This paper aims to explore the factors that influence the effectiveness of governance networks. Governance networks, or area-based policy networks, are a web of interdependent public, private and nonprofit organizations that work together to address policy problems within a certain geographical area. Governance networks exert important influence over the governance of mega-regions, cities, and neighborhoods. The effectiveness of governance networks can be measured by examining the impacts that governance networks produce on the cities or neighborhoods in which they are embedded.

Evaluating network effectiveness and studying its determinants have been an important topic in public management research. Provan and Milward (1995) provided a complete framework to conceptualize how network structural characteristics and environmental factors interact to affect network effectiveness. They examined factors such as network centralization, external control, resource munificence and stability. Some scholars focused on the role of agency in affecting network effectiveness. For example, O’Toole and Meier (1999) developed the O’Toole-Meier model and tested the effects of a number of network management variables on network performance in a series of studies. Although the current literature has deepened our understanding of network effectiveness, it focuses mainly on service-delivery networks with limited attention to other types of networks. In addition, with a few exceptions, the current literature employs single-case analysis or comparative case study as the main way to produce theory, which compromises the generalizability of conclusions.

This paper contributes to the current literature by developing a configurational theory of network effectiveness. Based on the model proposed by Provan and Milward (1995), this paper employed linear regression and fuzzy set Qualitative Comparative Analysis (fsQCA) to examine the determinants of the effectiveness of neighborhood governance networks. This mix methods approach helped to produce more generalizable conclusions and to explore complex interactions among explanatory variables. Contrary to some studies (Provan & Milward, 1995; Raab et al., 2013), the findings failed to support the positive effect of network centralization on effectiveness. However, network density, another form of integration, was found to positively affect network effectiveness. This finding suggests that structural characteristics may exert different impacts on different types of networks and in different contexts. This paper developed the configurational propositions from Provan and Mildward’s paper by employing the fsQCA analysis. Two configurations of structural and environmental factors that led to network effectiveness and one configuration that led to network ineffectiveness were found. For example, resource munificence was found to be a necessary but insufficient condition to network effectiveness – it had to be combined with factors such as network stability in order to achieve effectiveness. The configurations help us to achieve a more fine-grained understanding of the complex interactions among factors and to make more discriminating diagnosis of problems with the functioning of networks.