JOURNAL ARTICLE
Social networks that matter: Explaining the social participation of university students.
Published In: Citizenship Teaching & Learning, 2024, v. 19, n. 3. P. 325 1 of 3
Database: Education Source Ultimate 2 of 3
Authored By: Dávila, María Celeste; Zlobina, Anna; Belli, Simone 3 of 3
Abstract
This study examines the influence of social relationships on the development of social participation among university students, focusing on how previous participation experiences affect this dynamic. Surveying 827 Spanish social sciences undergraduates, the research found that students' social networks contain relatively few socially engaged individuals, mostly friends and acquaintances rather than family, and that these networks are associated with past social participation but only weakly predict future engagement. The study distinguishes between civic participation (e.g., volunteering, association membership) and political participation, finding that social network composition more strongly predicts civic than political participation, with friendships playing the most significant role. Contrary to expectations, relationships formed on campus did not have a greater impact than those off campus, and previous participation experience did not moderate the influence of social networks on future participation intentions. These findings suggest that fostering diverse and emotionally close peer networks may enhance civic engagement among university students, while highlighting the need to reconsider the university's role in promoting active citizenship.
Additional Information
- Source:Citizenship Teaching & Learning. 2024/09, Vol. 19, Issue 3, p325
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:17511917
- DOI:10.1386/ctl_00167_1
- Accession Number:181483719
- Copyright Statement:Copyright of Citizenship Teaching & Learning is the property of Intellect Ltd. 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.