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

AuthorThomas J. Holt,Bryanna Fox
Published date01 July 2021
Date01 July 2021
Subject MatterArticles
CRIMINAL JUSTICE AND BEHAVIOR, 2021, Vol. 48, No. 7, July 2020, 943 –963.
Article reuse guidelines:
© 2020 International Association for Correctional and Forensic Psychology
University of South Florida
Michigan State University
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 criminologi-
cal 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
major focus of criminological inquiry over the last century has been on the etiology of
juvenile delinquency (Loeber & Farrington, 1998). A number of individual-level risk
factors have been shown to increase the risk of juvenile delinquency, including delinquent
peer association (Akers, 1998), low self-control (Gottfredson & Hirschi, 1990; Pratt &
Cullen, 2000), victimization/abuse (Fox et al., 2015; Lauritsen & Laub, 2007), and
structural-level factors such as neighborhood disorganization and socioeconomic status
(SES; Bursik & Grasmick, 1993; Pratt & Cullen, 2005). Research also suggests that these
risk factors “do not operate in isolation” (Kendziora & Osher, 2004, p. 182) and that most
justice-involved youth display multiple risk factors that span across theoretical domains
(Loeber & Farrington, 1998; Thornberry et al., 1995). Moreover, these risk factors appear
to operate in a cumulative fashion, resulting a nonlinear effect on risk of future offending
(Herrenkohl et al., 2000; Loeber & Farrington, 1998).
These findings have led to a notable shift in the juvenile justice field over the past two
decades, with researchers aiming to “identify the key risk factors for offending, and tool
AUTHORS’ NOTE: Correspondence concerning this article should be addressed to Bryanna Fox, Department
of Criminology, University of South Florida, 4202 E. Fowler Avenue, SOC 107, Tampa, FL 33620; e-mail:
969754CJBXXX10.1177/0093854820969754Criminal Justice and BehaviorFox, Holt / Juvenile Hacker Risk Factors
prevention methods designed to counteract them” (Farrington, 2000, p. 1; Farrington et al.,
2012). This approach is focused on prevention, not punishment, and aims to develop strate-
gies to specifically counteract these risk factors (Farrington, 2000). In a related vein,
research on the heterogeneity of those who offend has also spurred efforts to identify the
distinct risk factors among justice-involved youth (Fox & DeLisi, 2018; Muniz et al., 2019).
Together, these efforts suggest that we can identify unique risk factors among youth and
develop tailored prevention and intervention strategies for youth with varied risk factors,
rather than the “one-size-fits-most” approach to juvenile justice (Andrews & Bonta, 2010;
Farrington & Welsh, 2006).
A recent challenge to the treatment and prevention of delinquency is that computers and the
internet have enabled crime to occur in ways that cannot be as easily monitored by parents,
teachers, and police (Holt & Bossler, 2015; Yar, 2005). Cybercrime, or the misuse of technol-
ogy to offend, has become a global problem, although available research on the risk factors
varies by cybercrime type (Holt & Bossler, 2015). There is a particularly small body of
research on the risk factors for computer hacking, broadly defined as the use of specialized
knowledge of technology to gain access to sensitive data and computer networks (Holt &
Bossler, 2015; Taylor, 1999). Hacking is a unique form of cybercrime, as the same action and
knowledge can be used to secure a system from compromise or illegally gain access to critical
system functions and data (Holt, 2007; Jordan & Taylor, 1998). In fact, the computer security
industry is predicated on understanding the malicious application of hacking to defend sensi-
tive networks from compromise by hackers and nation-state actors (Holt & Bossler, 2015).
As a consequence, social scientists typically use specific questions to assess computer
hacking in general population surveys (see Holt & Bossler, 2015, for review). Often overtly
illegal but simplistic forms of hacking have been measured in the past, such as password
guessing and adding or changing data (Holt & Bossler, 2015; Leukfeldt, 2017). More seri-
ous forms of hacking like malware use and theft of personal information have very low
prevalence rates and are often absent from juvenile surveys (Holt & Bossler, 2015; Leukfeldt,
2017). Similarly, questions related to simple forms of digital piracy have been treated as
distinct from hacking due to the ease with which music and media piracy are performed in
the modern computing age (Holt & Bossler, 2015). As a result, researchers have been lim-
ited in their ability to assess involvement in legitimate or legally questionable hacks, imped-
ing our understanding of pathways to hacking and variations to involvement in legal and
illicit hacks across populations.
Despite these measurement issues, hacking has increasingly been linked to serious crimi-
nal activity over the past three decades, particularly economic harms stemming from the
loss of sensitive data and degradation of computer hardware and software functionality
(Furnell, 2002; Grabosky, 2016; Holt, 2007). There has also been a growing concern over
the increased engagement of youth in hacking, with the near ubiquitous access to home
computing and internet connectivity since the 1980s (Calce & Silverman, 2008; Slatalla &
Quittner, 1995). Qualitative examinations have found youth with early access and interest
in computers may become involved in hacking (Holt, 2007; Taylor, 1999). Initial hacks
may often be simplistic in nature, such as guessing passwords to remotely access user

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