Emotional ambivalence as an interpretive mechanism in human–computer interaction with generative artificial intelligence: evidence from PLS-SEM, regression-based machine learning, and fsQCA

Generative artificial intelligence (GenAI) is increasingly embedded in interactive digital systems, yet user adoption remains uneven because users evaluate it as both useful and uncertain. This study examines GenAI adoption as a human–computer interaction process shaped by cognitive evaluations and affective responses in academic and knowledge-intensive settings. Using survey data from 645 respondents, mainly undergraduate and postgraduate users in higher-education contexts, the study applies Partial Least Squares Structural Equation Modeling, SHAP-based machine learning, and fuzzy-set Qualitative Comparative Analysis. The findings show that attitude toward use and perceived control are the strongest proximal drivers of adoption intention. Trust and perceived AI competency influence intention mainly through attitude and perceived control, while anxiety shows a small positive direct association that should be interpreted cautiously. The study contributes by explaining mediation patterns, contradictory effects, and configurational heterogeneity in GenAI adoption.