Performances in Indian Information Technology Industry. ... present in software firms can be measured and ... of foreign companies by Indian companies rose.
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
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36 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 37
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.
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38 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 39
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
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40 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 41
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
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42 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
•
•
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.
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 43
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
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44 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 45
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
Independent and Control Variables
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
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46 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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*
Independent and Control Variables
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
Independent and Control Variables
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
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International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011 47
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*
Independent and Control Variables
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
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48 International Journal of Asian Business and Information Management, 2(2), 34-49, April-June 2011
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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Bollen, L., Vergauwen, P., & Schnieders, S. (2005). Linking intellectual capital and intellectual property to company performance. Management Decision, 43(9), 1161–1185. doi:10.1108/00251740510626254 Bontis, N., Keow, W. C. C., & Richardson, S. (2000). Intellectual capital and business performance in Malaysian industries. Journal of Intellectual Capital, 1(1), 85–100. doi:10.1108/14691930010324188 Bozzolan, S., Favotto, F., & Ricceri, F. (2003). Italian annual intellectual capital disclosure: An empirical analysis. Journal of Intellectual Capital, 4(4), 543–558. doi:10.1108/14691930310504554 Chen, M., Cheng, S., & Hwang, Y. (2005). An empirical investigation of the relationship between intellectual capital and firms’ market value and financial performance. Journal of Intellectual Capital, 6(2), 159–176. doi:10.1108/14691930510592771
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Garcia-Meca, E. (2005). Bridging the gap between disclosure and use of intellectual capital information. Journal of Intellectual Capital, 6(3), 427–440. doi:10.1108/14691930510611157
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Oliver, J. L. H., & Porta, J. I. D. (2006). How to measure IC in clusters: Empirical evidence. Journal of Intellectual Capital, 7(3), 354–380. doi:10.1108/14691930610681456 Pulic, A. (1998). Measuring the performance of intellectual potential in knowledge economy. Retrieved from http://www.vaic-on.net/start.htm Seleim, A., Ashour, A., & Bontis, N. (2004). Intellectual capital in Egyptian software firms. The Learning Organization, 11(4-5), 332–346. doi:10.1108/09696470410538233
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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.
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