Indian software and pharmaceutical sector IC and financial performance

Abstract

Purpose
This paper seeks to estimate and analyze the relationship between intellectual capital and corporate conventional financial performance measures of Indian software and pharmaceutical companies for a period of five years from 2002 to 2006.

Design/methodology/approach
Annual reports, especially the profit and loss accounts and balance sheets of the selected companies for the relevant years have been used to obtain the data. International literatures on intellectual capital with specific reference to measurement tools and techniques have been reviewed. Value Added Intellectual Coefficient TM (VAIC) method is applied for measuring the value based performance of the companies. Corporate conventional performance financial measures used in this analysis are: profitability; productivity; and market valuation. It is an empirical study using multiple regression analysis for the data analysis. The intellectual capital (human capital and structural capital) and physical capital of the arbitrarily selected companies have been analyzed and their impact on corporate performance has been measured using multiple regression technique.

Findings
The analysis indicates that the relationships between the performance of a company's intellectual capital and conventional performance indicators, namely, profitability, productivity and market valuation, are varied. The findings suggest that the performance of a company's intellectual capital can explain profitability but not productivity and market valuation in India.

Research limitations/implications
The study has been conducted on a small sample of 80 companies belonging to the India software and pharmaceutical sectors. For a better understanding, a larger data set covering all prominent industry segments will be helpful.

Practical implications
Intellectual capital is an area of interest to numerous parties, e.g. shareholders, managers, policy makers, institutional investors. This paper throws some light on the new performance indicator, which Indian managers can use in order to evaluate the corporate performance and benchmark it with global standards. This is useful particularly in the context of the “knowledge economic” environment.

Originality/value
The paper represents a pioneering attempt to understand the implications of the business performance of the Indian software and pharmaceutical sectors from an intellectual resource perspective.

Full text

Indian software and
pharmaceutical sector IC and
financial performance
Santanu Ghosh and Amitava Mondal
The University of Burdwan, Burdwan, West Bengal, India
Abstract
Purpose – This paper seeks to estimate and analyze the relationship between intellectual capital and
corporate conventional financial performance measures of Indian software and pharmaceutical
companies for a period of five years from 2002 to 2006.
Design/methodology/approach – Annual reports, especially the profit and loss accounts and
balance sheets of the selected companies for the relevant years have been used to obtain the data.
International literatures on intellectual capital with specific reference to measurement tools and
techniques have been reviewed. Value Added Intellectual Coefficient
TM
(VAIC) method is applied for
measuring the value based performance of the companies. Corporate conventional performance
financial measures used in this analysis are: profitability; productivity; and market valuation. It is an
empirical study using multiple regression analysis for the data analysis. The intellectual capital
(human capital and structural capital) and physical capital of the arbitrarily selected companies have
been analyzed and their impact on corporate performance has been measured using multiple
regression technique.
Findings – The analysis indicates that the relationships between the performance of a company’s
intellectual capital and conventional performance indicators, namely, profitability, productivity and
market valuation, are varied. The findings suggest that the performance of a company’s intellectual
capital can explain profitability but not productivity and market valuation in India.
Research limitations/implications – The study has been conducted on a small sample of 80
companies belonging to the India software and pharmaceutical sectors. For a better understanding, a
larger data set covering all prominent industry segments will be helpful.
Practical implications – Intellectual capital is an area of interest to numerous parties, e.g.
shareholders, managers, policy makers, institutional investors. This paper throws some light on the
new performance indicator, which Indian managers can use in order to evaluate the corporate
performance and benchmark it with global standards. This is useful particularly in the context of the
“knowledge economic” environment.
Originality/value – The paper represents a pioneering attempt to understand the implications of the
business performance of the Indian software and pharmaceutical sectors from an intellectual resource
perspective.
Keywords Intellectual capital, Profit, Productivity rate, India, Pharmaceuticals industry
Paper type Research paper
1. Introduction
Knowledge and information are prime resources in today’s “knowledge-economy”. The
economic enterprises are increasingly knowledge–based and technology driven
(Davenport and Prusak, 1998). Drucker identifies knowledge as the only meaningful
resource today. Knowledge, information, experience etc. which are collectively termed
as intellectual capital, constitute the foundation for success in the twenty-first century.
These intangible resources are the keys for creating and sustaining competitive edge.
Wiig (1997) states that knowledge and intellectual capital play a fundamental role in
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1469-1930.htm
Indian software
369
Journal of Intellectual Capital
Vol. 10 No. 3, 2009
pp. 369-388
q Emerald Group Publishing Limited
1469-1930
DOI 10.1108/14691930910977798

the modern enterprises. Firms, by means of managing their intellectual capital can
outperform other companies. By managing knowledge and intellectual capital
companies like Skandia Insurance, Tellia, Microsoft, Intel, etc. have become global
leaders in their respective fields. Stewart (1997) explains the term “intellectual capital”
as intellectual material – knowledge, information, intellectual property, experience –
that can be put to use to create wealth. It is a collective brainpower. Intellectual capital
can be both the end result of a knowledge transformation process and the knowledge
that is transformed into intellectual property.
Firms in knowledge economy face a real challenge in the matter of accounting
for the investment and performances of intangibles. The conventional performance
measurement techniques fail to measure and monitor multiple dimensions of
performance. They concentrate only on financial aspects of the organization.
Benefits of intellectual capital such as management efficiency, customer relation,
R&D, innovations etc. are difficult to measure and quantify. This suggests that
traditional measures of a corporate performance, which are based on conventional
accounting principles, may be unsuitable in the new economic world in which
competitive advantage is driven by intellectual capital (Edvinsson and Malone,
1997, Pulic, 1998). The use of traditional performance measurement techniques may
lead investors and other stakeholders to make inappropriate decisions when
companies have a large proportion of their investment in intangible assets (Firer
and Stainbank, 2003)
Considering this, Pulic (1998), has suggested a measure named Value Added
Intellectual Coefficient
TM
(VAIC). Following his proposition of VAIC, Firer and
Stainbank (2003) have tried to examine whether or not, this newly proposed measure of
intellectual capital can explain the corporate profitability, productivity and the market
value of the firm in the context of South Africa.
The present study is a modest attempt to examine the above stated association in
the Indian context. More specifically, the present analysis is based on a sample of 80
publicly listed Indian companies in the high knowledge- intensive business sectors –
software (50 companies) and pharmaceutical (30 companies) (see Table I).
The remaining parts of this paper include a brief summery of the relevant
literature (section 2), a description of the development of hypotheses (section 3), the
specification of the research methodology (section 4), sample and analysis of the
descriptive statistics (section 5) and discussion of the results (section 6) and
conclusions (section 7).
2. Literature review
The term intellectual capital includes inventions, ideas, general know-how, design
approaches, computer programs and publications. An ex-editor of the business
magazine Fortune, Thomas Stewart describes intellectual capital as “something that
cannot be touched, although it slowly makes you rich”. According to Jacob
Ben-Simchon(2005) the term “intellectual capital” is used to enclose all of the
non-tangible or non-physical assets and resources of an organization, as well as its
practices, patents and the implicit knowledge of its members and their network of
partners and contracts. While Stewart (1997) defines it as “packaged useful
knowledge”, Sullivan (2000) describes the same as “knowledge that can be converted
into profit”. Roos and Roos (1997) view it as the “sum of knowledge” of its members
and practical translation of this knowledge into brands, trademarks and processes.
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Serial no. Name of the company Industry type
1 Infosys Technologies Limited Software
2 Tata Consultancy Services Software
3 Wipro Limited Software
4 Sonata Software Limited Software
5 D – Link (India) Limited Software
6 I – Flex Business solutions Software
7 Four Software Software
8 Rolta India Limited Software
9 3i Infotech Software
10 Blue Star Infotech Limited Software
11 NIIT Technologies Limited Software
12 Mastek Limited Software
13 Kale Consultant Limited Software
14 Mphasis Limited Software
15 Nucleus Software Exports Limited Software
16 Subex Azure Limited Software
17 Tulip IT Services Limited Software
18 Zenser Technologies Limited Software
19 Aztecsoft Software
20 Satyam Computer Services Limited Software
21 CMC Limited Software
22 Datamatics Technologies Limited Software
23 Tanla Limited Software
24 Polaries Software Lab Limited Software
25 Geometric Software Solutions Limited Software
26 Tech Mahendra Software
27 Aftek Infosys Limited Software
28 Cranes Software Limited Software
29 Educomp Solutions Software
30 FCS Software Software
31 Financial Technologies Limited Software
32 Geodesic Information Software
33 HCL Technologies Limited Software
34 Hexaware Technologies Limited Software
35 INFOTECH Software
36 Karuturi Networks Limited Software
37 Kernex Microsystems (India) Limited Software
38 Mascon Global Limited Software
39 Megasoft Limited Software
40 Patni Computer Services Limited Software
41 Prithvi Information Solutions Ltd. Software
42 R Systems International Limited Software
43 Mastek Limited Software
44 Redington (India) Limited Software
45 Sasken Software
46 Tricom India Limited Software
47 Visesh Infotecniques Limited Software
48 VisualSoft Technologies Limited Software
49 Micro Technology limited Software
50 ICSA – India Limited Software
(continued)
Table I.
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Edvinsson and Malone (1997) explain it as the possession of knowledge, applied
experience, organizational technology, customer relations and professional skills that
provide a company with a competitive edge in the market.
One of the most popular models for classifying intellectual capita (IC) has been
offered by Saint-Onge (1996). It divides intellectual capital into three parts: human
capital, structural capital, and customer capital.
A slight variant of this model developed by Dr Nick Bontis re-states customer
capital as relational capital to include relationships with suppliers.
Human capital is recognized as the largest and the most important intangible asset
in an organization. Ultimately it provides the goods or services that customers require
or the solutions to their problems. It includes the collective knowledge, competency,
experience, skills and talents of people within an organization. It also includes an
organization’s creative capacity and its ability to be innovative. Although investment
in human capital is growing, there is still no standard measure of its effectiveness in
companies’ balance sheets.
Structural capital is the supportive infrastructure for human capital – it is the capital,
which remains in the factory or office when the employees leave at the end of the day. It
Serial no. Name of the company Industry type
51 Abbott India Limited Pharmaceutical
52 Dabur Pharma Limited Pharmaceutical
53 Indoco Remedies Limited Pharmaceutical
54 Nicholas Phiramal India Limited Pharmaceutical
55 Ajanta Pharma Limited Pharmaceutical
56 Biocon Pharmaceutical
57 Dr Reddy’s Laboratories Pharmaceutical
58 Lupin Limited Pharmaceutical
59 Sun Pharmaceutical Industries limited Pharmaceutical
60 Aurobindo Pharma Limited Pharmaceutical
61 Cipla Pharmaceutical
62 Matrix Pharmaceutical
63 Unichem Laboratories Limited Pharmaceutical
64 Marksans Pharma Limited Pharmaceutical
65 Alembic Limited Pharmaceutical
66 Dishman Pharmaceuticals and Chemicals Limited Pharmaceutical
67 Divi’s Laboratories Limited Pharmaceutical
68 J. B. Chemicals and Pharmaceuticals Limited Pharmaceutical
69 Jupiter Bioscience Limited Pharmaceutical
70 Natco Pharma limited Pharmaceutical
71 Nectar Life Sciences Limited Pharmaceutical
72 Panacea Biotech Pharmaceutical
73 Plethico Pharmaceuticals Limited Pharmaceutical
74 Shasun Chemicals and Drugs Limited Pharmaceutical
75 Torrent Pharmaceuticals Limited Pharmaceutical
76 Wanbury Limited Pharmaceutical
77 FDC Limited Pharmaceutical
78 Pfizer Pharmaceutical
79 Merck Limited Pharmaceutical
80 Wockhardt Limited Pharmaceutical
Table I.
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includes organizational ability, processes, data and patents. Unlike human capital, it is
the property of the firm, which can be traded, reproduced and shared by, and within, the
organization. Relational capital is a company’s relationship with its customers and with
its network of suppliers, strategic partners and shareholders. The value of these assets is
determined by the company’s reputation or image (MERITUM guidelines).
These elements of IC are summed up in the definition of CIMA (2001):
IC is the possession of knowledge and experience, professional knowledge and skill, good
relationships, and technological capacities, which when applied will give organizations
competitive advantage.
Measurement of intellectual capital and its contribution
Today IC is a critical success factor, not only for knowledge-intensive organizations,
but also for most of the other types of organizations. The intellectual capital of an
organization may include knowledge and skills of employees, the culture and values as
well as its immaterial properties in addition to the organizational infrastructure that
supports the efforts of employees. As different items of an organization’s intellectual
capital generally appear or are considered to be effective as against specific contexts
only, researcher like Roos (2003) has voiced his doubt in the matter of justification of
adding them together for reporting purposes. The problem is further aggravated by the
fact that in most of the cases IC may not arise out of any formal transaction and, hence,
their valuations as well as reporting become difficult under the existing rule of
transaction-based accounting. But owing to its immense strategic importance for an
organization, the IC value should be properly measured (e.g. Drucker, 2000). There are
plenty of methods available to measure the success of physical capital. For measuring
the effectiveness or efficiency of the use of the physical capital the well known
conventional tools like Profit, ROI, ROE, and ROA can be used, but these are
considered to be ineffective for measuring the performance of intellectual capital.
Because these conventional performance measures fail to capture and monitor multiple
dimensions of corporate performance, they concentrate almost only on financial
aspects of the organizations. This suggests that conventional financial measures are
not adequate in the context of the current information age (Amaratunga et al., 2001). In
the early 1990s, multi-dimensional performance measurement models were developed,
to overcome the weaknesses of the existing financial measures. Such models place
greater focus on intangible resources. Commonly used models include:
(1) The Balanced Scorecard (Kaplan and Norton, 1996).
(2) Skandia’s IC Navigator (Edvinsson and Malone, 1997).
(3) Sveiby’s The Intangible Asset Monitor (Sveiby, 1997).
(4) Intellectual Capital Services’ IC-Index
TM
(Roos and Roos, 1997).
(5) Value added intellectual coefficient, VAIC (Pulic, 1998).
These models focus on the impact of IC on shareholders’ goal.
Influence of intellectual capital on corporate performance
Davenport and Prusak (1998) note that technological advances in data processing,
communication and transportation, as well as customer demand and strategists’
planning have made the world economy to change very fast. Teece (2000) states that
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intangible assets of the firm and its IC are the keys to achieving sustainable
competitive advantage and drive economic growth. Reeds (2000) finds that intellectual
capital is a strong predictor of a company’s performance.
Bontis et al. (2000) have tried to analyze the three elements of intellectual capital
namely – the human, structural and customer elements, as well as their
interrelationships. The main conclusions that could be drawn from the study is that
human and customer capital are significant factors in the way in which businesses are
run and that structural capital has a positive influence on business performance.
The study conducted by Riahi-Belkaoui (2003) also has focused upon the empirical
relationship between intellectual capital and the performance of selected multi-national
companies of the USA. The result suggests that intellectual capital is positively
associated with financial performance.
Sofian et al. (2005) also has examined the impact of IC on management accounting
practices. More specifically, the study focuses on the issue of performance
measurement in the context of the IC being important player for generating revenue
for the firm. Results this investigation suggest that IC has countable influence on the
corporate performance.
Chen et al. (2005) have tried to examine the relationship between the value creation
efficiency and firm’s market valuation and financial performance. They have found
that the intellectual capital has a positive influence on the market value and the
financial performance of the firms.
Tan et al. (2007) have reported a positive association between intellectual capital of
firms and their financial performances.
Kamath (2007) has analyzed the human capital and the physical capital of 98
scheduled commercial banks operating in India in order to examine their impact on the
value based performance during a period of five years from 2000 to 2004. This study
confirms that the observed vast differences in performance of different segments of
Indian banks are mainly due to the underlying differences in HC.
The studies mentioned above clearly indicate the usefulness of intellectual capital
and this motivates the present researchers to undertake an empirical study on the impact
of the intellectual capital on the corporate financial performance in the Indian context.
3. Development of hypotheses
Under the knowledge economic perspective in which the economic activities are
characterized by the dominance of modern technology, the role of knowledge assets or
for that matter, the role of intellectual capital needs to be appropriately recognized. But
the traditional financial accounting reporting system has not been able to account for
those assets on a well-accepted basis. Hence, there is a need to examine the extent to
which such measures are adequate to capture the contribution of intellectual capital
resources like human resources, firm’s reputation among customers i.e. the customer
relationship and the research and development.
The present study explores this issue empirically by analyzing the relationship
between a relevant measure of intellectual capital and some of the selected commonly
used measures of a company’s performance namely:
.
profitability;
.
productivity; and
.
market valuation.
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Intellectual capital and financial performance
According to the resource based view, firms may gain competitive advantage and can
achieve superior financial performance through the acquisition, holding and
subsequent use of strategic assets (Wernerfelt, 1984). Both tangible and intangible
assets are perceived as potential strategic assets (Riahi-Belkaoui, 2003;). Among the
invisible assets, IC is generally considered to be a vital strategic asset. According to
Riahi-Belkaoui (2003), IC refers to the specific and valuable knowledge that belongs to
the organization. This qualification of intellectual capital as a strategic asset rests on a
potential link between intellectual capital on the one hand and the firm performance on
the other (Seethamraju, 2000). Further, many scholars now argue that in comparison
with the tangible resources the intellectual capital or intangible resources are more
likely to be the key resources for many enterprises which help them in acquiring the
required competitive advantage or to ensure market dominance (Brernan and Connell,
2000; Marr, 2004).
According to Patton (2007), the productivity of a firm lies more on its IC and system
capabilities than on its hard assets. Bontis et al. (2000) argues that leveraging
knowledge assets is the key to a firm’s prosperity. Based on these studies, therefore, it
may be argued that a firm with higher intellectual capital performance is expected to
have higher rate of profitability and also it may experience higher productivity.
Thus, in our study we predict a positive relationship between financial performance
as measured by either profitability or productivity or both and the intellectual capital
performance of the related Indian software and Pharmaceutical companies. We,
therefore, hypothesize that,
H1. The higher the performance of a company’s intellectual capital, the greater
will be the company’s profitability.
H2. The higher the performance of a company’s intellectual capital, the greater
will be the company’s productivity.
Intellectual capital and market valuation
Researchers have argued that management and efficient utilization of IC helps
companies to be more innovative and productive. Low (2000) finds that firms with rich
intangible bases can enhance their respective market values by efficient utilization of
those intangibles. Being unable to satisfy the traditional definition of assets, most of
the IC components except goodwill, are not regarded as assets. However, the cost of
acquiring IC is considered as expenses under the existing system of accounting and
reporting. Thus, in so far as the valuation and reporting is concerned, by excluding IC,
traditional accounting system is believed to undervalue the companies. Because,
according to Riahi-Belkaoui (2003) and Firer and Williams (2003), in an efficient market
investors are seen to place higher value for companies with greater IC. Therefore, it
may be argued that the IC is expected to play an important role in enhancing corporate
value. In our empirical study, we predict a positive association between value creation
efficiency of IC and the market value of Indian software and pharmaceutical
companies. Thus we hypothesize that:
H3. Companies with greater Intellectual ability tend to have higher market to
book value.
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4. Research methodology
The first part of this section describes the proxies used to measure dependable
variables, independent variables and control variables. The multiple regression
equations are outlined in the last part of this section.
Measurement of dependent variables
For the purpose of conducting the analysis in the present study, three dependent
variables are taken into account, namely-profitability, productivity, and market
valuation. These are denoted respectively as:
(1) Return on assets (ROA).
(2) Assets turnover ratio (ATO).
(3) Market to book value ratio (MB).
Presently, there is no specific theoretical perspective or adequate empirical evidence
that supports the superiority of any specific proxy measure over the others. It is,
therefore, decided that for the purposes of the present study, the commonly used proxy
measures will be applied. Consequently, the proxy measures for each dependent
variable are defined as follows:
(1) Profitability (ROA): Profitability shows the degree to which a firm’s revenues
exceed over the costs. It is the ratio of the net income (less preference dividends)
divided by book value of total assets as reported in the 2002 -2006 annual
reports;
(2) Productivity (ATO): Productivity describes how efficiently inputs are converted
into outputs. It is the ratio of total revenue to total book value of assets as
reported in the 2002-2006 annual reports; and
(3) Market valuation (MB): Market valuation describes the degree to which a firm’s
market value exceeds its book value. It is the ratio of total market capitalization
(average share price times number of outstanding common shares) to book
value of net assets as reported in the 2002-2006 annual reports.
Measurement of independent variables
The VAIC methodology developed by Ante Pulic (1998, 2000) forms the underlying
measurement basis for the independent variable in the present study. In his words
VAIC is an analytical procedure designed to enable management, shareholders and
other relevant stakeholders to effectively monitor and evaluate the efficiency of VA by
a firm’s total resources and each major resource component. VAIC is the sum of three
indicators. These are:
(1) Capital employed efficiency (CEE) – the indicator of VA efficiency of capital
employed;
(2) Human capital efficiency (HCE) – the indicator of VA efficiency of human
capital; and
(3) Structural capital efficiency (SCE) – the indicator of VA efficiency of structural
capital.
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Following equation (1) formalizes the VAIC relationship algebraically:
VAICi ¼ CEEi þ HCEi þ SCEi ð1Þ
where:
VAIC ¼ VA intellectual coefficient for company i;
CEEi ¼ capital employed efficiency coefficient for company i;
HCEi ¼ human capital efficiency coefficient for company i; and
SCEi ¼ structural capital efficiency for company i.
Pulic (1998, 2000) states that higher the VAIC coefficient, the better will be the
efficiency of VA by a firm’s total resources. The first step in calculating CEE, HCE and
SCE is to determine a firm’s total VA.
This may be done with the help of computation by the following algebraic equation:
VAi ¼ Ii þ DPi þ Di þ Ti þ Mi þ Ri þ WSi ð2Þ
where: VA for firm i is computed as the sum of interest expenses (Ii); depreciation
expenses (DPi); dividends (Di); corporate taxes (Ti); equity of minority shareholders in net
income of subsidiaries (Mi); profits retained for the year (Ri) and wages and salaries (WSi).
Alternatively VA can be calculated by deducting operating expenses (materials,
maintenance and other external costs) from operating revenues (Pulic, 2000).
According to Pulic (2000), CEE is the ratio of total VA divided by the total amount
of capital employed (CE), where capital employed is defined as the book value of a
firm’s net assets. Equation (3) presents the CEE relationship algebraically:
CEEi ¼ VAi=CEi ð3Þ
where:
CEEi ¼ capital employed efficiency coefficient for company i;
VAi ¼ VA for firm i; and
CEi ¼ book value of the net assets for firm i.
Consistent with the views of other leading IC researchers (for example, Edvinsson and
Malone, 1997; Sveiby, 1997), Pulic (1998, 2000) present authors propose to use “total
salary and wage costs” as the indicator of a firm’s human capital (HC). HCE, therefore,
is calculated as the ratio of total VA divided by the total salary and wages spent by the
firm on its employees. Equation (4) shows this relationship algebraically:
HCEi ¼ VAi=HCi ð4Þ
where:
HCEi ¼ human capital efficiency coefficient for company i;
VAi ¼ VA for firm i. and
HCi ¼ total salary and wage costs for firm i.
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In order to calculate SCE, it is first necessary to determine the value of a firm’s
structural capital (SC). Pulic (1998) proposes that a firm’s “total VA less its human
capital” is an appropriate proxy of a firm’s SC. That is:
SCi ¼ VAi 2 HCi ð5Þ
where:
SCi ¼ Structural capital for company i;
VAi ¼ VA for firm i; and
HCi ¼ total salary and wages spent by the firm i.
Based on prior empirical research findings, Pulic (1998) argues that there is a
proportionate inverse relationship between HC and SC in the value creation process.
According to him, the less HCl participates in value creation, the more SC is involved.
Consequently, Pulic (1998) suggests the following formula for calculating SCE which is
the ratio of a firm’s SC divided by the total VA:
SCEi ¼ SCi=VAi ð6Þ
where:
SCEi ¼ structural capital efficiency coefficient VA for company i;
SCi ¼ Structural capital for company i;and
VAi ¼ VA for firm i.
Recently, VAIC method has gained popularity among the researchers to measure
intellectual ability of companies. Schneider (1999) supports the adoption of this
technique as an effective method of measuring intellectual capital efficiency because:
.
VAIC places an emphasis on the value of employees, a key component of
intellectual capital.
.
VAIC enables one to collect evidences of the intellectual capital leverage, which is
the key to the success of the value generating processes.
.
VAIC is easy to calculate. Because it involves the use of readily available
accounting information, which are reported in the annual reports of the firms.
.
The methodology used in the calculation of VAIC is relatively straightforward.
Control variables
For the purpose of the empirical analysis, this study uses correlation and multiple
regressions as the underlying statistical tests. In conducting the liner multiple
regression analysis following control variables have been included:
.
Size of the firm (LCAP) : Size of the firm as measured by the natural log of total
market capitalization (Firer and Stainbank, 2003; Firer and Williams, 2003) is
used here to control for the impact of size on wealth creation through economies
of scale, monopoly and bargaining power (Chandler, 1990; Porter, 1980;
Riahi-Belkaoui, 2003).
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.
Leverage (Lev): Financial leverage as measured by total debt divided by book
value of total assets is used to control for the impact of debt servicing on
corporate performance and wealth creation (Riahi-Belkaoui, 2003).
.
Physical capital intensity (PC): Physical capital intensity as measured by a ratio of
a company’s fixed assets to its total assets (Firer and Stainbank, 2003; Firer and
Williams, 2003;) is used to control for the impact of fixed assets on corporate
performance. The assumption is that company’s fixed assets have significant
impact on company’s financial performance.
Linear multiple regression
To analyze the respective relationships defined in prior sections linear multiple
regressions analysis is performed based on the following general models:
Equation 1:
ROA ¼
a
þ
b
1
ðVAICÞþ
b
2
ðPCÞþ
b
3
ðLCAPÞþ
b
4
ðDERÞþ
b
5
ðATOÞþ1
Equation 2:
ATO ¼
a
þ
b
1
ðVAICÞþ
b
2
ðPCÞþ
b
3
ðLCAPÞþ
b
4
ðDERÞþ1
Equation 3:
MB ¼
a
þ
b
1
ðVAICÞþ
b
2
ðPCÞþ
b
3
ðLCAPÞþ
b
4
ðDERÞþ
b
5
ðATOÞþ
b
6
ðROAÞþ1
where:
VAIC ¼ Intellectual capital performance as measured by the value added
intellectual capital coefficient
TM
.
PC ¼ Physical capital intensity as measured by fixed assets divided by total
assets.
LCAP ¼ Company size as measured by the natural log of market capitalization of
the company.
Risk ¼ The risk profile of the company as measured by the debt-equity ratio.
ROA ¼ Company profitability as measured by the company’s return on assets.
ATO ¼ Company productivity as measured by the asset-turn over ratio.
MB ¼ Market valuation of the company as measured by the ratio of market
capitalization to book value of net assets.
In addition to the previous, while
a
and 1 represent respectively the intercept and the
error terms,
b
1
2
b
6
are the slope coefficients representing the influence of the
associated independent variables over the dependent one.
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5. Sample selection and descriptive statistics
The sample of the present study consists of 80 leading Indian software and
pharmaceutical companies, which are listed on the Bombay Stock Exchange (BSE) and
the National Stock Exchange (NSE) during the study period. Data have been collected
from the annual reports of the selected companies for the financial years 2002-2006.
Table II presents the mean and standard deviation of the dependent variables,
independent variables and control factors for each year of the study period separately.
The overall financial performance of sample companies is moderate as they are seen
to have earned profit at 13 to 16 per cent between the years 2002 to 2006. The average
performance of the intellectual capital is seen to have gone up significantly (from
4.715029 to 6.217981) during the first three years of the study period (2002-2004). A stiff
fall (4.829877) of the same is observed in the year 2005, which is followed by a small
rise (4.842955) in the next year. Thus, the mean of sample companies’ intellectual
capital performance suggests that the sample companies are generally effective in
generating value from their intellectual capital base. Similarly, the mean value of
profitability brings out the strength of financial performance of the sample firms
during the study period. The mean of productivity, however, shows a decreasing trend
except between the years 2002-2003. The mean values of market valuation also indicate
that investors valued the sample firms in excess of the book value of net assets of the
respective companies. Finally the leverage level of sample companies is moderate
during the study period.
6. Discussion of results
Linear multiple regression results
Table III contains the multiple regression results for five years. The results show that
independent variables collectively explain 7.7 to 49.7 per cent of the variance in return
on assets, which are statistically significant.
From the results it is also seen that VAIC and few control variables significantly
influence profitability. Following table depicts the significant factors that influence
profitability at a glance.
Table IV clearly shows that VAIC has significant positive influence over
profitability. In case of Assets turnover ratio and company size, measured by LCAP, no
consistent relationship with profitability over the study period is found. While the
former is found to be significantly and positively related with the dependent variable
i.e. profitability in the year 2005 and negatively related in the years 2002 and 2004, the
company size has a positive influence on profitability only in the years 2003 and 2005.
Therefore, based on the above analysis it may not be unwise to claim that as
investment on intellectual capital has a positive impact on profit earning capability of
the firm, it may use intellectual capital as a vehicle to enhance its profitability. On the
other hand company size and assets turnover ratio have very little contribution to the
profitability.
Table V contains multiple regression results of assets turnover and intellectual
capital performance as measured by VAIC.
The results are statistically insignificant except in one isolated case. Regression
results of the year 2005 show that intellectual capital performance and company size
significantly and positively influence company performance when the latter is
measured by assets turnover ratio. But results of other four years are not significant.
Therefore, it is very difficult to conclude that the present empirical results support the
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Year 2002 2003 2004 2005 2006
n ¼ 32 n ¼ 41 n ¼ 51 n ¼ 56 n ¼ 80
Variable name Mean SD Mean SD Mean SD Mean SD Mean SD
ROA 0.168145 0.0942240 0.138170 0.084761 0.134481 0.0709140 0.119682 0.0869911 0.165907 0.1013843
ATO 1.094074 0.7138015 1.164224 1.3414934 0.888350 0.5569972 0.803688 0.4725432 0.566032 0.6843383
MB 2.971414 3.0030466 1.800077 1.9338582 3.300799 3.7224292 2.762252 2.8950289 2.741219 2.2558240
Market capitalization (LCAP) 22.387137 2.0156481 22.148746 1.9274986 22.356195 3.5630714 23.036984 1.6153105 22.404040 3.9363842
VAIC 4.715029 2.6509776 5.181963 3.6935997 6.217981 6.1560048 4.829877 4.2914604 4.842955 3.1807472
PC 0.272146 0.1616813 0.250140 0.1584106 0.275487 0.1713614 0.256828 0.1490.254 0.288712 0.1800184
DER 0.230834 0.3457894 0.235402 0.3595304 0.285006 0.3913925 0.453170 0.8004352 0.315317 0.5109633
Table II.
Descriptive statistics of
dependent and
independent variables
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381

Year N Adjusted R
2
F-statistics Significance
Independent and
control variables Standard beta t-statistic Significance Standard error
2002 32 0.497 7.116 0.000
*
VAIC 0.428 3.174 0.004
*
0.005
DER 0.211 1.504 0.145 0.019
ATO 2 0.643 2 4.528 0.000
*
0.039
PC 2 0.183 2 1.324 0.197 0.081
LCAP 0.202 1.484 0.150 0.006
2003 41 0.253 3.713 0.008
*
VAIC 0.380 2.644 0.012
**
0.003
DER 2 0.061 2 0.422 0.675 0.009
ATO 2 0.063 2 0.387 0.701 0.038
PC 0.091 0.569 0.573 0.084
LCAP 0.380 2.613 0.013
**
0.006
2004 51 0.160 2.899 0.0.24
**
VAIC 0.318 1.932 0.60
***
0.002
DER 0.202 1.529 0.133 0.017
ATO 2 0.530 2 3.298 0.002
*
0.029
PC 2 0.040 2 0.280 0.781 0.059
LCAP 2 0.066 2 0.497 0.622 0.003
2005 56 0.297 5.647 0.000
*
VAIC 0.273 2.334 0.024
**
0.002
DER 2 0.172 2 1.447 0.154 0.013
ATO 0.310 2.512 0.015
**
0.023
PC 0.003 0.021 0.983 0.069
LCAP 0.221 1.865 0.068
***
0.006
2006 80 0.077 2.326 0.051
***
VAIC 0.316 2.880 0.005
*
0.003
DER 0.112 0.999 0.321 0.022
ATO 0.095 0.847 0.400 0.017
PC 2 0.006 2 0.058 0.954 0.062
Notes: Significance level:
*
1 percent;
**
5 per cent;
***
10 per cent
Table III.
Linear multiple
regression results of
profitability
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hypothesis that intellectual capital performance is positively related to a company’s
performance when measured by asset turnover ratio.
Table VI shows the multiple regression results, which bring out the influences of all
the selected independent variables mentioned above including the IC performance as
measured by VAIC methodology on the market to book value ratio.
From the empirical findings reported in the above table it is seen that intellectual
capital performance and market to book value ratio have no significant relationship.
Company size influences the dependent variable significantly in the years 2002, 2004,
2005 and 2006. The relationship is found to be positive in all the years excepting for the
year 2004. Profitability of the company and the market to book value ratio are
significantly and positively related in the years 2002 and 2006. Therefore, considering
the empirical findings mentioned previously, the present researchers fail to accept the
hypothesis that intellectual capital performance is significantly influenced by the
company performance, when the latter is measured by the market-to-book value ratio.
Thus, it may be argued that the Indian capital market appears to be inadequately
sensitive to the earning capability of the intellectual capital assets held by the
companies. The reason behind the above mentioned observed capital market
insensitivity may be the non-availability of adequate information about firm’s
investment in intellectual capital and its performance to the stakeholders. It may also
be noted here that the existing financial accounting and reporting regulations neither
suggest an appropriate methodology for valuation of the intellectual capital
investments nor provide an appropriate framework for reporting of the same in the
annual statements of the business firms. Hence, the question of using the information
about intellectual capital of the company by its shareholders and its consequential
influence on the market value of shares does not arise.
VAIC and corporate financial performance: comparisons of empirical evidences
Before presenting the concluding remarks, a brief comparative analysis of the
empirical findings of some selected similar earlier research studies conducted by other
researchers and those of the present study may help us to arrive at a better
understanding of the relationship between intellectual capital performance (as
measured by VAIC) and corporate financial performance.
Paula Kuujansivu has conducted an empirical study on 2,000 Finnish companies
covering 11 largest industries for the period 2001 to 2003, to identify the important
resources of various industry sectors. The study results show that the business
services companies are more efficient in utilizing all types of its resources and the
companies belonging to the public utility sectors are more efficient in utilizing their
intellectual capital only.
Tan et al. also have made an empirical study on 150 public companies listed on
Singapore Stock Exchange for the period 2000 to 2002. This study shows that
Year VAIC ATO LCAP
2002 þ 2 0
2003 þ 0 þ
2004 þ 2 0
2005 þþþ
Table IV.
Significant factors that
influence profitability
Indian software
383

Year N Adjusted R
2
F-statistics Significance
Independent and
control variables Standard beta t-statistic Significance Standard error
2002 32 0.056 1.456 0.243 VAIC 2 0.082 2 0.446 0.659 0.050
DER 0.253 1.344 0.190 0.389
PC 2 0.277 2 1.524 0.139 0.804
LCAP 0.247 1.371 0.182 0.064
2003 41 0.009 1.095 0.374 VAIC 0.261 1.635 0.111 0.058
DER 0.140 0.755 0.455 0.691
PC 2 0.243 2 1.362 0.182 1.514
LCAP 0.010 0.062 0.951 0.117
2004 51 2 0.046 0.456 0.768 VAIC 2 0.219 2 1.216 0.230 0.016
DER 0.171 0.962 0.341 0.253
PC 2 0.0.19 2 0.117 0.908 0.518
LCAP 0.075 0.506 0.615 0.023
2005 56 0.094 2.424 0.060
***
VAIC 0.237 1.844 0.071
***
0.014
DER 2 0.090 2 0.668 0.507 0.079
PC 0.097 0.725 0.472 0.425
LCAP 0.276 2.137 0.037
**
0.038
2006 80 0.032 1.653 0.170 VAIC 2 0.107 2 0.959 0.341 0.024
DER 2 0.203 2 1.811 0.074
***
0.150
PC 2 0.134 2 1.211 0.230 0.422
LCAP 0.021 2 0.187 0.852 0.019
Notes: Significance level:
*
1 per cent;
**
5 per cent;
***
10 per cent
Table V.
Linear multiple
regression results of
productivity
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Year N Adjusted R
2
F-statistics Significance
Independent and
control variables Standard beta t-statistic Significance Standard error
2002 32 0.593 8.531 0.000
*
VAIC 2 0.145 2 1.014 0.320 0.162
DER 0.086 0.505 0.618 1.483
ATO 2 0.236 1.795 0.085
***
0.554
ROA 0.472 2.678 0.013
**
5.619
PC 2 0.053 2 0.415 0.681 2.389
LCAP 0.685 5.365 0.000
*
0.190
2003 41 0.080 1.582 0.183 VAIC 2 0.053 2 0.302 0.764 0.091
DER 2 0.194 2 0.524 0.604 0.969
ATO 2 0.074 2 0.459 0.649 0.232
ROA 0.143 0.764 0.450 4.315
PC 2 0.213 2 1.200 0.239 2.167
LCAP 0.294 1.663 0.106 0.177
2004 51 0.087 1.798 0.122 VAIC 0.060 0.335 0.739 0.108
DER 0.62 0.331 0.742 1.776
ATO 0.033 0.233 0.817 0.944
ROA 0.212 1.367 0.179 8.155
PC 2 0.027 2 0.179 0.859 3.239
LCAP 2 0.398 2 2.847 0.007
*
0.146
2005 56 0.406 7.256 0.000
*
VAIC 0.057 0.504 0.617 0.076
DER 2 0.118 2 1.053 0.298 0.404
ATO 2 0.014 2 0.113 0.911 0.737
ROA 0.094 0.723 0.473 4.327
PC 2 1.128 2 1.172 0.247 2.120
LCAP 0.602 5.338 0.000
*
0.202
2006 80 0.335 7.627 0.000
*
VAIC 2 0.095 2 0.965 0.338 0.070
DER 2 0.237 2 2.484 0.015
**
0.422
ATO 0.049 0.505 0.615 0.317
ROA 0.439 4.448 0.000
*
2.196
PC 2 0.071 2 0.769 0.444 1.164
LCAP 0.360 3.876 0.000
*
0.053
Notes: Significance level:
*
1 per cent;
**
5 per cent;
***
10 per cent
Table VI.
Linear multiple
regression results of
market valuation
Indian software
385

corporate profitability and growths have significant positive influence on the
intellectual capital performance. However, according to this study, significant
differences among various industries have been observed in the matter of utilization of
intellectual capital.
The study of Richieri et al. on 1,000 biggest Brazilian companies shows that
intellectual capital has significant positive influences over the corporate profitability.
Zhang et al. report that in Chinese Auto Industry also intellectual capital is the
significant determinant of corporate financial performance. However, Firer and
William’s (2003), empirical study on 75 publicly traded firms in South Africa reports no
significant association between intellectual resources and corporate financial
performance (see Table VII).
A cursory look into the table clearly brings out the fact that in six out of eight
studies conducted under various country contexts the intellectual capital has positive
bearing upon the corporate financial performance. It is also interesting to note that in
case of a highly IC reliant industry in South Africa this influence is negligible. Similar
findings have been reported by Syed Nijibullah in a case of the banking industry in
Bangladesh.
From the above table it is also apparent that the empirical findings of the present
study are in full conformity with most of the earlier major studies. Hence, a significant
positive association between the intellectual capital performance measured by the
VAIC and the corporate financial performance is empirically established.
7. Conclusions
The principal purpose of the present study is to investigate the relationship between
performance of intellectual capital in a company and three dimensions of corporate
financial performance. These three dimensions are profitability, productivity and
market valuation. Intellectual capital performance of a company has been measured by
using VAIC methodology. Present analysis has been conducted on a sample of 80
knowledge intensive Indian companies, which include software and pharmaceutical
companies. Overall empirical findings, which are based on multiple regression analysis
between intellectual capital performance and conventional corporate financial
performance measures, clearly indicate that intellectual capital is the positive
predictor of profitability. These findings allow the present researchers to conclude that
the profitability of a firm can be significantly improved by means of managing the
intellectual capital properly. Empirical analysis also indicates that the Indian investors
are not influenced by intellectual capital performance of the selected companies. The
Researchers Year Country Industry type Results
Richieri et al. 2007 Brazil General Positive
Zhang et al. 2006 China Auto Positive
Paula Kujansivu 2005 Finland General Positive
Tan et al. 2007 Singapore General Positive
Firer and Williams 2003 South Africa Highly reliant on IC No strong association
Syed Nijibullah 2005 Bangladesh Banks No strong association
Chen et al. 2006 Taiwan General Positive
Present study 2008 India Software and pharmaceutical Positive
Table VII.
Summary of empirical
results
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study finds no direct association between the productivity and the intellectual capital
performance. However, a study involving a fairly large sample may be conducted to
reassess those relationships.
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Corresponding author
Santanu Ghosh can be contacted at: shantanu.kaizen@gmail.com
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