Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels

Ali Ahani, Mehrbakhsh Nilashi, Elaheh Yadegaridehkordi, Louis Sanzogni, A. Rashid Tarik, Kathy Knox, Sarminah Samad and Othman Ibrahim

Journal of Retailing and Consumer Services, 2019, vol. 51, issue C, 331-343

Abstract: Travelers can enjoy a wide range of choices with the assistance of online review websites such as TripAdvisor. Online reviews provided by customers are an important portion of hotels' online business worldwide as they have value in terms of understanding customers' observations of hotels' product and service features. Hotel managers seek to understand travelers' satisfaction and hotel preferences through online reviews to improve their marketing strategy and decision making. This research uses the travelers' generated content in online hotel reviews to provide reasonable and benchmarking understandings about customers' satisfaction and preferences. Hence, the aim of this study is identifying the important factors for hotel selection based on previous travelers' reviews on TripAdvisor. Accordingly, we develop a new method for the use of Multi-Criteria Decision-Making (MCDM) and soft computing approaches. Concentrating on the case study of the Canary Islands hotels, we show how this method can be applied to determine the satisfaction and preferences among travelers that impact their decision in hotel choices. The results help to identify four customer segments for Canary Islands hotels. These segments are “Highly Satisfied Travelers†, “Satisfied Travelers†, “Moderately Satisfied Travelers†, and “Unsatisfied Travelers†, showing that different travelers have various degrees of satisfaction with dissimilar preferences. We found that travelers' preference and satisfaction segmentation is a crucial stage in travelers' behavior analysis to improve the quality of hotels' products and services. This form of analysis can enhance hotel managers' understanding of different market segments according to customers’ satisfaction level and their preferences. The findings of this study will help managers to set priority instructions for improving the corresponding hotel features and use online customer reviews to improve customer satisfaction and hotel performance.

Keywords: Customer satisfaction; Preferences analysis; Online reviews; Hotel; Market segmentation; Canary islands; Multi-criteria decision making; Machine learning (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0969698918306787
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:51:y:2019:i:c:p:331-343

DOI: 10.1016/j.jretconser.2019.06.014

Access Statistics for this article

Journal of Retailing and Consumer Services is currently edited by Harry Timmermans

More articles in Journal of Retailing and Consumer Services from Elsevier
Bibliographic data for series maintained by Catherine Liu ().