The distribution of roles and functions for upscaling and outscaling innovations in agricultural innovation systems

The distribution of roles and functions for upscaling and outscaling innovations in agricultural innovation systems

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Article ID: iaor2013319
Volume: 115
Issue: 1
Start Page Number: 117
End Page Number: 128
Publication Date: Feb 2013
Journal: Agricultural Systems
Authors: , , ,
Keywords: innovation
Abstract:

The results show that the three functions are not evenly distributed over all participants in an innovation network. For each of these three functions there is a small group of people that form a core group of knowledge creators, institutional entrepreneurs and innovation brokers. The analysis of the main paths through these projects and events shows the close interaction between the lobbying and knowledge co‐creation functions. The ability to perform more than one innovation function over a longer period of time is extremely rare, but those people who can pull this off are very important for the success of an innovation network. This paper therefore concludes that the organisers of innovation networks should take try to organise their collaboration in such a way that it becomes easier for individuals to perform multiple roles within an innovation network. In this paper we use a network perspective to study the micro level of agricultural innovation systems and investigate the different roles and functions that collaborating actors have to perform to spread their innovation both horizontally and vertically. Based on a literature review, we distinguish between three separate network functions: (1) learning and knowledge co‐creation, (2) upscaling and institutional entrepreneurship and (3) outscaling and innovation brokerage. We investigate how these different functions have been performed in the case of the Northern Frisian Woodlands (NFWs) in the Netherlands over a period of 17years. We have constructed the two‐mode affiliation networks of the actors involved in various multidisciplinary research projects and lobbying events. We have analysed these networks using Social Network Analysis and measured the participation rates, relative degrees and the main paths through time with the Search Path Node Pairs algorithm.

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