Given the economic, societal and environmental relevance of innovation, this paper contrasts various models of innovation, compares how innovation is understood in mainstream economics and evolutionary economics of innovation and juxtaposes the concomitant policy rationales. By discussing two monitoring tools used by the European Commission to assess its member states. innovation performance, it argues that the science-push model of innovation is still highly influential in the EC STI policy circles, in spite of the significance of non-R&D types of knowledge in innovation processes. Then it explores various types of opportunity costs stemming from the persistent high-tech myth, considers possible historical and sociological reasons for its perseverance and discusses policy implications of the systemic view of innovation, with an emphasis on the case of the EU10 countries. Policy conclusions include: i) several policies affect innovation processes and performance, perhaps even more strongly than STI policies, and hence policy goals and tools need to be orchestrated across several policy domains; ii) STI policies should promote learning and knowledge-intensive activities in all sectors, including low- and medium-technology industries and services; iii) analysts and policy-makers need to avoid the trap of paying too much attention to simplifying ranking exercises; iv) new indicators that better reflect the evolutionary processes of learning and innovation would be needed to support analysis and policy-making; v) the choice of an economics paradigm to guide policy evaluation is likely to be decisive.
Innovation processes Sources, forms and types of knowledge Models of innovation High-tech myth Low- and medium-technology sectors Economics paradigms STI policy rationales Measurement of innovation Composite indicators Scoreboards League tables European Commission Central and Eastern European countries