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Do online review readers react differently when exposed to credible versus fake online reviews?

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  • Kim, Jong Min
  • Park, Keeyeon Ki-cheon
  • Mariani, Marcello M.

Abstract

Marketing research on online reviews has attempted to understand the antecedents and consequences of review manipulation. Building on the elaboration likelihood model (ELM), this study deploys a rare dataset that allows distinguishing credible from less credible (and likely fake) online reviews by means of the online review posting policy adopted by the movie review website Naver.com. We use text analysis entailing word embedding and topic modelling techniques such as Latent Dirichlet Allocation, to capture content depth across different types of online reviews (credible vs manipulated). Furthermore, we explore how differences in the textual content of credible vs manipulated online reviews affect customer purchase decisions. Our results highlight that less credible reviews tend to contain more superficial information compared to more credible reviews, and that different levels of source credibility lead to distinctively different impacts of online reviews on box office revenue.

Suggested Citation

  • Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello M., 2023. "Do online review readers react differently when exposed to credible versus fake online reviews?," Journal of Business Research, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:jbrese:v:154:y:2023:i:c:s0148296322008426
    DOI: 10.1016/j.jbusres.2022.113377
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    References listed on IDEAS

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    Cited by:

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    4. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "Fake review detection in e-Commerce platforms using aspect-based sentiment analysis," Journal of Business Research, Elsevier, vol. 167(C).

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