Journal article
Understanding the determinants of online review helpfulness: A meta-analytic investigation
Decision Support Systems, Vol.102, pp.1-11
10/2017
DOI: 10.1016/j.dss.2017.06.007
Abstract
Online consumer reviews can help customers reduce uncertainty and risks faced in online shopping. However, the studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived online review helpfulness. All review related determinants (i.e., review depth, review readability, linear review rating, quadratic review rating, review age) and two reviewer related determinants (i.e., reviewer information disclosure and reviewer expertise) are found to have inconsistent conclusions on how they affect perceived review helpfulness. We conduct a meta-analysis to examine those determinant factors in order to reconcile the contradictory findings about their influence on perceived review helpfulness. The meta-analysis results affirm that review depth, review age, reviewer information disclosure, and reviewer expertise have positive influences on review helpfulness. Review readability and review rating are found to have no significant influence on review helpfulness. Moreover, we find that helpfulness measurement, online review platform, and product type are the three factors that cause mixed findings in extant research.
•We review extant research about the determinant factors of perceived online review helpfulness.•We conducted a meta-analysis to reconcile the contradictory findings on the influence of determinants on review helpfulness.•Helpfulness measurement, online review source, and product type are found to be responsible for most of the mixed findings.•Our findings help both sellers and consumers better identify helpful online reviews and improve the efficiency of decision-making.
Details
- Title: Subtitle
- Understanding the determinants of online review helpfulness: A meta-analytic investigation
- Creators
- Hong Hong - School of Management, Xiamen University, Xiamen, Fujian, ChinaDi Xu - School of Management, Xiamen University, Xiamen, Fujian, ChinaG. Alan Wang - Department of Business Information Technology, Virginia Tech, Blacksburg, VA, United StatesWeiguo Fan - Department of Accounting and Information Systems, Virginia Tech, Blacksburg, VA, United States
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.102, pp.1-11
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.dss.2017.06.007
- ISSN
- 0167-9236
- eISSN
- 1873-5797
- Grant note
- DOI: 10.13039/501100004543, name: China Scholarship Council, award: 201506310121; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 71572122, 71671153, 71671154, 71531013
- Language
- English
- Date published
- 10/2017
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083238602771
Metrics
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