Expertise in Online Markets

• 成果类型: Article
• 署名作者: Despotakis, Stylianos; Hafalir, Isa; Ravi, R.; Sayedi, Amin
• 署名单位: Carnegie Mellon University; University of Washington; University of Washington Seattle
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2016.2530
• 发表日期: 2017
• 页码: 3895-3910
• 关键词: competitive strategy; segmentation; pricing; Sniping; Online auctions; signaling

摘要

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.