Seite - (000336) - in Knowledge and Networks

334 Our findings suggest that learning networks are likely to aggregate according to a set of factors that systematically shape learning choices. In the Swedish case, these factors include geographical proximity, personal social relations, urban and regional economic and demographic structure, and political-administrative institu- tions. Although the important role of geographical proximity and county govern- ment are not entirely surprising, we have shown that such factors produce clear global patterns of learning—the small world of Swedish municipalities. This global learning pattern is likely to have important consequences for how knowledge, ideas, and best practices spread to and among Swedish municipalities. References Agranoff, R. (2006). Inside collaborative networks: Ten lessons for public managers. Public Administration Review, 66, 56–65. doi:10.1111/j.1540-6210.2006.00666.x Amin, A., & Cohendet, P. (1999). Learning and adaptation in decentralized business networks. Society and Space, 17, 87–104. doi:10.1068/d170087 Arundel, A., & Hollanders, H. (2011). A taxonomy of innovation: How do public sector agencies innovate? INNO Metrics 2011–2012 report, Brussels, Belgium: European Commission, DG Enterprise. Retrieved June 28, 2015, from http://ec.europa.eu/enterprise/policies/innovation/ files/psi-studies/taxonomy-of-innovation-how_en.pdf Bäck, H. (2005). Borgmästarens makt [The power of mayors] . Kommunal ekonomi och politik, 9, 7–36. Retrieved May 15, 2015, from http://hdl.handle.net/2077/20675 Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28, 31–56. doi:10.1191/0309132504ph469oa Bell, G. G., & Zaheer, A. (2007). Geography, networks, and knowledge flow. Organization Science, 18, 955–972. doi:10.1287/orsc.1070.0308 Betsill, M. M., & Bulkeley, H. (2004). Transnational networks and global environmental gover- nance: The cities for climate protection program. International Studies Quarterly, 48, 471–493. doi:10.1111/j.0020-8833.2004.00310.x Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional studies, 39, 61–74. doi:10.1080/0034340052000320887 Cao, X. (2010). Networks as channels of policy diffusion: Explaining worldwide changes in capi- tal taxation, 1998–2006. International Studies Quarterly, 54, 823–854. doi:10.1111/j.1468-2478.2010.00611.x Cooke, P., & Morgan, K. (1993). The network paradigm: New departures in corporate and regional development. Environment and Planning D, 11, 543–564. doi:10.1068/d110543 Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28, 1557–1575. doi:10.1016/j.jedc.2003.04.002 Davis, G. F. (1991). Agents without principles? The spread of the poison pill through the intercor- porate network. Administrative Science Quarterly, 36, 583–613. doi:10.2307/2393275 Fleming, L., King, C., & Juda, A. I. (2007). Small worlds and regional innovation. Organization Science, 18, 938–954. doi:10.1287/orsc.1070.0289 Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41. doi:10.2307/3033543 Freeman, R. (2008). Learning in public policy. In R. E. Goodin, M. Moran, & M. Rein (Eds.), The Oxford Handbook of Public Policy (pp. 367–388). Oxford: University Press. doi:10.1093/oxfo rdhb/9780199548453.003.0017 C. Ansell et al.