Predicting Technology Acceptance: Basic Psychological Needs as Important Determinants of Intentions to Use an AI Assistant

This study examines whether Basic Psychological Need (BPN) satisfaction, a core construct of Self-Determination Theory in motivational psychology, predicts acceptance of AI assistants. Satisfaction of the three basic psychological needs — autonomy, competence, and relatedness — has been shown to foster motivation, engagement, and well-being in domains such as work and education. Although BPN satisfaction has received some attention in technology acceptance research, its relevance may still be underestimated given the motivational nature of technology adoption and use. Using data from an online study with 883 participants, we investigated the predictive validity of BPN satisfaction for intention to use (ITU) an AI assistant for personal banking. Further, we assessed the predictive validity of BPN satisfaction in comparison with and in addition to the two core predictors of ITU in the classic Technology Acceptance Model (TAM): perceived usefulness and ease of use. Hierarchical linear regression analyses showed that BPN satisfaction explained substantial variance in ITU (70%), comparable to perceived usefulness and ease of use (73%), and accounted for additional variance beyond these two established predictors. Our findings identify BPN satisfaction as an important complement to established technology acceptance models and highlight its relevance for human-computer interaction research and the design of AI systems.