网络干预:将网络科学应用于公共行政和政策的务实行动

IF 2.7 Q2 PUBLIC ADMINISTRATION
Michael D. Siciliano, Travis A. Whetsell
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引用次数: 0

摘要

公共管理和政策学者对治理网络和协作系统进行了广泛的理论和实证研究。这一奖学金的重点是理解网络形成的机制以及网络属性对个人和集体结果的影响。尽管有丰富的描述性工作和推理分析,很少有工作试图干预这些系统。在这篇文章中,我们发展了一个新的研究机构的基础,在我们的领域专注于网络干预。网络干预被定义为有目的地使用网络数据来确定加速行为改变、提高绩效和产生理想结果的策略(Valente, 2012)。我们将网络干预战略从公共卫生领域扩展到公共部门组织间和治理网络。基于对效率、有效性和公平性的基本追求,公共部门行为者对网络干预感兴趣。网络干预可以增加员工对组织变革的接受程度,改善治理系统的绩效,或促进成功政策在司法管辖区之间的传播。我们为学者和实践者提供了一种有用的方法来概念化网络干预在追求公共价值的过程中可能在哪里、为什么以及如何部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Interventions: Applying Network Science for Pragmatic Action in Public Administration and Policy
Public management and policy scholars have engaged in extensive development of theory and empirical study of networks and collaborative systems of governance. This scholarship has focused on understanding the mechanisms of network formation and the implications of network properties on individual and collective outcomes. Despite rich descriptive work and inferential analyses, little work has attempted to intervene in these systems. In this article, we develop the foundation for a new body of research in our field focused on network interventions. Network interventions are defined as the purposeful use of network data to identify strategies for accelerating behavior change, improving performance, and producing desirable outcomes (Valente, 2012). We extend network intervention strategies from the field of public health to public sector interorganizational and governance networks. Public sector actors have an interest in network interventions based on the fundamental pursuit of efficiency, effectiveness, and equity. Network interventions can increase the uptake of an organizational change among employees, improve the performance of a governance system, or promote the spread of a successful policy across jurisdictions. We provide scholars and practitioners with a useful way to conceptualize where, why, and how network interventions might be deployed in the pursuit of public value.
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来源期刊
CiteScore
5.30
自引率
6.20%
发文量
28
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