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Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

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  • Sinan Aral

    () (MIT Sloan School of Management, Cambridge, Massachusetts 02142)

  • Dylan Walker

    () (School of Management, Boston University, Boston, Massachusetts 02215)

Abstract

We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society. This paper was accepted by Alok Gupta, special issue on business analytics .

Suggested Citation

  • Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
  • Handle: RePEc:inm:ormnsc:v:60:y:2014:i:6:p:1352-1370
    DOI: 10.1287/mnsc.2014.1936
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    1. repec:eee:proeco:v:216:y:2019:i:c:p:287-304 is not listed on IDEAS
    2. Ravi Bapna & Akhmed Umyarov, 2015. "Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks," Management Science, INFORMS, vol. 61(8), pages 1902-1920, August.
    3. repec:eee:ijrema:v:36:y:2019:i:1:p:3-19 is not listed on IDEAS
    4. repec:eee:wdevel:v:122:y:2019:i:c:p:306-324 is not listed on IDEAS
    5. Liangfei Qiu & Asoo Vakharia & Arunima Chhikara, 2019. "Multi-Dimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Working Papers 19-01, NET Institute.
    6. repec:eee:phsmap:v:508:y:2018:i:c:p:213-222 is not listed on IDEAS
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    10. repec:eee:phsmap:v:509:y:2018:i:c:p:256-264 is not listed on IDEAS
    11. Ruoxuan Xiong & Susan Athey & Mohsen Bayati & Guido Imbens, 2019. "Optimal Experimental Design for Staggered Rollouts," Papers 1911.03764, arXiv.org.
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