JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM)

AI LITERACY IN ACADEMIC LIBRARIANSHIP: COMPREHENSIVE LITERATURE REVIEW AND EMERGING TRENDS

Fri, 19 Sep 2025 00:00:00 +0800

The rapid integration of artificial intelligence (AI) into academic libraries has created both opportunities and challenges for librarians, particularly in terms of AI literacy. Despite the transformative potential of AI to enhance library services, many academic librarians lack the necessary competencies to effectively adopt and leverage these technologies. This gap in AI literacy poses significant barriers to equitable access and responsible AI use, threatening the ability of libraries to remain relevant in an increasingly digital and AI-driven world. To address this issue, this study aims to comprehensively review existing research on AI literacy in academic librarianship, identify best practices for enhancing AI literacy among librarians and the key themes for AI Literacy for academic librarian. The study using systematic analysis of recent literature from Scopus. Findings indicate that successful AI literacy initiatives often involve structured reskilling programs, interdisciplinary collaboration, and the integration of ethical frameworks into training curricula. These findings contribute to both theory and practice, offering a foundation for empowering academic librarians to navigate the complexities of AI adoption while promoting equity and ethical responsibility in library services.

VALIDATING AN INSTRUCTIONAL COMPETENCY MODEL FOR ASSISTANT HEADTEACHERS: PRELIMINARY EVIDENCE FROM A PILOT STUDY USING CFA AND SEM

Erna Suwardi, Muhamad Suhaimi Taat, Roslee Talip, Mohd Sobrye Paimin — Tue, 30 Sep 2025 00:00:00 +0800

This study aimed to develop and validate an Instructional Competency Model tailored for Assistant Headteachers (GPK) in Malaysian primary schools, across both rural and urban school settings in Sabah. Grounded in the KOMPAS 2.0 framework, the model conceptualised instructional competency as a multidimensional construct comprising four latent domains: Curriculum Focused Instructional Leadership (IBK), Research Culture (PP), Holistic Monitoring (PM), and Digital Ecosystem Culture (PED). A pilot survey involving 100 GPK across various districts in Sabah was conducted. The sample comprised 58 respondents from rural schools and 42 from urban schools, with balanced demographic characteristics. Data were analysed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to assess construct validity and model fit. The CFA results demonstrated excellent fit indices (χ²/df = 0.446, CFI = 1.000, RMSEA = 0.041), with standardised factor loadings ranging from 0.80 to 0.87. All constructs achieved strong composite reliability and average variance extracted. SEM analysis confirmed significant path coefficients from the main construct to its four dimensions, with Curriculum Focused Instructional Leadership (β = 0.87) emerging as the strongest contributor. These results confirm the robustness and contextual relevance of the model, while acknowledging the limitations of a pilot study. The validated model contributes theoretical novelty by extending instructional leadership theory into middle leadership domains and by adapting KOMPAS 2.0 specifically for Assistant Headteachers. It offers a practical framework that can be applied within Malaysia and in comparable international contexts, providing insights for education policymakers, school administrators, and training providers.

CLICK, MEASURE, SUCCEED: A BIBLIOMETRIC JOURNEY AND EMERGING RESEARCH GAPS IN INFORMATION SYSTEMS (IS) EFFECTIVENESS

Nor Asiah Taib, Izlawanie Muhammad, Suhaila Abdul Hamid — Mon, 11 Aug 2025 00:00:00 +0800

Information Systems (IS) effectiveness has emerged as a critical research area in the face of rapid digital transformation and the increasing reliance on technology across various sectors. Despite the field’s maturity, theoretical development and research focus remain fragmented, necessitating a comprehensive synthesis of its evolution. This study presents a bibliometric analysis titled “Click, Measure, Succeed: A Bibliometric Journey of Theoretical Contributions and Research Gaps in Information Systems Effectiveness” to uncover historical trends, influential works, key contributors, and potential research gaps. The problem underpinning this research lies in the limited understanding of how theoretical contributions—particularly the DeLone and McLean Information Systems Success Model (D&M ISSM), the Technology Acceptance Model (TAM), and the Task-Technology Fit (TTF) model have shaped and evolved within the IS effectiveness domain over time. To address this, bibliographic data were extracted from the Scopus database using defined keywords, yielding a refined dataset of 1,121 publications spanning from 1975 to 2025. The data were cleaned and standardized using OpenRefine, analyzed quantitatively via Scopus Analyzer, and visualized through VOSviewer version 1.6.20, enabling the mapping of co-authorship, keyword co-occurrence, and citation networks. Key findings reveal a notable publication surge after 2020, with the United States, Indonesia, and Malaysia leading in output. Seminal works by DeLone and McLean dominate citation metrics, underscoring the enduring influence of the D&M ISSM. In contrast, research grounded in TAM and TTF increasingly highlights user acceptance and alignment between tasks and technology as critical factors in the success of IS. Keyword clustering reveals four thematic concentrations: system quality, service performance, user satisfaction, and digital transformation. Despite growing interest, collaboration among countries remains limited, and theoretical diversification is still narrow. This study contributes to academic discourse by mapping the intellectual structure of IS effectiveness research and identifying gaps that warrant further exploration, particularly in emerging economies and across disciplinary boundaries. By consolidating fragmented knowledge, this research aids scholars in navigating past contributions while paving strategic directions for future inquiry into IS effectiveness.

QUANTITATIVE STUDY ON VIRTUAL DOM DIFF PERFORMANCE WITH DIFFERENT KEY ATTRIBUTE USAGE: A CASE OF VUE.JS

Yao Lei, Khairul Anwar Sedek, Azlan Ismail — Fri, 19 Sep 2025 00:00:00 +0800

As the complexity of modern web applications continues to grow, frontend performance has become a critical factor influencing user experience. Vue.js, as a mainstream framework, has gained widespread adoption thanks to its MVVM framework model and virtual DOM technology. Its Diff algorithm minimizes operations to update the real DOM, significantly improving page response efficiency. As the unique identifier for virtual nodes, the key attribute directly influences the Diff algorithm's node matching and reuse strategies, thereby impacting rendering performance. While extensive research has focused on performance comparisons of frontend frameworks, there remains a lack of systematic empirical analysis on the performance implications of Vue.js's internal key attribute usage (unique ID, index, or none) across different scenarios. This paper conducts a quantitative study using Vue 2.0 as the platform, constructs three typical pages (simple page, high-frequency large-data list page, and nested structure page), and builds a minimum-maximum inverse normalization and linear weighted scoring model to analyze overall performance. Additionally, by combining analysis of variance (ANOVA) and Tukey post-hoc tests, the performance differences under different settings are statistically validated. The research results indicate that in complex high-data-volume pages and nested page scenarios, selecting a unique ID as the key attribute can effectively improve page rendering performance, providing theoretical and practical support for front-end performance optimization.

AUGMENTED REALITY INTEGRATED ROBUST FUSION MODEL FOR SIGN LANGUAGE RECOGNITION USING COMPUTER VISON AND MACHINE LEARNING

A F M Saifuddin Saif , Zainal Rasyid Mahayuddin, Kevin Van Winkle — Sun, 14 Sep 2025 00:00:00 +0800

Sign language recognition (SLR) interprets sign language into text, bridging the communication gap between the deaf-mute community who use sign language and those who do not. Recent advancements in computer vision, deep learning, and augmented reality have shown significant progress in the field of motion and gesture recognition, however, large variations in hand actions, facial, and body postures, and the absence of region-specific datasets prevent universally accurate effective sign language recognition. This research developed an efficient model for SLR which includes an RGB-MHI attention module, and the Faster R-CNN deep learning architecture integrated with augmented reality. The proposed model was validated on two benchmark datasets, achieving an accuracy of 98.97% on the AUTSL dataset and 96.7% on the BosphorusSign22k dataset. Furthermore, the model was tested on a self-created dataset named "Amar Vasha" based on Bangla Sign Language (BdSL) to ensure cross-domain adaptability. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on all three benchmarks.

ESTABLISHING THE FUTURE DIRECTIONS OF EDUCATIONAL GAMIFICATION: A BIBLIOMETRIC PERSPECTIVE

Li Zi Jia, Noorminshah A Iahad, Hasnain Zafar Baloch — Thu, 25 Sep 2025 00:00:00 +0800

The integration of gamification in educational settings has been extensively discussed and utilized to enhance student engagement and learning outcomes. This study provides a bibliometric analysis of the existing literature, assessing the impact and themes prevalent in the research on gamification in education. This study aims to identify the major themes and trends in gamification in education over the past decade, evaluate the impact of significant publications and contributors, and suggest future research directions in this evolving field. Utilizing the Scopus database, we conducted a comprehensive bibliometric analysis. Key bibliometric indicators such as publication trends, citation analysis, and thematic evolution were examined using tools like BiblioMagika and VOSviewer. The analysis revealed a significant increase in publications over the years, with notable contributions from diverse geographic locations and institutions. The research highlights the dynamic and complex nature of gamification in education and its application across various educational settings. Several key themes, including student motivation, engagement strategies, and educational outcomes, were identified. Gamification in education continues to grow as an influential field of study. Future research should focus on longitudinal studies to assess the long-term impacts of gamification, explore under-researched areas such as adaptive learning through gamification, and develop more sophisticated gamification strategies that cater to diverse learning environments.

THE EVOLUTION OF RESEARCH ON PHISHING: A BIBLIOMETRIC ANALYSIS (2005–2025)

Norlizawati Ghazali, Ina Suryani Ab Rahim, Syed Zulkarnain Syed Idrus — Mon, 11 Aug 2025 00:00:00 +0800

Phishing has emerged as one of the most pervasive cyber threats in the digital age, evolving in both technical sophistication and psychological manipulation. While considerable research has been conducted on phishing detection and user susceptibility, a comprehensive bibliometric analysis of this field remains limited. This study addresses that gap by examining the evolution of phishing-related research from 2005 to 2025 through a systematic bibliometric approach. Using data extracted from the Scopus database (n = 1196) and analyzed through Scopus Analyzer, OpenRefine, and VOSviewer, the study explores annual publication trends, identifies the most cited articles, highlights prolific authors and contributing countries, and maps keyword co-occurrence, co-authorship, and co-citation networks. There has been a significant surge in publication output post-2016, with peak contributions between 2020 and 2024, likely influenced by global digitalization and the COVID-19 pandemic. The most cited works emphasize machine learning, persuasion tactics, and phishing susceptibility. India, the United Kingdom and the United States emerged as leading contributors, with the United States exhibiting the highest total link strength in collaborative networks. Among the 2000+ keywords identified, “phishing,” “machine learning,” “cybersecurity,” “phishing detection,” and “social engineering” were most prominent. Keyword co-occurrence mapping illustrates a dual focus on technical detection and human-centered analysis. The co-authorship analysis shows moderate international collaboration, concentrated among select academic hubs, while co-citation analysis reveals key intellectual influencers shaping the field. The findings present an in-depth overview of the intellectual structure and worldwide development of phishing research, highlighting the interdisciplinary nature of the field and informing future research directions for cybersecurity scholars and practitioners.

FROM SIMULATION TO STRATEGY: DISCRETE-EVENT APPROACHES TO PRIME MOVER ALLOCATION IN CONTAINER TERMINALS

Fri, 19 Sep 2025 00:00:00 +0800

The increasing complexity of container terminal operations has amplified the challenges associated with the efficient allocation of prime movers, which serve as the vital transport link between quay cranes and yard cranes. Inefficient allocation can lead to congestion, prolonged vessel turnaround times, and higher operational costs, ultimately undermining port competitiveness. Addressing this challenge requires robust analytical frameworks capable of capturing the dynamic, stochastic, and interdependent nature of container terminal systems. This study aims to examine how discrete-event simulation (DES) and related analytical approaches have been applied to optimize prime mover allocation in container terminals. By utilizing Scopus AI analytics, the study conducted a structured review based on a comprehensive search string that integrated key concepts of simulation, resource allocation, optimization, and efficiency in port logistics. The search produced a systematically organized corpus of scholarly works, encompassing journal articles, conference proceedings, and technical reports published between 2000 and 2025. The analysis was guided by Scopus AI features including the Summary, Expanded Summary, Concept Maps, Topic Experts, and Emerging Themes, allowing for both bibliometric mapping and narrative synthesis of the research landscape. The findings highlight three major contributions of DES to prime mover allocation: (i) operational optimization through simulation-based frameworks that reduce congestion and improve quay crane productivity, (ii) strategic planning via predictive simulation models and integrated scheduling, and (iii) sustainability-focused approaches that minimize fuel consumption and emissions. Emerging themes such as machine learning enhanced DES, decision support systems, and Boolean-algebraic methods underscore the evolution of research toward intelligent and adaptive allocation strategies. The study advances theory by framing prime mover allocation as both an operational and strategic systems problem, while offering practical insights for improving terminal efficiency, resilience, and sustainability. Limitations include dataset scope and limited empirical validation, with future research needed on predictive simulation, digital twin integration, and comprehensive sustainability metrics.

TRENDS IN C++ PROGRAMMING EDUCATION: A BIBLIOMETRIC ANALYSIS

Sun, 24 Aug 2025 00:00:00 +0800

This bibliometric analysis investigates global research trends in C++ programming education by examining scholarly outputs from the Scopus database. The study addresses a growing concern over the lack of comprehensive insights into the evolution of C++ education research, particularly in terms of thematic focus, publication patterns, and geographical distribution. To bridge this gap, a systematic search strategy was employed using three keyword variations related to C++, retrieving a total of 2,044 documents. The analysis utilized Scopus Analyzer for data filtering and statistical insights, OpenRefine for data cleaning and normalization, and VOSviewer for visualization of co-authorship networks, keyword co-occurrence, as well as citation mapping. The findings reveal that educational themes are closely connected with core topics such as programming pedagogy, curriculum design, and digital learning tools. The United States (US), the United Kingdom (UK), and China emerged as the leading contributors, with the US dominating in both document count and citation impact. Keyword mapping shows a consistent shift from basic language instruction to applied learning contexts like game-based education and computational thinking. Despite a strong research presence in developed nations, collaboration across countries remains limited, indicating potential for broader global engagement. In conclusion, this study highlights key publication trends and research gaps, offering valuable direction for educators, curriculum developers, and policy-makers aiming to strengthen and innovate C++ programming education worldwide.

A DECADE OF GIG ECONOMY RESEARCH (2014-2025): A BIBLIOMETRIC AND SCIENTIFIC MAPPING OF DIGITAL LABOR SCHOLARSHIP

Hu Li, Noorminshah A. Iahad — Thu, 25 Sep 2025 00:00:00 +0800

This study investigates how the academic discourse on the gig economy has evolved between 2014 and June 2025, particularly in response to the rise of digital labor platforms and algorithmic work coordination. Despite growing scholarly attention, the literature remains conceptually fragmented and lacks conceptual clarity and methodological coherence. Drawing on 1,303 Scopus-indexed publications, this study applies a comprehensive bibliometric and scientific mapping approach using tools such as OpenRefine, BiblioMagika, Biblioshiny, and VOSviewer, through co-authorship analysis, keyword co-occurrence mapping, thematic clustering, and trend visualization, the study uncovers both structural foundations and dynamic shifts in the field. The findings highlight a notable surge in gig economy research after 2020, largely influenced by the proliferation of digital labor platforms and the acceleration of remote work and algorithmic coordination during the COVID-19 pandemic. Research focus has shifted from early descriptive accounts to more critical investigations into algorithmic management, digital trust, governance, and platform-enabled labor control. Five major thematic clusters were identified, revealing increasing interdisciplinarity and convergence with domains such as information systems, innovation policy, and digital transformation. While the study is limited by its reliance on Scopus and exclusion of full-text analysis, it provides a timely and methodologically robust overview of the field. It also identifies conceptual blind spots and regional disparities in current scholarship. This paper contributes to the literature on digital labor and platform technologies by clarifying the knowledge base of gig economy studies and offering strategic insights for future research, platform governance, and the management of digital work ecosystems in both emerging and advanced economies.

STRATEGIC UTILIZATION OF RECORDS IN THE MALAYSIAN PUBLIC SECTOR: DETERMINANTS AND IMPLICATIONS

Fri, 19 Sep 2025 00:00:00 +0800

This conceptual paper explores the strategic use of records in the Malaysian public sector, emphasizing their role as vital assets for governance, transparency, and service delivery. It addresses the theoretical and empirical gaps in understanding how records users and records value influence the strategic use of records and examines the mediating role of records value. A systematic literature review approach was adopted to synthesize existing theories, models, and empirical studies related to records management and strategic use of records. The review followed four stages identification, screening, eligibility, and synthesis drawing on established frameworks such as the Life Cycle Concept of Records, Technology Acceptance Model, Sociotechnical System Theory, and Governance Recordkeeping Model. The review revealed limited empirical research on determinants and impacts of strategic records use, particularly in Malaysia. While previous studies examined aspects such as decision-making, good governance, and evidence provision, few integrated both user-related and value-based determinants within a comprehensive model. The proposed research framework identifies records user and records value as key predictors of strategic use of records, with records value also acting as a mediator. The study highlights the need for context-specific instruments that can measure determinants and predict outcomes of strategic records use in the Malaysian public sector. Implementing such tools can guide policy, improve accountability, and embed records management into decision-making processes to enhance efficiency and sustainable governance. This paper advances theoretical understanding by linking user engagement and perceived value to strategic records use, while also proposing a mediating effect of records value. It offers a localized conceptual framework tailored to Malaysia’s governance environment, providing a foundation for future empirical testing using quantitative methods.

A BIBLIOMETRIC ANALYSIS OF RESEARCH TRENDS IN ARTIFICIAL INTELLIGENCE (AI) AND FINANCE (2015-2025)

Mon, 18 Aug 2025 00:00:00 +0800

This study explores the research trends in the field of Artificial Intelligence (AI) and Finance through a detailed bibliometric analysis of relevant literature published between 2015 and 2025. In recent years, AI-driven innovations such as blockchain technology, digital platforms, and intelligent financial systems have transformed the financial sector, prompting a surge in academic interest. Despite this growing body of research, a systematic understanding of its structure, development, and key contributors remains limited. To address this gap, the study applies a bibliometric methodology, using the Scopus database to extract scholarly publications with the keywords "AI," "digital," "technology," and "finance" in the title. The dataset was refined using OpenRefine to ensure consistency and data cleanliness and analyzed using Scopus Analyzer and VOSviewer software to uncover key patterns in publication volume, subject areas, influential authors and countries, popular keywords, and collaborative networks. The final dataset comprises 1,388 English-language journal articles. The findings reveal a significant growth in scholarly output beginning in 2020, with strong growth observed in 2023 and 2024, reflecting increasing global interest in the digital transformation of financial services. China emerged as the dominant contributor, followed by the United States, the United Kingdom, and Malaysia. Keyword co-occurrence mapping highlights prominent themes such as digital finance, fintech, digital inclusive finance, and blockchain. Highly cited works focus on the role of digital finance in sustainability and the impact of AI on financial systems and policy. The analysis also shows increasing collaboration among authors and institutions across regions. In summary, this bibliometric study maps the development of research in AI and finance, identifies emerging themes and guiding future research directions in this interdisciplinary field.

EXPLORING THE SYNERGY BETWEEN AI AND HUMAN DECISION-MAKING TO BOOST EMPLOYEE PERFORMANCE

Thu, 25 Sep 2025 00:00:00 +0800

This conceptual paper explores the integration of Artificial Intelligence (AI) tools, human decision-making processes, and trust in AI tools as key factors influencing employee performance. Grounded in Socio-Technical Systems Theory (STS), this study emphasizes the interplay between technical (AI tools) and social (human decision-making) components within organizations. Specifically, AI tools enhance productivity by automating tasks and generating actionable insights, while human decision-making ensures ethical oversight and contextual understanding. Furthermore, trust in AI tools is identified as a mediating factor critical to fostering collaboration and acceptance of AI tools. Thus, this paper underscores the importance of user-centric AI design, continuous employee training, and fostering a collaborative culture to optimize AI integration. By emphasizing a balanced socio-technical approach, the proposed framework highlights that organizations can maximize the potential of AI while preserving a human-centered workplace. Ultimately, this approach not only enhances employee performance but also promotes sustainable organizational growth in the evolving AI-driven landscape.

CORPORATE TAXONOMY MAPPING FOR KNOWLEDGE ASSET MANAGEMENT IN MALAYSIAN KNOWLEDGE-BASED ORGANIZATIONS: A CONCEPTUAL FRAMEWORK FOR SPECIAL LIBRARY PRACTICE

Wan Ainol Mursyida Ahmad Tarmizi, Sharifah Fahimah Saiyed Yeop — Fri, 19 Sep 2025 00:00:00 +0800

This paper proposes a conceptual framework for corporate taxonomy mapping aimed at enhancing knowledge asset management (KAM) in Malaysian knowledge-based organizations (KBOs), with a focus on the strategic role of special libraries. Drawing on a qualitative synthesis of literature, the study identifies key components, including knowledge asset identification, taxonomy structuring, dynamic maintenance, and the role of special librarians as knowledge stewards. The framework integrates knowledge management theory, AI-enabled taxonomy systems, and librarianship practice to address challenges in organizing and retrieving both tacit and explicit knowledge. The proposed model provides a strategic approach to taxonomy governance, aligning knowledge structures with institutional objectives and dynamic information needs. It is intended to guide special libraries in becoming active enablers of sustainable knowledge ecosystems within KBOs.

GAMIFICATION IN SOCIAL COMMERCE: A SYSTEMATIC LITERATURE REVIEW

Li Ya, Noorminshah A Iahad — Tue, 30 Sep 2025 00:00:00 +0800

As social media and e-commerce increasingly converge, social commerce platforms have integrated gamification mechanisms to enhance user engagement, stimulate social interaction, and encourage behaviors such as information sharing and purchasing. While gamification has been extensively studied in domains such as education, health, and marketing, its application in social commerce remains fragmented and theoretically underdeveloped. To address this gap, this study conducts a systematic literature review of 15 peer-reviewed articles retrieved from Scopus and Web of Science as of May 2025. Guided by three research questions, the review examines which theoretical frameworks have been applied to gamification in social commerce, what methodological approaches dominate, and which gamification features are most frequently studied along with their behavioral outcomes. The findings reveal that quantitative surveys are the predominant methodology, with limited use of qualitative, experimental, or longitudinal designs, thus constraining the field’s ability to capture evolving user experiences. Theoretical engagement is moderate, with Self-Determination Theory and Hedonic Motivation Theory most frequently employed, though often in isolation rather than within integrative models that connect psychological, social, and technological perspectives. In terms of design, research has focused heavily on achievement-based elements such as points, badges, and leaderboards, while immersive and social interaction features remain underexplored despite their potential to foster deeper and more sustainable engagement. This review contributes to theory by highlighting the need for multi-layered frameworks that integrate individual motivation with social influence and platform-level factors, to methodology by underscoring the importance of diversified research designs, and to practice by offering guidance for platform designers to move beyond extrinsic rewards toward layered gamification strategies that support intrinsic motivation, community building, and long-term user loyalty.

DIGITIZING YANGJIABU WOODCUT NEW YEAR PRINTS: AUGMENTED REALITY APPLICATIONS IN INTANGIBLE CULTURAL HERITAGE PRESERVATION

Run Li , Muhammad Zaffwan Idris — Mon, 11 Aug 2025 00:00:00 +0800

This article is a case study on the digitization of Yangjiabu woodcut New Year prints. The article first compares the basic information of Yangjiabu woodcut New Year prints, including their characteristics and classification. Consequently, it analyzes the digitization case study of Yangjiabu woodcut New Year prints. Finally, it examines the Augmented Reality (AR) applications of other intangible Cultural Heritages (CH) and proposes suggestions and outlooks for the AR applications of Yangjiabu woodcut New Year prints. According to past literature, it can be observed that Yangjiabu Woodcut New Year prints underwent innovations and changes in themes and content during different periods, necessitating their creation to reflect the characteristics and themes of the times. The development strategy for Yangjiabu woodcut New Year prints in AR should focus on enhancing the expression of cultural connotations and the user's interactive experience. It aims to integrate the virtual and real worlds through lightweight mobile devices.

MECHANISMS OF CONSUMER FINTECH ADOPTION: EVIDENCE FROM A STREAMLINED TAM3 IN CHONGQING

Zhai Runjia, Nik Hadiyan Nik Azman — Fri, 19 Sep 2025 00:00:00 +0800

As the primary network medium for integrating digital technology into daily life, FinTech faces variations in institutional and infrastructural conditions caused by regional heterogeneity, as well as differences in digital literacy resulting from population diversity. This results in a diverse range of FinTech application types and usage purposes worldwide. Considering the current central hub of FinTech users globally, a dynamic cognitive gap exists in the adoption behaviour of Chinese consumers. This study was conducted in Chongqing, where government and non-governmental organisations actively participate in FinTech activities. Using the Technology Acceptance Model 3, a cross-sectional design was employed, with 368 valid online samples collected over one month, and analysed through SEM structural equation modelling. The findings indicated that Image and Result demonstrability influence the intention and behaviour of Chongqing residents to adopt FinTech by affecting perceived usefulness. External Control Perception, Computer Playfulness, and Objective Usability also impact these aspects by affecting Perceived ease of use. The study revealed that the penetration of FinTech services such as insurance and wealth management remains inadequate. Additionally, the frequency of Fintech use and the proportion of elderly users showed no significant differences compared to other populations. This research enhances global FinTech adoption theory, emphasising the need to strengthen the verifiability of FinTech results and perceived external control to facilitate universal application.

THE IMPORTANCE OF DIGITAL RECORD-KEEPING AMONG URBAN AGRICULTURE COMMUNITIES: A REVIEW

Noryana Ahmad Khusaini, Munifah Siti Amira Yusuf — Fri, 19 Sep 2025 00:00:00 +0800

Urban agriculture has emerged as a critical component of sustainable city development, contributing to food security, environmental resilience, and community well-being. As urban agricultural practices become more technologically integrated and data-driven, the role of digital record-keeping has gained increasing prominence. This review examines the significance of digital record-keeping in urban agriculture societies, focusing on its impact on improving efficiency and productivity, market access and economic benefits, sustainability, environmental benefits, and economic implications. The paper also discusses challenges such as a lack of digital skills, high implementation costs, inadequate technological infrastructure, limited access to reliable connectivity, data privacy, security, and many more, while offering recommendations for enhancing digital adoption in this sector. By analysing current trends, this review underscores the transformative potential of digital tools in shaping resilient and informed urban agricultural communities.

DEVELOPING A MODEL OF MULTI-CRITERIA DECISION MAKING FOR CUSTOMER UNCERTAINTY IN ONLINE PURCHASING (MCDM-UnOP)

Tue, 30 Sep 2025 00:00:00 +0800

The growth of the internet market has attracted more customers to purchase products online. Despite its popularity, there is a significant issue concerning customer uncertainty when making decisions to purchase online. Therefore, this research aims to develop, validate, and prioritize a model of multi-criteria decision making for customer uncertainty in online purchasing, known as the MCDM-UnOP model. This study used a mixed methods approach. The MCDM-UnOP model was developed based on the results of a comprehensive literature review and qualitative focus group interviews. A questionnaire was employed as the research instrument to collect quantitative data from internet users. A total of 348 usable questionnaires were collected and proceeded to the data analysis stage. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) using the split half method were conducted to validate the MCDM-UnOP model. The Analytical Hierarchy Process (AHP) technique was applied to prioritize the criteria associated with customer uncertainty in online purchasing. The validation process identified six key MCDM-UnOP criteria: security, shipping and delivery, privacy, product quality, financial risk, and psychological factors. These six criteria were measured using 34 sub criteria. The results of the AHP analysis indicated that security was the most important criterion, followed by privacy, shipping and delivery, product quality, and financial risk. The least important criterion was psychological factors. The findings provide valuable implications for decision makers in the online business industry to develop effective strategic marketing tools and to achieve a competitive advantage.

A BIBLIOMETRIC ANALYSIS OF ARTIFICIAL INTELLIGENCE IN PERFORMANCE EVALUATION: TRENDS AND GAPS IN CONSTRUCTION RESEARCH

Nurshikin Mohamad Shukery, Fuziah Ismail, Nur Emma Mustaffa, Tantish Kamaruddin — Mon, 11 Aug 2025 00:00:00 +0800

The integration of Artificial Intelligence (AI) into performance evaluation across various domains has emerged as a transformative research area, promising improved accuracy, objectivity, and efficiency in decision-making processes. However, despite growing interest, the literature remains fragmented, and a comprehensive understanding of global research trends is lacking. This study aims to bridge that gap by conducting a bibliometric analysis to map and evaluate the development, focus areas, and collaborative networks of research related to performance evaluation using AI. The study employs a robust methodology by retrieving bibliographic data from Scopus, refining and cleaning it using OpenRefine and Mendeley for consistency and accuracy, and analyzing it through VOSviewer to visualize keyword co-occurrences, country collaborations, and citation networks. A total of 801 relevant documents were identified and analyzed, spanning a diverse range of industries and applications. The findings reveal a sharp increase in publication trends since 2019, with India, China, and the United States emerging as the top contributing countries. “Machine learning,” “performance evaluation,” and “artificial intelligence” were the most frequently occurring keywords, indicating their centrality in the discourse. Highly cited papers focused on domains such as structural engineering, cybersecurity, and healthcare, reflecting the interdisciplinary application of AI in performance contexts. The visualization maps show strong international collaborations and evolving research clusters. This study concludes that AI-based performance evaluation is a growing field characterized by diverse applications and expanding global interest. However, there remains a need for more harmonized and cross-disciplinary research efforts. These insights provide researchers, practitioners, and policymakers with a clearer understanding of the research landscape and future directions in the application of AI for performance measurement.

COMPARING ARTIFICIAL NEURAL NETWORKS & LOGISTIC REGRESSION FOR BANKRUPTCY PREDICTION: A RESEARCH SYNTHESIS

Nor Hafizah Abdul Razak, Muhammad Fairus Ahmad Shukri, Mazura Mat Din — Fri, 19 Sep 2025 00:00:00 +0800

Predicting corporate bankruptcy accurately is crucial for reducing financial risks and protecting stakeholders. Logistic Regression (LR) has long been valued for its interpretability, while Artificial Neural Networks (ANNs) have gained prominence for capturing complex, nonlinear financial relationships. This study synthesizes comparative evidence to assess their relative strengths and limitations. A systematic review of peer-reviewed studies published between 2018 and 2024 was conducted using Scopus, Web of Science, and IEEE Xplore. Studies were included if they directly compared ANNs and LR in bankruptcy prediction, reporting performance metrics such as accuracy, recall, and handling of class imbalance. Attention was also given to the application of visualization and interpretability tools. The findings indicate that ANNs generally outperform LR in predictive performance, with accuracy reaching up to 96% and recall above 95%, especially when advanced preprocessing techniques such as the Synthetic Minority Oversampling Technique (SMOTE) and feature selection are applied. ANNs are particularly effective in modelling nonlinear and high-dimensional data but face challenges related to overfitting, computational demand, and lack of transparency. LR, though often less accurate, remains robust for its statistical rigor, ease of implementation, and interpretability. In certain contexts, LR has also demonstrated higher precision, showing that performance may vary depending on the characteristics and quality of the underlying data. Overall, ANNs are preferable in complex, data-rich environments, while LR is more suitable where transparency and regulatory compliance are critical. Future research should explore hybrid models that integrate ANN’s predictive power with LR’s explainability, supported by explainable AI and real-time decision-support tools.

ANALYSIS OF MACHINE LEARNING MODELS FOR EFFICIENT WATER QUALITY PREDICTION

Sun, 14 Sep 2025 00:00:00 +0800

Water plays a vital role in every aspect of human life, including the metabolism of organisms, industrial manufacturing of goods, and so on. Since water is essential to humanity and is used to improve our way of life, it will be a front-line challenge to humanity if it is heavily contaminated by their activities. In this study, a system that determines an efficient Machine Learning (ML) Model for better water quality prediction is proposed. The performance of the proposed model is evaluated in two different directions: the classification accuracy and the model accuracy in terms of quality (precision) and quantity (recall) of the prediction output. It is identified that the classification accuracy of the Random Forest algorithm appears to be the best model for water quality prediction on the obtained dataset. Random Forest outperforms other algorithms with the best accuracy score of 0.87 as against the XGBoost with 0.86. The model will be useful for treatment plants by automating and training the procedure of determining the quality of the water sample since the water quality of the water sample must be identified to determine the right amount of chemicals to be introduced for the water sample to be potable. In the near future, this work is aimed to be deployed with Flask to provide an interactive interface for users with a non-technical background to use the standard parameters in assessing the quality of water being used for daily activities.

THE DEVELOPMENT OF INTELLIGENCE BOOK RECOMMENDATION MODEL USING NEURAL COLLABORATIVE FILTERING METHOD

Thu, 25 Sep 2025 00:00:00 +0800

The arrival of the digital era has significantly reshaped how readers discover and interact with books, diminishing the effectiveness of conventional recommendation approaches, such as bestseller rankings and expert reviews, in reflecting personalized tastes. This study addresses the limitations of traditional methodologies, specifically the cold start and data sparsity concerns, by developing an intelligent book recommendation system that utilizes Neural Collaborative Filtering algorithms. The aim is to achieve higher recommendation accuracy by leveraging advanced techniques in user–item interaction modelling. Data is acquired from many sources, pre-processed, and evaluated using deep learning models that detect nonlinear patterns. The system's performance is evaluated using accuracy, precision, and recall scores, with a focus on mitigating cold start and data sparsity problems. The system provides reliable recommendations to existing users. Consequently, this study makes a significant contribution to the power of neural collaborative filtering in transforming customized book suggestions into the digital world.

RESEARCH TREND ON CHATBOT IN CONSTRUCTION: A BIBLIOMETRIC ANALYSIS

Mon, 11 Aug 2025 00:00:00 +0800

The rapid advancement of Artificial Intelligence (AI) technologies has catalyzed transformative changes across various sectors, including the construction industry. Among these innovations, Chatbot has gained significant attention for its potential to enhance communication, automation, and decision-making processes. Despite its growing relevance, scholarly exploration of Chatbots within the construction domain remains fragmented and under-characterized. Thus, this study aims to address this gap through a comprehensive bibliometric analysis, offering a data-driven overview of current research trends, influential contributions, and thematic structures related to Chatbots in the construction industry. Utilizing a systematic search strategy, three key terms, “chatbot,” “ChatGPT,” and “construction,” were applied to extract data from the Scopus database. The initial search yielded 537 relevant publications. Data cleansing and refinement were performed using OpenRefine to ensure analytical precision and consistency, followed by detailed quantitative and network-based analyses conducted via Scopus Analyzer and VOSviewer software. The results reveal a noticeable increase in publication activity from 2023 onwards, with significant contributions from countries such as the United States, China, and Malaysia. Thematic clustering highlights core research areas, including AI-assisted project scheduling, prompt engineering, and educational applications of Chatbots in engineering curricula. High-impact authors and journals were also identified, delineating the intellectual structure of the field. Citation and keyword co-occurrence analyses further emphasize the interdisciplinary nature of the topic, linking AI, natural language processing, and construction management. In conclusion, this study offers a foundational mapping of the emerging research landscape at the intersection of Chatbot and the construction industry. It also provides valuable insights for academics, practitioners, and policymakers seeking to harness AI-driven tools for innovation in construction processes and education.

RELIABILITY ANALYSIS OF STUDENT ENGAGEMENT MODEL IN USING COLLABORATIVE TECHNOLOGY

Fri, 19 Sep 2025 00:00:00 +0800

This paper examines the reliability of a student engagement model within the context of collaborative technology use in secondary education. As digital tools such as online discussion platforms, shared documents, and virtual collaboration spaces become increasingly integrated into teaching and learning, it is essential to assess how effectively student engagement can be measured in these environments. The model focuses on three dimensions of engagement: behavioral, emotional, and cognitive. Data were collected from secondary school students who participated in collaborative learning activities using digital tools. Statistical analyses, including Cronbach’s alpha, were conducted to evaluate the reliability and how well the components of student engagement fit together as a model. The results indicate high reliability across all dimensions confirming that the model provides a consistent and valid measure of student engagement in technology-supported collaborative learning. These findings offer valuable insights for educators and researchers aiming to assess and enhance student engagement through digital collaboration in secondary schools.

AI REVOLUTION IN LITERARY TRANSLATION: A BIBLIOMETRIC ANALYSIS OF TRENDS, CHALLENGES, AND POTENTIALS

Mon, 18 Aug 2025 00:00:00 +0800

AI has significantly affected literary translation through tools such as Neural Machine Translation and Large Language Models, providing greater speed and accessibility. However, their grasp of literary subtleties, like metaphor, tone, and culture, remains subject to debate. This study uses a bibliometric analysis of 254 peer-reviewed articles from 2005 to 2025 in the Scopus database, employing OpenRefine, VOSviewer, and Scopus Analyser. Findings indicate a surge in publications after 2020, reaching a peak in 2024. Most research comes from technical disciplines; Computer Science (26.6%), Engineering (21.7%), and Mathematics (10.3%), with limited input from the Arts and Humanities (1.8%). China leads in publications (41.3%), followed by India and the U.S. Keyword analysis reveals a focus on AI methods, often neglecting translation-specific challenges. The study calls for interdisciplinary collaboration and culturally sensitive AI to preserve literary depth.

ENHANCING MOBILE INTERFACE INTERACTION DESIGN FOR THE ELDERLY IN BEIJING: ADDRESSING ACCESSIBILITY, USABILITY, AND CULTURAL NEEDS

Peng Ling, Ahmad Rashidi — Thu, 25 Sep 2025 00:00:00 +0800

The rapid proliferation of mobile technology has dramatically reshaped daily life across all age groups; however, the elderly population often struggles with accessibility and usability challenges in mobile applications. This study investigates the critical role of interface interaction design in enhancing the digital experiences of elderly users in Beijing, China. Through a combination of quantitative surveys involving 200 senior participants and qualitative interviews with 7 experienced interface designers, the research identifies the physical, cognitive, and emotional barriers that older adults face when using mobile touchscreen devices. Results highlight that while some elderly users adapt enthusiastically to digital technologies, many experience difficulties due to poor motor skills, impaired vision, and limited digital literacy. The findings reveal that features such as simplified navigation, customizable font sizes, voice-activated assistance, and culturally sensitive design elements significantly improve user satisfaction and engagement among seniors. Correlation analysis underscores the importance of voice control integration and intuitive design to reduce cognitive load and foster greater technology acceptance. Based on these insights, the study offers actionable recommendations for developing elderly-friendly mobile applications that prioritize inclusivity, ease of use, and user empowerment. This research not only addresses existing gaps in elderly-centered interface design but also advocates for a user-centered, iterative design approach to bridge the digital divide and promote digital inclusion among Beijing’s aging population. Future research directions suggest deeper exploration into emotional connectivity, personalized learning supports, and cross-cultural adaptation of mobile interfaces for senior citizens globally.

MIXED VARIABLES CLASSIFICATION: A COMPREHENSIVE REVIEW

Kartini Kasim, Hashibah Hamid, Ayu Abdul-Rahman — Fri, 19 Sep 2025 00:00:00 +0800

Classification involving mixed categorical and continuous variables presents unique challenges due to the differences in data scales and statistical properties. These types of data are commonly encountered across various research fields. In this study, we introduce a systematic review of classification methods designed to handle mixed-type variables. A total of 52 journal articles were selected through a systematic review process based on the related topics, which were identified and screened from the Web of Science (WoS) as well as Scopus databases. The selected articles were reviewed and categorised into two main groups of classification methods: parametric and non-parametric approaches. Among parametric approaches, methods based on the location model were among the most frequently used. In contrast, non-parametric approaches, such as classification trees and machine learning methods, were often employed when distributional assumptions were not met. This review also presents the use of classification methods for mixed-type variables across diverse fields such as medical, psychology, agriculture, and biology. Therefore, this review may serve as a useful reference for researchers working with mixed-type data and emphasises the importance of ongoing methodological development to address the complexity of such data in real-world applications.

COGNITIVE FEEDBACK LOOPS IN AI SYSTEMS

Vignes Thurasingam — Tue, 30 Sep 2025 00:00:00 +0800

In the field of psychology, cognitive structures can be defined as an information processing system that encompasses the acquisition of sensory information, its storage, retrieval, and use in making complex decisions in an individual's life. In psychological understanding, cognition or thinking involves mental activities such as comprehension, problem-solving, forward thinking, and decision-making. Cognition represents an individual's belief about something derived from the thinking process about a particular matter. Through cognitive processes, humans can acquire and manipulate knowledge by engaging in activities such as recalling, analysing, understanding, evaluating, and imagining a particular subject. The capacity and ability of cognition are generally defined as human intelligence. However, the presence of artificial intelligence (AI) systems in the field of psychology brings about a new transformation and new challenge. With continuous and unlimited two-way feedback interaction, humans can correct model errors, reduce bias, and inject domain expertise, while AI systems can support human thinking with real-time information and pattern recognition. Cognitive loops are heavily influenced by cognitive psychology principles, especially those related to perception, attention, memory, and learning. By modelling AI systems to reflect human cognitive processes, collaboration between humans and machines becomes more intuitive and effective. This paper explores the theoretical foundations, design strategies, and practical implications of cognitive feedback loops in AI systems. By fostering a symbiotic relationship between human cognition and machine intelligence, such systems hold the potential to create AI that is not only more accurate but also more aligned with human reasoning and values.

A STUDY ON THE DIGITAL COMPETENCE CHARACTERISTICS OF VOLUNTEER CHINESE LANGUAGE TEACHERS IN SECONDARY SCHOOLS IN THAILAND BASED ON O*NET JOB ANALYSIS METHOD

Zhulin, Abdul Talib Mohamed Hashim — Mon, 11 Aug 2025 00:00:00 +0800

This study uses the O*NET job analysis questionnaire to investigate the work skills and work styles required for volunteer Chinese language teachers in secondary schools in Thailand. The analysis results of 265 subjects show that: (1) The important work skills of volunteer Chinese language teachers in secondary schools in Thailand include digital technology operation ability, digital teaching problem-solving ability, digital teaching strategy thinking ability, digital teaching guidance and evaluation ability, and digital teaching integration and application ability, and (2) The important work styles of volunteer Chinese language teachers in secondary schools in Thailand are basic professional characteristics and teacher social cognitive style.

ANALYTICS OF ACADEMIC PROGRAM DISTRIBUTION IMBALANCES BY STATE AND THEIR CONSEQUENCES FOR GRADUATE EMPLOYABILITY IN MALAYSIA (2018-2023)

Fri, 19 Sep 2025 00:00:00 +0800

This study was conducted to analyze the imbalance in the distribution of academic programs by state and its implications on graduate employability in Malaysia from 2018 to 2023. The primary objective of the study is to assess whether states offering a higher number and diversity of academic programs tend to record better graduate employability rates compared to those with limited program offerings. Secondary data were obtained from official sources such as the Department of Higher Education Malaysia, the Department of Statistics Malaysia, and the MyGRAD Tracer Study. A descriptive and correlational analysis approach was applied using data on the distribution of academic programs and graduate employability rates from 2018 to 2023. The findings show a consistent positive correlation between academic program distribution and graduate employability in most years. States such as Selangor, Kuala Lumpur, and Johor recorded both a high number of academic programs and employability rates exceeding 85%. However, in 2020, a negative correlation was observed due to the COVID-19 pandemic's impact on the labor market, resulting in a decline in employability even in advanced economies. States such as Kelantan, Perlis, and Labuan recorded consistently lower employability rates throughout the study period, in line with their limited program offerings. In conclusion, the study supports the hypothesis that a balanced and strategically aligned distribution of academic programs, especially those responsive to industry needs, plays a significant role in improving graduate employability across Malaysian states.