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1.
Co-Creation and Destination Image : A Bibliometric and Thematic Analysis Using CiteSpace
Tuyen Tran, original scientific article

Abstract: Co-creation has gained increasing scholarly attention as an approach for involving tourists, residents, and stakeholders in shaping destination image. However, existing research is fragmented, lacking an integrated review of its conceptual foundations, thematic evolution, and implications. This study aims to address this gap by mapping the intellectual structure and emerging trends of co-creation and destination images. A bibliometric and thematic analysis was conducted on 72 peer-reviewed publications indexed in Scopus, using CiteSpace for co-citation and keyword co-occurrence mapping, complemented by Scopus AI for trend identification. The analysis revealed four thematic clusters: destination branding, stakeholder engagement, emotional experience, and digital interaction. Results demonstrate that co-creation operates as a multidimensional, layered process, enhancing emotional attachment, destination loyalty, and brand value, rather than a single experiential design activity. The study concludes that co-creation plays a structurally significant role in destination branding and stakeholder engagement, as demonstrated by the dominant clusters in the bibliometric and thematic analyses. By synthesising existing knowledge and identifying research gaps, this paper offers a comprehensive reference for academics and practitioners, reinforcing the relevance of co-creation in destination management.
Keywords: co-creation, destination image, bibliometric analysis, destination branding, CiteSpace
Published in RUP: 06.03.2026; Views: 39; Downloads: 0
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2.
Image-based analysis of tourist destination perceptions : a deep learning and spatial–temporal study in Slovenia
Dejan Paliska, Aleksandra Brezovec, Gorazd Sedmak, 2026, original scientific article

Abstract: In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning.
Keywords: tourist destination image, user-generated content, deep learning, spatial-temporal analysis, destination marketing strategy
Published in RUP: 18.02.2026; Views: 181; Downloads: 4
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