Are students ready for robots in higher education? Examining the adoption of robots by integrating UTAUT2 and TTF using a hybrid SEM-ANN approach

Robots have been heavily utilized in many industries, including education. However, the determinants driving robot adoption for educational purposes, particularly in developing countries, are not yet well understood. Additionally, the existing literature does not examine the fitness between robot capabilities and education-related tasks. Therefore, this research integrates the extended unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF) to examine robot adoption in higher education. Based on 177 responses collected from university students, the proposed model is verified using a hybrid structural equation modeling and artificial neural network (SEM-ANN) approach. The findings indicated that effort expectancy is positively affected by individual technology fit and task technology fit. Further, performance expectancy is significantly driven by task technology fit, but not individual technology fit. The results also supported the role of performance expectancy, social influence, facilitating conditions, and hedonic motivation in affecting behavioral intention, with a variance of 65% in the latter. With a normalized value of 94.5%, the ANN results revealed that social influence is the most important factor affecting robot adoption. These empirical findings provide several theoretical contributions and will help higher education institutions to promote students' adoption of robots while enhancing their practical value.