EASTERN JOURNAL OF EUROPEAN STUDIES
Volume 12, Special Issue, August 2021
Regional innovation strategy for resilience and
transformative industrial path development:
evolutionary theoretical perspectives on innovation
Bjørn T. ASHEIM*, Sverre J. HERSTAD**
Many countries in the developed world are currently experiencing low rates of
economic growth, which is furthermore regionally and socially unequal. This
increasing inequality seems to have deepened during the Corona crisis. When
economic policies are discussed in these demanding times, the strategic question
remains whether the best strategy is to seek to bounce back to the ‘old normal’, or to
use this critical time as a conjuncture to departure on a development path to a ‘new
normal’ that is more innovative, sustainable, and inclusive. In light of this, the main
aim of this paper is to look for new perspectives on theory and policy with important
implications for promoting the most radical forms of industrial path development, viz.
path diversification based on unrelated knowledge combinations and new path
creation, securing the adaptability of an economy to become more resilient.
Keywords: regional innovation policy, resilience, transformative path development,
Many countries in the developed world are currently experiencing low rates
of economic growth, which is furthermore regionally and socially unequal. This
increasing inequality seems to have deepened during the Corona crisis. When
economic policies are discussed in these demanding times, the strategic question
remains whether the best strategy is to seek to bounce back to the ‘old normal’, i.e.
* Bjørn T. ASHEIM is Professor at the University of Stavanger, Business School and Centre
of Innovation Research, University of Stavanger, Stavanger, Norway; e-mail:
** Sverre J. HERSTAD is Professor at the Department of Sociology and Human Geography,
University of Oslo, Norway; and School of Business and Social Sciences, Inland Norway
University of Applied Sciences; e-mail: firstname.lastname@example.org.
44 | Bjørn T. ASHEIM, Sverre J. HERSTAD
Eastern Journal of European Studies | Volume 12(SI) 2021 | ISSN: 2068-6633 | CC BY | www.ejes.uaic.ro
to the situation we had before the outbreak of the Corona crisis, or to use this critical
time as a conjuncture to departure on a development path to a ‘new normal’ that is
more innovative, sustainable and inclusive, making sure that the technological and
other changes that such a policy would require would not cause greater social and
regional inequalities. As the Corona crisis can be seen in many ways as a forewarning
of the coming climate crisis (many regions and countries are already experiencing
disastrous consequences of extreme weather conditions), this provides compelling
arguments that a push forward to a ‘new normal’ is the only viable strategy to secure
a safer and more stable future.
A potential impact of the Corona crisis is to strengthen the legitimacy of
governmental intervention and the role of the welfare state. Western and many
emerging economies are now entirely dependent on massive state aid. Moreover, it
looks as if the organisational and institutional framework of Nordic and Western
European welfare states has been best equipped to handle the crisis with a well-
developed health sector and robust mechanisms to secure the economic well-being
of citizens. This might give the welfare state a competitive advantage to pursue the
necessary ‘mission-oriented’ innovation policies required to combat the grand social
challenges that lie ahead of us.
The aim of such mission-oriented policies is to build a more resilient society
that is more innovative, socially and regionally inclusive and environmentally
sustainable. Boschma (2015) provides an evolutionary based definition of (regional)
resilience as to simultaneously secure adaptation and adaptability. Adaptation
represents changes within preconceived paths, while adaptability concerns developing
new paths, i.e. departing from existing paths. While adaptation fundamentally
reproduces existing structures, adaptability represents transformative changes through
breakthrough innovations and new industrial development. In contrast to an
engineering-based concept of resilience, meaning back to the ‘old normal’, the
evolutionary perspective focuses more on the long-term evolutions of regions and their
ability to reconfigure their industrial, technological and institutional structures for a
more resilient society. The evolutionary meaning of resilience implies both a more
short-term perspective of how to secure adaptation of existing structures and the
longer-term perspective of promoting transformative changes in the economy to
contribute to solving grand societal challenges. Overcoming this trade-off which, in
organisational theory, is known as the tension between exploration and exploitation
(March 1991) is key for regions to become resilient.
The evolutionary perspective on regional resilience can be concretised by
referring to the literature on new path development, which differentiates between
path upgrading (climbing global value chains, renewal and niche development), path
importation, path diversification (related and unrelated) and new path creation.
According to Boschma, related diversification (and upgrading, our addition) secures
adaptation, capturing (current) value creation potential, while unrelated
Regional innovation strategy for resilience and transformative industrial path development: | 45
Eastern Journal of European Studies | Volume 12(SI) 2021 | ISSN: 2068-6633 | CC BY | www.ejes.uaic.ro
diversification (and new path creation (our addition)), promoting (new) value
creation potential, guarantees adaptability (Boschma, 2015).
Historically, innovation has been the most important source of increased
productivity and value creation, and, thus, for making societies wealthier. Reflecting
on this, there is strong agreement that innovation is the key factor in promoting
economic restructuring, resilience and sustainable development. As expressed in the
Lisbon strategy of the EU, innovation-based growth strategies were for long thought
of as identical to ‘horizontal’ policies emphasizing general framework conditions
and promoting R&D and university-industry linkages in accordance with the linear
view of innovation (Jensen et al., 2007; Bonaccorsi et al., 2009). Increasingly, a
broader and more comprehensive view has evolved. It sees innovation as dependent
on complex processes of interactive learning involving different types of knowledge
(analytical, synthetic, symbolic) distributed among different types of actors
(researchers, investors, managers, employees). In this perspective, technological
advances through R&D is considered a necessary yet not sufficient condition as
innovation-based development (Binz et al., 2016; Coenen et al., 2016; Mazzucato
and Semieniuk, 2017) also depends on experience-based knowledge and specialized
industrial capabilities built cumulatively by other means than university R&D, e.g.
through continuous experimentation in contexts of problem solving and application
(Herstad et al., 2015). This underscores the need for different policy areas and
initiatives to operate in tandem and form a holistic innovation policy able to mobilize
actors on a broad basis and, in this way, create – through learning – the resources
that industrial development is to build on.
In light of this, the main aim of this paper is to look for new perspectives on
theory and policy with important implications for promoting the most radical forms
of industrial path development, viz. path diversification based on unrelated
knowledge combinations and new path creation securing the adaptability of an
economy. Basically, this concerns expanding the access to diversity in knowledge
exploration and exploitation sub-systems. We argue that there are two main routes
to achieve this: by stimulating experimentation with (previously) unrelated
knowledge combinations and by sourcing non-local knowledge. While the great and
lasting achievements of evolutionary economic geography in terms of research on
regional economic development and diversification must be acknowledged, with
some exceptions, the potentials of unrelated knowledge combinations and non-local
knowledge to create new development paths have not represented the main focus of
its studies. Instead, the main contribution from evolutionary economic geography
(EEG) has been to unravel theoretically and document empirically the importance of
related variety for the ability of economies to self-transform from within by
gradually diversifying into new activities that are closely related to existing ones
(regional branching) (Boschma and Frenken, 2011; Frenken and Boschma, 2007).
Related variety and regional branching will normally represent the dominant
paths to regional economic diversification in the short and medium term (Boschma,
46 | Bjørn T. ASHEIM, Sverre J. HERSTAD
Eastern Journal of European Studies | Volume 12(SI) 2021 | ISSN: 2068-6633 | CC BY | www.ejes.uaic.ro
2017). However, the time might have come to broaden the scope to look for sources
of more radical paths of diversification. The risk of lock-in grows with increasing
specialization of the regional and national economy (cf. Grabher (1993, p. 256) trap
of rigid specialisation). The more perfectly adapted the economy is, the less diversity
of knowledge exploration and exploitation exists, and the weaker will its (market-
based) adaptability be. In turn, this increases the importance of non-market
mechanisms (e.g. policy) to support the search and development of diversity in
knowledge exploration and exploitation. This scenario accurately describes the
reality of many developed economies. If a sustainable and resilient economic
restructuring and diversification is to be achieved, the search for innovation and
industrial policies to promote such a development has to extend beyond the
perspectives of related variety and regional branching, i.e. gradual change based
directly on current industrial capabilities.
The second section of this paper critically summarizes the baseline
argumentation of evolutionary economic geography and the closely related ‘product
space’ approach. We demonstrate how this argumentation provides support for the
emerging, yet historically well-grounded ‘new industrial policy’ logic, as reflected
in concepts such as ‘smart specialization’ (Foray, 2014a, b; Bonaccorsi et al., 2009;
The Expert Group, 2006), and the ‘entrepreneurial’ (Mazzucato, 2013; 2016) or
‘developmental’ (Block, 2008; Wade, 2017) state. Based on this, the third section
sketches out the building blocks for a theory of new path development through
unrelated knowledge combinations and non-local knowledge, while the fourth
discusses the related questions of policy logics and policy mixes. Finally, the
conclusion emphasizes that policies seeking to promote radical innovation and new
path creation involve intervention along three complementary dimensions, each of
which contains numerous questions and issues that demand research and careful
consideration in policy design:
1. Exploration: Policy guidance, logics, infrastructures and tools providing
incentives for actors to explore combinations of (previously) unrelated
2. Anchoring and exploitation: Policy guidance, logics, infrastructures and tools
creating incentives for entrepreneurs (corporate as well as institutional or
individual) to ‘grow’ new (unrelated) activities in the economy in spite of limited
initial resource support (when they deviate from current specializations).
3. The creation of ‘policy spaces’ where failure is legitimate, and coordination
(across levels of governance and policy fields) combined with monitoring based
on dedicated infrastructures for evaluation and research provide the basis for
policy learning (and adjustment) involving various ministries and research
Regional innovation strategy for resilience and transformative industrial path development: | 47
1. The evolutionary point of departure
The point of departure is represented by the evolutionary perspectives on how
resources created as externalities of current industrial configurations channel
development in certain directions at the expense of others. In contrast to orthodox
economic thinking, here, the role of government is not limited to correcting ‘market
failures’ that arise because natural monopolies, collective goods and externalities do
not provide individual actors with full information, clearly articulated preferences
and well-protected property rights to negotiate with each other until a Pareto
optimum is reached (Groenewegen and van der Steen, 2007; Peneder, 2017). Instead,
it is one where governments must facilitate, guide and sometimes actively steer
development processes that are cumulative, collective and often fundamentally
uncertain (Hodgson, 1999). This involves interacting with (reinforcing or
counteracting) processes of novelty creation, selection and subsequent accumulation
of productive resources that are endogenous to the industrial system itself (Nelson
and Winter, 2002; Nelson and Winter, 1982) and therefore specific to each territorial
economy. This is a demanding task with high requirements regarding the quality of
governments (Rodríguez-Pose and Garcilazo, 2015) and involves risks of regulatory
capture (Laffont and Tirole, 1991). Hence, a well-functioning institutional
framework is paramount for it to work (Farole et al., 2010; 2011).
The basic premise is that economies learn from what they are currently doing
and are constrained in their ability to do differently by what they are not (Hausmann,
2016; Hidalgo et al., 2007; Neffke et al., 2011). From this follows that the entry and
exit of activities reflect as much specific conditions in the economy in question as it
reflects the actual value creation potentials of different economic activities. A second
basic premise is that resources are created through interactions between different
types of knowledge (analytical, synthetic, and symbolic) and generated in different
ways (spanning from R&D to ‘learning-by-doing’ in specific contexts of technology
application). Because it is not confined to certain activities (R&D) or actor groups
(high-tech firms, universities, venture capitalists, R&D specialists), research and
policy attention must be awarded to complex patterns of resource generation,
accumulation and integration occurring at the intersection between different actors
and occupations. By focusing on the breadth and nature of society-wide learning
rather than the depth and quality of scientific research, this positions recent
evolutionary thinking in opposition to the emphasis of mainstream innovation
studies and policy during the 2000s on the R&D efforts, technology portfolios and
university-industry linkages of territorial economies (Jensen et al., 2007).
In line with this, territorial economies are increasingly described in terms of
their employment compositions (Boschma et al., 2014; Neffke et al., 2011; Boschma
and Iammarino, 2009; Frenken et al., 2007) or export baskets (Saviotti and Frenken,
2008; Hidalgo et al., 2007). In this perspective, each industry or export specialization
is assumed to reflect a specific industrial ‘capability’ that is also a learning domain
48 | Bjørn T. ASHEIM, Sverre J. HERSTAD
composed of actor groups that span from researchers to venture capitalists, industrial
owners and banks, managers, politicians and workers, in general. The localized
nature of these processes stems from how the more generally accumulated
experience-based knowledge and interpersonal network ties formed through career
paths of researchers, managers and workers are shaped by current industrial
configurations. Notably, it is also evident from venture capital preferences for
investments close to home, in technology and market areas where it has prior
experiences and the strongest networks.
This suggests that high ‘relatedness density’, i.e. territorial specialization in
several different-yet-related learning domains, increases the capacity of regions to
diversify into new higher value added activity because new structures can be built
more easily by using existing components (Hausmann, 2016; Boschma, 2017).
Entrepreneurial new firm formation occurs most intensively, with the highest
probabilities of success, when drawing, in different ways, on spillovers (Acs et al.,
2009) from similar or ‘related’ incumbent industries is illustrative of this (Andersson
and Klepper, 2013; Neffke et al. 2011). Moreover, incumbent firms capture learning
benefits when the surrounding economy allow recruitment from different-yet-related
industries (Aarstad et al., 2016; Herstad et al., 2015; Timmermans and Boschma,
2014; Solheim et al., 2020). By implication, strong territorial specialization should
not only weaken the innovation performances of individual firms (as exposure to
new insights and skills from different yet related industries is limited), but also
reduce the capacity of economies to enter into new activities (as the scope for
‘related’ diversification is limited). Conversely, high diversity (unrelated variety)
should come with the benefit of portfolio effects that protect regions against sector-
specific shocks, and the disadvantage of limited cross-fertilization between different
activities due to lack of ‘fit’ between the resources that each create, and depend on.
Thus, the ability of different economies to self-transform from within can be
summarized as follows:
- Specialized regions (low related and unrelated variety) have a limited capacity
to self-transform from within through diversification unless new capabilities are
introduced exogenously. Thus, endogenous development is limited to path
extension (positive lock-in to current specialized activities) or path exhaustion
(negative lock-in to current activities);
- Regions characterized by ‘related variety’ have a particularly strong capacity to
‘self-transform’ from within by developing new activities in areas that are
related to current activities (path branching) and by strengthening the innovation
capacities and productivity performances of individual firms (cross-
fertilization). Yet, diversification is incremental, and conservative;
- Unrelated variety is the absence of a single and clearly defined regional path in
a context of diverse economic activity. It is characteristic of urban economies,
where diversity reproduces itself as a portfolio advantage that shelters economies
from sector-specific shocks yet limits inter-industry learning and
Regional innovation strategy for resilience and transformative industrial path development: | 49
interdependencies due to labour market segmentation and weak inter-industry
collaborative linkages (cf. The notion of urban fragmentation, see Tödtling and
Trippl, 2005; Herstad, 2017). Urban diversity does facilitate innovation and
growth in knowledge-intensive services (Herstad et al., 2019), and comes with
a large potential for radical innovation and new path creation more generally.
Yet, large initial cognitive distances between different pieces mean that
harnessing this potential beyond the services sectors that concentrate in cities
requires policy intervention.
1.1. New path development
Without the requisite knowhow and routines (distributed over various actor
groups and shaped by ongoing activities), it is difficult for economies to diversify
into entirely new fields, thus limiting diversification to fields that are closely
‘related’ to existing ones (Neffke et al., 2011). This applies even though high value
creation potential in a new field is paralleled by limited potentials in current ones.
This ‘chicken and egg’ dilemma (Hausmann, 2016) draws attention to the concept
of path dependency (Martin and Sunley, 2006), as captured by recently developed
typologies (Grillitsch and Trippl, 2016; Grillitsch et al., 2018) summarized in Table
1. Path extension is the continuation of existing industrial activities through
incremental innovations that build on established organizational practices and evolve
along established technological trajectories. It is foremost based on knowledge
created within these existing industries, partly through specialized R&D efforts
aiming to solve specific problems and partly as externalities of their broader business
activities. Limited generation of or access to new complementary knowledge and
deepening specialization constrain the innovation potential of regional industries and
may result in stagnation and decline (Isaksen and Trippl, 2016).
Path upgrading points to major intra-path changes, that is, changes of an
existing regional industrial path into a new direction. One can distinguish between
three subcategories of upgrading. First, such processes could be triggered by the
infusion of new technologies or major organizational changes (renewal). The
introduction of new digital technologies (e.g. Industry 4.0) would be an example of
renewal by using these new technologies to increase firms’ productivity. Second,
upgrading can be related to an improvement of the position of the regional industry
within global production networks (climbing GPNs). This may take place through
processes of value enhancement based on the development of more advanced
functions, more specialized skills, etc. (Coe et al., 2004; MacKinnon, 2012). Third,
upgrading might also be related to the development of niches in mature industries
(niche development). This could, for instance, be driven by the addition of symbolic
knowledge (e.g. in the food and beverage industry as well as in tourism).
This section is in parts based on Grillitsch et al., (2018).
50 | Bjørn T. ASHEIM, Sverre J. HERSTAD
Table 1. Types and mechanisms of new regional industrial path development
Types of path development
I – Climbing GPN
Major change of a regional industrial path related to
enhancement of position within global production
networks; moving up the value chain based on
upgrading skills and production capabilities
II – Renewal
Major change of an industrial path into a new
direction based on new technologies (e.g. Industry
4.0) or organizational innovations, or new business
III –Niche development
Development of niches through integration of
I - Related
Diversification into a new related industry for the
region building on competencies and knowledge of
existing industries (regional branching)
II - Unrelated
Diversification into a new industry based on unrelated
knowledge combinations (e.g. digital technologies
and other types of key enabling technologies (KET))
I - Path importation
Setting up of an established industry that is new to the
region (e.g. through non-local firms) and unrelated
with existing industries in the region
II - Path creation
Emergence and growth of entirely new industries
based on radically new technologies and scientific
discoveries or as an outcome of search processes for
new business models, user-driven innovation and
Source: Grillitsch et al., 2018
Path importation refers to the setting up of established industries that are new
to the region and not related to other industries present in the region. Such processes
could be triggered by the arrival of non-local companies, inflow of skilled
individuals with competences not previously available in the region or innovation
partnerships with distant sources. Inward investment by non-local companies is often
seen as a key route for path importation, particularly if these firms feature high value-
added functions and embed themselves in the regional economy by creating links to
regional actors (Aitken and Harrison, 1999; Smarzynska Javorcik, 2004;
Ebersberger and Herstad, 2012).
Table 1 conceptualizes related and unrelated path diversification depending
on the relatedness of the knowledge or sector used in the diversification process.
Related diversification – also referred to as regional branching - requires both
Regional innovation strategy for resilience and transformative industrial path development: | 51
knowledge and sector to be related, which is the most common form of industrial
development according to evolutionary economic geography (Hidalgo et al., 2018;
Neffke et al., 2011). Related diversification implies that new regional industrial
paths emerge building on the capabilities in existing industries. Such processes may
have several sources. An important mechanism is the diversification of incumbent
firms into related fields and industries based on the redeployment of existing assets
(Neffke and Henning, 2013). Branching can also occur through the foundation of
new firms based on the competencies in existing industries. Spinoffs from
incumbents have been found to play a key role for path branching (Klepper, 2007;
Klepper, 2001). An example would be a car manufacturer diversifying into the
production of trucks.
Unrelated diversification represents cases where either knowledge or sector is
unrelated. Path diversification based on unrelated knowledge combinations occurs
when firms combine their existing knowledge base with new, (previously) unrelated
knowledge. In this regard, the knowledge base concept, differentiating between
analytical, synthetic, and symbolic knowledge (Asheim, 2007), provides the
theoretical background to numerous empirical studies. The three knowledge bases
(analytical, synthetic and symbolic knowledge) differ substantially in many respects;
still, their combination is feasible and conducive for innovation processes (Grillitsch
et al., 2017). New combinations of knowledge bases seem to become increasingly
important as sources of new path development (Asheim et al., 2017), not the least in
connection with key enabling technologies (KET) (Bellandi et al., 2018). Evidence
suggests that such new combinations happen in innovation processes through
different mechanisms such as collaborations, recruitment, strategic alliances, or
acquisitions. The experimental and evolutionary character of new path development
is at the heart of this argument. The starting point is the search for competitiveness
and high value-creating activities driven by entrepreneurs. In order to become more
competitive, entrepreneurs experiment in innovation processes, where the
combination of unrelated but complementary knowledge offers a high potential for
introducing novelty. Successful experiments signal viable business opportunities to
A case of related knowledge but unrelated sector is the diversification of the
ski manufacturer Fischer (Austria) to the aviation industry by using its cutting-edge
knowledge of composite materials (Grillitsch et al., 2018). A case of related sector
but unrelated knowledge is the diversification of the food industry, which can be
characterized as a traditional industry resting on synthetic knowledge, into functional
foods by introducing knowledge from biotechnology (Asheim and Coenen, 2005).
The introduction of science-based, analytical knowledge from biotechnology has led
to the generation of high value-added functional foods with particular health benefits
(Zukauskaite and Moodysson, 2016). A similar example would be traditional textile
and shoes industries moving into technical textile and shoes by adding
52 | Bjørn T. ASHEIM, Sverre J. HERSTAD
nanotechnology (analytical knowledge base) to the traditional (synthetic) knowledge
base of the industry.
Table 2. Unrelated diversification: New combinations of related and/or
e.g. car -> trucks
e.g. food -> functional food
based on biotechnology
e.g. ski -> aviation based on
New path creation
e.g. attracting and anchoring
actors/knowledge from outside
Source: Authors’ representation
When both knowledge and sector are unrelated to existing industries in the
region, this is not diversification but new path creation. Path creation into new
industries represents the most radical form of change. It is brought about by the
emergence and growth of industries based on new technological and organizational
knowledge. There is a growing consensus that chance or historical accidents should
not be overemphasized as causes of new path creation, because they often emerge
‘in the context of existing structures and paths of technology, industry and
institutional arrangements’ (Martin and Simmie, 2008), such as an excellent
scientific base (Martin and Sunley, 2006). The emergence of new high-tech and
knowledge-intensive industries often hinges on the establishment of new companies
and spin-offs (Bathelt et al., 2010). This typically requires the acquisition and
anchoring of actors and knowledge from extra-regional scales (Binz et al., 2016;
Isaksen and Trippl, 2017). While path upgrading, regional branching and path
importation represent strategies to capture (current) value creation potential,
unrelated path diversification and new path creation are strategies to create new
value creation potential.
1.2. Regional knowledge dynamics and international linkages
Localized knowledge dynamics are not only local (Bathelt et al., 2004). On
the contrary, they simultaneously shape and are themselves shaped by the extra-
regional linkages that regions maintain (Herstad, 2017; Ebersberger et al., 2014,
Grillitsch and Nilsson, 2015). Spillovers due to relatedness density may provide
firms with resource support for internationalization (Fernhaber et al., 2008; Herstad
and Ebersberger, 2015), and strengthen the capacity of regions to absorb, assimilate
and transform this knowledge into impetuses for further innovation and growth
Regional innovation strategy for resilience and transformative industrial path development: | 53
(Boschma and Iammarino, 2009). Presumably, this works through the two
complementary mechanisms that are i) effective local diffusion of spillovers in
support of internationalization, such as information about international opportunities
and human resources in support of their pursuit (e.g. Fernhaber et al., 2008), and ii)
regional absorptive capacity (Meyer and Sinani, 2009; Herstad and Ebersberger,
2015; Boschma and Iammarino, 2009). If this is the case, as the available evidence
suggests, it means that highly specialized economies are in a disadvantaged position
in terms of establishing and capitalizing on the non-local knowledge flows that they
are particularly dependent on. This suggests that there is a ‘double disadvantage’ to
strong territorial specialization where firms become trapped (cf. Laursen et al., 2012)
by specialized home regions where local spillovers stimulate further specialization.
1.3. New perspectives on innovation policy
Based on this line of reasoning, it is commonly argued that innovation policies
should circumvent the ‘chicken and egg’ dilemma by supporting new industrial
activities that are ‘related’ to current ones (Balland et al., 2019) yet have higher value
added potentials and higher levels of complexity (in terms of skills and technology).
This is because such strategies are those with the highest probability of success.
Notably, ‘technology intensity’, the traditional guiding principle for higher value
added and protection against price-based competition is here substituted by two other
concepts: First, the concept of ‘relatedness density’, which captures the dynamic
interplay between industries. Second, the concept of ‘complexity’, which is the
inverse of ubiquity and thus addresses the risk of price-based competition due to
imitation (Hidalgo and Hausmann, 2009; Boschma, 2016; Mewes and Broekel,
While this line of reasoning is a welcome response to the fundamental
limitations of the Lisbon strategy, there are both conceptual and empirical reasons to
question whether excessive emphasis is currently put on ‘relatedness density’ as the
new guiding principle for policies. At the outset, it can be argued that advice for
policy to focus on supporting diversification into ‘related activities’ is very close to
saying that it should only work towards reinforcing evolutionary processes already
at play, i.e. strengthening ‘branching’ at the fringes of the current development path.
Doing so assumes that ‘high relatedness density’ is always a welcome state where
negative lock-ins do not exist, and raises questions concerning the additionality, and
thus long-term social returns, of policy intervention (cf. Heggedal, 2015).
Empirical research also gives reason to nuance the ‘relatedness density’
argument as applied to policy. For instance, different ‘varieties of capitalism’ foster
different patterns of diversification by representing institutional incentives and
constraints for collective action that are strongly embedded in the very fabric of
societies, and thus difficult to change (Boschma and Capone, 2016; Herrmann and
Peine, 2011; Filippetti and Guy, 2020). Moreover, complex and cumulative
54 | Bjørn T. ASHEIM, Sverre J. HERSTAD
manufacturing industries depend more on regional specialization than the now
rapidly growing services industries where innovation appears to depend foremost on
exposure to diversity of insights and experiences (Herstad, 2018a; Solheim et al.,
2018; Jøranli and Herstad, 2017). Finally, studies have argued that R&D intensity
increases the firm-level capacity to exploit diverse experience-based knowledge
(Solheim and Herstad, 2018), and the capacity of regions to engage in unrelated
diversification (Xiao et al., 2018). This is particularly notable in the light of research
suggesting that related variety foremost promotes short-term growth, whereas
unrelated variety is important for economies to keep growing in the long run (Saviotti
and Frenken, 2008).
This leads to a final, general point. Growth is driven by processes of
technological change through which not only new products but also entirely new
industries are created. Logically, as illustrated by the examples provided above, this
means that historical data on relatedness between established product technologies
and industry domains may be of limited value as indicators of what will be the most
dynamic complexes of ‘relatedness’ in the future. If this fundamental limitation to
the use of historical product space configurations and current relatedness matrixes
for the purpose of developing forward-looking policy advice is not acknowledged, it
advises regions to attempt ‘backward convergence’ towards the clusters of
dynamically related industrial capabilities that provided the most potent impetuses
for growth in other regions, in the past. Little room is then left for entirely new
product technologies and new industries to redefine regional ‘landscapes of
relatedness’, and thus for radical innovation based on new combinations of
(previously) unrelated knowledge. Thus, Zhu et al. (2017) note in their study of
Chinese exports: ‘Rather than focusing on path dependent regional diversification,
analyses (…) should pay more attention to the role of firm agency, local social and
institutional contexts, policy making and extra-regional linkages in reducing regions’
reliance on relatedness’ (Zhu et al., 2017).
2. The building blocks of a theory
This line of reasoning suggests that one has to acknowledge basic evolutionary
processes at play in the economy, and then look beyond these processes in order to
find theoretical and ontological arguments to underpin new policy perspectives.
Much like orthodox economics, evolutionary theories lack a social ontology. As a
result, ‘social agency’ is underdeveloped. This limits the scope for new policy
initiatives for new path development that goes beyond what has worked in the past.
Moreover, it does not allow space for preparing for serendipity and ‘black swans’,
the unexpected and sometimes pleasant outcomes of economic activity. It also limits
the scope for the more long-term perspective of promoting economic activities that
have higher knowledge and technology complexity than previous industries but are
Regional innovation strategy for resilience and transformative industrial path development: | 55
yet located outside or at the periphery of the current regional ‘revealed related
In this perspective, regional circumstances may demand policies aimed to
explore (previously) unrelated combinations of knowledge and technology for the
purpose of growing the new complexes of ‘high relatedness density’ that constitute
a new development path (e.g. new path creation). These may give higher long-term
benefits but are admittedly associated with higher risks compared to the ‘safer’
strategy of the more short-term perspective of supporting industries with high
relatedness to the existing knowledge base (Balland et al., 2019, see Figure 1).
Clearly, a strategy based on the exploration and exploitation of (previously)
unrelated knowledge combinations demand that mechanisms ensuring that more
knowledge is diffused between sectors than that occurring through existing
collaborative ties and labour market segments are at play. Thus, it draws attention to
the need for regional knowledge diffusion infrastructures, i.e. regional innovation
systems, to serve in support of such exploration.
We also expect that unrelated knowledge combinations will increase in
importance with the availability and improved accessibility to general-purpose
technologies or key enabling technologies (KET), such as ICT, biotech, nanotech, etc.,
providing the basis for more transformative forms of new path development in the
future (Asheim, 2019). This further underscores the importance of regional research
institutions engaging with regional actors on a long-term, committed basis. For
example, as part of the regions’ smart specialization strategy, in Emilia-Romagna,
emphasis has been put on introducing general purpose technologies such as
nanotechnologies, advanced materials, microelectronics, photonics, industrial
biotechnologies, advanced production systems and digital technologies into the
knowledge bases and innovation processes of traditional firms that largely depend on
an experience-based DUI mode of innovation. The aim is to foster the emergence of
new and highly innovative industrial systems. They do this by reinforcing the regional
innovation system by promoting investment in industrial R&D, research-industry
collaboration, and an increase in highly educated jobs to make the manufacturing-
oriented system competitive in a knowledge based global economy (Bertini, 2017).
In exploiting such opportunities, entrepreneurial discovery can play a key role.
However, potential entrepreneurs are not only innovative entrepreneurs in the
business sector but also institutional entrepreneurs in universities and the public
sector as well as place-based leaders and actors in the civil society (Grillitsch and
Sotarauta, 2020). General-purpose technologies will also, normally, be more
available globally than locally, and if sourced from outside the region, the region
does not have to develop ‘new technologies from scratch’ (Balland et al., 2019, p.
1260). This underlines the importance of supporting non-local, global knowledge
sourcing and links, and designing policies to facilitate this taking place, e.g. by
transnational and inter-regional benchlearning. Consequently, such ‘high risk/high
benefit’ alternatives will not represent a ‘casino’ strategy ‘(Ballard et al., 2019, p.
56 | Bjørn T. ASHEIM, Sverre J. HERSTAD
1260) but transformative activities based on unrelated knowledge combinations that
go beyond just sectoral renewal as the ‘high benefit/low risk’ alternative represents
(Asheim, 2019). Hidalgo et al. (2007, p. 482) emphasize how structural
transformation through adaptation to proximate opportunities are fundamentally
different from the adaptability that is required when a country hits a dead-end
production needs to shift to far-away positions in the product space.
Figure 1. Framework for Smart Specialization policy
Source: Balland et al., 2019, p. 1259
Thus, research increasingly focuses on the potential of unrelated
diversification as a more important source of economic diversification and new path
development, especially in a long-term perspective (Grillitsch et al., 2018; Saviotti
and Frenken, 2008). Pinheiro et al. (2018) found that countries at low levels of
development tend to be close to products that are of a low level of complexity,
meaning that they see an option set with a negative correlation between relatedness
and complexity (i.e. the most unrelated products are the most complex). At high
levels of development, countries are confronted with an opposite option set: higher
complexity products are among the most related. So, the low hanging fruits of
developing countries mostly include products of low complexity, whereas the low
hanging fruits of developed countries mostly include sophisticated, complex
products. The authors found that unrelated diversification tends to happen at an
intermediary stage of economic development, given that the countries have higher
An example of such dead-ends would be industries reaching a stage of ’path exhaustion’.
Regional innovation strategy for resilience and transformative industrial path development: | 57
levels of human capital (e.g. South Korea in the 1980s). Data shows that countries
that enter more unrelated activities experience an increase in short-term economic
growth of 0.5% per annum compared to those with similar levels of income, human
capital, capital stock per worker, and economic complexity. However, in another
article, Xiao et al. (2018) found that increasing innovation capacity leads to reduced
importance of relatedness for industrial diversification. They conclude that ‘high
innovation capacity allows an economy to break from its past and to develop truly
new industry specializations’ and, consequently, that ‘innovation capacity is a
critical factor for economic resilience and diversification capacity’ (p. 1). These
findings seem to contradict the findings of Pinheiro et al. (2018) and lend empirical
support to OECD’s claim that, due to its long term and pro-active innovation policy,
the high-tech economy of Sweden is the most resilient economy in Europe, as it
always comes out of crises by doing something more advanced and complex (OECD,
2013). This underlines the critical importance of public policy in promoting
especially long term and transformative forms of new path development in advanced
3. Innovation policy in the evolutionary landscape
From the perspective of policy formulation, three points from the above are
essential. First, that skills and organizational routines developed as externalities of
past and current industrial activities represent resources that are unique to regional
economies, and, depending on complexity, difficult for other regions to imitate.
These resources i) can be reconfigured into new activities; yet, ii) their specific
content imposes constraints on how new activities can be developed without active
intervention. Second, while stimulated by co-location, industry itself has a limited
capacity to explore new combinations of (previously) unrelated knowledge: Labour
market segmentation and lock-ins to established networks with defined ‘insiders’
and ‘outsiders’ (Giuliani, 2007) entail that knowledge foremost diffuses, and
diversification predominantly occurs at the intersection between industries already
identified as ‘related’. Firms will prefer to recruit from where they find skills that are
relevant to their current activities, and employees will respond to the wage structures
that form as a result. Collaboration networks will be established between partners
with capabilities that are complementary today, and new activities will emerge and
will be most attractive to investors, where alternative value in these skills and
capabilities is most transparent (low risk due to high relatedness). Third, and finally,
the ability of national economies to establish international linkages to upgrade value
chain positions or access and exploit complementary knowledge is also shaped, or
limited, by current configurations and the ‘related variety’ that is already revealed.
Logically, this first draws attention to the efforts aimed at increasing the
capacity to explore new combinations of knowledge and capabilities. In line with
this, the concept of entrepreneurial discovery processes is essential in the literature
58 | Bjørn T. ASHEIM, Sverre J. HERSTAD
on smart specialization that advises regions to foster strategic technological
diversification around their core activities (McCann and Ortega-Argilés, 2013). The
concept links closely to the related variety argument (related diversification), yet, at
the same time, it also emphasizes policy frameworks which facilitate processes of
entrepreneurial discovery across different domains (Foray, 2014a; McCann and
Ortega-Argilés, 2014): Activities that are transformative because they involve
unrelated, as clearly delineated from related, diversification. The rapid assimilation
of the concept into European regional cohesion policy (and the slower diffusion of
the concept into R&D, industrial and innovation policy more generally) suggests that
it resonated with a broadly and strongly felt need for theoretical perspectives
allowing regions to prioritize their development efforts (vertical policies). This shift
of emphasis in the field of regional development echoes the New industrial
(innovation) policy literature, in which the state has been rehabilitated as an
economic actor and enjoined to work in and through networks to catalyse innovation-
based development (Morgan, 2017; Lauridsen, 2017; UNCTAD, 2018).
The important point here is that smart specialization has become a focal point
for different strands of literature that emphasize the role of government in policies
that take as their point of departure the different resource conditions and histories of
different regions (Peneder, 2017). This raises, in itself, multiple questions
concerning the design and implementation of innovation policy and tools supportive
of the entrepreneurial discovery process, including that of how to organize for the
policy learning required when governments are to take on more active roles
(Groenewegen and van der Steen, 2007). At the same time, it forces one to
acknowledge that, in some regions, further diversification may not be possible unless
new capabilities are acquired. Here, the policy challenge is not so much one of
strengthening connections across (previously related or unrelated) domains, as one
of overcoming the above-mentioned ‘chicken and egg’ dilemma where the initial
absence of knowhow provides barriers to its future accumulation. Such situations are
characterized by a large discrepancy between potential social returns through
dynamic implications (of the new capability if introduced), and the returns that
private actors investing in the technology can expect. While Hausmann (2016) notes
that ‘in those circumstances, it would be appropriate for the government to promote
the acquisition of key capabilities’, neither evolutionary economic geography, nor
the product space approach of Hausmann et al. (2009) provide insights into how
countries initially acquired their capabilities, nor how they should act to acquire new
ones with potential for reinvigorating evolutionary dynamics.
This crucial point warrants attention. If a ‘capability’ is conceptualized as
stemming not from discrete R&D but from interactions between a wide range of
actor groups (researchers, investors, managers, employees) and types of knowledge
(analytical, synthetic, symbolic), acquiring a capability is not equal to investing in
research. Adding a capability to an economy entails anchoring and growing activities
that foster broad-based learning in the economy and feed on global networks. This
Regional innovation strategy for resilience and transformative industrial path development: | 59
involves upscaling beyond research to develop the knowhow that is constitutive of
‘capability’. These challenges are particularly pressing in, third, those cases where
the transformation of entire socio-economic systems are needed (cf. the concept of
transformational innovation policy as used in the sustainability transition literature,
see Weber and Rohracher, 2012). As the role of policy in such transformations
cannot be understood from the perspective of bottom-up entrepreneurial discovery,
nor from the orthodox perspective of hands-off, they draw attention to the ‘new
industrial policy’ literature.
Initially, this literature drew heavily on historical evidence, in particular from
Japan (post-war industrialization guided by MITI) and the US (the military-industrial
complex in general and the Advanced Research Projects Agency (ARPA) in
particular). Currently, it is drawing inspiration from sustainability transitions in
countries such as China, Germany (Fischer et al., 2016; Joas et al., 2016; Reichardt
and Rogge, 2016; Fornahl et al., 2012), Great Britain (Simmie et al., 2014) and
Denmark (Simmie, 2012) when emphasizing that the role of governments and policy
extends far beyond that of correcting ‘failures’. It includes the creation of shared
visions regarding the direction of change, coordination across different levels of
governance (regional and national) and multiple policy fields (e.g. education, R&D
and innovation), and, importantly, the establishment of spaces for experimentation
and learning (Groenewegen and van der Steen, 2007), where failure is legitimate. In
that sense, it acknowledges the complex, multi-faceted and distributed nature of
‘capability’ as conceptualized in evolutionary theory. Using concepts such as the
‘entrepreneurial’ (Mazzucato, 2013), ‘developmental’ (Block, 2008; Wade, 2017) or
‘embedded’ (Morgan, 2017) state, and emphasizing how industrial policy is
commonly concealed as public health or security policy to avoid conflict with
official industrial policy as prescribed by orthodox economics, this literature
underscores the importance of directionality as clearly delineated from both
neutrality and picking winners. Moreover, it emphasizes the transformative forces
created when governments are willing to intervene across the entire innovation value
chain spanning basic research to market creation (Mazzucato, 2016; Guerzoni and
Raiteri, 2015; Weber and Rohracher, 2012; Peneder, 2017).
At this point, the literature on NIP and smart specialization gains support from
evolutionary theory when suggesting that neither specific projects nor specific
policies or tools should be implemented and evaluated on an individual basis.
Instead, the benchmark for success, at the project level, is not value creation from
the project itself, but whether it contributes a constituent component of a new
capability. This, in turn, draws attention to the policy mix discussion, in which the
concept of complementarities is essential at the theoretical level, yet difficult to
acknowledge and capture in empirical research and real-life policy design (Flanagan
et al., 2011; Reichardt and Rogge, 2016; Rogge and Reichardt, 2016).
‘Complementarity’ must be understood here as ‘super-modularity’, meaning that the
60 | Bjørn T. ASHEIM, Sverre J. HERSTAD
effect of one set of policies, or policy tools, is greater in the presence of other policies
and tools that have a specific content (Herstad et al., 2010).
The first dimension concerns complementarities between supply-side and
demand-side intervention. Here, the idea is that supply-side intervention, such as
funding of strategic basic or applied research has a stronger impact on structural
change in the economy if complemented by demand-side intervention, such as public
procurement for innovation or market regulation that gives actors incentives to
engage in activities that lead to the accumulation of knowhow and the development
of industrial capabilities. The classic example is that of complementarities between
federal R&D funding (‘supply-side’) and defence contracts (‘demand-side’) in the
US during the Cold War. This dimension remains highly relevant, and a recent study
found supply-side technology policies effective only when interacting positively
with demand (Guerzoni and Raiteri, 2015). The attractiveness of complementary (to
investments in basic and applied research) demand-side intervention stems from
incentives provided for commercial actors (firms with their investors, employees and
partners) to develop the knowhow and capabilities required for viable commercial
activity on a large scale.
The second dimension along which complementarities (or contradictions)
must be considered derives from this and concerns firm-level versus system-level
types of policies (Herstad et al., 2010). Arguably, structures and incentives seeking
to broaden and speed up knowledge diffusion between firms should be viewed as
strongly, and fundamentally, complementary to efforts aimed at strengthening
knowledge development and accumulation within them (as this increases the amount
of knowledge externalities they dispatch, and the capacity to absorb incoming
externalities). This follows directly from the market failure argument in orthodox
economics, from which effective knowledge diffusion between firms, i.e. well-
functioning systems, can be expected to reduce their willingness to invest in
generating knowledge, i.e. market ‘failures’ (Herstad, 2018b). It also follows from
evolutionary thinking, as overcoming the ‘chicken and egg’ dilemma demands that
firms invest in the generation of knowhow that is new to the economy in question in
spite of the high risk that this involves and the constraints met among actors that
span from investors to employees due to the initial absence of requisite knowhow.
The balance between initiatives seeking to build R&D capacity in firms and
those seeking to strengthen applied R&D in the science system is related to this. The
strong policy emphasis of the last decade on technology commercialization and
university-industry linkages is commonly reflected in industry R&D funding
schemes that stimulate contractual R&D sourcing from universities or other public
research institutions. This is at odds with the ‘market failure’ argument for R&D
support to industry, as it provides firms with the option of downscaling their internal
R&D efforts, and with the ‘system failure’ argument in that contractual R&D
involves limited knowledge transfers (Fey and Birkinshaw, 2005). Moreover, it is in
conflict with research finding that firms capture the strongest learning effects when
Regional innovation strategy for resilience and transformative industrial path development: | 61
they balance strong intramural R&D efforts with selective rather than large-scale
extramural R&D (Grimpe and Kaiser, 2010; Cassiman and Veugelers, 2006;
Ebersberger and Herstad, 2011; Gkypali et al., 2017). Of particular importance in
the context of path dependency and structural change, studies have suggested that
in-house basic research is critical for the capacity of firms to generate radical
innovations (Añón Higón, 2016; Herstad, 2018a) and indicated that external R&D
purchases may lead to downscaling of intramural R&D efforts over time (Teirlinck
et al., 2010).
As the main point here is related to the policy implications for industrial learning
and capability building in new technology and market domains, it is notable how
Landini and Malerba (2017) find radical technological change generating particularly
strong complementarities between support for learning at the firm level and other,
more exploration-oriented initiatives. This is because the former favours the
accumulation of skills and routines (in new areas) that is critical for activities to grow
and impact (on skill-formation and further diversification in) the economy, while the
latter speeds up the search for new opportunities (Landini and Malerba, 2017).
Finally, it is well established that internal R&D capacity is an important
determinant of firms’ capacity to engage in international networking (Herstad et al.,
2014; Herstad and Ebersberger, 2015) and for their capacity to access, assimilate and
exploit complementary international knowledge (Chen et al., 2016) – not least among
technology-based SMEs (de Jong and Freel, 2009). In this sense, the traditional market
failure argument of orthodox economics align with the ‘chicken and egg’ dilemma
emphasized in evolutionary theory: Both imply that incentives for knowledge
development and accumulation in firms are strongly complementary to the efforts
aimed at increasing the intensity and scope of knowledge diffusion between firms and
industries (Herstad et al., 2010). This calls for system-level policies for exploring
related and unrelated knowledge combinations (Coenen et al., 2016).
A final issue concerns the geographical configuration of innovation systems.
Here, two closely related aspects must be considered. A territorial innovation
system consists of the relationship between an industrial structure (containing
specialized knowhow and capabilities) and supportive research institutions
(linking different industries and providing complementary research-based
knowledge). The first aspect concerns that of within-economy centralization (a
strong national innovation system) versus decentralization (to build diverse
regional innovation systems supportive of regional entrepreneurial discovery
processes). Arguably, centralization provides the basis for a limited number of
large research institutions to co-evolve (through domain-specific learning) with
(dominant incumbent) industries (Narula, 2002; Herstad and Sandven, 2017;
Claussen, 2009). This, in turn, provides the basis for the research system to resolve
the tension between research quality and relevance to industry by focusing on
activities that are supportive of incumbents, and should therefore strengthen the
economy-level capacity for related diversification as leading research institutions
62 | Bjørn T. ASHEIM, Sverre J. HERSTAD
link (related knowledge diffusion) and support (complementary technological
development) related (incumbent) industries located in different regions.
In essence, centralization strengthens the capacity for path extension and path
branching at the national level and dampens tensions between research excellence and
industry relevance. However, centralization also comes with reduced sensitivity to
diversity and the risk of large ‘cognitive distance’ between research institutions and
firms that operate on the periphery or outside current (related) specializations.
Presumably, this reduces the capacity of national innovation systems to support
experimentation with unrelated knowledge combinations and provide regions with
research system support that, in line with smart specialization strategies, reflect their
specific industrial structures and resource endowments. Strong (exploration-oriented)
regional collaboration networks are particularly important in diverse regions (De Noni
et al., 2017), as the learning potential associated with ‘unrelated variety’ does not
necessarily materialize from the local labour market mobility that occurs most
intensively within segments of industries already identified as skill-related.
The second dimension concerns the relationship between networks at different
geographical levels. At the micro-level, research has demonstrated that innovation and
productivity performance at the firm level is less dependent on regional and national
collaboration than on international collaboration (Fitjar and Rodríguez-Pose, 2013;
Fitjar and Rodríguez-Pose, 2011). Research has also suggested that it is foremost
through labour market mobility that regional conditions are beneficial for individual
firms (Cotic-Svetina et al., 2008; Power and Lundmark, 2004). At the same time,
research has also demonstrated that the knowledge dynamics of regions depend on a
good balance between extra-regional networks providing access to complementary
skills and capabilities, and intra-regional networks ensuring that knowledge diffuses
locally (De Noni et al., 2017; Herstad et al., 2010). Without strong regional networks,
the local diffusion of knowledge accessed globally is limited to that occurring as a
result of labour market mobility or informal ‘information buzz’ (the importance of
which, some argue, is exaggerated, see Fitjar and Rodríguez-Pose, 2016). As regional
knowledge diffusion tends to be asymmetrical (Giuliani 2007), occurring most
intensively between industries already identified as skill-related and benefiting firms
with advantaged positions in local networks more so than others, regions cannot
depend solely on ‘local buzz’ and labour market mobility for local knowledge
diffusion from global network nodes (Asheim et al., 2007; Herstad and Brekke, 2012).
This further underscores the role of decentralized, ‘constructed’ knowledge diffusion
infrastructures, i.e. regional innovation systems, that operate in parallel with (and
complementary to) strong and broad international networks (Asheim and Isaksen,
2002; Herstad and Sandven, 2017).
Regional innovation strategy for resilience and transformative industrial path development: | 63
4. Unrelated knowledge combinations and transformative path creation:
Towards a research and policy agenda
Using the ‘foster (regional) related variety’ argument of mainstream
evolutionary theory as our point of departure, we have argued above that the current
landscape of rapid technology and market change, globalization and ongoing
sustainability transitions puts a greater demand on innovation and industrial policy
to explore the innovation and growth potential of non-local knowledge and
(previously) unrelated knowledge combinations. While accepting that related
diversification dominates the evolutionary process itself and that the strategies
supporting it remain important in the short and medium term, our analysis points to
fundamental limitations of related diversification as the main industrial development
strategy. They may fail to capture latent local opportunities and emerging
opportunities at the international level, increasing the risk that dramatic changes in
technology or in the international economy itself (e.g. sustainability transitions)
translate into negative lock-ins at the level of territorial economies.
Accordingly, policy needs to step outside the comfort zone of related
diversification and stimulate activities that have a higher transformative potential
because they are not only characterized by high complexity, but also, at the outset,
located at the periphery of the current relatedness complex of territorial economies.
Our approach is compatible with the evolutionary theory in that it introduces a
distinction between the (current) revealed relatedness of industries, product domains
or technologies; and the future (potential) relatedness between (currently) unrelated
industries, product domains or technologies – including those not yet existing.
Consequently, a parallel distinction can be made between the degree of relatedness
density that characterizes current industrial activities and the technologies, products
and industries that, in the future, may form entirely different complexes of high
relatedness density – and, thus, new development paths characterized by their own
distinct forces of development and gradual transformations ‘from within’. To
approach the complex question of policy design and implementation supportive of
the growth of the new critical mass required for new path creation, including that of
the policy ‘mix’ and supportive infrastructures, let us consider three basic functions
that need to be served.
The first is exploration of new opportunities, as captured by the concept of
‘entrepreneurial discovery processes’ in the literature on smart specialization. As
pointed out above, a bottom-up approach focusing on exploration of (previously)
unrelated knowledge combinations on a systematic basis will normally demand that
third-party knowledge diffusion infrastructures (i.e. regional innovation systems
built around motivated and competent regional research institutions) serve in support
of knowledge diffusion between (currently) unrelated industry segments, and
provide access to complementary technology (e.g. key enabling technologies). This
facilitator and mediator function must be exerted on an ongoing basis, in close
64 | Bjørn T. ASHEIM, Sverre J. HERSTAD
contact with industry, and go well beyond that of providing contractual R&D
services to firms. This underscores the importance of regional research organizations
and draws attention to the trade-off between centralization for research quality and
decentralization for proximity, receptiveness and relevance (Herstad and Sandven,
2017). From this follows the first fundamental question of how the capacity of
research and higher education institutions to serve as infrastructures for exploring
(previously) unrelated knowledge combinations is influenced by the structure of the
research system and the framework conditions (governance, funding, recruitment
policies, defined roles and missions) under which institutions are operating.
The second is anchoring and upscaling new activities. Structural change,
generally, and new path creation, in particular, only come about when new activities
(single firms or, more often, clusters of ‘newly related’ firms) grow large enough to
redefine what ‘related’ industries in the economy in question (i.e. critical mass)
actually are. When, at the outset, new activities are unrelated to current ones, external
anchoring points (human resources, investors, partners and markets, research system
support) are weak, demanding ‘self-anchoring’ through upscaling and organizational
development at the level of individual firms. This includes the (early) formation of
(strong) linkages to markets and partners abroad, reinforcing centrifugal forces and
increasing the need for centripetal forces to balance them off. On the one hand, this
can be approached as a practical question concerning complementarities between
supply-side (research and education), firm-level (support for intramural R&D) and
demand-side (public procurement, market regulation) intervention. It is reasonable
to assume that incentives for firms to invest in generating new application-specific
knowhow are particularly important when related knowhow is not available in the
surrounding economy and the risk of exit therefore is high (Neffke et al., 2011). On
the supply side, long-term ‘competence programs’ based on the model from Sweden
may provide important ‘platform’ support for new activities in selected technological
fields and thus provide external anchoring points. Yet, this question extends beyond
that of certain types of programs or the specific ‘policy mix’ to more fundamental
ones of ‘directionality’, commitment, complementarity and coordination. They span
from how the ‘mix’ of innovation policy tools to tax legislation, labour and financial
market regulation, public legitimacy and the general policy discourse influence the
decision of entrepreneurs (corporate as well as individual) to ‘grow’ new activities
in a given economy and link them up to global networks even though relatedness to
current specializations is limited.
The third is the need for policy experimentation and coordination under
conditions where failure must be legitimate, success must be determined by the
cumulative impacts of different projects and initiatives instead of their individual
(technological or ‘value creation’) performances, while learning and adjustment are
essential (Smiths et al., 2010; Groenewegen and van der Steen, 2007). This raises
not only questions concerning the division of labour and subsequent coordination
between different ministries and levels of governance. It extends into the more
Regional innovation strategy for resilience and transformative industrial path development: | 65
fundamental issue of criteria and methodologies allowing monitoring and evaluation
to provide the basis for learning and development, as well as adjustments of policies
in ways that reflect the specific objectives and regional conditions at hand.
These general principles for promoting the most transformative forms of new
path development do neither call for ‘picking the winners’ strategies of supporting
national champions nor for general policies in the form of tax reliefs and subsidies
for R&D investments. These alternatives are either too specific, selecting specific
industries under conditions of rapid change and high uncertainty, or too general,
attempting to support all industries (Asheim, 2019) and thereby letting evolution run
its own course instead of directing it. What is needed are top-down policies that
strengthen the macro level innovation supporting infrastructure, e.g. global
innovation linkages, and national and regional innovation systems, as in the
innovation policy programs of the Swedish governmental innovation agency,
VINNOVA, as well as building capacity on the meso and micro levels (institutions,
organizations and actors). Such policies do not represent a linear model, but a
strategic approach that can be turned into a mission-oriented policy providing
directionality to build resources and solve societal challenges. At the same time, such
efforts must be complemented with bottom-up experimentation by the regional triple
helix stakeholders of the local entrepreneurial discovery process to decide on which
specific economic activities to develop. Thus, the entrepreneurial discovery process
of the smart specialisation policy would be an additional instrument, which could
promote the development of more transformative forms of new path development.
In turn, this top-down – bottom-up policy approach should be supplemented by a
combination of sector-neutral, horizontal and non-neutral, vertical innovation
policies. Building and upgrading R&D capacity for developing key enabling
technologies would be an example of a sector-neutral and horizontal policy aimed
at strengthening the exploration capacity of firms and innovation systems, while
strategic public demand side policies (e.g. public procurement of innovation) to
create markets for sustainable production would represent a non-neutral, vertical
policy to increase the exploration and exploitation capacity of firms and innovation
systems that would promote unrelated diversification and radical innovations.
Acknowledgement: This paper is partly based on an unpublished scoping paper
written for the FORINNPOL program of the Research Council of Norway, January
2018. The economic support is gratefully acknowledged, as is the support provided
by the REDINN project to Sverre J. Herstad for work with the paper.
66 | Bjørn T. ASHEIM, Sverre J. HERSTAD
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