Tourism Management

Loneliness is a growing public health concern, particularly among older adults. Tourism research on healthy aging has largely centered on positive well-being; limited attention has been given to whether travel can alleviate loneliness. Drawing on longitudinal data from China and the United States, this study conceptualizes leisure travel as a form of activation and examines its relationship with loneliness among older adults. The findings indicate that greater participation in leisure travel is associated with lower levels of loneliness among this group over time. Travel also offsets age-related declines in daily social participation and the accompanying increase in loneliness, highlighting travel's activating role in later life. Leisure travel further serves as a compensatory resource for older adults with relational deficits. This mechanism varies across cultural contexts. By moving beyond the focus on positive emotion, this study demonstrates tourism's therapeutic potential for reducing loneliness and promoting well-being in aging populations.

Accurate forecasting of tourist flows is crucial for urban planning, transportation management, and overcrowding prevention. Tourism demand is highly sensitive to external stimuli, often leading to abrupt and irregular fluctuations that are difficult to quantify using existing methods. To address this issue, we propose an event-aware dynamic spatiotemporal network (EAD-STN) that integrates large language model embeddings with dynamic spatiotemporal graph modeling. External factors are encoded into high-dimensional representations to capture their impacts on visitor demand. The model further incorporates both dynamic spatial adjacency and LLM-derived semantic adjacency among attractions, enabling a more comprehensive characterization of inter-attraction relationships. Experiments on real-world datasets from Beijing and Shanghai demonstrate that EAD-STN consistently outperforms state-of-the-art baselines. By leveraging large language models to quantify complex external influences and semantic relationships, this framework provides a new perspective for improving the accuracy and interpretability of tourism flow forecasting.

Generative AI tools can now create photorealistic destination images at low cost, yet whether such images motivate travel as effectively as human-generated images remains unclear. Drawing on the heuristic-systematic model, this research examines differences between AI-generated and human-generated tourism images through three studies. Study 1 shows that AI-generated images receive higher ratings of perceived aesthetics but lower ratings of perceived authenticity, narrative transportation, and visit intention than human-generated images. Study 2 identifies 19 computable visual indicators across three dimensions and finds that AI-generated images exhibit stronger visual schema characteristics, whereas human-generated images show richer visual structure. Study 3 uses XGBoost and SHAP analysis to identify the visual features associated with different psychological responses. These findings connect computational visual analysis with tourism consumer research by identifying differences in audience responses to AI-generated and human-generated images and the visual features associated with those responses.

Mixed methods research has a prominent place in tourism and hospitality scholarship, yet adoption has advanced faster than methodological selectivity, which leaves many studies combining qualitative and quantitative strands without explaining why both strands were necessary, how the strands were related, or what integrated inference their combination alone made possible. Drawing on a critical review of 222 mixed methods studies published in leading tourism and hospitality journals, this article identifies dominant patterns, strengths and weaknesses, and underused possibilities. In response, the article offers a stage-based guide that links problem formulation, purpose selection, design fit, sampling bridges, strand roles, integration, divergence handling, reporting architecture, and contribution development. In doing so, the article advances a more demanding standard for mixed methods scholarship and argues that the next phase of tourism and hospitality research should shift from routine method combination toward integrated inferences that are conceptually sharper, methodologically clearer, and practically more useful.

Tourism destinations increasingly seek embodied, multisensory, and culturally meaningful experiences whose significance extends beyond immediate consumption. Yet tourism management still has a limited vocabulary for explaining how embodied cultural practices generate value across destination systems. This Progress paper uses dance as a focused and theoretically revealing case to address this gap. Rather than treating dance merely as entertainment, heritage representation, or event programming, we conceptualize it as relational, embodied cultural infrastructure through which patterned movement can organize encounters, carry cultural meaning across generations, and contribute to the character and capabilities of destinations. Drawing on tourism's anthropological lineage and interdisciplinary dance scholarship, we develop the Dance Ecosystem for Destinations—a seven-biome framework that distinguishes encounter-level value realization, portfolio-level accumulation and circulation, and cross-cutting technological mediation. The framework expands tourism management's conceptual and diagnostic capacity by linking these biomes to questions of experience design, stewardship, governance, value distribution, and evaluation.

This article conceptualises hot war tourism as the touristification of unresolved conflict: configurations in which conflict continues materially to shape civilian vulnerability, security and visitor access while conflict-affected places and experiences acquire experiential, representational or market value. A critical integrative review clarifies its boundaries with dark, battlefield, military, risk, recovery and solidarity-oriented tourism and identifies five dimensions: temporal immediacy, spatial proximity, commodification, moral and motivational ambiguity, and governance and responsibility. Ukraine and Syria illustrate contrasting active-war witnessing and recovery–normalisation configurations. A relational framework locates war-related tourism according to the degree of unresolved conflict and the intensity of touristification, while a multi-actor governance framework specifies responsibilities for access, risk disclosure, representation, consent, platform accountability, humanitarian non-interference and escalation. The concept links tourism value production to unresolved harm and provides a basis for analysing when conflict-related visitation requires facilitation, restraint or restriction.