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Fake Reviews in Online Platforms and the Effort to Fight Them

Author

Listed:
  • Juan Pedro Aznar-Alarcón
  • Oriol Anguera-Torrell

Abstract

This paper proposes a model in which oligopolistic firms selling through an online platform can invest in creating positive fake reviews to increase their reputation and negative ones to harm that of their competitors. Therefore, oligopolistic firms’ demand depends on the amount of positive and negative fake reviews. In this context, the online platform optimally chooses the effort to fight fake reviews and the fee it charges to online sellers for each transaction. The novelty of the model lies in incorporating the online platform’s role in fighting fake reviews and its interplay with sellers’ strategic behaviour. The model’s main result is that the platform’s effort has a positive impact not only on consumers’ surplus but also on the oligopolistic firms’ profitability. In its turn, the platform’s optimal effort depends on exogenous parameters, including the demand’s sensitivity to fake reviews. JEL Classifications: D21, D43, L13, L81

Suggested Citation

  • Juan Pedro Aznar-Alarcón & Oriol Anguera-Torrell, 2023. "Fake Reviews in Online Platforms and the Effort to Fight Them," Studies in Microeconomics, , vol. 11(2), pages 235-245, August.
  • Handle: RePEc:sae:miceco:v:11:y:2023:i:2:p:235-245
    DOI: 10.1177/23210222211051470
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    References listed on IDEAS

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    1. Keisuke Hattori & Keisaku Higashida, 2012. "Misleading advertising in duopoly," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 45(3), pages 1154-1187, August.
    2. Paolo Garella & Emmanuel Petrakis, 2008. "Minimum quality standards and consumers’ information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 36(2), pages 283-302, August.
    3. Hamilton, Stephen F., 2009. "Informative advertising in differentiated oligopoly markets," International Journal of Industrial Organization, Elsevier, vol. 27(1), pages 60-69, January.
    4. Maria Alipranti & Evangelos Mitrokostas & Emmanuel Petrakis, 2013. "Comparative versus Informative Advertising in Oligopolistic Markets," Working Papers 1301, University of Crete, Department of Economics.
    5. Boris Knapp, 2021. "Fake Reviews and Naive Consumers," Vienna Economics Papers 2102, University of Vienna, Department of Economics.
    6. Lirong Chen & Wenli Li & Hao Chen & Shidao Geng, 2019. "Detection of Fake Reviews: Analysis of Sellers’ Manipulation Behavior," Sustainability, MDPI, vol. 11(17), pages 1-13, September.
    7. Erickson, Gary M., 2009. "An oligopoly model of dynamic advertising competition," European Journal of Operational Research, Elsevier, vol. 197(1), pages 374-388, August.
    8. Boris Knapp, 2021. "Fake Reviews and Naive Consumers," Vienna Economics Papers vie2102, University of Vienna, Department of Economics.
    9. Matsumura, Toshihiro & Sunada, Takeaki, 2013. "Advertising competition in a mixed oligopoly," Economics Letters, Elsevier, vol. 119(2), pages 183-185.
    10. 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.
    11. 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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    More about this item

    Keywords

    Fake reviews; online reputation; online platforms; oligopolistic competition;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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