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

Murad Moqbel and Aarushi Jain

Journal of Retailing and Consumer Services, 2025, vol. 84, issue C

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.

Keywords: Online product reviews; Cognitive appraisal theory; Language expectancy theory; Deceptive counterfeit products; Positive emotions; Text mining; Online ratings (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:84:y:2025:i:c:s0969698924004454

DOI: 10.1016/j.jretconser.2024.104149

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