More and more tourists are becoming active members of social networking sites (SNS) and are tending to use SNS during different trip stages. Thus, SNS use allows their members to find a great variety of tourism information; share experiences by posting various pictures, photos, and blogs; tag photos; and read and leave comments on network friends' posts. By getting tagged in a friend's photos or getting some positive comments, the tourists using SNS can perceive social support from both friends and other network members, and obtain positive emotions triggering from maintaining relationships with family members and close friends or sharing opinions and ideas with people with similar interests. All this can change and shape the satisfaction with tourism experience of the tourists. This study aims to fill the gap in understanding the relationship between SNS use and satisfaction with tourism experience by exploring the effect of psychological factors caused by SNS use on tourists' experience. More specifically, this research attempts to investigate the mediating effects of perceived social support, positive emotions, and commitment. The statistical results show that SNS use positively affect satisfaction with tourism experience by mediating positive emotions and commitment and perceived social support, and commitment also positively influences satisfaction with tourism experience by mediating positive emotions. With these results, this study extends the research stream of tourism experience by figuring out the mediating role of positive emotions caused by SNS use. Additionally, these findings provide some practical implications regarding marketing techniques that use SNS to increase satisfaction with tourism experience of tourists.

SummaryAnti-obesity effects of a mixture of thiamin, arginine, caffeine, and citric acid (TACO) were investigated in non-insulin dependent diabetic KK mice. Feeding of either argi nine or caffeine significantly suppressed an increase in hepatic lipid contents in fasted-refed KK mice. In addition, each component admixed with a low-calorie diet effectively reduced adipose tissue weight in KK mice previously fed a high-calorie diet. The decrease in adipose tissue weight was greater with a mixture of arginine and caffeine, and much greater with TACC than with arginine or caffeine alone. Moreover, plasma insulin concentration was sig nificantly lower in mice fed TACC than in control mice. The anti-obesity effects of TACC were also shown when it was supplemented with a tea beverage. Adipose tissue weight, he patic triglyceride contents, and plasma insulin concentration were significantly lower in mice given TACC-supplemented tea than in control mice. These results suggest that TACC is effective in reducing adipose tissue mass as well as improving disorders in lipid metabolism.

Given recent developments in information and communication technology, the number of individual tourists enjoying free travel without the advice of travel agencies is increasing. Therefore, such tourists can visit more tourist destinations and create more complex movement patterns than mass tourists. These tourist movement patterns are a key factor in understanding tourist behavior and they contain various information that is important for tourism marketers. In this vein, this study aims to investigate tourist movement patterns in Europe. We acquired 122 data points from posts on the NAVER blog, which is the most famous social media platform in Korea. These data were transformed into matrix data for social network analysis and analyzed for centrality. The results suggest that Korean backpackers in Europe tend to enter Europe through London and Paris. Venezia and Firenze are also key cities.

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The advancement of technology following the Fourth Industrial Revolution and the surge in demand for untact services caused by COVID-19 will gradually expand the scope of service automation through artificial intelligence (AI) technologies. In practice, numerous hotels are adopting AI-based service technologies, but it is still in its early stage to provide guidelines for the overall service strategy for this technology. Therefore, this study conducted a study on the failure of AI based self-service technologies (SSTs), service recovery, and the psychological expectations of customers’ SST. An online survey was conducted on respondents who had experience using AI based SSTs, and a total of 370 responses were used for analysis. As a result of structural model analysis through AMOS, it was found that adequate service recovery and low expectations for SSTs were satisfied with SSTs and hotels as a result, even if service failures were experienced. These findings provide specific practical guidelines for many hotels promoting AI-based service automation.