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Cited by 182

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Inside the Matrix: Integrating the Paradigms of Intergovernmental and Network Management

Agranoff, Robert, McGuire, Michael·2003·International Journal of Public Administration

Abstract

The basic research concepts of intergovernmental and network management are examined with regard to blending the two into a unified theory of collaborative management. A substantial literature in both fields has emerged and is ripe for conceptually enhancing interorganizational management in the public sector. The collaborative management concepts forwarded emanate from the authors' comprehensive study of collaborative public management in 237 cities. The major concerns that are found to be ready for knowledge unification include: (1) multiple network complexity and the emergence of governance through policy networks, (2) strategic behavior and its implementation through networks, (3) the role of government institutions in network governance, and (4) the use of myriad policy instruments and multiorganizational activity. Each of these research arenas are representative of the kind of knowledge base that can be blended and extended to help public managers understand how to operate in the vertical/horizontal matrix of intergovernmental network management.

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Metadata

Authors
Agranoff, R., & McGuire, M.
Venue
International Journal of Public Administration
Published
2003-10
Volume
26
Issue
12
Pages
1401-1422
ISSN
  • 0190-0692 (ISSN-L)
  • 1532-4265
OpenAlex
W2029676486
Cited by
182
References
1
FWCI
6.43
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Inside the Matrix: Integrating the Paradigms of Intergovernmental and Network Management. https://app.cochat.ai/papers/10.1081/pad-120024403
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Agranoff, R., & McGuire, M. (2003). Inside the Matrix: Integrating the Paradigms of Intergovernmental and Network Management. International Journal of Public Administration. https://doi.org/10.1081/pad-120024403

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.