Cyber-bullying and cyber-victimization among undergraduate student teachers through the lens of the General Aggression Model

Accepted Manuscript Cyber-bullying and Cyber-victimization among Undergraduate Student Teachers through the Lens of the General Aggression Model

Constantinos M. Kokkinos, Nafsika Antoniadou PII:

S0747-5632(19)30147-5

DOI:

10.1016/j.chb.2019.04.007

Reference:

CHB 5983

To appear in:

Computers in Human Behavior

Received Date:

30 August 2018

Accepted Date:

08 April 2019

Please cite this article as: Constantinos M. Kokkinos, Nafsika Antoniadou, Cyber-bullying and Cyber-victimization among Undergraduate Student Teachers through the Lens of the General Aggression Model, Computers in Human Behavior (2019), doi: 10.1016/j.chb.2019.04.007

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ACCEPTED MANUSCRIPT

Cyber-bullying and Cyber-victimization among Undergraduate Student Teachers through the Lens of the General Aggression Model

Constantinos M. Kokkinos1 & Nafsika Antoniadou1

1Department

of Primary Education, School of Education Sciences,

Democritus University of Thrace, Greece

Corresponding author: Professor Constantinos M. Kokkinos, Department of Primary Education, School of Education Sciences, Democritus University of Thrace, N. Hili, GR 68100, Alexandroupolis, Greece. Email: [email protected]

ACCEPTED MANUSCRIPT Running Head: CYBER-BULLYING UNIVERSITY TRHOUGH GAM

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Abstract Cyber-bullying (CB) is a prevailing phenomenon among University students, but scarcely investigated, thus leaving its correlates largely unexplored. Recently, integrated theoretical models were proposed for the explanation of the phenomenon, investigating complex interactions between personal and contextual factors. The present study was purported to investigate the association between several individual and contextual variables (attachment, Big Five personality traits, Internet use frequency, problematic Internet use, οnline disinhibition, loneliness, psychopathology) and CB/Cyber-victimization (CV) among 175 Greek University undergraduates, based on the General Aggression Model. Results showed that participants who used the Internet more frequently scored higher in CB. Significant positive correlations were observed between both CB and CV with Neuroticism, loneliness, anxiety, hostility, online disinhibition, and Problematic Internet Use, and positive with Agreeableness. Furthermore, CB was negatively correlated with Conscientiousness and CV negatively with depression. CB was predicted by low Agreeableness, frequent Internet use and higher levels of negative outcomes when using the Internet, while CV was predicted by low Agreeableness, high Extraversion, high loneliness and high compulsive Internet use. Overall, findings of this study indicate that CB and CV may involve students who make problematic Internet use, have specific personality traits and face various social difficulties and psychopathological symptoms. Keywords: cyber-bullying/victimization; Internet use; personality; attachment; loneliness; psychopathology symptoms

ACCEPTED MANUSCRIPT Cyber-bullying and Cyber-victimization among Undergraduate Student Teachers through the Lens of the General Aggression Model 1. Introduction Cyber-bullying1 (CB) is one of the harmful behaviors related to the abuse of technology. It involves the use of Information and Communication Technologies (ICT) (e.g., e-mail, blogs, instant messages, text messages) as a support for deliberate, repeated and hostile behavior exhibited by an individual or group, with the intent of harming others (Hinduja & Patchin, 2009). Until recently, high school population had been the most studied age-group, and although evidence suggests that CB behaviors are frequent among University students (incidents range from 10% to 50% among young adults) (e.g., Kokkinos, Antoniadou, & Markos, 2014; Peluchette, Karl, Wood, & Williams, 2015), with clearly documented psychological and emotional effects (e.g., Kowalski, Limber, & Agatston, 2012; Tokunaga, 2010), relative research is still sparse (Varghese & Pistole, 2017). University students have higher chances for CB participation compared to other age-groups, due to heavy and unsupervised use of ICT, experience seeking, frequent display of personal lives in social media, as well as formation of tight and competitive social cliques (e.g., political) (e.g., Jones & Scott, 2012). The literature on traditional bullying has substantially informed CB research, but CB has been claimed to be a unique phenomenon although closely related to and with significant overlap with face-to-face bullying (Antoniadou & Kokkinos, 2015; Olweus, 2012). For example, Englander (2008) refers to CB as the "perfect crime"

CB= Cyber-bullying, CV= Cyber-victimization, ICT= Information and Communication Technologies, CMC= Computer Mediated Communication, PIU= Problematic Internet Use, FFM= Five Factor Model, GAM= General Aggression Model, CBVEQ= Cyber-bullying/victimization Experiences Questionnaire, ΒSI= Brief Symptom Inventory, GPIUS2= Generalized Problematic Internet Use Scale 2. 1

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ACCEPTED MANUSCRIPT due to the ease of its execution and the minimal consequences for the perpetrator. Furthermore, it should be noted that often in CB situations both the perpetrator and the victim are one and the same person (e.g., bully/victims). University students' tendency to participate in CB with a dual role is reflected in a study of Akbulut and Eristi (2011), since the researchers found that cyber-victimization (CV) was an important predictive factor for student involvement in CB. In the physical context, it is difficult for a victim to defend him/herself, due to limited social, cognitive, verbal or physical skills. However, via the Internet, the victim's deficits may be hidden or even offset (e.g., Kowalski, Giumetti, Schroeder, & Lattanner, 2014). Therefore, a range of personal characteristics that have been associated with face-to-face bullying/victimization may not apply to CB. Thus, whereas traditional bullies are most likely to possess dominant and impulsive personality characteristics (Olweus, 1993), technology may attract more socially anxious personalities and those who would not engage in offline bullying (Kowalski & Limber, 2007). Consequently, the interventions intended to target those engaged in traditional bullying may not effectively address CB. The recognition of significant factors implicated in CB/CV requires a theoretical basis (e.g., Kowalski et al., 2014). There are multiple factors that may influence the risk of victimization such as the perpetrator’s characteristics, environmental factors, or victim behavior. As previous studies have suggested, although all young adults have access to the Internet, and may make heavy use for personal, academic and professional purposes, not all of them make Problematic Internet Use (PIU). PIU in general, and CB and CV involvement in specific, have been linked to the complex interaction of personal and contextual factors. Recently, integrated theoretical models such as the General Aggression Model (GAM) have

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ACCEPTED MANUSCRIPT been proposed for the study of the factors implicated in CB/CV, but supporting evidence among University students is still limited (e.g., Watts, Wagner, Velasquez, & Behrens, 2017). Based on the GAM (Kowalski et al., 2014), which provides a useful comprehensive theoretical framework integrating both person and situational factors and has been utilized in previous research on bullying behaviors (e.g., Gullone & Robertson, 2008; Vannucci, Nocentini, Mazzoni, & Menesini, 2012), it is hypothesized that the path to a CB encounter for a perpetrator or a victim starts with person and situational factors. These factors affect the present internal state of the individual, perhaps activating hostile thoughts, negative affect, and heightened arousal. The GAM proposes the contribution of person factors such as gender, personality traits, psychological states, technology use and situational factors such as provocation (e.g., CV), and perceived opportunities to act aggressively (e.g., online disinhibition effect). All these factors impact the CB/CV behavior and may have a different effect than in traditional bullying/victimization, since the used means (i.e., ICT) differ significantly that physical environments (e.g., Kowalski et al., 2014). The GAM has been recognized as a valuable theoretical framework to explain CB among University students (e.g., Wong, Cheung, & Xiao, 2018), but to our knowledge it has not been applied in studies in this age group. In the present study, specific variables were studied based on the GAM. First, factors that have repeatedly been found to correlate with aggression were considered, namely gender, attachment and personality. Secondly, factors specifically related to online behavior were also taken into consideration. It should be noted that although the GAM is an explanatory theoretical framework for all types of aggression, researchers have proposed (e.g., Kowalski et al., 2014) that a range of personal

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ACCEPTED MANUSCRIPT characteristics that have been associated with face-to-face bullying/victimization may not apply to CB. Therefore, when considering which situational factors proposed by the GAM to investigate, Internet-related variables were chosen (i.e., frequency of Internet use, PIU and online disinhibition), while in terms of personal characteristics, the selection was based on variables that could be hidden or offset during online communication, and psychopathological symptoms that have been associated with frequent and problematic Internet use (see following sections). Therefore, the present study is purported to investigate the association between several variables related to the context where CB/CV takes place (frequency of Internet use, PIU and online disinhibition), as well as individual variables (attachment, Big Five personality traits, loneliness, psychopathology symptoms) in a sample of University undergraduates. It should be noted that the current study is an extension of a previous one, conducted by Kokkinos et al. (2014), which investigated the psychological profile (personality characteristics, social skills and psychological symptoms) of Greek university students who reported CB/CV experiences. 1.1 Internet Use and Online Disinhibition University students use the Internet as a major means of communication in their daily routines (Ellison, Steinfield, & Lampe, 2007). Social interaction with the use of computers is frequently referred as Computer-mediated communication (CMC) and refers to any form of interpersonal communication facilitated using a computer which requires the interaction of two individuals. In several occasions, online environments can have a liberating effect, which was described by Suler (2004) as online disinhibition effect. There is evidence to suggest that online disinhibition may be socially beneficial (e.g., greater emotional and physical intimacy with peers and romantic partners compared to face-to-face interaction) (Horrigan, 2009), or toxic.

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ACCEPTED MANUSCRIPT For example, online disinhibition is associated to cyber aggression, since CB behaviors are considered as a product of the minimal social cues, or anonymity, available on the online media through which the behavior occurs. Since they make the heaviest, and the most unsupervised, use of ICT, young adults are at risk of exhibiting Internet related problems. Davis (2001) conceptualized PIU as a multidimensional overuse of the Internet that results in negative psychological, social and behavioral problems for the individual, because of Internet usage. Generalized PIU may be higher among University students than older adults and can manifest with excessive and compulsive Internet use, negative Internet influences, use of the Internet for mood alteration, etc. (Chong, Chye, Huan, & Ang, 2014). 1.2 Individual Variables Studies investigating aggression have traditionally considered gender, attachment and personality as pivotal factors, due to their significant influence on a person’s behavior throughout his/her life. According to the theories of individual differences and socio-cultural learning, it is hypothesized that innate and stable personal characteristics are strongly related to a person’s social behavior, such as aggression, during CMC (e.g., Leary & Hoyle, 2009; Revelle, Wilt, & Condon, 2010). Regarding gender, findings among University students are inconsistent (e.g., Aricak, 2009; Chapell, Hasselman, Kitchin, Lomon, Maclver, & Sarullo, 2006). In several studies, male University students have been found to participate more frequently in the incidents with the role of the aggressor (Hong, Li, Mao, & Stanton, 2007), while females have been found to report more CV experiences (Lindsay & Krysik, 2012).

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ACCEPTED MANUSCRIPT Parental caregiving plays a vital role in the development of “internal working models” according to the attachment theory (Bowlby, 1969). Attachment is related to effective social relationships (Varghese & Pistole, 2017); anxiously attached individuals are more prone to partner-directed aggression as they are likely to handle conflict by acting out, whereas those avoidantly attached deal with conflict by withholding affection, rather than acting aggressively toward their partners (Miga, Hare, Allen, & Manning, 2010). In addition to face-to-face partner-directed aggression, electronic technologies offer anxiously attached youth another way to deal with partner conflict. When faced with difficulties in creating and maintaining close relationships, insecurely attached University students may increase their time connecting to the Internet (Varghese & Pistole, 2017). In terms of CB and CV, insecure attachment has been linked to involvement as a bully, as a victim, or both, but regardless its use in understanding maladaptive social behaviors (Varghese & Pistole, 2017), studies have mainly investigated children and adolescent samples (Ševčíková, Macháčková, Wright, Dědková, & Černá, 2015). Personality has been a well-studied factor for predicting Internet use (e.g., Kokkinos & Saripanidis, 2017). Most research studying Internet use has applied the Five Factor Model of personality (FFM; Costa & McCrae, 1992), suggesting that most of the variability in personality can be captured by five overarching dimensions. Landers and Lounsbury (2006) found that three of the Big Five personality traits – Agreeableness, Conscientiousness, and Extraversion– were inversely related to Internet use. On the contrary, McElroy et al. (2007) found that Openness to Experience was positively correlated to Internet use, and Neuroticism showed a trend in predicting Internet use, after controlling for computer anxiety, self-efficacy, and gender. In terms of CB and CV, personality has been identified as a significant

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ACCEPTED MANUSCRIPT implicated factor (e.g., GAM; Kowalski et al., 2014). In general, high Extraversion, low Consciousness and low Agreeableness have been related to CV, but findings among University students are still scarce (Kokkinos & Saripanidis, 2017). Since the early days of research on the Internet use, it has been documented that it is linked to poor psychological adjustment. Although the direction of the relationship is unclear, evidence had demonstrated that improper use of the Internet (e.g., excessive use, participation in aggressive behavior) is related to various psychopathology symptoms such as loneliness, depression, and anxiety. These findings have been also replicated among University students (e. g., Kokkinos et al., 2014; Lindsay & Krysik, 2012). Similarly, psychopathological symptoms could lead to frequent and PIU, since the Internet may provide an attractive alternative for individuals who are emotionally isolated, socially alienated, or excessively shy. The experience of loneliness may be manifested with difficulty in establishing close intimate relationships, having few friends, experiencing frustration and dissatisfaction with existing relationships, less interest in developing social affairs and reduced active involvement in friendships (Nowland, Talbot, & Qualter, 2018). Nevertheless, research findings assessing the importance of loneliness in online behavior are inconsistent. An association between loneliness and use of technological devices such as mobile phones and computers has been documented (Sahin, 2012), since studies show that these individuals spend more time online and share more personal information (Ceyhan & Ceyhan, 2008). Empirical evidence suggests that online communications reduce loneliness by providing more opportunities to connect with others thus increasing control over communication (Valkenburg & Peter, 2011) and that the Internet is a means used by shy and socially anxious individuals who wish to expand their social networks in order to decrease feelings of loneliness (Lo,

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ACCEPTED MANUSCRIPT 2019). Douglas et al. (2008) report that feelings of loneliness and social isolation are antecedents of heavy Internet use, with some studies confirming this association among undergraduates (e.g., Morahan-Martin & Schumacher, 2003). Nevertheless, the use of technological communication has also been linked to a decrease in offline social interactions and weak, more superficial social relationships (Subrahmanyam & Lin, 2007). Contrary, regardless of the causal direction between Internet use and loneliness, the latter is conceptualized as perceived social isolation (Cacioppo, Hawkley, & Thisted, 2010), which may be associated with the propensity to perpetrate or be the target of CB. Though few studies have specifically addressed the relationship between CB and loneliness, Sahin (2012) reports a relationship between loneliness and CV in secondary school children. Though Sahin (2012) did not find a relationship between loneliness and CB, cyber-bullies often seek and rely on social support (Srabstein & Piazza, 2008), suggesting that they might feel lonely or rejected. Thus, CB may represent a form of empowerment or aggression against those perceived to have rejected their advances. With regards to University students, Kokkinos and Saripanidis (2017) found that those who are lonely and depressed are more likely to experience Facebook CV. Uncontrolled or compulsive Internet use can also be associated with depression (Cheng et al., 2018). Individuals who tend to suffer from depressive symptoms and sadness, sometimes seek to compensate for their lack of social contacts by finding refuge on the Internet, where they manage to develop relationships but become more vulnerable to threats such as CV (van den Eijnden, Vermulst, Van Rooij, & Meerkerk, 2006; Ybarra & Mitchell 2004). Kowalski et al. (2014) have underscored the association between psychological states with CB/CV and claimed that victims may have higher scores in measures of depression and anxiety. These

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ACCEPTED MANUSCRIPT suggestions have been replicated by several studies which have found that University students involved in CV have high depression and anxiety scores (e.g., Schenk & Fremouw, 2012). Such individuals may provide ideal targets for cyber-bullies, since they have difficulty handling attacks (e.g., Kokkinos & Saripanidis, 2017). Regarding CB, Kokkinos et al. (2014) found that increased levels of depression predicted both involvement in and experiencing of cyber-aggression. On the other hand, a study by Selkie, Kota, Chan and Moreno (2015) shows that depression can be the consequence of a CB/CV, especially among those involved as bully-victims. 1.3 The present study Since evidence regarding the involvement of Greek University students in CB/CV is still limited, relative studies are largely exploratory in nature. Ιn the previous study by Kokkinos et al. (2014) the contribution of several personal characteristics and psychological symptoms (i.e., psychopathic traits, sensation seeking, empathy, hostility, interpersonal sensitivity, social anxiety, and depression) in students’ CB/CV involvement was explored. This study serves as an extension to that previous investigation (Kokkinos et al., 2014) by exploring, in addition to the role of psychopathology symptoms (depression, anxiety, hostility), the role of personality (big5), problematic online behaviors (e.g., online disinhibition), emotional responses (i.e., loneliness, attachment style) since they have been found to affect cyber aggression. Furthermore, although researchers are currently investigating the associations between psychological factors and CB/CV, the literature has focused on the examination of the effects of one or two psychological variables on these behaviors, and not the simultaneous effects of multiple variables. Since behaviors do not have simple causes but are multiply determined by many interacting individual and contextual variables, the present study aims to examine the role of gender,

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ACCEPTED MANUSCRIPT attachment style, big five personality traits, frequency of Internet use, problematic Internet use, online disinhibition, loneliness, and psychopathology symptoms in CB/CV among university undergraduate students. This investigation was based on the GAM, according to which person and situational factors, are considered as the starting point for a CB or CV incident (Kowalski et al., 2014). Specifically, the factors that are examined in this study, have been found to impact CB/CV involvement by significantly affecting the state of the individual. According to the GAM, the person factors are regarded as the consistent characteristics that the individuals brings to the situation and which affect his/her inclination to be implicated in CB/CV, whereas the situational factors, are characteristics of the environment which restricts or offers an opportunity for CB/CV involvement. 2. Material and Methods A cross-sectional correlational study design was used. 2.1 Participants A convenience sample of 175 (83 males, 92 females) undergraduate students enrolled at the Department of Primary Education at a peripheral Northern Greek university, aged between 18 to 37 years (M = 19.56, SD = 2.36) participated in the study. On average, participants reported using the Internet 6 days per week (M = 6.05, SD = 2.21). 2.2 Measures 2.2.1 Demographic Characteristics and Internet Use. Participants’ gender, age, and year of studies were requested. An 8-point scale (1=Less than once per day to 8=Every day) asked them about the number of days they spend on the Internet weekly only for personal reasons. Based on the scale’s mean, participants were

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ACCEPTED MANUSCRIPT divided into 3 groups according to their Internet use (high = 79, moderate = 28, low = 68). 2.2.2 Cyber-bullying/victimization. Cyber-bullying/victimization Experiences Questionnaire (CBVEQ; Antoniadou, Kokkinos, & Markos, 2016), measures the frequency (1=never to 5=every day) of behaviors on two 12-item scales, namely CB and CV, for the last 30 days (α =.83 for both scales). CBVEQ is, to the knowledge of the authors, the only Greek questionnaire investigating CB/CV that has been thoroughly examined for its validity and reliability, with consistently excellent results (Antoniadou et al., 2016). 2.2.3 Personality. The 60-item NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992; Panayiotou, Kokkinos, & Spanoudis, 2004) measured participants’ level of agreement (1= Strongly disagree to 5= Strongly agree) for statements on five 12-item scales: Neuroticism (α = .80), Extraversion (α = .76), Openness (α = .62), Agreeableness (α = .65) and Conscientiousness (α = .83). The validity and reliability of the FFI has been confirmed in previous studies among adults (e.g., Kokkinos, Baltzidis, & Xynogala, 2016; Kokkinos & Saripanidis, 2017; Murray, Rawlings, Allen, & Trinder, 2003). 2.2.4 Loneliness. The 20-item UCLA Loneliness Scale, version 3 (Russell, 1996) measured participants’ frequency of feelings (1= Never to 4= Always), (α for the overall scale was .92). The scale has been used with College students, suggesting that the it is highly reliable, with significant correlations with other measures of loneliness, while factor analysis indicated that items load into a global bipolar loneliness factor (Russell, 1996). 2.2.5 Psychopathology Symptoms. Depression (α = .80), Hostility (α = .75), and Anxiety (α = .79) subscales from the Brief Symptom Inventory (ΒSI; Derogatis,

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ACCEPTED MANUSCRIPT 1975; Loutsiou-Ladd, Panayiotou, & Kokkinos, 2008) measured (0= Not at all to 4= Extremely) participants’ distress during the last week. The psychometric properties of BSI have been examined in previous studies among Greek-speaking adults, with results indicating adequate validity and reliability (Loutsiou-Ladd et al., 2008). 2.2.6 Online Disinhibition. The 7-item Social Confidence (α = .76) and the 8item Socially Liberating (α = .78) subscales of the Internet Behavior and Attitudes Scale (Antoniadou & Kokkinos, 2013; Morahan-Martin & Schumacher, 2003) were used to assess Internet disinhibitory effects, on a four-point scale (1= Strongly disagree to 4= Strongly agree). The scales have been previously used both combined and separately for the assessment of online disinhibition (Niemz, Griffiths, & Banyard, 2005) with adequate psychometric properties (Antoniadou & Kokkinos, 2013; Antoniadou et al., 2016). 2.2.7 Problematic Internet Use. The 15-item Generalized Problematic Internet Use Scale 2 (GPIUS2; Caplan, 2010) measured participants’ level of agreement (1= Strongly disagree to 6= Strongly agree) on five 3-item scales: Preference for Online Social Interaction (α = .81), Mood Regulation (α = .87), Cognitive Preoccupation (α = .78), Compulsive Internet Use (α = .78), and Negative Outcomes (α = .71). Studies in various countries have indicated the good internal consistency and adequate construct validity of the scale (e.g., Assunção & Matos, 2017). 2.2.8 Attachment Style. Hazan and Shaver’s (1987) three descriptions corresponding to the basic attachment styles (secure, avoidant, ambivalent) were used to measure participants’ close relationships. The three prototypical descriptions of Hazan and Shaver (1987) have been used in numerous studies and have influenced following instruments due to their ability to successfully assess adult attachment.

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ACCEPTED MANUSCRIPT Students who selected the first description were classified as securely attached (55.2%), while those who selected the second and third descriptions were classified as insecurely attached (44.8%). The Greek translations of the scales were used. Confirmatory Factor Analysis (Mplus version 6.1; Muthén & Muthén, 2010) confirmed all the a priori structures of the scales (Table 1). The fit of these models was mediocre (i.e., CFI), but overall still acceptable (Chen, 2007; Kline, 2011; Steenkamp & Baumgartner, 1998). Table 1 Fit Statistics for Confirmatory Factor Analytic Models. Scale NEO-FFI CBVEQ UCLA LS IBAS GPIUS2 BSI

Model Correlated 5-factor Correlated 2-factor

SB-χ2 1373.21 355.16

df 1264 245

CFI .92 .91

TLI .92 .90

SRMR .07 .07

RMSEA (90%CI) .045 (.023 - .062) .053 (.043 - .062)

Single-factor

262.94

159

.92

.91

.07

.061 (.048 - .073)

Correlated 2-factor Correlated 5-factor Correlated 3-factor

111.86 64.83 121.47

87 48 83

.95 .98 .95

.94 .97 .94

.06 .05 .06

.042 (.015 - .061) .047 (.011 - .072) .052 (.032 - .070)

Note: NEO-FFI = NEO Five-Factor Inventory, CBVEQ = Cyber-bullying/victimization Experiences Questionnaire, UCLA LS= University of California, Los Angeles Loneliness Scale, IBAS= Internet Behavior and Attitudes Scale, GPIUS2= Generalized Problematic Internet Use Scale 2, ΒSI= Brief Symptom Inventory.

2.3 Procedure The study was conducted during the fall semester of 2013-2014. Students gave their consent for participation. Specifically, participants were invited to take part in the research after they were reassured that (a) participation was voluntary, (b) all processed data would be anonymous, (c) their decision to participate (or not) would not affect their academic evaluation, (d) they were free to discontinue participation at any time, and (e) they were free to omit any questions they did not wish to answer. No extra course credits were provided to the participating students. Students completed the questionnaire in approximately 40-45 minutes. 2.4 Data Analytic Strategy

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ACCEPTED MANUSCRIPT Analyses were conducted using the IBM SPSS 21. Analysis of variance (ANOVA) was applied to investigate the differences among means of different groups and bivariate correlations were calculated among variables using Pearson r coefficient. Finally, multiple linear regression analyses that simultaneously included all the predictor variables were run to determine which variables were unique and significant predictors of the outcome. 3. Results 3.1 Descriptive Statistics Overall, participants had low to medium scores in dimensions of PIU, and low scores in both online disinhibition dimensions (Social Confidence, Socially Liberating). Involvement in CB/CV was infrequent, while the most highly rated personality characteristics were Extraversion, Agreeableness and Consciousness. Finally, loneliness, depression, anxiety and hostility rates tended to be low (Table 2). Table 2 Descriptive Statistics

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ACCEPTED MANUSCRIPT Measure

Scale

GPIUS2

POSI Mood CogP CompIU NegO SCon SLib CB CV N E O A C Lon Dep Anx Host

IBAS CBVEQ NEO-FFI

UCLA LS BSI

Range

Mean

1-6 1-6 1-6 1-6 1-6 0-3 0-3 1-5 1-5 1-5 1-5 1-5 1-5 1-5 1-4 0-4 0-4 0-4

1.68 2.98 2.59 2.74 2.13 .98 .23 1.25 1.35 2.94 3.34 3.19 3.52 3.71 1.99 1.05 .92 1

Standard Deviation .93 1.44 1.19 1.18 1 .60 .36 .33 .42 .62 .56 .48 .48 .59 .52 .69 .66 .70

Note. GPIUS2= Generalized Problematic Internet Use Scale 2, POSI= Preference for Online Social Interaction, Mood= Mood regulation, CogP= Cognitive Preoccupation, CompIU= Compulsive Internet Use, NegO= Negative Outcomes, IBAS= Internet Behavior and Attitudes Scale, SCon= Social Confidence, SLib= Socially Liberating, CBVEQ= Cyber-bullying/victimization Experiences Questionnaire, CB= Cyber-Bullying, CV= CyberVictimization, NEO-FFI= NEO Five-Factor Inventory, N= Neuroticism, E= Extraversion, O= Openness to Experiences, A= Agreeableness, C= Conscientiousness, UCLA LS= University of California, Los Angeles Loneliness Scale, Lon= Loneliness, ΒSI= Brief Symptom Inventory, Dep= Depression, Anx= Anxiety, Host= Hostility.

3.2 Group Differences Gender. One-way ANOVAs showed that females scored higher than males in Agreeableness F(1, 173) = 7.46, p ≤ .01 and Anxiety F(1, 171) = 4.53, p ≤ .05, whereas males had significantly higher scores than females in Online disinhibition (both Social Confidence F(1, 173) = 4.04, p ≤ .05 and Social Liberating scales F(1, 173) = 15.58, p ≤ .01), as well as on the Preference for Online Social Interaction F(1, 172) = 4.13, p ≤ .05. Attachment. Securely attached participants scored higher in Extraversion F(1, 172) = 17.82, p ≤ .01, Agreeableness F(1, 172) = 9.14, p ≤ .05, and Conscientiousness F(1, 172) = 9.02, p ≤ .05 than insecurely attached, whereas the latter had higher scores in Neuroticism F(1, 172) = 13.58, p ≤ .01, Loneliness F(1, 172) = 42.50, p ≤ .01, 16

ACCEPTED MANUSCRIPT Depression F(1, 170) = 17.21, p ≤ .01 and the Negative Outcomes F(1, 171) = 8.29, p ≤ .05. Internet Use. Participants who weekly used the Internet more frequently scored higher in CB F(2, 154) = 5.39, p ≤ .01, Social Confidence F(2, 154) = 7.63, p ≤ .01, and Compulsive Internet Use F(2, 153) = 7.64, p ≤ .01, than both those who used the Internet moderately and less frequently, while in terms of Cognitive Preoccupation, participants who made high Internet use had higher scores only from those students that used the Internet less frequently F(2, 153) = 7.87, p ≤ .01. 3.3 Bivariate Correlations CB was significantly correlated with CV (.66). CB was positively correlated with Neuroticism, loneliness, anxiety, hostility, online disinhibition, and all the five dimensions of Problematic Internet Use. It was negatively correlated with Agreeableness and Conscientiousness. Similar were the correlations for CV, although it was not correlated with Conscientiousness but with depression (.26) and had higher correlation with loneliness (.40).

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Table 3 Correlations among Cyber-bullying, Cyber-victimization, ICT Use, Personality, and Psychological Symptoms

2. Mood 3. CogP 4. CompIU 5. NegO 6. SCon 7. SLib 8. CB 9. CV 10. N 11. E 12. O 13. A 14. C 15. Lon 16. Dep 17. Host 18. Anx

1. POSI .41** .45** .41** .43** .59** .64** .32** .36** .22** -.13 -.12 -.15* -.10 .48** .30** .17* .21**

2

3

.46** .45** .44** .50** .38** .27** .31** .23** .01 .13 -.14 -.16* .31** .34** .36** .27**

.72** .41** .55** .31** .29** .25** .26** -.04 -.15 -.00 -.11 .17* .25** .18* .32**

4

5

.62** .49** .38** .34** .56** .37** .47** .39** .39** .21** .26** .02 -.05 -.09 .01 -.09 -.15* -.15 -.34** .23** .37** .29** .36** .28** .36** .33** .30**

6

.50** .25** .23** .13 -.02 -.15* -.03 .00 .22** .22** .15 .18*

7

8

9

10

11

12

13

14

15

16

17

.28** .30** .66** .12 .19* .28** -.07 .00 .06 -.36** -.02 -.02 .03 -.08 .12 ** ** * -.15 -.28 -.34 -.18 .28** .00 -.13 -.22** -.12 -.23** .28** -.03 .17* .33** .25** .40** .51** -.43** .07 -.41** -.36** .17* .15 .26** .58** -.37** .03 -.22** -.28** .57** .22** .33** .26** .35** -.15 .16* -.35** -.25** .35** .57** .16* .16* .25** .45** -.20** .01 -.15* -.18* .31** .61** .53**

Note. POSI= Preference for Online Social Interaction, Mood= Mood regulation, CogP= Cognitive Preoccupation, CompIU= Compulsive Internet Use, NegO= Negative Outcomes, SCon= Social Confidence, SLib= Socially Liberating, CB= Cyber-Bullying, CV= Cyber- Victimization, N= Neuroticism, E= Extraversion, O= Openness to Experiences, A= Agreeableness, C= Conscientiousness, Lon= Loneliness, Dep= Depression, Host= Hostility, Anx= Anxiety. Lon= Loneliness, ΒSI= Brief Symptom Inventory, Dep= Depression, Anx= Anxiety, Host= Hostility. * p< .05, **p< .01.

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ACCEPTED MANUSCRIPT 3.4 Regression Analysis. Two five-step hierarchical multiple regressions were run to determine the extent to which CB and CV can be predicted by the variables under study. Four blocks of independent variables were sequentially entered in the model (gender and attachment, personality traits, Internet use variables, online disinhibition, psychological symptoms). The order of the variables was based on the GAM. Although some variables did not correlate with CB/CV (e.g., Extraversion), they were nevertheless included in the regression analysis since previous studies have shown that when taken simultaneously into consideration, several factors have an interwoven effect which becomes non-significant when they are considered one by one (Kokkinos & Saripanidis, 2017). The results showed that 31% of the variance in CB was predicted by low Agreeableness, high weekly Internet use and higher levels of negative outcomes when using the Internet (Table 4). Moreover, 37% of the variance in CV was predicted by low Agreeableness, high Extraversion, high loneliness and high compulsive Internet use (Table 4). Table 4 Summary of Hierarchical Regression Analysis for variables predicting Cyber-bulling and Cyber-victimization CB B (95%CI) Step 1 Gender Att Step 2 N E A O C Step 3 Lon Anx Host Dep Step 4 Week POSI

CV R2 .08

ΔR2 .08***

.11

.03*

.02 (-.05, .10)

B (95% CI)

R2 .14

ΔR2 .14***

.20

.06***

.24

.04**

.30

.06***

.10 (.01, .20) .19 (.07, .30)*** -.27 (-.39, -.14)***

-.11 (-.21, -.01)* .15

.04** .19 (.05, .33)**

.06 (-.02, .14) .28

.13***

.06 (.01, .11)***

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ACCEPTED MANUSCRIPT Mood CogP CompIU NegO Step 5 SCon SLib

.10 (.06, .15)*** .12 (.07, .17)*** .31

.02*

.37

.07***

Note. CB= Cyber-Bullying, CV= Cyber- Victimization, Att= Attachment, N= Neuroticism, E= Extraversion, O= Openness to Experiences, A= Agreeableness, C= Conscientiousness, Lon= Loneliness, Dep= Depression, Host= Hostility, Anx= Anxiety, POSI= Preference for Online Social Interaction, Mood= Mood regulation, CogP= Cognitive Preoccupation, CompIU= Compulsive Internet Use, NegO= Negative Outcomes, Week= Weekly Internet Use, SCon= Social Confidence, SLib= Socially Liberating, * p< .05, **p< .01, ***p< .001.

4. Discussion The present study was purported to explore the association between a number of individual and contextual variables (attachment, Big Five personality traits, frequency of Internet use, PIU, οnline disinhibition, loneliness and psychopathology symptoms) and CB/CV involvement in a sample of University undergraduates, based on the GAM. Regarding the incidence of CB/CV, participants’ reported experiences were relatively infrequent. As discussed by Cowie, Bauman, Coyne, Myers, Porhola and Almeida (2013), the incidence of CB among University students may range from very low (e.g., 1%) to very high percentages (e.g., 55%). The significant difference between studies could be attributed to the CB/CV definition each researcher adopts, as well as to the assessment methods. The synthesis of each study’s sample may also affect the results; for example, as Cowie and her colleagues report (2013), if the sample includes high-risk students (e.g., students with different sexual orientation, students involved in off-line bullying and victimization, or students with disabilities), it is likely that results will report higher rates. Overall, incidence findings are comparable with those of previous studies among Greek University students (e.g., Kokkinos et al., 2014; Kokkinos & Saripanidis, 2017). Findings replicated the high correlation of CB with CV, suggesting that those students who engage in CB are more likely to get cyber-victimized. This trend has 20

ACCEPTED MANUSCRIPT been observed in many studies with adolescents, University students and other adults, probably due to the opportunity that the victim has, to avenge the perpetrator through the Internet (Akbulut & Eristi, 2011). As Kowalski et al (2014) note, based on the GAM, the ICT are essentially situational factors that are related to CB/CV participation. Indeed, the importance of Internet use patterns in terms of CB/CV involvement was highlighted in this study. Participants who (weekly) used the Internet more frequently scored higher in CB, while on a similar vein, high weekly Internet use was found to be a significant predictor of CB, and problematic Internet dimensions were predictive of both CB and CV. Although CB has been constantly linked to problematic Internet use, the role of this factor has been less discussed in terms of victimization. As it has been suggested, the victim’s online behavior affects his/her assault and thus should be the focus of relative interventions (Feinstein, Bhatia, & Davila, 2014). With respect to personality, both CB and CV were positively correlated with Neuroticism and negatively with Agreeableness, while CB was negatively correlated with Conscientiousness. In line with previous findings, both CB and CV were predicted by low Agreeableness, while additionally CV was predicted by high Extraversion. The GAM proposes that personality characteristics function as “knowledge structures” (Kowalski et al., 2014 p. 39) which navigate the individual through stored scripts and schemas and influence the situations in which the different participants (e.g., bullies, victims, uninvolved) will be drawn. As Kowalski et al. (2014) observe, although previous research has shed light on several personal factors, the link of personality traits to CB/CV involvement has not been thoroughly investigated. Although CB has been previously linked to Neuroticism, it is interesting that its predicted value became non-significant when other variables were entered in

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ACCEPTED MANUSCRIPT the regression model, thus suggesting that Internet-related variables may outperform other factors in the prediction of the phenomenon. Nevertheless, Internet-related behavior is affected by the various personality traits (e.g., Amichai-Hamburger & Vinitzky, 2010). Also, it is noteworthy that although CV did not correlate with Extraversion, it was predicted by this specific personality trait. This finding could be attributed to the interplay of several variables in the regression model (e.g., Agreeableness) and is in line with previous studies which support the interwoven effect of factors, especially personality traits, since it has been suggested that individuals with high Extraversion and low Consciousness and Agreeableness scores are likely to be victimized through online social network sites (see Kokkinos & Saripanidis, 2017). These findings show that stable predispositions seem to account for a significant amount of variance in both CB/CV, suggesting that an individual’s personality should be considered when studying both perpetrators and victims of cyber-aggression. However, it should be noted that since the scales used to measure Openness to Experience and Agreeableness had slightly lower alphas than the widely recommended, the results regarding the use of these scales should be treated with caution. As Mastor et al. (2000) argued, in collectivistic cultures (as is the case of Greece) individuals may perceive the Openness to experience factor in a different manner from those in individualistic cultures, which may be reflected in the psychometric properties of the scale. Agreeableness on the other hand has been questioned in terms of its temporal stability since it is the only big five factor to be more ephemeral than enduring (Caruso, 2000), considered more ''state'' than ''trait''. In terms of online disinhibition tendencies, although these did not remain a significant predictor after the addition of other variables in the regression model, both CB and CV had positive significant correlations with socially liberating and social

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ACCEPTED MANUSCRIPT confidence inclinations. These findings suggest that students who gain social confidence online, and find the anonymity of being online liberating, are more likely to get involved in cyber-aggression. It is notable that in this study, males had significantly higher scores than females in online disinhibition, and that such tendencies had statistically significant and high correlations with PIU dimensions. These conclusions are in line with previous research which suggest that pathological Internet users tend to be males, are more likely to gain social confidence online and to find the anonymity of being online liberating (Morahan-Martin & Schumacher, 2003). As noted in the GAM, CB is associated with several psychological and social negative outcomes for both bullies and victims (Kowalski et al., 2014). In this study, both CB and CV were positively correlated with loneliness, while high scores in this variable predicted CV. Students who tend to be lonely and suffer from sadness may find refuge on the Internet to develop relationships, and may become more vulnerable to CV. A recent study by Schenk, Fremouw and Keelan (2013), found that University cyber-bullies and cyber-bully/victims report greater psychopathology symptoms than control participants, suggesting a disparity in psychological functioning between CB/CV participants compared to their uninvolved peers. It is interesting to note that loneliness had a significant positive correlation with Preference for Online Social Interaction (POSI) and use of the Internet with the intent of altering one’s mood. These tendencies may function as vicious cycles, since, as findings from longitudinal studies suggest, the relationship between Internet use and psychological well-being might be bidirectional (van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008). Regarding the correlations of CB with loneliness, anxiety, and hostility, as the GAM proposes, if an online interaction is perceived as stressful by the user, or if the

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ACCEPTED MANUSCRIPT user regards that s/he does not have sufficient resources (social or other) to deal with it, s/he may respond with CB (Kowalski et al., 2014). Although both CB and CV had similar correlations with several variables under investigation, such as anxiety, only CV was positively correlated with depression. In line with previous suggestions, findings replicate that depressed University students are more likely to make problematic Internet use (e.g., Kokkinos & Saripanidis, 2017) and experience CV due to their vulnerabilities (van den Eijnden et al. 2006; Ybarra & Mitchell 2004). As previous findings have shown, the relationship between Internet use and psychological well-being has been found to be bidirectional (van den Eijnden et al, 2008). Finally, although tendencies for hostility were expected among cyber-bullies, results of this study showed that both CB and CV were positively correlated with hostility, thus verifying the overlap of the two behaviors at this sample. Although the high percentage of cyber-bully/victims found in many studies could be misinterpreted as playful behaviors that are not actually aimed at hurting the recipient, this high correlation of hostility with both phenomena indicates that actions may be indeed related with malicious intent. Students with hostility symptoms tend to have outbursts of anger and be irritable, which could lead them to extreme responses to provocation. A similar finding was reported by Schenk et al. (2013), who found that University cyber-bully/victims scored higher than control participants on hostility. 5. Conclusions Given the increased dependence of University students on technologies, both for academic purposes and personal communications, the nature of CB, and research on it, the present study provides evidence regarding the characteristics of the individuals involved, by examining prospective risk factors of CB/CV. The

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ACCEPTED MANUSCRIPT researchers that have proposed the examination of CB/CV under the GAM, suggest that it is useful to understand the relation of the incidents with meaningful behavioral and psychological variables; indeed, findings of this study confirm the complex interaction of CB/CV with significant personal and contextual factors (Kowalski et al., 2014) and have several implications for interventions. According to Molluzzo and Lawler (2011), incidents of CB/CV, are often underestimated by educational community leaders, resulting in increased incidence. CB episodes are perceived as a normal part of the social interaction of young people, especially University students, and the physical and mental health consequences of these experiences are underestimated, since they are considered painless. Nevertheless, studies investigating the views of University students contradict these notions, since they have been found to consider CB as a major problem that requires immediate intervention (Adamopoulou & Theologi, 2011). Similarly, Chen and Huang (2014) report that CB is one of the most important concerns of students when navigating the Internet due to its consequences. In general, the incidents may render students’ life grim and can have a negative impact on their academic achievement (Van Brunt, 2012). Overall, findings of this study indicated that CB and CV may overlap, and that both are more likely to involve students who; a) make problematic and uninhibited Internet use, b) have specific personality traits and c) face various social difficulties and mental health problems. Along with previous evidence, these findings could be taken into consideration by University authorities to prevent and battle CB incidents. This need is especially vital in Greece, where regardless the incidence and consequences of the phenomenon, relative initiatives are non-existent. As Kowalski et al (2014) note, a strong theoretical base is crucial for designing intervention programs

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ACCEPTED MANUSCRIPT which effectively address influential personal and environmental factors such as those that were indicated in the present study. Regarding the overlap of CB with CV, University counsellors should keep in mind that victims could become bullies as well, and that bullies are also vulnerable to counterattacks (Zalaquett & Chatters, 2014). Victims in specific, as shown by previous studies, experience a range of negative emotions which could lead them to retaliation and revenge (Cassidy et al., 2017). Therefore, University counsellors could aim at assisting victims in the effective coping of the provocations (e.g., provide them with clear guidelines and avenues of reporting the incident and offer them support; Cassidy et al., 2017) and bullies towards achieving satisfying social relations in an acceptable manner. Since counterattacking bullies may be a dysfunctional coping strategy that students have adopted long before entering the University, it is crucial that a collaboration is made with high school counselors in order to devise joint actions (e.g., spread awareness during University orientation visits, organize workshops on CB for students and parents) (Zalaquett & Chatters, 2014). In addition, as Schenk and Fremouw (2012) propose that discussion with friends may also serve as a functional coping strategy. Self-control can be an important skill in preventing and dealing with CB incidents, since according to Higgins, Ricketts and Vegh (2008), University students with high self-control are able to identify and avoid relative dangers. Therefore, in this sense, prevention and intervention efforts could first target the whole organization, and afterwards individual students who make PIU and possess certain personality traits and face various social and psychological difficulties. One of the most significant conclusions of this study relates to PIU since involvement in CB/CV was predicted by several of its dimensions. As Englander (2008) says, although most young people can use new technologies, they are unable to

26

ACCEPTED MANUSCRIPT protect themselves when faced with cyber-threats such as CB. Based on these findings, University authorities could aim at the prevention of the incidents through appropriate ICT use. Proper online social conduct should be included in the University syllabus (Dickerson, 2005), while University personnel at all levels ought to be knowledgeable regarding ICT use (Cassidy et al., 2017). Counsellors in specific, could effectively prevent and address improper conduct, by being aware of the relative regulations on Internet crimes (Tezer, 2017) and by being familiar enough with technology to be able to recommend relative technological strategies (e.g., blocking, reporting) (Myers & Cowie, 2017). Technological help could also be provided by the University’s IT department (e.g., helping the victim uncover the identity of an anonymous bully). Also, counsellors could educate freshmen students regarding the University’s policies and these educational meetings could include parents and siblings, who frequently have an active role in young students’ lives (Xiao & Wong, 2013). Student awareness about proper ICT use could be also spread through posters and bulletin boards in the residence halls and in commuter lounges (Myers & Cowie, 2017). Nevertheless, PIU is not only a matter of ICT education, since patterns of Internet use are linked to pivotal personal characteristics, such as personality. For this reason, it is not surprising that involvement in CB/CV is stable in an individual’s life. Specifically, findings of previous studies indicate that individuals who have been identified as cyber-bullies or cyber-victims while in high school, tend to adopt the same roles while in University (e.g., Watts et al., 2017). These consistencies show that, since an early stage in life, students follow dysfunctional patterns of behavior that sustain the vicious cycle of aggression and victimization.

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ACCEPTED MANUSCRIPT As this study indicated, CB/CV frequently involves lonely and depressed students, difficulties that could be exacerbated when young adults transition to tertiary education. Freshmen University students face various academic and social challenges, especially if they have moved away from home and they have lost their social supporting system. Unfortunately, relative provisions are limited, recent and uncoordinated in Greek Universities. Authorities could make greater efforts in order to support freshmen University students, by providing them with tutoring regarding academic skills (in order to battle academic stress), counselling (for social skills training, anger management etc), and ample opportunities for offline social interactions (e.g., orientation day, excursions etc). Experiencing CB can worsen these symptoms, since, as previous studies showed, the incident may elicit a range of negative emotions such as sadness, stress about re-occurrence, panic attacks, anxiety, depression, humiliation, isolation, social avoidance, and even suicidal thoughts (Cassidy et al., 2017). University counselling centers have a vital role in the prevention and intervention of CB among individuals with such psychological symptoms. First, freshmen should be screened for such symptoms when they enter the University, so that they can receive support and guidance on time (Myers & Cowie, 2017). Secondly, the University should have specific procedures and policies in place regarding CB and all personnel should be educated in order to recognize its importance (Cassidy et al., 2017). Finally, the counselling center of the University should be properly prepared (in terms of staff, awareness and procedures) to help the victim address the negative feelings and thoughts (Cassidy et al., 2017). It is worth mentioning that in the study of Faucher, Jackson, and Cassidy (2014), in which CB victims were asked what help they would like from the University to combat CB, the most frequent answer was counselling and

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ACCEPTED MANUSCRIPT support services, followed by establishing an anonymous phone-in line for reporting incidents, developing a more respectful university culture, expelling cyber-bullies, and developing a strong policy against CB. Finally, students with psychological symptoms in general and CB victims in specific could receive help from their peers who could be encouraged by University authorities to act as bystanders (Webber & Ovedovitz, 2018). Generally, peer group interventions and peer support systems involving community cohesion, support for new students, active listening, conflict resolution and problem solving have increased likelihood of success and these could be organized with the involvement of student unions (Myers & Cowie, 2017). Bullies, as this study replicated, may have high scores in socially dysfunctional characteristics, such as hostility and neuroticism, which could also be related to other forms of aggression. Although such individuals can easily be the ones to blame and punish, researchers propose that University authorities consider them as vulnerable to adverse mental health outcomes (Selkie et al., 2015). Therefore, if a student admits a CB act, the University counsellor should discuss with him the potential triggers of the behavior, as well as help him develop healthier ways to communicate and deal with conflict (Zalaquett & Chatters, 2014). This procedure should be conducted in a nonjudgmental way and with extreme caution, especially considering that University students are independent adults with freedom of speech (and not elementary/high school students that can be supervised and monitored) and that Universities have an equal responsibility towards both the victim and the perpetrator to ensure they get their education and degrees (Myers & Cowie, 2017). In conclusion, this study adds significantly to our understanding of CB/CV among University students, since as previous researchers have noted, the cyberbullying literature lacks a solid theoretical foundation (Kowalski et al., 2014).

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ACCEPTED MANUSCRIPT Although integrated theoretical models were proposed recently for the explanation of the phenomenon, evidence is still scarce among University students. Therefore, this study contributed to existing knowledge by investigating complex interactions between personal and contextual factors, based on the GAM. Findings of this study, as well as others on young adults’ involvement in problematic behaviors, are essentially important in terms of their psychosocial functioning. They show that CB and CV go hand in hand with psychological problems such as depression, anxiety and loneliness. Regardless of the direction of the relation of these variables with CB/CV, this link has been previously found in studies among University students and it is now replicated with a Greek sample. Although legal actions have been suggested as part of University authorities’ actions against CB (see Watts et al., 2017), prevention focusing on counselling services is vital, especially in countries such as Greece where a wide range or economic and social difficulties hinder young adults’ well-being. Studies, including this one, suggest that several young adults face social and psychological difficulties while studying, including CB/CV. Although CB/CV is only one of the challenges that these students face, the minimization of their incidence, as well as their confrontation is of high importance since the experiences can multiply their hardships. This study is not without limitations; the sample was convenient and, as such, originated from specific geographic regions. Future investigations in wider and randomly selected samples could replicate the findings. As already reported, this study was conducted during the fall semester of 2013-2014. However, although this could be considered as a limitation, evidence from the Hellenic Statistical Authority, which publishes data on the use of new technologies by households and their members, suggests that the use of ICT among this age group has not been

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ACCEPTED MANUSCRIPT differentiated in Greece since 2013. Specifically, in the 2013 annual report it is specified that “More than 9 out of 10 persons aged 16 – 24 access the Internet” (Hellenic Statistical Authority, 2014), while in the 2018 report, the authority posts similar percentages (approx. 93%) (Hellenic Statistical Authority, 2018). Therefore, in this respect, findings of the study should not be considered outdated. Furthermore, the use of self-report questionnaires could have resulted in socially desirable responses. Future studies could use multiple informants for the investigation of the phenomenon. In terms of the theoretical basis of the study, several, but not all factors proposed by GAM were included. Future studies could aim at investigating all factors implicated in the proposed theory (Kowalski et al., 2014). Finally, the study is crosssectional in nature, thus precluding the authors from making assumptions regarding the relation of the studied variables.

Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

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ACCEPTED MANUSCRIPT Highlights   

CB/CV correlated with personality, psychopathology, and Internet use. Agreeableness, weekly Internet use and negative Internet outcomes predicted CB. Agreeableness, Extraversion, loneliness and compulsive Internet use predicted CV.