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Cited by 301

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Promotional Elasticities and Category Characteristics

Narasimhan, Chakravarthi, Neslin, Scott A., Sen, Subrata K.·1996·Journal of Marketing

Abstract

The authors study the relationships between product category characteristics and average brand promotional elasticity within the category. They present a framework for understanding these relationships and use it to generate hypotheses. The authors also consider three types of promotions and seven category characteristics. They study 108 product categories and use data compiled from weekly scanner data, scanner panel data, and survey data. Their results indicate that promotional elasticities are higher for categories with relatively fewer number of brands, higher category penetration, shorter interpurchase times, and higher consumer propensity to stockpile. The authors find no statistically significant relationship between promotional elasticity and either impulse buying or private label market share. They discuss the reasons for these results and their managerial implications.

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Crossref · 10.1177/002224299606000202

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Metadata

Authors
Narasimhan, C., Neslin, S. A., & Sen, S. K.
Venue
Journal of Marketing
Published
1996-04
Volume
60
Issue
2
Pages
17-30
ISSN
  • 0022-2429 (ISSN-L)
  • 1547-7185
OpenAlex
W2316299968
Cited by
301
References
1
FWCI
33.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Promotional Elasticities and Category Characteristics. https://app.cochat.ai/papers/10.1177/002224299606000202
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Narasimhan, C., Neslin, S. A., & Sen, S. K. (1996). Promotional Elasticities and Category Characteristics. Journal of Marketing. https://doi.org/10.1177/002224299606000202

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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.