Emotion, Reputation Learning, and Audience Networks: Implications for Reputation Management in the Government Sector

The accepted definition of bureaucratic reputation refers to audience networks as carriers of an agency's reputation. However, scholars tend to view these audiences as a unitary actor or downplay differences between them. The premise underlying this paper is that these views lack empirical plausibility because they ignore variations among audience networks over the cognitive and emotional processing of reputation information and direct experience with agencies, as well as contextual attributes embedded in inter-audience power relations. Any serious reference to “audience networks” requires an in-depth understanding of audiences’ motivations because different human motivations involve various psycho-sociological processes that shape people's views and actions in significant ways. In line with recent advances to endogenize beliefs in models of politics and economics, this paper selects emotion from the numerous forms of motivation where preferences depend directly on one's or others' beliefs (e.g., emotion, reciprocity, image concerns, conformity). It formulates and experimentally tests a model of reputation learning by members of audience networks to gauge the extent to which enthusiasm, anger, and fear impact the kind of information audience members seek, gather, and integrate within different emotional contexts to shape their final decision regarding a government agency's reputation. The paper concludes by discussing the implications the findings may have on reputation management.