Digital learning anxiety and UTAUT predictors of university students’ intention to use generative artificial intelligence in Ghana

This study investigates the factors associated with university students’ intention to use Generative Artificial Intelligence (GenAI) in Ghana, with particular attention to the role of psychological barriers in shaping technology use intention. This study extends the UTAUT model by incorporating the concept of digital learning anxiety in understanding the psychological dimension of students using generative AI. The study employed a quantitative approach using a cross-sectional survey design, with data analyzed through covariance-based structural equation modeling (CB-SEM). The sample consisted of 350 university students in Ghana. Structural equation modeling and a bias-corrected percentile bootstrapping method were conducted in AMOS (v 23) to estimate the path coefficients. The results showed that performance expectancy and effort expectancy were positively associated with students’ intention to use GenAI. Additionally, a significant but small indirect association was found between effort expectancy and behavioral intention through digital learning anxiety. However, the findings indicate the insignificant influence of social influence. This study contributes to the literature by providing context-specific evidence from Ghanaian higher education on the role of Digital Learning Anxiety in students’ intention to use GenAI. However, because the study used cross-sectional data, this indirect relationship should be interpreted as an association rather than evidence of a causal mechanism. The findings suggest that students’ intention to use GenAI is associated with both their perceptions of the technology and their psychological responses to its use. These findings provide context-specific evidence on GenAI acceptance among the students surveyed.