Future robots are expected to be autonomous actors, even capable of moral reasoning. Yet how they can provide transparent explanations while being socially intelligent during morally relevant interactions deserves a close examination. Our mixed-methods lab study on a human-robot moral debate on the footbridge dilemma showed that quantitatively, a robot’s perceived competence was significantly higher with transparency cues (additional information presented on a screen). The robot’s perceived warmth and mind were not influenced by transparency cues, but they did significantly change over time (pre- vs. post-debate). The change in the robot’s perceived mind and social attributes after the debate correlated with people’s trust in the robot; transparency cues did not correlate with trust. Qualitatively, the robot was described to be logical, unemotional, and intentional in making moral decisions; participants focused on its gaze and speech. While transparency may help in theory, if people do not observe relevant cues while attributing intentionality to the robot and its gaze, transparency cues may not be useful during critical decision-making though the robot can seem competent. We discuss the implications and call for broadening the notion of transparency to investigate how robots can be transparent communicators by appealing to both cognition and affect in morally sensitive interactions.

As robots may take a greater part in our moral decision-making processes, whether people hold them accountable for moral harm becomes critical to explore. Blame and punishment signify moral accountability, often involving emotions. We quantitatively looked into people’s willingness to blame or punish an emotional vs. non-emotional robot that admits to its wrongdoing. Studies 1 and 2 (online video interaction) showed that people may punish a robot due to its lack of perceived emotional capacity than its perceived agency. Study 3 (in the lab) demonstrated that people were neither willing to blame nor punish the robot. Punishing non-emotional robots seems more likely than blaming them, yet punishment towards robots is more likely to arise online than offline. We reflect on if and why victimized humans (and those who care for them) may seek out retributive justice against robot scapegoats when there are no humans to hold accountable for moral harm.

Earlier research [1] suggests that a social robot can contribute to gaining self-management skills for children with diabetes. In the current study, the positive effect of different robot characteristics of a social robot is investigated in a natural environment with end-users. The NAO robot played an interactive trivia game with eight children with diabetes, which was video-recorded. After the study children filled in a questionnaire and parents were interviewed. Combining the data provided several findings: movements of the robot were entertaining, not all children appreciated eye contact and expressions contributed to bonding. These results can be used to improve interactions between children and human-like robots.

This paper presents a study that tests whether the similarity-attraction paradigm applies to persuasive technology. That is, the notion that similarity leads to more positive evaluations and persuasion of an interactive system was tested in an online study in which participants were provided with automated persuasive messages that had either a dominant or a submissive interaction style. The system with a dominant interaction style was expected to be more persuasive than the system with a submissive interaction style. Moreover, people with dominant personalities were expected to be persuaded more by a dominant system, while people with submissive personalities were expected to be persuaded more by a submissive one. Results showed that the dominant system was more persuasive than the submissive one, but also that the dominant system was perceived as less likeable than the submissive one. Expectations regarding the similarity-attraction paradigm were confirmed for people's evaluations of the system, but not for the system's persuasiveness. The current work could ultimately help creating technologies that adapt their persuasive messages to their users.

In this paper, we investigate the effect of an external human-machine interface (eHMI) and a conspicuous external vehicle appearance due to visible sensors on pedestrian interactions with automated vehicles (AVs). Recent research shows that AVs may need to explicitly communicate with the environment due to the absence of a driver. Furthermore, in interaction situations, an AV that looks different and conspicuous owing to an extensive sensor system may potentially lead to hesitation stemming from mistrust in automation. Thus, we evaluated in a virtual reality study how pedestrian attitude, the presence/absence of an eHMI, and a conspicuous sensor system affect their willingness to cross the road. Results recommend the use of an eHMI. A conspicuous appearance of automated-driving capability had no effect for the sample as a whole, although it led to more efficient crossing decisions for those with a more negative attitude towards AVs. Our findings contribute towards the effective design of future AV interfaces.

Abstract Self-tracking technologies bring a new set of experiences into our lives. Through sensors and ubiquitous measurements of bodily performance, a new form of automation experience shapes our understanding of our body and our behavior. While for many individuals self-tracking has an important role in their daily lives, a theoretical understanding of the level and behavioral manifestations of commitment to self-tracking is still missing. This paper introduces the concept of commitment to self-tracking and presents the development and first validation of a new 12-item behavior-based scale for its measurement, the Commitment to Self-Tracking (C2ST) scale. Using online survey data from individuals wearing self-tracking technology (N = 300), we explore the underlying factor structure of the scale and determine its reliability and validity. An analysis of the survey data indicates that commitment to self-tracking positively correlates with autonomous motivation for tracking and negatively correlates with controlled motivation. The C2ST scale brings insights on how self-tracking technology, as a novel automation experience, is affecting users’ everyday behaviors. Overall, by emphasizing the feasibility of defining commitment behaviorally, the paper concludes with implications for theory and practice and suggests directions for future research.

As robots are taking a more prominent role in our daily lives, it becomes increasingly important to consider how their presence influences us. Several studies have investigated effects of robot behavior on the extent to which that robot is positively evaluated. Likewise, studies have shown that the emotions a robot shows tend to be contagious: a happy robot makes us feel happy as well. It is unknown, however, whether the affect that people experience while interacting with a robot also influences their evaluation of the robot. This study aims to discover whether people's affective and evaluative responses to a social robot are related. Results show that affective responses and evaluations are related, and that these effects are strongest when a robot shows meaningful motions. These results are consistent with earlier findings in terms of how people evaluate social robots.

In recent years, much attention has been given to developing robots with various social skills. An important social skill is navigation in the presence of people. Earlier research has indicated preferred approach angles and stopping distances for a robot when approaching people who are interacting with each other. However, an experimental validation of user experiences with such a robot is largely missing. The current study investigates the shape and size of a shared interaction space and evaluations of a robot approaching from various angles. Results show an expected pattern of stopping distances, but only when a robot approaches the middle point between two persons. Additionally, more positive evaluations were found when a robot approached on the side of the participant compared to other participant's side. These findings highlight the importance of using a smart path planning method for robots when joining an interaction between users.

This article explores the relation between consistency of social cues and persuasion by an artificial agent. Including (minimal) social cues in Persuasive Technology (PT) increases the probability that people attribute human-like characteristics to that technology, which in turn can make that technology more persuasive (see, e.g., Nass, Steuer, Tauber, & Reeder, 1993). PT in the social actor role can be equipped with a variety of social cues to create opportunities for applying social influence strategies (for an overview, see Fogg, 2003). However, multiple social cues may not always be perceived as being consistent, which could decrease their perceived human-likeness and their persuasiveness. In the current article, we investigate the relation between consistency of social cues and persuasion by an artificial agent. Findings of two studies show that consistency of social cues increases people’s recognition and recall of artificial agents’ emotional expressions, and make those agents more persuasive. These findings show the importance of the combined meaning of social cues in the design of persuasive artificial agents.

Earlier research showed that artificial social agents can influence human behavior. This article argues that, especially under certain circumstances, people are sensitive to persuasion by (artificial) social agents. For example, when people feel socially excluded, they are motivated to increase their social connections with others. It was hypothesized that socially excluded people would attribute more human-likeness and be persuaded more by an artificial agent than socially included people. These hypotheses were investigated in two studies in which participants were either socially included or excluded, after which they performed an energy-saving task while receiving social feedback from an artificial agent. Results did not support the expectation that socially excluded people ascribe more human-likeness to an artificial agent, but they did show the expected effects on behavior change, indicating the importance of including a person’s psychological state in the design of human–agent interactions. Also, in line with earlier findings, female participants were more susceptible to the agent’s feedback than male participants, indicating that a user’s gender may also determine the effectiveness of persuasive technology.

In human-robot interaction research, much attention is given to the extent to which people perceive humanlike attributes in robots. Generally, the concept anthropomorphism is used to describe this process. Anthropomorphism is defined in different ways, with much focus on either typical human attributes or uniquely human attributes. This difference has caused different measurement tools to be developed. We argue that anthropomorphism can best be described as a continuum ranging from low to high human likeness, and should be measured accordingly. We found that anthropomorphic characteristics can be invariantly ordered according to the ease with which these can be ascribed to robots.

A fundamental characteristic of ambient persuasive technology is the ability to persuade users outside of conscious attention. One method of influencing people outside of their conscious awareness is subliminal priming: Presenting a stimulus for less than 50 milliseconds so people can not consciously perceive it but they are able to process the information unconsciously. Earlier research has shown that subliminal feedback on energy consumption leads to more optimal choices in an energy-related choice task compared with no feedback. Would subliminal feedback always be effective in influencing people's choices, or do people need to be motivated to reach a specific goal for it to work? In the current research, we investigate if persuasion by subliminal feedback comprises a goal-striving related process in which people strive for a goal and use the subliminal information to reach that goal. In an experimental study, half of the participants were primed with the goal to perform well, and the other half was not primed with this goal. Next, half of the participants received subliminal feedback in a learning task, whereas the other half received no feedback. Results indicated that participants primed with a performance goal made more correct choices in the task when given the subliminal feedback compared with given no feedback. Participants who were not primed with a goal were not influenced by this feedback. This finding indicates the need for a behavior-relevant goal to make subliminal information effective, which in turn has important implications for research and design of ambient persuasive technology.