Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

Abstract

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 SociBot TM , 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.

ENGAGE-DEM: A Model of Engagement of People With Dementia

“…If the agent is a person, the attributes influencing the quality of interaction can be the length of the relationship (early encounters or later interactions), the character of the person, the aesthetics of the person (Dion et al, 1972), the degree of mutuality of his/her behavior. If the agent is a social robot, the characteristics influencing the quality of interaction can be the term of exposure to the interaction (i.e., short-term vs long-term), the social cues that the robot displays (Ghazali et al, 2017a and2018a), the language it uses (Ghazali et al, 2017a and2017b), its appearance (Wu et al, 2012;Wrobel et al, 2013;Ghazali et al, 2018b), the reciprocity of its behavior (Leite et al, 2014), and its contingent responsiveness (Díaz-Boladeras, 2017).…”

Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance

“…To limit the 'carryover effects' of the within-subject experimental design [52], the order of the three interactive social cues conditions was randomized for each participant. For instance, some participants interacted with the robot showing no interactive social cue in their first session, followed by the robot showing a high number of interactive social cues in their second session, and a robot showing a low number of interactive social cues in their last session.…”

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

“…More recently, special attention has been paid to the social responses to persuasive robots. Related research informs us regarding social responses to persuasive robots such as engagement, psychological reactance, trust and liking [12][13][14].…”

“…On the other hand, we can expect that users may have the interest to touch a robot or show affective response (such as highly engage) with a robot that makes them happy. Related studies [13,14,25] demonstrated how social cues displayed by robots could influence the affective responses toward persuasive robots. Persuasive robots with minimal social cues, for example, evoked less reactance compared to persuasive robots with enhanced social cues in a decision-making game [26].…”

“…When someone complies with the attempts, it can be seen through his/her behaviour that he/she acts in accordance with the advice given. In this study, we measured trusting behaviours using the compliance score, reflecting how many times the users comply with the order or advice given by the persuasive robot as used in the earlier study [14]. Liking describes the feelings of friendliness, kind and nice towards the robots [45].…”