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Profiling diverse reviewer segments using online reviews of service industries

Author

Listed:
  • Nima Jalali

    (Belk College of Business, The University of North Carolina at Charlotte)

  • Sangkil Moon

    (Belk College of Business, The University of North Carolina at Charlotte)

  • Moon-Yong Kim

    (Belk College of Business, The University of North Carolina at Charlotte
    Hankuk University of Foreign Studies)

Abstract

Consumers often rely on online review sites (e.g., Yelp, TripAdvisor) to read reviews on service products they may consider choosing. Despite the usefulness of such reviews, consumers may have difficulty finding reviews suitable for their own preferences because those reviews possess significant preference heterogeneity among reviewers. Differences in the four dimensions of individual reviewers (Experience, Productivity, Details, and Criticalness) lead them to have differential impacts on various consumers. Thus, this research is aimed at understanding the profiles of multiple influential reviewer segments in the service industries under two fundamental evaluation principles (Quality and Likeness). Based on the results, we theorize that reviewer influence arises from writing either reviews of high quality (Quality) or reviews reflecting a significant segment of consumers’ common preferences (Likeness). Using two service product categories (restaurants and hotels), we empirically identify and profile four specific influential review segments: (1) top-tier quality reviewers, (2) second-tier quality reviewers, (3) likeness (common) reviewers, and (4) less critical likeness reviewers. By using our approach, product review website managers can present product reviews in such a way that users can easily choose reviews and reviewers that match their preferences out of a vast collection of reviews containing all kinds of preferences.

Suggested Citation

  • Nima Jalali & Sangkil Moon & Moon-Yong Kim, 2023. "Profiling diverse reviewer segments using online reviews of service industries," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 130-148, June.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:2:d:10.1057_s41270-022-00163-w
    DOI: 10.1057/s41270-022-00163-w
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