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Understanding Willingness-to-Pay Formation of Repeat Bidders in Sequential Online Auctions

Author

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  • Paulo B. Goes

    (Management Information Systems, Eller College of Management, University of Arizona, Tucson, Arizona 85721)

  • Gilbert G. Karuga

    (Accounting and Information Systems Area, University of Kansas, Lawrence, Kansas 66045)

  • Arvind K. Tripathi

    (Information Systems and Operations Management, Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

A growing number of vendors are using a sequence of online auctions to sell large inventories of identical items. Although bidding strategies and bidder behavior in single auctions have been extensively studied, limited research exists on bidding in sequential auctions. We seek to explain how bidders in such an environment learn from the information, and form and update their willingness to pay (WTP). Using a large data set from an online auction retailer, we analyze the evolution of the bidders' WTP as well as the effect of auction design on bidders' WTP in sequential auctions. We see our study in the context of a longitudinal field experiment, in which we were able to track actions of repeat bidders over an extended period of time. Our results show that bidders' WTP in sequential auctions can be explained from their demand characteristics, their participation experience in previous auctions, outcomes in previous auctions, and auction design parameters. We also observe, characterize, and measure what we call a modified demand reduction effect exhibited across different auctions, over time, by multiunit demand bidders. Our findings are important to enable better auction mechanism design, and more sophisticated bidding tools that explore the rich information environment of sequential auctions.

Suggested Citation

  • Paulo B. Goes & Gilbert G. Karuga & Arvind K. Tripathi, 2010. "Understanding Willingness-to-Pay Formation of Repeat Bidders in Sequential Online Auctions," Information Systems Research, INFORMS, vol. 21(4), pages 907-924, December.
    • Handle: RePEc:inm:orisre:v:21:y:2010:i:4:p:907-924
    DOI: 10.1287/isre.1080.0216
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    Cited by:

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    2. Ghate, Archis, 2015. "Optimal minimum bids and inventory scrapping in sequential, single-unit, Vickrey auctions with demand learning," European Journal of Operational Research, Elsevier, vol. 245(2), pages 555-570.
    3. Shi, Yang & Zhao, Ying, 2019. "Modeling Advertisers' Willingness to Pay in TV Commercial Slot Auctions," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 120-133.
    4. Yixin Lu & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2019. "Information Transparency in Business-to-Business Auction Markets: The Role of Winner Identity Disclosure," Management Science, INFORMS, vol. 65(9), pages 4261-4279, September.
    5. Arvind K. Tripathi & Young-Jin Lee & Amit Basu, 2022. "Analyzing the Impact of Public Buyer–Seller Engagement During Online Auctions," Information Systems Research, INFORMS, vol. 33(4), pages 1264-1286, December.
    6. Pownall, Rachel A.J. & Wolk, Leonard, 2013. "Bidding behavior and experience in internet auctions," European Economic Review, Elsevier, vol. 61(C), pages 14-27.
    7. Zhuang, Hejun & Popkowski Leszczyc, Peter T.L., 2022. "Optimal seller strategy in overlapping auctions," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    8. Cem Ozturk, O. & Karabatı, Selçuk, 2017. "A decision support framework for evaluating revenue performance in sequential purchase contexts," European Journal of Operational Research, Elsevier, vol. 263(3), pages 922-934.
    9. Eric Overby & Karthik Kannan, 2015. "How Reduced Search Costs and the Distribution of Bidder Participation Affect Auction Prices," Management Science, INFORMS, vol. 61(6), pages 1398-1420, June.
    10. Yili Hong & Chong (Alex) Wang & Paul A. Pavlou, 2016. "Comparing Open and Sealed Bid Auctions: Evidence from Online Labor Markets," Information Systems Research, INFORMS, vol. 27(1), pages 49-69, March.
    11. Chen, Xi & Ghate, Archis & Tripathi, Arvind, 2011. "Dynamic lot-sizing in sequential online retail auctions," European Journal of Operational Research, Elsevier, vol. 215(1), pages 257-267, November.
    12. Yue Yuan & Mary E. Deily & Yuliang Yao, 2022. "Willingness to Pay for Status Signals in Online Luxury Markets," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 668-680, February.
    13. May Truong & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2022. "Buyers’ Strategic Behavior in B2B Multichannel Auction Markets: When an Online Posted Price Channel Is Incorporated into a Dutch Auction System," Information Systems Research, INFORMS, vol. 33(4), pages 1344-1367, December.
    14. Vidyanand Choudhary & Shivendu Shivendu, 2017. "Targeted Couponing in Online Auctions," Information Systems Research, INFORMS, vol. 28(3), pages 490-510, September.
    15. Nicole Koschate-Fischer & Katharina Wüllner, 2017. "New developments in behavioral pricing research," Journal of Business Economics, Springer, vol. 87(6), pages 809-875, August.
    16. Verwer, S. & Zhang, Y., 2011. "Revenue Prediction in Budget-constrained Sequential Auctions with Complementarities," ERIM Report Series Research in Management ERS-2011-020-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    17. Yixin Lu & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2019. "Dynamic Decision Making in Sequential Business-to-Business Auctions: A Structural Econometric Approach," Management Science, INFORMS, vol. 65(8), pages 3853-3876, August.
    18. Kevin Yili Hong & Alex Chong Wang & Paul A. Pavlou, 2013. "How does Bid Visibility Matter in Buyer-Determined Auctions? Comparing Open and Sealed Bid Auctions in Online Labor Markets," Working Papers 13-05, NET Institute.

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