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AbstractAdvice taking and related research is dominated by deterministic weighting indices, specifically ratio‐of‐differences‐based formulas for investigating informational influence. Their arithmetic is intuitively simple, but they pose several measurement problems and restrict research to a particular paradigmatic approach. As a solution, we propose to specify how strongly peoples' judgments are influenced by externally provided evidence by fitting corresponding mixed‐effects regression models. Our approach explicitly distinguishes between endogenous components, such as updated beliefs, and exogenous components, such as independent initial judgments and advice. Crucially, mixed‐effects regression coefficients of various exogenous sources of information also reflect individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. The formal derivation of the proposed weighting measures is accompanied by a detailed elaboration on their most important technical and statistical subtleties. We use this modeling approach to revisit empirical findings from several paradigms investigating algorithm aversion, sequential collaboration, and advice taking. In summary, we replicate and extend the original finding of algorithm appreciation and initially demonstrate a lack of evidence for both systematic order effects in sequential collaboration as well as differential weighting of multiple pieces of advice. In addition to opening new avenues for innovative research, appropriate modeling of information sampling and utilization has the potential to increase the reproducibility and replicability of behavioral science. Furthermore, the proposed method is relevant beyond advice taking, as mixed‐effects regression weights can also inform research on related cognitive phenomena such as multidimensional belief updating, anchoring effects, hindsight bias, or attitude change.

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.

The worldwide spread of a new coronavirus (SARS-CoV-2) since December 2019 has posed a severe threat to individuals' well-being. While the world at large is waiting that the released vaccines immunize most citizens, public health experts suggest that, in the meantime, it is only through behavior change that the spread of COVID-19 can be controlled. Importantly, the required behaviors are aimed not only at safeguarding one's own health. Instead, individuals are asked to adapt their behaviors to protect the community at large. This raises the question of which social concerns and moral principles make people willing to do so. We considered in 23 countries (N = 6948) individuals' willingness to engage in prescribed and discretionary behaviors, as well as country-level and individual-level factors that might drive such behavioral intentions. Results from multilevel multiple regressions, with country as the nesting variable, showed that publicized number of infections were not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors. Instead, psychological differences in terms of trust in government, citizens, and in particular toward science predicted individuals' behavioral intentions across countries. The more people endorsed moral principles of fairness and care (vs. loyalty and authority), the more they were inclined to report trust in science, which, in turn, statistically predicted prescribed and discretionary behavioral intentions. Results have implications for the type of intervention and public communication strategies that should be most effective to induce the behavioral changes that are needed to control the COVID-19 outbreak.

peer-reviewed ; The worldwide spread of a new coronavirus (SARS-CoV-2) since December 2019 has posed a severe threat to individuals' well-being. While the world at large is waiting that the released vaccines immunize most citizens, public health experts suggest that, in the mean time, it is only through behavior change that the spread of COVID-19 can be controlled. Importantly, the required behaviors are aimed not only at safeguarding one's own health. Instead, individuals are asked to adapt their behaviors to protect the community at large. This raises the question of which social concerns and moral principles make people willing to do so. We considered in 23 countries (N = 6948) individuals' willingness to engage in prescribed and discretionary behaviors, as well as country-level and individual-level factors that might drive such behavioral intentions. Results from multilevel multiple regressions, with country as the nesting variable, showed that publicized number of infections were not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors. Instead, psychological differences in terms of trust in government, citizens, and in particular toward science predicted individuals' behavioral intentions across countries. The more people endorsed moral principles of fairness and care (vs. loyalty and authority), the more they were inclined to report trust in science, which, in turn, statistically predicted prescribed and discretionary behavioral intentions. Results have implications for the type of intervention and public communication strategies that should be most effective to induce the behavioral changes that are needed to control the COVID-19 outbreak.

The worldwide spread of a new coronavirus (SARS-CoV-2) since December 2019 has posed a severe threat to individuals' well-being. While the world at large is waiting that the released vaccines immunize most citizens, public health experts suggest that, in the meantime, it is only through behavior change that the spread of COVID-19 can be controlled. Importantly, the required behaviors are aimed not only at safeguarding one's own health. Instead, individuals are asked to adapt their behaviors to protect the community at large. This raises the question of which social concerns and moral principles make people willing to do so. We considered in 23 countries (N = 6948) individuals' willingness to engage in prescribed and discretionary behaviors, as well as country-level and individual-level factors that might drive such behavioral intentions. Results from multilevel multiple regressions, with country as the nesting variable, showed that publicized number of infections were not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors. Instead, psychological differences in terms of trust in government, citizens, and in particular toward science predicted individuals' behavioral intentions across countries. The more people endorsed moral principles of fairness and care (vs. loyalty and authority), the more they were inclined to report trust in science, which, in turn, statistically predicted prescribed and discretionary behavioral intentions. Results have implications for the type of intervention and public communication strategies that should be most effective to induce the behavioral changes that are needed to control the COVID-19 outbreak. ; Peer reviewed

The worldwide spread of a new coronavirus (SARS-CoV-2) since December 2019 has posed a severe threat to individuals' well-being. While the world at large is waiting that the released vaccines immunize most citizens, public health experts suggest that, in the meantime, it is only through behavior change that the spread of COVID-19 can be controlled. Importantly, the required behaviors are aimed not only at safeguarding one's own health. Instead, individuals are asked to adapt their behaviors to protect the community at large. This raises the question of which social concerns and moral principles make people willing to do so. We considered in 23 countries (N = 6948) individuals' willingness to engage in prescribed and discretionary behaviors, as well as country-level and individual-level factors that might drive such behavioral intentions. Results from multilevel multiple regressions, with country as the nesting variable, showed that publicized number of infections were not significantly related to individual intentions to comply with the prescribed measures and intentions to engage in discretionary prosocial behaviors. Instead, psychological differences in terms of trust in government, citizens, and in particular toward science predicted individuals' behavioral intentions across countries. The more people endorsed moral principles of fairness and care (vs. loyalty and authority), the more they were inclined to report trust in science, which, in turn, statistically predicted prescribed and discretionary behavioral intentions. Results have implications for the type of intervention and public communication strategies that should be most effective to induce the behavioral changes that are needed to control the COVID-19 outbreak.