Marketing Letters

, Volume 6, Issue 2, pp 123–136 | Cite as

Estimation of innovation diffusion models with application to a consumer durable

  • S. R. Dalal
  • S. Weerahandi
Article

Abstract

A class of birth processes having a variety of practical applications in penetration of new services and products is considered. Typically, statistical inferences on these models are performed by means of simple error structures placed on the deterministic analogs of the underlying stochastic processes. Motivated by the poor performance of conventional estimation methods, the problem of estimating the parameters of these models is readdressed. We develop necessary formulae for performing the maximum likelihood estimation and weighted least squares estimation methods, and demonstrate their superiority through analyses of some real data and simulation studies.

Key words

penetration of a service maximum likelihood estimation parametric bootstrap beta binomial distribution 

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References

  1. Bailey, N.T.J. (1950). “A Simple Stochastic Epidemic.”Biometrika, 37, 193–202.Google Scholar
  2. Bass, F.M. (1969). “A New Product Growth Model for Consumer Durables.”Management Science, 15, 215–227.Google Scholar
  3. Bhat, U.N. (1984).Elements of Applied Stochastic Processes (2nd ed.) New York: Wiley.Google Scholar
  4. Dalal, S.R., and S. Weerahandi. (1992). “Some Approximations for the Moments of a Process used in Diffusion of New Products.”Statistics and Probability Letters, 15(1), 181–189.Google Scholar
  5. Efron, B. (1982).The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia: Society for Industrial and Applied Mathematics.Google Scholar
  6. Eliashberg, J., and R. Chatterjee. (1986). “Stochastic Issues in Innovation Diffusion Models.” In V. Mahajan and Y. Wind (eds.),Innovation Diffusion Models of New Product Acceptance. Cambridge, MA: Ballinger.Google Scholar
  7. Horsky, D., and L.S. Simon. (1983). “Advertising and the Diffusion of New Products.”Marketing Science, 2, 1–17.Google Scholar
  8. Mahajan, V., C.H. Mason, and V. Srinivasan. (1986). “An Evaluation of Estimation Procedures for New Product Diffusion Models.” In V. Mahajan and Y. Wind (eds.),Innovation Diffusion Models of New Product Acceptance. Cambridge, MA: Ballinger.Google Scholar
  9. Mahajan, V., and Y. Wind. (1986).Innovation Diffusion Models of New Product Acceptance. Cambridge, MA: Ballinger.Google Scholar
  10. Robinson, B., and C. Lakhani (1975). “Dynamic Price Models for New Product Planning.”Management Science, 21, 70–77.Google Scholar
  11. Schmittlein, D.C., and V. Mahajan. (1982). “Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance.”Marketing Science, 1, 57–78.Google Scholar
  12. Srinivasan, V., and C.H. Mason. (1986). “Nonlinear Least Squares Estimation of New Product Diffusion Models.”Marketing Science, 5, 169–178.Google Scholar
  13. Sultan, F., J.U. Farley, and D.R. Lehmann. (1990). “A Meta-Analysis of Applications of Diffusion Models.”Journal of Marketing Research, 27, 70–77.Google Scholar
  14. Weerahandi, S., and S.R. Dalal. (1992). “A Choice Model Approach to the Diffusion of a Service: Forecasting Fax Penetration by Market Segments.”Marketing Science, 11(1), 39–53.Google Scholar
  15. Weerahandi, S., and S. Moitra. (1995). “Using Survey Data to Predict Adoption and Switching for Services,”Journal of Marketing Research (forthcoming).Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • S. R. Dalal
    • 1
  • S. Weerahandi
    • 2
  1. 1.BellcoreMorristown
  2. 2.BellcoreMorristown

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