Use of a Multitheoretic Model to Understand and Classify Juvenile Computer Hacking Behavior

Abstract

Criminological inquiry has identified a range of risk factors associated with juvenile delinquency. However, little research has assessed juvenile computer hacking, despite the substantial harm and opportunities for delinquent behavior online. Therefore, understanding the applicability of criminological risk factors among a cross-national sample of juvenile hackers is important from a theoretical and applied standpoint. This study aimed to address this gap using a logistic regression and latent class analysis (LCA) of risk factors associated with self-reported hacking behavior in a sample of more than 60,000 juveniles from around the globe. Results demonstrated support for individual- and structural-level predictors of delinquency, although distinct risk factors for hacking among three subtypes are identified in the LCA. This study examines criminological risk factors for juvenile hacking in a cross-national sample and provides insight into the distinct risk factors of hacking, so more tailored prevention and treatment modalities can be developed.

Keywords

cybercrime hacking juvenile justice prevention latent class analysis

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