The purpose of this paper is to know which hotels mostly rely on Booking.com, investigating the level of presence on Booking.com around the world by country, hotel size, hotel category and managerial form. Neither the company nor the hotels provide this information, so the authors use the number of reviews as an indicator of estimated sales.
Data from 33,996 hotels worldwide are 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.
The results show that three factors clearly influence the usage level of Booking.com: independent vs chain hotels, small vs large hotels and low vs high category hotels worldwide. The authors also observe that hotels from Europe are the ones that rely more on Booking.com.
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 the authors 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 on hotel sales through Booking.com. This new approach could be extended to the analysis of other online travel agencies (OTAs), which use similar review systems.
