Measurement of Intellectual Capital
Intellectual Capital is essential and very important in order to compare
different companies, to estimate their real value and even to control their
improvement year by year. Also to improve the way in which companies manage its
intellectual resources that generate value and give back some benefits in
consequences maximizing advantages for the company (Jurczak, 2008). But to
measure Intellectual Capital is necessary to determine exactly what the
Measurement Methods are, which are the best and which the company should choose
to evaluate its assets in proper way. Properly using Intellectual Capital Measurement
Methods can cause the creation of competitive advantage and in consequence
create development of the whole company at the present day.
most popular and widely used non financial measurement methods are The Balanced
Scorecard, VAIC™, Skandia’s IC Navigator, Intellectual Capital Navigator
IC-Index™, The Technology Broker’s IC Audit, Sveiby’s The Intangible Asset
Monitor (IAM). The financial methods use financial criteria to evaluate the
intangible assets and they give only a global value. The most commons are:
Economic Value Added (EVA™), Market to Book ratio, Calculated Intangible Value,
Market Value Added (MVA), Tobin’s Q Ratio. But VAIC™ developed by Pulic is
different and more detailed method. This method uses the links between the
activities of the company, the resources used and the financial outcome.
Pulic (1998, 2000) developed the “Value Added Intellectual Coefficient” (VAICTM)
to measure the IC of companies. The VAICTM method is designed to
provide information about the value creation efficiency of tangible and
intangible assets within a company. However, Value Added Intellectual
Coefficient (VAICTM) may be a better indicator and method
of reflecting the market value of business (Young et al. 2009). VAICTM
is used to measure the value creation efficiency of a company using
accounting-based figures. VAICTM 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).
Kamath (2007) also confirmed that VAICTM is a management and control
tool that is “designated to monitor and measure the IC performance and
potential of the firm”. This measuring tool has been used in many studies
(Firer and Williams, 2003; Mavridis, 2004, 2005; Goh, 2005; Mohiuddin et al.
2006; Tan et al. 2007; Yalama and Coskun, 2007; Kamath, 2008; Zeghal
and Maaloul, 2010)
Firer and Williams
(2003) identified several advantages of using VAICTM. Firstly, VAICTM
provides a standardized and consistent basis for measurement, thereby,
enabling the effective conduct of an international comparative analysis using a
large sample size across various industrial sectors. Secondly, all data used in
the VAICTM calculation is based on audited information and
therefore, calculations are objective and verifiable. Finally, VAICTM
is a straightforward technique that enhances cognitive understanding and
enables ease of calculation by various internal and external stakeholders. Due
to ease of calculation feature VAICTM has enhanced the universal
acceptance of many traditional measures of corporate performance such as return
on assets (ROA), market-to-book value (MB). Additionally, issues have also been
raised about difficulties in verifying information used in calculating the
indicators of other IC measures. Other IC measures like (Skandia Navigator, Economic
Value Added (EVATM), market value added (MVATM)are
limited as only internal parties can calculate them or rely upon sophisticated
models, analysis and principals. But, IC measures are limited in that they: (a)
utilize information associated with a select group of firms (for example stock
data) (b) involve unique financial and non-financial indicators that can be
readily combined into a single comprehensive measure; and/or (c) are customized
to fit the profile of individual firm (Roos and Roos, 1997; Edvinsson, 1997;
Sullivan, 2000). Consequently, the ability to apply alternative IC measures
consistently across a large and diversified sample for comparative analysis is
main objective of this paper is to examine the impact of Intellectual Capital
(IC) on the performance of Indian software Industry.
and time period
sample for the above study is taken from Business Standard (BS) 1000 on the
basis of net sales. 51 software companies have been
selected for the above study. The time period for the study is eleven years
i.e. 2001-2011. The span of more than a decade would be helpful to establish
the consistency and predictability for research conclusions.
data is collected through secondary sources. The relevant data required for
present research is collected from Electronic database ‘PROWESS’ of Centre for
Monitor Indian Economy (CMIE). This database was chosen because all the
information required for the above study was readily available in this.
on Assets (ROA) and Return on Net Worth (RONW) have been taken as the dependent
return on assets (ROA) is used as a dependent variable in this study, because
it reﬂects the proﬁtability of ﬁrms. Therefore, it is an
indicator to measure whether the ﬁrm has been performing proﬁtably
as compared to the previous year or not. It is measured as the ratio of
operating income to total assets of the firm.
on Equity (RONW) is the ratio of the net profits after taxes divided by net
worth as disclosed in the respective annual reports of the ﬁrm. The ratio is useful as a measure of how well a company
is utilizing the shareholder investment to create returns for them, and can be
used for comparison purposes with competitors in the same industry.
the present study for measuring the value of IC, Pulic, (1998) model has been
applied. IC has been deﬁned variedly, but the most commonly accepted
deﬁnition classiﬁes it into human, structural and customer
capital (Pulic, 1998). The ﬁrst measure is that which is used to measure
the efficiency of the capital employed (VACA). This is the ratio of the VA to
the total CE by the ﬁrm, the total capital is taken as the book value of
the ﬁrms net assets during a given period:
VACA= VA / CA
where VACA, value added
capital coefficient for ﬁrm ;VA, value added for the ﬁrm ; CA,
book value of the net assets for ﬁrm .
The VA is measured by
where VA, value added
for ﬁrm computed as sum of I, interest expense; DP, depreciation
expenses; D, dividends; T, corporate taxes; M , equity of minority shareholders
in net income of subsidiaries; R, proﬁts retained for the year.
The next step is to
determine the efficiency of the human CE on the value creation of the
ﬁrm. This is obtained by estimating the ratio of human capital
coefficient for the ﬁrm VAHU; this is the ratio of VA of the ﬁrm
to the expenditure made by the ﬁrm on its human capital. These expenses
are reﬂected in the salaries and wage cost of the ﬁrm in their
VAHU= VA / HC
where VAHU, human
capital coefficient for the ﬁrm; VA, value added for the ﬁrm;
HC, total salary and
wage costs for the ﬁrm.
The next measure
captures the efficiency of the structural capital on the VA by the ﬁrm.
This is the ratio of SC and VA of the ﬁrm represented as STVA. The SC is
calculated as follows:
where SC, structural
capital for the firm; VA, value added for the firm; HC, total salary and wage
costs for the firm.
Then the relationship
is shown as:
where SCVA, structural
capital VA for the firm; SC, structural capital for the firm; VA, value added
for the firm.
value added intellectual coefficient for the firm; VACA, value added capital
coefficient for firm; VAHU, human capital coefficient for the firm; STVA,
structural capital value added for the firm.
is measured using three important components, namely value added capital
coefficient (VACA), human capital coefficient (VAHU) and structural capital
value added (SCVA), which comprehensively measures the value added (VA) of the
ﬁrm by using its important resources such as human resources, customer
capital and structural capital.
model is not free from limitations. Andriessen (2004) has drawn attention
towards the limitations of VAIC regarding the basic assumptions and validity of
the model. Calculation of VAIC shortened the data by removing negative book
value of equity or firms with negative human and structural capital (Firer and
control variables are included in the analysis. Size of the firm (SIZE) is
determined through natural logarithm of firm’s book value of total assets
(Firer and Williams, 2003; Ghosh and Mondal, 2009; Zeghal and Maaloul, 2010;
Chu et al. 2011; Wang, 20011). Age of the firm (AGE) is calculated as
the difference between 2011 and the founding year of the organisation
(Taliyang, 2011). Leverage (LEV) is calculated as ratio of the total debt to
book value of assets of the firm (Kamath, 2008; Ghosh and Mondal, 2009; Zeghal
and Maaloul, 2010; Ahangar, 2011; Chu, et al. 2011) and Physical Capital
intensity (PC) is measured by the ratio of a company’s fixed assets to its
total assets (Firer and Williams, 2003; Ghosh and Mondal, 2009; Ahangar, 2011;
Pal and Soriya, 2012).
the data is of panel nature consisting of both time series and cross sectional
data, hence the Panel Data regressions are used for the purpose of analysis. For
conducting the empirical research above mentioned four models have been run
Return on Assets
= Return on Net Worth
Value Added Intellectual Coefficient
Value Added Capital Coefficient
Value Added Human Capital
Structural Capital Value Added
Discussion of Results
of Panel Data regression Unit root test
Lin and Chu unit root test was applied before running the Panel Data regression,
to check the stationarity of the data. It is applicable on panel and pooled
data (Levin et al., 2002). Results of the test lead to reject the hypothesis of
the unit root. To have better results both fixed and random effect models are
applied on the panel data. Results of both the models are checked through
applying Hausman Specification Test (Hausman, 1978). In case where both models
are found significant then Random Effect Model results are taken into
2: Showing the results where ROA is the dependent variable
level and *10%level
2 presents the results of GLS regression where ROA is the dependent variable.
Assessment of the table reveals that adjusted R2 software industry
is 24.09 percent. It indicates that the model does have good explanatory power.
VAIC and components of VAIC (Physical Capital, Human Capital and Structural
Capital) all are found to be positively and significantly related with ROA at 1%
level of significance. But, physical capital is the major factor affecting the
software industry with highest coefficient (8.55).
This indicates that capital employed (physical and financial) still remains
important for stockholders and stakeholders.
the control variables, Physical capital intensity and age are found to be
negative and insignificantly related with ROA. It seems that the old firms have
still not realised the importance and need of intellectual capital and hence is
found insignificant with the performance. Moreover, they have established
themselves over time and may be are able to retain their employees. But to
survive in such a competitive era one has survive and hence for survival
investment in intellectual capital is necessary. Furthermore, Leverage is
negatively but significantly related with ROA. But, size has positive but
insignificant affect on ROA. The results further indicate that the big firms no
doubt enjoy the benefits of large scale but still they are not paying much
attention towards intellectual capital.
results of the present study are in confirmation with the other studies by Chen
et al. (2005), Tan et al. (2007), Razafindrambinina and Anggreni
(2008), Gan and Saleh (2008) Ting and Lean (2009), Sharabati et al. (2010)
and Uadiale and Uwuigbe (2011) in which it is clearly revealed that there was a
significant positive relationship between VAIC and ROA at 1% level of
significance. But the results are contradictory with the findings of Firer
& Williams (2003) who found a mixed association between IC and performance.
Similarly, Kujansivu & Lonnqvist (2005) also found no clear relation
between intellectual capital and performance. But the majority of the studies
reviewed found a significant and positive relation between IC and performance.
Hence, this shows that intellectual capital has attained universal acceptance.
3: Showing the results where RONW as the dependent variable
level and *10%level
3 presents the results of panel regression where RONW is the dependent variable.
Assessment of the table reveals that adjusted R2 is 16.14 percent
indicating that the model does not have good explanatory power. VAIC and
variables of VAIC (physical, human and structural) are found to have positive
and significant impact on RONW. Amongst the control variable physical capital
intensity is found to be positive but insignificantly related with RONW. But,
leverage is negative but significant. Age and size both are found to be
negatively and insignificantly related with the dependent variable. The results
are similar with the results of ROA.
findings of the present study correspond with the results of Sharabati et al.
(2010) who reported that the intellectual capital variables and sub-variables
had a substantive and significant relationship with business performance.
Similarly, Firer and Williams (2003) and Razafindrambinina
and Anggreni (2008) also claimed that physical capital was the most influencing
components to increase the future performance of the organisations. Additionally,
Gan and Saleh (2008) also claimed that physical capital efficiency was the most
significant variable related to profitability among all the components.
the results are contradictory with the findings of Ahangar (2011) who suggested
that human capital was very efficient than structural capital and physical
capital in terms value creation efficiency. This inconsistency may be due to
geographical biasness as the present study is conducted in India and the former
was conducted in Iran. Moreover, Ahangar (2011) draws analysis on the data from
a single company but the present study uses a data of 51 companies.
the basis of adjusted R2 it can be concluded that model 1 is a
better fit model. Though the number of significant variables are same in both
the models but on the basis of R2 this conclusion can be drawn.
the empirical findings reported in the above tables shows that Intellectual
capital and profitability are positively related. The results are supported by
various studies. Hence, a significant positive association
between the intellectual capital performance measured by the VAIC and the
financial performance is empirically established.
principal purpose of the present study is to investigate the relationship
between performance of intellectual capital and three dimensions of financial
performance measured by Return on Assets (ROA) and Return on Net Worth (RONW).
Intellectual capital performance of a company has been measured by using VAIC
methodology. Present analysis has been conducted on a sample of 51 knowledge
intensive Indian software companies. Overall empirical findings, which are
based on Panel Regression analysis between intellectual capital performance and
corporate financial performance measures, clearly indicate that intellectual
capital is the positive predictor of profitability. India being a developing
country and second largest populated country has a wide prospective for growth.
As such the Indian managers should understand the importance of intellectual
capital and should try to disclose more information on intangible assets.
Moreover this study signals the need intellectual capital and suggests that
management of IC should be improved for enhancing the market value of
companies. These findings allow the present researchers to conclude that the
companies should invest in intellectual capital to stand for the gain.
Ahangar, R.G. (2011),
“The Relationship between Intellectual Capital and Financial Performance:
An Empirical Investigation in an Iranian Company”, African Journal of Business
Management, Vol. 5, No. 1, pp. 88-95.
Ahmed Riahi-Belkaoui, (2003), "Intellectual
capital and firm performance of US multinational firms: A study of the
resource-based and stakeholder views", Journal of Intellectual Capital,
Vol. 4, No. 2, pp.215 – 226.
Andriessen, D. (2004), “Making
Sense of Intellectual Capital: Designing a Method for the valuation of
Intangibles”, Elsevier Butterworth-Heinemann, Oxford.
Azad, N., Fard, D. J., Talab, M. B.
T. (2012), 4th International Business and Social Science Research
Conference, Dubai, UAE.
Ben-Simchon, J. (2005),
Reporting Intellectual Capital in Research Intensive SME’s, IN HOLLAND
University, The Netherlands.
Bontis, N. (1998), “Intellectual
Capital: An Exploratory study that develops measures and models”, Journal of
Management Decisions, Vol.36, No. 2, pp. 63-76.
Bouteiller Ch. (2002),
“The Evaluation of Intangibles: Advocating for an Option Based Approach”, VIth Alternative Perspectives
on Finance Conference, Hamburg, August.
Brooking, A. (1996),
“Intellectual capital: core asset for the third millennium enterprise”, New
York, Thomas Business Press.
Calisir, F., Gumussoy, C.
A., Bayraktaroglu, A. E., Deniz, E. (2010), "Intellectual capital in the
quoted Turkish ITC sector", Journal of Intellectual Capital, Vol.
11, No.4, pp.538 – 554.
Chen, M.C., S.J. Chang & Y. Hwang
(2005), “An Empirical Investigation of the Relationship between Intellectual
Capital and Firm’s Market Value and Financial Performance,” Journal of
Intellectual Capital, Vol.6, No. 2, pp.159-176.
Chu, S.K.W., K.H. Chan
& W.W.Y. Wu (2011), “Charting Intellectual Capital Performance of the
Gateway to China”, Journal of Intellectual Capital, Vo. 12 No. 2, pp.
CIMA (2001) Understanding corporate
value: managing and reporting intellectual capital, Cranfield University,
Publisher: Chartered Institute of Management Accountants (CIMA), pp.
Dahmash F.N., Durand R.B., Watson J.(2009), “The value relevance and reliability of reported goodwill and
identifiable intangible assets”, British
Accounting Review, Vol. 41, No. 2, pp. 120-137.
Edvinsson, L. and Malone, M.S.
(1997), Intellectual Capital: The Proven Way to Establish Your Company’s Real
Value by Measuring its Hidden Brainpower, Judy Piatkus, London.
Firer, S. & S.M.
Williams (2003), “Intellectual Capital and Traditional Measures of Corporate
Performance,” Journal of Intellectual Capital, Vol. 45, No. 3,
G. Roos, S. Pike, L. Fernstrom (2005), Managing
Intellectual Capital in Practice, Butterworth-Heinemann, New York, pp. 19.
Gan, K. & Z. Saleh
(2008), “Intellectual Capital and Corporate Performance of Technology-Intensive
Companies: Malaysia Evidence”, Asian Journal of Business and Accounting,
Vol.1, No. 1, pp. 113-30.
Ghosh, S. & A.
Mondal (2009), “Indian Software and Pharmaceutical Sector Intellectual Capital
and Financial Performance”, Journal of Intellectual Capital, Vol. 10 No.
3, pp. 1469-930.
Goh, P.C. (2005)
“Intellectual Capital Performance of Commercial Banks in Malaysia”, Journal
of Intellectual Capital, Vol. 6, No. 3, pp. 385-96.
Harrison, S. & Sullivan Sr, P.,
(2000),"Profiting from intellectual capital: Learning from leading
companies", Journal of Intellectual Capital, Vol. 1, No. 1, pp.
Hausman, J.A. (1978)
“Specification Tests in Econometrics”, Econometrica, Vol. 46, No. 6, pp.
Hudson, W., (1993), “Intellectual Capital: How to Build it, Enhance it, Use it”,
John Wiley and Sons, New York.
Jurczak, J. (2008), Intellectual Capital Measurement Methods, Vol. 1
No. 1, pp. 37–45.
(2007) "The Intellectual Capital Performance of Indian Banking
Sector", Journal of Intellectual Capital, Vol. 8, No. 1, pp.
Kamath, G.B. (2008),
“Intellectual Capital and Corporate Performance in Indian Pharmaceutical
Industry,” Journal of Intellectual Capital, Vol. 9, No. 4, pp. 684-704.
Pal, K. & Soriya, S.
(2012),"IC performance of Indian pharmaceutical and textile
industry", Journal of Intellectual Capital, Vol. 13, No.1, pp. 120
Levin, A., Lin, C. and Chu, J.
(2002), “Unit root tests in panel data: asymptotic and finite-sample
properties”, Journal of Econometrics, Vol. 108, No. 1, pp. 1-24.
Makki ,M. A. M., Lodhi, S. A. &
Rahman, R.(2008), “Intellectual Capital Performance of Pakistani Listed
Corporate Sector, International Journal of Business and Management, Vol.
3, No. 10, pp. 45-51.
Mavridis, D. (2004, 2005), “Intellectual
Capital Drivers in the Greek Banking Sector,” Management Research News,
Vol. 28, No. 5, pp. 43-62.
Mouritsen, J., Bukh, P.N. & Marr, B.
(2004), “Reporting on Intellectual Capital: why, what and how. Measuring
Business Excellence, Vol. 8, No. 1, pp. 46-54.
Muhammad, N.M.N. and Ismail, M.K.A.
(2009), “Intellectual capital efficiency and firms’ performance: study on
Malaysian financial sectors”, International Journal of Economics and Finance,
Vol. 1, No. 2, pp. 206-12.
Pulic, A. (1998, 2000)
“VAIC– An Accounting Tool for IC Management”, International Journal of
Technology Management, Vol. 20, No. 5-8, pp. 702-14.
& T. Anggreni (2008) An Empirical Research on the Relationship between
Intellectual Capital and Corporate Financial Performance on Indonesian Listed
Roos , G . , Pike , S . & Fernstorm
, L . ( 2005 ), “Managing intellectual capital in practice”, Elsevier , Oxford.
Roos, J., Roos, G.,
Dragonetti, N.C., & Edvinsson, L. (1997), “Intellectual capital: navigating
the new business landscape”, London, Macmillan Press.
Salleh, A.L. and Selamat, F. (2007).
"Intellectual Capital Management in Malaysian Public Listed
Companies", International Review of Business Research Paper, Vol.
3, No. 1, pp. 266-278.
Sharabati, A.A., S.N.
Jawad & N. Bontis (2010) “Intellectual Capital and Business Performance in
the Pharmaceutical Sector of Jordan”, Management Decision, Vol. 48, No.
1, pp. 105-31.
Stewart, T. A. (1997,
1999), “Intellectual Capital: The New Wealth of Organisations”,
Doubleday/Currency, New York, NY.
Sullivan, P. (2000) “Value-Driven
Intellectual Capital - How to Convert Intangible Corporate Assets into Market
Value, New York. John Wiley and Sons.
Taliyang, S.M. (2011) “Determinants
of Intellectual Capital Disclosure among Malaysian Listed Companies”, Unpublished
master’s thesis, UUM, Malaysia.
Tan, H.P., D. Plowman & P. Hancock
(2007), “Intellectual Capital and Financial Returns of Companies,” Journal
of Intellectual Capital, Vol. 8, No.1, pp. 76-95.
Ting, W.K.I. & H.H. Lean (2009),
"Intellectual Capital Performance of Financial Institutions in
Malaysia", Journal of Intellectual Capital, Vol. 10, No. 4, pp.
Uadiale, O. M. & U.
Uwuigbe (2011), “Intellectual Capital and Business Performance: Evidence from
Nigeria”, Interdisciplinary Journal of
Research in Business, Vol. 1, No. 10,
pp. 49- 56.
M.S. (2011), “Measuring the Intellectual Capital and Their Effect on Financial
Performance: Evidence from Capital Market in Taiwan,” CIBMP annual
conference on Innovations in Business and Management, London, UK.
Wiig, M.K. (1997),
“Integrating intellectual capital and knowledge management”, Long Range
Planning, Vol. 30, No. 5, pp. 399-405.
Yalama, A. & M. Coskun
(2007) "Intellectual Capital Performance of Quoted Banks on the Istanbul
Stock Exchange Market", Journal of Intellectual Capital, Vol. 8,
No. 2, pp. 256-271.
Young, C.S., H.Y. Su, S.C. Fang &
S.R. Fang (2009) “Cross-Country Comparison of Intellectual Capital
Performance of Commercial Banks in Asian Economies”, The Service Industries
Journal, Vol. 29, No. 11, pp. 1565-79.
Zeghal, D. & A.
Maaloul (2010) “Analyzing Value Added as an Indicator of Intellectual Capital
and its Consequences on Company Performance,” Journal of Intellectual
Capital, Vol. 11, No. 1, pp. 39-60.