JOURNAL ARTICLE
The road to learning "who am I" is digitized: A study on consumer self‐discovery through augmented reality tools.
Published In: Journal of Consumer Behaviour, 2023, v. 22, n. 5. P. 1112 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Ambika, Anupama; Belk, Russell; Jain, Varsha; Krishna, Rajneesh 3 of 3
Abstract
Today, digital tools offer multiple avenues for consumers to learn about themselves. Self‐discovery or knowing "who am I" is fundamental to our everyday experience. However, there is a paucity of research that investigates the "how and why" of self‐discovery, made possible by technological advancements. Adopting the theoretical tenets of extended self, possible selves, storied selves, and the twin metaphors of self and identity, we follow a multimethod qualitative approach to explore consumer self‐discovery in the context of AR‐based makeup and grooming apps and filters. We establish a framework for AR‐facilitated self‐discovery by analyzing the data obtained via Netnography and 22 in‐depth interviews. The findings suggest that digital tools enable the discovery of previously unknown facets of the consumers' self‐concept. Theoretically, this study demystifies the process of technology‐enabled self‐discovery, which is related to better life decisions and consumer well‐being. Brands may apply these insights to inculcate the discovery components into the AR design, which can facilitate the adoption of new products. Finally, this study highlights the possible challenges to be avoided to ensure consumer well‐being while using AR‐enabled digital tools. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Consumer Behaviour. 2023/09, Vol. 22, Issue 5, p1112
- Document Type:Article
- Subject Area:Sociology
- Publication Date:2023
- ISSN:1472-0817
- DOI:10.1002/cb.2185
- Accession Number:171582686
- Copyright Statement:Copyright of Journal of Consumer Behaviour is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.