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Augmented-Reality-Enhanced Product Comparison in Physical Retailing

Published: 08 September 2019 Publication History

Abstract

Augmented reality technology has experienced great improvement in recent years and it has been successfully applied to industry and entertainment settings. However, its application in everyday contexts such as shopping is still very limited. One of the requirements to seamlessly incorporate augmented reality into everyday tasks is to find intuitive, natural methods to make use of it. Due to the inherent capabilities of augmented reality to work as a visual aid to explore and extend the knowledge a user has of the surroundings, this paper proposes the combination of AR technology and product advisors in a novel approach for product comparison. The user's awareness of the differences between multiple physically present objects is enhanced through virtual augmentations, supporting an intuitive way of comparing two or more products while shopping. To assess the validity of the concept, a prototype for an AR-based shopping assistant for comparing vacuum cleaners has been implemented and evaluated in a user study, testing different methods of visual comparison and interaction.

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Cited By

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  • (2024)Enhancing Customer Shopping Experience Through AR Mini-GamesXR and Metaverse10.1007/978-3-031-50559-1_2(16-28)Online publication date: 23-Jan-2024
  • (2023)Unveiling Augmented Reality Trends in the Retail Industry: A Systematic and Bibliometric ReviewParadigm: A Management Research Journal10.1177/0971890723120665627:2(192-210)Online publication date: 4-Dec-2023
  • (2023)Creating Omni-Channel In-Store Shopping Experiences through Augmented-Reality-Based Product Recommending and ComparisonInternational Journal of Human–Computer Interaction10.1080/10447318.2022.216365040:10(2578-2603)Online publication date: 25-Jan-2023
  • Show More Cited By

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cover image ACM Other conferences
MuC '19: Proceedings of Mensch und Computer 2019
September 2019
863 pages
ISBN:9781450371988
DOI:10.1145/3340764
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 08 September 2019

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Author Tags

  1. augmented reality
  2. comparison visualization
  3. natural interaction
  4. physical object comparison

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MuC'19
MuC'19: Mensch-und-Computer
September 8 - 11, 2019
Hamburg, Germany

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Cited By

View all
  • (2024)Enhancing Customer Shopping Experience Through AR Mini-GamesXR and Metaverse10.1007/978-3-031-50559-1_2(16-28)Online publication date: 23-Jan-2024
  • (2023)Unveiling Augmented Reality Trends in the Retail Industry: A Systematic and Bibliometric ReviewParadigm: A Management Research Journal10.1177/0971890723120665627:2(192-210)Online publication date: 4-Dec-2023
  • (2023)Creating Omni-Channel In-Store Shopping Experiences through Augmented-Reality-Based Product Recommending and ComparisonInternational Journal of Human–Computer Interaction10.1080/10447318.2022.216365040:10(2578-2603)Online publication date: 25-Jan-2023
  • (2021)Omnichannel Retail Customer Experience with Mixed-Reality Shopping Assistant SystemsAdvances in Visual Computing10.1007/978-3-030-90439-5_40(504-517)Online publication date: 4-Oct-2021
  • (2021)Acceptance of an AR-Based In-Store Shopping Advisor - the Impact of Psychological User CharacteristicsHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85623-6_28(457-479)Online publication date: 30-Aug-2021
  • (2020)In-Store Augmented Reality-Enabled Product Comparison and RecommendationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412266(180-189)Online publication date: 22-Sep-2020

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