Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data

Iman Raoofpanah, Zamudio, César and Christopher Groening

Journal of Retailing and Consumer Services, 2023, vol. 72, issue C

Abstract: This study addresses gaps in online consumer review research. First, drawing on information overload and configuration theories, the authors posit that reviewer characteristics provide a context within which review readers consider the content of the review. Second, the authors employ a finite mixture model that uncovers distinct online reader segments based on their heterogeneous use of review and reviewer characteristics in determining review helpfulness. Third, the authors use demographic ZIP code-level data to provide more nuanced segment descriptions. Thus, businesses can use the study's approach to gain a better understanding of how their customer segments evaluate the helpfulness of reviews.

Keywords: Online reviews; Helpful; Finite mixture modeling; Segmentation; Review readers (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:72:y:2023:i:c:s0969698923000474

DOI: 10.1016/j.jretconser.2023.103300

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