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Buyer Uncertainty About Seller Capacity: Causes, Consequences, and a Partial Solution

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  • John J. Horton

    (Department of Information, Operations, and Management Sciences, Leonard N. Stern School of Business, New York University, New York, New York 10012)

Abstract

Employers in an online labor market often pursue workers with little capacity to take on more work. The pursuit of low-capacity workers is consequential, as these workers are more likely to reject employer inquires, causing a reduction in the probability that a job opening is ultimately filled. In an attempt to shift more employer attention to workers with greater capacity, the market-designing platform examined in this paper introduced a new signaling feature into the market. It was effective, in that when a worker signaled having high capacity, he or she received more invitations from employers, rejected a smaller fraction of those invitations, quoted lower prices to do the work, and was more likely to be hired. A back-of-the-envelope calculation suggests the signaling feature alone could increase market surplus by as much as 6%, both by increasing the number of matches formed and by helping to allocate projects to workers with lower costs.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:8:p:3518-3540
    DOI: 10.1287/mnsc.2018.3116
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    Cited by:

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    5. Alan Benson & Aaron Sojourner & Akhmed Umyarov, 2020. "Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market," Management Science, INFORMS, vol. 66(5), pages 1802-1825, May.
    6. Ni Huang & Gordon Burtch & Yumei He & Yili Hong, 2022. "Managing Congestion in a Matching Market via Demand Information Disclosure," Information Systems Research, INFORMS, vol. 33(4), pages 1196-1220, December.
    7. Peng Shi, 2023. "Optimal Matchmaking Strategy in Two-Sided Marketplaces," Management Science, INFORMS, vol. 69(3), pages 1323-1340, March.
    8. Yash Kanoria & Daniela Saban, 2021. "Facilitating the Search for Partners on Matching Platforms," Management Science, INFORMS, vol. 67(10), pages 5990-6029, October.
    9. Kostas Bimpikis & Ozan Candogan & Daniela Saban, 2019. "Spatial Pricing in Ride-Sharing Networks," Operations Research, INFORMS, vol. 67(3), pages 744-769, May.
    10. Ni Huang & Gordon Burtch & Yili Hong & Paul A. Pavlou, 2020. "Unemployment and Worker Participation in the Gig Economy: Evidence from an Online Labor Market," Information Systems Research, INFORMS, vol. 31(2), pages 431-448, June.
    11. Jin Liu & Xingchen Xu & Yongjun Li & Yong Tan, 2023. ""Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets," Papers 2308.05201, arXiv.org.
    12. Moshe A. Barach & Joseph M. Golden & John J. Horton, 2020. "Steering in Online Markets: The Role of Platform Incentives and Credibility," Management Science, INFORMS, vol. 66(9), pages 4047-4070, September.
    13. Kazakova, E. & Sandomirskaia, M. & Suvorov, A. & Khazhgerieva, A. & Shavshin, R., 2023. "Platforms, online labor markets, and crowdsourcing. Part 2. Crowdsourcing," Journal of the New Economic Association, New Economic Association, vol. 61(4), pages 128-144.
    14. Chen Liang & Yili Hong & Pei-Yu Chen & Benjamin B. M. Shao, 2022. "The Screening Role of Design Parameters for Service Procurement Auctions in Online Service Outsourcing Platforms," Information Systems Research, INFORMS, vol. 33(4), pages 1324-1343, December.
    15. Tobias Lehmann & Camille Terrier & Rafael Lalive, 2023. "Costs and Benefits of Congestion in Two-Sided Markets: Evidence from the Dating Market," Working Papers 964, Queen Mary University of London, School of Economics and Finance.
    16. 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.
    17. Rebecca Alcock & Justin J. Boutilier & Auyon Siddiq, 2022. "Shield-Net: Matching Supply with Demand for Face Shields During the COVID-19 Pandemic," Interfaces, INFORMS, vol. 52(6), pages 485-507, November.
    18. Mueller-Langer, Frank & Gómez-Herrera, Estrella, 2022. "Mobility restrictions and the substitution between on-site and remote work: Empirical evidence from a European online labour market," Information Economics and Policy, Elsevier, vol. 58(C).
    19. Yili Hong & Jing Peng & Gordon Burtch & Ni Huang, 2021. "Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets," Information Systems Research, INFORMS, vol. 32(3), pages 786-800, September.
    20. Michelangelo Rossi, 2019. "How Does Competition Affect Reputation Concerns? Theory and Evidence from Airbnb," CESifo Working Paper Series 7972, CESifo.
    21. Christopher T. Stanton & Catherine Thomas, 2020. "The Gig Economy Beyond Local Services and Transportation," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 21(03), pages 21-26, September.
    22. Itai Ashlagi & Mark Braverman & Yash Kanoria & Peng Shi, 2020. "Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations," Management Science, INFORMS, vol. 66(5), pages 2163-2193, May.

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