Mixed-methods, networks and the geography of innovation
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.
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