Identifying With Political Actions Plays a Greater Role in Predicting Collective Action Intention Among the Advantaged Than Identifying With Political Opinions

ABSTRACT This research aims to investigate what motivates members of advantaged groups to join collective action efforts aimed at reducing discrimination, as understanding this motivation is crucial for designing effective interventions and campaigns that aim to mobilise these groups in the fight against discrimination. Across three studies conducted in Germany ( N Study 1 = 326; N Study 2 = 136) and the UK ( N Study 3 = 184), we investigated the psychological mechanisms linking moral conviction to collective action intention among members of advantaged groups. Each study focused on a distinct social issue: xenophobia, sexism, and racism. Specifically, we examined the mediating roles of different types of politicised identification (solidarity‐based and opinion‐based), perceived collective efficacy, and perceived closeness with discriminated groups. Results consistently showed that solidarity‐based identification was a stronger and more reliable predictor of collective action intention than collective efficacy, closeness with discriminated individuals, or opinion‐based identification. By focusing on solidarity‐based identification, social movements can better harness the potential of these groups to contribute to meaningful social change, thereby enhancing the overall effectiveness of efforts to combat xenophobia, sexism, and racism.

Authors

Institutions

Publication Details

Journal
Journal of Community & Applied Social Psychology
Published
2025-10-20
DOI
https://doi.org/10.1002/casp.70190
Primary Topic
Social and Intergroup Psychology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifying With Political Actions Plays a Greater Role in Predicting Collective Action Intention Among the Advantaged Than Identifying With Political Opinions

Sandra Obradović, Nihan Albayrak‐Aydemir, Daniel Busacca Dolleo
Journal of Community & Applied Social Psychology
Social and Intergroup Psychology
article

Identifying With Political Actions Plays a Greater Role in Predicting Collective Action Intention Among the Advantaged Than Identifying With Political Opinions

Sandra Obradović, Nihan Albayrak‐Aydemir, Daniel Busacca Dolleo
article en

Abstract

ABSTRACT This research aims to investigate what motivates members of advantaged groups to join collective action efforts aimed at reducing discrimination, as understanding this motivation is crucial for designing effective interventions and campaigns that aim to mobilise these groups in the fight against discrimination. Across three studies conducted in Germany ( N Study 1 = 326; N Study 2 = 136) and the UK ( N Study 3 = 184), we investigated the psychological mechanisms linking moral conviction to collective action intention among members of advantaged groups. Each study focused on a distinct social issue: xenophobia, sexism, and racism. Specifically, we examined the mediating roles of different types of politicised identification (solidarity‐based and opinion‐based), perceived collective efficacy, and perceived closeness with discriminated groups. Results consistently showed that solidarity‐based identification was a stronger and more reliable predictor of collective action intention than collective efficacy, closeness with discriminated individuals, or opinion‐based identification. By focusing on solidarity‐based identification, social movements can better harness the potential of these groups to contribute to meaningful social change, thereby enhancing the overall effectiveness of efforts to combat xenophobia, sexism, and racism.

Journal of Community & Applied Social PsychologyVol. 35(6)
Boğaziçi University (TR), London School of Economics and Political Science (GB)
Openalex Percentile: Top 60%
Social and Intergroup Psychology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.