Initial expectations, interactions, and beyond with social robots

Impact of politeness and performance quality of android robots on future interaction decisions: a conversational design perspective

Despite robots being applied in various situations of modern society, some people avoid them or do not feel comfortable interacting with them. Designs that allow robots to interact appropriately with people will make a positive impression on them resulting in a better evaluation of robots, which will solve this problem. To establish such a design, this study conducted two scenario-based experiments focusing on the politeness of the robot's conversation and behavior, and examined the impressions caused when the robot succeeds or slightly fails at a task. These two experiments revealed that regardless of whether the partner is a robot or a human, politeness not only affected the impression of interaction but also the expectations for better task results on the next occasion. Although the effect of politeness on preference toward robot agents was smaller than those toward human agents when agents failed a task, people were more likely to interact with polite robots and human agents again because they thought that they would not fail the next time. This study revealed that politeness motivates people to interact with robots repeatedly even if they make minor mistakes, suggesting that the politeness design is important for encouraging human-robot interaction.

The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams

OBJECTIVE

Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions.

BACKGROUND

In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork.

METHOD

This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer.

RESULTS

When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human-robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human-robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust.

CONCLUSION

Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust.

APPLICATION

The study can help to better understand the interaction mechanism of the human-robot team and can better serve the management and cooperation of the human-robot team by appropriately adjusting the robots' fairness and social status.

Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI's inherent conscious or moral status.

Sequential model based on human cognitive processing to robot acceptance

Robots have tremendous potential, and have recently been introduced not only for simple operations in factories, but also in workplaces where customer service communication is required. However, communication robots have not always been accepted. This study proposes a three-stage (first contact, interaction, and decision) model for robot acceptance based on the human cognitive process flow to design preferred robots and clarifies the elements of the robot and the processes that affect robot acceptance decision-making. Unlike previous robot acceptance models, the current model focuses on a sequential account of how people decide to accept, considering the interaction (or carry-over) effect between impressions established at each stage. According to the model, this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed during the interaction with robot (politeness of its conversation and behavior) on robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior, the greater the acceptance rate. Importantly, there was no interaction between these two factors. The results indicating that the impressions of the first contact and interaction are additively processed suggest that we should accumulate findings that improving the appearance of the robot and making its communication behavior more human-like in politeness will lead to a more acceptable robot design.

It's not what you think: shaping beliefs about a robot to influence a teleoperator's expectations and behavior

In this paper we present a novel design approach for shaping a teleoperator's expectations and behaviors when teleoperating a robot. Just as how people may drive a car differently based on their expectations of it (e.g., the brakes may be poor), we assert that teleoperators may likewise operate a robot differently based on expectations of robot capability and robustness. We present 3 novel interaction designs that proactively shape teleoperator perceptions, and the results from formal studies that demonstrate that these techniques do indeed shape operator perceptions, and in some cases, measures of driving behavior such as changes in collisions. Our methods shape operator perceptions of a robot's speed, weight, or overall safety, designed to encourage them to drive more safely. This approach shows promise as an avenue for improving teleoperator effectiveness without requiring changes to a robot, novel sensors, algorithms, or other functionality.

Differences in Social Expectations About Robot Signals and Human Signals

In our daily lives, we are continually involved in decision-making situations, many of which take place in the context of social interaction. Despite the ubiquity of such situations, there remains a gap in our understanding of how decision-making unfolds in social contexts, and how communicative signals, such as social cues and feedback, impact the choices we make. Interestingly, there is a new social context to which humans are recently increasingly more frequently exposed-social interaction with not only other humans but also artificial agents, such as robots or avatars. Given these new technological developments, it is of great interest to address the question of whether-and in what way-social signals exhibited by non-human agents influence decision-making. The present study aimed to examine whether robot non-verbal communicative behavior has an effect on human decision-making. To this end, we implemented a two-alternative-choice task where participants were to guess which of two presented cups was covering a ball. This game was an adaptation of a "Shell Game." A robot avatar acted as a game partner producing social cues and feedback. We manipulated robot's cues (pointing toward one of the cups) before the participant's decision and the robot's feedback ("thumb up" or no feedback) after the decision. We found that participants were slower (compared to other conditions) when cues were mostly invalid and the robot reacted positively to wins. We argue that this was due to the incongruence of the signals (cue vs. feedback), and thus violation of expectations. In sum, our findings show that incongruence in pre- and post-decision social signals from a robot significantly influences task performance, highlighting the importance of understanding expectations toward social robots for effective human-robot interactions.

Should robots be polite? Expectations about politeness in human-robot interaction

Interaction with artificial social agents is often designed based on models of human interaction and dialogue. While this is certainly useful for basic interaction mechanisms, it has been argued that social communication strategies and social language use, a "particularly human" ability, may not be appropriate and transferable to interaction with artificial conversational agents. In this paper, we present qualitative research exploring whether users expect artificial agents to use politeness-a fundamental mechanism of social communication-in language-based human-robot interaction. Based on semi-structured interviews, we found that humans mostly ascribe a functional, rule-based use of polite language to humanoid robots and do not expect them to apply socially motivated politeness strategies that they expect in human interaction. This study 1) provides insights for interaction design for social robots' politeness use from a user perspective, and 2) contributes to politeness research based on the analysis of our participants' perspectives on politeness.

Drivers of post-adoption of e-wallet among academics in Palestine: An extension of the expectation confirmation model

E-wallet is one of the latest innovations in the field of payments. However, despite numerous studies on the adoption of e-finance systems, the post-adoption phase is largely neglected. In this paper, we use the extended Expectation Confirmation Model (ECM) to address this gap by focusing on the study of consumers’ continuous intentions regarding the use of an e-wallet service. We conducted an electronic questionnaire-based survey among 503 e-wallet users in Palestine. Using structural equation modeling to analyze the conceptual model of the study, our results confirm that satisfaction, trust, and perceived usefulness have a significant impact on consumers’ continuous intention regarding e-wallet. In addition, the study found that perceived security has an insignificant impact on consumer satisfaction. The study has several implications: E-wallet providers should improve their services in terms of performance, privacy, and security to ensure customer loyalty in this competitive industry.

Socially assistive robotics and older family caregivers of young adults with Intellectual and Developmental Disabilities (IDD): A pilot study exploring respite, acceptance, and usefulness

Introduction

The need for caregiver respite is well-documented for the care of persons with IDD. Social Assistive Robotics (SAR) offer promise in addressing the need for caregiver respite through ‘complementary caregiving’ activities that promote engagement and learning opportunities for a care recipient (CR) with IDD. This study explored the acceptability and usefulness of a SAR caregiver respite program responsive to feedback from both young adults with IDD and their older family caregivers (age 55+).

Method

Young adults with IDD and caregiver dyads (N = 11) were recruited. A mixed methods research design was deployed in three phases: Phase I with four focus groups to inform the program design; Phase II for program demonstration and evaluation with pre- and post-surveys; and Phase III with post-program interviews for feedback and suggestions.

Results

Both young adults with IDD and their caregivers scored favorably the social presence of, social engagement, and satisfaction with robot Pepper. Though there was no significant improvement of caregiving burden/stress as well as well-being of the young adults with IDD based on surveys, results from interviews suggested that the SAR may offer physical/emotional respite to caregivers by providing companionship/friendship as well as promoting independence, safety/monitoring, and interactive engagement with children.

Discussion

SAR has potential in providing respite for older family caregivers. Future studies need a longer program design and larger sample size to develop a promising intervention and test its feasibility and efficacy.

The Influence of Robot Designs on Human Compliance and Emotion: A Virtual Reality Study in the Context of Future Public Transport

As robots enter everyday environments, they start performing tasks originally performed by humans. One field of application is the public transport sector. The deployment of autonomous transport systems comes with a lack of human contact persons for help, guidance, and crowd management. This elicits challenges regarding redirecting and managing passengers. Current solutions on platforms can be replaced or enriched with service robots whose task includes crowd management as well as social interaction. This study investigates how the human-likeness of a robot influences the compliance and emotions of public transport users. A Virtual Reality experiment was conducted (N=33) to evaluate two different robot designs in a bus stop boarding scenario. The two robot designs differ in terms of humanoid appearance. In different experimental trials, participants had to perform a given task that was nullified by instructions from one of the two robots. Additionally, the dissonance of the situation was altered so that the environment either justified the robot's interference or not. Compliant behavior, pleasure, and arousal ratings, as well as task processing times were recorded. The experiment included an individual interview and a post-study questionnaire. The results suggest that future deployment of service robots has the potential to redirect passengers. In dissonant situations, clear reasoning must be given to make the robot effective. However, the robot's visual appearance has a more substantial impact on arousal and subjective preferences than on evoked behavior. The study implies that the presence of a service robot can influence peoples’ choices and gives hints about the importance of giving a reason. However, objectively, the level of the robot's humanoid appearance did not make a difference.

Robotic role theory: an integrative review of human–robot service interaction to advance role theory in the age of social robots

Purpose

Social robots increasingly adopt service roles in the marketplace. While service research is beginning to unravel the implications for theory and practice, other scientific disciplines have amassed a wealth of empirical data of robots assuming such service roles. The purpose of this paper is to synthesize these findings from a role theory perspective with the aim of advancing role theory for human–robot service interaction (HRSI).

Design/methodology/approach

A systematic review of more than 10,000 articles revealed 149 empirical HRSI-related papers across scientific disciplines. The respective articles are analyzed employing qualitative content analysis through the lens of role theory.

Findings

This review develops an organizing structure of the HRSI literature across disciplines, delineates implications for role theory development in the age of social robots, and advances robotic role theory by providing an overarching framework and corresponding propositions. Finally, this review introduces avenues for future research.

Originality/value

This study pioneers a comprehensive review of empirical HRSI literature across disciplines adopting the lens of role theory. The study structures the body of HRSI literature, adapts traditional and derives novel propositions for role theory (i.e. robotic role theory), and delineates promising future research opportunities.

Novelty Experience in Prolonged Interaction: A Qualitative Study of Socially-Isolated College Students' In-Home Use of a Robot Companion Animal

Social distancing policies such as limits on public gatherings and contact with others were utilized around the world to slow the spread of COVID-19. Yet, decreased social interactions may also threaten people's well-being. In this project, we sought to understand novelty-relevant experiences surrounding in-home companion robot pets for adults that were living in some degree of social isolation due to the COVID-19 pandemic. After 6-weeks of participants living with the robot companion, we conducted semi-structured interviews (N = 9) and six themes emerged from our iterative analysis (expectations versus reality, ontological comparisons, interactions, third-party influence, identity, and comfort). Findings suggest that novelty is a complex phenomenon consisting of various elements (i.e., imagined novelty, technology novelty, and relational novelty). Each component influences the user's experience. Our findings also suggest that our understanding of novelty as a nonlinear resource may hold important implications for how we view human-robot relationships beyond initial encounters.

Does the Correspondence Bias Apply to Social Robots?: Dispositional and Situational Attributions of Human Versus Robot Behavior

Increasingly, people interact with embodied machine communicators and are challenged to understand their natures and behaviors. The Fundamental Attribution Error (FAE, sometimes referred to as the correspondence bias) is the tendency for individuals to over-emphasize personality-based or dispositional explanations for other people’s behavior while under-emphasizing situational explanations. This effect has been thoroughly examined with humans, but do people make the same causal inferences when interpreting the actions of a robot? As compared to people, social robots are less autonomous and agentic because their behavior is wholly determined by humans in the loop, programming, and design choices. Nonetheless, people do assign robots agency, intentionality, personality, and blame. Results of an experiment showed that participants made correspondent inferences when evaluating both human and robot speakers, attributing their behavior to underlying attitudes even when it was clearly coerced. However, they committed a stronger correspondence bias in the case of the robot–an effect driven by the greater dispositional culpability assigned to robots committing unpopular behavior–and they were more confident in their attitudinal judgments of robots than humans. Results demonstrated some differences in the global impressions of humans and robots based on behavior valence and choice. Judges formed more generous impressions of the robot agent when its unpopular behavior was coerced versus chosen; a tendency not displayed when forming impressions of the human agent. Implications of attributing robot behavior to disposition, or conflating robot actors with their actions, are addressed.

RALF: an adaptive reinforcement learning framework for teaching dyslexic students

Dyslexia is a learning disorder in which individuals have significant reading difficulties. Previous studies found that using machine learning techniques in content supplements is vital in adapting the course concepts to the learners' educational level. However, to the best of our knowledge, no research objectively applied machine learning methods to adaptive content generation. This study introduces an adaptive reinforcement learning framework known as RALF through Cellular Learning Automata (CLA) to generate content automatically for students with dyslexia. At first, RALF generates online alphabet models as a simplified font. CLA structure learns each rule of character generation through the reinforcement learning cycle asynchronously. Second, Persian words are generated algorithmically. This process also considers each character's state to decide the alphabet cursiveness and the cells' response to the environment. Finally, RALF can generate long texts and sentences using the embedded word-formation algorithm. The spaces between words are proceeds through the CLA neighboring states. Besides, RALF provides word pronunciation and several exams and games to improve the learning performance of people with dyslexia. The proposed reinforcement learning tool enhances students' learning rate with dyslexia by almost 27% compared to the face-to-face approach. The findings of this research show the applicability of this approach in dyslexia treatment during Lockdown of COVID-19.

User Experience in Social Robots

Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and culture. However, introducing this technology for use in conventional environments is not trivial. For users to accept social robots, a positive user experience is vital, and it should be considered as a critical part of the robots' development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society. The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on PRISMA guidelines. Our findings revealed that the most common methods to evaluate UX in social robots are questionnaires and interviews. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards self-service systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industry.

Machines Like Us and People Like You: Toward Human-Robot Shared Experience

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from "human-robot interaction" to "human-robot shared experience." We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition.

Studying Dynamics of Human Information Gathering Behaviors Using Social Robots

A novel social interaction is a dynamic process, in which participants adapt to, react to and engage with their social partners. To facilitate such interactions, people gather information relating to the social context and structure of the situation. The current study aimed to deepen the understanding of the psychological determinants of behavior in a novel social interaction. Three social robots and the participant interacted non-verbally according to a pre-programmed “relationship matrix” that dictated who favored whom. Participants' gaze was tracked during the interaction and, using Bayesian inference models, resulted in a measure of participants' social information-gathering behaviors. Our results reveal the dynamics in a novel environment, wherein information-gathering behavior is initially predicted by psychological inflexibility and then, toward the end of the interaction, predicted by curiosity. These results highlight the utility of using social robots in behavioral experiments.

The wizard and I: How transparent teleoperation and self-description (do not) affect children’s robot perceptions and child-robot relationship formation

It has been well documented that children perceive robots as social, mental, and moral others. Studies on child-robot interaction may encourage this perception of robots, first, by using a Wizard of Oz (i.e., teleoperation) set-up and, second, by having robots engage in self-description. However, much remains unknown about the effects of transparent teleoperation and self-description on children’s perception of, and relationship formation with a robot. To address this research gap initially, we conducted an experimental study with a 2 × 2 (teleoperation: overt/covert; self-description: yes/no) between-subject design in which 168 children aged 7–10 interacted with a Nao robot once. Transparency about the teleoperation procedure decreased children’s perceptions of the robot’s autonomy and anthropomorphism. Self-description reduced the degree to which children perceived the robot as being similar to themselves. Transparent teleoperation and self-description affected neither children’s perceptions of the robot’s animacy and social presence nor their closeness to and trust in the robot.

Relationship Development with Humanoid Social Robots: Applying Interpersonal Theories to Human-Robot Interaction

Humanoid social robots (HSRs) are human-made technologies that can take physical or digital form, resemble people in form or behavior to some degree, and are designed to interact with people. A common assumption is that social robots can and should mimic humans, such that human-robot interaction (HRI) closely resembles human-human (i.e., interpersonal) interaction. Research is often framed from the assumption that rules and theories that apply to interpersonal interaction should apply to HRI (e.g., the computers are social actors framework). Here, we challenge these assumptions and consider more deeply the relevance and applicability of our knowledge about personal relationships to relationships with social robots. First, we describe the typical characteristics of HSRs available to consumers currently, elaborating characteristics relevant to understanding social interactions with robots such as form anthropomorphism and behavioral anthropomorphism. We also consider common social affordances of modern HSRs (persistence, personalization, responsiveness, contingency, and conversational control) and how these align with human capacities and expectations. Next, we present predominant interpersonal theories whose primary claims are foundational to our understanding of human relationship development (social exchange theories, including resource theory, interdependence theory, equity theory, and social penetration theory). We consider whether interpersonal theories are viable frameworks for studying HRI and human-robot relationships given their theoretical assumptions and claims. We conclude by providing suggestions for researchers and designers, including alternatives to equating human-robot relationships to human-human relationships.

Children’s Evaluations of a Therapy Dog and Biomimetic Robot: Influences of Animistic Beliefs and Social Interaction

Social robots are being used increasingly across a range of settings, including in the context of therapeutic interactions with children. While research has shown that interaction with live therapy dogs can be calming and enjoyable for children, it is currently unclear whether social robots can produce similar outcomes. In this study, 11–12-year old children completed a questionnaire about their biophilic beliefs and attitudes to dogs and robots before engaging in two separate free-play, non-goal directed, non-therapeutic sessions with an interactive biomimetic MiRo-E robot and a living therapy dog in a controlled setting. Behavioural observations of social interaction, initiation and reaction behaviours by the child and dog/robot showed that participants spent a similar amount of time engaging in positive social touch with the robot and the dog, but overall more time interacting with the robot. This may be because the robot was more responsive to the children’s initiation behaviours. In self-report, participants significantly preferred the session with the living dog. However, overall enjoyment was high and more positive emotions were reported following interaction with the robot. The more participants attributed mental attributes and animacy to the dog/robot, the more they enjoyed the interactions, demonstrating that participants’ animistic beliefs were an important factor in their evaluations. Levels of social interaction did not correlate with enjoyment, suggesting that the nature of the interaction was less important than pre-existing participant attitudes in producing reported positive outcomes. Although there were some differences in behaviour and evaluations, these preliminary results suggest that MiRo-E provides a useful comparison to therapy dogs and may be a suitable alternative for use in interventions with children.

Applicant Fairness Perceptions of a Robot-Mediated Job Interview: A Video Vignette-Based Experimental Survey

It is well-established in the literature that biases (e. g., related to body size, ethnicity, race etc.) can occur during the employment interview and that applicants' fairness perceptions related to selection procedures can influence attitudes, intentions, and behaviors toward the recruiting organization. This study explores how social robotics may affect this situation. Using an online, video vignette-based experimental survey (n = 235), the study examines applicant fairness perceptions of two types of job interviews: a face-to-face and a robot-mediated interview. To reduce the risk of socially desirable responses, desensitize the topic, and detect any inconsistencies in the respondents' reactions to vignette scenarios, the study employs a first-person and a third-person perspective. In the robot-mediated interview, two teleoperated robots are used as fair proxies for the applicant and the interviewer, thus providing symmetrical visual anonymity unlike prior research that relied on asymmetrical anonymity, in which only one party was anonymized. This design is intended to eliminate visual cues that typically cause implicit biases and discrimination of applicants, but also to prevent biasing the interviewer's assessment through impression management tactics typically used by applicants. We hypothesize that fairness perception (i.e., procedural fairness and interactional fairness) and behavioral intentions (i.e., intentions of job acceptance, reapplication intentions, and recommendation intentions) will be higher in a robot-mediated job interview than in a face-to-face job interview, and that this effect will be stronger for introvert applicants. The study shows, contrary to our expectations, that the face-to-face interview is perceived as fairer, and that the applicant's personality (introvert vs. extravert) does not affect this perception. We discuss this finding and its implications, and address avenues for future research.

Using robot animal companions in the academic library to mitigate student stress

Purpose

The purpose of this paper is to interrogate the relationship between self-reported levels of acute stress, perceived social support and interactions with robot animals in an academic library. The authors hypothesized that (1) participants would report lower stress and higher positive affect after their interaction with a robot support animal and (2) perceived supportiveness of the robot support animal would positively predict the amount of stress reduction the participants reported.

Design/methodology/approach

The authors hosted a robot petting zoo in the main library at a mid-sized Midwestern university during finals week. Participants were asked to rate their stress level prior to interacting with the robot pets (T1) and then after their interaction they were asked about their current stress level and the perceived supportiveness of the robot animal (T2). Data were analyzed using paired samples t-tests for the pretest and post-test scores.

Findings

The results showed a significant decrease in acute stress between T1 to T2, as well as a significant increase in happiness and relaxation. Participants reported feeling less bored and less tired after their interactions with the robot support animals. The findings also reveal that the degree to which individuals experienced a reduction in stress was influenced by their perceptions of the robot animal's supportiveness. Libraries could consider using robot pet therapy.

Originality/value

This study reveals the benefit of robot support animals to reduce stress and increase happiness of those experiencing acute stress in a library setting. The authors also introduce the concept of socially supportive contact as a type of unidirectional social support.