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Fuzzy segmentation in postmodern consumers

Author

Listed:
  • Pierpaolo D'Urso

    (Dipartimento di Scienze Sociali ed Economiche, La Sapienza, Roma, Italy)

  • Marta Disegna

    (Free University of Bozen-Bolzano, School of Economics and Management, Italy.)

  • Riccardo Massari

    (Dipartimento di Scienze Sociali ed Economiche, La Sapienza, Roma, Italy)

  • Linda Osti

    (Free University of Bozen-Bolzano, School of Economics and Management, Italy.)

Abstract

The aim of this paper is to create a nexus between postmodern consumer behaviour and fuzzy clustering, and to propose a suitable clustering method to segment postmodern consumers. 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 process. Unlike other fuzzy-based applications, which use fuzzy theory only on some parts of the clustering process, the clustering technique we propose and apply is fuzzy in every single step of the clustering process. By totally embracing the fuzzy theory the procedure we propose is capable of analysing the uncertainty and vagueness that characterise the experiences and perceptions of postmodern consumers.

Suggested Citation

  • Pierpaolo D'Urso & Marta Disegna & Riccardo Massari & Linda Osti, 2014. "Fuzzy segmentation in postmodern consumers," BEMPS - Bozen Economics & Management Paper Series BEMPS20, Faculty of Economics and Management at the Free University of Bozen.
    • Handle: RePEc:bzn:wpaper:bemps20
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    File URL: https://repec.unibz.it/bemps20.pdf
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    References listed on IDEAS

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    1. Renato Coppi & Paolo Giordani & Pierpaolo D’Urso, 2006. "Component Models for Fuzzy Data," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 733-761, December.
    2. Huang, Jen-Hung & Peng, Kua-Hsin, 2012. "Fuzzy Rasch model in TOPSIS: A new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries," Tourism Management, Elsevier, vol. 33(2), pages 456-465.
    3. Ngai, E. W. T. & Wat, F. K. T., 2003. "Design and development of a fuzzy expert system for hotel selection," Omega, Elsevier, vol. 31(4), pages 275-286, August.
    4. Li, Xiang (Robert) & Meng, Fang & Uysal, Muzaffer & Mihalik, Brian, 2013. "Understanding China's long-haul outbound travel market: An overlapped segmentation approach," Journal of Business Research, Elsevier, vol. 66(6), pages 786-793.
    5. Ahmad, Shimi Naurin & Richard, Marie-Odile, 2014. "Understanding consumer's brand categorization across three countries: Application of fuzzy rule-based classification," Journal of Business Research, Elsevier, vol. 67(3), pages 278-287.
    6. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    7. Coppi, Renato & D’Urso, Pierpaolo & Giordani, Paolo, 2012. "Fuzzy and possibilistic clustering for fuzzy data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 915-927.
    8. Firat, A Fuat & Venkatesh, Alladi, 1995. "Liberatory Postmodernism and the Reenchantment of Consumption," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 22(3), pages 239-267, December.
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    Citations

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    Cited by:

    1. 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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    More about this item

    Keywords

    Fuzzy clustering; Fuzzy numbers; Likert–type scale; Segmentation; Postmodernity;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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