Dujuan Wang , Qianyang Xia , Yi Feng , T.C.E. Cheng
{"title":"Unravelling the effects of two inconsistencies on online review helpfulness: Evidence from TripAdvisor","authors":"Dujuan Wang , Qianyang Xia , Yi Feng , T.C.E. Cheng","doi":"10.1016/j.dss.2025.114450","DOIUrl":null,"url":null,"abstract":"<div><div>Facing the challenge of information overload, some travel websites have introduced systems for travelers to vote on helpful reviews, prompting researchers to focus on the determinants of review helpfulness. While evaluations from multiple reviews may provide travelers with more perspectives, inconsistent information within the reviews may cause confusion. Studies exploring the effects of multiple inconsistencies on review helpfulness are relatively rare. Grounded in the heuristic-systematic model, we explore the relationships between systematic cues, i.e., review and rating inconsistencies, and review helpfulness. We also investigate how reviewer expertise and hotel rank moderate these inconsistency-helpfulness links, serving as heuristic cues. Applied to a real-world hotel dataset collected from TripAdvisor, our findings show that review inconsistency negatively influences review helpfulness, while rating inconsistency positively affects it. Furthermore, we find that reviewer expertise negatively moderates the review and rating inconsistency-helpfulness links, while hotels that rank low positively moderate both links. These findings offer both theoretical insights for research and practical implications for consumers, reviewers, and platform managers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"193 ","pages":"Article 114450"},"PeriodicalIF":6.7000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Support Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016792362500051X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Facing the challenge of information overload, some travel websites have introduced systems for travelers to vote on helpful reviews, prompting researchers to focus on the determinants of review helpfulness. While evaluations from multiple reviews may provide travelers with more perspectives, inconsistent information within the reviews may cause confusion. Studies exploring the effects of multiple inconsistencies on review helpfulness are relatively rare. Grounded in the heuristic-systematic model, we explore the relationships between systematic cues, i.e., review and rating inconsistencies, and review helpfulness. We also investigate how reviewer expertise and hotel rank moderate these inconsistency-helpfulness links, serving as heuristic cues. Applied to a real-world hotel dataset collected from TripAdvisor, our findings show that review inconsistency negatively influences review helpfulness, while rating inconsistency positively affects it. Furthermore, we find that reviewer expertise negatively moderates the review and rating inconsistency-helpfulness links, while hotels that rank low positively moderate both links. These findings offer both theoretical insights for research and practical implications for consumers, reviewers, and platform managers.
期刊介绍:
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).