IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v200y2024ics0040162523007837.html
   My bibliography  Save this article

Decoding digital nomad destination decisions through user-generated content

Author

Listed:
  • Lacárcel, Francisco Javier S.
  • Huete, Raquel
  • Zerva, Konstantina

Abstract

Digital nomads are engaged in a complex quest to select their next destination. In this context, user-generated content (UGC) on social media is a pivotal source to glean insights into digital nomads' destination choices. Accordingly, this study investigates the principal topics that influence digital nomad's destination choice. To this end, data-mining techniques are applied to analyze user-generated content (UGC) from the social platform X (former Twitter). Based on the results, we identify a total of 11 topics associated with digital nomads' location preferences that can be grouped into 3 clusters (positive, negative, and neutral). Specifically, we find six positive topics (employment, retirement, gastronomy, co-working, work motivation, culture), one neutral topic (customer service), and four negative topics (connectivity, work hours, visa issues, loneliness). The results suggest that job flexibility, the attraction of travel, and cultural immersion emerge as positive factors influencing destination choice. By contrast, connectivity concerns, visa management, feelings of isolation, and emotional adjustments stand out as considerable impediments for digital nomads. We spotlight the long-term pursuit of quality of life and technological connectivity as the main drivers of digital nomads in their destination choice. The paper concludes with a formulation of 33 research questions related to digital nomad destination decisions to be addressed in further research.

Suggested Citation

  • Lacárcel, Francisco Javier S. & Huete, Raquel & Zerva, Konstantina, 2024. "Decoding digital nomad destination decisions through user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
  • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523007837
    DOI: 10.1016/j.techfore.2023.123098
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523007837
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2023.123098?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:tefoso:v:200:y:2024:i:c:s0040162523007837. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.