“Am I at Risk of Cyberbullying”?

Aggression and Violent Behavior 23 (2015) 36–51

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Aggression and Violent Behavior

“Am I at risk of cyberbullying”? A narrative review and conceptual framework for research on risk of cyberbullying and cybervictimization: The risk and needs assessment approach

Anna Costanza Baldry a,⁎, David P. Farrington b, Anna Sorrentino a,c a Department of Psychology, Second University of Naples, Italy b Institute of Criminology, University of Cambridge, United Kingdom c Department of Humanities, University of Naples Federico II, Italy article info abstract

Article history: Bullying and its electronic evolution, cyberbullying, are widespread problems among children and adolescents. Received 17 April 2015 Numerous studies have been conducted that address the prevalence, nature, and possible impact of Accepted 13 May 2015 cyberbullying and strategies to prevent it (Patchin & Hinduja, 2013). Some recent papers have reviewed existing Available online 22 May 2015 instruments designed to measure cyberbullying (Berne et al., 2013; Vivolo-Kantor, Martell, Holland, & Westby, 2014), while others have reviewed risk factors (Kowalski et al., 2014). Keywords: The aim of the present study is to present what is known about risk factors associated with cyberbullying and Cyberbullying Cybervictimization cybervictimization by using an ecological framework, addressing the importance of adopting a risk and needs as- Risk factors sessment approach to identify early who is at risk and tailor interventions. Risk and needs assessment We conducted a review of cyberbullying risk factors, as well as of assessment and measurement instruments and risk and needs assessment to identify which papers fulﬁlled the purpose of this study. Multiple online databases (i.e., PsychInfo, SocIndex and PubMed) were searched to identify relevant studies. The keyword search criteria were: (bull* or cyberbull* or school viol* or juvenile delinquency) AND (risk* or threat* or assess*) AND (measure* or *method*) between 2000 and February 2015. Article titles and abstracts were reviewed, and all articles that ap- peared relevant were retrieved in full-text format and evaluated for inclusion in the review. In addition, articles accessed electronically were hand-searched for other relevant studies. A total of 7199 potential articles were located. Of these, only 53 were considered to be directly relevant and used for the purpose of the present work. Because of the large variability of methods, construct deﬁnitions, measures and item wording used in the different studies, a meta-analysis was not possible, therefore a narrative review approach was adopted to identify risk factors according to the ecological theoretical framework. Results regarding risk factors showed that individual as well as socio-family related factors were associated with cyberbullying and cybervictimization with some slight differences for boys and girls. The strongest risk factor that was associated with cyberbullying is school bullying. This review is of relevance because it is innovative in proposing a conceptual framework for developing a risk and needs assessment tool for cyberbullying and cybervictimization. © 2015 Elsevier Ltd. All rights reserved.

Contents

1. Introduction...... 37 1.1. Riskfactorsforcyberbullying:staticanddynamic...... 38 1.2. Relevanceofthecurrentstudy...... 38 2. Designandmethod...... 39 2.1. Literaturesearch...... 39 2.2. Selectionprocedureforrelevantpublishedarticles...... 39 2.3. Theory driven selection method of classiﬁcationofriskfactors:theecologicalframework...... 39 2.4. NarrativeSchemeReviewingSystem(NSRS)...... 39

⁎ Corresponding author at: Department of Psychology, Second University of Naples, Viale Ellittico, 31, 81100 Caserta, Italy. E-mail address: [email protected] (A.C. Baldry).

3. Findings:riskfactors...... 40 3.1. Riskfactorsforcyberbullying...... 40 3.1.1. Individual level risk factors (‘ontogenetic’)...... 40 3.1.2. Interpersonallevelriskfactors(microsystem)...... 44 3.1.3. Communitylevelriskfactors(mesosystem)...... 44 3.2. Riskfactorsforcybervictimization...... 44 3.2.1. Individual level risk factors (‘ontogenetic’)...... 44 3.2.2. Interpersonallevelriskfactors(microsystem)...... 48 3.2.3. Communitylevelriskfactors(mesosystem)...... 48 4. Discussion...... 48 4.1. Designandresearchlimitationsofthenarrativereview...... 48 4.2. Cyberbullying and cybervictimization proﬁles...... 49 4.3. A new approach for predicting and managing risk and needs of cyberbullying and cybervictimization: a risk and needs assessment tool andfutureresearch...... 49 Acknowledgment...... 50 References...... 50

1. Introduction bystanders who can in turn share the attack, prolonging the victim's distress (Dooley et al., 2009; Tokunaga, 2010). The problem is not online In the last 10 years, a growing interest has developed in the media communication per se, but its use; as clearly stated by Kowalski and col- and among researchers and policy makers in the massive development leagues in their extensive review, “certain features of online communica- of online technology and its use and impact, and in particular in young- tion including reproducibility, lack of emotional reactivity, perceived sters. Online communication is not only any more a daily way to work, uncontrollability, relative permanence, and 24/7 accessibility, make it but it is mainly used to communicate and interact with known and un- more likely for online misbehavior to occur” (Kowalski, Giumetti, known people (peers and adults). And this is particularly of relevance Schroeder, & Lattanner, 2014: 2, emphasis added). for youngsters. If in 2010 the number of children and teenagers having Once studies have extensively researched the nature and proportion access to the internet at home was 66% (reported by Tokunaga, 2010), of the problems, in the last decade they addressed characteristics linked this proportion is growing yearly. National UK statistics report an in- to such problems (Berne et al., 2013; Vivolo-Kantor, Martell, Holland, & crease in 2012 where 21 million households (80%) had internet access, Westby, 2014), trying to answer the following questions: which are the compared with 19 million (77%) in 2011 (Office for National Statistics, features of those children who bully or are bullied online? 2012). Competition between internet providers and reduction of prices As pointed out by Tokunaga (2010) and Slonje et al. (2013),whatis of IT (Internet Technology) devices have decreased prices and in- often missing in studies on cyberbullying is a clear sound theoretical creased dissemination especially in young people; access to a computer foundation guiding studies. If based on valuable theories, then methods, and/or smartphones is overwhelming. This is all good news, when variables and procedures can be adopted to test hypotheses, and reliable thinking about the free world, and the advantages of online, technolog- and coherent information can be available, helping to develop useful ical communication and search. However, research and news reports cost-effective intervention strategies. Kowalski et al. (2014),intheirex- do show another picture of web 2.0 communication. The dark side of tensive review, have overcome this limitation by adopting the General children's use of internet and associated technology is the risk of Aggression Model (GAM) to review existing (supporting) studies ad- being bullied online or bullying others, so called cyberbullying, leading dressing the different aspects related to this theory, adopted from stud- to short and long term negative consequences (Patchin & Hinduja, ies on aggression (based on theories by Bandura, 1986; Crick & Dodge, 2006; Topçu, Erdur-Baker, & Capa-Aydin, 2008; Ybarra, Mitchell, 1994) in relation to victimization and perpetration of cyberbullying. Wolak, & Finkelhor, 2006) and ultimately even suicide or attempted Another attempt to address and understand cyberbullying has been suicide (Van Geel, Vedder, & Tanilon, 2014); (for a review on conse- adopted in another recent review conducted by Mehari, Farrell, and Le quences, Tokunaga, 2010). (2014). These authors suggest that the means through which aggres- Several conceptual definitions have been provided on cyberbullying, sion takes place may be best conceptualized as a new dimension on summarized by Tokunaga (2010); this author provides an integrative which aggression can be classified, rather than addressing cyberbullying definition which tries to capture all relevant aspects: “Cyberbullying is as a “distinct counterpart to existing forms of aggression” (Mehari et al., any behavior performed through electronic or digital media by individ- 2014: 2). Therefore, research on cyberbullying should be considered uals or groups that repeatedly communicates hostile or aggressive mes- within the context of theoretical and empirical knowledge on aggres- sages intended to inflict harm or discomfort to others” (Tokunaga, 2010: sion among youngsters. 278). This definition as well as all the similar ones provided by other Another useful theoretical approach is the ecological system theory, authors (Besley, 2009; Finkelhor, Mitchell, & Wolak, 2000; Juvonen & based on Bronfenbrenner's ecological framework (Bronfenbrenner, Gross, 2008; Li, 2008; Patchin & Hinduja, 2006; Slonje & Smith, 2008; 1979, 1986, 1994) extensively used in the context of school bullying Smith et al., 2008; Willard, 2007; Ybarra & Mitchell, 2004)havesimilar in the review by Hong and Espelage (2012). The authors adopted this features to the definition of traditional bullying (Olweus, 1993)with framework to present the relationship between different individual, regard to intention of harming and in regard to imbalance of power. interpersonal and more broadly societal dimensions to show onset, With regard to ‘repeated actions’, cyberbullying can differ. Some authors development and recurrence of bullying and victimization among claim that it is enough to experience one or two actions to be defined as youngsters. The review showed empirical findings on the risk factors cyberbullying (Gámez-Guadix, Orue, Smith, & Calvete, 2013; Juvonen & associated with bullying and peer victimization at school within the Gross, 2008; Kowalski & Limber, 2007; Li, 2007; Vieno, Gini, & context of different levels of the ecological framework, addressing Santinello, 2011; Ybarra & Mitchell, 2004). A single cyber-attack (a risks and needs of those involved in bullying (either as victims or video, a comment, a picture) can remain online or in a mobile phone perpetrators), concluding that there is no one single risk factor, or for quite some time, therefore prolonging the harm to the victims cause to explain bullying, but that risk factors at all levels can have (Dooley, Pyżalski, & Cross, 2009; Raskauskas & Stoltz, 2007; Slonje, a role and influence and these vary from individual to individual, Smith, & Frisén, 2013) and increasing access to potential cyber- and from context to context. 38 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

The current narrative review aims to increase the understanding of taking place now or in the future; it increases the likelihood but its ab- risk factors for cyberbullying and cybervictimization and their relation- sence does not eliminate the risk (Hart, 2008; Hoge, Vincent, & Guy, ship and interaction to specify who is most at risk and provide a possible 2012). The assessment of the influence of risk factors on a given behav- framework for a risk and needs assessment approach to cyberbullying ior and the likelihood of its occurrence or reoccurrence is called risk and and cybervictimization (see Fig. 1). needs assessment. We therefore will explain cyberbullying and cybervictimization 1.1. Risk factors for cyberbullying: static and dynamic and their associated risk factors according to the risk and needs assessment approach, which is well known and adopted in the field of Risk factors for the purpose of the risk and needs assessment criminology, especially with regard to juvenile delinquency, but approach can be distinguished in two main categories: static risk factors, has never been used or adopted in the field of cyberbullying, within that cannot change and will always remain the same for youngsters [e.g. the ecological system framework, as we will discuss in the final gender, prior involvement in bullying or other antisocial behavior, prior part of this review. victimization, to some extent, impulsivity; past exposure to violence], and dynamic risk factors, that can change with time or as a consequence 1.2. Relevance of the current study of some form of intervention [e.g. school policies to prevent and reduce cyberbullying, parental supervision, academic achievement, empathy, This narrative review identifies various risk factors associated with time spent in online activities]. Some dynamic risk factors, in this frame- cyberbullying and cybervictimization for boys and girls based on orig- work, can be considered as ‘vulnerable’ aspects of the child so that even inal studies and significant reviews addressing risk factors associated if they are associated with the cyberbullying, they can be considered as with cyberbullying. This study also suggests the adoption of a risk and needs that can be addressed, to modify a risk that is changeable. Needs needs assessment approach (Baldry & Sorrentino, 2015; Borum & are considered as dimensions related to the individual, to his or her re- Douglas, 2003; Hoge et al., 2012), for best developing tailored, effi- lationships and involvement in cyberbullying, either as a cyberbully or cient and effective intervention strategies to manage risk and reduce cybervictim or as a bystander, that require attention and intervention. the risk of (re)occurrence of misbehavior. In particular, we examined These needs, according to the risk and needs assessment approach, if risk factors that have been found to be positively linked and associated not addressed, might not change spontaneously and will continue to in- with perpetration or victimization of cyberbullying. By adopting an fluence a juvenile's attitudes and behavior such as involvement in ecological theoretical framework (Bronfenbrenner, 1979, 1986), we cyberbullying and its justification or minimization. looked at the different levels of each (risk) factor and classified it ac- Any level of risk factors does not determine the occurrence or re- cordingly: individual (onto-), interpersonal (micro-), community occurrence of a given behavior; risk factors are not necessarily causally (meso-) and societal (macro-) and searched for consistency of find- related to a given behavior. A risk factor can explain a relationship: its ings and provided a possible explanatory model. We then used a new presence increases the likelihood of such behavior (i.e. cyberbullying) approach to address cyberbullying, the risk and needs assessment

Level of identification of Risk Factors Cyber bullying -victimization risk factors Ecological levels

Cultural norms and beliefs about violence, societal responses on safe youth use of Macrosystem Culture/Society internet, protecting issues protection issues Policies on cyber bullying, prevention programs at the Community school and community levels Support on strategies for prevention of violence

School Mesosystem School climate, rules on the use of cyber-communication, intervention programs, Peer and friends management strategies in cases of bullying

Family Peer interactions, friendship, isolation, attitudes towards cyberbullying Individulal (personality and biological) Monitoring, information, Microsystem procedures to increase ICT security, child’s supervision off/online activities, parental styles

Use of new technologies, numbers of hours online, involvement in bullying, low Ontogenetic self-esteem, empathy, moral disengagement, impulsivity

Fig. 1. Risk factors for cyberbullying and cybervictimization according to the ecological framework (adapted from Bronfenbrenner (1979). A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 39 approach, widely used in criminal behavior and highly developed and 2.3. Theory driven selection method of classification of risk factors: the used in the field of juvenile delinquency (Baldry & Kapardis, 2013; ecological framework Hoge et al., 2012). By classifying identifiedriskfactorsasstaticordy- namic factors (also identifiedas ‘needsfactors’), not only practitioners Once studies were selected for inclusion in the sample, to conduct and policy makers, but also researchers, can build and promote inter- the narrative review for classification of risk factors we adopted the ventions based on the actual needs of children at risk or potentially at ecological conceptual framework (Bronfenbrenner, 1979, 1986). By risk, and develop tailored interventions which can reduce the risk of classifying risk factors according to this theoretical framework, it is pos- cyberbullying in a cost-effective manner (Welsh & Farrington, 2007). sible to look at the relationship between risk factors and involvement in cyberbullying and cybervictimization and establish the likelihood of some children being more at risk than others, at one or more of the 2. Design and method four possible levels underlying the ecological theory — the individual, the interpersonal, the community, and the societal (Bronfenbrenner, 2.1. Literature search 1979, 1986; Hong & Espelage, 2012). At the individual level, originally identified as the ontogenetic level, A systematic search was conducted for all works on characteristics personal history and biological factors are found to influence how of cyberbullying or cybervictimization published between 2000 and children behave, and these factors show they can increase the likelihood February 2015. of becoming a victim or a perpetrator of cyberbullying. Among these To this aim, PsychInfo, SocIndex with Full Text and PubMed elec- factors we searched for gender, age, use of new technologies, and evi- tronic databases were used. We used as keyword search criteria: dence of internet addiction. (bull*, vict*, cyberbull*, cybervict*, school viol* or juvenile delinquency) At the interpersonal (microsystem) level, personal relationships such AND (risk* or threat* or assess*) AND (measure* or *method*). All as family, friends and peers may influence the risks of becoming a search terms were used in combination with each other in order to re- perpetrator or a victim of cyberbullying. For example, at the family trieve relevant publications. The abstracts of all relevant articles were level, we looked at whether studies investigated the role of parental screened for eligibility for inclusion. Full publications, whose abstracts monitoring of online activities, sharing information and procedures to were considered appropriate for the present study, were retrieved and increase ICT (Information and Communications Technology) security, reviewed in order to identify the individual and contextual risk factors the child's supervision in off/online activities, and parental styles that associated with involvement in cyberbullying and cybervictimization. can play a role and cyberbullying. With regard to peers and friends, In addition, articles accessed electronically were hand-searched for positive or negative perceived peer interactions, close friendship, other relevant studies. isolation, and peer attitudes towards cyberbullying were also included. Because of the large variability in methods, constructs and item At the community and school level (mesosystem), the context in wording across the studies included in our review, a meta-analysis which social relationships occur, such as schools or neighborhoods, was not possible. Also, specifically with regard to risk assessment or could also influence cyberbullying. Risk factors searched for include threat assessment methods, no specific instruments were found, so a theschoolclimate,thepresenceofrulesontheuseofcyber- theoretical review framework was adopted to specify a conceptual communication, school curricula including (or not) intervention framework for the development of a risk and needs assessment ap- programs, and management strategies in cases of bullying or proach to cyberbullying and cybervictimization. cyberbullying. Also, at the community level, more general policies on cyberbullying, prevention programs, the presence of services and strategies for prevention of violence can be important. 2.2. Selection procedure for relevant published articles Finally, societal/cultural risk factors (macrosystem)influence whether cyberbullying is encouraged or inhibited. These include The selection procedure is summarized in Fig. 2. A total of 7199 the growth of the economy, widespread cultural norms and beliefs potential publications matching the initial search criteria were iden- about violence, societal responses on safe youth use of internet and tified; of those, 147 studies were considered relevant for abstract companies' online protection policies. screening. However only 87 studies of those that were retrieved Unfortunately, as far as we could find, perhaps also due to the and reviewed were eligible; 60 were not actually dealing with risk difficulty of assessing and evaluating society-community level risk factors for cyberbullying or cybervictimization. Thirty-four additional factors for youngsters' involvement in cyberbullying both as perpe- publications were excluded because they were duplicates and did not trator and victim, no studies have been published measuring the meet our inclusion criteria, resulting in a final total of 53 articles com- macrosystem role in cyberbullying. Because of the lack of publica- plying fully with our inclusion criteria (see Fig. 2 for more detailed tions of society-community level risk factors, we included studies information). of at least one of the other three ecological levels above mentioned In summary, publications included in this study met the following and focused our review on risk factors within the context of the ontoge- criteria: netic (individual), micro- (interpersonal), or meso-system (community) (a) Evaluated or investigated risk factors for youngsters' involve- levels. ment in cyberbullying as perpetrator and/or victim; (b) assessed either static or dynamic risk factors for involvement in cyberbullying; 2.4. Narrative Scheme Reviewing System (NSRS) (c) assessed cyberbullying through survey/questionnaire administra- tion; (d) included participants aged 10–20; (e) was developed and pub- The current study not only reviewed existing knowledge about risk lished between 2000 and February 2015; (f) included measurement factors for cyberbullying and cybervictimization, but also investigated information on risk factors for cyberbullying or cybervictimization and these factors according to the ecological framework for possible use in (g) were published in English. the risk and needs assessment approach. We created a Narrative Studies that did not meet the above mentioned criteria were exclud- Scheme Reviewing System (NSRS) procedure to classify variables (i.e. ed. In particular, we excluded publications that (i) did not include any risk factors) according to our given theoretical framework. This proce- form of measurement information; (ii) did not investigate risk factors dure implies that all relevant studies selected, including the most rele- for cyberbullying and/or cybervictimization; (iii) analyzed only the con- vant existing reviews addressing risk factors and cyberbullying, were sequences following youngsters' involvement in cyberbullying; (iv) in- read and factors were categorized according to their level (individual, vestigated risk factors and incidence only of school bullying. interpersonal or community). The narrative approach is appropriate 40 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

RECORDS IDENTIFIED THROUGH DATABASE SEARCHING, RECORDS RECORDS EXCLUDED (N=7052) SCREENED (N=7199)

FULL-TEXT ARTICLES ASSESSED FOR FULL-TEXT ARTICLES EXCLUDED, WITH ELIGIBILITY (N=147 ) REASONS (N=60)

DUPLICATES OF SAME STUDIES IN ARTICLES ACCEPTED AGAINST DIFFERENT PUBLICATIONS, OR NOT SELECTION CRITERIA (N=87) FULLY COMPLYING WITH REQUIREMENTS (N=34)

ELIGIBLE ARTICLES REPORTING RISK FACTORS FOR INVOLVEMENT IN CYBER BULLYING AS VICTIM OR PERPETRATOR (N=53)

Fig. 2. Flow diagram of all stages of literature review. because it is theory driven and makes it possible to compare different et al. (2010), Huang and Chou (2010), Li (2006), and in the review of studies that used different methods and definitions for risk factors and 131 studies conducted by Barlett and Coyne (2014).Genderisconsid- cyberbullying. This procedure also makes it possible to further classify fac- ered a static risk factor that does not change over time. tors according to whether they are static or dynamic risk factors in their nature. This aspect is hardly taken into consideration in the other studies. 3.1.1.2. SES. Wang, Iannotti, and Nansel (2009) found a positive relationship between SES (socio-economic status) and cyberbullying. When 3. Findings: risk factors analyzing data from the 2005 Health Behavior Questionnaire in the USA with a sample of 7182, grade 6–10 students, it emerged that higher Tables 1 & 2 present the results of the narrative review. We orga- levels of socio-demographic status are associated with higher preva- nized studies and findings according to which level the risk factor lence rates of cyberbullying. SES is considered a dynamic factor since, belongs to, according to the ecological framework, excluding the to some extent, it can change in time. cultural societal level since no studies were found directly measuring cyberbullying or cybervictimization in relation to this. For each 3.1.1.3. School commitment. Kowalski and Limber (2013) in their study of study we provided details about the methodology, the sample and 931 students from 6th to 12th grades in the US and Ybarra and Mitchell countries and the type of relationship with either cyberbullying or (2004) in their US phone interview study with 1501, 10 to 17 year old cybervictimization, separately. Direct numeric comparisons are not students, both found that cyberbullying is associated with low school possible because different studies in many cases use different mea- commitment; this risk factor can be considered to be dynamic. sures for the same or similar constructs. Most studies reviewed are cross-sectional but some are longitudinal. For each group of risk factors 3.1.1.4. Technology use. At the individual level, different studies found we also specified whether these are considered static or dynamic. that technology use is highly associated with cyberbullying. All studies including such risk factors as ICT expertise found significant correla- 3.1. Risk factors for cyberbullying tions. Higher internet expertise is associated with a higher level of cyberbullying others as identified by Sticca, Ruggieri, Alsaker, and 3.1.1. Individual level risk factors (‘ontogenetic’) Perren (2013) in their longitudinal study conducted with a Swiss sample of 835 students. The same positive correlation was reported by 3.1.1.1. Gender. Overall gender differences indicated that boys are more Walrave and Heirman (2011) in Belgium with 1378 students aged 12 involved as cyberbullies than girls. This was reported in several studies. to 18 years old and by Hinduja and Patchin (2008) in the US. The Lapidot-Lefler and Dolev-Cohen's (2015) online study in Israel conduct- same positive correlation applies for Internet use with Kowalski et al. ed with 465 grade 7–12 students reported higher rates for boys. The (2014), Casas, Del Rey, and Ortega-Ruiz (2013), Sticca et al. (2013), same was found by Erdur-Baker (2010) in his Turkish study with 276 Mishna, Khoury-Kassabri, Gadalla, and Daciuk (2012), Walrave and students from 7th to 9th grades. The same was reported by Sourander Heirman (2011), Erdur-Baker (2010), Hinduja and Patchin (2008), A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 41

Table 1 Summary of risk factors for cyberbullying according to the ecological framework's levels.

Study Sample Method Ontogenetic Positive association with cyber perpetration N Age/grade Country Individual-level risk factor

Gendera Lapidot-Leﬂer and Dolev-Cohen 465 7th–12th grades Israel Online survey Being a boy (2015) Barlett and Coyne (2014)c Review of 109 studies Meta-analysis Being a boy Erdur-Baker (2010)d 276 10–14 years Turkey Self-reported questionnaire Being a boy Huang and Chou (2010)d 545 7th–9th grades Taiwan Anonymous survey Being a boy Sourander et al. (2010)d 2.215 13–16 years Finland Questionnaire Being a boy Kowalski and Limber (2007)d 3.767 6th–8th grades USA Self-reported questionnaire Being a girl Li (2006)d 264 7th–9th grades Canada Anonymous survey Being a boy

SESa Wang et al. (2009)d 7.182 6th–10th grades USA Health Behavior in Positive relationship with SES School-Aged Children 2005 Survey School commitmentb Kowalski and Limber (2013)d 931 6th–12th grades USA Anonymous survey Low school commitment Ybarra and Mitchell (2004)d 1.501 10–17 years USA Interview via telephone Low school commitment

Technology useb

Self-reported ICT expertiseb Sticca et al. (2013)d,e 835 Mean age = 13.2 Switzerland Longitudinal study Higher self-reported ICT expertise Walrave and Heirman (2011)d 1.318 12–18 years Belgium Self-reported survey Higher self-reported ICT expertise Hinduja and Patchin (2008)d 1.378 b18 years USA Online survey Higher self-reported ICT expertise

Internet useb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Reported more internet use Casas et al. (2013)d 893 11–19 years Spain Self-reported questionnaire Internet addiction Sticca et al. (2013)d,e 835 Mean age = 13.2 Switzerland Longitudinal study Reported more internet use Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Reported more internet use Walrave and Heirman (2011)d 1.318 12–18 years Belgium Self-reported survey Reported more internet use Erdur-Baker (2010)d 276 10–14 years Turkey Self-reported questionnaire Girls reported more frequent use Hinduja and Patchin (2008)d 1.378 b18 years USA Online survey Reported more internet use Ybarra and Mitchell (2004)d 1.501 10–17 years USA Interview via telephone Reported more internet use

Risky online behaviorsb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Reported more risky internet use Casas et al. (2013)d 893 11–19 years Spain Self-reported questionnaire Reported more risky internet use Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Reported more risky internet use Erdur-Baker (2010)d 276 10–14 years Turkey Self-reported questionnaire Boys reported more risky internet use Personalityb

Empathyb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low empathy Casas et al. (2013)d 893 11–19 years Spain Self-reported questionnaire Low empathy Topçu and Erdur-Baker (2012)d 795 13–18 years Turkey Self-reported questionnaire Boys with low levels of empathy Steffgen et al. (2011)d 2.070 7th–13th grades Luxemburg Self-reported survey Cognitive empathyb Low empathy Ang and Goh (2010)d 396 12–18 years Singapore Self-reported survey Girls with low levels of cognitive empathy Affective empathyb Boys with low levels of both cognitive and affective empathy Steffgen and König (2009) 2.070 7th–13th grades Luxemburg Self-reported survey Low empathy

Self-esteemb Modecki et al. (2013)d 1.364 12–14 years Australia Longitudinal study Low levels of self-esteem Patchin and Hinduja (2010)d 1.963 10–16 years USA Self-reported survey Low levels of self-esteem

Self-controlb Bayraktar et al. (2014) 2.092 12–18 years Czech Republic Online survey Low levels of self-control

Valuesb

Moral disengagementb Kowalski et al. (2014)c Review of 131 studies Meta-analysis High levels of moral disengagement Cappadocia et al. (2013)d,e 1.972 9th–12th grades Canada Longitudinal study High levels of antisocial behaviors Menesini et al. (2013)d 390 14–18 years Italy Self reported questionnaire Immoral and disengaged behaviors Pozzoli et al. (2012) 663 Mean age = 9 Italy Self reported questionnaire High levels of moral disengagement Bauman (2010)d 221 5th–8th grades USA Self-reported survey High levels of moral disengagement Pornari and Wood (2010)d 339 7th–9th grades UK Questionnaire High levels of moral disengagement

(continued on next page) 42 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

Table 1 (continued)

Study Sample Method Ontogenetic Positive association with cyber perpetration N Age/grade Country Individual-level risk factor

Rule breakingb Sticca et al. (2013)d,e 835 Mean age = 13.2 Switzerland Longitudinal study Rule breaking behaviors

Normative beliefs about aggressionb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Presence of normative beliefs about aggression

Psychological statesb

Depressionb Modecki et al. (2013)d,e 1.364 12–14 years Australia Longitudinal study Early depressed mood

Maladaptive behaviorsb

Alcohol drinkingb Cappadocia et al. (2013)d,e 1.972 9th–12th grades Canada Longitudinal study High level of alcohol drinking

Bullyingb

Bulliesa,b Hemphill and Heerde (2014)e 927 10–11 years Australia Longitudinal study Being a school bully Kowalski et al. (2014)c Review of 131 studies Meta-analysis Being a school bully Modecki et al. (2014)c Review of 80 studies Meta-analysis Being a school bully Cappadocia et al. (2013)d,e 1.972 9th–12th grades Canada Longitudinal study Being a school bully Kowalski and Limber (2013)d 931 6th–12th grades USA Anonymous survey Being a school bully Sticca et al. (2013)d,e 835 Mean age = 13.2 Switzerland Longitudinal study Being a school bully Del Rey, Elipe and 274 12–18 years Spain Self-reported questionnaire Being a school bully Ortega-Ruiz(2012) Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Being a school bully Gradinger et al. (2009)d 761 14–19 years Austria Self-report questionnaire Boys involvement as school bullies Vandebosch and Van Cleemput 2.052 11–18 years Belgium Online Survey Being a school bully (2009)d Hinduja and Patchin (2008)d 1.378 b18 years USA Online survey Being a school bully Smith et al. (2008)d 533 11–16 years UK Self-reported questionnaire Being a school bully Raskauskas and Stoltz (2007)d 84 13–18 years USA Self-reported questionnaire Being a school bully

Victimsa,b Kowalski et al. (2012)d 4.531 6th–12th grades USA Survey Girls victims of school bullying Ybarra and Mitchell (2004)d 1.501 10–17 years USA Interview via telephone Being a traditional victim

Cybervictimsa,b Kowalski et al. (2014)c Review of 131 studies Meta-analysis Being a cybervictim Wright and Li (2013)d,e 261 6th–8th grades USA Longitudinal study Being a cybervictim Vandebosch and Van Cleemput 2.052 11–18 years Belgium Online Survey Being a cybervictim (2009)d

Study Sample Method Microsystem Positive association with cyber perpetration N Age/grade Country Interpersonal-level risk factor

Peer group risk factorsb

Antisocial inﬂuencesb Cappadocia et al. (2013)d,e 1.972 9th–12th grades Canada Longitudinal study Fewer pro-social peer inﬂuences

Peer rejectionb Bayraktar et al. (2014) 2.092 12–18 years Czech Republic Online survey High level of peer rejection Wright and Li (2013)d,e 261 6th–8th grades USA Longitudinal study High level of peer rejection

Perceived peer supportb Calvete et al. (2010)d 1.431 12–17 years Spain Self-reported questionnaire Lack of perceived peer support Williams and Guerra (2007)d 3.339 5th, 8th, 11th USA Electronic questionnaire Lack of perceived peer support grades

Family risk factorsb

Parental supportb Hemphill and Heerde (2014)e 927 10–11 years Australia Longitudinal study Low parental support Wang et al. (2009)d 7.182 6th–10th grade USA Health Behavior in Low parental support School-Aged Children (HBSC) 2005 Survey

Emotional bondb Ybarra and Mitchell (2004)d 1.501 10–17 years USA Interview via telephone Poor emotional bond with a caregiver A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 43

Table 1 (continued)

Study Sample MethodMicrosystem Ontogenetic Positive association with cyber perpetration N Age/grade Country Interpersonal-levelIndividual-level riskrisk factor factor Parental attachmenta,b Bayraktar et al. (2014) 2.092 12–18 years Czech Republic Online survey Poor parental attachment

Family management (rules and children monitoring)a,b Hemphill and Heerde (2014)e 927 10–11 years Australia Longitudinal study Poor family management Kowalski et al. (2014)c Review of 131 studies Meta-analysis Poor family management Ybarra and Mitchell (2004)d 1.501 10–17 years USA Interview via telephone Poor family management

Parents' involvement with the child's internet useb Vandebosch and Van Cleemput 2.052 11–18 years Belgium Online survey Poor parents involvement (2009)d

Study Sample Method Mesosystem Positive association with cyber perpetration N Age/grade Country Community-level risk factor

School risk factorsb

Perception of school climateb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Negative school climate Casas et al. (2013)d 893 11–19 years Spain Self-reported questionnaire Lack of teachers' support, clear rules and school safety

Williams and Guerra (2007)d 3.339 5th, 8th, 11th USA Electronic questionnaire Negative school climate grade Connection to schoolb Williams and Guerra (2007)d 3.339 5th, 8th, 11th USA Electronic questionnaire Low perception of being grades connected to school School safetya,b Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low levels of school safety

Note: Unless differently speciﬁed, no gender differences were found or investigated. a Denotes the static risk factors associated with youngsters' involvement in cyberbullying. b Denotes the dynamic risk factors associated with youngsters' involvement in cyberbullying. c Denotes reviews on risk factors for youngsters' involvement in cyberbullying. d Denotes publications cited in reviews on risk factors for youngsters' involvement in cyberbullying. e Denotes longitudinal studies.

Ybarra and Mitchell (2004) all reporting positive correlations with reported lower levels of self-control compared to non-cyberbullies when cyberbullying: more time online, more cyberbullying. Risky online behav- asked about several aspects of their lives (Bayraktar et al., 2014). Accord- ior: The review by Kowalski et al. (2014) concluded that most studies ing to Modecki et al. (2013) cyberbullies among Australian students also showed a significant correlation between engaging in so called ‘risky be- present a so called ‘early depressed mood’. Though personality risk factors havior online’ and being a cyberbully. Risky behavior include, among are difficult to address and modify, with special dedicated intervention and other, surfing websites not suitable for children, and communicating in treatment they could be changed, so we consider them to be dynamic. social networks with unknown people. Specifically, this correlation is found in a Spanish study by Casas et al. (2013) with 893, 11 to 19 year 3.1.1.6. Values. Another set of individual variables that have been old students, in a Canadian self-reported survey conducted with 2186 frequently found to be associated with cyberbullying is moral disengage- students (Mishna et al., 2012) and in Turkey with 276, 10 to 14 year old ment, reported in different study designs (correlational and longitudinal students (Erdur-Baker, 2010). All ICT related risk factors are clearly dy- alike), consistently showing the same positive correlation (Bauman, namic, given that the type of behavior online can be changed. 2010; Cappadocia, Craig, & Pepler, 2013; Kowalski et al., 2014; Menesini, Nocentini, & Camodeca, 2013; Pornari & Wood, 2010; 3.1.1.5. Personality individual risk factors. With regard to individual risk Pozzoli, Gini, & Vieno, 2012). Differently measured and defined moral factors related to the personality of children, the most reported risk fac- disengagement refers to a denial of wrongdoing, of moral detachment tor associated with cyberbullying is low empathy. Kowalski et al. (2014) about antisocial action, and blaming the victim, which is higher in in their review systematically found an association between low empa- cyberbullies. Cyberbullies tend also to break rules more often (Sticca thy and cyberbullying (as did Casas et al., 2013; Topçu and Erdur-Baker, et al., 2013). In the same line of (distorted) values, cyberbullies also re- 2012; Steffgen, König, Pfetsch, & Melzer, 2011). More specifically Ang ported normative beliefs about aggression, indicating minimization and and Goh (2010) found that girls with lower levels of cognitive empathy justification (Kowalski et al., 2014). Other maladaptive behaviors, such and boys with low levels of both cognitive and affective empathy as alcohol drinking, is reported by Cappadocia et al. (2013) in Canada. are more likely to cyberbully others. Steffgen and König (2009) in These values can also be changed so they are dynamic. Luxembourg also found this correlation. Also, self-esteem was studied by Modecki, Barber, and Vernon (2013), who found in their Australian 3.1.1.7. Bullying in school. Most studies looking at possible risk factors for longitudinal study that cyberbullies report lower level of self-esteem cyberbullying also measured school bullying and consistently found a (Patchin & Hinduja, 2010). Cyberbullies in the Czech Republic also positive association. Hemphill and Heerde (2014), Modecki, Minchin, 44 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

Harbaugh, Guerra, & Runions (2014), Kowalski et al. (2014), Cappadocia Williams & Guerra, 2007), specifically defined as lack of teacher support et al. (2013), Kowalski and Limber (2013), Sticca et al. (2013), Del Rey, and lack of clear rules and school safety, was reported in the Spanish self- Elipe and Ortega-Ruiz (2012), Mishna et al. (2012), Gradinger, report study conducted with 893 adolescents (Casas et al., 2013)tobe Strohmeier, and Spiel (2009), Vandebosch and Van Cleemput (2009), associated with involvement in cyberbullying. Also, perceiving not Hinduja and Patchin (2008), Smith et al. (2008) and Raskauskas and being connected or bonded to the school (Williams & Guerra, 2007), Stoltz (2007), all reported that cyberbullies are also school bullies. Very and general lack of school safety (Kowalski et al., 2014), are all reported few of these studies are longitudinal (with the exception of Cappadocia as associated with cyberbullying. Any school-related aspect related to et al., 2013; Sticca et al., 2013) so it is not possible to say whether one rules and regulations is dynamic. However, where the school is located starts as a school bully and then goes online or whether the two behav- and in which area, for example a disruptive area with high levels of de- iors happen at the same time. The studiesalsodonotreportdataabout linquency, are more to be considered as static risk factors. respondents' reporting whether they bullied and cyberbullied the same or different children. 3.2. Risk factors for cybervictimization Gender differences are not always reported in studies with regard to gender differences between school and online bullying involvement; With regard to risk factors associated with cybervictimization, we Gradinger et al. (2009) looked at gender differences and reported that will here present results following again the three levels: individual, boys are more likely than girls to be school bullies and cyberbullies. interpersonal and community. We report from the literature review Being a victim of school bullying is also considered a risk factor for crosssectional or longitudinal associations with involvement in cybervictimization for girls, as the two behaviors have been found to cybervictimization, as presented in Table 2. be associated by Kowalski, Morgan, and Limber (2012) in their US study with over 4000 students. The same positive association was 3.2.1. Individual level risk factors (‘ontogenetic’) found in the telephone interview study done by Ybarra and Mitchell (2004). Being a cybervictim is also associated with cyberbullying, as 3.2.1.1. Gender. Overall gender differences indicate that girls are more found by Kowalski et al. (2014), Wright and Li (2013) and involved as cybervictims than boys. This was reported in several studies. Vandebosch and Van Cleemput (2009). This relationship reflects the Bayraktar et al. (2014)' online survey conducted with 2092 students “role inversion hypothesis” which emphasizes the overlap between aged 12–18 years reported higher rates of cybervictimization among bullies and victims (Ybarra & Mitchell, 2004). Clearly any bullying be- girls. The same was found by Holt, Fitzgerald, Bossler, Chee, and Ng havior is dynamic in nature. (2014) in their study with 4315 students from primary and secondary schools in Singapore. The same gender difference was reported by 3.1.2. Interpersonal level risk factors (microsystem) Sourander et al. (2010),andKowalski and Limber (2007), in which the same conclusions were drawn. 3.1.2.1. Peer group risk factors. The role and influence of the interpersonal social context is found in several studies as being associated with 3.2.1.2. Low school achievement. Hinduja and Patchin (2008) in their on- cyberbullying. Who children hang around with and the lack of prosocial line survey involving 1378 respondents aged less than 18 years found peer influences were found to be predictors in the Canadian longitudi- that cybervictimization is associated with low school achievement. Sim- nal study of Cappadocia et al. (2013) of future cyberbullying involve- ilarly, Wang et al. (2014) in their study with 1023 5th grade students ment. This is true also for peer rejection, with cyberbullies more likely and the review of 25 studies conducted by Tokunaga (2010) found to be rejected than non-cyberbullies (Bayraktar et al., 2014; Wright & that cybervictimization is associated with students' low academic Li, 2013) and experiencing greater social isolation as well as lack of per- achievement. This risk factor can be considered dynamic, since it can ceived peer support. This emerged in the systematic review (Kowalski change over time. et al., 2014) referring to the Spanish (Calvete, Orue, Estévez, Villardón, & Padilla, 2010) and to the US (Williams & Guerra, 2007) studies. Clearly 3.2.1.3. Technology use. At the individual level, several studies found that peer factors can be considered dynamic risk factors that can change in technology use is highly associated with cybervictimization. Internet use time or with tailored intervention. can be considered a risk factor for cybervictimization as summarized by Kowalski et al. (2014) in their review and in the studies by Zhou 3.1.2.2. Family risk factors. At the interpersonal level, lack of parental et al. (2013), Mishna et al. (2012), Erdur-Baker (2010), Vandebosch support is also considered a risk factor. Hemphill and Heerde (2014), and Van Cleemput (2009) and Hinduja and Patchin (2008). They all re- in Australia, and Wang et al. (2009) in the US, reported that parents of port the existence of a positive correlation with cybervictimization, cyberbullies provide less support and also lack emotional bonding that is, the more time youngsters spend online the more they are at (Ybarra & Mitchell, 2004). These parents are reported not only having risk of being victimized online. Risky online behavior: The review poor parental attachments (Bayraktar et al., 2014), but also the family concluded by Kowalski et al. (2014) showed a significant correlation management in general is reported as lacking some kind of rules and between being involved in so called ‘risky online behavior’ and any forms of monitoring of the children's lives and of online activities being a cybervictim. These correlations were found in Mishna et al. specifically. This is reported not only in the overall review by Kowalski (2012), Walrave and Heirman (2011), Erdur-Baker (2010), Katzer, et al. (2014), but also in the Australian longitudinal study by Hemphill Fetchenhauer, and Belschak (2009), Mesch (2009) and Hinduja and and Heerde (2014) and in Ybarra and Mitchell's (2004) survey. When Patchin (2008), all reporting that cybervictimization is related to in- parents are poorly involved with the child's internet use, Vandebosch volvement in risky online behavior. and Van Cleemput (2009) have found children more at risk of cyberbullying. Parental involvement in the reduction of cyberbullying 3.2.1.4. Personality. With regard to individual risk factors related to a is essential and possible due to the fact that these risk factors are youngster's personality, the most reported risk factor associated with dynamic. cybervictimization is low self-esteem. Bayraktar et al. (2014),intheiron- line survey involving 2092 students aged 12–18 years from the Czech 3.1.3. Community level risk factors (mesosystem) Republic, and Modecki et al. (2013) in their longitudinal study conducted with 1364 Australian students aged 12–14 years, found that 3.1.3.1. School risk factors. The role of schools, their policies and school cybervictimization is related to low levels of self-esteem. The same pos- climate all have been reported to have a relationship with the risk of itive correlation was found by Kowalski and Limber (2013), Patchin and cyberbullying. A negative school climate (Kowalski et al., 2014; Hinduja (2010),andKatzer et al. (2009). Perceived social intelligence can A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 45

Table 2 Summary of risk factors for cybervictimization according to the ecological framework's levels.

Study Sample Method Ontogenetic Positive association with cyber victimization N Age/grade Country Individual-level risk factor

Gendera Bayraktar et al. (2014) 2.092 12–18 years Czech Online survey Being a girl Republic Holt et al. (2014) 4.315 Primary/secondary Singapore Self-report questionnaire Being a girl schools Zhou et al. (2013) 1.438 10th–12th grades China Anonymous survey Being a boy Erdur-Baker (2010)d 276 14–18 years Turkey Self-reported questionnaire Being a boy Sourander et al. (2010)d 2.215 13–16 years Finland Questionnaire Being a girl Kowalski and Limber 3.767 6th–8th grades USA Self-reported questionnaire Being a girl (2007)d

School problemsb

Academic achievementb Hinduja and Patchin 1.378 b18 years USA Online survey More school problems (2008)d Wang et al. (2014) 1.023 5th grade USA Paper and pencil survey Lower academic achievement Tokunaga (2010)c Review of 25 Meta-analysis Low academic commitment studies/articles Technology useb

Internet useb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Reported more internet use Zhou et al. (2013) 1.438 10th–12th grades China Anonymous survey Reported more internet use Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Reported more internet use Erdur-Baker (2010)d 276 14–18 years Turkey Self-reported questionnaire Girls reported more internet use Vandebosch and Van 2.052 11–18 years Belgium Online survey Reported more internet use Cleemput (2009)d Hinduja and Patchin 1.378 b18 years USA Online survey Reported more internet use (2008)d Ybarra and Mitchell 1.501 10–17 years USA Interview via telephone Reported more internet use (2004)d Risky internet useb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Reported more risky internet use Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Reported more risky internet use Walrave and Heirman 1.318 12–18 years Belgium Self-reported survey Reported more risky internet use (2011)d Erdur-Baker (2010)d 276 14–18 years Turkey Self-reported questionnaire Boys reported more risky internet use Katzer et al. (2009)d 1.700 5th–11th grades Germany Survey Reported more risky internet use Mesch (2009)+ 935 12–17 years USA Survey Reported more risky internet use Hinduja and Patchin 1.378 b18 years USA Online survey Reported more risky internet use (2008)d

Personalityb

Perceived social intelligenceb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low perceived social intelligence Hunt et al. (2012)d 218 8–15 years Australia Personal Experiences Checklist Low perceived social intelligence (PECK) Schultze-Krumbholz and 71 7th, 8th, 10th Germany Self-reported questionnaire Low perceived social intelligence Scheithauer (2009)d grades

Schultze-Krumbholz and 71 7th, 8th, 10th Germany Self-reported questionnaire Empathyb Low levels of empathy Scheithauer (2009)d grades

Bayraktar et al. (2014) 2.092 12–18 years Czech Online survey Self-esteemb Low levels of self-esteem Republic Kowalski and Limber 931 6th–12th grades USA Anonymous survey Low levels of self–esteem (2013)d Modecki et al. (2013) 1.364 12–14 years Australia Longitudinal study Developmental decrease in self-esteem Patchin and Hinduja 1.963 10–16 years USA Self-reported survey Low levels of self-esteem (2010)d Katzer et al. (2009)d 1.700 5th–11th grades Germany Survey Low levels of self-concept

Emotional controlb Hemphill and Heerde 927 10–11 years Australia Longitudinal study Poor emotional control (2014)e Hemphill et al. (2014)e 673 12–13 years Australia Longitudinal study Emotional dysregulation

Valuesb

Moral disengagementb Kowalski et al. (2014)c Review of 131 studies Meta-analysis High levels of moral disengagement

(continued on next page) 46 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

Table 2 (continued)

Study Sample Method Ontogenetic Positive association with cyber victimization N Age/grade Country Individual-level risk factor

Psychological statesb

Depressionb Cappadocia et al. (2013)e 1.972 9th–12th grades Canada Longitudinal study High level of depression Gámez-Guadix et al. 845 13–17 years Spain Longitudinal study High level of depression (2013)e Kowalski and Limber 931 6th–12th grades USA Anonymous survey High level of depression (2013)d Modecki et al. (2013) 1.364 12–14 years Australia Longitudinal study Early depressed mood

Social anxietyb Kowalski et al. (2014)c Review of 131 studies Meta-analysis High level of social anxiety

Angerb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Feeling angry

Psychosocial problemsb Tokunaga (2010)c Review of 25 Meta-analysis Presence of psychosocial studies/articles problem

Maladaptive behaviorsb

Substance useb Gámez-Guadix et al. 845 13–17 years Spain Longitudinal study Substance use (2013)e Hinduja and Patchin 1.378 b18 years USA Online survey Substance use (2008)d

Bullyingb

Victimsa,b Hemphill et al. (2014)e 673 12–13 years Australia Longitudinal study Being a school victim Hemphill and Heerde 927 10–11 years Australia Longitudinal study Being a school victim (2014)e Holt et al. (2014) 4.315 Primary/secondary Singapore Self-report questionnaire Being a school victim schools Kowalski et al. (2014)c Review of 131 studies Meta-analysis Being a school victim Cappadocia et al. (2013)e 1.972 9th–12th grades Canada Longitudinal study Being a school victim Del Rey et al. (2012) 274 12–18 years Spain Self-reported questionnaire Being a school victim Mishna et al. (2012)d 2.186 10–17 years Canada Self-reported questionnaire Being a school victim Gradinger et al. (2009)d 761 14–19 years Austria Survey Being a school victim Vandebosch and Van 2.052 11–18 years Belgium Online Survey Being a school victim Cleemput (2009)d Hinduja and Patchin 1.378 b18 years USA Online survey Being a school victim (2008)d Smith et al. (2008)d 533 11–16 years UK Anonymous self-reported Being a school victim questionnaire Raskauskas and Stoltz 84 13–18 years USA Self-reported questionnaire Being a school victim (2007)d Bulliesa,b Kowalski et al. (2012)d 4.531 6th–12th grades USA Survey Boys involvement as bullies Hinduja and Patchin 1.378 b18 years USA Online survey Being a school bully (2008)d Ybarra and Mitchell 1.501 10–17 years USA Interview via telephone Being a school bully (2004)d Cyberbullyinga,b Cappadocia et al. (2013)d 1.972 9th–12th grades Canada Longitudinal study Being a cyber perpetrator Jose et al. (2012)d,e 1.700 11–16 years New Longitudinal study Being a cyber perpetrator Zeland Walrave and Heirman 1.318 12–18 years Belgium Self-reported survey Being a cyber perpetrator (2011)d

Study Sample Method Microsystem Positive association with cybervictimization N Age/grade Country Interpersonal-level risk factor

Peer group risk factorsb

Antisocial inﬂuencesb Hemphill and Heerde 927 10–11 years Australia Longitudinal study Association with antisocial (2014)e friends

Bayraktar et al. (2014) 2.092 12–18 years Czech Online survey Peer rejectionb High level of peer rejection Republic Katzer et al. (2009)d 1.700 5th–11th grades Germany Survey Low level of popularity Perceived peer supportb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low peer support Wang et al. (2009)d 7.182 6th–10th grades USA Health Behavior in School-Aged Low peer support Children (HBSC) 2005 Survey A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 47

Table 2 (continued)

Study Sample MethodMicrosystem Ontogenetic Positive association with cyber victimization N Age/grade CountryInterpersonal-level Individual-level risk risk factor factor

Family risk factorsb

Parental control of technologyb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low levels of parental control Aoyama et al. (2012)d 133 9th–12th grades Japan Self-reported survey Low levels of parental control Mesch (2009)d 935 12–17 years USA Survey Low levels of parental web sites monitoring Parents' rules on allowed online activitiesb Mesch (2009)d 935 12–17 years USA Survey Lack of parental clear rules on allowed online activities Communication with parents Özdemir (2014) 337 15–18 years Turkey Survey Less communication with parents Parental attachmentb Bayraktar et al. (2014) 2.092 12–18 years Czech Online survey Poor parental attachment Republic Emotional parent–child relationshipb Katzer et al. (2009)d 1.700 5th–11th grades Germany Survey Parents anxious concerned

Family troubleb Tokunaga (2010)c Review of 25 studies/articles Meta-analysis Presence of family trouble

Study Sample Method Mesosystem Positive association with cyber victimization N Age/grade Country Community-level risk factor

School risk factorsb

Perception of school climateb Kowalski et al. (2014)c Review of 131 studies USA Meta-analysis Negative school climate Wang et al. (2014) 1.023 5th grade Paper and pencil survey Poor perception of school climate

School safetyb Kowalski et al. (2014)c Review of 131 studies Meta-analysis Low perception of school safety

Note: Unless differently speciﬁed, no gender differences were found or investigated. a Denotes the static risk factors associated with youngsters' involvement in cybervictimization. b Denotes the dynamic risk factors associated with youngsters' involvement in cybervictimization. c Denotes reviews on risk factors for youngsters' involvement in cybervictimization. d Denotes publications cited in reviews on risk factors for youngsters' involvement in cybervictimization. e Denotes longitudinal studies.

be considered a risk factor for cybervictimization, as reported by 3.2.1.6. Psychological status. With regard to youngsters' psychological Kowalski et al. (2014) in their review of 131 studies, and in particular status, depression is associated with cybervictimization. This relationship in the study of Hunt, Peters, and Rapee (2012) conducted with 218 is highlighted by several studies including longitudinal ones, such as that Australian students aged 8–15 years and by Schultze-Krumbholz and of Cappadocia et al. (2013) conducted with 1972 9th to 12th grade Cana- Scheithauer (2009) involving 71 German students. They found that dian students, the Spanish study by Gámez-Guadix et al. (2013) with cybervictimization is related to low levels of perceived social intelli- 845 students aged 13–17 years, and the Australian study conducted by gence, namely children thinking of themselves as not clever or smart. Modecki et al. (2013) with 1364 students aged 12–14 years. All these Emotional control has also been found as an individual risk factor for studies found that cybervictimization is related to a high level of depres- cybervictimization. The longitudinal studies by Hemphill and Heerde sion later in time. Kowalski et al. (2014) in their meta-analysis of (2014) and Hemphill, Tollit, Kotevski, and Heerde (2014), conducted 131 studies also identified risk factors for cybervictimization social with respectively 927 Australian students aged 10–11 years and 673 anxiety and anger. Also the presence of psychosocial problems is a Australian students aged 12–13 years, found that cybervictimization is risk factor for cybervictimization as in the review by Tokunaga related to youngsters' poor emotional control.Alsoempathy is found to (2010). Such risk factors can be considered dynamic ones, because be a risk factor for being victimized online, as the study conducted by they could be treated and are potentially changeable. Schultze-Krumbholz and Scheithauer (2009). 3.2.1.7. Maladaptive behaviors. In their longitudinal study with 845 3.2.1.5. Morality. At the individual level a risk factor related to youngsters' Spanish students aged 13–17 Gámez-Guadix et al. (2013) found that values is moral disengagement. In their review of 131 studies, Kowalski students' substance use was linked to future cybervictimization. The et al. (2014) found that cybervictimization is correlated with high levels same relationship was found by Hinduja and Patchin (2008). of moral disengagement. Moral values, and in particular moral disengagement, is a dynamic risk factor for cybervictimization, that must be taken 3.2.1.8. Bullying in school. Studies found that involvement in school into account when planning and implementing prevention and interven- bullying is a risk factor for cybervictimization. The majority of tion programs focused on cybervictims. studies highlighted the existence of an overlap between the role of 48 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 traditional victim and cybervictim. Kowalski et al. (2014) in their trouble: Tokunaga's (2010) review of 25 studies systematically showed review systematically found an association between traditional vic- the existence of a relationship between cybervictimization and the timization and cybervictimization. Hemphill et al. (2014) and presence of family troubles. Cappadocia et al. (2013) in their longitudinal studies conducted respectively with 673 Australian students aged 12-13 years and 1.972 3.2.3. Community level risk factors (mesosystem) Canadian 9th–12th grade students, found that being involved in tra- Community level risk factors have rarely been studied. What has ditional bullying as a victim was related to cybervictimization. The been done so far is mainly concerned with school risk factors. same relationship was found by Holt et al. (2014), Hemphill and Heerde (2014), Del Rey, Elipe, and Ortega-Ruiz (2012), Mishna 3.2.3.1. School risk factors. Studies identified risk factors for et al. (2012), Gradinger et al. (2009) Vandebosch and Van cybervictimization by focusing on the schools' role in preventing or Cleemput (2009) Hinduja and Patchin (2008) Smith et al. (2008) otherwise facilitating youngsters' involvement in cyberbullying as and Raskauskas and Stoltz (2007). However, some studies highlight- victims. As the Kowalski et al. (2014) review highlights, youngsters' ed, as a risk factor for cybervictimization, students' involvement as perception of school climate is a risk factor for cybervictimization, that is, traditional bullies, supporting the “role inversion hypothesis” (Ybarra a perceived negative school climate is associated with cybervictimization. & Mitchell, 2004) mentioned earlier. The Kowalski et al. (2012) This result is supported by Wang et al. (2014) in a study of 1023, study, conducted with 4.531 6th–12th grade American students, 5th grade American students, who found a relationship between found a positive association between cybervictimization and being cybervictimization and a negative school climate. School safety: a school bully. Hinduja and Patchin (2008) and Ybarra and Mitchell Kowalski et al. (2014) found that cybervictimization is related to (2004) supported these results. Also, youngsters' cyberbullying is a a low perception of school safety. risk factor for cybervictimization, meaning that a cyberbully is often also victimized online. Cappadocia et al. (2013),intheirlongi- 4. Discussion tudinal study with 1.972 Canadian students, found that cyberbullying and cybervictimization were related to each other. This overlap was 4.1. Design and research limitations of the narrative review also found by Jose, Kljakovic, Scheib, and Notter (2012) and Walrave and Heirman (2011) studies. Narrative reviews do not summarize effect sizes as in a meta- analysis. In addition, using a predetermined theoretical framework, 3.2.2. Interpersonal level risk factors (microsystem) adopted for the criteria of selection of articles, might have left out some studies which indirectly contribute to the understanding of 3.2.2.1. Peer group risk factors. At an interpersonal level, several studies cyberbullying. Also, we only searched and analyzed English language have found that peer influence can constitute a risk factor for papers, and this might have left out other studies published in other lan- cybervictimization. Peer antisocial influence is positively associated guages, or unpublished work. Regardless of these limitations, we think with cybervictimization; in fact Hemphill and Heerde (2014) in their that our study is a significant further theoretical contribution for better longitudinal study conducted with 927 Australian students aged 10-11 understanding cyberbullying and cybervictimization. Our narrative re- years, found that youngsters involvement as cyber victims was correlat- view is innovative and unique because it enables the advancement of ed with involvement with antisocial friends. Also peer rejection is asso- knowledge about cyberbullying and cybervictimization by adopting a ciated with cybervictimization. Bayraktar et al. (2014) and Katzer et al. risk and needs assessment approach, in the ecological theory frame- (2009) in studies conducted respectively with 2.092 students aged work. Hong and Espelage (2012) conducted an ecological system anal- 12-18 years and 1.700 5th–11th grade students, reported a positive re- ysis but looked at studies on bullying and victimization in school and lationship between cybervictimization and high levels of peer rejection. did not classify studies by systematically looking at what role they can With regard to perceived peer support, Kowalski et al. (2014) in their re- play in predicting the occurrence or reoccurrence of the misbehavior. view concluded that cybervictimization and low levels of perceived Addressing each level of a risk factor and assessing whether it is static peer support were significantly correlated. The same association was or dynamic, instead, is a step forward for accurate interventions. found by Wang et al. (2009) in their study conducted with 7.182 6th– Compared to other reviews, Kowalski et al. (2014) adopts the General 10th grade American students. Aggression Model and is not specifically addressing risks and needs as the current paper, while the work by Tokunaga (2010) focused on com- 3.2.2.2. Family risk factors. Several studies investigated parents' role and paring and providing a meta synthesis of 25 reviewed studies up to involvement in cybervictimization, in particular parental control of 2009 on definitions and characteristics of cyberbullying but this was technology, meaning parents' habits and aptitude to check and super- done without a clear theoretical framework. vise children's understanding of technology and use of online activ- Cyberbullying is a complex phenomenon that has unique features in ities. Kowalski et al.'s (2014) summary of findings shows that comparison with school bullying, though it is highly connected. As with cybervictimization is associated with reported low levels of parental such antisocial, disruptive behavior or aggression in general, it is not control of technology (see also Aoyama, Utsumi, & Hasegawa, 2012; possible to identify a single causal association between one characteris- Mesch, 2009). Parents' rules on allowed online activities are associated tic and the (antisocial) behavior. To find multiple possible causes, longi- with a higher risk of cybervictimization: a lack of clear parental rules on tudinal studies are needed, and not very many were found. A multiple what to do online is positively associated with cybervictimization, as number of characteristics, referred to as risk factors, can play a role found by Mesch (2009) by surveying 935 American students aged and interact. In the present review, we looked at what is known in the 12–17 years. Low communication with parents is a risk factor for literature with regard to risk factors associated with cyberbullying and cybervictimization. Özdemir (2014) found a positive association be- cybervictimization, and we did this with two main frameworks in tween cybervictimization and less communication with parents, in a mind. On the one hand, we used the ecological theoretical framework survey with 337 Turkish students aged 15–18 years. Also parental at- to identify and look at risk factors (Bronfenbrenner, 1979, 1986). As tachment was found to be a risk factor for cybervictimization, as report- outlined, this theoretical framework is of high relevance and usefulness ed by Bayraktar et al. (2014). This study found that cybervictimization is for studying cyberbullying since it takes into consideration the com- associated with poor parental attachment. This emotional parent–child plexity associated with the disruptive youngsters' behavior. relationship, and in particular parental anxious concerns, is a risk factor On the other hand, what we did was to look at these different levels for cybervictimization; Katzer et al. (2009) found a positive relationship with a risk factor approach. What is making a child more at risk? Which between cybervictimization and parental anxious concerns. Family are the vulnerable aspects of his or her life that can promote or facilitate A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51 49 the occurrence or re-occurrence of cyberbullying either as a victim or as 4.3. A new approach for predicting and managing risk and needs of a perpetrator? This approach was used to identify and then classify risk cyberbullying and cybervictimization: a risk and needs assessment tool factors, according to whether they are static or dynamic. Dynamic risk and future research factors can be viewed as ‘needs’. They can change over time and are sus- ceptible to intervention. From our narrative review we conclude that one of the most useful ways to view and understand cyberbullying and cybervictimization is by adopting a ‘risk and needs assessment ap- 4.2. Cyberbullying and cybervictimization profiles proach’. Risk assessment for this context is a procedure used to establish whether cyber menaces are likely to take place, or take With these two frameworks in mind, results from the NSRS (Narra- place again, and even more specifically, whether there is a risk of re- tive Scheme Reviewing System) procedure showed that there is no one cidivism of something that is already taking place. Using a dynamic single risk factor or level of risk that is more relevant than another; sev- assessment approach means not only simply identifying and sum- eral seem to be associated. Also, what emerged of interest is that risk ming up risk factors present in individuals at school, at home, or in factors associated with cyberbullies and with cybervictims are rather their use of the internet, but also understanding that certain behav- similar, as if being or becoming a cyberbully or cybervictim are not sep- iors can be a dynamic combination of individual, environmental arate processes (Kowalski et al., 2014; Vandebosch & Van Cleemput, and social factors that interact with one another in a specific moment 2009; Wright & Li, 2013). Also, school victims as well as cybervictims at time for that single individual. could reverse roles; they share the same characteristics because to Assessing risk factors and their presence and intensity is not enough to some extent they are the same children and will invert roles (Ybarra & reduce risk, since their impact is relative to that child, that period of his or Mitchell, 2004). her life, and in combination with other factors; risk assessment is rather Looking back at Fig. 1 presented at the beginning of this paper, we complex and cannot be solved just by using an instrument/tool. However, can list risk factor relevant and associated with the risk of being a linking identification and understanding of risk factors at any of the indi- cyberbully or a cybervictim. Cyberbullies are more likely to be boys, vidual, interpersonal, community or societal levels, as we did, with have low school achievement and commitment, use internet devices an assessment approach of such risks to address and manage them, a lot, lack emotional and cognitive empathy, do not have a great idea as in the field of juvenile delinquency or of general violence or specif- of themselves, are more impulsive, tend to break rules, conform to ic types (e.g. intimate partner violence, stalking), is a promising and social supportive norms about aggression, are also involved in bul- innovative way to address cyberbullying and prevent its (re)occur- lying in school, and tend to be morally disengaged. They are more rence. We propose to use this approach to address different levels isolated and have no or scarce parental support or supervision of needs directly related to the individual, his or her personal rela- that is specifically related to internet use. They attend schools that tionships or at the community/school level, to develop a dedicated do not have clear rules or policies against cyberbullying or use of risk and needs assessment tool. This will facilitate risk management the web, and cyberbullies do not in any case feel bonded to their strategies and develop interventions to reduce the impact of risk fac- school. tors by addressing the different levels of needs (dynamic risk factors) With regard to cybervictims, they can be more likely girls, tend to and increase peer, parent, school, and community commitment and have low school achievement, are online more than non cybervictims, involvement. so they are more exposed to risk, they lack social skills, have a low This innovative approach would constitute a step forward to fu- self-esteem, report depression symptoms, as well as social anxiety, ture research, and for the development of a risk and needs assess- anger and also maladaptive behavior, they are likely to be victimized ment tool. Many studies have investigated the prevalence and at school but also to be bullies and cyberbullies. They lack social sup- nature of cyberbullying, and risk factors are becoming well port at school, are rejected by other children and also at home have established (Kowalski et al., 2014). What we found innovative, parents with whom they do not communicate a lot, give poor sup- which had not been studied and addressed yet, and that we did try port and with whom they do not have strong bonds. Parents of to achieve in the current review, is to use of a risk and needs assess- cybervictims tend to have little knowledge about internet use and ment approach,asinthefield of criminology to address and intervene safety regulations and have no or very little control over their in an efficient way with regard to (cyber)bullying to try to reduce it children's activities online. The schools that cybervictims attend within an ecological framework. lack a supportive climate and these victims feel detached from To develop such instruments, longitudinal studies should be set their schools. up within a specific theoretical framework, assessing the presence Overall, based on our findings it turned out that, with a few excep- of the different (four) levels of possible risks and needs, measuring tions, cyberbullies share similar risk factors with those found in past also involvement in cyberbullying and cybervictimization. Data on studies on school bullies and the same applies to cybervictims and vic- these risk and needs factors should be collected at least in a 6– tims (Hong & Espelage, 2012; Olweus, 1993). 9 month or more follow-up period of time, with reliable instruments These profiles show us that most of the risk factors associated measuring the different dimensions. Those risk and needs factors with cyberbullying and cybervictimization are dynamic in nature. that predict most strongly (one standard deviation above the They can change ‘naturally’ with time but more likely and most effec- mean) will be included in a new instrument that could comprise tively with tailored interventions. For this reason we conclude that, 10–15 risk and needs factors. The developed and validated instru- to better understand and address cyberbullying, we need to develop ments, as in the case of the risk and needs assessment tools devel- a risk and needs assessment tool, for understanding who is most at oped for of juvenile delinquency (Borum, 2003) and other violent risk,andwhattheneedsarethathavetobeaddressedandchanged. behaviors (Hart, 2008), would give us the opportunity to assess the Some differences were discovered. With regard to cyberbullying and risk of cyberbullying and cybervictimization. We think that this cybervictimization a unique risk factor has to do with online activi- type of risk and needs assessment tool would be of use also for self- ties and time spent online. Also, what was found contrary to the lit- assessment, to give the opportunitytostudentstounderstandif erature on school bullies is that cyberbullies report a higher level of they are at risk of being or becoming victims or perpetrators of SES. This might be due to the fact that they have more electronic de- cyberbullying and whether what is going on is of low, medium or vices, and the most updated ones that serve different communica- high risk. This assessment tool would constitute a first step towards tion features, increasing in this regard the risk of cyberbullying and effective intervention programs, addressing the identified children cybervictimization. and their needs. 50 A.C. Baldry et al. / Aggression and Violent Behavior 23 (2015) 36–51

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Cyber bullying: The role of traditional bullying and empathy. The Good, the Bad and the Challenging. Conference Proceedings, 2,1041–1047. 本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

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