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Heterogeneity in diffusion of innovations modelling: A few fundamental types

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  • Guseo, Renato
  • Guidolin, Mariangela

Abstract

Heterogeneity of agents in aggregate systems is an important issue in the study of innovation diffusion. In this paper, we propose a modelling approach to latent heterogeneity, based on a few fundamental types, which avoids cumbersome integrations with not easy to motivate a priori distributions. This approach gives rise to a discrete non-parametric Bayesian mixture model with a possibly multimodal distributional behaviour. The result is inspired by two alternative theories: the first is based on the Rosenblueth two-point distributions (TPD), and the second is related to Cellular Automata models. From a statistical point of view, the proposed reduction allows for the recognition of discrete heterogeneous sub-populations by assessing their significance within a realistic diffusion process. An illustrative application is discussed with reference to Compact Cassettes for pre-recorded music in Italy.

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  • Guseo, Renato & Guidolin, Mariangela, 2015. "Heterogeneity in diffusion of innovations modelling: A few fundamental types," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 514-524.
    • Handle: RePEc:eee:tefoso:v:90:y:2015:i:pb:p:514-524
    DOI: 10.1016/j.techfore.2014.02.023
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    Cited by:

    1. Gagliardi, Dimitri & Ramlogan, Ronnie & Navarra, Pierluigi & Dello Russo, Cinzia, 2018. "Diffusion of complementary evolving pharmaceutical innovations: The case of Abacavir and its pharmacogenetic companion diagnostic in Italy," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 223-233.
    2. Furlan, Claudia & Mortarino, Cinzia, 2018. "Forecasting the impact of renewable energies in competition with non-renewable sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1879-1886.
    3. Guseo, Renato, 2016. "Latent heterogeneity effects in modelling individual hazards: A non-proportional approach," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 89-93.
    4. Furlan, Claudia & Guidolin, Mariangela & Guseo, Renato, 2016. "Has the Fukushima accident influenced short-term consumption in the evolution of nuclear energy? An analysis of the world and seven leading countries," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 37-49.
    5. Jarunee Wonglimpiyarat, 2016. "Technological Change of the Innovation Payment System," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-20, August.
    6. Guseo, Renato & Schuster, Reinhard, 2021. "Modelling dynamic market potential: Identifying hidden automata networks in the diffusion of pharmaceutical drugs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    7. Karakaya, Emrah, 2014. "Finite Element Model of the Innovation Diffusion: An Application to Photovoltaic Systems," INDEK Working Paper Series 2014/6, Royal Institute of Technology, Department of Industrial Economics and Management.

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