Companies have the opportunity to better engage potential customers by presenting products to them in a highly immersive virtual reality (VR) shopping environment. However, a minimal amount is known about why and whether customers will adopt such fully immersive shopping environments. We therefore develop and experimentally validate a theoretical model, which explains how immersion affects adoption. The participants experienced the environment by using a head-mounted display (high immersion) or by viewing product models in 3D on a desktop (low immersion). We find that immersion does not affect the users’ intention to reuse the shopping environment, because two paths cancel each other out: Highly immersive shopping environments positively influence a hedonic path through telepresence, but surprisingly, they negatively influence a utilitarian path through product diagnosticity. We can explain this effect via low readability of product information in the VR environment and expect VR’s full potential to develop when the technology is further advanced. Our study contributes to literature on immersive systems and IS adoption research by introducing a research model for the adoption of VR shopping environments. A key practical implication of our study is that system designers need to pay special attention to the current state of technology when designing VR applications.

Augmented Reality (AR) gains increased attention as a means to provide assistance for different human activities. Hereby the suitability of AR does not only depend on the respective task, but also to a high degree on the respective device. In a standardized assembly task, we tested AR-based in-situ assistance against conventional pictorial instructions using a smartphone, Microsoft HoloLens and Epson Moverio BT-200 smart glasses as well as paper-based instructions. Participants solved the task fastest using the paper instructions, but made less errors with AR assistance on the Microsoft HoloLens smart glasses than with any other system. Methodically we propose operational definitions of time segments and other optimizations for standardized benchmarking of AR assembly instructions.

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.

A limiting factor of current smart glasses-based augmented reality (AR) systems is their small field of view. AR assistance systems designed for tasks such as order picking or manual assembly are supposed to guide the visual attention of the user towards the item that is relevant next. This is a challenging task, as the user may initially be in an arbitrary position and orientation relative to the target. As a result of the small field of view, in most cases the target will initially not be covered by the AR display, even if it is visible to the user. This raises the question of how to design attention guiding for such “off-screen gaze” conditions.