During the previous field study with a robot and its interaction with mall visitors, we observed a surprising event during which a leaflet-distributing robot was abused, although it was subsequently helped by one of its previous abusers. After analyzing 72.25 hours of video data, we identified 47 cases where a robot dropped a leaflet and classified them according to following three criteria: 1) interaction between the potential helper or others with the robot before it dropped the leaflet, 2) the nature of the interaction (abused or not), and 3) whether it was helped. Using the Trajectory Equifinality Model (TEM), we analyzed 19 cases where the robot was helped. We identified the following interaction process that started with individuals who paid attention to the robot, whether they had abusive or non-abusive interactions with it, whether they noticed its failure, and finally whether they helped it. The presence of others encouraged the person to focus on the robot, and the interactions with it led to helping, regardless whether the interaction was abusive. The absence of others when the robot dropped the leaflet encouraged helping. The findings of this study will motivate interaction designs for social robots that can leverage human help.

Service and social robot in public scenarios will face various tasks in future applications, such as guiding people or admonishing them to provide assistance or convey social norms. Robots in public spaces might also incorporate roles of authority figures who might admonish people (e.g., security or guard robots). However, recent investigations showed that people ignore the admonishment of robots. Thus, in this work, we are looking at the reasons why people might ignore robots based on the Cognitive Dissonance Theory (CDT). We present the results of two consecutive field observations where a robot admonishes participants (i.e., pedestrians in a shopping mall) and requests them to stop using a smartphone while walking, which is considered an unmoral behavior. In the first field observation, we approached 160 participants over four days and conducted semi-structured interviews with 19 of them. Approximately half of the people ignored the robot, and half of them followed the instructions. Our interview results show that people who ignore the robot indeed use trivialization as a cognitive dissonance reduction strategy to justify ignoring the robot. Based on our analysis of the results, we developed a counter-trivialization strategy that anticipates this dissonance reduction strategy. We admonished 167 participants in our second field observation over four days, and our results show that significantly fewer people ignore the instructions of the robot when the robot uses a counter-trivialization strategy.

We reveal the process of children engaging in such serious abuse as kicking and punching robots. In study 1, we established a process model of robot abuse and used a qualitative analysis method specialized for time-series data: the Trajectory Equifinality Model (TEM). With the TEM method, we analyzed interactions from nine children who committed serious robot abuse from which we developed a multi-stage model: the abuse escalation model. The model has four stages: approach, mild abuse, physical abuse, and escalation. For each stage, we identified social guides (SGs), which are influencing events that fuel the stage. In study 2, we conducted a quantitative analysis to examine the effect of these SGs. We analyzed 12 hours of data that included 522 children who visited the observed area nearby the robot, coded their behaviors, and statistically tested whether the presence of each SG promoted the stage. Our analysis confirmed the correlations of four SGs and children's behaviors: the presence of other children related a new child to approach the robot (SG1); mild abuse by another child related a child to do mild abuse (SG2); physical abuse by another child related a child to conduct physical abuse (SG3); and encouragement from others related a child to escalate the abuse (SG5).