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2.3. SAMPLE AND MEASUREMENT 33 this reason, contractibility is a formative construct 18 . Leaving one or more dimensions out (as often happens with reflective items, for example, to boost scale reliability) alters the content and meaning of the construct. Contractibility is measured as the weighted summation of goal clarity, measurability of outputs and knowledge of the transformation process. For the calculation of the weights of the individual measures, I use a MIMIC model. The MIMIC model is presented in appendix A at the end of this chapter on page 52. The remainder of the current subsection describes the contractibility measure primarily from a conceptual instead of a statistical viewpoint. The measurement of the individual underlying constructs (goal clarity, measurability of outputs and knowledge of transformation process) will be discussed next. Goal clarity is a multi-item construct, evaluated by four questionnaire items. These questions, stemming from Rainey (1983) and Kruis (2008) have been used together to measure goal ambiguity by Kruis (2008). I use these items to measure goal clarity (the exact opposite of ambiguity) by reversing the instrument and averaging the scores. The factor analysis and the Cronbach’s alpha results agree with this approach, as shown in table 2.5 - Panel A. Measurability of outputs is a construct evaluated by four items, including an item asking directly about the measurability of outputs and questions about the goal consistency and completeness of the output measures. The items load onto one factor with good scale reliability. The items and statistics are summarized in table 2.5 - Panel B. The knowledge of the transformation process variable consists of two questionnaire items, which were averaged to compute the knowledge measure. The items ask about the transparency of the transformation process for the manager and his knowledge about adjustments of the transformation process if necessary. As shown in table 2.5 - Panel C, the two items combined have insufficient scale reliability (Cronbach’s alpha 0.58) 19 . However, because the factor analysis shows high component loadings and a high level of variance explained, both items have been included in the knowledge of the transformation processvariable. Together, goal clarity, measurability of outputs and knowledge of transformation process form the necessary conditions for contractibility of performance. The measures are summed according to the weights that are calculated with the MIMIC model, which can be found in appendix A at the end of this chapter on page 52. 18 For the differences between formative and reflective indicators, and for a discussion of the consequences of these differences for model specification and measurement, see Bisbe et al. (2007), Diamantopoulos & Siguaw (2006), and Jarvis et al. (2003). 19 Hair et al. (2010:92) claim that a Cronbach’s alpha value of 0.60 is the lower limit of acceptability. Also Hair et al. (2010) argue that we need to consider multiple diagnostics to assess a measure’s internal consistency, including inter-item correlations and confirmatory factor analysis.
34 CHAPTER 2. RPE AT THE BUSINESS UNIT MANAGER LEVEL Table 2.5: Items for Contractibility Panel A - Items for Goal Clarity (Q17-20) Item description Component loadings a. Clarity of goals 0.758 b. Specificity of goals 0.734 c. Difficulty of explaining goals to out- 0.572 siders (reversed) d. Clarity of goals to insiders 0.682 Percentage variance explained 47.6% Cronbach’s alpha 0.630 a. Panel B - Items for Measurability of Outputs (Q21-23) Match between performance measures and business unit goals 0.731 b. Objective measurability of goals 0.724 c. Match between performance measures system and results 0.809 d. Goals consistency performance mea- 0.760 surement system Percentage variance explained 57.3% Cronbach’s alpha 0.744 Panel C - Items for Knowledge of the Transformation Processes (Q24) a. Transparency of the transformation process 0.839 b. Understanding of adjustments of the transformation process 0.839 Percentage variance explained 70.3% Cronbach’s alpha 0.580
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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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Chapter 5 Conclusion 5.1 Summary, C
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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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