The increasing complexity of Internet of Things (IoT) environments requires adaptive mechanisms for efficient service allocation across distributed infrastructures. Existing approaches are often focused on specific optimization algorithms or isolated Quality of Service (QoS) parameters, lacking a unified framework for decision-making across IoT, Fog/Edge, and Cloud layers. This paper proposes a modular QoS-aware decision-making framework that integrates QoS profiles, key performance indicators (KPIs), utility functions, and multi-criteria decision-making mechanisms to support both static and dynamic service allocation. The framework considers service execution latency, energy consumption, network throughput, and network coverage as decision criteria and enables adaptive balancing of conflicting QoS requirements. Its applicability is demonstrated through a smart transportation use case involving multiple service allocation scenarios across CRU, Fog, and Cloud infrastructures. The results confirm that different allocation strategies exhibit distinct QoS trade-offs and demonstrate the suitability of the proposed framework for adaptive and resource-efficient service allocation in layered IoT architectures.
The increasing use of digital messaging platforms such as WhatsApp, Instagram, and Facebook has made real-time customer communication a key activity for Small and Medium Enterprises (SMEs). However, many SMEs struggle to access and apply business knowledge during live interactions, often relying on fragmented information and keyword-based retrieval that does not capture user intent. This study examines how a mobile keyboard–based Retrieval-Augmented Generation (RAG) system supports SME customer response work practices from an Information Systems perspective. The system organizes business knowledge into semantic chunks, represents them as vector embeddings, retrieves relevant information using similarity-based methods, and generates context-aware responses through a Large Language Model. It is implemented as a lightweight keyboard interface embedded directly within messaging applications. The system was evaluated using the RAGAS framework on 37 test queries and compared with a keyword-based baseline. The results show high faithfulness (0.997) and answer correctness (0.881), with an average response time of approximately 5 seconds. A preliminary User Acceptance Testing session with one SME stakeholder suggests that the system may help reduce response effort and support more consistent use of business knowledge in customer communication.
This research investigates the expanding use of generative AI (Mahmoud et al., 2025) tools among computer programming students. Building on a 2023 study, a 2024 survey of 182 undergraduate and graduate students explored usage patterns, perceived effectiveness, and attitudes towards their academic use. Thematic analysis revealed a significant increase in AI adoption, with students frequently using tools for code generation, debugging, and explanation. While students value the speed and efficiency AI offers, they express significant concerns about output accuracy, over-reliance, and the impact on developing independent reasoning skills. Notably, attitudes shifted from favouring full allowance of AI tools to partial allowance for programming assignments, with distinct usage patterns observed across different study levels. These findings underscore the urgent need for educational institutions to establish clear guidelines. A balanced approach is required to integrate AI tools constructively while mitigating risks to academic integrity and deep learning.
Mobile Language Learning Applications (MLLAs) are gaining widespread use in higher education because of the flexible nature of practice opportunities with languages. The majority have easy-to-use interfaces, but few enable long-term engagement, retention, and productive learning. There is much empirical work that isolates usability or outcomes singularly without noting how user experience, motivation, and pedagogy intersect in the mobile environment. This study investigates MLLA user experience among university students in Indonesia using a mixed-methods design. Structural Equation Modelling (SEM) was used to investigate the inter-plays of usability, acceptance, engagement, and retention. Qualitative data provided information about user views and context-based limitations. Findings suggest that while overall MLLAs are usable and widely accepted, they tend to lack instructional intensity and intrinsic motivation support. Motivation and perceived usefulness significantly impact engagement, but redundant repetition, superficial individualization, and minimal interaction reduce retention. With reference to TPACK, FRAME, and SDT, the study highlights learner-centered design featuring effective pedagogy, social interaction, and adaptive functionality as being vital. To transcend surface gamification, MLLAs must support deep, long-term language learning. These results provide actionable recommendations for developers and instructors seeking to optimize mobile language learning by synergizing technology, pedagogy, and learner psychology.
This study examines how consultant competencies influence ERP project success by shaping critical success factors specific to the implementation and early stabilization stages of ERP projects. Thirteen ERP implementation critical success factors and 17 consultant competencies were operationalized based on prior research. Survey data from 227 SAP consultants were analysed using multiple regression to estimate competency effects on ERP implementation critical success factors and the effects of both critical success factors and competencies on project success. The results indicate that consultant competencies affect project success primarily through critical success factors, while direct competency effects are generally weak. Software Hardware Compatibility shows the strongest positive association with project success. User Training and Software Testing are significant predictors but exhibit negative relationships, suggesting that these practices may become more salient in projects facing implementation difficulty, rework, or instability. The study extends prior ERP research by specifying a structured competency to critical success factor to success pathway that clarifies the role of consultants and helps explain mixed findings regarding training, testing, and teamwork in ERP implementations.
Smart agriculture aims to improve agricultural sustainability output and earnings, increase resilience to climate change, mitigate greenhouse gas emissions, and promote Sustainable Development Goal 2. Understanding the internal organizational factors influencing digital transformation and digital maturity among farmers is crucial to achieving smart agriculture. This study collected a sample of 287 farmers from Indonesia, Malaysia, Thailand, Vietnam, and Cambodia across 15 agricultural zones. The study examines collective ambition through the lens of the theory of acceptance model (TAM) and organizational commitment through the unified theory of acceptance and use of technology (UTAUT), using partial least square structural equation modelling (PLS-SEM). The findings show that collective ambition and organizational commitment have a significant and positive impact on digital transformation and digital maturity. The integration of TAM and UTAUT validates collective views of technology usability, social influence, and effort expectations as essential social mechanisms for coordinated action, sustained engagement, and enduring digital transitions.
Mobile Language Learning Applications (MLLAs) are gaining widespread use in higher education because of the flexible nature of practice opportunities with languages. The majority have easy-to-use interfaces, but few enable long-term engagement, retention, and productive learning. There is much empirical work that isolates usability or outcomes singularly without noting how user experience, motivation, and pedagogy intersect in the mobile environment. This study investigates MLLA user experience among university students in Indonesia using a mixed-methods design. Structural Equation Modelling (SEM) was used to investigate the inter-plays of usability, acceptance, engagement, and retention. Qualitative data provided information about user views and context-based limitations. Findings suggest that while overall MLLAs are usable and widely accepted, they tend to lack instructional intensity and intrinsic motivation support. Motivation and perceived usefulness significantly impact engagement, but redundant repetition, superficial individualization, and minimal interaction reduce retention. With reference to TPACK, FRAME, and SDT, the study highlights learner-centered design featuring effective pedagogy, social interaction, and adaptive functionality as being vital. To transcend surface gamification, MLLAs must support deep, long-term language learning. These results provide actionable recommendations for developers and instructors seeking to optimize mobile language learning by synergizing technology, pedagogy, and learner psychology.
This study aimed to elaborate on the predictors of behavioral intention, actual behavior, perceived impact of AI on work engagement, and the perceived impact of AI on teaching and learning in Asian higher education institutions (HEIs). We extended the Unified Theory of Acceptance and Use of Technology (UTAUT) to test the relationships among these variables. In total, 516 lecturers from three different universities contributed to the dataset. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the respondents’ data through both measurement and structural models. The findings indicate that actual behavior has a significant positive relationship with the perceived impact of AI on teaching and learning and the perceived impact of AI on work engagement. Behavioral intention is strongly correlated with actual behavior. Performance expectancy is the most prominent link to behavioral intention, followed by social influence and facilitating conditions. The results of the study underscore the importance of fostering a conducive environment through adequate facilitating conditions and aligning performance expectations to drive behavioral intention and perceived engagement with AI technologies in HEIs.
This research investigates the expanding use of generative AI (Mahmoud et al., 2025) tools among computer programming students. Building on a 2023 study, a 2024 survey of 182 undergraduate and graduate students explored usage patterns, perceived effectiveness, and attitudes towards their academic use. Thematic analysis revealed a significant increase in AI adoption, with students frequently using tools for code generation, debugging, and explanation. While students value the speed and efficiency AI offers, they express significant concerns about output accuracy, over-reliance, and the impact on developing independent reasoning skills. Notably, attitudes shifted from favouring full allowance of AI tools to partial allowance for programming assignments, with distinct usage patterns observed across different study levels. These findings underscore the urgent need for educational institutions to establish clear guidelines. A balanced approach is required to integrate AI tools constructively while mitigating risks to academic integrity and deep learning.
This study reviews state-of-the-art research on the application of the meta-analysis based Modified Unified Theory of Acceptance and Use of Technology model (Meta-UTAUT) to provide a comprehensive understanding of how the model has been applied across diverse contexts. From 1,881 citations of the original model, 36 Scopus-and Web of Science-indexed studies were analysed. Findings reveal that mobile payment is the most examined system, with financial technology as the predominant domain of application. Across studies, attitude consistently emerged as a strong determinant of behavioural intention and use behaviour, underscoring its central role in technology adoption. Behavioural intention and facilitating conditions were the most influential predictors of use behaviour, while performance expectancy and effort expectancy contributed substantially to shaping attitude. Many studies extended the Meta-UTAUT framework by incorporating external variables to enrich explanations of attitude, intention, and behaviour, deepening theoretical understanding of technology adoption. This review highlights recurring methodological limitations, including single-subject sampling, cross-sectional designs, and sampling methods, indicating the need for more rigorous approaches to strengthen theoretical refinement and improve generalisability. This study is the first to offer an extensive synthesis of Meta-UTAUT applications, providing valuable implications for researchers and guiding future inquiry toward more rigorous and contextually diverse investigations.
The use of relevant and structured instruments for measuring digital development is essential for policy-making in digitalization. The aim of the research is to compare structural adequacy of the global digital development indexes by means of multicriteria decision-making (MCDM). Theoretical contribution is to develop an evaluation framework and propose a novel methodological integration. Nine criteria were used to quantify six indexes: the Network Readiness Index (NRI), the E-Government Development Index (EGDI), the Digital Economy and Society Index (DESI), the ICT Development Index (IDI), the IMD World Digital Competitiveness Ranking (IMD) and the Global Digital Index (GDI). The criteria's objective weights were evaluated using the Method based on the Removal Effects of Criteria (MEREC) and the weights alterations effect was considered using the Shannon entropy method. The final prioritization was consolidated using five MCDMs scores: Combined Compromise Solution (CoCoSo), Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS), Additive Ratio Assessment (ARAS), COmbinative Distance-based ASsessment (CODAS) and Evaluation based on Distance from Average Solution (EDAS). Practical contribution and originality are presented by proposing first time evaluation framework of digital development indexes based on a recently proposed MEREC and selecting the most appropriate index (NRI) in a neutral MCDM context.
The article discusses the optimization of batch processing in a public institution through the implementation of a proprietary centralized batch management system. The research is based on the analysis of processing log entries from 2018 to 2023 and the implementation of a proprietary information interface developed in C# and connected to an Oracle database. The study highlights the importance of operator roles, structured work orders, and socio-technical alignment between technology and organizational processes. The analysis confirms that advanced planning significantly reduces processing time, thereby improving operational efficiency. However, the impact on overall process success is limited, as reliability appears to depend on additional organizational and infrastructural factors.
Organizational change is an ongoing and dynamic process that enables institutions to adapt to both internal and external developments. This study explores key factors that influence employees' readiness to embrace change, focusing specifically on the roles of Empowering Leadership and Technology Readiness. The research examines how these factors directly and indirectly affect Readiness for Change, with Organizational Commitment acting as a mediating variable, as guided by a carefully developed conceptual and empirical model. A quantitative approach was used, employing a structured questionnaire survey distributed to change champions and change ambassadors at BPS Headquarters, Provincial Offices, and Regional Offices across Indonesia. Data from 432 valid responses were analyzed using Structural Equation Modeling (SEM) to test the proposed hypotheses and evaluate the overall model. Findings reveal that both Empowering Leadership and Technology Readiness have significant positive effects, both direct and indirect, on Readiness for Change. Organizational Commitment plays a critical mediating role in these relationships. This study contributes to theoretical literature by empirically demonstrating how leadership style and technological adaptability jointly enhances readiness in a public sector institution within a developing country. It highlights the human-centered dimensions necessary for sustaining transformation in bureaucratic settings.
The internationalization of higher education, especially in terms of student mobility, is a key indicator of the quality of higher education. Additionally, increasing the number of students participating in credit mobility is one of the strategic goals of the European higher education area. To help more students take advantage of student mobility programs, it is essential to understand the factors that influence student mobility at both the institutional and individual levels. This paper proposes the Institutional Student Mobility Ecosystem (ISME) for credit mobility. It is based on a systematic literature review of 321 initially retrieved sources, with 22 analyzed in detail. The results are supplemented by the analysis of 11 policy and professional documents. The proposed ISME identifies student decisional factors, supporting mechanisms and stakeholders as key enablers of student mobility. Additionally, it outlines the outcomes for students, HEIs and society that result from student mobility. This model provides valuable groundwork for researchers in the field of student mobility, facilitating further in-depth analysis of specific elements within the student mobility ecosystem.
Generative Artificial Intelligence (GenAI) is rapidly transforming higher education, yet its impact on learning experiences remains contested. Existing research often isolates either cognitive outcomes (e.g., comprehension, creativity) or affective outcomes (e.g., motivation, engagement), leaving a gap in integrated analyses that also account for heterogeneity across student groups. This study investigates both dimensions simultaneously by examining university students’ perceptions of GenAI, focusing on learning, creativity, motivation, and engagement, alongside perceived risks such as overreliance, ethical concerns, and difficulties in verifying accuracy. Data were collected from 93 students and analyzed through Spearman’s correlations and unsupervised clustering (k-means) with PCA visualization. Findings indicate low to moderate positive correlations between GenAI usage and learning outcomes, particularly problem-solving and motivation. Cluster analysis reveals diverse usage–perception profiles, including paradoxical cases where frequent users report limited cognitive benefit. These results align with Technology Acceptance Model (TAM) and UTAUT assumptions of perceived usefulness and performance expectancy, while also showing that digital literacy moderates these relationships, especially in critical thinking and responsible use. The study contributes by integrating cognitive and affective outcomes, revealing latent profiles beyond averages, and bridging adoption models with responsible AI frameworks. Practical implications highlight the need for AI literacy training, ethical policies, and instructional design to foster effective and responsible GenAI integration in higher education.
Despite continued efforts to digitize public services, many local government websites in emerging contexts still underperform in delivering satisfactory user experiences. This study develops an integrated evaluation framework that combines the ISO 25010 software quality model with the Technology Acceptance Model (TAM) to jointly assess system quality and user acceptance. We analyzed survey data from 524 users in Lombok Tengah, Indonesia, using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance Performance Map Analysis (IPMA). The results indicate that functional suitability, usability, and reliability significantly shape perceived usefulness, whereas reliability, security, and performance efficiency drive perceived ease of use. Both perceived usefulness and perceived ease of use positively influence user satisfaction and behavioral intention, with satisfaction emerging as the strongest predictor. IPMA highlights performance efficiency and security as priority areas for improvement. The study contributes to e-government literature by proposing a dual layer model that links system level attributes to user-level perceptions and outcomes, and by translating statistical effects into actionable priorities for local governments seeking to enhance the quality and adoption of digital public services in semi urban developing regions.
The rapid growth of the Internet of Medical Things (IoMT) has introduced critical cybersecurity challenges, highlighting the need for robust and accurate intrusion detection systems (IDS). This study presents a hybrid machine learning (ML) framework to strengthen intrusion detection in IoMT networks using the CIC-IoMT2024 dataset. The framework combines Information Gain (IG) and Principal Component Analysis (PCA) for feature selection and dimensionality reduction, while SMOTEENN and SMOTETomek are applied to address severe class imbalance. The processed data are classified using Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Multi-Layer Perceptron (MLPC), and Logistic Regression (LR), with hyperparameters optimized through Bayesian Optimization. Performance is evaluated using Accuracy, Precision, Recall, F1-Score, and AUC. Experimental results reveal that the optimized XGB classifier with SMOTEENN achieves a peak accuracy of 99.811%. This top-tier performance surpasses several existing benchmarks, validating the effectiveness of integrating IG-PCA with advanced resampling and optimization strategies. This work contributes a lightweight, scalable, and highly accurate IDS, offering a practical and efficient solution for enhancing security in resource-constrained, next-generation medical IoT systems.
Generative Artificial Intelligence (GenAI) is rapidly transforming higher education, yet its impact on learning experiences remains contested. Existing research often isolates either cognitive outcomes (e.g., comprehension, creativity) or affective outcomes (e.g., motivation, engagement), leaving a gap in integrated analyses that also account for heterogeneity across student groups. This study investigates both dimensions simultaneously by examining university students' perceptions of GenAI, focusing on learning, creativity, motivation, and engagement, alongside perceived risks such as overreliance, ethical concerns, and difficulties in verifying accuracy. Data were collected from 93 students and analyzed through Spearman's correlations and unsupervised clustering (k-means) with PCA visualization. Findings indicate low to moderate positive correlations between GenAI usage and learning outcomes, particularly problem-solving and motivation. Cluster analysis reveals diverse usage-perception profiles, including paradoxical cases where frequent users report limited cognitive benefit. These results align with Technology Acceptance Model (TAM) and UTAUT assumptions of perceived usefulness and performance expectancy, while also showing that digital literacy moderates these relationships, especially in critical thinking and responsible use. The study contributes by integrating cognitive and affective outcomes, revealing latent profiles beyond averages, and bridging adoption models with responsible AI frameworks. Practical implications highlight the need for AI literacy training, ethical policies, and instructional design to foster effective and responsible GenAI integration in higher education.
Active, technology-supported learning accelerated during and after COVID-19, yet evidence from non-programming computer science courses remains limited. This paper contributes (i) a focused review of flipped classroom (FC) studies in CS program (2020-2024) and (ii) a three-year case study of how the flipped classroom enhances the teaching of IT Service Management (ITSM) as a discipline in the computer science program in an online university environment, during and after the COVID-19 pandemic. The FC design combined pre-lecture micro-videos and auto graded quizzes with in-classroom clarification and post classroom activities (project). Using LMS telemetry, course outcomes, and an end of semester survey across three academic years (2021/22-2023/24), we examined engagement-achievement links with non-parametric, rank based correlations (Spearman ρ), regularized logistic regression, and comparisons across empirically defined engagement tertiles. Results show consistent, practically meaningful associations between quality weighted engagement (quiz participation and performance) and both passing and final grades, with survey perceptions aligning to the behavioral signals. While strictly non-causal, the pattern is robust across methods and suggests actionable uses: early identification of at-risk students and design guidance that emphasizes short, well scaffolded videos and steady formative assessment.
Despite continued efforts to digitize public services, many local government websites in emerging contexts still underperform in delivering satisfactory user experiences. This study develops an integrated evaluation framework that combines the ISO 25010 software quality model with the Technology Acceptance Model (TAM) to jointly assess system quality and user acceptance. We analyzed survey data from 524 users in Lombok Tengah, Indonesia, using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance Performance Map Analysis (IPMA). The results indicate that functional suitability, usability, and reliability significantly shape perceived usefulness, whereas reliability, security, and performance efficiency drive perceived ease of use. Both perceived usefulness and perceived ease of use positively influence user satisfaction and behavioral intention, with satisfaction emerging as the strongest predictor. IPMA highlights performance efficiency and security as priority areas for improvement. The study contributes to e-government literature by proposing a dual layer model that links system level attributes to user-level perceptions and outcomes, and by translating statistical effects into actionable priorities for local governments seeking to enhance the quality and adoption of digital public services in semi urban developing regions.
