Citation

Jelić M, Biruski DC, Ajduković D. Coll. Antropol. 2013; 37(1): 1-10.

Affiliation

University of Zagreb, Faculty of Humanities and Social Sciences, Department of Psychology, Zagreb, Croatia. mjelic@ffzg.hr

Copyright

(Copyright © 2013, Croatian Anthropological Society)

DOI

unavailable

PMID

23697243

Abstract

After a violent conflict many post-conflict communities remain ethnically divided and normalization of the inter-group relations is hindered not only by in-group norms and interpretation of past events, but also by collective guilt. Although collective guilt has proved to be an important indicator of post-conflict social repair, more research is needed to define its predictors. This study, conducted in an ethnically divided community, confirmed that collective guilt acceptance and collective guilt assignment in our sample are typical for the post-conflict pattern of intergroup relations--people readily assign guilt to the outgroup but are unwilling to accept the guilt of their in-group. This suggests that the process of community social reconstruction has not considerably progressed. Our findings also suggest that the two aspects of collective guilt--assignment and acceptance--are predicted by similar sets of variables. Both collective guilt assignment and collective guilt acceptance are influenced by identification with in-group which influence is fully or partially mediated with the justification of the in-group's wrongdoings. This indicates that in the post-conflict setting relationship towards the in-group may be more important for experiencing collective guilt than the relationship towards the out-group. Relationship towards out-group, although not crucial, also plays a role in experiencing collective guilt. Specifically, both affect towards and cognitions about out-group members predict collective guilt assignment (with cognition being stronger predictor than positive affect), whereas only (absence of) positive affect predicts acceptance of collective guilt and the cognitive aspect is not predictive.

Language: en