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

• 
  By Aarushi Jain
  Assistant Professor

  Co-Authors
  Murad Moqbel, Associate Professor Of Information Systems, University Of Texas Rio Grande Valley
  

  Journal : Journal of Retailing and Consumer Services
  Publisher : Elsevier

Article citation: Moqbel, M., & Jain, A. (2025). Mining the truth: A text mining approach to understanding perceived deceptive counterfeits and online ratings. Journal of Retailing and Consumer Services, 84, 104149.

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.