Open innovation, national innovation systems, QCA, causal complexity, IUS, innovation policy


This paper argues that innovation policy research can benefit from utilizing new research methods as they might lead to different policy recommendations. It demonstrates this by using a set-theoretic fsQCA method to analyse the data on innovation policies in the European Union (EU). It shows that the use of correlation-based statistical methods is not appropriate for the evaluation of innovation policies due to their causally complex nature that correlational statistical methods cannot unravel. This paper demonstrates this by focusing on the special importance of linkages among actors and innovation commercialisation through entrepreneurship and the notion that they represent a necessary condition for innovation success. Results confirm that the single factor of Linkages & entrepreneurship is the necessary condition for innovation success, thus emphasizing the importance of an open innovation framework for innovation policy-making. Results also show three combinations of sufficient conditions (but no single factor) lead to innovation success. They confirm the causal complexity of innovation policy and confirm that using different research methods will lead to different policy recommendation.