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Fuzzy segmentation of postmodern tourists

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  • D'Urso, Pierpaolo
  • Disegna, Marta
  • Massari, Riccardo
  • Osti, Linda

Abstract

In postmodern tourism, the experiences of each tourist could not be summarized only through a unique perspective but multiple and disjointed perspectives are necessary. The aim of this paper is to create a nexus between postmodern tourist and fuzzy clustering, and to propose a suitable clustering procedure to segment postmodern tourists. From a methodological perspective, the main contribution of this paper is related to the use of the fuzzy theory from the beginning to the end of the clustering process. Furthermore, the suggested procedure is capable of analysing the uncertainty and vagueness that characterise the experiences and perceptions of postmodern consumers. From a managerial perspective, fuzzy clustering methods offer to practitioners a more realistic multidimensional description of the market not forcing consumers to belong to one cluster. Moreover, the results are easy and comprehensible to read since they are similar to those obtained with more traditional clustering techniques.

Suggested Citation

  • D'Urso, Pierpaolo & Disegna, Marta & Massari, Riccardo & Osti, Linda, 2016. "Fuzzy segmentation of postmodern tourists," Tourism Management, Elsevier, vol. 55(C), pages 297-308.
  • Handle: RePEc:eee:touman:v:55:y:2016:i:c:p:297-308
    DOI: 10.1016/j.tourman.2016.03.018
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    4. Canavan, Brendan & McCamley, Claire, 2020. "The passing of the postmodern in pop? Epochal consumption and marketing from Madonna, through Gaga, to Taylor," Journal of Business Research, Elsevier, vol. 107(C), pages 222-230.
    5. Pierpaolo D’Urso & Marta Disegna & Riccardo Massari, 2020. "Satisfaction and Tourism Expenditure Behaviour," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 1081-1106, June.
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    9. 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.
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