Revisiting Review Depth in Search for Helpful Online Reviews

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dc.contributor.authorDorwat, Shardul
dc.contributor.authorNamvar, Morteza
dc.contributor.authorAkhlaghpour , Saeed
dc.date.accessioned2022-12-27T19:02:43Z
dc.date.available2022-12-27T19:02:43Z
dc.date.issued2023-01-03
dc.description.abstractThis study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.272
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102903
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining, and Machine Learning for Social Media
dc.subjectconsumer decision-making
dc.subjectreadability
dc.subjectreview depth
dc.subjectreview helpfulness
dc.subjecttwo-sided argument
dc.titleRevisiting Review Depth in Search for Helpful Online Reviews
dc.type.dcmitext
prism.startingpage2200

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