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Awareness, persuasion, and adoption: Enriching the Bass model

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  • Colapinto, Cinzia
  • Sartori, Elena
  • Tolotti, Marco

Abstract

In the context of diffusion of innovations, we propose a probabilistic model based on interacting populations connected through new communication channels. The potential adopters are heterogeneous in the connectivity levels and in their taste for innovation. The proposed framework can model the different stages of the adoption dynamics. In particular, the adoption curve is the result of a micro-founded decision process following the awareness phase. Eventually, we recover stylized facts pointed out by the extant literature in the field, such as delayed adoptions and non-monotonic adoption curves.

Suggested Citation

  • Colapinto, Cinzia & Sartori, Elena & Tolotti, Marco, 2014. "Awareness, persuasion, and adoption: Enriching the Bass model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 1-10.
    • Handle: RePEc:eee:phsmap:v:395:y:2014:i:c:p:1-10
    DOI: 10.1016/j.physa.2013.10.001
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    References listed on IDEAS

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    1. 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.
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    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Lawrence Blume & Steven Durlauf, 2003. "Equilibrium Concepts for Social Interaction Models," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 193-209.
    5. Barucci, Emilio & Tolotti, Marco, 2012. "Social interaction and conformism in a random utility model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1855-1866.
    6. Dai Pra, Paolo & Tolotti, Marco, 2009. "Heterogeneous credit portfolios and the dynamics of the aggregate losses," Stochastic Processes and their Applications, Elsevier, vol. 119(9), pages 2913-2944, September.
    7. 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.
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    Cited by:

    1. Tolotti, Marco & Yepez, Jorge, 2020. "Hotelling-Bertrand duopoly competition under firm-specific network effects," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 105-128.
    2. Zhao, Narisa & Cheng, Xiaokang & Guo, Xianda, 2018. "Impact of information spread and investment behavior on the diffusion of internet investment products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 427-436.
    3. Hashemi, Fariba & Gallay, Olivier & Hongler, Max-Olivier, 2021. "Opinion formation dynamics — Swift collective disillusionment triggered by unmet expectations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    4. Fanelli, Viviana & Maddalena, Lucia, 2020. "A nonlinear dynamic model for credit risk contagion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 174(C), pages 45-58.
    5. Simone Marsiglio & Marco Tolotti, 2020. "Motivation crowding‐out and green‐paradox‐like outcomes," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 22(5), pages 1559-1583, September.
    6. Paolo Pellizzari & Elena Sartori & Marco Tolotti, 2015. "Optimal Policies In Two-Step Binary Games Under Social Pressure And Limited Resources," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-16, August.

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