Uncovering the effects of textual features on trustworthiness of online consumer reviews: A computational-experimental approach

Abstract

Abstract Online consumer reviews are word-of-mouth exchanges on the Internet that can be harnessed for decision support. Combining computational and experimental methods, the current two-part research uncovered the effects of textual features on trustworthiness of consumer reviews on TripAdvisor. Taking a bottom-up approach, Study 1 employed text mining and human rating methods to explore the salient review topics that impact review trustworthiness. Study 2 took a top-down approach by examining the textual features that drive the effects of review topics identified in Study 1 and testing them across two product categories—hotel and restaurant—in an online experiment. The findings indicate that review trustworthiness has a moderating effect on review adoption in that highly trustworthy reviews are more likely to be adopted by consumers to aid in their judgement formation. This research also explicated the role of three textual features—namely, attribute salience, review valence, and content concreteness—in review trustworthiness.