Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. (May 2021) - Yadegaridehkordi, Elaheh; Nilashi, Mehrbakhsh; Nizam Bin Md Nasir, Mohd Hairul; Momtazi, Saeedeh; Samad, Sarminah; Supriyanto, Eko; Ghabban, Fahad
Abstract: This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers' reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices. Highlights: The use of online reviews in green hotel choice and customer segmentation. The use of TOPSIS in ranking the features of green hotels. The use of CART in predicting travellers' preferences. The use of TripAdvior data for method evaluation.