In an era where environmental conservation is increasingly critical, identifying pathways through which technological innovations like virtual reality tourism (VRT) can promote sustainable behaviors is vital. This study investigates the impact of ‘ecological presence’, a newly proposed sub-dimension of presence in VRT, on tourists’ environmentally responsible behavior (TERB). Through structural equation modeling and fuzzy set qualitative comparative analysis of data from 290 participants, we unveil that ecological presence—defined as the authenticity and immersion of tourists in virtual ecological environments—significantly bolsters biospheric values, environmental self-identity, and personal norms. Additionally, our findings indicate that ecological presence in VRT indirectly promotes TERB, predominantly through the mediation of enhanced biospheric values and environmental self-identity. Notably, ecological presence, biospheric values, and environmental self-identity constitutes a sufficient condition for achieving a high level of TERB. This research highlights the potential of VRT as an innovative tool for tourism administrators to foster environmental stewardship, offering a novel approach to leveraging technology for conservation efforts.

The inherent vulnerability of tourism poses a substantial challenge to maintaining the productivity and stability of livelihoods among rural tourism households. Although tourism seasonality significantly influences the livelihood activities of rural tourism households, research exploring how they adapt their livelihood strategies to achieve optimal outcomes is limited. Employing an exploratory sequential mixed-methods design that combines thematic analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA), we investigated the relationship between the livelihood strategies and outcomes of rural tourism households under the influence of tourism seasonality in the Lijiang scenic spot of China. The results indicate that livelihood strategies adopted by rural tourism households include “Tourism Persistence”, “Seasonal Employment”, and “Seasonal Farming” during the off-season and “Extending Working Hours”, “Increasing Staffing Input”, and “Increasing Capital Input” during the peak season. Furthermore, these strategies form flexible combinations to realize livelihood outcomes, with “Extending Working Hours” being a necessary condition. The findings also revealed that the four configurations of seasonal livelihood strategies in the two patterns significantly contributed to high livelihood outcomes. One is named “Peak-Season Driven Pattern”; the other is named “Peak-Off Blend-Driven Pattern”. These findings provide theoretical and practical insights for sustainable livelihood research.

Efficient order allocation and inventory management are essential for the success of supply chain operations in today’s dynamic and competitive business environment. This research introduces an innovative decision-making model incorporating dependability factors into redesigning and optimizing order allocation and inventory management systems. The proposed model aims to enhance the overall reliability of supply chain operations by integrating stochastic factors such as demand fluctuations, lead time uncertainty, and variable supplier performance. The system, named Dynamic Reliability-Driven Order Allocation and Inventory Management (DROAIM), combines stochastic models, reliability-based supplier evaluation, dynamic algorithms, and real-time analytics to create a robust and flexible framework for supply chain operations. It evaluates the dependability of suppliers, transportation networks, and internal procedures, offering a comprehensive approach to managing supply chain operations. A case study and simulations were conducted to assess the efficacy of the proposed approach. The findings demonstrate significant improvements in the overall reliability of supply chain operations, reduced stockout occurrences, and optimized inventory levels. Additionally, the model shows adaptability to various industry-specific challenges, making it a versatile tool for practitioners aiming to enhance their supply chain resilience. Ultimately, this research contributes to existing knowledge by providing a thorough decision-making framework incorporating dependability factors into order allocation and inventory management processes. Practitioners and experts can implement this framework to address uncertainties in their operations.

In the digital economy era, more opportunities and challenges for the sustainable tourism have been recognized by major economics. As the core of regional economic integration, improving tourism image for the urban agglomerations has been considered as a critical thrust in driving regional tourism economy. The studies of tourism image are of the great significance for guiding corresponding construction practices. Trying to bridge the theoretical discussion and regional policy practice, our research is hereby taking the Beibu Gulf urban as a travel destination for a comprehensive tourism image perception analysis. The network analysis and the text mining devices are combined to analyze the tourism image perception characteristics of the selected urban agglomeration. The result reveals that the cognitive tourism image perceptions of the Beibu Gulf urban agglomeration can be divided into multiple dimensions. The coastal tourism resources are the most distinctive in the urban agglomeration. We hereby suggest the policymakers should take the result into account in practice for promoting the high-quality tourism development.