Editorial to the special section—Technology acceptance models: What we know and what we (still) do not know

The rapid technological advancements and the digitalization in almost all areas of our lives, including education, have turned the attention of researchers to the factors that explain a person's technology acceptance. This attention resulted in several theoretical models that describe both the behavioral intention and the use of technologies, such as the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT). Over the last three decades, the body of empirical research on these models has increased, yet abounded in contradictory findings, in particular on the generalizability and comparability of these models (Nistor, 2014; Scherer, Siddiq, & Tondeur, 2019). Some reasons for the divergent findings may lie in the cultural specificity of the factors hypothesized to explain technology acceptance and adoption, the validity of measures used to represent them and the specificity to certain technologies (Marangunić & Granić, 2015; Scherer & Teo, 2019). Given the enormous influence TAMs have and will have on the design and distribution of almost any technology in education, including learning analytics tools and collaborative environments, it is critical to bring to attention the current issues and challenges surrounding them in order to identify future needs and research directions. These goals lie in the heart of this special section—it highlights persistent findings on technology acceptance across samples, domains, technologies, countries and other contexts, identifies commonalities in and differences between TAMs and reviews the contributions of these models to teaching and learning. Specifically, the authors present empirical studies and theoretical reviews in order to (a) classify and extend the set of constructs and models of technology acceptance, (b) classify and extend samples of students and teachers, (c) explain contrary findings and (d) review overarching issues in TAMs and research (see Table 1). The first set of papers presents several empirical studies and one review is aimed at organizing the constructs and extending the models describing technology acceptance. In the extant literature, some core variables were identified that explain variation in the usage of intentions and technology use. In the TAM, these variables are perceived usefulness, perceived ease of use and attitudes toward technology (Scherer et al., 2019). In later versions of this model, external variables were added to further explain the variation in perceived usefulness and ease of use (Marangunić & Granić, 2015). These extensions describe technology acceptance as a complex process that is influenced not only by individual attitudes and perceptions but also by contextual and situational features, such as the facilitating conditions, subjective norms and technological complexity (Abdullah & Ward, 2016). However, labeled and organized differently, these variables are also contained in the UTAUT and are assumed to explain usage intentions directly (Venkatesh, Thong, & Xu, 2016). Categorizing the list of explanatory variables, Kemp, Palmer, and Strelan (2019) distinguished between the primary categories of attitude and effect, social factors, usefulness and visibility, perceived behavioral control, instructional attributes, cognitive engagement and social attributes. This framework provides a taxonomy of technology acceptance constructs that supports the development of assessments and ultimately the crafting of validity arguments of their underlying constructs. Eraslan Yalcin and Kutlu (2019) extended the list of external variables in the TAM by a measure of the interface design of a learning management system (LMS). Collecting the data from university students with different technology experiences, the authors showed that the interface design—a design feature that determines the complexity of an LMS—explained variation in both perceived usefulness and ease of use over and above subjective norms and computer self-efficacy. Sánchez-Prieto, Huang, Olmos-Migueláñez, García-Peñalvo, and Teo (2019) studied preservice teachers’ acceptance of mobile devices and extended the external variables in the TAM by the emotional attachment to mobile devices and the resistance to change. Performing structural equation modeling, the authors found that the resistance to change explained variation in almost all relevant TAM variables, while emotional attachment only explained variation in the perceived ease of use. Finally, Lemay, Doleck, and Bazelais (2019) connected college students’ social media to use their political beliefs and engagement and emphasizing the situated nature of technology acceptance. The second set of papers presents empirical studies of technology acceptance that extended or classified samples of students and teachers. Garone et al. (2019) identified subsamples of university teachers on the basis of their LMS acceptance. Their cluster analysis revealed three groups of teachers, each of which indicated different needs for professional development. Similarly, Martín-García, Martínez Abad, and Reyes-González (2019) showed how data mining procedures, such as decision trees and cluster analysis, can be utilized to identify subsamples of university teachers that would have otherwise been unobservable. In contrast, Ursavaş, Yalçın, and Bakır (2019) distinguished between directly observable groups of pre and inservice teachers. The authors highlighted the role of subjective norms in both subsamples and brought to attention that any comparison of technology acceptance measures across subsamples requires the testing of measurement invariance—a testing procedure needed to ensure meaningful group comparisons (eg, Sass & Schmitt, 2013). Finally, Yuen, Cheng, and Chan (2019) extended their study of secondary school students’ LMS acceptance by a longitudinal component. This extension allowed them to examine the growth and stability of technology beliefs and usage over time. The third set of papers presents empirical studies that were aimed at explaining some of the contradictory findings in the technology acceptance research. Liu, Wang, and Koehler (2019) addressed probably one of the most discussed findings, that is, the oftentimes missing link between usage intentions and technology use. Differentiating between student- and teacher-centered usages of educational technology, the authors provided one possible explanation and further extended it by considering technological pedagogical content knowledge, facilitating conditions and experience with technology as moderating factors. Nistor, Stanciu, Lerche, and Kiel (2019) proposed attitude strength as a relevant technology acceptance variable and showed that indirect effects of attitude strength on usage intentions existed via different types of expectancies. The latter explained the missing intention-use link in their study. Finally, Granić and Marangunić (2019) took some overarching perspectives on TAMs and research and reviewed the state-of-the-art in education. Their systematic review revealed that the TAM dominated the research on technology acceptance and most empirical studies focused on the original TAM, yet not its extensions by external variables. Moreover, the core variables, perceived usefulness and ease of use, were consistent predictors of usage intentions or technology use. At the same time, the authors pointed to the lack of testing the generalizability of the TAM across study contexts as a major shortcoming in this research area. The scope and diversity of the papers in this special section are innovative and forward-looking by providing a clear and sound rationale for continuing research in technology acceptance. Moving on, the editors encourage all authors and readers to recognize and exploit the immense possibilities in research disciplines in which technology acceptance is less often associated, with an aim to deepen its impact and extend its influence on the study of phenomena hitherto unreported in the literature.