Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

Yadegaridehkordi, Elaheh and Nilashi, Mehrbakhsh and Md Nasir, Mohd Hairul Nizam and Momtazi, Saeedeh and Samad, Sarminah and Supriyanto, Eko and Ghabban, Fahad (2021) Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques. Technology in Society, 65. p. 101528. ISSN 0160-791X, DOI https://doi.org/10.1016/j.techsoc.2021.101528.

Full text not available from this repository.
Official URL: https://doi.org/10.1016/j.techsoc.2021.101528

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. © 2021 Elsevier Ltd

Item Type: Article
Funders: UNSPECIFIED
Uncontrolled Keywords: Green hotels; Decision making; Segmentation; Online travel reviews; Choice behaviour; Multi-criteria decision making (MCDM)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Software Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 30 Apr 2021 03:22
Last Modified: 30 Apr 2021 03:22
URI: http://eprints.um.edu.my/id/eprint/25916

Actions (login required)

View Item View Item
UM Research Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.