Eva Martin-Fuentes, Juan Pedro Mellinas, Carles Mateu
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引用次数: 0
Abstract
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.
期刊介绍:
Tourism and Hospitality Research is firmly established as a leading and authoritative, peer-reviewed journal for tourism and hospitality researchers and professionals. Tourism and Hospitality Research covers: • Hospitality and tourism operations • Marketing and consumer behaviour • HR management • Social Media and Marketing • Technology • Planning and development • Policy • Performance and financial management • Strategic implications • Environmental aspects • Forecasting and prediction • Revenue management • Impact assessment and mitigation • Globalisation • Research methodologies • Leisure and culture • Risk Management • Change Management