Learning About Computers: An Analysis of Information Search and Technology Choice

Erdem, T; Keane, M; Oncu, S; Strebel, J

Learning About Computers: An Analysis of Information Search and Technology Choice

Erdem, T Keane, M Oncu, S Strebel, J

Permalink

Publisher:: Springer
Publication Type:: Journal Article
Citation:: Quantitative Marketing and Economics, 2005, 3 (3), pp. 207 - 247
Issue Date:: 2005-01

Closed Access

	Filename	Description	Size
	2005004560.pdf		251.47 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Erdem, T	en_US
dc.contributor.author	Keane, M	en_US
dc.contributor.author	Oncu, S	en_US
dc.contributor.author	Strebel, J	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Quantitative Marketing and Economics, 2005, 3 (3), pp. 207 - 247	en_US
dc.identifier.issn	1570-7156	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5347
dc.description.abstract	We estimate a dynamic model of how consumers learn about and choose between different brands of personal computers (PCs). To estimate the model, we use a panel data set that contains the search and purchase behavior of a set of consumers who were in the market for a PC. The data includes the information sources visited each period, search durations, as well as measures of price expectations and stated attitudes toward the alternatives during the search process. Our model extends recent work on estimation of Bayesian learning models of consumer choice behavior in environments characterized by uncertainty by estimating a model of active learningi.e., a model in which consumers make optimal sequential decisions about how much information to gather prior to making a purchase. Also, following the suggestion of Manski (2003), we use our data on price expectations to model consumers price expectation process, and, following the suggestion of McFadden (1989a), we incorporate the stated brand quality information into our likelihood function, rather than modeling only revealed preference data. Our analysis sheds light on how consumer forward-looking price expectations and the process of learning about quality influence the consumer choice process. A key finding is that estimates of dynamic price elasticities of demand exceed estimates that ignore the expectations effect by roughly 50%. This occurs because our estimated expectations formation process implies that consumers expect mean reversion in price changes. This enhances the impact of a temporary price cut. Finally, while our work focuses specifically on the PC market, the modeling approach we develop here may be useful for studying a wide range of high-tech, high-involvement durable goods markets where active learning is important.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	Quantitative Marketing and Economics	en_US
dc.subject.classification	Marketing	en_US
dc.title	Learning About Computers: An Analysis of Information Search and Technology Choice	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.for	150505 Marketing Research Methodology	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1505 Marketing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	3	en_US
pubs.volume	3	en_US

Abstract:

We estimate a dynamic model of how consumers learn about and choose between different brands of personal computers (PCs). To estimate the model, we use a panel data set that contains the search and purchase behavior of a set of consumers who were in the market for a PC. The data includes the information sources visited each period, search durations, as well as measures of price expectations and stated attitudes toward the alternatives during the search process. Our model extends recent work on estimation of Bayesian learning models of consumer choice behavior in environments characterized by uncertainty by estimating a model of active learningi.e., a model in which consumers make optimal sequential decisions about how much information to gather prior to making a purchase. Also, following the suggestion of Manski (2003), we use our data on price expectations to model consumers price expectation process, and, following the suggestion of McFadden (1989a), we incorporate the stated brand quality information into our likelihood function, rather than modeling only revealed preference data. Our analysis sheds light on how consumer forward-looking price expectations and the process of learning about quality influence the consumer choice process. A key finding is that estimates of dynamic price elasticities of demand exceed estimates that ignore the expectations effect by roughly 50%. This occurs because our estimated expectations formation process implies that consumers expect mean reversion in price changes. This enhances the impact of a temporary price cut. Finally, while our work focuses specifically on the PC market, the modeling approach we develop here may be useful for studying a wide range of high-tech, high-involvement durable goods markets where active learning is important.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/5347