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

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).