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3.4. ANALYSES 75 3.4 Analyses This section presents the analyses. I analyse the noise-reducing effect of RPE with structural equation modelling (SEM). I use the structural model estimation of SEM to be able to estimate the indirect relation between environmental uncertainty and noise in the performance evaluation, via RPE use. In addition to the aforementioned noise-reducing effect of RPE use and the effect of environmental uncertainty, via RPE use, on noise, I include the noise-drivers and control variables for size and industry. The result is the statistical model shown in figure 3.3. 3.4.1 Correlations Table 3.9 presents the correlation matrix of all of the included variables. The table shows three significant correlations between the dependent and independent variables. Noise is negatively correlated with RPE-Use. Additionally, the correlations among noise and goal ambiguity, and noise and measurability of outputs are significant. The latter two correlations are consistent with the hypothesized directions. Furthermore, several significant correlations among the independent variables exist. RPE-Use is positively correlated with environmental uncertainty, and measurability of outputs. Additionally, I find significant correlations between the measures of goal ambiguity and measurability of outputs, as well as between the measures for business unit size and firm size. However, these correlations are not strong. 3.4.2 Multivariate Analyses To test the noise-reducing effect of RPE, I analyse the model that is presented in figure 3.3. In addition to the expected paths, as presented in the causal model (figure 3.2 on page 66), I also include the control variables for size and business unit sector in the model. I use maximum likelihood estimation in AMOS 18 to estimate the structural model. Due to sample size limitations and to reduce the model complexity, I do not estimate the measurement model and the structural model simultaneously. The measurement is determined outside the model, using principal component analysis and scale reliability analysis, as presented in section 3.3.3. I run two analyses. First, the model is tested with the RPE-Use measure. This model is referred to as the base model. Second, an alternative measure for RPE use is used as a robustness check. Both models fit the data and support the noise-reduction hypothesis (i.e., RPE use reduces the amount of noise in the performance evaluation). The results of these analyses are discussed in section 3.4.2.1. ‘Analyses with RPE-Use Measure’ and section 3.4.2.2. ‘Analyses with RPE-based-Targets Measure’.
76 CHAPTER 3. NOISE REDUCTION EFFECTIVENESS RPE use ✻ Positive Environmental Uncertainty - Goal Ambiguity - Measurability of Outputs - Emphasis on Personal Level Performance Measures - Emphasis on Disaggregate Performance Measures Size & Sector Figure 3.3: Statistical Model Negative (H1) Positive 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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Page 40 and 41: 2.3. SAMPLE AND MEASUREMENT 31 Comp
Page 42 and 43: 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
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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 80 and 81: 3.3. SAMPLE AND MEASUREMENT 71 Tabl
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Page 86 and 87: 3.4. ANALYSES 77 Table 3.9: Pearson
Page 92 and 93: 3.4. ANALYSES 83 Larcker & Rusticus
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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
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Page 110 and 111: 4.3. SAMPLE AND MEASUREMENT 101 Tab
Page 112 and 113: 4.4. ANALYSES 103 Table 4.8: Items
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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
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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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