Skip to main content

Bidder behaviours on eBay: collectibles and commodities

Abstract

In the past decade, online auctioning has become one of the most successful business innovations. eBay is the global leader in this marketspace. This paper focuses on understanding bidder behaviour in order to provide insight into the design of information systems to support online auctions. In contrast to previous work that uses discrete bidding information, we implement a functional viewpoint of the bidding path. The similarity between paths is measured in the functional space using L2 distance, which allows us to utilize K-means and PAM clustering methods. We apply this method to a large sample of collectible (1968 Camaro) and commodity (Digital Camera) eBay auction data to explore current bidding behaviours. We identify two different clusters of bidding behavior across both items which is associated with a specific winning percentage. Sniping exists as the dominant behaviour, but a hybrid approach has emerged (Sentry + Sniping).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    For these plots see http://selfsynchronize.com/hayne/ebay/SixClusters.pdf

References

  1. Avery, C. (1998). Strategic jump bidding in English auctions. Review of Economic Studies, 65, 185–210.

    Article  Google Scholar 

  2. Bajari, P., & Hortacsu, A. (2003). The winner’s curse, reserve prices and endogenous entry: empirical insights from eBay auctions. The RAND Journal of Economics, 34(2), 329–335.

    Article  Google Scholar 

  3. Bapna, R., Goes, P., & Gupta, A. (2000). A theoretical and empirical investigation of multi-item on-line auctions. Information Technology and Management, 1(1), 1–23.

    Article  Google Scholar 

  4. Bapna, R., Goes, P., & Gupta, A. (2001). On-line auctions: insights and analysis. Communications of the ACM, 44(11), 42–50.

    Article  Google Scholar 

  5. Bapna, R., Goes, P., & Gupta, A. (2003). Replicating online Yankee auctions to analyze auctioneers’ and bidders strategies. Information Systems Research, 14(3), 244–268.

    Article  Google Scholar 

  6. Bapna, R., Goes, P., Gupta, A., & Jin, Y. (2004). User heterogeneity and its impact on electronic auction market design: an empirical exploration. MIS Quarterly, 28(1), 21–43.

    Google Scholar 

  7. Bertsimas, D., Hawkins, J., & Perakis, G. (2009). Optimal bidding in online auctions. Journal of Revenue and Pricing Management, 8, 21–41.

    Article  Google Scholar 

  8. Chiang, K., & Kung, A. (2005). Bidding dynamics in multi-unit auctions: empirical evidence from online auctions of certificates of deposit. Journal of Financial Intermediation, 14(2), 239–252.

    Article  Google Scholar 

  9. Dewan, S., & Hsu, V. (2001). Trust in electronic markets: price discovery in generalist versus specialty online auctions. Working Paper. January 31, 2001.

  10. eBay (2009). eBay 2009 Annual Report. Available at www.ebay.com.

  11. Granka, L. A., Joachims, T., & Gay, G. (2004). Eye-tracking analysis of user behavior in WWW search. In Proceedings of SIGIR conference.

  12. Gregg, D. & Walczak, S. (2003). E-commerce Auction Agents and Online-Auction Dynamic, Electronic Markets, 13(3):242–250.

    Google Scholar 

  13. Hasker, K., Gonzalez, R., & Sickles, R. C. (2001). An analysis of strategic behavior in eBay auctions. The Singapore Economic Review, 54(3), 441–472.

    Google Scholar 

  14. Hayne, S., Smith, C. A. P., & Vijayasarathy, L. (2002). Predicting sniping in eBay auctions. INFORMS Conference, San Jose, CA, November.

  15. Hayne, S., Smith, C. A. P., & Vijaysarathy, L. (2003). Who wins on eBay: an analysis of bidders and their bid behaviours. Journal of Electronic Markets, 134, 459–470.

    Google Scholar 

  16. Herschlag, M., & Zwick, R. (2002). “Internet Auctions: A Popular and Professional Literature Review,” Quarterly Journal of Electronic Commerce 1(2), 161–186.

    Google Scholar 

  17. Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley.

    Google Scholar 

  18. Lucking-Reiley, D. (1999). Using Field Experiments to Test Equivalence Between Auction Formats: Magic on the Internet. American Economic Review, 89(5), 1063–1080.

    Article  Google Scholar 

  19. Lucking-Reiley, D., Bryan, D., Prasad, N., & Reeves, D. (2007). Pennies from eBay: the determinants of price in online auctions. Journal of Industrial Economics, 55(2), 223–233.

    Article  Google Scholar 

  20. Malone, T. W., Yates, J., Scott, M., & Benjamin, R. (1987). Electronic markets and electronic hierarchies. Communications of the ACM, 30(6), 484–494.

    Article  Google Scholar 

  21. McAfee, R. P., & McMillan, J. (1987). Auctions and bidding. Journal of Economic Literature, 25, 699–738.

    Google Scholar 

  22. Peng, J., & Müller, H. (2008). Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions. The Annals of Applied Statistics, 2(3), 1056–1077.

    Article  Google Scholar 

  23. Ray, S., & Turi, R. H. (1999). Determination of number of clusters in K-means clustering and application in color image segmentation. In N. R. Pal, A. K. De & J. Das (Eds.), Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT’99). Calcutta, India, 137–143.

  24. Rimbey, B., & Guilfoyle, N. (2000). Beware of online auction snipers. TechTV, Available at http://www.techtv.com/news/business/story/0,24195,12508,00.html.

  25. Roth, A., & Ockenfels, A. (2002). Last minute bidding and the rules for ending second-price auctions: theory and evidence from a natural experiment on the internet. American Economic Review, 92(4), 1093–1103.

    Article  Google Scholar 

  26. Schwartz, R., & Francioni, R. (2004). Equity markets in action: The fundamentals of liquidity, market structure & trading. New York: Wiley.

    Google Scholar 

  27. Shmueli, G., & Jank, W. (2005). Visualizing online auctions. Journal of Computational and Graphical Statistics, 14, 299–319.

    Article  Google Scholar 

  28. Sinclair, J., & Hanks, J. (2006). eBay inventory the smart way: How to find great sources and managed your merchandise to maximize profits on the world’s #1 auction site. New York: AMACOM.

    Google Scholar 

  29. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of data clusters via the Gap statistic. Journal of the Royal Statistical Society. Series B Statistical Methodology, 63(Part 2), 411–423.

    Google Scholar 

  30. Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance, 16(10), 8–37.

    Article  Google Scholar 

  31. Vishwanath, A., & Barnett, G. (2005). An empirical investigation into the structure of bidding in online auctions. Journal of Electronic Markets, 15(3), 261–268.

    Article  Google Scholar 

  32. Ward, S., & Clark, J. (2002). Bidding behavior in on-line auctions: an examination of the eBay Pokemon card market. International Journal of Electronic Commerce, 6(4), 139–155.

    Google Scholar 

Download references

Acknowledgements

This research was partially supported by the National Science Foundation. The authors are grateful for the contribution of C.A.P Smith to this project.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Stephen C. Hayne.

Additional information

Responsible editor: Martin Spann

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hayne, S.C., Bugbee, B. & Wang, H. Bidder behaviours on eBay: collectibles and commodities. Electron Markets 20, 95–104 (2010). https://doi.org/10.1007/s12525-010-0036-9

Download citation

Keywords

  • Online auctions
  • Bidding strategy
  • Clustering
  • Sniping

JEL

  • C1
  • D44
  • L1