Digging for acceptance theory

When you hit on a good theory, you have discovered academic gold. In this journal we regularly feature papers which have mined technology acceptance theories, in particular based on the original Technology Acceptance Model (TAM) (Davis et al., 1989), and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). These original models have drawn from other theories and both have developed into updated versions (TAM2 and Extended UTAUT) but they remain a popular place to start when digging down into how some computer technologies for learning fly and others fail.

In the beginning we had TAM. The model was designed in doctoral study by Davis in 1985 and first published in 1989 (Davis 1989), focussing on end-users of information systems. The early paper by Davis, Bagozzi & Warshaw (1989) began its abstract with the delightful statement which identified the purpose of the model: “Computer systems cannot improve organizational performance if they aren’t used” p. 982. This model made a lot of sense in that we could easily identify with the notions of Perceived Usefulness (PU) and Perceived Ease of Use (PEU) as attitudinal perceptions which might affect the outcome variable: behavioural intention to use a particular technology. This model was widely embraced in education as the foundation theory for many papers on the adoption of learning technology. Variations were proposed which looked at factors affecting the input variables of PU and PEU such as subjective norms, self-efficacy and facilitating conditions, and the relationship between Behavioural Intention as the outcome variable with actual use of the technology. TAM2 developed by Venkatesh and Davis (2000) added various factors to show details of the proposed relationships in the TAM model.

When relating the model to interactive learning, authors such as Scherer et al. (2019) suggest the TPACK (Technological Pedagogical Content Knowledge) framework (Koehler & Mishra 2005) , fills the areas left unexplored by TAM, aiming to identify the different kinds of knowledge teachers need not just to adopt but to integrate in order to introduce technology effectively into learning and teaching. Subsequent research and discussions have added detailed situational and contextual understanding to the TPACK framework (see particularly Mishra, 2019), this brings contextual knowledge including organizational constraints to bear on the adoption process. Mei et al. (2018) explored the relationship between TAM and TPACK for teachers wanting to integrate technology into their teaching.

Meanwhile in 2003 Venkatesh, together with Morris, Davis G. and Davis F.D. produced the UTAUT model based on a review of eight contributing theories, including TAM. This model drew together the input variables of performance expectancy, effort expectancy, social influence and facilitating conditions in relation to the output variable behavioural intention to use. This model also introduced moderators: gender, age, experience and the extent to which use of the technology was voluntary. Different studies using this model vary in relation to the impact of the moderators. There is also debate concerning whether facilitating conditions, including technical support for example, may or may not be a contributing variable in behavioural intention (Teo & Noyes, 2014). The extension of this UTAUT model (UTAUT2) was developed by Venkatesh et al. (2012) to incorporate the variables hedonic motivation, price value and habit when using the model in relation to consumer acceptance of new technology.

Acceptance of new technology is an overarching theme for our time, and this will affect every discipline. In relation to interactive learning environments we should consider well the implications of using a great theory originally developed in the information systems domain without questioning its specific and varying context in learning institutions. We have recently seen the huge impact of a global pandemic which has driven acceptance of learning technologies fitted to remote learning. How did this affect the model variables? Was PEU important when there were few alternatives other than that chosen by the institution? Following the pandemic impact, has this changed the way institutions make technology decisions, how are they judging PU? Further it is important not simply to dig up a great theory and force fit it to our sample. Acceptance of learning technology depends on multiple factors, not least the different standpoints and experience of teachers, learners and institutions. As we explore in detail, including in this issue, the key trends of Virtual Reality, Augmented Reality and other forms of Artificial Intelligence in learning, let’s carefully measure out the theory gold and make sure it fits the mould.