s 0219649211002791

7/27/2019 s 0219649211002791

1/12

An Empirical Study of the Impact of IntellectualCapital on Business Performance

Samuel Kai Wah Chu * ,z, Kin Hang Chan y,x, Ka Yin Yu * ,{ ,Hing Tai Ng * ,jj and Wai Kwan Wong * ,**

*Faculty of Education University of Hong Kong, HKSAR

yInstitute for China Business School of Professional and Continuing Education

University of Hong Kong, HKSAR [email protected] [email protected] {[email protected]

[email protected] **kwan [email protected]

Abstract. This empirical study examines the intellectualcapital (IC) performance of Hong Kong companies and its associ-ation with business performance. Data were collected from con-stituent companiesof theHang Seng Index listed on theHong KongStock Exchange (2005 2008). An IC measurement, Value AddedIntellectual Coecient (VAIC TM ), was utilised to evaluate the ICinvestment of the companies.

Four accounting ratios: market-to-book value (MB), returnon 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 andits components in order to explain the variance in business per-formance measures.

No conclusive evidence was found to support the associationsbetween VAIC TM as an aggregate measure and the four nancialindicators. However, components of VAIC TM were found topredict a substantial variance in business performance. CapitalEmployed Eciency (CEE) was found to be a key factor inpredicting business nancial performance. Structural CapitalEciency (SCE) was found to have a signi cant eect onbusinesses' market valuation, as measured by MB, and on prof-

itability, as measured by ROE. Negative correlations were foundbetween Human Capital Eciency (HCE) and the nancialindicators. The ndings indicate a gap between the traditionalaccounting perspective and the value creation perspective, whichis central to the VAIC TM methodology in measuring IC.

It is believed that the ndings of this research provideinsights for business stakeholders of Hong Kong companies inutilising IC, particularly the noted impact of structural capital.While our ndings indicate the importance of IC for corpor-ations, as shown by the signi cant eect of SCE on ROE,physical and nancial assets may still be considered as the keyresources in delivering business success.

Keywords : Intellectual capital; VAIC TM ; nancial performance;value creation; Hang Seng Index.

1. Introduction

\Knowledge-based economy" is a term which has beenused widely to describe today`s global economy. Knowl-edge-based resources have been described as the mainsources in sustaining the competitive advantage of acompany ( Ting and Lean , 2009). Placing the emphasis on

knowledge production rather than on the production of physical goods has been suggested to make up value cre-ation ( Pulic , 2008). This transformation of values hascreated a new perspective in viewing the resources of acompany. In recent years, intellectual capital (IC) hasbeen viewed as a factor that has an impact on businessperformance and critical in the value creation process in aknowledge-based economy ( Sveiby, 1997; Lynn , 1998;Pulic , 1998). IC has also been viewed as the roots of acompany's value ( Edvinsson and Malone , 1997). As a resultof the increasing recognition of the role of IC in business,researchers have become keen on assessing its impact on thebusiness performance of companies.

The more traditional and commonly used measures of business performance include the assessment of pro-ductivity, protability and market evaluation ( Firer andWilliams , 2003). Productivity measures the output con-version of input, while protability refers to the degree towhich the revenue of a business exceeds its costs. Finally,market evaluation describes the degree to which themarket value of a business surpasses its book value.

While a growing body of research has shown the posi-tive association of IC with the business performance of acompany ( Ting and Lean , 2009), this link needs to be

Journal of Information & Knowledge Management, Vol. 10, No. 1 (2011) 11 21#.c World Scientic Publishing Co.DOI: 10.1142/S0219649211002791

11
http://dx.doi.org/10.1142/S0219649211002791http://dx.doi.org/10.1142/S0219649211002791

7/27/2019 s 0219649211002791

2/12

conrmed in dierent geographic settings and industries(Cabrita and Bontis, 2008). Earlier studies on IC in HongKong have been focused on voluntary reporting (Guthrie,et al. , 2006; Petty and Cuganesan, 2005) and IC per-formance ( Young et al. , 2009). A preliminary study on the

impact of IC on organisational performance in Hong Konghas shown a lack of association between IC and nancialperformance indicators, which is contrary to the evidencein other parts of the world ( Chan , 2009b). This presentresearch builds on the studies that have been done so far inHong Kong. The study investigates the associationbetween IC and business performance among constituentcompanies of the Hang Seng Index (HSI) listed on theHong Kong Stock Exchange from 2005 2008. Specically,multiple regression was used to examine the association of IC, measured by the Value Added Intellectual Coecient(VAIC TM ), with traditional business performance indi-cators. The ndings of this study expand on the currentknowledge base regarding the relevance of IC in corporateperformance. Furthermore, the ndings oer insights forbusiness entities in Hong Kong, which positions itself as aknowledge-based economy.

1.1. Business performance indicators

In a resource-based view of business, benets that aremeasured with consideration to both tangible and intangibleassets have gained acceptance ( Canibano et al. , 2000).

However, measures of nancial performanceremain themostdominant model in examining business performance (Hofer,1983). Financial indicators have been assumed to reect thefullment of economic goals of a business entity, and thesemake up a component of business performance indicators(Venkatraman and Ramanujam , 1986). A number of accounting- and market-based measures have been utilisedas proxy measures to measure productivity, pro tability andmarket evaluation. Earlier studies by Firer and colleagues(Firer and Williams , 2003; Firer and Stainbank, 2003) on ICand business performance have utilised three measures,namely, return on assets (ROA), asset turnover (ATO), andmarket to book value (MB). ROA was represented by theratio of the net income to the book value of total companyassets. ATO was the ratio of the total revenue to thetotal book value of assets, while MB was the ratio of thetotal market capitalisation to the book value of net assets.Additionally, return on equity (ROE) has been commonlyused in nancial reporting and refers to the ratio of netincome to the total shareholders' equity ( Chan , 2009b).

1.2. Intellectual capital ( IC )

Physical capital, which refers to \the traditional inputs of land, labour and capital" ( Goh, 2005, p. 386), has always

been a crucial indicator for valuing a company's businessin the past. However, although it has been regarded ascritical to a company's operations, it may not truly reectthe changes and conditions in today's businesses(Mohiuddin et al. , 2006). Conventional accounting indi-

cators may not have adequately considered IC elements,resulting in an unexplained market premium ( Edvinssonand Sullivan , 1996), which was also noted by Pulic (2008).

The rst time IC was discussed in the business contextwas in the 1990s ( Yalama and Coskun , 2007). As explainedby Stewart (1997), IC is the greatest source of value andcompetitive advantage. This summation of knowledge isvalue added for the company and is used in the businesscreation process ( Ze ghal and Maaloul , 2010). Salleh andSelamat (2007) described IC as the aggregation of humancapital, structural capital and customer capital. IC has alsobeen viewed as the result of, or the intellectual propertygenerated from, the process of knowledge transformation(Ting and Lean , 2009) As stated by Appuhami (2007,p. 14), \the intellectual capital of a rm plays a signicantrole in the modern approach of value creation". To sum-marise, IC may be referred to as the sum of knowledgewithin an organisation, which involves value creation andgives competitive advantage to business organisations. Ascommented by Stewart (1997, p. 56), IC has become sovital that it would be fair to say that an organisation thatis not managing knowledge is \not paying attention tobusiness".

In general, IC can be classied into two major cat-egories: human capital and structural capital ( Edvinsson ,1997; Edvinsson and Malone , 1997; Bontis , 2004). Humancapital \is in the heads of employees", while structuralcapital is \what is left in the organisation when people gohome in the evening" ( Roos and Roos , 1997, p. 415). Theexamples that Ting and Lean (2009, p. 590) used toidentify human capital include \innovation capacity,creativity, know-how and previous experience, teamworkcapacity, employee exibility, tolerance for ambiguity,motivation, satisfaction, learning capacity, loyalty, formal

training and education". For structural capital, Bontiset al. (2000) gave examples such as databases, organis-ational charts, process manuals, strategies and routines.Properly managed IC has been regarded as the key drivingfactor for sustainable corporate success ( Yalama andCoskun , 2007; Ting and Lean , 2009). However, althoughIC has been regarded as the key factor inuencing thefuture value of a rm ( Yalama and Coskun , 2007), usingtraditional nancial indicators may not be sucient toillustrate the value of IC, as it may only reect theaccountant`s view towards the performance of the rm

and may be misleading to stakeholders ( Kamath , 2008).VAIC TM may be a better indicator and method of

12 S. K. W. Chu et al.

7/27/2019 s 0219649211002791

3/12

reecting the market value of businesses ( Young et al. ,2009).

1.3. Value added intellectual coecient (VAIC TM )

Value Added Intellectual Coecient (VAIC TM ) i s amethod developed by Ante Pulic to measure the valuecreation eciency of a company using accounting-basedgures ( Pulic , 2000). Companies with a higher VAIC TM

indicate that they have a higher value creation in using allavailable resources, i.e. IC, human capital, structuralcapital and physical capital.

VAIC TM is considered as a \universal indicator showingabilities of a company in value creation and representing ameasure for business eciency in a knowledge-basedeconomy" ( Pulic , 1998, p. 9). Also, as stated in Kamath

(2007, p. 98), VAIC TM is a management and control toolthat is \designated to monitor and measure the IC per-formance and potential of the rm". This indicator hasbeen widely applied in various research studies (seeTable 1) as a means of measuring IC ( Zeghal and Maaloul ,2010; Chan , 2009a; Kamath , 2008; Tan et al. , 2007;Yalama and Coskun , 2007; Mohiuddin et al. , 2006; Shiu,2006; Goh, 2005; Mavridis , 2005, 2004; Firer and Williams ,2003). This method is \designed to provide information

about the value creation eciency of tangible and intangi-ble assets within a company during operations" ( Tan et al. ,2007, p. 91). The concept of \value added" is incorporatedin the formulation of VAIC TM . Value added per pro-fessional may be regarded as the purest measure to produce

economic value in a knowledge-based company ( Sveiby,2001).The formulation of VAIC TM matches the denition of

IC. First of all, VAIC TM is obtained by adding HumanCapital Eciency (HCE), Structural Capital Eciency(SCE) and Capital Employed Eciency (CEE), thusincorporating the concept of classifying IC into humancapital and structural capital. Secondly, at present thereis more emphasis on the skills and knowledge of theemployees than on the physical assets of a company.(Muhammad , 2009) Due to the active role of value cre-ation in the process ( Pulic , 2000), employees' expenses areseen as an investment rather than as a cost in the calcu-lation of VAIC TM .

1.4. Prior studies using the VAIC TM

methodology

A number of studies have used theVAIC TM methodology toexamine IC, and its associations with other business per-formance measures have not been consistent. For instance,

Table 1. Examples of research studies adopting VAIC TM .

Author(s) Purpose of study

Ze ghal and Maaloul(2010)

To analyse the role of value added as an indicator of IC and its impact on the rm'seconomic, nancial and stock market performance in a sample of 300 UK companies.

Chan (2009a) To investigate if IC has an impact on the nancial aspects of organisational performanceand to identify whether IC components may be the drivers for the leading nancialindicators of listed companies in Hong Kong.

Kamath (2008) To study the relationship between the IC components, namely human, structural andphysical capital, with the traditional measures of performance of company in theIndian drug and pharmaceutical industry.

Tan, Plowman andHancock (2007)

To investigate, using the Pulic framework, the association between the IC of 150 publiclisted companies on the Singapore Exchange and their nancial performance.

Yalama and Coskun(2007) To test the eect of IC on the protability of quoted banks on the Istanbul StockExchange Market (ISE) in Turkey using VAIC TM and DEA.Mohiuddin, Najibullah

and Shahid (2006)To evaluate the IC performance of commercial banks in Bangladesh and to rank the

banks with high level of human capital eciency (HCE), capital employed eciency(CEE) and structural capital eciency (SCE).

Shiu (2006) To investigate the co-relation of VAIC TM and corporate performance in technologicalrms in Taiwan.

Goh (2005) To measure the IC performance of commercial banks in Malaysia from 2001 to 2003.Mavridis (2005) To study the intellectual performance of Greek listed companies on the Athens Stock

Exchange under the distinctive aspect of being a \globalised" or localised rm.Mavridis (2004) To analyse the IC of the Japanese banking sector and discuss the impact on the banks'

value-based performance.Firer and Williams

(2003)To study 75 South African publicly listed rms and to examine the correlation between

the eciency of value added by the companies major components and theprotability, productivity and market valuation of the companies.

Impact of Intellectual Capital 13

7/27/2019 s 0219649211002791

4/12

Ze ghal and Maaloul (2010) found a positive relationshipbetween IC and nancial performance in high-technologyindustries. Similarly, a study on Taiwan's companies foundthat IC investment had a positive impact on a rm's marketvalue and nancial performance ( Chen et al. , 2005). How-

ever, Firer and Williams (2003) found that physical capitalwas the most signicant underlying resource of corporateperformance in South Africa, and Chan (2009b) found noconclusive evidence to support a denitive associationbetween IC and nancial performance among Hong Kongcompanies.

Other studies ( Chen et al. , 2005; Kujansivu, 2005;Shiu, 2006b) have found both human and physical capitalto be positively associated with nancial performance.More specically, structural capital has been found to be acritical link that enabled IC to be measured at the organ-isational level, which means that, forexample, if a companyhasgood systems andprocedures, then IC eciency is likelyto 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 marketindexes in Hong Kong which indicates the overall marketperformance of the Hong Kong Stock Exchange (HKSE).HSI is commonly regarded as the representative of the

state of the Hong Kong economy and representsapproximately 67.3% of the total market capitalisation inHong Kong between 2005 and 2008 ( Hang Seng Indexes ,2008). On a year-to-year basis, the number of HSI con-stituent companies varies. The study sample includedall HSI constituent companies over a 4-year period(2005 n 33, 2006n 36, 2007n 43, 2008n 42).Data were collected from 154 published annual reportsand are referred to in this study as company-year cases.Three cases, which obtained either a negative book valueor a negative VAIC TM , were considered as problematic

and removed from the nal sample size (N

151) to avoidthe eect of outliers. Table 2 summarises the distributionof company-year cases according to business sectors.

2.2. Independent variables

VAIC TM and its components (HCE, SCE, CEE) wereused separately as independent variables. This is todierentiate the classication of IC into human capitaland structural capital ( Muhammad , 2009). Calculation of VAIC TM using accounting-based gures involves vesteps ( Pulic , 2000; Chan , 2009a), which has been illus-

trated in detail by other researchers ( Zeghal and Maaloul ,2010; Chan , 2009a; Kamath , 2008). For simplicity, the

practical procedures of calculating VAIC TM is demon-strated 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 prots; EC total employeeexpenses, which is viewed as investment; D depreciation;A amortisation. Secondly, the Human Capital Eciency(HCE) and Structural Capital Eciency (SCE) werecalculated.

HCE VA =HC

SCE VA HC =VA

where HC human capital, measured by total employeeexpenses. Since IC can only be operable with the support of nancial and physical capital, Capital Employed Eciency

(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 TM , which acted as an inde-pendent variable aecting the traditional nancial per-formance of companies, was obtained by summing up HCE,SCE and CEE.

2.3. Dependent variables

Four traditional nancial indicators were used as depen-dent variables and served as proxy measures of pro-ductivity, pro tabilityand market valuation. These includemarket-to-book value (MB), return on assets (ROA), assetturnover (ATO) and return on equity (ROE), which havebeen used in earlier studies ( Firer and Williams , 2003;Chan , 2009a).

2.4. Data analysis

Regression analysis was conducted to investigate the

association between IC performance and business per-formance, where VAIC TM and its components served as

Table 2. Sample distribution by sectors.

Sectors Frequency/company-year %

Commerce and industry 85 56.3Finance 32 21.2Properties 22 14.6Utilities 12 7.9Total 151* 100

Notes : Company-year after removing three sets of problematic data.


7/27/2019 s 0219649211002791

5/12

independent variables, while the MB, ROA, ATO, andROE were dependent variables. The formulas and rules forextracting gures from nancial reports used in thisresearch are detailed in the Appendices. Firm size (FSIZE)and rm leverage (DEBT) were added to the models as two

control variables ( Firer and Williams , 2003; Chan , 2009a),which helped reduce the eect of unknown variables(Shuttleworth , 2008).

2.5. Research hypotheses

The overarching research question asks: What is the impact of IC on corporate nancial performance , in relation to productivity , protability and market valuation ,of major companies in Hong Kong ? Considering thatearlier studies have generated inconclusive answers to thisresearch question (see Section 1.4), a number of hypoth-eses were generated to clarify the evidence.

2.5.1. Association of an aggregate VAIC TM measure with nancial performance indicators

The aggregate measure based on the components of VAIC TM represents a total measure of IC. In the followinghypotheses, the association of this aggregate measure witheach nancial indicator is examined.

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

H1b. VAICTM

is positively associated with pro t-ability as measured by ROA.H1c. VAIC TM is positively associated with pro-

ductivity as measured by ATO.H1d. VAIC TM is positively associated with return on

equity as measured by ROE.

2.5.2. Association of VAIC TM components with nancial performance indicators

The three components of VAIC TM reect the classicationof IC into physical, human and structural capital. Earlier

studies have shown that the associations of these com-ponents with nancial performance indicators are notuniform ( Chan , 2009b; Chen et al. , 2005). Thus, the fol-lowing hypotheses were generated:

H2a. HCE is positively associated with market valua-tion as measured by MB.

H2b. HCE is positively associated with protability asmeasured by ROA.

H2c. HCE is positively associated with productivity asmeasured by ATO.

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

H3a. SCE is positively associated with market valua-tion as measured by MB.

H3b. SCE is positively associated with protability asmeasured by ROA.

H3c. SCE is positively associated with productivity as

measured by ATO.H3d. SCE is positively associated with return onequity as measured by ROE.

H4a. CEE is positively associated with market valua-tion as measured by MB.

H4b. CEE is positively associated with protability asmeasured by ROA.

H4c. CEE is positively associated with productivity asmeasured by ATO.

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

2.6. Regression models

Eight regression models were used where the rst fourmodels investigated the association between the aggregateVAIC TM measure and the four dependent variables. Thelast four models were used to analyse each of the threecomponents of VAIC TM and the dependent variables.These models are illustrated in the regression equations inTable 3.

3. FindingsTables 4 to 11 reveal the correlations between the depen-dent and independent variables obtained by conductingthe Pearson product-moment correlation analysis. Stat-istical values such as standardised coecients ( ) andcoecient of determinations (R-square) are used in thefollowing discussion to illustrate the predictive capabilityand explanatory power of the models.

Table 3. Regression models.

Model Regression equation

1 MBi 1VAIC TM 2FSIZE 3DEBT 2 ROAi 1VAIC TM 2FSIZE 3DEBT 3 ATOi 1VAIC TM 2FSIZE 3DEBT 4 ROEi 1VAIC TM 2FSIZE 3DEBT 5 MBi 1HCE 2SCE 3CEE 4FSIZE

5DEBT 6 ROAi 1HCE 2SCE 3CEE 4FSIZE

5DEBT 7 ATOi 1HCE 2SCE 3CEE 4FSIZE

5DEBT 8 ROEi 1HCE 2SCE 3CEE 4FSIZE

5DEBT


7/27/2019 s 0219649211002791

6/12

3.1. The associations between VAIC TM

and the nancial indicators

The coecients of determination ( R 2) of hypotheses H1a

to H1d indicated limited explanatory power for the var-iances in the dependent variables. Even Model 2 (Table 5),

Table 4. Multiple regression results of Model 1: MB i = 1VAIC TM 2FSIZE 3DEBT.

Standardised t -value p VIFcoecients ( )

VAIC TM 0.026 0.305 0.760 1.112Firm size 0.162 1.952 0.053 1.057Debt 0.110 1.289 0.200 1.121R 2 = 0.045

Notes : H1a. VAIC TM is positively associated with marketvaluation as measured by market-to-book value.

Table 5. Multiple regression results of Model 2: ROA i = 1VAIC TM 2FSIZE 3DEBT.

Standardised t -value p VIF

coecients ( )VAIC TM 0.192 2.355 0:020 1.112Firm size 0.092 1.161 0.248 1.057Debt 0:232 2:837 0:005 1.121R 2 0:121

Notes : H1b. VAIC TM is positively associated with prot-ability as measured by return on assets. Statistically sig-nicant at p < 0:05; statistically signicant at p < 0:01.

Table 6. Multiple regression results of Model 3: ATO i 1VAIC TM 2FSIZE 3DEBT.


VAIC TM 0.173 2.035 0.044 1.112Firm size 0.028 0.342 0.733 1.057Debt 0.151 1.772 0.079 1.121R 2 = 0.042

Notes : H1c. VAIC TM is positively associated with pro-ductivity as measured by asset turnover. Statisticallysignicant at p < 0:05.

Table 7. Multiple regression results of Model 4: ROE i 1VAIC TM 2FSIZE 3DEBT.


VAIC TM 0.155 1.862 0.065 1.112Firm size 0:269 3:316 0:001 1.057Debt 0.143 1.714 0.089 1.121R 2 0:083

Notes : H1d. VAIC TM is positively associated with prot-

ability as measured by return on equity. Statisticallysignicant at p < 0.01.

Table 8. Multiple regression results of Model 5: MB i 1HCE 2SCE 3CEE 4FSIZE 5DEBT.


HCE 0:303 3:665 0.000 1.777SCE 0.272 3.193 0.002 1.889CEE 0.716 10.154 0.000 1.294Firm size 0.092 1.398 0.164 1.121Debt 0.336 4.772 0.000 1.293R 2 0:443

Notes : H2a. HCE; H3a. SCE; H4a. CEE is positivelyassociated with market valuation as measured by MB.Statistically signicant at p < 0:01; statistically sig-nicant at p < 0:001.

Table 9. Multiple regression results of Model 6: ROA i

1HCE 2SCE 3CEE 4FSIZE 5DEBT.


HCE 0:038 0:443 0.658 1.777SCE 0.225 2.527 0:013 1.889CEE 0.597 8.110 0:000 1.294Firm size 0.035 0.515 0.608 1.121Debt 0:046 0:622 0.535 1.293R 2= 0.393

Notes : H2b. HCE; H3b. SCE; H4b. CEE is positivelyassociated with pro tability as measured by ROA.Statistically signicant at p < 0:05; statistically sig-nicant at p < 0:001.

Table 10. Multiple regression results of Model 7: ATO i 1HCE 2SCE 3CEE 4FSIZE 5DEBT.


HCE 0:159 2:179 0:031 1.777SCE 0:248 3:286 0:001 1.889CEE 0.671 10.739 0:000 1.294Firm size 0:007 0:129 0.898 1.121Debt 0:014 0:220 0.826 1.293R 2= 0.563

Notes : H2c. HCE; H3c. SCE; H4c. CEE is positivelyassociated with productivity as measured by ATO.Statistically signicant at p < 0:05; statistically signi-cant at p < 0:01; statistically signicant at p < 0:001.


7/27/2019 s 0219649211002791

7/12

which had the highest coecient of determination(R 2 0:121; 12% explained variance), was unable to meetthe threshold of Cohen's minimum standard of 14% forlarge eect size (Grissom and Kim , 2005).

Statistically, VAIC TM obtained the strongest signi-cance level on ROA and ATO (for ROA, Model 2, p 0:020; for ATO, Model 3, p 0:044). However, thecoecients of determination were not adequate to supportthe ability of VAIC TM to predict the variance in ROA andATO. Viewing R 2 in a reverse manner, that is the coe-cient 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 3were too weak to claim that associations existed, and itwas of little practical eect and importance to theresearch. Hypotheses H1a, H1b, H1c and H1d weretherefore not substantiated by the ndings.

The limited association between VAIC TM and theconventional nancial indicators is consistent with thendings of Chan (2009b) on the HSI constitutent compa-nies from 2001 to 2005. This earlier study showed that therewas no strong association between corporate intellectual

ability and the traditional nancial performance measures.One possible explanation suggested by Chan (2009b) isthat local companies, when enhancing protability, areplacing more emphasis on other types of strategic assetsthan on IC. The ndings of this present study indicate thatIC investment, as a means of enhancing business perform-ance, persists to be weak in Hong Kong.

3.2. The associations between VAIC TM

components and nancial indicators

HCE, SCE and CEE were found to be better predictors of

the variance in the nancial indicators relative to theaggregate VAIC TM measure. The ndings are summarised

in Tables 6 to 10, showing higher amounts of explainedvariances.

Results showed that HCE was a statistically signicantmoderate predictor for MB with negative association(Model 5; 0:303; p 0:000). MB may be viewed

as an indicator to show how investors value the samplecompanies. It seems that investors have long perceived theexpenses spent on employees as a cost, rather than as aninvestment. This is consistent with the implication statedby Chan (2009b) that expenditures on human resourceshas been consistently perceived as an expense rather thanas an investment. The results reveal the phenomenon thatthe higher the employee expenses, the lower the marketvaluation of the company.

As seen in Model 7, HCE was a statistically signicantpredictor for ATO with small, negative association(Model 7; 0:159; p 0:031), while CEE was astatistically signicant predictor for ATO with moderate,positive association (Model 7; 0:671; p 0:000).These show that when enhancing productivity, localcompanies may exhibit a tendency to employ physical andnancial assets rather than human assets. In the tra-ditional view of productivity, given the same amount of input, a greater number of employees may result indecreasing marginal output. In contrast, from the valuecreation perspective, human capital may be looked uponas a depository of knowledge. The pool of knowledgecontained in it, when used eectively, becomes IC for valuecreation ( Pulic , 2008), contributing to the enhancementof a company's overall productivity. This contradictionreveals the gap between the traditional accounting per-spective and the value creation perspective when assessinghuman capital. Apart from the ROA, HCE was also foundto be able to predict MB, ATO and ROE at dierentsigni cance levels, although in negative correlations.Hence, the hypotheses H2a, H2b, H2c and H2d were notsubstantiated.

The statistical associations between SCE and nancialindicators provided some interesting insights. The

empirical results show that SCE exhibited an inuence onMB and ROE as indicated by the positive association withMB (Model 5; 0:272; p 0:002), although thestrength of this association may not be as strong as theother predictor, physical capital, in the same model. Itdoes, however, indicate that investors appeared to beconsidering structural capital an important factor whenmaking investment decisions. This result is further sup-ported by the nding that SCE was a moderate predictorwith very high statistical signicance to ROE (Model 8;

0:575; p 0:000), as ROE acts as one of the im-

portant indicators for investors to measure the nancialconditions of businesses.

Table 11. Multiple regression results of Model 8: ROE i 1HCE 2SCE 3CEE 4FSIZE 5DEBT.


HCE 0:237 2:593 0:010 1.777SCE 0.575 6.112 0:000 1.889CEE 0.439 5.641 0:000 1.294Firm Size 0:382 5.272 0:000 1.121Debt 0.342 4.396 0:000 1.293R 2 0:320

Notes : H2d. HCE; H3d. SCE; H4d. CEE is positivelyassociated with pro tability as measured by ROE.Statistically signicant at p < 0:05; statistically signi-cant at p < 0:001.


7/27/2019 s 0219649211002791

8/12

The statistical association found between SCE andROE is a very interesting nding in this research. Such anding may imply that the sample companies surveyedwere able to utilise their structural capital, i.e. strategy,proprietary computer systems, routines and procedures, to

yield higher prots from the shareholders' equity. Amongthe independent variables and control variables, SCE wasfound to be the strongest predictor of ROE as evidencedby the highest beta value in Model 8, more so than that of physical capital (CEE).

The results in Model 8 provide the strongest evidenceyet to suggest that structural capital may be playing amore important role in prot generation and hence,shareholders' equity, for companies in Hong Kong whencompared with other VAIC TM components. It also illus-trates that the `management' of the sample companieshave been very much guided by the deployment of struc-tural capital in achieving protability, more so thanphysical capital. This nding contrasts with that of a prior and similar study, where physical capital eciency (CEE)was found to be a better predictor for ROE ( Chan , 2009b).Also, it contrasts with ndings in Ting and Lean (2009)that SCE had a negative, though not signicant, eect onROA. As SCE had a positive and signicant eect on MB,ROA and ROE, hypotheses H3a, H3c and H3d weresubstantiated.

Overall, and apart from Model 8, CEE was found to bethe best predictor for the four nancial measurementswhen examining the associations of the three VAIC TM

components with MB, ROA, ATO and ROE. This ndingis consistent with the traditional accounting point of viewthat physical and nancial assets are critical when eval-uating business performance, and it also supports Tingand Lean (2009) in that capital employed has beenimportantly utilised in generating high value returns.Hypotheses H4a, H4b, H4c andH4d were supported by theregression results.

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

Firm size appears to be a signicant predictor for ROE(Model 8; p 0:000), while rm leverage is a highlysignicant predicator for MB (Model 5; p 0:000) andROE (Model 8; p 0:000). This is consistent withearlier ndings by Chan (2009b) which showed that thereis a positive association between rm leverage and ROE.However, this suggests a dierent conclusion from that of Firer and Williams (2003), who found that rm size andleverage contributed very little to the explanatory powerof the linear multiple regression results and were of

statistical signicance in only isolated cases. It is alsoinconsistent with the ndings of Kamath (2008) that therewas no signicant association between the size and lever-age of rms with their market valuation.

Moreover, the positive correlation between rm lever-

age and ROE shows that companies with high gearingratio tend to have higher protability. As suggested byChan (2009b), such a nding may imply that Hong Konglisted companies are maintaining a high level of invest-ment, 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 prac-titioners and business stakeholders into the utilisation of IC by businesses in Hong Kong. The statistical associ-ations found between structural capital and nancialindicators may be the clearest evidence yet to show thatstructural capital, one of the key components of IC, has animpact on business performance in the companies sur-veyed in Hong Kong. The utilisation and eectivedeployment of structural capital is becoming an importanttool for the managers of these companies to achieveprotability. Some interesting research questions follow:\What is the major structural capital in local companiesthat can aect corporate protability the most?"; \Wouldit be technologies or routines and procedures?" It alsoremains to be claried whether or not business companiesare aware of the importance of structural capital to theirnancial performance. Further studies may also examinehow local businesses may cultivate their structural capitalin order to ensure a higher return. There might bepotential dierences in structural capital utilisationbetween dierent industries or sectors, which may beinuenced by company size or information technology.Knowledge management practices may further improvestructural capital usage, but this is yet to be substantiatedby empirical ndings. 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 neighboringcountries?" are also worth investigating. We believe thatthere remains a vast body of potential research, the nd-ings of which would be of utmost importance and interestnot only to scholars and IC practitioners, but also tobusiness management, investors and other stakeholders aswell. A coordinated research eort into IC in Hong Kongmay help answer at least some of these questions.


7/27/2019 s 0219649211002791

9/12

Appendix 1

Appendix 2

Rules of extracting gures from nancial reportsOperating Prot (OP)

. Obtained from \Consolidated Income Statement" or \Consolidated P & L"

. Extracted from the eld of \Operating Prot"

. If OP is not clearly specied in the income statement, then used \prot before tax and other interests" as the rule,excluding nance-related charges and income, share of P&L from jointly controlled companies and associatedcompanies and other non-operational income and charges.

Employee costs

. Total spending on employees

. Including salaries, directors' remuneration, retirement benets and other related expenses.

Depreciation and Amortization (D and A)

. Sum of the \deprecation" or \amortization" of assets.

VAIC TM calculation

VA OP EC D A

HCE VA/HCSCE (VA HC)/VACEE VA/CE

VAIC HCE SCE CEE

Notes : CE Total assets Intangibleassets.

Financial Indicators Calculation

MB Market capitalization/shareholders' equityROA Operating prot/total assetsATO Total revenue/total assetsROE Net income/shareholders' equity

FSIZE log (market capitalization)DEBT Total debt/total assets

Notes : Shareholders' equity refers to total equity in B/S.Market capitalisation = Market price # of shares.

Currency conversion

USD against HKD: 1 USD 7:8 HKDRMB against HKD: Year Average exchange rate

2008 0:88RMB 1HKD2007 0:94RMB 1HKD2006 1RMB 1HKD2005 1:04RMB 1 HKD

Source : People's bank of China.


7/27/2019 s 0219649211002791

10/12

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

. \Prot attributable to shareholders" in the \Consolidated Income Statement".

Total revenue

. \Turnover" or \Revenue" specied 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".

References

Appuhami, BAR (2007). The impact of intellectual capi-tal on investors' capital gains on shares: An empiricalinvestigation of Thai banking, nance & insurancesector. International Management Review , 3(2), 1425.

Bontis, N (2004). National intellectual capital index: AUnited Nations initiative for the Arab region. Journal of Intellectual Capital , 5(1), 1339.

Bontis, N, WCC Keow and S Richardson (2000). Intel-lectual capital and business performance in Malaysianindustries. Journal of Intellectual Capital , 1(1), 85100.

Cabrita, M and N Bontis (2008). Intellectual capital andbusiness performance in the Portuguese bankingindustry. International Journal of Technology Man-agement , 43, 212237.

Canibano, L, M Garc a-Ayuso and P S a nchez (2000).Accounting for intangibles: A literature review. Journal of Accounting Literature , 19, 102130.

Chan, KH (2009a). Impact of intellectual capital onorganisational performance: An empirical study of companies in the Hang Seng Index (Part 1). The Learning Organization , 16(1), 421.

Chan, KH (2009b). Impact of intellectual capital onorganisational performance: An empirical study of com-

panies in the Hang Seng Index (Part 2). The Learning Organization , 16(1), 2239.

Chen, MC, SJ Cheng and Y Hwang (2005). An empirical

investigation of the relationship between intellectualcapital and rms' market value and nancial perform-ance. Journal of Intellectual Capital , 6(2), 159176.

Edvinsson, L (1997). Developing capital at Skandia. Long Range Planning , 30(3), 366373.

Edvinsson, L and M Malone (1997). Intellectual Capital:Realizing Your Company's True Value by Finding itsHidden Brainpower. New York: Harper Collins.

Edvinsson, L and P Sullivan (1996). Developing a modelfor managing intellectual capital. European Manage-ment Journal , 14(4), 356364.

Firer, S and L Stainbank (2003). Testing the association

between intellectual capital and entity performance:Empirical evidence from South Africa. Meditari , 12,2345.

Firer, S and SM Williams (2003). Intellectual capital andtraditional measures of corporate performance. Journal of Intellectual Capital , 4(3), 348360.

Goh, PC (2005). Intellectual capital performance of thecommercial banks in Malaysia. Journal of Intellectual Capital , 6(3), 385396.

Grissom, RJ and JJ Kim (2005). Eect Sizes for Research:A Broad Practical Approach, Mahwah, NJ: LawrenceErlbaum Associates.

Guthrie, J, R Petty and F Ricceri (2006). The voluntaryreporting of intellectual capital: Comparing evidence


7/27/2019 s 0219649211002791

11/12

from Hong Kong and Australia. Journal of Intellectual Capital , 7(2), 254271.

Hang Seng Indexes (2008). Press Releases , http://www.hsi.com.hk/HSI-Net/HSI-Net, accessed on 20 August, 2010.

Hofer, CW (1983). ROVA: A new measure for assessingorganizational performance. In Advances in Strategic Management , R Lamb (ed.), 43 55. New York: JAIPress.

Kamath, GB (2007). The intellectual capital performanceof Indian banking sector. Journal of Intellectual Capital , 8(1), 96123.

Kamath, GB (2008). Intellectual capital and corporateperformance in Indian pharmaceutical industry,Journal of Intellectual Capital , 9(4), 684704.

Kujansivu, P (2005). Intellectual capital performancein Finnish companies. Proceedings of the 2005 3rd Conference on Performance Measurement and Management Control , 114.

Lynn, B (1998). Intellectual capital. CMA Magazine ,72(1), 1015.

Mavridis, DM (2004). The intellectual capital perform-ance of the Japanese banking sector. Journal of Intel-lectual Capital , 5(1), 92115.

Mavridis, DM (2005). Intellectual capital performancedeterminants and globalization status of Greek listedrms. Journal of Intellectual Capital , 6(1), 127140.

Mohiuddin, M, S Najibullah and AI Shahid (2006). Anexploratory study on intellectual capital performance of the commercial banks in Bangladesh. The Cost and Management , 34(6), 4054.

Muhammad, NMN (2009). Intellectual capital eciencyand rm's performance: Study on Malaysian nancialsectors. International Journal of Economics and Finance , 1(2), 206212.

Petty, R and S Cuganesan (2005). Voluntary disclosureof intellectual capital by Hong Kong companies:Examining size, industry and growth eects over time.Australian Accounting Review , 15(2), 4050.

Pulic, A (1998). Measuring the performance of intellectualpotential in knowledge economy, in 2nd World Con-gress on Measuring and Managing Intellectual Capital ,McMaster University, Hamilton.

Pulic, A (2000). VAIC - an accounting tool for ICmanagement. International Journal of Technology Management , 20(58), 702714.

Pulic, A (2008). The principles of intellectual capital e-ciency a brief description, in Inspired by Knowledge in Organisations: Essays in Honor of Professor Karl-Erik Sveiby on his 60th Birthday 29th June 2008 .

G. Ahonen (ed.). Helsinki, Finland: Swedish School of Economics and Business Administration.

Roos, G and J Roos (1997). Measuring your company'sintellectual performance. Long Range Planning , 30(3),413426.

Salleh, AL and F Selamat (2007). Intellectual capitalmanagement in Malaysian public listed companies.International Review of Business Research Paper , 3(1),266278.

Shiu, HJ (2006a). The application of the value addedintellectual coecient to measure corporate perform-ance: Evidence from technological rms. International Journal of Management , 23(2), 356365.

Shiu, HJ (2006b). Application of the VAIC method tomeasure corporate performance: A quintile regressionapproach. Journal of American Academy of Business ,8(2), 156160.

Shuttleworth, M (2008). Controlled Variables , http://www.experiment-resources.com/controlled-variables.html, accessed on 19 June, 2010.

Stewart, TA (1997). Intellectual Capital: The New Wealth of Organizations . New York: Doubleday/Currency.

Sveiby, K (1997). The New Organizational Wealth:Managing and Measuring Knowledge Based Assets . SanFrancisco: Berrett-Koehler.

Sveiby, KE (2001). Measuring Competence , http://www.sveiby.com/articles/MeasureCompetence.html, acces-sed on 19 June, 2010.

Tan, HP, D Plowman and P Hancock (2007). Intellectualcapital and nancial returns of companies. Journal of

Intellectual Capital , 8(1), 7695.Ting, WKI and HH Lean (2009). Intellectual capital

performance of nancial institutions in Malaysia.Journal of Intellectual Capital , 10(4), 588599.

Yalama, A and M Coskun (2007). Intellectual capital per-formance of quoted banks on theIstanbul stock exchangemarket. Journal of Intellectual Capital , 8(2), 256271.

Young, CS, HY Su, SC Fang and SR Fang (2009). Cross-country comparison of intellectual capital performanceof commercial banks in Asian economies. The Services Industries Journal , 29(11), 15651579.

Venkatraman, N and V Ramanujam (1986). Measurement

of business performance in strategy research: A com-parison of approaches. The Academy of Management Review, 11(4), 801814.

Ze ghal, D and A Maaloul (2010). Analysing value addedas an indicator of intellectual capital and its con-sequences on company performance. Journal of Intel-lectual Capital , 11(1), 3960.


7/27/2019 s 0219649211002791

12/12

This article has been cited by:

1. Kin Hang Chan [email protected] Samuel Kai Wah Chu [email protected] Wendy W. Y. [email protected]. 2012. Exploring the Correlation Between Knowledge Management Maturity and IntellectualCapital Efficiency in Mainland Chinese Listed Companies. Journal of Information & Knowledge Management 11 :03. .[Abstract ] [References ] [PDF ] [PDF Plus ]
http://www.worldscientific.com/doi/pdfplus/10.1142/S0219649212500177http://www.worldscientific.com/doi/pdf/10.1142/S0219649212500177http://www.worldscientific.com/doi/ref/10.1142/S0219649212500177http://dx.doi.org/10.1142/S0219649212500177

s 0219649211002791

Documents

Transcript of s 0219649211002791