Expertise in Online Markets

Stylianos Despotakis (), Isa Hafalir, R. Ravi () and Amin Sayedi ()
Additional contact information
Stylianos Despotakis: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
R. Ravi: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Amin Sayedi: Foster School of Business, University of Washington, Seattle, Washington 98195

Management Science, 2017, vol. 63, issue 11, 3895-3910

Abstract: We examine the effect of the presence of expert buyers on other buyers, the platform, and the sellers in online markets. We model buyer expertise as the ability to accurately predict the quality, or condition, of an item, modeled as its common value. We show that nonexperts may bid more aggressively, even above their expected valuation, to compensate for their lack of information. As a consequence, we obtain two interesting implications. First, auctions with a “hard close” may generate higher revenue than those with a “soft close.” Second, contrary to the linkage principle, an auction platform may obtain a higher revenue by hiding the item’s common-value information from the buyers. We also consider markets where both auctions and posted prices are available and show that the presence of experts allows the sellers of high-quality items to signal their quality by choosing to sell via auctions.

Keywords: competitive strategy; segmentation; pricing; sniping; online auctions; signaling (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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https://doi.org/10.1287/mnsc.2016.2530 (application/pdf)

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Working Paper: Expertise in Online Markets
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:63:y:2017:i:11:p:3895-3910

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