Long rating scales trigger positive ratings: When referent thinking beats relative thinking

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.

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Long rating scales trigger positive ratings: When referent thinking beats relative thinking

Cited by 2 publications

References 6 publications

How consumers process online hotel ratings

How consumers process online hotel ratings

Understanding Booking.com’s rating drop in the context of online hotel reviews

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