The Impact of Electronic Reverse Auctions on Supplier Performance: The Mediating Role of Relationship Variables

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on Supplier Performance: The Mediating Role of Relationship Variables

AUTHORS

Craig R. Carter

is an associate professor of supply chain management in the College of Business Administration at the University of Nevada in Reno, Nevada.

Lutz Kaufmann

is a professor and The Herbert Quandt Endowed Chair in International Management at the WHU–Otto Beisheim School of Management in Vallendar, Germany.

The use of electronic reverse auctions (ERAs) by buying organizations has increased dramatically over the past five years. Both anecdotal and empirical evidence have shown that ERAs can lower purchase prices. However, researchers are only just beginning to investigate how ERAs impact perceptions of opportunism as compared to sealed bids and tradi- tional negotiations. Further, researchers have yet to examine how perceptions of opportunism surrounding ERAs might in turn affect such outcome variables as trust, commitment, conflict, and ultimately nonprice attributes of supplier performance.

The authors address this gap in the research by developing a theoretically grounded model of the interrelationships among these five variables, and empirically testing the model through a survey of buying organizations that rely heavily on ERAs to select and source from suppliers. The authors’ findings suggest that increased levels of opportunism harm supplier nonprice performance, through both their more obvious impact on dysfunctional conflict and their more latent effects on relationship trust and commitment.

INTRODUCTION

Buying organizations are increasingly using electronic reverse auctions (ERAs), defined by Beall et al. (2003, p. 7) as ‘‘an online, real-time dynamic auction between a buyer organization and a group of prequalified suppliers who compete against each other to win the business to supply goods or services that have clearly defined specifications for design, quantity, quality, delivery, and related terms and conditions,’’ to competitively source both goods and services from suppliers (Hartley et al. 2004; Millet et al.

2004; Hur et al. 2005). These ERAs have been reported to significantly reduce the purchase prices of material and service, with reports in some cases of 20 and even 30-plus percent reductions in price in some industries (Carbone 2003; Mayhew-Smith 2004; Anonymous 2005). At the same time, recent studies have suggested that ERAs can lead to increased perceptions of opportunistic behavior on the part of both buyers and suppliers (Jap 2003; Carter et al. 2004). As has been suggested by transaction cost theory and empirical research in the marketing channels and supply chain literature, such perceptions of opportunism might in turn affect trust, commitment and conflict in buyer–supplier relationships. Further, such changes in the relationship may ultimately impact supplier performance. Thus, while ERAs might reduce purchase prices, they may also negatively affect other aspects of supplier performance, as suppliers attempt to recoup profits by providing lower levels of service and product quality.

Several recent studies have examined the antecedents to successful ERA implementation and use (Mabert and Skeels 2002; Smeltzer and Carr 2002; Millet et al. 2004;

Talluri and Ragatz 2004). In addition, researchers have compared perceptions of opportunism and trust among sealed bids, negotiations and ERAs (Jap 2003; Gattiker et al. 2005); however, the authors are unaware of any research that has in turn examined how perceptions of opportunistic behavior might then affect other aspects of the buyer–supplier relationship, including, in the end,

Financial support for this study was provided by CAPS Research. The authors thank the four anonymousJSCMreviewers for their many helpful comments.

The Journal of Supply Chain Management: A Global Review of Purchasing and SupplyCopyright

&February 2007, by

the Institute for Supply Management, Inc.^TM

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supplier performance. Further, enhanced supplier performance can not only benefit the purchaser in terms of improved inbound delivery but can also benefit the supplier in terms of increased revenues (Stank, Goldsby and Vickery 1999); however, ‘‘there has been relatively little investigation (concerning) . . . suppliers’ performance’’ (Prahinski and Benton 2004, p. 40) in the supply management discipline. The objective of this paper is thus to examine how perceptions of opportunism surrounding ERAs might consequently influence relationship trust, purchaser commitment to the relationship, conflict between purchasers and suppliers and ultimately supplier performance. This is accomplished through the development of a model based on transaction cost economics and social exchange theory, along with empirical findings from the marketing channels and supply chain management literature.

The remainder of the paper is organized as follows.

In the next section, a brief review of the literature on auctions, and more specifically on ERAs, is presented.

The authors then outline the study’s theoretical model through the introduction of five hypotheses, which examine the interrelationships among opportunism, trust, commitment, conflict and performance. Next, the authors describe the study’s methodology, and afterwards present the results of the tests of the study’s hypotheses.

The final sections contain a discussion of the theoretical and managerial implications of the study’s findings, along with future research that is needed in the ERA arena.

LITERATURE REVIEW

In this section of the paper, the authors provide a brief overview of the extant research relating broadly to ERAs and more specifically to the effects of ERA usage on opportunism and trust in the buyer–supplier relationship.

The authors refer the reader to McAfee and McMillan (1987) for a more in-depth review of auctions in general and to Kaufmann and Carter (2004) for a more in-depth review of ERAs.

A limited amount of research examining the use of auctions by buying organizations has appeared in the field of economics, particularly with respect to government procurement auctions (Luton and McAfee 1986;

Branco 1997; Kjerstad and Vagstad 2000; Wang 2000;

Bajari 2001). However, this research has operationalized these auctions as sealed bids, where only a single bid is submitted, as compared with the dynamic, real-time environment of ERAs, and has focused upon government rather than private sector auctions.

Given its relatively recent advent, the topic of ERAs in purchasing has only recently received attention among supply chain management scholars. Hong and Hartley (2001) provide a succinct review of the relatively scarce trade literature that was available at the time their paper was written, and posit that ERAs might increase

perceptions of opportunism and decrease nonprice performance aspects of the relationship such as delivery and flexibility. Pearcy et al. (2002) and Jap (2003) provide an outline for utilizing a survey methodology to examine the relationship between ERA use and supplier cooperation, suggesting that ERA usage might decrease the willingness of suppliers to cooperate as a result of perceptions that buyers are opportunistically driving down prices, due in part to a lack of transparency surrounding ERAs. Talluri and Ragatz (2004) develop a framework for crafting and applying multiattribute ERAs, which takes into account both purchaser and supplier perspectives and interests including perceptions of fairness in the bidding process.

As is common during the nascent stages of most research streams, much of this initial work in the ERA arena has been conceptual (e.g., Pearcy et al. 2002) and prescriptive (e.g., Mabert and Skeels 2002; Talluri and Ragatz 2004) in nature. More recently, however, researchers have begun to collect and analyze data relating to ERAs.

In a descriptive treatment of ERA issues and themes, Jap (2002) considers unethical behaviors and perceptions of opportunism of purchasers and suppliers, suggesting that these may harm the buyer–supplier relationship. Carter et al. (2004) perform an extensive field study and qualitative analysis of both buyer and supplier organizations involved in ERAs, and find that heightened perceptions of opportunism, along with decreased levels of trust and commitment, might result from ERA usage. Through a quasi-experimental design, Jap (2003) finds that ERAs result in increased perceptions of opportunism as compared with the use of sealed bids. Somewhat similarly, Gattiker et al. (2005) show that ERAs are associated with lower levels of trust than are face-to-face negotiations.

While Jap (2003) and Gattiker et al. (2005) begin to examine the relationship consequences of ERA use, the authors are unaware of any research that has investigated the ensuing impact of perceptions of opportunism and trust on other relationship outcomes such as conflict, commitment and, in the end, performance. Thus, the motivation of this research is to extend the findings of Jap and Gattiker et al., by examining how ERA usage influ- ences supplier performance, through the effects of opportunism and trust, along with commitment and conflict, as described through the development of the study’s hypotheses in the next section of the paper.

HYPOTHESES

Williamson (1981, p. 554) defines opportunistic behavior as, ‘‘self-interest seeking behavior with guile.’’ In an interorganizational context supplier opportunism may be observed, for example, when suppliers provide false information or make hollow promises. For a more in- depth and current review of opportunism, the authors refer the reader to Wathne and Heide (2000). The trade

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press has touted that one of the major negative behavioral consequences of ERA use has been a heightened sense of opportunistic behavior, both by buyer and supplier organizations (e.g., Anonymous 2002). Academic research has begun to corroborate these assertions. Jap (2003), for example, found that the use of ERAs was more likely to result in perceptions of opportunism than were sealed bids, and Gattiker et al. (2005) found that ERAs lead to lower levels of trust than did face-to-face negotiations.

Trust in a supply chain relationship has been defined in the extant literature as ‘‘a willingness to rely on an exchange partner in whom one has confidence’’ (Moor- man et al. 1993, p. 82) and as ‘‘the firm’s belief that another company will perform actions that will result in positive outcomes for the firm as well as not take unex- pected actions that result in negative outcomes’’ (Ander- son and Narus 1990, p. 45). Within the context of ERAs, Jap (1999, p. 465) defines the trust of one firm in another firm as ‘‘the ability to predict the actions of the other party in the relationship reliably and the belief that the other party will not act opportunistically to do so.’’ The authors similarly define trust as the ability to rely on an exchange partner to meet promises and expectations.

This definition is congruent with extant operationaliza- tions and definitions of trust in interorganizational relationships (Zucker 1986; Anderson and Narus 1990;

Moorman et al. 1993; Ganesan 1994; Morgan and Hunt 1994; Siguaw et al. 1998; Joshi and Stump 1999).

Ring and Van de Ven (1992, 1994) assert that moral integrity and goodwill lead to increased levels of trust in interorganizational relationships. Conversely, a lack of such integrity and goodwill, or in other words, opportunism, will likely result in decreased trust in a buyer–

supplier relationship. The assertions of these authors, which are based on transaction cost analysis and the notion of psychological contracts, are also supported by social exchange theory. Blau (1964), for example, suggests that opportunism is negatively related to trust. Morgan and Hunt (1994) also posit that perceptions of opportunism will bring about lower levels of trust. These theories and empirical findings lead to the study’s first hypothesis:

H1: Supplier opportunism surrounding ERAs is negatively related to relationship trust.

Morgan and Hunt (1994) propose that commitment and trust can act as key mediating variables in buyer–

supplier relationships by encouraging managers in these organizations to deem potentially high-risk actions as being prudent because of the belief that their partners will not act opportunistically. Dyer (1996) advocates that trust is a necessary prerequisite to developing commitment in buyer–supplier relationships, and Siguaw, Simpson, and Baker (1998, p. 103) argue that ‘‘commitment to a relationship would not be established without a foundation of trust in place.’’ Narayandas and Rangan (2004) find support for this assertion through the use of field studies

and qualitative analysis, and Morgan and Hunt (1994) empirically demonstrate that trust is an antecedent to commitment using a large-scale survey of marketing managers. Interestingly, as noted by Kwon and Suh (2004, p. 4), ‘‘Although the literature often mentions a relationship between trust and commitment, there is a lack of empirical testing of such relationships in the supply context.’’ This leads to the second hypothesis:

H2: Relationship trust surrounding ERAs is positively related to the commitment of the buying organization.

An early discussion of functional conflict is presented by Deutsch (1969, p. 19), writing that functional conflict provides a ‘‘medium through which problems can be aired and solutions arrived at.’’ Conversely, dysfunctional conflict occurs when conflict is not resolved or is not likely to be resolved, as is ‘‘characterized by mutual goal interference where one party perceives the other to be blocking its goal attainment and reacts accordingly to block the other’s goal attainment’’ (Frazier 1983, p. 72).

Similarly, Stern and El-Ansary (1977, p. 283) define channel conflict as ‘‘a situation in which one channel member perceives another channel member to be engaged in behavior that is preventing or impeding him from achieving his goals.’’

Anderson and Narus (1990, p. 45) define ‘‘functionality of conflict’’ as ‘‘an evaluative appraisal of the results of recent efforts to resolve disagreements.’’ The authors operationalize functionality of conflict as the extent to which disagreements between manufacturers and distri- butors increase or decrease the productivity of the relationship. Brown et al. (2000, pp. 53–54) include

‘‘harmonization of conflict’’ as a relational norm, defining it as ‘‘the extent to which channel members achieve mutually satisfying resolution of their conflicts.’’ Dys- functional conflict would, on the contrary, be characterized by tense and frequent disagreements, and clashes between parties (Assael 1969).

Bowersox et al. (1980, p. 81) also comment on dysfunctional conflict within logistics systems. They suggest that dysfunctional conflict exists when conflict harms logistics performance or at least does not improve aspects of distribution performance such as lead times and on- time deliveries. While conflict is never absent in business- to-business exchanges (Dwyer et al. 1987), buyer–supplier relationships that have a greater presence of dysfunctional conflict are less likely to work out differences, and this should lead to decreased performance in their relationship. Moore (1998, p. 29), for example, finds that conflict can hamper the performance of interorganizational relationships, as it can prevent ‘‘a buyer from relying on a third party to fulfill future obligations.’’

Dysfunctional conflict may also be preceded by perceptions of opportunistic behavior in the buyer–supplier relationship. Dahlstrom and Nygaard (1999), for example,

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find a negative association between opportunism and cooperation, and also find that opportunism is significantly related to maladaption costs, which are ‘‘embodied in communication and coordination failures between parties to a contract’’ (p. 162). Further, Dwyer and Oh (1987) find a negative relationship between opportunism and participative decision making. Such participative decision making, which involves cooperative interaction to develop convergent goals, is almost directly opposed to the dysfunctional conflict described above, the latter of which involves tense clashes over how to conduct business and significant disagreements in the working relationship.

Given the conceptualizations of Deutsch (1969), Frazier (1983), Assael (1969), Brown et al. (2000) and Bowersox et al. (1980), and the findings of Dahlstrom and Nygaard (1999) and Dwyer and Oh (1987), the authors introduce the following hypotheses:

H3: Supplier opportunism surrounding ERAs is positively related to dysfunctional conflict.

H4: Dysfunctional conflict surrounding ERAs is negatively related to supplier performance.

Finally, it has been posited that commitment to a relationship should lead to higher performance levels (e.g., Stern and El-Ansary 1992). Within an organizational context, Bashaw and Grant (1994) find a positive relationship between worker career commitment and sales performance. From an interorganizational standpoint, Holm, Eriksson and Johanson (1996) find a positive relationship between relationship commitment and relationship profitability, and Siguaw, Simpson and Baker (1998) find that a distributor’s commitment to a relationship results in greater satisfaction with the distributor’s financial performance. These findings and assertions lead to the study’s final hypothesis:

H5: Buyer commitment surrounding ERAs is positively related to supplier performance.

The relationships among the study’s hypotheses are displayed in Figure 1. Next, the authors describe the methodology used to test these hypotheses.

METHODOLOGY

The authors employed a Web-based survey to test the study’s hypotheses. The study’s five constructs were based on existing scales: opportunism (John 1984; Anderson 1988; Gundlach et al. 1995; Brown et al. 2000), relationship trust (Ganesan 1994; Morgan and Hunt 1994; Jap 1999; Joshi and Stump 1999), buyer commitment (Anderson and Narus 1990; Morgan and Hunt 1994), dysfunctional conflict (Brown and Day 1981; Gaski 1984;

Moore 1998) and supplier performance (Carter 2000, 2005; Shin, Collier and Wilson 2000). A combination of five academics and four practitioners were used to pretest the survey and assess the face validity of the constructs (Heeler and Ray 1972). Several minor changes were made to the survey based on feedback received from this pre- testing, including rewording ambiguous scale items.

Following Beall et al. (2003), the authors identified what Beall and coresearchers refer to as ‘‘power users’’ of ERAs:

buying organizations that had conducted at least 21 ERAs within the prior 2 years. These buying organizations were identified through CAPS Research and a Web-based search. The authors further narrowed the sampling frame to for-profit organizations. Despite the fact that public and governmental institutions have a long history of using auctions, as indicated in the literature review, these public sector auctions more commonly take the form of sealed bids as opposed to the dynamic, real-time bidding of ERAs.

Given the need to target specific organizations that had experience in conducting ERAs, different channels were used to identify the key informants who would participate in the study. First, executive-level supply managers who participated in earlier, exploratory case studies conducted by the authors were asked to participate or forward the survey to purchasers in their organizations who were the most knowledgeable of and had actively participated in the planning and execution of an ERA. Second, executive participants of the CAPS Purchasing Round- tables, whose organizations were power users of ERAs, received an email inviting their organization to participate in the survey. Again, these executives were asked to forward the survey to knowledgeable informants as H1

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H2 (+)

H4 (–)

H5 Supplier (+)

Opportunism

Relationship Trust

Supplier Nonprice Performance Buyer

Commitment

Dysfunctional Conflict H3

(+) Figure 1

THE IMPACT OF ELECTRONIC REVERSE AUCTIONS ON SUPPLIER PERFORMANCE

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indicated above. Potential respondents were provided with a Web link and password that allowed them to complete the online survey. Respondents were assured that their responses to the survey would not be shared outside the research team; however, respondents were required to provide an email address in order to participate, which helped to ensure that respondents could not participate repeatedly (Cobanoglu et al. 2001).

The time period for the survey was 8 weeks. Responses were tracked by weekly reports from the service provider that hosted the online survey. Two friendly reminders were sent by email to all companies that had indicated their interest, but who had not yet provided their data.

The first reminder was sent after 3 weeks, and the second reminder was sent after 6 weeks. As an incentive for participating in the survey, respondents were promised an executive summary published at least 6 months ahead of any other kind of publication of the survey results.

Respondents were asked to consider a specific online, reverse auction in which they had participated in the past 24 months. The term ‘‘reverse auction’’ was defined for the respondents as ‘‘an online real time dynamic process of soliciting and collecting bids from multiple suppliers where some degree of bid visibility exists and where multiple bids are permitted from each supplier.’’ Respon- dents were asked to answer the survey’s questions based on an auction that was their most successful/least successful/largest in dollar volume/smallest in dollar volume.

This selection criterion for the auction was randomly assigned to respondents in order to increase the variability and representativeness of their responses.

RESULTS Survey Response

A total of 76 companies were contacted and provided completed surveys. Six hundred and fifteen Web links were sent to these companies, and 343 usable surveys completed, for a 55.9 percent response rate. Thus, each firm completed an average of 4.53 surveys. Respondents represented a diverse group of industries including auto- motive and trucking (11.31 percent), chemicals (8.14 percent), aerospace (6.79 percent), mining and processing (5.43 percent), pharmaceuticals (4.98 percent), utilities (4.98 percent) and airlines (3.62 percent).

A respondent’s company was assessed as a control vari- able against each of the study’s latent constructs. No significant relationship was found (the most significant relationship, between opportunism and a respondent’s company, had apvalue of 0.6416). Additionally, it is unlikely that a respondent’s company affiliation would explain a significant amount of variation in any of the study’s constructs, given the relatively large number of companies responding to the survey (76) and the relatively small ratio of companies to respondents (4.53).

The product categories that were purchased in the respondents’ reverse auctions were quite varied and ranged from general commodities and commodity-like products, such as diesel fuel, electricity, fiberglass and desktop computers, to somewhat more specialized items like aviation headsets and static antennas. Service categories were also varied, and included hotel stays, janitorial services and air and motor carrier transportation. The median auction volume was $975,000. Sixty-three percent (62.7 percent) of the auctions provided the suppliers with price-only visibility, 33 percent (33.2 percent) provided suppliers with rank-only visibility and 4 percent (4.1 percent) allowed suppliers to view both the low-price bid and their current rank in the auction. The composi- tion of responses based on the earlier-mentioned selection criteria were as follows: most successful ERA: 23.28 percent; least successful ERA: 25.41 percent; largest dollar volume ERA: 24.70 percent; smallest dollar volume ERA:

26.61 percent. For those questions that pertained directly to the buyer–supplier relationship, 50.90 percent of the survey respondents replied based on their experiences with winning suppliers and 49.10 percent based on their experiences with losing suppliers.

Nonresponse Bias. Nonresponse bias was tested by comparing the answers of early respondents with those of late respondents to the survey (Lambert and Harrington 1990). The assumption of this test is that late respondents are more like nonrespondents than are early respondents (Armstrong and Overton 1977). A natural break point occurred approximately halfway through the data collection process, and the authors classified the first half of respondents from each company (based on chronological order of responses) as early respondents and the second half of respondents as late respondents.

A multivariatet-test was then computed along the survey items used to measure the study’s five constructs, to test for significant differences between the two groups. There were no statistically significant differences between early and late respondents (p50.3017).

Key Informant Issue. The authors took two measures to assure that survey respondents were knowledgeable and appropriate (e.g., key) informants (Campbell 1955). First, the survey request was sent to an executive supply manager, who was asked to forward the survey to ‘‘the commodity or buying manager or buyer who hasthe most knowledge of and actively participated in the planning and execution of an online reverse auction event’’

(John and Reve 1982). Second, the survey included questions that addressed the respondent’s tenure in the supply management function and experience concerning ERAs (Kumar et al. 1993). Respondents had participated in an average (mean) of 18.9 ERAs (median515.6,

range5320), and had worked in the supply management function for 9.0 years on average (median56.0,

range532.0), suggesting that they possessed sufficient

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experience and knowledge of ERAs in particular and the supply management function in general.

Analyses

The authors followed Anderson and Gerbing’s (1988) two-stage procedure to analyze the model shown in Figure 1. In the first stage, a confirmatory factor analysis was conducted to assess the study’s measurement model.

Items with low standardized factor loadings or high normalized residuals were deleted in the course of developing the measurement model. The Appendix displays both the deleted scale items and the scale items that were retained and used to measure the model’s constructs, along with standardized factor loadings and construct reliability measures. The authors employed the chi-square to degrees-of-freedom ratio (w²/df), Bentler’s (1989) com- parative fit index (CFI), Bentler and Bonett’s (1980) non- normed fit index (NNFI) and the root mean square error of approximation (RMSEA) (Steigler 1990) to assess overall measurement model fit. Values of less than 3.00 for the w²/df ratio (Bollen and Long 1993), values of 0.90 or above for the CFI and NNFI and values of less than 0.08 for the RMSEA indicate an appropriate fit between the model and the data (Browne and Cudeck 1993; Baumgartner and Homburg 1996).

Thew²/df ratio, CFI, NNFI and RMSEA are equal to 1.39, 0.99, 0.98 and 0.04, respectively. Further, all factor loadings are highly significant (p<0.0001), indicating convergent validity (Gerbing and Anderson 1988; Bagozzi et al. 1991). All constructs have internal reliability coef- ficients far in excess of the 0.70 recommended minimum for established scales (Van de Ven and Ferry 1978;

Churchill 1979; Flynn et al. 1990). Finally, the authors assessed discriminant validity by performing one-at-a- time chi-square difference tests between a constrained model with fixed factor correlations equal to 1.0 and the

original unrestricted confirmatory factor analysis model.

A significantly worse fit was found for each of these restricted models as opposed to the unrestricted models, providing strong evidence of discriminant validity (Gerbing and Anderson 1988). In addition, the variance extracted for the study’s constructs ranged from 0.68 to 0.77, far in excess of the 0.50 threshold recommended by Fornell and Larcker (1981). Together, these findings suggest an excellent fit between the data and the theoretical measurement model.

Next, the measurement model was modified to repre- sent the structural model displayed in Figure 1. The results from the test of the full structural equation model are shown in Figure 2. The values of thew²/df ratio, CFI, NNFI and RMSEA for the full structural model are equal to 1.78, 0.97, 0.96 and 0.06, respectively, indicating a strong fit between the theoretical model and the data.

The results of the tests of the model’s hypotheses are also displayed in Figure 2. All hypothesized paths are statistically significant (p<0.0001), suggesting significant negative relationships between supplier opportunism and relationship trust, and between dysfunctional conflict and supplier nonprice performance, and significant positive relationships between relationship trust and buyer commitment, supplier opportunism and dysfunctional conflict, and between buyer commitment and supplier nonprice performance.¹

H1****

(–0.40)

H2****

(0.91)

H4****

(-0.41) H5****

(0.45) Supplier

Opportunism

Relationship Trust

Supplier Nonprice Performance Buyer

Commitment

Dysfunctional Conflict

O2 O3 O4 T1 T2 T3 SP1 SP3 SP4

DC1 DC2 DC3

C1 C3

SP5 SP6 SP8

H3****

(0.67) Figure 2

STRUCTURAL EQUATION MODELING RESULTS.

P<0.0001,

x²/df51.78, CFI50.97, NNFI50.96, RMSEA50.06

1One reviewer raised a valid concern regarding a potential relationship between trust and dysfunctional conflict. The authors did not include this relationship in their model because (a) the theoretical justification for this relationship is not overly strong and (b) the authors’ earlier case studies did not support this relationship. In addition, a post hoc review of the modification indices revealed that the path between relationship trust and dysfunctional conflict did not result in a meaningful improvement to the model.

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DISCUSSION

The study’s model uncovers two sets of mediated relationships between perceptions of opportunism and supplier nonprice performance, each of which hold important implications for both theory and practice. The firstset of relationships suggests that perceptions of supplier opportunism lower relationship trust. Lower levels of relationship trust in turn result in lower levels of purchaser commitment, which ultimately lead to decreased supplier nonprice performance. Thesecondset of relationships suggests that perceptions of supplier opportunism increase dysfunctional conflict in the relationship, which then worsens supplier nonprice performance.

Trust and commitment are constructs in a buyer–

supplier relationship that require time to establish.

Further, trust, and to a less extent, commitment, are more underlying and even more latent than is dysfunctional conflict. While trust and commitment of course exist, they are not as obvious as is dysfunctional conflict.

Further, while conflict can occur over time, it is also often related to a one-time opportunistic event, and is certainly more visible. This first set of relationships can thus be thought of as ‘‘underlying, intangible resources’’ (e.g., Penrose 1959; Barney 1991), which occur at the interorganizational, buyer–supplier level, and which can be eroded as a result of perceptions of opportunism. The relationships displayed in the lower part of the model can be viewed as the more visible, tangible consequences of opportunism.

Within the ERA context of this study, managers must attempt to avoid perceptions of supplier opportunism, which lead to increases in dysfunctional conflict. Avoid- ance tactics, which might include better communication of an ERA’s motivations and rules (Beall et al. 2003) and the appropriate combination of auction parameters (Car- ter and Stevens forthcoming), can be accomplished in a relatively short period of time. Conversely, efforts to reduce the negative effects that opportunism has on relationship trust, commitment and ultimately supplier nonprice performance will likely require longer periods of time so that both parties will have the opportunity to outlive difficult situations that can damage relationship trust (Kaufmann and Carter 2006).

This key finding, that supplier nonprice performance may be harmed in terms of both more visible and less tangible means, suggests that managers should first and foremost work to reduce perceptions of opportunism in the buyer–supplier relationship, specifically when utilizing ERAs to source from suppliers. This is critical in the short and in the long run, as perceptions of supplier opportunism in the short term increase the chances of dysfunctional conflict and result in lower supplier nonprice performance and in the long run perceptions of supplier opportunism lead to decreased trust in the buyer–supplier relationship, which then affects

commitment and ultimately supplier performance.

Efforts to reduce perceptions of opportunism can benefit both buyers and suppliers, and these efforts should be made by managers from both organizations.

On the purchaser side, supply managers should require openness from their suppliers, create high degrees of behavioral transparency by measuring suppliers’

overall performance, provide clear feedback to the suppliers, and reward their openness. This can be accomplished by more clearly delineating the rules of the auction and by providing supplier training regarding the software and platform that a buying organization uses to conduct the ERA. Further, clarification questions submitted by a supplier should be answered and shared with all the suppliers that have been invited to participate in an ERA.

In addition, managers from both buyer and supplier organizations should develop tactics to openly and con- structively settle disagreements. While conflict in buyer–

supplier relationships is inevitable, the constructive resolution of such conflict will likely not harm, and may even improve supplier performance. Conversely, this study’s findings suggest that dysfunctional conflict, characterized by ‘‘significant,’’ ‘‘tense’’ ‘‘clashes’’ with suppliers, will appreciably harm supplier performance. Conflict might be made more functional by lowering communication barriers and to some degree increasing the degree of formalization within companies and between supply chain members. While this latter suggestion might seem counterintuitive, formally stating goals and desired outcomes can help clarify expectations and responsibilities (Menon et al. 1996). In the case of ERAs, an explicit statement of the buying firm’s objectives in conducting the ERA, the rules of the actual event and the follow-up procedures, including whether subsequent negotiations will be held with suppliers, will likely reduce perceptions of opportunism not only for suppliers but also for purchaser personnel, and in turn will lower the degree of dysfunctional conflict.

Suppliers should also have an interest in increasing the transparency surrounding their performance in terms of providing product or services, to enable them to credibly communicate that they act in accordance with all agree- ments. This will also allow suppliers to demonstrate that they deliver much more than a certain product or service for the price quoted during the auction: suppliers may create opportunities to differentiate themselves during the ongoing contract phase by making clear that the absence of opportunism creates value in itself. This may even lead to the chance for such suppliers to shape spend specifications for the next contract, and might conceiva- bly avoid the possibility that the future contract is awarded through an ERA mechanism. For example, Jap and Anderson (2003) find that the development of trust as a safeguarding mechanism against opportunism can

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enhance future expectations in interorganizational alliances. Supply managers also need to inform personnel in their own organization, such as product developers, plant engineers and logisticians, about the importance of closely monitoring perceptions of opportunistic behavior on the part of the supplier, so that this can be proactively addressed. Finally, suppliers can reduce buyers’ perceptions of opportunism by charging reasonable prices for change orders and promising delivery dates and quality levels that they can reasonably expect to achieve.

LIMITATIONS AND FUTURE RESEARCH

The study’s sampling frame consisted of relatively early users of ERAs, which may affect the generalizability of the findings. Future research might replicate this study, to determine whether these findings hold now that the use of ERAs has broadened and auctions may be considered more of an accepted business practice. In addition, the authors only collected data from buying organizations across the study’s five constructs. Thus, it was not possible to test the model from the supplier’s perspective. A valuable complement to this study’s findings would be a replication using a sample of suppliers or even a dyadic sample of purchasers and their suppliers.

Given the short- and long-term manner in which perceptions of opportunism can affect supplier nonprice performance, future research might employ a longitudi- nal design to examine how the constructs from this model change from one time period, immediately following the use of an ERA, and a second, future time period. Such investigations could extend the channels research of Gundlach et al. (1995) and more recently Rokkan et al. (2003). While the authors treated supplier opportunism as an antecedent of trust and commitment, a multiperiod investigation may well lead to the finding that increased relationship trust and purchaser commitment in period 1 results in lower levels of supplier opportunism in period 2. A multiperiod analysis could also shed some light on the robustness of relationship trust and purchaser commitment by looking at how relationships that are characterized by high levels of trust and commitment are affected by occasional supplier opportunism.

The authors operationalized conflict as dysfunctional conflict, which included ‘‘tense’’ ‘‘clashes’’ with suppliers over disagreements. As noted by Menon et al. (1996, p. 300) in their examination of intraorganizational conflict, one gap in the channels literature is that, ‘‘empirical research in the marketing strategy area continues to view (conflict) as abnormal deviant behavior that needs to be controlled or resolved.’’ It is quite possible that disagreements that are resolved in more constructive ways would improve rather than harm supplier performance. In the future, researchers might operationalize and simulta- neously incorporate functional conflict into models

that examine ERA performance, perhaps based on a modification of the scales used by Menon et al. (1996) to examine conflict within organizations.

Finally, researchers might also investigate how different configurations of ERAs affect perceived opportunism and other attributes of the buyer–supplier relationship. Carter and Stevens (forthcoming), for example, use a laboratory experiment and find that the number of participating suppliers and the level of supplier visibility can affect supplier perceptions of opportunism during the auction process. Similarly, different auction configurations might potentially affect purchasers’ perceptions of opportunism by suppliers after the auction has occurred and business has been awarded.

REFERENCES

Anderson, E. ‘‘Transaction Costs as Determinants of Opportunism in Integrated and Independent Sales Forces,’’

Journal of Economic Behavior and Organization, (9), April 1988, pp. 247-264.

Anderson, J.C. and D.W. Gerbing. ‘‘Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,’’Psychological Bulletin, (103:3), 1988, pp. 411-423.

Anderson, J.C. and J.A. Narus. ‘‘A Model of Distributor Firm and Manufacturer Firm Working Partnerships,’’Journal of Marketing, (54:1), 1990, pp. 42-58.

Anonymous. ‘‘E-Auction Playbook II: E-Auction Participants Strive for Fair, Ethical Events,’’Purchasing, (131:20), 2002, pp. 35-37.

Anonymous. ‘‘Buyers Use More than One Way to Cut Component Costs,’’Purchasing, (134:5), 2005, pp. 18-22.

Armstrong, J.S. and T.S. Overton. ‘‘Estimating Nonresponse Bias in Mail Surveys,’’Journal of Marketing Research, (14:3), 1977, pp. 396-400.

Assael, H. ‘‘Constructive Role of Interorganizational Conflict,’’

Administrative Science Quarterly, (14:4), 1969, pp. 573-582.

Bagozzi, R.P., Y. Yi and L.W. Phillips. ‘‘Assessing Construct Validity in Organizational Research,’’Administrative Science Quarterly, (36:3), 1991, pp. 421-458.

Bajari, P. ‘‘Comparing Competition and Collusion: A Numerical Approach,’’Economic Theory, (18), 2001, pp. 187-205.

Barney, J.B. ‘‘Firm Resources and Sustained Competitive Advantage,’’Journal of Management, (17:1), 1991, pp. 99-120.

Bashaw, R.E. and E.S. Grant. ‘‘Exploring the Distinctive Nature of Work Commitments: Their Relationships with Personal Characteristics, Job Performance, and Propensity to Leave,’’

Journal of Personal Selling and Sales Management, (14:2), 1994, pp. 41-56.

Baumgartner, H. and C. Homburg. ‘‘Applications of Structural Equation Modeling in Marketing and Consumer Research: A Review,’’International Journal of Research in Marketing, (13:2), 1996, pp. 139-161.

Beall, S., C. Carter, P. Carter, T. Germer, T. Hendrick, S.D. Jap, L.

Kaufmann, D. Maciejewski, R. Monczka and K. Petersen.The Role of Reverse Auctions in Strategic Sourcing, CAPS Research, Tempe, AZ, 2003.

Bentler, P.M.EQS Structural Equations Program Manual, BMDP Statistical Software, Los Angeles, CA, 1989.

Bentler, P.M. and D.G. Bonett. ‘‘Significance Tests and Goodness-of-Fit in the Analysis of Covariance Structures,’’

Psychological Bulletin, (88:3), 1980, pp. 588-606.

(9)

Blau, P.M.Exchange and Power in Social Life, John Wiley & Sons, New York, 1964.

Bollen, K.A. and J.S. Long. ‘‘Introduction.’’ In K.A. Bollen and J.S. Long (Eds.),Testing Structural Equation Models, Sage Publications, Newbury Park, CA, 1993, pp. 1-9.

Bowersox, D.J., M.B. Cooper, D. Lambert and D.A. Taylor.

Management in Marketing Channels, McGraw-Hill Book Company, New York, 1980.

Branco, F. ‘‘Sequential Auctions with Synergies: An Example,’’

Economics Letters, (54:2), 1997, pp. 159-163.

Brown, J.R. and R.L. Day. ‘‘Measures of Manifest Conflict in Distribution Channels,’’Journal of Marketing Research, (18:3), 1981, pp. 263-274.

Brown, J.R., C.S. Dev and D.J. Lee. ‘‘Managing Marketing Channel Opportunism: The Efficacy of Alternative Governance Mechanisms,’’Journal of Marketing, (64:2), 2000, pp. 51-65.

Browne, M.W. and R. Cudeck. ‘‘Alternative Ways of Assessing Model Fit.’’ In K.A. Bollen and J.S. Long (Eds.),Testing Structural Equation Models, Sage Publications, Newbury Park, CA, 1993, pp. 136-162.

Campbell, D.T. ‘‘The Informant in Quantitative Research,’’

American Journal of Sociology, (60:1), 1955, pp. 339-342.

Carbone, J. ‘‘Debate Rages Over Use of E-Auctions for Components,’’Purchasing, (132:19), 2003, pp. 48-52.

Carter, C.R. ‘‘Ethical Issues in International Buyer-Supplier Relationships: A Dyadic Examination,’’Journal of Operations Management, (18:2), 2000, pp. 191-208.

Carter, C.R. ‘‘Purchasing Social Responsibility and Firm Performance: The Key Mediating Roles of Organizational Learning and Supplier Performance,’’International Journal of Physical Distribution and Logistics Management, (35:3), 2005, pp. 177-194.

Carter, C.R., L. Kaufmann, S. Beall, P. Carter, T. Hendrick and K.J. Petersen. ‘‘Reverse Auctions — Grounded Theory from the Buyer and Supplier Perspective,’’Transportation Research E, (40:3), 2004, pp. 229-254.

Carter, C.R. and C.K. Stevens. ‘‘Electronic Reverse Auction Configuration and Its Impact on Buyer Price and Supplier Perceptions of Opportunism: A Laboratory Experiment,’’Journal of Operations Management, forthcoming.

Churchill, G.A., Jr. ‘‘A Paradigm for Developing Better Measures of Marketing Constructs,’’Journal of Marketing Research, (16:1), 1979, pp. 64-73.

Cobanoglu, C., B. Warde and P.J. Moreo. ‘‘A Comparison of Mail, Fax and Web-Based Survey Methods,’’International Journal of Market Research, (43:3), 2001, pp. 441-452.

Dahlstrom, R. and A. Nygaard. ‘‘An Empirical Investigation of Ex Post Transaction Costs in Franchised Distribution Channels,’’Journal of Marketing Research, (36:2), 1999, pp. 160-170.

Deutsch, M. ‘‘Conflicts: Productive and Destructive,’’Journal of Social Issues, (25:1), 1969, pp. 7-41.

Dwyer, F.R. and S. Oh. ‘‘Output Sector Munificence Effects on the Internal Political Economy of Marketing Channels,’’Journal of Marketing Research, (24:4), 1987, pp. 347-368.

Dwyer, F.R., P.H. Schurr and S. Oh. ‘‘Developing Buyer–Supplier Relationships,’’Journal of Marketing, (51:2), 1987, pp. 11-27.

Dyer, J.H. ‘‘Specialized Supplier Networks as a Source of Competitive Advantage: Evidence from the Auto Industry,’’

Strategic Management Journal, (17:4), 1996, pp. 271-291.

Flynn, B.B., S. Sakakibara, R.G. Schroeder, K.A. Bates and E.J. Flynn. ‘‘Empirical Research Methods in Operations Management,’’Journal of Operations Management, (9:2), 1990, pp. 250-284.

Fornell, C. and D. Larcker. ‘‘Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,’’

Journal of Marketing Research, (18:1), 1981, pp. 39-50.

Frazier, G.L. ‘‘Interorganizational Exchange Behavior in Marketing Channels: A Broadened Perspective,’’Journal of Marketing, (47:4), 1983, pp. 68-78.

Ganesan, S. ‘‘Determinants of Long-Term Orientation in Buyer–

Seller Relationships,’’Journal of Marketing, (58:2), 1994, pp. 1-19.

Gaski, J.F. ‘‘The Theory of Power and Conflict in Channels of Distribution,’’Journal of Marketing, (48:3), 1984, pp. 9-29.

Gattiker, T.F., X. Huang and J.L. Schwarz. ‘‘Why do Sellers Dislike Internet Reverse Auctions?: Some Experimental Results,’’Proceedings of the 16th Annual North American Research Symposium, 2005, pp. 183-204.

Gerbing, D.W. and J.C. Anderson. ‘‘An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment,’’Journal of Marketing Research, (25:2), 1988, pp. 186-192.

Gundlach, G.T., R.S. Achrol and J.T. Mentzer. ‘‘The Structure of Commitment in Exchange,’’Journal of Marketing, (59:1), 1995, pp. 78-92.

Hartley, J.L., M.D. Lane and Y. Hong. ‘‘An Exploration of the Adoption of E-Auctions in Supply Management,’’IEEE Transactions on Engineering Management, (51:2), 2004, pp. 153-161.

Heeler, R.M. and M.L. Ray. ‘‘Measure Validation in Marketing,’’

Holm, Desiree Blankenburg, K. Eriksson and J. Johanson.

‘‘Business Networks and Cooperation in International Business Relationships,’’Journal of International Business Studies, (27:5), 1996, pp. 1033-1053.

Hong, Y. and J.L. Hartley. ‘‘An Exploration of On-line Reverse Auctions as a Procurement Tool,’’Proceedings of the 12th Annual North American Research Symposium on Purchasing and Supply Management, 2001, pp. 79-90.

Hur, D., V.A. Mabert and J.L. Hartley.Getting the Most Out of Reverse e-Auction Investment, Working Paper, Bowling Green State University, College of Business Administration, 2005.

Jap, S.D. ‘‘Pie Expansion Efforts: Collaboration Processes in Buyer–Supplier Relationships,’’Journal of Marketing Research, (36:4), 1999, pp. 461-475.

Jap, S.D. ‘‘Online Reverse Auctions: Issues, Themes, and Prospects for the Future,’’Journal of the Academy of Marketing Science, (30:4), 2002, pp. 506-525.

Jap, S.D. ‘‘An Exploratory Study of the Introduction of Online Reverse Auctions,’’Journal of Marketing, (67:3), 2003, pp. 96-107.

Jap, S.D. and E. Anderson. ‘‘Safeguarding Interorganizational Performance and Continuity Under Ex Post Opportunism,’’

Management Science, (49:12), 2003, pp. 1684-1701.

John, G. ‘‘An Empirical Investigation of Some Antecedents of Opportunism in a Marketing Channel,’’Journal of Marketing Research, (21:3), 1984, pp. 278-288.

John, G. and T. Reve. ‘‘The Reliability and Validity of Key Informant Data from Dyadic Relationships in Marketing Channels,’’Journal of Marketing Research, (19:4), 1982, pp. 517-524.

Joshi, A.W. and R.L. Stump. ‘‘The Contingent Effects of Specific Asset Investments on Joint Action in Manufacturer–Supplier Relationships: An Empirical Test of the Moderating Role of Reciprocal Asset Investments, Uncertainty, and Trust,’’Journal of the Academy of Marketing Science, (27:3), 1999, pp. 291-305.

Kaufmann, L. and C.R. Carter. ‘‘Deciding on the Mode of Negotiation: To Auction or Not to Auction Electronically,’’

Journal of Supply Chain Management, (40:2), 2004, pp. 15-26.

(10)

Kaufmann, L. and C.R. Carter. ‘‘International Supply

Relationships — Determinants of Non-financial Performance,’’

Journal of Operations Management, (24:5), 2006, pp. 653–675.

Kjerstad, E. and S. Vagstad. ‘‘Procurement Auctions with Entry Bidders,’’International Journal of Industrial Organization, (18), 2000, pp. 1243-1257.

Kumar, N., L.W. Stern and J.C. Anderson. ‘‘Conducting Interorganizational Research Using Key Informants,’’Academy of Management Journal, (36:6), 1993, pp. 1633-1651.

Kwon, I. and T. Suh. ‘‘Factors Affecting the Level of Trust and Commitment in Supply Chain Relationships,’’Journal of Supply Chain Management, (40:2), 2004, pp. 4-14.

Lambert, D.M. and T.C. Harrington. ‘‘Measuring Nonresponse Bias in Customer Service Mail Surveys,’’Journal of Business Logistics, (11:2), 1990, pp. 5-25.

Luton, R. and R.P. McAfee. ‘‘Sequential Procurement Auctions,’’

Journal of Public Economics, (31), 1986, pp. 181-195.

Mabert, V.A. and J.A. Skeels. ‘‘Internet Reverse Auctions:

Valuable Tool in Experienced Hands,’’Business Horizons, (45:4), 2002, pp. 70-76.

Mayhew-Smith, A. ‘‘Under the Hammer,’’Electronics Weekly, 2138, March 17, 2004, p. 26.

McAfee, R.P. and J. McMillan. ‘‘Auctions and Bidding,’’Journal of Economic Literature, (25:2), 1987, pp. 699-738.

Menon, A., S.G. Bharadwaj and R. Howell. ‘‘The Quality and Effectiveness of Marketing Strategy: Effects of Functional and Dysfunctional Conflict in Intraorganizational Relationships,’’

Journal of the Academy of Marketing Science, (24:4), 1996, pp. 299-213.

Millet, I., D.H. Parente, J.L. Fizel and R.R. Venkataraman.

‘‘Metrics for Managing Online Procurement Auctions,’’

Interfaces, (34:3), 2004, pp. 171-179.

Moore, K.R. ‘‘Trust and Relationship Commitment in Logistics Alliances: A Buyer Perspective,’’International Journal of Purchasing and Materials Management, (34:1), 1998, pp. 24-37.

Moorman, C., R. Deshpande and G. Zaltman. ‘‘Factors Affecting Trust in Market Research Relationships,’’Journal of Marketing, (57:1), 1993, pp. 81-101.

Morgan, R.M. and S.D. Hunt. ‘‘The Commitment–Trust Theory of Relationship Marketing,’’Journal of Marketing, (58:3), 1994, pp. 20-38.

Narayandas, D. and V.K. Rangan. ‘‘Building and Sustaining Buyer–Seller Relationships in Mature Industrial Markets,’’

Journal of Marketing, (68:3), 2004, pp. 63-77.

Pearcy, D.H., L.C. Giunipero and L.M. Dandeo. ‘‘The Impact of Purchase Type on the Reverse Auction Process — A Conceptual Model,’’Proceedings of the 13th Annual North American Research Symposium on Purchasing and Supply Management, 2002, pp. 327-346.

Penrose, E.T.The Theory of the Growth of the Firm, Wiley, New York, 1959.

Prahinski, C. and W.C. Benton. ‘‘Supplier Evaluations:

Communication Strategies to Improve Supplier Performance,’’

Journal of Operations Management, (22:1), 2004, pp. 39-62.

Ring, P.S. and A.H. Van de Ven. ‘‘Structuring Cooperative Relationships Between Organizations,’’Strategic Management Journal, (13:7), 1992, pp. 483-498.

Ring, P.S. and A.H. Van de Ven. ‘‘Developmental Processes of Cooperative Interorganizational Relationships,’’Academy of Management Review, (19:1), 1994, pp. 80-118.

Rokkan, A.I., J.B. Heide and K.H. Wathne. ‘‘Specific Investments in Marketing Relationships: Expropriation and Bonding,’’

Shin, H., D.A. Collier and D.D. Wilson. ‘‘Supply Management Orientation and Supplier/Buyer Performance,’’Journal of Operations Management, (18:3), 2000, pp. 317-333.

Siguaw, J.A., P.M. Simpson and T.L. Baker. ‘‘Effects of Supplier Market Orientation on Distributor Market Orientation and the Channel Relationship: The Distributor Perspective,’’Journal of Marketing, (62:3), 1998, pp. 99-111.

Smeltzer, L.R. and A.S. Carr. ‘‘Reverse auctions in industrial marketing and buying,’’Business Horizons, (45:2), 2002, pp. 47-52.

Stank, T.P., T.J. Goldsby and S.K. Vickery. ‘‘Effect of Service Supplier Performance on Satisfaction and Loyalty of Store Manages in the Fast Food Industry,’’Journal of Operations Management, (17:4), 1999, pp. 429-447.

Steigler, J.H. ‘‘Structural Model Evaluation and Modification:

An Interval Estimation Approach,’’Multivariate Behavioral Research, (25), 1990, pp. 173-180.

Stern, L.W. and A. El-Ansary.Marketing Channels, Prentice-Hall, Englewood Cliffs, NJ, 1977.

Stern, L.W. and A. El-Ansary.Marketing Channels, 4th ed., Prentice-Hall, Englewood Cliffs, NJ, 1992.

Talluri, S. and G.L. Ragatz. ‘‘Multi-Attribute Reverse Auctions in B2B Exchanges: A Framework for Design and Implementation,’’

Journal of Supply Chain Management, (40:1), 2004, pp. 52-60.

Van de Ven, A.H. and D. Ferry.Measurement and Assessment of Organizations, Wiley, New York, 1978.

Wang, R. ‘‘Bidding and renegotiation in procurement auctions,’’European Economic Review, (44), 2000, pp. 1577-1597.

Wathne, K.H. and J.B. Heide. ‘‘Opportunism in Interfirm Relationships — Forms, Outcomes, and Solutions,’’Journal of Marketing, (64), 2000, pp. 36-51.

Williamson, O.E. ‘‘The Economics of Organization: The Transaction Cost Approach,’’American Journal of Sociology, (87:3), 1981, pp. 548-577.

Zucker, L.G. ‘‘Production of Trust: Institutional Sources of Economic Structure 1840–1920.’’ In B.M. Staw and L.L.

Cummings (Eds.),Research in Organizational Behavior, Vol. 8, JAI Press, Greenwich, CT, 1986, pp. 53-111.

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QUESTIONNAIRE SCALE ITEMS^a

Standardized Factor Loading^b Opportunism (0.90)^c

To what extent have the (winning/losing) suppliers changed their willingness to do the following since the auction?^d . . . Make false accusations (O1) (DELETED: low factor loading)

. . . provide false information (O2) 0.87

. . . expect your firm to pay more than your fair share of the costs to correct a problem (O3) 0.86

. . . make hollow promises (O4) 0.88

Trust (0.88)^c

Please indicate the extent to which you agree or disagree with the following statements concerning the (winning/losing) suppliers of this auction.^e

. . . The promises made between our firm and them are reliable (T1) 0.91

. . . Our firm and these suppliers are very honest in dealing with each other (T2) 0.79

. . . Our firm and these suppliers trust each other (T3) 0.82

Commitment (0.81)^c

. . . The relationship that we have with these suppliers is something that we are very committed to (C1)

0.95

. . . The relationship that we have with these suppliers is something we intend to maintain indefinitely (C2) (DELETED: High standardized residual with C1, C3, and DC3)

. . . The relationship that we have with these suppliers is something we are willing to make long- term investments in (C3)

0.68 Dysfunctional Conflict (0.88)^c

. . . The relationship between our firm and these suppliers can best be described as tense (DC1) 0.84 . . . We have significant disagreements in our working relationship with these suppliers (DC2) 0.83 . . . We frequently clash with these suppliers on issues relating to how we should conduct our

business (DC3)

0.84

Nonprice Performance (0.95)^c

To what extent has the (winning/losing) suppliers changed their willingness to do the following since the auction?^d

. . . Uphold commitment dates (P1) 0.88

. . . Respond to product delivery problems (P2) (DELETED: high standardized residuals with P4 and P5)

. . . Provide a quick turnaround on failure analysis (P3) 0.90

. . . Respond to buyer requests (P4) 0.87

. . . Give you preferred access in times of high demand (P5) 0.90

. . . Give you first access to innovations (P6) 0.84

. . . Allow you to delay payment (P7) (DELETED: low factor loading)

. . . Improve the quality of their components (P8) 0.89

aThe unit of analysis was a specific electronic reverse auction (ERA) in which the respondent had managed and participated in. In order to increase variance in responses, the survey was randomized along two variables: (1) their most/least successful ERA, and (2) their largest/smallest dollar volume ERA. To further improve the variance in responses, respondents were asked to answer the questions concerning buyer–supplier relationships regarding either winning or losing suppliers. Questions concerning winning/losing suppliers were randomized consistently across survey items.

bAll loadings factor are significant (t410.78,p<0.0001)

cComposite reliability given in parentheses.

dThese items are measured on a seven-point Likert scale where 15much less willing and 75much more willing.

eThese items are measured on a seven-point Likert scale where 15strongly disagree and 75strongly agree.

Appendix