Learning About Computers: An Analysis of Information Search and Technology Choice
We estimate a dynamic model of how consumers learn about and choose between different brands ofpersonal computers (PCs). To estimate the model, we use a panel data set that contains the search and purchasebehavior of a set of consumers who were in the market for a PC. The data includes the information sources visitedeach period, search durations, as well as measures of price expectations and stated attitudes toward the alternativesduring the search process. Our model extends recent work on estimation of Bayesian learning models of consumerchoice behavior in environments characterized by uncertainty by estimating a model of active learning?i.e., amodel in which consumers make optimal sequential decisions about how much information to gather prior tomaking a purchase. Also, following the suggestion of Manski (2003), we use our data on price expectations tomodel consumers? price expectation process, and, following the suggestion of McFadden (1989a), we incorporatethe stated brand quality information into our likelihood function, rather than modeling only revealed preferencedata.Our analysis sheds light on how consumer forward-looking price expectations and the process of learning aboutquality influence the consumer choice process. A key finding is that estimates of dynamic price elasticities ofdemand exceed estimates that ignore the expectations effect by roughly 50%. This occurs because our estimatedexpectations formation process implies that consumers expect mean reversion in price changes. This enhancesthe impact of a temporary price cut. Finally, while our work focuses specifically on the PC market, the modelingapproach we develop here may be useful for studying a wide range of high-tech, high-involvement durable goodsmarkets where active learning is important.
Year of publication: |
2004
|
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Authors: | Keane, M. P ; Strebel, J. ; Oncu, S. ; Erdem, T. |
Publisher: |
Springer |
Saved in:
freely available
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