JOURNAL ARTICLE
Predicting Loyalty: Examining the Role of Social Identity and Leadership in an Extreme Operational Environment—A Swedish Case.
Published In: Armed Forces & Society (Sage Publications Inc.), 2024, v. 50, n. 3. P. 607 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Engelkes, Torbjörn; Sverke, Magnus; Lindholm, Torun 3 of 3
Abstract
This study investigates how social identity fusion—defined as a strong sense of oneness with an in-group—and developmental leadership relate to military personnel's willingness to behave loyally toward their closest workgroup, unit, and mission. Using survey data from 152 Swedish military personnel deployed in the United Nations mission in Mali, results showed that social identity fusion was positively associated mainly with willingness to make moderate and extreme sacrifices for these groups, while developmental leadership was positively related to both sacrificial and active loyal behaviors across most loyalty domains. The findings suggest that leadership styles fostering trust and commitment, alongside strong identification with organizational groups, may enhance loyalty in military contexts, though the study notes limitations including its cross-sectional design and specific sample. These insights contribute to understanding factors influencing loyalty in extreme operational environments and highlight the importance of considering multiple loyalty domains and social identities within military organizations.
Additional Information
- Source:Armed Forces & Society (Sage Publications Inc.). 2024/07, Vol. 50, Issue 3, p607
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2024
- ISSN:0095-327X
- DOI:10.1177/0095327X221150948
- Accession Number:177758755
- Copyright Statement:Copyright of Armed Forces & Society (Sage Publications Inc.) is the property of Sage Publications Inc. 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.