Juggling Multiple Networks in Multiple Streams
Table 1 : Provan and Kenis ’ key predictors of effectiveness of network governance forms
Trust |
Number of |
Goal Consensus |
Need for Network- |
|
Participants |
|
Level
Competencies
|
Shared governance High density Few High Low Lead organization
Network Administrative Organization ( NAO )
Low density , highly centralized Moderate density , NAO monitored by members
Moderate number Moderately low Moderate
Moderate to many Moderately high High
Administrative Organization ” that is capable of simultaneous monitoring and management of the many dimensions , actors , and connections in the policy environment . Such a role would require the capacity to dynamically engage at many different levels of governance and many elements of the policy process simultaneously . This inference resonates with Laumann and Knoke ’ s ( 1987 ) finding that effective policy intervention is predicated by larger teams of media and communication monitors based in influential ( public and private ) organizations .
Juggling
This issue touches on the very nature of theories of the policy process . Theories applied in behavioral research are typically linear , at best with a feedback loop : a number of inputs ( say , “ attitudes ” and “ beliefs ”) are transformed through a number of conditioners ( say , “ social norm ” and “ self-efficacy ”) to produce intermediary (“ intention ”) and final ( behavioral ) change ( e . g ., the theory of planned behavior and the theory of reasoned action , Madden , Ellen , and Ajzen1992 ). In more complex behavioral systems there may be iterative and more incremental steps , and sometimes the models may take the shape of a cycle .
This , then , is also how policy development is typically modeled . A policy cycle can variably exist of as little as three steps ( problem — solution — evaluation ), four stages ( agenda setting — policy formation — policy implementation — policy review ) with as many as 15 subprocesses , to retrospective policy analyses that yield dozens of policy development instances , phases , and events . In Figure 3 , we can see the Google image yield for the search term “ policy cycle .”
All of these represent the policy process as displaying a curved linearity in which one stage — sometimes under conditions — necessarily leads to the next stage , just like the behavioral theories
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