The Moderating Effect of Internet Ethics on the Relationship Between Cyberbullying Victimization and Perpetration Among Korean Adults*

Article information

Psychiatry Investig. 2025;22(1):47-56
Publication date (electronic) : 2025 January 15
doi : https://doi.org/10.30773/pi.2024.0127
1Department of Psychology, Graduate School, Dankook University, Cheonan, Republic of Korea
2Department of Psychology and Psychotherapy, College of Health Science, Dankook University, Cheonan, Republic of Korea
Correspondence: Sung-Man Bae, PhD Department of Psychology and Psychotherapy, College of Health Science, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan 31116, Republic of Korea Tel: +82-41-550-8142, E-mail: spirit73@hanmail.net
*This paper has revised and submitted the first author’s master’s thesis.
Received 2024 April 9; Revised 2024 July 7; Accepted 2024 August 14.

Abstract

Objective

Cyberbullying is increasing every year and poses a serious problem worldwide; although the rate of adult cyberbullying is increasing every year, still cyberbullying studies mainly focused on youths. This study examined the moderating effect of Internet ethics on the relationship between cyberbullying victimization and perpetration among adults.

Methods

An online self-report survey was conducted with 601 participants aged 20 to 59. A final total of 593 participants were included in the analysis. Confirmatory factor analysis of the Internet Ethics Scale was performed using AMOS 22.0, and the moderating effect was verified using PROCESS Macro v3.5.

Results

First, cyberbullying victimization was found to positively predict perpetration. These results indicate that the higher the cyberbullying victim experience, the more the cyberbullying behavior increases. Second, the moderating effect of Internet ethics on the relationship between cyberbullying victimization and perpetration was significant. Third, in the relationship between cyberbullying victimization and perpetration, the moderating effects of respect, responsibility, justice, and non-maleficence, which are subfactors of Internet ethics, were found to be significant.

Conclusion

This study demonstrated the preventive effect of Internet ethics on the relationship between cyberbullying victimization and perpetration among adults. Based on this, a theoretical basis for the intervention of education and programs for adult cyberbullying prevention was provided.

INTRODUCTION

The Internet usage rate in South Korea is 96.5%, which is the highest among Organization for Economic Cooperation and Development countries [1] and is increasing every year [2]. Although the Internet provides convenience and increases interpersonal interaction, it also leads to dysfunctions such as privacy invasion, hacking, and malicious comments [3-5]. Among these issues, cyberbullying, which refers to all violent acts in the Internet environment, is a serious problem that occurs worldwide [6,7].

Cyberbullying is defined as an intentional and repetitive act of threatening, harassing, and embarrassing others using electronic devices [8,9]. Many researchers have attempted to explain cyberbullying using the three elements of traditional bullying proposed by Olweus [10]: intention, repetition, and an imbalance of power between perpetrator and victim [11,12]. However, cyberbullying must be distinguished from traditional bullying because it is conducted non-face-to-face, using information and communication technology [13,14]. In particular, anonymity and non-face-to-face interactions cause disinhibition of individual aggressive behavior and expression of desire and, in fact, reduces fear of punishment [15,16]. In addition, the psychological pain is aggravated by the fact that it is difficult for the victim to identify the perpetrator [17], and it is easy for offenders to avoid taking responsibility for their actions, the frequency and intensity of the cyberbullying perpetration increases [18,19].

Meanwhile, according to the 2020 cyberbullying fact-finding survey conducted with 1,500 adults between the ages of 20 and 60 [20], the rate of cyberbullying victims and perpetrators among all adults were 65.8%, regardless of age; this rose every year and the cyberbullying perpetration rate was 40.9%—more than four times higher than that of youth (9.5%). Despite the seriousness of adult cyberbullying, research on cyberbullying has been conducted mainly among youth and college students; in other words, research on adult cyberbullying remains insufficient [7,21,22].

Victims of cyberbullying not only experience psychological distress such as depression, anxiety, stress, low self-esteem, and suicidal ideation, but also report functional decline in various areas of life, including low academic achievement, poor work ability, and difficulties in interpersonal relationships [23-26]. In addition, it may cause externalization problems such as increasing individual aggression and developing a permissive attitude toward violence, conversely leading individuals to become cyberbullying perpetrators themselves [27,28].

However, experiencing violence does not mean that all victims become perpetrators [29,30]. In a longitudinal study that verified the predictive factors of cyberbullying victimization and perpetration among university students, only 23.0% of cyberbullying victims were found to engage in cyberbullying perpetration [31]. In another study, cyberbullying victim experience did not predict cyberbullying perpetration [32].

Cyberbullying risk factors include victim experience, low self-control, and school life maladjustment. Particularly, cyberbullying victimization is one of the major risk factors predicting cyberbullying perpetration [7,33,34]. On the other hand, since the experience rate of overlapping cyberbullying bullyvictims is very high, there is a limit to explaining the causal relationship. Nevertheless, several studies have confirmed that cyberbullying victimization can lead to perpetrator behavior by mediating various variables such as internalization of anger [27], anxiety [35], moral disengagement [36], self-control [37]. Also, according to general strain theory, the strain caused by negative stimuli such as violence may cause depression, anger, and frustration, and individuals may engage in deviance and criminal acts to relieve strain [38]. Anger toward the perpetrator stimulates revenge and weakens self-control, promoting aggressive behavior [39]. Several studies have confirmed that cyberbullying victims may become cyberbullying perpetrators because of the motive of revenge against the perpetrator [27,34]. Therefore, this study intends to assume that the victimization precedes the perpetration based on previous studies and the general strain theory.

Although the roles of social participation, parental attachment, stress coping strategies, and peer relationships were identified as variables that can alleviate the relationship between victim experience and perpetrator behavior [22,35,37,40], but these results are limited to adolescents. Considering that adults are capable of more mature thinking than adolescents and that external control is difficult in Internet use [41], additional research on cognitive variables that can recognize cyberbullying issues and control one’s behavior related to online is needed.

For the reason that, researchers have emphasized the role of individual ethics in preventing and reducing cyberbullying [42,43]. Ethics is a concept related to individual morality. Generally, people with low ethical levels are more likely to engage in violent or delinquent behavior [44]. In the Internet environment, the role of individual beliefs or attitudes is emphasized because the power of control due to social ties is weakened [45,46]. On the other hand, since traditional normative ethics alone have limitations in coping with all the problems arising in the Internet environment, the concept of ‘Internet ethics’, which focuses on the standards of behavior related to the Internet, has been proposed [47]. Internet ethics is a norm necessary to properly judge and act on what is ethical and unethical in the information society, and consists of four sub-factors: respect, responsibility, justice, and non-maleficence [48]. Respect refers to recognizing and respecting the differences between oneself and others, and responsibility refers to performing one’s duty and being attentive to the needs of others. Justice is related to fairness, altruism, and compliance with laws and rules, also non-maleficence concerns the intention to not harm others.

According to previous studies, the more that cyberbullying is perceived as a serious ethical problem, the less likely are incidents of cyberbullying to occur [49-51]. A high level of Internet ethics in adults aged 20–50 years has a negative effect Internet violence [52]. In particular, the moderating effect of Internet ethics is found to be significant in the relationship between low self-control and cybercrime among college students [53]. The perception of the problems of cyberattacks moderates the relationship between exposure to violent online media and cyberattacks [54]. In addition, the moderating effects of Internet ethics is found in the relationship between cyberbullying victim experiences and cyberbullying using smartphones among college students [55].

Among demographic variables, sex and age have been related to cyberbullying perpetration. However, the degree of cyberbullying perpetration according to sex was different in each study [56-58], and the younger the age, the more the cyberbullying perpetration [59,60]. Additionally, cyberbullying perpetration behavior has been found to increase as Internet usage time increases [61,62]. Therefore, it is necessary to control for sex, age, and Internet usage time to explain cyberbullying perpetration behavior.

In summary, a negative relationship between Internet ethics and cyberbullying perpetration was confirmed; however, the moderating effect of Internet ethics on the relationship between cyberbullying victimization and perpetration among adults remains unclear. In addition, as most Internet Ethics Scales (IESs) used in previous studies were developed for adolescents, they consist of questions that are inadequate for adults [63]. This study modified the existing IES for use among adults and verified its validity. This study aimed to examine the moderating effect of Internet ethics on the relationship between cyberbullying victimization and perpetration.

METHODS

Participants and survey

In this study, an online survey was conducted using Google questionnaires with 601 adults between the ages of 20 and 59 years and residing in the Republic of Korea. The online survey was conducted from August 1 to 8. The questionnaire consisted of a total of 68 questions, all of which were self-reported, and generally took 15 minutes to complete. Prior to the start of the survey, it was informed that anonymity and confidentiality were guaranteed, and that the survey could be discontinued at any time if respondents felt uncomfortable. Only those who voluntarily consented to this were allowed to participate in the survey.

For accurate results, cases with fixed responses to all questions in the survey were excluded from the analysis. Accordingly, eight participants were excluded and a final total of 593 participants were included in the analysis. All research procedures, including data collection, were approved by the Institutional Review Board of Dankook University (approval number:2022-06-003-005). Of the 593 participants, 286 (48.2%) were male and 307 (51.8%) were female. The distribution by age group was 152 participants in their 20s (25.6%), 167 in their 30s (28.2%), 140 in their 40s (23.6%), and 134 in their 50s (22.6%), with an average age of 38.06 years (SD=10.41). The details are presented in Table 1.

Sociodemographic characteristics (N=593)

Measures

Cyberbullying victimization

Cyberbullying victimization was measured using the Korean version of the Cyberbullying Experiences Survey (KCES) developed by Doane et al. [64] and validated in Korean by Kim [65]. The items included “someone has posted a nude photo of me online,” “someone has treated me badly online,” and “someone has abused me online,” etc. A total of 16 questions are rated on a 6-point Likert scale (1 point: not at all to 6 points: every day/almost every day); the higher the total score, the greater the cyberbullying victimization. In the study of Kim [65], the total Cronbach’α was 0.94, and in this study, the total Cronbach’α was 0.94.

Cyberbullying perpetration

Cyberbullying perpetration was also measured using the K-CES [65]. The survey items included “I have sent unwanted sexual messages to someone online,” “I have made fun of someone online,” “I have tried to elicit information that the person I communicated with online did not want to share,” etc. A total of 13 questions were rated on a 6-point Likert scale (1 point: not at all to 6 points: every day/almost every day); the higher the total score, the greater the cyberbullying perpetration. In the study of Kim [65], the total Cronbach’α was 0.96, and in this study, the total Cronbach’α was 0.95.

Internet ethics

For Internet ethics, the Cyber Ethical Consciousness Scale developed by Cho [66] was modified and supplemented. First, content validity was checked by three experts in related fields (an information sociology professor, a social welfare professor, and a psychology professor), and certain questions with low content validity or that raised concerns about overlap with dependent variables were deleted. In addition, some questions were modified such that they were not limited to a specific age group, with a final total of 29 questions included. The IES consists of nine questions regarding respect, nine questions regarding responsibility, seven questions regarding justice, and four questions regarding non-maleficence; each question was evaluated on a 5-point Likert scale (1 point: not at all to 5 points: very much so). The higher the total score, the higher the level of Internet ethics. The questions consist of “Insulting others on the Internet is a crime,” “I seem to generally respect others on the Internet,” “Even if anonymity is guaranteed, you should not use bad language with strangers,” “The harm suffered on the Internet is weaker than the damage on off-line,” etc.

Confirmatory factor analysis (CFA) was performed to validate the factor structure of the scale using IBM AMOS version 22.0 (IBM Corp., Armonk, NY, USA). In this study, the goodness of fit of the factor structure was evaluated by comprehensively considering the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). In addition, modification indices (MI) and factor loadings were used to improve the fit of the factor structure. First, according to Kline [67] suggestion that inter-question correlations should be considered, covariance was set when the MI were 10 or higher. In the case of factor loadings for each question, the minimum recommended value is 0.30, and 0.40 or higher was considered desirable [68,69]. As a result of the analysis, the factor loading of responsibility question 6 was found to be -0.131 and was therefore excluded. Finally, the goodness of fit of the confirmatory model was CFI=0.955, TLI=0.943, and RMSEA=0.048, which satisfied all the criteria for an excellent model. The total Cronbach’α was 0.93, with 0.84 for respect, 0.67 for responsibility, 0.89 for justice, and 0.60 for non-maleficence.

Statistical analysis

The IBM SPSS Statistics version 23.0 (IBM Corp.), IBM AMOS version 22.0, and PROCESS Macro for SPSS version 3.5 (https://www.processmacro.org/index.html) were used for data analysis. First, frequency and descriptive statistical analyses were conducted using IBM SPSS Statistics version 23.0. Second, correlation analysis was conducted using Pearson’s correlation coefficient to determine the relationship between each variable. Third, a CFA was conducted using AMOS 22.0 to verify the construct validity of the IES. Fourth, sex, age, and Internet usage time were set as control variables, and sex was coded as 0=male and 1=female. Finally, to verify the moderating effect, the PROCESS Macro 1 model was used, and the independent and moderating variables were mean-centered to minimize multicollinearity [70]. In addition, to confirm the detailed interaction effect, the significance of the simple regression line between the independent and dependent variables according to the condition value of the moderating variable was verified [71].

RESULTS

Descriptive statistics and correlation analysis

Pearson’s correlation analysis revealed that cyberbullying victimization showed a significant positive correlation with cyberbullying perpetration (r=0.673, p<0.001). Cyberbullying victimization showed a significant negative correlation with Internet ethics (r=-0.520, p<0.001), respect (r=-0.571, p<0.001), responsibility (r=-0.319, p<0.001), justice (r=-0.426, p<0.001), and non-maleficence (r=-0.506, p<0.001). In addition, Internet ethics (r=-0.534, p<0.001), respect (r=-0.583, p<0.001), responsibility (r=-0.351, p<0.001), justice (r=-0.453, p<0.001), and non-maleficence (r=-0.464, p<0.001) were all found to have significant negative correlations with cyberbullying perpetration. Table 2 presents descriptive statistics and correlation analyses of the major variables.

Descriptive statistics and correlations of variables (N=593)

The moderating effect of internet ethics

Cyberbullying victimization (B=0.494, t=22.590, p<0.001) and Internet ethics (B=-0.055, t=-4.504, p<0.001) were significantly related to cyberbullying perpetration. In addition, the interaction effect of the two variables (B=-0.012, t=-11.066, p<0.001) significantly predicted cyberbullying perpetration. Cyberbullying victimization (B=0.392, t=17.464, p<0.001) and respect (B=-0.242, t=-7.522, p<0.001) were significantly related to cyberbullying perpetration. In addition, the interaction effect of the two variables (B=-0.039, t=-15.553, p<0.001) significantly predicted cyberbullying perpetration. Cyberbullying victimization (B=0.654, t=36.905, p<0.001) and responsibility (B=-0.112, t=-2.398, p<0.05) were significantly related to cyberbullying perpetration. Furthermore, the interaction effect of the two variables (B=-0.012, t=-3.051, p<0.01) significantly predicted cyberbullying perpetration. Cyberbullying victimization (B=0.612, t=32.185, p<0.001) and justice (B=-0.083, t=-1.992, p<0.05) were significantly related to cyberbullying perpetration. Additionally, the interaction effect of the two variables (B=-0.021, t=-5.869, p<0.001) significantly predicted cyberbullying perpetration. Finally, cyberbullying victimization (B=0.518, t=20.999, p<0.001) and non-maleficence (B=-0.216, t=-3.101, p<0.01) were significantly related to cyberbullying perpetration. Moreover, the interaction effect of the two variables (B=-0.052, t=-9.130, p<0.001) significantly predicted cyberbullying perpetration. Table 3 presents the results of the study.

The moderating effect of internet ethics in the relationship between cyberbullying victimization and cyberbullying perpetration (N=593)

Since the interaction effect was significant for both Internet ethics and subfactors, the effect of the independent variable on the dependent variable was analyzed at the specific value of the moderating variable (Mean -1SD, Mean, Mean +1SD) (Table 4). The group with low Internet ethics (B=0.704, t=40.634, p<0.001) showed a stronger positive relationship between cyberbullying victimization and perpetration than the group with high Internet ethics (B=0.316, t=8.881, p<0.001) (Figure 1). In the group with high Internet ethics, the effect of cyberbullying victimization on perpetration was lower. Respect, responsibility, justice, and non-maleficence showed similar trends (Figures 2-5).

Moderated results for engagement across levels of internet ethics (N=593)

Figure 1.

The moderating effect of Internet ethics.

Figure 2.

The moderating effect of respect.

Figure 3.

The moderating effect of responsibility.

Figure 4.

The moderating effect of justice.

Figure 5.

The moderating effect of non-maleficence.

DISCUSSION

This study aimed to examine the moderating effect of Internet ethics on the relationship between cyberbullying victimization and perpetration among adults aged between 20s to 50s in republic of Korea. The main results of this study are as follows:

First, it was found that males were more likely to engage in cyberbullying perpetration than females, which supports previous studies’ findings that males are more likely to show aggressive attitudes and behaviors in the Internet space than females [72-74]. It has been shown that the more time spent on the Internet, the greater the increase in cyberbullying perpetration [61,62], suggesting that efforts to control Internet usage time are necessary to prevent cyberbullying perpetration [75,76]. Age did not show a significant relationship with cyberbullying perpetration, and these results contradict previous studies showing that cyberbullying perpetration increases as age decreases [59,60]. These results suggest that cyberbullying perpetration occurs significantly among those in their 20s to 50s but not in a specific age group.

Second, cyberbullying victimization positively impacted cyberbullying perpetration. This finding supports general strain theory, which states that people become involved in deviance and crime as a strategy to relieve strain [7,38]. Victims of cyberbullying experience various psychological maladjustments such as depression, anxiety, and stress, and may commit cyberbullying to relieve these negative emotions [27,53,77]. In addition, victim experiences can stimulate hostility and desire for revenge, and develop a permissive attitude toward violence, increasing aggressive behavior [28,78]. The characteristics of cyberspace, such as non-face-to-face interaction or anonymity, enables victims to express aggression more easily [15].

Thirdly, Internet ethics demonstrated a mitigating effect on cyberbullying perpetration. This finding aligns with prior research indicating that awareness of Internet ethics assists individuals in identifying cyberbullying as unethical and in curtailing such behavior [49,50,52]. While the main effect of Internet ethics on reducing cyberbullying is statistically significant, its impact is further magnified when considering its interaction with cyberbullying victimization. This implies that while a general adherence to Internet ethics contributes to reducing cyberbullying incidents, Internet ethics educational interventions tailored to those who have experienced cyberbullying victimization could effectively deter them from cyberbully-victims.

Fourth, Internet ethics moderated the relationship between cyberbullying victimization and perpetration. These results support several previous studies claiming that a higher level of Internet ethics can serve as a protective factor against cyberbullying perpetration [51,53,79,80]. The Internet is more freely available to adults than it is to adolescents, and it is more difficult to control deviant behavior in the Internet environment than it is in reality because of the nature of the Internet; therefore, internal factors such as personal beliefs and attitudes play an important role in cyberbullying [46]. A high level of Internet ethics contributes to recognizing various crimes and violence occurring on the Internet as negative acts, and can thus reduce cyberbullying perpetration [50,55,81]. Therefore, proper education on moral norms and responsibilities that must be observed in the Internet space can help individuals suppress inappropriate behavior and more strongly recognize Internet ethical issues [82-84].

Fifth, all sub-factors of Internet ethics such as respect, responsibility, justice, and non-maleficence showed moderating effects on the relationship between cyberbullying victimization and perpetration. Respect was found to weaken the relationship between cyberbullying victimization and perpetration more than the other subfactors. Respect in the context of Internet ethics refers to respecting the rights and personalities of others and not slandering them [48]. Cyberbullying occurs through disrespectful attitudes towards others [11,85]. Therefore, people with a high level of respect are more likely not to harm others to relieve their negative feelings. Responsibility refers to fulfilling one’s duties and being attentive to the needs of others [48]. In the Internet environment, it is easier to avoid responsibility for ones’ actions than in the real world through anonymity, non-face-to-face interaction, transcendence of time and space, and open access to information; accordingly, one can rationalize one’s negative actions or lower one’s sense of guilt [15,46]. Therefore, individuals with a high level of responsibility are expected to anticipate the consequences of their actions and take responsibility for them, and are therefore less likely to engage in cyberbullying. Justice refers to an attitude that fairness, altruism, and law, and sometimes transcends it [48]. The higher the tendency to pursue fairness and rightness, the stronger the tendency to act ethically. Therefore, cyberbullying perpetration is expected to decrease when negative influences such as anger, desire for revenge, and aggression caused by cyberbullying victimization are controlled. Nonmaleficence relates to not harming others [48]. All negative actions that can cause harm to others, such as slandering or criticizing others in the Internet space, distributing pornography, and illegal websites, are related to a low level of non-maleficence. It is suggested that non-maleficence reduces cyberbullying by controlling permissive attitudes towards violence.

In this study, it was confirmed in adults of a comprehensive age group that moral beliefs such as Internet ethics suppress the reproduction of cyberbullying perpetrators. This suggests that in order to reduce cyberbullying, it is necessary to cultivate individuals’ Internet ethics. As mentioned in the introduction, the rate of adults experiencing cyberbullying in South Korea is increasing every year. However, little has been done to prevent this. According to the 2021 cyberbullying survey [86], eight out of ten adults were unaware of the possibility of legal punishment following cyberbullying, 89.5% of teenagers reported receiving cyberbullying prevention education, and 90.4% of adolescents reported receiving no prevention education. This means that the sensitivity of adults to cyberbullying is significantly lower than that of adolescents. To prevent cyberbullying, it suggests that it is necessary to conduct regular Internet ethics education for adults at the regional and national levels. In addition, along with this education, people should try to be sensitive to cyberbullying issues personally, and avoid criticizing others in spaces such as social media and YouTube under the guise of freedom of expression, and avoid sympathizing with criticism. This study is significant in that it identified the protective factor value of Internet ethics and verified the detailed role through sub-factors, thereby emphasizing the need for cyberbullying prevention education for adults and providing a theoretical basis for an Internet ethics education program in a timely manner.

The limitations of this study and suggestions for follow-up studies are as follows: the frequency of cyberbullying victimization and perpetration were investigated without setting a specific period. However, because the degree of cyberbullying perpetration can differ depending on the time point of cyberbullying victimization, it is necessary to examine their relevance by including the time points of victimization and perpetration in future research. Finally, in future studies, it is necessary to comprehensively review by adding additional mediation variables. In particular, it is necessary to confirm the influence of Internet ethics according to the level of negative emotions such as anger and depression, that the general strain theory emphasizes. This can provide a broader understanding of victims and information on the direction of intervention.

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Se-Ri Park, Sung-Man Bae. Data curation: Se-Ri Park, Sung-Man Bae. Formal analysis: Se-Ri Park. Investigation: Se-Ri Park. Methodology: Se-Ri Park, Sung-Man Bae. Project administration: Se-Ri Park, Sung-Man Bae. Supervision: Sung-Man Bae. Validation: Se-Ri Park. Visualization: Se-Ri Park. Writing—original draft: Se-Ri Park. Writing—review & editing: Sung-Man Bae.

Funding Statement

None

Acknowledgements

None

References

1. OECD. Individuals using the internet; 2020 [Internet]. Available at: https://www.oecd.org/en/publications/oecd-digital-economy-outlook-2020_bb167041-en.html.
2. National Information Society Agency. Internet usage survey of Korea 2021 [Internet]. Available at: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=99870&bcIdx=24378&parentSeq=24378. Accessed May 19, 2022.
3. Alam SS, Hashim NMHN, Ahmad M, Wel CAC, Nor SM, Omar NA. Negative and positive impact of internet addiction on young adults: empericial study in Malaysia. Intang Cap 2014;10:619–638.
4. Castells M. Technopoles of the world: the making of 21st century industrial complexes London: Routledge; 2014.
5. Wall DS. Cybercrime: the transformation of crime in the information age [Internet]. Available at: https://ssrn.com/abstract=1066922. Accessed June 7, 2022.
6. Gaffney H, Farrington DP, Espelage DL, Ttofi MM. Are cyberbullying intervention and prevention programs effective? A systematic and meta-analytical review. Aggress Violent Behav 2019;45:134–153.
7. Kowalski RM, Limber SP, McCord A. A developmental approach to cyberbullying: prevalence and protective factors. Aggress Violent Behav 2019;45:20–32.
8. Chun J, Lee J, Kim J, Lee S. An international systematic review of cyberbullying measurements. Comput Human Behav 2020;113:106485.
9. Lee DN, Ryu JY. [Tackling school-related online violence in the era of the COVID-19 pandemic: legislative issues and challenges]. J Law Educ 2021;33:161–185. Korean.
10. Olweus D. A profile of bullying at school. Educ Leadersh 2003;60:12–17.
11. Hinduja S, Patchin JW. Cyberbullying: an exploratory analysis of factors related to offending and victimization. Deviant Behav 2008;29:129–156.
12. Kowalski RM, Limber SP, Agatston PW. Cyberbullying: bullying in the digital age (2nd ed) New York: John Wiley & Sons; 2012.
13. Slonje R, Smith PK, Fris?n A. The nature of cyberbullying, and strategies for prevention. Comput Human Behav 2013;29:26–32.
14. Smith PK. Cyberbullying and cyber aggression (2nd ed). In: Jimerson S, Nickerson A, Mayer MJ, Furlong MJ, editors. Handbook of school violence and school safety: international research and practice. New York: Routledge, 2012, p.93-103.
15. Hinduja S, Patchin JW. Bullying beyond the schoolyard: preventing and responding to cyberbullying (2nd ed) Thousand Oaks: Corwin Press; 2014.
16. Lee SS, Jun SH. [An study on the causes of university students’ cyber bullying: three smart phone uses of texting, SNS, and internet]. Korean Crim Rev 2015;26:187–207. Korean.
17. Menesini E, Nocentini A, Palladino BE, Frisén A, Berne S, Ortega-Ruiz R, et al. Cyberbullying definition among adolescents: a comparison across six European countries. Cyberpsychol Behav Soc Netw 2012;15:455–463.
18. Choi JH, Kim DY. [The moderating effect of parent-child communication and self-control on the relationship between cyberbullying damage experience and suicidal ideation in female adolescents]. Korean J Appl Dev Psychol 2019;8:55–67. Korean.
19. Hinduja S, Patchin JW. Cyberbullying: a review of the legal issues facing educators. Prev Sch Fail 2011;55:71–78.
20. National Information Society Agency. The survey on cyberbullying 2020 [Internet]. Available at: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=68302&bcIdx=23180&parentSeq=23180. Accessed May 19, 2022.
21. Balakrishnan V. Cyberbullying among young adults in Malaysia: the roles of gender, age and internet frequency. Comput Hum Behav 2015;46:149–157.
22. Jun D, Kim D. [A study on adult’s cyberviolence]. Korean J Local Gov Adm Stud 2016;30:25–43. Korean.
23. Camerini AL, Marciano L, Carrara A, Schulz PJ. Cyberbullying perpetration and victimization among children and adolescents: a systematic review of longitudinal studies. Telemat Inform 2020;49:101362.
24. Kanwal H, Jami H. Exploring modes, strategies, and psychosocial consequences of cyberbullying perpetration and victimization among university students. Pak J Psychol Res 2019;34:787–817.
25. Martínez-Monteagudo MC, Delgado B, Díaz-Herrero Á, García-Fernández JM. Relationship between suicidal thinking, anxiety, depression and stress in university students who are victims of cyberbullying. Psychiatry Res 2020;286:112856.
26. Oksanen A, Oksa R, Savela N, Kaakinen M, Ellonen N. Cyberbullying victimization at work: social media identity bubble approach. Comput Hum Behav 2020;109:106363.
27. Ak Ş, Özdemir Y, Kuzucu Y. Cybervictimization and cyberbullying: the mediating role of anger, don’t anger me! Comput Hum Behav 2015;49:437–443.
28. Kim BS. [A study on the factors that influence adult cyberbullying-focusing on the mediation effect on the attitude to cyberbullying]. Inf Policy 2021;28:57–80. Korean.
29. Choi B, Park S. Who becomes a bullying perpetrator after the experience of bullying victimization? The moderating role of self-esteem. J Youth Adolesc 2018;47:2414–2423.
30. Marsh HW, Wen Z, Hau KT. Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction. Psychol Methods 2004;9:275–300.
31. Giumetti GW, Kowalski RM, Feinn RS. Predictors and outcomes of cyberbullying among college students: a two wave study. Aggress Behav 2022;48:40–54.
32. Fanti KA, Demetriou AG, Hawa VV. A longitudinal study of cyberbullying: examining riskand protective factors. Eur J Dev Psychol 2012;9:168–181.
33. Festl R, Quandt T. The role of online communication in long-term cyberbullying involvement among girls and boys. J Youth Adolesc 2016;45:1931–1945.
34. Walrave M, Heirman W. Cyberbullying: predicting victimisation and perpetration. Child Soc 2011;25:59–72.
35. Choi J. [Influence of cyber bullying victimization on cyber bullying: mediating effects of anxiety and moderation effects of stress coping strategy]. Crisisonomy 2015;11:195–214. Korean.
36. Merlici IA, Maftei A. The moral maze of cyberbullying: navigating the roles of victims, bystanders, and perpetrators in the cycle of harm. Deviant Behav 2024;Mar. 11. [Epub]. https://doi.org/10.1080/01639625.2024.2327563.
37. Oh S, Shin J. [The effect of cyber violence victimization on perpetration of children in the upper elementary grades: the mediating effect of self-control, and the moderating effect of child-parent attachment]. Forum Youth 2021;68:5–31. Korean.
38. Agnew R, White HR. An empirical test of general strain theory. Criminol 1992;30:475–500.
39. Kim K, Yoon H. [Associations between adolescents’ victimization of violence, tolerance toward violence, and cyber violence offending behavior]. J Korean Soc Child Welf 2012;39:213–244. Korean.
40. Leung ANM, Wong N, Farver JM. Cyberbullying in Hong Kong Chinese students: life satisfaction, and the moderating role of friendship qualities on cyberbullying victimization and perpetration. Pers Individ Differ 2018;133:7–12.
41. Cohen-Almagor R. Social responsibility on the internet: addressing the challenge of cyberbullying. Aggress Violent Behav 2018;39:42–52.
42. Leonard LN, Cronan TP, Kreie J. What influences IT ethical behavior intentions—planned behavior, reasoned action, perceived importance, or individual characteristics? Inf Manag 2004;42:143–158.
43. Peace AG, Galletta DF, Thong JY. Software piracy in the workplace: a model and empirical test. J Manag Inf Syst 2003;20:153–177.
44. Chu BW. [Online moral disengagement and the tasks of internet ethics education]. J Ethics 2012;1:119–141. Korean.
45. Agnew R. Social control theory and delinquency: a longitudinal test. Criminol 1985;23:47–61.
46. Rhee K, Kim MJ. [Are real ethics and internet ethics truly different?]. Commun KIISE 2012;30:9–14. Korean.
47. Kim KH, Cha EJ. [The influence of internet addiction on cyber delinquency among middle school students: testing the mediating effect of internet ethics]. Health Soc Welf Rev 2012;32:364–401. Korean.
48. Choo BW. [The formulation plan of cyber ethics]. Korea J Inf Soc 2001;3:164–190. Korean.
49. Bae SM. The relationship between exposure to risky online content, cyber victimization, perception of cyberbullying, and cyberbullying offending in Korean adolescents. Child Youth Serv Rev 2021;123:105946.
50. Kim SH, Lee CB, Kim SS, Kim TH, Jeong IS, Jo SJ. [The effects of the sense of cyber ethics on cyber bullying among adolescents: focusing on the moderating effects of parental control]. J Korean Public Police Security Stud 2022;19:17–34. Korean.
51. Molluzzo JC, Lawler J. A study of the perceptions of college students on cyberbullying. Inf Syst Educ J 2012;10:84–109.
52. Kim MK, Park JH. [Factors influencing the level of internet ethics and its relationship with internet violence among adults]. J Consum Policy Stud 2008;33:65–91. Korean.
53. Lee SS. [Testing the interaction effect between causal and countermeasure factors across three types of cybercrime]. J Korean Public Police Secur Stud 2018;15:209–234. Korean.
54. Bae SM. The moderating effect of the perception of cyber violence on the influence of exposure to violent online media on cyber offending in Korean adolescents. Sch Psychol Int 2021;42:450–461.
55. Jang HY, Lee SS. [An integrated test of interaction effect between causes and internet ethics of smart phone cyber bullying]. Inf Policy 2019;26:46–61. Korean.
56. De Pasquale C, Martinelli V, Sciacca F, Mazzone M, Chiappedi M, Dinaro C, et al. The role of mood states in cyberbullying and cybervictimization behaviors in adolescents. Psychiatry Res 2021;300:113908.
57. Ortega-Barón J, Buelga S, Cava MJ, Torralba E. [School violence and attitude toward authority of students perpetrators of cyberbullying]. Rev Psicodida?ct 2017;22:23–28. Spanish.
58. Wong DS, Chan HCO, Cheng CH. Cyberbullying perpetration and victimization among adolescents in Hong Kong. Child Youth Serv Rev 2014;36:133–140.
59. Ševčíková A, Šmahel D. Online harassment and cyberbullying in the Czech Republic: comparison across age groups. J Psychol 2009;217:227–229.
60. Young K, Govender C. A comparison of gender, age, grade, and experiences of authoritarian parenting amongst traditional and cyberbullying perpetrators. S Afr J Educ 2018;38(Supplement 1):S1–S11.
61. Lampridis E. Stereotypical beliefs about cyber bullying: an exploratory study in terms of myths. Univers J Educ Res 2015;3:135–147.
62. Musharraf S, Bauman S, Anis-Ul-Haque M, Malik JA. General and ICT self-efficacy in different participants roles in cyberbullying/victimization among Pakistani university students. Front Psychol 2019;10:1098.
63. Kim KM, Kim SS. [Development and validation of the information and communication ethics index of adolescents]. J Curric Eval 2014;17:191–221. Korean.
64. Doane AN, Kelley ML, Chiang ES, Padilla MA. Development of the cyberbullying experiences survey. Emerg Adulthood 2013;1:207–218.
65. Kim SK. [Validation of the Korean version cyberbullying experiences survey (K-CES)] [dissertation]. Daegu: Kyungpook National University; 2021. Korean.
66. Cho KI. [The research for the leveles and moral of teenagers in cyber space lncluding the development of evalutation methods for them]. Proc Korean Soc Comput Inf Conf 2013;17:151–156. Korean.
67. Kline RB. Principles and practice of structural equation modeling (4th ed) New York: Guilford Press; 2015.
68. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis (7th ed) New York: Prentice Hall; 2010.
69. Merenda PF. A guide to the proper use of factor analysis in the conduct and reporting of research: pitfalls to avoid. Meas Eval Counsel Dev 1997;30:156–164.
70. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach (2nd ed) New York: Guilford Press; 2017.
71. Frazier PA, Tix AP, Barron KE. Testing moderator and mediator effects in counseling psychology research. J Couns Psychol 2004;51:115–134.
72. Buelga S, Iranzo B, Cava MJ, Torralba E. Psychological profile of adolescent cyberbullying aggressors. Int J Soc Psychol 2015;30:382–406.
73. Erdur-Baker Ö. Cyberbullying and its correlation to traditional bullying, gender and frequent and risky usage of internet-mediated communication tools. New Media Soc 2010;12:109–125.
74. Li Q. Cyberbullying in schools: a research of gender differences. Sch Psychol Int 2006;27:157–170.
75. Gámez-Guadix M, Borrajo E, Almendros C. Risky online behaviors among adolescents: longitudinal relations among problematic internet use, cyberbullying perpetration, and meeting strangers online. J Behav Addict 2016;5:100–107.
76. Zsila Á, Orosz G, Király O, Urbán R, Ujhelyi A, Jármi É, et al. Psychoactive substance use and problematic internet use as predictors of bullying and cyberbullying victimization. Int J Ment Health Addict 2018;16:466–479.
77. Yoo SM, Kim JM, Kim CG. [The effects of psychological characteristics of adolescents on cyberbullying perpetration: focusing on the mediatig effect of cyberbullying victimization]. J Emot Behav Disord 2017;33:63–82. Korean.
78. Choi Y, Kim HS. [The mediating effects of displaced aggression in the relationship between child abuse experiences and cyberbullying tendencies in early adolescents]. Fam Fam Ther 2018;26:321–341. Korean.
79. Kumazaki A, Suzuki K, Katsura R, Sakamoto A, Kashibuchi M. The effects of netiquette and ICT skills on school-bullying and cyber-bullying: the two-wave panel study of Japanese elementary, secondary, and high school students. Procedia Soc Behav Sci 2011;29:735–741.
80. Sung DK, Kim DH, Lee YS, Lim SW. [A study on the cyber-violence induction factors of teenagers: focused on individual inclination, cyber violence damage experience, and moral consciousness]. J Cybercomm Acad Soc 2006;19:79–129. Korean.
81. Han JH, Chang HS. [The behavioral model of digital music piracy on the web]. J Inf Syst 2007;16:135–158. Korean.
82. Kim W. [Comparison of online flip-learning learning effects on the cultivation of internet ethics awareness for university students]. J Korean Assoc Comput Educ 2021;24:89–96. Korean.
83. Hurlburt G. Toward applied cyberethics. Comput 2018;51:80–84.
84. Lee YH, Kim JD, Park JH. [A study on development of common criteria for evaluation of internet ethics index]. J Internet Comput Serv 2016;17:75–85. Korean.
85. Tokunaga RS. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput Hum Behav 2010;26:277–287.
86. National Information Society Agency. The survey on cyberbullying 2021 [Internet]. Available at: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=68302&bcIdx=24353&parentSeq=24353. Accessed May 19, 2022.

Article information Continued

Figure 1.

The moderating effect of Internet ethics.

Figure 2.

The moderating effect of respect.

Figure 3.

The moderating effect of responsibility.

Figure 4.

The moderating effect of justice.

Figure 5.

The moderating effect of non-maleficence.

Table 1.

Sociodemographic characteristics (N=593)

Variable Value
Sex
 Male 286 (48.2)
 Female 307 (51.8)
Age (year)
 20–29 152 (25.6)
 30–39 167 (28.2)
 40–49 140 (23.6)
 50–59 134 (22.6)
Education
 High school graduate or less 73 (12.3)
 Enrolled in university 71 (12.0)
 University graduate 405 (68.3)
 Above graduate school 44 (7.4)
Monthly income (million won)
 <2 112 (18.9)
 ≥2 to <4 343 (57.8)
 ≥4 to <6 109 (18.4)
 ≥6 to <8 19 (3.2)
 ≥8 to <10 5 (0.8)
 ≥10 5 (0.8)
Times of internet usage (hr)
 <1 31 (5.2)
 ≥1 to <2 163 (27.5)
 ≥2 to <3 233 (39.3)
 ≥3 to <4 90 (15.2)
 ≥4 to <5 31 (5.2)
 ≥5 45 (7.6)

Data are presented as number (%).

Table 2.

Descriptive statistics and correlations of variables (N=593)

1 2 3 4 5 5-1 5-2 5-3 5-4 6
1
2 -0.035
3 0.073 0.030
4 -0.230*** -0.115* 0.269***
5 0.274*** 0.063 -0.239** -0.520***
5-1 0.237** 0.290 -0.237** -0.571*** 0.928***
5-2 -0.196** 0.181* -0.286*** -0.319*** 0.883*** 0.718***
5-3 0.288*** 0.038 -0.220** -0.426*** 0.916*** 0.781*** 0.813***
5-4 0.239** 0.104* -0.256** -0.506*** 0.736*** 0.680*** 0.514*** 0.531***
6 -0.288** -0.190* 0.379*** 0.673*** -0.534*** -0.583*** -0.351*** -0.453*** -0.464*** -
M 1.52 2.43 2.94 25.31 109.51 36.04 30.29 28.20 15.60 17.99
SD 0.50 1.10 1.32 11.91 16.65 5.99 4.64 5.19 3.02 9.51
W -0.07 0.11 0.11 2.01 -0.62 -0.36 -0.90 -1.25 -0.29 2.72
K -2.00 -1.31 -1.31 4.32 0.06 -0.46 1.10 1.53 -0.73 7.38
*

p<0.05;

**

p<0.01;

***

p<0.001.

1, sex; 2, age; 3, Internet usage time; 4, cyberbullying victimization; 5, Internet ethics; 5-1, respect; 5-2, responsibility; 5-3, justice; 5-4, non-maleficence; 6, cyberbullying perpetration; M, mean; SD, standard deviation; W, skewness; K, kurtosis

Table 3.

The moderating effect of internet ethics in the relationship between cyberbullying victimization and cyberbullying perpetration (N=593)

Variable Dependent variable: Cyberbullying perpetration
B SE t 95% CI R2
Sex -0.370 0.347 -1.066* -1.053 to -0.112 0.813***
Age 0.163 0.156 1.047 -0.143 to 0.470
Internet usage time 0.371 0.134 2.769** 0.108 to 0.634
Cyberbullying victimization 0.494 0.022 22.590*** 0.451 to 0.537
Internet Ethics -0.055 0.012 -4.504*** -0.079 to -0.031
Cyberbullying victimization×Internet ethics -0.012 0.001 -11.066*** -0.014 to -0.010
Sex -0.758 0.314 -2.410* -1.374 to -0.140 0.842***
Age 0.051 0.143 0.359 -0.230 to 0.333
Internet usage time 0.510 0.123 4.133*** 0.268 to 0.752
Cyberbullying victimization 0.392 0.022 17.464*** 0.348 to 0.436
Respect -0.242 0.032 -7.522*** -0.305 to -0.179
Cyberbullying victimization×Respect -0.039 0.003 -15.553*** -0.044 to -0.034
Sex -0.644 0.386 -1.671* -1.402 to -0.014 0.775***
Age 0.114 0.172 0.666 -0.223 to 0.452
Internet usage time 0.332 0.148 2.251* 0.042 to 0.622
Cyberbullying victimization 0.654 0.018 36.905*** 0.619 to 0.689
Responsibility -0.112 0.047 -2.398* -0.203 to -0.020
Cyberbullying victimization×Responsibility -0.012 0.004 -3.051** -0.020 to -0.004
Sex -0.459 0.376 -1.221* -1.198 to -0.279 0.786***
Age 0.118 0.167 0.703 -0.211 to 0.446
Internet usage time 0.332 0.144 2.313* 0.050 to 0.614
Cyberbullying victimization 0.612 0.019 32.185*** 0.574 to 0.649
Justice -0.083 0.042 -1.992* -0.166 to -0.001
Cyberbullying victimization×Justice -0.021 0.004 -5.869*** -0.028 to -0.014
Sex -1.329 0.358 -3.713*** -2.032 to -0.626 0.796***
Age 0.106 0.163 0.647 -0.215 to 0.426
Internet usage time 0.474 0.140 3.374** 0.198 to 0.750
Cyberbullying victimization 0.518 0.025 20.999*** 0.470 to 0.567
Non-maleficence -0.216 0.070 -3.101** -0.353 to -0.079
Cyberbullying victimization×Non-maleficence -0.052 0.006 -9.130*** -0.063 to -0.041
*

p<0.05;

**

p<0.01;

***

p<0.001.

SE, standard error; CI, confidence interval

Table 4.

Moderated results for engagement across levels of internet ethics (N=593)

Moderating variables Levels Effect SE t 95% CI
Internet ethics Mean -1SD 0.692 0.018 38.806*** 0.657 to 0.727
Mean 0.494 0.022 22.590*** 0.451 to 0.537
Mean +1SD 0.297 0.036 8.311** 0.227 to 0.367
Respect Mean -1SD 0.625 0.017 37.722*** 0.592 to 0.657
Mean 0.392 0.022 17.464*** 0.348 to 0.436
Mean +1SD 0.159 0.034 4.635*** 0.092 to 0.227
Responsibility Mean -1SD 0.709 0.022 32.309*** 0.666 to 0.752
Mean 0.654 0.018 36.905*** 0.619 to 0.689
Mean +1SD 0.599 0.028 21.263** 0.544 to 0.655
Justice Mean -1SD 0.719 0.021 37.740*** 0.679 to 0.760
Mean 0.612 0.019 32.185*** 0.574 to 0.649
Mean +1SD 0.504 0.031 16.209** 0.443 to 0.565
Non-maleficence Mean -1SD 0.675 0.018 37.957*** 0.640 to 0.710
Mean 0.518 0.025 20.999*** 0.470 to 0.567
Mean +1SD 0.361 0.039 9.359** 0.286 to 0.437
**

p<0.01;

***

p<0.001.

SD, standard deviation; SE, standard error; CI, confidence interval