Space tourism is a growing industry sector that faces challenges of cost, risk, environmental impact, and sustainability. However, few studies address space tourism in an Asian culture, particularly in the context of artificial intelligence (AI), which is an increasingly significant topic b oth in the tourism sector and in society overall. To address the research gap, this work establishes an analytical framework which contrasts t hree varieties of space tourism using partial least squares, multi-group analysis, and fuzzy-set Qualitative Comparative Analysis. It surveyed 1,000 prospective space travelers from South Korean who are eager to take part in space to urism to examine AI's role in enhancing sustainable space tourism. Findings indicate that recognizing AI benefits are crucial for sustainable on-Earth, suborbital, and orbital space tourism, particularly the latter. The study offers both conceptual and applied knowledge to enhance the sustainability of space tourism.0000

Given the COVID-19 pandemic has led to substantial job losses in the hospitality and tourism industry, it is especially critical to address the psychological consequences among individuals experiencing unemployment in the wake of this unprecedented global crisis. The purpose of this study was to explore the relationships between COVID-19-related unemployment and stress, quality of life, and life satisfaction perceived by hospitality and tourism workers. This study showed that job loss due to the COVID-19 pandemic significantly relate to the extent to which individuals evaluated stress, quality of life, and life satisfaction. By focusing on psychological consequences of unemployment, this study provides a fertile ground for discussion about the long-debated well-being of employees in the industry. Findings can also serve as guidance for the development and implementation of policies and interventions that can help reduce the impact of unemployment.

Recognizing the critical importance of data and its vital role in advancing the tourism industry, the European Union has begun to invest significantly in the creation of a common European Tourism Data Space (ETDS), an infrastructure to facilitate the sharing of data among tourism stakeholder organizations. In order to inform the design and governance strategies of the ETDS, a series of binary logistic regression models are evaluated to understand the key factors that influence tourism stakeholders' decisions to share various categories of tourism ecosystem data (i.e., Human Capital, Natural Capital, Built Capital, Economic/Social Capital, Environmental Impact, Economic Impact, and Social Impact data) with third parties. Results based on online questionnaire data obtained from European tourism organizations (n=209) indicate that organizational resources, typology, motivations, and effort expectancy all play varying roles in tourism stakeholder data sharing practice. Importantly, this research provides preliminary empirical evidence to support the on-going development of the ETDS. Additionally, this investigation provides the opportunity to revisit the formative theories related to organizational data sharing and to re-evaluate them within the context of today's rapidly evolving tourism sector.