IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-030-72469-6_44.html
   My bibliography  Save this book chapter

Anticipated Booking on Touristic Attractions: Flamenco Show in Seville

In: Culture and Tourism in a Smart, Globalized, and Sustainable World

Author

Listed:
  • Fernando Toro Sánchez

    (University of Seville)

Abstract

Flamenco is considered such an UNESCO Universal Heritage since 1990. As a tourist attraction in most destinations in Spain, it has special significance in Andalusia and especially in high growth destinations such as Seville. The anticipation of the reservation in a “Tablao” flamenco in Seville and in two periods distinguished by the milestone of the realization of marketing actions by own online channels versus that of external channels and Online Travel Agencies (OTAS) before. To do this, we use a sample of 2759 bookings to a local flamenco show in Seville, of daily representation and that are offered by various channels both online and offline and which the anticipation on the reservation is evaluated. Hence, a logarithmic scale regression is presented in addition to an analysis of associations between different categorical variables. To determine this partnership, machine learning techniques have been used with the Big ML program that leads us to conclusions about advance bookings that can be of practical use in the marketing actions of local tourist attraction operators. As a consequence, a good practice own channels drives to get a better results on booking anticipation based on branding techniques of branding: the purpose of tourist marketing is to anticipate the reservations.

Suggested Citation

  • Fernando Toro Sánchez, 2021. "Anticipated Booking on Touristic Attractions: Flamenco Show in Seville," Springer Proceedings in Business and Economics, in: Vicky Katsoni & Ciná van Zyl (ed.), Culture and Tourism in a Smart, Globalized, and Sustainable World, pages 673-686, Springer.
  • Handle: RePEc:spr:prbchp:978-3-030-72469-6_44
    DOI: 10.1007/978-3-030-72469-6_44
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Booking anticipation; Machine learning; Tourism experience; Cultural sensibility; Tourism digital marketing;
    All these keywords.

    JEL classification:

    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-030-72469-6_44. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.