Buy it Now: An Analysis of the Effects of Buy Prices in Auction Listings

<a href="https://embed.stanford.edu/iframe/?url=https%3A%2F%2Fpurl.stanford.edu%2Fqb045ky7925" class="su-underline">Show Content</a>

Abstract/Contents

Abstract: The rise of online auction platforms in the past decade has presented a wealth of new data and puzzling effects to researchers. New listing strategies and parameters created to give both sellers and bidder flexibility have also led the way for new types of listings. This paper explores the potential behavioral effects of simultaneously listing an auction with a fixed price option using data from eBay. It looks specifically at how the fixed price option—known as the Buy it Now (BIN) option on eBay—affects bidder behavior and how this translates into seller outcomes. It looks at (1) whether or not the BIN price has an effect on the final sale price of an auction, and (2) whether this effect can be attributed to anchoring. Two main results arise from this study. The first is that BIN prices have a positive effect on expected seller revenue, especially when the BIN price is more persistent and when the item is more unique. In the case of the former, this is caused by the higher probability of BIN exercise and in the later, this is caused in part by the pure existence of the BIN price. Second, bids anchor to the BIN price, which can help explain the mechanism through which final sale prices rise with the BIN price. This result is significant because in contrast to previous literature, it suggests that sellers might still benefit by setting a BIN option with effectively zero probability of exercise.

Type of resource	text
Date created	May 2015

Author	Ding, Anthony
Primary advisor	Einav, Liran
Degree granting institution	Stanford University, Department of Economics

Subject	Internet auctions
Subject	online markets
Subject	anchoring
Subject	bidder behavior
Subject	Stanford Department of Economics
Genre	Thesis

Location	https://purl.stanford.edu/qb045ky7925

Use and reproduction: User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.

Preferred Citation: Ding, Anthony. (2015). Buy it Now: An Analysis of the Effects of Buy Prices in Auction Listings. Stanford Digital Repository. Available at: https://purl.stanford.edu/qb045ky7925

Stanford University, Department of Economics, Honors Theses

Loading usage metrics...