Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/119096
DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorHuang, Fen_US
dc.creatorGuo, Pen_US
dc.creatorWang, Yen_US
dc.date.accessioned2026-06-03T01:49:38Z-
dc.date.available2026-06-03T01:49:38Z-
dc.identifier.issn0894-069Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/119096-
dc.language.isoenen_US
dc.publisherJohn Wiley & Sonsen_US
dc.subjectPricingen_US
dc.subjectQuality inferenceen_US
dc.subjectReporting biasen_US
dc.subjectReviewsen_US
dc.titleCustomer reviews subject to reporting bias : its influence on customers, firms, and platformen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1002/nav.70056en_US
dcterms.abstractCustomers 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.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationNaval research logistics, First published: 10 February 2026, Early View, https://doi.org/10.1002/nav.70056en_US
dcterms.isPartOfNaval research logisticsen_US
dcterms.issued2026-
dc.identifier.scopus2-s2.0-105029756020-
dc.identifier.eissn1520-6750en_US
dc.description.validate202606 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG001732/2026-04-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the National Natural Science Foundation of China (72101048, 72032001, 72571049), the Research Grants Council of Hong Kong (RGC Reference Number: CityU 11507124, PolyU 15503022), and Faculty of Business, The Hong Kong Polytechnic University (Project ID: P0049231).en_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000 00 00 (to be updated)en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
Open Access Information
Status embargoed access
Embargo End Date 0000 00 00 (to be updated)
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.