Technology acceptance model in educational context: A systematic literature review

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.

Over the past three decades, the Technology Acceptance model (TAM) has garnered considerable attention in higher education. COVID-19 boosted the development of TAM as multiple studies were rapidly undertaken during the pandemic. This, however, created a gap in our current understanding of the directions and trends of TAM advancement. The purpose of this study is to obtain insight into the advancement of TAM throughout the pandemic. It would assist researchers in comprehending the advancement and direction of TAM studies in higher education, such as gaining an understanding of the prevalent external variables for TAM, the statistical analysis employed, research methodologies, the technologies studied, and the geographic location of the research conducted. Finally, research gaps and future directions for TAM studies are presented. A systematic review utilizing PRISMA was conducted on 104 sampled publications. It was found that self-efficacy, subjective norms, experience, and enjoyment were the external variables most frequently used in TAM, while internal motivation received minimal attention. The existing studies have focused mainly on student samples, so further investigation is needed into lecturers, higher education personnel, and mixed groups. Further study is also required on qualitative and mixed methods, with the partial least square structural equation model currently dominating statistical analysis. Future technologies such as 5G, AI, cloud computing, augmented reality, virtual reality, and BYOD represent new TAM-related research gaps. The majority of studies have been undertaken in Asian countries, such as China and those in southeast Asia. This new systematic literature review provides insight into the trend of TAM advancement in the sustainability of higher education during the pandemic, the identified research gaps, and recommendations for future research directions. These findings also serve as a reference for future research by enhancing the foundation established by previous reviews and research on TAM, thereby facilitating the model’s ongoing expansion.

One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM). This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction. An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM. The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered. Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.

This study investigates the integration of technology acceptance and pedagogical deep learning in Islamic Religious Education (IRE), where deep learning is conceptualized as a pedagogical approach that emphasizes meaningful understanding, reflective thinking, and enjoyable engagement, rather than artificial intelligence-based deep learning. Although previous studies have extensively examined technology acceptance and deep learning independently, empirical studies integrating the Technology Acceptance Model (TAM) with pedagogical deep learning in the context of Islamic education are still limited. To address this gap, this study uses a quantitative survey design involving 321 teachers and students selected through simple random sampling from a population of 1,287 participants. Data were collected using a validated Likert scale questionnaire (Cronbach's Alpha = 0.874) and analyzed through descriptive statistics and simple linear regression using SPSS version 25. The results of the study indicate that acceptance of TAM-based learning media has a significant and positive effect on the implementation of pedagogical deep learning (R² = 0.542, t = 12.884, p <0.001), showing that 54.2% of the variance in deep learning practices is explained by technology acceptance. Descriptive analysis shows that Perceived Usefulness recorded the highest average score among TAM constructs (M = 4.26), while Meaningful Learning emerged as the most dominant dimension in pedagogical learning (M = 4.23). Theoretically, this study expands TAM by positioning technology acceptance as a driver of deep, reflective, and value-oriented pedagogical learning in Islamic education. Practically, these findings provide evidence-based guidance for educators and schools in designing technology-supported IRE that promotes meaningful, attentive, and enjoyable learning experiences.

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 study aims to examine the influence of two key psychological factors—trust and self-efficacy—on the acceptance and use of Big Data Analytics (BDA) by auditors at the Indonesian Supreme Audit Agency (SAA) and its impact on audit judgment. Using the Technology Readiness and Acceptance Model (TRAM) approach, this study integrates constructs from the Technology Acceptance Model (TAM) such as perceived usefulness and perceived ease of use, as well as behavioural variables such as intention to use and actual usage. The study employs a quantitative method with Partial Least Squares-Structural Equation Modelling (PLS-SEM) analysis on data collected from 137 SAA's auditors across Indonesia. The results indicate that trust significantly influences perceived usefulness and perceived ease of use, while self-efficacy only significantly influences perceived ease of use but not perceived usefulness. Furthermore, perceived usefulness and perceived ease of use positively influence intention to use, which in turn strongly influences actual usage. Finally, actual usage significantly improved the quality of audit judgment. These findings theoretically contribute by expanding the TRAM model in the context of public sector auditing and providing practical implications for the Indonesian SAA to drive digital transformation through increased trust in technology and strengthening auditors' competencies in using BDA.

Aim of this paper is to review technology (IS) acceptance theories and models, recognizing empirical evidence available to support the suitability of each theoretical model in explaining academicians’ acceptance of online learning technology. Understanding the factors influencing system usage is crucial for decision-makers to recognize potential user needs and concerns, which could be addressed during the development phase of a system. Thus, for decades, researchers have been trying to understand why people accept new technologies. As a result, a wide variety of theories and models explaining the concept of technology acceptance. Some prominent theoretical models explaining technology acceptance are, “Theory of Reasoned Action”, “Diffusion of Innovation theory”, “Theory of Planned Behavior”, “Social Cognitive Theory”, “Technology Acceptance Model”, “Model of PC Utilization”, “Motivational Model”, “Unified Theory of Acceptance and Use of Technology”, “UTAUT 2”, “UTAUT 3”. The concept of academic’s acceptance of online learning technology can be explained through several determinants that are operationalized through above information systems models.

Mobile health apps (MHAs) are increasingly used in modern health care provision. The technology acceptance model (TAM) is the most widely used framework for predicting health care technology acceptance. Since the advent of this model in 1989, technology has made generational advancements, and extensions of this model have been implemented. This systematic review aimed to re-examine TAM models to establish their validity for predicting the acceptance of modern MHAs, reviewing relevant core and extended constructs, and the relationships between them. In this systematic review, MEDLINE, Embase, Global Health, APA PsycINFO, CINAHL, and Scopus databases were searched on March 8, 2024, with no time constraints, for studies assessing the use of TAM-based frameworks for MHA acceptance. Studies eligible for data extraction were required to be peer-reviewed, English-language, primary research articles evaluating MHAs with health-related utility, using TAM as the primary technology acceptance evaluation framework, and reporting app use data. Data were extracted and grouped into 5 extended TAM construct themes. Quality assessment was conducted using the Joanna Briggs Institute (JBI) tools. For cross-sectional methodologies (9/14, 64%), the JBI checklist for analytical cross-sectional studies was used. For non-cross-sectional studies (5/14, 36%), the JBI checklist most relevant to the specific study design was used. For mixed methods studies (1/14, 7%), the JBI checklist for qualitative studies was applied, in addition to the JBI checklist most suited to the quantitative design. A subsequent narrative synthesis was conducted in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. A total of 2790 records were identified, and 14 were included. Furthermore, 10 studies validated the efficacy of TAM and its extensions for the assessment of MHAs. Relationships between core TAM constructs (perceived usefulness, perceived ease of use, and behavioral intention) were validated. Extended TAM constructs were grouped into 5 themes: health risk, application factors, social factors, digital literacy, and trust. Digital literacy, trust, and application factor extended construct themes had significant predictive capacity. Application factors had the strongest MHA acceptance predictive capabilities. Perceived usefulness and extended constructs related to social factors, design aesthetics, and personalization were more influential for those from deprived socioeconomic backgrounds. TAM is an effective framework for evaluating MHA acceptance. While original TAM constructs wield significant predictive capacity, the incorporation of social and clinical context-specific extended TAM constructs can enhance the model's predictive capabilities. This review's findings can be applied to optimize MHAs' user engagement and minimize health care inequalities. Our findings also underscore the necessity of adapting TAM and other acceptability frameworks as the technological and social landscape evolves. PROSPERO CRD42024532974; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024532974.

This study examines the acceptance of ICT-based sports devices by the older adults, focusing on psychological improvements through exercise participation. A systematic literature review was conducted on papers related to the types of ICT devices and their acceptance by the older adults, selecting 13 quantitative studies after a rigorous selection process. These studies applied various theoretical models such as the Technology Acceptance Model(TAM), Senior Technology Acceptance Model(STAM), Unified Theory of Acceptance and Use of Technology(UTAUT), and theTranstheoretical Model(TTM) to evaluate device acceptance behavior. Results showed that using ICT-based sports devices significantly enhanced the physical and emotional health of the older adults, particularly increasing self-efficacy. Device ease of use was critical for the older adults’ acceptance of technology, impacting their confidence in using new technologies effectively. The findings suggest that embracing ICT-based sports devices can significantly enhance the quality of life for the older adults, highlighting the importance of effective technology solutions and a multidisciplinary approach to policy development.

This study examines the Chromebookgate procurement scandal in Indonesia as a case of corruption-driven public sector fraud and its implications for the adoption of educational technology. Using a qualitative-conceptual approach with a case study strategy, the research relies on secondary data from audit reports, anti-corruption investigations, government documents, media coverage, and recent academic literature. The analysis is conducted within the frameworks of Broken Window Theory and the Technology Acceptance Model (TAM). The findings identify three major forms of fraud: price mark-up, non-compliance with technical specifications, and procedural irregularities involving conflicts of interest. These practices were tolerated and normalized within procurement governance, illustrating a “broken window” effect in which minor violations escalated into systemic corruption. Furthermore, the fraud undermined technology adoption in schools by reducing perceived usefulness, lowering perceived ease of use, and eroding public trust key constructs of TAM. The study contributes theoretically by integrating criminological and information systems perspectives to explain how procurement fraud not only generates financial losses but also diminishes the effectiveness of digital education policies. Practically, the results highlight the importance of zero tolerance toward procurement irregularities, the integration of technology acceptance evaluation in auditing, and stronger transparency and stakeholder participation in procurement governance. These insights are relevant for policymakers, auditors, and practitioners seeking to strengthen anti-fraud mechanisms and ensure the sustainability of digital transformation in education).

Project-based learning is a teaching method used in classrooms that integrate authentic and real-world problems in the learning environment. In light of the Industrial Revolution 5.0 where humans are working alongside advanced technology and A.I. generated assistance, the research introduces a project-based learning approach in ESL classrooms with the assistance of AI technologies. The project aims to analyse the usefulness and ease of use of AI-based language learning applications in project-based learning among undergraduates in a public university according to the Technology Acceptance Model (TAM). The research focuses on two main theories as a theoretical framework: project-based learning (PBL) and Technology Acceptance Model (TAM). It is a 6-week proposal writing project conducted with undergraduate students from a public university in Selangor. Project-based learning was introduced, and the participants were informed that they can make full use of any AI-based technologies to support their project. Submission of the proposal was scheduled for the sixth week in which it will be presented in class. A set of questionnaires was used as the instrument to obtain their perception of using AI-based technologies in project-based learning upon task completion. 50 respondents were involved in the study. The findings indicate that students had a positive view of using AI applications in their projects, preferred group work for its enjoyment, challenge, and manageability, and found AI language applications easy to use and beneficial for their learning. Finally, the project’s adaptability makes it suitable for various educational contexts, and integrating AI technology in ESL classrooms helps students complete language tasks effectively.

This study aims to analyze the factors influencing students’ acceptance of the Accurate accounting application using the Technology Readiness and Acceptance Model (TRAM) approach. The model integrates personality dimensions from the Technology Readiness Index (TRI)—optimism, innovativeness, discomfort, and insecurity—with the main constructs of the Technology Acceptance Model (TAM), namely perceived ease of use and perceived usefulness. Data were collected through questionnaires distributed to 92 students of Politeknik YKPN Yogyakarta who had participated in the Certified Accurate Professional (CAP) certification exam and analyzed using the SEM-PLS method. The results show that optimism and innovativeness have a positive and significant effect on perceived ease of use, while optimism also has a significant effect on perceived usefulness. In contrast, discomfort and insecurity were found to have no significant effect on either perception. These findings highlight that positive personality traits such as optimism and innovativeness play a dominant role in shaping users’ perceptions of technology acceptance, whereas psychological barriers tend to be insignificant. This research provides empirical contributions for higher education institutions in developing strategies to enhance students’ technological readiness, particularly through training and learning activities based on accounting applications, to support success in certification examinations.

The research sought to assess mathematics teachers’ acceptance of Information and Communication Technology (ICT) integration into teaching and learning at the secondary schools. This study was a cross-sectional survey that gathered data with structured questionnaire. The population was mathematics (core and elective) teachers of secondary schools within the Ashanti region of Ghana. Purposive sampling was used to select mathematics teachers from 41 secondary schools in the region. In all, there were 207 usable questionnaires for the study. Structural Equation Modelling (SEM) was run in Amos (v.23) to estimate the path coefficients of the various hypotheses, using Technology Acceptance Model (TAM). The study established that perceived ease of use predicted both perceived usefulness and attitude towards use; perceived usefulness predicted attitude towards use and behavioral intention; attitude towards use of technology predicted the behavioral intention to adopt technology in teaching and learning, while behavioral intention also predicted actual usage of ICT in teaching and learning of mathematics. As an extension to the original TAM, the study found school related factors as percussor to perceived usefulness and perceived ease of use. Similarly, ICT training was found to greatly influence perceived usefulness and perceived ease of use. Keywords: ICT, mathematics education, structural equation modelling, technology acceptance model

This study tests the applicability of the 'Technology Acceptance Model' (TAM) in Kuwaiti ministries. The aim of the study was to understand the usage of Information Systems (IS) in Kuwait in governmental organizations. The study did not find significant statistical evidence for the relationships between the TAM's core concepts, except in the case of the relationship between 'Perceived Ease of Use' (PEOU) and 'Perceived Usefulness' (PU). National cultural differences, the context of public organizations, and the nature/type of IS usage have been examined as possible explanations of why the TAM was not supported by the evidence. Managerial and research implications are also offered.

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.