Understanding German Consumers’ Intention to Adopt COVID-19 Infection Prevention Measures: A Moral Decoupling Perspective

Abstract

Getting consumers to adopt infection prevention measures is important for society to overcome the coronavirus pandemic. This research adopts a moral decoupling perspective to examine how consumers in Germany respond to perceived transgressions of COVID-19 infection prevention regulations. Focusing on two nonpharmaceutical measures (mask wearing, social distancing) as well as a pharmaceutical one (vaccination), two empirical studies indicate that transgression relevance influences intention to adopt the measure (in parallel) through judgment of performance and judgment of morality. Type of transgression moderates the effect of transgression relevance on morality, but not on performance. In addition, effects weaken as a person’s fear of infection increases. Effects are robust, though, when controlling for moral decoupling and moral delegation (Study 1), and additionally for psychological reactance and political orientation (Study 2). Implications for research and practice evolve around new insights into how to get consumers to adopt infection prevention measures more effectively.

Keywords

consumers COVID-19 Germany moral decoupling public health

When a new virus broke out in late 2019, governments around the globe responded by implementing various policies and regulations aimed at slowing down the spreading of the disease so that health care systems would not become overwhelmed (Kenyon, 2020). In Germany, COVID-19 infection prevention measures (CIPMs) promoted during the early stages of the pandemic included nonpharmaceutical “social distancing” (i.e., maintaining a minimum spatial distance to others) and mask wearing (i.e., using nose-mouth covers). Over time, as safe and effective vaccines were rolled out, additional pharmaceutical measures included vaccination (Spinelli et al., 2021). While those measures slowed down the spreading of the virus, they negatively impacted business and society (Bapuji et al., 2020). People found adopting the measures uncomfortable, questioned their effectiveness, got tired of them, and lost trust in institutional policies (Oksanen et al., 2020). As a consequence, people started ignoring or actively boycotting regulations (Lin, 2020), worsening the epiodemiological (Worldometers, 2021) and economical situation (Statistisches Bundesamt, 2020). To promote the adoption of CIPMs, law makers issued sanctions and fines for offenders (Murphy et al., 2020). Taken together, infection prevention regulations and consumer responses to those impaired business operations (Bapuji et al., 2020), disrupted retailing (Pantano et al., 2020), and triggered a shift toward a new economic reality (Roggeveen & Sethuraman, 2020). The number and magnitude of adverse effects for business and society lend urgency to the question of why and when people adopt or do not adopt CIPMs.

A substantial body of literature has examined the issue of why and when consumers comply with regulations aimed at protecting their health in a variety of contexts, including smoking (Lazuras et al., 2009), alcohol consumption (Adams & Effertz, 2010), drug use (Chaloupka et al., 1999), and nutrition (Cohn et al., 2012). Extant studies on possible drivers of individual adoption decisions identified demographic factors (Bish & Michie, 2010), individual differences (Xu & Cheng, 2021), health concerns (Ozdemir et al., 2020), beliefs on the effectiveness of measures (Clark et al., 2020), and fear of sanctions and fines (Murphy et al., 2020). More recently, scholars have highlighted the potential of moral factors to better explain a person’s intention to adopt health protective measures, especially within the context of the COVID-19 pandemic (Chan, 2021; Díaz & Cova, 2021). Different than studies focusing on moral foundations (Chan, 2021) or moral values (Díaz & Cova, 2021), we adopt a moral reasoning perspective, specifically, moral decoupling (Bhattacharjee et al., 2013) to better understand individual adoption of CIPMs in a German consumer context.

A key component of moral decoupling and a critical issue underlying morality in a business context is the balance between self-interest and the greater good (see Campbell & Winterich, 2018, for a conceptual framework). Consistent with Campbell and Winterich’s (2018) framework, moral decoupling captures a process where consumers engage in moral reasoning to separate judgments of performance from judgments of morality (Bhattacharjee et al., 2013). In doing so, moral decoupling enables consumers to support a transgression while simultaneously condemning it. For example, it enables consumers to continue buying from firms which abuse their employees or to keep supporting public figures even after they were included in a public scandal (Orth et al., 2019). In the context of COVID-19, possible transgressions may include not wearing a mask, not keeping a minimum distance from others, and not getting vaccinated against the virus. In these cases, decoupling involves separating the subjective ability of a measure to protect against infection (judgment of performance) from aspects of the greater good for society (judgment of morality).

Consumer research on moral decoupling has proven the capacity of this model to better understand transgressions in a number of highly diverse contexts, including corporate social responsibility (Chang et al., 2017), corporate misconduct (Cowan & Yazdanparast, 2019; Haberstroh et al., 2017), brand endorser missteps (Lee & Kwak, 2016), consumer shoplifting (Babin & Babin, 1996), counterfeit buying (Chen et al., 2018; Orth et al., 2019), and charitable giving (Weber, 2019). These studies suggest that moral decoupling may be particularly well suited for explaining consumer intention to adopt CIPMs because—unlike alternative accounts—it avoids moral compromise and dissonance (Cowan & Yazdanparast, 2019), as well as negative emotion (Orth et al., 2019).

Yet, while moral decoupling has a proven ability to explain consumer response to corporate transgressions (Chang et al., 2017; Fehr et al., 2019; Haberstroh et al., 2017) as well as a person’s own immoral actions (Chen et al., 2018; Orth et al., 2019), applications in the health behavioral domain are lacking. More specifically, while it seems plausible that consumers may engage in moral decoupling in response to transgressions relating to infection prevention measures, corresponding research is missing. This gap in research is exacerbated as it is unknown what individual and situational difference variables promote and inhibit adoptive behaviors.

This study extends Bhattacharjee and colleagues’ (2013) moral decoupling model to the context of the COVID-19 pandemic to examine why and when consumers adopt CIPMs. By doing so, this study makes the following contributions to research and practice. First, it extends research on health protective measures in general (Ferrer & Mendes, 2018) and COVID-19 in specifics (Chan, 2021; Díaz & Cova, 2021) by offering a novel process explanation for how judgment of performance and judgment of morality mediate effects of transgression relevance on behavioral intention. Second, our research adds to the body of research on decoupling (Bhattacharjee et al., 2013; Chen et al., 2018; Haberstroh et al., 2017; Orth et al., 2019) by showing that—for issues of high personal relevance with direct personal consequences—such as health and illness—moral decoupling reaches its limits as an explanatory framework. Third, drawing from the Health Action Process (HAP) model (Schwarzer, 1992), we identify individual and situational boundary conditions for the process, specifically, type of transgression and individual fear of infection.

Conceptual Framework and Hypotheses

Morality, Moral Reasoning, and Moral Decoupling

Most people do not intend to harm others with their consumption behavior; nonetheless, many consumption choices do cause harm (Nielsen & McGregor, 2013). Even when people engage in immoral actions and know about the immoral aspect of the action, they do not necessarily feel guilty because morality is not a fixed dimension, but rather functions like a slider scale, to be adjusted as convenient (Shu et al., 2011). Capturing a significant component of a person’s self-concept, morality is an important force in society, as it influences people’s attitudes, values, and behavioral intentions (Cowan & Yazdanparast, 2019). Morality is involved in everyday activities and people continuously evaluate what the “right” thing to do is (Nyberg, 2008).

Psychological theories of morality address how humans determine the best way to live, interact with others, consume, and respond to perceived transgressions. Across the different formulations of morality, a number of theoretical accounts have been put forward to better understand individual intention and behavior, including moral disengagement (Bandura et al., 1996), ethical blind spots (Sezer et al., 2015), moral intensity (Jones, 1991), and moral reasoning (Paxton & Greene, 2010). The role of moral reasoning has been debated in current psychological research on moral judgment. Accordingly, moral reasoning captures adjustments in a person’s thinking, thereby impacting moral judgment (Paxton & Greene, 2010). As a consequence, moral reasoning enables people to justify immoral behaviors and reevaluate them as moral (Mulder & van Dijk, 2020). A recent refinement to moral reasoning is moral decoupling, a process where consumers separate judgment of performance from judgment of morality (Bhattacharjee et al., 2013). For example, when Tiger Woods was exposed for having extramarital affairs, many fans still admired him as an exceptional athlete even though they loathed his behavior as a husband (Bhattacharjee et al., 2013).

This selective dissociation of performance aspects from morality aspects suggests that moral decoupling may be particularly suited for understanding consumer response to ethical transgressions. By decoupling, people can admit that another person, a firm, or even themselves act in an unethical manner, but they can still acknowledge performance aspects of the behavior (Fehr et al., 2019). Different than other strategies, moral decoupling does not involve condoning the immoral action, and therefore does not challenge people to compromise their moral standards (Bhattacharjee et al., 2013), which avoids evoking dissonance (Cowan & Yazdanparast, 2019). Furthermore, moral decoupling feels less wrong (Bhattacharjee et al., 2013), and reduces the experience of negative emotion during judgment and decision-making (Orth et al., 2019).

A Moral Decoupling Perspective on Adopting CIPMs

Over the course of the COVID-19 pandemic, governments mandated a number of preventive measures, such as lockdowns, prohibition of larger gatherings, prescription of masks, social distancing, and vaccination. All measures have a proven ability to slow down the spread of the virus (Humphrey et al., 2020), but their effectiveness hinges on compliance within a society being high (Sailer et al., 2021). In Germany, extant measures initially focused on nonpharmaceutical ones (mask wearing and social distancing), and later shifted to getting people vaccinated (Bundesministerium für Gesundheit, 2021b).

From a moral decoupling perspective, not adopting CIPMs (i.e., not wearing a mask, not keeping a proper distance from others, and refusing to get vaccinated) can be seen as transgressing moral rules and norms. For example, some people justify unwillingness to wear masks based on civil liberty arguments, arguing that this takes precedence over infection control (Taylor & Asmundson, 2021). Regardless of moral hierarchies (Chan, 2021; Clark et al., 2020), the perceived relevance of transgressions should impact a person’s intention to adopt the measure through individual judgments of performance and morality. In the context of CIPMs, judgment of morality captures a person’s evaluation of possible harm to others (by not adopting measures), whereas judgment of performance captures their evaluation of the effectiveness of measures.

People constantly judge others (and themselves) in regard to personalities, abilities, and behavior (Chen et al., 2018). Behavioral judgment is situation-dependent (Chen et al., 2018) and consists of two aspects: one assesses behavioral qualities that can be measured with great exactness; the other is more subjective and considers the psychological relevance of people’s behavior (Leising et al., 2014). Human judgment occurs for everyday behaviors as well as for behaviors during crisis situations, including moral actions (Musschenga, 2008). When judging moral transgressions, consumers face a dilemma as they have to weigh performance against moral aspects of the behavior (Bhattacharjee et al., 2013). By dissociating judgment of morality from judgment of performance, a moral decoupling model should provide a novel and unique explanation for consumer adoption of CIPMs. Identifying boundary conditions (the fear to become infected and the transgression type) should enhance insights into when effects are more and less likely to occur. Figure 1 holds the research model.

Figure 1.

Research model.

The Mediating Role of Judgment of Morality

We expect a consumer’s judgment of morality to channel at least some of the effect of transgression relevance on behavioral intention. Specifically, when a person perceives transgressions relating to CIPMs as more relevant, this person should be more likely to find it morally wrong to not keep the distance to others, to not wear a mask, and to not get vaccinated. In turn, judgment of morality should exert a positive effect on intention to adopt the measure.

Moral judgment can be defined as the degree to which an individual evaluates a behavior to be (un)ethical (Chen et al., 2018; May & Pauli, 2002). Furthermore, this judgment can be driven by intuitive emotional responses or an intentional and effortful conscious process (Paxton & Greene, 2010). The latter is thought to involve two steps: First, information that is seen as having moral relevance is selected from the behavior in question. Then, this information is weighted and judged based on personal moral rules for evaluating behavior (Jeanty, 2008). Moral judgment is situation-dependent (Chen et al., 2018), typically relying on reasoning based on a number of coexisting moral standards, such as compassion and justness (Jeanty, 2008). However, judgments can also be made automatically when individuals encounter immoral actions (Lee & Kwak, 2016). Consequently, forming moral judgments is influenced by contextual factors (Valdesolo & Desteno, 2006). We will revisit this aspect later when discussing transgression type as a possible moderator of the transgression relevance–morality relationship.

Studies on moral decoupling have consistently reported a negative effect of transgression relevance on judgment of morality (Bhattacharjee et al., 2013; Chang et al., 2017; Fehr et al., 2019; Haberstroh et al., 2017). Outside the moral decoupling domain, other studies have similarly established that a transgression’s relevance negatively impacts a person’s judgment of morality (Donovan, 2018). This effect has been attributed to people being motivated to maintain a positive view of themselves, finding it difficult to reevaluate a behavior as moral as the relevance of the transgression increases (Lee & Kwak, 2016). People commonly view the behavior of not complying with governmental rules and regulations as wrong. Specifically for CIPMs, people consider it a moral obligation to adopt preventive measures for protecting not only themselves, but additionally for protecting others and for the greater good of society (Shanka & Gebremariam Kotecho, 2021). Accordingly, people who find that not adopting CIPMs presents a relevant transgression should form less positive moral judgments. We therefore expect a negative effect of a transgression’s relevance on judgment of morality.

Regarding behavioral consequences, higher levels of moral judgment generally lead to higher incidence of moral and ethical intention and actual behavior (Reynolds & Ceranic, 2007). More specifically, research on moral decoupling (Cowan & Yazdanparast, 2019; Haberstroh et al., 2017; Reynolds & Ceranic, 2007) shows that judgment of morality positively influences intention. In line with this thought, we expect that judgment of morality increases an individual’s intention to adopt infection prevention measures. Formally,

Hypothesis 1a (H1a): Judgment of morality will mediate the transgression relevance–intention relationship to the extent that transgression relevance will negatively influence judgment of morality, which will positively influence intention to adopt CIPM.

The Mediating Role of Judgment of Performance

While we expect at least some of the effect of transgression relevance on behavioral intention to be channeled through judgment of morality, judgment of performance may play a similar role, representing a parallel mediator. Specifically, when people consider transgressions relating to CIPMs to be relevant, they should be more likely to judge corresponding measures (mask wearing, distance keeping, and getting vaccinated) to be more effective. In turn, judgment of performance should positively influence intention to adopt measures.

When forming intentions, people routinely judge the effectiveness of adopting the behavior under consideration (Fehr et al., 2019). Performance judgments can include economic, social, and hedonic effectiveness (Chen et al., 2018). Judging performance thus involves evaluating measures based on a person’s belief in their effectiveness or functionality, a subjective process (Fehr et al., 2019). While judgment of performance often parallels judgment of morality (Orth et al., 2019), performance judgments should be accounted for separately because they reflect a distinct and vital part of the evaluation (Earle & Siegrist, 2006). Furthermore, the relationship between unethical and immoral behavior and performance may be more complex than just weighing between good and bad (Fehr et al., 2019).

Based on the central tenet that separating moral from performance aspects facilitates performance judgment, moral decoupling research has established a generally positive effect of transgression relevance on judgment of performance (Bhattacharjee et al., 2013; Chang et al., 2017; Fehr et al., 2019; Haberstroh et al., 2017). Unlike previous studies, the present research examines a behavior with possible direct consequences for a consumer’s own health and wellbeing. In the context of COVID-19, medical research has substantiated the effectiveness of nonpharmaceutical interventions (mask wearing, distance keeping) and vaccination as means to prevent infection with the coronavirus (Spinelli et al., 2021). Relying on scientific evidence, many people believe that adopting CIPMs is effective in preventing infection (Brüggenjürgen et al., 2021). Although some people disagree (i.e., the so-called “Querdenker” [maverick thinkers] movement in Germany; Plümper et al., 2021), those individuals may also view transgressions to CIPMs as less relevant. We therefore expect a positive effect of transgression relevance on judgment of performance. In turn, judgment of performance should have a positive influence on intention to adopt CIPMs, in line with extant studies (Chen et al., 2018; Haberstroh et al., 2017). Formally,

Hypothesis 1b (H1b): Judgment of performance will mediate the transgression relevance–intention relationship to the extent that transgression relevance will positively influence judgment of performance, which will positively influence intention to adopt CIPM.

The Moderating Roles of Transgression Type and Fear of Infection

We expect the effect of transgression relevance (through morality and performance judgments) on consumer intention to adopt CIPMs to depend on two individual and situational difference variables: type of transgression and a person’s fear of infection. We base this prediction on the HAP approach (Schwarzer, 1992), which identifies critical factors impacting health protective intention and behavior. These factors include perceived vulnerability to a health threat (e.g., fear of infection) and self-regulatory strategies (e.g., self-control), which are considered important both generally (Schwarzer, 1992) and specifically in COVID-19 (Hamilton et al., 2020).

Regarding self-regulatory strategies, people adjust judgment and behavior by monitoring internal and external cues in a given situation (Snyder, 1974). The impact of self-monitoring on judgment is supported by research on consumer ethics (Kavak et al., 2009), moral reasoning strategies (Pagano & DeBono, 2011), and CIPMs (Hamilton et al., 2020). Examining the CIPMs central to this study suggests a number of critical differences relating to self-monitoring. These differences include the cognitive effort required for implementing a measure, visibility of the measure to others, reliance on the cooperation of others for successfully implementing the measure, and necessary equipment.

First, transgressions relating to pharmaceutical measures (vaccination) may yield divergent effects when compared to nonpharmaceutical measures. Once a person is fully vaccinated, less to none self-regulation is required in daily life. Furthermore, whether a person is vaccinated is not readily observable to others, whose cooperation is also not required for the measure to be effective. Similarly, no equipment is necessary. Among the nonpharmaceutical measures, social distancing requires a prolonged cognitive effort on part of the consumer who has to constantly monitor their own spatial position as well as that of others. Wearing a mask, in contrast, is a one-time effort, usually prompted by prominent signage at the point of entry into an environment. In addition, wearing a mask serves as a social signal that reminds others of the need to adopt the protective measure (Seres et al., 2020).

Taken together, adopting a self-regulatory perspective suggests a number of important differences between CIPMs (i.e., demand for self-monitoring, visibility to and reliance on others, necessary equipment). Integrating these differences with reports of a moderating role of transgression type in other moral decoupling contexts (Donovan, 2018; Lee & Kwak, 2016) suggests that the effects of transgression relevance on judgmental outcomes may vary between measures. Specifically, the negative effect on judgment of morality should be greater for measures requiring prolonged self-monitoring, investment of cognitive resources, and cooperation of others, such as social distancing. Similarly, the positive effect of transgression relevance on performance judgment should be muted with measures imposing low self-regulation demands, such as mask wearing and pharmaceutical measures. Formally,

Hypothesis 2a (H2a): Transgression type will moderate the negative effect of transgression relevance on judgment of morality such that the effect will be stronger when the transgression relates to distance keeping versus mask wearing/vaccination.

Hypothesis 2b (H2b): Transgression type will moderate the positive effect of transgression relevance on judgment of performance, such that the effect will be stronger when the transgression relates to distance keeping versus mask wearing/vaccination.

Further in line with Schwarzer’s (1992) HAP model, and regarding perceived vulnerability to the health threat, we expect that a person’s fear of infection moderates the effects of evaluative judgments on intentions to adopt CIPMs. During crisis situations, people commonly experience anxiety-related responses including a pronounced fear of infection (Taylor & Asmundson, 2021). Not surprisingly, during the early stages of the COVID-19 pandemic, with numbers of infections rising and no immediate remedies available (World Health Organization [WHO], 2019), public anxieties and fears increased substantially (Lin, 2020). The experience of fear, a discrete and intense emotional episode (Yik et al., 2011), is an established driver of people’s intentions and health protective behaviors (Witte & Allen, 2000). In the context of COVID-19, a number of studies showed that individual intention to adopt CIPMs is driven, at least partially, by a person’s fear of infection (Clark et al., 2020; Pakpour et al., 2020; Yıldırım et al., 2021; Zickfeld et al., 2020). Findings are inconclusive, however, in that some studies report strong direct effects of fear (Harper et al., 2020; Yıldırım et al., 2021), whereas others report moderate to weak effects (Clark et al., 2020; Zickfeld et al., 2020).

Different than previous research on direct effects of fear, we conceptualize fear as a moderator of the association between judgments (of performance and morality) and behavioral intention. Possibly reconciling inconclusive reports on the strength of direct effects, positing an interactive effect of fear is in line with reports that affective states can moderate cognitive influences on decision-making and behavior in many settings (Raghunathan & Pham, 1999), including health (Ferrer & Mendes, 2018). In fact, individuals interpret cognitions based on their feelings, regardless of whether those feelings are integral to the evaluative judgment or merely incidental (Clore et al., 2001). Fear specifically has been shown to differentially affect the effectiveness of intentional drivers involving personal risk (Raghunathan & Pham, 1999). Underlying this differential impact of levels of fear on adaptive intentions and behaviors is a transition from the use of evaluative judgments (such as performance and morality) to a more direct impact of emotion as levels of fear increase (Witte & Allen, 2000). In other words, when fear is nonexistent or at low levels, people rely on evaluative judgments to form behavioral intention; this influence of judgments decreases at moderate levels of fear, to disappear entirely at very high to extreme levels. Formally,

Hypothesis 3a (H3a): Fear of infection will moderate the negative effect of judgment of morality on the intention to adopt CIPMs such that the effect will decrease with increasing levels of fear.

Hypothesis 3b (H3b): Fear of infection will moderate the positive effect of judgment of performance on the intention to adopt CIPMs such that the effect will decrease with increasing levels of fear.

Controls

A number of individual and situational difference factors may impact evaluative judgments and behavioral intention, presenting boundary conditions. Possible influences include individual differences (i.e., moral decoupling, moral delegation, psychological reactance, and political orientation) and demographic factors (i.e., age and biological sex).

While we fully expect that a moral decoupling process can explain effects of transgression relevance on behavioral intention, accounting for moral decoupling as an individual and situational difference variable may offer additional explanatory power. Specifically, consumers vary in their tendency to categorize a behavior as immoral (Chen et al., 2018), selectively dissociating judgment of performance from judgment of morality (Fehr et al., 2019; Haberstroh et al., 2017). Recent studies have shed some light on contextual factors that impact when people decouple morality from performance, such as thinking about money versus personal comfort (Fehr et al., 2019), regulatory focus and emotional state (Cowan & Yazdanparast, 2019), or diffusion of responsibility (Tsang, 2002). While moral decoupling has not been examined in a health context, effects as an individual difference variable cannot be ruled out. We thus include the variable as a control.

Experimental economics scholars suggest that moral delegation can impact decision-making in a variety of contexts (Bartling & Fischbacher, 2012; Oexl & Grossman, 2013). Referring to the conscious process of shifting responsibility for a decision to another person or entity, moral delegation aims at diffusing or shirking responsibility to avoid negative consequences of a decision (Bartling & Fischbacher, 2012). A major motive for delegating accountability is self-interest and the evaluation of the behavior as possibly immoral (Hamman et al., 2010). Delegating thus allows the person to detach themselves from possible consequences, feel less responsible, and avoid possible punishment (Oexl & Grossman, 2013). While moral delegation has not been examined in the context of moral decoupling and personal health, it cannot be ruled out as an alternative predictor and will be included as a control.

The idea that individuals appreciate freedom in making decisions (e.g., on health protective measures) is a central tenet of psychological reactance theory (Brehm, 1966). When a person feels their freedom is restricted (i.e., through a behavior mandated by authorities), they will experience reactance, a blend of negative emotion (anger) and antagonistic cognition (Dillard & Shen, 2005). Motivating people to regain the freedom lost, reactance can lead not only to protest but also additionally to even greater engagement in the constrained behavior—a boomerang effect (Miron & Brehm, 2006). Because reactance can influence the adoption of CIPMs such as mask wearing (Taylor & Asmundson, 2021), distancing (Díaz & Cova, 2021), and vaccination (Sprengholz, Felgendreff et al., 2021), we include this variable as a control in our study.

An individual’s political orientation can be defined as the beliefs and values people hold about the way society is and how it should be (Kroh, 2007). Differentiating between left-right and liberal-conservative, political orientation accounts for divergent beliefs on individual rights versus a democratic sense of the common good, and impacts a person’s conformity with social norms (Jost et al., 2018). Important, political orientation is also a significant factor in people’s adoption of CIPMs, with a more liberal (rather than conservative) tendency being associated with greater engagement in mask wearing behavior (Xu & Cheng, 2021), social distancing compliance (Barbieri & Bonini, 2021), and willingness to get vaccinated (Huynh & Senger, 2021). To rule out a possible biasing effect of political orientation, we include the variable as a control.

Finally, a number of demographic factors have been shown to influence how people respond to CIPMs, specifically a person’s age and biological sex (Zhang et al., 2020). Research has shown that gender plays a crucial role in moral reasoning (Elm et al., 2001). Because it is conceivable that a person’s age and sex impact their intention to adopt preventive measures in a moral reasoning context, we include both variables as controls in our study.

Study 1

To initially test the effect of CIPM transgressions on consumers’ intention to adopt measures, Study 1 employed a 2 (transgression type: wearing no mask vs. keeping no distance) × 2 (transgression relevance: low vs. high) between-subjects experimental design. For organizational reasons, the experimental design was broken down into two subsamples, one relating to social distancing and the other relating to mask wearing. The two subsamples were collected in temporal sequence and were collapsed after preliminary analyses (see below).

Data were collected between late May and early June of 2020, over a time period of 2 weeks. At that time, initial infection rates in Germany had started a slight decrease, with regional rates varying between 16 and 700 newly infected people per day (per 100.000 inhabitants); daily death rates ranged from 8 to 60 (Robert Koch Institut, 2020). Infection prevention regulations in place at the time of data collection included mandatory mask wearing in all public places, shopping outlets, government buildings, city centers, and public transportation. Social distancing regulations applied to these locations as well with numerous signs constantly reminding citizens (Die Bundesregierung, 2020). Hospitality businesses were allowed to reopen under conditions of digital “contact tracing” (recording customer data for identifying social contacts in case of an infection). Retail outlets had to limit the number of customers and employers on their premises, to ascertain a minimum distance of 2 m between individuals (Die Bundesregierung, 2020). Fines for noncompliance ranged from 500€ to 4.000€ (Bußgeldkatalog, 2021).

Method

Snowball sampling initiated via social media posts (e.g., Facebook, Instagram) generated a total sample of 186 (N_SD = 110, N_MW = 76) (M_age = 37) shoppers in Germany, who were screened to shop for groceries at least occasionally. No incentives were offered for participation. Of the participants, 73.1% (n = 136) were female, in line with statistics on the general population of grocery shoppers in Germany (Statistisches Bundesamt, 2019). Because the sample included disproportionally many young consumers with possibly diverging consumption behaviors, age was included as a control in subsequent analyses.

Upon accessing the online questionnaire, participants were randomly assigned to one of the two conditions varying in transgression relevance (see Appendix B for stimuli). Instructed to carefully read a short text on the transgressive behavior, participants proceeded to complete measures of study constructs and demographic information. To adapt original English language scales to the German context, established translation-back translation routines were employed.

All measures consisted of established and validated scales,¹ using 7-point multi-item Likert-type scales (1 = fully disagree; 7 = fully agree). Measures of transgression relevance, judgment of morality, judgment of performance, and moral decoupling were adapted from Bhattacharjee et al.’s (2013) study. To assess intention to adopt the infection prevention measure, we adapted the scale originally developed and validated by Putrevu and Lord (1994). Fear of infection was assessed through two items taken from the Fear of the Coronavirus Questionnaire (FCQ: Mertens et al., 2020) and the COVID Stress Scales (CSS: Taylor et al., 2020). Moral delegation was assessed using a single item adapted to the specific context. Table 1 holds items and key statistics for all measures.

Table 1.

Scale Items and Summary Statistics for Multi-Item Construct Measures in the Empirical Studies.

	Study 1		Study 2
	Sample 1	Sample 2	Study 2
Model constructs	Cronbach’s alphaM (SD)Variance extractedIFC>
Transgression relevance (adapted from Ingram et al., 2005) Not keeping a distance to others does not cause any harm. It is very unlikely, that it is harmful to not keep a distance to others. Not keeping a distance to others when grocery shopping is harmful for customers and personnel. (r).	.77 5.93 (1.22) 70.65% .79	—	—
It is unlikely that not wearing a mask when grocery shopping is harmful. Not wearing a mask when grocery shopping does not cause any harm. Not wearing a mask when grocery shopping is harmful for customers and personnel. (r)	—	.62 4.10 (1.02) 57.60% .57	—
Not getting vaccinated against COVID-19 does not cause harm to other people. It is very unlikely that a personal decision against COVID-19 vaccination will harm anyone else. As a consumer, not getting vaccinated against COVID-19 harms other people. (r)	—	—	.84 2.42 (1.51) 76.74% .79
Judgment of performance (adapted from Bhattacharjee et al., 2013) Social distancing measures are effective in preventing the spread of the coronavirus. Social distancing measures are effective to protect myself. Social distancing measures are effective to protect others.	.94 5.35 (1.46) 88.90% .94	—	—
Wearing a mask is an effective measure for infection prevention.^a	—	4.95 (1.51)	—
Vaccination against COVID-19 is an effective measure to contain the pandemic. COVID-19 vaccination is an effective protection of my health. COVID-19 vaccination also protects other people.	—	—	.92 5.91 (2.51) 86.85% .93
Judgment of morality (adapted from Bhattacharjee et al., 2013) It is morally wrong to not keep the distance to another person. Due to the current circumstances, I think that it is morally wrong when a person comes close to me.	.85 5.32 (1.67) 86.65% .93	—	—
It is morally wrong to not wear a mask when grocery shopping.^a	—	5.01 (1.52)	—
I think it is morally wrong not to get vaccinated. It is morally reprehensible to decide against vaccination when the majority gets vaccinated.	—	—	.91 4.41 (4.06) 91.61% .96
Moral decoupling (adapted from Bhattacharjee et al., 2013) When it comes to social distancing measures, medical and moral reasons should be strictly separated.^a	3.85 (2.03)	—	—
When it comes to wearing a mask, medical and moral reasons should be strictly separated.^a	—	4.24 (1.52)	—
When deciding for or against vaccination, moral and medical reasons should be strictly separated. If people don’t get vaccinated, it doesn’t affect my opinion of them. The assessment of people’s willingness to be vaccinated should be done independently of moral aspects.	—	—	.58 4.06 (3.21) 54.79% .69
Moral delegation^a It is not my responsibility to decide over (not) keeping a distance. That is the responsibility of others (government, store owners, etc.).	3.17 (2.16)	—	—
It is not my responsibility to decide over (not) wearing masks. That is the responsibility of others (government, store owners, etc.).	—	4.97 (1.94)	—
The decision for or against vaccination is not made by me, but by others (e.g., the state).	—	—	2.35 (1.60)
Intention to adopt (adapted from Putrevu & Lord, 1994) It is very likely, that I will continue to support the social distancing measures. I comply with the social distancing measures.	.80 5.92 (1.30) 83.46% .91	—	—
It is very likely, that I will wear a mask while grocery shopping. Even without an official policy, I would wear a mask while grocery shopping. My willingness to wear a mask while grocery shopping is very high.	—	.69 5.37 (1.03) 65.53% .69	—
It is very likely that I will continue to support COVID-19 vaccination as a pandemic containment measure (e.g., booster/refresher vaccination).^a	—	—	5.95 (1.71)
Fear of infection^a (adapted from Mertens et al., 2020; Taylor et al., 2020) I am very afraid to be infected with the coronavirus.	2.78 (1.76)	2.92 (1.62)	3.80 (1.78)
Political orientation (adapted from Kroh, 2007) Please indicate to which political position you belong. (7 = political left to 1 = political right)	—	—	2.95 (1.50)
Salzburger State Reactance Scale (adapted from Sprengholz, Felgendreff, et al., 2021) I feel restricted in my personal freedom. I feel frustrated. I feel annoyed. I feel disregarded.	—	—	.92 2.74 (3.59) 80.19% .89

Note. “r” refers to reverse-coded items. IFC = item-to-factor correlations.

For single-item measures, only M and SD are provided.

Analyses and Results

To test our hypotheses on the mediating roles of performance and morality (H1) and the moderating roles of transgression type (H2) and fear of infection (H3), we employed moderated mediation analyses (Hayes, 2018, Model 21, number of bootstrap samples: 5,000). Transgression relevance was the independent variable; judgment of performance and judgment of morality were two parallel mediators, transgression type (dummy coded 1 = social distancing, 2 = mask wearing) and fear of infection were the moderators, and intention to adopt the measure was the dependent variable. Moral decoupling, moral delegation, age, and sex were included as covariates. All continuous variables were mean-centered (Hayes, 2018).

The results (Table 2) indicate significant effects of transgression relevance on judgment of morality (B = −.51, SE = .10, p = .001), which in turn influenced intention to adopt the measure significantly (B = .09, SE = .04, p = .028). This mediating effect of morality on the transgression relevance–intention relationship is in support of H1a. Furthermore, results show a significant effect of transgression relevance on judgment of performance (B = .52, SE = .09, p = .001), which had a significant influence on intention (B = .14, SE = .05, p = .009), supporting H1b.

Table 2.

Testing for Moderated Mediation (Study 1).

Predictors		B	SE	t	p value
Outcome: JP (r² = .32)
TR		.52	.09	6.10	.000
Transgression type		.66	.27	2.47	.015
TR × Type		−.28	.18	−1.59	.113
Moral decoupling		−.06	.05	−1.18	.238
Moral delegation		−.05	.05	−.99	.323
Age		.34	.10	3.41	.001
Sex		−.16	.09	−1.73	.085
Outcome: JM (r² = .23)
TR		−.51	.10	4.90	.000
Transgression type		.69	.33	2.09	.038
TR × Type		−.50	.22	−2.28	.024
Moral decoupling		−.10	.07	−1.49	.139
Moral delegation		−.10	.06	−1.71	.088
Age		.06	.12	0.50	.621
Sex		−.05	.11	−.47	.636
Outcome: intention to adopt measures (r² = .61)
TR		.29	.05	6.13	.000
JP		.14	.05	2.65	.009
JM		.09	.04	2.22	.028
Fear of infection		.19	.04	4.61	.000
JP × Fear of infection		−.10	.02	−3.86	.000
JM × Fear of infection		−.05	.02	−2.10	.037
Moral decoupling		.01	.03	0.38	.704
Moral delegation		.01	.03	0.43	.667
Age		.28	.07	4.23	.000
Sex		−.00	.06	−0.01	.991
Direct effect of TR on intention to adopt measuresB = .29, SE = .05, 95% CI = [.20, .39]
Conditional indirect effects of TR on intention to adopt measures through JP and JM at values of the moderators (transgression type, fear)
Mediator	Transgression type	Fear of infection	Effect	Boot LLCI	Boot ULCI
JP	Social distancing	−1.84	.20	.08	.36
		−0.84	.14	.05	.26
		2.16	−.04	−.14	.05
	Mask wearing	−1.84	.11	.02	.25
		−0.84	.08	.01	.17
		2.16	−.02	−.11	.02
JM	Social distancing	−1.84	.13	.02	.24
		−0.84	.09	.01	.18
		2.16	−.01	−.12	.10
	Mask wearing	−1.84	.04	−.02	.15
		−0.84	.03	−.02	.10
		2.16	−.00	−.08	.04

Note. N = 186. HAYES PROCES Model 21 (Hayes, 2018): Bootstrap sample = 5,000. Significant (p < .05) coefficients in bold. TR = transgression relevance; JP = judgment of performance; JM = judgment of morality; CI = confidence interval; LLCI = lower level confidence interval; ULCI = upper level confidence interval.

Transgression type had direct significant influences on morality (B = .69, SE = .33, p = .038) and performance (B = .66, SE = .27, p = .015). In addition, transgression type moderated the influence of transgression relevance on judgment of morality (B = −.50, SE = .22, p = .024). This significant interaction effect supports H2a, indicating that people evaluate transgressions of mask wearing regulations as less morally wrong than those of social distancing. The nonsignificant Transgression relevance × Transgression type interaction effect on performance does not support H2b.

Results further show a significant direct effect of fear on intention to adopt measures (B = .19, SE = .04, p = .001) indicating that higher levels of fear increase intentions to follow the rules and adopt CIPMs. Significant effects on intention by the Morality × Fear of infection interaction term (B = −.05, SE = .02, p = .038), as well as the Performance × Fear of infection interaction term (B = −.10, SE = .02, p = .001) support H3a and H3b. Those findings indicate that the effects of both mediators on behavioral intention get weaker as a person is more afraid to become infected. Through performance, the indirect effect of transgression relevance on intention was significant for both social distancing (B = .20, SE = .07, 95% CI = [.08, .36]) and mask wearing (B = .11, SE = .06, 95% CI = [.02, .25]) at low levels of fear, getting weaker at intermediate levels (social distancing, B = .14, SE = .05, 95% CI = [.05, .26]; mask wearing, B = .08, SE = .04, 95% CI = [.01, .17]), and nonsignificant at high levels of fear. Indirect effects of transgression relevance through morality were significant only for social distancing combined with low (B = .13, SE = .06, 95% CI = [.02, .24]) and intermediate (B = .09, SE = .04, 95% = CI [.01, .18]) levels of fear. At high levels of fear and regardless of transgression type, indirect effects were nonsignificant.

The significant positive effects of age on performance (B = .35, SE = .10, p = .001) and intention to adopt CIPMs (B = .17, SE = .07, p = .027) indicate that older (rather than younger) participants tended to evaluate CIPMs as a good performance and were more likely to adopt CIPMs. The significant negative effect of sex on performance (B = −.18, SE = .09, p = .046) indicated that female participants were less likely to evaluate CIPM performance as positively as male participants.

Discussion of Study 1 Findings

This study was designed to test a moral decoupling model in the context of nonpharmaceutical CIPMs at an early stage of the pandemic. The findings support the central claim that judgments of morality and performance channel effects of transgression relevance on people’s intention to adopt protective measures. Findings on the roles of transgression type and fear of infection as possible moderators of effects are more mixed. While the effects of both cognitive judgments on intention to adopt get weaker as levels of fear increase, there is no clear evidence that transgression type moderates transgression effects on performance. In addition, Study 1 has a few limitations in terms of sample size and composition, an omission of possibly important control variables (psychological reactance and political orientation), and a neglect of pharmaceutical measures (vaccination). To address these limitations and enhance confidence in the generalizability of findings, a second study was designed.

Study 2

To partially replicate and extend Study 1, Study 2 employed a survey, testing CIPM transgression effects in the context of a pharmaceutical measure (vaccination against the coronavirus). Vaccination is considered an effective measure not only for oneself but also additionally for curbing the disease (Sah et al., 2021). Accordingly, not getting vaccinated can be viewed as a transgression, similar to the nonpharmaceutical ones examined in Study 1. Nevertheless, vaccination is dissimilar to social distancing, in that getting vaccinated is an individual decision, requiring no cooperation from others. Different from both mask wearing and social distancing, a state of immunization is not readily observable by other people. Finally, getting vaccinated requires no prolonged cognitive effort (unlike distance keeping) and cannot be implemented momentarily, for example, when entering a retail environment.

Study 2 data were collected in early November 2021, at a time when roughly two thirds of the German population had been fully vaccinated. With infection rates on the rise to unprecedented levels, officials pleaded with the nonvaccinated, and recommended booster shots for all others (Bundesministerium für Gesundheit, 2021a). Previously established rules for mask wearing and social distancing were still in place, but the vaccination effort was considered pivotal. Thus, vaccination was chosen as the focal CIPM for this study. To address Study 1 limitations, psychological reactance and political orientation were included as controls, and a consumer sample was purchased from a commercial panel provider.

Method

A total of 240 consumers recruited from Prolific participated in an online survey, with four data sets subsequently dropped because individuals admitted not having provided truthful answers. Final data used for statistical analyses thus included 236 German consumers (M_age = 32.81; SD = 12.55; 72.9% females) with 90.7% being vaccinated and 1.3% having recovered from COVID-19.

Measures were identical to the ones employed in Study 1, specifically, for transgression relevance, morality, performance, and moral decoupling (Bhattacharjee et al., 2013); intention (Putrevu & Lord, 1994); fear of infection (Mertens et al., 2020; Taylor et al., 2020); and moral delegation. In addition, we assessed vaccination policy reactance using four items of the Salzburger State Reactance Scale (SSR; Sittenthaler et al., 2015; Sprengholz, Betsch, & Böhm, 2021). Finally, political orientation was assessed using a 7-point semantic differential ranging from political left to political right (Kroh, 2007).¹ Table 1 holds items and key statistics for all measures.

Analyses and Results

To test hypothesized effects (specifically H1a, H1b, H3a, and H3b), we replicated the analytical approach employed in Study 1, specifically, moderated mediation analyses (Hayes, 2018, Model 14, number of bootstrap samples: 5,000). Transgression relevance was the independent variable; judgments of performance and morality were two parallel mediators, fear of infection was the moderator, and intention to adopt the measure was the dependent variable. Moral decoupling, moral delegation, reactance, political orientation, age, and sex were included as covariates. All continuous variables were mean-centered. Table 3 holds full results.

Table 3.

Testing for Moderated Mediation (Study 2).

Predictors	B	SE	t	p value
Outcome: JP (r² = .58)
TR	.54	.06	−9.48	.000
Moral decoupling	−.03	.06	−.48	.634
Moral delegation	−.02	.04	−.46	.647
Political orientation	−.02	.04	−.54	.591
Reactance	−.22	.05	−4.91	.000
Age	−.01	.06	−.08	.936
Sex	−.14	.07	−2.09	.038
Outcome: JM (r² = .48)
TR	−.64	.08	−7.62	.000
Moral decoupling	−.19	.08	−2.41	.017
Moral delegation	.02	.06	.27	.791
Political orientation	.08	.06	1.26	.207
Reactance	−.20	.07	−3.08	.002
Age	.18	.09	1.91	.058
Sex	−.03	.10	−.28	.779
Outcome: Intention to adopt measures (r² = .70)
TR	.16	.07	2.24	.028
JP	.62	.07	8.52	.000
JM	.10	.05	2.00	.042
Fear of infection	−.01	.04	−.27	.787
JP × Fear of infection	−.02	.03	−.66	.508
JM × Fear of infection	.03	.02	1.18	.236
Moral decoupling	.04	.06	.65	.572
Moral delegation	.03	.04	.68	.674
Political orientation	−.12	.04	−2.71	.007
Reactance	−.20	.05	−4.14	.000
Age	.00	.07	.03	.948
Sex	−.06	.07	−.93	.314
Direct effect of TR on intention to adopt measuresB = −.17, SE = .07, 95% CI = [−.31, −.03]
Conditional indirect effects of TR on intention to adopt measures through JP and JM at values of the moderator (fear)
Mediator	Fear of infection	Effect	Boot LLCI	Boot ULCI
JP	−1.67	.34	.22	−.21
	−.00	.32	.19	−.21
	1.66	.30	.13	−.18
JM	−1.67	.03	−.07	.13
	−.00	.06	.00	.12
	1.66	.09	.01	.18

Note. N = 236. HAYES PROCES Model 14 (Hayes, 2018): Bootstrap sample = 5,000. Significant (p < .05) coefficients in bold. TR = transgression relevance; JP = judgment of performance; JM = judgment of morality; CI = confidence interval; LLCI = lower level confidence interval; ULCI = upper level confidence interval.

The results indicate that transgression relevance influenced judgment of morality significantly (B = −.51, SE = .10, p = 001), which had a significant effect on intention, the dependent variable (B = .09, SE = .04, p = .028). This mediating effect of morality supports H1a. Furthermore, results show that transgression relevance influenced performance (B = .52, SE = .09, p = .001), which in turn showed to have a significant effect on intention (B = .14, SE = .05, p = .009), thus supporting H1b.

Results imply that transgression relevance had significant indirect effects on intention through performance regardless of fear levels (M_+SD: B = .34, SE = .06, 95% CI = [.22, .46]; M: B = .32, SE = .07, 95% CI = [.19, .46]; M_−SD: B = .30, SE = .09, 95% CI = [.13, .49]). Even though effects appeared to decrease with increasing levels of fear, no significant moderating effect of fear could be found. Therefore, H3b is not supported. In contrast to the effects through performance, indirect effects through morality were the highest when fear was high (M + SD) (B = .09, SE =. 04, 95% CI = [.01, .18]) and were nonsignificant when fear was low (M − SD) (see lower part of Table 3), thus contradicting H3a.

Importantly, these effects were observed in the presence of significant negative effects of reactance on both judgments (morality: B = −.24, SE = .07, p = .002; performance: B = −.22, SE = .05, p = .001), as well as on intention (B = −.20, SE = .05, p = .001). Furthermore, a person’s sex significantly impacted performance judgments negatively (B = −.14, SE = .07, p = .038), in line with a marginal effect in Study 1. Moral decoupling negatively influenced morality judgments (B = −.19, SE = .08, p = .017), indicating that increased engagement in decoupling leads to decreased condemnation of not getting vaccinated. Political orientation had a negative effect on intention (B = −.12, SE = .04, p = .007), indicating (given our coding of the variable) that individuals considering themselves to be more politically right were generally less likely to adopt the CIPM.

General Discussion

Two empirical studies, an experiment and a survey, show that adopting a moral decoupling perspective can provide valuable insights into why and when people adopt CIPMs. Seated in the context of consumers in Germany, the two studies distinguish between nonpharmaceutical (mask wearing and distance keeping) and pharmaceutical measures. Taken together, the findings indicate that at least some of the effect of CIPM transgressions on behavioral intention is mediated through judgment of performance and judgment of morality, clarifying the “Why.” Clarifying the “When,” transgression type moderates the effects of transgression relevance on morality and performance (Study 1). A person’s fear of infection weakens effects of both judgments on intention, but only in Study 1. In addition, transgression relevance maintains a direct influence on consumers’ intention to adopt infection prevention measures. Moral decoupling effects are robust when controlling for a number of important individual and situational difference variables, including moral decoupling, moral delegation, reactance, political orientation, age, and biological sex. Table 4 holds more detailed findings and conclusions, with specific implications discussed in the following sections.

Table 4.

Research Program Overview, Findings, and Implications.

	Study 1	Study 2	Conclusions	Implications
Transgression	Mask wearingDistance keeping	Vaccination
Sample	186 consumers	236 consumers
Hypothesized relationships	Findings
H1a: Morality mediates the transgression relevance–intention relationship.	√	√	Consumers are more likely to adopt CIPMs when they find relevant transgressions to be immoral.	To get consumers to adopt CIPMs, moral issues involved in transgressions should be emphasized (e.g., consequences for society).
H1b: Performance mediates the transgression relevance–intention relationship.	√	√	Consumers are more likely to adopt CIPMs when they find them to be effective.	To get consumers to adopt CIPMs, the effectiveness of the measures (for both the individual and society) should be emphasized.
H2a: Transgression type moderates the transgression relevance–morality relationship.	×	—	Consumers evaluate transgressing CIPMs as immoral regardless of the type.	Many consumers acknowledge that CIPM transgressions are immoral in general; others should be targeted to further increase awareness.
H2b: Transgression type moderates the transgression relevance–performance relationship.	√	—	Consumers’ judgment of whether CIPMs are effective depends on the type (mask wearing, distancing).	Consumers should be better informed about how social distancing and mask wearing are effective in preventing infection.
H3a: Fear moderates the morality–intention relationship.	√	×	For nonpharmaceutical CIPMs, morality aspects have a stronger effect with people who are in fear of getting infected.	Political attempts during the early stages of the pandemic to scare the general public into adopting nonpharmaceutical CIPMs appear justifiable ex post. As levels of fear decrease (over time, due to vaccination and availability of remedies), appeals to adopt nonpharmaceutical measures due to morality aspects will become less effective.
H3b: Fear moderates the performance–intention relationship.	√	×	Perceived effectiveness of nonpharmaceutical CIPMs has a stronger effect with people who are in fear of getting infected, especially for social distancing.	Over time, as people’s fear of infection decreases, emphasizing the effectiveness of social distancing has a lesser impact on intention to adopt.
Nonhypothesized effects
Transgression relevance influences intention.	(−)	(−)	Some fraction of the influence of transgression relevance on behavioral intention is channeled through mechanisms other than decoupling.	Future research should investigate additional mediators, possibly including the control variables examined here.
Age influences performance.	(+)	n.s.	Older people find social distancing and mask wearing to be more effective.	To stimulate adoption of nonpharmaceutical measures with younger people, they need to be educated about the measures’ effectiveness.
Age influences intention.	(+)	n.s.	Older people are more likely to wear masks and distance themselves from others	To stimulate adoption of nonpharmaceutical measures with younger people, additional aspects should be communicated, going beyond effectiveness and morality.
Fear influences intention.	(+)	n.s.	People who are afraid of infection are more likely to wear masks and distance themselves from others.	While it appears unethical to increase levels of fear, accounting for aspects beyond morality and effectiveness of nonpharmaceutical measures (e.g., reactance, political orientation) may increase adoption rates.
Moral decoupling influences morality.	n.s.	(−)	People who separate judgment of performance from morality are less likely to evaluate nonvaccination as immoral.	To increase vaccination rates (through morality), communication should pursue a “coupling” strategy, emphasizing that morality aspects should not be separated from performance aspects.
Reactance influences morality.	—	(−)	People who perceive their decision freedom to be restricted . . .. . . evaluate nonvaccination as less immoral.	To get people to acknowledge morality aspects of nonvaccination, communication should better explicate boundaries between a person’s liberty rights and the need to adhere to societal norms.
Reactance influences performance.	—	(−)	. . . evaluate vaccination as less effective.	To get people to acknowledge performance aspects of nonvaccination, communication should better explicate boundaries between a person’s liberty rights and the need to adhere to societal norms.
Reactance influences intention.	—	(−)	. . . are less likely to get vaccinated.	To increase vaccination rates, communication should clearly explain limits of individual rights due for the greater good of society.
Political orientation influences intention.	—	(−)	People on the political “right” are less likely to get vaccinated.	To increase vaccination rates, communication should clearly explain that tradeoffs between individual liberty rights and the greater good of society constitute a pillar of democracy.
Sex influences performance.	—	(−)	Females are more likely to evaluate vaccination as effective.	Communication should specifically target males to get them to acknowledge the effectiveness of vaccination.

Note. √: hypothesis was supported; ×: hypothesis was not supported; —: relationship was not tested in that study. (Direction) of significant nonhypothesized effects. CIPMs = COVID-19 infection prevention measures.

Implications for Practice and Theory

This study aids policy makers and business managers in better understanding consumer response to CIPMs transgressions, enabling them to conceive and implement means for increased consumer adoption of preventive measures. The findings on nonpharmaceutical measures are additionally important for managers, as companies have a natural interest in getting their customers to adopt preventive measures so as to avoid official punishment (i.e., fines and store closures) and negative media reports (i.e., on “superspreader” events). Specific implications detailed in Table 4 (right-most column) center on making practical use of our findings on why and when consumers do and do not adopt infection prevention measures. A prominent way for law enforcement and legislators to make use of this relationship is through public health communications messaging (Chan, 2021).

It is not surprising that a consumer’s judgment of performance mediates effects; it seems intuitive that people are more likely to adopt prevention measures when they believe them to be effective. However, the finding of a parallel mediation through judgment of morality offers a second avenue for persuading consumers by appealing to their sense of ethics. This avenue appears to be especially effective regarding social distancing, as indicated by our finding of a moderating role of transgression type. At a fundamental level, our findings suggest that decision-makers should adopt a two-pronged approach, simultaneously highlighting performance and morality. Further significance lies with our finding that effects vary between nonpharmaceutical and pharmaceutical measures. For example, effects vary regarding the roles of fear and the control variables. Particularly, the relationship between fear of infection and behavioral intention observed for the nonpharmaceutical measures might be useful in managing consumers’ adherence to governmental policies as it suggests that more fear leads to more compliance with mask wearing and distance keeping regulations. Consumers should be kept well-informed—but not scared—about the massive and rapid spread of the virus and its possible consequences, both short and long term (Carfì et al., 2020). Our findings might aid businesses in getting customers to comply with regulations by obtaining state-of-art knowledge and evidence about individual and situational factors which affect whether customers comply with regulations. In summary, our detailed implications provide firms, legislators, law enforcement, and consumer advocacy groups with insights into how to more effectively and persuasively get consumers to adopt infection prevention measures.

From a theoretical point of view, our study offers at least three contributions to the literature concerned with understanding business and society. This study extends Bhattacharjee and colleagues’ (2013) moral decoupling model to the context of the COVID-19 pandemic to examine why and when consumers adopt CIPMs. By doing so, this study makes the following contributions to research and practice. First, it extends research on health protective measures in general (Ferrer & Mendes, 2018) and COVID-19 in specifics (Brüggenjürgen et al., 2021; Carfì et al., 2020; Chan, 2021; Clark et al., 2020; Díaz & Cova, 2021) by offering a novel process explanation for how judgment of performance and judgment of morality mediate effects of transgression relevance on behavioral intention.

Second, our study successfully applies the moral decoupling model to a public health and consumer context, thereby extending the body of research on decoupling (Bhattacharjee et al., 2013; Chen et al., 2018; Haberstroh et al., 2017; Orth et al., 2019) and adding to research assessing moral aspects of consumer behavior (Babin & Babin, 1996; Strutton et al., 1997). Our finding that consumers’ intention to adopt infection prevention measures is influenced not only by transgression relevance directly but also by the judgment they form about the morality and the performance aspect of the transgression is in line with previous studies on decoupling in the context of counterfeit purchase (Chen et al., 2018; Orth et al., 2019). However, our finding extends evidence obtained in other contexts (Bhattacharjee et al., 2013; Chen et al., 2018; Fehr et al., 2019; Haberstroh et al., 2017; Lee & Kwak, 2016; Orth et al., 2019) by providing insight into consumer behavior during a health crisis. Specifically, we show that in the context of health, an issue of high personal relevance, moral decoupling reaches its limits as moral decoupling (as a variable) has little influence on intentions.

Third, drawing from the HAP model (Schwarzer, 1992), we identify individual and situational boundary conditions for the process, specifically, type of transgression and individual fear of infection. According to the results, people with little fear are more likely to adopt measures when they think the measures are effective. People who are more fearful are less influenced by their belief in the effectiveness of the measures, leading to a decreased likelihood to adopt the measures. The direct positive effect of fear of infection on behavioral intention is consistent with reports that adhering to infection prevention regulations is significantly influenced by fear of COVID-19 (Harper et al., 2020). Furthermore, this finding is in line with previous research indicating that (extensive) fear is a strong motivator of behavior, which can even override reason (Williams, 2012). Together, those findings suggest that corresponding factors should be included in examinations of consumption behavior as they can lead to divergent behavioral intention.

Limitations and Future Research

As with any research, this study has a few limitations, which offer opportunities for future research. First, Study 1 data were collected at a time when regulations, especially the mandatory mask wearing in public, were still relatively new and people hoped that normal life would be restored soon. In contrast, Study 2 data were collected at a time when vaccination had become widely available, taking away from the importance of nonpharmaceutical measures. As such, it cannot be ruled out that differences between studies trace back to temporal effects rather than differences between nonpharmaceutical and pharmaceutical measures. Future research could adopt a temporal perspective, further detailing differences between measures, and accounting for interaction effects between CIPMs. A research agenda on how vaccinations might affect mask wearing and social distancing behavioral intentions (and vice versa) should not only include a person’s state of immunization but also additionally assess factors such as current infection rates and effective governmental regulations (e.g., 2G or 3G)² at the time of data collection.

Furthermore, Germany, the cultural context of the study, may present a limitation. In Germany, compared with other countries, the course of the COVID-19 pandemic was initially more controlled. Thus, levels of fear may have been relatively low. Intentions to adopt preventive measures may be higher in countries more affected by the virus, especially regarding mortality rates. This line of thought is in line with the divergent findings of Study 2. Perhaps more important, Germany has comparatively large numbers of “Maverick thinkers,” followers of Rudolph Steiner’s anthroposophical ideas, and naturopathic medicine, all skeptical of the CIPMs examined here. Further relating to ideology, Germany’s political landscape is more diverse, with currently five sizable parties representing the people, compared with the two major parties more typical for the United States. Given significant effects of a person’s political orientation and psychological reactance, future research could test our moral decoupling model across countries and cultural contexts to explore its robustness.

Finally, our results included a residual direct effect of transgression relevance on behavioral intention, likely indicating another pathway yet to be identified. Future research may find it valuable to further detail this direct effect, possibly investigating the role of affective mediators. Overall, this study establishes an initial foundation for future research adopting a moral decoupling perspective on consumer adoption of health protective measures and behaviors.

Footnotes

Appendix A

Correlations and Average Variance Extracted (AVE) for Multi-Item Study Measures.

Study 1
Measure	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) Transgression relevance	.63
(2) Judgment of performance	−.60**	—
(3) Judgment of morality	−.52**	.43**	—
(4) Transgression type	.29**	−.19**	−.11	1
(5) Moral decoupling	.37**	−.30**	−.24**	.08	1
(6) Moral delegation	.22**	−.21**	−.21**	.39**	.07	1
(7) Fear of infection	−.32**	.34**	.29**	.04	−.21**	−.09	1
Study 2
Measure	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) Transgression relevance	.77
(2) Judgment of performance	.72**	.87
(3) Judgment of morality	.66**	.61**	.92
(4) Political orientation	−.01	−.01	−.00	1
(5) Moral decoupling	−.49**	−.36**	−40**	.12	.55
(6) Moral delegation	.01	−.03	−.00	−.04	.03	1
(7) Fear of infection	.25**	.21**	.19**	.00	−.11	.09	1
(8) Reactance	−.56**	−.59**	−.49**	.07	.21**	.05	−.06

Note. Values in diagonal cells indicate AVE.

Correlation is significant at the .05 level (two-tailed). **Correlation is significant at the .01 level (two-tailed).

Appendix B

Appendix C

Acknowledgements

The authors thank the participants of the 2020 Consumer Psychology seminar for the excellent research assistance provided.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ulrich R. Orth

Notes

Author Biographies

Rebekka A. Böhm (MSc, Maastricht University, Netherlands) is a doctorate candidate at A&F Marketing—Consumer Psychology at Kiel University (CAU), Germany. Her research interests center on consumer behavior related to aspects of morality and sustainability. She has published in conference proceedings and Journal of Consumer Behavior.

Ulrich R. Orth (doctorate and habilitation, Munich University of Technology, Germany) is a professor of marketing and Chair of A&F Marketing—Consumer Psychology at Kiel University (CAU), Germany. His research interests center on consumer behavior and psychology related to ethical issues, brands, and retailing. His articles have been published in journals such as European Journal of Marketing, International Journal of Research in Marketing, Journal of Advertising, Journal of Business Ethics, Journal of Business Research, Journal of Consumer Behavior, Journal of Marketing, Journal of Retailing, Journal of Service Research, Psychology & Marketing, among others. He is an associate editor for the European Journal of Marketing and serves on several other editorial review boards.

References

Adams

Effertz

(2010). Effective prevention against risky underage drinking—The need for higher excise taxes on alcoholic beverages in Germany. Alcohol and Alcoholism, 45(4), 387–394. https://doi.org/10.1093/alcalc/agq031

Babin

B. J.

Babin

L. A.

(1996). Effects of moral cognitions and consumer emotions on shoplifting intentions. Psychology and Marketing, 13(8), 785–802. https://doi.org/10.1002/(SICI)1520-6793(199612)13:8<785::AID-MAR5>3.0.CO;2-B

Bandura

Barbaranelli

Caprara

G. V.

Pastorelli

(1996). Mechanisms of moral disengagement in the exercise of moral agency. Journal of Personality and Social Psychology, 71(2), 364–374. https://doi.org/10.1037/0022-3514.71.2.364

Bapuji

de Bakker

F. G. A.

Brown

J. A.

Higgins

Rehbein

Spicer

(2020). Business and society research in times of the corona crisis. Business and Society, 59(6), 1067–1078. https://doi.org/10.1177/0007650320921172

Barbieri

P. N.

Bonini

(2021). Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy. Economia Politica, 38(42), 1–22. https://doi.org/10.1007/s40888-021-00224-w

Bartling

Fischbacher

(2012). Shifting the blame: On delegation and responsibility. Review of Economic Studies, 79(1), 67–87. https://doi.org/10.1093/restud/rdr023

Bhattacharjee

Berman

J. Z.

Reed

(2013). Tip of the hat, wag of the finger: How moral decoupling enables consumers to admire and admonish. Journal of Consumer Research, 39(6), 1167–1184. https://doi.org/10.1086/667786

Bish

Michie

(2010). Demographic and attitudinal determinants of protective behaviours during a pandemic: A review. British Journal of Health Psychology, 15(4), 797–824. https://doi.org/10.1348/135910710X485826

Brehm

J. W.

(1966). A theory of psychological reactance. Academic Press.

10.

Brüggenjürgen

Stricker

H. P.

Krist

Ortiz

Reinhold

Roll

Rotter

Weikert

Wiese-Posselt

Willich

S. N.

(2021). Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model. Journal of Public Health. https://doi.org/10.1007/s10389-021-01566-2

11.

Bußgeldkatalog. (2021). Corona-Bußgeldkatalog: Das kosten Verstöße [Catalog of Fines]. https://www.bussgeldkatalog.org/corona/

12.

Bundesministerium für Gesundheit. (2021a). Auffrischungsimpfung [Federal Ministry of Health]. https://www.zusammengegencorona.de/impfen/aufklaerung-zum-impftermin/auffrischungsimpfung/

13.

Bundesministerium für Gesundheit. (2021b). Coronavirus-Pandemie (SARS-CoV-2): Chronik bisheriger Maßnahmen und Ereignisse [Federal Ministry of Health]. https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html

14.

Campbell

M. C.

Winterich

K. P.

(2018). A framework for the consumer psychology of morality in the marketplace. Journal of Consumer Psychology, 28(2), 167–179. https://doi.org/10.1002/jcpy.1038

15.

Carfì

Bernabei

Landi

(2020). Persistent symptoms in patients after acute COVID-19. Journal of the American Medical Association, 324(6), 603–605. https://doi.org/10.1001/jama.2020.12603

16.

Chaloupka

F. J.

Grossman

Bickel

W. K.

Saffer

(1999). The economic analysis of substance use and abuse. University of Chicago Press. https://doi.org/10.7208/chicago/9780226100494.001.0001

17.

Chan

E. Y.

(2021). Moral foundations underlying behavioral compliance during the COVID-19 pandemic. Personality and Individual Differences, 171, 110463. https://doi.org/10.1016/j.paid.2020.110463

18.

Chang

D. R.

Jang

Lee

E. Y.

Lee

Chang

(2017). When a good company transgresses: A study of the influences of CSR, moral decoupling, and ethnocentrism. Journal of Global Fashion Marketing, 8(1), 40–53. https://doi.org/10.1080/20932685.2016.1255853

19.

Chen

Teng

Liao

(2018). Counterfeit luxuries: Does moral reasoning strategy influence consumers’ pursuit of counterfeits? Journal of Business Ethics, 151(1), 249–264. https://doi.org/10.1007/s10551-016-3255-y

20.

Clark

Davila

Regis

Kraus

(2020). Predictors of COVID-19 voluntary compliance behaviors: An international investigation. Global Transitions, 2, 76–82. https://doi.org/10.1016/j.glt.2020.06.003

21.

Clore

G. L.

Gasper

Garvin

(2001). Affect as information. In Forgas

J. P.

(Ed.), Handbook of affect and social cognition (pp. 121–144). Lawrence Erlbaum.

22.

Cohn

E. G.

Larson

E. L.

Araujo

Sawyer

Williams

(2012). Calorie postings in chain restaurants in a low-income urban neighborhood: Measuring practical utility and policy compliance. Journal of Urban Health, 89(4), 587–597. https://doi.org/10.1007/s11524-012-9671-0

23.

Cowan

Yazdanparast

(2019). Consequences of moral transgressions: How regulatory focus orientation motivates or hinders moral decoupling. Journal of Business Ethics, 170, 115–132. https://doi.org/10.1007/s10551-019-04268-8

24.

Díaz

Cova

(2021). Reactance, morality, and disgust: The relationship between affective dispositions and compliance with official health recommendations during the COVID-19 pandemic. Cognition and Emotion, 36, 120–136. https://doi.org/10.1080/02699931.2021.1941783

25.

Die Bundesregierung. (2020). 6. Mai 2020: Regeln zum Corona-Virus [The Federal Government]. https://www.bundesregierung.de/breg-de/leichte-sprache/6-mai-2020-regeln-zum-corona-virus-1755252

26.

Dillard

J. P.

Shen

(2005). On the nature of reactance and its role in persuasive health communication. Communication Monographs, 72(2), 144–168. https://doi.org/10.1080/03637750500111815

27.

Donovan

L. M.

(2018). Consumer response to transgression relevance and proactive disclosure. International Journal of Business and Social Science, 9(7), 13–25. https://doi.org/https://doi.org/10.30845/ijbss.v9n7p2

28.

Earle

T. C.

Siegrist

(2006). Morality information, performance information, and the distinction between trust and confidence. Journal of Applied Social Psychology, 36(2), 383–416. https://doi.org/10.1111/j.0021-9029.2006.00012.x

29.

Elm

D. R.

Kennedy

E. J.

Lawton

(2001). Determinants of moral reasoning: Sex role orientation, gender, and academic factors. Business & Society, 40(3), 241–265. https://doi.org/10.1177/000765030104000302

30.

Fehr

Welsh

Yam

K. C.

Baer

Wei

Vaulont

(2019). The role of moral decoupling in the causes and consequences of unethical pro-organizational behavior. Organizational Behavior and Human Decision Processes, 153, 27–40. https://doi.org/10.1016/j.obhdp.2019.05.007

31.

Ferrer

R. A.

Mendes

W. B.

(2018). Emotion, health decision making, and health behaviour. Psychology and Health, 33(1), 1–16. https://doi.org/10.1080/08870446.2017.1385787

32.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

33.

Haberstroh

Orth

U. R.

Hoffmann

Brunk

(2017). Consumer response to unethical corporate behavior: A re-examination and extension of the moral decoupling model. Journal of Business Ethics, 140(1), 161–173. https://doi.org/10.1007/s10551-015-2661-x

34.

Hamilton

Smith

S. R.

Keech

J. J.

Moyers

S. A.

Hagger

M. S.

(2020). Application of the health action process approach to social distancing behavior during COVID-19. Applied Psychology: Health and Well-Being, 12(4), 1244–1269. https://doi.org/10.1111/aphw.12231

35.

Hamman

J. R.

Loewenstein

Weber

R. A.

(2010). Self-interest through delegation: An additional rationale for the principal-agent relationship. American Economic Review, 100(4), 1826–1846. https://doi.org/10.1257/aer.100.4.1826

36.

Harper

C. A.

Satchell

L. P.

Fido

Latzman

R. D.

(2020). Functional fear predicts public health compliance in the COVID-19 pandemic. International Journal of Mental Health and Addiction, 19(5), 1–14. https://doi.org/10.1007/s11469-020-00281-5

37.

Hayes

A. F.

(2018). Introduction to mediation, moderation, and conditional process analysis: A regression approach (2nd ed.). Guilford Press.

38.

Humphrey

Thommes

E. W.

Fields

Coudeville

Hakim

Chit

Cojocaru

M. G.

(2020). Testing, tracing and social distancing: Assessing options for the control of COVID_19. medRxiv. https://doi.org/10.1101/2020.04.23.20077503

39.

Huynh

H. P.

Senger

A. R.

(2021). A little shot of humility: Intellectual humility predicts vaccination attitudes and intention to vaccinate against COVID-19. Journal of Applied Social Psychology, 51(4), 449–460. https://doi.org/10.1111/jasp.12747

40.

Ingram

Skinner

S. J.

Taylor

V. A.

(2005). Consumers’ evaluation of unethical marketing behaviors: The role of customer commitment. Journal of Business Ethics, 62(3), 237–252. https://doi.org/10.1007/s10551-005-1899-0

41.

Jeanty

(2008). Social cognitive theory of moral thought & action. In Kurtines

W. M.

Gewirtz

Lamb

J. L.

(Eds.), Handbook of moral behavior and development (Vol. 1, pp. 69–128). Psychology Press.

42.

Jones

T. M.

(1991). Ethical decision making by individuals in organizations: An issue-contingent model. Academy of Management Review, 16(2), 366–395. https://doi.org/10.5465/amr.1991.4278958

43.

Jost

J. T.

van der Linden

Panagopoulos

Hardin

C. D.

(2018). Ideological asymmetries in conformity, desire for shared reality, and the spread of misinformation. Current Opinion in Psychology, 23, 77–83. https://doi.org/10.1016/j.copsyc.2018.01.003

44.

Kavak

Gürel

Eryiğit

Tektaş

Ö. Ö.

(2009). Examining the effects of moral development level, self-concept, and self-monitoring on consumers’ ethical attitudes. Journal of Business Ethics, 88(1), 115–135. https://doi.org/10.1007/s10551-009-0114-0

45.

Kenyon

(2020). Flattening-the-curve associated with reduced COVID-19 case fatality rates- an ecological analysis of 65 countries. Journal of Infection, 81(1), 98–99. https://doi.org/10.1016/j.jinf.2020.04.007

46.

Kroh

(2007). Measuring left-right political orientation: The choice of response format. Public Opinion Quarterly, 71(2), 204–220. https://doi.org/10.1093/poq/nfm009

47.

Lazuras

Eiser

J. R.

Rodafinos

(2009). Predicting smokers’ non-compliance with smoking restrictions in public places. Tobacco Control, 18(2), 127–131. https://doi.org/10.1136/tc.2008.025841

48.

Lee

J. S.

Kwak

D. H.

(2016). Consumers’ responses to public figures’ transgression: Moral reasoning strategies and implications for endorsed brands. Journal of Business Ethics, 137(1), 101–113. https://doi.org/10.1007/s10551-015-2544-1

49.

Leising

Gallrein

A. M. B.

Dufner

(2014). Judging the behavior of people we know: Objective assessment, confirmation of preexisting views, or both? Personality and Social Psychology Bulletin, 40(2), 153–163. https://doi.org/10.1177/0146167213507287

50.

Lin

C.-Y.

(2020). Social reaction toward the 2019 novel coronavirus (COVID-19). Social Health and Behavior, 3(1), 1–2. https://doi.org/10.4103/shb.shb_11_20

51.

May

D. R.

Pauli

K. P.

(2002). The role of moral intensity in ethical decision making: A review and investigation of moral recognition, evaluation, and intention. Business & Society, 41(1), 84–117. https://doi.org/10.1177/0007650302041001006

52.

Mertens

Gerritsen

Duijndam

Salemink

Engelhard

I. M.

(2020). Fear of the coronavirus (COVID-19): Predictors in an online study conducted in March 2020. Journal of Anxiety Disorders, 74, 102258. https://doi.org/10.1016/j.janxdis.2020.102258

53.

Miron

A. M.

Brehm

J. W.

(2006). Reactance theory–40 years later. Zeitschrift Fur Sozialpsychologie, 37(1), 9–18. https://doi.org/10.1024/0044-3514.37.1.9

54.

Mulder

L. B.

van Dijk

(2020). Moral rationalization contributes more strongly to escalation of unethical behavior among low moral identifiers than among high moral identifiers. Frontiers in Psychology, 10, Article 2912. https://doi.org/10.3389/fpsyg.2019.02912

55.

Murphy

Williamson

Sargeant

McCarthy

(2020). Why people comply with COVID-19 social distancing restrictions: Self-interest or duty? Australian & New Zealand Journal of Criminology, 53, 477–496. https://doi.org/10.1177/0004865820954484

56.

Musschenga

A. W.

(2008). Moral judgement and moral reasoning. In Musschenga

A. W.

(Ed.), The contingent nature of life (pp. 131–146). Springer. https://doi.org/10.1007/978-1-4020-6764-8_12

57.

Nielsen

McGregor

S. L. T.

(2013). Consumer morality and moral norms. International Journal of Consumer Studies, 37(5), 473–480. https://doi.org/10.1111/ijcs.12032

58.

Nyberg

(2008). The morality of everyday activities: Not the right, but the good thing to do. Journal of Business Ethics, 81(3), 587–598. https://doi.org/10.1007/s10551-007-9530-1

59.

Oexl

Grossman

Z. J.

(2013). Shifting the blame to a powerless intermediary. Experimental Economics, 16(3), 306–312. https://doi.org/10.1007/s10683-012-9335-7

60.

Oksanen

Kaakinen

Latikka

Savolainen

Savela

Koivula

(2020). Regulation and trust: 3-month follow-up study on COVID-19 mortality in 25 European countries. JMIR Public Health and Surveillance, 6(2), Article e19218. https://doi.org/10.2196/19218

61.

Orth

U. R.

Hoffmann

Nickel

(2019). Moral decoupling feels good and makes buying counterfeits easy. Journal of Business Research, 98, 117–125. https://doi.org/10.1016/j.jbusres.2019.01.001

62.

Ozdemir

Chaudhry

Finkelstein

E. A.

(2020). Adoption of preventive behaviour strategies and public perceptions about COVID-19 in Singapore. International Journal of Health Policy and Management, 11, 579–591. https://doi.org/10.34172/ijhpm.2020.199

63.

Pagano

DeBono

K. G.

(2011). The effects of mood on moral judgment: The role of self-monitoring. Journal of Applied Social Psychology, 41(12), 2928–2942. https://doi.org/10.1111/j.1559-1816.2011.00864.x

64.

Pakpour

A. H.

Griffiths

M. D.

Chang

K. C.

Chen

Y. P.

Kuo

Y. J.

Lin

C. Y.

(2020). Assessing the fear of COVID-19 among different populations: A response to Ransing et al. (2020). Brain, Behavior, and Immunity, 89, 524–525. https://doi.org/10.1016/j.bbi.2020.06.006

65.

Pantano

Pizzi

Scarpi

Dennis

(2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209–213. https://doi.org/10.1016/j.jbusres.2020.05.036

66.

Paxton

J. M.

Greene

J. D.

(2010). Moral reasoning: Hints and allegations. Topics in Cognitive Science, 2(3), 511–527. https://doi.org/10.1111/j.1756-8765.2010.01096.x

67.

Plümper

Neumayer

Pfaff

K. G.

(2021). The strategy of protest against Covid-19 containment policies in Germany. Social Science Quarterly, 102(5), 2236–2250. https://doi.org/10.1111/ssqu.13066

68.

Putrevu

Lord

K. I.

(1994). Comparative and noncomparative advertising: Attitudinal effects under cognitive and affective involvement conditions. Journal of Advertising, 23(2), 77–91. https://doi.org/10.1080/00913367.1994.10673443

69.

Raghunathan

Pham

M. T.

(1999). All negative moods are not equal: Motivational influences of anxiety and sadness on decision making. Organizational Behavior and Human Decision Processes, 79(1), 56–77. https://doi.org/10.1006/obhd.1999.2838

70.

Reynolds

S. J.

Ceranic

T. L.

(2007). The effects of moral judgment and moral identity on moral behavior: An empirical examination of the moral individual. Journal of Applied Psychology, 92(6), 1610–1624. https://doi.org/10.1037/0021-9010.92.6.1610

71.

Robert Koch Institut. (2020). Mai 2020: Archiv der Situationsberichte des Robert Koch-Instituts zu COVID-19 [Robert Koch Institute]. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Archiv_Mai.html

72.

Roggeveen

A. L.

Sethuraman

(2020). How the COVID-19 pandemic may change the world of retailing. Journal of Retailing, 96(2), 169–171. https://doi.org/10.1016/j.jretai.2020.04.002

73.

Sah

Vilches

T. N.

Moghadas

S. M.

Fitzpatrick

M. C.

Singer

B. H.

Hotez

P. J.

Galvani

A. P.

(2021). Accelerated vaccine rollout is imperative to mitigate highly transmissible COVID-19 variants. eClinicalMedicine, 35, Article 100865. https://doi.org/10.1016/j.eclinm.2021.100865

74.

Sailer

Stadler

Botes

Fischer

Greiff

(2021). Science knowledge and trust in medicine affect individuals’ behavior in pandemic crises. European Journal of Psychology of Education, 37, 279–292. https://doi.org/10.1007/s10212-021-00529-1

75.

Schwarzer

(1992). Self-efficacy in the adoption and maintenance of health behaviors: Theoretical approaches and a new model. In Schwarzer

(Ed.), Self-efficacy: Thought control of action (pp. 217–243). Hemisphere.

76.

Seres

Balleyer

Cerutti

Danilov

Friedrichsen

Liu

(2020). Face masks increase compliance with physical distancing recommendations during the COVID-19 pandemic. Journal of the Economic Science Association, 7, 139–158. https://doi.org/10.1007/s40881-021-00108-6

77.

Sezer

Gino

Bazerman

M. H.

(2015). Ethical blind spots: Explaining unintentional unethical behavior. Current Opinion in Psychology, 6, 77–81. https://doi.org/10.1016/j.copsyc.2015.03.030

78.

Shanka

M. S.

Gebremariam Kotecho

(2021). Combining rationality with morality–integrating theory of planned behavior with norm activation theory to explain compliance with COVID-19 prevention guidelines. Psychology, Health and Medicine. https://doi.org/10.1080/13548506.2021.1946571

79.

Shu

L. L.

Gino

Bazerman

M. H.

(2011). Dishonest deed, clear conscience: When cheating leads to moral disengagement and motivated forgetting. Personality and Social Psychology Bulletin, 37(3), 330–349. https://doi.org/10.1177/0146167211398138

80.

Sittenthaler

Traut-Mattausch

Steindl

Jonas

(2015). Salzburger State Reactance Scale (SSR Scale): Validation of a scale measuring state reactance. Zeitschrift fur Psychologie/Journal of Psychology, 223(4), 257–266. https://doi.org/10.1027/2151-2604/a000227

81.

Snyder

C. R.

(1974). Acceptance of personality interpretations as a function of assessment procedures. Journal of Consulting and Clinical Psychology, 42(1), 150. https://doi.org/10.1037/h0036033

82.

Spinelli

M. A.

Glidden

D. V.

Gennatas

E. D.

Bielecki

Beyrer

Rutherford

Chambers

Goosby

Gandhi

(2021). Importance of non-pharmaceutical interventions in lowering the viral inoculum to reduce susceptibility to infection by SARS-CoV-2 and potentially disease severity. The Lancet Infectious Diseases, 21(9), 296–301. https://doi.org/10.1016/S1473-3099(20)30982-8

83.

Sprengholz

Betsch

Böhm

(2021). Reactance revisited: Consequences of mandatory and scarce vaccination in the case of COVID-19. Applied Psychology: Health and Well-Being, 13(4), 986–995. https://doi.org/10.1111/aphw.12285

84.

Sprengholz

Felgendreff

Böhm

Betsch

(2021). Vaccination policy reactance: Predictors, consequences, and countermeasures. Journal of Health Psychology. https://doi.org/10.1177/13591053211044535

85.

Statistisches Bundesamt. (2019). Einkommen, Konsum, Lebensbedingungen [Federal Statistical Office]. Statistisches Jahrbuch 2019. https://www.destatis.de/DE/Themen/Querschnitt/Jahrbuch/jb-einkommenKonsumLeben.pdf?__blob=publicationFile

86.

Statistisches Bundesamt. (2020). EU-monitor COVID-19. https://www.destatis.de/Europa/EN/Topic/COVID-19/COVID-19-article.html

87.

Strutton

Pelton

L. E.

Ferrell

O. C.

(1997). Ethical behavior in retail settings: Is there a generation gap? Journal of Business Ethics, 16(1), 87–105. https://doi.org/10.1023/A:1017991525118

88.

Taylor

Asmundson

G. J. G.

(2021). Negative attitudes about facemasks during the COVID-19 pandemic: The dual importance of perceived ineffectiveness and psychological reactance. PLOS ONE, 16(2), Article e0246317. https://doi.org/10.1371/journal.pone.0246317

89.

Taylor

Landry

C. A.

Paluszek

M. M.

Fergus

T. A.

McKay

Asmundson

G. J. G.

(2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders, 72, 102232. https://doi.org/10.1016/j.janxdis.2020.102232

90.

Tsang

J. A.

(2002). Moral rationalization and the integration of situational factors and psychological processes in immoral behavior. Review of General Psychology, 6(1), 25–50. https://doi.org/10.1037/1089-2680.6.1.25

91.

Valdesolo

Desteno

(2006). Manipulations of emotional context shape moral judgment. Psychological Science, 17(6), 476–477. https://doi.org/10.1111/j.1467-9280.2006.01731.x

92.

Voorhees

C. M.

Brady

M. K.

Calantone

Ramirez

(2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4

93.

Weber

(2019). Understanding the millennials’ integrated ethical decision-making process: Assessing the relationship between personal values and cognitive moral reasoning. Business and Society, 58(8), 1671–1706. https://doi.org/10.1177/0007650317726985

94.

Williams

K. C.

(2012). Fear appeal theory. Research in Business and Economics Journal, 5, 44–56.

95.

Witte

Allen

(2000). A meta-analysis of fear appeals: Implications for effective public health campaigns. Health Education and Behavior, 27(5), 591–615. https://doi.org/10.1177/109019810002700506

96.

World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019

97.

Worldometer. (2021). COVID-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/

98.

Cheng

(2021). 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, 175, 110706. https://doi.org/10.1016/j.paid.2021.110706

99.

Yik

Russell

J. A.

Steiger

J. H.

(2011). A 12-point circumplex structure of core affect. Emotion, 11(4), 705–731. https://doi.org/10.1037/a0023980

100.

Yıldırım

Geçer

Akgül

. (2021). The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychology, Health and Medicine, 26(1), 35–43. https://doi.org/10.1080/13548506.2020.1776891

101.

Zhang

J. J. Y.

Lee

K. S.

Ang

L. W.

Leo

Y. S.

Young

B. E.

(2020). Risk factors for severe disease and efficacy of treatment in patients infected with COVID-19: A systematic review, meta-analysis, and meta-regression analysis. Clinical Infectious Diseases, 71(16), 2199–2206. https://doi.org/10.1093/cid/ciaa576

102.

Zickfeld

J. H.

Schubert

T. W.

Herting

A. K.

Grahe

Faasse

(2020). Correlates of health-protective behavior during the initial days of the COVID-19 outbreak in Norway. Frontiers in Psychology, 11, Article 564083. https://doi.org/10.3389/fpsyg.2020.564083