key: cord-1006796-jukgkn8l authors: Granić, Andrina title: Educational Technology Adoption: A systematic review date: 2022-04-05 journal: Educ Inf Technol (Dordr) DOI: 10.1007/s10639-022-10951-7 sha: 9370859a27eb1d5fe0c2cdae0e49420db7a7ea2e doc_id: 1006796 cord_uid: jukgkn8l During the past decades a respectable number and variety of theoretical perspectives and practical approaches have been advanced for studying determinants for prediction and explanation of user’s behavior towards acceptance and adoption of educational technology. Aiming to identify the most prominent factors affecting and reliably predicting successful educational technology adoption, this systematic review offers succinct account of technology adoption and acceptance theories and models related to and widely applied in educational research. Recognised journals of the Web of Science (WoS) database were searched with no time frame limit, and a total of 47 studies published between 2003 and 2021 were critically analysed. The key research findings revealed that in educational context a vast majority of selected studies explore the validity of Technology Acceptance Model (TAM) and its many different extensions (N=37), along with TAM’s integrations with other contributing theories and models (N=5). It was exposed that among numerous predictors, thematically grouped into user aspects, task & technology aspects, and social aspects, self-efficacy, subjective norm, (perceived) enjoyment, facilitating conditions, (computer) anxiety, system accessibility, and (technological) complexity were the most frequent predictive factors (i.e. antecedents) affecting educational technology adoption. Considering types of technologies, e-learning was found to be the most common validated mode of delivery, followed by m-learning, Learning Management Systems (LMSs), and social media services. The results also revealed that the majority of analysed studies were conducted in higher education environments. New directions of research along with potential challenges in educational technology acceptance, adoption, and actual use are discussed as well. Over the last half-century, a vast number of adoption theories and technology acceptance models, along with a plethora of their extensions and modifications has been advanced. Aiming to explore their applicability, as well as to enhance their predictive validity, proposed theories and models have been extensively used in assessment of various Information and Communication Technology (ICT) products and services. Commonly, technology adoption is a term that refers to the acceptance, integration, and embracement of any types of new technology. Technology acceptance, as the first step of technology adoption, is an attitude towards technology, and it is influenced by various factors. According to the Innovation Diffusion Theory (IDT) (Rogers, 1962 (Rogers, , 1995 , adoption is a decision to make full use of technology innovation as the best course of action available. The key to adoption is that the adopter (individual or organization) must perceive the idea, behavior, or product as new or innovative. As for technology adoption research at the individual level, numerous theories and models have been used to predict and explain human behavior towards technology acceptance, adoption and usage. Education presents an area of great interest in incorporating new technologies, thus technology acceptance and adoption theories and models are often used to inform research in educational context. Such setting is characterised by a great variety of potential users of various types of technology embraced in the process of learning, teaching, and assessment. Some of the most influential theoretical approaches involve (listed in chronological order with relevant illustrative example research): • Technology Acceptance Model (TAM) (Davis, 1986 (Davis, , 1989 , the widely used reliable model, to explore new facilitating technologies in educational context, ranging from social media platforms (Yu, 2020) to the technology aimed at helping the learning process through teaching assistant robots (Park and Kwon, 2016) , simulators (Lemay, Morin, Bazelais & Doleck, 2018) , and virtual reality (Jang, Ko, Shin & Han, 2021) ; • Decomposed Theory of Planned Behavior (DTPB) (Taylor & Todd, 1995) to understand university students' adoption of WhatsApp in learning (Nyasulu & Chawinga, 2019) , to explore factors that influence teachers' intentions to integrate digital literacy (Sadaf & Gezer, 2020) , as well as to examine factors that impact the acceptance and usage of e-assessment by academics (Alruwais, Wills & Wald, 2017) ; • Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Mor- ris, Davis & Davis, 2003) to study core factors affecting the university students' attitude towards adoption of online classes during COVID-19 (Tiwari, 2020) , to explore the factors that influence preservice teachers' acceptance of ICT integration in the classroom (Birch & Irvine, 2009) , and students' usage of e-learning systems in developing countries (Abbad, 2021) ; • Extended UTAUT (UTAUT2) (Venkatesh, Thong & Xu, 2012) to evaluate acceptance of blended learning in executive education (Dakduk, Santalla-Banderali & van der Woude, 2018) , and to examine preservice teachers' acceptance of learning management software (Raman & Don, 2013) . Several reviews and meta-analysis that summarize empirical research have been focused on specific topics in the field of education, for example: (i) particular technology adoption model, like the meta-analysis dealing with TAM in prediction of teachers' adoption of technology (Scherer, Siddiq & Tondeur, 2019) , and the quantitative meta-analysis to identify the most commonly used external factors of TAM in the context of e-learning adoption (Abdullah & Ward, 2016) ; (ii) specific type of users, like reviews conducted to understand factors influencing academics' adoption of learning technologies (Liu, Geertshuis & Grainger, 2020) , to explore factors that affect teachers' acceptance and use of ICT in the classroom (Gamage & Tanwar, 2018) , as well as to study factors affecting students' adoption and continuation of technology use in online learning (Panigrahi, Srivastava & Sharma, 2018) ; (iii) particular technology and mode of delivery, like reviews carried on to explore factors affecting blended learning adoption and implementation in higher education (Anthony, et al. 2020) , to study technical factors affecting users' intentions to use mobile phones as learning tools (Alghazi, Wong, Kamsin, Yadegaridehkordi & Shuib, 2020) , as well as to examine the most prominent external factors affecting learning management systems (LMSs) adoption in higher educational institutions (Al-Nuaimi & Al-Emran, 2021). Besides, some theoretical work aimed to identify determinants of learning technology acceptance, but it was more focused on original constructs of reviewed technology adoption theories, like in the study conducted by Kaushik and Verma (2020) . However, to the best of authors' knowledge, currently there is hardly a holistic view of factors that affect and reliably predict successful acceptance and adoption of technology engaged in educational process. Understanding these aspects can be beneficial and can help in an improvement of both, research and educational practices. Hence, this concept-centric review aims at addressing this concern with the following two main research questions (RQs): • RQ1. Which technology acceptance and adoption theories and models are widely applied in educational research? • RQ2. Which are the most prominent predictive factors (i.e. antecedents) affecting educational technology adoption? The research scope of this systematic review is narrowed and piloted towards understanding the most recognized and applied theoretical models, as well as the most influential predictive factors affecting various technologies used to support the process of knowledge transfer and acquisition. Due to massive work worldwide, this study is used to offer succinct account of predominant predictors in educational technology adoption, and certainly cannot be all-encompassing. With the aim to filter and narrow the search, but at the same time to cover representative literature from recognised journals, the Web of Science (WoS) Current Contents Connect (CCC) database was searched. The search was not limited to a precise timespan. To denote different technology acceptance models and theories, the search was conducted using relevant terms connected with Boolean operators "OR" and "AND", specifically ("theor*" OR "model") AND ("technolog*") AND ("adoption" OR "acceptance"). To locate education related studies, ("education*" OR "learn*") search terms were joined with the aforementioned ones by means of the operator "AND". Truncation was used to cover all variations of some keywords, for example, the search term "technolog*" was used to search for literature that included the word "technology" as well as "technologies". It was searched for studies that have specified search terms in publication title (the filter "TITLE" was selected). For the purpose of this review, specified inclusion criteria enabled selection of studies that report on technology acceptance and adoption theories and models in which some type of ICT products and services to support the process of learning and teaching was used (in this context indicating all classes of technologies, interactive systems, environments, tools, applications, services, platforms, and devices). To be included, studies had to report on empirically evaluated research model and related research hypothesis. Besides, studies must be published as peer-reviewed journal articles written in English language. On the subject of exclusion criteria, studies that do not clearly and credibly describe model/theory constructs or variables, and the relationships among them, were not considered as valid to be selected and included in the analysis. In addition, theoretical studies published as peer-reviewed journal articles, specifically reviews and meta-analysis, were excluded as well. The literature search was conducted in August 2021. No time frame period was specified; 1998-2021 is the full range of the CCC database search engine. In this inquiry, 71 publications that included specified search terms in the publication title were identified. Considering only peer-reviewed journal articles written in English, the number of 67 journal and review articles was reached. Title, abstract and full text of the filtered literature were screened to ensure publication suitability and relevance. Accordingly, the qualified publications were retained and eleven unrelated ones were excluded, thus narrowing the number and leaving for further detailed analysis 56 publications. Out of 56 identified journal articles, 47 publications were found to be compliant with the purpose of this study, while 9 publications offered theoretical work which summarized empirical research focused on specific topics in educational technology acceptance and adoption. In view of the identified theoretical work, the majority of studies offered meta-analysis and reviews of Technology Acceptance Model (TAM) based studies in education (N=6), specifically (Dimitrijević & Devedžić, 2021; Granić & Marangunić, 2019; Kemp, Palmer & Strelan, 2019; Scherer et al., 2019; Al-Emran, Mezhuyev & Kamaludin, 2018; Abdullah & Ward,2016) , while just few publications addressed other acceptance models and theories, in particular Unified Theory of Acceptance and Use of Technology (UTAUT) (Bervell & Umar, 2017) , Senior Technology Exploration, Learning and Acceptance (STELA) model (Tsai, Rikard, Cotton & Shillair, 2019) , along with Straub's (2009) study in a context of informal learning which examined adoption processes through the lenses of Innovation Diffusion Theory (IDT), Concerns-Based Adoption Model (CBAM), TAM and UTAUT. The analysis of 47 publications found to be compliant with the purpose of this study is presented and discussed in the following. Considering the history of publishing, Fig. 1 shows the trend of publication frequency which started in 2003, and can be followed until 2021. The majority of studies has been published in the last decade thus reflecting an increased attention given to the researched domain. It can be noticed that there are only three identified studies in 2021, but this is connected with the fact that the search was undertaken in August 2021, and several potentially relevant articles/studies are not published yet. The interest of researchers worldwide in educational technology acceptance and adoption is evident (see Fig. 2 ). Most of the identified studies were conducted in Taiwan (N=7), followed by relevant research carried out in South Korea and USA (N=4), Spain (N=3), Canada, China, Hong Kong, Malaysia, Pakistan, Singapore and Turkey (N=2). In the rest of illustrated countries only single studies were piloted (alphabetical order): Azerbaijan, Cyprus, France, Hungary, Lebanon, Libya, Netherlands, Nigeria, Oman, Philippines, South Africa, UK, United Arab Emirates, as well as Qatar & USA. This research revealed a diversity of ICT products and services employed in educational context, here referring to all classes of technologies, interactive systems, environments, tools, applications, services, platforms, and devices used in the selected research. Considering types of technologies and modes of delivery used to support the process of learning and teaching, it is noticeable that almost half of the analysed studies (N=20) validated e-learning technologies, in selected research referred to as e-learning systems (Hanif, Jamal & Imran 2018 ), e-learning platforms (Song & Kong, 2017) , e-learning environments (Esteban-Millat, Martinez-Lopez, Pujol-Jover, Gazquez-Abad & Alegret 2018), e-learning tools (Tarhini, Hone, Liu & Tarhini 2016) , web-based learning systems (Calisir, Gumussoy, Bayraktaroglu & Karaali 2014) , Internet-based learning systems (Saade & Bahli, 2005) , or just e-learning (Abdou & Jasimuddin, 2020) . Many studies dealt with mobile learning (N=6) in which context mobile computing devices (Lai, 2020) , mobile technology and apps (Briz-Ponce & Garcia-Penalvo, 2015) , tablet personal computers (Moran, Hawkes & El Gayar, 2010) , or just m-learning (Iqbal & Bhatti, 2015) was validated. Learning Management Systems (LMSs) in general, along with specific LMSs in particular, like Blackboard (Yi & Hwang, 2003) , Moodle (Nagy, 2018) , and Moodle gamification training platform (Vanduhe, Nat & Hasan, 2020) , were also frequently researched (N=6). Besides, some studies (N=5) counted on social media services/platforms at large (Al-Rahmi, Shamsuddin, Alturki, Aldraiweesh, Yusof, Al-Rahmi & Aljeraiwi, 2021), as well as on WeChat (Yu, 2020) and YouTube (Lee & Lehto, 2013) in particular. Since educational possibilities of virtual reality (VR) and augmented reality (AR) are getting more attention, few studies (N=3) were focused on VR technology (Lin and Yeh, 2019) , VR and AR technology (Jang, Ko, Shin & Han, 2021) , while one earlier study concerned virtual world Second Life (Chow, Herold, Choo & Chan, 2012) . Use of computer technology in general was examined in a couple of studies (N=2) (e.g. Teo, 2010) , while a number of single studies considered also assistive technology (Nam, Bahn & Lee, 2013) , collaborative technology, specifically Google applications for collaborative learning (Cheung & Vogel, 2013) , simulation-based learning environment (Lemay, Morin, Bazelais & Doleck, 2018) , university communication model (UCOM) which works similar to Massive Open Online Course (MOOC) (Tawafak, Romli & Arshah, 2018) , as well as Open Educational Resources (OER) (Kelly, 2014) . Figure 3 provides insight into a variety of validated technologies and modes of delivery. Another aspect refers to different types of involved participants/users. In a great majority of analysed research (N=29) university students were the most commonly chosen sample group, since most data from web-based questionnaires and/or mailed surveys was collected from the universities (e.g. Salloum, Alhamad, Al-Emran, Monem & Shaalan, 2019; Park, 2009) . Several studies involved employees (N=7) from a variety of organizations/companies, specifically faculty & educational stakeholders (Aburagaga, Agoyi & Elgedawy, 2020) , bank officials (Abdou & Jasimuddin, 2020) , business workforce (Lee, Hsieh & Hsu, 2011) , blue-collar workers (Calisir et al., 2014) , health nurses (Chen, Yang, Tang, Huang & Yu, 2008) , along with employees from four international agencies of the United Nations (Roca, Chiu & Martinez, 2006) , as well as from four industries, specifically manufacturing, information technology, marketing and government agencies (Lee, Hsieh & Chen, 2013) . Quite a few studies engaged teachers (N=5), to be specific pre-service (Teo, 2010) and in-service teachers (Jang et al., 2021) , special education teachers (Nam et al., 2013) , as well as K-12 educators (Kelly, 2014) . A small number of research also involved other partici- (Vanduhe et al., 2020) , older adults (Lai, 2020) , and senior high school students (Prasetyo, Ong, Concepcion, Navata, Robles, Tomagos, Young, Diaz, Nadlifatin & Redi, 2021) . Finally, in one study information about the type of participants who took part in the conducted research was not provided (see Fig. 4 ). It can be seen that sample size varied from the smallest sample of 72 students (Lin & Yeh, 2019) to the largest one of 2574 students involved in the study conducted by Esteban-Millat et al. (2018) . However, the domination of smaller sample sizes up to 400 participants (N=30) compared to the number of larger sample sizes is notable. The conducted review clearly indicated that the vast majority of identified research used TAM model (N=42), in particular the core TAM (N=1), the extended TAM (N=36), along with some studies which integrated TAM with other individual models/theories aiming to advance TAM's explanatory power (N=5), in particular with: • Innovation Diffusion Theory (IDT) proposed by Rogers (1962 Rogers ( , 1995 Oliver (1980) , in work conducted by Roca, Chiu, and Martinez (2006) . Besides TAM-based research, a few studies explored also the core (N=2) and the extended (N=2) UTAUT model, along with a single research which employed extended UTAUT2 model (refer to Fig. 5 ). This study revealed that, aiming to increase the predictive validity of TAM and UTAUT, in most selected studies (N=44) the models have been extended with different predictive (antecedent) factors. In view of UTAUT model on the one hand, those factors are related to the behavioral intention (BI) variable/construct. On the other hand, when considering TAM, the majority of identified factors represent antecedents of the two core variables of TAM, perceived ease of use (PEU) and perceived usefulness (PU), while a minor number predicts behavioral intention (BI). Among selected research, only three studies have used original models without any modifications and enhancements, in particular the core TAM (Chipps, Kerr, Brysiewicz & Walters, 2015) and the core UTAUT (Lai, 2020; Yakubu & Dasuki, 2019 ). In addition, besides a variety of introduced predictors for the core TAM constructs, as well as TAM's and UTAUT's behavioral intention variable, the results exposed also a number of incorporated supplementary factors which aimed to moderate relationships among TAM's constructs. Consequently, categorization of identified factors from models' modifications and enhancements included in this review is conducted, and three pools of factors affecting educational technology adoption are documented: • antecedents of perceived ease of use (PEU) and perceived usefulness (PU), • behavioral intention (BI) antecedents, and. • moderating factors. To shed-light-on, numerous identified predictive factors are thematically grouped into: (i) user aspects (individual attributes, and pleasure & usefulness), (ii) task & technology aspects, and (iii) social aspects. The categorised antecedents of TAM Antecedents of Perceived Ease of Use and Perceived Usefulness. By analysing the selected publications, self-efficacy was found as the most widely introduced predictive factor of TAM (N=16). In various empirical studies conducted in educational context it was revealed that self-efficacy, i.e. an individual judgement of one's capability to use computer (e.g. Salloum et al., 2019; Teo, 2009) , Internet (e.g. Nagy, 2018) , m-learning (e.g. Park, Nam & Cha, 2012) , e-learning (e.g. Chen et al., 2008) or specific application (e.g. Yi & Hwang, 2003) , had a significant impact on the perceived usefulness and the perceived ease of use. Another widely researched predictive factors were subjective norm (N=9), defined as the degree to which an individual believes that people who are important to him/her think he/she should or should not perform the behavior in question, as well as perceived enjoyment (N=8) considered as the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated. It has been revealed that the subjective norm (Song & Kong, 2017) , and enjoyment (Salloum et al., 2019) , positively and significantly influence students' perceived usefulness of e-learning, as well as perceived ease of use of e-learning systems (Hanif et al., 2018; Chang, Hajiyev & Su, 2017) The results indicated that system quality (e.g. Salloum et al., 2019) and system accessibility (e.g. Park et al., 2012; Hanif et al., 2018) , along with technological complexity (e.g. Teo, 2009 ) have a significant influence on perceived ease of use. Besides, facilitating conditions, which originally provide resource factors (such as time and money needed) and technology factors regarding compatibility issues that may constrain usage, were indicated to be an essential factor that affect e-learning system (e.g. Song & Kong, 2017) or computer technology (e.g. Teo, 2009) acceptance. Finally, while the perceived playfulness, which operationalizes the question of how intrinsic motives affect the individual's acceptance of technology, had a direct impact on the variables perceived usefulness and perceived ease of use (e.g. Padilla-Melendez, del Aguila-Obra & Garrido-Moreno, 2013), anxiety as a personal trait explained as evoking anxious or emotional reactions when it comes to performing a behavior, negatively affects the two core TAM variables (e.g. Chang et al., 2017; Calisir et al., 2014) Behavioral Intention Antecedents. Both self-efficacy and subjective norm were among frequently employed factors affecting attitude towards technology and behavioral intention. The results indicated that self-efficacy was found to have a direct effect and a positive influence on behavioral intention to use e-learning (e.g. Tarhini, Hone & Liu, 2014; Yi & Hwang, 2003) , m-learning (e.g. Moran et al., 2010; Park et al., 2012) , as well as collaborative technology (e.g. Cheung & Vogel, 2013) , and computers (e.g. Nam et al., 2013; Teo, 2009) . Subjective norm, as another important construct in providing an understanding of the determinants of usage in educational context, is shown to have strong influence on the behavioral intention to use e-learning systems/platforms (e.g. Song & Kong, 2017) . It has been revealed that subjective norm represented by peers significantly moderate the relationship between attitude and intention toward the technology (Cheung & Vogel, 2013) Furthermore, perceived playfulness is found to be one of the key drivers for the adoption and use of blended learning system depending of user's gender (Padilla- Nagy, 2018 Chang et al., 2017 . Saade & Bahli, 2005 . Chang et al., 2017 . Chen et al., 2008 . Esteban-Millat et al., 2018 . Aburagaga et al., 2020 . Yu, 2020 . Park et al., 2012 . Iqbal & Bhatti, 2015 . Jang et al., 2021 . (perceived) enjoyment (N=8) (perceived) playfulness (N=3) (perceived) system accessibility (N=4) (perceived) system quality (N=3) (perceived) content quality (N=2) information quality (N=2) content richness (N=1) relative advantages (N=2) result demonstrability (N=2) confirmation (N=1) perceived mobility value (N=1) perceived e-government learning value (N=1) perception of external control (N=1) Salloum et al., 2019 . Padilla-Melendez et al., 2013 . Hanif et al., 2018 . Prasetyo et al., 2021 . Calisir et al., 2014 . Salloum et al., 2019 . Lee & Lehto, 2013 . Lee et al., 2011 . Hanif et al., 2018 . Roca et al., 2006 . Huang et al., 2007 . Shyu & Huang, 2011 . Hanif et al., 2018 . (technological) complexity (N=4) compatibility (N=3) trialability (N=2) task-technology fit (N=2) task importance (N=1) task equivocality (N=1) fidelity (N=1) vividness (N=1) user interface (N=1) perceived resource (N=1) access devices (N=1) infrastructure (N=1) Internet access factors (N=1) Teo, 2010 . Lee et al., 2011 . Lee et al., 2011 . Lee & Lehto, 2013 . Schoonenboom, 2014 . Lemay et al., 2018 . Lee & Lehto, 2013 . Prasetyo et al., 2021 . Cheung & Vogel, 2013 . Aburagaga et al., 2020 . Aburagaga et al., 2020 . Chen et al., 2008 . Table 1 Predictors of the two core TAM variables (PEU and PU) along with relevant example research Education and Information Technologies Melendez et al., 2013) . Also, direct and indirect effect of perceived playfulness on the intention to use a computer-assisted training program has been confirmed (Lin & Yeh, 2019) . Finally, the research has exposed that system accessibility was one of the dominant exogenous constructs affecting behavioral intention to use mobile learning (e.g. Park et al., 2012) Moderating Factors. Although the majority of selected research has been focused on finding PEU, PU and BI antecedents, there is also a growing need for understanding incorporated supplementary factors aiming to moderate the relationships among Song & Kong, 2017 . Song & Kong, 2017 . Vanduhe et al., 2020 . Al-Rahmi et al., 2019 . Calisir et al., 2014 . Jang et al., 2021 . Vanduhe et al., 2020 . Briz-Ponce & Garcia-Penalvo, 2015 . Chen et al., 2008 . Tarhini et al., 2016 . Aburagaga et al., 2020 . Jang et al., 2021 . TAM variables, on the one hand, as well as those which have an impact on the model itself, on the other. In the investigation of the moderating effect of gender and age on e-learning acceptance Tarhini and colleagues (2014) have found that age moderates the effect of perceived ease of use, perceived usefulness and self-efficacy on behavioral intention, and that gender moderates the effect of perceived ease of use and social norms on behavioral intention. Yet, unexpectedly, no significant moderating effect of age on the relationship between social norms and behavioral intention was found; results also revealed no moderating of gender on perceived usefulness or self-efficacy and behavioral intention. Padilla-Melendez et al. (2013) argued that there exist gender differences in attitude and intentions to use. The main contribution of their study is provided evidence that there exist gender differences in the effect of playfulness in the student attitude toward technology and the intention to use it. In females, playfulness influences attitude toward using the system. In males, playfulness influences attitude moderated by perceived usefulness When examining the moderating effect of individual-level cultural values on users' acceptance of e-learning in developing countries, Tarhini et al. (2016) demonstrated that the relationship between social norms and behavioral intention was particularly sensitive to differences in individual cultural values, with significant moderating effects observed for all studied cultural dimensions, in particular masculinity/femininity, individualism/collectivism, power distance and uncertainty avoidance. As a final point, in an empirical study of the use of the General Extended Technology Acceptance Model for E-learning (GETAMEL) to determine the factors that affect students' intention to use an e-learning system, Chang and colleagues (2017) found that technological innovation significantly moderates the relationship between subjective norm and perceived usefulness, as well as perceived usefulness and behavioral intention to use e-learning. Although TAM proved to be a powerful model applicable to various technologies and contexts at the individual level, research also revealed its successful integration with other contributing theories and models within a range of different application fields . To evaluate students' adoption of smartwatches for educational purposes, TAM has been successfully combined with Goodhue and Thompson's (1995) Task-Technology Fit (TTF) (Al-Emran, 2021), and Rogers (1975) Protection Motivation Theory (PMT) (Al-Emran, Granić, Al-Sharafi, Nisreen & Sarrab, 2021) . In addition, the Innovation Diffusion Theory (IDT) has been combined with TAM in an empirical investigation on university students' intention to use e-learning systems (Al-Rahmi et al., 2019) , to investigate factors affecting business employees' behavioral intentions to use the e-learning system (Lee et al., 2011) , as well as to explore diffusion and adoption of an open source learning platform (Huang, Wang, Yang & Shiau, 2020) . The Information Systems Success Model (ISSM), as one of the post-adoption theories, has been integrated with TAM to help in determining factors which affected acceptance of e-learning platforms during the COVID-19 pandemic (Prasetyo et al., 2021) , and in exploring students' behavioral intention to use social media, specifically the perception of their academic performance and satisfac-tion (Al-Rahmi et al., 2021) . Lastly, to understand e-learning continuance intention, TAM has been integrated with ISSM and Oliver's (1980) Expectation-Confirmation Theory (ECT) (Roca et al., 2006) . In the conducted review, specific criteria were used to search the WoS CCC database for relevant studies to be included and analysed. The applied research approach allowed to capture and cover only a representative selection of studies published in numerous recognized journals, and undoubtedly cannot be all-inclusive. Specification of other search criteria along with a selection of other databases might bring more and/or slightly different selection of relevant journal articles and proceeding papers. Hence, this review should be regarded as an attempt to explore relevant challenges and emerged topics in educational technology adoption field during the past. Finally, it should be noted that this study does not describe or pass any judgement on research methods and approaches employed in the analysed literature since this is out of the scope of this review. Over the past decades a variety of theoretical perspectives have been advanced to provide an understanding of the determinants of acceptance, adoption and usage of various technologies used to support the process of knowledge transfer and acquisition. However, it has been shown that over the years TAM has emerged as a leading scientific paradigm for studying the determinants affecting human behavior and usage of various technologies through beliefs about two factors: the perceived ease of use and the perceived usefulness (Al-Emran & Marangunić & Granić, 2015) . Moreover, the conducted review once again exposed TAM predomination in educational context as well; refer also to (Granić & Marangunić, 2019) . This study confirmed that TAM is the most widely used powerful and valid model for prediction and explanation of user's behavior towards acceptance and adoption of various technologies used to support the process of learning and teaching. Continuous development of new technologies, along with a growing number and diversity of users in educational context, opens new directions of research that could raise understanding of the technology acceptance, adoption, and actual use. Thus, despite the fact that extensive work has already been conducted, there is still a huge potential for further advancements, exploration and practice in this field of research. In light of current research findings, future work could follow new research directions: • to explore predictive validity of technology acceptance models and theories when applied to various supporting ICT technologies employed in a number of emerging teaching strategies, like flipped learning, gamification-based learning, and visual scaffolding, favourable communication support, like chats, discussion forums, and discussion boards, as well as relevant facilitative tools, like blogs and wikis used in educational context; • to further empirically validate predictive factors (antecedents) influencing the acceptance and adoption of technology in education which have not been so widely explored, for example perceived playfulness which has been associated with a high level of perceived usefulness (Lin & Yeh, 2019) , social media usage which has indicated a positive and constructive influence on satisfaction and academic performance (Al-Rahmi et al., 2021) , as well as psychological influence factors such as conformity behavior and self-esteem due to their positive and direct effect on perceived ease of use, perceived usefulness, perceived enjoyment and continuance intention (Yu, 2020 ); • to explore some possibly significant predictive factors that still have not been adequately examined, but could be important in understanding educational technology adoption as for example, the factor dealing with task & technology aspects, that can be described as cost-effective/pennyworth, here referring to employment of efficient solutions in educational context with relatively limited budget (e.g. simulation, VR, AR, visual scaffolding/visualization); • to advance the explanatory power of individual technology acceptance and adoption models by reviewing and integrating them with already established theories and models from other fields, like social psychology - Bagozzi and Warshaw's (1990) Theory of Trying (TofT), cognitive psychology - Bhattacherjee's (2001) Expectation-Confirmation Model (ECM), along with information technology - Goodhue and Thompson's (1995) Task-Technology Fit (TTF). The data of the systematic review consist of articles published in journals and conferences. Many of these are freely available online, others can be accessed for a fee or through subscription. Using the UTAUT model to understand students' usage of e-learning systems in developing countries. Education and Information Technologies The use of the UTAUT model in the adoption of e-learning technologies: An empirical study in France based banks Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors Assessing faculty's use of social network tools in Libyan higher education via a Technology Acceptance Model Evaluating the use of smartwatches for learning purposes through the integration of the Technology Acceptance Model and Task-Technology Fit Is it still valid or outdated? A bibliometric analysis of the Technology Acceptance Model and its applications from 2010 to 2020 Examining the roles of students' beliefs and security concerns for using smartwatches in higher education Technology Acceptance Model in m-learning context: A systematic review Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools Learning management systems and technology acceptance models: A systematic review. Education and Information Technologies The influence of Information System Success and Technology Acceptance Model on social media factors in education Integrating Technology Acceptance Model with Innovation Diffusion Theory: An empirical investigation on students' intention to use e-learning systems Validating Factors that impact the acceptance and usage of e-assessment by academics in Saudi universities blended learning adoption and implementation in higher education: a theoretical and systematic review Trying to consume Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research Understanding information systems continuance: An Expectation-Confirmation Model Preservice teachers' acceptance of ICT integration in the classroom: Applying the UTAUT model An empirical assessment of a Technology Acceptance Model for apps in medical education Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the Technology Acceptance Model Examining the students' behavioral intention to use e-learning in Azerbaijan? The General Extended Technology Acceptance Model for E-Learning approach Applying the technology acceptance model to explore public health nurses' intentions towards web-based learning: A cross-sectional questionnaire survey Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning Survey of university students' perceptions of learning management systems in a low-resource setting using a Technology Acceptance Model Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education Acceptance of blended learning in executive education A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation. MIT Sloan School of Management perceived usefulness, perceived ease of use, and user acceptance of information technology Information systems success: The quest for the dependent variable Utilitarian and experiential aspects in acceptance models for learning technology Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development An extension of the technology acceptance model for online learning environments Factors affecting teachers' use of ICTs in the classroom: A systematic review of the literature Task-technology fit and individual performance Technology acceptance model in educational context: A systematic literature review Extending the Technology Acceptance Model for use of e-learning systems by digital learners A derivation of factors influencing the diffusion and adoption of an open source learning platform Elucidating user behavior of mobile learning -A perspective of the extended An investigation of university student readiness towards m-learning using Technology Acceptance Model. International Review of Research in Open and Distributed Learning Augmented reality and virtual reality for learning: An examination using an extended Technology Acceptance Model Determinants of digital learning acceptance behavior: A systematic review of applied theories and implications for higher education Path analysis of educator perceptions of open educational resources using the Technology Acceptance Model. International Review of Research in Open and Distance Learning A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models Investigating older adults' decisions to use mobile devices for learning, based on the unified theory of acceptance and use of technology User acceptance of You Tube for procedural learning: An extension of the Technology Acceptance Model An investigation of employees' use of e-learning systems: applying the technology acceptance model Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting employees' intentions to use e-learning systems Modeling students' perceptions of simulation-based learning using the Technology Acceptance Model How motion-control influences a VR-supported technology for mental rotation learning: from the perspectives of playfulness, gender difference and Technology Acceptance Model Understanding academics' adoption of learning technologies: A systematic review Technology acceptance model: a literature review from 1986 to 2013 Tablet personal computer integration in higher education: Applying the Unified Theory of Acceptance and Use Technology model to understand supporting factors Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model Acceptance of assistive technology by special education teachers: A structural equation model approach Using the decomposed theory of planned behaviour to understand university students' adoption of WhatsApp in learning. E-Learning and Digital Media A cognitive model of the antecedents and consequences of satisfaction decisions Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario Online learning: Adoption, continuance, and learning outcome -A review of literature The adoption of teaching assistant robots: a technology acceptance model approach. Program-Electronic Library and Information Systems An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model Determining factors affecting acceptance of e-learning platforms during the COVID-19 pandemic: Integrating extended Technology Acceptance Model and DeLone & McLean IS Success Model preservice teachers' acceptance of learning management software: an application of the UTAUT2 Model Understanding e-learning continuance intention: An extension of the Technology Acceptance Model A protection motivation theory of fear appeals and attitude change The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model Exploring factors that influence teachers' intentions to integrate digital literacy using the decomposed theory of planned behaviour Exploring students' acceptance of e-learning through the development of a comprehensive Technology Acceptance Model The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others Elucidating usage of e-government learning: A perspective of the extended Technology Acceptance Model Investigating students' acceptance of a statistics learning platform using Technology Acceptance Model Understanding technology adoption: Theory and future directions for informal learning Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended Technology Acceptance Model Examining the moderating effect of individuallevel cultural values on users' acceptance of E-learning in developing countries: A structural equation modeling of an extended technology acceptance model Continued intention to use UCOM: Four factors for integrating with a Technology Acceptance Model to moderate the satisfaction of learning Understanding information technology usage: A test of competing models Modelling technology acceptance in education: A study of pre-service teachers A path analysis of pre-service teachers' attitudes to computer use: applying and extending the technology acceptance model in an educational context Measuring the impact of students" attitude towards adoption of online classes during COVID 19: Integrating UTAUT Model with perceived cost Senior technology exploration, learning, and acceptance (STELA) model: from exploration to use -a longitudinal randomized controlled trial continuance intentions to use gamification for training in higher education: Integrating the Technology Acceptance Model (TAM), social motivation, and Task Technology Fit (TTF) Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology User acceptance of information technology: Toward a unified view Factors affecting the adoption of e-learning technologies among higher education students in Nigeria A structural equation modelling approach. Information Development Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the Technology Acceptance Model Extending the learning Technology Acceptance Model of WeChat by adding new psychological constructs Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations The authors declare no conflicts of interest.No ethics review was required to undertake this literature review.