Beyond the Hype: Drivers and Barriers to Augmented Reality Use in University Teaching

Abstract

The COVID-19 pandemic accelerated the shift toward digital teaching in universities and renewed interest in Augmented Reality (AR). AR technology overlays digital content onto the physical environment, offering opportunities to make learning more interactive and engaging. Research suggests that such experiences can increase student motivation and improve learning outcomes. Despite this potential, adoption of AR in higher education remains low. This thesis investigates the factors that influence academics' decisions to adopt AR and provides practical guidance to support its implementation. The primary goal of this research is to identify and examine the factors that influence academics' acceptance of AR in higher education, with attention to cross-cultural differences and the post-COVID-19 context. The main research question asks: what factors are most likely to influence the acceptance and use of AR applications by academics in higher education institutions in the post-COVID-19 period? Four sub-questions address the roles of personal characteristics, cross-national differences between Australia and Saudi Arabia, qualitative reasons behind academics' attitudes, and the effectiveness of practical adoption resources. The research employed a sequential explanatory mixed-methods design organised into three phases. Phase 1 began with a pilot study involving 35 academics from Australia and Saudi Arabia. The aim was to develop and validate a survey instrument based on an extended version of the UTAUT2 framework, referred to as UTAUT2-ARHE. The price value construct was excluded because it lacks relevance in educational settings where technology costs are typically borne by institutions rather than individual users (Ain et al., 2016). Two constructs were introduced to the model: content quality and resistance to change. These additions captured pedagogical and dispositional concerns that academics commonly raise when considering AR adoption. The refined survey was completed by 436 academics from universities worldwide. Three quantitative analyses were conducted. First, a descriptive analysis was performed to provide context for subsequent analyses and for Phase 2 qualitative analysis. Second, a global analysis using covariance-based structural equation modelling (CB-SEM) identified performance expectancy, effort expectancy, social influence, hedonic motivation, and content quality as significant predictors of intention to use AR. The model explained 66.3% of the variance in behavioural intention. Facilitating conditions and resistance to change showed no direct effect on intention at the global level. Third, a comparative analysis using partial least squares structural equation modelling (PLS-SEM) examined sub-samples from Australia (n=157) and Saudi Arabia (n=203). Performance expectancy, effort expectancy, and hedonic motivation predicted intention in both countries. However, social influence and content quality were significant predictors only in the Saudi Arabian sample. The effect of hedonic motivation on intention was also stronger among Saudi academics. Phase 2 integrated quantitative and qualitative approaches. PLS-SEM analysis revealed that facilitating conditions and resistance to change operated through indirect pathways. Facilitating conditions positively influenced perceptions of both performance expectancy and effort expectancy, while resistance to change negatively affected effort expectancy. Age emerged as a moderator of the relationship between facilitating conditions and intention. Two focus groups involving 12 academics provided qualitative insights that supported and enriched the quantitative findings. Participants recognised AR as a valuable tool for increasing student engagement and clarifying complex concepts. They also identified barriers including limited institutional support, poorly targeted training programmes, and established teaching routines. Several participants noted that the pandemic had made them more receptive to experimenting with new technologies. Phase 3 translated these findings (from Phase 1 and 2) into practice. Two resources were developed: an Educators' Checklist for Implementing AR and a Summary of Managerial Guidance to facilitate AR Adoption. These tools were evaluated through an online survey completed by 53 academics. The evaluation assessed perceived usefulness, clarity, and practical applicability. The UTAUT2-ARHE model explained 66.3% of the variance in behavioural intention. Performance expectancy, effort expectancy, social influence, hedonic motivation, and content quality emerged as significant direct predictors. However, facilitating conditions and resistance to change had indirect effect on behavioural intention. This thesis contributes to technology acceptance research by confirming core theoretical propositions and extending them to the AR context in higher education. The findings indicate that academics are more likely to adopt AR when they perceive it as useful and enjoyable. However, positive intentions alone are insufficient. Successful adoption requires institutional commitment, training that addresses discipline-specific needs, and access to high-quality content aligned with curriculum objectives. In the absence of these supporting conditions, AR is likely to remain underutilised in university teaching.