Search Results (288)

Artificial intelligence (AI) is expanding opportunities for adaptive and personalised learning that responds to learners’ abilities, interests, and educational needs. However, formal gifted education programmes may still offer limited opportunities for individualised learning pathways. In this study, gifted education programmes refer to formal provision for identified gifted students that supports differentiated depth, complexity, pace, challenge, enrichment, and talent development. This study proposes an AI-Supported Double-Helix Framework for Personalised Learning in Gifted Education and examines preliminary empirical support for its hypothesised relationships. The framework integrates the Unified Theory of Acceptance and Use of Technology (UTAUT), AI-Supported Adaptive Learning (ASAL), Personalised Learning Experience (PLE), and curriculum integration. Using a quantitative cross-sectional design, PLS-SEM was applied to data from 437 gifted secondary school students. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and measurement invariance. Performance Expectancy showed the strongest positive association with ASAL, which was positively associated with PLE. PLE was positively associated with perceived Double-Helix Framework Implementation (DHFI), which was subsequently associated with perceived Learning Effectiveness, Talent Development, and Future Readiness. A secondary multi-group analysis indicated that most structural relationships were comparable across gender groups. The study extends UTAUT by linking technology adoption factors with AI-supported adaptation, personalised learning, curriculum integration, and perceived developmental outcomes. It also situates these processes within the context of Education 4.0 through adaptive, data-informed, and future-oriented learning. However, the cross-sectional self-report design provides preliminary empirical support only for statistical relationships among the measured constructs. It does not establish the framework’s educational effectiveness or causal effects on learning. Longitudinal, experimental, behavioural, and performance-based studies are needed to evaluate its educational effectiveness and practical implementation. Full article

The rapid adoption of generative artificial intelligence tools, particularly ChatGPT, is transforming teaching and learning in higher education. This study proposes an explainable artificial intelligence (XAI) framework that integrates the Unified Theory of Acceptance and Use of Technology (UTAUT2), machine learning, and explainability techniques to examine students’ intentions to use ChatGPT in academic contexts. Survey data were analyzed using Ordinary Least Squares regression, Random Forest, SHAP, Necessary Condition Analysis (NCA), Importance–Performance Map Analysis (IPMA), and K-Means clustering. The results indicate that Habit, Performance Expectancy, Hedonic Motivation, Social Influence, and Facilitating Conditions significantly influence behavioral intention, explaining 67.6% of the variance. Habit emerged as the strongest predictor, whereas Price Value had negligible influence. XAI analyses revealed that Effort Expectancy acts as a necessary condition for high adoption levels despite its limited direct effect. Four distinct student profiles were identified, highlighting heterogeneous patterns of AI integration and informing strategies for responsible and effective educational adoption. These findings provide evidence-based guidance for integrating AI literacy, responsible AI practices, and pedagogically meaningful ChatGPT use in higher education curricula. Full article

This study investigates how integrated smart urban systems enhance resource efficiency in Saudi Arabia’s rapidly urbanising cities. Despite growing global interest in smart cities, there remains a critical gap in empirical research that simultaneously examines smart energy, water, and waste systems within a unified analytical framework, particularly in emerging urban contexts, while the mediating role of governance efficiency remains underexplored. Adopting a quantitative survey design, this study employs Structural Equation Modelling (SEM) to analyse 384 valid data from 384 stakeholders across selected urban areas. A stratified sampling approach ensures representation of policymakers, urban planners, and infrastructure managers, and measurement constructs are adapted from validated scales to ensure reliability and validity. The study examines relationships between smart energy systems, smart water management, smart waste monitoring, governance efficiency, and resource efficiency outcomes, with governance efficiency conceptualised as a mediating variable enhancing the effectiveness of smart urban systems. The study hypothesises that integrated smart technologies significantly improve resource efficiency, with smart infrastructure as a key predictor; structural model robustness is assessed using standard SEM fit indices, with CFI (0.93), TLI (0.91), and RMSEA (0.052). This research contributes to theory by integrating smart urban systems and governance into a unified framework, extending smart city and circular economy literature, while offering practical insights aligned with Saudi Vision 2030 to support sustainable urban development. It is recommended that policymakers prioritise integrated smart infrastructure, strengthen institutional frameworks, and promote public awareness to maximise resource optimisation. Future research should adopt longitudinal designs, expand across multiple cities, and incorporate behavioural and policy variables to enhance generalizability and deepen insights into smart urban resource efficiency. Full article

India is the most populous country in the world, with a rising urban population and witnessing a surge in noncommunicable diseases such as cancer that impose significant pressure on healthcare professionals, hospitals, and city planners. Over the last decade, India has pursued a vision to transform urban areas into smart cities and roll out digital infrastructure. Rapid urbanization coupled with an ageing population has accelerated demand for precision cancer care (PCC) solutions using advanced technologies such as digital twins (DT), leveraging the developing digital infrastructure in urban India. The fast evolution of e-health technologies and digital infrastructure outstrips healthcare professional readiness, creating challenges for DT adoption for PCC. Past studies on DT adoption primarily focused on organizational adoption and technical implementation challenges. This study addresses this research gap by leveraging the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the factors that influence the adoption intention of DT for PCC in urban India. The article reports actionable insights and implications for researchers, healthcare professionals, top management of hospitals, smart city planners, government agencies, and urban policymakers. The study will assist stakeholders such as cancer care organizations, technology service providers, and city planners in planning, designing, and implementing DT for PCC. Full article

The rapid growth of e-commerce has intensified last-mile delivery challenges in Thailand, where rising parcel volumes contribute to failed deliveries and increased carbon emissions. Smart parcel lockers offered a promising solution, though their adoption depends on consumer acceptance. However, existing research has not sufficiently examined how trialability, performance expectancy, and perceived risk operate alongside core TAM beliefs within an integrated framework, particularly in emerging markets. This study extends the Technology Acceptance Model (TAM) with constructs from Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of smart parcel locker adoption, incorporating trialability, performance expectancy, and perceived risk. A quantitative survey was conducted with 397 Thai consumers with prior online shopping and parcel delivery experience. Data were analyzed using covariance-based structural equation modeling (CB-SEM). The model explained 79.3%, 94.3%, and 82.7% of the variance in perceived ease of use, attitude, and intention. Trialability is the strongest predictor, working through perceived ease of use, while attitude and performance expectancy together drove intention. Contrary to traditional TAM, perceived usefulness did not significantly affect attitude, and perceived risk had no significant effect. These findings suggest that, for simple self-service delivery technologies, first-hand experience is more influential than emphasizing usefulness or safety concerns. This contributes to technology acceptance theory and offers practical guidance to increase smart parcel locker usage. Full article

Artificial intelligence-enabled technologies offer new opportunities to support healthy aging and the long-term care needs of older adults. However, inclusive practices are paramount to ensuring that accessibility, usability, privacy, and equitable use are factored into the design and deployment of these emerging technologies. This article explores the role of co-design in health technology development and highlights the application of three methodological tools—the NIH Stage Model for Behavioral Intervention Development, the Unified Theory of Acceptance and Use of Technology, and Goal Attainment Scaling—to create a smart-home-based socially assistive robot (SAR) for the care of individuals living with Alzheimer’s disease and related dementias (ADRD). Ten participants (five caregiver–care recipient dyads) from an ongoing mixed-methods pilot feasibility study trialed the SAR in their homes for 1–6 months, with the robot personalized to their daily functioning, home layout, and caregiving needs. Qualitative analysis of monthly interviews derived themes pertaining to technical design, care protocol design, training management, and complementary care. These themes, combined with goal attainment analysis, offered several insights that allowed us to iteratively scale and refine the technology tailored to ADRD care. The study offers a practical framework for future co-design efforts aimed at enhancing the adoption of AI-enabled health technologies among older adults. Full article

Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)-based hybrid AI systems are increasingly transforming higher education, yet it remains unclear whether they are adopted through similar or distinct technology adoption pathways. This study compares the adoption of these two AI paradigms among postgraduate students using the Unified Theory of Acceptance and Use of Technology (UTAUT). It proposes the Adaptive Dual-AI Model (ADAM), which extends UTAUT by examining architecture-sensitive AI adoption within a unified framework. The model incorporates Technology Readiness (TR) and AI Awareness (AIA) as mediating variables and generational differences (Gen Z and Gen Y) as moderating factors. Data were collected from 639 postgraduate students enrolled in Saudi universities, of which 619 valid responses were retained after data screening. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to evaluate the proposed model. The results indicate that the two AI paradigms follow distinct technology adoption pathways. Behavioural intention toward standalone LLMs was primarily associated with Performance Expectancy (PE) and Effort Expectancy (EE), whereas the adoption of RAG-based hybrid AI systems was more strongly associated with indirect relationships involving Technology Readiness and AI Awareness. Multi-group analysis further revealed that Gen Z learners exhibited stronger associations with LLM adoption, whereas Gen Y learners demonstrated stronger relationships involving RAG-based hybrid AI systems. These findings support the proposed architecture-sensitive perspective of ADAM, suggesting that differences in AI architecture are associated with distinct technology adoption patterns. From a practical perspective, the findings provide guidance for higher education institutions in selecting complementary AI technologies according to learning objectives, learner characteristics, and evidence requirements. More broadly, the study contributes to technology adoption research by extending UTAUT to heterogeneous AI ecosystems and offers practical insights for designing adaptive, personalised, and evidence-aware learning environments. Full article

Generative artificial intelligence (GenAI) is widely considered to hold transformative potential for public administration, but its adoption in local governments remains uneven, weakly institutionalized, and shaped by informal employee experimentation. This study advances the literature by examining GenAI adoption during a rare moment of institutional restructuring in Vietnam, where the 2025 administrative reform abolished the district tier and shifted responsibilities to ward- and commune-level government. To address the unique dynamic of GenAI adoption, this research extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating trust in technology, perceived risk, technological awareness, and perceived organizational support. Survey data were collected from 302 civil servants across eight post-merger ward- and commune-level administrative units in a transitioning Vietnamese province. The study employs a dual-methodological approach, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to evaluate both net effects of individual variables and the underlying causal complexity. The PLS-SEM results show that performance expectancy, social influence, technological awareness, and perceived risk are positively associated with GenAI adoption intention, while effort expectancy and trust in technology operate indirectly through performance expectancy rather than exerting a direct effect. This suggests that, in the context of local government reform, trust matters primarily when it strengthens employees’ belief that GenAI can improve work performance. Perceived organizational support attenuates the positive relationship between perceived risk and adoption intention, suggesting a buffering role in reducing risk-driven experimentation under institutional uncertainty toward more structured use. The fsQCA findings reveal causal asymmetry and equifinality: adoption intention emerges through multiple pathways anchored in perceived usefulness, social influence, and facilitating conditions, whereas non-adoption is associated with low awareness and weak organizational support. The study contributes to public administration, digital governance and technology acceptance research by showing how GenAI adoption is shaped not only by individual perceptions, but also by institutional change. Full article

Economic and technical barriers to agricultural technology adoption are widely documented, but less is known about how social and psychological factors shape farmers’ intention to adopt smart irrigation systems in post-communist transition economies. This study examines smart irrigation adoption intentions among 368 farmers in Albania, an EU-candidate country characterised by smallholder farming, land fragmentation and uneven technological diffusion. Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and motivation perspectives, we analyse how social influence, self-efficacy and task-technology fit shape adoption intentions. Partial Least Squares Structural Equation Modelling (PLS-SEM) shows that social influence is the strongest direct predictor of intention to use smart irrigation systems, while self-efficacy and task-technology fit also contribute to farmers’ perceptions and intentions. Perceived usefulness has a stronger role than perceived ease of use, suggesting that farmers prioritise practical benefits such as water efficiency, productivity and farm-level utility over usability alone. The findings show how social embeddedness, farmer confidence and task compatibility shape smart irrigation intentions in a post-communist agricultural context, and suggest that policy should strengthen peer learning, farmer champions and community-based diffusion mechanisms. Full article

Teacher adoption of virtual reality (VR) in education appears constrained despite the technology’s potential as an immersive multimodal learning environment. This cross-sectional online survey, based on convenience and snowball sampling, examined adoption perceptions among 408 Romanian teachers from primary, secondary, and tertiary levels using an integrated Technology Acceptance Model and Theory of Planned Behavior framework. Seven constructs were measured on five-point Likert scales and analyzed through internal consistency indices, confirmatory factor analysis, HTMT discriminant validity assessment, structural equation modeling, Spearman correlations, nonparametric group comparisons, supplementary manifest-score regression, and descriptive thematic coding of open-ended responses. The seven-factor CFA model showed acceptable fit (CFI = 0.945, TLI = 0.936, RMSEA = 0.068), and composite reliability and AVE supported convergent validity across all constructs. However, HTMT indicated limited discriminant validity between attitude toward using VR and attitude toward the behavior of adopting VR (HTMT = 0.928). In the SEM model, perceived usefulness showed the largest standardized association with attitude toward using VR, while behavioral intention was mainly associated with attitudinal evaluations and subjective norm; perceived behavioral control showed a weaker standardized path. All scales showed acceptable internal consistency (Cronbach’s $\alpha =0.81$–0.95), and construct means exceeded the scale midpoint (range: 3.45–4.03), indicating generally positive but differentiated perceptions. Supplementary manifest-score regression was consistent with the SEM results: the unified attitude factor showed the strongest statistical association with behavioral intention, followed by subjective norm and perceived behavioral control. Descriptive thematic coding of open-ended responses identified training, infrastructure, equipment access, curriculum-aligned content, cost, technical support, and time as recurrent perceived conditions associated with self-reported VR adoption intentions. The findings suggest that educational VR adoption should be interpreted through self-reported human factors and perceived implementation conditions, including perceived control, access to immersive equipment, practical training, and institutional support. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

In an increasingly complex financial landscape, individual financial behavior is shaped by a range of cognitive, technological, and psychological factors. Existing research on financial behavior often examines financial literacy, FinTech adoption, and financial attitude separately, with limited attention to their combined effects or to whether these relationships remain consistent across contrasting economic environments. To address this gap, this study examines the associations of financial literacy and FinTech adoption with financial behavior, both directly and indirectly through the mediating role of financial attitude. Grounded in the Theory of Planned Behavior and the Technology Acceptance Model, the study proposes an integrated behavioral model using primary data from two contrasting contexts: Lebanon, a financially constrained and unstable environment, and the United Arab Emirates (UAE), a stable, high-income country with advanced FinTech infrastructure. Data were collected through a survey of 400 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that financial literacy and FinTech adoption are positively associated with financial behavior in both countries. Moreover, financial attitude significantly mediates both relationships. Measurement invariance was established prior to cross-country comparisons, and the multi-group analysis indicated that the structural relationships were generally comparable across Lebanon and the UAE despite descriptive differences in several path coefficients. The study contributes to the behavioral finance and sustainable finance literature by integrating cognitive, technological, and psychological predictors within a unified framework, validating the mediating role of financial attitude, and providing cross-national evidence from two contrasting economic contexts. The findings suggest that strengthening financial literacy alongside responsible FinTech adoption may support more sustainable and inclusive financial behaviors, particularly in environments characterized by economic instability and unequal access to financial services. Practical and policy implications are offered for educators, FinTech providers, financial institutions, and policymakers, emphasizing the importance of context-sensitive initiatives that promote financial resilience, financial inclusion, and the development of sustainable financial systems. Full article

Framing the study within the Unified Theory of Acceptance and Use of Technology (UTAUT) and the extended online privacy concern model, this study investigates the effects of effort expectancy, social influence, privacy concerns, and perceived risk on e-commerce consumer behavior, while positioning trust in AI as a key mediating factor. The data were collected from 250 active e-commerce users in Bangladesh, and the analysis was done with a hybrid approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) analysis. The SEM results indicate that effort expectancy and social influence have significant positive effects on consumer behavior. Social influence also shows a significant positive effect on trust in AI. Trust in AI exhibits a significant positive effect on consumer behavior. However, perceived risk does not show a significant effect on either trust in AI or consumer behavior. Privacy concerns demonstrate a significant positive relationship with both trust in AI and consumer behavior, contrary to the hypothesized negative relationships. The mediation analysis shows that trust in AI significantly mediates the relationship between social influence and consumer behavior, while no significant mediation effects are observed for privacy concerns or perceived risk. The ANN results further confirm the dominance of social influence as the most important predictor of both trust in AI and consumer behavior, followed by effort expectancy and trust in AI, while perceived risk shows minimal predictive relevance. Overall, the findings suggest that consumer adoption of AI-enabled e-commerce is primarily driven by benefit-oriented factors rather than risk-based considerations in the present context. The study contributes to the literature by extending UTAUT and privacy calculus theory to AI-mediated commerce and by demonstrating the value of combining SEM and ANN to capture both explanatory relationships and predictive importance. From a managerial perspective, the results highlight the importance of strengthening social influence mechanisms, improving system usability, and building trust in AI systems to enhance consumer engagement in AI-driven e-commerce environments. Full article

Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values. Full article

Mobile applications are increasingly promoted as instruments for improving agricultural information access, advisory delivery, market participation, and decision support, particularly for smallholder farmers in developing nations. Research, however, has been dominated by general technology-acceptance and diffusion constructs, while the design-sensitive and infrastructural mechanisms studied in human–computer interaction (HCI) have received comparatively little attention. In this paper we develop a parsimonious HCI model of mobile application use in agriculture. Drawing on the technology acceptance model, the unified theory of acceptance and use of technology, diffusion of innovations, socio-technical systems theory, and human–computer interaction for development (HCI4D), the model proposes that agricultural application use is driven by five antecedent domains: perceived agronomic value, inclusive usability and accessibility, contextual and cultural fit, trust and transparency, and social and institutional embeddedness. Each plays a distinct role across three use stages: adoption intention, sustained use, and decision impact. Contextual constraints (infrastructure and farmer characteristics) moderate these relationships. We develop six testable propositions from the model. The model is conceptual: it is offered as a framework for empirical testing rather than as a validated account of farmer behavior. The paper contributes an HCI-sensitive specification of mobile application use under agricultural field conditions: a “field-ready” conception of mobile HCI. Full article