Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 71 references
TL;DR
The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI, and institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour.
Abstract
This study examines the determinants influencing lecturers’ intention to use and adopt artificial intelligence (AI) in higher education. As AI technologies become increasingly integrated into teaching and learning, understanding lecturers’ perceptions is critical for successful adoption. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study investigates the roles of AI self-efficacy, perceived usefulness, relative advantage, and institutional support.A quantitative research design was employed using a systematic random sampling approach. Data were collected from 371 lecturers in public universities in Jordan through a structured questionnaire. All constructs were measured using validated items on a five-point Likert scale, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI. Furthermore, institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour. The findings also confirm that intention is a significant predictor of AI adoption behaviour.Theoretically, this study extends the UTAUT model by incorporating AI-specific determinants and emphasising the moderating role of institutional support, providing deeper insights into technology adoption in educational contexts. Practically, the findings highlight the importance of institutional support in developing lecturers’ AI-related skills and competencies. Targeted training programs and supportive environments can facilitate the effective and sustained use of AI tools in higher education.
The rapid integration of artificial intelligence (AI) into organizational practices has intensified scholarly interest in employees’ readiness and behavioral intentions toward AI adoption, particularly in emerging economies such as Vietnam. While prior studies have largely emphasized technological and organizational determinants, relatively limited attention has been paid to individual-level and contextual factors shaping AI adoption within developing digital environments. This study examines the determinants of employees’ behavioral intentions to adopt AI tools in Vietnamese technology firms, drawing on an integrated framework that combines the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, with the inclusion of personality traits and social influence. A quantitative research design was employed, using survey data collected from 231 employees working in technology-related roles across Vietnam. Structural Equation Modeling (SEM) was applied to test the proposed relationships among key constructs. The findings reveal that perceived ease of use plays a pivotal mediating role, significantly influenced by organizational support, environmental factors, and individual personality traits. Moreover, perceived usefulness and attitude toward using AI emerge as the strongest direct predictors of behavioral intention. Notably, and contrary to much of the existing literature, technological characteristics and social influence do not exhibit statistically significant effects on employees’ adoption intentions in this context. These findings contribute empirical evidence from an emerging market and suggest that internal organizational readiness and individual psychological factors may outweigh purely technological considerations in shaping AI adoption. From a practical perspective, the study highlights the importance of employee-centered training, supportive organizational cultures, and targeted change management strategies for enhancing AI adoption in Vietnamese technology firms.
Nhung Thi Hong Tran, T. Nguyen, Hong Duyen· Tạp chí Khoa học Đại học Côn...· 0 citations
The findings reveal that AI self-efficacy has a positive and significant effect on perceived ease of use and perceived usefulness, and perceived usefulness significantly influence behavioral intention, which subsequently has a positive effect on the actual use of AI tools.
Alfionita Mariska Saputri, R. Kurniasih, Siti Zulaikha Wulandari et al.· The International Conference...· 0 citations
Universities increasingly have access to artificial intelligence, but access alone says little about whether these tools become part of everyday academic work. This article examines behavioural intention as the link between university teachers' and administrators' evaluations of AI and their subsequent use of it. It brings the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behavior into conversation with research on AI literacy, perceived risk and organisational support. Performance expectancy, effort expectancy, social influence, AI literacy and risk perceptions are expected to shape intention, while facilitating conditions affect both intention and the likelihood that intended use becomes sustained practice. The article distinguishes between two possible consequences of adoption: changes in teaching quality and changes in management efficiency. Neither consequence can be inferred from usage alone; each depends on the task, the quality of human review and the institutional arrangements surrounding the tool. Against the policy and organisational background of Shenyang, the article sets out propositions and measurement priorities for a later empirical study. Its main contribution is to clarify the sequence from appraisal to intention, from intention to use, and from use to educational or administrative outcomes.
Nan-Nan Hu, N. Noordin, Wong Siew Ping· Global Media and Social Scie...· 0 citations
Generative artificial intelligence (GenAI) has entered classrooms faster than most institutions have been able to formulate policy, yet its instructional value ultimately depends on whether teachers choose to use it. This study examined the determinants of teachers' behavioral intention (BI) to use GenAI in teaching, drawing on an integrated Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Five predictors were specified: perceived ease of use (PEoU), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), and anxiety (ANX). A cross-sectional survey was administered to 500 in-service teachers drawn from three Chinese educational institutions spanning medical higher education, vocational higher education, and primary education. Each construct was operationalized as a composite mean of its constituent items, and the model was estimated using the Regression module of SmartPLS 4 with bootstrapping (5,000 subsamples) to obtain confidence intervals. Collinearity diagnostics were acceptable (VIF = 1.046-1.238). The model accounted for 32.7% of the variance in behavioral intention (adjusted R² = .320). Perceived usefulness was the strongest predictor (β = .228, p < .001), followed by facilitating conditions (β = .206, p < .001), perceived ease of use (β = .198, p < .001), and social influence (β = .195, p < .001). Contrary to expectations, anxiety exerted no significant effect (β = .018, p = .631), with a bootstrap confidence interval that spanned zero. The findings indicate that teachers' adoption decisions are jointly driven by instrumental value and institutional provisioning, and that generalized technology anxiety is not, in itself, a barrier among teachers who already have practical exposure to GenAI. Implications for professional development design and institutional AI policy are discussed.
Artificial Intelligence (AI) has emerged as a transformative technology with significant implications for teaching, learning, and academic administration. As AI-enabled tools become increasingly integrated into educational environments, understanding the factors that shape users’ intention to adopt these technologies has become imperative, particularly in developing countries where empirical evidence remains limited. Grounded in the Technology Acceptance Model (TAM), this study investigates the behavioral intentions toward AI adoption among students and educators in Nepalese secondary schools and higher education institutions. The proposed model extends TAM by incorporating trust and perceived risk as additional determinants of adoption intention. A quantitative research design was employed using a structured questionnaire administered to 321 respondents comprising secondary-level students, university students, school teachers, and faculty members from public and private educational institutions in Nepal. Data were analyzed using descriptive statistics and Structural Equation Modeling (SEM) to examine the relationships among perceived usefulness, perceived ease of use, trust, perceived risk, and behavioral intention toward AI adoption. The findings reveal that perceived usefulness, perceived ease of use, and trust exert significant positive effects on behavioral intention, whereas perceived risk negatively influences AI adoption intentions. The results indicate growing acceptance of AI technologies among educational stakeholders, although concerns regarding privacy, ethical issues, academic integrity, and overdependence on AI remain substantial challenges. This study extends the applicability of TAM in the educational context of a developing economy. It offers valuable implications for policymakers, educational institutions, and technology developers to foster digital literacy, promote responsible AI use, and establish supportive regulatory frameworks for sustainable AI integration in education.
S. Adhikari· Nepalese Journal of Manageme...· 0 citations
The proposed framework suggests that students who perceive AI tools as useful and beneficial are more likely to develop positive attitudes toward their use, and students who perceive AI tools as useful and beneficial are more likely to develop positive attitudes toward their use.
Mohd Rozaini Abdul Rahim, W. Hasan, Mastura Roni et al.· International journal of res...· 0 citations
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