Preventing radicalization leading to violence: Insights from the significance quest theory and its 3N model.

  • Published In: Journal of Community & Applied Social Psychology, 2023, v. 33, n. 3. P. 608 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Da Silva, Caroline; Amadio, Nicolas; Sarg, Rachel; Domingo, Bruno; Tibbels, Sarah; Benbouriche, Massil 3 of 3

Abstract

Radicalization leading to violence is a major societal issue all over the globe. In order to prevent its increase and expansion, measures need to be taken at different instances and levels. In the present narrative review, to inform evidence‐based practices, we bring together numerous applied recommendations made by scholars studying the psychological underpinnings of radicalization within the framework of the Significance Quest Theory and its 3N model. The applied recommendations target at least one of the three elements of the 3N model (i.e., need, narrative, and network) in at least one of the three levels of prevention (i.e., primary, secondary, and tertiary). In the discussion, we highlight which of these are still lacking empirical evaluation, which might be problematic and why, and how policymakers, practitioners, and researchers can work together to provide an integrative model of intervention addressing both the need for significance and the influence of radical narratives and groups. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Community & Applied Social Psychology. 2023/05, Vol. 33, Issue 3, p608
  • Document Type:Article
  • Subject Area:Military History and Science
  • Publication Date:2023
  • ISSN:1052-9284
  • DOI:10.1002/casp.2667
  • Accession Number:163565959
  • Copyright Statement:Copyright of Journal of Community & Applied Social Psychology is the property of Wiley-Blackwell 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.