Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions

Ethics 4.0: Ethical Dilemmas in Healthcare Mediated by Social Robots

This study examined people's moral judgments and trait perception toward a healthcare agent's response to a patient who refuses to take medication. A sample of 524 participants was randomly assigned to one of eight vignettes in which the type of healthcare agent (human vs. robot), the use of a health message framing (emphasizing health-losses for not taking vs. health-gains in taking the medication), and the ethical decision (respect the autonomy vs. beneficence/nonmaleficence) were manipulated to investigate their effects on moral judgments (acceptance and responsibility) and traits perception (warmth, competence, trustworthiness). The results indicated that moral acceptance was higher when the agents respected the patient's autonomy than when the agents prioritized beneficence/nonmaleficence. Moral responsibility and perceived warmth were higher for the human agent than for the robot, and the agent who respected the patient's autonomy was perceived as warmer, but less competent and trustworthy than the agent who decided for the patient's beneficence/nonmaleficence. Agents who prioritized beneficence/nonmaleficence and framed the health gains were also perceived as more trustworthy. Our findings contribute to the understanding of moral judgments in the healthcare domain mediated by both healthcare humans and artificial agents.

Adoption of a deep learning-based neural network model in the psychological behavior analysis of resident tourism consumption

With the development of society and the continuous progress of science and technology, it has become the mainstream measure to promote the development of the social economy through science and technology. Therefore, to improve the current situation of tourism consumption, improve the consumer sentiment of tourists, and promote the development of the tourism economy, the convolutional neural network (CNN) technology model is used to analyze the tourist's consumer psychology and behavior. Based on this, the user's consumption situation is analyzed, thus providing support for the intelligent improvement of tourism consumption. First, the basic characteristics of tourism consumption mood and behavior are introduced, and the methods to improve the tourism consumption mood and behavior are briefly introduced. Then, the CNN algorithm is employed to identify consumers' travel consumption behaviors and emotions. To improve the recognition effect, the algorithm is combined with skeleton node behavior recognition and video image behavior recognition. Finally, the performance of the designed algorithm is tested. The accuracy of the human behavior recognition (HBR) algorithm is more than 0.88. Compared with the detection effect of the HBR algorithm, the combined algorithm adopted in this work can reduce the image processing time and improve the detection efficiency. The multithread method can effectively reduce the complexity of the model and improve the recognition accuracy. The test results on different data sets show that the proposed algorithm can better adapt to the changes in identification samples and obtain more accurate recognition results compared with similar algorithms. In summary, this study not only provides technical support for the rational analysis of consumer sentiment and consumer behavior but also contributes to the comprehensive development of the tourism market.

Effect of robot’s vertical body movement on its perceived emotion: A preliminary study on vertical oscillation and transition

The emotion expressions of social robots are some of the most important developments in recent studies on human–robot interactions (HRIs). Several research studies have been conducted to assess effective factors to improve the quality of emotion expression of the robots. In this study, we examined the effects of a robot’s vertical oscillation and transition on the quality of its emotion expression, where the former indicates the periodic up/down movement of the body of the robot, while the latter indicates a one-time up or down movement. Short-term and long-term emotion expressions of the robot were studied independently for the four basic emotions described in the circumplex model of emotions: joy, anger, sadness, and relief. We designed an experiment with an adequate statistical power and minimum sample size of human subjects based on a priori power analysis. Human subjects were asked to evaluate the robot’s emotion expressions by watching its video with/without vertical movement. The results of the experiment showed that for the long-term emotions, the speed of vertical oscillation corresponded to the degree of arousal of the emotion expression as noted in the circumplex model; this indicated that fast oscillations improved the emotion expression with a higher degree of arousal, such as joy and anger, while slow or no oscillations were more suited to emotions with a lower degree of arousal, such as sadness and relief. For the short-term emotions, the direction of the vertical transition corresponded to the degree of valence for most of the expressed emotions, while the speed of vertical oscillation reflected the degree of arousal. The findings of this study can be adopted in the development of conversational robots to enhance their emotion expression.

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications

Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

The Acceptance of Telepresence Robots in Higher Education

While telepresence robots have increasingly become accepted in diverse settings, the research on their acceptance in educational contexts has been underdeveloped. This study analyzed how the use intention of telepresence robots can be influenced by perceived usefulness, perceived ease of use, subjective norm, and perceived risk for students, faculty, and staff in higher education. Survey data were collected from 60 participants with direct operator experience with a variety of telepresence robots deployed in a large research university in the Midwest region of the United States. Path analysis results indicated that perceived usefulness was the only significant direct predictor of use intention of telepresence robots. Both perceived ease of use and subjective norm had a significant positive effect on perceived usefulness. Subjective norm also had a significant positive indirect effect on use intention, mediated by perceived usefulness. Perceived risk had a negative effect on perceived ease of use. These findings indicated that the usefulness of robots was central to operators’ decisions to use telepresence robots. Therefore, design choice for telepresence robots should prioritize usefulness. Secondly, the design of telepresence robots should minimize complexity for the end user and minimize cognitive demand. Having nominal difficulty of use would also facilitate multiple embodiments by combining telepresence robots with other technologies to support more rich social interactions.

Who Likes Artificial Intelligence? Personality Predictors of Attitudes toward Artificial Intelligence

We examined how individuals' personality relates to various attitudes toward artificial intelligence (AI). Attitudes were organized into two dimensions of affective components (positive and negative emotions) and two dimensions of cognitive components (sociality and functionality). For personality, we focused on the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness) and personal innovativeness in information technology. Based on a survey of 1,530 South Korean adults, we found that extraversion was related to negative emotions and low functionality. Agreeableness was associated with both positive and negative emotions, and it was positively associated with sociality and functionality. Conscientiousness was negatively related to negative emotions, and it was associated with high functionality, but also with low sociality. Neuroticism was related to negative emotions, but also to high sociality. Openness was positively linked to functionality, but did not predict other attitudes when other proximal predictors were included (e.g. prior use, personal innovativeness). Personal innovativeness in information technology consistently showed positive attitudes toward AI across all four dimensions. These findings provide mixed support for our hypotheses, and we discuss specific implications for future research and practice.

Perceived Service Quality in HRI: Applying the SERVBOT Framework

Services are intangible in nature and as a result, it is often difficult to measure the quality of the service. In the service literature, the service is usually delivered by a human to a human customer and the quality of the service is often evaluated using the SERVQUAL dimensions. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment value and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robot's service quality. The paper proposes the SERVBOT model to assess a social robot's service quality. It identifies, reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional engagement and future intentions to use the social robot in a concierge service setting. The model was tested using student sampling, and a total of 94 responses were collected for the study. The findings indicate empathy and entertainment value as key predictors of emotional engagement. Further, emotional engagement is a strong predictor of future intention to use a social robot in a service setting. This study is the first to propose the SERVBOT model to measure social robot's service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends on the literature by exploring the key factors that influence the use of social robots (i.e., emotional engagement).

Human-Robot Confluence: Toward a Humane Robotics

The new humanoid robots not only perform tasks, but also can activate interactions and social relationships with other robots and with humans. In this view, the diffusion of humanoid robots with a physical structure reminiscent of the human body, endowed with decision-making abilities, and capable of externalizing and generating emotions, is opening a new line of research with a main objective of understanding the dynamics of social interactions generated by the encounters between robots and humans. However, this process is not easy. To be accepted by society, robots have to "understand" people and to adapt themselves to complex real-life social environments. This goal underlines the importance for research of aspects such as communication, acceptance, and ethics that require the collaboration between multiple disciplines, including psychology, neuroscience, design, mechatronics, computer science, philosophy, sociology, anthropology, biomechanics, and roboethics. This special issue seeks to gather knowledge from these disciplines with respect to human-robot confluence (HRC) in the application of robots in everyday life, including robot training partners and industrial collaborative robots (Cobots). It covers a wide range of topics related to HRC, involving theories, methodologies, technologies, and empirical and experimental studies. The final goal is to support researchers and developers in creating robots that not only have a humanoid body but that are really "humane": accessible, sympathetic, generous, compassionate, and forbearing.

The Role of Human–Machine Interactive Devices for Post-COVID-19 Innovative Sustainable Tourism in Ho Chi Minh City, Vietnam

In this research article, we aim to study the proposed role of human–machine interactive (HMI) technologies, including both artificial intelligence (AI) and virtual reality (VR)-enabled applications, for the post-COVID-19 revival of the already depleted tourism industry in Vietnam’s major tourist destination and business hub of Ho Chi Minh City. The researchers aim to gather practical knowledge regarding tourists’ intentions for such service enhancements, which may drive the sector to adopt a better conclusive growth pattern in post-COVID-19 times. In this study, we attempt to focus on travelers who look for paramount safety with the assurance of empathetic, personalized care in post-COVID-19 times. In the current study, the authors employ structural equation modeling to evaluate the intentions of tourists both structurally and empirically for destination tourism with data collected from tourists with previous exposure to various kinds of these devices. The study shows that human–machine interactive devices are integrating AI and VR and have a significant effect on overall service quality, leading to tourist satisfaction and loyalty. The use of such social interactive gadgets within tourism and mostly in hospitality services requires an organization to make a commitment to futuristic technologies, along with building value by enriching service quality expectations among fearful tourists. This research shows that tourists mainly focus on the use of such HMI devices from the perspective of technology acceptance factors, qualitative value-enhancing service and trustworthy information-sharing mechanisms. The concept of the tour bubble framework is also discussed in detail. The analysis of this discussion gives us a more profound understanding of the novel opportunities which various administrative agencies may benefit from to position these devices better in smart, sustainable destination tourism strategies for the future so that, collectively, service 5.0 with HMI devices can possibly bring back tourism from being disintegrated. Such service applications are the new social innovations leading to sustainable service and a sophisticated experience for all tourists.

A Study on the Role of Web 4.0 and 5.0 in the Sustainable Tourism Ecosystem of Ho Chi Minh City, Vietnam

This research discusses the role of Web 4.0 and 5.0 devices on the commercial sustainability for tourism in Ho Chi Minh City (HCMC), Vietnam. The study adopts a tourist perspective regarding the future implementation of Web 4.0 and 5.0 devices, focusing on the service quality of various tourism-associated services and assessing, in return, the extent to which the overall satisfaction and long-term patronage of tourists is affected. Usually, researchers conduct this test in various popular establishments where devices are intended for experimental use and tourists are supposed to have previous exposure to them, so that their perceptual thinking regarding the service quality of these devices can be studied. The tourists act as respondents for various establishments. We also asked subjective questions in a short interview to 10 professionals working in these establishments, which helped us in the quantitative survey process. As a mixed method study, it has covered both quantitative and qualitative aspects of the items and provided a holistic result. The results indicated that Web 4.0 and 5.0, along with the service quality offered by personnel working in the establishments, showed significant variance in holistic service evaluation for tourist satisfaction and loyalty. Very few service quality factors had a distinct variance in the output. After using a regression analysis for Web 4.0 and 5.0 devices and service quality, we found that Web 4.0 and 5.0 showed insignificant negative results. This study contributes to the incremental research on Web 1.0 to 5.0, specifically detailing how it affects tourists’ satisfaction and patronage. Moreover, from a management point of view, this study can help solve the financial riddle of various establishments in HCMCs tourism ecosystem for optimum fund management and better productivity.

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

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.