The mediating role of engagement in the relationship between performance expectancy, effort expectancy, and students' behavioral intention to use ChatGPT.

Background/Objectives: Artificial intelligence (AI) tools for fracture detection in radiographs are increasingly approved for clinical use but remain underutilized. Understanding physician attitudes before implementation is essential for successful integration into emergency care workflows. This study investigates the acceptance of an AI-based fracture detection tool among physicians in emergency care settings, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods: A cross-sectional, pre-implementation survey was conducted among 92 physicians across three hospitals participating in the SMART Fracture Trial (ClinicalTrials.gov: NCT06754137). The questionnaire assessed the four core UTAUT constructs—performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC)—and additional constructs such as attitude toward technology (AT), diagnostic confidence (DC), and workflow efficiency (WE). Responses were collected on a five-point Likert scale. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were performed to assess predictors of behavioral intention (BI). Results: PE was the strongest predictor of BI (β = 0.5882, p < 0.001), followed by SI (β = 0.391, p < 0.001), FC (β = 0.263, p < 0.001), and EE (β = 0.202, p = 0.001). These constructs explained a substantial proportion of variance in BI. WE received the lowest ratings, while internal consistency for SI and BI was weak. Moderator analyses showed prior AI experience improved EE, whereas more experienced physicians were more skeptical regarding WE and DC. However, none of the moderators significantly influenced BI. Conclusions: Physicians’ intention to use AI fracture detection is primarily influenced by perceived usefulness and ease of use. Implementation strategies should focus on intuitive design, targeted training, and clear communication of clinical benefits. Further research should evaluate post-implementation usage and user satisfaction.

This study investigates the determinants of artificial intelligence (AI) tool adoption among professional designers using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the guiding theoretical framework. As AI increasingly reshapes creative industries, understanding the factors that drive designers’ intention to adopt and actual use of AI tools has become both theoretically and managerially relevant. Data were collected through a cross-sectional survey administered to 170 professional designers, including graphic, UI/UX, and product designers. The proposed research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that performance expectancy, effort expectancy, social influence, hedonic motivation, and price value have significant positive effects on designers’ behavioral intention to adopt AI tools. In contrast, facilitating conditions do not significantly influence behavioral intention but exert a strong positive effect on actual use behavior. Habit and behavioral intention also significantly predict use behavior, with habit emerging as the strongest determinant of sustained usage. The model explains a substantial proportion of variance in both behavioral intention and use behavior, confirming the strong predictive power of the UTAUT2 model in a creative professional context. This study contributes to the growing literature on AI adoption by extending UTAUT2 to the design industry and highlighting the joint role of utilitarian, hedonic, and habitual factors. Managerially, the findings provide actionable insights for AI tool developers and design organizations seeking to foster effective and sustained AI adoption. Keywords: Artificial Intelligence, UTAUT2, Technology Adoption, Designers, Use Behavior

This study aims to investigate the factors influencing university students’ intention and behavior toward eLearning in Kathmandu Valley, Nepal. The research framework used in this study was the Unified Theory of Acceptance and Usage of Technology (UTAUT). The most common factors associated with UTAUT are social influence, facilitating conditions, habit, performance expectancy, effort expectancy, behavioral intention, and use behavior. Data were collected from 385 university students through a closed-ended questionnaire through social media platforms. The demographic information of respondents was summarized using SPSS version 25 software, while structural equation modeling was performed using SmartPls version 3 to identify the factors that influence behavioral intention and use behavior of eLearning. The data analyses revealed that performance expectancy, effort expectancy, facilitating conditions, social influence, and habit all significantly influence the behavioral intention of eLearning, with facilitating conditions being the most significant factor. Similarly, habit, facilitating conditions, and behavioral intention also significantly influence the use behavior of eLearning, with facilitating conditions as the most significant factor. It suggests that students are more likely to utilize eLearning tools when they have access to various technical devices and receive sufficient support from educational institutions. Therefore, universities should prioritize accessibility, feedback mechanisms, and seamless integration of eLearning into curricula. Peer support, technical assistance, and promotion of the benefits of eLearning are also essential for fostering engagement. By focusing on these aspects, eLearning adoption can be optimized, leading to improved academic performance and learning outcomes among university students in Kathmandu Valley.

This study examines the factors influencing the adoption and use of artificial intelligence (AI) tools to enhance writing skills among English as a Foreign Language (EFL) learners in Oman, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). The objectives were to assess the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on students’ behavioral intention and actual AI usage, and to test the moderating role of prior AI experience. A cross-sectional quantitative design was employed, with data collected from 255 undergraduate female EFL students through a validated questionnaire. Structural equation modeling (SEM) and confirmatory factor analysis were used to validate the measurement model and test hypothesized relationships. Findings indicate that behavioral intention and facilitating conditions significantly predicted actual AI tool use, while performance expectancy, effort expectancy, and social influence strongly shaped behavioral intention. Mediation tests confirmed that behavioral intention served as a key pathway linking UTAUT constructs to actual adoption, and moderation analysis showed that prior AI experience strengthened the intention–usage relationship. This research contributes to a context-specific, evidence-based framework for AI adoption in EFL writing, offering novel insights for educators, institutions, and technology designers to integrate AI ethically and effectively in language learning.

Purpose This study investigates the factors shaping Chinese university students' behavioral intentions to adopt artificial intelligence (AI) for English as a Foreign Language (EFL) writing development. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and second language acquisition principles, it aims to understand how performance expectancy, effort expectancy, social influence, facilitating conditions, perceived learning resources, perceived enjoyment, and attitude influence students' willingness to use AI in their writing processes. Design/methodology/approach Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and second language acquisition principles, a cross-sectional survey was administered to 415 Chinese university students. Data were analysed using structural equation modeling (SEM) to examine the relationships among performance expectancy, effort expectancy, social influence, facilitating conditions, perceived learning resources, perceived enjoyment, attitude, and behavioral intention. Findings Path analysis revealed that performance expectancy, effort expectancy, facilitating conditions, and attitude significantly predict behavioral intention, with attitude being the strongest direct predictor. Surprisingly, social influence, perceived enjoyment, and perceived learning resources did not exert significant direct effects. Attitude fully mediated the effect of effort expectancy and partially mediated performance expectancy, while serving as an indirect-only mediator for social influence and perceived enjoyment. Originality/value This study challenges the universal applicability of Western technology acceptance models by highlighting the central role of attitude as a cultural-cognitive mediator in collectivist, exam-driven contexts. It proposes a cultural attunement model for AI adoption in EFL education and offers practical implications for pedagogy, policy, and culturally responsive AI tool design.

Artificial Intelligence (AI) provides new tools and approaches for English as a Foreign Language (EFL) learning, yet it also brings new risks and challenges, such as AI anxiety. With the gradual adoption of AI in EFL learning, AI anxiety has brought about a variety of issues. In order to help educators understand students’ concerns and promote the use of AI for secondary school students’ EFL learning, this study took the Unified Theory of Acceptance and Use of Technology (UTAUT) and relevant aspects of AI anxiety as the theoretical foundation. Subsequently, this study analyzed the situation of AI anxiety among secondary school students and its relationship with students’ Behavioral Intention to use AI EFL learning tools. Data were collected through an online platform, with 293 valid responses from secondary school students in Beijing, China. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the data. The validity and reliability of the scale were satisfied with nine constructs: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention, AI Learning Anxiety, Job Replacement Anxiety, Sociotechnical Blindness Anxiety, and AI Configuration Anxiety. The results indicated: (1) Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions could all positively predict Behavioral Intention in different degrees, and Social Influence had the strongest effect; and (2) AI Learning Anxiety and Job Replacement Anxiety might indirectly and negatively predict Behavioral Intention through intermediate variables. Based on the analysis, the study suggests that educators should not only cultivate students’ AI literacy through comprehensive AI education, but also guide students to form correct emotions through scientific psychological interventions so that they can better use AI EFL learning tools.

Machine translation (MT) has emerged as a widely-used foreign language learning tool that could enhance language learning proficiency and productivity. However, the factors influencing college students’ acceptance of MT in foreign language learning remain insufficiently understood. Additionally, the existing literature seems to fail to examine the fitness between MT and foreign language learning tasks. Thus, this study integrates the Unified Theory of Acceptance and Usage of Technology (UTAUT) and Task-Technology Fit (TTF) models to investigate students’ acceptance of MT in foreign language learning. This study adopted a survey-based quantitative research approach, employing a convenience sampling method to collect 313 valid responses. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the hypothesized relationships. Results showed that performance expectancy, effort expectancy and social influence significantly influenced behavioral intention, and the behavioral intention to use MT had an impact on actual use behavior among students. Moreover, experience has proved to be a moderator that has positively impacted the relationship between performance expectancy and the intention use of MT, and TTF moderated the relationship between performance expectancy and behavioral intention, as well as the relationship between effort expectancy and behavioral intention. The theoretical and practical implications are provided for future researchers and practitioners to enhance students’ effective use of MT in their foreign language learning activities.

Background: This research investigates the perceptions, benefits, and challenges of generative artificial intelligence (AI) tools among students of Technical and Vocational Education and Training (TVET). A sample of 200 students from various institutions in Malaysia including Technical and Vocational Education and Training (TVET) institutions and universities in the fields of engineering, information technology, business studies and hospitality were surveyed for this study. You were selected for this study due to your experience with generative AI in your academic and real-world learning experiences. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study addresses whether performance expectancy, effort expectancy, social influence and facilitating conditions are key factors in students' intentions to adopt and use generative AI. The findings demonstrate the importance of generative AI in improving TVET education but also spotlight challenges to its mainstream implementation. The study then draws recommendations for educators and policymakers, on how to ensure informed and effective AI use in TVET settings based on these findings.Objectives: The purpose of this research was to investigate the factors affecting the implementation of Generative AI among TVET students using UTAUT model, its advantages and disadvantages, as well as the role of institutional support and ethical concerns such as plagiarism and data privacy.Methods: Quantitative survey approach applied to collect comprehensive data on the adoption of generative AI among TVET students. A total of 200 students across multiple disciplines, including engineering, IT, and hospitality participated in this study. The participants were selected based on their exposure on generative AI tools to ensure relevance in assessing adoption factors. Data collection was conducted through a structured questionnaire based on the UTAUT model, covering constructs key such as performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey aimed to capture students’ perceptions, experiences, and challenges related to AI adoption in their fields of study. Additionally, regression technique was used to analysed the data and identify relationships between UTAUT constructs and adoption behaviour.Results: The findings of the study focus on the validation of the UTAUT constructs and the analysis of survey responses. The descriptive statistics (mean, standard deviation) and inferential statistics (correlation, regression analysis) were applied to understand the impact of various factors on AI adoption.Conclusions: This study highlights the potential of generative AI tools to change the landscape of TVET education. These tools can make the learning experience richer by improving the learning efficiency, enhancing creativity and improving problem-solving skills. Nevertheless, successful adoption is contingent on overcoming major obstacles, including technological literacy gaps, institutional support, and ethical considerations. The statistical analysis showed that performance expectancy and facilitating conditions were significant determinants of students' behavioral intention to adopt AI. It further stresses the importance of organized policies and training programs to promote responsible AI use. They can help facilitate an environment where generative AI can be maximized for TVET, ultimately ensuring that students are being equipped with essential digital skills necessary for a technology-driven future.

Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly supported by AI. However, lack of studies focus on nursing students as AI users and the behavioural intention to accept and utilise AI. Aim: This study investigated the factors influencing nursing students’ acceptance and use of AI based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and Methods: A cross-sectional study was conducted at one of the oldest and most prominent universities, collecting data from April to May 2022. The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. Additionally, the study explored the moderating effects of age and gender on the UTAUT model. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0 for descriptive statistics and SmartPLS version 4 for Partial Least Squares (PLS) structural equation modeling. Results: The findings indicated that PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education. Regarding moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect. Conclusion: This study provides a foundation for its integration to enhance learning outcomes and prepare students for technology-driven healthcare. It highlights the importance of evidence-based strategies tailored to meet diverse educational needs, ensuring effective adoption and utilisation.

PurposeThis study aims to evaluate the users’ behavioral intention toward the acceptance and adoption of digital payment FinTech services in India. The study also compares the differences in Gen Y and Gen Z’s intention to adopt digital payment FinTech services.Design/methodology/approachThe present study adopted both the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM) as its theoretical base and also added financial literacy and customer satisfaction. The data was analyzed by applying structural equation modeling using SmartPLS 4.FindingsThe outcomes of the study imply that customer satisfaction, effort expectancy and performance expectancy had a significant effect on behavioral intention. Moreover, effort expectancy, performance expectancy and perceived enjoyment had a significant influence on customer satisfaction, and effort expectancy and performance expectancy is significantly influenced by perceived enjoyment, while self-efficacy significantly influenced perceived enjoyment. Also, financial literacy does not moderate the relationship between effort expectancy, performance expectancy, facilitating condition and behavioral intention. Furthermore, the association of effort expectancy → customer satisfaction; perceived enjoyment → customer satisfaction; and perceived enjoyment → effort expectancy is moderated by age factor.Originality/valueThis study contributes by developing a more cohesive and unified model for assessing users’ behavioral intention toward acceptance and adoption of FinTech services by adopting constructs from the UTUAT and TAM and incorporating financial literacy and customer satisfaction to expand and enhance the theoretical prospect of the existing literature.

Generative artificial intelligence (GAI) has attracted attention in education as a tool to help college students learn mathematics. This study analyzed the factors influencing their use of GAI by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) and focusing on mathematics motivation. This study involved 331 Chinese college students and used partial least squares structural equation modeling (PLS-SEM) for data analysis. The results showed that college students' behavioral intention to use GAI to support their mathematics learning was directly influenced by performance expectancy, social influence, personal innovativeness, and mathematics motivation. Mathematics motivation, facilitating conditions, individual demand, and behavioral intention, had direct effects on college students' use of GAI in mathematics. The most significant factor influencing both intention and behavior was mathematics motivation. Effort expectancy and individual demand did not affect the intention to use GAI in mathematics learning. In addition, there were important positive moderating effects, including individual demand, of mathematics motivation in the structural model on usage behavior and behavioral intention regarding usage behavior. The results of this study could help to identify the key influences on college students' use of new technologies in mathematics learning and provide informative insights for the application of AI technologies in mathematics learning in the future.

PurposeThis study aims to explore the factors affecting investment behaviour in cryptocurrencies among Jordanian investors. Specifically, it aims to assess how various motivational and behavioural drivers impact the intention to use cryptocurrencies, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The choice of Jordan as the research context is particularly relevant due to the lack of adequate regulations on cryptocurrency investment.Design/methodology/approachThis study uses a quantitative research approach, using an online survey as the primary method for data collection. The final data set consists of 285 responses collected through a self-administered questionnaire to cryptocurrency users in Jordan. Next, structural equation modelling (SEM) was used to test the developed theoretical framework based on the UTAUT2 model.FindingsThe findings reveal that performance expectancy, trust, hedonic motivation and price value significantly enhance the intention to invest in cryptocurrencies, with performance expectancy acting as a mediator. Effort expectancy is not directly related to behavioural intention; however, it positively impacts performance expectancy, validating the mediation hypothesis. Trust affects both the intention to use and the performance expectancy, reinforcing its role as a mediator in cryptocurrency adoption. Hedonic motivation and price value also positively affect the intention to use cryptocurrency. In contrast, social influence and facilitating conditions do not significantly impact behavioural intention, suggesting that cryptocurrency adoption decisions are less influenced by external opinions or the availability of necessary conditions. The findings also show that the demographic profiles of the cryptocurrency users were young, educated males, which suggests a demographic skew in cryptocurrency usage in Jordan.Originality/valueThis study innovatively adapts the UTAUT2 model, focusing on the mediating role of performance expectancy between effort expectancy, trust, and behavioural intention. This study pioneers by examining the mediation effect of performance expectancy, showing how users' ease in using cryptocurrencies positively affects their belief in positive outcomes, subsequently influencing their behavioural intention to use cryptocurrencies. Moreover, this study sheds light on the factors driving cryptocurrency adoption in developing countries like Jordan. It also underscores the demographic trends in cryptocurrency use and proposes targeted recommendations for policymakers and cryptocurrency platforms to foster more inclusive and informed investment environments.

Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches

Rail ticket system is a train ticket booking system online that have been implemented by PT.KAI. To find out how far the success rate of acceptance of rail ticket system for the users of transport services , PT.KAI needs to test the rail ticket system . Tests conducted to determine the factors that influence user acceptance rail ticket system . T esting model in this study is using a model of the Unified Theory of Acceptance and Use of Technology (UTAUT), this model to analyze the influence of performance expectancy (expectation of performance), effort expectancy (expectation of business) and social influence (social factors) on behavioral intention (intention utilization) and the effect of facilitating conditions (conditions that facilitate) and behavioral intention (intention utilization) to use behavior (behavioral usage) on a rail ticket system. Based on this study will be known factors that influence the behavioral intention and use behavior and the factors that do not affect the behavioral intention. With the research is expected to provide recommendations were appropriate and beneficial for PT. KAI in developing online train ticket booking system in the future. Based on the obtained data processed F values of 5.175 and 3.09 F table this means F values > F table with a significant level of 0,000 F below 0.05 that is jointly independent variable performance expectancy, effort expectancy and social influence significantly influence the dependent variable behavioral intention. The amount of independent influence performance expectancy, effort expectancy and social influence views on the value of R square of 0.139 Retrieved value F values 8.303 and F table by 3.94 this means F values > F table with a significant level of 0.000 is below 0.05, it indicates that is jointly variable Facilitating Conditions and Behavioral Intention significantly affect Behavior Use variables, the influence of the independent performance expectancy, effort expectancy and social influence can be seen where the value of R square of 0.146. With this research is expected to provide recommendations which ar e appropriate and beneficial for PT. KAI in developing online train ticket booking system in the future . Key Words : Rail ticket system , UTAUT, SPSS

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R2 = 0.745), SI (β = 0.070, R2 = 0.745), and BI (β = 0.792, R2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R2 = 0.745) and FC (β = −0.004, R2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R2 = 0.317), SI (β = 0.462, R2 = 0.317), and EE (β = 0.139, R2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.