TY - JOUR
T1 - Fake online reviews: Literature review, synthesis, and directions for future research
AU - Wu, Yuanyuan
AU - Ngai, Wai Ting
AU - Wu, Pengkun
AU - Wu, Chong
N1 - Funding Information:
The authors are grateful for the constructive comments of the three anonymous referees on an earlier version of this paper. Yuanyuan Wu was supported in part by the Joint PhD Programmes (PolyU-HIT) leading to Dual Awards. Pengkun Wu was supported in part by the Humanities and Social Sciences Fund of Ministry of Education of China , the Natural Science Foundation of Hunan Province ( 2019JJ50403 ) and the Fundamental Research Funds for the Central Universities .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/5
Y1 - 2020/5
N2 - Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.
AB - Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.
KW - Antecedents and consequences
KW - Electronic commerce
KW - Fake review
KW - Literature review
KW - Online review
UR - http://www.scopus.com/inward/record.url?scp=85081257189&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.dss.2020.113280
DO - https://doi.org/10.1016/j.dss.2020.113280
M3 - Journal article
SN - 0167-9236
VL - 132
JO - Decision Support Systems
JF - Decision Support Systems
M1 - 113280
ER -