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Performance comparison of two diffusion models in a saturated mobile phone market

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  • Chang, Byeong-Yun
  • Li, Xu
  • Kim, Yun Bae

Abstract

This research examines a performance comparison of the Lotka–Volterra (LV) and extended Bass models in the saturated mobile phone market of the Republic of Korea. A three species LV model is developed and applied to Korean mobile phone service providers in terms of competitive impact. Fitting the historical data of the Korean mobile communication service market, the results show that the goodness of fit of the three species LV model in the case of competition among three companies is better than that of the extended Bass model. The advantages and disadvantages of the two models are discussed based on the results of empirical tests.

Suggested Citation

  • Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    • Handle: RePEc:eee:tefoso:v:86:y:2014:i:c:p:41-48
    DOI: 10.1016/j.techfore.2013.08.029
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    References listed on IDEAS

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    1. Hermann Simon & Karl-Heinz Sebastian, 1987. "Diffusion and Advertising: The German Telephone Campaign," Management Science, INFORMS, vol. 33(4), pages 451-466, April.
    2. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    3. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. F. R. Oliver, 1964. "Methods of Estimating the Logistic Growth Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 13(2), pages 57-66, June.
    6. Meade, Nigel & Islam, Towhidul, 1995. "Forecasting with growth curves: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 11(2), pages 199-215, June.
    7. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    8. Bass, Frank M, 1980. "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 51-67, July.
    9. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    10. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    11. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
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    Cited by:

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    3. Khan, Nokhaiz Tariq & Jung, Gisun & Kim, Jaebum & Kim, Yun Bae, 2019. "Evolving competition between low-cost carriers and full-service carriers: The case of South Korea," Journal of Transport Geography, Elsevier, vol. 74(C), pages 1-9.
    4. Cerqueti, Roy & Quaranta, Anna Grazia & Ventura, Marco, 2016. "Innovation, imitation and policy inaction," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 22-30.

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