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A Tourist Segmentation Based on Motivation, Satisfaction and Prior Knowledge with a Socio-Economic Profiling: A Clustering Approach with Mixed Information.

D’Urso, P., De Giovanni, L., Disegna, M., Massari, R. and Vitale, V., 2021. A Tourist Segmentation Based on Motivation, Satisfaction and Prior Knowledge with a Socio-Economic Profiling: A Clustering Approach with Mixed Information. Social Indicators Research, 154, 335-360.

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DOI: 10.1007/s11205-020-02537-y

Abstract

© 2020, The Author(s). The popularity of the cluster analysis in the tourism field has massively grown in the last decades. However, accordingly to our review, researchers are often not aware of the characteristics and limitations of the clustering algorithms adopted. An important gap in the literature emerged from our review regards the adoption of an adequate clustering algorithm for mixed data. The main purpose of this article is to overcome this gap describing, both theoretically and empirically, a suitable clustering algorithm for mixed data. Furthermore, this article contributes to the literature presenting a method to include the “Don’t know” answers in the cluster analysis. Concluding, the main issues related to cluster analysis are highlighted offering some suggestions and recommendations for future analysis.

Item Type:Article
ISSN:0303-8300
Uncontrolled Keywords:Fuzzy clustering; Mixed data; “Don’t know” answers; Visitors
Group:Bournemouth University Business School
ID Code:34916
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:30 Nov 2020 16:44
Last Modified:15 Aug 2021 08:27

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