This paper examines the features of successful forms of innovation in rural areas characterized by geographical distance and sparse population. The core questions are: how firms compensate for the lack of a dense local network; how rural clusters are emerging and changing; how firms acquire knowledge for innovation processes; how firms face the challenges of globalization, notably at the level of value chains. The topics will be examined through an analysis of the development of one agricultural area in northern Finland which has industrialized since the mid-1990s in the wake of the growth of the national information and communication technology (ICT) cluster. The findings reveal that the knowledge needed for innovation in small and medium-sized enterprises (SMEs) stems from regional sources-clients of localized networks as well as regional educational and knowledge institutes-whereas the leading firms of the regional networks acquire knowledge from clients, non-local knowledge institutes and national technology programmes.

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AcknowledgementsWe would like to acknowledge the Regional Council of Ostrobothnia, and especially Mr.Jerker Johnson, as well as the other members of the research team Smart specialisering strategi Österbotten for co-operation. Åge Mariussen acknowledges the support of Nordland County Council VRI program through the RISCK project. We would also like to thank the two anonymous referees for valuable comments.

In the quantitative, macro-oriented triple helix literature, synergy is measured indirectly, through patent data, firm data and other secondary statistical sources. These macro-level quantitative studies do not open up for understanding how different processes of cooperation create different outcomes, in terms of synergies. This article presents an alternative method of measuring quantitatively how different networks of innovation in a variety of ways create different types of complex synergies. This opens up for an empirical analysis of variations of synergy formation, seen as innovation networks with different structures, formed within and between helices, regions and geographical levels. Data was collected through a snapshot survey in 10 regional cases in the Baltic Sea Region. The analysis presents how different networks of innovation within and between helices are formed by different combinations of expectations, experiences and gaps.

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