Testing the Elaboration Likelihood Model of Persuasion on the Acceptance of Health Regulations in a Video Human-Robot Interaction Study

Abstract

Social robots in public places could be a useful tool to guide and remind people to adhere to general regulations (e.g., wearing a mask, keeping social distance during a pandemic). Additionally, robots could be a useful assistive tool for public order offices, such as reducing risks of infection for employees. However, it is uncertain whether and how robots could enhance regulation adherence. To this extent, we present the results of a 2 (distraction: yes/no) between- by 2 (argument: strong/weak) within-mixed HRI video study (n=83) investigating the argument's persuasiveness based on the Elaboration Likelihood Model of persuasion (ELM). Participants watched a video of a robot persuading people to wear a mask using either a strong or a weak argument. As a distraction, participants had to either count the word mask in the video or not. Our results show that the distraction had no influence, while the argument's strength significantly influences the perceived robot's persuasiveness.