Nursing Students' Awareness, Perceptions, and Readiness for Artificial Intelligence Integration: An Extended UTAUT Analysis (Preprint)

The growing integration of artificial intelligence (AI) in public sector governance has significantly transformed tax administration by enhancing efficiency, accuracy, and taxpayer engagement. In India, the adoption of AI based tax filing systems marks a shift toward automated, data-driven compliance mechanisms aimed at reducing errors and administrative burden. However, taxpayer acceptance of such systems depends on multiple technological and behavioral factors. This study examines the determinants influencing the adoption of AI-based tax filing systems among Indian taxpayers using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative, cross-sectional research design was employed, with primary data collected from 300 Indian taxpayers through a structured questionnaire. Key constructs analyzed include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust in AI-based tax systems, with behavioral intention as the dependent variable. Data analysis was conducted using SPSS and AMOS, applying reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling. The results indicate that performance expectancy and trust in AI systems are the most significant predictors of behavioral intention, followed by facilitating conditions and effort expectancy. Social influence also shows a positive but comparatively weaker effect. The proposed model explains 68 percent of the variance in behavioral intention. The study highlights that effective implementation of AI-based tax filing systems requires not only technological sophistication but also institutional trust, transparency, and digital readiness. The findings offer valuable implications for policymakers and tax authorities in designing inclusive, citizen-centric AI-enabled tax governance frameworks.

<p>The emergence of generative Artificial Intelligence (AI) is disrupting every sector of the global economy and the information society. The education industry, similarly, has both educators and students exploring ways to utilize AI. This study investigates the adoption of AI in higher education among undergraduate mathematics education students using the four constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research focuses on Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, and their influence on students’ Behavioural Intention to adopt AI tools. Gender was included as a moderating variable in the relationships between these constructs and behavioural intention. A sample of 142 undergraduate mathematics education students participated in an online survey to measure their perceptions and intentions of AI adoption in their academic work. The findings revealed that all four independent variables and gender had significant direct effects on Behavioural Intention to use AI. This indicates that students perceive AI tools as valuable for enhancing academic performance, easy to use, influenced by social factors such as peers, and supported by adequate facilitating conditions such as technical infrastructure. However, gender did not emerge as a significant moderator in any of the relationships between the UTAUT constructs and Behavioural Intention. This suggests that male and female students exhibit similar adoption patterns toward AI technologies in this context. These results contribute to the growing body of literature on technology adoption in education by confirming the applicability of the UTAUT model within a mathematics education-focused cohort of students while highlighting that gender differences may not play a critical role in shaping intentions toward AI adoption. Future research could explore additional moderating variables or extend this analysis across other disciplines for broader generalizability.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0188/a.php" alt="Hit counter" /></p>

PurposeThis study aims to investigate the factors that influence behavioral intention (BI) and usage of e-wallets by extending the unified theory of acceptance and use of technology (UTAUT) with constructs, namely, mobile self-efficacy, perceived enjoyment and satisfaction.Design/methodology/approachThis quantitative study used partial least squares structural equation modeling on a sample of 576 mobile e-wallet users surveyed online.FindingsThe key findings indicate that the model can explain 58.8% of the variance in behavioral intention and 53.8% in usage. Moreover, mobile self-efficacy has a significant influence on perceived enjoyment. Perceived enjoyment significantly affects satisfaction, effort expectancy and performance expectancy. Furthermore, effort expectancy significantly influences customer satisfaction in contrast to performance expectancy. In addition, although performance expectancy, social influence and satisfaction significantly impact consumers’ behavioral intention, effort expectancy and facilitating conditions condition have an insignificant influence on consumers’ behavioral intention. E-wallet stakeholders can use the findings of this study to make strategic decisions regarding the e-wallet ecosystem.Originality/valueAlthough previous studies have independently addressed the impact of mobile self-efficacy, perceived enjoyment and satisfaction on consumers’ behavioral intention and usage behavior, the expanded framework with the possible relationships proposed in this study has never been adequately studied in previous research in the context of e-wallets in developing countries based on an empirical analysis. This study represents one of the first attempts to improve the UTAUT by empirically analyzing these relationships.

Background Artificial Intelligence (AI) is transforming service-oriented industries, yet its adoption in developing countries remains underexplored, particularly from the employee perspective. Employees’ perceptions, intentions, and organizational support are pivotal for effective AI integration in hospitality. Objective This study investigates the determinants of AI adoption among employees in Egypt’s hotel sector, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). It examines the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on AI usage, the mediating role of behavioral intention, and the moderating role of gender. Methods A quantitative design was applied using a structured online survey administered to 346 frontline employees in five-star hotels in Greater Cairo. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested direct and indirect effects. Results Findings reveal that effort expectancy, social influence, and facilitating conditions directly and positively influence AI usage, while performance expectancy affects adoption indirectly via behavioral intention. BI mediates the link between UTAUT constructs and AI use, with gender moderating the intention–usage relationship: male employees exhibit stronger adoption tendencies. Conclusions The study underscores the role of user-friendly systems, inclusive training, and gender-sensitive strategies, extending UTAUT by establishing BI as a central mediator and revealing demographic contingencies.

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.

Information and communication technologies (ICTs) has become a tool which cannot be ignored in terms of public administrations, which provides advantage to the administrations as far as they can be adapted. The use of ICTs become widespread in the health sector as it is in all other sectors. In hospitals, hospital information systems (HIS) are used to keep records of patients and hospitals securely, to improve appointment, in-hospital management, decision support and workflow processes. Therefore, HISs are also used to increase efficiency and productivity, to reduce error rates, to increase service quality, to reduce service costs and to realize the specific purposes such as ensuring patient satisfaction. It is necessary that the end users should adopt HISs to obtain the expected benefits and to implement HIS successfully in public hospitals. The adoption of a technology product is also a sociological phenomenon at the same time. In this regard, the issue of adoption in the relevant literature is addressed in the context of a wide variety of models and many variables. This study is also a study of technology adoption. The subject of the study is the adoption of HISs in public hospitals in Turkey. In this context. The study aimed to determine the factors affecting the adoption of HISs by the personnel working in public hospitals in Turkey, in accordance with the Unified Theory of Acceptance and Use of Technology (UTAUT) model in the literature on technology adoption. In the study, the universe of the study consisted of the personnels (physicians, nurses, health officers, medical secretaries] which were working in public hospitals in the Bursa and Balıkesir Metropolitan Municipalities at the time of the study. According to the results of the study, performance expectancy, effort expectancy and social influence variables have positive and significant effects on the behavioral intention of hospital staff for using of HISs. In addition, facilitating conditions and behavioral intention variables have a positive and significant effect on usage behavior. On the other hand, it was found that gender has a moderator effect on the relationship between performance expectancy, effort expectancy and behavioral intention. Experience has a moderator effect on the relationship between the social influence and the behavioral intention while age has a moderator effect on the relationship between facilitating conditions and use behavior.

Artificial intelligence (AI) is spreading rapidly in organizational settings, yet limited research examines how culture shapes leaders' readiness to adopt these technologies. The current study addresses that gap by exploring cross‐cultural differences in AI acceptance among 434 leaders from Western and Eastern regions, guided by the unified theory of acceptance and use of technology (UTAUT). A cross‐sectional, quantitative design, supplemented by short, open‐ended responses, assessed five UTAUT constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Results showed that Western leaders report significantly higher average effort expectancy, social influence, facilitating conditions, and behavioral intention than Eastern leaders, although both groups find AI beneficial. Multiple regression analyses reveal that Western leaders' intention to adopt AI is primarily related to ease of use, whereas Eastern leaders intention is related to organizational support and peer encouragement. Open‐ended responses demonstrate that leaders across regions share ethical and privacy concerns, but Western participants emphasize security and training, while Eastern leaders highlight transparency and real‐time insights. These results imply that AI implementation strategies require cultural adaptation, such as prioritizing the quality of user interfaces and training for Western leaders and ensuring organizational endorsements for Eastern contexts. By identifying how leaders evaluate and integrate AI, the current research delivers practical insights for multinational organizations and deepens theoretical dialogues on leadership and technology acceptance. These findings also address current leadership journal calls by spotlighting AI bias, inclusivity, and ethical governance in distinct regional settings.

Dalam era perkembangan teknologi informasi, metode baru dalam pembelajaran telah muncul, memberikan perspektif berbeda terhadap konsep pendidikan. Universitas Brawijaya merespon dengan menerapkan LMS BRONE, platform pembelajaran daring berbasis Moodle. Namun, pengguna masih mengeluhkan beberapa masalah seperti harus login berkali-kali, pengingat tenggat waktu tugas yang tidak konsisten, dan tampilan yang tidak efisien. Populasi ditetapkan sebagai mahasiswa Universitas Brawijaya S1 dan Diploma yang pernah menggunakan LMS BRONE dalam proses pembelajaran. Metode penelitian yang diaplikasikan yaitu kuantitatif dengan pengumpulan data melalui penyebaran kuesioner dengan teknik purposive sampling kepada mahasiswa Universitas Brawijaya dan berhasil mendapatkan 194 responden. Penelitian ini menggunakan model Unified Theory of Acceptance and Use of Technology (UTAUT) untuk mengeksplorasi faktor-faktor yang mempengaruhi penerimaan pengguna terhadap LMS BRONE di Universitas Brawijaya. Hasil analisis data menggunakan PLS-SEM dengan alat bantu SmartPLS menunjukkan bahwa lima hipotesis diterima. Dari analisis jalur, dapat disimpulkan bahwa Performance Expectancy, Effort Expectancy, dan Social Influence memiliki pengaruh signifikan terhadap Behavioral Intention, sementara Facilitating Conditions tidak berpengaruh signifikan terhadap Behavioral Intention, tetapi berpengaruh signifikan terhadap Use Behavior. Behavioral Intention juga berpengaruh signifikan terhadap Use Behavior. Hasil penelitian ini mendukung teori UTAUT dan memberikan wawasan penting tentang penerimaan pengguna LMS BRONE di Universitas Brawijaya. Secara keseluruhan, penerimaan pengguna terhadap LMS BRONE di Universitas Brawijaya dapat dikategorikan positif. LMS BRONE memudahkan aksesibilitas materi, pengumpulan tugas, dan membantu dalam manajemen waktu. Namun, diperlukan perbaikan dalam konsistensi penggunaan oleh dosen dan optimalisasi fitur untuk perangkat mobile. Upaya sosialisasi dan pelatihan penggunaan LMS BRONE juga diperlukan agar adopsi sistem ini lebih optimal di kalangan pengguna. Abstract In the era of information technology development, new learning methods have emerged, offering different perspectives on educational concepts. Universitas Brawijaya has responded by implementing BRONE LMS, an online learning platform based on Moodle. However, users have reported several issues such as the need to log in repeatedly, inconsistent task deadline reminders, and an inefficient interface. The population was defined as undergraduate and diploma students at Universitas Brawijaya who had used the BRONE LMS in the learning process. The research applied a quantitative method by collecting data through a questionnaire distributed via purposive sampling to Universitas Brawijaya students, resulting in 194 respondents. This study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to explore the factors influencing user acceptance of the BRONE LMS at Universitas Brawijaya. The data analysis, conducted using PLS-SEM with SmartPLS software, showed that five hypotheses were supported. From the path analysis, it was concluded that Performance Expectancy, Effort Expectancy, and Social Influence had a significant impact on Behavioral Intention, while Facilitating Conditions did not significantly affect Behavioral Intention but did significantly influence Use Behavior. Behavioral Intention also had a significant impact on Use Behavior. The results of this study support the UTAUT theory and provide valuable insights into user acceptance of the BRONE LMS at Universitas Brawijaya. Overall, user acceptance of the BRONE LMS at Universitas Brawijaya can be categorized as positive. The BRONE LMS facilitates access to materials, assignment submissions, and helps with time management. However, improvements are needed in the consistency of use by lecturers and optimization of features for mobile devices. Efforts in socialization and training for the use of the BRONE LMS are also necessary to enhance system adoption among users.

This study investigated the factors determining the behavioral intention of young Saudi entrepreneurs to use crowdfunding to finance their small enterprises. The capacity of behavioral intention to predict the use behavior of crowdfunding platforms was also assessed. Partial least squares-based structural equation modeling method (PLS-SEM) was used to analyze responses collected from 270 young Saudi entrepreneurs (qualified students) attending the Community College of Abqaiq in Saudi Arabia, which is affiliated with King Faisal University. The unified theory of acceptance and use of technology (UTAUT) with extensions of three constructs was employed. The findings revealed that performance expectancy, social influence, perceived trust and perceived risks predicted the behavioral intention of young Saudi entrepreneurs to use crowdfunding platforms to finance their small enterprises, whereas effort expectancy, facilitating conditions and trialability had no significant predictive effect on the same behavioral intention. It was further reported that behavioral intention could also predict the use behavior of young Saudi entrepreneurs on crowdfunding platforms. The overall results indicated that the model explained 55.4% of the variance in behavioral intention and 38.3% of the variance in use behavior of young Saudi entrepreneurs.

Purpose – The purpose of this paper is to understanding early childhood student teachers’ self‐reported acceptance and use of interactive whiteboard (IWB), by employing the Unified Theory of Acceptance and Use of Technology (UTAUT) as the research framework.Design/methodology/approach – A total of 112 student teachers enrolled in science‐related papers from Bachelor of Early Childhood Education completed the questionnaire, measuring their responses to performance expectancy, effort expectancy, social influence and facilitating condition and behavioural intention. Structural equation modelling was used as the main technique for data analysis.Findings – The results of this study showed that performance expectancy and effort expectancy were found to have a direct and statistically significant positive effect on behavioural intention. Of the four variables in the UTAUT model, the model accounted for 41 per cent of the variance in behavioural intention to use IWB among student teachers. The findings obtained i...

This study explores the adoption of augmented reality (AR) in digital insurance services through the lens of the unified theory of acceptance and use of technology (UTAUT). As insurers increasingly turn to digital solutions to improve operational efficiency and customer engagement, AR emerges as a promising yet under-researched technology with applications in claims processing, risk communication, and policy visualization. By adapting core UTAUT constructs – performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), behavioral intention (BI), and actual use (AU) – the research employs a scenario-based quantitative design to assess user perceptions and BIs in a simulated AR insurance environment. Data were collected via an online survey targeting individuals with prior insurance experience and basic digital literacy. The structural model was tested using structural equation modeling (SEM). Results reveal that EE, SI, and FC have significant positive effects on BI, and that BI strongly predicts AU. However, PE does not significantly influence BI, suggesting that perceived ease of use and environmental support outweigh functional expectations in early-stage technology contexts like AR insurance. The findings provide both theoretical and practical contributions. Theoretically, the study extends UTAUT to a novel application domain, offering insights into user behavior around emerging InsurTech tools. Practically, it highlights the importance of intuitive design, social endorsement, and infrastructural readiness for fostering adoption. These insights can inform insurers aiming to integrate AR into their digital service strategies.

Digital mental health interventions are increasingly used to address gaps in access to psychological services in Saudi Arabia. However, cultural, technological, and usability factors may shape public acceptance of these tools. This study examines the willingness of Saudis to use the Labayh mental health application, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), focusing on the roles of performance expectancy, effort expectancy, social influence, and facilitating conditions. A cross-sectional online survey was administered to adult participants (n = 307). Validated UTAUT measures were used, and multiple regression analyses tested the effects of the 4 constructs on behavioral intention to use the application. The findings supported 3 of the 4 UTAUT constructs. Performance expectancy was the strongest predictor of behavioral intention. Effort expectancy also had a significant effect, indicating that ease of use increased adoption. Social influence significantly predicted intention, particularly among younger respondents, underscoring the role of peers, family, and clinicians. By contrast, facilitating conditions were not significant, likely reflecting a ceiling effect whereby most participants already possessed sufficient access and technical readiness, making this factor less relevant. These results indicate that acceptance of digital mental health applications in Saudi Arabia is primarily driven by perceived usefulness, ease of use, and social endorsement, whereas infrastructure plays a limited role. Efforts to increase adoption should prioritize culturally sensitive communication, usability-centered design, and engagement with trusted social and clinical influencers to encourage sustained uptake across diverse groups of users nationwide. No clinical trial registration applies.

INTRODUCTION: With the accelerating aging of the population and the integrated development of artificial intelligence (AI) technology, AI health voice assistants (AI-HVAs) present a novel approach for enhancing health management among older adults. However, the adoption of this technology by the elderly population still faces multiple barriers, including cognition, trust, and user experience, and its adoption mechanisms have yet to be fully elucidated. OBJECTIVES: This study aims to construct an AI-HVA adoption model applicable to China's elderly population, focusing on revealing the dual driving role of “experience (experiential rationality)” and “trust (relational rationality)” in the decision-making process of elderly users. METHODS: Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study introduces two key variables—"Perceived AI Experience (PAIE)" and "Perceived AI Trust (PAIT)"—to form a dual-path hypothesis of "internal experience-external influence." Through a questionnaire survey of 413 elderly users, structural equation modeling (SEM) was employed to analyze the data and examine the influence relationships among variables. RESULTS: (1)Internal Experience Path:PAIE significantly and positively influenced Performance Expectancy (PE) and Effort Expectancy (EE), and also directly promoted Behavioral Intention (BI). This indicates that the quality of the interaction experience is a key antecedent for elderly users forming perceptions of usefulness and ease of use . (2) External Influence Path:Social Influence (SI) did not exert a direct effect on BI but required mediation through PAIT, highlighting the pivotal bridging role of trust in the adoption decision . (3) BI and Facilitating Conditions (FC) jointly significantly promoted Usage Behavior (UB), supporting the applicability of the UTAUT model in the context of AI technology adoption among the elderly. CONCLUSION: This study extends the explanatory boundaries of the UTAUT model in the field of digital technology adoption by older adults, revealing the complex psychological processes underlying their acceptance of AI-HVAs. On a practical level, the findings provide important insights for the age-friendly design, trust-building, and promotion strategies of AI health products.

There are different theoretical models regarding the determinants of consumers’ intention to use technology and their use behavior. The Unified theory of acceptance and use of technology-2 (UTAUT-2) model explains the variance in behavioral intention and technology use variables in pursuit of a consumer-centric model. This research study aims to figure out the determinants and impacts of individuals’ intention to use mobile banking and their use behaviors within the context of Turkey. The data of the study was obtained from 684 participants aged 18 years or older in April-June 2018 via an online survey method. Findings of the research study reveal that the behavioral intention of using mobile banking is influenced by trust, social influence, price value, facilitating conditions, performance expectancy, and habit; whereas habit and trust affect both use behavior and behavioral intention. Furthermore, moderator impacts of trust and age as well as hedonic motivation and age on behavioral intention, whereas moderator impacts of habits and age on use behavior are determined. No significant relationship is found between the behavioral intention of using mobile banking and the use behavior.

University Customer Care Center (UC3) is a complaint service system for active academic civitas of the University of Jember. Implementation of UC3 is expected to optimize complaints complaint related to performance evaluation at the University of Jember. One of the keys to the successful implementation of information technology, The purpose of the study is to know the factors that influence the acceptance of the academic civitas in using the UC3 application. This research will use the Unified Theory of Acceptance and Use of Technology (UTAUT) method by utilizing the Performance Expectancy (PE) variable, Effort Expectancy (EE), Social Influence (SI), and Facilitating Condition (FC) to see how it affects the intensity of application usage UC3 And the influence of moderator variables (Age and Experience) to the relationships of those variables. Based on the results of data analysis PLS-SEM known that the PE, EE, and SI variables have a positive and significant effect on the Behavioral Intention (BI) variables as well as the FC and BI variables significantly positive and significant to the Use Behavior (UB) variables. Age influences inter-variable relationships for ages 26-35 years, 36-45 years, and >45 years, while Experience influences the relationship between variables for usage intensity ≥3 times. Based on the results of the five hypotheses received can be interpreted that the UC3 application was accepted by the academic and the acceptance is still not fully.