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Xenophobia and anti-immigrant attacks rose during the COVID-19 pandemic, yet this may not be solely due to the disease threat. According to theories of frustration and scapegoating, situational obstructions and deprivation can motivate prejudice against outgroups. Using a global natural quasi-experimental design, this study tests whether the restrictiveness of national lockdowns can explain higher individual-level perceptions of immigrant threat. Data of 45,894 participants from 23 countries were analyzed. Both lockdown duration and lockdown severity were positively associated with individuals’ perceived threat of immigrants. The lockdown effects were independent of objective and subjective measures of disease threat, and there was no evidence that disease threat drives people’s prejudice toward immigrants. Subgroup analysis suggested the lockdown effects were reliable in Europe and the Americas, but not in Asia. These findings suggest a need to mitigate frustration and scapegoating when implementing lockdowns, and to distinguish the influence of societal restrictions from disease threat.

Understanding how individual beliefs and societal values influence support for measures to prevent SARS-CoV-2 transmission is vital to developing and implementing effective prevention policies. Using both Just World Theory and Cultural Dimensions Theory, the present study considered how individual-level justice beliefs and country-level social values predict support for vaccination and quarantine policy mandates to reduce SARS-CoV-2 transmission. Data from an international survey of adults from 46 countries (N =6424) were used to evaluate how individual-level beliefs about justice for self and others, as well as national values—that is, power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence—influence support for vaccination and quarantine behavioral mandates. Multilevel modeling revealed that support for vaccination and quarantine mandates were positively associated with individual-level beliefs about justice for self, and negatively associated with country-level uncertainty avoidance. Significant cross-level interactions revealed that beliefs about justice for self were associated more strongly with support for mandatory vaccination in countries high in individualism, whereas beliefs about justice for others were more strongly associated with support for vaccination and quarantine mandates in countries high in long-term orientation. Beliefs about justice and cultural values can independently and also interactively influence support for evidence-based practices to reduce SARS-CoV-2 transmission, such as vaccination and quarantine. Understanding these multilevel influences may inform efforts to develop and implement effective prevention policies in varied national contexts.Shared national values and one’s own beliefs about justice simultaneously influence support for policies that aim to encourage COVID-19 vaccination and quarantine.

Previous studies suggest that entrepreneurs play a key role in the success of their ventures. But relatively little is currently known about how they produce such effects. The present research provides data suggesting that two modes of entrepeneurs’ self‐regulation—locomotion and assessment—enhance a firm's success through their effects on the components of alertness. This mediational model was tested and supported with data from 120 entrepreneurs. Locomotion was positively related to the scanning and search component, while assessment was positively related to the association and evaluation components. These findings are discussed in terms of the role of founders’ self‐regulation in the performance of their companies.

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant.