Advertisement

Bystanders Against Cyberbullying: a Video Program for College Students

  • Ashley N. DoaneEmail author
  • Sarah Ehlke
  • Michelle L. Kelley
Original Article
  • 113 Downloads

Abstract

In the present study, we tested the effectiveness of a Theory of Planned Behavior-based cyberbullying bystander video on three outcomes: joining the bully, helping the victim, and doing nothing as compared to a control group. University students (M = 23.68 years old; female = 78.7%), were randomly assigned to a cyberbullying bystander (n = 113) or an alcohol control (n = 112) video. The bystander video showed students defining and depicting four types of cyberbullying (malice, public humiliation, deception, and unwanted contact) with bystanders present and giving suggestions for positive responses when witnessing cyberbullying. Participants completed surveys prior to viewing the video, immediately following the video, and 1 month later. Positive attitudes, favorable injunctive norms, and perceived behavioral control regarding doing nothing when witnessing cyberbullying were significantly lower for the cyberbullying than control group immediately following the video. Further, the experimental group reported higher intentions to help the victim and lower intentions to do nothing when witnessing cyberbullying compared to the control group. Although mean differences for some of these effects were in the same direction at 1-month follow-up, no significant differences were found. Findings are discussed in terms of implications and future directions for cyberbullying intervention programs targeting improvements in bystander behavior.

Keywords

Cyberbullying Bystanders Prevention Intervention College students 

Cyberbullying has received increasing research attention in recent years. Most initial studies focused on the roles of victims and perpetrators, particularly among youth (see Kowalski et al. 2014 for a meta-analysis/review). However, more recent research has demonstrated that cyberbullying continues to be highly prevalent and is associated with negative consequences, such as mental health problems, among college students (e.g., Selkie et al. 2015; Varghese and Pistole 2017). Two studies that used exploratory and confirmatory factor analyses validated a multifactor cyberbullying experiences survey for college students and found that in the previous year, 88% were victims and 78% were perpetrators of malice (e.g., sending mean messages), 78% were victims and 53% were perpetrators of deception (e.g., having someone share personal information while pretending to be someone else), 73% were victims and 38% were perpetrators of public humiliation (e.g., posting an embarrassing picture or message publicly), and 66% were victims and 29% were perpetrators of unwanted contact (e.g., sending unwanted sexual pictures or messages; Doane et al. 2013). Recent studies have also begun to address the role of cyberbullying bystanders (i.e., witnesses of cyberbullying incidents).

Although witnessing a cyberbullying incident is common, most youth and college student bystanders do not take any action (Gahagan et al. 2016; Huang and Chou 2010; Li 2010; Kowalski et al. 2012). For specific types of cyberbullying, such as posting negative messages or sharing pictures or videos, there may be an unlimited audience, emphasizing that bystanders have a critical role in cyberbullying. Although researchers have begun to consider the role that bystanders play in reducing cyberbullying perpetration and/or victimization among youth and college students (e.g., Doane et al. 2016; Holfeld 2014; Menesini et al. 2012; Williford et al. 2014), few cyberbullying programs that include bystander components have used a viable framework to test constructs that may be expected to change bystander behavior (Tokunaga 2010). In the present study, we tested the effectiveness of a cyberbullying bystander video grounded in the Theory of Planned Behavior (TPB; Ajzen 1991) that was designed to improve attitudes, norms, perceived behavioral control (PBC), positive bystander behavior intentions, and positive bystander behavior. Findings from this initial study may provide information for future programs that can incorporate a cyberbullying bystander video.

Bystanders in Cyberbullying Research

Prevalence rates for bystanders who witness cyberbullying vary due in part to differences in methodology. Based on self-reports, 28–76% of youth (Li 2010; Van Cleemput et al. 2014; Vandebosch and Van Cleemput 2009) and 24–55% of college students and young adults (Balakrishnan 2015; Gahagan et al. 2016; Kowalski et al. 2012; Whittaker and Kowalski 2015) indicated they have witnessed cyberbullying. There are three primary ways bystanders can respond to incidents of bullying: avoiding it, reinforcing the bullying or joining in, and defending/supporting the victim (Barlińska et al. 2015; Bastiaensens et al. 2014; Steffgen et al. 2018). Studies have found 59–70% of adolescent and college student bystanders report they did nothing in response to witnessing cyberbullying (Gahagan et al. 2016; Li 2010; Van Cleemput et al. 2014). Common explanations for doing nothing are that the situation was not their business or problem, their actions would not help, and fear of retaliation if they did intervene (Alipan, Skues, Theiler, and Wise 2019; Rigby 2008; Van Cleemput et al. 2014). Further, sometimes bystanders do not know whether an incident is cyberbullying, as the context in which the event occurs is not always known (e.g., teasing among friends). The lack of certainty around whether events constitute cyberbullying also has been suggested as a reason why adolescent and emerging adult bystanders may not intervene (Alipan et al. 2019; Holfeld 2014; Machackova and Pfetsch 2016). Fewer adolescents engage in positive bystander behaviors, including supporting the victim and/or confronting the bully (Li 2010; Van Cleemput et al. 2014). It is generally least common for adolescents to engage in negative bystander behavior, such as engaging in cyberbullying or reinforcing the bully (Li 2010; Van Cleemput et al. 2014).

Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB)

Given that many cyberbullying incidents are witnessed and most bystanders do nothing in response, a better understanding of the types of cyberbullying events and other variables that may be associated with responses is critical for intervention. Practical models that have been used to explain cyberbullying perpetration include the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB). TRA (Ajzen 1985) posits that attitudes (i.e., one’s approval of the behavior) and subjective norms (i.e., perception of pressure from others to perform a behavior) positively predict behavioral intentions (i.e., one’s plans to engage in the behavior in the future). More recently, measurement of subjective norms has included an injunctive norms component (i.e., perception of others’ approval of the behavior) and a descriptive norms component (i.e., perception of others’ engagement in the behavior; Ajzen 2006; Fishbein and Ajzen 2010). A social norms approach posits that by correcting misperceptions, it is possible to change behavioral intentions, and in turn, predict changes in actual behavior. TPB extended TRA with the introduction of PBC (i.e., perception of one’s ability to execute the behavior), which is also expected to influence one’s behavioral intentions.

In a study of adolescents, cyberbullying attitudes, norms, and PBC positively predicted intentions to cyberbully, which in turn predicted engagement in cyberbullying (Heirman and Walrave 2012). Similarly, in a study of college students, Doane et al. (2014) found that TRA constructs predicted intentions to cyberbully and intentions predicted cyberbullying perpetration across four distinct types of cyberbullying (deception, public humiliation, malice, and unwanted contact). In addition, higher empathy for victims was associated with less favorable attitudes as well as lower injunctive norms and descriptive norms regarding cyberbullying perpetration. In fact, the effects of empathy on all four types of cyberbullying perpetration were fully mediated by TRA constructs.

Using the Integrative Model (a combination of TRA and TPB components) and Social Cognitive Theory (the interaction of personal, behavioral, and environmental determinants; Bandura 2001) as theoretical frameworks, focus groups with adolescents revealed willingness to defend a victim depended on the context (e.g., specific victim and bully characteristics and circumstances; DeSmet et al. 2014). Most adolescents viewed helping the victim as appropriate, but to a lesser extent, not responding to the event also was viewed as acceptable. Self-efficacy was highest for providing comfort to victims and generally lowest for confronting bullies, especially in situations in which support was lacking. In a study of Flemish adolescents who were cyberbullying bystanders, the relationship between favorable injunctive norms (i.e., perception that peers approved of cyberbullying) and joining the cyberbully was mediated by social pressure to join the cyberbully (Bastiaensens et al. 2016). Descriptive norms regarding peers’ cyberbullying behavior did not predict social pressure to join the cyberbully or joining the cyberbully, however. Overall, research suggests that intervention programs that increase TPB constructs regarding positive bystander behaviors and decrease TPB constructs regarding negative bystander behaviors may, in turn, improve bystander behaviors.

In addition, in a study of adolescents, higher levels of empathy were related to helping cyberbullying victims, whereas lower levels of empathy were associated with joining the bully or doing nothing (Van Cleemput et al. 2014). Machackova and Pfetsch (2016) found that affective (i.e., emotional response toward others; Davis 1994) and cognitive empathy (i.e., understanding the perspective of others; Davis 1994) predicted bystander support for victims of traditional bullying, but only affective empathy predicted providing support to cyberbullying victims. In a longitudinal study, adolescents in the affective (i.e., brief video demonstrating the effects of cyberbullying on the victim) and cognitive empathy (i.e., same video, but focused on victim’s emotions and behavior) activation conditions were less likely to engage in negative bystander behavior than those in the control condition (Barlińska et al. 2013). In a follow-up study, adolescents were less likely to forward the message, but only if the cognitive empathy activation prime was immediately before they had the opportunity to forward the message (Barlińska et al. 2015). Results from these studies suggest that building empathy may improve active helping during bystander intervention.

Cyberbullying Prevention/Intervention Programs

Generally, cyberbullying prevention/intervention efforts have been evaluated with youth and have focused on changes in victimization and/or perpetration (e.g., Menesini et al. 2012; Lee et al. 2013; Palladino et al. 2016; Salmivalli et al. 2011; Williford et al. 2014). Wölfer et al. (2014) tested a TPB-based program, which used role playing and other strategies designed to increase cyberbullying knowledge, promote empathy, and improve norms and behavioral control in a sample of adolescents. Both a short version (i.e., four sessions in 1 day) and long version (i.e., 10 weekly sessions) of the program decreased cyberbullying and increased perspective-taking skills. However, length of the program did not influence program outcomes. In one of the few cyberbullying prevention programs for college students, Doane et al. (2016) developed a TRA-based 10-min video that provided informational slides and had student actors depict victims’ experiences of cyberbullying. Compared to baseline, participants who viewed the video online reported improved attitudes, injunctive and decriptive norms of cyberbullying perpetration, and increased knowledge and empathy toward cyberbullying victims for some forms of cyberbullying. Additionally, compared to control participants, those who viewed the video reported less favorable attitudes and injunctive norms and lower perpetration in various cyberbullying behaviors 1 month later.

Previous techniques used in cyberbullying preventions/interventions have varied and focused on the assessment of changes in victimization and perpetration. To our knowledge, only one study (DeSmet et al. 2018) of adolescents has assessed change in bystander behavior and predictors of bystander behavior, specifically. Although some cyberbullying programs have been based on TRA/TPB (e.g., Doane et al. 2016; Wölfer et al. 2014), included a bystander component (e.g., Doane et al. 2016; Menesini et al. 2012; Palladino et al. 2016; Williford et al. 2014), included a video (e.g., Doane et al. 2016; Menesini et al. 2012), involved peers (e.g., Doane et al. 2016; Menesini et al. 2012), and/or were delivered online (e.g., Doane et al. 2016; Lee et al. 2013), combining previous program characteristics into a single program with a primary focus on bystanders may be effective for promoting positive bystander behaviors (e.g., helping the victim) and decreasing negative/passive bystander reactions (e.g., joining the bully, doing nothing) among college students.

Relative to youth, many college student and adult cyberbullying behaviors are sexual in nature (e.g., sexting; unwanted sexual contact; bashing pages, including calling people “sluts”; revenge porn; e.g., Doane et al. 2013; Bauman and Baldasare 2015; Ehman and Gross 2019; Kowalski et al. 2012; Lee 2017). Further, well-publicized instances of cyberbullying with devastating consequences have involved events that were sexual in nature. For this reason, the present study developed and tested a cyberbullying bystander intervention video program for college students based on TPB (Ajzen 1991) as compared to an alcohol intervention control group. Some scenarios in the video program depicted cyberbullying of a sexual nature. Given that TPB indicates that changes in attitudes, norms, and PBC should ultimately change behavior, we designed the cyberbullying video to target these predictors with the goal of changing bystander behavior.

Hypothesis 1

We hypothesized that immediately following the intervention, TPB components (i.e., attitudes, injunctive norms, PBC, and intentions) would be lower for joining the bully and doing nothing and higher for helping the victim in the experimental group compared to the control group.

Hypothesis 2

It was hypothesized that empathy toward cyberbullying victims would be higher for the experimental group than the control group immediately after the video.

Hypothesis 3

We hypothesized that TPB components (i.e., attitudes, injunctive and descriptive norms, PBC, intentions, and behavior) would be lower for joining the bully and doing nothing and higher for helping the victim at 1-month follow-up for the experimental group compared to the control group.

Hypothesis 4

It was hypothesized that empathy toward cyberbullying victims would be higher for the experimental group compared to the control group at 1-month follow-up.

Method

Participants and Procedure

A total of 225 students were recruited via a psychology research pool at a large southeastern university in the USA. On average, participants were 23.68 years old (SD = 7.50). The majority were female (n = 177, 78.7%), and most were Black (n = 96, 42.7%) or White (n = 88, 39.1%). Informed consent was obtained from all individual participants included in the study. After consenting to participate in an online study, participants were randomly assigned to the cyberbullying video (experimental group, n = 113) or an alcohol video (control group, n = 112). Participants completed an online survey prior to viewing the online video (baseline), immediately following the video (T2), and at a 1-month follow-up assessment (T3). A total of 111 participants (49.3%) completed the T3 survey (experimental condition: n = 62, 55.9%; control condition n = 49, 44.1%). Participants were awarded research credit; those that completed the 1-month follow-up were also entered into a gift card raffle. The study was approved by the university’s Institutional Review Board.

Program Content

The bystander intervention video (18.5 min) was developed by the first author with input from the Cyberbullying Research and Awareness Group (CRAG; a college student organization for students of all majors led by the first author). Cyberbullying instances reported in previous studies were discussed with CRAG student members who then suggested ways to make the previously identified cyberbullying events realistic, engaging, and up-to-date for the current bystander intervention video program.

The TPB-based bystander video (see Fig. 1) began with interviews of college students regarding their definitions of cyberbullying, types of cyberbullying witnessed, their responses, and whether there was anything else they could have done in response to the event. The purpose of the interviews was to show peer perspectives on actual cyberbullying events and ideas for positive bystander behavior. To improve empathy toward victims, they described how the victims were affected (e.g., being upset, cutting one’s hair after others made fun of it). Although the norm is to do nothing when witnessing cyberbullying, to improve attitudes and norms, these students described how they took action when witnessing actual cyberbullying (e.g., talked to the victim to make her feel better, asked perpetrator to remove video) and what else could have been done (e.g., telling the perpetrator their behavior is not cool or funny, reporting it, comforting the victim). The interviews were interspersed between slides containing cyberbullying information with study citations, including a definition (i.e., intentionally and repeatedly harming others through the use of electronic devices, such as computers or cell phones; Hinduja and Patchin 2009), a description of four types of cyberbullying (malice, public humiliation, deception, and unwanted contact) identified in previous research (Doane et al. 2013), and effects of cyberbullying on victims (i.e., to improve empathy for victims). Next, student actors performed common cyberbullying incidents with bystanders present. The scenarios were developed to cover the four types of cyberbullying and were cyberbullying situations that would most likely be witnessed by others. Scenario 1 (public humiliation, malice) involved a female student talking to her friend about sending her new boyfriend a nude picture of herself after they had sex. She finds out that she has been cyberbullied when the friend discovers that her nude picture has been posted on a bashing page with mean comments. To improve attitudes and perceived norms regarding cyberbullying, in the next scene, two male students are looking at the picture and comments and expressing their disapproval. Finally, to increase empathy toward the victim, the female is “interviewed” and tearfully talking about how this event significantly affected her. In scenario 2 (deception), a male student thinks he is talking to an attractive female and reveals personal information. They agree to meet with each other for the first time to have sex. When he shows up to the room to meet her, two guys are waiting for him with a video camera. They reveal that it was them all along and make fun of him. The two males then cyberbully the male student by posting the video on YouTube, where we see a student viewing it and expressing her disapproval of the cyberbullies’ behavior (i.e., to improve attitudes and perceived norms regarding cyberbullying). In Scenario 3 (unwanted contact), three students exchange phone numbers to work on a group project. The male student begins texting inappropriate messages to one of the young women. Despite her telling him she has a boyfriend, he continues to cyberbully her by sending a picture of his penis. When the girl explains what he did to the other female group member, the other female tells her to stop responding to him (i.e., helps the victim). To increase empathy toward cyberbullying victims, scenario 4 (malice) depicts a female student (i.e., cyberbullying victim) sitting alone and reading many mean messages about her (with voiceovers reading them). She is “interviewed” discussing how these messages hurt her deeply. After the scenarios, to improve attitudes, perceived norms, and PBC regarding positive bystander behavior, students are shown discussing what can be done when they witness cyberbullying (such as the scenarios they watched) and how these actions can make a difference. Suggestions included not ignoring it or assuming it is not your business (you can prevent something bad from happening to the victim), do not “like” it (i.e., joining the bully) because it is just as bad as the perpetrator’s actions, report it at school, come together with others to support victims, and “having courage to be the first” to do something about it. To further improve PBC, the video ends with individual student responses to the question, “What would you want others to do if you were cyberbullied?” Students indicated they would want someone to stand up for them, help, and comfort them (e.g., kind words, counteract the negative comments), and that it would be uncool to do nothing.
Fig 1

The application of the Theory of Planned Behavior to the cyberbullying bystander video

The comparison group watched an 8.75-min alcohol video that was developed as part of a separate study. This program was selected because many colleges address alcohol use, and we expected it to be unrelated to cyberbullying. The program consisted of a narrated powerpoint presentation highlighting the helpfulness of specific protective behavioral strategies (PBS) and provided normative feedback about PBS use designed to increase students’ intentions to use PBS in future drinking occasions. This intervention encourages those who abstain from alcohol to continue to do so as the only full-proof strategy to reduce alcohol-related harm.

Measures

All cyberbullying survey questions were grouped by four types of cyberbullying behaviors (i.e., having participants focus on similar incidents together) that were previously identified by exploratory/confirmatory factor analyses in the development of the Cyberbullying Experiences Survey (CES; Doane et al. 2013): malice (e.g., mean message), deception (e.g., having someone else share personal information while pretending to be someone else ), public humiliation (e.g., embarrassing picture), and unwanted contact (e.g., unwanted sexual message). The CES consists of 21-item cyberbullying victimization and 20-item cyberbullying perpetration scales with four factors each that have been validated in two college student samples and have good convergent validity with other measures of cyberbullying (Doane et al. 2013). Between the victimization and perpetration scales, 12 behaviors overlap and 17 behaviors do not overlap, for a total of 29 different cyberbullying behaviors: malice (6 items), deception (3 items), public humiliation (11 items), and unwanted contact (9 items). For each type of cyberbullying separately (i.e., behaviors from each factor were presented before each question), participants answered a series of questions regarding witnessing cyberbullying, three types of bystander behavior (joining the bully, helping the victim, doing nothing), and empathy for victims. Total scores that averaged across the four types of cyberbullying (malice, deception, public humiliation, unwanted contact) for each bystander response (joined the bully, helped the victim, do nothing) were computed to simplify the presentation of the results. Cronbach’s alpha estimates for these scores are presented in Table 1. Due to concerns with underreporting and social desirability (see Kowalski et al. 2014 for a discussion of this issue), the term “cyberbullying” and its definition were intentionally excluded from all measures. All TPB measures in the present study were based on TRA/TPB measures regarding cyberbullying perpetration (Doane et al. 2014; Doane et al. 2016) and Ajzen’s (2006) suggestions. Students in both conditions also answered 23 questions regarding alcohol use that were not used in the present study.
Table 1

Descriptive information

 

Baseline

 

Immediate post

 

1-month post

 
 

CB

n = 113

Alcohol

n = 112

 

CB

n = 112

Alcohol

n = 111

 

CB

n = 62

Alcohol

n = 49

 
 

M

SD

M

SD

α

M

SD

M

SD

α

M

SD

M

SD

α

Attitudes

Join bully

0.28

0.51

0.25

0.48

0.77

0.17

0.48

0.18

0.46

0.86

0.21

0.45

0.28

0.67

0.71

Help victim

3.32

1.49

3.27

1.61

0.91

3.56

1.60

3.43

1.67

0.97

3.36

1.69

3.34

1.59

0.95

Do nothing

1.48

1.25

1.21

1.17

0.88

1.02

1.21

1.10

1.24

0.97

1.33

1.22

1.16

1.17

0.92

Injunctive norms

Join bully

0.50

0.67

0.40

0.48

0.67

0.26

0.61

0.25

0.48

0.88

0.31

0.55

0.42

0.74

0.85

Help victim

3.23

1.42

3.20

1.54

0.91

3.51

1.55

3.38

1.65

0.97

3.31

1.60

3.41

1.52

0.93

Do nothing

1.47

1.20

1.29

1.12

0.87

1.07

1.25

1.17

1.24

0.97

1.25

1.22

1.20

1.17

0.93

Perceived behavioral control

Join bully

0.96

1.26

0.72

0.94

0.84

0.68

1.29

0.46

0.95

0.94

1.14

1.65

0.87

1.34

0.92

Help victim

3.41

1.48

3.36

1.53

0.90

3.73

1.64

3.45

1.75

0.97

3.45

1.71

3.35

1.66

0.95

Do nothing

1.94

1.61

1.77

1.51

0.91

1.45

1.68

1.53

1.55

0.96

1.82

1.78

1.72

1.56

0.94

Intentions

Join bully

0.20

0.43

0.17

0.31

0.73

0.12

0.37

0.10

0.34

0.85

0.13

0.32

0.16

0.55

0.88

Help victim

1.86

1.14

1.92

1.19

0.84

2.59

1.32

2.33

1.38

0.97

1.95

1.28

1.96

1.21

0.90

Do nothing

1.26

1.10

1.15

1.01

0.86

0.85

1.00

0.98

1.02

0.95

1.22

1.09

1.18

1.00

0.92

Empathy toward victims

4.15

1.03

4.04

1.19

0.84

4.26

1.09

4.25

1.05

0.94

4.14

0.92

4.33

0.85

0.84

Self: witness cyberbullying

0.85

0.82

0.68

0.67

0.93

--

--

--

--

--

0.62

1.00

0.70

0.86

0.97

 

n = 102

n = 99

    

n = 46

n = 43

 

Joined the bully

0.34

0.64

0.24

0.36

0.61

--

--

--

--

--

0.16

0.33

0.21

0.53

0.88

Helped the victim

1.34

1.07

1.33

1.12

0.82

--

--

--

--

--

1.03

1.09

1.12

1.06

0.89

Did nothing

1.85

1.28

1.70

1.25

0.87

--

--

--

--

--

1.79

1.30

1.77

1.21

0.85

Peer: witness cyberbullying

0.92

0.99

0.73

0.76

0.95

--

--

--

--

--

0.75

1.17

0.87

1.10

0.98

 

n = 97

n = 94

    

n = 41

n = 39

 

DN: joined the bully

0.80

0.83

0.41

0.46

0.69

--

--

--

--

--

0.53

0.77

0.59

0.71

0.78

DN: helped the victim

1.23

0.89

1.24

0.98

0.71

--

--

--

--

--

1.21

1.01

1.22

0.94

0.74

DN: Did nothing

1.95

1.21

1.75

1.22

0.85

--

--

--

--

--

1.88

1.20

1.91

1.14

0.89

CB cyberbullying condition, DN descriptive norms

Witnessing Cyberbullying Behavior and Bystander Responses

To assess the frequency of witnessing each cyberbullying behavior (i.e., 29 items) in the past month at baseline and T3, participants were asked, e.g., “In the past month, how often have you witnessed someone [e.g., malice: “being mean to someone electronically;” deception: “getting someone to share personal information with him/her electronically while pretending to be someone else;” public humiliation: “posting an embarrassing picture of someone electronically where other people could see it,” unwanted contact: “sending an unwanted sexual message to someone electronically”]?” Responses ranged from 0 (never) to 5 (every day/almost every day). Three bystander responses (joined the bully, helped the victim, did nothing) were assessed only for the types of behavior (out of four) that participants indicated they had witnessed in the past month (12 items total). Participants responded to, “In the past month, when witnessing these types of behaviors, how often did you [join the person doing these things, help the person who experienced these things, do nothing]?” on a response scale that ranged from 0 (never) to 4 (always).

Descriptive Norms Regarding Bystander Behavior

To assess perceived descriptive norms about bystander responses at baseline and T3, participants were first asked, “In the past month, how often have your peers witnessed someone [e.g., malice: “being mean to someone electronically;” deception: “getting someone to share personal information with him/her electronically while pretending to be someone else;” public humiliation: “posting an embarrassing picture of someone electronically where other people could see it,” unwanted contact: “sending an unwanted sexual message to someone electronically”]?” Responses ranged from 0 (never) to 5 (every day/almost every day). For each of the four types of cyberbullying their peers witnessed, participants responded to, “In the past month, when they witnessed these types of behaviors, my peers [joined the person doing these things, helped the person who experienced these things, did nothing]” on a response scale that ranged from 0 (never) to 4 (always).

Bystander Intentions

Intentions to engage in the three types of bystander responses were examined for the four types of cyberbullying behavior (12 items total) at baseline, T2, and T3. For example, participants responded to, “In the next month, if you witness these types of behaviors, how often will you [join the person doing these things, help the person who experienced these things, do nothing]?” on a response scale that ranged from 0 (never) to 4 (always).

Attitudes About Bystander Behavior

Twelve items (three bystander responses to four types of cyberbullying behavior) examined attitudes about bystander responses at baseline, T2, and T3. For instance, participants answered, “In the next month, if I witness these types of behaviors, I would (strongly disapprove-strongly approve) of me [joining the person doing these things, helping the person who experienced these things, doing nothing]” on a response scale of 0 (strongly disapprove) to 5 (strongly approve).

Injunctive Norms Regarding Bystander Behavior

Similarly, at baseline, T2, and T3, 12 items examined injunctive norms regarding each bystander response for each type of cyberbullying behavior (i.e., “In the next month, my peers would (strongly disapprove-strongly approve) of me [joining the person doing these things when I witness these types of behaviors, helping the person experiencing these things when I witness these types of behaviors, doing nothing when I witness these types of behaviors]”). Responses ranged from 0 (strongly disapprove) to 5 (strongly approve).

PBC of Bystander Behavior

PBC for each bystander response to each type of cyberbullying behavior was measured with 12 items (e.g., “In the next month, if I witness these types of behaviors, I would know how to [join the person doing these things, help the person experiencing these things, do nothing]”) at baseline, T2, and T3. Responses ranged from 0 (strongly disagree) to 5 (strongly agree).

Empathy Toward the Cyberbullying victim

Based on the Empathetic Responsiveness Questionnaire (i.e., a measure of affective empathy; Olweus and Endresen 1998), empathy toward cyberbullying victims was assessed with one item for each type of cyberbullying behavior: “I feel sorry for a person who has experienced these types of behaviors” at baseline, T2, and T3. Participants responded using a scale from 0 (strongly disagree) to 5 (strongly agree).

Results

Data Analysis

Due to non-normal distributions for most of the dependent variables, we used bias corrected and accelerated bootstrapping with 1,000 samples for all statistical tests (Efron and Tibshirani 1993). This approach was selected because it is recommended to use corrections that are robust against normality violations for analyses (Erceg-Hurn and Mirosevich 2008).

Independent samples t tests and chi-square analyses were used to examine differences between the experimental and control conditions on demographic information and baseline variables. Due to small sample sizes and low witnessing frequency means (see Table 1), descriptive information was examined for frequency of witnessing cyberbullying behaviors, frequency of responses when witnessing cyberbullying, perception of the frequency of peers witnessing cyberbullying, and perception of frequency of peers’ responses to cyberbullying when witnessed (i.e., descriptive norms).

Due to attrition at T3, we conducted analysis of covariance (ANCOVA) analyses for T2 (n = 225) and T3 (n = 111) separately in order to use the full baseline/T2 sample. Although the full T2 sample results are reported, we found similar results with only the T3 sample at T2 (see Online Resources 1). These ANCOVA models, which used bias corrected and accelerated bootstrapping with 1000 samples, compared the experimental and control groups (independent variable) on all dependent variables (TPB constructs regarding joining the bully, helping the victim, and doing nothing as well as empathy toward cyberbullying victims) immediately following the video (hypotheses 1–2) and 1 month later (hypotheses 3–4). Although all participants were contacted for the follow-up 1 month after they completed the baseline survey, the range of days to complete the follow-up survey was 30 to 69 days (M = 39.92 days, SD = 8.14). Number of days to complete the follow-up was negatively correlated with attitudes toward doing nothing at the follow-up (r = − 0.21, p = 0.025). Participants who responded later to the T3 assessment may be less likely to recall important characteristics from the video. For these reasons, number of days to complete the follow-up was included as a covariate (CV) for the T3 ANCOVA model for attitudes toward doing nothing. All models controlled for age (CV) and baseline scores (CV).

Preliminary Analyses

There were no significant differences between the experimental and control groups on any demographic variable (gender, race/ethnicity, and age) or baseline study variables. Compared to those who did not complete the follow-up (T3), those who completed the follow-up were significantly older [completed: M = 25.54, SD = 8.47; did not complete: M = 21.86, SD = 5.92; t(196.26) = − 3.77, p < 0.001]. Therefore, age was controlled for in all models. There were no other significant differences on demographic information, experimental condition, or baseline variables between those who completed the follow-up and those who did not.

Descriptive Information

Descriptive information for outcome variables and witnessing behaviors at baseline, T2, and T3 are displayed in Table 1.

Immediate Intervention Effects at Baseline

Table 2 displays the results for the immediate post-test (T2) results.
Table 2

Analysis of covariance for condition (IV) and baseline scores (CV) on iimmediate post video scores testing effects of the video program

     

95% CI

 

Variable

CB

Adj.M

T2

Alc

Adj.M

T2

M D

SE

Lower limit

Upper limit

Partial η2

Attitudes

Join bully

0.16

0.20

− 0.04

0.04

− 0.11

0.04

0.004

Help victim

3.54

3.45

0.09

0.11

− 0.14

0.32

0.003

Do nothing

0.90

1.22

0.32

0.09

0.50

0.12

0.047

Injunctive norms

Join bully

0.22

0.29

− 0.07

0.05

− 0.17

0.03

0.007

Help victim

3.51

3.38

0.13

0.10

− 0.08

0.33

0.006

Do nothing

0.99

1.26

0.27

0.10

0.46

0.07

0.032

Perceived behavioral control

Join bully

0.58

0.55

0.03

0.09

− 0.15

0.21

0.000

Help victim

3.71

3.47

0.24

0.11

− 0.01

0.45

0.022

Do nothing

1.36

1.62

0.26

0.12

0.48

0.03

0.020

intentions

Join bully

0.11

0.12

− 0.01

0.03

− 0.07

0.05

0.000

Help victim

2.62

2.30

0.32

0.11

0.10

0.55

0.037

Do nothing

0.81

1.03

0.22

0.09

0.37

0.06

0.031

Empathy toward victims

4.21

4.30

-0.09

0.07

− 0.23

0.06

0.006

CI bias corrected and accelerated confidence intervals based on 1000 bootstrapped samples. Significant differences between conditions are determined by 95% confidence intervals that do not contain zero and are italicized. Each model controls for baseline scores and age. CB cyberbullying condition, Alc alcohol condition, T2 = immediate post

Hypothesis 1

Immediately following the video, when controlling for baseline scores and age, the experimental group scored significantly lower than the control group on positive attitudes, favorable injunctive norms, and PBC regarding doing nothing, lower on intentions to do nothing, and higher on intentions to help the victim. No other dependent variables significantly differed between groups. Similar to the full sample, the same analyses on the smaller T3-only sample revealed that there were significant group differences in positive attitudes, favorable injunctive norms, and intentions to do nothing; however, differences in PBC regarding doing nothing and intentions to help the victim were no longer significant (see Online Resource 1).

Hypothesis 2

Empathy toward cyberbullying victims did not significantly differ between the experimental and control groups immediately following the intervention.

Intervention Effects at Follow-up

Table 3 displays the 1-month post (T3) results.
Table 3

Analysis of covariance for condition (IV) and baseline scores (CV) on 1-month post (T3) scores testing effects of the video program (n = 111)

     

95% CI

 

Variable

CB

Adj. M

T3

Alc

Adj. M

T3

M D

SE

Lower limit

Upper limit

Partial η2

Attitudes

Join bully

0.22

0.26

− 0.04

0.09

− 0.26

0.13

0.002

Help victim

3.34

3.37

− 0.03

0.27

− 0.55

0.53

0.000

Do nothinga

1.16

1.37

− 0.21

0.15

− 0.52

0.08

0.018

Injunctive norms

Join bully

0.30

0.43

− 0.13

0.11

− 0.34

0.10

0.012

Help victim

3.28

3.44

− 0.16

0.25

− 0.64

0.31

0.004

Do nothing

1.14

1.33

− 0.19

0.15

− 0.49

0.08

0.013

Perceived behavioral control

Join bully

1.01

1.04

− 0.03

0.26

− 0.55

0.46

0.000

Help victim

3.45

3.36

0.09

0.28

− 0.47

0.64

0.001

Do nothing

1.64

1.95

− 0.31

0.23

− 0.79

0.17

0.017

Intentions

Join bully

0.13

0.15

− 0.02

0.07

− 0.17

0.12

0.001

Help victim

1.96

1.95

0.01

0.21

− 0.37

0.45

0.000

Do nothing

1.09

1.35

− 0.26

0.14

− 0.55

0.02

0.030

Empathy toward victims

4.15

4.31

− 0.16

0.15

− 0.44

0.14

0.010

CI, bias corrected and accelerated confidence intervals based on 1000 bootstrapped samples. Significant differences between conditions are determined by 95% confidence intervals that do not contain zero. Each model controls for baseline scores and age. a, ANCOVA model controlled for number of days to complete follow-up. CB cyberbullying condition, Alc alcohol condition, T3 1-month post

Hypothesis 3–4

There were no significant differences between the experimental and control groups for any dependent variables at T3.

Discussion

Hypothesis 1

The present study developed and tested the effectiveness of a cyberbullying bystander video designed to decrease negative/passive bystander behaviors (joining the bully, doing nothing) and increase positive bystander behaviors (helping the victim). Immediately after viewing a bystander video, experimental group participants held less favorable attitudes toward doing nothing, were less likely to believe that others would approve of their behavior if they did nothing, had lower PBC that they could do nothing, and had lower intentions to do nothing after witnessing cyberbullying than control participants. Additionally, compared to control participants, the cyberbullying bystander video group reported higher intentions to help the victim if they witness a cyberbullying incident.

Prior to viewing the video, participants generally already disapproved of joining the bully, perceived that their peers disapproved of joining the bully, did not know how to join the bully, and did not intend to join the bully. Thus, lack of group differences in these variables may have been in part due to a floor effect. Results suggest the importance of focusing more on enhancing knowledge about how to effectively intervene and help the victim, which may increase PBC regarding helping the victim, and in turn may result in more likelihood of engaging in proactive bystander behavior.

Hypothesis 2

Empathy toward victims of cyberbullying, a predictor of cyberbullying bystander behavior (e.g., Barlińska et al. 2013; Machackova and Pfetsch 2016; Van Cleemput et al. 2014) and a predictor of TRA constructs for cyberbullying perpetration (Doane et al. 2014), did not significantly differ between conditions post video. This finding is in contrast to Doane et al. (2016) who found empathy toward victims of two of four forms of cyberbullying were higher for the cyberbullying video group post video compared to an assessment-only control group. However, the video in the current study placed more emphasis on behaviors such as sending someone nude photos that were then shared with others, whereas the earlier video focused on suicide as a possible consequence of cyberbullying. Thus, participants in the current study may have “blamed” the victim. If this was the case, this would support results by Schacter et al. (2016) who found victim blaming was higher and empathy toward the victim was lower when the victim posted more personal information compared to when the victim posted less personal information. Participants may have used moral disengagement strategies (e.g., displacement of responsibility, attribution of blame) to justify doing nothing when witnessing cyberbullying (see Van Cleemput et al. 2014 for a discussion). The lack of differences in empathy may also reflect a ceiling effect, as on average, participants in both conditions reported high levels of empathy toward victims prior to the video (see Table 1). In addition to empathy, future interventions should address victim blaming and moral disengagement.

Hypotheses 3–4

Although the program was brief, cost-effective, and appeared to exert several effects in theory-consistent ways and average scores for several of the variables (e.g., doing nothing attitudes, norms, perceived behavior control, and intentions) differed between groups in the expected direction, we failed to detect significant differences between experimental and control groups at 1-month follow-up. Some TPB construct effects may have eroded. For example, the adjusted mean difference between experimental and control groups on intentions to help the victim was 0.32 at immediate post, compared to 0.01 at 1-month follow-up. A similar decrease in group differences was observed for attitudes to do nothing (− 0.32 vs. − 0.21) and for injunctive norms to do nothing (− 0.27 vs. − 0.19). However, the adjusted mean difference was actually larger at 1-month follow-up for PBC to do nothing (− 0.26 vs. − 0.31) and intentions to do nothing (− 0.22 vs. − 0.26). Thus, our failure to find significant effects at 1-month follow-up may in part be due to a lack of statistical power. It is also possible that the participants who completed the full study were less affected by the video than the participants who completed only baseline/T2. Although the purpose of the present study was to examine the isolated effects of only a brief video, integrating the video into a more comprehensive program with discussions, role plays, repeated sessions, and other effective intervention components (e.g., Banyard et al. 2007; Midgett et al. 2015) may have larger, more sustained effects on empathy toward victims, TPB components, and cyberbullying bystander behavior.

Although the time frame for the assessment of witnessing cyberbullying frequency was consistent (i.e., past month) at baseline and the 1-month follow up, fewer participants reported witnessing at least one cyberbullying incident at 1-month follow-up (80.2%) compared to baseline (88.9%). Therefore, the opportunity to intervene may have been slightly lower. Previous cyberbullying bystander studies that have experimentally manipulated contextual factors (e.g., private versus public, Barlińska et al. 2013; severity, relationship to other witnesses, Bastiaensens et al. 2014), self-reported bystander behavior (e.g., relationship with victim or perpetrator, Macháčková et al. 2013), and qualitative methods (e.g., relationship with victim or perpetrator, Macháčková et al. 2013) have demonstrated that willingness to intervene may depend on the context. Context was not assessed here.

Limitations and Future Research

Several study limitations should be noted. One limitation is that the sample was only college student volunteers from a single university. The university climate can support or discourage cyberbullying (Myers and Cowie 2017). Further, although cyberbullying is prevalent among college students, whether results may generalize to non-students is not known. It is important, however, to consider the context and cultural environment in which cyberbullying occurs, as interventions for more fixed groups and designed with specific cultures may be more effective.

In addition, we did not examine actual bystander behavior; rather, bystander behavior was examined using non-established self-report measures. To keep the study at a reasonable length for participants, we used shortened measures for empathy toward cyberbullying (i.e., one item assessing only affective empathy extracted from a previous bullying study) and PBC (i.e., only assessed one’s capability of engaging in bystander behaviors, not one’s control over engaging in bystander behaviors; Ajzen 2006). Cronbach’s alphas were slightly below acceptable levels for three scales at baseline. However, only one of these measures (injunctive norms regarding joining the bully; α = 0.67) was used to evaluate the program. In addition, a greater focus on empathy training could be addressed in future programs. Future studies should also incorporate in vivo measures of actual intervention behaviors following a cyberbullying experience and include larger samples and longer follow-ups to determine the duration of the program effects and to examine mediators and moderators of efficacy. Longer or more sustained programs or boosters may be needed to sustain change. It is also possible that bystanders were less likely to intervene in the types of scenarios that we created.

Another limitation is that we do not know whether participants were attentive to the videos. In addition, no pretest was conducted comparing the experimental video to the control video. The videos differed in length, presentation format, level and type of emotion, and complexity of program content and message. Future research should consider the effect of these differences on outcome variables. Although our scenarios included male and female victims, perpetrators, and bystanders, we did not assess whether variations in victim/perpetrator/bystander sex impacted the dependent variables which is important to consider in future research on bystander behavior.

To our knowledge, this study was the first attempt to develop and test a TPB-based cyberbullying bystander video. Future research should evaluate programs based on TPB or other applicable theories, such as the Model of Bystander Intervention (Latané and Darley 1970), which has been applied to traditional bullying bystanders (e.g., Nickerson et al. 2014).

Conclusion

Relative to controls, immediately after viewing the cyberbullying bystander video, participants reported they were less likely to do nothing and more likely to help the victim. Although in the expected direction, these effects were no longer significant at the 1-month follow-up. Although the video alone may not be enough to have a large effect on bystander intentions or behavior over a sustained period of time, incorporating a bystander video similar to the program used in the present study into a larger-scale intervention with discussions or other activities, and repeated messages over time may be effective in increasing positive cyberbullying bystander behavior, such as helping cyberbullying victims.

Notes

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Supplementary material

42380_2019_51_MOESM1_ESM.pdf (296 kb)
ESM 1 (PDF 295 kb)

References

  1. Ajzen, I. (1985). From intentions to actions: a theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Germany: Springer-Verlag.Google Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.  https://doi.org/10.1016/0749-5978(91)90020-T
  3. Ajzen, I. (2006). Constructing a TpB questionnaire: conceptual and methodological considerations. http://people.umass.edu/aizen/pdf/tpb.measurement.pdf. Accessed February 28, 2010.
  4. Alipan, A., Skues, J. L., Theiler, S., & Wise, L. (2019). Defining cyberbullying: a multifaceted definition based on the perspectives of emerging adults. International Journal of Bullying Prevention, 1–14.  https://doi.org/10.1007/s42380-019-00018-6.
  5. Balakrishnan, V. (2015). Cyberbullying among young adults in Malaysia: the roles of gender, age and Internet frequency. Computers in Human Behavior, 46, 149–157.  https://doi.org/10.1016/j.chb.2015.01.021.CrossRefGoogle Scholar
  6. Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3, 265–299.  https://doi.org/10.1207/S1532785XMEP0303_03.CrossRefGoogle Scholar
  7. Banyard, V. L., Moynihan, M. M., & Plante, E. G. (2007). Sexual violence prevention through bystander education: an experimental evaluation. Journal of Community Psychology, 35, 463–481.  https://doi.org/10.1002/jcop.20159.CrossRefGoogle Scholar
  8. Barlińska, J., Szuster, A., & Winiewski, M. (2013). Cyberbullying among adolescent bystanders: role of the communication medium, form of violence, and empathy. Journal of Community & Applied Social Psychology, 23, 37–51.  https://doi.org/10.1002/casp.2137.CrossRefGoogle Scholar
  9. Barlińska, J., Szuster, A., & Winiewski, M. (2015). The role of short- and long-term cognitive empathy activation in preventing cyberbystander reinforcing cyberbullying behavior. Cyberpsychology, Behavior, And Social Networking, 18, 241–244.  https://doi.org/10.1089/cyber.2014.0412.CrossRefGoogle Scholar
  10. Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., & De Bourdeaudhuij, I. (2014). Cyberbullying on social network sites: an experimental study into bystanders’ behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior, 31, 259–371.  https://doi.org/10.1016/j.chb.2013.10.036.CrossRefGoogle Scholar
  11. Bastiaensens, S., Pabian, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., & De Bourdeaudhuij, I. (2016). From normative influence to social pressure: how relevant others affect whether bystanders join in cyberbullying. Social Development, 25, 193–211.  https://doi.org/10.1111/sode.12134.CrossRefGoogle Scholar
  12. Bauman, S., & Baldasare, A. (2015). Cyber aggression among college students: demographic differences, predictors of distress, and the role of the university. Journal of College Student Development, 56, 317–330.  https://doi.org/10.1353/csd.2015.0039.CrossRefGoogle Scholar
  13. Davis, M. H. (1994). Empathy: a social psychological approach. Dubugue, IA: Westview Press.Google Scholar
  14. DeSmet, A., Bastiaensens, S., Van Cleemput, K., Poels, K., Vandebosch, H., Deboutte, G., et al. (2018). The efficacy of the Friendly Attac serious digital game to promote prosocial bystander behavior in cyberbullying among young adolescents: a cluster-randomized controlled trial. Computers in Human Behavior, 78, 336–347.  https://doi.org/10.1016/j.chb.2017.10.011.CrossRefGoogle Scholar
  15. DeSmet, A., Veldeman, C., Poels, K., Bastiaensens, S., Van Cleemput, K., Vandebosch, H., & De Bourdeaudhuij, I. (2014). Determinants of self-reported bystander behavior in cyberbullying incidents amongst adolescents. Cyberpsychology, Behavior, and Social Networking, 17, 207–215.  https://doi.org/10.1089/cyber.2013.0027.CrossRefGoogle Scholar
  16. Doane A. N., Kelley M. L., Chiang E. S., & Padilla M. A. (2013) Development of the Cyberbullying Experiences Survey. Emerging Adulthood 1(3):207-218.Google Scholar
  17. Doane A. N., Pearson M. R., & Kelley M. L. (2014) Predictors of cyberbullying perpetration among college students: An application of the Theory of Reasoned Action. Computers in Human Behavior 36:154-162.Google Scholar
  18. Doane A. N., Kelley M. L., & Pearson M. R. (2016) Reducing cyberbullying: A theory of reasoned action-based video prevention program for college students. Aggressive Behavior 42(2):136–146.Google Scholar
  19. Ehman, A. C., & Gross, A. M. (2019). Sexual cyberbullying: review, critique, & future directions. Aggression & Violent Behavior, 44, 80–87.  https://doi.org/10.1016/j.avb.2018.11.001.CrossRefGoogle Scholar
  20. Erceg-Hurn, D. M., & Mirosevich, V. M. (2008). Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. American Psychologist, 65, 591–601.  https://doi.org/10.1037/0003-066X.63.7.591.CrossRefGoogle Scholar
  21. Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap. New York: Chapman and Hall.Google Scholar
  22. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: the reasoned action approach. New York, NY: Psychology Press (Taylor & Francis).Google Scholar
  23. Gahagan, K., Vaterlaus, J. M., & Frost, L. R. (2016). College student cyberbullying on social networking sites: conceptualization, prevalence, and perceived bystander responsibility. Computers in Human Behavior, 55, 1097–1105.  https://doi.org/10.1016/j.chb.2015.11.019.CrossRefGoogle Scholar
  24. Heirman,W., & Walrave, M. (2012). Predicting adolescent perpetration in cyberbullying: an application of the Theory of Planned Behavior. Psicothema, 24(4), 614–620.Google Scholar
  25. Hinduja, S., & Patchin, J. W. (2009). Bullying Beyond the Schoolyard: Preventing and Responding to Cyberbullying. Thousand Oaks, CA: Corwin Press.Google Scholar
  26. Holfeld, B. (2014). Perceptions and attributions of bystanders to cyber bullying. Computers in Human Behavior, 38, 1–7.  https://doi.org/10.1016/j.chb.2014.05.012.CrossRefGoogle Scholar
  27. Huang, Y. Y., & Chou, C. (2010). An analysis of multiple factors of cyberbullying among junior high school students in Taiwan. Computers in Human Behavior, 26, 1581–1590.  https://doi.org/10.1016/j.chb.2010.06.005.CrossRefGoogle Scholar
  28. Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140, 1073–1137.  https://doi.org/10.1037/a0035618.CrossRefPubMedGoogle Scholar
  29. Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Reese, H. H. (2012). Cyber bullying amongst college students: evidence from multiple domains of college life. In L. A. Wankel & C. Wankel (Eds.), Misbehavior online in higher education (pp. 293–321). Bingley: Emerald Group Publishing.Google Scholar
  30. Latané, B., & Darley, J. M. (1970). The unresponsive bystander: Why doesn’t he help? Englewood Cliffs, NJ: Prentice Hill.Google Scholar
  31. Lee, E. B. (2017). Cyberbullying: prevalence and predictors among African American young adults. Journal of Black Studies, 48, 57–73.  https://doi.org/10.1177/0021934716678393.CrossRefGoogle Scholar
  32. Lee, M., Zi-Pei, W., Svanström, L., & Dalal, K. (2013). Cyber bullying prevention: Intervention in Taiwan. PLoS ONE, 8, e64031.  https://doi.org/10.1371/journal.pone.0064031.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Li, Q. (2010). Cyberbullying in high schools: a study of students’ behaviors and beliefs about this new phenomenon. Child and Adolescent Aggression and Maltreatment, 19, 372–392.  https://doi.org/10.1080/10926771003788979.CrossRefGoogle Scholar
  34. Macháčková, H., Dedkova, L., Sevcikova, A., & Cerna, A. (2013). Bystanders’ support of cyberbullied schoolmates. Journal of Community & Applied Social Psychology, 23, 25–36.  https://doi.org/10.1002/casp.2135.CrossRefGoogle Scholar
  35. Machackova, H., & Pfetsch, J. (2016). Bystanders’ responses to offline bullying and cyberbullying: the role of empathy and normative beliefs about aggression. Scandinavian Journal of Psychology, 57, 169–176.  https://doi.org/10.1111/sjop.12277.CrossRefPubMedGoogle Scholar
  36. Menesini, E., Nocentini, A., & Palladino, B. E. (2012). Empowering students against bullying and cyberbullying: evaluation of an Italian peer-led model. International Journal of Conflict and Violence, 6, 314–321.  https://doi.org/10.4119/UNIBI/ijcv.253.CrossRefGoogle Scholar
  37. Midgett, A., Doumas, D., Sears, D., Lundquist, A., & Hausheer, R. (2015). A bystander bullying psychoeducation program with middle school students: a preliminary report. The Professional Counselor, 5, 486–500.  https://doi.org/10.15241/am.5.4.486.CrossRefGoogle Scholar
  38. Myers, C., & Cowie, H. (2017). Bullying at university: the social and legal contexts of cyberbullying among university students. Journal of Cross-Cultural Psychology, 48, 1172–1182.  https://doi.org/10.1177/0022022116684208.CrossRefGoogle Scholar
  39. Nickerson, A. B., Aloe, A. M., Livingston, J. A., & Feeley, T. H. (2014). Measurement of the bystander intervention model for bullying and sexual harassment. Journal of Adolescence, 37(4), 391–400.  https://doi.org/10.1016/j.adolescence.2014.03.003.CrossRefPubMedGoogle Scholar
  40. Olweus, D., & Endresen, I. M. (1998). The importance of sex-of-stimulus object: age trends and sex differences in empathic responsiveness. Social Development, 7, 370–388.  https://doi.org/10.1111/1467-9507.00073.CrossRefGoogle Scholar
  41. Palladino, B. E., Nocentini, A., & Menesini, E. (2016). Evidence-based intervention against bullying and cyberbullying: evaluation of the NoTrap! program in two independent trials. Aggressive Behavior, 42, 194–206.  https://doi.org/10.1002/ab.21636.CrossRefPubMedGoogle Scholar
  42. Rigby, K. (2008). Children and bullying: How parents and educators can reduce bullying at school. Boston, MA: Blackwell/Wiley.Google Scholar
  43. Salmivalli, C., Kärnä, A., & Poskiparta, E. (2011). Counteracting bullying in Finland: the KiVa program and its effects on different forms of being bullied. International Journal of Behavioral Development, 35, 405–411.  https://doi.org/10.1177/0165025411407457.CrossRefGoogle Scholar
  44. Schacter, H. L., Greenberg, S., & Juvonen, J. (2016). Who’s to blame? The effects of victim disclosure on bystander reactions to cyberbullying. Computers in Human Behavior, 57, 115–121.  https://doi.org/10.1016/j.chb.2015.11.018.CrossRefGoogle Scholar
  45. Selkie, E. M., Kota, R., Chan, Y. F., & Moreno, M. (2015). Cyberbullying, depression, and problem alcohol use in female college students: a multisite study. Cyberpsychology, Behavior, and Social Networking, 18, 79–86.  https://doi.org/10.1089/cyber.2014.0371.CrossRefGoogle Scholar
  46. Steffgen, G., Costa, A. P., & Slee, P. (2018). The copying of bystanders with cyberbullying in an adolescent population. In P. T. Slee, G. Skrzypiec, & C. Cefai (Eds.), Child and adolescent wellbeing and violence prevention in schools (pp. 129–137). Abingdon, Oxon: Routledge.Google Scholar
  47. Tokunaga, R. S. (2010). Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26, 277–287.  https://doi.org/10.1016/j.chb.2009.11.014.CrossRefGoogle Scholar
  48. Van Cleemput, K., Vandebosch, H., & Pabian, S. (2014). Personal characteristics and contextual factors that determine “helping,” “joining in,” and “doing nothing” when witnessing cyberbullying. Aggressive Behavior, 40, 383-396. https://doi.org/10.1002/ab.21534.
  49. Vandebosch, H., & Van Cleemput, K. (2009). Cyberbullying among youngsters: profiles of bullies and victims. New Media & Society, 11, 1349–1371.  https://doi.org/10.1177/1461444809341263.CrossRefGoogle Scholar
  50. Varghese, M. E., & Pistole, M. C. (2017). College student cyberbullying: self-esteem, depression, loneliness, and attachment. Journal of College Counseling, 20, 7–21.  https://doi.org/10.1002/jocc.12055.CrossRefGoogle Scholar
  51. Whittaker, E., & Kowalski, R. M. (2015). Cyberbullying via social media. Journal of School Violence, 14, 11–29.  https://doi.org/10.1080/15388220.2014.949377.CrossRefGoogle Scholar
  52. Williford, A., Elledge, L. C., Boulton, A. J., DePaolis, K. J., Little, T. D., & Salmivalli, C. (2014). Effects of the KiVa Antibullying program on cyberbullying and cybervictimization frequency among Finnish youth. Journal of Clinical Child & Adolescent Psychology, 42, 820–833.  https://doi.org/10.1080/15374416.2013.787623.CrossRefGoogle Scholar
  53. Wölfer, R., Schultze-Krumbholtz, A., Zagorscak, P., Jäkel, A., Göbel, K., & Scheithauer, H. (2014). Prevention 2.0: Targeting cyberbullying @ school. Prevention Science, 15, 879–887.  https://doi.org/10.1007/s11121-013-0438-y.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Chowan UniversityMurfreesboroUSA
  2. 2.Department of PsychologyOld Dominion UniversityNorfolkUSA

Personalised recommendations