Journal of Information Systems and Digital Technologies

FROM E-GOVERNMENT TO DIGITAL GOVERNMENT: RECENT ADVANCES AND LESSONS LEARNED FROM THE KOREAN EXPERIENCE

2026-07-13T16:59:05+08:00

Despite significant progress in e-government initiatives across many developing countries, as reflected in the UN e-Government Development Index (EGDI), several nations—particularly in Sub-Saharan Africa—continue to face substantial challenges. These include inadequate IT infrastructure, low digital literacy, severe budget constraints, bureaucratic resistance to change, among others. Sharing lessons from successful countries in building digital government can provide valuable guidance to these nations. This can help them to consolidate best practices and overcome common pitfalls. To this end, this study highlights the key success factors, experiences, and lessons learned from the development of Korea's digital government. Beginning with the computerization and informatization eras and progressing to the current digital transformation era, Korea has pursued advancements over the past 40 years that have laid the foundation for its emergence as one of the leading countries in e-government and the digital revolution. This workr explores and emphasizes the socio-technical development pillars and elaborates on the key services provided. It also summarizes the challenges encountered and lessons learned. The paper employs qualitative methods to examine how the core policies of Korean e-government were formulated and implemented. It includes document analysis and reviews of key stakeholder websites.

THE COMPUTATIONAL CONVERGENCE OF AI AND ISLAMIC DIGITAL HUMANITIES

2026-06-22T09:43:32+08:00

This study investigates the accelerating paradigm shift driven by Artificial Intelligence (AI) and Deep Learning architectures within Arabic computational linguistics and Islamic scriptural hermeneutics. Contemporary literature indicates that Natural Language Processing (NLP) provides robust frameworks for parsing complex Arabic morpho-syntactic structures and semantic layers, alongside automating translation and manuscript digitization. Specialized deep learning architectures—specifically domain-adapted Arabic BERT variants—have significantly mitigated systemic grammatical and semantic ambiguities. Conversely, deploying these systems introduces profound ethical, ontological, and theological vulnerabilities, including data-driven algorithmic biases and epistemic hallucinations within sacred text interpretations. To secure a resilient and doctrinally sound implementation, this paper proposes a governance matrix derived from the teleological objectives of Islamic jurisprudence (Maqasid al-Shariah). Through comprehensive case studies, empirical benchmarks, and comparative cross-lingual analyses, this research demonstrates that maximizing AI's utility for Islamic cultural heritage requires a strictly regulated equilibrium balancing technical automation with expert human oversight and foundational Islamic principles.

TOWARDS AN ISLAMIC BEHAVIORAL MODEL OF TECHNOLOGY ACCEPTANCE

2026-05-22T05:38:34+08:00

This conceptual paper explores the integration of the Islamic concept of qalbun salim the sound and spiritually purified heart into the Technology Acceptance Model (TAM). While TAM emphasizes perceived usefulness and ease of use as determinants of technology adoption, it overlooks the ethical, spiritual, and moral dimensions that are central to Islamic decision-making. Drawing upon Qur’anic verses and classical Islamic scholarship, this paper presents qalbun salim as a reflective, evaluative faculty that filters technological choices through the lens of divine guidance and maqasid al-shariah (objectives of Islamic law). Rather than altering TAM’s empirical structure, this study offers an interpretive integration that repositions the heart as a locus of moral cognition in the technology acceptance process. This framework aims to enrich the discourse on ethical digital behavior from an Islamic epistemological standpoint. It also lays the foundation for interdisciplinary engagement between Islamic thought, behavioral science, and digital ethics.

DATA SHARING ACT 2025 AND THE CONSTITUTIONAL SAFEGUARDS ON INFORMATIONAL AUTONOMY IN MALAYSIA

2026-05-22T04:55:25+08:00

Malaysia’s rapid digitalisation of public administration has significantly expanded the scale and intensity of personal data processing within government digital services. Platforms such as MySejahtera, PADU, MyIdentity, MyDigital ID and one stop centre MyGov apps have become central to service delivery, policy implementation, and data-driven governance. The new Data Sharing Act 2025 (DSA 2025) is a significant piece of legislation designed to facilitate inter-agency data sharing and enhance administrative efficiency. However, the centralisation and dissemination of personal data within the public-sector raise important constitutional and governance questions concerning informational autonomy and state accountability. Although the Federal Constitution does not expressly recognise a right to privacy, Malaysian courts have interpreted Article 5 (1) on life and personal liberty, together with Article 8(1) on equality before the law, as comprising elements of autonomy, dignity, and protection against arbitrary state action. This paper adopts a doctrinal legal methodology to examine whether the institutional design and operational mechanisms of the DSA 2025 adequately align with these constitutional principles. It analyses the Act’s data-sharing architecture, executive discretion, and oversight arrangements, and evaluates the extent to which existing constitutional safeguards constrain state power over the processing of citizens’ personal data. Drawing on comparative insights from Germany, the European Union, the United Kingdom, and Canada, the paper argues that effective public-sector data governance requires principles constraints that extend beyond administrative efficiency. It proposes the development of a Malaysian Doctrine of Informational Autonomy, grounded in Articles 5(1), 8(1), and constitutional supremacy, to provide a coherent framework for assessing the constitutional and governance-based analytical framework, this paper contributes to ongoing debates on digital governance, trust, accountability, sovereignty and the protection of individual informational liberty in Malaysia’s evolving data-driven services.

FACTORS AFFECTING CYBERSECURITY RISK BEHAVIOUR AMONG MALAYSIAN PAYTECH USERS

2026-05-08T15:30:28+08:00

The increased uptake of financial technology (FinTech) and digital payment platforms has heightened the importance of investigating the cybersecurity risk behaviour of users. The research focuses on analysing the role of essential constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) in eliciting cybersecurity risk behaviour among PayTech users in Malaysia, with an emphasis on the moderator of trust. A structured, self-administered survey was employed as a quantitative research design, targeting online payment system users aged 18 to 65 years who actively use this system. Hypothesized relationships were tested using Structural Equation Modeling (SEM). Results show that performance expectancy and effort expectancy have a significant effect on cybersecurity behaviour, but social influence and facilitating conditions are minor predictors of that behaviour. Trust was identified as mediating between several relationships, highlighting its importance in influencing user behaviour. The results provide empirical evidence on the determinants of secure online transaction behavior and will guide policymakers, developers, and financial institutions in improving system usability, user trust building, and the adoption of safer digital financial services.

A CONCEPTUAL BUSINESS MODEL FOR SAFESPHERE

2025-12-25T22:51:53+08:00

The goal of this paper is to present a conceptual SafeSphere Smart Home Security System business model embedded within a digital platform aligned with the United Nations Sustainable Development Goals (SDG). Although smart home technologies are expanding, adoption remains limited due to ecosystem fragmentation and multiple device communication protocols that complicate integration for users. To address this, the SafeSphere model proposes a smart home gateway that acts as a unified hub to manage diverse protocols, enhancing device interoperability and simplifying user experience. The value proposition centres on safety, convenience and compatibility through features such as AI-powered intrusion detection, PDPA-compliant local data storage and centralized control of cameras and locks via a single mobile application. Guided by the principle of Rahmatan lil ‘Alamin, SafeSphere aims to promote universal well-being by offering accessible security solutions for all communities. The business model includes affordable and scalable packages for B40 and M40 households to encourage wider adoption and reduce digital inequality. The gateway approach also provides a standardized communication layer that benefits both users and hardware providers. The purpose of this paper is to develop an initial business model and platform that address the key challenges, pains and desired gains of four main customer segments: homeowners, small businesses operators, schools institutions and hardware providers. The initiative aligned with SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice and Strong Institutions), by promoting secure digital innovation and strengthening digital trust. The paper adopts the Design Thinking (DT) methodology to understand user pain points and generate innovative solutions. Business modelling tools such as the Business Model Canvas (BMC), Value Proposition Design Canvas (VPC) and Environmental Map (EM) are used to conceptualize and evaluate the proposed business model.

UNVEILING PUBLIC PERCEPTION OF TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING (TVET) ON SOCIAL MEDIA USING A HYBRID SENTIMENT ANALYSIS APPROACH

2026-05-08T15:45:09+08:00

The public perception significantly impacts the effectiveness and societal acceptance of Technical and Vocational Education and Training (TVET) in Malaysia. Social media platforms such like Facebook becoming more important which makes it easier to get the real-time public input on education policy and programs. However, evaluating sentiment in informal and bilingual discussion poses significant challenges. This study presents a hybrid sentiment analysis approach that integrates lexicon-based approach (VADER, TextBlob, SentiWordNet, AFINN) with machine learning classifiers (Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Random Forest and K-Nearest Neighbors) to classify and evaluate sentiments in 1,304 Facebook posts and comments related to TVET. The dataset was systematically pre-processed, translated, and subjected to feature extraction by Term Frequency-Inverse Document Frequency (TF-IDF). Accuracy served as the sole evaluation metric to ensure consistent comparison among lexicon-only, machine learning-only, and hybrid approaches. The hybrid combination of TextBlob and SVM achieved the highest accuracy at 72%, surpassing both standalone lexicon and machine learning approaches. This finding indicates that hybrid approaches are more effective in handling informal, code-switched sentiment data. The study offers a scalable framework for sentiment analysis in education policy, providing practical implications for TVET stakeholders aiming to evaluate public opinion and enhance engagement strategies.

AI FROM AN ISLAMIC PERSPECTIVE

2025-09-13T14:59:03+08:00

This paper explores Artificial Intelligence (AI) from an Islamic ethical perspective by drawing upon the Qur’an, classical tafsir, and contemporary scholarship. Rather than treating AI solely as a technological or theological issue, the paper highlights how Islamic principles—justice, accountability, remembrance (dhikr), reflection (fikr), and gratitude (shukr)—can guide the ethical development and use of AI. Using a descriptive research design, the study synthesizes global concerns about AI, including bias, manipulation, and autonomy, with Islamic teachings on morality, responsibility, and societal well-being. The objective is to propose a framework that integrates AI ethics with Islamic epistemology, offering guidance for Muslims and contributing to broader discussions on responsible AI.

AN INTELLIGENT SYSTEM TO IDENTIFY FAKE VIDEOS ON ONLINE SOCIAL NETWORKS USING MACHINE LEARNING

2025-09-22T13:54:11+08:00

Advances in emerging technologies have led to the wide dissemination of fake videos on online social networks. This research employs the hybrid machine learning and deep learning algorithms to recognise video forgery on social media. This research presents a hybrid deep learning and machine learning approach for detecting manipulated videos. The proposed model employs the OpenCV deep neural network (DNN) face detector to locate facial regions in video frames, after which a pretrained ResNet50 convolutional neural network is applied to extract deep features, and a Support Vector Machine (SVM) is used to classify authentic and fake content in a binary fashion. The model was trained and evaluated on a dataset of publicly available 128 short-form videos gathered from TikTok and Facebook platforms. The five-fold stratified cross-validation results generated an average accuracy of 89.1%, precision of 87.6%, recall of 96.1%, F1-score of 91.5%, and an AUC of 0.95 for the SVM model. Furthermore, the comparative analyses showed that SVM outperformed Logistic Regression, Random Forest, Gradient Boosting, and K-Nearest Neighbour classifiers. The findings demonstrate that combining automated face detection with deep feature extraction and classical machine learning significantly improves fake-video detection and contributes to preserving authenticity in digital communication.

ADVANCING SUSTAINABLE WASTE MANAGEMENT ON CAMPUS THROUGH AN INTELLIGENT REVERSE VENDING MACHINE

2025-09-03T14:01:10+08:00

Global plastic pollution poses a critical environmental threat, impacting Sustainable Development Goals (SDGs) related to responsible consumption and climate action. Inconsistent recycling habits, particularly within high-consumption environments like university campuses, significantly contribute to this issue. Single-use plastic bottles are a major campus waste component, often ending up in landfills, exacerbating environmental degradation. This highlights an urgent need for innovative, engaging recycling solutions within educational settings and beyond. To address this, we developed an AI-driven Reverse Vending Machine (RVM) prototype and a cross-platform mobile application. The RVM utilizes a Raspberry Pi 4 as its central control unit, integrating capacitive proximity, photoelectric, ultrasonic, and infrared sensors for comprehensive bottle detection and monitoring. The system employs a YOLOv5 model for object detection, trained on a robust dataset of 2,000 labelled images, and a Flutter-based mobile application for user interaction and reward redemption. The development followed an Agile methodology, emphasizing iterative testing and refinement. A campus pilot study confirmed the system's efficacy. The YOLOv5 model achieved high accuracy (99.0% mAP@0.5, 98.4% precision, 96.8% recall). The system was highly responsive, with a detection-to-reward cycle under 1.2 seconds. User feedback was positive (SUS score: 81.3), and a student survey showed 76% willingness to use the RVM, with 48% motivated by vouchers, indicating strong acceptance and potential for behavioral change. This AI-driven RVM offers a technically feasible and highly accepted solution for sustainable waste management, providing an effective, user-centric approach to combat plastic pollution and offering a blueprint for broader global recycling efforts.