Acceptance of AI-Assisted Drug Information Systems by Pharmacists: A UTAUT-Based Cross-Sectional Study

Abstract

AI-assisted drug information systems promise faster, more comprehensive answers to medication queries, but their clinical value depends on whether pharmacists accept and use them. This study examined the determinants of pharmacists' acceptance of an AI-assisted drug information system using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A cross-sectional survey of 412 practising pharmacists across community, hospital and clinical settings measured performance expectancy, effort expectancy, social influence, facilitating conditions, trust and perceived risk against behavioural intention to use, analysed by partial least squares structural equation modelling (PLS-SEM). The model explained 68.4% of variance in behavioural intention. Performance expectancy was the strongest predictor (β = 0.34, p < .001), followed by trust (β = 0.27) and effort expectancy (β = 0.21); perceived risk was a significant negative determinant (β = −0.16). Trust partially mediated performance expectancy. Acceptance is driven primarily by perceived clinical benefit and trust, not ease of use alone.