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Prescribing Response Strategies to Manage Customer Opinions: A Stochastic Differential Equation Approach

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  • Mingwen Yang

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195;)

  • Zhiqiang (Eric) Zheng

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Vijay Mookerjee

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Today, the reputation of a firm is profoundly influenced by user opinions expressed in online consumer reviews. Managing these opinions is, therefore, critical for the success of firms. We study the problem of devising an appropriate opinion management strategy (or response strategy ) for a firm to respond to online customer reviews. To unravel the underlying mechanics of the problem, we develop a stochastic differential equation model that describes the evolution of review ratings over time for a given response strategy employed by the firm. This model is validated using data on online customer reviews and firm responses from two of the world’s largest online travel agents. When pitted against popular benchmark models, such as autoregressive moving average, generalized autoregressive conditional heteroscedasticity, moving average, exponential smoothing, and naive method, our approach not only achieves comparable (often better) predictive performance, it is also able to incorporate the response strategy into the data-generation process underlying the review ratings. Our approach, therefore, is not just predictive, but, more importantly, one that can be used in a prescriptive sense, namely to prescribe a response strategy that controls review ratings in a desired manner. We operationalize the theoretical response strategy in our stochastic model to an operational prescription that a firm can implement and show the applicability of our approach for different business objectives, such as mean control, mean-variance control, and service-level control. Finally, we demonstrate the flexibility of the stochastic differential equation model by extending it to encompass multiple state variables. The online appendices are available at https://doi.org/10.1287/isre.2018.0805 .

Suggested Citation

  • Mingwen Yang & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2019. "Prescribing Response Strategies to Manage Customer Opinions: A Stochastic Differential Equation Approach," Information Systems Research, INFORMS, vol. 30(2), pages 351-374, June.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:2:p:351-374
    DOI: 10.1287/isre.2018.0805
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    as
    1. Keane, Michael P., 2010. "Structural vs. atheoretic approaches to econometrics," Journal of Econometrics, Elsevier, vol. 156(1), pages 3-20, May.
    2. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    3. Claes Fornell & Birger Wernerfelt, 1988. "A Model for Customer Complaint Management," Marketing Science, INFORMS, vol. 7(3), pages 287-298.
    4. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    5. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    6. Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2020. "Buying reputation as a signal of quality: Evidence from an online marketplace," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 965-988, December.
    7. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    8. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    9. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    10. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    11. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    12. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    13. Judith A. Chevalier & Yaniv Dover & Dina MayzlinDina Mayzlin, 2018. "Channels of Impact: User Reviews When Quality Is Dynamic and Managers Respond," Marketing Science, INFORMS, vol. 37(5), pages 688-709, September.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    15. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    16. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    17. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    18. Paul A. Pavlou & Angelika Dimoka, 2006. "The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation," Information Systems Research, INFORMS, vol. 17(4), pages 392-414, December.
    19. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    20. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    21. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    22. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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    Cited by:

    1. Mingwen Yang & Zhiqiang (Eric) Zheng & Vijay Mookerjee, 2021. "The Race for Online Reputation: Implications for Platforms, Firms, and Consumers," Information Systems Research, INFORMS, vol. 32(4), pages 1262-1280, December.
    2. T. Ravichandran & Chaoqun Deng, 2023. "Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints," Information Systems Research, INFORMS, vol. 34(1), pages 319-341, March.
    3. Zibo Liu & Zhijie Lin & Ying Zhang & Yong Tan, 2022. "The Signaling Effect of Sampling Size in Physical Goods Sampling Via Online Channels," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 529-546, February.
    4. Zhao, Yan & Wen, Lingling & Feng, Xiangnan & Li, Ran & Lin, Xiaolin, 2020. "How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    5. Yan Song & Xin Tian, 2020. "Managerial Responses and Customer Engagement in Crowdfunding," Sustainability, MDPI, vol. 12(8), pages 1-13, April.

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