The dissertation investigates the form of network governance in the context of U.S. homeless service networks (namely continuum of care programs; CoCs). This research examines CoC homeless service networks by applying the institutional collective action (ICA) perspectives to understand the forms of network governance as a reflection of network context. The ICA perspective has been applied to understand the rational behavior of network members for the network governance form to mitigate the collective action problems. The ICA perspective helps understand why network members accept specific governance structures with their expectation to maximize the benefits and minimize the costs and uncertainty in their process of collaboration. This dissertation uses the data of CoC networks and point in time data from the U.S. Department of Housing and Urban Development in 2019 and Census. The data developed for this study offers the homeless incidences, geographical characteristics, and governance structure based on the contact information. For an in-depth understanding, interview by CoC leaders was integrated. This dissertation consists of four essays about 1) Literature review on network governance and the theoretical argument in the ICA framework, 2) Background and network governance of the U.S. homeless service networks, 3) Factors affecting the choice of network administrative organization (NAO) form, and 4) Interviews by the representatives of CoC networks. The findings inform us of the governance structure for the effective service provisions and coordination of actions of network members and about why and how network organizations choose a form of network governance.

This dissertation examines the factors and combinations of factors that affect the performance of continuum of care homeless service networks, applying the configurational approach of contingency theory to data sets drawn from federal sources. The study accepts the two key assumptions from the theory: (1) that there are multiple paths to high performance and (2) key conditions have a joint influence on network performance. The data analysis in this study has two parts. The first employs OLS regression to examine the causal relationship between independent variables and the performance of permanent supportive housing (PSH) programs. This study also applies fuzzy set qualitative comparative analysis (fsQCA) to identify multiple combinations of factors that influence the performance of PSH programs. The results identify key factors and multiple combinations of factors that lead to high and low network performance. Federal CoC funding emerges as a core condition for high and low performance, but all relevant conditions can be critical factors depending on how they interact with other relevant conditions. This analysis helps expand the utility of existing contingency theory by using it to explain the dynamic interactions between factors in the context of public service networks. Ultimately, this dissertation confirms that fsQCA can be a useful method for evaluating the performance of public service networks and helping them provide more services more effectively.