A social cognitive perspective in cyberbullying prevention

Vassilis Barkoukis, Lambros Lazuras & Haralambos Tsorbatzoudis

Department of Physical Education and Sport Science,

Aristotle University of Thessaloniki

Definition of cyberbullying

Nowadays, youth spend more time online than ever before. The use

of the cyber world offers young people a huge database with information

facilitating learning and exploration (Blais et al., 2008). In addition, it

provides to young people with tremendous opportunities for

communication and interaction with other people beyond the traditional

face-to-face social networks (Mishna et al., 2010). However, there are also

important risks that should be taken into account, such as cyberbullying,

online harassment and cyberstalking (Mishna et al., 2012). Online

harassment refers to isolated episodes of molestation through internet,

while cyberstalking reflects the use of electronic means, such as the

internet, to stalk an individual or a group of individuals (Brighi et al., 2009;

Wolak et al., 2007). On the other hand, cyberbullying has been defined as

the distorted and inappropriate use of electronic means, such as the internet

and the mobile phones, to repeatedly attack a person, usually defenseless,

in order to hurt him/her and cause damage to his/her reputation (Smith et

al., 2008). Since cyberbullying is a repeated behavior, an important issue

pertaining to the severity of the incidents is their frequency. Brighi et al.

(2009) summarizing prior research on the severity of the cyberbullying

suggest that in order to define the severity of the cyberbullying incidents a

relative small time frame of two months should be used, a frequency of at

least two or three cyberbullying acts in this time frame is required to judge

an incident as a serious one.

Education as a venue towards cyberbullying prevention

Cyberbullying so far has led to suicide attempts and changing of school

environment. Taking also into consideration that a) the use of electronic

means by youth increases every year increasing the number of the possible

interactions and b) children start using these technologies in smaller ages

which is associated with a less mature use of them, it is obvious that

education and prevention acts should be prepared and applied (Dooley et

al., 2009). Barkoukis and Panagiotou (2012) suggested that the school can

offer an appropriate environment for the application of prevention

intervention targeting cyberbullying mainly because a) schools offer access

to the population that is mainly involved, either as victim or as an

aggressor, in cyberbullying and b) the school’s primary objective is to

educate children and promote social and moral values. This view is also

supported by Farrell et al. (2001) who argued that school-based programs

should be included in prevention intervention strategies against youth

violence and aggression.

In addition, Daunic et al. (2006) suggested the application of

prevention interventions to schools in order to reduce risk factors in youth

and maintain their health and safety. Cullinan (2002) indicated that these

interventions should be both universal and selective. Universal

interventions target all students and aim to increase awareness and educate

them how to avoid risks and improve safety, and maintain health through

the development of healthy lifestyles. On the other hand, selective

interventions involve activities targeting groups of student at risk and aim

to deal with the specific behavior at hand. For instance, a selective

intervention could try to resolve a severe cyberbullying incident appeared

in a school and could include activities such as discussions with the bully,

discussions with the victim, role playing etc.

Several interventions have been developed to combat various forms

of aggression, such as traditional bullying (Baldry & Farrington 2004;

Olweus & Limber, 1999; Salmivalli, 2001) and cyberbullying (Knol et al.,

2012; Lazuras et al., 2012; Ortega et al., 2012; Smith et al., 2012; Wölfer et

al., 2012). The application of these interventions indicated that there were

specific aspects associated with their effectiveness. These aspects involved

a) a sound theoretical basis, b) a strong commitment from the school

(administration and educators) and members of the research team, c) a

whole-school approach involving the development of a school ethos against

aggression, the assistance of educators, the provision of information to

parents and education to students, d) the use of robust measures and

sophisticated analyses, e) the development of safe and supportive school

environments, f) educators’ training on successful management of

aggressive behaviours, g) the use of practices and strategies both at

individual and peer levels, h) the integration of cognitive-behavioural

strategies to achieve long-term change, and i) the support from parents (see

Vreeman & Carroll, 2007 for a detailed description of the characteristics of

successful interventions).

Traditional social cognitive frameworks towards the development of

prevention interventions

A social cognitive perspective has been proliferated among the best

practices to tackle aggressive and violent behaviors in adolescence

(Thornton et al., 2000). Such a perspective involves learning, thinking, and

reasoning, and as such it fits well with the educational objectives of the

school (Barkoukis & Panagiotou, 2012; Boxer & Dubow, 2002). The social

cognitive perspective applied so far to tackle aggressive and violent

behaviors in adolescence have typically employed four approaches, a)

attribution retraining, b) social problem solving and skills training, c) pairs

therapy and d) instruction and curriculum change. Each approach is based

on a specific social cognitive background and offers a set of practices,

guidelines and strategies derived from this background (Hudley & Graham,

1995).

More specifically, attribution retraining is based on the notion that

dysfunctional cognitive interpretations of the social world and

subsequent behavioral intentions provide clues to understanding

aggressive behavior (Hudley & Graham, 1995). An aggressive child

perceives neutral stimuli as aggressive acts against him, feels angry and

tends to justify himself/herself to respond accordingly. However, when

these stimuli are attributed to unintentional acts, then aggressive impulses

can be controlled (Krieglmeyer et al., 2009). Based on these, attribution

retraining has been proliferated as an appropriate strategy for antisocial

behavior change as it can help training children infer unintentionality in

situations of attributional ambiguity and, hence, decrease the experience of

anger and the possibility in retaliating with bullying behaviors. Such a

prevention intervention program includes role play, story reading,

brainstorming, and discussion of personal experiences, through which

students are trained to search for, interpret, and properly categorize the

verbal and behavioral cues presented in ambiguous situations. The

attribution retraining approach is thought to enhance causal thinking on

subsequent social behavior which is also important for preventing

cyberbullying behaviors.

The second social cognitive approach typically used to tackle

adolescents’ aggressive behaviors involves social problem solving skills

training. A consistent finding in the aggression literature is that social

rejection leads to aggressive behaviors (see Leary et al., 2006 for an

extensive review). Also, social rejection is associated with a lack of the

social problem solving skills needed for effective interactions with peers,

such as negotiate conflict and influence by peers (Nangle et al., 2002). Prior

evidence has documented that training of social problem solving skills can

improve the social functioning of children manifesting bullying and

antisocial behaviors (Webster-Stratton et al., 2001). The focus of this type

of interventions is the increase of the awareness on the social consequences

of bullying behaviors and the improvement of social acceptance for peer-

rejected adolescents. This is mainly done by teaching students to choose the

appropriate solutions that will produce effective interpersonal behavior in

ambiguous situations (Hudley & Graham, 1995).

The interventions employing the social problem solving skills

training approach have been grouped in those using a) social skills training,

b) cognitive–behavioral skills training, and c) multicomponent cognitive–

behavioral skills training. The social skills training focused interventions

included activities such as instruction, modeling, behavioral rehearsal,

feedback, and discussion. Through these activities the intervention aimed to

enhance social skills, such as participation, cooperation, and

communication. In cognitive-behavioral skills training intervention the

basic aim was to promote students’ social problem solving skills. This type

of interventions included activities such as listening to and observing

others, discussing others thoughts, feelings, and motives in problem

situations, drawing pictures, playing with puppets, dialoguing and role

playing. Through these activities the interventions aimed to enhance

problem-solving skills, such as generating alternative solutions, thinking of

consequences of actions, and pairing solutions and consequences. Finally,

multicomponent cognitive–behavioral skills training interventions tried to

combine the previously used procedures to anger control and other

cognitive –behavioral variables, such as social problem solving, self-

instruction, relaxation, perspective taking and self-regulation. This type of

interventions include activities such as development of group roles, control

of arousal in anger situations, cooperative work for the preparation of

material promoting anger control and social problem solving skills,

dialoguing, discussion, role playing and goal setting in order to develop

self-statements helping in anger control (Nangle et al., 2002).

The third social cognitive approach employed to tackle bullying

and aggression involves pair therapy or pairing. This approach is based on

the view that deviant behaviors are initiated by problems integrated into the

self and as such they require individual therapy. The aim of this type of

interventions is to a) offer students knowledge on the causes and

consequences of bullying and aggression, b) develop students’ risk

management skills in order to be able to effectively interact with peers and

c) establish a low personal meaning for bullying and aggressive behaviors.

The focus of this approach is the teaching of interpersonal negotiation and

intimate sharing of experience in pairs of at-risk students (typically each

pairs consists of a bully and a victim). Typically these interventions are

employed as counselling programs to students identified as having deviant

behaviors. Under adult guidance the selected students work in pairs towards

defining and understanding the problem at hand, developing alternative to

bullying and aggression responses, selecting the best strategy in dealing

with provoking situations and evaluating the outcomes of the alternative

behaviors. Likewise, multicomponent cognitive–behavioral skills training

interventions, in pair therapy besides discussing the pairs are actively

engaging in a number of activities such as role playing, videotaping and

playing with puppets. These activities are thought to enhance the friendship

between the students and more effectively deal with the bullying behavior.

The final social cognitive approach to develop interventions against

bullying and aggression involves changing the instructional styles and the

curriculum. Prior evidence has indicated that aggressive and bullying

behaviors were associated with low academic performance. Hence, these

changes are thought to have a positive influence on the academic

performance of students. The focus of this type of interventions is to

enhance students’ self-esteem and develop positive attitudes towards school

and school achievement. Through this way students will be more reluctant

in manifesting deviant behaviors. The proposed curriculum changes involve

an emphasis on positive role models, on the development of values for

academic achievement and on the establishment of a psychologically safe

school and class climate (Hudley & Graham, 1995). Regarding

instructional style, a teaching approach fostering mastery orientation is

thought to develop students’ moral and social skills resulting in lower

bullying and aggressive behaviors (Telama & Polvi, 2007). The TARGET

intervention program, developed by Ames (1992), offers guidelines and

practices towards the development of a mastery-oriented class climate. The

program consists of six dimensions (i.e., Task, Authority, Recognition,

Grouping, Evaluation, and Time), whose initials form the acronym

TARGET. The Task dimension suggests that a variety of drills is used and

students are allowed to work at their own level. Alternative drills should be

provided to students and opportunities for goal setting. The Authority

dimension involves students’ participation in decision making and offering

them opportunities to lead an activity in the class. The Recognition

dimension has to do with the type and the timing of the rewards provided to

students. According to guidelines pertaining to this dimension rewards

should be provided for self-improvement, achieving personal goals and

exerting effort during the class. Also extra-curricular activities should be

recognized and praised. The Grouping dimension suggests the use of

cooperative teaching approaches, where students work together in small

and heterogeneous groups in order to promote students’ social interaction.

The Evaluation dimension suggests the use of self-referenced criteria for

students’ evaluation, such as personal improvement, achieving personal

goals, participating in the lesson, and exerting effort. In addition, self-

evaluation through keeping personal records and monitoring of the goal-

setting process should be encouraged. Finally, the Time dimension reflects

the organization of the class. Maximizing learning time, using behavior

‘protocols’, maintaining discipline in the class and allowing students to

work on their goals are fundamental practices in this TARGET dimension

(Barkoukis et al., 2008).

Contemporary Social Cognitive Approaches to Cyberbullying

Prevention

The aforementioned prevention strategies provide a general

overview of social cognitive approaches that have been developed over the

last decades and have provided useful recommendations for prevention

strategies against adolescent aggression and bullying in educational

settings. This section will advance our discussion about social cognitive

approaches to cyberbullying prevention by presenting related research and

evidence-based practices used in the field of adolescent risk behaviour.

Specifically, the section will unfold through two major parts respectively

addressing the roles of individual differences (empathy and moral

disengagement), and social cognitions (self-efficacy, attitudes, social

norms, and behavioural intentions). It is noteworthy that these variables

have not been extensively addressed in cyberbullying research to date, but

provide useful avenues for future study and evidence-based preventive

strategies.

Individual Differences in Cyberbullying prevention: The Roles of

Empathy and Moral Disengagement.

Empathy is a cardinal aspect of human behaviour that facilitates

social interactions, and helps people communicate emotions and feelings

more effectively (Cohen & Strayer, 1996; Davis, 1983; Mehrabian &

Epstein, 1972). Empathy is defined as the person’s ability to perceive and

understand other people’s emotional states, and accordingly shape or

modify his/her behavioural reactions (Preston & de Waal, 2002). Unlike

attitudes, beliefs, and other more transient states of social cognition,

empathy is best conceived as a stable attribute that is likely to be shaped

along the individual’s personality and affect different types of social

behaviours (Loudin et al., 2003; Strayer, 1987). Furthermore, instead of

being a unitary construct, researchers have noted that empathy is

multidimensional and can be distinguished between a cognitive and an

affective part (Davis, 1983). The former reflecting the ability to understand

and cognitively process other people’s emotions and the latter representing

the capacity to understand other people’s feelings through an emotional and

less cognitively-bound channel (vicarious emotional sharing; Shamay-

Tsoory et al., 2009; Smith, 2006).

In relation to aggressive behaviours, empathy has been extensively

addressed in studies of adolescent violence and traditional bullying (e.g.,

Bartholow et al., 2005; Lovett & Sheffield, 2007). The available evidence

suggests that lower levels of empathy are strongly related to higher levels

of aggression and bullying incidents in the school environment (e.g.,

Endresen & Olweus, 2002; Jolliffe & Farrington, 2006; Olweus, 1993).

Thus, it is no surprise that empathy has figured prominently in the most

recent studies of cyberbullying behaviour. In particular, a study by Ang and

Goh (2010) showed that both male and female adolescents with lower

empathy levels, scored higher in cyberbullying behaviour. Furthermore, the

role of empathy in cyberbullying was addressed by Schultze-Krumbholz

and Scheithauer (2009), who found that both cyberbullyies and

cyberbullying victims reported lower empathy levels, as compared to

adolescents not involved in cyberbullying incidents. In terms of preventive

strategies, the aforementioned studies suggest that empathy training should

be included in the agenda of educators and policy makers interested in

reducing the incidents of cyberbullying behaviour (Ang & Goh, 2010).

More specifically, it appears that addressing both cognitive and affective

aspects of empathy will impact involvement in cyberbullying incidents

(Ang & Goh, 2010; Campbell, 2005).

Moral disengagement represents another aspect of individual

differences that has been associated with bullying and cyberbullying

behaviours in young people. The term moral disengagement was addressed

by Bandura in his social-cognitive theory of the moral self (Bandura, 1986,

1991). Bandura argued that moral reasoning guides behaviour through

specific self-regulatory processes, such as moral disengagement. This

process can be described in several stages whereby the individual

cognitively ‘moralizes’ behaviours that would otherwise by accepted as

immoral ones or as against personal moral values (Bandura et al., 1996;

McAlister, Bandura, & Owen, 2006). Bandura (2002) further argued that

engaging in moral disengagement may help individuals self-justify for their

behaviour and reduce any cognitive dissonance and tension between their

(moral) attitudes and (immoral) behaviour.

In relation to bullying behaviours, several studies have shown that

adolescents with higher levels of moral disengagement are more likely to

be involved in traditional bullying, cyber-bullying, or both modes of

bullying either as aggressors, or as victims (Bauman & Pero, 2011; Perren

& Gutzwiller-Helfenfinger, 2012; Pornari & Wood, 2010). Therefore, any

attempts to prevent cyberbullying behaviour in educational contexts should

take into account individual differences in moral disengagement and

accordingly promote the skills and increase awareness so that students are

more morally engaged with others/cyberbullying victims (Bauman, 2010).

Moving beyond individual differences: Social cognitions, intentions,

and their effects on behaviour

Individual differences provide a useful and holistic approach to our

understanding of cyberbullying behaviour, and can be effectively targeted

by related prevention strategies and school-based interventions.

Nevertheless, their direct impact on actual behaviour is not clear. Scholars

have argued that individual differences are likely to directly influence

behaviour, but they actually represent distal predictors of behaviour, whose

effects tend to be mediated by more proximal variables (Armitage &

Conner, 2001; Conner & Armitage, 1998). Ajzen’s (1991) theory of

planned behaviour (TPB) provides a good example of how social

cognitions (attitudes, norms, and self-efficacy) and intentions mediate the

effects of individual differences and personality traits on behaviour. In

support of this argument, Lazuras and Ourda (2012) found that self-efficacy

beliefs mediated the effects of empathy on cyberbullying intentions among

Greek adolescents.

The mediating properties of social cognitions in personality-

behaviour relationships have been also addressed by the more recently

developed Integrative Model of Behavioural Prediction (IM; Fishbein &

Yzer, 2003). This theory is an extension of the original TPB approach and

clearly discusses the ways through which individual differences exert their

influence on intentions and actual behaviour. As in TPB, the main postulate

of the integrative model is that personality attributes and individual

characteristics influence behaviour through attitudes, affective, normative,

and self-efficacy beliefs, and behavioural intentions (see also Armitage &

Conner, 2000; 2001). We will now consider the effects of each of these

potential mediators and discuss their role in the prevention of cyberbullying

among adolescents.

To begin with, attitudes represent core evaluations of target

behaviours (e.g., smoking, drinking, cyberbullying) and are more transient

than, say, individual differences and personality traits, thus are more

malleable and more easily changed by tailor-made interventions (Ajzen,

2001; Ajzen & Fishbein, 2005). Attitudes are typically formed through

classical conditioning and learning, as well as by social interactions with

family members and peers and persuasion (Olson & Fazio, 2001; Wood,

2000). They can be captured by self-reports, but more recent approaches to

attitude theory suggest that people may be reluctant to express their

attitudes towards sensitive issues (e.g., racism, prejudice, aggression), and

therefore have suggested the use of more implicit methodologies to capture

individual attitudes (e.g., the Implicit Association Test; De Houwer, 2006;

Greenwald et al., 2002; McConnell & Leibold, 2001; Wilson et al., 2000).

Nevertheless, despite the exact method used to capture attitudinal beliefs,

researchers have noted that what’s important is the way attitudes can

change and accordingly guide behaviour (Fazio & Olson, 2003; Glasman &

Albarracin, 2006; Greenwald et al., 2009).

To this end, educational approaches against adolescent violence

and aggression have much to offer, mainly by intervening early and

influence the attitude formation process (Merrell et al., 2008). Besides,

attitudes have been found to significantly predict bullying behaviour among

children and adolescents and researchers have called for school-based

interventions targeting attitudes (e.g., Rigby, 2005). In relation to

cyberbullying, this implies that a good preventive strategy would be to

educate children about cyberbullying and its effects from an early stage,

long before cyberbullying incidents are likely to occur (e.g., in elementary

school). Of course, such educational approaches should not only tap the

negative aspects of cyberbullying, but also teach young people how to

effectively use new social media for social interaction, exchange of

information, and social integration. This approach would ensure that

negative attitudes towards cyberbullying are forged, while at the same time

positive attitudes towards the effective and responsible use of digital

communication applications are shaped. Finally, it is important to consider

attitude formation approaches to reduce bystander effects. That is,

preventive efforts should also promote positive attitudes towards

intervening (where possible) to prevent cyberbullying, or if such an

intervention is impossible due to external obstacles, to report cyberbullying

incidents to the respective authorities (e.g., school counsellors, educators,

school principal; Campbell, 2005).

Social norms represent another important aspect of social cognition

approaches. Traditionally, much of adolescent risk-behaviour, including

unsafe sex, careless driving, drug, alcohol, and tobacco use, has been

largely attributed to normative influences, such as peer pressure,

advertisement, social approval, and biased frequency (prevalence) estimates

(Kobus, 2003; Reyna & Farley, 2006; Petraitis et al., 1995; Steinberg,

2004). Normative influences on adolescent behaviour have been described

by a wide range of theories, including the theory of planned behaviour

(Ajzen, 1991), the social cognitive theory (Bandura, 1986), and the

prototype-willingness model (Gibbons et al., 1998). Still, before discussing

how normative influences can be addressed in the context of cyberbullying

prevention, it is noteworthy to provide some basic information about the

different types of social norm beliefs.

Initially, much attention was paid to the effects of subjective

norms, which represent perceived social pressure or approval by significant

others (Rivis & Sheeran, 2003). This process is relevant to behaviours that

are enacted because of affiliation motives (e.g., to gain approval and be

accepted by peers), and lower self-esteem (Cialdini & Goldstein, 2004;

Lazuras et al., 2009). Another main type of social norms, however, is

descriptive norms. This concept was addressed extensively by Cialdini and

colleagues (Cialdini, 2007; Cialdini et al., 1990; Nolan et al., 2008), and

reflects beliefs about the prevalence of the perceived frequency of a given

behaviour within a target population (e.g., among peers, classmates, or even

in the general population). It has been argued that unlike subjective norms,

which require some sort of action planning and goal-oriented behaviour

(e.g., I will act in this way so that I am liked more by my friends),

descriptive norms may operate more automatically and predict behaviour

without any prior planning or consideration of goals (Bargh & Chartrand,

1999; Lazuras et al., 2009; Nolan et al., 2008; Rivis & Sheeran, 2003). Put

simply, the mere perception of other people acting in a specific way should

be enough to initiate action. This approach rests on evolutionary

explanations of social influence (e.g., Sundie et al., 2006), but has been also

supported by research on implicit social cognition and priming effects on

behaviour (e.g., ‘perceiving is for doing’; Bargh & Chartrand, 1999).

Therefore, descriptive norms can operate at both conscious and

unconscious (implicit) levels, and, thus, have ‘dual-process’ impact on

behaviour.

Finally, the most recently developed concept of normative beliefs

is ‘moral norms’, which describe personal moral standards and principles

towards a given behaviour (Armitage & Conner, 2001). Moral norms do

not represent influence from others rather they reflect personal moral

values. So, unlike subjective and descriptive norms, moral norms guide

behaviour by reminding people that they’re acting in discord with their

moral principles and personal values. Several studies have shown that

moral norms predict intentions and behaviour across behavioural domains,

and even more so in behaviours with a strong moral character (Conner &

Armitage, 1998; Godin et al., 2005).

In terms of cyberbullying prevention, social norms can provide a

useful tool. Firstly, as already noted, adolescents are susceptible to

normative influences, thus, manipulations of social norms by related

interventions can have an impact on the perceived social approval and

prevalence of cyberbullying behaviour. It is important to note that both

subjective and descriptive norms should be addressed, as selecting either of

them could lead to a boomerang effect. Specifically, one study showed that

merely targeting prevalence rates of a target behaviour (i.e., targeting

descriptive norms), can lead to opposite effects, unless coupled with

subjective norms training (i.e., tapping the social approval aspect; Schulz et

al., 2007). Thus, interventions to reduce cyberbullying effects would be

more likely to achieve their goal by tapping both descriptive and subjective

norms. Finally, perhaps a greater impact of normative education could be

achieved by incorporating moral norms in related interventions. Although

not actually addressing normative influences (i.e., influences exerted by

others to the person), moral norms may be jointly addressed and provide a

more coherent and holistic approach to norms training: along with teaching

adolescents about the social disapproval of cyberbullying and providing

information about its prevalence, educators can also forge moral norms and

remind them to the students (i.e., bolster the belief that engaging in

cyberbullying is against individual moral values and principles, and that

intervening to prevent cyberbullying incidents is the ‘right thing’ to do).

Even if adolescents possess ‘desirable’ traits, attitudes, and

normative beliefs against cyberbullying, they often have to fight with

impulses generated at ‘the spur of the moment’, mainly due to external

pressures. In simple words, are negative attitudes and social norms against

cyberbullying enough to predict whether an adolescent can cope effectively

with direct social pressure to engage in cyberbullying? In most cases, it

turns out that another important variable that should be addressed in

preventive strategies related to adolescent risk-taking is self-efficacy. This

term was central to Bandura’s (1989) social cognitive theory, who argued

that self-efficacy beliefs play a central role in self-regulatory processes and

the exercise of behavioural control and coping strategies. Self-efficacy has

been successfully incorporated in other theoretical models, including the

TPB (see Ajzen, 2005) and the transtheoretical model of change (TTM,

Prochaska & DiClemente, 1983), and numerous studies have shown that

efficacy beliefs are strong predictors of adolescent risk-taking tendencies

and behaviour (Armitage & Conner, 2000; DeVries et al., 1988; Petraitis et

al., 1995).

Within the context of cyberbullying behaviour, self-efficacy

training can be really useful for the following reasons. Firstly, teaching

young people that they can cope with cyberbullying incidents is an

important aspect of their social empowerment. Namely, adolescents may

feel unable to abstain from cyberbullying incidents (especially in the face

or explicit social pressures or coercion by peers), or feel reluctant to

intervene and prevent cyberbullying, because they simply believe that they

cannot do it. Thus, self-efficacy training is important because it can bolster

a ‘can do’ attitude, which may, in turn, determine actual behaviour.

Secondly, learning specific coping strategies is essential for promoting self-

regulation and reduce bystander effects (i.e., What to do in a cyberbullying

incident? How to handle emotions and behaviour? Where to report the

incident?). In fact, such an approach will further expand the impact of self-

efficacy training by providing specific guidelines and steps that will

facilitate the process of intervention against cyberbullying.

The aforementioned variables (attitudes, norms, and self-efficacy)

can directly predict behaviour, but their effects on behaviour can also be

indirect, mediated by behavioural intentions (Azjen & Fishbein, 2005;

Fishbein & Yzer, 2003). Specifically, more negative attitudes and social

norms towards cyberbullying, and greater efficacy to cope with, report, or

intervene to prevent cyberbullying incidents can lead to the formation of

stronger intentions, which, in turn, predict actual behaviour. Intentions

represent the person’s motivation or eagerness to execute a specific course

of action, and are said to be the most proximal predictor of actual behaviour

(Azjen, 1991, 2001). Thus, building stronger social cognitions against

cyberbullying through educational interventions, can lead to the formation

of stronger intentions to intervene, report, or abstain from cyberbullying

incidents. Nevertheless, intentions do not always predict actual behaviour

(Armitage & Conner, 2001; Webb & Sheeran, 2006), either because other

goals intervene or because individuals simply forget their intentions, or are

distracted by other stimuli (Gollwitzer, 1999; Gollwitzer & Sheeran, 2006).

One way to ensure an effect on behaviour, however, is to furnish intentions

with specific action plans (Orbell et al., 1997; Sheeran & Orbell, 1999).

These action plans are often referred to as ‘implementation intentions’ and

represent the individual’s specific and detailed plan of action under specific

circumstances. So, unlike just stating a strong intention to intervene or

report cyberbullying to authorities, or a weak intention to engage in

cyberbullying, we should expect greater outcomes by having adolescents

form specific action plans on how they will realize their stated intentions

under specific circumstances.

Before proceeding with a detailed analysis of how we can help

adolescents form such implementation intentions, it must be explicitly

stated that implementation intentions would work only if strong intentions

have been formed in the first place (Gollwitzer & Sheeran, 2006).

Therefore, it is crucial that educational or school-based interventions have

firstly met the requirements of building strong anti-cyberbullying intentions

(a collective term used here to denote strong intentions to abstain, report, or

intervene to prevent cyberbullying and weak intentions to engage in

cyberbullying) by forging attitudes, normative beliefs, and self-efficacy

skills in the ways discussed earlier (Gollwitzer, 1999; Sheeran et al., 2005;

Sniehotta, 2009). Once this requirement is met, then building

implementation intentions usually requires two basic steps. Firstly, we need

to identify the conditions under which adolescents will feel more at risk to

observe incidents of, or engage in cyberbullying. These could include

several situations (e.g., chatting with friends in Facebook) the details of

which can be drawn from the adolescents’ reported experiences with

similar incidents in the past. Secondly, once the risk-conducive situations

are identified, it is important to form ‘if-then’ plans. The ‘if’ part of these

plans describes the specific situations (e.g., and if I am chatting with friends

in Facebook and witness a cyberbullying incident...), whereas the ‘then’

part reflects the action-initiation plan (e.g., then, I will ask my friends to

stop it; Gollwitzer, 1999; Gollwitzer & Sheeran, 2006).

Gollwitzer et al. (2005) provide numerous examples of how such

if-then plans can be formed. For instance, someone could describe an

action-initiation (e.g., I will report the cyberbullying incident), or an

avoidance plan (e.g., I will abstain from the cyberbullying incident), or

furnish his/her intentions with both types of action plans. The existing

evidence shows that furnishing behavioural intentions with implementation

intentions strengthens the intention-behaviour link in several domains

(Gollwitzer & Sheeran, 2006; Sniehotta, 2009). Therefore, considering

implementation intentions in cyberbullying prevention can further enhance

the outputs and impact or related interventions targeting social cognitions

against cyberbullying. This is even more so in efforts to boost self-efficacy

training, as it is relevant to providing specific guidelines for action

initiation (e.g., how to cope with or report cyberbullying).

Overall, this section described how social cognitive variables

derived from leading theoretical models in social psychology can be

utilized for the prevention of cyberbullying behaviour in educational

settings. The roles of attitude formation, normative education and self-

efficacy skills training were discussed in terms of both direct and indirect

effects on behaviour. While these variables are likely to predict behaviour

directly, or through the formation of anti-cyberbullying intentions, we

should recall that a complete approach to prevention interventions should

also incorporate the concept of implementation intentions that will teach

adolescents how to form specific action-initiation plans against

cyberbullying. This will further enhance the impact of shaping anti-

cyberbullying attitudes or norms, and will also help in de-normalizing

cyberbullying, and creating a stronger ‘can do’ belief.

References

Ajzen I. (1991), The theory of planned behaviour. Organizational

Behaviour & Human Decision Processes, 50, 179-211.

Ajzen I. (2001), Nature and operation of attitudes. Annual Review of

Psychology, 52, 27-58.

Ajzen I., & Fishbein M. (2005)., The influence of attitudes on behavior. In

Albarracín D. Johnson B. T. & Zanna M. P. edited by, The handbook of

attitudes. Erlbaum, Mahwah, NJ.

Ames C. (1992), The relationship of achievement goals to student

motivation in classroom settings. In Roberts G. ed. Motivation in sport

and exercise. Human Kinetics, Champaign, IL.

Ang R.P., & Goh D.H. (2010), Cyberbullying among adolescents: The role

of affective and cognitive empathy, and gender. Child Psychiatry and

Human Development, 41, 387-397.

Armitage C.J., & Conner M. (2000), Social cognition models and

health behaviour: A structured review. Psychology & Health, 15,

173-189. Armitage C.J., & Conner M. (2001), Efficacy of the theory of planned

behaviour: A meta-analytic review. British Journal of Social

Psychology, 40, 471-499.

Baldry A.C. & Farrington D.P. (2004), Evaluation of an intervention

program for the reduction of bullying and victimization in schools.

Aggressive Behavior, 30, 1-15.

Bandura A. (1986), Social Foundations of Thought and Action: A Social

Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ.

Bandura A. (1991). Social cognitive theory of moral thought and action. In

Kurtines, W.M. & Gewirtz J.L. edited by, Handbook of Moral

Behavior and Development, Erlbaum, Hillsdale, NJ.

Bandura A. (2002), Selective moral disengagement in the exercise of moral

agency. Journal of Moral Education, 31, 101–119.

Bandura A., Barbaranelli C., Caprara G. V., & Pastorelli C. (1996),

Mechanisms of moral disengagement in the exercise of moral agency.

Journal of Personality and Social Psychology, 71, 364–374.

Bargh J.A., & Chartrand T.L. (1999), The unbearable automaticity of

being. American Psychologist, 54, 462-479.

Barkoukis V., & Panagiotou C. (2012), Didactic approaches to tackle

cyberbullying. In Tsorbatzoudis H, Lazuras L, & Barkoukis V edited

by, Cyberbullying in Greece: An interdisciplinary approach, COPY

CITY, Thessaloniki.

Barkoukis V., Tsorbatzoudis H., & Grouios G. (2008), Manipulation of

motivation climate in physical education: Effects of a seven-month

intervention. European Physical Education Review, 14, 373-394.

Bartholow B.D., Sestir M.A., & Davis E.B. (2005), Correlates and

consequences of exposure to video game violence: Hostile personality,

empathy, and aggressive behavior. Personality & Social Psychology

Bulletin, 31, 1573-1586.

Bauman S. (2010), Cyberbullying in a rural intermediate school: An

exploratory study. Journal of Early Adolescence, 30, 803-833.

Bauman S., & Pero H. (2011) Bullying and cyberbullying among deaf

students and their hearing peers: An exploratory study. Journal of Deaf

Studies & Deaf Education, 16, 236-253.

Blais J., Craig W.M., Pepler D.J., & Connolly J. (2008), Adolescents

online: The importance of Internet activity choices to salient

relationship. Journal of Youth and Adolescence, 37, 49–58.

Boxer P., & Dubow E.F. (2002), A social-cognitive information-processing

model for school-based aggression reduction and prevention programs:

Issues for research and practice. Applied & Preventive Psychology, 10,

177-192.

Brighi A., Guarini A., & Genta M. L. (2009), Bullying in the digital age. In

Genta M. L., Brighi A. & Guarini A. edited by, Bullying and

cyberbullying in adolescence. Carocci Editions, Rome.

Campbell M.A. (2005), Cyberbullying: An old problem in a new guise?

Australian Journal of Guidance and Counselling, 15, 68-76.

Cialdini R.B. (2007), Descriptive social norms as underappreciated sources

of social control. Psychometrika, 72, 263-268.

Cialdini R.B., & Goldstein N.J. (2004), Social influence: Compliance and

conformity. Annual Review of Psychology, 55, 591-621.

Cialdini R.B., Reno R.R., & Kallgren C.A. (1990), A focus theory of

normative conduct: Recycling the concept of norms to reduce littering

in public places. Journal of Personality and Social Psychology, 58,

1015-1026.

Cullinan D. (2002), Students with emotional and behavior disorders: An

introduction for teachers and other helping professionals. Upper

Saddle River, NJ7 Pearson.

Cohen D. & Strayer J. (1996), Empathy in conduct disordered and

comparison youth. Developmental Psychology, 32, 988-998.

Conner M., & Armitage C.A. (1998), Extending the theory of planned

behaviour: A review and avenues for further research. Journal of

Applied Social Psychology, 28, 1429-1464.

Daunic A.P., Smith S.W., Brank E.M., & Penfield R.D. (2006). Classroom-

based cognitive–behavioral intervention to prevent aggression: Efficacy

and social validity. Journal of School Psychology, 44, 123–139.

Davis M.H. (1983), Measuring individual differences in empathy: Evidence

for a multidimensional approach. Journal of Personality & Social

Psychology, 44, 113-126.

De Houwer J. (2006), What are implicit measures and why are we using

them. In Wiers R.W. & Stacy A.W. edited by, The handbook of implicit

cognition and addiction. Sage Publishers, Thousand Oaks, CA.

De Vries H., Dijkstra M., & Kuhlman P. (1988), Self-efficacy: The third

factor besides attitude and subjective norm as a predictor of

behavioural intentions. Health Education Research, 3, 273-282. Dooley J.J., Pyzalski J., & Cross D. (2009), Cyberbullying versus face-to-

face bullying: A theoretical and conceptual review. Zeitschrigt fur

Psychologie, 217, 182-188.

Endresen I.M., Olweus D. (2002), Self-reported empathy in Norwegian

adolescents: Sex-differences, age trends, and relationship to bullying.

In: Stipek D & Bohart A. edited by, Constructive and Destructive

Behavior. Implications for Family, School, & Society. American

Psychological Association, Washington.

Farrell A.D., Meyer A.L., Kung E.M., & Sullivan T.N. (2001),

Development and evaluation of school-based prevention programs.

Journal of Clinical Child Psychology, 30, 207-220.

Fazio R.H., & Olson M.A. (2003), Implicit measures in social cognition

research: Their meaning and use. Annual Review of Psychology, 54,

297-327.

Fishbein M., & Yzer M.C. (2003), Using theory to design effective health

behaviour interventions. Communication Theory, 13, 164-183.

Gibbons F.X., Gerrard M., Blanton H., & Russell D.W. (1998), Reasoned

action and social reaction: Willingness and intention as independent

predictors of health risk. Journal of Personality and Social Psychology,

74, 1164-1180.

Glasman L.R., & Albarracin D. (2006), Forming attitudes that predict

future behavior: A meta-analysis of the attitude-behavior relation.

Psychological Bulletin, 132, 778-822.

Godin G., Conner M., & Sheeran P. (2005), Bridging the intention-

behavior gap: The role of moral norm. British Journal of Social

Psychology, 44, 497-512.

Gollwitzer P.M. (1999), Implementation intentions: Strong effects of

simple plans. American Psychologist, 54, 493-503.

Gollwitzer P.M., Bayer U., & McCulloch K. (2005), The control of the

unwanted. In Hassin R. Uleman J.& Bargh J.A. edited by, The new

unconscious. Oxford University Press, Oxford.

Gollwitzer P.M., & Sheeran P. (2006) Implementation intentions and goal

achievement: A meta-analysis of effects and processes. Advances in

Experimental Social Psychology, 38, 69-119.

Greenwald A.G., Banaji M.R., Rudman L.A., Farnham S.D., Nosek B.A.,

& Mellott D.S. (2002), A unified theory of implicit attitudes,

stereotypes, self-esteem, and self-concept. Psychological Review, 109,

3-25.

Greenwald A.G., Poehlman T.A., Uhlmann E.L., & Banaji M.R. (2009),

Understanding and using the implicit association test: Meta-analysis

and predictive validity. Journal of Personality & Social Psychology,

97, 17-41.

Hudley C., & Graham S. (1995), School-based interventions African-

American boys for aggressive. Applied & Preventive Psychology, 4,

185-195.

Joliffe D., & Farrington D. (2006), Examining the relationship

between low empathy and bullying. Aggressive Behavior, 32,

540-550. Knol K., Plichta P., & Pyzalski J. (2012), Cyberbullying among polish

adolescents in a broader perspective. Positive intervention and

prevention strategies. Paper presented at the conference on

Cyberbullying in Adolescence, Bologna, 20 February.

Kobus K. (2003), Peers and adolescent smoking. Addiction, 98, 37-55.

Krieglemeyer R., Wittstadt D., & Strack F. (2009), How attribution

influences aggression: Answers to an old question by using an implicit

measure of anger. Journal of Experimental Social Psychology, 45, 379-

385.

Lazuras L. Barkoukis V & Tsorbatzoudis H. (2012), Cyberbullying among

Greek adolescents: Findings from a school based study. Paper

presented at the conference on Cyberbullying in Adolescence, Bologna,

20 February.

Lazuras L., Rodafinos A., & Eiser J.R. (2009), Predicting Greek

adolescents’ intentions to smoke: A focus on normative processes.

Health Psychology, 28, 770-778.

Leary M.R., Twenge J.M., & Quinlivan E. (2006), Interpersonal rejection

as a determinant of anger and aggression. Personality and Social

Psychology Review, 10, 111–132.

Loudin J.L., Loukas A., & Robinson, S. (2003), Relational aggression in

college students: Examining the roles of social anxiety and empathy.

Aggressive Behaviour, 29, 430-439.

Lovett B.J., & Sheffield R.A. (2007), Affective empathy deficits in

aggressive children and adolescents: A critical review. Clinical

Psychology Review, 27, 1-13.

McAlister A.L., Bandura A., & Owen S.V. (2006), Mechanisms of moral

disengagement in support of military force: The impact of Sept.9.11.

Journal of Social & Clinical Psychology, 25, 141-165.

McConnell A.R., & Leibold J.M. (2001), Relations among the implicit

association test, discriminatory behaviour, and explicit measures of

racial attitudes. Journal of Experimental Social Psychology, 37, 435-

442.

Mehrabian A., & Epstein N. (1972), A measure of emotional empathy.

Journal of Personality, 40, 525-543.

Merrell K.W., Gueldner B.A., Ross S.W., & Isava D.M. (2008), How

effective are school bullying intervention programs? A meta-analysis of

intervention research. School Psychology Quarterly, 23, 26-42.

Mishna F., Cook C., Gadalla T., Daciuk J., & Solomon S. (2010), Cyber

bullying behaviours among middle and high school students. The

American Journal of Orthopsychiatry, 80, 362–374.

Mishna F., Khoury-Kassabri M., Gadalla T., & Daciuk J. (2012), Risk

factors for involvement in cyber bullying: Victims, bullies and bully–

victims. Children and Youth Services Review, 34, 63–70.

Nangle D.W., Erdley C.A., Carpenter E.M., Newman J.E. (2002), Social

skills training as a treatment for aggressive children and adolescents: a

developmental–clinical integration. Aggression and Violent Behavior,

7, 169–199.

Nolan J. M., Schultz W., Cialdini R. B., Goldstein N. J., & Griskevicius V.

(2008), Normative social influence is underdetected. Personality &

Social Psychology Bulletin, 34, 913-923.

Olson M.A., & Fazio R.H. (2001), Implicit attitude formation through

classical conditioning. Psychological Science, 12, 413-417.

Olweus D. (1993), Bullying at school: What we know and what we can do.

Oxford: Blackwell. Olweus D., & Limber S. (1999), Blueprints for violence prevention:

Bullying Prevention Program. Institute of Behavioral Science,

University of Colorado, Boulder, USA.

Orbell S., Hodgkins S., & Sheeran P. (1997), Implementation intentions

and the theory of planned behaviour. Personality & Social Psychology

Bulletin, 23, 945-954.

Ortega R., Del Rey R., & Casas J.A. (2012), The impact of the ConRed

Program: a proposal to prevent cyberbullying. Paper presented at the

conference on Cyberbullying in Adolescence, Bologna, 20 February.

Perren S., & Gutzwiller-Helfenfinger E. (2012), Cyberbullying and

traditional bullying in adolescence: Differential roles of moral

disengagement, moral emotions, and moral values. European Journal

of Developmental Psychology, 9, 195-209.

Petraitis J., Flay B.R., & Miller T.Q. (1995), Reviewing theories of

adolescent substance use: Organizing pieces in the puzzle.

Psychological Bulletin, 117, 67-86.

Preston S.D., & de Waal F.B.M. (2002), Empathy: Its ultimate and

proximate bases. Behavioural and Brain Sciences, 25, 1-72.

Prochaska J.O., & DiClemente C.C. (1983), Stages and processes of self-

change of smoking: Toward an integrative model of change. Journal of

Consulting and Clinical Psychology, 51, 390-395.

Pornari C.D., & Wood J. (2010), Peer and cyber aggression in secondary

school students: The role of moral disengagement, hostile attribution

bias, and outcome expectancies. Aggressive Behaviour, 36, 81-94.

Reyna V.F., & Farley F. (2006), Risk and rationality in adolescent decision

making. Psychological Science in the Public Interest, 7, 1-44.

Rigby K. (2005), Why do some children bully at school? The contribution

of negative attitudes towards victims and the perceived expectations of

friends, parents, and teachers. School Psychology International, 26,

147-161.

Rivis A., & Sheeran P. (2003), Descriptive norms as an additional predictor

in the theory of planned behaviour: A meta-analysis. Current

Psychology, 22, 218-233.

Salmivalli C. (2001) Peer-led intervention campaign against school

bullying: Who considered it useful, who benefited? Educational

Research, 43, 263–279.

Schulz P.W., Nolan J.M., Cialdini R.B., Goldstein N., & Griskevicius V.

(2007) The constructive, destructive, and reconstructive power of social

norms. Psychological Science, 18, 429-434.

Schultze-Krumbholz A., & Scheithauer H. (2009). Social-behavioural

correlates of cyberbullying in a German student sample. Journal of

Psychology, 217, 224-226.

Shamay-Tsoory S.G., Aharon-Peretz J., & Perry D. (2009), Two systems

for empathy: A double dissociation between emotional and cognitive

empathy in inferior frontal gyrus versus ventromedial prefrontal

lesions. Brain, 132, 617-627.

Sheeran P., & Orbell S. (1999), Augmenting the theory of planned

behaviour: Roles of anticipated regret and descriptive norms. Journal

of Applied Social Psychology, 29, 2107-2142.

Sheeran P., Webb T., & Gollwitzer P.M. (2005). The interplay between

goal intentions and implementation intentions. Personality & social

Psychology Bulletin, 31, 87-98.

Smith P.K., Mahdavi J., Carvalho M., Fisher S., Russell S., & Tippett N.

(2008), Cyberbullying: Its nature and impact in secondary school

pupils. Journal of Child Psychology and Psychiatry, 49, 376–385.

Smith P.K., Thompson F., & Robinson S. (2012), Approaches to tackling

cyberbullying in England. Paper presented at the conference on

Cyberbullying in Adolescence, Bologna, 20 February.

Sniehotta F.F. (2009), Towards a theory of intentional behavior change:

Plans, planning, and self-regulation. British Journal of Health

Psychology, 14, 261-273.

Steinberg L. (2004) Risk taking in adolescence: What changes, and why?

Annals of the New York Academy of Sciences, 1021, 51-58.

Sundie J.M., Cialdini R.B., Griskevicius V., & Kenrick D.T. (2006).

Evolution and social psychology. In Schaller M. Simpson J.& Kenrick

D.T. edited by, Evolution and social psychology. Psychosocial Press,

Madison, CT.

Strayer J. (1987), Empathy and its development. In Eisenberg, N. &

Strayer, J. Edited by, Cambridge studies in social and emotional

development. Cambridge University Press, New York..

Telama R. & Polvi S. (2007). Facilitating prosocial behaviour in physical

education. In Liukkonen, J. Vanden Auweele Y. Vereijken B. Alferman

D. & Theodorakis Y. edited by, Psychology for physical educators,

student in Focus. Human Kinetics, Champaign Il.

Thornton T.N., Craft C.A., Dahlberg L.L., Lynch B.S., & Baer K. (2000),

Best practices of youth violence prevention: A sourcebook for

community action. Atlanta, GA: Centers for Disease Control and

Prevention.

Vreeman R.C. & Carroll A.E. (2007), A systematic review of school-based

interventions to prevent bullying. Archives of Pediatrics and

Adolescent Medicine, 161, 78-88.

Webb T.L., & Sheeran P. (2006), Does changing behavioural intentions

engender behaviour change? A meta-analysis of the experimental

evidence. Psychological Bulletin, 132, 249-268.

Webster-Stratton C., Reid J., & Hammond M. (2001), Preventing conduct

problems, promoting social competence: A parent and teacher training

partnership in Head Start. Journal of Clinical Child Psychology, 30,

283–302.

Wilson T.D., Lindsey S., & Schooler T.Y. (2000), A model of dual

attitudes. Psychological Review, 107, 101-126.

Wolak J., Mitchell K.J., & Finkelhor D. (2007), Does online harassment

constitute bullying? An exploration of online harassment by known

peers and online-only contacts. Journal of Adolescent Health, 41, S51–

S58.

Wölfer R. Schultze-Krumbholz A., Jäkel A., Zagorscak P. & Scheithauer

H. (2012). Medienhelden” against Cyberbullying: Results of the

German research team. Paper presented at the conference on

Cyberbullying in Adolescence, Bologna, 20 February.

Wood W. (2000), Attitude change: Persuasion and social influence. Annual

Review of Psychology, 51, 539-570.