This study examines the new Booking.com rating system, which has suffered a significant drop in scores awarded to accommodation. We aim to determine the extent of these declines and identify the factors that make them more pronounced in some hotels than in others. Our findings reveal a consistent, much more significant drop in scores than reflected in recently published studies that minimized the effects of the changes. Contrary to the predictions made in other studies, the highest-rated hotels have also suffered drops in their scores. Machine learning models identified "facilities" as the item that plays the most relevant role in consumers' global satisfaction and contributes to predicting the magnitude of drops in scores with the new system. Implications for both hoteliers and academics utilizing Booking.com's score data are identified, particularly for studies comparing data from different periods.
机构:
Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China
Lo, Ada S.
Yao, Sharon Siyu
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机构:
Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China
机构:
Hong Kong Polytech Univ, Sch Hotel & Tourism Management, TST East, Kowloon, Hong Kong, Peoples R ChinaUniv Surrey, Sch Hospitality & Tourism Management, Guildford, Surrey, England
Park, Sangwon
Woo, Mina
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Sogang Univ, Grad Sch Business, 35 Baekbeomro, Seoul 04107, South KoreaUniv Surrey, Sch Hospitality & Tourism Management, Guildford, Surrey, England