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Factors Affecting Technological Diffusion Through Social Networks: A Review of the Empirical Evidence
[Just a Few Seeds More: Value of Network Information for Diffusion.” SSRN Scholarly Paper ID 3062830]

Author

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  • Hoi Wai Jackie Cheng

Abstract

Network theory-based interventions could be particularly effective for promoting technology adoption when information friction serves as the major obstacle to technology diffusion. To inform policy makers interested in such interventions, this paper systematically reviews empirical evidence on determinants of how social networks shape technology diffusion. It identifies three sets of factors that individually and jointly affect technological diffusion on social networks: Population characteristics, including those describe overall network structures and key economic agents’ network positions and technology sophistication; technology parameters; and information propagation mechanisms. Accurate social network assessment—crucial for the formulation of network interventions—relies on making careful selection out of the many measures of network characteristics and layers of socioeconomic interactions to examine, and on accurately defining the scope and size of network data to collect. Evidence indicates effective network interventions should aim to introduce new technologies first to economic agents with high centrality or clustering, sufficient resemblance to average population, and whom are incentivized to communicate with others.

Suggested Citation

  • Hoi Wai Jackie Cheng, 2022. "Factors Affecting Technological Diffusion Through Social Networks: A Review of the Empirical Evidence [Just a Few Seeds More: Value of Network Information for Diffusion.” SSRN Scholarly Paper ID 30," The World Bank Research Observer, World Bank, vol. 37(2), pages 137-170.
  • Handle: RePEc:oup:wbrobs:v:37:y:2022:i:2:p:137-170.
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    File URL: http://hdl.handle.net/10.1093/wbro/lkab010
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    Cited by:

    1. Läpple, Doris & Maertens, Annemie & Barham, Bradford L., 2023. "Communication and advice-taking: Evidence from a laboratory experiment," Economics Letters, Elsevier, vol. 228(C).
    2. Zhang, Jianhua & Ballas, Dimitris & Liu, Xiaolong, 2024. "Global climate change mitigation technology diffusion: A network perspective," Energy Economics, Elsevier, vol. 133(C).

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