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Market Segmentation using Bagged Fuzzy C–Means (BFCM): Destination Image of Western Europe among Chinese Travellers

Listed author(s):
  • Pierpaolo D'Urso


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

  • Girish Prayag


    (Department of Management, Marketing and Entrepreneurship, University of Canterbury, New Zealand)

  • 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)

Market segmentation offers several strategic and tactical advantages to marketers. Hierarchical and non-hierarchical segmentation methods have several weaknesses but remain widely applied in tourism studies. Alternative segmentation methods such as fuzzy, mixture models, and Bagged Clustering are relatively less popular. In this study, we propose a novel method, the Bagged Fuzzy C–Means (BFCM) algorithm, for segmenting tourism markets. A sample of 328 Chinese travellers revealed the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of perceived images for Western Europe. BFCM is able to identify stable clusters, inheriting this feature from Bagged clustering method. Furthermore, fuzzy allocation allows to idetify travellers whose profiles match with more than one cluster. Destination marketers need to proactively manage the image of Western Europe to attract the increasingly discerning Chinese traveller. Information provision and on-line presence strategies will be critical for destination success.

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Paper provided by Faculty of Economics and Management at the Free University of Bozen in its series BEMPS - Bozen Economics & Management Paper Series with number BEMPS13.

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Length: 50 pages
Date of creation: Oct 2013
Handle: RePEc:bzn:wpaper:bemps13
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  1. Heungsun Hwang & Wayne Desarbo & Yoshio Takane, 2007. "Fuzzy Clusterwise Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 181-198, June.
  2. 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.
  3. Willem Heiser & Patrick Groenen, 1997. "Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima," Psychometrika, Springer;The Psychometric Society, vol. 62(1), pages 63-83, March.
  4. Sara Dolnicar & Friedrich Leisch, 2010. "Evaluation of structure and reproducibility of cluster solutions using the bootstrap," Marketing Letters, Springer, vol. 21(1), pages 83-101, March.
  5. 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.
  6. Keller, Kevin Lane, 2003. " Brand Synthesis: The Multidimensionality of Brand Knowledge," Journal of Consumer Research, Oxford University Press, vol. 29(4), pages 595-600, March.
  7. Evgenia Dimitriadou & Sara Dolničar & Andreas Weingessel, 2002. "An examination of indexes for determining the number of clusters in binary data sets," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 137-159, March.
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