Abstract
We investigate the ‘fluidity’, i.e., entry and exit of inventors, as well as changes of their relationships in nine German regions. The levels of entry and exit of inventors are rather high and most links among inventors are only short term. Fluidity of inventors and links leads to some fragmentation of the regional inventor networks but does not significantly affect other network characteristics. Entry of new inventors and growing numbers of links are positively related to the performance of the respective regional innovation system.
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Notes
There are two main reasons why embeddedness in networks may have a positive effect on the performance of actors. First, interaction with others may be an important channel for transferring (tacit) knowledge (Owen-Smith and Powell 2004; Storper and Venables 2004). Particularly, face-to-face contact promotes the development of personal trust that can be regarded as an important precondition for fruitful R&D cooperation. Second, the formation of links in R&D networks implies a process of screening and selection. Assuming that actors choose cooperation partners according to their abilities, actors included in a network have been positively evaluated. This positive selection of relatively able cooperation partners should have a positive effect on the probability of success (Granovetter 1973; Storper and Venables 2004; Wilhelmsson 2009).
The huge variation of the organization and the performance of innovation activities across the regions of a country (OECD 2010; Asheim et al. 2011) clearly demonstrates that the regional innovation system is an important topic for empirical analysis (Feldman and Kogler 2010). Although a number of factors that shape innovation activities are at the national and the sectoral level (Lundvall 2007) conditions for innovation activities may be a particularly relevant starting point for policy attempts to foster regional development (see Asheim et al. 2011; OECD 2009).
There are only very few studies that include the numbers or share of new actors in their analysis of the evolution of networks (see for example Ramlogan and Consoli 2014). While some studies analyze reasons for the discontinuation of R&D collaboration between organizations, assessments of the shares of discontinuing relationships and the reasons for abandoning cooperative ties are rare (see for example Thune and Gulbrandsen 2014; Greve et al. 2009; Park and Russo 1996). While some studies have investigated the effect of cooperative relationship on the performance of individual firms (e.g., Belderbos et al. 2015), we are also not aware of any other analysis of the effect of fluidity of actors and links on the performance of the respective innovation system.
Main reasons for the necessity to build trust are the considerable levels of uncertainty and asymmetric information that are characteristic for R&D cooperative relationships (Gilsing and Nooteboom 2005; Noteboom 2002). The uncertainty follows from the very nature of R&D as a discovery procedure. Since the result of this discovery procedure is unknown ex ante, it cannot be completely specified in an R&D contract, leaving room for opportunistic behavior of cooperation partners. Asymmetric information arises when there is incomplete knowledge about the abilities and future behavior of a potential cooperation partner. Trust is also needed because any cooperative R&D effort involves a considerable transfer of information and knowledge between partners that may be regarded sensitive. When engaging in cooperative R&D, actors need to trust that their partners will not use this information in an undesirable way (Gilsing and Nooteboom 2005; Tomkins 2001).
Examples are Greve et al. (2009), Park and Russo (1996) and Thune and Gulbrandsen (2014). Main reasons for abandoning a cooperative relationship are obviously completion of the R&D project and failure. According to Park and Russo (1996), the average duration of a cooperative R&D project between organizations is less than five years. Belderbos et al. (2015) investigate the relationship between the dynamics of R&D cooperation and innovation performance in a panel of Spanish firms. They conclude from their analysis that it is more the persistent collaboration that has a positive effect on firm innovativeness while the effect of discontinued cooperation was insignificant.
Albert et al. (2000) give two reasons why the performance of large scale-free networks should be highly stable with regard to fluctuations of actors and links. First, since most actors in such type of network have only a few links (Albert et al. 2000), the probability that a randomly removed actor has a central position in the network is rather low. Second, assuming that new actors tend to gravitate to well-embedded actors (‘preferential attachment’) there is a high probability that these new actors are at least as well connected in the network as the discontinued actors.
The number of patents that is recorded in RegPat (version March 2018) for the same regions and period of time is only about 53% of the number of patents that we find in our data base. Quite remarkably, this share varies considerably across the regions of our sample.
By harmonizing the data, we corrected for misspellings and compared the obtaining individuals regarding their first name, second name and ZIP code. If all of these three criteria were identical, we assumed that the individuals are identical.
A comparison of regional innovation networks constructed with different data sources (Fritsch et al. 2018) finds that patent data tend to underestimate links of private sector firms, while universities and other public research institutions are well-represented in patent data.
The number of such co-applications per region and time period ranges between 9 and 165. There is also a considerable problem of identifying cooperative relationships between organizations if member of such organizations file patent applications as private inventors. This is a particularly relevant case since in Germany, the professor’s privilege that allowed university researchers for file inventions for patenting on their own account was only abolished in the year 2002 while our period of analysis is 1994–2008. Moreover, even after this regulatory change university professors are still entitled to patent as private inventors if their university is not interested in the exploitation of their invention (e.g., because it wants to avoid paying the patent fees; see von Proff et al. 2012).
These periods are 1994–96, 1997–99, 2000–02, 2003–05 and 2006–08. Using longer time-periods (e.g., 5 year periods) does not lead to any basically different results.
In some further analyses we investigated the effect of omitting those inventors—so-called key players (Borgatti 2006)—from the network that have an particularly important role in keeping the network together so that their removal leads to the most significant fragmentation of the network. We found, that the network parameters remained rather stable when 5 or 10% of the most important key players are omitted.
Isolates are not included in the calculation of the average component size.
Empirical analyses of factors that may determine the reoccurrence of inventors in a subsequent period suggest that having been part of the largest component in t − 1 has the strongest impact while the number of an actor’s patents as well as his or her number of links is only a minor importance. For details see the Working Paper version of this paper under https://econpapers.repec.org/paper/jrpjrpwrp/2017-009.htm.
The shares of applicants that are present in two successive periods are in about the same range. Takíng all applicants together, the average share is 25.54%. There are, however, rather pronounced differences in this respect between types of applicants. While the share of reappearing private persons that cannot be assigned to a certain organization is rather low (14.44%) the share for organizations (firms and public research organizations) is much higher (33.85%). For larger universities the share is close to 100%.
A study by Ramlogan and Consoli (2014) on collaborative research in medicine finds that the share of new collaborations over all collaborations is always above 70% in all years of the observation period.
The share of links between applicants that persist in the successive period is 8.37%.
The share of discontinued inventors is the number of inventors that have been present in period t − 1 but not in the current period (t0) divided by the number of inventors in t − 1. The share of new inventors is the number of inventors that are present in the current period (t0) but were not part of the network in the previous period (t − 1) divided by the total number of inventors in t0. The net change of the number of inventors between t − 1 and t0 in measured in percent.
The squared form of the fluidity measures is never statistically significant, indicating absence of non-linear relationships.
Differences in the performance level of East- and West German regions are assigned to the regional fixed effects. Introducing a dummy variable for location in East-Germany in the models results in a significantly negative sign for the performance of East German RIS. For a comparison of East- and West German RIS in the years 1995–2001 see Fritsch and Graf (2011).
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Appendix
Appendix
Shares of inventors by number of patents (all periods)
Shares of inventors by number of degrees (all periods)
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Fritsch, M., Zoellner, M. The fluidity of inventor networks. J Technol Transf 45, 1063–1087 (2020). https://doi.org/10.1007/s10961-019-09726-z
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DOI: https://doi.org/10.1007/s10961-019-09726-z