JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

IMPACT OF TECHNOLOGY IN HUMANITARIAN ASSISTANCE AND DISASTER RELIEF OPERATION TOWARDS ASSOCIATION OF SOUTHEAST ASIAN NATIONS

2025-12-02T16:26:44+08:00

This study examines the transformative role of emerging technologies in enhancing Humanitarian Assistance and Disaster Relief (HADR) operations across the ASEAN region. The need for rapid, coordinated and data-driven responses is critical due to ASEAN's vulnerability to natural disasters. This paper evaluates the technological readiness and integration within ASEAN's disaster management ecosystem, using the Sendai Framework for Disaster Risk Reduction (DRR) as a guiding structure. A qualitative research approach was employed, involving expert interviews with stakeholders from eight ASEAN nations and relevant agencies, including NADMA and MMEA. The findings highlight a growing demand for technology attributes such as interoperability, real-time data acquisition, geospatial intelligence, autonomous systems and resilient communication networks. Technologies such as artificial intelligence (AI), drones, Internet of Things (IoT), and blockchain are identified as enablers for enhanced situational awareness and effective resource coordination. Despite their potential, challenges persist in infrastructure limitations, fragmented data systems, cybersecurity vulnerabilities and funding constraints. Therefore, the paper proposes a Hybrid Technology Framework aligned with the four priorities of the Sendai Framework. The model leverages the DIKW Model to integrate big data analytics with operational decision-making in HADR contexts. The proposed framework is proven to improve the efficiency, scalability and resilience of ASEAN’s disaster response mechanisms. The ASEAN HADR operations must embrace IR4.0 technologies to shift from reactive to proactive disaster management. This transition will not only bridge existing capability gaps but also promote regional cooperation, enhance institutional capacity and foster community-based resilience.

MAXIMIZING TAX COLLECTION EFFICIENCY THROUGH STRATEGIC CUSTOMS TECHNOLOGY ADOPTION: A REVIEW OF GLOBAL TRENDS AND APPLICATIONS

2025-11-20T12:12:43+08:00

Governments are accelerating customs digitalization to safeguard revenue while enabling faster, risk‑based clearance. This paper refines and synthesizes recent evidence on how strategic customs technologies translate into measurable gains in tax collection efficiency (TCE). We conduct a structured narrative review of 2020–2025 peer‑reviewed studies and flagship policy reports (Scopus/WoS; WCO/OECD/World Bank/IMF), integrating insights through the Technology Acceptance Model (TAM) and Institutional Theory. The synthesis indicates that integrated e‑customs/Single Window, API‑based interoperability, and AI‑enabled risk engines are consistently associated with shorter clearance times, higher audit hit‑rates, improved valuation accuracy, and greater post‑clearance recovery—conditional on user‑centred design, training/ICT support, robust data governance, and enabling legal frameworks. For middle‑income administrations, benefits are maximized by phased use‑case pilots, data‑quality programs, and human‑in‑the‑loop analytics with transparent KPIs (e.g., Time Release Study (TRS), audit yield, recovery per audit, cost‑to‑collect). Originality stems from integrating behavioural (TAM) and institutional lenses to convert global evidence into an actionable 12–24‑month roadmap tailored to administrations such as the Royal Malaysian Customs Department (RMCD).

DRIVER DISTRACTION ASSOCIATED WITH MOBILE PHONE TACTILE TASKS: A DRIVING SIMULATOR-BASED STUDY

2025-12-02T16:14:06+08:00

The mobile phone is an extremely beneficial tool for humanity, capable of performing a multitude of functions beyond the mere transmission and reception of telephone communications. Regrettably, it has the capacity to cause significant distractions for drivers in numerous ways. The aim of the study is to measure the distraction in terms of participants’ response time for different tactile tasks and driving scenarios. In this research, 54 participants executed several secondary tasks (i.e., tactile tasks) while simultaneously engaging in the DRT throughout a simulated driving environment. For the comparison of driving scenarios, three scenarios were used: free flow (40 km/h) with medium traffic volume, free flow (40 km/h) without traffic and traffic jams. For different tactile tasks, apart from the 2-character number, other tactile tasks such as 5, 9, 14, and 20 number of characters were also assessed. The study findings indicated that participants responded to lesser stimuli when dealing with more difficult tactile tasks. Besides, the results found that drivers were more distracted when dealing with more demanding tasks of using mobile phones (i.e., tactile tasks) as compared to baseline. In addition, participants attended the worst in terms of stimuli and higher response time in the traffic jam scenario as compared to other scenarios. Besides, the study discovered that the novice drivers’ group was identified to respond at a significantly faster rate than the experienced drivers’ group. In terms of gender, male drivers were generally faster in terms of response time than female drivers, with no significant differences.

STRATEGIES FOR ENHANCING DIGITAL LEARNING ENVIRONMENTS: TEACHERS’ PERSPECTIVES ON OVERCOMING CHALLENGES AND MAXIMIZING ENGAGEMENT

2025-12-02T16:02:56+08:00

This study explores the creation of positive digital learning environments (DLEs) by integrating digital technologies and teaching strategies that promote personalized and interactive learning. A well-designed DLE includes digital media, online platforms, and intelligent learning systems that work together to enhance education. However, challenges such as inconsistent implementation across schools, especially between urban and rural areas, highlight the need for effective strategies to ensure equal access to these tools. Teachers play a crucial role in this transformation, yet many face difficulties in effectively using digital tools despite positive attitudes towards technology in education. Professional development and strong leadership are essential to overcoming these challenges, but time constraints and varying levels of motivation among teachers can limit the effectiveness of such programs. Infrastructure also plays a vital role, with the Malaysian government recognizing the need to improve technological resources in schools. However, disparities in access to technology, particularly in rural areas, remain a significant concern. This study uses thematic analysis to identify key factors that contribute to a positive digital learning environment. Sixty pre-service teachers from diverse educational backgrounds participated, providing insights into effective strategies for digital learning. The analysis revealed themes such as engagement and interaction, infrastructure and accessibility, effective classroom management, teacher competency and creativity, support systems, and the importance of a positive learning environment. The findings emphasize the need for engaging content, reliable technology, well-prepared teachers, and strong support systems to create effective DLEs. The results highlight both the potential and challenges of digital education, stressing the importance of addressing existing gaps to ensure that all students benefit from high-quality digital learning experiences. This study suggests that collaborative efforts among educators, administrators, parents, and policymakers are crucial for building a successful and inclusive digital learning environment.

SYSTEM INTEGRATION IN CUSTOMS FOR TAX COLLECTION EFFICIENCY: A BIBLIOMETRIC AND SCIENCE-MAPPING ANALYSIS

2025-11-21T10:52:16+08:00

This study examines the integration of systems for tax collection efficiency in customs administration, addressing critical gaps in understanding the intellectual structure and theoretical foundations of this rapidly evolving field. Despite the growing importance of digital transformation in tax administration globally, existing knowledge remains theoretically fragmented, lacking coherent frameworks that bridge technological innovation with administrative effectiveness. Furthermore, the theoretical gap is evident in the absence of integrated models explaining how system interoperability influences tax collection outcomes. Accordingly, a comprehensive bibliometric analysis of 706 Scopus publications (1981-2025) was conducted using OpenRefine for data harmonization and VOSviewer for visualization. The analysis uniquely identifies four distinct theoretical clusters that evolve in isolation: technological determinism in system design, institutional theory in administrative reform, economic efficiency models in revenue optimization, and governance theory in compliance management. At the same time, publications increased sharply after 2018, reflecting urgent needs for frameworks to guide pandemic-driven digitization. Additionally, the study's novelty lies in demonstrating the evolution of system integration research from purely technical implementations to complex socio-technical systems that require interdisciplinary foundations. Moreover, substantial research contributions emerged from the United States (US) (18.27%), China (16.43%), and the United Kingdom (UK) (6.09%). Keyword clustering reveals insufficient theoretical development at the intersection of computer science (11.8%) and social sciences (24.4%), particularly in human-technology interaction within tax administration contexts. This study uniquely establishes a conceptual map that reveals how research has been conducted within disciplinary silos, with limited cross-pollination between technological and administrative theories. In addition, this fragmentation creates a significant gap that hinders the development of a comprehensive framework. Nonetheless, findings guide future research toward integrated theoretical models that better explain how digital transformation drives tax collection efficiency in customs administration.

CORRELATION BETWEEN HARDNESS AND MICROSTRUCTURAL EVOLUTION OF P22 STEEL SUBJECTED TO ANNEALING

2025-11-20T12:16:18+08:00

This study investigates the correlation between hardness and microstructural evolution in P22 carbon steel subjected to annealing. Brinell hardness testing revealed a reduction from 161.3 BHN in the as-received condition to 121.3 BHN after annealing, corresponding to an approximate 25% decrease. Optical microscopy of the as-received sample showed irregularly shaped grains with elongated, clustered carbide precipitates concentrated along grain boundaries, which contributed to higher hardness by impeding dislocation motion. In contrast, the annealed microstructure exhibited coarser, more homogeneous grains with spheroidized and uniformly dispersed carbides, consistent with thermal stability achieved through recovery and recrystallization. Complementary SEM analysis further revealed that machining induced grooves and work-hardened surface features present in the as received specimen disappeared after annealing, leaving a uniform recrystallized surface morphology. The combined hardness and microstructural observations demonstrate that annealing promotes softening by reducing dislocation density and redistributing carbides, thereby enhancing ductility at the expense of strength. These findings provide insight into the role of annealing in balancing mechanical performance and microstructural stability of P22 steel for high-temperature service applications.

TOWARDS A CONCEPTUAL MODEL FOR AI ADOPTION IN MEDICAL LIBRARIES: INTEGRATING TOE, DOI, COM-B, AND UTAUT MODEL

2025-12-02T16:21:30+08:00

Artificial intelligence (AI) is transforming library operations through automation and enhanced efficiency, yet its adoption in medical libraries, particularly in Malaysia, remains insufficiently studied. The objective of this study is to propose a comprehensive conceptual framework to explain AI adoption in medical library settings by integrating the COM-B Model, the Technology–Organization–Environment (TOE) framework, and Diffusion of Innovation (DOI) theory. The framework highlights three determinant categories: capability, opportunity and motivation. These factors are proposed to influence intention, which in turn drives adoption behavior. Developed through a narrative literature review and theory synthesis, the framework addresses the limitations of single-theory approaches and offers both theoretical and practical contributions. It provides guidance for medical library administrators and policymakers to enhance AI readiness and establishes a foundation for future empirical research in healthcare education contexts.

VALIDATION OF THE ADAPTED MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE (MSLQ) FOR ONLINE LEARNING: INSIGHTS FROM EXPERTS REVIEWS

2025-12-02T16:09:33+08:00

Adapting Adapting the Motivated Strategies for Learning Questionnaire (MSLQ) for online learning environments is essential to ensure its relevance and accuracy in assessing students’ motivation and self-regulated learning (SRL) strategies in digital contexts. The MSLQ has been widely used in traditional learning settings. However, the rapid growth of online education necessitates its contextual refinement. The main objective of this study was to adapt and validate the MSLQ to suit online learning environments, ensuring that its terminology, structure, and constructs accurately reflect the digital learning experience. This study employed a three-phase expert validation process involving content analysis to adapt the MSLQ. Three experts in Information Technology, Language Education, and Content Creator were engaged to review and evaluate the modified questionnaire. The phases included preparation of the adapted instrument, evaluation through expert discussions, and implementation of feedback. Key constructs such as “class,” “teacher,” and “discussion” were modified to reflect online learning terminology (e.g., “learning management system,” “instructor,” “forum”), and each modification was reviewed for contextual appropriateness. The results revealed significant consensus among experts on the importance of using context-specific terminology and providing clear operational definitions to ensure the instrument’s clarity and applicability. However, some terms, such as “online readings” and “online discussion,” prompted critical feedback, highlighting the balance needed between specificity and generalizability. Final adaptations were made based on expert input, resulting in a more refined and validated MSLQ suited for online learning contexts. The refined MSLQ offers a practical and contextually appropriate tool for assessing SRL and motivation in online settings, with future studies recommended to expand expert involvement and conduct longitudinal testing to strengthen its predictive value and generalizability.

THE EVOLUTION AND IMPACT OF ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE ANALYSIS OF FUTURE APPLICATIONS AND CHALLENGES

2025-11-21T11:14:16+08:00

This paper provides a comprehensive review of the evolution, current state, and future implications of artificial intelligence (AI) across multiple domains. Although AI’s technological progress has accelerated rapidly, a critical gap remains in integrating ethical and governance frameworks to ensure responsible and human-centered innovation. Drawing from literature and policy developments between 2018 and 2025, this review explores AI’s operational mechanisms, its distinction from human cognition, and the socio-ethical challenges in education, healthcare, law, and industry. Key findings highlight (1) the persistent gap between human intuition and machine reasoning; (2) AI’s growing global economic influence, and (3) emerging concerns of fairness, privacy, and accountability. Sustainable advancement requires alignment with UNESCO’s Recommendation on the Ethics of Artificial Intelligence, the European Union Artificial Intelligence Act, and IEEE’s Ethically Aligned Design framework. The study contributes by synthesizing technological, ethical, and regulatory perspectives to guide responsible, human-centered AI.

BEYOND THE ALGORITHM ON HOW AI WILL REDEFINE HUMAN PROGRESS: FROM DIRECTION TO EMPOWERMENT

2025-11-21T10:38:38+08:00

This position paper presents a critical analysis of the evolution of Artificial Intelligence in Education (AIEd) through three distinct paradigms: AI-directed (learner-as-recipient), AI-supported (learner-as-collaborator), and AI-empowered (learner-as-leader). Through a comprehensive review of literature spanning three decades (1990-2021), the study examines the theoretical foundations, implementation approaches, and practical applications of each paradigm. The analysis reveals a progressive shift from technology-centered to learner-centered approaches, highlighting the transformation of learners' roles from passive recipients to active leaders in their educational journey. The paper identifies critical challenges in integrating AI with educational theories and proposes a framework for future AIEd development that emphasizes human agency, personalized learning, and ethical considerations. The findings suggest that successful AIEd implementation requires balancing technological advancement with pedagogical principles, while maintaining focus on human-centered learning experiences. This work contributes to the field by providing a structured framework for understanding AIEd's evolution and guiding future development toward more effective, learner-centered educational technologies.