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4.4. ANALYSES 103 Table 4.8: Items for Business Unit Size & Firm Size (Q31-32) Item description Component loadings a. Business unit FTE (Ln) 0.866 b. Business unit revenue (Ln) 0.866 Percentage variance explained 75.0% Cronbach’s alpha 0.634 c. Firm FTE (Ln) 0.952 d. Firm revenue (Ln) 0.952 Percentage variance explained 90.6% Cronbach’s alpha 0.892 Table 4.9: Descriptive Statistics Mean Min Max Std. Dev. SkewnessKurtosis RFMO 2.64 1.00 7.00 1.21 0.73 -0.09 RPE-Use 3.37 1.00 5.00 1.00 -0.85 0.31 Information asymmetry 5.29 2.00 7.00 0.91 -0.61 0.57 Goal ambiguity 2.34 1.00 5.00 0.57 0.43 1.19 Measurability 4.48 2.00 6.00 0.69 -0.45 0.38 Decentralization 4.31 1.00 7.00 1.13 -0.12 -0.02 Business unit size 10.27 2.30 14.97 2.26 -1.56 2.70 Firm size 13.41 3.69 19.41 2.43 -0.44 1.34 as presented in table 4.10. The table shows an insignificant Levene’s test for homogeneity of variances, which tests whether the variance in RFMO scores is the same for each of the four sectors. An insignificant Levene’s test indicates that the assumption of homogeneity of variance is not violated. 4.4 Analyses This section describes the analyses. Here I discuss the bivariate correlations and the multivariate analyses. I also analyse the opportunism-mitigating effect of RPE by performing OLS regression analyses. In addition to the opportunism-mitigating effect of RPE, I include a set of factors driving opportunism (i.e., information asymmetry, goal ambiguity, measurability of outputs, and decentralization of decision rights), dummies to control for the sector in which the business unit operates, and size control variables. This results in the statistical model shown in figure 4.3.
104 CHAPTER 4. OPPORTUNISM MITIGATION EFFECTIVENESS Table 4.10: One-way ANOVA of RFMO-levels across sectors ANOVA between groups ANOVA F-statistic 1.706 Prob. 0.17 Levene’s test for homogeneity of variances Levene’s statistic 0.897 Prob. 0.44 Table 4.11: Pearson Correlation Table 1. 2. 3. 4. 5. 1. RFMO 2. RPE-Use .063** 3. Information asymmetry .043 -.026 4. Goal ambiguity .244*** -.081 -.202*** 5. Measurability -.278*** .165** .242*** .586*** 6. Decentralization .053 -.016 .342*** -.176*** .173*** 4.4.1 Correlations *** 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=274 Table 4.11 presents the correlation matrix of the variables. The correlation table shows several significant associations between the dependent and independent variables. Contrary to my expectations, RPE-Use is positively correlated with RFMO 10 . Additionally, significant correlations exist between RFMO and goal ambiguity (positive) as well as between RFMO and measurability of outputs (negative). The latter two relations are theory-consistent. Furthermore, a strong and significant correlation between goal ambiguity and measurability of outputs exists. However, this correlation does not cause multi-collinearity, as shown in the next subsection. No significant relations exist between RFMO and information asymmetry, nor between RFMO and decentralization. 10 In the conclusions and discussion section of this chapter, I seek an explanation for the positive relation between RFMO and RPE-Use.
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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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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
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2.3. SAMPLE AND MEASUREMENT 35 Emph
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2.4. ANALYSES 37 2.4 Analyses This
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2.4. ANALYSES 39 Table 2.8: RPE use
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2.4. ANALYSES 41 2.4.2 Correlations
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2.4. ANALYSES 43 Concerning the con
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2.4. ANALYSES 45 Table 2.13: Result
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2.4. ANALYSES 47 Table 2.15: Summar
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2.5. CONCLUSIONS AND DISCUSSION 49
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2.5. CONCLUSIONS AND DISCUSSION 51
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Page 72 and 73: 3.2. DEVELOPMENT OF THE MODEL 63 3.
Page 74 and 75: 3.2. DEVELOPMENT OF THE MODEL 65 RP
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Page 90 and 91: 3.4. ANALYSES 81 Table 3.11: Result
Page 92 and 93: 3.4. ANALYSES 83 Larcker & Rusticus
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Page 96 and 97: Chapter 4 Does Relative Performance
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Page 118 and 119: 4.4. ANALYSES 109 RPE and RFMO. Mor
Page 120 and 121: 4.4. ANALYSES 111 room for manageri
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Page 128 and 129: 5.1. SUMMARY, CONCLUSIONS AND DISCU
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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
Page 144 and 145: Q23 Please indicate whether you agr
Page 146 and 147: Noise Q34 NOISE (primary measure) P
Page 148 and 149: References 1. Abernethy, M.A., Bouw
Page 150 and 151: 26. Diamantopoulos, A. & Siguaw, J.
Page 152 and 153: 52. Himmelberg, C.P. & Hubbard R.G.
Page 154: 83. Towry, K. 2003. ‘Control in a
Page 157 and 158: 148 beïnvloeden, veroorzaken ruis
Page 160 and 161: Biography Hilco van Elten (1979) ho
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