The impact of life domains on cyberbullying perpetration in Iran: A partial test of Agnew's general theory of crime

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The impact of life domains on cyberbullying perpetration in Iran: A partial test of Agnew's general theory of crime Saeed Kabiria, Seyyedeh Masoomeh Shamila Shadmanfaatb, Jaeyong Choic, Ilhong Yund,

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a

University of Mazandaran, Iran University of Guilan, Iran c Angelo State University, United States of America d Chosun University, South Korea b

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A B S T R A C T

Keywords: Theoretical integration Life domains Cyberbullying Moral identity Iran

Purpose: This study extends prior research by examining the direct and indirect effects of four of Agnew's life domains (i.e., self, family, school, and peer) through constraints against and motivations for cyberbullying as well as the interaction effects among these life domains using an Iranian sample. Methods: Using self-report data on cyberbullying from a sample of 785 high school students in Iran, a series of multivariate models were estimated to examine direct, indirect, and moderating effects discussed in Agnew's integrated theory. Results: The results showed that the four life domains have direct and indirect effects on cyberbullying. Interaction effects among the life domains are also detected. Conclusions: This research finds strong support for Agnew's integrated theory with respect to cyberbullying.

1. Introduction Cyberbullying, defined as an aggressive act using electronic forms of contact to support intentional, repeated, and hostile behavior by an individual or group that is intended to harm others (Menesini et al., 2012), has become a social problem globally. The prevalence of cyberbullying is increasing by the emergence of various information and communication technologies. In particular, the potential for cyberbullying is significantly related to the increasing penetration of network computers and mobile phones (Smith et al., 2008). Worldwide evidence has shown a pervasiveness of cyberbullying ranging from 10 to 40% for victimization and 3–50% for perpetration (Arslan, Savaser, Hallett, & Balci, 2012; Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Lee & Shin, 2017; Selkie, Fales, & Moreno, 2016; Ybarra & Mitchell, 2004). Hinduja and Patchin (2008) report that 16% of girls and 18% of boys have perpetrated cyberbullying. Another study indicates that over 30% of students have been involved in cyberbullying behavior (Mishna, Khoury-Kassabri, Gadalla, & Daciuk, 2012). Similarly, Iranian researchers have noted the prevalence of cyberbullying among university students in Iran, subsequently finding it to be a social problem (Shadmanfaat et al., 2019; Shadmanfaat, Kabiri, Hayden, & Cochran, 2019). In particular, social media provide a rich context for Iranian sports fans to learn hostile and aggressive online behaviors, with its use

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leading the prevalence of cyberbullying perpetration to increase over time (Shadmanfaat, Kabiri, Willits, & Shadmanfaat, 2019). A range of variables can influence cyberbullying perpetration, including parenting practices (Vazsonyi, Machackova, Sevcikova, Smahel, & Cerna, 2012), school climate (Chan & Wong, 2019), peer affiliation (Hinduja & Patchin, 2013), moral capabilities (Menesini, Nocentini, & Camodeca, 2013; Perren & Gutzwiller-Helfenfinger, 2012), self-control (Li, Holt, Bossler, & May, 2016), social learning (Shadmanfaat, Kabiri et al., 2019), moral identity (Wang, Yang, Yang, Wang, & Lei, 2017), strain (Patchin & Hinduja, 2011), and perceived deterrence (Shadmanfaat, Howell, et al., 2019; Shadmanfaat, Kabiri et al., 2019). Agnew's (2005) general theory of crime was developed to integrate the risk factors mentioned above and offer a coherent and understandable theoretical framework to explain why individuals offend. The theory posits that five life domains (i.e., self, family, school, peers, and work) influence the likelihood of criminal acts both directly and indirectly through motivations for crime and constraints against crime. Although several studies have examined Agnew's integrated general theory of crime (Choi & Kruis, 2019; Cochran, 2017; Muftić, Grubb, Bouffard, & Maljević, 2014; Ngo & Paternoster, 2014; Ngo, Paternoster, Cullen, & Mackenzie, 2011; Zhang, Day, & Cao, 2012), empirical research on this theory remains limited. The current study seeks to extend and contribute to this small area of scholarship by

Corresponding author at: Department of Police Administration, Chosun University, 309 Pilmun-daero Dong-gu, Gwangju 501-759, South Korea. E-mail address: [email protected] (I. Yun).

https://doi.org/10.1016/j.jcrimjus.2019.101633 Received 25 August 2019; Received in revised form 24 September 2019; Accepted 24 September 2019 0047-2352/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Saeed Kabiri, et al., Journal of Criminal Justice, https://doi.org/10.1016/j.jcrimjus.2019.101633

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Moral identity is defined as “the cognitive schema a person holds about his or her moral character” (Aquino, Freeman, Reed, Lim, & Felps, 2009, p. 124) which includes behavioral scripts, moral values, goals, and traits (Aquino & Reed, 2002). As Hardy (2006) notes, “generally, it entails integration between the moral and self-systems such that there is some degree of unity between one's sense of morality and one's sense of identity” (p. 208). As such, we echo Hardy (2006) in conceptualizing moral identity in terms of the centrality of moral traits to fans' identities. Aquino et al. (2009) argue that the desire for selfconsistency means that moral identity acts as a source of moral motivation. To the extent that morality is a core identity characteristic, the individual is likely to act in ways that are congruent with their moral values and beliefs (see Hardy, 2006). With regard to the general theory of crime, and the importance of self capabilities, morality as a core identity characteristic is likely to motivate law-abiding behavior. Aquino and Reed (2002) argue that “the motivational driver between moral identity and behavior is the likelihood that a person views certain moral traits as being essential to his or her self-concept” (p. 1425). They view moral identity as the vehicle through which a person accesses selfregulatory schemas that shape behavior in ways that conform to their moral values/beliefs. The chronic accessibility of self-regulatory schemas, according to Aquino and Reed (2002), is dependent on the degree to which morality forms a core part of their identity. People with higher measures of moral identity are found to be less antisocial and criminal (Hardy, Bean, & Olsen, 2015a; Hardy, Nadal, & Schwartz, 2017; Kavussanu, Stanger, & Ring, 2015b) and less aggressive (Hardy, Walker, Olsen, Woodbury, & Hickman, 2014; Hardy, Walker, Rackham, & Olsen, 2012). Individuals with high moral identity also tend to be prosocial in different social domains (e.g., civic engagement and donation behaviors) (Gotowiec & van Mastrigt, 2019; Hardy et al., 2015a; Reynolds & Ceranic, 2007). The family domain can be represented by one's attachment to their parents, the quality of parenting, monitoring and supervision, neglect/ abuse, and family conflict (Agnew, 2005). When the family function is effective, deviant motivations are weak, whereas those constraints that regulate individuals' behaviors are strong. Conversely, poor parenting and ineffective monitoring increase the likelihood of the incidence of criminal behavior by promoting deviant motivations for crime and diminishing constraints against it (Cochran, 2017). A work by Paez (2018) reveals that satisfaction with family relationships is a significant predictor of engagement in cyberbullying as well as traditional bullying. Agnew (2005) notes that school domain variables are moderately linked to offending. The school domain consists of negative bonding to a teacher or school, poor academic performance, strict school discipline, educational aspirations/goals, time spent on homework, and support and monitoring by teachers (Agnew, 2005). Inadequate school supervision or ineffective school socialization may diminish constraints against crime and foster deviant motivations for offending (Muftić et al., 2014). Poor monitoring in schools or low attachment to teachers may enhance the likelihood of cyberbullying perpetration. Specifically, students can learn rule-breaking acts from socializing agents in schools, and strain from school life (e.g., low academic performance) can generate negative emotions that can lead to online deviance (see Paez, 2018). Choi and Kruis' (2019) study, therefore, provides empirical support for the link between poor teacher attachment and cyberbullying. The general theory of crime proposes that having delinquent peers, high levels of conflict with friends, poor quality friendships, and spending much time with peers in unstructured and unsupervised activities are robust predictors of offending (Agnew, 2005). Zhang et al. (2012) find that affiliation with deviant peers increases motivations for crime by providing role models as well as reinforcement in the form of punishments and rewards. The importance of social rewards from delinquent peers can also promote delinquency. With regard to cyberbullying, Choi and Kruis (2019) reveal that cyberbullying perpetration

examining the key propositions of the theory. Specifically, we examine the direct, indirect, and moderating effects of these life domains on cyberbullying perpetration among Iranian sports fans. We are aware of no other tests of Agnew's theory in which both the direct and the indirect effects of life domains through motivations for crime and constraints against crime are tested against cyberbullying. The current study is an attempt to bridge the gap in this line of research. 2. Literature review 2.1. Agnew's general theory of crime Agnew's general theory is an inductive framework developed to integrate the main risk factors of crime and advance a clear and parsimonious theory of crime. The theory suggests that crime and delinquent behaviors are most likely to occur when motivations for crime are high and constraints are low (Agnew, 2005). Motivations and constraints are the byproducts of a multitude of individual and social/environmental factors subsumed under the five life domains of self, family, school, peer, and work (Choi & Kruis, 2019). According to Agnew's general theory, people refrain from committing crime 1) when they are fearful of getting caught and punished (i.e., external control), 2) when they have a lot to lose if they are punished (i.e., stakes in conformity), and 3) when they believe that crime is wrong and produces shame and embarrassment (i.e., internal control) (Ngo et al., 2011). Agnew states that constraints (e.g., external control, internal control, and stakes in conformity) inhibit individuals from engaging in criminal behavior (Cochran, 2017). The theory also posits that individuals are motivated toward crime as a result of factors or forces that either pull them (e.g., learning criminality through the socialization process) or push them toward crime (e.g., strain and negative emotions, which produce deviant motivations for crime) (Ngo et al., 2011). Agnew (2005) advances a causal process linking propositions from general strain theory and social learning theory to motivations for crime. The theory suggests that deviant motivations stem from strain and negative emotions that pressure people to violate the rules. Individuals may be pressured into crime when they are presented with negative or noxious stimuli. For example, when individuals are constrained from achieving their goals or when their valued possessions are threatened to be taken away, they may be motivated to commit crime (Ngo et al., 2011). Agnew (2005) also notes that deviant motivation could originate from social forces that may attract a person to carry out crime through social learning mechanisms (e.g., favorable definitions of crime, social and personal reinforcement, and encouragement from significant others) (Cochran, 2017). Individuals can be reinforced to perpetrate cyberbullying and be exposed to successful role models such as family members, peers, or significant others involved in cyberbullying perpetration and thus develop positive perceptions of aggressive online behaviors (Ngo et al., 2011). Agnew (2005) contends that deviant motivations for crime, constraints against crime, and criminal behaviors are influenced by individual traits and social environments that can be subsumed under these five life domains (Grubb & Posick, 2018). In the self domain, an individual's characteristics such as self-control and irritability are the key variables that directly influence constraints against and motivations for crime (Ngo et al., 2011). Agnew (2005) points out two opposing sources of motivations and constraints: selfcontrol and the internalization of conventional norms. Effective socialization can result in the internalization of conventional social norms and values (Cochran, 2017). Cochran (2017) notes that failure to instill pro-social norms and values diminishes constraints and allows criminal motivations to arise. To mirror Agnew's discussion on the internalization of conventional social norms, we add moral identity into our test of the theory. We propose that a person with high moral identity is more likely to conform to social norms and regulate his/her behaviors based on internalized norms. 2

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that only few interaction terms in their negative binomial regression analyses are statistically significant in predicting traditional bullying and cyberbullying perpetration. Considering that the results from the small number of studies on the conditioning effects of life domains have yielded conflicting results, there remains a need to replicate previous research.

is significantly linked to peer delinquency and peer attachment. Similarly, Paez's (2018) study shows that low levels of acceptance by their peers are positively and significantly associated with the likelihood of cyberbullying perpetration. Finally, the work domain comprises unemployment, discipline, working conditions, attachment and commitment to work, poor supervision, and association with coworkers who are criminally involved (Muftić et al., 2014). Likewise, having low-quality work conditions reduces constraints against and enhances motivations for crime (Cochran, 2017). Agnew (2005) summarizes several key propositions of his integrated general theory: (1) low constraints against and high motivations for crime enhance the likelihood of offending; (2) individual traits and the social environment directly influence motivations for and constraints against crime; (3) the five life domains can influence each other; and (4) there are significant interactions between these life domains, Put simply, the theory claims that such life domains have direct, indirect, and moderating effects on offending by affecting constraints against and motivations for crime (Muftić et al., 2014; Ngo et al., 2011; Zhang et al., 2012).

2.3. Cyberbullying perpetration Hinduja and Patchin (2010) contend that cyberbullying often takes place to harm someone through new communication technologies. The delinquent user can send contemptuous and destructive messages to a third person or public group that has a large number of online users (Hinduja & Patchin, 2013). Evidence suggests that cyberbullying has mental, behavioral, and emotional consequences for victims (Baier, Hong, Kliem, & Bergmann, 2018; Kim, Colwell, Kata, Boyle, & Georgiades, 2018; Kim, Kimber, Boyle, & Georgiades, 2019; Patchin & Hinduja, 2006) such as educational failure, low self-confidence, social anxiety, social isolation, self-harm, and symptoms of depression (Albdour, Hong, Lewin, & Yarandi, 2019; Cassidy, Faucher, & Jackson, 2013; Cénat, Blais, Lavoie, Caron, & Hébert, 2018; Foody, Samara, & Carlbring, 2015; Hu et al., 2019; Kim et al., 2019), which may even lead to suicide in severe cases (Brailovskaia, Teismann, & Margraf, 2018; Hinduja & Patchin, 2010, 2019; Kim et al., 2019). Previous studies have established that cyberbullying perpetration is significantly associated with the variables subsumed under the five life domains discussed above. It is more likely when the quality of parenting (monitoring, attachment, nurturance) and school socialization (monitoring, attachment, discipline, support) are ineffective (Guo, 2016; Lee, Hong, Yoon, Peguero, & Seok, 2018; Paez, 2018). People with low selfcontrol (e.g., physical [as opposed to mental], impulsive, insensitive, risk-taking, short-sighted) are more likely to demonstrate cyberbullying and analogous behaviors (Li et al., 2016; You & Lim, 2016). Moreover, recent research has revealed that moral identity has a significant effect on cyberbullying perpetration (Wang et al., 2017; Yang, Wang, Chen, & Liu, 2018). Having deviant friends, namely spending time with riskseeking and law-breaking peers, increases the possibility of cyberdeviance (Holt, Bossler, & May, 2012). Cyberbullying perpetration is also more likely when peers endorse cyberaggression and are involved in bullying when using social media apps (Shim & Shin, 2016). Constraints such as shame (internal control) and embarrassment (informal control) as well as legal sanctions (e.g., formal control) prevent individuals from engaging in offending (Cochran, Chamlin, Wood, & Sellers, 1999; Kelley, Fukushima, Spivak, & Payne, 2009; Spivak, Fukushima, Kelley, & Jenson, 2011). These personal and social controls remind a person that involvement in cyberbullying can result in losing the respect of significant others, blame from parents, exclusion from circles of friends, legal sanctions, and feelings of guilt (Hinduja & Patchin, 2013; Patchin & Hinduja, 2018; Perren & GutzwillerHelfenfinger, 2012; Shadmanfaat, Howell, et al., 2019; Shadmanfaat, Kabiri et al., 2019; Slonje, Smith, & Frisén, 2012).

2.2. Indirect and conditioning effects of life domains Several studies have attempted to examine the indirect and moderating effects discussed in Agnew's (2005) integrated theory. Empirical research on the indirect effects of life domains remains very limited, and two exceptions are worth noting. In the first, Zhang et al. (2012) used longitudinal data from a sample of high-school youth in Columbia, South Carolina to examine whether life domains promote delinquency by diminishing constraints against delinquency and by enhancing motivations for delinquency. Notably, their structural equation modeling (SEM) analysis showed that those who are strongly attached to their parents and those who are under close supervision by parents are more likely to perceive constraints against delinquency (i.e., parental deterrence), which, in turn, are less likely to engage in delinquent behaviors. Parental attachment and parental supervision also diminish motivations for delinquency (e.g., beliefs favorable to delinquency), and this, in turn, reduces the likelihood of engagement in delinquent behaviors. In a second study, Cochran (2017) tested whether the effects of life domains on academic dishonesty among college are mediated by constraints against and motivations for academic dishonesty. His ordinary least squares regression models indicated that the effects of some life domain variables (e.g., moral condemnation of cheating and moral socialization) are fully mediated through constraints and motivations, lending some support for Agnew's key proposition. These studies notwithstanding, assessing the indirect effects of life domains is scarce and deserving of empirical scrutiny. The current study seeks to address this gap in the literature. Regarding the conditioning effects of life domains, there exists a growing empirical literature, although the findings from this line of research are often conflicted regarding the direction and significance of interaction effects between life domains. For instance, Ngo et al. (2011) used data from the Maryland Boot Camp Experiment and tested whether various combinations among the life domain variables (e.g., low self-control × high school dropout or unmarried × bad job) are predictive of re-arrest among participants. Their study did not yield strong support for the interaction effects between life domains, challenging the proposition from Agnew's integrated theory. On the other hand, other studies have provided support for the conditioning effects of life domains. Muftić et al.'s (2014) study demonstrated that low self-control and violent attitudes interact to influence violent and property offending and that low self-control interacts with delinquent peers to influence violent offending. However, most of the interaction terms in their study are not significant to predict both violent offending and property offending. Similarly, Choi and Kruis's (2019) study also provide limited support for Agnew's conditioning hypothesis by showing

2.4. Current study The present study explores the possible direct, indirect, and moderating effects of the five life domains on cyberbullying perpetration. Agnew's general theory of crime suggests that those variables within each domain can have both direct and indirect effects on crime as well as interactive effects with each other (Choi & Kruis, 2019). Those who experience ineffective parenting or poor school socialization may develop low levels of moral identity and be less susceptible to attachment in moral values, which could lead to deviant motivations for cyberbullying. At the same time, they may start to develop friendships with deviant peers and engage in cyberbullying. Similarly, poor life conditions (i.e., poor parenting, poor school socialization) may generate strain and negative emotions such as anger, which in turn enhance the 3

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someone online, (2) how often have you threatened to hurt someone online, (3) how often have you posted mean or hurtful comments about someone online, (4) how often have you posted mean or hurtful pictures about someone online, and (5) how often have you posted mean or hurtful videos about someone online? Responses ranged from 0 (never) to 4 (almost daily).

likelihood of cyberaggression. In addition, Agnew (2005) contends that the life domains have a reciprocal effect on one another; for example, individuals experiencing poor parenting are more likely to engage in delinquency when they have low moral identity and low self-control, suffer negative school experiences and mix with delinquent peers (Ngo et al., 2011). Deviant peer affiliation can also differ across levels of moral identity (Hertz & Krettenauer, 2016) and low self-control (Choi & Kruis, 2019; Kabiri et al., 2019; Vitulano, Fite, & Rathert, 2010). Drawing on Agnew's general theory of crime, we aim to explain Iranian high school students' cyberbullying perpetration. Specifically, this study examines the direct, indirect, and moderating roles of the life domains on cyberbullying perpetration. Moreover, we consider moral identity as an important self domain construct that is strongly predictive of offending (Hardy et al., 2012; Hardy, Bean, & Olsen, 2015b; Hardy & Carlo, 2011; Kavussanu & Ring, 2017; Kavussanu, Stanger, & Ring, 2015a; Patrick, Bodine, Gibbs, & Basinger, 2018; Wang et al., 2017). Reflecting the theoretical framework mentioned above, the study hypotheses are as follows:

3.2. Independent variables 3.2.1. Self domain Agnew's (2005) general theory of crime postulates that individual capability like low self-control and the super trait of “irritability” constitute the key variables that directly reduce the constraints against and the motivations for crime. Here, The self domain consisted of low self-control and low moral identity. Moral identity was measured using five items previously developed (Aquino & Reed, 2002). Participants were given a list of moral qualities (caring, compassionate, fair, friendly, generous, helpful, hardworking, honest, and kind) and were asked questions about this set of traits. Consistent with Aquino and Reed (2002) and Reed and Aquino (2003), we explained the purpose of these items by providing the following description: The person with these characteristics could be you, or it could be someone else. For a moment, visualize in your mind the kind of person who has these characteristics. Imagine how that person would think, feel, and act. When you have a clear image of what this person would be like, answer the following statements: (1) It would make me feel good to be a person who has these characteristics, (2) being someone who has these characteristics is an important part of who I am, (3) I would be ashamed to be a person who had these characteristics, (4) having these characteristics is not really important to me, and (5) I strongly desire to have these characteristics. Responses ranged from 5 (completely disagree) to 1 (completely agree) where higher scores represent low moral identity. The ability to exercise self-control was measured using an abridged version of the self-control scale utilized by Wikström, Oberwittler, Treiber, and Hardie (2012). The six items presented to participants tap particularly, but not exclusively, into the impulsivity and risk-taking components of the construct, which have been shown to be the most predictive of crime involvement (Svensson, 2015; Wikström & Svensson, 2010). The items are (1) I often act on the spur of the moment without stopping to think, (2) I often try to avoid things that I know will be difficult, (3) I lose my temper pretty easily, (4) when I am really angry, other people better stay away from me, (5) I often take a risk just for the fun of it, and (6) sometimes I find it exciting to do things that are dangerous. Responses ranged from 1 (strongly agree) to 4 (strongly disagree), and they were coded such that high scores indicate a poor ability to exercise self-control.

Hypothesis 1. The life domains have significant direct effects on cyberbullying perpetration. Hypothesis 2. Deviant motivations for and constraints against crime have significant direct effects on cyberbullying perpetration. Hypothesis 3. Deviant motivations for and constraints against crime mediate the relationships between the life domains and cyberbullying perpetration. Hypothesis 4. The effect of each life domain on cyberbullying perpetration is indirectly carried through the other life domains. Hypothesis 5. The life domains interact with one another in affecting cyberbullying perpetration.

3. Methods To test Agnew's general theory of crime on cyberbullying perpetration, we used a cross-sectional sample of 785 high school students in Iran who participated in this project in spring 2019. The data source for this study was 7476 high school students (Rasht = 4455 students; Anzali = 3021 students). The obtained list from the Rasht and Anzali education departments served as the sampling frame for the present study. From this list, we utilized the Cochran method of sample size estimation (e.g., Cochran, 1977), and 800 students were randomly selected.1 Once this sampling frame was drawn, students were invited to the school exam hall. Following approved institutional review board requirements, the purpose of the study was discussed and voluntary consent was provided by the participating students. The self-administered questionnaires were then distributed and 785 completed questionnaires were returned, yielding a 98.1% response rate. In total, 30.1% of our respondents were 16 years old (first year of high school), 36.6% were 17 years old (second year of high school), and 33.4% were 18 years old (third year of high school). All respondents were male students.

3.2.2. Family domain In the domain of “family,” Agnew's (2005) general theory of crime posits that weak bonds between the parent(s) and juvenile, poor parental supervision and discipline, a lack of parental social support, family conflict and child abuse, and criminal parents and siblings are the main causes of crime. Concerning the current study, the family domain included three key variables: poor monitoring, poor attachment, and harsh discipline. Poor monitoring assessed how closely parents or guardians monitored the behavior of their children (Simons, Wu, Conger, & Lorenz, 1994). The scale was composed of five items: (1) how often do your parents/guardians know who you are with when you are away from home, (2) in the course of a day, how often do your parents/ guardians know where you are, (3) how often do your parents/guardians ask about things that happened during a normal day at school, (4) how often do your parents/guardians ask you about what happened during your free time, and (5) how often do your parents/guardians have extra time to sit down and listen to you when you talk about what happened during your free time? Responses ranged from 1 (never) to 5 (always) with higher scores indicating greater parental monitoring.

3.1. Dependent variable 3.1.1. Cyberbullying perpetration Cyberbullying perpetration was measured using the five items developed by Hinduja and Patchin (2010). Participants were asked the following questions regarding the past six months before the questionnaires were answered: (1) how often have you spread rumors about 1 According to the Cochran formula for sample size estimation, we selected approximately 400 students for each city. To ensure the achievement of the appropriate sample size, 50 additional questionnaires were distributed.

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We used the five-item self-report scale developed by Simons et al. (1994) to form an indicator of harsh parenting. The students were asked a series of questions: (1) how often have your parents/guardians disagreed with you, (2) when you have had disagreements, how often have your parents/guardians discussed them calmly with you (reverse coded (, (3) how often have your parents/guardians argued heatedly or shouted at you, (5) how often have your parents/guardians ended up threatening you, and (6) how often have the arguments between you and your parents/guardians ended up being physical (e.g., hitting, shaking, shoving)? Responses ranged from 1 (never) to 5 (always). The measure of poor attachment was derived from Udris' (2017) scale. It comprised the following five items: (1) my parents/guardians encourage me to talk about my difficulties, (2) I tell my parents/guardians about my problems and troubles, (3) I like to get my parents/ guardians' point of view on things I'm concerned about, (4) my parents/ guardians accept me as I am, and (5) when I am angry about something, my parents/guardians try to be understanding. Responses ranged from 1 (completely agree) to 5 (completely disagree).

captured deviant peer affiliation developed by Hong, Kim, and Piquero (2017). Respondents were asked how many of their close friends had carried out the following acts in the past six months: (1) smoking cigarettes, (2) drinking alcohol, (3) playing truant, (4) teasing or calling names, (5) physical fighting, (6) hitting someone severely, (7) bullying someone at school, (8) taking away someone's money or things, (9) stealing someone's money or things, (10) cyberbullying, and (11) purposely damaging or destroying property that did not belong to them. Each item was answered on a five-point Likert-type scale from 0 (none of them) to 4 (all of them).

3.2.5. Constraints Agnew (2005) asserts that delinquency is most likely when the constraints against crime are low. Constraints are the products of a multitude of individual and social-environmental factors which he organized into five distinct life domains: self, family, peer, school, and work. Agnew (2005) describes constraints as those factors that deter, inhibit, and/or dissuade individuals from engaging in criminal behavior; he organizes these constraints into one of three spheres: external controls, internal controls, and “stakes in conformity. Here, Three dimensions of constraints were measured: internal control (shame), informal control (embarrassment), and formal control (formal sanctions). The constructs of constraints were measured through the certainty and severity of cyberbullying developed by Cochran (2017). To measure shame, students were asked (1) would you feel ashamed of yourself if you posted mean or hurtful comments, pictures, or videos of others and (2) would you feel ashamed of yourself if you threatened others online? Responses ranged from 4 (definitely would not) to 1 (definitely would). The severity of shame was captured by asking students the following: (1) how big of a problem would feeling ashamed of yourself be for you if you posted mean or hurtful comments, pictures, or videos of others and (2) how big of a problem would feeling ashamed of yourself be for you if you threatened others online? Responses ranged from 4 (no problem at all) to 1 (a very big problem). To measure the certainty of embarrassment, students were asked (1) would most of the family members whose opinions you value lose respect for you if you engaged in cyberbullying, (2) would most of your close friends whose opinions you value lose respect for you if you engaged in cyberbullying, and (3) would most of your important others such as teachers and school staff whose opinions you value lose respect for you if you engaged in cyberbullying? Responses ranged from 4 (definitely would not) to 1 (definitely would). The severity of embarrassment was captured by asking students the following: (1) how big of a problem would it be for you if most of your close friends whose opinions matter to you lost respect for you because you engaged in cyberbullying, (2) how big of a problem would it be for you if your family members whose opinions matter to you lost respect for you because you engaged in cyberbullying, (3) and how big of a problem would it be for you if important others such as teachers and school staff whose opinions matter to you lost respect for you because you engaged in cyberbullying? Responses ranged from 4 (no problem at all) to 1 (a very big problem). The severity of formal sanctions was measured using the two items used by Cochran (2017), Shadmanfaat, Howell, et al. (2019), and Shadmanfaat, Kabiri et al. (2019). Participants were asked whether they thought they would get caught if they (1) posted mean or hurtful comments, videos, or pictures, (2) spread rumors about others, and (3) threatened to hurt others online. Responses ranged from 4 (definitely would not) to 1 (definitely would). The certainty of formal sanctions was measured using a three-item scale. Respondents were asked whether they would be in great trouble if they got caught (1) posting mean or hurtful comments, videos, or pictures, (2) spreading rumors about others online, and (3) threatening to hurt others online. Responses ranged from 4 (no trouble at all) to 1 (very much trouble).

3.2.3. School domain Within the domain of “school,” Agnew (2005) argue that negative treatment by teachers, low attachment to teachers/school, poor academic performance, little time on homework, and poor supervision and discipline are the major correlates of offending. Regarding the current study, the school domain consisted of poor school support, ineffective disciplinary structure, school disorganization, and poor school attachment. Poor school support was measured using the seven-item scale adapted from Cornell, Shukla, and Konold (2015). The items included (1) most teachers and other adults at this school care about all students, (2) most teachers and other adults at this school want all students to do well, (3) most teachers and other adults at this school listen to what students have to say, (4) most teachers and other adults at this school treat students with respect, (5) teachers support me when I have problems, (6) teachers are willing to listen to my problems, and (7) teachers go out of their way to address my needs. Responses ranged from 1 (completely agree) to 5 (completely disagree). The scale of the ineffective disciplinary structure proposed by Cornell et al. (2015) was adopted to measure the school's discipline. The scale contained six items: (1) the punishment for breaking school rules is the same for all students, (2) students at this school only get punished when they deserve it, (3) students get suspended without good reason (reverse coded), (4) the adults at this school are too strict (reverse coded), (5) the school rules are fair, and (6) when students are accused of doing something wrong, they get a chance to explain it. Responses ranged from 1 (completely agree) to 5 (completely disagree). The authors also adopted the six-item school disorganization scale from Moon and Alarid (2015). The sample items were (1) students at my school frequently call each other names, (2) students move around or make noise during class, (3) students frequently get into fights, (4) there is much violence in my school, (5) there is much stealing in my school, and (6) many things are broken or vandalized in my school. Responses ranged from 1 (completely agree) to 5 (completely disagree). The school attachment items were drawn from Dornbusch, Erickson, Laird, and Wong (2001) and Watts, Province, and Toohy (2019). Respondents were asked about whether during the current or most recent school year, (1) they had trouble getting along with fellow students, (2) they felt like a part of their school, (3) they believed their fellow students were prejudiced, (4) they were happy to be at their school, and (5) they felt safe at their school. Responses ranged from 1 (completely agree) to 5 (completely disagree). 3.2.4. Peer domain For the domain of “peers,” Agnew (2005) states that having deviant affiliations, spending much time with peers in unstructured and unsupervised activities are the main factors that leading to deviant behaviors. The peer domain was assessed using the 11-item measure that 5

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self domain (p < .05). Moreover, constraints and motivations are related to the peer domain. (See Table 3.) To fully investigate the direct and indirect paths of the life domains on cyberbullying perpetration, OLS regression and structural equation modeling with bootstrapping2 was employed. Regarding the direct effect of Agnew's general theory of crime on cyberbullying perpetration, six models were tested to examine the direct effect of four of the life domains, namely the self domain (model 1), family domain (model 2), school domain (model 3), and peer domain (model 4), as well as motivations (model 5) and constraints (model 6). With regard to the self domain, model 1 shows that low self-control (β = 0.27, p < .01) and low moral identity (β = 0.32, p < .01) have significant effects on fans' cyberbullying perpetration. Model 2 indicates that cyberbullying perpetration is significantly linked to deviant peer affiliation (β = 0.20, p < .01). Likewise, the inclusion of the family domain variables, namely poor parental monitoring (β = 0.17, p < .01), harsh parental discipline (β = 0.10, p < .01), and poor parental attachment (β = 0.11, p < .01), increases the predictive power of model 2 by 8%. Furthermore, with regard to the school domain, the addition of poor school support (β = 0.10, p < .01), ineffective disciplinary structure (β = 0.11, p < .01), school disorganization (β = 0.09, p < .01), and poor school attachment (β = 0.12, p < .01) raises the analytical power of model 3 by 8%. Model 4 shows that poor school support (β = 0.08, p < .01), ineffective disciplinary structure (β = 0.09, p < .01), school disorganization (β = 0.10, p < .01) and poor school attachment (β = 0.13, p < .01) have significant effects on fans' cyberbullying perpetration. Model 5 shows the significant effects of constraints on cyberbullying activities, highlighting that low shame (β = 0.15, p < .01), low embarrassment (β = 0.14, p < .01), and low formal sanctions (β = 0.10, p < .01) have robust effects on cyberbullying; hence, the predictive power of the main model is intensified by 6%. Finally, pertaining to deviant motivations, model 6 indicates that both social learning-based deviant motivation (β = 0.18, p < .01) and strain-based deviant motivation (β = 0.09, p < .01) boost the predictive power of the main model by 3%. In sum, model 6, which includes all the proposed variables, accounts for 52% of the variance in cyberbullying perpetration. In sum, the model is shown that four life domains which are derived for Agnew's (2005) general theory of crime have a significant and direct effect on bullying perpetration. The indirect paths of Agnew's general theory of crime were examined using structural equation modeling. As discussed above, Table 4 shows that ineffective school socialization (β = 0.21, p < .01), ineffective parenting (β = 0.19, p < .01), low self-control (β = 0.11, p < .01), low moral identity (β = 0.15, p < .01), deviant peer affiliation (β = 0.09, p < .01), low shame (β = 0.14, p < .01), low embarrassment (β = 0.14, p < .01), low informal sanctions (β = 0.10, p < .01), strain-based deviant motivation (β = 0.09, p < .01), and social learning-based deviant motivation (β = 0.18, p < .01) have significant effects on cyberbullying. Likewise, ineffective school socialization (β = 0.13, p < .01), ineffective parenting (β = 0.14, p < .01), low self-control (β = 0.06, p < .01), low moral identity (β = 0.07, p < .01), and deviant peer affiliation (β = 0.04, p < .01) have significant effects on cyberbullying perpetration. Moreover, social learning-based deviant motivation is significantly associated with ineffective school socialization (β = 0.10, p < .01), ineffective parenting

3.2.6. Deviant motivations Agnew (2005) states that, while constraints restrain one from criminal/deviant behavior, motivations lure, entice, or pressure individuals toward crime/deviance. These motivations are shaped from both social learning process and strains. Deviant motivations included two key variables: strain-based motivation and social learning-based motivation. Strain-based deviant motivation was measured using four items: cyberbullying gives me a strong motivation because (1) it relieves my tensions, (2) cyberbullying people who deserve it makes me feel good, (3) it helps me to escape from daily strain, and (4) when I am angry or frustrated from life events, it reduces my negative emotions. Responses ranged from 1 (completely agree) to 5 (completely disagree). To capture social learning-based motivation, students were asked to what extent they agree with the following six items: cyberbullying gives me a strong motivation because (1) it helps me fit into the group (group of friends) better, (2) it helps me enhance my personal/social image in the eyes of the people around me, (3) it is consistent with my ethical standards and personal definition, (4) the people around me do that in similar situations and I've figured out it's not wrong, (5) the people around me (whose opinions I value) do not regard cyberbullying as wrong behavior and this is one of my main reasons for engaging in cyberbullying, and (6) important others (close friends, family members, role models), which I know well, are involved in cyberbullying and I try to imitate their behavior in a similar situation. Responses ranged from 1 (completely agree) to 5 (completely disagree). 3.3. Analytic strategy The analyses proceeded in several steps. First, bivariate correlations were conducted to examine the initial associations between the independent and dependent variables. Second, to examine the direct effects of the life domains, motivations for, and constraints against crime on cyberbullying perpetration, an OLS regression was implemented. Third, structural equation modeling was employed to test the indirect effects of the life domains on self-reported cyberbullying perpetration. Finally, the moderating effects were explored using Hayes' (2018) PROCESS version 3.1 macro in SPSS. 3.4. Validity and reliability of the measurement instruments All scales were found to have high internal consistency (α > 0.70; Composite Reliability [CR] > 0.70) (Nunally, 1978), as shown in Table 1. We also tested their discriminant validity by exploring the average variance shared between a construct and its measures. These indices were found to be above 0.50 as recommended (Fornell & Larcker, 1981). First-order confirmatory factor analysis for these scales was also conducted, and the factor loadings for all the items were significant (above 0.50). Moreover, the confirmatory factor analysis revealed good fit indices for all (Kline, 2015). 4. Results Table 2 reports the zero-order correlations between the independent and dependent variables. As the results indicate, there are moderately strong correlations (r > 0.25) among the self domain (low self-control and low moral identity), peer domain (deviant peer affiliation), family domain (harsh discipline, poor monitoring, and attachment), school domain (poor school support, attachment, school disorganization, and ineffective disciplinary structure), motivations (strain- and social learning-based motivations), constraints (shame, embarrassment, informal sanctions), and cyberbullying perpetration (p < .01). Likewise, there are significant correlations (r > 0.07) between the family domain and school domain, on the one hand, and the self domain, the peer domain, motivations, and constraints, on the other (p < .05). In addition, the peer domain is significantly correlated (r > 0.08) with the

2 Following Preacher and Hayes's (2008) recommendation, we used bootstrapping tests for indirect effects and mediational hypotheses, because this approach does not rely on distributional assumptions that are likely violated in the case of testing for indirect effects (Preacher & Hayes, 2008). In other words, an increasingly popular method of testing the indirect effect is bootstrapping. Bootstrapping is a non-parametric method based on resampling with replacement which is done many times, e.g., 5000 times. From each of these samples the indirect effect is computed and a sampling distribution can be empirically generated (Cheung & Lau, 2008).

6

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Table 1 Validity and reliability of research's measurement instruments AVE (MSV)

CR

α

0.724–0.814

0.609 (0.280)

0.886

0.886

0.708–0.768 0.805–0.862

0.559 (0.180) 0.682 (0.108)

0.884 0.896

0.883 0.895

0.780–0.788 0.756–0.866

0.614 (0.347) 0.661 (0.347)

0.761 0.795

0.749–0.791 0.760–0.752

0.600 (0.489) 0.655 (0.489)

0.818 0.851

0.736–0.807 0.683–0.782

0.606 (0.374) 0.533 (0.374)

0.822 0.773

0.775 0.761 0.791 0.859 0.817 0.845 0.821 0.822 0.773

0.712–0.854

0.631 (0.113)

0.949

0.949

0.705–0.853 0.692–0.752

0.607 (0.210) 0.513 (0.181)

0.885 0.863

0.689–0.741 0.722–0.835 0.738–0.793

0.549 (0.323) 0.564 (0.247) 0.570 (0.323)

0.858 0.866 0.869

0.687–0.815 0.772–0.862 0.722–0.835 0.748–0.806

0.543 0.653 0.643 0.598

0.876 0.904 0.915 0.912

0.883 0.863 0.895 0.857 0.865 0.869 0.926 0.876 0.903 0.912 0.912

Factor loadings Minimum - Maximum Cyberbullying perpetration Motivations Social learning based deviant motivation Strain based deviant motivation Constraints Low shame Certainty of shame Severity of shame Low embarrassment Certainty of embarrassment Severity of embarrassment Low formal sanctions Certainty of formal sanctions Severity of formal sanctions Peer domain Deviant peer affiliation Self-domain Low moral identity Low self-control Family domain (ineffective parenting) Poor parental monitoring Harsh discipline Low parental attachment School domain (ineffective school socialization) School disorganization Poor school attachment Ineffective school disciplinary structure Poor school support

(0.214) (0.237) (0.233) (0.237)

shame of cyberbullying. The low embarrassment of cyberbullying is significantly predicted by ineffective school socialization (β = 0.14, p < .01), ineffective parenting (β = 0.10, p < .01), low self-control (β = 0.11, p < .01), low moral identity (β = 0.13, p < .01), and deviant peer affiliation (β = 0.14, p < .01). Further, ineffective school socialization (β = 0.04, p < .01), ineffective parenting (β = 0.04, p < .01), low self-control (β = 0.02, p < .01), and low moral identity (β = 0.01, p < .05) have indirect effects on the low embarrassment of cyberbullying. Additionally, low self-control (β = 0.17, p < .01) has a significant direct effect on the low informal sanctions of cyberbullying. The model also shows that deviant peer affiliation is directly associated with ineffective school socialization (β = 0.09, p < .05), ineffective parenting (β = 0.13, p < .01), low self-control (β = 0.12, p < .01),

(β = 0.11, p < .05), low self-control (β = 0.09, p < .05), low moral identity (β = 0.11, p < .01), and deviant peer affiliation (β = 0.10, p < .01), while ineffective school socialization (β = 0.03, p < .01), ineffective parenting (β = 0.04, p < .01), low self-control (β = 0.01, p < .01), and low moral identity (β = 0.01, p < .05) indirectly affect cyberbullying perpetration. Furthermore, strain-based deviant motivation is significantly linked to ineffective school socialization (β = 0.23, p < .01) and ineffective parenting (β = 0.22, p < .01). Additionally, ineffective school socialization (β = 0.19, p < .01), ineffective parenting (β = 0.18, p < .01), and low self-control (β = 0.16, p < .01) have significant effects on the low shame of cyberbullying, while ineffective school socialization (β = 0.03, p < .01) and ineffective parenting (β = 0.03, p < .01) have indirect effects on the perceived low

Table 2 The Zero-order correlations between independent and dependent variables (N = 785)

1. Cyberbullying perpetration 2- Low self-control 3- Low moral identity 4- Deviant peer affiliation 5- Poor parental monitoring 6- Parental harsh discipline 7- Poor parental attachment 8- Poor school support 9- Ineffective disciplinary structure 10- School disorganization 11- Poor school attachment 12- Low shame 13- Low embarrassment 14- Low formal sanctions 15- Social learning based deviant motivations 16- Strain based deviant motivations

M

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

5.61 11.83 11.37 11.50 12.83 11.66 10.85 15.22 11.71 14.27 13.03 7.83 13.14 13.04 12.89

4.20 3.89 3.80 8.87 3.73 3.76 3.54 5.34 4.67 3.81 3.94 2.75 4.35 4.11 4.83

– 0.38⁎⁎ 0.41⁎⁎ 0.30⁎⁎ 0.37⁎⁎ 0.32⁎⁎ 0.33⁎⁎ 0.33⁎⁎ 0.32⁎⁎ 0.29⁎⁎ 0.35⁎⁎ 0.40⁎⁎ 0.37⁎⁎ 0.25⁎⁎ 0.37⁎⁎

0.35⁎⁎ 0.20⁎⁎ 0.23⁎⁎ 0.23⁎⁎ 0.20⁎⁎ 0.11⁎⁎ 0.11⁎⁎ 0.07⁎ 0.15⁎⁎ 0.19⁎⁎ 0.23⁎⁎ 0.18⁎⁎ 0.19⁎⁎

0.18⁎⁎ 0.18⁎⁎ 0.18⁎⁎ 0.18⁎⁎ 0.13⁎⁎ 0.15⁎⁎ 0.16⁎⁎ 0.18⁎⁎ 0.25⁎⁎ 0.24⁎⁎ 0.13⁎⁎ 0.20⁎⁎

0.18⁎⁎ 0.14⁎⁎ 0.14⁎⁎ 0.14⁎⁎ 0.11⁎⁎ 0.05 0.14⁎⁎ 0.12⁎⁎ 0.22⁎⁎ 0.10⁎⁎ 0.17⁎⁎

0.40⁎⁎ 0.48⁎⁎ 0.16⁎⁎ 0.11⁎⁎ 0.05 0.09⁎⁎ 0.23⁎⁎ 0.18⁎⁎ 0.08⁎ 0.15⁎⁎

0.43⁎⁎ 0.09⁎⁎ 0.07⁎ 0.09⁎⁎ 0.06⁎ 0.18⁎⁎ 0.16⁎⁎ 0.06 0.17⁎⁎

0.12⁎⁎ 0.10⁎⁎ 0.06 0.12⁎⁎ 0.17⁎⁎ 0.16⁎⁎ 0.09⁎⁎ 0.14⁎⁎

0.35⁎⁎ 0.38⁎⁎ 0.44⁎⁎ 0.21⁎⁎ 0.17⁎⁎ 0.10⁎⁎ 0.13⁎⁎

0.42⁎⁎ 0.44⁎⁎ 0.21⁎⁎ 0.15⁎⁎ 0.09⁎⁎ 0.09⁎⁎

0.38⁎⁎ 0.14⁎⁎ 0.11⁎⁎ 0.04 0.13⁎⁎

0.20⁎⁎ 0.22⁎⁎ 0.14⁎⁎ 0.16⁎⁎

0.20⁎⁎ 0.20⁎⁎ 0.16⁎⁎

0.11⁎⁎ 0.14⁎⁎

0.09⁎⁎

9.14

3.65

0.30⁎⁎

0.12⁎⁎

0.14⁎⁎

0.15⁎⁎

0.22⁎⁎

0.19⁎⁎

0.21⁎⁎

0.20⁎⁎

0.21⁎⁎

0.18⁎⁎

0.21⁎⁎

0.15⁎⁎

0.13⁎⁎

0.07⁎

Note: ⁎ p < .05. ⁎⁎ p < .01. 7

15

0.09⁎⁎

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Table 3 Ordinary least squares regression predicting high school students' cyberbullying perpetration (N = 785)

Self domain Low self-control Low moral identity Peer domain Deviant peer affiliation Family domain Poor parental monitoring Parental harsh discipline Poor parental attachment School domain Poor school support Ineffective disciplinary structure School disorganization Poor school attachment Constraints Low shame Low embarrassment Low formal sanctions Deviant motivations Social learning based deviant motivation Strain based deviant motivation R2

Self domain

Peer domain

Family domain

School domain

Constraints

Deviant motivations

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

b (β)

b (β)

b (β)

b (SE)

b (β)

b (β)

0.29⁎⁎ (0.27) 0.35⁎⁎ (0.32)

0.26⁎⁎ (0.24) 0.33⁎⁎ (0.29)

0.19⁎⁎ (0.18) 0.28⁎⁎ (0.26)

0.17⁎⁎ (0.16) 0.23⁎⁎ (0.21)

0.13⁎⁎ (0.12) 0.18⁎⁎ (0.16)

0.12⁎⁎ (0.11) 0.16⁎⁎ (0.15)

0.33⁎⁎ (0.20)

0.08⁎⁎ (0.16)

0.06⁎⁎ (0.14)

0.05⁎⁎ (0.11)

0.04⁎⁎ (0.09)

0.19⁎⁎ (0.17) 0.11⁎⁎ (0.10) 0.13⁎⁎ (0.11)

0.17⁎⁎ (0.15) 0.11⁎⁎ (0.10) 0.10⁎⁎ (0.09)

0.13⁎⁎ (0.12) 0.09⁎⁎ (0.08) 0.10⁎ (0.08)

0.12⁎⁎ (0.10) 0.07⁎⁎ (0.06) 0.08⁎⁎ (0.07)

0.08⁎⁎ 0.09⁎⁎ 0.10⁎⁎ 0.13⁎⁎

0.06⁎ (0.07) 0.07⁎⁎ (0.08) 0.10⁎⁎ (0.09) 0.09⁎⁎ (0.08)

0.05⁎⁎ 0.07⁎⁎ 0.08⁎⁎ 0.07⁎⁎

0.24⁎⁎ (0.15) 0.14⁎⁎ (0.14) 0.11⁎⁎ (0.10)

0.22⁎⁎ (0.14) 0.13⁎⁎ (0.13) 0.10⁎⁎ (0.10)

0.49

0.16⁎⁎ (0.18) 0.10⁎⁎ (0.09) 0.52

0.23

0.27

0.35

0.43

(0.10) (0.11) (0.09) (0.12)

(0.07) (0.08) (0.08) (0.06)

Note: ⁎ p < .05. ⁎⁎ p < .01.

change = 7.48, p < .01). Additionally, ineffective school socialization has no significant moderating role in the relationship between the peer domain and social learning-based deviant motivation. In sum, when ineffective school socialization raises the effect of low self-control, shame, embarrassment, and strain-based deviant motivation on cyberbullying will increase, too. Table 7 reports the moderating effects of low self-control and low moral identity as self domain constructs. The findings are mixed, with low self-control having a significant moderating effect on the relationship between low informal sanctions and cyberbullying (b = 0.02, R2 change = 0.01, F change = 5.39, p < .01); likewise, low moral identity intensifies the effect of low shame (b = 0.04, R2 change = 0.01, F change = 12.24, p < .01) and low embarrassment (b = 0.02, R2 change = 0.01, F change = 4.69, p < .01) on cyberbullying. However, low self-control and low moral identity have no significant moderating effects on the relationships among deviant peer affiliation, social learning-based deviant motivation, and cyberbullying. Lastly, Table 8 presents the moderating effect of the peer domain. The data indicate that the inclusion of the interaction term of deviant peer affiliation × low embarrassment (b = 0.01, p < .01) amplifies the predictive power of the main effects by 1% (R2 change = 0.01, F change = 6.64, p < .05), whereas the addition of the deviant peer affiliation × social learning-based deviant motivation interaction term does not significantly increase the predictive power of the main model.

and low moral identity (β = 0.09, p < .01). Likewise, ineffective school socialization (β = 0.02, p < .05) and ineffective parenting (β = 0.02, p < .05) have indirect effects on deviant peer association. Similarly, ineffective school socialization (β = 0.18, p < .01) and ineffective parenting (β = 0.20, p < .01) have significant direct effects on low moral identity. The model accounts for 51% of the variance in cyberbullying perpetration, 9% of the variance in social learning-based deviant motivation, 12% of the variance in strain-based deviant motivation, 13% of the variance in low shame, 13% of the variance in low embarrassment, 3% of the variance in low informal sanctions, 8% of the variance in deviant peer affiliation, and 9% of the variance in moral identity. For the fitted model, the summary statistics (i.e., CMIN/DF (1.334), GFI (0.993), CFI (0.983), and RMSEA (0.021)) are above the critical values and represent the goodness-of-fit for the proposed model (Fig. 1). Table 5 (family domain), Table 6 (school domain), Table 7 (self domain), and Table 8 (peer domain) provide the moderating effects of these life domains. As Table 5 shows, ineffective parenting (family domain) has a significant moderating effect on the relationship between the other life domains and cyberbullying perpetration. For example, model 2 indicates the interaction effect of ineffective parenting × low moral identity. The interaction term has a significant effect on fans' cyberbullying perpetration (b = 0.01, p < .05). The inclusion of the interaction term increases the predictive power of the main effects by 1% (R2 change = 0.01, F change = 6.85, p < .05). Although the moderating effects of parenting on both social learning- and strainbased deviant motivation are not significant, when ineffective parenting is high, the direct effect of the self domain and peer domain on cyberbullying do tend to increase. For the school domain, Table 6 shows that ineffective school socialization has a significant moderating role in the relationships among the self domain, constraints, and deviant motivation. For instance, model 1 reveals that ineffective school socialization has a significant moderating role in the relationship between low self-control and cyberbullying perpetration; the inclusion of the interaction term boosts the predictive power of the main effects by 1% (R2 change = 0.01, F

5. Discussion The current study examined the utility of Agnew's general theory of crime for explaining cyberbullying perpetration in Iran. We aimed to explain why high school students engage in cyberbullying. Agnew (2005) contends that adverse life conditions such as poor parenting, poor school socialization, having deviant peers, poor quality of work, and low self-control increase the likelihood of offending by affecting motivation for and constraints against crime. Further, the theory suggests that there are theoretical direct, indirect, and moderating effects of the four life domains on criminality. 8

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Table 4 Direct and indirect effects of Life Domains on high school students' cyberbullying Perpetration (N = 785) Independent variable

Age Ineffective school socialization Ineffective parenting Low self-control Low moral identity Deviant peer affiliation Low shame Low embarrassment Low informal sanctions Strain based deviant motivation Social learning based deviant motivation R squared of model Ineffective school socialization Ineffective parenting Low self-control Low moral identity Deviant peer affiliation R squared of model Ineffective school socialization Ineffective parenting Low self-control Low moral identity Deviant peer affiliation R squared of model Ineffective school socialization Ineffective parenting Low moral identity R squared of model Ineffective school socialization Ineffective parenting Low self-control Low moral identity Deviant peer affiliation R squared of model Ineffective school socialization Ineffective parenting Low self-control R squared of model Ineffective school socialization Ineffective parenting Low self-control Low moral identity R squared of model Ineffective school socialization Ineffective parenting R squared of model

Dependent variable

Direct effect

Indirect effect

R squared

b

β

b

β

0.11 0.07⁎⁎ 0.09⁎⁎ 0.12⁎⁎ 0.16⁎⁎ 0.04⁎⁎ 0.22⁎⁎ 0.13⁎⁎ 0.10⁎⁎ 0.10⁎⁎ 0.16⁎⁎

0.02 0.21 0.19 0.11 0.15 0.09 0.14 0.14 0.10 0.09 0.18

– 0.04⁎⁎ 0.07⁎⁎ 0.07⁎⁎ 0.08⁎⁎ 0.02⁎⁎

– 0.13 0.14 0.06 0.07 0.04

0.04⁎⁎ 0.06⁎ 12⁎ 0.14⁎⁎ 0.05⁎⁎

0.10 0.11 0.09 0.11 0.10

0.01⁎⁎ 0.02⁎⁎ 0.02⁎⁎ 0.01⁎ –

0.03 0.04 0.01 0.01 –

0.07⁎⁎ 0.09⁎⁎ – – –

0.23 0.22 – – –

– – – – –

– – – – –

Low shame Low shame Low shame

04⁎⁎ 0.06⁎⁎ 0.11⁎⁎

0.19 0.18 0.16

0.01⁎⁎ 0.01⁎⁎ –

0.03 0.03 –

Low Low Low Low Low

0.05⁎⁎ 0.05⁎⁎ 0.12⁎⁎ 0.14⁎⁎ 0.07⁎⁎

0.14 0.10 0.11 0.13 0.14

0.01⁎⁎ 0.04⁎⁎ 0.02⁎⁎ 0.01⁎

0.04 0.04 0.02 0.01

Low informal sanctions Low informal sanctions Low informal sanctions

– – 0.18⁎⁎

– – 0.17

– – –

– – –

Deviant Deviant Deviant Deviant

0.06⁎ 0.13⁎⁎ 0.28⁎⁎ 0.20⁎

0.09 0.13 0.12 0.09

0.01⁎ 0.02⁎ – –

0.02 0.02 – –

0.05⁎⁎ 0.09⁎⁎

0.18 0.20

– –

– –

Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying Cyberbullying

perpetration perpetration perpetration perpetration perpetration perpetration perpetration perpetration perpetration perpetration perpetration

R2 = 0.51 Social Social Social Social Social

learning learning learning learning learning

Strain Strain Strain Strain Strain

based based based based based

based based based based based

dmotivation deviant motivation deviant motivation deviant motivation deviant motivation

R2 = 0.09 deviant deviant deviant deviant deviant

motivation motivation motivation motivation motivation

R2 = 0.12

R2 = 0.13 embarrassment embarrassment embarrassment embarrassment embarrassment

R2 = 0.13

R2 = 0.03 peer peer peer peer

affiliation affiliation affiliation affiliation

R2 = 0.08 Low moral identity Low moral identity

R2 = 0.08

Note: ⁎ p < .05. ⁎⁎ p < .01.

Mazerolle, & Piquero, 2001; Lin & Yi, 2016; Mazerolle & Maahs, 2000; Paez, 2018). Similarly, these adverse conditions can provide a deviant socialization context that more likely increases the likelihood of cyberbullying perpetration (Hong et al., 2017; Simons, Simons, Chen, Brody, & Lin, 2007; Simons, Whitbeck, Conger, & Conger, 1991). Concurring with previous research, the results from the current study showed that cyberbullying perpetration is more likely when the family and school domains do not function properly. In our study, the self domain was represented by self-control and moral identity. Agnew (2005) notes that personal traits such as low selfcontrol and irritability can increase the possibility of offending. We considered moral identity as a self domain construct. Aquino, Reed, Thau, and Freeman (2007) state that when morality is central to an individual's identity, it enhances his or her sense of responsibility to behave in accordance with his or her moral beliefs and values (Hardy & Carlo, 2011); the likelihood of cyberbullying can thus be reduced accordingly (Wang et al., 2017; Yang et al., 2018). Our findings are in line with previous research that has reported links among moral identity,

We hypothesized that high school students' cyberbullying perpetration is associated with four life domains (self, family, school, and peer). Additionally, we tested the ability of motivations for and constraints against cyberbullying as mediators of the relationship between the life domain constructs and cyberaggression. Finally, the interaction effects among the four life domains were examined to predict cyberbullying. Briefly, our study found full support for the theory (Agnew, 2005) and prior works that have shown significant links between these life domains and offending (Choi & Kruis, 2019; Cochran, 2017; Muftić et al., 2014; Ngo et al., 2011; Ngo & Paternoster, 2014; Zhang et al., 2012). The current study thus established the direct, indirect, and moderating effects of the life domains as well as motivations for and constraints against crime on cyberbullying perpetration. Scholars have indicated that poor attachment with parents and schools, ineffective monitoring by parents and teachers, harsh parental discipline, and an ineffective disciplinary structure at school generate strain, which can foster deviant motivations for cyberbullying (Bao, Haas, & Pi, 2004; Brezina, Piquero, & Mazerolle, 2001; Capowich, 9

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S. Kabiri, et al.

.09* .09

.12 .09

Social Learning based Deviant Motivation

Strain based Deviant motivation

Low Self-control

Age

.11* .02

.18** .11**

.22**

.10** .12**

.09**

.11**

Ineffective Parenting

.19**

.13**

.23**

.51

.08 Deviant Peer Affiliation

.18** .09*

Cyberbullying Perpetration

.09**

.21** Ineffective School Socialization

.15** .14**

.11** .10** .19**

.14** .14**

.11**

.18**

.10** .17**

.09* .14** Low Moral Identity

.20**

.16**

Low Embarrassment

Low Shame

Low informal Sanctions

.08 .13

.13**

.14

.03

Model Fit Summary: CMIN/DF= 1.334, GFI = .993, TLI = 983, CFI = .993 RMSEA= .021

Fig. 1. The effect of parenting and morality on fans' cyberbullying perpetration.

self domain variable in explaining cyberbullying offending. Evidence shows that cyberbullying perpetration is significantly linked to low self-control (Hinduja & Patchin, 2008; Li et al., 2016; You & Lim, 2016). The results of our study showed that cyberbullying is both directly and indirectly associated with low self-control. Concerning the indirect path, students low in self-control are more likely to join deviant peer friendships and perceive a low level of formal

moral emotions, and cyberbullying (Menesini et al., 2013; Perren & Gutzwiller-Helfenfinger, 2012; Wachs, 2012). One important contribution of the current study is testing the direct and indirect effects of moral identity on cyberbullying perpetration. Agnew's (2005) general theory of crime suggests that irritability and low self-control are important self characteristics promoting the likelihood of offending. Our study reveals that moral identity could be considered as an important

Table 5 Family Domain Moderation effect on high school students' cyberbullying Perpetration (N = 785)

Ineffective parenting (IP) Low self-control (LSC) IP * LSC Low moral identity (LMI) IP * LMI Deviant peer affiliation (DPA) IP * DPI Low Shame (LS) IP * LS Low embarrassment (LE) IP * LE Social learning based deviant motivation (SLDM) IP * SLDM Strain based deviant motivation (SDM) IP * SDM R2 R2 change F Change Low (−1 SD below the mean) Moderate (mean) High (+1 SD above the mean)

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

b (SE)

b (β)

b (β)

b (SE)

b (β)

b (β)

b (β)

0.17** (0.02) 0.30** (0.13) 0.01* (0.01)

0.17** (0.01)

0.18** (0.02)

0.17** (0.01)

0.17** (0.02)

0.18** (0.02)

0.18** (0.02)

0.36** (0.03) 0.01* (0.01) 0.10** (0.02) 0.01* (0.01) 0.46** (0.05) 0.04** (0.01) 0.27** (0.01) 0.01** (0.01) 0.25** (0.03) 0.001 (0.01)

0.26 +0.01 6.09* 0.22** (0.04) 0.30** (0.03) 0.37** (0.04)

0.30 +0.01 6.85* 0.27** (0.05) 0.36** (0.03) 0.44** (0.04)

0.24 +0.01 4.05* 0.07** (0.02) 0.10** (0.02) 0.13** (0.02)

10

0.32 +0.04 49.36** 0.15** (0.07) 0.46** (0.05) 0.77** (0.06)

0.29 +0.02 10.42** 0.14** (0.04) 0.27** (0.03) 0.40** (0.04)

0.28 +0.003 3.21 – – –

0.22** (0.04) 0.001 (0.01) 0.23 +0.003 3.10 – – –

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Table 6 School Domain Moderation effect on high school students' cyberbullying Perpetration (N = 785)

Ineffective school socialization (ISS) Low self-control (LSC) ISS * LSC Low moral identity (LMI) ISS * LMI Deviant peer affiliation (DPA) ISS * DPI Low shame (LS) ISS * LS Low embarrassment (LE) ISS * LE Social learning based deviant motivation (SLDM) ISS * SLDM Strain based deviant motivation (SDM) ISS * SDM R2 R2 change F Change Low (−1 SD below the mean) Moderate (mean) High (+1 SD above the mean)

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

b (SE)

b (β)

b (β)

b (SE)

b (β)

b (β)

b (β)

0.12** (0.01) 0.35** (0.03) 0.01* (0.01)

0.12** (0.01)

0.12** (0.01)

0.11** (0.01)

0.12** (0.01)

0.12** (0.01)

0.11** (0.01)

0.37** (0.03) 0.002 (0.01) 0.11** (0.01) 0.002 (0.01) 0.47** (0.05) 0.01** (0.01) 0.28** (0.03) 0.01** (0.01) 0.27** (0.03) 0.001 (0.01)

0.30 +0.01 7.48* 0.26** (0.04) 0.34** (0.03) 0.43** (0.04)

0.30 +0.001 1.20* – – –

0.25 +0.003 3.24 – – –

0.28 +0.01 7.03** 0.35** (0.07) 0.47** (0.05) 0.59** (0.07)

0.27 +0.01 4.93** 0.22** (0.04) 0.28** (0.03) 0.35** (0.04)

0.28 +0.001 0.50 – – –

0.20** (0.04) 0.01 (0.01) 0.23 +0.01 10.01 0.10 (0.05) 0.20** (0.04) 0.30** (0.04)

cyberbullying perpetration. For example, the effect of the self domain (low self-control and low morality) is contingent on family and school performance. Poor parenting and ineffective school socialization intensify the effect of the self domain constructs on cyberbullying. Moreover, the effect of deviant peer affiliation on cyberbullying strengthens when students have low moral identity and self-control. The current study makes an important contribution to the criminological literature by examining the validity of propositions derived from Agnew's general theory of crime using a unique sample of high school students in Iran. However, a few limitations of the study need to be acknowledged. First, the generalizability of the results may be limited given the single-country sample used. Regional differences in cyberbullying perpetration may exist, and the external validity of the findings could be specific to Iranian students. Future research should thus assess the generalizability of the findings over time as well as using

sanctions. This social and attitudinal climate subsequently leads them to engage in cyberbullying as a means to achieve short-term desires. Deviant affiliation is one of the most important correlates of cyberbullying (Burton, Florell, & Wygant, 2013; Hinduja & Patchin, 2013; Lee & Shin, 2017; Shim & Shin, 2016). Agnew (2005) argues that having deviant peers increases the possibility of crime. Our findings indicated that deviant peer affiliation has a direct and indirect effect on cyberbullying perpetration. Having deviant peer associations decreases the constraints against cyberbullying as well as heightens the deviant motivations for it. Agnew's (2005) general theory proposes a significant interaction between the life domains and offending. Consistent with prior works that have found the moderating effects of the life domains on criminality (Choi & Kruis, 2019; Muftić et al., 2014), we demonstrated that the self, family, school, and peer domains all interact with each other in

Table 7 Self Domain Moderation effect on high school students' cyberbullying Perpetration (N = 785)

Low self-control (LSC) Deviant peer affiliation (DPA) LSC * DPA Low informal sanctions (LPA) LSC * LPA Social learning based deviant motivation (SLDM) LSC * SLDM Low moral identity (LMI) Deviant peer affiliation (DPA) LMI * DPI Low shame (LS) LMI * LS Low embarrassment (LE) LMI * LE Social learning based deviant motivation (SLDM) LMI * SLDM R2 R2 change F Change Low (−1 SD below the mean) Moderate (mean) High (+1 SD above the mean)

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

b (SE)

b (β)

b (β)

b (SE)

b (β)

b (β)

b (β)

0.35** (0.03) 0.11** (0.02) 0.01 (0.01)

0.37** (0.03)

0.34** (0.03)

0.41** (0.02) 0.11** (0.01) 0.002 (0.01)

0.37** (0.03)

0.37** (0.03)

0.39** (0.03)

0.20** (0.03) 0.02** (0.01) 0.27** (0.03) 0.01 (0.01)

0.47** (0.05) 0.04** (0.01) 0.28** (0.03) 0.02* (0.01)

0.20 +0.002 2.84 – – –

0.19 +0.01 5.39** 0.12* (0.05) 0.20** (0.03) 0.28** (0.05)

0.24 +0.001 1.01 – – –

11

0.22 +0.001 0.30 – – –

0.28 +0.01 12.24** 0.32** (0.07) 0.47** (0.05) 0.63** (0.06)

0.25 +0.01 4.69** 0.21** (0.04) 0.28** (0.03) 0.34** (0.04)

0.27** (0.03) 0.01 (0.01) 0.26 +0.001 1.35 – – –

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climates supporting excellence in achievement and ethical character; (3) adults foster ethical skills across activities (e.g., curriculum and extra curriculum) focusing on skills in ethical sensitivity, judgment, focus, and action; (4) adults encourage their children to exercise selfauthorship and self-regulation; and (5) adults work together to build communities that coordinate support and relationships across institutions to foster resiliency (Narvaez & Lapsley, 2009). The findings from the peer domain suggest that the increased levels of monitoring and supervision on peer relationships can act as an inhibitor of cyberbullying perpetration. Additionally, given that life domains are indirectly related to cyberbullying by constraints and motivations, prevention efforts to promote internal control, informal control, and formal control hold the potential to reduce cyberbullying among sports fans by teaching children to see the relationship between their actions and long-term consequences (e.g., SNAP program). Cyberbullying penetrations can also be intervened by implementing strategies to mitigate the influence of deviant motivations on the decision to engage in offending by getting sports fans to stop angry feelings and think different ways to cope with their negative emotions.

Table 8 Self domain moderation effect on high school students' cyberbullying perpetration (N = 785)

Deviant peer affiliation (DPA) Low embarrassment (LE) DPI * LE Social learning based deviant motivation (SLDM) DPI * SLDM R2 R2 change F Change Low (−1 SD below the mean) Moderate (mean) High (+1 SD above the mean)

Model 1

Model 2

b (SE)

b (β)

0.10** (0.02) 0.30** (0.03) 0.01* (0.01)

0.11** (0.02)

0.28** (0.03)

0.20 +0.01 6.64* 0.22** (0.05) 0.30** (0.03) 0.39** (0.04)

0.01 (0.01) 0.20 +0.003 3.44 – – –

other methods and samples. Second, measuring offending using selfreport methods can suffer from social desirability bias (Jennings & Reingle, 2019). Although we informed participants of the anonymous and confidential nature of the data at several points, future research should attempt to measure cyberbullying perpetration using a combination of methods. Third, our exclusion of females from the sample should be noted as a limitation. Gender is an important variable that shapes life domains outlined in the general theory of crime. Although the information on female respondents was not available in our study, future research can consider gender to help unpack the relationship between life domains and offending.

References Agnew, R. (2005). Why do criminals offend? A general theory of crime and delinquency. Los Angeles, CA: Roxbury. Albdour, M., Hong, J. S., Lewin, L., & Yarandi, H. (2019). The impact of cyberbullying on physical and psychological health of Arab American adolescents. Journal of Immigrant Minority Health, 21(4), 706–715. Aquino, K., Freeman, D., Reed, I., Lim, V., & Felps, W. (2009). Testing a social-cognitive model of moral behavior: The interactive influence of situations and moral identity centrality. Journal of Personality and Social Psychology, 97(1), 123–141. Aquino, K., Reed, A., Thau, S., & Freeman, D. (2007). A grotesque and dark beauty: How moral identity and mechanisms of moral disengagement influence cognitive and emotional reactions to war. Journal of Experimental Social Psychology, 43(3), 385–392. Aquino, K., & Reed, I. (2002). The self-importance of moral identity. Journal of Personality and Social Psychology, 83(6), 1423–1440. Arslan, S., Savaser, S., Hallett, V., & Balci, S. (2012). Cyberbullying among primary school students in Turkey: Self-reported prevalence and associations with home and school life. Cyberpsychology, Behavior and Social Networking, 15(10), 527–533. Baier, D., Hong, J. S., Kliem, S., & Bergmann, M. C. (2019). Consequences of bullying on adolescents’ mental health in Germany: Comparing face-to-face bullying and cyberbullying. Journal of child and family studies, 28(9), 2347–2357. Bao, W.-N., Haas, A., & Pi, Y. (2004). Life strain, negative emotions, and delinquency: An empirical test of general strain theory in the People's Republic of China. International Journal of Offender Therapy and Comparative Criminology, 48(3), 281–297. Baumeister, R. F., Gailliot, M., DeWall, C. N., & Oaten, M. (2006). Self-regulation and personality: How interventions increase regulatory success, and how depletion moderates the effects of traits on behavior. Journal of Personality, 74(6), 1773–1802. Brailovskaia, J., Teismann, T., & Margraf, J. (2018). Cyberbullying, positive mental health and suicide ideation/behavior. Psychiatry Research, 267, 240–242. Brezina, T., Piquero, A. R., & Mazerolle, P. (2001). Student anger and aggressive behavior in school: An initial test of Agnew's macro-level strain theory. Journal of Research in Crime and Delinquency, 38(4), 362–386. Burton, K. A., Florell, D., & Wygant, D. B. (2013). The role of peer attachment and normative beliefs about aggression on traditional bullying and cyberbullying. Psychology in the Schools, 50(2), 103–115. Capowich, G. E., Mazerolle, P., & Piquero, A. (2001). General strain theory, situational anger, and social networks: An assessment of conditioning influences. Journal of Criminal Justice, 29(5), 445–461. Cassidy, W., Faucher, C., & Jackson, M. (2013). Cyberbullying among youth: A comprehensive review of current international research and its implications and application to policy and practice. School Psychology International, 34(6), 575–612. Cénat, J. M., Blais, M., Lavoie, F., Caron, P.-O., & Hébert, M. (2018). Cyberbullying victimization and substance use among Quebec high schools students: The mediating role of psychological distress. Computers in Human Behavior, 89, 207–212. Chan, H. C., & Wong, D. S. (2019). Traditional school bullying and cyberbullying perpetration: Examining the psychosocial characteristics of Hong Kong male and female adolescents. Youth and Society, 51(1), 3–29. Cheung, G., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296–325. Choi, J., & Kruis, N. E. (2019). The effects of life domains on cyberbullying and bullying: Testing the generalizability of Agnew's integrated general theory. Crime & Delinquency, 65(6), 772–800. https://doi.org/10.1177/0011128718814860. Cochran, J. K. (2017). The effects of life domains, constraints, and motivations on academic dishonesty: A partial test and extension of Agnew's general theory. International Journal of Offender Therapy and Comparative Criminology, 61(11), 1288–1308. Cochran, J. K., Chamlin, M. B., Wood, P. B., & Sellers, C. S. (1999). Shame, embarrassment, and formal sanction threats: Extending the deterrence/rational choice model to

6. Policy implications Despite the limitations, we can draw several important policy implications to reduce cyberbullying perpetration among high school students. In order to decrease adolescent's cyberbullying perpetration based on Agnew's (2005) general theory of crime, we should make changes in life domains. Reflecting the effect of the family domain on cyberbullying engagement, policymakers should design and provide programs for effective parenting. Additionally, our results have shown that school climate is relevant to cyberbullying perpetration (Tanrikulu & Campbell, 2015; Varjas, Henrich, & Meyers, 2009). School administrators and teachers should provide a secure and safe environment for students to feel less distressed. There should be efforts to establish attachments to teachers and school. Regarding the self domain, this study is focused on self-control and moral identity as self characteristics associated with cyber aggression. Some researchers have suggested that parenting is crucial in fostering children's levels of self-control and that parents should attend parenting courses that allow them to learn about how they can effectively monitor and discipline their children (Wright & Beaver, 2005; Wright, Beaver, Delisi, & Vaughn, 2008). Contrary to Gottfredson and Hirschi's (1990) stability hypothesis, evidence reveals that self-control can be strengthened over time (Baumeister, Gailliot, DeWall, & Oaten, 2006; Muraven, 2010). School administrators can incorporate resiliency training and cognitive-behavioral programs to help children to recognize their angry feelings and consider positive alternatives before acting given that one's level of self-control can be strengthened through repeated exercises. Study findings related to morality also have some relevance to policy. Policymakers should design and implement programs to promote ethics and moral emotions. One particular program, an Integrative Ethical Education model is particularly promising to strengthen one's moral capabilities (Narvaez & Lapsley, 2009). This model proposes five steps for ethical character development: (1) adults establish caring relationships with their children; (2) adults develop 12

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suicidal ideation in adolescents. The Canadian Journal of Psychiatry, 64(2), 126–135. Kline, R. B. (2015). Principles and practice of structural equation modeling: Guilford publications. Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073. Lee, C., & Shin, N. (2017). Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents. Computers in Human Behavior, 68, 352–358. Lee, J. M., Hong, J. S., Yoon, J., Peguero, A. A., & Seok, H. J. (2018). Correlates of adolescent cyberbullying in South Korea in multiple contexts: A review of the literature and implications for research and school practice. Deviant Behavior, 39(3), 293–308. Li, C. K., Holt, T. J., Bossler, A. M., & May, D. C. (2016). Examining the mediating effects of social learning on the low self-control—Cyberbullying relationship in a youth sample. Deviant Behavior, 37(2), 126–138. Lin, W.-H., & Yi, C.-C. (2016). Family and school strains and their effects on adolescent deviance in Taiwan: A mediating study. Youth and Society, 48(4), 496–520. Mazerolle, P., & Maahs, J. (2000). General strain and delinquency: An alternative examination of conditioning influences. Justice Quarterly, 17(4), 753–778. Menesini, E., Nocentini, A., & Camodeca, M. (2013). Morality, values, traditional bullying, and cyberbullying in adolescence. British Journal of Developmental Psychology, 31(1), 1–14. Menesini, E., Nocentini, A., Palladino, B. E., Frisén, A., Berne, S., Ortega-Ruiz, R., ... Luik, P. (2012). Cyberbullying definition among adolescents: A comparison across six European countries. Cyberpsychology, Behavior and Social Networking, 15(9), 455–463. Mishna, F., Khoury-Kassabri, M., Gadalla, T., & Daciuk, J. (2012). Risk factors for involvement in cyber bullying: Victims, bullies and bully–victims. Children Youth Services Review, 34(1), 63–70. Moon, B., & Alarid, L. F. (2015). School bullying, low self-control, and opportunity. Journal of Interpersonal Violence, 30(5), 839–856. Muftić, L. R., Grubb, J. A., Bouffard, L. A., & Maljević, A. (2014). The impact of life domains on juvenile offending in Bosnia and Herzegovina: Direct, indirect, and moderating effects in Agnew's integrated general theory. Journal of Research in Crime and Delinquency, 51(6), 816–845. Muraven, M. (2010). Building self-control strength: Practicing self-control leads to improved self-control performance. Journal of Experimental Social Psychology, 46(2), 465–468. Narvaez, D., & Lapsley, D. K. (2009). Moral identity, moral functioning, and the development of moral character. Psychology of learning Motivation, 50, 237–274. Ngo, F. T., & Paternoster, R. (2014). Contemporaneous and lagged effects of life domains and substance use: A test of Agnew's general theory of crime and delinquency. Journal of Criminology, 2014, 1–20. Ngo, F. T., Paternoster, R., Cullen, F. T., & Mackenzie, D. L. (2011). Life domains and crime: A test of Agnew's general theory of crime and delinquency. Journal of Criminal Justice, 39(4), 302–311. https://doi.org/10.1016/j.jcrimjus.2011.03.006. Nunally, J. C. (1978). Psychometric theory. New York: McGraw-Hill. Paez, G. R. (2018). Cyberbullying among adolescents: A general strain theory perspective. Journal of School Violence, 17(1), 74–85. Patchin, J. W., & Hinduja, S. (2006). Bullies move beyond the schoolyard: A preliminary look at cyberbullying. Youth Violence and Juvenile Justice, 4(2), 148–169. Patchin, J. W., & Hinduja, S. (2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43(2), 727–751. Patchin, J. W., & Hinduja, S. (2018). Deterring teen bullying: Assessing the impact of perceived punishment from police, schools, and parents. Youth Violence and Juvenile Justice, 16(2), 190–207. Patrick, R. B., Bodine, A. J., Gibbs, J. C., & Basinger, K. S. (2018). What accounts for Prosocial behavior? Roles of moral identity, moral judgment, and self-efficacy beliefs. The Journal of Genetic Psychology, 179(5), 231–245. Perren, S., & Gutzwiller-Helfenfinger, E. (2012). Cyberbullying and traditional bullying in adolescence: Differential roles of moral disengagement, moral emotions, and moral values. European Journal of Developmental Psychology, 9(2), 195–209. Preacher, K., & Hayes, A. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. Reed, I., & Aquino, K. F. (2003). Moral identity and the expanding circle of moral regard toward out-groups. Journal of Personality and Social Psychology, 84(6), 1270–1286. Reynolds, S. J., & Ceranic, T. L. (2007). The effects of moral judgment and moral identity on moral behavior: An empirical examination of the moral individual. Journal of Applied Psychology, 92(6), 1610–1624. Selkie, E. M., Fales, J. L., & Moreno, M. A. (2016). Cyberbullying prevalence among US middle and high school–aged adolescents: A systematic review and quality assessment. Journal of Adolescent Health, 58(2), 125–133. Shadmanfaat, S. M., Howell, C. J., Muniz, C. N., Cochran, J. K., Kabiri, S., & Fontaine, E. M. (2019). Cyberbullying perpetration: An empirical test of social learning theory in Iran. Deviant Behavior, 1–16. https://doi.org/10.1080/01639625.2019.1565513 (In Press). Shadmanfaat, S. M., Kabiri, S., Hayden, S., & Cochran, J. K. (2019). A longitudinal study of Iranian fans' cyberbullying: The utility of social learning theory. Deviant Behavior, 1–21. https://doi.org/10.1080/01639625.2019.1637996 (In press). Shim, H., & Shin, E. (2016). Peer-group pressure as a moderator of the relationship between attitude toward cyberbullying and cyberbullying behaviors on mobile instant messengers. Telematics and Informatics, 33(1), 17–24. Simons, R. L., Simons, L. G., Chen, Y. F., Brody, G. H., & Lin, K. H. (2007). Identifying the psychological factors that mediate the association between parenting practices and delinquency. Criminology, 45(3), 481–517. Simons, R. L., Whitbeck, L. B., Conger, R. D., & Conger, K. J. (1991). Parenting factors,

academic dishonesty. Sociological Inquiry, 69(1), 91–105. Cornell, D., Shukla, K., & Konold, T. (2015). Peer victimization and authoritative school climate: A multilevel approach. Journal of Educational Psychology, 107(4), 1186–1201. Dornbusch, S. M., Erickson, K. G., Laird, J., & Wong, C. A. (2001). The relation of family and school attachment to adolescent deviance in diverse groups and communities. Journal of Adolescent Research, 16(4), 396–422. Foody, M., Samara, M., & Carlbring, P. (2015). A review of cyberbullying and suggestions for online psychological therapy. Internet Interventions, 2(3), 235–242. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford, CA: Stanford University Press. Gotowiec, S., & van Mastrigt, S. (2019). Having versus doing: The roles of moral identity internalization and symbolization for prosocial behaviors. The Journal of Social Psychology, 159(1), 75–91. Grubb, J. A., & Posick, C. (2018). Applying Agnew's integrated theory of crime and delinquency to victimization risk: A contemporaneous and longitudinal examination. Criminal Justice Review, 43(3), 289–308. Guo, S. (2016). A meta-analysis of the predictors of cyberbullying perpetration and victimization. Psychology in the Schools, 53(4), 432–453. Hardy, S. (2006). Identity, reasoning, and emotion: An empirical comparison of three sources of moral motivation. Motivation & Emotion, 30(3), 205–213. Hardy, S., Bean, D., & Olsen, J. (2015a). Moral identity and adolescent prosocial and antisocial behaviors: Interactions with moral disengagement and self-regulation. Journal of Youth & Adolescence, 44(8), 1542–1554. Hardy, S., Nadal, A., & Schwartz, S. (2017). The integration of personal identity, religious identity, and moral identity in emerging adulthood. Identity, 17(2), 96–107. Hardy, S. A., Bean, D. S., & Olsen, J. A. (2015b). Moral identity and adolescent prosocial and antisocial behaviors: Interactions with moral disengagement and self-regulation. Journal of Youth and Adolescence, 44(8), 1542–1554. Hardy, S. A., & Carlo, G. (2011). Moral identity: What is it, how does it develop, and is it linked to moral action? Child Development Perspectives, 5(3), 212–218. Hardy, S. A., Walker, L. J., Olsen, J. A., Woodbury, R. D., & Hickman, J. R. (2014). Moral identity as moral ideal self: Links to adolescent outcomes. Developmental Psychology, 50(1), 45. Hardy, S. A., Walker, L. J., Rackham, D. D., & Olsen, J. A. (2012). Religiosity and adolescent empathy and aggression: The mediating role of moral identity. Psychology of Religion and Spirituality, 4(3), 237–248. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). New York, NY: Guilford Publications. Hertz, S. G., & Krettenauer, T. (2016). Does moral identity effectively predict moral behavior?: A meta-analysis. Review of general psychology, 20(2), 129–140. Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29(2), 129–156. Hinduja, S., & Patchin, J. W. (2010). Bullying, cyberbullying, and suicide. Archives of Suicide Research, 14(3), 206–221. Hinduja, S., & Patchin, J. W. (2013). Social influences on cyberbullying behaviors among middle and high school students. Journal of Youth and Adolescence, 42(5), 711–722. Hinduja, S., & Patchin, J. W. (2019). Connecting adolescent suicide to the severity of bullying and cyberbullying. Journal of School Violence, 18(3), 333–346. Holt, T. J., Bossler, A. M., & May, D. C. (2012). Low self-control, deviant peer associations, and juvenile cyberdeviance. American Journal of Criminal Justice, 37(3), 378–395. Hong, J. S., Kim, D. H., & Piquero, A. R. (2017). Assessing the links between punitive parenting, peer deviance, social isolation and bullying perpetration and victimization in south Korean adolescents. Child Abuse and Neglect, 73, 63–70. Hu, H.-F., Liu, T.-L., Hsiao, R. C., Ni, H.-C., Liang, S. H.-Y., Lin, C.-F., . . . Lee, M.-J. (2019). Cyberbullying victimization and perpetration in adolescents with high-functioning autism spectrum disorder: correlations with depression, anxiety, and suicidality. Journal of Autism Developmental Disorders(In Press), 1–11. doi:https://doi.org/10. 1007/s10803-019-04060-7. Jennings, W. G., & Reingle, J. M. (2019). Criminological and criminal justice research methods. New York: Wolters Kluwer. Kabiri, S., Willits, D. W., & Shadmanfaat, S. M. (2019). A multitheoretical framework for assessing performance-enhancing drug use: Examining the utility of self-control, social learning, and control balance theories. Journal of Drug Issues(In press), 1–19. doi:https://doi.org/10.1177/0022042619839935. Kavussanu, M., & Ring, C. (2017). Moral identity predicts doping likelihood via moral disengagement and anticipated guilt. Journal of Sport and Exercise Psychology, 39(4), 293–301. Kavussanu, M., Stanger, N., & Ring, C. (2015a). The effects of moral identity on moral emotion and antisocial behavior in sport. Sport, Exercise, Performance & Psychology, 4(4), 268–279. Kavussanu, M., Stanger, N., & Ring, C. (2015b). The effects of moral identity on moral emotion and antisocial behavior in sport. Sport, Exercise, Performance Psychology, 4(4), 268–279. Kelley, M. S., Fukushima, M., Spivak, A. L., & Payne, D. (2009). Deterrence theory and the role of shame in projected offending of college students against a ban on alcohol. Journal of Drug Education, 39(4), 419–437. Kim, S., Colwell, S. R., Kata, A., Boyle, M. H., & Georgiades, K. (2018). Cyberbullying victimization and adolescent mental health: Evidence of differential effects by sex and mental health problem type. Journal of youth and adolescence, 47(3), 661–672. Kim, S., Kimber, M., Boyle, M. H., & Georgiades, K. (2019). Sex differences in the association between cyberbullying victimization and mental health, substance use, and

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S. Kabiri, et al.

Wright, J., & Beaver, K. (2005). Do parents matter in creating self-control in their children? A genetically informed test of Gottfredson and Hirschi's theory of low selfcontrol. Criminology, 43(4), 1169–1202. Wright, J., Beaver, K., Delisi, M., & Vaughn, M. (2008). Evidence of negligible parenting influences on self-control, delinquent peers, and delinquency in a sample of twins. Justice Quarterly, 25(3), 544–569. Yang, X., Wang, Z., Chen, H., & Liu, D. (2018). Cyberbullying perpetration among Chinese adolescents: The role of interparental conflict, moral disengagement, and moral identity. Children and Youth Services Review, 86, 256–263. Ybarra, M. L., & Mitchell, K. J. (2004). Online aggressor/targets, aggressors, and targets: A comparison of associated youth characteristics. Journal of Child Psychology and Psychiatry, 45(7), 1308–1316. You, S., & Lim, S. A. (2016). Longitudinal predictors of cyberbullying perpetration: Evidence from Korean middle school students. Personality and Individual Differences, 89, 172–176. Zhang, Y., Day, G., & Cao, L. (2012). A partial test of Agnew's general theory of crime and delinquency. Crime and Delinquency, 58(6), 856–878.

social skills, and value commitments as precursors to school failure, involvement with deviant peers, and delinquent behavior. Journal of Youth and Adolescence, 20(6), 645–664. Simons, R. L., Wu, C. I., Conger, R. D., & Lorenz, F. O. (1994). Two routes to delinquency: Differences between early and late starters in the impact of parenting and deviant peers. Criminology, 32(2), 247–276. Slonje, R., Smith, P. K., & Frisén, A. (2012). Processes of cyberbullying, and feelings of remorse by bullies: A pilot study. European Journal of Developmental Psychology, 9(2), 244–259. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376–385. Spivak, A. L., Fukushima, M., Kelley, M. S., & Jenson, T. S. (2011). Religiosity, delinquency, and the deterrent effects of informal sanctions. Deviant Behavior, 32(8), 677–711. Svensson, R. (2015). An examination of the interaction between morality and deterrence in offending: A research note. Crime & Delinquency, 61(1), 3–18. Tanrikulu, I., & Campbell, M. (2015). Correlates of traditional bullying and cyberbullying perpetration among Australian students. Children & Youth Services Review, 55, 138–146. Udris, R. (2017). Psychological and social factors as predictors of online and offline deviant behavior among Japanese adolescents. Deviant Behavior, 38(7), 792–809. Varjas, K., Henrich, C. C., & Meyers, J. (2009). Urban middle school students' perceptions of bullying, cyberbullying, and school safety. Journal of School Violence, 8(2), 159–176. Vazsonyi, A. T., Machackova, H., Sevcikova, A., Smahel, D., & Cerna, A. (2012). Cyberbullying in context: Direct and indirect effects by low self-control across 25 European countries. European Journal of Developmental Psychology, 9(2), 210–227. Vitulano, M. L., Fite, P. J., & Rathert, J. L. (2010). Delinquent peer influence on childhood delinquency: The moderating effect of impulsivity. Journal of Psychopathology and Behavioral Assessment, 32(3), 315–322. Wachs, S. (2012). Moral disengagement and emotional and social difficulties in bullying and cyberbullying: Differences by participant role. Emotional and Behavioural Difficulties, 17(3–4), 347–360. Wang, X., Yang, L., Yang, J., Wang, P., & Lei, L. (2017). Trait anger and cyberbullying among young adults: A moderated mediation model of moral disengagement and moral identity. Computers in Human Behavior, 73, 519–526. Watts, S. J., Province, K., & Toohy, K. (2019). The kids aren't alright: School attachment, depressive symptoms, and gun carrying at school. American Journal of Criminal Justice, 44(1), 146–165. Wikström, P. H., & Svensson, R. (2010). When does self-control matter? The interaction between morality and self-control in crime causation. European Journal of Criminology, 7(5), 395–410. Wikström, P.-O. H., Oberwittler, D., Treiber, K., & Hardie, B. (2012). Breaking rules: The social and situational dynamics of young people's urban crime. Oxford, UK: Oxford University Press.

Saeed Kabiri earned his master degree in Sociology at University of Guillan (2012) and also earned his PhD degree in social problems of Iran at University of Mazandaran (2017). He has published several papers about the sociology and criminology of sports. His current research interests involve sport criminology. Kabiri’s recent research has been published in multiple peer-reviewed journals such as Deviant Behavior, Journal of Drug Issues, and the International Journal of Offender Therapy and Comparative Criminology. Seyyedeh Masoomeh Shamila Shadmanfaat earned his master degree in Sociology at University of Guillan (2016) has published several papers about sociology and criminology of sport with a focus on gender differences. Her current research interests involve Gender sport criminology. Shadmanfaat’s recent research has been published in multiple peer-reviewed journals such as Deviant Behavior, Journal of Drug Issues, and the International Journal of Offender Therapy and Comparative Criminology. Jaeyong Choi, Ph.D. is an Assistant Professor in the Department of Security Studies and Criminal Justice at Angelo State University. His research interests include criminological theory, police legitimacy, media, and criminal justice, and fear of crime. His recent work has been published in several outlets including Crime and Delinquency, Policing: An International Journal of Police Strategies & Management, and the Prison Journal. Ilhong Yun, Ph.D., is a Professor in the Department of Police Administration at Chosun University. His research interests lie in the area of biosocial criminology, comparative criminal justice, and policing. His recent work has been published in several outlets including Journal of Criminal Justice, Policing: An International Journal of Police Strategies & Management and the International Journal of Offender Therapy and Comparative Criminology.

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