The program

The program utilizing the IMB framework is designed for adolescents. It is facilitated in a group setting, with the goal of increasing online risk perception, ultimately resulting in behavior change.

Interventions adopting the IMB model are usually tailored to individuals’ current information, motivation, and behavioral skills (Kalichman et al., 2002; Osborn & Egede, 2010). This makes the intervention particularly effective since it targets specific risk behaviors and perceptions (DiClemente, Crosby, & Kegler, 2009; Fisher & Fisher, 1992; Suls & Wallston, 2008), which are seen to be personally relevant, making it more likely that participants will internalize and reflect on the risks and adjust their risk perception. This tailoring is an important aspect to the program. Thus, an initial baseline questionnaire should be administered to the group of adolescents being targeted by the intervention in order to screen their online behaviors, perceptions, and experiences.

This questionnaire should ask adolescents about their online risk perception and various online risk behaviors. It can focus on their experience and involvement in both cyberaggression and cyberbullying as victims, perpetrators, and witnesses, but there is scope for flexibility in terms of what is included. In our pilot study, victimization and perpetration of eight cyberaggression behaviors were explored, including name calling, spreading rumors, and impersonation, according to the categories of behaviors outlined by Patchin and Hinduja (2006) and Willard (2007). Cyberbullying was assessed subjectively, by asking adolescents whether they had ever been cyberbullied, as our previous focus group data from schools in South Africa indicated that adolescents had a good understanding of what constituted cyberbullying actions. Items relating to the emotional effects of these experiences were also included to gauge the severity of these online experiences. The different sections of the baseline questionnaire and example items are shown in Table 17.1.

The baseline questionnaire can be administered in a class session, and due to the highly personal and potentially sensitive information provided by students, it should be anonymous, perhaps utilizing an online survey service such as Survey Monkey (https://www.surveymonkey.com). Moreover, Survey Monkey can automatically generate simple results relating to prevalence rates that are needed to plan the intervention, without needing any complicated and time-consuming analyses by those undertaking the intervention. This can reduce the time between the baseline questionnaire and the intervention, enabling practitioners to focus their efforts on preparing the tailored activities. Although variation of online behaviors and experiences within the same class is to be expected, the focus in the intervention should be on the students’ most commonly reported behaviors and experiences. To protect students’ confidentiality and anonymity, it is best to use closed-ended questions and not ask for details about specific incidents. Practitioners should omit any details regarding specific incidents from the intervention, and be sensitive to ensuring confidentiality of what students may have reported in the baseline questionnaire.

The information in the baseline questionnaire leads to the development of a focused, targeted intervention, as the practitioner now has an understanding of the key issues. This information is used to inform the content of the program, with participants (i) receiving relevant information about cyberaggression and cyberbullying, (ii) being motivated to make a change in their behaviors, and (iii) enhancing their practical skills and self-confidence to elicit behavior change.

For the information component of the model, participants receive feedback about the prevalence rates of cyberaggression and cyberbullying in their grade and discuss the possible consequences of victimization and perpetration. The different types of behaviors that are experienced (e.g., online threats, name-calling, etc.) are also discussed in more detail. Multimedia learning is recommended (Mayer, 2005), through the use of short video clips, newspaper articles, images, and current research to guide discussions. Participants are also informed about the prevalence rates of witnessing cyberaggression and cyberbullying and reflect on their role as bystanders. During these discussions, the practitioner’s role is to present the key issues and facilitate group discussion in a multimedia environment. The practitioner could also introduce alternative perspectives in order to challenge views and correct any myths and misconceptions that might be held (e.g., victim blaming attitudes).

Understanding the short- and long-term effects of cyberaggression and cyberbullying and discussing these from multiple viewpoints (as victims, perpetrators, and witnesses) can motivate behavior change and create negative social norms relating to these behaviors. It can also increase empathy, encourage help seeking, and engage bystanders. Thus, reflecting on and discussing the issues they face is important in enhancing both personal and social motivation among adolescents. Two activities were particularly effective in this regard. Firstly, students are asked to draw cards containing various cyberaggression or cyberbullying scenarios from the perspective of victims, perpetrators, or witnesses, which were developed for the intervention (see Box 17.1 for examples). For each scenario, students discuss in pairs (i) how they would feel, and (ii) what they could do, if this happened to them. A class discussion follows. This activity encourages students to think about both consequences and strategies to address the issues. Secondly, students are asked to advise one another about an incident of cyberaggression or cyberbullying they were currently (or previously) experiencing, each of which was written anonymously on a piece of paper at the start of the workshop. Advising someone else about a real-life scenario potentially gives participants the opportunity to problem solve about issues that they might themselves encounter online in the future. It also allows participants to give one another relevant advice, promoting peer support and positive social norms in the class.

Although similar, these exercises are quite distinct. The first is more general and allows students to reflect on emotions and effects of behaviors and allows them to begin thinking about possible solutions. This leads to the second exercise, which is more personal as it relates to a peer’s real experience. Both activities address personal and social motivation as well as behavioral skills through practical knowledge and by enhancing self-confidence in online contexts. It also encourages participation of all students in the intervention. If time allows, students can work in groups and think of strategies that could be useful to address the scenarios they have been given in both exercises more broadly, such as in their class and in their school. Coming up with problem-solving strategies related to cyberaggression and cyberbullying is very useful as the students’ ideas could feed into broader school strategies.

In addition to these exercises aimed at enhancing self-confidence related to online safety issues, practitioners should also discuss practical skills (e.g., privacy settings, blocking websites) and the available resources and support both within and outside the school (e.g., reporting mechanisms at school). The main aspects of the intervention program are summarized in Table 17.2. Further resources relating to the intervention (i.e., sample questionnaire, teacher guide, and activity plans) can be found at https://www.buckingham.ac.uk/cyberbullying.

This intervention, tailored to adolescents’ own behaviors and experiences, along with a focus on addressing all three components of the IMB model, encourages adolescents to take ownership of their online safety, to be mindful of their online behaviors and their potential impact on others, and to take action as bystanders through increased self- confidence and understanding of the support available to them. The steps in the program thus include (i) administering an initial questionnaire to screen adolescents’ perceptions and experiences, (ii) developing a focused intervention using the information provided by participants and addressing all three components of the IMB model, and (iii) facilitating the workshop by promoting discussion and problem solving (see Fig. 17.2). Practitioners can re-administer the baseline questionnaire (or parts thereof) periodically to assess further areas to work on, especially given the pace with which technology evolves, as well as to evaluate the effectiveness of the intervention. A tailored intervention provides practitioners with the flexibility to customize the program to the current issues being experienced by participants, ensuring that the workshop is appropriate, relevant, and comprehensively addresses the issues through the IMB framework.

The study was conducted at an all-female high school in Cape Town, South Africa, due to reports of differences in online activities and risk experiences between males and females (e.g., Mitchell, Finkelhor, Jones, & Wolak, 2012; Ybarra, Alexander, & Mitchell, 2005). Extensive data about online risk behaviors and perceptions were collected for 91 grade 8 and 9 students at the end of the 2013 school year. The intervention took place 7 months later, and was conducted with the original participants, now in grades 9 and 10. The intervention was tailored for these participants. The new grade 8 students of 2014 were also included in the intervention, enabling them to benefit from it. However, since they did not complete the baseline questionnaire, their intervention was not tailored, allowing an exploratory comparison of the effectiveness of a tailored versus non-tailored intervention.

A total of 177 females between the ages of 13–16 years (M = 14.5) participated in the intervention, representing early and middle adolescence (see Table 17.3). Each participating grade (8, 9 and 10) consisted of three classes. The first author facilitated one 50-min workshop with each of the 9 classes. The intervention received the necessary ethical approval. Consent and assent for participation in the initial questionnaire and the intervention workshop was obtained from the parents and adolescents, respectively. No parent withheld consent and none of the adolescents chose not to participate in the intervention.

The baseline questionnaire: Behaviors and perceptions to target in the intervention

The initial questionnaire showed that 71% of females had experienced cyberaggression, of whom 35% indicated that they had ever been cyberbullied. Most participants knew the identity of the perpetrator (87%) and were most likely to tell a friend about the experience (44%). Half also admitted that they had perpetrated cyberaggression (51%). Most (72%) had witnessed cyberbullying, with 16% witnessing it often. The majority also knew someone who had been cyberbullied (64%). Unsurprisingly, given these findings, 68% of participants believed that cyberbullying was currently a very serious issue. Further examples of cyberaggression experiences reported at baseline are shown in Table 17.4. Reliability analyses of the scale showed a Cronbach’s alpha of .82.

The severity of these online experiences is reflected in the emotional distress reported by some adolescents (see Table 17.5). Examples of online risk perception items are also shown in Table 17.5, showing the participants’ concerns and their perceptions of how controllable the risks are, which were addressed in the intervention through enhancing behavioral skills.

Assessing the intervention’s effectiveness

The intervention was evaluated using the online risk perception scale from the baseline questionnaire. This 15-item scale was developed from existing risk perception literature (e.g., Benthin et al., 1993; Boholm, 1998; Turow & Nir, 2000). Validity testing of the scale showed that the items reflected four categories of risk perception, including: (i) costs vs. benefits of the internet (e.g., “The benefits of the internet are far bigger than any dangers”); (ii) knowledge of risks (e.g., “People on the internet are usually honest about who they are”); (iii) fear and controllability of risks (e.g., “I cannot control the things that happen to me on the internet”); and (iv) desire for regulation (e.g., “It is important that adults keep a watch over teenagers’ internet behaviors”). Participants indicated their agreement or disagreement on a 5-point Likert scale, with a midpoint neutral option. The scale demonstrated adequate reliability (Cronbach’s alpha = .78).

The risk perception scale was administered either before or after the intervention. One randomly selected class per grade completed the questionnaire before the intervention (control group), the other two classes completing it after the intervention (intervention group). It was not feasible in the time available for participants to complete the instrument both before and after the intervention, and participants might have recalled their responses, potentially confounding the results. Moreover, this would have reduced the time available for the intervention. Thus, independent groups with 50 participants in the control and 124 in the intervention were used to evaluate the effectiveness of the IMB Skills model as an intervention for cyberaggression and cyberbullying.

An overall online risk perception score was calculated for each participant. Negatively worded items were reverse scored and response options were assigned scores of − 2, − 1, 0, + 1, and + 2, resulting in overall scores ranging from − 30 to + 30. A higher score reflected higher risk perception (i.e., greater appreciation of risk) and a lower score reflected lower risk perception.

Two verification analyses were conducted prior to the main analysis. Firstly, online risk perception of the new grade 8 students were compared with those of the previous year (i.e., 2014 students vs. 2013 students) using independent samples t-tests. Since the new students did not complete the baseline questionnaire, it was important to ensure that they did not significantly differ from the previous grade 8’s. The results confirmed that there was no significant difference in online risk perception between the two groups (2013 grade 8’s: M = 1.42; 2014 grade 8’s: M = 1.88).

Secondly, the differences in risk perception between the (i) baseline questionnaire, (ii) control and (iii) intervention groups were compared (one-way ANOVA). There was a significant difference between the groups, F(2, 255) = 5.10, p = .007, η² = .04. This was due to the intervention group having significantly higher risk perception scores (M = 3.52, SD = 5.69, SE = 0.51) compared to both other groups, providing initial evidence that the intervention increased online risk perception. No significant difference was found between the baseline questionnaire (M = 1.40, SD = 6.86, SE = 0.76) and the control group (M = 0.60, SD = 6.44, SE = 0.91). As both occurred prior to the intervention, this ruled out a significant maturation effect over the 7-month period between data collection and the intervention.

The control and intervention groups were then compared for each grade. Main effects were found for both group (control and intervention) and grade (8, 9, and 10). The intervention group had significantly higher risk perception than the control, as established in the verification analysis above, F(1, 174) = 11.65, p = .001, η² = .07. Risk perception was also significantly lower with each subsequent grade for both groups combined, i.e., risk perception in grade 8 was highest (M = 3.90, SE = 1.29), followed by grade 9 (M = 1.87, SE = − 0.84) and grade 10 (M = − 0.25, SE = 0.92), F(2, 174) = 5.96, p = .003, η² = .07. With the difference between grades 8 and 10 being significant (p = .017), this suggests that risk perception decreased with age. A significant negative correlation between grade and risk perception further demonstrates this (r = − 0.21, p = .007). This pattern has also been established in previous research: although risk taking and risky decision making generally decrease with age (Gardner & Steinberg, 2005), mid-adolescence has been identified as a period of heightened vulnerability in risky behavior (Steinberg, 2008, 2010). The current study likewise indicates that middle adolescence may also be a particularly vulnerable period for cyberaggression and cyberbullying. Although the interaction was nonsignificant, there was a larger proportional difference in risk perception scores between the control and intervention groups in the higher grades (see Fig. 17.3), possibly because they received the tailored intervention. This suggests that the intervention may be especially effective during middle adolescence when it is tailored to participants’ risk behaviors and experiences. During the intervention itself, many participants were surprised by the prevalence rates of cyberaggression and cyberbullying in their grade. A tailored intervention likely makes it more difficult to dismiss the personal relevance of the issues, which is important in enhancing risk perception. Knowing that others are encountering similar experiences may also be powerful in influencing risk perception.

Despite the small effect sizes of these findings, preliminary analysis suggests that the intervention had some positive effect on risk perception. The individual items in the risk perception scale show this (see Table 17.6). For example, fewer adolescents in the intervention group thought that the benefits of the internet outweigh any risks. This is a positive shift as the intervention is aimed at enhancing understanding and appreciation of risks in order to make informed behavioral choices when using technology.

These results provide preliminary evidence for the IMB model as an effective framework for online safety efforts, which should form part of broader strategies (see later). It also highlights the importance of tailored interventions and targeting those at middle adolescence in particular. Further interventions with additional pre- and post-test groups are underway to clarify these links.