MODELING THE CONSUMER ACCEPTANCE OF RETAIL SERVICE ROBOTS

This study uses the Computers Are Social Actors (CASA) and domestication theories as the underlying framework of an acceptance model of retail service robots (RSRs). The model illustrates the relationships among facilitators, attitudes toward Human-Robot Interaction (HRI), anxiety toward robots, anticipated service quality, and the acceptance of RSRs. Specifically, the researcher investigates the extent to which the facilitators of usefulness, social capability, the appearance of RSRs, and the attitudes toward HRI affect acceptance and increase the anticipation of service quality. The researcher also tests the inhibiting role of pre-existing anxiety toward robots on the relationship between these facilitators and attitudes toward HRI. The study uses four methodological strategies: (1) incorporating a focus group and personal interviews, (2) using a presentation method of video clip stimuli, (3) empirical data collection and multigroup SEM analyses, and (4) applying three key product categories for the model’s generalization— fashion, technology (mobile phone), and food service (restaurant). The researcher conducts two pretests to check the survey items and to select the video clips. The researcher conducts the main test using an online survey of US consumer panelists (n = 1424) at a marketing agency. The results show that usefulness, social capability, and the appearance of a RSR positively influence the attitudes toward HRI. The attitudes toward HRI predict greater anticipation of service quality and the acceptance of the RSRs. The expected quality of service tends to enhance the acceptance. The relationship between social capability and attitudes toward HRI is weaker when the anxiety toward robots is higher. However, when the anxiety is higher, the relationship between appearance and the attitudes toward HRI is stronger than those with low anxiety. This study contributes to the literature on the CASA and domestication theories and to the human-computer interaction that involves robots or artificial intelligence. By considering social capability, humanness, intelligence, and the appearance of robots, this model of RSR acceptance can provide new insights into the psychological, social, and behavioral principles that guide the commercialization of robots. Further, this acceptance model could help retailers and marketers formulate strategies for effective HRI and RSR adoption in their businesses

A systematic review of Augmented Reality content-related techniques for knowledge transfer in maintenance applications

Augmented Reality (AR) has experienced an increasing trend in applied research in the last few years. This emerging trend is focused in content-related challenges: mainly creation (Authoring), adaptation (Context-Awareness) and improvement (Interaction-Analysis) of augmented content. Research in these techniques has enabled Academia to recognise Augmented Reality capability for knowledge transfer, either from AR systems to users or between users. But to the best of author’s knowledge, there are no specific literature review in these areas, neither on their relations with AR knowledge transfer ability. Therefore, this paper aims to identify these relations through an analysis of state-of-the-art techniques in Authoring (A), Context-Awareness (CA) and Interaction-Analysis (IA) in the context of maintenance applications. In order to do so, a Systematic Literature Review (SLR) has been conducted on 74 application-relevant papers from 2012 to 2017. It comprised a thematic analysis to establish the relation between maintenance applications, research in A, CA and IA and AR knowledge transfer modes. Its results helped to classify AR maintenance-applications by technological readiness levels. They also revealed the potential of AR for users’ knowledge capture, and future research required for full knowledge management capabilities. Furthermore, the SLR method proposed could be extended to correlate AR systems and applications by their knowledge management capabilities in any AR application context

Ontology-based augmented reality content-related techniques and their impact in knowledge capture and re-use within maintenance diagnosis

This PhD thesis aims to study ontology-based AR content-related methods and their impact in knowledge transfer, capture and re-use for cost-effective human knowledge integration in digital diagnostic systems. Industry 4.0 has revealed the importance of maintainers’ knowledge capture and re-use in diagnostics systems for providing satisfactory solutions in cases where those systems cannot (e.g. nofault-found). Augmented Reality (AR) utilises content-related techniques to transfer knowledge to maintainers for improving efficiency and effectiveness of diagnosis tasks. Academic literature has shown that AR can also be utilised for knowledge capture and re-use, but this has only been demonstrated in simple, step-by-step repair operations. In diagnosis research, ontology-based methods are applied to capture and re-use knowledge from unstructured and heterogenous sources like humans. Nevertheless, these methods have not made use of AR potential to contextualise knowledge and so, improve efficiency and effectiveness of knowledge capture and re-use diagnosis operations...[cont.]Manufacturin

A Diagrammatic Framework for Intuitive Human Robot Interaction: An Augmented Reality Approach

This thesis explores the area of human robot interaction and develops a framework which is designed to allow ordinary people to control and program complex robots in an intuitive way that is both reliable and accurate. Our framework bridges the experience gap which is typically found between non-technical experts and complex robotic control technologies. The principle mechanism behind our framework is Augmented Reality (AR) and we use this to provide a diagrammatic service to users. <br> <br> The service uses a range of diagrammatic markers including marker-less AR objects, which can be created and connected together and used to command and control the robot and engage in two way communications in the form of a diagram. The diagrams are expressed as three-dimensional augmented reality objects, so that users can annotate arbitrary locations in a given environment with instructions through a video see-through camera. The robots automatically pick up these instructions and perform the respective actions at the specified locations. A key feature of this framework is its ability to translate diagrams into robot-related action tasks, giving each task a context through spatial references. Other features of the framework include scalability and the generic nature where it is conjectured to be applicable for a range of different robots and multimedia devices. <br> <br> In later chapters we report on two case studies where our framework is applied to a set of command and control tasks to measure performance across situation-awareness, task completion-time and cognitive-load. Our results show that our model leads to greater situation awareness and improved task completion times, when compared to conventional interaction methods, such as a gamepad controller. Finally, we conclude the thesis with an overview of our contributions and an account of future work which will integrate our model into multi-modal hybrids and extend the case studies to compare against other interaction methods