Collaborative Governance in Theory and Practice is a research paper published in Journal of Public Administration Research and Theory (2007). On theSindex it has a DataRank of 1.3. It has been cited 7,402 times.
AbstractOver the past few decades, a new form of governance has emerged to replace adversarial and managerial modes of policy making and implementation. Collaborative governance, as it has come to be known, brings public and private stakeholders together in collective forums with public agencies to engage in consensus-oriented decision making. In this article, we conduct a meta-analytical study of the existing literature on collaborative governance with the goal of elaborating a contingency model of collaborative governance. After reviewing 137 cases of collaborative governance across a range of policy sectors, we identify critical variables that will influence whether or not this mode of governance will produce successful collaboration. These variables include the prior history of conflict or cooperation, the incentives for stakeholders to participate, power and resources imbalances, leadership, and institutional design. We also identify a series of factors that are crucial within the collaborative process itself. These factors include face-to-face dialogue, trust building, and the development of commitment and shared understanding. We found that a virtuous cycle of collaboration tends to develop when collaborative forums focus on “small wins” that deepen trust, commitment, and shared understanding. The article concludes with a discussion of the implications of our contingency model for practitioners and for future research on collaborative governance.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
1.3
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.