- E-ISSN2288-7709
- KCI Candidate
14권 1호
초록
Abstract
Purpose: This study proposes an implementation framework for an intelligent safety management information system that integrates public big data and AI to preemptively respond to hazardous chemical leakage accidents in industrial complexes. The research aims to bridge the gap between technical analytics and managerial practice, establishing a new paradigm for intelligent disaster management within the framework of sustainable corporate governance. Research design, data and methodology: The research design utilizes public APIs from the Korea Meteorological Administration to collect multivariate time-series data. The methodology employs the Long Short-Term Memory (LSTM) algorithm to predict dynamic pollutant dispersion paths. A key methodological distinction lies in the standardization of complex predictive data into a unified 'Environmental Risk Index (ERI)' and the automation of real-time response processes through user-centered UI/UX design. Results: The findings demonstrate that the proposed system provides management with objective, data-driven evidence for high-stakes decision-making through risk quantification. Furthermore, it offers a cost-effective ESG management model for small and medium-sized enterprises (SMEs) by leveraging public data infrastructure instead of costly proprietary sensors, proving its practical efficiency in resource-limited environments. Conclusions: This system functions as a strategic information asset that significantly enhances organizational resilience and ensures industrial site safety. The study concludes that the integration of AI-driven predictive models into managerial information systems is essential for proactive risk control and sustainable corporate governance, providing a robust strategic mechanism for modern industrial safety.
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Purpose: This study aims to examine how the COVID-19 pandemic, as an exogenous shock, affected prepayment behavior in the Korean mortgage-backed securities(MBS) market and to empirically identify how financial-market uncertainty influences borrower repayment decisions. Research design, data and methodology: This analysis utilizes monthly pool-level data (2004.06~2024.12) on MBS issued by the Korea Housing Finance Corporation, with the constant prepayment rate(CPR) as the dependent variable. A 2-step System GMM model is employed, incorporating COVID-19 period dummy variables, interest-rate spread, housing-price index, consumer prices, stock index, seasoning period, and seasonal moving dummies as control variables. Results: The empirical findings reveal that prepayment levels declined overall during the COVID-19 period, associated with heightened liquidity-holding strategies among households due to income and employment instability. In addition, the market-volatility indicator(VIX) shows a statistically significant negative relationship with prepayment, indicating a pronounced suppressing effect during the early and middle stages of the pandemic, which later weakened. Conclusions: The influence of COVID-19 is assessed as a short-term shock, suggesting that future crisis responses require more sophisticated policy frameworks that consider financial psychology and market uncertainty.
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Purpose: The purpose of this study is to compare and analyze the structural characteristics of AI-based industrial safety information systems with those of conventional industrial safety management systems, and to identify a paradigm shift in industrial safety management from an information systems perspective. Research design, data and methodology: The analytical framework is a risk-aware, information-flow–based decision-making structure, which enables a comparative analysis of conventional industrial safety management systems and AI-based systems. Results: The analysis reveals that conventional systems are characterized by reactive post-incident management that focuses on documentation and inspections. In contrast, AI-based industrial safety information systems exhibit a cyclical structure that integrates real-time data collection, AI-driven analysis, immediate alerts and responses, and continuous feedback and organizational learning. This structural distinction indicates a transformation in which industrial safety management has shifted from regulation-compliance–oriented post-incident management to a data-driven prediction and proactive management system. Conclusions: This research conceptualizes the AI-based safety information system as a fundamental paradigm shift in industrial safety management structures. The findings elucidate a transition from compliance-oriented reactive protocols to data-driven proactive systems. Consequently, this study suggests that future safety frameworks must prioritize optimized information flows and structural management to ensure sustainable organizational safety.
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Purpose: This study investigates the relationship between Environmental, Social, and Governance (ESG) ratings and corporate dividend policy in the Korean capital market. While prior studies generally document a positive ESG–dividend nexus, empirical evidence remains mixed, particularly in emerging markets characterized by distinct ownership and governance structures. Research design, data and methodology: Using a comprehensive panel of non-financial firms listed on the KOSPI and KOSDAQ from 2012 to 2024 and ESG ratings provided by the Korea Corporate Governance Service (KCGS), we examine whether and under what conditions ESG performance influences firms’ dividend payout behavior. Results: Employing firm-fixed effects regressions with clustered standard errors, we find that ESG composite ratings are positively associated with both dividend payout ratios and dividend yields. However, the magnitude of this association varies substantially across monitoring environments and dividend regimes. Analyses using disaggregated environmental, social, and governance component scores further reveal that governance quality exhibits the strongest and most consistent association with dividend outcomes. Conclusions: These findings indicate that ESG performance enhances shareholder payout capacity in Korea primarily under institutional settings characterized by weaker external monitoring or higher information asymmetry. By demonstrating that the ESG–dividend nexus is heterogeneous and context-dependent, this study helps reconcile mixed prior evidence and offers new insights into how sustainability practices interact with corporate payout policies in emerging markets.
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Purpose: This study explores the educational potential of culture-text-based English learning by using coffee culture and advertising language as core instructional materials in a university liberal arts English course. It examines how such instruction shapes learners’ motivation, participation, emotional engagement, and identity formation. Research design, data and methodology: A qualitatively driven mixed-methods design was implemented over a 15-week semester with 78 Korean EFL learners. Data were collected through pre- and post-course questionnaires, reflective journals, project artefacts, and semi-structured interviews. Descriptive statistics were used to examine motivational trends, and thematic analysis was applied to the qualitative data. Results: Learners reported increased motivation, confidence, and willingness to participate. They came to reinterpret English not as a test-oriented subject but as a resource for expressing personal meaning. Coffee culture and advertising discourse functioned as symbolic resources for reflecting on lifestyle, values, and identity, while positive emotions such as enjoyment and familiarity supported sustained engagement. Conclusions: The findings suggest that culture-text-based English learning can meaningfully connect language study with learners’ everyday lives and self-narratives. Incorporating culturally grounded and identity-relevant content into liberal arts English education may therefore promote more active, confident, and emotionally engaged English use.
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Purpose: Rapid population aging has intensified structural pressures on caregiving systems, resulting in workforce shortages, high turnover, and persistent caregiver burnout. Although welfare technologies such as care robots, wearable devices, and digital care platforms are widely promoted as solutions, many fail to achieve sustained adoption or industrialization. This study aims to explain why welfare technologies repeatedly remain confined to pilot implementation despite technical viability. Drawing on consumer behavior theory, service-dominant logic, emotional labor theory, and well-being economics, this paper reframes caregivers as consumers and primary value evaluators of welfare technology. It argues that caregivers’ emotional and eudaimonic value expectations are often implicit in everyday care practices and therefore remain unarticulated within prevailing technology design, procurement, and evaluation frameworks. When these implicit needs are excluded, welfare technologies may deliver functional improvements while generating compliance without long-term engagement. The study proposes a caregiver-centered conceptual framework that links needs articulation, perceived value in use, and welfare technology industrialization. Illustrative cases are employed to contextualize the framework and to demonstrate the persistence of unmet emotional and eudaimonic needs across different care settings and stages of technology exposure. The analysis highlights how mis-specifying caregivers as passive intermediaries rather than consumers leads to recurring adoption failure. Conclusions: The findings suggest that recognizing caregivers as consumers and systematically integrating their value perceptions into design and policy processes is a necessary condition for sustainable welfare technology markets beyond function-oriented innovation.
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Purpose: This paper examines whether corporate carbon intensity is priced in the cross-section of stock returns using firm-level data from the Korean equity market. The analysis is motivated by growing investor attention to climate transition risks and by mixed empirical evidence on carbon risk pricing in the existing literature. Research design and methodology: We develop a Bayesian hierarchical asset-pricing framework that allows the pricing of carbon intensity to vary across industries while controlling for firm-specific characteristics, unobserved firm-level heterogeneity, and aggregate time effects. The hierarchical structure enables partial pooling across sectors and firms, while a heavy-tailed return specification accommodates the empirical distribution of stock returns. Results: The posterior distribution of the average carbon-intensity coefficient is positive but economically modest, with substantial uncertainty and a credible interval that includes zero, indicating limited evidence of a strong uniform carbon risk premium across firms. At the same time, posterior means of industry-specific carbon effects are predominantly positive, suggesting cross-sectoral variation in how carbon exposure is priced, albeit with considerable uncertainty at the individual industry level. Conclusions: Taken together, the results imply that carbon risk is not priced uniformly in the Korean stock market. Instead, carbon exposure is associated with a weak and heterogeneous return premium that operates through sector-dependent channels rather than as a pervasive market-wide effect. These findings underscore the importance of accounting for heterogeneity and uncertainty when assessing the financial implications of firms’ carbon intensity.
