Gender Differences in Online Harassment Perpetration among University Students in Korea: Impact of Patriarchal Family and Moderators

Abstract

The influence of the patriarchal family environment on deviance has been limited to offline harassment perpetration. This study examines its impact on online harassment perpetration, incorporating moderating effects of differential association with peers engaging in online harassment and low self-control. Students attending universities in Seoul, South Korea, were surveyed about online harassment perpetration via smartphones. The OLS regression analysis of a sample of 301 students reveals that the patriarchal family environment is significantly associated with online harassment perpetration, with a notably more pronounced effect among men. Additionally, peer association showed enhancing effects, but this was significant only among women.

Keywords

online harassment patriarchal family differential association with peers who harass online low self-control gender difference

Introduction

As cybercrime has emerged as a new social problem, numerous attempts to explain the nature of this novel phenomenon have developed it into a major research area in criminology. Online harassment, a subtype of cybercrime, has become a common problem for many individuals and groups in contemporary society (Marcum & Higgins, 2019; Patchin & Hinduja, 2022). Defined as threats or engaging in other offensive behaviors online (Jones et al., 2013), online harassment includes sending threatening or unwanted sexual content through various online platforms (Finkelhor et al., 2000; Holt et al., 2012). Additionally, as online activities have become a part of daily life with the development of smartphones and various spaces for online interaction, social network services (SNS) are the most common places for online harassment with exclusion, alienation, and bullying, perpetrated mainly in online spaces by real-life friends (Marcum et al., 2014).

Statistics underscore the prevalence of these behaviors and gender differences among perpetrators. The 2022 School Crime Supplement (SCS) to the National Crime Victimization Survey reported that approximately 39.0% of students aged 12–18 were bullied. Among those, 21.6% indicated that the bullying took place online or through text (National Center for Education Statistics, 2024). In South Korea, a national survey on victimization and perpetration of cyber violence conducted in 2023 revealed that 40.8% of adolescents and 8.0% of adults experienced cyberbullying. Among adolescents who reported experiencing cyberbullying, 19.3% acknowledged engaging in it. A breakdown by gender indicated that 25.0% of boys and 13.2% of girls reported perpetration. Similarly, among adults who experienced cyberbullying, 2.2% admitted perpetrating it, with 2.5% of men and 1.9% of women reporting perpetration (Korea Communications Commission & National Information Society Agency, 2023).

Research interest in these behaviors has grown since Olweus’s early attempts to define cyberbullying (Olweus, 1993), which is a form of cyber-deviant behavior that is synonymous with online harassment (Choi et al., 2022). Studies have applied traditional criminological frameworks, such as strain, social learning, and low self-control to elucidate the underlying cause of online harassment within anonymous relationships in online expanded networks (Holt et al., 2012; Jun, 2020; Lee et al., 2021; Li et al., 2016; Lianos & McGrath, 2018; Patchin & Hinduja, 2011; Wang & Ge, 2021). This study adds to the literature by introducing the patriarchal family environment as a risk factor for online harassment perpetration.

Patriarchal factors, such as the patriarchal family environment and patriarchal peer support, are primarily used to explain men’s sexual violence against women (Franklin et al., 2012; Goodson et al., 2021; Hunnicutt, 2009). These factors have also been shown to predict other violent offenses among young adults and adolescents (Eitle et al., 2014; Jung et al., 2024) and bullying in Korea (Lee, 2011). Research indicates that individuals in patriarchal family environments tend to exhibit higher risk preferences and lower perceptions of risk (Hirtenlehner et al., 2014), while bullying often functions as a tool of patriarchal dominance (Olthof et al., 2011; Sijtsema et al., 2009). From this, it can be inferred that patriarchal environments foster violence or harassment as an externalization of criminogenic traits, such as higher risk preferences, and as a means of gaining status, power, and fame among peers. Despite the increasing convergence between online and offline forms of harassment attributable to the widespread use of the Internet, there has been a notable absence of studies examining the association between the patriarchal family environment and online harassment perpetration (see Holt & Steinmetz, 2021). Therefore, this study primarily focuses on exploring this relationship.

This study emphasizes understanding gender differences in these associations. According to the power-control theory, traditional gender roles within the patriarchal family environment tend to discourage delinquency among adolescent girls while encouraging delinquency among adolescent boys (Hagan et al., 1987). Our study aims to analyze gender differences in risk factors for online harassment perpetration and discuss implications and potential countermeasures. Specifically, we focus on the patriarchal family environment as a primary risk factor for online harassment perpetration and investigate whether its effects differ between men and women. Additionally, we examine whether the moderating effects of differential association with peers engaging in online harassment and low self-control differ by gender. In the subsequent sections, we review existing literature on online harassment to formulate research questions related to these gender differences.

Literature Review

Patriarchal Family Environment as a Possible Cause of Online Harassment Perpetration

Conventional criminology has historically overlooked female criminality and the role of patriarchy in crimes by and against women (Lilly et al., 2018), where patriarchy is defined as the “hierarchical organization of social institutions and social relationships that allows men to maintain positions of power, privilege, and leadership” (DeKeseredy & Schwartz, 1993, p. 396). The second wave of the women’s movement in the 1960s and 1970s challenged this oversight, initially focusing on explaining the narrowing gender gap in crime rates and later drawing attention to patriarchy (Daly & Chesney-Lind, 1988). Early feminists emphasized the importance of patriarchy in explaining crimes against women, particularly domestic and sexual violence (Dobash & Dobash, 1979; Yllö & Bograd, 1988), arguing that male violence against women stems from a desire to maintain dominance in a patriarchal society. This perspective suggests that men, empowered by patriarchy in a gender-unequal society, commit violence to exercise power and control over women.

This argument extends to explaining general violence perpetrated by men, including male-on-male violence, claiming that violence is a tool to dominate and express masculinity (Messerschmidt, 1993; Morales et al., 2016). Previous studies have argued that men who pursue hegemonic masculinity are more likely to commit crimes such as violence (Mahalik et al., 2005; Messerschmidt, 1986; Richardson & Hammock, 2007). Additionally, Carrera-Fernández et al. (2018) indicate that bullying in secondary education settings during 2008–2009 is a “strongly gendered phenomenon.” Boys performing hegemonic masculinity often bully male peers and utilize them as instruments of control to assert dominance over girls, while simultaneously portraying themselves as protector heroes. In contrast, girls performing hegemonic femininity tend to control their female peers through everyday meanness, positioning themselves within sexual hierarchies that emphasize decent sexuality, appearance, and behavior to construct and control desirable femininity (Carrera-Fernández et al., 2018). Accordingly, the patriarchal family environment, which reinforces hegemonic masculinity for boys and traditional femininity for girls, may contribute to delinquent behaviors. In other words, men with a sense of patriarchy use violence as a tool to achieve their goals of subjugating, dominating, and controlling opponents who challenge their authority. This is particularly evident in male bullying offenses, as bullying is a process of pursuing social control to gain dominant power in peer groups (Olweus, 1993; Smith & Brain, 2000).

Interest in gender differences related to the patriarchal family environment is largely due to the emergence of Hagan’s power-control theory (Hagan et al., 1979, 1987). This theory asserts that the effect of the patriarchal family environment differs between boys and girls. Specifically, the theory argues that imbalanced power relations between parents, constructed by their labor force participation, produce patriarchal gender-role beliefs and lead to differences in parental control and supervision of sons and daughters. Sons, receiving less supervision than daughters, are socialized to face danger with toughness, whereas daughters are socialized to avoid risk and internalize the submissive role of their mothers. Thus, in patriarchal households, sons are more likely to commit delinquency, whereas daughters are less likely to commit delinquency. Most empirical tests of power-control theory have indicated that the effect of the patriarchal family environment is more significant in predicting delinquency in boys than girls (Blackwell & Piquero, 2005; Eitle et al., 2014; Grasmick et al., 1996; Hadjar et al., 2007; Lee, 2011).

Since the power-control theory was designed to explain the gender gap in common forms of deviant behaviors, few studies have examined the applicability of the patriarchal family environment and power-control theory to cybercrime, a specific type of crime (Holt & Steinmetz, 2021). With the rise in internet use and the expansion of online interaction platforms, the online network is increasingly seen as an extension or integral part of reality, allowing people to connect with offline acquaintances (Näsi et al., 2011). Although online harassment presents unique risk factors due to its specific nature, studies have found significant overlaps and connections between offline bullying and cyberbullying (Baldry et al., 2016; Casas et al., 2013). It is generally argued that bullying is a computational act for gain rather than impulse (Reijntjes et al., 2013) in that it is an act to secure social dominance, fame, and status (Olthof et al., 2011; Sijtsema et al., 2009). In other words, in modern society, where online interaction intersects with offline interaction, online harassment can be seen as a result of patriarchal authoritarian thinking about domination or punishment, similar to offline harassment.

Differential Association with Peers Who Harass Online and Low Self-Control as Moderators and Gender Differences in Their Moderating Effects

Many previous studies have argued that differential association with other lawbreakers is the most significant risk factor for crime and delinquency (Warr & Stafford, 1991). Social learning theory argues that criminal behavior, like other behaviors, is learned through social interactions with others, particularly with deviant peers (Akers, 1985). When a person primarily associates with lawbreaking friends, they learn and internalize definitions favorable to violating the law, eventually leading to criminal behavior. Differential association with peers who engage in cyber delinquency has been identified as one of the most significant risk factors for cyber delinquency (Higgins et al., 2007; Holt et al., 2012; Miller & Morris, 2016; Skinner & Fream, 1997), including online harassment and cyberbullying (Jones et al., 2013; Kim et al., 2022; Li et al., 2016; Sasson & Mesch, 2017). According to social norms theory, people tend to adopt the norms prevalent among their peers (Berkowitz, 2005). Thus, individuals who associate with peers engaging in online harassment learn attitudes, beliefs, and definitions favorable to such behavior, leading them to commit these acts individually or collectively, influenced by their learned norms. Although online harassment is often anonymous, perpetrators might also want to share their experiences of committing harassment to gain acceptance and validation from their peers.

A general theory of crime argues that adolescents with low self-control are more likely to commit delinquency and crimes (Gottfredson & Hirschi, 1990). Indeed, studies have found that adolescents are prone to commit crimes because they struggle with delaying instant gratification and impulsivity (Gibbs & Giever, 1995; Pratt & Cullen, 2000). Low self-control may also be an important risk factor for online offenses. The anonymity of cyberspace creates an ideal opportunity to commit crimes, where individuals may act more impulsively. Consequently, those with low self-control can easily commit offenses with a single click, unable to adequately control their behavior. In contrast, people with high self-control are less likely to commit offenses by controlling themselves, even in crime-inducing environments. Previous studies have suggested that low self-control is a major risk factor for various cybercrimes, such as online harassment and cyberbullying (Holt et al., 2012; Kim et al., 2022; Lianos & McGrath, 2018).

Research on gender differences in the effects of the two predictors, differential association with peers and low self-control, has been mixed. First, although some studies have shown that the influence of differential association with deviant peers on deviance is more significant for boys (Mears et al., 1998; Piquero et al., 2005), most studies indicate that girls are more significantly influenced in comparison to boys (Haynie et al., 2014; Heimer, 1996; McGloin & O’Neill Shermer, 2009; Steketee et al., 2013; Zimmerman & Messner, 2010). Second, the influence of low self-control on deviant behaviors, including online harassment, is generally similar between males and females (Baek et al., 2016; Burton Jr et al., 1998; Shekarkhar & Gibson, 2011); however, some studies have found evidence of gender differences indicating that low social control is only a significant predictor of crime for boys and men (Burton Jr et al., 1998; Steketee et al., 2013).

The influence of differential association with peers and low social control on online harassment may also differ by gender. Given that boys’ offenses are more related to the presence of low self-control (Choi & Kruis, 2020), it may be that boys find the online environment, which allows for immediate and direct harassment, to be an ideal opportunity for perpetuating online harassment. In contrast, girls who are more influenced by deviant peers (Marcum et al., 2014) may be more likely to engage in online harassment collectively with friends or, even when acting individually, tend to follow their peers’ norms. In other words, deviant peers may predict online harassment offenses among girls, whereas low self-control may predict offenses among boys.

Current Study

This study examines the patriarchal family environment as the main cause of online harassment. It posits that individuals raised in patriarchal environments are more likely to commit online harassment driven by motives of power and dominance. In addition, differential association with peers engaging in online harassment and low self-control—variables frequently highlighted in earlier studies on online harassment—are treated as moderating factors with enhancing effects. In other words, this integrated perspective posits that individuals within patriarchal family environments are more likely to commit online harassment under the conditions of peers engaging in online harassment and low self-control. Based on these considerations, the research questions for this study are as follows.

Research Question 1

How is the patriarchal family environment associated with online harassment perpetration among men and women, respectively?

Research Question 2

Does differential association with peers engaging in online harassment enhance the association between the patriarchal family environment and online harassment perpetration?

Research Question 2a

If so, does the moderating role of differential association with peers engaging in online harassment vary between men and women?

Research Question 3

Does low self-control enhance the association between the patriarchal family environment and online harassment perpetration?

Research Question 3a

If so, does the moderating role of low self-control vary between men and women?

Methods

Participants

This study examines online harassment via smartphones, utilizing data from a 2018 survey conducted among young adult university students attending universities in Seoul, South Korea. University students were chosen as the target sample due to their high frequency of smartphone usage, particularly among individuals in their late teens and twenties (Pew Research Center, 2021), and their accessibility within this age group. The survey was conducted over two weeks in July 2018, with approval from the Institutional Review Board of Soongsil University. Participants were informed that their participation was completely voluntary, and their responses would be treated as confidential and protected by the law. All respondents provided consent, and the questionnaire was administered in Korean.

Five universities in Seoul were randomly selected from each cardinal direction—north, south, east, west, and center. After selecting the target universities, the researchers employed a quota sampling method and conducted in-person surveys, considering participants’ gender, birth year, and major as demographic subgroups. The demographic composition of these subgroups was determined using the 2017 Statistical Yearbook of Education in Korea. Five graduate and undergraduate students from Soongsil University’s Department of Information Sociology visited the selected campuses, recruited respondents in person, and conducted paper-based surveys. Respondents self-reported their responses, with approximately 60 students randomly chosen per university. As an incentive, participants received a coffee coupon upon completing the survey. Among the 304 respondents who completed the study, 301 smartphone users were selected as the final participants for the analysis.

Measures

As the dependent variable in this study, online harassment perpetration was measured using representative online harassment behaviors, such as insult/defamation, stalking, and sexual harassment (Bossler et al., 2012; Slaughter & Newman, 2022; Willard, 2007). Respondents were asked whether they had engaged in the following while using their smartphones in the previous year: (1) cursing, using insulting language, or spreading rumors about others; (2) continually sending messages or posting comments despite the person’s lack of consent; and (3) sexually harassing others by sending obscene messages, photos, and video clips (0 = no, 1 = yes). The sum of the three parameters was used.

According to the power-control theory, the patriarchal family environment, the independent variable of this study, is reflected by the amount of authority each parent has in the workplace (Hagan et al., 1987). However, we tried to identify this variable more substantively and directly by measuring the power relationships between mothers and fathers, as well as between parents and children, as suggested by Hirtenlehner et al. (2014) and Lee (2011). Therefore, we used three questions to measure the patriarchal family environment: “My father dominates my mother,” “My father’s authority is so great that other family members are subservient to him,” and “My parents do not consider their children’s opinions; instead, they make decisions arbitrarily.” Respondents answered these questions on a five-point scale ranging from “strongly disagree (=1)” to “strongly agree (=5).” The summed score of these responses was used, resulting in Cronbach’s alpha of 0.845, indicating high reliability.

To measure differential association with peers engaging in online harassment, one of the moderating variables of this study, we adapted the measurement used by previous literature into a binary format (Warr, 2002; Warr & Stafford, 1991). We asked respondents whether they had a close friend who had committed each type of online harassment in the previous year (i.e., insult/defamation, stalking, sexual harassment; zero = no, 1 = yes). The sum of the three parameters was used for the analysis.

To measure low self-control, we used a shortened version of the scale developed by Grasmick et al. (1993), which originally consisted of 24 questions (6 characteristics of low self-control × 4 questions). For practical reasons related to the questionnaire’s length, we selected two questions from each of six characteristics: impulsivity, risk-seeking, simple tasks, physical activity, self-centeredness, and volatile temper. For example, questions included “I often act on impulse,” which respondents answered on a five-point scale ranging from “strongly disagree (=1)” to “strongly agree (=5).” The summed score of the 12 questions was used, yielding a Cronbach’s alpha of 0.815, indicating high reliability.

We also included measures of gender, age, and perceived family socioeconomic status. For gender, respondents were asked if they identified as female or male. For age, respondents answered questions about their birth year, which we recoded as actual ages. For perceived family socioeconomic status, respondents assessed their family’s status based on a scale of 1–5: “low (=1),” “middle-low (=2),” “middle (=3),” “middle-high (=4),” and “high (=5).”

Plan of Analysis

This study analyzed four research questions using two analytical models segmented by gender. The baseline model examined the effect of the patriarchal family environment on online harassment by gender, controlling for socio-demographic variables. The full model incorporated interaction terms involving differential association with peers engaging in online harassment and low self-control and tested the moderating effects, controlling for socio-demographic variables. The interaction terms were mean-centered to mitigate multicollinearity. We conducted Ordinary Least Squares regression analysis using SPSS 24.0.

Results

Table 1 presents the descriptive analysis of the sample used in this study, which includes 149 men (49.5%) and 152 women (50.5%) with an average age of 21.48 years. On average, respondents perceived their family socioeconomic status as middle, with an average score of 3.20. Examining the main variables, the average score for the patriarchal family environment was higher for women (5.55) than for men (5.30). The average score for the association with peers engaging in online harassment, one of the moderators in this study, was higher for men (0.76) compared to women (0.39). Women (38.10) had lower self-control than men (37.55). Finally, the average score for the dependent variable, online harassment perpetration, was higher for men (0.37) than for women (0.09).

Table 1.

Descriptive Statistics.

	All	Male	Female	Range
Age	21.48	21.99	20.98	18–27
Perceived family socioeconomic status	3.20	3.26	3.15	1–5
Patriarchal family environment	5.43	5.30	5.55	3–15
Association with peers engaging in online harassment	0.57	0.76	0.39	0–3
Low self-control	37.83	37.55	38.10	12–60
Online harassment	0.23	0.37	0.09	0–3

The OLS regression results presented in Table 2 show that the effects of the independent variable and interaction variables depend on gender. In the baseline model, after controlling for socio-demographic variables, the patriarchal family environment shows statistically significant associations with online harassment perpetration in both men and women. To be specific, while holding all socio-economic variables in the model constant, a one-unit increase in the patriarchal family environment is, on average, associated with 0.075 units increase in online harassment perpetration among males (p < .001) and 0.020 units increase in online harassment perpetration among females (p < .05). The patriarchal family environment, including socio-demographic variables, explained 10.9% of online harassment in men, which was more explanatory than the 4.5% in women. For men, perceived family economic status had a significant effect on online harassment, indicating that lower economic levels were associated with higher online harassment perpetration. In addition, age did not have a significant influence on online harassment for either men or women.

Table 2.

OLS Regression Results Predicting Online Harassment Perpetration Across Gender.

Variables	Baseline Model				Full Model
	Male		Female		Male		Female
	b	β	b	β	b	β	B	β
Age	-.010	-.027	.022	.124	.014	.038	.019	.106
Perceived family socioeconomic status	-.172*	-.186	.008	.015	-.077	-.083	.010	.019
Patriarchal family environment	.075***	.280	.020*	.163	.017	.064	.017	.142
Association with peers engaging in online harassment	-	-	-	-	.279***	.391	.146***	.315
Low self-control	-	-	-	-	.019**	.209	.007	.127
Patriarchal family*Peers	-	-	-	-	.034	.163	.023*	.170
Patriarchal family*Low self-control	-	-	-	-	.001	.044	.002	.148
F-value	5.871***		2.315*		13.425***		6.728***
R²	.109		.045		.499		.249

Note. *p < 0.05; **p < 0.01; ***p < 0.001.

The full model examines the interaction effect of the two moderating variables—the differential association with peers engaging in online harassment and low self-control—on the relationship between the patriarchal family environment and online harassment, stratified by gender. The R-square value increased by approximately 39.0% in the male model and 20.4% in the female model. Moreover, the independent associations of the main variables with online harassment show that the patriarchal family environment is not significantly associated with online harassment in either men or women. In addition, the differential association with peers engaging in online harassment exhibits a positive and significant association with online harassment among both men and women (p < .001). The findings reveal that low self-control is positively and significantly associated with online harassment among men (p < .01), suggesting that lower self-control of men corresponds to an increased likelihood of online harassment perpetration.

Examining the results of the interaction effect between the patriarchal family environment and moderating variables, which is the main focus of this study, the interaction effects between the patriarchal family environment and differential association with peers engaging in online harassment are positively significant only for women (p < .05). This significant interaction implies that the greater the differential association with peers engaging in online harassment experienced by young adult women, the stronger the impact of their patriarchal family environment on their online harassment perpetration. In contrast, the interaction between the patriarchal family environment and low self-control shows no significant effect for either women or men.

Discussion and Conclusion

The current study investigates the patriarchal family environment as a primary determinant of online harassment perpetration, exploring whether individuals in patriarchal family environments are more likely to commit online harassment. It also considers differential association with peers engaging in online harassment and low self-control—two extensively studied risk factors—as moderating variables to explore their interaction with the patriarchal family environment. Above all, this study examines potential gender differences in the effect of the patriarchal family environment and the interaction effects of moderating variables.

The impact of the patriarchal family environment on online harassment revealed statistically significant results across both genders, especially among men, when not accounting for the moderating effects of differential association with peers engaging in online harassment and low self-control in the model. This result aligns with previous research, suggesting that males in patriarchal environments are more likely to commit online harassment compared to those in egalitarian family environments, driven by motives of dominance and power (Caravita et al., 2012; Lee, 2011). Additionally, as suggested by the power-control theory, sons in patriarchal families may engage in riskier behaviors with less fear of consequences, such as official punishment, due to less control from their parents (Hagan et al., 1987).

However, in contrast to the power-control theory's assertion that girls in patriarchal households are less likely to commit delinquency compared to those in egalitarian households, this study’s findings indicate that young adult women from patriarchal households are more likely to engage in online harassment than their counterparts from less patriarchal households. One potential explanation for this could be that while power-control theory focuses on real-world delinquency among adolescents, this study examines online harassment perpetration among college students in their twenties. Young adults in their twenties often experience greater freedom from parental supervision compared to adolescents and perceive online harassment perpetration as more feasible due to the anonymity it provides. Therefore, women college students from patriarchal backgrounds may be addressing the strains of their repressive family environments during adolescence through online interactions as adults.

Another emphasis of this study is the independent and interaction effects of the two moderators across genders. First, differential association with peers engaging in online harassment was the most positively significant factor for both men and women. This suggests that the influence of peers who engage in online harassment is substantial not only for women who are involved in collective forms of harassment but also for men who engage in individual forms of harassment. Even if men individually commit online harassment, they may be influenced by friends who have engaged in similar behaviors.

However, the interaction effect of peers engaging in online harassment was significant only among women. This aligns with previous research on the interaction effect of differential association with peers engaging in online harassment on the association between general strain and delinquency (Botchkovar & Hughes, 2010; Morash & Moon, 2007). Given the comparatively subdued effect of the patriarchal family on women’s online harassment, it becomes apparent that the young adult women in our sample exhibit an increased likelihood of engaging in deviant behaviors, especially when they associate with deviant peers. Considering the propensity of women engaging in relational offenses compared to men, peers engaging in online harassment may play a vital and supportive role for women from patriarchal families to engage in such activities. They may choose to perpetrate online harassment as a convenient tool of rebellion against parental control, given its lower detection risk when compared to offline activities. Additionally, since the peer association variable measures the behaviors of a best friend rather than a broader peer group, this finding indicates that the influence may stem from a close relationship with a best friend, not necessarily from a larger peer group. This nuanced understanding suggests that the influence of a close friend may be pivotal for young adult women from patriarchal families to engage in online harassment behaviors.

Low self-control had a significant independent effect on online harassment only among men (Mears et al., 1998; Piquero et al., 2005; Steketee et al., 2013), indicating that impulsive men with low self-control are more likely to act on the opportunity to harass in an online setting. This suggests that men’s online harassment is more likely driven by direct and impulsive individual aggression, in contrast to the more collective forms seen among women.

The interaction effect of low self-control on online harassment was not significant, despite low self-control being a significant factor among men. This means that young adult men with lower social control from patriarchal families were not necessarily more likely to engage in online harassment compared to those with higher self-control in similar family environments. The significant effect of low self-control in the model might have offset the effect of the patriarchal family variable, rendering the interaction term insignificant. Young adult men within patriarchal families who have a sense of power may commit online harassment as a rational strategy to dominate others, where higher self-control could potentially make the impact of the patriarchal family environment on online harassment perpetration stronger. In addition, the interaction effect of low self-control was also not significant among young adult women.

However, several limitations should be noted. First, our measurement of patriarchal families limits the applicability of our findings to power-control theory. While this study measured patriarchal families in terms of family culture and relationships, it is noteworthy that different occupational positions between spouses have been deemed a more precise means of categorizing household typologies, thereby distinguishing between patriarchal and egalitarian families (Blackwell, 2000; Hagan et al., 1987). Second, we did not explicitly include a “don’t know” option for questions regarding differential association with peers engaging in online harassment. Participants were instructed to respond with “yes” or “no” based on their knowledge. We recognize that some respondents might have preferred a “don’t know” option or refrained from responding, which could influence the accuracy of reported data. Future research should consider incorporating a “don’t know” option to provide participants with a more nuanced response choice and improve the reliability of peer behavior assessments. Lastly, measuring peer association and the dependent variable as dichotomous variables is a limitation, as it does not capture the frequency of these variables. By only determining whether or not an individual engaged in specific behaviors, we miss out on important information regarding how often these behaviors occur. Frequency data can provide a more nuanced understanding of the severity and persistence of the problem behavior. e.g., individuals who frequently engage in online harassment may require more intensive intervention compared to those who engage in these behaviors infrequently. The lack of frequency data limits our ability to differentiate between varying levels of engagement in online harassment, which has significant implications for designing effective intervention programs. Future research should consider including frequency measures to address this limitation.

This study suggests that the causes of online harassment and the moderating variables that promote online perpetration risk may differ by gender among young adults. These results indicate that men and women may require distinct intervention and prevention strategies. When other risk factors are not considered, patriarchal family dynamics have a significant impact on online harassment for both genders, with a notably more pronounced effect among men. This finding suggests the need for targeted interventions that address patriarchal influences on male online behavior. For instance, educational programs that impart a profound understanding of equitable relationships, gender equality, and inclusivity to individuals may prevent the intergenerational transmission of patriarchal cultures among men.

Furthermore, in the case of men, low self-control tends to attract attention because it increases the possibility of online harassment. Therefore, interventions tailored to men may also prioritize the potential impact of low self-control. e.g., it could provide participants with strategies to identify impulsive behavior triggers and develop effective self-regulation techniques. In addition, the influence of deviant peers plays a significant role in young women’s online harassment because their deviant behaviors are usually committed collectively. Thus, intervention programs in colleges or communities aimed at women who are susceptible to peer influence should focus on providing opportunities for positive relationship building and facilitating communication with diverse individuals. These programs may create opportunities to disrupt the formation of deviant peer groups (e.g., Cooperative Learning; see Van Ryzin & Roseth, 2019).

This study ultimately underscores the need to consider gender differences in understanding online harassment perpetration. Notably, it is the first to empirically test the role of the patriarchal family environment as a risk factor for online harassment within the framework of power-control theory, revealing significant gender differences. Furthermore, the findings on the moderating effects of differential association with peers engaging in online harassment and low self-control—demonstrating that the influence of peer association is only significant for women—underscore the necessity for further research on gender-specific factors related to online harassment perpetration.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Soongsil University Institutional Review Board and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Soongsil University Institutional Review Board (No. SSU-202407-HR-525-1).

ORCID iD

Sohee Jung

Author Biographies

Dr. Seong-Sik Lee is a professor in the Department of Information Sociology at Soongsil University in South Korea. His main areas of research include cyber criminology, juvenile delinquency, and criminological theory. He has published more than 120 articles and books in his field. His recent publications include “SNS Online Victimization and Fear of Crime: Including the Moderating Effects of Disorder, Collective Efficacy, and Gender Difference” (with Dr. Cheong Sun Park) in Victim & Offenders (2022).

Sohee Jung is a doctoral student in the Department of Criminology and Criminal Justice at the University of South Carolina (M.A. at Soongsil University in South Korea). Her research interests include technology-faciliated sexual violence, gender-based violence, and juvenile delinquency. Her recent work has been published in the Journal of Criminal Justice, Crime and Delinquency, and the Journal of Experimental Criminology.

References

Akers

R. L.

(1985). Deviant behavior: A social learning approach (Subsequent edition). Wadsworth Pub Co.

Baek

Losavio

M. M.

Higgins

G. E.

(2016). The impact of low self-control on online harassment: Interaction with opportunity. Journal of Digital Forensics, Security and Law, 11(3), 6. https://doi.org/10.15394/jdfsl.2016.1417

Baldry

A. C.

Farrington

D. P.

Sorrentino

(2016). Cyberbullying in youth: A pattern of disruptive behaviour. Psicologia Educativa, 22(1), 19–26.

Berkowitz

A. D.

(2005). An overview of the social norms approach. Changing the Culture of College Drinking: A Socially Situated Health Communication Campaign, 1(1), 193–214.

Blackwell

B. S.

(2000). Perceived sanction threats, gender, and crime: A test and elaboration of power-control theory. Criminology, 38(2), 439–488. https://doi.org/10.1111/j.1745-9125.2000.tb00896.x

Blackwell

B. S.

Piquero

A. R.

(2005). On the relationships between gender, power control, self-control, and crime. Journal of Criminal Justice, 33(1), 1–17. https://doi.org/10.1016/j.jcrimjus.2004.10.001

Bossler

A. M.

Holt

T. J.

May

D. C.

(2012). Predicting online harassment victimization among a juvenile population. Youth & Society, 44(4), 500–523. https://doi.org/10.1177/0044118X11407525

Botchkovar

E. V.

Hughes

L. A.

(2010). Strain and alcohol use in Russia: A gendered analysis. Sociological Perspectives, 53(3), 297–319.

Burton

J. , V. S.

Cullen

F. T.

Evans

T. D.

Alarid

L. F.

Dunaway

R. G.

(1998). Gender, self-control, and crime. Journal of Research in Crime and Delinquency, 35(2), 123–147.

10.

Caravita

S. C.

Gini

Pozzoli

(2012). Main and moderated effects of moral cognition and status on bullying and defending. Aggressive Behavior, 38(6), 456–468.

11.

Carrera-Fernández

M. V.

Lameiras-Fernández

Rodríguez-Castro

(2018). Performing intelligible genders through violence: Bullying as gender practice and heteronormative control. Gender and Education. Available at: https://www.tandfonline.com/doi/abs/10.1080/09540253.2016.1203884

12.

Casas

J. A.

Del Rey

Ortega-Ruiz

(2013). Bullying and cyberbullying: Convergent and divergent predictor variables. Computers in Human Behavior, 29(3), 580–587.

13.

Choi

Kruis

Lee

(2022). Empathy, self-control, and online harassment: A partial test of agnew’s social concern theory. Computers in Human Behavior, 136(1), 107402.

14.

Choi

Kruis

N. E.

(2020). Gender, self-control, and opportunity: Applying the general theory of crime to online harassment. International Journal of Cyber Criminology, 14(1), 267–282.

15.

Daly

Chesney-Lind

(1988). Feminism and criminology. Justice Quarterly, 5(4), 497–538.

16.

DeKeseredy

W. S.

Schwartz

M. D.

(1993). Male peer support and woman abuse: An expansion of DeKeseredy’s model. Sociological Spectrum, 13(4), 393–413.

17.

Dobash

R. E.

Dobash

(1979) Violence against wives: A case against the patriarchy (15). Free Press.

18.

Eitle

Niedrist

Eitle

T. M.

(2014). Gender, race, and delinquent behavior: An extension of power-control theory to American Indian adolescents. Deviant Behavior, 35(12), 1023–1042. https://doi.org/10.1080/01639625.2014.903756

19.

Finkelhor

Mitchell

K. J.

Wolak

(2000). Online victimization: A report on the nation’s youth.

20.

Franklin

C. A.

Bouffard

L. A.

Pratt

T. C.

(2012). Sexual assault on the college campus: Fraternity affiliation, male peer support, and low self-control. Criminal Justice and Behavior, 39(11), 1457–1480.

21.

Gibbs

J. J.

Giever

(1995). Self-control and its manifestations among university students: An empirical test of Gottfredson and Hirschi’s general theory. Justice Quarterly, 12(2), 231–255.

22.

Goodson

Franklin

C. A.

Bouffard

L. A.

(2021). Male peer support and sexual assault: The relation between high-profile, high school sports participation and sexually predatory behaviour. Journal of Sexual Aggression, 27(1), 64–80.

23.

Gottfredson

M. R.

Hirschi

(1990). A general theory of crime. Stanford University Press.

24.

Grasmick

H. G.

Hagan

Blackwell

B. S.

Arneklev

B. J.

(1996). Risk preferences and patriarchy: Extending power-control theory. Social Forces, 75(1), 177–199.

25.

Grasmick

H. G.

Tittle

C. R.

Bursik Jr

R. J.

Arneklev

B. J.

(1993). Testing the core empirical implications of Gottfredson and Hirschi’s general theory of crime. Journal of Research in Crime and Delinquency, 30(1), 5–29.

26.

Hadjar

Baier

Boehnke

Hagan

(2007). Juvenile delinquency and gender revisited: The family and power-control theory reconceived. European Journal of Criminology, 4(1), 33–58. https://doi.org/10.1177/1477370807071729

27.

Hagan

Simpson

Gillis

A. R.

(1987). Class in the household: A power-control theory of gender and delinquency. American Journal of Sociology, 92(4), 788–816. https://doi.org/10.1086/228583

28.

Hagan

Simpson

J. H.

Gillis

A. R.

(1979). The sexual stratification of social control: A gender-based perspective on crime and delinquency. British Journal of Sociology, 30(1), 25. https://doi.org/10.2307/589499

29.

Haynie

D. L.

Doogan

N. J.

Soller

(2014). Gender, friendship networks, and delinquency: A dynamic network approach. Criminology, 52(4), 688–722.

30.

Heimer

(1996). Gender, interaction, and delinquency: Testing a theory of differential social control. Social Psychology Quarterly, 12(1), 39–61.

31.

Higgins

G. E.

Fell

B. D.

Wilson

A. L.

(2007). Low self-control and social learning in understanding students’ intentions to pirate movies in the United States. Social Science Computer Review, 25(3), 339–357.

32.

Hirtenlehner

Blackwell

B. S.

Leitgoeb

Bacher

(2014). Explaining the gender gap in juvenile shoplifting: A power-control theoretical analysis. Deviant Behavior, 35(1), 41–65. https://doi.org/10.1080/01639625.2013.822217

33.

Holt

T. J.

Bossler

A. M.

May

D. C.

(2012). Low self-control, deviant peer associations, and juvenile cyberdeviance. American Journal of Criminal Justice, 37(1), 378–395.

34.

Holt

T. J.

Steinmetz

K. F.

(2021). Examining the role of power-control theory and self-control to account for computer hacking. Crime & Delinquency, 67(10), 1491–1512. https://doi.org/10.1177/0011128720981892

35.

Hunnicutt

(2009). Varieties of patriarchy and violence against women: Resurrecting “patriarchy” as a theoretical tool. Violence Against Women, 15(5), 553–573.

36.

Jones

L. M.

Mitchell

K. J.

Finkelhor

(2013). Online harassment in context: Trends from three youth internet safety surveys (2000, 2005, 2010). Psychology of Violence, 3(1), 53.

37.

Jun

(2020). A study on the cause analysis of cyberbullying in Korean adolescents. International Journal of Environmental Research and Public Health, 17(13), 4648.

38.

Jung

Boehme

H. M.

Kaminski

R. J.

(2024). An integrated test of power-control theory by delinquency types: Assessing additional factors predicting adolescents risk-taking preferences. Crime & Delinquency.

39.

Kim

Lee

Jennings

W. G.

(2022). A path from traditional bullying to cyberbullying in South Korea: Examining the roles of self-control and deviant peer association in the different forms of bullying. Journal of Interpersonal Violence, 37(9–10), 5937–5957.

40.

Korea Communications Commission & National Information Society Agency . (2023). 2023 cyber violence survey. KCCNISA. Available at: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=68302&bcIdx=26483&parentSeq=26483

41.

Lee

S.-S.

(2011). Patriarchy families, differential socialization and delinquency: A gender difference. Studies on Korean Youth, 22(3), 97–117.

42.

Lee

S.-S.

Song

Park

(2021). Exploring risk and protective factors for cyberbullying and their interplay: Evidence from a sample of SouthSouth Korean college students. International Journal of Environmental Research and Public Health, 18(24), 13415. https://doi.org/10.3390/ijerph182413415

43.

C. K.

Holt

T. J.

Bossler

A. M.

May

D. C.

(2016). Examining the mediating effects of social learning on the low self-control—cyberbullying relationship in a youth sample. Deviant Behavior, 37(2), 126–138.

44.

Lianos

McGrath

(2018). Can the general theory of crime and general strain theory explain cyberbullying perpetration? Crime & Delinquency, 64(5), 674–700.

45.

Lilly

J. R.

Cullen

F. T.

Ball

R. A.

(2018). Criminological theory: Context and consequences. Sage publications.

46.

Mahalik

J. R.

Aldarondo

Gilbert-Gokhale

Shore

(2005). The role of insecure attachment and gender role stress in predicting controlling behaviors in men who batter. Journal of Interpersonal Violence, 20(5), 617–631.

47.

Marcum

C. D.

Higgins

G. E.

(2019). Examining the effectiveness of academic scholarship on the fight against cyberbullying and cyberstalking. American Journal of Criminal Justice, 44(1), 645–655.

48.

Marcum

C. D.

Higgins

G. E.

Freiburger

T. L.

Ricketts

M. L.

(2014). Exploration of the cyberbullying victim/offender overlap by sex. American Journal of Criminal Justice, 39(1), 538–548.

49.

McGloin

J. M.

O’Neill Shermer

(2009). Self-control and deviant peer network structure. Journal of Research in Crime and Delinquency, 46(1), 35–72.

50.

Mears

D. P.

Ploeger

Warr

(1998). Explaining the gender gap in delinquency: Peer influence and moral evaluations of behavior. Journal of Research in Crime and Delinquency, 35(3), 251–266.

51.

Messerschmidt

J. W.

(1986). Capitalism, patriarchy, and crime: Toward a socialist feminist criminology. Rowman & Littlefield.

52.

Messerschmidt

J. W.

(1993). Masculinities and crime: Critique and reconceptualization of theory. Rowman & Littlefield.

53.

Miller

Morris

R. G.

(2016). Virtual peer effects in social learning theory. Crime & Delinquency, 62(12), 1543–1569.

54.

Morales

J. F.

Yubero

Larrañaga

(2016). Gender and bullying: Application of a three-factor model of gender stereotyping. Sex Roles, 74(1), 169–180.

55.

Morash

Moon

(2007). Gender differences in the effects of strain on the delinquency of South Korean youth. Youth & Society, 38(3), 300–321.

56.

Näsi

Räsänen

Lehdonvirta

(2011). Identification with online and offline communities: Understanding ICT disparities in Finland. Technology in Society, 33(1–2), 4–11.

57.

National Center for Education Statistics . (2024). Student reports of bullying: Results from the 2022 school crime supplement to the national crime victimization survey. National Center for Education Statistics. Available at: https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2024109

58.

Olthof

Goossens

F. A.

Vermande

M. M.

Aleva

E. A.

van der Meulen

(2011). Bullying as strategic behavior: Relations with desired and acquired dominance in the peer group. Journal of School Psychology, 49(3), 339–359. https://doi.org/10.1016/j.jsp.2011.03.003

59.

Olweus

(1993). Bullying at school: What we know and what we can do. Blackwell Publishing.

60.

Patchin

J. W.

Hinduja

(2011). Traditional and nontraditional bullying among youth: A test of general strain theory. Youth & Society, 43(2), 727–751.

61.

Patchin

J. W.

Hinduja

(2022). Cyberbullying among tweens in the United States: Prevalence, impact, and helping behaviors. The Journal of Early Adolescence, 42(3), 414–430.

62.

Pew Research Center . (2021). Mobile fact sheet. Pew Research Center. Available at: https://www.pewresearch.org/internet/fact-sheet/mobile/

63.

Piquero

N. L.

Gover

A. R.

MacDonald

J. M.

Piquero

A. R.

(2005). The influence of delinquent peers on delinquency: Does gender matter? Youth & Society, 36(3), 251–275.

64.

Pratt

T. C.

Cullen

F. T.

(2000). The empirical status of gottfredson and hirschi’s general theory of crime: A meta analysis. Criminology, 38(3), 931–964.

65.

Reijntjes

Vermande

Olthof

Goossens

F. A.

Van De Schoot

Aleva

Van Der Meulen

(2013). Costs and benefits of bullying in the context of the peer group: A three wave longitudinal analysis. Journal of Abnormal Child Psychology, 41(1), 1217–1229.

66.

Richardson

D. S.

Hammock

G. S.

(2007). Social context of human aggression: Are we paying too much attention to gender? Aggression and Violent Behavior, 12(4), 417–426.

67.

Sasson

Mesch

(2017). The role of parental mediation and peer norms on the likelihood of cyberbullying. The Journal of Genetic Psychology, 178(1), 15–27.

68.

Shekarkhar

Gibson

C. L.

(2011). Gender, self-control, and offending behaviors among Latino youth. Journal of Contemporary Criminal Justice, 27(1), 63–80.

69.

Sijtsema

J. J.

Veenstra

Lindenberg

Salmivalli

(2009). Empirical test of bullies’ status goals: Assessing direct goals, aggression, and prestige. Aggressive Behavior, 35(1), 57–67. https://doi.org/10.1002/ab.20282

70.

Skinner

W. F.

Fream

A. M.

(1997). A social learning theory analysis of computer crime among college students. Journal of Research in Crime and Delinquency, 34(4), 495–518.

71.

Slaughter

Newman

(2022). New frontiers: Moving beyond cyberbullying to define online harassment. Journal of Online Trust and Safety, 1(2), 1.

72.

Smith

P. K.

Brain

(2000). Bullying in schools: Lessons from two decades of research. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 26(1), 1–9.

73.

Steketee

Junger

Junger-Tas

(2013). Sex differences in the predictors of juvenile delinquency: Females are more susceptible to poor environments; males are influenced more by low self-control. Journal of Contemporary Criminal Justice, 29(1), 88–105.

74.

Van Ryzin

M. J.

Roseth

C. J.

(2019). Effects of cooperative learning on peer relations, empathy, and bullying in middle school. Aggressive Behavior, 45(6), 643–651.

75.

Wang

(2021). Does victimization predict cyberbullying perpetration? Examining depression as a mediator and self-control as a moderator. Personality and Individual Differences, 183(1), 111135.

76.

Warr

(2002). Companions in crime: The social aspects of criminal conduct. Cambridge University Press.

77.

Warr

Stafford

(1991). The influence of delinquent peers: What they think or what they do? Criminology, 29(4), 851–866.

78.

Willard

N. E.

(2007). Cyberbullying and cyberthreats: Responding to the challenge of online social aggression, threats, and distress. Research Press.

79.

Yllö

K. E.

Bograd

M. E.

(1988). Feminist perspectives on wife abuse. National conference for family violence researchers, 2nd, 1984. U of New Hampshire, NH. US .

80.

Zimmerman

G. M.

Messner

S. F.

(2010). Neighborhood context and the gender gap in adolescent violent crime. American Sociological Review, 75(6), 958–980.