The Society of Convergence Knowledge Transactions ISSN:2287-8920(Print) 융복합지식학회논문지

This study was conducted to identify factors influencing the formation of nurses' professional identity in the rapidly digitalizing healthcare environment, aiming to confirm the effects of nurses' self-leadership and digital health literacy on nursing professionalism perception. The study was a descriptive survey research, targeting 165 hospital nurses who were conveniently sampled based on the sample size calculated using the G*Power 3.1.9.4 program. Data on socio-demographic characteristics, self-leadership , digital health literacy , and nursing professionalism perception were collected via questionnaire. The collected data were analyzed using the SPSS WIN 22.0 program with frequency analysis, t-test, ANOVA, Pearson correlation analysis, and hierarchical multiple regression. The results showed that nurses' self-leadership, digital health literacy, and nursing professionalism perception had significant positive correlations with each other (r=.395~.610). The hierarchical multiple regression revealed that the most influential factor on nursing professionalism perception was self-leadership (β=.471, p<.001), followed by digital health literacy (β=.265, p<.001). Furthermore, working in a hospital with 200 to less than 300 beds was found to be significantly associated with lower nursing professionalism perception (β=-.323, p<.01). This study confirmed that nurses' self-leadership and digital health literacy are significant predictors influencing the formation of nursing professionalism perception. This suggests that to solidify nurses' professional identity in the digital healthcare environment, educational and organizational support to enhance individual-level self-directed leadership and digital competencies is necessary.

This paper explores strategies to improve user experience (UX) through sentiment analysis of user reviews for Naver Pay and Kakao Pay, which dominate the domestic FinTech market. As mobile FinTech services become more sophisticated, user demands have expanded beyond simple transactions to encompass usability, security, and connectivity. Consequently, accurately identifying user feedback to improve UX is essential; however, traditional sentiment analysis, which relies primarily on simple positive and negative classification, has limitations in deriving specific contexts of user complaints. To overcome these limitations, this study adopted an in-depth analysis methodology utilizing TF-IDF-based keyword extraction combined with the LLM SOLAR 2. The analysis revealed that both services exhibited a high negative sentiment ratio of over 90%, but the underlying causes differed. For Naver Pay, the primary complaints were pop-up ads obstructing the payment flow and complex authentication procedures during external integration. For Kakao Pay, functional errors in stock charts and a decline in trust due to concerns over personal data leaks were the main issues. Based on these findings, this study proposes four practical UX improvement strategies: separating payment UIs from advertising areas, dualizing notification settings tailored to user proficiency, establishing adaptive interfaces, and introducing a security dashboard for data transparency, thereby contributing to the qualitative enhancement of FinTech services.

As the need for non-face-to-face psychological support for children and adolescents has surged in the post-pandemic era, traditional in-person counseling or simple video-based sessions have faced limitations in facilitating emotional openness. This study systematically analyzed the emotional characteristics and participation patterns of children in the mindfulness program based on the avatar communication platform. As a result of analyzing 18 children at the local children’s center, the participants showed defensive attitudes such as "I don’t know" in the beginning, but experienced emotional relaxation quickly through avatar movement manipulation and visual tools. In particular, the age-specific analysis revealed a clear differentiation and transition in both the themes of concern and the depth of self-expression according to developmental stages: lower elementary school students focused on stress relief through play, higher elementary students on lifestyle reflection and self-regulation, and middle school students on family attachment and academic concerns. Furthermore, the virtual space became a channel to safely vent and purify intense anger and negative emotions suppressed in reality. These findings suggest that the metaverse platform acts as a core mechanism for lowering psychological barriers rather than merely being a digital tool. This study is meaningful in confirming that the mindfulness program is an effective psychological support system tailored to the characteristics of children’s developmental stages, and suggesting practical emotional support strategies.

Oyster shells are generated in large quantities as marine waste, yet their recycling rate remains low, causing environmental and economic burdens. In this study, oyster-shell powder was used as a substrate to prepare antibacterial powders through ion-exchange coating with antibacterial metal ions (Zn, Ni, Cu, and Ag). The structural and chemical characteristics of the prepared powders were evaluated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Brunauer–Emmett–Teller (BET) surface area analysis, and X-ray photoelectron spectroscopy (XPS). Antibacterial performance was examined by the disk diffusion method against Escherichia coli, Salmonella spp., and Staphylococcus aureus. Among the tested samples, the Ag-coated oyster-shell powder exhibited clear antibacterial activity against all tested strains, whereas the Zn-, Ni-, and Cu-coated powders showed no distinct antibacterial activity or only negligible effects under the present experimental conditions. These results suggest that Ag-coated oyster-shell powder has potential for application in functional coatings and industrial antibacterial materials.

Organizations are increasingly leveraging generative AI (GenAI) for employee knowledge management; however, ensuring consistent GenAI adoption and enhanced task performance necessitates a strategic organizational communication approach. This study examines the need for structured communication by elucidating the mechanisms linking communication gaps regarding GenAI to employee task performance. Drawing upon prior research in sociology, nursing, and business administration, a research model was developed and validated through a survey of employees in organizations utilizing GenAI. Hypothesis testing was applied using AMOS 22.0 and Process 3.1 macros. Hypothesis testing revealed that communication uncertainty and ambiguity negatively affect task performance via prospective anxiety, while individual technology readiness attenuates these adverse effects. These results contribute to the formulation of strategies aimed at optimizing GenAI-driven performance by clarifying the organizational and individual factors critical for successful task achievement in GenAI-enabled environments.

In this study, we analyzed the interconnections among stock markets and the efficiency resulting from volatility using time series data of the KOSPI200, HSI, Nikkei225, EuroStoxx, and S&P500 indices before and after the COVID-19 pandemic. The main findings are summarized as follows. First, the Granger causality analysis showed that there were no significant interrelationships among the Asian indices (KOSPI200, HSI, and Nikkei225) either before or after the pandemic. In contrast, both the EuroStoxx and S&P500 indices were found to lead all Asian indices during both periods. Second, the permanent component of volatility, rather than the transitory component, was found to be more statistically significant, indicating that volatility information tends to be relatively efficient. Third, the risk premium associated with asymmetric volatility was statistically significant in the Asian markets (KOSPI200, HSI, and Nikkei225) after the pandemic. This suggests that although the KOSPI200 market is relatively less efficient, substantial information is embedded in asymmetry, which is gradually reflected in volatility and subsequently affects returns. In conclusion, although it is difficult to conclude that the impact of volatility on returns is highly efficient, the information contained in volatility itself tends to reflect the permanent component more quickly than the transitory component. The market shock caused by COVID-19 significantly affected both transitory factors and asymmetry. In particular, when asymmetric information is incorporated, both the influence on the risk premium and the impact of transitory volatility tend to increase.

This study aimed to examine the relationship between self-leadership and career preparation behavior among university students and to investigate the roles of resilience and self-esteem in this relationship. Data were collected from 245 students enrolled in four-year universities in Korea, and the analyses were conducted using SPSS 29.0 and PROCESS Macro Model 6. The results indicated that self-leadership significantly enhanced career preparation behavior. In addition, self-esteem was found to be a significant mediating factor explaining the relationship between self-leadership and career preparation behavior. In contrast, resilience did not show a significant mediating effect when examined independently. However, the indirect effect became significant when resilience was linked sequentially to self-esteem. These findings suggest that career preparation behavior among university students is shaped through the interaction of multiple psychological factors rather than by a single variable alone. The study highlights the importance of considering both self-regulatory abilities and positive psychological resources in supporting students’ career development.

The intensification of climate change and the rapid advancement of artificial intelligence (AI) technologies have created new opportunities for the preservation of non-Western architectural heritage. Rather than empirically validating the performance of AI-based conservation systems, this study focuses on conceptually organizing their design framework and potential applications. Using the Taj Mahal in India as a case study, the paper proposes a three-stage architecture consisting of data collection, analysis, and decision-making. In the data collection stage, satellite imagery, unmanned aerial vehicles (UAVs), and Internet of Things (IoT) data are integrated. In the analysis stage, various deep learning models, including U-Net, YOLO, and Mask R-CNN, are employed to detect and assess heritage deterioration. The decision-making stage is built upon a digital twin framework that supports predictive conservation and management. In addition, the study presents a multi-threat matrix, design scenarios that reflect the Mughal, Islamic, and Indian architectural context, and AI bias mitigation strategies informed by postcolonial perspectives. Finally, a five-stage validation roadmap is proposed to facilitate future field-based research and practical implementation.

This study examined the effects of grit, positive psychological capital, and self-leadership on nursing professionalism among nursing students, and sought to provide foundational data for the development of intervention strategies to enhance nursing professionalism. The participants were 171 nursing students, and data were collected through a self-administered online survey from September 1 to September 20, 2025. The collected data were analyzed using descriptive statistics, frequency analysis, t-test, one-way ANOVA, Scheffé test, Pearson’s correlation analysis, and multiple regression analysis with SPSS version 25.0. The results showed that nursing professionalism was significantly and positively correlated with grit, positive psychological capital, and self-leadership. In the multiple regression analysis, self-leadership and grit were identified as significant predictors of nursing professionalism. These variables explained 30.3% of the variance in nursing professionalism. These findings suggest that educational programs designed to enhance self-leadership and grit are needed to improve nursing professionalism among nursing students.

This study examined changes in news discourse on healthcare artificial intelligence before and after the enforcement of the AI Basic Act in Korea. A total of 3,371 news articles published between January 1, 2023, and May 10, 2026, were collected from BIGKinds and divided into two periods based on the enforcement date of January 22, 2026. TF-IDF analysis, latent Dirichlet allocation topic modeling, and keyword network analysis were conducted to identify major keywords, latent topics, and semantic structures. Before the enforcement of the Act, the discourse mainly focused on biohealth research and development, next-generation medical products, healthcare data utilization, generative AI, and digital healthcare platforms, indicating a technology- and industry-oriented perspective. After the enforcement of the Act, the discourse shifted toward AI medical device and pharmaceutical policy development, support for medically underserved areas, personal information protection, life-cycle-based management, healthcare big data governance, and region-based precision medicine safety nets. These findings suggest a shift toward trust-based governance emphasizing safety, publicness, accountability, equity, and human-centered care.