This is a joint contribution from Matthieu Stigler (I previously posted Messages 15 and 44), Dominique Barjolle (Message 61) and Sylvain Quiédeville from the Research Institute of Organic Farming (FiBL) in Switzerland. Part of FibL's work in the IMPRESA project (http://www.impresa-project.eu/) is to prepare a case study manual which will document the methodological framework to be used later in the project for six case studies of scientific research-based innovation.
Before the conference ends, we would like to ask the participants their views about the potential of social network analysis (SNA) for ex post impact assessment (epIA), which will be very useful for us to prepare the IMPRESA case-studies guidelines.
In a nutshell, SNA analyses links between individuals in a network, usually through network maps and various indices of clustering and concentration, centrality and power within the network. In this way, SNA gives a picture of how information can be transmitted (or not) among actors. This appeared to us very promising in the case of agricultural R&D, where the diffusion of innovations can be facilitated/hindered depending on the type of network structure. As SNA was further advocated for epIA by several authors (see Davies, 2005; CGIAR, 2008), this led us see positively its inclusion as an epIA method in the e-conference background document (Ruane 2014).
However, our initial enthusiasm for SNA for epIA has somehow decreased now, and we see several problems to use SNA for epIA:
-SNA is clearly interesting to get an idea of how information is transmitted. It does, however, not say anything about the effect of receiving that information on the actors, whether the information will change their behaviour and, if yes, based on which mechanisms. It is, however, precisely these elements which SNA cannot describe that are of main interest in an epIA.
-We agree SNA can help identify key actors in a network, which will prove helpful for qualitative data collection such as focus group discussions, key informant surveys, etc. But SNA is a costly operation, and a simple search of key players through informal contacts can be much cheaper and faster.
-SNA is a static approach, providing few insights into the dynamics of innovation diffusion and learning. It is true that SNA can be made dynamic by surveying the network multiple times, but this seems again costly, and particularly difficult in a pure ex-post situation.
But we would be happy to hear the participants' views on these points. Are we understating the potential of SNA? Can SNA be used for epIA? At which stage? Has anyone particular experience of using SNA for epIA?
Matthieu Stigler, Dominique Barjolle and Sylvain Quiédeville
Institut de recherche de l¹agriculture biologique (FiBL)
Ackerstrasse 113,
Case postale 219
5070 Frick,
Switzerland
www.fibl.org
e-mail: matthieu.stigler (at) gmail.com
References:
-CGIAR. 2008. Impact assessment of policy-oriented research in the CGIAR: Evidence and insights from case studies. CGIAR Independent Science and Partnership Council (ISPC). http://www.sciencecouncil.cgiar.org/fileadmin/templates/ispc/documents/Impact_Assessment/SC_IA_PORIA2008.pdf (1.1 MB)
-Davies, R. (2005). Scale, complexity and the representation of theories of change: Part II. Evaluation, 11(2), 133-149.
doi:10.1177/1356389005055528
-Ruane, J. (2014) Background document to the FAO e-mail conference on "Approaches and methodologies in ex post
impact assessment of agricultural research: Experiences, lessons learned and perspectives", FAO, http://www.fao.org/docrep/019/as549e/as549e.pdf
[To contribute to this conference, send your message to [log in to unmask] For further information, see http://www.fao.org/nr/research-extension-systems/res-home/news/detail/en/c/217706/ ].
########################################################################
To unsubscribe from the Impact-L list, click the following link:
https://listserv.fao.org/cgi-bin/wa?SUBED1=Impact-L&A=1
|