User heterogeneity and its impact on electronic auction market design: an empirical exploration (1).(Research Article)

Abstract

While traditional information systems research emphasizes understanding of end users from perspectives such as cognitive fit and technology acceptance, it fails to consider the economic dimensions of their interactions with a system. When viewed as economic agents who participate in electronic markets, it is easy to see that users' preferences, behaviors, personalities, and ultimately their economic welfare are intricately linked to the design of information systems. We use a data-driven, inductive approach to develop a taxonomy of bidding behavior in online auctions. Our analysis indicates significant heterogeneity exists in the user base of these representative electronic markets. Using online auction data from 1999 and 2000, we find a stable taxonomy of bidder behavior containing five types of bidding strategies. Bidders pursue different bidding strategies that, in aggregate, realize different winning likelihoods and consumer surplus. We find that technological evolution has an impact on bidders' strategies. We demonstrate how the taxonomy of bidder behavior can be used to enhance the design of some types of information systems. These enhancements include developing usercentric bidding agents, inferring bidders' underlying valuations to facilitate real-time auction calibration, and creating low-risk computational platforms for decision making.

Keywords: Electronic markets, online auctions, bidding strategies, user behavior taxonomy, smart agents, valuation discovery, calibration, simulation

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Introduction

Using information technology as a key ingredient, electronic markets match globally dispersed buyers and sellers, facilitate transactions, and provide the necessary regulatory institutional infrastructure (Bakos 1998). Electronic markets can also be characterized as economic information systems. Consequently, we argue their design has to incorporate the economic dimensions of users' interactions with the system. In this paper, we use business-to-consumer (B2C) online auctions as a representative electronic market and adopt a data-driven, inductive approach for developing a taxonomy of bidder behavior. Bidders pursue different bidding strategies that realize different chances of winning and different levels of consumer surplus. We demonstrate how the taxonomy of bidder behavior can be used to enhance the design of economic information systems. This includes developing user-centric bidding agents, inferring bidders' underlying valuations to facilitate real-time auction calibration, and creating low-risk computational platforms for decision making.

Our objective is to show how individuals use economic information systems in different ways. This is particularly apparent in the instance of online auctions because the outcomes map directly to users' pockets. Nonetheless, the necessity of considering the economic impact of an information system's design is being recognized across a broad range of applications. For instance, Ba et al. (2001) illustrate the importance of considering the interests and incentives of users in such areas as distributed decision support systems, knowledge management, and e-business supply-chain coordination.