Successful innovations depend on the creation and integration of new knowledge – technological, strategic and market related. An extensive theoretical and empirical literature deals with the determinants of internal and external knowledge sourcing, and their effects on innovation performance. Evolutionary economic theory, for instance, highlights the interactive learning and cumulative processes among actors involved in innovation (Jensen et al., 2007; Nelson and Winter, 2002). Within this perspective, the combination of diverse knowledge sources, such as inter-sectoral knowledge, is especially valued as a means to generate radical innovations (Nemet, 2012; Rosenberg and Nelson, 1994). More recently, one strand of literature has gained attention by encouraging an ‘open innovation model’, which advocates the use of external knowledge sources to accelerate innovation and market diffusion (Chesbrough and Crowther, 2006; Gassmann et al., 2010). Similarly, management studies provide evidence that a wider and more diverse pool of knowledge sources fosters innovation as it enables building new competences through the combination of complementary knowledge sets from internal and external sources (Bergek et al., 2013; Teece and Pisano, 1994).
A commonly shared idea is that internal and external knowledge sourcing activities are simultaneously carried out and complementary, i.e., mutually beneficial as their returns are positively correlated. Internal knowledge sources strengthen the impact of external knowledge sources by improving knowledge assimilation and optimising the focus or intensity of external knowledge search, which is usually known as absorptive capacity (Cohen and Levinthal, 1990; Zahra and George, 2002). At the same time, external knowledge sources enhance the impact of internal knowledge by introducing new knowledge and practices, and by creating the possibility of innovative combinations of knowledge. These contribute to preventing inertia and obsolescence (Cockburn and Henderson, 1998; Kogut and Zander, 1992; Leonard-Barton, 1992; Nelson and Winter, 2002). Previous studies have also built up empirical evidence both for the positive impact of accumulated knowledge when tapping external knowledge (Arora and Gambardella, 1994; Escribano et al., 2009; Hansen and Birkinshaw, 2007; Wu and Shanley, 2009), as well as on the benefits of external to internal knowledge sources (Cassiman and Veugelers, 2002; Cohen et al., 2002; Tether and Tajar, 2008; Vega-Jurado et al., 2009). Yet, next to the potentially positive effects on innovation of combining knowledge sources, comes the risk of inhibitory effects due to path
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dependency (Acemoglu et al., 2012; Sydow et al., 2009), search myopia (Levinthal and March, 1993) or core rigidities (Leonard-Barton, 1992).
The impact of complementarity between internal and external knowledge sourcing on innovation performance remains controversial due to mixed evidence from the few empirical studies done so far. Beneficial effects of complementarity have been found for the combination of internal R&D and suppliers as knowledge sources for process innovations (Reichstein, 2006), internal R&D and knowledge flows from research institutes and universities (Cassiman and Veugelers, 2006), and between absorptive capacity and external knowledge flows in sectors marked by knowledge breakthroughs and tight intellectual property rights (Escribano et al., 2009). In contrast, other research has pointed to a substitution effect between internal R&D and external knowledge sources, as well as decreasing returns of the benefits from the latter. (Laursen and Salter, 2006). Further research indicates that internal R&D has a weak effect on promoting exploitation of external knowledge sources and a negative relationship with knowledge sourced from competitors (Lhuillery, 2011). Along the same lines, Vega-Jurado et al. (2009) indicate a significant variation of knowledge sourcing complementarity between product and process innovation, but still no positive effect in terms of innovation output.
5.2.2 Knowledge sourcing for environmental innovation
External knowledge sources play an especially important role in environmental innovation for several reasons. First, empirical evidence suggests that external (inter-sectoral and international sources) have a higher impact on innovation in environmental technologies than on other technologies (Acemoglu et al., 2012; Cruz-González et al., 2015; De Marchi, 2012;
Ghisetti et al., 2015; Lanjouw and Mody, 1996; Leoncini et al., 2016). Hence, if environmental innovation is comparatively more dependent on external knowledge sources than incumbent technologies, external knowledge sourcing strategy gains relevance. Second, incumbent technologies have a much larger knowledge base and thus enjoy increasing returns to scale in generating further innovations (Acemoglu et al., 2012; Dangerman and Schellnhuber, 2013; van den Bergh, 2013). This means that optimising knowledge sourcing for environmental technology involves a double challenge, namely to compete with incumbent technologies while also seeking to explore them as external knowledge sources.
Third, the use of external (competing) knowledge sources can backfire by feeding further development of the knowledge base of the incumbent technologies.
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Among all environmental innovations, extensive research has been gathered for renewable energy on the impact of external knowledge sources on innovation. Previous studies focused on renewable energy innovation have sought to identify, trace and assess the mechanisms capable to induce or hamper the impact of external knowledge. This has involved examination of the link between knowledge and innovation in renewable energy with a focus mainly at the technology and country levels. At the technological level, a key element is comparing the benefits of investment in knowledge production across technologies and sectors. The underlying argument is that resources should be aimed at the alternatives with best return on investment, for example, by focusing on technologies with larger extent of knowledge spillovers, broader applications, and limited crowding-out effects (Noailly and Shestalova, 2013; Popp et al., 2010; Popp and Newell, 2012). At the country level, studies have concentrated on the relative influence of domestic and foreign knowledge production in renewable energy. Empirical evidence points to an important impact of international knowledge flows on renewable energy innovation, not only through traditional market channels, but also through knowledge spillovers (de la Tour et al., 2011; Glachant et al., 2013; Kirkegaard et al., 2009; Popp, 2009). Nevertheless, despite the central role of external knowledge in fostering renewable energy development, empirical investigation of the impact that different knowledge sourcing strategies have on innovation remains underexplored.
5.2.3 Knowledge sourcing strategy and renewable energy innovation
Renewable energy innovation depends on a broad range of knowledge sources as it usually involves the combination of diversified and complex knowledge areas (Garrone et al., 2014;
Nemet, 2012; Noailly and Smeets, 2015; Popp, 2016). This explains the importance of external knowledge sourcing as a means to increase the likelihood of gathering complementary knowledge capable of fostering innovation.
Two core features of knowledge sourcing strategies are “breadth” and “depth”
(Aharonson and Schilling, 2016; Ferreras-Méndez et al., 2015; Ghisetti et al., 2015; Laursen and Salter, 2006, 2014; Leiponen and Helfat, 2009; Love et al., 2014). The first component,
“breadth”, refers to the range of knowledge sources upon which an innovator draws in order to access external knowledge. The basic assumption is that a larger number of knowledge sources improves innovative performance as it means access to a more diverse knowledge base. Early research showed that the likelihood of uncertain innovation success can be enhanced by increasing the variety of technological sources (Nelson, 1959; (Kogut and Zander, 1992). More recently, the idea of open innovation further emphasized the value of
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external knowledge sourcing by arguing it is a necessary strategy to increase R&D productivity in the face of growing competition and faster technology development cycles (Cassiman and Valentini, 2015; Chesbrough, 2003; Chesbrough and Crowther, 2006). In the context of renewable energy technologies, the role of external knowledge sources is particularly relevant as they require a systemic change, including not only technological innovations but also organizational (e.g., grid management) and institutional ones - e.g., specific regulations (Popp et al., 2010; Trancik, 2015).
The second component of knowledge sourcing, “depth”, denotes the intensity of use of external knowledge sources. Innovative organisations often draw deeply from only a restricted number of external knowledge sources (Bodas Freitas et al., 2013; Cassiman and Veugelers, 2006; von Hippel, 1994). The development of each of these intensively used knowledge sources involves the construction of an interaction pattern, where shared communication channels, goals and working routines are built over time. Despite the additional costs involved, this knowledge sourcing strategy is expected to pay-off as it grants access to more distant and complex knowledge, and spurs virtuous cycles of exchanges with external knowledge.
Motivated by the foregoing arguments, we will test if the following hypotheses are supported for renewable energy innovation:
H1: Breadth of external knowledge sourcing has a positive effect on innovation performance of research groups focused on renewable energy.
H2: Depth of external knowledge sourcing has a positive effect on innovation performance of research groups focused on renewable energy.
Building on previous studies (Cruz-González et al., 2015; Ghisetti et al., 2015; Laursen and Salter, 2006, 2014), we further examine whether some organisations ‘over-search’, leading to a negative impact on their innovation performance, or become too deeply dependent on external sources for innovation. Moreover, as external knowledge sourcing has a cost in terms of money, resources and attention, one may expect decreasing returns to set in at some stage (Ghisetti et al., 2015; Laursen and Salter, 2006). These considerations give rise to a third hypothesis that will be tested:
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H3: Breadth and depth of external knowledge sourcing have an inverted U-shape effect on innovation performance of research groups focused on renewable energy.
An issue particularly relevant to renewable energy innovation is the impact of public support to innovation in the form of government funding of private or public R&D, given that there is broad empirical evidence of its impact on innovation in general (Arnold, 2012;
Defazio et al., 2009; Jaffe, 2002; Jaffe et al., 2015). For innovation in renewable energy, public support in the form of direct or indirect subsidies has played a key role in the emergence and deployment of new technologies (Bettencourt et al., 2012; Johnstone et al., 2009; Popp and Newell, 2012; Rodríguez et al., 2015). Still, to our knowledge, little work has been carried out to investigate the relationship between the degree of public support for R&D (henceforward referred to as ‘public support) and the impact of external knowledge sourcing on innovation. Given the combination of the importance of basic, scientific research for renewable energy innovation and the broadness of the knowledge basis for it, we expect to find public support to have a moderating effect on external knowledge sourcing.
Organisations that enjoy greater public support may be able to multiply the innovation benefits of external knowledge sourcing, for example, by reducing the costs of knowledge management. We therefore investigate whether, conditional on hypothesis 1 and 2, the interaction of breadth and depth with public support is associated with greater innovation success. Accordingly, our hypothesis can be stated as:
H4: Public support to R&D positively moderates the impact of breadth and depth on innovation performance of research groups focused on renewable energy.
5.3 Study design