The linear model of innovation has been superseded by a variety of theoretical models that view the innovation process as systemic, complex, multi-level, multi-temporal, involving a plurality of heterogeneous economic agents. Accordingly, the emphasis of the policy discourse has shifted over time. It has gone from a focus on direct public funding of basic research as an engine of innovation, to the creation of markets for knowledge goods, to, eventually, the acknowledgement that knowledge transfer very often requires direct interactions among innovating actors. In most cases, these interventions attempt to facilitate the match between “demand” and “supply” of the knowledge needed to innovate. A complexity perspective calls for a different framing, one focused on the fostering of process characterized by multiple agency levels, multiple temporal scales, ontological uncertainty and emergent outcomes. The article explores what it means to design interventions in support of innovation processes inspired by a complex systems perspective. It does so by analyzing two different examples of coordinated interventions: an innovative public policy funding networks of innovating firms, and a private initiative supporting innovation in the mechanical engineering industry thanks to the set up of a technology broker. Relying on two unique datasets recording the interactions of the various organizations involved in these interventions, the article combines social network analysis and qualitative research in order to investigate the dynamics of the networks and the roles and actions of specific actors in fostering innovation processes. Building upon this comparative analysis, some general implications for the design of coordinated interventions supporting innovation in a complexity perspective are derived.
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Paper provided by University of Modena and Reggio E., Faculty of Economics "Marco Biagi" in its series Department of Economics with number
0619.
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