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Modeling the Diffusion of New Durable Goods: Word-of-Mouth Effect Versus Consumer Heterogeneity

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Research traditions in marketing

Part of the book series: International Series in Quantitative Marketing ((ISQM,volume 5))

Abstract

The spread of new products in a population has been the subject of renewed interest over the past 15 years, stimulated in part by Robinson and Lakhani’s [1975] study on the pricing implications of the Bass diffusion model [1969]. Recent reviews have summarized the main articles in the area (for example, de Palma, Droesbeke, and Lefèvre [1991], Lilien, Kotler, and Moorthy [1992, pp. 461-80], and Mahajan, Muller, and Bass [1990]). However, as Mahajan, Muller, and Bass (p. 11) observed, most reported work has consisted of “refinements and extensions of the Bass diffusion model” without alteration of the basic premise of the diffusion curve, that is, sales as the result of the combination of both independent and imitative buying over time. Essentially, most work has considered adoption time as a deterministic event based upon the traits of consumers, the amount of information available to them, and their utility functions. Consequently, knowledge of those determinants implies a perfect prediction of adoption times.

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References

  • Abd-Ella, Mokhtar M.; Hoichberg, Eric O.; and Warren, Richard D. 1981, “Adoption farm behavior in family farm systems: An Iowa study,” Rural Sociology, Vol. 46, N° 1 (Spring), pp. 42–61.

    Google Scholar 

  • Abramowitz, Milton and Stegun, Irene A., eds. 1972, Handbook of Mathematical Functions, Dover Publications, New York.

    Google Scholar 

  • Bass, Frank M. 1969, “A new product growth model for consumer durables,” Management Science, Vol. 15, N° 5 (January), pp. 215–27.

    Article  Google Scholar 

  • Chatterjee, Rabikar and Eliashberg, Jehoshua 1990, “The innovation diffusion process in a heterogeneous population: A micromodeling approach,” Management Science, Vol. 36, N° 9 (September), pp. 1057–74.

    Article  Google Scholar 

  • Coleman, James S.; Katz, Elihu; and Menzel, Herbert 1966, Medical Innovation: A Diffusion Study, The Bobbs-Merrill Company, Indianapolis, IN.

    Google Scholar 

  • de Palma, André; Droesbeke, Jean-Jacques; and Lefèvre, Claude 1991, Modèles de Diffusion en Marketing, Presses Universitaires de France, Paris.

    Google Scholar 

  • Dolan, Robert J. and Jeuland, Abel P. 1981, “Experience curves and dynamic demand models: Implications for optimal pricing strategies,” Journal of Marketing, Vol. 45, N° 1 (Winter), pp. 52–62.

    Article  Google Scholar 

  • Easingwood, Christopher J.; Mahajan, Vijay; and Muller, Eitan 1983, “A nonuniform influence innovation diffusion model of new product acceptance,” Marketing Science, Vol. 2, N° 3 (Summer), pp. 273–95.

    Google Scholar 

  • Eliashberg, Jehoshua and Chatterjee, Rabikar 1986, “Stochastic issues in innovation diffusion models,” in Innovation Diffusion Models of New Product Acceptance, eds. Vijay Mahajan and Yoram Wind, Ballinger Publishing Company, Cambridge, MA, pp. 151–99.

    Google Scholar 

  • Feder, Gershon and O’Mara, Gerald T. 1982, “On information and innovation diffusion: A Bayesian approach,” American Journal of Agricultural Economics, Vol. 64, N° 1 (February), pp. 145–7.

    Article  Google Scholar 

  • Gatignon, Hubert A. and Robertson, Thomas S. 1986, “Integration of consumer diffusion theory and diffusion models: New research directions,” in Innovation Diffusion Models of New Product Acceptance, eds. Vijay Mahajan and Yoram Wind, Ballinger Publishing Company, Cambridge, MA, pp. 37–59.

    Google Scholar 

  • Goldberger, Arthur S. 1973, “Correlations between binary outcomes and probabilistic predictions,” Journal of the American Statistical Association, Vol. 68, N° 341 (March), p. 84.

    Article  Google Scholar 

  • Goldberger, Arthur S. 1991, A Course in Econometrics, Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Heeler, Roger M. and Hustad, Thomas P. 1980, “Problems in predicting new product growth for consumer durables,” Management Science, Vol. 26, N° 10 (October), pp. 1007–20.

    Article  Google Scholar 

  • Jeuland, Abel P. 1981, “Parsimonious models of diffusion of innovation: Derivations and comparisons,” Working paper N° 81–102, Graduate School of Business, University of Chicago, IL, June.

    Google Scholar 

  • Kalbfleisch, John D. and Prentice, Ross L. 1980The Statistical Analysis of Failure Data John Wiley & Sons, New York.

    Google Scholar 

  • Lattin, James M. and Roberts, John H. 1989, “The role of individual-level risk-adjusted utility in the diffusion of innovation,” working paper, Graduate School of Business, Stanford University, July.

    Google Scholar 

  • Lekvall, P. and Wahlbin, C. 1973, “A study of some assumptions underlying innovation diffusion functions,” Swedish Journal of Economics, Vol. 75, N° 4 (December), pp. 362–77.

    Article  Google Scholar 

  • Lenk, Peter J., and Rao, Ambar G. 1990, “New models from old: Forecasting product adoption by hierarchical Bayes procedures,” Marketing Science, Vol. 9, N° 1 (Winter), pp. 42–57.

    Article  Google Scholar 

  • Lilien, Gary L.; Kotler. Philip; and Moorthy, K. Sridhar 1992, Marketing Models, Prentice-Hall, Inc., Englewood Cliffs, NJ.

    Google Scholar 

  • Mahajan, Vijay; Muller, Eitan; and Bass, Frank M. 1990, “New product diffusion models in marketing: A review and directions for research,” Journal of Marketing, Vol. 54, N° 1 (January), pp. 1–26.

    Article  Google Scholar 

  • Mahajan, Vijay; Muller, Eitan; and Srivastava, Rajendra K. 1990, “Determination of adopter categories by using innovation diffusion models,” Journal of Marketing Research, Vol. 27, N° 1 (February), pp. 37–50.

    Article  Google Scholar 

  • Mahajan, Vijay, and Wind, Yoram 1986, “Innovation diffusion models of new product acceptance: A reexamination,” in Innovation Diffusion Models of New Product Acceptance, eds. Vijay Mahajan and Yoram Wind, Ballinger Publishing Company, Cambridge, MA, pp. 3–25.

    Google Scholar 

  • Mansfield, Edwin 1961, “Technical change and the rate of imitation,” Econometrica, Vol. 29, N° 4 (October) pp. 741–66.

    Article  Google Scholar 

  • Massy, William F.; Montgomery, David B.; and Morrison, Donald G. 1970, Stochastic Models of Buying Behavior, The MIT Press, Cambridge, MA.

    Google Scholar 

  • Mood, Alexander M.; Graybill, Franklin A.; and Boes, Duane C. 1974, Introduction to the Theory of Statistics, third edition, McGraw-Hill International Book Company, Auckland.

    Google Scholar 

  • Nelder, John A. 1962, “An alternative form of a generalised logistic equation,” Biometrics, Vol. 18, N° 4 (December), pp. 614–6.

    Article  Google Scholar 

  • Oren, Shmuel S. and Schwartz, Rick G. 1988, “Diffusion of new products in risk-sensitive markets,” Journal of Forecasting, Vol. 7, N° 4 (October/December), pp. 273–87.

    Article  Google Scholar 

  • Robinson, Bruce, and Lakhani, Chet 1975, “Dynamic price models for new-product planning,” Management Science, Vol. 21, N° 10 (June), pp. 1113–22.

    Article  Google Scholar 

  • Rogers, Everett M. 1983, Diffusion of Innovations, third edition, The Free Press,New York.

    Google Scholar 

  • Sinha, Rajiv K. and Chandrashekaran, Murali 1992, “A split hazard model for analyzing the diffusion of innovations,” Journal of Marketing Research,Vol. 29, N° 1 (February), pp. 116–27.

    Article  Google Scholar 

  • von Bertalanffy, Ludwig 1957, “Quantitative laws in metabolism and growth,” Quarterly Review of Biology, Vol. 32, N° 3 (September), pp. 217–31.

    Article  Google Scholar 

References

  • Bass, Frank M. and Krishnan, Trichy V. 1992, “A generalization of the Bass model: Decision variable considerations,” Working paper #50–6–92, School of Management, University of Texas at Dallas.

    Google Scholar 

  • Jamieson, Linda F. and Bass, Frank M. 1989, “Adjusting stated intention measures to predict trial purchase of new products: A comparison of models and methods,” Journal of Marketing Research, Vol. 26, No. 3 (August), pp. 336–345.

    Article  Google Scholar 

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© 1992 Springer Science+Business Media New York

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Bemmaor, A.C. (1992). Modeling the Diffusion of New Durable Goods: Word-of-Mouth Effect Versus Consumer Heterogeneity. In: Laurent, G., Lilien, G.L., Pras, B. (eds) Research traditions in marketing. International Series in Quantitative Marketing, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1402-8_6

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  • DOI: https://doi.org/10.1007/978-94-011-1402-8_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4615-2

  • Online ISBN: 978-94-011-1402-8

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