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Reducing Cyberbullying: A Theory of Reasoned Action-Based Video Prevention Program for College Students Ashley N. Doane1*, Michelle L. Kelley2, and Matthew R. Pearson3

1Psychology Department, Chowan University, Murfreesboro, North Carolina 2Psychology Department, Old Dominion University, Norfolk, Virginia 3Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Few studies have evaluated the effectiveness of cyberbullying prevention/intervention programs. The goals of the present study were to develop a Theory of Reasoned Action (TRA)-based video program to increase cyberbullying knowledge (1) and empathy toward cyberbullying victims (2), reduce favorable attitudes toward cyberbullying (3), decrease positive injunctive (4) and descriptive norms about cyberbullying (5), and reduce cyberbullying intentions (6) and cyberbullying behavior (7). One hundred sixty-seven college students were randomly assigned to an online video cyberbullying prevention program or an assessment-only control group. Immediately following the program, attitudes and injunctive norms for all four types of cyberbullying behavior (i.e., unwanted contact, malice, deception, and public humiliation), descriptive norms for malice and public humiliation, empathy toward victims of malice and deception, and cyberbullying knowledge significantly improved in the experimental group. At one-month follow-up, malice and public humiliation behavior, favorable attitudes toward unwanted contact, deception, and public humiliation, and injunctive norms for public humiliation were significantly lower in the experimental than the control group. Cyberbullying knowledge was significantly higher in the experimental than the control group. These findings demonstrate a brief cyberbullying video is capable of improving, at one-month follow-up, cyberbullying knowledge, cyberbullying perpetration behavior, and TRA constructs known to predict cyberbullying perpetration. Considering the low cost and ease with which a video-based prevention/intervention program can be delivered, this type of approach should be considered to reduce cyberbullying. Aggr. Behav. 42:136–146, 2016. © 2015 Wiley Periodicals, Inc.

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Keywords: cyberbullying; prevention; attitudes; norms; college students

INTRODUCTION

Cyberbullying, defined as “willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices” (Hinduja & Patchin, 2009, p. 5), has been a frequent topic in the media over the past few years. Reports have shown that youth (e.g., De Nies, Donaldson, & Netter, 2010; Michels, 2008; Smith-Spark & Vandoorne, 2013) and a college student (Friedman, 2010) committed suicide after beingcyberbullied.Despite growing media attention to the issue ofcyberbullying, few cyberbullying prevention/intervention programs have been developed and evaluated (e.g., Lee, Zi-Pei, Svanstr€om, & Dalal, 2013; Menesini, Nocentini, & Palladino, 2012; Williford et al., 2014; W€olfer et al., 2014). Furthermore, these prevention programs have been developed for youth. In the present study, we developed and tested a cyberbullying prevention program for college students based on constructs outlined by the Theory of Reasoned Action (TRA; Ajzen, 1985).

Cyberbullying Prevalence

Although not as extensive as research on youth (Tokunaga, 2010), a few studies have examined college students’ experiences of cyberbullying. In three studies that assessed cyberbullying experiences during college, approximately 9–11% of college students reported having been cyberbullied at their university (Kraft & Wang, 2010; Schenk & Fremouw, 2012; Walker, Sockman, & Koehn, 2011). Other studies have found higher rates of cyberbullying victimization among

�Correspondence to: Ashley N. Doane, Psychology Department, Chowan University, 1 University Place, Murfreesboro, NC 27855. E-mail: doanea@chowan.edu

Received 16 November 2014; Accepted 30 April 2015

DOI: 10.1002/ab.21610 Published online 9 September 2015 in Wiley Online Library (wileyonlinelibrary.com).

AGGRESSIVE BEHAVIOR Volume 42, pages 136–146 (2016)

© 2015 Wiley Periodicals, Inc.

college students (54%, Aricak, 2009; 55%, DilmaSc, 2009; 22%, MacDonald & Roberts-Pittman, 2010; 18%, Whittaker & Kowalski, 2015). Perpetration rates have varied from approximately 9–23% (20% Aricak, 2009; 23%, DilmaSc, 2009; 9%, MacDonald & Roberts- Pittman, 2010; 12%, Whittaker & Kowalski, 2015). In a recent study of college students, Kokkinos, Antonia- dou, and Markos (2014) identified 11% of the sample as cyberbullying victims (only), 14% as cyberbullying perpetrators (only), and 33% as both cyberbullying victims and perpetrators. However, definitions, regions, modes of communication included (e.g., cell phones, email), specific types of behavior assessed, and assess- ment time frames have varied considerably across these studies. More recently, the Cyberbullying Experiences Survey (CES), a comprehensive measure of cyberbully- ing, was developed. The CES assesses 21 victimization and 20 perpetration behaviors that reflect four distinct types of cyberbullying: deception, malice, public humiliation, and unwanted contact (Doane, Kelley, Chiang, & Padilla, 2013). Using this multidimensional assessment of cyberbullying, approximately 78% of participants reported experiencing and 53% reported perpetrating at least one instance of cyberbullying involving deception, 88% experienced and 78% perpe- trated cyberbullying involving malice, 73% experienced and 38% perpetrated cyberbullying involving public humiliation, and 66% experienced and 29% perpetrated cyberbullying involving unwanted contact.

Consequences of Cyberbullying

Research has shown that cyberbullying victims and perpetrators are at greater risk for mental health and school problems. Cyberbullying victimization in sam- ples of youth is associated with school problems (e.g., suspension, detention, skipping school, and carrying a weapon to school; Ybarra, Diener-West, & Leaf, 2007), emotional problems (e.g., depressive symptoms; Bo- nanno & Hymel, 2013; Perren, Dooley, Shaw, & Cross, 2010; Ybarra, 2004), suicidal ideation (Bonanno & Hymel, 2013; Hinduja & Patchin, 2010), and suicide attempts (Hinduja & Patchin, 2010). In addition, cyberbullying perpetration is also related to depressive symptoms (Bonanno & Hymel, 2013), suicidal ideation (Bonanno & Hymel, 2013; Hinduja & Patchin, 2010), and suicide attempts (Hinduja & Patchin, 2010). In a study of college students, Aricak (2009) found

that cyberbullying victims reported higher somatization, phobic anxiety, paranoid ideation, anxiety, symptoms of obsessive–compulsive disorder, and depression than students not involved in cyberbullying. In addition, those who were both victims and perpetrators of cyberbullying reported higher somatization, anxiety, hostility, and paranoid ideation, as well as more

psychotic symptoms than college students not involved in it. Given the frequency of cyberbullying and the negative consequences associated with cyberbullying, low-cost, easily implemented, effective cyberbullying prevention programs are needed for college students.

Theory of Reasoned Action

The proposed study was guided by the TRA. TRA posits that attitudes toward a particular behavior (i.e., the degree to which it is positively or negatively evaluated), injunctive norms regarding it (i.e., the perception of others’ approval or disapproval of performing it), and descriptive norms (i.e., the perception that others actually engage in it) influence behavioral intentions, which in turn has a direct effect on the behavior itself (Fishbein & Ajzen, 2010). Based on this theory, decreasing positive attitudes toward the behavior and decreasing positive perceived norms about it are expected to decrease intentions to perform the behavior; ultimately, reducing intentions to perform the behavior should reduce the likelihood of it being carried out. Violence prevention researchers have argued for the

need to assess attitude change (e.g., Limber, Nation, Tracy, Melton, & Flerx, 2004; Weisz & Black, 2001). Bullies tend to have more positive attitudes towards violence and low empathy toward victims of bullying (Olweus, 1993a). Therefore, Olweus (1993a, 1993b) recommends that bullying interventions focus on changing the attitudes and behavior of bullies and having students empathize with victims. Recent studies on children (Elledge et al., 2013) and college students (Barlett & Gentile, 2012; Boulton, Lloyd, Down, & Marx, 2012; Doane, Pearson, & Kelley, 2014) have found positive relationships between favorable attitudes toward cyberbullying and cyberbullying perpetration. Using the four-factor cyberbullying perpetration scale

of the CES, Doane et al. (2014) found that attitudes, descriptive norms, and injunctive norms significantly predicted cyberbullying intentions, and cyberbullying intentions predicted cyberbullying behavior. Although there were some differences between types of cyber- bullying behavior in terms of which specific TRA constructs predicted cyberbullying perpetration, positive attitudes toward cyberbullying was the strongest predictor of cyberbullying intentions, and cyberbullying intentions were substantially related to cyberbullying perpetration. Another study examined the Theory of Planned Behavior (TPB; Ajzen, 2012), which includes the TRA constructs as well as perceived behavioral control, or the degree to which a person believes they are able to perform a particular behavior (Heirman & Walrave, 2012). Likewise, they found that attitudes, subjective norms (i.e., injunctive norms), and perceived behavioral control were significant predictors of

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Cyberbullying Prevention: Theory of Reasoned Action 137

cyberbullying intentions, which in turn predicted cyberbullying perpetration. Thus, at least two studies provide support that modifying TRA constructs may be an effective means of reducing cyberbullying perpetration.

Empathy

In addition to TRA constructs, empathy has been associated with cyberbullying. Studies measuring empathy in general (Schultze-Krumbholz & Scheitha- uer, 2009) and empathy specifically related to cyber- bullying (Doane et al., 2014; Steffgen, K€onig, Pfetsch, & Melzer, 2011) found higher levels of empathy to be associated with lower levels of cyberbullying. When examining affective and cognitive empathy separately, relationships with cyberbullying perpetration were inconsistent (Ang & Goh, 2010; Renati, Berrone, & Zanetti, 2012; Topcu & Erdur-Baker, 2012). Doane et al. (2014) found the negative associations between empathy and cyberbullying perpetration to be fully mediated by TRA constructs such that higher empathy was associated with less positive attitudes, lower injunctive norms, and lower descriptive norms for all four types of cyberbully- ing assessed.

Cyberbullying Prevention Programs for Youth

Several recent multidisciplinary and international efforts have demonstrated the effectiveness of youth cyberbullying prevention programs. These programs vary in length from one-day programs (e.g., W€olfer et al., 2014) to those that take place over the course of the school-year (e.g., ViSC Social Competence Program, Gradinger, Yanagida, Strohmeier, & Spiel, 2015; KiVa Antibullying Program, Williford et al., 2014). Moreover, programs also vary in terms of whether they are taught by trained teachers (Cross, Campbell, Slee, Spears, & Barnes, 2013; Gradinger et al., 2015; W€olfer et al., 2014), involve a combination of peer educators and teacher-led instruction (e.g., Menesini et al., 2012), or are self-guided but allow interaction with other students (e.g., Lee et al., 2013). In some instances, they are delivered in the community (e.g., “Keep it Tame”; Spears & Zeederberg, 2013) or both in school and the community (e.g., Cyber Friendly School Project; see Cross et al., 2013; Campbell, Cross, Spears, & Slee, 2010). Although the results of many of these programs are preliminary, in general, they have shown benefits in terms of improved cyberbullying knowledge and perspective-taking skills and reduced aggressive behavior.

Prevention Program Characteristics

Although cyberbullying prevention programs have largely relied on in-person instruction and interactions,

videos have been used in a variety of prevention programs across other fields, including programs targeting problems such as workplace violence (e.g., Peek-Asa, Casteel, Mineschian, Erickson, & Kraus, 2004), substance abuse or tobacco use (e.g., Ferketich, Kwong, Shek, & Mae, 2007; Ramirez, Gallion, Espinoza, & Chalela, 1999), pathological gambling (e.g., Doiron & Nicki, 2007), and eating disorders (e.g., Heinze, Wertheim, & Kashima, 2000; Withers, Twigg, Wertheim, & Paxton, 2002; Withers & Wertheim, 2004). Prevention programs including videos have been shown to be effective in increasing empathy toward victims of rape (e.g., Foubert & Cowell, 2004; O’Donohue, Yeater, & Fanetti, 2003). Furthermore, participants in Foubert and Cowell’s study rated the video aspect of the program as the most powerful part of the program. In traditional bullying prevention programs, Olweus

(1993a) recommends using videos of bullying exam- ples to clarify bullying behavior. The video commonly used in the school-based Olweus Bullying Prevention Program is 11 min in length and consists of four bullying situation vignettes (Olweus, Limber, & Mihalic, 1999). In addition to providing bullying information, the bullying video “elicits emotional, ‘gut feeling’ reactions from the audience” (p. 28). In a study of Italian youth, Baldry and Farrington (2004) evaluated a bullying and victimization intervention program which consisted of three videos, a booklet, role-playing, and discussions. Youth reported signifi- cantly less bullying and victimization at post-inter- vention than pre-intervention. Internet-based prevention programs have been

used to target many areas, including smoking (see Walters, Wright, & Shegog, 2006 for a review), HIV (e.g., Bowen, Williams, Daniel, & Clayton, 2008; Roberto et al., 2008), drug abuse (e.g., Schwinn, Schinke, & di Noia, 2010), and depression (e.g., Van Voorhees et al., 2009). Conn (2010) recommends increasing the use of Internet-based health preven- tion programs due to their lower cost, higher consistency, greater accessibility (i.e., both tempo- rally and with physical location), and the ability for program participants to remain anonymous. Extrap- olating from the results of previous studies, a video- based cyberbullying prevention program that con- tains brief informational segments combined with short depictions of common cyberbullying incidents that show victim responses, and peers commenting on the inappropriateness of these actions, may be effective in reducing positive cyberbullying attitudes and behavior. Moreover, the technology currently exists to widely disseminate this type of program at low cost.

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138 Doane et al.

Present Study

Based on the TRA and previous research, the purpose of the present study was to evaluate the success of a video-based cyberbullying prevention program devel- oped by the first author presented in an online format (Doane, 2011). It was predicted that: (1) compared to baseline, positive attitudes toward cyberbullying, pos- itive injunctive and descriptive norms concerning cyberbullying, and intentions to cyberbully would be significantly lower and cyberbullying knowledge and empathy toward cyberbullying victims would be significantly higher in the experimental group immedi- ately after completing the program. In addition, it was hypothesized that: (2) at one-month follow-up, positive attitudes toward cyberbullying, positive injunctive and descriptive norms concerning cyberbullying, intentions to cyberbully, and cyberbullying perpetration would be significantly lower and cyberbullying knowledge and cyberbullying victim empathy would be significantly higher in the experimental than in the control group after controlling for baseline scores, gender, and age.

METHOD

Participants

At a large university in southeastern Virginia, an e- mail invitation to participate in a study on negative experiences via electronic devices was distributed to freshmen (n ¼ 3,187) and sophomores (n ¼ 3,128) who were 18–23 years old. Students were randomly assigned to either the video-based cyberbullying prevention program or no prevention program (assessment-only). Of the 375 students (190 in the experimental group, 185 in the control group) who participated in the initial part of the study (baseline), 167 students (73 in the experimental group, 94 in the control group; 68.7% females, 31.3% males; Mage ¼ 19.02, SD ¼ .91) com- pleted both study time points (baseline and one-month follow-up). The majority self-reported their ethnic group as White (62.9%) or African American (18.0%). Students were entered into a raffle for a $25 Amazon. com gift certificate for completing the first assessment. For completing the one-month follow-up assessment, participants were entered into a total of 31 raffles (one $50 Amazon.com gift certificate and 30 $15 gift certificates for Amazon.com, Starbucks, Walmart, iTunes, or Subway). For each assessment, participants enrolled in psychology courses were also offered research credit. This study was approved by the university’s Institutional Review Board prior to data collection. Prior to beginning the study, participants read a study description and provided informed consent. More information regarding the 375 students who

completed the baseline portion of the present study has been reported elsewhere (Doane et al., 2014).

Measures Cyberbullying knowledge. Cyberbullying know-

ledge was assessed by a five-item multiple choice quiz based on video content. A sample item is, “Which of the following are individuals who have been cyberbullied at greater risk for?” with the following choices: A. School related problems, B. Alcohol and drug use, C. Attempted suicide, D. All of the above, E. A and C only. CES. The 20-item perpetrator scale of the CES

(Doane et al., 2013) was used to measure four types of cyberbullying behavior: malice (six items, as ¼ .89–.90, e.g., “Have you sent a rude message to someone electronically?”), deception (three items, as ¼ .85–.89, e.g., “Have you pretended to be someone else while talking to someone electronically?”), public humilia- tion (three items, as ¼ .76–.93, e.g., “Have you posted an embarrassing picture of someone electronically where other people could see it?”), and unwanted contact (eight items, as ¼ .96, e.g., “Have you sent an unwanted pornographic picture to someone electroni- cally?”). Cyberbullying behavior items are answered on a six-point scale ranging from “Never” (0) to “Everyday/Almost Everyday” (5). For the present study, behavior was assessed for the past month. Convergent validity with Ybarra et al.’s (2007) measure of Internet harassment and the Cyberbullying Assess- ment Instrument (Hinduja & Patchin, 2009) has been established. Empathy toward victims. Based on a study by

Endreson and Olweus (2001), to assess empathy toward victims (as ¼ .90–.99), participants reported the degree to which they feel sorry for a person who has experienced each of the 20 cyberbullying behaviors in the CES. For example, “I feel very sorry for a person who has been [teased by others electronically]” was answered on a six-point scale ranging from “Does not apply at all” (0) to “Applies exactly” (5). Four composite scores were formed by averaging across the items in each CES subscale. We have found support for convergent validity of these empathy subscales as they have been shown to be negatively associated with cyberbullying perpetration (Doane et al., 2014). Attitudes, injunctive norms, descriptive

norms, and intentions. Based on recommenda- tions by Ajzen (2006), questions were administered that assess attitudes toward cyberbullying, perceived norms concerning cyberbullying, and intentions to engage in cyberbullying behavior. Each group of items comprised all 20 items from the four CES perpetration subscales (Doane et al., 2013). To measure attitudes toward a behavior, Ajzen suggests using adjective scales

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Cyberbullying Prevention: Theory of Reasoned Action 139

characterizing instrumental (e.g., harmful-beneficial), experiential (e.g., unenjoyable-enjoyable), and overall evaluation (e.g., bad-good). Thus, the item “For me, to [tease someone electronically] in the forthcoming month is” was given for all 20 CES perpetration items and answered on the three six-point scales (0–5) with the anchors listed above. Attitude composite scores (as ¼ .77–.96) were computed by averaging these sets of attitudes items separately for each CES subscale. Higher scores indicated more positive attitudes toward cyberbullying. Injunctive norms (as ¼ .88–.96) were measured by

repeating the stem “My peers would ______ of my [teasing someone electronically] in the forthcoming month” for each item with a six-point response scale ranging from “disapprove” (0) to “approve” (5). To report descriptive norms (as ¼ .88–.97), participants selected responses on a six-point scale from “completely false” (0) to “completely true” (5) to the stem “My peers [tease others electronically]” for each item. For both injunctive and descriptive norms, four composite scores were computed by averaging across the 20 CES items. Higher scores indicated more favorable injunctive and descriptive norms regarding cyberbullying. The stem “I intend to [tease someone electronically]

within the next month” was answered on a six-point scale ranging from “extremely unlikely” (0) to “extremely likely” (5) for each CES item to measure intentions to engage in cyberbullying (as ¼ .80–.97). The 20 CES items from the four subscales were averaged across to create composite scores. Higher composite scores indicated higher intentions to engage in cyberbullying in the next month. Support for convergent validity of these measures has been established, as attitudes toward cyberbullying, injunctive norms, descriptive norms, and intentions were significantly associated with all four CES perpetration subscales (Doane et al., 2014).

Procedure Program development and content. A video-

based cyberbullying program (approximately 10 min) was developed for students in the experimental group to view during the online prevention program. During the development phase of the study, a cyberbullying researcher not associated with the project, faculty members, and graduate students reviewed the video content and actor scripts and made suggestions. Once the scripts were finalized, young actors from the participat- ing university were recruited and assigned parts. The first author supervised practices and identified appro- priate set designs. The video-based program was directed, filmed, edited, and the final product developed by the award-winning video production team at the participating university.

Given previous support for empathy and TRA constructs as predictors of cyberbullying perpetration (e.g., Doane et al., 2014), our goal was to improve these constructs with the video content. Thus, the cyberbully- ing prevention video alternated between (1) four brief flashes in which actual news stories were summarized about teenagers who were cyberbullied and eventually committed suicide (i.e., to improve victim empathy and attitudes toward cyberbullying); (2) brief attention- grabbing informational slides with voiceovers that presented key information about cyberbullying (e.g., definition of cyberbullying, the different types of cyberbullying, the modes used for cyberbullying, common outcomes associated with cyberbullying, and the prevalence of cyberbullying; i.e., to increase cyberbullying knowledge); and (3) six short, memo- rable, realistic vignettes that consisted of narration and depictions of common cyberbullying events (e.g., receiving mean text messages; i.e., to improve victim empathy and cyberbullying attitudes and norms). The six vignettes are based on actual cyberbullying events and common cyberbullying events identified in previous research. To increase victim empathy and reduce positive

cyberbullying attitudes, four vignettes are from the victims’ point-of-view and involve common modes of electronic communication used for cyberbullying (e.g., instant messaging). These scripts illustrate how upset- ting cyberbullying can be. For instance, one video segment shows a female actor sitting at her laptop in her dorm room with multiple instant message windows open with hurtful messages from other people. She then describes how upset she becomes when she receives these messages. To decrease favorable norms, five actors discussed

how cyberbullying is unacceptable and not “cool.” The rationale for including young actors discussing the inappropriateness of cyberbullying is that: (1) students may think that their peers believe cyberbullying is unacceptable, and (2) they may perceive that their peers’ frequency of cyberbullying behavior is lower. In other words, the video may decrease injunctive norms and descriptive norms about cyberbullying behavior. For example, one scenario shows a group of students sitting around and talking about their friends’ experiences, and how cyberbullying is “stupid” and “immature.” Pilot study. Prior to testing the program, a pilot

study was conducted with 57 college students to determine if the cyberbullying video appeared effective in facilitating the study goals. Results of the pilot test revealed favorable attitudes toward cyberbullying, favorable injunctive and descriptive norms about cyberbullying, and intentions to cyberbully were significantly lower and cyberbullying knowledge and

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140 Doane et al.

empathy toward victims were significantly higher immediately after viewing the video online than at baseline. Evaluation design. After pilot testing, the pre-

vention program was evaluated using two designs. Specifically, the experimental group viewed the cyber- bullying prevention video and participated in a pre-, immediate post-, one-month follow-up design (i.e., completed three assessments), whereas the control group did not view the video (i.e., assessment only) and participated in a pre-, one-month follow-up design (i.e., completed two assessments) at the same time as the pre- and one-month follow-up assessments for the exper- imental group. In the spring of 2011, all freshmen and sophomores

enrolled at the participating university who were traditional college age (i.e., 18–23-years-old) were invited to participate via their university e-mail address which included a link to the study. Both the cyberbully- ing prevention group and the control group completed electronic surveys that assessed cyberbullying knowl- edge, cyberbullying attitudes, injunctive and descriptive norms concerning cyberbullying, intentions to engage in cyberbullying, cyberbullying behavior, and empathy toward cyberbullying victims at baseline and one month after baseline. To assess immediate effects of the program, only the experimental group completed the measures of knowledge, cyberbullying attitudes, in- junctive norms, descriptive norms, intentions to cyber- bully, and cyberbullying victim empathy immediately after completing the video-based prevention program.

RESULTS

Data Analysis Plan

Given that most of the outcome variables in the present study were not normally distributed, it is important to use a method that is robust to normality violations (Erceg- Hurn & Mirosevich, 2008); thus, all analyses were conducted with bootstrapping (Efron & Tibshirani, 1993). Rather than relying on a theoretical sampling distribution that is assumed to be normal, bootstrapping creates an empirical sampling distribution by resampling from the sample with replacement. Then, statistical tests are based on these empirically derived sampling distributions.

Preliminary Analyses

Demographic comparisons were conducted between the study participants and the university population, between students who participated only in the baseline survey and those who completed both time points, and between the experimental and control group. Further- more, participants who completed both assessments

were compared to participants who did not complete both assessments on all study variables (knowledge and for all four types of cyberbullying: attitudes, injunctive norms, descriptive norms, intentions, behavior, and empathy toward victims). Compared to the larger population of traditional-aged

freshman and sophomore classes at the university, participants in the present study were more likely to be female and White but did not differ significantly on age. Students who completed only the first assessment did not significantly differ from those who completed both assessments in age, gender, or ethnicity. Similarly, the experimental group and control group did not signifi- cantly differ in age, gender, ethnicity, or any of the 25 study variables at baseline. Moreover, participants who did and did not complete the one-month follow-up assessment only differed on three out of the 25 study variables; attitudes toward public humiliation were slightly more positive among dropouts (M ¼ .33, SD ¼ .78) compared to completers (M ¼ .20, SD ¼ .47), empathy toward victims of malice was slightly higher in the completers (M ¼ 3.87, SD ¼ 1.45) compared to dropouts (M ¼ 3.46, SD ¼ 1.60), and empathy toward victims of public humiliation was slightly higher in the completers (M ¼ 3.97, SD ¼ .78) than the dropouts (M ¼ 3.65, SD ¼ .78). Immediate Intervention Effects

To examine the immediate effects of the intervention, paired samples t-tests with bias corrected and accel- erated bootstrapping (i.e., 1,000 samples) were used to compare pretest scores with immediate post-interven- tion scores on attitudes, injunctive norms, descriptive norms, behavioral intentions, empathy, and cyberbully- ing knowledge. Significant effects are determined by 95% bootstrapped confidence intervals of the mean difference that do not contain zero. There were significant decreases on attitudes and injunctive norms for all four types of cyberbullying behavior (see Table I). Although descriptive norms for all types of cyberbully- ing behavior decreased between pretest and immediate post-treatment, only the changes in malice and public humiliation cyberbullying facets reached statistical significance. Surprisingly, behavioral intentions were essentially unchanged immediately following the inter- vention. Empathy for a victim of all types of cyberbullying increased, but only the malice and deception facets were statistically significant. Cyber- bullying knowledge significantly increased immediately following the intervention.

One-Month Follow-Up

To assess the effects of the intervention one month later, analyses of covariance (ANCOVA) with bias corrected

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and accelerated bootstrapping (i.e., 1,000 samples) were used to examine between group differences on study outcomes at one-month follow-up while controlling for baseline scores, gender, and age. Table II summarizes the results of the one-month follow-up data. There were significant intervention effects on cyberbullying behavior involving malice and public humiliation, but decreases in cyberbullying behavior involving unwanted contact and deception did not reach statistical significance. Interven- tion participants showed significantly less approving

attitudes of cyberbullying behavior involving unwanted contact, deception, and public humiliation and lower injunctive norms for cyberbullying behavior involving public humiliation. Cyberbullying knowledge also im- proved significantly. No other between-group differences met statistical significance at one-month follow-up.

DISCUSSION

The purpose of the present study was to examine the efficacy of an online cyberbullying prevention video, based on the Theory of Reasoned Action (TRA). As we have previously demonstrated that TRA constructs account for between 54.3% and 87.2% of the variance in cyberbullying perpetration across the four types of cyberbullying behavior examined in the present study (Doane et al., 2014), we first tested our prevention program by examining immediate changes in empathy toward cyberbullying victims and the TRA constructs. Across the four types of cyberbullying behavior, we found small changes in empathy (.19 < ds < .24), moderate changes in attitudes (.24 < ds < .48), moder- ate-to-large changes in injunctive norms (.27 < ds < .73), small-to-moderate changes in descriptive norms (.12 < ds < .50), and small, non-significant changes in cyberbullying intentions (.02 < ds < .14). We also observed a large improvement in cyberbullying knowl- edge (d ¼ .85). Overall, the immediate pre-/post-tests suggested that our prevention program was successful at changing many of the known antecedents to cyberbully- ing behavior. Importantly, our between-subject analyses at one-month follow-up revealed that students random- ized to the cyberbullying prevention program reported significantly less perpetration of cyberbullying behavior involving malice or public humiliation. In addition, the prevention group was distinguished from the control group as having significantly less positive attitudes toward three of the four types of cyberbullying behavior, lower injunctive norms of public humiliation cyberbul- lying, and higher cyberbullying knowledge. Overall, we found that a brief, video-based cyberbul-

lying prevention program was effective at reducing specific types of cyberbullying and changing a number of factors that may lead to the maintenance of types of cyberbullying behavior. Although the effects of the program on cyberbullying perpetration at one-month follow-up can be considered small (.002 < partial h 2s < .060), considering the brevity of the prevention

video (10 min), we find these results promising for the field of cyberbullying prevention. These results dem- onstrate both the promise of low-cost, video-based cyberbullying prevention programs for college students as well as prevention programs that are aimed at effecting TRA constructs.

TABLE I. Paired Samples t Tests Testing the Immediate Effects of Video Program

95%CIs

Variable Pre M

Post M MD SE

Lower Limit

Upper Limit d

Attitudes Unwanted contact

.11 .03 .08 .03 .03 .15 .24

Malice .25 .05 .19 .04 .12 .28 .48 Deception .17 .04 .13 .05 .05 .23 .26 Public humiliation

.17 .04 .13 .04 .06 .21 .30

Injunctive norms Unwanted contact

.24 .12 .12 .04 .03 .20 .27

Malice .73 .41 .32 .07 .20 .46 .73 Deception .43 .23 .20 .05 .11 .30 .37 Public humiliation

.51 .26 .25 .08 .12 .40 .31

Descriptive norms Unwanted contact

.37 .30 .07 .06 �.01 .18 .12

Malice 1.32 .90 .42 .08 .27 .58 .50 Deception .60 .49 .11 .06 �.001 .22 .16 Public humiliation

.89 .60 .11 .06 .13 .41 .36

Intentions Unwanted contact

.04 .03 .02 .04 �.04 .08 .04

Malice .18 .11 .06 .07 �.01 .14 .14 Deception .04 .03 .02 .04 �.05 .09 .04 Public humiliation

.06 .05 .01 .05 �.08 .11 .02

Empathy Unwanted contact

4.15 4.45 �.30 .14 �.57 .03 .20

Malice 3.96 4.26 �.31 .14 �.56 �.03 .22 Deception 4.00 4.39 �.39 .15 �.66 �.09 .24 Public humiliation

4.03 4.33 �.29 .15 �.56 .01 .19

Knowledge 2.00 3.31 �1.31 .14 �1.57 �1.02 .85 CI ¼ bias corrected and accelerated confidence intervals based on 1,000 bootstrapped samples. Significant effects are determined by 95% confidence intervals that do not contain zero, and are in boldtype face for emphasis.

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142 Doane et al.

Despite strong relationships among the TRA con- structs (Doane et al., 2014), the intervention changed attitudes and norms but did not significantly change cyberbullying intentions. Despite the promise of our TRA-based cyberbullying prevention program, the fact that cyberbullying intentions were unchanged either immediately or one-month following the intervention

presents a significant limitation of TRA-based ap- proaches to cyberbullying prevention. According to TRA, cyberbullying intentions are the most proximal antecedents to cyberbullying behavior. However, our prevention program resulted in behavior change in the absence of changing intentions. We consider a few explanations for these findings. As shown in Table II,

TABLE II. Analysis of Covariance for Condition (IV) and Baseline Scores (CV) on One-Month Post Prevention Scores Testing Effects of the Video Program

95%CIs

Variable Control Adj. M

Video Adj. M MD SE

Lower Limit

Upper Limit

Partial h 2

Attitudes Unwanted contact

.12 .04 .08 .04 .01 .13 .024

Malice .37 .21 .16 .09 �.05 .35 .017 Deception .24 .08 .15 .07 .04 .23 .028 Public humiliation

.20 .05 .15 .05 .05 .24 .039

Injunctive norms Unwanted contact

.27 .26 .01 .10 �.21 .20 .000

Malice .90 .63 .28 .13 �.003 .54 .027 Deception .48 .40 .07 .13 �.20 .31 .002 Public humiliation

.67 .32 .35 .13 .12 .57 .040

Descriptive norms Unwanted contact

.46 .51 �.05 .12 �.28 .17 .001

Malice 1.55 1.28 .27 .17 �.05 .61 .015 Deception .67 .64 .03 .14 �.26 .30 .000 Public humiliation

.84 .84 .00 .16 �.32 .28 .000

Intentions Unwanted contact

.09 .05 .03 .04 �.03 .11 .002

Malice .41 .27 .14 .09 �.01 .28 .013 Deception .12 .09 .03 .04 �.05 .10 .001 Public humiliation

.09 .07 .02 .03 �.03 .08 .001

Behavior Unwanted contact

.10 .08 .03 .05 �.07 .13 .002

Malice .67 .36 .31 .09 .15 .48 .060 Deception .26 .14 .12 .07 �.02 .26 .017 Public humiliation

.25 .12 .14 .08 .001 .28 .013

Empathy Unwanted contact

3.93 4.11 �.18 .23 �.60 .32 .004

Malice 3.78 3.88 �.11 .23 �.55 .40 .002 Deception 3.86 3.89 �.03 .25 �.51 .49 .000 Public humiliation

3.81 3.98 �.18 .23 �.59 .25 .004

Knowledge 2.20 2.80 �.62 .17 �.93 �.30 .089 CI ¼ bias corrected and accelerated confidence intervals based on 1,000 bootstrapped samples. Significant effects are determined by 95% confidence intervals that do not contain zero, and are in boldtype face for emphasis. Each model controls for baseline scores, gender, and age.

Aggr. Behav.

Cyberbullying Prevention: Theory of Reasoned Action 143

cyberbullying intentions for all four types of cyberbully- ing behavior were relatively low compared to cyber- bullying perpetration in both the intervention and control groups. Thus, social desirability biases may result in underreporting of cyberbullying intentions. However, one might expect that social desirability biases would similarly result in an underreporting of cyberbullying perpetration. Alternatively, the low self-reported cyber- bullying intentions may reflect a limitation of applying TRA to understand cyberbullying perpetration. Accord- ing to the Prototype Willingness Model (Gibbons, Gerrard, Blanton, & Russell, 1998), another proximal antecedent to behavior is willingness to engage in the behavior. Although many perpetrators of cyberbullying may not explicitly intend to cyberbully, they may be willing to engage in cyberbullying if the opportunity arises, which would suggest that willingness may also predict cyberbullying perpetration beyond that which is explained by behavioral intentions.

Limitations

Despite the important implications of our theory-based cyberbullying prevention program, there are a number of limitations of the present study that must be considered. Given the low incentives provided for participation, less than 3% of students invited to participate in the study actually participated, and less than 45% of those who completed the baseline survey completed the one-month follow-up survey. Further, our participants were more likely to be White and female than expected based on the demographics at the participating university; thus, it is difficult to know to what extent our college student sample reflects the college student population at large and whether our findings would generalize to this population. It is also not clear whether the high drop-out rate resulted in an overestimation or underestimation of the efficacy of the cyberbullying prevention program. Future studies that provide greater participation incentives and longer-term follow-ups will be better able to ascertain the short- and long-term efficacyofcyberbullyingpreventionprograms. In addition, although the one-month follow-up effects

were assessed by comparing the experimental group to the control group while controlling for group differences in baseline scores, it is important to note that the immediate effects of the program were assessed using a pre-post design without a control group immediate follow-up assessment. Thus, the immediate effects of the program should be interpreted with caution. Further- more, it is possible that the additional assessment for the experimental group (i.e., immediately after the video) could have had an effect. Given the small effect sizes observed at one-month follow-up, it may also be worthwhile to integrate the video as part of a larger scale cyberbullying prevention program.

Research Implications

Cyberbullying research has focused primarily on the prevalence, correlates, and conceptualization of cyber- bullying (see Kiriakidis & Kavoura, 2010, for a review). Although a number of recommendations have been made for cyberbullying intervention/prevention (e.g., Couvil- lon & Ilieva, 2011; Slonje, Smith, & Fris�en, 2013), only a few studies have evaluated cyberbullying intervention/ prevention programs (e.g., Gradinger et al., 2015; Lee et al., 2013; W€olfer et al., 2014). To our knowledge, the present study was the first to evaluate a theory-based cyberbullying prevention program for college students. This study should be replicated by evaluating

prevention videos that target emerging adults. The effectiveness of presenting similar video content in an in-person format and peer-delivered presentations with skits and information should be examined. In addition, resources for victims including tips for prevention and information on seeking help when cyberbullied could be included in future programs. Integrating the video as part of a larger scale on-campus cyberbullying prevention program as well as increased exposure to anti- cyberbullying messages throughout the school year may strengthen its effectiveness. In addition, longer- term follow-up assessments should be included. Fur- thermore, the video content may be potentially modified for use with middle and high school students. The program developed for the present study is

appropriate and relevant for emerging adults and has the ability to be universal. A brief, Internet format for a cyberbullying prevention program is a low-cost option that would enable greater accessibility across a wide variety of settings and target populations than would traditional prevention programs. This program could serve as a model for future cyberbullying prevention programs that may change attitudes toward cyberbully- ing and reduce cyberbullying behavior.

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