Implementation from a Networked Perspective

Background:  In responding to complex social problems, administrators frequently implement policy solutions that are vague and span multiple public and private entities.  In doing so, the type of governance model utilized to guide implementation influences incentives, opportunities and constraints which become the context of the implementation and influences the extent to which outcomes are achieved.  Attention to the implementation process is important for providing a contextual understanding of outcomes.

The networked governance model shifts policy implementation from the sole public entity responsible for the policy to a collaborative network consisting of actors from public and private entities who have a stake in the outcome of the policy.  There has been a rise in the networked governance approach since the global financial crisis of 2008 when resources for public services were constrained.  The networked approach provides a structural mechanism for shared information, resources and expertise across policy partners.  This study examined the implementation of a statewide system change for children’s behavioral health from a networked perspective.

Methods:  A mixed-method approach was employed to describe the network structure and to attribute the structure to network effectiveness as perceived by the respondents. Archival data were collected to identify the actors in the network (N=107) and construct an “affiliation network.” The data was also used to develop a “name roster” for the second phase of the research, a survey of network members. The survey yielded a 53% response rate.

Multi-level network analysis techniques were employed to describe the patterns of relationships that comprise the overall network structure(s).  Four relationship variables of working together (operations), sharing information, sharing resources and trust were the focus of the examination.  The data were analyzed using the UCINET software program to run appropriate correlations, regression analyses, and generate visual depictions of the network’s density and centralization of actors.

Results:   The findings reveal two network structures across the four relationship variables. Higher density and decreased centrality observed in the operations and sharing information variables indicate a network structure similar to early formation of a group.  Whereas, decreased density and increased centrality observed in the strengthened network relations of sharing resources and trust indicates a network structure similar to an implementation stage and is considered more likely to be effective.  Central actors perceived higher levels of effectiveness than those who are not as central.  The results also indicate the presence of one of the relationship variables can predict the presence of another.

Implications:  This study described the network structure(s) present in the implementation of a statewide system change.  Applying techniques of network analysis to policy implementation research draws upon the theoretical developments from the 1980s (i.e. top down vs. bottom up approaches) and utilizes technological advancements to understand how a network structure can shape the incentives, opportunities and constraints within the implementation of a public policy.  By employing network analysis techniques to the study of policy implementation, we can pull the complexity of the actors’ interactions into a separate study that can, through multi-level network analysis, be used to explain and predict network behaviors.