Large research infrastructures (RIs) s find themselves balancing the needs of many stakeholders striving for excellence in areas beyond the epistemic. One instance of this is the need to measure their impact, especially impact brought about through innovation. CASEIA is a pilot for a comprehensive impact study, applicable to future innovation interventions at RIs. As a qualitative study, it offers nuance and enrichment that can complement quantitative econometric methods such as M. Florio’s socio-economic cost-benefit analysis. We have tested our methodology, instruments and analytical framework on three case-studies. The main result from CASEIA is a study framework that can be scaled up to do just that.
We present a methodology to measure innovation impact qualitatively in an ex-post analysis. We present a novel analytical framework within which this impact can be understood, based on the non-orthogonal dimensions of Serendipity, spin off, spillover, skills and learning, social structures; and broader social impact. We also draw on an understanding of the taxonomy of Serendipity and the theory of social learning in looking for enablers and constraints in RI innovation research.
In our pilot study, our attention is focused on the impact of three innovation projects rooted in large infrastructures carrying out fundamental physics research. Our methodological and analytical toolbox can readily be extended to an ex-ante analysis, allowing us to search for indicators that an innovation intervention, such as seed-capital, will boost the impact of that innovation.
