Search Results (382)

This study investigates employee perceptions of artificial intelligence (AI) in the field of recruitment and selection in Saudi Arabian companies. While the use of recruitment systems based on AI is growing, their acceptance by organisations is influenced by whether employees consider these systems to be useful, transparent, and fair, and how this affects their professional judgement. This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Socio-Technical Theory (STT) and explores a two-path explanatory model. Perceived usefulness is linked to perceived AI effectiveness in the AI-related pathway and is associated with transparency of AI, trust in AI systems, and organisational support for AI use. On the HR pathway, there is a negative association between technostress and HR support and readiness, while clarity of integration of AI is positively associated with HR support and readiness. The sample comprised 450 purposively selected respondents from Jeddah, Riyadh and Dammam, and data were analysed using Structural Equation Modelling (SEM) in AMOS. The perceived effectiveness of AI and HR’s support/readiness for AI was positively and significantly correlated with employee perceptions of AI in recruitment and selection. The relationships of these data should be interpreted as structural relationships; since the data are cross-sectional, they cannot be interpreted as causation or mediation. Further methodological issues include common method bias since all constructs were assessed via self-report and in the same questionnaire. Full article

Artificial Intelligence (AI) and Media and Information Literacy (MIL) are recognised as vital components of adult secondary education. However, we know little about how adult learners engage with AI in authentic educational contexts, given that AI adoption is a complex sociotechnical process. In this study, AI for MIL (AI/MIL) refers to AI-supported tools that guide learners in interpreting and engaging with digital media and information. We report findings from an ethnographic study involving eight adult students in Greek secondary education. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we followed participants over an eight-month educational intervention through participant observation, interviews, and field notes. Our data show that learners moved from anxiety toward more agentic forms of AI engagement. This ethnographic reinterpretation of UTAUT highlights an interplay among technological, emotional, and sociocultural factors that shape AI/MIL adoption. Beyond traditional technology acceptance models, we underscore negotiated trust, critical AI literacy, ethical awareness, and human agency within AI-mediated information ecosystems. These findings have implications for designing participatory, transparent, and ethically grounded AI initiatives in educational settings. Full article

Against the background of AI penetration in C2C second-hand commerce, AI chatbots have emerged as vital operational tools for individual sellers. Prior technology adoption literature predominantly emphasizes positive acceptance drivers yet overlooks the simultaneous existence of benefit and risk perceptions, and cannot fully explain individual sellers’ paradoxical adoption intentions. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Threat Avoidance Theory (TTAT) to construct an integrated dual mediation model, adopting perceived effortlessness and perceived risk as parallel mediators. Online questionnaire surveys were distributed to individual sellers operating on Chinese C2C second-hand trading platforms. After screening and data cleaning, a final valid sample of 261 respondents was retained. Partial least-squares structural equation modelling (PLS-SEM) was employed to empirically test the proposed research model. The results demonstrate that personal factors (perceived busyness, desire for control, AI acceptance) and situational factors (product standardization, social influence, platform safeguards) jointly shape individual sellers’ dual perceptions. These antecedents exert differentiated impacts on the two mediating constructs, which subsequently predict individual sellers’ intentions to adopt AI chatbots. This research clarifies the balancing mechanism of positive and negative perceptions in technology decision-making. The framework reconciles conflicting psychological evaluations of intelligent systems, deepens the understanding of paradoxical adoption behaviors, and offers practical implications for optimizing intelligent services within C2C second-hand ecosystems. Full article

The integration of AI into higher education provides new opportunities for supporting learning in interdisciplinary contexts and fostering entrepreneurial development. However, previous research has mainly focused on AI technology adoption, with limited attention paid to its broader educational outcomes. In particular, how AI-assisted learning contributes to students’ entrepreneurial intention within an interdisciplinary course context remains underexplored. This study develops an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model by incorporating AI-assisted learning engagement as a mediating factor. A questionnaire survey was conducted among 300 undergraduate students enrolled in a food product development course within a food science and engineering program at a university in China. Using PLS-SEM, the results reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions positively influence AI-assisted learning engagement and entrepreneurial intention. Furthermore, AI-assisted learning engagement significantly mediates the relationships between UTAUT factors and entrepreneurial intention. This study contributes to AI education and entrepreneurial learning research by demonstrating how AI-assisted learning within interdisciplinary contexts can promote students’ entrepreneurial intention through learning engagement. Full article

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

Background/Objectives: This study examines the continued use of smart healthcare applications among older adults by integrating the Health Belief Model (HBM) with the Unified Theory of Acceptance and Use of Technology (UTAUT). Perceived health threat and health management self-efficacy were considered health-related antecedents, while technology anxiety was examined as a moderator. Methods: Data were collected from 224 South Korean adults aged 60 years or older with prior experience using smart healthcare applications. Confirmatory factor analysis and structural equation modeling were used to assess the measurement and structural models. Indirect effects were tested using 5000 bootstrap resamples, and moderation was examined through hierarchical regression and simple slope analysis. Results: Perceived health threat was positively associated with performance expectancy but not with effort expectancy. Health management self-efficacy was positively associated with both performance and effort expectancy. Performance expectancy, effort expectancy, social influence, and facilitating conditions were all positively associated with behavioral intention, which showed the strongest relationship with actual use behavior. Technology anxiety significantly weakened the relationship between social influence and behavioral intention, whereas its moderating effects on performance expectancy and effort expectancy were not significant. Conclusions: The findings provide an integrated explanation of continued smart healthcare application use among older adults by linking health-related beliefs, UTAUT factors, and technology anxiety. Sustained use may be promoted by strengthening self-efficacy, improving usability, and reducing technology-related anxiety through accessible design and appropriate support. 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

The rapid institutionalization of generative artificial intelligence (GenAI) in higher education has created an urgent need for empirical evidence on how structured course-level integration relates to student engagement and perceptions of learning. This study examines a usefulness-centered conceptual framework combining elements of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) with the digital competence perspective of DigComp 2.2. A structured pedagogical pilot intervention requiring all students to use generative AI tools was implemented in an undergraduate Public Service Management course (n = 76). Students completed AI-supported group assignments and an immediate post-intervention questionnaire comprising 19 Likert-scale items, four demographic questions, and four optional open-ended questions that are not analyzed in the present paper. Because most variables were non-normally distributed, non-parametric statistical methods were applied, including Spearman’s rank correlations, Mann–Whitney U tests, and Kruskal–Wallis tests. Perceived learning usefulness was strongly and positively associated with both frequency of AI use and satisfaction with the learning process. Ethical attitudes were also positive, but more weakly associated with frequency of use. Demographic group differences were observed mainly in specific usage patterns rather than in general attitudes towards AI-supported learning. These exploratory findings suggest that perceived learning usefulness remains relevant in mandatory AI-integration contexts. Pedagogical scaffolding—including prompt literacy, verification practices, and reflective documentation—provides a structured framework for guided and responsible use of generative AI tools in higher education. Full article

The financial services sector is undergoing rapid digital transformation, yet the adoption of online financial services (OFS) remains uneven due to perceptual barriers such as security concerns and limited digital and financial literacy. Existing research, often based on aggregate models such as the Technology Acceptance Model (TAM), tends to overlook heterogeneity in user perceptions and leaves unresolved whether the digital divide is primarily generational or socio-economic. This study addresses these gaps through a quantitative, descriptive–analytic, and exploratory design based on a cross-sectional survey of 225 respondents. Integrating TAM, UTAUT, Diffusion of Innovations, Perceived Risk, and Trust, the analysis combines correlational assessment, non-parametric group comparisons, and hierarchical cluster analysis. Three main findings emerge. First, Perceived Risk appears statistically decoupled from the core adoption-related perceptions of usefulness and ease of use, suggesting a more latent role among current users. Second, two distinct divides are identified: an age-based divide affecting perceived ease of use, and a stronger education- and income-based divide affecting perceived usefulness and facilitating conditions. Third, three perceptual profiles are identified, challenging the “average user” assumption. Profile membership is significantly associated with education, but not age, suggesting that the main barrier to inclusive digital finance is increasingly one of literacy rather than generation. Full article

Introduction: Fitness short videos have become an increasingly popular form of digital health communication, yet the factors influencing users’ continued usage remain insufficiently understood. This study integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Health Belief Model (HBM), and the Theory of Planned Behavior (TPB) to examine the determinants of users’ continuous intention and actual use of fitness short videos. Methods: A cross-sectional questionnaire survey was conducted using a convenience sampling approach among Chinese fitness short video users. A total of 550 valid questionnaires were collected. Structural equation modeling (SEM) and bootstrapping analysis were employed to examine the proposed research model and test the hypothesized relationships among constructs. Results: The results indicate that performance expectancy, hedonic motivation, habit, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, perceived behavioral control, and behavioral intention significantly influenced users’ continued fitness short video usage. Habit exerted significant effects on both behavioral intention and actual use behavior. In contrast, attitude, subjective norm, and self-efficacy did not show significant direct effects. Conclusions: This study extends current understanding of continuous fitness short video usage by integrating technology acceptance, health belief, and planned behavior perspectives into a unified framework. The findings provide theoretical insights into digital health behavior and offer practical implications for the design of fitness short-video platforms and digital health intervention strategies. Full article

Background: The growth of e-commerce has intensified last-mile delivery challenges, including failed deliveries, delivery-time uncertainty, and pressure on urban logistics. Smart parcel lockers (SPLs) offer a technology-enabled out-of-home delivery solution, yet limited evidence explains consumer adoption in Saudi Arabia. This study examines which factors motivate Saudi consumers to adopt SPLs, how trust shapes adoption intention, and whether perceived risk affects intention, using an extended UTAUT2 framework. Methods: Data were collected through an online self-administered questionnaire from 415 residents in Saudi Arabia, and the model was analyzed using partial least squares structural equation modelling (PLS-SEM). Results: Performance expectancy was the strongest determinant of behavioral intention. Effort expectancy, social influence, trust, facilitating conditions, and hedonic motivation also had significant positive effects, whereas price value and perceived risk did not directly influence intention. Conclusions: The study contributes to SPL adoption literature by validating an extended UTAUT2 model in an underexamined Saudi context and highlighting the role of trust in technology-enabled LMD acceptance. Full article

Citizen-sourced non-emergency reporting is an increasingly important component of smart-city governance, yet cities often lack systematic methods for evaluating alternative participation designs before deployment. This study develops a design-oriented pre-deployment evaluation framework for assessing citizen adoption of a proposed reporting application and comparing alternative incentive mechanisms. Drawing on an extended UTAUT framework, the study introduces perceived value (PV) as a mediating mechanism linking incentive framings to behavioral intention. A scenario-based survey experiment with 580 participants in the Greater Toronto Area compared the following four conditions: control, monetary incentive, recognition, and donation-based community-benefit incentive. Data were analyzed using PLS-SEM and bootstrapped mediation analysis. Results indicate that performance expectancy and attitude are the strongest predictors of behavioral intention, while trust was significantly associated with both cognitive and affective evaluations of the reporting system. Social influence becomes non-significant once these evaluations are included. Recognition and donation-based community-benefit incentives significantly increased perceived value and showed both direct and indirect effects on performance expectancy and behavioral intention. At the behavioral-intention stage, indirect effects accounted for approximately 80% of their respective total effects, indicating complementary mediation with indirect effects predominating. By contrast, the specific $2 city-credit condition showed no significant direct, indirect, or total effect. Rather than proposing a new acceptance theory, the study demonstrates how technology acceptance modeling can be repurposed as a practical decision-support framework for evaluating alternative civic-technology designs before system launch. Limitations related to the pre-deployment setting and intention-based measures are acknowledged, and future research directions involving field deployment and longitudinal participation analysis are discussed. 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