Aug 2026· The International Conference on Sustainable Economics Management and Accounting Proceeding· Vol 2, pp. 2582-2590· 0 citations· 9 references
TL;DR
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.
Abstract
This study examines the role of AI self-efficacy in enhancing employees' actual use of artificial intelligence (AI) tools through the mediating roles of perceived ease of use, perceived usefulness, and behavioral intention within the Technology Acceptance Model (TAM). As AI technologies become increasingly integrated into workplace activities, understanding the factors that drive employees' actual AI usage has become an important issue, particularly in the banking sector. A quantitative research design was employed using survey data collected from 100 employees of commercial banks in Banyumas Raya, Indonesia, who had experience using AI tools such as ChatGPT, Google Gemini, and Microsoft Copilot. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings reveal that AI self-efficacy has a positive and significant effect on perceived ease of use and perceived usefulness. Furthermore, perceived ease of use and perceived usefulness significantly influence behavioral intention, which subsequently has a positive effect on the actual use of AI tools. The findings also confirm the mediating roles of perceived ease of use and perceived usefulness in linking AI self-efficacy to behavioral intention. This study extends the Technology Acceptance Model by incorporating AI self-efficacy as an antecedent of technology acceptance and provides practical implications for commercial banks in strengthening employees' AI capabilities to encourage effective and sustainable AI adoption.
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
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.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
The rapid proliferation of artificial intelligence (AI) in the banking sector necessitates a deeper understanding of the integration between biological and artificial brains. This study aims to investigate the factors influencing bank employees’ acceptance and self-reported use behavior of AI technologies. The research employs a quantitative approach, utilizing data collected via convenience sampling from 290 bank employees operating in Ankara, Turkey. The proposed theoretical model was tested using a dual-stage analysis: descriptive statistics and validity and reliability tests were conducted via SPSS, while the hypotheses and structural relationships were validated through Structural Equation Modeling (SEM) using AMOS software. The results of the SEM analysis indicate that Social Influence, Hedonic Motivation, and Price Value are significant positive predictors of bank employees’ Behavioral Intention to use AI. Furthermore, the findings demonstrate that Behavioral Intention serves as a critical and significant determinant in explaining the self-reported Use Behavior of these technologies within the organizational setting. Despite the global trend toward AI integration, there is a notable gap in the literature regarding the empirical measurement of AI adoption among bank employees in the Turkish context. This study fills this gap by providing localized empirical evidence and contributes to the broader “AI-Human Integration” discourse by highlighting the specific drivers of technology acceptance in a high-stakes service environment like banking.
Eyüp Kağnici, M. Kaya· Managing Sustainable Develop...· 0 citations
The rapid integration of artificial intelligence (AI) into the education sector has raised a question that goes beyond technology itself: When is AI's use beneficial to employees instead of a burden? The study builds on the theories of Self-Determination Theory (SDT), Social Exchange Theory (SET), and the Technology Acceptance Model (TAM) and proposes the following mediation model: AI Adoption Leadership Style influences Task Performance and Job Stress, with Perceived Job Security and Psychological Need satisfaction serving as the mediating psychological mechanisms.Based on the theories of SDT, SET, and TAM, this study proposes the following mediation model: AI Adoption Leadership Style affects Task Performance and Job Stress, while Perceived Job Security and Psychological Need satisfaction act as the psychological mediation pathway. A total of 359 respondents from the education sector of Punjab, Pakistan, were selected to collect the data and analyzed using partial least squares structural equation modeling (PLS-SEM) software, namely SmartPLS 4. Strong reliability and validity were obtained in the measurement model and good fit was achieved for the structural model (R² = 0.44-0.49, SRMR = 0.044). AI Adoption Leadership Style significantly and positively affected Psychological Need (β = 0.668) and Perceived Job Security (β = 0.663), and had significant direct effect on Task Performance (β = 0.433) and Job Stress (β = -0.503). The results showed that the mediation model for the relationship between Perceived Job Security and Task Performance was complementary partial mediation (β = 0.336) and the mediation model for the relationship between Psychological Need and Job Stress was complementary partial mediation (β = -0.246). All eight hypotheses were supported with both stress related pathways appearing in the hypothesized negative direction. The findings contribute to the existing human-resource and technology-adoption literature and have important implications for educational leaders who are tasked with leading change through the use of AI in an under-researched context of education in South Asia.
Muhammad Habib, Shahzaib Imran, Sheraz Anjum et al.· Kashmir Journal of Academic...· 0 citations
In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI self-efficacy and digital human resource management (HRM) practices in translating AI adoption into employee performance outcomes. Survey data were collected from firms located in major Chinese cities (Beijing, Shenzhen, Xi’an, and Zhengzhou), resulting in 750 valid responses for analysis. The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes. Theoretically, this study advances knowledge management studies by highlighting the complementary roles of individual cognitive beliefs and HR systems in enabling AI-driven learning and capability development. Practically, the findings suggest that organizations should embed AI technologies in HR systems that foster learning, knowledge utilization, and continuous innovation.
This study examines students' adoption of Artificial Intelligence (AI) in business education through an extended Technology Acceptance Model (TAM), positioning AI as an intelligent and adaptive socio-technical system rather than a conventional digital tool.
Drawing on data from 521 undergraduate business students in private universities in Cairo, Egypt, the study employs structural equation modelling to investigate the psychological, contextual, and cognitive factors of AI usage.
Findings reveal that perceived usefulness and perceived ease of use remain central drivers of students' attitudes toward AI; however, AI-specific factors (including job relevance, self-efficacy, and perceived resource availability) significantly shape these perceptions. Notably, the results highlight the importance of contextual constraints in developing economies, where institutional support and access to technological resources play a critical role in facilitating AI adoption.
By extending TAM to the context of AI-enabled learning, this study contributes to the literature by emphasizing the dual role of AI as both a technological tool and a cognitive partner in the learning process. The findings offer implications for the design of digitally enhanced curricula, particularly in emerging markets, and provide insights into how higher education institutions can better align with the demands of an AI-driven workforce.
Rehab EmadEldeen, Ahmed F. Elbayuomi, M. Farghaly et al.· Frontiers in Education· 0 citations