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2.6. CONTRACTIBILITY MIMIC MODEL (APPENDIX A) 55 Overall, the MIMIC model fits the data. All of the formative and reflective indicators are significantly different from zero at a confidence level of 5%, as displayed in table 2.17 - Panel A. This finding implies that all of the indicators need to be included in the index. Table 2.17 also provides goodness-of-fit statistics for the model. To assess the fit of a SEM, one must assess multiple measures (Kline 2005). As the indicators of model fit, I use the chi 2 , the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI), the root mean square residual (RMR) and the root mean square error of approximation (RMSEA). Kline (2005) suggests that the following values indicate a good fit: an insignificant chi 2 ; a GFI, AGFI, and CFI greater than 0.90; a RMR close to 0; and an RMSEA less than 0.045. As shown in table 2.17 - Panel B, the indicators show adequate model fit. Table 2.17: Results of MIMIC Analysis Panel A - Contractibility MIMIC Coefficient Std. Error Critical Ratio Prob. Goal clarity > Contractibility 1.000 Measurability > Contractibility 0.892 0.370 2.413 0.02 of outputs Transformation process Contractibility > > Contractibility 0.431 0.212 2.027 0.04 Difficulty to assess work 0.238 0.058 4.129 0.00 Contractibility > Agreement output quality 0.184 0.046 4.027 0.00 Contractibility > Job manceperfor- 0.193 0.052 3.697 0.00 ‘a’ : variable based on directional expectation; significance calculated as onetailed p-value Panel B - Goodness-of-Fit Statistics Contractibility MIMIC Chi 2 = 6.064 goodness-of-fit statistics Degrees of freedom = 6 GFI = 0.994 Probability level = .416 AGFI = 0.978 CFI = 1.000 Multivariate normality RMR = 0.011 Kurtosis 2.046 (c.r. 1.862) RMSEA = 0.006
56 CHAPTER 2. RPE AT THE BUSINESS UNIT MANAGER LEVEL To estimate contractibility, I calculate the factor score weights in AMOS. Table 2.18 gives these weights for predicting the unobserved variable from the (formative and reflective) observed variables, with which contractibility is calculated. Table 2.18: Weights for Construction of Contractibility Measure Goal clarity .585 Measurability of outputs .522 Knowledge of transformation process .252 Difficulty to reach agreement output quality (reversed) .807 Difficulty to assess work (reversed) .862 Job performance .319 Based on the estimated weights, contractibility is calculated using the formula presented below. Contractibility is added to the analyses in section 4 of this chapter. Contractibility = 0.585 * goal clarity + 0.522 * measureability of outputs + 0.252 * knowledge of transformation process + 0.807 * difficulty to reach agreement output quality + 0.862 * difficulty to assess work + 0.319 * job performance
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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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Page 22 and 23: Chapter 2 The Use of Relative Perfo
Page 24 and 25: 2.1. INTRODUCTION 15 is not a promi
Page 26 and 27: 2.2. THEORETICAL BACKGROUND 17 The
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Page 30 and 31: 2.2. THEORETICAL BACKGROUND 21 who
Page 32 and 33: 2.2. THEORETICAL BACKGROUND 23 suff
Page 34 and 35: 2.3. SAMPLE AND MEASUREMENT 25 2.2.
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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
Page 62 and 63: 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
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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
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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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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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