Introduction: This study explores the well-being dimensional components of event tourists and their identification processes in validating the well-being occurrence mechanism of event tourism and the correlation between the well-being of event tourists and the frequency and length of event tourism.
Methods: This study adopted a sequential mixed-methods design that followed a pragmatic paradigm through a photo interview with event tourists and festival travel organizers (N=16). The qualitative research method provided evidence to explore the framework of content and dimensional identification of event tourists' well-being according to Seligman's PERMA model. The quantitative research phase (N=475) focused on identifying and validating the PERMA model in the event tourist well-being dimension through descriptive statistical analysis and validated factor analysis, followed by a one-way analysis of covariance to explore the effects of the frequency and endurance of FSE tourism.
Results: The results show quantitative differences in the well-being dimensions and framework presentation of the PERMA model (Positive emotion, Engagement, Relationship, Meaning, and Achievement). R (relationship) and A (achievement) are identified and validated as dimensions of well-being outcomes for event tourists, while single-day or short trips of 2-3 days were most significant for event tourists' perceived well-being.
Conclusion: This study provides an empirical argument, thus providing an empirical argument for uncovering the deeper influencing and exhibiting factors of the PERMA theoretical framework and a research paradigm for PERMA theory in more tourism behaviors and psychology. Second, this study provides an in-depth explanation of the five dimensions of well-being in the PERMA model. The findings show the salience of the relationship and achievement in FSE tourism well-being, providing theoretical insight into existing studies integrating positive psychology models for in-depth tourism well-being research.
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http://dx.doi.org/10.2147/PRBM.S413012 | DOI Listing |
Sports Med
December 2024
Ultra Sports Science Foundation, Pierre-Bénite, France.
Background: Antarctic expeditions, although supported by scientific knowledge, face various challenges, with little research conducted to explore the physical demands that explorers experience.
Objective: To summarise physiological, psychological, body composition and nutritional changes faced during trek expeditions in the Antarctic's continental portion.
Design: Systematic review.
PLoS One
December 2024
Warnell School of Forestry, University of Georgia Athens, Athens, Georgia, United States of America.
Remotely-sensed risk assessments of emerging, invasive pathogens are key to targeted surveillance and outbreak responses. The recent emergence and spread of the fungal pathogen, Batrachochytrium salamandrivorans (Bsal), in Europe has negatively impacted multiple salamander species. Scholars and practitioners are increasingly concerned about the potential consequences of this lethal pathogen in the Americas, where salamander biodiversity is higher than anywhere else in the world.
View Article and Find Full Text PDFEnviron Pollut
December 2024
State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228, China. Electronic address:
PLoS One
December 2024
School of Business Management, College of Business, Universiti Utara Malaysia, Sintok, Malaysia.
Tourism advertising and tourism promotion have over the years been the core functions of tourism departments and major tourist sites. In relation to the progressing development of new media, the mobile short-form videos, which are short, focused, and have an engaging content, appear to be a useful means of advertising tourist destinations. In the digital era, short videos have become a new communication tool between destinations and consumers.
View Article and Find Full Text PDFSci Rep
December 2024
School of Hotel and Tourism Management, Shunde Polytechnic University, Foshan, Guangdong, 528333, China.
This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists' purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists' attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists' purchases.
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