Mapping inventors' networks to trace knowledge flows among E U regions

Conventional theories on innovation have tried to explain the technological trajectory of firms stressing the discontinuities existing in the firm innovation process (D’Aveni, 1994; Tushmann and Anderson, 1986). As highlighted over the years by the Schumpeterian tradition, radical innovations emerge erratically by chance, when dynamic entrepreneurs, exploring new market opportunities, introduce “new combinations” moving the entire economic system far from equilibrium (Schumpeter, 1934, 1947). However, a great deal of technological change and product improvements consist of marginal and incremental innovations (Arrow, 1962; Freeman, 1994; Malerba, 1992). This was not acknowledged in the innovation literature during 1980s and 1990s, where the focus was prevalently on basic radical inventions and innovations (Clark et al., 1984, Jewkes et al., 1958). After the end of the 1970s the economic importance of marginal technical improvements for sustaining innovation in firms becomes largely acknowledged (Basalla, 1988; Dosi, 1982; Rosenberg, 1976; 1982). As argued by Mokyr (2000): “Much if not most creativity comes from the manipulation of what is already known, rather than in addition of totally new knowledge” (p. 18). Often innovations are only fed by a continuous re-combination of flows of pre-existing knowledge, coming from different sectors or firms through cumulative learning processes, as Pavitt (1984, 1999) authoritatively showed. A critical aspect is how old and new knowledge is integrated by firms, and applied to new domains. Within the economic system there is an overwhelming amount of old knowledge that firms reuse and re-combine for new needs. Old knowledge might be recombined to new uses in other domains, or the firms might acquire existing knowledge from outside to feed their internal innovation activities, along with an open innovation strategy (Asheim and Isaksen, 2002; Chesbrough, 2003a and b). Generative collaborations within an innovative ecosystem or regional innovation system (Asheim et al., 2011) may enlarge the space of possibilities and identify new systems of use alongside the discovery of new functionalities and the recombination of new and old knowledge within a process of innovation cascades (Bonaccorsi, 2011; Lane 2011). The new literature about technological change has emphasized the role of knowledge recombination as one of the most important sources of technological novelty and invention (Weitzman, 1998; Strumsky et al., 2012 and 2012; Youn et al., 2014). Youn et al. (2014), for instance, showed that after a huge creation of new patent codes (indicating the introduction of novel technologies) occurred between 1800-1850, the subsequent pattern of inventions was mainly based on the recombination of existing codes, occupying a practically infinite space of technological configuration. Patents (Jaffe et al., 1993) are the main expression of this technology novelty and, in fact, new patents nowadays are typically associated with old existing technological codes. As Fleming (2001) affirms, “the source of technological novelty and uncertainty lies within the combination of new components and new configurations of previously combined components” (pg. 130), while in the history of patent analysis there is a very limited role for the development of original technologies (Strumsky et al., 2012). In literature, there is considerable evidence that the production of scientific and technological knowledge is becoming more and more a collective phenomenon (Allen, 1983; Freeman, 1991; Gay et al., 2008). As Powell and Giannella (2010, p. 4) define collective invention as a “technological advance driven by knowledge sharing among a community of inventors who are often employed by organizations with competing intellectual property interests”. Collaborations enable organizations and regions to share, transfer, and assimilate knowledge by supporting knowledge creation and recombination process, reduce the costs of innovation, extent the depth and breadth of local knowledge base by facilitating the access to diversified knowledge, and foster externalities and knowledge spillover. This study investigates the extension of collaborative invention processes across time and geographical boundaries. Our first research question was to investigate in EU the relative decline of individual patents and the emergence of co-patenting activity (Hall et al, 2001; Fleming and Frenken, 2007). Economic geography and regional science have an established tradition of studying the importance of geographical proximity for innovation and the formation of networks (Rallet and Torre, 1999; Boschma and Frenken, 2010; Cassi and Plunket, 2015). In the example of the collaboration networks of inventors in German biotechnology, Ter Wal (2013) has demonstrated that the role of geographic distance as mechanisms of tie formation and network evolution shifts over time as the technological regime of the industry changes. Several studies have stressed the spatial dependence of invention process and the critical role of spatial proximity even though other forms of proximity (such as cognitive, cultural, organizational, and institutional) have been recognized as important (Marrocu et al., 2013). However, collaborations over long-distance remain a critical driver for accelerating knowledge creation (Wilhelmsson, 2009). Our second research question explores the innovation performance of inventors considering their scalar geographical localization (intra-regional, inter-regional, extra-EU). In this light, our analysis confirms the patenting distribution across European regions within a strong innovative area, commonly named “blue banana” (Foddi and Usai, 2013). Our third research question explores the inter-regional collaboration in EU lagging-behind regions, which is expected to balance the local lack of resources and structures for innovation. The need for co-invention opportunities drives the consideration of a higher collaboration propensity of peripheral regions. Finally, in rapidly developing industries, the development of collaboration strategies to identify new opportunities and learn about new technology (Powell, 1998) is almost inevitable. In this study, the increasing relevance of innovative industries such as bio, nano, green, laser and optical technologies is analyzed.