Factors influencing professional knowledge workers’ adoption intention towards AI-driven telemedicine

Abstract

This study investigates factors influencing professional knowledge workers’ intention to adopt AI-driven telemedicine in Malaysia, addressing key gaps in understanding its acceptance. Data were collected through cross-sectional research methodology via a questionnaire from 130 respondents, and Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to evaluate the proposed model. Results indicate that openness to change significantly affects adoption intentions through both Reasons For (β = 0.451) and Reasons Against (β = 0.370), while attitude shows minimal influence. The model explains 82.5% of the variance in adoption intentions, highlighting strong relationships among openness to change, perceived benefits, perceived barriers, and adoption behavior for professional knowledge workers’ intention to adopt AI-driven telemedicine in Malaysia. However, the study’s focus on AI-driven telemedicine limits generalizability to other healthcare domains, and its cross-sectional design, along with potential social desirability bias, suggests the need for longitudinal research. Practically, the findings offer actionable insights for healthcare organizations, policymakers, and the Malaysian government to enhance telemedicine adoption through collaborative AI-driven initiatives. This research contributes originality by integrating Behavioural Reasoning Theory (BRT) and the Health Belief Model (HBM) to explain adoption behavior.