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

1School of Communication, San Diego State University, San Diego, California, USA2Department of Communication and World Languages, Glendale Community College, Glendale, Arizona, USA3Hugh Downs School of Human Communication, Arizona State University, Tempe, Arizona, USA4Department 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 ofempirically basedmethods to confront it. This study used social cognitive theory to design and test an interventionmessage aimed at persuadingcollege students to abstain from retaliation, seek social support, save evidence, and notify authorities—important victim responses identifiedand recommended in previous research. Using a posttest-only control group design, this study tested the effectiveness of an interventionmessage in changing college students’ perceived susceptibility to and perceived severity of cyberbullying as well as their self-efficacy, responseefficacy, attitudes, and behavioral intentions toward each recommended response in future episodes of cyberbullying. Results indicated that theintervention message caused participants in the experimental condition to report significantly higher susceptibility, but not perceived severity, tocyberbullying than those in the control condition. The intervention message also caused expected changes in all outcomes except self-efficacyfor not retaliating and in all outcomes for seeking social support, saving evidence, and notifying an authority. Implications for message designand future research supporting evidence-based anti-cyberbullying health communication campaigns are discussed.

Cyberbullying is a serious public health concern (Centers forDisease Control and Prevention, 2016). Emphasizing thecommunicative aspect of cyberbullying, Roberto and Eden(2010) defined it as the “deliberate and repeated misuse ofcommunication technology by an individual or group tothreaten or harm others” (p. 201). As a problem of modernlife, cyberbullying has garnered significant attention. Thegeneral 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 relativelylarge body of data highlighting the widespread dangerousnature of the behavior (Kowalski, Giumetti, Schroeder, &Lattanner, 2014).

Although most studies have focused on minors, cyberbullyingcan 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 similarrates (Crosslin & Golman, 2014; Foody, Samara, & Carlbring,2015; Zalaquett & Chatters, 2014). Because cyberbullying hasbeen examined as a youth problem, its prevalence in adults isunknown (Foody et al., 2015) despite three decades of interest inworkplace 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 beenassociated 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 ofdepression, paranoia and anxiety (Schenk, Fremouw, &Keelan, 2013). These problems underscore the need todevelop and disseminate specific behaviors that can empowervictims and minimize morbidity. This study utilized socialcognitive theory (SCT; Bandura, 1986) to design and test amessage aimed at persuading potential victims to enact spe-cific recommended behaviors if they are cyberbullied. To ourknowledge, no investigation has incorporated these behaviorsinto an empirically tested, theory-based intervention message.Filling this gap contributes to health communicationresearch, 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) toinform evidence-based persuasive message design (Jacobs,Jones, Gabella, Spring, & Brownson, 2012), and supportingthe development of cyberbullying prevention programs(Ramirez, Palazzolo, Savage, & Deiss, 2010). Theoreticalunderpinnings are discussed, followed by an overview ofmessage design and study results.

Address correspondence to Matthew W. Savage, School ofCommunication, San Diego State University, San Diego, CA92182-4560, USA. E-mail: [email protected]

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

Journal of Health Communication, 22: 124–134, 2017Copyright © Taylor & Francis Group, LLCISSN: 1081-0730 print/1087-0415 onlineDOI: 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 theSocial Networking Safety Promotion and CyberbullyingPrevention Program (Arizona Attorney General, 2016), MediaHeroes (Wölfer et al., 2014), and Cyberbullying: A PreventionCurriculum (Limber, Kowalski, & Agatston, 2008). These pro-grams all consistently recommend four responses to victims: Donot retaliate, seek social support, save evidence, and notifyauthorities. A review of webpages providing cyberbullyingadvice 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 passiveand therefore potentially ineffective strategy (Patchin &Hinduja, 2006), others find it worthwhile, as it can stop theconflict from escalating and prevent victims from becomingbullies 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 than90% of adolescent cyberbullying victims do not inform adultsof 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 beconfiscated (Aboujaoude et al., 2015). But although informingan 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 teachablebehavior with potentially significant rewards, as an extensiveliterature documents the relational, health, and psychologicalbenefits of such support (Burleson & MacGeorge, 2002).

Save Evidence and Notify Authorities

Because saving evidence is typically only useful if an authorityis notified, saving evidence and notifying authorities are linkedbehaviors. Holding on to cyberbullying evidence is a behaviorthat most victims report knowing how to do (Juvoven & Gross,2008). Notifying authorities when cyberbullied, however, ismore complicated, primarily because of the need to interactwith an external resource, which raises fears similar to thosethat prevent adolescent victims from approaching parents orteachers. 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 thataims to buffer the psychosocial impact of a cyberbullying event.Notifying authorities establishes official documentation andmay lead to a formal investigation.

SCT and Message Design

SCT describes how an individual’s knowledge acquisition andbehaviors are largely a function of observing others interact insocial settings or in the media (Bandura, 2008). When peopleobserve a model performing a behavior and the subsequentconsequences of that behavior, they use this information toguide their own behaviors (Bandura, 1977, 1986, 2001).Observers do not learn new behaviors solely by trying themand either succeeding or failing but rather by replicating others’actions depending on whether those actions and their outcomesresulted in reward or punishment. The theory can be applied topersuasive message development through the use of storytellingand narratives that foster behavior change via peer modeling(Hinyard & Kreuter, 2007; Noar et al., 2015). In this study, SCTwas used to design an intervention message that aimed toencourage certain anti-cyberbullying strategies by illustratinghow one can successfully navigate an instance of being cyber-bullied by adopting the recommended responses. The theoryhas been utilized in multiple arenas, including health promotionand 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 ofthis review (for a review, see Rosenthal & Zimmerman, 2014).The effectiveness of an SCT approach can be determined bychanges in perceptions of common outcome variables generallyconsistent 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 dependedon 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) moraldisengagement. Each was incorporated into an interventionmessage created to persuade cyberbullying victims to not retali-ate, to seek social support, to save evidence, and to notifyauthorities. Figure 1 shows the intervention message designedusing SCT components. Exemplars of SCT application aredescribed in the text below, but because of space considerationsthese descriptions are not exhaustive.

Observational Learning

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

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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 learningof each recommended response. The intervention message wasdesigned at a seventh-grade reading level. Scholars have hadsuccess promoting observational learning by using social normsapproaches to address bullying behaviors (Perkins, Craig, &Perkins, 2011).

Psychological Determinants of Behavior

Two psychological determinants of behavior were integratedinto the intervention message: outcome expectations and self-efficacy. First, outcome expectations represent beliefs about thelikelihood and perceived value of various behaviors(Viswanath, 2008). Providing information about consequenceis 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 messageare enhanced when the protagonist experiences reduced nega-tive consequences as a result of adopting the recommendedbehaviors. 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 theknowledge and skills to perform a task. Thus, the interventionmessage stated, “If I can deal with a cyberbully, you can too.”Such motivational statements can increase confidence in one’sability to model the recommended behaviors, thereby promot-ing self-efficacy. A step-by-step list also followed the narrativesummarizing each recommended behavior with instructionsconsistent with how each behavior was modeled in the narra-tive. Easy-to-follow directions for adopting behavioral changeenhance self-efficacy (Bandura, 2001).

Environmental Determinants of Behavior

Behavioral change is likely when the environment encouragesand allows the new behaviors (Bandura, 2004). Facilitationdescribes when new resources make recommended behaviorseasier to enact. Research shows that efficacious people are moresuccessful at finding opportunities in the environment and cir-cumventing constraints (Kelder et al., 2016). In the interventionmessage, for example, the narrative described resources in one’senvironment that simplify the adoption of each recommendedbehavior. Resources to foster each recommended behavior wereenhanced in the step-by-step list following the narrative.

Self-Regulation

Self-regulation is a distinct behavioral skill whereby exercise ofcontrol allows one to more successfully perform recommendedbehaviors (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 incrementaland long-term outcomes and determining paths to reachingthem. Self-monitoring involves the systematic observation ofone’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 behaviorsled to successful performance. Scholarship suggests the utilityof 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 andcruel. Bussey, Fitzpatrick, and Raman (2015) demonstrated thatcyberbullying rates were positively associated with moral dis-engagement proneness. Such findings demonstrate the need tohelp victims distinguish humor (e.g., teasing) from bullying sothat a cyberbully is not given moral justification via theassumption that it is humorous. In the intervention message,the narrative described that the protagonist determined thatbeing cyberbullied was not a joke and reinforced that otherswould provide empathy in the situation. This served to huma-nize the cyberbullying episode and enhance adoption of therecommended behaviors.

Dependent Variables and Hypotheses

The effectiveness of SCT in causing behavioral change can bemeasured by changes in outcome variables across behavior changetheories (Noar & Zimmerman, 2005), including susceptibility,severity, self-efficacy, response efficacy, attitudes, and behavioralintentions (see Witte, 1992). Susceptibility is the likelihood that athreat will occur. Severity is a perception of how bad or harmful anact, experience, or threat is evaluated. Self-efficacy refers to anindividual’s perceived ability and confidence to enact a recom-mended response. Response efficacy refers to a belief that arecommended response will be effective. Attitudes are generalevaluations 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). Theseoutcomes served as dependent variables and were used to comparethe effects of the intervention message.

We hypothesized (Hypotheses 1–2) that those exposed to theintervention message would report higher perceived suscept-ibility to (Hypothesis 1) and higher perceived severity of(Hypothesis 2) cyberbullying than those exposed to the controlmessage. Furthermore, we hypothesized (Hypotheses 3–6) thatthose exposed to the intervention message would report higherself-efficacy beliefs, higher response efficacy, more favorableattitudes, and greater intentions regarding not retaliating(Hypothesis 3), seeking social support (Hypothesis 4), savingevidence (Hypothesis 5), and notifying authorities (Hypothesis6) 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/

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

The study was completed online with participants receivingincentive research credit. Following online study recommenda-tions (Birnbaum, 2004), participants completed all proceduresin a setting of their choice via a secure website. Participantswere 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 experimentalgroup was exposed to an intervention message (see Figure 1)designed as a realistic cyberbullying scenario but written innarrative form, illustrating the recommended responses withapplicable reinforcing rewarding outcomes. A concise step-by-step list of the four recommended responses followed. Thecontrol 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 thecontrol group on the topic and generate a comparison. Attentioncontrol messages have been used in persuasion research tofoster 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 Diagnosisscale and measured on a 5-point Likert scale (1 = stronglydisagree, 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 ηp2

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* .01Severity 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 .00Response efficacy .81 3.28 0.84 3.41 0.80 F(1, 676) = 5.36 .021* .01Attitude .88 3.71 0.90 3.84 0.88 F(1, 676) = 8.10 .005** .01Behavioral 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*** .01Response efficacy .78 3.26 0.84 3.42 0.70 F(1, 675) = 8.73 .003** .01Attitude .87 3.65 0.89 3.84 0.84 F(1, 675) = 9.27 .002** .01Behavioral 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* .01Response efficacy .81 3.48 0.84 3.69 0.75 F(1, 674) = 12.08 .001** .02Attitude .89 4.19 0.81 4.31 0.83 F(1, 674) = 8.49 .004** .01Behavioral 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** .01Response efficacy .82 3.25 0.85 3.47 0.76 F(1, 676) = 14.24 <.001*** .02Attitude .88 3.63 0.95 3.81 0.93 F(1, 676) = 6.79 .009** .01Behavioral 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 measuredwith a 3-item scale that assessed participants’ perceived like-lihood of experiencing cyberbullying (e.g., “I am at risk ofbeing cyberbullied”). Self-efficacy, response efficacy, attitudes,and behavioral intentions were measured regarding each of thefour behavioral recommendations (not retaliating, seekingsocial support, saving evidence, and notifying an authority).Self-efficacy measured participants’ confidence in their abilityto 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 likelihoodof success when enacting each of the recommended responseswith three items (e.g., “[Insert strategy] works to prevent cyber-bullying”). Attitudes were measured using 4-item semanticdifferential scales developed (Himmelfarb, 1993) and used inpersuasion research (Roberto et al., 2009), including items thatasked 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 Iintend to [insert strategy]”). Behavioral intention is highlycorrelated with actual behavior (Albarracin, Johnson,Fishbein, & Muellerleile, 2001; Downs & Hausenblas, 2005),making it suitable for self-report.

Cyberbullying Perpetration and VictimizationSingle-item dichotomous measures (yes/no) asked “In the last12 months, did you ever repeatedly use communication tech-nology to deliberately hurt or embarrass others in an unfriendlyway?” and “In the last 12 months, did anyone ever repeatedlyuse communication technology to deliberately hurt or embar-rass you in an unfriendly way?” (see Roberto, Eden, Savage,Ramos-Salazar, & Deiss, 2014a, 2014b).

Technology UseAccess to communication technology (a personal computer andcell phone), easy access to e-mail and the Internet, and whetherparticipants 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 beencyberbullied in the past 12 months. Cyberbullying victimization didnot differ significantly across college years, χ2(4) = 6.00, p = .20. Atotal of 22% of freshmen, 25% of sophomores, 24% of juniors, and17% 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% ofwomen reported victimization.Also, 14.6% (n=107) of participantsreported having been a cyberbullying perpetrator in the past12 months. Cyberbullying perpetration did not differ significantlyacross college class level, χ2(4) = 7.14, p = .13. In all, 18% offreshmen, 18% of sophomores, 15% of juniors, and 10% of seniors

reported perpetrating. Perpetration did not differ significantly bysex, χ2(2) = 3.84, p = .15. A total of 16% of men and 13% ofwomen reported perpetration. Table 1 shows descriptive data andprovides raw means to compare conditions for all outcomevariables.

Hypotheses

A series ofmultivariate analyses of covariance (MANCOVAs)wereused to analyze the effect of condition (control or experimental)across sets of dependent variables while controlling for technologyuse (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 Mahalanobisdistance (p < .001; Tabachnick & Fidell, 2007) were removed.Pairwise deletion was used to analyze all available data.Univariate effects were interpreted as follow-up when significantmultivariate effects were present to determine support for thehypotheses. Partial eta squared was used to estimate effect size(Richardson, 2011). Table 1 presents detailed results.

For Hypotheses 1–2, perceived susceptibility and severityserved as the dependent variables. Bartlett’s test of sphericityindicated a significant (p < .001) correlation (r = .23). A sig-nificant multivariate main effect emerged for condition. Forperceived susceptibility, results revealed a significant univariatemain effect for condition. In support of Hypothesis 1, partici-pants in the experimental condition reported significantlyhigher susceptibility to cyberbullying than those in the controlcondition. For perceived severity, no significant univariateeffects 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 dependentvariables in each analysis (r2 = .50–.55). A significant multivariatemain effect emerged for condition in eachMANCOVA. Hypothesis3 was partially supported, as univariate results indicated that theintervention message caused expected changes in all outcomes fornot retaliating except self-efficacy. Hypotheses 4–6 were supported,as univariate results in each MANCOVA demonstrated that theintervention message caused expected changes in all outcomes forseeking 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 relationshipsbetween 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 cyberbullyingperpetration and victimization (0 = no, 1 = yes) in Step 1. All Step 2predictors were significantly correlated with intention. Results arepresented in Table 2. All models were significant, with largeexplained proportions of variance (finalR2 = .43–.47). In all models,

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significant R2 changes indicated that the addition of perceivedsusceptibility, 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 recommendedbehavior. With one exception, susceptibility was not a significantpredictor. Effect sizes using part correlations squared indicated thatperceived severity, self-efficacy, and response efficacy eachexplained 3%–14% of unique variance. Consistent results emergedwhen the same analyses were conducted with attitude toward eachrecommended behavior as the criterion variable.

Discussion

An anti-cyberbullying intervention message was designed usingSCT, and the experimental group exposed to the interventionmessage was expected to have higher perceived susceptibilityand severity regarding cyberbullying. In addition, the experi-mental group was expected to have superior self-efficacy;superior response efficacy; as well as more positive attitudesabout, and intentions to act on, the following recommendations:avoid retaliation, seek social support, save evidence, and notifyauthorities. Results show the persuasiveness of the interventionmessage in reaching the majority of these goals and offerinsight into message design.

Cyberbullying is a prevalent and serious problem amongcollege students. Current data on the association between ageand victimization do not indicate when cyberbullying ends,which makes it important to explore cyberbullying in youngadults. Perpetration (15%) and victimization (21%) frequencieswithin our sample were comparable to those in other collegesamples (Foody et al., 2015). Furthermore, no differences inperpetration and victimization were seen across class levels,suggesting that cyberbullying is not a holdover phenomenonfrom childhood and high school that tapers off during college.

Findings for perceived susceptibility and severity to cyber-bullying warrant attention, as they may play a complex role infuture persuasive message design. As expected, the experimen-tal group conveyed higher perceived susceptibility than thecontrol group. In line with SCT, reading a story about a collegestudent’s cyberbullying experience strengthened participants’perception that it could happen to them. As Bandura (1977)suggested in his discussion of knowledge, learning about aparticular health issue initiates a cognitive process wherebyindividuals exposed to persuasive messaging contemplate theirlikelihood of encountering the issue personally. However, withone exception, post hoc analyses showed that perceived sus-ceptibility was not a significant predictor of intention or atti-tude, which suggests that message designers might consider itless in future anti-cyberbullying persuasive appeals. The experi-mental group did not report a higher perception of the severityof cyberbullying than the control group, which represents alimitation of the present study, but post hoc analysis showedthat severity was a significant and strong predictor of attitudeand intention. These results are consistent with Roberto andcolleagues (2014a), who found that only severity but not sus-ceptibility emerged as predictors of attitude and intention. Themanipulation of severity in the experimental message may have

Table 2. Step 2 results of stepwise regressions: Predicting beha-vioral intention by sets of predictors

Model and predictors (R2/adjusted R2; ΔR2) B (SE B) β p sr2

Not retaliating (.46/.45***;ΔR2 = .34***)

<.001

Intercept .21 (.26)Condition .13 (.05) .07* .01 .01Victimization .09 (.07) .04 .19 .00Perpetration −.24 (.08) −.10** .002 .01Technology use −.01 (.05) −.01 .80 .00Sex .34 (.05) .19*** <.001 .03Susceptibility .00 (.03) .00 .91 .00Severity .19 (.03) .21*** <.001 .03Self-efficacy .27 (.03) .27*** <.001 .06Response efficacy .38 (.04) .34*** <.001 .14Seeking social support (.47/.46***; ΔR2 = .38***)

<.001

Intercept −.71 (.27)Condition .07 (.05) .04 .17 .01Victimization .07 (.07) .03 .38 .00Perpetration −.08 (.08) −.03 .33 .00Technology use −.01 (.05) .02 .40 .00Sex .23 (.05) .12*** <.001 .01Susceptibility .01 (.03) .01 .75 .00Severity .27 (.03) .27*** <.001 .06Self-efficacy .35 (.05) .26*** <.001 .05Response efficacy .39 (.04) .31*** <.001 .07Saving evidence (.43/.42***;ΔR2 = .35***)

<.001

Intercept −.16 (.27)Condition .06 (.05) .04 .24 .00Victimization .02 (.07) .01 .78 .00Perpetration −.14 (.08) −.06 .08 .00Technology use .01 (.05) .01 .90 .00Sex .21 (.05) .12*** <.001 .01Susceptibility .04 (.03) .04 .722 .00Severity .17 (.03) .19*** <.001 .03Self-efficacy .41 (.04) .32*** <.001 .08Response efficacy .35 (.04) .30*** <.001 .07Notifying an authority (.46/.46***; ΔR2 = .40***)

<.001

Intercept −.19 (.28)Condition .05 (.06) .02 .42 .00Victimization .11 (.08) .04 .17 .00Perpetration −.07 (.09) −.03 .41 .00Technology use −.03 (.05) −.02 .54 .00Sex .21 (.06) .11*** <.001 .01Susceptibility −.10 (.03) −.09** .003 .01Severity .26 (.03) .26*** <.001 .05Self-efficacy .37 (.04) .30*** <.001 .06Response efficacy .40 (.04) .31*** <.001 .07

Note. Final model (Step 2) results are reported here. Δ refers to R2 change fromcontrol variables in Step 1 (condition, victimization, perpetration, technologyuse, and sex) to variables of interest added in Step 2 (susceptibility, severity,self-efficacy, and response efficacy).

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

130 M. W. Savage et al.

been inadequate. Research should focus on amplifying theseverity of cyberbullying in future message design given itsimportance in predicting attitudes and intentions toward recom-mended responses. In addition, scholars should consider howmen and women may perceive messages differently given thesignificant association of sex with attitudes and intentionstoward recommended responses across the post hoc results.

Each recommended behavior was examined across all dependentvariables. The first behavior was avoiding retaliation. Three of thedependent variables demonstrated significant differences betweenthe experimental and control groups: Participants exposed to theexperimental message understood the utility of not retaliating, had apositive attitude about not retaliating, and reported an intention tonot retaliate. However, findings regarding self-efficacy indicate thatthey did not report an ability to restrain themselves from retaliating.This is certainly a limitation. An explanationmay be that subjects donot trust that they can exhibit self-restraint in themoment when theyare cyberbullied. Individuals form their self-efficacy from varioussources, including their experience with a specific behavioraldomain (Bandura, 1997). Therefore, previous cyberbullying experi-ence may stand to inoculate against attitudes and intentions to adoptprosocial behaviors due to cognitive dissonance (e.g., Breen &Matusitz, 2008). Self-efficacy will be important for messagedesigners to foster when designing anti-cyberbullying persuasiveappeals, particularly any messages targeted to former perpetrators.1

Cyberbullying perpetration predictors (e.g., Roberto et al., 2014b)could be used as a basis for creating efficacymessages. Furthermore,experts advise that some strategies should be enacted (seek socialsupport, save evidence, notify authorities), whereas retaliatingshould not be enacted. This active/passive approach may be per-ceived as a confusing conflation of strategies (Larsen & Augustine,2008). Thus, exploring whether self-efficacy can be improved via amessage that encourages nonretaliation framed as an active strategy,such as logging off or blocking the perpetrator, is worthwhile.

The second recommended behavior was social support seeking.Given the numerous positive outcomes for sharing troubles withclose others (Burleson & MacGeorge, 2002), there is a need toencourage this behavior in instances of cyberbullying. As intended,the experimental message was effective at causing changes in allsocial support dependent variables. The experimental group feltstronger self-efficacy, stronger response efficacy, more positiveattitudes, and a greater intention to seek social support if cyberbul-lied. These findings suggest that using an SCTapproach works wellto promote social support and might be considered within the con-text of emerging intervention research that aims to correct socialnorms (Perkins et al., 2011).

The third recommended behavior involved saving evidence. Ashypothesized, the experimental message was effective at causing

changes in all social support dependent variables. Although signifi-cant differences emerged between groups, research suggests thatyoung adults may already know how to save evidence (Juvoven &Gross, 2008). Initial data exploration using t tests revealed a ceilingeffect for self-efficacy (there was little room for the experimentalgroup to demonstrate an increase beyond the control group); differ-ences only emerged when we accounted for technology use as acovariate. Saving evidence is likely a strategy that college studentsare accustomed to; improving self-efficacy toward this may be lessimportant. Future evaluation work should continue to account fortechnology-related covariates.

Notifying an authority was the final recommended behavior.All dependent variables were impacted in an expected mannerregarding this strategy. Those in the experimental groupreported more confidence in their ability to notify authoritiesand thought that this would be an effective strategy to helpthem. They had more positive attitudes and were more likely tonotify an authority if cyberbullied. In addition to illustratinghow to notify an authority, we included a reporting tool fromthe website wiredsafety.org (Wired Safety, 2009). However, thewebsite no longer takes reports. Future interventions will ben-efit from determining and promoting easy online reportingmechanisms. Message designers will find that cyberbullying.org/report (Cyberbullying Research Center, 2016) includes anupdated resource list for notifying authorities.

These findings support the effectiveness of an SCT model topersuade college students to adopt recommended behavioralresponses to cyberbullying and have theoretical implications. Thenarrative and presentation of recommended strategies in the inter-vention message were developed utilizing all components of SCT’sunderpinnings (Bandura, 1977, 1986). This extends previous SCTresearch by demonstrating specifically how SCT can be applied toadult-level cyberbullying interventions. Lent and Brown (2006)argued that SCT is topic specific in that it requires a high level oftailoring to fit a particular domain and it is difficult to look at SCTasa one-size-fits-all theory; thus, every new context of SCTapplicationfurthers the theory’s explanatory power. This study provides a start-ing place for message development; future studies should examinewhich parts of SCT-based messages are more or less effective thanothers.

Strengths, Limitations, and Future Directions

This study contributes to the literature on cyberbullying in severalways. Most cyberbullying research has been conducted within thefields of psychology, education, and criminal justice. Yet commu-nication is inherent to cyberbullying because messages that harm,threaten, taunt, harass, or embarrass the victim are defining char-acteristics. The communication discipline is particularly well suitedto help respond to this problem by developing anti-cyberbullyingmessages that could be incorporated into formal interventions andcampaigns (see also Ramirez, Eastin, Chakroff, &Cicchirillo, 2008;Roberto et al., 2014a). The use of a theoretically driven message toaffect participants’ perceptions, attitudes, and intentions representsother strengths. This study is among the first to test a theoreticallydriven persuasive anti-cyberbullying message. Although futureresearchers should elucidate how best to affect self-efficacy for

1In addition, in an earlier version of this article, cyberbullying perpe-tration and victimization were included as predictors in 2 × 2 × 2MANCOVAs rather than covariates. Results showed that no interactioneffects were significant; significant main effects indicated that those whoreported cyberbullying perpetration displayed less self-efficacy, less favor-able attitudes, and less intention to enact all of the recommended strategies(with the exception of attitudes toward not retaliating). Readers interestedin these findings for message targeting implications may contact the firstauthor.

Cyberbullying Victimization 131

avoiding retaliation, SCT was generally successful for designing aconvincing message about implementing anti-cyberbullying strate-gies. Future studies could revise this message, test it in other popula-tions, devise other messages varying in SCT constructs, or draw onother communication theories (Ramirez et al., 2010).

The posttest-only control group design and random assign-ment constrain major threats to internal validity (Shadish,Cook, & Campbell, 2001), but limitations warrant discussion.First, using one experimental group rather than multiple con-ditions restricts conclusions. Future studies including addi-tional message conditions (e.g., strong, moderate, weak,control) could speak to how robust an intervention must beto change outcomes. This could clarify specific componentsof SCT causing changes in outcome variables. Although otherstudies have examined select constructs from SCT(Viswanath, 2008), we filled a gap in the literature by apply-ing the complete theory in message design and formativeevaluation. Second, the use of a single message among oneage group could be remedied, although the college-age popu-lation is understudied and experiences cyberbullying.Although testing multiple messages in a particular domainhas much heuristic and theoretical value, the purpose herewas to examine a specific message crafted to reflect SCT’sfull explanatory power. Still, future research should examinehow message variations impact outcomes across ages. Third,this study was limited to behavioral recommendations toaddress future victimization. Scholars should advance persua-sive messaging that aids perpetration prevention too. Fourth,this was not a long-term study of actual behavior, andalthough the pattern of effects contributes to this area ofresearch, effect sizes were small. Kirk (1996) argued thatthe practical significance of small effects is valuable, andAllen (1991) argued that small effect sizes can be importantwhen there might be a cumulative effect over time in inter-vention/campaign contexts. The ultimate efficacy test of thisor any intervention message is a prospective long-term ran-domized study, something the field lacks.

Conclusion

Cyberbullying is a serious problem that may worsen as digitaltools connect people more intimately, exposing them along theway to all manner of aggression and intrusion. Sadly, fewproven interventions exist. This study supports continuedapplied research to help those who confront cyberbullyingvictimization. SCT, which has been proven effective in otherhealth contexts, provided the theoretical framework for design-ing the anti-cyberbullying message, but other theoretical per-spectives that examine long-term efficacy deserve inquiry too.Such investigations can over time lead to data-driven, theory-based, effective persuasive message design and interventions.

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