Advancing the Strategic Messages Affecting Robot Trust Effect: The Dynamic of User- and Robot-Generated Content on Human-Robot Trust and Interaction Outcomes.

Human-robot interaction (HRI) will soon transform and shift the communication landscape such that people exchange messages with robots. However, successful HRI requires people to trust robots, and, in turn, the trust affects the interaction. Although prior research has examined the determinants of human-robot trust (HRT) during HRI, no research has examined the messages that people received before interacting with robots and their effect on HRT. We conceptualize these messages as SMART (Strategic Messages Affecting Robot Trust). Moreover, we posit that SMART can ultimately affect actual HRI outcomes (i.e., robot evaluations, robot credibility, participant mood) by affording the persuasive influences from user-generated content (UGC) on participatory Web sites. In Study 1, participants were assigned to one of two conditions (UGC/control) in an original experiment of HRT. Compared with the control (descriptive information only), results showed that UGC moderated the correlation between HRT and interaction outcomes in a positive direction (average Δr = +0.39) for robots as media and robots as tools. In Study 2, we explored the effect of robot-generated content but did not find similar moderation effects. These findings point to an important empirical potential to employ SMART in future robot deployment.

The Role of Reciprocity in Verbally Persuasive Robots.

The current research examines the persuasive effects of reciprocity in the context of human-robot interaction. This is an important theoretical and practical extension of persuasive robotics by testing (1) if robots can utilize verbal requests and (2) if robots can utilize persuasive mechanisms (e.g., reciprocity) to gain human compliance. Participants played a trivia game with a robot teammate. The ostensibly autonomous robot helped (or failed to help) the participants by providing the correct (vs. incorrect) trivia answers. Then, the robot directly asked participants to complete a 15-minute task for pattern recognition. Compared to no help, results showed that a robot's prior helping behavior significantly increased the likelihood of compliance (60 percent vs. 33 percent). Interestingly, participants' evaluations toward the robot (i.e., competence, warmth, and trustworthiness) did not predict compliance. These results also provided an insightful comparison showing that participants complied at similar rates with the robot and with computer agents. This result documents a clear empirically powerful potential for the role of verbal messages in persuasive robotics.

Gender differences in justice evaluations: Evidence from fMRI.

Justice research examining gender differences has yielded contrasting findings. This study enlists advanced techniques in cognitive neuroscience (fMRI) to examine gender differences in brain activation patterns in response to procedural and distributive justice manipulations. We integrate social role, information processing, justice, and neuroscience literature to posit and test for gender differences in 2 neural subsystems known to be involved in the appraisal of self-relevant events. Results indicate that the relationship between justice information processing and neural activity in areas representing these subsystems is significantly influenced by gender, with greater activation for females than males during consideration of both procedural and distributive justice information. In addition, we find evidence that gender and distributive injustice interact to influence bargaining behavior, with females rejecting ultimatum game offers more frequently than males. Results also demonstrate activation in the ventromedial prefrontal cortex (vmPFC) and ventral striatum brain regions during procedural justice evaluation is associated with offer rejection in females, but not in males. Managerial implications based on the study's support for gender differences in justice perceptions are discussed.

Reciprocity in computer-human interaction: source-based, norm-based, and affect-based explanations.

Individuals often apply social rules when they interact with computers, and this is known as the Computers Are Social Actors (CASA) effect. Following previous work, one approach to understand the mechanism responsible for CASA is to utilize computer agents and have the agents attempt to gain human compliance (e.g., completing a pattern recognition task). The current study focuses on three key factors frequently cited to influence traditional notions of compliance: evaluations toward the source (competence and warmth), normative influence (reciprocity), and affective influence (mood). Structural equation modeling assessed the effects of these factors on human compliance with computer request. The final model shows that norm-based influence (reciprocity) increased the likelihood of compliance, while evaluations toward the computer agent did not significantly influence compliance.