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U-Shaped Conformity in Online Social Networks

Author

Listed:
  • Monic Sun

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Xiaoquan (Michael) Zhang

    (Department of Decision Sciences and Managerial Economics, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong)

  • Feng Zhu

    (Harvard Business School, Harvard University, Boston, Massachusetts 02163)

Abstract

We explore how people balance their needs to belong and to be different from their friends by studying their choices of wall color in a virtual house on a leading Chinese social-networking site. The setting enables us to randomize both the popular color and the adoption rate at the individual level so that our experimental design minimizes informational social influence, homophily, and group-identity signaling to the general public. We find that there exists significant social influence within a user’s friend circle. While learning about the most popular color among a user’s friends generally increases the likelihood for the user to adopt that color, conformity first decreases and then increases with the adoption rate of that choice, which ranges from 50% to 100%. In addition, users who are of a minority or lower socioeconomic status or are newer are more likely to conform upon learning about the popular choice. Our findings are consistent with optimal distinctiveness and middle-status conformity theories and have implications for designing normative marketing campaigns.

Suggested Citation

  • Monic Sun & Xiaoquan (Michael) Zhang & Feng Zhu, 2019. "U-Shaped Conformity in Online Social Networks," Marketing Science, INFORMS, vol. 38(3), pages 461-480, May.
    • Handle: RePEc:inm:ormksc:v:mksc:y:2019:i:3:p:461-480
    DOI: 10.1287/mksc.2018.1133
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    Cited by:

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    3. Haibing Gao & Subodha Kumar & Yinliang (Ricky) Tan & Huazhong Zhao, 2022. "Socialize More, Pay Less: Randomized Field Experiments on Social Pricing," Information Systems Research, INFORMS, vol. 33(3), pages 935-953, September.
    4. Han, Xintong & Li, Yushen & Wang, Tong, 2023. "Peer recognition, badge policies, and content contribution: An empirical study," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 691-707.
    5. Maggie Rong Hu & Xiaoyang Li & Yang Shi & Xiaoquan (Michael) Zhang, 2023. "Numerological Heuristics and Credit Risk in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 34(4), pages 1744-1760, December.
    6. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    7. Mina Ameri & Elisabeth Honka & Ying Xie, 2019. "Word of Mouth, Observed Adoptions, and Anime-Watching Decisions: The Role of the Personal vs. the Community Network," Marketing Science, INFORMS, vol. 38(4), pages 567-583, July.

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