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Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews

Author

Listed:
  • Raksmey Sann

    (Department of Tourism Innovation Management, Faculty of Business Administration and Accountancy, Khon Kaen University, Khon Kaen 40000, Thailand)

  • Pei-Chun Lai

    (Department of Hotel and Restaurant Management, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Shu-Yi Liaw

    (Department of Business Administration, National Pingtung University of Science and Technology, Pingtung 912, Taiwan)

  • Chi-Ting Chen

    (Department of Hospitality Management, School of Tourism, Ming Chuan University, Taoyuan City 333, Taiwan)

Abstract

Purpose : This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating ). Design/methodology/approach : First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings : Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter , when staying at large hotels; (ii) Value for Money and Service Encounter , when staying at medium-sized hotels; (iii) Room Space and Service Encounter , when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money , Room Space , or Service Encounter , and to stay at small hotels. Practical implications : By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value : The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.

Suggested Citation

  • Raksmey Sann & Pei-Chun Lai & Shu-Yi Liaw & Chi-Ting Chen, 2022. "Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews," Sustainability, MDPI, vol. 14(3), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1800-:d:742338
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    Citations

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    Cited by:

    1. Nguyen The Hien & Yen-Lun Su & Raksmey Sann & Le Thi Phuong Thanh, 2022. "Analysis of Online Customer Complaint Behavior in Vietnam’s Hotel Industry," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    2. Ram Narayan & Anita Gehlot & Rajesh Singh & Shaik Vaseem Akram & Neeraj Priyadarshi & Bhekisipho Twala, 2022. "Hospitality Feedback System 4.0: Digitalization of Feedback System with Integration of Industry 4.0 Enabling Technologies," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    3. Harman Preet Singh & Mohammad Alshallaqi & Mohammed Altamimi, 2023. "Predicting Critical Factors Impacting Hotel Online Ratings: A Comparison of Religious and Commercial Destinations in Saudi Arabia," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    4. Raksmey Sann & Pei-Chun Lai & Chi-Ting Chen, 2022. "Crisis Adaptation in a Thai Community-Based Tourism Setting during the COVID-19 Pandemic: A Qualitative Phenomenological Approach," Sustainability, MDPI, vol. 15(1), pages 1-16, December.
    5. Wei Fu & Shengnan Wei & Jue Wang & Hak-Seon Kim, 2022. "Understanding the Customer Experience and Satisfaction of Casino Hotels in Busan through Online User-Generated Content," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    6. Harman Preet Singh & Ibrahim Abdullah Alhamad, 2022. "A Novel Categorization of Key Predictive Factors Impacting Hotels’ Online Ratings: A Case of Makkah," Sustainability, MDPI, vol. 14(24), pages 1-25, December.

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