Linking e-service quality and markups: The role of imperfect information in the supply chain

Journal of Operations Management 25 (2007) 14–41 www.elsevier.com/locate/jom

Linking e-service quality and markups: The role of imperfect information in the supply chain Elliot Rabinovich * Supply Chain Management, W. P. Carey School of Business, PO Box 874706, Arizona State University, Tempe, AZ 85287-4706, USA Received 14 July 2004; received in revised form 1 September 2005; accepted 17 November 2005 Available online 3 April 2006

Abstract Consumers’ access to the Internet has greatly expanded their ability to compare offers across a wide array of retailers. In some particular industries (i.e., music), the Internet has also provided consumers with unprecedented opportunities to consider retail offers involving physical goods (i.e., CDs) alongside specialized services. As shown in this paper, these circumstances have important implications for the design and management of customer relationships. These circumstances also permeate relationships across retail and wholesale echelons in music supply chains. In particular, an empirical analysis shows that online consumer access to information on CD retail markups compels retailers to market a level of service quality that is consistent with that markup information. However, limitations in consumer access to markup data, available only to wholesalers and to Internet retailers, allow retailers to inversely link their markups to the fulfillment service quality offered to consumers with wholesaler support. # 2006 Elsevier B.V. All rights reserved. Keywords: E-commerce; Logistics/distribution; E-services; Service operations; MIS/operations interface; Supply chain management

1. Introduction In the 1990s, the arrival of Internet commerce rocked the recording industry’s supply chains. Music became available for sale in all formats over the Web, and buyers gained access to a vast array of titles at increasingly lower prices. This posed a serious challenge for the sale of compact disks (CDs) on the Web (Hamilton, 2002), forcing some online retailers, under pressure from suppliers, to lower CD markups (or margins) indiscriminately. However, in their efforts to catch up with industry price leaders, many online retailers failed to consider offering complementary services as a way to sustain or even expand their markups in their CD sales. This

* Tel.: +1 480 965 5398; fax: +1 480 965 8629. E-mail address: [email protected]. 0272-6963/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jom.2005.11.013

misstep likely resulted from a poor understanding of how to align retail margins with the quality of complementary e-services. These e-services are diverse. Some of them involve Web-based cues (e.g., testimonials, samples) necessary to assist likely buyers in making purchase decisions (Shapiro and Varian, 1998), while others comprise the fulfillment of orders (Rabinovich and Bailey, 2004). However, irrespective of this diversity, e-services involve quality levels that depend on how effectively buyers can search, find, and obtain products that meet their needs (Boyer et al., 2002). Despite recent advances in service research (Hill et al., 2002; Verma et al., 2002; Bretthauer, 2004), the operations management literature offers no discernible insights into how e-service quality and Internet-retail margins are aligned. As reviewed in Table 1 (‘‘Main knowledge contribution’’ column), work in this literature has studied linkages between e-service quality

Table 1 A review and comparison of empirical e-service and Internet-retailing research in operations management

Conceptualization and empirical assessment of linkages between e-service quality and perceptions of performance, satisfaction, and loyalty among consumers

Contributed Theoretical Foundation

Contributed Empirical Approach

Exemplar Study

Study uses customer values to identify mean and fundamental objectives underlying e-service quality. A mean objective found was product information maximization. A fundamental objective was the decrease of time to deliver orders

None

Interviews with over 100 individuals. The interviews centered on the individuals’ perceived values about purchasing goods and services on the Internet vs. alternative channels

Keeney (1999)

Web content created by product users is rich in perceptions about product quality. These perceptions are split evenly among positive, neutral, and negative and offered comparisons among products

None

Data collection centered on Usenet group postings on a particular firm’s products. The data were perceptual and focused on the online user echelon

Finch (1999)

Order procurement (web navigation, product information, and price) and order fulfillment (product availability, timeliness of delivery, and ease of return) have significant association with loyalty

None

Secondary data on customer perceptions of service received from 52 Web retailers. Data collected at the retailer and customer echelons of the supply chain

Heim and Sinha (2001)

A model of e-service delivery and customer retention was developed. The model illustrates a fundamental hypothesis: service operations strategy aligned properly with the Internet target market requirements determines key buying factors and supports retention

Service-profit chain Product-process alignment in service operation management

Evidence from a case study positioned at the Internet-retailer echelon of the supply chain

Boyer et al. (2002)

Internet site usefulness and the number of hours working on a computer are linked to the degree to which the Web lowers perceived purchasing costs among users. Also, the perceived importance of standard purchasing, Internet site usefulness, and accuracy of information are linked to perceptions of website improvements in accounting accuracy

Technology acceptance Organizational behavior Models of service strategy content

Survey-based data collection from 416 customers of a major Internet retailer of office supplies. The data collection is centered on the online customer echelon

Boyer and Olson (2002)

An approach is developed and implemented for measuring the magnitudes of switching costs and the loyalty for users of online brokerage services. A main result suggests that high levels of service quality increase customer acquisition rates and loyalty

Switching cost theory Customer behavior theory on brand loyalty e-service theory

Objective data were extracted from click-stream data observed in encounters between 2900 customers and 11 online brokers. The data collection is centered on the online consumer echelon

Chen and Hitt (2002)

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Main Knowledge Contribution

15

16

Table 1 (Continued ) Contributed Theoretical Foundation

Contributed Empirical Approach

Exemplar Study

Customers’ service views and preferences affect their use of the Web as a purchasing medium. The views and preferences point to variation between loyal and opportunistic (price-sensitive) customers

Technology acceptance Organizational behavior Service strategy content

Survey-based data collection from 416 customers of an online retailer of office supplies. Data collection is centered on the online customer echelon

Olson and Boyer (2003)

E-service encounters with customers were examined to understand how they influence a non-profit organization’s perceived performance in its economic and mission orientations. Economic orientation pertains to sales while mission orientation pertains to the public’s education. Greater activity among users of the organization Internet site’s sale and educational services led to improvements in performance perceptions among e-service users

Service operations management theory Organizational behavior theory applied to a non-profit context

Survey-elicited perceptions of e-service utilization and performance from 242 patrons of the Chicago Symphony Orchestra. The data were collected at the customer echelon

Olson et al. (2005)

Internet customers have higher satisfaction levels with the order fulfillment process of convenience goods (e.g., groceries) and shopping goods (e.g., apparel) than specialty goods (e.g., computers)

Customer satisfaction theory

Secondary data on customer perceptions regarding service received from 256 online retailers. Data are collected at the retailer and customer echelons

Thirumalai and Sinha (2005)

Customer experience and the method involved in picking customer orders (DC based as opposed to store based) have a positive link with perceived service quality, product quality, and time savings, as well as with behavioral intentions of re-purchase

Theories on service quality, service operations strategy, and online buyer behavior

Perceptual data provided by 2985 shoppers responding to surveys administered to customers of five online grocers. Data collection centered on the customer echelon of the grocery supply chain

Boyer and Hult (2006)

The perceived quality of an online retailer’s site, products, and e-services will motivate shoppers to say positive things about the retailers, refer other customers to the retailers, and remain loyal to the retailers

Theories on service market orientation, service operations strategy, technology adoption, and service quality

Perceptual data provided by 2442 shoppers responding to surveys administered to customers of four online grocers. Data collection centered on the customer echelon of the grocery supply chain

Boyer and Hult (2005)

Three areas of e-service creation are identified: (1) the foundation of service, (2) customer-centered service, and (3) value-added service

None

Survey of 70 companies’ responsiveness Voss (2000) to inquiries via email sent through the companies’ websites and two focus groups of 30 customers

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Conceptualization of e-service quality roles in generating competitive advantage for retailers

Main Knowledge Contribution

Service operations A conceptual framework underscores the importance management of pursuing a service quality strategy to compete in e-commerce. Also, it suggests that there are economic advantages to be gained when service quality involves information about products sold, relative to differentiation involving physical services, such as fulfillment

Hallowell (2001)

Presents an operations management analysis of e-retail failures and proposes factors that profitable e-retailers need. Fulfillment processes and the ability to efficiently match customer demand with product availability are key for Internet-retailer success

None

Case studies of 12 Internet retailers. The case studies relied on archival records

Starr (2003)

Conceptualizing Internet content as the means to deliver customer service, the article underscores Internet sites’ interaction value and their design and functionality applications to create customer service

Theories on service marketing and competitive dynamics applied to a growth model

Interviews with officials from 10 companies, content analysis of 30 company websites, and case studies based on visits and interviews at six firms

Piccoli et al. (2004)

Online consumer purchases allow for greater transaction efficiency (in the form of list price discounts) when stock ownership in music CD supply chains is postponed to upstream echelons

Transaction cost theory Internet economics theory

Objective archival data were collected from 14 Internet retailers across 840 different CD titles

Rabinovich et al. (2003)

Increases in shipping and handling prices charged by Internet retailers lead to improvements in physical distribution service quality. However, product prices charged by Internet retailers have a negative effect on physical distribution service quality

Market microstructure Service operations management theory Search cost theory

Objective data extracted from 808 transactions between 25 online retailers and 229 shoppers via non-intervening techniques using Web-structured formats. The approach straddles the online retailer and customer echelons of the supply chain

Rabinovich and Bailey (2004)

Retail markups-e-service quality links are assessed. E-service quality and markups, as well as linkages between them are defined by availability of information about them in the market

Information economics. Service operations management theory

Objective data extracted from transactions across Internet CD retailer, customer, and wholesaler echelons via non-intervening techniques

Present study

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Development of empirical links between retail-pricing policies and e-service quality and supply chain management

Evidence from case studies positioned at the Internet-retailer echelon of the supply chain

17

18

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

and perceptions of performance, satisfaction, and loyalty among consumers. Research in this stream of literature has also conceptualized some e-service quality roles in generating competitive advantage for retailers and has empirically linked retailpricing policies to e-service quality and supply chain management. The goal of this paper is to contribute to the operations management literature through a study of the relationship between e-service quality and retail markups offered by CD retailers within a supply chain context. To meet this goal, the paper incorporates information economics theories to arguments used previously in operations management to study e-services (reviewed in the ‘‘Contributed theoretical foundation’’ column of Table 1). One of these information economics theories predicts the emergence of Internet-retailer strategies in which markups and e-service quality are positively related (Varian, 2000). This view suggests that, to target e-service-sensitive customers, retailers would offer superior e-service quality accompanied by high markups. In turn, to cater to buyers who care little about eservices, retailers would offer low markups along with inferior e-service quality. A second theory builds on work by Salop and Stiglitz (1977) to suggest that markups and e-service quality are inversely related because retailers price items to take advantage of disparities in consumers’ knowledge about transaction prices and service quality. According to this view, markets include both uninformed and informed buyers. While uninformed buyers choose not to search and observe only one offer before purchasing, informed buyers search extensively for bargains and observe multiple offers. If enough uninformed buyers limit their searching, it pays for retailers to target this uninformed group and charge a high premium but offer inferior service quality. Alternatively, firms would cater to informed bargain hunters and offer low premiums and superior service quality. Thus, because buyers differ in terms of their accessibility to information, this view suggests that markets are characterized by retailers, which settle for low markups in conjunction with superior e-service quality, and by retailers, which obtain high markups along with relatively inferior e-service quality. It is not yet known, however, whether these views by Varian and Salop and Stiglitz have a concurrent applicability on retailers’ adoption of e-service quality and markup strategies. This paper examines such a possibility: in studying the links between e-service quality and retail markups, it explains why and how

Internet retailers would pursue strategies defined by both of these theories. To that end, Section 2 (1) integrates the Varian and the Salop-Stiglitz theories to a service operation conceptualization of markups and e-service quality and (2) develops a framework linking e-service quality and margins. This framework relies on theoretical service operations models and the Varian and SalopStiglitz views to explain ways retailers align margins and e-service quality to position themselves competitively in both online markets and the supply chains that support those markets. The development of this framework is essential in articulating the propositions necessary to understand retailer strategies that reconcile these service concepts and information economic views. Section 3 details these propositions and, in doing so, frames online retail conduct not previously considered by e-service authors. These researchers have mainly focused on studying buyers’ stated preferences about e-service quality (e.g., Keeney, 1999; Heim and Sinha, 2001) or have studied subjective evidence regarding the e-service quality– profit link among retailers (e.g., Hallowell, 2001, 2002). Thus far, scholars have not endeavored to empirically examine why and how retailers actually link their premiums and e-service quality. This paper’s methodological and operational approach relies on nonreactive, objective data to generate results that take us beyond the current state of empirical understanding in the literature (see Table 1’s ‘‘Contributed empirical approach’’ column). Sections 4 and 5 detail the methodological and operational approach. The final two sections discuss the statistical analyses and results used to examine the propositions, as well as the conclusions, contributions, implications, and future research opportunities stemming from this paper. 2. E-service quality and markups in Internet retailing supply chains In justifying their offers, retailers must learn not only how to price their transactions with buyers and suppliers (Rabinovich et al., 2003) but also how to add value through e-services at the heart of those transactions (Zeithaml, 2002). To that end, retailers may draw from Roth and Menor (2003) service operations management framework and seminal formulations put forth by Sasser et al. (1978), Fitzsimmons and Sullivan (1982), and Roth and Van der Velde (1991) to (1) conceptualize their e-services and (2) design unique dimensions necessary to deliver e-service quality to customers.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

2.1. Defining the e-service concept and designing its quality dimensions Roth and Menor (2003) framework leads us to define the e-service concept in terms of facilitating goods (e.g., CDs) and explicit and implicit service elements. Explicit elements comprise backroom tasks provided to buyers during their fulfillment encounters with retailers (Stewart, 2003a). Implicit elements complement explicit elements because they involve storefront information about goods. This information allows retailers to represent their offers more accurately, match their product supply and demand more effectively, and, thus, enhance the explicit service elements provided to customers during the fulfillment of their orders (Hill et al., 2002). In turn, the design of the delivery system of this eservice concept, along with its explicit and implicit service elements, is defined by infrastructural choices necessary to achieve e-service quality. These choices depend on two primary quality dimensions concerning tasks and tangibles in service encounters enabled by information technology (Stewart, 2003a). A primary dimension of e-service quality centers on fulfillment tasks (Wolfinbarger and Gilly, 2003). Termed Physical Distribution Service (PDS) quality (Rabinovich and Bailey, 2004), this dimension embodies three sub-dimensions reflecting explicit service capabilities in inventory availability and order delivery. PDS quality is first characterized by the availability of inventory to match consumer orders (by either retailer- or supplier-owned inventory). Second, PDS quality depends on the prompt delivery of ordered goods to consumer destinations. Third, it is a function of how well online retailers meet the performance promises they make to buyers during the initial stages of their encounters. The latter sub-dimension of PDS quality is an outcome of the former two sub-dimensions (Rabinovich and Bailey, 2004; Berry et al., 1994). While the quality of PDS, like that of other services, must ultimately be judged by how well actual operating performance matches promised performance (Kellogg and Nie, 1995), a holistic assessment of PDS quality must predict this outcome from the operational ability to source and deliver orders during the fulfillment process (Melnyk et al., 2004). Another primary dimension of e-service quality, ‘‘search efficiency’’, is a function of the implicit service buyers receive through their access to tangible information about items available at online retail sites (Zeithaml et al., 2002). As information online becomes

19

increasingly available, search costs for products and product-related information decline (Bakos, 1997). Thus, search efficiency affects the judgments that both consumers and Internet retailers make regarding the quality of their e-service encounters (Zeithaml et al., 2000; Wolfinbarger and Gilly, 2003; Liu and Arnett, 2000). Scholars have also identified system availability and privacy as two other dimensions of e-service quality. ‘‘System availability’’ is a function of the proper operation of the Internet site, and ‘‘privacy’’ reflects the retailer’s ability to ensure the security of customers’ transactions (Zeithaml et al., 2002, 2004). However, buyers do not perceive system availability and privacy to be significant in predicting e-service quality (Zeithaml et al., 2004) because these dimensions appear to be subsumed in the search efficiency obtained by buyers. This concurs with evidence suggesting consumers are prone to trust websites that allow them to search and find multiple offers easily (Jarvenpaa and Tractinsky, 1999). More important, system availability depends on the time a retailer has been in operation. In turn, Privacy depends on the visibility and recognition a retailer has achieved. As a retailer’s visibility and recognition grows, buyers’ become more willing to join the retailer’s network of customers and trust the retailer with their purchases and personal information (Stewart, 2003b). Thus, the study of e-service quality must focus on two primary dimensions, namely PDS quality and search efficiency. However, it must also include measures of retailer longevity and visibility and recognition to account for system availability and privacy. To do so, the study must position retailers and e-service quality dimensions in a supply chain context. This setting incorporates retailers’ transactions with not only consumers, as the recipients of e-services, but also wholesalers, as the retailers’ most immediate suppliers. As a result, this setting allows us to consider integration choices put forth by Roth and Menor (2003) for the design of e-service delivery. Such choices are pivotal in an online context, where operational policies are tied to functional boundaries of service operations spanning supply chain echelons beyond those occupied by the retailer (McDermott et al., 2001). 2.2. Targeting markets by linking e-service quality to retail markups: competing theories The conceptualization and design of e-services and e-service quality dimensions are essential prerequisites to connect retailers’ infrastructural and integration

20

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

choices to markups and, ultimately, to articulate conducts that will allow retailers to profitably target their markets with their e-services (Boyer et al., 2002). It is possible that Internet retailers adopting a particular conduct may find themselves targeting markets in which buyers are freely able to search and to find the lowest-priced goods. Thus, according to information economics theory, these retailers would have no choice but to price uniformly at cost and to render their e-services irrelevant (Bakos, 1997, 1998). In practice, however, prices in most online markets appear not to have converged to a uniform equilibrium, where all retailers price at cost and reduce their markups to zero (Clay et al., 2001). One explanation is that searching for offers on the Internet is a more intricate process than initially thought. Customers spend sizeable resources searching and processing Internet-retail offers, many of which involve multiple dimensions of e-service quality (Clemons et al., 2002). According to information economics theorists (Varian, 1980), these consumer search costs, coupled with offers involving many e-service features, imply that Internet retailers could justify their prices by their ability to markup products in order to target buyers across two segments. The first segment includes service-conscious patrons willing to pay high markups, while the second segment includes price-sensitive customers with little regard for service quality. Thus, in some cases, retailers would target buyers by providing superior e-service quality in return for high markups, while, in others, retailers would offer low

markups coupled with inferior levels of e-service quality. Based on this logic, Varian (2000) predicted the emergence of online retail conduct where superior eservice quality accompanies high markups and inferior e-service quality is coupled with low markups. This conduct concurs with market-targeting principles presented by Roth and Van der Velde (1991), who proposed that quality be aligned with market requirements (Fig. 1). Information economics’ tenets by Salop and Stiglitz (1977) also showed that sellers could discriminate among buyers and target different market segments with services and premiums. By offering a variety of margins and service quality levels, sellers could exploit disparities in buyers’ knowledge about markups and service attributes. But, unlike Varian, Salop and Stiglitz posited that sellers could charge markups that run contrary to the quality of services they afford. As shown in Fig. 1, this view suggests the emergence of inferior service quality/high markup (i.e., ‘‘bad’’) firms catering to uninformed buyers (or ‘‘tourists’’) and superior service quality/low markup (i.e., ‘‘good’’) firms catering to informed buyers (or ‘‘natives’’). Thus, whereas the Varian theory predicts that retailers align markups to e-service quality offered to buyers, Salop and Stiglitz argue that, theoretically, disparities in buyers’ access to information would invert such alignment. This latter view concurs with theory presented by Stewart and Chase (1999), who posited that e-service strategies are linked to differences in knowledge among buyers (Fig. 1).

Fig. 1. E-service quality and markup strategies for the targeting of Internet-retail markets: a competing theory framework.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

3. Integrating the competing theories: the role of imperfect information in linking e-service quality and markups Although Fig. 1 juxtaposes two views linking e-service quality to markups, it fails to explain why a retailer would adopt a conduct involving these two views, or how the retailer might do so. To overcome this deficiency, it is necessary to study linkages between markups and e-service quality, while considering heterogeneity in the access to information about prices and service outputs and processes in transactions between retailers and customers (Karmarkar and Pitbladdo, 1995). A key information economics tenet recognizes that information in transactions has degrees of imperfection because managing access to it can favor sellers to the detriment of their buyers (Stigler, 1961). Incorporating this tenet into the competing-theory framework in Fig. 1 makes it possible to assess why an online retailer’s conduct may be anchored in dissimilar information access regarding markups and several dimensions of e-service quality offered during transactions with customers. 3.1. Why is information on markups imperfect in Internet-retailing markets? Markup information available to consumers in Internet-retailing markets is imperfect because customers are restricted in their ability to assess how retail prices diverge from retailers’ costs in sourcing the products. On one hand, buyers may be able to obtain information on each item’s retail price, relative to its list price, which corresponds to the product’s suggested retail price. According to Rabinovich et al. (2003), the disparity between retail and list prices corresponds to the item’s list price discount (i.e., the item’s retail price minus its list price), and information about this disparity is widely available to consumers. In fact, many online retailers use it to attract price-conscious buyers. For example, a retailer may attract bargain hunters by claiming that certain items are priced 30% below their list prices. In this case, the retailer’s list price discount would equal to a negative 30%. Moreover, this information allows customers to infer markups, because an item’s list price discount relates directly to that item’s markup (Rabinovich et al., 2003). Since list price discounts are widely accessible on the Web, they yield symmetric markup information for retailers and buyers.

21

Conversely, information on actual markups (measuring the disparity between retail and wholesale product prices) is asymmetric — that is, it is only available to online retailers, not to consumers. Since retailers control access of wholesale prices to consumers, retailers can align actual markups to quality in their e-services, thereby obtaining the greatest benefit for themselves, not the consumers (Kirmani and Rao, 2000). 3.2. Why is information on e-service quality imperfect in Internet-retailing markets? Information on e-service quality is imperfect because customers’ access to the two principal dimensions of e-service quality is not uniform during their transactions. Findings by Roth et al. (1997) suggest that this accessibility defines retailers’ representations in the design of their e-service delivery. Also, this accessibility impacts buyers’ perceptions of service quality (Soteriou and Chase, 1998). A buyer who knows what results are wanted from a product can often make sure that the item yields these results before buying it online. Thus, the buyer may assess search efficiency in retailer encounters prior to purchasing the item. Since search efficiency builds on information buyers use to look for and to find goods for subsequent purchasing, this dimension’s design centers on a service representation defined by perfectly accessible search qualities (Karmarkar and Pitbladdo, 1995). However, PDS quality and the information buyers need to assess it remain unknown until after this eservice quality dimension is rendered. PDS quality hinges on a representation comprising experiential aspects that buyers can assess only after they have incurred monetary expenses and delays necessary to receive their orders (Harvey, 1998). This is because online buyers can only estimate the expected PDS results that they will receive prior to purchasing. Also, the accuracy of those PDS expectations depends on the retailers’ ability to project and share precise information about inventory availability and order delivery. This leaves buyers with no recourse but to assess PDS quality based on how the results meet expectations after they have purchased (Rabinovich and Bailey, 2004). 3.3. How do imperfections in markup and e-service quality information shape Internet-retail conduct? The search and experiential aspects, along with their service representation designs for search efficiency and PDS quality, define the causality of these e-service

22

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

quality dimensions with respect to actual markups and list price discounts and, thus, shape the conduct of online retailers during their encounters with customers (Goldstein et al., 2002). During these encounters, customers and Internet retailers engage in loosely coupled contacts from the time customers land on the retailers’ home pages until customers receive their orders (Boyer et al., 2002). Like contacts in offline settings, these contacts are defined by the timing of creation behind infrastructural choices in the e-service concept — evidenced by search efficiency and PDS quality. These contacts also depend on their interdependence with customers. This interdependence is defined by how extensively customers can shape their search efficiency and PDS quality outcomes (Kellogg and Chase, 1995). In the case of search efficiency, its search-cost outcomes result from customers actively being able to look for and process available product information at retail websites in order to find suitable items prior to committing their purchases. Search efficiency outcomes not only require interdependency with consumers, but also precede the price premiums online retailers are able to command in their transactions with consumers. Thus, search efficiency impacts retailers’

ability to mark up their items (Lynch and Ariely, 2000; Karmarkar and Pitbladdo, 1995) — it affects retailers’ pricing policy decisions on list price discounts and actual markups in transactions with consumers. On the other hand, PDS quality outcomes result from a service representation composed of experiential aspects. The outcomes will only be apparent to buyers after they have paid and waited for their orders to arrive. Also, once customers have made their purchases, they will have virtually no influence or control over PDS quality outcomes. Since prices charged by retailers chronologically precede PDS quality outcomes, and since those outcomes require no interdependence with customers, retailers will set up premiums for PDS quality that consumers can neither observe nor influence in advance. Thus, retailers’ pricing policy decisions regarding list price discounts and actual markups are expected to impact PDS quality in transactions with consumers (Rabinovich and Bailey, 2004). Fig. 2 illustrates the role that information imperfections in e-service quality and markups play in Internetretail conduct, according to the framework in Fig. 1 and the causalities developed above. The theoretical structure in Fig. 2 decouples symmetric markup information from asymmetric markup information

Fig. 2. Imperfections in markup and e-service quality information and Internet-retailer strategies.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

and separates search attributes from experiential attributes in e-service quality. This theoretical structure draws from contingency theory to explain how an online retailer’s conduct might be based on coexisting markup information that is either directly or inversely aligned to e-service quality dimensions — in accordance with the views by Varian (1980, 2000) and Salop and Stiglitz (1977). The structure casts a diverse set of retail conducts that may originate from multiple contingencies. These contingencies are rooted in the information economics and service operations management tenets presented above: they contrast markup information symmetry against information asymmetry among consumers and differentiate between consumers’ exposure to experiential and to search attributes in e-service quality. Also, in line with equifinality principles put forth by Van de Ven and Drazin (1985) and Gresov (1989), these contingencies will yield several equally feasible options for retail conduct in Internet markets, as defined by the Varian and the Salop-Stiglitz views. To discern these retail conduct options from multiple contingencies, the theoretical structure in Fig. 2 also controls for potential effects on list price discounts and actual markups originating from the two secondary dimensions of e-service quality (system availability and privacy) developed in Section 2.1. Empirical applications of information economics theory point to a direct link between these dimensions and retail-pricing decisions, reflected in list price discounts and actual markups (Pan et al., 2002). In addition, Fig. 2 incorporates two contextual attributes (CD popularity and CD time in market) identified by e-services authors (e.g., Rabinovich et al., 2003) and information economists (e.g., Latcovitch and Smith, 2001) as determinants of list price discounts and actual markups in online CD markets. The addition of these dimensions and attributes to the model helps preserve the substantive and generalized validity necessary to develop and to test the propositions in a manner that is consistent with conditions surrounding online transactions (Gresov and Drazin, 1997). 3.4. Theoretical propositions On the Internet, symmetric markup information, in the form of list price discounts, is amenable to searching by consumers at virtually no costs. Moreover, information economics theory suggests that a consumer’s ability to scour the market effortlessly, acquiring and comparing list price discounts, compels sellers to offer a quality of service that is consistent with that information (Tellis and Wernerfelt, 1987). For instance, if a retailer

23

posts a retail price approximating or exceeding the list price, but offers inferior e-service quality, the retailer is unlikely to receive new or repeat business from eservice-conscious customers. Moreover, this retailer will forfeit revenue from price-sensitive consumers, who will be unlikely to buy at prices near or greater than the list price. If price-sensitive consumers account for a significant portion of demand in the market, then retailers who provide inferior e-service quality, but who also signal prices that are close to or exceed list prices, will be unable to remain in the market unless they lower their prices in relation to the product list prices (Kirmani and Rao, 2000). Alternatively, to remain viable by luring and retaining e-service-conscious buyers, the retailers should improve their e-service quality to match their high prices in relation to the product list prices (Karmarkar and Pitbladdo, 1995). This view has received indirect empirical support from studies examining retailing markets, both online (Alba et al., 1997) and offline (e.g., Geistfeld, 1982; Hjroth-Andersen, 1984). These studies suggest that while markets exhibit great variation, there is a positive link between service quality and price information available to buyers. The strength of this association depends directly on how accessible price information is to buyers. Thus, when buyers can freely access price information, Internet retailers will be compelled to provide e-service quality that concurs with that information in order to uphold their market base and remain competitive. Because list price discounts give buyers the chance to assess retail prices relative to those in the industry, and since they allow buyers to infer markup information as a function of the difference of the items’ retail prices minus their list prices, list price discounts are expected to relate directly to search efficiency and PDS quality. Specifically, since search efficiency depends on information that buyers seek, access, and assess prior to purchasing, search efficiency gains are likely to increase list price discounts. Proposition 1a summarizes this effect, consistent with Varian’s view: Proposition 1a. In transactions in Internet-retailing markets, an improvement in search efficiency would lead to an increase in list price discounts. In turn, PDS quality comprises experiential aspects that consumers can assess only after purchasing from online retailers, and that allow for virtually no customer interactivity. Since PDS quality involves a postpurchase and a non-interdependent set of results for consumers, buyers’ search costs are not necessarily the dominant influence on pricing decisions by Internet

24

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

retailers (Rabinovich and Bailey, 2004). However, retailers may still mark up their product prices in relation to their list prices to signal a superior PDS quality that cannot be evaluated as part of the online search experience, but that can be easily assessed with respect to list price discounts upon purchasing (Harvey, 1998). These service operations management arguments concur with Varian’s view and suggest transactions where list price discounts have a positive impact on PDS quality. Since list price discounts correspond to each item’s retail price minus its list price, list price discounts’ positive effect on PDS quality will involve a rise in the items’ retail prices, which will converge to list prices. Thus, as summarized in Proposition 1b, increases in retail prices in relation to list prices will yield PDS quality improvements in online retailer transactions with customers. Proposition 1b. In transactions in Internet-retailing markets, an increase in list price discounts would lead to an improvement in PDS quality. The attributes underlying search efficiency yield information on verifiable item traits that will reduce a buyer’s dissatisfaction from disappointing purchases (Alba et al., 1997). In providing search efficiency, online retailers implicitly set product prices to (1) cover costs incurred to maintain and convey information on product qualities, (2) address consumers’ value from this dimension of e-service quality, and (3) match prices available from other firms, including wholesalers and other retailers. Online retailers do not charge buyers for search efficiency piecemeal. Instead, retailers use search efficiency to holistically add value beyond what wholesalers could offer. Furthermore, search efficiency allows online retailers to increase their prices, relative to those charged by their wholesalers, if competing retailers cannot easily replicate the same dimension and make the same degree of search efficiency available to consumers at minimum search costs (Chellappa and Kumar, 2005). Thus, actual markups will reflect a rise in retail charges over wholesale prices when Internet retailers offer traits in their search efficiency dimension of eservice quality that allow buyers to match goods with their interests in a manner that exceeds what is available elsewhere, either on or off the Internet. Search efficiency is important for the completion of Internet transactions because it can reduce ‘‘lack-of-fit’’ costs for buyers before the purchase (Bakos, 1997; Lynch and Ariely, 2000). As summarized in Proposition 2, and in line with Varian’s view, search efficiency will lead to

high retail prices relative to wholesale prices because it enables an Internet retailer to ensure a greater likelihood that customers’ purchases will be close to their ideal specifications. Proposition 2. In transactions in Internet-retailing markets, an improvement in search efficiency would lead to an increase in actual markups. But what if markup information is asymmetric and eservice quality is experiential? In this case, Salop (1977) showed that sellers segregate customers into: (1) inefficient searchers (those buyers exposed to high search costs due to imperfect information), and (2) efficient searchers (those buyers not exposed to search costs caused by imperfect information). In online retailing, this exposure to search costs can segment markets. This is because variations in buyers’ search aversion lead buyers to place different values on asymmetric information on actual markups in relation to e-service quality based on experiential attributes (Zettelmeyer, 2000). Thus, when e-service quality comprises experiential attributes, such as PDS quality, Internet retailers may offer, in some cases, actual markups that are relatively high and, subsequently, afford experiential e-service quality that is inferior to that of other competitors in order to target inefficient searchers exclusively. After all, these buyers are not sensitive to asymmetric information on markups vis-a`-vis the experiential e-service quality they receive after paying for these markups. These consumers’ search inefficiency leads them to give no consideration to the experiential quality of e-services or to assess this quality only after purchasing. In choosing where to purchase, these buyers are unlikely to seek pricing information for items and retailer ratings on experiential e-service quality from information agents such as Bizrate.com or sources like Consumer Reports. There may be other cases in which Internet retailers offer efficient searchers actual markups that are relatively low. Subsequently, retailers will afford a level of experiential quality in e-services that is superior to what these search-prone, highly e-service-conscious consumers are likely to experience if they had purchased at other Internet market sites. In these instances, retailers realize that there is a cluster of consumers with the knowledge, the patience, and the wherewithal to compile, compare, and act upon pricing information and experiential e-service quality. For example, these savvy consumers could opt to cancel their orders or return them and shop elsewhere if they realize that the experiential eservice they receive post-purchase does not merit the price paid.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Thus, these arguments concur with the SalopStiglitz view and suggest that there is an inverse correspondence between actual markups and PDS quality. Since buyers are offered actual markups before they are afforded PDS quality in a non-interactive fashion, Proposition 3 states that: Proposition 3. In transactions in Internet-retailing markets, an increase in actual markups would lead to a decrease in PDS quality. 4. Research methodology Beyond its dynamic landscape, several aspects of Internet CD retailing make it well suited to test the propositions. Focusing on CD retailing contributed to the study’s external validity, as defined by Cook and Campbell (1994), because findings in this setting have implications for a broader e-commerce spectrum. Relative to other sellers, online CD retailers are highly constrained in their ability to markup their items (Rabinovich et al., 2003). This condition provides a critical setting to test the true ability of sellers to align their list price discounts and actual markups with eservice quality. Results necessary to assess the propositions in this market should also be evident in other areas, where retailers have a greater autonomy to manage their prices in relation to e-service quality. There are two reasons why an online retailer is highly constrained in its ability to price its CDs. First, CDs are standardized items. Although e-service quality might act as a differentiator in retailing offers, CDs are readily amenable to searching and comparing by online buyers, who could assess many offers and find the lowest-priced CDs (Daripa and Kapur, 2001). Second, a few large firms dominate CD recording and a few major wholesalers handle the distribution of CDs for subsequent sale to consumers through a vast number of retailers. As a result, the concentration of sales is very high in the upstream echelons of the CD supply chains, but very low in the retail echelon (Harchaoui and Hamdad, 2000; Gallaway and Kinnear, 2001). Under these conditions, online CD retailers are likely to have little leeway in setting their prices above the prices they take from their upstream supply chain vendors. A focus on Internet CD retailing also contributed to the internal validity of this study’s findings (Rosenthal and Rosnow, 1991). First, this area of e-commerce is among the most prominent and diverse, and it enabled the inclusion of a number of retailers. This condition helped minimize selection bias and reduced matura-

25

tion threats resulting from focusing on individual retail firms in the sample. Second, studying online CD retailing incorporated a variety of titles available across retailer and wholesaler echelons in the supply chain. Thus, it contributed to preventing possible biases in the study that might have resulted from a disproportionate attention to items that are not representative of wider market demand conditions. Third, unlike other similar commodities (e.g., books), CDs are physically homogenous products, i.e., CD volume dimensions and weight are uniform across titles. Hence, volume and weight do not bias PDS quality across individual CDs. This condition helped avert instrumentation threats to the validity of the empirical examination of the propositions. 4.1. Context: Internet retailers as intermediaries in the music CD supply chain The methodology used to test the propositions builds on a field study of Internet CD retailers. These retailers act as intermediaries by selling inventories of titles sourced from the wholesaler echelon, once consumer orders arrive at the retail sites. Thus, the study examines transactions that are positioned in supply chains spanning wholesaler, Internet retailer, and consumer echelons. Moreover, the study imposed several controls that ensured a reliable and precise empirical testing of the Internet-retailing conduct underlying the propositions. First, the study only considered CD orders originating from a single wholesaler and, thus, controlled for differences in marginal costs that may affect markups. The firm selected for the study is a major U.S. wholesaler of entertainment products (e.g., CDs, videos). A confidentiality agreement prevents the disclosure of the firm’s identity. Second, the study ensured uniformity in the link between PDS quality and actual markups and list price discounts by controlling for PDS costs in each of the orders. First, the weight of the orders was constant: each order comprised only one CD. Second, all orders shared a unique delivery destination. Third, all orders’ fulfillment was carried out through drop-shipping arrangements. In these arrangements, the wholesaler owned, and held at a single facility, the CDs needed to fulfill the orders, and then shipped the CDs at the retailers’ request to the delivery destination. In return, the wholesaler collected uniform shipping and handling fees from the retailers for each order fulfilled. Finally, to control for potential price discrimination policies by Internet retailers with respect to consumers

These retailers offered comprehensive information on physical distribution terms and policies prior to receiving shopper purchases. Several information aspects were considered: product availability status; expected order delivery time; identity of carrier involved in order delivery; order tracking; unconditional receipt and issuance of refund, if products are returned; ability to cancel order prior to shipment and at no charge for the shopper; and disclosure of shipping and handling price prior to payment. b The coverage obtained from this sampling approach is consistent with previous Internet studies. A study by Brynjolfsson and Smith (2000) included 20 book titles and 20 CD titles, out of a total of about 3 million titles carried by online retailers at the time data was collected. Furthermore, Chen and Hitt (2002) sampled transactions generated by less than 0.1% of Internet users at the time stock brokerage transactions were collected. Clemons et al. (2002) sampled 939 unique online airline ticket transactions over 4 days, out of hundreds of thousands ticket transactions carried out on the Internet over the same time period in the U.S. alone.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

a

26

Table 2 Sampled Internet CD retailers and sampled, available, and fulfilled music CDs

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

that may bias list price discounts and actual markups measures, all field-study data were collected through commercial transactions involving a single shopper. This shopper ordered a sample of CD titles across the set of studied Internet retailers. 4.2. Data collection 4.2.1. Retailer selection Data collection initially focused on 21 online retailers served by the wholesaler. While these retailers all offered a wide CD selection, all provided varying levels of information on CD attributes on their sites, and all offered fairly common and uniform distribution services, not all of them provided the same information on physical distribution terms and policies. Because such inconsistencies likely influence buying preferences (Esper et al., 2003), they may bias measurements for actual markup and list price discount. To account for this, the study included 11 of the retailers who offered fully comprehensive information on their physical distribution terms and policies (Table 2). Based on the methodology proposed by Sampler (1998), these firms afford external validity to the study’s results. At the time data were collected, these 11 retailer sites accounted for 75.11% of the total visits to Internet sites selling CDs, as recorded by comScore Networks. 4.2.2. Title sampling and purchasing A random sample of 339 titles was drawn from an original set of 11,134 CDs. This original set was sold under drop-shipping arrangements between the wholesaler and the sampled retailers during the second half of 2001. By relying on real purchases to sample the titles, the study could test the propositions under real market conditions and substantiate the study’s external validity. The 339 CDs were assigned to the 11 participating retailers using a uniform distribution (see ‘‘Sampled CDs’’ column in Table 2). To preserve independence among observations, each title was assigned exclusively to only one of the 11 retailers. To minimize longitudinal biases in assessing the propositions, the shopper bought the CDs right after the titles were sampled. To preclude a selection bias that could have threatened the study’s internal validity, only those titles that all retailers could process are included in the scope of this study. As Table 2 indicates, 291 titles were purchased independently of each other during four rounds in March, April, June, and September 2002, respectively. Of these 291 titles, 16 could not be

27

fulfilled, leaving a final group of 275 titles in the study (‘‘Fulfilled CDs’’ column in Table 2). In collecting the data, it was also critical to preserve the internal validity in assessing the links tying PDS quality with actual markups and list price discounts. To accomplish this, the shopper chose uniform PDS options across all purchases: all titles were bought individually, using the standard U.S. Postal Service ground delivery option. This, in addition to an emphasis on wholesaler drop-shipping from a single stocking facility to a common delivery destination, ensured an instrumentally unbiased assessment of the links involving PDS quality, actual markups, and list price discounts. Also, these conditions helped preserve uniform marginal costs for PDS quality across purchases. Finally, two tests assessed whether the sample differed systematically from the original set of 11,134 CDs. First, the list-price average of the 275 CDs fulfilled was compared against the list-price average of those titles in the original set of 11,134 CDs that were not part of the study. An independent-sample t-test failed to show a statistical difference between the list-price averages across the two groups (t-value = 1.248, p = 0.212). Second, the average transit time to deliver the 275 CDs was compared against the average transit time to deliver 1348 CD orders to online buyers. The 1348 orders were sampled from the transactions comprising the original 11,134 CDs. Again, a t-test did not detect a statistical disparity in the transit time averages across both groups (t-value = 0.239, p = 0.811). 5. Operationalization and testable hypotheses 5.1. Construct operationalization The propositions that constitute the model in Fig. 2 involve four constructs and matching measures. Testing these propositions also required the use of measures of retailer longevity and visibility and recognition to control for markup variation caused by system availability and privacy. In addition, to control for theoretical effects on actual markups and list price discounts by CD popularity and CD time in market, the testing of the propositions required the inclusion of measures for these two contextual attributes. The remainder of this section and Table 3 specify these measurements to Internet CD retailing and describe their operationalization timeline and process. 5.1.1. Actual markups and list price discounts Actual markups and list price discounts were obtained at the time the shopper purchased each CD

28

Table 3 Operationalization summary and timeline of constructs and measurements

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

a

According to comScore Networks, Amazon accounted for the highest share (almost 50%) of recorded visits to any Internet retailing site selling CDs at the time data for this research were collected. This information is based on unit shipments (minus returns) from manufacturers to a wide range of accounts, including non-retail record clubs, mail-order houses, retail stores, units shipped for Internet fulfillment, and direct marketing sales on television. c To address internal validity, the number of visits to each site spanned the 4 months during which the shopper’s purchases took place. External validity in this measurement was addressed by collecting data through a panel of global Web users, assembled by comScore to track their Web browsing, buying, and other behavior. This panel’s members generated 2.6 million visits per month to 30,000 purchase-enabled retail sites tracked by comScore. b

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

at the retail sites. The markup measure (MARKUP) captures the difference between the CD price each retailer charged to the shopper and the wholesale price each retailer paid for the CD. MARKUP is measured in percentages to normalize it across heterogeneous wholesale prices for the studied CDs. Thus, as the percentage difference between retail price and wholesale price for a CD rises, MARKUP increases. The list price discounts measure (DISCOUNT) corresponds to the percentage difference between each CD’s retail price and the CD’s list price specified by the wholesaler. An increase in DISCOUNT reflects an increase in retail price for a CD, relative to its list price. 5.1.2. PDS quality Following work by Rabinovich and Bailey (2004), PDS quality is operationalized through time-based measures gathered after the shopper placed each order. This operationalization encompasses measures of inventory availability and delivery timeliness that lead to overall fulfillment reliability. Inventory availability relates to a measure of speed in locating and processing inventory for each order. This measure (AVAILABILITY) is inversely equal to the time needed to source the CDs from the wholesaler’s facility, after the shopper placed the orders. In turn, delivery timeliness is reflected in the speed to distribute orders, after goods have been sourced and released from inventory. Its measure (TIMELINESS) is an inverse function of the time to transport each order to the shopper’s destination, after the CDs have been released (or shipped) from inventory. The reliability measure (RELIABILITY) corresponds to the ratio listed at the bottom of Table 3. RELIABILITY spans the entire PDS process because it captures each retailer’s ability to match the actual performance in sourcing and in delivering each order with the performance expectations the retailer promised before each order was placed. Thus, RELIABILITY reveals how favorably retailers match actual and promised times to source and deliver each CD ordered by the shopper. Moreover, since RELIABILITY encompasses speed in sourcing and delivery, it is endogenous with respect to AVAILABILITY and TIMELINESS. While AVAILABILITYand TIMELINESS yield only intermediate PDS quality measures, RELIABILITY yields ultimate PDS quality measures. This is because an increase in RELIABILITY denotes a more favorable correspondence between actual and signaled times to source, as well as, deliver the ordered CDs. Thus, in line with Section 2.1, if AVAILABILITY or TIMELINESS increases, so will RELIABILITY.

29

5.1.3. Search efficiency The operationalization of search efficiency captured Internet-retailer conduct reflected in information availability for a specific CD at the retailer’s site. A latent variable (EFFICIENCY) measured this construct across transactions between each retailer and the shopper. Building on work by Rabinovich et al. (2003), this variable is reflected on four information indicators measured right before CDs were purchased from the retailers (Table 3). 5.1.4. Secondary dimensions of e-service quality Following Sections 2.1 and 3.3, Table 3 incorporates system availability as a retail measure (SYSTEM_AVAIL) equivalent to the time each online retailing site was in operation at the time each transaction with the shopper took place. Table 3 also incorporates privacy as a retail measure (PRIVACY) consistent with its conceptualization in Section 2. Therefore, PRIVACY is a function of site visibility and recognition in the market (Stewart, 2003b): it equals the number of visits to each retail site as a percentage of total visits to all retailers selling CDs on the Web. These visits were estimated during the 4 months when the shopper’s purchases took place. 5.1.5. CD time in market and popularity CD time in market (CD_VINTAGE) corresponds to the difference between each CD’s order date by the shopper and the CD’s release date. Based on work by Rabinovich et al. (2003), CD popularity is captured through a latent variable (CD_POPULARITY) with four indicators measured for each CD immediately before the shopper purchased it at its corresponding retailer (see Table 3). 5.2. Testable hypotheses The empirical analysis relies on path coefficients to evaluate the propositions through testable hypotheses. As illustrated in Fig. 3, the assessment of Propositions 1a and 2 suggests statistical tests of whether or not the corresponding path coefficients from EFFICIENCY to DISCOUNT and from EFFICIENCY to MARKUP are less than or equal to zero — i.e., whether or not to reject H1a NULL:gDISCOUNT EFFICIENCY  0 and H2 NULL:gMARKUP EFFICIENCY  0, 0, respectively. Likewise, Propositions 1b and 3 are evaluated by statistically testing the path coefficients tying the measures corresponding to list price discounts and actual markups with the measures for PDS quality. To capture PDS quality and account for endogeneity

30

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Fig. 3. Empirical model.

between the fulfillment-reliability measure and the measures for inventory availability and delivery timeliness, the model relies on RELIABILITY as an overarching PDS quality measure that is endogenous with respect to AVAILABILITY and to TIMELINESS. Therefore, in line with procedures outlined by Baron and Kenny (1986), the testing of the theorized effects between list price discounts and PDS quality (Proposition 1b) compiles the coefficient corresponding to the direct path connecting DISCOUNT to PDS quality’s terminal measure (RELIABILITY) with the coefficients in two indirect paths linking DISCOUNT to RELIABILITY via AVAILABILITY and via TIMELINESS. Proposition 1b will obtain statistical support if the following testable null hypothesis (H1b NULL) is rejected: bRELIABILITY

DISCOUNT

þ ½bAVAILABILITY

 ½bRELIABILITY

AVAILABILITY 

þ ½bTIMELINESS  ½bRELIABILITY

DISCOUNT  TIMELINESS 

0

compiles the coefficient corresponding to the direct path connecting MARKUP to RELIABILITY with the coefficients in two indirect paths linking MARKUP to RELIABILITY via AVAILABILITY and via TIMELINESS. Statistical validity support for Proposition 3 will depend on whether the following testable null hypothesis (H3 NULL) is rejected: bRELIABILITY

MARKUP

þ ½bAVAILABILITY

 ½bRELIABILITY

AVAILABILITY 

þ ½bTIMELINESS

MARKUP 

 ½bRELIABILITY

TIMELINESS   0

MARKUP 

(2)

6. Statistical analyses and results

DISCOUNT 

(1)

Similarly, the testing of the theorized effects between actual markups and PDS quality (Proposition 3)

As shown in Table 2, hypothesis testing relies on groups of transactions that involve common Internet retailers. Observations from these transactions are nested within retailers in a structure comprising two levels (transactions and Internet retailers). Following research by Hox (1998, 2002), both levels should be

Table 4 Correlation and standard deviation coefficients at the transactional and Internet-retailer levelsa

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

a Coefficients in the lower left-hand portion of the matrix correspond to transactional-level covariation. Coefficients in the upper right-hand portion correspond to Internet-retail-level covariation. All coefficients were calculated using a pseudobalanced approach (Muthe´n, 1989). Also, to obtain similar standard deviations across the measures and to facilitate a more efficient fitting of the modelimplied covariance matrix, the scaled standard deviations listed at the bottom and at the top were used in the transactional- and Internet-retail-level analyses (Bentler, 1995; Bollen, 1989). b A mirror image of Amazon’s CD sales ranking was used. This measure prevents indeterminacy from developing in the CFA. c A natural log scale was used to measure the indicator and preserve its normality, linearity, and Tau equivalency in its relationship with its factor. d A quadratic scale was used to measure the indicator and preserve its normality, linearity, and Tau equivalency in its relationship with its factor. e Because this measure is captured at an Internet retail-level, no standard deviations or correlation coefficients can be estimated for this measure at the transactional level of analysis. Transactionallevel correlation and standard deviation cells involving this measure were marked as not applicable (NA).

31

32

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

considered separately in a multilevel structural equation model (MSEM). An MSEM analysis accounts for the fact that the transactional observations in the nested structure are not independent. A stronger covariance of measures may follow when transactions involve the same retailer instead of multiple retailers. If the levels of analysis are confounded and the observations do not account for these sources of dependency, standard-error estimates will be smaller than they should be. This will lead to spuriously significant results in testing the hypotheses. In addition, confounding the levels of analysis will yield erroneous conclusions drawn from a data set that purports greater homogeneity than the data actually have. Moreover, if the analysis aggregated all transaction measures at the Internet-retailer level, the number of observations would be reduced and the hypothesis testing would unnecessarily lose statistical power. Conversely, if retailer measures (i.e., SYSTEM_AVAIL and PRIVACY) were disaggregated to a transactional level, the number of observations concerning the measures would be overstated. This condition would generate spuriously significant coefficients in the paths involving SYSTEM_AVAIL and PRIVACY. The approach used to generate MSEM results was a maximum-likelihood estimation. The first analysis level assessed transaction variability within each retailer and pooled across the sample. At this transactional level, the MSEM evaluates, for each transaction, the variance of most construct measures, except for the retail-level measures (SYSTEM_AVAIL and PRIVACY), with respect to their corresponding retailer mean and across the retailers. The second level accounts for variability among retailers. Accordingly, it examines retailer-level covariation based on all construct measures. Thus, the MSEM considers two covariation levels: one at the transactional (intra-retailer) level, and a second one at the Internet retailer (inter-retailer) level. Table 4 lists the standard deviations and correlations corresponding to these levels. Following Anderson and Gerbing (1988) two-step procedure and Hox (2002) MSEM approach, the values in Table 4 were

used to assess the measurement model for the latent variables. Then, these values enabled the hypotheses’ testing at the transactional and Internet retailer levels of covariation (Fig. 4). 6.1. Measurement model The goal of the measurement model is to assess whether the indicators chosen to reflect the two latent variables (EFFICIENCY and CD_POPULARITY) yield a statistically reasonable representation of these variables. Since EFFICIENCY and CD_POPULARITY are transactional-level measures, a confirmatory factor analysis (CFA) was first conducted at the transactional level of covariation to achieve this goal. Obtained from the transactional-level correlation and standard deviation coefficients in the lower left half of Table 4, these CFA results are presented in Table 5. Next, a CFA was performed at the Internet-retailer level. Both latent variables were examined again, but in this instance, the measures for the indicators for the latent variables were positioned at the retailer level to obtain their correlation and standard deviation coefficients (in the upper right half of Table 4). These coefficients led to the CFA model results presented in Table 5. The results in Table 5 suggest a statistically reliable level of predictability and measurement quality in the CFA across the transactional and the Internet-retailer levels. The fit indices for the overall CFA model and for each construct suggest that the measurement model accurately reflected the underlying variance–covariance structure tying the indicator variables according to the criteria by Hu and Bentler (1999). Moreover, since all loadings are statistically significantly different from zero (0.05 level) and none of the standardized residuals is above or below 2.0 and 2.0, the results provide evidence of convergent validity (Anderson and Gerbing, 1988) and unidimensionality (Steenkamp and Van Trijp, 1991). Also, the composite reliability measures obtained independently for EFFICIENCY and CD_POPULARITY provide evidence of convergence validity

Fig. 4. An overview of the MSEM analysis procedure.

Table 5 Multilevel measurement model

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

a

A mirror image of Amazon’s CD sales ranking was used in modeling CD popularity. This measure prevents indeterminacy from developing in the CFA. A natural log scale was used to measure the indicator and preserve its normality, linearity, and Tau equivalency in its relationship with its factor. c Denotes loading and error-variance statistical significance at the 0.05 level. All loadings are significantly different from zero, providing evidence of convergent validity for the measurement model (Anderson and Gerbing, 1988). d A quadratic scale was used to measure the indicator and preserve its normality, linearity, and Tau equivalency in its relationship with its factor. b

33

34

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

across both covariation levels (Reines-Eudy, 2000). In turn, the values for the average variance extracted (AVE) obtained for the overall CFA model at the transactional and at the Internet-retailer levels and their relationship to the shared variance values in the same models, compare favorably with the criteria for discriminant validity prescribed by Fornell and Larcker (1981). Discriminant validity in the measurement model was further assessed by constraining the estimated correlation parameter between EFFICIENCY and CD_POPULARITY to equal 1.0. Subsequently, a difference test was performed using the x2 value obtained for this model and that obtained for an unconstrained model in which the latent variables were allowed to covary freely (Anderson and Gerbing, 1988). This difference test yielded a x2 value of 119.932 with 1 d.f. at the transactional level and 15.157 with 1 d.f. at the Internetretailer level. Since these values exceeded the test’s critical value (3.840, 1 d.f., p = 0.05), it was possible to conclude that the fit measure of the constrained model was significantly worse than the fit measure of the unconstrained model. The probability that EFFICIENCY and CD_POPULARITY accurately represent the same construct is less than 0.05, and consequently, a strong indication of discriminant validity was present in the measurement model. 6.2. Structural analysis at the transactional level Having confirmed the adequacy of the measurement model, this analysis portion examines the propositions empirically by testing the hypotheses at the transactional level of covariation. The testing is performed in accordance with the model in Fig. 3 and is based on the correlation and standard deviation coefficients among variables on the lower left half of Table 4. The results from the structural analysis at the transactional level are presented in Table 6, along with indices confirming a statistically sound and reliable fit of the model with respect to the data. The statistical results of the hypothesis testing are briefly summarized next, while a discussion of the results and their implications is presented in the next section. The standardized path coefficients (in the third column of Table 6) represent the strength of the potential relationships between constructs, and are used to test the hypotheses, as outlined in Fig. 3. The path coefficient from EFFICIENCY to DISCOUNT is positive and significantly different from zero (0.05 level), rejecting H1a NULL and supporting

Proposition 1a. Thus, an increase in search efficiency elevates Internet-retail prices for CDs closer to their list prices. Consistent with Varian’s view, this relationship suggests that e-service quality is aligned with symmetric markup information in transactions in Internetretailing markets. The empirical results in Table 6 also support the rejection of H1b NULL. Although the model failed to find coefficients that were significantly different from zero (0.05 level) in the paths linking DISCOUNT to AVAILABILITY and to TIMELINESS, it did find that the path coefficient between DISCOUNT and RELIABILITY was positive and significantly different from zero (0.05 level). It appears that mediation by AVAILABILITY and by TIMELINESS has no bearing on the assessment of Proposition 1b at the transactional level, when all PDS quality measures are considered jointly. In addition to this result, no AVAILABILITY or TIMELINESS mediation was found when testing procedures recommended by Baron and Kenny (1986) were performed. The individual and standardized effects of DISCOUNT on AVAILABILITY and on TIMELINESS (0.071 with p = 0.342 and 0.118 with p = 0.249) were not significantly different from zero. On the other hand, DISCOUNT was found to have a positive and significant standardized effect on RELIABILITY (0.342 with p = 0.023), when considered separately from AVAILABILITY and from TIMELINESS. Thus, these results support Proposition 1b and, in line with Varian’s theory, suggest that e-service quality is aligned with symmetric markup information in transactions in Internet-retailing markets. In particular, as CD retail prices in Internet transactions increase in relation to the CDs’ list prices, the PDS quality afforded by the Internet retailers goes up. The path coefficient from EFFICIENCY to MARKUP is also positive and different from zero at the 0.05 level and, therefore, H2 NULL is rejected. This result supports Proposition 2 and Varian’s direct alignment between actual markups and search efficiency in e-service quality. From Table 6, the standardized coefficient in the MARKUP-RELIABILITY path was negative and significantly different from zero (0.05 level). In turn, the standardized effects of MARKUP on AVAILABILITY and on TIMELINESS were not found to be significantly different from zero. The compilation of these results supports the rejection of H3 NULL. Moreover, it provides an initial indication that AVAILABILITY and TIMELINESS mediation does not influence the assessment of Proposition 3 at the transactional level, when all PDS quality measures are considered jointly.

Table 6 Structural equation model results at the transactional and the Internet-retail level of covariation

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41 a The results from the LaGrange multiplier test are univariate. Each probability measure reflects the likelihood that the difference in each path coefficient across the two groups of covariation is equal to zero. To conclude that each pair of path coefficients across the two groups of covariation are statistically different, the probability measure must be less than, or equal to 0.05. b p < 0.05. c In line with research by Rabinovich et al. (2003), the structural equation model was also analyzed while allowing MARKUP to determine DISCOUNT. The transactional-level coefficients obtained for the model’s paths were consistent with those reported in this table.

35

36

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Testing for AVAILABILITY and TIMELINESS mediation following procedures recommended by Baron and Kenny (1986) lent further support to this initial indication. The individual and standardized effects of MARKUP on AVAILABILITY and on TIMELINESS (0.147 with p = 0.199 and 0.037 with p = 0.415) were not significantly different from zero. In contrast, MARKUP was found to have a negative and significant standardized effect (0.471 with p = 0.003) on RELIABILITY, when considered separately from AVAILABILITY and from TIMELINESS. Thus, the results support Proposition 3 and concur with the Salop-Stiglitz view. Since MARKUP is a measure of asymmetric information on online retail premiums and RELIABILITY in PDS quality is experiential, retailers inversely align MARKUP and RELIABILITY to target profits from buyers who have a low valuation of actual markups relative to the PDS quality they receive. 6.3. Structural analysis at the Internet-retailer level This part of the analysis assessed whether retail conditions moderate the path coefficients obtained at the transactional level and whether this effect contributed to validate the propositions. To that end, it used the same paths as those used to test the hypotheses at the transactional level. It then used a covariance structure at the retail level (in Table 4) to obtain path coefficients for comparison against the coefficients obtained initially at the transactional level. LaGrange multiplier tests showed that the coefficients do not differ significantly across both covariation levels (Table 6). Thus, the results showed no divergence between retailer and transactional levels in testing the hypotheses. The MSEM at the Internet-retailer level also assessed the paths from the retailer measurements (SYSTEM_AVAIL and PRIVACY) to MARKUP and DISCOUNT. This assessment builds on the correlation structure in the upper right portion of Table 4. Results (in the fourth column in Table 6) showed that the coefficients linking SYSTEM_AVAIL and PRIVACY to MARKUP and DISCOUNT were positive but not significantly different from zero. 7. Conclusion The Web has revolutionized commercial transactions between sellers and buyers. Nowhere has this shift been more evident than in retailing markets, where the Internet has allowed consumers wider access to prices and premiums for many goods and e-services. This

study shows that these changes have permeated the management of customer and supplier relationships by Internet retailers. Specifically, this paper develops and tests a framework that integrates divergent theoretical relationships between markups and e-service quality, showing that participants’ access to information on markups in the online music industry plays a key role in e-service quality. When customers can access markup information (through list price discounts), retailers provide eservice quality directly aligned to that information. That is, when products exhibit prices that approximate or exceed list prices, retailers will offer buyers more information about the qualities of items. Moreover, as charges in retail transactions approach or surpass list prices, the quality of PDS in the transactions will be increasingly favorable in relation to the early PDS promises made to consumers. Directly aligning list price discounts with e-service quality helps uphold a costumer base in retailing markets. In the long run, building a costumer base is important not only for retailers in competitive markets, such as those found on the Web, but also for wholesalers who distribute the products sold in those markets. If wholesalers cannot rely on online channels to distribute their goods to consumers, they may become dependent on other channels, where they may not hold an advantage. In turn, when data on premiums are available to retailers and wholesalers, but not to consumers, retailers will align these data (through actual markups) with the product information they provide to consumers. This conduct is justified by retailers’ ability to add value to transactions with consumers in a manner that exceeds what wholesalers, or other retailers, may offer. By disclosing specific information on an array of products, retailers add value because they help consumers improve how they search and make buying decisions. In so doing, retailers provide a compelling online experience for experiential, browsing-prone customers, who are likely to exhibit lower price sensitivity and who are willing to pay higher premiums. Furthermore, retailers reduce the potential for disappointing purchases for consumers who may exhibit a more utilitarian, directed-search behavior. On the other hand, the study found an inverse alignment between actual markups and PDS quality, as measured by the percentage difference between the expected and actual time spent in sourcing and delivering consumer orders. Thus, the evidence suggests that the Salop-Stiglitz theory in Internetretailing markets is present in those dimensions of e-

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

service quality that are available for searching on the retailers’ websites prior to purchasing by consumers, but can only be assessed by consumers after they have purchased and have received the service. In some transactions, retailers choose to offer high actual markups to provide the appearance of being ‘‘good firms’’ by promising a superior PDS quality based on information displayed at their websites. However, they obtain profits by providing actual services (in coordination with wholesalers) that often fall well below the service quality they signal to consumers who display inefficient Internet searching capabilities. In other transactions, retailers signal information on PDS quality that is inferior to their capability. In so doing, the retailers are able to adhere to service plans that will justify their minimal actual markups to their wholesalers and to those consumers with efficient Internet searching skills. In sum, the results in this paper offer compelling evidence about links between service quality and pricing policies in online CD retailing. Note that retailers in this industry are subject to severe price competition and are highly constrained from unjustifiably marking up their product prices. This backdrop provides an acid test for the links between markups and e-service quality. The fact that the study finds evidence of these linkages in this price-competitive environment makes its findings all the more remarkable because they provide a strong indication that these linkages exist in other online settings, where competition does not so constrain retailers’ ability to price their products. 7.1. Research limitations and opportunities for future research Despite the support obtained from positioning the study’s empirical setting in the online CD-retailing market, the uncovered links between markups and e-service quality will not perfectly match those in other markets. The phenomena studied should thus be examined in other settings. Future work may analyze how alternative product and market characteristics influence links between markups and e-service quality. For example, these links might be amplified in markets for customized items (e.g., clothing), where e-services depend on pictorial displays. Such links may also strengthen in online retail markets that involve highpriced goods, such as jewelry and some electronics, where services that certify authenticity and guarantee product quality are critical to complete the transactions. Second, this research arrives early in the Internet’s development as a medium for conducting transactions

37

that span consumers, retailers, and wholesalers. Thus, some of the industry conditions considered here may not correspond to the realities of future Internet commerce. Future research may focus on studying how changes in industry conditions relate to the links between markups and e-service quality analyzed in this paper. Research extensions may also assess whether the adoption of online retail strategies that run contrary to the management of customer and supplier relationships uncovered in this paper contribute to long-run failure among retailers. Future work may also consider the effect that linkages between markups and e-service quality have on the ability of upstream supply chain firms to develop new business models that overlap with, or replace altogether, those models first developed by downstream retailers. For example, research may study whether links between markups and e-service quality drive wholesalers to take over those e-services offered by retailers. 7.2. Academic and managerial contributions and implications As called for by Amundson (1998), this paper incorporates principles from information economics theory into the service operations management literature to provide an understanding of why and how Internet retailers link premiums and e-service quality in their offers to consumers. This focus on objectively studying Internet-retail conduct extends service operation management research that has mainly focused on investigating customers’ stated preferences regarding eservice quality (e.g., Thirumalai and Sinha, 2005) or that has only considered subjective evidence about the e-service quality–profit link among retailers (e.g., Hallowell, 2001). The results in this paper provide empirical support to theoretical insights and propositions developed by Boyer et al. (2002): Internet retailers offer consumers levels of e-service quality linked directly to their transaction premiums. However, the results also highlight conditions under which Internet retailers inversely align their premiums with the e-service quality they provide. This adds to the conclusions by service operations empiricists suggesting an unequivocally direct and proportional relationship between e-service quality and Internet-retail prices (Rabinovich and Bailey, 2004). Retail premiums and e-service quality decisions appear not to run in the same direction when pricing information is not symmetrically available to consumers and when the e-service quality dimensions

38

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

are available for searching prior to purchasing by consumers, but can only be assessed by consumers after they have purchased and have received the service. Moreover, this study contributes to research methods in service operations management by advancing the use of non-intervening techniques at accessing, recording, and extracting data through the use of Internet technology. The use of this data collection methodology for testing the research propositions demonstrates how to perform a non-reactive and objective quantification of measurements across individual Internet-retailing transactions over time (through the course of online retail encounters) and space (across multiple supply chain locations), but without relying on contrived observations that would likely bias these measurements (Webb et al., 1981). With this methodological contribution, the paper goes beyond traditional emphases on the single Internet retailer (e.g., Starr, 2003) or customer echelon (e.g., Yang et al., 2004) to consider concomitant interactions across consumer, online retailer, and wholesaler echelons. Simultaneously, this methodological technique addresses data collection limitations in generalizability and in impartiality that are prevalent in some operations management studies relying on methodologies based on single-event observations in case studies (Voss et al., 2002) and on direct interviews with informants (e.g., Graham et al., 2004). According to Sechrest (1979), the methodological approach used in this paper is well suited to obtain an optimal coverage, as well as the measurement gradation and objectivity, necessary to study the conduct followed by a representative scope of Internet retailers responding directly to an array of purchases in their markets. From a managerial standpoint, this research highlights the importance of a service-oriented strategy in order to succeed in markets where profits were seemingly doomed to vanish not too long ago. Internet retailers will not succeed solely by cutting costs and aiming to become price leaders. Price leadership will yield suboptimal results for retailers, even in online markets where it is easy for buyers to search for the best offers. The lowest-priced retailers in these markets can lure bargain hunters, but price leaders will forego margins from buyers who either (a) seek great service quality or (b) take no advantage of the Web’s search capabilities — due, perhaps, to their familiarity with and loyalty towards particular online merchants (Heim and Sinha, 2001; Boyer and Hult, 2006). This paper also highlights e-service operations strategies that improve the bottom line and thus should be of interest for managers in online retail settings.

This research isolated a specialized set of strategies in terms of search efficiency and PDS quality because these e-service quality dimensions seem to carry great value for managers and buyers. Understanding the links between markups and e-service quality in the highly competitive CD retail context should give online retailers in less hostile areas a starting point for designing their service operations and deciding what type of information they should share with consumers about those operations. This process has been useful for offline firms (Hart, 1988; Cook et al., 2002), and it is likely that online retail managers will obtain similar benefits from this study’s benchmarks (Menor et al., 2002). Finally, the findings reported in this paper have implications for the development of sound relationships between Internet retailers and their suppliers in demand chain management settings analogous to that considered in this paper’s research methodology (Frohlich and Westbrook, 2002). Experts have attributed success in these kinds of relationships to the fact that Internet retailers have been financially prepared to share their margins with suppliers offering superior skills in supporting PDS quality (Rabinovich, 2005). However, retailers need not share their margins under all conditions. Retailers should consider sharing their markups when the PDS quality support they obtain from their suppliers allows them to retain buyers who are insensitive to asymmetric information on markups vis-a`-vis the experiential PDS quality they receive, while attracting savvy consumers who are prone to continue buying online. Acknowledgements The W.P. Carey School of Business and the Center for Services Leadership at Arizona State University provided financial support to fund this research. The staff at the music CD wholesaler and at the Recording Industry Association of America, as well as Janet McCabe (Senior Director) at comScore Networks Inc. provided valuable input for this paper. I would like to thank Thomas Choi and Manus Rungtusanatham at Arizona State University and Joseph P. Bailey and Philip T. Evers at the University of Maryland for their helpful comments on this paper. I also wish to acknowledge Bryan Ashenbaum’s data collection support and Robert Haynes’ copyediting assistance during the development of this paper. Finally, I am grateful to the Editor in Chief, the Associate Editor, and the reviewers for their valuable comments on an earlier draft of this paper.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

References Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., Wood, S., 1997. Interactive home shopping: incentives for consumers, retailers, and manufacturers to participate in electronic marketplaces. Journal of Marketing 61 (3), 38–53. Amundson, S.D., 1998. Relationships between theory-driven empirical research in operations management and other disciplines. Journal of Operations Management 16 (4), 341–359. Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin 103 (3), 411–423. Bakos, J.Y., 1997. Reducing buyer search costs: implications for electronic marketplaces. Management Science 43 (12), 1676– 1692. Bakos, J.Y., 1998. The emerging role of electronic market places on the Internet. Communications of the ACM 41 (8), 35–42. Baron, R.M., Kenny, D.A., 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51 (6), 1173–1182. Bentler, P.M., 1995. EQS Structural Equations Program Manual. Multivariate Software Inc., Encino, CA. Berry, L.L., Parasuraman, A., Zeithaml, V.A., 1994. Improving service quality in America: lessons learned. Academy of Management Executive 8 (2), 32–52. Bollen, K.A., 1989. Structural Equations with Latent Variables. John Wiley, New York, NY. Boyer, K.K., Hallowell, R., Roth, A.V., 2002. E-services: operating strategy — a case study and a method for analyzing operational benefits. Journal of Operations Management 20 (2), 175–188. Boyer, K.K., Hult, G.T.M., 2006. Customer behavioral intentions for online purchases: an examination of fulfillment method and customer experience level. Journal of Operations Management 24 (2), 124–147. Boyer, K.K., Hult, G.T.M., 2005. Extending the supply chain: integrating operations and marketing in the online grocery industry. Journal of Operations Management 23 (6), 642–661. Boyer, K.K., Olson, J.R., 2002. Drivers of Internet purchasing. Production and Operations Management 11 (4), 180–198. Bretthauer, K.M., 2004. Service management. Decision Sciences 35 (3), 325–332. Brynjolfsson, E., Smith, M.D., 2000. Frictionless commerce? A comparison of Internet and conventional retailers. Management Science 46 (4), 563–585. Chellappa, R.K., Kumar, K.R., 2005. Examining the role of ‘‘free’’ product augmenting online services in pricing and customer retention strategies. Journal of Management Information Systems 22 (1), 355–377. Chen, P.-Y., Hitt, L.M., 2002. Measuring switching costs and the determinants of customer retention in Internet enabled businesses: a study of the on-line brokerage industry. Information Systems Research 13 (3), 255–274. Clay, K., Krishnan, R., Wolff, E., 2001. Prices and price dispersion on the web: evidence from the online book industry. The Journal of Industrial Economics 49 (4), 521–538. Clemons, E.K., Hahn, I.H., Hitt, L.M., 2002. Price dispersion and differentiation in on-line travel: an empirical investigation. Management Science 48 (4), 534–549. Cook, L.S., Bowen, D.E., Chase, R.B., Dasu, S., Stgewart, D.M., Tansik, D.A., 2002. Human issues in service design. Journal of Operations Management 20 (2), 159–174.

39

Cook, T.D., Campbell, D.T., 1994. The design and conduct of quasi experiments and true experiments in field settings. In: Dunnette, M.D. (Ed.), Handbook of Industrial and Organizational Psychology. Rand-McNally, Chicago, IL, pp. 223–326. Daripa, A., Kapur, S., 2001. Pricing on the Internet. Oxford Review of Economic Policy 17 (2), 202–216. Esper, T.L., Jensen, T.D., Turnipseed, F.L., Burton, S., 2003. The last mile: an examination of effects of online retail delivery strategies on consumers. Journal of Business Logistics 24 (2), 177–203. Finch, B.J., 1999. Internet discussions as a source for consumer product, customer involvement, and quality of information: an exploratory study. Journal of Operations Management 17 (5), 535– 556. Fitzsimmons, J., Sullivan, R.S., 1982. Service Operations Management. McGraw-Hill, New York. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 23 (1), 39–50. Frohlich, M.T., Westbrook, R., 2002. Demand chain management in manufacturing services: web-based integration, drivers, and performance. Journal of Operations Management 20 (6), 729–745. Gallaway, T., Kinnear, D., 2001. Unchained melody: a price discrimination-based policy for addressing the MP3 revolution. Journal of Economic Issues 35 (2), 279–287. Geistfeld, L., 1982. The price-quality relationship revisited. Journal of Consumer Affairs 16 (2), 334–346. Goldstein, S.M., Johnston, R., Duffy, J., Rao, J., 2002. The service concept: the missing link in service design research? Journal of Operations Management 20 (2), 121–134. Graham, G., Burnes, B., Lewis, G.J., Langer, J., 2004. The transformation of the music industry supply chain: a major label perspective. International Journal of Operations and Production Management 24 (11), 1087–1103. Gresov, C., 1989. Exploring fit and misfit with multiple contingencies. Administrative Science Quarterly 34 (3), 431–453. Gresov, C., Drazin, R., 1997. Equifinality: functional equivalence in organization design. Academy of Management Review 22 (2), 403–428. Hallowell, R., 2001. Scalability: the paradox of human resources in ecommerce. International Journal of Service Industry Management 12 (1), 34–43. Hallowell, R., 2002. Virtuous Cycles: Improving Service and Lowering Costs in E-Commerce. Harvard Business School Publishing, Publication 9-800-155, Boston, MA. Hamilton, A., 2002. The pirates of primetime. Time 159 (8), 54–56. Harchaoui, T.K., Hamdad, M., 2000. The prices of classical recorded music: a hedonic approach. International Journal of Industrial Organization 18 (3), 497–514. Hart, C.W.L., 1988. The power of unconditional service guarantees. Harvard Business Review 67 (4), 54–61. Harvey, J., 1998. Service quality: a tutorial. Journal of Operations Management 16 (5), 583–597. Heim, G.R., Sinha, K.K., 2001. Operational drivers of customer loyalty in electronic retailing: an empirical analysis of electronic food retailers. Manufacturing and Service Operations Management 3 (3), 264–271. Hill, A.V., Collier, D.A., Froehle, C.M., Goodale, J.C., Metters, R.D., Verma, R., 2002. Research opportunities in service process design. Journal of Operations Management 20 (2), 189–202. Hjroth-Andersen, C., 1984. The concept of quality and the efficiency of markets for consumer products. Journal of Consumer Research 11 (2), 708–718.

40

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41

Hox, J., 1998. Multilevel modeling: when and why. In: Balderjahn, I., Mathar, R., Schader, M. (Eds.), Classification, Data Analysis, and Data Highways. Springer-Verlag, Berlin, Germany, pp. 147–154. Hox, J., 2002. Multilevel Analysis: Techniques and Applications. Lawrence Erlbaum Associates Publishers, Mahwah, NJ. Hu, L., Bentler, P.M., 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6 (1), 1–55. Jarvenpaa, S., Tractinsky, N., 1999. Consumer trust in an Internet store: a cross-cultural validation. Journal of Computer-Mediated Communication 5 (2), available electronically at http://www.ascusc.org/jcmc/vol5/issue2/jarvenpaa.html. Karmarkar, U.S., Pitbladdo, R., 1995. Service markets and competition. Journal of Operations Management 12 (3–4), 397–411. Keeney, R.L., 1999. The value of Internet commerce to the customer. Management Science 45 (4), 533–542. Kellogg, D.L., Chase, R.B., 1995. Constructing an empirically derived measure of customer contact. Management Science 41 (11), 1734– 1749. Kellogg, D.L., Nie, W., 1995. A framework for strategic service management. Journal of Operations Management 13 (4), 323– 337. Kirmani, A., Rao, A.R., 2000. No pain, no gain: a critical review of the literature on signaling unobservable product quality. Journal of Marketing 64 (2), 66–79. Latcovitch, S., Smith, H., 2001. Pricing, sunk costs and market structure online: evidence from book retailing. Oxford Review of Economic Policy 17 (2), 217–234. Liu, C., Arnett, K.P., 2000. Exploring the factors associated with web site success in the context of electronic commerce. Information and Management 38 (1), 23–34. Lynch, J.G., Ariely, D., 2000. Wine online: search costs affect competition on price, quality, and distribution. Marketing Science 19 (1), 83–103. McDermott, C.M., Kang, H., Walsh, S., 2001. A framework for technology management in services. IEEE Transactions on Engineering Management 48 (3), 333–341. Melnyk, S.A., Stewart, D.M., Swink, M., 2004. Metrics and performance measurement in operations management: dealing with the metrics maze. Journal of Operations Management 22 (3), 209– 217. Menor, L.J., Tatikonda, M.V., Sampson, S.E., 2002. New service development: areas for exploitation and exploration. Journal of Operations Management 20 (2), 135–157. Muthe´n, B.O., 1989. Latent variable models in heterogeneous populations. Psychometrika 54 (4), 557–585. Olson, J.R., Belohlav, J.A., Boyer, K.K., 2005. Operational, economic, and mission elements in not-for-profit organizations: the case of the Chicago Symphony Orchestra. Journal of Operations Management 23 (2), 125–142. Olson, J.R., Boyer, K.K., 2003. Factors influencing the utilization of Internet purchasing in small organizations. Journal of Operations Management 21 (2), 225–245. Pan, X., Ratchford, B.T., Shankar, V., 2002. Can price dispersion in online markets be explained by differences in e-tailer service quality? Journal of the Academy of Marketing Science 30 (4), 443–445. Piccoli, G., Brohman, M.K., Watson, R., Parasuraman, A., 2004. Netbased customer service systems: evolution and revolution in web site functionalities. Decision Sciences 25 (3), 423–455.

Rabinovich, E., 2005. Consumer direct fulfillment performance in Internet retailing: emergency transshipments and demand dispersion. Journal of Business Logistics 26 (1), 79–112. Rabinovich, E., Bailey, J.P., 2004. Physical distribution service quality in Internet retailing: service pricing, transaction attributes, and firm attributes. Journal of Operations Management 21 (6), 651– 672. Rabinovich, E., Bailey, J.P., Carter, C.R., 2003. A transaction-efficiency analysis of an Internet retailing supply chain in the music CD industry. Decision Sciences 34 (1), 131–172. Reines-Eudy, R., 2000. Using structural equation modeling to test for differential reliability and validity: an empirical demonstration. Structural Equation Modeling 7 (1), 124–141. Rosenthal, R., Rosnow, R.L., 1991. Essentials of Behavioral Research: Methods and Data Analysis, 2nd ed. McGraw-Hill, New York, NY. Roth, A.V., Chase, R.B., Voss, C., 1997. Service in the US: Progress Towards Global Service Leadership. Severn Trent Plc, London, UK. Roth, A.V., Menor, L., 2003. Insights into service operations management: a research agenda. Journal of Operations Management 12 (2), 145–164. Roth, A.V., Van der Velde, M., 1991. Operations as marketing: a competitive service strategy. Journal of Operations Management 10 (3), 303–328. Salop, S.C., 1977. The noisy monopolist: imperfect information, price dispersion and price discrimination. The Review of Economic Studies 44 (3), 393–406. Salop, S.C., Stiglitz, J.E., 1977. Bargains and rip offs: a model of monopolistically competitive price dispersion. The Review of Economic Studies 44 (3), 493–510. Sampler, J.L., 1998. Redefining industry structure for the information age. Strategic Management Journal 19 (4), 343–355. Sasser, W.E., Olsen, R.P., Wyckoff, D.D., 1978. Management of Service Operations. Allyn and Bacon, Boston, MA. Sechrest, L., 1979. Unobtrusive Measures Today. Josey-Bass, San Francisco, CA. Shapiro, C., Varian, H.R., 1998. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, Boston, MA. Soteriou, A.C., Chase, R.B., 1998. Linking the customer contact model to service quality. Journal of Operations Management 16 (4), 495–508. Starr, M.K., 2003. Application of POM to e-business: B2C e-shopping. International Journal of Operations and Production Management 23 (1), 105–124. Steenkamp, J.B., Van Trijp, H., 1991. The use of LISREL in validating marketing constructs. International Journal of Research in Marketing 8 (4), 283–299. Stewart, D.M., 2003a. Piecing together service quality: a framework for robust service. Production and Operations Management 12 (2), 246–265. Stewart, D.M., Chase, R.B., 1999. The impact of human error on delivering service quality. Production and Operations Management 8 (3), 240–263. Stewart, K.J., 2003b. Trust transfer on the World Wide Web. Organization Science 14 (1), 5–17. Stigler, G.J., 1961. The economics of information. The Journal of Political Economy 69 (3), 213–225. Tellis, G.J., Wernerfelt, B., 1987. Competitive price and quality under asymmetric information. Marketing Science 6 (3), 240– 253.

E. Rabinovich / Journal of Operations Management 25 (2007) 14–41 Thirumalai, S., Sinha, K.K., 2005. Customer satisfaction with order fulfillment in retail supply chains: implications of product type in electronic B2C transactions. Journal of Operations Management 23 (3–4), 291–303. Van de Ven, A.H., Drazin, R., 1985. The concept of fit in contingency theory. In: Staw, B.M., Cummings, L.L. (Eds.), Research in Organizational Behavior, 7. JAI Press, Greenwich, CT, pp. 333–365. Varian, H., 1980. A model of sales. American Economic Review 70 (4), 651–659. Varian, H., 2000. Market structure in the network age. In: Brynjolfsson, E., Kahin, B. (Eds.), Understanding the Digital Economy: Data, Tools, and Research. The MIT Press, Cambridge, MA, pp. 137– 150. Verma, R., Fitzsimmons, J., Heineke, J., Davis, M., 2002. New issues and opportunities in service design research. Journal of Operations Management 20 (2), 117–120. Voss, C., 2000. Developing an e-service strategy. Business Strategy Review 11 (1), 21–33. Voss, C., Tsikriktsis, N., Frohlich, M., 2002. Case research in operations management. International Journal of Operations and Production Management 22 (2), 195–219. Webb, E.T., Campbell, D.T., Schwartz, R., Sechrest, L., Grove, J.B., 1981. Non Reactive Measures in the Social Sciences, 2nd ed. Houghton Mifflin Company, Boston, MA.

41

Wolfinbarger, M., Gilly, M.C., 2003. eTailQ: dimensionalizing, measuring, and predicting etail quality. Journal of Retailing 79 (3), 183–198. Yang, Z., Jun, M., Peterson, R.T., 2004. Measuring customer perceived online service quality: scale development and managerial implications. International Journal of Operations and Production Management 24 (11), 1149–1174. Zeithaml, V.A., 2002. Service excellence in electronic channels. Managing Service Quality 12 (3), 135–139. Zeithaml, V.A., Parasuraman, A., Malhotra, A., 2000. A conceptual framework for understanding e-service quality: implications for future research and managerial practice. MSI Monograph, Report # 00-115. Marketing Science Institute, Cambridge, MA. Zeithaml, V.A., Parasuraman, A., Malhotra, A., 2002. Service quality delivery through websites: a critical review of extant knowledge. Journal of the Academy of Marketing Science 30 (4), 362–375. Zeithaml, V.A., Parasuraman, A., Malhotra, A., 2004. E-S-QUAL: a multiple-item scale for assessing electronic service quality. MSI Monograph, Report # 04-112. Marketing Science Institute, Cambridge, MA. Zettelmeyer, F., 2000. The Strategic use of consumer search cost. Unpublished manuscript, University of California, Berkeley.