The work carried out at the DVRC laboratory in collaboration with IREST raises new questions about the world of tourism. Whether it be graph theory techniques, statistical tools, textual analysis, machine learning or data mining, the Digital Tourism Working Group is constantly enriching its research output.
This study examines how hegemonic ideologies shape women's participation in tourism in a theocracy. Applying Gramsci's theory of hegemony and a critical poststructural feminist lens, it analyses how Iran's politico-religious structures influence women's roles, employment, and mobility in tourism. Qualitative findings reveal strategies of compliance, negotiation, and resistance expressed through entrepreneurship, networking, and workplace practices. Concepts of ‘war of position’ and ‘passive revolution’ explain how women create space for agency without direct confrontation, reshaping visibility and legitimacy incrementally. The study advances tourism scholarship by situating women's agency within hegemonic structures of theocratic governance and extending Gramscian theory to show how gendered consent and resistance operate in tourism, while calling for gender-transformative policies that address inequality and support women's situated agency.
Cinematic realities creatively produce imagined scenic environments that constitute seemingly `authentic' destinations that can attract tourists' attention. Not all is as it seems, however; hence we ask, ``How can paradoxes of authenticity fuel consumers' reception of products of the film industry?''. We use paradox theory to address this question. Virtual ethnography is used to capture tourists' perception of multifaceted contexts of authenticity in the Moroccan film industry. We categorize authenticity as multiform (real, fake and fake-authentic) through film case analysis. We reveal and discuss three paradoxical forms of authenticity: (i) authentic reality; (ii) merging fake reality; and (iii) genuinely authentically fake. Our findings highlight that, despite a fake reality, tourists interplay fakery with authenticity, transforming the imitation into something more original than the source.
Overtourism presents complex and often hidden challenges for urban environ- ments, impacting residents, infrastructure, and visitor satisfaction. This study proposes a novel, data-driven methodology to detect and analyze latent over- tourism?the early, subtle warning signs of excessive tourism?before visible breakdowns occur. By leveraging user-generated content from Tripadvisor, a tem- poral circulation multidigraphs is modeled to capture tourist mobility. Using frequent subgraph mining algorithms, the approach identifies recurring tourist movement patterns across different urban scales. These patterns are then ana- lyzed in both spatial and temporal dimensions to detect hotspots and evaluate dynamic attractiveness through a Huff-based probabilistic model. The approach is applied to three cities of varying sizes revealing consistent tourist flows and areas under increasing pressure, suggesting early overtourism.
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