The role of the parent–child relationship and parental control in the link between adolescent cyber-victimization,risky internet behavior,and self-control

Abstract

This study extends existing research on adolescent cyber-victimization by highlighting the role of the parent–child relationship as an important protective factor. Drawing on data from a large, self-reported survey of ninth-grade students (N = 1.546) living in the four largest cities in Czechia and employing structural equation modeling (SEM), the study assesses the direct and indirect effects of the parent–child relationship and parental control on cyber-victimization, considering the mediating roles of risky internet behavior, time spent on social networks, and self-control. Four types of cyber-victimization—mocking and teasing, pornography, fearful content, and sexual harassment—were analyzed. Parental control showed a weak positive effect on cyber-victimization, whereas a better parent–child relationship was consistently associated with less frequent cyber-victimization across all analyzed types. In addition, indirect effects were mediated either through self-control alone or through combinations of lower self-control, higher level of risky internet behavior, and more time spent on social networks. While excessive parental control may be counterproductive, a balanced and supportive parent–child relationship appears to reduce cyber-victimization both directly and indirectly by fostering stronger self-control.

Keywords

Cyber-victimization parent–child relationship parental control risky internet behavior self-control SEM

Introduction

The rapid increase in internet use in recent years has facilitated people's everyday lives in terms of consuming and sharing information, maintaining and widening social contacts, enhancing self-esteem, or avoiding loneliness and depression (Hunt et al., 2018; Wellman and Haythornthwaite, 2002; Yavich et al., 2019). It has also noticeably changed the way in which adolescents socialize, since virtually unlimited daily access to the internet allows them to establish relationships with their peers (Mei et al., 2016; Mesch, 2022). However, the inappropriate use of the internet and social networks presents a risk to adolescents of becoming victims of cybercrime, that is, online aggressions that intentionally annoy, offend, or harm (Álvarez-García et al., 2018; Álvarez-García et al., 2019; Gardella et al., 2017). These acts can include cyberstalking, cyber-harassment, hate crimes, and threats of a sexual or violent nature (Mikkola et al., 2024).

While recent official and self-reported data indicate declining trends in general crime, such as property and violent crimes, among adolescents across Europe (McAra and McVie, 2018; McCarthy, 2021), crime rates associated with the use of the internet and information and communication technology are on the rise (Bossler and Berenblum, 2019; Mikkola et al., 2024). It is estimated that 50%–70% of adolescents experience cyber-victimization (Garaigordobil, 2015; Gardella et al., 2017; Peluchette et al., 2015). In particular, Athanasiou et al. (2018) reported the prevalence of cyberbullying at 37% in Romania, 27% in Greece, 24% in Germany, and 21% in Poland. Moreover, it is assumed that a significant number of adolescents do not report that they were victimized or may not even realize that they have become victims of cybercrime (Ngo et al., 2020).

Cyber-victimization is associated with a number of negative outcomes, including depression (Tran et al., 2023), low academic performance (Gardella et al., 2017), suicidal thoughts, and anxiety (Wright, 2016). Moreover, given adolescents’ widespread access to the internet, limited parental control, and more sophisticated criminal methods, researchers expect the number of cybercrime victims to increase in the future (Mikkola et al., 2024; Ngo et al., 2020). It is therefore important to focus on why adolescent cyber-victimization occurs, what factors influence it, and what preventive measures may reduce the risk of victimization.

When explaining cyber-victimization, many studies have drawn from lifestyle/routine activity theory (Bossler and Holt, 2009; Marcum et al., 2010; Pratt et al., 2010; Reyns, 2015; Yar, 2005) and individual self-control stemming from the general theory of crime (Bossler and Holt, 2010; Peker, 2017; Reyns et al., 2018; van Wilsem, 2013). Subsequently, some studies sought to integrate both approaches (Holt et al., 2016; Holt et al., 2020; Mikkola et al., 2024; Ngo and Paternoster, 2011; Reyns et al., 2019). More importantly, their goal was to assess whether low self-control directly influences cyber-victimization or whether a part of its influence is mediated through risky internet behavior (Álvarez-García et al., 2019; Kabiri et al., 2021; Partin et al., 2022).

Nonetheless, several methodological and substantive limitations of these studies highlight the need for further analysis. In particular, Kabiri et al. (2021) and Partin et al. (2022) utilized small convenience samples of university students, making their conclusions more relevant to young adults. This likely led the authors not to consider parental effects in their analyses entirely, as opportunities for family intervention or prevention of cyber-victimization may already be limited for this social group. An exception is the study by Álvarez-García et al. (2019), which pointed to the potential influence of parental control on adolescent cyber-victimization. However, the authors relied on summation indexes and simplified mediation approaches (cf. Partin et al., 2022), which provide only a limited understanding of the complex relationships among the analyzed variables. Moreover the effect of the parent–child relationship remains overlooked, even though it has been shown to influence the level of individual self-control in childhood (Li et al., 2019; Park et al., 2022), as well as the risk of cyber-victimization itself (Elsaesser et al., 2017).

This paper examines the role of parent–child relationship and parental control in adolescent cyber-victimization considering the mediating effects of risky internet behavior, time spent on social networks, and self-control. Using a large sample of ninth-grade students from elementary schools and equivalent classes in grammar schools participating in the self-reported, school-based Urban Youth Victimization Survey in Czechia, we employ a structural equation modeling (SEM) approach to test our hypotheses. Before estimating the models and presenting the results of the analysis, we discuss previous research on the relationship between cyber-victimization, time spent on social networks, risky internet behavior, and self-control, as well as the role of parental effect in this context.

The link between cyber-victimization, risky internet behavior, and self-control

Research that considers the interconnection between cyber-victimization, risky internet behavior, time spent on social networks, and self-control draws on several theoretical traditions. These include Cohen and Felson's (1979) Routine Activity Theory (RAT), Lifestyle-Routine Activity Theory (LRAT), which integrates RAT with Lifestyle Theory (Hindelang et al., 1978), and the General Theory of Crime (Gottfredson and Hirschi, 1990).

RAT argues that victimization occurs when motivated offenders, suitable targets, and a lack of capable guardianship converge in time and space, thus emphasizing situational conditions under which victimization occurs. LRAT, by contrast, highlights how individuals’ everyday routines and lifestyle patterns shape their exposure to motivated offenders and their vulnerability to victimization (Pratt and Turanovic, 2016). This aligns with the broader criminological literature on unstructured activities, which argues that spending large amounts of time in loosely regulated or unsupervised contexts may heighten adolescents’ exposure to risky interactions and crime (Barnes et al., 2007; Mahoney and Stattin, 2000). Although these theories were originally developed to explain conventional crimes, prior research suggests they may also apply to online environments (Felson, 2016; Holt and Bossler, 2008). For example, some authors have found that frequent internet and Facebook usage is positively correlated with cyber-victimization (Álvarez-García et al., 2015; Mishna et al., 2012; Peluchette et al., 2015). However, according to Bossler and Holt (2009), Reyns et al. (2019), and Marcum et al. (2010), merely spending more time on the computer does not seem to increase the risk of cybercrime significantly; rather, the number of hours spent in chat rooms and instant messaging affects cyber-victimization. In addition, other authors (Mishna et al., 2012; Walrave and Heirman, 2011) link cyber-victimization to sharing one's personal password, disclosing personal information, communicating with strangers, clicking on suspicious links and attachments, or downloading and viewing pornography. Thus, it is specifically an individual's risky internet behavior and routines that seem to increase their risk of victimization (Choi and Lee, 2017; Ngo et al., 2020; Pratt et al., 2010; Reyns et al., 2019; van Wilsem, 2013).

Gottfredson and Hirschi's (1990) general theory of crime was originally formulated to explain delinquent, criminal, and analogous behavior, arguing that individuals with low self-control (i.e., those who are impulsive, self-centered, lazy, hostile, and unable to delay gratification) are more prone to engage in criminal acts. In addition, individuals with lower self-control are likely to be more engaged in risky activities, making them more vulnerable to victimization compared to those with relatively high self-control (Ren et al., 2017; Schreck, 1999; Turanovic et al., 2015). This also seems to apply to the online environment. Some studies have found a recurring association between self-control and risky internet behavior on the one hand (e.g., Yu, 2014; Jeske et al., 2016) and cyber-victimization on the other (Bossler and Holt, 2010; Holt et al., 2016; Ngo and Paternoster, 2011; Reyns et al., 2018). For example, lower self-control appears to be associated with spending time in chatrooms (Reyns et al., 2018), sharing personal information (Yu, 2014), or using unsecured wireless networks (Jeske et al., 2016). As for cyber-victimization, Reyns et al. (2019) found that individuals with low self-control were about 50% more likely to be harassed or receive nude/explicit content. The authors also reported a weaker yet significant association with hacking (cf. Bossler and Holt, 2010; Peker, 2017).

Examining the mediating effect of risky internet behavior on the link between self-control and cyber-victimization was a logical step when integrating (L)RAT and the general theory of crime (Mikkola et al., 2024). For example, using a sample of Iranian college women, Kabiri et al. (2021) found evidence for the mediating effect of risky internet activities, although it was only for one form of online victimization, namely cyberstalking (cf. Reyns et al., 2018). More recently, Partin et al. (2022) observed an indirect effect of self-control on cyber-victimization through risky internet behavior among students enrolled in criminology and criminal justice courses at a university in the southern USA, while employing summation indices of self-control, risky internet behavior, and cyber-victimization, each consisting of over a dozen indicators to measure these phenomena. Similar conclusions were drawn by Álvarez-García et al. (2019). However, working with a sample of compulsory secondary education students aged 11–18 likely prompted the authors to consider the effect of parental control in their analysis, as adolescents are more often subject to parental influence than young adults. The study suggested that parental restriction and supervision act as protective factors against cyber-victimization, while confirming the mediating role of impulsivity—one of the dimensions of self-control—and risky internet behavior. In addition, some studies have also discussed the mediating role of unstructured spare time between parental supervision, self-control, and offline offending (Buil-Gil, 2025; Osgood and Anderson, 2004), however, we found no comparable evidence testing this mechanism in online settings. Overall, research on the role of parental control in the link between self-control, time spent on social networks, risky internet behavior, and cyber-victimization remains limited, and to our knowledge, there is virtually no evidence discussing the effect of the parent-child relationship in this context.

The role of parental control and the parent–child relationship

Although we could assume that parental monitoring and supervision decrease risky internet behavior and cyber-victimization, existing studies yield mixed results. While some authors observed the expected negative association of parental control with cyber-victimization, especially in early childhood and adolescence (Baldry et al., 2019; Floros et al., 2013; Khurana et al., 2015), others found it to be negligible or non-existent (Mesch, 2009; Mishna et al., 2012). In addition, some studies reported the positive relationship between the aforementioned phenomena, suggesting that parental control is not effective enough because children can access the internet or use mobile devices outside the home (Álvarez-García et al., 2015). These inconsistencies may partly stem from heterogeneity in how cyber-victimization is defined across studies; however, even research employing conceptually similar indicators of cyberbullying victimization (e.g., Baldry et al., 2019; Mishna et al., 2012) reports associations with parental control that vary in both strength and direction.

Similar inconsistencies appear when examining the mediating effect of self-control and risky internet behavior in the link between parental control and cyber-victimization. According to Álvarez-García et al. (2019), higher parental control seems to decrease impulsivity and restrict risky internet activities, which further reduces the likelihood of cyber-victimization. Conversely, Lau and Allan (2013) did not find parental control to significantly decrease online risks and suggested that a restrictive parenting style is not effective enough in reducing risky online behaviors. Moreno-Ruiz et al. (2019) drew on reactance theory and found that a highly restrictive family environment constraining children's freedom increases their motivation to disobey parents and engage in riskier (internet) activities, possibly leading to cyber-victimization. Sasson and Mesch (2014) and Shin and Ismail (2014) assessed excessive parental control as contraproductive (cf. Wright, 2017), especially in families with limited communication and little affection. This aligns with the results of other studies that indicate that the parent–child relationship, in terms of warmth, support, and communication, may decrease the probability of both problematic internet use and cyber-victimization (Elsaesser et al., 2017; Lereya et al., 2013; Park et al., 2014; Lukavska et al., 2020). Moreover, according to some authors, this relationship seems to be even more effective in cyber-victimization prevention than parental supervision and restrictions (Buelga et al., 2017; Law et al., 2010; Moreno-Ruiz et al., 2019).

In addition, parental socialization and child-rearing practices seem to directly influence individual self-control (Gottfredson and Hirschi, 1990; Ngo and Paternoster, 2011). Specifically, parents who monitor and correct their children's behavior and maintain a close relationship with them instill conformity and self-control during their early life period (Vazsonyi and Huang, 2010). With respect to risk-taking behavior, Liu et al. (2019) found that parent–child conflict and lower parental support were associated with lower self-control among adolescents, which in turn was linked to their risk-taking behavior. This association has been observed not only offline but also in the online environment. For instance, Li et al. (2014) found that parental behaviors, such as positive parenting and teaching rules, were associated with higher self-control and lower internet addiction.

The current study and hypotheses

Drawing on the aforementioned literature, the aim of this study is to examine how the parent–child relationship and parental control contribute to adolescent cyber-victimization, taking into account the mediating effects of self-control, time spent on social networks, and risky internet behavior. Prior research in this area has often relied on young adult samples and simplified mediation approaches, and has produced mixed findings regarding the role of parental control (see Álvarez-García et al., 2019; Kabiri et al., 2021; Mishna et al., 2012; Partin et al., 2022; Sasson and Mesch, 2014), while also suggesting that the parent–child relationship may be a more consistent protective factor (e.g., Moreno-Ruiz et al., 2019; Buelga et al., 2017).

Figure 1 shows the proposed model. We tested the following hypotheses:

Better parent–child relationships and parental control will be associated with less frequent cyber-victimization.

The parent–child relationships will have a somewhat stronger association with cyber-victimization than parental control.

Better parent–child relationships and parental control will be associated with stronger self-control, which in turn will reduce time spent on social networks and decrease risky internet behaviors, ultimately lowering the frequency of cyber-victimization.

Figure 1.

The proposed model.

Method

Data

The study is based on a secondary analysis of data from the Urban Youth Victimization Survey (UYVS), conducted in Czechia in 2015. This self-reported, school-based survey focused on ninth-grade students living in the four largest cities in the country home to over 150,000 inhabitants—Prague, Pilsen, Brno, and Ostrava. The survey design and questionnaire drew on the methodology of the second and third waves of the International Self-Report Delinquency Study (ISRD) (Marshall et al., 2013), meaning that several indicators were adopted or adapted from the ISRD instruments, and the overall data collection procedures followed ISRD guidelines. The data collection was conducted by the Department of Sociology at the Faculty of Arts, Charles University. The sampling process involved randomly selecting classes from the complete list of classes provided by the Ministry of Education, Youth and Sports and stratifying them by both school type and city. Because this procedure ensured random selection within strata and no systematic bias in the composition of the selected classes was detected, the data were not weighted. School principals approved of their schools’ participation in the survey. Both children and parents were informed in advance about the research being conducted at the school and its nature, and they were given the opportunity to decline participation. All participants took part in the study voluntarily. The response rate was 64% at the school level and 78% at the individual level, primarily due to student absences on the day of data collection. In total, the sample consisted of 1.546 children distributed across 85 classes in 69 schools, comprising 54 middle schools and 15 grammar schools. The number of students varied between 346 and 410 per city. Girls comprised almost 50% of the sample, and the average age of the children was 15.0 years (SD = 0.48; range: 13–17). The main topics investigated in the questionnaire included various forms of victimization (prevalence and incidence) and their circumstances, different forms of delinquent and antisocial behavior, possible risk and protective factors from various areas—family background, school, peers, leisure time, and residential location—and individual dispositions and attitudes.

Measures

Cyber-victimization

Cyber-victimization was measured by a set of indicators addressing unpleasant experiences that adolescents may encounter when using the internet, social networks, or a mobile phone. The term cyber-victimization is used here in a broader sense, encompassing both direct interpersonal aggression and indirect or passive exposure to harmful or distressing online content. Such exposure can be regarded as a form of victimization because it is non-consensual and may cause psychological harm or distress, even in the absence of a specific perpetrator (Flood, 2009; Smahel et al., 2020). Specifically, respondents were asked to report on a 5-point scale, where 1 = “never,” 2 = “once or twice,” 3 = “sometimes,” 4 = “often,” and 5 = “very often,” how often in the previous 12 months: (1) Someone mocked or teased them through e-mail, social networks (such as Facebook), chat, websites, or text messages; (2) They unintentionally encountered pornography (images or videos with sexual content) while using the internet; (3) They unintentionally came across images or videos on the internet that made them feel anxious or scared; and (4) Someone made inappropriate sexual advances toward them or asked for intimate photos. In the subsequent analysis, we considered cyber-victimization both as a summation index, where maximum values refer to frequent cyber-victimization, and as separate indicators. This approach allowed us to build on previous studies that used summation indexes in their analyses, while also providing a more detailed examination of differences between specific types of cyber-victimization and assessing the robustness of our results (see Analytic Strategy section).

Risky internet behavior

Risky internet behavior consists of a set of binary indicators capturing adolescents’ potentially unsafe conduct associated with the use of the internet or social networks. While some of the following practices may be relatively safe in certain circumstances, they are nonetheless regarded as risky because they enable constant connectivity and foster impulsive interactions in less regulated digital environments and beyond, thereby increasing adolescents’ vulnerability (Aizenkot, 2020; Bayraktar et al., 2016; Walrave and Heirman, 2011). The respondents were asked to answer “yes” or “no” to the following statements: (1) In my social media profile, I provide my email address; (2) In my social media profile, I provide my phone number; (3) I have sent my photo or video to someone I have never met in person; (4) I add people to my friends list who I have never met in person; (5) I have personally met someone whom I first met online¹; and (6) I often access the internet and social networks from my mobile phone or tablet. By summing positive responses to the statements, we calculated a summation index, in which 0 indicates that the adolescent does not engage in any risky activities, whereas 6 indicates he or she engages in all of them.

Time spent on social networks

To measure time spent on social networks, we asked respondents how many hours per day they spend on social media (such as Facebook) on average, with following response categories: 1 = “none or almost none,” 2 = “no more than 1 h,” 3 = “1 to 2 h,” 4 = “2 to 3 h,” 5 = “3 to 4 h,” 6 = “4 to 5 h” and 7 = “more than 5 h.”

Self-control

The self-control scale used in this study is based on Grasmick et al.'s (1993) adaptation of the scale developed by Hirschi and Gottfredson (1993). It is widely recognized as a common tool for assessing the subjective level of self-control (Pechorro et al., 2023). In the survey data used for this study, the scale was reduced² from its original 24 items to 13 items, which measure four dimensions of self-control: risk-seeking, self-centeredness, temper, and impulsivity (see Table 1). Each item was assessed using response categories 1 = “strongly agree,” 2 = “somewhat agree,” 3 = “somewhat disagree,” and 4 = “strongly disagree,” where a higher score indicated a stronger level of self-control. The internal consistency of the scale is high (Cronbach's alpha = 0.84).

Table 1.

Reflective latent constructs: Confirmatory factor analysis.

Questions	Factor loadings
Relationship with parents (5-point scale)
I can confide in my parents with any problem	0.727
My parents always make sure that I lack nothing	0.750
I often do things together with my parents	0.645
My parents show me how much I mean to them	0.804
Self-control (4-point scale)
Risk-seeking	0.648
I like to occasionally test myself, perhaps with a somewhat risky act	0.750
Sometimes I take risks simply because I enjoy it	0.857
Excitement and adventure are more important to me than safety	0.732
Self-centeredness	0.766
I try to prioritize myself first, even if it might cause trouble for other people	0.667
When what I do bothers other people, it's their problem, not mine	0.709
I like to acquire things I want, even if I know it causes problems for other people	0.742
Temper	0.779
I lose patience quite quickly	0.681
When I get really angry, it's better for others to avoid me	0.665
When I fundamentally disagree with someone, it's usually difficult for me to address it calmly without getting angry	0.693
Impulsivity	0.870
I act impulsively without thinking too much about it	0.652
I do what brings me immediate satisfaction, even at the cost of sacrificing longer-term goals	0.669
I care more about what's happening around me now than what will happen in the distant future	0.420
I don’t really worry too much about what will happen with me in the future	0.408
Fit statistics
CFI	0.978
TLI	0.974
RMSEA	0.029
SRMR	0.029
N	1257
Clusters	85

Note. CFA (Mplus 8); standardized coefficients (STDYX); MLR estimation; given the multidimensional nature of self-control, it entered the analysis as a second-order latent variable (Muthén and Muthén, 2017).

Parental effect

Finally, to measure parental effect, we used two binary variables capturing parental control and a set of indicators assessing the quality of the parent–child relationship. We asked respondents whether their parents were interested in what they were doing while on the internet and whether their parents attempted to warn them about the various risks associated with using the internet and social networks (response categories 1 = “yes” and 0 = “no”). The parent–child relationship is included in the analysis as a latent variable (Cronbach's alpha = 0.81) consisting of four statements assessing perceived trust and affection, as well as the provision of care and engagement in joint activities (see Table 1). Each item was assessed using response categories 1 = “strongly disagree,” 2 = “somewhat disagree,” 3 = “neither agree nor disagree,” 4 = “somewhat agree” and 5 = “strongly agree,” where a higher score indicated a better child's relationship with parents.

All variables used in the subsequent analysis are presented in Table 2, along with their descriptive statistics. Given the gender-specific differences in cyber-victimization (e.g., Sun and Fan, 2018), we include gender as a control variable. Furthermore, in Table 1, we present our latent variables (self-control and parent–child relationship) together with the results of a confirmatory factor analysis (CFA) with factor loadings and fit statistics. The fit statistics of the measurement models were acceptable (see Ruiz et al., 2010), suggesting that the model fit the data well.

Table 2.

Descriptive statistics.

Variables	Mean	SD	Min	Max
Cyber-victimization	1.75	0.65	1	5
Mocking and teasing	1.43	0.76	1	5
Pornography	2.25	1.24	1	5
Fearful content	1.69	0.92	1	5
Sexual harassment	1.61	1.07	1	5
Risky internet behavior	2.68	1.54	0	6
Time spent on social networks	3.45	1.85	1	7
Parents interested	0.38	0.48	0	1
Parents warn	0.68	0.47	0	1
Relationship with parents
I can confide in my parents with any problem	4.08	1.04	1	5
My parents always make sure that I lack nothing	4.44	0.81	1	5
I often do things together with my parents	3.82	1.12	1	5
My parents show me how much I mean to them	4.10	1.01	1	5
Self-control
I like to occasionally test myself, perhaps with a somewhat risky act	2.58	0.87	1	4
Sometimes I take risks simply because I enjoy it	2.57	0.92	1	4
Excitement and adventure are more important to me than safety	2.65	0.94	1	4
I try to prioritize myself first, even if it might cause trouble for other people	3.00	0.82	1	4
When what I do bothers other people, it's their problem, not mine	2.84	0.91	1	4
I like to acquire things I want, even if I know it causes problems for other people	3.22	0.77	1	4
I lose patience quite quickly	2.43	0.94	1	4
When I get really angry, it's better for others to avoid me	2.47	0.96	1	4
When I fundamentally disagree with someone, it's usually difficult for me to address it calmly without getting angry	2.60	0.93	1	4
I act impulsively without thinking too much about it	2.54	0.78	1	4
I do what brings me immediate satisfaction, even at the cost of sacrificing longer-term goals	2.74	0.79	1	4
I care more about what's happening around me now than what will happen in the distant future	2.23	0.78	1	4
I don’t really worry too much about what will happen with me in the future	3.10	0.93	1	4
Gender	0.49	0.50	0	1

Note. The variables are coded such that higher values indicate more frequent cyber-victimization, a higher level of risky internet behavior, more time spent on social networks, a better relationship with parents, and stronger self-control. Parental interest and efforts to warn their children are coded so that 1 = “yes” and 0 = “no.” Gender is coded as 1 = “boy” and 0 = “girl.”

Analytic strategy

Previous studies (e.g., Álvarez-García et al., 2019; Partin et al., 2022) mostly used summation indexes of aforementioned phenomena and applied mediation approaches such as path analysis and the PROCESS mediation macro, which face significant statistical limitations. They analyze each pathway or effect in the model independently, focusing on estimating and testing the direct and indirect effects without accounting for the overall interdependencies and covariances among the variables. Therefore, no information was provided on whether the unified structural model accurately fit the data (cf. Kabiri et al., 2021). Additionally, measurement error was ignored, which could have led to biased estimates of the relationships between the analyzed variables. This is because the presence of measurement error generally weakens the observed associations, reduces statistical power, and may even result in failing to detect significant effects that actually exist (Mitchell, 1992; Nunkoo and Ramkissoon, 2012; Sarstedt et al., 2020). Therefore, in order to test our hypotheses, we used SEM in Mplus (version 8). Given that the data we used in our study had a nested structure (pupils nested in classes), we also controlled for within-cluster correlation.

Moreover, by using summation indexes, the studies overlooked the very nature of the analyzed concepts. In particular, they failed to distinguish between reflective and formative latent variables in their models (Bollen and Lennox, 1991; Diamantopoulos and Winklhofer, 2001; Freeze and Raschke, 2007; Riefler and Roth, 2008). Reflective latent variables, such as self-control, cause the observed indicators, meaning that the indicators are seen as manifestations or reflections of the underlying latent variable. In contrast, formative latent variables, such as cyber-victimization, are caused by their formative indicators, which are not necessarily correlated (e.g., an individual may frequently experience online sexual harassment while never encountering fearful content) and cannot be easily interchanged; consequently, their internal consistency cannot meaningfully be assessed. In the event that a formative latent variable serves as an outcome or endogenous variable in a model, it complicates the estimation of the effects of its determinants as well as the variance explained (Cadogan and Lee, 2013). To address this, researchers often create a formative composite index (see Álvarez-García et al., 2019; Kabiri et al., 2021; Partin et al., 2022). According to Cenfetelli and Bassellier (2009: 690), such aggregation of indicators “allows the researcher to focus attention on a single structural effect.” However, more advantageous approach is to treat the formative indicators separately in one model, ensuring that significant relationships with their determinants are not overlooked, thereby minimizing the risk of losing information or misinterpreting empirical findings (Cadogan and Lee, 2013).

Therefore, we first estimated the model with cyber-victimization as a composite index to reflect the aggregate approach used in previous studies (e.g., Partin et al., 2022; Álvarez-García et al., 2019). While we acknowledge that this is not the optimal conceptual solution, it allows a direct comparison with the second model, inspired by Cadogan and Lee (2013), in which the four types of cyber-victimization and their model paths are examined separately.

Results

Bivariate correlations

Bivariate correlations between all latent and manifest variables (Spearman's ρ) are presented in Table A1 in the Appendix. As expected, risky internet behavior (ρ = 0.34) and time spent on social networks (ρ = 0.29) showed moderate positive associations with the cyber-victimization index. Conversely, self-control exhibited a moderate negative association (ρ = −0.22) with cyber-victimization, indicating its role as an important protective factor. The pattern was similar across the separate indicators of cyber-victimization, with the exception of exposure to fearful content, for which no direct association with self-control was observed. Additionally, we found a moderately strong positive association between risky internet behavior and time spent on social networks (ρ = 0.45). In contrast, self-control showed moderate negative associations with both risky internet behavior (ρ = −0.32) and time spent on social networks (ρ = −0.27).

Parental warnings showed a weak negative association with pornography-related cyber-victimization (ρ = −0.06) and weak positive associations with sexual harassment (ρ = 0.08) and risky internet behavior (ρ = 0.05). In addition, parental interest and warnings had weak positive associations with exposure to fearful content (both ρ = 0.11). Parental interest and warnings also appeared to exhibit weak positive associations with self-control (ρ = 0.09 and ρ = 0.08, respectively). Overall, the effect of parental control was rather inconsistent, showing either weak or negligible correlations with the other variables in the analysis. In contrast, a good parent-child relationship showed a moderate negative association with cyber-victimization index (ρ = −0.19). Further, it showed weak-to-moderate negative associations with separate indicators of cyber-victimization, risky internet behavior, and time spent on social networks (ranging from ρ = −0.17 to ρ = −0.09) and a positive association with self-control (ρ = 0.12).

SEM

First, we estimated the model (Table 3; M1) using the cyber-victimization index as applied in previous studies (Álvarez-García et al., 2019; Kabiri et al., 2021; Partin et al., 2022). Parental interest in children's online activities showed a negligible direct effect on cyber-victimization. However, it was associated with slightly stronger self-control (beta = 0.071), indicating that adolescents whose parents expressed interest in their online activities tended to report marginally higher levels of self-control. Parental efforts to warn their children about the various risks associated with using the internet and social networks were weakly and positively related to both more frequent cyber-victimization (beta = 0.052) and higher level of risky internet behavior (beta = 0.061). Conversely, a better parent–child relationship was moderately and directly associated with less frequent cyber-victimization (beta = −0.162) and with stronger self-control (beta = 0.145). Thus, we only partially corroborated Hypothesis 1, as the effect of parental interest is negligible, and parental efforts to warn their children operate in the opposite direction. In contrast, the findings align with Hypothesis 2, as the effect of the parent–child relationship on cyber-victimization is stronger than that of parental control.

Table 3.

Cyber-victimization index: Structural equation modeling.

M1	Cyber-victimization (frequent)			Risky internet behavior (higher level)			Time spent on social networks (more)			Self-control (strong)
M1	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta
Risky internet behavior (higher level)	0.082 (0.015)	<.001	0.192	–	–	–	–	–	–	–	–	–
Time spent on social networks (more)	0.047 (0.013)	<.001	0.134	0.306 (0.026)	<.001	0.367	–	–	–	–	–	–
Self-control (strong)	−0.190 (0.054)	<.001	−0.126	−0.954 (0.127)	<.001	−0.270	−1.310 (0.161)	<.001	−0.308	–	–	–
Parents interested	0.017 (0.034)	.625	0.012	0.060 (0.090)	.501	0.019	0.065 (0.104)	.529	0.017	0.064 (0.030)	.034	0.071
Parents warn	0.073 (0.034)	.031	0.052	0.202 (0.097)	.038	0.061	−0.035 (0.113)	.755	−0.009	−0.012 (0.032)	.702	−0.013
Relationship with parents (good)	−0.140 (0.029)	<.001	−0.162	−0.046 (0.074)	.533	−0.023	−0.021 (0.082)	.794	−0.009	0.083 (0.025)	<.001	0.145
Gender (boy)	−0.140 (0.043)	<.001	−0.107	0.102 (0.093)	.272	0.033	−0.921 (0.114)	<.001	−0.249	−0.026 (0.031)	.399	−0.030
R ²			0.189			0.270			0.155			0.029

Note. N = 1257; clusters = 85; Beta = standardized coefficients (STDYX). CFI = 0.968; TLI = 0.961; RMSEA = 0.029; SRMR = 0.030.

Further, a higher level of risky internet behavior (beta = 0.192) and more time spent on social networks (beta = 0.134) were associated with more frequent cyber-victimization. On the other hand, we found that stronger self-control was associated with less frequent cyber-victimization, (beta = −0.126), with a total effect of beta = −0.241 (Table 4; M1). Self-control decreased the frequency of cyber-victimization directly (beta = −0.126) or indirectly by reducing risky internet behavior, time spent on social networks, or both factors (beta = −0.115). The indirect effect of the parent–child relationship on cyber-victimization (beta = −0.041) was mediated either through self-control alone or through combinations of lower self-control, a higher level of risky internet behavior, and more time spent on social networks.

Table 4.

Total, direct, and indirect effects of self-control and parental effect on cyber-victimization index.

M1	Self-control (strong)		Parents interested		Parents warn		Relationship with parents (good)
M1	Beta	P	Beta	P	Beta	P	Beta	P
Total effect	−0.241	<.001	0.002	.930	0.065	.007	−0.204	<.001
Direct effect	−0.126	<.001	0.012	.625	0.052	.029	−0.162	<.001
Indirect effect (total)	−0.115	<.001	−0.010	.379	0.013	.242	−0.041	<.001

Note. Standardized coefficients (STDYX).

The total effect of the parent–child relationship on cyber-victimization was −0.204. The indirect effect of parental control on cyber-victimization was negligible; therefore, we only partially corroborate Hypothesis 3.

With respect to the model with separate cyber-victimization indicators (Table 5; M2), a higher level of risky internet behavior was associated with more frequent victimization across the analyzed types of cyber-victimization. More time spent on social networks was associated with more frequent mocking and teasing and sexual harassment. Similar to M1 (Table 3), more time spent on social networks was associated with a higher level of risky internet behavior, and both were linked to lower self-control. Self-control was a protective factor across all analyzed types of cyber-victimization; however, its effect on mocking and teasing as well as on fearful content was only indirect (Table 6; M2).

Table 5.

Cyber-victimization separate indicators: Structural equation modeling.

M2	Mocking and teasing			Pornography			Fearful content			Sexual harassment			Risky internet behavior (higher level)			Time spent on social networks (more)			Self-control (strong)
M2	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta	b (SE)	P	Beta
Risky internet behavior (higher level)	0.071 (0.015)	<.001	0.145	0.072 (0.029)	.014	0.090	0.046 (0.021)	.027	0.077	0.137 (0.024)	<.001	0.198	–	–	–	–	–	–	–	–	–
Time spent on social networks (more)	0.067 (0.014)	<.001	0.163	0.018 (0.027)	.505	0.027	0.023 (0.014)	.102	0.046	0.082 (0.020)	<.001	0.142	0.305 (0.026)	<.001	0.367	–	–	–	–	–	–
Self-control (strong)	−0.037 (0.061)	.552	−0.021	−0.430 (0.120)	<.001	−0.152	−0.009 (0.081)	.912	−0.004	−0.280 (0.087)	<.001	−0.115	−0.949 (0.126)	<.001	−0.270	−1.302 (0.160)	<.001	−0.309	–	–	–
Parents interested	0.045 (0.038)	.243	0.029	−0.009 (0.070)	.895	−0.004	0.115 (0.053)	.029	0.061	−0.083 (0.052)	.109	−0.038	0.060 (0.090)	.500	0.019	0.065 (0.104)	.528	0.017	0.064 (0.030)	.034	0.071
Parents warn	0.087 (0.054)	.108	0.054	−0.004 (0.073)	.956	−0.002	0.147 (0.047)	<.002	0.075	0.061 (0.054)	.260	0.027	0.202 (0.097)	.038	0.061	−0.035 (0.113)	.755	−0.009	−0.012 (0.032)	.702	−0.013
Relationship with parents (good)	−0.144 (0.030)	<.001	−0.145	−0.144 (0.058)	.012	−0.088	−0.131 (0.052)	.012	−0.109	−0.140 (0.050)	.005	−0.100	−0.046 (0.074)	.535	−0.022	−0.021 (0.082)	.796	−0.009	0.084 (0.025)	<.001	0.145
Gender (boy)	0.002 (0.046)	.963	0.001	0.230 (0.080)	.004	0.093	−0.228 (0.068)	<.001	−0.125	−0.565 (0.066)	<.001	−0.265	0.101 (0.093)	.274	0.033	−0.922 (0.114)	<.001	−0.249	−0.026 (0.031)	.394	−0.030
R ²			0.104			0.064			0.065			0.251			0.270			0.157			0.029

Note. N = 1257; Clusters = 85; Beta = standardized coefficients (STDYX). CFI = 0.970; TLI = 0.960; RMSEA = 0.027; SRMR = 0.029.

Table 6.

Total, direct, and indirect effects of self-control and parental effect on cyber-victimization separate indicators.

M2		Self-control (strong)		Parents interested		Parents warn		Relationship with parents (good)
M2		Beta	P	Beta	P	Beta	P	Beta	P
Mocking and teasing	Total effect	−0.127	<.001	0.026	.284	0.062	.057	−0.169	<.001
	Direct effect	−0.021	.550	0.029	.240	0.054	.105	−0.145	<.001
	Indirect effect (total)	−0.106	<.001	−0.003	.762	0.009	.311	−0.024	.013
Pornography	Total effect	−0.195	<.001	−0.015	.621	0.006	.828	−0.119	<.001
	Direct effect	−0.152	<.001	−0.004	.895	−0.002	.956	−0.088	.014
	Indirect effect (total)	−0.043	.003	−0.011	.148	0.008	.324	−0.031	<.001
Fearful content	Total effect	−0.048	.178	0.060	.029	0.080	<.001	−0.118	.005
	Direct effect	−0.004	.912	0.061	.027	0.075	<.002	−0.109	.011
	Indirect effect (total)	−0.044	.005	−0.001	.869	0.005	.221	−0.009	.095
Sexual harassment	Total effect	−0.235	<.001	−0.047	.071	0.040	.096	−0.140	<.001
	Direct effect	−0.115	<.001	−0.038	.109	0.027	.259	−0.100	.004
	Indirect effect (total)	−0.120	<.001	−0.009	.413	0.013	.223	−0.040	<.001

Note. Standardized coefficients (STDYX).

When decomposing the types of cyber-victimization, we found that parental control was associated only with more frequent victimization by exposure to fearful content (beta = 0.061 for parental interest and beta = 0.075 for parental efforts to warn their children), while other effects proved to be negligible. A better parent–child relationship was consistently associated with less frequent cyber-victimization across all analyzed types (beta = −0.145, −0.088, −0.109, −0.100). Victimization by exposure to fearful content was associated only directly with a better parent–child relationship, whereas for the other types of cyber-victimization indirect effects were also found either through self-control alone or through various combinations of self-control with risky internet behavior, time spent on social networks, or both.

Discussion

Cyber-victimization significantly influences an individual's mental well-being, as well as other aspects of quality of life (Gardella et al., 2017; Tran et al., 2023; Wright, 2016). At the same time, it is challenging to address this issue through official channels, as victims of cybercrime often do not even realize that they should be defending themselves against inappropriate online conduct (Ngo et al., 2020). Continued research into the risk and protective factors of cyber-victimization among young people is therefore becoming increasingly urgent.

The aim of our study was to examine the role of parental effect on adolescent cyber-victimization, while considering the mediating effect of self-control, time spent on social networks, and risky internet behavior. In particular, addressing limitations of previous studies (e.g., Álvarez-García et al., 2019; Partin et al., 2022), we extended the model with the parent–child relationship, as this was suggested to be more effective in the prevention of cyber-victimization than parental control (Buelga et al., 2017; Law et al., 2010; Moreno-Ruiz et al., 2019).

Our analysis revealed several significant findings. When considering the model with the composite index of cyber-victimization, we found the effect of parental interest in their children's online activities to be negligible. Parental efforts to warn their children were weakly but positively associated with more frequent cyber-victimization and a higher level of risky internet behavior. This is in line with other studies (Sasson and Mesch, 2014; Shin and Ismail 2014) that highlight the counterproductive nature of excessive parental control, which may motivate an adolescent to behave in opposition to their parents (Baldry et al., 2019; Sasson and Mesch, 2017). When breaking down cyber-victimization into its separate indicators, the data revealed that the positive effect of parental efforts to warn their children applied to being a victim of fearful content. To interpret such findings, we considered research on adolescents’ fear of crime (Cops, 2010, 2013; May et al., 2015; May et al., 2002), which suggests that excessive parental control serves as a mechanism through which parental altruistic fear is transferred to the child. Consequently, it is plausible that a child's heightened sensitivity to certain online content may result in greater fear or anxiety. While these interpretations emphasize potentially counterproductive effects of parental control, an alternative explanation should also be considered. Parental warnings may reflect parents’ awareness of their child's existing risky online activities or prior negative experiences. In this sense, warnings could be reactive rather than preventive. Given the cross-sectional design of the study, the direction of these associations cannot be conclusively determined.

More importantly, our data showed a moderate negative overall direct effect of the parent–child relationship on cyber-victimization—which remained significant across all types of cyber-victimization—and a positive overall direct effect on self-control. Thus, a better parent–child relationship was associated with less frequent cyber-victimization across all analyzed types and with stronger self-control. In addition to the direct relationship between the mentioned variables, three of the studied types of cyber-victimization—mocking and teasing, pornography, and sexual harassment—were also indirectly associated with the parent–child relationship. This association occurred either through self-control alone or through various combinations of self-control, risky internet behavior, and time spent on social networks. In line with previous studies (Buelga et al., 2017; Law et al., 2010; Moreno-Ruiz et al., 2019), our data thus suggest that maintaining a good parent–child relationship proves to be a more effective approach in preventing cyber-victimization than (excessive) parental control. This effect operates either directly or indirectly through fostering self-control. Our findings also resonate with criminological research showing that unstructured spare time can mediate the effects of parental supervision and self-control on delinquency (Buil-Gil, 2025). Time spent on social networks may represent a digital analogue of such unstructured contexts, helping to explain its mediating role between the parent–child relationship, self-control, and cyber-victimization.

Beside the parental effect, we observed a higher level of risky internet behavior to be associated with more frequent cyber-victimization, and this result was consistent for both the cyber-victimization index and the analyzed types of cyber-victimization. Conversely, more time spent on social networks was associated with a lower likelihood of experiencing mocking and teasing and sexual harassment. Additionally, we found self-control to be a direct protective factor against cyber-victimization for sexual harassment and pornography, while the relationship between self-control and being a victim of mocking and teasing or fearful content was indirect. This further supports Cadogan and Lee's (2013) approach, which suggests that researchers should examine indicators of formative latent factors separately, as the construction of composite indexes can lead to a loss of information or misinterpretation of the data.

Despite the important findings this study has brought to light, it also has some limitations that must be acknowledged. First, the sample was restricted to urban ninth-grade adolescents around the age of 15 years old. The relationships between time spent on social networks, risky internet behavior, and cyber-victimization may vary with age, affecting both their strength and relevance across different victimization types. Consequently, further research should strive to validate these findings for other adolescent age groups. Second, the study focused on forms of cyber-victimization experienced by adolescents as psychological or emotional harm, while other types of online victimization such as cyber fraud or cyber-dependent offences were beyond the scope of the present analysis. Future research could extend this approach by integrating interpersonal, exposure-based, and financially motivated forms of online victimization within a single analytical framework. Third, the cross-sectional nature of the data prevented us from determining causal patterns of the analyzed relationships. Nevertheless, the observed associations are consistent with existing theories and previous research. Lastly, the data were collected approximately ten years ago, and the digital landscape has since evolved considerably. Certain online practices that were once considered uncommon or risky—such as frequent mobile access to social networks—have become largely normative among adolescents today. Nevertheless, the indicators used in our analyses capture broader behavioral tendencies, including online self-disclosure, which represents one of the key mechanisms through which online victimization may occur (e.g., Aizenkot, 2020). Moreover, several of these measures continue to be employed in recent studies examining adolescents’ self-control, risky internet behavior, and cyber-victimization (e.g., Partin et al., 2022; Álvarez-García et al., 2019), suggesting that they retain their conceptual relevance in contemporary digital environments. Similarly, as new technologies emerge and adolescents spend an increasing amount of time online, parental control has largely shifted from the offline to the online environment (Modecki et al., 2022), while at the same time making it more challenging for parents to engage in offline interactions with their children and strengthen mutual emotional bonds (Zhu et al., 2022). In this respect, the indicators employed in our study remain conceptually relevant, as they continue to reflect these developments and allow for an assessment of whether it is parental control or the quality of the parent–child relationship that exerts a stronger influence on adolescents’ experiences of cyber-victimization.

Conclusion

Our study highlights the complex dynamics of cyber-victimization and its associations with parent-child relationship and parental control. Key findings suggest that excessive parental control may unintentionally heighten a child's sensitivity to online threats, emphasizing the need for a balanced and supportive parent–child relationship, which reduces cyber-victimization both directly and indirectly through individual self-control. Additionally, while reducing risky internet behaviors and fostering self-control may contribute to lower levels of cyber-victimization, regulating time spent on social networks appears to address only some types of cyber-victimization. Future research should investigate these dynamics across a wider range of cyber-victimization types to further refine prevention strategies and better equip parents, educators, and policymakers to address cyber-victimization risks.

Supplemental Material

sj-docx-1-euc-10.1177_14773708261447207 - Supplemental material for The role of the parent–child relationship and parental control in the link between adolescent cyber-victimization, risky internet behavior, and self-control

Supplemental material, sj-docx-1-euc-10.1177_14773708261447207 for The role of the parent–child relationship and parental control in the link between adolescent cyber-victimization, risky internet behavior, and self-control by Eva Krulichová in European Journal of Criminology

Footnotes

Acknowledgments

The author thanks to Petra Raudenská from the Institute of Sociology of the Czech Academy of Sciences for her valuable advice on data analysis.

ORCID iD

Eva Krulichová

Ethical considerations

This study was based on anonymized data, which did not involve the collection of personal information from participants. As a result, no ethics approval or informed consent was required.

Consent to participate

The data collection was approved by school principals. Children and their parents were informed in advance by school representatives about the research and had the opportunity to opt out. Participation in the survey was voluntary, and informed consent was obtained implicitly through voluntary completion of the questionnaire.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study is from the project “Research of Excellence on Digital Technologies and Well-being CZ.02.01.01/00/22_008/0004583” which is co-financed by the European Union.

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.

Data sharing

The dataset used in this study is not publicly accessible but may be made available by the corresponding author upon reasonable request for academic, non-commercial research, or scholarly educational purposes. Requests should be submitted by email to the corresponding author and should include (a) a brief description of the intended research or educational use, (b) the applicant's institutional affiliation, and (c) confirmation that the data will not be redistributed. All requests will be assessed on a case-by-case basis.

Supplemental material

Supplemental material for this article is available online.

Notes

Appendix

Table A1.

Correlation matrix for all latent and manifest variables.

	1		1A		1B		1C		1D		2		3		4		5		6		7		8
(1) Cyber-victimization (frequent)	I
(1A) Mocking and teasing	0.55	<.001	I
(1B) Pornography	0.75	<.001	0.20	<.001	I
(1C) Fearful content	0.58	<.001	0.24	<.001	0.23	<.001	I
(1D) Sexual harassment	0.63	<.001	0.36	<.001	0.24	<.001	0.23	<.001	I
(2) Risky internet behavior (higher level)	0.34	<.001	0.26	<.001	0.18	<.001	0.13	<.001	0.36	<.001	I
(3) Time spent on social networks (more)	0.29	<.001	0.25	<.001	0.12	<.001	0.12	<.001	0.34	<.001	0.45	<.001	I
(4) Self-control (strong)	−0.22	<.001	−0.11	<.001	−0.18	<.001	−0.03	.258	−0.20	<.001	−0.32	<.001	−0.27	<.001	I
(5) Parents interested	0.02	.432	0.04	.116	−0.05	.059	0.11	<.001	0.02	.404	0.01	.635	0.04	.095	0.09	<.001	I
(6) Parents warn	0.03	.287	0.05	.054	−0.06	.019	0.11	<.001	0.08	<.001	0.05	.040	0.02	.357	0.08	.003	0.36	<.001	I
(7) Relationship with parents (good)	−0.19	<.001	−0.15	<.001	−0.12	<.001	−0.09	<.001	−0.17	<.001	−0.10	<.001	−0.10	<.001	0.12	<.001	0.16	<.001	0.21	<.001	I
(8) Gender (boy)	−0.19	<.001	−0.11	<.001	0.04	.156	−0.20	<.001	−0.36	<.001	−0.06	.015	−0.26	<.001	−0.02	.380	−0.19	<.001	−0.24	<.001	0.14	<.001	I

Note. Spearman’s correlation coefficients; Latent variables were constructed as indexes by averaging the values of relevant indicators.

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