Abstract

http://ssrn.com/abstract=2266053
 
 

References (27)



 


 



On Information Distortions in Online Ratings


Omar Besbes


Columbia Business School - Decision Risk and Operations

Marco Scarsini


LUISS, Dipartimento di Economia e Finanza

May 16, 2013

Columbia Business School Research Paper No. 13-36

Abstract:     
Consumer reviews and ratings of products and services have become ubiquitous on the internet. This paper analyzes the implications of the sequential nature of reports on the accuracy of their representation of the true underlying quality of the service they rate. We consider a sequence of consumers arriving sequentially over time and reporting a grade for some service. Upon arrival to the system, the consumer develops a sincere rating and also observes the average of past ratings. She then provides a report based on these two pieces of information. We analyze how different behavioral models with regard to how consumers account for existing reviews impact the gap one may observe between reports' statistics and those of the true underlying quality. We show that biases arise: in general, the long-run average might be different from the the true average quality of the service. We establish that the worst-case gap can be (arbitrarily) large for herding reporting mechanisms while it is always bounded for compensating mechanisms. Despite such distortions, we show that structure exists with regard to the relative ordering of alternatives: the long-run averages are monotone in the stochastic order of the true rating. We then analyze the potential for manipulation as a function of the reporting behavior.

Number of Pages in PDF File: 26

Keywords: online reviews, quality biases, stochastic approximation, stochastic order, sequential analysis, manipulation

working papers series





Download This Paper

Date posted: May 17, 2013  

Suggested Citation

Besbes, Omar and Scarsini, Marco, On Information Distortions in Online Ratings (May 16, 2013). Columbia Business School Research Paper No. 13-36. Available at SSRN: http://ssrn.com/abstract=2266053 or http://dx.doi.org/10.2139/ssrn.2266053

Contact Information

Omar Besbes (Contact Author)
Columbia Business School - Decision Risk and Operations ( email )
New York, NY
United States

Marco Scarsini
LUISS, Dipartimento di Economia e Finanza ( email )
Viale Romania 32
Rome, RM 00197
Italy
Feedback to SSRN


Paper statistics
Abstract Views: 590
Downloads: 165
Download Rank: 106,938
References:  27

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo4 in 0.328 seconds