This study focuses on seven key measures to combat the pandemic. Three measures refer to the standard measures formulated by German health and governmental authorities (“AHA-rules”): social distancing in terms of keeping a minimum distance of 1.5 m from other persons in public (i.e., distance rule), performing hygiene (i.e., appropriate coughing and sneezing as well as washing hands regularly and thoroughly), and wearing face masks in certain areas of public life (BZgA, 2021). The fourth measure concerns the restriction of physical contacts, which has been implemented on a large scale locally and globally (e.g., Bönisch et al., 2020; Lai et al., 2021). Complementing this, the fifth measure is a technical approach to the pandemic, namely the active use of a contact-tracing app. The German version of this app, which is the focus here, informs its users about critical contacts with infected individuals. This measure is intended to detect and interrupt chains of infection. However, infected people decide for themselves whether they want to share their infection status via the app. Therefore, besides just using the app, sharing one's infection status is an important sixth measure. It should be noted that these app-related measures are less preventive than other measures because neither using the app nor sharing one's infection status directly helps prevent a COVID-19 infection, but they are helpful to others and to combat the pandemic on a broad societal level (cf. Kaspar, 2020). Finally, the seventh measure is vaccination against the coronavirus. Mass vaccination campaigns are taking place around the world and the relative effectiveness of available vaccinations has been confirmed (e.g., Dagan et al., 2021), but it also depends on virus variants (e.g., Lopez Bernal et al., 2021).

Previous research and several health theories highlight the critical role of personal characteristics when it comes to the motivation to comply with COVID-19 measures. For example, the Health Belief Model explains and predicts various health behaviors such as influenza vaccination, risky sexual behaviors, and cancer prevention. The model postulates that in addition to individual belief components such as perceived vulnerability to and severity of disease, personal characteristics, including age, gender, and personality traits, act as modifying factors shaping individual beliefs and behavior (Champion and Skinner, 2008). Also, the Protection Motivation Theory focuses on cognitive aspects to explain health-related behaviors (Rogers, 1983). Protection motivation (i.e., the motivation to perform health-beneficial behaviors) is formed through coping appraisal and threat appraisal processes, including evaluations of maladaptive and adaptive behaviors. Accordingly, threat appraisal considers the rewards of not performing the recommended behavior as well as the severity of and vulnerability to negative consequences of performing maladaptive behavior. Coping appraisal evaluates perceived self-efficacy and response efficacy of the adaptive behavior, as well as response costs. The applicability of this theory to COVID-19 measures has been demonstrated (Kaspar, 2020). But importantly, the core components of the theory are additionally complemented by various influential sources of information, which include personal characteristics (Rogers, 1983; Floyd et al., 2000). Similarly, the Theory of Reasoned Action and the Theory of Planned Behavior aim to predict individual behavior, including health behavior. In addition to the main components – attitude, subjective norm, and perceived control – demographic variables, personality traits, and other individual differences are considered as factors influencing behavioral motivation (Montaño and Kasprzyk, 2008).

Many of these theories have two things in common: first, they emphasize behavioral motivation as the key outcome variable determining subsequent health-related behavior. Indeed, motivational psychology describes motivation as an internal process that determines the choice, strength, persistence, and actualization of behavior (Becker-Carus and Wendt, 2017), and some authors argue that behavioral motivation is the most reliable predictor of actual behavior (Montaño and Kasprzyk, 2008). Second, personal characteristics, such as personality traits, are included but are not the focus, as they are considered marginal variables that may play an additional or moderating role in behavioral intention. In contrast, the present study shifts the research focus to these personal characteristics. In general, many models in the field of motivation psychology emphasize that the current motivation of a person to strive for a certain goal (such as preventing an infection with the coronavirus) is modulated by personal and situational factors (cf. Heckhausen and Heckhausen, 2006). In addition, according to the accentuation hypothesis formulated by Caspi and Moffitt (1993), personal characteristics “are accentuated when environmental events disrupt previously existing social equilibria” (p. 247) and that they “should predict behavior best in novel, ambiguous, and uncertain circumstances” (p. 267). This description fits very well the overall societal and unpredictable changes brought about by the pandemic, with their impact on the (health) behavior of each individual. Therefore, a significant role of personal characteristics can be assumed when it comes to COVID-19 protective measures. Based on previous research, we propose a research model encompassing several personal characteristics that may jointly contribute to people's motivation to comply with COVID-19 measures. As shown in Figure 1, we consider demographic variables (age and gender), individual risk perception (perceived severity of and vulnerability to infection), general personality traits (Big Five and Dark Triad), individual conspiracy mentality, health-related locus of control beliefs, and general positive and negative affect. The role of these variables in the context of COVID-19 measures and our specific hypotheses are presented in the following section.

Recruitment was via convenience sampling. The link to the study was distributed through social media platforms including Instagram, Facebook, WhatsApp, and LinkedIn, as well as through a survey platform of a German journal (Psychologie Heute). At the beginning of the study, participants were informed that the data of this study will be used for research purposes and that all data would be collected anonymously. Thus, no identifying information was collected. Participants who prematurely stopped the survey were not included in the analyses and all of their data were deleted from the dataset. Informed consent to participate in this study was provided by clicking a corresponding box, and participation was voluntary in all cases. The study ran from 10 November 2020 to 29 December 2020.

A total of 1,026 German-speaking participants took part in this online study. Thirteen people were excluded because of early termination, lack of consent, or because they were under 18. In addition, those who reported “diverse” as their gender were excluded (n = 6) because this subgroup was too small to be reasonably integrated into gender-based analyses. The final sample thus consisted of 1,007 participants, 793 female, and 214 male, with a mean age of M = 37.74 years (SD = 13.86). The most frequently named educational qualification was a master's degree (n = 366), followed by a bachelor's degree (n = 215), a higher education entrance qualification (n = 194), a completed vocational training (n = 155), a secondary school diploma (n = 64), a main school diploma (n = 11), and no school diploma (n = 2).

After the participants were informed about the study content and gave their consent, they provided demographic information, including age, gender, and education level. They were then asked about their motivation to comply with seven COVID-19 measures (distance rule, hygiene rules, face mask rule, use of a contact-tracing app, sharing one's infection status via a contact-tracing app, reducing physical contacts, and getting vaccinated). Then they reported their perceived severity of and vulnerability to a coronavirus infection, general personality (Big Five and Dark Triad), conspiracy mentality, HLOC, and general affect.

First, intercorrelations between the independent variables of the regression models were calculated to assess construct validity and identify critical construct overlap. Second, bivariate correlations between independent and dependent variables of the regression models were calculated as a reference for subsequent results of the regression models. Third, a multiple regression analysis was calculated for each of the seven protective measures. In this context, statistical assumptions being relevant were tested (cf. Poole and O'Farrell, 1971), and bootstrapping was used for inferential tests, as routinely suggested (cf. Hayes and Cai, 2007). Fourth, one-sample t-tests were calculated to test whether the mean motivation to comply with each measure deviated from the scale's midpoint to draw conclusions about above- or below-average compliance. Fifth, a repeated measures ANOVA (Greenhouse-Geisser applied) and Bonferroni-adjusted pairwise comparisons were calculated. Finally, a multiple regression analysis with conspiracy mentality as the dependent variable was calculated on an ad hoc basis for exploratory reasons.

As expected, age showed positive but small bivariate correlations with five of the seven protective measures, with the exception of using the contact-tracing app and sharing one's infection status through the app. In contrast, some associations disappeared when the joint contribution of all personal characteristics was examined simultaneously via multiple regression models. In this case, age was positively related to the motivation to comply with social distancing and reducing physical contacts, indicating that distancing measures are particularly important for seniors. Given their higher health risk, they might be more motivated to avoid contact with others to minimize their risk of infection. Also, larger social gatherings might play a greater role in younger individuals, decreasing their motivation in this regard. This is consistent with some previous findings showing that older adults reported higher compliance with COVID-19 protective measures (Brouard et al., 2020; Dohle et al., 2020; Zettler et al., 2022) and specifically engaging in physical distancing (Zettler et al., 2022). However, age was not associated with other preventive measures in the regression models, i.e., hygiene rules, wearing face masks, and vaccination readiness, although this would seem reasonable given the increased risk of the elderly to a potentially severe disease course (Jordan et al., 2020). In contrast to a non-significant bivariate correlation, higher age was significantly associated with lower motivation to share one's infection status via the contact-tracing app in the multiple regression model. Since sharing the infection status, as opposed to simply downloading the app, requires an active use and a certain familiarity with the app, the general lower willingness of older adults to use smartphone apps may come into play here (Cho, 2015). Older participants may also be more concerned about the security of their personal information.

In general, age effects may be even more pronounced when comparing young and very old individuals, but none of the previous studies in this field realized such a specific comparison. Although the present sample had an age range of 18–82 years (mean 38), only 1% of the participants were over 70 years old, representing a group being at higher risk of developing severe health consequences (cf. Wyper et al., 2020). Many of the samples studied have such restrictions in terms of the age range of participants, which could reduce the magnitude of the association between age and potentially related variables (cf. Mendoza and Mumford, 1987). In addition, the relevance of age in the context of COVID-19 measures may depend in part on the practical feasibility of each measure (e.g., availability and use of a contact-tracing app, vaccination for all or only for certain age groups, or social distancing in care facilities) and the median age of populations, which considerably varies across countries (cf. Pew Research Center, 2020). Consequently, future research should emphasize cross-national comparisons and context variables that may moderate the relevance of age to comply with COVID-19 measures.

The results regarding gender were identical for the bivariate correlations and the multiple regression analyses. In line with previous research on gender-related differences in compliance with COVID-19 measures (e.g., Abdelrahman, 2020; Brouard et al., 2020; Dohle et al., 2020; Zettler et al., 2022), women reported higher motivation to comply with social distancing and the hygiene rules. Accordingly, in a study comparing eight Western countries, Galasso et al. (2020) showed that women differed from men in having a higher “belief that COVID-19 represents a very serious health risk, in their agreement with restraining public health rules, and in their compliance with them” (p. 27,290). However, results were based on composite scores, and hence no differentiated picture across measures was presented. In the present study, motivation to comply with wearing face masks, reducing physical contact, using the contact-tracing app, and actively sharing one's infection status was unrelated to gender, indicating that gender differences are less relevant in this context. In contrast, men reported higher motivation to get vaccinated. This result is in line with meta-analytic findings according to which more men stated that they would get vaccinated. This phenomenon appears to be stable across countries and more pronounced in samples with health care workers (Zintel et al., 2022). However, the reasons for this finding are less clear and should be addressed by future research. One reason might be that even before the current COVID-19 pandemic and related vaccines, women typically reported more side effects after vaccinations than men (for a review, see Flanagan et al., 2017). Klugar et al. (2021) found that women reported an increased risk of side effects after either mRNA-based or viral vector-based COVID-19 vaccines.

These age- and gender-related results exemplarily show that different measures to combat the pandemic are strongly linked to sociodemographic variables, associated with varying levels of acceptance and different reasons for acceptance of measures. Consequently, public presentations of study results and associated recommendations for appropriate health behavior should consider individual differences in the subjective threat appraisal and the objective risk situation. Target group-specific approaches and education could increase the overall acceptance of the different measures across heterogeneous groups.

Perceived severity of coronavirus infection showed a positive but rather low bivariate correlation with all the seven protective measures. In contrast, multiple regression models showed a positive association between perceived severity of infection and participants' motivation to follow hygiene rules, reduce physical contact, and get vaccinated. In addition, perceived vulnerability to coronavirus infection showed a positive association with all COVID-19 measures at both the bivariate correlation level and the multiple regression level. Moreover, the perceived vulnerability was the most relevant factor regarding all measures except app use in the regression models. It is important to note that this pattern of results remarkably differs from what Kaspar (2020) previously reported regarding people's motivation for social distancing, app use, and sharing one's infection status via the app. While Kaspar (2020) used a regression model incorporating all core variables of the Protection Motivation Theory (Rogers, 1983) and some context-specific variables, the present model shifted the focus to context-independent personal characteristics. Therefore, the relevance of perceived severity and vulnerability must be interpreted in the context of the specific research model. With respect to the current and dynamic pandemic, it is also particularly important to consider the point in time a study is conducted. In this case, perceived vulnerability to infection was the most relevant factor, even outweighing the severity of infection. However, this result could change with new variants of the virus. When this study was conducted, the now steadily expanding Omicron variant was not foreseeable. Since this variant appears to have higher transmissibility (Karim and Karim, 2021) but milder effects on health (Nealon and Cowling, 2022) than earlier variants, the role of perceived vulnerability to and severity of infection might change. For example, in the context of Protection Motivation Theory, Floyd et al. (2000) argued that “the threat-appraisal process is addressed first, since a threat must be perceived or identified before there can be an evaluation of the coping options” (p. 410). In other words, if an infection with a new virus variant is assessed as hardly or not at all severe, possible coping strategies such as social distancing or vaccination may no longer be considered at all, and associations/correlations between severity assessment and motivation to follow certain measures disappear. Such a potential moderation effect, which could be caused by different variants of the SARS-CoV-2 virus, has not yet been empirically investigated but seems very reasonable.

At the same time, individual vaccination status also significantly affects the perceived severity of a potential infection as the number of vaccinations received and their timing appear to play a significant role in possible infections and severe courses of disease (e.g., Abu-Raddad et al., 2021; Andrews et al., 2021; Doria-Rose et al., 2021). In this respect, the results of the present and previous studies may be subject to greater temporal changes than is the case in other health-related areas with more time-stable contextual factors (e.g., skin cancer screening or caries prophylaxis).

Nevertheless, three things are important: first, we should not make the mistake of underestimating the validity of previously reported results, although they may change over time. Future research should rather specifically identify the role of time-varying and time-invariant factors. Until then, it remains unclear whether perceived vulnerability and severity are more likely to be traits or states. Second, the population should be accurately informed and kept up to date with respect to actual virus transmissibility (and the measures that reduce it) because vulnerability to infection appears to be particularly relevant to intended adherence to COVID-19 protective measures. This requires ongoing outreach by scientific institutions and political bodies, guided by consistent communication criteria. Third, among all the constructs (traits) examined, the construct of perceived vulnerability is likely to be particularly sensitive to contextual factors. Future research models should therefore consider situational factors that might moderate the relationship between perceived vulnerability and compliance with COVID-19 measures. Corresponding factors could significantly increase the explanatory power of research models in the future, even though the explanatory power of the present model is already remarkable, at least with regard to some of the COVID-19 measures investigated here.

Contrary to our hypotheses, the present data suggest that the Big Five do not play a central role in the context of COVID-19 protective measures, neither in the multiple regression models nor at the level of bivariate correlations between traits and motivation to comply with measures. We found that extraversion was negatively related to the reduction of physical contacts at both the bivariate correlation level and the multiple regression level. This is consistent with the finding of a negative association between extraversion and general rule-following (Clark et al., 2020). However, extraversion showed no relationship to other measures in the present study, whereas other studies found positive relations between extraversion and motivation to wear a face mask and to follow hygiene rules (Aschwanden et al., 2021).

The level of reported neuroticism showed no significant bivariate correlation with any of the seven protective measures. However, we found that neuroticism was negatively associated with motivation to get vaccinated in the respective multiple regression model. In other studies, neuroticism has been associated with lower caution in avoiding COVID-19 (Aschwanden et al., 2021), but neuroticism was positively associated with social distancing (Abdelrahman, 2020) and willingness to reduce contacts by staying at home (Götz et al., 2020), which was not replicated in this study.

Agreeableness showed a positive bivariate correlation with using the contact-tracing app and sharing one's infection status through this app. This result is not surprising as this app does not help individual users to actively prevent infection, but its use is important to fight the pandemic on a broad societal level. Thus, the use of this app indicates solidarity, which reflects facets of agreeableness, namely the motivation to compromise and cooperate. In the multiple regression models, however, agreeableness was negatively and exclusively related to the distance rule. Interestingly, agreeableness was positively related to physical distancing in another study using a regression model that includes a different set of independent variables (Aschwanden et al., 2021).

In contrast, conscientiousness was positively related to the hygiene rules in this study and the study of Aschwanden et al. (2021). This positive relation was present at both the bivariate correlation level and the multiple regression level. In addition, conscientiousness showed a positive but small bivariate correlation with participants' motivation to comply with the distance rule. It is not implausible that permanently maintaining a minimum distance from other people is associated with more pronounced conscientiousness.

Similar to neuroticism, the degree of reported openness to experience showed no significant bivariate correlation with any of the seven protective measures. However, we found that openness was negatively related to both using the national contact-tracing app and the willingness to share one's infection status via this app in the multiple regression models. This is a novel but an unexpected result as these COVID-19 measures were not captured by other studies on personality. It seems counterintuitive that general openness to new experiences is associated with less openness to technology-based interventions to address the pandemic. It is important to reiterate that using the app and sharing one's infection status via the app does not create a preventive effect for users, which might explain this result. However, these measures are helpful for other people and the fight against the pandemic on a broader societal level. In general, technology is likely to play a central role in controlling future endemic and pandemic scenarios, and the app use is not a new approach, as it has already been demonstrated in the context of the Ebola epidemic in West Africa between 2014 and 2015 (for a review, see Tom-Aba et al., 2018). Consequently, and contrary to the finding here, it appears necessary to promote a certain “true” openness in society to such technological measures by adequately addressing concerns about the privacy of one's data and the usefulness of the technology.

Indeed, besides the trait openness, general trust in app providers and perceived vulnerability to data misuse seem to be significant determinants of people's willingness to use a contact-tracing app and to share one's infection status (Kaspar, 2020), while perceived usefulness mainly predicts one's intention to use technology according to the well-established technology-acceptance model (Venkatesh and Bala, 2008). In summary, the current literature, including the present study, paints a very mixed picture regarding the Big Five in the context of COVID-19 protective measures. Some differences between studies could be partially attributed to different research models and divergent study groups. Overall, however, the Big Five appear to play a relatively weak role when several other factors are considered simultaneously.

Similarly, Dark Triad characteristics were also only weakly related to the measures when included simultaneously with other factors in the multiple regression models. In this case, Machiavellianism was negatively related to the willingness to share one's infection status via the contact-tracing app. Subclinical narcissism did not show a significant relation to the measures. Subclinical psychopathy showed a negative relation to the distance rule and physical contact reduction. At the level of bivariate correlations, there were more correlations with the protective measures, although these were generally very small, albeit statistically significant. It is noteworthy that in the case of a significant relation between Dark Triad traits and protective measures (regardless of whether in the context of multiple or bivariate regressions), this always turned out to be negative for all three “dark” traits. In particular, subclinical psychopathy showed negative correlations with all measures except vaccination readiness. In this respect, it seems important to include these personality traits in future studies and to consider them in practice, as they might be inhibiting factors for the success of COVID-19 measures.

In contrast to personality traits, conspiracy mentality was a strong factor that was negatively correlated with all measures and showed negative associations with all measures (except hygiene rules) in the multiple regression models. This result supports some but not all of the mixed results found in other studies (Freeman et al., 2020; Allington et al., 2021; Bruder and Kunert, 2022), and it is particularly important because several specific conspiracy theories and beliefs have already emerged in connection with the COVID-19 pandemic (cf. Juanchich et al., 2021). Moreover, the sample studied here had a strong academic background, with 58% of participants reporting at least a bachelor's degree. Nevertheless, even in such an academic sample, the level of conspiracy beliefs seems to play a truly significant role in the acceptance of COVID-19 measures. A look at the concrete operationalization of the construct in the present study shows that trust in political actors and in public communication plays a key role (e.g., “I think that many very important things happen in the world, which the public is never informed about” and “I think that politicians usually do not tell us the true motives for their decisions”).

In addition, we conducted an exploratory multiple regression analysis in which conspiracy mentality served as the dependent variable: conspiracy mentality was negatively related to perceived vulnerability to infection but not to the perceived severity of infection. Similarly, Romer and Jamieson (2020) found in their survey that conspiracy beliefs were inversely related to the perceived possibility that the respondents or someone in their families will become infected with the coronavirus. Accordingly, with an increasingly pronounced conspiracy mentality, the assessment that one can be infected with the virus decreases. This is remarkable because the virus does not care about the mindset the individual host has. At the conceptual level, this result shows the importance of distinguishing between the concepts of vulnerability to and severity of infection, which mirrors the different threat assessment components in protection motivation theory (cf. Rogers, 1983). In addition, conspiracy mentality was higher in women, in participants with pronounced beliefs that they are in control of their health (internal HLOC), and in participants who experienced high negative affect. Interestingly, conspiracy mentality was also positively related to Machiavellianism and subclinical psychopathy, replicating findings of other studies focusing on different populations such as U.K. citizens (Hughes and Machan, 2021). Surprisingly, conspiracy mentality was also positively related to agreeableness and conscientiousness in the regression model. While the positive association with conscientiousness does not seem implausible, since the tendency to engage with and defend alternative theories of system interrelations and processes requires a certain persistence, the positive association with agreeableness (including trust in others and belief in the goodness of people) seems unexpected. Since the corresponding bivariate correlation was not significant and also showed a negative sign, this finding seems to be the result of the regression model in which special suppression effects occurred.

Given these results, we may conclude that transparent communication of political decisions is required, which ideally should be evidence-based to counteract the significant role of conspiracy beliefs in the COVID-19 pandemic. In this context, the communication of scientific research practices and results in a way that is appropriate to the target audience plays also an important role. Rational and ridiculing arguments were found to be effective in reducing conspiracy beliefs (Orosz et al., 2016). Importantly, Hornsey et al. (2018) have pointed out that their data from a large cross-cultural study suggest communication solutions rather than repetition of evidence to effectively address vaccination skepticism. Furthermore, effective mechanisms should be installed to curb the rapid spread of misinformation in the sense of an “infodemic” (cf. Eysenbach, 2020). In principle, all media professionals have a special responsibility when it comes to the appropriate examination and dissemination of information that is important for the assessment of protective measures and associated behavioral tendencies. Indeed, Romer and Jamieson (2021b) found that the use of conservative media (e.g., Fox News) and social media (e.g., Facebook and Twitter) by U.S. residents was positively related to conspiracy beliefs related to the COVID-19 pandemic, whereas reliance on mainstream print (e.g., New York Times and Washington Post) predicted a decrease in such conspiracy beliefs. However, it should not be overlooked that individual characteristics, such as tendencies toward analytical thinking and spirituality (Gligorić et al., 2021), as well as the educational level (Georgiou et al., 2019), strongly interact with conspiracy beliefs, and thus measures taken by policymakers, academics, and the media can hardly lead to a reduction of conspiracy belief among all people.

Health-related control beliefs allow an estimation of the extent to which people consider their health a result of their actions (internal HLOC) or a result of external circumstances and chances that cannot be influenced (external HLOC). Overall, these variables showed a weak relation to compliance with COVID-19 measures in concert with all the other factors. At least, internal HLOC showed a negative bivariate correlation with reductions in physical contacts and vaccination readiness, as well as a negative relation to vaccination readiness in the multiple regression model. In a parallel study, Murphy et al. (2021) also found that the internal locus of control was stronger among vaccine skeptics. Apparently, people who are particularly convinced that they are in control of their health and that one can become healthy or stay sick through one's own behavior seem to be more opposed to the injection of a foreign substance. This appears consistent with one's control beliefs in that the production and effects of vaccines are beyond one's control. Nonetheless, this ignores the proven efficacy of licensed vaccines. However, it is important to note that at the start of this study (November 2020), no COVID-19 vaccine had been definitively approved for use in Germany, but results were already available demonstrating the vaccine's efficacy, and a vaccination start was officially envisioned (Bundesgesundheitsministerium, 2020). In this respect, there was no comprehensive experience with the vaccines at the time of the study, which may have affected the results.

In addition, we found that external HLOC was positively correlated with participants' motivation to comply with the distance rule, wear a face mask, and reduce physical contacts. In contrast, external HLOC showed only a positive relation to the hygiene rules in the multiple regression model. As people with high external HLOC have a general expectation that they have no or only little influence on factors that determine their health (Wallston and Wallston, 1981), they may be particularly devoted to those measures that they can obviously control themselves, such as proper coughing and sneezing and hand washing. Other measures such as the use of apps and vaccinations may be unrelated to external HLOC because these measures are based on technological and scientific progress, as well as concrete realizations over which individual users actually have no control.

The current and ongoing pandemic is a major strain on everyone and has been shown to negatively impact the emotional state of many people (e.g., Cao et al., 2020; Salari et al., 2020). Several studies have focused on state affect as a dependent variable in the context of the pandemic (e.g., Hardin et al., 2021). Some studies also included state affect as an independent variable, for example, to explain computer-mediated communication during the pandemic (Meier et al., 2021). However, the role of general (trait) affect has been widely neglected so far in the context of COVID-19 research. Based on the present results, we must conclude that trait affect is indeed not a relevant factor, at least as far as COVID-19 measures are concerned. Positive as well as negative trait affect did not show any significant relationship with the seven measures within the multiple regression model. Even at the level of bivariate correlations, only a few very small correlations were found. This result is nonetheless insightful because it seems that motivation to comply with various measures is less related to basic emotional factors than to cognitive factors (e.g., risk perception and conspiracy mentality).

Some limitations of the study should be noted. First, the data collected in this online study were self-report data, and social desirability could have potentially biased the responses. However, as shown by meta-analytic results, computerized surveys lead to significantly more reporting of socially undesirable behaviors (Gnambs and Kaspar, 2015).

Second, behavioral motivation was measured but not actual behavior. Motivation to follow COVID-19 measures does not necessarily mean that this behavior is performed. Several obstacles could jeopardize the transition from intention to action, such as technical problems with the contact-tracing app, temporary unavailability of face masks, problems organizing vaccination appointments, or social influence through the peer group. Nevertheless, from a research point of view, our approach was reasonable. In reference to the COVID-19 measures, monitoring actual behavior would mean to directly accompany and observe people in their daily life, which is difficult to realize. Additionally, given that “dark” traits such as conspiracy mentality, psychopathy, and Machiavellianism were negatively related to guideline adherence, it is questionable whether people in whom these traits are strongly pronounced would be willing to participate in a behavioral study. Consequently, self-selection among participants could occur, resulting in a sample of individuals who are highly motivated anyway and hence producing range restriction in collected data and biased statistical results. In addition, participants might be especially motivated to behave in socially desirable ways when they are overtly observed. Against this background, interviewing individuals seems to be the method of choice not only for reasons of research economics, which is evident in many current studies.

Third, the present research model was limited to personal characteristics. As stated above, motivation psychology emphasizes that a person's current motivation to strive for a certain goal is influenced by personal and situational factors (cf. Heckhausen and Heckhausen, 2006). Hence, it can be assumed that specific situational factors also explain a considerable proportion of the interpersonal variance in the motivation to comply with COVID-19 measures. These factors include, for example, the social environment, regional differences concerning applicable regulations, and job-related factors that could facilitate or impede the implementation of measures (e.g., social distancing, reducing physical contacts, and wearing a face mask).

Fourth, the present study focused on a comprehensive but simple research model, resulting in a multiple regression analysis. Many other studies that have been conducted previously or in parallel used the same or a similar methodological approach (e.g., Abdelrahman, 2020; Clark et al., 2020; Götz et al., 2020; Zajenkowski et al., 2020; Zettler et al., 2022), which does not specify relationships between model variables in terms of mediator and moderator variables. This makes sense at the beginning of such research, which has now been going on for just 2 years, especially since existing models do not cover all potentially relevant personal characteristics that may impact behavioral motivation regarding COVID-19 protective measures. As outlined in the introduction, relevant theories in the field of protection motivation (e.g., Protection Motivation Theory, Theory of Reasoned Action, and Theory of Planned Behavior) consider personal characteristics only as marginal variables. However, future studies may formulate and test more specific personality-oriented models based on the findings presented here and in other studies. At least, initial studies have examined indirect associations between conspiracy mentality and COVID-19-related preventive behaviors (e.g., Romer and Jamieson, 2020; Dijkstra, 2021), providing fruitful starting points.

Finally, the results are based on cross-sectional and correlational data. Therefore, no conclusion can be drawn about causal relationships between independent and dependent variables. This is true for most studies in the field, including those on indirect effects. Importantly, some authors propose double randomization to investigate a mediation hypothesis in terms of causal direction (e.g., Bullock et al., 2010; Green et al., 2010), leading to a sequence of experiments (cf. Kaspar and Cames, 2016). Here, participants are randomly assigned to levels of the independent variable to examine its effect on the observed mediator. Subsequently, participants are randomly assigned to different levels of the mediator to examine its effect on the observed dependent variable. However, in addition to the challenges of manipulating independent and mediator variables, ethical considerations might in principle militate against the manipulation of variables such as perceived vulnerability to infection and severity of infection.