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Dynamics of social factors in technological substitutions

Author

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  • Brice Dattée

    (UCD - University College Dublin [Dublin], Ecole Centrale Paris)

  • Henry Weil

    (MIT Sloan - Sloan School of Management - MIT - Massachusetts Institute of Technology)

Abstract

Diffusion models of technological innovations are often based on an epidemic structure which has a good fit to historical data but whose communication assumptions lack explanatory power. They assume a simplified decision process, uniform decision criteria across adopters categories, and a fully interconnected social structure. The objective of this paper is to show that the dynamics of social factors during technological substitutions have significant effects on substitution patterns. The success of a paradigmatic shift is not only a function of technological characteristics but also depends on change agents and many social dynamics. Such complexity requires analysis at several levels of granularity. We start with cognitive processes at the individual level using concepts from cognitive psychology and decision making under uncertainty and then move to interpersonal communications at the aggregate social level. We show that population heterogeneity generates different decision criteria and a social topology which greatly affect perceptions and the formation of expectations. The structure of interpersonal networks also explains how the relevance and credibility of information impact the critical mass dynamics of technology adoption. A more complete model accounting for social interactions provides a useful framework for understanding complex substitution patterns and reducing the risk of misreading the market.

Suggested Citation

  • Brice Dattée & Henry Weil, 2007. "Dynamics of social factors in technological substitutions," Post-Print hal-02312753, HAL.
  • Handle: RePEc:hal:journl:hal-02312753
    DOI: 10.1016/j.techfore.2007.03.003
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    Cited by:

    1. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2016. "Forecasting technology substitution based on hazard function," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 259-272.
    2. Seebauer, Sebastian, 2015. "Why early adopters engage in interpersonal diffusion of technological innovations: An empirical study on electric bicycles and electric scooters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 146-160.
    3. Cowan, Kelly R. & Daim, Tugrul U., 2011. "Review of technology acquisition and adoption research in the energy sector," Technology in Society, Elsevier, vol. 33(3), pages 183-199.
    4. Jadwiga Biegańska & Elżbieta Grzelak-Kostulska & Michał Adam Kwiatkowski, 2021. "A Typology of Attitudes towards the E-Bike against the Background of the Traditional Bicycle and the Car," Energies, MDPI, vol. 14(24), pages 1-21, December.
    5. Yang, Kuo-Pin & Chou, Christine & Chiu, Yu-Jen, 2014. "How unlearning affects radical innovation: The dynamics of social capital and slack resources," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 152-163.
    6. Birgit Kopainsky & Katharine Tröger & Sebastian Derwisch & Silvia Ulli‐Beer, 2012. "Designing Sustainable Food Security Policies in Sub‐Saharan African Countries: How Social Dynamics Over‐Ride Utility Evaluations for Good and Bad," Systems Research and Behavioral Science, Wiley Blackwell, vol. 29(6), pages 575-589, November.
    7. Groesser, Stefan N., 2014. "Co-evolution of legal and voluntary standards: Development of energy efficiency in Swiss residential building codes," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 1-16.

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