In generative artificial intelligence we trust: unpacking determinants and outcomes for cognitive trust

Abstract Amid the pervasive integration of AI technologies across societal and industrial domains, understanding users’ trust in these systems becomes increasingly crucial. This study addresses the growing need to understand users’ trust in Generative Artificial Intelligence (GenAI) and explores the societal implications of this type of trust. Based on the socio-technical systems theory, this work employs the FAT (Fairness, Accountability, Transparency) framework and humanness factors of AI, anthropomorphism, social presence, and emotions, as antecedents of users’ human-like trust, which is proposed to influence users’ attitudes, perceived performance, and behavioral intentions. Structural equation modeling analysis ( N = 244) reveals that fairness significantly enhances trust, while accountability and transparency do not. Social presence and emotions positively impact trust, whereas anthropomorphism shows no significant effect. Furthermore, trust shapes users’ attitudes, perceived performance, and behavioral intentions toward GenAI systems. This study contributes to the AI adoption and user trust literature by illuminating the main antecedents of human-like trust and showing its impact on user acceptance from a social-technical perspective. Beyond the academic contribution, this research highlights the broader societal relevance of user trust in GenAI, particularly regarding public concerns over black box issues and humanness features of GenAI systems.