Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2011
Abstract
Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the mean consumer rating of that product. Hence, manipulation decreases the informativeness of online reviews. Furthermore though consumers understand the existence of manipulation, they can only partially correct it based on their expectation of the overall level of manipulation. Hence, vendors are able to change the final outcomes by manipulating online reviewers. In addition, we demonstrate that at the early stages, after an item is released to the Amazon market, both price and reviews serve as quality indicators. Thus, at this stage, a higher price leads to an increase in sales instead of a decrease in sales. At the late stages, price assumes its normal role, meaning a higher price leads to a decrease in sales. Finally, on average, there is a higher level of manipulation on Barnes & Noble than on Amazon.
Keywords
Online word of mouth, Manipulation, Self-selection, Price, Time-series
Discipline
Databases and Information Systems | Information Security
Research Areas
Cybersecurity
Publication
Decision Support Systems
Volume
50
Issue
3
First Page
614
Last Page
626
ISSN
0167-9236
Identifier
10.1016/j.dss.2010.08.012
Publisher
Elsevier
Embargo Period
12-3-2020
Citation
HU, Nan; LIU, Ling; and SAMBAMURTHY, Vallbh.
Fraud detection in online consumer reviews. (2011). Decision Support Systems. 50, (3), 614-626.
Available at: https://ink.library.smu.edu.sg/sis_research/5399
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.dss.2010.08.012