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2.4. ANALYSES 41 2.4.2 Correlations Table 2.10 presents the correlation matrix of all of the metric variables 28 . The correlation table shows no significant theory-consistent associations between the dependent and independent variables. However, some significant correlations among the independent variables exist. I find a significant correlation between the two size variables (i.e., business unit size and firm size). This finding might raise concerns regarding multicollinearity, because the Pearson correlation coefficient is 0.479. However, as discussed in the next paragraph (2.4.3), the multivariate models do not suffer from multicollinearity. Table 2.10: Pearson Correlation Table 1. 2. 3. 4. 5. 6. 7. 1. RPE-Use 2. RPE-based-Targets .519*** 3. Common Uncertainty -.040 .004 4. Information Asym. .004 .014 -.039 5. Contractibility .205* .144** -.005 -.019 6. Firm-level Measures -.057 -.010 -.031 .086 -.064 7. Business Unit Size .002 .023 .068 .057 -.025 .066 8. Firm Size .039 .042 -.052 .135** .118* -.040 .479*** *** Correlation is significant at the 0.01 level (2-tailed). ** Correlation is significant at the 0.05 level (2-tailed). * Correlation is significant at the 0.10 level (2-tailed). Listwise N=244 2.4.3 Multivariate Analyses This subsection presents the ordinary least squares regression analyses for the two dependent variables (RPE-Use and RPE-based-Targets). Additionally, Tobit and Logit regression analyses are included as additional analyses to improve and buttress the findings. The Tobit models have a slightly better fit because Tobit was designed to estimate limited dependent variables, such as the RPE use variables. However, the qualitative results are similar to those of the OLS models. All models are significant and show no indications of multicollinearity 29 . 28This matrix excludes the sector controls. Because these controls are nominal variables, a Pearson correlation would not be appropriate. 29Variance Inflation Factors in the models are below 2.5.
42 CHAPTER 2. RPE AT THE BUSINESS UNIT MANAGER LEVEL 2.4.3.1 Analyses with RPE-Use Measure First, I test the model with RPE-Use, the broad measure of RPE that captures both implicit and explicit influence of peer performance on the performance evaluation. I find partial support for the hypotheses, as shown in table 2.11 30 . The OLS estimation shows that the model is significant and fits the data, albeit marginally. The reported R 2 is 7.5% (Adjusted R 2 = 3.1%). This model’s ANOVA F-statistic is 1.718 (p = 0.063). As an additional analysis, a subsample of for-profit business units is analysed. The results are presented in appendix B at the end of this chapter on page 57. Potentially because of the limited size of the for-profit subsample, the resulting model is not significant (N = 224, ANOVA F-statistic is 1.461, p = 0.148). Table 2.11: Results of OLS Regression Analysis (RPE-Use) Coefficient Std. Error t-Stat. Prob. Constant 3.445 0.715 4.816 0.00 Common uncertainty (H1) 0.358 0.269 1.333 0.09a Interaction information asymmetry * comparability (H2) 0.126 0.096 1.332 0.09a Interaction uncertainty * information -0.115 0.110 -1.070 0.14a asymmetry (H3) Information asymmetry 0.073 0.071 1.032 0.30 Comparability -1.771 0.961 -1.841 0.06 Contractibility 0.049 0.021 2.287 0.02 Firm-Level Measures -0.919 0.349 -2.629 0.00 Size of BU 0.025 0.032 0.800 0.42 Size of firm -0.003 0.029 -0.121 0.90 Dummy production BU 0.122 0.194 0.626 0.53 Dummy financial services BU 0.071 0.154 0.460 0.64 Dummy not-for-profit BU 0.078 0.205 0.382 0.70 R 2 = 0.075 F-statistic = 1.718 Adjusted R 2 = 0.031 Prob(F-statistic) = 0.063 Included observations: 267 ‘a’ : variable based on directional hypothesis significance calculated as one-tailed p-value Both the effects of common uncertainty (hypothesis H1) and the interaction between information asymmetry and comparability of the business unit (hypothesis H2) hold in this model, although the effects are only marginally significant at t-values of 1.333 and 1.332, respectively. The combined effect of uncertainty and information asymmetry (H3) is not supported by the analysis. 30 The presented p-values in tables 2.11 & 2.13 are one-tailed if the underlying hypothesis is directional. The one-tailed findings are marked as ‘a’ .
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2.4. ANALYSES 41<br />
2.4.2 Correlations<br />
Table 2.10 presents the correlation matrix of all of the metric variables 28 . The correlation<br />
table shows no significant theory-consistent associations between the dependent and<br />
independent variables. However, some significant correlations among the independent variables<br />
exist. I find a significant correlation between the two size variables (i.e., business unit<br />
size and firm size). This finding might raise concerns regarding multicollinearity, because<br />
the Pearson correlation coefficient is 0.479. However, as discussed in the next paragraph<br />
(2.4.3), the multivariate models do not suffer from multicollinearity.<br />
Table 2.10: Pearson Correlation Table<br />
1. 2. 3. 4. 5. 6. 7.<br />
1. RPE-Use<br />
2. RPE-based-Targets .519***<br />
3. Common Uncertainty -.040 .004<br />
4. Information Asym. .004 .014 -.039<br />
5. Contractibility .205* .144** -.005 -.019<br />
6. Firm-level Measures -.057 -.010 -.031 .086 -.064<br />
7. <strong>Business</strong> Unit Size .002 .023 .068 .057 -.025 .066<br />
8. Firm Size .039 .042 -.052 .135** .118* -.040 .479***<br />
*** Correlation is significant at the 0.01 level (2-tailed).<br />
** Correlation is significant at the 0.05 level (2-tailed).<br />
* Correlation is significant at the 0.10 level (2-tailed).<br />
Listwise N=244<br />
2.4.3 Multivariate Analyses<br />
This subsection presents the ordinary least squares regression analyses for the two dependent<br />
variables (RPE-Use and RPE-based-Targets). Additionally, Tobit and Logit regression<br />
analyses are included as additional analyses to improve and buttress the findings.<br />
The Tobit models have a slightly better fit because Tobit was designed to estimate limited<br />
dependent variables, such as the RPE use variables. However, the qualitative results are<br />
similar to those of the OLS models. All models are significant and show no indications of<br />
multicollinearity 29 .<br />
28This matrix excludes the sector controls. Because these controls are nominal variables, a Pearson<br />
correlation would not be appropriate.<br />
29Variance Inflation Factors in the models are below 2.5.
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