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

114 hult, boyer, and ketchen, Jr.Non-response biasThe extrapolation procedure suggested by Armstrong and Overton (1977) was used to assesspotential non-response bias. When assessing the first quartile versus the last quartile of the respondentsin each sample group (semi extended, fully extended, de-coupled, and centralized extendedstrategies), we found no significant differences on any of the seven summated measures in the survey(i.e., service quality, product quality, eBusiness-quality, attitude toward Internet ordering, online accessibility,sacrifice, and behavioral intentions). Thus, our inference is that the data are free fromsystematic difference bias. Thus, non-response bias does not appear to be an inhibiting factor inanalyzing the hypothesized relationships.Statistical powerFor each operational logistics strategy type, we conducted power analysis per the guidelinesof Cohen, Cohen, West, and Aiken (2003) to determine the probability of finding the sample R2 tobe greater than zero with α = 0.01. We achieved excellent statistical power (β > 0.99, p < 0.01) ineach scenario.MeasuresThe measures used in this study are included in the Appendix. Three categories of measureswere employed to assess the fit of the service, product, and eBusiness quality-profile with operationallogistics strategy types and their effects on customer repurchase intentions: (1) profile measures (i.e.,service-, product-, and eBusiness-quality measures), (2) customers’ behavioral repurchase intentions,and (3) control variables (i.e., attitude toward Internet ordering, online accessibility, and sacrifice).Quality profile measuresThree measures were used to develop the ideal profiles for each strategy type: service quality,product quality, and eBusiness quality. Service quality was measured via ten items based on Parasuraman,Zeithaml, and Berry’s (1985) ten original dimensions of service quality (reliability, understanding,responsiveness, competence, security, courtesy, access, tangibles, credibility, and communication)(cf. Gotlieb, Grewal, and Brown 1994; Mentzer, Flint, and Hult 2001). Product qualitywas measured via six items adapted from works by Brucks, Zeithaml, and Naylor (2000), Carsky,Dickson, and Canedy (1998), and Garvin (1987). eBusiness quality was measured via seven itemsadapted from Agarwal and Prasad (1999) and Boyer and Olson (2002).Repurchase intentionsGiven the customer focus of our study, we opted to use customers’ behavioral intentions as theoutcome variable. This is consistent with previous studies on logistics-based online retail phenomena(Esper et al. 2003; Keller et al. 2002). Zeithaml, Berry, and Parasuraman (1996) develop an argumentthat positive behavioral intentions are reflected in a firm’s ability to get its customers to: (1) say positivethings about them; (2) recommend them to other consumers; (3) remain loyal to them; (4) spendmore with the company; and (5) pay price premiums. Similar to Cronin, Brady, and Hult (2000), weused four items to operationalize customers’ behavioral intentions that are similar to the domains assessedin the first four of these five outcomes (cf. Samiee and Anckar 1998). It is important to notethat although we do not have access to objective performance data, we found that the summated