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This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

This article aims to develop a new theory that can better explain and predict how and when humans interact with commercial robots. To this end, utility maximization theory (UMT) along with four principles and propositions that may guide how human-to-commercial robot interactions are developed.

This article conceptualizes UMT by drawing from social exchange, conservation of resources, and technology-driven theories.

This article proposes UMT, which consists of four guiding principles and propositions. First, it is proposed that the human must invest sufficient resources to initiate a human-to-commercial robot interaction. Second, the human forms an expectation of utility gain maximization once a human-to-commercial robot interaction is initiated. Third, the human severs a human-to-commercial robot interaction if the human is unable to witness maximum utility gain upon the interaction. Finally, once the human severs a human-to-commercial robot interaction, the human seeks to reinvest sufficient resources in another human-to-commercial robot interaction with the same expectation of utility maximization.

This article is one of the few studies that offers a theoretical foundation for understanding the interactions between humans and commercial robots. Additionally, this article provides several managerial implications for managing effective human-to-commercial robot interactions.

Recent service studies suggest focusing on the service triad consisting of technology-customer-frontline employee (FLE). This study empirically investigates the role of service robots in this service triad, with the aim to understand the augmentation or substitution role of service robots in driving utilitarian and hedonic value and ultimately customer repatronage.

In study 1, field data are collected from customers (n = 108) who interacted with a service robot and FLE in a fast casual dining restaurant. Structural equation modeling (SEM) is used to test hypotheses about the impact of service robots' anthropomorphism, social presence, value perceptions and augmentation opportunities in the service triad. In study 2, empirical data from a scenario-based experimental design (n = 361) complement the field study by further scrutinizing the interplay between the service robot and FLEs within the service triad.

The study provides three important contributions. First, the authors provide empirical evidence for the interplay between different actors in the “customer-FLE-technology” service triad resulting in customer repatronage. Second, the empirical findings advance the service management literature by unraveling the relationship between anthropomorphism and social presence and their effect on perceived value in the service triad. And third, the study identifies utilitarian value of service robots as a driver of customer repatronage in fast casual dining restaurants.

The results help service managers, service robot engineers and designers, and policy makers to better understand the implications of anthropomorphism, and how the utilitarian value of service robots can offer the potential for augmentation or substitution roles in the service triad.

Building on existing conceptual and laboratory studies on service robots, this is one of the first field studies on the service triad consisting of service robots – customers – frontline employees. The empirical study on service triads provides evidence for the potential of FLEs to augment service robots that exhibit lower levels of functional performance to achieve customer repatronage. FLEs can do this by demonstrating a high willingness to help and having excellent interactions with customers. This finding advocates the joint service delivery by FLE – service robot teams in situations where service robot technology is not fully optimized.

Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adoption. To enhance trust, firms add human-like features to robots; yet, anthropomorphism theory is ambiguous about their appropriate implementation. This study therefore aims to investigate what is more effective for fostering trust: appearance features that are more human-like or social functioning features that are more human-like.

In an experimental field study, a humanoid service robot displayed gaze cues in the form of changing eye colour in one condition and static eye colour in the other. Thus, the robot was more human-like in its social functioning in one condition (displaying gaze cues, but not in the way that humans do) and more human-like in its appearance in the other (static eye colour, but no gaze cues). Self-reported data from 114 participants revealing their perceptions of trust, anthropomorphism, interaction comfort, enjoyment and intention to use were analysed using partial least squares path modelling.

Interaction comfort moderates the effect of gaze cues on anthropomorphism, insofar as gaze cues increase anthropomorphism when comfort is low and decrease it when comfort is high. Anthropomorphism drives trust, intention to use and enjoyment.

To extend human–robot interaction literature, the findings provide novel theoretical understanding of anthropomorphism directed towards humanoid robots.

By investigating which features influence trust, this study gives managers insights into reasons for selecting or optimizing humanoid robots for service interactions.

This study examines the difference between appearance and social functioning features as drivers of anthropomorphism and trust, which can benefit research on self-service technology adoption.

The purpose of this paper is to provide a novel training-responding controlling approach for human–robot interaction. The approach is inspired by the processes of muscle memory and conditioned reflex. The approach is significant for dealing with the problems of robot’s redundant movements and operator’s fatigue in human–robot interaction system.

This paper presented a directional double clustering algorithm (DDCA) to achieve the training process. The DDCA ensured that the initial clustering centers uniformly distributed in every desired cluster. A minimal resource allocation network was used to construct a memory responding algorithm (MRA). When the human–robot interaction system needed to carry out a task for more than one time, the desired movements of the robot were given by the MRA without repeated training. Experimentally demonstrated results showed the proposed training-responding controlling approach could successfully accomplish human–robot interaction tasks.

The training-responding controlling approach improved the robustness and reliability of the human–robot interaction system, which presented a novel controlling method for the operator.

This approach has significant commercial applications, as a means of controlling for human–robot interaction could serve to point to the desired target and arrive at the appointed positions in industrial and household environment.

This work presented a novel training-responding human-robot controlling method. The human-robot controlling method dealt with the problems of robot’s redundant movements and operator’s fatigue. To the authors’ knowledge, the working processes of muscle memory and conditioned reflex have not been reported to apply to human-robot controlling.

Retailers are increasingly considering the introduction of service robots in their stores to support or even replace service staff. Service robots can execute service scripts during the service encounter that can influence customer interactions and the overall experience. While the role of service agents is well documented, more research is needed to understand customer responses to a technology-infused servicescape and to investigate the value of service robots as interaction partners. The purpose of this study is to examine the degree of customer immersion in human-human or human-robot interactions across different stages of the service experience and to understand how immersion affects store visit duration under each condition.

An experimental field study was developed to test the research hypotheses. The study was conducted in a retail store selling premium Italian leather goods with 50 respondents randomly allocated to one of two experimental conditions, interaction with a service robot or interaction with a human sales associate. Participants’ biometrics were collected to measure their immersion in the service encounter and to assess its impact on store visit duration.

The interaction with a service robot increases the level of customer immersion during the service encounter’s welcome and surprise moments. Immersion positively affects visit duration. However, participants exposed to a robot sales associate reported a shorter visit duration as compared to those who interacted with a human sales associate.

This study contributes to the emerging service and retail marketing literature on service robot applications applying a neuroscientific approach to the study of human–robot interactions across different moments of the service encounter. For managers, this study shows the conditions under which service robots can be successfully implemented in retail stores in accordance with the type of task performed and the degree of immersion generated in customers.

In recent developments in Artificial Intelligence (AI) and especially in robotics we can observe a tendency towards building intelligent artefacts that are meant to be social, to have ‘human social’ characteristics like emotions, the ability to conduct dialogue, to learn, to develop personality, character traits, and social competencies. Care, entertainment, pet and educational robots are conceptualised as friendly, understanding partners and credible assistants which communicate ‘naturally’ with users, show emotions and support them in everyday life. Social robots are often designed to interact physically, affectively and socially with humans in order to learn from them. To achieve this goal, roboticists often model the human‐robot interaction on early caregiver‐infant interactions. In this paper I want to analyse prominent visions of these ‘socio‐emotional’ machines as well as early prototypes and commercial products with regard to the human‐machine interface. By means of this I will ask how feminist critiques of technology could be applied to the field of social robotics in which concepts like sociality or emotion are crucial elements while, at the same time, these concepts play an important role in feminist critiques of technology.

Service robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots.

A set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings.

Based on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots.

Robots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.

There is growing interest in the use of human-like social robots, able to undertake complex tasks whilst building consumer engagement. However, further exploration is needed on the optimal level of humanoid appearance for service robots. In particular, the literature is limited with respect to mitigating disconfirmed expectations for robots high in human-likeness. This paper aims to address this gap by testing the effect of robot appearance, disconfirmed expectations and warmth (vs competence) on customers’ responses.

The study adopts a mixed-method design by presenting a focus group (Study 1) that guides two laboratory experiments (Studies 2 and 3). Studies 2 and 3 test for the moderating effect of warmth (vs competence) and the mediating roles of perceived eeriness and disconfirmed expectations.

The findings show that a robot high (vs low) in human-likeness leads to higher negative customers’ responses, which is explained by disconfirmed expectations rather than perceived eeriness. However, when customers interact with a warm (vs competent) robot high in human-likeness, this negative effect vanishes.

The paper investigates boundary conditions and underlying mechanisms that affect customers’ experiences. Although the study adopts high realistic experiments, a limitation lies in not measuring customers’ actual behaviours in the field.

This study provides new insights on how the appearance and characteristics of social robots influence the consumers’ experience. By doing so, this study offers managers actionable insights (i.e. enhancing warmth) to lessen the risk of disconfirmed expectations.

The paper offers new explanations as to why human-like robots can generate negative responses from customers. Moving beyond the “uncanny valley” hypothesis, this study shows the key role of disconfirmed expectations in explaining consumers’ negative responses towards humanoid robots. Moreover, it sheds light on the moderating role of warmth (vs competence), which can mitigate such negative effects.

This research investigates and conceptualizes non-dyadic human–robot interactions (HRI).

The authors conducted a netnographic study of the Facebook group called “iRobot – Roomba,” an online brand community dedicated to Roomba vacuums. Their data analysis employed an abductive approach, which extended the grounded theory method.

Dyadic portrayals of human–robot interactions can be expanded to consider other actants that are relevant to the consumption experiences of consumer robots. Not only humans but also nonhumans, such as an online brand community, have a meaningful role to play in shaping interactions between humans and robots.

The present study moves theoretical discussions on HRI from the individual level grounded in a purely psychological approach to a more collective and sociocultural approach.

If managers do not have a proper assessment of human–robot interactions that considers different actants and their role in the socio-technical arrangement, they will find it more challenging to design and suggest new consumption experiences.

Unlike most previous marketing and consumer research on human–robot interactions, we show that different actants exert agency in different ways, at different times and with different socio-technical arrangements.