How Valuable Is a Good Reputation? A Sample Selection Model of Internet Auctions

Livingston, Jeffrey A.

doi:10.1162/0034653054638391

Cited by 212 publications

(129 citation statements)

References 12 publications

(19 reference statements)

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“…The coefficient for bid surplus in Equation 5 is negative (α 31 < 0), so α 31 × φ i1-2 > 0, which indicates that as consumers' bid rate increases, they become more likely to bid early, assuming that they bid. This finding is consistent with previous results in auction literature (Ba and Pavlou 2002;Brinkman and Siefert 2001;Dewan and Hsu 2001;Houser and Wooders 2006;Livingston 2005). The reduction of quality uncertainty makes consumers less likely to depend on other bidders' bidding information.…”

Section: Impact Of Credibility Indicators: Hsupporting

confidence: 92%

Internet Auction Features as Quality Signals

Srinivasan

Sun

2009

Journal of Marketing

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Internet auction companies have developed innovative tools that enable sellers to reveal more information about their credibility and product quality to avoid the "lemons" problem. On the basis of signaling and auction theories, the authors propose a typology of Internet auction quality and credibility indicators, adopt and modify Park and Bradlow's (2005) model, and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty. This empirical evidence demonstrates how Internet auction features affect consumer participation and bidding decisions, what modifies the credibility of quality indicators, and how different buyers react to indicators. The signaling-based hypotheses provide coherent explanations of consumers' bidding behavior. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.

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Section: Impact Of Credibility Indicators: Hsupporting

confidence: 92%

Internet Auction Features as Quality Signals

Srinivasan

Sun

2009

Journal of Marketing

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“…However, the results of prior studies are not directionally consistent. For example, Eaton (2002) finds that negative feedback reduces the probability of sale of electronic guitars only for sellers with more than 20 feedback postings; Livingston (2002) finds that experienced sellers with positive feedback earn premiums over new sellers with no feedback, but cannot establish any effect of negative feedback. Cabral and Hortaçsu (2005) identify a significant effect of reputation on pricing power only after eBay changed its display in 2003.…”

Section: Related Workmentioning

confidence: 99%

The Dimensions of Reputation in Electronic Markets

2009

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We analyze how different dimensions of a seller's reputation affect pricing power in electronic markets. We do so by using text mining techniques to identify and structure dimensions of importance from feedback posted on reputation systems, by aggregating and scoring these dimensions based on the sentiment they contain, and using them to estimate a series of econometric models associating reputation with price premiums. We find that different dimensions do indeed affect pricing power differentially, and that a negative reputation hurts more than a positive one helps on some dimensions but not on others. We provide the first evidence that sellers of identical products in electronic markets differentiate themselves based on a distinguishing dimension of strength, and that buyers vary in the relative importance they place on different fulfilment characteristics. We highlight the importance of textual reputation feedback further by demonstrating it substantially improves the performance of a classifier we have trained to predict future sales. This paper is the first study that integrates econometric, text mining and predictive modeling techniques toward a more complete analysis of the information captured by reputation systems, and it presents new evidence of the importance of their effective and judicious design.

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“…Earlier work about reputation systems focuses on the e-commerce scenario that reflects such an interaction system with buying and selling products, e.g., [2][3][4].…”

Section: Centralized Communication Protocols That Allow Users Tomentioning

confidence: 99%

Privacy-Respecting Reputation for Wiki Users

2011

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Wikis are popular tools for creation and sharing of content. Integrated reputation systems allow to assess expertise and reliability of authors and thus to support trust in the wiki content. Yet, results from our empirical study indicate that the disclosure of user reputation evokes privacy issues. As a solution for this conflict between the need to evaluate trustworthiness of users and protecting their privacy, we present a privacy-respecting reputation system for wikis that we realized as OpenSource-Extension for the wiki software MediaWiki.

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How Valuable Is a Good Reputation? A Sample Selection Model of Internet Auctions

Cited by 212 publications

References 12 publications

Internet Auction Features as Quality Signals

Internet Auction Features as Quality Signals

The Dimensions of Reputation in Electronic Markets

Privacy-Respecting Reputation for Wiki Users

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