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The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach

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Author Info

  • Rabik Ar Chatterjee

    (School of Business Administration, University of Michigan, Ann Arbor, Michigan 48109-1234)

  • Jehoshua Eliashberg

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6371)

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    Abstract

    A model of the innovation diffusion process is developed using a micromodeling approach that explicitly considers the determinants of adoption at the individual level in a decision analytic framework, and incorporates heterogeneity in the population with respect to initial perceptions, preference characteristics, and responsiveness to information. The micromodelling approach provides a behavioral basis for explaining adoption at the disaggregate level and the consequent pattern of diffusion at the aggregate level. The analytical implications of the model are compared and contrasted with the traditional, aggregate-level, diffusion models. An advantage of our approach is its micro-theory driven flexibility in accommodating various patterns of diffusion. Examples are provided of conditions under which the model yields diffusion patterns identical to those of some well-known aggregate models. A pilot study is reported, outlining procedures for data collection and estimation of the individual-level parameters, and providing a preliminary test of the predictive performance of the model. Measurement of the individual parameters prior to product launch enables potential applications of the model for segmentation of the target population in terms of their expected adoption times.

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    File URL: http://dx.doi.org/10.1287/mnsc.36.9.1057
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 36 (1990)
    Issue (Month): 9 (September)
    Pages: 1057-1079

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    Handle: RePEc:inm:ormnsc:v:36:y:1990:i:9:p:1057-1079

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    Related research

    Keywords: marketing: new products; marketing: diffusion of innovations; marketing: measurement; decision analysis: applications;

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    Cited by:
    1. Weisenfeld, Ursula & Ott, Ingrid, 2011. "Academic discipline and risk perception of technologies: An empirical study," Research Policy, Elsevier, vol. 40(3), pages 487-499, April.
    2. Steven Lim, 2012. "Estimating the Final Size of an Online User Base," Working Papers in Economics 12/15, University of Waikato, Department of Economics.
    3. Sumitro Banerjee & Miklos Sarvary, 2009. "How incumbent firms foster consumer expectations, delay launch but still win the markets for next generation products," Quantitative Marketing and Economics, Springer, vol. 7(4), pages 445-481, December.
    4. Malmi, Teemu, 1999. "Activity-based costing diffusion across organizations: an exploratory empirical analysis of Finnish firms," Accounting, Organizations and Society, Elsevier, vol. 24(8), pages 649-672, November.
    5. Francesco Bogliacino & Giorgio Rampa, 2009. "Quality Risk Aversion, Conjectures, and New Product Diffusion," Quaderni di Dipartimento 092, University of Pavia, Department of Economics and Quantitative Methods.
    6. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    7. Laciana, Carlos E. & Rovere, Santiago L. & Podestá, Guillermo P., 2013. "Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1873-1884.
    8. Eun-Ju Lee & David Eastwood & Jinkook Lee, 2004. "A Sample Selection Model of Consumer Adoption of Computer Banking," Journal of Financial Services Research, Springer, vol. 26(3), pages 263-275, December.
    9. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics, Springer, vol. 8(1), pages 91-121, March.
    10. Gopalakrishnan, S. & Damanpour, F., 1997. "A review of innovation research in economics, sociology and technology management," Omega, Elsevier, vol. 25(1), pages 15-28, February.
    11. Marshall, Pablo & Dockendorff, Monika & Ibáñez, Soledad, 2013. "A forecasting system for movie attendance," Journal of Business Research, Elsevier, vol. 66(10), pages 1800-1806.
    12. Ingrid Ott & Ursula Weisenfeld, 2009. "Self-selection, socialization, and risk perception of technologies: An empirical study," Kiel Working Papers 1555, Kiel Institute for the World Economy.
    13. McRoberts, Neil & Franke, A.C., 2008. "A diffusion model for the adoption of agricultural innovations in structured adopting populations," Working Papers 61117, Scotland's Rural College (formerly Scottish Agricultural College), Land Economy & Environment Research Group.
    14. Alexander Frenzel Baudisch & Hariolf Grupp, 2006. "Evaluating the market potential of innovations: A structured survey of diffusion models," Jenaer Schriften zur Wirtschaftswissenschaft 21/2006, Friedrich-Schiller-Universität Jena, Wirtschaftswissenschaftliche Fakultät.
    15. Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics, Springer, vol. 1(4), pages 371-407, December.
    16. Haruvy, Ernan & Prasad, Ashutosh, 2005. "Freeware as a competitive deterrent," Information Economics and Policy, Elsevier, vol. 17(4), pages 513-534, October.
    17. Teck H. Ho & Noah Lim & Colin Camerer, 2005. "Modeling the Psychology of Consumer and Firm Behavior with Behavioral Economics," Levine's Bibliography 784828000000000476, UCLA Department of Economics.
    18. Liu, John, 2000. "On the dynamics of stochastic diffusion of manufacturing technology," European Journal of Operational Research, Elsevier, vol. 124(3), pages 601-614, August.
    19. Rubel, Olivier, 2013. "Stochastic competitive entries and dynamic pricing," European Journal of Operational Research, Elsevier, vol. 231(2), pages 381-392.
    20. Emmanouilides, Christos J. & Davies, Richard B., 2007. "Modelling and estimation of social interaction effects in new product diffusion," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1253-1274, March.
    21. Erick Leroux & Pierre-Charles Pupion & Jean-Michel Sahut, 2014. "ERP Diffusion and Mimetic Behaviors," Working Papers 2014-355, Department of Research, Ipag Business School.
    22. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer, vol. 7(2), pages 145-165, October.
    23. TINA M. Edgar A. Ghossoub, . "Economic Development and the Welfare Costs of Inflation It has been widely observed that the role of money in the ?nancial system varies across developing and advanced countries. While the connections," Working Papers 0034, College of Business, University of Texas at San Antonio.

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