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Psychology and Marketing
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Self‐quantification and consumer well‐being: A meta‐analytic review

Authors: Anuj Jain; Debasis Pradhan; Abhisek Kuanr; Shiwangi Singh;

Self‐quantification and consumer well‐being: A meta‐analytic review

Abstract

AbstractSelf‐quantification technology is increasingly and irrevocably transforming consumers' relationships with their own minds and bodies. However, existing research findings on the contribution of self‐quantification to consumer well‐being are disparate. Given the popularity of self‐quantification technology among consumers in the post‐pandemic era and its inherent transformative nature, it is surprising that this gap remains unaddressed. To resolve this inconsistency and to examine how and when self‐quantification influences consumer well‐being, we conduct a meta‐analysis of consumer well‐being in the context of self‐quantification technology. Our findings reveal that self‐quantification positively influences consumer well‐being. However, self‐quantification also negatively affects consumer well‐being through body image and self‐esteem. The systematic moderation effects of cultural dimensions (e.g., uncertainty avoidance and individualism), prior experience, data sharing, and sample characteristics on the relationship between self‐quantification and consumer well‐being are also confirmed. While uncertainty avoidance, prior experience, and data sharing accentuate the positive effects of self‐quantification on consumer well‐being, an individualistic culture attenuates this influence. This study contributes to the consumer well‐being literature and extends objectification theory in the context of self‐quantification. These findings will guide practitioners and policymakers in devising strategies and policies to allow self‐quantification technology to be used in a way that enhances consumers' health and well‐being.

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Top 10%
Green
hybrid