Mechanism of Bystander-Blaming

Abstract

Contemporary victimology recognizes that an understanding of the mechanism of blaming requires a comprehensive approach that includes the victim, the offender, and the bystander. However, most of the existing research on blaming focuses on the victim and the offender, ignoring the issue of bystander-blaming. This study highlights the bystander and investigates bystander-blaming by exploring some theoretical explanations, including counterfactual thinking, defensive attribution, and gender differences. The study included 363 young male and female participants, who read vignettes describing the behavior of the victim and the bystander in a rape scenario and answered questions regarding bystander-blaming. The results show that both counterfactual thinking and defensive attribution play a role in bystander-blaming. This article addresses the theoretical and practical implications of these findings.

Keywords

bystander blaming counterfactual thinking defensive attribution theory gender differences

Introduction

Since Kitty Genovese’s much discussed murder, the bystander’s behavior and the factors that influence it have roused a substantial interest both in theory and research (Barnett, 1999; Cohen, 1993; Gini, Pozzoli, Borghi, & Franzoni, 2008; Latane & Darley, 1970; Lickel, Schmader, & Hamilton, 2003; Oliner & Oliner, 1992; Staub, 1992). While some of these theories and research constitutes attribution of blame to the bystander (Levy & Ben-David, 2008), there have been no empirical attempts to understand the mechanisms that induce bystander-blaming. The majority of studies that examine blame attribution focus on victim-blaming; yet, according to contemporary victimology, a crime scene entails the victim, the offender, and the bystander (Levy & Ben-David, 2008). It ascribes equal significance to factors associated with victim- and offender-blaming and to factors associated with the perception of bystanders’ responsibility (Levy & Ben-David, 2008). The significance of this issue lies not merely in understanding the attitudes toward the bystanders but also in the acknowledgment of the possibility that bystander-blaming affects victim- and offender-blaming (Critcher & Pizarro, 2008; Gini et al., 2008). Therefore, current research examines Levy and Ben-David’s (2008) suggestion to study bystander-blaming using variables that have been frequently applied in victim—and offender—blaming studies, such as defensive attribution theory (Kelley, 1971; Shaver, 1970; Walster, 1966) and counterfactual thinking (Kahneman & Tversky, 1982).

In addition, this study examines bystander-blaming in context of rape. Rape is one of the most harmful crimes (Koss & Harvey, 1991), its psychological consequences for the victim are severe (Maddox, Lee, & Barker, 2011) and it usually raises high levels of victim-blaming (Brems & Wagner, 1994; Sleath & Bull, 2012). This type of extreme events elicits the need to blame a number of actors and factors (Kelley, 1971). It is therefore possible to assume that in cases of rape there is a tendency to attribute the blame for the event also to the bystander.

The Bystander

Almost every incident of human suffering includes a perpetrator who causes the suffering, a victim who suffers from the perpetrator’s actions, and a bystander who is aware of the perpetrator’s harm-doing and the suffering of the victim (Cohen, 1993). A bystander is an individual who is present during the victimizing act but does not take part in the situation or the event. This individual is a spectator or an audience member (Barnett, 1999) who passively lets the victimization happen by standing idly by (Hill, 2010). The concept of “bystander” incorporates not only the bystander’s status regarding the event, but it also defines behavior, including the decision whether or not to interfere. Thus, most definitions of the bystander refer to both of these aspects (Barnett, 1999). For instance, Oliner and Oliner (1992, p. 4) define a bystander as an individual who did nothing to help the victim and did not stand up to the perpetrator. Staub (1992) states that the bystander is neither victim nor offender; nevertheless, the bystander’s behavior forms the circumstances that lead to the event. Sometimes, the bystander is even unaware of being part of a chain of events that leads to victimization (Lickel et al., 2003).

There are several types of bystanders. Sometimes bystanders witness the crime or hear the victim’s cries for help. These bystanders are defined as “real bystanders.” In contrast, bystanders who learn of the victim’s suffering through the media (Sheleff, 1978), gossip (Cohen, 1993), or the stories of offenders or victims—and these comprise the majority—are considered “metaphorical bystanders” (Cohen, 1993). The bystanders may also be differentiated from one another according to the nature of their relationship with the victim (Sheleff, 1978): strangers, acquaintances, or family members (Markey, 2000; Sheleff, 1978; Suedfeld, 2000).

Bystander-Blaming

The discussion on bystander-blaming has been conducted until now only on the theoretical level. Levy and Ben-David (2008), who raised this issue, have stated that although the common term is an innocent bystander, researchers and the public seldom consider the bystander blame-free (Levy & Ben-David, 2008). In essence, some degree of bystander-blaming is inherent in the questions raised by researchers with regard to bystander behavior, as well as in the questions’ underlying assumptions. For instance, one of the most frequently raised questions concerning the bystander is “Why didn’t the bystander try to help the victim?” It is possible that this question is based on the belief that bystander intervention or an attempt to help the victim may change the end result and prevent the victimization and suffering of the victim (Levy & Ben-David, 2008). However, blame is implied not only by such questions regarding bystander behavior but also by explanations for a failure to get involved. Thus, for example, Latane and Darley (1970) state that the diffusion of responsibility among numerous bystanders negatively influences their motivation to help the victim. This claim actually embodies the tendency to blame the bystander.

A more direct example of the inclination to blame the bystander is found in the literature on bystander behavior during World War II and the Holocaust. Jaspers (2001), who considers bystander passivity blameworthy, distinguishes between two types of guilt: moral and metaphysical. Bystanders who could help the victim(s) but did not do enough to help or to prevent the crime bear moral guilt. Such bystanders have failed to fulfill their responsibility to do everything possible to save others. Metaphysical guilt, on the other hand, is a burden for those bystanders who could do nothing to prevent the victimization and were not able to save the victim. It must be noted that in the case of metaphysical guilt, interfering with the scene of a crime in an attempt to save the victim could expose the bystander to a grave risk.

According to Jaspers (2001), a bystander’s decision to take a neutral stand allows the aggressor to commit the crime; thus, making the bystander an accomplice. Scheffler (2004) agrees with Jaspers, stating that when a person is in a position to prevent the chain of events that lead to victimization but does nothing to prevent it, then the fact that he or she did not start the chain or did not know what would happen at the end of it does not justify or excuse one’s behavior. While both Jaspers (2001) and Scheffler (2004) raise strong theoretical points regarding bystander blame, in reality it is difficult, and sometimes even impossible, to determine whether a bystander’s action could have prevented the victimization, whether the avoidance of interference made the victimization possible, and whether the bystander did everything possible to prevent the crime. The difficulty in distinguishing between blameworthy and blameless bystanders suggests that bystander-blaming factors are not objective but rather subjective (Levy & Ben-David, 2008).

Only a few studies have examined the issue of blame toward those that may fit the definition of “bystander.” One of these is the study conducted by Lickel et al. (2003), which pointed out that in cases of school shootings, people blamed not only the attacker of students and teachers, but also their parents. Likewise, in examining the social response toward mothers of incest victims, Feigenbaum (1997) found that mothers are often blamed and experience negative social reactions.

Some research has focused on how bystander-blaming influences the attribution of blame to victims and offenders. For example, Gini et al. (2008) examined attitudes toward bystanders in bullying situations and how bystander behavior influences victim-blaming. They found that participants approved of bystanders who tried to help victims of bullying and perceived bystander passivity in a negative light. The research also showed that victim-blaming was affected by perception of the bystanders’ behavior.

Defensive Attribution, Gender Differences, and Bystander-Blaming

Shaver’s (1970) defensive attribution theory maintains that the tendency to blame the victim depends on the similarity between the victim and the observer who is judging the victim’s behavior. Shaver distinguishes between situational and personal similarity. In situational similarity, there is a high likelihood that the observer could be involved in similar circumstances. Personal similarity refers to similarities in attitudes, personal traits, and demographic characteristics (Herzog, 2008). According to Shaver (1970), when observers feel situational and personal similarity, they tend to attribute less blame to the victim. Although there are some studies (Kahn, 1977; Krebs, 1975) that do not support Shaver’s defensive attribution theory, most of the studies that examined this issue did indeed find a negative correlation between similarities and blaming (Grubb & Harrower, 2008). Bell, Kuriloff, and Lottes’s. (1994) research results show that the more the subjects perceive themselves as being similar to the victim of a rape, the less blame they are likely to attribute to that person. More specifically, related to our study, Bell and colleagues found that because more women than men see themselves as being similar to a female victim of rape, women tend to blame these victims less.

These gender differences in victim-blaming are well documented, and research shows that men tend to attribute more blame and responsibility to the rape victim than women (Fulero & Delara, 1976; Furnham & Boston, 1996). Men also tend to minimize the damage (Davies, Pollard, & Archer, 2001) and to identify with the offender (Gerber, Cronin, & Steigman, 2004). These findings apply with regard to female victims of both rape and domestic violence (Levy, 2011), and also to male victims of rape (Whatley & Riggio, 1993), and victims in general (Tang, Pun, & Cheung, 2002). The reason for gender differences in rape-victim blaming may be due to the fact that women feel threatened by the possibility of rape (Ferraro, 1996; Gordon & Riger, 1989; Parker & Ray, 1990), while men do not feel they are potential rape victims, and therefore there is no situational similarity between male research participants and the victim. In contrast, in cases of custody battles, men tend to feel that they could be in the same situation as divorcing males (situational similarity), and consequently they are affected by the existence of personal similarity (Elkins, Philips, & Konopaske, 2002). Thus, a connection between defensive attribution and gender differences was found in many instances of victim-blaming.

It may be supposed that, whereas male subjects’ lesser identification with victims makes them more likely to blame them, their greater identification with bystanders would make them less inclined to place blame. However, there are no findings yet to support this notion. Nevertheless, based on the defensive attribution theory and the findings about victim-blaming, it seems reasonable to assume a negative correlation between perceived similarities—both situational and personal—and bystander-blaming.

Counterfactual Thinking and Bystander-Blaming

Another theory that explains blame attribution is the counterfactual thinking theory, which sees the tendency to blame as a way to process information. We use counterfactual thinking (or put differently, “if-only” thinking) in an attempt to understand the causes for negative events (e.g., Alicke, Buckingham, Zell, & Davis, 2008; Goldinger, Kleider, Azuma, & Beike, 2003; Miller & Gunasegaram, 1990). Through counterfactual thinking, one can change the outcome of an event by mentally changing the antecedents of that event (Roese & Olson, 1997). For example, one can think in regard to a female rape victim: “If only she had not walked this way, nothing bad would have happened to her.” The ability to change the past, even if the change is only mental, helps us to learn how to prevent negative events in the future (Morris & Moore, 2000).

There are two types of counterfactual thinking: upward and downward. Upward counterfactual thinking focuses on how one could achieve a better outcome than the one that was achieved in reality. Downward counterfactual thinking raises the possibility that things could have ended much worse than they actually did (Mandel, 2003). These types of counterfactual thinking have different effects on emotions and conclusions. Upward counterfactual thinking presents the possibility of a more favorable outcome (Galinsky, Moskowitz, & Skurnik, 2000), and therefore it amplifies negative feelings, such as regret, shame and guilt (Mandel, 2003). Downward counterfactual thinking makes one feel better by comparing the actual outcome to more grave possibilities (Galinsky et al., 2000). Research shows that severe and negative events, such as rape and murder, usually lead to upward counterfactual thinking (Branscombe, Owen, Garstka, & Coleman, 1996; Reis, 2001; Spellman & Mandel, 1999), which increases the tendency to blame the victim (Branscombe et al., 1996; Turley, Sanna, & Reiter, 1995).

As the content of the counterfactual thought is predicated on normal everyday activities, to generate counterfactual thought, the behavior prior to the event should be perceived as being unusual, abnormal, or unexpected (Teigen, Evensen, Samoilow, & Vatne, 1999). The counterfactual thought changes the outcome by mutating the unusual behavior that preceded the event. Research has frequently shown that outcomes that follow an exceptional chain of events are more likely to be imagined otherwise than outcomes of routine and regular sequences (Kahneman & Miller, 1986; Kahneman & Tversky, 1982; Wells, Taylor, & Turtle, 1987). However, Ball (2002) found that it is possible to mutate and generate counterfactual thoughts with regard to different kinds of sequences, whether exceptional or routine. Ball’s findings show that the direction of undoing is influenced not only by the normality of events but also by the availability of alternatives. Based on Ball’s findings, we can assume that when alternatives are available for the sequence of events involving bystanders, the research participants are more likely to blame the bystander.

Current Research

Previous research has highlighted the existence of bystander-blaming (Feigenbaum, 1997; Jaspers, 2001; Latane & Darley, 1970; Levy & Ben-David, 2008; Lickel et al., 2003; Scheffler, 2004) and its significance (Levy & Ben-David, 2008). However, few studies have considered the factors contributing to bystander- blaming. To examine this issue, the current research examines the effect that gender, age, and mutability have on bystander-blaming (Kahneman & Miller, 1986; Kahneman & Tversky, 1982; Roese & Olson, 1997; Wells et al., 1987). Based on previous studies reviewed in this article, the hypotheses were set as follows:

Hypothesis 1: There is a difference in bystander-blaming in accordance with the similarity between the research participants and the bystander: Participants who are characterized by having both situational and personal similarity with bystanders will blame them less than participants who have only situational similarity with the bystander. Specifically:

Hypothesis 1a: Young participants will blame a young bystander less than more mature participants.

Hypothesis 1b: Female participants will blame the bystander (a female) less than male participants.

Hypothesis 2: The bystander-blaming differ in accordance with the degree of mutability: In cases characterized by high level of mutability, the bystander will be blamed less than in cases of low mutability.

Method

Participants

The study included 363 participants (M_age = 22.09, SD = 1.38; age range = 19-24). The vast majority of participants were single (93.9%), 4.4% were married, 0.3% were divorced, and 1.4% did not answer the question. Moreover, the majority were female (87.3%) and only 12.1% male and 0.6% missing values. All the participants were students from a large Israeli University. The majority of the participants (98.6%) were undergraduate students and only 1.4% were graduate (MA) students. Among the undergraduate students, 75% were in their first year, 18.1% in their second, 5.7% in their third, and 1.2% in their fourth and final year (1.1% missing values). In all, 59.5% of the participants worked in addition to their studies at the university and 38% were unemployed (2.5% missing values). Criminology Department requirements for 1st-year students accounted for 34.4% of the participants answering the questionnaire. They completed the questionnaires during their free time. Other participants participated voluntarily and were approached by researchers during the brakes between the classes. In general, the participants in this research were students from 53 different academic programs. The response rate was 91%.

Measurements

Demographic characteristics

This part of the questionnaire gathered information about gender, age, marital status, educational level, academic program, and employment.

Vignettes

For the measurement of counterfactual thinking and similarity, two vignettes were designed: low and high mutability. The assumption was that the difference in levels of mutability in bystander behavior would create two levels of counterfactual thinking, where the vignette with the higher level of mutability would stimulate more counterfactual thoughts with regard to bystander behavior than the one with the lower level (Kahneman & Tversky, 1982). Both vignettes included a triad: the rape victim (female), the offender (male), and the bystander (female). The story in the vignettes described a woman (victim) who was supposed to be accompanied by her friend (the bystander), but for some reason the bystander did not accompany her. The victim was raped on her way home. The two vignettes were as follows:

Low degree of mutability: The bystander and the victim usually carpool, but earlier on the day of the rape the bystander was needed at home. The bystander (a lawyer/a student) left the office before the end of the work day, and therefore she was not there when the victim went home. On her way home the victim was raped.

High degree of mutability: The bystander (who works at the hospital/the student who volunteers at the hospital) was walking with the victim toward a bus stop, but got a phone call from her husband that he was going to pick her up at her workplace. She went back, leaving the victim to walk alone to the bus stop. After the bystander left, the victim was raped.

The bystander in the vignettes was designed to fit Scheffler’s (2004) definition: She was a part of the chain of events that preceded the victimization, but she did not start this chain and did not know what the outcome of her actions would be. The vignette also included a manipulation of bystander’s age-related status. For example, in the “low-mutability” vignettes the mature bystander was characterized as victim’s coworker or a lawyer and the young bystander was characterized as law student or intern. In vignettes with high degree of mutability, the mature bystander was described as a person who works at the hospital and the young bystander was described as student who volunteers at the hospital. Ten graduate students rated the degree of the bystander’s behavior mutability, the victim’s respectability, and the crime severity in both vignettes. The results of these ratings showed that there was no significant difference in victim’s respectability and rape’s brutality between the two stories. With regard to the bystander behavior mutability, the ratings reflected the difference in levels of mutability.

Defensive attribution: Similarity

The vignettes also make it possible to measure similarity. To assess the applicability of Shaver’s (1970) defensive attribution theory, we manipulated the bystander’s age and age-related status (employed or student) from one version of the questionnaire to another. Similar to Shaver’s (1970) research, a young bystander (student) is assumed to be “personally similar” to young female participants, whereas a mature bystander (a working person) is considered “personally dissimilar.”

At the same time, in regard to bystanders’ behavior, the situation described in each vignette was commonly experienced by most people. Thus, both vignettes enabled us to control the degree of both personal and situational similarity between the bystander and our participants. To avoid labeling and its influence on results, the relationship between the bystander and the victim was presented in the vignettes as friendship.

Bystander-blaming

The bystander-blaming was measured by 10 questions that examined two dimensions: behavioral and personal blame. These 10 questions were chosen from 55 questions that Dexter, Penrod, Linz, and Saunders (1997) used to assess attributions about the victim and the situation in a rape scenario. The questions were translated into Hebrew by the method of back translation described by Butcher (1982). The questions were translated from English to Hebrew and then back into English by two bilingual criminologists, who were instructed not to make any changes in the context or wording of the items. The response alternatives were based on a 7-point Likert-type scale, where “1” represented “strongly disagree” and “7” “strongly agree.” To examine the internal structure of the questionnaire, factor analysis with a direct oblimin rotation was performed separately for each vignette. The factor analysis will be presented in results.

Procedure

Each participant’s questionnaire included both scenarios: low- and high-mutability vignettes. The order of the vignettes differed to avoid order effect. Approximately half of the participants answered on a questionnaire with vignettes that described young victim and young bystander, and the other half answered a questionnaire with vignettes that described an older victim and older bystander; participants were randomly assigned to these conditions. After reading each scenario, respondents answered questions regarding bystander-blaming. Participants required approximately 20 min to complete the questionnaire.

All analyses were carried out using SPSS Version 17. The statistical approach adopted for the analysis of the data was repeated measures ANOVA. When assumptions of univariate analysis of variance (ANOVAs) were not met (either in terms of the sphericity test or the test of equality of error variances), a more conservative multivariate solution to the repeated-measures problem (Greenhouse–Geisser) was adopted. In all repeated measures, the p values for Greenhouse–Geisser corrected degrees of freedom are reported. The cases with missing value were deleted listwise.

Results

We identified two factors in a bystander-blaming scale (BBS), in a two-stage process. The choice of a two-factor model was based on Janoff-Bulman’s (1979) research, which described two types of blame: behavioral and characterological. As we used different questions to assess bystander-blaming than Janoff-Bulman, it seems that instead of characterological blame the more appropriate term is personal blame. High scores on behavioral blame scale indicate high level of blaming, but high score on personal blame scale indicate low level of blaming. These two factors (behavioral blame and personal blame) account for 55.25% of the variance in the first vignette, and 64.69% in the second. (For detailed description, see Table 1 in the appendix.)

Bystander-Blaming, Counterfactual Thinking, and Gender

To examine whether bystander behavior mutability and gender created differences in the tendency for bystander-blaming, we conducted a mixed ANOVA: 2 (low vs. high mutability) × 2 (male/female). The bystander behavior mutability was a repeated-measures factor and gender was a between-subjects factor. The dependent variable, the bystander-blaming, included two measures: behavioral blaming and personal blaming. The Greenhouse–Geisser adjustment was used to correct for violation of the assumption of sphericity for the repeated-measures factor. Multivariate analysis shows main effect for mutability, F(2, 357) = 16.16, p < .00. Table 1 presents univariate analysis for each measure of bystander-blaming and shows that there is a significant effect for mutability with regard to both measures. Where there was high mutability, participants attributed more behavioral and personal blame to the bystander than in cases of low mutability. Thus, the degree of mutability in bystander behavior affected attribution of blame to the bystander.

Table 1.

Repeated-Measures Results on Differences in Bystander-Blaming by Low and High Mutability (n = 363).

Bystander-blaming measures	Mutability		F	MSE	df
	Low	High
	M (SD)	M (SD)
Behavioral blame	2.15 (.09)	2.54 (.11)	16.68***	11.77	1,358
Personal blame	4.14 (.12)	3.61 (.11)	29.00***	21.23

Note. MSE = mean square error.

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

The results of a between-subject multivariate test show that there is a main effect for gender, F(2, 357) = 4.09, p < .05. However, univariate tests show (Table 2) that the significant difference in bystander-blaming between men and women is present only in personal blaming: Women have a more positive perception of the bystander than men. There were no significant differences in attribution of behavioral blame and also no significant interaction between degrees of mutability in bystander behavior and gender, F(2, 357) = 0.18, p = .83.

Table 2.

Repeated-Measures Results on Differences in Bystander-Blaming by Gender (n = 360).

Bystander-blaming measures	Men (n = 44)	Women (n = 316)	F	MSE	df
Bystander-blaming measures	M (SD)	M (SD)	F	MSE	df
Behavioral blame	2.34 (.16)	2.34 (.06)	0.001	0.001	1,358
Personal blame	3.61 (.18)	4.15 (.07)	8.01**	8.010

Note. MSE = mean square error.

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

Bystander-Blaming and Similarity

To examine the hypothesis that personal similarity reduces the tendency for bystander-blaming, we used repeated-measures ANOVA and included only the female participants. The hypothesis was that the differences in bystander age-related status (young/mature) would influence bystander-blaming. The results of a multivariate test, F(2, 313) = 4.34, p < .05, support this hypothesis and show that there is a main effect for personal similarity. Univariate statistics show that personal similarity creates a significant difference in both behavioral and personal blaming: The similar (younger) bystander was blamed less than the dissimilar (mature) bystander (Table 3).

Table 3.

Repeated-Measures Results on Differences in Bystander-Blaming by Personal Similarities (n = 363).

Bystander-blaming measures	Bystander		F	MSE	df
	Young (n = 169)	Mature (n = 147)
	M (SD)	M (SD)
Behavioral blame	2.23 (.08)	2.48 (.09)	4.51*	10.39	1,314
Personal blame	4.28 (.09)	3.99 (.10)	4.98*	13.63

Note. MSE = mean square error.

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

Discussion

The main aim of this article was to address the previously unexplored subject of blaming the bystander (Levy & Ben-David, 2008). Based on the literature regarding victim-blaming, the assumption was that bystander-blaming may be influenced by factors similar to those that play a role in victim-blaming: counterfactual thinking (Kahneman & Tversky, 1982; Roese & Olson, 1997), gender, and defensive attribution (Shaver, 1970). In the current study, we examined the direct and indirect attribution of blame to the bystander in the context of rape. The measures of blaming included two aspects: behavioral and personal blaming.

With regard to counterfactual thinking, previous research has shown that mutation of the usual routine facilitates the imagining of alternative antecedent conditions by which one could have prevented the unwanted outcome (Kahneman & Tversky, 1982; Petrocelli, Percy, Sherman, & Tormala, 2011). Based on these findings, it was hypothesized that mutability influences blame attribution. The current study findings support this hypothesis, since they show that participants tended to attribute more blame, both behavioral and characterological, to the bystander when there was a high degree of bystander behavior mutability. These findings are consistent with previous studies on blaming the victim, which showed that counterfactual thinking influences attribution of blame to victims (Alicke et al., 2008; Branscombe & Weir, 1992; Goldinger et al., 2003; Miller & Gunasegaram, 1990; Turley et al., 1995; Wells & Gavanski, 1989).

However, these findings are not consistent with those of Alicke et al. (2008), who found that the attribution of blame was dependent not only on the degree of mutability but also on the cause of the deviation from the routine. Their findings indicate that when the behavior of the individual was negative (being late because of a drug deal), there was a significant difference in attribution of blame as a function of mutability. When the reason was positive, mutability did not affect the attribution of blame (Alicke et al., 2008). It is important to note that Alicke et al. (2008) studied the blame attributed to the offender or the victim, and not to the bystander. In addition, the difference between the scenarios in their study was not the degree of mutability, but the nature of its cause. In our study, the reason for the bystander behavior was relatively neutral. In our low-mutability scenario, the bystander went home earlier for some unknown reason, and in the high-mutability one, the bystander left the victim halfway to the bus station, because her husband called her and told her that he was coming for her. In both cases, the reason for the bystander leaving the victim alone was not a negative one. Unlike Alicke et al. (2008), our findings show a significant difference between the low- and the high-mutability scenarios. It is possible that this discrepancy is due to the differences in research design. Alicke et al. studied the attribution of blame to the protagonist, the change in whose actions began the chain of events; in our research, the change was in the bystander’s action, and another person (the victim) suffered from this change. In addition, in the former study what was different about the scenarios was not the degree of mutability, but the nature of its cause.

We also examined whether the defensive attribution theory can provide an explanation for bystander-blaming. Shavers’ (1970) research on defensive attribution and blame showed that participants’ judgment depends on the similarity they perceive between themselves and the person whose action they are judging. Until now, most of the studies regarding defensive attribution has focused on victim-blaming (see the review by Bell et al., 1994) and has not examined whether the defensive attribution theory can explain bystander-blaming. Since the research on victim-blaming regards participants as quasi-bystanders (Crandall & Eshleman, 2003; Lerner, 1965, 1977; Rayburn, Mendoza, & Davison, 2003; Shaver, 1970), the present study examines defensive attribution’s role in these quasi-bystanders’ (the research participants’) blaming of the bystanders in the crime scenario. The hypothesis was that the tendency to blame the bystander is a function of personal similarity; thus, young participants tend to blame bystanders less if they are young than if they are old. This finding matches Shaver’s (1970) findings which showed that older (dissimilar) drivers were blamed more for their involvement in accidents than younger (similar) drivers. Thus, the results support the defensive attribution hypothesis and show that the similar bystander is blamed less than the different one.

We also examined gender differences in bystander-blaming. Because the role of bystander is much more relevant for men than the role of rape victim, the question was whether this relevancy would decrease the tendency of men to blame the bystander. The findings show an interesting complexity. We found a significant difference between men and women in the personal blaming (depending on personal factors), but there were no significant differences in the behavioral blaming (depending on behavioral factors). The findings regarding personal blaming are compatible with previous research findings showing that men express less favorable attitudes toward victims than women (Jimenez & Abreu, 2003; Tang et al., 2002; White & Kurpius, 1999; Xenos & Smith, 2001). However, it is possible that this complexity reflects the fact that the bystanders in this study’s vignettes were females. Thus, although there was the possibility of situational similarity, there was no personal similarity between male participants and the bystanders in this scenario. In other words, we may assume that the lack of difference between men and women in the attribution of behavioral blame to the bystander can be explained as a result of the males’ perception that they can be in a role of bystander as easily as females. Yet, the lack of personal similarity allowed men to attribute more personal blame. Thus, defensive attribution can explain the difference in blaming, not merely of offenders and victims but also of bystanders. In addition, this finding is important and contributes to our understanding of the differences between bystander and victim-blaming, especially in light of previous research (Burt & DeMello, 2003; Howard, 1984) showing that men tend to be more likely to blame not just female victims but also male victims. Therefore, it seems that the role in the crime scene (victim/bystander/offender) is more significant with regard to blaming than the gender of the victim or the bystander.

On the whole, the study presented in this article adds an important aspect to the literature regarding bystander-blaming. Nonetheless, a few limitations of this study should be noted. It should be considered that this study examined a very specific type of situation and type of bystander. Rape is considered to be one of the most injurious criminal acts (Koss & Harvey, 1991) and usually raises stronger responses than other crimes (Brems & Wagner, 1994). It is a unique situation with regard to bystander-blaming: Males can just as easily be bystanders as females, but they may have difficulty identifying with rape victims. Also the victim in the vignettes did nothing to provoke the offender and was not acquainted to the offender. Thus, the vignettes presented the participants with a classic case of stranger rape. It is possible that the uniqueness of the situation may limit our ability to generalize the findings of this study to different types of rape such as acquaintance or date rape, and additional research is needed to examine the mechanism of bystander-blaming in other types of rape, and even in other types of criminal events that are more gender-neutral, like burglary. In addition, when dealing with complex situations with severe outcomes people typically attribute the blame to multiple actors and factors (Kelley, 1971), and it is possible that differences in the harmfulness of the crime (homicide vs. theft) may influence the tendency to blame bystanders.

Moreover, we scripted passive bystanders who were not strangers to the victims, did not anticipate that their behavior opened the possibility of victimization, and were not present at the time of victimization. It would be interesting to examine attitudes toward different types of bystanders, such as active bystanders, bystanders who are related to the offender, or who are not related to either the victim or the offender. In addition, the sample in this study was highly homogeneous with regard to age, level of education, and marital status: The participants were all young students and almost all of them were single. It is possible that other types of participants will assign blame differently. Although this research’s goal was to examine a psychological mechanism of bystander-blaming, the use of convenience sampling, the homogeneity of the sample and the unbalanced gender distribution may affect the external validity of the study. To further our understanding of bystander–blaming, future research should examine the differences between different groups using a more comprehensive and representative data collection.

Conclusion

The current research raises and examines the important issue of bystander-blaming. The notion of bystander-blaming is not new, but it has not been tested until now. This study opens a new direction in research on attribution and blame. The contribution of this study is both theoretical and practical. Theoretically, it emphasizes the need to examine the attribution of blame in a criminal act as part of a triad, which includes victim, offender, and bystander. Practically, the study draws attention to the bystanders, who may be blamed even if their part in the criminal act itself is insignificant. People who are close to the victim are a significant component of the victim’s social support (Ahrens, Rios-Mandel, Isas, & Lopez, 2010). Attribution of blame to the bystander may minimize the bystander’s ability to support the victim and prevent a positive contribution to the rehabilitation process. Thus, assistance to the victims—including treatment and rehabilitation—may need to include assistance to the bystander, at least to bystanders, such as family members and relatives of the victims. Therefore, it is important to initiate more research in this field and to initiate actions that aim to raise awareness regarding bystander-blaming.

Footnotes

Appendix

Table 1.

Direct Oblimin Factor Loadings of Bystander-Blaming (n = 363).

Items	Vignette 1		Vignette 2
	Factor loadings		Factor loadings
	1	2	1	2
First factor—Behavioral blame
1. Could have behaved differently during the incident	.768	−.139	.858	−.281
2. The attack on the victim was a result of bystander’s actions	.675	−.107	.740	−.217
3. Could have predicted the incident	.608	−.151	.701	−.082
4. Could have prevented the incident	.663	−.011	.834	−.228
5. Could have done something else from what she did	.832	−.079	.886	−.267
Second factor—Personal blame
6. Emphasize with the bystander	−.063	.824	−.149	.848
7. Put yourself into bystanders’ shoes	−.066	.848	−.198	.861
8. Could have fitted to your circle of friends	−.177	.646	−.189	.702
9. Bystander’s intelligence	−.107	.764	−.299	.762
M	2.15	4.33	2.51	3.88
Variance	1.27	1.84	1.83	1.75
α Cronbach	.77	.77	.85	.80
% of variance	31.13	24.11	40.95	22.2

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.

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