The purpose of this article is to know which hotels most rely on Booking.com, investigating the level of presence on Booking.com through the number of reviews per room by country, hotel size, hotel category, and managerial form. Neither the company nor the hotels provide this information, so we use the number of reviews as an indicator of estimated sales. Design/methodology/approach Data from 33,996 hotels worldwide is downloaded from Booking.com using a web browser automatically controlled, developed in Python, that simulated a user navigation (clicks and selections). The comparison between independent hotels and hotels belonging to a chain is performed by a Student's-t distribution test and the comparison of hotel categories and hotel size is analyzed by a one-way ANOVA test. Findings The results show that three factors clearly influence the use level of Booking.com: independent vs. chain hotels, small vs. large hotels, and low vs. high category hotels worldwide. We observe also that hotels from Europe are the ones that more rely on Booking.com. Originality/Value The originality of this research is to identify the factors that make hotels to have a greater (lesser) dependence on Booking.com within each destination and geographical area. Moreover, the use of big data from hotels worldwide allows us to know the level of use of Booking.com in dozens of countries, especially those with the highest tourist activity. This work expands the capabilities of big data in the hospitality industry research and, with a simple ratio, this study counteracts the lack of public data of hotel sales through Booking.com. This new approach could be extended to the analysis of other OTAs, which use similar reviews systems.
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