Optimal Design of the Online Auction Channel: Analytical, Empirical, and Computational Insights

Ravi Bapna and Paulo Goes
Department of Operations and Information Management,U-41 IM, School of Business,University of Connecticut, Storrs, CT 06269, e-mails: ravi.bapna@business.uconn.edu, paulo.goes@business.uconn.edu

Alok Gupta
Information and Decision Sciences Department, Carlson School of Management,
University of Minnesota, 321 19th Avenue South, Minneapolis, MN 55455, e-mail: gupta037@umn.edu

Gilbert Karuga
Accounting and Information Systems Department, School of Business, University of Kansas, 1300 Sunnyside Avenue, Lawrence, KS 66045, email: gkaruga@ku.edu

Abstract. The focus of this study is on business-to-consumer (B2C) online auctions made possible by the advent of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.

Subject Areas: Emerging Supply Chain Channels, Online Auctions, and Simulation.