IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v25y2024i4d10.1007_s10799-023-00392-0.html
   My bibliography  Save this article

Review content type and hotel review helpfulness: direct and moderating effects

Author

Listed:
  • Rongqin Liu

    (Southwestern University of Finance and Economics)

  • Yun Zhang

    (Southwestern University of Finance and Economics)

  • Chuan Luo

    (Southwestern University of Finance and Economics)

  • Shangyu Tan

    (Southwestern University of Finance and Economics)

  • Yunqu Gong

    (Southwestern University of Finance and Economics)

Abstract

Helpful online reviews can benefits customers with less stress from information overload and valuable cues for decision-making, and thus many scholars have discussed what the determinants of review helpfulness. However, limited studies explore how specific review content impacts review helpfulness especially in the hotel industry. Using a latent Dirichlet allocation method, we identified review content type regarding hotel attributes with five topics (room experience, location convenience, personalization & uniqueness, event management & staff attitude, and cleanliness & smell) by extracting dimensions from 166, 546 reviews on Ctrip.com. Then we explored the direct and moderating effects of review content type on review helpfulness. Our findings indicated that customers give the most weight to event management & staff attitude in hotel reviews, followed by personalization & uniqueness, cleanliness & smell, and location convenience/room experience. Moreover, the effects of some characteristics of reviews (e.g., review extremity, review photo) and reviewers (e.g., reviewer expertise, reviewer reputation) on review helpfulness vary in different types of review content. These findings could provide foundation on exploring the influence of specific review content on review helpfulness, as well as offer valuable guidelines for hotels to allocate resources effectively.

Suggested Citation

  • Rongqin Liu & Yun Zhang & Chuan Luo & Shangyu Tan & Yunqu Gong, 2024. "Review content type and hotel review helpfulness: direct and moderating effects," Information Technology and Management, Springer, vol. 25(4), pages 383-406, December.
    • Handle: RePEc:spr:infotm:v:25:y:2024:i:4:d:10.1007_s10799-023-00392-0
    DOI: 10.1007/s10799-023-00392-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-023-00392-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10799-023-00392-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    2. Cheng, Yi-Hsiu & Ho, Hui-Yi, 2015. "Social influence's impact on reader perceptions of online reviews," Journal of Business Research, Elsevier, vol. 68(4), pages 883-887.
    3. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    4. Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    5. Yang, Jun & Mai, Enping (Shirley), 2010. "Experiential goods with network externalities effects: An empirical study of online rating system," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1050-1057, September.
    6. Richard F. J. Haans & Constant Pieters & Zi-Lin He, 2016. "Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research," Strategic Management Journal, Wiley Blackwell, vol. 37(7), pages 1177-1195, July.
    7. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    8. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    9. 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.
    10. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    11. Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    12. Eric P. S. Baumer & David Mimno & Shion Guha & Emily Quan & Geri K. Gay, 2017. "Comparing grounded theory and topic modeling: Extreme divergence or unlikely convergence?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1397-1410, June.
    13. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    14. Carlo Schwarz, 2018. "ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation," Stata Journal, StataCorp LP, vol. 18(1), pages 101-117, March.
    15. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
    16. Chen, Chun-Chih & Chuang, Ming-Chuen, 2008. "Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design," International Journal of Production Economics, Elsevier, vol. 114(2), pages 667-681, August.
    17. Casaló, Luis V. & Flavián, Carlos & Guinalíu, Miguel & Ekinci, Yuksel, 2015. "Avoiding the dark side of positive online consumer reviews: Enhancing reviews' usefulness for high risk-averse travelers," Journal of Business Research, Elsevier, vol. 68(9), pages 1829-1835.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    4. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    5. Meek, Stephanie & Wilk, Violetta & Lambert, Claire, 2021. "A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews," Journal of Business Research, Elsevier, vol. 125(C), pages 354-367.
    6. 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.
    7. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    8. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    9. Guha Majumder, Madhumita & Dutta Gupta, Sangita & Paul, Justin, 2022. "Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis," Journal of Business Research, Elsevier, vol. 150(C), pages 147-164.
    10. Kong, Juan & Lou, Chen, 2023. "Do cultural orientations moderate the effect of online review features on review helpfulness? A case study of online movie reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    11. Yang, Luming & Xu, Min & Xing, Lin, 2022. "Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    12. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    13. Yi, Jisu & Oh, Yun Kyung, 2022. "The informational value of multi-attribute online consumer reviews: A text mining approach," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    14. Lien Thi Kim Nguyen & Hao-Hsuan Chung & Kristine Velasquez Tuliao & Tom M. Y. Lin, 2020. "Using XGBoost and Skip-Gram Model to Predict Online Review Popularity," SAGE Open, , vol. 10(4), pages 21582440209, December.
    15. Hernández-Ortega, Blanca, 2020. "When the performance comes into play: The influence of positive online consumer reviews on individuals' post-consumption responses," Journal of Business Research, Elsevier, vol. 113(C), pages 422-435.
    16. Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.
    17. Miyea Kim & Jeongsoo Han & Mina Jun, 2020. "Do same-level review ratings have the same level of review helpfulness? The role of information diagnosticity in online reviews," Information Technology & Tourism, Springer, vol. 22(4), pages 563-591, December.
    18. Kübler, Raoul V. & Lobschat, Lara & Welke, Lina & van der Meij, Hugo, 2024. "The effect of review images on review helpfulness: A contingency approach," Journal of Retailing, Elsevier, vol. 100(1), pages 5-23.
    19. Xian Wang & Huixian Li & Qingyi Wang & Alison Noble, 2023. "Consumers’ Concerns Regarding Product Quality: Evidence From Chinese Online Reviews," SAGE Open, , vol. 13(1), pages 21582440231, March.
    20. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infotm:v:25:y:2024:i:4:d:10.1007_s10799-023-00392-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.