Insights and analyses of online auctions
Communications of the ACM
Information Technology and Management
An auctioning reputation system based on anomaly
Proceedings of the 12th ACM conference on Computer and communications security
Cheating in online auction - Towards explaining the popularity of English auction
Electronic Commerce Research and Applications
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
Presumptive selection of trust evidence
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Online reputation systems: Design and strategic practices
Decision Support Systems
Enhancing remote participation in live auctions: an 'intelligent' gavel
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirical analysis of online auction fraud: Credit card phantom transactions
Expert Systems with Applications: An International Journal
Bazaar: strengthening user reputations in online marketplaces
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Price comparison: A reliable approach to identifying shill bidding in online auctions?
Electronic Commerce Research and Applications
Journal of Theoretical and Applied Electronic Commerce Research
Survey: Combating online in-auction fraud: Clues, techniques and challenges
Computer Science Review
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
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Online auctions allow the seller to remain anonymous and to easily change identities. Buyers must rely on the seller's description of a product and ability to deliver the product as promised. Internet auction environments make opportunistic behavior more attractive to sellers because the chance of detection and punishment is decreased. In this research, we show how fee structures at eBay, the largest online auction house, motivate shilling behavior. We distinguish between two different types of shilling that exhibit different motivation and behavior: shilling can be used to make the bidders pay more for an item, competitive shilling, and shilling that can be used to avoid paying auction house fees, reserve price shilling. We then use auction data gathered using an Internet-based data collection software agent to examine reserve price shilling using a probit model. We give evidence of reserve price shilling and then show factors that lead to this behavior.