Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews
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 cap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/20/2/80 |
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| author | Junsung Park Heejun Park |
| author_facet | Junsung Park Heejun Park |
| author_sort | Junsung Park |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e02dff06db249e09bd0c245dc437638 |
| institution | OA Journals |
| issn | 0718-1876 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj-art-3e02dff06db249e09bd0c245dc4376382025-08-20T02:21:03ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762025-04-012028010.3390/jtaer20020080Understanding the Impact of Inconsistency on the Helpfulness of Online ReviewsJunsung Park0Heejun Park1School of Business Administration, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of KoreaDepartment of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of KoreaThis 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.https://www.mdpi.com/0718-1876/20/2/80online customer reviewaspect-based sentiment analysisinconsistencyreviewer expertisereview helpfulness |
| spellingShingle | Junsung Park Heejun Park Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews Journal of Theoretical and Applied Electronic Commerce Research online customer review aspect-based sentiment analysis inconsistency reviewer expertise review helpfulness |
| title | Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews |
| title_full | Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews |
| title_fullStr | Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews |
| title_full_unstemmed | Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews |
| title_short | Understanding the Impact of Inconsistency on the Helpfulness of Online Reviews |
| title_sort | understanding the impact of inconsistency on the helpfulness of online reviews |
| topic | online customer review aspect-based sentiment analysis inconsistency reviewer expertise review helpfulness |
| url | https://www.mdpi.com/0718-1876/20/2/80 |
| work_keys_str_mv | AT junsungpark understandingtheimpactofinconsistencyonthehelpfulnessofonlinereviews AT heejunpark understandingtheimpactofinconsistencyonthehelpfulnessofonlinereviews |