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

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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Institutions

Publication Details

Journal
Tourism and Hospitality Research
Published
2024-09-15
DOI
https://doi.org/10.1177/14673584241283901
Citations
5
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
5.66

Funders

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article

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

Eva Martín-Fuentes, Carles Mateu, Juan Pedro Mellinas
5 citations
Tourism and Hospitality Research
Digital Marketing and Social Media
5.66
article

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

Eva Martín-Fuentes, Carles Mateu, Juan Pedro Mellinas
article en
5 citations

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.

Tourism and Hospitality ResearchVol. 26(1)
Universitat de Lleida (ES), Universidad de Murcia (ES)
Ministerio de Ciencia e Innovación
Openalex Percentile: Top 4%
Digital Marketing and Social Media
5.66
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