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Agile policy development for digital government: an exploratory case study

Published:17 June 2013Publication History

ABSTRACT

Our goal with this paper is to describe "agile sprinting" as a management approach to digital government policy making. We seek to demonstrate through this descriptive case study how inter-agency groups can meld principles and concepts from the agile start-up literature with high-performance team concepts to update or create digital government governance models and related governance structures. The resulting melding of concepts in our case is identified as "agile policy development." We will use the case of a number of federal government agencies' experiences with using agile startup methods when creating/reviewing digital governance models for the Federal Government's Digital Government Strategy [14]. After describing the design and execution of the sprints, the paper's findings focus on what worked and did not work during the sprints based on participant observation and feedback throughout the process. Implications for practice indicate that using agile concepts were helpful in accelerating the policy development process at least in this instance of Digital Government. It remains to be seen whether this experience was an anomaly or can be replicated with other groups examining different policy topics. There are a number of theoretical issues to examine in future studies concerning policy development processes, team dynamics and organization change.

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  1. Agile policy development for digital government: an exploratory case study

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      cover image ACM Other conferences
      dg.o '13: Proceedings of the 14th Annual International Conference on Digital Government Research
      June 2013
      318 pages
      ISBN:9781450320573
      DOI:10.1145/2479724

      Copyright © 2013 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 June 2013

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      dg.o '13 Paper Acceptance Rate 28 of 37 submissions, 76%Overall Acceptance Rate 130 of 213 submissions, 61%

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