So far neither an elaborate theory of social innovation nor of its economic dimensions exists. There is a relative lack of academic research exploring social innovations' dimensions and antecedents in general, and its economic dimensions in particular. Until now, attention is primarily afforded to defining and understanding the construct, establishing theoretical models of social innovation, and providing detailed analysis of specific case studies.
At the same time, it appears that well-documented research on economic innovation as well as underlying theories, concepts and business models cannot easily be applied to social innovation. Social innovations are not per se led by profit motives, but rather guided by social demands. A first step towards understanding and operationalising the economic dimensions of social innovation requires a better, more robust theoretical foundation by means of a Go to Middle-range Theorytheory of middle-range (WP1).
In order to go beyond theoretical considerations and to develop a theory relevant for social innovation actors and target groups, testing through simulation and foresight through Go to SI Behaviour Scenariosscenario building (WP2) will be applied, to verify the elaborated middle-range theory, modes of policy production and business models.
Middle-range Theory (MRT)
MRT was developed in sociology by Robert K. Merton in the late 1940s as a way of connecting high-level social theory with empirically observable patterns. As such it stands between high-level social theory (e.g. hermeneutics) and low-level general laws or principles.
Modelling & Simulation
Simulation modelling and analysis is the process of creating and experimenting with a computerised mathematical model (Chung, 2004) imitating the behaviour of eal-world processes or systems over time. Simulation modelling is specifically useful for policy makers and strategic management, gaining insight into general future developments.
Scenarios are narrative descriptions («stories») of potential futures that focus on relationships between events and decision points. They direct attention to driving forces, possible avenues of evolution and the span of contingencies that may be confronted. Thus they are particularly useful when many factors need to be considered and the degree of uncertainty about the future is high.