The early results of a social network analysis of the KM4Dev main discussion group
Palabras clave:
social network analysis, KM4dev, communities of practice, knowledge seeking, knowledge sharingResumen
This paper presents the early results of a social network analysis of the KM4Dev Main Discussion Group. Ten complete years of data, and two years of incomplete data, were provided for analysis. Data was in an XML format and required a considerable iterative data cleansing exercise. Ultimately this process left 703 identified individuals in the network. These people comprise the node-set for the public bounded or contained network, for which activity and various network measures can be applied. Gloor's (2006) Contribution Index was used to attribute and partition the network. 113 key participants were identified as being crucial to the health of the active public network; however, this group appears to be in decline. Overall the Main Discussion Group of the KM4Dev community appears to be a ?knowledge seeking? network rather than a ?knowledge sharing? network.Citas
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Borgatti, S. and Foster, P., 2003. The network paradigm in organizational research: a review and typology. Journal of Management, 29 (1), 991?1013.
Borgatti, S, Mehra, A., Brass, D. and Labianca, G., 2009. Network analysis in the social sciences. Science, 323 (5916), 892?95.
Carrington, P., Scott, J., and Wassermann, S. eds., 2005. Models and methods in social network analysis. Cambridge: Cambridge University Press.
Cyram, 2011. NetMiner® 4, Cyram Co. Ltd, Seoul, South Korea.
de Nooy,W., Mrvar, A., and Batagelj, A., 2005. Exploratory social network analysis with Pajek. New York: Cambridge University Press.
Dunbar, R., 2010. How many friends does one person need? Dunbar?s number and other evolutionary quirks. London: Faber and Faber.
Durland, M. and Fredericks, K., eds., 2006. Social network analysis in program evaluation. Minnesota: Wiley.
Farmer, N., 2008. The invisible organisation. How informal networks can lead organizational change. Farnham: Gower.
Gloor, P., 2006. Swarm creativity: competitive advantage through collaborative innovation networks. Oxford: Oxford University Press.
Hansen, D., Shneiderman, B., and Smith, M., 2011. Analyzing social media networks with NodeXL. Insights from a connected world. Burlington: Elsevier.
Johnson, D., 2009. Managing knowledge networks. Singapore: Cambridge University Press.
Marschall, N., 2007. Methodological pitfalls in social network analysis. Why current methods produce questionable results. Milton Keynes: Lightning Source UK Ltd.
Monge, P. and Contactor, N., 2003. Theories of communication networks. New York: Oxford University Press.
Kilduff, M. and Tsai, W., 2005. Social networks and organisations. London: Sage.
Rainie, L. and Wellman, B., 2012. Networked: the new social operating system. Cambridge, MA: MIT Press.
Scott, J., 2005. Social network analysis: a handbook. 2nd ed. London: Sage.
Shadbolt, N. and Milton, N., 1999. From knowledge engineering to knowledge management. British Journal of Management, 10, 309?322.
Wassermann, S. and Faust, K., 1999. Social network analysis. Cambridge: Cambridge University Press.
Wellman, B., 2011. Is Dunbar?s number up? British Journal of Psychology, 103, 176?6.
Wiig, K., 2004. People-focused knowledge management. Oxford: Butterworth-Heinemann. Wu, M., 2010, The 90-9-1 rule in reality [online]. 18 March, Lithium. Available from: http://
lithosphere.lithium.com/t5/Building-Community-the-Platform/The-90-9-1-Rule-in-Reality/bap/5463 [Accessed 16 July 2012].
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2019-09-07
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