Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence

Повышение качества покупательского опыта в традиционных магазинах с помощью приложения — помощника по покупкам на основе дополненной реальности, использующего персонализированные рекомендации и объяснимый искусственный интеллект
Robert Zimmermann, Daniel Mocencahua Mora, Douglas Cirqueira, Markus Helfert, Marija Bezbradica, Dirk Werth, Wolfgang Weitzl, René Riedl‬, Andreas Auinger
2022-04-11

augmented reality shopping assistantdesign science researchexplainable artificial intelligenceomnichannel retailpersonalized recommendations
Purpose The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences. Design/methodology/approach The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment ( n = 252), providing both qualitative and quantitative data. Findings Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact. Research limitations/implications This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications. Practical implications This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey. Originality/value This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.
1
A smartphone-based augmented reality shopping assistant using personalized recommendations and explainable AI positively affects customers’ perceptions of brick-and-mortar shopping experiences.
2
Improved shopping experience did not necessarily increase customers’ intentions to purchase or visit brick-and-mortar stores, contrary to established theoretical expectations.
3
The artifact was developed through design science research and evaluated in an online experiment with 252 participants using qualitative and quantitative data.
4
The findings are limited by online evaluation; actual technology use in physical stores requires further investigation.
5
The study identifies artifact improvements and proposes novel design principles for developing more advanced augmented reality shopping assistants.

A smartphone-based augmented reality shopping assistant application for brick-and-mortar retail stores

The application’s impact on customers’ brick-and-mortar shopping experience, including the effects of personalized recommendations and explainable artificial intelligence

Publication Details
Publication Date
2022-04-11
Journal
Publisher
ISSN
Cited by
128
Access Type
Author Information
Authors
Robert Zimmermann
Daniel Mocencahua Mora
Douglas Cirqueira
Markus Helfert
Marija Bezbradica
Dirk Werth
Wolfgang Weitzl
René Riedl‬
Andreas Auinger
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%