Analyzing the Adoption Intention of Artificial Intelligence and Robotics to Enhance the Quality of Work-Life Among Healthcare Professionals: A Hybrid SEM-ANN Approach

Abstract

The COVID-19 pandemic has accelerated the adoption of robotics and Artificial Intelligence (AI) in healthcare, offering both opportunities and challenges. This study explores factors influencing healthcare professionals' intention to adopt such technologies and their effects on work-life quality. Guided by the cognitive-affective-conative framework, a conceptual model was tested using data from 328 professionals via a hybrid Structural Equation Modelling (SEM) and Artificial Neural Network (ANN) approach. Results show that utilitarian value, control beliefs, and anthropomorphism positively affect repatronage intention, while perceived risks and ethical dilemmas exert a negative influence. Social presence was not a significant factor. Interaction comfort moderates the link between repatronage intention and work-life quality. Utilitarian value emerged as the strongest predictor. The study offers practical insights for healthcare administrators on supporting technological adoption and contributes to the literature by applying a novel SEM-ANN approach to uncover non-linear dynamics in digital adoption behaviour.