Unravelling risk-trust interplay in artificial intelligence-based systems for e-learning platforms: evidence from CB-SEM and fsQCA

Purpose This study aims to investigate the interplay between perceived risk and initial trust in shaping learners’ satisfaction and intention to use Artificial Intelligence-based Systems (AIS) in e-learning platforms. It addresses a key gap by theorising multidimensional risk factors and early-stage trust antecedents in AI-enabled personalised learning environments characterised by algorithmic opacity, continuous data-driven personalisation, and heightened privacy concerns, particularly in emerging economies. Design/methodology/approach Grounded in perceived risk theory and the initial trust model, the study analyses data from 371 higher-education students from an emerging economy. A complementary multi-method quantitative design combines covariance-based structural equation modelling (CB-SEM) to test hypothesised net effects with fuzzy-set qualitative comparative analysis (fsQCA) to identify configurational pathways leading to intention to use AIS. Findings CB-SEM shows that time, performance, psychological and privacy risks significantly influence satisfaction and intention to use AIS, while trust propensity and structural assurance shape initial trust. Although initial trust exhibits no direct linear effect on intention, fsQCA reveals eight equifinal configurations through which satisfaction and structural assurance compensate for elevated risk perceptions demonstrating that AIS adoption is shaped by a risk–trust–satisfaction interplay rather than a single causal pathway. Research limitations/implications While the cross-sectional design limits causal inference, the study advances IS theory by demonstrating how risk, trust and satisfaction jointly shape AIS adoption and by showing how configurational mechanisms complement net-effect explanations. Originality/value This study contributes by integrating CB-SEM and fsQCA within a complementary multi-method quantitative design and by revealing how satisfaction and structural assurance jointly compensate for risk perceptions in shaping AIS adoption.