Inhibiting factors in a hypothetical shoplifting situation - a contribution to crime prevention
Current Issues of Business and Law
This paper addresses the relation between public urban safety and crime. It is assumed that crime prevention is a crucial tool to promote urban safety and that crime prevention programmes or measures must be designed and implemented based on knowledge about what tends to inhibit people from committing a certain crime. It is also assumed that inhibiting factors are, quite frequently, socially constructed and imposed, meaning that an individual hypothetical decision of offending, or not offending, is influenced by socially constructed ethical values, moral values and perceived social cost. In other words, it is assumed that an individual decision is not unique, in that it is felt, fought and implemented to cope with a particular situation. Results of an exploratory empirical study support these assumptions. A sample of 200 inhabitants of Lisbon was used in an inquiry into what kind of factors would inhibit each respondent from committing several hypothetical offences, namely shoplifting. Data was analysed using three methods of multivariate analysis: Multiple Correspondence Analysis, Cluster Analysis and Categorical Regression. Results show that decisions are influenced by a complex set of factors. First, each decision is influenced by ethical and moral values and by perceived social cost which are mainly socially configured. Secondly, clear and significant differences are found between groups configured by inhibiting factors. Ethical and moral values and perceived social cost did not appear as unique in that each individual constructs them from a given situation. This means that socially configured ethical and moral values, not a unique situational morality, as well as perceived social cost can inhibit shoplifting. Implications for crime prevention design and implementation are analysed and discussed.
Urban safety,Shoplifting,Crime prevention,Multidimensional profiles,Multiple correspondence analysis,Categorical regression