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Individual Learning Behaviour in Collaborative Networks

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Learning in Public Policy

Part of the book series: International Series on Public Policy ((ISPP))

Abstract

This chapter examines the conditions under which individuals are likely to engage with other participants in learning activities during collaborative processes of innovation in the public sector. Drawing on the statistical network methodology of Exponential Random Graph Modelling we show that the formation of tightly clustered learning alliances in collaborations is not something straightforward. Furthermore, the analyses demonstrate that the decision of an individual to show learning behaviour towards another actor in the collaboration mainly depends on whether this other actor shows good, and exemplary, collaborative behaviour or if the other actor sits in a position where his or her involvement accrues power within the collective.

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References

  • Agger, A., & Sørensen, E. (2016). Managing collaborative innovation in public bureaucracies. Planning Theory.

    Article  Google Scholar 

  • Agranoff, R. (2006). Inside collaborative networks: Ten lessons for public managers. Public Administration Review, 66(special issue), 56–65.

    Article  Google Scholar 

  • Agranoff, R., & McGuire, M. (2001). Big questions in public network management research. Journal of Public Administration Research and Theory, 11(1), 295–326.

    Article  Google Scholar 

  • Alpay, L., Giboin, A., & Dieng, R. (1998). Accidentology: An example of problem solving by multiple agents with multiple representations. In M. W. van Someren, P. Reimann, H. P. A. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 152–174). Amsterdam: Pergamon.

    Google Scholar 

  • Ansell, C., & Torfing, J. (2014). Collaboration and design: New tools for public innovation. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 1–19). New York, NY: Routledge.

    Google Scholar 

  • Axelrod, R. (1984). The evolution of cooperation. New York, NY: Basic Books.

    Google Scholar 

  • Bardach, E. (1998). Getting agencies to work together: The practice and theory of managerial craftsmanship. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Bason, C. (2014). Design attitude as an innovation catalyst. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 209–229). New York, NY: Routledge.

    Google Scholar 

  • Bressers, N. (2014). The impact of collaboration on innovative projects: A study of Dutch water management. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 148–170). New York, NY: Routledge.

    Google Scholar 

  • Burt, R. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Calanni, J. C., Siddiki, S. N., Weible, C. M., & Leach, W. D. (2014). Explaining coordination in collaborative partnerships and clarifying the scope of the belief homophily hypothesis. Journal of Public Administration Research Theory, 25(1), 901–927.

    Google Scholar 

  • Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Studies, 23(3), 279–313.

    Article  Google Scholar 

  • Dawson, C. (2002). Practical research methods. A user-friendly guide to mastering research techniques and projects. Trowbridge, UK: Cromwell Press.

    Google Scholar 

  • Ferguson, R. F., & Stoutland, S. E. (1999). Reconceiving the community development field. In R. F. Ferguson & W. T. Dickens (Eds.), Urban problems and community development (pp. 33–76). Washington, DC: Brookings Institution.

    Google Scholar 

  • Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.

    Google Scholar 

  • Gioia, D. A., & Sims, H. P., Jr. (1986). Cognition-behaviour connections: Attribution and verbal behaviour in leader-subordinate interactions. Organizational Behaviour and Human Decision Processes, 37(2), 197–229.

    Article  Google Scholar 

  • Goodreau, S. M. (2007). Advances in exponential random graph (p*) models applied to a large social network. Social Networks, 29(1), 231–248.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Henry, A., Lubell, M., & McCoy, M. (2011). Beliefs systems and social capital as drivers of policy network structure: The case of California regional planning. Journal of Public Administration Research and Theory, 21(3), 419–444.

    Article  Google Scholar 

  • Ingold, K. (2011). Network structures within policy processes: Coalitions, power, and brokerage in Swiss climate policy. Policy Studies Journal, 39(3), 435–459.

    Article  Google Scholar 

  • Kanter, R. M. (1994). Collaborative advantage. Harvard Business Review, 72(4), 96–108.

    Google Scholar 

  • Keast, R., & Waterhouse, J. (2014). Collaborative networks and innovation: The negotiation-management nexus. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 148–170). New York, NY: Routledge.

    Google Scholar 

  • Koppenjan, J. F. M., & Klijn, E. H. (2004). Managing uncertainties in networks: A network approach to problem solving and decision-making. London, UK: Routledge.

    Google Scholar 

  • Lee, Y., Lee, I. W., & Feiock, R. C. (2012). Interorganizational collaboration networks in economic development policy: An exponential random graph model analysis. Policy Studies Journal, 40(3), 547–573.

    Article  Google Scholar 

  • Levi, M., & Stoker, L. (2000). Political trust and trustworthiness. Annual Review of Political Science, 3(1), 475–507.

    Article  Google Scholar 

  • Lubell, M. (2007). Familiarity breeds trust: Collective action in a policy domain. Journal of Politics, 69(1), 237–250.

    Article  Google Scholar 

  • Lubell, M., Robins, G., & Wang, P. (2014). Network structure and institutional complexity in an ecology of water management games. Ecology and Society, 19(4), 1–14.

    Article  Google Scholar 

  • Lubell, M., Scholz, J., Berardo, R., & Robins, G. (2012). Testing policy theory with statistical models of networks. Policy Studies Journal, 40(3), 351–374.

    Article  Google Scholar 

  • Marin, A., & Hampton, K. N. (2007). Simplifying the personal network name generator: Alternatives to traditional multiple and single name generators. Fields Methods, 19(2), 163–193.

    Article  Google Scholar 

  • Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system on trust for management: A field quasi-experiment. Journal of Applied Psychology, 84(1), 123–136.

    Article  Google Scholar 

  • Montin, S., Johansson, M., & Forsemalm, J. (2014). Understanding innovative regional collaboration: Metagovernance and boundary objects as mechanisms. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 148–170). New York, NY: Routledge.

    Google Scholar 

  • Oliver, C. (1991). Strategic responses to institutional processes. Academy of Management Review, 16(1), 145–179.

    Article  Google Scholar 

  • Rolls, D. A., Sacks-Davis, R., Jenkinson, R., McBryde, E., Pattison, P., Robins, G., & Hellard, M. (2013). Hepatitis C transmission and treatment in contact networks of people who inject drugs. PLoS ONE, 8(11), e78286.

    Article  Google Scholar 

  • Sabatier, P. A., & Jenkins-Smith, H. C. (1993). Policy change and learning: An advocacy coalition approach. Boulder, CO: Westview.

    Google Scholar 

  • Scharpf, F. W. (1978). Interorganizational policy studies: Issues, concepts and perspectives. In K. I. Hanf & F. W. Scharpf (Eds.), Interorganizational policy making: Limits to coordination and central control (pp. 345–370). London, UK: Sage.

    Google Scholar 

  • Scott, T. A. (2015). Analysing policy networks using valued exponential graph models: Do government-sponsored collaborative groups enhance organizational networks. Policy Studies Journal, 44(2), 215–244.

    Article  Google Scholar 

  • Simon, H. A. (1955). A behavioural model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.

    Article  Google Scholar 

  • Snijders, T. A. B., Van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.

    Article  Google Scholar 

  • Sørensen, E., & Torfing, J. (2011). Enhancing collaborative innovation in the public sector. Administration and Society, 43(8), 842–868.

    Article  Google Scholar 

  • Stevens, V. (2017). Replication data for: Stevens, V. (2017). Individual learning behaviour in collaborative processes of innovation. In C. A. Dunlop, C. M. Radaelli, & P. Trein (Eds.), Learning in public policy: Analysis, modes and outcomes. Basingstoke: Palgrave Macmillan. https://doi.org/10.7910/dvn/mtelu8,HarvardDataverse,V1,UNF:6:/CI0WcdoQrpiqKid/MSLTg==.

  • Stevens, V., & Verhoest, K. (2016). A next step in collaborative policy innovation research: Analysing interactions using exponential random graph modelling. The Innovation Journal: The Public Sector Innovation Journal, 21(2), 1–20.

    Google Scholar 

  • Teleford, Q. T., Simpson, S. L., Burdette, J. H., Hayasaka, S., & Laurienti, P. (2011). The brain as a complex system: Using network science as a tool for understanding the brain. Brain Connectivity, 1(4), 295–308.

    Article  Google Scholar 

  • Thomas, C. W. (2003). Bureaucratic landscapes: Interagency cooperation and the preservation of biodiversity. Cambridge, MA: MIT Press.

    Google Scholar 

  • Tummers, L. (2012). Policy alienation of public professionals: The construct and its measurement. Public Administration Review, 72(4), 516–525.

    Article  Google Scholar 

  • Van den Bossche, P., Gijselaers, W., Segers, M., Woltjer, G., & Kirschner, P. (2011). Team learning: Building shared mental models. Instructional Science, 39(3), 283–301.

    Article  Google Scholar 

  • Waldorff, S. B., Kristensen, L. S., & Ebbesen, V. E. (2014). The complexity of governance: Challenges for public sector innovation. In C. Ansell & J. Torfing (Eds.), Public innovation through collaboration and design (pp. 148–170). New York, NY: Routledge.

    Google Scholar 

  • Weible, C. W., & Sabatier, P. A. (2005). Comparing policy networks: Marine protected areas in California. Policy Studies Journal, 33(1), 181–201.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Belgian Science Policy under grant BR/132/A4/BRAIN-TRAINS. In addition, many thanks to Inger Baller for her help with the ERGM analyses.

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Authors and Affiliations

  1. Erasmus School of Social and Behavioural Sciences, Erasmus Universiteit Rotterdam, Rotterdam, The Netherlands

    Vidar Stevens

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  1. Vidar Stevens
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Correspondence to Vidar Stevens .

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Editors and Affiliations

  1. Department of Politics, University of Exeter, Exeter, Devon, United Kingdom

    Claire A. Dunlop

  2. Department of Politics, University of Exeter, Exeter, Devon, United Kingdom

    Claudio M. Radaelli

  3. Institute of Political, Historical, and International Studies, University of Lausanne, Lausanne, Switzerland

    Philipp Trein

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Stevens, V. (2018). Individual Learning Behaviour in Collaborative Networks. In: Dunlop, C., Radaelli, C., Trein, P. (eds) Learning in Public Policy. International Series on Public Policy . Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-76210-4_5

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