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Paper info: Exploring innovation networks: two simulations, two perspectives and the mechanisms that drive innovation performance


Exploring innovation networks: two simulations, two perspectives and the mechanisms that drive innovation performance


Doina Olaru,
Sara Denize
University of Western Sydney
Sara Denize and
Sharon Purchase
University of Western Australia
Sharon Purchase

Place of Publication

The paper was published at the 24th IMP-conference in Uppsala, Sweden in 2008.


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Business networks are complex systems, made up of interdependent organisations whose managers are each trying to accomplish their own goals whilst simultaneously responding to the actions of others. Understanding the complex interactions that occur within networks requires perspectives that consider whole systems and the complex interdependencies that exist between its parts. Case research methodologies provide the requisite rich perspectives and have been crucial in developing our understanding of business networks. In this paper we show how simulation can be used to complement case-based research to enrich our understanding of the market forms. We develop competing simulations of a prototypical innovation network using two different modelling approaches. We then use these simulations to show how actors' choices impact the distribution of resources within the network and which subsequently produce variations in the innovative performance of the network. In doing this we are able to provide a unique perspective on the mechanisms that produce different network eventualities (i.e. innovation network success and failure).The alternative simulation approaches we used consider this same phenomenon from two quite different standpoints. First, we develop the theoretical evidence supporting a simple "prototypical" innovation network and the mechanisms that drive it and we explore this network using fuzzy set theory. The model shows how network links, bonds and ties impact on innovation performance, but from a macro system wide orientation. In the competing model we use an agent-based approach (in Netlogo) to reproduce a similar innovation network. But with this approach it is possible to consider the impact on performance for individual actors as well as the overall network. We argue that there are deep mechanisms in innovation networks which drive their performance and that these consistently emerge despite the different simulation strategies and their perspective/frame of reference.