Effects of robot facial characteristics and gender in persuasive human-robot interaction

• Main Authors: Ghazali, Aimi S., Ham, Jaap, Barakova, Emilia I., Markopoulos, Panos P.
• Format: Article
• Language: English; English
• Published: Frontiers Media 2018
• Subjects: H Social Sciences (General); H61 Theory. Method; HA29 Theory and method of social science statistics; T Technology (General)
• Online Access: http://irep.iium.edu.my/72721/8/72721%20Effects%20of%20robot%20facial%20characteristics.pdf; http://irep.iium.edu.my/72721/7/72721%20Effects%20of%20robot%20facial%20characteristics%20SCOPUS.pdf; http://irep.iium.edu.my/72721/; https://www.frontiersin.org/articles/10.3389/frobt.2018.00073/full

The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBotTM, a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.