Advanced Search
MyIDEAS: Login

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

Contents:

Author Info

  • 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)

Registered author(s):

    Abstract

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://pro1.unibz.it/projects/economics/repec/bemps13.pdf
    Download Restriction: no

    Bibliographic Info

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

    as in new window
    Length: 50 pages
    Date of creation: Oct 2013
    Date of revision:
    Handle: RePEc:bzn:wpaper:bemps13

    Contact details of provider:
    Postal: VIA SERNESI, 1 - 39100 BOLZANO
    Phone: +39 0471 315 000
    Fax: +39 0471 315 009
    Email:
    Web page: http://www.unibz.it/en/economics/research/workingpapers/default.html
    More information through EDIRC

    Related research

    Keywords: Bagged Clustering; Fuzzy C–means; Chinese travellers; Tourism market segmentation; Western Europe; Likert–type scales; fuzzy coding.;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. 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.
    2. Evgenia Dimitriadou & Sara Dolničar & Andreas Weingessel, 2002. "An examination of indexes for determining the number of clusters in binary data sets," Psychometrika, Springer, vol. 67(1), pages 137-159, March.
    3. Keller, Kevin Lane, 2003. " Brand Synthesis: The Multidimensionality of Brand Knowledge," Journal of Consumer Research, University of Chicago Press, vol. 29(4), pages 595-600, March.
    4. 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.
    5. Heungsun Hwang & Wayne Desarbo & Yoshio Takane, 2007. "Fuzzy Clusterwise Generalized Structured Component Analysis," Psychometrika, Springer, vol. 72(2), pages 181-198, June.
    6. 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, vol. 62(1), pages 63-83, March.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:bzn:wpaper:bemps13. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (bemps administrator).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.