Introduction: Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication. Methods: A total of 157 participants [80% female; mean age = 27.82 years (SD = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors. Results: Three latent classes were identified: high compliance (25%) with all recommendations; public compliance (51%), with high compliance regarding public but not personal behaviors; and low compliance (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (RRR) = 0.08 (0.01; 0.85)], younger age [RRR = 0.72 (0.57; 0.93)], and lower public stigma [RRR = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [RRR = 0.76 (0.59; 0.98)]. Discussion: With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.

BackgroundIn crisis communication, warning messages are key to informing and galvanizing the public to prevent or mitigate damage. Therefore, this study examines how risk appraisal and individual characteristics influence the intention to comply with behavioral recommendations of a warning message regarding three hazard types: the COVID-19 pandemic, violent acts, and severe weather.MethodsA cross-sectional survey examined 403 German participants from 18 to 89 years (M = 29.24; 72% female). Participants were allocated to one of three hazard types (COVID-19 pandemic, violent acts, severe weather) and presented with warning messages that were previously issued via an official warning app. Four components of risk appraisal—perceived severity (PS), anticipated negative emotions (AE), anticipatory worry (AW), and risk perception (RP)—were assessed before and after presenting the warning message. Path models were calculated to predict the intention to comply with the warning message, controlling for age, gender, and previous hazard experience.ResultsFor the COVID-19 pandemic, higher age (β = 0.18) predicted warning compliance (R2 = 0.05). AE (β = 0.20) predicted compliance in the case of violent acts (R2 = 0.09). For severe weather, PS (β = 0.28), age (β = 0.29), and female gender (β = 0.34) lead to higher compliance (R2 = 0.27). Changes across risk appraisal components were not consistent, as some facets decreased after the receipt of a warning message.DiscussionRisk appraisal has shown a marginal yet differential influence on warning message compliance in different types of hazards. Regarding the COVID-19 pandemic, the impact of sociodemographic factors on compliance should be studied more intensively. Moreover, integrating intermediary variables, such as self-efficacy, is necessary.

Background: Warning apps can provide personalized public warnings, but research on their appraisal and impact on compliance is scarce. This study introduces a virtual city framework to examine affective reactions when receiving an app-based warning, and subsequent behavioral intentions. Methods: In an online experiment, 276 participants (M = 41.07, SD = 16.44, 62.0% female) were randomly allocated to one of eight groups (warning vs. no warning, thunderstorm vs. no thunderstorm, video vs. vignette). Participants were guided through a virtual city by a mock-up touristic app (t1). Then, the app issued a warning about an impending thunderstorm (t2), followed by a virtual thunderstorm (t3). The virtual city tour was presented via vignettes or videos. ANCOVAs were used to investigate trajectories of momentary anxiety, hierarchical regressions analyzed the impact of momentary anxiety on information seeking. Results: Participants who received a warning message and were confronted with a thunderstorm showed the highest increase in momentary anxiety, which predicted information seeking intentions. Conclusions: The findings underscore the importance of affective appraisal in processing warning messages. The virtual city framework is able to differentiate the impact of warning versus event in an online context, and thus promising for future warning research in virtual settings.