Journal of Business logistics, Vol. 28, no. 2, 2007 ... - Global Initiatives

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118 hult, boyer, and ketchen, Jr.Discriminant validity was assessed by calculating the shared variance between pairs of constructsand verifying that it was lower than the average variances extracted for the individual constructs(Fornell and Larcker 1981). Shared variance was calculated as:2γ = 1 −ψwhere γ 2 = shared variance between constructs, and with the diagonal element of ψ indicatingthe amount of unexplained variance. Since η and ε were standardized, γ 2 was equal to the squaredcorrelation between the two constructs. In all but one case (i.e., the behavioral intentions scale), theaverage variances extracted were higher than 50% (cf. Fornell and Larcker 1981) (ranging from43.7% to 70.7%; see Table 2). The shared variances between pairs of all possible scale combinationsindicated that the variances extracted were higher than the associated shared variance in all cases (seeTable 2 for shared variances and Table 3 for average variances extracted).In the interest of thoroughly examining discriminant validity, we conducted one additional testof discriminant validity (Anderson 1987; Bagozzi and Phillips 1982). This test entailed analyzing allpossible pairs of constructs in a series of two-factor CFA models using LISREL 8.72. Each modelwas run twice – once constraining the φ coefficient to unity and once freeing this parameter. A χ 2 -difference test was performed on the nested models to assess if the Δχ 2 was significantly lower forthe unconstrained models (Anderson and Gerbing 1982). The critical value (Δχ 2 > 3.84) was(Δdf=1)exceeded in all cases. Thus, the seven constructs and their purified 33 indicators were found to bereliable, valid, and robust across samples.Testing for potential common method biasBefore moving on to the hypothesis testing, we conducted an examination of potential commonmethod variance problems. We used Harmon’s One-Factor test to examine common method bias(Podsakoff and Organ 1986). As such, the 33 remaining items were factor analyzed via SPSS 11.0with a principal component extraction method and varimax rotation to examine if one single factorwould emerge and/or if one general factor would account for most of the covariance in the variables.Rotated factors cannot be used to test for common method bias since the variance explained is allocatedto a particular rotated factor, not to the first and strongest factor. The unrotated solution allocatesthe most variance to the first factor, the second most to the next factor, and so on. If a largemajority of the variance is explained by the first factor it is an indication that common method biasmay be an issue. Using a varimax rotation, the factors had variances explained ranging from 4.45% to21.99%. Without rotation, the first factor explained 34.29% of the total 71.01%. Thus, our inferenceis that common method bias does not appear to be an inhibiting factor in this study.Hypothesis Testing and ResultsIn the testing of our predictions, we followed the literatures on configuration theory (Doty,Glick, and Huber 1993; Miller 1997) and profile deviation analysis (Drazin and Van de Ven 1985;Venkatraman and Prescott 1990). The data were standardized (mean-centered with a mean of zeroand a standard deviation of 1) to remove the effects of different measurement units and potentialmulticollinearity (Gresov 1989; Jaccard and Turrisi 2003).
Journal of Business Logistics, Vol. 28, No. 2, 2007 119The first step in the profile deviation analysis was to identify ideal quality-based profiles thatcould be used as the benchmark against which the fit of all profiles in the sample with operationallogistics strategy type could be examined (Doty, Glick, and Huber 1993; Vorhies and Morgan 2003).To identify the ideal profiles in each strategy type, we examined the frequencies of the outcomevariable (customers’ behavioral intentions) coupled with the general guidelines in profile deviationstudies of selecting about 10% of the customers to be included in the ideal profile (Venkatraman andPrescott 1990). For each operational logistics strategy, we selected a cut-off point within the top 10%of the customers where a significant drop-off in repurchase intentions was apparent (this resulted ina range of 16 to 70 cases being included in each ideal profile). One concern was that our tests couldbe self-fulfilling prophecies because one dataset provided both the ideal profiles and the rest of thedata. To be clear, each of the four ideal profiles was developed based on the perceptions of a firm’sbest customers. Our hypotheses in essence examine the expectation that the closer the perceptions ofa firm’s customers in general align with those held by the firm’s best customers, the stronger the repurchaseintentions of customers in general. These hypotheses did not have to be supported becauseit is possible that each firm would attract a diverse array of customers (i.e., what the marketing fieldcalls different market segments).In testing the hypotheses, we calculated the mean scores of the top customers for each operationallogistics strategy type on the three constructs of service-, product-, and eBusiness qualityto form an ideal quality profile (Venkatraman 1989; Vorhies and Morgan 2003). For the remainingcustomers, we calculated the Euclidian distance of each case from the ideal quality profile (EDQ)for its operational logistics strategy type across the three quality dimensions (Drazin and Van de Ven1985; Venkatraman 1990). The following formula was used:EDQ=N∑j( X sj− Xij2)where X sj= the score for a customer case on the jth dimension, X ij= the mean for the ideal profilealong the j th dimension, and j = the number of profile dimensions (i.e., 1, 2, and 3).These calculations result in a profile deviation score that represents the degree to which thequality profile is similar to that of the ideal profile for each strategic type. The profile deviation scorewas then regressed, using the OLS method, on repurchase intentions. We also included three controlvariables: attitude toward Internet ordering, online accessibility, and sacrifice (see the Appendix forthe scale items). For our hypotheses to be supported, the results should indicate that deviation fromthe ideal quality profile is negatively related to the outcome variable for each of the operational logisticsstrategy types (Drazin and Van de Ven 1985; Gresov 1989).Prior to testing the hypotheses, it was important to validate two assumptions regarding our strategytype conceptualization (Vorhies and Morgan 2003). First, configuration theory (Doty, Glick, andHuber 1993) coupled with research on strategy types (Conant, Mokwa, and Varadarajan 1990; Slaterand Olson 2000) assumes that any one of the operational logistics strategy types can lead to superiorrepurchase intentions among customers. As such, we examined – via analysis of variance tests withunequal cell sizes (ANOVA) – that repurchase intentions variations between cases in our dataset werenot simply a function of strategy type. The ANOVA results revealed no significant differences be-
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Journal of Business logistics, Vol. 28, no. 2, 2007 ... - Global Initiatives

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