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Modeling the diffusion of complex innovations as a process of opinion formation through social networks

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  • Valentina A Assenova

Abstract

Complex innovations– ideas, practices, and technologies that hold uncertain benefits for potential adopters—often vary in their ability to diffuse in different communities over time. To explain why, I develop a model of innovation adoption in which agents engage in naïve (DeGroot) learning about the value of an innovation within their social networks. Using simulations on Bernoulli random graphs, I examine how adoption varies with network properties and with the distribution of initial opinions and adoption thresholds. The results show that: (i) low-density and high-asymmetry networks produce polarization in influence to adopt an innovation over time, (ii) increasing network density and asymmetry promote adoption under a variety of opinion and threshold distributions, and (iii) the optimal levels of density and asymmetry in networks depend on the distribution of thresholds: networks with high density (>0.25) and high asymmetry (>0.50) are optimal for maximizing diffusion when adoption thresholds are right-skewed (i.e., barriers to adoption are low), but networks with low density (

Suggested Citation

  • Valentina A Assenova, 2018. "Modeling the diffusion of complex innovations as a process of opinion formation through social networks," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-18, May.
  • Handle: RePEc:plo:pone00:0196699
    DOI: 10.1371/journal.pone.0196699
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    1. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
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    Cited by:

    1. Tommy Pan Fang & Andy Wu & David R. Clough, 2021. "Platform diffusion at temporary gatherings: Social coordination and ecosystem emergence," Strategic Management Journal, Wiley Blackwell, vol. 42(2), pages 233-272, February.

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