Emotions and Reputation Learning by Audience Networks: A Research Agenda in Bureaucratic Politics

Abstract

Audiences that observe and interact with government agencies play a crucial role in shaping these agencies' reputations. However, existing research often treats these audience networks as monolithic, overlooking the inherent diversity in their cognitive and emotional processing of reputational information. This approach fails to account for the variations in how audiences experience and evaluate agencies. To address this gap, we propose a new research agenda focused on the role of emotions in bureaucratic politics. We introduce a novel theoretical framework of Reputation Learning, informed by Affect-as-Information Theory and Affective Intelligence Theory, to explore the downstream effects of emotions as content and as process in shaping judgment formation and information processing. Specifically, we identify emotion-based components of bureaucratic reputation and examine how emotions influence audience decision-making processes and perceptions of government agencies. We conclude by outlining four key contributions of this framework to advancing the study of emotions in bureaucratic politics.