IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v33y2022i1p224-243.html
   My bibliography  Save this article

An Economic Analysis of Rebates Conditional on Positive Reviews

Author

Listed:
  • Jianqing Chen

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

  • Zhiling Guo

    (School of Computing and Information Systems, Singapore Management University, Singapore 178902)

  • Jian Huang

    (School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China)

Abstract

Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We find that the seller’s optimal price and rebate decisions critically depend on both the review-posting and moral costs. The seller adopts the no-rebate strategy when the review-posting cost is low but the moral cost is high, the low-rebate strategy when the review-posting cost is high or when the review-posting cost is intermediate and the moral cost is high, and the high-rebate strategy when the review-posting cost is not too high and the moral cost is low. Our results suggest that it is not always profitable for strategic sellers to adopt the conditional-rebate strategy. Even if the conditional-rebate strategy is adopted, it does not always result in fake reviews. Furthermore, we find that, compared with the benchmark of no rebate, conditional rebates do not always hurt consumer surplus or social welfare. When a low (high) rebate is offered, if the review-posting cost is not too low (not very high), the conditional-rebate strategy can even lead to higher consumer surplus and social welfare. Our findings shed new light on the platform-policy debate about the fake-review phenomenon induced by conditional rebates.

Suggested Citation

  • Jianqing Chen & Zhiling Guo & Jian Huang, 2022. "An Economic Analysis of Rebates Conditional on Positive Reviews," Information Systems Research, INFORMS, vol. 33(1), pages 224-243, March.
  • Handle: RePEc:inm:orisre:v:33:y:2022:i:1:p:224-243
    DOI: 10.1287/isre.2021.1048
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2021.1048
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2021.1048?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. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    2. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    3. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
    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. Xianghua Lu & Sulin Ba & Lihua Huang & Yue Feng, 2013. "Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews," Information Systems Research, INFORMS, vol. 24(3), pages 596-612, September.
    6. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    7. Yabing Jiang & Hong Guo, 2015. "Design of Consumer Review Systems and Product Pricing," Information Systems Research, INFORMS, vol. 26(4), pages 714-730, December.
    8. 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.
    9. Yang Liu & Juan Feng & Xiuwu Liao, 2017. "When Online Reviews Meet Sales Volume Information: Is More or Accurate Information Always Better?," Information Systems Research, INFORMS, vol. 28(4), pages 723-743, December.
    10. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    11. Rajiv Lal & Miklos Sarvary, 1999. "When and How Is the Internet Likely to Decrease Price Competition?," Marketing Science, INFORMS, vol. 18(4), pages 485-503.
    12. Lingfang (Ivy) Li & Erte Xiao, 2014. "Money Talks: Rebate Mechanisms in Reputation System Design," Management Science, INFORMS, vol. 60(8), pages 2054-2072, August.
    13. Gerstner, Eitan & Hess, James D, 1991. "A Theory of Channel Price Promotions," American Economic Review, American Economic Association, vol. 81(4), pages 872-886, September.
    14. Lin Hao & Yong Tan, 2019. "Who Wants Consumers to Be Informed? Facilitating Information Disclosure in a Distribution Channel," Service Science, INFORMS, vol. 30(1), pages 34-49, March.
    15. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    16. Jana Gallus, 2017. "Fostering Public Good Contributions with Symbolic Awards: A Large-Scale Natural Field Experiment at Wikipedia," Management Science, INFORMS, vol. 63(12), pages 3999-4015, December.
    17. Steffen Zimmermann & Philipp Herrmann & Dennis Kundisch & Barrie R. Nault, 2018. "Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand," Information Systems Research, INFORMS, vol. 29(4), pages 984-1002, December.
    18. 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.
    19. Gary Charness & Celia Blanco-Jimenez & Lara Ezquerra & Ismael Rodriguez-Lara, 2019. "Cheating, incentives, and money manipulation," Experimental Economics, Springer;Economic Science Association, vol. 22(1), pages 155-177, March.
    Full references (including those not matched with items on IDEAS)

    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. Jürgen Neumann, 2021. "When Biased Ratings Benefit the Consumer - An Economic Analysis of Online Ratings in Markets with Variety-Seeking Consumers," Working Papers Dissertations 77, Paderborn University, Faculty of Business Administration and Economics.
    3. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    4. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2022. "Promotions of online reviews from a channel perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    5. Guo, Qiaozhen & Chen, Ying-Ju & Huang, Wei, 2022. "Dynamic pricing of new experience products with dual-channel social learning and online review manipulations," Omega, Elsevier, vol. 109(C).
    6. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    7. Duan, Yongrui & Liu, Tonghui & Mao, Zhixin, 2022. "How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    8. Hongpeng Wang & Rong Du & Jin Li & Weiguo Fan, 2020. "Subdivided or aggregated online review systems: Which is better for online takeaway vendors?," Electronic Commerce Research, Springer, vol. 20(4), pages 915-944, December.
    9. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    10. Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
    11. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    12. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    13. Yabing Jiang & Hong Guo, 2015. "Design of Consumer Review Systems and Product Pricing," Information Systems Research, INFORMS, vol. 26(4), pages 714-730, December.
    14. Zhang, Tao & Li, Gang & Cheng, T.C.E. & Lai, Kin Keung, 2017. "Welfare economics of review information: Implications for the online selling platform owner," International Journal of Production Economics, Elsevier, vol. 184(C), pages 69-79.
    15. 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.
    16. Cui Zhao & Xiaoshuai Peng & Zhendong Li, 2023. "The influence of online customer reviews on two-stage product strategy in a competitive market," Annals of Operations Research, Springer, vol. 326(1), pages 411-503, July.
    17. Bikram P. Ghosh & Michael R. Galbreth, 2023. "The weight of the crowd, social information credibility, and firm strategy," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1079-1095, April.
    18. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    19. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    20. Wang, Jiayun & Shum, Stephen & Feng, Gengzhong, 2022. "Supplier’s pricing strategy in the presence of consumer reviews," European Journal of Operational Research, Elsevier, vol. 296(2), pages 570-586.

    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:orisre:v:33:y:2022:i:1:p:224-243. 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.