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Why the Bass Model Fits without Decision Variables

Author

Listed:
  • Frank M. Bass

    (University of Texas at Dallas, Northwestern University)

  • Trichy V. Krishnan

    (University of Texas at Dallas, Northwestern University)

  • Dipak C. Jain

    (University of Texas at Dallas, Northwestern University)

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.

Suggested Citation

  • Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
  • Handle: RePEc:inm:ormksc:v:13:y:1994:i:3:p:203-223
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    File URL: http://dx.doi.org/10.1287/mksc.13.3.203
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