SPECIAL SESSIONS

Mixed-methods, networks and the geography of innovation

Session organizers:

Bastien Bernela (bastien.bernela@univ-poitiers.fr), Marie Ferru (marie.ferru@univ-poitiers.fr), Michel Grossetti (rgros@univ-tlse2.fr)

 

The geography of innovation community has mainly developed on the basis of quantitative studies. The traditional search for the determinants of the spatial concentration of innovation activities and the measurement of spillovers has motivated the sophistication of econometric modelling and quantitative social network analysis (Massard and Mehier, 2009; Autant-Bernard et al., 2007; Snijders et al., 2010; Balland, 2012). The improvement of quantitative techniques associated to a better access of data (such as patent or European projects database) has led to the multiplication of empirical studies; but the geography of innovation scholars, facing the comprehensiveness of the data used, have to assume relationships within networks (Bernela and Levy, 2017). Most of recent studies lack relational data sufficiently precise to reveal the role of networks and to decrypt complex interactions inherent to the spatial dynamics of innovation.

One of the main empirical challenge in this literature lies therefore in the build-up of relevant relational data. In this context, the mobilization of “different data sources, ranging from the collection of primary data by qualitative research or questionnaires, to a multitude of secondary data sources” (Boschma and Fornhal (2011, p. 1297) appears as a possible answer to this challenge. More precisely, the mixed method approach constitutes a promising methodological framework by giving new insights for the geography of innovation. This method is used for research that involves collecting, analyzing and integrating quantitative and qualitative research and data in a single study (Small, 2011). It therefore uses the combination of the two approaches in order to exploit the strengths of each: statistical and systemic results but misinterpretation risks on the quantitative side vs. “decoding” of complex processes, behaviors, or trajectories but illustrative and contextual analysis on the qualitative side (Starr, 2012).

Recently, authors argue mixed method can complete quantitative social network analysis by reintroducing through qualitative data “the real-life experience, bibliographical events that leave traces, qualitative data give a different thickness and a better understanding of quantitative data” (De Federico de la Rúa and Comet, 2011); they serve to take the context into account (Edwards, 2010), to bind content analysis of network structure (D’Angelo et al., 2016) and to “explore in depth the reasons for change” (ibidem). Mixed methods can be used for different topics within the geography of innovation, as start-up creation (Grossetti and Barthe, 2008), science-industry partnerships (Grossetti and Bès, 2001; Ferru, 2010, 2014), spatial trajectory of scientists (Bernela and Milard, 2016), clusters’ life cycle, etc. This special session addresses to conceptual, methodological or empirical contributions that question or use mixed methods for the geography of innovation.

 

References

Autant-Bernard, C., Mairesse, J., & Massard, N. (2007). Spatial knowledge diffusion through collaborative networks. Papers in Regional Science, 86, 341-350.

Balland, P.A. (2012). Proximity and the Evolution of Collaboration Networks: Evidence from Research and Development Projects within the Global Navigation Satellite System (GNSS) Industry. Regional Studies, 46(6), 741-756.

Bernela, B., & Levy, R. (2017). Collaboration networks in a French cluster: do partners really interact with each other? Papers in Regional Science, 96(1), 115138.

Bernela, B., & Milard, B. (2016). Co-authorship Network Dynamics and Geographical Trajectories-What Part Does Mobility Play? Bulletin of Sociological Methodology, 131(1), 5-24.

Boschma, R., & Fornahl, D. (2011). Cluster evolution and a roadmap for future research. Regional Studies, 45(10), 1295-1298.

D’Angelo, A., Ryan, L., & Tubaro, P. (2016). Visualization in mixed-methods research on social networks. Sociological Research Online, 21.

De la Rúa, A.D.F., & Comet, C. (2011). Personal networks, social networks. Bulletin of Sociological Methodology, 110(1), 5-10.

Edwards, G. (2010). Mixed-Method Approaches to Social Network Analysis. ESRC National Centre for Research Methods Review.

Ferru, M. (2010). Formation process and geography of science–industry partnerships: the case of the University of Poitiers. Industry and Innovation, 17(6), 531-549.

Ferru, M. (2014). Partners connection process and spatial effects: New insights from a comparative inter-organizational partnerships analysis. European Planning Studies, 22(5), 975-994.

Grossetti, M., & Barthe, J. F. (2008). Dynamique des réseaux interpersonnels et des organisations dans les créations d’entreprises. Revue française de sociologie, 49(3), 585-612.

Grossetti, M., & Bès, M. P. (2001). Interacting individuals and organizations: a case study on cooperations between firms and research laboratories. In Economics with heterogeneous interacting agents (pp. 287-301). Springer Berlin Heidelberg.

Massard, N. & Mehier, C. (2009). Proximity and innovation through an « accessibility to knowledge » lens. Regional Studies, 43(1), 77-88.

Small, M. (2011). How to conduct a mixed methods study: Recent trends in a rapidly growing literature. Annual Review of Sociology, 37, 57–86.

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

Starr, M. (2012). Qualitative and mixed-methods research in economics: surprising growth, promising future. Journal of Economic Surveys, 28(2), 238–264.

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