Public service users’ responses to performance information: Bayesian learning or motivated reasoning?
研究公共服务用户如何处理绩效信息，通过实验发现用户更多是保守贝叶斯学习而非动机推理，绩效报告有助于纠正错误信念并提升问责性。
Abstract Although performance information is widely promoted to improve the accountability of public service provision, behavioral research has revealed that motivated reasoning leads recipients to update their beliefs inaccurately. However, the reasoning processes of service users have been largely neglected. We develop a theory of public service users’ motivated reasoning about performance information stemming from their identification with the organization providing their services. We address a significant challenge to studying motivated reasoning—that widely used existing research designs cannot rule out alternative cognitive explanations, especially Bayesian learning, such that existing findings could be driven by strong prior beliefs rather than biased processing of new information. We use a research design incorporating Bayesian learning as a benchmark to identify departures from accuracy motivated reasoning process. We assess the empirical implications of the theory using a preregistered information provision experiment among parents with children using public schools. To assess their identity-based motivated reasoning, we provide them with noisy, but true, performance information about their school. Overall, we find no evidence of directionally motivated reasoning. Instead, parents change their beliefs in response to performance feedback in a way that largely reflects conservative Bayesian learning. Performance reporting to service users is less vulnerable to motivational biases in this context than suggested by the general literature on motivated reasoning. Furthermore, exploratory findings show that performance information can correct erroneous beliefs among misinformed service users, suggesting that investment in reporting performance to service users is worthwhile to inform their beliefs and improve accountability.