How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy

Abstract

Consumers often use shopbots to search for information when making purchase decisions in Internet markets. Although they have varying sensitivity to shopbot bias, consumers generally prefer accurate market representation. However, in choosing the accuracy of market representation, shopbots must balance the desires of consumers with the costs of providing their services and with the desires of the vendors, who are often the largest source of their revenue. In this paper, we study how accurately shopbots represent a market and analyze the strategies shopbots adopt to achieve market representativeness. We theoretically identify two important drivers in shopbot vendor coverage strategy – how many vendors it covers (shopbot size) and which vendors it covers (shopbot affiliation) – and analytically show how the drivers affect shopbot market representativeness. We report the results of a large-scale study in which we collected 2.2 million vendor price listings from eight shopbots and develop metrics for measuring shopbot size, shopbot affiliation, and shopbot market representativeness. We found that (1) shopbots do not represent markets equally well; (2) size drives a shopbot's market representativeness positively whereas affiliation drives a shopbot's market representativeness negatively; (3) shopbots follow differnet vendor representative strategies to pursue market representativeness.

Keywords:

• electronic markets
• shopbots
• market representativeness
• vendor coverage strategy

Additional information

Notes on contributors

Gove Allen

About the Authors

Gove Allen received his Ph.D. from the University of Minnesota in 2001 and is currently an assistant professor of information system at Brigham Young University's Marriott School of Management. Dr. Allen has consulted major corporations on implementing information technology including AT&T, Hewlett Packard, Micron, Intel, 3M, American Express and the Kennedy Space Center. His research as appeared in such outlets as MIS Quarterly, Information Systems Research, and Communications of the AIS. He is currently an associate editor for Electronic Commerce Research and Applications. Dr. Allen maintains webSQL.org, a site for teaching database management.

Jianan Wu

Jianan Wu is Associate Professor of Marketing. He joined the E. J. Ourso College of Business of LSU in the fall of 2006. Before joining LSU, he served as an Assistant Professor (1998–2004) and a tenured Associate Professor (2004–2006) of Marketing at A. B. Freeman School of Business of Tulane University. He earned a B.S. in Mathematics from Xiangtan University (China), M.S. in Mathematics from Huazhong University of Science of Technolegy (China), M.S. in Statistics in 1994 and Ph.D. in Business Administration in 1998 from the Pennsylvania State University. His research interests are in the areas of e-commerce and marketing models. His research has been published in top academic journals such as Marketing Science, Information Systems Research, and Journal of Marketing Research, etc. He is on the editorial review board of Journal of Interactive Marketing (U.S.A.) and Journal of Marketing Science (China).