Measuring Intellectual Capital and Its Impact on Financial Performance: Empirical Evidence from CNX Nifty Companies

Abstract

During the recent years, value of financial assets has grown exponentially when compared to physical assets indicating that intangibles are growing in importance in their contribution to economic growth. The evidence in support of this phenomenon can be found in the increasing gap between market and book valuation of firms. The present study attempts to measure the intellectual capital (IC) of publicly listed firms in India and empirically examine the relationship among IC, financial performance and market valuation of these firms. Value creation efficiency of the firms listed on CNX Nifty over the period ranging from 2004–2005 to 2013–2014 has been estimated using Pulic’s Value Added Intellectual Coefficient (VAIC). It was observed that firms operating in sectors such as financial services, mining and energy had the highest VAIC scores. Further, there was a positive association between VAIC and all the measures of financial performance—profitability, productivity and market valuations. Efficiency of physical capital employed had a significant positive relation with profitability, market valuation as well as productivity. Human capital efficiency was found to have a strong positive association with profitability, while structural capital efficiency did not have any significant impact on any of the measures of financial performance.

Keywords

Intellectual capital financial performance Value Added Intellectual Coefficient human capital efficiency physical capital efficiency structural capital efficiency

Introduction

Knowledge being the new engine of corporate development has become one of the greatest clichés of recent years. But there is unanimous consensus to the fact that companies which have achieved unprecedented success in the past have been those which have relied majorly on technologies, skills and knowledge of their employees rather than on physical assets such as plant and machinery. The traditional factors of production, such as labour and capital, are increasingly being replaced by intellectual assets in the modern economy. As competitors have equal access to them, the conventional factors at best yield only the cost of capital. Companies operating in sectors, such as informal technology, pharmaceuticals, banking and telecommunications, are typically characterized by a high intellectual capital (IC) base and have market valuations many times over their book value (Sveiby, 1997). According to Lev and Radhakrishnan (2002), such high valuations cannot be attributed to monopoly power or competition constraining regulations; such organizations are manifested by unique systems and processes employed in the investment, production and sales activity of the enterprise along with incentives and compensation system governing its human resources. Much emphasis has been laid on the substantial role played by intangibles as a means for better understanding of the value creation process in private, public and not for profit enterprises (Grasenick & Low, 2004). The necessity and importance of measuring intangibles has increasingly been accepted by the business, research as well as academic communities. But it is indeed unfortunate that traditional accounting continues to remain focused on tangible assets undermining the importance as well as relevance of intangible assets. At best, the only intangible assets that have found place in corporate financial statements are in the nature of intellectual property, such as patents, trademarks and acquired items like goodwill. In fact, in a joint study conducted by Ballow et al., (2004) almost 94 per cent of the senior executives recognized the importance of comprehensive management of intellectual assets but at the same time mere 5 per cent of the executives claimed to have built up robust systems to monitor the diverse aspects of performance of these assets. Failure to measure and account for IC assets can lead to inefficient allocation of resources as well as their mismanagement. Although some companies are implementing different measurement tools for IC valuation, there is still a lot to be achieved in terms of consolidation and application of standard measurement and reporting techniques.

Given the importance of IC, research works have been conducted in different countries to empirically examine the role of IC in improving financial performance. Such studies are limited in the Indian context. Most of the existing studies in India have tested the relationship but have been restricted to a few knowledge intensive sectors such as information technology, pharmaceuticals, banking, etc. Comprehensive studies encompassing diverse sectors have been absent in the Indian context. In the present economic scenario, intangible information and relationship resources are being utilized by companies the way conventional assets such as machinery, property and assets have been used for developing a business. As the geographical boundaries for trade shrink and competition heightens, control of physical resources no longer remains the defining factor for most business models. Hence, it is not readily obvious why this relationship should only be tested for knowledge-intensive sectors such as IT, pharmaceuticals and banking and not carried across other industrial sectors such as manufacturing, real estate, infrastructure, etc. Moreover, such previous studies provide mixed results, not clearly indicating the relationship between IC and traditional measures of financial performance. Thus, in the present study, the authors extend this analysis to a set of diverse Indian firms and attempt to explore the relationship between IC and financial performance of publicly listed firms in India using Pulic’s Value Added Intellectual Coefficient (VAIC).

The remaining article is organized as follows: the second section describes the evolution and developments in the field of IC along with the previous studies on the relationship among IC efficiency, financial performance and market valuation of companies. The third and the fourth sections lay down the objectives and the rationale for the study, respectively. The next section provides the theoretical framework and describes the methodology, the period and sample for study and data sources. The sixth section illustrates the empirical results of the study along with subsequent analysis and finally the last section concludes the entire study.

Review of Literature

Evolution and State of the Art

The term ‘intellectual capital’ was first popularized in the management literature in mid-1990s but the concept was initially coined as early as in 1969 by John Kenneth Galbraith (Feiwal, 1975). In the following years, pioneer work in the field of IC management was published in a number of popular management books (Brooking, 1996; Stewart, 1997) to enrich the understanding of IC and explore its varied facets. Itami and Roehl (1987) described IC as intangible assets comprising of technology, goodwill and brand name, organization culture and information about the customer that can contribute to a firm’s competitive power. Barney (1991) referred to IC as firm’s resources including its assets, capabilities, organizational processes, firm attributes and information knowledge that collectively contribute to improved organizational efficiency and sustainable competitive advantage. Brooking (1996) defined IC to include all intangible assets—market, infrastructure, human-centred and intellectual property facilitating the smooth functioning of the company. Broadly speaking, there is no generally accepted definition of IC. But one of the most succinct and widely accepted definitions is that given by Stewart (1997) describing IC as ‘packaged useful knowledge’. He explains that this includes an organization’s processes, technologies, patents, employees’ skills and information about customers, suppliers and stakeholders. Various other definitions use concepts such as ability, skill, expertise and other forms of knowledge that are useful in organizations. Many authors refer to it as the difference between the market value and book value of the firm, associated with the hidden value of many firms.

Over the past three decades, with the developments in the knowledge society, a remarkable shift has been witnessed in major industrial sectors in favour of the knowledge resources. The concept is gaining momentum as economists worldwide are realizing that production-based growth is increasingly being replaced by knowledge-driven growth so as to cope up with the pace of economic development. Intellectual capital is becoming a preemptive resource for firms for achieving sustainable competitive advantage and continuous growth, irrespective of the underlying nature of activity. Following are some of the reasons which underline the importance of IC (Marr, Gray & Neely, 2003):

help organizations formulate their strategy;

assess strategy execution;

assist in diversification and expansion decisions;

use these as a basis for compensation; and finally

communicate measures to external stakeholders.

Empirical Evidence on IC Measurement and its Impact on Financial Performance in Developed Countries

Bornemann (1999) established a positive correlation between IC and economic performance in Australian firms; Chen, Cheng and Hwang (2005) empirically investigated the relationship between value creation efficiency and firm’s financial performance and market valuation using VAIC. The results revealed that firm’s IC has a positive influence on its market valuation and financial performance and may also indicate its future performance. Chu, Chan and Wu (2011), using VAIC, measured IC of firms listed on Hong Kong Stock Exchange and found a positive association between IC and firm performance. Joshi, Ubha and Sidhu (2012) examined the IC performance of the Australian financial sector for the period 2006–2008 and the study established a strong relation between human cost and value addition made by the Australian banks. Rossi and Celenza (2014) empirically examined the relationship between the efficiency of IC and business performance in the Italian manufacturing sector over the period 2002–2011. The findings revealed that IC was a major driver in the performance of Italian manufacturing sector. Mantoh and Hirth (2015) examined the influence of intangible assets on traditional measures of financial performance using a sample of 189 publicly listed German companies and observed that capital employed and human capital efficiency (HCE) together contributed to the profitability and productivity of firms.

Empirical Evidence on IC Measurement and its Impact on Financial Performance in Developing Countries

Goh (2005) measured the IC performance of commercial banks in Malaysia and found that Malaysian banks scored relatively high in terms of HCE when compared with structural capital efficiency (SCE) and capital employed efficiency (CEE). Najibullah (2005) failed to establish any strong association between VAIC and measures of financial performance in case of banks in Bangladesh. Sharabati, Jawad and Bontis (2010) conducted a study on pharmaceutical companies in Jordan where the findings revealed that IC is impacting business performance positively. They further suggested that managers should consider the publication of IC report so as to improvise the monitoring of IC phenomenon. Ting and Lean (2009) examined the IC performance and its relationship with financial institutions in Malaysia for the period 1999–2007. The study revealed a positive relationship between VAIC and profitability. In India, Kamath (2007) estimated and analyzed the value creation efficiency of Indian banks using VAIC for the period 2000–2004. The study revealed vast differences in the intellectual and value creation performances of the Indian banks. Foreign banks outperformed the public sector banks with respect to HCE. In terms of CEE, public sector banks were the top performers. The study provided useful implications for various stakeholders such as policymakers, regulators, shareholders, etc. wherein decision-making can be improved and a better allocation of intellectual resources can be achieved for better performances. Kamath (2008) analyzed the relationship between IC components and traditional measures of performance of a company namely productivity, profitability and market valuation. Value Added Intellectual Coefficient for top 25 drug and pharmaceutical companies India was analyzed for 10-year period ranging between 1996 and 2006. The analysis based on correlation and simple linear regression revealed that HCE had a major impact on the productivity and profitability of firms. Pal and Soriya (2012) carried out a comparison of performance of IC on Indian pharmaceutical and textile industries. Value Added Intellectual Coefficient was calculated on a select sample of 105 pharmaceutical companies and 102 textile companies. Correlation and regressions models were used on panel data for the analysis. Results indicated that profitability and IC were positively associated but no significant relationship was observed between IC with productivity and market–book value ratio (M/B ratio) in both the industries. Mondal and Ghosh (2012) empirically investigated the relationship between IC and financial performance of 65 Indian banks for a period of 10 years ranging from 1999 to 2008. Return on Assets (ROA) and Return on Equity (ROE) have been used to measure the profitability while Assets Turnover Ratio (ATO) has been used to measure the productivity of sample firms. The results of the study suggest that bank’s IC is vital for its competitive advantage. Whereas HCE showed a strong impact on financial performance, SCE and physical capital are important for bank’s productivity. In a recent study of 22 IT firms in India, Shaban and Kavida (2013) found that the relationship between the performance of a company’s IC and conventional performance indicators is varied. Profitability and IC are positively associated, whereas there is no significant relationship among IC, productivity and market valuation. Ghosh and Maji (2015) empirically investigated the basic propositions of VAIC and extended VAIC technique for Indian banking and electronic sector. Their study established significant and positive influence of VAIC and its components, except for SCE, on corporate performance of the sample firms.

The existing literature clearly indicates that there is a strong positive association between IC and financial performance in case of developed countries. However, the findings reveal mixed results in the case of developing countries not clearly establishing the importance of IC in driving business performance. In addition, research in India so far has been primarily restricted to sectors such as information technology, pharmaceuticals and banking. The present study will contribute to the existing literature as it seeks to empirically examine this relationship using a more comprehensive and diversified sample of 50 companies listed on CNX Nifty over the period ranging from 2004–2005 to 2013–2014.

Objectives of the Study

Researchers and economists firmly believe that in the present global economic scenario, the most crucial value generating resources are intangible in nature. Thus, if companies use measures capturing intangibles, it would enable them to generate more value relevant information. An understanding of the value generated process always results in a more efficient allocation of resources. As reflected in the review of literature above, firms in developed countries have witnessed positive association between IC and measures of financial performance. On the other hand, studies in the developing economies do not establish a clear relationship. Especially, in the Indian context, the limited existing literature provides mixed results and is restricted to a few sectors namely information technology, banking and pharmaceuticals. Keeping in view the gaps in literature, the following two objectives guided this research:

To empirically measure IC of publicly listed firms in India and investigate its relationship with measures of financial performance.

To empirically determine which component of IC is the best predictor of company performance.

Rationale

The term IC is usually taken as a misnomer, often understood to be of relevance to only high-technology industries and information and communication technology companies. But it is important to understand that IC is essentially relevant to every business organization. Rapid technology advancements, fierce competitive environment, deregulations, product innovations, etc. have made firms increasingly rely on leveraging IC, so as to develop strategies for sustained competitive advantage. Vergauwen, Roberts and Vandemaele (2009) determined that all components of IC have a positive influence on tangible performance, although to varying degrees. Thus, organizations need to measure value-based performance so that they can manage and use it to improve financial performance and competitive advantage (Lindgren, Saghaug & Knudsen, 2009). In the Indian context, the economy has a couple of intrinsic advantages such as a huge domestic market, stable macroeconomic policies and most important of all, large low-cost and skilled labour force. In addition, the nation is bestowed with abundant natural resources and a critical mass of well-qualified workers engaged in sectors such as science and engineering, banking, hospitality, manufacturing, etc. Therefore, it becomes all the more relevant to extend the concept of IC measurement to facilitate a smooth transition into a knowledge-based economy—an economy that creates, disseminates and uses knowledge to enhance its growth and development (Dahlman & Utz, 2005). The present study would provide valuable information on measurement of IC efficiency of public listed firms in India. The study further seeks to empirically examine the relationship, if any, between components of IC and financial performance. By identifying the crucial performance indicators, the study can guide the firms in strategy formulation for improvements in future performance as well as provide useful information on developing the IC base of the nation.

Methodology

Research Design and Data Description

The present study is based on an explanatory research design as the objective is to carry out a quantitative analysis of the secondary data of sample companies. The sample comprises 50 publicly listed companies in India represented by CNX Nifty for the period 2004–2014. CNX Nifty is a diversified stock index representing 13 sectors of the economy. It accounts for 66.17 per cent of free-float market capitalization of the listed stocks as on 31 March 2015.¹ Data has been collected through PROWESS, a database on financial performance of Indian companies compiled by Centre for Monitoring Indian Economy. The primary source of this database is the annual reports of the individual companies comprising of annual financial statements—mainly profit and loss account and balance sheet.

The period of study ranges from 2004–2005 to 2013–2014. This period is the last among the three phases of economic growth and has special economic significance.² During this period, the gross domestic product (GDP) growth accelerated to more than 8 per cent per annum (Bhalla, 2011). As a result, India had become the second largest growing economy in the world in 2008. Some of the major factors driving this growth trend have been identified as technology, expansion of telecommunication and internet services, growth of information technology and information technology-enabled services and a consistent growth of the service sector in India (Bag & Gupta, 2012). However, growth rates moderated as the economy was struck by global financial crisis in 2007–2008.³ But the economy quickly underwent the recovery path with GDP growth rates returning to 8 per cent in 2009–2010 and 9.3 per cent in 2010–2011 (Bhatt, 2011). Growth plummeted again in the following years with GDP growth rate declining to 6.3 per cent in 2011–2012, 5 per cent in 2012–2013 and 4.7 per cent in 2013–2014. Overall, after two decades of economic reforms, the growth trends from 2003 onwards have positioned India as a high growth economy with the potential to make a big leap from a developing country to a developed one (Manwani, 2010). Hence, the study of measurement of IC and its impact on financial performance of firms in India during this period becomes all the more relevant.

Theoretical Framework

Research has affirmed that companies that measure and report intangibles may experience substantial gains. Edvinsson (1997), former corporate director for IC at Swedish financial services company Skandia, claims that a reduction in the cost of capital of 1 per cent was directly attributable to the company’s ability to measure and report its intangibles. Although it is being rapidly realized that it is important to understand and measure value of IC in today’s typical firm, identifying the IC of a company is not easy, and requires a strategy to be defined beforehand (Johnson, 1999). Acknowledgement of the importance of knowledge is not enough; it must also be managed and tangible results obtained. As Harrison and Sullivan (2000) state, ‘calculating the value of intangibles, companies based on their ability to develop and maintain cash flows by converting their ideas and innovations into revenue streams is fundamental to adequately assessing and quantifying the value of these firms’. Here, the managers face mainly two kinds of problem. The first is to define the intellectual material which must be accounted for. The second aspect is to define the type of value of IC that can be estimated, considering the underlying potential of all the elements of a business that can generate wealth.

Nevertheless, during 1990s, pioneer work has been done in the field of IC. Scholars as well as practitioners have developed models such as The Balanced Scorecard (Kaplan & Norton, 1996), Skandia’s IC Navigator (Edvinsson & Malone, 1997), Intellectual Capital Services’ IC-Index^TM (Roos & Roos, 1997), VAIC developed by Pulic (1998) to address the issue of measurement of IC. These models have emphasized on the strategic nature of IC and have laid focus on IC as a source of sustainable competitive advantage.

Value Added Intellectual Coefficient and Its Aggregates

The present study is based on Pulic’s VAIC as a measure of IC of a firm. The VAIC as designed by Ante Pulic and his colleagues at the Austrian IC Research Centre (Pulic, 1998, 2000; Pulic & Bornemann, 1999) is being deployed in the present study to empirically examine the association between IC and three traditional measures of key notions of corporate performance (i.e., profitability, productivity and market valuation). Value Added Intellectual Coefficient intends to measure the extent of value addition on the basis of intellectual resources. The major components of VAIC can be further classified into three broad categories—physical capital, human capital and structural capital. The sum of these three aggregates is the value of VAIC. Human capital is defined as the set of knowledge, skills and experiences that the employees take with them when they leave the organization. It is the lifeline of any organization and it is in this resource where the roots of creativity and innovation are vested. Structural capital, on the other hand, is the knowledge that stays with the firm. It consists of organizational infrastructure, processes, information systems, production techniques and relational capital developed by the organization over the years. Capital employed can be explained as the total funds deployed in the form of assets (both fixed and variable) owned by a firm. Thus, VAIC is an indirect measure of value added (VA) by both tangible as well as intangible assets of a firm and is being increasingly used in business (Pulic, 1998). As a measure of IC efficiency, it has several advantages when compared with other measures. First, it provides a standardized and consistent basis of measure (Pulic & Bornemann, 1999). Second, computation of VAIC is based on audited financial statements which can be considered authentic and verifiable (Pulic, 1998, 2000). In contrast, other IC measures have invited considerable criticism owing to underlying subjectivity in the variables. Another merit of VAIC is that it is a relatively easy technique and simple to understand enhancing cognitive understanding. Finally, VAIC is being gradually and universally accepted and is used in business applications (International Business Efficiency Consulting, Inc., 2002; Nova Kreditna Banka Meriba, 2000; Williams 2001).

Calculating VAIC

As explained by Riahi-Belkaoui (2003), the calculation of VA is discussed below:

R = S - B - D P - W - I - D D - T

(1)

where R is retained earnings: It is the portion of the net income which is retained by a corporation for the purpose of reinvestment or payment of debt rather than distributed to shareholders as dividend. S is net sales revenues: This is the operating revenue generated by a company by selling its products or rendering its services. B is bought-in material or cost of goods sold: This includes the cost of the materials and direct labour used in producing the good. ‘DP’ is depreciation; W is wages (employee salaries); ‘DD’ is dividends; and T is taxes.

Rearranging equation (1), we get

S - B = D P + W + I + D D + T + R

(2)

S - B - D P = W + I + D D + T + R

(3)

Equation (2) is the gross-value added approach and equation (3) is the net-value added approach. Value added is expressed as the net value created by firms during the year and is explained below:

V A = S - B - D P = W + I + T + N I

(4)

The major components of the firm resources, namely, capital employed (CE), human capital (HU) and structural capital (SC) are:

CE = capital employed

HC = wages and salaries

SC = Value added less human capital

Thus,

\begin{matrix} V A I C = C a p i t a l E m p l o y e d E f f i c i e n c y (C E E) + H u m a n C a p i t a l E f f i c i e n c y (H C E) \\ + S t r u c t u r a l C a p i t a l E f f i c i e n c y (S C E) \end{matrix}

(5)

where CEE = VA/CE, HCE = VA/HU and SCE = SC/VA.

Capital employed efficiency is an indicator of VA efficiency of capital employed; HCE is an indicator of VA efficiency of human capital; and SCE is an indicator of VA efficiency of structural capital.

Method

To carry out the empirical investigation, correlation analysis followed by panel data regression has been conducted (using E-Views 7.0). In order to select the relevant panel data technique, Hausman test was conducted (results can be provided on request) so as to decide on the appropriateness of Fixed Effects Model (FEM) or Random Effects Model (REM). Based on the results, the null hypothesis was rejected and accordingly fixed effects panel data regression technique was used to examine the impact of IC on financial performance of the sample firms.

Defining Variables

Independent Variables

The independent variables chosen for the study are VAIC and its components, that is, CEE, HCE and SCE. Value Added Intellectual Coefficient is used as a measure of corporate intellectual ability. Value Added Intellectual Coefficient provides an easy-to-calculate, standardized and consistent basis of measure, enabling effective comparative analyses across firms and countries; the data used in the calculation of VAIC is based on annual financial statements.

Dependent Variables

Market-to-book value ratio (M/B): measure of market value of a company relative to its book value.

M/B ratio = Share price of the stock/Book value of the share.

Return on Assets (ROA): measures profitability of a company relative to its assets. It is expressed in percentage terms and indicates how efficiently assets are being utilized for the purpose of generating income.

ROA = Net Income/Total Assets.

Asset Turnover Ratio (ATO): measures the efficiency in terms of a company’s ability to generate sales from its assets. It is an indicator of efficiency in deployment of assets.

ATO = Net Sales/Average Total Assets.

Empirical Model

Intellectual Capital and Market Valuation

Intellectual capital, the knowledge-based equity of corporations, has increasingly gained acceptance as a source of competitive advantage. Sullivan (2000) indicates that IC is that knowledge that can be converted into future profits and includes resources such as ideas, technologies, processes, designs and informatics programmes. It is expected that IC will play a vital role in increasing a firm’s market value In addition, it is believed that in efficient markets, investors will place higher value for firms with greater IC (Firer & Williams, 2003; Riahi-Belkaoui, 2003). Accordingly, the study seeks to empirically examine whether IC and its components have an impact on market valuation of firms. It has been empirically established in the previous research works that if investors place different values for these three indicators, then the model using these three components of VAIC will have greater explanatory power than the model using the aggregate one. Therefore, the following regression equations have been proposed to examine the relationship between IC and individual components of VAIC:

M / B_{i t} = β_{i t} + β_{2} V A I C_{i t} + u_{i t}

(6)

M / B_{i t} = β_{i t} + β_{2} C E E_{i t} + β_{3} H C E_{i t} + β_{3} S C E_{i t} + u_{i t}

(7)

Intellectual Capital and Financial Performance

Existing research has provided ambiguous results in terms of establishing a causal relationship between IC and financial performance of firms. Riahi-Belkaoui (2003) established a positive relationship between IC and financial performance, while Firer and Williams (2003) failed to find any relationship between IC and traditional measures of firm performance. Chen et al. (2005) using the same methodology inferred that IC has a significant impact on profitability. Accordingly, the study makes the below-mentioned regression models to revisit the postulates of the previous studies and present some conclusive findings:

R O A_{i t} = β_{i t} + β_{2} V A I C_{i t} + u_{i t}

(8)

R O A = β_{i t} + β_{2} C E E_{i t} + β_{3} H C E_{i t} + β_{3} S C E_{i t}

(9)

Intellectual Capital and Productivity

While the role of physical assets is well established in the literature and in practice, it is the role of intangible assets as strategic resources that needs and deserves investigation (Grant, 1991; Itami & Roehl, 1987; Mahoney & Pandian, 1992). Patton (2007) affirmed that productivity of a firm to a large extent depends upon its IC rather than physical assets. Hence, the following hypotheses are proposed:

The regression equation so formed is:

A T O_{i t} = β_{i t} + β_{2} V A I C_{i t} + u_{i t}

(10)

A T O_{i t} = β_{i t} + β_{2} C E E_{i t} + β_{3} H C E_{i t} + β_{3} S C E_{i t} + u_{i t}

(11)

Data Analysis

Table 1 provides information on the average VAIC scores of the sample firms over the period ranging from 2004–2005 to 2013–2014. The firms are listed in the order of their VAIC values—from largest to smallest. The firms are further classified into three categories—top performers (VAIC is more than 30), average performers (VAIC is more than 10 but less than 30) and poor performers (VAIC is less than 10). Twelve per cent of the sample firms are observed to be the top performers in terms of IC efficiency, namely, ICICI Bank, IDFC Ltd, Coal India, NMDC Ltd, DLF Ltd and Sesa Sterlite. Around 16 per cent of the firms have average performance in terms of IC efficiency. Firms belonging to financial services, mining and energy are witnessed to have best performances. The findings present some very interesting revelations, breaking the myth that the concept of IC is more relevant for knowledge-intensive industries such as IT, pharmaceuticals and banking. But here it is found that some of the top IT companies (by market capitalization) such as Tata Consultancy Services Ltd, Infosys Ltd, HCL Technologies, Tech Mahindra Ltd and Wipro Ltd have the least VAIC scores. The results are in close conformity with a recent study by Kamath (2015) where IC efficiency of 30 firms listed on Bombay Stock Exchange (BSE) was measured. Another important finding is that 70 per cent of the sample firms fall in the category of poor performers having an average VAIC score of less than 10. This presents a strong case for improvement in IC performance of these firms. Finally, HCE is observed to be a major component of VAIC, thus emphasizing the crucial role of human assets in the value creating process.

Table 1.

Measurement of VAIC and Its Components

S. No.	Company Name	VAIC	CEE	HCE	SCE	Performance
1	ICICI Bank Ltd	56.80	0.07	55.75	0.98	Top performers
2	IDFC Ltd	51.19	0.10	50.79	0.30	Top performers
3	Coal India	37.85	0.31	36.58	0.97	Top performers
4	NMDC Ltd	34.91	0.49	33.45	0.97	Top performers
5	DLF Ltd	32.21	0.11	31.14	0.96	Top performers
6	Sesa Sterlite Ltd	29.20	0.43	27.82	0.95	Top performers
7	GAIL (India) Ltd	15.45	0.22	14.31	0.92	Average performers
8	Reliance Industries Ltd	14.16	0.13	13.11	0.92	Average performers
9	Indusind Bank Ltd	13.70	0.08	12.70	0.92	Average performers
10	Axis Bank	13.02	0.07	12.03	0.92	Average performers
11	Jindal Steel & Power Ltd	12.95	0.15	11.90	0.89	Average performers
12	Cairn India	12.80	0.14	11.74	0.93	Average performers
13	Bajaj Auto	11.52	0.61	9.35	1.56	Average performers
14	ITC Ltd	11.34	0.44	9.99	0.91	Average performers
15	Bank of Baroda	10.88	0.06	9.94	0.88	Average performers
16	Hindustan Unilever Ltd	9.92	2.19	5.41	2.32	Poor performers
17	Oil & Natural Gas Corp. Ltd	9.38	0.32	8.18	0.88	Poor performers
18	Ultratech Cement Ltd	9.28	0.21	8.21	0.85	Poor performers
19	Power Grid Corp. of India Ltd	9.22	0.10	8.24	0.87	Poor performers
20	HDFC Bank	9.08	0.08	1.91	7.09	Poor performers
21	Ambuja Cements Ltd	9.05	0.28	7.91	0.86	Poor performers
22	NTPC Ltd	9.00	0.14	7.99	0.87	Poor performers
23	Punjab National Bank	8.86	0.15	7.86	0.85	Poor performers
24	HDFC Bank Ltd	8.72	0.07	7.79	0.87	Poor performers
25	Housing Development Finance Corp. Ltd	8.65	0.11	7.67	0.86	Poor performers
26	Bharti Airtel	8.28	0.21	7.22	0.85	Poor performers
27	Sun Pharmaceutical Industries Ltd	8.27	0.16	7.32	0.79	Poor performers
28	Tata Power Co. Ltd	8.15	0.11	7.18	0.86	Poor performers
29	Maruti Suzuki India Ltd	7.90	0.22	6.85	0.83	Poor performers
30	Hero Motorcycles	7.01	0.65	8.13	–1.77	Poor performers
31	Grasim Industries	6.95	0.21	5.93	0.82	Poor performers
32	Asian Paints Ltd	6.87	0.74	5.33	0.80	Poor performers
33	Kotak Mahindra Bank Ltd	6.55	0.09	5.65	0.81	Poor performers
34	Tata Steel Ltd	6.43	0.17	5.45	0.81	Poor performers
35	Hindalco Industries Ltd	6.31	0.11	5.67	0.52	Poor performers
36	ACC Ltd	6.17	0.27	5.12	0.78	Poor performers
37	Cipla	5.84	0.27	4.80	0.76	Poor performers
38	Bharat Petroleum	5.51	0.15	4.61	0.74	Poor performers
39	Lupin Ltd	4.82	0.35	3.75	0.72	Poor performers
40	Larsen & Toubro Ltd	4.71	0.28	3.71	0.72	Poor performers
41	Mahindra & Mahindra Ltd	4.65	0.32	3.62	0.72	Poor performers
42	United Spirits Ltd	4.48	0.17	3.61	0.70	Poor performers
43	Bharat Heavy Electricals	4.37	0.90	3.24	0.23	Poor performers
44	Dr. Reddy’s Laboratories	4.23	0.23	3.34	0.66	Poor performers
45	Tata Consultancy Services Ltd	3.32	2.39	1.78	–0.84	Poor performers
46	Tata Motors Ltd	3.25	0.17	2.51	0.56	Poor performers
47	Infosys Ltd	3.16	0.87	2.22	0.08	Poor performers
48	HCL Technologies	3.08	0.69	1.91	0.47	Poor performers
49	Tech Mahindra Ltd	3.01	0.93	1.69	0.39	Poor performers
50	Wipro Ltd	2.72	0.77	1.58	0.37	Poor performers

Source: Computed by the authors.

Table 2.

Correlation Results

		CEE	HCE	SCE	M/B Ratio	ROA	ATO
Capital employed efficiency	Pearson correlation	1	–0.002	–0.234**	0.541**	0.122**	0.500**
	Sig. (2-tailed)		0.765	0.000	0.000	0.002	0.000
	N	633	632	632	602	631	623
Human capital efficiency	Pearson correlation	–0.002	1	0.161**	–0.047	0.011	–0.257**
	Sig. (2-tailed)	0.765		0.000	0.254	0.779	0.000
	N	632	632	632	601	630	622
Structural capital efficiency	Pearson correlation	–0.234**	0.161**	1	–0.180**	0.095*	–0.206**
	Sig. (2-tailed)	0.000	0.000		0.000	0.018	0.000
	N	632	632	632	601	630	622
	Sig. (2-tailed)	0.000	0.136	0.056	0.000	0.000	0.000
	N	633	632	632	602	631	623
Market–book value ratio	Pearson correlation	0.541**	–0.047	–0.180**	1	–0.224**	0.440**
	Sig. (2-tailed)	0.000	0.254	0.000		0.000	0.000
	N	602	601	601	602	602	594
Return on assets	Pearson correlation	0.122**	0.011	0.095*	–0.224**	1	–0.136**
	Sig. (2-tailed)	0.002	0.779	0.018	0.000		0.001
	N	631	630	630	602	631	623
Assets turnover ratio	Pearson correlation	0.500**	–0.257**	–0.206**	0.440**	–0.136**	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.001
	N	623	622	622	594	623	623

Source: Computed by the authors.

Notes: (i) **Correlation is significant at the 0.01 level (2-tailed).

(ii) *Correlation is significant at the 0.05 level (2-tailed).

Correlation statistics between the dependent as well as the independent variables are presented in Table 2. Capital employed efficiency is observed to have a significant positive association with all the measures of corporate performance namely represented by M/B ratio, ROA and ATO. This means that efficient utilization of capital employed can lead to better market valuations, higher profitability and improved productivity. The results are in close conformity with previous studies such as Gan and Saleh (2008), Chu et al. (2011) and Shaban and Kavida (2013). Human capital efficiency is found to be negatively correlated with M/B ratio and ATO but the association is not significant with M/B ratio. Similarly, SCE is negatively correlated with market valuation and productivity but has a significant positive association with ROA. This can be interpreted to mean that both efficiency of human capital employed as well as procedures and systems forming structural capital fail to have any positive impact on market valuation and productivity. The findings emphasize the important role played by physical capital employed in generation of improved financial results. Stakeholders undoubtedly rely more upon improving utilization of capital deployed when compared with human and structural productivity for increasing returns. None of the variables have correlation measuring more than 0.8; thus, multicollinearity is not reported and the results can be relied upon.

The empirical findings in Table 3 present the panel regression results for relationship between M/B ratio and VAIC as an aggregate measure and the results indicate a statistically significant positive relationship. The value of adjusted R² is 0.75801 which establishes the robustness of the model. Further, results in Table 4 report a significant positive impact of VAIC on profitability of firms as measured by ROA. Table 5 lists the regression results for relationship between VAIC and productivity as measured by ATO. It is found to have a significant positive influence on productivity of firms. The relatively high value of adjusted R² (0.91081) is symbolic of reliability of the empirical model. Thus, overall VAIC is found to have a significant positive influence on all the measures of corporate performance selected for the present study, namely, market valuation (M/B ratio), profitability (ROA) and productivity (ATO).

Table 3.

Regression Results—M/B Ratio and VAIC

Variable	Coefficient	Std Error	t-statistics	Probability
C	1.028709	0.097877	10.51026	0.0000
VAIC	0.112742	0.046222	2.439156	0.0151*

Source: Computed by the authors.

Notes: (i) Adjusted R² = 0.75801.

(ii) *Significant at α = 0.95.

Table 4.

Regression Results—ROA and VAIC

Variable	Coefficient	Std Error	t-statistics	Probability
C	1.973699	0.18603	10.60956	0.0000
VAIC	0.479334	0.087404	5.484119	0.0000**

Source: Computed by the authors.

Notes: (i) Adjusted R² = 0.46818.

(ii) **Significant at α = 0.99.

Table 5.

Regression Results—ATO and VAIC

Variable	Coefficient	Std Error	t-statistics	Probability
C	–0.665128	0.135797	–4.89795	0.0000
VAIC	0.119281	0.06375	1.871073	0.0620***

Source: Computed by the authors.

Notes: (i) Adjusted R² = 0.910814.

(ii) ***Significant at α = 0.90.

In order to understand the importance of individual components of VAIC in driving business performance, the model is further analyzed and results are listed in Table 6. It can be observed that CEE and HCE have a strong positive association with ROA, thereby implying that an increase in the efficiency of physical capital employed and human capital can contribute towards higher profitability. In case of relationship of components of VAIC with M/B ratio and ATO respectively, CEE is found to be the only variable having a significant positive association with market valuation and productivity. Efficiency of human resources is observed to have a non-significant impact on market valuation and productivity of sample firms. In fact, HCE has a negative but non-significant association with ATO and M/B ratio. Thus, the chances are that an excellent and innovative management team contributing to increased profits might miss its reporting in accounts of a firm even though it would have played a crucial role in improving the productivity. Kamath (2008) revealed that Indian markets fail to reflect the performance of firms in terms of IC efficiency because the perception of stakeholders is skewed towards tangible assets with respect to measurement of performance. The prime reason explaining this phenomenon could be the nature of financial reporting and accounting standards governing business in India. The regulations fail to provide an adequate framework for measuring and reporting of intellectual assets. Reporting of IC in financial statements is based on voluntary disclosures. Further, the findings fail to establish any significant association between SCE and any measure of financial performance.

Table 6.

Regression Results: Financial Performance Variables and VAIC Components

Independent Variables	Dependent Variables
Independent Variables	Coefficient	Std Error	t-statistics	Probability	Adj. R²	F-statistics
Return on Assets
C	2.892299	0.354832	8.151178	0.0000*	0.471697	8.406294
CEE	0.186094	0.085418	2.178635	0.0299*
HCE	0.237943	0.122601	1.940789	0.0529***
SCE	0.319816	0.319456	1.001128	0.3173
Assets Turnover Ratio
C	0.198198	0.253039	0.783272	0.4339	0.765452	91.19893
CEE	0.322116	0.060236	5.347546	0.0000**
HCE	–0.080755	0.087273	–0.92531	0.3553
SCE	–0.127731	0.226828	–0.56312	0.5736
Market–Book Value Ratio
C	1.637863	0.193401	8.468725	0.0000*	0.429789	27.37572
CEE	0.201194	0.046384	4.33756	0.0000*
HCE	–0.031545	0.066261	–0.47608	0.6343
SCE	0.011456	0.194176	0.058999	0.9530

Source: Computed by the authors.

Notes: (i) *Significant at α = 0.95.

(ii) **Significant at α = 0.99.

(iii) ***Significant at α = 0.90.

Previous studies too have failed to establish the importance of SCE in improving corporate performance (Firer & Williams, 2003; Ghosh & Mondal, 2009; Shaban & Kavida, 2013; Ting & Lean, 2009). The reason explaining this can be attributed to the inappropriateness of VAIC methodology in computation of SC (Ståhle, Ståhle & Aho, 2011). In fact, this is identified as one of the most important limitations of VAIC technique. Nevertheless, this measure of IC valuation is widely acceptable considering its objectivity and simplicity.

Thus, on evaluation of the individual components of VAIC, results strongly advocate the importance of CEE as a value driver for improving overall corporate performance. Human capital efficiency is found to be only significant in its relation with profitability, while SCE failed to have any significant association with any measure of performance. The F-statistics in Table 6 establishes the overall significance of model depicted in equations (7), (9) and (11). These results are in conformation with previous studies in India and other parts of the globe (Ghosh & Maji, 2015; Ghosh & Mondal, 2009; Firer & Williams, 2003; Mehralian, Rajabzadeh, Reza Sadeh & Reza Rasekh, 2012; Shaban & Kavida, 2013; Ting & Lean, 2009). The investors and other stakeholders are more comfortable perceiving value based on performance measures driven by tangible assets rather than intellectual assets.

Conclusion

Management of IC can result in significant benefits to an organization that can aid in formulation of business strategy, process design as well as providing competitive advantage. Given the importance of IC, the present study endeavoured to measure the IC of publicly listed companies in India using Pulic’s VAIC technique. Further, the study examined the relationship of VAIC and its components, namely, CEE, HCE and SCE with measures of corporate performance, that is, ROA, M/B ratio and ATO. The findings reveal a significant positive influence of VAIC on all measures of corporate performance. As regards the components of VAIC, HCE was found to have a significant association with profitability, while CEE was observed to have a significant positive relation with profitability, market valuation as well as productivity. Structural capital efficiency failed to have any significant influence on any measure of corporate performance. The findings of the present study are very encouraging and can be of particular interest to policymakers, business communities and to members of academic communities. Development of IC if aligned with national objectives and recognized in national accounts can always lead to better implementation of national policies. Given the pro-growth reforms of the government, it becomes essential to unleash the underlying potential of human capital of firms. As HCE was observed to have insignificant association with productivity and market valuation, the situation becomes particularly alarming for a country like India where demographics favour a large pool of young talent. The policymakers need to align education guidelines with industry requirements for improving industrial production and efficiency. A well-educated and properly trained workforce will always create, share, disseminate and utilize knowledge more effectively and efficiently. In this direction, academicians need to play an integral role to impart skill-based education. Simultaneously, organizations need to inculcate a culture which promotes knowledge management activities, dynamic learning and strategic planning. According to Shahid (2009), corporate leaders in Indian firms are not laying emphasis on promoting enterprise-wide IC management strategies. Moreover, IC recording and reporting in India is negligible and the average number of items reported is deplorable (Joshi and Ubha, 2009). Thus, managers need to adopt full disclosure policies which can further aid in leveraging the latent potential. Mandatory disclosure norms by the regulatory authority would be a step in the right direction in this context.

Future research should focus on identifying and evaluating the existing IC measurement techniques presently being used by Indian firms. In addition, alternative IC measurement techniques can be evaluated in order to identify a standardized measurement method which can be universally applied.

Footnotes

Acknowledgements

The authors are grateful to anonymous referees of the journal for their extremely useful suggestions for improving the quality of the article. The usual disclaimers apply.

Notes

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