Using Structural Equation Modeling to Explore Patients’ and Healthcare Professionals’ Expectations and Attitudes Towards Socially Assistive Humanoid Robots in Nursing and Care Routine

Abstract

AbstractHealthcare systems around the world are currently witnessing various challenges, including population aging and workforce shortages. As a result, the existing, overworked staff are struggling to meet the ever-increasing demands and provide the desired quality of care. One of the promising technological solutions that could complement the human workforce and alleviate some of their workload, are socially assistive humanoid robots. However, despite their potential, the implementation of socially assistive humanoid robots is often challenging due to low acceptance among key stakeholders, namely, patients and healthcare professionals. Hence, the present study first investigated the extent to which these stakeholders accept the use of socially assistive humanoid robots in nursing and care routine, and second, explored the characteristics that contribute to higher/lower acceptance within these groups, with a particular emphasis on demographic variables, technology expectations, ethical acceptability, and negative attitudes. In study 1, conducted on a sample of 490 healthcare professionals, the results of structural equation modeling showed that acceptance is driven primarily by aspects of ethical acceptability, although education and technology expectations also exert an indirect effect. In study 2, conducted on a sample of 371 patients, expectations regarding capabilities and attitudes towards the social influence of robots emerged as important predictors of acceptance. Moreover, although acceptance rates differed between tasks, both studies show a relatively high acceptance of socially assistive humanoid robots. Despite certain limitations, the study findings provide essential knowledge that enhances our understanding of stakeholders’ perceptions and acceptance of socially assistive humanoid robots in hospital environments, and may guide their deployment.