An IC measurement, Value Added Intellectual Coefficient

(VAIC™), was utilized to evaluate the IC investment of the companies. Four accounting

ratios: market-to-book value (MB), return on assets (ROA), asset turnover (ATO) and return

on equity (ROE) were used as the indicators of business performance. Regression analyses

were conducted to test the ability of IC and its components to explain the variance in business

performance measures.

No conclusive evidence was found to support the associations between VAIC™ as an

aggregate measure and the four financial indicators. However, components of VAIC™ were

found to predict a substantial variance in business performance. Capital Employed Efficiency

(CEE) was found to be a key factor in predicting business financial performance. Structural

Capital Efficiency (SCE) was found to have a significant effect on businesses‟ market

valuation, as measured by MB, and on profitability, as measured by ROE. Negative

correlations were found between Human Capital Efficiency (HCE) and the financial

indicators. The findings indicate a gap between the traditional accounting perspective and the

value creation perspective, which is central to the VAIC™ methodology in measuring IC.

It is believed that the findings of this research provide insights for business stakeholders of

Hong Kong companies in utilizing IC, particularly the noted impact of structural capital.

While our findings indicate the importance of IC for corporations, as shown by the significant

effect of SCE on ROE, physical and financial assets may still be considered as the key

resources in delivering business success.

Keywords:

Intellectual capital, VAIC™, financial performance, value creation, Hang Seng Index



1. Introduction

“Knowledge-based economy” is a term which has been used widely to describe today`s

global economy. Knowledge-based resources have been described as the main sources in

sustaining the competitive advantage of a company (Ting & Lean, 2009). Placing the

emphasis on knowledge production rather than on the production of physical goods has been

suggested to make up value creation (Pulic, 2008). This transformation of values has created

a new perspective in viewing the resources of a company. In recent years, intellectual capital

(IC) has been viewed as a factor that has an impact on business performance and is critical in

the value creation process in a knowledge-based economy (Sveiby, 1997; Lynn, 1998; Pulic,

1998). IC has also been viewed as the roots of a company‟s value (Edvinsson & Malone,

1997). As a result of the increasing recognition of the role of IC in business, researchers have

become keen on assessing its impact on the business performance of companies.

The more traditional and commonly used measures of business performance include the

assessment of productivity, profitability, and market evaluation (Firer & Williams, 2003).

Productivity measures the output conversion of input, while profitability refers to the degree

to which the revenue of a business exceeds its costs. Finally, market evaluation describes the

degree to which the market value of a business surpasses its book value.

While a growing body of research has shown the positive association of IC with the business

performance of a company (Ting & Lean, 2009), this link needs to be confirmed in different

geographic settings and industries (Cabrita & Bontis, 2008). Earlier studies on IC in Hong

Kong have been focused on voluntary reporting (Guthrie, Petty, & Ricceri, 2006; Petty &

Cuganesan, 2005) and IC performance (Young, Su, Fang, & Fang, 2009). A preliminary study

on the impact of IC on organizational performance in Hong Kong has shown a lack of

association between IC and financial performance indicators, which is contrary to the

evidence in other parts of the world (Chan, 2009b). This present research builds on the

studies that have been done so far in Hong Kong. The study investigates the association

between IC and business performance among constituent companies of the Hang Seng Index

(HSI) listed on the Hong Kong Stock Exchange from 2005-2008. Specifically, multiple

regression was used to examine the association of IC, measured by the Value Added

Intellectual Coefficient (VAIC™), with traditional business performance indicators. The

findings of this study expand on the current knowledge base on the relevance of IC in



corporate performance. Furthermore, the findings offer insights for business entities in Hong

Kong, which positions itself as a knowledge-based economy.

1.1 Business Performance Indicators

In a resource-based view of business, benefits that are measured considering both tangible

and intangible assets have gained acceptance (Canibano, Garcia-Ayuso, & Sanchez, 2000).

However, measures of financial performance remain to be the most dominant model in

examining business performance (Hofer, 1983). Financial indicators have been assumed to

reflect the fulfillment of economic goals of a business entity, and these make up a component

of business performance indicators (Venkatraman & Ramanujam, 1986). A number of

accounting- and market-based measures have been utilized as proxy measures to measure

productivity, profitability, and market evaluation. Earlier studies by Firer and colleagues

(Firer & Williams, 2003; Firer & Stainbank, 2003) on IC and business performance have

utilized three measures, namely, Return on Assets (ROA), Asset Turnover (ATO), and Market

to Book Value (MB). ROA was represented by the ratio of the net income to the book value

of total company assets. ATO was the ratio of the total revenue to the total book value of

assets, while MB was the ratio of the total market capitalization to the book value of net

assets. Additionally, Return on Equity (ROE) has been commonly used in financial reporting

and refers to the ratio of net income to the total shareholders‟ equity (Chan, 2009b).

1.2 Intellectual Capital (IC)

Physical capital, which refers to “the traditional inputs of land, labor and capital” (Goh, 2005,

p. 386), has always been a crucial indicator for valuing a company‟s business in the past.

However, although it has been regarded as critical to a company‟s operations, it may not truly

reflect the changes and conditions in today‟s businesses (Mohiuddin et al., 2006).

Conventional accounting indicators may not have adequately considered IC elements,

resulting in an unexplained market premium (Edvinsson & Sullivan, 1996), which was also

noted by Pulic (2008).

The first time IC was discussed in the business context was in the 1990s (Yalama & Coskun,

2007). As explained by Stewart (1997), IC is the greatest source of value and competitive

advantage. This summation of knowledge is value added for the company, and is used in the

business creation process (Zéghal & Maaloul, 2010). Sallen and Selamat (2007) described IC



as the aggregation of human capital, structural capital and customer capital. IC has also been

viewed as the result of, or the intellectual property generated from, the process of knowledge

transformation (Ting and Lean, 2009). As stated by Appuhami (2007, p. 14), “the Intellectual

capital of a firm plays a significant role in the modern approach of value creation”. To

summarize, IC may be referred to as the sum of knowledge within an organization, which

involves value creation and gives competitive advantage to business organizations. As

commented by Stewart (1997, p. 56), IC has become so vital that it would be fair to say that

an organization that is not managing knowledge is “not paying attention to business”.

In general, IC can be classified into two major categories: human capital and structural

capital. (Edvinsson, 1997; Edvinsson & Malone, 1997; Bontis, 2004). Human capital “is in

the heads of employees”, while structural capital is “what is left in the organization when

people go home in the evening” (Roos & Roos, 1997, p. 415). The examples that Ting and

Lean (2009, p. 590) used to identify human capital include “innovation capacity, creativity,

know-how and previous experience, teamwork capacity, employee flexibility, tolerance for

ambiguity, motivation, satisfaction, learning capacity, loyalty, formal training and education”.

For structural capital, Bontis et al. (2000) gave examples such as databases, organizational

charts, process manuals, strategies and routines. Properly managed IC has been regarded as

the keydriving factor for sustainable corporate success (Yalama & Coskun, 2007; Ting &

Lean, 2009). However, although IC has been regarded as the key factor influencing the future

value of a firm (Yalama & Coskun, 2007), using traditional financial indicators may not be

sufficient to illustrate the value of IC, as it may only reflect the accountant`s view towards the

performance of the firm and may be misleading to stakeholders (Kamath, 2008). VAIC™

may be a better indicator and method in reflecting the market value of businesses (Young,

Fang & Fang, 2009).

1.3 Value Added Intellectual Coefficient (VAIC™)

Value Added Intellectual Coefficient (VAIC™) is a method developed by Ante Pulic to

measure the value creation efficiency of a company using accounting-based figures (Pulic,

2000). Companies with a higher VAIC™ indicate that they have a higher value creation in

using all available resources, i.e., IC, human capital, structural capital, and physical capital.



VAIC™ is considered as a “universal indicator showing abilities of a company in value

creation and representing a measure for business efficiency in a knowledge-based economy”

(Pulic, 1998, p.9). Also, as stated in Kamath (2007, p. 98), VAIC™ is a management and

control tool that is “designated to monitor and measure the IC performance and potential of

the firm”. This indicator has been widely applied in various research studies (see Table 1.1)

as a means of measuring IC (Zéghal & Maaloul, 2010; Chan, 2009a; Kamath, 2008; Tan,

Plowman & Hancock 2007; Yalama & Coskun, 2007; Mohiuddin, Jahibllah & Shahid, 2006;

Shiu, 2006; Goh 2005; Mavridis, 2005; Mavridis, 2004; Firer & Williams, 2003). This

method is “designed to provide information about the value creation efficiency of tangible

and intangible assets within a company during operations” (Tan, Plowman & Hancock, 2007,

p. 91). The concept of “value added” is incorporated in the formulation of VAIC™. Value

added per professional may be regarded as the purest measure to produce economic value in a

knowledge-based company (Sveiby, 2001).

The formulation of VAIC™ matches the definition of IC. First of all, VAIC™ is obtained by

adding Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE) and Capital

Employed Efficiency (CEE), thus incorporating the concept of classifying IC into human

capital and structural capital. Secondly, at present there is more emphasis on the skills and

knowledge of the employees than on the physical assets of a company (Muhammad, 2009).

Due to the active role of value creation in the process (Pulic, 2000), employees‟ expenses are

seen as an investment rather than as a cost in the calculation of VAIC™.

1.4 Prior studies using the VAIC™ methodology

A number of studies have used the VAIC™ methodology in examining IC, and its

associations with other business performance measures have not been consistent. For instance,

Zéghal and Maaloul (2010) found a positive relationship between IC and financial

performance in high-technology industries. Similarly, a study on Taiwan‟s companies found

that IC investment had a positive impact on a firm‟s market value and financial performance

(Chen, Cheng & Hwang, 2005). However, Firer and Williams (2003) found that physical

capital was the most significant underlying resource of corporate performance in South Africa,

and Chan (2009b) found no conclusive evidence to support a definitive association between

IC and financial performance among Hong Kong companies.



Other studies (Chen et al., 2005; Kujansivu, 2005; Shiu, 2006b) have found both human and

physical capital to be positively associated with financial performance. More specifically,

structural capital has been found to be a critical link that enabled IC to be measured at the

organisational level, which means that, for example, if a company has good systems and

procedures, then IC efficiency is likely to be high (Bontis et al., 2000).

2. Research Methods

2.1 Samples and Data Collection

The Hang Seng Index (HSI) is one of the stock market indexes in Hong Kong which indicates

the overall market performance of the Hong Kong Stock Exchange (HKSE). HSI is

commonly regarded as the representative of the state of the Hong Kong economy, and

represents approximately 67.3% of the total market capitalization in Hong Kong between

2005 and 2008 (Hang Seng Indexes, 2008). On a year-to-year basis, the number of HSI

constituent companies varies. The study sample included all HSI constituent companies over

a 4-year period (2005 n = 33, 2006 n = 36, 2007 n = 43, 2008 n = 42). Data were collected

from 154 published annual reports, and are referred to in this study as company-year cases.

Three cases, which obtained either a negative book value or a negative VAIC™, were

considered as problematic and removed from the final sample size (N =151) to avoid the

effect of outliers. Table 2.1 summarizes the distribution of company-year cases according to

business sectors.

Table 2.1 Sample distribution by sectors

Sectors Frequency/company-year %

Commerce and industry 85 56.3

Finance 32 21.2

Properties 22 14.6

Utilities 12 7.9

Total 151* 100

Notes: * company-year after removing three sets of problematic data

2.2 Independent Variables

VAIC™ and its components (HCE, SCE, CEE) were used separately as independent

variables. This is to differentiate the classification of IC into human capital and structural

capital (Muhammad, 2009). Calculation of VAIC™ using accounting-based figures involves

five steps (Pulic, 2000; Chan, 2009a), which has been illustrated in detail by other researchers



(Zéghal & Maaloul, 2010; Chan, 2009a; Kamath, 2008). For simplicity, the practical

procedures of calculating VAIC™ is demonstrated in this paper. First of all, the Value Added

(VA) of the company had to be extracted.

VA = OP + EC + D + A

where OP = operating profits; EC = total employee expenses, which is viewed as

investment; D = depreciation; A = amortization. Secondly, the Human Capital Efficiency

(HCE) and Structural Capital Efficiency (SCE) were calculated.

HCE = VA / HC

SCE = ( VA – HC ) / VA

where HC = human capital, measured by total employee expenses. Since IC can only be

operable under the support by financial and physical capital, Capital Employed Efficiency

(CEE) was added to the formula.

CEE = VA / CE

VAIC = HCE + SCE + CEE

where CE = capital employed, which is the book value of tangible assets. Finally, VAIC™,

which acted as an independent variable affecting the traditional financial performance of

companies, was obtained by summing up HCE, SCE and CEE.

2.3 Dependent Variables

Four traditional financial indicators were used as dependent variables, and served as proxy

measures of productivity, profitability, and market valuation. These include market-to-book

value (MB), return on assets (ROA), asset turnover (ATO) and return on equity (ROE) which

have been used in earlier studies (Firer & Williams, 2003; Chan, 2009a).

2.4 Data Analysis

Regression analysis was conducted to investigate the association between IC performance

and business performance, where VAIC™ and its components served as independent

variables, while the MB, ROA, ATO, and ROE were dependent variables. The formulas and

rules for extracting figures from financial reports used in this research are detailed in the

appendices. Firm size (FSIZE) and firm leverage (DEBT) were added to the models as two

control variables (Firer & Williams, 2003; Chan, 2009a), which helped reduce the effect of

unknown variables (Shuttleworth, 2008).



2.5 Research Hypotheses

The overarching research question asks: What is the impact of IC on corporate financial

performance in relation to productivity, profitability, and market valuation, of major

companies in Hong Kong? Considering that earlier studies have generated inconclusive

answers to this research question (see Section 1.4), a number of hypotheses were generated to

clarify the evidence.

2.5.1 Association of an aggregate VAIC™ measure with financial performance indicators

The aggregate measure based on the components of VAICTM

represents a total measure of IC.

In the following hypotheses, the association of this aggregate measure with each financial

indicator is examined.

H1a. VAIC™ is positively associated with market valuation as measured by MB

H1b. VAIC™ is positively associated with profitability as measured by ROA.

H1c. VAIC™ is positively associated with productivity as measured by ATO.

H1d. VAIC™ is positively associated with return on equity as measured by ROE.

2.5.2 Association of VAIC™ components with financial performance indicators

The three components of VAICTM

reflect the classification of IC into physical, human, and

structural capital. Earlier studies have shown that the associations of these components with

financial performance indicators are not uniform (Chan, 2009b; Chen, Cheng, & Hwang,

2005). Thus, the following hypotheses were generated:

H2a. HCE is positively associated with market valuation as measured by MB.

H2b. HCE is positively associated with profitability as measured by ROA.

H2c. HCE is positively associated with productivity as measured by ATO.

H2d. HCE is positively associated with return on equity as measured by ROE.

H3a. SCE is positively associated with market valuation as measured by MB.

H3b. SCE is positively associated with profitability as measured by ROA.

H3c. SCE is positively associated with productivity as measured by ATO.

H3d. SCE is positively associated with return on equity as measured by ROE.

H4a. CEE is positively associated with market valuation as measured by MB.



H4b. CEE is positively associated with profitability as measured by ROA.

H4c. CEE is positively associated with productivity as measured by ATO.

H4d. CEE is positively associated with return on equity as measured by ROE.

2.6 Regression Models

Eight regression models were used where the first four models investigated the association

between the aggregate VAIC™ measure and the four dependent variables. The last four

models were used to analyze each of the three components of VAIC™ and the dependent

variables. These models are illustrated in the regression equations in Table 2.2.

Table 2.2 Regression models

Model Regression equation

1 MBi = β1VAIC™+β2FSIZE+β3DEBT

2 ROAi = β1VAIC™+β2FSIZE+β3DEBT

3 ATOi = β1VAIC™+β2FSIZE+β3DEBT

4 ROEi = β1VAIC™+β2FSIZE+β3DEBT

5 MBi = β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

6 ROAi = β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

7 ATOi = β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

8 ROEi = β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

3. Findings

Tables 3.1 to 3.8 reveal the correlations between the dependent and independent variables

obtained by conducting the Pearson product-moment correlation analysis. Statistical values

such as standardized coefficients (β) and coefficient of determinations (R-square) are used in

the following discussion to illustrate the predictive capability and explanatory power of the

models.

3.1 The Associations between VAIC™ and the Financial Indicators

The coefficients of determination (R2) of hypotheses H1a to H1d indicated limited

explanatory power for the variances in the dependent variables. Even model 2 (Table 3.2),

which had the highest coefficient of determination (R2=0.121; 12% explained variance), was

unable to meet the threshold of Cohen‟s minimum standard of 14% for large effect size



(Grissom & Kim, 2005).



Table 3.1: Multiple regression results of Model 1: MBi=β1VAIC™+β2FSIZE+β3DEBT

Standardized

coefficients (β)

t-value p VIF

VAIC™ -0.026 -0.305 0.760 1.112

Firm Size 0.162 1.952 0.053 1.057

Debt 0.110 1.289 0.200 1.121

R2 = 0.045

Note: H1a. VAIC™ is positively associated with market valuation as measured by Market to Book

Value

Table 3.2: Multiple regression results of Model 2: ROAi=β1VAIC™+β2FSIZE+β3DEBT

Standardized

coefficients (β)

t-value p VIF

VAIC™ 0.192 2.355 0.020* 1.112

Firm Size 0.092 1.161 0.248 1.057

Debt -0.232 -2.837 0.005** 1.121

R2 = 0.121

Notes: H1b. VAIC™ is positively associated with profitability as measured by Return on Assets

*statistically significant at p < .05; ** statistically significant at p < .01

Table 3.3: Multiple regression results of Model 3: ATOi=β1VAIC™+β2FSIZE+β3DEBT

Standardized

coefficients (β)

t-value p VIF

VAIC™ -0.173 -2.035 0.044* 1.112

Firm Size -0.028 -0.342 0.733 1.057

Debt -0.151 -1.772 0.079 1.121

R2 = 0.042

Notes: H1c. VAIC™ is positively associated with productivity as measured by Asset Turnover.

*statistically significant at p < .05



Table 3.4: Multiple regression results of Model 4: ROE i=β1VAIC™+β2FSIZE+β3DEBT

Standardized

coefficients (β)

t-value p VIF

VAIC™ 0.155 1.862 0.065 1.112

Firm Size -0.269 -3.316 0.001** 1.057

Debt 0.143 1.714 0.089 1.121

R2 = 0.083

Notes: H1d. VAIC™ is positively associated with profitability as measured by Return on Equity

**statistically significant at p < .01

Statistically, VAIC™ obtained the strongest significance level on ROA and ATO (for ROA,

Model 2, p=0.020*; for ATO, Model 3, p=0.044*). However, the coefficients of

determination were not adequate to support the ability of VAIC™ to predict the variance in

ROA and ATO. Viewing R2

in a reverse manner, that is the coefficient of non-determination,

there was a high amount of unexplained variance of the dependent variable by the

independent variables in Model 2 (88%) and Model 3 (96%). As such, the explanatory

powers of Models 2 and 3 were too weak to claim that associations existed, and it was of

little practical effect and importance to the research. Hypotheses H1a, H1b, H1c and H1d

were therefore not substantiated by the findings.

The limited association between VAIC™ and the conventional financial indicators is

consistent with the findings of Chan (2009b) on the HIS constitutent companies from 2001 to

2005. This earlier study showed that there was no strong association between corporate

intellectual ability and the traditional financial performance measures. One possible

explanation suggested by Chan (2009b) is that local companies, when enhancing profitability,

are placing more emphasis on other types of strategic assets than on IC. The findings of this

present study indicate that IC investment, as a means of enhancing business performance,

persists to be weak in Hong Kong.

3.2 The Associations between VAIC™ Components and Financial Indicators

HCE, SCE, and CEE were found to be better predictors of the variance in the financial

indicators relative to the aggregate VAIC™ measure. The findings are summarized in Tables

3.5 to 3.8, showing higher amounts of explained variances.



Table 3.5: Multiple regression results of Model 5: MBi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

Standardized

coefficients (β)

t-value p VIF

HCE -0.303 -3.665 0.000*** 1.777

SCE 0.272 3.193 0.002** 1.889

CEE 0.716 10.154 0.000*** 1.294

Firm Size 0.092 1.398 0.164 1.121

Debt 0.336 4.772 0.000*** 1.293

R2 = 0.443

Notes: H2a. HCE; H3a. SCE; H4a. CEE is positively associated with market valuation as measured

by MB

**statistically significant at p < .01; ***statistically significant at p < .001

Table 3.6: Multiple regression results of Model 6: ROAi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

Standardized

coefficients (β)

t-value p VIF

HCE -0.038 -0.443 0.658 1.777

SCE 0.225 2.527 0.013* 1.889

CEE 0.597 8.110 0.000*** 1.294

Firm Size 0.035 0.515 0.608 1.121

Debt -0.046 -0.622 0.535 1.293

R2 = 0.393

Notes: H2b. HCE; H3b. SCE; H4b. CEE is positively associated with profitability as measured by

ROA

*statistically significant at p < .05; ***statistically significant at p < .001



Table 3.7: Multiple regression results of Model 7: ATOi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

Standardized

coefficients (β)

t-value p VIF

HCE -0.159 -2.179 0.031* 1.777

SCE -0.248 -3.286 0.001** 1.889

CEE 0.671 10.739 0.000*** 1.294

Firm Size -0.007 -0.129 0.898 1.121

Debt -0.014 -0.220 0.826 1.293

R2 = 0.563

Notes: H2c. HCE; H3c. SCE; H4c. CEE is positively associated with productivity as measured by ATO

*statistically significant at p < .05; **statistically significant at p < .01; ***statistically significant at

p < .001

Table 3.8: Multiple regression results of Model 8: ROEi=β1HCE+β2SCE+β3CEE+β4FSIZE+β5DEBT

Standardized

coefficients (β)

t-value p VIF

HCE -0.237 -2.593 0.010* 1.777

SCE 0.575 6.112 0.000*** 1.889

CEE 0.439 5.641 0.000*** 1.294

Firm Size -0.382 -5.272 0.000*** 1.121

Debt 0.342 4.396 0.000*** 1.293

R2 = 0.320

Notes: H2d. HCE; H3d. SCE; H4d. CEE is positively associated with profitability as measured by

ROE

*statistically significant at p < .05; ***statistically significant at p < .001

Results showed that HCE was a statistically significant moderate predictor for MB with

negative association (Model 5; β=-0.303; p=0.000***). MB may be viewed as an indicator to

show how investors value the sample companies. It seems that investors have long perceived

the expenses spent on employees as a cost, rather than as an investment. This is consistent

with the implication stated by Chan (2009b) that expenditures on human resources has been

consistently perceived as an expense rather than as an investment. The results reveal the

phenomenon that the higher the employee expenses, the lower the market valuation of the



company.

As seen in model 7, HCE was a statistically significant predictor for ATO with small,

negative association (Model 7; β=-0.159; p=0.031*), while CEE was a statistically significant

predictor for ATO with moderate, positive association (Model 7; β=0.671; p=0.000***).

These show that when enhancing productivity, local companies may exhibit a tendency to

employ physical and financial assets rather than human assets. In the traditional view of

productivity, given the same amount of input, a greater number of employees may result in

decreasing marginal output. In contrast, from the value creation perspective, human capital

may be looked upon as a depository of knowledge. The pool of knowledge contained in it,

when used effectively, becomes IC for value creation (Pulic, 2008), contributing to the

enhancement of a company‟s overall productivity. This contradiction reveals the gap between

the traditional accounting perspective and the value creation perspective when assessing

human capital. Apart from the ROA, HCE was also found to be able to predict MB, ATO and

ROE at different significance levels, although in negative correlations. Hence, the hypotheses

H2a, H2b, H2c and H2d were not substantiated.

The statistical associations between SCE and financial indicators provided some interesting

insights. The empirical results show that SCE exhibited an influence on MB and ROE as

indicated by the positive association with MB (Model 5; β=0.272; p=0.002**), although the

strength of this association may not be as strong as the other predictor, physical capital, in the

same model. It does, however, indicate that investors appeared to be considering structural

capital an important factor when making investment decisions. This result is further

supported by the finding that SCE was a moderate predictor with very high statistical

significance to ROE (Model 8; β=0.575; p=0.000***), as ROE acts as one of the important

indicators for investors to measure the financial conditions of businesses.

The statistical association found between SCE and ROE is a very interesting finding in this

research. Such a finding may imply that the sample companies surveyed were able to utilize

their structural capital, i.e., strategy, proprietary computer systems, routines and procedures,

to yield higher profits from the shareholders‟ equity. Among the independent variables and

control variables, SCE was found to be the strongest predictor of ROE as evidenced by the

highest beta value in Model 8, more so than that of physical capital (CEE).



The results in Model 8 provide the strongest evidence yet to suggest that structural capital

may be playing a more important role in profit generation and hence, shareholders‟ equity, for

companies in Hong Kong when compared with other VAIC™ components. It also illustrates

that the „management‟ of the sample companies have been very much guided by the

deployment of structural capital in achieving profitability, more so than physical capital. This

finding contrasts with that of a prior and similar study, where physical capital efficiency

(CEE) was found to be a better predictor for ROE (Chan, 2009b). Also, it contrasts with

findings in Ting and Lean (2009) that SCE had a negative, though not significant, effect on

ROA. As SCE had a positive and significant effect on MB, ROA and ROE, hypotheses H3a,

H3c and H3d were substantiated.

Overall, and apart from Model 8, CEE was found to be the best predictor for the four

financial measurements when examining the associations of the three VAIC™ components

with MB, ROA, ATO and ROE. This finding is consistent with the traditional accounting

point of view that physical and financial assets are critical when evaluating business

performance, and it also supports Ting and Lean (2009) in that capital employed has been

importantly utilized in generating high value returns. Hypotheses H4a, H4b, H4c and H4d

were supported by the regression results.

The control variables, i.e., firm size and firm leverage, were found to be in association with

business performance. Firm size appears to be a significant predictor for ROE (Model 8;

p=0.000***), while firm leverage is a highly significant predicator for MB (Model 5;

p=0.000***) and ROE (Model 8; p=0.000***). This is consistent with earlier findings by

Chan (2009b) which showed that there is a positive association between firm leverage and

ROE. However, this suggests a different conclusion from that of Firer and Williams (2003),

who found that firm size and leverage contributed very little to the explanatory power of the

linear multiple regression results, and were of statistical significance in only isolated cases. It

is also inconsistent with the findings of Kamath (2008) that there was no significant

association between the size and leverage of firms with their market valuation.

Moreover, the positive correlation between firm leverage and ROE shows that companies

with high gearing ratio tend to have higher profitability. As suggested by Chan (2009b), such

a finding may imply that Hong Kong listed companies are maintaining a high level of



investment, possibly with the assistance of borrowing or leverage, to enhance ROE for

shareholders.

4. Conclusion and Further Studies

The research results provide new insights for IC practitioners and business stakeholders into

the utilization of IC by businesses in Hong Kong. The statistical associations found between

structural capital and financial indicators may be the clearest evidence yet to show that

structural capital, one of the key components of IC, has an impact on business performance in

the companies surveyed in Hong Kong. The utilization and effective deployment of structural

capital is becoming an important tool for the managers of these companies to achieve

profitability. Some interesting research questions follow: “What is the major structural capital

in local companies that can affect corporate profitability the most?”; “Would it be

technologies or routines and procedures?” It also remains to be clarified whether or not

business companies are aware of the importance of structural capital to their financial

performance. Further studies may also examine how local businesses may cultivate their

structural capital in order to ensure a higher return. There might be potential differences in

structural capital utilization in different industries or sectors, which may be influenced by

company size or information technology. Knowledge management practices may further

improve structural capital usage, but this has yet to be substantiated by empirical findings. As

this research focused on local companies, questions such as: “Does the higher levels of usage

of structural capital apply only to Hong Kong?”, and “How does Hong Kong compare with

her neighboring countries?” are also worth investigating. We believe that there remains a vast

body of potential research, the findings of which would be of utmost importance and interest

not only to scholars and IC practitioners, but also to business management, investors and

other stakeholders as well. A coordinated research effort in IC in Hong Kong may help

answer at least some of these questions.



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Appendix 1

VAIC™ Calculation

VA = OP + EC + D + A

HCE = VA / HC

SCE = ( VA – HC ) / VA

CEE = VA / CE

VAIC = HCE + SCE + CEE

Note: CE = Total Assets – Intangible Assets

Financial Indicators Calculation

MB = Market Capitalization / Shareholders‟ Equity

ROA = Operating Profit / Total Assets

ATO = Total Revenue / Total Assets

ROE = Net Income / Shareholders‟ Equity

FSIZE = log (Market Capitalization)

DEBT = Total Debt / Total Assets

Notes: Shareholders’ equity refers to total equity in B/S

Market Capitalization = Market Price* # of shares

Appendix 2

Currency Conversion

USD against HKD: 1 USD = 7.8 HKD

RMB against HKD: Year Average exchange rate

2008 0.88 RMB=1 HKD

2007 0.94 RMB=1 HKD

2006 1 RMB=1 HKD

2005 1.04 RMB=1 HKD

Source: People‟s Bank of China



Rules of extracting figures from financial reports

Operating Profit (OP)

Obtained from “Consolidated Income Statement” or “Consolidated P & L”

Extracted from the field of “Operating profit”

If OP is not clearly specified in the income statement, then used “profit before tax and

other interests” as the rule, excluding finance related charges and income, share of P&L

from jointly controlled companies and associated companies and other non-operational

income and charges

Employee Costs

Total spending on employees

Including salaries, directors‟ remuneration, retirement benefits and other related

expenses

Depreciation and Amortization (D and A)

Sum of the “deprecation” or “amortization” of assets

Market Price

Closing price on the last trading day of the year

Extracted from Yahoo! Finance

Number of shares

Number of shares which were issued and fully paid

Extracted from the “Share Capital” under “notes to the accounts”

Net income

“Profit attributable to shareholders” in the “Consolidated Income Statement”

Total Revenue

“Turnover” or “Revenue” specified in the “Consolidated Income Statement”



Intangible Assets

Figure of Intangible Assets extracted based on the “Hong Kong Accounting Standard

38”

Including license, goodwill, leasehold land and land use right, brand name and other

rights

http://app1.hkicpa.org.hk/ebook/HKSA_Members_Handbook_Master/volumeII/hkas38.pdf

Total Equity

“Total Equity” in “Consolidated Balance Sheet”

Current, Non-current Assets and Liabilities

All extracted from the “Consolidated Balance Sheet”

http://app1.hkicpa.org.hk/ebook/HKSA_Members_Handbook_Master/volumeII/hkas38.pdf

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Chu 2011 Empirical Study of Impact of Intellectual Capital on Business Performance