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Research Network is a collaborative Canadian initiative to undertake and disseminate research results concerning the diverse nature of regional and local innovation systems across the country. The network was established on the premise that, because of their diversity, regional innovation systems in Canada could not be treated as similar, and that single nation-wide innovation policies would not be successful. This project is, itself, an experiment in research management, in that, because of its regional focus in a geographically diverse federation, it is organized as a federation of regional innovation studies networks. A consequence is that the network has been productive and has provided opportunities for graduate students to meet their peers (and future colleagues) across the country.

Manual standard for data collection on technological innovation. This can lead to methodological problems some of which exist in regional systems of innovation within OECD countries. Studies need to be carried out to improve the accuracy of the Manual and to provide input for future revisions of it.

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Governments in OECD countries are turning more and more towards creating networked entities as a means of organising cross-sector and multidisciplinary research. Yet, there is little discussion of how such networks operate and how they differ in evaluation terms from other research entities (individuals and organisations). This particularly relates to the policy objectives of networks. In this paper, we use the literature on evaluation, impact and value as a lens through which to focus on the nature and benefits of formal research networks. This paper seeks to refine our concepts of research networks and, in defining the concept of formal research networks, to map the policy issues in evaluating networks. We argue that, to do this, it is important that two extant literatures (stakeholder theory and organisational environments) be introduced into the analysis of network operations. We focus particularly on the significance of environmental complexity for network evaluation.