Earlier theorizing suggested that an (artificial) agent that combines persuasive strategies will be more persuasive. Therefore, the current research investigated whether a robot that uses two persuasive strategies is more persuasive than a robot that uses only one. Two crucial persuasive strategies that humans use in face-to-face persuasion are gazing and gestures, and therefore we studied the combined and individual contribution of these two persuasive strategies (gestures and gazing) on the persuasiveness of a storytelling robot. A robot told a classical persuasive story about the consequences of lying to forty-eight participants, and was programmed to use (persuasive) gestures (or not) and gazing (or not). Next, we asked participants to evaluate the character in the story thereby assessing the robot’s persuasiveness. Results presented evidence a robot’s persuasiveness is increased when gazing is used. When the robot used gestures, its persuasiveness only increased when it also used gazing. When the robot did not use gazing, using gestures diminished the robot’s persuasiveness. We discuss the implications for theory and design of robots that are more persuasive.

In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots’ acceptance. Implications for the development of persuasive robots are discussed.

In the near future, robotic agents might employ persuasion to influence people’s behavior or attitudes, just as human agents do in many situations. People can comply with these requests, but, people can also experience psychological reactance, which may lead to the complete opposite of the proposed behavior. In this study we are interested in the social nature of psychological reactance. Social agency theory proposes that more social cues lead to a more social interaction. We argue that this also holds for psychological reactance. Therefore, we expect a positive relationship between the level of social agency of the source of a persuasive message and the amount of psychological reactance the message arouses. In an online experiment, participants read an advice on how to conserve energy when using a washing machine. The advice was either provided as text-only, as text accompanied by a still picture of a robotic agent, or as text accompanied by a short film clip of the same robotic agent. Confirming our expectations, results indicated that participants experienced more psychological reactance when the advice was accompanied by the still picture or when the advice was accompanied by the short film clip as compared to when the advice was provided as text-only. This indicates that stronger social agency of the messenger can lead to more psychological reactance. Furthermore, our results confirmed earlier research about the effects of controlling language on psychological reactance. Implications are discussed.

Earlier research investigating persuasive technology—technology designed to influence human behavior or attitude—indicates that persuasive technology can stimulate energy-efficient behavior. However, most applications of persuasive technology need people’s focal attention to be successful, and people may often not have these cognitive resources available. The current research investigates a form of persuasive technology that is less obvious and easier to process: ambient lighting as persuasive technology. In an experimental study, participants could conserve energy while setting temperatures on a central heating panel and receive feedback about their energy consumption in each task. We tested the effect of feedback through a lamp that gradually changed color dependent on energy consumption and compared these effects to more widely used factual feedback. Half of the participants received lighting feedback, and half of the participants received numerical feedback. To investigate whether ambient feedback is easier to process than numerical feedback, half of the participants performed a cognitive load task in addition to the focal task. Results indicated that feedback through lighting has stronger persuasive effects than numerical feedback. Furthermore, ambient lighting feedback seemed easier to process than numerical feedback because cognitive load interfered with processing numerical feedback, but not with processing lighting feedback. Implications for theory and design of energy consumption feedback systems, persuasive lighting, and (ambient) persuasive technology are discussed.