Abstract
Abstract In the quantitative, macro-oriented triple helix literature, synergy is measured indirectly, through patent data, firm data and other secondary statistical sources. These macro-level quantitative studies do not open up for understanding how different processes of cooperation create different outcomes, in terms of synergies. This article presents an alternative method of measuring quantitatively how different networks of innovation in a variety of ways create different types of complex synergies. This opens up for an empirical analysis of variations of synergy formation, seen as innovation networks with different structures, formed within and between helices, regions and geographical levels. Data was collected through a snapshot survey in 10 regional cases in the Baltic Sea Region. The analysis presents how different networks of innovation within and between helices are formed by different combinations of expectations, experiences and gaps.
Highlights
The article responds to the call by Cai and Etzkowitz (2020) for new methodological approaches in understanding helix dynamics, as well as Meyer et al (2014: 170): via free access more enriched indicators that are multilayered and multi-dimensional are required to unpick the situation from different and differing angles, allowing for the heterogeneity of the different actors to be voiced and heard.State of the art studies use qualitative case-studies and quantitative analysis of secondary macro-level statistical sources, such as patents, co-authorships, citation indexes etc. to build analysis of synergy indicators between helices (Leydesdorff and Etzkowitz, 1998; Leydesdorff et al, 2017b; Meyer et al, 2014)
Our research questions are: How can we build indicators which measures synergies within and between helices in innovation networks through primary data collected from informants participating in these processes? What can we learn from this approach? The approach presented in this article use primary micro-level data which was collected in our studies of connectivity between organizations within and between helices in selected regions
Connectivity between actors, measured through expectations, experiences and importance is an important feature in the approach, which has been used in analyzing both triple helix (TH) (Mäenpää, 2020) and quadruple helix (QH) arrangements (Mariussen et al, 2019; Vilkė et al, 2020; Gedminaitė-Raudonė et al, forthcoming 2021)
Summary
The article responds to the call by Cai and Etzkowitz (2020) for new methodological approaches in understanding helix dynamics, as well as Meyer et al (2014: 170): via free access more enriched indicators that are multilayered and multi-dimensional are required to unpick the situation from different and differing angles, allowing for the heterogeneity of the different actors to be voiced and heard.State of the art studies use qualitative case-studies and quantitative analysis of secondary macro-level statistical sources, such as patents, co-authorships, citation indexes etc. to build analysis of synergy indicators between helices (Leydesdorff and Etzkowitz, 1998; Leydesdorff et al, 2017b; Meyer et al, 2014). Our research questions are: How can we build indicators which measures synergies within and between helices in innovation networks through primary data collected from informants participating in these processes? Connectivity analysis has been developed since 2013 (Virkkala et al, 2014, Virkkala et al, 2017; Mäenpää, 2020) in the context of a regional innovation development policy called Smart Specialization Strategy It has been developed both as an analytical approach and as policy model, originally in cooperation with regional development authorities in Ostrobothnia, Finland. Our informants are a variety of actors in different helices Based on these variations, we argue that our data captures core aspects of the dynamic where expectations and experiences create and shape perceptions of importance, indicating synergies within and between helices in innovation networks. Our analysis is related to and draw upon Niklas Luhmann’s analysis of how social systems emerge through creation and protection of expectations (for a comparison of our approach and Luhmann, see section 3.2)
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