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2.4. ANALYSES 45 Table 2.13: Results of OLS Regression Analysis (RPE-based-Targets) Coefficient Std. Error t-Stat. Prob. Constant 4.352 0.673 6.459 0.00 Common uncertainty (H1) 0.964 0.283 3.402 0.00a Interaction information asymmetry * 0.001 0.105 0.000 0.49a comparability (H2) Interaction uncertainty * information asymmetry (H3) 0.229 0.116 1.978 0.03 a Information asymmetry -0.026 0.076 -0.345 0.73 Comparability -3.851 1.023 -3.765 0.00 Contractibility 0.067 0.023 2.900 0.00 Firm-Level Measures 0.085 0.412 0.208 0.84 Size of BU 0.012 0.036 0.355 0.72 Size of firm -0.008 0.032 -0.252 0.80 Dummy production BU -0.279 0.204 -1.372 0.17 Dummy financial services BU -0.102 0.165 -0.616 0.54 Dummy not-for-profit BU -0.307 0.205 -1.502 0.13 R 2 = 0.122 F-statistic = 2.653 Adjusted R 2 = 0.076 Prob(F-statistic) = 0.002 Included observations: 242 ‘a’ : variable based on directional hypothesis significance calculated as one-tailed p-value 2.4.3.3 Logit Model Robustness Check To further buttress the findings of the base model analysis, I conduct two Logit analyses. I regress the hypothesized and control variables on dichotomized versions of both measures of RPE use. The Logit model predicts whether individual respondents are either RPE adopters or non-/low-RPE users 31 , based on their score on the hypothesized and control variables. The logic behind using a Logit regression is that a fundamental distinction may exist between high (or intensive) RPE use and no/less intensive RPE use. As presented in section 2.4.1, almost all organizations consider the performance of peers when evaluating their employees. However, relying on RPE to a greater degree may be a deliberate choice that is driven by noise- and opportunism-reduction considerations, as predicted by the theory. In contrast, relying less on RPE by merely incorporating ‘the world that’s out there’ into the background of the performance evaluation, may simply be ‘common practice’, not driven by the specific problem-solving capabilities of RPE per se. Logit regression analyses require the existence of two distinct groups and focus the analyses on this distinction. 31 The mean scores of the RPE-Use and RPE-based-Targets scales are used as cut-off values. The cases with observed RPE scores at or below the mean are considered non-/low-RPE users. Higher scores indicate a high degree of RPE adoption. Additionally, analyses are run using the medians and modes of the RPE scales. These analyses yield similar results.
46 CHAPTER 2. RPE AT THE BUSINESS UNIT MANAGER LEVEL Table 2.14: Results of Tobit Regression Analysis (RPE-based-Targets) Coefficient Std. Error t-Stat. Prob. Constant 4.197 0.683 6.146 0.00 Common uncertainty (H1) 1.016 0.297 3.423 0.01a Interaction information asymmetry * comparability (H2) -0.015 0.111 -0.144 0.44a Interaction uncertainty * information 0.261 0.122 2.129 0.02a asymmetry (H3) Information asymmetry -0.037 0.080 -0.466 0.64 Comparability -3.984 1.069 -3.725 0.00 Contractibility 0.071 0.024 2.897 0.00 Firm-Level Measures -0.073 0.433 -0.171 0.86 Size of BU -0.020 0.038 -0.510 0.61 Size of firm -0.009 0.033 -0.270 0.79 Dummy production BU 0.185 0.186 -0.998 0.31 Dummy financial services BU 0.241 0.188 1.285 0.19 Dummy not-for-profit BU -0.222 0.188 -1.176 0.24 McKelvey-Zavoina statistic = 0.135 Left censored observations: 19 Right censored observations: 0 Uncensored observations: 223 Total observations: 242 ‘a’ : variable based on directional hypothesis significance calculated as one-tailed p-value However, a drawback of this method is that it ignores a great deal of information about RPE use within these groups, which renders the analyses statistically less powerful. The Logit model that predicts RPE-adoption based on the RPE-Use measure is significant (chi 2 = 25.8, p = 0.011). A significant chi 2 indicates that the predictors have a significant effect. The model fit is also confirmed by an insignificant Hosmer & Lemeshow statistic (p = 0.593). Similar to the base model, the explained variance indicators are low. The Cox & Snell R 2 is 0.09 and the Nagelkerke R 2 is 0.121. The model correctly predicts 61.2% of the cases whereas an ‘empty’ model that contains no predictors only corrects 53.1%. This difference is a small but significant increase. The second model, which uses a dummy variable for RPE-based-Targets, also fits the data. The model is significant (chi 2 = 33,2, p = 0.001, Hosmer & Lemeshow p = 0.390). The Cox & Snell R 2 is 0.127 and the Nagelkerke R 2 is 0.172. The model correctly predicts 70.6% of the cases, whereas an ‘empty’ model that contains no predictors correctly predicts 61.6%. This finding is, again, a small but significant increase. The test statistics are summarized in table 2.15. Qualitatively, both Logit models yield results that are largely similar to those of the overall OLS findings. Similar to the OLS analysis of RPE-Use, the Logit analyses of both the
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Relative Performance Evaluation in
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4.3. SAMPLE AND MEASUREMENT 101 Tab
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4.4. ANALYSES 103 Table 4.8: Items
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4.4. ANALYSES 105 RPE use ✻ Posit
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4.4. ANALYSES 107 The model does sh
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4.4. ANALYSES 109 RPE and RFMO. Mor
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4.4. ANALYSES 111 room for manageri
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4.5. CONCLUSIONS AND DISCUSSION 113
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4.5. CONCLUSIONS AND DISCUSSION 115
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Chapter 5 Conclusion 5.1 Summary, C
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5.1. SUMMARY, CONCLUSIONS AND DISCU
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5.2. IMPLICATIONS FOR PRACTICE 125
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5.3. LIMITATIONS AND SUGGESTIONS FO
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Appendix Overview of Questionnaire
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Q5 How often do the following thing
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Q14 When serving your clients, how
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Q23 Please indicate whether you agr
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Noise Q34 NOISE (primary measure) P
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References 1. Abernethy, M.A., Bouw
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26. Diamantopoulos, A. & Siguaw, J.
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52. Himmelberg, C.P. & Hubbard R.G.
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83. Towry, K. 2003. ‘Control in a
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148 beïnvloeden, veroorzaken ruis
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Biography Hilco van Elten (1979) ho
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