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All Journal Aptisi Transactions on Management International Transactions on Education Technology (ITEE)
Yao, Goh
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Leveraging AI-Powered Automation for Enhanced Operational Efficiency in Small and Medium Enterprises (SMEs) Andayani, Dwi; Indiyati, Dian; Mayang Sari, Meri; Williams, Jack; Yao, Goh
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2363

Abstract

This study explores the potential of AI-powered automation in enhancing opera- tional efficiency within Small and Medium Enterprises (SMEs). The primary objective is to identify how automation tools driven by artificial intelligence (AI) can streamline business processes, reduce operational costs, and improve productivity. The methodology includes a quantitative analysis of SMEs that have implemented AI-based solutions, supported by qualitative interviews with key stakeholders. The Results indicate significant improvements in operational workflows, particularly in areas such as supply chain management, customer service, and financial operations. The findings demonstrate that SMEs adopting AI technologies experience reduced human error, faster decision-making pro- cesses, and improved customer satisfaction. However, challenges such as initial investment costs and technical expertise remain. The study concludes that with proper implementation and strategic planning, AI-powered automation can be a key driver of success for SMEs in competitive markets.
Enhancing Student Engagement with AI-Driven Personalized Learning Systems Zaharuddin; Chen Yu; Yao, Goh
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.662

Abstract

This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both students and educators, this research identifies key factors that contribute to improved student motivation, interaction, and academic performance. The findings suggest that AI-driven systems not only provide tailored learning experiences but also foster a deeper connection between students and their learning content. The paper concludes with recommendations for future research and practical applications in educational institutions to further optimize the use of AI for enhancing student engagement.
Risk Management Model for Compliance and Security in Blockchain Powered Payment Platforms Novalita Savitri, Agnes; Hardini, Marviola; Yao, Goh
APTISI Transactions on Management (ATM) Vol 9 No 2 (2025): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v9i2.2475

Abstract

Blockchain technology has revolutionized financial services by enabling decen- tralized, transparent, and tamper-resistant payment platforms. However, these innovations bring significant challenges related to regulatory compliance and security management, which threaten platform adoption and user trust. This study aims to develop and empirically validate a comprehensive risk management model that integrates both regulatory oversight and security auditing dimensions specific to blockchain-powered payment systems. A cross-sectional survey was conducted among 215 industry practitioners involved in blockchain payment platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study tested hypothesized relationships among regulatory over- sight, smart contract auditing, perceived compliance and security risks, risk mit- igation intent, and platform adoption intention. The results demonstrate that regulatory oversight and smart contract auditing significantly increase perceived compliance and security risks. These heightened risk perceptions positively in- fluence intentions to mitigate risks, which in turn significantly drive platform adoption. The model explains 58% and 42% of the variance in risk mitigation intent and platform adoption intention, respectively, confirming its strong ex- planatory power. This research contributes a validated, unified risk manage- ment framework that guides policymakers, platform operators, and auditors in addressing intertwined compliance and security risks. The findings support the advancement of safer, more trustworthy blockchain payment systems, fostering broader adoption and aligning with evolving regulatory landscapes. 
Co-Authors Chen Yu Dian Indiyati Dwi Andayani Marviola Hardini Mayang Sari, Meri Novalita Savitri, Agnes Williams, Jack Zaharuddin