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Innovation Diffusion in a Dynamic Potential Adopter Population

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  • Vijay Mahajan

    (Ohio State University)

  • Robert A. Peterson

    (University of Texas at Austin)

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    Abstract

    Existing single-adoption diffusion models assume a static (constant) ceiling on the number of adopters, that is, a constant population of potential adopters, over the entire time frame of the diffusion process. However, for most innovations this assumption is tenuous. Rather, the ceiling, or the potential adopter population is more likely to be dynamic. The present paper relaxes this assumption and presents a dynamic diffusion model. To illustrate the application of this model, data from two innovations are analyzed.

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    File URL: http://dx.doi.org/10.1287/mnsc.24.15.1589
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 24 (1978)
    Issue (Month): 15 (November)
    Pages: 1589-1597

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    Handle: RePEc:inm:ormnsc:v:24:y:1978:i:15:p:1589-1597

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    Related research

    Keywords: marketing; marketing: buyer behavior; health care: epidemiology;

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    Cited by:
    1. 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.
    2. Chih-Jou Chen & Chia-Chin Chang & Shiu-Wan Hung, 2011. "Influences of Technological Attributes and Environmental Factors on Technology Commercialization," Journal of Business Ethics, Springer, vol. 104(4), pages 525-535, December.
    3. Pablo Marshall, 2000. "Difusion De Internet En Chile," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 3(2), pages 143-163.
    4. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    5. Luigi De Cesare & Andrea Di Liddo & Stefania Ragni, 2002. "Numerical solution of some optimal control problems arising from innovation diffusion," Computing in Economics and Finance 2002 221, Society for Computational Economics.
    6. de Bondt, Gabe & Marqués-Ibáñez, David, 2004. "The high-yield segment of the corporate bond market: a diffusion modelling approach for the United States, the United Kingdom and the euro area," Working Paper Series 0313, European Central Bank.
    7. Fuentelsaz, Lucio & Gomez, Jaime & Polo, Yolanda, 2003. "Intrafirm diffusion of new technologies: an empirical application," Research Policy, Elsevier, vol. 32(4), pages 533-551, April.
    8. Sadou, Karim, 2002. "L’impact des externalités de réseau sur le processus de décision du consommateur," Economics Papers from University Paris Dauphine 123456789/5855, Paris Dauphine University.
    9. Kalika, Michel & Pallud, Jessie & Elie-dit-Cosaque, Christophe, 2008. "The Influence of Work Environment On It-Specific Individual Differences: An Empirical Study," Economics Papers from University Paris Dauphine 123456789/8266, Paris Dauphine University.
    10. Yann Duval & Arlo Biere, 2001. "Product diffusion and the demand for new food products," Agribusiness, John Wiley & Sons, Ltd., vol. 18(1), pages 23-36.
    11. Crompton, Paul, 2001. "The diffusion of new steelmaking technology," Resources Policy, Elsevier, vol. 27(2), pages 87-95, June.
    12. Martin Hewing, 2012. "A Theoretical and Empirical Comparison of Innovation Diffusion Models Applying Data from the Software Industry," Journal of the Knowledge Economy, Springer, vol. 3(2), pages 125-141, June.
    13. Luigi De Cesare & Andrea Di Liddo & Stefania Ragni, 2003. "Numerical Solutions to Some Optimal Control Problems Arising from Innovation Diffusion," Computational Economics, Society for Computational Economics, vol. 22(2), pages 173-186, October.

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