Personal Characteristics and Contextual Factors ThatDetermine “Helping,” “Joining In,” and “Doing Nothing”When Witnessing CyberbullyingKatrien Van Cleemput*, Heidi Vandebosch, and Sara Pabian

Department of Communication Studies, University of Antwerp, Antwerp, Belgium

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .In this article, we investigated several determinants of bystanders’ reactive behaviors when confronted with cyberbullying usingself‐reported data from 2,333 Flemish 9–16 year olds. Structural equation modeling showed that adolescents that had joined in onthe cyberbullying were older, had lower levels of empathy and were more likely to have been involved in cyberbullying ortraditional bullying as perpetrators. Adolescents who had helped the victim were younger, had higher levels of empathy and weremore likely to have been a victim of cyberbullying or traditional bullying in the past months. Adolescents that did nothing whenthey witnessed cyberbullying, were also older, showed lower levels of empathy and were less likely to have been a victim oftraditional bullying. Social anxiety was not related to joining in, helping and remaining passive. In the second part of the analysis,we found that bystanders’ passive behavior could be explained in more detail by moral disengagement theory and othercontextual factors. In the discussion, the implications of the findings for research on cyberbullying are addressed. Aggr. Behav.9999:XX–XX, 2014. © 2014 Wiley Periodicals, Inc.

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Keywords: cyberbullying; bystanders; adolescents; moral disengagement; empathy

INTRODUCTION

Schools, policy makers, and researchers were firstfaced with cyberbullying, or bullying through informa-tion and communication technologies, among youngpeople around 2004. Since then, a growing amount ofresearch has focused on the prevalence of cyberbullying,the profiles of perpetrators, the outcomes for victims, thedifferences and similarities with traditional bullying(Kowalski, Limber, & Agatston, 2008; Patchin &Hinduja, 2012; Tokunaga, 2010; Veenstra, Vandebosch,& Walrave, 2012). In accordance with definitions oftraditional bullying, cyberbullying among adolescentshas been described as harmful behavior that is: (1)intentional, (2) carried out “repeatedly and over time,”and (3) takes place in an interpersonal relationshipcharacterized by an imbalance of power (e.g., Olweus,1999, p. 10; Patchin & Hinduja, 2006, p. 152; Smithet al., 2008, p. 376). There is however an on‐goingdiscussion on the implications of the involvement ofcommunication technologies with regard to the nature ofthe bullying and its impact on the victim. The bullying is,for instance, not restricted to time and space, the bully canoperate semi‐anonymously, and instances of bullying canbecome visible for a large audience (for an extensivediscussion, see: Dooley, Pyżalski, & Cross, 2009; Grigg,

2010; Menesini & Nocentini, 2009; Vandebosch & VanCleemput, 2008).Recently, in research on cyberbullying, there has been a

turn toward the prevention of cyberbullying withresearchers guiding the development and evaluation ofprograms against cyberbullying (e.g., Menesini, Nocen-tini, & Palladino, 2012; Ortega‐Ruiz, Del Rey, & Casas,2012; Paul, Smith, & Blumberg, 2010). In order to createprograms that are evidence‐based and have a high chanceof being effective, we believe that following establishedmethods that are used in the field of health promotion isnecessary.A crucial step in health promotion strategies, forinstance the interventionmappingprotocol (Bartholomew,Parcel, Kok, Gottlieb, & Fernandez, 2011), is mapping thepersonal and environmental determinants of the “un-healthy” behavior that the program planners want tochange. This study focuses on the behavior of the

�Correspondence to: Katrien Van Cleemput, Department of Communica-tion Studies, University of Antwerp, Sint‐Jacobstraat 2, 2000 Antwerp,Belgium. E‐mail: katrien.vancleemput@uantwerpen.be

Received 12 December 2012; Accepted 15 February 2014

DOI: 10.1002/ab.21534Published online XX Month Year in Wiley Online Library (wileyonli-nelibrary.com).

AGGRESSIVE BEHAVIORVolume 9999, pages 1–14 (2014)

© 2014 Wiley Periodicals, Inc.

adolescents that are confronted with acts of cyberbullyingtaking place between other adolescents, or the “by-standers”ofcyberbullying. In traditionalbullyingresearch,scholars have argued that bullying is a groupprocess (Gini,2006; Salmivalli, 2010; Sutton & Smith, 1999) character-ized by bullies aiming for social status through anti‐socialbehavior toward peers. Their negative behavior is oftenreinforced by bystanders who (seem to) approve their actsby joining in or remaining silent. This explains whyintervention programs (primarily) aiming at changingbystanders’ behavior prove to be effective in reducingbullying (Polanin, Espelage, & Pigott, 2012; Salmivalli,Kärnä, & Poskiparta, 2011). Only recently, cyberbullyingresearchers have started to explore the determinants ofbystanders’ reactive behaviors (Barlińska, Szuster, &Winiewski, 2013; Freis&Gurung, 2013;Li&Fung, 2012;Macháčková, Dedkova, Sevcikova, & Cerna, 2013).The aim of this study is to further investigate the

personal and context‐related determinants of differentpossible reactive behaviors of bystanders of cyberbully-ing: “joining in,” “helping the victim,” or “doingnothing.” The analyses in this article were performedon a dataset that was collected among 2,333 Flemish 9–16 year olds in the context of an ongoing longitudinalstudy on cyberbullying perpetration and victimization. Ithowever also contained several questions on witnessingcyberbullying and different general personality traits,that could be important for bystander behavior as well.

BYSTANDER BEHAVIOR IN TRADITIONALBULLYING AND CYBERBULLYING

Following the classification of bullying roles bySalmivalli et al.’s well‐known distinction of bullyingroles (Salmivalli, Lagerspetz, Björkqvist, Österman, &Kaukiainen, 1996; Salmivalli et al., 2011), bystanders canprovide support to the victim, assist/reinforce the bully orignore/do nothing about the bullying. Research ontraditional bullying and cyberbullying has shown thatbystanders often remain passive (Atlas & Pepler, 1998;Hawkins, Pepler, & Craig, 2001; Huang & Chou, 2010).In the literature on traditional bullying, bystanders’reactive behaviors have been explained by referring topersonality traits, socio‐psychological mechanisms, andcontextual factors (Gini, Albiero, Benelli, & Altoe, 2008;Oh & Hazler, 2009; Pöyhönen, Juvonen, & Salmivalli,2010, 2012; Thornberg & Jungert, 2013; Tsang, Hui, &Law, 2011). To our knowledge, in only three studies,similar factors with regard to cyberbullying have beeninvestigated. First, Barlińska et al. (2013) conducted threeexperiments among Polish 11–18 year olds. Their mostimportant findings were that public violence was morelikely to entice negative bystander behavior (¼activelytaking part in bullying) than private violence, and that

previous involvement in cyberbullying as perpetrator wasrelated to negative bystander behavior. Empathy activa-tion (by letting students watch a video of a victim ofcyberbullying) reduced negative bystander behavior.Second, in their survey study among 156 Czech children(12–18 year olds), Macháčková et al. (2013) found thatsupporting a victim of cyberbullying was predicted by ageneral tendency toward prosocial behavior, existingrelationships with the victim, upset feelings evoked bywitnessing cyberbullying and direct requests for help bythe victim. Fear of intervening played no role. Finally, intheir survey among 545 Taiwanese junior high students,Huang andChou (2010) askedwhy the bystanders reactedto cyberbullying in a certain way. Most students did notreport the cyberbullying and the most important reasonswere that they considered the cyberbullying not theirbusiness or no big deal, that the people involved were nottheir friends, and that reporting to parents was useless orwould get them in trouble.

SOCIO‐DEMOGRAPHIC AND PSYCHOLOGICALPREDICTORS OF BYSTANDERS’ BEHAVIOR

Age and Gender Differences

With regard to demographic factors, studies ontraditional bullying have shown that younger childrenare more willing to help victims of bullying than olderadolescents (Bellmore, Ma, You, & Hughes, 2012; Rigby& Johnson, 2006; Rigby & Slee, 1991). Bellmore et al.(2012) suggest several reasons why early adolescents areless likely to intervene than primary school children: theyare less likely to report thebullying toanadult because theyvalue independence more highly in this developmentalperiod, and they are more sensitive to acceptance by theirpeer groupwhich inhibits them from “taking sides.”As forgender differences, girls seem to bemorewilling to defendvictims of bullying than boys (Goossens, Olthof, &Dekker, 2006; Sainio, Veenstra, Huitsing, & Salmivalli,2010; Sutton & Smith, 1999). With regard to cyberbully-ing, in both the studies by Macháčková et.al. (2013) andBarlińska et al. (2013) gender was not a significantpredictor of “supportive” and/or “negative” bystanderbehavior. Age was only included in Macháčková et al.’sstudy (2013), and also did not significantly predictsupportive behavior of bystanders. Based on these results,we expect that there are no significant age and genderdifferences for each of the reactive behaviors.

H1a: Gender is not a significant predictor of joining inwhen witnessing cyberbullying.

H1b: Gender is not a significant predictor of helpingwhen witnessing cyberbullying.

H1c: Gender is not a significant predictor of doingnothing when witnessing cyberbullying.

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2 Van Cleemput et al.

H2a: Age is not a significant predictor of joining inwhen witnessing cyberbullying.

H2b: Age is not a significant predictor of helpingwhen witnessing cyberbullying.

H2c: Age is not a significant predictor of doingnothing when witnessing cyberbullying.

Empathy

Several psychological characteristics have been associ-ated with bystanders’ reactive behaviors for traditionalbullying. A first, well‐documented, characteristic isempathy. Empathy can be described as “a fundamentalsocial skill which allows the individual to anticipate,understand, and experience the point of view of the otherpeople” (Davis & Franzoi, 1991, p. 73). Empathy is amultidimensional concept that refers to bothcognitiveandaffective aspects (Davis, 1994; Davis & Franzoi, 1991;Eisenberg & Fabes, 1991; Olweus & Endresen, 2001).Olweus and Endresen (2001) distinguish three aspects:perspective taking (referring to the cognitive processesinvolved in understanding the internal state of others),empathic concern (referring to feelings of sympathy,compassion, and concern), and empathic distress orpersonal distress (referring to feelings of discomfort,uneasiness, and distress) (Olweus & Endresen, 2001,p. 371).Of these three aspects, empathic concern has beenidentified as an important predictor of bullying perpetra-tion (Caravita, Di Blasio, & Salmivalli, 2009; Jolliffe &Farrington, 2006; Stavrinides, Georgiou, & Theofanous,2010) and cyberbullying perpetration (Ang&Goh, 2010;Renati, Berrone,&Zanetti, 2012; Schultze‐Krumbholz&Scheithauer, 2009; Steffgen, König, Pfetsch, & Melzer,2011). With regard to bystanders’ behavior, it is thereforeexpected that low empathic concern will also be apredictor of “joining in” cyberbullying.

H3a: Empathic concern is a negative predictor of‘joining in’ by adolescent bystanders whenwitnessing cyberbullying

For “defending” or “helping” the victim, researchershave, on the contrary, found that adolescents who self‐nominate (or are nominated) more often as defenders ofvictims are more empathic (Bellmore et al., 2012; Giniet al., 2008; Nickerson, Mele, & Princiotta, 2008;Pöyhönen et al., 2010). Since we did not find studies thathave investigated this relationship for cyberbullyingspecifically, H3b is based on the findings in traditionalbullying research.

H3b: Empathic concern is a positive predictor of‘helping the victim’ by adolescent bystanderswhen witnessing cyberbullying

The relationship between remaining passive andempathy is less clear. Nickerson et al. (2008) foundlower levels of empathy for “outsiders” than “defenders”while Gini et al. (2008) found that bystanders thatremained passive did not have lower levels of empathythan those that helped the victim.

RQ1: To what extent is empathic concern a predictorof “doing nothing” by adolescent bystanders whenwitnessing cyberbullying?

Social Self‐Efficacy and Social Anxiety

Social‐self efficacy and social anxiety have also beenstudied often in relation to bullying and bystanderbehavior. Several studies have shown that adolescentswho tend to help victims of bullying have a higher levelof social self‐efficacy (Caravita, Pöyhönen, & Salmivalli,2009; Pöyhönen et al., 2012; Warden & Mackinnon,2003). Self‐efficacy refers to “people’s beliefs about theircapabilities to exercise control over their own level offunctioning and over events that effect their lives”(Bandura, 1991, p. 257). Social self‐efficacy morespecifically relates to people’s beliefs in their abilitiesto act in social situations, for instance when confrontedwith bullying. While Bandura (1991) originally used theterm in relation to a specific context, self‐efficacy canalso be used in a more general way and refer to apersonality trait (Shelton, 1990).In our dataset, no information is available on social

self‐efficacy, but the dataset does contain informationon social anxiety, a concept that is highly related tosocial self‐efficacy (Gaudiano & Herbert, 2006; Rudy,Davis, & Matthews, 2012). Social anxiety consists ofdifferent aspects: “fear of negative evaluation,” “socialavoidance and distress,” and “social avoidance specificto new situations or new people” (La Greca & Lopez,1998). Because of the inverse relationship betweensocial self‐efficacy and social anxiety, it is expected thatadolescents who tend to defend the victim have a lowerlevel of social anxiety. Not reacting is expected to berelated to a higher level of social anxiety. Therelationship between “joining in” and social anxietyis less clear. Bullies tend to have lower levels of socialanxiety (Craig, 1998). However, it is possible thatpeople find it difficult to resist the bully’s or the grouppressure to join in because they fear being evaluatednegatively by the bully, or because they want to avoidfurther social conflict. We should also mention, thatsocial anxiety is often discussed in the context ofvictimization. Researchers suggest there is a circularrelation between victimization and social anxiety:adolescents that are socially anxious are more oftenchosen as targets by bullies, and being victimized

Aggr. Behav.

Bystander Behavior in Cyberbullying 3

increases their social anxiety (Craig, 1998; Juvonen,Graham, & Schuster, 2003).

RQ2: To what extent are “joining in,” “helping thevictim,” or “doing nothing” predicted by therespondents’ level of social anxiety?

Involvement in Cyberbullying and TraditionalBullying as Perpetrator or Victim

Finally, tested whether involvement in cyberbullyingand traditional bullying as a perpetrator or as a victimpredicts bystander behavior. In the study by Barlińskaet al. (2013), previous involvement in cyberbullying asperpetrator was related to joining in with the perpetrator.

RQ3a: To what extent are “joining in,” “helping thevictim,” or “doing nothing” related to involvementin cyberbullying as perpetrator?

RQ3b: To what extent are “joining in,” “helping thevictim,” or “doing nothing” related to involvementin traditional bullying as perpetrator?

RQ4a: To what extent are “joining in,” “helping thevictim,” or “doing nothing” related to involvementin cyberbullying as victim?

RQ4b: To what extent are “joining in,” “helping thevictim,” or “doing nothing” related to involvementin traditional bullying as victim?

SOCIO‐COGNITIVE PROCESSES INBYSTANDER BEHAVIOR

While people with certain personality traits do seemmore prone to defend the victim, ignore the bullying, oreven join in, in the literature, more attention has been paidto the socio‐cognitive processes that explain why peoplepassively standby in specific situations. Researchers havefirst referred to theories on the bystander effect. “Thebystander effect” refers to the observation that people areless likely to help a person in distress if more otherwitnesses are present (Fischer et al., 2011; Latané &Darley, 1970; Latané & Nida, 1981). Latané and Darley(1970) originally identified three psychological mecha-nisms that could lead to the effect: (1) diffusion ofresponsibility (tendency to divide the personal responsi-bility to help by the number of bystanders), (2) evaluationapprehension (fear of being judged by others when actingpublically), and (3) pluralistic ignorance (tendency torely on the overt reactions of others present, for example,if they do not react, also stay passive). Research onoffering help through E‐mail messages and in chat boxesindicates that this effect also holds in online environ-ments (Barron & Yechiam, 2002; Blair, Foster Thomp-son, & Wuensch, 2005; Markey, 2000).

A second socio‐cognitive process that is often referredto is “moral disengagement.” Moral disengagementstems from social cognitive theory (Bandura, 1991;Bandura, Barbaranelli, Caprara, & Pastorelli, 1996), andrefers to the socio‐cognitive process through whichpeople rationalize and justify harmful acts against others.Bandura describes eight types of rationalizations thatlead to moral disengagement (Bandura, 1991, pp. 32–35;Obermann, 2011). Moral justification means that anti‐social conduct is made acceptable by portraying it asserving a higher purpose. Euphemistic labeling refers tothe process by which anti‐social conduct is labeled insuch a way that it is made more acceptable. One could forinstance label bullying behavior as “teasing.” Whenbehavior is contrasted with behavior that is even worse, itis called advantageous comparison. Displacement ofresponsibilities means that people ascribe the responsi-bility to another person, diffusion of responsibilitymeansthat when an individual feels part of a larger group, he canfeel as if he is only partly responsible. Anotherdisengagement practice is distortion of consequences,and refers to minimizing the harm that is inflicted toothers. Finally, dehumanization refers to treating peopleas less than humans (who do not deserve to be treatedmorally right) and attribution of blame refers to accusingthe victim of provoking the behavior. While moraldisengagement is commonly used as a framework toexplain bullying perpetration (online and offline) (seee.g., Almeida, Correia, & Marinho, 2009; Menesini,Codecasa, Benelli, & Cowie, 2003; Pornari & Wood,2010), in his study on passive bystander behavior,Obermann (2011) found that passive bystanders ofbullying also tend to morally disengage.

RESEARCH QUESTIONS

RQ5: What reasons do adolescents provide forremaining passive when confronted with onlinebullying as bystander?

RQ6:How canwe understand these reasons departingfrom theories on moral disengagement?

METHODS

Sample

In October–November 2011, we conducted a surveyamong 519 primary school children (9 to 11 year‐olds)and 1,814 students from secondary education (12–16year‐olds) in Flanders. The respondents were selected onthe basis of a stratified random cluster sample. Thesampling criteria were grade and type of schooling(general, technical, and vocational secondary education).

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4 Van Cleemput et al.

Because of an overrepresentation of certain years andtypes of schooling, all results were weighted based on theproportions of type of schooling per grade in thepopulation. The surveys were administered at school,during school hours in the presence of a researcher.Informed consent was obtained from the respondents andtheir parents before the survey was administered. Eightstudents did not receive parental consent and did notparticipate in the study.

MeasuresObserved variables. The questionnaire consisted

of different sections in which adolescents were askedabout their experience with traditional bullying andcyberbullying, but also about their general Internet andmobile phone use, socio‐demographic and personalitytraits. We explained to the respondents what was meantwith the term “bullying” (“pesten” in Dutch): “We call itbullying when people say or do mean or hurtful thingsmore than once, when the bully wants to make the otherperson feel bad and when it is difficult for the victim todefend himself or herself. Friends teasing each other isnot bullying.” We then explained them that bullying canalso occur through the internet and mobile phones,provided some examples, and asked them how often theybullied, were bullied and witnessed bullying through theinternet and mobile phones in the past 6 months. Thestudents could answer “never,” “once in the past 6months,” “a few times in the past 6 months,” “once amonth,” “a few times a month,” and “several times aweek.” The students that indicated that they hadwitnessed bullying at least once in the past 6 months,were directed to several other questions. First, they wereasked who the victim was (or victims were), what hadhappened and through which technology(s) the bullyingoccurred. Then they were asked how they had reacted tothe bullying by choosing between: “I joined thebullying,” “I helped the victim,” and “I did nothing.”Finally they were asked why they had done nothing.They could choose between several pre‐indicatedanswers, or give another reason in an open field.Latent variables. Question batteries using Likert

scales with four categories were used for measuringempathy, social anxiety, and impulsiveness. Empathywas measured using six items of Olweus and Endresen’s(2001) empathic responsiveness scale, referring toempathic concern. Cronbach’s alpha for this scale was.883. This scale does not include cognitive aspects ofempathy. The items referring to personal distress werenot used as it has been shown that personal distress isconceptually different from empathy. In experiencingpersonal distress the individual focuses on his or her ownaffective state (“self‐focus”) instead of on the person thatis suffering (“other‐focus”) (Davis, 1994; Eisenberg &

Fabes, 1991, p. 37; Eisenberg & Strayer, 1990, p. 7).Social anxiety was measured using the Social AnxietyScale for Adolescents (SAS‐A: La Greca & Lopez,1998). This scale consists of three factors: Fear ofNegative Evaluation (FNE), Social Avoidance andDistress in General (SAD‐G), and Social Avoidancespecific to new situations or unfamiliar peers (SAD‐NEW). One item “I only talk to people I know reallywell” did not load sufficiently high on factor SAD‐NEW(<.300) and was not included in further analysis.Cronbach’s alpha was .895 for the FNE scale and, .783for the SAD‐NEW factor, and .723 for the SAD‐G factor.The measurement model was tested inMplus 6 (Byrne,

2012; Muthén & Muthén, 2010) using confirmatoryfactor analysis. The items were treated as categoricalvariables. Therefore weighted least square parameterestimates were used (WLSMV estimator). We predictedthat social anxiety consisted of three factors: FNE, SAD‐G, and SAD‐NEW, and that empathic concern was a one‐dimensional construct. Empathic concern and socialanxiety were allowed to covary. The results indicated agood fit for the measurement model, except for theChi‐square (due to its sensitivity to sample size):comparative fit index (CFI)¼ .964, root mean squareerror of approximation (RSMEA)¼ .052, [.049–.054],x2¼ 1980.25 (df¼ 249, P¼.00). The factor loadings arepresented in Table I. Missing data were handled usingmaximum likelihood estimation.

RESULTS

General Results

With regard to involvement in cyberbullying, 11.1% ofthe respondents indicated that they had been bullied atleast once in the past 6 months. The exact samepercentage (11.1%) indicated that they had bulliedsomeone in the same time period. There was an overlapbetween cyberbullying perpetration and victimizationwith 3.8% of the respondents indicating that they hadbeen both a bully and a victim in the past 6 months. Amuch higher figure (35.2%) was found for havingwitnessed cyberbullying during the past 6 months. Whenasked for the technologies through which they sawbullying occur,1 67.4% of respondents who answered thisquestion (N¼ 808) indicated that they witnessed bully-ing on social network sites, 51.0% indicated that they sawpeople being bullied through short message system(SMS), and 31.2% witnessed bullying on InstantMessaging. Other technologies were indicated less asvenues for cyberbullying: e‐mail (15.9%), online games

1The respondents could indicate multiple responses.

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Bystander Behavior in Cyberbullying 5

(11.8%), mobile phone voice calls (9.8%), fora or blogs(6.3%), Youtube (6.0%), and other applications (5.0%).Of cyberbullying witnesses, 65.4% of the respondents

who answered this question (N¼ 815) indicated that thevictim(s) were other students in their school. With regardto the way they reacted to the cyberbullying, only a smallpercentage (4.6%) indicated that they had (on at least oneoccasion) joined the bully, 45.1% indicated that they hadhelped the victim and the largest group (58.6%) remainedpassive (Table II).In total, only 64 of the 796 respondents that answered

this question, indicated to have engaged in two (N¼ 62)

or three (N¼ 2) behaviors. The largest overlap (N¼ 47)was between joining in the bullying and doing nothing,six respondents indicated that they had both joined in andhelped when they witnessed cyberbullying in differentoccasions and 16 respondents both helped and remainedpassive. The reasons the students provide for “doingnothing,” are discussed below.

Part One: Structural Models ExplainingBystander Behavior

We tested the structural model that was presented inFigure 1 for each of the bystander behaviors in Mplus6.2

Only the respondents who indicated that they hadwitnessed cyberbullying were included in the analysis(N¼ 836). We used WLSMV (weighted least squareparameter estimate) as estimator. Descriptive statisticsand zero‐order correlations for the study variables arepresented in Table III. Table IV displays the coefficientsof the structural models. The estimates for the paths fromgender and age to empathy and social anxiety were thesame for the three models and are therefore displayedonly once. The coefficients in Table IV for the bystanderbehaviors (binary variables) are probit regressioncoefficients. Missing values were handled using maxi-mum likelihood estimation.The fit indices showed a good fit for each of the models

(Table V), except for the Chi‐square (due to its sensitivityto sample size).Age and gender. Our hypotheses with regard to

gender (H1a‐c) were supported: gender was not asignificant predictor of “joining in,” “helping,” or “doingnothing.” Age, in contrary to what we hypothesized(H2a‐c) did predict each of the bystander behaviors: olderadolescents were more likely to join in on the bullying,more likely to do nothing and less likely to help thevictim.Emphatic concern and social anxiety. H3a

and H3b were supported: adolescents that showed lowerlevels of emphatic concern were more likely to join in onthe bullying, while adolescents with higher levels ofemphatic concern were more likely to help the victim.With regard to RQ1, model 3 showed that doing nothingwas predicted by a lower level of emphatic concern.Social anxiety was not related to any of the bystanderbehaviors (RQ2).Involvement in cyberbullying and traditional

bullying. Research questions RQ3a‐b and RQ4a‐brelated to the relationship between involvement incyberbullying and traditional bullying on the one handand bystander behavior on the other hand. Adolescents

TABLE I. Standardized and Unstandardized RegressionCoefficients for the Confirmatory Factor Analysis of EmpathicConcern and Social Anxiety

Observed Variable Latent Construct b B SE

Item 1 Empathic concern .84 1.00Item 2 .86 1.06 .01Item 3 .76 0.90 .02Item 4 .77 0.92 .02Item 5 .89 1.07 .01Item 6 .81 0.96 .01Item 7 Fear of negative evaluation (FNE) .76 1.00Item 8 .83 1.09 .02Item 9 .75 0.98 .02Item 10 .67 0.88 .02Item 11 .80 1.06 .02Item 12 .86 1.13 .02Item 13 .69 0.91 .02Item 14 .85 1.12 .02Item 15 SAD‐NEW .69 1.00Item 16 .69 1.00 .03Item 17 .78 1.12 .03Item 18 .64 0.92 .04Item 19 .74 1.07 .03Item 20 SAD‐G .71 1.00Item 21 .71 1.01 .02Item 22 .64 0.91 .02Item 23 .69 0.98 .02FNE Social anxiety .76 0.80SAD‐NEW .89 0.62SAD‐G .94 0.67

TABLE II. Table of Frequency for Multiple Response Set ofReactive Behaviors When Confronted With Cyberbullying asBystander

Behavior

Responses

Percent of CasesN %

Join in 37 4.2 4.6Help 359 41.7 45.1Do nothing 467 54.1 58.6Total responses 863 100 108.4Total respondents 798

2The three bystander behaviors could not be integrated in one structuralmodel because of linear dependency between these behaviors.

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6 Van Cleemput et al.

who bullied online or offline were more likely to join inwhen they witnessed cyberbullying. Victims of cyber-bullying and traditional bullying were more likely to helpother victims. Finally, doing nothing was negativelypredicted by being a victim of traditional bullying.

Part Two: Reasons for Remaining PassiveWhen Witnessing Cyberbullying

As mentioned above, more than half of the studentswho had witnessed cyberbullying in the past 6 monthsindicated that they had remained passive. These studentswere also asked why they had done nothing when

confronted with bullying. The researchers provided sixpossible answers and also gave the respondents theopportunity to indicate a different or additional reason inan open question. Of the possible reasons that wereprovided by the researchers, “because it is not mybusiness,” “because I could get bullied myself,” and“because I did not know how to help the victim”were thethree most indicated answers (Table VI).Through the open‐ended question, 93 respondents

provided a reason (or an additional reason) as to why theydid nothing when someone was being cyberbullied. Theanalysis consisted of an open coding phase and an axial

Fig. 1. Structural model with hypotheses and research questions. Schematic representation of the structural model for bystander behavior withhypotheses and research questions. This model will be tested for each of the three bystander behaviors. Latent constructs are shown in ellipses, andobserved variables are shown in rectangles.

TABLE III. Descriptive Statistics and Zero‐Order Correlations for Indicator Variables

Variable 1 2 3 4 5 6 7 8

1. Sex (girl¼ 1)2. Age .10��

3. Social anxiety .05 �.064. Empathy .26�� �.22�� .14��

5. CBP �.02 .05 �.04 �.15��

6. CBV .07� �.16�� .16�� .07� .16�

7. TBP �.11�� .01 .00 �.19� .52�� .068. TBV .02 �.26�� .25�� .09� .18� .43�� .22�

9. Join in �.08 .12�� �.03 �.16�� 0.38� �.04 .37�� .0010. Help .09� �.21�� .05 .35�� �.09� .20�� �.14�� .17��

11. Do nothing �.10�� .13�� �.02 �.27�� �.04 �.16�� .01 �.18��

M 0.61 13.51 2.24 3.07 1.35 1.36 1.62 1.71SD 0.48 1.53 0.54 0.56 0.85 0.91 1.06 1.26Range 0–1 9–17 1–4 1–4 1–6 1–6 1–6 1–6

Note. CBP, cyberbullying perpetration; CBV, cyberbullying victimization; TBP, traditional bullying perpetration; TBV, traditional bullying victimization.�� P<.01, �P<.05.

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Bystander Behavior in Cyberbullying 7

coding phase, in accordance with the first two analyticalphases in the Grounded theory approach3 (Forrester,2010; Holliday, 2007). The data were coded by tworesearchers and codes were adjusted until agreement wasreached. In Table VII, the main codes and sub codes thatresulted from the axial coding phase are presented.Throughout the answers provided by the researchers

and in the open answers, we found illustrations of four ofthe mechanisms of moral disengagement that wereoutlined in the introduction. The first mechanism isdiffusion of responsibility. In the options provided by the

researchers, it became apparent that a significantproportion of the students did not feel personallyresponsible for reacting against the cyberbullying. Inthe fixed answers, the most indicated response was“because it is not my business” (49% of respondentsindicated this as one of the reasons for not intervening).A second mechanism, that was illustrated by the

TABLE IV. Standardized Coefficients of the Structural Models

Standardized Coefficient Coefficient SE z‐Score P‐Value R2

Probit regression coefficients for the bystander behaviorsModel 1: Join in .34Sex (girl¼ 1) �.18 �.39 .21 �1.87 NSAge .25 .16 .07 2.12 .03Empathic concern �.23 �.28 .13 �2.12 .03Social anxiety .13 .14 .13 1.10 NSCBP .16 .19 .08 2.57 .01TBP .19 .20 .06 3.33 .00CBV �.16 �.19 .12 �1.63 NSTBV �.02 �.02 .08 �0.21 NS

Model 2: Help .36Sex (girl¼ 1) .00 .01 .11 0.07 NSAge �.21 �.13 .03 �3.66 .00Empathic concern .42 .51 .07 7.51 .00Social anxiety .01 �.01 .07 �1.91 NSCBP .01 �.01 .08 �0.15 NSTBP �.12 �.12 .07 �1.78 NSCBV .18 .21 .06 3.41 .00TBV .12 .10 .05 2.18 .03

Model 3: Do nothing .19Sex (girl¼ 1) �.02 �.03 .12 �0.26 NSAge .13 .08 .04 2.19 .03Empathic concern �.32 �.36 .07 �5.13 .00Social anxiety .01 .01 .01 0.83 NSCBP �.08 �.10 .08 �1.20 NSTBP .07 .07 .07 1.10 NSCBV �.09 �.10 .06 �1.76 NSTBV �.16 �.13 0.05 �2.85 .00

Linear regression coefficients for social anxiety and emphatic concern (same for model 1, 2, and 3)Social anxiety .03Sex .16 .31 0.10 3.25 .00Age �.07 �.05 0.03 �1.53 NS

Empathic concern .20Sex .30 .54 .08 6.91 .00Age �32. �.16 .02 �7.17 .00

Note. Probit regression coefficients give the change in the z‐score for a one unit change in the predictor.

TABLE V. Fit Indices

x2 df CFI RMSEA (90% CI)

Join in 784.54, P¼.00 386 .967 .036 [.032–.039]Help 784.19, P¼.00 386 .967 .036 [.032–.039]Do nothing 782.40, P¼.00 386 .967 .036 [.032–.039]

Note. CFI, comparative fit index; RSMEA, root mean square error ofapproximation.

3Grounded theory is typically and inductive approach to theory. In thisapproach, the two steps that were taken in this study are followed by atheory construction phase. In this study, theory construction is not the maingoal.

Aggr. Behav.

8 Van Cleemput et al.

respondents, was displacement of responsibility: 13.0%of the respondents explicitly put the responsibility to actwith the victim himself or herself. Third, in the openanswers, five students indicated that the victim hadprovoked the bullying. This is in line with the moraldisengagement strategy attribution of blame. The fourthmoral disengagement strategy that was illustrated in theopen answers was distortion of consequences: severalstudents indicated that they did not intervene becausethey thought the bullying was not serious.In addition to these moral disengagement strategies,

the students also provided other reasons why they didnot intervene. These reasons are mostly in line withprevious studies on traditional bullying and cyberbully-ing that asked bystanders specifically about their reasonsfor not intervening (Cappadocia, Pepler, Cummings, &

Craig, 2012; Huang & Chou, 2010). First, a largenumber of respondents indicated that they did notinterfere out of fear of being bullied themselves (31.8%of respondents indicated this as one of the reasons fornot intervening). Also in the open answers severalrespondents indicated that they feared becoming avictim themselves. Second, in line with Cappadociaet al.’s study (2012), a number of respondents alsoindicated that they lacked the knowledge, skills or self‐efficacy to help the victim: 30.6% indicated that they didnot intervene “because I did not know how to help thevictim” and 15.4% “because I cannot help the victim.”Third, the reason that was the most often referred to inthe open questions was “I don’t know the victim.” Asimilar argument could be found in Huang and Chou’sstudy (2010) on cyberbullying where participants stated

TABLE VI. Table of Frequency for Multiple Response Set of Six Reasons for “Doing nothing” When Confronted WithCyberbullying

Responses

Percent of CasesN %

“I did nothing when I saw someone being bullied in the past 6 months…because it is not my business” 260 31.1 49.0because I could get bullied myself” 169 20.1 31.8because I did not know how to help the victim” 163 19.4 30.6because of another reason:…” 95 11.4 18.0because I cannot help the victim” 82 9.8 15.4because it is the victim’s responsibility to act” 69 8.2 13.0

Total responses 838 100 157.9Total respondents 531

Note. The respondents could indicate multiple reasons.

TABLE VII. Main and Sub Codes After Axial Coding of Open Answers

Main Category Sub Category Example

Difficult to interpret situation Don’t know who started (3), mutual fighting(2) don’t know why victim was bullied (2)

Girl, age 14. “Because you never know how itall started”

Did not want to get involved (5) Boy, age 11. “I didn’t want to meddle in”It was not necessary to get involved Victim can defend himself (4), bullying not

serious (4)Girl, age 11. “Because it was not so serious

and the person that was bullied is able tostand up for himself”

Out of fear Fear of losing friends (1), fear to become avictim myself (2)

Because of relationship with perpetrator Know perpetrator (1), don’t know perpetrator(4), like perpetrator (2)

Girl, age 15. “It would pass anyway after awhile, and it were my friends who did it,so… ”

Because of relationship with victim Don’t know victim (32), dislike victim (6) Girl, age 14. “I did not know her. I just saw iton Facebook. It felt sorry for this person,but I did not even know in which schoolshe is”

Timing Someone else already intervened (2), notaware of the bullying at moment it tookplace (6)

Boy, age 12. “The teacher had already dealtwith the problem before I arrived at schooland knew about it”

Victim’s behavior Victim provoked it (5), agree with bullies (2) Girl, age 15. “The bullies were right, I just didnot join them”

Aggr. Behav.

Bystander Behavior in Cyberbullying 9

that they did not intervene because the people involvedwere not their friends.Two additional reasons for not intervening that were

expressed by the respondents in this study were related totiming and assessment of the situation. Five students alsoindicated that they did not react because they found itdifficult to assess the situation. They for instance wrotethat they did not know how the bullying started, or who“caused it.” With regard to timing, some studentsindicated that they became aware of the bullying whena teacher was already involved (two students) or when thebullying had already stopped (four students).

CONCLUSION AND DISCUSSION

The goal of this article was to increase currentknowledge on the determinants of different reactivebehaviors of bystanders of cyberbullying: “joining in,”“helping the victim,” and “doing nothing.”While similarresearch questions have been formulated for traditionalbullying, research on reactive behaviors for cyberbully-ing is still scarce. Two main topics were central in thispaper. First, we explored to what extent sex, age,empathy, social anxiety, and previous experience withcyberbullying could explain bystanders’ behavior. Next,based on a series of closed and open questions we tried toreveal the socio‐cognitive processes and contextualfactors that determine remaining passive when witness-ing cyberbullying. In this study, 35.2% of the adolescentshad witnessed cyberbullying in the past 6 months. Whileit could be expected that a lot of students would havewitnessed bullying on social network sites (because of itssemi‐public nature), the high percentage for witnessingSMS bullying is less easy to explain. For witnessing SMSbullying, either the bully or the victim has to disclose themessage to a third person. Further research should beconducted to figure out what practices surround harmfultext messages.In the first part of our analyses, we found similar results

for age to those in studies on traditional bullying:younger adolescents in our study were more likely toindicate that they had helped the victim, while the boysand older adolescents were more likely to indicate thatthey had joined in or remained passive. In interventionprograms, we thus need to take into account that at acertain age, adolescents start to feel more inhibited abouthelping the victim (which is probably due to the greaterimportance of the peer group and their striving forindependence). In accordance with previous studies onbystander behavior in cyberbullying gender was not asignificant predictor. Empathic concern was the mostimportant predictor of each of the reactive behaviors:adolescents with high levels of empathic concern weremore likely to help victims of cyberbullying, while

adolescents with low levels of empathic concern weremore likely to join in on the bullying or remain passive. Inthis study, we did not collect data on cognitive aspects ofempathy, which is a limitation that will be commented onbelow. Social anxiety overall was not a good predictor forbystanders’ reactive behaviors. Further research shouldinclude social self‐efficacy rather than social anxiety.Adolescents who admitted that they had bullied orcyberbullied someone in the past three months moreoften had also joined in on cyberbullying incident. Beinga victim of cyberbullying or traditional bullying was apositive predictor of helping the victim. Victim oftraditional bullying were less likely to remain passivewhen they witnessed cyberbullying. Finally, it needs tobe remarked that the explanatory value of the model for“doing nothing” was much lower than that of the othertwo models. The fact that different types of people withdifferent experiences tend to do nothing when theywitness cyberbullying could be better explained by thesocio‐cognitive processes that are going on in theadolescents’ minds or by looking at context‐relatedfactors.In the second part, these socio‐cognitive processes and

contextual factors were further explored. In their answersto the question why they remained passive whenwitnessing cyberbullying, it is clear that adolescentshave very different reasons for not intervening incyberbullying. While most of these reasons are similarto those that are found in studies on traditional bullying,others can provide indications for the differencesbetween being a bystander of traditional bullying andbeing a bystander of cyberbullying that are related to theaffordances of communication technologies.Just like bystanders of traditional bullying, some

adolescents who witness cyberbullying have a tendencytomorally disengage. Four types of moral disengagementcould be illustrated by our data: diffusion of responsibili-ty, displacement of responsibility, distortion of con-sequences and attribution of blame. The respondents alsomentioned other reasons such as fear of retaliation andlack of social skills or efficacy to intervene. While thissecond reason is not specific for cyberbullying, severalinterventions in cyberbullying (such as reporting acomment or picture to the software provider) do requireadditional knowledge and skills and are sometimes noteven within reach for adolescents (it can for instance bevery difficult to stop a defamatory picture from “goingviral”).Third, the reason that was the most often referred to in

the open questions was “I don’t know the victim.” Asimilar argument could be found in Huang and Chou’sstudy (2010) on cyberbullying where participants statedthat the people involved were not their friends. This ispartly an argument that could be categorized as

Aggr. Behav.

10 Van Cleemput et al.

displacement of responsibility. However, the fact thatsome technologies, for instance social network sites,provide extra opportunities to maintain ties with whomwe are only weakly tied (Ellison, Steinfeld, & Lampe,2007; Mesch & Talmud, 2010; Van Cleemput, 2010,2012) also means that the respondents would probablynot have been confronted with the bullying if thisoccurred offline. It is possible that these instances occurtoo far out of the youngsters’ social circle for them tointervene in an effective way, without complicatingthings further. Several respondents also indicated thatthey did not intervene because it was difficult for them toassess the situation: they did not know who started it, orwhether it was just “a fight” between two otheradolescents. The lack of (non‐verbal) social cuesafforded by communication technologies (Sproull &Kiesler, 1986;Walther, Loh, & Granka, 2005) can indeedmake it more difficult for bystanders of cyberbullying toassess how a message was meant by a perpetrator and tounderstand how a victim feels about this message.Moreover, previous research on cyberbullying haspointed toward a strong connection between traditionalbullying and cyberbullying, with online and offline actsof bullying alternating, making it even more difficult forbystanders of cyberbullying to assess certain situations.A final response of the adolescents that refers todifferences between being a bystander of cyberbullyingversus being a bystander of traditional bullying is thatthey had not intervened because of the timing of theevents. Because of the asynchronous nature of severaltechnologies, it is possible to witness cyberbullying thathas taken place in the past and is already “solved,” by thetime it is witnessed. A question that might arise iswhether these respondents can be considered truebystanders of cyberbullying. The fact that these respon-dents have indicated that they have witnessed thecyberbullying implies that there is still something to bewitnessed (e.g., a page that is not taken down, a picturethat is still circulating…). In this case, they still havemultiple behavioral options: they can still join the bully(e.g., by liking or sharing the material), help the victim(e.g., provide support, provide advice on how the makethematerial disappear) and donothing.We therefore arguethat they can be considered bystanders of cyberbullying.Several limitations of this study need to be addressed.

As was indicated above, the operationalization of certainconcepts was not optimal: only affective aspects ofempathy were measured and not cognitive empathy.While in research on traditional bullying and empathy,the relationship between affective empathy and bullyingis clear, smaller or no associations are often found forperspective taking (Caravita, Di Blasio, et al., 2009;Jolliffe & Farrington, 2006; Stavrinides et al., 2010). Thissuggests that it is important for bystanders to “feel” for

the victim, and not only to understand how he or she isfeeling. For cyberbullying however, the role of cognitiveempathy should be reconsidered, especially as severalrespondents indicated that they found it difficult to assesswhether certain situation were actually cyberbullying.Next, in our dataset we could not perform analyses on

single incidents of cyberbullying because we asked therespondents about their experiences with witnessingcyberbullying in general. This made it not possible toinclude incident‐specific information (technologythrough which bullying occurred, technology throughwhich bystander reacted (or not), relationship to thevictim and bully, etc.).A final limitation is that the data rely on self‐report.

Even though we emphasized the anonymity of thequestionnaire, the issue of social desirability cannot beruled out. This issue has already been acknowledged intraditional bullying research in the past. When comparedwith figures obtained through other data collectionmethods (peer nominations and observations), figuresobtained through self‐reports are more concurrent withsocially desirable behavior. With regard to helpingbehavior (or defending behavior), the figures for self‐report are approximately between 35% and 55%(Obermann, 2011; Sutton & Smith, 1999), which is inaccordance with the 41% in this study. For observationdata (approximately 10–25%, see Hawkins et al., 2001;O’Connell, Pepler, & Craig, 1999) and peer reports,lower figures have been reported (17.3% in Salmivalliet al., 1996). In Sutton and Smith’s study (1999), only42% of the students who nominated themselves asdefenders were also peer‐nominated as defenders. Withregard to reinforcing the bully or assisting the bully, thefigures in studies using self‐reports (4.2% in this study,4% in: Nickerson et al., 2008) are lower than figuresobtained through peer nominations (19.5% reinforcersand 6.8% assistants in Salmivalli et al.’s’ study (1996))and observations (21% actively modeled bullying inO’Connell et al. (1999)).While this article provided some knowledge on

youngsters’ reactive behaviors when witnessing cyber-bullying—mainly by confirming findings from tradition-al bullying—there are still large gaps to fill in theliterature. First, what exactly it means to be a bystander ofcyberbullying, in contrast to a bystander of traditionalbullying, should be explored further. This could forinstance happen through qualitative research or bycombining data on traditional bullying and cyberbullyingin one study. Second, the technological characteristics,determine bystander behavior (for instance the number ofcues transferred, the size of the audience, synchronousversus asynchronous communication, etc.). Because ofthe multitude of technologies that adolescents use and thedifferent affordances that one technology can offer,

Aggr. Behav.

Bystander Behavior in Cyberbullying 11

experimental designs in which each of these factors areisolated or survey designs that are incidence‐based, areprobably the most suitable methods. Finally, futureresearch should look into what effects the online andoffline behaviors of bystanders of cyberbullying have onbullies’ and other bystanders’ behaviors and on theimpact of the cyberbullying on victims.

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