Psychological Predictors of Bystanders’ Intention to Help Cyberbullying Victims Among College Students: An Application of Theory of Planned Behavior

Abstract

The goal of the present study was to evaluate the Theory of Planned Behavior (TPB) as an explanation for bystanders’ intention to help cyberbullying victims among college students. Participants completed an online survey in which their intention, attitude, subjective norm, and perceived behavioral control toward helping cyberbullying victims were assessed. In addition to these traditional TPB variables, empathy toward cyberbullying victims and anticipated regret from not helping victims were included in the model. Results showed that empathy and anticipated regret significantly predicted intention to help cyberbullying victims over and above the traditional TPB variables. Results also showed that gender altered how traditional TPB variables, empathy, and anticipated regret predict bystander’s intention to help cyberbullying victims: Empathy and anticipated regret were most robust predictors for males and females, respectively. These results suggest that the TPB is a useful theoretical framework for understanding bystanders’ intention to help cyberbullying victims. Implications for developing effective prevention and intervention strategies are discussed.

Keywords

cyberbullying bystanders theory of planned behavior anticipated regret empathy gender

Bullying is defined as a target intimidation of a victim by a socially or physically stronger person in an attempt to make the victim belittled or threatened (Juvonen & Graham, 2014). Cyberbullying refers to bullying conducted through an electronic medium such as online social media or mobile phones (Gradinger et al., 2010). More specifically, Slonje and Smith (2008) defined cyberbullying as “an aggressive, intentional act or behavior that is carried out by a group or an individual repeatedly and over time [through modern technological devices, and specifically mobile phones or the internet], against a victim who cannot easily defend him or herself” (p. 1).

Although previous research on characteristics and predictors of perpetrators and victims is in abundance, research on bystanders in cyberbullying is less common (Shultz et al., 2014). Thus, despite the potential effectiveness, developing prevention and intervention strategies utilizing bystanders for cyberbullying remains an important challenge.

Research has shown the similarities and the differences in the role of bystanders and their responses between cyberbullying and traditional bullying (Barlińska et al., 2013). Regardless of the environment, bystander responses can take one of the three forms: assisting or reinforcing the bully (negative bystander responses), supporting the victim (positive bystander responses), and remaining an outsider (Salmivalli et al., 1996). For example, positive bystander responses can take the forms of defending or comforting the victim, challenging the bully, and reporting the incident to others (DeSmet et al., 2018), which can be achieved either online or offline. Some previous research found that positive bystander responses were more frequent in cyberbullying than in traditional bullying (e.g., Macaulay et al., 2019). On the other hand, it might be expected that because of the presence of authority figure in some traditional bullying situations, bystanders may respond more positively in traditional bullying than in cyberbullying (Patterson et al., 2016).

One distinctive feature in online environment that should not be ignored in this context is the anonymity and autonomy that the online environment provides to the bystanders (Wong-Lo & Bullock, 2014). In traditional bullying, bystanders are often present during bullying incidents, whereas in cyberbullying, bystanders do not have to be physically present at the moment the incident occurs as long as the cyberbullying content is still available online (Cleemput et al., 2014). This increased anonymity could provide bystanders with a higher level of autonomy and perceived control to positively respond to the cyberbullying incidents while maintaining their privacy (Bastiaensens et al., 2015). Online contents that include bullying materials can reach a broader audience even in a single text message and each of the recipients can become a bystander. Hence, the number of bystanders can be larger in cyberbullying than in traditional bullying (Heirman et al., 2016). Because of the differences in the way bystanders can respond to bullying incidents, traditional approaches for intervention in offline bullying might not be suitable for cyberbullying context.

Our goal in this study was to develop effective theory-based intervention strategies for cyberbullying. Although previous research has identified various factors related to cyberbullying bystanders (see below), there is a scarcity of theory-based approaches in understanding bystanders’ responses in cyberbullying. Among some existing explanatory theories, we believe the Theory of Planned Behavior (TPB; Ajzen, 1991) is promising for this purpose (DeSmet et al., 2016). Nevertheless, previous research utilizing the TPB-based approach is limited and there is the need for more investigation in various contexts and age groups as well as the inclusion of other explanatory variables that could further improve its utility. We attempted to fill in the gap by investigating the determinants of bystander behavior in the context of cyberbullying among college students. More specifically, we sought to answer how the TPB could explain the bystander’s intention to help the cyberbullying victim in college students.

Prevalence and Consequences of Cyberbullying

Although cyberbullying victimization is widespread in middle and high school students, the prevalence of cyberbullying is not limited to adolescents (Brody & Vangelisti, 2016). The reports on prevalence rates for cyberbullying are heterogeneous and vary across different age groups. Especially, since substantially less research has examined cyberbullying among adults, the reports on its prevalence vary to a greater extent. Kowalski et al. (2018), for example, surveyed 3,699 adults aged 18 years old or older and found that 20% of the participants experienced cyberbullying victimization in their lifetime. Rosenthal et al. (2016) surveyed young adults aged between 21 and 30 years old and found that 44% of them had lifetime experience of bullying or meanness through Facebook.

With respect to college students, with which a majority of cyberbullying research on adults has been conducted, the victimization rates range from 2.5% to 90.9% (Peluchette et al., 2015; Schenk & Fremouw, 2012). Similarly, the rates of cyberbullying perpetration range from 2.2% to 43.7% (Kokkinos et al., 2014; Selkie et al., 2015). Finally, the rates of witnessing cyberbullying range from 27.5% to 68.8% (Alhabash et al., 2013; Gahagan et al., 2016; Smith & Yoon, 2013). Among 196 college students from a northwestern university, for example, 46% indicated that they have witnessed cyberbullying on social network sites and the majority of them (61%) did not intervene (Gahagan et al., 2016). For all of these statistics, it may be that several factors, such as the college, the sample size, the definition of cyberbullying, and the timeframe of the study, impacted the diversity of the prevalence rates (Jenaro et al., 2018).

The consequences of cyberbullying on victims can also vary among cases. In some instances, the harm can be so severe that the victims ultimately committed a suicide (Nikolaou, 2017). In addition, character development of young victims can be also negatively impacted, which could lead to personality disorders, lack of self-confidence, social misbehavior, and anxiety and depression (Extremera et al., 2018; Nikolaou, 2017). Some studies reported that the extent of the harm and social difficulties that victims of cyberbullying experience are larger than that of traditional bullying (Campbell et al., 2012; Wang et al., 2011); while others have reported the consequences of the two forms of bullying are not substantially different (Kowalski & Limber, 2013; Sticca & Perren, 2013). Nevertheless, it is imperative to acknowledge the psychological impact of cyberbullying on its victims.

Cyberbullying Factors

Previous research has identified various factors that have an impact on bystander’s behavior in relation to a cyberbullying instance. These factors include but are not limited to (a) the relationship of the bystander with victims or perpetrators (DeSmet et al., 2014; Macháčková et al., 2013; Pabian et al., 2016; Patterson et al., 2017), (b) social factors such as behavioral attitude, subjective norms and social skills (DeSmet et al., 2018), (c) personality factors such as self-efficacy, empathy, moral disengagement, and impulsivity (Barlińska et al., 2018; DeSmet et al., 2018; Erreygers et al., 2016; Kyriacou & Zuin, 2018), (d) bystanders’ previous experience as a victim of bullying/cyberbullying (e.g., Cleemput et al., 2014), and (e) the situational factors such as the number of bystanders and the seriousness of the bullying situation or the severity of the incident (e.g., Bastiaensens et al., 2014; Brody & Vangelisti, 2016, Macaulay et al., 2019; Patterson et al., 2017).

Another important line of previous research is an investigation of decision-making processes of bystanders’ helping of cyberbullying victims. Using a hypothetical task utilizing the behavioral economic framework, Hayashi and Tahmasbi (2020) demonstrated that social discounting, a behavioral economic index of generosity towards others, plays an important role in bystanders’ decision to help cyberbullying victims. They found that the likelihood of helping victims decreased as a hyperbolic function of the social distance between the bystanders and the victims.

Despite these previous studies, it is still largely unknown what exact factors are critical for encouraging bystanders to intervene and help cyberbullying victims (Barlińska et al., 2018). This is particularly concerning because previous research has found that bystanders who witness a cyberbullying situation usually do not take any action (Song & Oh, 2018). An important challenge, therefore, is to better understand the mechanism underlying bystanders’ behavior of helping (and not helping) cyberbullying victims. To achieve this goal, we believe it is important to utilize established theories to explain such a behavior. Evaluating relationships between the behavior of helping victims and theoretically proposed variables can provide guidelines for measurements and suggest causal paths (DeSmet et al., 2014), which ultimately could lead to theory-based interventions (Steinmetz et al., 2016). As Barlett (2017) succinctly stated, “if the psychological mechanisms essential to a behavior can be learned and understood with replicated effects to validate theory, then professionals can use this empirical evidence to inform interventions” (p. 269).

Theory of Planned Behavior

Among various theoretical approaches, we believe the TPB (Ajzen, 1991) is particularly promising because the TPB has been applied to various behaviors and meta-analytic reviews have demonstrated its efficacy in predicting behavior (e.g., Armitage & Conner, 2001). The TBP is a well-validated behavioral prediction model that posits that behavior is determined by one’s intention to perform the behavior and intention, in turn, is affected by attitude, subjective norm, and perceived behavioral control. Attitude refers to how positively/negatively the behavior is evaluated. Subjective norm refers to the degree to which important others approve or disapprove the behavior. Perceived behavioral control refers to the perception of how easy/difficult performing the behavior is. By incorporating these variables, the TPB can offer a more holistic view of human behavior (Shevlin & Goodwin, 2019).

Although several previous studies have applied the TPB to cyberbullying, most of these applications targeted cyberbullying perpetration (Heirman & Walrave, 2012; Jafarkarimi et al., 2017; Pabian & Vandebosch, 2014; Rashid et al., 2017; cf. Doane et al., 2014). In Heirman and Walrave (2012), for example, the TPB model accounted for 45% of the variance in adolescents’ intention to engage in cyberbullying perpetration. All three TPB variables were significant and independent predictors of the intention, yet attitude toward cyberbullying perpetration was the most robust predictor.

With respect to bystanders’ helping of cyberbullying victims, there are only few previous studies that employed the TPB-based approach (DeSmet et al., 2016; Doane et al., 2020). In DeSmet et al. (2016), for example, bystanders’ attitude toward negative bystander behavior (e.g., laughing at victims and passive bystanding) and self-efficacy (perceived behavioral control) over defending victims were significant predictors of bystanders’ intention to help cyberbullying victims, although their attitude toward positive bystander behavior (e.g., defending victims) was not a significant predictor.

Taken together, although previous research has made important progresses and have applied theory-based approaches, such as the TPB, to cyberbullying perpetration and bystanders’ helping of victims, the literature on such applications is still scarce, particularly on the TPB’s efficacy in predicting bystanders’ intention to help cyberbullying victims. This gap in the literature needs to be filled in to develop effective theory-based intervention strategies.

In addition, according to Ajzen (1991), the TPB is open to the addition of other explanatory variables as long as the additional variables can make a significant contribution over and above the standard TPB variables. In the present study, we investigated two explanatory variables, empathy and anticipated regret, in addition to the standard TPB variables. We also investigated whether the standard TPB variables and the explanatory variables would differentially predict bystanders’ intention to help cyberbullying victims among female and male participants.

Empathy, Anticipated Regret, and Gender

Empathy refers to the degree to which one is sensitive to, and understands of, other people’s mental states (Smith, 2006). As mentioned previously, previous research has shown that empathy toward cyberbullying victims is positively associated with bystanders’ helping of victims (e.g., Cleemput et al., 2014; Erreygers et al., 2016). In addition, a recent experimental study demonstrated the effectiveness of an anti-cyberbullying video in improving college students’ attitudes and perceived norms toward cyberbullying perpetration as well as empathy toward cyberbullying victims (Doane et al., 2016; but see Doane et al., 2020). Given the empirical evidence, it would be worthwhile to investigate whether empathy toward cyberbullying victims as an additional variable can make a significant contribution to predicting bystanders’ helping of cyberbullying victims over and above the standard TPB variables.

Regret is an aversive cognitive emotion experienced when realizing that our current situation would have been better if we had acted differently. According to the regret management theory, we act in a way that reduces the regret we would experience to avoid blaming ourselves (Zeelenberg & Pieters, 2007). Consistent with this notion, making individuals aware of future regret (i.e., anticipated regret) has been effective in altering various behaviors, particularly when individuals feel a regret from not engaging in the behavior (see Brewer et al., 2016, for a meta-analysis). In addition, another meta-analysis revealed that anticipated regret when integrated into the TPB is a significant predictor over and above the standard TPB variables (Sandberg & Conner, 2008). These extensive literatures linking anticipated regret and bystanders’ helping of cyberbullying victims would provide a compelling rationale to examine the predictive utility of anticipating regret over and above the standard TPB variables.

Finally, previous research has investigated the role of gender in predicting bystanders’ helping of cyberbullying victims, but the results are mixed. Some studies (Bastiaensens et al., 2014, 2016; Macháčková et al., 2013; Olenik-Shemesh et al., 2017) found that females are more likely to help cyberbullying victims, whereas others (Cleemput et al., 2014; Erreygers et al., 2016) found no gender difference in helping cyberbullying victims. Nevertheless, it is important to note that all the previous studies were conducted with children or early adolescents. No previous study, to the best of our knowledge, has investigated the role of gender in college students’ helping of cyberbullying victims.

Present Study

Following DeSmet et al. (2016), the present study further examined the utility of the TPB framework in predicting bystanders’ intention to help cyberbullying victims. Unlike DeSmet et al. (2016), which combined the TPB, Theory of Reasoned Action (Fishbein & Ajzen, 1975), and Social Cognitive Theory (Bandura, 1998), the present study exclusively employed the TPB as the theoretical model. We employed this approach such that the present study can be directly comparable to those in the rich literature on the TPB. In addition, unlike DeSmet et al. (2016), which employed adolescences as participants, the present study employed college students as the target population. The following hypotheses were tested:

H_1a: Positive attitude toward helping cyberbullying victims would significantly predict bystanders’ intention to help victims in college students.

H_1b: Positive subjective norm toward helping cyberbullying victims would significantly predict bystanders’ intention to help victims in college students.

H_1c: Greater perceived behavioral control toward helping cyberbullying victims would significantly predict bystanders’ intention to help victims in college students.

Another difference between the present study and DeSmet et al. (2016) is that the present study included empathy and anticipated regret as additional predictors for the TPB model to account for bystanders’ intention to help cyberbullying victims. Based on the previous studies reviewed previously, the following additional hypotheses were tested in the present study:

H₂: Greater empathy toward cyberbullying victims would significantly predict bystanders’ intention to help victims over and above the standard TPB variables in college students.

H₃: Greater anticipated regret from not helping cyberbullying victims would significantly predict bystanders’ intention to help victims over and above the standard TPB variables in college students.

Finally, the present study differed from DeSmet et al. (2016) in its investigation of gender differences in predicting bystanders’ intention to help cyberbullying. Because this was an exploratory investigation, we did not have a specific hypothesis to be tested. Instead, the following research question was addressed:

RQ₁: Do the standard TPB variables, empathy toward cyberbullying victims, and/or anticipated regret from not helping cyberbullying victims differentially predict bystanders’ intention to help cyberbullying victims among female and male college students?

Method

Participants and Procedure

One-hundred eighty-eight undergraduate students participated for course credit. They were recruited from multiple introductory courses in psychology and information sciences and technology at a public university in the Northeastern United States, whose racial/ethnic makeup of the student population in 2018–2019 was as follows: Asian (5.7%), Black/African American (5.3%), Hispanic/Latino (7.0%), International (10.7%), Native Hawaiian/Pacific Islander (3.3%), White (65.5%), and Unknown/Other (2.6%). The sample was composed of 101 females and 87 males. Their mean age and years of higher education were 20.0 (SD = 5.0) and 1.4 (SD = 1.1), respectively.

Surveys were hosted online by Qualtrics (Provo, UT). After clicking the “Agree to participate” bottom as a part of the informed consent, the participants completed a demographic questionnaire as well as the questionnaires on cyberbullying. The Institutional Review Board at the university that the first author is affiliated with reviewed the study protocol and deemed the study exempt.

Measures

Demographics and cyberbullying experience.

In addition to their age, gender, and years of higher education, the participants answered two questions about their cyberbullying experience both as a victim and as a perpetrator, with response options of never, once, a few times, several times, and many times. For analysis purposes, the responses were dichotomized into 0 (no experience) or 1 (at least once).

Standard TPB variables.

Intention to help cyberbullying victims was assessed with three items (e.g., “If I see someone bullied online, I plan to help the person”), with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). For all scales, means across items were calculated such that all scales have the score between 1 and 7.

Attitude toward helping cyberbullying victims was assessed with four semantic differential items (e.g., “For me, helping someone bullied online is” 1: Bad, 7: Good). Subjective norm of helping cyberbullying victims was assessed with three items (e.g., “Those people who are important to me would approve of me helping someone bullied online”), with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). Perceived behavioral control of helping cyberbullying victims was assessed with two items (e.g., “I have complete control over whether I will help someone bullied online”), with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree).

Additional variables.

Empathy toward cyberbullying victims was assessed with five items (“It often makes me distressed when I see someone bullied online”), with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). The items were adopted from Doane et al. (2014). Anticipated regret from not helping cyberbullying victims was assessed with two items (“If I do not help someone bullied online, I would feel regret”), with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree). The items were created for the present study based on the items used in previous studies (Gauld et al., 2014; Parker et al., 1996). The appendix shows all items of the questionnaires used in the present study. Table 1 shows Cronbach’s alphas of all scales calculated with the current sample.

Table 1.
Descriptive Statistics, Cronbach’s Alpha, and Pearson Correlation Coefficients of Demographic Variables and Cyberbullying-related Measures.

Variables Mean/Count α 1 2 3 4 5 6 7 8 9 10

1. Age 20.0 (5.0) – –

2. Gender (M = 1) M: 87/F: 101 – .04 –

3. Victimization (Y = 1) Y: 106/N: 77 – .16* –.04 –

4. Perpetration (Y = 1) Y: 46/N: 133 – .00 .11 .36 –

5. Attitude 6.0 (1.3) .91 .01 –.23 –.20 –.13 –

6. Subjective norm 6.1 (1.2) .90 –.05 –.08 –.12 –.04 .59 –

7. PBC 6.3 (1.0) .84 .07 –.14 –.11 –.07 .41 .35 –

8. Empathy 5.5 (1.4) .91 –.05 –.24 –.14 –.21 .46 .35 .18* –

9. Anticipated regret 5.0 (1.7) .95 –.04 –.14 –.12 –.15 .46 .36 .16* .68** –

10. Intention 5.2 (1.5) .96 –.02 –.05 –.18* –.16* .58 .46 .28 .65 .68** –

Notes. The numbers in parentheses are standard deviations.

M = Male. F = Female. Y = Yes (experienced). N = No (not experienced). PBC = Perceived behavioral control. α = Cronbach’s alpha.

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

Data Analysis

Correlational analyses were conducted by calculating Pearson correlation coefficients. For hierarchical linear regression predicting the intention to help cyberbullying victims, the demographic variables (age, gender, and experiences of victimization and perpetration) were entered in Step 1. The standard TPB variables (attitude, subjective norm, and perceived behavioral control) were entered in Step 2. The additional variables (empathy and anticipated regret) were entered in Step 3. For the analysis of gender differences, multiple linear regression with all predictors was conducted separately for each gender. The assumptions of multiple linear regression were evaluated and no violation for linear relationship, multivariate normality of residuals, homoscedasticity, and no multicollinearity was observed. All statistical analyses were performed with SPSS Version 26. The statistical significance level was set at .05.

Results

Table 1 shows descriptive statistics, Cronbach’s alphas, and Pearson correlation coefficients of demographics and cyberbullying-related measures. All cyberbullying-related measures were significantly correlated with intention to help cyberbullying victims (p’s < .05).

Table 2 shows results of the hierarchical linear regression analysis predicting intention to help cyberbullying victims. The first model with demographic variables and cyberbullying experiences accounted for 4.3% of the variance, F(4, 163) = 1.83, p = .125. In this model, no variable was a significant predictor of intention to help victims. In the second model, traditional TPB variables were entered and additional 29.8% of the variance was accounted for, ΔF(3, 160) = 24.15, p < .001. In this model, attitude (β = .39, t = 4.69, p < .001) and subjective norm (β = .25, t = 3.18, p = .002) were significant predictors of intention to help victims. In the third model, empathy and anticipated regret were entered and additional 28.2% of the variance was accounted for, ΔF(2, 158) = 59.11, p < .001. In this model, gender (β = .14, t = 2.68, p = .008), attitude (β = .19, t = 2.92, p = .004), subjective norm (β = .12, t = 2.01, p = .047), empathy (β = .31, t = 4.61, p < .001), and anticipated regret (β = .37, t = 5.69, p < .001) were significant predictors. Overall, this model accounted for 62.3% of the variance in intention to help cyberbullying victims, F(9, 158) = 29.04, p < .001, Adjusted R² = .60, and Cohen’s f²’s for the differences between Models 1 and 2 and between Models 2 and 3 = .43 and .39, respectively.

Table 2.
Hierarchal Linear Regression Predicting Intention to Help Cyberbullying Victims.

Variable B SE B β t R ² F ΔR² ΔF

Model 1 .04 1.83

Age .00 .02 .00 .01

Gender –.11 .23 –.04 −.45

Victimization –.47 .26 –.15 −1.83

Perpetration –.31 .29 –.09 −1.09

Model 2 .34 11.84* .30 24.15*

Age –.00 .02 –.01 −.12

Gender .21 .20 .07 1.02

Victimization –.11 .22 –.04 −.52

Perpetration –.28 .24 –.08 −1.15

Attitude .47 .10 .39 4.69*

Subjective norm .32 .10 .25 3.18

PBC .00 .10 .00 .03

Model 3 .62 29.04* .28 59.11*

Age .00 .02 .01 .22

Gender .42 .16 .14 2.68

Victimization –.08 .17 –.03 −.51

Perpetration –.02 .19 –.01 −.12

Attitude .23 .08 .19 2.92

Subjective norm .16 .08 .12 2.01*

PBC .04 .08 .03 .52

Empathy .34 .07 .31 4.61*

Anticipated regret .34 .06 .37 5.69*

Notes. PBC = Perceived behavioral control.

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

Finally, Table 3 shows how gender interacts with the standard TPB variables, empathy, and anticipated regret in predicting bystanders’ intention to help cyberbullying victims. The results of the multiple linear regression analyses conducted separately for each gender revealed that different predictors were significant for each gender. For females, empathy (β = .20, t = 2.38, p = .020) and anticipated regret (β = .57, t = 7.08, p < .001) were significant predictors of intention to help cyberbullying victims, and the model accounted for 63.7% of the variance in intention, F(8, 81) = 17.15, p < .001, Adjusted R² = .60, and Cohen’s f² for the entire model = 1.75. For males, on the other hand, attitude (β = .26, t = 2.62, p = .011) and empathy (β = .42, t = 4.04, p < .001) were significant predictors of the intention to help cyberbullying victims, and the model accounted for 68.8% of the variance in intention to help cyberbullying victims, F(8, 69) = 19.02, p < .001, Adjusted R² = .65, and Cohen’s f² for the entire model = 2.21.

Table 3.
Multiple Linear Regression Predicting Intention to Help Cyberbullying Victims for Females and Males.

Female Male

Variable B SE B β t B SE B β t

Age –.03 .02 –.09 −1.33 .03 .02 .09 1.22

Victimization –.04 .21 –.01 −.19 –.21 .27 –.07 −.79

Perpetration –.04 .24 –.01 −.15 .02 .27 .01 .06

Attitude .15 .10 .12 1.41 .31 .12 .26 2.62*

Subjective norm .12 .10 .10 1.24 .21 .12 .16 1.77

PBC .04 .12 .03 .32 .03 .11 .02 .31

Empathy .22 .09 .20 2.38* .47 .12 .42 4.04*

Anticipated regret .53 .08 .57 7.08* .10 .09 .11 1.14

R² = .64, F = 17.79* R² = .69, F = 19.02*

Note. *p < .05. ***p < .001.

Discussion

Summary and Significance

The present study examined whether the extended TPB model can be applicable to bystanders’ intention to help cyberbullying victims among college students. The present results suggest that the TPB provides a sound theoretical framework for predicting bystanders’ intention to help victims. First, when examined in isolation, the standard TPB variables of attitude and subjective norm (but not perceived behavioral control) were significant unique predictors of the intention to help victims (i.e., supporting H_1a and H_1b but not H_1c). Second, when empathy and anticipated regret were included, the additional variables significantly predicted bystanders’ intention to help victims over and above the standard TPB variables (i.e., supporting H₂ and H₃). In this comprehensive model, attitude, subjective norm, empathy, and anticipated regret were significant unique predictors. These findings suggest that positive attitude and norm toward helping victims, greater empathy toward victims, and greater anticipated regret from not helping victims would increase the strength of intention to do so. In combination with previous studies demonstrating the TPB variables can predict cyberbullying perpetration (e.g., Heirman & Walrave, 2012) and bystanders’ helping of cyberbullying victims among adolescents (DeSmet et al., 2016), the present findings contribute to the literature on cyberbullying by providing the evidence of potential usefulness of theory-based approaches, particularly the TPB, for reducing cyberbullying.

The results of the present study also confirm recent research demonstrating attitudes and norms toward helping cyberbullying victims (e.g., DeSmet et al., 2016) as well as empathy toward victims (e.g., Barlińska et al., 2018; Erreygers et al., 2016) are important in predicting bystanders’ helping of cyberbullying victims. Nevertheless, the present study, unlike DeSmet et al. (2016), found that perceived behavioral control was not a significant predictor of intention to help victims. Perhaps, this could be because the level of perceived behavioral control was relatively higher in the present sample (see Table 1).

At any rate, the present study, which employed a regression analysis of all these variables, revealed that empathy toward victims is a more robust predictor of intention to help victims than attitudes and norms. In addition, among all the variables investigated in the present study, anticipated regret was the most robust predictor of bystanders’ intention to help cyberbullying victims (when females and males are analyzed together). To our knowledge, this is the first report that demonstrated the importance of anticipated regret in the context of bystanders’ intention to help cyberbullying victims, and this has important implications for the development of effective prevention and intervention strategies (discussed below).

Another important contribution of the present study is that, when analyzed separately, different variables significantly predict bystanders’ intention to help cyberbullying victims for females and males (RQ₁). Although empathy was a significant predictor for both females and males, anticipated regret and attitude, respectively, were significant predictors only for females and males. This gender difference has important implications for prevention and intervention strategies that utilize bystanders to help cyberbullying victims (see below). In addition, future research should investigate the mechanism(s) of this gender difference, which should further contribute to the development and refinement of the effective prevention and intervention strategies.

Implications for Prevention and Intervention Strategies

Previous research has shown that the TPB is a useful framework to develop interventions that influence behavioral intentions as well as behaviors themselves. An intervention that influences the attitude, subjective norm, and/or perceived behavioral control of a particular behavior is expected to influence the intention to engage in the behavior, which in turn influences the behavior itself (Steinmetz et al., 2016). As an example, Doane et al. (2016) demonstrated that a cyberbullying prevention program based on the Theory of Reasoned Action, whose extension led to the TPB (Ajzen, 1991), was effective in reducing cyberbullying perpetration in college students. In their study, an anti-cyberbullying video successfully improved attitudes and perceived norms toward cyberbullying perpetration as well as empathy toward cyberbullying victims.

Despite the potential effectiveness of improving traditional TPB variables as a prevention/intervention strategy, however, it is practically very challenging to actually improve ones’ attitudes toward a target behavior, alter the perception of important others toward the behavior, and increase the perception of control over the target behavior (Koch, 2014). Indeed, a recent study conducted by Doane et al. (2020) found that, although an anti-cyberbullying video improved bystanders’ intention to help cyberbullying victims, the video failed to improve any of attitudes, subjective norms, perceived behavioral control toward helping victims as well as empathy toward victims.

Therefore, while previous research has reported some promising results, it is still important to develop different solutions that utilize different strategies. In this regard, one of the major findings of the present study that empathy was the most robust and the second most robust predictor of the intention to help cyberbullying victims for males and females, respectively, suggests the importance of the development and refinement of empathy-focused strategies. As an example, an experimental study by Barlińska et al. (2018) demonstrated that activating cognitive empathy (mental perspective taking) toward cyberbullying victims increased bystanders’ likelihood of helping victims in adolescents. It is possible that a similar strategy is also effective in increasing bystanders’ helping of victims in college students.

Another major finding of the present study that has some important implications for prevention and intervention strategies is that, for females, anticipated regret was such a robust predictor of intention to help cyberbullying victims (sr² = .225, meaning that 22.5% of the variance in intention to help victims can be accounted for by this single variable). Because regret is considered to be a cognitive emotion (Pieters & Zeelenberg, 2007), prevention and intervention strategies that improve anticipated regret act on both cognitive and emotional processes, which may increase the effectiveness of such strategies (Connolly & Reb, 2005). Indeed, with respect to the relative effectiveness of anticipated regret-based interventions, Parker et al. (1996) reported that an intervention targeting anticipated regret was more effective than those targeting traditional TPB variables, suggesting the potential effectiveness and efficacy of a regret-based approach for cyberbullying. Second, because experiencing regret is aversive, we are likely to act in a way that minimizes the occurrence of a regret in the future (Zeelenberg, 1999). Therefore, simply asking people to extend their temporal perspective and think about how they would feel if they engage or do not engage in the behavior can be a cost-effective intervention to alter the behavior: Anticipated-regret manipulation simply involves making participants pay attention to the possible regret that their behavior may cause, rather than actually altering the levels of regret they might experience (Koch, 2014).

Despite its wide use for other societal problems, such as physical health (e.g., Abraham & Sheeran, 2004) and public safety (e.g., Hayashi et al., 2019), anticipated regret has not been utilized for reducing cyberbullying. Given the present finding that anticipated regret is the most robust predictor for females, it is possible that experimentally manipulating it can be an effective strategy. For example, based on previous studies (e.g., Parker et al., 1996), a video-based approach that would induce the emotion of regret from not helping a cyberbullying victim may be promising. Future research should evaluate the effectiveness of the regret-based strategies.

Limitations and Future Research

Several limitations of the present study need to be discussed. First, the present study employed self-reported data for cyberbullying-related measures. It is at least possible that some self-reported data do not accurately represent what the participants would actually do (cf. Fisher, 1993). Second, no formal definition of cyberbullying was provided to the participants. Therefore, what constitutes cyberbullying might have not been identical across participants. Third, the behaviors described in the scales of the present study were relatively general, and the scales for perceived behavioral control and anticipated regret had only two items. It is advisable for future research to examine the reliability of the scales by replicating the present findings with more items with specific descriptions (cf. Fishbein & Ajzen, 2010). Fourth, the present study employed a relatively small convenience sample. Although the effect sizes observed are considered to be large (Cohen’s f² ≥ .39; cf. Selya et al., 2012) and the use of college students is not necessarily a limitation because college students are also an important target population, future research should evaluate the generalizability of the present findings with a larger and diverse sample. Fifth, no information on the race/ethnicity of the participants was collected. It is advisable for future research to collect such information and analyze data as a function of such an important variable.

Finally, the scales of the present study did not specify particular online platforms (e.g., social media, email, and/or discussion forum), and the participants were asked about their intention to help victims in a general situation. It is possible that specific features of platform, such as anonymity, could potentially impact the bystanders’ intention to help (Bastiaensens et al., 2015). In addition, even if a particular feature is embedded in a platform, it is not guaranteed that all users respond to the feature in the same manner. In this sense, we believe that investigating how online technology and its feature that guides user’s actions would influence bystanders’ intention to help cyberbullying victims is an interesting avenue for future research.

Conclusion

The overarching goal of the present study was to evaluate the usefulness of a well-validated theoretical framework, the TPB, for understanding bystanders’ decision to help cyberbullying victims. By incorporating two additional variables, empathy toward cyberbullying victims and anticipated regret from not helping victims, the present study improves our knowledge on factors influencing bystanders’ intention to help cyberbullying victims. Particularly, the novel finding that anticipated regret was the most robust predictor of bystanders’ intention to help victims is noteworthy. The present study contributes to the literature on cyberbullying by providing important implications that could facilitate the development of effective prevention and intervention strategies for the urgent societal issue of cyberbullying.

Footnotes

Appendix

Cyberbullying Experience (never, once, a few times, several times, or many times)

Intention (1: Strongly disagree, 7: Strongly agree)

Attitude

Subjective Norm (1: Strongly disagree, 7: Strongly agree)

Perceived Behavioral Control (1: Strongly disagree, 7: Strongly agree)

Empathy (1: Strongly disagree, 7: Strongly agree)

Anticipated Regret (1: Strongly disagree, 7: Strongly agree)

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Yusuke Hayashi

Author Biographies

Yusuke Hayashi, PhD, is an associate professor of Psychology at the Pennsylvania State University, Hazleton. His research focuses on various technology-related societal problems, such as texting while driving, text-message dependency, media multitasking, and cyberbullying. Using a behavioral economic approach, he is interested in better understanding the decision-making mechanisms underlying these problems and developing effective prevention and intervention strategies using the improved knowledge.

Nargess Tahmasbi, PhD, is an assistant professor of Information Science and Technology at the Pennsylvania State University, Hazleton. Her research is in online social media analytics, mainly focused on the two areas of crowdsourcing and cyberbullying detection. Due to her interest in multidisciplinary research, her studies sometimes span across other disciplines. Thus, apart from computational data-driven studies in automated detection of cyberbullying, she is also interested in identifying the antecedents of such behavior in online environments.

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Variable	B	SE B	β	t	R ²	F	ΔR²	ΔF
Model 1					.04	1.83
Age	.00	.02	.00	.01
Gender	–.11	.23	–.04	−.45
Victimization	–.47	.26	–.15	−1.83
Perpetration	–.31	.29	–.09	−1.09
Model 2					.34	11.84***	.30	24.15***
Age	–.00	.02	–.01	−.12
Gender	.21	.20	.07	1.02
Victimization	–.11	.22	–.04	−.52
Perpetration	–.28	.24	–.08	−1.15
Attitude	.47	.10	.39	4.69***
Subjective norm	.32	.10	.25	3.18**
PBC	.00	.10	.00	.03
Model 3					.62	29.04***	.28	59.11***
Age	.00	.02	.01	.22
Gender	.42	.16	.14	2.68**
Victimization	–.08	.17	–.03	−.51
Perpetration	–.02	.19	–.01	−.12
Attitude	.23	.08	.19	2.92**
Subjective norm	.16	.08	.12	2.01*
PBC	.04	.08	.03	.52
Empathy	.34	.07	.31	4.61***
Anticipated regret	.34	.06	.37	5.69***

	Female				Male
Variable	B	SE B	β	t	B	SE B	β	t
Age	–.03	.02	–.09	−1.33	.03	.02	.09	1.22
Victimization	–.04	.21	–.01	−.19	–.21	.27	–.07	−.79
Perpetration	–.04	.24	–.01	−.15	.02	.27	.01	.06
Attitude	.15	.10	.12	1.41	.31	.12	.26	2.62*
Subjective norm	.12	.10	.10	1.24	.21	.12	.16	1.77
PBC	.04	.12	.03	.32	.03	.11	.02	.31
Empathy	.22	.09	.20	2.38*	.47	.12	.42	4.04***
Anticipated regret	.53	.08	.57	7.08***	.10	.09	.11	1.14
	R² = .64, F = 17.79***				R² = .69, F = 19.02***