Abstract

This study identifies "always-the-same-rating" reviewers (ASRs), that is, reviewers who give the same star rating for all reviewed products and who write many reviews on Amazon. This study identifies ASRs in 29 product categories by analyzing 230 million individual reviews on Amazon. The findings of this study show that: 1) all product categories contain reviews written by ASRs; 2) the majority of ASRs (99.99%) give the same star rating for all reviewed products in all categories; and 3) the rating distribution of ASRs' reviews is extremely skewed toward the five-star rating (98.02%). The digital music category, in particular, shows a high share and volume of ASRs among all categories, making it an ideal focal category for further empirical analysis of ASRs. This study empirically demonstrates that star rating, the helpfulness of reviews, the length of headline and review, prior reviews, and holidays are potential indicators of reviews written by ASRs. The finding shows that reviews from verified and nonverified ASRs respond differently to some potential indicators. This article is the first step toward identifying irregular reviewer groups and their abnormal rating patterns, which would help in the segmentation of online consumers and a better understanding of online consumer review behaviors.

Department(s)

Economics

Keywords and Phrases

Behavior; Big Data; data mining; digital marketing; electronic commerce; online product review; Pipelines; Product design; Python; Quality assessment; social computing; Stars; Uncertainty

International Standard Serial Number (ISSN)

2329-924X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Institute of Electrical and Electronics Engineers; Computer Society; Systems, Man, and Cybernetics Society, All rights reserved.

Publication Date

01 Jan 2023

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Included in

Economics Commons

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