research hypothesis of cyberbullying

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Research Article

How childhood psychological abuse affects adolescent cyberbullying: The chain mediating role of self-efficacy and psychological resilience

Roles Writing – review & editing

* E-mail: [email protected]

Affiliation School of Education Science, Nanjing Normal University, Jiangsu, China

Roles Investigation, Writing – original draft

Affiliation School of Computing, Nanjing University of Information Science & Technology, Jiangsu, China

• Haihua Ying, 

• Published: September 9, 2024
• https://doi.org/10.1371/journal.pone.0309959
• Peer Review
• Reader Comments

Despite the recognition of the impact of childhood psychological abuse, self-efficacy, and psychological resilience on cyberbullying, there is still a gap in understanding the specific mechanisms through which childhood psychological abuse impacts cyberbullying via self-efficacy and psychological resilience.

Based on the Social Cognitive Theory, this study aims to investigate the link between childhood psychological abuse and cyberbullying in adolescents, mediated by the sequential roles of self-efficacy and psychological resilience. The sample consisted of 891 students ( M = 15.40, SD = 1.698) selected from four public secondary schools in Jiangsu Province, Eastern China. All the participants filled in the structured self-report questionnaires on childhood psychological abuse, self-efficacy, psychological resilience, and cyberbullying. The data were analyzed using SPSS 24.0 and structural equation modeling (SEM) in AMOS 24.0.

The findings of this study are as follows: (1) Childhood psychological abuse is positively associated with adolescent cyberbullying; (2) Self-efficacy plays a mediating role between childhood psychological abuse and adolescent cyberbullying; (3) Psychological resilience plays a mediating role between childhood psychological abuse and adolescent cyberbullying; (4) Self-efficacy and psychological resilience play a chain mediation role between childhood psychological abuse and adolescent cyberbullying.

This study contributes to a deeper understanding of the underlying mechanisms linking childhood psychological abuse to adolescent cyberbullying, shedding light on potential pathways for targeted interventions and support programs to promote the well-being of adolescents in the face of early adversity.

Citation: Ying H, Han Y (2024) How childhood psychological abuse affects adolescent cyberbullying: The chain mediating role of self-efficacy and psychological resilience. PLoS ONE 19(9): e0309959. https://doi.org/10.1371/journal.pone.0309959

Editor: Amgad Muneer, The University of Texas, MD Anderson Cancer Center, UNITED STATES OF AMERICA

Received: February 6, 2024; Accepted: August 21, 2024; Published: September 9, 2024

Copyright: © 2024 Ying, Han. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The rapid development of the internet has brought many conveniences to our lives, but it has also brought numerous negative impacts, such as internet addiction [ 1 ], online fraud [ 2 ], and cyberbullying [ 3 ]. Among these, cyberbullying has been referred to as an “invisible fist”, with its harm being greater than traditional bullying and having a wider impact [ 4 ]. Cyberbullying is characterized by deliberate, repetitive, and malicious acts which are carried out using modern communication technologies, aimed at causing harm to others [ 5 , 6 ]. It comprises two dimensions: cyberbullying victimization and cyberbullying perpetration [ 7 ]. This pervasive issue is recognized globally [ 8 ], as evidenced by data from 2019, which revealed that one-third of young people from 30 countries consistently reported being victims of cyberbullying [ 9 ]. In China, the number of underage internet users reached 183 million in 2020, with 24.3% of minors reporting experiencing cyber violence, according to the “Research Report on Internet Usage among Minors in China in 2020” [ 10 ]. Adolescents are particularly vulnerable to cyberbullying [ 11 ]. The survey results indicate that approximately 52.2% of adolescents in China have experienced at least one incident of cyberbullying in the past year [ 12 ]. Cyberbullying not only impacts the psychological well-being of adolescents, but also lead to their difficulties in social adaptation and potentially tragic outcomes [ 13 ]. Therefore, it is of great significance to explore the factors influencing adolescent cyberbullying for prevention and intervention.

Cyberbullying is influenced by both environmental factors and individual factors [ 14 ]. Childhood psychological abuse is an important environmental factor influencing cyberbullying [ 15 ]. Child psychological abuse refers to the series of inappropriate fostering methods that are repeatedly and continuously adopted by the fosterer during the process of children’s growth, including intimidation, neglect, disparagement, interference, and indulgence [ 16 ]. Previous research has established a positive correlation between childhood psychological abuse and adolescent cyberbullying [ 17 , 18 ]. High levels of childhood psychological abuse have been associated with higher levels of cyberbullying, while low levels of childhood psychological abuse can hinder adolescent cyberbullying [ 19 ]. Self-efficacy and psychological resilience are two individual factors that have been extensively explored in relation to cyberbullying [ 20 ]. Self-efficacy refers to an individual’s confidence and expectation in their ability to take effective action and accomplish tasks in specific situations [ 21 ]. Psychological resilience is defined as the adaptive ability to maintain an active life despite adversity and stressful events [ 22 ]. They have been found to exhibit a negative correlation with adolescent cyberbullying. For example, Özdemir and Bektaş suggested that self-efficacy plays a negative role in cyberbullying [ 23 ]. Similarly, Clark and Bussey observed a noteworthy negative association between self-efficacy and cyberbullying among adolescents [ 24 ]. Güçlü-Aydogan et al. posited that psychological resilience has a negative impact on cyberbullying [ 20 ]. The findings highlight the importance of considering both self-efficacy and psychological resilience in understanding adolescent cyberbullying.

Despite scholars proposing the influence of these factors on adolescent cyberbullying, the specific mechanisms through which childhood psychological abuse affects adolescent cyberbullying via self-efficacy and psychological resilience remain understudied. To address this research gap, this study aims to investigate the interactive effects of childhood psychological abuse, self-efficacy, psychological resilience on adolescent cyberbullying, thereby providing a holistic understanding of the relationship between these factors. Furthermore, the study endeavors to investigate the impact of childhood psychological abuse on adolescent cyberbullying, with a specific focus on the mediating roles of self-efficacy and psychological resilience. This study seeks to address the following questions: First, what is the relationship between childhood psychological abuse and adolescent cyberbullying? Second, does self-efficacy mediate the relationship between childhood psychological abuse and adolescent cyberbullying? Third, does psychological resilience mediate the relationship between childhood psychological abuse and adolescent cyberbullying? Fourth, is there a serial mediation effect of self-efficacy and psychological resilience between childhood psychological abuse and adolescent cyberbullying? This study is significant as it addresses a gap in the existing literature and provides insights into the determinants of adolescent cyberbullying. Moreover, by exploring the mediating mechanisms through which childhood psychological abuse impacts adolescent cyberbullying, this study provides valuable guidance for educators and parents seeking to reduce adolescent cyberbullying.

The structure of the remaining sections of this article is as follows. Section 2 provides an overview of the theoretical background and hypothesis development. Section 3 details the materials and methods, encompassing participants, the research process, research instruments, and statistical analysis. Section 4 covers common method variance, descriptive statistics, correlation analysis, examination of the model, and testing for mediation effects. Section 6 presents the findings, limitations, and implications.

2. Theoretical background and hypothesis development

2.1 theoretical background.

Social Cognitive Theory (SCT), originally proposed by Bandura [ 21 ], provides a robust theoretical framework for this study. The theory includes three elements: environment, personal factors, and behavior [ 25 ]. Environment is defined as the external influences that affect an individual’s behavior, such as social norms, cultural values, and physical surroundings, while personal factors refer to an individual’s cognitive, affective, and biological characteristics, including beliefs, emotions, and genetic predispositions [ 26 ]. Behavior encompasses the actions and responses exhibited by an individual in various situations [ 21 ]. Unlike some other theories that focus solely on either environmental or personal determinants of behavior, SCT emphasizes the dynamic interaction between environment, personal factors, and behavior. It posits that individuals are not simply passive recipients of environmental influences, but rather they actively engage with and interpret their surroundings. Personal factors, such as cognitive processes and emotional states, play a crucial role in mediating the impact of the environment on behavior. Similarly, an individual’s behavior can also influence and modify their environment and personal factors. In this study, childhood psychological abuse is considered an environmental factor, while self-efficacy and psychological resilience as two personal factors. Cyberbullying, heralded as individuals’ social behavior, can also be explained by environmental and personal factors [ 27 ]. Childhood psychological abuse has a significant impact on the development of individuals’ self-efficacy. An enhanced sense of self-efficacy enables individuals to effectively cope with academic and social challenges, engage actively in demanding learning tasks, and develop psychological resilience [ 28 ]. Moreover, self-efficacy significantly reduces the occurrence of cyberbullying by bolstering individuals’ confidence and coping abilities, while psychological resilience lowers the risk of becoming a victim of cyberbullying by improving individuals’ adaptability to adversity [ 20 ]. By employing this theoretical framework, we can gain a comprehensive understanding of the association between childhood psychological abuse and cyberbullying, elucidating the mediating roles of self-efficacy and psychological resilience. This theoretical model in the study is visually represented in Fig 1 .

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https://doi.org/10.1371/journal.pone.0309959.g001

2.2 Hypothesis development

2.2.1 childhood psychological abuse and cyberbullying..

Numerous studies have provided compelling evidence of the link between childhood psychological abuse and subsequent engagement in cyberbullying behaviors [ 15 , 29 , 30 ]. Research has proposed that adverse experiences of psychological abuse in childhood can impact brain function states, such as persistent stress and heightened neurotic anxiety, prompting individuals to suppress and bury these feelings in their subconscious, ultimately leading to engaging in cyberbullying behavior [ 31 ]. Research has also proposed that childhood psychological abuse can have an impact on psychological development, thus leading to cyberbullying [ 19 , 32 ]. For instance, Xu and Zheng demonstrated that childhood emotional abuse can damage an individual’s self-esteem and self-confidence, making them seek to control and gain a sense of power through cyberbullying [ 33 ]. Moreover, Li et al. identified that childhood psychological abuse may lead to inner feelings of anger in individuals, causing them to seek comfort and escape from reality in online environments, ultimately leading them to release these negative emotions by bullying others online [ 34 ]. Based on the evidence presented in the literature, it is hypothesized:

• H1: Childhood psychological abuse is positively associated with adolescent cyberbullying.

2.2.2 Self-efficacy as a mediator.

There is a well-established negative relationship between childhood psychological abuse and self-efficacy [ 35 ]. For example, Soffer et al. conducted a study that revealed individuals who experienced childhood psychological abuse reported lower levels of self-efficacy in various domains, such as academic, social, and personal domains [ 36 ]. This suggests that the negative experiences associated with abuse can undermine an individual’s belief in their capabilities. Supporting this notion, Hosey emphasized the detrimental effects of childhood psychological abuse on an individual’s self-efficacy beliefs [ 37 ]. Their research highlighted the long-lasting impact of abuse on self-efficacy. Furthermore, Bentley and Zamir conducted a longitudinal study that found the negative relationship between childhood psychological abuse and self-efficacy persisted over time [ 38 ]. This suggests that the effects of abuse on self-efficacy may endure throughout adolescence and beyond. Taken together, these studies provide compelling evidence that childhood psychological abuse can significantly impact an individual’s self-efficacy.

Studies have explored the relationship between self-efficacy and cyberbullying [ 23 , 39 ]. Clark and Bussey conducted a study examining the relationship between self-efficacy and cyberbullying victimization and revealed that higher levels of self-efficacy were associated with higher rates of defending behavior during cyberbullying episodes [ 24 ]. Similarly, Bussey et al. investigated the relationship between self-efficacy and cyberbullying defending and indicated that individuals with a high level of self-efficacy were more likely to defend cyberbullying [ 40 ]. Ferreira et al. surveyed 676 students from the fifth to twelfth grade and found that self-efficacy significantly impacted cyberbullying behavior, with students exhibiting higher self-efficacy demonstrating more proactive problem-solving behavior, thereby reducing instances of cyberbullying [ 41 ]. Additionally, Ybarra and Mitchell found that self-efficacy plays a crucial role in moderating the negative effects of cyberbullying [ 42 ]. Their studies revealed that individuals with higher self-efficacy were better able to cope with and overcome the negative consequences of cyberbullying.

The above views indicate that childhood psychological abuse may negatively affect individuals’ self-efficacy, which in turn, may contribute to an increased likelihood of engaging in cyberbullying behavior. Based on these, the following assumption is proposed:

• H2: Self-efficacy may play a mediating role in the association between childhood psychological abuse and adolescent cyberbullying.

2.2.3 Psychological resilience as a mediator.

It has been found that psychological resilience can be influenced by childhood psychological abuse [ 43 ]. Yang et al. carried out a cross-sectional survey among 1607 adolescents and proposed that childhood psychological abuse may contribute to the development of psychological resilience during the learning process [ 44 ]. Additionally, Arslan conducted a survey involving 937 adolescents from various high schools and emphasized that childhood psychological abuse was a consistent predictor of psychological resilience [ 45 ]. These findings collectively support the notion that childhood psychological abuse may have a positive impact on the psychological resilience of adolescents.

Studies have shown that psychological resilience can influence cyberbullying [ 46 , 47 ]. Students with higher levels of resilience were less likely to engage in cyberbullying behaviors [ 48 ]. Hinduja and Patchin have argued that students with more psychological resilience were less likely to report being online victims, and among those who did report being victims, their psychological resilience worked as a “buffer,” preventing negative effects at school [ 49 ]. Similarly, Güçlü-Aydogan et al. investigated the role of psychological resilience in mitigating the impact of cyberbullying and found adolescents who exhibit higher levels of psychological resilience are capable of surviving adversity and uncertainty through the use of healthy, effective, and adaptable coping mechanisms, which may result in reduced cyber victimization [ 20 ]. Zhang et al. have demonstrated that students who experienced more childhood psychological abuse have lower psychological resilience, which plays a crucial role in bullying victimization [ 50 ]. Therefore, this study speculates that there is a positive relationship between adolescents’ psychological resilience and their cyberbullying, and psychological resilience may play an intermediary role between childhood psychological abuse and cyberbullying.

Psychological resilience is believed to be influenced by self-efficacy [ 51 ]. Bandura proposed a comprehensive framework for understanding the role of self-efficacy in promoting psychological resilience [ 21 ]. Individuals with higher levels of self-efficacy are better equipped to navigate and overcome challenges, leading to greater psychological resilience [ 52 ]. Sabouripour et al. [ 28 ] revealed that individuals with higher levels of self-efficacy demonstrated greater psychological resilience when facing health challenges. Therefore, it is believed that childhood psychological abuse may influence cyberbullying via the serial variables of self-efficacy and psychological resilience. Given this, the following hypotheses are proposed:

• H3: Psychological resilience plays a mediating role in the association between childhood psychological abuse and adolescents’ cyberbullying.
• H4: Self-efficacy and psychological resilience play a chain mediating role in the association between childhood psychological abuse and adolescent cyberbullying.

Based on Social Cognitive Theory and the above hypotheses, this study aims to apply SCT to explore the relationship between childhood psychological abuse and adolescents’ cyberbullying. Specifically, we will examine the mediating roles of self-efficacy and psychological resilience. A theoretical model ( Fig 1 ) will be constructed to investigate these relationships.

3. Materials and methods

3.1 participants.

This study utilized G*power 3.1 software [ 53 ] to calculate the required sample size, with an effect size set at 0.3 and α set at 0.05. The results indicated that in order to achieve a statistical power of 0.95, a total of 145 participants were needed. Furthermore, based on the requirement of Structural Equation Modeling (SEM) [ 54 ] that the appropriate sample size should be at least ten times the total observed variables, it was determined that a minimum of 800 participants would be necessary. The survey initially identified schools for sample collection based on convenience sampling principles. However, to ensure representativeness, cluster sampling was subsequently employed at the class level to select the 1,000 samples from 4 secondary schools (2 public junior high schools and 2 public senior high schools) in Jiangsu province, China. The selected public schools for this study exhibit diversity in terms of student backgrounds, academic achievements, and socio-economic statuses, thereby approximating the overall student population in the region. A total of the 1000 questionnaires were distributed, and after excluding the invalid questionnaires with missing answers or consistent responses, 891 valid questionnaires were collected, resulting in an effective response rate of 89.1%. Participants were aged 13 to 18 years old (M = 15.40, SD = 1.698), with 408 (45.8%) being boys, and 483 (54.2%) being girls. In terms of grade, the participants included 152 (17.1%) in the 7th grade, 167 (18.7%) in the 8th grade, 148 (16.6%) in the 9th grade, and 164 (18.4%) in the 10th grade, 113 (12.7%) in the 11th grade, 147 (16.5%) in the 12th grade.

3.2 Procedure

The study was conducted in accordance with the approved guidelines from the Ethical Review Committee of Hohai University (Protocol Number: Hhu10294-240125). Additionally, consent was obtained from the principals, students, and their parents in the participating schools. Before the survey, students were informed about the confidentiality of the survey results and their intended use solely for research purposes in class. They were also assured that measures had been implemented to safeguard their privacy. The questionnaires were then distributed and thoroughly explained to the participants. After 15 minutes, the trained research assistants collected the questionnaires on the spot, and subsequently, the data from the questionnaires were meticulously sorted and analyzed to derive meaningful conclusions.

3.3 Research instrument

3.3.1 childhood psychological abuse scale..

The measurement of childhood psychological abuse was conducted using Pan et al.’s scale [ 16 ], which comprises 23 items capturing five dimensions: intimidation, neglect, disparagement, interference, and indulgence. For example, one item on the scale is “My parents interrogate me about the details of my interactions with friends.” A 5-point Likert scale was employed, with scores ranging from 0 to 4, indicating “none” to “always”, and higher scores reflecting higher childhood psychological abuse. The scale has been demonstrated to possess good reliability and validity [ 55 ].

3.3.2 Self-efficacy scale.

Self-efficacy was measured using the scale developed by Wang et al. [ 56 ], which is based on Schwarzer and Jerusalem’s General Self-Efficacy Scale [ 57 ]. This scale consists of 10 items, presented in a single structure, with statements such as “I can calmly face challenges because I trust my ability to handle problems.” A 4-point Likert scale was utilized, with scores ranging from 1–4, representing “strongly disagree” to “strongly agree” respectively. Higher scores indicate higher levels of self-efficacy. The scale has good reliability and validity in previous study [ 58 ].

3.3.3 Psychological resilience scale.

The psychological resilience scale, developed by Hu and Gan [ 59 ], was utilized to evaluate the psychological resilience levels of adolescents. This scale comprises 27 items, encompassing five dimensions: goal focus, emotional control, positive cognition, interpersonal assistance, and family support. For example, one item states, “I believe that everything has its positive aspects”. The scale is rated on a 5-point Likert scale, with scores ranging from 1(strongly disagree) to 5(strongly agree), and higher scores indicating a stronger sense of psychological resilience. The scale demonstrates good reliability and validity, which has been validated by Xiao et al. [ 60 ].

3.3.4 Cyberbullying scale.

The measurement of adolescents’ cyberbullying was carried out using the revised Chinese version of the Cyberbullying Scale by You [ 7 ]. This scale comprises two subscales: the cyberbullying victimization scale (12 items, such as “Someone has shared or used my photos or videos online without my consent”) and the cyberbullying perpetration scale (8 items, such as “When conversing with someone online and things don’t go my way, I may resort to using offensive language to insult them”). The scale utilizes a 4-point rating, ranging from 1 (Never happened) to 4 (Frequently happened), with higher scores indicating a higher frequency of cyberbullying. Studies have demonstrated good reliability and validity among Chinese adolescents [ 61 , 62 ].

3.4 Statistical analysis

The collected data were analyzed using SPSS 24.0 and AMOS 24.0. Initially, the Harman single-factor test was conducted in SPSS 24.0 to assess common method variance. Subsequently, correlation analysis was performed on the variables of childhood psychological abuse, self-efficacy, psychological resilience, and cyberbullying in SPSS 24.0. Then, the measurement model and structural model were assessed using factor loadings, Cronbach’s α, CR, AVE, and goodness-of-fit. Finally, the mediation test was conducted utilizing AMOS 24.0. To ascertain the statistical significance of the mediating effects posited by the hypotheses, a bootstrapping method was employed, with the generation of 95% confidence intervals to provide a robust evaluation of these effects.

4.1 Common method bias analysis

To mitigate the influence of common method bias, in addition to ensuring anonymous responses during the survey, Harman’s single-factor test was conducted [ 63 ]. Exploratory factor analysis was performed on the 80 items of the questionnaire, and an unrotated principal component analysis revealed the presence of 11 factors with eigenvalues greater than 1. However, the first factor accounted for only 32.534% of the variance, which is below the critical threshold of 40% [ 64 ], indicating that there is no significant evidence of common method bias.

4.2 Correlation analyses

Table 1 shows the results of the correlation analysis. Specifically, there is a significant positive correlation between childhood psychological abuse and cyberbullying (r = 0.398, p < 0.01); There is a significant negative correlation between childhood psychological abuse and both self-efficacy (r = -0.162, p < 0.01); Childhood psychological abuse and psychological resilience established a significant negative relationship (r = -0.445, p < 0.01); Self-efficacy was significantly and negatively related to adolescent psychological resilience (r = 0.459, p < 0.01); Self-efficacy was significantly and negatively related to adolescent cyberbullying(r = -0.309, p < 0.01); Psychological resilience was significantly and negatively related to adolescent cyberbullying(r = -0.490, p < 0.01). Among these correlations, the highest correlation is observed between psychological resilience and cyberbullying, while the lowest correlation is observed between childhood psychological abuse and self-efficacy.

https://doi.org/10.1371/journal.pone.0309959.t001

4.3 Measurement model

The fit indices for the measurement model were assessed to examine how well the model fits the data. Jackson et al. have suggested that a model fits the data when the goodness-of-fit index is between 1 and 3 for x 2 / df, greater than 0.9 for GFI, AGFI, NFI, TLI, and CFI, less than 0.08 for SMSEA [ 54 ]. Childhood psychological abuse showed a good model fit: χ2/df = 2.939 (X 2 = 567.167 df = 193), RMSEA = 0.047, TLI = 0.970, NFI = 0.966, CFI = 0.977, GFI = 0.946, AGFI = 0.923. Self-efficacy showed a good model fit: χ2/df = 2.847 (X 2 = 54.093, df = 19), RMSEA = 0.046, TLI = 0.986, NFI = 0.991, CFI = 0.994, GFI = 0.988, AGFI = 0.964). Psychological resilience also meets the requirement with χ2/df = 3.097 (X 2 = 607.072, df = 196), RMSEA = 0.049, TLI = 0.962, NFI = 0.969, CFI = 0.979, GFI = 0.951, AGFI = 0.905, together with cyberbullying (χ2/df = 2.996, X2 = 245.708, df = 82, RMSEA = 0.047, TLI = 0.983, NFI = 0.989, CFI = 0.993, GFI = 0.974, AGFI = 0.933). All the data support the robustness of the measurement model.

Additionally, in the measurement model, the standardized factor loadings are significant and ideally above 0.50, indicating that the items are good indicators of their respective constructs [ 65 ]. The values of Cronbach’s α and CR are over 0.7, indicating the acceptable reliability [ 66 ]. The AVE values surpassed the recommended threshold of 0.5, signifying satisfactory convergent validity, and the AVE value reaching 0.36 shows acceptable convergent validity [ 67 ]. The square root of the AVE should be greater than the correlations with other constructs, indicating that the constructs have discriminant validity [ 68 ].

As presented in Table 2 , the value of Cronbach’s α ranged from 0.931 to 0.974, indicating high reliability. The standardized factor loadings covered a range between 0.528 and 0.890 ( p < .001), while the values of CR and AVE ranged from 0.932 to 0.975 and from 0.482 to 0.660 respectively, indicating acceptable convergent validity. In Table 3 , the square root of AVE for each construct was greater than the correlation with other constructs, indicating acceptable levels of discriminant validity.

https://doi.org/10.1371/journal.pone.0309959.t002

https://doi.org/10.1371/journal.pone.0309959.t003

4.4 Structural model

The structural model was evaluated using the goodness-of-fit indices and path coefficients. The fit indices for the structural model are as follows: X 2 / df = 1.403 (X 2 = 1135.419, df = 809), GFI = 0.913, AGFI = 0.901, CFI = 0.973, TII = 0.971, NFI = 0.913, RMSEA = 0.033. All the values met the recommended thresholds [ 54 ], indicating a good fit for the structural model. Additionally, as shown in Fig 2 , all the path coefficients were statistically significant (P < 0.01) by performing a bootstrap procedure with 5000 resamplings. Therefore, the structural model was supported by these empirical data.

https://doi.org/10.1371/journal.pone.0309959.g002

4.5 Testing for mediation effect

The study employed structural equation modeling to examine the mediating effects among the four variables. The bootstrap proposed by MacKinnon [ 69 ] was used for significance testing, with a sample size of 5000 and a confidence level of 95%. A mediating effect is considered statistically significant when the bootstrap 95% confidence interval of the indirect effects estimated by the bias-corrected percentile method does not include zero [ 69 ]. Data analysis was performed using Amos 24.0 software. The results of the mediation analysis for the mediating effect of self-efficacy and psychological resilience on the relationship between childhood psychological abuse and cyberbullying are presented in Table 4 . The direct effect of childhood psychological abuse on adolescent cyberbullying is significant ( β = 0.296, P < 0.001), supporting the acceptance of H1. Self-efficacy and psychological resilience mediate the relationship between childhood psychological abuse and cyberbullying, with a total indirect effect of 0.214 ( P < 0.001). Specifically, the indirect effect is composed of three pathways: The pathway of childhood psychological abuse → self-efficacy→ cyberbullying had an indirect effect of 0.025 with a 95% confidence interval of [0.007, 0.053]; The pathway of childhood psychological abuse → self-efficacy→ psychological resilience → cyberbullying had an indirect effect of 0.028 with a 95% confidence interval of [0.013, 0.049]; The pathway of childhood psychological abuse → psychological resilience → cyberbullying had an indirect effect of 0.162 with a 95% confidence interval of [0.112, 0.227]. The Bootstrap 95% confidence intervals for all three indirect effects do not include zero, indicating that all three indirect effects are statistically significant. These results provide support for H 2, H3, and H4.

https://doi.org/10.1371/journal.pone.0309959.t004

In addition, the indirect effect percentage of self-efficacy and psychological resilience as partial mediators were examined. As indicated in Table 4 , among the three significant indirect mediators, the indirect effect of self-efficacy accounts for 11.5% of the total indirect effect, while the indirect effect of psychological resilience accounts for 75.7% of the total indirect effect. Besides, the indirect effect of self-efficacy and psychological resilience accounts for 12.8% of the total indirect effect. This indicates that the indirect effect of psychological resilience is the greatest. The specific pathways of childhood psychological abuse acting on cyberbullying through self-efficacy and psychological resilience are detailed in Fig 2 .

5. Discussion

Empirical evidence suggests that childhood psychological abuse, self-efficacy, and psychological resilience have an impact on cyberbullying. However, there is still a gap in understanding the specific mechanisms through which childhood psychological abuse impacts cyberbullying via self-efficacy and psychological resilience. This research aimed to construct a mediation model to investigate whether childhood psychological abuse would be indirectly correlated with adolescents’ cyberbullying through self-efficacy and psychological resilience. The findings, limitations and implications are presented as follows.

5.1 Findings

The results of the study revealed a direct and positive link between childhood psychological abuse and adolescents’ cyberbullying. This not only corroborates Kircaburun et al.’s research [ 70 ], which identified a positive correlation between childhood psychological abuse and adolescents’ cyberbullying but also aligns with the notion proposed by Zhang et al. [ 30 ] that psychological abuse contributes to the occurrence of cyberbullying. One potential explanation is that individuals who have experienced abuse may struggle to regulate their emotions, increasing the likelihood of displaying aggressive behavior in online settings. Adolescents who experienced greater psychological abuse during childhood are more inclined to exhibit negative online behaviors [ 19 ]. This study further underscores the significance of childhood psychological abuse as a predictive factor for cyberbullying.

The results of the study identified self- efficacy as one significant partial mediating role between childhood psychological abuse and adolescents’ cyberbullying. This finding is consistent with previous research suggesting a negative association between childhood psychological abuse and self-efficacy [ 35 , 38 ], as well as a negative association between self-efficacy and cyberbullying [ 23 , 24 ]. These findings provide support for the idea that childhood psychological abuse plays a crucial role in shaping the perception of self-efficacy, which subsequently influences adolescents’ engagement in cyberbullying behaviors. This finding adds further evidence to the understanding of the role of self-efficacy in the link between childhood psychological abuse and cyberbullying.

The results of the study demonstrated that psychological resilience plays a significant partial mediating role between childhood psychological abuse and adolescents’ cyberbullying. This finding is consistent with previous research suggesting a negative association between childhood psychological abuse and psychological resilience [ 44 , 45 ], as well as a negative association between psychological resilience and cyberbullying [ 20 , 48 ]. One potential reason is that childhood psychological abuse can lead to a sense of helplessness, frustration, and negative emotions in children, hindering the development of psychological resilience. Individuals with lower psychological resilience may have difficulty seeking help when facing adversity and may resort to negative behaviors to avoid problems, leading to an increase in cyberbullying. These findings provide support for the idea that childhood psychological abuse plays a crucial role in shaping the perception of psychological resilience, which subsequently influence adolescents’ engagement in cyberbullying behaviors.

The results of the study further showed that both self-efficacy and psychological resilience functioned as a chain mediating role between childhood psychological abuse and adolescents’ cyberbullying. In other words, adolescents with high childhood psychological abuse scores tend to perceive lower self-efficacy, leading to an overall lower belief in their ability to effectively cope with and overcome challenges. This, in turn, is associated with lower levels of psychological resilience, resulting in increased engagement in cyberbullying behaviors. This finding further elucidates the mechanisms by which environmental systems and individual factors influence adolescents’ cyberbullying and advances the previous research by shedding light on how childhood psychological abuse can increase adolescents’ cyberbullying. It is worth noting that although both serial mediation and self-efficacy as mediators were established, their percentages were only 12.8% and 11.5%, respectively, which were lower than the mediating effect of psychological resilience. This indicates that psychological resilience has a more significant impact on cyberbullying behaviors. This suggests that when intervening in adolescent cyberbullying behaviors at the family level, cultivating their perception of psychological resilience should be given greater priority compared to enhancing their self-efficacy.

5.2 Implications

The findings of this study have significant implications for both theory and practice in understanding and addressing adolescents’ cyberbullying.

From a theoretical perspective, this study contributes to the existing literature by unravelling the intricate relationship between childhood psychological abuse and adolescent cyberbullying with the application of the Social Cognitive Theory. By identifying self-efficacy and psychological resilience as pivotal mediators, the study provides a conceptual framework that enhances our comprehension of the psychological processes underpinning cyberbullying behaviors. This understanding is crucial for developing psychological interventions and educational programs aimed at bolstering self-efficacy and fostering resilience among adolescents. Moreover, the findings of the study offer insights into the buffering effects of positive psychological attributes against the adverse outcomes of childhood maltreatment, enriching the existing literature on the subject and guiding future research endeavors in the field of developmental psychology and educational studies.

On a practical level, these findings offer valuable insights for designing effective interventions to prevent and address cyberbullying among adolescents. Specifically, by addressing childhood psychological abuse, enhancing self-efficacy, and fostering psychological resilience, we can reduce the likelihood of adolescents engaging in or being affected by cyberbullying. To address childhood psychological abuse, parents need to increase self-awareness and understand the impact of their emotions and behaviors on their children. They can learn positive parenting techniques such as active listening, respect, and expressing love. Additionally, establishing a positive parent-child relationship, including positive communication and emotional support, as well as clear rules and boundaries, can help reduce the occurrence of psychological abuse [ 71 ]. In enhancing self-efficacy, both schools and parents play crucial roles. Schools can design tasks that are challenging yet fair, enabling adolescents to experience success and bolster their sense of self-efficacy. Teachers should complement this by offering timely recognition and encouragement, nurturing greater confidence in their abilities. Meanwhile, parents should lead by example, exhibiting positive and proactive attitudes and behaviors. By doing so, they create an environment that allows adolescents to observe and imitate these behaviors, providing them with opportunities to practice and excel in various tasks, thereby, contributing to the development of their self-efficacy. To foster psychological resilience, parents should assist children in cultivating positive values and building self-confidence. Schools should prioritize student growth and development by establishing appropriate evaluation systems and avoiding excessive competition. Students themselves should strive to establish positive interpersonal relationships with their peers, fostering mutual support and respect.

5.3 Limitations

It is important to recognize several limitations inherent in this study. Firstly, the use of a cross-sectional design precludes the establishment of causal relationships between variables. It is recommended that future research employ longitudinal or experimental designs to validate the causal hypotheses. Secondly, the reliance on self-reported data from middle school students introduces the possibility of biases, such as social desirability. Future studies should consider gathering data from multiple sources, such as parents or peers, to enhance the robustness of findings. Lastly, there are other unexplored factors in this study, such as self-control and self-esteem, which could potentially mediate the relationship. Future studies should focus on investigating the role of these factors in developing targeted interventions to reduce the occurrence of cyberbullying among adolescents.

6. Conclusion

The findings of this study can be summarized as follows: (1) Childhood psychological abuse, self-efficacy, psychological resilience, and cyberbullying are significantly correlated with each other. Specifically, childhood psychological abuse is significantly positively correlated with cyberbullying, while self-efficacy and psychological resilience are significantly negatively correlated with cyberbullying; (2) Childhood psychological abuse influences cyberbullying indirectly through self-efficacy and psychological resilience respectively; (3) Childhood psychological abuse can affect cyberbullying through the mediating chain role of self-efficacy and psychological resilience.

Supporting information

https://doi.org/10.1371/journal.pone.0309959.s001

Acknowledgments

The authors wish to thank Jingtao Wu for providing technical support in data analysis for this research.

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• > Cyberbullying and its impact on young people's emotional...

Article contents

The nature of cyberbullying, the impact of cyberbullying on emotional health and well-being, technological solutions, asking adults for help, cyberbullying and its impact on young people's emotional health and well-being.

Published online by Cambridge University Press:  02 January 2018

The upsurge of cyberbullying is a frequent cause of emotional disturbance in children and young people. The situation is complicated by the fact that these interpersonal safety issues are actually generated by the peer group and in contexts that are difficult for adults to control. This article examines the effectiveness of common responses to cyberbullying.

Whatever the value of technological tools for tackling cyberbullying, we cannot avoid the fact that this is an interpersonal problem grounded in a social context.

Practitioners should build on existing knowledge about preventing and reducing face-to-face bullying while taking account of the distinctive nature of cyberbullying. Furthermore, it is essential to take account of the values that young people are learning in society and at school.

Traditional face-to-face bullying has long been identified as a risk factor for the social and emotional adjustment of perpetrators, targets and bully victims during childhood and adolescence; Reference Almeida, Caurcel and Machado 1 - Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 bystanders are also known to be negatively affected. Reference Ahmed, Österman and Björkqvist 7 - Reference Salmivalli 9 The emergence of cyberbullying indicates that perpetrators have turned their attention to technology (including mobile telephones and the internet) as a powerful means of exerting their power and control over others. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 Cyberbullies have the power to reach their targets at any time of the day or night.

Cyberbullying takes a number of forms, to include:

• flaming: electronic transmission of angry or rude messages;

• harassment: repeatedly sending insulting or threatening messages;

• cyberstalking: threats of harm or intimidation;

• denigration: put-downs, spreading cruel rumours;

• masquerading: pretending to be someone else and sharing information to damage a person’s reputation;

• outing: revealing personal information about a person which was shared in confidence;

• exclusion: maliciously leaving a person out of a group online, such as a chat line or a game, ganging up on one individual. Reference Schenk and Fremouw 11

Cyberbullying often occurs in the context of relationship difficulties, such as the break-up of a friendship or romance, envy of a peer’s success, or in the context of prejudiced intolerance of particular groups on the grounds of gender, ethnicity, sexual orientation or disability. Reference Hoff and Mitchell 12

A survey of 23 420 children and young people across Europe found that, although the vast majority were never cyberbullied, 5% were being cyberbullied more than once a week, 4% once or twice a month and 10% less often. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 Many studies indicate a significant overlap between traditional bullying and cyberbullying. Reference Perren, Dooley, Shaw and Cross 5 , Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Kowalski and Limber 14 , Reference Ybarra and Mitchell 15 However, a note of caution is needed when interpreting the frequency and prevalence of cyberbullying. As yet, there is no uniform agreement on its definition and researchers differ in the ways they gather their data, with some, for example, asking participants whether they have ‘ever’ been cyberbullied and others being more specific, for example, ‘in the past 30 days’.

Research consistently identifies the consequences of bullying for the emotional health of children and young people. Victims experience lack of acceptance in their peer groups, which results in loneliness and social isolation. The young person’s consequent social withdrawal is likely to lead to low self-esteem and depression. Bullies too are at risk. They are more likely than non-bullies to engage in a range of maladaptive and antisocial behaviours, and they are at risk of alcohol and drugs dependency; like victims, they have an increased risk of depression and suicidal ideation. Studies among children Reference Escobar, Fernandez-Baen, Miranda, Trianes and Cowie 2 - Reference Kaltiala-Heino, Rimpalä, Rantanen and Rimpalä 4 , Reference Kumpulainen, Rasanen and Henttonen 16 and adolescents Reference Salmivalli, Lappalainen and Lagerspetz 17 , Reference Sourander, Helstela, Helenius and Piha 18 indicate moderate to strong relationships between being nominated by peers as a bully or a victim at different time points, suggesting a process of continuity. The effects of being bullied at school can persist into young adulthood. Reference Isaacs, Hodges and Salmivalli 19 , Reference Lappalainen, Meriläinen, Puhakka and Sinkkonen 20

Studies demonstrate that most young people who are cyberbullied are already being bullied by traditional, face-to-face methods. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Dooley, Pyzalski and Cross 21 - Reference Riebel, Jaeger and Fischer 23 Cyberbullying can extend into the target’s life at all times of the day and night and there is evidence for additional risks to the targets of cyberbullying, including damage to self-esteem, academic achievement and emotional well-being. For example, Schenk & Fremouw Reference Schenk and Fremouw 11 found that college student victims of cyberbullying scored higher than matched controls on measures of depression, anxiety, phobic anxiety and paranoia. Studies of school-age cyber victims indicate heightened risk of depression, Reference Perren, Dooley, Shaw and Cross 5 , Reference Gradinger, Strohmeier and Spiel 22 , Reference Juvonen and Gross 24 of psychosomatic symptoms such as headaches, abdominal pain and sleeplessness Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 and of behavioural difficulties including alcohol consumption. Reference Mitchell, Ybarra and Finkelhor 25 As found in studies of face-to-face bullying, cyber victims report feeling unsafe and isolated, both at school and at home. Similarly, cyberbullies report a range of social and emotional difficulties, including feeling unsafe at school, perceptions of being unsupported by school staff and a high incidence of headaches. Like traditional bullies, they too are engaged in a range of other antisocial behaviours, conduct disorders, and alcohol and drug misuse. Reference Sourander, Brunstein, Ikomen, Lindroos, Luntamo and Koskelainen 6 , Reference Hinduja and Patchin 26

The most fundamental way of dealing with cyberbullying is to attempt to prevent it in the first place, through whole-school e-safety policies Reference Campbell 27 - Reference Stacey 29 and through exposure to the wide range of informative websites that abound (e.g. UK Council for Child Internet Safety (UKCCIS; www.education.gov.uk/ukccis ), ChildLine ( www.childline.org.uk )). Many schools now train pupils in e-safety and ‘netiquette’ to equip them with the critical tools that they will need to understand the complexity of the digital world and become aware of its risks as well as its benefits. Techniques include blocking bullying behaviour online or creating panic buttons for cyber victims to use when under threat. Price & Dalgleish Reference Price and Dalgleish 30 found that blocking was considered as a most helpful online action by cyber victims and a number of other studies have additionally found that deleting nasty messages and stopping use of the internet were effective strategies. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Kowalski and Limber 14 , Reference Juvonen and Gross 24 However, recent research by Kumazaki et al Reference Kumazaki, Kanae, Katsura, Akira and Megumi 31 found that training young people in netiquette did not significantly reduce or prevent cyberbullying. Clearly there is a need for further research to evaluate the effectiveness of different types of technological intervention.

Parents play an important role in prevention by banning websites and setting age-appropriate limits of using the computer and internet. Reference Kowalski and Limber 14 Poor parental monitoring is consistently associated with a higher risk for young people to be involved in both traditional and cyberbullying, whether as perpetrator or target. Reference Ybarra and Mitchell 15 However, adults may be less effective in dealing with cyberbullying once it has occurred. Most studies confirm that it is essential to tell someone about the cyberbullying rather than suffer in silence and many students report that they would ask their parents for help in dealing with a cyberbullying incident. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Stacey 29 , Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 On the other hand, some adolescents recommend not consulting adults because they fear loss of privileges (e.g. having and using mobile telephones and their own internet access), and because they fear that their parents would simply advise them to ignore the situation or that they would not be able to help them as they are not accustomed to cyberspace. Reference Smith, Mahdavi, Carvalho, Fisher, Russell and Tippett 10 , Reference Hoff and Mitchell 12 , Reference Kowalski and Limber 14 , Reference Stacey 29 In a web-based survey of 12- to 17-year-olds, of whom most had experienced at least one cyberbullying incident in the past year, Juvonen & Gross Reference Juvonen and Gross 24 found that 90% of the victims did not tell their parents about their experiences and 50% of them justified it with ‘I need to learn to deal with it myself’.

Students also have a rather negative and critical attitude to teachers’ support and a large percentage consider telling a teacher or the school principal as rather ineffective. Reference Aricak, Siyahhan, Uzunhasanoglu, Saribeyoglu, Ciplak and Yilmaz 32 , Reference DiBasilio 33 Although 17% of students reported to a teacher after a cyberbullying incident, in 70% of the cases the school did not react to it. Reference Hoff and Mitchell 12

Involving peers

Young people are more likely to find it helpful to confide in peers. Reference Livingstone, Haddon, Anke Görzig and Ólafsson 13 , Reference Price and Dalgleish 30 , Reference DiBasilio 33 Additionally, it is essential to take account of the bystanders who usually play a critical role as audience to the cyberbullying in a range of participant roles, and who have the potential to be mobilised to take action against cyberbullying. Reference Salmivalli 9 , Reference Cowie 34 For example, a system of young cyber mentors, trained to monitor websites and offer emotional support to cyber victims, was positively evaluated by adolescents. Reference Banerjee, Robinson and Smalley 35 Similarly, DiBasilio Reference DiBasilio 33 showed that peer leaders in school played a part in prevention of cyberbullying by creating bullying awareness in the school, developing leadership skills among students, establishing bullying intervention practices and team-building initiatives in the student community, and encouraging students to behave proactively as bystanders. This intervention successfully led to a decline in cyberbullying, in that the number of students who participated in electronic bullying decreased, while students’ understanding of bullying widened.

Although recommended strategies for coping with cyberbullying abound, there remains a lack of evidence about what works best and in what circumstances in counteracting its negative effects. However, it would appear that if we are to solve the problem of cyberbullying, we must also understand the networks and social groups where this type of abuse occurs, including the importance that digital worlds play in the emotional lives of young people today, and the disturbing fact that cyber victims can be targeted at any time and wherever they are, so increasing their vulnerability.

There are some implications for professionals working with children and young people. Punitive methods tend on the whole not to be effective in reducing cyberbullying. In fact, as Shariff & Strong-Wilson Reference Shariff, Strong-Wilson and Kincheloe 36 found, zero-tolerance approaches are more likely to criminalise young people and add a burden to the criminal justice system. Interventions that work with peer-group relationships and with young people’s value systems have a greater likelihood of success. Professionals also need to focus on the values that are held within their organisations, in particular with regard to tolerance, acceptance and compassion for those in distress. The ethos of the schools where children and young people spend so much of their time is critical. Engagement with school is strongly linked to the development of positive relationships with adults and peers in an environment where care, respect and support are valued and where there is an emphasis on community. As Batson et al Reference Batson, Ahmad, Lishner, Tsang, Snyder and Lopez 37 argue, empathy-based socialisation practices encourage perspective-taking and enhance prosocial behaviour, leading to more satisfying relationships and greater tolerance of stigmatised outsider groups. This is particularly relevant to the discussion since researchers have consistently found that high-quality friendship is a protective factor against mental health difficulties among bullied children. Reference Skrzypiec, Slee, Askell-Williams and Lawson 38

Finally, research indicates the importance of tackling bullying early before it escalates into something much more serious. This affirms the need for schools to establish a whole-school approach with a range of systems and interventions in place for dealing with all forms of bullying and social exclusion. External controls have their place, but we also need to remember the interpersonal nature of cyberbullying. This suggests that action against cyberbullying should be part of a much wider concern within schools about the creation of an environment where relationships are valued and where conflicts are seen to be resolved in the spirit of justice and fairness.

Acknowledgement

I am grateful to the COST ACTION IS0801 for its support in preparing this article ( https://sites.google.com/site/costis0801 ).

Declaration of interest

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• Volume 37, Issue 5
• Helen Cowie (a1)
• DOI: https://doi.org/10.1192/pb.bp.112.040840

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Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue

• Published: 12 August 2022
• Volume 4 , pages 175–179, ( 2022 )

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• Selma Therese Lyng 2  

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Introduction

School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897 ). However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann ( 1972 ) and Olweus ( 1978 ). Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. ( 2021 ) found that there were only 83 articles with the term “bully” in the title or abstract published in the Web of Science database prior to 1989. The numbers of articles found in the following decades were 458 (1990–1999), 1,996 (2000–2009), and 9,333 (2010–2019). Considering cyberbullying more specifically, Smith and Berkkun ( 2017 , cited in Smith et al., 2021 ) conducted a search of Web of Science with the terms “cyber* and bully*; cyber and victim*; electronic bullying; Internet bullying; and online harassment” until the year 2015 and found that while there were no articles published prior to 2000, 538 articles were published between 2000 and 2015, with the number of articles increasing every year (p. 49).

Numerous authors have pointed out that research into school bullying and cyberbullying has predominantly been conducted using quantitative methods, with much less use of qualitative or mixed methods (Hong & Espelage, 2012 ; Hutson, 2018 ; Maran & Begotti, 2021 ; Smith et al., 2021 ). In their recent analysis of articles published between 1976 and 2019 (in WoS, with the search terms “bully*; victim*; cyberbullying; electronic bullying; internet bullying; and online harassment”), Smith et al. ( 2021 , pp. 50–51) found that of the empirical articles selected, more than three-quarters (76.3%) were based on quantitative data, 15.4% were based on a combination of quantitative and qualitative data, and less than one-tenth (8.4%) were based on qualitative data alone. What is more, they found that the proportion of articles based on qualitative or mixed methods has been decreasing over the past 15 years (Smith et al., 2021 ). While the search criteria excluded certain types of qualitative studies (e.g., those published in books, doctoral theses, and non-English languages), this nonetheless highlights the extent to which qualitative research findings risk being overlooked in the vast sea of quantitative research.

School bullying and cyberbullying are complex phenomena, and a range of methodological approaches is thus needed to understand their complexity (Pellegrini & Bartini, 2000 ; Thornberg, 2011 ). Indeed, over-relying on quantitative methods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012 ; Patton et al., 2017 ). Qualitative methods are particularly useful for better understanding the social contexts, processes, interactions, experiences, motivations, and perspectives of those involved (Hutson, 2018 ; Patton et al., 2017 ; Thornberg, 2011 ; Torrance, 2000 ).

Smith et al. ( 2021 ) suggest that the “continued emphasis on quantitative studies may be due to increasingly sophisticated methods such as structural equation modeling … network analysis … time trend analyses … latent profile analyses … and multi-polygenic score approaches” (p. 56). However, the authors make no mention of the range or sophistication of methods used in qualitative studies. Although there are still proportionately few qualitative studies of school bullying and cyberbullying in relation to quantitative studies, and this gap appears to be increasing, qualitative studies have utilized a range of qualitative data collection methods. These methods have included but are not limited to ethnographic fieldwork and participant observations (e.g., Eriksen & Lyng, 2018 ; Gumpel et al., 2014 ; Horton, 2019 ), digital ethnography (e.g., Rachoene & Oyedemi, 2015 ; Sylwander, 2019 ), meta-ethnography (e.g., Dennehy et al., 2020 ; Moretti & Herkovits, 2021 ), focus group interviews (e.g., Odenbring, 2022 ; Oliver & Candappa, 2007 ; Ybarra et al., 2019 ), semi-structured group and individual interviews (e.g., Forsberg & Thornberg, 2016 ; Lyng, 2018 ; Mishna et al., 2005 ; Varjas et al., 2013 ), vignettes (e.g., Jennifer & Cowie, 2012 ; Khanolainen & Semenova, 2020 ; Strindberg et al., 2020 ), memory work (e.g., Johnson et al., 2014 ; Malaby, 2009 ), literature studies (e.g., Lopez-Ropero, 2012 ; Wiseman et al., 2019 ), photo elicitation (e.g., Ganbaatar et al., 2021 ; Newman et al., 2006 ; Walton & Niblett, 2013 ), photostory method (e.g., Skrzypiec et al., 2015 ), and other visual works produced by children and young people (e.g., Bosacki et al., 2006 ; Gillies-Rezo & Bosacki, 2003 ).

This body of research has also included a variety of qualitative data analysis methods, such as grounded theory (e.g., Allen, 2015 ; Bjereld, 2018 ; Thornberg, 2018 ), thematic analysis (e.g., Cunningham et al., 2016 ; Forsberg & Horton, 2022 ), content analysis (e.g., Temko, 2019 ; Wiseman & Jones, 2018 ), conversation analysis (e.g., Evaldsson & Svahn, 2012 ; Tholander, 2019 ), narrative analysis (e.g., Haines-Saah et al., 2018 ), interpretative phenomenological analysis (e.g., Hutchinson, 2012 ; Tholander et al., 2020 ), various forms of discourse analysis (e.g., Ellwood & Davies, 2010 ; Hepburn, 1997 ; Ringrose & Renold, 2010 ), including discursive psychological analysis (e.g., Clarke et al., 2004 ), and critical discourse analysis (e.g., Barrett & Bound, 2015 ; Bethune & Gonick, 2017 ; Horton, 2021 ), as well as theoretically informed analyses from an array of research traditions (e.g., Davies, 2011 ; Jacobson, 2010 ; Søndergaard, 2012 ; Walton, 2005 ).

In light of the growing volume and variety of qualitative studies during the past two decades, we invited researchers to discuss and explore methodological issues related to their qualitative school bullying and cyberbullying research. The articles included in this special issue of the International Journal of Bullying Prevention discuss different qualitative methods, reflect on strengths and limitations — possibilities and challenges, and suggest implications for future qualitative and mixed-methods research.

Included Articles

Qualitative studies — focusing on social, relational, contextual, processual, structural, and/or societal factors and mechanisms — have formed the basis for several contributions during the last two decades that have sought to expand approaches to understanding and theorizing the causes of cyber/bullying. Some have also argued the need for expanding the commonly used definition of bullying, based on Olweus ( 1993 ) (e.g., Allen, 2015 ; Ellwood & Davies, 2010 Goldsmid & Howie, 2014 ; Ringrose & Rawlings,  2015 ; Søndergaard, 2012 ; Walton, 2011 ). In the first article of the special issue, Using qualitative methods to measure and understand key features of adolescent bullying: A call to action , Natalie Spadafora, Anthony Volk, and Andrew Dane instead discuss the usefulness of qualitative methods for improving measures and bettering our understanding of three specific key definitional features of bullying. Focusing on the definition put forward by Volk et al. ( 2014 ), they discuss the definitional features of power imbalance , goal directedness (replacing “intent to harm” in order not to assume conscious awareness, and to include a wide spectrum of goals that are intentionally and strategically pursued by bullies), and harmful impact (replacing “negative actions” in order to focus on the consequences for the victim, as well as circumventing difficult issues related to “repetition” in the traditional definition).

Acknowledging that these three features are challenging to capture using quantitative methods, Spadafora, Volk, and Dane point to existing qualitative studies that shed light on the features of power imbalance, goal directedness and harmful impact in bullying interactions — and put forward suggestions for future qualitative studies. More specifically, the authors argue that qualitative methods, such as focus groups, can be used to investigate the complexity of power relations at not only individual, but also social levels. They also highlight how qualitative methods, such as diaries and autoethnography, may help researchers gain a better understanding of the motives behind bullying behavior; from the perspectives of those engaging in it. Finally, the authors demonstrate how qualitative methods, such as ethnographic fieldwork and semi-structured interviews, can provide important insights into the harmful impact of bullying and how, for example, perceived harmfulness may be connected to perceived intention.

In the second article, Understanding bullying and cyberbullying through an ecological systems framework: The value of qualitative interviewing in a mixed methods approach , Faye Mishna, Arija Birze, and Andrea Greenblatt discuss the ways in which utilizing qualitative interviewing in mixed method approaches can facilitate greater understanding of bullying and cyberbullying. Based on a longitudinal and multi-perspective mixed methods study of cyberbullying, the authors demonstrate not only how qualitative interviewing can augment quantitative findings by examining process, context and meaning for those involved, but also how qualitative interviewing can lead to new insights and new areas of research. They also show how qualitative interviewing can help to capture nuances and complexity by allowing young people to express their perspectives and elaborate on their answers to questions. In line with this, the authors also raise the importance of qualitative interviewing for providing young people with space for self-reflection and learning.

In the third article, Q methodology as an innovative addition to bullying researchers’ methodological repertoire , Adrian Lundberg and Lisa Hellström focus on Q methodology as an inherently mixed methods approach, producing quantitative data from subjective viewpoints, and thus supplementing more mainstream quantitative and qualitative approaches. The authors outline and exemplify Q methodology as a research technique, focusing on the central feature of Q sorting. The authors further discuss the contribution of Q methodology to bullying research, highlighting the potential of Q methodology to address challenges related to gaining the perspectives of hard-to-reach populations who may either be unwilling or unable to share their personal experiences of bullying. As the authors point out, the use of card sorting activities allows participants to put forward their subjective perspectives, in less-intrusive settings for data collection and without disclosing their own personal experiences. The authors also illustrate how the flexibility of Q sorting can facilitate the participation of participants with limited verbal literacy and/or cognitive function through the use of images, objects or symbols. In the final part of the paper, Lundberg and Hellström discuss implications for practice and suggest future directions for using Q methodology in bullying and cyberbullying research, particularly with hard-to-reach populations.

In the fourth article, The importance of being attentive to social processes in school bullying research: Adopting a constructivist grounded theory approach , Camilla Forsberg discusses the use of constructivist grounded theory (CGT) in her research, focusing on social structures, norms, and processes. Forsberg first outlines CGT as a theory-methods package that is well suited to meet the call for more qualitative research on participants’ experiences and the social processes involved in school bullying. Forsberg emphasizes three key focal aspects of CGT, namely focus on participants’ main concerns; focus on meaning, actions, and processes; and focus on symbolic interactionism. She then provides examples and reflections from her own ethnographic and interview-based research, from different stages of the research process. In the last part of the article, Forsberg argues that prioritizing the perspectives of participants is an ethical stance, but one which comes with a number of ethical challenges, and points to ways in which CGT is helpful in dealing with these challenges.

In the fifth article, A qualitative meta-study of youth voice and co-participatory research practices: Informing cyber/bullying research methodologies , Deborah Green, Carmel Taddeo, Deborah Price, Foteini Pasenidou, and Barbara Spears discuss how qualitative meta-studies can be used to inform research methodologies for studying school bullying and cyberbullying. Drawing on the findings of five previous qualitative studies, and with a transdisciplinary and transformative approach, the authors illustrate and exemplify how previous qualitative research can be analyzed to gain a better understanding of the studies’ collective strengths and thus consider the findings and methods beyond the original settings where the research was conducted. In doing so, the authors highlight the progression of youth voice and co-participatory research practices, the centrality of children and young people to the research process and the enabling effect of technology — and discuss challenges related to ethical issues, resource and time demands, the role of gatekeepers, and common limitations of qualitative studies on youth voice and co-participatory research practices.

Taken together, the five articles illustrate the diversity of qualitative methods used to study school bullying and cyberbullying and highlight the need for further qualitative research. We hope that readers will find the collection of articles engaging and that the special issue not only gives impetus to increased qualitative focus on the complex phenomena of school bullying and cyberbullying but also to further discussions on both methodological and analytical approaches.

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Acknowledgements

We would like to thank the authors for sharing their work; Angela Mazzone, James O’Higgins Norman, and Sameer Hinduja for their editorial assistance; and Dorte Marie Søndergaard on the editorial board for suggesting a special issue on qualitative research in the journal.

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Horton, P., Lyng, S.T. Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue. Int Journal of Bullying Prevention 4 , 175–179 (2022). https://doi.org/10.1007/s42380-022-00139-5

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Defining cyberbullying: a qualitative research into the perceptions of youngsters

Affiliation.

• 1 Department of Communication Studies, University of Antwerp, Antwerpn, Belgium. [email protected]
• PMID: 18721100
• DOI: 10.1089/cpb.2007.0042

Data from 53 focus groups, which involved students from 10 to 18 years old, show that youngsters often interpret "cyberbullying" as "Internet bullying" and associate the phenomenon with a wide range of practices. In order to be considered "true" cyberbullying, these practices must meet several criteria. They should be intended to hurt (by the perpetrator) and perceived as hurtful (by the victim); be part of a repetitive pattern of negative offline or online actions; and be performed in a relationship characterized by a power imbalance (based on "real-life" power criteria, such as physical strength or age, and/or on ICT-related criteria such as technological know-how and anonymity).

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SYSTEMATIC REVIEW article

Cyberbullying among adolescents and children: a comprehensive review of the global situation, risk factors, and preventive measures.

• 1 School of Political Science and Public Administration, Wuhan University, Wuhan, China
• 2 School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
• 3 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom

Background: Cyberbullying is well-recognized as a severe public health issue which affects both adolescents and children. Most extant studies have focused on national and regional effects of cyberbullying, with few examining the global perspective of cyberbullying. This systematic review comprehensively examines the global situation, risk factors, and preventive measures taken worldwide to fight cyberbullying among adolescents and children.

Methods: A systematic review of available literature was completed following PRISMA guidelines using the search themes “cyberbullying” and “adolescent or children”; the time frame was from January 1st, 2015 to December 31st, 2019. Eight academic databases pertaining to public health, and communication and psychology were consulted, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. A total of 63 studies out of 2070 were included in our final review focusing on cyberbullying prevalence and risk factors.

Results: The prevalence rates of cyberbullying preparation ranged from 6.0 to 46.3%, while the rates of cyberbullying victimization ranged from 13.99 to 57.5%, based on 63 references. Verbal violence was the most common type of cyberbullying. Fourteen risk factors and three protective factors were revealed in this study. At the personal level, variables associated with cyberbullying including age, gender, online behavior, race, health condition, past experience of victimization, and impulsiveness were reviewed as risk factors. Likewise, at the situational level, parent-child relationship, interpersonal relationships, and geographical location were also reviewed in relation to cyberbullying. As for protective factors, empathy and emotional intelligence, parent-child relationship, and school climate were frequently mentioned.

Conclusion: The prevalence rate of cyberbullying has increased significantly in the observed 5-year period, and it is imperative that researchers from low and middle income countries focus sufficient attention on cyberbullying of children and adolescents. Despite a lack of scientific intervention research on cyberbullying, the review also identified several promising strategies for its prevention from the perspectives of youths, parents and schools. More research on cyberbullying is needed, especially on the issue of cross-national cyberbullying. International cooperation, multi-pronged and systematic approaches are highly encouraged to deal with cyberbullying.

Introduction

Childhood and adolescence are not only periods of growth, but also of emerging risk taking. Young people during these periods are particularly vulnerable and cannot fully understand the connection between behaviors and consequences ( 1 ). With peer pressures, the heat of passion, children and adolescents usually perform worse than adults when people are required to maintain self-discipline to achieve good results in unfamiliar situations. Impulsiveness, sensation seeking, thrill seeking, and other individual differences cause adolescents to risk rejecting standardized risk interventions ( 2 ).

About one-third of Internet users in the world are children and adolescents under the age of 18 ( 3 ). Digital technology provide a new form of interpersonal communication ( 4 ). However, surveys and news reports also show another picture in the Internet Age. The dark side of young people's internet usage is that they may bully or suffer from others' bullying in cyberspace. This behavior is also acknowledged as cyberbullying ( 5 ). Based on Olweus's definition, cyberbullying is usually regarded as bullying implemented through electronic media ( 6 , 7 ). Specifically, cyberbullying among children and adolescents can be summarized as the intentional and repeated harm from one or more peers that occurs in cyberspace caused by the use of computers, smartphones and other devices ( 4 , 8 – 12 ). In recent years, new forms of cyberbullying behaviors have emerged, such as cyberstalking and online dating abuse ( 13 – 15 ).

Although cyberbullying is still a relatively new field of research, cyberbullying among adolescents is considered to be a serious public health issue that is closely related to adolescents' behavior, mental health and development ( 16 , 17 ). The increasing rate of Internet adoption worldwide and the popularity of social media platforms among the young people have worsened this situation with most children and adolescents experiencing cyberbullying or online victimization during their lives. The confines of space and time are alleviated for bullies in virtual environments, creating new venues for cyberbullying with no geographical boundaries ( 6 ). Cyberbullying exerts negative effects on many aspects of young people's lives, including personal privacy invasion and psychological disorders. The influence of cyberbullying may be worse than traditional bullying as perpetrators can act anonymously and connect easily with children and adolescents at any time ( 18 ). In comparison with traditional victims, those bullied online show greater levels of depression, anxiety and loneliness ( 19 ). Self-esteem problems and school absenteeism have also proven to be related to cyberbullying ( 20 ).

Due to changes in use and behavioral patterns among the youth on social media, the manifestations and risk factors of cyberbullying have faced significant transformation. Further, as the boundaries of cyberbullying are not limited by geography, cyberbullying may not be a problem contained within a single country. In this sense, cyberbullying is a global problem and tackling it requires greater international collaboration. The adverse effects caused by cyberbullying, including reduced safety, lower educational attainment, poorer mental health and greater unhappiness, led UNICEF to state that “no child is absolutely safe in the digital world” ( 3 ).

Extant research has examined the prevalence and risk factors of cyberbullying to unravel the complexity of cyberbullying across different countries and their corresponding causes. However, due to variations in cyberbullying measurement and methodologies, no consistent conclusions have been drawn ( 21 ). Studies into inconsistencies in prevalence rates of cyberbullying, measured in the same country during the same time period, occur frequently. Selkie et al. systematically reviewed cyberbullying among American middle and high school students aged 10–19 years old in 2015, and revealed that the prevalence of cyberbullying victimization ranged from 3 to 72%, while perpetration ranged from 1 to 41% ( 22 ). Risk and protective factors have also been broadly studied, but confirmation is still needed of those factors which have more significant effects on cyberbullying among young people. Clarification of these issues would be useful to allow further research to recognize cyberbullying more accurately.

This review aims to extend prior contributions and provide a comprehensive review of cyberbullying of children and adolescents from a global perspective, with the focus being on prevalence, associated risk factors and protective factors across countries. It is necessary to provide a global panorama based on research syntheses to fill the gaps in knowledge on this topic.

Search Strategies

This study strictly employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We consulted eight academic databases pertaining to public health, and communication and psychology, namely: Web of Science, Science Direct, PubMed, Google Scholar, ProQuest, Communication & Mass Media Complete, CINAHL, and PsycArticles. Additional records identified through other sources included the references of reviews and two websites, Cyberbullying Research Center and United Nations Children's Fund. With regard to the duration of our review, since most studies on cyberbullying arose around 2015 ( 9 , 21 ), this study highlights the complementary aspects of the available information about cyberbullying during the recent 5 year period from January 1st, 2015 to December 31st, 2019.

One researcher extracted keywords and two researchers proposed modifications. We used two sets of subject terms to review articles, “cyberbullying” and “child OR adolescent.” Some keywords that refer to cyberbullying behaviors and young people are also included, such as threat, harass, intimidate, abuse, insult, humiliate, condemn, isolate, embarrass, forgery, slander, flame, stalk, manhunt, as well as teen, youth, young people and student. The search formula is (cyberbullying OR cyber-bullying OR cyber-aggression OR ((cyber OR online OR electronic OR Internet) AND (bully * OR aggres * OR violence OR perpetrat * OR victim * OR threat * OR harass * OR intimidat * OR * OR insult * OR humiliate * OR condemn * OR isolate * OR embarrass * OR forgery OR slander * OR flame OR stalk * OR manhunt))) AND (adolescen * OR child OR children OR teen? OR teenager? OR youth? OR “young people” OR “elementary school student * ” OR “middle school student * ” OR “high school student * ”). The main search approach is title search. Search strategies varied according to the database consulted, and we did not limit the type of literature for inclusion. Journals, conference papers and dissertations are all available.

Specifically, the inclusion criteria for our study were as follows: (a). reported or evaluated the prevalence and possible risk factors associated with cyberbullying, (b). respondents were students under the age of 18 or in primary, junior or senior high schools, and (c). studies were written in English. Exclusion criteria were: (a). respondents came from specific groups, such as clinical samples, children with disabilities, sexual minorities, specific ethnic groups, specific faith groups or samples with cross-national background, (b). review studies, qualitative studies, conceptual studies, book reviews, news reports or abstracts of meetings, and (c). studies focused solely on preventive measures that were usually meta-analytic and qualitative in nature. Figure 1 presents the details of the employed screening process, showing that a total of 63 studies out of 2070 were included in our final review.

Figure 1 . PRISMA flow chart diagram showing the process of study selection for inclusion in the systematic review on children and adolescents cyberbullying.

Meta-analysis was not conducted as the limited research published within the 5 years revealed little research which reported odds ratio. On the other hand, due to the inconsistency of concepts, measuring instruments and recall periods, considerable variation could be found in research quality ( 23 ). Meta-analysis is not a preferred method.

Coding Scheme

For coding, we created a comprehensive code scheme to include the characteristics. For cyberbullying, we coded five types proposed by Willard ( 24 – 26 ), which included verbal violence, group violence, visual violence, impersonating and account forgery, and other behaviors. Among them, verbal violence is considered one of the most common types of cyberbullying and refers to the behavior of offensive responses, insults, mocking, threats, slander, and harassment. Group violence is associated with preventing others from joining certain groups or isolating others, forcing others to leave the group. Visual violence relates to the release and sharing of embarrassing photos and information without the owners' consent. Impersonating and account forgery refers to identity theft, stealing passwords, violating accounts and the creation of fake accounts to fraudulently present the behavior of others. Other behaviors include disclosure of privacy, sexual harassment, and cyberstalking. To comprehensively examine cyberbullying, we coded cyberbullying behaviors from both the perspectives of cyberbullying perpetrators and victims, if mentioned in the studies.

In relation to risk factors, we drew insights from the general aggression model, which contributes to the understanding of personal and situational factors in the cyberbullying of children and adolescents. We chose the general aggression model because (a) it contains more situational factors than other models (e.g., social ecological models) - such as school climate ( 9 ), and (b) we believe that the general aggression model is more suitable for helping researchers conduct a systematic review of cyberbullying risk and protective factors. This model provides a comprehensive framework that integrates domain specific theories of aggression, and has been widely applied in cyberbullying research ( 27 ). For instance, Kowalski and colleagues proposed a cyberbullying encounter through the general aggression model to understand the formation and development process of youth cyberbullying related to both victimization and perpetration ( 9 ). Victims and perpetrators enter the cyberbullying encounter with various individual characteristics, experiences, attitudes, desires, personalities, and motives that intersect to determine the course of the interaction. Correspondingly, the antecedents pertaining to cyberbullying are divided into two broad categories, personal factors and situational factors. Personal factors refer to individual characteristics, such as gender, age, motivation, personality, psychological states, socioeconomic status and technology use, values and perceptions, and other maladaptive behaviors. Situational factors focus on the provocation/support, parental involvement, school climate, and perceived anonymity. Consequently, our coders related to risk factors consisting of personal factors and situational factors from the perspectives of both cyberbullying perpetrators and victims.

We extracted information relating to individual papers and sample characteristics, including authors, year of publication, country, article type, sampling procedures, sample characteristics, measures of cyberbullying, and prevalence and risk factors from both cyberbullying perpetration and victimization perspectives. The key words extraction and coding work were performed twice by two trained research assistants in health informatics. The consistency test results are as follows: the Kappa value with “personal factors” was 0.932, and the Kappa value with “situational factors” was 0.807. The result shows that the coding consistency was high enough and acceptable. Disagreements were resolved through discussion with other authors.

Quality Assessment of Studies

The quality assessment of the studies is based on the recommended tool for assessing risk of bias, Cochrane Collaboration. This quality assessment tool focused on seven items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other sources of bias ( 28 ). We assessed each item as “low risk,” “high risk,” and “unclear” for included studies. A study is considered of “high quality” when it meets three or more “low risk” requirements. When one or more main flaw of a study may affect the research results, the study is considered as “low quality.” When a lack of information leads to a difficult judgement, the quality is considered to be “unclear.” Please refer to Appendix 1 for more details.

This comprehensive systematic review comprised a total of 63 studies. Appendices 2 , 3 show the descriptive information of the studies included. Among them, 58 (92%) studies measured two or more cyberbullying behavior types. The sample sizes of the youths range from several hundred to tens of thousands, with one thousand to five thousand being the most common. As for study distribution, the United States of America, Spain and China were most frequently mentioned. Table 1 presents the detail.

Table 1 . Descriptive information of studies included (2015–2019).

Prevalence of Global Cyberbullying

Prevalence across countries.

Among the 63 studies included, 22 studies reported on cyberbullying prevalence and 20 studies reported on prevalence from victimization and perpetration perspectives, respectively. Among the 20 studies, 11 national studies indicated that the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 14.6 to 52.2% and 6.3 to 32%, respectively. These studies were conducted in the United States of America ( N = 4) ( 29 – 32 ), South Korea ( N = 3) ( 33 – 35 ), Singapore ( N = 1) ( 36 ), Malaysia ( N = 1) ( 37 ), Israel ( N = 1) ( 38 ), and Canada ( N = 1) ( 39 ). Only one of these 11 national studies is from an upper middle income country, and the rest are from highincome countries identified by the World Bank ( 40 ). By combining regional and community-level studies, the prevalence of cyberbullying victimization and cyberbullying perpetration ranged from 13.99 to 57.5% and 6.0 to 46.3%, respectively. Spain reported the highest prevalence of cyberbullying victimization (57.5%) ( 41 ), followed by Malaysia (52.2%) ( 37 ), Israel (45%) ( 42 ), and China (44.5%) ( 43 ). The lowest reported victim rates were observed in Canada (13.99%) and South Korea (14.6%) ( 34 , 39 ). The reported prevalence of cyberbullying victimization in the United States of America ranged from 15.5 to 31.4% ( 29 , 44 ), while in Israel, rates ranged from 30 to 45% ( 26 , 42 ). In China, rates ranged from 6 to 46.3% with the country showing the highest prevalence of cyberbullying perpetration (46.30%) ( 15 , 43 , 45 , 46 ). Canadian and South Korean studies reported the lowest prevalence of cyberbullying perpetration at 7.99 and 6.3%, respectively ( 34 , 39 ).

A total of 10 studies were assessed as high quality studies. Among them, six studies came from high income countries, including Canada, Germany, Italy, Portugal, and South Korea ( 13 , 34 , 39 , 46 – 48 ). Three studies were from upper middle income countries, including Malaysia and China ( 37 , 43 ) and one from a lower middle income country, Nigeria ( 49 ). Figures 2 , 3 describe the prevalence of cyberbullying victimization and perpetration respectively among high quality studies.

Figure 2 . The prevalence of cyberbullying victimization of high quality studies.

Figure 3 . The prevalence of cyberbullying perpetration of high quality studies.

Prevalence of Various Cyberbullying Behaviors

For the prevalence of cyberbullying victimization and perpetration, the data were reported in 18 and 14 studies, respectively. Figure 4 shows the distribution characteristics of the estimated value of prevalence of different cyberbullying behaviors with box plots. The longer the box, the greater the degree of variation of the numerical data and vice versa. The rate of victimization and crime of verbal violence, as well as the rate of victimization of other behaviors, such as cyberstalking and digital dating abuse, has a large degree of variation. Among the four specified types of cyberbullying behaviors, verbal violence was regarded as the most commonly reported behaviors in both perpetration and victimization rates, with a wide range of prevalence, ranging from 5 to 18%. Fewer studies reported the prevalence data for visual violence and group violence. Studies also showed that the prevalence of impersonation and account forgery were within a comparatively small scale. Specific results were as follows.

Figure 4 . Cyberbullying prevalence across types (2015–2019).

Verbal Violence

A total of 13 studies reported verbal violence prevalence data ( 15 , 26 , 34 , 37 – 39 , 42 , 43 , 47 , 48 , 50 , 51 ). Ten studies reported the prevalence of verbal violence victimization ranging from 2.8 to 47.5%, while seven studies claimed perpetration prevalence ranging from 1.5 to 31.8%. Malaysia reported the highest prevalence of verbal violence victimization (47.5%) ( 37 ), followed by China (32%) ( 43 ). China reported that the prevalence of verbal violence victimization ranged from 5.1 to 32% ( 15 , 43 ). Israel reported that the prevalence of verbal violence victimization ranged from 3.4 to 18% ( 26 , 38 , 42 ). For perpetration rate, Malaysia reported the highest level at 31.8% ( 37 ), while a study for Spain reported the lowest, ranging from 3.2 to 6.4% ( 51 ).

Group Violence

The prevalence of group violence victimization was explored within 4 studies and ranged from 5 to 17.8% ( 26 , 34 , 42 , 43 ), while perpetration prevalence was reported in three studies, ranging from 10.1 to 19.07% ( 34 , 43 , 47 ). An Israeli study suggested that 9.8% of respondents had been excluded from the Internet, while 8.9% had been refused entry to a group or team ( 26 ). A study in South Korea argued that the perpetration prevalence of group violence was 10.1% ( 34 ), while a study in Italy reported that the rate of online group violence against others was 19.07% ( 47 ).

Visual Violence

The prevalence of visual violence victimization was explored within three studies and ranged from 2.6 to 12.1% ( 26 , 34 , 43 ), while the perpetration prevalence reported in four studies ranged from 1.7 to 6% ( 34 , 43 , 47 , 48 ). For victimization prevalence, a South Korean study found that 12.1% of respondents reported that their personal information was leaked online ( 34 ). An Israel study reported that the prevalence of outing the picture was 2.6% ( 26 ). For perpetration prevalence, a South Korean study found that 1.7% of respondents had reported that they had disclosed someone's personal information online ( 34 ). A German study reported that 6% of respondents had written a message (e.g., an email) to somebody using a fake identity ( 48 ).

Impersonating and Account Forgery

Four studies reported on the victimization prevalence of impersonating and account forgery, ranging from 1.1 to 10% ( 15 , 42 , 43 ), while five studies reported on perpetration prevalence, with the range being from 1.3 to 9.31% ( 15 , 43 , 47 , 48 , 51 ). In a Spanish study, 10% of respondents reported that their accounts had been infringed by others or that they could not access their account due to stolen passwords. In contrast, 4.5% of respondents reported that they had infringed other people's accounts or stolen passwords, with 2.5% stating that they had forged other people's accounts ( 51 ). An Israeli study reported that the prevalence of being impersonated was 7% ( 42 ), while in China, a study reported this to be 8.6% ( 43 ). Another study from China found that 1.1% of respondents had been impersonated to send dating-for-money messages ( 15 ).

Other Behaviors

The prevalence of disclosure of privacy, sexual harassment, and cyberstalking were also explored by scholars. Six studies reported the victimization prevalence of other cyberbullying behaviors ( 13 , 15 , 34 , 37 , 42 , 43 ), and four studies reported on perpetration prevalence ( 34 , 37 , 43 , 48 ). A study in China found that 1.2% of respondents reported that their privacy had been compromised without permission due to disputes ( 15 ). A study from China reported the prevalence of cyberstalking victimization was 11.9% ( 43 ), while a Portuguese study reported that this was 62% ( 13 ). In terms of perpetration prevalence, a Malaysian study reported 2.7% for sexual harassment ( 37 ).

Risk and Protective Factors of Cyberbullying

In terms of the risk factors associated with cyberbullying among children and adolescents, this comprehensive review highlighted both personal and situational factors. Personal factors referred to age, gender, online behavior, race, health conditions, past experiences of victimization, and impulsiveness, while situational factors consisted of parent-child relationship, interpersonal relationships, and geographical location. In addition, protective factors against cyberbullying included: empathy and emotional intelligence, parent-child relationship, and school climate. Table 2 shows the risk and protective factors for child and adolescent cyberbullying.

Table 2 . Risk and protective factors of cyberbullying among children and adolescents.

In terms of the risk factors associated with cyberbullying victimization at the personal level, many studies evidenced that females were more likely to be cyberbullied than males ( 13 , 26 , 29 , 38 , 43 , 52 , 54 , 55 , 58 ). Meanwhile, adolescents with mental health problems ( 61 ), such as depression ( 33 , 62 ), borderline personality disorder ( 63 ), eating disorders ( 41 ), sleep deprivation ( 56 ), and suicidal thoughts and suicide plans ( 64 ), were more likely to be associated with cyberbullying victimization. As for Internet usage, researchers agreed that youth victims were probably those that spent more time online than their counterparts ( 32 , 36 , 43 , 45 , 48 , 49 , 60 ). For situational risk factors, some studies have proven the relationship between cyberbullying victims and parental abuse, parental neglect, family dysfunction, inadequate monitoring, and parents' inconsistency in mediation, as well as communication issues ( 33 , 64 , 68 , 73 ). In terms of geographical location, some studies have reported that youths residing in city locations are more likely to be victims of cyberbullying than their peers from suburban areas ( 61 ).

Regarding the risk factors of cyberbullying perpetration at the personal level, it is generally believed that older teenagers, especially those aged over 15 years, are at greater risk of becoming cyberbullying perpetrators ( 55 , 67 ). When considering prior cyberbullying experiences, evidence showed that individuals who had experienced cyberbullying or face-to-face bullying tended to be aggressors in cyberbullying ( 35 , 42 , 49 , 51 , 55 ); in addition, the relationship between impulsiveness and cyberbullying perpetration was also explored by several pioneering scholars ( 55 , 72 , 80 ). The situational factors highlight the role of parents and teachers in cyberbullying experiences. For example, over-control and authoritarian parenting styles, as well as inharmonious teacher-student relationships ( 61 ) are perceived to lead to cyberbullying behaviors ( 74 , 75 ). In terms of differences in geographical locations, students residing in cities have a higher rate of online harassment than students living in more rural locations ( 49 ).

In terms of the protective factors in child and adolescent cyberbullying, scholars have focused on youths who have limited experiences of cyberbullying. At the personal level, high emotional intelligence, an ability for emotional self-control and empathy, such as cognitive empathy ability ( 44 , 55 ), were associated with lower rates of cyberbullying ( 57 ). At the situational level, a parent's role is seen as critical. For example, intimate parent-child relationships ( 46 ) and open active communication ( 19 ) were demonstrated to be related to lower experiences of cyberbullying and perpetration. Some scholars argued that parental supervision and monitoring of children's online activities can reduce their tendency to participate in some negative activities associated with cyberbullying ( 31 , 46 , 73 ). They further claimed that an authoritative parental style protects youths against cyberbullying ( 43 ). Conversely, another string of studies evidenced that parents' supervision of Internet usage was meaningless ( 45 ). In addition to conflicting roles of parental supervision, researchers have also looked into the role of schools, and posited that positive school climates contribute to less cyberbullying experiences ( 61 , 79 ).

Some risk factors may be protective factors under another condition. Some studies suggest that parental aggressive communication is related to severe cyberbullying victims, while open communication is a potential protective factor ( 19 ). Parental neglect, parental abuse, parental inconsistency in supervision of adolescents' online behavior, and family dysfunction are related to the direct or indirect harm of cyberbullying ( 33 , 68 ). Parental participation, a good parental-children relationship, communication and dialogue can enhance children's school adaptability and prevent cyberbullying behaviors ( 31 , 74 ). When parental monitoring reaches a balance between control and openness, it could become a protective factor against cyberbullying, and it could be a risk factor, if parental monitoring is too low or over-controlled ( 47 ).

Despite frequent discussion about the risk factors associated with cyberbullying among children and adolescents, some are still deemed controversial factors, such as age, race, gender, and the frequency of suffering on the internet. For cyberbullying victims, some studies claim that older teenagers are more vulnerable to cyberbullying ( 15 , 38 , 52 , 53 ), while other studies found conflicting results ( 26 , 33 ). As for student race, Alhajji et al. argued that non-white students were less likely to report cyberbullying ( 29 ), while Morin et al. observed no significant correlation between race and cyberbullying ( 52 ). For cyberbullying perpetration, Alvarez-Garcia found that gender differences may have indirect effects on cyberbullying perpetration ( 55 ), while others disagreed ( 42 , 61 , 68 – 70 ). Specifically, some studies revealed that males were more likely to become cyberbullying perpetrators ( 34 , 39 , 56 ), while Khurana et al. presented an opposite point of view, proposing that females were more likely to attack others ( 71 ). In terms of time spent on the Internet, some claimed that students who frequently surf the Internet had a higher chance of becoming perpetrators ( 49 ), while others stated that there was no clear and direct association between Internet usage and cyberbullying perpetration ( 55 ).

In addition to personal and situational factors, scholars have also explored other specific factors pertaining to cyberbullying risk and protection. For instance, mindfulness and depression were found to be significantly related to cyber perpetration ( 76 ), while eating disorder psychopathology in adolescents was associated with cyber victimization ( 41 ). For males who were familiar with their victims, such as family members, friends and acquaintances, they were more likely to be cyberstalking perpetrators than females or strangers, while pursuing desired closer relationships ( 13 ). In the school context, a lower social likability in class was identified as an indirect factor for cyberbullying ( 48 ).

This comprehensive review has established that the prevalence of global childhood and adolescent victimization from cyberbullying ranges from 13.99 to 57.5%, and that the perpetration prevalence ranges from 6.0 to 46.3%. Across the studies included in our research, verbal violence is observed as one of the most common acts of cyberbullying, including verbal offensive responses, insults, mocking, threats, slander, and harassment. The victimization prevalence of verbal violence is reported to be between 5 and 47.5%, and the perpetration prevalence is between 3.2 and 26.1%. Personal factors, such as gender, frequent use of social media platforms, depression, borderline personality disorder, eating disorders, sleep deprivation, and suicidal tendencies, were generally considered to be related to becoming a cyberbullying victim. Personal factors, such as high school students, past experiences, impulse, improperly controlled family education, poor teacher-student relationships, and the urban environment, were considered risk factors for cyberbullying perpetration. Situational factors, including parental abuse and neglect, improper monitoring, communication barriers between parents and children, as well as the urban environment, were also seen to potentially contribute to higher risks of both cyberbullying victimization and perpetration.

Increasing Prevalence of Global Cyberbullying With Changing Social Media Landscape and Measurement Alterations

This comprehensive review suggests that global cyberbullying rates, in terms of victimization and perpetration, were on the rise during the 5 year period, from 2015 to 2019. For example, in an earlier study conducted by Modecki et al. the average cyberbullying involvement rate was 15% ( 81 ). Similar observations were made by Hamm et al. who found that the median rates of youth having experienced bullying or who had bullied others online, was 23 and 15.2%, respectively ( 82 ). However, our systematic review summarized global children and adolescents cyberbullying in the last 5 years and revealed an average cyberbullying perpetration rate of 25.03%, ranging from 6.0 to 46.3%, while the average victimization was 33.08%, ranging from 13.99 to 57.5%. The underlying reason for increases may be attributed to the rapid changing landscape of social media and, in recent years, the drastic increase in Internet penetration rates. With the rise in Internet access, youths have greater opportunities to participate in online activities, provided by emerging social media platforms.

Although our review aims to provide a broader picture of cyberbullying, it is well-noted in extant research that difficulties exist in accurately estimating variations in prevalence in different countries ( 23 , 83 ). Many reasons exist to explain this. The first largely relates poor or unclear definition of the term cyberbullying; this hinders the determination of cyberbullying victimization and perpetration ( 84 ). Although traditional bullying behavior is well-defined, the definition cannot directly be applied to the virtual environment due to the complexity in changing online interactions. Without consensus on definitions, measurement and cyberbullying types may vary noticeably ( 83 , 85 ). Secondly, the estimation of prevalence of cyberbullying is heavily affected by research methods, such as recall period (lifetime, last year, last 6 months, last month, or last week etc.), demographic characteristics of the survey sample (age, gender, race, etc.), perspectives of cyberbullying experiences (victims, perpetrators, or both victim and perpetrator), and instruments (scales, study-specific questions) ( 23 , 84 , 86 ). The variety in research tools and instruments used to assess the prevalence of cyberbullying can cause confusion on this issue ( 84 ). Thirdly, variations in economic development, cultural backgrounds, human values, internet penetration rates, and frequency of using social media may lead to different conclusions across countries ( 87 ).

Acknowledging the Conflicting Role of the Identified Risk Factors With More Research Needed to Establish the Causality

Although this review has identified many personal and situational factors associated with cyberbullying, the majority of studies adopted a cross-sectional design and failed to reveal the causality ( 21 ). Nevertheless, knowledge on these correlational relationships provide valuable insights for understanding and preventing cyberbullying incidents. In terms of gender differences, females are believed to be at a higher risk of cyberbullying victimization compared to males. Two reasons may help to explain this. First, the preferred violence behaviors between two genders. females prefer indirect harassment, such as the spreading of rumors, while males tend toward direct bullying (e.g., assault) ( 29 ) and second, the cultural factors. From the traditional gender perspective, females tended to perceive a greater risk of communicating with others on the Internet, while males were more reluctant to express fear, vulnerability and insecurity when asked about their cyberbullying experiences ( 46 ). Females were more intolerant when experiencing cyberstalking and were more likely to report victimization experiences than males ( 13 ). Meanwhile, many researchers suggested that females are frequent users of emerging digital communication platforms, which increases their risk of unpleasant interpersonal contact and violence. From the perspective of cultural norms and masculinity, the reporting of cyberbullying is also widely acknowledged ( 37 ). For example, in addition, engaging in online activities is also regarded as a critical predictor for cyberbullying victimization. Enabled by the Internet, youths can easily find potential victims and start harassment at any time ( 49 ). Participating in online activities directly increases the chance of experiencing cyberbullying victimization and the possibility of becoming a victim ( 36 , 45 ). As for age, earlier involvement on social media and instant messaging tools may increase the chances of experiencing cyberbullying. For example, in Spain, these tools cannot be used without parental permission before the age of 14 ( 55 ). Besides, senior students were more likely to be more impulsive and less sympathetic. They may portray more aggressive and anti-social behaviors ( 55 , 72 ); hence senior students and students with higher impulsivity were usually more likely to become cyberbullying perpetrators.

Past experiences of victimization and family-related factors are another risk for cyberbullying crime. As for past experiences, one possible explanation is that young people who had experienced online or traditional school bullying may commit cyberbullying using e-mails, instant messages, and text messages for revenge, self-protection, or improving their social status ( 35 , 42 , 49 , 55 ). In becoming a cyberbullying perpetrator, the student may feel more powerful and superior, externalizing angry feelings and relieving the feelings of helplessness and sadness produced by past victimization experiences ( 51 ). As for family related factors, parenting styles are proven to be highly correlated to cyberbullying. In authoritative families, parents focus on rational behavioral control with clear rules and a high component of supervision and parental warmth, which have beneficial effects on children's lifestyles ( 43 ). Conversely, in indulgent families, children's behaviors are not heavily restricted and parents guide and encourage their children to adapt to society. The characteristics of this indulgent style, including parental support, positive communication, low imposition, and emotional expressiveness, possibly contribute to more parent-child trust and less misunderstanding ( 75 ). The protective role of warmth/affection and appropriate supervision, which are common features of authoritative or indulgent parenting styles, mitigate youth engagement in cyberbullying. On the contrary, authoritarian and neglectful styles, whether with excessive or insufficient control, are both proven to be risk factors for being a target of cyberbullying ( 33 , 76 ). In terms of geographical location, although several studies found that children residing in urban areas were more likely to be cyberbullying victims than those living in rural or suburban areas, we cannot draw a quick conclusion here, since whether this difference attributes to macro-level differences, such as community safety or socioeconomic status, or micro-level differences, such as teacher intervention in the classroom, courses provided, teacher-student ratio, is unclear across studies ( 61 ). An alternative explanation for this is the higher internet usage rate in urban areas ( 49 ).

Regarding health conditions, especially mental health, some scholars believe that young people with health problems are more likely to be identified as victims than people without health problems. They perceive health condition as a risk factor for cyberbullying ( 61 , 63 ). On the other hand, another group of scholars believe that cyberbullying has an important impact on the mental health of adolescents which can cause psychological distress consequences, such as post-traumatic stress mental disorder, depression, suicidal ideation, and drug abuse ( 70 , 87 ). It is highly possible that mental health could be risk factors, consequences of cyberbullying or both. Mental health cannot be used as standards, requirements, or decisive responses in cyberbullying research ( 13 ).

The Joint Effort Between Youth, Parents, Schools, and Communities to Form a Cyberbullying-Free Environment

This comprehensive review suggests that protecting children and adolescents from cyberbullying requires joint efforts between individuals, parents, schools, and communities, to form a cyberbullying-free environment. For individuals, young people are expected to improve their digital technology capabilities, especially in the use of social media platforms and instant messaging tools ( 55 ). To reduce the number of cyberbullying perpetrators, it is necessary to cultivate emotional self-regulation ability through appropriate emotional management training. Moreover, teachers, counselors, and parents are required to be armed with sufficient knowledge of emotional management and to develop emotional management capabilities and skills. In this way, they can be alert to the aggressive or angry emotions expressed by young people, and help them mediate any negative emotions ( 45 ), and avoid further anti-social behaviors ( 57 ).

For parents, styles of parenting involving a high level of parental involvement, care and support, are desirable in reducing the possibility of children's engagement in cyberbullying ( 74 , 75 ). If difficulties are encountered, open communication can contribute to enhancing the sense of security ( 73 ). In this vein, parents should be aware of the importance of caring, communicating and supervising their children, and participate actively in their children's lives ( 71 ). In order to keep a balance between control and openness ( 47 ), parents can engage in unbiased open communication with their children, and reach an agreement on the usage of computers and smart phones ( 34 , 35 , 55 ). Similarly, it is of vital importance to establish a positive communication channel with children ( 19 ).

For schools, a higher priority is needed to create a safe and positive campus environment, providing students with learning opportunities and ensuring that every student is treated equally. With a youth-friendly environment, students are able to focus more on their academic performance and develop a strong sense of belonging to the school ( 79 ). For countries recognizing collectivist cultural values, such as China and India, emphasizing peer attachment and a sense of collectivism can reduce the risk of cyberbullying perpetration and victimization ( 78 ). Besides, schools can cooperate with mental health agencies and neighboring communities to develop preventive programs, such as extracurricular activities and training ( 44 , 53 , 62 ). Specifically, school-based preventive measures against cyberbullying are expected to be sensitive to the characteristics of young people at different ages, and the intersection of race and school diversity ( 29 , 76 ). It is recommended that school policies that aim to embrace diversity and embody mutual respect among students are created ( 26 ). Considering the high prevalence of cyberbullying and a series of serious consequences, it is suggested that intervention against cyberbullying starts from an early stage, at about 10 years old ( 54 ). Schools can organize seminars to strengthen communication between teachers and students so that they can better understand the needs of students ( 61 ). In addition, schools should encourage cyberbullying victims to seek help and provide students with opportunities to report cyberbullying behaviors, such as creating online anonymous calls.

Conclusions and Limitations

The comprehensive study has reviewed related research on children and adolescents cyberbullying across different countries and regions, providing a positive understanding of the current situation of cyberbullying. The number of studies on cyberbullying has surged in the last 5 years, especially those related to risk factors and protective factors of cyberbullying. However, research on effective prevention is insufficient and evaluation of policy tools for cyberbullying intervention is a nascent research field. Our comprehensive review concludes with possible strategies for cyberbullying prevention, including personal emotion management, digital ability training, policy applicability, and interpersonal skills. We highlight the important role of parental control in cyberbullying prevention. As for the role of parental control, it depends on whether children believe their parents are capable of adequately supporting them, rather than simply interfering in their lives, restricting their online behavior, and controlling or removing their devices ( 50 ). In general, cyberbullying is on the rise, with the effectiveness of interventions to meet this problem still requiring further development and exploration ( 83 ).

Considering the overlaps between cyberbullying and traditional offline bullying, future research can explore the unique risk and protective factors that are distinguishable from traditional bullying ( 86 ). To further reveal the variations, researchers can compare the outcomes of interventions conducted in cyberbullying and traditional bullying preventions simultaneously, and the same interventions only targeting cyberbullying ( 88 ). In addition, cyberbullying also reflects a series of other social issues, such as personal privacy and security, public opinion monitoring, multinational perpetration and group crimes. To address this problem, efforts from multiple disciplines and novel analytical methods in the digital era are required. As the Internet provides enormous opportunities to connect young people from all over the world, cyberbullying perpetrators may come from transnational networks. Hence, cyberbullying of children and adolescents, involving multiple countries, is worth further attention.

Our study has several limitations. First, national representative studies are scarce, while few studies from middle and low income countries were included in our research due to language restrictions. Many of the studies included were conducted in schools, communities, provinces, and cities in high income countries. Meanwhile, our review only focused on victimization and perpetration. Future studies should consider more perspectives, such as bystanders and those with the dual identity of victim/perpetrator, to comprehensively analyze the risk and protective factors of cyberbullying.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author/s.

Author Contributions

SH, CZ, RE, and WZ conceived the study and developed the design. WZ analyzed the result and supervised the study. CZ and SH wrote the first draft. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2021.634909/full#supplementary-material

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Keywords: cyberbullying, children, adolescents, globalization, risk factors, preventive measures

Citation: Zhu C, Huang S, Evans R and Zhang W (2021) Cyberbullying Among Adolescents and Children: A Comprehensive Review of the Global Situation, Risk Factors, and Preventive Measures. Front. Public Health 9:634909. doi: 10.3389/fpubh.2021.634909

Received: 29 November 2020; Accepted: 10 February 2021; Published: 11 March 2021.

Reviewed by:

Copyright © 2021 Zhu, Huang, Evans and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Wei Zhang, weizhanghust@hust.edu.cn

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Research article
• Open access
• Published: 14 December 2021

Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

• Håkan Källmén 1 &
• Mats Hallgren   ORCID: orcid.org/0000-0002-0599-2403 2  

Child and Adolescent Psychiatry and Mental Health volume  15 , Article number:  74 ( 2021 ) Cite this article

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To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

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HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

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Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

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Social media ostracism and creativity: moderating role of emotional intelligence

• Muhammad Waqas Amin 1 &
• Jiuhe Wang 1  

BMC Psychology volume  12 , Article number:  484 ( 2024 ) Cite this article

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The goal of this study is to learn more about social media ostracism, a stressor associated with online social networks, defined by feelings of rejection, exclusion, or ignoring. We investigate the connection between social media ostracism and worker creativity. We suggest that psychological safety and psychological rumination serve as intermediaries in this relationship. Furthermore, we investigate emotional intelligence as a relationship regulator. To verify our hypothesis, we gathered data with the help of the HR department from 244 workers of nine Chinese organizations. Our research shows that psychological rumination and social media exclusion are significantly correlated, but only in workers with low emotional intelligence. Furthermore, for individuals with strong emotional intelligence, we did not discover a statistically negative association between psychological safety and social media exclusion. Findings suggest that psychological safety and psychological rumination serve as mediating factors in the relationship between employee creativity and social media exclusion. This study illuminates the negative aspects of social media ostracism and reveals how it might hinder creativity. It also emphasizes how emotional intelligence functions as a moderator. Organizations may learn a lot from this study on how to lessen the negative impacts of social media exclusion on employee creativity.

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Introduction

Social media usage in the workplace and its outcomes become a hot topic for researchers. Individuals use social media to communicate, interact, share information, and become a part of his/her favorite social communities [ 1 , 2 , 3 ]. Many modern organizations encourage their employees to use enterprise social media (ESM) for knowledge sharing, collaboration, and receiving information from other employees within the organization, considered a primary contributor to the success, competitiveness, and growth of the organization [ 4 , 5 ]. Although social media use has positive effects, Social media is repeatedly exemplified by numerous stressors. That is, scholars investigated cyberbullying and information overload as prominent stressors [ 6 ]. As more individuals engage in online interaction and social media use, the potential for experiencing exclusion in these virtual spaces has increased. Understanding the impact of social media–based ostracism on creativity is crucial, as creativity is a vital driver for innovation, problem-solving, personal growth, and creating robust, novel, and valuable ideas at the workplace [ 7 ]. Employees’ creativity is considered a critical component of innovation, which encompasses both the invention of the latest ideas and their accomplishment [ 8 ]. The existing literature has primarily focused on the general effects of ostracism ( family ostracism, workplace ostracism) on various psychological and behavioral outcomes [ 9 , 10 ]. Understanding the connection between SMO and employees’ creativity is supremely significant in the digital age. However, the impact of SMO on employees’ psychology and cognitive approach has been overlooked. This represents a significant research gap that warrants further investigation. Predominantly, we focus on the role of social media stressors (SMO) on employee’s behavior and creativity. The prominence of SMO relates in a way like, that organization must realize, how it can help or hinder creativity.

Research has repeatedly backed the idea that social media stressors have a detrimental effect on users. Organizations appear to be experiencing a revival in ostracism. SMO refers to the act of excluding someone in a social setting [ 11 ]. It can lead to feelings of social rejection and loneliness. Ostracism, however, is thought to be a phenomenon that exists everywhere and in a variety of circumstances [ 12 ]. As a result, previous research has revealed that Internet users experience cyber-ostracism. However, given that ESM can be used as a tool for employees to collaborate on projects, share information, and ask questions, thereby fostering teamwork and innovation [ 5 , 13 , 14 ]. Employee perception of social media usage can be influenced by SMO. If an employee frequently experiences exclusion or negativity on social media, it can affect their self-esteem, overall job satisfaction, and perception of their employer or colleagues. They may feel isolated, unappreciated, or less motivated in their work. This can have negative effects on individuals’ psychological well-being, including a decrease in self-esteem and an increase in negative emotions. Therefore, we concentrate on SMO and also define it, as the degree to which social media users think that they are excluded, redundant, or ignored by their social media friends. Yet, scarce studies have examined the linkage between SMO and employees’ creativity. Unfortunately, this study gap exists given the importance of stressors related to ESM in the workplace [ 15 ]. The mechanism by which SMO hinders employees’ capacity for creativity remains undermined. Hence, scholars urged for additional research on social media-related stressors, as well as their impact associated with the workplace [ 14 ]. Hence, an investigation of the link between SMO and employee creativity will aid in the foundation of managerial implications as well as the growth of theory.

Our study deals with the issue of social media stressor namely SMO. We squabble that different types of social media stressors have different effects on employees’ behavior. In the notion of ego depletion theory, this study is to examine the mechanism involving between SMO and creativity. Ego depletion theory according to [ 16 ] “a temporary reduction in the self’s capacity to engage in volitional action caused by the prior exercise of volition” (p. 1235). Ego depletion was first introduced by social psychology. Numerous domains, including personality, consumer behavior, cognitive psychology, decision-making, and organizational behavior, continue to apply and explore them [ 17 , 18 , 19 ]. It describes the circumstance in which an individual performs worse on self-control tasks after having completed a self-control-requiring task in the past. According to the theory, future regulatory efforts becoming more and more challenging as resources are depleted. As a result, the more regulatory duties individuals must undertake throughout the day, the more tricky it is for them to retain effort, tenacity, and, eventually, appropriate performance on a variety of jobs. Moreover, such depleted individuals are also more inclined to deceive and misread organizational performance, mislead and undermine coworkers, and be vocally aggressive toward colleagues, supervisors, and subordinates [ 20 ]. Hence our research projected that the exhibition of SMO provides a foundation for psychological cost, like, psychological safety and psychological rumination, which limits employees to present creativity in their job tasks.

From the above argument, a potential inconsistency emerges. We further move forward to study such circumstances in which SMO is interconnected with psychological safety, psychological rumination, and creativity. The value of resource depletion is not the same for all individuals [ 20 ]. However, this condition may not apply to all individuals. People do have diverse personalities, and depending on the circumstances, they act in different ways. So, a person’s reaction to stress is determined by their traits. Although depilation of individual resources due to SMO always prevails, individuals having high emotional intelligence may stay away from the adverse effect of recourse depletion. Emotional intelligence comprises the ability to understand and manage emotions effectively and the flexibility to acclimatize unpredicted situations [ 21 ]. Individuals with higher emotional intelligence may be better equipped to cope with the negative emotions associated with social media stressors such as SMO [ 11 ] and are expected to tolerate a smaller amount of resource loss when dealing with SMO., in turn, we propose that emotional intelligence can moderate the significant affect of SMO on psychological rumination, psychological safety, and indirect negative impact of SMO on employees creativity passing through psychological rumination and psychological safety and allowing individuals to maintain their creativity levels.

The ongoing study aims to examine the relationship among social media stressors such as (SMO), psychological states including (psychological rumination, and psychological safety), and creativity through a survey of Chinese employees, contributing to the existing literature in various important ways. Firstly, adding to on-hand literature on ESM and social media stressors by incorporating it with ostracism literature. Secondly, our research is the primary effort to look at how social media stressors—specifically, SMO—affect employees’ creativity. In this approach, we help widen the scope of social media stressors and their effect on workers’ creativity. Thirdly, we integrate SMO with ego depletion theory. By doing this, we acquire tools such as the depletion of resources (self-control) and identify psychological safety and rumination as a mechanism through which SMO is allied to creativity. Fourthly, we implement a contingency view and introduce emotional intelligence as a significant edge on SMO. Finally, this study adds to the literature on ego depletion theory by incorporating and experimentally assessing its function in propagating the blow of SMO on creativity.

The following segment of the study begins with a review of pertinent literature. Following that, three mediating and two moderating assumptions are provided. Section  3 is highly structured on our study technique, including data gathering methodology, and sample demographic information, in addition to constructing operationalization. The findings of the data analysis are presented in Sect.  4 . Section  5 reviews the findings, gives theoretical and practical suggestions, and defines the study’s limitations. Section  6 concludes this research.

Theoretical background and hypothesis development

• Social media ostracism

Ostracism refers to the act of excluding an individual or a group from social acceptance, inclusion, or participation [ 22 ]. Researchers argued that ostracism commonly prevails in social settings including social media [ 11 ]. Social media is used to connect, interact, share ideas, contact, and knowledge sharing among employees [ 23 , 24 ]. When coworkers on social media platforms shun others by not responding to their messages, neither seeing nor appraising and giving likes to their postings, or acting as, if a person is not a member of their organization’s social media network, it can cause SMO for employees.

Ostracism tends to in result adverse consequences as it causes a sense of " social pain” [ 25 ]. The theoretical background of ostracism can be traced to various psychological theories and frameworks. One prominent theory is the human need theory, which posits that humans have basic psychological needs necessary for their well-being and functioning. According to this theory, developed by [ 26 ], the need for belongingness and social acceptance is fundamental to human nature. Ostracism on social media platforms can often take the form of cyberbullying [ 27 ]. When individuals are deliberately excluded from online activities or targeted by negative comments, and messages, they can experience emotional distress and harm to their self-esteem. It is very imperative to note that while social media can be a platform for ostracism [ 11 ], it can also serve as a tool to address and combat it. By promoting positive online behavior, encouraging empathy and inclusivity, and actively working on creating safe and supportive digital spaces, individuals and platforms can diminish the pessimistic impact of ostracism on social media. SMO refers to the act of shunning individuals within an online platform [ 11 , 28 ]. This can happen through various means, including blocking, or excluding someone from conversations, groups, or events. SMO can have significant effects on individuals’ well-being and sense of social belonging. Given the nature of ostracism, we discover numerous aspects of SMO. Like, Due to their hectic schedules, social media users generally don’t reply to other users’ comments. Irrelevant connections could be the cause of social media exclusion. That instance, if employees post about hedonistic events on ESM, other users are likely to ignore the posts. A person’s sense of connection to their connections is jeopardized by being ignored. Hence, SMO is a complex phenomenon that people might encounter and it can be challenging to grasp how it starts and ends, thus it is a part of everyday life for social media users. Employees try to build and sustain interpersonal connections with their colleagues on social media to get the maximum payback of social media for job performance [ 29 , 30 ], and fulfillment of their social needs projected by social media customers [ 31 ]. Consequently, ostracism from social media contacts leads to a disruption in the individual’s needs, thoughts, and emotions, triggering various cognitive and emotional reactions. However, the intuition of SMO is not essentially to ruin workers [ 32 ]. It is just an exemption of optimistic commitment in online networks eventually it is apposite in the social media context [ 11 ]. Although SMO is treated as a trifling issue, it has an important effect on ostracized employees’ psychological emotions and cognitive behavior [ 11 ]. In the light of ego depletion theory [ 33 ], for example: targeted personnel, who are perceived to be ignored, avoided, or rejected by other colleagues on social media, are more likely to feel psychological pressure, discomfort, and less work engagement resulting in insignificant creativity. Furthermore, staff use social media to intermingle, and exchange information as well as to communicate with others to fulfill their needs through interpersonal exchange. However, for ostracised employees, the use of social media creates an intimidating environment, resulting in psychological pressure and insignificant creativity.

The majority of research has been done in behavioral labs and has been framed from an “intrinsic motivation” perspective. This viewpoint contends that an individual’s intrinsic motivation is affected by the situation in which they accomplish a task, which in turn affects how creatively they can express themselves [ 34 ]. Additionally, creativity has been categorized into two main dimensions: originality and effectiveness. The former focuses on the novelty and uniqueness of ideas, while the latter assesses their value and utility in addressing a problem or meeting a goal [ 3 , 35 ]. Numerous cognitive abilities have been found to influence creativity, such as high levels of fluency, flexibility, and originality of thinking. The ability to generate a large number of ideas and to think beyond conventional constraints contributes significantly to creative output. Intrinsic motivation, driven by genuine interest and enjoyment, is found to be crucial for initiating and sustaining creative endeavors. Extrinsic motivators, such as rewards and recognition, may also influence creativity, but their effects are more context-dependent. Employees are thought to be most creative when they have a high level of intrinsic motivation—that is when they are passionate about a professional activity and want to engage in it for the sake of the activity itself [ 36 , 37 , 38 ].

Social media provide a platform for social interactions, collaborative environments, and diverse perspectives that can enhance creative thinking by fostering the exchange of ideas, challenging assumptions, sharing knowledge, and promoting interdisciplinary approaches [ 39 ]. Social media used by employees in the organization ( ESM ) is different from public social media(PSM). ESM platforms have become increasingly popular tools for collaboration and communication within organizations. ESM use has been found to positively influence individual creativity [ 40 ]. Studies highlight that these platforms provide opportunities for idea generation, knowledge sharing, and informal discussions, where cognitive resources can be quickly and conveniently accessed [ 41 ]. Collaborative features of these platforms facilitate the exchange of diverse perspectives, enhancing the likelihood of new and innovative ideas. Research also suggests that social interactions on ESM platforms increase intrinsic motivation, helping employees overcome creative blocks and fear of criticism [ 41 , 42 , 43 ]. Employees are bound to join ESM at any cost, while users on PSM are free to use or switch to other platforms. Employees can share hedonic stuff, and information with colleagues on the ESM platform [ 44 , 45 ]. Meanwhile, PSM provides a wide range of social, cognitive, and hedonic activity platforms, to build social associations, sharing photos and videos with social media users [ 46 , 47 ]. Thus, ESM compelled employees to interact with organizational members having competing interests. Hence, conflict of interest my lead employees keep silent on ESM, resulting in not giving a response to any post or message and deliberately not taking part in any routine discussion. Thus, ignoring others may lead to psychological stress, and insecurity and affecting work engagement [ 48 ]. However, the emergence of SMO – the deliberate exclusion or rejection of individuals within these online communities – has raised concerns about its potential impact on one’s creativity [ 49 ]. Employees are less likely to contribute creative ideas in performing their tasks when they experience rejection. To the paramount of our knowledge, none of the studies has been carried out to investigate employees’ creativity using social media stressors. Hence, our motivation behind this unique study is to investigate how employees’ creativity is being affected by social media stressors namely SMO.

Ego depletion theory

Ego depletion theory, also known as self-control theory, was developed by psychologists [ 50 ]. The supposition postulates employee self-control or willpower resources are limited and can easily be depleted with repeated or prolonged use, resulting in a decrease in the employee’s ability to regulate oneself effectively, suggests that when individuals engage in acts of self-control, such as resisting the temptation or suppressing emotions, their self-control reserves become diminished, leading to reduced employees self-control performance in subsequent tasks [ 51 , 52 ]. For instance [ 50 ], found that participants who initially resisted eating tempting cookies performed worse on an unsolvable puzzle task compared to those who did not resist temptation. These findings suggest that self-control depletion affects cognitive resources needed for subsequent self-control tasks. This theory has been extensively studied and researched in the field of psychology [ 53 ].

The concept of ego depletion is rooted in the psychodynamic and cognitive approaches to employees’ behavior. According to [ 54 ] psychoanalytic theory suggests that the self is tranquil of three parts: the id, ego, and superego. The ego, being the rational part, is responsible for self-control and decision-making. Building upon this idea, ego depletion theory proposes that the self-control capacity is a central aspect of employees’ behavior, contributing to the regulation of emotions, impulses, and thoughts [ 50 ]. Explain the foremost efficacy of ego depletion theory: Resources gained from personnel and social activities are well sheltered and appropriate. Personal resources include a sense of self, personal characteristics, and energies. Meanwhile, social resources take account of social support, which comes from communication, relation, and interaction with other groups or individuals [ 55 ]. Individuals make an effort to acquire, hold onto, and preserve their social resources because they are the primary route to acquiring valuable personal resources. According to the ego depletion theory, when there is a possibility of losing resources, people would experience psychological discomfort. An individual who practices ego depletion tends to exhibit cognitive biases, miscalculate their ability, and feel a weakened sense of self-control over the future [ 56 ]. Depleting cognitive resources impairs individual creative performance. Individuals are less successful at reacting promptly when resources (limited) run out, even in an apparent unconnected domain of action. Ego depletion can discourage self-regulation, leading to impulsive decisions and a decline in creative performance. Numerous studies show that depleted individuals cannot carry out daily tasks and respond in a broad range as they would be able to if not depleted. Once depleted, workers’ ability to show self-control or reveal proper behavior based on their limited resources befalls challenging, resulting in inappropriate conduct [ 17 ]. Therefore, individuals under stressful conditions play defensively to protect their emotional, social, and cognitive resources.

In addition, ego depletion theory suggests that the impact of a stressor differs from person to person depending on their attributes and characteristics. Moreover, individuals give more value to their social connections and if they are unable to receive perceived social care, thus, this situation has an unfavorable impact on individuals’ emotional and psychological state. Consequently, social media stressors like SMO have a negative impact on individuals’ psychological, emotional, and cognitive states [ 57 ]. When employees experience ostracism on social media, it can deplete their psychological and cognitive resources. In light of ego depletion, susceptible willpower resources are depleted [ 50 ]. Emotional intelligence may support individuals to trim down the adverse effect of stressors on employees’ psychological discomfort state (rumination and safety) and respectively on employees’ cognitive approach. We argue based on ego depletion theory, that SMO may linked with individual psychological discomfort state (rumination and safety), and affect individual creativity. Meanwhile, the depressing effect of SMO may be moderated by emotional intelligence. Fig.  1 shows the conceptual model.

Social media ostracism, psychological rumination, and psychological safety

Social media within the workplace can be used as a tool for employees to collaborate on projects, share information, and ask questions, thereby fostering teamwork and innovation. Employees can use ESM platforms to connect with colleagues and industry professionals, which can enhance their networking opportunities and career growth prospects [ 40 ]. SMO refers to the exclusion or isolation of individuals on social media platforms. This can be a result of various factors, such as not receiving likes or comments on posts, being ignored by social media groups or conversations, and being targeted by negative or hurtful comments [ 58 ]. Employee perception of social media usage can be influenced by SMO. If an employee experiences frequent exclusion or negativity on ESM, it can affect their self-esteem, overall job satisfaction, and perception of their employer or colleagues. They may feel isolated, unappreciated, or less motivated in their work [ 11 , 59 ]. When individuals experience SMO, such as being excluded from enterprise online social groups or being subject to cyberbullying, the end product can be psychologically struck personally (i.e. psychological rumination, psychological safety). Psychological rumination refers to the repetitive thinking about negative experiences, which can have detrimental effects on mental well-being. Rumination can further intensify feelings of social exclusion and negatively impact overall psychological functioning [ 60 ]. Psychological safety refers to an individual’s perception of safety within a group or social environment, where one feels free to express oneself without fear of negative consequences such as rejection or humiliation [ 61 ].

When someone experiences SMO, it can have a profound impact on their mental and emotional well-being. Being excluded or ignored on social media may trigger feelings of sadness, loneliness, and low self-esteem [ 11 , 62 ]. These negative emotions can then lead to psychological rumination and a decrease in psychological safety, where individuals excessively think about the event, replaying it in their minds and analyzing what they might have done wrong. This means that individuals who experience more frequent instances of SMO are more likely to engage in greater rumination and decrease psychological safety. The constant exposure to social media interactions, where exclusion or negative feedback is prevalent, may contribute to a heightened focus on negative experiences, leading to an increase in psychological rumination and a decrease in psychological safety. When individuals repeatedly encounter ostracism on social media, it may erode their sense of belonging, acceptance, and value in their online communities. This can result in heightened feelings of insecurity, anxiety, and a lower sense of psychological safety. The fear of further SMO may prevent individuals from freely expressing themselves or fully engaging in online enterprise social interactions [ 63 ], limiting their participation and potentially negatively impacting their mental health [ 64 ], it may lead to a decrease in their level of psychological safety. Humans have a fundamental need for social belonging and inclusion. According to ego depletion theory [ 65 ], When individuals are excluded on social media platforms, it can lead to negative psychological consequences including feelings of sadness, anger, loss of self-control, and lower self-esteem decrease in psychological safety. Hence, SMO have psychological effect on employees. Thus, we propose that.

Social media ostracism is positively associated with the psychological rumination of social media users.

Social media ostracism is negatively associated with the psychological safety of social media users.

Linking psychological rumination, psychological safety to creativity

SMO spoils individuals’ valuable psychological and self-control resources, thereby rooting psychological rumination and decreasing psychological safety. Ego depletion theory predicts employees whose self-control and self-regulatory resources have been depleted, lead to inappropriate behavior [ 66 ], self-control requires mental effort. When individual employees’ limited self-control resources are depleting they get psychological pressure. Accordingly, this study argues that psychological rumination and a decrease in psychological safety caused by SMO affect employees’ creativity.

According to [ 67 ], Creativity is stated as providing novel, valuable ideas and solutions for any problem. It is influenced by various factors such as psychological processes and the external environment. Furthermore, cognitive processes involve creative thinking. Hence, various mental processes (i.e. problem framing, analogical reasoning, flexibility, and divergent thinking) arise in creativity [ 35 , 68 ]. Moreover, social factors such as cultural norms, social interactions, and environmental conditions (organizational environment) foster creativity. Creative ideas emerge through employee interactions, brainstorming, collaborations, and exposure to diverse perspectives and ideas [ 8 ]. Consequently, it is noted that creativity is an emergent property of complex systems, that arises from interaction of different social, environmental, and cognitive factors. When an individual is psychologically, mentally, socially, and environmentally stable, can be a creative thinker and generate novel ideas, and solutions for problems. In this current perspective, we use ego-depletion theory to enlighten employees’ creativity.

Employees’ psychological and emotional resources are affected by SMO, thereby stimulating psychological rumination and affecting individuals’ psychological safety. Ego depletion theory indicates that individuals whose resources are depleted are less likely to feel confident, relaxed, and happy [ 69 ] therefore, Employees always have negative thoughts and insecurity. Hence, individuals who engage in persistent rumination are more likely to experience cognitive and emotional burdens that could hinder their creative abilities. The excessive focus on negative thoughts and emotions may restrict cognitive flexibility. Furthermore, when individuals feel psychologically safe, they are expected to take risks, share ideas, and think innovatively. Thus, SMO drags employees into a depressing psychological and emotional situation, thereby messing up their cognitive creative approach. Hence we proposed the following hypothesis.

Psychological rumination is negatively associated with the creativity of social media users.

Psychological safety is positively associated with the creativity of social media users.

Psychological rumination and psychological safety will mediate the association between social media ostracism and employees’ creativity.

Moderating role of emotional intelligence

Emotional intelligence is the aptitude to identify, understand, and deal with one’s own emotions as well as the emotions of others [ 21 ]. It is crucial in regulating individuals’ responses to various social situations [ 70 ] such as SMO. Individuals with higher levels of emotional intelligence may be better equipped to regulate their emotions and engage in adaptive coping strategies when faced with negative thoughts and emotions. Emotional intelligence influences how individuals greet, and consider any event that takes place in their routine lives [ 71 ]. This includes staying calm under pressure, managing stress, and handling conflicts or difficult conversations with emotional maturity. Conversely, individuals having weak emotional intelligence will bear high pressure.

According to ego depletion theory, the depletion of self-control, willpower, and decision-making resources differs from person to person [ 72 ]. Individuals having different traits, respond differently to their loss of limited recourses [ 11 ]. However, in our context Employee suffering from SMO may identify different degrees of psychological rumination and psychological safety because of their different level of emotional intelligence [ 73 ]. Therefore SMO plays a vital role in diminishing employees ' control over resource loss, employees who have high emotional intelligence are less likely to welcome ostracism effects or immediate recovery from resource loss. However, workers with high emotional intelligence are well equipped to handle the adverse effects of SMO, are more likely to prevent themselves from leading to rumination, and maintain their sense of psychological safety. On the other hand, workers with low levels of emotional intelligence are more susceptible to the negative shock of SMO. Indeed, emotional intelligence overcomes stressful conditions like SMO [ 74 ].

Based on a previous study, employees face negative behavior and events on social media [ 14 ]. Besides personality traits, every social media user faces psychological frustration encountered by SMO, including stressful situations and negative emotions [ 75 ]. However, individuals with higher levels of emotional intelligence can be better equipped to regulate their emotions and engage in adaptive coping strategies when faced with negative emotions and stressful situations [ 74 ]. As a result, they are more resilient to the harmful effects of rumination and psychological well-being. For that reason, when individuals have strong self-control over emotions, a sense of psychological frustration cannot lead to psychological rumination and a decrease in psychological safety. Based on ego depletion theory this study argues that emotional intelligence enhances workers’ capability to deal with SMO. Consequently, we assert that individuals with high emotional intelligence are better prepared to navigate and minimize the negative effects of SMO. Moreover, experience low psychological frustration, thereby decreasing the adverse consequence of SMO on psychological rumination and psychological safety. So, we proposed.

Emotional intelligence moderates the positive association between social media ostracism and psychological rumination, such that when the emotional intelligence level is high, the positive relation is weaker and when the emotional intelligence level is low, the positive relation becomes strong.

Emotional intelligence moderates the negative relationship between SMO and psychological safety, such that when the emotional intelligence level is high, the negative relation is weaker and when the emotional intelligence level is low, the negative relation becomes strong.

Conceptual and hypothesized model

Methodology

Sample and data collection.

To evaluate our hypothesis for this study, we created a questionnaire. We took up an online survey methodology to achieve a maximum and diverse sample response rate. China is rich in using social media so we conducted this study in China. According to a foremost professional consulting company, Towers Watson’s report states ESM is widely used by Chinese employees, accordingly 49% of respondents claimed ESM provides a platform to establish a sense of belonging, interaction, and communication because of its cost-effectiveness [ 76 ].

We further collaborated and contacted several companies’ HR managers. The backbone of every company is the HR department and HR managers are much more familiar with the company’s routines, and policies and also have employees’ personal and performance records. Initially, we chose 15 companies and explained our research purpose (i.e. creative performance of employees using ESM ). As such, among these 15 companies, 9 companies are willing to participate in our research work. Further, we made a promise to share the final report of our research and provide them with suggestions once we finished our research. We further, conducted random interviews of several selected companies’ employees before distributing the questionnaire. The purpose of the interview is to investigate whether or not employees of these companies utilize social media to interact and converse with their coworkers. Therefore, it is been confirmed that employees employ social media to be in touch with both internal employees and external customers.

We designed and generated an online survey link and distributed it to the targeted sample companies’ workers with the help of the HR department. HR department’s collaborative attitude generally increased the response rate. After three weeks, 301 responses were received, out of them 57 responses were incomplete and inaccurate so, discarded, finally we got 244 useful responses. By employing a chi-square test to match the first and last 25% of respondents on all variables, we were able to evaluate potential non-response bias [ 77 ]. No significant difference has been reported, signifying that non-response bias for this research was not a serious concern. Nevertheless, it’s very important to note that the cooperative behavior of the HR department may have direct to optimistic bias towards social media use amongst respondents. To address this issue we took three initiatives. Firstly, we established our study ambition with HR executives, as we would like them to understand our intention for this study was to find out whether and how social media usage manipulates employees’ creativity, rather than give support to the significant effects of social media. All of them agreed and verified similar goals. Secondly, managers only helped to regulate our online survey link within the organization and identified it as a university research project. Thirdly, we mentioned at the start instant of a questionnaire for better understanding that this questionnaire was established to familiarize how ESM users perform creatively. Furthermore, the secrecy of this survey was highlighted. Table  1 shows the sample’s demographic information.

All of the construct items were taken and measured from the existing literature to increase the reliability of empirical findings [ 78 , 79 , 80 , 81 , 82 , 83 ]. All the respondents of this study were Chinese, so to cope with the language barrier we were required to translate our English questionnaire into Chinese. Finally, we requested two Chinese native speakers, proficient in English, but they are not a part of our study but to help us translate our English questionnaire into Chinese language. To check the accuracy of the translation we further invited two experts, unfamiliar with the original English version questionnaire and they translated the questionnaire back into English. No systematic error was found between the original and English-translated versions. Hence, we considered the Chinese questionnaire an accurate reflection of the original English questionnaire to test our constructs. Furthermore, for a better understanding of respondents, all the possible descriptions of constructs were briefly explained in the questionnaire. We used a 5-point Likert scale arranged from 1 " strongly agree " to 7 " strongly disagree " to measure all items, except one item used a 4-point Likert scale arranged from 1 “never” to 4 “always”.

Measurement items for SMO were adopted by the ostracism scale established by [ 11 ]. Scholars already adopted this scale in the Chinese context [ 11 , 84 ]. The scale was initially designed to measure ostracism at the workplace, this current study demands to modify workplace ostracism items in a social media context. Respondent rated ten items under SMO. A psychological safety measurement five-item scale was adopted from [ 79 ]. We modified the items by just changing “team” to “organization”. Psychological rumination was measured by a 5-item Ruminative response scale. It is a rating scale developed to evaluate people’s behavior and thoughts when they are depressed [ 82 , 83 ]. To measure emotional intelligence, measurement sixteen-item items scale was adopted from [ 85 ]. Four-item scale from [ 81 ] was used to measure employees’ creativity. The related literature for survey items is précised in Appendix A . Several variables have been controlled that can affect this study’s results. This is followed by previous social media studies to control various variables that are demographic traits (i.e. age, gender, and education ) [ 11 , 86 , 87 ], usage frequency (i.e. hourly, daily, or weekly ) [ 87 ], users familiarity and several friends also affects employees behavior ( below 5 years, 5–10 years, 11–15 years, 16 years or above) [ 87 ], several contacts on ESM ( under 100, 101–200, 201–300, 301–400, 401 or above) [ 88 ], Nature of job may impact on workers social media usage, for instance, accounting professional use less social media than sales and marketing professionals [ 89 ].

Data analysis and results

Common method bias.

The entire data collected for the in-progress study is self-reported, resulting there might be a chance for common method bias. Checking the possible severity of common method bias we used several methods. First, Harman’s single-factor test was carried out. Results showed that common method bias was not an issue for our data, neither a single nor a general factor reports the variance < 50%. Secondly, confirmatory factor analysis pointed out that five-factor model suggests better fit [(X2 (714) = 1552.921, Tucker–Lewis index (TLI) = 0.94, comparative fit index (CFI) = 0.94, and root-mean-square error of approximation (RMSEA) = 0.07)] than a single factor (X2 (724) = 5392.758, TLI = 0.65, CFI = 0.68, and RMSEA = 0.16). These results indicate that no possibility of common method bias was found in our data. Third, significant moderating effects of emotional intelligence, indicated that data is free from common method bias. Collectively, the result shows that in our data common method bias is not a significant issue and also strengthens the legality of our findings.

Measurement model

The scale’s convergent validity, discriminant validity, and reliability were assessed using confirmatory factor analysis. The satisfactory fit between the measurement model and date base was declared in the CFA result (χ2 = 1552.921, d.f. = 714, TLI = 0.94, CFI = 0.94, and RMSEA = 0.07). Table  2 shows that all the item loading is higher than the suggested benchmark of 0.50. To test the convergent validity and composite reliability of constructs, Cronbach’s alpha and average variance reliability (AVE) are been assessed. The table shows that the values of composite reliability and Cronbach alpha for each construct are higher than the benchmark value of 0.70 and the AVE score is also ranged from 0.57 to 0.81. Thus, the result indicates that the convergent validity is satisfactory.

Discriminant validity is appraised by comparing the square root AVE and correlation for each construct [ 1 , 90 ]. Discriminant validity requirements were also satisfactory. Table  2 illustrates that the square root of AVE is greater than the correlation between each construct. Therefore, the measurement model is obsessed with adequate reliability, convergent validity and discriminate validity.

We further conducted additional tests followed by previous studies [ 40 , 76 ] to address the potential effect of multicollinearity among all the constructs. In this test, we found the value of variance inflation factor (VIF) range between 1.10 and 1.21 is less than the threshold of 5 [ 91 ] there is no significant concern of multicollinearity in our dataset.

Structural model

The tables illustrate the outcome of regression analysis, conducted to test our hypothesis. Our research suggests that SMO, psychological rumination, psychological safety, and emotional intelligence have a significant impact on employees’ creativity.

To analyze hypothesis relationships, we employed the AMOS version 24. The findings presented in Table  3 reveal the results of the hypothesis testing. Both Hypothesis 1 and Hypothesis 2 posit that SMO has a favorable impact on psychological rumination and negativity about the psychological safety experienced by users of social media. The outcomes displayed in Table  3 confirm these hypotheses, as they demonstrate a positive correlation between SMO and psychological rumination among social media users (β = 0.19, P  < 0.001). Furthermore, our Hypothesis 2 is also sustained, indicating a negative association between SMO and the psychological safety of social media users (β = -0.21, P  < 0.001). Consequently, both Hypothesis 1 and Hypothesis 2 are validated. In addition, the results also uphold Hypothesis 3 and Hypothesis 4, showing a negative relationship between psychological rumination and creativity (β = -0.18, P  < 0.001)., as well as a positive connection between psychological safety and creativity among social media users (β = 0.24, P  < 0.001).

Table  4 shows, that Hypothesis 5 suggests that the link between SMO and employee creativity is mediated by psychological rumination and psychological safety. According to Baron and Kenny [ 92 ] following conditions must be fulfilled for mediation. First, the Mediator must be significantly affected by the independent variable. Second, the significant effect of the independent variable must be visible on the dependent variable. Third, the influence of the mediator on dependent variables must be significant. When all three conditions are fulfilled, the independent variable has less effect on the dependent variable in the presence of a mediator. The findings demonstrate significant relation between SMO and psychological rumination and psychological safety respectively (β = 0.19, P  < 0.001, β = -0.21, P  < 0.001). therefore, the first condition of mediation is fulfilled. SMO has a significant relation with employee creativity which fulfills the second condition (β = -0.16, P  < 0.001). Psychological rumination and psychological safety significantly affect employee creativity (β = -0.20, P  < 0.001, β = 0.22, P  < 0.001), thereby supporting the third condition. The results indicate that psychological rumination and psychological safety partially mediate the link between SMO and employee creativity. Consequently, Hypothesis 5 is confirmed.

According to Hypothesis 6 & 7, the presence of emotional intelligence influences the connection between SMO and psychological rumination, as well as psychological safety. Specifically, with a high level of emotional intelligence, this relationship is weakened, while at a low level of emotional intelligence, it becomes stronger. This hypothesis is supported by the significant interaction term between SMO and emotional intelligence (β = -0.12, P  < 0.01 & β = 0.11, P  < 0.05) respectively.

To provide additional support for the moderation of hypotheses 6 and 7, the outcome of the post-hoc analysis following the approach outlined by [ 93 ] exposes the following: employees’ emotional intelligence, dampen the positive relationship between SMO psychological rumination of social media users (effect = 0.43, SE = 0.08, [0.26, 0.60], effect = 0.17, SE = 0.06, [0.03, 0.30]). Conversely, when social media users possess a high level of emotional intelligence, the negative relationship between SMO and psychological safety is relatively weak then those having low emotional intelligence level (effect = -0.39, SE = 0.09, [-0.57, -0.21], effect = -0.17, SE = 0.07, [-0.31, -0.03]). These findings are visually depicted in Fig.  2 and Fig. 3 , reinforcing the post-hoc analysis’s contribution to providing auxiliary support for Hypotheses 6 and 7. Fig. 4 also shows hypothesis results.

 Interactive effect of SMO and emotional intelligence on psychological rumination

 Interactive effect of SMO and emotional intelligence on psychological safety

Results of AMOS analysis

Discussion, implications, and limitations

The study attempts to find the effect of social media ostracism on employees’ creative performance, considering the mediating role of psychological rumination and psychological safety, and the moderating role of emotional intelligence with the support of ego depletion theory. We aspire to identify the narrative perspective of SMO and explore its effect on employees’ creativity. SMO decreases employees’ interaction with other employees and exerts psychological pressure and insecurity. Based on ego depletion theory, our study presents psychological rumination and psychological safety as mediators between SMO and creativity. Findings indicate that SMO depletes self-regulatory resources, leading to psychological rumination and psychological safety which impairs individual ability to perform creatively. It significantly affects organizations and social media users’ psychological condition. The findings of this study indicate that such critical feelings of ostracism enhance employees’ psychological rumination and decrease their psychological safety, thereby impacting their creativity. The novel aspect of this study lies in its timely approach to examining the complex relationship between SMO, employee psychology, and their creative performance. Furthermore, the ongoing research proposes that emotional intelligence in this relationship is particularly insightful. Previous studies have primarily investigated the effect of general social ostracism on individual psychology and performance [ 94 , 95 ]. This study contributes to covering the existing gaps by groping the specific consequences of SMO on creativity, providing appreciated knowledge for organizations in managing the challenges associated with online interactions.

Theoretical implication

Numerous theoretical support for social media literature is a part of this study. This research significantly contributes to the existing literature on social media by addressing a research gap related to the understanding of stressors in the context of social media. While previous studies have predominantly concentrated on exploring the various stressors in social media and their effects on workers and students [ 96 , 97 ], the important stressor of ostracism has often been overlooked, with only a few studies considering its influence. However, it has been argued that exclusion is a prevalent phenomenon in various social settings, together with the realm of social media. Therefore, our research provides a platform for understanding social media stressors by specifically examining the impact of SMO on organizational creativity.

Our study builds upon earlier research that has explored the adverse effects of social media stressors by specifically investigating the impact of SMO on employee creativity. While existing literature has identified various stressors in the realm of social media [ 98 , 99 ], it has often overlooked the crucial stressor of ostracism and its potential implications for employee attitudes and cognitive behavior. Particularly within the organizational context, the implications of SMO have been disregarded. By addressing these gaps, our research contributes to the expanding body of study on social media stressors and broadens the understanding of its consequences on employee creativity. By examining the experiences of employees who feel excluded, ignored, or rejected by their coworkers on ESM platforms, we uncover valuable empirical insights. These findings reveal that SMO prompts employees to adopt passive behaviors, leading them to refrain from sharing information, share ideas, contribute to team projects contributing content, and finally, they are less likely to actively participate in discussions. This lack of collaboration can impede the flow of information, limit knowledge sharing, and hinder problem-solving efforts.

This study builds on the framework of ego depletion theory [ 33 ] and identifies and confirms a significant pathway through which SMO influences cognitive outcomes, specifically concerning employees’ creativity. The research findings highlight two key factors, namely psychological rumination and psychological safety, as mediating variables in this process. The study reveals that SMO impacts employees’ creativity by depleting their self-control and well-being resources, as suggested by ego-depletion theory. This depletion of resources subsequently leads to increased psychological rumination and a decrease in psychological safety. Consequently, employees are less willing to engage in cognitive activities such as sharing ideas and actively participating in discussions. The outcomes of our study provide valuable empirical evidence that supports the mediation of psychological rumination and psychological safety in linking SMO to employees’ creativity. By establishing this link, our research contributes a fresh perspective on understanding the influence of SMO on employees’ creativity.

In our study, we not only identify a critical moderator that alleviates the adverse effects of SMO but also provide empirical evidence to support our findings. Furthermore, we found that individuals with high emotional intelligence are less likely to perceive SMO and experience fewer resource losses. This reduced impact on resource loss, in turn, diminishes the direct influence of SMO on psychological rumination and psychological safety, ultimately leading to a lesser impact on employees’ creativity. By investigating the moderating role of emotional intelligence, our study contributes to the existing literature by expanding our understanding of the moderating conditions that mitigate the effects of SMO on social media users. These findings shed light on the complex dynamics and provide deeper insights into the influence of SMO on individuals’ psychological and cognitive behavior.

Our study makes a valuable contribution to the existing framework of ego depletion theory. This theory has been widely employed in organizational behavior research to elucidate the impact of workplace-related factors on individual resources, and their subsequent influence on employees’ behavior and work performance [ 55 , 69 , 100 , 101 , 102 ]. However, we extended prior research on ego depletion theory by demonstrating that resource depletion is a crucial factor not only in traditional workplace settings but also among information system users [ 101 ]. Our findings indicate that self-regulatory resource depletion plays a significant role in the relationship between SMO and employees’ creativity. In other words, when resources are depleted, SMO negatively affects employees’ creative output.

With the implementation of a moderated mediation model, our study provides empirical evidence that aligns with the core principles of ego depletion theory. These findings strengthen the inclusivity of this theory and highlight its relevance in comprehending the dynamics of SMO and its impact on employees’ creativity.

Managerial implication

This section of our study provides several practical implications for practitioners. The findings of our study expose that SMO is a key factor for employees to enthusiastically intermingle, make conversation, and share information using social media. Unluckily, this part has been neglected in the prior literature, in that way, managers are not much more aware of this situation that this social media stressor can diminish the value of social media acceptance in organizations. Particularly organizations approve social media (ESM) to communicate, interact, and share knowledge with internal and external members [ 5 , 23 , 41 ], our results drew attention toward the importance of SMO to enlighten its impact on employees’ psychological well-being and creativity. Hence, we advocate that organizations should flourish strategies to evade SMO by workers. A prior study recommended a series of trainings for social media users [ 103 ] and escalating workers’ face-to-face interaction to enlighten their understanding of social media usage [ 104 , 105 ]. This practice is expected to hold back workers from SMO, thus normally mounting the efficacy of ESM, and ultimately increasing their creativity. Based on our findings workers who experience SMO are likely to encounter psychological pressure, consequentially declining their cognitive capability.

Results of our research also point out that employees who perceive SMO are likely to encounter a shrinking of their self-control and willpower resources, resulting in psychological rumination and decreasing their psychological safety, consequently affecting their creativity. Given our result of the mediating effect of psychological rumination and psychological safety linking SMO with employees’ creativity, we advocate that organizations reduce psychological rumination and enhance psychological safety. One possible suggestion for organizations is to create a general atmosphere, offer supplementary social resources, and provide opportunities to individuals with high emotional intelligence to help workers achieve the required job resources.

In addition, our results disclose that workers with high-level emotional intelligence are less susceptible to SMO. Our results recommend that the HR department consider emotional intelligence in hiring procedures to facilitate employees handling stressors. Therefore, we auxiliary recommend that organizations consider interference during employment and work (e.g. enrollment tests, training programs, and social support).

Limitations

It is important to acknowledge the limitations of this study, as they provide opportunities for future research. Firstly, our data collection was conducted exclusively in China. While China serves as a representative example of an emerging economy, it possesses distinct cultural, behavioral, and value-related characteristics that may introduce biases to our findings. For instance, Chinese organizations often rely heavily on personal relationships for task completion, which may differ from Western contexts in terms of instrumental value. Consequently, the impact of ostracism may be more obvious in the Chinese context. Therefore, it is recommended that future studies apply the conceptual framework utilized in this study to other countries with diverse economic, political, and cultural environments to examine the generalizability of our findings.

Second, one important limitation of our study is the potential for common method bias due to the data collection method. We acknowledge that collecting data from the same source for all variables can introduce the possibility of common method bias [ 106 ]. Addressing this concern, we employed multiple methods and conducted rigorous tests to comprehensively evaluate the effect of common method bias in our study. We aimed to mitigate the impact of common method bias on our results. In summary, while we made efforts to address common method bias, the data collection method used in this study has limitations. Future research should employ alternative data collection methods to further mitigate the potential influence of common method bias and enhance the generalizability of the findings. For instance, gathering data from colleagues, subordinates, and co-workers in addition to the original data source would be advantageous when examining creativity.

Third, future research can also investigate the role of individual-level variables, such as personality traits or cultural orientations, in moderating the effects of SMO. Understanding how individual differences interact with contextual factors can enhance our understanding of the complex dynamics involved in SMO.

Lastly, another important limitation to consider is that while our theoretical model suggests that SMO is associated with creative performance through psychological rumination and psychological safety, there may be alternative theoretical mechanisms that explain this relationship. For instance, previous studies have highlighted the concept of social media stressors leading to social media fatigue and anxiety [ 11 , 107 ]. Therefore, it is recommended that future studies explore and examine our theoretical model using alternative variables and mechanisms.

This study investigates how SMO influences employees’ creativity. The mediating role of psychological rumination and psychological safety were investigated based on ego depletion theory. All hypotheses were supported, showing that the psychological condition of employees is very important in transmitting the consequence of SMO on employees’ creativity. Furthermore, the moderation effect of emotional intelligence plays a vital role in handling social media stressors and ultimately leads to employees’ creativity. This study adds value to SMO literature by integrating ego-depletion theory, using the mediation moderation model to discuss the outcome of SMO on employees’ creativity. Additionally, managers are advised to check employees’ psychological conditions and train them to handle stressors that would not affect on their creativity.

Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author Muhammad Waqas Amin. The data are not publicly available because it contains information that could compromise the privacy of the research participants.

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Amin, M.W., Wang, J. Social media ostracism and creativity: moderating role of emotional intelligence. BMC Psychol 12 , 484 (2024). https://doi.org/10.1186/s40359-024-01985-2

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Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14.

Preventing Bullying Through Science, Policy, and Practice.

• Hardcopy Version at National Academies Press

2 The Scope of the Problem

Although attention to bullying has increased markedly among researchers, policy makers, and the media since the late 1990s, bullying and cyberbullying research is underdeveloped and uneven. Despite a growing literature on bullying in the United States, a reliable estimate for the number of children who are bullied in the United States today still eludes the field ( Kowalski et al., 2012 ; Olweus, 2013 ). Estimates of bullying prevalence vary greatly, and there is little consensus on the value and accuracy of existing estimates.

This chapter describes the current state of research focused on estimating rates of bullying and cyberbullying in the United States and based on the findings from four major, federally funded, nationally representative samples. The committee considers overall trends in these prevalence estimates, as well as areas of inconsistencies and potential reasons for these discrepancies across the particular studies. The committee also draws upon other large-scale studies to provide insight into various demographic factors—such as gender, age, and ethnicity—as potential risk or protective factors for youth involvement in bullying. Although perceptions and interpretations of communications may be different in digital communities, the committee decided to address cyberbullying within a shared bullying framework rather than treating cyberbullying and traditional bullying as separate entities because there are shared risk factors, shared negative consequences, and interventions that work on both cyberbullying and traditional bullying. However, there are differences between these behaviors that have been noted in previous research, such as different power differentials, different perceptions of communication, and questions of how best to approach the issue of repetition in an online context. These differences suggest that although the Centers for Disease Control and Prevention (CDC) definition, developed in the context of traditional bullying, may not apply in a blanket fashion to cyberbullying, these two forms are not separate species. This chapter offers insights into the complexities and limitations of current estimates and underscores the challenges faced by policy makers, practitioners, advocates, and researchers. 1 Although exact estimates are challenging to identify and require more comprehensive measurement of bullying that addresses the current prevalence research limitations, it is clear that a sizable portion of youth is exposed to bullying.

Perspectives from the Field

“[Bullying is] emotionally, or mentally, or physically putting down someone and it happens everywhere, it never stops.”

—Young adult in a focus group discussing bullying (See Appendix B for additional highlights from interviews.)

• NATIONALLY REPRESENTATIVE STUDIES OF BULLYING IN THE UNITED STATES

Several national surveys provide insight into the prevalence of bullying and cyberbullying in the United States. In this section, the committee focuses specifically on the School Crime Supplement (SCS) of the National Crime Victimization Survey (NCVS), the National School-Based Youth Risk Behavior Survey (YRBS), the Health Behaviour in School-Aged Children (HBSC) survey, and the National Survey of Children's Exposure to Violence (NatSCEV) because their samples of youth are nationally representative and epidemiologically defined. The committee notes that there are a number of methodological differences in the samples and measurement across the four studies. The prevalence of bullying behavior at school ranged from 17.9 percent to 30.9 percent, whereas the prevalence of cyberbullying ranged from 6.9 percent to 14.8 percent of youth ( Centers for Disease Control and Prevention, 2014b ; Finkelhor et al., 2015 ; Iannotti, 2013 ; U.S. Department of Education, 2015 ; see Table 2-1 for a summary of these nationally representative surveys and Appendix C for detailed results from these surveys). The discussion below considers in greater detail the strengths and weaknesses of the methods employed by each of these surveys, in an effort to elucidate factors that may contribute to the variation in reported prevalence rates.

Comparison of Current National Data Sources on Bullying for School-Aged Children and Adolescents.

School Crime Supplement of the National Crime Victimization Survey

The SCS is a national survey of 4,942 students ages 12 through 18 in U.S. public and private elementary, middle, and high schools as well as home-schooled youth ( U.S. Department of Education, 2015 ). Created as a supplement to the NCVS and co-designed by the Department of Education, National Center for Education Statistics, and Bureau of Justice Statistics, the SCS survey collects information about victimization, crime, and safety at school ( U.S. Department of Education, 2015 ). The survey was designed to assist policy makers as well as academic researchers and practitioners at the federal, state, and local levels so they can make informed decisions concerning crime in schools. NCVS crime data come from surveys administered by field representatives to a representative sample of households in the United States throughout the year in person and over the phone ( U.S. Department of Education, 2015 ). 2 In 2015, the SCS administration tested two different ways of asking about bullying to better align with the CDC definition of bullying.

The SCS asked students a number of key questions about their experiences with and perceptions of crime and violence that occurred inside their school, on school grounds, on a school bus, or on the way to or from school. 3 Additional questions not included in the NCVS were added to the SCS, such as students' self-reports of being bullied and perceived rejection at school. This survey's approach to bullying and cyberbullying is far more intensive than the other national surveys; however, it is limited by its focus exclusively on reports of being bullied (being a target of bullying behavior), with no information on perpetration. Additional information is also available regarding differences in rates of being bullied and cyberbullied by student characteristics such as gender, race and ethnicity, school and grade level, school enrollment, geographic region, eligibility for reduced-price lunch, household income, and student-teacher ratio. Other characteristics of the events assessed include whether or not an adult was notified of the bullying incident, injury, frequency of bullying, form of bullying, and location of the bullying ( U.S. Department of Education, 2015 ). The SCS data showed that in 2013, 21.5 percent of students ages 12-18 were bullied on school property and 6.9 percent of students were cyberbullied anywhere ( U.S. Department of Education, 2015 ; see Appendix C , Tables C-1 through C-3 ). 4

Although the SCS provides the most recent and in-depth assessment of bullying and cyberbullying prevalence in the United States, it has several major limitations. The questions about being bullied or cyberbullied are only included in the SCS, a supplement to the NCVS; therefore, its sample size is only a fraction of that of the larger NCVS. 5 The SCS and NCVS data, similar to the other national datasets, are voluntary self-report surveys. These surveys focused on students ages 12-18 and on their experience being bullied; data are not available from younger children and from children who have bullied others or children who have witnessed bullying instances. The survey also fails to address rates of bullying among various subpopulations of youth, such as groups differentiated by their sexual orientation or gender identity, by weight status, or by religious minorities.

School-Based Youth Risk Behavior Survey

The YRBS is one component of the Youth Risk Behavior Surveillance System (YRBSS), an epidemiological surveillance system developed by the CDC to monitor the prevalence of youth behaviors that most influence health ( Centers for Disease Control and Prevention, 2014b ). The YRBS is conducted biennially and focuses on priority health-risk behavior established during youth (grades 9-12) that result in the most significant mortality, morbidity, disability, and social problems during both youth and adulthood. 6 State and local education and health agencies are permitted to supplement the national survey to meet their individual needs.

National YRBS

Bullying and cyberbullying estimates include responses by student characteristics, such as gender, race and ethnicity, grade level, and urbanicity of the school. 7 , 8 The data showed that 19.6 percent of children ages 14-18 were bullied on school property and 14.8 percent of children ages 14-18 were electronically bullied ( Centers for Disease Control and Prevention, 2014b ; see Appendix C , Table C-4 ). The data captured by the national YRBS reflect self-report surveys from students enrolled in grades 9-12 at public or private schools. As with the other nationally representative samples, it does not identify many subpopulations that are at increased risk for bullying such as lesbian, gay, bisexual, and transgender (LGBT) youth and overweight children. The YRBS gathers information from adolescents approximately ages 14-17; but it offers no nationally representative information on younger children ( Centers for Disease Control and Prevention, 2014b ). The survey gathers information on Hispanic, black, and white students but does not identify other races and ethnicities.

State and Local YRBS

The YRBSS is the only surveillance system designed to monitor a wide range of priority health risk behavior among representative samples of high school students at the state and local levels as well as the national level ( Centers for Disease Control and Prevention, 2014b ). 9 There is a smaller sample of middle school youth that is included in various state YRBS results, but national-level estimates are not available. The 2014 CDC report includes state- and local-level surveys conducted by 42 states and 21 large urban school districts. Of the 42 states that conducted their own YRBS survey, 26 asked questions about bullying and cyberbullying. 10 The state-specific results for bullying prevalence ranged from a high of 26.3 percent in Montana to a low of 15.7 percent in Florida ( Centers for Disease Control and Prevention, 2014b ). Whereas this state-level high is relatively similar to the prevalence of 19.6 percent reported by the national YRBS, the state-level low is less than a third of the national prevalence. For cyberbullying, the state results ranged from a high of 20.6 percent in Maine to a low of 11.9 percent in Mississippi. The national YRBS cyberbullying prevalence of 14.8 percent is about in the middle of these extremes ( Centers for Disease Control and Prevention, 2014b ).

At this time, the available state and local data are highly variable due to major limitations caused by self-reports, variable definitions of bullying, and the limited age range of students, making it difficult to gauge differences in bullying prevalence among states and in comparison to national estimates.

The Health Behaviour in School-Aged Children Survey

The HBSC survey is an international study that generally addresses youth well-being, health behavior, and their social context ( Iannotti, 2013 ). This research is conducted in collaboration with the World Health Organization Regional Office for Europe, and the survey is administered every 4 years in 43 countries and regions across Europe and North America. The HBSC survey collects data on a wide range of health behaviors, health indicators, and factors that may influence them. These factors are primarily characteristics of the children themselves, such as their psychological attributes and personal circumstances, and characteristics of their perceived social environment, including their family relationships, peer-group associations, school climate, and perceived socioeconomic status ( Iannotti, 2013 ).

The most recent survey focused solely on the United States was conducted in the 2009-2010 school year. The 2009-2010 HBSC survey included questions about nutrition; physical activity; violence; bullying; relationships with family and friends; perceptions of school as a supportive environment; and use of alcohol, tobacco, marijuana, and other drugs ( Iannotti, 2013 ). 11 , 12 Regarding bullying and cyberbullying, the HBSC asked questions only about the frequency with which children were bullied in the “past couple of months,” with follow-up questions about the frequency of a certain type of bullying a student experienced (called names or teased, left out of things, kicked or pushed, etc.). The survey found that 30.9 percent of children ages 10-16 were bullied at school and 14.8 percent of children ages 10-16 were bullied using a computer or e-mail ( Iannotti, 2013 ; see Appendix C , Tables C-6 and C-7 ). 13 The survey is the only nationally representative survey that asked students how often they bullied another student and the type of bullying they carried out. It found that 31.8 percent of students bullied others and 14.0 percent of students cyberbullied other children ( Iannotti, 2013 ). It is the only national survey that asked students to report on the reason they thought they were bullied (e.g., how often were you bullied for your race/color?; how often were you bullied for your religion?). (For additional detail, see Appendix C , Tables C-6 and C-7 ). Nevertheless, like the other surveys reviewed here, the HBSC survey is limited by the nature of self-reported and voluntary data from minors, as well as by its decision to limit questions only to frequency of incidents.

National Survey of Children's Exposure to Violence

The National Survey of Children's Exposure to Violence II (NatSCEV II) was designed to obtain up-to-date incidence and prevalence estimates for a wide range of childhood victimizations ( Finkelhor et al., 2015 ). The first such assessment, the National Survey of Children's Exposure to Violence I (NatSCEV I), was conducted in 2008. This updated assessment, conducted in 2011, asked students to report on 54 forms of offenses against them. The offenses include sexual assault, child maltreatment, conventional crime, Internet victimization, peer and sibling victimization, witnessing victimization, and indirect victimization ( Finkelhor et al., 2015 ). 14 While this survey asked questions regarding bullying-type incidents, many of the questions referred to the offenses as “assault” rather than bullying, which typically includes a wider scope of victimization. It addressed these offenses by age and gender of the child who was bullied. NatSCEV II found that 17.9 percent of children ages 1 month to age 17 had experienced an assault by a nonsibling peer, 1.8 percent of children had experienced a bias assault, and 6.0 percent experienced Internet/cell phone harassment ( Finkelhor et al., 2015 ; see Appendix C , Table C-5 ). It is not clear whether Internet or cell phone harassment meets the CDC definition of bullying.

Trends over Time

Although attention to bullying and cyberbullying has increased, the extent to which rates of bullying have changed in recent years is unclear ( Figures 2-1 and 2-2 ) ( Kowalski et al., 2012 ; Limber, 2014 ). As illustrated in Figure 2-1 , data from the SCS-NCVS indicate a sharp reduction in the percentage of 12-18 year olds who reported being bullied at school—from 27.8 percent to 21.5 percent in just 2 years ( U.S. Department of Education, 2015 ).

Trends in bullying over time as reported by national surveys. NOTES: HBSC = Health Behaviour in School-Aged Children; NatSCEV = National Survey of Children's Exposure to Violence, NCVS = National Crime Victimization Survey; SCS = School Crime Supplement (more...)

Trends in cyberbullying over time as reported by national surveys. NOTES: HBSC = Health Behaviour in School-Aged Children; NatSCEV = National Survey of Children's Exposure to Violence, NCVS = National Crime Victimization Survey; SCS = School Crime Supplement (more...)

While the YRBS and NatSCEV mirror this decline, neither found so large a change ( Finkelhor et al., 2015 ; Centers for Disease Control and Prevention, 2014b ; see Figure 2-1 ). Findings from the HBSC survey show an increase in bullying among 11-, 13-, and 15-year-old youth in the United States of about 1 percentage point between 2006 and 2010 ( Iannotti, 2013 ). As illustrated in Figure 2-2 , the trend in cyberbullying over time is even less clear. According to the SCS-NCVS data, the percentage of students ages 12-18 who were cyberbullied doubled between 2001 and 2007 but declined by 2 percentage points between 2011 and 2013 ( U.S. Department of Education, 2015 ). 15 While the HBSC survey and the YRBS also showed a decline in the percentage of students who have been cyberbullied, the NatSCEV showed an increase in the percentage of students who experienced Internet and/or cell phone harassment (see Figure 2-2 ).

Because the available national trend data are limited in the range of years for which data are available and because findings vary somewhat among the major national samples, it is difficult to gauge the extent to which bullying may have increased or decreased in recent years. Additional data points will be necessary to determine national trends in the prevalence rates for children and youth who are bullied.

• EXISTING ESTIMATES OF BULLYING IN THE UNITED STATES BY SUBPOPULATION

In an effort to understand the nature and extent of bullying in the United States, some studies have examined specific subpopulations or subsets of children involved in bullying incidents. Because the major national surveys that include bullying do not uniformly or fully address the bullying experience of subpopulations of interest, 16 in this section the committee also draws upon findings from meta-analyses and independent large-scale research. Although these studies are limited by inconsistent definitions, survey data based on self-reports, differing age ranges, and a lack of questions seeking responses from children who have bullied or have witnessed bullying incidents, they do provide valuable insight into particular risk factors or protective factors for involvement in bullying, insights that are generally not available from the surveys of nationally representative samples. The committee expands on risk and protective factors in Chapter 3 .

Prevalence of Bullying by Age

A majority of bullying research has shown that children's experiences with bullying vary significantly according to their age. Decreases with age in rates of being bullied were reported in the SCS.

As reported by Limber (2014) , a meta-analysis by Cook and colleagues (2010) found that the likelihood of both being bullied and perpetrating bullying behavior peaked in the early adolescent years (ages 12-14) before decreasing slightly in later adolescence ( Limber, 2014 ). Decreases with increasing grade level in rates of being bullied were also reported in the SCS-NCVS.

For example, whereas 27.8 percent of sixth graders reported being bullied at school in 2013, 23.0 percent of ninth graders and 14.1 percent of twelfth graders said they had been bullied ( U.S. Department of Education, 2015 ; see Figure 2-3 ). Although these data suggest that the overall chances of being bullied are particularly likely in middle childhood, children are more or less likely to be involved in specific forms of bullying at different ages, depending on their verbal, cognitive, and social development ( Limber, 2014 ).

Prevalence of bullying and cyberbullying among students, ages 12-18, by grade level, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Reports of being bullied through an electronic context appear to peak later than reports of being bullied by a more traditional context; the SCS, for example, reported a peak for cyberbullying in tenth grade ( U.S. Department of Education, 2015 ). According to a 2015 overview of teen's social media and technology use, the Pew Research Center found that 68 percent of teens ages 13-14 had access to a smartphone and 84 percent had access to a desktop or laptop computer, whereas 76 percent of teens ages 15-17 had access to a smartphone and 90 percent had access to a desktop or laptop computer ( Lenhart et al., 2015 ). Today's youth are often referred to as “digital natives” due to their upbringing immersed in technological tools including smartphones and social media, while adults are often referred to as “digital immigrants.” This report found that approximately three-fourths of teens ages 13-17 reported access to a cell phone and 94 percent of teens reported going online daily, including 24 percent who said they go online “almost constantly” ( Lenhart et al., 2015 ). Owning a mobile phone allows for ongoing access to the Internet, including social media and other communication tools that may foster opportunities for bullying. Approximately one-quarter of teens surveyed described themselves as “constantly connected” to the Internet ( Lenhart et al., 2015 ). Among teens 13-17 years old, most reported using several forms of social media including Facebook, Instagram, Snapchat, and Twitter (see Figure 2-4 ). A previous study found that older adolescents viewed Facebook as a powerful source of influence through four major processes: connection to others, comparison with peers, building an online identity, and an immersive multimedia experience ( Moreno et al., 2013 ).

Facebook, Instagram, and Snapchat top social media platforms for teens (n = 1,060 teens ages, 13-17). SOURCE: Adapted from Lenhart (2015, p. 2)

This increasing access to and use of technologies with age may help explain rising rates of cyberbullying as adolescents age. An older study of 10-17 year olds found an “online harassment” prevalence of approximately 9 percent ( Wolak et al., 2007 ). However, a more recent study, which focused on middle school adolescents, found a lower prevalence of cyberbullying: 5 percent reported being a perpetrator of cyberbullying, and 6.6 percent reported being a target of cyberbullying ( Rice et al., 2015 ).

Smith and colleagues (2008) found rates of cyberbullying to be lower than rates of traditional bullying, but appreciable, and reported higher cyberbullying prevalence outside of school than inside. It is possible that reported cyberbullying rates are lower than traditional bullying rates because much of technology use occurs outside of school and current approaches to measuring bullying are designed mostly to assess rates of traditional bullying in school ( Smith et al., 2008 ). Previous work has suggested that increased Internet use is associated with increased risk for cyberbullying ( Juvonen and Gross, 2008 ).

Although research has suggested that the prevalence of bullying among older adolescents is lower than that of younger adolescents, researchers have proposed that cyberbullying among older students may represent a continuation of behaviors from previous grades but with a focus on technological tools for more subtle bullying techniques ( Cowie et al., 2013 ).

Prevalence of Bullying by Gender

Research has confirmed that there are gender differences in the frequency with which children and youth are involved in bullying. A recent meta-analysis found that although boys and girls experienced relatively similar rates of being bullied, boys were more likely to bully others, or to bully others and be bullied, than girls were ( Cook et al., 2010 ; Limber, 2014 ). Research has suggested that there are gender differences in the frequency with which children and youth are involved in bullying. The SCS, YRBS, and NatSCEV found that rates for self-reports of being bullied range from 19.5 to 22.8 percent for boys and from 12.8 to 23.7 percent for girls ( Centers for Disease Control and Prevention, 2014b ; Finkelhor et al., 2015 ; U.S. Department of Education, 2015 ). All three of these national surveys found that girls were more likely to report being bullied than were boys (see Figure 2-5 for SCS data).

Prevalence of being bullied among 12-18 year olds by gender, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Research has suggested similarities and differences, beyond just overall frequency, in how often boys and girls experience different forms of bullying ( Felix and Green, 2010 ). As noted in Chapter 1 , there are two modes of bullying (direct and indirect) as well as different types of bullying (physical, verbal, relational, and damage to property). As illustrated in Figure 2-6 , being made fun of or called names and being the subject of rumors are the two most common forms of bullying experienced by children and youth, and both are much more frequently experienced than physical bullying ( Iannotti, 2013 ; Limber, 2014 ; U.S. Department of Education, 2015 ). For example, the 2013 SCS found that 13.2 percent of youth ages 12-18 reported being the subject of rumors and 13.6 percent said they had been made fun of, called names, or insulted, compared with 6.0 percent who reported being pushed, shoved, tripped, or spit on ( U.S. Department of Education, 2015 ; see Figure 2-6 ). Notions of gendered forms of bullying are common because physical aggression has been regularly associated with boys, whereas relational aggression has been considered to be the domain of girls ( Oppliger, 2013 ). For example, studies have shown that indirect aggression is normative for both genders, while boys are more strongly represented in physical and verbal aggression (see review by Card et. al., 2008). As for differences in different forms of cyberbullying, according to the 2013 SCS, girls experienced a higher prevalence of being bullied in nearly all types, except for receiving unwanted contact while playing online games and facing purposeful exclusion from an online community ( Limber, 2014 ; U.S. Department of Education, 2015 ; see Figure 2-7 ). However, because there is not yet a common definition of cyberbullying, there is no agreement on what forms of online harassment fall under the umbrella term of “cyberbullying.”

Prevalence of different types of bullying among students, ages 12-18, bullied in a school year, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Prevalence of different types of cyberbullying among students, ages 12-18, bullied in a school year, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

Limber and colleagues (2013) observed that age trends for self-reports of bullying others varied for boys and girls. Among boys, bullying others increased from grades 3 through 12, but among girls, rates of bullying others peaked in eighth grade ( Limber et al., 2013 ). Among older adolescents and college students, cyberbullying may be more common than traditional bullying. Prevalence rates of cyberbullying among young adults and college students have been estimated to be around 10-15 percent ( Kraft and Wang, 2010 ; Schenk and Fremouw, 2012 ; Wensley and Campbell, 2012 ).

Prevalence of Bullying by Race and Ethnicity

There has been only limited research on the roles that race and ethnicity may play in bullying ( Larochette et al., 2010 ; Peskin et al., 2006 ; Spriggs et al., 2007 ). 17 Data from the SCS indicate that the percentage of students who reported being bullied at school in 2013 was highest for white students (23.7%) and lowest for Asian students (9.2%), with rates for black students (20.3%) and Hispanic students (19.2%) falling between (see Figure 2-8 ; data from U.S. Department of Education, 2015 ). Data from the national YRBS were highest for white students (21.8%), next highest for Hispanic students (17.8%), and lowest for black students (12.7%) ( Centers for Disease Control and Prevention, 2014b ). The YRBS data did not include any other ethnicities/races.

Prevalence of being bullied and cyberbullied among students, ages 12-18, by race/ethnicity, as reported by the 2013 School Crime Supplement of the National Crime Victimization Survey. SOURCE: Data from U.S. Department of Education (2015).

It is challenging to interpret the percentages of children and youth who are bullied across different racial and ethnic groups, due to the limited information currently available on racial and ethnic differences in definitions of bullying and on whether and how bullying may vary according to the racial/ethnic diversity and density of schools and communities. See Chapter 3 for a discussion of contextual factors, including the school and community contexts, and their modulation of the relations between individual characteristics and prevalence of involvement in and consequences of bullying by race/ethnicity.

• DISPARITIES IN BULLYING PREVALENCE IN THE UNITED STATES AMONG VULNERABLE GROUPS

In addition to exploring standard demographic differences in bullying (i.e., gender, age, race/ethnicity), researchers have identified specific populations that are at increased risk for being bullied. This section reviews the research on groups for which there is consistent epidemiologic evidence of disparities in being the target of bullying, including LGBT youth, overweight/obese youth, and youth with disabilities. The committee also identified groups for which the evidence of increased risk is not currently consistent and which therefore warrant greater research attention ( U.S. Government Accountability Office, 2012 ). In this chapter, we report descriptive data on prevalence rates; see Chapter 3 for a discussion of factors that contribute to these disparities in rates of bullying (e.g., stigma) as well as research evidence on specific forms of bullying (e.g., bias-based bullying) that are more likely to occur among some of the groups covered in this section.

Differences in Bullying by Sexual Orientation and Gender Identity

LGBT youth, youth questioning their sexuality, and youth who do not conform to gender stereotypes frequently face bullying by their peers ( Eisenberg and Aalsma, 2005 ; Espelage et al., 2008 ; Garofalo et al., 1998 ; Rivers, 2001 ; Russell et al., 2014 ). The prevalence of bullying of lesbian, gay, and bisexual (LGB) males and females ranges from 25.6 percent to 43.6 percent ( Berlan et al., 2010 ).

Most research on bullying related to sexual orientation and gender identity comes from nonprobability samples. For example, the 2003 Massachusetts Youth Risk Behavior Survey found that 42.0 percent of sexual-minority youth reported being bullied in the 12 months prior to survey administration ( Hanlon, 2004 ). Similarly, the cross-sectional analysis of the 2001 questionnaire from the Growing Up Today study, a national longitudinal study involving 7,559 youths (ages 14-22) who were children of nurses participating in the Nurses' Health study found that the prevalence of bullying victimization was lowest in heterosexual female respondents (15.9%) and highest in gay male respondents (43.6%) ( Berlan et al., 2010 ). Girls identifying as “mostly heterosexual” and “mostly bisexual” were at increased risk for perpetrating bullying compared to heterosexual girls, while boys identifying as gay were less likely to perpetrate bullying than were heterosexual boys ( Berlan et al., 2010 ).

A growing body of research has aimed to assess the experiences of transgender youth specifically. The existing quantitative research suggests that most transgender youth experience regular bullying and harassment at school ( Grant et al., 2011 ; Kosciw et al., 2012 ; McGuire et al., 2010 ). For instance, in a sample of 5,542 adolescents sampled online, 82 percent of the transgender or gender nonconforming youth reported any bullying experience in the past 12 months, compared to 57 percent among cisgender boys and girls ( Reisner et al., 2015 ). 18

Measures of sexual orientation—including sexual attraction, sexual behavior, and sexual identity—have been recently incorporated into large surveillance systems, such as some state and local versions of the YRBSS, which have provided population-based estimates of bullying among LGB youth. Two of CDC's large surveillance systems—School Health Profiles and the School Health Policies and Practices studies—assess school health policies and practices relevant to LGB students including the prohibition of harassment and bullying ( Centers for Disease Control and Prevention, 2014a ). The results from these sources provide a means to assess sexual-orientation differences in bullying perpetration and victimization among youth by location within the United States ( Centers for Disease Control and Prevention, 2014a ). 19 Recent analyses by Olsen and colleagues (2014) were conducted by creating two datasets: one that combined 2009-2011 YRBS data from 10 states (Connecticut, Delaware, Hawaii, Illinois, Maine, Massachusetts, North Dakota, Rhode Island, Vermont, and Wisconsin) and the other that combined YRBS data from 10 school districts (Boston, Chicago, District of Columbia, Houston, Los Angeles, Milwaukee, New York City, San Diego, San Francisco, and Seattle). Adjusted prevalence rates for being bullied on school property were lowest for both heterosexual boys and girls (18.3% and 19.9%, respectively, based on the state dataset; 11.4% and 11.8%, respectively, based on the district dataset) and highest among gay boys (43.1% and 25.7%, respectively, based on the state and district datasets) and bisexual boys (35.2% and 33.2%, respectively, based on the state and district datasets) ( Olsen et al., 2014 ). Rates of being bullied on school property were intermediate for the lesbian girls (29.5% in the state dataset, and 14.0% in the district dataset) and bisexual girls (35.3% in the state dataset, and 18.8% in the district dataset).

Given the absence of measures of gender identity disaggregated from sex in these large state and local datasets, population-based estimates of the prevalence of bullying among transgender youth are not currently available. However, recent research has conducted cognitive testing to determine the most reliable and valid way of assessing gender identity among both adults ( GenIUSS Group, 2013 ) and youth (e.g., Conron et al., 2008 ). Further, population-based datasets have very recently begun to include measures of gender identity among youth (e.g., the 2013-2014 California Healthy Kids Survey), which will enable researchers to examine gender identity–related disparities in bullying using representative samples of youth.

Using data from the first wave (1994-1995 school year) of the National Longitudinal Study of Adolescent Health, which included 10,587 youth between 13 and 18, Russell and colleagues (2002) examined differences in experiencing, witnessing, and perpetrating violence, depending on the respondent's self-reported category of romantic attraction (same-sex, both-sex, or other-sex), a measure of sexual orientation. Youth who reported same-sex or both-sex attraction were more likely to experience and perpetrate the most dangerous forms of violence (e.g., pulling a gun or knife on someone, shooting or stabbing someone) and to witness violence ( Russell et al., 2002 ). These findings were not disaggregated by sex or gender identity.

Differences in Bullying Among Youth with Disabilities

Much of the existing data suggests that students with disabilities are overrepresented within the bullying dynamic ( McLaughlin et al., 2010 ; Rose, 2015 ; Rose et al., 2010 ), whether as children who have bullied ( Rose et al., 2009 ), children who have been bullied ( Blake et al., 2012 ; Son et al., 2012 ), or children who have both bullied and have been bullied ( Farmer et al., 2012 ). 20 Specifically, national prevalence data suggest that students with disabilities, as a whole, are up to 1.5 times more likely to be bullied than youth without disabilities ( Blake et al., 2012 ); this disproportionate bullying begins in preschool ( Son et al., 2012 ) and continues through adolescence ( Blake et al., 2012 ; Rose, 2015 ).

However, variability exists in reported prevalence rates of involvement for various subgroups of youth with disabilities. For example, Rose and colleagues (2015) conducted a prevalence study of a large sample of youth with and without disabilities in middle and high school ( n = 14,508) and determined that 35.3 percent of students with emotional and behavioral disorders, 33.9 percent of students with autism spectrum disorders, 24.3 percent of students with intellectual disabilities, 20.8 percent of students with another health impairment, and 19.0 percent of students with specific learning disabilities experienced high levels of victimization. In addition, 15.3 percent of youth with emotional and behavioral disorders, 19.4 percent of youth with autism spectrum disorders, 24.1 percent of youth with intellectual disabilities, 16.9 percent of youth with other health impairment, and 14.4 percent of youth with specific learning disabilities perpetrated bullying behavior. These estimates are in contrast to 14.5 percent of youth without disabilities who experienced high rates of being bullied and 13.5 percent who engaged in high rates of perpetration. The authors of this study acknowledge that the study has a number of limitations—mainly self-report, cross-sectional data, and data that were examined at the group level.

This literature on bullying and disabilities has several inconsistencies, which stem from differences in three basic factors: (1) measurement and definition, (2) disability identification, and (3) comparative groups. For instance, separating subclasses of youth with specific typographies of learning disabilities proves difficult, resulting in the general assessment of a combined class of specific learning disabilities ( Rose, 2015 ). This confounding factor leads to conflicting measures of bullying involvement, with some studies suggesting that rates of bullying perpetration are relatively comparable among youth with and without disabilities ( Rose et al., 2015 ), while others found that students with specific learning disabilities were almost six times more likely to engage in bully perpetration than their peers without disabilities ( Twyman et al., 2010 ). These conflicting results suggest further assessment or disaggregation of subgroups of youth with specific learning disabilities may be necessary to better understand bullying involvement among this subpopulation of youth.

Differences in Bullying by Weight Status

Weight status, specifically being overweight or obese, can be a factor in bullying among children and youth ( Puhl and Latner, 2007 ). The CDC defines childhood overweight as a body mass index (BMI) at or above the 85th percentile and below the 95th percentile of a CDC-defined reference population of the same age and sex. It defines childhood obesity as a BMI at or above the 95th percentile of this reference population for the same age and sex ( Centers for Disease Control and Prevention, 2015b ).

In 2012, 31.8 percent of U.S. children and youth 6 to 19 years of age were overweight or obese, using the CDC weight status categories. Eighteen percent of children 6 to 11 and 21 percent of youth 12 to 19 years of age were obese ( Centers for Disease Control and Prevention, 2015a ). Although the 2012 National Health and Nutrition Examination Survey (NHANES) data showed a decrease in obesity rates for children 2 to 5 years of age, the obesity rates for 2-19-year olds between 2003-2004 and 2011-2012 remained unchanged at 31.8 percent ( Ogden et al., 2014 ). Thus, weight-based bullying can affect a substantial number of youth.

In 2007, Puhl and Latner reviewed the growing literature on social marginalization and stigmatization of obesity in children and adolescents, paying attention to the nature and extent of weight bias toward overweight youth and the primary sources of stigma in their lives, including peers. 21 The researchers found that existing studies on weight stigma suggest that experiences with various forms of bullying is a common experience for overweight and obese youth; however, determining specific prevalence rates of bias is difficult because various assessment methods are used across the literature ( Puhl and Latner, 2007 ). For example, Neumark-Sztainer and colleagues (2002) examined the prevalence of weight-based teasing among middle and high school students ( n = 4,746) and found that 63 percent of girls at or above the 95th percentile for BMI and 58 percent of boys at or above the 95th percentile for BMI experienced “weight-based teasing.” However, in a recent longitudinal study of weight-based teasing ( n = 8,210), Griffiths and colleagues (2006) found that 34 percent of girls at or above the 95th percentile for BMI and 36 percent of boys at or above the 95th percentile for BMI reported being victims of “weight-based teasing and various forms of bullying” ( Griffiths et al., 2006 ). Griffiths and colleagues (2006) found that obese boys and girls were more likely to be victims of overt bullying one year later.

Janssen and colleagues (2004) found that among 5,749 children, ages 11-16, girls with a higher BMI were more likely to be targets of bullying behavior than their average-weight peers. They found that the likelihood of these girls being targeted in verbal, physical, and relational bullying incidents only increased as BMI rose. Among boys, however, the researchers found no significant associations between BMI and physical victimization. When they looked at the older portion of the sample, they found that among 15-16-year-old boys and girls, BMI was positively associated with being the perpetrator of bullying behavior compared with BMI among average-weight children ( Puhl and Latner, 2007 ). In this sample of 15 and 16 year olds, girls still faced an increased likelihood of both being bullied and being a perpetrator of bullying ( Puhl and Latner, 2007 ).

In their review of the literature on peer victimization and pediatric obesity, Gray and colleagues (2009) summarized evidence since 1960 on stigmatization, marginalization, and peer victimization of obese children. They concluded that obesity in children and youth places them at risk for harmful physical, emotional, and psychosocial effects of bullying and similar types of peer mistreatment. They also noted that “over time, a cyclical relationship may emerge between obese individuals and victimization such that children who are victimized are less likely to be active, which in turn leads to increased weight gain and a greater likelihood of experiencing weight-based victimization” ( Gray et al., 2009 , p. 722).

In summary, although numerous studies indicate that overweight and obese youth are at increased risk of being bullied, it can be difficult to attribute weight-based bullying to a single physical attribute, given that being overweight or obese often co-exists with other factors (see also the subsection below on “Youth with Intersectional Identities”). Additional research is needed to identify the relative importance of weight as a reason for being bullied or being a perpetrator of bullying among children and youth.

Other Disparity Groups Requiring More Research

Although most research on groups that are at disproportionate risk for bullying has focused on LGBT youth, overweight/obese youth, or youth with disabilities, some recent research has begun to identify other groups that may be at heightened risk. 22 Because this research is in its early stages, the evidence is not yet compelling on whether these groups do experience disparities in perpetrating or being targeted by bullying behavior. Consequently, the committee highlights the following groups as warranting further study to establish their risk status.

Socioeconomic Status

The literature on socioeconomic status and bullying contains conflicting results. Higher socioeconomic status has been associated with higher levels of perpetration ( Barboza et al., 2009 ; Shetgiri et al., 2012 ) but so has lower socioeconomic status ( Christie-Mizell et al., 2011 ; Garner and Hinton, 2010 ; Glew et al., 2005 ; Jansen et al., 2011 , 2012 ; Nordhagen et al., 2005 ; Pereira et al., 2004 ; Schwartz et al., 1997 ). Other studies found that socioeconomic status was not associated with perpetration ( Flouri and Buchanan, 2003 ; Zimmerman et al., 2005 ).

The evidence for an association between socioeconomic status and being bullied is similarly inconsistent. Specifically, some studies found that neither economic deprivation ( Wilson et al., 2012 ), family income ( Garner and Hinton, 2010 ), nor general socioeconomic status ( Magklara et al., 2012 ) predicted greater risk of being targeted by bullying behavior. Other studies found that insufficient parental income ( Lemstra et al., 2012 ) and low social class ( Pereira et al., 2004 ) predicted increased rates of being the target in bullying incidents. These conflicting results may be due in part to different measures and conceptualizations of socioeconomic status. In addition, other environmental or social–ecological factors that are often not included in evaluative models may account for the differences in these findings. For example, Barboza and colleagues (2009) argued that perpetration emerges as a function of social climate deficits, where social supports may mediate perpetration regardless of demographic characteristics, including socioeconomic status. Thus, further research is warranted on the mediating and moderating variables in the association between socioeconomic status and either bullying perpetration or being targeted for bullying. (See Chapter 3 for a more detailed discussion of moderation.)

Immigration Status

The results to date from research on the association between immigration status and bullying involvement are inconsistent. For example, Lim and Hoot (2015) investigated the bullying involvement of third and sixth grade students who were immigrants, refugees, or native born. The majority of these students who were refugees or immigrants came from Burma, Burundi, Iraq, Somalia, Thailand, and Yemen. The refugees and immigrants did not report higher levels of being bullied than the native-born American students. However, qualitative data suggested that youth with refugee status responded as “nonpassive victims,” meaning they would try to defend themselves when physically attacked, whereas immigrants and native-born youth who were bullied responded to bullying more passively. The inconsistencies in the results may be associated with age of the respondents, total sample size, nationality of the immigrants/refugees, or other environmental or social–ecological factors ( Hong et al., 2014 ), all of which require greater attention in future studies.

Minority Religious Affiliations

Few studies have specifically investigated the bullying involvement of youth from minority religious groups. However, evidence from other areas of violence suggests that youth from religious minorities may experience higher rates of being bullied than those who identify as Christians. For instance, the percentage of hate crimes in the United States that are grounded in religious affiliation has increased from 10 percent in 2004 to 28 percent in 2012 ( Wilson, 2014 ). Since schools are reflective of society as a whole, and bullying involvement is grounded in a social–ecological context that includes community and societal factors ( Hong and Espelage, 2012 ), this targeting of religious minorities may carry over into the school environment. However, this hypothesis requires empirical documentation.

Youth with Intersectional Identities

As noted in the earlier discussion of weight status as a factor in bullying, “intersectionality” refers to individuals with multiple stigmatized statuses (e.g., black lesbian youth). The majority of studies on bullying perpetration and targeting have examined identity groups in isolation, but there is increasing acknowledgement that multiple intersecting identities can exacerbate or attenuate health outcomes (e.g., Bowleg, 2008 ; McCall, 2005 ). An exception is the study by Garnett and colleagues (2014) , which analyzed the intersectionality of weight-related bullying with bullying for other reasons. Among 965 Boston youth sampled in the 2006 Boston Youth Survey, participants had been discriminated against or bullied (or assaulted) for any of four attributes (race or ethnicity, immigration status, perceived sexual orientation, and weight). Participants who were bullied for their race and weight had higher rates of being targeted for bullying behavior, compared with students who had two or more of the other characteristics ( Garnett et al., 2014 ). As discussed earlier, the extent to which intersecting identities affect the prevalence of bullying perpetration and targeting remains largely unknown and therefore represents an important area for future study.

Children and adolescents have mostly stated that the differences in their physical appearance contribute to the possibility of their being bullied ( Lunde et al., 2007 ). There is concern that students with characteristics, such as obesity, disabilities, food allergies, and gender issues could put them directly in the path of being more likely to be bullied ( Schuster and Bogart, 2013 ). These categories may intersect at the micro level of individual experience to reflect multiple interlocking systems of privilege and oppression at the macro, social-structural level ( Bowleg, 2012 ).

Is bullying more prevalent in urban schools than in suburban or rural schools? Because large-city urban schools are often located in inner-city areas of concentrated poverty and exposure to violence, theories of social disorganization suggest that bullying might be more common in such contexts ( Bradshaw et al., 2009 ). However, there is not much research in support of this hypothesis. Rural students have self-reported at least as much bullying in their schools as did urban youth ( Dulmus et al., 2004 ; Stockdale et al., 2002 ). Moreover, data from large national studies in the United States indicate that students in rural schools report somewhat more bullying than those in urban and suburban schools ( Nansel et al., 2001 ; Robers et al., 2013 ). In particular Robers and colleagues (2013) found, using 2011 National Center for Education Statistics data, that 25 percent of students in urban schools reported some bullying, compared with 29 percent in suburban schools and 30 percent in rural schools. One reason that has been suggested for this difference is that smaller rural schools, some of which have fewer school transitions (e.g., lacking a separate middle school between elementary and high school grades), may typically consolidate social reputations and provide fewer opportunities for targeted youth to redefine how they are perceived by peers ( Farmer et al., 2011 ).

What may differ by urbanicity of schools are the reasons for targeting certain individuals in a pattern of bullying behavior. For example, Goldweber and colleagues (2013) documented that urban African American youth were more likely to report race-based bullying by peers than were rural or suburban youth. As noted above in the section on “Prevalence of Bullying by Race and Ethnicity,” the connection between experiences of peer bullying and racial discrimination merits further study.

• ISSUES IN DEVELOPING ESTIMATES OF BULLYING IN THE UNITED STATES

Current efforts to estimate prevalence of bullying and cyberbullying behavior are characterized by disagreement and confusion. This chapter has pointed out the major challenges associated with generating accurate and reliable estimates of bullying and cyberbullying rates in the United States. The issues to be addressed are summarized here in terms of definitional issues and issues of measurement and sampling.

Definitional Issues

As attention to bullying behavior has grown in recent years, concerns have been raised that efforts to characterize bullying vary considerably and that a lack of a consistent definition “hinders our ability to understand the true magnitude, scope, and impact of bullying and track trends over time” ( Gladden et al., 2014 , p. 1). One such approach to measuring bullying includes providing an explicit definition or explanation of what is meant by bullying to study participants. In contrast, some approaches simply use the word “bullying” but do not define it, whereas others list specific behaviors that constitute bullying without using the term “bullying” ( Gladden et al., 2014 ; Sawyer et al., 2008 ). Even if the definition is provided, researchers must assume that respondents (who are often children) fully understand the broad and difficult concept of bullying—including its elements of hostile intent, repetition, and power imbalance and its various forms—when answering. However, research has shown that this level of comprehension might not be uniformly present for children of all age groups and cultures ( Monks and Smith, 2006 ; Smith et al., 2002 ; Strohmeier and Toda, 2008 ; Vaillancourt et al., 2008 ). For instance, 8-year-old children consider fewer negative behavior options to be bullying than do 14-year-old adolescents ( Smith et al., 2002 ). Furthermore, children hold very different definitions of bullying from those held by researchers. Bullying may also be understood and defined differently in different languages and cultures ( Arora, 1996 ). Smith and colleagues (2002) showed that terms used in different cultures differed remarkably in their meanings. For example, some terms captured verbal aggression, while others were connected instead with physically aggressive acts or with social exclusion. These definitional issues are also relevant to cyberbullying, as there is no uniform definition used across studies.

There is still a lot of variability when it comes to defining bullying: Parents, children, and schools or medical professionals can mean a wide range of different things when they use the term “bullying.” Bullying varies in different developmental stages, and we should acknowledge that it is not always obvious. Even so, bullying can be characterized as the kind of behavior that would actually be considered harassment if the people involved were over age 18. However you look at it, a standardized definition would help us more precisely target bullying behavior and consequences while avoiding misunderstandings.

—Summary of themes from service providers/community-based providers focus group (See Appendix B for additional highlights from interviews.)

Measurement and Sampling Issues

Measuring bullying and cyberbullying is very difficult. The variability in prevalence rates reflects a number of measurement and sampling issues. First, studies reporting prevalence rates of bullying problems may rely on different data sources, such as peer versus teacher nominations or ratings, observations by researchers, or self-report questionnaires. Particularly with children, the self-report strategy poses a unique problem in regard to possible underreporting or overreporting ( Solberg, 2003 ). Some children who bully other students will choose not to respond honestly on the relevant questionnaire items for fear of retribution from adults. To date, a majority of information is gathered via self-reports, which have limitations; however, the committee does not believe that official reports are necessarily a better or more reliable source of information. The committee also acknowledges that for studies examining the prevalence of bullying by a certain demographic category, such as obesity or sexual orientation, it is not possible to say who is the “most bullied” by comparing students with one set of demographic characteristics with other students with different demographic characteristics.

Second, research suggests that the approach to measuring bullying does affect the pattern of responses and in turn may influence the prevalence rates. For example, a study of over 24,000 elementary, middle, and high school age youth found significantly higher prevalence rates for bullying when it was assessed using a behavior-based approach (i.e., asking about the experience of specific forms and acts of bullying) than when it was measured using a definition-based approach ( Sawyer et al., 2008 ). A similar pattern occurs for cyberbullying, For example, one study used a definition that read “repeatedly [trying] to hurt you or make you feel bad by e-mailing/e-messaging you or posting a blog about you on the Internet (MySpace).” This study found the prevalence of cybervictimization to be 9 percent ( Selkie et al., 2015 ). Another study asked about “the use of the Internet, cell phones and other technologies to bully, harass, threaten or embarrass someone” and found cybervictimization prevalence to be 31 percent ( Pergolizzi et al., 2011 ).

Third, studies may differ with regard to the reference period used in measuring bullying. For example, a question may refer to a whole school year or one school term, the past couple of months, or over a lifetime. Response and rating categories may vary in both number and specificity as well. Such categories may consist of a simple yes or no dichotomy; of various applicability categories such as “does not apply at all” and “applies perfectly”; or of relatively vague frequency alternatives ranging from “seldom” to “very often” or from “not at all in the past couple of months” to “several times a week.”

Fourth, some studies use different criteria for differentiating students who have been bullied and students who have not, as well as students who have and have not bullied others. This variation in identification makes prevalence rates difficult to compare ( Solberg, 2003 ). A majority of studies do not ask questions about children who have bullied or children who have been bystanders, instead focusing on children who have been bullied. Taken together, these findings suggest that researchers need to be cautious about interpreting their findings in light of their measurement approach.

Estimates of bullying inform an evidence-based understanding about the extent of the problem and bring attention to the need to address the problem and allocate the funding to do so. Prevalence estimates provide information for policy makers, identify where education is needed, identify vulnerable populations, and help direct assistance and resources. As this chapter has explained, generating reliable estimates for the number of children who have bullied and the number who have been bullied is not an easy task. In some cases, the task is extraordinarily difficult. For example, existing research suggests disparities in rates of bullying by a variety of characteristics, including sexual orientation, disability, and obesity, mostly due to the lack of nationally representative data on these and other vulnerable groups. Bullying must be understood as a social problem characterized by numerous challenges to estimating its prevalence and the conditions associated with it. In summary, based on its review of the available evidence, the committee maintains that, despite the current imperfect estimates, bullying and cyberbullying in the United States is clearly prevalent and therefore worthy of attention.

• FINDINGS AND CONCLUSIONS

Finding 2.1: Estimates of bullying and cyberbullying prevalence reported by national surveys vary greatly, ranging from 17.9 percent to 30.9 percent of school-age children for the prevalence of bullying behavior at school and from 6.9 percent to 14.8 percent for the prevalence of cyberbullying. The prevalence of bullying among some groups of youth is even higher. For instance, the prevalence of bullying of lesbian, gay, bisexual, and transgender youth is approximately double that of heterosexual and cisgender youth. Finding 2.2: The extent to which rates of bullying and cyberbullying have changed in recent years is unclear. Finding 2.3: The four major national surveys that include bullying do not uniformly address all age groups and school levels. Finding 2.4: A majority of prevalence data collection is done through self-reports or observation. Finding 2.5: A majority of national studies do not ask questions about children who have bullied or children who have been bystanders. Finding 2.6: Many studies differ with regard to the reference period used in measuring bullying behavior (e.g., last month versus last 12 months). Finding 2.7: Studies use different definitional criteria for differentiating students who have been bullied and cyberbullied and students who have not, as well as students who bully and cyberbully and students who do not. Finding 2.8: Existing research suggests that there are disparities in rates of bullying by a variety of characteristics, including sexual orientation, disability, and obesity. However, there is a lack of nationally representative data on these and other vulnerable groups. Future research is therefore needed to generate representative estimates of bullying, including bias-based and discriminatory bullying, to accurately identify disparity groups.

Conclusions

Conclusion 2.1: Definitional and measurement inconsistencies lead to a variation in estimates of bullying prevalence, especially across disparate samples of youth. Although there is a variation in numbers, the national surveys show bullying behavior is a real problem that affects a large number of youth. Conclusion 2.2: The national datasets on the prevalence of bullying focus predominantly on the children who are bullied. Considerably less is known about perpetrators, and nothing is known about bystanders in that national data. Conclusion 2.3: Cyberbullying should be considered within the context of bullying rather than as a separate entity. The Centers for Disease Control and Prevention definition should be evaluated for its application to cyberbullying. Although cyberbullying may already be included, it is not perceived that way by the public or by the youth population. Conclusion 2.4: Different types of bullying behaviors—physical, relational, cyber—may emerge or be more salient at different stages of the developmental life course. Conclusion 2.5: The online context where cyberbullying takes place is nearly universally accessed by adolescents. Social media sites are used by the majority of teens and are an influential and immersive medium in which cyberbullying occurs.

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Additional information about strategies for overcoming these limitations can be found in Chapter 7 .

Households are selected through a stratified, multistage, cluster sampling process. Households in the sample are designed to be representative of all households as well as noninstitutionalized individuals ages 12 or older.

For the SCS, being “bullied” includes students being made fun of, called names, or insulted; being the subject of rumors; being threatened with harm; being pushed, shoved, tripped, or spit on; being pressured into doing things they did not want to do; being excluded from activities on purpose; and having property destroyed on purpose. “At school” includes the school building, school property, school bus, or going to and from school. Missing data are not shown for household income.

In 1995 and 1999, “at school” was defined for respondents as in the school building, on the school grounds, or on a school bus. In 2001, the definition for “at school” was changed to mean in the school building, on school property, on a school bus, or going to and from school.

The NCVS has a nationally representative sample of about 90,000 households comprising nearly 160,000 persons, whereas the sample size of the SCS is just 4,942 students.

The YRBS uses a cluster sampling design to produce a nationally representative sample of the students in grades 9-12 of all public and private school students in the 50 states and the District of Columbia.

The 2014 YRBS does not clarify whether this includes school events held off campus or the children's journey to and from school.

Electronically bullied includes being bullied through e-mail, chat rooms, instant messaging, Websites, or texting.

Each state-based and local-school-based YRBS employs a two-stage, cluster sample design to produce representative samples of students in grades 9-12 in the survey's jurisdiction.

States and cities could modify the national YRBS questionnaire for their own surveys to meet their needs.

The student survey was administered in a regular classroom setting to participating students by a school representative (e.g., teacher, nurse, guidance counselor, etc.).

Three versions of the self-report questionnaire were administered: one for fifth and sixth graders; one for students in seventh, eighth, and ninth grade; and one for students in tenth grade. The tenth grade questionnaire contained the complete set of questions asked.

This is the highest prevalence rate for both bullying and cyberbullying reports among the four national surveys.

For NatSCEV II, data were collected by telephone interview on 4,503 children and youth ages 1 month to 17 years. If the respondent was between the ages of 10-17, the main telephone interview was conducted with the child. If the respondent was younger than age 10, the interview was conducted with the child's primary caregiver.

The statistical standard for referring to “trends” is at least three data points in the same direction. In the SCS, the decrease from 2011 to 2013 is one data point, and conclusions should not be drawn at this point in time.

The committee's Statement of Task (see Box 1-1 ) requested “a particular focus on children who are most at risk of peer victimization—i.e., those with high-risk factors in combination with few protective factors . . .” At-risk subpopulations specifically named in the Statement of Task were “children with disabilities,” poly-victims, LGBT youth, and children living in poverty . . .”

The committee expands on this topic in Chapter 3 .

Reisner and colleagues (2015, p. 1) define cisgender youth as youth “whose gender identity or expression matches one's sex assigned at birth.”

The National YRBS data available at the time of publication did not include questions about sexual identity and sex of sexual contacts, but these topics are included in the YRBS report released in June 2016.

This section is adapted from a study ( Rose, 2015 ) commissioned by the committee for this report.

In this review, weight stigma included “verbal teasing (e.g., name calling, derogatory remarks, being made fun of), physical bullying (e.g., hitting, kicking, pushing, shoving), and relational victimization (e.g., social exclusion, being ignored or avoided, the target of rumors”) ( Puhl and Latner, 2007 , p. 558).

• Cite this Page Committee on the Biological and Psychosocial Effects of Peer Victimization: Lessons for Bullying Prevention; Board on Children, Youth, and Families; Committee on Law and Justice; Division of Behavioral and Social Sciences and Education; Health and Medicine Division; National Academies of Sciences, Engineering, and Medicine; Rivara F, Le Menestrel S, editors. Preventing Bullying Through Science, Policy, and Practice. Washington (DC): National Academies Press (US); 2016 Sep 14. 2, The Scope of the Problem.
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• Published: 13 September 2024

Relationship between physical exercise, bullying, and being bullied among junior high school students: the multiple mediating effects of emotional management and interpersonal relationship distress

• Qiang Zhang 1 &
• Wenjing Deng 2  

BMC Public Health volume  24 , Article number:  2503 ( 2024 ) Cite this article

Metrics details

This paper investigates the relationships between physical activity (PA), school bullying, emotion regulation self-efficacy (ERS), and interpersonal relationship distress (IRD) among junior high school students. It also examines the underlying mechanisms of school bullying to provide insights into reducing adolescent bullying and to lay the groundwork for preventing and controlling aggressive behaviors.

A survey was conducted on 484 students (240 males, 12.18 ± 0.8 years) from 4 secondary schools using the Physical Activity Rating Scale (PARS), Emotional Management Self-Efficacy Scale (EMSS), Interpersonal Relationship Distress Scale (IRDS), and Campus Bullying Scale (CBS) to examine the effects among the variables. A stratified random sampling method was used to select the sample, and data were collected with a structured questionnaire. The data were analyzed using SPSS 24.0 and AMOS 24.0 statistical software. The analysis included Pearson correlation analysis, structural equation modeling, and bias-corrected percentile Bootstrap methods.

(1) PA negatively predicts IRD, which in turn has an indirect effect on bullying (PA → IRD → Bullying), ES = -0.063. Additionally, EM and IRD act as mediators between PA and school bullying (PA → EM → IRD → Bullying), ES = 0.025. (2) PA negatively predicts IRD, which has an indirect effect on being bullied (PA → IRD → Being bullied), ES = -0.044. EM and IRD serve as chain mediators between PA and being bullied (PA → EM → IRD → Being bullied), ES = -0.071.

PA can positively predict bullying, but it can be mitigated through EM to reduce IRD, thereby decreasing the occurrence of campus bullying and being bullied.

Peer Review reports

School bullying is considered an aggressive, intentional, and repetitive behavior, occurring without clear motivation, inflicted by one or more students on others. It not only causes physical harm to the victims but also negatively affects their mental health [ 1 , 2 ]. School bullying is prevalent in some East Asian countries [ 3 ], having profound and lasting effects on the health and well-being of the victims [ 4 , 5 ]. In children and adolescents, school bullying is closely associated with depression, anxiety, and insomnia [ 6 ]. Severe bullying can lead to self-harm [ 7 ]. Bullying peaks between the ages of 11 and 13, during the transition from primary to secondary school [ 8 ]. Given the potential harm of bullying to mental health, it is necessary to explore the mechanisms of bullying and victimization to provide a theoretical basis for preventing bullying among middle school students.

Physical activity (PA) has demonstrated significant mental health benefits, including strong anti-depressive and anti-anxiety effects, improved self-efficacy, and enhanced mood regulation [ 7 , 9 ]. PA has also proven to be an effective intervention in anti-bullying programs for special populations with mental disorders, overweight, or obesity [ 10 , 11 ]. As a vital component of public health strategies against bullying, PA positively influences the psychological well-being of both perpetrators and victims. Studies abroad have confirmed a close relationship between school bullying, victimization, and the frequency and type of PA. Students who engage in PA at least four times a week show higher aggression scores than those with lower exercise frequencies [ 9 ]. Studies suggest that regularly exercising adolescents are more likely to become bullies and exhibit higher aggression compared to their non-exercising peers [ 12 ]. Nikolaou’s study suggests that individuals who frequently participate in competitive sports are more likely to become bullies but are less likely to be victimized [ 13 ]. It recommends increasing opportunities for adolescents to exercise while enhancing supervision of exercise content and venues. Other studies confirm that physical education classes protect against bullying, with regularly exercising girls showing lower levels of victimization [ 14 ]. Waasdor used binary logistic regression to examine the relationship between health-related behaviors and bullying, finding that PA significantly reduces the likelihood of students becoming victims [ 15 ]. Pacífico et al. systematically reviewed the relationship between bullying victimization, aggressive behavior, and participation in physical activities and sedentary behaviors, finding that victims are associated with reduced PA and increased sedentary time [ 11 ]. Based on these findings, we hypothesize that H1a: PA can increase the likelihood of bullying behavior. H1b: PA can decrease the likelihood of being bullied.

Recent studies have found that emotion regulation self-efficacy (ERS) and interpersonal relationship distress (IRD) are crucial in preventing school bullying. Muris defines ERS as an individual’s perceived ability to manage negative emotions, including the belief in one’s capacity to avoid or recover from such states [ 16 ]. Effective ERS is essential for mental and physical health and is considered a protective factor against negative emotions. For instance, self-talk can help regain a positive attitude or calm oneself during fear and anxiety. Bandura, in his self-efficacy and social cognitive theories [ 17 , 18 ], emphasizes that self-efficacy and self-regulation strategies are crucial for behavior change. Regular PA promotes both physical and psychological health, including a healthy lifestyle, body awareness, and confidence in physical skills. It also enhances safety, responsibility, patience, courage, and psychological balance [ 19 ]. Valois et al. surveyed over 3,800 students and found that PA is related to ERS [ 20 ]. Continuous PA can enhance self-efficacy, which, if not managed, can lead to decreased academic performance, social adaptation disorders, emotional depression, and an increased likelihood of deviant behavior. Moreover, regular physical activity offers multiple psychological benefits, such as enhanced self-control and self-esteem, both of which are closely linked to a decrease in bullying behaviors [ 21 ]. Participation in sports can mitigate the effects of bullying and is an effective strategy for promoting positive peer interactions and emotional regulation among adolescents.

IRD refers to the inability to establish and maintain meaningful relationships, lack of stable personal identity and self-awareness, and the use of avoidance strategies to manage strong emotions [ 22 ]. Méndez et al. found that poor relationships among students can result in bullying [ 9 ]. School bullying, a negative social interaction among adolescents, can be predicted by negative relationships with parents, teachers, and peers [ 23 ]. Gross’s emotion regulation self-efficacy theory emphasizes the impact of emotion management on social interactions and relationships [ 24 ]. PA can serve as an ERS strategy, improving emotion management through stress relief and emotional state enhancement, thereby improving interpersonal relationships.

In recent years, research has increasingly focused on how PA can improve adolescents’ emotion management and interpersonal relationships, thereby reducing bullying and victimization. González’s study confirmed that adolescents’ participation in group sports brings joy, improves poor interpersonal relationships, and promotes harmonious peer relationships [ 25 ]. Further research found that non-competitive physical activities convey values, promote prosocial attitudes, prevent bullying and victimization, and reduce the risk of aggressive incidents [ 26 ].

Based on these findings, we hypothesize H2a: PA can improve individuals’ ERS abilities, thereby reducing the occurrence of school bullying behaviors. H2b: PA can indirectly decrease the likelihood of individuals becoming victims of bullying by enhancing their ERS abilities. H3a: PA can alleviate IRD, thereby reducing the frequency of bullying behaviors. H3b: PA can further lower the risk of students being bullied by mitigating IRD.

According to Sullivan’s interpersonal theory [ 27 ], individuals seek interpersonal interactions during their adolescence. If they cannot effectively control their emotions, it may lead to psychological stress and social interaction difficulties [ 28 ]. Jun et al., in a cross-sectional survey study of 207 medical students, found that appropriate expression of anger can enhance their ERS ability, thereby improving interpersonal interaction skills [ 29 ]. ERS ability is a cognitive variable that influences behavioral and emotional processes [ 30 ]. Individuals with high levels of ERS ability believe they can achieve desired outcomes through their efforts, thus choosing effective coping strategies. Moreover, they exhibit more patience in the process of achieving their goals [ 30 , 31 ]. Therefore, the ability to manage emotions may enhance individuals’ confidence, stimulate motivation for communication with others, and enable them to handle interpersonal relationships more effectively, avoiding disharmony in relationships.

Based on the above, the hypotheses are proposed: H4a: PA can enhance ERS, thereby alleviating IRD and preventing school bullying. H4b: PA can enhance ERS, thereby reducing IRD and decreasing the risk of being bullied.

This study employed PA as the independent variable, school bullying and being bullied as the dependent variables, and ERS and IRD as mediating variables to develop a chain mediation model (Fig.  1 ). The model aims to elucidate how PA reduces IRD through the enhancement of ERS, thereby preventing school bullying. This theoretical framework clarifies the research objectives and provides direction for subsequent analysis.

Multiple mediation model of school bullying

Materials and methods

Participants.

From March to May 2023, we utilized a multi-stage cluster random sampling method to select two key junior high schools and two regular junior high schools in Shandong Province. Within each school, 1–2 classes from grades 6 through 9 were randomly chosen to participate in the survey, which was administered via the Wenjuanxing platform. Ultimately, 15 classes took part, and we issued 529 questionnaires, each taking an average of 8 min to complete. After excluding responses with repetitive patterns or completion times under 3 min, 45 invalid responses were discarded, resulting in 484 valid responses and a response rate of 91.5%. The sample included 240 male and 244 female students; 171 were only children, while 313 had siblings. The breakdown by grade was as follows: 87 students in grade 6, 137 in grade 7, 134 in grade 8, and 126 in grade 9. The average age of the participants was 12.18 years with a standard deviation of 0.8 years. Detailed demographic information is presented in Table  1 .

This study utilized a cross-sectional design and structured questionnaires to collect the necessary data. The questionnaires used in this study were revised in China, widely utilized, and demonstrated high reliability and validity. To further ensure their reliability and validity, we conducted additional reliability analysis and exploratory factor analysis. The study followed these procedures: Approval was first obtained from the Human Research Ethics Committee of Capital University of Physical Education and Sports. Afterward, researchers received consent from the principals of the selected schools and coordinated with grade-level directors to select the participating classes. Class teachers then distributed informed consent forms to students and their parents, explaining that participation was voluntary and confidentiality was assured. They also confirmed the number of participants. Finally, during physical education classes, teachers organized students to complete the questionnaires anonymously using the Wenjuanxing platform in the school information room. Researchers were present on-site to address any participant questions.

Instruments

Physical activity rating scale (pars).

The PARS revised by Liang (1994) [ 32 ], was employed to assess the PA levels of middle school students and investigate their exercise habits. This scale evaluates the intensity (e.g., “light exercise”), duration (e.g., “less than 10 minutes”), and frequency (e.g., “less than once a month”) of PA, with each dimension rated on a 5-point scale from 1 (low) to 5 (high). The amount of PA is calculated using the formula: PA = Intensity × Duration × Frequency. Both intensity and frequency are rated on a scale from 1 to 5, while duration is rated from 0 to 4. The possible scores range from 0 to 100 points. The activity levels are categorized as follows: 0–19 points indicate low activity, 20–42 points indicate moderate activity, and 43–100 points indicate high activity. In this study, Cronbach’s alpha for the scale was 0.807.

Emotion regulation self-efficacy scale (ERSS)

The ERSS, developed by Li [ 33 ], was used. This scale contains 17 items, covering four dimensions: Expressing Positive Emotions (EPM) with 4 items (e.g., “showing joy when something good happens”), Regulating Anger (AM) with 3 items, Regulating Depression (RD) with 4 items (e.g., “not feeling dejected when strongly criticized”), and Regulating Fear (RF) with 6 items (e.g., “not feeling scared in the dark”). In this study, Cronbach’s alpha for the total scale was 0.955, with the four dimensions being 0.911, 0.822, 0.907, and 0.889 respectively. The overall confirmatory factor analysis fit indices for the scale were: χ2/ df  = 5.03, CFI = 0.96, TLI = 0.92, RMSEA = 0.06, and SRMR = 0.05.

Interpersonal relationship distress scale (IRDS)

The IRDS developed by Deng & Zheng [ 34 ] was used. This questionnaire consists of 28 questions, with a binary response format (“Yes” or “No”). Higher scores indicate more severe IRD. The scale includes four dimensions, each with 7 items: Conversation Trouble (CT) (e.g., “finding it difficult to talk about personal troubles”), Interaction Trouble (IT) (e.g., “feeling uncomfortable when meeting strangers”), Trouble Treating Others (TTO) (e.g., “feeling excessive envy and jealousy towards others”), and Exposure to Heterosexual Distress (EHD) (e.g., “feeling unnatural when interacting with the opposite sex”). In this study, the Cronbach’s alpha for the questionnaire was 0.889. The confirmatory factor analysis fit indices were: χ2 /df  = 2.603, CFI = 0.89, TLI = 0.87, RMSEA = 0.05, and SRMR = 0.04.

Campus bullying scale (CBS)

The bullying subscale of the Olweus Bullying Questionnaire [ 35 ], revised by Zhang & Wu [ 36 ], consists of 12 items. It uses a 5-point Likert scale to measure the frequency of bullying behaviors. Six items assess bullying (BULLY) (e.g., “I spread rumors about some classmates to make others dislike them”), and six items assess victimization (VIC) (e.g., “others call me unpleasant nicknames, insult me, or mock me”). The frequency of occurrence is rated from 0 to 4, ranging from “never happened” to “several times a week”. In this study, the Cronbach’s alpha for the questionnaire was 0.843. Confirmatory factor analysis indicated good structural validity, with fit indices as follows: χ2/ df =  2.94, CFI = 0.97, TLI = 0.98, RMSEA = 0.05, and SRMR = 0.03.

Data analysis

The social statistical analysis software SPSS 24.0 was used for internal consistency testing and Pearson correlation analysis of PA, ERS, IRD, and school bullying. The AMOS 24.0 software was used for confirmatory factor analysis, mediation analysis, and Bootstrap analysis for difference testing. A significance level of α = 0.05 was set for statistical significance.

Control and testing for common method bias

All data in this study were self-reported by adolescents, which may be affected by common method bias. Therefore, in the study design and data collection process, measures were taken such as making the questionnaire anonymous, separating different questionnaires, reverse scoring some items, and emphasizing the confidentiality of the data for pre-program control. In addition, confirmatory factor analysis was used to test for common method bias in all self-reported items. The results showed a poor model fit, with χ2/ df  = 29.44, CFI = 0.47, GFI = 0.55, AGFI = 0.39, NFI = 0.46, and RMSEA = 0.24. This indicates that there is no serious common method bias issue in this study.

Means, standard deviations, and correlation analysis of variables

Descriptive statistics and correlation analysis (Table  2 ) reveal significant relationships: gender is significantly related to PA and interpersonal relationships ( r =-0.272, P  < 0.01; r  = 0.107, P  < 0.05). ERS is significantly related to bullying and IRD ( r =-0.134, P <0.01) ( r =-0.316, P <0.01), and ERS is positively correlated with PA ( r  = 0.161, P <0.01). IRD is negatively correlated with PA ( r =-0.132, P <0.01), and positively correlated with bullying and being bullied ( r  = 0.306, P <0.01) ( r  = 0.207, P <0.01). Given that the relationship between gender and bullying was not significant, subsequent analyses did not differentiate between male and female students.

Chain mediating mechanism analysis between PA and school bullying

To effectively control measurement errors, this study used structural equation modeling to test for multiple mediating effects. First, based on the hypothesized model, PA was used as the predictor variable, bullying as the outcome variable, and ERS and IRD as mediating variables for path analysis. Figure  2 presents the data fit results: χ2/ df  = 3.001, CFI = 0.96, GFI = 0.953, RMSEA = 0.064, and SRMR = 0.041. All fit indices fall within acceptable ranges, confirming the validity of the initially proposed model.

Chain mediations between PA and bullying. Note: BULLY = school bullying. Dash lines indicate an insignificant relationship

The path coefficients and significance levels are illustrated in the diagram. PA significantly affects ERS and IRD, with standardized path coefficients of 0.23 and − 0.19, respectively. ERS negatively impacts IRD, with a standardized path coefficient of -0.33. IRD positively affects bullying, with a standardized path coefficient of 0.33. Additionally, PA positively affects bullying, with a standardized path coefficient of 0.16. However, ERS does not significantly impact bullying.

This study used the Bootstrap procedure to test the significance of the mediating effects, drawing 5000 samples with a 95% confidence interval. A mediating effect is considered significant if the 95% confidence interval for the path coefficients does not include 0. According to the results in Table  3 , the path from PA to bullying is significant ( P  = 0.025), supporting hypothesis H1a. In contrast, the path from ERS to bullying is not significant ( P  = 0.103), which does not support hypothesis H2a.

Following Wen et al. [ 37 ], the interpretation of mediating effects depends on the signs of ab and c’. If ab and c’ have the same sign, the mediating effect is considered valid. This study found that both IRD and ERS act as suppressor variables in the relationship between PA and school bullying among adolescents: (1) PA reduces IRD, which indirectly decreases bullying (PA → IRD → Bullying), with a suppressor effect value of -0.063 (95% CI: -0.179, -0.028), supporting hypothesis H3a. (2) ERS and IRD also act as suppressors in the relationship between PA and school bullying (PA → ERS → IRD → Bullying), with a suppressor effect value of 0.025 (95% CI: -0.375, -0.037), supporting hypothesis H4a.

Path analysis of the chain mediation mechanism of PA and school bullying

In this analysis, PA is treated as the predictor variable, and being bullied is the outcome variable. ERS and IRD serve as mediating variables. The model’s fit indices are within acceptable ranges: χ2 /df  = 2.704, CFI = 0.965, GFI = 0.957, RMSEA = 0.059, and SRMR = 0.039, as illustrated in Fig.  3 . These results support the validity of the initially proposed model.

Chain mediations between PA and being bullied. Note: VIC = being bullied. Dash lines indicate an insignificant relationship

The figure illustrates the path coefficients and significance levels. PA significantly impacts ERS and IRD, with standardized path coefficients of 0.22 and − 0.19, respectively. ERS significantly negatively impacts IRD, with a standardized path coefficient of -0.33. IRD significantly positively affects being bullied, with a standardized path coefficient of 0.23. However, the effects of ERS and PA on being bullied are not significant.

Table  4 reveals that the paths from PA and ERS to being bullied are not significant ( P  = 0.054, 0.445), thus hypotheses H1b and H2b are not supported. The lack of a significant direct effect from PA to being bullied indicates that IRD and ERS fully mediate this relationship. This complete mediation consists of two pathways: (1) PA negatively affects IRD, which then indirectly impacts being bullied (PA → IRD → Being bullied), with a mediation effect of -0.044 (95% CI: -0.179, -0.028), supporting hypothesis H3b; (2) ERS and IRD mediate the relationship between PA and being bullied in a chain mediation model (PA → ERS → IRD → Being bullied), with a mediation effect of -0.071 (95% CI: -0.375, -0.037), supporting hypothesis H4b.

This study examines the predictive effects of PA on bullying and victimization, as well as the role of emotional management and interpersonal relationship issues in mediating this relationship. It reveals the mechanism by which PA predicts bullying and victimization through its influence on emotional management and interpersonal relationships.

Direct effects analysis of PA on bullying and being bullied

The study found that PA can positively predict school bullying, but its predictive effect on victimization is not significant. Empirical studies on the relationship between PA and bullying/victimization in China are relatively scarce. The results of this study are consistent with most related studies both domestically and internationally, indicating that higher levels of PA may be associated with the occurrence of school bullying. Upon entering middle school, adolescents face the challenge of re-establishing peer relationships. During this period, active physical activities may lead to more frequent participation in various social activities on campus, thereby increasing the risk of exposure to potential conflicts. Without adequate supervision, these conflicts may escalate into bullying behaviors. However, some studies do not distinguish between bullying and victimization, suggesting that the higher the level of physical participation, the less frequent the occurrence of school bullying. These studies suggest that physical participation can enhance cognitive functions, reduce sensitivity to hostile information, and decrease attention to dangerous behaviors. Haney Aguirre-Loaiza et al. first confirmed through experimental intervention the positive effects of PA on inhibitory control and emotional situation recognition [ 38 ]. Additionally, physical participation can improve the quality of peer relationships, further reducing the occurrence of school bullying. PA may also enhance the cognitive flexibility and emotional regulation abilities of victims, helping them better cope with the negative impacts of bullying experiences. This study found that the direct predictive effect of PA on bullying may be related to the personality changes of adolescents during puberty and the high level of activity brought by PA. In this context, although the positive effects of PA may not be as significant as expected, it still helps in understanding the role of PA in school bullying.

For victimization, this study did not find a significant direct effect between PA and being bullied, which is consistent with the findings of Ortega [ 39 ]. Some studies suggest that regular participation in PA can reduce the likelihood of becoming a victim of bullying. PA is not only an important way to convey values but also enhances communication skills and promotes prosocial attitudes. Therefore, students’ physical activities are considered a health-promoting practice, and physically active students are generally believed to be more capable of protecting themselves. Hermoso and his team explored the relationship between PA, sedentary behavior, and the experience of bullying among children and adolescents [ 40 ]. They found that not meeting PA guidelines and excessive sedentary behavior are risk factors for being bullied, while at least 60 min of moderate-to-vigorous PA per day leads to better health and quality of life. Previous cross-sectional studies have also shown that not meeting these PA guidelines significantly increases the risk of bullying among children and adolescents. Therefore, PA is seen as an effective tool for preventing and reducing the occurrence of bullying. Hermoso et al. believe that students lacking PA are more likely to be bullied due to factors such as insufficient motor skills, poor physical fitness, and lack of confidence in participating in physical activities [ 40 ]. Nevertheless, this study did not find a significant direct effect between PA and being bullied. The occurrence of school bullying is influenced by various factors, including family background, cultural adaptation, bullying experience, and parental educational background. For example, Jang et al. pointed out that long-term bullying victimization is a potential risk factor for the mental health of children from multicultural families, particularly among adolescents whose mothers are from Southeast Asia [ 58 ]. The study by Kim and Fong explored the relationship between the bullying victimization experiences of children from multicultural families and their cultural adaptation and life satisfaction [ 41 ]. They found that entering bullying victimization is associated with reduced emotional cultural adaptation, and both entering and exiting bullying victimization are related to life satisfaction. Park used an asymmetric fixed effects model to evaluate the effects of entering and exiting bullying victimization [ 42 ]. He found that the mother’s college education level enhances the psychological health benefits of exiting bullying victimization but does not mitigate the harmful effects of entering it. The protective effect of the mother’s college education level is particularly significant for girls.

In summary, the direct predictive effects of PA on school bullying and victimization have not yet reached a consensus. Due to the complexity of the direct predictive effects of PA on bullying and victimization, more research is needed to further confirm this relationship and consider other potential influencing factors.

Indirect effects Analysis of ERS and IRD

PA has a significant positive predictive effect on emotional management, consistent with most related studies [ 43 ]. Regular and sustained PA plays a crucial role in regulating emotions. When individuals are in an uncomfortable state, excellent emotional regulation abilities often lead to a reevaluation of existing cognitive elements related to interpersonal perception, memory, and thinking. This approach helps quickly find flexible and effective ways to avoid conflicts and contradictions in interpersonal interactions, aiding individuals in better integrating into groups [ 44 ].

According to the general aggression model, negative emotions such as anxiety, depression, and anger can bring negative experiences to adolescents, who may bully others to vent these unpleasant feelings [ 45 ]. However, this study did not confirm the hypothesis that emotional management negatively predicts bullying and victimization. Additionally, the effects of emotional regulation strategies vary in different contexts, and individuals can flexibly deploy these strategies according to changing situational demands [ 46 ]. Emotional regulation encompasses various strategies, such as cognitive reappraisal and emotional suppression. Individuals may use different strategies to cope with emotions, but this study did not distinguish between them, potentially obscuring the predictive role of emotional regulation self-efficacy on bullying and victimization. Additionally, differences in emotional regulation abilities among individuals and the ways male and female students handle positive and negative emotions may contribute to the non-significant relationship between emotional management and bullying/victimization.

The hypothesis that PA negatively predicts interpersonal relationship issues was confirmed in this study. Regular participation in PA increases opportunities for interaction among students, especially in group activities [ 47 ]. It promotes mutual interactions while reducing the occurrence of some negative emotions. Adolescents usually do not prefer to communicate with guardians such as family and teachers, leading to relatively less social support and help, which weakens their interpersonal communication skills during this period. When conflicts and disputes arise among students, they tend to use simple and rough methods to cope, leading to deteriorating peer relationships and escalating conflicts, which may trigger school bullying. The conclusion that interpersonal relationship issues positively predict school bullying and victimization was also confirmed in this study, consistent with most research results [ 25 , 47 , 48 ]. Negative interpersonal relationships among adolescents can directly predict aggressive behaviors. Previous studies have shown that poor conflict management skills in interpersonal relationships are a risk factor for bullying [ 49 ]. Longitudinal studies also show that reducing negative emotions like anxiety and depression and fostering positive peer perceptions can predict a reduction in victimization [ 50 ]. Adolescents with interpersonal relationship issues often experience frustration in real-life interactions. The widespread use of the internet and the development of mobile media lead them to escape reality, feel alienated from society, and seek fulfillment in the virtual world [ 51 ]. Adolescents during this period may view bullying as a reasonable way to solve problems and may become bullies in certain situations. Meanwhile, the excessive use of mobile phones, the internet, and other media takes up a lot of students’ time, making those who lack PA and are sedentary more likely to become victims of bullying [ 15 ].

This study also found that emotional management negatively predicts interpersonal relationship issues, similar to most domestic and international studies [ 52 , 53 , 54 ]. Liu proposed that emotional self-management ability, psychological resilience, and adolescent PA mutually promote each other, enhancing adolescents’ social communication skills [ 52 ]. With good emotional management strategies, individuals can transform negative emotions, stressful events, and disharmony into conditions that motivate self-development, reduce the experience of negative emotions, and handle interpersonal relationships more rationally, effectively alleviating awkwardness and discomfort in interactions [ 53 ]. Emotional management is closely related to the development of individual social cognitive characteristics and social communication skills [ 54 ]. Adolescents who frequently exhibit negative emotions may experience social withdrawal, lack of activity, and a lack of sports skills, leading to lower acceptance among peers, such as being less talkative or not fitting in. In contrast, good emotional management can help adolescents better handle various issues related to self-development and social interactions.

In summary, this study confirmed the positive predictive effect of PA on emotional management and its negative predictive effect on alleviating interpersonal relationship issues. This provides important guidance for the application of PA in preventing school bullying. By incorporating the challenges and stress of activity into daily physical education classes, students can learn and experience the effectiveness of emotional regulation during physical activities. Some studies have shown that participating in relaxation activities such as yoga [ 55 ] and meditation [ 56 ] can significantly promote emotional stability and enhance self-regulation abilities, helping students better control and adjust their emotions during exercise, thus more effectively coping with school pressures and conflicts. Additionally, encouraging participation in group activities [ 57 ] can significantly enhance students’ social skills, increase peer interactions, and reduce feelings of isolation and interpersonal conflicts. Implementing these strategies can help schools effectively reduce the impact of interpersonal relationship issues on bullying and victimization, and improve students’ social environment. Ultimately, this will help create a positive and healthy living environment for students, promoting their overall well-being and healthy development.

Strengths and limitations

This study, grounded in the theories of self-efficacy and interpersonal relationships, incorporates ERS and IRD as mediating variables. It separately models and explores the relationships and underlying mechanisms between physical activity (PA), bullying behavior, and being bullied. The findings provide empirical evidence and intervention recommendations for addressing school bullying, assisting educators and society in effectively tackling issues that impact students’ physical and mental health.

However, this study has some limitations. Firstly, as a cross-sectional study, it cannot infer causal relationships between variables. Future research could employ longitudinal tracking or experimental intervention designs to better explain the impact of PA on school bullying. Secondly, while this study considered the mediating roles of self-efficacy and IRD, other potential moderating factors such as cultural adaptation [ 41 ] and parental education level [ 58 ] might also influence the research outcomes. Future research could incorporate these factors to achieve a more comprehensive understanding of the complex relationship between bullying victimization and mental health. Additionally, the study’s participants were junior high school students from Eastern China, which may limit the generalizability of the findings. Future research should expand the sample to include students from more countries and regions. Lastly, this study explored the predictive mechanism of bullying based on students’ PA levels, but the practical implications of the findings need further strengthening. Future research should examine the effects of different forms, intensities, and types of PA on school bullying.

In the model predicting school bullying, PA significantly predicts bullying, ERS, and IRD. ERS negatively predicts IRD but does not significantly predict bullying. Additionally, IRD significantly predicts bullying. The prediction of school bullying by PA includes masking effects, with two primary pathways: (1) PA → IRD → bullying, and (2) PA → ERS → IRD → bullying. In the model predicting being bullied at school, PA does not significantly predict this outcome. However, PA significantly positively predicts ERS and negatively predicts IRD. ERS significantly negatively predicts IRD but does not significantly predict being bullied. IRD significantly predicts being bullied. PA fully mediates the prediction of being bullied, with the pathways being: (1) PA → IRD → being bullied, and (2) PA → ERS → IRD → being bullied.

Based on the results of this study, we conclude that PA is an effective way to improve students’ emotional regulation and interpersonal relationships, significantly reducing both bullying and victimization. Given the prevalence of school bullying and its negative impact on mental health, public health policies should prioritize increasing adolescent participation in moderate-to-vigorous physical activity. This not only enhances physical health but also effectively improves emotional regulation and develops students’ social interaction skills. Additionally, it is recommended that structured physical activity be incorporated into school curricula to create a relaxed and enjoyable learning environment, improving peer relationships and maximizing the prevention of school bullying.

Data availability

The datasets of this study are available from the corresponding author on reasonable request.

Abbreviations

Campus Bullying Scale

Comparative Fit Index

Conversation Trouble

Emotional Management Self-Efficacy Scale

Emotion Regulation Self-Efficacy

Expressing Positive Emotions

Exposure to Heterosexual Distress

Goodness of Fit Index

Interpersonal Relationship Distress Scale

Interpersonal Relationship Distress

Interaction Trouble

Moderate-to-Vigorous Physical Activity

Physical Activity

Physical Activity Rating Scale

Root Mean Square Error of Approximation

Regulating Fear

Regulating Stress

Standardized Root Mean Square Residual

Tucker-Lewis Index

Trouble Treating Others

Victimization

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Acknowledgements

We are grateful to the schools for approving our baseline survey and the intervention study and thank all the students who participated in our study.

This work was supported by the Key Project of Chongqing Academy of Educational Science (Grant No. 2021-16-238). The funding agency had no role in the design of the study, data collection, analysis, interpretation of data, writing of the manuscript, or the decision to publish.

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Zhang, Q., Deng, W. Relationship between physical exercise, bullying, and being bullied among junior high school students: the multiple mediating effects of emotional management and interpersonal relationship distress. BMC Public Health 24 , 2503 (2024). https://doi.org/10.1186/s12889-024-20012-y

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DOI : https://doi.org/10.1186/s12889-024-20012-y

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