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3.5. CONCLUSIONS AND DISCUSSION 85 3.4.3 Summary of the Findings The analyses show robust support for the main claim of RPE theory (i.e., RPE reduces the amount of noise in the performance evaluation). This finding is robust for two operationalizations of RPE that capture various applications of RPE in practice. The two RPE use measures include situations in which peer performance functions as an implicit standard when appraising the performance of the business unit manager as well as situations in which peer performance directly influences the difficulty of the performance target. Regardless of the operationalization of RPE use, I find that RPE affects the amount of noise in the performance evaluation. When RPE use increases, noise is reduced. Additionally, the analyses show the effects of a variety of factors on noise. For example, goal ambiguity increases the amount of noise in the performance evaluation, whereas increased measurability of the outputs of the BU and the emphasis on personal and disaggregated performance measures reduce the amount of noise. The analyses show no direct effect of environmental uncertainty on the amount of noise in the performance evaluation. 3.5 Conclusions and Discussion This chapter studies the noise-reduction claim that underlies RPE theory. Noise reduction is a primary explanation of RPE use in practice (see, for examples, Holmstrom 1982, Gibbons & Murphy 1990, Matsumara & Shin 2006, Albuquerque 2009, Gong et al. 2011, and chapter 2 of this thesis). Noise results from the inclusion of windfall gains and losses in the performance evaluation of the employee. By reducing noise in the performance evaluation, RPE use can benefit organizations. Noise reduction improves the contractual efficiency between the owner and the employee by improving the performance evaluation. According to RPE theory, RPE can restore the quality of the performance evaluation by filtering out the noise from the employee’s measured performance (Holmstrom 1982, Gibbons & Murphy 1990). RPE reduces noise by incorporating information about the attainable performance level into the evaluatee’s performance evaluation via peer comparison (Holmstrom 1982). RPE reduces noise by incorporating information about the attainable performance level into the evaluated employee’s performance evaluation via peer comparison (Holmstrom 1982). RPE compares the employee’s actual performance with the performance of a reference group that partially faces the same external events. The performance of the reference group informs the principal about the performance level that should be attainable for the agent under the external events that have occurred. The comparison with peer performance constructs a performance norm that is (at least partially) insulated from the impact of external events. The noise-reduction effect of RPE finds support within both the analytical and empirical literature. However, the support for this relationship is based on rather indirect analyses,
86 CHAPTER 3. NOISE REDUCTION EFFECTIVENESS leaving ample room for rival explanations. In chapter 2 of this thesis, the support for the noise-reduction explanation is based on the empirically significant relation between environmental uncertainty (i.e., a driver for noise) and the extent to which organizations rely on RPE. However, other explanations in the literature explain the relation between environmental uncertainty and RPE. For example, Holmstrom (1982) argues that RPE can be used as a means to pit employees or organizational parts against each other to create a sense of internal rivalry that increases the efficiency of the individual employees. Additionally, Gibbons & Murphy (1990:33-S) show that RPE is used to facilitate organizational learning. Both of the presented applications can be especially useful in a highly uncertain environment, where environmental turbulence demands that a firm exhibits its optimal performance to survive. As a result, the analyses in chapter 2 cannot empirically distinguish between the noise-reduction explanation and the two alternative explanations. To provide a more direct analysis of the noise-reduction explanation of RPE theory, this chapter analyses the effectiveness of the noise-reduction properties of RPE. This analysis assesses whether organizational reliance on RPE actually reduces the noise levels in the performance evaluation of employees. For the empirical analysis, I use a SEM model to analyse survey data of 325 business unit managers in the Netherlands. Overall, the results are consistent with the findings of the analytical agency literature and the conclusions of chapter 2. The analyses support the main claim of RPE theory (i.e., RPE can reduce the amount of noise in an agent’s performance evaluation). This finding is important because it further validates the noise-reduction explanation on RPE. The evidence for the noise-reduction claim is robust over two operationalizations of RPE use. Similar to chapter 2, this chapter relies on both a broad, unspecific measure of RPE use and on a measure that focuses on the explicit application of RPE to performance targets. Both measures yield qualitatively and quantitatively similar results. The SEM models explain 11-13% of the variance in the dependent variable ‘noise in the performance evaluation’. Additionally, both models indicate an adequate fit on a number of goodnessof-fit indicators, as suggested by Kline (2005). However, the models do have important limitations. These limitations lie in the measurement of the dependent variable. Firstly, the dependent variable noise in the performance evaluation is measured with a single item construct. Using single-item measures increases measurement error in the estimation and potentially reduces the validity of the model and the findings. Although pre-testing and robustness checks support the convergent validity of the single-item construct, the use of single-item constructs remains less than perfect. Secondly, the responses on the noise-measure may be biased to the extent that weaker performance managers may attribute negative performance to exogenous factors more than high performing managers. This potential bias may introduce some noise in my noise measure. Despite these limitations, I argue that the current study furthers our confidence in the cornerstone explanation of RPE theory. Because of more direct evidence on the implied causal mechanism underlying RPE theory, we can be more certain about the effectiveness of RPE in reducing noise in the performance evaluation.
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Relative Performance Evaluation in
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NYENRODE BUSINESS UNIVERSITEIT Rela
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Table of Contents Preface 1 General
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Preface Why do people make things h
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2 CHAPTER 1. GENERAL INTRODUCTION I
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4 CHAPTER 1. GENERAL INTRODUCTION o
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6 CHAPTER 1. GENERAL INTRODUCTION T
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8 CHAPTER 1. GENERAL INTRODUCTION F
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10 CHAPTER 1. GENERAL INTRODUCTION
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Chapter 2 The Use of Relative Perfo
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2.1. INTRODUCTION 15 is not a promi
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2.2. THEORETICAL BACKGROUND 17 The
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2.2. THEORETICAL BACKGROUND 19 Sinc
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2.2. THEORETICAL BACKGROUND 21 who
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2.2. THEORETICAL BACKGROUND 23 suff
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2.3. SAMPLE AND MEASUREMENT 25 2.2.
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2.3. SAMPLE AND MEASUREMENT 27 2.3.
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2.3. SAMPLE AND MEASUREMENT 29 can
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2.3. SAMPLE AND MEASUREMENT 31 Comp
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2.3. SAMPLE AND MEASUREMENT 33 this
Page 44 and 45: 2.3. SAMPLE AND MEASUREMENT 35 Emph
Page 46 and 47: 2.4. ANALYSES 37 2.4 Analyses This
Page 48 and 49: 2.4. ANALYSES 39 Table 2.8: RPE use
Page 50 and 51: 2.4. ANALYSES 41 2.4.2 Correlations
Page 52 and 53: 2.4. ANALYSES 43 Concerning the con
Page 54 and 55: 2.4. ANALYSES 45 Table 2.13: Result
Page 56 and 57: 2.4. ANALYSES 47 Table 2.15: Summar
Page 58 and 59: 2.5. CONCLUSIONS AND DISCUSSION 49
Page 62 and 63: 2.6. CONTRACTIBILITY MIMIC MODEL (A
Page 64 and 65: 2.6. CONTRACTIBILITY MIMIC MODEL (A
Page 66 and 67: 2.7. ANALYSES FOR FOR-PROFIT BUS (A
Page 68 and 69: Chapter 3 Does Relative Performance
Page 70 and 71: 3.1. INTRODUCTION 61 Holmstrom’s
Page 72 and 73: 3.2. DEVELOPMENT OF THE MODEL 63 3.
Page 74 and 75: 3.2. DEVELOPMENT OF THE MODEL 65 RP
Page 76 and 77: 3.3. SAMPLE AND MEASUREMENT 67 3.3
Page 78 and 79: 3.3. SAMPLE AND MEASUREMENT 69 3.3.
Page 80 and 81: 3.3. SAMPLE AND MEASUREMENT 71 Tabl
Page 82 and 83: 3.3. SAMPLE AND MEASUREMENT 73 Tabl
Page 84 and 85: 3.4. ANALYSES 75 3.4 Analyses This
Page 86 and 87: 3.4. ANALYSES 77 Table 3.9: Pearson
Page 92 and 93: 3.4. ANALYSES 83 Larcker & Rusticus
Page 96 and 97: Chapter 4 Does Relative Performance
Page 98 and 99: 4.1. INTRODUCTION 89 Moreover, chap
Page 100 and 101: 4.2. DEVELOPMENT OF THE MODEL 91 is
Page 102 and 103: 4.2. DEVELOPMENT OF THE MODEL 93 Sp
Page 104 and 105: 4.3. SAMPLE AND MEASUREMENT 95 4.3
Page 106 and 107: 4.3. SAMPLE AND MEASUREMENT 97 To i
Page 108 and 109: 4.3. SAMPLE AND MEASUREMENT 99 of R
Page 110 and 111: 4.3. SAMPLE AND MEASUREMENT 101 Tab
Page 112 and 113: 4.4. ANALYSES 103 Table 4.8: Items
Page 114 and 115: 4.4. ANALYSES 105 RPE use ✻ Posit
Page 116 and 117: 4.4. ANALYSES 107 The model does sh
Page 118 and 119: 4.4. ANALYSES 109 RPE and RFMO. Mor
Page 120 and 121: 4.4. ANALYSES 111 room for manageri
Page 126 and 127: Chapter 5 Conclusion 5.1 Summary, C
Page 128 and 129: 5.1. SUMMARY, CONCLUSIONS AND DISCU
Page 134 and 135: 5.2. IMPLICATIONS FOR PRACTICE 125
Page 136 and 137: 5.3. LIMITATIONS AND SUGGESTIONS FO
Page 138 and 139: Appendix Overview of Questionnaire
Page 140 and 141: Q5 How often do the following thing
Page 142 and 143: 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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