Year: 2025
Type: article
Abstract: This study investigates how review inconsistency influences perceived helpfulness in online restaurant reviews both in ratings and specific aspects of service attributes. Drawing on 106,464 Yelp reviews spanning 666 restaurants, we employed aspect-based sentiment analysis and Tobit regression to capture not only rating inconsistencies but also differences in sentiment toward décor, taste, service, and price. Results indicate that rating inconsistency negatively affects review helpfulness, suggesting that highly divergent ratings reduce credibility. However, aspect inconsistency shows mixed effects. Discrepancies in décor and taste positively influence perceived helpfulness by offering novel information, whereas service-related inconsistencies diminish review helpfulness, due to heightened consumer sensitivity to possible service failures. Reviewer expertise further strengthens the negative influence of inconsistency as readers expect experienced reviewers to provide objective feedback. These findings extend current research by shifting the analytical lens from individual reviews to sets of reviews, thereby capturing the relational dynamics that shape consumers’ perceptions of review credibility. The results also highlight the importance of analyzing review content by specific aspects to uncover nuanced effects. Practically, platforms can benefit from grouping reviews by attributes and alerting users to noteworthy inconsistencies, facilitating more informed consumer decision-making.
Authors: Junsung Park, Heejun Park
Institutions: Soongsil University, Yonsei University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4409667059