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

Raksmey Sann, Pei-Chun Lai, Shu-Yi Liaw and Chi-Ting Chen
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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

Sustainability, 2022, vol. 14, issue 3, 1-22

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.

Keywords: online complaining behavior; decision trees; hotel class; online complaining attributes; data mining algorithms; big data analytics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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