Fraud Detection in Online Consumer Reviews

Decision Support Systems, 2010

35 Pages Posted: 16 Jan 2009 Last revised: 13 Apr 2010

See all articles by Nan Hu

Nan Hu

Stevens Institute of Technology - School of Business

Ling Liu

University of Wisconsin Eau Claire

Vallabh Sambamurthy

Michigan State University - Department of Accounting & Information Systems

Date Written: January 31, 2008

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, Quality, Manipulation, Self-Selection, Price, Time-series, Panel

Suggested Citation

Hu, Nan and Liu, Ling and Sambamurthy, Vallabhajosyula, Fraud Detection in Online Consumer Reviews (January 31, 2008). Decision Support Systems, 2010. Available at SSRN: https://ssrn.com/abstract=1328143

Nan Hu (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Ling Liu

University of Wisconsin Eau Claire ( email )

Eau Claire, WI 54702
United States

Vallabhajosyula Sambamurthy

Michigan State University - Department of Accounting & Information Systems ( email )

270 North Business Complex
East Lansing, MI 48824-1034
United States

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