When do I profit? Uncovering boundary conditions on reputation effects in online auctions

https://doi.org/10.1016/j.im.2016.06.007Get rights and content

Highlights

  • This study investigates buyer decision-making in online auctions.

  • It examines the combined effects of information cues on price premium.

  • Classical and contemporary theories are used to develop competing hypotheses.

  • Hypotheses are tested using hierarchical linear modeling (HLM).

  • Results confirm most of the effects predicted by classical theories.

Abstract

Reputation profiles, based on customer feedback ratings, are important for achieving above average sales prices in online auctions. However, contradictory results in past research suggest that reputation effects may depend on information alternatives to customer feedback that sellers can provide to buyers. By explicitly modeling the competing assumptions of classical and contemporary approaches to buyer decision-making and using hierarchical linear modeling to analyse data from 363 online auctions, we found that sellers may benefit from carefully evaluating what information alternatives they combine with reputation profile to realize higher sales prices.

Introduction

In online auctions (e.g., eBay), which are largely characterized by unfamiliar sellers and an absence of merchant branding, it is difficult for buyers to ascertain product quality [1], [5], [18], [20], [73]. Moreover, unscrupulous sellers may misrepresent the products they are selling [34], [46], [55]. Consequently, in these settings, reputation systems based on customer feedback constitute an important informational resource for buyers that reduce their perceptions of transaction risk [1], [5], [13], [58], [74]. Customer feedback is solicited in a variety of ways that help otherwise unknown sellers establish a reputation. For example, eBay’s system uses individual evaluations left after each transaction to rate registered users [58]. Reputation systems such as these discourage fraudulent trading by ensuring that trading behaviors are made publicly available to an entire community via information technology (IT)-enabled summary statistics, referred to here as reputation profiles.

As it takes time to establish a good reputation, sellers must be rewarded or at least compensated for costs incurred during the reputation-building process. At the same time, the penalty for bad reputation should exceed any benefits sellers might expect to gain by behaving opportunistically—thus, “in equilibrium, a good reputation must command a price premium” [8,p. 82]. Price premium has been defined in the literature as the additional monetary amount buyers are willing to pay for a product above the average price received by multiple sellers for an identical product [1]. The present paper adopts this definition and henceforth uses “price premium” to refer to above average prices.

Many studies have investigated the relationship between reputation profile and online auction outcomes [13]. Although this literature stream suggests that a seller’s reputation profile is influential in buyer decision-making, past research presents contradictory results for the precise impacts of positive and negative feedback ratings on final sales prices [48]; see Table 1 for an overview). Although some studies show that negative feedback has little [13] or no impact [1], [3], [19], others find that it reduces final sales prices [31], [41], [45]. Furthermore, while many studies show that positive feedback increases final sales prices [1], [3], [31], [45], others find no such effect [41], [58], or that these effects taper off for established sellers with high percentages of positive feedback [8], [39].

Some previous studies have suggested that the ambiguous relationship found between feedback ratings and final sales prices may be due to boundary conditions on reputation profile’s influence on price premiums [10]. Buyers might have a different understanding of reputation profiles in the presence of other information provided by sellers. Consequently, to determine under which conditions reputation profile is effective, there is a need to examine the interplay between this informational resource and other types of information in the choice context (henceforth referred to as information alternatives; [13], [37], [67]. As contradictory findings limit knowledge creation [2], developing a clear understanding of the boundaries of the effects of reputation profile is important if sellers are to manage the information they provide effectively [21]. For example, the pragmatic question remains of whether sellers can, and should, leverage information alternatives that are under their direct control to influence the effect of reputation profile on price premiums. For sellers with a damaged reputation, leveraging information alternatives could help persuade reluctant buyers to transact with them, increasing the likelihood of sales and making it possible to restore reputation profiles more quickly. For reputable sellers, information alternatives may enhance positive reputation effects to increase price premiums. Thus, to tease out the underlying dynamics of these relationships, this study examines the following research question: In online auctions, what information alternatives are open to sellers to compensate for a poor reputation profile or to boost a good reputation profile?

The value in this work is in developing a more nuanced understanding of the relationships between customer-based reputation profile and seller-provided information alternatives in online auctions. We begin by reviewing the online auction literature on information alternatives to reputation profile in online auctions. Next, we frame our arguments by contrasting oft-used economic theories of rational choice (e.g., utility theory) with more contemporary perspectives. Previous information system (IS) research has drawn on economic theories of rational choice to predict the direct effects of information alternatives on final sales prices. Under classical assumptions − for example, that rational buyers have perfect information and unlimited computational capacity [61]—information alternatives would be expected to compensate for negative feedback and add to the effects of positive feedback on price premiums. However, some argue that due to bounded rationality, classical assumptions do not adequately explain buyer behavior in practice [6], [13]. Contemporary perspectives suggest that negative and positive feedback may interact with information alternatives in unexpected ways to influence buyers’ willingness to pay more or less for the same product from different sellers [33], [44], [65]. Although both classical and contemporary perspectives suggest that sellers’ choices with respect to information alternatives can significantly influence final sales prices, their competing assumptions have not been explicitly modeled or empirically examined. Because moderation hypotheses are relatively complex and, thus, entail a relatively high risk of error in reasoning, testing competing moderation hypotheses may help minimize errors in judgment. In addition, this approach sheds light on which set of assumptions best explains buyer behavior in online auctions.

To evaluate the relative utility of economic theories of rational choice and contemporary perspectives on buyer decision-making, we develop competing hypotheses of how seller-provided information in auction listings will interact with customer-based positive and negative feedback ratings to exert influences on price premium. Afterwards, we empirically test the hypothesized relationships in the context of new, electronic products. The paper concludes with a discussion of results, limitations, and implications for research and practice.

This study contributes to IS research on online auctions by helping resolve ambiguities found in past works that have historically examined reputation effects in isolation. By specifying information alternatives as boundary conditions on reputation effects, this study helps lay the foundations for enhancing sellers’ ability to manage information under their control effectively.

Section snippets

Literature review

Researchers studying online auctions have found that how a product is presented for sale may be a more important predictor of online buyers’ purchase intentions than the actual product being sold [24], [40], [64]. To this end, many online auction studies emphasize the significance of how different types of information contained in auction listings influence buyers’ willingness to pay more for the same product (presented in Table 2). More specifically, textual descriptions based on standardized

The separate influences of information alternatives on price premium

Classical economic theories of rational choice, such as utility theory [70], represent one approach to understanding buyer decision-making in online auctions. Utility theory suggests that rational buyers are motivated to maximize their expected utility (i.e., satisfaction) in making a choice over a set of available alternatives [4], [44]. The total utility that a buyer expects to gain from purchasing an item from a specific seller is revealed in its final sales price [43].

Utility theory has

Research method

Data from a total of 363 completed auctions across 232 sellers are collected between 1 September 2010 and 21 September 2010 from eBay, a leading online marketplace. eBay may be particularly appropriate for studying how buyers integrate multiple pieces of information as the multiplicity of sellers and products it offers makes large amounts of information available to buyers [1], [13], [55]. Consistent with past research (e.g., [1], [15], [46], [53], [55], [71], we selected electronic products

Analysis and results

Because individual auctions in our sample were nested within online sellers (i.e., N = 363 auctions at level 1 across J = 232 sellers at level 2), potentially they were not independent, resulting in correlated residuals. Misestimating standard errors can lead to erroneous conclusions when dependency of observations is not accounted for [12]. Accordingly, to adequately align the analytical model with the data, hierarchical linear modeling (HLM) was used to test the effects of predictors (all at the

Implications for research and practice

Our results confirm the linear effects, and most of the interaction effects, predicted by classical theories of economic choice [31], [36]. In explicitly modeling and testing the competing assumptions of classical and contemporary approaches, this study extends past work by yielding a more nuanced understanding of factors that bound the value of reputation in online auctions (see Fig. 2). Such understanding is necessary for sellers to manage the information they provide to buyers effectively

Limitations

Study limitations should be acknowledged. First, as our sample was limited to 363 completed auctions, a Type II error was more likely to occur than in a larger sample, which could have been obtained through the use of a computerized data collection program. However, consistent with previous research, we decided to collect our data manually to maintain full control over the data collection process Gonzales et al., 2009, ensuring that only fully homogeneous items were included in the analysis.

Conclusion

Past online auction research has advanced understanding of whether reputation can yield price premiums but has not examined the boundaries of this important relationship, resulting in conflicting findings and the need to improve knowledge in this area. Although classical perspectives on economic choice have contributed significantly to predicting separate influences of reputation, classical and contemporary assumptions lead to competing moderation hypotheses about the combined effects of

Michelle Carter is an Assistant Professor in the Carson College of Business at Washington State University. Her work has appeared in MIS Quarterly, MISQ Executive, European Journal of Information Systems, Communications of the AIS, and ACM Transactions on Management Information Systems, as well as several conference proceedings and book chapters. Her current research investigates the involvement of information technologies in identity, humanness, and social change, in an increasingly digital

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    Michelle Carter is an Assistant Professor in the Carson College of Business at Washington State University. Her work has appeared in MIS Quarterly, MISQ Executive, European Journal of Information Systems, Communications of the AIS, and ACM Transactions on Management Information Systems, as well as several conference proceedings and book chapters. Her current research investigates the involvement of information technologies in identity, humanness, and social change, in an increasingly digital world.

    Stefan Tams is an Assistant Professor of Information Systems at HEC Montréal, Canada. He received his PhD from the Department of Management at Clemson University. His research interests focus on the roles of aging, stress, and culture in technology-use behaviors, and on electronic commerce. His work has appeared or is scheduled to appear in several scientific journals, including Journal of Strategic Information Systems, Journal of the Association for Information Systems, and European Journal of Work and Organizational Psychology.

    Varun Grover is the William S. Lee (Duke Energy) Distinguished Professor of Information Systems at Clemson University. He has published extensively in the information systems field, with over 200 publications in major refereed journals. Nine recent articles have ranked him among the top four researchers based on number of publications in the top Information Systems journals, as well as citation impact (h-index). Dr. Grover is Senior Editor for MISQ Executive, and Senior Editor (Emeritus) for MIS Quarterly, the Journal of the AIS and Database. He is currently examining the impacts of digitalization on individuals and organizations. He is a recipient of numerous awards from University of South Carolina, Clemson, Association for Information Systems, Decision Sciences Institute, Anbar, PriceWaterhouse, and other organizations for his research and teaching. He is a Fellow of the Association for Information Systems.

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