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Consumer Learning, Brand Loyalty, and Competition
Abstract
In several markets, consumers can gain further information regarding how well a product fits their preferences only by experiencing it after purchase. This could then generate loyalty for the products tried first. This paper considers a model in which consumers learn in the first period about the product they buy and then make choices in the second period about the competing products, given what they learned in the first period. The paper finds that if the distribution of valuations for each product is negatively (positively) skewed, a firm benefits (is hurt) in the future from having a greater market share today—the brand loyalty characteristic. With negative skewness, two effects are identified: On one hand, marginal forward-looking consumers are less price sensitive than myopic consumers, and this is a force toward higher prices. On the other hand, forward-looking firms realize that they gain in the future from having a higher market share in the current period and compete more aggressively in prices. For similar discount factors for consumers and firms, the latter effect dominates. The paper also characterizes the importance of consumer learning effects on the market outcome.
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Cited by
Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence
Feng, J., Li, X., & Zhang, X. (. (2019) · Information Systems Research · doi:10.1287/isre.2019.0852 · Cited by 117
Metadata
- Authors
- Villas-Boas, J. M.
- Venue
- Marketing Science
- Published
- 2004-02
- Volume
- 23
- Issue
- 1
- Pages
- 134-145
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2103598033
- Cited by
- 244
- References
- 0
- FWCI
- 33.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Villas-Boas, J. M. (2004). Consumer Learning, Brand Loyalty, and Competition. Marketing Science. https://doi.org/10.1287/mksc.1030.0044
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.