CHAPTER 4 EMPIRICAL ANALYSIS AND DISCUSSION

Transcription

1 79 CHAPTER 4 EMPIRICAL ANALYSIS AND DISCUSSION 4.1 SAMPLE SELECTION AND DESCRIPTIVE STATISTICS The study begins by identifying industrial sectors heavily reliant on intellectual capital. The data covers four major Indian industrial sectors namely Banking, Information Technology, Pharmaceutical and Electronics. The sample is limited to these industries, given the exploratory nature of this study. All the companies chosen for study are listed and publicly traded on National Stock Exchange (NSE) in India during Data is collected from Centre for Monitoring Indian Economy (CMIE) Prowess Database. Taking into account the study s longitudinal objective the time period established for this study is from 2000 to 2010 (11 years). Researcher believes that 11 years is enough time to account for common environmental and economic changes and shift in industrial business cycle that can impact changes in the performance of firm and industry. Moreover the study span over a decade has been helpful in establishing the consistency and predictability of research conclusions. In order to ensure that data is useful for research purpose two sample selection criteria has been followed. The selection principles include (a) complete target information (b) excluding unusual information (this includes negative income, misreported or omitted data) Final sample consists of total 583 eleven year observations (Table 5). An effective sample of 583 company observations has been chosen on the basis of above sampling criterion. The companies are limited to four major sectors Banking,

2 80 Pharmaceutical, Information Technology and Electronics are highly intellectual capital intensive. Table 4.1 Sample selection procedure Firm Years Listed companies ( ) 1806 Less: Key variables omitted 358 Less: Negative Income 260 Less: Misreported/missing data 659 Final Sample (Total) 583 Table 4.2 Sample selection and sample firms profile and distribution by industry Industry Firm years Percentage of sample Banking Electronics Information Technology Pharmaceutical Total DESCRIPTIVE STATISTICS Table 4.3 presents the mean, variance and standard deviation, of the independent and dependent variables in the final useable sample. The mean values of 6.66 for intellectual capital performance measured by VAIC suggest that during the period the sample firms were effective in creating value from their intellectual capital base. The mean value of components of intellectual capital viz. VACA, VAHU, and STVA are 0.62,

3 , and 0.68 indicates that human capital is the most important factor contributing to value creation from intellectual capital base. The mean value of market to book value (MV/BV) of 3.58 indicates that investors generally value the sample industries in excess of their book value of net assets as reported in financial statements. The overall financial performance of the sampling is satisfactory and is indicated by the EPS value of The other variables on financial performance ROE, ROA, MCAP have means of 0.21, 0.09 and 9.42 Table 4.3 Descriptive statistics for the selected variables (for all industries) from Variable No. of observations Mean Variance Standard Deviation VACA VAHU STVA VAIC MV/BV ROE ROA EPS MCAP Table 4.4 Industry wise Descriptive Statistics of variables Bank Industry Electronics VACA VAHU STVA VAIC MV/BV ROE ROA EPS MCAP Mean SD Mean SD

4 82 Mean IT SD Pharmaceutical Mean SD Table 4.4 presents the mean, standard deviation of the independent and dependent variables studied. The mean value of VAIC for the banking sector for all the years together is This shows that banking industry has been successful in creating value from intellectual capital. It has generated a value of rupee for every one rupee invested. The other value creation efficiency indicators like VACA, VAHU, STVA has a mean value of 0.71, 9.51 and 0.86 respectively. The mean value of human capital efficiency of 9.51 indicates the high quality of human resources employed by the banking sector. Moreover being a high profile service industry, human capital has been a major contributor for overall excellence in the performance of this industry. The MV/BV ratio of 1.57 reveals that about 36% of the banking firm s market value is not reflected in financial statements. The overall financial performance is sound as indicated by the high EPS ratio of The other financial indicators like ROE, ROA and MCAP have mean value of 0.17, 0.01, The mean value for of VAIC for Pharmaceutical industry is The mean values of other efficiency indicators like VACA, VAHU and STVA are 0.43, 4.65, and 0.70 respectively. It has been understood that pharmaceutical industry has been able to create value from intellectual capital to the tune of 5.78 Rs for every one rupee invested. Similar to banking industry, human capital has been the highest contributor to enhancing the intellectual capital efficiency with respect to pharmaceutical industry too. The mean values of financial performance indicators are 3.16, 0.23, 0.12, 26.86,

5 83 and 9.23 respectively for MV/BV, ROE, ROA, EPS and MCAP. The MV/BV ratio of 3.16 reveals that 68.32% of Pharmaceutical firm s market value has not been reflected in financial statements. The Electronic industry has been able to create 3.43 for every one rupee invested as shown by intellectual capital efficiency measured by VAIC. The other measures of efficiencies like physical capital efficiency, human capital efficiency and structural capital efficiency have created values of 0.58, 2.31and 0.54 for every one rupee invested. The mean values of financial indicators like ROE, ROA, EPS and MCAP shows satisfactory financial performance of electronic sector. The mean values are 0.22, 0.08, and 8.94 respectively. The mean ratio of 2.74 for MV/BV reveals that 63.2% of Electronic firm s value has not been captured in the financial statement. Mean value of VAIC for IT industry reveal that Value addition brought about by VAIC is about This is the least value for VAIC when compared with all the other three industries. The information technology industry has been able to produce only 3.41 value addition for every one rupee invested. The other indicators of efficiency have mean value of 0.8 for VACA, 2.16 for VAHU and 0.45 for STVA. The highest value enhancer of intellectual capital is human capital. Mean value of Financial performance variables indicate the overall financial soundness of this sector. This is highlighted by the reasonably high value of mean EPS which is ROE, ROA and MCAP has recorded mean values of 0.22, 0.15, and The mean ratio of 6.93 for MV/BV shows that 85.56% of firm s value is hidden and not captured in the financial report The above analysis reveals that banking industry has the highest ranking for VAIC followed by Pharmaceutical industry, Electronic sector and finally Information technology Sector. Values of intellectual capital efficiency indicator VAIC suggests sample companies were capable of creating value

6 84 from intellectual capital base. Comparison of mean values of VACA, VAHU and STVA of different industries suggest that the sampling industries has been able to create values from human capital employed than from physical or structural capital. This proves that high-quality human resource has been the backbone for efficienct utilization of intellectual capital assets. India has a large pool of talented and educated high quality human resources who has contributed for this achievement. Moreover the governments policy on education and information technology has provided a huge impetus on improving the quality. These findings are being consistent throughout all the industries taken for study. The Mean ratio of Market to Book value (MV/BV) of various industries highlights the existence of an increasing gap between market value and book value of organizations and are consistent with the findings of many early researchers like Lev and Zarowin, 1999: Lev, 2001: Ze ghal, 2000; Pulic, 2004 Lev and Radhakrishnan, 2003: Chen et.al, 2005: Remarkable disparity between market value and book value may be due to poor valuation of intellectual capital. The edifice of future growth of any organization lies on the strength of intellectual capital. A wide gap in market value and book value is seen in the case of high-tech and knowledge based industries where huge amounts are invested in intellectual capital components like, R&D, Patents, network etc... The gap in MV/BV may be primarily due to two reasons (1) Those assets which are not included in the conventional balance sheet such as brand, image, knowledge, relationship etc. (2) The inability of conventional balance sheet to capture the value of future opportunities of a company. Knowledge Centric companies have huge hidden values which are not visible to traditional accounting methods and divergence specifically indicates something not accounted in the balance sheet.

7 CORRELATION ANALYSIS AND VERIFIFCATION OF HYPOTHESIS I Table 4.5 presents the Pearson s Correlation analysis for the dependent and independent variables in this study. Correlation measures the relationship between two variables and explains the linear relationship between them. Tabel 4.5 Correlation analysis for selected variables Correlation matrix(pearson) Variables VACA VAHU STVA MV/BV ROE ROA EPS MCAP VACA VAHU STVA MV/BV ROE ROA EPS MCAP VACA shows significant positive relationship with ROE (.391) and ROA (.167) but shares a negative relationship with MCAP (-.096). This demonstrates that an increase in value creation efficiency by physical capital would influence profitability. VAHU or human capital efficiency has significant positive association with MCAP (0.108) but not with ROE (-.145) and ROA (-.346). STVA - structural capital efficiency shows significant positive relationship with EPS (0.110) and MCAP (0.240), but is negatively correlated to ROE (0-.098) and ROA (-0.382). Thus it has been concluded

8 86 that physical capital shows significant positive correlation with ROE and ROA human capital shows significant positive correlation with MCAP and structural capital shows significant positive correlation with EPS and MCAP. Thus the hypotheses H1 stands partly accepted. 4.4 VERIFICATION OF HYPOTHESES II Hypothesis II states that there is a significant difference among the four industries with regard to the intellectual capital variables which includes VACA, VAHU and STVA. One way ANOVA has been conducted to determine whether intellectual capital performances of different industries are statistically different. Table 4.6 Summary of Intellectual capital variables VACA VAHU STVA R² F Pr > F < < < Industry < < < Highlighted values represent significance at 95% C.I The test used here is the Fisher's F test. Given that the probability corresponding to the F value in this case is 0.001, it means that we would take a 0.1% risk to conclude that the null hypothesis (no effect of industries on IC variables) is wrong. Thus from the above analysis it is clear that we reject the null hypotheses and accept the alternate hypotheses. In order to know which group means differ and how many group means differ from each other, Post- Hoc test (multiple comparison test) has been done. Tukey s (HSD) test has

9 87 been done to answer the above questions. Table 4.7 shows the analysis of difference between categories. Table 4.7: Industry/ Tukey (HSD)/Analysis of differences between categories with a confidence interval of 95 % Contrast Difference Standardized difference Critical value Pr > Diff Significant BANK vs. IT < Yes BANK vs. Elec < Yes BANK vs. Pharma < Yes Pharma vs. IT < Yes Pharma vs. Elec < Yes Elec vs. IT Yes Tukey's d critical value Table 4.8: Category grouping using LS means Category LS means Groups Bank A Pharmaceutical B Electronics C IT D As shown in the table 4.7 the Tukey s HSD (Honestly Significantly Different) test has been applied to all pair wise difference between means. The risk of 5% has been chosen to determine the critical value q, which is then compared to the standardized difference between means. Thus it is clear from the above analysis that all the pairs are significantly different from each other. Table 4.8 shows the category grouping which also makes it clear that IC variables of four industries chosen for study are significantly different.

10 88 It has been concluded with confidence that there is statistically significant difference among the four industries with regard to intellectual capital performance. Thus hypotheses H2 stands fully accepted. 4.5 VERIFICATION OF HYPOTHESES H3 AND H4 In order to study the multivariate relationship among the observed variables that measure intellectual capital and corporate performance PLSpath modeling method has been used. For the study purpose a Formative model has been developed. This model treats both intellectual capital performance and corporate performance as latent variables measured by manifest variables (indicators). Intellectual capital performance is measured using three indicators VACA, VAHU and STVA. Corporate performances are measured using MV/BV, ROE, ROA, EPS and MCAP. Each industry is studied separately and results are interpreted. PLS approach has recently attracted renewed interest from applied researchers after initial frequent application in the early 1980 s. PLS demands fewer requirements compared to that of other covariance structure analyses, but delivers consistent estimation results. This makes PLS a valuable tool for testing theories. Another quality of PLS is the ease of it to deal with both formative and reflective models within one structural equation model. This makes it a highly sought after tool for explorative analysis of structural equation models, thus offering a significant contribution to theory development.

11 EVALUATION OF FORMATIVE MODELS In the case of Formative measurement models, the direction of causality is reverse, in contrast to reflective models as far as the indicators form or that constitute the latent variables. The statistical evaluation criteria for reflective models cannot be directly passed on to a formative model (Diamantopoulos 1999, pp. 453). In case of formative models an indicator can become irrelevant if there is high multicolinearity among variables. So the first step in assessing any formative model is to find out whether the variables studied suffers from multicolinearity. Multicollinearity would mean that the indicators information is redundant. Manifest variables in the formative block have to be tested for multicollinearity. (Diamantopoulos & Winklhofer, 2001; Cassel, Hackl, & Westlund, 2000, Grewal, Cote, & Baumgartner, 2004). In order to assess the multicollinearity among indicator variables VIF (Variance inflation Factor) or tolerance values has to be calculated. Rule of thumb from econometrics state that VIF greater than 10 reveals critical level of multicolinearity. Table 4.9: Multicollinearity Statistics Statistic MV/BV ROE ROA EPS MCAP VACA VAHU STVA R² Tolerance VIF Above table reveals that VIF values of all variables are less than 10 and hence it has been concluded that there is no harmful multicolinearity among the variables selected for study.

12 PLS-PM FOR BANKING INDUSTRY sector Figure 4.1 given below shows the PLS-PM model for banking Figure PLS-PM for Banking Industry

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16 94 The above table shows various measurement aspects of PLS-PM model and its interpretations. A detailed interpretation and relevance of each measurement indicators are given below. Quality Index: According to PLS-PM structure each part of the model needs to be validated. This includes the measurement model, structural model and the overall model. The measurement model and the structural model are explained through the communality index and Redundancy index. Even though there is no overall fit index in PLS-Path Modeling, a global criterion of goodness of fit has been proposed by Tenenhaus et al. (2004): the GoF index. Such an Index has been developed to take care of both the model performance by measuring both measurement and structural model, thus provides a single measurement in overall prediction performance of the model. Hence GoF index is obtained as the geometric mean of average communality index and average R 2 values. Even though GoF models are mostly used in case of reflective models, for practical purpose GoF can be interpreted for formative models also as it provides a measure of overall fit V. Esposito Vinzi et al. (2010). This index is bound to be between 0 and 1. Since both GoF and Relative GoF are descriptive indices, there is no inference based threshold to judge the statistical significance of their values. As a rule of thumb value of relative GoF close to 0.9 or greater than that clearly speaks in favor of the model. Table 4.11 Goodness of Fit Index, GoF Index (Banking Industry) GoF GoF (Bootstrap) Standard error Relative Outer model

17 95 From Table 15 it is clear that the value of GoF for the outer model and relative model are 0.82 which is very close to 0.9 and hence speak in favor of the model. Cross Loadings: (Monofactorial manifest variables): This table allows to check whether a given manifest variable is monofactorial or not. This means whether the manifest variable is mostly related with the latent variable. Ideally the model is said t be well specified if it appears that the manifest variables are more related to its latent variables than other variables. Table 4.12 Cross-loadings (Monofactorial manifest variables) (Banking Industry) IC CP VACA VAHU STVA MV/BV ROE ROA EPS MCAP The result shows that the manifest variables like VACA, VAHU and STVA are more loaded in its own latent variable Intellectual capital than CP. The same is reflected in case of Corporate Performance also. The manifest variables MV/BV, ROE, ROA, EPS, and MCAP are loaded more on CP than IC. Thus it has been concluded that the model is well specified. The manifest variables measure the latent variables conclusively.

18 96 R 2 values of endogenous Latent Variable: Valid outer model permits the evaluation of inner model. The criteria used for evaluation is the coefficient of determination -R 2 of the endogenous latent variable. Chin (1998) describes R 2 values of 0.67, 0.33, and 0.19 in PLS path models as substantial, moderate, and weak, respectively. Table 4.13 Inner model (Dimension 1): Banking Industry R² R² F Pr > F (Bootstrap) Standard error Critical ratio (CR) Lower bound (95%) Upper bound (95%) Table 4.13 gives the inner model specification.r 2 value of 0.56 is very close to the substantial values given above. This clearly shows the confidence in the theory, i.e there is no doubt in the theoretical underpinnings and the model is capable of explaining the endogenous latent variables namely Corporate Performance. Thus it has been concluded that 56% of variance in Corporate Performance is explained by Intellectual Capital. Path Coefficient: This shows the regression weights of the path. f 2 measures the effect of size by means of Cohen s (1998)f 2. According to Cohen (1998), f 2 values of 0.02, 0.15, 0.35 signify small, medium and Large effects respectively. Table 4.14 shows the significance of path coefficient and the f 2 values. Table 4.14: Path coefficients (CP / 1) (Banking Industry) Latent variable Value Standard error T Pr > t f² IC

19 97 From the above table the t-statistics for path coefficient is which is more than the required Hence it has been proven that the path is valid at P <.05 level. High f 2 values indicate that the predictor latent variable i.e Intellectual Capital has a large effect at the structural level. Predictive Ability: Blind folding procedure is done to obtain the cross validated redundancy which fits the PLS path modeling approach like hand and glove (WOLD 1982). In analogy to f 2 the relative impact of predictive relevance can be assessed by means of redundancies (q 2 ) values: values of 0.02, 0.15, and 0.35 reveal a small, medium or large predictive relevance of latent variable which in turn explain the endogenous latent variable. Table 4.15 Model Assessment (with blind folding redundancies) (Banking Industry) Latent variable IC Type R² Exogenous Adjusted R² Mean Redundancies CP Endogenous Mean From the table 4.15 the mean redundancy for endogenous variable is This explains the medium predictive relevance of the model. It also explains the latent variable which in turn explains the endogenous variable corporate performance.

20 PLS-PM FOR ELECTRONIC INDUSTRY Figure 4.2 PLS PM for Electronic Industry

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23 101 Quality Index: According to PLS-PM structure each part of the model needs to be validated. This includes the measurement model, structural model and the overall model. The measurement model and the structural model are explained through the communality index and Redundancy index. Even though there is no overall fit index in PLS-Path Modeling, a global criterion of goodness of fit has been proposed by Tenenhaus et al. (2004): the GoF index. Such an Index has been developed to take care of both the model performance by measuring both measurement and structural model, thus provides a single measurement in overall prediction performance of the model. Hence GoF index is obtained as the geometric mean of average communality index and average R 2 values. Even though GoF models are mostly used in case of reflective models, for practical purpose GoF can be interpreted for formative models also as it provides a measure of overall fit V. Esposito Vinzi et al. (2010). This index is bound to be between 0 and 1. Since both GoF and Relative GoF are descriptive indices, there is no inference based threshold to judge the statistical significance of their values. As a rule of thumb value of relative GoF close to 0.9 or greater than that clearly speaks in favor of the model Table 4.17 Goodness of Fit (GoF) index Electronic industry GoF GoF (Bootstrap) Standard error Relative Outer model From Table 4.17 it is clear that the value of GoF for the outer model and relative model are.745 which is near to.9 and hence speak in favor of the model.

24 102 Cross Loadings: (Monofactorial manifest variables): This table allows to check whether a given manifest variable is monofactorial or not. This means whether the manifest variable is mostly related with the latent variable. Ideally the model is said to be well specified if it appears that the manifest variables are more related to its latent variables than other variables. Table 4.18 Cross-loadings (Monofactorial manifest variables / 1) (Electronic Industry) IC CP VACA VAHU STVA MV/BV ROE ROA EPS MCAP The result shows that the manifest variables like VACA, VAHU and STVA are more loaded in its own latent variable Intellectual capital than CP and manifest variables MV/BV, ROE, ROA, EPS, and MCAP are loaded more on its latent variables CP than IC. Thus it has been concluded that the model is well specified. The manifest variables measure the latent variables conclusively. R 2 values of endogenous Latent Variable: Valid outer model permits the evaluation of inner model. The criteria used for evaluation is the

25 103 coefficient of determination -R 2 of the endogenous latent variable. Chin (1998) describes R 2 values of 0.67, 0.33, and 0.19 in PLS path models as substantial, moderate, and weak, respectively Table 4.19: Inner model (Dimension 1): Electronic industry R² R² F Pr > F (Bootstrap) Standard error Critical ratio (CR) Lower bound (95%) Upper bound (95%) Table 4.19 gives the inner model specification.r 2 value of 0.88 is more than the substantial value. This clearly shows the confidence in the theory, i.e there is no doubt in the theoretical underpinnings and the model is capable of explaining the endogenous latent variable namely corporate Performance. Thus it has been concluded that 88% of variance in corporate performance is explained by intellectual capital. Path Coefficient: This shows the regression weights of the path. f 2 measures the effect of size by means of Cohen s (1998)f 2. According to Cohen (1998), f 2 values of 0.02, 0.15, and 0.35 signify small, medium and large effects respectively. Table 4.20: Path coefficients (CP / 1) (Electronic industry) Latent variable Value Standard error t Pr > t f² IC From the above table the t-statistics for path coefficient is which is more than the required Hence it has been proven that the path

26 104 is valid at P <.05 level. High f 2 values indicate that the predictor latent variable i.e Intellectual Capital has a large effect at the structural level. Predictive Ability: Blind folding procedure is done to obtain the cross validated redundancy which fits the PLS path modeling approach like hand and glove (WOLD 1982). In analogy to f 2 the relative impact of predictive relevance can be assessed by means of redundancies (q 2 ) values: values of 0.02, 0.15, and 0.35 reveal a small, medium or large predictive relevance of latent variable which in turn explain the endogenous latent variable. Table 4.21 gives the Model assessment with redundancies. Table 4.21: Model Assessment (with blind folding redundancies) (Electronic industry) Latent variable Type R² Adjusted R² IC Exogenous Mean Redundancies CP Endogenous Mean From the above table the mean redundancy for endogenous variable is This explains that the model has large predictive (since the value is more than.35) relevance of the latent variable which in turn explains the endogenous variable Corporate Performance. Hence analysis shows that model for electronic industry shows more predictive relevance than banking industry.

27 PLS-PM FOR INFORMATION TECHNOLOGY INDUSTRY: Figure 4.3: PLS-PM for Information Technology Industry

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31 109 Quality Index: According to PLS-PM structure each part of the model needs to be validated. This includes the measurement model, structural model and the overall model. The measurement model and the structural model are explained through the communality index and Redundancy index. Even though there is no overall fit index in PLS-Path Modeling, a global criterion of goodness of fit has been proposed by Tenenhaus et al. (2004): the GoF index. Such an Index has been developed to take care of both the model performance by measuring both measurement and structural model, thus provides a single measurement in overall prediction performance of the model. Hence GoF index is obtained as the geometric mean of average communality index and average R 2 values. Even though GoF models are mostly used in case of reflective models, for practical purpose GoF can be interpreted for formative models also as it provides a measure of overall fit V. Esposito Vinzi et al. (2010). This index is bound to be between 0 and 1. Since both GoF and Relative GoF are descriptive indices, there is no inference based threshold to judge the statistical significance of their values. As a rule of thumb value of relative GoF close to 0.9 or greater than that clearly speaks in favor of the model Table 4.23 Goodness of Fit (GoF) index Information Technology Industry GoF GoF (Bootstrap) Standard error Relative Outer model From Table 4.23 it is clear that the value of GoF for the outer model and relative model are.688. This value is much less when compared to

32 110 banking and electronic industry. Hence it can be concluded that in case of IT industry the model speaks moderately in favor of the model. Cross Loadings: (Monofactorial manifest variables): This table allows to check whether a given manifest variable is monofactorial or not. This means whether the manifest variable is mostly related with the latent variable. Ideally the model is said to be well specified if it appears that the manifest variables are more related to its latent variables than other variables Table 4.24 Cross-loadings (Monofactorial manifest variables / 1) (IT Industry) IC CP VACA VAHU STVA MV/BV ROE ROA EPS MCAP The result shows that the manifest variables like VACA, VAHU and STVA are more loaded in its own latent variable Intellectual capital than CP and manifest variables MV/BV, ROE, ROA, EPS, and MCAP are loaded more on its latent variables CP than IC. Thus it has been concluded that the model is well specified. The manifest variables measure the latent variables conclusively.

33 111 R 2 values of endogenous Latent Variable: Valid outer model permits the evaluation of inner model. The criteria used for evaluation is the coefficient of determination -R 2 of the endogenous latent variable. Chin (1998) describes R 2 values of 0.67, 0.33, and 0.19 in PLS path models as substantial, moderate, and weak, respectively Table 4.25 Inner model (Dimension 1): IT industry R² F Pr > F R² Standard Critical (Bootstrap) error ratio (CR) Lower bound (95%) Upper bound (95%) Table 4.25 gives the inner model specification.r 2 value 0f.76 is very close to the substantial value. This clearly shows the confidence in the theory, i.e there is no doubt in the theoretical underpinnings and the model is capable of explaining the endogenous latent variable namely corporate Performance. Thus it has been concluded that 76% of variance in corporate performance is explained by intellectual capital. Path Coefficient: This shows the regression weights of the path. f 2 measures the effect of size by means of Cohen s (1998)f 2. According to Cohen (1998), f 2 values of 0.02, 0.15, and 0.35 signify small, medium and large effects respectively Table 4.26 Path coefficients (CP / 1) (IT industry) Latent variable Value Standard error t Pr > t f² IC

34 112 From the above table the t-statistics for path coefficient is 20.9 which is more than the required Hence it has been proven that the path is valid at P <.05 level. High f 2 values indicate that the predictor latent variable i.e Intellectual Capital has a large effect at the structural level. Predictive Ability: Blind folding procedure is done to obtain the cross validated redundancy which fits the PLS path modeling approach like hand and glove (WOLD 1982). In analogy to f 2 the relative impact of predictive relevance can be assessed by means of redundancies (q 2 ) values: values of.02,.15, and.35 reveal a small, medium or large predictive relevance of latent variable which in turn explain the endogenous latent variable. Table 4.27 Model Assessment (with blind folding redundancies) (IT industry) Latent variable IC Type R² Adjusted R² Exogenous Mean Redundancies CP Endogenous Mean From the above table the mean redundancy for endogenous variable is.276 which explains that the model has medium predictive (since the value is less than.35) relevance of the latent variable which in turn explains the endogenous variable Corporate Performance. Hence analysis shows that model for electronic industry shows medium predictive relevance than banking industry and electronic industry.

35 PLS-PM for Pharmaceutical Industry Figure 4.4: PLS-PM for Pharmaceutical Industry

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39 117 Quality Index: According to PLS-PM structure each part of the model needs to be validated. This includes the measurement model, structural model and the overall model. The measurement model and the structural model are explained through the communality index and Redundancy index. Even though there is no overall fit index in PLS-Path Modeling, a global criterion of goodness of fit has been proposed by Tenenhaus et al. (2004): the GoF index. Such an Index has been developed to take care of both the model performance by measuring both measurement and structural model, thus provides a single measurement in overall prediction performance of the model. Hence GoF index is obtained as the geometric mean of average communality index and average R 2 values. Even though GoF models are mostly used in case of reflective models, for practical purpose GoF can be interpreted for formative models also as it provides a measure of overall fit Esposito Vinzi et al. (2010). This index is bound to be between 0 and 1. Since both GoF and Relative GoF are descriptive indices, there is no inference based threshold to judge the statistical significance of their values. As a rule of thumb value of relative GoF close to 0.9 or greater than that clearly speaks in favor of the model Table 4.29 Goodness of Fit (GoF) index Pharmaceutical Industry GoF GoF (Bootstrap) Standard error Relative Outer model From Table 4.28 it is clear that the value of GoF for the outer model and relative model are.81 which is very close to.9 and hence speak in favor of the model.

40 118 Cross Loadings: (Monofactorial manifest variables): This table allows to check whether a given manifest variable is monofactorial or not. This means whether the manifest variable is mostly related with the latent variable. Ideally the model is said to be well specified if it appears that the manifest variables are more related to its latent variables than other variables Table 4.30 Cross-loadings (Monofactorial manifest variables / 1) (Pharmaceutical Industry) IC CP VACA VAHU STVA MV/BV ROE ROA EPS MCAP The result shows that the manifest variables VACA, VAHU and STVA are more loaded in its own latent variable Intellectual capital than CP and manifest variables MV/BV, ROE, ROA, EPS, and MCAP are loaded more on its latent variables CP than IC. Thus it has been concluded that the model is well specified. The manifest variables measure the latent variables conclusively. R 2 values of endogenous Latent Variable: Valid outer model permits the evaluation of inner model. The criteria used for evaluation is the coefficient of determination -R 2 of the endogenous latent variable. Chin

41 119 (1998) describes R 2 values of 0.67, 0.33, and 0.19 in PLS path models as substantial, moderate, and weak, respectively Table 4.31: Inner model (Dimension 1): Pharmaceutical industry R² Standard R² F Pr > F (Bootstrap) error Critical ratio (CR) Lower bound (95%) Upper bound (95%) Table 4.29 gives the inner model specification.r 2 value 0f.72 is close to the substantial value. This clearly shows the confidence in the theory, i.e there is no doubt in the theoretical underpinnings and the model is capable of explaining the endogenous latent variable namely Corporate Performance. Thus it has been concluded that 72% of variance in corporate performance is explained by Intellectual capital. Path Coefficient: This shows the regression weights of the path. f 2 measures the effect of size by means of Cohen s (1998) f 2. Table 4.32: Path coefficients (CP / 1) (Pharmaceutical industry) Latent variable Value Standard error t Pr > t f² IC From the above table the t-statistics for path coefficient is which is more than the required Hence it has been proven that the path is valid at P <.05 level. High f 2 values indicate that the predictor latent variable i.e Intellectual Capital has a large effect at the structural level.

42 120 Predictive Ability: Blind folding procedure is done to obtain the cross validated redundancy which fits the PLS path modeling approach like hand and glove (WOLD 1982). In analogy to f 2 the relative impact of predictive relevance can be assessed by means of redundancies (q 2 ) values: values of.02,.15, and.35 reveal a small, medium or large predictive relevance of latent variable which in turn explain the endogenous latent variable. Table 4.33 Model Assessment (with blind folding redundancies) (Pharmaceutical industry) Latent variable IC Type R² Adjusted R² Exogenous Mean Redundancies CP Endogenous Mean From the above table the mean redundancy for endogenous variable is.325 which explains that the model has high predictive (since the value is close to.35) relevance of the latent variable which in turn explains the endogenous variable corporate performance. Hence analysis shows that model for pharmaceutical industry shows high predictive relevance DISCUSSION Central idea of this research is to provide evidence from the Indian industry with regard to relationship between Intellectual capital and corporate performance. Taking into account the transformation in the economic reality it is important that we treat IC at par with other resources like land, capital and other physical and financial assets. IC calculated in this study is not a

43 121 absolute value, but rather it is an index which incorporates the Vale addition or wealth created by the companies through employment of IC resources like Human capital, Structural Capital and Financial Capital. Thus VAIC method used in this study to quantify IC assets does not present a monetized value of IC but its efficiency to create value from different components. The study attempts to analyze the impact of Intellectual Capital on Corporate Performance and its predictive relevance. It is evident from the PLS-PM results that intellectual capital has an impact on corporate performance and can predict the latter, but at varying levels. Of all the industries studied the highest variance has been explained in case of electronic industry with R 2 value of.87 which explains IC contributing to 87% variance in CP. This is followed by IT industry with 76% variance explained, Pharmaceutical industry with 72% variance explained and finally the last in the list banking industry with 56% of variance. Path model results confirm the model fit and strongly support the theoretical foundation of the Intellectual Capital frame work. The study provides empirical evidence on the relevance of IC measure (VAIC ) put forth by Ante Pulic model with the corporate performance. The path coefficient of all path model are highly significant which reconfirms the model fit and impact of IC on CP. The study provides evidence from Indian industry that in case of knowledge based industries like electronics and Information technology IC is highly critical and impacts the performance more when compared to others. Hence we fully accept the hypotheses H3 and H4 that IC impacts corporate performance and it has predictive relevance.