Keywords

1 Introduction

The past couple of decades have seen a shift in innovation policy toward increasingly addressing grand societal challenges. Policy agendas are no longer solely aimed at increasing the supply of Research and Development expenditure (R&D), generating more entrepreneurial ventures, or strengthening national competitiveness in certain sectors. Instead, policy programs are increasingly crafted to accomplish systemic transformation of the economy toward environmental and social sustainability. Initiatives with such ambitions are sometimes described as the third generation of innovation policy (Schot & Steinmueller, 2018). This chapter departs from the increased awareness of the need for systemic transformation in policy and focuses on the following questions:

  • How can such policy programs be designed to facilitate the type of transformative change they are intended to accomplish?

  • What are the intrinsic barriers to transformation they must handle?

To address these questions we take a closer look at dominant theories regarding socio-technical transitions that have inspired the third generation of innovation policy. We compare these theories to cases of such innovation policy programs in different countries. Our overview sheds light on some remaining challenges and shortcomings of contemporary innovation policy, given its ambition to facilitate systemic transformation.

We begin the chapter with a condensed review of the trends shaping the innovation policy literature over the past decades. Next, we turn to the more recent literature on socio-technical transitions, which has recently gained increased attention in the innovation policy literature. We pay special attention to the role of interest groups and the power struggles related to innovation and system transformation. Relatedly, we discuss the role of policy instruments and argue that innovation policies may end up supporting established technological regimes rather than favoring the emergence of competing solutions, unless the above challenges are acknowledged and properly addressed. We then turn to some empirical examples of innovation policies aimed at transformative change, which serve as illustrations of our arguments. The chapter ends with a concluding discussion.

2 Background

Innovation policy can be defined as initiatives by the public sector aimed at increasing the amount and impact of innovation in society (Edler & Fagerberg, 2017). While it is at times hard to apply such a broad definition to a system composed of interdependent actors (Nilsson & Moodysson, 2015), the definition is nevertheless useful within the scope of this chapter.

Innovation policy has its origins in research policy. The linear model of innovation originally developed and diffused following Vannevar Bush’s work (1945) for President Franklin D. Roosevelt in Science: the endless frontier, laid the foundations for modern policies related to innovation. Bush argued that public investments in R&D would spill over to industry and in turn result in the development of new technologies that would subsequently benefit consumers and result in economic growth. This linear view of innovation remained dominant for more than half a century and is still very influential for governments aiming to support innovation and economic growth. Today, however, most research would refer to such efforts as R&D policies rather than innovation policies.

This first generation of innovation policy gained widespread acceptance and was used for many decades. It was eventually questioned in the 1980s by Kline and Rosenberg (1986), who proposed a different framework which they referred to as “the chain linked model.” In this model, the innovation process was instead conceived of as non-linear, iterative, interactive, and hence more unpredictable than the linear model. One important implication of this model is the idea that the impulse for innovation may come from other places than the organizations in society that pursue basic science. There is in this sense less unilateral emphasis on universities, research institutes, and corporate R&D departments than the perceived change agents. Similar ideas were advanced by Nelson and Winter (1982), Freeman (1987), and Lundvall (1992) and paved the way for a stream of academic literature using the terms innovation systems or systems of innovation. Policies inspired by the innovation system approach are often thought of as the second generation of innovation policy. In contrast to the first generation, these policies were often designed to support the linkages between knowledge creation and commercialization, and more devoted to bringing actors together in novel network constellations. These policies also put increased emphasis on intermediaries and their role as facilitators for change and innovation by providing good conditions and support to networks involving both academia and industry. The triple helix approach can be considered a framework rooted in the same tradition (Etzkowitz & Leydesdorff, 2000).

The third generation of innovation policy is more aimed toward certain grand challenges and is in this sense more outcome oriented, whereas the preceding generations were more aimed at (1) generating an increased supply of R&D and (2) creating conditions for commercialization. Several scholars have paved the way for the emergence of this approach. Schot and Steinmueller (2018) used the term “transformative change,” Borrás and Edler (2014) wrote about “socio-technical systems,” and Geels (2004) introduced the notion of “system innovation.” One important characteristic of these approaches, as well as the policies drawing on them, which distinguishes them from the first and second generations of innovation policy, is that they pay more careful attention to demand and adoption of innovation in society. Consequently, the networks, or “systems,” that these scholars and policymakers define when analyzing and supporting innovation in society, become more complex by also including civil society and the consumer market. Thus, the outer boundaries of the system become less straightforward to define, and thereby so does the scope of actors shaping the target population of any policy initiative. One way to handle this increased complexity is to focus less on single actors, networks, or aggregates of actors, and more on the universal norms and regulations that the literature refers to as institutions (Scott, 1995). The underlying assumption is that the institutional framework of a society defines the behavior of actors. These institutions are defined at different layers, and it is crucial for policymakers to understand the dynamics between these layers.

Although the third generation of innovation policy, as described above, draws on an eclectic set of related ideas, there are some central ideas upon which this policy rests. Below follows a review of these central ideas and their implications for transformation.

3 System Transformation

The idea that institutions of various type interplay at different layers in society, shaping and challenging collective action, has received widespread attention in the literature underpinning the third generation of innovation policy. Frank Geels (2004) coined this idea and his proposal of how to handle it in empirical research: the multi-level framework. The framework may be applied to specific industries or the economy as a whole. According to Geels, innovations are usually nurtured in what the framework refers to as niches, i.e. parts of the economy that are sheltered from direct opposition or competition. These may be R&D departments in a price-insensitive application such as within the military, within the public sector, or among universities. They may also be entrepreneurs subject to incubator programs or other constructions temporarily sheltering them from competition.

When technologies have been further developed within a niche, they subsequently enter various socio-technical regimes. A regime is an established and ordered part of the economy such as a specific industry. The regime is populated by other complementary and/or competing technologies; there are firms, customers, suppliers, and institutions maintaining power balance and order in the regime. Each actor in the regime posits capabilities and incentives making them more or less willing and able to accept a technology that comes from an alternative niche and tries to enter the regime. For this reason, regimes have intrinsic tendencies to foster stability and path dependence.

Lastly, we have the landscape level. Here, we have a collection of macro trends that affect the regime, but are beyond the direct control of actors and institutions in the regime. These include, for example, globalization, changes in demography, general technological advancements, alterations in policies, and external shocks such as wars or pandemics. While these macro trends and events cannot be influenced by the regime, they nevertheless influence the regime and the emergence of various innovations from different niches.

A system transformation, according to Geels, can be thought of as the successful emergence of an innovation from a niche, which survives and makes its way into an established regime, which in turn is altered to such an extent that its directionality is fundamentally changed.

3.1 Technology Transitions as Creative Destruction

Based on Geels’ (2004) framework and related literature, several barriers to successful system transformation can be identified. To any policymaker aiming to accomplish system transformation, these barriers and how policies relate to them are of critical importance. Below, we expand on some of these barriers as depicted in various literature concerning institutions and political economy. Thus, the following paragraphs should be read as an attempt to unpack and highlight some of the ideas underpinning the multi-level perspective, specifically with regard to its relevance for innovation policy.

Broadly speaking, the emergence of a significant innovation and efforts to penetrate a regime can be thought of as a Schumpeterian process of creative destruction. According to Schumpeter, innovation is the primary source of value creation in society as it enables the economy to transcend established trade-offs. This novel value is however created at the expense of established structures and industries that are to be displaced. The notion of creative destruction applies to a collection of factors, such as human capital, investments, and institutions. Similar arguments were advanced by Juma (2016) in Innovation and its Enemies. Through a collection of historical cases, Juma argues that the primary source of inertia related to innovation and renewal is resistance from established interest groups.

3.2 Institutions and the Role of Embedded Agency

As touched upon above, an established regime is governed by a collection of institutions, defined as formal and non-formal rules that structure the behavior of individuals and organizations (North, 1990; Scott, 1995). Institutions are crucial elements in any society since they lower transaction costs between agents, thereby by providing clarity and reducing ambiguities. At times, emerging technologies may not be compatible with existing institutions. Under such circumstances, institutions would either need to be altered or the (niche) innovation will be repelled by the regime.

Resistance to institutional change is often discussed under the paradox of embedded agency, which refers to the inherent paradox of how actors can change the very institutions they are themselves guided and controlled by. One strand of literature, referred to as institutional entrepreneurship, has looked at the various properties of actors and the environment that enable institutional change to come about (Battilana et al., 2009). Related literature makes use of the term institutional work in order to illustrate and explain how all actors in fact both influence and are influenced by institutions on a more constant basis (e.g., Garud & Karnoe, 2003). Among the core ideas of this literature, relevant for this chapter, is the observation that change agency requires both a certain degree of power and influence, and a certain degree of dissatisfaction with the current situation. Therefore, we should not expect to find change agents at the top of a hierarchy in an industry (regime) because they have fewer incentives to challenge the current situation, and neither at the bottom of the same hierarchy, because they have limited influence. Thus, change agents are most likely found in the mid-level of hierarchies. Below is a condensed review of some of the core mechanisms upon which these ideas are based, adapted to the specific context of innovation and system transformation.

3.3 Resistance and Regulatory Capture

The emergence of a more significant innovation (and subsequent system transformation) is contingent upon the ability of actors to either influence institutions and thus function as institutional entrepreneurs or the ability of vested interests to stop such influence from taking place.

The political economy literature provides insight into the workings of such negotiation processes. Here, it is assumed that various interest groups exert pressure on both the political process and informal rules (Epstein, 1980). Models of rent-seeking behavior often assume that vested interests have stronger incentives than the general public to influence policymakers. With more financial and relational resources, larger incumbent organizations are, according to these theories, more likely to gain the upper hand in the policymaking process and consequently, smaller organizations introducing radical innovations that may distort the positions of established players are likely to be unsuccessful. The costs of such dysfunctions in the political system are distributed over time and over an entire population, and hence, resistance is likely to be limited. This pattern is also at times referred to as “regulatory capture,” which suggests that established, resourceful interest groups can captivate the regulatory process and influence it in their favor at the expense of others (Mokyr, 1994). As a consequence of this unequal distribution of power and resources, policies may be captivated by established and dominant actors at the expense of those potential institutional entrepreneurs who intend to initiate change of a more divergent nature.

4 The Role of Policy in Technology Transitions: Empirical Illustrations

Innovation policies can be categorized as either a collection of support activities or various attempts to constructively deal with resistance and remove barriers. Traditionally, as briefly touched upon in the introduction, innovation policy has largely been a matter of various forms of R&D-related supporting activities (i.e., the first generation of innovation policy).

As stated in previous theory section on system transformation, there is an obvious risk that the policymaking process ends up captivate to the regime rather than supporting the emergence of radical innovation in a certain niche. In light of this, some scholars have argued that the political economy of innovation policy tends to generate an overemphasis on supporting activities and that insufficient attention is devoted to handling resistance from vested interest groups (Potts et al., 2016; Sandström et al., 2019). In other words, there is an apparent risk that policy measures intended to promote change and renewal instead end up supporting the continuation of an established regime. Looking into some of the most developed policies we have been able to identify, from different countries often recognized as forerunners in modern innovation policy, it is however not always obvious to what extent these policies actually differ from the traditional linear policies in their actual activities and outcomes. The remainder of this chapter addresses this problem.

In this section, we provide empirical illustrations of innovation policies aiming at system transformation across Western economies. We start with the Strategic Innovation Programs (SIP) in Sweden, continue with the SHOK programs in Finland, move on to the Top Sectors in the Netherlands, further toward the Austrian competence centers, and end with some illustrations from Canada.

4.1 The Strategic Innovation Programs (SIP) in Sweden

The Swedish Strategic innovation programs (SIP) are stated to be designed to create conditions for sustainable solutions to global challenges and to increase competitiveness in areas of high relevance to the Swedish economy. The program activities should be characterized by openness and transparency and implemented in public-private collaboration whereby problem formulation and program management are delegated to the program actors, while public agencies are responsible for the formal exercise of authority. Thus, the SIP programs appear as state-of-the-art examples of the third generation of innovation policy. The programs’ main activities consist of research and innovation projects (R&D projects) that are carried out in collaboration with a multitude of actors. The programs also carry out complementary activities to take a holistic approach to innovation in the targeted areas. The programs are offered public funding for up to 12 years, divided into four stages with intermediate evaluations. Seventeen strategic innovation programs have been granted funding in four rounds. Six programs were evaluated in 2020. These are presented in Table 1.

Table 1 Strategic Innovation Programs (evaluated in 2020)
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The Swedish Innovation Agency (Vinnova) estimates that the total budget for all the SIP programs (over 12 years) will amount to approximately 16 billion Swedish Krona (SEK) (approximately US$1.9 billion). Of this, an estimated 5.9 billion SEK consists of public funding through the SIP instrument and an additional 1.3 billion SEK through collaboration programs. Additional funding is expected to come from the private sector and other societal actors. Figure 1 shows how public funding has been allocated among different types of actors.

Fig. 1
A bar chart depicts the share of public funds in the projects of universities, institutes, S M E, large enterprises, the public sector, and others. University has 52% of public funds.

Share of Public Funding by Actor Type (Projects Funded 2014–2019). Source: Technopolis (2020)

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Universities and large institutes have received most public funding. Small- and medium-sized enterprises (SMEs) participate to a high degree in the programs Swelife and SIO Grafen, and to a relatively large extent also in the programs IoT Sweden, SES, and BioInnovation, while SME participation in Innovair is small. It is also noticeable that large companies receive relatively large volume of public funding.

Column A in Table 2 shows the 20 largest recipients of public funding in projects between 2014 and 2019, including coordination funds and by distribution of funding within individual recipient of public funding, followed by Chalmers University of Technology (CTH), LU, Linköping University (LIU), and the Royal Institute of Technology (KTH). GKN Aerospace (GKN) and Saab have received significant public funding, although most of this funding has been earmarked for specific demonstration projects. When we exclude funding for coordination of the different programs, there is little differences in the top 20 funding receivers, as shown in column B in Table 2. RISE remains the largest recipient of public funding and Chalmers University of Technology remains the second largest receiver.

Table 2 Largest recipients of public funding in Swedish Strategic Innovation Programs
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As this analysis shows, when looking into the details of how funds are distributed and which activities are actually carried out, SIP appears somewhat detached from what theory says about the third generation of innovation policy in support of system transformation. Money is primarily transferred to larger organizations such as universities and large industrial firms. As these actors can be considered part of the established socio-technical regime, who are also collaborating with each other, we would expect these efforts to strengthen the current socio-technical regime rather than niche experiments.

4.2 Strategic Centers for Science, Technology, and Innovation (SHOK) in Finland

In 2008, the Strategic Centers for Science, Technology, and Innovation (SHOK) were launched in Finland. The initiative was financed by The Finnish Funding Agency for Technology and Innovation, TEKES. During the period 2008–2012, TEKES spent €343 million on the program, approximately 40% of the total funding for the program.

The Finnish concept was established around 2006 as a type of partnership between the public and private sectors. The stated purpose was to increase the pace of innovation and renew the Finnish business community by developing new skills and generating system-changing, radical innovations. This aim was, in turn, based on a report on Finland’s competitiveness, which sought to explore how the Finnish economy could cope in a world characterized by increasing transformational pressure. Finland is a small and open economy, so the report advocated a need to niche and prioritize resources toward more knowledge-intensive industries. It also stated that there was a need to improve the commercialization of research and development. More cross-border cooperation, more venture capital, and new platforms would, in theory, lead to enhanced competitiveness. The predecessor to SHOK was the initiatives launched in Finland in the wake of the deep crisis in the early 1990s.

Once SHOK was initiated, its stated goal was to create research and clusters in Finland that are internationally competitive. The aim was that key actors in the innovation system were to dedicate their activities to stipulated goals, and that collaboration would increase at the regional level and at the same time attract human capital to Finland. The centers are declared to be founded to make a difference. The policy documents emphasize that resources need to be concentrated and focused on application in order to give Finland a comparative advantage in the targeted areas.

In the evaluation of SHOK, it is shown that SHOK has a natural focus on large companies, which is partly at the expense of smaller companies. Furthermore, large companies have had limited incentives to engage in research that goes beyond current operations. In addition to this, they have relatively great autonomy. It is also clear that the international elements have been limited. Thus, SHOK also seems to have suffered from a lack of attention to the potential of emerging niche experiments and the inherent tendency to conserve and strengthen existing regimes.

4.3 Top Sectors in the Netherlands

The Top Sectors initiative in the Netherlands’ aims to strengthen cooperation between academia and the private sector in a total of nine sectors. Innovation policy in the Netherlands changed around 2012. Targeted subsidies and innovation support were removed and instead focus on different sectors of the economy that would collaborate more with universities to become more innovative: agriculture and food, chemical industry, creative industries, high-tech materials, raw materials, life science and health, logistics, and water. Government, private sector, universities, and research centers work together in the Top Sector Alliance for Knowledge and Innovation (TKI) to make top sectors even stronger. The alliance looks for ways to get innovative products or services to the market.

One purpose of the top sector programs has been to combine academic and industrial research. Previously, large companies engaged in in-house research and did not work much with universities. At universities, there was a bias toward researching what was scientifically interesting but perhaps of limited interest to industry. A further aim has been to reduce the fragmentation of public support functions for innovation. A more holistic view of innovation has thus been the goal.

The setup can thus be seen as a form of self-organizing public-private partnership (PPP). In order to receive a grant, a university and a company must enter into a contract that shows that they will cooperate for a longer time period. The grant corresponds to 30% of the funds the company uses to support the university. Each top sector has a steering group with representatives from industry, academia, and the state. These consortiums arrange various activities linked to innovation, internationalization, and skills development (Technopolis., 2019).

The idea of the top sector programs is that the whole process begins with research. This is emphasized by Paul Merkus, innovation partnership manager at the University of Technology in Eindhoven: “The process starts out with pure science, the exploration of theories. After that, professors and engineers will look at whether or not an idea is feasible in practice. In the end, companies will market it” (Eindhoven University of Technology, 2019).

An evaluation carried out in 2017 pointed out that the top sector programs had reduced fragmentation and shifted the focus to collaborations rather than subsidies. One could also see some positive competence development and that the universities’ research was linked more closely to the needs of the business community. However, the programs had not led to radical innovation, mainly because they were so focused on already established actors and technologies (Dialogic, 2017).

4.4 Competence Centers for Excellent Technologies in Austria

At first glance, the competence programs do not appear to be related to SIP, SHOK, or similar initiatives. However, there are some similarities. These programs were launched in the 1990s to increase the elements of research and development in industry by trying to combine academic research and private-sector R&D. The programs ran over a ten-year period, 1999–2009; the resources were distributed across sectors and with clear requirements for co-financing from industry. The purpose was to stimulate academic scientists and industrial researchers and developers to work together on strategic and translational research projects, closer to industry than university groups would typically work, however concentrating on prototype research and not on products ready for the market.

In 2006, the programs were restructured and came to be known as COMET (Competence Centers for Excellent Technologies) and they were placed under the authority of the Austrian Research Promotion Agency. At that time, there were 18 active competence centers with a total of 270 partners in academia and 150 in industry. In 2012, there were 40 active centers with a total of 1500 researchers involved. The programs were divided into three categories based on budget and scope. K2 is the largest in scope and runs over 10 years, while K1 runs for 7 years and K projects receive funding for 3–5 years with the aim of potentially becoming a larger project in the future. Overall, the research within the COMET programs is applied in nature. Since the start in 2008, a total of 22 centers have been formed; in 2017, there were a total of more than 1600 employees and a total budget of more than €100 million.

According to the OECD, COMET has been successful in the sense that new skills have been developed. At the same time, it is noted that few new approaches to achieving innovation have been applied. The projects that aimed to create new working methods for innovation have often received limited resources and later been reduced in scope. “International comparisons suggest the success of the industry-led, co-operative research competence center model and its contribution to R&D, innovation skills and cluster growth. But effectively supporting scale-up businesses may require a different—more risk-tolerant—governance approach and a more entrepreneurial attitude towards center development” (OECD, 2018, p. 115).

4.5 Networks of Centers of Excellence (NCE) in Canada

This initiative can be traced back to the late 1980s and has had similar ambition to the competence programs in Austria. NCE programs aim to meet Canada’s needs to focus on a critical mass of research resources on social and economic challenges, commercialize and apply more of its homegrown research breakthroughs, increase private-sector R&D, and train highly qualified people. As economic and social needs change, programs have evolved to address new challenges. The programs support large-scale academic research networks.

There is a clear multidisciplinary approach through which natural sciences, engineering, social sciences, and health sciences meet. In total, the resources invested by industry, academia, and the state amount to about $90 million per year. To acquire skills in specific areas also seems to be an important task. Today, the initiative has developed into a number of national programs: Networks of Centers of Excellence, Centers of Excellence for Commercialization of Research, and Business-Led Networks of Centers of Excellence. Some investments focus more on creating knowledge and others on research or commercialization. The programs runs for anywhere from 4 years to more than 10 years and budgets vary between $1 million and up to $146 million (Government of Canada, 2021).

5 Discussion

The cases of third-generation innovation programs reviewed in this chapter show that many innovation programs across the European continent are mainly designed to build competencies. Several of the programs described appear to constitute various forms of continuations of industry-oriented public policies for competence development that were put in place in the 1990s. Some were implemented with the aim of enhancing the productivity of established industries after the recession in the early 1990s. An important objective seems to have been to transition established industries toward more knowledge-intense activities. Important to note, though, is that such renewal is not necessarily equivalent to the transition required to address the grand societal challenges.

A critical question regarding path renewal and the creation of a new directionality in the socio-technical regime concerns the formation of new competencies. Previous literature has pointed out that new technology can either build upon and enhance existing competencies or destroy the value of existing skill sets (Tushman & Anderson, 1986). For the emergence of a new regime—or new directionality within an existing regime—it is usually important to develop new skill sets that at least partially destroy established knowledge, hence calling for the formation of new competencies. This is one of the reasons why such transition meets resistance from actors in established regimes who thus find their current position in the regime fundamentally challenged.

As can be seen in the cases provided, many innovation programs appear to be directed toward large, established firms and universities that are supposed to collaborate with these large firms. Furthermore, these programs are often sector specific and country specific with limited participation of foreign actors. This observation indicates the strong preserving power of established regimes.

While the creation of new skill sets can take place by interacting with universities, our empirical cases point at a couple of delimitations. First, an explicit focus on large, established firms implies that entrepreneurial ventures are disregarded as sources of new capabilities. While path-breaking innovations may take place in large firms, however, previous research shows that small firms make up a significant portion of all innovation in an economy (Ejemo, 2011). Innovation may also take place via convergence of industries (Berglund & Sandström, 2017; Chandler, 1980). Traditional media outlets such as newspapers and T.V. channels are increasingly displaced by social media firms such as Facebook and Google. The explicit focus on industry boundaries and nationally oriented initiatives in many of these programs is, therefore, likely to inhibit rather than facilitate the emergence of new regimes.

Many of the policy programs described in this chapter are designed to increase collaboration, both between industry and universities and between different firms. In this sense, policies seem to be inspired by innovation systems research emphasizing the importance of dealing with fragmentation and bringing actors together. This is evident in both the second- and third-generation innovation policy.

It is reasonable to assume that collaborations can increase the productivity of firms in an established regime. In the encouragement of collaborations across firms, there is an inherent assumption that innovation is primarily a matter of dealing with transaction costs, helping firms to understand each other, build trust, etc. An alternative, and complementary, point of view would however be to regard innovation as processes of creative destruction (Schumpeter, 1942) whereby values are created and distributed in novel ways. The Schumpeterian perspective implies that innovation is largely a matter of conflicts rather than mutual understanding (Juma, 2016). An illustration of this is Uber’s entry into the taxi industry, which caused considerable turbulence across the world. The firm’s efforts to circumvent, alter, or influence regulations in the industry have been highly controversial (Laurell & Sandström, 2017 and generated strikes in many countries. One can speculate as to what the effects of an innovation program for collaboration would have been in this case.

Previous research has identified a collection of factor conditions that are likely to contribute to institutional change (Battilana et al., 2009; Garud & Karnoe, 2003). Institutional change is often required for the successful emergence of a new technology (Geels, 2004) and actors pursuing such efforts are frequently referred to as institutional entrepreneurs. These are more likely to succeed when there is widespread discontent with the current order of things and when they operate at the intersection of different fields or industries.

Our illustrations above have in common that they provide various forms of support to an established industry. In this sense, the conditions for institutional entrepreneurship are reduced by these policies. Institutional entrepreneurs are likely to be left outside a collaboration program as these programs are built upon the idea of collaboration rather than confrontation. Through a process of regulatory capture, innovation programs will therefore in many cases be captivated by established interest groups and thereby sustain their power rather than paving the way for new directionality in the regime.

As pointed out earlier, innovation policies can broadly be categorized as either providing support or proactively dealing with resistance. The political and economic logic of these two categories would imply that supporting policies receive more attention. Supporting policies in the forms of various R&D-support and innovation grants are associated with a concentrated and comparatively visible utility, while the costs are distributed across the entire population. Conversely, it is usually politically costly to remove barriers and deal with resistance from vested interest groups. The benefits of doing so are increased levels of entrepreneurship, more new firms, and potentially also new technologies being developed. Generally speaking, lost opportunities are hard to quantify as they, by definition, never materialize (Sandström et al., 2019; Potts et al., 2016).

When looking at the examples described in this chapter, few policy documents or descriptions explicitly deal with resistance. On the contrary, it is assumed that the main challenges to be addressed seem to be competence development and collaboration between established actors. There is, therefore, an apparent risk that these policies sustain an established socio-technical regime rather than paving way for the emergence of a new one.

The discussion above can be applied not only to national innovation efforts, but also to policies at the E.U. level. Large shifts in policy at the E.U. level are beyond the direct control of firms within a regime in a certain country. In Geels’ (2004) model, these shifts can in this sense be conceived of as changes on the landscape level. Actors within a certain regime are, therefore, likely to accept these policies and align themselves with them rather than trying to influence them to their favor. As large, established firms are usually more resourceful, we would expect them to be more likely to benefit from such changes in policy.

6 Conclusions

In this chapter, we have explored how contemporary innovation policies may affect the economy’s ability to achieve system transformation. Drawing upon Geels’ (2004) model for socio-technical transitions and applied literature on institutional theory and political economy, we have highlighted some of the mechanisms that may lead innovation programs to sustain rather than displace an established socio-technical regime. Empirical illustrations of ongoing and recent innovation policy initiatives also point to some of these mechanisms.

Socio-technical transitions usually require a process of creative destruction across several parts of the economy. New competencies may be required, institutions need to be altered, and at times the industry giants may be toppled by entrant firms or by large firms in related industries. A focus on established industries and national borders along with efforts being directed primarily to large firms rather than entrepreneurial ventures implies that existing competencies may be refined with this model, but they are—in principle—less likely to be overthrown with such a setup.

A similar logic can be observed with regard to the need for institutional change. Actors that are in harmony with an established institutional setup are less likely to alter those institutions and hence, innovation policies directed toward supporting these actors may be more likely to reinforce established institutions and related vested interests than to alter them. Our illustrations from innovation policies in different countries show how these initiatives primarily target large, established firms, which indicates the risk for path dependence described above.

We build our arguments by drawing on theories from political economy, such as regulatory capture. Assuming that policies and programs are shaped by the interest groups that are affected by the policies, we highlight the risk that policymaking may end up as support for established interest groups rather than supporting the emergence of those who could act as institutional entrepreneurs or disruptors. Policies and programs may thus be captivated by dominant actors in the established regime, who have superior financial and relational resources. The result would then be that innovation policies sustain the established socio-technical structures of industries rather than contributing to the emergence of new structures.