Essential ingredients for radical innovations? The role of (un‐)related variety and external linkages in Germany

Abstract

The role of radical innovations for the economy has received increasing attention by German policy-makers. This paper investigates how (un-)related variety and external linkages influence these innovations in German labour market regions. Evidence is found that related and unrelated knowledge capabilities both support the emergence of radical innovations, although strong related capabilities are especially important. External linkages have an inverted u-shape relation to radically new ideas and can act as substitute for missing unrelated competences in a region. The results shed new light on the emergence of radical innovations and thus have interesting scientific and practical implications. El papel de las innovaciones radicales para la economía ha recibido cada vez más atención por parte de los responsables políticos alemanes. Este artículo investiga la forma en que la variedad (no) relacionada y los vínculos externos influyen en estas innovaciones en las regiones del mercado laboral alemán. Se encontró evidencia de que las capacidades de conocimiento, tanto relacionadas como no relacionadas, apoyan el surgimiento de innovaciones radicales, aunque son especialmente importantes las capacidades fuertemente relacionadas. Los vínculos externos muestran una relación en forma de “U” invertida con las ideas radicalmente nuevas y pueden actuar como sustituto de las competencias no relacionadas que no existen en una región. Los resultados arrojan nueva luz sobre el surgimiento de innovaciones radicales y, por lo tanto, tienen interesantes implicaciones científicas y prácticas. ドイツの政策立案者らは、経済のための急進的イノベーションの役割に注目している。本稿では、関連性のある(または関連性のない)多様性および外部リンケージが、ドイツの労働市場地域において、経済のための急進的イノベーションにどのように影響を及ぼすかを検討する。特に関連性の強いケイパビリティが重要ではあるものの、関連の有無に関わらず知識のケイパビリティが急進的イノベーションの発生を支持していることを示すエビデンスが認められる。外部リンケージは、新しい急進的なアイデアと逆U字型の関係にあり、ある地域では欠落している関連のないコンピテンシーの代替として機能することができる。 During the last decades, innovations have been highlighted as key factor for economic growth (Rosenberg, 2004; Verspagen, 2005). Recently, it has been acknowledged that in particular radical innovations offer great economic potential (Castaldi, Frenken, & Los, 2015). Innovations that are radical in nature combine previously unconnected knowledge domains, which is more uncertain and riskier than combining knowledge that has been combined before (Fleming, 2001). In the event that such innovations are successful, they can form completely new markets and industries and provide the basis for long-term economic growth (Ahuja & Morris Lampert, 2001). A good example is, for instance, the new combination of the technological fields automotive, sensor-based safety systems, communication and high-resolution mapping which are combined for the first time in the self-driving car (Boschma, 2017). The possible catalysing role of radical innovations for the economy has also received increasing attention by policy-makers. For instance, the German government just recently established a public agency for the promotion of radical innovations (BMBF, 2018). Recently, the importance of the relatedness of technologies for technological change, economic competitiveness and diversification processes has been highlighted in a series of studies (Boschma & Iammarino, 2009; Breschi, Lissoni, & Malerba, 2003; Frenken, Van Oort, & Verburg, 2007; Hidalgo, Klinger, Barabási, & Hausmann, 2007). In spite of many papers on the drivers of innovation processes in general and especially the role of existing localized knowledge variety, the driving forces of radical innovations remain relatively unclear. Lately, scholars have started research endeavours in this regard. While Castaldi et al. (2015) find evidence that only unrelated variety positively influences technological breakthroughs, Miguelez and Moreno (2018) discover that not only unrelated knowledge competencies but also related one's favour breakthrough innovations. These results show that further analysis is required in order to understand the impact of knowledge variety on radical innovations in a more comprehensive way. Moreover, radical innovations may not solely draw from local knowledge sources since it can become redundant at some point and cause situations of lock-in (Boschma, 2005). Hence, actors might find complementary knowledge for radical innovation processes through linkages with actors from outside the region (Bathelt, Malmberg, & Maskell, 2004). Formal collaborations may be a specific channel to access this knowledge (Singh, 2008), which have been acknowledged to enhance innovativeness of regions and firms (Fitjar & Rodríguez-Pose, 2013). Although, De Noni, Ganzaroli, and Orsi (2017) have analysed the effects of technological variety and (non-)local linkages on regional inventive performance, it remains unclear how they influence radical innovation processes. This paper aims to shed further light on the determinants enhancing the emergence of radical innovations. In particular, it analyses how (un-)related variety and external linkages drive radical innovation processes. The paper contributes to this issue in several ways: First, we analyse radical innovations from two complementary perspectives, looking at the emergence as well as at the diffusion. Thereby, we include a new indicator to detect radical innovations in the research on regional diversification. Furthermore, we expand the analysis by inspecting the role of linkages with external actors through collaborations and how this influences radical innovation output in the region. Despite contributing to close a research gap, this study also has important implications for policy-makers and managers. The remainder of the paper is structured as follows: The next section gives an overview of the theoretical background and leads to the hypotheses. Section 3 describes the data and methods. The main empirical results are presented and discussed in Section 4 and Section 5 concludes and gives an outlook of possible future research endeavours. During the past decades, innovation processes have been acknowledged as important factor for economic growth (Rosenberg, 2004; Verspagen, 2005). Thereby, innovations are commonly understood as a cumulative process where existing knowledge is combined in unique ways to create something new (Arthur, 2007; Basalla, 1988). Weitzman (1998, p.333) defined the reconfiguration of existing knowledge in a unique fashion to form new artefacts as “recombinant innovation.” This process can lead to both incremental and radical innovations. 1 While the former are considered to develop mostly alongside well-known trajectories and particularly refine existing technologies, the latter introduce a novel artefact or technological approach, which can lead to a paradigm shift and thus radical change (Arthur, 2007; Dosi, 1982; Verhoeven, Bakker, & Veugelers, 2016). This radical change may open up new markets or even industries while causing old ones to disrupt (Henderson & Clark, 1990; Tushman & Anderson, 1986). Hence, radical inventions “serve as the basis of ‘future’ technologies, products and services” (Ahuja & Morris Lampert, 2001, p. 522). The search processes at the heart of these inventions find novelty through the recombination of former unconnected knowledge (Fleming, 2001; Hargadon, 2003; Nerkar, 2003). New combinations then are the result of such search processes, when actors discover a new purpose for their existing knowledge or they fuse together some external expertise with their own mind-set (Desrochers, 2001). These processes introducing novelty are difficult to engage in and also riskier in regard of commercialization since it is uncertain if the activities will have an economic impact in the future (Fleming, 2001; Schoenmakers & Duysters, 2010; Strumsky & Lobo, 2015). As radical innovations can be radical in terms of their degree of novelty as well as with regard to their impact, it is important to analyse them in both dimensions (Dahlin & Behrens, 2005). 2 Radically new ideas emerge through existing knowledge pieces, which are unevenly distributed over regions. However, radical innovations can help regions to obtain a competitive advantage. Hence, scholars and policy-makers seek to understand how regions can strengthen their ability to produce these innovations. To study the impact of localized knowledge on radical innovations we make use of the concept of knowledge variety. The knowledge created over time and embedded in organizations leads to variety of knowledge in an economy, which can be seen as a crucial factor of economic growth (Saviotti, 1996). Although knowledge is based in individual firms, the interaction with other firms in the region is important for the creation of new knowledge (Fleming, 2001). However, to be able to absorb new knowledge spilling over from other firms, actors need to be related to each other in terms of their knowledge to a certain extent (Cohen & Levinthal, 1990). Following the concept of Nooteboom (2000), two knowledge bases are viewed as related to one another if they have a certain degree of overlap and develop through similar skills and abilities. Knowledge variety can be divided into related and unrelated variety. Related variety describes the situation where actors in a region engage in industries with similar knowledge bases. Several empirical studies have shown for different dimensions (e.g., products, industries, technologies) and spatial units (e.g., countries, labour market that variety in related industries the basis for knowledge and economic growth (e.g., et 2007). and have a comprehensive of these the other unrelated variety describes the situation when a region firms from unrelated unrelated variety have been discussed more scholars & 2010; & scholars have started to variety and inventive processes. These studies find for different spatial dimensions that related variety especially general innovation output et Miguelez & & impact, Castaldi et al. (2015) find only a of unrelated variety. Miguelez and Moreno (2018) that both variety have a the effects of related and unrelated variety on radical innovations are from Following Miguelez and Moreno we that it is to have both related and unrelated knowledge capabilities in a region in order to up with radical innovations. et al. also have in their paper that related and unrelated variety not be considered as but each Moreover, (2017) has that it more that new activities on both related and unrelated First, the of related and unrelated variety the for possible new combinations & 2016). competences related can help to understand unconnected knowledge & Related variety the process of and to unrelated knowledge (Boschma, 2017). Hence, we the and unrelated variety in a region both have a on radical innovations. that related variety might be important since knowledge for the creation of new knowledge related actors (Cohen & Levinthal, 1990; 2001; et 2007). Related competences and to be able to absorb knowledge from unrelated et However, we that unrelated variety has a since knowledge combinations from unrelated can introduce more radical novelty & Frenken, it may radically new ideas by an increasing of possible new combinations related and unrelated industries & 2016). Although it is more uncertain and riskier to with if successful, new combinations of unrelated knowledge might the for technological et 2001). The is as follows: of unrelated variety in radical innovations is more than the of related variety. While knowledge variety in a region is the can be a specific channel to access to complementary In the light of and knowledge (Singh, 2008), is as important to strengthen the innovativeness of firms and regions (Fitjar & Rodríguez-Pose, 2013). actors not only engage in of and but also in order to the of their inventions with the to create radical (Singh, Several have the that complementary knowledge for radically new ideas might be found outside one's own region & and have that external knowledge can situations of regional lock-in (Boschma, 2005). This external knowledge can into a region through collaborations and support the emergence of radical innovations. This can if the knowledge is different but related to the local knowledge (Boschma & Iammarino, 2009; Miguelez & 2018). However, we that collaborations have an a of collaborations increasing for novel combinations of complementary at a certain point the of new with complementary knowledge and the of become to be & 2016). Hence, we the linkages have an inverted u-shape relation to radical innovations. while strong related variety that unrelated competences can be et these unrelated knowledge for radical novelty can be found through unrelated variety in a region or knowledge & 2018). in regions for radical innovations complementary knowledge from local unrelated actors or from actors outside their region. However, combining both and knowledge might be difficult to absorb for economic actors (Boschma, research endeavours in this regard have found that knowledge is if it is different but related (Boschma & Iammarino, 2009; Miguelez & 2018). Hence, we that unrelated variety and external linkages have a on radical innovations. the variety and linkages substitute each other in radical innovations. studies have on to radical innovations. can First, are several scholars that novel and unique have a overlap in the with past and (Dahlin & Behrens, 2005). and Morris point to the that radical innovations not on and and and find that are a good indicator to a and also find evidence that a with impact has with the of future another is to study on on that radical innovations from former knowledge domains, radical innovations can be by which are combined for the first is by their degree of novelty (Fleming, 2007; Strumsky & Lobo, et 2016). also two which can be as previously by & 2013). are in studies on the role of knowledge variety on radical innovations et Miguelez & on the impact an innovation has in the In we to expand this and an indicator for the emergence of radical new combinations of as For radical innovation output is by data from the 3 The of the analysis are and with at one German on are to German labour market regions as defined by and 4 This is that and are to the are to the which their technological they are 5 The the data to the which different This the of in the and a of technologies & 2017). 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