2026· International journal of research and innovation in social science· Vol 10, pp. 11274-11283· 0 citations
TL;DR
The results of the study show that performance expectancy, effort expectancy, and social influence have a positive and significant effect on behavioral intention in using AI, which suggests that intent of use is a major factor driving the actual use of AI in accounting learning.
Abstract
This study aims to analyze the factors that affect the use of Artificial Intelligence (AI) in accounting learning using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study used an explanatory quantitative approach with a survey method on 327 students of the Under Graduate of Accounting Program at Public Universities and Private Universities in Jakarta and Depok City. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the help of SmartPLS 4.0. The results of the study show that performance expectancy, effort expectancy, and social influence have a positive and significant effect on behavioral intention in using AI. In addition, facilitating conditions and behavioral intention have a positive and significant effect on use behavior. The greatest influence was found on the relationship of behavioral intention to use behavior, which suggests that intent of use is a major factor driving the actual use of AI in accounting learning. The research model also had good predictive ability with an R² value of 0.676 for behavioral intention and 0.758 for use behavior. These findings strengthen the relevance of the UTAUT model in explaining the adoption of AI in accounting education and provide implications for universities in optimizing the use of AI to improve the quality of learning.
This study examined the influence of artificial intelligence (AI) tool usage on entrepreneurial intentions among Technical and Vocational Education and Training (TVET) university students in Malaysia. Grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Theory of Planned Behavior (TPB), the study aims to identify key factors influencing AI tool usage, determine their relationship with entrepreneurial intention, and identify the most significant predictor. A quantitative research approach was adopted, and data were collected through an online questionnaire from 384 students across four Malaysian TVET universities (UTHM, UniMAP, UMP, and UTEM). The study applied multiple regression analysis and correlation analysis to evaluate the relationships between variables. The findings revealed that hedonic motivation was the strongest and most significant predictor of entrepreneurial intention, followed by subjective norm and effort expectancy. In contrast, performance expectancy showed a significant negative relationship, while perceived AI competencies were not statistically significant. The regression model explained 71.6% of the variance in entrepreneurial intention, indicating strong explanatory power. The results suggested that students were more influenced by enjoyment and social factors rather than perceived usefulness or skill confidence in adopting AI for entrepreneurship. The study concluded that fostering intrinsic motivation and positive social influence is essential in promoting AI-driven entrepreneurial intentions among TVET students, with important implications for curriculum design and policy development.
S. Chun, H. Azman, A. Azman· International journal of res...· 0 citations
The UTAUT Model was the strongest predictor of behavioral intention, followed by Personal Concern and Top Management Support, while Perceived Risk had no significant influence and the model demonstrated strong explanatory and predictive capability.
Roselyn Ann Quijano, Jimnanie Manigo· International Journal For Mu...· 0 citations
Empirical evidence on AI adoption in Islamic higher education institutions within emerging economies remains limited, as prior studies have predominantly focused on universities in developed countries and have rarely examined the applicability of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) in this unique educational context. Addressing this gap, this study extends the UTAUT2 framework to examine the determinants of AI adoption among students at Islamic universities in Pekanbaru, Indonesia. A quantitative approach was employed using survey data collected from 200 students through probability sampling and analyzed with Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results reveal that Performance Expectancy, Facilitating Conditions, Hedonic Motivation, and Habit significantly influence Behavioral Intention, whereas Effort Expectancy, Social Influence, and Price Value do not. Furthermore, Behavioral Intention has a strong positive effect on Use Behavior. The structural model demonstrates substantial explanatory power, with an R² of 0.721 for Behavioral Intention and 0.428 for Use Behavior, indicating that the proposed model effectively explains students' AI adoption behavior. This study contributes theoretically by validating and extending UTAUT2 in the underexplored context of Islamic higher education in an emerging economy, highlighting the dominant roles of perceived usefulness, intrinsic motivation, and habitual technology use. Practically, the findings provide evidence-based guidance for university leaders and policymakers to develop AI-supported learning environments through improved digital infrastructure, student engagement strategies, and sustainable AI integration in higher education.
M. L. Hamzah, M. A. Syahrin, Meinita Triana et al.· Jurnal Administrasi dan Mana...· 2 citations
The adoption of artificial intelligence in financial reporting is increasingly important as accounting practices move toward automation, data-driven analysis, and digital decision-making. This study aims to analyze the factors influencing users’ intention to adopt artificial intelligence in financial reporting using the Technology Acceptance Model. A quantitative explanatory approach was applied, involving 93 students from state Islamic higher education institutions with academic experience in accounting, finance, financial reporting, or digital technology. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed with Partial Least Squares Structural Equation Modeling. The measurement model showed adequate reliability and convergent validity, although the cross-loading results indicated potential conceptual overlap among several constructs. The structural model revealed that perceived ease of use had a positive and significant effect on attitude toward using and behavioral intention to use artificial intelligence. Perceived usefulness positively affected attitude but had a negative and significant effect on behavioral intention. Meanwhile, attitude toward using did not significantly affect behavioral intention and did not mediate the relationship between perceived usefulness, perceived ease of use, and intention to use artificial intelligence. These findings indicate that AI adoption in financial reporting is shaped more by direct perceptions of ease and practical usefulness than by user attitude alone. The study implies the need to strengthen AI literacy, technical readiness, and ethical awareness in financial reporting practices.
Sinta Ardhillatul Jannah, Yusnawati Yusnawati· Journal of Economics and Man...· 0 citations
The increasing use of accounting software in accounting education requires a better understanding of students’ acceptance of different accounting applications. However, previous studies have mainly focused on single-platform adoption, providing limited comparative evidence between accounting software systems. This study examines the behavioral intention of Generation Z students toward the use of Accurate and Zahir accounting software within the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The study focuses on three main determinants: Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC). A quantitative explanatory approach was employed using primary data collected through questionnaires from 119 undergraduate students who had completed computerized accounting practicum sessions using both applications. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 3. The results indicate that Facilitating Conditions and Performance Expectancy positively influence Behavioral Intention in both applications. However, Effort Expectancy influenced Behavioral Intention only in the Accurate application and showed no significant effect in the Zahir application. These findings suggest that Generation Z students may evaluate accounting software differently based on usability perceptions and learning experiences.
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.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
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