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

Journal of Business Logistics, Vol. 28, No. 2, 2007 105QUALITY, OPERATIONAL LOGISTICS STRATEGY, AND REPURCHASEINTENTIONS: A PROFILE DEVIATION ANALYSISbyG. Tomas M. HultMichigan State UniversityKenneth K. BoyerMichigan State UniversityandDavid J. Ketchen, Jr.Auburn UniversityThis research was conducted with Grant SES 0216839 from the National Science Foundation.INTRODCUTIONThe last decade has seen an increased focus on eBusiness as a method to create customer-drivenvalue in a firm’s logistics operations (e.g., Boyer and Olson 2002; Esper et al. 2003; Rodrigues,Stank, and Lynch 2004; Williams et al. 1997). Whether the eBusiness solution is incorporated asan enhancer to other core competencies (e.g., Dell, FedEx, and Omaha Steaks) or stressed as one ofthe core competencies of the firm (e.g., Amazon, Ebay, and Ocado), eBusiness should be integratedinto the firm’s logistics operations as a strategic resource (Mentzer, Flint, and Kent 1999). In fact,eBusiness resources have become crucial in many firms’ efforts to create value to customers andprofitability for firms (Jensen and Meckling 1992).While past supply chain studies have primarily evaluated eBusiness as a strategic resource or asa facilitator of logistics solutions (Williams et al. 1997), we seek to expand the application of eBusinessresources to end-customers in the online marketplace (Edwards, Peters, and Sharman 2001;Esper et al. 2003). In particular, we focus on eBusiness quality – along with service and product quality– within the context of four operational logistics strategies (i.e., semi extended, fully extended, decoupled,and centralized extended strategies) as they pertain to a firm’s order fulfillment. This focuscenters on determining the appropriate constellation of the service, product, and eBusiness qualitiesthat is the most suitable for each operational logistics strategy type in the retailer-customer linkage.

106 hult, boyer, and ketchen, Jr.The focus on the retailer-customer linkage is both theoretically interesting and practically valuablebecause of its increasingly important role in affecting firm outcomes (cf. Daugherty, Stank, andEllinger 1998; Mentzer 1993; Niraj, Gupta, and Narasimhan 2001). In addition, the retailer-customerlinkage within supply chains (Handfield and Nichols 2003) presents an opportunity for eBusinessinitiatives to create unique advantages vis-à-vis competitors (Carter and Narasimhan 1996). In particular,the “last mile of the supply chain” (i.e., the retailer-customer linkage in the online market)represents perhaps the most critical part of the chain to manage (Boyer, Frolich, and Hult 2004). Afirm’s ability to create value for customers in the last mile of the chain can be severely impeded byhow operational logistics strategies are implemented (e.g., Venkatesh, Kohli, and Zaltman 1995). Ourfocus is on the implementation of customer-driven quality elements (service, product, and eBusinessquality), which quality elements to emphasize for each logistics strategy, and the connection to customers’repurchase intentions from the online grocery firm.As such, in this study we examine a model of customer-driven quality elements and their linkto customers’ behavioral (repurchase) intentions within the online grocery industry. The theoreticalfoundation is composed of strategic choice theory (Child 1972), configuration theory (Doty, Glick,and Huber 1993; Miller 1997), and research from the Nordic School of Services Management (Grönroos1984; Grönroos and Gummesson 1985). Specifically, these three streams of research providedthe guide to identifying the three quality constructs (service-, product-, and eBusiness-quality) thatare analyzed as value creation elements for firms adopting different operational logistics strategies(Dresner and Xu 1995; Sterling and Lambert 1987; Wisner 2003). Based on the services marketingand services operations literature, the outcome examined is a customer’s repurchase intention froman online grocer (Esper et al. 2003).To provide managerially relevant implications, we studied the effect of these three quality constructson customers’ repurchase intentions among the customers of firms that have adopted vastlydifferent operational logistics strategies (semi extended, fully extended, de-coupled, and centralizedextended strategies). While each of these logistics strategies have proven viable and can result insuperior performance (Boyer, Frolich, and Hult 2004), each strategy is likely to need to be nurturedby a different emphasis of the three quality elements. Below, we provide a theoretical integrationof the relevant literature streams. This section is followed by a discussion of the methods (data collection,samples, and measures), analysis (e.g., profile deviation analysis), results (the regressionanalysis including control variables and the profile-deviation criterion along with multiple tests toverify the correctness of the results), and a discussion of the implications of the study as well as thefuture avenues for research.THEORETICAL INTEGRATIONWithin the marketing, logistics, and supply chain literatures, three quality characteristics (service-,product-, and eBusiness-quality) have been suggested to be critical antecedents of customers’repurchase intentions. Service quality is defined as “the consumer’s overall impression of the relativeinferiority/superiority of the organization and its services” (Bitner and Hubbert 1994, p. 77; Mentzer,Flint, and Hult 2001). Product quality is defined as “the consumer’s [subjective] judgment about aproduct’s overall excellence or superiority” (Zeithaml 1988, p. 3). eBusiness quality, in this study, isdefined as aspects of quality that are critically affected by taking orders online (Agarwal and Prasad

Journal of Business Logistics, Vol. 28, No. 2, 2007 1071999; Boyer and Olson 2002). While service and product quality are established concepts, eBusinessquality is a relatively new phenomenon that can be attributed to the Internet era. As such, companiessuch as Amazon.com, Dell, and Tesco thrive partly because of their ability to offer customers an onlineinterface to ordering their products that is user friendly. In fact, user friendly technology is oftenseen as a core competency by customers and firms alike. This new dimension of quality – labeledeBusiness quality in our research – has assumed a place alongside service and product quality in affectingcustomer repurchase intentions.One approach to understanding how the concepts of service-, product-, and eBusiness-quality,operational logistics strategy, and repurchase intentions relate to each other is to search for linearrelationships that apply across all members of a sample. A linear understanding of relationships iscommonly used in social science studies, and has been applied regularly in quality oriented studies.However, a key disadvantage to this approach is that important relationships may be masked bydata aggregation (Miller 1987). For example, if emphasizing service quality greatly enhances somecustomers’ intentions while having a small effect on others, these effects may “average out” to amodest level in an aggregate analysis. The result is that no firm’s situation is depicted as accuratelyas it could be.To offset this potential limitation and to advance the literature both managerially and methodologically,we adopt a focus on configurations – the simultaneous consideration of multiple interwovenfactors (Miller 1997). In our study, a configuration refers to a constellation of the threequality elements and operational logistics strategy (Meyer, Tsui, and Hinings 1993). Configurationaltheory contends that the closer a firm matches an “ideal constellation,” the better its outcomes. Morebroadly, a focus on such configurations avoids the aggregation problem identified by Miller (1987).As such, configuration theory allows for a more managerially relevant assessment of the preferredmakeup of service-, product-, and eBusiness- quality elements for specific operational logistics strategiesthan can be analyzed via linear modeling (cf. Vorhies and Morgan 2003).When fit among multiple elements is considered simultaneously and the effects on outcomesare examined, as in our study, configuration should be conceptualized and measured via profile deviationanalysis (Doty, Glick, and Huber 1993; Venkatraman 1990; Vorhies and Morgan 2003). Thisanalysis assesses the fit between the three quality elements and strategy as the degree to which thequality elements of a supply chain differ from those of an “ideal” profile for implementing a specificoperational logistics strategy (Vorhies and Morgan 2003; Zajac, Kraatz, and Bresser 2000). Below,we describe the conceptual background for the components of the profiles (quality and operationallogistics strategy). We then develop hypotheses relating the profiles and our outcome of interest (i.e.,a customer’s repurchase intention).Configurations of Quality and Operational Logistics StrategyIn light of configuration theory (Doty, Glick, and Huber 1993), coupled with guidance fromstrategic choice theory (Child 1972) and the quality literatures, two major constructs are relevant tounderstanding and assessing quality-based fit with strategy: operational logistics strategy types andquality-based supply chain characteristics including service-, product-, and eBusiness-quality. “Strategictype pertains to the planned patterns of organizational adaptation to the market through which abusiness seeks to achieve its strategic goals” (Vorhies and Morgan 2003, p. 102; cf. Conant, Mokwa,and Varadarajan 1990; Matsuno and Mentzer 2000).

108 hult, boyer, and ketchen, Jr.Strategic choice theory argues that the major decisions managers make for their firms have significantimplications for the firms’ outcomes (Child 1972). In that sense, strategic choice theory contrastswith other theories such as population ecology and institutional theory where external forcesare viewed as the dominant drivers of firm outcomes. Using strategic choice theory as the theoreticalfoundation, we identified four operationally based logistics strategies as appropriate in an organization’sefforts to extend their supply chains toward the customer: centralized extended strategy, fullyextended strategy, de-coupled strategy, and semi-extended strategy (Boyer, Frolich, and Hult 2004).As shown in Figure 1 and Table 1, order fulfillment location (store-based vs. distribution centerbased)and method of delivery to customer (direct vs. indirect) are the two key dimensions that differentiateamong the four strategies. The resultant strategies represent four different approaches to afundamental question: to what extent and how will a firm extend its supply chain to reach customers?Relative to the other approaches, a fully extended strategy maximizes activities close to the customer.Specifically, this strategy involves a firm fulfilling orders at the store level and then delivering goodsdirectly to customers. For example, Tesco.com, the online segment of British retailer Tesco, picksorders from individual stores and takes them to private homes. Their online business accounted forover $1.25 billion in revenues in 2005, but it represents just 2.6% of Tesco’s total sales. Using a fullyextended strategy enables Tesco to minimize capital investment in what is currently a niche marketfor the firm. This approach also offers a high level of customer convenience, but delivery costs arerelatively high and picking efficiency is low.Firms following a centralized extended strategy fulfill orders through distribution centers (“centralized”)and then deliver goods directly to customers (“extended”). One example is Office Depot,which delivered over $3 billion in office supplies directly from warehouses to customers’ offices andhomes in 2004. Firms following a centralized extended strategy face trade-offs. The strategy enablesfirms to enjoy high picking efficiency and provide high levels of customer convenience, but deliverycosts and capital investments are relatively high. In Office Depot’s case, however, the size of thefirm’s home delivery business accrues economies of scale that mitigate some of these costs.When following a de-coupled strategy, a firm fulfills orders at a distribution center and thenrelies on a delivery firm (such as DHL, Federal Express, or UPS) to send orders to customers. Suchan “arm’s length” approach entails relatively large fixed investments initially (for warehouses), butminimizes the variable costs involved in actual deliveries. This strategy has helped Dell becomethe dominant player in the personal computer industry. Other examples of firms using a de-coupledstrategy include Amazon, NetFlix, and Caremark.A semi-extended strategy involves fulfilling orders at the store-level. Customers place ordersover the Internet and then pick up their goods at a retail store. This approach requires little capitalinvestment and delivery costs are also minimal. However, picking efficiency is limited and some customersconsider visiting stores to be inconvenient. Examples of firms using a semi-extended strategyinclude REI, Circuit City, Rite-Aid, and Lowes Food.

110 hult, boyer, and ketchen, Jr.FIGURE 1OPERATIONAL LOGISTICS STRATEGIES FOR EXTENDING THE SUPPLY CHAINStore-basedOrder FulfillmentDistribution CenterLowLowIndirectSemiExtendedStrategyDe-CoupledStrategyDeliveryDeliveryCostCustomerConvenienceDirectFullyExtendedStrategyCentralizedExtendedStrategyHighHighLowLowPickingEfficiencyCapitalInvestmentHighHighHypothesis DevelopmentAs shown in Figure 2, using the logic established by Vorhies and Morgan (2003), we posit linkagesamong quality, operational logistics strategy, and customers’ repurchase intentions. To specifyand test the relationships outlined in Figure 2, we draw on the insights of Doty, Glick, and Huber(1993) and Venkatraman (1990) who suggest that when fit among multiple variables is consideredsimultaneously (as in the holistic study of the relationship between quality characteristics and operationallogistics strategy types) and links to criterion variables are assessed (i.e., repurchase intentions),fit should be conceptualized and assessed via profile deviation analysis (cf. Vorhies and Morgan2003). Profile deviation analysis views fit as the degree to which a particular case (a customer,in our study) matches an “ideal” profile (an optimal standing within a dataset) (Venkatraman 1990;Zajac, Kraatz, and Bresser 2000).34

Journal of Business Logistics, Vol. 28, No. 2, 2007 111FIGURE 2A MODEL OF CUSTOMERS’ BEHAVIORAL REPURCHASE INTENTIONSQuality Profile Characteristics• Service Quality• Product Quality• eBusiness QualityLogistics Strategies• Semi Extended Strategy• Fully Extended Strategy• De-Coupled Strategy• Centralized Extended StrategyQuality Profile Fitwith Strategic TypeDeviation from an idealquality profile that producessuperior performance byarranging resources in waysthat enable logistics strategyimplementation.Customers’BehavioralRepurchaseIntentionsThe expectation that product-, service-, and eBusiness-quality have a linkage with repurchaseintentions among online grocery firms can be traced to the theoretical grounding offered by theNordic School of Service Management (NSSM) (Grönroos 1982, 1984; Grönroos and Gummesson1985). The NSSM perspective lends itself well to online business encounters (such as online groceryshopping) signified by a considerable product component (Grönroos 1990). What happens in theseonline encounters will have a critical impact on the perceived service, product, and eBusiness qualitiesas well as customers’ future behavioral intentions. Customers’ behavioral repurchase intentions“can be viewed as indicators that signal whether customers will remain with or defect from the company”(Zeithaml, Berry, and Parasuraman 1996, p. 33).35Based on the NSSM school of thought, customers evaluate the online business encounter alongtwo dimensions, a technical and a functional dimension (e.g., Grönroos 1984). Related to the technicaldimension, what customers receive in their interactions with the firm is important to theirevaluation (e.g., Zeithaml 1988). This is often thought of as the quality of the product delivered (i.e.,the technical quality). It is what the customer is left with when the production process and onlineinteractions are over. Additionally, product quality has been found to have an impact on customer’sbehavioral intentions (Normann 1984).However, the technical dimension will not account for a consumer’s total evaluation of the onlinegrocery encounter. Customers will also be influenced by the way in which the product – the outcomeor end result of the encounter – is delivered to them. As such, customers are influenced by how theyreceive the products. This portion of the online grocery encounter is referred to as functional qualitywithin the NSSM framework. It incorporates the more subjective service and eBusiness qualities associatedwith the encounter (cf. Grönroos 1982, 1990). Based on the NSSM school of thought (e.g.,Grönroos 1990) coupled with subsequent research on service and eBusiness qualities, both quality

112 hult, boyer, and ketchen, Jr.attributes are linked with customers’ behavioral intentions (Boulding, Kalra, and Staelin 1999; Parasuraman,Zeithaml, and Malhotra 2004; Zeithaml, Berry, and Parasuraman 1996). In fact, the notionthat customers prefer greater service and eBusiness qualities, in addition to product quality, is ratherintuitive, especially if price and other tangible and intangible elements are held constant.In summary, we expect that the outcomes involving online grocery firms (in our case, customers’repurchase intentions) will be greater when their quality profiles are similar to those of theperformance-maximizing ideal profile of a certain operational logistics strategy type (as illustrated inFigure 2) (cf. Vorhies and Morgan 2003). As such, we hypothesize that:H 1: The more similar the quality profile of an online grocery firm pursuing a semi-extendedstrategy is to that of the ideal profile for a semi-extended strategy type, the greater therepurchase intentions of the firm customers.H 2: The more similar the quality profile of an online grocery firm pursuing a fully extendedstrategy is to that of the ideal profile for a fully extended strategy type, the greater therepurchase intentions of the firm customers.H 3: The more similar the quality profile of an online grocery firm pursuing a de-coupledstrategy is to that of the ideal profile for a de-coupled strategy type, the greater therepurchase intentions of the firm customers.H 4: The more similar the quality profile of an online grocery firm pursuing a centralizedextended strategy is to that of the ideal profile for a centralized extended strategy type, thegreater the repurchase intentions of the firm customers.METHODData Collection and SamplesThe overall sample consisted of customers of four different online/home delivery grocers.Online/home delivery grocers present an interesting area of application for the study of operationallogistics strategy types and quality issues (service, product, and eBusiness) because many such firmshave struggled (e.g., Webvan), suggesting that much remains unknown about this area. In addition,operational logistics strategy as a research focus is increasing in the logistics literature (Clinton andCloss 1997; Wisner 2003).The four strategies we discuss are based on inductive insights generated through our preliminaryfieldwork coupled with actual order fulfillment and delivery methods used by the firms in the sample.The fieldwork involved a series of interviews with industry experts and participant observation activitieswithin several firms (e.g., visiting firm locations, warehouses, transportation operations). Oncewe identified the four strategies, we sought out one firm that pursues each strategy to participate inthe study. Executives from each firm then verified the classification of their firm within the selectedstrategy type (between three to fifteen executives per firm verified the strategy classification). Collectively,this method ensured the correct classification of each firm within the appropriate strategytype. For example, if a firm fills orders by picking from a distribution center and the customers pick

Journal of Business Logistics, Vol. 28, No. 2, 2007 113up the “assembled basket” of products from the DC or another location, we classify the strategy as“de-coupled strategy.” The same logic applies to the other strategies – they are based on actual orderfulfillment and actual delivery methods, not perceptions by the researchers or executives.Each quadrant in Figure 1 is represented by the customers of one firm. All four firms weregenerous with their time and allowing us access to their customers, but prefer not to be identified byname given the competitive nature of the home delivery grocery business. While we cannot includeindividual sales and financial characteristics for sensitivity reasons, collectively the firms had annualonline/home delivery sales of more than $150 million in 2003 and an aggregate customer base ofmore than 100,000 customers who purchase at least once per month. The people in our final sampleaccount for over 34,000 purchases and $4 million total in home delivery sales over the length of theirrelationship with the Internet grocer. The general survey data collection principles recommendedby Dillman (2000) were followed. All measures in the study were pre-tested via discussion withnumerous managers at the participating companies as well as executives from other online grocers.Suggestions for wording and presentation were used to improve each of the measures prior todata collection.Semi extendedCustomers of this grocer were contacted by email and asked to fill out an online survey. Onefree delivery (a $4.95 value) was offered to customers as an incentive to complete the survey andcustomers were sent a reminder email one week after the first email if they had not yet filled out thesurvey. A total of 1,159 customers were contacted and 396 responses were received for a responserate of 34.2%.Fully extendedThis grocer was not comfortable providing us with any contact or past ordering information onany customers despite our standard and repeated assurances that all information would be held incomplete confidentiality. Thus, to obtain customer responses related to the fully extended grocer, thisgrocer posted a link on the checkout page for orders inviting customers to “opt-in” and fill out thesurvey. No incentive for participation was offered. A total of 10,418 unique customers (14,959 totalorders) were placed over a two-week period and we received 898 survey responses (8.5%).De-coupledCustomers of this grocer were contacted by email and asked to fill out an online survey. Thegrocer provided baseball hats with the grocer’s logo as an incentive to complete the survey – thechoice of incentives was partly driven by the desire to build the company’s brand given a lack ofbrick and mortar stores. A total of 2,000 customers were contacted and 461 responses were receivedfor a response rate of 23.0%.Centralized extendedCustomers of this grocer were contacted by email and asked to fill out an online survey. Thegrocer provided 20 pairs of movie tickets to be raffled off to customers participating in the survey. Areminder email was sent one week after the first if customers had not yet filled out the survey. A totalof 2,500 customers were contacted and 691 responses were received for a response rate of 27.6%.

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

116 hult, boyer, and ketchen, Jr.TABLE 2CORRELATIONS AND SHARED VARIANCES (N = 2,440)SQ PQ IQ ATT OAA SAC BISQ – 0.24 0.10 0.02 0.08 0.08 0.18PQ 0.49 – 0.16 0.04 0.10 0.20 0.28EQ 0.32 0.40 – 0.07 0.35 0.27 0.14ATT 0.14 0.21 0.26 – 0.05 0.10 0.07OA 0.28 0.32 0.59 0.23 – 0.16 0.08SAC 0.29 0.45 0.52 0.31 0.40 – 0.28BI 0.43 0.53 0.37 0.26 0.29 0.53 –Notes:Labels:All correlations are significant at the p < 0.01 level.Correlations are included below the diagonal.Shared variances are included above the diagonal.SQ = service qualityPQ = product qualityEQ = eBusiness qualityATT = attitude toward Internet orderingOA = online accessibilitySAC = sacrificeBI = behavioral intentionsTABLE 3BASIC STATISTICS AND CONFIRMATORY FACTOR ANALYSIS RESULTS(N = 2,440)Variable Mean StandardDeviationVarianceExtractedCompositeReliabilityCoefficientAlphaFactorLoadingsSQ 5.22 1.25 60.0% 0.94 0.94 0.57 to 0.85PQ 3.65 1.28 50.8% 0.79 0.80 0.41 to 0.88EQ 4.77 1.38 68.0% 0.93 0.92 0.68 to 0.93ATT 4.93 1.20 70.7% 0.88 0.88 0.77 to 0.90OA 5.27 1.02 60.0% 0.75 0.68 0.75 to 0.80SAC 4.58 1.52 60.0% 0.80 0.77 0.41 to 0.92BI 5.51 1.04 43.7% 0.69 0.70 0.50 to 0.78Fit Statistics26,452.17Degrees of Freedom 413DELTA2 0.95RNI 0.95CFI 0.95TLI 0.95RMSEA 0.0830

Journal of Business Logistics, Vol. 28, No. 2, 2007 117Confirmatory factor analysis (CFA)The next step in the analysis of the measurement properties was to conduct a CFA on the remaining33 items using the full sample (n = 2440). The model fits were evaluated using a series ofindices. The DELTA2 index (Bollen 1989), the relative noncentrality index (RNI) (McDonald andMarsh 1990), and the comparative fit index (CFI) (Bentler 1990) have been shown to be the moststable fit indices by Gerbing and Anderson (1992). Hu and Bentler (1999) suggested that the Tucker-Lewis index (TLI) (Tucker and Lewis 1973) and the root mean square error of approximation index(RMSEA) (Steiger and Lind 1980) be added in evaluating CFA and SEM analyses. Using this seriesof fit indices, the CFA resulted in DELTA2, RNI, CFI, and TLI all being 0.95, and RMSEA = 0.08 (χ2= 6,452.17, df = 413). Thus, the measurement structure of 7 factors and 33 items produced excellentfit statistics.Reliability and validity assessmentsWithin the CFA setting, composite reliability was calculated using the procedures outlined byFornell and Larcker (1981). The formula specifies that:CRη2( ∑λγi)=2( ∑λγ) + ( ∑εii)where CRη = composite reliability for scale η; λ yi= standardized loading for scale item γ i, andε i= measurement error for scale item γ i(coefficient alphas are also included in Table 3 for comparison).Additionally, the parameter estimates and their associated t-values were examined along withthe average variance extracted for each construct (Anderson and Gerbing 1982). Average varianceextracted was calculated using the following formula:Vη2i2i∑λγ=∑λγ+ ∑εiwhere V η= average variance extracted for η; λ yi= standardized loading for scale item γ i, and ε i=measurement error for scale item γ i. The composite reliabilities for the seven scales ranged from 0.69to 0.95 (coefficient alphas ranged from 0.68 to 0.94), the factor loadings ranged from 0.41 to 0.93 (allsignificant at p < 0.01), with average variances extracted ranging from 43.7% to 70.7%. In addition,the 33 purified items were found to be reliable and valid when evaluated based on each item’s errorvariance, modification index, and residual covariation. Also, the skewness and kurtosis results ofeach item indicated that the data were normally distributed.

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-

120 hult, boyer, and ketchen, Jr.tween the four operational logistics strategy groups on the repurchase intention variables. Second, wecompared repurchase intentions of deviation from two different ideal quality profiles, one developedfrom cases of the same strategic type and one developed regardless of the strategy type (Venkatraman1990; Vorhies and Morgan 2003). The results indicate that calibrating ideal quality profiles withinstrategy type produces greater beta coefficients (Cohen et al. 2003) and greater explanatory power(Chow 1960) in the regression models.Table 4 reports the standardized regression results for quality-based profile fit with strategic typeand its effect on repurchase intentions. Table 5 summarizes the means and standard deviations for theideal quality profiles based on the particular operational logistics strategy type (as well as providesthe scores for the average cases for each strategy type). The results for the control variables – attitudetoward Internet ordering, online accessibility, and sacrifice – are also included in Table 4.TABLE 4QUALITY PROFILE FIT WITH OPERATIONAL LOGISTICS STRATEGY TYPE ANDREPURCHASE INTENTIONS: STANDARDIZED REGRESSION RESULTSPredictor VariablesCriterion Variable: Repurchase IntentionsBeta t-value SignificanceVarianceInflationFactorSemi Extended Strategy (n = 394)Quality Profile Deviation -0.26 4.35 < 0.01 1.80Attitude Toward Internet Ordering 0.07 1.39 0.17 1.14Online Accessibility -0.02 0.42 0.68 1.39Sacrifice 0.37 6.69 < 0.01 1.49Adjusted R 2 = 0.32F-value = 40.60, p < 0.01Fully Extended Strategy (n = 896)Quality Profile Deviation -0.41 11.85 < 0.01 1.63Attitude Toward Internet Ordering 0.05 1.62 0.11 1.09Online Accessibility 0.02 0.55 0.59 1.47Sacrifice 0.29 8.39 < 0.01 1.59Adjusted R 2 = 0.41F-value = 140.28, p < 0.01De-Coupled Strategy (n = 461)Quality Profile Deviation -0.43 9.22 < 0.01 1.35Attitude Toward Internet Ordering 0.03 0.71 0.48 1.09Online Accessibility 0.02 0.53 0.59 1.27Sacrifice 0.26 5.59 < 0.01 1.34Adjusted R 2 = 0.37F-value = 58.04, p < 0.01Centralized Strategy (n = 689)Quality Profile Deviation -0.41 10.47 < 0.01 1.51Attitude Toward Internet Ordering 0.09 2.60 0.01 1.18Online Accessibility -0.06 1.67 0.10 1.32Sacrifice 0.27 6.89 < 0.01 1.48Adjusted R 2 = 0.37F-value = 90.24, p < 0.01

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

122 hult, boyer, and ketchen, Jr.DISCUSSION AND IMPLICATIONSOur study’s results should be viewed in light of its limitations. Different insights might havebeen generated had we asked respondents what their ideal points were and then compared their responsesto the ideal points. This could have mitigated the potential problems involved because somepeople tend to score items more positively. Next, only relatively general conclusions can be drawnfrom our results given that we do not examine all possible intricacies (such as cycle time and pickingcosts) that are associated with each strategy type. As a function of our focus on customers’ repurchaseintentions, we did not study performance effects related to high volume or high profitability customers(although the customers in the overall sample account for over 34,000 purchases and $4 million inhome delivery sales over the length of their relationship with the Internet grocers). As in any surveystudy, additional firms and customers and/or additional industries would enhance the generalizabilityof the results (and possible lead to refinement of the implications).Despite these limitations, our study provides new insights into what drives customers’ behavioralintentions. As shown in Figure 2, our specific focus was on identification of the composition ofquality profiles that drive superior outcomes in each of four operational logistics strategy types. Theprofile-deviator predictor, together with three control variables (attitude toward Internet ordering,online accessibility, and sacrifice) explained between 32% and 41% of the variance in customers’behavioral repurchase intentions (see Table 4). These R 2 values are similar to those found in otherconfiguration studies (Doty, Glick, and Huber 1993; Vorhies and Morgan 2003). As such, the overallimplication of our results is that managers should (a) decide which operational logistics strategy thefirm should follow, (b) adjust the firm’s activities to match the strategy’s requirements, (c) effectivelycommunicate to customers the value-added offered to them by the strategy, and (d) systematicallyassess the alignment of perceptions between best customers on one hand and customers in general onthe other. At the same time, more detailed and intricate implications can be found by examining theresults of each strategy type. In the next sections, we provide a discussion of the results, implications,and future research opportunities of each operational strategy type.Semi Extended StrategyIn the analysis of the semi extended strategy type, we find that the quality-based profile deviationpredictor had a beta = -0.26 (t-value = 4.35). The profile-deviator predictor – together with thecontrol variables – explained 32% of the variance in customers’ behavioral intentions (see Table 4).In examining the quality profile in Table 5, we find that service quality and eBusiness quality appearto be the most critical quality elements in achieving superior repurchase intentions among customers,with product quality slightly lower on the importance scale for customers. Thus, the implicationis that the operational logistics strategy signified by indirect delivery of products and a store-basedorder fulfillment method can be operated the most effectively by stressing service and eBusinessquality over product quality (i.e., stressing the functional component of NSSM – Grönroos 1984). Assuch, the functional quality of order fulfillment is more important to customers than what customersreceive in their interactions with the firm (i.e., the technical dimension of the NSSM school ofthought). As such, firms should spend their limited resources on developing a high-level of servicequality and eBusiness quality prior to investing heavily in product quality initiatives. However, atthe same time, customers buying groceries via the semi extended approach clearly expect the lowest

Journal of Business Logistics, Vol. 28, No. 2, 2007 123level of product quality, despite their preference for service and eBusiness quality. Future researchneeds to determine the lowest level of product quality acceptable to customers interacting with thefirm via the semi extended approach.Fully Extended StrategyIn the analysis of the fully extended strategy type, we find that the quality-based profile deviationpredictor had a beta = -0.41 (t-value = 11.85). The profile-deviator predictor – together with thecontrol variables – explained 41% of the variance in customers’ behavioral intentions. Within thisstrategy and two others (see Table 4), the control variables of Internet ordering and online accessibilitywere not significant. This may reflect that respondents in general were very familiar with theInternet and ordering online. In examining the quality profile in Table 5, we find that service qualityis the most critical element for fully extended types, followed by eBusiness quality and productquality. Thus, in the operational logistics strategy role signified by direct delivery of products anda store-based order fulfillment method, service quality, as a dimension of the functional componentof NSSM (Grönroos 1984) appears to be the most critical. The direct implication is that, assumingproduct and eBusiness quality can be held at a minimum level acceptable to customers, firms shouldspend their resources on improving service quality. As such, the functional quality of order fulfillment(in particular service quality) is more important to customers than the products they receivefrom the firm (i.e., the technical dimension of NSSM). However, to understand the roles of productquality within the framework of the fully extended strategy future research needs to tackle the issueof substitution of products by the firm’s representatives when the product ordered by the customeris not in stock.De-Coupled StrategyIn the analysis of the de-coupled strategy type, we find that the quality-based profile deviationpredictor had a beta = -0.43 (t-value = 9.22). The profile-deviator predictor – together with the controlvariables – explained 37% of the variance in customers’ behavioral intentions. In examining the idealquality profile in Table 5, we find that eBusiness quality is the most critical element, followed by servicequality and product quality. Thus, in the operational logistics strategy role signified by indirectdelivery of products and a distribution center-based order fulfillment method, eBusiness quality, asa dimension of the functional component of NSSM (Grönroos 1984) appears to be the most critical.As such, the implication is that the functional quality of the process (in particular eBusiness quality)is more important to customers than what they “take home” in the form of products (i.e., the technicaldimension of NSSM) or what type of service they receive. These results place a great deal ofemphasis on the firm to maintain user friendliness regarding the technology used to interact with thecustomers. Future research needs to examine what type of web interface or other technological interfacecustomers prefer when they shop for groceries online. It will become paramount to stay updatedtechnologically for firms pursuing a de-coupled strategy.Centralized Extended StrategyIn the analysis of the centralized extended strategy type, we find that the quality-based profiledeviation predictor had a beta = -0.41 (t-value = 10.47). The profile-deviator predictor – togetherwith the control variables – explained 37% of the variance in customers’ behavioral intentions. Inexamining the ideal quality profile in Table 5, we find that service quality is the most critical ele-

124 hult, boyer, and ketchen, Jr.ment, followed by eBusiness quality and product quality. Thus, the implication is that in the operationallogistics strategy role signified by direct delivery of products and a distribution center-basedorder fulfillment method, service quality (i.e., the NSSM school’s functional dimension) appears tobe the most critical. As such, the functional quality of the process (in particular service quality) ismore important to customers than the actual products (i.e., NSSM school’s technical dimension).However, the results also suggest that moving from being an average performer on product qualityto a top performer appears to be a very significant task compared with making the same moves withrespect to service and eBusiness qualities (see Table 5). Interestingly, the firm that used a centralizedextended strategy approach in our sample needed to achieve almost perfect service quality results(mean = 6.96, as perceived by its customers) as well as a rather high level of eBusiness quality. Thus,the implication is that the centralized extended strategy, which represents the newest form of grocerydelivery, is signified by perhaps the toughest logistics operations of the four types. As such, futureresearch needs to carefully examine what drives outcomes for centralized extended strategy typessince they are the most likely to evolve the most of the four types over the next several years.In summary, the roles of quality in traditional bricks-and-mortar firms have been researched extensivelyfor at least the last two decades. Yet, the important quality constructs and their roles relativeto the chosen operational logistics strategies of online firms have received scant research attention.This study offers an important step toward closing the gap between what we know and what we needto know regarding online customers’ preferences and intentions. Our results suggest that the closerthe perceptions of a firm’s customers in general match those of the firm’s best customers (defined inthis research as those customers that have the highest repurchase intentions) on service, product, andeBusiness qualities, the stronger the repurchase intentions of customers in general. The broad implicationis that firms should allocate their limited resources to improve in certain quality areas (i.e.,service, product, or eBusiness) as opposed to attempting to improve in all areas simultaneously. Suchtargeted resource allocation is likely to be much more effective in reaping advantages in the form ofcustomers continuing to purchase from their firms.

Journal of Business Logistics, Vol. 28, No. 2, 2007 125APPENDIXMEASURES AND SOURCES 1Service Quality (SQ) (Adapted from Gotlieb, Grewal, and Brown 1994; Parasuraman, Zeithaml, and Berry1985)SQ1 XYZ Company’s employees are reliable in providing the service I expect.SQ2 XYZ Company’s employees are understanding of my service needs.SQ3 XYZ Company’s employees are responsive to my service requests.SQ4 XYZ Company’s employees are competent in providing expected service.SQ5 I feel secure in my service encounters with XYZ Company’s employees.SQ6 XYZ Company’s employees are courteous in providing me service.SQ7 XYZ Company’s employees are accessible to answer my questions.SQ8 The tangible aspects of XYZ Company’s service (appearance of deliveryvans, staff, products, etc.) are excellent.SQ9 XYZ Company has good credibility in providing the service I need.SQ10 I can easy communicate with XYZ Company regarding my service needs.Product Quality (PQ) (Adapted from Brucks, Zeithaml, and Naylor 2000; Carsky, Dickson, and Canedy 1998;Garvin 1987)PQ1 XYZ Company has prestigious (high quality) products.PQ2 XYZ Company has an excellent assortment of products.PQ3 XYZ Company’s products are among the best.PQ4 XYZ Company has a sufficient range of product choices (I can get what I want). 2PQ5 The products are the same quality as I can get in the store. 2PQ6 The number of substitutions or out of stock items is reasonable.eBusiness Quality (EQ) (Adapted from Agarwal and Prasad 1999; Boyer and Olson 2002)EQ1 It is easy for me to remember how to perform tasks using XYZ Company’s website. 2EQ2 It is easy to get XYZ Company’s website to do what I want it to do.EQ3 My interaction with XYZ Company’s website is clear and understandable.EQ4 Overall, I believe that XYZ Company’s website is easy to use.EQ5 The site is easy to navigate.EQ6 The site has a logical sequence of steps for completing an order.EQ7 The XYZ Company’s web site is easy to search.Attitude Toward Internet Ordering (ATT) (Adapted from Agarwal and Prasad 1999)ATT1 I like using Internet ordering.ATT2 Internet ordering is fun to use.ATT3 Internet ordering provides an attractive ordering method.Online Accessibility (OA) (Adapted from Boyer and Olson 2002)OAA1 I can get on the site when I want to.OAA2 The site loads quickly (I don't have to wait long for new material).Sacrifice (SAC) (Adapted from Dodds, Monroe, and Grewal 1991; Heskett, Sasser, and Hart 1990; Zeithaml1988)SAC1 XYZ Company’s prices are low on the products that they offer.SAC2 The time needed to make a purchase from XYZ Company is low.SAC3 The effort required to make a purchase from XYZ Company is low.Customers’ Behavioral Intentions (BI) (Adapted from Cronin, Brady, and Hult 2000; Zeithaml, Berry, andParasuraman 1996)BI1 I would classify myself as a loyal customer of XYZ Company.BI2 I do not expect to switch to another online grocer to get better service in the future.BI3 I would continue to shop with XYZ Company even if I had to pay more.BI4 I would complain to other customers if I experienced a problem with XYZ Company’s service. 2BI5 I would complain to XYZ Company’s employees if I experienced a problem with their service. 2Notes:1 All items used a 7-point Likert-type scale ranging from “strongly disagree” to “strongly agree.”2 Item deleted after the item-level analysis across the four strategy groups.36

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132 hult, boyer, and ketchen, Jr.ABOUT THE AUTHORSG. Tomas M. Hult (Ph.D. University of Memphis) is Professor of Marketing, Supply ChainManagement, and International Business and Director of the Broad International Business Center(MSU-CIBER) at Michigan State University. He is also Executive Director of the Academy ofInternational Business. His research on supply chain management and strategic marketing has beenpublished in Academy of Management Journal, Journal of Marketing, Strategic Management Journal,Journal of Operations Management, Decision Sciences, Journal of the Academy of MarketingScience, and Journal of Retailing, among others. He is Deputy Editor-in-Chief of Journal of InternationalBusiness Studies and Associate Editor of Journal of Operations Management and DecisionSciences. He co-wrote the book Extending the Supply Chain: How Cutting-Edge Companies Bridgethe Critical Last Mile into Customers’ Homes, 2005. He is co-founder of Hult Ketchen InternationalGroup, LLC.Kenneth K. Boyer (Ph.D. The Ohio State University) is Professor of Operations Managementat Michigan State University. Prior to academe, Dr. Boyer worked as a project engineer at GeneralDynamics. Dr. Boyer’s research interests focus on the strategic management of operations, electroniccommerce, and the effective use of advanced manufacturing technologies. Currently, Dr. Boyer iscoordinating a study of online ordering and home delivery of groceries in Canada, United Kingdom,and the U.S. that is funded by the National Science Foundation. He has published articles in ManagementScience, Decision Sciences, Journal of Operations Management, and Business Horizons,among others. He co-wrote the book Extending the Supply Chain: How Cutting-Edge CompaniesBridge the Critical Last Mile into Customers’ Homes, 2005. He is an Associate Editor for both theJournal of Operations Management and Production and Operations Management.David J. Ketchen, Jr. (Ph.D. Pennsylvania State University) is the Lowder Eminent Scholar andthe Executive Director of the Lowder Center for Family Business and Entrepreneurship at AuburnUniversity. His research on supply chain management has appeared in a variety of outlets includingthe Academy of Management Journal, Journal of Operations Management and Organizational Dynamics.He has served as an associate editor for the Journal of Management, Journal of OperationsManagement, and Journal of International Business Studies. Dr. Ketchen belongs to the editorialboard of several journals, including Academy of Management Journal and Academy of ManagementReview. He is co-founder of Hult Ketchen International Group, LLC – a consultancy devoted tohelping firms improve their performance through melding strategy and supply chain management.