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Predicting product life cycle patterns

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  • Yair Orbach
  • Gila Fruchter

Abstract

In this paper, we propose a new model of adoption and repurchase due to upgrades driven by the utility of technology products that keep improving. The model is able to predict product life cycle patterns that could not be explained previously. Such patterns were used to challenge diffusion theory validity. Mathematically, the model is described as a nonlinear discrete system that depends on a small set of parameters. We investigate the dynamic properties of the nonlinear system using numerical stability analysis. We find domains in the parameters space in which the equilibrium point and the periodical orbits are stable. The domains correspond to population heterogeneity, tendency to upgrade, and the influence of industry response on market dynamics. We also implement our model to fit actual data of two real-world product life cycles with many irregularities and benchmark the results of our model vs. well-known models. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Yair Orbach & Gila Fruchter, 2014. "Predicting product life cycle patterns," Marketing Letters, Springer, vol. 25(1), pages 37-52, March.
  • Handle: RePEc:kap:mktlet:v:25:y:2014:i:1:p:37-52
    DOI: 10.1007/s11002-013-9239-0
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    References listed on IDEAS

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    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Carrasco, Jalmar M.F. & Ortega, Edwin M.M. & Cordeiro, Gauss M., 2008. "A generalized modified Weibull distribution for lifetime modeling," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 450-462, December.
    3. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    4. Lin, Chih-Min & Chen, Chiu-Hsiung, 2008. "CMAC-based supervisory control for nonlinear chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 35(1), pages 40-58.
    5. William E. Cox & Jr., 1967. "Product Life Cycles as Marketing Models," The Journal of Business, University of Chicago Press, vol. 40, pages 375-375.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    8. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    9. Rink, David R. & Swan, John E., 1979. "Product life cycle research: A literature review," Journal of Business Research, Elsevier, vol. 7(3), pages 219-242, September.
    10. Russell, Thomas, 1980. "Comments on "The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations."," The Journal of Business, University of Chicago Press, vol. 53(3), pages 69-73, July.
    11. Christoph H. Loch & Bernardo A. Huberman, 1999. "A Punctuated-Equilibrium Model of Technology Diffusion," Management Science, INFORMS, vol. 45(2), pages 160-177, February.
    12. S. Weerahandi & S. R. Dalal, 1992. "A Choice-Based Approach to the Diffusion of a Service: Forecasting Fax Penetration by Market Segments," Marketing Science, INFORMS, vol. 11(1), pages 39-53.
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    Cited by:

    1. Yaniv Shani & Gil Appel & Shai Danziger & Ron Shachar, 2020. "When and Why Consumers “Accidentally” Endanger Their Products," Management Science, INFORMS, vol. 66(12), pages 5757-5782, December.
    2. Mehmet N. Aydin & N. Ziya Perdahci, 0. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    3. Goksel Yalcinkaya & Tevfik Aktekin & Sengun Yeniyurt & Setiadi Umar, 2017. "How often should a firm modify its products? A Bayesian analysis of automobile modification cycles," Marketing Letters, Springer, vol. 28(1), pages 85-97, March.
    4. Mehmet N. Aydin & N. Ziya Perdahci, 2019. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 21(2), pages 229-240, April.

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