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When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type

Author

Listed:
  • Raffaele Filieri
  • Elisabetta Raguseo

    (Polito - Politecnico di Torino = Polytechnic of Turin)

  • Claudio Vitari

    (MTS - Management Technologique et Strategique - EESC-GEM Grenoble Ecole de Management)

Abstract

Online customer reviews (OCRs) have become increasingly important in travelers' decision-making. However, the proliferation of OCRs requires e-commerce organizations to identify the characteristics of the most helpful reviews to reduce information overload. This study focuses on OCRs of hotels and particularly on the moderating factors in the relationship between extreme ratings and review helpfulness. The study has adopted 11,358 OCRs of 90 French hotels from TripAdvisor.com. Findings highlight that large hotels are more affected by extreme reviews than small hotels. Extreme reviews are more helpful to consumers when reviews are long and accompanied by the reviewers' photos.

Suggested Citation

  • Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
  • Handle: RePEc:hal:gemptp:halshs-01923243
    DOI: 10.1016/j.chb.2018.05.042
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01923243
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    References listed on IDEAS

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    9. Baidyanath Biswas & Pooja Sengupta & Boudhayan Ganguly, 2022. "Your reviews or mine? Exploring the determinants of “perceived helpfulness” of online reviews: a cross-cultural study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1083-1102, September.
    10. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
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    12. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
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