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Modelling of innovation diffusion

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  • Arkadiusz Kijek
  • Tomasz Kijek

Abstract

Since the publication of the Bass model in 1969, research on the modelling of the diffusion of innovation resulted in a vast body of scientific literature consisting of articles, books, and studies of real-world applications of this model. The main objective of the diffusion model is to describe a pattern of spread of innovation among potential adopters in terms of a mathematical function of time. This paper assesses the state-of-the-art in mathematical models of innovation diffusion and procedures for estimating their parameters. Moreover, theoretical issues related to the models presented are supplemented with empirical research. The purpose of the research is to explore the extent to which the diffusion of broadband Internet users in 29 OECD countries can be adequately described by three diffusion models, i.e. the Bass model, logistic model and dynamic model. The results of this research are ambiguous and do not indicate which model best describes the diffusion pattern of broadband Internet users but in terms of the results presented, in most cases the dynamic model is inappropriate for describing the diffusion pattern. Issues related to the further development of innovation diffusion models are discussed and some recommendations are given.

Suggested Citation

  • Arkadiusz Kijek & Tomasz Kijek, 2010. "Modelling of innovation diffusion," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 20(3-4), pages 53-68.
    • Handle: RePEc:wut:journl:v:3-4:y:2010:p:53-68:id:169
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    References listed on IDEAS

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    Cited by:

    1. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2019. "Modeling Technological Substitution by Incorporating Dynamic Adoption Rate," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-24, February.
    2. Chorowski, Michał & Nowak, Andrzej & Andersen, Jørgen Vitting, 2023. "What makes products trendy: Introducing an innovation adoption model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    3. Doumax-Tagliavini, Virginie & Sarasa, Cristina, 2018. "Looking towards policies supporting biofuels and technological change: Evidence from France," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 430-439.
    4. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2020. "Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland," Energies, MDPI, vol. 13(1), pages 1-18, January.
    5. Kobos, Peter H. & Malczynski, Leonard A. & Walker, La Tonya N. & Borns, David J. & Klise, Geoffrey T., 2018. "Timing is everything: A technology transition framework for regulatory and market readiness levels," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 211-225.
    6. Javier Alonso & Alfonso Arellano, 2015. "Heterogeneity and diffusion in the digital economy: Spain’s case," Working Papers 1529, BBVA Bank, Economic Research Department.
    7. Kijek Tomasz, 2015. "Modelling Of Eco-innovation Diffusion: The EU Eco-label," Comparative Economic Research, Sciendo, vol. 18(1), pages 65-79, March.
    8. Duarte, Rosa & Sánchez-Chóliz, Julio & Sarasa, Cristina, 2018. "Consumer-side actions in a low-carbon economy: A dynamic CGE analysis for Spain," Energy Policy, Elsevier, vol. 118(C), pages 199-210.

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