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

Abstract

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).