The scientific goal of TechEvo was to undertake a comprehensive investigation of why regions enter and exit scientific and technological knowledge domains based on their past activities (path-dependency), their regional characteristics (place-dependency), and their embeddedness in regional, national and international networks. Further, TechEvo promised to develop new theoretical understandings that take into consideration previously overlooked factors and relationships in the study of regional innovation systems and technology evolution, and, simultaneously, to contribute to theoretical discussions elsewhere that have lacked empirical evidence, as well as to provide an online evaluation and planning tool for practical use to inform policy-makers in their quest to craft successful regional Smart Specialisation Strategies (S3).
The project has delivered on all these promises, and to some extent even significantly succeeded in these. The 18 already published manuscripts significantly advance the current state-of-the-art in this line of inquiry and have produced an array of contributions regarding theoretical, methodological, empirical, and practical (i.e. policy-relevant) advancements to the field of Evolutionary Economic Geography, and beyond. TechEvo has proven to be scientifically critical, conceptually innovative, and economically advantageous due to its far-reaching and all-inclusive approach that considered the heterogenous regional landscape that presents itself in the pan-European space.
The TechEvo core methodology, i.e. the “knowledge space” methodology, has established itself as a sound and widely applied empirical tool capable of assessing regional knowledge (scientific & technical) capabilities leading to future growth opportunities, which in turn is much needed for the more effective regional development and innovation policy instruments. TechEvo also provided a number of additional methodological advancements that are well beyond the current state-of-the-art, e.g. the concept of “knowledge entry-potential” (Kogler et al., 2022).
TechEvo also produced a number of highly relevant theoretical contributions, including an overview and discussion of the widely applied relatedness concept (Whittle and Kogler, 2019), and a contribution that highlights how evolutionary approaches to regional development can be improved upon with progressive empirical strategies (Kedron et al., 2020). Finally, TechEvo also contributed to current debates, e.g. on Artificial Intelligence (Buarque et al., 2020; Kogler et al., 2022). In summary, TechEvo insights and theoretical and empirical advancements are revolutionising the way regional innovation systems are analysed, and subsequently how strategies and policies utilise regional science-technology and innovation indicators in the development of more effective policy interventions.
