In the era of the Fourth Industrial Revolution, characterized by rapid digital advancements, the metaverse is emerging as a significant innovation driving business transformation. This study explores the impact of the metaverse on business models within the context of the Fourth Industrial Revolution, positioned at the intersection of digital transformation. By adopting a systematic literature review and bibliometric analysis as research methodologies, the study addresses two key research questions: the impact of the metaverse on business model transformation and the potential changes the metaverse may bring to future society from a business perspective. The findings emphasize that companies embracing the metaverse are increasingly shifting towards customer-centric models, with the metaverse showing potential to drive the transition of users into a tangible online life, particularly in sectors such as education, finance, and healthcare. This study serves as an academic milestone for further in-depth research in the metaverse field and offers essential insights for practitioners navigating the emerging digital convergence environment.

As companies increasingly utilize various information and communication technologies, vast amounts of data are being generated every day from their business activities. In oredr to leverage this data and create added value, companies have invested significant time and resources. In the process, information security issues have emerged as a major societal concern. We have already experienced through a series of recent incidents that if a company fails to properly manage the data it holds and it leaks to the wrong parties, it can cause significant harm not only to the company but also to its customers. In this study, we empirically explored the impact of a company's information security activities on its future value and present value. Many previous studies have explored the impact on corporate value through surveys of employees to highlight the importance of information security. However, this approach has significant limitations in objectively and systematically assessing the extent of a company's security activities. In this study, we identified the content and extent of a company's information security activities through text analysis of the "Sustainability Reports" published annually by companies. We then explored the impact of these security activities on corporate value. The text analysis of 171 reports from 57 companies included in the KOSPI 200 over three years revealed that only personal information protection activities had a statistically significant impact on future corporate value, and all information security activities (management/physical/technical activities) had a statistically significant impact on present corporate value. This research reaffirmed that information security activities significantly impact not only the current value of a company but also company's future value. In addition, it is expected that this study will provide concrete insights into which specific information security activities influence a company's value for those organizations aiming to establish security policies.

Recent issues have arisen in the informatization projects of public institutions and administrative agencies, such as the administrative computer network incident. In the case of security reviews for informatization projects, submission of a request for proposal (RFP) is required. Additionally, since January 1, 2013, it has been mandatory to disclose the RFPs needed for informatization projects, and it is recommended to detail the requirements in the RFP for successful project execution. This study collected RFPs over five years, from 2019 to 2023, and analyzed the security-related requirements. The analysis was conducted using BERTopic, a BERT-based topic modeling method known for its strong performance. As a result of the analysis, a total of 8 topics were derived, which were then interpreted by classifying them into two groups based on the similarity of keywords. Furthermore, time series analysis was conducted to identify the trends and patterns of requirements for each topic in the future. It is hoped that the results of this study will provide implications and help establish a successful roadmap in the field of informatization project policy.

Multihoming has received considerable attention from both practitioners and scholars. However, rather than treating it as the strategic behavior of complementors, previous research has evolved mainly from the perspective of platform owners and studied multihoming against the backdrop of platform dynamics or complementor management. Furthermore, studies have generally assumed that the primary reason behind the strategy is to expand market reach. In this study, we present quantitative research that offers alternative motivations behind the multihoming strategy and empirically captures its impact on complementor survival. Based on density dependence theory, we suggest that the adoption of multihoming behavior results from interactions with other complementors in the original platform. We find a U-shaped relationship between the density of the original platform and the complementor multihoming rate. Furthermore, we propose that there is a positive relationship between the number of platforms targeted by complementors and their mortality rate. However, large organizational size mitigates this effect. This study contributes to the platform literature by explaining the motivation and consequences of multihoming and by building a theoretical foundation for the strategical behavior of complementors and their impact on platform ecosystems.

The purpose of this study is to find an effective strategy for improving data literacy considering the propensity of each member of a public institution, and to suggest ways to improve job performance through data literacy. A questionnaire was designed to evaluate the level of data literacy and personal inclination of civil servants in Hwaseong City Hall, and the effect of individual inclination on data literacy, the effect of data literacy on job performance, and the mediating role of organizational culture were analyzed. Among various aspects of data literacy, the importance of data communication competency was found to have a positive effect on job performance. Although not statistically significant, data collection and data decision-making also showed a positive relationship with job performance. When considering the mediating role of data decision-making culture, the ability to collect data was found to be the most important factor. Emphasizing the importance of convenient access to necessary data, it has positively impacted the culture of data decision-making. Based on the results of this study, applicable implications can be drawn. First, voluntary participation in data-related activities is important, and group activities promoting exchange and cooperation are more effective than external events. Second, facilitating data collection and access is essential for creating a data-driven decision-making culture, and implementation of a data-driven management system that promotes data sharing and management along with user education is required for smooth utilization. Third, if you have data processing skills such as using tools such as Excel, expressing phenomena through data, and writing reports, you can greatly improve your job performance. Enabling data validation for key issues requested by managers can facilitate communication through data and facilitate decision-making. When establishing data literacy improvement strategies for public institutions, it is important to consider individual tendencies and organizational culture.

This study emphasizes the importance of ambidexterity, exploration and exploitation, as a strategy for the sustainable growth and goal achievement of small and medium-sized enterprises in an uncertain and rapidly changing business environment, and empirically analyzes the impact of this strategy on business model innovation and business performance. In addition, it examines how the external management environment affects this relationship. The results of the study confirm that the ambidexterity strategy has a positive impact on business performance through business model innovation, and that external management environment factors (funding, government support, R&D technical support) play an important role in moderating this relationship. This study theoretically extends the ambidexterity strategy to include internal and external factors, such as business model innovation and external management environment, by reflecting the structural characteristics of small and medium-sized enterprises. Through this, it contributes to improving business performance and provides practical management strategies for small and medium-sized enterprises to adapt to rapidly changing market environments and maintain competitiveness. This suggests that small and medium-sized enterprises can achieve sustainable growth by utilizing internal and external factors in a balanced manner.

In addition to the positive evaluations of LLMs' language understanding and generation capabilities, concerns have been raised about the potential leakage of sensitive information through questions entered into prompts. To address this, a method has been proposed that stores sensitive information in a separate knowledge graph to prevent LLMs from accessing it. However, storing various types of sensitive information in a single knowledge graph presents practical limitations. To account for the diversity and volume of sensitive information, multiple knowledge graphs need to be federated to construct correct answers. This study presents a GraphRAG-based approach by implementing a prototype chatbot using a composite knowledge graph, which consolidates multiple knowledge graphs that store different types of sensitive information to generate responses to input questions. The implemented chatbot converts natural language questions into query statements suitable for the knowledge graph via the LLM, which then executes queries on the knowledge graph to retrieve results. The LLM is solely involved in generating queries appropriate for the knowledge graph, while the chatbot handles the query execution and result extraction, thereby eliminating the possibility of sensitive information being introduced into the LLM. To validate the proposed method, the performance of the implemented chatbot was tested, confirming not only that correct responses were generated but also that the LLM effectively learned the content and structure of the composite knowledge graph using the approach developed in this study.