Human-Robot Teaming Configurations: A Study of Interpersonal Communication Perceptions and Affective Learning in Higher Education

Technology encourages collaboration in creative ways in the classroom. Specifically, social robots may offer new opportunities for greater innovation in teaching. In this study, we combined the established literature on co-teaching teams with the developing field of machine actors used in education to investigate the impressions students had of different team configurations that included both a human and a robot. Participants saw one of three teams composed of a human and a social robot with different responsibilities present a short, prerecorded lecture (i.e., human as lead teacher-robot as teaching assistant, robot as lead teacher-human as teaching assistant, human and robot as co-teachers). Overall, students rated the human-led team as more appealing and having more credibility than the robot-led team. The data suggest that participants would be more likely to take a course led by a human instructor than a social robot. Previous studies have investigated machine actors in the classroom, but the current findings are unique in that they compare the individual roles and power structures of human-robot teams leading a course

Mediated Communication and Customer Service Experiences: Psychological and Demographic Predictors of User Evaluations in the United States

People around the world who seek to interact with large organizations increasingly find they must do so via mediated and automated communication. Organizations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people’s satisfaction with these systems, we conducted a generally representative U.S. national survey (N = 1321). Here, we found that people still overwhelmingly like and trust in-person customer service over mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. But communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide

Human-Robot Interaction: Mapping Literature Review and Network Analysis

Organizations increasingly adopt social robots as additions to real-life workforces, which requires knowledge of how humans react to and work with robots. The longstanding research on Human-Robot Interaction (HRI) offers relevant insights, but the existing literature reviews are limited in their ability to guide theory development and practitioners in sustainably employing social robots because the reviews lack a systematic synthesis of HRI concepts, relationships, and ensuing effects. This study offers a mapping review of the past ten years of HRI research. With the analysis of 68 peer-reviewed journal articles, we identify shifting foci, for example, towards more application-specific empirical investigations, and the most prominent concepts and relationships investigated in connection with social robots, for example, robot appearance. The results offer Information Systems scholars and practitioners an initial knowledge base and nuanced insights into key predictors and outcome variables that can hinder and foster social robot adoption in the workplace

Mediated Communication and Customer Service Experiences: Psychological and Demographic Predictors of User Evaluations in the United States

People around the world who seek to interact with large organisations increasingly find they must do so via mediated and automated communication. Organisations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people's satisfaction with these systems, we conducted a representative U.S. national survey (N = 1321). We found that people overwhelmingly like and trust in-person customer service compared to mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. However, communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide

Human, Hybrid, or Machine? Exploring the Trustworthiness of Voice-Based Assistants

This study investigates how people assess the trustworthiness of perceptually hybrid communicative technologies such as voice-based assistants (VBAs). VBAs are often perceived as hybrids between human and machine, which challenges previously distinct definitions of human and machine trustworthiness. Thus, this study explores how the two trustworthiness models can be combined in a hybrid trustworthiness model, which model (human, hybrid, or machine) is most applicable to examine VBA trustworthiness, and whether this differs between respondents with different levels of prior experience with VBAs. Results from two surveys revealed that, overall, the human model exhibited the best model fit; however, the hybrid model also showed acceptable model fit as prior experience increased. Findings are discussed considering the ongoing discourse to establish adequate measures for HMC research

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.publishedVersio

Do People Perceive Alexa as Gendered? A Cross-Cultural Study of People’s Perceptions, Expectations, and Desires of Alexa

Mainly, the scholarly debate on Alexa has focused on sexist/anti-woman gender representations in the everyday life of many families, on a cluster of themes such as privacy, insecurity, and trust, and on the world of education and health. This paper takes another stance and explores via online survey methodology how university student respondents in two countries (the United States, n = 333; and Italy, n = 322) perceive Alexa's image and gender, what they expect from this voice-based assistant, and how they would like Alexa to be. Results of a free association exercise showed that Alexa's image was scarcely embodied or explicitly gendered. Rather, Alexa was associated with a distinct category of being-the VBA, virtual assistant, or digital helper-with which one talks, and which possesses praiseworthy technical and social traits. Expectations of Alexa and desires regarding Alexa's ideal performance are presented and compared across the two country samples

Do People Perceive Alexa as Gendered? A Cross-Cultural Study of People’s Perceptions, Expectations, and Desires of Alexa

Mainly, the scholarly debate on Alexa has focused on sexist/anti-woman gender representations in the everyday life of many families, on a cluster of themes such as privacy, insecurity, and trust, and on the world of education and health. This paper takes another stance and explores via online survey methodology how university student respondents in two countries (the United States, n = 333; and Italy, n = 322) perceive Alexa’s image and gender, what they expect from this voice-based assistant, and how they would like Alexa to be. Results of a free association exercise showed that Alexa’s image was scarcely embodied or explicitly gendered. Rather, Alexa was associated with a distinct category of being—the VBA, virtual assistant, or digital helper—with which one talks, and which possesses praiseworthy technical and social traits. Expectations of Alexa and desires regarding Alexa’s ideal performance are presented and compared across the two country samples