Understanding mobile e-wallet consumers’ intentions and user behavior

<sec> <title>BACKGROUND</title> Intelligent technologies are transforming healthcare delivery, necessitating that nursing curricula prepare students for digitally enhanced practice environments. However, empirical evidence examining nursing students' readiness for technology adoption, particularly through established theoretical frameworks, remains limited in Middle Eastern educational contexts. </sec> <sec> <title>OBJECTIVE</title> The objectives of this study were to (1) assess the level of awareness among nursing students regarding the use of artificial intelligence (AI) applications in nursing education; (2) examine nursing students' perceptions of the potential benefits and challenges associated with AI adoption, guided by the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT); and (3) evaluate nursing students' readiness and preparedness for artificial intelligence adoption by analysing the relationships between UTAUT-based theoretical constructs and behavioural intention to use AI technologies in educational and clinical training contexts. </sec> <sec> <title>METHODS</title> A cross-sectional survey was conducted among 314 undergraduate nursing students at a university in the United Arab Emirates. Data were collected using a validated questionnaire (Cronbach α=.89; content validity index=0.92) measuring UTAUT constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Analysis utilized IBM SPSS Statistics version 28.0, including descriptive statistics, Pearson correlation, and multiple regression </sec> <sec> <title>RESULTS</title> Students demonstrated high awareness (298/314, 94.9%) and training interest (261/314, 83.1%), with favourable perceptions of artificial intelligence's educational benefits. However, practical confidence remained lower (186/314, 59.2%), and three-quarters indicated needing substantial support. Performance expectancy (mean 3.96, SD 0.72) and facilitating conditions significantly predicted behavioural intention. The regression model explained 58% of variance in behavioural intention (R²=0.58; F6,307=71.24; P&lt;.001). Performance expectancy emerged as the strongest predictor (β=.38; P&lt;.001), followed by social influence (β=.20; P&lt;.001) and effort expectancy (β=.19; P=.001). A pronounced gap emerged between theoretical readiness (mean 3.78-4.09) and actual preparedness (mean 2.00-2.56), with insufficient training (mean 2.03, SD 0.76) and limited practical experience (mean 2.13, SD 1.34) as primary deficits. </sec> <sec> <title>CONCLUSIONS</title> The Extended UTAUT framework effectively explained nursing students' artificial intelligence adoption intentions. While students demonstrate positive attitudes, the readiness-preparedness gap highlights urgent needs for structured competency development, faculty training, and phased implementation strategies in nursing education. </sec>

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.

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.

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.

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.

As the Malaysian public sector undergoes digital transformation, its electronic records management faces challenges, including security issues, maintaining record integrity and record authenticity, audit trails, and trust in existing systems. Blockchain technology has the potential to solve these challenges through features such as distributed records, transparency, restricted immutability, and cryptographic security. However, the adoption of this technology in the Malaysian public sector is still underexplored. The main objective of the study is to identify factors that influence the adoption of blockchain technology in Electronic Records Management in the Malaysian public sector and to develop an adoption model. A quantitative method was used to collect data from 253 public-sector officials directly involved in electronic records management. The conceptual framework was developed by integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data analysis was conducted using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach to test the relationship between variables and confirm the study hypotheses. Results show that users' behavioral intentions have a significant effect on actual usage of blockchain technology (H10 is accepted), with an R² of 0.633, indicating that the independent construct explains 63.3% of the variance in behavioral intentions. Individual, organizational, and environmental factors, including performance expectations, effort expectations, social influences, and facilitating conditions, have a significant effect on users’ behavioral intentions, emphasizing that technology adoption requires a holistic approach. The study also finds that behavioral intentions act as a critical mediator before actual usage of the technology can occur.

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.

The effective integration of digital technology in differentiated and deep learning requires a clear understanding of the factors influencing teachers’ acceptance of instructional platforms. This study investigates the determinants of elementary school teachers’ adoption of Canva for developing differentiated learning media using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative design was employed, involving 133 Indonesian elementary teachers selected through purposive sampling. Data were collected through a validated questionnaire and analyzed using Structural Equation Modeling (SEM) with SmartPLS 4, including measurement and structural model evaluation based on path coefficients, p-values, R², and predictive relevance (Q²). The model explains 68% of the variance in behavioral intention (R² = 0.68), indicating substantial explanatory power. The results show that effort expectancy (β = 0.35, p &lt; 0.01), social influence (β = 0.28, p &lt; 0.01), and self-efficacy (β = 0.22, p &lt; 0.05) significantly and positively influence teachers’ intention to use Canva, whereas performance expectancy and facilitating conditions do not exhibit significant direct effects. These findings confirm the relevance of UTAUT in the Indonesian educational technology context and identify ease of use, social support, and digital confidence as key drivers of technology adoption. The study offers practical implications for professional development programs and educational policy aimed at strengthening technology integration for differentiated instruction.

Purpose: This study used the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) paradigm to investigate the factors influencing teachers' adoption of Digital Learning Resources (DLR). In addition to examining variations by gender, frequency of technology use, and academic field, this study assesses the influence of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HB) on behavioural intention (BI) and use behaviour (UB). Design/methodology/approach: Data were gathered from 502 teachers in nine taluks in Dakshina Kannada, India using a quantitative study approach and stratified random sampling. Google Forms and offline surveys were used to collect data, and structural equation modelling (SEM) was used for analysis. Group differences were also investigated using one-way analysis of variance (ANOVA) and independent sample t-tests. Findings: The findings showed that teachers’ BIs were highly influenced by PV, HM, SI, PE, and EE. Use behaviour is greatly influenced by behavioural aims and facilitating circumstances. FC did not affect BI, and HB had no discernible impact on BI or UB. Gender-based BI did not differ significantly according to the group analysis; however, the frequency of technology use and field of study did differ significantly. Practical implications: This study highlights the need for customised training, improved digital infrastructure, and targeted professional development to enhance the integration of digital tools in education. Originality/value: This study provides empirical insights into digital adoption patterns among teachers and offers valuable guidance to policymakers and teachers aiming to enhance technology-driven learning environments. Type of the paper: Original Article

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.

This study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework to systematically explore the multidimensional drivers of assistive devices (AD) usage behavior (UB) among people with visual impairments or blindness (PwVIB). The results indicate that four variables– Performance Expectation (PE), Effort Expectation (EE), Social Influence (SI), and Facilitating Conditions (FC) –significantly enhance Behavioral Intention (BI). Socioeconomic status exhibits significant moderating effects on UB, while BI plays a critical mediating role in the pathways through which PE, EE, SI, and FC influence UB. Based on these findings, the study proposes a dual-path strategy combining technological innovation and policy support to optimize AD and promote BI and UB. Specifically, increasing investments in technological R&D and improving policy frameworks, and establishing a systemic "economic support–targeted training–service extension" tripartite solution to comprehensively enhance AD, thereby integrating technological empowerment with social inclusive development.

This study analyzes students' preferences regarding mobile banking usage in the Jabodetabek area, involving 250 respondents selected through purposive sampling. The research employs the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate key factors influencing mobile banking adoption. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique is used to analyze variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior. The findings show that effort expectancy, facilitating conditions, and behavioral intention have a significant impact on mobile banking adoption. These results suggest that perceptions of ease of use, good facilitating conditions, and a clear intention to use mobile banking are crucial in shaping user behavior. However, performance expectancy and social influence do not have a significant impact on the intention to use. These findings provide insights for banks to enhance service quality and meet students' expectations.

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.

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.