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Social Learning in Networks of Friends versus Strangers

Author

Listed:
  • Jurui Zhang

    (College of Management, University of Massachusetts Boston, Boston, Massachusetts 02125)

  • Yong Liu

    (Eller College of Management, University of Arizona, Tucson, Arizona 85721)

  • Yubo Chen

    (School of Economics and Management, Tsinghua University, 100084 Beijing, China)

Abstract

Networks and the embedded relationships are critical determinants of how people communicate and form beliefs. The explosion of social media has significantly increased the scope and impact of social learning among consumers. This paper studies observational learning in networks of friends versus strangers. A consumer decides whether to adopt a product after receiving a private signal about product quality and observing the actions of others. The preference for the product has greater heterogeneity in the stranger-network than in the friend-network. We show that when the network is small, observing friends’ actions helps the consumer make more accurate inferences about quality. As the network grows, however, the stranger-network becomes more effective. Underlying these results are two competing effects of network heterogeneity on social learning. These are the individual preference effect , which allows one to make a better quality judgment when the preference element of past actions is more certain, and the social conforming effect , wherein private signals are underused in quality judgment as people follow others’ actions. We find cascading is more likely to occur in the friend-network than in the stranger-network. For a high-quality firm, the stranger-network generates greater sales than the friend-network when the network size is sufficiently large or the private signal is sufficiently accurate. We also examine the existence of experts and firms using advertising to influence consumers. Finally, we show how networks that are highly homogeneous or heterogeneous could impede observational learning.

Suggested Citation

  • Jurui Zhang & Yong Liu & Yubo Chen, 2015. "Social Learning in Networks of Friends versus Strangers," Marketing Science, INFORMS, vol. 34(4), pages 573-589, July.
  • Handle: RePEc:inm:ormksc:v:34:y:2015:i:4:p:573-589
    DOI: 10.1287/mksc.2015.0902
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    as
    1. Fudenberg, Drew & Tirole, Jean, 1991. "Perfect Bayesian equilibrium and sequential equilibrium," Journal of Economic Theory, Elsevier, vol. 53(2), pages 236-260, April.
    2. Mark Grinblatt & Matti Keloharju & Seppo Ikäheimo, 2008. "Social Influence and Consumption: Evidence from the Automobile Purchases of Neighbors," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 735-753, November.
    3. Yubo Chen & Jinhong Xie, 2005. "Third-Party Product Review and Firm Marketing Strategy," Marketing Science, INFORMS, vol. 24(2), pages 218-240, February.
    4. Preyas S. Desai, 2001. "Quality Segmentation in Spatial Markets: When Does Cannibalization Affect Product Line Design?," Marketing Science, INFORMS, vol. 20(3), pages 265-283, August.
    5. Hongbin Cai & Yuyu Chen & Hanming Fang & Li-An Zhou, 2009. "Microinsurance, Trust and Economic Development: Evidence from a Randomized Natural Field Experiment," NBER Working Papers 15396, National Bureau of Economic Research, Inc.
    6. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    7. Thisse, Jacques-Francois & Vives, Xavier, 1988. "On the Strategic Choice of Spatial Price Policy," American Economic Review, American Economic Association, vol. 78(1), pages 122-137, March.
    8. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    9. Eric L. Dey, 1997. "Undergraduate Political Attitudes," The Journal of Higher Education, Taylor & Francis Journals, vol. 68(4), pages 398-413, July.
    10. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    11. Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
    12. Hongbin Cai & Yuyu Chen & Hanming Fang, 2009. "Observational Learning: Evidence from a Randomized Natural Field Experiment," American Economic Review, American Economic Association, vol. 99(3), pages 864-882, June.
    13. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    14. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    15. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    16. Urbany, Joel E & Dickson, Peter R & Wilkie, William L, 1989. "Buyer Uncertainty and Information Search," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(2), pages 208-215, September.
    17. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    18. Enrico Moretti, 2011. "Social Learning and Peer Effects in Consumption: Evidence from Movie Sales," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(1), pages 356-393.
    19. Gershoff, Andrew D & Broniarczyk, Susan M & West, Patricia M, 2001. "Recommendation or Evaluation? Task Sensitivity in Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(3), pages 418-438, December.
    20. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    21. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    22. Michael L. Katz & Carl Shapiro, 1994. "Systems Competition and Network Effects," Journal of Economic Perspectives, American Economic Association, vol. 8(2), pages 93-115, Spring.
    23. Mark B. Vandenbosch & Charles B. Weinberg, 1995. "Product and Price Competition in a Two-Dimensional Vertical Differentiation Model," Marketing Science, INFORMS, vol. 14(2), pages 224-249.
    24. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    25. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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