Motives Behind Cyberbullying Perpetration: A Test of Uses and Gratifications Theory

Abstract

Drawing from Uses and Gratifications Theory (UGT), this study explored the interplay between personality traits (online disinhibition, moral disengagement, narcissism, and aggression) and cyberbullying perpetration motives (entertainment, revenge, harm, and dominance) through structural equation modeling. The participants were a convenience sample of 598 cyber bully-victims who were Turkish university students (229 females, 362 males, and seven unknown). The findings empirically supported UGT for conceptualization of cyberbullying perpetration. Almost half of the participants (49.7%) cyberbullied someone twice or more during the preceding 6 months, and males cyberbullied others significantly more than females. In terms of the structural interrelationships, (a) online disinhibition was the only personality trait related to cyberbullying others for entertainment; (b) moral disengagement and aggression were the two variables associated with the revenge motive of cyberbullying perpetration; (c) although online disinhibition was negatively related to cyberbullying others for harm, moral disengagement and aggression were positively linked to harm; and (d) moral disengagement and narcissism were the two personality trait variables associated with the dominance motive of cyberbullying perpetration. Results imply that UGT offers a new understanding about cyberbullying perpetration to researchers, theorists, and practitioners. Personality traits play an important role in cyberbullying perpetration motives and behaviors. Thus, personality traits should be considered in prevention and intervention efforts against cyberbullying. The earlier the personality characteristics of the young individuals are identified, the earlier they can be prevented from engaging in cyberbullying perpetration. Also, sports activities, social events, competitive contests, or leisure and recreational activities offered by university managements can help the university youth to keep away from cyberbullying perpetration and to satisfy motives in more appropriate ways.

Keywords

cyberbullying perpetration motives personality traits uses and gratifications theory university students structural equation modeling

Introduction

Based on the definition of Tokunaga (2010), cyberbullying perpetration can be defined as a person or a group of people targeting an individual or a group of individuals in a repeating, power-imbalanced, and harm-intended manner. Cyberbullying perpetration exists among university students. Lund and Ross (2017) reviewed 14 investigations reporting the incidence of cyberbullying perpetration between 3.8% and 9.9% among university students. According to Faucher, Jackson, and Cassidy’s (2014) review study, the occurrence of cyberbullying perpetration was between 3% and 40% among university students. Of note, these prevalence rates should be read with caution as the majority of the studies explored in these review studies are not systematic prevalence studies, and their sample representativeness is quite low. Nonetheless, these investigations point out the fact that the university student is engaged in cyberbullying perpetration.

In terms of gender, the majority of the research findings pointed out male university students as cyber bullies (e.g., Erdur-Baker, Tanrıkulu, & Topcu, 2016), while some studies reported no significant differences (e.g., Lund & Ross, 2017). Considering its negative impacts on mental health, psychological well-being, interpersonal relationships, and school success (Campbell, Slee, Spears, Butler, & Kift, 2013; Schenk, Fremouw, & Keelan, 2013), understanding more about cyberbullying perpetration behaviors can contribute to the prevention and intervention efforts against cyberbullying among university students.

To the question why young individuals engage in cyberbullying perpetration, Barlett Gentile Cyberbullying Model (Barlett, Chamberlin, & Witkower, 2017), General Strain Theory (Paez, 2018), and Theory of Planned Behavior (Pabian & Vandebosch, 2014) have been proposed as theoretical explanations. However, these theories have not considered the role of personality, although the extant literature has underlined that cyber bullies have distinct personality characteristics (e.g., Fanti & Henrich, 2015). Personality traits, therefore, can be surmised to explain why some individuals behave as cyber bullies. From this point of view, understanding more about the role of personality in cyberbullying perpetration can expand the existing theoretical and practical knowledge on cyberbullying prevention. Aiming to unravel the interplay among personality traits and cyberbullying perpetration motives among university students, this current research is based on Uses and Gratifications Theory (UGT) (Blumler & Katz, 1974).

Being in use since 1974, what makes UGT relevant today is its tenets which are still applicable to the communication media of the 21st century. The mechanism of UGT works as follows. Personality traits trigger individuals to become motivated to behave in a certain way, and individuals, being aware of their motives, intentionally choose to behave in a certain manner. Finally, by behaving in a certain manner, individuals obtain the gratifications they aimed at in advance. More specifically, UGT posits that individuals are (a) active in using communication media, (b) aware of their motives/needs, (c) intentionally choose certain communication media to gratify motives/needs, and (d) personality traits influence the motives/needs which then affect the gratifications attained from a behavior (Alonzo & Aiken, 2004; Ruggiero, 2000).

Based upon the UGT mechanism, certain personality traits such as online disinhibition, moral disengagement, narcissism, or aggression may trigger individuals to become motivated to harm others. Individuals, being aware of their motives as well as the potentials of the online technologies, may intentionally choose cyberbullying to harm others. Eventually, by bullying others online, individuals may obtain the gratifications they aimed at beforehand. Empirical evidence supports this reasoning. For example, certain personality characteristics including self-control, offline aggression, and self-esteem are related to bullying others online (e.g., Bayraktar, Machackova, Dedkova, & Cerna, 2014). Besides, young people reported deliberately cyberbullying others (Pettalia, Levin, & Dickinson, 2013), and the same individuals purposefully act as peer and cyber bullies (Dempsey, Sulkowski, Dempsey, & Storch, 2011; Tanrikulu & Campbell, 2015). And individuals favor cyberbullying because online tools provide them affordances, including lack of social cues, temporal delays, permanency of the digital data, anonymity, and audience permanency (Runions, 2013).

To conceptualize cyberbullying perpetration from the standpoint of UGT, previous reports concerning cyberbullying perpetration motives as well as the personality traits of the cyber bullies were considered. About the motives of the cyber bullies, entertainment (e.g., Rafferty & Vander Ven, 2014), dominance (e.g., Vandebosch & van Cleemput, 2008), revenge and harm (e.g., Fluck, 2014), easiness and anonymity (e.g., Compton, Campbell, & Mergler, 2014), and disliking the victim (e.g., Zhou et al., 2013) were among the most reported motives of cyberbullying perpetration. In addition, interpersonal problems (Akbulut & Erişti, 2011), inability to see the victim and avoiding from adult punishment (Compton et al., 2014), acceptance to a social group (Gradinger, Strohmeier, & Spiel, 2012), cyber sanctioning (Rafferty & Vander Ven, 2014), succorance, and a response, or defense against inferiority (Johnston et al., 2014), demonstrating technological skills (Vandebosch & van Cleemput, 2008), social popularity (Yaman & Peker, 2012), and attracting attention and looking cool (Zhou et al., 2013) were reported.

Regarding the personality traits of the cyber bullies, lower levels of empathy (e.g., Brewer & Kerslake, 2015), low self-esteem (e.g., Bayraktar et al., 2014), narcissism (e.g., Fanti & Henrich, 2015), moral disengagement (e.g., Tanrikulu & Campbell, 2015), aggressiveness (e.g., Fletcher et al., 2014), online disinhibition (e.g., Barlett, 2015), disregard for others and vengefulness (e.g., Gibb & Devereux, 2014), sensation seeking (Kokkinos, Antoniadou, & Markos, 2014), and emotional instability (Kokkinos, Antoniadou, Dalara, Koufogazou, & Papatziki, 2013) were reported. As is clear from the research reported above, much is known about the motives and personality characteristics of cyber bullies. Nonetheless, the possible connections among personality traits and cyberbullying perpetration motives have not yet been investigated in a structural equation model (SEM). If these connections are better understood, individuals with certain personality traits can be detected earlier, and professional help can be provided to keep certain individuals away from cyberbullying. By this way, cyber victimization can be prevented, and less individuals would suffer the negative impacts of cyberbullying. This study, therefore, aimed to examine the role of personality traits on cyberbullying perpetration motives among university students. By employing a SEM strategy, these relationships were empirically tested by this present research.

As all of the previously reported motives and personality traits of the cyber bullies could not be explored in a single research study, certain motives and personality traits had to be selected for this present investigation. Cyberbullying perpetration motives were first determined as the UGT postulates that motives are activated by personality traits (Alonzo & Aiken, 2004). Nonetheless, there were many motives previously reported, and it was necessary to follow a procedure. First, the definition of cyberbullying was considered as it already contains the two main motives of cyberbullying perpetration, which are harming others and domination (power imbalance) (e.g., Rafferty & Vander Ven, 2014; Tokunaga, 2010). Next, the rest of the motives to be selected for this present study had to be interrelated to harming others and domination. Otherwise, meaningful relationships among the motives could not be achieved. Hence, revenge (e.g., Shapka & Law, 2013) was chosen as the third motive because cyber bullies could fulfill the motive of revenge while harming and dominating others. Entertainment (e.g., Baas, de Jong, & Drossaert, 2013) was selected as the last motive reasoning that cyber bullies could have fun by ventilating anger and revenge while harming, dominating, and revenging others.

Assuming possible relationships with the four initially identified cyberbullying perpetration motives, four personality traits were chosen based on the limited but suggestive evidence revealed by the previous studies. Barlett (2015) and Wright (2014) reported that online disinhibition was a significant risk factor for cyberbullying perpetration. Therefore, online disinhibition was decided as the first personality trait. Online disinhibition refers to the emancipation from social constraints and societal inhibitions when individuals are online (Kerstens & Stol, 2012). Online disinhibition may encourage individuals to engage in risky online behaviors including cyberbullying. Therefore, a higher level of online disinhibition can be assumed to trigger individuals to cyberbully others with the motives of harm, domination, revenge, or entertainment.

Moral disengagement was the second personality trait selected for this study considering the previously reported linkages between moral disengagement and cyberbullying others (Kowalski, Giumetti, Schroeder, & Lattanner, 2014; Sticca & Perren, 2015). Moral disengagement enables individuals to rationalize and convince themselves that the necessary conditions can require engaging in some actions although they are personally unacceptable in daily life (Moore, Detert, Trevino, Baker, & Mayer, 2012). For instance, individuals who would normally not inflict any harm on others may feel weak after experiencing negative experiences in online or offline settings. Thinking that the features of the cyber environment such as anonymity or audience permanency may help them compensate for the feeling of weakness, such individuals may perpetrate others online. Thus, a higher level of moral disengagement can be anticipated to provoke individuals to cyberbully others with the motives of harm, domination, revenge, or entertainment.

The third personality trait chosen for this current research was narcissism. The previous evidence reveals significant associations between narcissism and cyberbullying others (Goodboy & Martin, 2015). According to Thomas (2012), narcissistic individuals are manipulative and aim for social status and authority over others in their human relationships. Narcissistic individuals can be surmised to prefer cyberbullying to build superiority in technology use by attacking online contacts, by manipulating online interactions, and by demonstrating that they are important and authoritative in cyber space. Hence, a higher level of narcissistic inclination can be expected to activate personality traits of the individuals to cyber bully others with the motives of harm, domination, revenge, or entertainment.

Regarding the previously reported significant relationship between aggression and cyberbullying perpetration (Bayraktar et al., 2014; Lonigro et al., 2015), aggression was the last personality trait in this present investigation. Aggressive individuals are inclined to overcome disappointments by force (Bergman, McIntyre, & James, 2007). Individuals may prefer cyberbullying as it offers less risky opportunities to relieve aggression. For example, punching someone to display aggression may result in several risky consequences for the individuals in physical settings. Yet, threatening, humiliating, or offending someone online to alleviate aggression by hiding behind anonymous identities may create much less risky consequences. Therefore, a higher level of aggression can trigger individuals to cyberbully others with the motives of harm, domination, revenge, or entertainment.

In this current research, the structural interplay between the four cyberbullying perpetration motives (harming others, domination, revenge, and entertainment) and the four personality traits (online disinhibition, moral disengagement, narcissism, and aggression) were tested with a university-aged sample. Regarding the principles of UGT, it was hypothesized that the personality traits of the cyber bullies would influence their cyberbullying perpetration motives. The hypothesized structural model of this present study can be seen in Figure 1.

Figure 1.

The hypothesized model.

Method

Procedure and Participants

After obtaining ethical clearance, an anonymous, self-report, and paper-based survey was conducted for university students enrolled to three of the state universities in Ankara during the 2014-2015 fall semester. The survey completion lasted about 25 min. The participants were explicitly informed about voluntary participation, anonymity, confidentiality, and their right to discontinue the survey at any time. Data were collected during regular teaching hours by the first author.

Of the 1,328 respondents, 47 were excluded due to missing data and non-independence of the observations, resulting in 1,281 participants eligible for this research. As this study’s focus was on cyberbullying perpetration, the participants had to be individuals having cyberbullied someone. Cyber bullies were identified considering the previous literature proposing that a cyberbullying behavior must be repeated twice or more (e.g., Langos, 2012). As can be seen in Table 1, while 38 (3.0%) of the participants were only cyber bullies, 598 (46.7%) were cyber bully-victims.

Table 1.

Participants by Their Cyberbullying Involvement.

	f	%
Cyber bully	38	3.0
Cyber victim	218	17.0
Cyber bully-victim	598	46.7
Perpetrated only once	23	1.8
Victimized only once	52	4.1
Perpetrated and victimized only once	25	1.9
Perpetrated more than once and victimized only once	32	2.5
Perpetrated only once and victimized more than Once	55	4.3
Not involved	240	18.7
Total	1,281	100

Note. Cyber bully group refers to the individuals who bullied others twice or more but were never victimized. Cyber victim group refers to the individuals who were victimized at least twice but never bullied others. Cyber bully-victim group refers to the individuals who both bullied others and got victimized by others more than twice.

Cyber bully-victims were chosen as the research group of this study for several reasons. Considering that differences in personality traits were previously noted between pure cyber bullies and cyber bully-victims (e.g., Bayraktar et al., 2014), t tests were performed to check whether there were significant differences between pure cyber bullies and cyber bully-victims in terms of the cyberbullying perpetration motives as well as the personality characteristics. As can be seen in Table 2, t-test results showed that cyber bully-victims had significantly higher scores in terms of entertainment, revenge, and harm. Yet, there were no significant differences between pure cyber bullies and cyber bully-victims in terms of dominance. For the t-test analyses conducted for personality traits (Table 2), results revealed no significant differences between pure cyber bullies and cyber bully-victims in terms of online disinhibition, moral disengagement, narcissism, and aggression. These findings suggested that pure cyber bullies and cyber bully-victims were quite different from each other. Thus, the pure bully group was not included in the sample of this current study. Of note, the readers should be cautious about the interpretation of these findings as the comparisons were made with the unequal number of participants in pure cyber bully and cyber bully-victim groups.

Table 2.

T-Test Results for Cyberbullying Perpetration Motives and Personality Traits.

	Pure Cyber bullies		Cyber bully-Victims		t test
	M	SD	M	SD	t test
Cyberbullying perpetration motives
Entertainment	6.11	6.14	9.12	6.41	−2.74**
Revenge	1.83	4.48	5.45	5.95	−4.60***
Harm	1.25	2.99	3.10	3.71	−3.55**
Dominance	2.31	4.25	3.86	5.37	−2.09
Personality traits
Online disinhibition	9.29	6.24	10.20	6.05	−0.89
Moral disengagement	10.39	7.56	12.37	7.45	−1.59
Narcissism	5.29	3.70	5.20	3.12	0.18
Aggression	19.08	8.80	21.54	8.80	−1.73

Note. The Bonferroni correction was applied to control Type 1 error on multiple comparisons (0.05/4 = 0.012).

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

In addition, cyber bully-victims were considered as a distinct group in the literature (e.g., Tanrikulu & Campbell, 2015), but investigations providing information on cyber bully-victims as a unique group are quite rare (e.g., Johnston et al., 2014). For these reasons, exploring cyber bully-victims as a homogeneous group could contribute to the scant knowledge in the extant literature.

Of the 598 cyber bully-victim participants, 229 (38.3%) were female and 362 were (60.5%) male, with seven (1.2%) of unreported gender, ranging in age from 17 to 27 with a mean age of 20.23 (SD= 1.84). About year levels, 135 (22.8%) were from preparatory school of English, 119 (20.1%) from first year, 136 (23.0%) from second year, 87 (14.7%) from third year, and 114 (19.3%) from fourth year.

Instruments

Updated by the authors of this current research, Revised Cyber Bullying Inventory (RCBI) for University Students is a newer revision of the Revised Cyber Bullying Inventory (Topcu & Erdur-Baker, 2010). RCBI for University Students measures cyberbullying in two independent but parallel sections with 12 shared items. While the first section investigated cyberbullying perpetration, the second section examined cyber victimization in the previous 6 months. Sample items are “Sending threatening, offending, embarrassing messages on the Internet” and “Spreading gossips and rumors online.” Response options were 1 = never, 2 = once, 3 = 2 to 3 times, and 4 = more than 3 times. On the basis of Topcu and Erdur Baker’s (2010) study, a unidimensional factor structure was tested and confirmed in this study, for cyberbullying perpetration section (χ² = 2.56, df = 2, p = .27; χ²/df = 1.28; goodness-of-fit index [GFI] = .99, comparative fit index [CFI] = .99, Tucker–Lewis Index (TLI) = .98, standardized root mean square residual [SRMR] = .02, root mean square error of approximation (RMSEA) = .03) and the cyber victimization section (χ² = 1.36, df = 2, p = .50; χ²/df = .68; GFI = .99, CFI = 1.00, TLI = 1.00, SRMR = .01, RMSEA = .00). Whereas Cronbach’s alpha was .80 for cyberbullying perpetration section, it was .73 for cyberbullying victimization section.

Developed by the authors of this research, Cyber bullying Perpetration Motives Scale (CBPMS), a 21-item self-report instrument, assesses cyberbullying perpetration motives. CBPMS includes four factors: revenge, entertainment, dominance, and harm. CBPMS is rated on a 5-point scale ranging from not at all (1) to very much (5). Example items include the following: “These types of behaviors were also done to me” and “I wanted to show my mastery in technology usage.” Item formats were designed by considering the existing instruments measuring TV viewing motives and Internet motives. Items were written based on the cyberbullying literature on entertainment, revenge, harm, and dominance. After generating an item pool, feedback was obtained from two experts who are highly experienced in cyberbullying research, four PhD candidates, and four university students. In accordance with their recommendations, the items were modified in terms of choice of words, spelling, grammatical structure, relevancy, and understandability. As a result, there were 22 items in the item pool.

Additional pilot data were collected from 277 university students, 122 of whom self-reported having been a cyber bully-victim in the previous 6 months. An exploratory factor analysis (EFA) was performed on this sample of 122 participants to explore the factor structure of CPBMS. Regarding the initial EFA findings, one of the items of CPBMS was deleted as it cross-loaded on two factors. For this reason, the EFA was run again with 21 items which revealed that a four-factor structure explained 67.11% of the total variance. To investigate whether the four-factor structure of CBPMS with 21 items was confirmed by the data of this current investigation, a confirmatory factor analysis (CFA) was performed. CFA results showed that the four-factor structure of CBPMS was confirmed (χ² = 268.95, df = 83, p = .00; χ²/df = 3.24; GFI = .94, CFI = .96, TLI = .95, SRMR = .05, RMSEA = .06). Cronbach’s alpha was found as .90 for CBPMS in this present study.

Adapted to Turkish by the authors of this research, Online Disinhibition Scale (ODS; Kerstens & Stol, 2012) explores individuals’ disconnection from social constraints and societal inhibitions when online. Involving seven items, ODS was rated on 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5). Example items are “I am more myself on the Internet than in real life” and “On the Internet, I talk about things I am anxious to talk about in real life.” Based on Kerstens and Stol’s (2012) findings, a single-factor construct was tested and confirmed in this study (χ² = 21.75, df = 11, p = .02; χ²/df = 1.97; GFI = .98, CFI = .98, TLI = .96, SRMR = .03, RMSEA = .06). Cronbach’s alpha was .82 for ODS.

Adapted to Turkish by the authors of this research, Propensity to Morally Disengage Scale (PDMS; Moore et al., 2012) measures people’s inclinations to persuade themselves that personally unacceptable behaviors can be condoned in some situations. PDMS is an eight-item self-report survey rated on a 7-point Likert-type scale ranging from strongly disagree (1) to strongly agree (7). Example items are “It is okay to spread rumors to defend those you care about” and “Some people have to be treated roughly because they lack feelings that can be hurt.” In line with Moore et al. (2012), a single-factor structure was tested and confirmed (χ² = 35.06, df = 20, p = .20; χ²/df = 1.75; GFI = .97, CFI = .93, TLI = .91, SRMR = .02, RMSEA = .04). Cronbach’s alpha was .71 for PDMS.

Adapted to Turkish by Temel (2008), Narcissistic Personality Inventory With 16 Items (NPI-16; Ames, Rose, & Anderson, 2006) assesses participants’ narcissistic tendencies. As with scoring, narcissistic responses are scored as 1, while non-narcissistic responses are scored as 0. Two sample items were “I like to be the center of attention (narcissistic response), I prefer to blend in with the crowd (non-narcissistic response)” and “I like having authority over people (narcissistic response), I don’t mind following orders (non-narcissistic response).” As recommended by Ames et al. (2006), a unidimensional factor structure was tested and confirmed in this study (χ² = 0.50, df = 2, p = .77; χ²/df = .25; GFI = .99, CFI = 1.00, TLI = 1.00, SRMR = .00, RMSEA = .00). Cronbach’s alpha was .74 for NPI-16.

Adapted to Turkish by the authors of this research, the 12-item Aggression Questionnaire (AQ-12; Bryant & Smith, 2001) examines participants’ inclinations of aggression. AQ-12 is rated on a 5-point Likert-type scale ranging from uncharacteristic of me (1) to extremely characteristic of me (5). Sample items include the following: “I have threatened people I know” and “Given enough provocation, I may hit another person.” Based on Bryant and Smith’s (2001) findings, a single-factor construct was tested and confirmed in this study (χ² = 2.12, df = 1, p = .14; χ²/df = 2.12; GFI = .99, CFI = .99, TLI = .98, SRMR = .01, RMSEA = .06). Cronbach’s alpha was .79 for AQ-12.

A demographic information form obtained data about the participants. The influence of gender (e.g., Erdur-Baker et al., 2016) as well as year level (Akbulut & Erişti, 2011; Francisco, Simao, Ferreira, & das Dores Martins, 2015) has been an important issue in the cyberbullying perpetration literature. For this reason, gender and year level differences were investigated because gender and year level differences could have an impact on the findings of this current investigation.

Results

Gender and Year Level Differences in Cyberbullying Perpetration and Victimization

The Bonferroni correction was applied to control the Type 1 error on multiple comparisons (0.05/2 = 0.025). According to the t-test results, males (M = 20.71, SD = 6.16) had significantly higher scores of cyberbullying perpetration than females (M = 18.69, SD = 5.31), t(535.83) = −4.24, p = .000, and males (M = 21.09, SD = 5.99) also had significantly higher scores of cyberbullying victimization compared with the females (M = 19.94, SD = 5.08), t(540.91) = −2.49, p = .01. Multivariate analysis of variance (MANOVA) results showed no statistically significant differences among the participants’ year levels on cyberbullying perpetration and cyberbullying victimization scores, F(8, 1.172), p = .25, Pillai’s trace = .02, partial eta squared = .01.

Model Testing

The interplay between personality traits and cyberbullying motives was examined by a SEM via AMOS version 21. Item parceling by random assignment method was applied to the variables of revenge, dominance, online disinhibition, moral disengagement, narcissism, and aggression except for entertainment and harm. Maximum likelihood estimation was used with 1,000 bootstrap samples, and the confidence interval (CI) was 95%. Assumptions regarding sample adequacy, independent observations, normality, linearity, homoscedasticity, and multicollinearity were tested and confirmed before the model test. Means, standard deviations, and correlations are presented in Table 3.

Table 3.

Means, Standard Deviations and Intercorrelations of the Study Variables.

	M (SD)	1.	2.	3.	4.	5.	6.	7.	8.
1. Entertainment	13.92 (6.42)	—
2. Revenge	11.26 (5.84)	.13**	—
3. Harm	6.96 (3.59)	.20**	.69**	—
4. Dominance	9.70 (5.21)	.26**	.61**	.61**	—
5. Online disinhibition	17.29 (6.04)	.17**	.13**	.07	.15**	—
6. Moral disengagement	20.25 (7.25)	.17**	.32**	.33*	.31**	.32**	—
7. Narcissism	5.14 (3.08)	.08*	.13**	.17**	.24**	.00	.19**	—
8. Aggression	32.44 (8.36)	.11**	.30**	.24**	.22**	.20**	.28**	.16**	—

p < .05. **p < .01, two-tailed.

In the first stage of the model test, the measurement model tested the relationships among the latent variables, including entertainment, revenge, harm, dominance, and their indicators. According to the results, the measurement model yielded a good fit to the data (χ² = 662.19, df = 296, p = .00; χ²/df = 2.24; GFI = .92, CFI = .95, TLI = .94, SRMR = .05, RMSEA = .04). The standardized factor loadings were all significant and ranged from .45 to 91.

In the second stage, the hypothesized structural model was tested in two steps. First, the full model was tested with all hypothesized relationships between variables of cyberbullying perpetration motives and variables of personality traits. The model resulted in a good fit to the data (χ² = 662.19, df = 296, p = .00; χ²/df = 2.24; GFI = .92, CFI = .95, TLI = .94, SRMR = .04 RMSEA = .04). In the second step, the trimmed model was tested with only the significant relationships identified in the first step. Paths from (a) moral disengagement to entertainment, (b) narcissism to entertainment, (c) aggression to entertainment, (d) online disinhibition to revenge, (e) narcissism to revenge, (f) narcissism to harm, (g) online disinhibition to dominance, and (h) aggression to dominance were deleted. Thus, the trimmed model was tested with the remaining eight significant paths. Figure 2 presents the coefficients with their standardized values for the trimmed model.

Figure 2.

The coefficients with their standardized values for the trimmed model.

According to the results, the trimmed model demonstrated a good fit to the data (χ² = 697.56, df = 304, p = .00; χ²/df = 2.23; GFI = .92, CFI = .95, TLI = .94, SRMR = .05 RMSEA = .04). The full model and the trimmed model shared quite close fit indices. Thus, the trimmed model was preferred as it was more parsimonious.

The findings of the final trimmed model (Figure1) were considered: (a) as the participants became more disinhibited online, they tended more to cyber bully others for entertaining themselves; (b) the higher the participants’ moral disengagement and aggression levels, the more likely they are to cyber bully others for taking revenge; (c) when the moral disengagement and aggressive tendencies of the participants are higher, the possibility of their harming others by cyberbullying becomes higher, but when online disinhibition is higher, the possibility of cyberbullying others for harm decreases; and (d) the more the participants became morally disengaged and the more they had narcissistic tendencies, they were more likely to cyber bully with the aim of dominating others. Table 4 presents the standardized coefficients for the final model.

Table 4.

The Standardized Coefficients for the Final Model.

			Standardized Coefficient	SE	t Values
Online disinhibition	—	Entertainment	.21***	.04	4.34
Moral disengagement	—	Revenge	.35***	.09	5.53
Aggression	—	Revenge	.19***	.03	4.40
Online disinhibition	—	Harm	−.11**	.02	−2.60
Moral disengagement	—	Harm	.46***	.05	5.90
Aggression	—	Harm	.09*	.01	2.02
Moral disengagement	—	Dominance	.37***	.10	6.07
Narcissism	—	Dominance	.13***	.10	3.31

p < .05. **p < .01. ***p < .001, two-tailed.

The squared multiple correlations regarding the final model are listed in Table 5. Accordingly, while the personality traits variables explained 4% of the variance in entertainment motive, they accounted for 20% in revenge motive, 21% in harm motive, and 19% in dominance motive.

Table 5.

The Squared Multiple Correlations for the Final Model.

	Entertainment	Revenge	Harm	Dominance
R ²	.04	.20	.21	.19

Testing for the Structural Invariance of the Final Model Across Gender

This current study identified gender differences in cyberbullying perpetration and cyberbullying perpetration motives. Therefore, the structural invariance of the final model for females and males was explored. Two models were tested. In the first model which Byrne (2010) named as the configural model, the parameter estimates of the model were freely tested across females and males. To avoid multivariate non-normality, the test was carried out with 1,000 bootstrapped samples with 95% CI. The results showed that the configural model yielded a good fit for females and males (χ² = 1,092.90, df = 608, p = .000; χ²/df = 1.80; GFI = .88, CFI = .93, TLI = .92, SRMR = .07 RMSEA = .03). In the second step, the configural model needed to be constrained in terms of its structural covariances (Byrne, 2010). Hence, the second model specified all loadings except for the six constrained covariances among the variables of personality traits. This constrained model can be seen in Figure 3. To prevent multivariate non-normality, the test was conducted with 1,000 bootstrapped samples with 95% CI. According to the results, this constrained model demonstrated a good fit to the data (χ² = 1,095.32, df = 614, p = .000; χ²/df = 1.78; GFI = .88, CFI = .93, TLI = .92, SRMR = .07 RMSEA = .03).

Figure 3.

The constrained model.

To explore invariance across females and males, a chi-square difference test (∆χ²) was performed by comparing the configural model with the constrained model (Byrne, 2010). Evidence of invariance should be obtained when the value of the chi-square difference test between the configural model and the constrained model is statistically nonsignificant. In this comparison, the configural model served as the baseline model contrasted by the constrained model. While the chi-square value of the configural model was 1,092.90₍₆₀₈₎, the chi-square value of the constrained model was 1,095.32₍₆₁₄₎. The comparison of the constrained model with the configural model resulted in a statistically nonsignificant chi-square difference, $Δ χ_{(6)}^{2} = 2.42,$ ns, revealing that the structural model was invariant across females and males.

Discussion

The main purpose of the present research was to analyze the relationships between personality traits and cyberbullying perpetration motives by SEM. To achieve this goal, Uses and Gratifications Theory (UGT) was considered as a means to understand why young individuals engage in cyberbullying perpetration. It is important to note that it was the first time UGT was utilized to theoretically explain cyberbullying perpetration, and that UGT was empirically supported for conceptualization of cyberbullying perpetration in this study.

The findings of this present research indicated that almost half of the participants (49.7%) cyberbullied someone twice or more during the preceding 6 months. Comparing this prevalence rate with the previously reported prevalence reports may be inaccurate. This is because such reports, including this present one, are mostly not systematic prevalence studies in addition to having a low level of sample representativeness. On the contrary, the rate of cyberbullying perpetration found in this current investigation is higher than the formerly reported rates among Turkish university students. For instance, Arıcak (2009) found that of his 695 university-aged Turkish participants, 19.7% reported having cyberbullied someone. This current research lacked empirical data to account for the detected high rate of cyberbullying perpetration. Yet, it can be speculated that the high usage frequency of information and communication technologies (ICTs) among university-aged Turkish individuals may be one of the reasons of the high rate of cyberbullying perpetration. For example, in 2015, the year the data of this present investigation were gathered, 70% of the university-aged Turkish individuals were using ICTs (TÜİK, 2015).

The results showed that there were no statistically significant year level differences among the participants on cyberbullying perpetration and cyberbullying victimization scores. This finding is in line with the previous research findings (Akbulut & Erişti, 2011; Francisco et al., 2015). The results also revealed that Turkish male university students cyber bullied others significantly more than females. This finding is compatible with both a group of international research done with university students (e.g., Francisco et al., 2015) and a group of research conducted with Turkish university students (e.g., Akbulut & Erişti, 2011; Ozden & Icellioglu, 2014). To the question why Turkish males act more as cyber bullies, Topcu and Erdur-Baker (2012) and Akbulut and Erişti (2011) pointed out gender socialization processes. Compared with the females, males’ aggressive behaviors can be more acceptable because Turkish society’s dominant patriarchal nature is more tolerant to males’ aggressive actions. In addition, that males had lower levels of empathy was reported as a possible reason why males perpetrate others online (Topcu & Erdur-Baker, 2012). Furthermore, Erdur-Baker et al. (2016) found that males had significantly higher levels of moral disengagement than females, and moral disengagement could explain why males cyber bully others more. However, there was not a significant difference between males (M = 12.63, SD = 7.74) and females (M = 11.96, SD = 6.96) in terms of moral disengagement levels, t(595) = −1.06, p = .29, in this present study. Therefore, future research, especially longitudinal ones, is needed to satisfactorily explain reasons for gender difference in cyberbullying perpetration especially for different societies.

Before discussing the findings about the links between personality traits and cyberbullying perpetration motives, it should be noted that due to the correlational nature of the study, this current research lacked empirical data to explain these links. For this reason, the readers should be aware of the fact that assumptions or speculations were made drawing from the existing literature. Although significant relations between each of the four personality traits and each of the four cyberbullying perpetration motives were expected in this study, the findings were complex. According to the findings, individuals with higher online disinhibition levels tended to cyber bully others for entertainment. Previously, a similar link was reported (e.g., Barlett, 2015; Wright, 2014). Dissociative anonymity and asynchronicity as two significant components of online disinhibition (Suler, 2004) may explain this link. Dissociative anonymity refers to splitting online and offline behaviors from each other by hiding or altering real identities and not fully accepting the impacts of their online behaviors because of the impact of being anonymous (Suler, 2004). Cyber bullies may think that they have the right to entertain themselves and may not be held responsible for the damaging impacts of their behavior because they are anonymous. Asynchronicity refers to the inability of synchronously communicating with others when online (Suler, 2004). Unable to directly witness the immediate consequences of their perpetration behaviors, cyber bullies may assume that they are having fun although the victims suffer.

The findings showed that moral disengagement and aggression, as personality traits, were related to the possibility of cyberbullying others for revenge. This finding was in line with the past research revealing significant associations between moral disengagement (Sticca & Perren, 2015), aggression (Lonigro et al., 2015), and cyberbullying others. Deriving from Bandura’s (2002) cognitive mechanisms on moral agency, cyber bullies may become motivated by feelings of vengefulness and may morally justify their bullying acts on their earlier offenders. They may reason that they have the right to fight against merciless bullies who victimized themselves in the past. Cyber bullies may also blame their victims by asserting that victims provoked the feeling of revenge by formerly targeting them. As for the link between aggression and cyberbullying others with the motive of revenge, aggressive individuals are reported being prone to be revengeful and tend to overcome frustrations with force (Bergman et al., 2007). These tendencies may cause victimized aggressors to engage in cyberbullying perpetration to take vengeance. In fact, König, Gollwitzer, and Steffgen (2010) reported that vengefulness is a common trait of cyber bullies having a tendency to victimize their earlier perpetrators who traditionally bullied them.

The results demonstrated that higher levels of moral disengagement and aggression were associated with the possibility of harming others by cyberbullying. Prior empirical support exists for the relationship between moral disengagement (e.g., Sticca & Perren, 2015), aggression (e.g., Bayraktar et al., 2014), and cyberbullying perpetration. Bandura’s (2002) cognitive mechanisms on moral agency can explain the association between moral disengagement and cyberbullying perpetration. Cyber bullies may believe that they are posing no harm or less harm on the victims because they cannot directly observe the harm they caused. Cyber bullies may also compare themselves with others and conclude that other individuals are physically, verbally, or relationally stronger than themselves. Cyber bullies may regard that unless they inflict harm on others, they will become victimized sooner or later. Moreover, dehumanizing their victims as “losers” or “freaks,” cyber bullies may think that such people naturally deserve being harmed as they are not normal human beings. In addition, cyber bullies may relabel their perpetration as “teaching a lesson” to the victim rather than a harmful conduct, assuming that their aim is not to harm the victim but to teach a lesson.

For the link between aggression and cyberbullying others with the motive of harm, cyberbullying ensures easier and less risky ways of displaying aggression to the cyber bullies because they can easily become anonymous and hide behind fake identities to target their victims. In parallel with this assumption, aggressors reported that they preferred cyberbullying as it was easy and less risky (e.g., Topcu, Yıldırım, & Erdur-Baker, 2013). Unexpectedly, online disinhibition was significantly but negatively correlated to engaging in cyberbullying to harm others. In other words, the higher the online disinhibition, the less likely the motive to harm. It is not easy to explain this finding. Yet, it can be speculated that individuals with higher levels of online disinhibition are more vulnerable against the risks in cyber space as online disinhibition leads them to behave more aggressive, more courageous, and more dissociative while online. Hence, greater levels of online disinhibition may be related to cyberbullying victimization rather than perpetration.

According to the findings, moral disengagement and narcissism were significantly and positively associated with cyberbullying others with the aim of domination. These linkages were previously reported between moral disengagement (e.g., Kowalski et al., 2014), narcissism (Goodboy & Martin, 2015), and cyberbullying perpetration. On the basis of Bandura’s (2002) explanations on cognitive mechanisms of moral disengagement, cyber bullies may like dominating online settings by demonstrating their skills and thus power in technology use. They may justify their harmful conducts thinking that they have the power of technological savviness to dominate the online environments, whereas others are using their physical power to dominate the physical environments. The characteristics of the individuals with higher tendencies of narcissism may account for the relationship between narcissism and dominating others by cyberbullying. Thomas (2012) postulates that narcissistically tended individuals are more likely to be manipulative and seeking for social status and authority over others in their interpersonal relationships. Compatible with Thomas’s (2012) proposition, cyber bullies may like to establish dominance by demonstrating their superiority in technology usage by embarrassing online contacts whom they dislike, by manipulating online communications, and by indicating that they are important and authoritative in cyber platforms.

Overall, the findings of this present study revealed that the personality traits of the cyber bullies are significantly connected to perpetrators’ motives, which validated the underlying mechanism of Uses and Gratifications Theory (UGT). That is, significant relationships exist between the motives and the personality traits of cyber bullies, and the type of the personality trait cyber bullies own has an impact on why cyber bullies perpetrate others. Considering the basic tenets of UGT, this present investigation demonstrated that cyber bullies are aware of their perpetration motives, personality traits of cyber bullies affect their perpetration motives, and cyber bullies intentionally choose certain communication technologies to gratify their motives.

Strengths, Limitations, and Future Research

The main strength of this investigation is providing empirical evidence about the validity of Uses and Gratifications Theory (UGT) as a theoretical map to understand cyberbullying perpetration. Focusing on cyberbullying perpetration behaviors, bringing together the research on personality traits of the cyber bullies and the motives of cyberbullying perpetration, extending the knowledge about the university students’ cyberbullying experiences, and examining the cyber bully-victims as a distinct group are the additional strengths of this study.

Notwithstanding these strengths, this study bears some limitations. First, personality trait variables and variables of motivations of the perpetrators of cyberbullying are limited to the selected variables in this study. In addition, the findings cannot be generalized due to the participant selection bias and low sample size. Besides, the limitations stemming from the self-report data of this current investigation are acknowledged. Finally, a cause–effect chain cannot be established due to the correlational nature of this research.

This study offers several implications for future research. First, future investigations are welcomed to explore the nature and the extent of cyberbullying perpetration among university students. In addition, evidence-based prevention and intervention programs specifically designed and validated for the university student would certainly contribute to the literature because very limited knowledge exists regarding how to prevent and intervene cyberbullying in the university campus. Furthermore, considering the extant limited number of research, learning more about the cyber bully-victims including their personality traits and perpetration motives would expand the knowledge on cyber bully-victims in cyberbullying incidents as a distinct group. On the contrary, future research should also examine the pure cyber bullies as a distinct group with regard to personality traits and cyberbullying perpetration motives. Exploring more about the nature of the personality traits and the perpetration motives of the pure cyber bullies, as well as the links between the personality traits and the perpetration motives of the pure cyber bullies, will certainly contribute to the cyberbullying literature.

Moreover, UGT brings a new understanding about cyberbullying perpetration to researchers, theorists, and practitioners. However, the viability of UGT with different samples awaits further examinations with different research methods and samples. More research based on UGT is needed to be able to explain the connections between personality traits and cyberbullying perpetration. In addition, considering the high percentage of the cyber bully-victims found in this present investigation, more research is required to understand why cyberbullying perpetration is very common among the Turkish university-aged youth. In addition, considering the continuous debate on why males are more likely to engage in cyberbullying perpetration, future investigations are warranted to examine the factors, mediators, or cultural components of gender differences in cyberbullying perpetration.

Implications for Practice

Preventive procedures to identify the personality traits of the individuals could be developed and implemented. Although this present study was carried out with university students, such preventive procedures could be practiced for different age groups. For instance, counselors or psychologists working at schools can measure the personality traits of the students in each class, identify the possible bullying perpetrators by considering the scores obtained from the personality scales/inventories, and offer preventive measures to the possible bullies. Regarding that the same students act as peer and cyber bullies (e.g., Tanrikulu & Campbell, 2015), such procedures can assist the school counselors or psychologists to determine the students who may engage in bullying in physical and/or cyber space.

Volk, Camilleri, Dane, and Marini (2012) and Thornberg (2010) argue that bullying can offer benefits to the perpetrators, including social status, permanently drawing attention, showing strength, being competitive, and taking part and staying in a social group. In other words, young individuals may use bullying perpetration as a means to gain benefits in their social groups. This argument seems applicable to the findings of this present research. The interplay between the personality traits and the motives may lead cyber bully-victims to employ cyberbullying perpetration to gain benefits. Such a conceptualization provides important implications for preventive practice. Mental health professionals and practitioners working at university level should lead the university managements to create opportunities for students to satisfy personal motives and obtain benefits. If alternative ways are offered, university students, rather than bullying others, can be able to engage in harmless acts to satisfy needs and obtain benefits. More specifically, sports activities, social events, competitive contests, or leisure and recreational activities offered by the university can help the university youth to obtain benefits by satisfying personal motives such as entertainment, dominance, drawing attention, or showing strength.

Conclusion

Certain strong connections seem to exist between personality traits and cyberbullying perpetration motives. These connections suggest that individuals’ personality traits have a fundamental impact on their cyberbullying behaviors. This signifies the importance of the need for reliable, valid, and up-to-date instruments intending to identify the personality traits of the individuals, especially children and young people. The earlier the personality traits of the children and young people are specified, the earlier they can be prevented from engaging cyberbullying perpetration. In this way, cyberbullying victimization can be more easily prevented, and fewer children and young individuals will suffer from cyberbullying.

Footnotes

Acknowledgements

Under the supervision of the second author, this research was conducted as a doctoral dissertation by the first author. This paper is based on the research conducted as a part of PhD studies of the first author at Middle East Technical University, Turkey. The first author is sincerely thankful to Prof. Dr. Özgür Erdur-Baker for her excellent academic mentoring.

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

Ibrahim Tanrikulu

Author Biographies

Ibrahim Tanrikulu, PhD, is an assistant professor in Psychological Counseling and Guidance Department/Faculty of Education at Gaziantep University, Gaziantep/Turkey. He earned his PhD in psychological counseling and guidance at the Middle East Technical University, Ankara, Turkey. His interests include prevention research on bullying, cyberbullying, sibling bullying, and online applications in counseling profession. In addition to the primary, middle, and high school students and university students, he conducts research on bullying prevention in preschool level.

Özgür Erdur-Baker, PhD, is a full professor in Department of Educational Sciences at Middle East Technical University, Ankara, Turkey. Her research interests include outcome and process research in counseling, peer bullying (traditional and cyberbullying), trauma, grief, and disaster psychology.

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