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Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings

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  • Hu, Ya-Han
  • Chen, Kuanchin

Abstract

The tourism industry has been strongly influenced by electronic word-of-mouth (eWOM) in recent years. Currently, there are only limited studies available that look into hotel review helpfulness. This present study addresses three hidden assumptions prevalent in online review studies: (1) all reviews are visible equally to online users, (2) review rating (RR) and hotel star class (HSC) affect review helpfulness individually with no interaction, and (3) characteristics of reviews and reviewer status stay constant.

Suggested Citation

  • Hu, Ya-Han & Chen, Kuanchin, 2016. "Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings," International Journal of Information Management, Elsevier, vol. 36(6), pages 929-944.
    • Handle: RePEc:eee:ininma:v:36:y:2016:i:6:p:929-944
    DOI: 10.1016/j.ijinfomgt.2016.06.003
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    References listed on IDEAS

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    Cited by:

    1. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    2. Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.
    3. Moro, Sérgio & Ramos, Pedro & Esmerado, Joaquim & Jalali, Seyed Mohammad Jafar, 2019. "Can we trace back hotel online reviews’ characteristics using gamification features?," International Journal of Information Management, Elsevier, vol. 44(C), pages 88-95.
    4. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.
    5. Ragini, J. Rexiline & Anand, P.M. Rubesh & Bhaskar, Vidhyacharan, 2018. "Big data analytics for disaster response and recovery through sentiment analysis," International Journal of Information Management, Elsevier, vol. 42(C), pages 13-24.
    6. Fink, Lior & Rosenfeld, Liron & Ravid, Gilad, 2018. "Longer online reviews are not necessarily better," International Journal of Information Management, Elsevier, vol. 39(C), pages 30-37.
    7. Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    8. Jiahua Du & Jia Rong & Sandra Michalska & Hua Wang & Yanchun Zhang, 2019. "Feature selection for helpfulness prediction of online product reviews: An empirical study," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.

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