IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v87y2018icp24-35.html
   My bibliography  Save this article

Manufactured opinions: The effect of manipulating online product reviews

Author

Listed:
  • Zhuang, Mengzhou
  • Cui, Geng
  • Peng, Ling

Abstract

Previous research assumes that consumers can detect and discount the manipulation of online product reviews or are oblivious to such practices. We posit that the equilibrium occurs due to the cues of manipulation, consumer suspicion and their expertise. Our analysis of hotel occupancy data shows that the effect of adding positive reviews and deleting negative reviews on sales exhibits an inverted U-curve. Moreover, weak brands suffer more from excessive adding. Our laboratory experiments find that adding affects consumer purchase intention, but it also arouses suspicion, which exerts a negative mediating effect. Deleting is more disguised and difficult to be suspected. Novices are more influenced by manipulations compared with their experienced counterparts. Thus, contrary to the popular belief of “fake it until you make it,” excessive adding leads to consumer distrust and may backfire. Deleting exacerbates information asymmetry and results in adverse selection, thus warrants restraint and regulation.

Suggested Citation

  • Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    • Handle: RePEc:eee:jbrese:v:87:y:2018:i:c:p:24-35
    DOI: 10.1016/j.jbusres.2018.02.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296318300912
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2018.02.016?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kirmani, Amna, 1990. "The Effect of Perceived Advertising Costs on Brand Perceptions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(2), pages 160-171, September.
    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. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. Campbell, Margaret C & Kirmani, Amna, 2000. "Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(1), pages 69-83, June.
    5. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    6. 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.
    7. Moorthy, Sridhar & Hawkins, Scott A., 2005. "Advertising repetition and quality perception," Journal of Business Research, Elsevier, vol. 58(3), pages 354-360, March.
    8. Joseph E. Stiglitz, 2002. "Information and the Change in the Paradigm in Economics," American Economic Review, American Economic Association, vol. 92(3), pages 460-501, June.
    9. Thales Teixeira & Rosalind Picard & Rana el Kaliouby, 2014. "Why, When, and How Much to Entertain Consumers in Advertisements? A Web-Based Facial Tracking Field Study," Marketing Science, INFORMS, vol. 33(6), pages 809-827, November.
    10. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    11. Reimer, Thomas & Benkenstein, Martin, 2016. "When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews," Journal of Business Research, Elsevier, vol. 69(12), pages 5993-6001.
    12. George A. Akerlof & Robert J. Shiller, 2015. "Phishing for Phools: The Economics of Manipulation and Deception," Economics Books, Princeton University Press, edition 1, number 10534.
    13. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    14. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    15. 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.
    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. Hui Zhao & Xiaoyuan Wang & Debing Ni & Kevin W. Li, 2023. "The Quality-Signaling Role of Manipulated Consumer Reviews," Group Decision and Negotiation, Springer, vol. 32(3), pages 503-536, June.
    2. Koukova, Nevena T. & Wang, Rebecca Jen-Hui & Isaac, Mathew S., 2023. "“If you loved our product”: Do conditional review requests harm retailer loyalty?," Journal of Retailing, Elsevier, vol. 99(1), pages 85-101.
    3. Harrison-Walker, L. Jean & Jiang, Ying, 2023. "Suspicion of online product reviews as fake: Cues and consequences," Journal of Business Research, Elsevier, vol. 160(C).
    4. Anna Kerkhof & Johannes Münster, 2021. "Detecting coverage bias in user-generated content," ECONtribute Discussion Papers Series 057, University of Bonn and University of Cologne, Germany.
    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. Plotkina, Daria & Munzel, Andreas & Pallud, Jessie, 2020. "Illusions of truth—Experimental insights into human and algorithmic detections of fake online reviews," Journal of Business Research, Elsevier, vol. 109(C), pages 511-523.
    7. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    8. Anna Kerkhof & Johannes Münster, 2021. "Detecting Coverage Bias in User-Generated Content," CESifo Working Paper Series 8844, CESifo.
    9. Pyle, Martin A. & Smith, Andrew N. & Chevtchouk, Yanina, 2021. "In eWOM we trust: Using naïve theories to understand consumer trust in a complex eWOM marketspace," Journal of Business Research, Elsevier, vol. 122(C), pages 145-158.
    10. 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.
    11. Costa Filho, Murilo & Nogueira Rafael, Diego & Salmonson Guimarães Barros, Lucia & Mesquita, Eduardo, 2023. "Mind the fake reviews! Protecting consumers from deception through persuasion knowledge acquisition," Journal of Business Research, Elsevier, vol. 156(C).
    12. 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.
    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. Naveen Kumar & Liangfei Qiu & Subodha Kumar, 2018. "Exit, Voice, and Response on Digital Platforms: An Empirical Investigation of Online Management Response Strategies," Information Systems Research, INFORMS, vol. 29(4), pages 849-870, December.
    15. Reimer, Thomas & Benkenstein, Martin, 2016. "When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews," Journal of Business Research, Elsevier, vol. 69(12), pages 5993-6001.
    16. 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.
    17. Travis Dyer & Eunjee Kim, 2021. "Anonymous Equity Research," Journal of Accounting Research, Wiley Blackwell, vol. 59(2), pages 575-611, May.
    18. Oksana Loginova & Andrea Mantovani, 2019. "Price competition in the presence of a web aggregator," Journal of Economics, Springer, vol. 126(1), pages 43-73, January.
    19. Li, Yuanshuo & Zhang, Zili & Pedersen, Susanne & Liu, Xudong & Zhang, Ziqiong, 2023. "The influence of relative popularity on negative fake reviews: A case study on restaurant reviews," Journal of Business Research, Elsevier, vol. 162(C).
    20. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.

    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:eee:jbrese:v:87:y:2018:i:c:p:24-35. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    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.