چه کسی است که این کار را انجام می دهد؟ عوامل پیش بینی و شخصیت و ارتباط مزاحمت سایبری در دانشگاه
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|30400||2014||9 صفحه PDF||سفارش دهید||7750 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Human Behavior, Volume 38, September 2014, Pages 8–16
Less is known about cyberbullying behaviors in college populations because studies on this topic traditionally have focused on adolescent populations, have not measured correlates of this behavior within college samples, or have methodological weaknesses limiting their findings. By using a more comprehensive measure of cyberbullying behaviors and examining what is associated with its occurrence, the current study aims to extend the knowledge about cyberbullying behaviors in college. Results showed that approximately 52% of college students report engaging in cyberbullying behaviors and indicated that victims of CBB and individuals high on a subclinical measure of psychopathy were more likely to report having engaged in CBB. It was also found that victims of CBB, men, and individuals high on subclinical psychopathy engaged in a wider range of cyberbullying behaviors. Age was the only factor associated with a decrease in CBB.
Instances of cyberbullying have been widely studied in adolescent populations (Li, 2006, Li et al., 2012 and Slonje and Smith, 2008). Adolescence is an important period of study because the most severe consequences of cyberbullying have typically been experienced within this population (e.g., Megan Meiers committing suicide due to experiencing harassment online; ABCNews, 2007). Based on these studies and others, cyberbullying behavior has been theorized to peak in early adolescence and then to decrease significantly after high school (Tokunaga, 2010), with studies that have examined cyberbullying in college finding rates ranging from 8% (Slonje & Smith, 2008) to a high of 9% (MacDonald & Roberts-Pittman, 2010) compared to 44% reported in adolescent populations (Calvete, Orue, Estévez, Villardón, & Padilla, 2010). However, rates of victimization have been higher, ranging between 22% (MacDonald & Roberts-Pittman, 2010) to over 50% (MTV, 2011). Given the discrepancy between the reported rates of victimization and bullying, and the lack of attention in this population generally, more research is needed to examine the occurrence of cyberbullying behaviors on college campuses. 1.1. Who engages in cyberbullying behavior? Many studies have attempted to create a profile of individuals who engage in cyberbullying behaviors (Li, 2006, Slonje et al., 2013, Tokunaga, 2010, Ybarra and Mitchell, 2004a and Ybarra and Mitchell, 2004b). However, the majority of these studies focus on adolescent samples, and the applicability of these factors to college-aged samples have not been established. It is important to include factors that are not only established in previous literature but also theoretically relevant to describing individuals who engage in cyberbullying behaviors. One such theoretical idea was introduced by Björkqvist, Lagerspetz, and Kaukialnen (1992) when they proposed that aggressive behavior does not decrease over time but instead individuals engage in different types of aggressive behavior dependent upon their abilities. This theory was refined to suggest that individuals engage in a risk/benefit analysis, such that they engage in aggressive behaviors that provide a high level of benefits (e.g., distress of a target) at relatively low risk (e.g., social exclusion or physical harm; Björkqvist, 1994 and Björkqvist et al., 1994). For individuals that can accurately read social situations, this means a movement towards more covert/indirect (e.g., manipulating an individual to ‘explode’ causing them to lose face in front of their peers) or relational (e.g., spreading gossip or otherwise attacking another’s social relationships) forms of aggression. The age of an individual, their gender, past experience with specific types of aggressive behavior, as well as personality traits may play an important role in determining an individual’s abilities, and thus the type of aggressive behavior that they engage in. 1.1.1. Age Physical forms of bullying typically decrease as a function of age, however, rates of relational bullying tend to remain stable or increase (Björkqvist et al., 1992). Compared to adolescents, adults typically engage in different forms of aggressive behavior ( Björkqvist, 1994 and Björkqvist et al., 1994). However, studies that have examined cyberbullying behaviors have found mixed evidence supporting this idea. Some studies have found that as age increases cyberbullying behaviors decrease ( Raskauskas and Stoltz, 2007, Slonje and Smith, 2008 and Williams and Guerra, 2007) while other studies find stable ( Patchin & Hinduja, 2006) or increasing rates ( Smith et al., 2008 and Vandebosch and Van Cleemput, 2009) of cyberbullying behavior across ages 11–18. Beyond this age range there is little information regarding the rates of cyberbullying behaviors so it is important to determine an accurate rate of the behavior in an older population. 1.1.2. Gender Women and men have been shown to engage in gender specific ways of engaging in relational aggression with men engaging in more overt forms (e.g., calling someone derogatory names to their face) and women engaging in more covert forms (e.g., spreading gossip about someone; Björkqvist et al., 1994). Rates of cyberbullying may mirror rates of relational aggression, with rates remaining relatively stable for women but increasing in men ( Archer and Coyne, 2005, Björkqvist et al., 1992 and Coyne et al., 2006). Some studies have found that men are more likely to report engaging in cyberbullying behaviors (Calvete et al., 2010, Li, 2006, Slonje and Smith, 2008 and Vandebosch and Van Cleemput, 2009) whereas other studies have found that women are more likely to engage in cyberbullying behavior (Dilmac, 2009, Kowalski and Limber, 2007, Rivers and Noret, 2010 and Sourander et al., 2010). Still other studies fail to find a significant gender difference in who reports engaging in the behavior (Kowalski et al., 2012, Patchin and Hinduja, 2006 and Smith et al., 2008). Drawing from the risk/benefit model proposed by Björkqvist et al. (1994) it is more likely that there is no significant gender difference in who engages in cyberbullying behaviors, however, most of the studies that have examined this have done so in younger (11–18) samples. 1.1.3. Past experience It is likely that one way that one way individuals gauge the potential risk/benefit ratio of aggressive behavior is by observing others engage in the behavior first (similar to the principles of observational learning proposed by Bandura, 1978). If a behavior is met with high levels of consequences but has no observable benefit, then that behavior is not likely to be repeated. However, if the behavior has little to no observable consequences and is perceived to cause the desired outcome (in this case distress), then the likelihood of that behavior being repeated would increase. In much the same manner, past experience with cyberbullying behavior (both perpetration and victimization) may be an important factor for predicting those individuals who engage in cyberbullying behaviors. Studies have found that previous experience with cyberbullying behaviors was a significant predictor of current cyberbullying behaviors in adolescents (Kraft and Wang, 2010, Ybarra et al., 2007 and Ybarra and Mitchell, 2004a) as well as future cyberbullying behaviors in college students (Barlett et al., 2013). 1.2. The Dark Triad and cyberbullying behavior Given cyberbullying’s antagonistic nature, three personality characteristics, typically labeled as the Dark Triad, may be associated with its occurrence. The Dark Triad (DT; Paulhus & Williams, 2002) consists of Machiavellianism, narcissism, and psychopathy, all measured at the subclinical level (i.e., these traits are typically measured in non-clinical or non-forensic populations). It is not correct to assume, however, that these traits are any less damaging to others and the individual than the same traits measured within a clinical population (Ray & Ray, 1982). All three personality styles are theorized to be distinct from each other, although all are related to general social malevolence (Furnham et al., 2013 and Paulhus and Williams, 2002). Importantly, each trait may influence the perception of risk/benefit associated with engaging in aggressive behaviors. 1.2.1. Machiavellianism Individuals who endorse more Machiavellian traits are characterized by cold and manipulative behaviors (Christie, Geis, & Berger, 1970) and engage in negative behaviors (physical or other forms of aggression) in order to gain and/or maintain influence over others. These individuals are also more likely to suspect ulterior motives of others (Rauthmann, 2012) and have been characterized as having the ‘darkest’ of the DT personalities (Rauthmann & Kolar, 2012). In relation to CBB, social group manipulation can be accomplished through relatively anonymous threats of real world aggression (e.g., threatening to seriously injure the victim in real life) or cyber-aggression (e.g., threatening to post humiliating images to a social network). Following from theoretical perspectives on Machiavellianism, individuals high on this trait may engage in CBB to solidify, maintain, or establish their place within their social network due to the relatively low risk associated with these types of behaviors and potentially large influence on their social network. As there is no established link between CBB and Machiavellianism, it is important to understand what influence these personality traits have on engagement of CBB in a college sample. 1.2.2. Narcissism Theoretical views about the traits associated with subclinical narcissism include feelings of grandiosity, a sense of entitlement, dominance and superiority over others (Raskin & Hall, 1979). Specific factors of this trait (i.e., narcissistic exploitativeness and entitlement) have been linked to physical aggression (Reidy, Zeichner, Foster, & Martinez, 2008), to anti-social behaviors on Facebook (Carpenter, 2012) as well as cyberbullying behaviors in adolescents (Ang, Tan, & Mansor, 2011). It is possible that individuals high on narcissism may engage in CBB because they feel socially invulnerable (i.e., they believe that their social status is such that there is a relatively low level of social risk associated with the behavior). Although established in adolescent samples, the relationship between narcissism and aggressive behaviors within a college population has not been established. It is thus important to understand what influence, if any, narcissistic personality traits have on engagement of CBB in a college sample. 1.2.3. Psychopathy Traits associated with subclinical psychopathy include high impulsivity and engagement in thrill seeking behavior, as well as low levels of empathy and low social anxiety (Paulhus & Williams, 2002). Subclinical psychopathy has been linked to traditional bullying behavior in adults (Baughman, Dearing, Giammarco, & Vernon, 2012), and the low levels of empathy exhibited by these individuals has been linked to both reactive and proactive aggressive behaviors (Fanti, Frick, & Georgiou, 2008), which in turn have been linked to bullying behavior (Law, Shapka, Domene, & Gagné, 2012) in adolescents. There have been no studies to date, however, that directly link psychopathy to engagement in CBB in either adolescent or college samples. 1.3. Cyberbullying/cyberaggression in college Cyberbullying behavior (CBB) is typically defined as the repeated use of technology to cause distress to others (Slonje et al., 2013 and Tokunaga, 2010) and is drawn from the accepted definition of traditional (i.e., face-to-face) bullying first proposed by Olweus (1993). However, Baldasare, Bauman, Goldman, and Robie (2012) found that college students do not necessarily agree with the definition. Participants in the study cited concerns regarding the shifting nature of technology (allowing for a greater diversity of behaviors to be defined as CBB), the highly subjective nature of the behavior (with participants stating that it was the victim’s perceptions of distress and not the intended consequences of the perpetrator that mattered), and an association of the term ‘cyberbullying’ with adolescent behavior (Baldasare et al., 2012). Due to its perceptually covert nature (i.e., individuals who engage in the behavior believe that they are anonymous to the victim; Baldasare et al., 2012), it makes conceptual sense to associate cyberbullying with other indirect forms of aggression. One possible reason for the low rates of reported perpetration in college samples may be due to social desirability or due to a desire to present the self in a positive manner (i.e., not one who engages in bullying type behaviors). Given these concerns, it is possible that college students underreport CBB when directly asked if they cyberbully or when the term ‘cyberbullying’ is associated with a measure via a definitional statement. One way to avoid definitional problems is to provide respondents a list of behaviors associated with the construct that does not contain the term itself. Utilizing an unlabeled behavioral interview may also prevent participants from associating the behaviors in question with the broader category (cyberbullying) and therefore reduce both age related and social stigma associated with the term. One such interview was developed by Calvete et al. (2010) and is utilized in the current study. Several factors (e.g.; age, sex, past experience) have been identified in adolescent samples as possible predictors of CBB. However, only two factors (past experience with CBB; Dilmac, 2009 and sex; Kowalski et al., 2012) have been studied in a college population. The personality traits known as the Dark Triad (narcissism, psychoticism, and Machiavellianism; Paulhus & Williams, 2002) have been measured in college samples (e.g., Jonason et al., 2009 and Jonason and Webster, 2012), but have not been studied in conjunction with cyberbullying behaviors. It is possible that these factors all influence not only the risk associated with cyberbullying behaviors but also the perceived benefits of engaging in the behavior. 1.4. Current study As discussed, much of the research on cyberbullying behaviors has focused on adolescent samples. This research often suggests that the majority of individuals ‘age out’ of the behavior as they enter college (e.g., Tokunaga, 2010). Empirical studies that have examined college samples have found small numbers of individuals who engage in cyberbullying behaviors (e.g., MacDonald & Roberts-Pittman, 2010). One possible reason for this discrepancy is that college students associate the term ‘cyberbully’ with adolescent behavior and underreport the behavior (as suggested by Baldasare et al., 2012). It is thus unclear the rate that CBB occurs on college campuses and it is not known which demographic factors may be related to cyberbullying behaviors within a college sample. It is also important to identify, what, if any, personality characteristics are predictive of engagement in cyberbullying behaviors. It is hypothesized that the Dark Triad (i.e., Machiavellianism, narcissism and psychopathy) would be predictive of engagement in cyberbullying behavior. Based on the evidence linking these personality traits to aggressive behavior, it is hypothesized that the Dark Triad (Machiavellianism (Hypothesis 1), narcissism (Hypothesis 2), and/or psychopathy (Hypothesis 3)) will predict cyberbullying behaviors.
نتیجه گیری انگلیسی
Cyberbullying behaviors have typically been associated with and studied in adolescent populations (e.g., Slonje et al., 2013 and Tokunaga, 2010). Although these studies have expanded our understanding of the phenomenon, this knowledge is necessarily limited to a specific population. The current study examined cyberbullying behaviors within a college population, one that has been studied only sporadically compared to adolescent populations (e.g., Baldasare et al., 2012, Kowalski et al., 2012 and MacDonald and Roberts-Pittman, 2010) or tangentially (i.e., used as a convenience sample where an adolescent population could not be accessed e.g., Barlett et al., 2013). It is important to understand the phenomenon of cyberbullying as it exists in a college population because these students may be at higher risk of psychiatric problems and dropping out of college (Baldasare et al., 2012). It is also possible that student learning will be negatively impacted, with students skipping classes or avoiding online discussion boards for fear of being attacked. As more and more colleges seek to move their campuses into the digital realm, aggressive acts committed within this realm may have increasingly severe real-world consequences.