Abstract
At the very heart of this book is the analysis of R&D cooperation and networking activities of firms in science-driven industries. As outlined before, we have used two official databases to gather data on nationally and supra-nationally funded R&D cooperation projects (cf. Sect. 4.2.3). These two data sources provided the basis for the construction of the German laser industry innovation network. Network analysis methods (cf. Sect. 5.2) provide us with a broad range of instruments to explore and analyze structural characteristics of networks (Wasserman and Faust 1994; Degenne and Forse 1999; Carrington et al. 2005; Borgatti et al. 2013). These methods can be used to analyze both network snap-shots at a particular point in time, and evolving network patterns over time. This chapter is divided into three sections. Section 8.1 gives an overview of the organizations involved in publicly funded R&D cooperation projects from various angles. Based on these findings, we explore the proportion of LSMs and PROs participating in two types of publicly funded research projects – “CORDIS” and “Foerderkatalog”. Then, we take an initial look at the large-scale topology of the German laser industry innovation network. Next we focus on the evolutionary change patterns of the German laser industry innovation network. In Sect. 8.2 we start our longitudinal exploration by analyzing a set of basic node-related and tie-related network measures over time. In Sect. 8.3 we provide an in-depth analysis of the network topology by testing for the existence of three distinct large-scale network properties. First, we analyze the overall degree distribution and check for the emergence of scale-free properties (Barabasi and Albert 1999). Then we test whether the German laser industry’s innovation network exhibits small-world properties by applying the method proposed by Watts and Strogatz (1998). Finally, we use different but complementary methodological approaches to check for the existence of a core-periphery structure (Borgatti and Everett 1999). We finish off the descriptive analysis by visualizing the evolution of the German laser industry innovation network over time.
In the long history of humankind (and animal kind, too), those who learned to collaborate and improvise most effectively have prevailed.
(Charles Darwin)
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Notes
- 1.
- 2.
This figure refers to our database extract provided by the CORDIS Service Team, European Commission (latest update: end of 2010).
- 3.
Each ID in Fig. 8.3 with the syntax: “LSMxxx” represents one of the 233 laser manufacturing firms. Note that the sequential ID number can be larger than the total number of firms in our sample.
- 4.
Public research organizations are symbolized by the following abbreviations: University = “RxxxU”, University of Applied Sciences = “RxxxA”, Technical University = “RxxxT”, Fraunhofer Institute = “RxxxF”, Max Planck Institute “RxxxM”, Helmholtz Institute “RxxxH”, Leibniz Institute “RxxxL” and other laser-related PROs = “RxxxD”.
- 5.
These types of networks are called scale-free networks (Barabasi and Bonabeau 2003, p. 52).
- 6.
To provide a solid benchmark for the real world network, we proceeded as follows: First we calculated the network size and density measures of each real world network on a yearly basis. Then we used the Erdös-Renyi procedure implemented in UCI-Net 6.2 (Borgatti et al. 2002) to generate random networks on an annual basis. Each annual random network corresponded exactly to its real-world equivalent in terms of network size and network density. Finally the annual degree distributions for both the real world network and the random network were accumulated and the results were plotted on a log-log scale.
- 7.
For details on the calculation and interpretation of both measures, see Sect. 5.2.3.
- 8.
To gain a more robust random benchmark this procedure has to be repeated several times. However, this may be dispensed with for the purpose of this analysis.
- 9.
For further details, see Sect. 5.2.3.
- 10.
Bipartite networks are based on the assumption that all members of a team form a fully connected clique (Uzzi and Spiro 2005, p. 453). We explicitly checked for this issue, as our network data is compiled on the basis of multi-partner R&D cooperation projects.
- 11.
We used NetDraw 2.0 to visualize the network (Borgatti 2002).
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Kudic, M. (2015). Evolution of the Industry’s Innovation Network. In: Innovation Networks in the German Laser Industry. Economic Complexity and Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-07935-6_8
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