Generic Vs. Personalised Robot Feedback in Health-Related Recommendation: Influence on User Satisfaction and Behavioural Change Intention

Social robots for healthcare application aimed at inspiring positive behavioural changes often operate on scripted behaviours, disregarding user context or inputs. This study expands existing research by examining how a person’s health risk (low vs. high) and types of robot feedback (generalised vs. personalised) impact users’ intention to follow robot’s recommendation in the context of fostering awareness of sun-protective behaviours to mitigate skin cancer risks. In our in-person human-robot interaction study using the temi service robot ( n = 98), personalised feedback was perceived as higher quality, fostering greater satisfaction towards the robot’s recommendations, and increasing users’ intention and likelihood to implement positive behavioural changes. Furthermore, risk scores serve as critical boundary conditions, with high-risk users perceiving personalised feedback as significantly higher quality than generic feedback. We also show that high-risk users reported heightened anxiety, resulting in a greater inclination to follow the robot’s recommendation and stronger intention to implement the recommended behavioural changes. These findings underscore the significance of providing personalised interaction experience and acknowledging individuals’ emotional states when designing social robots intended to motivating behavioural change for health benefits.