Essays on supplier responsiveness and buyer firm value - Nyenrode ...

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4.5.3 Normality of the Data We checked for skewness and kurtosis to determine whether the data were normally distributed in Chapter 2. For details on items skewness and kurtosis please refer to Appendix B. 4.5.4 Data Analysis We followed a two-step model for comparison with the model in Chapter 2. Therefore, we tried to emulate the methodology of the Chapter 2 model as far as possible. Below, we discuss the measurement model that was our first step and then discuss the structural model. 4.5.5 Measurement Model For the measurement model, we used the seven steps specified by Hair et al. (2006). First, we specified the model, then we identified the model, next we estimated the model, later we evaluated it, and finally, we respecified the model. Our specified model was a replica of the model in Chapter 2 using its scale. We used AMOS version 17 for our analysis. The four latent variables in the measurement model are supplier responsiveness, IdRR, buyer satisfaction, supplier brand value. We used all the items of the original model in Chapter 2. The items were later on reduced to 9 items following the instructions of the AMOS modification indices to improve the fit statistics. We achieved a good model fit and identification with 9 items, 45 distinct sample moments, 25 distinct sample parameters, and hence 20 degrees of freedom. Our sample size of 87 is above of the bare minimum of 50 needed to estimate a maximum likelihood model (MLE) (Hair et al., 2006). However, because of persistent Heywood cases that is a common problem when small sample sizes are used with MLE, we switched to generalized least squares for the confirmatory models estimation (Lisrel, 2012). We also used the options of fitting the saturated and independence models, minimization history, standardized estimates, and squared multiple correlations. Another criterion is that the squared average variance or standardized regression weights should be above 0.5 and preferably 0.7. All our measures as reflected in Table 4.2 fulfill this criteria. Examining the standardized regression weights of the CFA model closely, we find that none is very close to one. This implies that multicollinearity is not a problem with our data. The highest standardized regression weight is 0.849. From Table 4.3, we can observe that all the average variance extracted is above the 50% mark, which means that it is acceptable. 102
Table 4.2 Standardized Regression Weights for CFA Latent Variable Items Estimate Strategic Supplier Responsiveness CF 0.736 SIQ 0.846 CustNPD 0.866 IdRR IM 0.849 CCC 0.739 Supplier Brand Value AssBrand 0.812 RP 0.758 Buyer Satisfaction Sales 0.804 Fair 0.772 Table 4.3 Average Variance Extracted Item Squared multiple correlations CF 0.542 SIQ 0.715 CustNPD 0.750 Average Variance Extracted 66.90 % IM 0.721 CCC 0.546 Average Variance Extracted 63.35 % AssBrand 0.659 RP 0.574 Average Variance Extracted 61.65 % Fair Sales 0.596 0.646 Average Variance Extracted 62.12 % In Table 4.4, the construct reliabilities are presented. All are above the 0.7 Alpha Chronbach value recommended by Hair et al. (2006). Table 4.4 Construct Reliability Construct Construct Reliability Supplier Responsiveness .857 IdRR .774 Supplier Brand Value .762 Buyer Satisfaction .766 Our chi square statistic is 2 = 24.34, p>.228 with 20 degrees of freedom. The 2 to degrees of freedom ration is 1.2. Our chi square results are within the acceptable range as delineated by Hair et al. (2006). The model has a GFI value of 0.937, which is considered good. The RMSEA value is good at 0.050. The CFI value is acceptable at 0.906, and so is the IFI value at 0.930. 103
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ISBN 978-90-8980-055-8 © M. Abrahi
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Examination Committee Supervisor: P
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2.3 DEVELOPMENT OF THE CONCEPTUAL M
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5.1 RESEARCH FINDINGS 114 5.2 OVERA
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List of Tables Table 2.1 Representa
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PREFACE Oscar Wilde once said, “I
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Chapter 1 Introduction to the Thesi
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Figure 1.1 Mckinsey Survey Results
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1.4 Scope PhD thesis This thesis fo
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Netherlands. We used the maximum li
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2.1 Introduction A central tenant o
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only provide an assessment of the s
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Table 2.1 highlights several promin
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other hand, they later argue that m
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Therefore, there is a convergence i
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usiness performance, firms need to
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means is that the more better a str
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Supplier responsiveness or market o
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Naryandas, 1995). Further, idiosync
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They might also include the ease wi
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they ask about intimate supplier in
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Table 2.2 Modified Items of the MAR
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Table 2.2 Modified Items of the MAR
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market intelligence, offering flexi
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25. If the supplier were to find ou
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33.Does your strategic supplier als
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41.To what degree do those specific
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Supplier Brand Value 51.How likely
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Appendix B Scatter Plot Diagrams fo
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