Theoretical Framework: This study theorized that the overall effectiveness of interorganizational networks is influenced by the context in which the network operates, and network structure and process factors. The basis of this proposition was two models of interorganizational network effectiveness: 1) Core Dimensions of Connectivity (Varda, et al., 2008); and 2) Integrated Framework of Network Effectiveness (Turrini, et al., 2010).
Methods: This research utilized mixed methods, including an interview with the network manager, a web-based network survey administered to 18 network members, and outcome data for foster youth in California drawn from the National Youth in Transition Database (NYTD). Descriptive and social network analysis was used to assess the network’s context (e.g., system stability), structure (e.g., member connectivity), and process (i.e., ways members interact), and to examine the relationship between these factors and overall network performance at both the network-level (e.g., access to care) and client-level (e.g., youth outcomes).
Results: This exploratory study found that network performance, including factors like access to services, capacity to achieve goals and client outcomes were positively related to network context, structure and process determinants. Specifically, the context in which the network operated positively influenced the effects of network structure and process determinants. Secondly, indicators of network structure and process were positively associated with network-level outcomes, and network-level outcomes were positively associated with client-level outcomes. Finally, outcomes experienced by youth in Santa Clara County were better than youth served in other communities in California that did not offer the same network of care.
Conclusions and Recommendations: Results of this proof of concept study provide preliminary evidence of the utility of a networked approach to improving outcomes of adult foster youth. Policy makers’ and public administrators’ capacity to make budget and program planning decisions may be improved by the results of this study. However, future research should aim to use more complex social network analysis and advanced statistics with large samples of networks and clients to produce results that are broadly generalizable.