Earticle

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size  of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.

This study investigated technological and managerial barriers in technology startups through a survey of 151 companies, yielding 118 responses (78.1% response rate). Factor and multivariate analyses identified two distinct barriers: technological and managerial. Reliability analysis validated the measurement tool. Using MANCOVA, 12 hypotheses were tested, incorporating six independent variables. Results revealed significant disparities in technological and managerial barriers based on establishment type, commercialization goals, growth stage, and commercialization stage, with 5 hypotheses supported. This study highlights the crucial role of these variables in understanding barriers within technology-based startups.

How to create high levels of employee engagement and how to avoid burnout in the workplace is main issue in human resource management. According to Job Demands-Resources (JD-R) model, this study aims to investigate when self-efficacy plays as a mitigator on the impact of job demand on burnout, and explains why job resources are translated into work engagement. A sample of 237 Mongolian employees is used to test hypotheses. Results show that self-efficacy does offset the relationship between job demands and burnout. Meanwhile, self-efficacy plays as a mediator on the impact of job resources on work engagement. The implications of these findings for the context of JD-R model are discussed.

The aim of this research is to examine the impact of trust in reviews. Expertise, enjoyment, recency, and usefulness—four aspects of reviews—are designated as independent variables, and trust in reviews has been chosen as the mediating variable. The dependent variables are trust in firms and trust in products. For explaining the flow of trust, this study uses the theory of Trust Transfer. The study's findings demonstrated that customer trust in a product leads to consumer trust in a company, which is derived from trust in reviews. Reviews were found to be important from a practical standpoint. Furthermore, it was discovered that a product's category or features would have an impact on how reviews are trusted.

In modern society, as data plays a crucial role at the levels of businesses, industries, and nations, the utilization of data becomes increasingly important. Consequently, governments are prioritizing the development and implementation of plans to cultivate data workforce, viewing the data industry as a cornerstone of national strategy. To enhance domestic capabilities and nurture workforce in the data industry, it is deemed necessary to conduct an objective comparative analysis with major foreign countries. Therefore, this study aims to analyze cases of domestic and international data industries and explore methods for quantitatively comparing data industry workforce across nations. Initially, the study distinguishes between "data industry workforce" and "data job-related workforce," particularly focusing on professionals handling data-related tasks. Subsequently, it compares the workforce sizes of data job-related workforce across nations, utilizing standardized occupational classification codes based on the International Standard Classification of Occupations(ISCO). However, it should be noted that countries employing their own unique occupational classification systems often require matching job titles with similar meanings for accurate comparison. Through this study, it is anticipated that policymakers will be able to establish future directions for cultivating data workforce based on comparable status.

In order to verify the factors affecting the acceptance of Serious Games for Healthcare (SGHs), this study developed a hierarchical model of general and specific benefit and risk factors affecting the intention to use SGHs based on the valence framework. As a result based on 199 samples, it was revealed that perceived customization and perceived schedule flexibility had a positive effect on the perceived benefits, which, in turn, had a positive effect on the intention to use SGHs. However, among the specific risk factors, only privacy risk had a positive effect on perceived risk, but it did not have a effect on SGHs usage intention. The results related to the fact that the survey respondents were potential users of SGHs and the bias that may overestimate the benefits provided by SGHs called optimistic bias. Based on these findings, some implications were presented such as the spread and distribution of SGHs to the ordinary persons, improvement of negative perceptions of games, and the need for data-based services to refine customized services for SGHs.

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.

With the increase in online shopping and delivery food consumption since the pandemic, solving environmental problems caused by single-use packaging has become an important issue. ‘Upcycling’ is a combination of ‘Upgrade’ and ‘Recycle’, and it is the rebirth of obsolete or discarded objects by adding new value to them, and there are currently various upcycled products on the market. In order to activate upcycling, consumers’ awareness of the environment and their values for consumption are very important. This study aims to investigate the influence of students’ environmental concern, environmental experience, and consumption value on their purchase intention of upcycled products. Based on the results of previous studies on environmental concern, environmental experience, and consumption value, hypotheses were set, and a survey was conducted among university students nationwide to test the hypotheses. The results of this study are as follows First, environmental concern has a significant positive effect on purchase intention of upcycled products. It can be seen that the more environmental concerns such as global warming and waste disposal problems increase, the more positive attitudes toward upcycled products increase. Second, the research hypothesis that environmental knowledge will have a positive effect on the purchase intention of upcycled products is rejected. It was found that environmental knowledge is acquired through environmental education and many SNS, but it does not have a direct effect on the purchase intention of upcycled products. Third, it was found that the consumption value of college students has a positive effect on the purchase intention of upcycled products by increasing their positive perception of upcycled products. Fourth, college students’ purchase intention of upcycled products has a positive effect on their behavioral intention to purchase upcycled products. The results of the study provide implications for relevant organizations such as universities and companies to effectively design upcycling-related education. It is also expected to have a positive impact on the use of upcycled products by providing basic information on the characteristics of consumers who purchase upcycled products.

The Fourth Industrial Revolution has spurred widespread adoption of AI-based services, driving global interest in AI semiconductors for efficient large-scale computation. Text mining research, historically using LDA, has evolved with machine learning integration, exemplified by the 2021 BERTopic technology. This study employs BERTopic to analyze AI semiconductor-related patents and research data, generating 48 topics from 2,256 patents and 40 topics from 1,112 publications. While providing valuable insights into technology trends, the study acknowledges limitations in taking a macro approach to the entire AI semiconductor industry. Future research may explore specific technologies for more nuanced insights as the industry matures.

This study conducts topic modeling analysis on four electric scooter sharing platforms: Alpaca, SingSing, Kickgoing, and Beam. Using user review data, the study aims to identify key topics and issues associated with each platform, as well as uncover common themes across platforms. The analysis reveals that users primarily express concerns and preferences related to application usability, service mobility, and parking/accessibility. Additionally, each platform exhibits unique characteristics and challenges. Alpaca users generally appreciate convenience and enjoyment but express concerns about safety and service areas. SingSing faces issues with application functionality, while Kickgoing users encounter connectivity problems and device usability issues. Beam receives overall positive feedback, but users express dissatisfaction with application usability and parking. Based on these findings, scooter sharing service providers should focus on enhancing application features, stability, and expanding service coverage to meet user expectations and improve customer satisfaction. Furthermore, highlighting platform-specific strengths and providing tailored services can enhance competitiveness and foster continuous service growth and development.

In this research, a cohort of two children, aged 7-8 years, was selected to participate in a specialized three-week training program aimed at enhancing their working memory. The program consisted of three sessions, each lasting approximately 30 minutes. The primary goal was to investigate the impact and developmental trajectory of working memory in school-aged children. Working memory plays a significant role in young children’s learning and daily activities. To address the needs of this demographic, products should offer both educational and enjoyable activities that engage working memory. Digital educational tools, known for their flexibility, are suitable for both older individuals and young children. By updating software or modifying content, these tools can be effectively repurposed for young learners without extensive hardware changes, making them both cost-effective and practical. For example, memory training games initially designed for older adults can be adapted for young children by altering images, music, or storylines. Furthermore, incorporating elements familiar to children, like animals, toys, or fairy tales, can increase their engagement in these activities. Historically, working memory capabilities have been assessed predominantly through traditional intelligence tests. However, recent research questions the adequacy of these behavioral measures in accurately detecting changes in working memory. To bridge this gap, the current study utilized electroencephalography (EEG) as a more sophisticated and precise tool for monitoring potential changes in working memory after the training. The research findings were revealing. Participants showed marked improvement in their performance on n-back tasks, a standard measure for evaluating working memory. This improvement post-training strongly supports the effectiveness of the training program. The results indicate that such targeted and structured training programs can significantly enhance the working memory abilities of children in this age group, providing promising implications for educational strategies and cognitive development interventions.