IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v31y2020i3p950-971.html
   My bibliography  Save this article

A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?

Author

Listed:
  • Uttara M. Ananthakrishnan

    (Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Beibei Li

    (Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Michael D. Smith

    (Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The growing interest in online product reviews for legitimate promotion has been accompanied by an increase in fraudulent reviews. However, beyond algorithms for initial fraud detection, little is known about what review portals should do with fraudulent reviews after detecting them. In this paper, we address this question by studying how consumers respond to potentially fraudulent reviews and how review portals can leverage this knowledge to design better fraud management policies. To do this, we combine theoretical development from the trust literature with randomized experiments and statistical analysis using large-scale data from Yelp. We find that consumers tend to increase their trust in the information provided by review portals when the portal displays fraudulent reviews along with non-fraudulent reviews, as opposed to the common practice of censoring suspected fraudulent reviews. The impact of fraudulent reviews on consumers’ decision-making process increases with the uncertainty in the initial evaluation of product quality. We also find that consumers do not effectively process the content of fraudulent reviews (negative or positive). This result furthers the case for a decision heuristic that will incorporate the motivational differences between positive fraudulent reviews and negative fraudulent reviews to effectively aid consumers’ decision making.

Suggested Citation

  • Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    • Handle: RePEc:inm:orisre:v:31:y:2020:i:3:p:950-971
    DOI: 10.1287/isre.2020.0925
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2020.0925
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2020.0925?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. Smith, Vernon L & Walker, James M, 1993. "Monetary Rewards and Decision Cost in Experimental Economics," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 245-261, April.
    3. Gefen, David, 2000. "E-commerce: the role of familiarity and trust," Omega, Elsevier, vol. 28(6), pages 725-737, December.
    4. Eric J. Johnson & John W. Payne, 1985. "Effort and Accuracy in Choice," Management Science, INFORMS, vol. 31(4), pages 395-414, April.
    5. Erik Brynjolfsson & Michael D. Smith, 2000. "Frictionless Commerce? A Comparison of Internet and Conventional Retailers," Management Science, INFORMS, vol. 46(4), pages 563-585, April.
    6. 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.
    7. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2012. "Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science, INFORMS, vol. 31(3), pages 493-520, May.
    8. Wang Zhongmin, 2010. "Anonymity, Social Image, and the Competition for Volunteers: A Case Study of the Online Market for Reviews," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-35, May.
    9. repec:cup:judgdm:v:5:y:2010:i:5:p:411-419 is not listed on IDEAS
    10. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    11. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    12. D. Harrison McKnight & Vivek Choudhury & Charles Kacmar, 2002. "Developing and Validating Trust Measures for e-Commerce: An Integrative Typology," Information Systems Research, INFORMS, vol. 13(3), pages 334-359, September.
    13. Stefano DellaVigna & Eliana La Ferrara, 2010. "Detecting Illegal Arms Trade," American Economic Journal: Economic Policy, American Economic Association, vol. 2(4), pages 26-57, November.
    14. 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.
    15. Paul A. Pavlou & David Gefen, 2004. "Building Effective Online Marketplaces with Institution-Based Trust," Information Systems Research, INFORMS, vol. 15(1), pages 37-59, March.
    16. Michael Luca & Jonathan Smith, 2013. "Salience in Quality Disclosure: Evidence from the U.S. News College Rankings," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(1), pages 58-77, March.
    17. Siddharth Suri & Duncan J Watts, 2011. "Cooperation and Contagion in Web-Based, Networked Public Goods Experiments," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-18, March.
    18. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    19. 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)

    Citations

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


    Cited by:

    1. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    3. Yasui, Yuta, 2021. "Controlling Fake Reviews," MPRA Paper 108177, University Library of Munich, Germany.
    4. 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).
    5. Chan, Haksin & Yang, Morgan X. & Zeng, Kevin J., 2022. "Bolstering ratings and reviews systems on multi-sided platforms: A co-creation perspective," Journal of Business Research, Elsevier, vol. 139(C), pages 208-217.
    6. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    7. Mardumyan, Anna & Siret, Iris, 2023. "When review verification does more harm than good: How certified reviews determine customer–brand relationship quality," Journal of Business Research, Elsevier, vol. 160(C).

    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. 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.
    2. 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.
    3. 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.
    4. 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).
    5. Marios Kokkodis & Theodoros Lappas & Gerald C. Kane, 2022. "Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2943-2961, July.
    6. 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.
    7. 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.
    8. Tao Lu & May Yuan & Chong (Alex) Wang & Xiaoquan (Michael) Zhang, 2022. "Histogram Distortion Bias in Consumer Choices," Management Science, INFORMS, vol. 68(12), pages 8963-8978, December.
    9. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    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. 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).
    14. 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.
    15. 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).
    16. Marios Kokkodis, 2021. "Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online Work," Information Systems Research, INFORMS, vol. 32(3), pages 688-712, September.
    17. 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.
    18. 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.
    19. Danish H. Saifee & Zhiqiang (Eric) Zheng & Indranil R. Bardhan & Atanu Lahiri, 2020. "Are Online Reviews of Physicians Reliable Indicators of Clinical Outcomes? A Focus on Chronic Disease Management," Information Systems Research, INFORMS, vol. 31(4), pages 1282-1300, December.
    20. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.

    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:31:y:2020:i:3:p:950-971. 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.