A Decision Tree Model for Profiling Citizens’ Support for Self-Help Strategies

Noncompliance and deviancy among citizens are practices that are well-documented. However, there is no clear definition of the types of citizens who do so. Using self-reported data from 461 Israeli respondents, we propose a classification of such people based on their levels of support for self-help strategies in the context law enforcement, using a decision tree model. Three different profiles emerged from our data: (1) people who do not support self-help at all, (2) people who support self-help strategies, and (3) people who support illegal self-help strategies, including harming their alleged perpetrator. Our findings suggest that process-related factors such as trust and procedural justice play a major role in classifying people into these profiles, whereas outcome-related factors are less important. This study improves our understanding of the concept of self-help strategies and noncompliance, because it identifies the factors most important in predicting support for such behavior.