Effective public communications have been proposed as a remedy for citizens’ distrust in government. Recent studies pointed to the emotional effect of symbolic elements, entangled in government public communications (e.g., logos, images, and celebrities). Still, they did not examine the interaction between these symbols and the substantive information in communications about bureaucracies’ performance and policies. Exploring this interaction is important for understanding the theoretical mechanisms underlying the effect of symbolic communication on citizens’ trust. Also, it is essential to assess symbols’ potency to unduly compensate for unfavorable or logically unpersuasive information, and enable public organizations to escape justified public criticism. Building on the social psychology Elaboration Likelihood Model, I theorize that symbols may increase citizens’ trust by conducing citizens to pay less attention to logically unpersuasive information, and thus offsetting its negative effect. I test this indirect mechanism via a large survey experiment, focusing on the Israeli Environment Protection Ministry. The experimental results support the research hypotheses and suggest that the effect of symbolic elements is stronger when communications include logically unpersuasive information. I discuss the implications of these findings for democratic responsiveness and accountability.

This article challenges the depiction of bureaucracy as a hurdle to democratic responsiveness. It proposes that senior civil servants' (SCSs) dual position as professionals and citizens may enhance government permeability to salient public agendas. Building on social identity theory, we argue that salient public agendas may arouse SCSs' social identification with in‐groups and thereby elicit their motivation for policy change within their task domain. Employing a mixed‐methods design, we analyze SCSs' social identification with the participants of the large‐scale social protests that took place in Israel during the summer of 2011, and their motivation for policy change in response to the protest agenda. We find that SCSs' social identification with the protesters enhanced their motivation for policy change. In addition, SCSs' perception of a conflict between responsiveness to the protest agenda and their organizational or professional identities shaped their preferences for policy solutions more than their motivation for policy change.

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The public versus private nature of organizations influences their goals, processes, and employee values. However, existing studies have not analyzed whether and how the public nature of organizations shapes their responses to concrete social pressures. This article takes a first step toward addressing this gap by comparing the communication strategies of public organizations and businesses in response to large‐scale social protests. Specifically, we conceptualize, theorize, and empirically analyze the communication strategies of 100 organizations in response to large‐scale social protests that took place in Israel during 2011. We find that in response to these protests, public organizations tended to employ a “positive‐visibility” strategy, whereas businesses were inclined to keep a “low public profile.” We associate these different communication strategies with the relatively benign consequences of large‐scale social protests for public organizations compared with their high costs for businesses.

Artificial intelligence algorithms are increasingly adopted as decisional aides by public bodies, with the promise of overcoming biases of human decision-makers. At the same time, they may introduce new biases in the human-algorithm interaction. Drawing on psychology and public administration literatures, we investigate two key biases: overreliance on algorithmic advice even in the face of ‘warning signals’ from other sources (automation bias), and selective adoption of algorithmic advice when this corresponds to stereotypes (selective adherence). We assess these via three experimental studies conducted in the Netherlands: In study 1 (N=605), we test automation bias by exploring participants’ adherence to an algorithmic prediction compared to an equivalent human-expert prediction. We do not find evidence for automation bias. In study 2 (N=904), we replicate these findings, and also test selective adherence. We find a stronger propensity for adherence when the advice is aligned with group stereotypes, with no significant differences between algorithmic and human-expert advice. Studies 1 and 2 were conducted among citizens in a context where citizens can act as decision-makers. In study 3 (N=1,345), we replicate our design with a sample of civil servants. This study was conducted shortly after a major scandal involving public authorities’ reliance on an algorithm with discriminatory outcomes (the “childcare benefits scandal”). The scandal is itself illustrative of our theory and patterns diagnosed empirically in our experiment, yet in our study 3, while supporting our prior findings as to automation bias, we do not find patterns of selective adherence. We suggest this is driven by bureaucrats’ enhanced awareness of discrimination and algorithmic biases in the aftermath of the scandal. We discuss the implications of our findings for public sector decision-making in the age of automation. Overall, our study speaks to potential negative effects of automation of the administrative state for already vulnerable and disadvantaged citizens.