Earticle

The business environment from 2020 to 2024, marked by the COVID-19 pandemic, digital transformation, and strengthened ESG regulations, has dismantled industry boundaries, positioning inter-industry convergence as a critical driver of corporate competitiveness. This study aims to identify the success factors of inter-industry convergence strategies from a value chain perspective and empirically validate them across ICT, manufacturing, bio-health, content, and logistics industries. The impacts of digital technology adoption, value chain modularity and standardization, ESG sensitivity, network density and collaboration experience, and convergence strategy capability on the realization of convergence strategies were analyzed. Using data of 200 firms, confirmatory factor analysis and structural equation modeling confirmed high reliability and validity for all variables, with digital technology adoption and convergence strategy capability exerting the most significant influence on convergence success. ESG sensitivity and network density showed differentiated contributions across industries. This study suggests that firms should leverage digital technologies and collaboration networks to strengthen convergence strategies and build sustainable value chains aligned with ESG standards. Practically, ICT firms should prioritize AI and IoT technologies, while bio-health firms should focus on ESG-driven innovation. Policy-wise, support systems to foster inter-industry collaboration are essential.

ESG has increasingly established a foothold in the field of social welfare and is emerging as a significant paradigm. However, empirical evidence regarding its applicability and implementation within social welfare organizations remains limited. This study conducted an exploratory investigation to assess the applicability of ESG management to the social welfare sector. A structured survey was administered to the directors of 19 social welfare organizations in Daejeon Metropolitan City. The survey instrument was designed by identifying the primary functions and tasks of social welfare organizations and applying the PDCA Cycle framework. The analysis revealed results regarding stepwise promotion rates, institutional promotion levels, positive and negative perceptions, and detailed awareness and implementation tasks related to ESG management. Overall, the study found that ESG management adoption remains at an early stage, with substantial variation across institutions. Based on these findings, the study proposes core implementation strategies and detailed guidelines to facilitate the revitalization and effective integration of ESG management within the social welfare sector.

This study introduces the DAINOS Framework as an integrated management approach for hyper-scale AI projects in manufacturing, characterized by technical complexity and multi-organizational collaboration. As open innovation becomes more common, large-scale AI initiatives increasingly involve diverse stakeholders such as data providers, AI developers, infrastructure suppliers, and users. Traditional Work Breakdown Structures (WBS) often fail to clarify roles, facilitate functional communication, or coordinate collaboration effectively. To address these limitations, DAINOS is proposed as a role-based meta-framework that complements WBS. It consists of six domains—Data, AI, Infrastructure, Network, Organizing, and Service —and provides functional context to tasks through auxiliary labeling. This enables clear role alignment and structured collaboration. Grounded in systems engineering, complexity theory, and collaborative network theory, DAINOS was applied to a large-scale AI service development project involving 15 institutions. Results showed it functioned as a common language among stakeholders, reducing confusion and enhancing project governance. In qualitative assessments, stakeholders reported clearer role recognition, reduced inter-organizational friction, and more effective communication; for example, 15 participating institutions achieved streamlined decision-making processes and observed measurable improvements in collaboration efficiency. The study contributes both theoretical insights and practical tools for managing complex socio-technical systems. DAINOS also shows promise as a generalizable framework applicable to sectors such as aerospace, defense, and healthcare. Future work will focus on quantitative evaluations, cross-industry comparisons, and developing a maturity model for framework evolution. In summary, these research directions aim to further develop DAINOS into a robust and generalizable governance framework with practical applicability and scalability, thereby enhancing project governance and collaboration in hyper-scale AI projects and similar multi-organizational R&D initiatives, and ultimately contributing to diverse industries.

This study proposes a data-driven model for optimizing the location of Chinese restaurants, a critical determinant of success in the highly competitive food service industry. Using public data provided by the Korean government—including resident registration statistics and restaurant business records—the analysis quantifies the saturation of Chinese restaurants across administrative districts in South Korea. Two indices are defined: the Saturation Index (number of Chinese restaurants per 10,000 residents) and the Competitiveness Index (proportion of Chinese restaurants among all food service businesses). Data preprocessing and analysis were conducted in Python, with visualization facilitated by Pandas, Matplotlib, and Seaborn. The results reveal regional disparities: for example, Gwacheon and Sejong exhibited low saturation levels, indicating substantial market potential, while districts such as Gangnam in Seoul and Haeundae in Busan were oversaturated. The findings demonstrate the practical utility of public data in entrepreneurial decision-making and present a reproducible analytical framework that can be extended to other restaurant types or industries. This approach advances location selection from intuition-driven to evidence-based, quantitative analysis.