IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v67y2021i7p4420-4445.html
   My bibliography  Save this article

Online Review Solicitations Reduce Extremity Bias in Online Review Distributions and Increase Their Representativeness

Author

Listed:
  • Hülya Karaman

    (Department of Marketing, Lee Kong Chian School of Business, Singapore Management University, Singapore 178899)

Abstract

Representative online customer reviews are critical to the effective functioning of the Internet economy. In this study, I investigate the representativeness of online review distributions to examine how extremity bias and conformity impact it and explore whether online review solicitations alter representativeness. Past research on extreme distribution of online ratings commonly relied solely on observed public online ratings. One strength of the current paper is that I observe the private satisfaction ratings of customers regardless of whether they choose to write an online review or not. I show that both extremity bias and conformity exist in unsolicited online word-of-mouth (WOM) and introduce online review solicitations as a mechanism that can partially de-bias ratings. Solicitations increase all customers’ engagement in online WOM, but if solicited, those with moderate experiences increase their engagement more than those with extreme experiences. Consequently, although extremity bias still exists in solicited online WOM, solicitations significantly increase the representativeness of rating distributions. Surprisingly, the results demonstrate that without conformity, unsolicited online WOM would be even less representative of the original customer experiences. Furthermore, I document that both solicited and unsolicited reviews equally overstate the average customer experience (compared with average private ratings) despite stark differences in their rating distributions. Finally, I establish that solicitations for reviews on the company-owned website, on average, decrease the number of one-star reviews on a third-party review platform.

Suggested Citation

  • Hülya Karaman, 2021. "Online Review Solicitations Reduce Extremity Bias in Online Review Distributions and Increase Their Representativeness," Management Science, INFORMS, vol. 67(7), pages 4420-4445, July.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:7:p:4420-4445
    DOI: 10.1287/mnsc.2020.3758
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2020.3758
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2020.3758?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Yan Chen & F. Maxwell Harper & Joseph Konstan & Sherry Xin Li, 2010. "Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens," American Economic Review, American Economic Association, vol. 100(4), pages 1358-1398, September.
    3. 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.
    4. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    5. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    6. Elisa Montaguti & Scott A. Neslin & Sara Valentini, 2016. "Can Marketing Campaigns Induce Multichannel Buying and More Profitable Customers? A Field Experiment," Marketing Science, INFORMS, vol. 35(2), pages 201-217, March.
    7. 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.
    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. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Krügel, Jan Philipp & Paetzel, Fabian, 2024. "The impact of fraud on reputation systems," Games and Economic Behavior, Elsevier, vol. 144(C), pages 329-354.
    2. Kübler, Raoul V. & Lobschat, Lara & Welke, Lina & van der Meij, Hugo, 2024. "The effect of review images on review helpfulness: A contingency approach," Journal of Retailing, Elsevier, vol. 100(1), pages 5-23.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    2. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    3. 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.
    4. Ji Wu & Haichuan Zhao & Haipeng (Allan) Chen, 2021. "Coupons or Free Shipping? Effects of Price Promotion Strategies on Online Review Ratings," Information Systems Research, INFORMS, vol. 32(2), pages 633-652, June.
    5. Wei Chen & Bin Gu & Qiang Ye & Kevin Xiaoguo Zhu, 2019. "Measuring and Managing the Externality of Managerial Responses to Online Customer Reviews," Service Science, INFORMS, vol. 30(1), pages 81-96, March.
    6. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    7. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    8. Uttara Ananthakrishnan & Davide Proserpio & Siddhartha Sharma, 2023. "I Hear You: Does Quality Improve with Customer Voice?," Marketing Science, INFORMS, vol. 42(6), pages 1143-1161, November.
    9. Erfan Rezvani & Christian Rojas, 2022. "Firm responsiveness to consumers' reviews: The effect on online reputation," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 898-922, November.
    10. Apostolos Filippas & John Horton & Joseph M. Golden, 2017. "Reputation in the Long-Run," CESifo Working Paper Series 6750, CESifo.
    11. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    12. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    13. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    14. Dandan Qiao & Shun-Yang Lee & Andrew B. Whinston & Qiang Wei, 2020. "Financial Incentives Dampen Altruism in Online Prosocial Contributions: A Study of Online Reviews," Information Systems Research, INFORMS, vol. 31(4), pages 1361-1375, December.
    15. Apostolos Filippas & John J. Horton & Joseph M. Golden, 2019. "Reputation Inflation," NBER Working Papers 25857, National Bureau of Economic Research, Inc.
    16. Foster, Joshua, 2022. "How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence," Journal of Business Research, Elsevier, vol. 142(C), pages 791-807.
    17. 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.
    18. Arslan Aziz & Hui Li & Rahul Telang, 2023. "The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment," Information Systems Research, INFORMS, vol. 34(2), pages 590-608, June.
    19. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, C.E.P.R. Discussion Papers.
    20. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:67:y:2021:i:7:p:4420-4445. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.