Financial Reporting of Intellectual Capital and Company's ...

34 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011

Financial Reporting of Intellectual Capital and Company’s Performance in Indian Information Technology Industry Karam Pal, Guru Jambheshwar University of Science & Technology, India Sushila Soriya, Guru Jambheshwar University of Science & Technology, India

ABSTRACT This paper examines the relationship between Financial Reporting of Intellectual Capital and Company’s Performances in Indian Information Technology Industry. For the purpose of this study, sixty companies listed on NSE were taken for a period of 1999-00 to 2008-09. Value Added Intellectual Co-efficient (VAICTM) method developed by Pulic (1998) was used for the analysis of the data. The present study uses VAICTM model and regression equation for the evaluation of intellectual capital and their relationship with productivity, profitability, and market valuation of the companies. The result of the study supports the hypothesis that profitability of the company can be explained by the intellectual capital. However, there is no significant association of intellectual capital with productivity and market capitalization of the companies for the selected time period of year 1999-00 to 2008-09. Keywords:

Intellectual Capital, IT Sector, Market Valuation, Productivity, Profitability, Value Added Intellectual Co-Efficient (VAICTM)

INTRODUCTION Financial reporting of intellectual capital is the most debatable issue among the accounting professionals because of its intangible nature. Researchers have defined and measured various models to know the exact value of intellectual capital. Different measures are used to calcuDOI: 10.4018/jabim.2011040103

late the amount of intellectual assets present in the company’s annual reports particularly the balance sheet. For the convenience in its measurement, intellectual capital is divided into three major groups. These are human capital, structural capital and customer capital. Researchers have always been interested in knowing relationship between presence of intellectual capital in the company and its impact on the market value of the companies.

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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 35

Table 1. India IT/ITES Industry Size (2007-2012) (value in Crores) 2007

2008

2009

2010

2011

2012

CAGR 07-12

Domestic IT/ITES Market

90,014

110,177

133,100

158,053

182,991

209,698

18.4%

IT/ITES Export Revenue

156,594

186,142

218,104

250,087

284,666

320,278

15.4%

India IT/ITES Industry Size

246,609

296,319

315,207

408,139

467,657

529,976

16.5%

Many researches were carried out to assess the relationship between intellectual capital and its consequences with market valuation of the companies. Seleim, Ashour, and Bontis (2004) investigated Egyptian software firms to know the components of the intellectual capital i.e. human, structural and relational capital present in them. These components were very essential for the proper development of the theory and the model. The study found that intellectual capital which was widely present in software firms can be measured and utilized. Oliver and Porta (2006) developed a cluster model to analyze the components of the intellectual capital namely Intellectual Capital Cluster Index (ICCI®). It was developed to measure the intellectual capital on clusters. Intangible and tangible assets cannot be treated separately as both are necessary for the proper running of the organization. In fact intellectual capital is gaining more importance over the physical assets of the company. This study is an attempt to analyze the relationship of intellectual capital with profitability, productivity and the market valuation of the companies. The paper is divided into five sections. Section-1 gives overview of Indian IT Industry, Section-2 reviews literature of the exiting studies. Section-3 presents the methodology followed in this paper. Section-4 discusses the results and Section-5 concludes the paper.

1. AN OVERVIEW OF INDIAN IT INDUSTRY Information technology industry is one of the growing sectors in India making its presence well felt all over the world. The IT industry sector is one of the many knowledge based industries. The growth of the IT industry may be due to the presence of intellectual capital in it. Table 1 shows industry size of IT and IT enables services (ITES) from the year 2007 to 2012 with compound annual growth rate (CAGR). IT industry is major contributor to Indian economy in terms of foreign exchange services and employment opportunities. Indian IT companies are expanding their business at the global level by various mergers and acquisitions done by these companies. In terms of Gross Domestic Product (GDP), IT sector has increased its share from 1.2% in FY98 to 5.2% in FY07. Export earning was also approximately USD 40.0 billion with a growth rate of 36% in year FY08. This sector is also providing employment to a large part of the population. In the year 2006, out of total merger and acquisition, 23% were in IT industry. This industry is also one of the largest distributors of dividends to shareholders. Contribution of IT sector in the foreign earnings showed remarkable growth of 32.6% in FY07. This industry also became the largest employer in private sector having a growth



Figure 1. Production Performance of IT Industry

Compound Annual Growth Rate (CAGR) of 26% in the last decade. Traditionally Indian export was limited to only few products like gems, jewelleries and garments etc. But with the beginning of IT industry, India made its presence felt in the global arena for its products and services. Acquisition of foreign companies by Indian companies rose to 125 foreign acquisitions in the year 2006 with a value of about $10 million. It was about 23% of the total number of international acquisitions. The production performance of various industrial groups in the hardware and software sector in 2008-09 is given below in Figure 1.

2. REVIEW OF LITERATURE The term intellectual capital constitutes different variables which are difficult to measure in the quantitative terms. Indian Accounting Standard (AS) 26 specifies stringent criteria needed to be fulfilled by an intangible asset to be reported as intangible assets in company. To be reported under this standard the assets must fulfill the required conditions. Studies were conducted to check the inter relationship between components of intellectual capital i.e. human, structural and relationship capital. Bozzolan, Favotto, and Ricceri (2003) analyzed listed Italian companies to check their disclosure pattern regarding the intel-

lectual capital and the various reasons behind the disclosure. It was found that disclosure of companies occurred mainly with the external structure. Size and industry were also relevant to the disclosing the intellectual capital differences by the companies. The study examined the voluntary intellectual capital disclosure of the companies. Abeysekera and Guthrie (2005) examined annual reports of Sri Lanka’s Colombo Stock Exchange with the support of content analysis method. The study focused on the intellectual capitals that have covered a wide variety of intellectual capital items but not specifically mentioned under any heading. Kamath (2007) analyzed the Indian banking industry with 98 banks in India. VAIC was used to measure the performance of banking industry. The study confirmed the overall performance of banks in India and there was great diversity among the performances of the banks. Foreign banks were overall better performers in terms of human capital efficiency as compared to others. And public sector banks were better in case of capital employed efficiency. Chen, Cheng, and Hwang (2005) investigated Taiwanese companies to examine the association between intellectual capital efficiency and firms’ financial performance. The result of the study supported the argument that the intellectual capital was positively related with the market value and financial performance of



the company. It also highlighted the fact that investors were giving more importance to the firms with higher intellectual capital efficiency and in turn greater profitability. Bollen, Vergauwen, and Schnieders (2005) conducted a survey in German pharmaceutical industry to check the relationship between intellectual property and performance of the firms. For the purpose of this study, intellectual capital is divided into four main heads i.e. human capital, structural capital, relationship capital and intellectual property. The result highlighted that the intellectual property has major impact on the company’s overall performance which was influenced by human capital, structural capital and relational capital. It means that the pharmaceutical companies have to focus on the intellectual property of the company to enhance the company’s performance. Bontis, Keow, and Richardson (2000) investigated Malaysian industry with three components of intellectual capital i.e. human capital, structural capital and customer capital and their inter relationships with the help of questionnaire method. The study found that intellectual capital has significant relationship with the business performance in the industry. Garcia-Meca (2005) examined the disclosure of intellectual capital in Spanish companies and its usefulness in the investment decision making process. The Disclosure Index (DI) and Analyst Index (AI) were used for the study. The study confirmed that the information regarding the intellectual capital was disclosed by way of meetings with the analysts and later on this information was used for the earnings forecast. Boekestein (2006) analyzed a sample of 52 largest pharmaceutical companies to assess reporting of intangible assets and goodwill in company’s annual reports. The information provided in the balance sheet and the annual reports were more of qualitative kinds and not in quantitative form. From the study it was found that there was no significant relationship between the profitability and intangible assets of the company. Abeysekera and Guthrie (2003) examined 30 companies in Sri Lanka using content

analysis method. For study purpose, intellectual capital was divided into three parts namely internal, external and human capital. The result of the study indicated that external capital followed by the human capital were most reported items. It was found that the information was in qualitative form and not in numerical terms. The outcome of the study was that Sri Lankan companies were disclosing intellectual capital information despite of not using the term intellectual capital. Companies were disclosing lot of information about it in the sundry section of the annual reports. Mavridis (2004) examined a set of Japanese banks with the Value Added Intellectual Co-efficient (VAICTM) to know value added by the intellectual capital. Best Performance Index (BPI) was used to supplement the VAICTM model. Increase in the Best Performance Index (BPI) was complemented by both physical capital and human resource capital. The results highlighted that the banks which performed better than others were those which has used more intellectual capital than the physical capital. Kamath (2008) carried out the study to analyze the association between the intellectual capital components and pharmaceutical firms’ corporate performance. VAIC model was used for this purpose. The study was conducted on a sample size of 25 pharmaceutical firms for a period of 1996 to 2006. The study found that components of intellectual capital i.e. Value Added Capital Co-efficient (VACA), Human Capital Co-efficient (VAHU) and Structural Capital Co-efficient (SCVA) individually have a significant impact on the dependent variable. It was also found that human assets were important and have an impact on profitability and productivity in the industry. Bhasin (2006) studied different components of intellectual capital and has given diverse arguments both in support and against of not disclosing intellectual capital in the annual reports of the companies. The reasons discussed for the disclosure of intellectual capital was reduction of borrowing cost by providing transparency to the stakeholders.



Tan, Plowman and Hancock (2007) used Pulic’s model to evaluate 150 public listed companies on Singapore Exchange. Company’s ratios such as return on investment, earnings and shares’ performance on the stock market were used to measure the company’s performance. In Indian context only few studies were done to check the impact of financial reporting of intellectual capital on the traditional valuation methods. This study tries to find the association of intellectual capital with that of firms’ profitability, productivity and market valuation. This paper follows the methodology used by Firer and Stainbank (2003) to check the relationship between intellectual capital firms’ profitability, productivity and market valuation in South African context and Ghose and Mondal (2009) in pharmaceutical and software industry in India with a sample of 80 firms.

3. RESEARCH METHODOLOGY In research, the methodology needs to be cautiously designed to obtain results that are as objective as realistic. A well comprehensible modus operandi empowers the innovative researcher to revisit the study setting. Good methodology follows the standards of the established conventions. For the present paper, a number of obligatory inimitabilities of the research methodology are defined here: Objectives of the paper: • •

•

•

To evaluate the VAICTM of 60 companies in Indian IT sector for a period of ten year from 1999-00 to 2008-09; to study the relationship between intellectual capital and profitability in IT sector in India for a period of ten year from 1999-00 to 2008-09; to study the relationship between intellectual capital and productivity in IT sector in India for a period of ten year from 1999-00 to 2008-09; and to investigate the relationship between intellectual capital and market valuation

of the firm for a period of ten year from 1999-00 to 2008-09. Hypothesis: H1. Intellectual capital and company’s profitability is not associated with each other. H2. Intellectual capital and company’s productivity is not associated with each other. H3. Intellectual capital and market value of company is not associated with each other. Data Collection: For the purpose of this study, sixty companies listed on National Stock Exchange (NSE) are taken (Table 2). The data used for the study is secondary data. The data is collected from the Centre for Monitoring Indian Economy (CMIE), Prowess database. The time period for the study is from 1999-00 to 2008-09. For the purpose of this study, variables are divided into dependent, independent and control variables. VAICTM is considered as independent variable and dependent variables are Return on Assets (ROA), Assets Turnover Ratio (ATO) and Market to Book Value (MB). Control variables are LCAP (Market Capitalization of the company), Debt Equity Ratio (DER) and Physical intensity (PC) measured to know the amount of fixed assets of a company over its total assets. Statistical Tools for the study: Value Added Intellectual Co-efficient (VAICTM) method developed by Pulic (1998) used for the analysis of the data. This method measures the value added of a company by the presence of the intellectual capital in it. There are many advantages of using VAICTM model as the measuring technique as it is based on the information provided in the annual reports which are already audited and a reliable source of information. VAICTM method provides consistent results and so comparisons of different companies can be done in an effective way. The results of value added intellectual co-efficient can be analyzed easily as higher the value of VAIC better it is. Correlation and linear regression equations used to analyze the company’s performance in



Table 2. Name of the IT Companies Sl. No. 1

Name of the IT companies

Market Capitalization (In Crores)

3I Infotech Ltd.

973.25

2

Accel Frontline Ltd.

159.41

3

Allsec Technologies Ltd.

58.21

4

Axis-I T & T Ltd.

37.97

5

Blue Star Infotech Ltd.

58.67

6

C M C Ltd.

724.38

7

Cranes Software Intl. Ltd.

1110.09

8

Datamatics Global Services Ltd.

97.56

9

Educomp Solutions Ltd.

4845.21

10

F C S Software Solutions Ltd.

78.87

11

Financial Technologies (India) Ltd.

4974.43

12

Firstsource Solutions Ltd.

1141.63

13

Four Soft Ltd.

84.17

14

G T L Ltd.

2031.63

15

Geodesic Ltd.

1165.50

16

Geometric Ltd.

237.85

17

Glodyne Technoserve Ltd.

421.13

18

H C L Infosystems Ltd.

1930.99

19

H C L Technologies Ltd.

12775.02

20

Hexaware Technologies Ltd.

563.89

21

Hinduja Ventures Ltd.

393.98

22

I C S A (India) Ltd.

1086.58

23

Infosys Technologies Ltd.

84608.37

24

Infotech Enterprises Ltd.

915.88

25

K L G Systel Ltd.

351.76

26

K P I T Cummins Infosystems Ltd.

369.29

27

Kale Consultants Ltd.

46.15

28

Kernex Microsystems (India) Ltd.

124.73

29

Logix Microsystems Ltd.

121.23

30

Mastek Ltd.

722.35

31

Mindtree Ltd.

1203.84

32

Moser Baer India Ltd.

1709.70

33

Mphasis Ltd.

4042.87

34

Mro-Tek Ltd.

79.87

35

N I I T Ltd.

542.58

36

N I I T Technologies Ltd.

542.58 continued on following page



Table 2. continued Sl. No.

Name of the IT companies

Market Capitalization (In Crores)

37

Nucleus Software Exports Ltd.

431.95

38

Oracle Financial Services Software Ltd.

7691.24

39

Panoramic Universal Ltd.

236.76

40

Patni Computer Systems Ltd.

2487.32

41

Polaris Software Lab Ltd.

678.65

42

R Systems International Ltd.

95.99

43

Ramco Systems Ltd.

128.49

44

Rolta India Ltd.

3064.43

45

Saksoft Ltd.

85.38

46

Sasken Communication Technologies Ltd.

291.96

47

Smartlink Network Systems Ltd.

188.06

48

Softpro Systems Ltd.

87.88

49

T V S Electronics Ltd.

43.30

50

Tanla Solutions Ltd.

1497.47

51

Tata Consultancy Services Ltd.

67847.81

52

Tata Elxsi Ltd.

428.11

53

Tech Mahindra Ltd.

6539.59

54

Tricom India Ltd.

112.68

55

Tulip Telecom Ltd.

2087.95

56

Vakrangee Softwares Ltd.

269.59

57

Wipro Ltd.

50421.92

58

Zenith Computers Ltd.

35.48

59

Zenith Infotech Ltd.

329.89

60

Zensar Technologies Ltd.

259.97

form of profitability, productivity and market valuation of the company. Value Added (VA) = OUT – IN Where OUT = Output of the firm and IN = Input of the firm Ho and Williams (2003) used VAICTM model but did not included wages and salaries in the calculation of value added by the companies for the reason being the major role of human capital in the value added of the firms. Different researchers have given arguments in

favor of using wages and salaries items for the calculation of value added. Value Added of the companies is measured by the summation of the following items. VAi= Ii + DPi + Di + Ti +Mi + Ri + WSi Where: VAi is the value added by the company Ii is the interest expenses DPi is the depreciation expenses Di is the dividend paid Ti represents the taxes paid



Mi for equity of minority shareholders in net income of the subsidiaries Ri represents the profits retained of the company and WSi represents the wages and salaries of the personnel in the company. Chen, Cheng, and Hwang (2005) calculated value added in a broader term. Same method is followed for the calculation of value added of the selected companies. It is calculated by using the following formula. VAi = Wi + Ii + Ti + NIi Where: VAi is the value added by the company WSi represents the wages and salaries of the personnel in the company Ii is the interest expenses Ti represents the taxed paid NIi represents Net Income. Value added of the firm can be divided into three main factors Capital Employed Efficiency (CEE), Human Capital Efficiency (HCE) and Structural Capital Efficiency (SCE). It can be explained in the form of equation; VAICTM = CEEi + HCEi + SCEi Where CEEi = VAi/CEi Where: VAi is the value added efficiency of the firm i. CEi is capital employed by the firm measured by the book value of net assets of the firm i. HCEi = VAi/HCi VAi is the value added efficiency of the firm i. HCi is the sum of total salaries and wages of the firm i. SCEi = VAi/SCi VAi is the value added efficiency of the firm i. SCi is the structural capital of the firm i.

Where: SCi = VAi – HCi After calculation of VAICTM of firm the following regression equation is used to check the respective association between the Return on Assets (ROA), Assets Turnover Ratio and Market to Book value of the companies. Three equations are as follows (Firer & Stainbank, 2003; Ghose & Mondal, 2009). ATO = α + β1(VAICTM) + β2(PC) + β3(LCAP) + β4(DER) + ε (Equation 1) ROA = α + β1(VAICTM) + β2(PC) + β3(LCAP) + β4(DER) + β5(ATO) + ε (Equation 2) MB = α + β1(VAICTM) + β2(PC) + β3(LCAP) + β4(DER) + β5(ATO) + β6(ROA) + ε (Equation 3) Where: VAIC TM: Intellectual capital performance measured by Value Added Intellectual Co- efficient TM. ATO: Asset turnover ratio that shows company’s productivity. PC: Physical intensity measured by fixed assets divided by total assets. LCAP : Company size taken as natural log of market capitalization. DER: Leverage of the company measured by the debt equity ratio of that period. ROA: Return on assets measured to know the company’s profitability. MB: Market to book value of the company. The remaining are α and ε are intercept and residual terms respectively and β1 to β6 are the slope co-efficient. Explanation of the terms used: •

Assets Turnover Ratio (ATO) is the ratio of total assets to the book value of assets. It is used to show the efficiency of the company’s use of its assets in generating



•

•

sales of the company for the period 199900 to 2008-09. Return on Assets (ROA) is the ratio of net income (less preference dividends) to the book value of assets. It is the indicator of the profits generated relative to the total assets of the company for the period 199900 to 2008-09. Market to Book value (MB) is the ratio of market capitalization which is measured by the multiplying total number of outstanding shares with the share price of the company to book value of net assets for the period 1999-00 to 2008-09. This paper uses the average 365 days prices of the shares.

4. ANALYSIS AND INTERPRETATION Table 3 and 4 show the mean and standard deviation of the selected companies with the independent, dependent and control variables. Table 3 and 4 show the mean and standard deviation of the variables from the year 199900 to 2008-09. The mean value of the VAIC of different companies in the 2000 is 4.006 but started declining afterwards. The table shows that the presence of intellectual capital has declined in the reports of the companies but has increased moderately in the last second two years of the studies. Profitability of the IT companies measured by the return on assets (ROA) is also somewhat resolves around 12 percent from the year from the 2000 to 2003 but declines afterwards till 2009. The productivity (ATO) of the companies showing a declining trend up to 2003 and starts increasing after that but it is again declined from the year 2008. Market capitalization of the companies from the year 2000 started decreasing up to the year 2004 and increased till the year 2007 but again declined. The debt equity ratio of the companies has declined but again increased. Physical intensity of the companies measured by fixed assets by total assets is also increased till the year 2005 and declined afterwards and

it shows than the dependence on the physical assets has increased till the year 2005 and dependence decreased in the last four years. Results of the Linear Multiple Regressions: An important assumption of Linear Multiple Regressions is met by taking normally distributed data. Natural logarithm and inverse transformation are carried out for those variables which are not normally distributed. Data which are having negative or zero values are used by transforming the data. The key variables where data was not available for that particular period are excluded from the study. The problem of multi-collinearity is checked by taking Variance Inflation Factor (VIF) below 5. There was not the problem of multicollinearity as the VIF of the variables are below 5. Table 5 shows that productivity (ATO) of the companies explains 21.8 percent to 86.9 percent of the variables which is significant at 5% level and is influenced by the factors like VAIC, DER, and PC. But for year 2002 and 2003 is showing the significance level 1% but by this conclusion can be made that productivity of the companies is affected by other factors which may not be included in this study. So, more variables can be included in the study to get more accurate results. Table 6 shows the profitability measured by ROA is explaining 36.9 to 55.6% of the variances in the multiple regressions on return of assets of the companies. The variables which are significantly explaining the productivity are VAIC, DER and ATO. The results are significant in nine years except the year 2000. It means that the overall variables are significant in nine cases out of ten. VAIC is significant in seven cases. So the researcher can conclude that the profitability of the companies can be explained by the presence of intellectual capital in the companies. This result is contradictory to Boekestein (2006) as the study found no relation between intellectual capital and profitability in pharmaceuticals companies.



Table 3. Mean of the Selected Companies MEAN YEAR

VAIC

PC

DER

ATO

ROA

LCAP

MB

2000

5.391

0.239

0.874

0.741

0.121

3374.872

8.039

2001

4.533

0.257

0.531

0.869

0.142

5291.434

6.176

2002

3.254

0.308

0.545

0.917

0.087

2671.454

2.443

2003

2.793

0.326

0.624

0.966

0.075

2560.044

2.695

2004

2.957

0.491

0.712

1.072

0.101

2511.099

2.609

2005

3.112

0.513

0.698

1.083

0.118

4744.175

3.289

2006

3.290

0.317

0.593

1.085

0.122

5345.751

3.706

2007

3.872

0.337

0.803

0.992

0.137

6601.420

3.928

2008

3.624

0.433

0.819

0.960

0.109

6740.368

3.479

2009

3.458

0.466

0.900

0.943

0.096

4594.558

1.836

LCAP

MB

Table 4. Standard Deviation of the Selected Companies STANDARD DEVIATION YEAR

VAIC

PC

DER

ATO

ROA

2000

4.006

0.155

1.318

0.606

0.122

8596.010

8.039

2001

3.816

0.181

0.772

0.582

0.111

14678.959

8.456

2002

2.722

0.177

0.639

0.567

0.131

7818.886

3.129

2003

1.464

0.174

0.727

0.520

0.149

7522.377

2.472

2004

1.698

1.082

0.836

0.652

0.094

6856.661

2.022

2005

1.833

1.176

0.695

0.661

0.104

13296.181

3.069

2006

1.577

0.161

0.494

0.760

0.081

15916.802

2.962

2007

2.837

0.173

0.758

0.770

0.157

21201.355

3.702

2008

2.078

0.619

0.706

0.661

0.091

20440.139

2.774

2009

1.916

0.771

0.742

0.592

0.080

15077.563

1.623

Table 7 explains the market to book value (MB) of the companies. Market to book value is explained by the VAIC, PC, LCAP, DER, ATO and ROA. It is collectively significant only in all the ten years. But VAIC is significant only in three years out of ten years. So the conclusion can be made that market valuation of the companies is not explained by the presence of intellectual capital. This was supported by the previous studies (Firer & Stainbank, 2003; Ghose & Mondal,

2009). But on the contrary Chen, Cheng, and Hwang (2005) found that intellectual capital has positive impact on the market valuation and financial performance in Taiwan companies. Other variables in this study which explain the market valuation of the companies are ROA, PC and LCAP. It shows that intellectual capital is not taken into consideration for the market valuation of the company. From the results it can be concluded that investors of India give preference to physical assets and company’s



Table 5. Showing the results of the linear multiple regression of productivity

Year

N

Adjusted R

2000

11

0.869

2001

2002

2003

2004

2005

2006

2007

2008

2009

27

27

30

33

39

48

59

60

60

0.391

0.304

0.274

0.218

0.027

-0.052

-0.026

0.017

0.012

2

F- Statistics

Significance

Independent and Control Variables

Standard Beta

t- statistic

Significance

Standard error

14.267

0.012**

VAIC

-0.620

-4.733

0.009*

0.076

PC

0.020

0.124

0.907

0.353

LCAP

0.450

2.910

0.044**

0.085

5.166

3.838

3.741

3.224

1.414

0.420

0.628

1.251

1.179

0.004*

0.016**

0.016**

0.027**

0.241

0.793

0.645

0.301

0.330

DER

0.480

3.657

0.022**

0.039

VAIC

-0.207

-1.235

0.230

0.216

PC

0.100

0.612

0.547

0.169

LCAP

0.300

1.857

0.077***

0.066

DER

0.679

4.323

0.000*

0.110

VAIC

0.063

0.328

0.746

0.075

PC

-0.373

-2.092

0.048**

0.482

LCAP

-0.096

-0.503

0.620

0.013

DER

0.402

2.175

0.041**

0.025

VAIC

0.069

0.333

0.742

0.501

PC

0.444

2.633

0.014**

0.473

LCAP

0.023

0.117

0.908

0.095

DER

-0.355

-2.090

0.047**

0.174

VAIC

0.201

1.139

0.264

0.945

-2.146

PC

-0.350

0.041**

0.200

LCAP

0.150

0.901

0.375

0.052

DER

0.497

2.903

0.007*

0.125

VAIC

-0.224

-1.600

0.115

0.166

PC

-0.188

-1.381

0.173

0.264

LCAP

0.047

0.354

0.725

0.022

DER

0.267

1.960

0.055***

0.049

VAIC

-0.089

-0.541

0.591

0.875

PC

-0.101

-0.604

0.549

0.743

LCAP

-0.021

-0.135

0.893

0.142

DER

0.110

0.736

0.466

0.122

VAIC

0.114

0.777

0.440

0.315

PC

-0.042

-0.279

0.781

0.268

LCAP

0.130

0.942

0.350

0.026

DER

0.170

1.240

0.220

0.040

VAIC

-0.207

-1.533

0.131

0.151

PC

-0.133

-0.964

0.340

0.279

LCAP

0.036

0.270

0.788

0.022

DER

0.255

1.871

0.067***

0.049

VAIC

0.107

0.706

0.483

0.331

PC

0.048

0.362

0.718

0.072

LCAP

-0.038

-0.261

0.795

0.395

DER

0.281

2.131

0.038**

0.102



Table 6. Showing the results of the linear multiple regression of profitability

Year

N

Adjusted R

2000

11

-0.802

2001

2002

2003

2004

2005

2006

2007

27

27

30

33

39

48

59

0.488

0.556

0.535

0.369

0.538

0.513

0.424

2

F- Statistics

Significance


Standard Beta

t- statistic

Significance

Standard error

0.288

0.893

VAIC

-0.284

-0.228

0.834

1.935

PC

-0.075

-0.128

0.906

3.514

LCAP

0.690

0.681

0.545

1.493

5.963

7.519

7.661

4.740

14.733

10.915

9.256

0.001*

0.000*

0.000*

0.003*

0.000*

0.000*

0.000*

DER

0.421

0.414

0.706

0.808

ATO

-1.173

-0.633

0.572

4.967

VAIC

0.525

3.305

0.003*

0.021

PC

0.022

0.144

0.887

0.016

LCAP

0.235

1.477

0.154

0.007

DER

-0.353

-1.801

0.086***

0.014

ATO

0.603

3.088

0.006*

0.021

VAIC

0.568

3.672

0.001*

0.018

PC

-0.064

-0.409

0.687

0.127

LCAP

0.271

1.778

0.090***

0.003

DER

-0.408

-2.510

0.020**

0.007

ATO

0.413

2.426

0.024**

0.051

VAIC

-0.064

-0.381

0.706

0.029

PC

-0.017

-0.109

0.914

0.030

LCAP

0.343

2.140

0.043**

0.005

DER

-0.537

-3.641

0.001*

0.011

ATO

-0.582

-3.633

0.001*

0.011

VAIC

-0.035

-0.217

0.830

0.040

PC

-0.163

-1.031

0.312

0.009

LCAP

0.371

2.445

0.021**

0.002

DER

-0.400

-2.281

0.031**

0.006

ATO

0.346

2.038

0.051***

0.008

VAIC

0.625

6.333

0.000*

0.028

PC

0.092

0.966

0.339

0.045

LCAP

0.344

3.778

0.000*

0.004

DER

-0.342

-3.518

0.001*

0.008

ATO

0.303

3.258

0.002*

0.023

VAIC

-0.459

-4.112

0.000*

0.063

PC

-0.030

-0.263

0.794

0.054

LCAP

0.248

2.361

0.023**

0.010

DER

-0.309

-3.012

0.004*

0.009

ATO

0.396

3.814

0.000*

0.011

VAIC

-0.493

-4.512

0.000*

0.108

PC

-0.130

-1.198

0.237

0.083

LCAP

0.112

1.079

0.286

0.008

DER

-0.358

-3.329

0.002*

0.013

continued on following page



Table 6. continued

Year

2008

2009

N

60

60

Adjusted R

2

0.540

0.490

F- Statistics

14.860

12.340

Significance

0.000*

0.000*


Standard Beta

t- statistic

Significance

Standard error

ATO

0.371

3.562

0.001*

0.042

VAIC

0.598

6.349

0.000*

0.026

PC

-0.003

-0.031

0.975

0.047

LCAP

0.355

3.868

0.000*

0.004

DER

-0.296

-3.073

0.003*

0.008

ATO

0.298

3.233

0.002*

0.022

VAIC

-0.430

-3.946

0.000*

0.024

PC

-0.017

-0.176

0.861

0.005

LCAP

-0.240

-2.267

0.027**

0.029

DER

-0.375

-3.806

0.000*

0.008

ATO

0.477

4.922

0.000*

0.010

Table 7. Showing the results of the linear multiple regression of market valuation

Year

N

Adjusted R2

F- Statistics

Significance


Standard Beta

t- statistic

Significance

Standard error

2000

11

0.977

57.697

0.017**

VAIC

-1.098

-7.724

0.016**

0.165

2001

2002

2003

27

27

30

0.775

0.746

0.595

15.884

13.743

8.091

0.000*

0.000*

0.000*

PC

0.161

2.426

0.136

0.298

LCAP

0.146

1.185

0.358

0.136

DER

0.476

4.041

0.056***

0.070

ATO

-0.311

-1.394

0.298

0.447

ROA

0.056

0.861

0.48

0.049

VAIC

-0.432

-3.323

0.003*

0.193

PC

-0.08

-0.796

0.435

0.120

LCAP

0.693

6.236

0.000*

0.053

DER

0.256

1.833

0.082***

0.113

ATO

-0.067

-0.426

0.674

0.180

ROA

0.493

3.4

0.003*

1.589

VAIC

-0.334

-2.227

0.038**

0.397

PC

-0.007

-0.059

0.954

2.193

LCAP

0.748

6.046

0.000*

0.059

DER

0.23

1.638

0.117

0.130

ATO

0.196

1.349

0.193

0.999

ROA

0.373

2.261

0.035**

3.754

VAIC

0.104

0.665

0.513

0.857

PC

-0.149

-1.048

0.306

0.909

LCAP

0.567

3.472

0.002*

0.176

DER

0.361

2.107

0.046**

0.400

continued on following page



Table 7. continued

Year

2004

2005

2006

2007

2008

2009

N

33

39

48

59

60

60

Adjusted R2

0.67

0.585

0.607

0.573

0.575

0.429

F- Statistics

11.841

14.881

15.495

13.543

14.326

8.394

Significance

0.000*

0.000*

0.000*

0.000*

0.000*

0.000*


Standard Beta

t- statistic

Significance

Standard error

ATO

-0.045

-0.243

0.81

0.423

ROA

0.334

1.751

0.093***

6.103

VAIC

-0.086

-0.734

0.47

0.878

PC

-0.217

-1.863

0.074***

0.200

LCAP

0.582

4.805

0.000*

0.053

DER

0.232

1.68

0.105

0.141

ATO

0.089

0.673

0.507

0.184

ROA

0.203

1.457

0.157

4.187

VAIC

0.151

1.226

0.226

0.308

PC

-0.055

-0.607

0.547

0.372

LCAP

0.677

6.978

0.000*

0.034

DER

0.19

1.863

0.068***

0.077

ATO

-0.061

-0.634

0.529

0.203

ROA

0.058

0.449

0.655

1.118

VAIC

0.123

1.039

0.305

0.207

PC

-0.093

-0.848

0.401

0.364

LCAP

-0.484

-4.552

0.000*

0.007

DER

0.166

1.521

0.136

0.168

ATO

-0.103

-0.971

0.337

0.138

ROA

0.408

3.088

0.004*

0.750

VAIC

-0.008

-0.076

0.94

0.197

PC

-0.045

-0.471

0.639

0.129

LCAP

-0.534

-5.887

0.000*

0.013

DER

-0.373

-3.654

0.001*

0.021

ATO

0.067

0.666

0.508

0.072

ROA

-0.483

-4.006

0.000*

0.215

VAIC

0.053

0.446

0.657

0.281

PC

-0.087

-0.96

0.341

0.388

LCAP

0.658

6.6

0.000*

0.035

DER

0.241

2.399

0.020**

0.076

ATO

-0.089

-0.922

0.361

0.204

ROA

0.121

0.927

0.358

1.134

VAIC

0.062

0.472

0.639

0.285

PC

0.202

1.992

0.051***

0.054

LCAP

-0.414

-3.532

0.001*

0.312

DER

0.305

2.602

0.012**

0.090

ATO

-0.242

-1.962

0.055***

0.122

ROA

0.363

2.523

0.015**

1.412



profitability and company size are other major factors for the market valuation of the company. Our results are also supported by the study of Firer and Stainbank (2003) as they found that there was positive association with the profitability and it was negative in case of productivity. There was no explanatory power for the market valuation of the company in South African context. Contrary to our results Bontis, Knew, and Richardson (2000) found inter-relationship between structural capital one of the components of intellectual capital and company’s performance. That was explained by the reporting of structural capitals in by having competitive advantage through it and resulting into higher business performance. A similar type of study was done by Tan, Plowman, and Hancock (2007) to check the relationship between intellectual capital and company’s performance measured in terms of Return on Equity (ROE), Earning Per Share (EPS) and Annual Stock Return (ASR) and found correlation between the them.

in the company’s annual reports which may be the reason for increase in profitability of the selected IT firms. Summarizing the results, it can be concluded that intellectual capital should be an important factor for the increase in the profitability of the companies. Intellectual capital should be used as a competitive tool to increase companies’ current and future performance in terms of profitability.

5. FINDINGS AND CONCLUSION

Boekestein, B. (2006). The relationship between intellectual capital and intangible assets of pharmaceutical companies. Journal of Intellectual Capital, 7(2), 241–253. doi:10.1108/14691930610661881

The present study has analyzed the intellectual capital with Valued Added Intellectual Coefficient (VAICTM) model and then compared with companies’ performance in terms of productivity, profitability and market valuation of the companies. The results supports the hypothesis that intellectual capital has a positive relation with that of profitability but fails to explain the relation with productivity and market valuation of the companies. The results can be explained as there are other factors which may be the reason for market valuation of the companies other than the intellectual capital of the company. The productivity of the company may have influence from the proper management of the structural capital which is measured as a component of the intellectual capital. The presence of structural capital is largely present

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Karam Pal, who is presently working as Associate Professor in Finance at Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana), completed his PhD degree as JRF (UGC) from Maharishi Dayanand University, Rohtak in 1996. He has been enjoying the profession of teaching and research for last more than seventeen years. He has attended more than 55 conferences/seminars of national and international level and has more than 75 published research papers in the Journals of repute and 11 books to his credit. Sushila Soriya, who did her M.Com from Kurukshetra University Kurukshetra, is presently PhD Scholar at Haryana School of Business. She has authored more than five research papers.
