IDEAS home Printed from https://ideas.repec.org/a/eee/joreco/v84y2025ics0969698924004454.html
   My bibliography  Save this article

Mining the truth: A text mining approach to understanding perceived deceptive counterfeits and online ratings

Author

Listed:
  • Moqbel, Murad
  • Jain, Aarushi

Abstract

Research investigating the consequences of perceived deceptive counterfeit products is of pressing concern yet remains insufficiently explored in the current academic landscape. Through the lens of cognitive appraisal theory and language expectancy theory, the present study investigates how perceived deceptive counterfeit product reviews can impact consumer behavior in terms of product rating directly and indirectly (by impeding positive emotions). Utilizing the natural language processing text mining approach with 67,981 Amazon consumer reviews, the study reveals that perceived deceptive counterfeit product reviews reduce product rating directly as well as through the mediation of decreased positive emotions. Furthermore, the study finds a significant role of text length as a buffering factor in the relationship between perceived deceptive counterfeit product reviews and product ratings. Theoretical and practical implications are discussed.

Suggested Citation

  • Moqbel, Murad & Jain, Aarushi, 2025. "Mining the truth: A text mining approach to understanding perceived deceptive counterfeits and online ratings," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:joreco:v:84:y:2025:i:c:s0969698924004454
    DOI: 10.1016/j.jretconser.2024.104149
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969698924004454
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretconser.2024.104149?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.

    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:joreco:v:84:y:2025:i:c:s0969698924004454. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://www.journals.elsevier.com/journal-of-retailing-and-consumer-services .

    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.