Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal JURNAL EDUCATION AND DEVELOPMENT Economics and Business Journal Multidisciplinary Indonesian Center Journal
Han, Yonghwa
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Multidisciplinary Indonesian Center Journal

 1 
SUSTAINABLE AGILITY-DRIVEN CULTURE IN HUMAN RESOURCE MANAGEMENT: A PRIDE FRAMEWORK FOR FUTURE-READY ORGANIZATIONS Wulandari, Aghnia; Efendi, Suryono; Hasanudin; Han, Yonghwa
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.1571

Abstract

This research develops the PRIDE Framework to integrate five interdependent dimensions, People (People-Centric Excellence), Resilience, Innovation, Development, and Empowerment, into a unified model explaining how HRM practices drive both sustainability and agility. A systematic literature review guided by PRISMA principles was conducted, selecting peer-reviewed empirical and conceptual studies that examine HRM, organizational agility, and sustainability. Thematic synthesis and reflexive analysis produced a circular puzzle architecture representing these dimensions and their interconnections. Findings reveal that integrated people-centric policies, robust knowledge management, embedded innovation practices, continuous learning, and distributed decision-making create synergistic capabilities that enable rapid adaptation while maintaining long-term viability. Critical enablers include psychological safety, holistic wellness initiatives, adaptive learning programs, and outcome-based empowerment. The framework addresses gaps by showing sustainability and agility as complementary imperatives rather than competing priorities. Future research should validate the framework through multi-level and longitudinal studies, incorporate diverse language sources for broader context, and explore the impacts of AI-enabled HRM on each dimension. Limitations involve temporal and linguistic scope.
ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW ON ENHANCING ORGANIZATIONAL RESILIENCE FOR FUTURE GLOBAL FINANCIAL CRISES Han, Yonghwa; Nurwulandari, Andini; Hasanudin; Wulandari, Aghnia
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 3 No. 1 (2026): Vol. 3 No. 1 Edisi Januari 2026
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v3i1.1572

Abstract

This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.
Co-Authors Aghnia Wulandari ANDINI NURWULANDARI Efendi, Suryono Hasanudin Tri Waluyo