Beautify the blurry self: Low self‐concept clarity increases appearance management.
Published In: Journal of Consumer Psychology (John Wiley & Sons, Inc. ), 2023, v. 33, n. 2. P. 377 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wang, Jiaqian; Yu, Yiqi 3 of 3
Abstract
The current research examines how and why self‐concept clarity (i.e., having self‐aspects that are integrated into a well‐defined whole) shapes consumers' appearance management behaviors. Five (including four pre‐registered) studies and one supplemental study provide correlational and causal evidence for the link between low self‐concept clarity and appearance management (e.g., choice of appearance‐enhancing products, interest in cosmetic procedures, and beauty filters). Furthermore, we demonstrate that public self‐consciousness mediates this effect (Studies 3–4). We also find convergent process‐by‐moderation evidence that low self‐concept clarity increases appearance management only when the appearance management behavior is perceived to be socially acceptable (Study 5). In addition, we rule out global and appearance self‐esteem, private self‐consciousness, self‐improvement, and mood management as potential mechanisms. This research extends the literature on self‐concept, impression management, and appearance management and yields implications for beauty marketing, health communication, and consumer well‐being. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Consumer Psychology (John Wiley & Sons, Inc. ). 2023/04, Vol. 33, Issue 2, p377
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:1057-7408
- DOI:10.1002/jcpy.1298
- Accession Number:162595326
- Copyright Statement:Copyright of Journal of Consumer Psychology (John Wiley & Sons, Inc. ) 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.)
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