Jul 2026· 2026 11th International Conference on Applying New Technology in Green Buildings (ATiGB)· pp. 619-624· 0 citations· 25 references
Abstract
The rapid advancement of Artificial Intelligence (AI) is transforming organizational workflows and requiring employees to adapt to emerging technologies. This study aims to analyse the impact of AI literacy and trust in AI on work productivity, while examining whether AI anxiety serves as a mediating variable. A quantitative approach was employed using a cross-sectional survey design. Data were collected from 245 respondents working in the food and beverage sector in Jabodetabek, Indonesia, who have experience using AI-based systems. The instrument utilized a five-point Likert scale, and data were analysed through multiple regression within a mediation analysis framework. Results indicate that AI literacy has a strong positive and significant effect on work productivity $(\beta=0.637; \mathrm{p}<0.001)$, emerging as the strongest predictor in the model). However, AI anxiety does not mediate the relationship be-tween AI literacy or trust and productivity, as its effect on productivity is not significant $(\beta=-0.095; \mathrm{p}=0.291)$. These findings highlight the critical role of strengthening AI literacy to enhance employee productivity in the digital era. Organizations are encouraged to design training programs that go beyond technical skills, focusing on strategic AI utilization to optimize performance. The findings contribute to workforce development by highlighting the importance of AI capability building and psychological readiness in AI-driven operational environments associated with smart mobility ecosystems. Limitations include the small sample size and single-industry context, which limits the generalizability of findings across industries and smart mobility contexts. Future research should adopt longitudinal designs, explore additional variables such as perceived usefulness and organizational support, and extend to diverse sectors to deepen understanding of human–AI interaction dynamics.
This study examined GenAI acceptance among private vocational teachers in Thailand by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with three AI-specific constructs: AI Literacy, Trust, and Perceived AI Risk and tested Behavioral Intention as an antecedent of Digital Competency.
The rapid digital transformation in the banking sector presents critical challenges for rural banks (BPR), which lack the resources for structured adaptation. This issue creates an urgent need to understand the human-centered mechanisms and digital competencies interacting with technology that determine successful performance outcomes in this new era. This study aims to analyze the influence of digital skills and AI utilization on BPR employee performance, with digital competence and innovation as mediating variables. This study employed a quantitative explanatory survey method among BPR employees. Data were collected from 250 respondents selected through purposive sampling, using a structured questionnaire with a five-point Likert scale. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 3 software to test the hypotheses. The study findings prove that all nine hypotheses are accepted. Digital Skills (X1) and Artificial Intelligence (X2) have a positive and significant effect on Digital Competence (Z1), Innovation (Z2), and Employee Performance (Y). AI is the strongest direct driver of performance, accompanied by Digital Competence. Digital Competence and Innovation serve as critical mediators for performance improvement investments. This study presents an integrated empirical model for Indonesian rural banks. The main practical implication is the need to prioritize the development of human resource capabilities in technology procurement by implementing tiered training to build structured digital competencies, targeted integration of AI into core processes supported by training, and building an organizational culture that encourages technology-based innovation.
This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels, and contributes to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work.
Suman Kumar, M. Moslehpour, A. Walawalkar et al.· Journal of Information, Comm...· 0 citations
Artificial intelligence (AI) is redefining higher education through its support of personalized learning, intelligent tutoring, and collaboration. Yet, responsible utilization of AI is dependent on ethical awareness among the students, AI competencies, and self-efficacy towards the utilization of such technologies. This study analyzes the impact of Responsible AI Awareness on Learning Engagement via Trust in AI, AI Literacy, AI Usage Self-Efficacy, and Human–AI Collaboration in a sequential manner. A quantitative research approach was adopted in this study, with data collected from 431 postgraduate students through a structured survey instrument. The measurement and structural models of the research model were tested through SmartPLS 4 for reliability, validity, direct, and mediation effects using bootstrapping. The results reveal that Responsible AI Awareness positively influences Trust in AI and AI Literacy, which further positively affects AI Usage Self-Efficacy and Human–AI Collaboration resulting in enhanced Learning Engagement.
Pinnika Syam Yadav, Ashok Singh Malhi, Abhishek Sharma et al.· International journal of com...· 0 citations
With the growing integration of Artificial Intelligence (AI) in education, it is crucial to understand how future educators perceive and interact with AI tools. This study examines how education students’ self-efficacy and attitudes influence their trust in AI, addressing the gap in existing literature that often treats trust as a mediator rather than an outcome variable. A mixed-method sequential explanatory design was used. In the quantitative phase, 140 education students answered standardized scales on AI self-efficacy, attitude toward AI, and trust in AI. Data were analyzed using descriptive statistics and regression analysis. In the qualitative phase, semi-structured interviews were conducted and analyzed through Interpretive Phenomenological Analysis (IPA). Findings revealed that students demonstrated moderately high AI self-efficacy, positive attitudes, and high trust in AI. Regression results showed that both self-efficacy (β = 0.605, p < .001) and attitude (β = 0.872, p < .001) significantly predicted trust, with attitude emerging as the stronger predictor. Qualitative themes included: (1) AI as Academic Scaffolding, (2) Negotiating Dependence and Reliability, and (3) Maintaining Agency and Control. Results highlight that students’ trust in AI is shaped by their attitudes and reinforced by their confidence in using it. AI is most effective as a supportive tool when paired with critical thinking and responsible use. The study recommends developing institutional guidelines for ethical AI use in the academe, grounded in AI literacy, ethical awareness, and self-regulation—ensuring that future educators engage with AI confidently, responsibly, and in alignment with SDG-4’s vision of inclusive and quality education.
This study aims to investigate the key predictors of artificial intelligence literacy (AI literacy). It explores the mediating role of digital literacy (DL) in the relationship between psychological (interest, attitude and self-efficacy) and experiential (prior experience with AI tools) factors and AI literacy.
This study used a survey-based quantitative research methodology. Data were obtained from 754 students enrolled in various programs at the University of the Punjab. Structural equation modeling (SEM) was used to test direct and mediated relationships among constructs.
Interest, attitude, self-efficacy and prior experience significantly predicted AI literacy, both directly and indirectly through DL. Self-efficacy demonstrated the strongest total effect on AI literacy. Prior experience with AI tools significantly improved DL, which in turn enhanced AI literacy. The results highlight a dynamic interplay between psychological readiness and experiential learning, positioning DL as a crucial mediator in the development of AI literacy.
Educators and policymakers should design curricula that foster digital competence, encourage early exposure to AI tools and build learner self-efficacy. Structured, hands-on experience with AI technologies can support the development of confident and competent AI users.
This study presents a comprehensive model linking psychological and experiential variables to AI literacy through DL. It offers both theoretical advancement and practical guidance for educational stakeholders preparing learners for AI-integrated futures.
