Relationship of Firm Types to Perceptions of Simultaneous Multithreading Effectiveness - Statistics Project Example

Chapter Six: Data Analysis 6.1 Introduction Using the SPSS program, a series of correlation and regression analyses were conducted to examine the relationships of the responses for Questions 13 through 18 – which addressed perceptions of the effectiveness of SMTs on firm performance according to different applications of the tools – to firm type and individual strategic management tools currently used by the firms. From the research questions, two hypotheses for the analysis were developed: H1: Firm type is a predictor of general perceptions of the SMT effectiveness. H2: Strategic management tool type is a predictor of general perceptions of SMT effectiveness. In all instances, the Q13 through Q18 responses, which were according to a Likert Scale, were the dependent variables, with firm type and individual SMTs being the independent variables. 6.2 Correlation of Responses to Firm Type In order to perform a correlation test, the firm types were converted to a numeric value (1-10), according to the number of each firm represented in the survey; this arrangement was completely arbitrary, and was done simply as a matter of organisation. By numeric value, the firm types are: 1. Telecom firms 2. Energy and utility firms 3. Insurance firms 4. Building and construction firms 5. Cement manufacturers 6. Banks and financial firms 7. Petrochemical firms 8. Real estate firms 9. Industrial investment firms 10. Food industries The results of the correlation analysis are shown in Table 4. The correlation between firm type and the responses for questions 13 through 18 is very strong as a negative correlation (i.e. the correlation is stronger for lower-numbered firm types representing larger numbers of firms in the survey), and is virtually equal for each of the six factors represented by the questions. Correlations Firm type SMT efficiency Non use SMT comparison SMT on perfom. SMTon depart. SMT on firm as a whole SMT on inform. Firm type Pearson Correlation 1 -.897** -.895** -.897** -.891** -.896** -.896** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT efficiency Pearson Correlation -.897** 1 .976** .975** .923** .961** .965** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 Non use SMT comparison Pearson Correlation -.895** .976** 1 .962** .903** .939** .943** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on perfom. Pearson Correlation -.897** .975** .962** 1 .938** .976** .980** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMTon depart. Pearson Correlation -.891** .923** .903** .938** 1 .960** .955** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on firm as a whole Pearson Correlation -.896** .961** .939** .976** .960** 1 .985** Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on inform. Pearson Correlation -.896** .965** .943** .980** .955** .985** 1 Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 **. Correlation is significant at the 0.01 level (2-tailed). Table 4: Correlation Results, Firm Type to Q13-Q18 The correlation test was repeated for a Spearman rather than Pearson correlation, since this is considered a more accurate test than the more commonly-used Pearson test for data involving Likert Scale responses (Green & Salkind, 2010). Table 5 shows the results of this analysis, which were moderately stronger than those for the first test: Nonparametric Correlations Firm type SMT efficiency Non use SMT comparison SMT on perfom. SMTon depart. SMT on firm as a whole SMT on inform. Spearman's rho Firm type Correlation Coefficient 1.000 -.914** -.912** -.912** -.904** -.911** -.911** Sig. (2-tailed) . .000 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT efficiency Correlation Coefficient -.914** 1.000 .979** .972** .919** .954** .963** Sig. (2-tailed) .000 . .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 Non use SMT comparison Correlation Coefficient -.912** .979** 1.000 .958** .903** .935** .944** Sig. (2-tailed) .000 .000 . .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on perfom. Correlation Coefficient -.912** .972** .958** 1.000 .941** .976** .986** Sig. (2-tailed) .000 .000 .000 . .000 .000 .000 N 213 213 213 213 213 213 213 SMTon depart. Correlation Coefficient -.904** .919** .903** .941** 1.000 .964** .953** Sig. (2-tailed) .000 .000 .000 .000 . .000 .000 N 213 213 213 213 213 213 213 SMT on firm as a whole Correlation Coefficient -.911** .954** .935** .976** .964** 1.000 .984** Sig. (2-tailed) .000 .000 .000 .000 .000 . .000 N 213 213 213 213 213 213 213 SMT on inform. Correlation Coefficient -.911** .963** .944** .986** .953** .984** 1.000 Sig. (2-tailed) .000 .000 .000 .000 .000 .000 . N 213 213 213 213 213 213 213 **. Correlation is significant at the 0.01 level (2-tailed). Table 5: Nonparametric Correlation Results, Firm Type to Q13-Q18 6.3 Regression Analysis In order to determine the possibility of predicting a set of responses to Q13 through Q18 based on the type of firm (or conversely, predicting the type of firm from a given set of responses to those questions), a linear regression test was conducted. In general, the idea that firm type could be used to predict perceptions of SMT effectiveness was confirmed according to the Q13-Q18 factors, as shown by Table 6: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .916a .839 .834 1.215 a. Predictors: (Constant), SMT on inform., Non use SMT comparison, SMTon depart., SMT efficiency, SMT on perfom., SMT on firm as a whole ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1584.621 6 264.104 178.944 .000b Residual 304.036 206 1.476 Total 1888.657 212 a. Dependent Variable: Firm type b. Predictors: (Constant), SMT on inform., Non use SMT comparison, SMTon depart., SMT efficiency, SMT on perfom., SMT on firm as a whole Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 23.559 .562 41.909 .000 SMT efficiency -.409 .739 -.093 -.553 .581 Non use SMT comparison -1.639 .573 -.380 -2.862 .005 SMT on perfom. -.031 .790 -.007 -.040 .968 SMTon depart. -1.680 .462 -.373 -3.637 .000 SMT on firm as a whole -.190 .806 -.043 -.236 .814 SMT on inform. -.188 .858 -.042 -.219 .827 a. Dependent Variable: Firm type Table 6: Regression Results, Firm Type and Q13-Q18 Variables Except for the perception of SMT effectiveness on department performance, the regression analysis indicated that firm type could be used as a predictor of perceived SMT effectiveness. 6.4 Correlation of Responses to SMT Types The correlation, ANOVA, and regression analyses for Q13 and Q18 were repeated, using types of SMTs used currently (Q12) as the dependent variable. To conduct the analysis, the SMTs were coded in a similar fashion to the firm types in the first analysis: 1. SWOT Analysis 2. KPI Analysis 3. CSF Analysis 4. Benchmarking 5. Balanced Scorecards 6. PLC Analysis Tables 7 and 8 show the results of the correlation analyses. As can be seen, there was a positive correlation between the responses for Q12 (specific SMTs) and the responses for Q13-Q18. These results were positive, but were much less conclusive compared to the earlier correlation analyses for firm types. However, the negative correlation found in the firm type analysis was absent in the analysis for strategic management tools: Correlations Management tools SMT efficiency Non use SMT comparison SMT on perfom. SMTon depart. SMT on firm as a whole SMT on inform. Management tools Pearson Correlation 1 .058 .061 .031 .020 .018 .035 Sig. (2-tailed) .399 .374 .654 .773 .797 .614 N 213 213 213 213 213 213 213 SMT efficiency Pearson Correlation .058 1 .976** .975** .923** .961** .965** Sig. (2-tailed) .399 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 Non use SMT comparison Pearson Correlation .061 .976** 1 .962** .903** .939** .943** Sig. (2-tailed) .374 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on perfom. Pearson Correlation .031 .975** .962** 1 .938** .976** .980** Sig. (2-tailed) .654 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMTon depart. Pearson Correlation .020 .923** .903** .938** 1 .960** .955** Sig. (2-tailed) .773 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on firm as a whole Pearson Correlation .018 .961** .939** .976** .960** 1 .985** Sig. (2-tailed) .797 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on inform. Pearson Correlation .035 .965** .943** .980** .955** .985** 1 Sig. (2-tailed) .614 .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 **. Correlation is significant at the 0.01 level (2-tailed). Table 7: Correlation Results, SMTs to Q13-Q18 Nonparametric Correlations Management tools SMT efficiency Non use SMT comparison SMT on perfom. SMTon depart. SMT on firm as a whole SMT on inform. Spearman's rho Management tools Correlation Coefficient 1.000 .065 .071 .028 -.002 .009 .026 Sig. (2-tailed) . .347 .304 .685 .971 .896 .704 N 213 213 213 213 213 213 213 SMT efficiency Correlation Coefficient .065 1.000 .979** .972** .919** .954** .963** Sig. (2-tailed) .347 . .000 .000 .000 .000 .000 N 213 213 213 213 213 213 213 Non use SMT comparison Correlation Coefficient .071 .979** 1.000 .958** .903** .935** .944** Sig. (2-tailed) .304 .000 . .000 .000 .000 .000 N 213 213 213 213 213 213 213 SMT on perfom. Correlation Coefficient .028 .972** .958** 1.000 .941** .976** .986** Sig. (2-tailed) .685 .000 .000 . .000 .000 .000 N 213 213 213 213 213 213 213 SMTon depart. Correlation Coefficient -.002 .919** .903** .941** 1.000 .964** .953** Sig. (2-tailed) .971 .000 .000 .000 . .000 .000 N 213 213 213 213 213 213 213 SMT on firm as a whole Correlation Coefficient .009 .954** .935** .976** .964** 1.000 .984** Sig. (2-tailed) .896 .000 .000 .000 .000 . .000 N 213 213 213 213 213 213 213 SMT on inform. Correlation Coefficient .026 .963** .944** .986** .953** .984** 1.000 Sig. (2-tailed) .704 .000 .000 .000 .000 .000 . N 213 213 213 213 213 213 213 **. Correlation is significant at the 0.01 level (2-tailed). Table 8: Nonparametric Correlation Results, SMTs to Q13-Q18 6.5 Regression Analysis As in the first correlation analysis, the results suggested that the SMT being used could predict perceptions of effectiveness according to the responses to Q13-Q18, so in order to test this hypothesis, the linear regression test was carried out. As the correlation analysis indicated, prediction of perceptions according to type of SMT was indicated, and more conclusively than predictions according to firm type: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .172a .030 .001 1.494 a. Predictors: (Constant), SMT on inform., Non use SMT comparison, SMTon depart., SMT efficiency, SMT on perfom., SMT on firm as a whole ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 13.991 6 2.332 1.045 .397b Residual 459.821 206 2.232 Total 473.812 212 a. Dependent Variable: Management tools b. Predictors: (Constant), SMT on inform., Non use SMT comparison, SMTon depart., SMT efficiency, SMT on perfom., SMT on firm as a whole Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3.128 .691 4.525 .000 SMT efficiency .991 .909 .450 1.091 .277 Non use SMT comparison .433 .704 .200 .615 .540 SMT on perfom. -.975 .972 -.450 -1.003 .317 SMTon depart. -.037 .568 -.016 -.065 .948 SMT on firm as a whole -1.322 .991 -.603 -1.334 .184 SMT on inform. 1.026 1.055 .463 .973 .332 a. Dependent Variable: Management tools Table 9: Regression Results, SMT and Q13-Q18 Variables The results of the regression analysis also indicated that the predictive strength of SMTs is more than that of firm types, and that the model fit is more reliable. 6.6 Conclusion The results of the correlation and regression analyses are best described as mixed. The data analysis of the relationship of firm types to perceptions of SMT effectiveness indicated that likely perceptions could be determined by the firm type, but the conclusion is not a strong one; therefore, H1 is considered inconclusive – proven, but with a relatively large degree of uncertainty. The relationship of strategic management tools to perceptions of performance was much more clearly demonstrated by the analysis; thus H2 is considered proven. This result would seem logical even in the absence of the correlation and regression analysis; SMTs are intended to produce certain outcomes if used as designed. Comparing the perceived effectiveness in these results with the types of tools indicates to what degree the tools are used “as-is”. As indicated in the previous chapter, using SMTs without significant modifications or customisations appears to be normal management practise amongst Saudi firms; therefore, some conclusions can be drawn as to which tools are generally more or less effective. The implications of this are discussed in the following chapter. Read More