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The diffusion of mobile social networking: Further study

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  • Bemmaor, Albert C.
  • Zheng, Li

Abstract

In a recent study, Scaglione et al. (2015) analyzed the diffusion of mobile social networking in four G7 countries. Using Bass’s model and Bemmaor’s Gamma/Shifted Gompertz (G/SG) model, they found evidence of a left skew in the right-censored distributions of the times to adoption in three countries out of four. However, this conclusion relied on the skewness parameter of Bemmaor’s model. We reanalyze the data, making use of three special cases of the G/SG as well as the full version. Extending the data set to six countries, we show that (i) fitting the four models to the data does not allow us to discriminate between models, but (ii) forecasting the subsequent adoptions provides a strong support of right skewness in the data set: each country (except France) shows a substantial mass of later adopters of mobile social networking following an initial embrace of the access.

Suggested Citation

  • Bemmaor, Albert C. & Zheng, Li, 2018. "The diffusion of mobile social networking: Further study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 612-621.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:612-621
    DOI: 10.1016/j.ijforecast.2018.04.006
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    1. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Goldenberg, Jacob & Libai, Barak & Muller, Eitan, 2010. "The chilling effects of network externalities," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 4-15.
    4. 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.
    5. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    6. Trajtenberg, Manuel & Yitzhaki, Shlomo, 1989. "The Diffusion of Innovations: A Methodological Reappraisal," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(1), pages 35-47, January.
    7. Scaglione, Miriam & Giovannetti, Emanuele & Hamoudia, Mohsen, 2015. "The diffusion of mobile social networking: Exploring adoption externalities in four G7 countries," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1159-1170.
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