Download A social cognitive perspective in cyberbullying prevention

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 peerrejected 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, selfinstruction, 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, selfevaluation through keeping personal records and monitoring of the goalsetting 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; ShamayTsoory 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 personalitybehaviour 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 selfregulation 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 selfefficacy 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 selfefficacy 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 anticyberbullying 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). Classroombased 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-toface 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 intentionbehavior 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 AfricanAmerican 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, 379385.
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, 435442.
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 selfchange 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.