Reflections on the Systems of Innovation Approach

Science and Public Policy December 2004 0302-3427/04/060485-5 US$08.00 Beech Tree Publishing 2004 485

Science and Public Policy, volume 31, number 6, December 2004, pages 485–489, Beech Tree Publishing, 10 Watford Close, Guildford, Surrey GU1 2EP, England

Final remarks

Reflections on the systems of innovation approach

Charles Edquist

This concluding article takes up some central theoretical aspects about the concept of system of innovation that were launched at the onset of this special issue. Acknowledging that the system of innovation approach is still not a theory in its own right, further efforts for theory development will focus on the main activities performed by the system. Beyond the main function of the system, which is to produce innovation, the article exam-ines very briefly the most important of these ac-tivities. Last, the article summarises the findings about the system of innovation in relation to the European Union.

Charles Edquist is Professor of Innovation and Director of CIR-CLE (Centre for Innovation, Research and Competence in the Learning Economy), Division of Innovation, Department of Design Sciences, Lund Institute of Technology, Lund Univer-sity, Box 118 (Visiting address: Sölvegatan 26), SE 221 00 Lund, Sweden; Tel: +46 46 222 39 31; Fax: +46 46 222 80 60; E-mail: [email protected]; Websites: www. innovation.lth.se; www.circle.lu.se.

HE CENTRAL CONCEPT permeating all the articles in this issue of Science and Pubic Pol-icy is systems of innovation (SIs). These final

remarks will therefore reflect on this approach. As mentioned by Susana Borrás in the introduction, this notion gained a firm foothold with the edited vol-umes by Lundvall (1992) and Nelson (1993). Since then, it has received enormous attention in the aca-demic world and among policy-makers, and it is currently gaining ground in management circles.

When I, after some time, evaluated SIs (Edquist 1997), I pointed out nine characteristics that were shared in common by all the versions of the ap-proach at that time:

• they place innovation and learning processes at the centre of focus;

• they adopt an holistic and interdisciplinary perspective;

• they employ historical and evolutionary pers-pectives, rendering the notion of optimality irrelevant;

• they stress the differences among systems and that comparisons among them are important (since it is not possible to compare an existing system to an optimal one);

• they emphasise interdependence and non-linearity;

• they encompass product and process innovations, and sub-categories of these types of innovation;

• they emphasise the central role of institutions; • they are associated with conceptual diffuseness;

and • they are conceptual frameworks or ‘approaches’,

rather than formal theories.

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extremely useful for the analysis of innovation, and for policy-making in this field. Important reasons for this fruitfulness are the first seven points in the list of characteristics.

How to make this approach more theory-like

Scholars disagree on the strengths and weaknesses

eflections on the systems of innovation approach

Charles Edquist holds the Ruben Rausing Chair in Innova-tion at Lund Institute of Technology (LTH), Lund University.He is the Director of the Division of Innovation at LTH andthe Director of The Centre for Innovation, Research andCompetence in the Learning Economy at Lund University.His research interests include issues related to innovationprocesses, innovation systems, public innovation policiesand innovation management. He has published many booksin these fields.

86 Science and Public Policy December 2004

ystems of innovation: theory or approach?

welling on the last of these characteristics, the uestion to address here is whether ‘system of inno-ation’ represents a theory or an approach. A formal heory is an abstract structure expressed in a highly tylised form and crafted to enable us to explore, ind and verify proposed logical connections. It pro-ides convincing propositions as regards established nd stable relationships among variables. This re-uires, among other things, conceptual precision, larity and a clear identification of independent nd dependent variables and their relation to one nother.

In the mid-1990s, conceptual diffuseness was as-ociated with SIs (penultimate point above) and this ontinues to be the case. For example, it remains ery common in the literature on the subject that oth the category of ‘legal rules, norms and habits’ nd that of ‘formal structures that are consciously reated and have an explicit purpose’ are referred to s ‘institutions’, although the latter category ought to e called ‘organisations’ (players or actors). It is mportant to make a distinction between the two ategories of institutions and organisations, in order o be able to study the relationships between them — nd there are important such relationships in sys-ems of innovation. For example, ‘institutional rules’ for instance, the patent law) have a great impact on he actions of ‘organisational actors’ (for instance, irms that produce inventions and innovations).

Nor is the distinction between independent and ependent variables always clear. Both Lundvall 1992) and Nelson (1993) defined systems of nnovations in terms of determinants of, or factors nfluencing, innovation processes. They did not nclude the consequences of innovations in their efinitions, although such consequences are tremen-ously important for productivity growth, employ-ent and society at large.1 However, ‘systems of

nnovation’ are still often used in a very loose anner; the determinants and consequences of inno-

ations are included under its umbrella, especially y policy-makers.

Hence, my evaluation of 1997 remains pertinent: systems of innovation’ is not yet a formal theory in he sense of providing specific propositions regard-ng causal relationships between well-defined vari-bles. Instead, the term ‘approach’ remains more ppropriate. The approach is fairly advanced and

of the SI approach and on how they ought to be ad-dressed. According to some, the approach should not be made too rigorous nor ‘overtheorised’, as it should remain the basis for an inductive type of re-search strategy.2 Another position argues that the SI approach is ‘undertheorised’, that conceptual clarity should be increased and that the approach should be made more ‘theory-like’.3

Hence, the international community within inno-vation studies is currently divided on this issue. I belong to the second category and believe that it is important to advance the theoretical development of the SI approach, as was expressed by Susana Borrás in her article in this issue (Borrás, 2004). In Edquist (2004), I therefore tried to contribute further to in-crease the rigor and specificity of the SI approach.4

What is a fruitful path for developing the SI ap-proach into a formal theory? First, it is important to note that the state of the art with regard to innova-tion theory is generally quite underdeveloped. We do not have systematic and detailed knowledge re-garding the actual determinants and consequences of innovation. It is important to develop knowledge in this field; we must develop a better capacity to be able to explain innovation processes and measure their consequences.

Given our limited systematic knowledge about the determinants of innovations, case studies of the de-terminants (and consequences) of specific inno-vations or specific (and narrow) categories of innovations are very useful. In particular, compara-tive case studies have great potential, comparing innovation systems of various kinds, including the determinants (and consequences) of innovation processes within them.

Relevant questions for further investigation include: which activities from which organisations are important for the development and diffusion of specific innovations? Is it possible to distinguish between important activities and less important ones? Which institutional rules influence the organi-sations in carrying out these activities? Addressing these questions in an ordered and comparative man-ner could further develop the SI approach and con-tribute to the creation of partial theories about the relationships among variables within SIs.

As mentioned above, it is important to focus such analytical work on specific and narrow categories of innovations, as opposed to all innovations at the same time. The reason for this is that we can expect that the determinants (and the consequences) of innovations differ among different categories of innovations.


Science and Public Policy December 2004 487

It is therefore important to pursue the explanatory work at a meso- or micro-level of aggregation, meaning that the taxonomies of innovations become important.

Clearly defined concepts are necessary to identify empirical correspondence to theoretical constructs and to identify the data that should be collected. Conceptual specifications are therefore crucial for empirical studies, and it is important to increase the rigor and specificity of the SI approach. This can be achieved by clarifying the meaning of key concepts such as innovation, function, activities, components, organisations, and institutions, and the relations among them.5

One useful distinction is between product and process innovations. Product innovations are new, or better, products (or product varieties) being pro-duced and sold; it is a question of what is produced. The category of product innovations can include both new material goods and new intangible ser-vices. Process innovations are new ways of produc-ing goods and services; this is a matter of how existing products are produced. Process innovations can be divided into technological and organisational ones.6

We can expect these categories of innovations to have different determinants, just as they have differ-ent consequences. For example, process innovations lead to decreased employment, whereas product in-novations result in increased employment — even when compensation and substitution mechanisms have been taken into account. Both of these catego-ries of innovations are expected to lead to increased labour productivity; however, the mechanisms through which this occurs are different. In the case of process innovations, the decreased amount of labour required (per unit of output) results in prod-uctivity growth.7 For product innovations, the partial monopolies associated with new products and pat-ented products render it possible for firms to charge a higher price for the products.8

It is also important to distinguish between the de-velopment and diffusion of innovations.9 Develop-ment results in new products and processes that are totally new. In the Community Innovation Surveys (CIS), such new products are referred to as ‘new to the market’ products. They are to be distinguished from ‘new to the firm’ products, which have previ-ously been produced by other firms and are diffused to other firms, perhaps in other countries or regions.

On this basis, the CIS provides several indicators of the propensity to innovate for various categories of innovations. Similar surveys have also been car-ried out in non-European Union countries, meaning that we have access to indicators of the propensity to innovate that are comparable among different countries. This is important. For one thing, the no-tion of optimality is irrelevant in the SI approach. Therefore, we cannot identify an optimal or ideal system of innovation. Nor can we talk about an op-timal propensity to innovate. What remains for

analytical and policy purposes is to compare differ-ent systems of innovations with one another. We can compare the same system over time or different geographically (and/or sectorally) specified systems with each other.

Comparing systems is the only means of deter-mining what represents a high or a low propensity to innovate. Hence, it is the only way to identify the strengths and weaknesses in systems of innovation. Such an identification is very important for policy purposes.

As implicitly indicated above, I believe it is useful to draw clear distinctions between independent and dependent variables. In the field of innovation, this means that it is important to draw distinctions among the determinants of innovation, the propen-sity to innovate (or innovations as such), and the consequences of innovations.10

Activities in systems of innovation

This is exactly what we have set out to do in an ongoing research project comparing the national systems of innovation in ten small countries in Europe and Asia.11 We are using CIS data to meas-ure the propensity to innovate in the ten countries in a comparative perspective. On this basis, we attempt to estimate some consequences of innovation, in-cluding productivity growth. We also make a con-siderable effort to identify important determinants of innovation.

We hypothetically identify determinants of inno-vation with what we call ‘activities’ in systems of innovation.12 This is a way of addressing what actually happens in systems of innovation.

At a general level, the main function, or the overall function, in SIs is to pursue innovation processes, that is, to develop and to diffuse innovations. The factors influencing the development and diffusion of innovations are what I call ‘activities’ in SIs. Exam-ples are; R&D as a means of developing economi-cally relevant knowledge that can provide a basis for innovations; or the financing of the commercialisa-tion of such knowledge, that is, its transformation into innovations.

Comparing systems is the only means of determining what represents a high or a low propensity to innovate, hence it is the only way to identify the strengths and weaknesses in systems of innovation: such an identification is very important for policy purposes


488 Science and Public Policy December 2004

It is important to study the activities (factors, causes, determinants) in SIs in a systematic manner. The hypothetical activities listed below are not ranked in order of importance, but start with knowl-edge inputs in the innovation process, continue with the demand-side factors and the provision of con-stituents of SIs, and end with support services for innovating firms.

The following activities can be expected to be im-portant in most SIs:

• Provision of research and development (R&D), creating new knowledge, primarily in engineer-ing, medicine and the natural sciences.

• Competence building (provision of education and training, creation of human capital, production and reproduction of skills, individual learning) in the labour force to be used in innovation and R&D activities.

• Formation of new product markets. • Articulation of quality requirements emanating

from the demand side with regard to new products. • Creating and changing the organisations required

for the development of new fields of innovation, for instance, enhancing entrepreneurship to create new firms and intrapreneurship to diversify exist-ing firms, creating new research organisations, policy agencies, and so on.

• Networking through markets and other mecha-nisms, including interactive learning among dif-ferent organisations (potentially) involved in the innovation processes. This implies integrating new knowledge elements developed in different spheres of the SI and coming from outside with elements already available in the innovating firms.

• Creating and changing institutions — for instance, intellectual property rights laws, tax laws, environ-ment and safety regulations and R&D investment routines — that influence innovating organi-sations and innovation processes by providing incentives or obstacles to innovation.

• Incubating activities, for instance, providing ac-cess to facilities, administrative support, and so on for new innovating efforts.

• Financing of innovation processes and other ac-tivities that can facilitate the commercialisation of knowledge and its adoption.

• Provision of consultancy services of relevance for innovation processes, for instance, technology transfer, commercial information, and legal advice.

This list is provisional and will be subject to revision as our knowledge about determinants of innovation processes increases.

These activities in SIs should ideally be related to the propensity to innovate. The ambition should be to reveal which activities are important for the pro-pensity to innovate (in different respects) in different systems of innovation, and, if possible, how impor-tant they are.

The strong emphasis on activities here does not mean that we can disregard or neglect the ‘compo-nents’ of SIs — the organisations and the institutions — and the relations among them. Organisations or individuals perform the activities, and institutions provide incentives and obstacles influencing these activities. To understand and explain innovation processes, we must address the relationships be-tween activities and components, as well as among different kinds of components.

There are powerful reasons to integrate concep-tual and theoretical work with empirical studies in an effort to identify the determinants of the devel-opment and diffusion of innovations. Such integra-tion can be expected to lead to cross-fertilisation. The SI approach ought to be employed as a con-ceptual framework in specific empirical analyses of concrete conditions. Testable statements or hypotheses should be formulated on the basis of the approach, and these should be investigated empiri-cally by using both qualitative and quantitative observations. Theoretically based empirical work is the best means by which to straighten out the SI ap-proach conceptually and theoretically; the empirical work will, in this way, serve as a ‘disciplining’ device in an effort to develop the conceptual and theoretical framework.

Such work would increase our empirical knowl-edge about the relations among the main function, activities, organisations, and institutions in SIs. This knowledge could then serve as the basis for further empirical generalisations to develop the framework, including theoretical elements. In other words, em-pirically based theoretical work is also very fruitful. Independently of where one starts, the important thing is that there ought to be a close relationship between theoretical and empirical work.

The research approach suggested here does not imply that SIs are, or can be, consciously designed and planned. On the contrary, just as innovation processes are evolutionary, SIs evolve over time in a largely unplanned manner. Even if we knew all the determinants of innovation processes in detail (which we certainly do not now, and perhaps never will), we would not be able to control them and de-sign or ‘build’ SIs on the basis of this knowledge. Centralised control over SIs is impossible and inno-vation policy can only influence the spontaneous development of SIs to a limited extent.

This is an important aspect that comes up in all the articles that compose this special issue. The spontaneous nature of the innovation process means that there is limited ability to shape and control the innovation system. In the context of the European Union, the creation of many new transnational and supranational institutions and organisations indicates the need to improve the framework conditions for the innovation process, which is increasingly cutting across national borders.

However, these institution-building efforts do not mean a top-down steered creation of a system of


Science and Public Policy December 2004 489

innovation, fully controlled by policy-makers. The current large asymmetries and diversity in terms of both innovative performance and the impact of these institutions in the EU25 show how innovation re-mains essentially a socio-economic process.

Notes

1. However, they single out different determinants in their actual definitions of the concept, presumably reflecting what

they believe to be the most important determinants of inno-vation. Hence, they propose different definitions of the

concept, but use the same term. This reflects the lack

of a generally accepted definition of a national system of innovation.

2. See Lundvall et al (2002, page 221) and Lundvall (2003, page 9), where it is argued that the pragmatic and flexible character of the concept may be regarded as seen to be a great advantage. However, Lundvall et al (2002, page 221) also argue that efforts should be made to provide the con-cept with a stronger theoretical foundation.

3. For example, the OECD (Organisation for Economic Co-operation and Development) has expressed such a view: “There are still concerns in the policy making community that the NIS approach has too little operational value and is diffi-cult to implement” (OECD, 2002, page 11). A similar position is taken by Fischer (2001 pages 213–214).

4. If such an attempt reveals weaknesses associated with the approach, this is a good thing. Acknowledging such weak-nesses may lead to additional research and new insights into the operation of innovation systems.

5. Moving in this direction does not mean transforming the social sciences into something similar to natural science. For example, one cannot abstract from time and space, since there are no universal laws in the social sciences.

6. There are many different important relationships between product and process innovations. For example, product innovations that are investment goods become process in-novations when they are used in other firms — in a different ‘incarnation’ or ‘appearance’.

7. In this case, the denominator in the (productivity) ratio be-tween production value (or value added) and amount of labour needed is influenced.

8. In this case, the change in the numerator in the ratio is the

main mechanism resulting in measured productivity growth. 9. Another distinction, not addressed here, is among incremental

innovations, radical innovations and new techno-economic paradigms (or new general purpose technologies).

10. Though of course, a determinant in one context may be a consequence in another one.

11. The countries are Norway, Sweden, Finland, Denmark, the Netherlands, Ireland, Singapore, Hong Kong, Taiwan and South Korea.

12. The discussion of activities here is based upon Edquist (2004, section 7.4.3).

13. The activities in SIs are the same as the determinants of the main function. An alternative term to ‘activities’ could have been ‘sub-functions’. I chose ‘activities’ to avoid the connota-tion to ‘functionalism’ or ‘functional analysis’, as practised in sociology, which focuses on the consequences of a phe-nomenon rather than on its causes, which are in focus here.

References

Borrás, Susana (2004), “System of innovation theory and the European Union”, Science and Public Policy, 31(6), Decem-ber, pages 000–000.

Edquist, C (1997), “Systems of innovation approaches — their emergence and characteristics”, in Charles Edquist (editor), Systems of Innovation — Technologies, Institutions and Or-ganizations (Pinter Publishers/Cassell Academic, London).

Edquist, C (2004), “Systems of innovation — perspectives and challenges”, in J Fagerberg, D Mowery and R R Nelson, The Oxford Handbook of Innovation (Oxford University Press, Oxford).

Fischer, M F (2001), “Innovation, knowledge creation and sys-tems of innovation” Regional Science, 35, pages 199–216.

Lundvall, B-Å (editor) (1992), National Systems of Innovation: Towards a Theory of Innovation and Interactive learning (Pin-ter, London).

Lundvall, B-Å (2003), “National innovation systems: history and theory”, in Elgar Companion to Neo-Schumpeterian Econom-ics (Edward Elgar Publishing Limited, Cheltenham UK).

Lundvall, B-Å, B Johnson, E S Andersen and D Dalum (2002), “National systems of production, innovation and competence building”, Research Policy, 31(2), February, pages 213–231.

Nelson, R R (editor) (1993), National Systems of Innovation: a Comparative Study (Oxford University Press, Oxford).

OECD, Organisation for Economic Co-operation and Develop-ment (2002), Dynamising National Innovation Systems (OECD, Paris).

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Reflections on the Systems of Innovation Approach