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Why the Bass Model Fits without Decision Variables

Bass, Frank M., Krishnan, Trichy V., Jain, Dipak C.·1994·Marketing Science

Abstract

Over a large number of new products and technological innovations, the Bass diffusion model (Bass 1969) describes the empirical adoption curve quite well. In this study, we generalize the Bass model to include decision variables such as price and advertising. The generalized model reduces to the Bass model as a special case and explains why the Bass model works so well without including decision variables. We compare our generalized Bass model to other approaches from the literature for including decision variables into diffusion models, and our results provide both theoretical and empirical support for the generalized Bass model. We also show how our generalized Bass model can be used for product planning purposes.

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Metadata

Authors
Bass, F. M., Krishnan, T. V., & Jain, D. C.
Venue
Marketing Science
Published
1994-08
Volume
13
Issue
3
Pages
203-223
ISSN
  • 0732-2399 (ISSN-L)
  • 1526-548X
OpenAlex
W1983795980
Cited by
838
References
0
FWCI
7.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Why the Bass Model Fits without Decision Variables. https://app.cochat.ai/papers/10.1287/mksc.13.3.203
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Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the Bass Model Fits without Decision Variables. Marketing Science. https://doi.org/10.1287/mksc.13.3.203

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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.