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Managing Market Thickness in Online Business-to-Business Markets

Author

Listed:
  • Kostas Bimpikis

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Wedad J. Elmaghraby

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Ken Moon

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Wenchang Zhang

    (Kelley School of Business, University of Indiana, Bloomington, Indiana 47405)

Abstract

We explore marketplace design in the context of a business-to-business platform specializing in liquidation auctions. Even when the platform’s aggregate levels of supply and demand remain fixed, we establish that the platform’s ability to use its design levers to manage the availability of supply over time yields significant value. We study two such levers, each using the platform’s availability of supply as a means to incentivize participation from buyers who decide strategically when/how often to participate. First, the platform’s listing policy sets the ending times of incoming auctions (hence, the frequency of market clearing). Exploiting a natural experiment, we illustrate that consolidating auctions’ ending times to certain weekdays increases the platform’s revenues by 7.3% mainly by inducing a higher level of bidder participation. The second lever is a recommendation system that can be used to reveal information about real-time market thickness to potential bidders. The optimization of these levers highlights a novel trade-off. Namely, when the platform consolidates auctions’ ending times, more bidders may participate in the marketplace (demand-side competition); but ultimately auctions for substitutable goods cannibalize one another (supply-side competition). To optimize these design decisions, we estimate a structural model that endogenizes bidders’ dynamic behavior, that is, their decisions on whether/how often to participate in the marketplace and how much to bid. We find that appropriately designing a recommendation system yields an additional revenue increase (on top of the benefits obtained by optimizing the platform’s listing policy) by reducing supply-side cannibalization and altering the composition of participating bidders.

Suggested Citation

  • Kostas Bimpikis & Wedad J. Elmaghraby & Ken Moon & Wenchang Zhang, 2020. "Managing Market Thickness in Online Business-to-Business Markets," Management Science, INFORMS, vol. 66(12), pages 5783-5822, December.
  • Handle: RePEc:inm:ormnsc:v:66:y:12:i:2020:p:5783-5822
    DOI: 10.1287/mnsc.2019.3497
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    References listed on IDEAS

    as
    1. Gérard P. Cachon & Kaitlin M. Daniels & Ruben Lobel, 2017. "The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 368-384, July.
    2. Gabriel Y. Weintraub & C. Lanier Benkard & Benjamin Van Roy, 2008. "Markov Perfect Industry Dynamics With Many Firms," Econometrica, Econometric Society, vol. 76(6), pages 1375-1411, November.
    3. Mireia Jofre-Bonet & Martin Pesendorfer, 2003. "Estimation of a Dynamic Auction Game," Econometrica, Econometric Society, vol. 71(5), pages 1443-1489, September.
    4. John J. Horton, 2019. "Buyer Uncertainty About Seller Capacity: Causes, Consequences, and a Partial Solution," Management Science, INFORMS, vol. 65(8), pages 3518-3540, August.
    5. Ken Moon & Kostas Bimpikis & Haim Mendelson, 2018. "Randomized Markdowns and Online Monitoring," Management Science, INFORMS, vol. 64(3), pages 1271-1290, March.
    6. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    7. Marcelo Olivares & Christian Terwiesch & Lydia Cassorla, 2008. "Structural Estimation of the Newsvendor Model: An Application to Reserving Operating Room Time," Management Science, INFORMS, vol. 54(1), pages 41-55, January.
    8. Alexei Alexandrov & Martin A. Lariviere, 2012. "Are Reservations Recommended?," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 218-230, April.
    9. Matthew Backus & Gregory Lewis, 2016. "Dynamic Demand Estimation in Auction Markets," NBER Working Papers 22375, National Bureau of Economic Research, Inc.
    10. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    11. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2015. "Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design," Management Science, INFORMS, vol. 61(4), pages 864-884, April.
    12. Tunca, Tunay I., 2008. "Information precision and asymptotic efficiency of industrial markets," Journal of Mathematical Economics, Elsevier, vol. 44(9-10), pages 964-996, September.
    13. Gan, Li & Li, Qi, 2016. "Efficiency of thin and thick markets," Journal of Econometrics, Elsevier, vol. 192(1), pages 40-54.
    14. Kanoria, Yash & Saban, Daniela, 2017. "Facilitating the Search for Partners on Matching Platforms: Restricting Agents' Actions," Research Papers 3572, Stanford University, Graduate School of Business.
    15. Gad Allon & Achal Bassamboo, 2011. "Buying from the Babbling Retailer? The Impact of Availability Information on Customer Behavior," Management Science, INFORMS, vol. 57(4), pages 713-726, April.
    16. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    17. Pinker, Edieal & Seidmann, Abraham & Vakrat, Yaniv, 2010. "Using bid data for the management of sequential, multi-unit, online auctions with uniformly distributed bidder valuations," European Journal of Operational Research, Elsevier, vol. 202(2), pages 574-583, April.
    18. Kostas Bimpikis & Ozan Candogan & Daniela Saban, 2019. "Spatial Pricing in Ride-Sharing Networks," Operations Research, INFORMS, vol. 67(3), pages 744-769, May.
    19. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    20. Sang Won Kim & Marcelo Olivares & Gabriel Y. Weintraub, 2014. "Measuring the Performance of Large-Scale Combinatorial Auctions: A Structural Estimation Approach," Management Science, INFORMS, vol. 60(5), pages 1180-1201, May.
    21. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    22. Robert L. Bray & Haim Mendelson, 2015. "Production Smoothing and the Bullwhip Effect," Manufacturing & Service Operations Management, INFORMS, vol. 17(2), pages 208-220, May.
    23. Mohammad Akbarpour & Shengwu Li & Shayan Oveis Gharan, 2020. "Thickness and Information in Dynamic Matching Markets," Journal of Political Economy, University of Chicago Press, vol. 128(3), pages 783-815.
    24. James D. Dana, Jr. & Nicholas C. Petruzzi, 2001. "Note: The Newsvendor Model with Endogenous Demand," Management Science, INFORMS, vol. 47(11), pages 1488-1497, November.
    25. Steven Tadelis & Florian Zettelmeyer, 2015. "Information Disclosure as a Matching Mechanism: Theory and Evidence from a Field Experiment," American Economic Review, American Economic Association, vol. 105(2), pages 886-905, February.
    26. Xuanming Su & Fuqiang Zhang, 2009. "On the Value of Commitment and Availability Guarantees When Selling to Strategic Consumers," Management Science, INFORMS, vol. 55(5), pages 713-726, May.
    27. Haim Mendelson & Tunay I. Tunca, 2007. "Strategic Spot Trading in Supply Chains," Management Science, INFORMS, vol. 53(5), pages 742-759, May.
    28. Jun Li & Nelson Granados & Serguei Netessine, 2014. "Are Consumers Strategic? Structural Estimation from the Air-Travel Industry," Management Science, INFORMS, vol. 60(9), pages 2114-2137, September.
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