Innovation systems 101-lecture-4

LECTURE-4:
THEORETICAL FOUNDATIONS OF
NATIONAL SYSTEMS OF
INNOVATION
BY:
Dr. Lourenço Galvão Diniz Faria
Copenhagen University, Denmark
Dr. Nimita Pandey
Indian Institute of Science, Bangalore, India
CHAIR:
Professor Edward Lorenz
Aalborg University, Denmark
University of Johannesburg, South Africa

Lecture plan
1. National Systems of Innovation in
historical perspective
2. “Systemic” characteristic of national
systems
3. The notion of National boundaries
4. Spatiality of Knowledge
5. Tacitness of Knowledge
6. Production, trade and knowledge base as
determinants of national systems
7. Relationship between Knowledge, science
and innovation
8. Questions on NIS

1. National Innovation Systems in historical
perspective

1. National Innovation Systems in historical perspective
 NIS antecedents:
• The concept of NIS was influenced by many previous ideas;
• National System of Political Economy (Friedrich List, 1841)  national systems of
production, relevance of national institutions to development of productive forces.

 First known (written) mention to NIS*  Freeman, C. (1982) “Technological Infrastructure and
International Competitiveness”
• Draft paper presented at the OECD’s expert group on Science, Technology and
Competitiveness, but never published, according to some it was too provocative (later
printed for the first Globelics Conference – Rio 2003).
• Preface from Chris Freeman (2003): “As the last page only briefly indicates, any merit
that it has was derived from a sabbatical that I spent in Aalborg in the early 1980s where I
first learnt from colleagues there the expression ‘national system of innovation’ and its
content”  Importance of Innovation, Knowledge and Economic Dynamics Research Group
(IKE) group in Aalborg University, Denmark.
*Although Godin (2010) argues that the first mention to an “innovation system” was in
Freeman’s first edition of the book The Economics of Industrial Innovation (1974).

 Context of early 1980’s:
• Emergence of ICTs  new technological paradigm, very dynamic; early stages of knowledge-
based economy.;
• Mainstream economics theory fails to explain international trade leadership through price
fator competition; e.g. Japan’s rise as a major player on the technology-intensive exports
despite deterioration of relative prices and labour costs  Basis of IKE group establishment;
• Studies/empirical evidence corroborating the influence of technology on country’s trade
performance (e.g. Soete (1981);
• Freeman’s hypothesis  international trade leadership might be explained by technological
leadership;

 Freeman (1982, page 9)  “where it all started”:
“(...) what changes in the science-technology system of a country might help to explain its rise
to technology leadership over a considerable period and its corresponding rise in world market
leadership? Were such changes the result of deliberate national policies designed to improve
competitive performance? Are there any pointers to the type of policies, which might prove
more effective for the next wave of new technologies?”

 Freeman (1982, page 10)  first written mention to NIS:
“(…) creativity is an essential element of entrepreneurship, since it involves the bringing
together of what were previously disparate and scattered pieces of knowledge to create
something new. Sometimes the term ‘creativity’ is reserved for those abilities of the scientist,
which lead to new discoveries or of the artist, which lead to new works of art. (…) But when
we are considering national innovation systems (as opposed to global civilisation and the world
economy) then at least in the past they have not been so central to innovative success as
those types of creativity which are characteristic of the engineer in the work of invention and
design and of the entrepreneur. In these entrepreneurial/engineering types of creativity the
synthesis and creative application of information from a variety of different sources (including
the arts and sciences) is, critical.

 Other studies quickly followed in the 1980s:
• Lundvall (1985)  First use of the concept ‘innovation system’ in a publication with ISBN.
He also highlighted differences in the innovative capacity of ‘‘national systems of production’’.
• Freeman (1987)  The first explicit, deliberate definition of NIS is in Freeman’s book on
Japan: “(…) the network of institutions in the public and private sectors whose activities
and interactions initiate, import, modify and diffuse new technologies” (p. 1).
• Almost at the same time, Richard Nelson presented studies of the US NIS (1987, 1988).
• Freeman and Lundvall (1988) 
• Dosi, Freeman, Nelson, Silverberg, Soete (eds.)(1988)  The book Technology Change and
Economic Theory establishes the NSI concept as key to further research on issues of national
specialization, innovation, and economic performance.

 The concept experienced rapid dissemination in the 1990s:
• The first notable, widespread, and significant instance of a country adopting the concept was
Finland in 1992 (Vuori and Vuorinen, 1994; Miettinen, 2002). The NSI concept was part of
the country’s developmental and recovery strategy (Sharif, 2006).

 Therefore, the concept of NIS was initially formulated to be an alternative model to the
mainstream economics theory that neglected the role of innovation and learning to explain
economic growth and development (Lundvall, 2007).
 It is the result of a collective intelectual effort that included distinguished researchers such as
Chris Freeman, Benkt Lundvall, Giovanni Dosi, Richard Nelson, Luc Soete, and many others.
 Its popularity and use quickly increased. From the late 1980’s onwards, it has been adopted by
scholars and policy makers globally, including organizations such as OECD, Unctad, World Bank, EU
Commission. It also inspired other researchers to come up with related concepts, reshaping the
boundaries of IS.
 Main contributions: has shifted sources of “international competitiveness”; drew attention
towards nacional policy strategies.

 To know more about the history of the concept:
• Fagerberg, J., & Sapprasert, K. (2011). National innovation systems: the emergence of a
new approach. Science and public policy, 38(9), 669-679.
• Soete, L., Verspagen, B., & Ter Weel, B. (2010). Systems of innovation. In Handbook of
the Economics of Innovation (Vol. 2, pp. 1159-1180). North-Holland.
• Godin, B. (2010). National innovation system: A note on the origins of a concept. Project
on the Intellectual History of Innovation, manuscript.
• Sharif, N. (2006). Emergence and development of the National Innovation Systems concept.
Research policy, 35(5), 745-766.
• Lundvall, B. Å. (2007). National innovation systems—analytical concept and development
tool. Industry and innovation, 14(1), 95-119.

2. “Systemic” nature/characteristic of
national systems

2. “Systemic” nature/characteristic of national systems
 What is a system?
• “Anything that is not chaos” (Boulding, 1985);
• A system is constituted by a number of elements and their relationships;
• One of the basic structures of the universe: from the smallest scales to the level of clusters
of galaxies and beyond, from abstract structures to real ones;
 Examples:

 A system can be roughly described by its boundaries, structure and purpose.
 Purpose of IS: “A system of innovation is constituted by elements and relationships that interact
in the production, diffusion and use of new and economically useful knowledge” (Lundvall, 2016);
 Boundaries of IS: It builds upon the idea of localized patterns of learning, sharing and use of
knowledge  establishment of boundaries on the knowledge flows  contrast with neoclassical
view;
 Structure of IS (Lundvall, 2007): Firms are the key actors. To innovate, they interact within
themselves and with other supporting structures such as universities, labor and finantial markets,
education systems etc. These interactions (producing knowledge flows) are shaped by a set of

 Examples of NIS structure (OECD, 1999; Feinson, 2003):

 More examples of NIS structure (Bingwen & Huibo, 2010):

 What kind of system is an IS?
 Lundvall (2007):
“The ‘‘system’’ terminology may have had a negative impact on the use of the concept in
public policy. Certain policy makers have interpreted the ‘‘system’’ in a mechanistic way assuming
that the system can be easily constructed, governed and manipulated. The lack of clear definition
has contributed to such misinterpretations. One type of mechanistic interpretation is found in
regional development strategies based upon the assumption that ‘‘clusters’’ and ‘‘regional
systems’’ may be built from scratch through policy initiatives.” (pg. 100).
“The innovation process may be seen as an intricate interplay between micro and macro
phenomena where macro-structures condition micro-dynamics and vice versa new macro-
structures are shaped by micro-processes. In a dynamic context this means that we need to
understand systems as being complex and characterized by co-evolution and self-organizing.” (pg.
101).

 Katz (2006): An IS can be characterized as a complex adaptative system (CAS).
 Some characteristics of complex systems (Amaral and Ottino, 2004, Baranger, 2001):
• Openness: information flows across its boundaries.
• Complexity: their behavior cannot be easily inferred from their basic properties; it contains many
subsystems.
• Emergence: relationship between the details and the larger view  has traits, behaviors and properties
that arise as result of the interactions between the elements (i.e. are not apparent from the elements
in isolation).
• Interdependence and Nonlinearity: The parts of the system are interdependent and they respond very
differently to the same inputs, depending on small changes in their properties and organization 
“butterfly effect”. In linear systems, the effect is always directly proportional to cause (e.g. Kline and
Rosenberg, 1986).
• Self-organization: the parts of the system are able to coordinate their actions without centralized
planning.

 Why was nation the first level of analysis for the innovation systems?
• The NSI concept was originally intended to explain competitiveness and trade performance
between nations.
• Mainstream economic theory regarded new technologies as exogenous (“like manna falling from
heaven”), equally accessible for all actors, sectors, regions and nations.
• However, to innovate, firms, universities, research centers etc. depend on supporting
elements such as institutions, actors, knowledge bases, infrastructure which, at some level,
are nationally constrained  technology and knowledge are not freely available and accessible.

 The concept of national innovation systems presumes the existence of nation states (Lundvall,
2016);
 A standard, “ideal” definition of nation state: individuals defined by common cultural, ethnical
and linguistic characteristics – gathered into one single geographical space controlled by one
central state authority (without foreign nationalities)  difficult if not impossible to find all
these elements in our society today (Lundvall, 2007).
 Thus, we use the notion of nation as an archetype  basic definition, primordial image, from
which we derive the heterogenous NIS that we see in the real world.
 The key is to understand why the boundaries of IS can be nationally defined.

 Examples of key elements for IS that are nationally constrained (at some level) and thus affect
knowledge production, dissemination and application, and innovation performance (Lundvall, 2007,
2016):
• Production conditions  competitive conditions, market structures, availability of resources,
cooperation, heuristics of R&D processes;
• Institutional set-up  laws, regulations, IP systems, fiscal incentives, standards and technical
norms, agents’ behaviour, culture, language, routines, social organization, user-producer
colaborations, national plans;
• Supporting infrastructures  labs, machines and equipment, public R&D, finantial system,
education and training system;
• Knowledge base  tacit knowledge embedded “in the minds and bodies of agents” (Lundvall,
2016), strategic knowledge, technically-based interdependencies;

 One might argue that the “national” boundary of IS is losing importance to other dimensions
(regional, sectoral, or technological)  many interesting interactions in the context of modern
innovation cross national borders, particularly in an era of multinational companies, global value
chains, and ICTs (Lundvall, 2016).
 On the other hand, the NIS might be still relevant, especially for the policy discussions  As
long as nation-states exist as political entities with their own agendas related to innovation, it is
useful to work with national systems as analytical objects; plus, national characteristics still play
a role in shaping innovation processes (Lundvall, 2016; Sharif, 2006).

4. ‘Spatiality’ of knowledge
 The Endogenous growth theory (Pack, 1994) is a pioneering work to look at locational
aspects economic activities, with focus on R&D investments and as a determinant of
economic competitiveness (Durlauf et al., 2005; Fagerberg, 1994; Crescenzi, 2005).
 Innovation systems approach emphasises on the idea that firms interact with other
entities; mutual learning and co-production of knowledge is an inevitable phenomenon
(Dodgson, 2018; Tremblay, Singh and Lepore, 2017)
 ‘Knowledge and Spatiality –
• Knowledge generation and diffusion of knowledge
• knowledge application and exploitation
 To fuel innovation, there are interdependencies and interactions between the two
categories, through flows of knowledge, technology and human capital, involving dialogue
with institutions, policies at multi-levels.

4. ‘Spatiality’ of knowledge
 Localisation of Innovation
Industrial Districts (Marshall, 1920), Clusters (Porter, 1991), Regional Innovation Systems
(Cooke, 1992). Cluster or regions are vital in order to provide a conducive environment for
nurturing as well as sustaining competition and technological advancement (Zhou and Xin,
2009), which can be achieved through a systemic approach (Cooke, 2005; Lundvall, 1992)
 Globalisation of Innovation
Innovation is becoming more globalised (Archibugi and Michie, 1995); many firms are
‘reorganising’ innovation, ranging from R&D to marketing their products (Ernst, 2006;
Chaminade, 2009). From market and low cost production to quest for knowledge and
capabilities (Herstad et al., 2010).
 Global-Local interactions and Innovation
Generation of Innovation by a combination of close and distant interactions (Asheim and
Gentler, 2005; Owen-Smith and Powell, 2004); external linkages (Camagni, 1991); Local
buzz- global pipelines (Bathelt et al., 2004); local, regional to global level networks (Visser
and Atzema, 2006).

5. ‘Tacitness’ of knowledge

5. ‘Tacitness’ of Knowledge
 Process of Innovation: Non-linear, more systemic in nature
(Lundvall, 2002; Iammarino, 2005) involves different forms
of knowledge – tacit and codified (Nelson and Winter, 1982);
know‐what’, ‘know‐why’, ‘know‐how’ and ‘know‐who’ (
Lundvall and Johnson, 1994) and knowledge bases – analytical,
synthetic and symbolic (Laestadius, 2000; Asheim and
Gertler, 2005)
 The more ‘a knowledge’ is tacit in nature, it is more context-
specific and difficult to be transcribed; most of such knowledge
can be transferred through interactions in person and physical
presence. As a result, entities, industries, organisations
dependent on tacit knowledge, tend to locate in proximity, to
access and derive benefit from localized flow/buzz of knowledge
– Relevance of Agglomeration and Clusters
 The two types of (codified and tacit) are to be seen as
complements rather than substitutes to each other (Johnson,
Lorenz and Lundvall, 2002). This complementarity was in
fact stressed in the original writings by Polanyi (1967), but

5. ‘Tacitness’ of Knowledge
 In order to move beyond a binary discussion on the tacitness of some and the
codifyability of other types of knowledge, Lundvall and Johnson (1994) promoted an
alternative distinction between ‘know-what’, ‘know - why’, ‘know - how’ and ‘know-
who’.
 Some of the scholars like Hirsch-Kreinsen et al. (2005), Moodysson, et al. (2008)
and Asheim and Gertler (2005) have introduced an alternative conceptualization of
knowledge that explicitly takes into account the content of actual interactions taking
place in networks of innovators.  Value chains and Networks
 Points to Ponder:
 Why Tacit Knowledge matters?
 How can it be produced? Transferred?

6. Production, trade and knowledge base as
determinants of national systems

6. Production, trade and knowledge base as determinants of national
systems
 The learning processes, basis of innovation, are influenced by production, trade and the knowledge
base of a country.
 The interdependency between production and innovation goes both ways (Lundvall, 2016):
• Learning takes place in production processes – as ‘learning by doing’ or as ‘learning by using’, thus it
forms an important input into the process of innovation.
• On the other hand, the process of innovation might be the single most important factor restructuring
the system of production by introducing new sectors, breaking down the old linkages and establishing
new ones.
• Production is a repetitive process where routines tend to develop. The everyday experiences of
workers, production engineers and sales representatives influence the agenda determining the
direction of innovative efforts as they produce knowledge and insights forming crucial inputs to the

systems
 The interdependency between trade and innovation (Lundvall, 2016):
• ‘Learning by interacting’ will typically take place between parties linked together by flows of goods and
services.
• User-producer relationships  users as source of inputs for innovation.
• Global value chains and international knowledge flows  important source of information for the
knowledge base
• On the other hand, in a knowledge economy, innovation is crucial to develop and maintain
international trade positions  technological capabilities.

systems
 Knowledge base  knowledge available to feed the learning process and innovate.
• Searching is another important activity, creating inputs to the system of innovation. Organizations
don’t rely only on their productive routines to innovate, they also search for knowledge outside the
production routines.
• In academic or science-oriented organizations, we can find another input for the process of
innovation: exploratory learning. Exploring is less goal-oriented than profit-oriented search  it will
sometimes result in outcomes neither foreseen nor looked for by profit-oriented organizations. When
added to the knowledge base and used by profit-seeking organizations, this exploratory-based
knowledge adds a dimension of dynamism and radical change to the innovation system (Lundvall,
2016).  more codified, easier to flow.

7. Relationship between Knowledge, science and
innovation

7. Relationship between Knowledge, Science and Innovation
 Discovery State of Knowledge, Invention State of Knowledge, Innovation State of
Knowledge (Lane & Flagg, 2010)
 Science is more than just an outcome of knowledge, it is an organized activity, pursued
by scientists who act as a community. Mertonian norms often drive that activity and in
turn justify the actions of scientists. This knowledge is cumulative and is increasing at
as faster pace than imagined - technology and science reinforce each other and
technology gives science more powerful tools to work with (Mokyr, 2018).
 As Science grows, it is not only cumulative, but also due to rapid spurt in collaboration
and growth of networks it spans across continents through many projects and activities
(Wagener, 2018).

7. Relationship between Knowledge, Science and Innovation
 Innovations involve a series of scientific,
technological, organizational, financial and
commercial activities. (OECD 1992: 28)
 The role of institutions and actors in growth
of science: innovation systems comprises of
components and its relationships between
actors and institutions (Edquist and Johnson
1997).
 The interactions comprise of two modes of
learning: STI (science, technology and
innovation) and DUI (doing, using,
interacting), which are important for
innovation and have policy implications
(Lundvall and Lorenz, 2007)

Some provoking questions…
 Are the national boundaries of IS weakening over time?
• When the concept of NIS emerged, the world was a very different place: we didn’t have internet,
social media, software, digital devices were scarce and expensive;
• Impact of new technologies on (tacit) knowledge transfer and codification (advanced automation, 3D
printing, virtual reality, CAD software, machine learning, big data, 5G internet);
• Reshoring of manufacturing in Europe and U.S., rise of nationalism; industrial policies focusing on
bringing manufacturing activities back to their countries;
• Disruptive impact of COVID-19  substantial reduction of physical meetings  more codification of
tacit knowledge? Facilitating knowledge transfer globally?

Innovation systems 101-lecture-4

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