Keywords

Introduction

More than a half-century of research has resulted in the development of a vast number of adoption theories and technology acceptance models, along with a plethora of their extensions and modifications. To test their applicability and enhance their predictive validity, established theories and models have been widely used to facilitate assessment of diverse information and communication technology (ICT) products and services, including all kinds of technologies, systems, environments, tools, applications, services, and devices. In general, technology adoption is a term that refers to the acceptance, integration, and embracement of new technology. Technology acceptance, as the first step of technology adoption, is an attitude toward technology, and it is influenced by various factors. According to the Innovation Diffusion Theory (IDT) (Rogers, 1962, 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 toward adoption of various technologies.

An area of great interest in incorporating new technologies is the educational field. Educational settings involve a great variety of potential users of various ICT technologies embraced in the process of learning, teaching, and assessment. Thus, technology acceptance and adoption theories and models are often used to inform research in the context of education. Some of the most influential theoretical approaches involve (listed in chronological order) IDT (Rogers, 1962, 1995), Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991), Decomposed Theory of Planned Behavior (DTPB) (Taylor & Todd, 1995), Technology Acceptance Model (TAM) (Davis, 1986, 1989), Motivational Model (MM) (Davis, Bagozzi, & Warshaw, 1992), and Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003), along with extended UTAUT (UTAUT2) (Venkatesh, Thong, & Xu, 2012).

This chapter offers a comprehensive and up-to-date insight into main research findings in the area of educational technology acceptance and adoption. The chapter is divided into four sections. The second section provides a brief overview of basic concepts of major theories and models used in the educational field, along with some sample contributions. Most important research themes and findings, together with new developments in educational technology acceptance research, are introduced in the third section. The last section offers concluding remarks and further research directions.

Technology Acceptance Theories and Models in Educational Contexts

Over the past decades, a diversity of theoretical perspectives has been put forward to provide an understanding of the determinants of usage and adoption of different technologies to support the process of learning, teaching, and assessment. For example, IDT, proposed by Rogers (1962, 1995), is the oldest and very popular theory of adoption of innovations among individuals and organizations. In this context, “innovation” can be anything that is perceived as new from the perspective of the adopters and may be described by five characteristics: relative advantage, compatibility, complexity, traceability, and observability. IDT was used, for example, to identify the factors influencing the use of Moodle as a learning management systems (LMS) in the academic context (Pinho, Franco, & Mendes, 2021) and to investigate potential factors influencing students’ behavioral intentions to use e-learning systems (Al-Rahmi et al., 2019).

Davis et al. (1992) applied the motivational theory to study information technology adoption and use. MM, based on the psychological aspects of technology acceptance, hypothesizes that the individual’s behavior and her/his technology acceptance and usage are influenced by intrinsic and extrinsic motivation. An example of intrinsic motivation is perceived enjoyment, while perceived usefulness, perceived ease of use, and subjective norm can be considered as examples of extrinsic motivation. Accordingly, MM was employed to explore intrinsic (effort expectancy, anxiety, and attitude toward e-learning) and extrinsic (performance expectancy, social influence, and facilitating conditions) motivators aiming to explain why employees might accept the e-learning technology in the workplace (Yoo, Han, & Huang, 2012).

DTPB, introduced by Taylor and Todd (1995), suggests that the three predictors of the behavior intention and actual behavior adoption are attitude, subjective norms, and perceived behavior control. This model promotes decomposed belief structures since attitudinal, normative, as well as control beliefs are additionally decomposed into multidimensional belief constructs. DTPB was used, for example, to examine factors that impact the acceptance and usage of e-assessment by academics, specifically attitude (perceived ease to use, perceived usefulness, and compatibility), subjective norm (peer influence and superior influence), as well as perceived behavioral control factors (self-efficacy, resource-facilitating conditions, and information technology support) (Alruwais, Wills, & Wald, 2017).

Basic concepts of two major technology acceptance models and theories used in educational contexts, namely, UTAUT and TAM, are introduced in this section. To illustrate their broad potential and applicability, some relevant sample research is presented as well.

Technology Acceptance Model (TAM)

Theory of Reasoned Action (TRA)

Originating in the psychology-based Theory of Reasoned Action (TRA), TAM proposed by Davis (1986, 1989) has evolved to become the key model in understanding predictors of human behavior toward potential acceptance or rejection of technology in general and learning technology in particular. Assuming that individuals are usually rational, Fishbein and Ajzen (1975) developed TRA to predict and understand behaviors and attitudes. TRA suggests that a main predictor of behavior is an individual’s behavioral intention, while an individual’s intention is jointly determined by her/his attitude toward performing the behavior (attitude), as well as perceived social influence of people who are important to the individual (subjective norms). Behavioral intention has typically been defined as an individual’s subjective probability that he/she will perform a specified behavior and attitude as an individual’s degree of evaluative affect toward the target behavior, while subjective norm refers to the person’s perception that most people who are important to him/her think he/she should or should not perform the behavior in question (Davis, 1986).

Emergence of TAM

To develop a reliable model which could predict actual use of any technology, Davis adapted TRA since he considered attitudes, rather than behavioral intentions, as the main predictors of behavior. Davis suggested that the user’s motivation can be explained by three factors, in particular perceived ease of use, perceived usefulness, and attitude toward using. Thus, TAM specifies two beliefs, perceived usefulness and perceived ease of use, as determinants of attitude toward usage intentions and actual technology usage.

In his doctoral dissertation version of TAM, Davis (1986) hypothesizes that the attitude of a user toward the system (attitude toward using) is a major determinant of whether the user will use or reject the system (actual system use). The attitude of the user, in turn, is considered to be influenced by two major beliefs, perceived usefulness and perceived ease of use, with the perceived ease of use having a direct influence on the perceived usefulness. Perceived usefulness is defined as the degree to which the person believes that using the particular system would enhance her/his job performance, while the perceived ease of use is defined as the degree to which the person believes that using the particular system will be free of effort (Davis, 1986). Finally, both beliefs are hypothesized to be directly influenced by the system design characteristics.

Modifications and Extensions of TAM

During later stages, TAM was modified and extended to include new factors with significant influence on its two core variables. The strength of TAM and its many different versions that extend/modify the original model by simply adding other constructs (called “TAM++,” cf. Benbasat & Barki, 2007) is confirmed by numerous studies emphasizing its broad applicability to various technologies, users, and contexts.

TAM’s core variables, perceived ease of use and perceived usefulness, have been proven many times to be sound predictive factors that have affected acceptance of learning with technology as well. The intentions of a user toward using learning technology in a vast majority of research were explained by using or extending the TAM research model with numerous relevant constructs (predictive factors). While, for example, Farahat (2012), along with Chipps, Kerr, Brysiewicz, and Walters (2015), has tested application of the original (core) TAM in educational areas, Yu (2020) extended TAM with perceived enjoyment and two psychological constructs, conformity behavior and self-esteem, in order to test the acceptance of WeChat use in language learning, Lin and Yeh (2019) added perceived playfulness as an intrinsic motivator to explore the acceptance of virtual reality (VR) motion control technology for mental rotation learning, while Aburagaga, Agoyi, and Elgedawy (2020) used the extended TAM model to assess the faculty needs for adopting social networks into educational settings (used constructs: privacy, infrastructure, institutional support, and access devices).

Prevalence of TAM in Educational Technology Adoption

Overall, research has revealed that TAM is the most widely used powerful and valid model for prediction and explanation of user’s behavior toward acceptance and adoption of educational technology (Abdullah & Ward, 2016; Granić & Marangunić, 2019). Empirical evidence for the predictive validity of TAM has been provided in numerous educational technology acceptance studies, with the most recent research addressing electronic learning (Prasetyo et al., 2021), mobile learning (Lai, 2020), personal learning environments (PLEs) (Rejón-Guardia, Polo-Peña, & Maraver-Tarifa, 2020), virtual reality environments (VLEs) (Fussell & Truong, 2021), massive open online courses (MOOCs) (Al-Adwan, 2020), and learning management systems (LMSs) in general (Dampson, 2021), as well as, for example, open-source LMS Moodle (Vanduhe, Nat, & Hasan, 2020) and commercial LMS Blackboard (Ibrahim et al., 2017) in particular.

Furthermore, various acceptance studies have explored TAM’s applicability for different supportive facilitating technologies used in education, ranging from social media platforms (Al-Rahmi et al., 2021; Yu, 2020) to the technology aimed at helping the learning process through teaching assistant robots (Park & Kwon, 2016), simulators (Lemay, Morin, Bazelais, & Doleck, 2018), virtual reality (Lin & Yeh, 2019), and augmented reality technologies (Jang, Ko, Shin, & Han, 2021), among very many others.

Unified Theory of Acceptance and Use of Technology (UTAUT)

Emergence of UTAUT

Venkatesh et al. (2003) revised existing theories and models on acceptances of new technologies and proposed UTAUT by reviewing and integrating eight previously established user acceptance models, i.e., Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), TAM, MM, TPB, augmented TAM (A-TAM) (Davis, 1986, 1989; Davis, Bagozzi & Warshaw, 1989), Model of Personal Computer Utilization (MPCU) (Thompson, Higgins & Howell, 1991), IDT, and Social Cognitive Theory (SCT) (Bandura, 1986). Three main constructs that directly determine behavioral intention are proposed, namely, performance expectancy, effort expectancy, and social influences. Besides, behavioral intention and facilitating conditions are foreseen as predictors of actual behavior (usage). Accordingly, the core UTAUT (i.e., the four principal determinants of intention and usage) was used, for example, to explore the factors that influence preservice teachers’ acceptance of ICT integration in the classroom (Birch & Irvine, 2009), as well as to evaluate students’ e-learning acceptance in a postgraduate program (Mahande & Malago, 2019), and students’ usage of e-learning systems in developing countries (Abbad, 2021).

Usually, the original model is extended by simply adding additional constructs. For instance, to examine core factors affecting the university students’ attitude toward adoption of online classes during COVID-19 (Tiwari, 2020), the UTAUT model was modified with single construct perceived cost. Yet, a majority of studies extended the core model with multiple constructs. For example, in the investigation of university students’ behavioral intentions toward using m-learning in higher education, UTAUT was extended by incorporating the constructs of mobile self-efficacy, perceived enjoyment, satisfaction, and trust (Chao, 2019). In another study, the UTAUT model was applied to examine the effects of different factors that were identified from the literature on students’ acceptance of mobile learning applications in higher education, in particular perceived information quality, perceived compatibility, perceived trust, perceived awareness, availability of resources, self-efficacy, and perceived security (Almaiah, Alamri, & Al-Rahmi, 2019).

UTAUT2 as an Extension of UTAUT

Paying particular attention to the consumer use context, Venkatesh et al. (2012) extended the original UTAUT and developed UTAUT2. To formulate UTAUT2, they added three new constructs that directly determine behavioral intention, in particular hedonic motivation, price value, and habit. Also, besides behavioral intention and facilitating conditions, the model also postulated habit as an additional predictor of usage. Extended UTAUT (UTAUT2) was used, for instance, to evaluate acceptance of blended learning in executive student education (Dakduk, Santalla-Banderali, & van der Woude, 2018), as well as to explore preservice teachers’ acceptance of learning management software (Raman & Don, 2013). However, the construct price value is excluded in both studies since it has been used only to study consumer behavior in other technological conditions like e-commerce, e-banking, or online payment.

As already mentioned, TAM and UTAUT are the two most prominent theoretical approaches in educational contexts. Comparison of the two models is out of the scope of this chapter, but as concluding remarks of this section, few aspects should be noted. On the one hand, for practical, predictive applications of the model, fewest possible but still effective numbers of constructs/factors could be of great importance (called model’s parsimony). Evidently, TAM’s parsimony has been proven to be valid and powerful approach to explain technology acceptance. On the other hand, to obtain the most complete understanding of the validated technology, the level of parsimony may be sacrificed (Samaradiwakara & Gunawardena, 2014). It has been shown that UTAUT is rich in explaining behavioral intention and usage of technology. However, despite its good explanatory ability, it has been criticized for having too many independent constructs for predicting intentions and behavior (Bagozzi, 2007).

Major Findings in Educational Technology Acceptance Research

Educational settings involve a wide range of potential users of ICT products and services used to support the process of knowledge transfer and acquisition; thus, technology adoption investigation is often used to inform educational research. Major research themes and findings, along with recent developments in the field of educational technology acceptance and adoption, are given in the following.

Major Research Themes and Findings

Educational Technologies Validated and Users Involved

When it comes to the use of variety of technologies, it can be noted that the majority of acceptance studies in educational areas validated e-learning modes of delivery, referred to as e-learning systems, e-learning platforms, e-learning environments, and e-learning tools or just denoted as e-learning. Many studies also addressed mobile learning in which context mobile computing devices, mobile technology and applications, tablet personal computers, or just m-learning was considered.

Learning management systems (LMSs) in general, along with specific LMSs in particular, such as Blackboard and Moodle were also frequently researched. Besides, various studies in educational contexts counted on support of social media services and platforms at large, for example, WeChat and YouTube in particular. Educational affordances of virtual reality (VR) and augmented reality (AR) are attracting increasing attention; thus, several studies focus on VR, AR, and mixed reality technologies. Examination of technology acceptance work in the educational domain also includes validation of technology for collaborative learning, simulation-based learning environments, massive open online courses (MOOCs), as well as open educational resources (OER).

Regarding the type of users, in a great majority of research, university students were the most commonly chosen sample group (Abdullah & Ward, 2016; Granić & Marangunić, 2019). Various studies also involved employees from different types of organizations and companies, university teaching staff, as well as teachers from preservice, in-service, and special education.

Most Researched Predictors of Technology Acceptance

The research in educational technology acceptance and adoption has revealed that the great majority of acceptance studies use TAM (cf. Al-Emran & Granić, 2021; Granić & Marangunić, 2019), but an employment of UTAUT model is also well accepted, albeit in a considerably smaller number of studies. Besides, aiming to increase the predictive validity of TAM and UTAUT, the models have usually been extended with different predictive factors. When using UTAUT model, those factors are related to behavioral intention, while when using TAM the majority of factors represent predictors of the two core variables, perceived ease of use and perceived usefulness, with a small number predicting behavioral intention.

Numerous empirical studies conducted in educational contexts have revealed that self-efficacy, i.e., an individual judgment of one’s capability to use a specific technology, has a significant impact on the perceived usefulness and perceived ease of use. In addition, as one of the most commonly used predictors, self-efficacy was found to have a direct effect and a positive influence on behavioral intention to use e-learning, m-learning, and computers in educational settings in general.

Other widely researched predictive factors are subjective norm, defined as the degree to which an individual believes that people who are important to her/him think she/he should perform the behavior in question, as well as perceived enjoyment referring to the extent to which the activity of using a technology is perceived to be enjoyable in its own right. It has been revealed that subjective norm and enjoyment positively influence students’ perceived usefulness and perceived ease of use of e-learning systems. Besides, subjective norm, as an important construct in providing an understanding of the determinants of usage in educational contexts, is shown to have a strong influence on the behavioral intention to use e-learning systems and platforms. Another predictor dealing with societal aspects which significantly affects learning technology adoption (m-learning) is social influence, the degree to which an individual perceives that others believe that he or she should use the new system.

Furthermore, perceived playfulness is found to be one of the key drivers for the adoption and use of blended learning as well as computer-assisted training programs. While perceived playfulness, which questions how intrinsic motivation affects an individual’s acceptance of technology, has a direct impact on the variables of perceived usefulness and perceived ease of use, 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.

System quality and system accessibility, along with facilitating conditions which originally provide resource factors (such as time and money needed) and technology factors regarding compatibility issues that may constrain usage, are found to be essential factors that affect technology acceptance as well.

New Developments in Educational Technology Adoption

TAM and UTAUT have attracted significant attention in educational technology adoption research. To cover all significant components in determining technology adoption in educational settings, these models have been widely extended, as mentioned earlier, with other factors which have improved their overall predictability power. Furthermore, although both models proved to be applicable to various technologies and educational contexts at individual level, research has also revealed their successful integration with other relevant approaches from other fields.

Integration of TAM

Although TAM has proved to be a powerful model applicable to a variety of technologies and contexts at the individual level, research also reveals its successful integration with other contributing theories and models within a range of different application fields (Al-Emran & Granić, 2021). To advance the model’s explanatory power in the educational research, TAM has been integrated with other technology adoption (e.g., IDT and TPB) and post-adoption (e.g., Information Systems Success Model (ISSM) and ECT) theories and models, as well as with a number of additional approaches, for example, Task-Technology Fit (TTF), Protection Motivation Theory (PMT), and System Usability Scale (SUS), among many others. The integration of TAM with the aforementioned approaches together with related sample research is presented below.

IDT, as the most popular model in investigating innovation acceptance and adoption, was integrated with TAM to empirically explore university students’ intention to use e-learning systems (Al-Rahmi et al., 2019), investigate factors affecting business employees’ behavioral intentions to use the e-learning system (Lee, Hsieh, & Hsu, 2011), as well as explore diffusion and adoption of an open-source learning platform (Huang, Wang, Yang, & Shiau, 2020).

TPB (Ajzen, 1985, 1991), an extension of TRA which asserts that behavior is a direct function of behavioral intention and perceived behavioral control, was used with TAM to explain how perceptions influence m-learning adoption among university students (Gómez-Ramirez, Valencia-Arias, & Duque, 2019).

ISSM, introduced by DeLone and McLean (1992) as a robust theoretical basis for the study of technology post-adoption, was combined with TAM in a couple of recent studies, in particular to help determine 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 satisfaction (Al-Rahmi et al., 2021).

ECT, a post-adoption theory offered by Oliver (1980), was integrated with two other theories, TAM and ISSM, to understand and identify several attributes as likely predictors of e-learning continuance intention (Roca, Chiu, & Martinez, 2006).

TTF, a theoretical model proposed by Goodhue and Thompson (1995) which asserts that for information technology to have a positive impact on individual performance the technology must be utilized and must be a good fit with the tasks it supports, was used with TAM to explore the students’ behavioral intention to adopt smartwatches for learning activities (Al-Emran, 2021), as well as to explore continuance intention to use massive open online courses (MOOCs) (Wu & Chen, 2017).

PMT, a theory postulated by Rogers (1975) and considered as a special case of a more general category of theories that employ “expectancy” and “value” constructs, was integrated with TAM to study students’ behavioral intention to adopt smartwatch devices in learning activities (Al-Emran, Granić, Al-Sharafi, Nisreen, & Sarrab, 2021).

SUS, a reliable and low-cost attitude scale from the field of human-computer interaction (HCI) developed by Brooke (1986) and used for subjective assessments of technology usability (i.e., its ease of use in a particular context), was combined with TAM to evaluate perceived usability of the online learning platforms during COVID-19 (Pal & Vanijja, 2020).

Integration of UTAUT

UTAUT integration/enhancement with further models is mostly related to users’ continuance intention of using mobile banking and payment, or to employees’ adoption of e-government, but seldom used in education. In such a context, usually representative post-adoption theories and models have been considered. Some contributing theories along with relevant sample research are offered in the following:

Technology Acceptance Model (TAM), an extensively used powerful model in technology acceptance and adoption research in general, was integrated with UTAUT to explore and explain predictive factors that influence preservice teachers’ intention to use learning management system (LMS) in developing countries (Buabeng-Andoh & Baah, 2020).

Expectation-Confirmation Theory (ECT) (Oliver, 1980), a leading cognitive theory in the area of consumer satisfaction which seeks to explain post-purchase or post-adoption satisfaction as a function of expectations, perceived performance, and disconfirmation of beliefs, was used in a most recent study which extended UTAUT with the aim of exploring students’ perspectives regarding the acceptance of mobile learning in higher education (Alowayr & Al-Azawei, 2021).

Conclusion and Further Research Directions

Due to continuous development of new technologies, there is still a huge potential for further advancement, exploration, and practice in the field of educational technology adoption, despite the fact that extensive work has already been conducted. In light of current research findings, future work could follow new research directions listed below:

To explore predictive validity of technology acceptance models and theories when applied to various supporting ICT technologies employed in:

  • Emerging teaching strategies, for example, flipped learning, an active teaching-learning approach which has proved to motivate students to engage in out-of-classroom activities, as well as gamification-based learning, another approach to facilitating students’ participation and proactive behaviors

  • Encouraging communication support, for example, the broadly used online discussion forums and discussion boards, which extend the learning space beyond the classroom and provide asynchronous opportunities for peer-to-peer collaborations

To empirically validate predictive factors, i.e., determinants, influencing the acceptance and adoption of technology in education which have not been widely explored, for example, psychological influence factors such as:

  • Flow, perceived as an intrinsic motivation and a holistic experience of an individual when involved in the action

  • Conformity behavior, seen as the behavior that individuals tend to follow others or the phenomenon that their behaviors are greatly influenced by others

  • Self-esteem, understood as a sort of attitude toward an individual’s general subjective emotional assessment of her/his own value

To advance the explanatory power of individual technology acceptance and adoption models by considering contributions from established theories and models from other fields, for example:

  • Social psychology – Bagozzi and Warshaw’s (1990) Theory of Trying (TofT): Since intentions do not always lead to a specific action, the criterion of behavior in TRA is replaced with trying to reach a goal.

  • Positive psychology – Seligman’s (2011) PERMA Theory: As positive psychology is about the concept of well-being, the theory postulates five relevant elements: positive emotion, engagement, relationships, meaning, and accomplishment (PERMA).

  • Information technology – Thompson et al.’s (1991) MPCU: Due to intensive spread of information technologies, Triandis’ (1977) Theory of Interpersonal Behavior (TIB) is adapted and refined for ICT contexts and used to forecast individual acceptance and personal computer (PC) utilization.