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Consumer Learning from Own Experience and Social Information: An Experimental Study

Author

Listed:
  • Andrew M. Davis

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York, 14853)

  • Vishal Gaur

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York, 14853)

  • Dayoung Kim

    (Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California 92831)

Abstract

We investigate how different types of social information affect the demand characteristics of firms competing through service quality. We first generate behavioral hypotheses around both consumers’ learning behavior and firms’ corresponding demand characteristics: market share, demand uncertainty, and rate of convergence. We then conduct a controlled human-subject experiment in which a consumer chooses to visit one of two firms, each with unknown service quality, in a repeated interaction and is exposed to different information treatments from a social network: (1) no social information; (2) share-based social information, which details the percentage of people who visited each firm; (3) quality-based social information, which illustrates the percentage of people who received a satisfactory experience from each firm; or (4) full social information, which contains both share- and quality-based social information. A key insight from our study is that different types of social information have different effects on firms’ demand. First, promoting quality-based social information leads to a significantly higher market share, lower demand variability, and faster rate of convergence for a firm with significantly better service quality. Second, when the higher quality firm has only a marginal advantage over the other firm, promoting only share-based information leads to significantly higher market share and lower demand variability. A third important result is that providing only one type of social information can actually be more helpful to the higher quality firm than providing full social information.

Suggested Citation

  • Andrew M. Davis & Vishal Gaur & Dayoung Kim, 2021. "Consumer Learning from Own Experience and Social Information: An Experimental Study," Management Science, INFORMS, vol. 67(5), pages 2924-2943, May.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:5:p:2924-2943
    DOI: 10.1287/mnsc.2020.3691
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    References listed on IDEAS

    as
    1. Vishal Gaur & Young-Hoon Park, 2007. "Asymmetric Consumer Learning and Inventory Competition," Management Science, INFORMS, vol. 53(2), pages 227-240, February.
    2. Mirko Kremer & Brent Moritz & Enno Siemsen, 2011. "Demand Forecasting Behavior: System Neglect and Change Detection," Management Science, INFORMS, vol. 57(10), pages 1827-1843, October.
    3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    4. , & , & ,, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
    5. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    6. Uri Simonsohn & Dan Ariely, 2008. "When Rational Sellers Face Nonrational Buyers: Evidence from Herding on eBay," Management Science, INFORMS, vol. 54(9), pages 1624-1637, September.
    7. Joseph Hall & Evan Porteus, 2000. "Customer Service Competition in Capacitated Systems," Manufacturing & Service Operations Management, INFORMS, vol. 2(2), pages 144-165, November.
    8. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    9. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    10. 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.
    11. 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.
    12. 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.
    13. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
    14. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    15. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    16. Pnina Feldman & Yiangos Papanastasiou & Ella Segev, 2019. "Social Learning and the Design of New Experience Goods," Management Science, INFORMS, vol. 65(5), pages 1502-1519, April.
    17. 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.
    18. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    19. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    20. Ming Hu & Joseph Milner & Jiahua Wu, 2016. "Liking and Following and the Newsvendor: Operations and Marketing Policies Under Social Influence," Management Science, INFORMS, vol. 62(3), pages 867-879, March.
    21. Ruomeng Cui & Santiago Gallino & Antonio Moreno & Dennis J. Zhang, 2018. "The Operational Value of Social Media Information," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1749-1769, October.
    22. Dennis J. Zhang & Gad Allon & Jan A. Van Mieghem, 2017. "Does Social Interaction Improve Learning Outcomes? Evidence from Field Experiments on Massive Open Online Courses," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 347-367, July.
    23. Robert J. Meyer & Yong Shi, 1995. "Sequential Choice Under Ambiguity: Intuitive Solutions to the Armed-Bandit Problem," Management Science, INFORMS, vol. 41(5), pages 817-834, May.
    24. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    25. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    26. Bogaçhan Çelen & Kyle Hyndman, 2012. "Social Learning Through Endogenous Information Acquisition: An Experiment," Management Science, INFORMS, vol. 58(8), pages 1525-1548, August.
    27. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
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    2. Gregory DeCroix & Xiaoyang Long & Jordan Tong, 2021. "How Service Quality Variability Hurts Revenue When Customers Learn: Implications for Dynamic Personalized Pricing," Operations Research, INFORMS, vol. 69(3), pages 683-708, May.

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