Innovator: an expert system for new product launch decisions
Applied Artificial Intelligence
Modeling competitive bidding: a critical essay
Management Science
Characteristics of electronic markets
Decision Support Systems - Special issue on electronic commerce
Decision Support Systems - Special issue on economics of electronic commerce
Predictors of online buying behavior
Communications of the ACM
An empirical evidence of winner's curse in electronic auctions
ICIS '99 Proceedings of the 20th international conference on Information Systems
Implications of the Bidders' Arrival Process on the Design of Online Auctions
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand
Management Science
Jump Bidding Strategies in Internet Auctions
Management Science
Risk profile and consumer shopping behavior in electronic and traditional channels
Decision Support Systems
Consumer reactions to electronic shopping on the world wide web
International Journal of Electronic Commerce
Journal of Management Information Systems
A Model of Consumer Choice of the Internet as an Information Source
International Journal of Electronic Commerce
Price formation and its dynamics in online auctions
Decision Support Systems
Competitive information systems in support of pricing
MIS Quarterly
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 12.05 |
Auction mechanisms allocate consumers' demand revealing incentives. In this study, we highlight field experimental online auctions for their value in new product demand estimation. The immediate question is whether the information from online auctions can be utilized to get the full demand curve across various sales channels, since many firms utilize multi-channel strategies. Each channel may bear different transaction costs for its patrons. Hence, channel selection is the result of consumers' self-selection based on transaction cost economics. Consequently, a consumer's Willingness-To-Pay (WTP) in a selected channel reflects her/his depreciated pure WTP by the mixture of channel specific and individual-related characteristics determining transaction costs. We propose a skeleton model to resolve this self-selection bias, and this projects the partial demand observed in online auction channels to the whole demand curve. We discuss the kind of information that should be required to resolve this problem, and verify our approach using empirical testing. We demonstrate how online auction data, which firms have not yet capitalized on so far, can be a very valuable experimental resource for multi-channel firms' marketing strategies.