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Cited by 271

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Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks

Dubé, Jean-Pierre·2004·Marketing Science

Abstract

For several of the largest supermarket product categories, such as carbonated soft drinks, canned soups, ready-to-eat cereals, and cookies, consumers regularly purchase assortments of products. Within the category, consumers often purchase multiple products and multiple units of each alternative selected on a given trip. This multiple discreteness violates the single-unit purchase assumption of multinomial logit and probit models. The misspecification of such demand models in categories exhibiting multiple discreteness would produce incorrect measures of consumer response to marketing mix variables. In studying product strategy, these models would lead to misleading managerial conclusions. We use an alternative microeconomic model of demand for categories that exhibit the multiple discreteness problem. Recognizing the separation between the time of purchase and the time of consumption, we model consumers purchasing bundles of goods in anticipation of a stream of consumption occasions before the next trip. We apply the model to a panel of household purchases for carbonated soft drinks.

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Metadata

Authors
Dubé, J.-P.
Venue
Marketing Science
Published
2004-02
Volume
23
Issue
1
Pages
66-81
ISSN
  • 0732-2399 (ISSN-L)
  • 1526-548X
OpenAlex
W2141561277
Cited by
271
References
1
FWCI
38.17
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks. https://app.cochat.ai/papers/10.1287/mksc.1030.0041
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Dubé, J.-P. (2004). Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks. Marketing Science. https://doi.org/10.1287/mksc.1030.0041

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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.