The following Table 3.4 provides the data analysis procedure, which was based largely on the work of Arnold and Reynolds (2003) and Duman (2002).
Table 3.4 Procedure of Data Analysis
Exploration of loadings; removal of items with low loadings and high cross-loadings of retail store image and salesperson likeability.
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B. Across contexts: testing the relative importance of pre-satisfaction on purchase decision
C. Across groups of buyers: testing the moderating effect of salesperson likeability on relationship
6 Presentation of results Discussion of findings
First, item analysis was performed to describe the sample characteristics, investigate the item means and assess item-to-total correlations. Second, exploratory factor
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analysis was performed to explore whether the items load highly on their intended latent construct and have low cross-loadings. After the exploratory factor analysis, the reliability of the underlying factors was discussed in terms of Cronbach’s alphas.
Third, confirmatory analysis (CFA) was performed to ensure that the constructs are valid and reliable; this refers to the measurement part of the model. Many SEM researchers argue that the measurement model should be established before one can assess structural relationships (Anderson & Gerbing 1988; Steenkamp & Baumgartner 2000). Consequently, CFAs without any structural relationships are performed with AMOS 7.0 to check whether the items meet the criteria for convergent and discriminant validity, as well as construct reliability. In this phase, the presence of multicollinearity was also investigated through regression and correlation analysis.
The regression analyses were performed by using SPSS 15.0, whereas correlations are derived through AMOS 7.0. Fourth, prior to testing measurement invariance, it was customary to establish the baseline models separately for each group under study (Byrne 2001). These baseline models were used also to test the hypotheses.
Multiple group confirmatory analysis was then performed to check whether the items used are equivalent (invariant) across contexts. SEM researchers argue that analyses of the differences between structural relationships can only be meaningful when the items measure the same thing and to the same degree in each context (Byrne 2001;
Steenkamp & Baumgartner 2000); therefore, the establishment of measurement invariance across contexts was a logical prerequisite for testing the structural parameter estimates; i.e., structural invariance (Vandenberg & Lance 2000). In the study, invariance tests were conducted in order to investigate whether the relative importance of the antecedents varies between contexts, and between buyers. The investigation first tested whether certain factors had a more (or less) pronounced effect in either context. Then, it was investigated whether there were differences between salesperson likeability and retail store image in creating relationship orientation in the construction of purchase decisions. In doing so, it was determined whether or not salesperson likeability had a moderating effect on the relationships or retail store image. In the fifth stage, the same procedure outlined for the base model was followed for the extended model. Baseline models were used to test the hypotheses regarding trust, commitment and involvement.
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After the establishment of measurement invariance, results were examined to determine whether pre-purchase decisions strongly improve the model in totality.
Finally, an overview was presented to highlight the main findings.
The research procedure for the second study was somewhat shorter, as it was limited to the base model; the aim was to test the relationships within the perceived value framework rather than to replicate the rather complex extended model. Thus, the second study was used to test whether the relationships found in the first study were replicated. Structural invariance tests were performed, then, to check whether certain factors play a more profound role in either context. Finally, the second study was used to investigate the moderating influence of salesperson likeability on relationship orientation and, in turn, purchase decision.
Factor analysis: Since there are many items in both these questions, a factor analysis was undertaken to reduce the weight and come up with more important factors. The principal components factor analysis was performed with a varimax and orthogonal rotation. The latent root criteria (i.e., with only eigen values greater than 1.0 considered) indicated three factor structures. In addition, to minimise the cross-loadings in the factor matrix, items with cross-loadings of 0.30 or higher on two or more factors were eliminated (Hair et al. 1992). A reliability analysis (i.e., an examination of coefficient alphas) indicated when an additional item should be dropped from the scale. The final factor structure consisted of eight items retained across three dimensions. These dimensions followed previous categorisations of retail store image.
The two dimensions were labelled as 1) store image in general (SRI) and 2) store offerings (ORI). Factor analysis was done on retail store image on factors like store image in general (SRI)—such as its appearance Q4.1), physical condition (SRI-Q4.2), use of informative signage inside the store (SRI-Q4.4) and attractive and meaningful in-store point-of-sale promotions (SRI-Q4.21). The alpha Cronbach’s scoring for this factor analysis was α = 0.758 and the average mean was χ2 = 6.737.
The second factor derived from the principle component analysis was store offerings (ORI). This was extracted from questions such as whether the retailer offers good in-store service (ORI-Q4.6), excellent service (ORI-Q4.16), a good product profile and a versatile product offerings range Q4.17), always has good price deals (ORI-Q4.18) and, finally, an excellent warranty policy (ORI-Q4.22). According to
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respondents, other items in the scale were not as important and could not differentiate substantially; therefore, other items in the scale were deleted and the factor analysis resulted in α = 0.825. The analysis of this scale means respondents were concerned about product offerings, warranties, physical condition of the store, store appearance and after-sales service, whereas they don’t bother about other factors like convenience, advertising, reputation and reliability. The reason for getting this sort of result was that computer stores are termed ‘technical offering’ stores, where service, product offering, warranties, in-store promotions and store service matter a lot more than factors which might be important in consumer durable stores, such as grocery stores.