Online consumer reviews, as a major source of information and influence, are of great interest to marketing researchers and practitioners. This study investigates the effects of linguistic coordination on perceived review quality. Drawing on the elaboration likelihood model, the authors theorize that two types of linguistic coordination—topic matching (semantic component) and language style matching (lexical component)—have profound effects on perceived review quality. Utilizing natural language processing tools and a novel clustering technique to measure matching, empirical analyses based on an IMDb data set support the positive direct effects of both types of matching. Moreover, the authors find that there is a negative interaction between topic matching and language style matching in affecting perceived review quality. The findings contribute to the understanding of online review quality, and the application of natural language processing enriches the methodological tool kit available to researchers.
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