Segmenting tourists by wellness hotel attributes and demographics: a study of North American wellness tourists

다국어 초록 (Multilingual Abstract)

This study aims to segment North American wellness tourists according to wellness hotel attributes using a factor-cluster market segmentation approach. Using convenience sampling from the Wellness Tourism Association database and the Traveltowellness email list, survey data were collected from 2,912 tourists using a self-administered questionnaire. Based on the data collected, this article answers the following research questions: Which wellness hotel attributes are important for the North American wellness tourist? What segments of tourists can be distinguished based on these hotel attributes? What are the characteristics of these segments, and how are they different from one another? The authors derived three factors from sixteen motivational items by factor analysis based on destination attributes and relevant socio-demographic characteristics. Utilizing destination attributes and pertinent socio-demographic characteristics, a clustering method identified three discernible segments: high environmental wellness sensitivity, medium environmental wellness sensitivity, and low environmental wellness sensitivity. The findings indicate that individuals with higher incomes exhibit greater environmental wellness sensitivity. The most important hotel attributes found in this survey included a secure and hygienic hotel environment, a comprehensive water purification system throughout the hotel, availability of fitness activities in nature, and implementation of sustainable green practices. Additional examination of these characteristics and variables is necessary given the evolving circumstances of the COVID-19 pandemic during which the survey was conducted. This research is practically beneficial for tourism providers who are poised to confront intense competition in the years to come. This research makes a scholarly contribution to the field of segmentation theory by applying data-driven segmentation techniques to scenarios traditionally relying on conventional segmentation methods.