文献信息
文献标题：Juvenile Delinquency in the Virtual World: Similarities and Differences between Cyber-Enabled, Cyber-Dependent and Offline Delinquents in the Netherlands（虚拟世界中的青少年犯罪：荷兰使用网络犯罪、依赖网络犯罪和线下犯罪的异同）
文献作者：Josja J. Rokven等
文献出处：《 International Journal of Cyber Criminology》,2018.12(1): 27–46.
字数统计：英文3172单词，18497字符；中文5758汉字
外文文献
Juvenile Delinquency in the Virtual World: Similarities and Differences between Cyber-Enabled, Cyber-Dependent and Offline Delinquents in the Netherlands
Abstract This study examines similarities and differences between juvenile delinquents of self-reported cyber-enabled offenses, cyber-dependent offenses, and offline offenses. The study builds on past studies by examining a broad range of online and offline offenses among a national probability sample of
Dutch juveniles aged 12-17 years old. Results show that juveniles who report both offline and online offenses have the most high-risk profile. Within the group online delinquents, juveniles who commit both cyber-dependent and cyber-enabled offenses have the highest risk profile. The results further indicate that cyber-dependent delinquents are a distinct group from online delinquents.
Keywords: Online offending, cyber crime, cyber-dependent crime, cyber-enabled crime, risk and promotive factors.
Introduction
Since 2007 police census data have shown a sharp decline in juvenile crime in the Netherlands (Van der Laan & Goudriaan, 2016). Because of this crime drop, the urgency to deal with juvenile crime seems to have decreased, and the focus has shifted to more specific forms of crime, such as high impact crimes. However, official statistics relate primarily to traditional offline offenses. One possible explanation for the observed crime drop is that juveniles have shifted from committing traditional offline offenses to online offenses (Tcherni et al., 2016). With the digitalization of society, new ways to commit traditional offline offenses have emerged, as well as opportunities to commit new types of offenses online. This raises the question as to whether ‘street criminals’ have gone online, or whether we are dealing with a new type of delinquent.
Previous research distinguishes two types of online delinquency: cyber-enabled and cyber-dependent delinquency (Holt & Bossler, 2016; McGuire &Dowling, 2013). Cyber- enabled delinquency refers to ‘traditional’ offenses that are committed using Information Communication Technology (ICT), and includes acts such as online fraud, extortion, and online stalking. Cyber-dependent delinquency refers to offenses that can only be committed using ICT and that are primarily directed against computer or network resources. This includes acts such as hacking, distributing viruses, and orchestrating DDoS- attacks.
In an attempt to better understand what kind of individual is involved in online delinquency, scholars have now started to examine correlates of online delinquency and their differences and communalities with correlates of offline delinquency. The majority of previous studies have limited their focus to either one or, at most, a small number of online offenses. Regarding cyber-enabled delinquency, research has focused on offenses such as bullying and online harassment (e.g., Kerstens & Veenstra, 2015; Raskauskas & Stolz, 2007; Ybarra & Mitchell, 2004), online child pornography (for an overview, see Babchishin, Hanson & Hermann, 2010), and digital piracy (e.g., Brunton-Smith & McCarthy, 2016; Higgins, 2005; Higgins, Fell & Wilson, 2006; Wolfe & Higgins, 2009). With regard to cyber-dependent offenses, most studies have focused on hacking (e.g., Bossler & Burruss, 2011; Khey et al., 2009; Yar, 2005a). To
the best of our knowledge, only one study has focused on a broader range of online offenses, whilst also including offline offenses (Donner, Jennings & Banfield, 2015). Donner and colleagues found that online delinquents were often prone to offline delinquency as well. However, these findings are based on a sample from a specific population, namely undergraduate college students in the US. The question is whether their results can be generalized to a national probability sample of juveniles.
In the current study, we build on previous research by first investigating the extent to which cyber-enabled and cyber-dependent delinquents differ from each other, and secondly the extent to which they differ from offline delinquents. Studying types of online delinquents and their similarities and differences with traditional offline delinquents is important, because if online delinquents are similar to offline delinquents, the same prevention methods could be used for both. However, if differences emerge within and between groups, different approaches regarding prevention and forensic treatment may be required.
To study potential differences between cyber-enabled, cyber-dependent, and offline delinquents, we used the Youth Delinquency Survey (YDS), a cross-sectional self- reported study on a national probability sample of juveniles from the Netherlands (see Van der Laan, Blom & Kleemans, 2009). The YDS contains detailed information on both self-reported online and offline delinquency, and on risk and
promotive factors that are related to traditional offline delinquency. Risk factors increase the likelihood of delinquency, whereas promotive factors decrease this likelihood. Determining the differences between cyber-enabled and cyber-dependent delinquents, and determining the extent to which online delinquents differ from offline delinquents, is done by examining these risk and promotive factors. The main research questions of this study are: What distinguishes juvenile delinquents of cyber-enabled offenses from juvenile delinquents of cyber-dependent offenses? What distinguishes juvenile delinquents of online offenses from juvenile delinquents of offline offenses?
1.Theory
1.1.Risk Factor Model
The risk factor model is based on the (bio) social ecological model of Bronfenbrenner (1979).The general idea behind this model is that different domains influence the likelihood of antisocial and delinquent behavior (Farrington, 2003; Lipsey & Derzon, 1998; Loeber et al., 2008). The domains are generally organized into five broader categories: the individual, family, school, peers, and the community domain.
A variety of factors have been found to increase the likelihood of delinquent behavior. Individual risk fa
ctors include impulsivity or defective moral beliefs (Agnew, 2003; Farrington, 2003), unstructured routine activities without the supervision of parents (Osgood & Anderson 2004; Osgood et al., 1996), and (excessive) substance use (Felson, 1998). Another important risk factor is self-control. Self-control has been demonstrated to be one of the most influential correlates of traditional crime, and has also frequently been applied to various forms of cybercrime (e.g., Bossler & Burrus, 2011; Higgins, 2005). Next, certain online activities may place individuals at risk for online delinquency. Past studies suggest that more advanced forms of cyber-dependent crimes, such as hacking, may require higher levels of computer skills (Bossler & Burrus, 2011; Xu, Hu & Zhang, 2013). A factor that may favor the development of these skills is gaming; juveniles, who frequently play online games, may develop more online skills, which are necessary for the (successful) pursuit of cyber-crimes (Xu, Hu & Zhang, 2013).
In the family domain, poor parental bonding, little openness to parents, and the lack of parental supervision have been found to predict delinquency (Rutter, Giller &Hagell, 1998; Stattin & Kerr, 2000). In the school domain, poor academic performance and low attachment to school are examples of risk factors (Junger & Haen Marshall, 1997; Mason & Windle, 2002). The delinquent behavior of friends is considered an important risk factor in the peer domain (Warr, 1993; Weerman, 2011), and poverty and community disorganization are examples of risk factors in the community domain (Hawkins et al., 2000).
In addition to risk factors, scholars have also identified promotive factors (Farrington et al., 2008; Sameroff et al., 1998). Promotive factors reduce the
likelihood of negative outcomes, and can counterbalance risk factors. As such, promotive factors can (partially) explain why not all juveniles that are exposed to risk factors become involved in antisocial or delinquent behavior (Farrington & Welsh, 2007; Loeber et al., 2008). Examples of promotive factors are strong social bonds, pro-social norms, parental support, and a strong attachment to school (Catalano et al., 2004).
Research has shown that the accumulation of risk factors across multiple domains increases the likelihood of negative outcomes, including antisocial and delinquent behavior (Loeber et al., 2008; Stouthamer-Loeber et al., 2002). Studies on promotive factors show that an accumulation of promotive factors reduces the likelihood of negative outcomes (Farrington et al., 2008; Sameroff et al., 1998). Because promotive factors have the ability to buffer the negative influences of risks, scholars have also examined the cumulative impact of risk and promotive factors across different domains. Overall, the more risk factors and the fewer promotive factors present, the higher the likelihood that individuals engage in (serious) delinquent behavior (e.g., Stouthamer-Loeber et al., 2002; Van der Laan et al., 2010). As such, we expect juveniles who commit several types of offenses, both online and offline, the
most serious delinquents, to be characterized by the highest risk profile
(i.e., most risk factors and fewest promotive factors).
1.2.The Current Study
In this study, we first examine the differences between self-reported cyber-enabled and cyber-dependent delinquents on risk and promotive factors, distinguishing between factors in the individual, family, peer and school domain. Secondly, we test whether these online delinquents differ from offline delinquents on these factors. So far, research on online delinquency has mostly focused on a single type or limited number of online offenses, and/or are based on samples of student populations. Our study builds on and extends this body of literature by examining a broader range of cyber-enabled, cyber-dependent, and offline offenses among a national probability sample of juveniles. This way, we are able to provide a more comprehensive picture of different types of online delinquents, and their differences
with offline delinquents. For this purpose, we focus on a variety of risk and promotive factors derived from the risk factor model, and also investigate the cumulative impact of these risk and promotive factors across different domains.
2.Data and Methods
We used data from the most recent wave (2015) of the YDS. The YDS is a cross-sectional, self-report study, conducted every five years among a national probability sample of Dutch juveniles, aged between 10 and 23 years. Within the YDS, a random stratified sampling method was followed. The initial sample was divided into 30 strata, defined by age and ethnic origin, followed by a random selection of juveniles from the Municipal Population Register. Ethnic minorities (Turks, Moroccans, Surinamese and Antilleans/Arubans) and juveniles under twelve (10 and 11-year-olds) were oversampled, as these groups tend to be less likely to participate in survey research.
In the current study, we focused solely on minors aged between 12 to 17 years. We excluded juveniles under the age of twelve, as these individuals cannot be prosecuted by the juvenile justice system in the Netherlands. Young adults (18-to 23-year-olds) were also excluded, as these individuals are prosecuted by the adult justice system in the Netherlands. Between January and June 2015, a total of 2,207 juveniles, aged between 12 and 17 years, were approached to participate in the study. With an unweighted response rate of 61.8%, the final sample consisted of 1,365 juveniles. The sample was largely representative of the target populations. Juveniles from Turkish and Moroccan origin were less likely to participate in the study (response rates respectively 52.2% and 56.2%). However, this underre
presentation is small enough that the data can be considered representative for these groups as a whole (Engelen, Roels & de Heij, 2015). The data were gathered by means of Computer Assisted Personal Interviews (CAPI) and Computer Assisted Self Interviews (CASI), with CASI being used for questions about sensitive information, including self-reported delinquency.
2.1 Dependent Variables
To measure delinquency, we used 27 items on offline delinquency, and 10 items
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