Customer Reviews Subject to Reporting Bias: Its Influence on Customers, Firms, and Platform

ABSTRACT Customers tend to share extreme experiences more than moderate ones, a phenomenon known as reporting bias. Reporting bias diminishes the visibility of moderate experiences and polarizes customer opinions. It raises the following questions: How does reporting bias affect customers' evaluations of product quality? How should firms adjust their pricing strategies to address this reporting bias? What can online platforms do to mitigate its impact? We consider a firm selling a product of uncertain quality through an independent platform. Product quality can be high or low, and the probability of high quality is the firm's private information. Customers with heterogeneous preferences arrive sequentially and infer quality based on observed reviews. After consumption, customers decide whether to leave a review, with their review decisions subject to reporting bias. We assess the effectiveness of two common practices: review‐solicitation programs and platform interventions that automatically assign positive reviews to unreviewed transactions. We show that customers cannot learn the high‐quality probability from reviews subject to reporting bias: they make downward‐biased estimations if the high‐quality probability exceeds a threshold and upward‐biased estimations otherwise. The firm's optimal pricing ultimately converges to a static price that maximizes the expected current profit. Reporting bias hurts a high‐quality firm (i.e., a firm whose high‐quality probability is above the threshold) but benefits a low‐quality firm. While review‐solicitation programs can alleviate reporting bias, only a high‐quality firm is interested in participating. Platform intervention does not necessarily alleviate reporting bias and, worse yet, may harm high‐quality firms. Our findings suggest that online platforms should implement review‐solicitation programs to mitigate reporting bias. These programs enhance the quality of information for customers, facilitating more informed purchasing decisions and allowing high‐quality sellers to signal their product quality through participation.

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Publication Details

Journal
Naval Research Logistics (NRL)
Published
2026-02-10
DOI
https://doi.org/10.1002/nav.70056
Primary Topic
Consumer Market Behavior and Pricing
Type
article
Field-Weighted Citation Impact
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article

Customer Reviews Subject to Reporting Bias: Its Influence on Customers, Firms, and Platform

Pengfei Guo, Fengfeng Huang, Yulan Wang
Naval Research Logistics (NRL)
Consumer Market Behavior and Pricing
article

Customer Reviews Subject to Reporting Bias: Its Influence on Customers, Firms, and Platform

Pengfei Guo, Fengfeng Huang, Yulan Wang
article en

Abstract

ABSTRACT Customers tend to share extreme experiences more than moderate ones, a phenomenon known as reporting bias. Reporting bias diminishes the visibility of moderate experiences and polarizes customer opinions. It raises the following questions: How does reporting bias affect customers' evaluations of product quality? How should firms adjust their pricing strategies to address this reporting bias? What can online platforms do to mitigate its impact? We consider a firm selling a product of uncertain quality through an independent platform. Product quality can be high or low, and the probability of high quality is the firm's private information. Customers with heterogeneous preferences arrive sequentially and infer quality based on observed reviews. After consumption, customers decide whether to leave a review, with their review decisions subject to reporting bias. We assess the effectiveness of two common practices: review‐solicitation programs and platform interventions that automatically assign positive reviews to unreviewed transactions. We show that customers cannot learn the high‐quality probability from reviews subject to reporting bias: they make downward‐biased estimations if the high‐quality probability exceeds a threshold and upward‐biased estimations otherwise. The firm's optimal pricing ultimately converges to a static price that maximizes the expected current profit. Reporting bias hurts a high‐quality firm (i.e., a firm whose high‐quality probability is above the threshold) but benefits a low‐quality firm. While review‐solicitation programs can alleviate reporting bias, only a high‐quality firm is interested in participating. Platform intervention does not necessarily alleviate reporting bias and, worse yet, may harm high‐quality firms. Our findings suggest that online platforms should implement review‐solicitation programs to mitigate reporting bias. These programs enhance the quality of information for customers, facilitating more informed purchasing decisions and allowing high‐quality sellers to signal their product quality through participation.

Naval Research Logistics (NRL)
Hong Kong Polytechnic University (HK), University of Electronic Science and Technology of China (CN), City University of Hong Kong (HK)
Openalex Percentile: Top 66%
Consumer Market Behavior and Pricing
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