Investigating lifelong learners’ adoption of generative artificial intelligence using PLS-SEM and fsQCA within the UTAUT2 framework

Understanding how lifelong learners adopt generative artificial intelligence (GenAI) is critical for effectively integrating these tools into lifelong education systems. Drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine GenAI adoption among 436 lifelong learners. PLS-SEM reveals that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit significantly predict behavioural intention, which in turn predicts usage behaviour. fsQCA identifies four types of configurational pathways to high behavioural intention and four to high usage behaviour, revealing concurrent factor combinations that are associated with adoption. These findings offer evidence-based guidance for integrating GenAI in lifelong education.