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

Journal of Business Logistics, Vol. 28, No. 2, 2007 121TABLE 5MEAN SCORES OF IDEAL AND AVERAGE QUALITY PROFILE VARIABLESQuality CharacteristicsIdealProfile ScoresAverageProfile ScoresDifference inMean Scores for Idealand Average CustomersMean Std. Dev. Mean Std. Dev. Mean SignificanceSemi Extended StrategyService Quality 6.77 0.54 6.25 0.82 0.52 p < 0.01Product Quality 6.53 0.56 5.74 1.01 0.79 p < 0.01eBusiness Quality 6.73 0.48 5.92 1.07 0.81 p < 0.01Fully Extended StrategyService Quality 6.82 0.30 6.01 0.95 0.81 p < 0.01Product Quality 6.41 0.78 5.23 1.14 1.18 p < 0.01eBusiness Quality 6.60 0.69 5.82 1.14 0.78 p < 0.01De-Coupled StrategyService Quality 6.57 0.62 5.77 1.04 0.80 p < 0.01Product Quality 6.22 0.64 5.30 0.94 0.92 p < 0.01eBusiness Quality 6.69 0.68 6.34 0.76 0.35 p < 0.05Centralized Extended StrategyService Quality 6.96 0.07 6.15 0.76 0.81 p < 0.01Product Quality 6.55 0.46 5.05 0.94 1.50 p < 0.01eBusiness Quality 6.79 0.51 5.95 0.94 0.84 p < 0.01To examine whether our ideal profiles provide strong explanatory power, we also examinedalternative “non-ideal” models, where the “average customers” (those cases at the median on therepurchase intention scale) at each firm were used as the benchmark for profile deviation scores foreach operational logistics strategy. In all four cases, the results support the notion that calibratingideal quality profiles within strategy type produces stronger profile deviation coefficients (Cohen etal. 2003) and greater explanatory power (Chow 1960) than the average benchmarks (note that theideal profiles also achieve higher mean scores than the average profiles; see Table 5). For all models,the Variance Inflation Factors were lower than 1.80, indicating that multicollinearity does not inhibitthe analysis (Mason and Perreault 1991).H 1-4predicted that the more similar the quality profile is to that of the ideal profile for its operationallogistics strategy type, the greater is its outcome (measured as customers’ behavioral repurchaseintentions). Table 4 shows that, using OLS regression, the results reveal a significant, negativeeffect of the quality ideal profile for semi extended (β = -0.26, p < 0.01, Adjusted R 2 = 0.32), fullyextended (β = -0.041, p < 0.01, Adjusted R 2 = 0.41), de-coupled (β = -0.43, p < 0.05, Adjusted R 2 =0.37), and centralized extended (β = -0.41, p < 0.01, Adjusted R 2 = 0.37) strategy types with repurchaseintentions as the criterion variable. Thus, all four hypotheses were supported. Additionally, sacrifice(SAC), as a control variable, was significant in all four equations. However, the control variableof online accessibility (OA) was significant in only the centralized-extended strategy model, whileattitude toward Internet ordering (ATT) did not affect repurchase intentions in the four equations.33