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Research on information systems has identified a variety of factors across a range of adoption models that determine their acceptance. In this research, the unified theory of acceptance and use of technology (UTAUT), which integrates determinants across eight models, was utilised to analyse students’ intentions to use and their actual usage of Moodle, an e-learning system at Hashemite University, a public university in Jordan, one of developing countries. Four principal determinants of intention and usage were explored: performance expectancy, effort expectancy, social influence, and facilitating conditions. Data were collected from 370 undergraduate students and analysed using structural equation modelling techniques. The results indicated that performance expectancy and effort expectancy affected behavioural intentions to use Moodle whereas social influence did not. In addition, the results confirmed the direct impact of behavioural intentions and facilitating conditions on students’ use of Moodle. UTAUT thus provides a valuable tool that enables university decision makers, faculty members, and designers to understand the factors driving e-learning system acceptance and thus facilitate the adoption of the system by students. The study will help educational institutions prepare e-learning systems, which is especially important during a state of emergency such as that caused by COVID-19.

Virtual and augmented (VAR) technology is in the early stages of being adopted as a teaching platform in higher education. The technology can facilitate immersive learning in environments that are not usually physically accessible to students via 3D models and interactive 360° videos. To date, adoption rates of VAR technology for teaching have not been well described across a higher education institution. Further, there is an absence of information on the optimal VAR laboratory designs and cost per student. In this study, a purpose designed virtual reality laboratory was formed in 2017 at The University of Sydney, housing 26 Oculus Rift headset units. An evaluation was conducted on the design, costs, rates of teaching adoption and student experiences over five teaching periods (2.5 years). Over this period, 4833 students were taught in the laboratory across 7952 student visits. The laboratory was used most by the Faculty of Engineering (53%), followed by the Faculty of Arts & Social Science (23.8%) and Faculty of Science (23.2%). For engineering, the units of study using the laboratory represented only 1.4% of annual faculty subjects offered. This confirms that adoption was in the initial stage of innovation diffusion. The laboratory saw a 250% increase in student numbers over the period of evaluation and 71.5% of students surveyed (n = 295) reported enhanced learning outcomes. The cost per visit was only AU$ 19.50. These findings give confidence to higher education institutions that the right VAR technology infrastructure is a sound educational investment for the future.

Technological innovations such as blended learning (BL) are rapidly changing teaching and learning in higher education, where BL integrates face to face teaching with web based learning. Thus, as polices related to BL increases, it is required to explore the theoretical foundation of BL studies and how BL were adopted and implemented in relation to students, lecturers and administration. However, only fewer studies have focused on exploring the constructs and factors related to BL adoption by considering the students, lecturers and administration concurrently. Likewise, prior research neglects to explore what practices are involved for BL implementation. Accordingly, this study systematically reviews, synthesizes, and provides meta-analysis of 94 BL research articles published from 2004 to 2020 to present the theoretical foundation of BL adoption and implementation in higher education. The main findings of this study present the constructs and factors that influence students, lecturers and administration towards adopting BL in higher education. Moreover, findings suggest that the BL practices to be implemented comprises of face-to-face, activities, information, resources, assessment, and feedback for students and technology, pedagogy, content, and knowledge for lecturers. Besides, the review reveals that the ad hoc, technology acceptance model, information system success model, the unified theory of acceptance and use of technology, and lastly diffusion of innovations theories are the mostly employed theories employed by prior studies to explore BL adoption. Findings from this study has implications for student, lecturers and administrators by providing insights into the theoretical foundation of BL adoption and implementation in higher education.

Owing to the rapid advancements in artificial intelligence (AI) technologies, there has been increasing concern about how to promote the use of AI technologies in school settings to enhance students' learning performance. Teachers' intention to adopt AI tools in their classes plays a crucial role in this regard. Therefore, it is important to explore factors affecting teachers' intention to incorporate AI technologies or applications into course designs in higher education. In this study, a structural equation modeling approach was employed to investigate teachers' continuance intention to teach with AI. In the proposed model, 10 hypotheses regarding anxiety (AN), self-efficacy (SE), attitude towards AI (ATU), perceived ease of use (PEU) and perceived usefulness (PU) were tested, and this study explored how these factors worked together to influence teachers' continuance intention. A total of 311 teachers in higher education participated in the study. Based on the SEM analytical results and the research model, the five endogenous constructs of PU, PEU, SN, and ATU explained 70.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effect of ATU was 0.793, followed by SE, with a total effect of 0.554. As a result, the intentions of teachers to learn to use AI-based applications in their teaching can be predicted by ATU, SE, PEU, PU and AN. Among them, teachers' SE positively influenced teachers' PEU and ATU towards adopting AI-based applications, and also influenced PU through PEU. In addition, the relationship between teachers' SE and AN was negatively correlated, which indicated that enhancing teachers' SE could reduce their AN towards using AI-based applications in their teaching. Accordingly, implications and suggestions for researchers and school teachers are provided.

This study aimed to explore the experiences, perceptions, knowledge, concerns, and intentions of Generation Z (Gen Z) students with Generation X (Gen X) and Generation Y (Gen Y) teachers regarding the use of generative AI (GenAI) in higher education. A sample of students and teachers were recruited to investigate the above using a survey consisting of both open and closed questions. The findings showed that Gen Z participants were generally optimistic about the potential benefits of GenAI, including enhanced productivity, efficiency, and personalized learning, and expressed intentions to use GenAI for various educational purposes. Gen X and Gen Y teachers acknowledged the potential benefits of GenAI but expressed heightened concerns about overreliance, ethical and pedagogical implications, emphasizing the need for proper guidelines and policies to ensure responsible use of the technology. The study highlighted the importance of combining technology with traditional teaching methods to provide a more effective learning experience. Implications of the findings include the need to develop evidence-based guidelines and policies for GenAI integration, foster critical thinking and digital literacy skills among students, and promote responsible use of GenAI technologies in higher education.

Metaverse, which combines a number of information technologies, is the Internet of the future. A media for immersive learning, metaverse could set future educational trends and lead to significant reform in education. Although the metaverse has the potential to improve the effectiveness of online learning experiences, metaverse-based educational implementations are still in their infancy. Additionally, what factors impact higher education students’ adoption of the educational metaverse remains unclear. Consequently, the aim of this study is to explore the main factors that affect higher education students’ behavioral intentions to adopt metaverse technology for education. This study has proposed an extended Technology Acceptance Model (TAM) to achieve this aim. The novelty of this study resides in its conceptual model, which incorporates both technological, personal, and inhibiting/enabling factors. The empirical data were collected via online questionnaires from 574 students in both private and public universities in Jordan. Based on the PLS-SEM analysis, the study identifies perceived usefulness, personal innovativeness in IT, and perceived enjoyment as key enablers of students’ behavioral intentions to adopt the metaverse. Additionally, perceived cyber risk is found as the main inhibitor of students’ metaverse adoption intentions. Surprisingly, the effect of perceived ease of use on metaverse adoption intentions is found to be insignificant. Furthermore, it is found that self-efficacy, personal innovativeness, and perceived cyber risk are the main determinants of perceived usefulness and perceived ease of use. While the findings of this study contribute to the extension of the TAM model, the practical value of these findings is significant since they will help educational authorities understand each factor’s role and enable them to plan their future strategies.

Emergence of the use and application of Artificial Intelligence (AI) in higher education in India has opened new possibilities and challenges. Use of AI in will bring in effective change of governance in the entire internal architecture of Indian Institutes of higher education. The prospect of use of AI includes investigation of educational implications as to how teachers would enrich them, how students would learn, and how accurate and prompt decisions can be taken in the institutes of higher education. This is important since the workload has been multiplied due to massification of higher education. Such being the scenario, help of AI is highly essential. The question of adoption of AI in higher education is an important issue in this perspective. The purpose of this study is to explore how the stakeholders would be able to adopt it. For this, we have taken help of many adoption theories and models including ‘Unified Theory of Acceptance and Use of Technology’ (UTAUT) model. We have developed hypotheses and a conceptual model and got it validated through survey with the help of feedbacks from useable 329 respondents. It has been found that the model can help the authorities to facilitate adoption of AI in higher education.

This present study aims to investigate factors that impact behavioural intention of university students on e-learning use during the COVID-19 pandemic. An online questionnaire was utilised to gather data from 109 students enrolled in one of the universities in Indonesia. The Technology Acceptance Model (TAM) was the primary framework employed for analysis, in which system quality and e-learning experience were included as external constructs to seek out a much better model to improve the understanding of students’ intention to adopt e-learning. An extended TAM model was developed and tested in this study. The model consists of six constructs: system quality, e-learning experience, perceived ease of use, perceived usefulness, attitude toward use, and behavioural intention. Structural Equation Modelling (SEM) and SMART PLS 3.0 software were applied for data analysis. The findings informed that the proposed model has been successfully explained factors university students use of e-learning during the pandemic in Indonesia. It suggested that attitude toward e-learning use was the most prominent construct to predict university students’ behavioural intention to use e-learning during the pandemic. Finally, this study offers recommendations for future research and practices.

How and why individuals adopt innovations has motivated a great deal of research. This article examines individuals' computing adoption processes through the lenses of three adoption theories: Rogers's innovation diffusion theory, the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance and Use of Technology. Incorporating all three models, this article suggests technology adoption is a complex, inherently social, developmental process; individuals construct unique yet malleable perceptions of technology that influence their adoption decisions. Thus, successfully facilitating technology adoption must address cognitive, emotional, and contextual concerns. This article also focuses specific attention on adoption theory outside of a formal organization and the implications of adoption theory on informal environments.

Technology acceptance has become one of the dominant research trends in the domain of learning management systems (LMSs). While a plethora of several research studies conducted in this area, there is still a scarcity of knowledge concerning a holistic review and taxonomy of studies in this field. Thus, the main objective of this systematic review is steered toward understanding the most prevalent theoretical models and the most prominent external factors affecting the LMS adoption in higher educational institutions. Out of 732 collected studies between 2005 and 2020, a total of 68 studies were critically reviewed and analyzed. The main results indicated that the TAM, DeLone and McLean IS success model, UTAUT, TRA, DOI, and UTAUT2 have been dominating the theoretical landscape in LMS research. The results also elucidated that external factors linked to LMS acceptance models fall primarily into three macro-categories, including individual variables, contextual variables, and psychological/behavioral constructs driven from other theories. It is believed that the results of this review can serve as a departure point for synthesizing more advanced hybrid adoption theoretical models on the one hand, and a standardized inventory of factors affecting the LMS adoption on the other hand. Several theoretical contributions, practical implications, and future research paths were discussed.