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The dark side of social media in the workplace: A social comparison perspective.

Authors :
Huang, Xinyao
Fan, Peng
Source :
Computers in Human Behavior. Nov2022, Vol. 136, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Social media has widely permeated the workplace because of its potential benefits. Nowadays, it is not surprising that employees browse what friends are sharing on social media (e.g., Facebook and WeChat) during working hours. But interestingly, people tend to present themselves in a positive manner and thus selectively share positive information (e.g., positive profiles, images, or living situations) on social media platforms for others to browse. This would easily cause feelings like "how successful they are" and "I am so inferior to them" when employees browse social media information. What does this mean for their job performance? Findings from our two-wave longitudinal survey suggest that employees comparing themselves with friends who are better off on social media would fall into ego-depletion, which further reduces their job performance. Moreover, employees' preferences for online social interaction can exacerbate this process. Although short breaks obtained from using social media may help employees better engage in subsequent work tasks, the present research suggests managers be wary of the dark side of social media stemming from social comparison. This research contributes to the literature regarding employee social media use by focusing on its dark side from the lens of online social comparison. • Employees browsing social media information may fall into online social comparison. • The dark side of social media usage was examined from the lens of online social comparison. • Upward social comparison on social media can harm employee job performance via ego-depletion. • Preference for online social interaction can exacerbate the negative impact of social comparison. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07475632
Volume :
136
Database :
Academic Search Index
Journal :
Computers in Human Behavior
Publication Type :
Academic Journal
Accession number :
158389485
Full Text :
https://doi.org/10.1016/j.chb.2022.107377