Destination Image of DMO and UGC on Instagram: A Machine-Learning Approach

Roman Egger (), Oguzcan Gumus (), Elza Kaiumova (), Richard Mükisch () and Veronika Surkic ()
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Roman Egger: Salzburg University of Applied Sciences
Oguzcan Gumus: Salzburg University of Applied Sciences
Elza Kaiumova: Salzburg University of Applied Sciences
Richard Mükisch: Salzburg University of Applied Sciences
Veronika Surkic: Salzburg University of Applied Sciences

A chapter in Information and Communication Technologies in Tourism 2022, 2022, pp 343-355 from Springer

Abstract: Abstract Social media plays a key role in shaping the image of a destination. Although recent research has investigated factors influencing online users’ perception towards destination image, limited studies encompass and compare social media content shared by tourists and destination management organisations (DMOs) at the same time. This paper aims to determine whether the projected image of DMOs corresponds with the destination image perceived by tourists. By taking the Austrian Alpine resort Saalbach-Hinterglemm as a case, a netnographic approach was applied to analyse the visual and textual posts of DMO and user-generated content (UGC) on Instagram using machine learning. The findings reveal themes that are not covered in the posts published by marketers but do appear in UGC. This study adds to the existing literature by providing a deeper insight into destination image formation and uses a qualitative approach to assess destination brand image. It further highlights practical implications for the industry regarding DMOs’ social media marketing strategy.

Keywords: Machine learning; Instagram; Destination image (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-94751-4_31

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DOI: 10.1007/978-3-030-94751-4_31

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