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Mind the fake reviews! Protecting consumers from deception through persuasion knowledge acquisition

Author

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  • Costa Filho, Murilo
  • Nogueira Rafael, Diego
  • Salmonson Guimarães Barros, Lucia
  • Mesquita, Eduardo

Abstract

A growing body of research has shown that while computers can effectively detect fake reviews, humans are no more accurate than chance. Since consumers strongly trust online reviews, and fake reviews are pervasive, they often make suboptimal choices. However, whether consumers can learn to detect fake reviews and whether this knowledge would help them make better-informed decisions remain open questions. We propose that learning four distinctive features of fake reviews (one-sidedness, exaggeration, personal selling style, and generic descriptions) affects consumers’ trustworthiness in them and their perceived favorability, thus affecting their purchase intentions toward the target product. Five studies support our theoretical model. We also show that one-sidedness is the most discriminating among the four features and that simply activating consumers’ current knowledge is not enough to protect them from fake reviews.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:jbrese:v:156:y:2023:i:c:s0148296322010037
    DOI: 10.1016/j.jbusres.2022.113538
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    References listed on IDEAS

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    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. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print hal-02423574, HAL.
    3. Munzel, Andreas, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 96-108.
    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. Moon, Sangkil & Kim, Moon-Yong & Bergey, Paul K., 2019. "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms," Journal of Business Research, Elsevier, vol. 102(C), pages 83-96.
    7. 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.
    8. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    9. 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.
    10. Stefan Gössling & C. Michael Hall & Ann-Christin Andersson, 2018. "The manager's dilemma: a conceptualization of online review manipulation strategies," Current Issues in Tourism, Taylor & Francis Journals, vol. 21(5), pages 484-503, March.
    11. Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    12. Anne Hamby & David Brinberg, 2018. "Cause‐Related Marketing Persuasion Knowledge: Measuring Consumers' Knowledge and Ability to Interpret CrM Promotions," Journal of Consumer Affairs, Wiley Blackwell, vol. 52(2), pages 373-392, July.
    13. Bastos, Wilson & Moore, Sarah G., 2021. "Making word-of-mouth impactful: Why consumers react more to WOM about experiential than material purchases," Journal of Business Research, Elsevier, vol. 130(C), pages 110-123.
    14. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    15. Costa, Ana & Guerreiro, João & Moro, Sérgio & Henriques, Roberto, 2019. "Unfolding the characteristics of incentivized online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 272-281.
    16. Andreas Munzel, 2015. "Malicious practice of fake reviews: Experimental insight into the potential of contextual indicators in assisting consumers to detect deceptive opinion spam," Post-Print hal-02423578, HAL.
    17. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print halshs-01522497, HAL.
    18. Bambauer-Sachse, Silke & Mangold, Sabrina, 2013. "Do consumers still believe what is said in online product reviews? A persuasion knowledge approach," Journal of Retailing and Consumer Services, Elsevier, vol. 20(4), pages 373-381.
    19. Banerjee, Snehasish, 2022. "Exaggeration in fake vs. authentic online reviews for luxury and budget hotels," International Journal of Information Management, Elsevier, vol. 62(C).
    20. 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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