یک مدل فرایند از مزاحمت سایبری در نوجوانی

Cyberbullying is an emerging form of aggression that utilizes information and communication technologies (ICTs). While cyberbullying incidents attract considerable attention, research on the causes and psychosocial predictors of cyberbullying is still limited. The present study used an integrated theoretical model incorporating empathy, moral disengagement, and social cognitions related to cyberbullying. Structured questionnaires were administered to 355 randomly selected adolescents (M = 14.7, SD = 1.20). Linear regression analysis showed that social norms, prototype similarity and situational self-efficacy directly predicted cyberbullying expectations. Multiple mediation modelling indicated that normative influences mediated the effects of moral disengagement and affective empathy on cyberbullying expectations. These findings provide valuable information regarding the effect of both distal and proximal risk factors for cyberbullying in adolescence, highlight the relationship between normative processes and moral self-regulation, and set the basis for related educational and preventive interventions.

1.1. Cyberbullying Cyberbullying1 is an emerging form of aggression that takes place in cyberspace and is utilized by contemporary information and communication technologies (ICTs). Unlike traditional face-to-face bullying, cyberbullying provides total anonymity to the aggressor, and can reach a wide audience (e.g., a humiliating video against another person posted on social networking or file sharing websites can become visible to millions of web users; Beran and Li, 2007 and Patchin and Hinduja, 2006). Most importantly, cyberbullying can have a significant psychological impact on the victim, by leading to withdrawal and social exclusion, lower self-esteem and academic achievement, or even depression and suicide ideation and attempts (Hinduja and Patchin, 2010, Juvonen and Gross, 2008, Klomek et al., 2010 and Li, 2007). The rates of cyberbullying range between 12% and 25% in Europe, USA, and Canada, while the overall rates of other forms of online aggression may be even higher (Patchin and Hinduja, 2006, Slonje and Smith, 2008 and Ybarra and Mitchell, 2004). Because cyberbullying has only recently attracted research attention (most empirical studies on the subject being published after 2008), there are still important questions to be answered and accordingly inform evidence-based preventive strategies (Li, 2007 and Patchin and Hinduja, 2006). One such question is whether cyberbullying can be explained solely by individual characteristics and traits, or by the interplay between traits and social cognitions that facilitate behaviour initiation. So far, empirical research has feed out some relevant traits for cyberbullying, such as empathy, and has also identified the role of cognitive processes like moral disengagement, and personal beliefs, including attitudes, normative beliefs, and demographic characteristics, such as age and gender (Ang and Goh, 2010, Pornari and Wood, 2010 and Walrave and Heirman, 2011). Nevertheless, researchers have yet to examine the interplay among these risk factors, and, accordingly provide an integrated behavioural model for cyberbullying in young people. 1.2. Empathy Empathy is a cardinal aspect of human behaviour that facilitates and eases social interaction by allowing people to identify and communicate each other’s emotions (Cohen and Strayer, 1996, Davis, 1994 and Preston et al., 2002). Researchers have argued that empathy should be treated as a relatively stable attribute in a person’s life time that may affect different types of social behaviours (Loudin et al., 2003 and Strayer, 1987). Studies have shown that empathy comprises two rather distinct processes: a cognitive process reflecting one’s ability to identify and cognitively process another person’s emotional states, and an affective process that facilitates emotional understanding and communication through an emotional and less cognitively-bound channel, also termed “vicarious emotional sharing” (Davis, 1983 and Shamay-Tsoory et al., 2009). In relation to bullying, several studies have shown that lower levels of empathy are associated with higher frequency of bullying behaviours in children and adolescents (Bartholow et al., 2005, Endresen and Olweus, 2002, Joliffe and Farrington, 2006, Lovett and Sheffield, 2007 and Olweus, 1993). In a similar fashion, recent studies confirmed that empathy plays an important role in cyberbullying behaviour. Specifically, Ang and Goh (2010) showed that both male and female adolescents with lower empathy levels, reported higher cyberbullying scores, and Schultze-Krumbholz and Scheithauer (2009) found that both cyberbullying perpetrators and victims reported lower empathy levels, as compared to individuals not involved in cyberbullying. In a similar vein, Steffgen, König, Pfetsch, and Melzer (2011) found that cyberbullies had significantly lower scores on empathy than non-cyberbullies. It is noteworthy that the aforementioned studies did not employ the same measures of empathy. Thus, the findings actually show that the relationship between cyberbullying and empathy is independent of the methods used to assess this effect. 1.3. Moral disengagement In their course of life, individuals engage in behaviours that are in discord with their moral or personal values. In order to cope with and resolve this dissonance they cognitively re-process the moral values attached to the behaviours and accordingly initiate a moral disengagement mechanism (Bandura, 1986 and Bandura, 1991). This strategy allows the cognitive moralization of actions that would otherwise be considered immoral or against personal moral norms (Bandura et al., 1996 and McAlister et al., 2006). Thus, moral disengagement can ‘soothe’ the mental discomfort associated with disputes, arguments, and even more extreme forms of aggressive behaviours that may occur in the course of social interaction. Indeed, several studies have shown that there is a positive correlation between higher levels of moral disengagement and higher levels of aggressive behaviours (Bandura, 2002, Bandura et al., 1996 and Gini, 2006). In relation to bullying behaviour, the findings are mixed with some studies reporting that moral justification predicted only traditional bullying but not cyberbullying (Bauman and Pero, 2011 and Perren and Gutzwiller-Helfenfinger, 2012), whereas others have reported a significant correlation between moral disengagement and both traditional bullying and cyberbullying (Pornari & Wood, 2010). Given that cyberbullying studies have only recently emerged, further research is needed in order to establish the role of disengagement in the process of cyberbullying. 1.4. Attitudes, norms, regret and intentionality Cyberbullying is defined as a goal-directed behaviour that is intended to hurt others (Patchin and Hinduja, 2006 and Pyzalski, 2011). Cyberbullying may include a wide range of actions, including posting offensive and insulting messages on the web, harassment and mistreatment with online means (e.g., texting, instant messaging) altering or hacking personal accounts and information in social networking sites, and even posting embarrassing videos online, or creating libellous blogs against someone (Juvonen and Gross, 2008 and Li, 2007). At the very least, such actions require some sort of strategic delegation of time and effort. Thus, intentionality plays a key role in the occurrence of cyberbullying, and distinguishes cyberbullying from other more general forms of aggression in adolescence (Pyzalski, 2011 and Slonje and Smith, 2008). Goal intentions, and their psychosocial predictors, are important in understanding premeditated behaviours. Research on the Theory of Planned Behaviour (TPB; Ajzen, 1991 and Ajzen, 2002) has shown that attitudes, social norms, and self-efficacy beliefs explain a great deal of intention-formation across behavioural domains in adolescence (Conner and Armitage, 1998, Hamilton and White, 2008 and McMillan and Conner, 2003), including aggressive acts like peer sexual harassment and abuse (Li, Frieze, & Tang, 2010). Nevertheless, the correspondence between intentions and actual behaviour is far from being perfect (e.g., Webb & Sheeran, 2006). Therefore, researchers have suggested that the traditional TPB approaches are enriched with theory-driven variables that can explain specific behaviours in specific situations and social contexts (Armitage and Conner, 2001 and Conner and Armitage, 1998). To this end, several studies have shown that the tripartite of attitudes-social norms-self-efficacy can better predict intentions and behaviour if additional variables are assessed, such as anticipated regret, which reflects the feeling of remorse from following (or abstaining from) a specific course of action (Abraham and Sheeran, 2004 and Conner et al., 2006). Anticipated regret predicts intentions, and strengthens the link between intentions and behaviour (Abraham and Sheeran, 2004, Conner and Armitage, 1998 and Perugini and Bagozzi, 2001). Also, considering the role of descriptive norms (i.e., judgments of frequency and prevalence of target behaviours) over subjective norms (i.e., perceived social approval of target behaviours), might further enhance the predictive validity of normative influences on intentions (Rivis & Sheeran, 2003a). Normative influences can be understood in terms of stored social representations or prototypes, whereby more favorable evaluations of these prototypes predict stronger intentions to engage in prototype-relevant behaviours (Rivis & Sheeran, 2003b). Prototype evaluation has been studies in the context of TPB (e.g., Norman et al., 2007 and Rivis et al., 2006), and is also a main component of the Prototype/Willingness Model which is used to predict adolescent risk taking (Gerrard et al., 2005 and Gibbons et al., 1998). Finally, researchers have argued that the intention concept itself needs to be changed in order to better understand adolescent risk-taking. In particular, instead of asking questions referring to concrete plans (e.g., I intent to do X), it is advisable to assess intentionality through questions of behavioural expectations (e.g., I expect to do X), because “people often do not expect what they intend to do, and vice versa” (Davis & Warshaw, 1992, p. 392). Unlike personal planning, therefore, when asked about the perceived likelihood of performing a target action, adolescents may consider potential barriers to action, external influences, and personal skills and competences; thus, making behavioural expectations more valid predictors of future behaviour, than behavioural intentions (Davis and Warshaw, 1992, Rhodes and Matheson, 2005 and Warshaw and Davis, 1986). 1.5. A process-model approach to cyberbullying “Psychologists often conduct research to establish whether and to what extent one variable affects another. However, the discovery that two variables are related to each other is only one small part of the aim of psychology. Deeper understanding is gained when we comprehend the process that produces the effect.” ( Preacher & Hayes, 2008, p. 717) Preacher and Hayes (2008) assertion is highly relevant to the study of cyberbullying for the following reasons. Firstly, related research has already identified some psychosocial correlates of cyberbullying, but we need to put these associations in context in order to better understand the causal processes and mechanisms underlying the behaviour in question. Secondly, cyberbullying is a recent phenomenon, and related theories are still in its infancy. Therefore, by using theoretical developments in other fields of research (e.g., health behaviour, decision-making, and risk-taking) we can inform subsequent research and theory development in cyberbullying. Finally, by understanding the causal mechanisms of cyberbullying we can accordingly inform evidence-based preventive strategies. In the present study we suggest that such an integrative process-model approach will be of benefit to cyberbullying research, as it will shed light to the causal process that shape intentions to engage in cyberbullying. This is highly relevant to past research on the subject, because such a process-model approach will integrate previous findings and provide a theory-driven model of cyberbullying behaviour. To this end, the present study employs an integrated theoretical model to assess the predictors of adolescents’ cyberbullying intentions. This model pertains to the direct and indirect effects of individual traits and self-regulatory processes, and social cognitions relevant to cyberbullying (e.g., attitudes, norms, self-efficacy, anticipated emotions). Empathy is seen as a personality trait that can influence the decision to engage in cyberbullying directly, or indirectly, through the effects of more proximal predictors, such as personal attitudes or social norms. Past research on the theory of planned behaviour and related theoretical approaches have shown that individual traits can indeed influence intentions both directly and indirectly (e.g., Conner and Armitage, 1998, Fishbein, 2009 and Rhodes and Courneya, 2003). Furthermore, according to Bandura et al. (1996) moral disengagement induces aggressive behaviour indirectly, through aggression proneness. In this line it could be argued that moral disengagement could induce cyberbullying behaviour by encouraging cyberbullying intentions (seen as eagerness/proneness for cyberbullying). In this regard, it would be interesting and theoretically relevant to assess whether moral disengagement is associated directly with cyberbullying intentions, after controlling for the effects of other predictors of intentionality, such as relevant social cognitions (e.g., attitudes, norms, and efficacy). The need to further examine the relationships between moral and social cognitive mechanisms in relation to adolescent bullying behaviour was also addressed by previous research (e.g., Gini, 2006). Finally, it would be of interest to assess the unique direct effects of social cognitions towards cyberbullying. Several studies have shown that attitudes or social norms influence cyberbullying intentions and behaviour, but there are limited studies assessing the effects of these variables by taking into account the effects of other predictors of intentions (e.g., anticipated regret, prototype similarity). In line with these assumptions, it was hypothesized that: H1. Lower empathy scores and higher levels of moral disengagement would predict cyberbullying intentions. H2. Attitudes, descriptive social norms, prototype favourability and similarity, anticipated regret, and self-efficacy would directly predict cyberbullying intentions. H3. Empathy and moral disengagement would predict cyberbullying intentions indirectly, through the effects of social cognitions.

Table 1 presents the intercorrelations, means and standard deviations scores, and, where appropriate, the internal consistency reliability scores for the measures used in the study. Table 1. Intercorrelations, means, standard deviation, and internal consistency reliability scores of the measures used in the study. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1. BES cognitive – 0.39⁎ 0.28⁎ 0.04 −0.20⁎ 0.05 0.07 −0.16⁎ 0.23⁎ −0.18⁎ 0.27 0.21⁎ −0.19⁎ −0.09 2. BES affective – 0.25⁎ −0.08 −0.21⁎ −0.07 −0.07 −0.16⁎ 0.30⁎ −0.25⁎ 0.41⁎ 0.41⁎ −0.29⁎ −0.28⁎ 3. MD – −0.16⁎ −0.24⁎ −0.06 −0.08 −0.21⁎ 0.27⁎ −0.24⁎ 0.24⁎ 0.31⁎ −0.31⁎ −0.39⁎ 4. Classmate norms – 41⁎ 0.33⁎ 0.29⁎ 0.16⁎ −0.16⁎ 0.28⁎ −0.24⁎ −0.26⁎ 0.32⁎ 0.40⁎ 5. Close friend norm – 0.29⁎ 0.26⁎ 0.29⁎ −0.28⁎ 0.44⁎ −0.34⁎ −0.39⁎ 0.40⁎ 0.41⁎ 6. Perceived norm – 0.41⁎ 0.13⁎ −0.09 0.22⁎ −0.10 −0.09 0.19⁎ 0.26⁎ 7. Peer norms – 0.07 −0.10 0.17⁎ −0.13⁎ −0.19⁎ 0.29⁎ 0.31⁎ 8. PF (pos.) – −0.35⁎ 0.26⁎ −0.25⁎ −0.23⁎ 0.34⁎ 0.26⁎ 9. PF (neg.) – −0.31⁎ 0.36⁎ 0.39⁎ −0.21⁎ −0.27⁎ 10. PS – −0.32⁎ −0.49⁎ 0.43⁎ 0.64⁎ 11. Attitudes – 0.56⁎ −0.42⁎ −0.37⁎ 12. AR – −0.45⁎ −0.48⁎ 13. Self-efficacy – 0.51⁎ 14. CB expectation – M 34.37 39.38 50.65 1.97 1.42 33.62 2.50 2.95 4.74 1.80 5.90 5.75 1.62 2.13 SD 5.04 6.68 6.84 0.92 1.08 22.03 0.95 1.17 1.17 1.36 1.25 1.47 0.81 1.50 Cronbach’s α 0.65 0.74 0.71 – – – – 0.66 0.61 – 0.82 0.89 0.80 0.84 ⁎ p < 0.05; MD = moral disengagement; BES = Basic Empathy Scale; PF = prototype favorability; PS = prototype similarity; AR = anticipated regret; CB = cyberbullying; Cronbach’s alpha scores are relevant to the multi-item measures used in the study. Table options 3.1. Experiencing and reporting cyberbullying While 32.4% students reported they had either witnessed or personally experienced cyberbullying (victimized), only 12.5% reported the incident to someone. Those who reported the cyberbullying incident preferred to do so to their friends (44.7%), parents and siblings (44.7%), and to a lesser extent to official authorities, such as the police (10.5%). There were no gender differences in either experiencing/witnessing or reporting cyberbullying. 3.2. Direct effects on expectation to engage in cyberbullying A hierarchical linear regression was used to assess the direct effects of age, gender, moral disengagement, empathy, and social cognitions (attitudes, norms, prototype favourability and similarity, anticipated regret and self-efficacy) on cyberbullying expectations. The analysis was completed in two steps and an overall significant model emerged predicting (AdjR2) 54.9% of the variance (F (16, 284) = 22.6, p < 0.001) – a large multivariate effect size according to Cohen (1992). Tolerance levels were high (>0.508) and above the criterion of 0.300 for multicollinearity. Thus, the predictor variables in the present study were independent from each other, and multicollinearity risk was low. In the first step of the analysis we assessed the effects of age, gender, empathy facets, and moral disengagement. Moral disengagement (β = −0.360, p < 0.001) and affective empathy (β = −0.243, p < 0.001) significantly predicted cyberbullying expectations. Adding social cognitions in the second step of the analysis reduced the effects of moral disengagement, and turned the effect of affective empathy non-significant, thus suggesting a mediation effect. The significant predictors of expectations for cyberbullying in the final step of the analysis included moral disengagement (β = −0.173, p < 0.001), prototype similarity (β = 0.446, p < 0.001), perceived prevalence of cyberbullying among classmates or ‘classmate norms’ (β = 0.101, p = 0.04), frequency of witnessing or being aware about cyberbullying incidents committed by same age peers or ‘peer norms’ (β = 0.097, p = 0.04), and situational self-efficacy (β = 0.154, p = 0.003). The findings from the regression analysis are summarized in Table 2. Table 2. Psychosocial predictors of expectations to engage in cyberbullying (N = 355). Step Predictors B 95% CI (B) β AdjR2 1 Age −0.016 −0.146 to −0.114 −0.013 0.203 Gender −0.156 −0.510 to 0.197 −0.052 Moral disengagement −0.078 −0.102 to −0.054 −0.360⁎⁎⁎ BES affective −0.057 −0.085 to −0.028 −0.243⁎⁎⁎ BES cognitive 0.033 −0.003 to 0.069 0.106 2 Age −0.227 −0.826 to 0.373 0.009 0.549 Gender −0.04 −0.189 to 0.110 −0.017 Moral disengagement 0.591 0.399 to 0.783 −0.173⁎⁎⁎ BES affective 0.035 −0.036 to 0.106 −0.087 BES cognitive 0.506 0.349 to 0.663 −0.052 Classmate norms 0.17 0.007 to .333 0.101⁎ Close friend norms −0.051 −0.198 to 0.097 −0.035 Perceived prevalence 0.002 −0.005 to 0.008 0.025 Peer norms 0.158 0.005 to 0.311 0.097⁎ Prototype favorability (pos.) 0.085 −0.035 to 0.204 0.064 Prototype favorability (neg.) 0.076 −0.051 to 0.202 0.056 Prototype similarity 0.521 0.404 to 0.639 0.446⁎⁎⁎ Attitudes −0.074 −0.203 to 0.056 −0.059 Anticipated regret −0.061 −0.172 to 0.051 −0.06 Self-efficacy 0.301 0.100–0.502 0.154⁎⁎ BES = Basic Empathy Scale. ⁎ p < 0.05. ⁎⁎ p < 0.005. ⁎⁎⁎ p < 0.001. Table options 3.3. Analysis of indirect effects Multiple mediation analysis was used to assess the indirect effects of affective empathy and moral disengagement on cyberbullying expectations. According to Preacher and Hayes (2008), 95% confidence intervals and bootstrapping with 1000 resamples were used. The findings showed that the effect of affective empathy on expectations was mediated by prototype similarity (z = −4.36, p < 0.001) and situational self-efficacy (z = −3.89, p < 0.001), but not peer and classmate norms. Accordingly, the effect of moral disengagement was mediated by prototype similarity (z = −4.30, p < 0.001), situational self-efficacy (z = −3.55, p < 0.001), and classmate norms (z = −2.22, p = 0.02).