Determinants of Adoption and Rejection of Protective Measures During the SARS-CoV-2 Pandemic: A Longitudinal Study in Germany’s Second Wave

The rejection (or disapproval) of policies and the adoption (or uptake) of protective behaviors determine the transmission and containment of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). Various studies have addressed the potential influence of different factors on the implementation of protective measures, such as social distancing and good hand hygiene (Scholz & Freund, 2021; Stroebe et al., 2021; Vacondio et al., 2021). At the beginning of the pandemic, in March and April 2020, Dohle et al. (2020) aimed to identify whether sociodemographic variables, risk perception, and trust are important factors influencing the acceptance and implementation of protective measures. In two studies (Study 1: explorative, convenience sample; Study 2: confirmatory, representative sample), it was found that older and female individuals showed greater adoption and acceptance of measures. In terms of risk perception, it was shown that a higher perceived susceptibility (risk of becoming infected in comparison to another person of the same age and same gender) to the coronavirus disease (COVID-19) significantly predicted more frequent adoption of measures. However, the perceived risk of infection (conceptualized as the product of the subdimensions probability of getting infected and anticipated severity of consequences) did not turn out to be significant in either of their studies. Finally, a higher level of trust in science predicted higher adoption as well as higher acceptance; trust in politics, in contrast, predicted both variables in Study 1 but had no influence in Study 2. This work aims to delineate on why the perceived risk of infection—usually a key element in theories of health behavior—do not seem to play a consistent role in this context. One possible explanation could be that only one of the two subdimensions (probability of infection or severity of the disease) is particularly important. Other studies also point in this direction: different dimensions of cognitive risk perceptions are mostly unrelated to the intention and actual behavior of protective measures as Scholz and Freund (2021) showed. We will examine the longitudinal impact of probability, severity, and susceptibility to further investigate these diverse effects.

Since the beginning of the pandemic, restrictive policies have repeatedly been changed (Hale et al., 2020). As the study by Dohle et al. (2020) took place at the beginning of the pandemic, it remains unclear to what extent trust, risk, and sociodemographic factors can still explain the acceptance and adoption of protective measures at a later stage of the pandemic. Our data allow us to look at those aspects in October 2020 and January 2021. Second, in contrast to the cross-sectional design of Dohle et al. (2020), the panel design of the current study allows us to estimate the effect of individuals’ attitudes and behaviors over time. Moreover, additional factors such as affective risk (Loewenstein et al., 2001; Slovic et al., 2004) and risk attitude (E. U. Weber et al., 2002) are added herein to the predictors investigated by Dohle et al. (2020) to further explain behavior.

Often, affective risk perceptions are even more strongly associated with preventive behaviors than cognitive risk perceptions (Dillard et al., 2012; Janssen et al., 2014; Loewenstein et al., 2001). Affective risk was shown to be correlated with different protective behaviors during the swine flu (A/H1N1) pandemic in 2009 (Goodwin et al., 2011; Renner & Reuter, 2012; Setbon & Raude, 2010). Recently, Vacondio et al. (2021) also found that a higher level of worry was associated with higher engagement in protective COVID-19 behaviors. Loewenstein et al. (2001) argued that there is often a discrepancy between the cognitive evaluation of a threat and the fear experienced in connection with that risk. As such, this study aims to investigate whether affective risk is of greater importance than cognitive risk in association with COVID-19.

The second additional predictor evaluated in this study is risk attitude in the context of health and safety. Several authors have argued that risk-taking behavior is not a stable personality trait but is rather domain-specific (Hanoch et al., 2006; E. U. Weber et al., 2002). The Domain-Specific Risk-Taking (DOSPERT) scale (Blais & Weber, 2006; E. U. Weber et al., 2002) measures risk attitudes in five subdomains. The subscale of health and safety assesses willingness to engage in health-related risks such as “driving a car without wearing a seat belt” (Blais & Weber, 2006). Keinan et al. (2021) found that the health subscale was highly correlated with showing risky COVID-19-related behaviors. We presume that a higher health-related risk attitude will be related to lower adoption and acceptance of COVID-19 measures.

Data from the COVID-19 Snapshot Monitoring in Thuringia (COSMO-T) allow us to investigate the research question—that is, why risk perception does not seem to play a definite role in the context of COVID-19—and to analyze the stability of effect sizes in the risk–behavior relationship during the first winter of the pandemic. The study was designed as a longitudinal online survey with two measurement points. Data collection took place in October 2020 (T1) and January 2021 (T2). During the first data collection T1, the 7-day incidence increased from 16.3 (10/15/2020) to 43.1 (10/27/2020) per 100,000. At this time, there was no lockdown, but restrictions came into effect (e.g., restriction of large events and festivities when the 7-day-incidence reached 35 per 100,000). During the second data collection T2, the 7-day incidence increased from 251 (01/04/2021) to 326 (01/12/2021) per 100,000 (Robert Koch-Institut, 2021). Since November 2, 2020, contact restrictions, closure of restaurants, entertainment establishments and sports facilities were established and labeled as “lockdown light”. On December 16, the measures restricted non-essential retail and private gatherings of more than 5 people from two households. Schools and kindergartens were closed. A detailed overview with numbers of deaths, new cases per day and intensive care unit cases can be found in the Supplementary Materials. The contents of the questionnaires largely resemble those of the national COSMO and include various psychosocial constructs, COVID-19-specific queries, and sociodemographic items (Betsch et al., 2020).

Method

Results

Discussion

The present longitudinal data contribute to identifying predictors of the adoption and rejection of protective measures over the first winter months in the COVID-19 pandemic. Earlier studies (Scholz & Freund, 2021; Vacondio et al., 2021), including the study by Dohle et al. (2020), were conducted cross-sectionally at a time when the pandemic was beginning to unfold on an international scale. Our data were collected at a later point in the pandemic when the situation was not as new as it had been.

Further, cross-sectional studies do not allow for causal inferences (Caruana et al., 2015). With our longitudinal study design, it is still not possible to draw causal conclusions, but we can observe how perceptions identified at the beginning of the study are related to subsequent behavior. Finally, we added the risk attitude and a second dimension of risk perception to the predictors of protective measures. This complements the previous research and gives insights into evidence-based behavioral intervention planning based on psychological research.

It was shown that cognitive risk perceptions are not the major factors predicting the behavior under consideration, both short term and over time. Apart from severity, which predicted rejection, the other cognitive risk perceptions proved to be irrelevant for the two outcome variables in the present longitudinal study. For the cross-sectional analyses susceptibility and severity were significant. The lack of explanatory power of probability here shows that it is advisable to look at subdimensions of risk.

Within our longitudinal analyses, predictors other than cognitive risk have been shown to be relevant in explaining behavior. In terms of trust in politics, mixed results were found. Initial political trust had no influence on the adoption of protective measures three months later but turned out to be the greatest predictor of rejection. High levels of trust in institutions and support for politicians are of great importance in the context of a health crisis (Bish & Michie, 2010; Blair et al., 2017; Han et al., 2020; Rubin et al., 2009), because trust can legitimate decisions by authorities (Bennett et al., 2010). However, Dohle et al. (2020) did not find any influence of trust in politics on the adoption or acceptance of measures in their second, representative study, and in the present study, it only predicted the rejection of measures.

One reason for this contrasting result could be that trust in politics was of great importance at the beginning of the pandemic. Several authors described a “rally ‘round the flag” effect in the period from January to May 2020 (Baekgaard et al., 2020; Sibley et al., 2020; Yam et al., 2020). These studies showed an increase of support for national leaders across culturally diverse countries. However, our longitudinal data, which show (1) a general decline of trust in politics as well as (2) that trust becomes less important in predicting behavior and attitudes over time, suggest that this importance has vanished over time. The fact that then new behaviors have become routine may also make trust less important. Cross-sectional data of the national COSMO (Betsch et al., 2020) also show a decline of trust in the federal government from a rather moderate level at the beginning of the pandemic (April 2020) to a lower level, indicating rather little trust, one year later (April 2021).

More trust in science to deal well with COVID-19 was reflected in more frequent adoption as well as less rejection of protective measures. Average trust in science also remained constant, at a high level, over time. Trusted communicators should be involved in the process and should be considered providers of important information. This is of particular interest after vaccination—the most effective protective measure—has become available. As it is voluntary in most countries, it is important to increase the population’s confidence and to use trusted sources of information in vaccination campaigns.

Risk attitude and affective risk were the strongest longitudinal predictors of the adoption of protective measures: those who were generally more willing to take health risks adopted protective measures less frequently. This speaks for a differentiated targeting of risk-seeking people in this domain. The same was true when the virus was seen as non-frightening and non-worrisome. In terms of the rejection of measures, it was shown that the more the coronavirus was seen as frightening and worrisome, the smaller the level of rejection three months later. Risk attitude, however, did not predict rejection. According to the present data, emotional risk perception is of higher importance than cognitive risk perception when it comes to the personal risk assessment of COVID-19 and the related behavior. This is in line with other studies investigating different protective behaviors (Dillard et al., 2012; Janssen et al., 2014; Loewenstein et al., 2001; Renner & Reuter, 2012). In the present study the average cognitive risk assessment remained constant over time—even though the number of infections increased tenfold (Robert Koch-Institut, 2021). A possible reason might be the habituation of risk (Slovic, 1987). The ongoing exposure to the pandemic could have created a sense of familiarity and the risk is attenuated by habituation. Dryhurst et al. (2020) showed that the risk perception was high in ten different countries at the beginning of the pandemic in mid-March to mid-April. This is not applicable to our data at a later date, where risk perception was on a rather low to medium level. The level of risk perception may have come to a plateau. Further longitudinal research is needed to investigate the cognitive underpinnings of risk perception during the pandemic over time.

Furthermore, there was a difference between the predictors of adoption and rejection. On the one hand, personal aspects such as risk attitude were important for the adoption of protective measures—primarily a process that one undertakes for one's own protection. On the other hand, trust in policy—a process involving others—was most important for rejection. Rejection appears to be more of an attitudinal than a behavioral characteristic. Additionally, the two strongest factors were not important to the respective other.

Our analyses of the stability of effects over time have shown that our longitudinal analyses do not differ significantly from cross-sectional analyses—cross-sectional data are thus well suited to predict future behavior and to make stable recommendations for behavioral interventions. This is important in the context of the ongoing pandemic and can be considered when planning and implementing policies. It was shown that in cross-sectional analyses, cognitive risk perception became important for behavior. Affective risk perception, however, had an impact in both models.

Our results come with some limitations. First, the adoption of measures was self-reported and could be overestimated due to a social-desirability bias. Second, cognitive risk was measured with fewer items and rejection was measured via a single survey item asking whether the participant felt that the measures taken to manage the COVID-19 pandemic were exaggerated, and not how reasonable each protective measure is, as Dohle et al. (2020) did. Therefore, the results for this construct can only be seen as conceptual and not as a direct replication of the original results by Dohle et al. (2020). Nevertheless, our results show similar patterns, and we therefore conclude that the item is suitable to gain further insights. Third, even though the sample of the initial study was representative of the age × gender distribution of the Thuringian population, it was not representative in terms of education level. Furthermore, the analysis sample was not representative of the age × gender of the population because of the drop-out rate (see Supplementary Materials File 3: Drop Out Analyses.docx). The age range from 18 to 74 years was chosen because online surveys tend to show a self-selection bias, which may be more pronounced among the older age group (W. Weber et al., 2020). Last, the results may be restricted to Western societies and should be interpreted within its cultural–psychological perspective.