This paper reviews the current body of empirical research on persuasive technologies 95 studies. In recent years, technology has been increasingly harnessed to persuade and motivate people to engage in various behaviors. This phenomenon has also attracted substantial scholarly interest over the last decade. This review examines the results, methods, measured behavioral and psychological outcomes, affordances in implemented persuasive systems, and domains of the studies in the current body of research on persuasive technologies. The reviewed studies have investigated diverse persuasive systems/designs, psychological factors, and behavioral outcomes. The results of the reviewed studies were categorized into fully positive, partially positive, and negative and/or no effects. This review provides an overview of the state of empirical research regarding persuasive technologies. The paper functions as a reference in positioning future research within the research stream of persuasive technologies in terms of the domain, the persuasive stimuli and the psychological and behavioral outcomes.

Do Persuasive Technologies Persuade? - A Review of Empirical Studies

While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

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Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development.

Standardized metrics for assessing the success of robots is a necessity for a research field to compare and validate results. The Godspeed Questionnaire Series (GQS) is one of the most frequently used questionnaires in the field of Human-Robot Interaction (HRI) with over 160 citations as of October 2014. In this paper, we present a meta analysis of studies that used the GQS. The HRI community uses a large variety of robotic platforms and only the NAO robot seems to be used by multiple research groups. A qualitative meta analysis of 18 NAO studies reveals accumulated findings on perceived intelligence, likability, and anthropomorphism, but also reveals contradictions on how the robot's behaviour and task context impact GQS ratings. The paper closes with a reflection on how added value of data analysis and presentation could be achieved for the HRI community in future.

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Meta analysis of the usage of the Godspeed Questionnaire Series

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.

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Combining Robotic Persuasive Strategies: The Persuasive Power of a Storytelling Robot that Uses Gazing and Gestures

Demographic developments have challenged our research on how to assist elderly people by using robots. The KSERA (Knowledgeable SErvice Robots for Aging) project integrates smart home technology and a socially-assistive robot to extend independent living for elderly people, in particular those with COPD (Chronic Obstructive Pulmonary Disease). The social robot is the most visible component of the system playing the role of communication interface between the elderly, the smart home, and the external world. The robot’s behavior is determined in part by sensor information gathered through the smart home. To ensure user acceptance, we used user-centered design to implement the robot’s behavior. This paper describes the KSERA system, how it was developed based on user needs, treatment plans, and lab studies, and how we validated the approach through user studies and field trials. The key enabling technologies for successful socially-assistive robots include person- and self-localization abilities, person-aware navigation, speech recognition and generation, robot gestures, emulated emotions, eye contact and joint attention, and audio-video communication with family members and care givers.

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Socially Assistive Robots: A Comprehensive Approach to Extending Independent Living

Social agency theory suggests that when an (artificial) agent combines persuasive strategies, its persuasive power increases. Therefore, we investigated whether a robot that uses two persuasive strategies is more persuasive than a robot that uses only one. Because in human face-to-face persuasion two crucial persuasive strategies are gazing and gestures, the current research investigated the combined and individual contribution of gestures and gazing on the persuasiveness of a storytelling robot. A robot told a persuasive story about the aversive consequences of lying to 48 participants. The robot used persuasive gestures (or not) and gazing (or not) to accompany this persuasive story. We assessed persuasiveness by asking participants to evaluate the lying individual in the story told by the robot. Results indicated that only gazing independently led to increased persuasiveness. Using persuasive gestures only led to increased persuasiveness when the robot combined it with (the persuasive strategy of) gazing. Without gazing, using persuasive gestures diminished robot persuasiveness. The implications of the current findings for theory and design of persuasive robots are discussed.

René Bokhorst

Papers

Making robots persuasive: the influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power