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This study explores strategies to revitalize regional content and design a local transmedia platform based on AI and storytelling. Focusing on Asan and Suseong District in Korea, it compares local content utilization and policy approaches. Asan, despite its cultural assets such as Oeam Folk Village and Onyang Hot Springs, struggles with low digitalization and limited community engagement. In contrast, Suseong has successfully transformed urban spaces through the “Art Museum City” project using AI and transmedia storytelling. Through literature review, case analysis, and expert interviews, the study proposes a three-stage content model: narrativization, media expansion, and AI-driven interaction. It also outlines platform functions, user flow, and creator participation. To ensure long-term use, strategies such as community-based promotion, public- private collaboration, and sustainable governance are suggested. The research contributes a digital content framework centered on local identity and storytelling, with practical implications for regional cultural policy. Further studies should test this model and expand comparative analysis across diverse local contexts.

In education, Generative AI is being used as a way to increase competencies along with subject matter knowledge. As AI is being used in education, the potential for personalization and active education is being studied, and it is necessary to study the application and effectiveness of Generative AI in education as it develops in the fields of assisting, collaborating, and coaching. It is expected that Generative AI will be used in education not only as an educational content creation tool, but also as a tutor and assistant in a role-split interaction. Therefore, in the Chinese language class, we will look at the steps to utilize Generative AI after basic Chinese language acquisition as creating 'conversation scenarios' and creating interactive video content. A survey was conducted before the use of Generative AI, after the role-divided interaction, and after the educational content creation tool, and the results showed significant positive effects on class participation, interest, satisfaction, and motivation. Of these, interest and satisfaction had the highest effects. “Role-splitting interaction” had a higher effect on engagement and satisfaction, and ‘educational content creation tools’ had a higher effect on interest. The overall competency effect of using Generative AI was on communication and critical thinking, and the effect of role-splitting interaction was on content, critical thinking, creative innovation, and self-confidence compared to educational content creation tools, which showed an overall increase in self-confidence. Training should extend experience from knowledge transfer to validate knowledge and increase flexibility and the ability to utilize vast amounts of knowledge under different conditions.

Organizations face unprecedented challenges in today’s volatile global landscape, demanding a new Enterprise Risk Management (ERM) approach. While the COSO framework has been valuable, it may not fully address the complexities of today’s risk landscape, particularly concerning geopolitical power dynamics. This paper introduces the innovative Jupsang Leadership model to bridge this gap, drawing on Eastern and Western leadership theories. Validated by a survey of 890 managers in Asia, the Jupsang Leadership Orientation (JSLO) scale links COSO’s strategic, operational, reporting, and compliance aspects with Jupsang’s vision, relationship building, market awareness, vigilance, agility, and innovation facets. Jupsang Leadership offers a novel contingency model for ERM, promoting a self-adjusting mentality among employees for proactive risk handling. For top executives in both Western and Eastern contexts, JSLO provides a new lens for ERM leadership and sets the stage for further research, inspiring a new wave of exploration in this field.

This study empirically analyzed the impact of entrepreneurial ecosystem factors, including regional policy, entrepreneurial culture, infrastructure (incubation facilities), investment attraction environment, networks, market exploration, growth, and local attachment, on the firm performance of 75 startups supported by the University-Centered Entrepreneurship Program. The factor analysis results indicated that the eight factors (policy, culture, infrastructure, investment attraction environment, network, market exploration, growth, and local attachment) demonstrated high reliability and validity, distinctly categorized. Correlation analysis revealed significant positive (+) correlations between firm performance and factors such as networks, market exploration, growth, and local attachment. In the multiple regression analysis, infrastructure (β=0.481), networks (β=0.298), growth (β=0.349), and local attachment (β=0.247) were found to have significant positive (+) effects on firm performance. However, entrepreneurial culture unexpectedly exhibited a significant negative (-) coefficient (β=-0.351). Meanwhile, regional policy, investment attraction environment, and market exploration were not statistically significant in this model. These findings suggest that incubation infrastructure, networks, growth strategies, and local attachment directly contribute to short-term improvements in firm performance. On the other hand, entrepreneurial culture and regional policies may exert indirect effects through mediating variables. Therefore, future research should expand the sample and apply structural equation modeling (SEM) to investigate more sophisticated causal relationships. This study holds academic and practical significance by emphasizing the importance of “practical support systems,” “growth strategies,” and “community integration” in designing and managing regional entrepreneurial ecosystem policies and programs.

This study investigates the changes in factors influencing aircraft utilization based on aircraft types during the pandemic period. The analysis categorizes the periods into three phases: pre-pandemic, during the pandemic, and post-pandemic, while the aircraft types are classified into two groups (C-class and E-class or higher). Utilizing SHAP and Random Forest methodologies, the study analyzes the importance of various factors for each group and examines the significance of differences in importance across the specified periods and aircraft types through group difference testing. The findings reveal that for C-class aircraft, the importance of the USD/KRW exchange rate and the lagging composite index was notably high before the pandemic. However, during the pandemic, there was a substantial surge in the significance of the JPY/KRW exchange rate, and post-pandemic, the importance of economic indicators increased. E-class or higher aircraft exhibited a similar trend, with a significant rise in the importance of the CNY/KRW exchange rate observed after the pandemic. The Kruskal-Wallis test confirmed that most average changes among the groups were not statistically significant, except for the C-class aircraft in the Random Forest analysis. This indicates that there were no substantial changes in the underlying factors between the pre-pandemic and post-pandemic periods, suggesting that the impact of the pandemic on airline fleet utilization extends beyond short-term changes. Furthermore, the findings highlight the need for airlines to enhance their sensitivity to external environments and develop utilization strategies in volatile markets. Additionally, there is a possibility that long-term dependency on network strategies may become more pronounced than reliance on external factors like the pandemic. By providing an in-depth analysis of the pandemic's impact on aircraft utilization, this study contributes to the formulation of effective utilization strategies for airlines navigating an uncertain economic environment.