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The Economic Value of Online Reviews

Author

Listed:
  • Chunhua Wu

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Hai Che

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Tat Y. Chan

    (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

  • Xianghua Lu

    (School of Management, Fudan University, 200433 Shanghai, China)

Abstract

This paper investigates the economic value of online reviews for consumers and restaurants. We use a data set from Dianping.com , a leading Chinese website providing user-generated reviews, to study how consumers learn, from reading online reviews, the quality and cost of restaurant dining. We propose a learning model with three novel features: (1) different reviews offer different informational value to different types of consumers; (2) consumers learn their own preferences, and not the distribution of preferences among the entire population, for multiple product attributes; and (3) consumers update not only the expectation but also the variance of their preferences. Based on estimation results, we conduct a series of counterfactual experiments and find that the value from Dianping is about 7 CNY for each user, and about 8.6 CNY from each user for the reviewed restaurants in this study. The majority of the value comes from reviews on restaurant quality, and contextual comments are more valuable than numerical ratings in reviews.

Suggested Citation

  • Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    • Handle: RePEc:inm:ormksc:v:34:y:2015:i:5:p:739-754
    DOI: 10.1287/mksc.2015.0926
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    References listed on IDEAS

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    1. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    2. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    3. Tat Chan & Chakravarthi Narasimhan & Ying Xie, 2013. "Treatment Effectiveness and Side Effects: A Model of Physician Learning," Management Science, INFORMS, vol. 59(6), pages 1309-1325, June.
    4. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    5. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    6. Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    7. Tat Y. Chan & Barton H. Hamilton, 2006. "Learning, Private Information, and the Economic Evaluation of Randomized Experiments," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 997-1040, December.
    8. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    9. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    Full references (including those not matched with items on IDEAS)

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