Forgiveness and cyberbullying in adolescence: Does willingness to forgive help minimize the risk of becoming a cyberbully?

Forgiveness and cyberbullying in adolescence: Does willingness to forgive help minimize the risk of becoming a cyberbully?

Computers in Human Behavior 81 (2018) 209e214 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.c...

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Computers in Human Behavior 81 (2018) 209e214

Contents lists available at ScienceDirect

Computers in Human Behavior journal homepage:

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Forgiveness and cyberbullying in adolescence: Does willingness to forgive help minimize the risk of becoming a cyberbully? Cirenia Quintana-Orts, Lourdes Rey* laga, Spain Department of Personality, Assessment and Psychological Treatment, Faculty of Psychology, University of Ma

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Cyberbullying has received recent research attention due to the frequent use of social media and electronic devices among adolescents. This study examined forgiveness and cybervictimization as predictors of cyberbullying aggression in a sample of 1650 secondary school adolescents (50.5% females). Results of regression analyses indicated that cybervictimization was a significant predictor of indices of cyberbullying. The inclusion of forgiveness was found to significantly augment the prediction of cyberbullying aggression, even after accounting for sex and grade. Furthermore, the cybervictimization  forgiveness interaction term was found to significantly augment the prediction of cyberbullying aggression. Specifically, cybervictimized adolescents with high forgiveness, compared to those with low forgiveness, reported significantly lower levels of cyberbullying behaviors. Implications of the present findings are discussed in terms of the protective role of forgiveness for preventing aggressive behavior and for preventing individuals from becoming a bully after suffering victimization. The results suggest that anticyberbullying interventions also need to focus on promoting forgiveness in adolescents. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Cyberbullying Cybervictimization Forgiveness Aggression Adolescents

1. Introduction Cyberbullying is defined as repeated acts of aggressive behavior over time through the use of electronic devices (e.g., social networking sites, e-mail, etc.). Cyberbullying is becoming a significant problem for adolescents (El Asam & Samara, 2016; Ybarra & Mitchell, 2004a) and victims find it difficult to defend themselves (Palermiti, Servidio, Bartolo, & Costabile, 2017). Although cyberbullying is observed globally, studies that have addressed this phenomenon have shown differences in its prevalence, ranging from as high as 72% (Juvonen & Gross, 2008) to as low as 6.5% (Ybarra & Mitchell, 2004a). According to a recent systematic review, the vast majority of research has reported that between 10 and 40% of secondary school adolescents have experienced cyberbullying (Kowalski, Giumetti, Schroeder, & Lattanner, 2014). Cyberbullying is not only frequent but is also related to negative outcomes. In particular, being a victim of cyberbullying has a number of consequences for mental health (Kim, Colwell, Kata, Boyle, & Georgiades, 2017). Specifically, cybervictimization has been linked with eating disorders, depressive and anxiety

* Corresponding author. Department of Personality, Assessment and Psychologlaga, Spain. ical Treatment, University of M alaga, Campus de Teatinos s/n., 29071, Ma E-mail addresses: [email protected] (C. Quintana-Orts), [email protected] (L. Rey). 0747-5632/© 2017 Elsevier Ltd. All rights reserved.

symptomatology, poor self-esteem, suicidality, and substance abuse, among others (Bannink, Broeren, van de LooijeJansen, De € m, 2012; Waart, & Raat, 2014; Beckman, Hagquist, & Hellstro Olenik-Shemesh, Heiman, & Eden, 2012; Palermiti et al., 2017). In addition, like other forms of interpersonal conflict, being a victim of cyberbullying negatively affects an adolescent’s social and n, Ortega-Ruiz, 2015). emotional adjustment (Elipe, Mora-Mercha Thus, adolescents who experience cybervictimization can feel a variety of negative emotions such as shame, anger, sadness, frusn, Ortega-Ruiz, tration, guilt, and helplessness (Elipe, Mora-Mercha & Casas, 2015; Hinduja & Patchin, 2007; Ortega et al., 2012); the experience of negative emotions is associated with unforgiveness and difficulty in containing a desire for vengeance (Worthington, 2006). Those feelings might lead adolescents to bully back in or€nig, Gollwitzer, & Steffgen, 2010) and to der to exact revenge (Ko obtained equal power so as to defend oneself (Safaria, Tentama, & Suyono, 2016); these feelings and behaviors can have an effect on adolescent adjustment (Van Rensburg & Raubenheimer, 2015). Consistent with this line of reasoning, recent studies (Kowalski et al., 2014; Kwan & Skoric, 2013) indicate that the strongest predictor of engaging in cyberbullying is being a previous victim of cyberbullying. As such, vengefulness and negative emotions have been found to significantly predict whether a victim of bullying and/or cyberbullying turns into a bully; these victims even tend to


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choose a prior traditional perpetrator as the target for cyberbully€ nig et al., 2010; Peets, Hodges, & Salmivalli, 2013; Safaria ing (Ko et al., 2016; Watson, Rapee, & Todorov, 2015). Further, those victims who also cyberbully appear to report even worse adverse consequences than those who are only cyberbullies or cybervictims (Kowalski & Limber, 2013). Thus, understanding protective factors for cyberbullying among adolescents is essential for the identification of factors that might help adolescents cope with the impact of cybervictimization, and promote healthy development. Recently, research has suggested that forgiveness is a protective factor that can act to break the cycle of violence and improve general health (Akhtar & Barlow, 2016; Egan & Todorov, 2009; Hirsch, Webb, & Jeglic, 2012). Forgiveness is a strength established at the beginning of the 20th century by Peterson and Seligman (2004). It involves the reduction of negative emotions, thoughts, and behaviors, and an increase in more positive feelings, cognitions, and behaviors towards a perpetrator, event, and oneself, without there necessarily being restitution, retribution, or reconciliation (Webb, Toussaint, & Conway-Williams, 2012). Forgiveness is suggested to be an effective resource for ameliorating the aggressive states associated with being victimized, and reducing negative reactions to other people’s behavior (Van Rensburg & Raubenheimer, 2015; Watson et al., 2015). In particular, some scholars have argued that promoting forgiveness would combat school bullying and other violent behaviors, by reducing feelings of hurt experienced when one is bullied, and decreasing instances of victims becoming perpetrators themselves (Egan & Todorov, 2009; Hui, Tsang, & Law, 2011). Although the benefits of forgiveness have been well documented in different interpersonal transgressions (Akhtar & Barlow, 2016), very little research has been done in the context of bullying and, even less, in cyberbullying. Still, a handful of empirical research studies have reported that forgiveness is positively related to less emotional hurt in response to past bullying incidents (Egan & Todorov, 2009), and that forgiveness reduces aggressiveness and other conduct problems (Peets et al., 2013; Van Rensburg & Raubenheimer, 2015). Nevertheless, to date, none of the aforementioned studies focused on cyberbullying. Thus, this study examined the protective and buffering role of forgiveness, with regard to the effects of cybervictimization, on acts and behaviors of cyberaggression among adolescents. The current study attempts to increase knowledge in the research field by examining the link between cyberbullying and forgiveness, which is considered as a protective factor, in a sample of Spanish secondary students. Taking into account that cyberbullying is an increasing problem in adolescents (Kowalski et al., 2014), and that there is a scarcity of research examining the relationships between cyberbullying and forgiveness, the objectives of the present study were twofold: 1) to examine forgiveness as a predictor of cyberbullying aggression, even after accounting for variance attributed to cybervictimization; and 2) to examine whether there is a significant cybervictimization  forgiveness interaction effect that accounts for additional variance in cyberbullying aggression, beyond the main effects of both cybervictimization and forgiveness. All analyses will control for sex and grade. € Consistent with past findings (e.g., Ak, Ozdemir, & Kuzucu, 2015; Patchin & Hinduja, 2011), we expect cybervictimization to account for a significant amount of the variance in cyberbullying aggression. Further, based on the contention that cyberbullying is an extension of face-to-face bullying (Kowalski et al., 2014), and given the strong evidence for a relationship between forgiveness and bullying (e.g., Van Rensburg & Raubenheimer, 2015), we expect forgiveness to account for a significant amount of additional variance in cyberbullying aggression, beyond cybervictimization. Finally, consistent with the notion that forgiveness might help mitigate or weaken the

association between cybervictimization and cyberbullying aggression, we expect to find evidence for a significant cybervictimization  forgiveness interaction effect, that accounts for additional variance in predicting cyberbullying aggression, beyond cybervictimization and forgiveness. Specifically, we expect to find that the positive association between cybervictimization and cyberbullying aggression will be weaker for those high in forgiveness, compared to those low in forgiveness.

2. Methods 2.1. Participants The sample comprised 1650 students (825 males and 840 felaga, males) from six public secondary schools in the city of Ma Spain. The age of the participants ranged from 11 to 20 years, and the mean age was 14.10 years (SD ¼ 3.22). The students involved in the study were from the 1st year of E.S.O. (compulsory secondary education) to the 2nd year of Bachillerato (high school) (7th to 12th grades). The sample was predominantly Spanish (83.1%), but also included students from other European countries (9.3%), America (4.7%), Africa (2.3%), and Asia (0.6%). These figures are representative of the nationalities present in Andalusia (southern Spain).

2.2. Measures The instruments used in this study are as follows: - The Spanish version of the European Cyberbullying Intervention Project Questionnaire (ECIPQ; Del Rey et al., 2015; Ortega-Ruiz, Del Rey, & Casas, 2016) was utilized. This questionnaire is composed of 22 items assessing frequency of cyberbullying behavior. Each item is measured on a 5-point Likert ranging from 0 (no) to 4 (yes, more than once a week). The questionnaire evaluates two dimensions of cyberbullying; 11 items measure cybervictimization behaviors (e.g., ‘Someone has posted online my embarrassing photos or videos’) and 11 items measure cyberbullying aggression (‘I threatened someone with messages on the Internet’). These subscales have demonstrated adequate internal consistency in Spanish samples (Ortega-Ruiz et al., 2016). In the current study, the internal consistency using the Cronbach’s alpha was good for both subscales: cybervictimization behaviors (0.86) and cyberbullying aggression (0.82). According to the cut-off criteria for the cybervictimization subscale used by Elipe, De la Oliva, and Del Rey (2017), participants who mark ‘no’ or ‘once or twice’ are considered non-victims, those who mark at least one of the responses as ‘once or twice a month’ are defined as occasional victims, and those who respond to at least one of the items with a frequency of ‘once or twice a week’ or more, are considered severe victims. - The 10-item forgiveness subscale of the Values in ActionInventory of Strengths (VIA-Y; Park & Peterson, 2006) was also utilized. The Spanish version, comprising 10 items measured on a 5-point Likert scale, ranging from 1 (not at all) to 5 (completely), can be found on the Authentic Happiness website ( The forgiveness subscales from the VIA-Y have been shown to provide valid assessment of the trait of forgiveness and have reported good psychometric properties in Spanish samples nez,2010). Cronbach’s alpha in this study was 0.73. (Gime

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2.3. Procedure This research was part of a larger project (PPIT.UMA.B1.2017/23) which examined the relationship between strengths and health among adolescents. The selection of schools was based on convenience design, with cluster sampling (probability sampling based on whole classrooms), incorporating public schools that agreed to participate in the research. For each school, the head of school approved the aims of the research, and parental consent was given before student participation in the study. The instruments were collectively administered in the classroom of grades 1e4 of E.S.O. (compulsory secondary education) (7th to 10th grades) and grades 1e2 of Bachillerato (high school) (11th and 12th grades). Students were informed that the research was anonymous, confidential, and voluntary in nature; all students agreed to take part in the study. Under the supervision of the researcher and at least one teacher, the students individually completed the questionnaires which contain both examined scales (ECIPQ and VIA-Y); the average time taken to complete the questionnaire was 40 min. All data were collected during the second and third trimester of the 2016/2017 academic year, and data collection was in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki, 2013). Ethical approval for the study protocol was obtained from the Research Ethics Committee of the University of Malaga (Spain). 2.4. Analysis The data were analyzed using the SPSS statistics software package, version 24. Firstly, after calculating means, standard deviations (SDs), and internal consistency reliabilities for each measure, Pearson correlation analysis was conducted to establish whether forgiveness was related to cybervictimization behaviors and cyberbullying aggression. Next, to examine whether cybervictimization and forgiveness were predictors of cyberbullying aggression in adolescents, we conducted a set of hierarchical regression analyses. Finally, to test for a potential moderating effect of forgiveness, we used the procedure described by Hayes and Matthes (2009) to visually inspect the manner in which cybervictimization and forgiveness interacted with each other in predicting cyberbullying aggression. 3. Results In total, 11.6% of students reported being involved in cyberbullying as a perpetrator (occasional, 6.8%; severe, 4.8%), whereas 16.0% of the students reported at least one cybervictimization behavior (occasional, 7.8%; severe, 8.2%). Means, standard deviations, correlations, and reliability estimates for all study measures are presented in Table 1. Consistent with previous findings, cyberbullying aggression was found to be positively correlated with cybervictimization behaviors (r ¼ 0.57, p < 0.001) and negatively with forgiveness (r ¼ 0.18, p < 0.001). Concerning age, we examined differences between two groups

Table 1 Means, standard deviations, reliabilities, and correlations between study variables, among the total sample.

1. Forgiveness 2. Cybervictimization behaviors 3. Cyberbullying aggression

M (SD)




3.38 (0.77) 0.19 (0.38) 0.12 (0.26)


0.03 (0.86)

0.18*** 0.57*** (0.82)

Note. Internal consistency reliabilities are indicated in parentheses (along the diagonal in the correlation section). *** p < .001.


of age based on the World Health Organization (WHO, 2013) classification. The younger group was made up with those adolescents between 10 and 14 years old, and the older adolescent group presented individuals aged between 15 and 19 years. The results of the independent t-test showed age differences for cybervictimization (t (1648) ¼ 2.60, p < 0.05; d ¼ 0.13), cyberaggression (t (1648) ¼ 3.18, p < 0.01; d ¼ 0.18), and forgiveness (t (1648) ¼ 2.03, p < 0.05; d ¼ 0.10), with older adolescents obtaining higher scores than the younger group in both cybervictimization and cyberaggression (M ¼ 0.23, SD ¼ 0.41 for older adolescents and M ¼ 0.18, SD ¼ 0.36 for younger adolescents in cybervictimization; M ¼ 0.15, SD ¼ 0.30 for older adolescents and M ¼ 0.10, SD ¼ 0.24 for those younger adolescents in cyberaggression). Contrarily, younger adolescents obtained higher scores than older adolescents in forgiveness (M ¼ 3.41, SD ¼ 0.77 for younger adolescents and M ¼ 3.33, SD ¼ 0.76 for older adolescents). Nevertheless, these effect sizes lower than 0.2 can be considered small according to Cohen (1977). For the current study, independent t-test were used to examine likely gender differences in forgiveness, as well as in the two dimensions of cyberbullying (aggression and victimization). The analyzes revealed significant gender differences for forgiveness (t (1648) ¼ 3.51, p < 0.001, d ¼ 0.17), cyberaggression (t (1648) ¼ 2.58, p < 0.05, d ¼ 0.15) and cybervictimization (t (1648) ¼ 2.74, p < 0.01, d ¼ 0.16) with women obtaining higher scores than men in both forgiveness and cybervictimization (M ¼ 3.45, SD ¼ 0.75 for women and M ¼ 3.32, SD ¼ 0.77 for men in forgiveness; M ¼ 0.23, SD ¼ 0.40 for women and M ¼ 0.17, SD ¼ 0.35 for men in cybervictimization). By contrast, men obtained higher scores than women in cyberaggression (M ¼ 0.14, SD ¼ 0.31 for men and M ¼ 0.10, SD ¼ 0.20 for women). However, according to Cohen (1977) these effect sizes are lower than 0.2 cut-off point which is considered small. In order to examine whether forgiveness would add incremental validity, beyond cybervictimization, in predicting cyberbullying aggression among adolescents, we conducted a set of hierarchical regression analyses (Table 2). There was no evidence of multicollinearity, since the Variance Inflation Factors (VIF) ranged around 1.00. To avoid the possibility that associations between forgiveness and cyberbullying could be confounded by sociodemographic factors, both grade and sex were entered in Step 1. Within this predictor set, sex was the only statistically significant predictor, and was positively associated with cyberbullying aggression (b ¼ 0.08, t ¼ 3.94, p < 0.001). Step 1 showed that the control variables as a set accounted for a significant proportion of the variance (1%) in cyberbullying aggression (F(2,1647) ¼ 11.43,

Table 2 Results of the hierarchical regression analyses showing the amount of variance in cyberbullying aggression accounted for by cybervictimization and forgiveness, after controlling for grade and sex. Predictor

Total sample

b Step 1 Sex Grade Step 2 Cybervictimization Step 3 Forgiveness Step 4 Cybervictimization x forgiveness











0.08*** 0.03 0.55*** 0.15*** 0.20***

Note. Change in R2 is denoted as DR2. The beta reported in the table is the standardized regression coefficient for the final equation. *** p < .001.


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p < 0.001; DR2 ¼ 0.01, p < 0.001). Step 2 indicated that cybervictimization was a statistically significant predictor (b ¼ 0.55, p < 0.001), accounting for 31% of the variance in cyberbullying aggression (F(1,1646) ¼ 266.56, p < 0.001; DR2 ¼ 0.31, p < 0.001). When forgiveness was entered in Step 3, it was found to be a significant predictor (b ¼ 0.15, p < 0.001) and accounted for an additional 3% of the variance in cyberbullying aggression (F(1,1645) ¼ 223.16, p < 0.001). Finally, when the cybervictimization x forgiveness term was entered, it was found to account for a significant 4% of additional unique variance in cyberbullying aggression. The full prediction model was found to account for 39% of the variance in cyberbullying aggression, F(1,1644) ¼ 211.36, p < 0.001. In order to visually examine the manner in which cybervictimization and forgiveness interact with each other to predict cyberbullying aggression, we plotted the regression following the procedures described by Hayes and Matthes (2009). Fig. 1 depicts the plot of this interaction, showing that the relationship between cybervictimization and cyberbullying aggression weakened as levels of forgiveness increased. Specifically, there was a significant positive relationship between cybervictimization behaviors and cyberbullying aggression at low levels of forgiveness (b ¼ 0.50, t(1650) ¼ 28.72, p < 0.001). Similarly, at high levels of forgiveness, that relationship was also significant (b ¼ 0.26, t(1650) ¼ 13.90, p < 0.001). 4. Discussion Regarding the prevalence of the behaviors investigated in this study, the results for both cyberaggression (11.6%) and cybervictimization (16.0%) concur with previous studies that have reported relatively low rates of cyberbullying in Spanish contexts (Elipe et al., 2015, 2017; Garaigordobil & Machimbarrena, 2017; Romera, Cano, Garcia-Fernandez, & Ortega-Ruiz, 2016) compared with

other countries (Kowalski et al., 2014; Palermiti et al., 2017). According to some research the prevalence of cyberbullying could be related to some contextual factors such as the mobile penetration (i.e. mobile phones per hundred inhabitants) or the frequent of internet use, being Spain one of the countries with lower rates in  € rzig & Olafsson, both risk factors (Go 2013; Tsitsika et al., 2015). Nevertheless, these prevalence ranges confirm that cyberbullying is a prevalent problem within schools (El Asam & Samara, 2016), and underlines the importance of investigating and designing new approaches to prevent these behaviors. From this perspective, we conducted the current research among adolescent students to examine the protective and buffering role of forgiveness in relation to cyberbullying aggression. With respect to the relationships between the examined variables, cybervictimization behavior was positively and significantly related to cyberbullying aggression. These findings are consistent with earlier work showing that previous experience as a cybervictim is associated with a higher likelihood of turning into a bully (Ak et al., 2015; Safaria et al., 2016), leading to even worse adverse consequences for victims (Dukes, Stein, & Zane, 2009; Elipe et al., 2015), and increased aggressive behaviors (Cuevas, Finkelhor, Turner, & Ormrod, 2007; Hinduja & Patchin, 2007; Ybarra & Mitchell, 2004b). As suggested by Patchin and Hinduja (2011), one possible explanation for the observation that cybervictimized individuals tend to engage in bullying behaviors (both traditional and cyberaggression) could be the negative emotions that victimization produces, such as feelings of anger, which may reduce the ability to solve social problems effectively, and lead to victims processing social information in a hostile manner (Ak et al., 2015). Consequently, deficits in appropriate emotion regulation and expression could be risk factors that increase the likelihood of becoming a cyberbully. On the other hand, forgiveness was negatively and significantly related to cyberbullying aggression. Our findings confirm earlier

***p < .001 Fig. 1. Relationship between cybervictimization and forgiveness for the prediction of cyberbullying scores.

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research that indicated that high scores on forgiveness may inhibit aggression (Riek & Mania, 2012). In short, people who present with a greater dispositional tendency to forgive are found to have less negative affect, more conflict resolution and advice/support seeking strategies, and less revenge seeking (Flanagan, Hoek, Ranter, & Reich, 2012). Thus, adolescents might handle the affective aggressive states in a more successful way through the effects of forgiveness on regulating mood, thoughts, and behaviors (Flanagan et al., 2012; Watson et al., 2015). We next explored the unique contribution of forgiveness in explaining cyberbullying aggression, above and beyond cybervictimization, sex, and grade. The observation that being a cybervictim is a strong predictor of engaging in cyberbullying is not a new or unexpected finding (e.g., Kowalski et al., 2014). However, we found that forgiveness was a unique and significant protective factor for cyberbullying aggression in a relatively large sample of adolescents, independent of cybervictimization behaviors, grade, and sex. Our overall finding that forgiveness is a protective factor associated with victimization not only replicates past findings (e.g., Peets et al., 2013), but also extends those findings to cyberspace phenomena, which remain largely understudied. Accordingly, our findings reveal the value of applying forgiveness interventions that may help reduce of the likelihood that one will turn into a bully, even after being cyberbullied oneself. Additionally, we found evidence for a significant cybervictimization  forgiveness interaction effect in predicting cyberbullying aggression. Specifically, the plot of the relationships indicated that among cybervictimized students, those who forgive more, compared to those who forgive less, reported significantly less cyberaggression behaviors. That is to say, indices of being involved as a cyberbully were highest among cybervictims with low forgiveness. These results are consistent with prior studies that have found that people reporting higher victimization behaviors might have deficiencies in appropriate emotion expression (Ak et al., 2015), which, as a result, might increase victims’ motivations for revenge. By contrast, our results suggest that victimized adolescents with higher scores on forgiveness are less likely to present with higher cyberbullying aggression (Egan & Todorov, 2009; Safaria et al., 2016). Forgiveness seems to have an aggression-reducing effect by buffering the negative consequences of being victimized, at least in response to cybervictimization behaviors. Thus, when working with victims who may also be cyberbullies, learning to forgive others might help to manage the consequences of victimization, thereby reducing their aggressiveness, and reducing the likelihood of them exacting revenge by becoming a perpetrator themselves. Taken together, the present findings have implications for anticyberbullying interventions and educational guidelines. Current meta-analyses and systematic reviews have reported inconsistent findings regarding the effectiveness of anti-cyberbullying programs, reporting only modest reductions in peer victimization of bullying, and cyberbullying (e.g., Zych, Ortega-Ruiz, & Del Rey, 2015). Our results suggest that forgiveness could potentially be used as an important adjunct to current approaches for reducing cyberbullying aggression in bullied adolescents. Thus, as some authors have already emphasized (e.g., Egan & Todorov, 2009; Hui et al., 2011), it would be useful to include evidence-based interventions on forgiveness in the field of anti-bullying interventions; these should involve students, parents, teachers, and researchers. If forgiveness can be developed (e.g., Hui et al., 2011; Wade, Hoyt, Kidwell, & Worthington, 2014), it may be feasible to design anti-(cyber)bullying programs that focus on the development of forgiveness in both bullies and in victims who are at risk of become bullies. Forgiveness has relevance not only for reducing aggressive responses and behaviors, but also for assisting


adolescents to be successful in many areas of life: moral, emotional, social, and behavioral (Hui et al., 2011). There are some limitations of this study that should be acknowledged. First, although the current sample is relatively large, it was mainly drawn from one geographic location. As such, generalizing these results to other adolescent populations must be done with caution. In addition, given the cross sectional nature of the data, it is not possible to make definitive causal inferences. Therefore, future research should apply a prospective design that examines forgiveness as a predictor of changes in cyberbullying aggression across time. Furthermore, researchers interested in exploring these issues further should consider incorporating other measures of forgiveness, and cultural beliefs about aggression and forgiveness, to the investigation of the development of forgiveness in future research. Finally, it should be noted that the percentage of variance in cyberbullying aggression explained by forgiveness, as compared to cybervictimization, was very modest (explaining about 3% of the variance). However, according to Meyer et al. (2001), when the outcomes are important and explained by numerous factors, small effects should not be dismissed. Therefore, although being bullied through cyberspace might play a large role in elevating the risk of perpetrating cyberbullying, our findings clearly indicate that protective factors, such as forgiveness, also need to be considered in order to develop effective interventions to reduce aggressiveness and negative responses to other people’s behavior. Despite these limitations, this was the first study to examine the protective and buffering role of forgiveness in the relationship between cybervictimization and cyberbullying. Our findings set a solid framework for future research. 5. Conclusions Our results suggest that forgiveness is an important predictor and buffer of becoming a bully after suffering cybervictimization behaviors. In particular, we found that adolescents with better developed forgiveness were less likely to become perpetrators, which may have implications for managing the cycle of violence and peer victimization. These results are consistent with previous research which highlighted the implications of forgiveness in interpersonal transgressions; our findings confirm that lower levels of forgiveness can represent a risk for cyberbullying aggression. In addition, forgiveness appears to be a key element for addressing the limitations of traditional anti-(cyber)bullying programs, helping victims overcome interpersonal transgressions, and improving general health. Future studies in the field of cyberbullying should also focus on the practical implications of forgiveness in order to enrich the design and testing of approaches aimed at preventing aggressive behavior within schools. Acknowledgments This research was supported by the University of Malaga [PPIT.UMA.B1.2017/23]. References Akhtar, S., & Barlow, J. (2016). Forgiveness therapy for the promotion of mental well-being. Trauma, Violence, & Abuse, 1e7. 1524838016637079. € Ak, S¸., Ozdemir, Y., & Kuzucu, Y. (2015). Cybervictimization and cyberbullying: The mediating role of anger, don’t anger me! Computers in Human Behavior, 49, 437e443. Bannink, R., Broeren, S., van de LooijeJansen, P. M., De Waart, F. G., & Raat, H. (2014). Cyber and traditional bullying victimization as a risk factor for mental health problems and suicidal ideation in adolescents. PLos One, 9(4), e94026. https://


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