Students’ acceptance of e-learning: extending the technology acceptance model with self-regulated learning and affinity for technology

This study investigated the relations between the five important external factors outlined in the General Extended Technology Acceptance Model for E-Learning and the four core components in Technology Acceptance Model (Perceived Usefulness: PU, Perceived Ease of Use: PEOU, Behavioural Intention of Use: BIU, and Actual Use: AU). It also examined the extent to which these relations differed by academic majors (STEM versus non-STEM). A total of 745 Chinese undergraduates answered a Likert-scale questionnaire. The data were analysed using structural equation modeling, measurement invariance, and multi-group analysis. Except Computer Anxiety, the other four external factors were significantly and positively associated with either PU or PEOU. Although there were no significant differences in the total effects of the five external factors on PU, differences were found in the effects from Computer Self-efficacy, Subjective Norm, and Prior Experience on PEOU between STEM and non-STEM students. Furthermore, the effects of PEOU on PU and BIU were stronger for non-STEM undergraduates; whereas the path from PU to BIU was stronger for STEM undergraduates. The results suggested that different strategies should be implemented according to students’ academic majors to encourage Chinese undergraduates to actively adopt e-learning. For Chinese STEM undergraduates, emphasis should be placed to improve their perceptions of the usefulness aspect in e-learning via strategies such as incorporating gamified elements and engaging features into the learning activities. It is important to enable non-STEM students to operate and navigate the e-learning systems free of effort through essential orientation programs and quality technical support.

Digital technologies are changing and will continue to change how we learn and teach today and in the future. With the latest developments in the field of generative artificial intelligence (AI), particularly large language models (LLMs), the question of using AI-based tools in academic education is ruling the current discussions about the transformative impact of AI in higher education (HE). These discussions range from banning these technologies for learning and teaching in HE to guided study support. This study avoids taking up these multifarious and partly controversial debates. Instead, we show how students perceive using AI-based tools for automated text generation for their studies. Drawing on a synthesis of two theories: the 'Technology Readiness Index' (TRI) and 'Technology Acceptance Model' (TAM). The model is validated based on survey data collected among undergraduate first-semester students (N=111) of a computer science-related study programme in Germany in winter 2022/23. The students had to evaluate their relationship to that new technology focusing on their readiness for technology adoption and acceptance. By analysing the collected data with a partial least squares model, we find that the optimism toward the new technology positively influences technology acceptance, while discomfort with the technology negatively influences perceived ease of use. The paper concludes with recommendations for action for adopting LLMs in HE. A proper investment in building AI skills in academic teaching plays a valuable role in fostering the students' positive attitude and innovativeness towards this new technology. Additionally, there is a need for more education about the risks and challenges of using this technology to reduce the impact of factors such as discomfort on ease of use. This requires a factual discourse, away from the current hype-induced exaggerated and hyperbolic statements, for instance, in developing formal guidance for universities.

This paper examines the social, organisational and individual factors that may affect students' acceptance of e‐learning systems in higher education in a cross‐cultural context. A questionnaire was developed based on an extended technology acceptance model ( TAM ). A total sample of 1173 university students from two private universities in L ebanon and one university in E ngland participated in this study. After performing the satisfactory reliability and validity checks, the hypothesised model was estimated using structural equation modeling. The findings of this study revealed that perceived usefulness ( PU ), perceived ease of use ( PEOU ), social norms ( SN s), perceived quality of work life ( QWL ), computer self‐efficacy ( SE ) and facilitating conditions ( FC ) are significant determinants of behavioural intentions (BIs) and usage of e‐learning system for the L ebanese and B ritish students. QWL , the newly added variable, was found the most important construct in explaining the causal process in the model for both samples. Differences were found between L ebanese and B ritish students with regard to PEOU , SN , QWL , FC , SE and actual usage; however, no differences were detected in terms of PU and BI . Overall, the proposed model achieves acceptable fit and explains for 69% of the B ritish sample and 57% of the L ebanese sample of its variance which is higher than that of the original TAM . Our findings suggest that individual, social and organisational factors are important to consider in explaining students' BI and usage of e‐learning environments.

Moodle is widely used in higher education institutions in this digital age. With the growing popularity of Moodle use in education, this study aimed to research on the factors that influence student users’ intentions to adopt Moodle for learning purposes in Macau. A total of 564 students from nine departments at the University of Macau responded to a survey in which ten constructs from a framework that integrated the Diffusion of Innovation Theory and Technology Acceptance Model, were measured. The results of this study showed that the research model had a good fit. Two variables—usefulness and ease of use—had significantly influenced Macau students’ attitudes towards Moodle use. Other variables such as usefulness, attitude, and perceived behavioral control were found to be important determinants of students’ behavioral intentions. Furthermore, usefulness was significantly associated with ease of use, output quality, trialability, as well as subjective norm. Students’ perceptions on the ease of use was significantly influenced by technology complexity and trialability. On the whole, the proposed research model had explained 66% of the variance of Macau university students’ behavioral intentions to use Moodle. This study contributed to deepening our understanding of technology acceptance theories by contextualizing the current study within the Macau higher education.

This study examines student acceptance of mobile videos in online learning by applying the Technology Readiness and Acceptance Model (TRAM). The research investigates the relationships between technological readiness, perceived ease of use, perceived usefulness, and students’ intention to use mobile video-based learning tools. A quantitative research design was employed, with data collected from university students using a quota sampling technique. Structural Equation Modeling (SEM) was applied to test the proposed research model and examine both direct and indirect relationships among the constructs. The findings indicate that technology readiness has a significant positive effect on perceived ease of use, perceived usefulness, and students’ intention to use mobile videos in online learning. Furthermore, perceived ease of use and perceived usefulness are found to significantly influence students’ behavioral intention to adopt mobile video-based learning. These results highlight the critical role of individual readiness toward technology in shaping students’ acceptance of digital learning media. The study contributes to the literature on adoption of educational technology by confirming that technology readiness is a key antecedent of mobile learning acceptance. Practical implications suggest that educational institutions should strengthen students’ technological readiness to enhance the effectiveness and sustainability of mobile video-based online learning.

Massive Open Online Courses (MOOCs) are emerging as the new trend for modern higher education institutions. Student acceptance is viewed as the key determinant for the success of MOOCs. This study intends to examine factors influencing higher education students’ behavioral intention to adopt MOOCs. Thus, this study proposes the use of a modified Technology Acceptance Model (TAM). Data is collected via an online survey from a sample of 403 participants in Jordan. Structural equation modeling (SEM) is used to assess the accuracy of the research model. The results reveal that 1) students’ behavioral intention to adopt MOOCs is positively affected by the perceived ease of use and by the perceived usefulness, 2) self-regulated learning has both a negative direct and indirect (through perceived usefulness) influence on behavioral intention, 3) computer self-efficacy and perceived convenience have positive indirect effects on behavioral intention through the perceived usefulness and perceived ease of use, and 4) learning tradition has a negative indirect effect on behavioral intention through self-regulated learning. Based on the results, various implications (both practical and theoretical), and suggestions for future research, have been highlighted.

BackgroundElectronic learning is the process of remote teaching and learning through the use of electronic media. There is a dearth of research on the factors influencing e-learning acceptance in Ethiopia using the modified technology acceptance model (TAM). Previous research appears to have overlooked the mediating impact of factors on e-learning acceptability Therefore, the present study aimed to assess the acceptance of e-learning and its associated factors among postgraduate medical and health science students by applying TAM at first-generation universities in the Amhara region.MethodsThis institutional-based cross-sectional study was conducted from March 15 to April 20, 2023, at Amhara First Generation University, Ethiopia. A total of 659 students participated in the study. A self-administered questionnaire in the Amharic language was used to collect the data. SEM analysis was employed to test the proposed model and the relationships among factors using SPSS version 25 and AMOS version 26.ResultsThe proportion of postgraduate students who agreed to use e-learning was 60.7%, 95% CI (56.9–64.4). SEM analysis revealed that perceived ease of use (β = 0.210, p < 0.001), attitude (β = 0.377, p < 0.001) and perceived usefulness (β = 0.330, p < 0.001) had positive direct relationships with acceptance of e-learning. Perceived usefulness (β = 0.131, p < 0.001), and perceived ease of use (β = 0.029, p < 0.01) significantly mediate the relationship between self-efficacy, and acceptance of e-learning. Accessibility had a positive indirect effect on acceptance of e-learning through perceived ease of use (β = 0.040, p < 0.01). Facilitating condition had a positive indirect on acceptance of e-learning through perceived ease of use (β = 0.070, p < 0.01), and perceived usefulness (β = 0.084, p < 0.001).Conclusion and recommendationOverall, the proportion of postgraduate students who accepted e-learning is promising. Perceived ease of use perceived usefulness, and attitude had positive direct effects on the acceptance of e-learning. Facilitating conditions and self-efficacy had positive indirect effects on the acceptance of e-learning. Thus, implementers need to prioritize enhancing the provision of devices, students’ skills, and knowledge of e-learning by providing continuous support to improve students’ acceptance of the use of e-learning.

With the growing use of technology in medical education, a framework is needed to evaluate learners' and educators' acceptance of these technologies. In this context, the Technology Acceptance Model (TAM) offers a valuable theoretical framework, providing insights into the determinants influencing users' acceptance and adoption of technology. This review aims to systematically synthesize the body of research in medical education that uses the TAM. An electronic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach in February 2024 on the Embase, MEDLINE, PsycINFO, PubMed, and Web of Science databases, yielding 680 articles. Upon elimination of duplicates and applying the exclusion criteria, a total of 39 articles were retained. To evaluate the quality of the study, the Medical Education Research Study Quality Instrument score was calculated for each analysis with a qualitative component. Studies using TAM in medical education began in 2010, with the model's application relatively rare up to 2016. Most of the studies were quantitative, operationalizing the TAM as a survey instrument, but it was also used as a research framework in qualitative data analysis. Structural equation modeling, descriptive analysis, and correlation analysis were the most common data analysis approaches in the studies. E-learning and mobile learning were the predominant learning interventions explored, but there were indications that novel learning technologies such as augmented reality, virtual reality, and 3D printing were being investigated. The study's findings reveal an expanding scholarly engagement with using TAM in medical education. Although the TAM has been mostly used as a survey instrument, it can also be adapted as a qualitative research framework to analyze data. This systematic review provides a foundation for future research to understand the factors influencing users' acceptance of technology, especially in medical education.

Copyright 2016 ACM.University students are expected to self-regulate their learning while maintaining high motivation in their tertiary education. However, not all students possess the competency required to self-regulate their learning independently. Students need guidance and support in developing the skills to self-regulate their learning. The aim of this study was, therefore, to identify factors that influence university students' self-regulation of learning. Structural Equation Modelling (SEM) based Partial Least Square (PLS) analysis revealed that students' goal, employment prospects, self-efficacy, metacognitive knowledge, prior learning experience, peer influence and family influence have a significant effect on university students' self-regulation of learning. Employment opportunities and teaching staff significant were also found to have an indirect impact on students' self-regulation of learning. Based on these results, the role of the teaching staff, academic managers, teaching and learning support staff as well as students themselves are discussed, together with the advancement of future research and practice in the facilitation of self-regulated learning in higher education.

This study investigates how digital literacy moderates the relationships among intrinsic motivation, Technology Acceptance Model (TAM) perceptions, and mobile gaming adoption among African university students, addressing whether technology acceptance pathways operate uniformly across users with varying technological backgrounds. Combining TAM with Self-Determination Theory, the research conceptualized digital literacy as a fundamental moderator shaping how competency, relatedness, and autonomy influence perceived usefulness, perceived ease of use, behavioral intention, and actual usage. Using a quantitative cross-sectional design, data were collected from 310 Nigerian university students and analyzed through multi-group structural equation modeling. Participants were classified into high (34.8%), moderate (45.2%), and low (20.0%) digital literacy groups based on technology experience profiles across seven platforms. The findings reveal substantial literacy-contingent variation in adoption pathways. Competency effects on perceived ease of use were markedly stronger among low-literacy users, while autonomy significantly predicted perceived usefulness only among digitally experienced users. Perceived ease of use consistently dominated the formation of behavioral intention across all literacy levels. However, actual usage remained constrained by infrastructure and economic barriers, irrespective of user expertise. These findings challenge foundational assumptions within TAM and Self-Determination Theory about the universal applicability of adoption processes, demonstrating instead that motivational hierarchies are conditional on users’ digital proficiency. Hence, the results foreground the need for literacy-sensitive theoretical models and adaptive technology designs that align competency scaffolding, autonomy features, and social integration with users’ digital capacities and proficiency level. Policy interventions that address structural constraints are also necessary to translate adoption intentions into sustained usage within African higher education settings.

PurposeThis study aims to examine university students’ acceptance of e-learning systems in Pakistan. A Web-based learning system is a new form of utilizing technological features. Although, developed countries have initiated and established the concept for e-learning, developing countries require empirical support to implement e-learning.Design/methodology/approachThis paper further explains a conceptual model that is based on the technology acceptance model (TAM). Earlier theories such as the theory of reasoned action (TRA), theory of planned behaviour (TPB) and decomposed theory of planned behaviour (DTPB) have been conducted on user behavioural intention (BI). TAM is considered as the most relevant framework in a Web-based context. To analyse the present study’s hypothesized model, structural equation modelling (SEM) has been used to statistically analyse self-reported sample data from 424 university students.FindingsThe results revealed that TAM, with the combination of new constructs, explains university students’ acceptance of the e-learning system reasonably well. Additionally, work life quality (WLQ) and facilitating conditions (FC) have a greater influence on the BI and the actual use (AU) of the e-learning system, respectively.Originality/valueThe study has also provided valuable implications for academics and practitioners for ways to enhance the acceptance of the e-learning system in the higher education of Pakistan.

This research aims to identify the key factors that affect accounting students’ intention to adopt and the actual usage of Large Langauge Models (LLMs), including ChatGPT, in academic contexts. It also addresses ethical concerns that may arise from their use. Using a quantitative design, data were collected through an online survey involving 302 students from various universities in the Greater Jakarta area who had prior experience using LLMs. This research aims to address the gap in literature on AI-based technology acceptance within the accounting field by extending the Technology Acceptance Model (TAM) with trust and academic ethics. The study offers a theoretical contribution by deepening insights into technology acceptance within accounting education and a practical contribution by emphasizing the integration of ethical considerations in the use of LLMs in higher education. The study focuses on key constructs including perceived ease of use, perceived usefulness, trust, academic ethics, behavioral intention, and actual usage behavior. Data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique via SmartPLS 4 software. The results show that all examined factors positively influence students’ intention to use LLMs, with perceived usefulness stands out as the most significant driver. Furthermore, behavioral intention significantly predicts actual use, suggesting that students who see practical value in these tools are more likely to adopt them in their learning routines. What sets this research apart is its integration of motivational and ethical dimensions in examining technology acceptance within accounting education.

The incorporation of electronic technologies in educational institutions has demonstrated enhancements within the South African setting. This facilitated the transition from traditional pedagogical approaches to online and mixed formats. A comprehensive examination into the issues affecting the implementation of e-learning in secondary schools within the Gauteng North District has not yet been conducted. Research on educators' viewpoints regarding the determinants affecting the execution of e-learning in secondary school is scarce. This study aimed to investigate educators' views on the implementation of e-learning in secondary schools across the Gauteng Province, specifically in the Johannesburg North area. The study utilised the Technology Acceptance Model (TAM) as its theoretical foundation. A quantitative methodology was employed, involving 220 participants from 44 secondary schools in the Johannesburg North district who completed a questionnaire. The study employed a frequency distribution model to classify variables for respondent profiling. The questionnaire utilised a five-point Likert scale, ranging from "agree" to "strongly disagree." Data were analysed with the Statistical Package for Social Sciences (SPSS). The research hypothesis was examined using Structural Equation Modelling (SEM). Confirmatory Factor Analysis (CFA) was performed for each research item. The results demonstrate that perceived ease of use (PEU) and computer experience are critical determinants influencing the uptake of e-learning in secondary education. The research identified that the primary determinants affecting teachers' acceptance of e-learning in secondary schools within the Johannesburg North District are perceived ease of use and computer proficiency, therefore enhancing the current body of knowledge. This study illustrates that the perceived ease of use of technological systems substantially impacts the intention to adopt e-learning in South Africa, more so than perceived usefulness, as delineated by the TAM framework.

An online train ticketing system, called KAI Access, has been introduced by Indonesian Railways despite some other online platforms introduced earlier by private service providers. While the older generation may still have difficulty in using the online platform, the millennial generation, mostly university students, are faster in mastering the platform, including the online ticketing system. The purposes of this study are (1) to determine university students’ perception of the Technology Acceptance Model (TAM) variable and (2) to measure the effect of Perceived Ease of Use, Perceived Usefulness and Attitude towards Usage variables on Behavioral Intention. Questionnaires were distributed to university students who have used KAI Access. From 350 data collected, 335 data were valid for further analysis. Variance-based SEM PLS was used to determine the relationship between variables. The results show that Perceived Ease of Use and Perceived Usefulness have a direct positive and significant effect on Behavioral Intention, while Attitude towards Usage has no significant influence on Behavioral Intention. The implication for management is to provide a better ticketing system which can create users’ positive attitude to use it. Further research may compare several online ticketing service systems and involve other variables to predict consumers’ attitudes and behavioral intentions.

Background: The rapid emergence of generative artificial intelligence (AI) tools such as ChatGPT is transforming teaching and learning practices in higher education. This study assessed the awareness, attitudes, and usage patterns of ChatGPT among university students in Zambia and examined factors associated with students' attitudes toward the technology. Methods: A multi-institutional cross-sectional study was conducted among 1,829 university students in Zambia using a structured questionnaire adapted from instruments informed by the Technology Acceptance Model (TAM). Data were analysed using SPSS version 26.0, with statistical significance set at p < 0.05. Results: Among the 1,829 participants, 81.7% were aged 18–25 years, 52.5% were female, and 92.4% were unmarried. Overall, 96.8% of the students had heard of ChatGPT, and 85.6% reported having used it before this study. Among AI-usage constructs, 74.8% of respondents perceived high risks associated with AI use, 73.3% perceived ChatGPT as easy to use, 64.0% perceived it as useful, and 56.8% reported behavioural intention to use AI tools. In multivariable analysis, students aged ≥40 years were more likely to report positive attitudes toward ChatGPT compared with those aged 18–25 years (aOR = 5.91; 95% CI: 1.23–28.33; p = 0.026). Technology/social influence was also significantly associated with positive attitudes (aOR = 2.04; 95% CI: 1.55–2.68; p < 0.001). Conversely, perceived risks were associated with lower odds of positive attitudes (aOR = 0.57; 95% CI: 0.43–0.75; p < 0.001). Regarding ChatGPT use, perceived usefulness significantly predicted higher usage (aOR = 1.49; 95% CI: 1.17–1.89; p = 0.001), while perceived risks were associated with reduced usage (aOR = 0.61; 95% CI: 0.45–0.84; p = 0.003). Conclusion: Awareness and use of ChatGPT are widespread among university students in Zambia, with more than four out of five students reporting prior use. Perceived usefulness and social influence were positively associated with adoption, whereas perceived risks were linked to lower attitudes and reduced use. These findings highlight the need for higher education institutions to develop clear policies and integrate AI literacy into curricula to promote responsible and effective use of generative AI technologies in academic environments.