Electronic Commerce Research and Applications
Image Effects and Rational Inattention in Internet-Based Selling
International Journal of Electronic Commerce
Are online auction markets efficient? An empirical study of market liquidity and abnormal returns
Decision Support Systems
Electronic Commerce Research and Applications
Optimal Windows for Aggregating Ratings in Electronic Marketplaces
Management Science
Of values and functionality: the sequestering nonpositive reviews in an online feedback system
Proceedings of the 2011 iConference
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Reputation inflation detection in a Chinese C2C market
Electronic Commerce Research and Applications
Seller Strategies for Differentiation in Highly Competitive Online Auction Markets
Journal of Management Information Systems
Subjective review-based reputation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Input online review data and related bias in recommender systems
Decision Support Systems
Some clues to the determinants of feedback behaviour
Proceedings of the 13th International Conference on Electronic Commerce
Engineering Trust: Reciprocity in the Production of Reputation Information
Management Science
Social Media and Firm Equity Value
Information Systems Research
Seller Strategies for Differentiation in Highly Competitive Online Auction Markets
Journal of Management Information Systems
Sentiment analysis on evolving social streams: how self-report imbalances can help
Proceedings of the 7th ACM international conference on Web search and data mining
Web Intelligence and Agent Systems
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Most online feedback mechanisms rely on voluntary reporting of privately observed outcomes. This introduces the potential for reporting bias, a situation where traders exhibit different propensities to report different outcome types to the system. Unless properly accounted for, reporting bias may severely distort the distribution of public feedback relative to the underlying distribution of private transaction outcomes and, thus, hamper the reliability of feedback mechanisms. This study offers a method that allows users of feedback mechanisms where both partners of a bilateral exchange are allowed to report their satisfaction to “see through” the distortions introduced by reporting bias and derive unbiased estimates of the underlying distribution of privately observed outcomes. A key aspect of our method lies in extracting information from the number of transactions where one or both trading partners choose to remain silent. We apply our method to a large data set of eBay feedback. Our results support the widespread belief that eBay traders are more likely to post feedback when satisfied than when dissatisfied and are consistent with the presence of positive and negative reciprocation among eBay traders. Most importantly, our analysis derives unbiased estimates of the risks that are associated with trading on eBay that, we believe, are more realistic than those suggested by a naïve interpretation of the unusually high (99%) levels of positive feedback currently found on that system.