Explaining support for vigilantism and punitiveness

Assessing the role of perceived procedural fairness, ethnocentrism, authoritarianism and anomia

auteurs Anjuli Van Damme
  Lieven Pauwels
tijdschrift GofS (ISSN: )
jaargang 2012
aflevering Social conflicts, citizens and policing
onderdeel Artikelen
publicatie datum 14 september 2012
taal Dutch
pagina 31
keywords punitiveness, Support for vigilantism, structural equation modelling, the confidence hypothesis, procedural fairness/confidence in the criminal justice system
samenvatting

The purpose of this study is to investigate the interrelationships among ethnocentrism, authoritarianism, anomia, the lack of confidence in the criminal justice system, punitiveness and support for vigilantism in a cross-sectional sample of 1,078 Belgian university students enrolled at Ghent University during the academic year 2009-2010. The emphasis lies on confidence in procedural justice or perceived procedural fairness, a specific type of organisational justice perception that reflects how fairly organisational procedures of the criminal justice system are perceived. First, it is assessed to what extent ethnocentrism, authoritarianism and anomia can equally explain individual differences in perceived procedural fairness of the criminal justice system, punitiveness and support for vigilantism. Ethnocentrism, anomia and authoritarianism are from a theoretical point of view hypothesised as exogenous variables that especially (but not exclusively) have indirect effects on public support for vigilantism mainly because of their effects on perceived procedural fairness in the criminal justice system and punitiveness. Finally, it is investigated to what extent punitiveness can be seen as the key mediator of the effects of all exogenous mechanisms (ethnocentrism, authoritarianism, anomia) and perceptions of procedural fairness as an endogenous mechanism on public support for vigilantism. Direct and indirect effects between latent variables are assessed using a structural equation modelling approach (full LISREL models).