Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions

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.

[1]  Paul E. Johnson,et al.  Detecting deception: adversarial problem solving in a low base-rate world , 2001, Cogn. Sci..

[2]  Peter E. Kennedy A Guide to Econometrics , 1979 .

[3]  B. Klein,et al.  The Role of Market Forces in Assuring Contractual Performance , 1981, Journal of Political Economy.

[4]  Eric K. Clemons,et al.  Price Dispersion and Differentiation in Online Travel: An Empirical Investigation , 2002, Manag. Sci..

[5]  Chrysanthos Nicholas Dellarocas,et al.  Efficiency and Robustness of eBay-like Online Feedback Mechanisms in Environments with Moral Hazard , 2003 .

[6]  George A. Akerlof The Market for “Lemons”: Quality Uncertainty and the Market Mechanism , 1970 .

[7]  W. W. Muir,et al.  Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .

[8]  T. Liao Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models , 1994 .

[9]  Ho Geun Lee,et al.  Do electronic marketplaces lower the price of goods? , 1998, CACM.

[10]  C. Shapiro Premiums for High Quality Products as Returns to Reputations , 1983 .

[11]  K. Zimmermann,et al.  PSEUDO‐R2 MEASURES FOR SOME COMMON LIMITED DEPENDENT VARIABLE MODELS , 1996 .

[12]  Abraham Seidmann,et al.  Can online auctions beat online catalogs? , 1999, ICIS.

[13]  Atanu R. Sinha,et al.  The Impact of Discrete Bidding and Bidder Aggressiveness on Sellers' Strategies in Open English Auctions: Reserves and Covert Shilling , 2000 .

[14]  J. Geweke,et al.  Alternative computational approaches to inference in the multinomial probit model , 1994 .

[15]  V. Mittal,et al.  Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics , 2001 .

[16]  Paul R. Milgrom,et al.  Auctions and Bidding: A Primer , 1989 .

[17]  Susan Athey,et al.  Information and Competition in U.S. Forest Service Timber Auctions , 2001, Journal of Political Economy.

[18]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[19]  Michael H. Kutner Applied Linear Statistical Models , 1974 .

[20]  Christopher S. McIntosh,et al.  Qualitative Forecast Evaluation: A Comparison of Two Performance Measures , 1992 .

[21]  W. K. Vickery,et al.  Counter-Speculation Auctions and Competitive Sealed Tenders , 1961 .

[22]  BaSulin,et al.  Evidence of the effect of trust building technology in electronic markets , 2002 .

[23]  Robert F. Easley,et al.  Jump Bidding Strategies in Internet Auctions , 2004, Manag. Sci..

[24]  B. Depaulo,et al.  On-the-Job Experience and Skill at Detecting Deception1 , 1986 .

[25]  Luke M. Froeb,et al.  Mergers in Sealed versus Oral Auctions , 2000 .

[26]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[27]  C. Shapiro Consumer Information, Product Quality, and Seller Reputation , 1982 .

[28]  R. Mehra,et al.  Auctions: Theory and Applications , 1993 .

[29]  R. McKelvey,et al.  A statistical model for the analysis of ordinal level dependent variables , 1975 .

[30]  D. McFadden,et al.  Specification tests for the multinomial logit model , 1984 .

[31]  Andrew Whinston,et al.  Shill Bidding in English Auctions , 2001 .

[32]  J. Bakos Reducing buyer search costs: implications for electronic marketplaces , 1997 .

[33]  Jeremy I. Bulow,et al.  Toeholds and Takeovers , 1998, Journal of Political Economy.

[34]  Prabuddha De,et al.  Proceedings of the 20th international conference on Information Systems , 1999 .

[35]  Alok Gupta,et al.  Insights and analyses of online auctions , 2001, CACM.

[36]  Paul A. Pavlou,et al.  Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior , 2002, MIS Q..

[37]  W. Greene,et al.  Marginal Effects in the Bivariate Probit Model , 1996 .

[38]  P. Schultz,et al.  Why Do NASDAQ Market Makers Avoid Odd-Eighth Quotes? , 1994 .