Dynamics of Innovation Systems Simona Negro Marko Hekkert Utrecht University Innovation Studies Group Department of innovation and environmental sciences Copernicus Institute for Sustainable Development and Innovation

Introduction Innovation systems approach is a powerful heuristic framework Highlights the systemic nature of innovation processes –No innovation in isolation Very well diffused under policy makers (OECD etc) It has potential to contribute to ‘system innovations for sustainable development’

Theoretical approach Several authors study IS in relation to the (sub)functions of the IS that they consider important. These functions relate to what needs to be achieved by (in) the system to create and diffuse innovation

System Functions (1/2) The way a certain system function is fulfilled is determined by many activities of agents and by different institutions These agents do not act purposely to fulfill a system’s function, but act based on strategies to meet their own (or public) needs. –Free will but also bounded by institutional settings The way all these activities cumulate determines the functioning of the innovation system This cumulative outcome can be studied.

System Functions (2/2) Function 1: Entrepreneurial Activities Function 2. Knowledge Development Function 3. Knowledge Diffusion through networks Function 4. Guidance of the Search »Expectations »Legitimation »Vision for future Function 5. Market Formation Function 6. Resource Mobilisation Function 7. Support from Advocacy Coalitions »Lobby activities

Interactions

Aim Problem 1: Different lists of functions are used in literature Problem 2: No detailed mapping method to map function interaction Goal of our paper: –Testing one set of system functions –Testing whether cycles really take place –Studying which type of interactions often take place / reoccurring patterns

Methodology Method: event history analysis / process method 10 cases analyzed over time ( ) (all sustainable technological innovation systems) Combination of qualitative and quantitative data Reconstruct the evolution in narrative Make graphs of functional patterns over time

System Functions Biomass Digestio n NL Biomass Digestio n D Biomass Gasificati on Biomass Combustion Biofuel s PVTotal % per SF Function 1: Entrepreneurial Activities Function 2: Knowledge Development Function 3: Knowledge Diffusion Function 4: Guidance of the Search Function 5: Market Formation Function 6: Resource Mobilisation Function 7: Advocacy Coalition Results

Trends / Reoccurring patterns Very preliminary results –Guidance (F4) very important: often starting point of IS dynamics, often also starting point of new virtuous cycle –Guidance often leads to (soft) knowledge development (F2) (via resources formation, F6). Then whole range of different patterns –Entrepreneurial activities (F1) have a pivotal role in IS. Many other functions lead to EA but in turn also many SF are triggered by EA. –Legitimation / lobby / advocacy coalitions (F7) proved to be critical in changing existing legislation / creating alignment – requires well organised entrepreneurs –Market formation (F5) is often final barrier / driver

Virtuous and vicious cycles Strongly growing IS show continuous process of positive interaction between system functions Sometimes temporary vicious cycles take place, but are quickly terminated / taken over by virtuous cycles More problematic functioning IS show alternating cycles: virtuous – vicious. Some developments end due to vicious cycles Poorly functioning IS show hardly any positive interaction between SF. Also over long time periods several functions are missing

Implications More insights in these patterns may help to improve innovation policy – now instruments often strongly financially dominated May help to improve entrepreneurial strategies – provides insights in how to include the IS in business / innovation strategy