Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2023 Mar 24:1–13. Online ahead of print. doi: 10.1007/s12144-023-04477-5

The effects of moral norms and anticipated guilt on COVID19 prevention behaviors

Monique Mitchell Turner 1,2, Youjin Jang 1,2,✉, Rachel Wade 3, Ruth Jinhee Heo 1,2, Qijia Ye 4, Larry A Hembroff 2, Jong In Lim 1
PMCID: PMC10036969  PMID: 37359698

Abstract

Studies have shown that older adolescents have a low perceived personal risk of COVID-19, and yet their ability and willingness to engage in COVID-19 prevention behaviors is imperative for community health. Thus, health communication scholars need to consider alternative psycho-social predictors of prevention behaviors that will assist in protecting others in a pandemic. Based on Schwartz’s Norms Activation Model (NAM; Schwartz, 1977), we examined the relationship between moral norms and COVID-19 prevention behaviors (mask wearing and physical distancing). We predicted that anticipated guilt would mediate the relationship between moral norms and intention to engage in prevention behaviors, and that collective orientation would strengthen the association between moral norms and anticipated guilt. We tested predictions with data from a cross-sectional survey with a probability-based sample of college students at a large land grant university. These data indicated that moral norms were associated with behavioral intention, and this relationship was mediated by anticipated guilt. Collective orientation was found to moderate the relationship between moral norms and anticipated guilt in the context of physical distancing but not mask wearing. These findings suggest that making moral norms salient when designing an intervention is an effective strategy for older adolescents.

Supplementary information

The online version contains supplementary material available at 10.1007/s12144-023-04477-5.

Keywords: Moral norms, Anticipated guilt, Collective orientation, College students, COVID-19

In the early stages of the COVID-19 pandemic, colleges and universities across the U.S. tirelessly developed and assessed strategies to permit the safe opening of their campuses (Paltiel et al., 2020). These efforts impacted not only the safety of the college student audience, but also the residents of surrounding college towns. Indeed, the viral spread of COVID-19 was higher in counties with a college, even when controlling for population size (Borowiak et al., 2020; Christensen et al., 2020; Jang, 2020). This is due, in part, to characteristics unique to the older adolescent population (ages 18–25 years) combined with their living in the college context.

Older adolescent college students are a highly mobile demographic, often oscillating between college and permanent residences. Older adolescents are more likely engage in risky behaviors (Braitman & McCartt, 2010, Fergus et al., 2007, Windle, 2003) which some have explained to be the result of their underdeveloped brain (Steinberg, 2008). Neuroscientists propose that a mismatch between the subcortical brain systems (impulse-control) and the later maturity of the prefrontal cortex (decision making, self-control, and planning) explains risk-taking behaviors among this target population (Steinberg, 2008). Thus, universities are rife with people who have strong emotions and are risk-takers (Anderson, 2020; Cha, 2020). Moreover, this age group has lower perceived vulnerability to a host of issues and is more likely to engage in risky health behavior than older adults (Millstein & Halpern-Felsher, 2022). In the case of COVID-19, older adolescents were less likely to engage in all COVID-19 prevention behaviors relative to older groups (CDC, 2022). Given this, they could be considered a priority target for future infectious disease prevention campaigns.

This issue, however, is even more nuanced. In the case of older adolescents and COVID-19, their probabilistic (i.e., objective) risk is lower compared to other age groups, ceteris paribus (Boehmer et al., 2020). Thus, their lower personal risk perception could be seen as justified (e.g., Kollmann, et al., 2022, Reniers et al., 2016). In other words, for this demographic, traditional models of personal risk communication are likely to be ineffective.

Conventional health campaigns often aim to modify a population’s risk perception with the intention of increasing prevention behaviors, as risk perception is one determinant of behavior change (e.g., Janz & Becker, 1984; Witte, 1992). This is problematic in the case of COVID-19 for the older adolescent population, however, because the risks are often relatively lower. This creates a misperception that prevention messages do not apply to them and they could overlook their role in viral spread to more vulnerable populations via their highly mobile and social lifestyles. To appeal to this audience, then, their behaviors should be understood beyond the scope of traditional theories explaining prevention behaviors based on one’s risk perception (e.g., health belief model, Janz & Becker, 1984; extended parallel process model, Witte, 1992). Rather, it is imperative that we understand what motivates this targe audience to help others.

Actions taken by older adolescents can lower viral transmission and subsequently protect their community, family members, and other vulnerable people; that is, prosocial behavior. One theory explaining the underlying mechanisms behind prosocial behaviors is Schwartz (1977)’s norm activation model (NAM). According to the NAM, people tend to engage in prosocial behaviors when they think “it is a right thing to do” (i.e., perceived moral norms). Even though Schwartz (1968) suggested that activated moral norms arouse guilt from anticipated violation, little attention has been paid to the relationship between moral norms and guilt in the NAM literature.

The purpose of the current study is to test and expand the NAM with a large sample of older adolescent college students in the early stages of COVID-19. Using a cross-sectional survey administered in the first year of COVID-19, we measured key constructs, mediators, and moderators with the desire of building a model of pro-socially motivated COVID-19 prevention.

Prosocial behavior and moral norms

Prosocial behaviors encompass a broad range of behaviors that benefit other people and/or communities more so than the individual (De Groot & Steg, 2009). Prosocial behaviors have long been linked to morality (De Groot & Steg, 2009). Moral norms (i.e., personal norms) are derived from personal obligations internalized by an individual (Schwartz, 1977). People experience feelings of obligation in achieving self-expectations, and this perceived obligation constructs personal normative standards (Schwartz, 1977). Hence, the degree to which people believe they are morally responsible for given outcomes predicts their behavioral intentions. Moral norms are distinct from social norms in that people follow moral norms because they are internalized and tied to their own self-concept which includes one’s “moral code” (Schwartz, 1973). People comply with social norms because they want to avoid social sanctions (i.e., injunctive norms) or because they think it is what is done by the majority of people in the group they belong to (i.e., descriptive norms; Lapinski & Rimal, 2005).

The NAM (Schwartz, 1977) outlines the relationship between moral norms and behavior. The NAM has been applied to a wide variety of prosocial issues, but mainly pro-environmental behavior (Schwartz, 1973; Schwartz & Ben David, 1976; Schwartz & Clausen, 1970; Zuckerman & Reis, 1978). Some scholars have argued that pro-environmental behavior is a special case of prosocial behavior because such behaviors benefit others (e.g., future generations; De Groot & Steg, 2009), and there may be no direct personal benefit (Godin et al., 2005; Onwezen et al., 2013). Two studies have applied and expanded the NAM in the COVID-19 context by adding factors that account for behavioral intentions, but even these studies examined behaviors related to public transportation use and mask waste separation (Arkorful et al., 2021; Javid et al., 2021). Furthermore, another study attempted to explain compliance towards COVID-19 prevention guideline in general with the NAM but did not consider the role of emotion (Shanka & Kotecho, 2021), which is viewed as critical in this study.

Moral norms are closely associated with self-expectation and can be regarded as an expression of the core self (Schwartz, 1977). Individuals who have strong moral norms perceive stronger behavioral obligations. Moral standards represent an individual’s knowledge and internalization of moral norms and conventions (Tangney et al., 2007). The NAM posits that the relationship between moral norms (i.e., personal norms1) and behavior is affected by awareness of consequences and ascription of responsibility. Awareness of consequences is defined as individuals’ recognition that moral norms violations could result negative outcomes. In this context it is an awareness of the negative consequences that can occur when one fails to wear a mask (as one example). As Schwartz’s explanation about the relationships between awareness of consequences, ascription of responsibility, and moral norms were overly ambiguous, the NAM has been considered in both the moderation model and mediation models (see literature review in De Groot & Steg, 2009). Yet a common prediction is that awareness of consequences and ascription of responsibility sequentially activate moral norms which directly predict prosocial behavior (Black et al., 1985; De Groot & Steg, 2009; Steg et al., 2005; Stern & Dietz, 1994).

The NAM has been extended in several ways. Onwezen et al. (2013), for example, conducted an online survey with a sample of Dutch residents to examine anticipated guilt (and other variables) as a mediator of the relationship between moral norms and environmentally friendly behavior. Their data were consistent with guilt as a mediator. Moreover, their results indicated that awareness of consequences and ascription of responsibility can be left out of the analyses for reasons of simplicity—and still maintain good model fit. Given the expanse of literature on guilt as a moral emotion and as a predictor of prosocial behavior, we believe this would be a fruitful direction for understanding COVID-19 protective behaviors.

Anticipated guilt

Appraisal-based theories of emotion indicate that guilt is triggered when individuals are aware that they are causing harm and feel personally responsible for such harm (Lazarus, 1991; Onwezen et al., 2013). Importantly, these cognitive appraisals that cause guilt overlap with NAM’s antecedents of moral norms (awareness of consequences and ascription of responsibility). In fact, consistent with Onwezen et al. (2013), we believe that these appraisals can be left out of the model if guilt is measured. Guilt is a negative emotion that arises when individuals have violated moral standards or envision that moral transgression could occur in the future (Lazarus, 1991; Turner et al., 2021). Even the anticipation of guilt can be triggered as individuals reflect on the potential violation of moral rules of behavior (Huhmann & Brotherton, 1997; Lazarus, 1991).

When moral norms are made salient, anticipated guilt is activated through the process of thinking about a situation that violates (or violated) moral norms. In other words, it is posited that anticipated guilt mediates the relationship between moral norms salience and behavioral intention. Although some NAM studies did not statistically distinguish moral norms from emotion (e.g., guilt), others have shown that norms and emotions are distinct constructs (Bamberg et al., 2007; Hunecke et al., 2001), albeit positively correlated (Hunecke et al., 2001). As a self-conscious emotion that is evoked by self-reflection and self-evaluation, guilt is often accompanied by the thought of wishing one had not behaved wrongly and the desire to compensate for the transgressions (Lazarus, 1991; Roseman et al., 1994; Tangney et al., 2007). Consequently, people seek ways to alleviate unpleasant guilt. According to the negative state relief model (Baumann et al., 1981), people are motivated to perform altruistic behavior to alleviate a negative emotional state (e.g., guilt), thereby gaining positive feelings and self-gratification. When it comes to guilt, people are more likely to comply with behaviors that can reduce their negative, and self-conscious, feelings (Boster et al., 1999).

Accordingly, anticipated guilt, can serve as a mechanism of social influence and shape people’s behavior (O’Keefe, 2000). Research has shown that anticipated guilt was associated with a variety of intentions and behaviors that can benefit others and society in various contexts (Boudewyns et al., 2013; Elgaaied, 2012; Steenhaut & Van Kenhove, 2006; Wang, 2011). Anticipated affect and moral norms indeed had significant impacts on behavioral intention (Rivis et al., 2009). Therefore, in the context of COVID-19, it is reasonable to expect that individuals who experience stronger anticipated guilt would be more willing to adopt preventive behaviors to avoid potential moral transgressions that will elicit feelings of guilt.

Collective orientation

Still, the question lingers as to whether individual differences moderate the relationship between moral norms and anticipated guilt. Guilt results from an appraisal that individuals have done something (or could do something) wrong which could harm others (Lewis, 1971; Zeelenberg & Breugelmans, 2008). According to the guilt aversion model (Geanakoplos et al., 1989), people want to avoid feeling guilty so as to not let others down. Not engaging in the “right” behavior might let others down and could impact their relationship with others. If one is more oriented toward others, they would anticipate more guilt when they engage in a behavior that can harm the collective. For example, if I care about my community and I fail to wear a mask in public during COVID-19, I am more likely to anticipate feeling guilty because I knew that my behavior could potentially negatively affect an entire community (Cheng et al., 2020).

Collective orientation refers to the extent to which individuals value other people’s needs and desires (Triandis, 1995). Collective orientation is highly correlated with the construct of collectivism (Brewer & Chen, 2007). People in collective cultures tend to prioritize the goal of group solidarity/maintenance/health and are more influenced by group norms as they want to maintain their relationships and harmony with others (Markus & Kitayama, 1991; Triandis et al., 1988).

Individuals with a strong collective orientation experience more guilt when they violate norms (Bierbrauer, 1992; Lee & Paek, 2014). Collective orientation can strengthen the effect of perceived norms on attitudes toward a behavior and on behavioral intention (Lapinski et al., 2007). Specifically, people with a stronger collective orientation reported more positive attitude toward a behavior and a stronger behavioral intention irrespective of the prevalence information they were provided. Although the previous study (Lapinski et al., 2007) did not test the role of anticipated guilt, it is possible that participants showed more positive attitudes and higher intentions to follow the norms as a strategy to avoid feeling guilty (Cialdini & Kenrick, 1976).

To our knowledge, there have been limited attempts to test the relationships among moral norms, anticipated guilt, and collective orientation—as predictors of COVID-19 prevention behavior. In the early stages of the pandemic, Herbas-Torrico and Frank (2022) investigated the impact of emotional burdens (e.g., worries, depression) and collectivistic orientation on COVID-19 prevention behaviors in Bolivia. Their results signaled the importance of emotions and collective orientation on engagement in prevention measures. Moreover, Shanka and Kotecho (2021) used the NAM to predict COVID-19 prevention behaviors, finding that moral norms were a predictor of intentions, but did not examine the role of anticipated guilt or collective orientation.

Yet, there is evidence indicating guilt would mediate the relationship between moral norms and behaviors (Onwezen et al., 2013), while the relationship between norms and anticipated guilt would be strengthened by collective orientation (e.g., Bierbrauer, 1992; Lee & Paek, 2014). For example, people in the collectivistic culture also reported that they felt guiltier than those in the individualistic culture when they were exposed to the normative messages describing they were violating norms and causing harms to others (Lee & Paek, 2013).

Taken together, with regard to COVID-19 prevention measures, those with high levels of collective orientation prioritize their values in protecting others. And the more people orient themselves toward others, the more likely they experience guilt when their personal norms are violated. Anticipated guilt will influence one’s intention to behave “correctly,” which is adopting COVID-19preventive behaviors here in our paper.

Given the arguments we have outlined, the following hypotheses are posited (see Fig. 1):

  • H1. The relationship between moral norms and anticipated guilt will be moderated by collective orientation such that when one is more collective-oriented, the effect of moral norms on anticipated guilt will be strengthened.

  • H2. Anticipated guilt will mediate the relationship between moral norms and behavioral intention to adopt the preventive behaviors of COVID-19.

Fig. 1.

Fig. 1

Open in a new tab

The expected conceptual relationship between variables

The hypotheses were tested with the two types of COVID-19 preventive behaviors, mask wearing and physical distancing. In the emergence of the COVID-19 pandemic, mask wearing and physical distancing were the two primary behaviors recommended by CDC to minimize the risk of spreading the virus. While both prevention behaviors have been emphasized in the reduction of COVID cases, they have distinct characteristics from each other. For example, a study found that physical distancing was much harder to maintain compared to mask wearing, and the predictors of distancing and mask wearing behaviors were different (Mueller et al., 2021; Nikolov et al., 2020; Xu & Cheng, 2021). Demographics associated with adherence to mask wearing and physical distancing were also found to be different (Cohen et al., 2021). Given their distinct characteristics, the hypotheses were examined with those two behaviors separately.

Methods

Procedure and sample

The cross-sectional data were collected as a part of a campus survey regarding COVID-19 at a large land grant university in the United States. A stratified (by sex) random sample of 8,000 students enrolled for the Fall 2020 semester was obtained from the Registrar’s Office. Males were oversampled at a rate of 1.5 as males tend to respond at lower rates than females (Porter & Umbach, 2006). Students were invited to participate in the current study via email; a ten dollar gift card was offered as compensation. The survey instrument measured normative perceptions, emotions, risk perceptions, self-efficacy, collective orientation, intentions, ratings of information sources and channels, perceptions of college experience, and demographics. The instrument and all supporting materials were approved as exempt by the institution’s IRB. Data collection occurred between July 7–17, 2020. Respondents (N = 1,658) submitted a survey; after eliminating participants who (1) completed less than 60% of the survey and/or (2) did not complete most of the demographic information, the final sample was 1,618 (completion rate: 20.7%). The margin of error for this study was ± 2.4%. Only adult respondents aged 17 to 25 years were included for the analyses; hence the final sample size was 1,421.

More than half of the participants were female (56.1%). The participants’ average age was 20.02 (SD = 1.84). As for grade level, 33.5% of participants were undergraduate freshmen, 19.1% sophomores, 16.7% juniors, 19.8% seniors, 3.3% master’s students, 5.2% professional students, and 2.5% doctoral students. Participant self-identified racial ethnicity was: 70.5% European American, 10.0% Asian/Pacific Islander, 6.2% Hispanic, 4.6% African American, 3.6% Multi-Racial/Not reported/Other, 3.3% International Student, and 0.1% American Indian/Alaskan. In terms of the representativeness of the university at large, our sample closely mirrored the university in terms of biological sex and racial/ethnic representation2, but was underrepresented in terms of junior/senior students.

Measurement

All relevant attitude variables were measured with two or more Likert items using 5-point response options (from 1 = “strongly disagree” to 5 = “strongly agree”) except for behavior intentions which were measured on a 0 to 100 scale (see Appendix for survey instrument). To confirm the scales’ internal consistency, Cronbach’s alpha was calculated when the scale included more than three items. When the scale had two items, a correlation between items was calculated. Moreover, CFA was conducted for the scale with more than four items (i.e., collective orientation). Table 1 includes the descriptive statistics of all variables.

Table 1.

Descriptive statistics and correlations of main variables

1 2 3 4 5 6 7 8 9
1. Moral norms of physical distancing
2. Moral norms of mask wearing 0.77
3. Anticipated guilt of physical distancing 0.59 0.53
4. Anticipated guilt of mask wearing 0.55 0.60 0.65
5. Collective orientation 0.43 0.46 0.37 0.38
6. Self efficacy of physical distancing 0.41 0.35 0.28 0.26 0.28
7. Self efficacy of mask wearing 0.59 0.71 0.44 0.51 0.41 0.48
8. Behavioral intention of physical distancing 0.47 0.43 0.32 0.29 0.27 0.34 0.39
9. Behavioral intention of mask wearing 0.55 0.68 0.40 0.45 0.33 0.32 0.63 0.55
M 4.24 4.40 3.58 4.07 4.29 3.38 4.19 76.16 80.98
SD 0.94 0.95 1.25 1.15 0.66 1.15 1.01 23.67 24.33
Min. 1 1 1 1 1 1 1 0 0
Max. 5 5 5 5 5 5 5 100 100
Kurtosis 1.65 3.06 − 0.60 0.83 0.96 − 0.86 1.22 0.54 1.34
Skewness -1.40 -1.86 − 0.66 -1.30 − 0.91 − 0.36 -1.38 -1.04 -1.43
Open in a new tab

Note. All correlations between variables were significant at the 0.01 level

Moral norms

The moral norms construct was operationalized as one’s belief in what is the right thing to do related to preventive behaviors. Participants reported their moral norms with two items for each protective behavior (Anderson & Dunning, 2014) (wearing a mask r = .80, p < .01, physical distancing r = .81, p < .01). An example item includes “I believe wearing a mask in public is the right thing to do.”

Anticipated guilt

Anticipated guilt was operationalized as the extent to which participants would feel guilty when imagining a situation in which they did not engage in preventive behaviors (Turner et al., 2018), anticipated guilt from not practicing physical distancing was measured with one item, and anticipated guilt from not wearing a mask was measured with two items (r = .75, p < .01). An item used is “I would feel guilty if I went out in public without a mask.”

As some NAM studies integrate anticipated guilt and moral norms as one construct (Harland et al., 1999; Vining & Ebreo, 1992), we conducted the CFA to examine whether they were unidimensional. CFA results showed that anticipated guilt and moral norms of mask-wearing were not unidimensional, χ2 = 954.23, p = .00, CFI = 0.77, RMSEA = 0.54 [0.51, 0.57], SRMR = 0.12. Anticipated guilt and moral norms of physical distancing behaviors were not unidimensional, χ2 = 432.22, p = .00, CFI = 0.88, RMSEA = 0.36 [0.33, 0.39], SRMR = 0.09. Therefore, moral norms and anticipated guilt were operationalized as distinct constructs.

Collective orientation

Collective orientation was measured with four items (Lapinski et al., 2007) (ɑ = 0.74, χ2 = 0.05, p > .98, CFI = 1.00, SRMR = 0.001). An example item includes “I usually sacrifice my self-interest for the benefit of others around me.”

Behavior intentions

Participants were asked to indicate to what percentage they would engage in the prevention behaviors in the next semester on campus (from 0% = not at all to 100% = all the time). The scale was developed based on the list of COVID-19 prevention behaviors from the news article and Johns Hopkins Medicine website (Merrill, 2020; Johns Hopkins Medicine, 2020). Behavior intention of wearing masks on campus was measured with three items (ɑ = 0.80). Practicing physical distance was measured with two items (r = .59, p < .01). An example item includes “I will always wear a mask when I go to class.” CFA results indicated that preventive behaviors (practicing physical distancing and mask-wearing behavior together) were not unidimensional, χ2 = 2698.97, p = .00, CFI = 0.87, RMSEA = 0.23 [0.21, 0.25], SRMR = 0.08. Thus, these behaviors were analyzed separately.

Demographic information

After answering all the questions, participants reported their demographic characteristics.

Covariate: self-efficacy

Given that risk perception, self-efficacy and political orientation can drive COVID-19 behaviors, we engaged in steps to determine which should be used as covariates in this study. To identify the covariates for preventive behaviors, a series of statistical tests were conducted (Tabachinick & Fidell, 2012). According to Tabachinick and Fidell, the optimal covariates are (1) correlated to the dependent variables (i.e., physical distancing and mask wearing behaviors), (2) but not causally dependent on other independent variables (i.e., moral norms, anticipated guilt, and collective orientation), and (3) not significantly correlated to other covariates (Tabachinick & Fidell, 2012). We were not able to test the causality between independent variables and possible covariates (e.g., risk perception, self-efficacy, and partisanship) as the study design was cross-sectional. Still, it was found that all possible covariates were significantly correlated with each other. In this case, we included self-efficacy as the covariate in our model because it had the most robust correlations with both dependent variables (r = .39, p < .001 for self-efficacy of mask wearing and behavioral intention to wear mask; r = .33, p < .001 for self-efficacy of physical distancing and behavioral intention to keep physical distancing).

Results

To test hypothesis 1 and 2, a series of moderated mediation analyses were performed using PROCESS 3.5 (Model 7; Hayes, 2018) for each dependent variable. All the attitude variables were averaged and mean-centered for the analyses for the sake of interpretation (Shieh, 2011). In the context of physical distancing, the results showed that both moral norms (b = 0.70, SE = 0.04, p < .001) and collective orientation (b = 0.31, SE = 0.04, p < .001) had direct effects on anticipated guilt. The interaction effect of moral norms and collective orientation on anticipated guilt was statistically significant, b = 0.09, SE = 0.04, p < .05. As predicted, collective orientation was found to strength the relationship between moral norms and anticipated guilt. Thus, the data were consistent with the hypothesis 1 in the context of physical distancing.

Moral norms (b = 9.76, SE = 0.80, p < .001) and anticipated guilt (b = 1.41, SE = 0.55, p < .05) had direct effects on intention to keep physical distancing. The indirect effect of moral norms on intention to engage in physical distancing via anticipated guilt was statistically significant regardless of the level of collective orientation (Table 2). Therefore, it was concluded that the data were consistent with hypothesis 2 in the context of physical distancing (Fig. 2).

Table 2.

Results of moderated mediation analysis on intention to engage in physical distancing

Predictors DV: anticipated guilt DV: intention to engage in physical distancing
B SE △in R2 B SE
Constant 3.51*** 0.09 57.32*** 2.70
Moral norms 0.70*** 0.04 9.76*** 0.80
Anticipated guilt - - 1.41** 0.55
Collective orientation 0.31*** 0.05 - -
Moral norms* Collective orientation 0.09* 0.04 0.00* - -
Self-efficacy 0.03 0.03 4.02*** 0.53
 F(df) 169.46 (4, 1336) 155.20 (3, 1336)
R 2 0.34*** 0.26***
Conditional indirect effect of moral norms on intention via anticipated guilt (95% CI)
Level of collective orientation Effect Boot SE Boot LLCI Boot ULCI
M − 1 SD 0.64 0.04 0.57 0.72
M 0.70 0.04 0.63 0.77
M + 1 SD 0.76 0.05 0.66 0.86
Index of moderated mediation (95% CI)
Mediator Index Boot SE Boot LLCI Boot ULCI
Anticipated guilt 0.12 0.10 −0.05 0.35
Open in a new tab

Fig. 2.

Fig. 2

Open in a new tab

The moderated mediation models. Notes. The moderated mediation models were created using Hayes PROCESS 3.5 (model 7). The numbers are unstandardized coefficients

With regard to mask wearing behavior, the results suggested that perceived moral norms (b = 0.50, SE = 0.04, p < .001) and collective orientation (b = 0.20, SE = 0.04, p < .001) had direct effects on anticipated guilt. The interaction effect between perceived moral norms and collective orientation on anticipated guilt was not statistically significant (Table 3). Thus, the data were not consistent with the hypothesis 1 in the context of mask wearing.

Table 3.

Results of moderated mediation analysis on intention to wear a mask

Predictors DV: anticipated guilt DV: intention to wear a mask
B SE △in R2 B SE
Constant 3.44*** 0.15 45.17*** 3.27
Moral norms 0.50*** 0.04 12.24*** 0.77
Anticipated guilt - - 1.18* 0.51
Collective orientation 0.20*** 0.04 - -
Moral norms* Collective orientation −0.04 0.03 0.00 - -
Self-efficacy 0.16*** 0.03 7.33*** 0.66
F(df) 182.16 (4, 1357) 476.92 (3, 1358)
R 2 0.33*** 0.51***
Conditional indirect effect of moral norms on intention via anticipated guilt (95% ci)
Level of collective orientation Effect Boot SE Boot LLCI Boot ULCI
M − 1 SD − 0.64 0.30 0.04 1.22
M 0.03 0.29 0.04 1.17
M + 1 SD 0.69 0.28 0.04 1.13
Index of moderated mediation (95% CI)
Mediator Index Boot SE Boot LLCI Boot ULCI
Anticipated guilt −0.04 0.06 −0.19 0.05
Open in a new tab

Perceived moral norms (b = 12.24, SE = 0.77, p < .001) and anticipated guilt (b = 1.18, SE = 0.51, p < .05) were found to have direct effects on intention to wear a mask (Table 3). The results also indicated that the indirect effect of perceived moral norms on behavioral intention to wear a mask via anticipated guilt was statistically significant regardless of the level of collective orientation (Table 3). Therefore, the data were consistent with hypothesis 2 in the context of mask-wearing.

Discussion

COVID-19’s impact on economic, quality of life, and mental and physical health outcomes is difficult to enumerate. As global pandemics are on the rise (World Health Organization, 2022), it is imperative that public health scholars understand the psychosocial predictors of prevention behaviors. It is likely that those predictors vary for different audience segments. College students and older adolescents generally are a critical target audience in the spread of infectious diseases like COVID-19. Communities surrounding large universities were found to be more at risk for the spread of COVID-19 (Leidner et al., 2021). But studies have found that younger audiences are less likely to feel at risk for COVID-19 (Boehmer et al., 2020; Kollmann et al., 2022). Those studies showed, as would be predicted, older adolescents were less likely to engage in all COVID-19 prevention behaviors (Luo et al., 2021). Consequently, moral norms become a vital predictor of prevention behavior in contexts where people must engage in the behavior to protect others—given they may not perceive themselves to be at high personal risk. Specifically, we tested the extended version of Schwarz’s NAM in the COVID-19 context (De Groot & Steg, 2009; Schwartz, 1977), focusing on the role of moral norms and anticipated guilt.

Our data showed that as moral norms increase so does intention to wear a mask and to keep physically distant from others. This implies that in the college setting, it is important that people believe that mask wearing and physical distancing are the “right thing to do.” College-based prevention campaigns need to bolster the morality of the behavior and leverage this promising predictor. Anticipated guilt mediated the relationship between moral norms and COVID-19 behaviors regardless of the context as predicted. In other words, people who believe that engaging in prevention behaviors is the “right” thing to do are more likely to anticipate feeling guilty if they were to “forget” or “neglect” performing these behaviors, which resulted in higher intention to perform these behaviors.

Our finding also revealed that collective orientation was found to strengthen the relationship between moral norms and anticipated guilt in the context of physical distancing, but not mask wearing. Keeping a physical distance with others is harder than wearing a mask as it is not something you can do by yourself. In order to keep a physical distance with others, others also should be willing to keep distance from you. Thus, not keeping a physical distance might result in less guilt compared to not wearing a mask; our data also showed participants reported relatively less anticipated guilt when not keeping a physical distance (M = 3.58, SD = 1.25) compared to when not wearing a mask (M = 4.07, SD = 1.15). Although not keeping a physical distance would cause less guilt in general because it is a cooperative act, if you are more willing to do a right thing for people around you even when it is difficult (i.e., high collective orientation), you would feel stronger guilt. However, this might not be applicable for mask wearing. As it can be done by themselves, people tend to feel guilty when not wearing a mask regardless of whether they are oriented toward the community.

The current study contributes to extending the NAM theory by adding a cultural variable (i.e., collective orientation) and emotional variable (i.e., anticipated guilt). Although Schwartz (1968) stated the importance of guilt from anticipated violation, there has been little discussion about the role of guilt in the NAM literature. Moreover, previous NAM studies examining COVID-19 preventive behaviors (e.g., Arkorful et al., 2021; Javid et al., 2021; Shanka & Kotecho, 2021) left the role of cultural tendency out of consideration, even though previous studies regading COVID-19 have shown that the role of culture is important in understanding people’s engagement in COVID-19 preventive behaviors (e.g., Card, 2022; Herbas-Torrico & Frank, 2022). Our findings underline the importance of emotion and cultural variables.

Limitations

This study is not without limitations. First, it is based on cross-sectional data and therefore cannot make any claims about causality. That is, it is also possible that people felt stronger anticipated guilt about not engaging in COVID-19 preventive behaviors and this might have made moral norms salient to them. Second, this study provided data on one large university, and therefore, cannot necessarily be generalized to other university communities. That said, we did collect a large probability-based sample of students which increases our confidence in our data. Moreover, we examined theoretically sound relationships among variables, thus, there is little reason to believe that these variables would be associated with one university but unassociated with another university. Third, it should be noted that our response rate was around 20%. Literature suggested that several factors could influence student survey response rates across institutions, and one of the main determinants of student survey response rates is student body (Porter & Umbach, 2006). High ability students, women, and European American are more likely to respond compared to low ability students, men, and students of color. Although these response differentials could result in potential bias, we expect this bias would be minimal as our sample closely mirrored the university population at large. Fourth, it is also possible that social desirability bias influenced the results of the current study as it is related to prosocial behaviors, but our data are consistent in the main with previous NAM studies, leading us to discount the social desirability hypothesis. Fifth, for the sake of brevity of survey, we used the single-item (or two-item) measures for moral norms and anticipated guilt. Again, our data were consistent with the most theoretical predictions, so we argue that our measurements were reliable and valid in terms of predictive validity (Allen et al., 2022). Still, we encourage researchers would validate these measures of moral norms and anticipated guilt with more items when replicating the current study.

Future directions

Beyond examining and expanding an existing theory, these data were also considered to be pre-campaign formative research. These results can help health communicators and social marketers understand the university student audience segment. Particularly, our findings illuminate the psycho-social predictors of prosocial health behaviors that can be leveraged in future campaign efforts with this segment. Notably, the university students’ scores on moral norms, anticipated guilt, and collective orientation were above the scale mid-point. To some degree, this is not surprising given that studies on Generation Z have also uncovered an elevated level of collectivism, desire for authenticity, and a value of ethical behaviors (Galván Casas et al., 2020; Varkety Foundation, 2017). Hence, the recommendation would be to develop positive campaigns that remind students of what they believe to be “right,” to use collective language, and to show images of people helping others. Although guilt appeals have been shown to fail among high school first-year students (Bessarabova et al., 2015), subtle anticipated guilt could work here.

Supplementary information

Below is the link to the electronic supplementary material.

ESM 1 (22.5KB, docx)

(DOCX 22.4 KB)

Declarations

Three authors (MT, LH and YJ) of the current manuscript serve as consultants and a graduate assistant to the National Social Norms Center at Michigan State University, which is funded in part by the Anheuser-Busch Foundation. Although a financial conflict of interest was identified for management based on the overall scope of the project, the research finding included in this manuscript may not necessarily related to the interests of the Anheuser-Busch Foundation.

Informed consent

Informed consent was obtained from all individual participants included in the study before participating in the study. 

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research board at the University where the study was conducted and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. 

Welfare of animals

This article does not contain any studies with animals performed by any of the authors.

Footnotes

1

Schwartz used the word moral norms (Schwartz, 1968) and personal norms (Schwartz, 1977) in the NAM interchangeably. Most NAM literature have used the word personal norms to stress its the difference from social norms (e.g., De groot & Steg, 2009; Onwezen et al., 2013). Personal norms refer to one’s perception about appropriateness of a certain behavior based on his/her own private and internalized value (Schwartz, 1977), whereas social norms refer to one’s perception about appropriateness of a certain behavior based on group members or society’s standards (Lapinski & Rimal, 2005). However, in this study, we use the word moral norms as our purpose is to emphasize its foundation rooted in moral value.

2

The university population where data were collected is approximately 48.1% Male, and 51.9% Female. In terms of racial ethnicity, the population is 68.9% European American, 7.7% African American, 7.4% Asian/Pacific Islander, 6% International Students, 5.2% Hispanic, and 4.9% Others. In terms of year in school, the population is 19.1% Freshman, 21.7% Sophomore, 26.5% Junior, and 29.7% Senior.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Monique Mitchell Turner, Email: mmturner@msu.edu.

Youjin Jang, Email: jangyouj@msu.edu.

Rachel Wade, Email: wade.661@osu.edu.

Ruth Jinhee Heo, Email: heoruth@msu.edu.

Qijia Ye, Email: qijia.ye@asc.upenn.edu.

Larry A. Hembroff, Email: larry.hembroff@gmail.com

Jong In Lim, Email: limjong1@msu.edu.

References

  1. Allen MS, Iliescu D, Greiff S. Single item measures in psychological science. European Journal of Psychological Assessment. 2022;38(1):1–5. doi: 10.1027/1015-5759/a000699. [DOI] [Google Scholar]
  2. Anderson JE, Dunning D. Behavioral norms: variants and their identification. Social and Personality Psychology Compass. 2014;8(12):721–738. doi: 10.1111/spc3.12146. [DOI] [Google Scholar]
  3. Anderson, N. (2020). UNC-Chapel Hill pivots to remote teaching after coronavirus spreads among students during the first week of class. The Washington Post. Retrieved January 18, 2021, from: https://www.washingtonpost.com/local/education/unc-chapel-hill-coronavirus-cluster/2020/08/17/8ebce060-e0ab-11ea-8181-606e603bb1c4_story.html
  4. Arkorful, V. E., Lugu, B. K., & Shuliang, Z. (2021). Unearthing mask waste separation behavior in COVID-19 pandemic period: an empirical evidence from Ghana using an integrated theory of planned behavior and norm activation model. Current Psychology, 1–16. 10.1007/s12144-021-02313-2 [DOI] [PMC free article] [PubMed]
  5. Bamberg S, Hunecke M, Blöbaum A. Social context, personal norms and the use of public transportation: two field studies. Journal of Environmental Psychology. 2007;27(3):190–203. doi: 10.1016/j.jenvp.2007.04.001. [DOI] [Google Scholar]
  6. Braitman KA, McCartt AT. National reported patterns of driver cell phone use in the United States. Traffic Injury Prevention. 2010;11(6):543–548. doi: 10.1080/15389588.2010.504247. [DOI] [PubMed] [Google Scholar]
  7. Baumann DJ, Cialdini RB, Kendrick DT. Altruism as hedonism: helping and self-gratification as equivalent responses. Journal of Personality and Social Psychology. 1981;40(6):1039–1046. doi: 10.1037/0022-3514.40.6.1039. [DOI] [Google Scholar]
  8. Bessarabova E, Turner MM, Fink EL, Blustein NB. Extending the theory of reactance to guilt appeals: “You ain’t guiltin’me into nothin”. Zeitschrift für Psychologie. 2015;223(4):215–224. doi: 10.1027/2151-2604/a000223. [DOI] [Google Scholar]
  9. Bierbrauer G. Reactions to violation of normative standards: a cross-cultural analysis of shame and guilt. International Journal of Psychology. 1992;27(2):181–193. doi: 10.1080/00207599208246874. [DOI] [Google Scholar]
  10. Black JS, Stern PC, Elworth JT. Personal and contextual influences on househould energy adaptations. Journal of Applied Psychology. 1985;70(1):3–21. doi: 10.1037/0021-9010.70.1.3. [DOI] [Google Scholar]
  11. Boehmer TK, DeVies J, Caruso E, van Santen KL, Tang S, Black CL, Hartnett KP, Kite-Powell A, Dietz S, Lozier M, Gundlapalli AV. Changing age distribution of the COVID-19 pandemic—United States, May–August 2020. Morbidity and Mortality Weekly Report. 2020;69(39):1404–1409. doi: 10.15585/mmwr.mm6939e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Borowiak, M., Ning, F., Pei, J., Zhao, S., Tung, H. R., & Durrett, R. (2020). Controlling the spread of COVID-19 on college campuses. arXiv preprint arXiv:2008.07293.10.48550/arXiv.2008.07293 [DOI] [PubMed]
  13. Boster FJ, Mitchell MM, Lapinski MK, Cooper H, Orrego VO, Reinke R. The impact of guilt and type of compliance-gaining message on compliance. Communications Monographs. 1999;66(2):168–177. doi: 10.1080/03637759909376470. [DOI] [Google Scholar]
  14. Boudewyns V, Turner MM, Paquin RS. Shame-free guilt appeals: testing the emotional and cognitive effects of shame and guilt appeals. Psychology & Marketing. 2013;30(9):811–825. doi: 10.1002/mar.20647. [DOI] [Google Scholar]
  15. Brewer MB, Chen YR. Where (who) are collectives in collectivism? Toward conceptual clarification of individualism and collectivism. Psychological Review. 2007;114(1):133–151. doi: 10.1037/0033-295X.114.1.133. [DOI] [PubMed] [Google Scholar]
  16. Centers for Disease Control and Prevention (2022, February 25). How to protect yourself & others. Retrieved July 29, 2022, from: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html
  17. Cha, A. E. (2020). ‘Superspreading’ events, triggered by people who may not even know they are infected, propel coronavirus pandemic. The Washington Post. Retrieved January, 18, 2021, from https://www.washingtonpost.com/health/2020/07/18/coronavirus-superspreading-events-drive-pandemic/
  18. Card. K. G. (2022) Collectivism individualism and COVID-19 prevention: A cross-sectional study of personality culture and behavior among Canadians. Health Psychology and Behavioral Medicine 10(1), 415–438. 10.1080/21642850.2022.2069571 [DOI] [PMC free article] [PubMed]
  19. Cheng VCC, Wong SC, Chuang VWM, So SYC, Chen JHK, Sridhar S, Yuen KY. The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. Journal of Infection. 2020;81(1):107–114. doi: 10.1016/j.jinf.2020.04.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Christensen H, Turner K, Trickey A, Booton RD, Hemani G, Nixon E, Relton C, Danon L, Hickman M, Pollock EB. COVID-19 transmission in a university setting: a rapid review of modelling studies. MedRxiv. 2020 doi: 10.1101/2020.09.07.20189688. [DOI] [Google Scholar]
  21. Cialdini RB, Kenrick DT. Altruism as hedonism: a social development perspective on the relationship of negative mood state and helping. Journal of Personality and Social Psychology. 1976;34(5):907–914. doi: 10.1037/0022-3514.34.5.907. [DOI] [PubMed] [Google Scholar]
  22. Cohen DA, Talarowski M, Awomolo O, Han B, Williamson S, McKenzie TL. Systematic observation of mask adherence and distancing (SOMAD): findings from Philadelphia. Preventive Medicine Reports. 2021;23:101449. doi: 10.1016/j.pmedr.2021.101449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. De Groot JI, Steg L. Morality and prosocial behavior: the role of awareness, responsibility, and norms in the norm activation model. The Journal of Social Psychology. 2009;149(4):425–449. doi: 10.3200/SOCP.149.4.425-449. [DOI] [PubMed] [Google Scholar]
  24. Elgaaied L. Exploring the role of anticipated guilt on pro-environmental behavior–a suggested typology of residents in France based on their recycling patterns. Journal of Consumer Marketing. 2012;29(5):369–377. doi: 10.1108/07363761211247488. [DOI] [Google Scholar]
  25. Fergus S, Zimmerman MA, Caldwell CH. Growth trajectories of sexual risk behavior in adolescence and young adulthood. American Journal of Public Health. 2007;97(6):1096–1101. doi: 10.2105/AJPH.2005.074609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Galván Casas C, Catala ACHG, Carretero Hernández G, Rodríguez-Jiménez P, Fernández‐Nieto D, Rodríguez‐Villa Lario A, Navarro Fernández I, Ruiz-Villaverde R, Falkenhain-López D, Llamas Velasco M, García-Gavín J, Baniandrés O, González-Cruz C, Morillas-Lahuerta V, Cubiró X, Nart F, Selda-Enriquez I, Romaní G, Fustà-Novell J, Melian-Olivera X, García‐Doval A. Classification of the cutaneous manifestations of COVID‐19: a rapid prospective nationwide consensus study in Spain with 375 cases. British Journal of Dermatology. 2020;183(1):71–77. doi: 10.1111/bjd.19163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Geanakoplos J, Pearce D, Stacchetti E. Psychological games and sequential rationality. Games and Economic Behavior. 1989;1(1):60–79. doi: 10.1016/0899-8256(89)90005-5. [DOI] [Google Scholar]
  28. Godin G, Conner M, Sheeran P. Bridging the intention–behaviour gap: the role of moral norm. British Journal of Social Psychology. 2005;44(4):497–512. doi: 10.1348/014466604X17452. [DOI] [PubMed] [Google Scholar]
  29. Harland P, Staats H, Wilke HA. Explaining proenvironmental intention and behavior by personal norms and the theory of planned behavior. Journal of Applied Social Psychology. 1999;29(12):2505–2528. doi: 10.1111/j.1559-1816.1999.tb00123.x. [DOI] [Google Scholar]
  30. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. Guilford Press; 2018. [Google Scholar]
  31. Herbas-Torrico BC, Frank B. Explaining interpersonal differences in COVID-19 disease prevention behavior based on the health belief model and collective resilience theory: a cross-sectional study from Bolivia. BMC Public Health. 2022;22(1):1–24. doi: 10.1186/s12889-022-13068-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Huhmann BA, Brotherton TP. A content analysis of guilt appeals in popular magazine advertisements. Journal of Advertising. 1997;26(2):35–45. doi: 10.1080/00913367.1997.10673521. [DOI] [Google Scholar]
  33. Hunecke M, Blöbaum A, Matthies E, Höger R. Responsibility and environment: ecological norm orientation and external factors in the domain of travel mode choice behavior. Environment and Behavior. 2001;33(6):830–852. doi: 10.1177/00139160121973269. [DOI] [Google Scholar]
  34. Jang G. (2020). Covid-19 and college towns. Berkeley Economic Review. Retrieved January 18, 2021, from: https://econreview.berkeley.edu/covid-19-and-college-towns/
  35. Janz NK, Becker MH. The health belief model: a decade later. Health Education Quarterly. 1984;11(1):1–47. doi: 10.1177/109019818401100101. [DOI] [PubMed] [Google Scholar]
  36. Javid MA, Abdullah M, Ali N, Dias C. Structural equation modeling of public transport use with COVID-19 precautions: an extension of the norm activation model. Transportation Research Interdisciplinary Perspectives. 2021;12:100474. doi: 10.1016/j.trip.2021.100474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Johns Hopkins Medicine (2020). Staying safe from COVID-19. Retrieved January 18, 2021, from: https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/how-can-i-protect-myself-from-coronavirus
  38. Kollmann J, Kocken PL, Syurina EV, Hilverda F. The role of risk perception and affective response in the COVID-19 preventive behaviours of young adults: a mixed methods study of university students in the Netherlands. British Medical Journal Open. 2022;12(1):e056288. doi: 10.1136/bmjopen-2021-056288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lapinski MK, Rimal RN. An explication of social norms. Communication Theory. 2005;15(2):127–147. doi: 10.1111/j.1468-2885.2005.tb00329.x. [DOI] [Google Scholar]
  40. Lapinski MK, Rimal RN, DeVries R, Lee EL. The role of group orientation and descriptive norms on water conservation attitudes and behaviors. Health Communication. 2007;22(2):133–142. doi: 10.1080/10410230701454049. [DOI] [PubMed] [Google Scholar]
  41. Lazarus RS. Emotion and adaptation. Oxford University; 1991. [Google Scholar]
  42. Lee H, Paek HJ. Roles of guilt and culture in normative influence: testing moderated mediation in the anti-secondhand smoking context. Psychology Health & Medicine. 2014;19(1):14–23. doi: 10.1080/13548506.2013.772303. [DOI] [PubMed] [Google Scholar]
  43. Leidner AJ, Barry V, Bowen VB, Silver R, Musial T, Kang GJ, Ritchey MD, Fletcher K, Barrios L, Pevzner E. Opening of large institutions of higher education and county-level COVID-19 incidence—United States, July 6–September 17, 2020. Morbidity and Mortality Weekly Report. 2021;70(1):14–19. doi: 10.15585/mmwr.mm7001a4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lewis HB. Shame and guilt in neurosis. International Universities Press; 1971. [PubMed] [Google Scholar]
  45. Luo, Y., Cheng, Y., & Sui, M. (2021). The moderating effects of perceived severity on the generational gap in preventive behaviors during the COVID-19 pandemic in the US. International Journal of Environmental Research and Public Health, 18(4), 2011. 10.3390/ijerph18042011 [DOI] [PMC free article] [PubMed]
  46. Markus HR, Kitayama S. Culture and the self: implications for cognition, emotion, and motivation. Psychological Review. 1991;98(2):224–253. doi: 10.1037/0033-295X.98.2.224. [DOI] [Google Scholar]
  47. Merrill, C. (2020, May 8). Where do you fall on the “avoiding covid-19” scale?: Washingtonian (DC) Washingtonian. Retrieved January 13, 2022, from https://www.washingtonian.com/2020/05/05/where-do-you-fall-on-the-avoiding-covid-19-scale/
  48. Millstein S, Halpern-Felsher BL. Perceptions of risk and vulnerability. Journal of Adolescent Health. 2022;31(1):10–27. doi: 10.1016/S1054-139X(02)00412-3. [DOI] [PubMed] [Google Scholar]
  49. Mueller AS, Diefendorf S, Abrutyn S, Beardall KA, Millar K, O’Reilly L, Steinberg H, Watkins JT. Youth mask-wearing and social-distancing behavior at in-person high school graduations during the COVID-19 pandemic. Journal of Adolescent Health. 2021;68(3):464–471. doi: 10.1016/j.jadohealth.2020.12.123. [DOI] [PubMed] [Google Scholar]
  50. Nikolov, P., Pape, A., Tonguc, O., & Williams, C. (2020). Predictors of social distancing and mask-wearing behavior: panel survey in seven US states. arXiv preprint arXiv:2009.13103. 10.48550/arXiv.2009.13103
  51. O’Keefe DJ. Guilt and social influence. Annals of the International Communication Association. 2000;23(1):67–101. doi: 10.1080/23808985.2000.11678970. [DOI] [Google Scholar]
  52. Onwezen MC, Antonides G, Bartels J. The norm activation model: an exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. Journal of Economic Psychology. 2013;39:141–153. doi: 10.1016/j.joep.2013.07.005. [DOI] [Google Scholar]
  53. Paltiel AD, Zheng A, Walensky RP. Assessment of SARS-CoV-2 screening strategies to permit the safe reopening of college campuses in the United States. JAMA Network Open. 2020;3(7):e2016818–e2016818. doi: 10.1001/jamanetworkopen.2020.16818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Porter SR, Umbach PD. Student survey response rates across institutions: why do they vary? Research in Higher Education. 2006;47(2):229–247. doi: 10.1007/s11162-005-8887-1. [DOI] [Google Scholar]
  55. Reniers RL, Murphy L, Lin A, Bartolomé SP, Wood SJ. Risk perception and risk-taking behaviour during adolescence: the influence of personality and gender. PLoS One1. 2016;11(4):e0153842. doi: 10.1371/journal.pone.0153842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Rivis A, Sheeran P, Armitage CJ. Expanding the affective and normative components of the theory of planned behavior: a meta-analysis of anticipated affect and moral norms. Journal of Applied Social Psychology. 2009;39(12):2985–3019. doi: 10.1111/j.1559-1816.2009.00558.x. [DOI] [Google Scholar]
  57. Roseman IJ, Wiest C, Swartz TS. Phenomenology, behaviors, and goals differentiate discrete emotions. Journal of Personality and Social Psychology. 1994;67(2):206–221. doi: 10.1037/0022-3514.67.2.206. [DOI] [Google Scholar]
  58. Shieh G. Clarifying the role of mean centring in multicollinearity of interaction effects. British Journal of Mathematical and Statistical Psychology. 2011;64(3):462–477. doi: 10.1111/j.2044-8317.2010.02002.x. [DOI] [PubMed] [Google Scholar]
  59. Schwartz SH. Words, deeds and the perception of consequences and responsibility in action situations. Journal of Personality and Social Psychology. 1968;10(3):232–242. doi: 10.1037/h0026569. [DOI] [Google Scholar]
  60. Schwartz SH. Normative explanations of helping behavior: a critique, proposal, and empirical test. Journal of Experimental Social Psychology. 1973;9(4):349–364. doi: 10.1016/0022-1031(73)90071-1. [DOI] [Google Scholar]
  61. Schwartz, S. H. (1977). Normative influences on altruism. In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 221–279). Academic
  62. Schwartz, S., & Ben David, A. (1976). Responsibility and helping in an emergency: Effects of blame, ability and denial of responsibility. Sociometry, 406–415. 10.2307/3033505 [PubMed]
  63. Schwartz SH, Clausen GT. Responsibility, norms, and helping in an emergency. Journal of Personality and Social Psychology. 1970;16(2):299–310. doi: 10.1037/h0029842. [DOI] [Google Scholar]
  64. Schwartz SH, Howard JA. A normative decision-making model of altruism. In: Rushton JP, Sorrentino RM, editors. Altruism and helping behavior. Hillsdale, NJ: Erlbaum; 1981. pp. 89–211. [Google Scholar]
  65. Shanka, M. S., & Kotecho, G. M. (2021). Combining rationality with morality–integrating theory of planned behavior with norm activation theory to explain compliance with COVID-19 prevention guidelines. Psychology Health & Medicine, 1–11. 10.1080/13548506.2021.1946571 [DOI] [PubMed]
  66. Steenhaut S, Van Kenhove P. The mediating role of anticipated guilt in consumers’ ethical decision-making. Journal of Business Ethics. 2006;69(3):269–288. doi: 10.1007/s10551-006-9090-9. [DOI] [Google Scholar]
  67. Steg L, Dreijerink L, Abrahamse W. Factors influencing the acceptability of energy policies: a test of VBN theory. Journal of Environmental Psychology. 2005;25(4):415–425. doi: 10.1016/j.jenvp.2005.08.003. [DOI] [Google Scholar]
  68. Steinberg L. A social neuroscience perspective on adolescent risk taking. Developmental Review. 2008;28:78–106. doi: 10.1016/j.dr.2007.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Stern PC, Dietz T. The value basis of environmental concern. Journal of Social Issues. 1994;50(3):65–84. doi: 10.1111/j.1540-4560.1994.tb02420.x. [DOI] [Google Scholar]
  70. Tabachinick BB, Fidell LS. Using multivariate statistics. 6. Pearson Education; 2012. [Google Scholar]
  71. Tangney, J. P., Stuewig, J., & Mashek, D. J. (2007). Moral emotions and moral behavior. Annual Review of Psychology,58, 345–372. 10.1146/annurev.psych.56.091103.070145 [DOI] [PMC free article] [PubMed]
  72. Triandis HC. Individualism and collectivism. Westview Press; 1995. [Google Scholar]
  73. Triandis HC, Bontempo R, Villareal MJ, Asai M, Lucca N. Individualism and collectivism: cross-cultural perspectives on self-ingroup relationships. Journal of Personality and Social Psychology. 1988;54(2):323–338. doi: 10.1037/0022-3514.54.2.323. [DOI] [Google Scholar]
  74. Turner, M. M., Jang, Y., Barry, R., Heo, R., & Ye, Q. (2021). Examining the Effects of Guilt and Anger Appeals on Perceived Persuasiveness, Cognitions and Intentions [Paper presentation]. Paper presented at the 71st Annual International Communication Association (ICA) Conference 2021, Virtual
  75. Turner MM, Mabry-Flynn A, Shen H, Jiang H, Boudewyns V, Payne D. The effects of guilt-appeal intensity on persuasive and emotional outcomes: the moderating role of sponsor motive. Journal of Nonprofit & Public Sector Marketing. 2018;30(2):134–150. doi: 10.1080/10495142.2017.1326345. [DOI] [Google Scholar]
  76. Varkety Foundation (2017). Generation Z: Global citizenship survey. Retrieved October 29, 2021, from: https://www.varkeyfoundation.org/media/4487/global-young-people-report-single-pages-new.pdf
  77. Vining J, Ebreo A. Predicting recycling behavior from global and specific environmental attitudes and changes in recycling opportunities. Journal of Applied Social Psychology. 1992;22(20):1580–1607. doi: 10.1111/j.1559-1816.1992.tb01758.x. [DOI] [Google Scholar]
  78. Wang X. The role of anticipated guilt in intentions to register as organ donors and to discuss organ donation with family. Health Communication. 2011;26(8):683–690. doi: 10.1080/10410236.2011.563350. [DOI] [PubMed] [Google Scholar]
  79. Windle M. Alcohol use among adolescents and young adults. Alcohol Research & Health. 2003;27(1):79–85. [PMC free article] [PubMed] [Google Scholar]
  80. Witte K. Putting the fear back into fear appeals: the extended parallel process model. Communications Monographs. 1992;59(4):329–349. doi: 10.1080/03637759209376276. [DOI] [Google Scholar]
  81. World Health Organization (2022). WHO coronavirus (COVID-19) dashboard. Retrieved April 24, 2022, from: https://covid19.who.int
  82. Xu P, Cheng J. Individual differences in social distancing and mask-wearing in the pandemic of COVID-19: the role of need for cognition, self-control and risk attitude. Personality and Individual Differences. 2021;175:110706. doi: 10.1016/j.paid.2021.110706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Zeelenberg M, Breugelmans SM. The role of interpersonal harm in distinguishing regret from guilt. Emotion. 2008;8(5):589–596. doi: 10.1037/a0012894. [DOI] [PubMed] [Google Scholar]
  84. Zuckerman M, Reis HT. Comparison of three models for predicting altruistic behavior. Journal of Personality and Social Psychology. 1978;36(5):498–510. doi: 10.1037/0022-3514.36.5.498. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ESM 1 (22.5KB, docx)

(DOCX 22.4 KB)

Articles from Current Psychology (New Brunswick, N.j.) are provided here courtesy of Nature Publishing Group

RESOURCES