UTAUT Model Research Articles

Smart technology’s potential in smart destinations: a comprehensive UTAUT model with privacy and safety risk moderation

Smart technology’s potential in smart destinations: a comprehensive UTAUT model with privacy and safety risk moderation

Chronicling digital teaching at a South African historically disadvantaged comprehensive university: navigating institutional culture for agricultural sciences academics

ABSTRACT This article presents a pragmatic study that explored academics’ experiences of using digital learning environments to teach agricultural sciences at a South African historically disadvantaged university. The study aimed to investigate challenges faced in integrating technology and proposes a contextually responsive approach to digital platform adoption in rural university settings. A mixed methods approach was adopted, involving reflective activities, one-on-one semi-structured interviews, focus-group discussion, and document analysis. Triangulation enhanced credibility through classroom observations and quantitative data collection. Two main themes emerged: pedagogical effectiveness, highlighting efforts to align digital tools with academic standards, and institutional culture, highlighting cultural norms, social relationships, and contextual realities on academics’ experiences. Using UTAUT2 model, findings revealed that while academics recognised the potential benefits of using prescribed digital platforms, they often customise their teaching with casual/informal platforms to reflect their personal identities and meet the needs of agricultural science modules. The study introduces a Contextually Adaptive Tech-Enhanced Pedagogy model, designed to improve the personalisation and quality within digital learning environments at historically disadvantaged higher education institutions. This study contributes to the growing discussion on contextually adaptive digital teaching practices, offering insights for inclusive and transformative teaching experiences in agricultural sciences.

How did Turkish higher education respond to emergency distance education in the COVID-19 pandemic? Acceptance and use by Turkish academics

ABSTRACT The COVID-19 pandemic necessitated a rapid transition to online education, presenting adaptation challenges for faculty members unfamiliar with distance education methodologies. Certain institutions have demonstrated greater efficacy owing to prior experience in this domain. This study investigates faculty acceptance and utilisation of mandatory distance education employing the UTAUT2 model, with age and gender as moderating variables, analysed through structural equation modelling. Data from 3631 participants were collected using a 25-item scale encompassing seven subdimensions: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit and price value. The research assessed the impact of these dimensions on behavioural intention and usage, with gender moderating several relationships, while age did not exhibit a significant moderating effect. The findings, consistent with the extant literature, suggest that training and experience in distance education could enhance acceptance and utilisation among faculty members.

What is the influence of psychosocial factors on artificial intelligence appropriation in college students?

BackgroundIn recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian university students and uses an extended UTAUT2 model to examine various constructs that may impact AI acceptance and use.MethodThis study employed a quantitative approach with a survey-based design. A total of 482 students from public and private universities in Peru participated in the research. The study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the data and test the hypothesized relationships between the constructs.ResultsThe findings revealed that three out of the six hypothesized factors significantly influenced AI adoption among Peruvian university students. Performance expectancy (β = 0.274), social influence (β = 0.355), and AI learning self-efficacy (β = 0.431) were found to have significant positive effects on AI adoption. In contrast to expectations, ethical awareness, perceived playfulness, AI readiness and AI anxiety did not have significant impacts on AI appropriation in this context.ConclusionThis study highlights the importance of practical benefits, the social context, and self-confidence in the adoption of AI within Peruvian higher education. These findings contribute to the understanding of AI adoption in diverse educational settings and provide a framework for developing effective AI implementation strategies in higher education institutions. The results can guide universities and policymakers in creating targeted approaches to enhance AI adoption and integration in academic environments, focusing on demonstrating the practical value of AI, leveraging social networks, and building students’ confidence in their ability to learn and use AI technologies.

The impact of AI technology on oral English teaching behaviour under UTAUT model

The impact of AI technology on oral English teaching behaviour under UTAUT model

The impact of AI technology on oral English teaching behaviour under UTAUT model

The impact of AI technology on oral English teaching behaviour under UTAUT model

Understanding young adults’ use of mobile banking: the integration of the UTAUT, D&M, and PMT models

Understanding young adults’ use of mobile banking: the integration of the UTAUT, D&M, and PMT models

Examining UTAUT model for mobile food ordering applications (MOFAs): A case study of Food-panda application

The purpose of the study was to examine the effectiveness of the Mobile Food Ordering Application (MFOA) in a collectivist country like Pakistan. Data was gathered using an online survey-based approach from 354 MFOA users and was analyzed using the structural equation modeling technique through Smart PLS 3.0. The results show that consumers’ online reviews strongly influence customer satisfaction and continued intention. Similarly, price value and online tracking of food services are strongly associated with customer satisfaction. Consumer habits and facilitation conditions are significantly associated with consumer continued intention. Habit is also found to partially mediate consumer satisfaction and continued intention. The study did not find any support for performance expectancy, effort expectancy, social influence, price value, hedonic motivation, or online review with continued intention. Similarly, performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation were not associated with consumer satisfaction. The present work is the first of its kind that has empirically examined the effectiveness of MFOAs in Pakistan. It lays down useful practical implications for practitioners, policymakers, and academia.

Factors influencing students' attitude toward to use mobile learning applications using SEM-ANN hybrid approach

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

Analysis of e-banking use by commercial bank customers in Bukavu, DRC

Purpose: This study aims to identify the determinants of the adoption and use of e-banking by the customers of commercial banks in Bukavu, DRC. Method: A conceptual framework based on the UTAUT model was developed, followed by flat sorting and econometric analyses of data from a survey based on a sample of 215 customers. Logistic regression and ordered Logit enabled us to address our research question Results: Perceived usefulness and social influence, modulated by profession and level of education, favour the likelihood of adopting e-banking. The cost perceived, on the other hand, has a negative impact. Trust decreases the likelihood of using a wide range of e-banking services, while gender, profession and app rating have positive effects on use. Originality / relevance: In addition to a methodology combining the logit and ordered logit models, the originality of this work lies in the analytical aspect focused on the number of e-banking services used by customers in a developing country context.

UTAUT2 기반 인공지능 기술이 실제 시스템 사용을 통한 사용자 만족도에 미치는 영향

Artificial Intelligence (AI) is a computer system that performs perception, reasoning, learning, and language abilities similar to humans, making it a core technology of the Fourth Industrial Revolution. Particularly, generative AI, which creates new content such as text, images, and music through large-scale data learning, has garnered significant attention. This technology utilizes artificial neural networks and machine learning to understand user intentions, learn from data, and generate various types of content. AI is defined as a technology that implements human intelligence-related functions such as learning, cognition, and reasoning using the concepts and tools of computer science. This study aims to analyze the impact of AI-based UTAUT2 characteristics on user satisfaction. The research targets general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The purpose of this study is to analyze the impact of UTAUT2 characteristics in the context of artificial intelligence (AI) on user satisfaction. The research subjects were general consumers who use AI services, and statistical analysis was conducted using Smart PLS 4.0. The results of the study are as follows. First, performance expectancy had a significant positive effect on behavioral intention. Second, effort expectancy had a significant positive effect on behavioral intention. Third, social influence did not have a significant positive effect on behavioral intention. Fourth, facilitating conditions did not have a significant positive effect on behavioral intention. Fifth, trust had a significant positive effect on behavioral intention. Sixth, personal innovativeness had a significant positive effect on behavioral intention. Seventh, behavioral intention had a significant positive effect on actual system use. Eighth, actual system use had a significant positive effect on user satisfaction. Therefore, this study empirically verified the relationships between AI technology’s behavioral intention, actual system use, and user satisfaction based on the UTAUT2 model. Performance expectancy, effort expectancy, trust, and personal innovativeness were identified as significant factors influencing the acceptance intention of AI technology. Furthermore, behavioral intention was shown to lead to actual system use, which, in turn, positively influenced user satisfaction. Based on these findings, AI technology developers and companies should enhance user experience, build trust, and strengthen technical support to maximize AI technology adoption and user satisfaction.

IMPACTS OF AUGMENTED REALITY MARKETING ON GEN-Z FASHION CONSUMERS’ BEHAVIORAL INTENTION

Innovative technologies are significant for the textile and fashion industry. In this context, this research study focuses on understanding the impact of augmented reality (AR) technologies on Gen-Z fashion consumers’ behavioral intention by using a specific AR app. As a methodology, the present study utilized a questionnaire based on the UTAUT2 model, which was modified to incorporate a 5-point Likert Scale to gather essential research data. The questionnaire comprised twenty-three questions distributed across seven constructs. Using a sample of 310 male and female Gen-Z consumers, the findings indicate that perceived value (PV) positively influences their behavioral intention to use AR technology for fashion shopping. Findings indicate that the research model explained 75% of behavioral intention. All research results confirm the unified theory of acceptance and the use of the technology 2 model (UTAUT2) with its six dimensions. The perceived value (PV) dimension, added to the UTAUT2, has improved the model. In terms of its originality, this recent research study defines augmented reality technology's impacts on the behavioral intention of Gen-Z consumers by focusing on this specific group of users through an advanced AR application.

Unification of fintech platforms: reshaping the Bangladeshi financial frontier

Purpose This paper aims to explore the integration of technology platforms of Fintech service providers in Bangladesh and its outcome on the users’ acceptance and intention to use it. It has considered Bangla QR, a Bangladesh-specific unified payment platform, for the said purpose. Design/methodology/approach The study investigates the usage and acceptance of Bangla QR’s financial service application by collecting data on user demographics and usage patterns. The data have been collected from Bangla QR users who have firsthand experience using a unified payment system in Bangladesh. Structural equation modeling (SEM) has been used to analyze the data and investigate the relationships between latent constructs, such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM) and Behavioral Intention (BI). Findings Results reveal that the experience of users from earlier platforms and their expectation of improved technologies have a positive and significant relationship with the behavior intention of users regarding the acceptance of a fresh fintech platform. Specifically, PE, EE and FC have a significant effect on HM. Moreover, HM mediates the relationship between PE and BI, EE and BI and FC and BI. Startlingly, SI does not affect HM and BI. Research limitations/implications The study posits that users of Bangla QR are embracing the unified payment scheme willingly. Experience with new technology in the financial aspects has given them the confidence to continue this in the future. However, social awareness about the intrinsic worth of unified payment should be raised by financial institutions and regulators in Bangladesh. The lack of Bangla QR’s accessibility limits the number of respondents, and its newness hinders the researchers from focusing on price value and habit constituting BI. Originality/value First, this is the first paper to explore the fintech users’ intention to continue a unified payment platform in Bangladesh. Second, it uses the UTAUT2 model with modifications as per the requirement of the study. Finally, this study theorizes specific reasons to develop the overall scenario of a unified payment system in the future in Bangladesh.

Event planners’ intentions to use advanced ICTs: Extension of the UTAUT model

This study investigates event planners’ intentions to adopt augmented reality presentations (ARP) using the UTAUT model. Furthermore, this study aims to shift the focus from attendees’ to planners’ perspectives on technology adoption, extending the UTAUT model by introducing attendees’ engagement expectancy as a potential factor and understanding the roles of personal cognitive and affective responses. Data was collected from a sample of event planners in the U.S. A total of 156 valid responses were analyzed using PLS-SEM. Findings showed that event planners’ expectations of attendees’ engagement, performance expectancy, social influence, and facilitating conditions positively influenced their behavioral intentions to use ARP. Furthermore, this study revealed the potential antecedent role of perceived innovativeness and technology anxiety in the UTAUT model. Consequently, this research offers theoretical and practical implications for adopting various advanced ICTs across the hospitality and tourism industry beyond the events field.

Adoption of artificial intelligence in higher education: an empirical study of the UTAUT model in Indian universities

Adoption of artificial intelligence in higher education: an empirical study of the UTAUT model in Indian universities

فجوة الذكاء الاصطناعي بين المهاجرين والمواطنين الرقميين في مصر

The study explores the artificial intelligence (AI) divide in Egypt while considering the moderating influence of age/generation. The research compares digital natives born in the digital age vis-à-vis digital immigrants who grew up in less technologically adapted environments. The study uses the UTAUT2 model to measure how determinants of AI acceptance and digital self-efficacy impact the intentions and behaviors of 400 surveyed individuals. The results indicate that all AI acceptance determinants positively affect users’ intentions in both generations. However, social influence is more prevalent among digital immigrants, while hedonic motivation more significantly affects digital natives. Both generations prioritize performance expectancy, as well as habit and facilitating conditions, and highlight the low impact of effort expectancy. Digital self-efficacy dimensions also impact AI acceptance and use differently for each respectivegeneration. Interestingly, the study revealed an AI divide between generations. However, it proposed that governments and tech developers could reduce this divide by making AI more inclusive for all ages, which includes ensuring technology access for adults and individuals with special needs.

Pathways to Resilient and Sustainable Online Education in the New Normal: Interpreting University Teachers’ Willingness to Use Online Teaching Platforms Consistently

In the new normal, online education has become a norm and is considered equally important as traditional face-to-face education. Although online teaching has improved accessibility, reduced management costs, and expanded educational opportunities, there are also some issues, such as whether technology and internet access are widespread and whether the quality of online education is better or worse compared to previous modes. With the rapid development of online education, it is crucial to investigate the key factors influencing university teachers’ continuous usage of online teaching platforms and their working mechanisms, which is of great significance for improving the level of educational informatization and promoting educational modernization. This study mainly explores the continuous usage intention of online teaching platforms by Chinese university teachers under the new normal and its influencing factors, aiming to provide theoretical support and practical insights for improving educational informatization, promoting educational modernization, and designing and optimizing online teaching platforms. This study builds a new theoretical framework based on the technology acceptance model (TAM), UTAUT model, and the theory of planned behavior (TPB). From April to November 2024, 343 questionnaires were collected from Chinese university teachers. Combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the key factors influencing teachers’ continuous usage of online platforms and their configuration relationships are systematically discussed. Not only are the key factors verified, but the configuration relationships are also revealed, providing deeper insights for universities to design, promote, and optimize online teaching platforms and offering theoretical support and practical guidance for promoting sustainable educational development.

Factors Influencing Customer Behavioral Intention of Mobile Money: A Case Study in Vietnam

This study explores the factors influencing the behavioural intention of Mobile Money services in Vietnam. To address the research problem, the author has combined the UTAUT2 model [1] with the Push-Pull-Mooring model [2]. The data collection was conducted from March to July 2024 across three regions of Vietnam: the North, Central, and South. The collection methods included both direct surveys and online surveys via Google Forms, distributed through social networks such as Facebook and Zalo. A total of 1,784 participants responded, of which 1,576 valid responses were obtained. Among them, 369 respondents had never heard of Mobile Money, while 1,180 were already aware of the service. Key findings are: (i) The study develops a research model regarding factors influencing the behavioural intention of Mobile Money services in the context of Vietnam, which consists of three groups of factors. The pull factor is reflected in Performance expectancy, Effort expectancy, facilitating conditions, and transaction trends. The push factor is represented by perceived costs and perceived risks, while the mooring factor is reflected in social influence and trust. (ii) The push factor has the strongest negative impact on consumer intentions to use the service, while the pull factors positively affect the behavioural intention. Mooring factor has both a positive impact on intention and play a moderating role in the relationship between the pull and push factor and the behavioural intention of MM service. Keywords: Fintech, network provider, mobile finance, mobile money, mobile payment, telco account.

Understanding graphic designers’ usage behavior of generative artificial intelligence tools

PurposeThe rapid development of generative artificial intelligence has witnessed its widespread integration across various industries, contributing to enhanced productivity. However, a comprehensive exploration of the underlying factors influencing the behavior of graphic designers in employing such tools remains incomplete. This research aims to amalgamate the IDT theory with the UTAUT2 model to construct a structural model, delving into the factors affecting graphic designers’ behavior in using GenAI tools.Design/methodology/approachA survey was conducted with 394 respondents, and the results were analyzed using PLS-SEM.FindingsThe findings reveal that most factors proposed in both the UTAUT2 and IDT theories exert positive influences. Notably, the study highlights that AI anxiety significantly influences designers’ usage behavior.Originality/valueThis research provides a theoretical foundation and practical guidance for both graphic designers and AI developers.

The impact of the COVID-19 pandemic on e-learning adoption in an emerging market: a longitudinal study using the UTAUT model

Purpose The COVID-19 pandemic provided unprecedented impetus to the evolution of the e-learning learning ecosystem by compelling students to adopt e-learning systems. This paper aims to use the UTAUT model to provide insight into the differences in factors influencing the adoption of e-learning systems before and after the pandemic. Design/methodology/approach This longitudinal study uses two surveys conducted among graduate students in the city of Bengaluru in India. One prior to the start of the COVID-19 pandemic and a second in its aftermath. PLS-SEM is used to analyze both data sets to draw insights into the constructs that influence Behavioral intention to adopt e-learning systems. The moderating effect of gender is also analyzed. Findings Pre COVID-19, Facilitating Conditions, Performance Expectancy and Effort Expectancy (quadratic behavior) were dominant factors influencing the adoption of e-learning technologies. Post pandemic, Performance Expectancy and Social Influence are drivers of e-learning adoption. Effort Expectancy and Facilitating Conditions grouped as Ease of Use is a significant driver of e-learning adoption post pandemic. Gender is found to not have a moderating influence. Originality/value The unique longitudinal study of the differences in factors influencing students’ intention to adopt e-learning pre- and post-COVID-19 can prove useful to policy makers in the higher education sector. Academics can use the post-pandemic e-learning model’s findings in multiple contexts such as generational cohorts, educational contexts and social contexts.