Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

Abstract

In reservation-based industries, accurate booking cancellation forecast is of foremost importance to estimate demand. By combining data science tools and capabilities with human judgement and interpretation it is possible to demonstrate how the semiautomatic analysis of the literature can contribute to synthetize research findings and identify research topics on the subject of booking cancellation forecasting. The data used was obtained through keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias, but also enhances the fact that data visualization and text mining techniques facilitate abstraction, expedite analysis and contribute to the improvement of reviews. Results show that albeit the importance of bookings’ cancellation forecast, further research on the subject is still needed. By detailing the full experimental procedure of the analysis, this work aims to encourage other authors to conduct automated literature analysis as a means to understand current research in their working fields.