Online Reviews and Information Overload: The Role of Selective, Parsimonious, and Concordant Top Reviews

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Publication History

Received: March 25, 2019
Revised: June 24, 2020; May 17, 2021; September 4, 2021; November 1, 2021
Accepted: November 2, 2021
Published Online as Accepted Author Version: February 16, 2022
Published Online as Articles in Advance: August 29, 2022
Published in Issue: September 1, 2022

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By empowering customers to make fitting purchases, user reviews play an important role in reducing inefficiencies in the provisioning of product information. Because of the abundance of reviews and the signals they provide, this information may become confusing and risks overloading customers. Consequently, review hosting platforms have adjusted their designs to feature a signal “distilled” from a selective set of “top reviews” and their valences. The expected ease with which customers process this signal is intended to increase their satisfaction, thus reducing dispersion in their subsequent review ratings. In this study, we analyze the influential role that top reviews and their valence play under various scenarios: when customers are overloaded by a large number of reviews, when top reviews themselves are not parsimonious in number, and when the signals from top reviews are not in concordance with that from all the other reviews. We find that the valence of top reviews plays a central role in mitigating information overload. However, the influence of those top reviews diminishes when they too pose an overload risk but is strengthened when their signal is reaffirmed by signals from all other reviews. Finally, the impact of top reviews is weaker for less popular products. 

Additional Details
Author Wael Jabr and Mohammad Saifur Rahman
Year 2022
Volume 46
Issue 3
Keywords Information overload, top reviews, signaling theory, information theory, information provisioning
Page Numbers 1517-1550
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