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From its beginnings, innovation has been strongly linked to economic growth in particular (Bernal 1939), but also to national security (Bush 1945). Therefore, the fundamental question for innovation research has always been to explain how innovations occur (Fagerberg 2005, p.9), implicitly making the normative claim that innovations are good and we need more of them. Recently, that goal has been updated

towards gaining “systematic and reliable knowledge about how best to influence innovation and exploit its effects to the full” (Fagerberg et al. 2013, p.1). To provide an overview of the innovation-policy relationship, it is helpful to organize current work in innovation studies into a two-by-two table. On one dimension, we can distinguish between those works that look at how innovation functions at the macro scale (such as innovation systems, networks or clusters) and those who look at the micro scale (such as individuals, firms, or organizations). On the other dimension, we can look at whether the work is oriented towards studying the causes of innovation or studying its effects. Mapping these two dimensions onto a table would yield an overview of the innovation studies literature as seen below with a few key works cited as examples in each quadrant.12

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The works cited in this table are not meant to provide an extensive and complete overview of the field, and neither are they meant to portray the paragons of the discipline – I cite them in order to provide examples that contribute to understanding the main distinctions drawn in the typology. The fit between the table and the clusters identified by Fagerberg et al. (2012) is also not a perfect one-to-one fit: the Organizing Innovation cluster also deals with works that look at the macro-scale (such as the industry level), but it could be argued that there is an emphasis on providing knowledge that is useful from the perspective of the individual organization. The point I want to make here is that the table is a helpful heuristic for understanding the argument of the chapter and to point out and conceptualize a blind spot in

Table 3-1. An overview of the innovation studies literature13

Macro-scale Micro-scale

Causes Innovation Systems

Freeman (1987) Lundvall (1992) Nelson (1993) Braczyk et al. (1998) Mazzucato (2013) Organizing Innovation Teece (1986)

Cohen & Levinthal (1990) Henderson & Clark (1990) Tidd et al. (1997)

Effects Economics of R&D

Marx (1993) Schumpeter (1943) Nelson & Winter (1982) Freeman & Soete (1997) Porter (1990)

Reacting to innovation Christensen (1997)

Innovation Governance

Source: Adapted from Fagerberg et al. (2012).

According to Fagerberg, Fosaas, and Sapprasert (2012), the innovation studies literature clusters into three linked but distinct groups: the Economics of R&D (Research and Development), Organizing Innovation, and Innovation Systems. These clusters fit the two-by-two typology, filling out three of the four quadrants. The Organizing Innovation cluster looks at the way individual organizations, firms or industries organize in order to become more innovative, benefit better from innovation, or defend themselves from the pressures of innovation. The scope for policy in the political sense is limited in this cluster as the works here tend to view innovation from the perspective of the individual organization – policy here mostly has to do with managerial or strategic decision- making. The Innovation Systems cluster has its beginnings in the national systems of

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The cluster analysis is drawn from Fagerberg et al. (2012). I have made two changes to their groupings: Christensen (1997) has been moved to the “Reacting to Innovation” quadrant and Nelson & Winter (1982) has been moved to the “Economics of R&D” quadrant. The logic of the first move is that while his books investigate both the causes and effects of disruption, their purpose is primarily to inform

management decisions. The logic of the second move is that this work is thematically closer related to the other works in the Economics of R&D cluster as the goal of the book is to challenge neoclassical

economic theory and its unsatisfactory explanations of how innovation impacts the dynamics of competition among firms.

innovations literature and is broadly concerned with studying institutional setups at the macro level, concerning themselves with the flow of technology and information among people, enterprises and institutions in national or regional settings. Like the Organizing Innovation cluster, the focus is on the causes of innovation in order to produce more innovation and better benefit from it. In this quadrant the emphasis is very much on an active role for policymakers in the construction of innovation systems, but the analyses tend to care more about what policies can do to foster innovation and not the policy problems that innovations may bring about. Finally, the Economics of R&D cluster focuses on the systemic impacts of innovation and how these systems change over time as a consequence thereof. These works are mostly concerned with providing better explanations of economic change as a consequence of innovation. In this quadrant, the literature does question what innovation does, but the replies tend to look at the macro- scale and restrict themselves to economic analysis. Although policymakers and other strategists are cast in roles having to respond to these external pressures and with less scope for initially shaping these themselves, policy is not the main focus of these studies and fades into the background behind economics.

What about the final quadrant: the micro-scale effects of innovation? This is where we would expect to find studies on the political and regulatory challenges that innovations raise, among other things. To the extent that Christensen’s studies on disruptive innovation deal with the effects of disruption on individual firms, other organizations or even entire sectors and industries, then he also has a home here. But there is a marked lack of studies that go into the politics of innovation: when it comes to answering the question “what does innovation do?” the replies within innovation studies have tended to look at the macro-scale and focused on economic analysis. There is an

opportunity to devote more attention to the micro-scale, and in particular to the impact of innovation on policymaking, that is, the regulatory challenges raised by some types of innovations. This is the area that Innovation Governance (IG) should concern itself with.

The innovation-policy relationship can be understood cyclically: innovations can impact policy, and policy can impact innovation. Over time, this creates a cycle of iteration and feedback loops between policy systems and innovation systems that account for their continual development. From the previous discussion about Table 3-1, it is clear that the majority of research in innovation studies that deal with both innovation and policy focus on gaining a greater understanding of the actions, mechanisms or institutional setups that are the most conducive to producing innovation. These inquiries are valuable and have proven their policy relevance (Lundvall & Borrás 2005; Mazzucato 2013). However, when it comes to answering the question “what does innovation do to policy?” the replies within innovation studies have been more muted.

Are there studies that take this cyclical relationship more seriously? Within the more economics-leaning branch of the discipline, attention to co-evolutionary processes is growing (Dosi 2013). In a different discipline, Science and Technology Studies (STS) have long been indicating the co-constitutive effects and feedback loops between science and policy (Jasanoff 1987; Latour 1998), but this interaction seems less fully developed when we look at innovation and policy. Even when attention does get

directed specifically to interaction in the innovation-policy “dance” or in the effects of

non-technological regulations, it is always with a view to enabling more innovative activities (Kuhlmann et al. 2010; Paraskevopoulou 2012). If the self-proclaimed theoretical core of innovation studies is “the conceptualization of innovation as an

interactive process” (Lundvall 2013, p.33), then when it comes to the political system, it is fair to say that it has mostly been treated as a case of policies acting on innovations and not the reverse.

That current work in innovation studies emphasizes the promotion of innovation is entirely unsurprising given the history of the field and its links to economic growth. However, when we reverse the causal arrow in the relationship between innovation and

policy, we open a Pandora’s Box of political and societal implications of innovation that

deserve more attention. Until that box is opened, we are essentially neglecting one half of the innovation-policy relationship. There is an opportunity here for policy studies and innovation studies to come together in order to flesh out what interaction in the innovation-policy relationship looks like, particularly in the case of innovations acting on policy. This research program can be called “innovation governance”, and one of the goals of the dissertation is to take the initial steps towards defining this area.

I am not alone in arguing for more attention to this issue. For example, Martin (2010; 2012) has called for a more sophisticated model of the interaction between policy research and policy-making, and Steinmueller (2013, p.161) has argued for the need for innovation studies to address the “uneasy relationship with public administration as well as politics.” This program can also be seen as a development of Mazzucato’s (2013) call for reimagining the role of the state in innovation. In spite of the advances made within the Innovation Systems literature, the state is still primarily seen as a fixer of markets – which is especially true when it comes to the European Commission (Mazzucato et al. 2015). But in addition to this much bigger role for the state in directing mission-oriented investments in innovative activities, it should be clear that states and regulatory agencies play a major role in defining and constituting

innovations by writing the rules to which they must adhere. These rules are often reactions to innovative developments that have caught regulators off guard, and they are immensely important for distributing the costs and benefits of innovations among societal groups. For instance, in reviewing the societal impacts of planned obsolescence in consumer goods and financial innovations, Soete (2013) decries how regulators succumbed to short-termism and vested interests that led to “destructive creation” in both cases. In order to secure truly innovative and beneficial “creative destruction,” Soete argues for stronger and more independent public agencies staffed with high- quality personnel. Presumably this staff will need to be better at distinguishing between different types of innovative activities and reacting to them appropriately. According to Soete (2013, p.142), “society sorely missed an appropriate innovation assessment tool.” It is ironic, then, that the sub-discipline of technology assessment (which has always resided on the fringes of innovation studies and STS and from where we took the notion of Collingridge dilemmas discussed in the previous chapter) is not being suggested as harbouring the seed of such a hypothetical tool.

IG should exceed the ambitions of technology assessment (TA), however. The purpose of TA is to contribute to the formation of public and political opinion on the societal aspects of technologies via a structured, deliberative process. While it is important to pay attention to how opinions are formed, IG should go a step further by studying how policy actors wield these opinions as well as scientific facts to compete for influence and control of the policy processes that regulate disruptions. This cannot be a task left solely to innovation studies – it entails a closer partnership with political science to situate policy actors in their institutional settings.

3.3

The politics of innovation: innovation as viewed from