Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/335631 
Year of Publication: 
2026
Citation: 
[Journal:] Information Technology & Tourism [ISSN:] 1943-4294 [Volume:] 28 [Issue:] 1 [Article No.:] 16 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2026
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
With the rapid development of the internet, social media platforms enable tourists to extensively research, compare, and evaluate potential destinations. Concurrently, these platforms have provided new channels for destinations to present themselves and engage with potential visitors. Therefore, the closer destinations’ self-presentation is to the actual experience of the tourists, the higher the likelihood that tourists’ expectations are met. This study presents an approach for analyzing customer preferences and comparing them with the self-representation of tourism destinations or providers, based on images uploaded to Instagram. Compared to existing approaches, the proposed methodology enables a more fine-grained analysis of preference profiles for individual customer segments by applying clustering techniques to the multidimensional preference profile and executing a cluster-specific congruence analysis. The results demonstrate that the data reveal distinct segments within the tourist groups of the studied destinations and differences between the external perception and self-presentation for specific customer segments could be identified, constituting a valuable input to a refinement of marketing strategies for tourism destinations.
Subjects: 
Self-presentation
External perception
Instagram
Image recognition
Convolutional neural networks
Perception congruence
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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