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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

Author

Listed:
  • Pierpaolo D’Urso

    (Sapienza University of Rome)

  • Livia Giovanni

    (Luiss University)

  • Marta Disegna

    (Bournemouth University)

  • Riccardo Massari

    (Sapienza University of Rome)

  • Vincenzina Vitale

    (Sapienza University of Rome)

Abstract

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.

Suggested Citation

  • Pierpaolo D’Urso & Livia Giovanni & Marta Disegna & Riccardo Massari & Vincenzina Vitale, 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: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(1), pages 335-360, February.
    • Handle: RePEc:spr:soinre:v:154:y:2021:i:1:d:10.1007_s11205-020-02537-y
    DOI: 10.1007/s11205-020-02537-y
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