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Theory-Based Formative Research on an Anti-Cyberbullying Victimization Intervention Message MATTHEW W. SAVAGE1, DOUGLAS M. DEISS, JR.2, ANTHONY J. ROBERTO3, and ELIAS ABOUJAOUDE4

1School of Communication, San Diego State University, San Diego, California, USA 2Department of Communication and World Languages, Glendale Community College, Glendale, Arizona, USA 3Hugh Downs School of Human Communication, Arizona State University, Tempe, Arizona, USA 4Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA

Cyberbullying is a common byproduct of the digital revolution with serious consequences to victims. Unfortunately, there is a dearth of empirically basedmethods to confront it. This study used social cognitive theory to design and test an interventionmessage aimed at persuading college students to abstain from retaliation, seek social support, save evidence, and notify authorities—important victim responses identified and recommended in previous research. Using a posttest-only control group design, this study tested the effectiveness of an intervention message in changing college students’ perceived susceptibility to and perceived severity of cyberbullying as well as their self-efficacy, response efficacy, attitudes, and behavioral intentions toward each recommended response in future episodes of cyberbullying. Results indicated that the intervention message caused participants in the experimental condition to report significantly higher susceptibility, but not perceived severity, to cyberbullying than those in the control condition. The intervention message also caused expected changes in all outcomes except self-efficacy for not retaliating and in all outcomes for seeking social support, saving evidence, and notifying an authority. Implications for message design and future research supporting evidence-based anti-cyberbullying health communication campaigns are discussed.

Cyberbullying is a serious public health concern (Centers for Disease Control and Prevention, 2016). Emphasizing the communicative aspect of cyberbullying, Roberto and Eden (2010) defined it as the “deliberate and repeated misuse of communication technology by an individual or group to threaten or harm others” (p. 201). As a problem of modern life, cyberbullying has garnered significant attention. The general media initially recognized tragic cases of cyberbully- ing-related suicides (e.g., Alvarez, 2013; BBC News, 2014; Stelter, 2008). Scholarly work has since produced a relatively large body of data highlighting the widespread dangerous nature of the behavior (Kowalski, Giumetti, Schroeder, & Lattanner, 2014).

Although most studies have focused on minors, cyberbullying can occur from elementary school to college. Among adolescents, cyberbullying victimization rates range from 20% to 40% (Moreno, 2014; Tokunaga, 2010). Studies of college students show similar rates (Crosslin & Golman, 2014; Foody, Samara, & Carlbring, 2015; Zalaquett & Chatters, 2014). Because cyberbullying has been examined as a youth problem, its prevalence in adults is unknown (Foody et al., 2015) despite three decades of interest in workplace bullying as a serious problem (Baum, Catalano, Rand, & Rose, 2009) and despite interest in cyberstalking as a possible adult

version of cyberbullying (e.g., Spitzberg & Hoobler, 2002). Because of the dearth of data in adults, it is unclear when cyberbul- lying stops being a serious problem.

College students’ cyberbullying victimization has been associated with depressive symptomatology (Feinstein, Bhatia, & Davila, 2014). In contrast, cyberbullying perpetra- tion has been associated with lower self-esteem (Na, Dancy, & Park, 2015); anger and stress (Zalaquett & Chatters, 2014); and higher scores on psychological measures of depression, paranoia and anxiety (Schenk, Fremouw, & Keelan, 2013). These problems underscore the need to develop and disseminate specific behaviors that can empower victims and minimize morbidity. This study utilized social cognitive theory (SCT; Bandura, 1986) to design and test a message aimed at persuading potential victims to enact spe- cific recommended behaviors if they are cyberbullied. To our knowledge, no investigation has incorporated these behaviors into an empirically tested, theory-based intervention message. Filling this gap contributes to health communication research, theory, and practice by helping meet calls for the- ory-based message design research (Harrington, 2015), ela- borating on message design using SCT (Noar et al., 2015) to inform evidence-based persuasive message design (Jacobs, Jones, Gabella, Spring, & Brownson, 2012), and supporting the development of cyberbullying prevention programs (Ramirez, Palazzolo, Savage, & Deiss, 2010). Theoretical underpinnings are discussed, followed by an overview of message design and study results.

Address correspondence to Matthew W. Savage, School of Communication, San Diego State University, San Diego, CA 92182-4560, USA. E-mail: [email protected]

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uhcm.

Journal of Health Communication, 22: 124–134, 2017 Copyright © Taylor & Francis Group, LLC ISSN: 1081-0730 print/1087-0415 online DOI: 10.1080/10810730.2016.1252818

Recommended Behaviors for Victims

Strategies for handling cyberbullying victimization are recom- mended in school-based prevention programs and online web- sites. Aboujaoude, Savage, Starcevic, and Salame (2015) reviewed school-based prevention programs, including the Social Networking Safety Promotion and Cyberbullying Prevention Program (Arizona Attorney General, 2016), Media Heroes (Wölfer et al., 2014), and Cyberbullying: A Prevention Curriculum (Limber, Kowalski, & Agatston, 2008). These pro- grams all consistently recommend four responses to victims: Do not retaliate, seek social support, save evidence, and notify authorities. A review of webpages providing cyberbullying advice to parents, youth, school personnel, and authorities (e.g., stopbullying.gov, stopcyberbullying.org, wiredsafety.org, safeteens.com) showed a consensus on these four strategies. Thus, these behaviors were investigated herein.

Do Not Retaliate

Although some researchers consider not retaliating a passive and therefore potentially ineffective strategy (Patchin & Hinduja, 2006), others find it worthwhile, as it can stop the conflict from escalating and prevent victims from becoming bullies themselves. Research from the field of conflict manage- ment supports the latter claim (Roloff & Parks, 2002).

Seek Social Support

Seeking social support is similarly advantageous. More than 90% of adolescent cyberbullying victims do not inform adults of their victimization (Aricak et al., 2008; Dehue, Bolman, & Vollink, 2008; Juvoven & Gross, 2008; Slonje & Smith, 2008), perhaps out of embarrassment or fear that their device might be confiscated (Aboujaoude et al., 2015). But although informing an authority figure such as a parent or teacher is unlikely, victims find it easier to consult with friends (Aricak et al., 2008; Dehue et al., 2008; Slonje & Smith, 2008; Topcu, Erdur-Baker, & Capa-Aydin, 2008). This suggests a teachable behavior with potentially significant rewards, as an extensive literature documents the relational, health, and psychological benefits of such support (Burleson & MacGeorge, 2002).

Save Evidence and Notify Authorities

Because saving evidence is typically only useful if an authority is notified, saving evidence and notifying authorities are linked behaviors. Holding on to cyberbullying evidence is a behavior that most victims report knowing how to do (Juvoven & Gross, 2008). Notifying authorities when cyberbullied, however, is more complicated, primarily because of the need to interact with an external resource, which raises fears similar to those that prevent adolescent victims from approaching parents or teachers. Once notified, law enforcement offices may investi- gate claims, but laws vary greatly across states and jurisdic- tions, as does the level of protection (Aboujaoude et al., 2015). A more convenient way to notify authorities may be by reach- ing out to an Internet service provider or an information tech- nology office. These impersonal reporting strategies are distinct

from soliciting social support from friends, family, or peers that aims to buffer the psychosocial impact of a cyberbullying event. Notifying authorities establishes official documentation and may lead to a formal investigation.

SCT and Message Design

SCT describes how an individual’s knowledge acquisition and behaviors are largely a function of observing others interact in social settings or in the media (Bandura, 2008). When people observe a model performing a behavior and the subsequent consequences of that behavior, they use this information to guide their own behaviors (Bandura, 1977, 1986, 2001). Observers do not learn new behaviors solely by trying them and either succeeding or failing but rather by replicating others’ actions depending on whether those actions and their outcomes resulted in reward or punishment. The theory can be applied to persuasive message development through the use of storytelling and narratives that foster behavior change via peer modeling (Hinyard & Kreuter, 2007; Noar et al., 2015). In this study, SCT was used to design an intervention message that aimed to encourage certain anti-cyberbullying strategies by illustrating how one can successfully navigate an instance of being cyber- bullied by adopting the recommended responses. The theory has been utilized in multiple arenas, including health promotion and disease prevention (e.g., Plotnikoff, Costigan, Karunamuni, & Lubans, 2013; Van Zundert, Nijhof, & Engels, 2009; Young, Plotnikoff, Collins, Callister, & Morgan, 2014), marketing (Phipps et al., 2013), and others that are beyond the scope of this review (for a review, see Rosenthal & Zimmerman, 2014). The effectiveness of an SCT approach can be determined by changes in perceptions of common outcome variables generally consistent across theories of behavior change (Noar & Zimmerman, 2005), including susceptibility, severity, self-effi- cacy, response efficacy, attitudes, and behavioral intentions.

Although there are alternative ways to organize SCT con- structs (e.g., Kelder, Hoelscher, & Perry, 2016), we depended on five major theoretical components as outlined by McAlister, Perry, and Parcel (2008): (a) observational learning, (b) psy- chological determinants of behavior, (c) environmental deter- minants of behavior, (d) self-regulation, and (e) moral disengagement. Each was incorporated into an intervention message created to persuade cyberbullying victims to not retali- ate, to seek social support, to save evidence, and to notify authorities. Figure 1 shows the intervention message designed using SCT components. Exemplars of SCT application are described in the text below, but because of space considerations these descriptions are not exhaustive.

Observational Learning

SCT emphasizes the capacity to learn by witnessing examples and the process of observational learning: (a) attention, (b) retention, (c) production, and (d) motivation (Bandura, 2008). In the intervention message, a cyberbullying narrative was utilized to capture readers’ attention by depicting a relevant cyberbullying experience. Following recommendations for per- suasive narrative construction (Green & Brock, 2000), the story

Cyberbullying Victimization 125

Fig. 1. Intervention message designed using the social cognitive theory framework.

126 M. W. Savage et al.

was designed to promote the process of observational learning of each recommended response. The intervention message was designed at a seventh-grade reading level. Scholars have had success promoting observational learning by using social norms approaches to address bullying behaviors (Perkins, Craig, & Perkins, 2011).

Psychological Determinants of Behavior

Two psychological determinants of behavior were integrated into the intervention message: outcome expectations and self- efficacy. First, outcome expectations represent beliefs about the likelihood and perceived value of various behaviors (Viswanath, 2008). Providing information about consequence is a behavioral change technique modeled after SCT (Abraham & Michie, 2008). In weighing outcome expectations, indivi- duals typically seek to minimize costs and maximize benefits. For example, outcome expectations in the intervention message are enhanced when the protagonist experiences reduced nega- tive consequences as a result of adopting the recommended behaviors. Second, self-efficacy refers to an individual’s con- fidence in and ability to adopt a behavior (Bandura, 1997; Betz, 2013). It increases when individuals believe they possess the knowledge and skills to perform a task. Thus, the intervention message stated, “If I can deal with a cyberbully, you can too.” Such motivational statements can increase confidence in one’s ability to model the recommended behaviors, thereby promot- ing self-efficacy. A step-by-step list also followed the narrative summarizing each recommended behavior with instructions consistent with how each behavior was modeled in the narra- tive. Easy-to-follow directions for adopting behavioral change enhance self-efficacy (Bandura, 2001).

Environmental Determinants of Behavior

Behavioral change is likely when the environment encourages and allows the new behaviors (Bandura, 2004). Facilitation describes when new resources make recommended behaviors easier to enact. Research shows that efficacious people are more successful at finding opportunities in the environment and cir- cumventing constraints (Kelder et al., 2016). In the intervention message, for example, the narrative described resources in one’s environment that simplify the adoption of each recommended behavior. Resources to foster each recommended behavior were enhanced in the step-by-step list following the narrative.

Self-Regulation

Self-regulation is a distinct behavioral skill whereby exercise of control allows one to more successfully perform recommended behaviors (Bandura, 1997). Two factors known to bolster self- regulation (Bandura, 1986, 1991; Vohs & Baumeister, 2011) were utilized in the intervention message: goal setting and self- monitoring. Goal setting involves establishing ideal incremental and long-term outcomes and determining paths to reaching them. Self-monitoring involves the systematic observation of one’s own behavior. In the intervention message, for example, verbiage called to “make it your goal” to adopt the recom- mended behaviors as well as described how self-monitoring

during and after adopting the set of recommended behaviors led to successful performance. Scholarship suggests the utility of tapping into self-regulation to achieve behavior change (e.g., Ramdass & Zimmerman, 2011).

Moral Disengagement

Bandura (1991) described how when people learn moral stan- dards for their behavior, it can lead to being less violent and cruel. Bussey, Fitzpatrick, and Raman (2015) demonstrated that cyberbullying rates were positively associated with moral dis- engagement proneness. Such findings demonstrate the need to help victims distinguish humor (e.g., teasing) from bullying so that a cyberbully is not given moral justification via the assumption that it is humorous. In the intervention message, the narrative described that the protagonist determined that being cyberbullied was not a joke and reinforced that others would provide empathy in the situation. This served to huma- nize the cyberbullying episode and enhance adoption of the recommended behaviors.

Dependent Variables and Hypotheses

The effectiveness of SCT in causing behavioral change can be measured by changes in outcome variables across behavior change theories (Noar & Zimmerman, 2005), including susceptibility, severity, self-efficacy, response efficacy, attitudes, and behavioral intentions (see Witte, 1992). Susceptibility is the likelihood that a threat will occur. Severity is a perception of how bad or harmful an act, experience, or threat is evaluated. Self-efficacy refers to an individual’s perceived ability and confidence to enact a recom- mended response. Response efficacy refers to a belief that a recommended response will be effective. Attitudes are general evaluations or positive versus negative feelings toward a recom- mended response (Kim & Hunter, 1993; O’Keefe, 2002). Behavioral intentions refers to an individual’s likelihood of adopt- ing a recommended response (Ajzen & Fishbein, 1980). These outcomes served as dependent variables and were used to compare the effects of the intervention message.

We hypothesized (Hypotheses 1–2) that those exposed to the intervention message would report higher perceived suscept- ibility to (Hypothesis 1) and higher perceived severity of (Hypothesis 2) cyberbullying than those exposed to the control message. Furthermore, we hypothesized (Hypotheses 3–6) that those exposed to the intervention message would report higher self-efficacy beliefs, higher response efficacy, more favorable attitudes, and greater intentions regarding not retaliating (Hypothesis 3), seeking social support (Hypothesis 4), saving evidence (Hypothesis 5), and notifying authorities (Hypothesis 6) than those exposed to the control message.

Method

Participants and Procedures

Participants (N = 734; 55.3% women) with a mean age of 20.63 (SD = 2.43) were recruited from a large university in the south- western United States. Participants were White/Caucasian (79.6%), Asian/Asian American (6.0%), African American/

Cyberbullying Victimization 127

Black (5.1%), Native American (1.9%), Pacific Islander (1.3%), or other (13.2%).

The study was completed online with participants receiving incentive research credit. Following online study recommenda- tions (Birnbaum, 2004), participants completed all procedures in a setting of their choice via a secure website. Participants were randomly assigned to the experimental (n = 375) or con- trol (n = 359) condition then exposed to their respective mes- sage before completing an online survey. The experimental group was exposed to an intervention message (see Figure 1) designed as a realistic cyberbullying scenario but written in narrative form, illustrating the recommended responses with applicable reinforcing rewarding outcomes. A concise step-by- step list of the four recommended responses followed. The control group was exposed to an attention control message (see Figure 2) that included a simple definition of cyberbully- ing. Because cyberbullying is a novel concern to college stu- dents, a simple definition was all that was needed to prime the control group on the topic and generate a comparison. Attention control messages have been used in persuasion research to foster engagement with the topic using basic information (Noar, Harrington, & Aldrich, 2009; Roberto, Krieger, & Beam, 2009). They are regularly used in intervention studies (e.g., Neil & Christensen, 2009) that use SCT (e.g., Stacey, James, Chapman, Courneya, & Lubans, 2015) and are

recommended in evidence-based strategies (Flay et al., 2005) for public health research (Jacobs et al., 2012).

Measures

All measures, unless noted, were adapted from Witte, Cameron, McKeon, and Berkowitz’s (1996) Risk Behavior Diagnosis scale and measured on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Reliability estimates for all vari- ables were acceptable or better (see Table 1). Perceived severity

Table 1. Reliability, descriptive statistics, and MANCOVA results by condition for each set of dependent variables

Reliability Control Experimental Univariate results

Variable (multivariate result) α M SD M SD F(df) p ηp 2

Overall (Λ = .99, F(2, 672) = 3.18, p = .042*) Susceptibility to CB .84 2.56 0.93 2.73 0.89 F(1, 673) = 6.09 .01* .01 Severity of CB .93 3.34 1.05 3.36 0.94 F(1, 673) = 1.14 .29 .00

Not retaliate (Λ = .98, F(4, 673) = 3.01, p = .018*) Self-efficacy .85 3.23 0.91 3.22 0.92 F(1, 676) = 0.02 .89 .00 Response efficacy .81 3.28 0.84 3.41 0.80 F(1, 676) = 5.36 .021* .01 Attitude .88 3.71 0.90 3.84 0.88 F(1, 676) = 8.10 .005** .01 Behavioral intention .95 3.45 0.92 3.59 0.93 F(1, 676) = 7.36 .007** .01

Social support (Λ = .98, F(4, 672) = 4.11, p = .003**) Self-efficacy .70 3.59 0.72 3.72 0.73 F(1, 675) = 12.31 <.001*** .01 Response efficacy .78 3.26 0.84 3.42 0.70 F(1, 675) = 8.73 .003** .01 Attitude .87 3.65 0.89 3.84 0.84 F(1, 675) = 9.27 .002** .01 Behavioral intention .95 3.27 1.04 3.48 0.95 F(1, 675) = 9.44 .002** .01

Save evidence (Λ = .98, F(4, 671) = 3.50, p = .008**) Self-efficacy .86 4.07 0.74 4.15 0.71 F(1, 674) = 4.10 .043* .01 Response efficacy .81 3.48 0.84 3.69 0.75 F(1, 674) = 12.08 .001** .02 Attitude .89 4.19 0.81 4.31 0.83 F(1, 674) = 8.49 .004** .01 Behavioral intention .94 3.77 0.94 3.93 0.91 F(1, 674) = 5.19 .023* .01

Notify authority (Λ = .98, F(4, 673) = 4.21, p = .002**) Self-efficacy .81 3.46 0.83 3.59 0.83 F(1, 676) = 8.78 .003** .01 Response efficacy .82 3.25 0.85 3.47 0.76 F(1, 676) = 14.24 <.001*** .02 Attitude .88 3.63 0.95 3.81 0.93 F(1, 676) = 6.79 .009** .01 Behavioral intention .96 3.20 1.06 3.34 1.02 F(1, 676) = 9.45 .002** .01

Note. Each MANCOVA controlled for technology use, CB perpetration, and CB victimization. All means are uncorrected, using all participant responses. MANCOVA = multivariate analysis of covariance; CB = cyberbullying.

*p < .05. **p < .01. ***p < .001.

Fig. 2. Control group message: Attention control message.

128 M. W. Savage et al.

was measured with a 3-item scale that assessed participants’ perceived seriousness of cyberbullying (e.g., “I believe cyber- bullying is severe”). Perceived susceptibility was measured with a 3-item scale that assessed participants’ perceived like- lihood of experiencing cyberbullying (e.g., “I am at risk of being cyberbullied”). Self-efficacy, response efficacy, attitudes, and behavioral intentions were measured regarding each of the four behavioral recommendations (not retaliating, seeking social support, saving evidence, and notifying an authority). Self-efficacy measured participants’ confidence in their ability to adopt each of the recommended responses with three items (e.g., “I would be able to [insert strategy] if I am cyberbullied”). Response efficacy measured participants’ perceived likelihood of success when enacting each of the recommended responses with three items (e.g., “[Insert strategy] works to prevent cyber- bullying”). Attitudes were measured using 4-item semantic differential scales developed (Himmelfarb, 1993) and used in persuasion research (Roberto et al., 2009), including items that asked participants to choose between opposite adjectives: bad/ good, useless/useful, harmful/helpful, and detrimental/benefi- cial. Higher scores indicated more positive attitudes. Behavioral intention for each recommended response was mea- sured with four items (e.g., “The next time I am cyberbullied I intend to [insert strategy]”). Behavioral intention is highly correlated with actual behavior (Albarracin, Johnson, Fishbein, & Muellerleile, 2001; Downs & Hausenblas, 2005), making it suitable for self-report.

Cyberbullying Perpetration and Victimization Single-item dichotomous measures (yes/no) asked “In the last 12 months, did you ever repeatedly use communication tech- nology to deliberately hurt or embarrass others in an unfriendly way?” and “In the last 12 months, did anyone ever repeatedly use communication technology to deliberately hurt or embar- rass you in an unfriendly way?” (see Roberto, Eden, Savage, Ramos-Salazar, & Deiss, 2014a, 2014b).

Technology Use Access to communication technology (a personal computer and cell phone), easy access to e-mail and the Internet, and whether participants had social media accounts were measured dichot- omously. These items were summed to measure technology use (Roberto et al., 2014a, 2014b).

Results

Descriptive Statistics

Approximately 21% (n = 154) of participants reported having been cyberbullied in the past 12 months. Cyberbullying victimization did not differ significantly across college years, χ2(4) = 6.00, p = .20. A total of 22% of freshmen, 25% of sophomores, 24% of juniors, and 17% of seniors were victims. Victimization did not differ signifi- cantly by sex, χ2(2) = 0.62, p = .74. In all, 22% of men and 20% of women reported victimization.Also, 14.6% (n=107) of participants reported having been a cyberbullying perpetrator in the past 12 months. Cyberbullying perpetration did not differ significantly across college class level, χ2(4) = 7.14, p = .13. In all, 18% of freshmen, 18% of sophomores, 15% of juniors, and 10% of seniors

reported perpetrating. Perpetration did not differ significantly by sex, χ2(2) = 3.84, p = .15. A total of 16% of men and 13% of women reported perpetration. Table 1 shows descriptive data and provides raw means to compare conditions for all outcome variables.

Hypotheses

A series ofmultivariate analyses of covariance (MANCOVAs)were used to analyze the effect of condition (control or experimental) across sets of dependent variables while controlling for technology use (Roberto et al., 2014a), cyberbullying perpetration (no or yes), and cyberbullying victimization (no or yes). Prior to analyses, cases (n = 18) identified as within-cell outliers using the Mahalanobis distance (p < .001; Tabachnick & Fidell, 2007) were removed. Pairwise deletion was used to analyze all available data. Univariate effects were interpreted as follow-up when significant multivariate effects were present to determine support for the hypotheses. Partial eta squared was used to estimate effect size (Richardson, 2011). Table 1 presents detailed results.

For Hypotheses 1–2, perceived susceptibility and severity served as the dependent variables. Bartlett’s test of sphericity indicated a significant (p < .001) correlation (r = .23). A sig- nificant multivariate main effect emerged for condition. For perceived susceptibility, results revealed a significant univariate main effect for condition. In support of Hypothesis 1, partici- pants in the experimental condition reported significantly higher susceptibility to cyberbullying than those in the control condition. For perceived severity, no significant univariate effects emerged. Thus, Hypothesis 2 was not supported.

For Hypotheses 3–6, self-efficacy, response efficacy, attitude, and behavioral intention served as the dependent variables in sepa- rate analyses for each recommended response: not retaliating (Hypothesis 3), seeking social support (Hypothesis 4), saving evi- dence (Hypothesis 5), and notifying authorities (Hypothesis 6). Bartlett’s test of sphericity was significant (p < .001) in all analyses, indicating significant average correlations between the dependent variables in each analysis (r2 = .50–.55). A significant multivariate main effect emerged for condition in eachMANCOVA. Hypothesis 3 was partially supported, as univariate results indicated that the intervention message caused expected changes in all outcomes for not retaliating except self-efficacy. Hypotheses 4–6 were supported, as univariate results in each MANCOVA demonstrated that the intervention message caused expected changes in all outcomes for seeking social support, saving evidence, and notifying an authority. Univariate effect sizes for all results were small (ηp

2 = .01–.02).

Post Hoc Analysis

Four stepwise regression models examined the relationships between perceived susceptibility, perceived severity, self-efficacy, and response efficacy with behavioral intention for each recom- mended behavior in Step 2 while controlling for experimental con- dition, sex (0 =male, 1 = female), technology use, and cyberbullying perpetration and victimization (0 = no, 1 = yes) in Step 1. All Step 2 predictors were significantly correlated with intention. Results are presented in Table 2. All models were significant, with large explained proportions of variance (finalR2 = .43–.47). In all models,

Cyberbullying Victimization 129

significant R2 changes indicated that the addition of perceived susceptibility, perceived severity, self-efficacy, and response effi- cacy in Step 2 explained 34%–40% of variance beyond the controls. Perceived severity, self-efficacy, and response efficacy were signifi- cant predictors of behavioral intention toward each recommended behavior. With one exception, susceptibility was not a significant predictor. Effect sizes using part correlations squared indicated that perceived severity, self-efficacy, and response efficacy each explained 3%–14% of unique variance. Consistent results emerged when the same analyses were conducted with attitude toward each recommended behavior as the criterion variable.

Discussion

An anti-cyberbullying intervention message was designed using SCT, and the experimental group exposed to the intervention message was expected to have higher perceived susceptibility and severity regarding cyberbullying. In addition, the experi- mental group was expected to have superior self-efficacy; superior response efficacy; as well as more positive attitudes about, and intentions to act on, the following recommendations: avoid retaliation, seek social support, save evidence, and notify authorities. Results show the persuasiveness of the intervention message in reaching the majority of these goals and offer insight into message design.

Cyberbullying is a prevalent and serious problem among college students. Current data on the association between age and vi

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