Information technologies in general and artifical intelligence (AI) in particular try to shift operational task away from a human actor. Machine learning (ML) is a discipline within AI that deals with learning improvement based on data. Subsequently, retailing and wholesaling, which are known for their high proportion of human work and at the same time low profit margins, can be regarded as a natural fit for the application of AI and ML tools. This article examines the current prevalence of the use of machine learning in the industry. The paper uses two disparate approaches to identify the scientific and practical state-of-the-art within the domain: a literature review on the major scientific databases and an empirical study of the 10 largest international retail companies and their adoption of ML technologies in the domain are combined with each other. This text does not present a prototype using machine learning techniques. Instead of a consideration and comparison of the particular algorythms and approaches, the underling problems and operational tasks that are elementary for the specific domain are identified. Based on a comprehensive literature review the main problem types that ML can serve, and the associated ML techniques, are evaluated. An empirical study of the 10 largest retail companies and their ML adoption shows that the practical market adoption is highly variable. The pioneers have extensively integrated applications into everyday business, while others only show a small set of early prototypes. However, some others show neither active use nor efforts to apply such a technology. Following this, a structured approach is taken to analyze the value-adding core processes of retail companies. The current scientific and practical application scenarios and possibilities are illustrated in detail. In summary, there are numerous possible applications in all areas. In particular, in areas where future forecasts and predictions are needed (like marketing or replenishment), the use of ML today is both scientifically and practically highly developed.

Despite the advances in machine perception, semantic scene understanding is still a limiting factor in mixed reality scene composition. In this paper, we present TransforMR, a video see-through mixed reality system for mobile devices that performs 3D-pose-aware object substitution to create meaningful mixed reality scenes. In real-time and for previously unseen and unprepared real-world environments, TransforMR composes mixed reality scenes so that virtual objects assume behavioral and environment-contextual properties of replaced real-world objects. This yields meaningful, coherent, and humaninterpretable scenes, not yet demonstrated by today’s augmentation techniques. TransforMR creates these experiences through our novel pose-aware object substitution method building on different 3D object pose estimators, instance segmentation, video inpainting, and pose-aware object rendering. TransforMR is designed for use in the real-world, supporting the substitution of humans and vehicles in everyday scenes, and runs on mobile devices using just their monocular RGB camera feed as input. We evaluated TransforMR with eight participants in an uncontrolled city environment employing different transformation themes. Applications of TransforMR include real-time character animation analogous to motion capturing in professional film making, however without the need for preparation of either the scene or the actor, as well as narrative-driven experiences that allow users to explore fictional parallel universes in mixed reality. We make all of our source code and assets available ¹ . ¹ TransforMR code release: https://github.com/MohamedKari/transformr

Manipulating their environment is one of the fundamental actions that humans, and actors more generally, perform. Yet, today’s mixed reality systems enable us to situate virtual content in the physical scene but fall short of expanding the visual illusion to believable environment manipulations. In this paper, we present the concept and system of Scene Responsiveness, the visual illusion that virtual actions affect the physical scene. Using co-aligned digital twins for coherence-preserving just-in-time virtualization of physical objects in the environment, Scene Responsiveness allows actors to seemingly manipulate physical objects as if they were virtual. Based on Scene Responsiveness, we propose two general types of end to-end illusionary experiences that ensure visuotactile consistency through the presented techniques of object elusiveness and object rephysicalization. We demonstrate how our Daydreaming illusion enables virtual characters to enter the scene through a physically closed door and vandalize the physical scene, or users to enchant and summon far-away physical objects. In a user evaluation of our Copperfield illusion, we found that Scene Responsiveness can be rendered so convincingly that it lends itself to magic tricks. We present our system architecture and conclude by discussing the implications of scene-responsive mixed reality for gaming and telepresence.

Purpose: Due to recent developments of indoor location-tracking technologies, brick-and-mortar retailers are now able to add location-based marketing capabilities to their existing retail apps, providing retailers with new opportunities to interact with customers inside of their stores. The aim of this study is to identify factors influencing the customers' adoption intention of location-based retail apps for stationary retailing.Design/methodology/approach: Based on the technology acceptance model, a conceptual model was developed. A Web-based survey was conducted in Germany with a final sample of 501 cases. Structural equation modelling was performed to test the hypotheses.Findings: The results confirmed the positive relationship between attitude and the customers' intention to use location-based retail apps. Perceived usefulness and perceived enjoyment promote a positive attitude toward location-based retail apps, while privacy concerns and fear of spam hinder the formation of a favorable attitude. Subjective norms have a positive effect on customers' usage intention.Practical implications: The results help retailers who want to establish location-based retail apps at the point of sale (e.g. in the context of omni-channel strategies).Originality/value: As previous research about location-based apps has mainly used empirical data from either Chinese or American samples, less is known about the perception of European customers. Research indicates that existing findings might not be transferable to European countries due to differences in values, social norms or regulations. Also, the failure of Shopkick in Germany illustrates the need for more research. Based on this, using a German sample to answer the research question, this study aims to provide a European perspective on location-based retail apps, and thereby extend existing research.