Department(s)
Graziadio Business School
Document Type
Article
Version Deposited
Accepted manuscript
Publication Date
2023
Keywords
need to belong, self-disclosure, trait reactance, envy, online social network obsessive-compulsive disorder
Abstract
Through online social networks, individuals establish and maintain social connections to satisfy their need to belong. Recent research suggests that taken too far, one’s need to belong can increase envy and lead to maladaptive social media behavior aligned with obsessive-compulsive disorder. This study examines the role of two personality traits, one’s intrinsic need to belong and trait reactance, on feelings of envy and the self-disclosure processes that lead to obsessive-compulsive disorder on social networks. A sample of 354 U.S. adult users of Facebook completed a survey measuring individuals’ need to belong, trait reactance, envy, self-disclosure, and online social network obsessive-compulsive disorder. Regression analyses reveal that need to belong and trait reactance both independently and interactively relate to envy, and that self-disclosure mediates the relationship between envy and obsessive-compulsive disorder on social networks. Those with low trait reactance appear at the lowest risk of online social network OCD no matter their need to belong. The highest risk profile for online OCD is found in those with both high trait reactance and high need to belong. Overall, our findings support further exploration of one’s intrinsic need to belong and trait reactance as personality indicators of risk for online social network OCD.
Publication Title
Cyberpsychology, Behavior, and Social Networking
Volume
26
Issue
8
DOI
10.1089/cyber.2022.0272
Recommended Citation
Shanahan, D. E., Russell, C. A., and Alderman, J. (2023). The role of personality, self-disclosure, and envy in maladaptive social media engagement. Cyberpsychology, Behavior, and Social Networking. ahead of print. doi: 10.1089/cyber.2022.0272
Comments
Publication can be accessed at this link: https://doi.org/10.1089/cyber.2022.0272