公共部门决策中的偏差和噪音管理：来自医疗保健领域的实验证据

IF 5 2区管理学 Q1 MANAGEMENT

Public Management Review Pub Date : 2024-02-27 DOI:10.1080/14719037.2024.2322159

Nicola Belle, Paola Cantarelli, Sophie Y. Wang

引用次数: 0

摘要

在对 2,647 名医生进行的六次随机在线实验中，我们测试了在公共机构中参与处方决策的医生是否会因选择结构的不同而在处方决策中出现偏差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The management of bias and noise in public sector decision-making: experimental evidence from healthcare

In six randomized online experiments with 2,647 medical doctors we test whether – depending on the choice architecture – physicians engaged in prescribing decisions in public organizations fall pre...

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来源期刊

Public Management Review Multiple-

CiteScore

10.90

自引率

16.30%

发文量

期刊介绍： Public Management Review seeks to: -explore the developing field of public management -embrace research both about the strategic and operational management of public services and about social/public policy development and implementation -encourage in particular work which either presents new empirical knowledge about public management and/or developed theory -encourage questioning both of the legitimacy and hegemony of the ’new public management’ paradigm and its alternatives, and of the developing pluralism in public management which encompasses the governance of inter-sectoral relationships between government, non-profit and for-profit organisations in the provision of public services. The remit of Public Management Review is to promote the dissemination and discussion of such research about public management. Its specific target audience is the academic and research community. Public Management Review is an international journal, seeking to draw together and learn lessons from the development of public management across the world rather than being parochially focused upon one area and encourages cross-national and comparative research papers. The journal promotes inter-disciplinary work. Much of the most important work about public management is coming at the cusp of traditional disciplines. Public Management Review promotes such cross-boundary learning and conceptualisation.