Exploring factors in AI-based healthcare services adoption: integrating cognitive, social and technical perspectives

Purpose As artificial intelligence (AI) integrates into healthcare, understanding the factors influencing its adoption becomes increasingly pivotal. Healthcare’s distinctive context requires considering both AI emotional interaction capabilities and users’ cognitive abilities. Social influences also shape behavior through interpersonal interaction. Additionally, despite trust being widely recognized as critical, its mediating role between cognitive, technical and social factors and the adoption of AI-based healthcare remains insufficiently explored. This study develops an integrated “cognitive-technical-social” framework, aiming to systematically examine the mechanisms through which these factors influence trust in AI and adoption intentions. Design/methodology/approach Drawing on data from 319 valid questionnaires, this research employs partial least squares structural equation modeling (PLS-SEM) to test the proposed relationships. Findings The results reveal that social influence, digital technology self-efficacy and AI empathy significantly and positively affect both trust in AI and adoption intentions. AI literacy also positively influences adoption intentions. Moreover, trust in AI partially mediates the impact of social influence and AI empathy on adoption intentions, highlighting the critical role of trust in facilitating user adoption of AI-based healthcare services. Originality/value By integrating cognitive, technical and social dimensions into a theoretical framework, this research offers novel insights into the mechanisms of AI acceptance within healthcare settings. The results also provide actionable guidance for healthcare professionals and AI developers seeking to design more effective, empathetic and socially supported AI-based healthcare solutions.