The role of inter-sectoral knowledge spillovers in technological innovations: The case of lithium-ion batteries

Technological Forecasting & Social Change 148 (2019) 119718

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The role of inter-sectoral knowledge spillovers in technological innovations: The case of lithium-ion batteries

T

Annegret Stephana, , Catharina R. Beninga, Tobias S. Schmidtb, Marius Schwarza, Volker H. Hoffmanna ⁎

a

Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Management, Technology, and Economics, Group for Sustainability and Technology, Weinbergstrasse 56/58, 8092 Zurich, Switzerland b Swiss Federal Institute of Technology Zurich (ETH Zurich), Department of Humanities, Social and Political Sciences, Energy Politics Group, Haldeneggsteig 4, 8092 Zurich, Switzerland

ARTICLE INFO

ABSTRACT

JEL classifications: O33 O38 Q28 Q42 Q55 L52

Innovation is critical for economic growth and addressing societal and environmental problems. Therefore, many policy interventions aim to accelerate and redirect technological change. Most modern technologies have value chains spanning multiple sectors, and thus are likely to require cross-sectoral knowledge spillovers. However, knowledge spillovers between sectors in a technology's value chain have hardly been analyzed. We analyze the role of the sectoral diversity and sectoral distance of knowledge for subsequent knowledge generation within one specific technology. More specifically, we investigate how the sectoral diversity and distance of prior knowledge affect the technological importance, sectoral diversity, and sectoral distance of subsequent knowledge. Our regression analyses of global patent data of lithium-ion batteries show that (1) higher sectoral diversity increases the importance of newly created knowledge, whereas higher sectoral distance does not significantly increase the importance of newly created knowledge; (2) both higher sectoral diversity and distance of prior knowledge increase the sectoral diversity of subsequent knowledge; and (3) higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge, whereas higher sectoral diversity of prior knowledge does not significantly increase the distance of subsequent knowledge. We discuss our findings and derive implications for research, R&D managers and policymakers.

Keywords: Knowledge spillovers Sector Technology value chain Lithium-ion batteries Patents Knowledge diversity and distance

1. Introduction Technological change—i.e., the invention, innovation, and diffusion of new technologies—is one of the critical drivers for economic growth and a key lever for addressing societal and environmental problems. For instance, mitigating climate change requires a fundamental shift in the rate and direction of technological change in the energy sector (IEA, 2015). Many countries have enacted technology policies to support innovations in specific low-carbon energy technologies—such as photovoltaic and wind power, or electricity storage (REN21, 2016). Policymakers face the hard task of designing policies that (cost-) effectively induce technological change in key technologies (Anadon et al., 2011; Battke and Schmidt, 2015; Stephan et al., 2016). To date, however, it is debatable which policy designs and mixes are most effective. Most modern (Arthur, 2007; Murmann and Frenken, 2006; Tushman and Rosenkopf, 1992) and particularly (low-carbon) energy technologies (Stephan et al., 2017; Zhang and Gallagher, 2016), consist

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of different components and subsystems and are themselves integrated into various larger systems. The production and use of these technologies and their components require different knowledge bases and production processes. The value chains of these technologies therefore typically link different sectors that provide the different knowledge bases and production competencies required (Jacobsson and Bergek, 2011; Malerba, 2002; Stephan et al., 2017). For example, biomass gasification links sectors such as agriculture, forestry, and chemicals (Hellsmark, 2010); or battery technology value chain encompasses sectors such as chemicals, electronics, and transportation (Stephan et al., 2017). Hence, innovation in one sector can depend on innovation in others. Innovations in these technologies thus require sectoral interaction (Stephan et al., 2017), so that knowledge—the key driver for innovation—can flow between these different sectors. In other words, for innovation in most modern (low-carbon energy) technologies to flourish, inter-sectoral knowledge spillovers must occur. Improving our understanding of the patterns of innovation and mechanisms behind

Corresponding author. E-mail address: [email protected] (A. Stephan).

https://doi.org/10.1016/j.techfore.2019.119718 Received 17 May 2017; Received in revised form 17 June 2019; Accepted 13 August 2019 0040-1625/ © 2019 Elsevier Inc. All rights reserved.

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inter-sectoral knowledge spillovers in specific technologies can hence inform the current policy debate. Starting with the work of Usher (1929) and Gilfillan (1935), innovation scholars have shown substantial interest in knowledge spillovers. More recently, attention has focused on the effects of two characteristics of knowledge (Battke et al., 2016; Fleming, 2001; Lettl et al., 2009; Nemet and Johnson, 2012; Schoenmakers and Duysters, 2010). “Technological diversity” describes the extent to which knowledge originates from a variety (i.e. a large number) of technologies, while “technological distance” reflects the degree of difference between those source technologies and the focal technology (Jaffe and de Rassenfosse, 2016). However, analyses in this field have focused on knowledge flows between different technological fields, rather than those between the different sectors that make up an individual technology's value chain. These extant analyses typically use technological fields such as patent classes (i.e. the content of knowledge) to approximate knowledge flows between sectors (i.e. the industrial location of knowledge) (Jaffe, 1986; Nemet, 2012; Nemet and Johnson, 2012; Schoenmakers and Duysters, 2010). If we transpose this approach to cross-sectoral knowledge spillovers, we (implicitly) assume that sectors and technological fields match up perfectly—an assumption that is also widespread in other analyses of knowledge spillovers across sectors (Peri, 2005; Verspagen, 1997; Verspagen and De Loo, 1999). The implication is that sectors' knowledge bases precisely correspond with their production activities (and do not exceed them). However, sectors and technological fields differ, as organizations in a given sector often know more than they produce (Brusoni et al., 2001; Granstrand et al., 1997; Patel and Pavitt, 1997; Pohl and Yarime, 2010; Stephan et al., 2017)1 and their knowledge may serve as an input for various technologies even if they don't actually produce them (Jaffe, 1986; Scherer, 1982a). This can be driven by strategic rationales such as hedging against technological uncertainties (Brusoni et al., 2001; Lee and Veloso, 2008; Pohl and Yarime, 2010; Takeishi, 2002). At the same time, studies that have investigated inter-sectoral knowledge flows empirically, typically using sector classifications, have either focused on flows within economies rather than within the value chains of technologies (Scherer, 1984, 1982b, 1982a) or omitted the role of diversity and distance of inter-knowledge spillovers for individual technologies (Bergeron et al., 1998; Stephan et al., 2017). We are only aware of one recent study that compares patterns of intersectoral learning in three different clean energy technologies (Malhotra et al., 2019). Consequently, the patterns of inter-sectoral knowledge spillovers within individual technologies remain underexplored. To address this gap, we investigate the role of the sectoral diversity and sectoral distance of knowledge for subsequent knowledge generation within one specific technology. More specifically, we investigate how the sectoral diversity and distance of prior knowledge affect the importance, sectoral diversity, and distance of subsequent knowledge within a technology. A detailed understanding of where which kind of knowledge is likely to flow to can help policymakers support technological innovations most effectively and decide on the appropriate policy design. To investigate our research question, we analyze lithium-ion batteries (“LIBs”) as an example of a low-carbon energy technology for three main reasons. First, given their potential to integrate high shares of renewable power production and to electrify mobility, the development of LIBs is socially desirable (IPCC, 2011) and policymakers in several countries have hence started to promote LIBs (REN21, 2016). Second, LIB technology cuts across different sectors (Malhotra et al.,

2019; Stephan et al., 2017) such as chemicals, electronics, or transportation. Third, besides improvements in cost and performance (Crabtree et al., 2015), LIBs have exhibited substantial knowledge development within the last 30 years (Stephan et al., 2017). We employ negative binomial and fractional logit regression analyses of global LIB patent data. We classify each patent's assignee into its sector in terms of production. Our final database comprises 11,705 LIB patents. Our paper provides two main contributions. First, we apply a sectoral perspective to knowledge diversity and distance and derive hypotheses regarding their role in subsequent knowledge creation. In doing so, we extend previous literature on knowledge spillovers by analyzing spillovers within the sectors active in individual technologies' value chains. Second, we identify patterns of knowledge spillovers in LIB technology. We find that knowledge diversity increases the importance of knowledge, that both diversity and distance breed diversity, and that distance breeds distance. The remainder of this paper is structured as follows. In Section 2 we provide the theoretical background and derive our hypotheses. Section 3 describes data and methodology, while our results are presented and discussed in Section 4. Section 5 concludes with implications for research and policymakers and discusses limitations. 2. Theory and hypotheses 2.1. Concepts and definitions In this article, we follow Stephan et al. (2017) and adopt a value chain perspective on a technology. We understand a technology's value chain as “a collection of activities spanning across different firms that develop, produce and use a technology” (Stephan et al., 2017, p. 710).2 Most modern technologies consist of different components and subsystems (Arthur, 2007; Murmann and Frenken, 2006; Tushman and Rosenkopf, 1992), and are also integrated in various larger systems. The development, production, and use of these different technological artifacts typically require different capabilities, such as knowledge bases and production competences that are provided by different sectors (Malerba, 2002; Pavitt, 1984). These differences are also reflected in the sectors' industry classifications (OECD/Eurostat, 2005). In this paper, we understand a sector as an “aggregation of actors having similar production competences and outputs” (Stephan et al., 2017, p. 711). Sectors can, therefore, be distinguished by their (process) knowledge and practices, which typically relate to different scientific disciplines (Stephan et al., 2017). The sectors in a technology's value chain are linked not only by the exchange of physical objects (such as materials and artifacts), but also by knowledge flows (Pavitt, 1984). In the field of batteries, for example, important knowledge flows occur between the electronics and chemicals sectors (Stephan et al., 2017). Fig. 1 illustrates our conceptual understanding of the different sectors active in a technology's value chain, and the knowledge spillovers that can occur. The production process of the different technological artifacts (components, subsystem, assembled system, and various integrated systems) from raw materials to final use is organized into sequential and parallel activities carried out by different sectors. Knowledge spillovers within and between sectors typically take place in a less linear way than the organization of production. Note that while Fig. 1 demonstrates the basic principle by focusing on the sectors responsible for the production and use of a technology, a comprehensive depiction would also include other sectors that are involved in the technology's development, such as research or finance. The literature on inter-sectoral knowledge spillovers3 has shown that they can have positive macroeconomic effects such as domestic

1

In the field of batteries, for instance, many inventions regarding cathodes and anodes are developed in the transportation sector, even though the actual components are typically produced in the chemicals sector (Stephan et al., 2017). Note that individual transportation and battery companies can differ largely in strategy and focus (Pohl and Yarime, 2010).

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Adapted from Porter (1985). We use the terms “knowledge flow” and “knowledge spillover” interchangeably. 3

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Fig. 1. Knowledge spillovers within and between different sectors involved in a technology's value chain (adapted from Stephan et al. (2017)). The technology's artifacts from raw materials to final use (i.e., components, subsystem, assembled system, integrated systems) are provided by different sectors. The sectors are linked by the exchange of physical goods, but also by knowledge spillovers.

economic or productivity growth (Scherer, 1984, 1982a, 1982b; Schmookler, 1966). Moreover, relations in a value chain—such as buyer-supplier (Isaksson et al., 2016) or user-producer (Gehringer, 2016; Hippel, 1976) relationships, and their geographic proximity (Gehringer, 2016; Todo et al., 2016)—can affect innovation. The mechanisms behind the knowledge spillovers between the different sectors in a technology's value chain, however, have yet to be fully explored. For example, a technology's development might result from the integration of knowledge from numerous or highly different sectors.

influence on subsequent knowledge generation, and therefore play an important role in the realms of organizations' search strategies (Nemet and Johnson, 2012; Schoenmakers and Duysters, 2010) and public policy design (Battke et al., 2016; Nemet and Johnson, 2012). Both characteristics refer to the origin of knowledge—i.e., where knowledge comes from—and are typically measured using the relevant prior knowledge flows. Note that while some analyses have clearly distinguished between these two aspects4 (Kaplan and Vakili, 2015; Trajtenberg et al., 1997), others focus on just one (Lettl et al., 2009; Lin and Chang, 2015), while still others incorporate both into their understanding of knowledge diversity (Battke et al., 2016). The recombinant property of knowledge, especially the recombination of technologically diverse or distant knowledge, relates to various effects and has been investigated with different units of analyses such as firms, inventions/technological fields, inventors, or geographies. For example, on a firm level, diversity has been analyzed with regard to the firm's innovative (Lin and Chang, 2015; Nesta and Saviotti, 2005) and financial (Lin and Chang, 2015) performance.5 On an invention level, diversity and distance have been analyzed with regard to its invention's importance6 (Kaplan and Vakili, 2015; Lettl et al.,

2.2. On knowledge spillovers and relevant constructs Previous studies have shown that new knowledge arises when old knowledge is newly reconfigured, or when novel ideas are combined with existing knowledge (Arthur, 2009; Fleming, 2001; Schilling and Green, 2011). Especially in complex technologies, knowledge creation can be “viewed as a collective process made possible by the development of continuous accumulation of highly differentiated but complementary competences and technological knowledge” (Costantini et al., 2015, p. 300). Knowledge spillovers between different domains, therefore, facilitate technological development. Moreover, they have become increasingly important over time (Mowery and Rosenberg, 1998). Previous analyses of knowledge spillovers have investigated user-producer R&D relationships (Scherer, 1982b, 1982a), compared different means of knowledge transfer (Krammer, 2014), or focused on a knowledge exchange or learning perspective (Acemoglu et al., 2016; Bergeron et al., 1998; Malerba, 2002; Verspagen, 1997). The latter can occur via different channels such as R&D collaborations or patent citations (Wang et al., 2017). Two important concepts featured in the growing literature on knowledge spillovers are technological diversity and technological distance (Jaffe and de Rassenfosse, 2016). These characteristics have a decisive

4 Extant work in a wide array of research fields diverges in its understanding of diversity and distance. See also the conceptual paper by A. Stirling (Stirling, 2007) for the different aspects related to diversity. 5 Firms that diversify their knowledge base can increase both their innovative (Lin and Chang, 2015; Nesta and Saviotti, 2005) and financial performance (Lin and Chang, 2015). 6 The literature describes these inventions as “important” (Nemet, 2012), “radical” (Schoenmakers and Duysters, 2010), “impactful” (Lettl et al., 2009), or “of economic value” (Kaplan and Vakili, 2015). Because all these studies use patents' forward citation frequency as a proxy, we stick with the notion of importance in this paper.

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2009; Nemet, 2012; Schoenmakers and Duysters, 2010) and the direction of subsequent knowledge flows (Battke et al., 2016; Trajtenberg et al., 1997), respectively.7 On a geographical level, diversity (typically called “variety”) has been analyzed with regard to the innovative performance of nations (Porter and Stern, 2001) or the economic growth of regions (Frenken et al., 2007) or cities (Feldman and Audretsch, 1999).8 While studies observe many positive effects of diversity, they also note that it typically comes at a high cost (Todo et al., 2016), involves uncertainty (Fleming, 2001), does not necessarily result in novelty (Kaplan and Vakili, 2015), and can negatively affect internal knowledge generation processes (Gkypali et al., 2017). Other work looks at optimal levels for diversity (van den Bergh, 2008). Despite this wide adoption of diversity and distance, the literature diverges on terminology and a clear distinction between these two characteristics.9 In this paper, we focus on the two characteristics of diversity and distance, which extant studies show are relevant for a better understanding of the mechanisms behind knowledge spillovers. However, in extension to previous literature, we specifically focus on an individual technology and acknowledge that the technology's value chain encompasses different sectors (Section 2.1). This is especially relevant as the value chains of many modern technologies encompass different sectors that are involved in knowledge creation and spillovers (Stephan et al., 2017). Hence, a better understanding of the mechanisms of knowledge creation and spillovers between the sectors active in a technology's value chain should help policymakers who want to foster the growth of these technologies more effectively. In adopting this approach, we take into account that sectors and technological fields do not perfectly correspond (Jaffe, 1986; Scherer, 1982a), i.e., that sectors often know more than they produce. For example, in LIB technology, the sectors integrating LIBs into larger systems, such as electronics or transportation, have developed much of the knowledge relevant for the LIB components that are produced by the chemicals sector, which is one of their suppliers (Stephan et al., 2017).10 In this paper, we draw a clear distinction between the two characteristics of diversity and distance (Kaplan and Vakili, 2015; Trajtenberg et al., 1997). We aim to understand how diversity and distance affect subsequent knowledge generation. For subsequent knowledge generation, we distinguish between the importance of knowledge, and the diversity and distance of subsequent knowledge flows. These characteristics are relevant for both organizations and policymakers. Important knowledge is highly valuable to the organization that creates it, and innovation policy (e.g., through R&D support) typically aims at inducing the creation of important knowledge. Diverse and distant subsequent knowledge flows represent positive externalities, which are beneficial for technology development (Noailly and Shestalova, 2017), and are thus of interest for policymakers. We therefore apply the characteristics of both diversity and distance to both prior and subsequent knowledge flows. New knowledge can hence originate from, and flow to, the

various and (more or less) different sectors involved. Fig. 2 illustrates the constructs, nomenclature, and design of our study. We investigate whether the sectoral diversity of prior knowledge (Diversityprior) and the sectoral distance of prior knowledge (Distanceprior) affect subsequent knowledge generation. We understand Diversityprior as the variety of sectors from which knowledge originates—i.e., knowledge that originates from a large number of sectors is more diverse than knowledge that originates from a small number of sectors. We understand Distanceprior as the difference between the sectors from which knowledge originates and the sector of the focal knowledge. Knowledge that comes from one or more sectors that are different from the focal knowledge's sector (e.g., knowledge flows from the chemical to the transportation sector) thus has a higher distance than knowledge that builds upon more similar sectors.11 In adopting this approach, we transpose the literature's understanding of diversity and distance in terms of technological fields (Trajtenberg et al., 1997) to the context of sectors. For subsequent knowledge, we distinguish between the technological impact (Importance), sectoral diversity (Diversitysubsequent), and sectoral distance (Distancesubsequent) of subsequent knowledge flows. We consider knowledge to be important if it has a significant impact on the technology's development, and hence if it engenders many subsequent knowledge flows (Jaffe and de Rassenfosse, 2016). Diversitysubsequent and Distancesubsequent follow the same rationale as Diversityprior and Distanceprior, but refer to subsequent knowledge flows. 2.3. Hypotheses In this paper, we analyze the role of the sectoral diversity and sectoral distance of prior knowledge for the importance, sectoral diversity, and sectoral distance of subsequent knowledge, respectively. We derive hypotheses on the six relations that we investigate (indicated by the arrows in Fig. 2), starting with hypotheses regarding the number of subsequent knowledge flows, i.e., Importance, and continuing with those regarding where the newly created knowledge flows to, i.e., Diversitysubsequent and Distancesubsequent. Based on the idea of the combinatorial nature of knowledge and hence innovation processes, the literature generally argues that the recombination of knowledge specifically benefits radical or breakthrough, i.e., important, ideas (Arthur, 1989; Gilfillan, 1935; Nelson and Winter, 1982; Schumpeter, 1934). Empirical studies show that important inventions are induced by the incorporation of knowledge from various—i.e., diverse—technological domains (Schoenmakers and Duysters, 2010). For example, this effect has been observed in inventions that originate from corporations (Lettl et al., 2009) and for inventions in energy technologies (Nemet, 2012).12 The sectors active in a technology's value chain differ in their knowledge bases (Malerba, 2002; Pavitt, 1984), which is also indicated by different industry classifications (OECD/Eurostat, 2005). Thus, if a large number of sectors are involved in a technology's value chain, new knowledge can integrate these sectors' different knowledge bases. This newly created knowledge can exhibit high sectoral diversity that potentially covers different technological domains. For example, empirical evidence shows that important energy inventions—among others—have benefitted from chemical knowledge (Nemet, 2012). We hence hypothesize:

7 Individual inventions that build upon technologically diverse sources are likely to be important (Kaplan and Vakili, 2015; Schoenmakers and Duysters, 2010)—especially those in the energy field (Nemet, 2012) or corporate inventions (Lettl et al., 2009). In case of the integration of distant knowledge, knowledge is likely to flow to many (Trajtenberg et al., 1997) and other (Battke et al., 2016) technologies. 8 Note that the integration of new technologies into cities is more likely if these technologies are related to what is already there (Boschma et al., 2015), i.e. if they are diverse, but not too distant. 9 While some authors have focused on the breadth of prior knowledge and refer to this as “diversity” (Lettl et al., 2009), others also incorporate the idea of difference (i.e. distance) into their understanding of technological diversity (Battke et al., 2016; Nemet, 2012; Nemet and Johnson, 2012), and still others have clearly distinguished between the two characteristics of diversity and distance (Kaplan and Vakili, 2015; Trajtenberg et al., 1997). 10 In aircraft technology, system integrators also have knowledge in the area of their suppliers (Brusoni et al., 2001).

H1a. A higher sectoral diversity of prior knowledge increases the importance of the newly created knowledge. 11 Our understanding of distance hence measures the degree to which knowledge is diversified into unrelated areas. 12 Note that another study identifies the opposite effect (Nemet and Johnson, 2012). However, this paper analyzes an aggregation of technologies, which is why we consider the former work, which analyzed energy technologies, as being more relevant for this paper.

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Fig. 2. Constructs and research framework.

A similar argument holds true if less numerous but very distant sectors are involved in a technology's value chain. Important inventions arise not only from combinations of various technologies, but also through the interaction between unfamiliar technologies (Ahuja and Lampert, 2001), previously unconnected (Hargadon, 2003; Schoenmakers and Duysters, 2010), or completely unknown knowledge domains (Hargadon and Sutton, 1997). Despite the high uncertainty (Fleming, 2001) and costs (Todo et al., 2016) that accompany the integration of unfamiliar knowledge, “[the] rewards can be extraordinary” (Corradini and De Propris, 2017, p. 196). For example, the incorporation of knowledge from geographically distant firms, which are assumed to have less familiar knowledge than neighboring firms, increases a firm's innovative capabilities (Todo et al., 2016). Since distant sectors that are also within one value chain can have very unfamiliar knowledge bases as they use different production techniques, come from different scientific fields etc., we hypothesize:

Besides the general effect of diversity and distance on importance, i.e., the number of subsequent knowledge flows, we are also interested in where subsequent knowledge flows to, i.e., to numerous or different other sectors. We consider three aspects to be relevant for new knowledge that originates from inter-sectoral knowledge flows. While the first aspect relates to the mechanism that underlies the integration of diverse and distant knowledge, the other two relate to the characteristics embodied in diverse and distant knowledge itself. First, new knowledge that incorporates knowledge from numerous or different sectors might originate from a coordination process between the different sectors that are involved in the development and production of a technology, including activities such as assimilation, adaption, or a relevance check from the other sectors.13 This newly

13 Note that firm-level literature shows that the incorporation of technologically proximate knowledge from other firms is particularly crucial for the development of the technology concerned (Rosenkopf and Nerkar, 2001). However, we do not know whether or not these other firms are in different sectors.

H1b. A higher sectoral distance of prior knowledge increases the importance of the newly created knowledge. 5

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Table 1 Hypotheses. Dependent variables

Independent variables

a) Diversityprior b) Distanceprior

H1: Importance

H2: Diversitysubsequent

H3: Distancesubsequent

H1a: A higher sectoral diversity of prior knowledge increases the importance of the newly created knowledge. H1b: A higher sectoral distance of prior knowledge increases the importance of the newly created knowledge.

H2a: A higher sectoral diversity of prior knowledge increases the sectoral diversity of subsequent knowledge. H2b: A higher sectoral distance of prior knowledge increases the sectoral diversity of subsequent knowledge.

H3a: A higher sectoral diversity of prior knowledge increases the sectoral distance of subsequent knowledge. H3b: A higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge.

created knowledge is, by definition, important for those sectors involved, making it more likely to flow back to the sectors involved in its original creation. This mechanism results in the general effect that diversity breeds diversity and distance breeds distance, which is further supported by the following aspects. Second, if a large number of sectors are involved in a technology, inventors can integrate the different knowledge bases, and the resulting knowledge is, therefore, likely to be more original and more generally applicable (Corradini and De Propris, 2017; Trajtenberg et al., 1997), and to flow to unrelated fields (Corradini and De Propris, 2017). This diverse knowledge is hence more likely to be of high interest for many sectors, and also distant ones—i.e., diversity breeds both diversity and distance. Finally, a similar argument can be made if less numerous but very distant sectors are involved in one technology. Those sectors have very unfamiliar knowledge bases that typically incorporate knowledge from other technological areas themselves. The incorporation of unfamiliar knowledge bases into new knowledge—i.e., a high prior sectoral distance—might provide additional or novel concepts (Arthur, 2007) and result in breakthroughs (Ahuja and Lampert, 2001). This can produce two effects: (i) other sectors might rather know about these “outside the box” ideas than about incremental improvements; (ii) these “outside the box” ideas might also be especially relevant for numerous sectors—including the distant ones. This means that these novel concepts or breakthrough ideas are more likely to flow to numerous and distant sectors—i.e., distance breeds diversity and distance. Based on these three aspects, we hypothesize:

(Jaffe et al., 1993) and are “a result of a process of accumulation and the production of technological knowledge” (Bergeron et al., 1998, p. 735). While other data sources such as R&D expenditures and their relation to productivity growth (e.g., Griliches, 1990, 1979), user-producer relationships (e.g., Scherer, 1982a, 1982b and subsequent work) or R&D cooperation (Wang et al., 2017) have also been widely used as a proxy for knowledge creation and knowledge flows respectively, we chose patents/patent citations for the following reasons. Patent citations theoretically relate to the flows of knowledge (as opposed to the so-called “rent spillovers” covered by economic transactions (Verspagen, 1997)), previous empirical work has found that they can serve as a valid proxy for the intensity of knowledge flows, especially in an aggregated form (Trajtenberg et al., 2000), and patent data are available (Griliches, 1990)—which probably also explains why they have increasingly been used in social science to measure knowledge flows (Jaffe and de Rassenfosse, 2016).14 A recent paper, combining patent data with data from interviews with R&D managers, shows that patents are a good proxy to analyze inter-sectoral learning, also in the LIB field (Malhotra et al., 2019). However, other measures that relate to the successful transfer of ideas, such as the composition and success of R&D cooperation projects, or co-application analyses, might also serve as appropriate proxies—but are subject to future research (see also Section 5.3). 3.1.1. Research case The research case should fulfill three conditions. First, it should consist of a technology whose progress is desired from a policy perspective, since we aim to derive policy implications. Second, the technology should cut across different sectors, as this is the precondition for knowledge flows within this technology and between the different sectors. Third, it should also exhibit substantial technological progress, as this is the basis for knowledge development and, thereby, patent activity. We investigate LIBs over the period 1985–2005. LIBs are well suited to our purpose from both a theoretical and a methodological perspective. First, LIBs are expected to play an important role in future energy systems by supporting the shift towards low-carbon power production (IEA, 2015). LIBs can support the balancing of volatile renewable power generation and thereby boost the proportion of renewables that contribute to major electricity grids. Policymakers in several countries have hence started to promote progress in LIBs (REN21, 2016). Despite their past development, however, LIBs still need further improvements in order to meet the needs of future electricity and transportation systems (Crabtree et al., 2015). Second, LIBs consist of different components and subsystems and are integrated in different larger systems. The development, production, and use of LIBs, therefore, cut across different sectors (for a graphical illustration, see Stephan et al. (2017)). LIBs' main components (cathode, anode, electrolyte, separator, etc.) are synthesized from raw

H2a. A higher sectoral diversity of prior knowledge increases the sectoral diversity of subsequent knowledge. H2b. A higher sectoral distance of prior knowledge increases the sectoral diversity of subsequent knowledge. H3a. A higher sectoral diversity of prior knowledge increases the sectoral distance of subsequent knowledge. H3b. A higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge. Therefore, we expect positive effects of knowledge that exhibits high prior sectoral diversity and distance on the importance, diversity, and distance of subsequent knowledge flows. Table 1 summarizes our hypotheses. 3. Data and methodology 3.1. Data We conducted a regression analysis of patent families (hereafter simply referred to as “patents”). Patent data have been widely used as a measure for knowledge and knowledge flows (Battke et al., 2016; Griliches, 1998; Jaffe, 1989; Jaffe and de Rassenfosse, 2016; Nemet, 2012; Nemet and Johnson, 2012; Rosenkopf and Almeida, 2003; Scherer, 1982a). Patents serve as a paper trail of explicit knowledge

14 Jaffe and de Rassenfosse (2016) present an extensive discussion of the use of patent citations as proxy for knowledge flows.

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Fig. 3. Global LIB patents per year.

material extracted by the mining sector and produced in the chemical sector. These main components are assembled into battery cells and battery packs in the electronics sector, and enhanced by peripheral components such as housing, wiring, and cooling systems produced in the metals and electronics sectors. The battery is then integrated into larger systems such as electricity grids or electric vehicles, in various sectors such as electric, instruments, or transportation. The sectors involved in the development, production and use of LIBs differ substantially in their (process) knowledge and practices. Third, LIBs have made substantial technological progress over the last 30 years. Improvements in cost and performance (Crabtree et al., 2015) demonstrate technological progress. Moreover, the continuous increase in the number of LIB patents per year (Fig. 3) indicates knowledge development in this technology.

et al., 2017). We used either standard industry classification (four-digit SIC)16 codes or, if they were unavailable, manually coded publicly available company information such as industry reports, company websites, and company databases with regard to sectors. Both SIC codes and manual classification match with our definition of a sector based on production knowledge. We focused on the sectors relevant to the technologies' development, production, and use.17 Two independent researchers cross-checked classification and coding. We focused on the 1500 most important assignees in terms of knowledge creation, and only considered patents assigned by organizations. We determined the most important organizations by ordering them according to the importance of their inventions (most frequently cited patents). As some organizations (i.e., conglomerates) might produce in several sectors relevant for the respective technologies, multiple classifications were allowed. Hence, we assigned these organizations to multiple sectors.18 Table 2 shows the sectors considered, with their respective SIC codes. Our final databases of focal patents from 1985 until 2005 comprise 11,705 LIB patents (Appendix B), and 53,909 backward and 47,543 forward citations.

3.1.2. Data selection, retrieval, and processing Patent data was retrieved from the Thomson Innovation database, which covers the most important patent offices worldwide. Relevant patents were selected using sequential data-retrieval rounds based on a keyword- and classification-based (International Patent Classifications, IPC) search string (Appendix A).15 We stopped data-retrieval rounds when low levels (<5%) of false positives and false negatives were reached (see also Stephan et al. (2017)). Forward- and backward-citation information was linked by a MATLAB-based matching algorithm, which identified all citations created and received from one patent to another in each technology during the period covered. We considered a maximum citation lag of five years to ensure equal opportunities for each patent to cite and be cited. Since the patents used as regression inputs date from the period 1985–2005, we extended the window used for citation information back to 1980 (to capture backwards citations) and forward to 2010 (to capture forwards citations). We determined each patent's sector of origin by classifying its assignee by sector of production activity (Bergeron et al., 1998; Verspagen, 1997). While the literature has typically used patent classification as a proxy for sector (Jaffe, 1986; Nemet, 2012; Nemet and Johnson, 2012; Schoenmakers and Duysters, 2010), our approach acknowledges that sectors might know more than they produce. This is relevant especially in the case of LIBs, since sectors develop knowledge that exceeds their production activities. For example, the transportation sector develops patents in the area of the active materials, e.g., the cathode or anode, which are produced by the chemicals sector (Stephan

3.2. Method To test our hypotheses, we regress the importance of knowledge, the sectoral diversity, and the distance of subsequent knowledge flows on the sectoral diversity and distance of prior knowledge flows (Fig. 2). We approximate knowledge with patents, and knowledge flows with patent citations. As we are interested in knowledge flows between different sectors, but within each technology, we focus exclusively on citations of patents that are also assigned to the respective technology,19 i.e., citations within the same technology (Lettl et al., 2009). Table 3 and Appendix E display the descriptive statistics20 and the correlation of the variables. 16

We used SIC 1–8 of each organization. Two researchers independently identified the relevant sectors on a fourdigit level. We classified each patent into the four-digit SIC code(s) in which it was active. To construct the indicators and hence the analysis, we aggregated the sectors into the respective two-digit sectors shown in Table 2. 18 We thereby regard an organization as a unit and assume that knowledge exists within this unit. This furthermore means that also the patents of these organizations are classified into the multiple sectors. 19 These citations make up a share of 40.1% of all citations. 20 Note that the number of observations is lower for Diversityprior and Distanceprior than for Importance, as patents without forward citations cannot be considered. 17

15 We included keywords in our search as the IPC classification system might not reflect the entire economic activity that we wanted to cover (Costantini et al., 2015).

7

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Herfindahl-Hirschman index of concentration. Accordingly, a patent that is cited by patents from a theoretically infinite number of sectors is regarded as fully diverse (diversity = 1), as opposed to a patent that is cited by patents from only one sector, which is considered to be fully concentrated (diversity = 0). In cases of patent assignees with multiple sectoral assignments, we adapt the equation of Trajtenberg et al. (1997) by weighting the patent according to its number of sectoral assignments (NASSIGNEDisf). For example, each citing patent that is assigned to two different sectors is weighted with 0.5, and considered twice in our calculation. An example is shown in Appendix C.

Table 2 Sectors used, with their respective one- and two-digit SIC codes. One-digit SIC

Two-digit SIC

Sector

1

10 13 15 17 28 30 33 34 35 36 37 38 39 49 82/87

Metal mining Oil and gas extraction Building contractors Trade contractors Chemicals Rubber and plastics Primary metal Fabricated metal Industrial equipment Electronic and electric Transportation equipment Instruments Miscellaneous manufacturing Electric services Research laboratories and organizationsa

2 3

4 8

Ni

Diversitysubsequent , i = 1

s= 1

NCITINGi

NCITINGis = f =1

a The sector "Research laboratories and organizations" encompasses organizations that are solely devoted to research activities, such as universities and pure research companies. Patents filed by the research departments of firms assigned to other sectors are classified into the sectors of the respective firm.

NASSIGNEDisf =

NCITINGis NCITINGi

2

(1)

NASSIGNEDisf NASSIGNEDif

1, patent f isassignedtosector s 0, else

NCITINGi : Number of patents that cite patent i

3.2.1. Dependent variables 3.2.1.1. Importance of Knowledge. In line with literature, we use a patent's number of forward citations as a proxy for importance—i.e., its impact on subsequent technological progress (Jaffe and de Rassenfosse, 2016; Nemet, 2012; Nemet and Johnson, 2012; Rosenkopf and Almeida, 2003; Schoenmakers and Duysters, 2010; Verhoeven et al., 2016).21 A patent's number of forward citations is highly correlated with its value and importance for the technology (Albert et al., 1991; Hall et al., 2000), i.e., its economic value (Kaplan and Vakili, 2015), and can relate to the technology's life cycle, especially the likelihood of a successful market launch (Wagner and Wakeman, 2016). More specifically, as we want to measure the importance of the patent for the respective technology, we only consider those forward citations that refer to the same technology (Lettl et al., 2009)—i.e., that refer to other patents within our database. Furthermore, based on other studies on knowledge spillovers, we impose a five-year citation window (Huenteler et al., 2016b; Wagner and Wakeman, 2016).

NASSIGNEDif : Numberofsectors patent f citingpatent i isassignedto Ni : Numberofrelevantsectorsoftheparticulartechnologyofpatent i i : Correspondstothefocalpatent s: Correspondstothesector f : Correspondstotheforwardcitingpatent 3.2.1.3. Sectoral distance of subsequent knowledge. The sectoral distance of subsequent knowledge (Distancesubsequent) reflects the degree of difference between the sectors in which subsequent knowledge resides and the focal knowledge's sectors. We, therefore, measure the extent to which the focal patent is cited by (more or less) distant sectors based on the equations given in Trajtenberg et al. (1997). Eq. (2) describes our calculation. For each focal patent i, we determine its sectoral distance from citing patents (DISTANCEif) by comparing the sectors of the citing patents' assignees with those of the focal patent. More specifically, we weigh each citing patent with a distance value based on the focal patent's sector, and average the distances according to the number of citing patents (NCITINGi). To this end, we compare the assigned SIC codes of the focal and cited patents to determine the weights, i.e., distance values (DISTANCEifc). If the SIC codes are similar on a twodigit level (e.g., 36 and 36), their distance is assumed to be zero. If they are different but related—that is, similar on the one-digit level but different on the two-digit level (e.g., 36 and 38), their distance is set to 0.5. If they differ even on a one-digit level (e.g., 36 and 28), their distance is set to 1. Therefore, if all citing patents stem exclusively from unrelated sectors, the distance is regarded as maximal, whereas if all citations occur only within the focal patent's own sector, the distance is regarded as zero. In the case of multiple assignments, we adapt the equation of Trajtenberg et al. (1997) and use an average distance value for each citation. We determine all possible combinations of SIC codes of the focal and cited patent, weigh them with the distance values, and finally average them according to the number of combinations (DISTANCEifc). For example, each distance value of a citation of which the focal and the citing patent are assigned to two different sectors each is divided by four. An example is shown in Appendix C.

3.2.1.2. Sectoral diversity of subsequent knowledge. We understand the sectoral diversity of subsequent knowledge (Diversitysubsequent) as the variety of sectors in which subsequent knowledge resides. In order to measure it, we use the extent to which the focal patent is cited by patents from different sectors. To this end, we make use of the equations given in Trajtenberg et al. (1997), who use one minus the Herfindahl-Hirschman index of concentration.22,23 Higher values of Diversitysubsequent therefore represent lower concentration, and vice versa. Diversitysubsequent of each focal patent i across sectors is calculated via Eq. (1). For each focal patent i, we determine Diversitysubsequent by focusing on the sectors of the citing patents. More specifically, for each sector s we calculate the ratio between the citing patents f that reside within this sector (NCITINGis) and the total number of citing patents (NCITINGi), which—when squared and summed up—results in the 21 Note that while this approach is widely applied, the literature also discusses this approach and provides other measures, e.g., measures that also include indirect citations (Corredoira and Banerjee, 2015). 22 The number of cited sectors therefore plays the same role as the firms' sales in the Herfindahl-Hirschman index's traditional context. 23 The Herfindahl-Hirschman index has also been used in more recent literature in order to determine patents', firms', or macro-level technological capabilities with regard to depth versus breadth (Aharonson and Schilling, 2016).

NCITINGi

Distancesubsequent , i = f =1

8

DISTANCEif NCITINGi

(2)

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Table 3 Descriptive statistics of dependent and independent variables. LIB

Obs.

Means

Std. dev.

Min

Maxa

Operationalization

Dependent variables Importance Diversitysubsequent

11,705 8829

4.06 0.47

5.97 0.25

0 0

75 0.87

8829

0.46

0.26

0

1

Independent variables Diversityprior Distanceprior

Total count of forward citations of focal patent within a five-year citation window Calculation of the extent to which the focal patent is cited by patents from different sectors Calculation of the extent to which the focal patent is cited by patents from (more or less) distant sectors

11,705 11,705

0.48 0.46

0.25 0.27

0 0

0.88 1

Control variables Backwardcitations Forwardcitations Triadic

Calculation of the extent to which the focal patent cites patents from different sectors Calculation of the extent to which the focal patent cites patents from (more or less) distant sectors

11,705 11,705 11,705

4.61 4.06 0.11

6.01 5.97 0.31

1 0 0

116 75 1 (1238)

11,705

2.62

1.08

0

5

11,705 11,705

1999 265

4.65 126.6

1985 1

2005 441

Total count of backward citations of focal patent within a five-year citation window Total count of forward citations of focal patent within a five-year citation window Binary variable to declare patent as triadic. Patent is triadic if it was filed at EPO, USPTO, and JPO Citation lag averaged over all cited patents of the focal patent within a five-year citation window Year in which the patent was filed (1985–2005) Squared standardized value of the priority date = (prioritydate-1984)2

11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705 11,705

0.01 0.01 0.02 0.00 0.21 0.01 0.04 0.01 0.31 0.70 0.07 0.17 0.00 0.01 0.07

0.12 0.08 0.15 0.06 0.41 0.12 0.20 0.07 0.46 0.46 0.25 0.38 0.01 0.11 0.26

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Distancesubsequent

Citationlag Prioritydate Prioritydate2 Dummy variables for sectors Metal mining Oil and gas extraction Building contractors Trade contractors Chemicals Rubber and plastics Primary metal Fabricated metal Industrial equipment Electronic and electric Transportation equipment Instruments Miscellaneous manufacturing Electric services Research laboratories and organizations a

(167) (83) (253) (41) (2468) (164) (475) (66) (3598) (8155) (799) (2047) (1) (141) (846)

The numbers in brackets show how many patents fall into the respective category (binary variable = “1”). NCOMBINATIONif

DISTANCEif = c=1

backward instead of forward citations. The calculations of both variables and examples can be found in Appendix D and Appendix C, respectively.

DISTANCEifc NCOMBINATIONif

DISTANCEifc 1, SICsofcombination m are equal on the 2 =

digit

3.2.3. Rationale behind the choice of indicators for sectoral diversity and sectoral distance of prior and subsequent knowledge Many studies on (technological) innovation that investigate the origins of technological knowledge using patent data apply the understanding and indicators of diversity and/or distance as presented in Trajtenberg et al. (1997) and subsequent work (e.g., Baron and Delcamp, 2015; Hall et al., 2001; Jaffe and de Rassenfosse, 2016; Kaplan and Vakili, 2015; Lettl et al., 2009; Lin and Chang, 2015).26 Extant work in this field has, however, also applied other indicators. To measure technological variety, for example, studies have counted the number of sectors (Schoenmakers and Duysters, 2010) or technological fields (Nesta and Saviotti, 2005), and therefore neglected the aspect of concentration. To measure technological relatedness (i.e., distance), extant work has used indicators that measure the technological relatedness of the knowledge recorded in a patent, or held by a firm (Breschi et al., 2003; Corradini and De Propris, 2017; Nesta and Saviotti, 2005), typically compared to the general relation between different technological knowledge domains. Still others use patent counts in their econometric analyses, after classifying patents into different technological classes (e.g., internal, external, near knowledge) (Battke et al., 2016; Nemet, 2012; Nemet and Johnson, 2012). Given that we want to measure the two new theoretical constructs that we introduce (i.e., sectoral diversity and sectoral distance), our

level

0.5, SICsofcombination m are equal on the 1 digit 0, else

level

NCOMBINATIONif = NASSIGNEDi * NASSIGNEDif NASSIGNEDi : Number of sectors patent i is assigned to c: Correspondstothesectoralcombinationbetweenpatent i andpatent f 3.2.2. Independent variables Sectoral diversity and distance of prior knowledge The sectoral diversity of prior knowledge (Diversityprior) describes the variety of sectors that knowledge is based upon, measured via the extent to which cited patents are distributed across sectors.24 Diversityprior is measured based on the same rationale as Diversitysubsequent. However, we use the patent's backward rather than forward citations.25 For the sectoral distance of prior knowledge (Distanceprior), we use a similar measure to Distancesubsequent but consider the focal patent's 24

Note that we only consider citations from the same technology. Trajtenberg et al. (1997) use the notion of “originality” when referring to diversity across technological domains. 25

26

9

Some of these studies apply modifications of the indicators.

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indicators have to build on indicators that fulfill the following requirements. They have to clearly delineate diversity from distance, and hence reflect our own understanding of these two concepts. In addition, they have to be well established in the literature on (technological) innovation, and make use of patent data as a very comprehensive data source. Given that the indicators used by Trajtenberg et al. (1997) and many other extant studies (see above) for measuring technological diversity and technological distance fulfill these requirements, we chose to adapt them for measuring sectoral diversity and sectoral distance.

Importance/ Diversitysubsequent / Distancesubsequent = b1*Diversityi + b2 *Distanceprior , i + b3*backwardcitationsi + b4 *forwardcitationsi + b5*triadici + b6*citationlagi + b7*prioritydatei + b8*prioritydatei 2 + b9 +*

3.2.4. Controls We control for differences in patent and citation propensity over time and therefore include priority date, which represents the date of the earliest filing of a patent from the respective patent family (Popp et al., 2011), and priority date squared to control for non-linear effects. We expect a positive and an inverse u-shaped relation between knowledge importance and priority date and priority date squared respectively (Battke et al., 2016). We furthermore include the mean citation lag, since the likelihood of a patent being cited might change over time (Battke et al., 2016; Criscuolo and Verspagen, 2008; Nemet and Johnson, 2012). Moreover, we control for contrasts in the sectors' innovation behavior (Archibugi, 1988; Dumont and Tsakanikas, 2002; Iammarino and McCann, 2006; Malerba, 2002; Patel and Pavitt, 1994; Pavitt, 1984) such as their propensity to patent (Cohen et al., 2000; Graham et al., 2009; Johnstone and Haščič, 2010), and include dummy variables for each sector. For the regression models that investigate Diversitysubsequent and Distancesubsequent (H2 and H3), we furthermore control for a patent's general value by including triadic, which indicates whether a patent has been filed simultaneously in the US, Europe, and Japan (Dechezleprêtre et al., 2013; Grupp, 1998). Additionally, we control for the total counts of backward citations and total counts of forward citations, as they might bias our measures of sectoral diversity and distance. A higher number of citations is more likely to be distributed among different sectors than a low number of citations. This is indicated by relatively high positive correlations (Appendix E), especially between the citation counts and the respective forward and backward diversity measures.

industry sectorsi

(3)

3.2.6. Sensitivity analyses Besides the main regression model, we present four additional regression models as sensitivities (Appendix F). They focus on three specific aspects. First, we changed the assumptions considering knowledge spillovers between multiply assigned organizations (i.e., conglomerates). In our main model, we assume that knowledge flows with equal probability from each of the focal patent's sectors to each of the citing patent's sectors. In sensitivity analysis 1, we take the opposite view and assume that—in those cases where firms are active in similar sectors—knowledge only flows between these overlapping sectors. We hence release the assumption that knowledge always exists within all business units of an organization. Second, we add additional controls for conglomerates and geographic areas. In our main model, we classify organizations that are active in multiple areas relevant for LIBs' development, production, and use into multiple sectors. In sensitivity analysis 2, we control for the number of sectoral assignments of the patents' assignees in each of the focal, citing, and cited patents. We furthermore do not consider country-specific effects in our main model. In sensitivity analysis 3, we add dummies for the most relevant geographic areas—Japan, the US, and Europe—in order to test for the potential relevance of specific (national) innovation systems. Third, we change the regression technique and apply other regression models for count and proportional data in sensitivity analysis 4. 4. Results and discussion 4.1. Results We describe the effects of the sectoral diversity and distance of prior knowledge on the importance (H1a/b), sectoral diversity (H2a/b), and sectoral distance (H3a/b) of subsequent knowledge for LIBs. Table 4 shows the results on the main regression specifications for the different models, while Appendix F shows the sensitivity analyses. The sizes of the coefficients have to be compared with caution, as the underlying variables differ substantially (different calculations, binary versus continuous variables, etc.). We therefore focus on general significant trends rather than on effect sizes. Values obtained for the models' goodness-of-fit are in line with previous analyses on knowledge spillovers (Battke et al., 2016). Our results support H1a, as the coefficient for Diversityprior is positive and significant. Knowledge that originates from many sectors therefore results in a high number of subsequent knowledge flows. Our results regarding Importance therefore confirm the findings regarding the role of diversity between different technological fields in energy technologies (Nemet, 2012). As the coefficient for Distanceprior in H1 is not significant, a higher sectoral distance of prior knowledge flows does not necessarily increase the importance of knowledge (see discussion below). We can support both hypotheses H2a and H2b. The highly significant and positive coefficients for Diversityprior and Distanceprior indicate that both a higher sectoral diversity and higher sectoral distance of prior knowledge increase the sectoral diversity of subsequent knowledge flows. However, we do not find evidence for H3a, as the coefficient for Diversityprior is not significant. This means that higher

3.2.5. Regression models In order to explain the effect of the sectoral diversity and distance of prior knowledge on the importance of knowledge, (H1a/b), the sectoral diversity (H2a/b), and distance (H3a/b) of subsequent knowledge, we employ two different regression models, since the dependent variables have different characteristics. We use a negative binomial regression model to test for H1a/b, as the numbers of forward citations (dependent variable) are overdispersed count data. We use robust standard errors to avoid heteroscedasticity and model the variance as a function of its means as proposed by Long and Freese (2006). For H2a/b and H3a/b, we employ a fractional logit model, since our dependent variables are proportional variables varying between 0 and 1 inclusive. The fractional logit uses the logit link function (logit transformation of the response variable) assuming a binomial distribution. We again use robust standard errors and model the variance as a function of its means. Eq. (3) describes our basic regression models for the different hypotheses regarding Importance, Diversitysubsequent, and Distancesubsequent.27

27 The controls forward citations and triadic are only considered in H2a/b and H3a/b as the number of forward citations is the dependent variable for H1a/b and triadic is another measure for the value of a patent such as the dependent variable for H1a/b.

10

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Table 4 Results of regressions on knowledge spillovers. Dependent variables Independent variables Diversityprior Distanceprior Controls Triadic Citationlag Prioritydate Prioritydateb Backward citations Forward citations Dummy variables for sectors Metal mining Oil and gas extraction Building contractors Trade contractors Chemicals Rubber and plastics Primary metal Fabricated metal Industrial equipment Electronic and electric Transportation equipment Instruments Miscellaneous manufacturing Electric services Research laboratories and organizations Constant Overdispersion (ln α) N Goodness of fita,b

H1: Importance

H2: Diversitysubsequent

H3: Distancesubsequent

0.608⁎⁎⁎ 0.115

0.336⁎⁎⁎ 0.280⁎⁎⁎

0.085 0.728⁎⁎⁎

0.010 0.160⁎⁎⁎ −0.007⁎⁎⁎ 0.072⁎⁎⁎

0.046 −0.012 0.021 −0.001⁎ −0.003 0.049⁎⁎⁎

−0.035 −0.014 0.011 −0.001⁎ −0.009⁎⁎⁎ 0.009⁎⁎⁎

0.363⁎⁎⁎ 0.298⁎ 0.321⁎⁎⁎ −0.020 0.290⁎⁎⁎ 0.027 0.125⁎ 0.289⁎ 0.269⁎⁎⁎ 0.164⁎⁎⁎ 0.177⁎⁎⁎ 0.170⁎⁎⁎ 1.131⁎⁎⁎ 0.153 0.169⁎⁎⁎ −41.86

0.885⁎⁎⁎ 1.276⁎⁎⁎ 0.827⁎⁎⁎ 1.099⁎⁎⁎ 0.834⁎⁎⁎ 0.237⁎⁎⁎ 0.180⁎⁎⁎ 0.374⁎⁎⁎ 0.321⁎⁎⁎ −0.251⁎⁎⁎ 0.171⁎⁎⁎ 0.181⁎⁎⁎ 0.387⁎⁎⁎ 0.963⁎⁎⁎ 0.981⁎⁎⁎ −22.89

8829 0.142

8829 0.320

0.037 −0.052 −0.295⁎⁎ 0.335 0.115⁎⁎ −0.108 −0.264⁎⁎⁎ 0.035 −0.012 −0.088⁎⁎ −0.042 −0.026 −1.456⁎⁎⁎ −0.476⁎⁎⁎ 0.025 −317.3⁎⁎⁎ 0.134⁎⁎⁎ 11,705 0.173

a

Gof measured by Cragg-Uhler (Nagelkerke) R2 for count models. Gof measured by correlation2 between observations and predictions. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001. b

prior sectoral diversity does not increase the subsequent sectoral distance. Knowledge spillovers might therefore underlie patterns other than those described in Section 2 (see discussion below). Our regression results (significant coefficients for Distanceprior) support H3b. A higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge. While significance levels of the coefficients for controls differ between the different regression models, they generally show the expected signs. The role of sector affiliation differs for the different dependent variables, indicated by an increase in the number of significant sector dummies when comparing Importance with Diversitysubsequent and Distancesubsequent. Our findings are supported in 16 out of 18 cases in the sensitivity analyses (Appendix F). Hence, our findings are robust even when changing the assumptions regarding knowledge spillovers between organizations active in multiple sectors (sensitivity analysis 1), when controlling for multiple sector assignment (sensitivity analysis 2) and geographic differences (sensitivity analysis 3). Only two effects—the distance of prior knowledge on the importance of knowledge (H1b) and of the diversity of prior knowledge on the distance of subsequent knowledge (H3a)—might change once we abandon our assumption of how knowledge flows between organizations active in different sectors (sensitivity analysis 1). However, as the multiple assignment per se does not affect the results (sensitivity analysis 2), and as the findings of the main analysis (which represent the less extreme case in the realm of the assumptions regarding knowledge spillovers between organizations active in different sectors) do not support H1b and H3a, we consider the findings of the main analysis to be robust. Moreover, while the regression models that we chose for the main analyses are most appropriate (Section 3.2.4), all findings are also supported in cases where we apply other widely used models (sensitivity analysis 4). In conclusion, we find that in the LIB technology's value chain, a

higher sectoral diversity increases the importance of the newly created knowledge. A higher sectoral distance of prior knowledge, however, does not significantly increase the importance of the newly created knowledge. Furthermore, both higher sectoral diversity and distance of prior knowledge increase the sectoral diversity of subsequent knowledge. Similarly, higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge, whereas higher sectoral diversity of prior knowledge does not. This means that the integration of knowledge from various sectors active in the LIB value chain increases the impact of the respective newly created knowledge, and that the integration of knowledge from various and/or distant sectors results in positive inter-sectoral externalities. Besides, our results also indicate the importance of distinguishing between diversity and distance, and applying them to both prior and subsequent knowledge flows. The differences in the results for the coefficients for Diversityprior and Distanceprior show that the two constructs measure different aspects, which also have different effects. For example, while the integration of diverse knowledge increases the importance of newly created knowledge, the integration of distant knowledge does not. Moreover, the differences in the results for the coefficients between the different hypotheses demonstrate that differentiating subsequent knowledge flows by their direction (H2, H3) yields interesting insights. For example, while the integration of distant knowledge does not increase the importance of the newly created knowledge, it does result in diverse subsequent knowledge flows—i.e., positive externalities—that foster technology development. 4.2. Discussion Based on our findings, we discuss below three aspects in more detail: the insignificance of the coefficient for H1b, the insignificance of 11

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the coefficient for H3a, and the relation of our findings to LIBs' technology and value chain characteristics. First, we do not find support for H1b, i.e., the positive role of prior sectoral distance for the importance of knowledge. While we hypothesized a positive relationship based on potential rewards when integrating unfamiliar knowledge (Ahuja and Lampert, 2001; Corradini and De Propris, 2017; Todo et al., 2016), one explanation for the insignificant results might be the difficulty of successfully translating distant knowledge into important new knowledge: “incorporating external knowledge is hard to do well and is risky” (Nemet and Johnson, 2012, p. 197). The ultimate limit on recombinant growth is not the transfer of ideas from unfamiliar knowledge bases, but the process of translating such concepts into usable (i.e., important, in our case) forms (Weitzman, 1998). This latter task might be more difficult the more distant the integrated knowledge is, as “learning performance is greatest when the object of learning is related to what is already known” (Cohen and Levinthal, 1990, p. 131). Firms might be limited by their absorptive capacity (Cohen and Levinthal, 1990) and unable to turn external (distant) knowledge into new usable knowledge. While some sectors in the LIB value chain (e.g., primary and fabricated metal) may draw on similar knowledge bases, the knowledge relevant for others differs substantially—and may even originate in completely different scientific disciplines (Stephan et al., 2017) (e.g., chemicals and transportation). Knowledge spillovers might be difficult between firms that originate from these very distant sectors. Second, we do not find support for H3a, i.e., the positive role of prior sectoral diversity for subsequent sectoral distance. Whereas our hypothesis H3a is mainly based on the generality or applicability of knowledge, the results indicate that such general knowledge might not necessarily be useful for distant sectors. One explanation for the insignificant effect might be that diverse knowledge that incorporates knowledge from different sectors may well be very general in these sectors' domain, but still be too specific or too unfamiliar for very distant sectors.28 Empirical work has shown that firms struggle to utilize those heterogeneous resources (in this case, the technological knowledge of alliance partners) that are very different to their own resources, as opposed to more familiar heterogeneous resources (Nooteboom et al., 2007). If the technological cognitive distance is too high, it can prevent firms from appropriating the diverse but still unfamiliar knowledge (Nooteboom et al., 2007)—again, due to the firm's limited absorptive capacity (Cohen and Levinthal, 1990). In the LIB case, for example, diverse knowledge that is developed in the chemical sector might integrate ideas from chemistry and electronics, and would be relatively diverse (and relatively general in chemical/electrochemical terms). However, this knowledge might still be hard to interpret and evaluate, and/or of limited use for distant sectors such as transportation. Third, we find that the integration of knowledge from various sectors increases the impact of the respective newly created knowledge, and that the integration of knowledge from various and/or distant sectors results in positive inter-sectoral externalities within the LIB technology. Hence, the interaction between different sectors active in a technology's value chain, and LIB technology in particular, can substantially benefit a technology's development. While the recombinant nature of knowledge and its effects has provided the basis for our hypotheses, the results might also arise from characteristics specific to the LIB technology and its value chain. For example, the complexity of a technology's architecture—i.e., its various interdependent components and subsystems (Huenteler et al., 2016a; Nightingale, 2000; Rosenberg,

1982)—might relate to knowledge flows. LIBs are complex (Stephan et al., 2017), i.e., their components and subsystems are highly interrelated. Moreover, for complex technologies, the innovation process over the technology life-cycle is dominated by product innovations (Huenteler et al., 2016b). Both interdependencies and product innovation are likely to induce spillovers between the sectors active in a technology's value chain. Firms from different sectors therefore have an incentive to interact—e.g., to coordinate interfaces, standards, etc. Furthermore, and relatedly, the number of production steps and applications of a technology might affect knowledge creation patterns. This is related to the number of sectors active in a technology's value chain. In this realm, the sectors that use the technology can play a particularly important role. LIBs have various applications in different sectors (Schmidt et al., 2016; Stephan et al., 2017).29 Feedback from/to these different using sectors is therefore needed (Stephan et al., 2017), which is also true for other complex technologies (Schmidt and Huenteler, 2016). Moreover, knowledge creation is unequally distributed across the LIB value chain in the analyzed period. Our descriptive results (Table 3) show that the electronics sector creates by far most knowledge.30 New knowledge creation by other sectors is therefore likely to build on knowledge from the electronics sector, which would inevitably require inter-sectoral knowledge spillovers. The generalizability of our findings may therefore be confined to technologies with technology and value chain characteristics similar to those of LIB technology. 5. Implications and conclusion 5.1. Implications for research on knowledge spillovers Through our analysis, we extend previous literature on knowledge spillovers by focusing on spillovers within the sectors active in individual technologies' value chains and aiming to understand patterns of inter-sectoral knowledge flows. This is especially relevant for most modern technologies, since their value chains span different sectors. Moreover, we believe this is salient because inter-sectoral knowledge flows across sectors have become important for technological innovations (Mowery and Rosenberg, 1998). We assume that the importance of these spillovers may even increase as new technologies become more complex and integrated. Firms that work in complex technologies are typically more vertically integrated (conglomerates), from either a production (Armour and Teece, 1980; Novak and Eppinger, 2001) or a knowledge perspective (Brusoni et al., 2001)—i.e., they have production or knowledge activities in several sectors. Our general suggestion, therefore, is for further investigation of knowledge spillovers between the different sectors involved in the development, production, and use of individual technologies. Our analysis yields six more specific implications. First, we recommend that additional technologies should be investigated, since our analysis only yields findings regarding LIBs and potentially also similar technologies. While such further work might widen the external validity of our findings, it could also help to pinpoint technology-specific patterns. Specifically, technology or value chain characteristics might affect the occurrence and effect of knowledge spillovers between the sectors active in a technology's value chain. One productive starting point might be to explore different levels of technological complexity; the number of production steps; and the number and roles of sectors involved. 29 Their ability to serve different mobile and stationary applications in different sectors (Dunn et al., 2011) makes them a so-called “multi-purpose technology” (Battke and Schmidt, 2015). 30 Note that this dominance is likely to wane, as indicated by the decrease of the sector's relative patent activity compared to other sectors in Japan (Stephan et al., 2017).

28

While not focusing on diversity Griliches (1979) has already argued that “the usefulness of somebody else's research to you is [presumably] highest if he is in the same four-digit SIC classification [compared to knowledge from someone who is in the same three- or two-digit classification]” (Griliches, 1979, p. 104). 12

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Second, researchers could also examine the extent to which our findings relate to the technology life-cycle or the sectoral composition and structure of the value chain. Innovation patterns can change during a technology's life-cycle (Huenteler et al., 2016a, 2016b), which could also mean that the patterns identified for diversity and distance might change. Moreover, the composition of the value chain, e.g., the individual sectors (Malerba, 2004) or types of sectors (Breschi and Malerba, 1997; Iammarino and McCann, 2006; Pavitt, 1984), might affect innovation processes (Stephan et al., 2017) and hence the patterns identified. In the LIB field, for example, while some sectors have been active already at the beginning of the technology's life-cycle, others such as transportation entered later (Stephan et al., 2017). Third, future research might want to further investigate the aspect that technological fields and sectors do not perfectly correspond. While the literature has identified and discussed the differences (Jaffe, 1986; Scherer, 1982a; Stephan et al., 2017), further analysis could yield interesting insights with regard to industry structure and evolution. This might include a comparison between the different indicators that have been typically used, patent classes and industry classifications, or developments over time. Fourth, research could develop a better understanding of how intersectoral spillovers take place in order to identify levers and barriers for knowledge flows. Like most extant work on spillovers in the fields of clean energy (Huenteler et al., 2016a; Nemet, 2012), storage (Noailly and Shestalova, 2016), and (lithium-ion) battery technologies (Battke et al., 2016), our study relies on quantitative (patent data) analyses. Hence, it is limited in its explanatory power regarding a more detailed understanding of enablers and mechanisms behind the inter-sectoral knowledge flows in the LIB technology. A recent study combining patent with interview data (Malhotra et al., 2019) highlights that the importance of inter-sectoral knowledge flows varies between different technologies. Hence, future research should analyze the role of sectoral difference in other technologies too. Fifth, researchers could also analyze knowledge flows to non-LIB sectors. While we focused on spillovers within the sectors active in the LIB value chain, knowledge is also likely to come from, and flow to, other technologies (Battke et al., 2016), whose value chains could also cut across non-LIB sectors. Finally, we generally recommend making a distinction between the two constructs diversity and distance. Our analysis clearly demonstrates that these constructs are different. Moreover, we recommend applying diversity and distance to both dependent and independent variables—i.e., to also take the direction of subsequent knowledge flows into account.

Moreover, R&D managers can strategically locate their research facility close to firms from other sectors to benefit from geographically bound knowledge spillovers (Audretsch, 1998). Firms from the chemical sector, for example, can build their R&D centers close to electronics firms, as Umicore and BASF have (Malhotra et al., 2019). However, not only is diversity costly (Todo et al., 2016) and uncertain (Fleming, 2001), our findings indicate that R&D managers still struggle to optimize it. Either they miss the target—because integrating (overly) distant knowledge does not necessarily help them—or they have to retain their employees for an inter-disciplinary environment so they can also utilize very unfamiliar knowledge. Both education and on-the-job training would have to provide employees with the skills required to understand and interpret each other's knowledge and research culture. In the LIB field, this could mean that education programs cover aspects related to the entire battery value chain (e.g., electrochemical studies also transmit knowledge regarding resource extraction, or the application of electrochemical devices). Policymakers who wish to foster innovation in particular technologies aim to design policies that will reach this goal efficiently and effectively. We find that knowledge that builds upon inter-sectoral knowledge flows will cause further inter-sectoral knowledge flows—i.e., it generates positive inter-sectoral externalities in LIB technology. We assume similar patterns for technologies that are similar either technologically, or in terms of their value chains. If policymakers want to foster these positive inter-sectoral externalities, they should specifically enable inter-sectoral knowledge exchange, e.g., by supporting sector coordination.31 For this purpose, they could implement technology-push instruments that provide R&D support for intersectoral consortia or collaboration projects rather than for individual firms. They could, for example, design tendering requirements accordingly, or support platforms such as interdisciplinary associations. For LIBs, this would mean linking the actors from different sectors—as has successfully happened for LIBs in Japan (Keller and Negoita, 2013). Note that if policymakers specifically want to support knowledge spillovers to distant sectors, their coordination activities must include those distant sectors. However, policymakers also need to consider that technology-push effects are typically regionally limited (Peters et al., 2012). Hence, they should consider local conditions in order to benefit from innovation effects and spillovers. Besides fostering individual technologies, our analysis also has implications for policymakers who want to stimulate their local economies by supporting a specific sector. If that sector is actively contributing to one or several complex technologies, inter-sectoral knowledge spillovers will be particularly strong if the relevant sectors are located in geographic and cultural proximity (Audretsch, 1998). In their localization efforts, policymakers therefore need to look beyond the sectors they originally aimed to strengthen and include additional sectors that are relevant for innovation in key technologies to which the targeted sector contributes. In order to get the most benefit from the desired effects, policymakers, therefore, should support the local settlement of the current and prospective (most) relevant sectors, e.g., chemicals, industrial equipment, electronics, instruments, and transportation in the LIB field. However, policymakers should be aware that the patterns identified might be subject to individual technology and value chain characteristics. The literature has shown that technologies with different

5.2. Implications for R&D managers and policymakers For R&D managers, our findings indicate that integrating knowledge from other sectors is a double-edged sword. On the one hand, R&D managers can increase their innovative and probably also economic performance by integrating such knowledge. On the other hand, the integration of knowledge is likely to spill over to other sectors—i.e., it could represent a negative inter-sectoral externality to the firm. If R&D managers want to integrate diverse knowledge, they should think about aspects such as the structure of their research group, their “outside” activities, or their geographic location. More specifically, they could employ workers trained in other sectors, participate in inter-sectoral conferences or platforms, or seek partnerships across sectors. In the LIB field, recent examples show that firms have realized the need for crosssectoral collaboration (see also Malhotra et al., 2019). For example, they have started to develop inter-sectoral joint ventures, such as the Gigafactory by Tesla and Panasonic, or enter long-term collaborations with chemical and electronics firms (Malhotra et al., 2019). Moreover, firms have started to vertically integrate into upstream activities, e.g., many car manufacturers have started making batteries (BMW, 2018; Daimler, 2019), and become conglomerates. These conglomerates, in particular, should enable communication across their various divisions.

31 This is also indicated by the success of LIB development in Japan. Japan has fostered knowledge spillovers between actors from different sectors of the LIB value chain during the analyzed period (Keller and Negoita, 2013), and had achieved a dominant market share of 57% by 2010 (Lowe et al., 2010). Strong ties in form of joint ventures between automotive companies and battery suppliers are also present in Japan (Pohl and Yarime, 2010). A country comparison is beyond the scope of this paper, but a more detailed discussion on LIB knowledge creation in Japan can be found in Stephan et al. (2017).

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characteristics, such as their design complexity, follow different innovation paths (Huenteler et al., 2016a; Schmidt and Huenteler, 2016). In cases where policymakers want to foster technologies that differ substantially from LIBs, we recommend that they analyze the respective innovation patterns.

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5.3. Conclusion This paper elaborates on the role of knowledge diversity and distance across the sectors involved in a technology's value chain for new knowledge creation. More specifically, we analyze whether the sectoral diversity and distance of prior knowledge affect the importance and direction (sectoral diversity and distance of subsequent knowledge) of subsequent knowledge generation. We develop six hypotheses, which we test based on regression analyses of LIB patents. We find that (1) while a higher sectoral diversity of prior knowledge increases the importance of the newly created knowledge, a higher sectoral distance of prior knowledge does not significantly increase the importance of the newly created knowledge; (2) both higher sectoral diversity and higher sectoral distance of prior knowledge increase the sectoral diversity of subsequent knowledge; and (3) higher sectoral distance of prior knowledge increases the sectoral distance of subsequent knowledge, whereas higher sectoral diversity of prior knowledge does not significantly increase the distance of subsequent knowledge. Our findings therefore highlight general patterns of inter-sectoral knowledge spillovers in LIB technology. We assume these findings to be generalizable at least to technologies with similar technology and value chain characteristics. Furthermore, we emphasize the importance of distinguishing between the two constructs of diversity and distance, and of differentiating between subsequent knowledge flows based on their direction. These aspects yield implications for future research and policymakers. However, our analysis is not without its limitations. As our study only covers the illustrative case of LIBs, similar studies in other technological fields with different characteristics or specific countries might enhance our results. Furthermore, we use patent citations as a proxy for knowledge spillovers, but this approach also has its limitations (Jaffe and de Rassenfosse, 2016; Trajtenberg and Jaffe, 2002). By using patents, we neglect both tacit knowledge and explicit knowledge that is not patented. Additionally, the use of patent citations for knowledge flows is also subject to limitations (Alcácer and Gittelman, 2006; Criscuolo and Verspagen, 2008; a more detailed discussion can be found in Jaffe and de Rassenfosse (2016)). For example, knowledge might flow via other channels such as R&D cooperations (Wang et al., 2017). Analyzing other data sources, such as the composition and success of R&D cooperation projects or co-applications, might shed further light on the question investigated in this paper. Further research might use other data sources or different constructs in order to validate our results. Acknowledgements We would like to thank the participants of the DRUID Academy Conference 2016 in Bordeaux, France as well as Fanny Frei, Julian Koelbel, Jan Ossenbrink, Juliana Subtil, Marloes Maathuis and Claude Renaux (statistical consulting), Tom Albrighton (language editing) and the anonymous reviewers for their valuable feedback and support. This research is part of the activities of the Swiss Competence Center for Energy Research (SCCER CREST), which is financially supported by Innosuisse the Swiss Innovation Agency under Grant No. 1155000154. Appendix. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.techfore.2019.119718. 14

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Annegret Stephan is a postdoctoral researcher at the Department of Management, Technology and Economics at ETH Zurich (Switzerland). Her research centers on energy technology innovation with an economic and innovation system perspective. She holds a Diploma (MSc. equivalent) in Business Engineering from the Karlsruhe Institute of Technology (KIT) (Germany), and a PhD from ETH Zurich (management, technology and economics). She also studied at the University of Technology Sydney (Australia), and spent time as a visiting scholar at the Department of Science, Technology, Engineering and Public Policy (STEaPP) of the University College London (UCL) (UK). Catharina R. Bening is a postdoctoral researcher at the Department of Management, Technology and Economics at ETH Zurich (Switzerland). In her research, she focuses on energy technology innovations, as well as on circular economy. She holds a Lic. oec. (MSc. equivalent) and a PhD (economics) from the University of St. Gallen (Switzerland). She also studied at the National University of Singapore (Singapore), the Erasmus University Rotterdam (Netherlands), and spent one research year at the Department of Comparative Human Development and at the Economics Department of the University of Chicago (US). Tobias S. Schmidt is Assistant Professor for Energy Politics at the Department of Humanities, Social and Political Sciences of ETH Zurich (Switzerland). His research focusses on the interaction of energy policy and its underlying politics with technological change in the energy sector. He holds a BSc. and a Diploma (MSc. equivalent) in electrical engineering from the Technical University of Munich (Germany), and a PhD from ETH Zurich (management, technology and economics). During his postdoc, he spent time as a visiting scholar at Stanford University's Precourt Energy Efficiency Center (PEEC) and acted as consultant to the United Nations Development Program (UNDP). Marius Schwarz is a PhD candidate at the Department of Management, Technology and Economics at ETH Zurich (Switzerland). In his research, he focusses on the diffusion of renewable energy technologies and its interaction with policy instruments. He holds a MSc. in mechanical engineering and business administration from the RWTH Aachen University (Germany). He also studied at Linköping University (Sweden) and wrote his master thesis at the Department of Management, Technology and Economics of ETH Zurich (Switzerland). Volker H. Hoffmann is Professor for Sustainability and Technology at the Department of Management, Technology and Economics of ETH Zurich (Switzerland). His research focuses on technological innovations, institutional dynamics, and organizational strategies as key drivers for the de-carbonization of the economy. He holds graduate degrees in chemical engineering from ETH Zurich and in business administration from the University of Hagen (Germany), and a PhD in technical sciences from ETH Zurich. He was a guest researcher at the Massachusetts Institute for Technology (MIT) (US) and worked as project manager at McKinsey & Company (Germany) before joining the faculty of ETH Zurich.

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