Connecting the Dots: Applying Complexity Theory, Knowledge Management and Social Network Analysis to Policy Implementation

摘要: INTRODUCTION It has been more than 25 years since the publication of Lipsky's Street Level Bureaucracy (1980), and while his work is cited repeatedly in studies policy implementation, little theoretical done to deepen our understanding fundamental mechanisms that lead emergence behaviors resulting implementation success or failure. This article brings together complexity theory as a language for understanding, social network analysis (SNA) method examining extending (Mischen, 2007) treats outcome knowledge management. The "Dots" Several authors have made important connections between (Carroll & Burton, 2000; Costa, Rodrigues, Travieso, Villas Boas, 2007), (Choi Brower, 2006), management 2007; Sandfort, 1999), (Bardzki Reid, 2004; McElroy, 2003; Ruggles Little, Tasaka, 2002), but there no attempt integrate all four concepts. Additionally, applied public sector (Elliot Kiel, 1999; Morcol, 2002; Rhodes MacKechnie, 2003) are apply specifically implementation. Likewise, used extensively study intraorganizational behavior, not linked particular. Successful critical successful Choo (1998) describes "knowing organization" one able successfully manage sensemaking, creation decision making processes an organization. As noted by both Lipsky (1980) Pressman Wildavsky (1984), decision-making process. In street-level bureaucracy, large numbers frontline workers make decisions concurrently concerning clients. While "first generation" (KM) focused largely on role information technology, recent KM scholars argued develops shared complex adaptive systems 2002) should be integrated with theories organizational learning (Easterby-Smith Lyles, 2003). A system (CAS) which number moderately connected interdependent agents co-evolve when they find themselves far-from-equilibrium. For new structures order created, must pushed away from equilibrium condition, otherwise changes will temporary revert its stable state (Mitleton-Kelly, Through feedback loops, these self-organize create behavioral "paths" within limited space possibilities. What emerges pattern behavior influenced greatly historicity locality system. other words, adapt their environments learning, McElroy (2003) argues provides missing how cognition happens systems, lacking theory. Organizations interorganizational networks because involve interactions agents. Various systems--everything neural ant colonies--have studied perspective (Kauffman, 1995; Waldrop, 1992). Social differ biological (Newman Park, dense level historicity: humans remember past. Therefore, ability individuals result past allows adaptations human occur at much quicker pace (Holland, 1995). …