In the landscape of Covid-19 pandemic, people’s well-being, to some extent, can be affected through virtual reality tourism because it has the opportunity to enhance their level of well-being and destination recovery. To verify this empirically an investigation was conducted among people who used Quanjingke, the largest tourism-related virtual reality website in China, during the pandemic. The specific the aim of this paper is to prove the effectiveness of virtual reality tourism in promoting people’s subjective well-being. Hence, an integrated model with the constructs of peripheral attribute, core attribute and pivotal attribute, presence, perceived value, satisfaction, and subjective well-being was proposed and tested. The results indicate that attributes of virtual reality tourism have a positive effect on presence during virtual reality experiences, which positively influences perceived value. The values of virtual reality tourism perceived by tourists result in their satisfaction. Eventually, it was found that tourists’ subjective well-being is improved due to their satisfaction with virtual reality tourism. Practical suggestions are also provided based on the findings.

This research is designed to examine the relationships between brand awareness, consumer emotion, which involves pleasure and arousal, brand trust, and customers’ intention to revisit in the setting of foodservice business for the public. Moreover, this study assesses and evaluates the moderating role of the two types of foodservice businesses, which include name-brand coffee shops and family restaurants, amid the course of the revisit intention formation. The structural equation modeling (SEM) analysis is used to test the research model. The statistical results indicate that brand awareness is a helpful originator of brand trust, arousal, and pleasure, which in part mediates the relationship between brand trust and brand awareness. Brand trust positively affects customers’ revisit intention. The moderating effect of the foodservice business type is significant for the purpose of forecasting and estimating in the relationship between trust and arousal and between brand trust and revisit intention.

Purpose: This study aims to develop an analytically grounded destination decision-support framework that integrates GIS-based spatial analysis with explainable machine learning to identify tourism supply-demand gaps and assess suitability for national-scale 5A scenic-spot cultivation screening. Design/methodology/approach: A multi-threshold two-step floating catchment area (2SFCA) method was used to identify tourism gaps at the 50 × 50 km grid scale. Eleven variables representing tourism resource endowment, public service facilities, transport accessibility and ecological quality were used in four machine learning models. Cross-validation selected the primary suitability model, and ecologically constrained grids were excluded using institutional nature reserve boundaries. Shapley additive explanations (SHAP) and GeoSHAP were used to identify key drivers and reveal nonlinear effects and spatial heterogeneity. Findings: Tourism gaps account for about 20% of grid cells and show stable clustering across distance thresholds, mainly in the Tibetan Plateau, the northern continuous belt and the southwest plateau mountains. XGBoost was retained for suitability prediction and interpretation. Tourism attractions and trunk accessibility were the dominant drivers, jointly contributing over 61%. Tourism attractions show pronounced spatial non-stationarity, whereas trunk accessibility has weaker spatial moderation. After applying ecological constraints, only a limited number of high-suitability grids with existing 4A scenic spots remained. Originality/value: By integrating tourism gaps, institutional ecological constraints and explainable machine learning within an analytically grounded decision-support framework, this study improves the transparency of national-scale priority-area screening for sustainable tourism planning.