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

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