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?:abstract
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With the rise of COVID-19, decision support systems (DSS) increasingly display crowding information (CI) (e.g. how crowded a medical practice is) to encourage physical distancing when users select locations. Despite important implications for containing COVID-19, little is known about the causal effect of CI on user selection behaviour and how the immediacy of CI (e.g. “updated 2 minutes ago“) as well as users’ health anxiety further influence the effect of CI. Drawing on literature on digital choice environments and construal level theory, we conducted a multi-national online experiment to investigate the effect of CI on selecting differently crowded medical practices. Our results demonstrate that present (vs. absent) CI in DSS increases the likelihood of users selecting less crowded medical practices, while the effect is strongest when employed with real-time (vs. historical average) CI and, surprisingly, when users’ health anxiety is low (vs. high). Overall, our study adds to the growing body of research on IS in the age of pandemics and provides actionable insights for DSS providers and policymakers to endow users with information to identify and select less crowded locations, thus containing COVID-19 through improved physical distancing without paternalistically restricting users’ freedom of choice.
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