An overview of evolutionary innovation models

Since the 1970s, the view on innovation of political and corporate decision makers has gradually changed from a linear perspective (demand-pull and technology-push) to more complex models considering a diversity of influencing actors as well as the importance of feedback effects during the process from the idea creation to the release of the novelty on markets (Kline and Rosenberg 1986, Etzkowitz and Leydesdorff 1999). The theoretical founders of these more complex and more realistic approaches to the analysis of the phenomenon of innovation were among others, Rosenberg (1976), Nelson and Winter (1982), Rothwell and Zegveld (1981) or McKelvey (1994). On the basis of their theoretical underpinnings different lines of models have evolved to understand the process of the production of new knowledge and to foster innovation. This survey paper presents an introduction to the conceptual framework of innovation systems (Freeman 1989, Lundvall 1992, Nelson 1993) and the triple helix model (Etzkowitz and Leydesdorff 1999) as approaches to under-stand innovation processes within their specific socio-economic and political contexts from two different systemic perspectives. To comprehend the mechanisms shaping industrial innovation processes on a more operational level, evolutionary economists have described the phenomenon of innovation networks, which will be introduced subsequently. Before we do so, we will take a closer look at the sources and diffusion mechanisms of the most crucial resource of innovation, namely knowledge. By introducing the new paradigm of knowledge production, the mode 2 (Gibbons et al. 1994; Cohendet and Joly 2001) we take account of the particular knowledge flows between agents of the innovation process. This will lay the foundation for a better understanding of the processes steering the innovation systems, the triple helix and innovation networks. We will argue that these notions are of fundamental importance for informing innovation policy confronted with the innate uncertainty within complex socio-economic environments determined by: (1) a high degree of transdisciplinarity, (2) a variety of stakeholders, (3) alternative communication channels, (4) environmental and societal challenges of global dimension as well as (5) novel technologies like artificial intelligence blurring the boundaries between agency and medium. 1.0 Opening It is widely recognized that ‘‘the most fundamental resource in the modern economy is knowledge and, accordingly, the most important process is learning’’ (Lundvall 1992; 1). Knowledge has always played a crucial role in economies and the skills to create artefacts have been passed on and improved from generation to generation, building up competences and skills that facilitated economic growth. With the onset of industrialization, however, life conditions changed rapidly and living environments were not as stable as they used to be. Consequently, the ability to adapt to new conditions, i.e. the ability to learn became much more fundamental (Lundvall 1994). Since the early 1980s, when traditional industries struggled with excess capacity and falling productivity, governments ran into deficits and income declined, the new post-industrial vision of a knowledge-based economy came to life. It became obvious that economic wealth was more and more decoupled from material growth REELER Working Paper Series 2 and rather tended to be secured through the creation, production, distribution and consumption of knowledge and knowledge-based products (Harris 2001). 2.0 A new mode of knowledge production – mode 2 Considering this fundamental role of knowledge in contemporary economies raises the question of where this knowledge is produced and stored and who controls and distributes it. While this power has for quite some time been the privilege of public scientific institutions, a new mode of knowledge production is now undermining this monopoly: the mode 2 (Gibbons et al. 1994; Cohendet and Joly 2001). This new way of knowledge production is said to be “socially distributed, application-oriented, trans-disciplinary, and subject to multiple accountabilities” (Nowotny et al. 2003; 179). Whereas mode 1 implied pure science being generated in an exclusive environment and sometimes afterwards applied, transferred or managed otherwise, a mode 2 knowledge generation is motivated by topics and research designs that are embedded in the social system. Research agendas are not exclusively fixed in a top-down manner, but become more and more a product of social discourse. This requires a dialogue between research subjects and objects and at the same time blurs traditional boundaries between disciplines and between basic and applied research (Gibbons et al. 1994, Nowotny et al. 2003). In short: research is becoming more directly relevant for society. 2.1 Knowledge for society To create solutions to problems instead of creating publications seems to be a purely positive characteristic of mode 2 knowledge production. Especially with view on the currently pressing global challenges like climate change, resource depletion, distributional questions or the demographic development it seems imperative to put most effort in research addressing a transformative system change based on what has been termed transformative science (Grunwald 2015, Schneidewind et al. 2016). This approach, which targets radical transformation of existing institutions and routines, would surely not have reached such high priority on a research agenda designed by an exclusive research community steered by current politicians in line with the powerful incumbent industries simply because the currently powerful do not have an interest in changing anything. Furthermore, findings of technological impact assessment and more recently of responsible research and innovation studies show that discursive deliberation involving different societal stakeholders increases critical reflection of advantages and disadvantages, potential and risks of new technologies (Schneidewind et al. 2016). Another gain from the new mode of knowledge production obviously is the tendency of increasing collaboration of formerly distant disciplines (Nowotny et al. 2003). Using interand transdisciplinary approaches to explore theoretical or practical issues has been found to fundamentally improve creativity and lead to more creative solutions (see, e.g., Paci and Usai 1999, Kanter 2000, Blackwell et al. 2009). At the same time, mode 2 comes along with an intensification of research collaboration between universities, industry, government organizations and hospitals (Godin and Gingras 2000). Together with the emergence of other knowledge production communities such as think-tanks, activist groups or management consultants that engage in research (Nowotny et al. 2003) these new configurations contribute to the development of alternative ways to produce knowledge. In line with the evolutionary approach to economic development, such variety of information flows create highly innovative environments, for “innovation often demands the gathering and storing of types of information different from those obtained by scientists” (Kline and Rosenberg 1986; 281). Understanding innovation processes: An overview of evolutionary innovation models 3 2.2 Quality issues The new paradigm of mode 2 knowledge production has been widely welcomed due to the de-scribed advantages. However, it has been adopted with mixed feelings in other circles. Some universities and traditional scholars oppose to the celebration of the “democratization” of knowledge production for reasons connected to the quality, the freedom and the openness of basic research. While, firstly, the quality of advanced scientific research (mode 1) is traditionally secured by the peer-review process of scientific publications, results from mode 2 knowledge production often need to fulfil other criteria quite different from the objective of scientific excellence. Quality of mode 2 knowledge will rather have to be evaluated on the basis of its relevance for the users or its adequacy to solve a specific problem (Fujigaki and Leydesdorff 2000). The second argument against the establishment of the new way of knowledge production is the fear of a commercialization of scientific research (Nowotny et al. 2006). Where public expenditure for academic research is ac-companied or taken over by private investment, commercial interests are expected to be served increasingly which threatens scientific autonomy and freedom. The third reason why mode 2 is regarded with skepticism by some is connected to the latter. The reduction of the entire knowledge production to the creation of solutions, termed solutionism, may run risk of destroying the traditionally basic force of scientific progress and its warranty to openness: the desire to under-stand the world (Strohschneider in Grunwald 2015). 2.3 Reconciliation of the modes The locus of production of the fundamental resource in modern knowledge-based societies is a highly debated issue. Before the diversification of knowledge production was coined as mode 2 in the 1990s (Gibbons et al. 1994) several authors have described similar phenomena with notions such as finalized science (Böhme et al. 1973, see also Schäfer 2012) or post-normal science (Funtowicz and Ravetz 1993, Elzinga 1996). Although mode 1 is by no means the “normal” or “intuitive” procedure of knowledge production the observation of a trend of departure from it has created anxiety among established science communities (i.e. typical mode 1 institutions) (Weingart 1997). But in the context of the increasing knowledge base in our modern societies and especially with view on the globalized challenges humanity is facing, the competition between the different modes is probably obsolete. Instead of fostering resentment against transformative approaches to socially relevant scientific questions, academic science should seek to take advan