Cyberstalking Perpetration Among Young Adults: An Assessment of the Effects of Low Self-Control and Moral Disengagement

Abstract

The current study sought to explain cyberstalking perpetration using low self-control and moral disengagement frameworks. Self-report survey data collected from a Mechanical Turk sample of 1,500 young adults aged 18 to 25 years old revealed that approximately 22% of the sample had engaged in cyberstalking perpetration during their lifetime. Findings also generally supported the self-control and moral disengagement frameworks. Respondents with higher levels of low self-control were more likely to engage in cyberstalking perpetration, as were those respondents who had a higher moral disengagement score. The interaction between low self-control and moral disengagement, however, did not yield a significant effect.

Keywords

perpetration cyberstalking Mechanical Turk low self-control moral disengagement

Introduction

Sustained interest in cyberstalking exists, ranging from published scholarly articles to media coverage to streaming services crafting the plots of movies and series around unwanted pursuit behaviors facilitated by technological innovations (e.g., You, Unfriended). Despite this interest, relatively little is known about cyberstalking, particularly those who perpetrate this behavior. Although the definitions across state and federal statutes and scholarly research vary, generally, cyberstalking refers to the repeated pursuit of an individual via communication technologies that causes the target to experience fear and/or a substantial emotional response (e.g., Fissel, 2018; Nobles et al., 2014; van Baak & Hayes, 2018).

The emerging body of research suggests that cyberstalking is a societal problem that impacts a substantial number of individuals each year (e.g., Baum et al., 2009; Fissel, 2019; Reyns et al., 2018). One estimate suggests that nearly 41% of college student respondents had been cyberstalked in their lifetime (Reyns et al., 2012). Research has also illustrated that many cyberstalking victims experience serious negative health harms as a result of their victimization. Specifically, some victims have experienced negative psychological effects, ranging from fear and anxiety (Worsley et al., 2017) to sleep disorders (Dreßing et al., 2014) to depression (Dreßing et al., 2014). Recent research also revealed that some cyberstalking victims experience school (e.g., lower grades), work (e.g., quit or got fired), and social consequences (e.g., increased fights with friends or family), in addition to suffering negative health impacts (Fissel & Reyns, 2020).

Collectively, the extant findings support the argument that cyberstalking is a societal ill necessitating resources directed at its prevention. However, researchers know little about who engages in cyberstalking perpetration, how often, and why. This gap in the literature is largely the result of the difficulty in collecting data and the few theoretically grounded studies of cyberstalking perpetration.

The current study makes two contributions to the nascent cyberstalking research. First, while the existing cyberstalking perpetration research is typically atheoretical or guided by a single theory (e.g., Cavezza & McEwan, 2014; Dreßing et al., 2014; Reyns et al., 2012) the present study used a multi-theoretical framework to situate participation in cyberstalking. This proposed framework allows for an understanding of how multiple perspectives may simultaneously contribute to explaining why individuals engage in cyberstalking perpetration, offering a more complete explanation. To this end, the current study assessed the extent to which measures of low self-control and moral disengagement influenced cyberstalking perpetration. In the limited research examining cyberstalking perpetration from a theoretical approach, a link between low self-control and cyberstalking has been repeatedly observed (e.g., Marcum et al., 2014; Reyns, 2019). While no known study has utilized a moral disengagement framework to explore cyberstalking perpetration, existing research has found a link between moral disengagement and other cyber-based perpetration (e.g., Bussey et al., 2015; Orue & Calvete, 2019; Renati et al., 2012; Wasch, 2012). Second, most of what is known about cyberstalking perpetration is derived from samples consisting solely of college students. Further, these samples are typically drawn from just one institution of higher education, limiting the ability to generalize findings outside of that single college or university. To address this limitation, the current study utilized a community sample of young adults, that included both college students and non-students, across the United States. The data include several items tapping various aspects of cyberstalking perpetration that provide an indication of who engages in cyberstalking, how often, and why.

Cyberstalking Perpetration

Although relatively scant, the existing research does provide some basic understanding of cyberstalking perpetration. Among the first to examine cyberstalking perpetration, Reyns et al. (2012) observed that less than 5% of respondents in their sample of 974 college students had ever repeatedly engaged in one or more online pursuit behaviors, including contact, harassment, sexual advances, or threats of violence. Similarly, in a study of 1,617 high school students, approximately 5% reported contacting someone online repeatedly in the previous year after they were asked to stop (Marcum et al., 2014). Much higher perpetration estimates have also been observed among samples of adults and college-aged Facebook users (Lyndon et al., 2011). Strawhun et al. (2013), for example, used a sample consisting of 248 undergraduate students. The authors utilized the Electronic Use Pursuit Behavioral Index (EUPBI), which included 42 cyberstalking behaviors and an open-ended question asking respondents to indicate if they ever stalked someone electronically. While a majority of the respondents indicated participation in many of the behaviors analogous to cyberstalking (e.g., sending threatening, insulting, or harassing messages), the repetition required for cyberstalking was not assessed. The findings from the open-ended question, however, revealed that 26.5% of respondents admitted to cyberstalking someone (Strawhun et al., 2013). However, due to the self-identifying nature of the question it is difficult to know the behaviors captured by this item. Generally, the existing literature reveals that between approximately 5% and 50% of research participants have engaged in cyberstalking perpetration. This wide range in perpetration prevalence estimates highlights how sensitive these numbers are to differences in definition and measurement across studies.

In addition to both conceptualization and operationalization issues, the anonymity afforded in cyberspace adds complexity to identifying who engages in cyberstalking perpetration. The existing research, though, has revealed some preliminary findings regarding typical characteristics of those who engage in cyberstalking behaviors. First, research has indicated that cyberstalkers are most often male in studies with samples consisting of adults (e.g., D’Ovidio & Doyle, 2003; Dreßing et al., 2014; Moriarty & Freiberger, 2008) and college students (e.g., Marcum et al., 2017; Ménard & Pincus, 2012; Reyns et al., 2012).

Second, there is mixed evidence regarding the nature of the victim-offender relationship. Studies of samples consisting of adults have typically found that most cyberstalking victims did not know their offender (e.g., Bocij, 2002; Moriarty & Freiberger, 2008; Short et al., 2015). Studies with college student samples, however, tend to find that the perpetrator is most likely to be a former intimate partner (e.g., Alexy et al., 2005) or an acquaintance (e.g., Paullet et al., 2009). Nonetheless, some research using college student samples also found the perpetrator was most often a stranger (e.g., Reyns et al., 2012).

Third, research also provides mixed evidence regarding the racial identity of cyberstalkers. D’Ovidio and Doyle (2003) used data collected from 201 closed cases involving cyberstalking investigated by the New York City Police Department and found that approximately 74% of the cyberstalkers were White, 13% were Asian, 8% were Hispanic, and 5% were Black. Reyns et al. (2012), in contrast, found that non-White undergraduate students engaged in cyberstalking more often than White students.

As illustrated in the past research, findings regarding the characteristics of cyberstalkers vary based on the composition of the sample. Most notable is the victim-offender relationship, with college students most often being cyberstalked by someone they know. While racial differences are also noted, there are too few studies to draw any solid conclusions. Due to the potential behavioral differences between general adults and college students, logically, one cannot assume patterns reported amongst college students will mirror findings from general adult samples.

Theoretical Explanations of Cyberstalking Perpetration

Low Self-Control

Researchers have begun to examine theoretical explanations for cybercrime perpetration (e.g., Bossler & Burruss, 2012; Holt et al., 2012). Those focusing specifically on cyberstalking have found a link between low self-control and cyberstalking perpetration. In short, Gottfredson and Hirschi’s (1990) general theory of crime suggests that individuals with relatively low self-control are at an increased likelihood of engaging in criminal behavior (Gottfredson & Hirschi, 1990). Individuals with low self-control are impulsive, self-centered, quick tempered, short sighted, risk seeking, and prefer physical tasks. Cyberspace is conducive for many of these traits, particularly the immediate nature of online behaviors.

Marcum et al. (2014) were among the first to assess the relationship between low self-control and cyberstalking perpetration. Analyses of a sample of 1,617 high school students in rural North Carolina revealed that higher levels of low self-control, captured using Schreck’s (1999) nine-item measure, were associated with an increased likelihood of repeatedly contacting someone via the Internet even after being asked to stop such contact. Specifically, for each unit change as low self-control increased, the likelihood of cyberstalking perpetration increased 18% (Marcum et al., 2014). Using an older sample of 890 undergraduate and graduate students enrolled at a mid-sized university in the Southeast, Marcum et al. (2017) examined the impact of the general theory of crime on cyberstalking behaviors within the context of romantic relationships. One model was estimated using a measure of low self-control based on Grasmick et al. (1993) scale. The results revealed support for the general theory of crime, as students with low self-control were more likely to attempt to log into a significant other’s social networking account without their knowledge (Marcum et al., 2017).

Two additional studies using college student samples revealed support for the impact of low self-control on cyberstalking perpetration. van Baak and Hayes (2018) examined the impact of low self-control on cyberstalking perpetration—captured by three online pursuit behaviors—among a sample of 662 undergraduate students enrolled in criminal justice classes at a university in the American southeast. Findings revealed that individuals with lower levels of low self-control had a 7% reduction in odds of engaging in cyberstalking perpetration. Reyns (2019) found, in a sample of 759 college students enrolled in a large university in the Midwest, that students with low self-control were over twice as likely to perpetrate cyberstalking compared to those students with high levels of self-control. These studies provide promising support for the utility of low self-control in explaining cyberstalking perpetration. Though, we see that differences in definition and samples produce varying levels of effect sizes. Further, these studies focus primarily on student samples. Given the ubiquity of cell phone ownership and internet use in the United States (e.g., Horrigan & Dugan, 2015; Pew Research Center, 2016) it is important to examine the impact of low self-control on cyberstalking perpetration in a general adult sample.

Moral Disengagement

Cybercrime researchers have also recently found a link between moral disengagement and cybercrime perpetration. Bandura’s (1986, 1991) social cognitive theory is a theory of moral agency that explains the relationships between moral thought and moral actions. One self-regulatory mechanism, moral disengagement, allows individuals to selectively disengage from internal standards. Individuals participate in behaviors that challenge their own morals while still believing in them, without experiencing negative reactions, due to their ability to disengage with their morals (Paciello et al., 2008). Bandura (2002) presented eight types of moral disengagement that may be utilized when participating in undesirable behavior.¹

The first set of mechanisms include practices that are labeled cognitive restructuring. First, moral justification refers to the process of restructuring behavior as to justify the morality of the behavior (Bandura, 2002). Specifically, the unfavorable behavior is “made personally and socially acceptable by portraying it as serving socially worthy or moral purposes” (Bandura, 2002, p. 103). Second, euphemistic labeling allows someone to shift misconduct into something reasonable and reduce their own personal responsibility (Bandura, 2002). Individuals can also use advantageous comparison to compare their misconduct with behavior that is deemed even more unacceptable, thus, making their conduct appear decent (Bandura, 2002).

The next category of mechanisms includes techniques that minimizes one’s responsibility. Displacement of responsibility allows individuals to view their behavior as orders from others, removing personal responsibility (Bandura, 2002). Similarly, diffusion of responsibility refers to when individuals spread the responsibility across multiple agents, thus, their own behaviors appear harmless (Bandura, 2002). Disregarding or distortion of consequences occurs when people ignore or minimize the harm caused by their behaviors (Bandura, 2002).

The final two mechanisms of moral disengagement are related to how the victim is viewed. Dehumanization is when someone removes the human qualities from a victim, while attribution of blame occurs when an individual views themselves as a victim (Bandura, 2002). Each of these eight mechanisms of moral disengagement, Bandura argues, allow individuals to engage in unacceptable behavior without viewing themselves as being morally wrong.

Bandura’s concept of moral disengagement has been used to explain a variety of traditional antisocial behaviors, including bullying (e.g., Hymel et al., 2005; Obermann, 2011), aggressive behavior (e.g., Gini et al., 2014; Paciello et al., 2008), and gang-related activity (e.g., Wood et al., 2009). Scholars have also argued that moral disengagement has additional utility in explaining a variety of online antisocial and deviant behaviors, as cyberspace may make it easier to use moral disengagement techniques (Runions & Bak, 2015).

Moral disengagement and cyberspace

Bandura (2002) originally argued that it would be easier to harm others if that harm was not observed by the perpetrator or if there was a delay in time between the behavior and the harm. Cyberspace provides such a distance between offender and victim, allowing the perpetrator to behave without directly witnessing any harm (Pornari & Wood, 2010). Technological advances often provide greater opportunity and ease for antisocial behavior, while some advances, such as the Internet, may even serve to promote moral disengagement (Bauman, 2010; Runions & Bak, 2015).

There are at least three ways in which cyberspace may encourage moral disengagement. First, contemporary media tends to report on newsworthy stories that are either entertaining in some way or exhibit extreme cases. Thus, high-profile cases of cybercrime typically receive the most widespread attention (e.g., murder that occurs after cyberstalking). Such a process may provide individuals the opportunity to employ advantageous comparison, justifying their own behavior (Runions & Bak, 2015). Second, cyberspace provides ample opportunity for displacement or diffusion of responsibility. Social networking sites provide the opportunity to widely share information (i.e., reposting or distribution of content to a wider audience). This feature allows users to share harmful, humiliating, or offensive content without taking responsibility, as they were not the ones who originally posted the material (Runions & Bak, 2015). Finally, in contrast to face-to-face interactions, emotional cues and humanistic qualities are virtually non-existent with text-based communications, making it possible to dehumanize others (Runions & Bak, 2015). Overall, cyberspace allows individuals to more easily disengage from internal moral standards, suggesting that the concept of moral disengagement may be central to explaining perpetration in online antisocial behavior such as cyberstalking.

Empirical research supports these ideas, as numerous scholars have assessed the impact of moral disengagement on cyberbullying using samples consisting of adolescents, with all finding evidence that higher levels of moral disengagement are associated with an increased likelihood of cyberbullying perpetration (e.g., Bussey et al., 2015; Orue & Calvete, 2019; Renati et al., 2012; Wasch, 2012). Others have also found that moral disengagement was positively associated with cyberaggression perpetration, which included three behaviors, such as sending insulting or threatening messages (Pornari & Wood, 2010). To date, however, no study has examined the impact of moral disengagement on cyberstalking perpetration.

Interaction of Self-Control and Moral Disengagement

Finally, low self-control and moral disengagement may interact and impact the likelihood of cyberstalking perpetration. This is because individuals who have high levels of moral disengagement have already justified their unfavorable behaviors through the methods of moral engagement and, thus, are less motivated to use self-control.

Li et al. (2014) recognized the importance of testing how these two constructs—low self-control and moral disengagement—may interact with one another to impact offending. In a study on self-reported aggression using a sample of 946 university students in China, findings revealed that trait self-control, captured via a 13-item measure, and moral disengagement, measured using the Chinese version of the Moral Disengagement Scale, independently influenced self-reported aggression. Specifically, trait self-control negatively predicted physical aggression, verbal aggression, anger, and hostility, meaning that as self-control increased, likelihood of engaging in aggression decreased. Moral disengagement was found to positively predict all forms of aggression; more morally disengaged students were more likely to engage in aggression. Moreover, the interaction between self-control and moral disengagement was significant for self-reported verbal aggression and hostility (Li et al., 2014). Unfortunately, no other known studies have examined this interaction.

The Current Study

The current study builds upon the existing literature and contributes to the understanding of cyberstalking perpetration in three main ways. First, the relatively large sample in the current study contains respondents aged 18- to 25-years-old, which addresses a shortcoming of previous research that relies primarily upon small college student samples. Second, the measure of cyberstalking perpetration used in the current study includes behaviors that capture a greater range of online pursuit behaviors. Consequently, the measure represents an improvement from previous work, which included limited cyberstalking behaviors, in terms of content validity. Finally, the current study is grounded in a multi-theoretical framework, including concepts central to the low self-control and moral disengagement perspectives. This multi-theoretical approach allows us to explore how multiple factors could be working simultaneously—and potentially interacting—to more fully explain why individuals engage in cyberstalking. Derived from self-control and moral disengagement frameworks, and building upon the past research, three hypotheses are tested.

Research Question 1: Does low self-control impact the likelihood of engaging in cyberstalking perpetration?

Hypothesis 1: Individuals with higher levels of low self-control will be more likely to perpetrate cyberstalking.

Research Question 2: Does moral disengagement impact the likelihood of engaging in cyberstalking perpetration?

Hypothesis 2: Individuals with higher levels of moral disengagement will be more likely to perpetrate cyberstalking.

Research Question 3: Does the interaction between low self-control and moral disengagement impact the likelihood of engaging in cyberstalking perpetration?

Hypothesis 3: Individuals with higher levels of low self-control and moral disengagement will be more likely to perpetrate cyberstalking.

Data

The data used to answer the three research questions and test the three hypotheses were collected via an online self-report survey from 1,500 Amazon’s Mechanical Turk (MTurk) workers as part of a larger study on cyberstalking between late November 2017 and January 2018. In addition to having an active MTurk worker account, individuals were eligible to participate only if they were between 18 and 25 years of age, English speaking, and resided in the United States at the time of the survey. An online self-report questionnaire was administered to participants via Qualtrics. While each respondent was given 1 hour to complete the questionnaire, the average competition time was approximately 12 minutes. Individuals who successfully completed² the entire questionnaire were paid $0.35.³

Measures

Dependent variable

To capture cyberstalking perpetration,⁴ respondents were asked to indicate the number of times they ever engaged in any of the following behaviors using communication technologies toward the same individual: (1) persistently harassed or annoyed someone; (2) made unwanted sexual advances; (3) threatened physical harm; (4) spied on or monitored someone’s activities; (5) tracked someone’s whereabouts; and (6) posted or threatened to post inappropriate, unwanted, or personal information about someone.⁵ Response options included 0 times, 1 time, 2 times, 3 to 6 times, 7 to 10 times, and more than 10 times. The responses to each item were summed and then dichotomized to represent repeatedly engaged in online pursuit behaviors. Values of 0 and 1 were recoded as 0 (did not engage in cyberstalking perpetration), while scores of 2 and greater were coded as 1 (engaged in cyberstalking perpetration).

Independent variables

Two theoretically relevant variables were created in the current study to capture low self-control and moral disengagement. Low self-control was measured using the Grasmick et al. (1993) low self-control scale. The 24 items were measured with a Likert scale ranging from 1 = “Strongly Disagree” to 4 = “Strongly Agree.” The items were first summed and then an index was created by dividing the summed values by four (Cronbach’s alpha = 0.890). Higher values represent higher levels of low self-control.

Moral disengagement is a five-item measure based on Bandura et al. (1996). Respondents were asked to indicate the extent to which they agreed with the following statements about cyberstalking: (1) most people who get cyberstalked deserve it; (2) cyberstalkers don’t mean to hurt anyone; (3) being cyberstalked is no big deal; (4) I can understand why someone would cyberstalk others; and (5) I think cyberstalkers should be punished⁶ (Cronbach’s alpha = 0.749). Each item was measured on a Likert scale ranging from 1 = “Totally False” to 5 = “Totally True.” The item responses were summed and then an index was created by dividing the summed scores by five. An exploratory factor analysis was conducted, and the items loaded onto a single factor, accounting for 51.9% of the variance. Higher values represent higher levels of moral disengagement.

Control variables

Measures capturing three types of victimization experiences were included in this study as control variables due to previous literature on the victim-offender overlap. Cyberbullying victimization was created by asking respondents to indicate if “someone has ever repeatedly made fun of you online, repeatedly picked on you online, or posted something about you online that you did not like?” Response options were no (coded 0) or yes (coded 1; representing experiencing at least one incident of cyberbullying victimization).

To capture cyberstalking victimization,⁷ respondents were asked a series of questions to determine if they were victims of cyberstalking within the previous 12 months. First, respondents indicated if they had experienced any of the following pursuit behaviors through the use of communication technologies: (1) unwanted contact or attempted contact; (2) harassment or annoyance; (3) unwanted sexual advances; (4) threats of harm; (5) were spied on or had their activities monitored; (6) had their whereabouts tracked; and (7) had inappropriate, unwanted, or personal posts about them shared or threatened to share (Cronbach’s alpha = 0.737). Those who experienced at least one type of these pursuit behaviors were asked to indicate the number of times they experienced each type, if they experienced a substantial emotional response due to the pursuit behaviors, and if they feared for their safety or the safety of someone close to them due to the pursuit behaviors. To be considered a victim of cyberstalking, the respondent had to experience: (1) repeated pursuit behaviors (either one of the behaviors two or more times or at least two behaviors at least one time each) and (2) a substantial emotional response and/or fear for their safety or the safety of someone close to them as a result of the repeated contacts or behaviors. Those who met the criteria were coded 1, representing having experienced cyberstalking victimization in the previous 12-months, while those who did not were coded 0.

Offline stalking victimization⁸ was created by asking respondents to indicate “. . .if one person has ever done any of the following that caused you to have a substantial emotional response or fear for your safety or the safety of someone close to you.” The pursuit behaviors included: (1) followed you around and/or watched you; (2) sneaked into your home, car, or any other place and did unwanted things to let you know they had been there; (3) waited for you at your home, work, school, or any other place when you did not want them to; (4) showed up, rode, or drove by places where you were when they had no business being there; (5) left or sent unwanted cards, letters, presents, flowers, or other items; and (6) harassed or repeatedly asked your friends or family about your whereabouts (Cronbach’s alpha = 0.755). A dichotomous variable was created with individuals who experienced two or more of the pursuit behaviors coded as 1 and labeled as victims of offline stalking, while all others were coded 0.

Personal characteristics including age, race, gender identity, student status, and relationship status, were also included in the analyses due to their potential impact on cyberstalking perpetration. Age was created as a continuous variable based on the respondent’s numerical age ranging from 18 to 25 years old. Respondents were asked to select the racial category (-ies) that best described them. Due to low base rates, the racial categories were collapsed into a dichotomous variable capturing race (0 = non-White, 1 = White).

Respondents were asked to select which of the following options best described their gender identity: (1) man; (2) woman; (3) transgender man; (4) transgender woman; (5) genderqueer or gender non-conforming; (6) questioning; and (7) not listed. Dummy variables were created to capture gender identity: man (reference group), woman, and other gender identity. The other gender identity variable included responses of transgender man, transgender woman, genderqueer or gender non-conforming, questioning, and not listed due to low base rates for these categories.

Student status was created by asking respondents to select the option that best represented them: (1) high school student; (2) trade school student; (3) vocational school student; (4) college or university student; and (5) not a student. A dichotomous variable was created to capture those respondents who were currently a student (options 1–4; coded 1) and those who were not a student (option 5; coded 0). Given that the majority of existing studies on cyberstalking utilized student samples (e.g., Ménard & Pincus, 2012; Reyns et al., 2012; van Baak & Hayes, 2018), it was important to include this variable to determine if student status impacted the likelihood of participating in cyberstalking perpetration.

Finally, relationship status was captured by asking respondents to identify their current primary relationship status from the following list: (1) single; (2) casually dating or hooking up; (3) steady or exclusive relationship; (4) married, civil union, domestic partnership; and (5) divorced, separated, or widowed. This variable was coded dichotomously (0 = single; 1 = non-single).

Analytic Strategy

The analyses followed a multi-step procedure. First, all summary statistics for the study variables were produced. Second, the bivariate associations between the independent variables, the control variables, and the dependent variables were assessed. Due to the nature of the study variables, two types of bivariate statistics were calculated—point-biserial correlations (r_pb) and Phi coefficients (ϕ)—to determine the strength and direction of the association between two variables. Finally, given the dichotomous nature of the dependent variable (cyberstalking perpetration), four separate binary logistic regression models were estimated. The first model included low self-control, victimization experiences, and personal characteristics. The second model included moral disengagement, victimization experiences, and personal characteristics. The third model included low self-control, moral disengagement, victimization experiences, and personal characteristics. The final model included the same variables as model three, plus an interaction between low self-control and moral disengagement.⁹

Results

Descriptive Statistics

Table 1 presents the descriptive statistics for the study variables. Over 22% (N = 333) of the sample met the criteria for cyberstalking perpetration. The mean low self-control value was 2.19 (SD = 0.49), indicating that, on average, respondents scored slightly above the middle of the scale (i.e., toward higher levels of low self-control). The average moral disengagement score was 1.87 (SD = 0.76), which represents a fairly low moral disengagement score.

Table 1.

Descriptive Statistics of All Study Variables (N = 1,500).

Variable (coding)	Min	Max	M (SD)
Dependent variables
Cyberstalking perpetration (0 = did not participate; 1 = participated)	0	1	0.22 (0.42)
Theoretically relevant variables
Low self-control	1	4	2.19 (0.49)
Moral disengagement	1	5	1.87 (0.76)
Control variables
Victimization experiences
Cyberbullying victimization (0 = did not experience; 1 = experienced)	0	1	0.23 (0.42)
Cyberstalking victimization (0 = did not experience; 1 = experienced)	0	1	0.32 (0.47)
Offline stalking victimization (0 = did not experience; 1 = experienced)	0	1	0.20 (0.40)
Personal characteristics
Age	18	25	22.83 (1.82)
Race (0 = non-White; 1 = White)	0	1	0.64 (0.48)
Gender identity
Man (reference group)	0	1	0.31 (0.46)
Woman	0	1	0.65 (0.48)
Other gender identity	0	1	0.04 (0.19)
Student status (0 = not a student; 1 = student)	0	1	0.65 (0.48)
Relationship status (0 = single; 1 = non-single)	0	1	0.66 (0.48)

Respondents’ experiences varied depending on type of victimization. Nearly a quarter of respondents had been previously cyberbullied in their lifetime, 32% had been cyberstalked within the previous 12 months, and 20% had been stalked offline. The average age of the sample was 22.83 years old (SD = 1.82). Those who identified as women represented the majority of the sample (65%), followed by men (31%), and other gender (4%). Approximately 64% of the sample identified as White. Nearly two-thirds of the sample were enrolled in school at the time of the survey and 66% of the sample identified as being non-single.

Bivariate Analyses

Table 2 displays the bivariate relationships among the study variables. As illustrated, most of the independent variables were significantly related to the dependent variable—cyberstalking perpetration. First, low self-control (r_pb = 0.24, p < .001) and moral disengagement (r_pb = 0.35, p < .001) were both positively related to cyberstalking perpetration, although the magnitudes of the associations were moderate. Each type of victimization experience—cyberbullying victimization (ϕ = 0.16, p < .001), cyberstalking victimization (ϕ = 0.26, p < .001), and offline stalking victimization (ϕ = 0.24, p < .001)—was also found to be significantly and positively related to cyberstalking perpetration.

Table 2.

Bivariate Associations Among All Study Variables (N = 1,500).

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
(1) Cyberstalking perpetration	1.00
(2) Low self-control	0.24***	1.00
(3) Moral disengagement	0.35***	0.34***	1.00
(4) Cyberbullying victimization	0.16***	0.12***	0.03	1.00
(5) Cyberstalking victimization	0.26***	0.15***	0.09***	0.25***	1.00
(6) Offline stalking victimization	0.24***	0.14***	0.10***	0.24***	0.38***	1.00
(7) Age	0.02	−0.09***	−0.08**	−0.04	−0.08**	0.02	1.00
(8) Race	−0.04	−0.07**	−0.04	0.05	−0.05	−0.07**	0.11***	1.00
(9) Man	0.06*	0.16***	0.28***	0.01	−0.10***	−0.08**	−0.07**	−0.02	1.00
(10) Woman	−0.08**	−0.15***	−0.27***	−0.06*	0.06*	0.08**	0.08**	0.02	−0.92***	1.00
(11) Other gender identity	0.06*	0.00	−0.00	0.12***	0.09***	−0.01	−0.02	−0.02	−0.13***	−0.28***	1.00
(12) Student status	0.11***	0.05*	0.13***	−0.01	0.06*	0.04	−0.30***	−0.14***	0.12***	−0.11***	−0.00	1.00
(13) Relationship status	−0.03	−0.05*	−0.14***	0.04	0.07**	0.09***	0.09***	0.11***	−0.18***	0.17***	0.01	−0.03	1.00

Note. Point-biserial correlations (r_pb) were employed when assessing the association between continuous and discrete variables while Phi (ϕ) was employed when assessing the association between two dichotomous variables.

p < .05. **p < .01. ***p < .001.

Gender identity was significantly correlated with cyberstalking perpetration. Identifying as a man (ϕ = 0.06, p = .029) or other gender identity (ϕ = 0.06, p = .022) were both positively associated with cyberstalking perpetration, while identifying as a woman (ϕ = −0.08, p = .002) was negatively associated with cyberstalking perpetration. Finally, student status (ϕ = 0.11, p < .001) was significantly and positively related to cyberstalking perpetration. While the magnitude of these relationships is generally relatively weak to moderate in strength, they indicate that, to some degree, low self-control, moral disengagement, victimization experiences, and personal characteristics are associated with cyberstalking perpetration.

Multivariate Analyses

To assess the extent to which the bivariate associations observed in Table 2 were susceptible to confounding, we estimated four logistic regression models. Model 1 in Table 3 presents the influence of low self-control on cyberstalking perpetration, while controlling for victimization experiences and personal characteristics. Low self-control remained positively associated with cyberstalking perpetration, indicating that those with higher levels of low self-control were more likely to engage in cyberstalking perpetration (OR = 2.61, p < .001). Age (OR = 1.14, p < .001) and student status (OR = 1.81, p < .001) were also positively associated with cyberstalking perpetration. Older respondents and those who were enrolled in school were significantly more likely to cyberstalk others. Further, those who had experienced cyberbullying (OR = 1.44, p < .05), cyberstalking (OR = 2.43, p < .001), and offline stalking victimization (OR = 2.09, p < .001) were more likely to engage in cyberstalking perpetration.

Table 3.

Binary Logistic Regression Models Predicting Cyberstalking Perpetration (N = 1,500).

	Model 1			Model 2			Model 3			Model 4
	b (SE)	OR	OR 95%CI	b (SE)	OR	OR 95%CI	b (SE)	OR	OR 95%CI	b (SE)	OR	OR 95%CI
Theoretically relevant variables
Low Self-control (LSC)	0.96*** (0.15)	2.61	1.96–3.47	—	—	—	0.58*** (0.16)	1.79	1.31–2.44	0.96* (0.41)	2.62	1.17–5.87
Moral disengagement (MD)	—	—	—	1.05*** (0.10)	2.86	2.37–3.45	0.96*** (0.10)	2.60	2.15–3.16	1.39** (0.44)	4.01	1.69–9.49
LSC × MD	—	—	—	—	—	—	—	—	—	−0.18 (0.18)	0.83	0.58–1.19
Control variables
Cyberbullying victimization	0.37* (0.16)	1.44	1.06–1.96	0.53*** (0.16)	1.70	1.23–2.35	0.50** (0.17)	1.65	1.20–2.29	0.50** (0.17)	1.66	1.20–2.29
Cyberstalking victimization	0.89*** (0.15)	2.43	1.81–3.27	0.91*** (0.16)	2.48	1.82–3.37	0.86*** (0.16)	2.37	1.74–3.23	0.87*** (0.16)	2.39	1.75–3.25
Offline stalking victimization	0.74*** (0.16)	2.09	1.51–2.88	0.71*** (0.17)	2.04	2.45–2.85	0.69*** (0.17)	1.99	1.42–2.79	0.69*** (0.17)	2.00	1.42–2.80
Age	0.13*** (0.04)	1.14	1.05–1.23	0.13** (0.04)	1.14	1.05–1.23	0.13*** (0.04)	1.14	1.05–1.24	0.13*** (0.04)	1.14	0.58–1.19
Race (White)	−0.00 (0.14)	1.00	0.75–1.32	−0.05 (0.15)	0.95	0.71–1.28	−0.02 (0.15)	0.98	0.73–1.32	−0.02 (0.15)	0.98	0.73–1.31
Gender identity (Man)
Woman	−0.27 (0.15)	0.77	0.57–1.03	0.03 (0.16)	1.03	0.75–1.42	0.08 (0.16)	1.08	0.79–1.49	0.08 (0.16)	1.08	0.79–1.49
Other gender identity	0.29 (0.33)	1.34	0.70–2.54	0.49 (0.35)	1.63	0.82–3.26	0.52 (0.36)	1.69	0.84–3.39	0.53 (0.36)	1.69	0.84–3.41
Student status (student)	0.59*** (0.16)	1.81	1.33–2.45	0.47** (0.16)	1.59	1.16–2.18	0.47** (0.16)	1.61	1.17–2.20	0.48** (0.16)	1.62	1.17–2.22
Relationship status (non-single)	−0.24 (0.15)	0.79	0.59–1.05	−0.12 (0.15)	0.89	0.66–1.20	−0.11 (0.15)	0.89	0.66–1.20	−0.11 (0.15)	0.90	0.66–1.21
Constant	−7.12*** (1.04)	0.00	0.00–0.01	−7.19*** (1.02)	0.00	0.00–0.01	−8.47*** (1.09)	0.00	0.00–0.00	−9.39*** (1.43)	0.00	0.00–0.00
Log-likelihood	−685.55			−641.69			−634.87			−634.37
Pseudo R²	0.14			0.19			0.20			0.20

Note. Reference group are italicized and in parentheses.

p < .05. **p < .01. ***p < .001.

Model 2 in Table 3 displays the results of moral disengagement regressed on cyberstalking perpetration, while controlling for victimization experiences and personal characteristics. As illustrated, individuals with higher moral disengagement scores had an increased likelihood of participation in cyberstalking perpetration (OR = 2.86, p < .001). Additionally, the likelihood of engaging in cyberstalking perpetration increased as respondents aged (OR = 1.14, p < .01), were currently enrolled in school (OR = 1.59, p < .01), or if they had experienced cyberbullying (OR = 1.70, p < .001), cyberstalking (OR = 2.48, p < .001), or offline stalking (OR = 2.04, p < .001) victimization.

Model 3 in Table 3 assessed the impact of low self-control and moral disengagement on cyberstalking perpetration, while controlling for personal characteristics and victimization experiences. As illustrated, those with higher levels of low self-control were still more likely to engage in cyberstalking perpetration (OR = 1.79, p < .001), as were respondents who had higher moral disengagement scores (OR = 2.60, p < .001). Age (OR = 1.14, p < .001) and student status (OR = 1.61, p < .01) remained positively associated with cyberstalking perpetration. In terms of victimization experience, cyberbullying (OR = 1.65, p < .01), cyberstalking (OR = 2.37, p < .001), and offline stalking victimization (OR = 1.99, p < .001) were also still associated with cyberstalking perpetration in a positive direction.

Finally, to assess the extent to which low self-control and moral disengagement interacted to affect the likelihood of engaging in cyberstalking, Model 4 in Table 3 includes an interaction term between the two variables as well as the personal characteristics and victimization experiences. As illustrated, low self-control and moral disengagement were still positively associated with cyberstalking perpetration (OR = 2.62, p < .05; OR = 4.01, p < .01, respectively), but the interaction term was not statistically significant.

Discussion

The purpose of the current study was to assess the predictive utility of multiple theoretical frameworks, specifically low self-control and moral disengagement, to understanding cyberstalking perpetration. Several conclusions can be drawn from the findings. First, the data revealed that about 22% of the sample had engaged in cyberstalking perpetration within their lifetime. This prevalence estimate is higher than many of the estimates reported in previous studies. One explanation for this is that the measure of cyberstalking perpetration in the current study captures a wide collection of behaviors. In previous studies, cyberstalking perpetration measures often captured only three or four behaviors (e.g., Reyns et al., 2012; van Baak & Hayes, 2018), with some only capturing one (e.g., Marcum et al., 2014). Whereas the current study asked the participants to reveal their participation in six online pursuit behaviors.

Second, the associations observed in the bivariate analyses remained significant in the multivariate logistic regression models and revealed support for our first hypothesis; those respondents with higher levels of low self-control were more likely to engage in cyberstalking perpetration. Across all models, low self-control reached statistical significance, with fairly large effect sizes. These findings are in line with existing studies that have employed different samples (e.g., Marcum et al., 2014; Reyns, 2019; van Baak & Hayes, 2018). When combined with research illustrating the effect of low self-control on a variety of other online antisocial behaviors (e.g., Bossler & Burruss, 2012; Buzzell et al., 2006; Higgins & Wilson, 2006; Holt et al., 2012; Marcum et al., 2014; Vazsonyi et al., 2012), it appears that low self-control is an important construct to consider when assessing online deviant behavior.

Third, our second hypothesis also received support. Across all models, individuals with higher levels of moral disengagement were more likely to engage in cyberstalking perpetration. We anticipated this finding given Bandura’s (2002) belief that individuals would find it easier to harm another if the perpetrator did not witness that harm or if there was a delay between the perpetrator’s behavior and the harm caused, both of which are provided by cyberspace. Although our analyses represent the first assessment of the relationship between moral disengagement and cyberstalking perpetration, our findings are consistent with those observed in the cyberbullying literature (e.g., Bussey et al., 2015). As with low self-control, it appears that moral disengagement is a highly relevant theoretical construct to include in empirical examinations of cyberstalking perpetration.

Fourth, our analyses in Model 3 revealed that both low self-control and moral disengagement were independently and significantly related to cyberstalking perpetration but that the magnitude of the associations differed across the theoretical constructs. Thus, while both theoretical constructs appear to be affecting the likelihood of engaging in cyberstalking, moral disengagement may be influencing a greater degree of the variance in the outcome. This finding is consistent with previous research that found moral disengagement to have a stronger influence than trait self-control on physical aggression, verbal aggression, anger, and hostility (Li et al., 2014).

Fifth, while our analyses indicated a consistently strong independent influence of low self-control and moral disengagement, they did not appear to interact to impact the likelihood of cyberstalking perpetration. Although our expectations for an interaction were based on prior literature (e.g., Li et al., 2014), we speculate there may have been a lack of interactive effect in the current study due to cultural differences. More specifically, the items used to capture trait self-control and moral disengagement were developed in Li et al.’s (2014) study specifically for Chinese adolescents. Another explanation for the lack of interactive effective is based on Bandura’s idea that individuals regulate their behaviors based on anticipated social consequences. Given that cyberstalking could leave digital evidence, individuals use self-control, even when morally disengaged, to avoid social consequences.

Although included as a collection of control variables, the victimization experience measures evinced interesting findings that reveal a potential victim-offender overlap. The victim-offender overlap has been well established across several types of crime and indicates that, in general, one’s chances of being victimized of a particular offense increases with one’s perpetration of the offense, and vice-versa (Lauritsen & Laub, 2007; Lauritsen et al., 1991). The victim-offender overlap in terms of cybercrime, however, has received limited empirical examination (see, however, Holt & Bossler, 2013; Marcum et al., 2014). In all models in the current study, the analyses illustrated that experiencing cyberbullying, cyberstalking, and offline stalking victimization were associated with a greater likelihood of participating in cyberstalking perpetration. Additionally, the collection of offline and online victimization experiences reduced the magnitude of the effect of low self-control on cyberstalking perpetration. Consequently, our findings indicate that assessments of cyberstalking perpetration should also consider the extent to which the victim-offender overlap is affecting any observed associations. Finally, given the variance in magnitude exhibited across the different victimization experiences it is especially important that scholars account for cyberstalking victimization in assessing potential etiological factors underpinning cyberstalking perpetration.

The other collection of control variables, personal characteristics, also yielded some significant findings across the models. For instance, age was observed to be a significant predictor in each model, revealing that older respondents had a greater likelihood of having ever engaged in cyberstalking perpetration. This observation is consistent with Reyns et al. (2012) study that found cyberstalkers were more frequently 21 or older compared to those who were younger than 21, though, the difference was not statistically significant. Generally, age has not be found to be a significant predictor of cyberstalking perpetration (e.g., Marcum et al., 2014, 2017; van Baak & Hayes, 2018).

The current finding is also contrary to the body of literature on the life course pattern of antisocial behavior in general, which suggests involvement tends to decrease with age. A recent study, however, examined cyber offending over the life course and compared it with traditional offending in terms of the impact of life circumstances (Weulen Kranenbarg et al., 2018). The authors observed that employment and education were not significantly related to cyber offending, but they were both associated with reductions in traditional offending (Weulen Kranenbarg et al., 2018). Although there are several arguments that can be made to explain why cybercrime does not reflect the life course research findings, one plausible reason could be that cybercrime is more cognitive in nature than it is physical. Thus, the physical toll associated with traditional or street crime is not present and so the aging-out process may not take place. An additional reason could be due to the minimal punishment—relative to offline offending—associated with cybercrime. Thus, cyber perpetrators do not receive negative social consequences that could potentially lead to a reduction in offending over time (Weulen Kranenbarg et al., 2018). Future research should utilize samples that include individuals older than young adults to further unpack the relationship between age and cyberstalking perpetration.

Surprising findings were also observed in terms of the associations between gender identity and cyberstalking perpetration. Given the fairly consistent finding that men are typically cyberstalking perpetrators (e.g., Cavezza & McEwan, 2014; D’Ovidio & Doyle, 2003; Dreßing et al., 2014; Moriarty & Freiberger, 2008; WHOA, 2009), it was expected that negative relationships between the other gender identities and cyberstalking perpetration would be observed. Analyses revealed, however, that identifying as a woman or other gender identity did not impact likelihood of cyberstalking perpetration. We speculate that this may be due to recent findings that participation in cyber deviance may not be gendered (e.g., van Baak & Hayes, 2018).

Finally, student status was observed to consistently affect cyberstalking perpetration as well. Across all models, those who were currently enrolled in school were more likely to have ever perpetrated cyberstalking compared to those who were not students. One could speculate, based on a social learning perspective (Akers, 1973), that those who are enrolled in school are exposed to an environment characterized by unique peer influences. Thus, already existing perceptions about cyberstalking could be reinforced, or new opinions could be formed, based on interactions within the school environment. Given that student samples have been utilized in the majority of existing studies on cyberstalking perpetration, this relationship has never been explored.

Limitations and Future Directions

The current study adds to the scant literature on cyberstalking perpetration in numerous ways; however, the conclusions should be tempered by four limitations. First, our measure of cyberstalking did not account for the emotional response experienced by the victim. In recent work, cyberstalking has been defined as repeated pursuit behavior via communication technologies that causes the individual to experience a substantial emotional response or feel fear for their safety or the safety of someone close to them (e.g., Fissel, 2019; Nobles et al., 2014). However, in the current study respondents were only asked about their own behaviors and not the reactions of the victims (i.e., substantial emotional response or fear). The extent to which this omission affected the results reported herein is an empirical question that should be assessed by future research. Replication is certainly necessary due to the debate surrounding the fear requirement (see Dietz & Martin, 2007) and the fact that some scholars have not included emotional response as a requirement of cyberstalking (e.g., Reyns et al., 2012).

Second, the sample for this study was limited to respondents who were between 18 and 25 years old at the time of the survey. While this inclusion criterion was guided by prior literature indicating that cyberstalkers tend to be young adults (e.g., Reyns et al., 2012), the extent to which our findings generalize to other age groups is unknown. Future research is encouraged to assess our observations using samples comprised of other age groups and especially those who are older than 25, as prior literature has focused primarily on college students.

Third, due to the cross-sectional research design of the current study temporal ordering cannot be fully established. For example, it is plausible that the victimization experiences could have occurred after the cyberstalking perpetration or that the theoretically relevant variables (low self-control and moral disengagement) were somehow affected by engaging in cyberstalking perpetration. While temporal ordering is always a concern in a cross-sectional study, the theoretically relevant variables have been shown to be relatively stable across the life course in a wide variety of studies and thus are unlikely to have been affected by cyberstalking. Although temporal ordering remains a concern in the current study, the findings should be considered as a springboard for future research on cyberstalking wherein a longitudinal design is employed.

A fourth limitation is related to the operationalization of moral disengagement in the current study. While the extant literature has put forth eight elements underlying moral disengagement, the items employed in the current study captured five. The primary reason for this lack of coverage is because the items used to capture moral disengagement were not originally included in the questionnaire to tap this construct. Consequently, it remains an empirical question as to whether our findings would change substantively if moral disengagement was measured using all eight elements outlined in the relevant literature. Future research is encouraged to examine this question.

Concluding Statement

Given the relatively recent arrival of the internet to our social world, research on how it is employed in an antisocial fashion, though burgeoning, is in its infancy. The paucity of research is especially germane in the case of cyberstalking perpetration. Of the research that does exist, our knowledge of the prevalence of cyberstalking has hitherto been based primarily on college student samples. Additionally, few empirical studies have contextualized their analyses within a theoretical framework. The current study’s use of a community-based sample and analyses that were situated within a multi-theoretical scaffolding provided a much-needed advancement to the existing literature on cyberstalking. Overall, with the outlined limitations in mind, our analyses illustrate that cyberstalking perpetration occurs at rates similar to numerous other deviant online behaviors and that those with low self-control and who more readily disengage from internal standards of morality are more likely to cyberstalk others. These observations await replication and expansion in future research.

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.

ORCID iD

Erica R. Fissel

Notes

Author Biographies

Erica Fissel is an assistant professor in the Department of Criminal Justice at the University of Central Florida, with a secondary joing appointment with the Violence Against Women Faculty Research Cluster. Her primary research interests currently focus on interpersonal victimization that occurs in cyberspace, including cyberstalking and intimate partner cyber abuse. Her research explores correlates of victimization and perpetration, the reporting and help-seeking behaviors of victims, and the consequences of victimization. Some of her recent work has been published in Journal of Quantitative Criminology, Journal of Interpersonal Violence, Crime and Delinquency, Violence Against Women, and Violence and Victims.

Bonnie Fisher is a Distinguished Professor in the School of Criminal Justice at the University of Cincinnati. Her research interests span victimological topics ranging from the measurement of interpersonal violence against college students to the identification of theory-based predictors of interpersonal victimization to the evaluation of prevention strategies, and most recently, to the design and implementation of a longitudinal study of interpersonal violence against and by emerging adults. She was the Co-PI, with David Cantor, on the 2015 and 2019 Association of American University’s Campus Climate Survey on Sexual Assault and Misconduct. Her recent publications can be found in Justice Quarterly, Journal of Interpersonal Violence, Violence Against Women, and American Journal of College Health.

Joseph L. Nedelec, Ph.D., is an Associate Professor in the School of Criminal Justice at the University of Cincinnati. His research interests include biosocial criminology, evolutionary psychology, intelligence, quantitative behavior genetics, and cybercrime. Dr. Nedelec’s work has been published in a variety of journals including Criminology, Evolution and Human Behavior, Intelligence, Journal of Criminal Justice, Journal of Quantitative Criminology, Personality and Individual Differences, PLOS One, among others.

References

Akers

R. L.

(1973). Deviant Behavior: A Social Learning Approach. Wadsworth.

Alexy

E. M.

Burgess

A. W.

Baker

Smoyak

S. A.

(2005). Perceptions of cyberstalking among college students. Brief Treatment and Crisis Intervention, 5(3), 279–289.

Bandura

(1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Bandura

(1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50(2), 248–287.

Bandura

(2002). Selective moral disengagement in the exercise of moral agency. Journal of Moral Education, 31(2), 101–119.

Bandura

Barbaranelli

Caprara

G. V.

Pastorelli

(1996). Mechanisms of moral disengagement in the exercise of moral agency. Journal of Personality and Social Psychology, 71(2), 364.

Baum

Catalano

Rand

Rose

(2009). Stalking victimization in the United States. U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics, NCJ 224527.

Bauman

(2010). Cyberbullying in a rural intermediate school: An exploratory study. The Journal of Early Adolescence, 30(6), 803–833.

Bocij

(2002). Victims of cyberstalking: An exploratory study of harassment perpetrated via the internet. First Monday, 8(10), 1–12.

10.

Bossler

A. M.

Burruss

G. W.

(2012). The general theory of crime and computer hacking: Low self-control hackers? In Cyber Crime: Concepts, Methodologies, Tools and Applications (pp. 1499–1527). IGI Global.

11.

Bussey

Fitzpatrick

Raman

(2015). The role of moral disengagement and self- efficacy in cyberbullying. Journal of School Violence, 14(1), 30–46.

12.

Buzzell

Foss

Middleton

(2006). Explaining use of online pornograpy: A test of self-control theory and opportunities for deviance. Journal of Criminal Justice and Popular Culture, 13(2), 96–116.

13.

Cavezza

McEwan

T. E.

(2014). Cyberstalking versus off-line stalking in a forensic sample. Psychology, Crime & Law, 20(1), 955–970.

14.

Dietz

N. A.

Martin

P. Y.

(2007). Women who are stalked: Questioning the fear standard. Violence Against Women, 13(7), 750–776.

15.

D’Ovidio

Doyle

(2003). A study on cyberstalking: Understanding investigative hurdles. FBI Law Enforcement Bulletin, 73, 10–17.

16.

Dreßing

Bailer

Anders

Wagner

Gallas

(2014). Cyberstalking in a large sample of social network users: Prevalence, characteristics, and impact upon victims. Cyberpsychology, Behavior, and Social Networking, 17(2), 61–67.

17.

Fissel

E. R.

(2018). The reporting and help-seeking behaviors of cyberstalking victims. Journal of Interpersonal Violence. Advance online publication. https://doi.org/10.1177/0886260518801942

18.

Fissel

E. R.

(2019). Victimization of the 21st Century: An Examination of Cyberstalking Victimization Using a Target Congruence Approach. Doctoral dissertation, University of Cincinnati.

19.

Fissel

E. R.

Reyns

B. W.

(2020). The aftermath of cyberstalking: School, work, social, and health costs of victimization. American Journal of Criminal Justice, 45(1), 70–87.

20.

Gini

Pozzoli

Hymel

(2014). Moral disengagement among children and youth: A meta-analytic review of links to aggressive behavior. Aggressive Behavior, 40(1), 56–68.

21.

Gottfredson

M. R.

Hirschi

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

22.

Grasmick

H. G.

Tittle

C. R.

Bursik

R. J.

Jr. 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.

23.

Henson

Reyns

B. W.

(2016). Taking stock: The current status of cyberstalking research. In Holt

T. J.

(Ed.), Crime online: Correlates, causes, and context (pp. 199–224). Carolina Academic Press.

24.

Higgins

G. E.

Wilson

A. L.

(2006). Low self-control, moral beliefs, and social learning theory in university students’ intentions to pirate software. Security Journal, 19(2), 75–92.

25.

Holt

T. J.

Bossler

A. M.

(2013). Examining the relationship between routine activities and malware infection indicators. Journal of Contemporary Criminal Justice, 29(4), 420–436.

26.

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(3), 378–395.

27.

Horrigan

J. B.

Duggan

(2015). Home broadband 2015. Retrieved from Pew Research Center: http://www.pewinternet.org/2015/12/21/2015/Home-Broadband-2015/

28.

Hymel

Rocke-Henderson

Bonanno

R. A.

(2005). Moral disengagement: A framework for understanding bullying among adolescents. Journal of Social Sciences, 8(1), 1–11.

29.

Lauritsen

J. L.

Laub

J. H.

(2007). Understanding the link between victimization and offending: New reflections on an old idea. Crime Prevention Studies, 22, 55–75.

30.

Lauritsen

J. L.

Sampson

R. J.

Laub

J. H.

(1991). The link between offending and victimization among adolescents. Criminology, 29(2), 265–292.

31.

Nie

Boardley

I. D.

Situ

Dou

(2014). Moral disengagement moderates the predicted effect of trait self-control on self-reported aggression. Asian Journal of Social Psychology, 17(4), 312–318.

32.

Lyndon

Bonds-Raacke

Cratty

A. D.

(2011). College students’ Facebook stalking of ex-partners. Cyberpsychology, Behavior, and Social Networking, 14(12), 711–716.

33.

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, 29(3), 538–548.

34.

Marcum

C. D.

Higgins

G. E.

Nicholson

(2017). I’m watching you: Cyberstalking behaviors of university students in romantic relationships. American Journal of Criminal Justice, 42(2), 373–388.

35.

Marcum

C. D.

Higgins

G. E.

Ricketts

M. L.

(2014). Juveniles and cyber stalking in the United States: An analysis of theoretical predictors of patterns of online perpetration. International Journal of Cyber Criminology, 8(1), 47–56.

36.

Ménard

K. S.

Pincus

A. L.

(2012). Predicting overt and cyber stalking perpetration by male and female college students. Journal of Interpersonal Violence, 27(11), 2183–2207.

37.

Moriarty

L. J.

Freiberger

(2008). Cyberstalking: Utilizing newspaper accounts to establish victimization patterns. Victims & Offenders, 3(2–3), 131–141.

38.

Nobles

M. R.

Reyns

B. W.

Fox

K. A.

Fisher

B. S.

(2014). Protection against pursuit: A conceptual and empirical comparison of cyberstalking and stalking victimization among a national sample. Justice Quarterly, 31(6), 986–2014.

39.

Obermann

M. L.

(2011). Moral disengagement in self-reported and peer-nominated school bullying. Aggressive Behavior, 37(2), 133–144.

40.

Orue

Calvete

(2019). Psychopathic traits and moral disengagement interact to predict bullying and cyberbullying among adolescents. Journal of Interpersonal Violence, 34(11), 2313–2332.

41.

Paciello

Fida

Tramontano

Lupinetti

Capra

G. V.

(2008). Stability and change of moral disengagement and its impact on aggression and violence in late adolescence. Child Development, 79(5), 1288–1309.

42.

Paolacci

Chandler

Ipeirotis

P. G.

(2010). Running experiments on Amazon Mechanical Turk. Judgement and Decision Making, 5(5), 411–419.

43.

Paullet

K. L.

Rota

D. R.

Swan

T. T.

(2009). Cyberstalking: An exploratory study of students at a mid-Atlantic university. Issues in Information Systems, 10(2), 640–649.

44.

Pew Research Center. (2016). Home Internet access. http://www.pewresearch.org/data-trend/media-and-technology/internet-penetration/

45.

Pornari

C. D.

Wood

(2010). Peer and cyber aggression in secondary school students: The role of moral disengagement, hostile attribution bias, and outcome expectancies. Aggressive Behavior, 36(2), 81–94.

46.

Renati

Berrone

Zanetti

M. A.

(2012). Morally disengaged and unempathic: Do cyberbullies fit these definitions? An exploratory study. Cyberpsychology, Behavior, and Social Networking, 15(8), 391–398.

47.

Reyns

B. W.

(2019). Online pursuit in the twilight zone: Cyberstalking perpetration by college students. Victims & Offenders, 14(2), 183–198.

48.

Reyns

B. W.

Fisher

B. S.

Randa

(2018). Explaining cyberstalking victimization against college women using a multitheoretical approach: Self-control, opportunity, and control balance. Crime & Delinquency, 64(13), 1742–2764.

49.

Reyns

B. W.

Henson

Fisher

B. S.

(2012). Stalking in the twilight zone: Extent of cyberstalking victimization and offending among college students. Deviant Behavior, 33(1), 1–25.

50.

Ribeaud

Eisner

(2010). Are moral disengagement, neutralization techniques, and self-serving cognitive distortions the same? Developing a unified scale of moral neutralization of aggression. International Journal of Conflict and Violence, 4(2), 298–315.

51.

Runions

K. C.

Bak

(2015). Online moral disengagement, cyberbullying, and cyber-aggression. Cyberpsychology, Behavior, and Social Networking, 18(7), 400–405.

52.

Schreck

C. J.

(1999). Criminal victimization and low self-control: An extension and test of a general theory of crime. Justice Quarterly, 16(3), 633–654.

53.

Short

Guppy

Hart

J. A.

Barnes

(2015). The impact of cyberstalking. Studies in Media and Communication, 3(2), 23–37.

54.

Strawhun

Adams

Huss

M. T.

(2013). The assessment of cyberstalking: An expanded examination including social networking, attachment, jealousy, and anger in relation to violence and abuse. Violence and Victims, 28(4), 715–730.

55.

Sykes

G. M.

Matza

(1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22(6), 664–670.

56.

van Baak

Hayes

B. E

. (2018). Correlates of cyberstalking victimization and perpetration among college students. Violence and Victims, 33(6), 1036–1054.

57.

Vazsonyi

A. T.

Machackova

Sevcikova, Smahel

Cerna

(2012). Cyberbullying in context: Direct and indirect effects by low self-control across 25 European countries. European Journal of Developmental Psychology, 9(2), 210–227.

58.

Wasch

(2012). Moral disengagement and emotional and social difficulties in bullying and cyberbullying: Differences by participant role. Emotional and Behavioral Difficulties, 17(3–4), 347–360.

59.

Weulen Kranenbarg

Ruiter

Van Gelder

J. L.

Brenasco

(2018). Cyberoffending and traditional offending over the life-course: An empirical comparison. Journal of Developmental and Life-Course Criminology, 4(3), 343–364.

60.

Wood

Moir

James

(2009). Prisoners’ gang-related activity. The importance of bullying and moral disengagement. Psychology, Crime & Law, 15(6), 569–581.

61.

Working to Halt Online Abuse (WHOA). (2009). Online harassment/cyberstalking statistics. http://www.haltabuse.org/resources/stats/index.shtml.

62.

Worsley

J. D.

Wheatcroft

J. M.

Short

Corcoran

(2017). Victims’ voices: Understanding the emotional impact of cyberstalking and individuals’ coping responses. Sage Open, 7(2), 215824401771029.