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

Kim, J. M., Park, K. K.-c. and Mariani, M. M. (2023) Do online review readers react differently when exposed to credible versus fake online reviews? Journal of Business Research, 154. 113377. ISSN 0148-2963

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To link to this item DOI: 10.1016/j.jbusres.2022.113377


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 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.

Item Type:Article
Divisions:Henley Business School > Leadership, Organisations and Behaviour
ID Code:108345


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