Online Reviews and Information Overload: The Role of Selective, Parsimonious, and Concordant Top Reviews
2022 | Management Information Systems Quarterly | Citations: 0
Authors: Jabr, Wael; Rahman, Mohammad
Abstract: By empowering customers to make fitting purchases, user reviews play an importan ...
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Abstract: 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.
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Semantic filters:
inverse propensity weighting
Topics:
information overload electronic commerce online review missing data decision making
Methods:
computational algorithm experimental group longitudinal research field experiment autocorrelation analysis
