Employee Commitment on Workplace Quality and Its Effect on Firm Performance - Coursework Example

Employee Commitment on Workplace Quality and Its Effect on Firm Performance INTRODUCTION As economy’s growth continues to go in an upward spiral, the level of competition also is spreading into abundance making the best compete against the best. Key players in the market and competition continue to increase in number every year. It is in this regard that companies are beginning to look forward to their competitive advantage in the market and one way to obtain it is to be able to provide the best working environment. It cannot be denied that more managers today are beginning to believe the idea that a remarkably better working environment is essential to business success. The trust between employees and the management is the basic foundation in creating a better place to work. Specific practices and policies cannot exactly compare the level of trust that an employee can give to the company. In order to investigate how exactly better practices for people can significantly contribute to business success of a firm, this paper was able to conduct a study in line with finding the relationship that exists between employee commitment to work and its overall impact to the quality and performance of the firm. There was an analysis showing a remarkable conclusion that those firms found on the 100 Best lists in a popular financial press have relatively better firm performance. The only drawback of this analysis was its limitations considering that most of those analyses already conducted were theoretical and more experimental rather than into scrutinizing the level of information why for instance those companies on the list of 100 best are better than those companies not on the list. It is in this regard that this study was formulated in order to study and investigate the relationship between employee commitment and the quality of working environment and performance of the firm. In order to work this out, resource-based review of the company was employed in order to find out the level of company’s competitive advantage as far as its human resource activities are concerned. Thus, the Sunday Times list of the “100 Best Companies to Work For in the UK – Mid Companies” was employed in order to be able to find how the quality of workplace environment has significantly contributed effects to positive employee commitment. It is therefore put into assumption that those firms on the list of 100 best have higher market values than those companies not on the list. This is an empirical study applied to the UK. Thus, in order to find out the objective of this study, the following research question was investigated: Do companies with employees with high commitment have better performance than those with employees with less commitment to work? We were able to test the significant relationship that connects the line of employee commitment and firm performance. According to Wright et al. (2001), there is a significant role that human resource activities played on the competitive advantage of the firm and in particular the firm performance. This study begins with the introduction of theory and research underlying the prediction of human resource activities if it provides valuable competitive advantage for a company. The next step involves the identification how employee commitment would affect the level of employee behaviour. In order to recognize the union of employee commitment and firm value, the investigation on financial and market performance of companies involved in the study were initiated in which the bottom line was to investigate further the underlying effect that different levels of employee commitment may bring to the firm. Finally, an investigation if companies with better employee commitment have larger market returns than companies with worse employee commitment was conducted. In totality, the these series of analysis of information would just provide some support to prove that employee commitment is a source of competitive advantage which can improve the firm performance. LITERATURE REVIEW Strategic human resource management The interest of the study is to find the relationship that exists between employee commitment and firm performance, then the first part needs to look into strategic human resource management literature. “According to the resource-based view of the firm, firms create sustainable competitive advantages by manipulating and controlling their capabilities and resources effectively.” (Barney, 1991) “Companies able to fulfill good employee relations may see the improvement of performance for higher worker quality. Anecdotal evidence suggests that companies do seem to change in terms of employee relations. Correspondingly, firms that have accomplished good employee commitment have perused such various strategies as different human resource systems, cultures, leadership, etc.” (Fulmer et al, 2003) “Human resource activities can create a competitive advantage by developing a skilled workforce that carries out the company’s business strategy effectively as a result. This competitive advantage leads to improved performance, higher profitability, and greater market values.” (Brian and Norman, 2003) Employee commitment The assumed impact of employee commitment on performance is the reason why studies related to employee commitment and performance have been overflowing for so many years now (Keiningham and Vavra, 2001). Commitment is defined by Meyer and Allen (1997) into three different categories: affective orientation, cost-based and obligation or moral responsibility. According to Buchanan (1974), commitment is in a way of giving focus to the goals and values of the organization. On the other hand, Becker (1960) believed that it is the effect when a person puts his interest into action. DuBrin (2008) defined commitment as an extension of motivation. According to Shore and Wayne (1993) from the social exchange view of commitment, there is a strong impact to work behaviour of the employee if there is a feeling of strong commitment for them from the organization. This means that organization needs to enhance its strong commitment to the employee in order to get the desired commitment from its employees in return. Fisher et al. (2010) were able to conduct a study to find out the link between employee attitudes and behaviours with business performance. Their findings suggest that employee commitment, job satisfaction and organisational citizenship behaviour were highly associated with financial performance of the company. Employee satisfaction on the other hand was found to be related to service quality and customer satisfaction (Yee, et. al, 2008). This is related to the findings of Allan and Grisafe (2001). The study of Cohen (1998) on working commitment of nurses suggests that work commitment and occupational commitment can be significant predictors of work outcomes. Chang (2006) had a study regarding Korean employees and the study was related in particular to compensation, HR practices and employee commitment. The finding of the study suggests that there is a strong positive connection of compensation and HR practices to employee commitment. Employee performance is affected by many factors and one of them which is certain to contribute an effect is employee’s own level of commitment (Fink, 1992). It is argued that when an individual has high morale, he or she participates in the work place with full-charged enthusiasm and a sense of commitment (Bruce, 2003). According to a study, successful companies have different profiles of employee commitments compared to less successful organizations (Gostick and Elton, 2004). Work group commitment is found far stronger than organisational commitment suggests that the former has strong influence over employee commitment (Moreland and Levine, 2001). METHODOLOGY The important assumption of this study is that the firms included on the Sunday Times list of the “100 Best Companies to Work For in the UK – Mid Companies” have exemplary employee commitment. The assumption was made so as to test the developed projection that firms with higher committed employees have better firm performance than companies with lower committed employees. Companies chosen in the studies were those coming from an industry based on their firm size, and operating performance. So as to come up with objective analysis, quantitative method was used in the study. Regression analysis was employed to measure or predict the employee commitment impact on firm performance. Firm performance is assessed in two ways: market value and returns on assets (ROA). A regression equation is used to examine how employee commitment benefited market values. The multiple regression models were created in order to find out the projection in the following working model. MV = α + βLIST + βBVE + βEARNINGS + ε The dependent variable MV is the market value of company. The primary variable is LIST, which is a dummy variable taking the value of 1 for companies on the list and 0 otherwise. EARNINGS is represented the value of firm and I is represented the book value of equity. In this study, a major limitation is the assumption that inclusion on the Sunday Times list of the “100 Best Companies to Work For in the UK – Mid Companies” represents employee commitment. In reality, companies arise from the list may not have better employee commitment. Also, some companies with good employee commitment did not finish the survey probably required in the listing process of the Sunday Times. Although such self-selection bias and measurement error is present, we believe that these problems would make it harder to determine significant outcomes. However, our inability to obviate these concerns should be indicated as a study limitation. There were 19 companies randomly chosen from “100 Best Companies to Work for in the UK-Mid Companies and 22 companies from those not found on the list. RESULT The data sets used comprised from those in 2005 to 2009. There were 10 steps involved in the regression analysis. Step 1 The first step was the regression analysis using the data set from 2009. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value (Sig) obtained which was P-value = 0.000. There were three predictors used in the analysis; company earnings, book value of equity and LIST, which represented the value of “1” if the company belongs to 100 best on the list and “0” otherwise. The result of the analysis of these predictors showed that only earnings and book value of equity have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.361. A more detailed presentation of the result of this analysis is shown in Appendix A. Step 2 The second step was the regression analysis using the data set from 2008. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. Of the three predictors used in the analysis, company earnings and book value of equity were found out to have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.720. A more detailed presentation of the result of this analysis is shown in Appendix B. Step 3 The third step was the regression analysis using the data set from 2007. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. Of the three predictors used in the analysis, company earnings and book value of equity were found out to have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.747. A more detailed presentation of the result of this analysis is shown in Appendix C. Step 4 The fourth step was the regression analysis using the data set from 2006. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. Of the three predictors used in the analysis, company earnings and book value of equity were found out to have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 1.000. A more detailed presentation of the result of this analysis is shown in Appendix D. Step 5 The fifth step was the regression analysis using the data set from 2005. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. Of the three predictors used in the analysis, company earnings and book value of equity were found out to have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.518. A more detailed presentation of the result of this analysis is shown in Appendix E. Step 6 The sixth step was the regression analysis using the data sets all together from 2005 to 2009. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. Of the three predictors used in the analysis, company earnings and book value of equity were found out to have significant contribution to the model with P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.377. A more detailed presentation of the result of this analysis is shown in Appendix F. Step 7 It seems that from the results obtained from step 1 to step 6, LIST turned out to be a not significant predictor in the model. Thus, the other way of looking if there was significant contribution of LIST variable in the model was to consider the significant difference of earnings between the 100 best companies and the other. As shown in Appendix G, there was no significant correlation between the earning of 100 best companies and non-100 best considering that the P-value obtained was P-value = 0.944. However, a P-value = 0.000 in the test of mean difference suggested that there was a significant difference between the means of earnings of 100 best companies and non-100 best. The average earning obtained by those companies from 100 best was ₤1,195.66 denoted as Earnings1. The average earning obtained by those companies not from 100 best was ₤298.92 denoted as Earnings2. Step 8 The other way of looking if there was significant contribution of LIST variable in the model was to consider the significant difference of book value of equity between the 100 best companies and the other. As shown in Appendix H, there was no significant correlation between the book value of equity of 100 best companies and non-100 best considering that the P-value obtained was P-value = 0.576. However, a P-value = 0.000 in the test of mean difference suggested that there was a significant difference between the means of book value of equity of 100 best companies and non-100 best. The average book value of equity obtained by those companies from 100 best was ₤4,993.99 denoted as I1. The average book value of equity obtained by those companies not from 100 best was ₤1,148.79 denoted as I2. Step 9 The result in step 8 was a contradiction to the results presented in step 1 to step 6. As noticed, earning and book value of equity were only predictors found out to have significant contribution in the regression model. However, as presented in step 8, there was a significant difference in the earning and book value of equity between the companies coming from 100 best and those companies not in the 100 best. This clearly suggests that as a matter of fact, the model must be able to predict the performance of companies in the 100 best from those that are not. The proponent suspected that earning and book value of equity must be significantly correlated. Thus, a test of correlation of earning and book value of equity was initiated. As shown in Appendix I, the correlation value of earning and book value of equity equals 0.884. To test if this was statistically significant, at 95% level of confidence this result was indeed statistically significant since the P-value = 0.000. Step 10 Knowing that there was a significant correlation between earning and book value of equity, it is a prerequisite in the regression analysis to drop at least one of the variables with significant correlation to other variables. Thus, the book value of equity was dropped leaving earning and LIST as predictors to proceed to another regression analysis. From the ANOVA analysis, it showed that at least one of the predicting variables was a significant predictor of the model. This can be explained by the P-value obtained which was P-value = 0.000. The two predictors used in the analysis, company earnings and LIST were found out to have significant contribution to the model. The earning has P-value = 0.000. On the other hand, the LIST obtained a P-value = 0.007. To find out how fit the regression model was, around 86.60% of variation in the model can be accounted by the independent variables considering that the R square was 0.866. The following is the actual model obtained from the regression analysis. MV = ₤283.438 + ₤4.055 (Earning) + ₤1,058.101 (LIST) Where ₤283.438 was the constant value, in which it was found out insignificant in the regression model considering that in the analysis of coefficient, its P-value was 0.260. A more detailed presentation of the result of this analysis is shown in Appendix J. DISCUSSION This section discusses the results of this study together with their implications. In order to understand the result and fully appreciate its implications, the following statistical method used in the study needs to be understood first. What is Regression Analysis? The regression analysis is a parametric test of association between independent and dependent variables. In this study, there were two groups of companies involved with corresponding data sets for earnings, book value of equity and LIST which the values correspond to “1” or “0” respectively. The goal is to find out whether companies belonging to 100 best companies or not can be a significant predictor of the regression model which is equivalent to market value of the company. In this case, companies belonging to 100 best companies can be quantitatively determined by “1” and “0” otherwise. Considering that there were three independent variables or predictors considered in this study, the regression analysis was defined multiple. The multiple regression analysis is used to predict the dependent variables y given the independent variables xs. Aside from making predictions, relationship between the dependent variables and the different independent variables can also be observed. For instance, in this case the dependent variable considered was market value of the company and the independent variables were earning, book value of equity and LIST. Using the regression analysis is very important when predicting y dependent variable with 2 or more independent variables xs. This is to find out if there is a relationship that exists between dependent variables and among the independent variables. Another reason of using the multiple regression analysis is to know the extent of influence that the independent variables have on the dependent variables through the r square x 100% coefficient of determination and to know whether the correlation is positive or negative as indicated in the value of r. The following are steps used in order to determine the result given by the regression analysis. Step 1: Determination of hypothesis. In the study, every test conducted undergone the formulation of hypotheses. There were two hypotheses involved; the null (Ho) and the alternative (Ha). In the case of the study, the following are hypotheses used in order to determine results of the analysis. There were tests employed in the regression analysis and the most commonly used were the test of overall significance of the model and the test of significance of each coefficient. The test of overall significance of the model has the following working hypotheses. Ho: B1 = B2 = B3 = …Bn (The independent variables do no have influence on the dependent variables.) Ha: At least one of the independent variables have influence on the dependent variable. The test of significance of each coefficient in the model has the following working hypotheses. Ho: Coefficient x have significant influence on the dependent variable y. Ha: Coefficient x have no significant influence on the dependent variable y. Step 2: Chosen significance level, α = 0.05. The level of significance of α = 0.05 means every statistically significant result should have 95% level of confidence. Step 3: Rejection rule. The basic rule needs to reject Ho or the null hypothesis if the p-value is less than or equal to 0.05. Step 4. In order to determine the result of the regression analysis, a statistical software SPSS version 17 was used. Step 5. Decision. This step emphasises the decision to be made in order to obtain result or conclusion. Step 6. Conclusion. This normally states the fact if the level of significance is obtained, then the null hypothesis needs to be rejected which further implies that the independent variables as a group are related to dependent variables. In the same way, the null hypothesis needs to be rejected in testing the level of significance of individual independent variables to dependent variables if the level of significance is obtained. Inferential statistics (Test of difference) Another way of looking at how the status of a company together with its employee commitment can affect its market value is through comparing if there are significant differences on their earning and book value of equity. Using the t-test of difference can actually reveal some important information. The same steps were actually done. Step 1: Determination of hypothesis. The test of significant difference between the mean has the following working hypotheses. Ho: M1 = M2 = M3 = …Mn (The means of earnings or book value of equity from the two groups of companies have no significant difference or they are equal.) Ha: The means of earnings or book value of equity from the two groups of companies are not equal. Step 2: Chosen significance level, α = 0.05. The level of significance of α = 0.05 means every statistically significant result should have 95% level of confidence. Step 3: Rejection rule. The basic rule needs to reject Ho or the null hypothesis if the p-value is less than or equal to 0.05. Step 4. In order to determine the result of the regression analysis, a statistical software SPSS version 17 was used. Step 5. Decision. This step emphasises the decision to be made in order to obtain result or conclusion. Step 6. Conclusion. This normally states the fact if the level of significance is obtained, then the null hypothesis needs to be rejected which further implies that the means of two or more compared groups have significant difference. Correlation problem It was also of great importance to find out any correlation among the independent variables used in the regression analysis. The purpose of finding this is to be able to drop at least one of the variables with strong correlation with other variables. This is to ensure to eliminate redundancy of information in the analysis and to give more accurate information and interpretation from the obtained results. The correlation value ranges from 0 to 1. The closer it is to 1, the better since the degree of correlation is high. However, so as to be able to tell that the obtained correlation value is significant say at 95% level of confidence, then α = 0.05. Now, the obtained P-value must be less than or equal to the value of chosen α in order to say that the obtained correlation value is statistically significant. Analysing the results In order to understand smoothly the whole flow of the discussion, let us try to recall the main objective of this study: To find out if companies with employees with high commitment have better performance than those with employees with less commitment to work. In order to find results for this objective, the proponent analysed “100 Best Companies to Work For in the UK – Mid Companies” to show the presence of positive employee commitment concerning the quality of workplace environment. The basic assumption of the study is to that inclusion on the Sunday Times list of the “100 Best Companies to Work For in the UK – Mid Companies” represents employee commitment. Therefore, firms on the list have greater market values than companies do not on the list. In order to test the hypothesis and its underlying assumption, the used of statistical tool such as regression analysis was initiated. The following was the working regression model used in the regression analysis. MV = α + βLIST + βBVE + βEARNINGS + ε The results suggested that the obtained regression model was statistically significant. The statistically significant results only imply that the data used in the analysis were able to meet specific criteria, restrictions, evaluations and even more with respect to the statistical test used. Thus, a statistically significant result implies an understanding that there was a good reason to believe that the obtained result is reliable and can further prove the hypothesis and its underlying assumption. Due to statistically significant result, companies with employees with high commitment were found out to have better performance than those with employees with less commitment to work. This information can also be significantly proven by other existing related studies. Meyer and Herscovitch (2001) defined commitment of employee as something that allows strong connection between the individual and something that of his or her focus followed by various mind-sets that are responsible for the creation of behaviour. Neininger et al. (2010) found out that organizational commitment was influential to job satisfaction and turnover. According to them, team commitment on the other hand was influential to team performance and altruism. Studying on employee commitment and motivation, Meyer et al. (2004) found that a motivated behaviour can be the effect of different mindsets that also have important implications for predicting work behaviour. There was a significant correlation of personal characteristics to company commitment according to Fukami and Larson (1984). They found out that supervisory positions tend to have dual loyalty, commitment to company and commitment to union. Cravens and Oliver (2006) believed that employees are very important in creating corporate reputation. Employee commitment therefore is needed to be understood. In other study, they were able to cite management strategy such as the balanced scorecard in order to discuss motivational aspect such as incentive system prior to obtaining corporate reputation. This was related to what Allan and Grisafe (2001) have studied; organizational commitment of employee and its impact to customer satisfaction and repeat purchase behaviour. Herrington et al. (2009) on the other hand found out that the strength of employee and firm relationship can be predicted by cooperation, balanced power, communication, attachment, shared goals and values, trust and absence of conflict. From their study on employees and supervisors at two commercial banks in the United Arab Emirates, Shaw et al. (2003) found out that organizational commitment and guest worker status interactions were able to predict overall performance, altruism and supporting dissonance perspective. CONCLUSION The study wants to show the presence of positive employee commitment concerning the quality of workplace environment. It is in this regard that the list of “100 Best Companies to Work For in the UK – Mid Companies” represents employee commitment. Therefore, firms on the list have greater market values than companies do not on the list. The statistically significant result of the analysis suggests that companies on the list of 100 best have significantly high market value compared to companies not on the list. This can be predicted using the regression model. What makes the model reliable to predict this information is due to the fact that it was proven statistically significant. What is even more good about this result is that it is also presented that the market value of a certain company can be directly influenced by employee commitment under the assumption that companies on the list of 100 best have higher employee commitment than those not on the list. REFERENCES Allen, N. J. and Grissaffe, D. B. (2001) “Employee commitment to the organization and customer reactions: mapping the linkages.” Human Resource Management Review, Vol. 11 (3): 209-236. Barney, J.B. 1991. Firm resources and sustained competitive advantage. Journal of Management 17: 99-120. Becker, H. S. (1960) “ Notes on the concept of commitment.” American Journal of Sociology, Vol. 66: 32-42. Brian Ballou, Norman H. Godwin, and Rebecca Toppe Shortridge. 2003. Firm value and employee attitudes on workplace quality. Accounting Horizons 17: 329-341. Bruce, A. (2003) Building a high morale workplace. New York: McGraw-Hill. Buchanan, B. (1974) “Building organizational commitment: The socialization of managers in work organizations.” Administrative Science Quarterly, Vol. 9: 533-546. Chang, E. (2006) “Individual pay for performance and commitment HR practices in South Korea.” Journal of World Business, Vol. 41 (4): 368-381. Cohen, A. (1998) “An examination of the relationship between work commitment and work outcomes among hospital nurses.” Scandinavian Journal of Management, Vol. 14 (1-2): 1-17. Cravens, K. S. and Oliver, E. O. (2006) “Employees: the key link to corporate reputation management.” Business Horizons, Vol. 49 (4): 293-302. DuBrin, A. (2008) Essentials of management. 8th ed. OH: South-Western Cengage Learning. Fink, S. L. (1992) High commitment workplaces. New York: Greenwood Publishing. Fisher, R., McPhail, R., and Menghetti, G. (2010) “Linking employee attitudes and behaviors with business performance: A comparative analysis of hotels in Mexico and China.” International Journal of Hospitality Management, Vol. 29 (3): 397-404. Fukami, C. V. and Larson, E. W. (1984) “Commitment to company and union: Parallel models.” Journal of Applied Psychology, Vol. 69 (3): 367-371. Fulmer, I., B.Gerhart, and K. Scott. 2003. Are the 100 Best better? An empirical investigation of the relationship between being a “great place to work” and firm performance. Academy of Management Journal 39: 802-835. Gostick, A., and Elton, C. (2004) A carrot a day: a daily dose of recognition for your employees. Layton: O. C. Tanner. Herignton, C., Johnson, L. W. and Scott, D. (2009) “Firm –employee relationship strength – A conceptual model.” Journal of Business Research, Vol. 62 (11): 1096-1107. Keiningham, T. and Vavra, T. (2001) The customer delight principle: Exceeding customers’ expectations for bottom-line success. USA: McGraw-Hill. Meyer, J. P. and Allen, N. J. (1997) Commitment in the workplace: theory, research and application. London: Sage Publications. Meyer, J. P., Becker, T. E., and Vandenberghe, C. (2004) “Employee commitment and motivation: A conceptual analysis and integrative model.” Journal of Applied Psychology, Vol. 89 (6): 991-1007. Meyer, J. P. and Herscovitch, L. (2001) “Commitment in the workplace: toward a general model.” Human Resource Management, Vol. 11 (3): 299-326. Moreland, R. L. and Levine, J. M. (2001) “Groups at work: Theory and research” in M. E. Turner (ed.). Socialization in organizations and work groups. New Jersey: Lawrence Erlbaum Asociates. Neininger, A., Willenbrock, N. L., Kauffeld, S. and Henschel, A. (2010) “Effects of team and organizational commitment – A longitudinal study.” Journal of Vocational Behavior, Vol. 76 (3): 567-579. Shaw, J. D., Dlery, J. E. and Abdulla, M. H. A. (2003) “Organizational commitment and performance among guest workers and citizens of an Arab country.” Journal of Business Research, Vol. 56 (12): 1021-1030. Shore, L. M., and Wayne, S. J. (1993) “Commitment and employee behaviour: Comparison of affective commitment and continuance commitment with perceived organizational support.” Journal of Applied Psychology, Vol. 78 (5): 774-780. Wright, B. Dunford, and S.A. Snell. 2001. Human resources and the resource based view of the firm. Journal of Management 27: 701-721. APPENDIX Appendix A. Regression result using 2009 data set. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .48671 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.364E9 3 7.881E8 3.327E9 .000a Residual 8.765 37 .237 Total 2.364E9 40 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.167 .105 -1.586 .121 Earnings 1.000 .000 .211 10951.832 .000 BVE 1.000 .000 .814 41355.905 .000 List .149 .161 .000 .924 .361 a. Dependent Variable: MV Appendix B. Regression result using 2008 data set. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .00225 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.953E9 3 6.509E8 1.288E14 .000a Residual .000 37 .000 Total 1.953E9 40 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .001 .000 1.069 .292 Earnings 1.000 .000 .249 2594784.345 .000 BVE 1.000 .000 .780 7949964.410 .000 List .000 .001 .000 .361 .720 a. Dependent Variable: MV Appendix C. Regression result using 2007 data set. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .00371 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.736E9 3 5.787E8 4.204E13 .000a Residual .000 36 .000 Total 1.736E9 39 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .000 .001 .585 .562 Earnings 1.000 .000 .227 1130916.443 .000 BVE 1.000 .000 .792 3918290.995 .000 List .000 .001 .000 -.325 .747 a. Dependent Variable: MV Appendix D. Regression result using 2006 data set. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .00023 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.925E9 3 6.418E8 4.56232 .000a Residual .000 35 .000 Total 1.925E9 38 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.860E-13 .000 .000 1.000 Earnings 1.000 .000 .235 1.445E7 .000 BVE 1.000 .000 .775 4.703E7 .000 List 2.274E-12 .000 .000 .000 1.000 a. Dependent Variable: MV Appendix E. Regression result using 2005 data set. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .00172 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.809E9 3 6.029E8 2.033E14 .000a Residual .000 32 .000 Total 1.809E9 35 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .000 .000 1.263 .216 Earnings 1.000 .000 .192 1781524.641 .000 BVE 1.000 .000 .820 7410824.881 .000 List .000 .001 .000 -.653 .518 a. Dependent Variable: MV Appendix F. Regression result using data set from 2005 to 2009 together. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .21885 a. Predictors: (Constant), List, Earnings, BVE ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 9.790E9 3 3.263E9 6.814E10 .000a Residual 9.244 193 .048 Total 9.790E9 196 a. Predictors: (Constant), List, Earnings, BVE b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.033 .021 -1.564 .119 Earnings 1.000 .000 .224 47308.122 .000 BVE 1.000 .000 .797 165617.026 .000 List .030 .033 .000 .885 .377 a. Dependent Variable: MV Appendix G. Significant difference between earnings from 100 best companies on the list and those not on the list. Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 Earnings1 1195.6576 87 2027.74612 217.39720 Earnings2 298.9223 87 981.41650 105.21889 Paired Samples Correlations N Correlation Sig. Pair 1 Earnings1 & Earnings2 87 -.008 .944 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Earnings1 - Earnings2 896.73529 2259.56022 242.25028 415.157 1378.312 3.702 86 .000 Appendix H. Significant difference between book value equity from 100 best companies on the list and those not on the list. Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 BVE1 4993.9984 87 7455.64705 799.32927 BVE2 1148.7902 87 2524.23073 270.62594 Paired Samples Correlations N Correlation Sig. Pair 1 BVE1 & BVE2 87 -.061 .576 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 BVE1 - BVE2 3845.20816 8015.26909 859.32705 2136.92253 5553.493 4.475 86 .000 Appendix I. Correlation of earnings and book value equity. Paired Samples Correlations N Correlation Sig. Pair 1 Earnings & BVE 197 .884 .000 Appendix J. Regression result after dropping book value of equity. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .930a .866 .864 2602.30972 a. Predictors: (Constant), List, Earnings ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 8.477E9 2 4.238E9 625.861 .000a Residual 1.314E9 194 6772015.867 Total 9.790E9 196 a. Predictors: (Constant), List, Earnings b. Dependent Variable: MV Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 283.438 250.635 1.131 .260 Earnings 4.055 .123 .906 33.027 .000 List 1058.101 389.625 .075 2.716 .007 a. Dependent Variable: MV Read More