Innovation Systems

NATIONAL INNOVATION SYSTEMS: Experiences from Finland, Sweden and Australia Compared By Prof. G. Roos and O. Gupta

Disclaimer This document should be considered preliminary in nature and may require revision. No references may be made to the document without referring to the original authors. Distribution of this document is strictly prohibited.

Report Prepared by

INTELLECTUAL CAPITAL SERVICES

April 2004 © 2004 Intellectual Capital Services

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TABLE OF CONTENTS TABLE OF CONTENTS............................................................................................................................2 EXECUTIVE SUMMARY.......................................................................................................................10 Purpose.......................................................................................................................................................10 What makes a successful NIS?.................................................................................................................10 Finland .......................................................................................................................................................11 Lessons from Finland ..................................................................................................................................13 Sweden........................................................................................................................................................15 Lessons from Sweden .................................................................................................................................17 How does Australia compare ...................................................................................................................18 Challenges for the Australian NIS ...........................................................................................................19 Recommendations for future development.............................................................................................20 1

INTRODUCTION ....................................................................................................................23

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NATIONAL INNOVATION SYSTEMS ................................................................................26

2.1

Introduction...............................................................................................................................26

2.2 2.2.1 2.2.2 2.2.3 2.2.4

Basic Terms and Concepts .......................................................................................................26 Defining National Innovation Systems........................................................................................26 Learning and Innovation..............................................................................................................27 System..........................................................................................................................................28 Nation ..........................................................................................................................................28

2.3 2.3.1 2.3.2 2.3.3

The Elements of a National Innovation System .....................................................................29 The Structure of the Production System ......................................................................................29 The Institutional Set-Up...............................................................................................................30 Institutions and Organisations .....................................................................................................31

2.4

The Conceptual Boundaries of a National Innovation System .............................................31

2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.5.5 2.5.6 2.5.7

Activities and Relations of the Key Elements of a NIS ..........................................................33 The R&D System: Resources, Competencies and Organisation of R&D Activities...................34 The Role of Government .............................................................................................................35 Inter-Firm Relationships ..............................................................................................................38 The Financial System...................................................................................................................39 Education and Training System...................................................................................................43 The Management System: Internal Organisation of Firms..........................................................44 Relation between Labour and Capital..........................................................................................45

2.6 2.6.1 2.6.2

Sectoral, Regional and Local Systems of Innovation.............................................................46 Sectoral Systems of Innovation ...................................................................................................46 Regional Innovation Systems ......................................................................................................47

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2.6.3 2.6.4 2.6.5

Local Innovation Systems............................................................................................................49 Comparing Local, Regional and National Innovation Systems...................................................49 Characteristics of Successful Regions .........................................................................................52

2.7 2.7.1 2.7.2 2.7.3 2.7.4 2.7.5 2.7.6

Alternative Innovation System Approaches to Economic Growth.......................................53 The Linear Model ........................................................................................................................53 The “Chain-Linked Model” (Interactive Model) .........................................................................54 Technological Systems ................................................................................................................54 Socio-Technical Networks...........................................................................................................55 Competence Blocs .......................................................................................................................56 Relationship between the Theories ..............................................................................................58

2.8

Attempts to Operationalize the National Innovation Systems Approaches.........................59

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COMPARING EXPERIENCES FROM FINLAND, SWEDEN AND AUSTRALIA........61

3.1 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.1.7 3.1.8

Finland .......................................................................................................................................62 Finland’s National Innovation System - Background..................................................................62 Institutional Profile of Finland’s National Innovation System ....................................................62 Development of Science and Technology Policy in Finland.......................................................67 Finland from a Cluster Policy Perspective...................................................................................84 Finnish Adaptation of Foreign Institutional, Organizational & Functional Models....................85 Main Features of Finland’s Homogenization with International Developments .........................86 The Influence of International R&D Statistics in Finnish Policy Design....................................87 Evidence on the Role of Finnish Public Policy on Government Funding and Imperfect Capital Markets .......................................................................................................................................88 Reviewing the Public Provision of Business Support Services in Finland..................................88 The Influence of Experts and Professionalism on Finnish Science and Technology Policy .......90 Finnish Best Practices in Incubator Infrastructure and Incubator Support ..................................90

3.1.9 3.1.10 3.1.11 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.2.5 3.2.6 3.2.7

Sweden .......................................................................................................................................93 Swedish National Innovation System - Background ...................................................................93 Institutional Profile of the NIS.....................................................................................................93 Development of the Swedish Innovation System ........................................................................96 Sweden from a Cluster Policy Perspective ................................................................................102 A Critical Assessment of the Measures Taken in Sweden to Overcome Barriers to Innovation within SME ...............................................................................................................................103 The East Gothia Regional Innovation System in Sweden .........................................................108 Using the Triple Helix Perspective to Compare the Development of Two Nordic Regions .....109

3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5

Australia ..................................................................................................................................112 Australian National Innovation System: Background ...............................................................112 Development of Science and Technology Policy in Australia ..................................................113 Institutional Profile of the Australian NIS .................................................................................113 The Key Commonwealth Programs Influencing Business R&D...............................................117 Different Perspectives on the National Innovation System of Australia ...................................126

3.4 3.4.1 3.4.2

Comparing Finland, Sweden and Australia .........................................................................130 Innovation Activity ....................................................................................................................130 Growth Competitiveness............................................................................................................131

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3.4.3 3.4.4 3.4.5 3.4.6 3.4.7 3.4.8 3.4.9 3.4.10 3.4.11

Scientific Specialisation.............................................................................................................132 Technological Specialization.....................................................................................................132 R&D Spending...........................................................................................................................135 Graduates ...................................................................................................................................137 Services and Innovation.............................................................................................................139 Export Specialisation .................................................................................................................141 Labour Mobility.........................................................................................................................144 Venture Capital Markets............................................................................................................145 Relationships between Public and Private Sector Funding and Organisation of R&D .............146

3.5 3.5.1 3.5.2 3.5.3

Discussion on Australia’s NIS Policies and Programs.........................................................151 A Critical View on the Mismatches in Australia’s NIS.............................................................151 Lessons from Finland.................................................................................................................156 Lessons from Sweden ................................................................................................................159

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POLICY DEVELOPMENT FOR AUSTRALIA.................................................................161

4.1

General Policy Making Guideline for Achieving Growth ...................................................161

4.2

Examples of Good Policy Practices .......................................................................................162

4.3

Broad Policy Lessons from Sweden and Finland.................................................................164

4.4

What Australians Suggest ......................................................................................................165

4.5

Summary..................................................................................................................................168

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REFERENCES........................................................................................................................170

APPENDIX A: INNOVATION, INSTITUTIONS, INDUSTRIAL DYNAMICS AND ECONOMIC GROWTH – THEORETICAL BACKGROUND ................................................................................199 A.1 Innovation Systems for Economic Growth: Theories of Economic Growth...............................199 A.1.1 Neo-Classical Growth Theory.........................................................................................................199 A.1.2 Keynesian Demand Growth Theory................................................................................................199 A.1.3 New Growth Theory........................................................................................................................200 A.1.4 Institutional and Evolutionary Growth Theory ...............................................................................200 A.1.5 Property Rights................................................................................................................................201 A.1.6 Evolutionary-Institutional Economics and the Concept of Industrial Dynamics ............................201 A.2 Evolutionary Theory of the Firm and Systems of Innovation......................................................203 APPENDIX B: COMMONALITIES AND SIMILARITIES OF INNOVATION SYSTEMS APPROACHES .......................................................................................................................................204 B.1 Characteristics of Innovation Systems............................................................................................204 B.2 What do Innovation Systems Deliver?............................................................................................209 APPENDIX C: A CLOSER LOOK AT INNOVATION.....................................................................212 C.1 What Do We Mean by “Innovation”? ............................................................................................212 C.2 Diffusion of Innovation: Impact on Economic Growth ................................................................214 C.3 Models of Innovation........................................................................................................................214 C.3.1 First Generation: Technology-Push.................................................................................................215

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C.3.2 Second Generation: Market-Pull .....................................................................................................216 C.3.3 Third Generation: Coupling Model .................................................................................................216 C.3.4 Fourth Generation: Integrated Model ..............................................................................................217 C.3.5 Fifth Generation: Systems Integration and Networking ..................................................................218 C.4 Levels of Analysis .............................................................................................................................219 C.4.1 Firm-Level .......................................................................................................................................219 C.4.2 Regional-Level ................................................................................................................................220 C.4.3 National-Level .................................................................................................................................220 APPENDIX D: THE INNOVATIVE FIRM .........................................................................................222 D.1 What is Innovativeness? ..................................................................................................................222 D.1.1 Individual-level innovativeness.......................................................................................................222 D.1.2 Firm-level innovativeness ...............................................................................................................222 D.2 Firm-Level Innovative Capacity .....................................................................................................223 D.2.1 Towards a Definition of Innovation Capacity .................................................................................223 D.2.2 The Importance of Framework Conditions .....................................................................................225 D.2.3 Types of Firms and Gaps in Innovation Capacities.........................................................................227 D.3 Innovative Firm Networks...............................................................................................................228 D.3.1 Firms Rarely Innovate Alone ..........................................................................................................228 D.3.2 General and Country-Specific Networking Patterns .......................................................................230 APPENDIX E: INNOVATION SYSTEMS AND PERFORMANCE ................................................233 E.1 Introduction ......................................................................................................................................233 E.2 Link between Innovation and Business Performance ...................................................................233 E.2.1 Innovation Transforms Internal Capabilities of a Firm ...................................................................233 E.2.2 Innovation is Necessary but not Sufficient for Business Performance ............................................233 E.2.3 Firm-Level Evidence .......................................................................................................................233 E.2.4 Regional-Level Evidence.................................................................................................................234 E.2 Fallacies of Measuring National Innovation Systems....................................................................236 E.3 Measurement of Innovative Activities ............................................................................................237 E.3.1 Common Measures ..........................................................................................................................238 E.3.2 Measuring the Efficiency of Regional Innovation Activities ..........................................................238 E.3.3 Different Approaches towards Measuring the Effectiveness of National Systems of Innovation...239 E.3.4 Measuring Sectoral Systems of Innovation .....................................................................................241 E.3.5 Final Word of Caution .....................................................................................................................243

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List of Figures

Figure 1 Components of a typical NIS........................................................................................................34 Figure 2 Basic information about Finland and Sweden ..............................................................................61 Figure 3 Institutional Profile of Finland's NIS (source: www.research.fi)..................................................62 Figure 4 Key Flows between Actors of Finland's NIS (source: OECD, 1999)...........................................63 Figure 5 Tekes, the National Technology Agency, Jan 2002......................................................................64 Figure 6 Tekes and three roles of Government (Source: Rand Europe) .....................................................64 Figure 7 TekesTechnology Programs .........................................................................................................65 Figure 8 STPC Statements ..........................................................................................................................67 Figure 9 Impact of Tekes Activities (source: Tekes) ..................................................................................72 Figure 10 Dynamics of Finnish Industrial Clusters (source: Tekes)...........................................................73 Figure 11 The Finnish IT Cluster................................................................................................................74 Figure 12 Milestones of Finnish Technology Policy ..................................................................................75 Figure 13 R&D in Finland, 1985-2001 (source: Statistics Finland) ...........................................................76 Figure 14 Finnish Innovation System: Sources and Funding 2001.............................................................76 Figure 15 Allocation of R&D Funds in Resources .....................................................................................77 Figure 16 Tekes and the Focus on Research ...............................................................................................77 Figure 17 Finland's National Research and Technology Policy (Source: Tekes) .......................................78 Figure 18 Structure of the Swedish National Innovation System ...............................................................94 Figure 19 Triple Helix configurations (developed on the basis of Etzkowitz and Leydesdorff, 2000, p. 111) ....................................................................................................................................................110 Figure 20 Australian NIS: Advisory Organisations Education and Science.............................................114 Figure 21 The Australian Science Funding System ..................................................................................114 Figure 22 Projected Innovation Index: Selected Years, 1995-2005..........................................................131 Figure 23 Technological (dis)similarities among groups of countries based on patenting. (Source: OECD) ...........................................................................................................................................................133 Figure 24 Business Expenditure on R&D as a percentage of GDP - International Comparison ..............135 Figure 25 Business Expenditure on R&D as a percentage of GDP - Time series.....................................136 Figure 26 R&D Intensities by Industry (1997) .........................................................................................137 Figure 27 Degrees as a Share of Total Degrees Awarded.........................................................................138 Figure 28 Percentage of R&D Expenditure by Broad Discipline .............................................................138 Figure 29 Share of services in business R&D, 1980 and 1998. Source: OECD, ANBERD database, May 2000 ...................................................................................................................................................140 Figure 30 Business expenditure on innovation (expenditure on innovation as a share of total sales (1996) ...........................................................................................................................................................141 Figure 31 Mobility of employees with higher education degrees by providing........................................145 Figure 32 Comparative venture capital markets........................................................................................146 Figure 33 Share of R&D expenditure relative to industry structure .........................................................153 Figure 34 Australian R&D Expenditure $Million.....................................................................................154 Figure 35 Highlighting the need to increase private sector funding of CRCs...........................................155 Figure 36 A suggestion for simplifying the Australian NIS .....................................................................156 Figure 37 Identifying policies for growth through network alignment: a process ....................................162

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Figure 38 Distinguishing between product and process innovation..........................................................213 Figure 39 Dimensions and types of innovation.........................................................................................214 Figure 40 The Five Generations of Innovation Process ............................................................................215 Figure 41 Technology push (1950s - mid 1960s)......................................................................................215 Figure 42 Market pull (late 1960s - early 1970s)......................................................................................216 Figure 43 ‘Coupling’ model (mid 1970s - early 1980s)............................................................................217 Figure 44 Integrated innovation process (mid 1980s - 1990s) ..................................................................218 Figure 45 Independent variables related to organisational innovativeness. Source: Rogers (1995:380)..222 Figure 46 The dimensions of innovative capacity.....................................................................................224 Figure 47 Levels of firms’ innovation capacity ........................................................................................226 Figure 48 Firms collaborating on R&D are more innovative (OECD, 1999) ...........................................229 Figure 49 Types of firm networks.............................................................................................................230 Figure 50 Innovative capacity, innovativeness and competitiveness........................................................236 Figure 51 Examples of indicators of NSIs performance ...........................................................................240 Figure 52 Analytical framework to measure sectoral systems of innovation (Oosterwijk, 2003) ............242

List of Tables

Table 1 Definition of NISs ..........................................................................................................................27 Table 2 Differences in financial systems according to Zysman (1983). .....................................................41 Table 3 Differences in the financial system according to Berglöf (1990). .................................................42 Table 4 Differences in the financial system according to Dosi (1990). ......................................................43 Table 5 The six ‘actors’ in the competence bloc approach .........................................................................57 Table 6 Levels of NIS Analysis (Micro, Meso and Macro) ........................................................................59 Table 7 Members of the Science and Technology Policy Council of Finland............................................66 Table 8 Population growth 1970-97 (thousand inhabitants) .....................................................................111 Table 9 Employment 1996 and employment change 1987-1996:.............................................................111 Table 10 The World Competitiveness Scoreboard 2003...........................................................................132 Table 11 Specialisation patterns in science, selected scientific fields, 1981 and 1995 (Source: OECD) .134 Table 12 Export specialisation by manufacturing industry, 1980-94 (source: OECD).............................142 Table 13 Specialisation patterns in patenting activity, selected areas (source: OECD)............................143 Table 14 Overview of different sources and modes of funding for research and development in selected countries, 1999 (Source: OECD + National documentation).............................................................147 Table 15 Summary of policy priorities, structures and implementation structures in selected countries (2001).................................................................................................................................................148 Table 16 % GERD performed by the Higher Education Sector (Source: Source: OECD Main Science and Technology Indicators, 2000) ............................................................................................................149 Table 17 General University Funds as a Percentage of Civil GBAORD, 1994-2001 (Source: OECD Main Science and Technology Indicators, 2000) ........................................................................................149 Table 18 Alternative University Funding Structures ................................................................................150 Table 19 Typology of good policy practices.............................................................................................163 Table 20 Distinguishing features of DI, OI, and PT research. Adapted from Wolfe (1994:413) .............219 Table 21 A typology of SMEs (OECD, 1999) ..........................................................................................227 Table 22 Possible priorities for innovation support in SMEs (OECD, 1999) ...........................................228 Table 23 Relative importance of technology transfer channels (OECD, 1999)........................................228 7

Table 24 Propensity of innovative firms to engage in international exchange of technology ..................231 Table 25 Distribution of foreign and domestic collaborating partners, 1997............................................231 Table 26 Main variables in comparing Dutch telecom and biotech sectors (Oosterwijk, 2003) ..............243

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Abbreviations

AAD AGSO AIMS ANSTO APA APEC ARC BAA BERD BIF BIOCOG CERN CRC CSIRO DETYA DSTO EAC ECR EU GAMS HECS ICT IP ISIG ISR MNC MNE MNRF NCGP NHMRC NIS NOIE NSFC NSI NTBF OECD PELS PMSEIC R&D RFCD RIS RSI S&T SME SRC SSI STPC TS

Australian Antarctic Division Australian Geological Survey Organisation Australian Institute of Marine Sciences Australian Nuclear Scientific and Technology Organisation Australian Postgraduate Award Asia-Pacific Economic Cooperation Australian Research Council Backing Australia's Ability Business Expenditure on Research and Development Biotech nology Innovation Fund Biotechnology Consultative Group European Organization for Nuclear Research Cooperative Research Centre Commonwealth Scientific and Industrial Research Organisation Department of Education, Training and Youth Affairs Defence Science and Technology Organisation Expert Advisory Committee Early Career Researcher European Union Grant Application Management System Higher Education Contribution Scheme Information and Communication Technologies Intellectual Property Innovation Summit Implementation Group Department of Industry, Science and Resources Multi-National Corporation Multi-National Enterprise Major National Research Facilities National Competitive Grants Program National Health and Medical Research Council National Innovation System National Office of the Information Economy National Natural Science Foundation of China National System of Innovation New Technology-Based Firms Organization for Economic Cooperation and Development Postgraduate Education Loan Scheme Prime Minister's Science Engineering and Innovation Council Research and Development Research field, course or discipline Regional Innovation System Regional Systems of Innovation Science and Technology Small and Medium-sized Enterprise Special Research Centre Sectoral System of Innovation Science and Technology Policy Council Technological System

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Innovation Systems Paper

EXECUTIVE SUMMARY This report is prepared by Intellectual Capital Services for the Australian Business Foundation. The report reviews and compares National Innovation Systems in Finland, Sweden and Australia. The OECD has estimated that innovation is the key driver for economic growth in developed countries with at least 50 per cent of growth directly attributable to it. Furthermore, growth in the world economy will be increasingly dominated by knowledge-intensive goods and services. A key element to competitiveness in the knowledge based economy is ''interconnectedness" or linkages. The nation that fosters an infrastructure of linkages (networks) among firms, universities and governments, gains competitive advantage through quicker information diffusion and product deployment, i.e. nations need National Innovations Systems (NIS). NIS can be broadly defined as all economical, political and other social institutions affecting learning, searching, and exploring activities, i.e. a nation’s universities and research bodies, financial system, its monetary policies, and internal organization of private firms. 8.International Links & Infrastructure R&D & Business links, Recruit & Retain Companies Imports/Exports & Infrastructure (Physical & Info/comms) 3.Public& non-profit R&D: University Govt R&D (CSIRO,DSTO) Non-profit

4.Public Good Health & Medical -Environ ment -Arts & Culture - Defence -Space

Private Res. 2.Education Teaching Higher Degrees Tertiary Workforce Dev VET Primary & Secondary

5.Linkages Technology Transfer Cooperative Research Incubators Technology Diffusion Innovation Awareness Conferences 1.People and Culture Education levels Innovative/creative Risk Tolerance Entrepreneurship Attitudes to S&T

6.Clusters Cluster Networks MNC’s, Large Co’s SME’s Emerging Exporters Innovative Companies R&D Performing firms Start-ups/Spinoffs Industry Bodies Advisor Services Investors/ creditors

9.IP Patents etc

10. Risk Finance Retained Earnings, VC Debt Equity Grants

7.Domestic and International Customers Leading Customers Direct Customers Endusers/ Stakeholders Government Procurement International Customers

11.Rewards/ Incentives Tax Rates R&D break CGT Options

12. Government Policy, Funding and Procurement Institutions Education Funding Bodies R&D Funding Bodies Science, Technology & Innovation PolicyAdvisory Bodies Standards, Regulations, & contract legal systemFiscal & Tax PolicyTrade/Tariff &procurement Policies Federal and Regional Government Decision Making Processes

Constitution of National Innovation Systems

Purpose The purpose of this report is to analyse the national innovation systems of Finland, Sweden and Australia and based on the comparisons of the Nordic countries with Australia, to identify potential policy options for improving Australia’s innovation capacity.

What makes a successful NIS? Successful NIS are leaders in managing the transition towards a new innovation policy paradigm and share these characteristics: - Above average rate of investment in education, research and innovation; - High business and government expenditure on R&D; - An increasingly diversified base of R&D performers; - Improved linkages between science and industry; - High level of networking among innovators; - A supportive financial system.

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Australia has a lot to learn from the two Nordic examples of Sweden and Finland which have been classified as showcases for development of their national innovation systems. The Finnish and Swedish NIS are briefly described here below.

Finland Finland was among the first countries to adopt the concept of ’National Innovation System’ as a basis for its technology and innovation policy. Figure 3 Institutional Profile of Finland's NIS (source: www.research.fi)

Key Flows between Actors of Finland's NIS (source: OECD, 1999) Key organisations in the system include: Academy of Finland; National Technology Agency, TEKES; Public R&D organisations; technology transfer agencies; and capital providers. TEKES, the National Technology Agency of Finland, is the principal organisation for implementing technology policy and is subordinate to the Ministry of Trade and Industry. It supports companies engaged in risk-bearing product development projects with grants and loans, and finances the projects of research institutes and universities in applied technical research. TEKES launches, co-ordinates and funds technology programs to be implemented together with companies, research institutes, and universities and has expertise abroad including co-ordinating international co-operation in research and technology. The public R&D organisations include universities and polytechnics, national research institutes and the Technical Research Centre of Finland (VTT). The combined expenditure of these organisations is about 30% of the total national expenditure on R&D. The private sector's expenditure on R&D is approximately 2% of GDP and is growing. There are very strong linkages between the R&D efforts of business and universities and other public sector R&D groups. The Finnish NIS has always had a strong focus on regional development through technology transfer and there is a diverse range of capital providers for innovation, both private and public. SITRA is one of them and provides: - capital for start-up technology firms (SITRA is always a minority investor);

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- services to match SMEs with 'business angels'; - funds for research projects for existing companies, both large and small; - funds for training projects; - funds for technology transfer; and - funds for foreign venture capital funds. One important feature of the Finnish NIS is the operation and role of the Science and Technology Policy Council (STPC). Chaired by the Prime Minister, the STPC has several important facilitating roles in innovation policy making: it acts as a coordinating body between the ministries on R&D issues, it provides a platform for policy discussion among ministers, industry, funding organisations, labour unions, universities and government officials. The Council defines the over all guidelines for government R&D funding.

Private

R&D at companies 3,284

From abroad 115 VTT 214 (68)

Public

Academy of Finland 184 Universities 834 (364)

Business Angels 380 Venture capitalists: Private 287 Industry Investment Ltd 38 (42) Sitra 64 Finnvera 332 (44) Tekes Finpro 386 55 (30)

Innofin Ministries, 5 (4) TE-Centres, sectoral research 287 (209)

Basic research Applied research

Business R&D

Business development Marketing Internationalisation

Finnish Innovation System: Sources and Funding 2001

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Lessons from Finland Develop a coordinated NIS: – Finland has made considerable advances in generating a coordinated national innovation system. The high frequency of consultation, deliberation and effective linkage creation between government, its programs, and industry has been productive. – The Finnish process begins with the recognition that in the knowledge-based economy, knowledge can be fostered to produce economic benefits. – The “spotter” system employed in incubators encourages flows of knowledge out of the laboratories into fledgling firms. – Undoubtedly, the management of the incubator is critical to the success of such incubated firms – but it is the coordinated flow-on benefits that follow in Finland which are impressive. Namely entrepreneurial training, linkages to prestigious Tekes grants, and the informal imprimatur to investment through Sitra and Finnish Industry Investment Ltd for small and emerging firms. – It is significant that international Technology Transfer is the primary province of the investment houses and their links back through the research system. – The evaluation of government programs to determine their impact on outputs – jobs, turnover and exports has become a significant tool, which adds to the integrity of performance of both the programs and the firms. – The lesson of using the sale of utilities to generate a substantial endowment fund for establishing government investment agencies (Sitra and Finnish Industry Investment Ltd) cannot go unnoticed, but is probably not reproducible. – Australia might learn considerably from the simple membership listings of some of the Science Parks – involving universities, the government, regional governments, financial capital funds, and firms. – Australia can learn from the industrial supply chain clustering built up from the connections between the technology parks, firms and investment funds in addition to the firms involved. – Unfortunately, the Finnish examples provide no clear picture of the “ideal” incubator model. BUT they do offer an interesting contrast between on the one hand the Viikki Biocentre – A Research Cluster, and the Innopoli/Oteniemi – An Incubator for Emerging Firms – both based at universities, but providing markedly different patterns of association and linkage, in the former case to international companies as well as indigenous ones. – The strategic plans offered on a triennial basis through the Finnish Science and Technology Council provide the blueprint for continued integration and development of the Finnish system and could readily be emulated in Australia if it can be agreed that this is considered a viable “way to go”. – The considerable achievements of the Finnish Government in stimulating the economy to reach the highest levels of R&D expenditure from the private sector, and public/private combined will need to be carried forward in new growth firms. Venture Capital: – Until the mid-1980s the banking system model was based on continental Europe’s central bank system and a weakly developed risk capital market, presenting weak conditions for nurturing entrepreneurship and financing new small and medium-sized enterprises. – After that time, a vibrant venture capital market emerged as a result of the liberalization of the financial sector. – This provided unparalleled financing opportunities for innovative high-tech firms, which are now able to enter the market already at a relatively early stage of product development.

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–

The amount of venture capital investments increased more than ten-fold between 1995 and 2000. It is estimated that about one-third of private equity investment in Finland during this period went to ICT. – Most notable is the outward investment view taken by Sitra – primarily as a mechanism to gain expertise for the Finnish venture capital market. – It is the direct linkages to the United States that are held in highest regard, and this is intended to be emulated in the international cooperation platform of Tekes. – Funds management in the Australian venture capital market could also benefit from the influx of US know-how in this field. – It is equally significant that investment in new ventures is generally shared amongst the institutions with the universities as the central player in most cases, and a particular emphasis on regional development plans – the economy and employment. Helsinki Stock Exchange: – A further notable development has been the growth and internationalization of the Helsinki Stock Exchange. With the ratio of market capitalization to GDP at below 20 percent and limited foreign portfolio investment until the early 1990s, the stock market was not a very important source of capital. – After Nokia’s breakthrough, foreign investors discovered the Finnish market. The market capitalization rate had risen to well over 200 percent by 2000, around 70 percent of shares were held by foreign owners, and many companies other than Nokia had significant foreign ownership. – Recently merged with the Swedish Stock Exchange Ensure Supply of Skilled Labour: – Success of the Finnish ICT industry was further dependent on the availability of a skilled labor supply. – The Nokia case shows that the initial breakthrough in the telecommunications sector was made possible by the availability of specialized skills, largely built up as a result of the mix of technical solutions chosen by the many competing telecom operators. – The 1980s were characterized by a shortage of the labor skills needed by Nokia and other high-tech firms, and the companies invested substantial funds on specialized in-house training programs, sometimes in collaboration with Finnish universities (Blomström and Kokko 2001). – By the early 1990s, the shortage of educated manpower had come to the attention of the government, and a broad expansion program in higher education was initiated. – The total intake in universities nearly doubled in the five years between 1993 and 1998, and the number of students in polytechnics tripled over the same period. – This increase in the supply of labor has been essential for the expansion of the ICT cluster. – It should be noted that Finnish high-tech companies still suffer from a chronic shortage of educated labor, and total employment in the cluster would certainly be much higher without this restriction. Encourage networking: – Networking between industry and science is so well developed in Finland that in the mid1990s, 40 percent of all innovative firms reported that they cooperated with universities or public research institutions, which is among the highest in OECD. – Collaboration reaches well beyond university participation in corporate research programs. In many of the current high-tech fields including ICT, technology development is so fast that the skills demanded by companies cannot be found in textbooks. – Industry is therefore actively involved in training and knowledge transfer to the universities, and a large number of internships are provided to link theoretical studies to practice.

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–

The ICT cluster is a case in point, where Nokia has acted as a catalyst in creating vertical relationships with suppliers and subcontractors, covering not only production but also research and product development. In many cases, this networking has been mandated by Tekes (which is co-financing Nokia’s research) and it has often necessitated substantial transfers of technology from Nokia to its partners, at least in the initial stages of the relationship. – The networked production paradigm, enhanced by cooperative long-term relations, can be seen behind much of the superior performance of Nokia and the Finnish ICT sector in general. This is not only a feature of Nokia.s operations: networking solutions have become increasingly common in the ICT industry at large. Take a systems approach: – A systems approach is necessary to use existing resources efficiently and to identify bottlenecks and obstacles to growth and development. – It is not enough to support the development of firm-specific assets in the chosen cluster: demand, supporting and related industries, and conditions in the factor market need to be taken into account as well. Establish a broad foundation for policy debate: – It is not sufficient to delegate responsibility to any individual ministry: the main actors, including industry, universities, labor market organizations and other central players should be represented in the policy discussion.

Sweden The Swedish NIS is characterised by internationalised research; industrial orientation towards resourceintensive industries; rapid adoption of new techniques; high expenditures on education; and a relatively costly financial system. Large authorities aided by small ministries dominate the governmental part of this system. The authorities are independent units whose task is to carry out the plans of the government, but also to initiate relevant projects of their own, aiming at a specific goal. Most of the competence is allocated to the authorities rather than the ministries. Sweden invests more in R&D than any other country in relation to its GDP. As a result, Sweden is a world leader in scientific output per head of population, measured in terms of scientific publications. In addition, Sweden plays a prominent role in registering patents. Despite considerable investment in R&D, Sweden's long-term economic growth rate is low.

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Structure of the Swedish National Innovation System The Swedish NIS recently underwent restructuring in order to reduce the number of agencies and clarify their mission. Former NUTEK has been divided into: – Swedish Business Development Agency (NUTEK) – Swedish Agency for Innovation Systems (VINNOVA) – Institute for Growth Studies (ITPS) NUTEK is Sweden’s central public authority for questions related to economic development. Its functions include financing for companies, regional economic development, information and advice services, as well as networking and meeting places. It aims at cluster building in its functions. Seed-financing is one of the main instruments of NUTEK. It does not finance R&D. Swedish Agency for Innovation Systems, Vinnova, funds needs-based R&D to support innovation systems and sustainable development and growth by means of problem-oriented research and the development of effective innovation systems. It is funded by the Government (AUD200 m/Year + AUD 60 m (20032005) for R&D institutes + AUD 15 m/Year for long-term strengthening of R&D institutes) and activities comprise support for R&D in technology, transport, communication, and the labour market. Institute for Growth Studies, ITPS, aims at increasing the competence in future oriented growth policy, by analysing the economic and technical changes, evaluating political actions and ensuring the quality and availability of data related to growth politics. The ALMI Group aims at stimulating and motivating SMEs for continuous growth and development, mainly by offering loans to SMEs. ALMI also offers management programs, business-development consultation and advice for the companies, from its 21 regional offices spread out over the country. The state and county councils own the regional ALMI companies. In the beginning of 2001 the ALMI mother company was merged with NUTEK. The challenge is now to combine a culture of a company with a culture of a public agency effectively. The Swedish Research Council (Vetenskapsrådet), which is to support fundamental research in all scientific fields, comprises three separate councils; the liberal arts and social sciences, natural sciences and technology and medicine and also an education committee. These four bodies distribute funds within

16


their own areas of responsibility. The Swedish Research Council has particular responsibility for maintaining the quality of Swedish research and providing analyses of research policy and advice on research issues for the Government. Other public-sector sources of research funding include various research foundations. The Swedish Foundation for Strategic Research supports research in natural science, engineering and medicine. Its total annual disbursements in 2000 were about SEK1000 million. The Foundation for Knowledge and Competence Development (KK-Foundation) is to promote information technology, research at Sweden’s institutes of higher education, and bridge the gap between the academic and the business worlds. Since 1994 it has invested SEK 1,500 million in approximately 500 projects.

Lessons from Sweden Competitiveness requires flexibility: – Sweden had a very competitive system for a long period after the Second World War and grew very fast, but the policy environment was designed to benefit a small number of large actors at the expense of domestic heterogeneity, competition, and entrepreneurship. – The concentration of economic power in the hands of government, labor unions, and a small number of large multinational companies created an environment where the distribution of profits overtook growth as the main objective, and where costs increased rapidly since no party was interested in disruptive conflicts. – Despite the fact that this was already obvious in the mid-1970s, no change in the overall policy environment took place until the early 1990s. Various interest groups wanted to keep the system intact, with detrimental effects on long run growth. Increasing dependence on the international environment: – Globalization has made governments more and more dependent on the international environment. By setting up international production that exploits the comparative advantages of several countries, MNCs can maximize their efficiency. They may also force governments to adjust to the competition between alternative locations, and create a more favorable business environment. – Sweden is probably the best example of this. – The stagnant Swedish model survived as long as multinational corporations were still tied to their home country by various restrictions on the international mobility of goods, services, capital, and labor. – As these restrictions were reduced, in some cases at the regional rather than the global level, the high costs and other weaknesses of the Swedish model became critical, and motivated firms to move attractive jobs out of the country. – This, in turn, forced the government to start reforming the system. Leveraging opportunities for change: – Although many of the weaknesses of the Swedish model were well known and widely discussed for many years, nothing happened until the financial crisis interrupted the fixed positions between various interest groups. – Many of the reforms introduced in the wake of the crisis would not have been politically viable only a few years earlier. – The point to note is that crises provide rare opportunities for reforms in societies with strong established interest groups. – Continuous monitoring of an economy’s relative strengths and weaknesses is particularly important at these times: the economies that have a ready-made blueprint for reform are clearly in a stronger position than countries where the reform agenda must be decided in the turbulent environment following the crisis.

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How does Australia compare This section looks at how Australia compares to the two show cases outlined above. An Innovation Index developed by the US Council on Competitiveness paints a very contrasting picture between Australia, Finland and Sweden. Whereas both Sweden and Finland are placed within the top 6, the Index rated Australia 12th out of 17 major OECD countries. The index, based on per capita measures, are: total R&D personnel, total R&D investment, the percentage of R&D funded by private industry, the percentage of R&D performed by the university sector, spending on higher education, the strength of intellectual property protection, openness to international competition, and, finally, a nation’s per capita GDP (Porter and Stern, 1999). A projection to the year 2005 based on 1995 data only lifts Australia ranking to 11th out of 17 countries. Looking at the growth competitiveness ranking, Finland posted improvements in its overall macroeconomic stability characterized by an increase in its government surplus, an increase in its national savings rate, and further reduction of its inflation rate and interest rate spread. Yet despite generally stable economic indicators, Finland posted the fourth worst deterioration in recession expectations (rank #69) as negative sentiment deepened over the economy’s prospects in the immediate 12-month period. Significantly, Sweden’s position in third place is unchanged, but underlying its ranking is one of the most striking improvements in scores, particularly in the area of public institutions. Sweden posted increases in the scores pertaining to the extent of organized crime and the perception of favoritism in the decisions of government officials. But like the United States, Sweden’s continued leadership in the technology index (rank #4) belies a notable decline in patents granted.

That Australia stays in 10th place underplays significant improvements in the public trust in politicians and perceptions of the extent of distortive government subsidies, as well as overall quality of public institutions. What did decline is the tertiary enrollment rate from 79.8 percent to 63.3 percent, which by itself accounts for a drop of 7 positions in the technology index and 2 positions in the overall Growth Competitiveness Index.

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Australia

Finland

Sweden

Middling

LEADER

Strength

Human Capital

+

+

+

IC Technologies

0

+

+

Entrepreneurship

+

-

-

Science base & linkage to industry

-

+

+

Innovation

-

+

+

Growth and Business Competitiveness

Australian business expenditure on R&D as a share of GDP is markedly lower than the OECD average and was falling between 1995-96 and 1998-99 while the average for OECD countries continued to rise. 2001-02 was the second successive year of significant increase but total business expenditure on R&D is still markedly lower than the OECD average. Sweden tops the list with Finland at 6th position (Figure 24). Australia’s total expenditure on Research and Development (R&D) was around $8.8 billion in 1998-99. To put that in context, at the time it represented about one thirtieth of the level of R&D investment in the USA, the world’s largest economy or only some 16.5 per cent more than IBM’s expenditure on research, development and engineering. Australia’s expenditure on R&D as a share of Gross Domestic Product (GDP) in 1998-99 was 1.49%, placing it in the mid-range of Organisation for Economic Cooperation and Development (OECD) nations. However, its R&D intensity has been falling in recent years (until very recently), running counter to the general OECD trend.

Challenges for the Australian NIS Australia shows some adverse features of the NIS, which the performance comparison showed: Private sector R&D is low relative to other countries Australia is a weak R&D performer in medium and high technology industries relative to other countries, does less engineering and software R&D than more innovative countries and produces significantly less engineering graduates than major industrialised countries but more biologists. Public and private sectors mismatch on R&D patterns and many research institutions have poor linkages with potential users of research. 19


Insufficient attention is paid to the development of human capital, for example entrepreneurship, and low average company size impedes the ability to compete in new industries and innovate. There is a lack of collaboration between researchers, government and industry in Australia, which can be remedied through the Finnish model: – Finland made major advances in developing coordinated NIS with frequent consultation & highly developed linkages between industry & government. – Finland also has a system of evaluation of government programs which has been described as adding to the integrity of the performance of both the programs & companies. The Australian NIS also suffers from an innovation-commercialization gap, with a SME dominated industry struggling with R&D absorption and commercialization. Poor commercialisation outcomes are also a reflection of the fact that public sector R&D priorities tend to be science rather than industry driven, i.e. public sector R&D does not always meet industry needs. Broad empirical evidence from Australia shows that larger firms are more likely to innovate than smaller firms. – This has major implications for Australia given its SME dominated economy. – The large firms comprise multinational companies who historically prefer to perform R&D activities close to headquarters and export technology into Australia. This picture is however a bit mixed, as MNC’s account for a relatively high proportion of total R&D spend, but at the same time, Australia is a weak attractant of MNC R&D. Moreover, Australia has weaknesses in its innovation culture. – Australian managers used new products and services less than world competitors. – Major weaknesses in management style and capability (Strategic response capability, Entrepreneurialism, Global business perspective, Australian managers are not skilled at managing issues that they cannot touch or see, which is a drawback since innovation is an abstract concept, and creative and lateral thinking concepts are precursors to innovative and creative behaviours.) Further problems characteristic to the Australian SMEs are the difficulties in matching their skill base and commercial imperatives with the public sector. Most SMEs are simply too small to conduct R&D or absorb high tech ideas from the science base. The ability of most SME's to absorb any new technology remains a huge issue that needs to be addressed for all parts of the Australian NIS.

Recommendations for future development 1. It is never too late to start: – The Swedish example clearly demonstrates that there are opportunities for relative latecomers to catch up. – With suitable policies and investments in human and physical capital, even Australia is able to upgrade its industry from raw material intensive and labor intensive activities to knowledge and technology intensive sectors. – The Swedish experiences also highlight the great importance of political will and commitment in providing a suitable environment for growth and development 2. When resources are scarce, focus on specific industry clusters: – The discussion about Finnish development suggests that small countries with limited public resources for investment may need to focus on specific industry clusters. – To facilitate specialization and positive externalities, it is necessary to promote linkages, knowledge flows, and technology diffusion within the cluster. 3. Develop regional innovation systems: – The positive experiences Finland made with focus on particular clusters also highlights the growing importance of the innovation system approach within ‘regional development’.

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–

4.

5.

6.

7.

The Finnish network of higher education institutions, technology centres, centres of expertise and other similar operational players has promoted innovation in the regions to the extent that they are now referred to as regional innovation systems. Take a stakeholder approach: – There is a need to establish a broad foundation for policy debate as has been successfully observed in Finland. – Finland made major advances in developing a coordinated national innovation system with frequent consultation and highly developed linkages between industry and government. – It is not sufficient to delegate responsibility to any individual ministry: all the main actors, including industry, universities, labor market organizations and other central players must be represented in the policy discussion. – Finland also has a system of evaluation of government programs which has been described as adding to the integrity of the performance of both the programs and the companies. Remain flexible: – Discussion of Swedish experiences points in particular to the need for flexibility. – Reform of the Swedish model did not commence until the financial crisis of 1991-93 when it forced the government to reduce the tax burden and to cautiously increase the emphasis on growth rather than distribution in its overall policies. Adopt an international perspective: – To remain competitive, it is necessary to adopt an international perspective on their business environment. – There is no optimal model that will fit in spite of changing international conditions, but instead a need for continuous reform as demand, technology, and competition change. – The explicit monitoring of competing economies and comparison against the best performers in various policy areas – benchmarking – is probably the only way to measure the strength of the national system. Support local ventures: – One key difference between Finland and Australia is the way Finnish companies and the government support local ventures, resulting in the successful development of a range of biotech, telecommunications and hi-tech manufacturing companies. – This is a mindset that must be encouraged and developed in Australian businesses and government agencies so local suppliers are considered not only in light of the value they represent, but for the longer term economic benefits that derive from their contracts. – Finland applies a highly co-operative partnering approach to industry development, with a range of private/public alliances driving growth. – This is particularly noticeable in Finnish public sector decision-making, in which the Prime Minister heads a Science and Technology Policy Council that is responsible for setting the national agenda. With representation from both the government and private enterprises, this council is more than just a bureaucratic think-tank or committee. It is a major force in decisions about how to best grow the industry and the economy. – In comparison, it has been argued that Australia's councils and committees would appear to be ‘toothless tigers’ that spend months in discussions and preparing reports that historically have been largely ignored.

21


The figure below sums up what best practice in national research and technology policy look like. High Highinvestment investmentininR&D R&D(3,5 (3,5%%of ofGDP) GDP) Public Publicand andhigh highclass classuniversity universitysystem systemwith withstrong strongemphasis emphasison onengineering engineeringand and sciences sciences

Coordinated Coordinatedresearch researchand anddevelopment developmentfunding funding High HighIndustrial IndustrialR&D R&Dspending spendingencouraged encouragedthrough throughindustry industrypolicy policy[moving [movingtowards towards system integrators not sub-suppliers] and firm policy [encourage R&D system integrators not sub-suppliers] and firm policy [encourage R&Dspending] spending] Public Publicventure venturecapital capitalorganisation organisationfor forseed seed and andearly earlyphase phasefunding fundingof ofinnovations innovations Government Governmentprocurement procurementspending spending[primarily [primarilyhealth healthand anddefence] defence]used usedas asaa demanding demandingleading leadingedge edgecustomer customerto todrive drivedevelopment developmentofofcompetitive competitiveleading leading national nationalsystems systemssuppliers suppliers Offset Offsetinindefence defencemanaged managedcarefully carefullyas aspart partof ofthe thenational nationalinnovation innovationsystem system Intensive Intensivenetworking networkingand andco-operation co-operationbetween betweencompanies companiesand andpublic publicsector sector organisations organisationsencouraged encouraged

To increase exports, broaden the national industrial base, generate new jobs and expand welfare.

World Worldleading leadingapplied appliedresearch researchthrough throughnational nationalresearch researchcentres centresincluding includingdefence defence

Advanced Advancedliberalisation liberalisationand andderegulation deregulationpolicies; policies;support supportfor foropen openstandards. standards. Peer Peerreviews reviewsand andaxiological axiologicalperformance performancemeasurement measurementsystems systemsused usedextensively extensively

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1

INTRODUCTION

Background

In recent years, Australia has seen an awakening around the world of the critical importance of innovation in providing high-value jobs and sustaining economic development. The OECD has estimated that innovation is the key driver for economic growth in developed countries with at least 50 per cent of growth directly attributable to it. Furthermore, growth in the world economy will be increasingly dominated by knowledge-intensive goods and services. A key element to competitiveness in the knowledge based economy is ''interconnectedness" or linkages. The nation that fosters an infrastructure of linkages (networks) among and between firms, universities and governments, gains competitive advantage through quicker information diffusion and product deployment. Australia’s ability to innovate faster than its competitors is critical to providing high-value jobs and sustaining future economic development to maintain its current quality of life. There are some key questions to be addressed if it is to optimise its innovation performance: – How can it stimulate business to innovate more in-house and to use new knowledge from outside to support that? – How can it stimulate business to transform new knowledge from universities and R&D institutes into new products for the market? – How does it address the innovation skills and culture gaps? – What is the right balance of university education, public good research and industrially relevant disciplines? – How can it commercialise more R&D in public-dominated R&D disciplines? – How can industry, Federal and State/Territory Governments cooperatively work to build a more effective National Innovation System and improve the linkages? – How can breaks in the chain from basic research over applied research, development, productification to market be avoided? Traditionally, the concept of innovation has been linked to technological change and physical outcomes. More recently, however, the innovation process is being identified with intangible assets, such as the investment in R&D, R&D as such or human capital endowment. As a consequence, innovation is now understood as a complex process, structured in both the tacit and the specific knowledge of people, and hence closely related with its environment. Failure to invest in the generation and application of knowledge can set in train a vicious cycle of slow growth and little innovation. Innovation policy is ultimately not about achieving static gains to output but about achieving a dynamic, high growth path in a diverging world. Thus, for a country such as Australia, success or failure in innovation policy is vital to the path of future prosperity.

Australia can learn from Finland and Sweden

Australia has a lot to learn from the two Nordic examples of Sweden and Finland which have been classified as showcases for development of their national innovation systems. For Australia, the Finnish experience is especially pertinent because so much of this remote, sparsely populated country resembles Australia’s own culture and history - it, too, is a small country with large neighbours, an agrarian past and a commodities-based economy. The people have a history of egalitarianism, an independent streak and the can-do mentality that arises from centuries of making do in an environmentally challenging life. In terms of world indices, Finland outranks the US on the UN Technical Achievement Index, it competes with the US and Singapore on the IMD competitiveness rating system, it's one of the lowest on the UN's social injustice scale and it's second highest on the EU wellbeing index. International reviews have found Finnish science and technology policy to be both systematic and successful.

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Finland and Sweden both make interesting countries to study from the viewpoint of high-tech versus lowtech since the industrial structures have undergone rather radical transformation in the 1990s. In Finland this is mainly due to the rapid growth of the electronics industry with ICT and Nokia in the forefront. In the most recent aggregate OECD statistics from 1999, this is reflected in the doubling of Finland’s share of total exports of high-tech products during the 1990s, mainly at the expense of the relative decline of low-tech products. On the welfare ledger, Finland has retained free, high-quality education from kindergarten to university, universal pensions and generous unemployment payments, parental leave to die for, flexible leave arrangements, reasonable working hours, the least population of poor in the world and free public health, although the health system is struggling. While the experiences of Finland and Sweden differ in many respects, they do suggest some policy lessons and conclusions for countries such as Australia. First and foremost, the experiences of both countries demonstrate that there are opportunities for relative latecomers to catch up. With suitable policies and investments in human and physical capital, even small economies are able to upgrade their industry from raw material intensive and labor intensive activities to knowledge and technology intensive sectors. Many of the policy prescriptions followed in the two countries are very orthodox – in particular, the emphasis on human capital, institutions, and outward orientation – but some of their experiences also highlight the great importance of political will and commitment in providing a suitable environment for growth and development. In both countries, policy reform has been important, and the support for reform has been strong because of severe economic circumstances. In both Finland and Sweden, reform was driven by the deep financial crisis of the early 1990s, and the realization that the multinational companies that dominate industry may decide to move production away from their home country if the business environment is not competitive. Both of these phenomena are clearly related to the increasing globalization of the world economy. Report Objectives

The purpose of this report is to analyse and investigate the national innovation systems of Finland, Sweden and Australia and based on the comparisons of the Nordic countries with Australia, to identify potential policy options for improving Australia’s innovation capacity. Priorities for this paper include: Map and measure the structure and performance of Australia's national innovation system and draw comparisons with Finland and Sweden; Provide government policy guidelines about innovation particularly about the commercialisation of public and private sector research; Develop best practice guidelines and supporting information aimed at improving individual innovation ability; Provide actionable advice on innovation, the management of technology and research commercialisation. It is hoped that the comparison with the Nordic countries will provide guidance on some of the following issues of concern to Australia: - The role of government in the National Innovation System; - The role of venture capital in encouraging innovation; - How to foster growth in small and medium sized enterprises, in particular, hightechnology firms; - How to encourage new business formation; - How to encourage industry to be more innovative; - How to improve linkages within the National Innovation System.

Structure of the Report

The report that follows consists of two main sections and six accompanying sections: The first section of this report involves reviewing the academic literature on national innovation systems. More specifically the following issues are covered: - Basic terms and concepts of national innovation systems; 24


- Elements of a national innovation system; - Conceptual boundaries of a national innovation system; - The characteristics of and relationship between the key elements of national innovation systems; - Sectoral, regional and local interpretations of innovation systems; - Alternative innovation system approaches to explain economic growth. The second main section reviews the experiences from Finland, Sweden and Australia and introduces their respective national innovation systems. Their institutional profiles are examined as well as their historical developments. Furthermore, the three countries are compared on a number of quantitive and qualitative dimensions as to the effectiveness of their national innovation systems. A special focus is placed on mismatches within the Australian system and these are used as a basis for recommendations based on the experiences from Finland and Sweden. Throughout the report there is a strong motivation for underpinning the report’s subject matter with academic grounding and foundations. For this reason several topics and literature reviews of importance to this report have been included as six appendices: - Appendix A addresses innovation system from a theoretical perspective looking at the older economic models and their treatment of innovation. - Appendix B describes innovation systems in general, their history, and discusses their strengths and weaknesses as frameworks for analysing economic processes. The overview in this section deals with commonalities among innovation systems approaches. - Appendix C takes a closer look at the concept of innovation itself, with a review of its definition from an economic perspective. Innovation’s impact on economic growth is discussed and several generations of innovation models are presented. Studies of innovation on the firm, regional and national level are also discussed. - Appendix D the question “what is innovation” is answered, as the constructs: (i) innovation, (ii) innovativeness and (iii) capacity to innovate are explored. - Appendix E reviews the link between innovation and performance and discusses the approaches and associated difficulties in measuring the performance of national innovation systems.

Approach and Methodology

This review is based on an international literature review of national innovation systems in its broadest sense, concentrating mainly (but not exclusively) on literature published in the period 1980-2003. The effort is based on the integration of research in a number of disciplines related to innovation systems such as innovation management, clustering, networking, science and technology policy, to name a few. The assumptions made about the research agenda of this document are that it will: – be multidisciplinary and interdisciplinary; – be pluralist and inclusive; – involve multiple perspectives; – ultimately provide policy contributions relevant to Australia. A special focus has been placed on capturing the experiences from Finland and Sweden. Literature on national innovation systems from most other nations has been excluded from this review.

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2

NATIONAL INNOVATION SYSTEMS

2.1

Introduction

The approach of National Systems of Innovation (NSI) or National Innovation Systems (NIS)1 focuses on the country-specific factors influencing the process of technological change. The NIS approach is widely regarded not as a formal and established theory, but as a "conceptual framework” for the analysis of the innovative capabilities embedded in a society. The study of NIS is important because such capabilities contribute to the competitive processes of firms, and, consequently, to the economic performance of a nation as a whole. Drawing upon the literature on the microeconomic dynamics of innovation (Freeman and Soete 1997), the NIS approach assumes that the innovative capabilities of a firm depends on its ability to communicate and interact with a variety of external sources of knowledge (e.g. competing firms, suppliers, users, scientific institutes, other supporting institutions and so on) as well as on the ability to co-ordinate a variety of interdependent sources of knowledge within the firm itself (e.g. R&D, production, marketing/sales). On this basis, the NIS approach combines an "institutionalist” and a “systemic” view to innovation. Institutions and their interaction mechanisms are regarded as the fundamental sources of innovation, and therefore economic success of firms. The remainder of this section reviews the literature describing the elements and functionings of NIS’s. Parts of this section draw heavily on the seminal work undertaken by Senker and Marsili (1999) in their literature review on national innovation systems.

2.2

Basic Terms and Concepts

2.2.1

Defining National Innovation Systems

Although no single definition has yet imposed itself, there is a semantic core that appears in most of the definitions used. Table 1 outlines some definitions present in innovation literature. “. . . The network of institutions in the public- and private-sectors whose activities and interactions initiate, import, modify and diffuse new technologies” (Freeman, 1987) “. . . The elements and relationships which interact in the production, diffusion and use of new, and economically useful knowledge . . . and are either located within or rooted inside the borders of a nation state” (Lundvall, 1992) “. . . The set of institutions whose interactions determine the innovative performance of national firms” (Nelson and Rosenberg, 1993) “. . . The national system of innovation is constituted by the institutions and economic structures affecting the rate and direction of technological change in the society” (Edquist and Lundvall, 1993) “. . .A national system of innovation is the system of interacting private and public firms (either large or small), universities, and government agencies aiming at the production of science and technology within national borders. Interaction among these units may be technical, commercial, legal, social, and financial, in as much as the goal of the interaction is the development, protection, financing or regulation of new science and technology” (Niosi et al., 1993) “. . . The national institutions, their incentive structures and their competencies, that determine the rate and direction of technological learning (or the volume and composition of change generating activities) in a country” (Patel and Pavitt, 1994) “. . . That set of distinct institutions which jointly and individually contribute to the development and 1

The terms NIS and NSI are used interchangeably in this report.

26


diffusion of new technologies and which provides the framework within which governments form and implement policies to influence the innovation process. As such it is a system of interconnected institutions to create, store and transfer the knowledge, skills and artifacts which define new technologies” (Metcalfe, 1995) Table 1 Definition of NISs The focus upon national systems reflects the fact that national economies differ regarding the structure of the production system and regarding the general institutional set-up. Basic differences such as historical experiences, languages and culture should be reflected in national characteristics, which can then be taken as an argument for a national analysis. From the above we can conclude that Lundvall (1992) focuses on organisational matters, related to the processes of learning and innovations. But, he also recognises the importance of institutional factors in explaining differences in economic growth between countries. That is, both the economic structure and the institutional set-ups play a role in the process of learning and developing the innovative ability that contributes to economic growth. Other mainstream authors using the concept of NIS include Nelson (1988) who focuses on the production of knowledge and innovation and upon the innovation system in a more narrow sense and Freeman (1982) who focuses upon the interaction between the production system and the process of innovation. The conceptual bases of the NIS approach rests on a broadly shared definition of the basic terms involved in the concept of NIS (Lundvall 1992; Nelson and Rosenberg 1993; Edquist 1997). These terms are: ‘learning’, ‘innovation’, ‘system’ and ‘nation’. The following sections explore the definitions of each term.

2.2.2

Learning and Innovation

Nelson and Rosenberg (1993) interpret the term ‘innovation’ in a rather broad sense. Innovation encompasses ”the processes by which firms master and get into practice product design and manufacturing processes that are new to them, if not to the universe or even to the nation” (p. 4). This definition refers to the process of technological change leading to the introduction and commercialisation of new products and production processes (i.e. innovation strictly defined), and their diffusion in the economy. Nelson and Rosenberg (1993) observe that including diffusion in the definition of a NIS is important for two reasons: (a) the diffusion of new technologies involves significant processes of local and incremental learning, and (b) the economic benefits of innovation are acquired rarely by first innovators and spillover across other firms. With respect to the previous definition, Lundvall (1992) emphasises that innovation is the outcome of learning processes though which economically useful knowledge is accumulated. In Lundvall’s definition, learning is viewed as a complex process that involves new knowledge as well as new combinations of existing knowledge. As a consequence, learning is fundamentally an interactive and cumulative process. Learning processes, Lundvall (1992) also observes, draw upon a variety of sources of knowledge and are carried out in a variety of activities in a society. In this respect, the author distinguishes three forms of learning: i) ”learning”, in a strict sense, that originates in routine activities associated with the production, distribution and consumption functions of firms, in the form of learningby-doing (Arrow 1962), learning-by-using (Rosenberg 1982), and learning-by-interacting (Lundvall 1988); ii) ”searching” through more formalised learning activities carried out by firms in their departments for market analysis and R&D laboratories; and iii) ”exploring” which consists of the research activities undertaken in academic or science-oriented organisations outside the private sector. All these forms of learning fall within the concept of NIS. While the definition of innovation assumed by Nelson and Rosenberg (1993) refers mainly to the process of technological change, the definition taken by Lundvall (1992) is more general and encompasses also the processes of organisational and institutional learning. In a society, learning takes place not only in the R&D system and in the production system, but also, for example, in the marketing system and in the finance system (Lundvall 1992). Not only do important processes of organisational change occur within a

27


firm, as necessary conditions for, or as consequences of, technological innovations, but also, as stressed by Johnson (1992), a more general process of institutional change occurs in a society. This process interacts, with positive and negative feedbacks, with the dynamic of technological change. Technological change often imposes a pressure for institutional change and, symmetrically, institutional change may provide incentives for technological change. However, mismatching problems in the coevolution of technological and socio-institutional change, especially in a period of radical technical change, may hamper the process of innovation (Freeman and Perez 1988). These problems emerge because of the inertia of behavioural rules and cultural elements embedded in institutions, which, change rather incrementally and slowly over time (Johnson 1992). However, Edquist (1997) notices that although the NIS approach recognises the importance of institutional and organizational change, it focuses mainly on the features of technological change within a given institutional set-up. For a more detailed discussion and review on innovation literature the reader is asked to refer to Appendix C.

2.2.3

System

Because innovation involves important forms of interactive learning, Lundvall suggests that it needs to be addressed within a ”systems approach” (Lundvall 1992). Such an approach, observes Edquist (1997), is common to all the authors in the NIS. In general terms, a ”systems approach” assumes that the overall performance of a complex of elements depends not only on the characteristics of the single elements, but on how these elements mutually constrain and influence one another. In order to describe a system of innovation, it is not sufficient to specify its elements or constituent parts. Edquist (1997) suggests that the emphasis should be placed on the interdependent and generally non-linear relationships between elements. According to Lundvall (1992, p.2), ”a system of innovation is constituted by elements and relationships which interact in the production, diffusion and use of new, and economically useful knowledge”. The essential elements of a system of innovation are identified by Lundvall (1992) in (a) the institutional setup and (b) the structure of production of the economy. Both factors, he argues, have an impact on the process of interactive learning of the individual firm, of a group of firms, and of a nation. Edquist (1997) notices that although both elements are considered by Lundvall as important, the focus is placed mainly on the informal institutional set-up (i.e. social and cultural elements of institutions), and the structure of production. Conversely, Nelson and Rosenberg (1993), observes Edquist (1997), view as more important the formal institutions (or organisations) supporting R&D activities. In their definition, a system of innovation consists of ”the set of institutions whose interactions determine the innovative performance ... of national firms”, and these institutions are viewed as ”institutional actors” creating policies for innovations (Nelson and Rosenberg 1993, p.4).

2.2.4

Nation

As stressed by Johnson (1992), the processes of interactive learning necessary to building up innovative capabilities in a firm relies on communication and interaction among people with different skills and types of knowledge, at different levels of aggregation: inside the firm, among firms, and outside the industrial system. Communication and interaction among people involve relationships of trust and intimate dialogue, relationships which depend on both geographic, social and cultural proximity (Johnson 1992). A nation state defines the boundaries, not only in geographic terms, but also for relatively homogenous patterns of social and cultural values shaping the institutional set up of a system of innovation (Lundvall, 1992). Most authors agree that the boundaries involve not only a cultural dimension, but also a political dimension influencing technological change (Lundvall 1992; Edquist 1997). The choice of national boundaries is then not only a matter of geographic and cultural/ideological delimitation, but reflects also the specific role of the state and the power attached to it. Johnson (1992) identifies various dimensions of institutional differences among nations which are important in relation to technical change: (i) differences in national cultures are reflected in different sets of rules, including rules for breaking and changing 28


existing rules, many of which apply to economic activities; (ii) differences in national ideologies underlies differences in the social acceptance of change, and in particular of technical change; (iii) differences in national government lead to differences in a variety of aspects related to the functions of the public sector as producer, regulator and user of innovation. Because of difference in public policies, important differences across nations emerge in a variety of factors in a NIS, such as standards, regulations, communication infrastructures, formal education system, property rights, regulation of money and banking, level of aggregate demand, and so on (Johnson 1992). As stressed by Edquist (1997) an important reason to study NIS is that ”most public policies influencing the innovation system or the economy as a whole are still designed and implemented at the national level” (p. 12). Most authors agree on the importance for technological change of differences among nations in the institutional set-up, with particular emphasis on public policies, concludes Edquist (1997). In addition, however, Lundvall (1992) argues that “the focus upon national systems reflects the fact that national economies differ regarding the structure of production ... [as well as] ... regarding the general institutional set-up” (Lundvall 1992, p. 13). In a broad general definition, it can be concluded according to Johnson (1992) that a ”national system of innovation simply means all the interrelated, institutional and structural factors, in a nation, which generate, select and diffuse innovation”. Authors in the NIS approach have also highlighted the limits of a geographic delimitation of systems of innovation based on national states. Nelson and Rosenberg (1993) suggest that one of the main issues in the empirical studies on NISs is to investigate whether the concept of a ”national” system of innovation makes any sense today. They argue, in particular, that the geographic scope of the influence on R&D activities by supporting institutions may differ across sectors and not necessarily overlap among them. In addition, for some sectors, supporting institutions may act supranationally. Lundvall (1992), by referring to a more general notion of institutional set-up, observes two main limitations in the concept of ”national” innovation system. First, a nation may not be characterised by the cultural and social homogeneity that the NIS approach assumes. Second, in the case of ”multinational” or ”federal” states, it might be difficult to locate the borders of a ”national” system of innovation. (Lundvall 1992). Most authors also agree on the fact that phenomena of globalisation and regionalisation affect the definition and relevance of national boundaries for a system of innovation (Lundvall 1992; Edquist 1997). Regionalisation implies, on the one hand, that national boundaries may be too broad a unit of analysis. The emergence of networks among firms and other institutions located in geographically delimited areas within a nation, reveals the importance of regional systems of innovation. On the other hand, national boundaries may be too narrow a unit of analysis. The process of globalisation can blur national boundaries and institutional factors influencing innovative activities may operate independently on their specific location (see also Section 4 below).. At the same time, processes of economic integration (e.g. EU) may lead to supra-national systems of innovation (e.g. a European system of innovation). As stressed by Edquist (1997) regional, national and supra-national systems of innovation are to be considered as complementary levels of analysis of the determinants of technological change.

2.3

The Elements of a National Innovation System

As mentioned before, Lundvall (1992) identifies two key elements as part of a NIS: a) the structure of the production system and b) the general institutional set-up. The production structure in the industrial system defines how production activities are vertically and horizontally linked within the industrial system. The institutional set-up encompasses ‘formal institutions’ (i.e. organisations such as firms, universities etc.) and ‘informal institutions’ such as rules, norms, traditions, laws etc. (Edquist and Johnson 1997). Although both variables are generally regarded of some relevance for innovation, some of them are viewed as being more important than others by various authors. A further important factor is obviously the linkages between the national innovation system and the international structure. This need to be managed as well.

2.3.1

The Structure of the Production System

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Andersen (1992) stresses the importance of the production and linkage structure in the economy for the process of technological change. The impact of the production pattern of a nation on innovation acts through various mechanisms, Andersen argues. First, the structure of production influences the pattern of learning as an unintentional outcome of a firm’s production activities, such as the process of learning-bydoing (Arrow 1962). Second, as the natural trajectories of learning vary across industrial sectors (Nelson and Winter 1982), the specialisation pattern in the production structure influences innovation patterns. Third the structure of production defines the linkage structure in the economy, i.e accordingly to an inputoutput table (Andersen 1992). The linkage structure is important for the informal processes of ”learning”, in the definition of Lundvall (1992), as by-product of a firm’s normal production activities. In this respect, Andersen (1992) observes that the existence in the economy of subsystems of vertically integrated industries identifies the potential linkages structure among users and producers within the industrial system. This structure identifies relatively stable information channels. Specifically, the technological distance along the linkage structure between users and producers determines the ease with which selected and persistent relationships are established and learning results are transferred (Ibid). Users represent an important source of knowledge for the development of new products as they provide producers with feedback on product specificities and requirements. Eventually, such an informal relationship develops into institutionalised forms of co-ordinated R&D activities (Lundvall 1988). Andersen (1992) concludes by observing that the linkage structure is also relevant for the more formal process of learning by searching.

2.3.2

The Institutional Set-Up

Edquist (1997) argues that there is agreement among NIS authors that institutions are central for a system of innovation given the interactive nature of learning processes. However, he suggests that the definition of institutions different authors adopt is rather heterogeneous (Edquist 1997). Johnson (1992) argues that in a general sense, institutions represent forms of behavioural regularities in societies. In the simplest form, these regularities are intended as habits (or routines) of individuals. When habits and routines become generalised and are shared by groups of individuals, they give rise to social regularities in behaviour. These regularities are represented by norms, customs, traditions, rules and laws, some of which are formal and explicit, such as laws and regulations, while others are informal and implicit, such as common laws and social norms (Johnson 1992). In this general definition, institutions provide guideposts for individual and collective behaviour and regulate/co-ordinate how individuals and organisations relate to each other. Johnson (1992) suggests that institutions serve important functions for technical change. Innovation draws on learning processes which are interactive and take place in an uncertain and complex environment. In relation to these characteristics of learning processes, institutions serve diverse functions: they i) act as informational devices to reduce uncertainty; ii) manage conflicts, iii) co-ordinate the production and the use of knowledge, iv) provide incentive systems and help to mediate conflicts between individual incentives and collective incentives to enable interactive learning and v) govern cognitive processes and help individuals to form a common conceptual basis and ‘language’ to understand, communicate, and acquire knowledge in an interactive process of learning (Johnson 1992). In these functions, institutions provide the stability necessary to maintain and reproduce existing knowledge. They also contribute to the production and diffusion of knowledge by shaping communication and interaction among people with different skills and types of knowledge (Johnson 1992). In addition, Johnson argues that institutions affect the process of ‘creative forgetting’ which is necessary for the growth of knowledge; this is also stressed by Lundvall (1992). For example, institutional factors - such as tax rules, capital markets, ownership structure - affect the process of shutting down old activities at the firm level and within firms (Johnson 1992). In all these functions, the institutional set-up plays a central role in constraining, and therefore determining, the rate and directions of innovation (Johnson 1992; Lundvall 1992).

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2.3.3

Institutions and Organisations

The previous definition of institutions made by Lundvall and Johnson in 1992 includes also ‘formal institutions’, such as firms, banks, universities, governments agencies and so forth. In these institutions, a set of behavioural rules has been already formalised through the creation of specific organisations (Johnson 1992). This broad definition is generally referred to as the institutional set-up of a NIS. However, Edquist and Johnson (1997) introduce a distinction between organisations and institutions. Drawing from the work of North, Edquist and Johnson argue that organisations are formal structures that are consciously created and act with an explicit purpose (i.e. ‘formal institutions’ in the sense above of Johnson (1992)). Institutions define regular patterns of individual or collective behaviour formally and informally embedded in norms, habits, rules etc. Institutions may develop spontaneously and not necessarily with an explicit purpose (Edquist and Johnson 1997). Having distinguished between institutions and organisations, Edquist and Johnson (1997) suggest that is important to develop taxonomies of institutions and organisations in order to understand their role in a NIS. In particular, they argue that institutions can be distinguished between ‘basic’ institutions, such as constitutional or ground rules, and ‘supporting’ institutions that define and specify certain aspects of the basic rules. In addition, one can distinguish between ‘hard’ institutions which bind individual and collective behaviour, and ‘soft’ institutions which provide rules of thumb and suggestions (Edquist and Johnson 1997). Edquist and Johnson (1997) also observe that organisations can be distinguished in private organisations (e.g. firms, industry associations, scientific and professional societies etc.) and public organisations (e.g. regulatory agencies, technology support agencies, policy organisations etc.). Another distinction is made on the basis of the activities performed, that is organisations for (i) knowledge production (e.g. universities), (ii) knowledge distribution (e.g. science parks) and (iii) knowledge regulation (e.g. patent offices) (Edquist and Johnson 1997). Although Edquist and Johnson make a distinction between organisations and institutions and suggest that this distinction is conceptually useful, often authors working in the NIS tradition ignore this distinction, and use the term ‘institutions’ indifferently. Moreover, Edquist and Johnson’s distinction between institutions and organisations may be problematic as both are closely inter-related, as the authors themselves notice, and therefore difficult to disentangle. Edquist (1997) however argues that authors in the NIS approach attribute a diverse emphasis to the two elements of institutions or organisations involved in the definition of the general term ”institutions”. Edquist observes that Nelson and Rosenberg (1993) refer essentially to organisations by stressing the importance in a NIS of ”institutional actors” which they identify in firms and industrial research laboratories and in ”supporting institutions”, such as universities, government laboratories, technology policy agencies, and so on. Conversely, Edquist (1997) notices, Lundvall (1992) refers mainly to institutions which provide agents and collectives with behavioural guide-posts. In such a definition, as stressed by Johnson (1992), institutions also include routines, technological trajectories and technological paradigms which shape and constraint the innovative activities of various agents (Nelson and Winter 1982).

2.4

The Conceptual Boundaries of a National Innovation System

One of the weaknesses of the NIS approach is a certain degree of “conceptual ambiguity”, as Edquist (1997) suggests. The NIS approach rests on broad definitions, such as innovation and institutions. In addition, it is proposed as an explicitly multi-disciplinary approach encompassing the study of the general determinants of technological change and all the scientific, technical, institutional, social and cultural dimensions of economic development. Lundvall (1992) argues that the NIS approach needs to account for the complexity of the process of technological change and therefore it needs to be multi-disciplinary. This need for diversity, observes Edquist (1997), is common to all the systems of innovation approaches, but leads to a certain vagueness of the boundaries of a NIS. As a consequence the NIS approach is ”conceptually diffuse” and no clear demarcation seems to be made between a system and its surrounding context (Edquist 1997).

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The two opposite extremes to the boundaries of a NIS can be identified in the literature, however. A system of innovation is often seen to be broader than the R&D system and the system of technology diffusion. It has to account also for the institutions and structural factors influencing the impact of new technologies on productivity and economic growth. At the same time, a system of innovation has to be smaller than the overall economic system (Edquist and Lundvall 1993). Within these two extremes, a distinction is generally made between a system of innovation in the narrow sense and a system of innovation in the broad sense (Freeman 1992; Lundvall 1992; Edquist 1997). The narrow definition of a NIS, used by Nelson, encompasses the set of (formal) institutions which are more directly involved with scientific and technical activities. The focus is on innovation as the outcome of the processes of learningby-searching of private institutions and learning-by-exploring of public institutions. The supply-side of the innovation process is mainly addressed in Freeman (1992) and Lundvall (1992). Nelson and Rosenberg (1993) stress that the basic dimensions which need to be explored in empirical studies on NIS are: i) the allocation of R&D activity and the sources of its funding; ii) the characteristics of firms and the important industries; iii) the role of universities; and iv) government policies expressly aimed to incentive and regulate industrial innovation. Lundvall takes a broad approach to the NIS. In the broad approach, a NIS encompasses all institutions and structural factors which affect the introduction and diffusion of new products, processes and systems in a national economy (Freeman 1992). This definition includes all parts and aspects of the economic structure and the institutional set up which affect learning as a by-product of production activities (i.e. learning-by doing, learning-by using and learning-by interacting), as well as more intentional and formal processes of learning (i.e. learning-by-searching and learning by exploring) (Lundvall 1992). This more general definition of innovation used by Lundvall (1992), extended to comprehend the process of leaning as a by-product of firm’s normal activities, leads to the explicit consideration of the production system among the elements of a NIS (Andersen 1992). Such an extension of the boundaries of a NIS implies also that more emphasis should be given to the interaction between the supply side (R&D labs, scientific and technical institutions) and the demand side (users, marketing organisations) in the process of technical change (Freeman 1992). Accordingly, the nature of the relationship between users and producers and its impact on innovation is regarded as one of the essential features of a NIS (Lundvall 1992b). In addition, the supply side also considers how the capital market and the labour market influence the process of technological change. In short, in the narrow definition, a NIS is composed as follows: i) institutions actively engaged in the production and diffusion of new technologies (e.g. private and public R&D laboratories, quality control and testing facilities, etc.); ii) institutions regulating the production and diffusion of new technologies (e.g. national standard institutes, patent offices, etc.); iii) institutions supporting the access and dissemination of scientific and technical knowledge (e.g. scientific and technical information services, science parks, publications, libraries, universities, etc.); iv) institutions providing qualified people, and a variety of craft and technical skills (the educational system and the industrial training system); v) institutions formulating and implementing science and technology policy (e.g. ministries, national research councils, etc.) (Freeman 1992; Nelson 1993). In the broad definition of a NIS of innovation the following elements are also considered in relation to their impact on the process of technological change: vi) the production system (inter-industry linkages and production structure); vii) the marketing system (in-house departments, marketing organisations); viii) users of innovations (firms, communities, government, consumers); ix) the finance system (banks, stock markets); x) labour markets (unions, industrial relations); xi) institutions formulating and implementing antitrust and trade policies

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xii)

institutions regulating the use of innovations (e.g. regulations on pharmaceuticals) and their impact on the environment and natural resources xiii) informal and implicit institutions (e.g. social norms, culture, etc.) (Lundvall 1992). xiv) International institutions like EU R&D organisation affects a NIS Another source of difficulty in defining the boundaries of a NIS derives from the increasing internationalisation of economic activities. As stressed by Chesnais (1992) foreign direct investment and the operations of multinational companies (MNEs), may influence the structure and organisation of a NIS. Such a process of internationalisation, the author argues, gives rise to two main issues. First, it may weaken the ability of governments to increase the innovativeness of domestic firms by supporting relevant national institutions and coherent internal demand conditions. Second, the operations of multinational enterprises (in terms of investment/disinvestment, take-over of domestic firms, location of R&D departments, and of other activities, etc.) may potentially affect innovative processes in domestic firms. With respect to this second issue, Chesnais (1992) suggests that MNEs may contribute to a NIS through diverse mechanisms: i) the transfer of technology from the parent firm to subsidiaries, ii) the learning processes which occur as by-product of production activities in subsidiaries, and iii) the building up of R&D capacity, and the training of scientific and technical personnel in host countries. However, the extent to which processes of knowledge accumulation take place in a MNE’s subsidiaries depends essentially on the organisational form of subsidiaries and their degree of technological dependence on the parent firm (Chesnais 1992).

2.5

Activities and Relations of the Key Elements of a NIS

Taking a broad definition perspective of NIS, the complexity of a NIS typical of a developed country is reflected in Figure 1 which identifies major elements and some of the common sub-elements. Such a large and broad open system are derivatives of nations’s history and economical development and have emerged over time rather than been carefully designed2. Consequently, it is not unusual to find systemic mismatches and interface issues that can be improved for such a system as a whole to function more effectively. Indeed, these form part of our later discusson on systematic mistmatches in the Australian NIS. The complexity of such a system almost guarantees that no one person or group of people understands the dynamics of the whole.

2

See Appendix B.1 for detailed discussion on the highly path-dependent and cumulative nature of socio-economic activities.

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8.International Links & Infrastructure R&D & Business links, Recruit & Retain Companies Imports/Exports & Infrastructure (Physical & Info/comms) 3.Public& non-profit R&D: University Govt R&D (CSIRO,DSTO) Non-profit

4.Public Good Health & Medical -Environ ment -Arts & Culture - Defence -Space

Private Res. 2.Education Teaching Higher Degrees Tertiary Workforce Dev VET Primary & Secondary

5.Linkages Technology Transfer Cooperative Research Incubators Technology Diffusion Innovation Awareness Conferences 1.People and Culture Education levels Innovative/creative Risk Tolerance Entrepreneurship Attitudes to S&T

7.Domestic and International Customers

6.Clusters Cluster Networks MNC’s, Large Co’s SME’s Emerging Exporters Innovative Companies R&D Performing firms Start-ups/Spinoffs Industry Bodies Advisor Services Investors/ creditors

9.IP Patents etc

10. Risk Finance Retained Earnings, VC Debt Equity Grants

Leading Customers Direct Customers Endusers/ Stakeholders Government Procurement International Customers

11.Rewards/ Incentives Tax Rates R&D break CGT Options

12. Government Policy, Funding and Procurement Institutions Education Funding Bodies R&D Funding Bodies Science, Technology & Innovation PolicyAdvisory Bodies Standards, Regulations, & contract legal systemFiscal & Tax PolicyTrade/Tariff &procurement Policies Federal and Regional Government Decision Making Processes

Figure 1 Components of a typical NIS When assuming a “systems approach” to innovation, it is actually not sufficient to just enumerate the institutions composing a NIS in order to understand the innovative performance of national firms (Lundvall 1992; Edquist 1997)3. Innovative capabilities depend on the ability to combine multiple inputs which originate in a network (system) of interdependent institutions. The relationships between elements also need to be addressed. In particular, Lundvall (1992) identifies some fundamental activities and relationships among the institutions composing a NIS. Lundvall (1992) argues that basic differences in history, language and culture are reflected in national idiosyncrasies in the following interdependent dimensions: the R&D system, the role of the public sector, interfirm relationships, the institutional set-up of the financial system, national education and training system, internal organisation of firms. Although not explicitly addressed in the book edited by Lundvall (1992), the importance of the institutional set-up of the labour market is stressed by Edquist and Lundvall (1993). In the remainder of this section the major characteristics of the various sub-systems composing a NIS are illustrated as per the literature review by Senker and Marsili (1999).

2.5.1

The R&D System: Resources, Competencies and Organisation of R&D Activities

The R&D system is defined by formal institutions which are directly concerned with the production and diffusion of new scientific and technological knowledge (Freeman 1992). It includes both private and public institutions: - firms: in-house R&D laboratories, quality control and testing facilities; - university laboratories; - national research institutes and agencies; - domestic and international consortia and alliances of firms; and - domestic university-industry formal research collaborations (Freeman 1992, Nelson 1993). 3

For a detailed discussion on innovation performance the reader is referred to Appendix E.

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As suggested by Nelson and Rosenberg (1993) an important feature of a NIS is represented by the allocation and funding of R&D activities. This dimension is analysed especially in terms of the relationship between the private sector and the public sector and their specific contribution to basic and applied research. The following statistics are generally analysed in empirical studies of NIS: a) Distribution of R&D activities between the industrial sector and the public sector, for basic and applied research. b) Distribution of industrial and government R&D financial support to the industrial sector and the public sector, for basic and applied research (Nelson 1993). A second important feature concerns the organisation of R&D activities (Freeman 1992, Mowery and Rosenberg 1993). Within a firm the process of learning-by-searching is institutionalised through the creation of specialised R&D departments and laboratories. Learning-by-searching within R&D labs also benefits from new scientific and technical developments which originate outside the firm. While this contribution may often take the form of knowledge spillovers, new organisational approaches to exploit the outcomes of R&D activities outside a firm have been identified in three complementary categories of research collaboration prevalent in the US NIS: (i) research collaboration between domestic and foreign firms, with a focus on development activities, production and marketing; (ii) research collaboration among domestic firms performing less applied research, which is less closely linked to specific commercial products; and (iii) domestic university-industry research collaboration for more fundamental research. Mowery and Rosenberg (1993) stress how university-industry research collaborations are important to strengthen the link between basic and more applied research, for example through the establishment of research facilities, financed by companies and the public sector, on university campuses to conduct research with potential commercial value. In addition, university-industry research collaborations are important to face the increasingly interdisciplinary character of technological and research activities (Mowery and Rosenberg 1993).

2.5.2

The Role of Government

Nelson and Rosenberg (1993) and Gregersen (1992) argue that an important feature of a NIS is the specific role played by the government in relation to the process of technological change. The public sector assumes various functions in a NIS, as a producer of R&D and human resources, regulator and user of innovations (Gregersen 1992). However, the role of government in a NIS not only refers to the design and implementation of policies with a direct impact on the production and diffusion of innovation, through the funding, regulation and demand for innovations. More generally, public policies have an impact on all the subsystems composing a NIS, such as the financial system, the educational system, the labour market, etc. (Dalum, Johnson and Lundvall 1992). In this section, however, the more direct role of the public sector in relation to technological change is considered. Technology Policy

Freeman (1992) and Nelson (1993) emphasise the role of the public sector in supporting innovation though technology policy measures. These are implemented by diverse government institutions including: - ministries; - national research councils; and - state enterprises. Funding of industrial research Government R&D funding provides direct support for industrial research and public research (government intramural laboratories, research and development centres administered by universities and colleges, university research, non profit institutions). Government funding to industrial research differs across industrial sectors and is mainly concentrated in areas of military and energy technologies (aerospace, telecommunication, electronics, nuclear technologies) and directed largely to development activities (Mowery and Rosenberg 1993).

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Intramural R&D in governmental institutes Studies on NIS stress the role of military R&D expenditure in intramural government departments and agencies, in the post-war period, as a source of spillover for commercial applications (see Mowery and Rosenberg 1993 among others in Nelson (1993)). Mowery and Rosenberg (1993) and Keck (1993) suggest however, that the relationship between military and civilian technologies has recently changed.. Military-funded research programmes are now designed to support the commercial development by firms of civilian technologies with potential applications in military technologies.4 Because these programmes involve both high private and public returns, they address explicitly the commercial competitive strengths of domestic firms. Most programmes exclude foreign firms (Mowery and Rosenberg 1993). Funding of basic research in universities The role and content of public funding to university research has been extensively illustrated by Mowery and Rosenberg (1993) for the US NIS. Government funding for academic research is mainly oriented towards basic research and serves different functions. It represents ”demand” for scientific research through contractual arrangements and grants for specific research projects within universities. It enlarges the pool of scientific personnel through programmes for financial aid in higher education and graduate fellowships. It supports the acquisition of the physical equipment and facilities essential to research by providing expensive scientific equipment and advanced instrumentation for universities. Government financial aid is increasingly important to support industry-university research collaborations. Governments are particularly concerned about the degree of openness of the system to foreign firms, because in this kind of collaboration, both private and public returns are high. Some forms of industryuniversity collaborations involve foreign firms. The consequent transfer of technology may lead to a potential loss of competitiveness for domestic firms5 (Mowery and Rosenberg 1993). Mowery and Rosenberg (1993) conclude that the variety of financial interventions from the public sector has been proven to be effective for the US NIS, (i) to strengthen the university commitment to basic research and build up a strong knowledge base for innovation, (ii) to reinforce the links between research and teaching activities, and (iii) to more effectively relate basic research to industrial applications. Other technology-related policies Antitrust policy and trade policies represent further means by which government influences industrial innovation. For example, Mowery and Rosenberg (1993) argue that reduced antitrust restrictions on collaboration in research might have contributed to the increase in the number of research consortia in the US industry in the late 1980s.6 They also notice that technology and trade policies have increasingly merged as issues of intellectual property entered into international trade negotiations. It has also been observed by Odagiri and Goto (1993), for the Japanese NIS, that trade policies can also be designed to encourage the import of advanced technology. Restrictions on imports and on direct foreign investment can be devised so that foreign firms can exploit their technological superiority only by selling their technology to domestic firms. However, import-substitution policies may reduce market competition and prevent the inflow of capital7 (Odagiri and Goto 1993).

Regulations, Standards and Property Rights

The public sector, in its function as a regulator, defines the “room for innovative manoeuvre” by setting up technical standards and regulations and protecting technical activities. Gregersen (1992) observes that the content of regulations, generally defined, (e.g. technical characteristics and timing of standards), and 4

The National Centre for Manufacturing Sciences, and the Sematech consortia are examples of partly military funded research programmes in civil technologies in the US (Mowery and Rosenberg 1993). The Airbus consortia is an example in Europe (Keck 1993). 5 The Industrial Liaison Program at M.I.T has faced this kind of criticism in the US (Mowery and Rosenberg 1993). 6 Mowery and Rosenberg (1993) notice that in the early 1980s the Justice Department in the US adopted less restricted guidelines and review procedures for mergers and acquisitions. In particular, they suggest, the number of research consortia in U.S. industry has grown since ”the 1984 National Cooperative Research Act, which reduced the antitrust penalties for collaboration among firms in precommercial research” (p. 59). 7 The Japanese Ministry of International Trade and Industry (MITI) combined successfully an import substitution trade policy with a technology policy promoting the domestic technology. This task was achieved by distributing subsidies to firms mainly through joint or co-operative research efforts (Odagiri and Goto 1993).

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the type of regulations (e.g. anticipatory/reactive character, process/product control) which are established in a certain system, depend on institutional negotiations among qualified agents and experts within both the private and public sector. Such negotiations involve social and cultural factors which are specific to a national context. Therefore regulations are an important dimension of a NIS (Gregersen 1992). More recently, Smith (1997) has emphasised the infrastructural character of standards, regulations, and protection of technical activities, and their importance for the process of innovation. In particular, he otices that technical standards contribute to defining the characteristics or performance of products and therefore the shape and focus the innovative activities of firms. Standardisation may be imposed by the public sector, in its position of provider of physical infrastructure (e.g. energy system), or through regulation. Additionally, standards may be imposed by a dominant firm and diffused as an emergent form of coordination within the private sector. Regulations are designed to address the risk associated with most technologies. Such a risk may concerns life or health (e.g. consumer and worker safety), economic loss, or environmental risk. Although the regulatory system is mainly public, it also involves many private agencies. All these activities, Smith says, are important for technological change because help to establish a ”co-ordinated acceptance” by innovative agents of technical norms and standards, and the social acceptance of the consequences and risks associated with new technologies (Smith 1997). The protection of technical activities (e.g. patent acts, property rights) aims more explicitly at economic efficiency due to the partially non-appropriable nature of technological knowledge (Gregersen 1992). As noticed by Mowery and Rosenberg (1993) the character of the appropriability regime may influence not only the overall rate of innovation, but also the role that new small firms play in the process of innovation. A permissive intellectual property regime (e.g. liberal licensing and cross-licensing policies) aid technology diffusion and reduce the burden on young firms of litigation over innovation. At the same time, it reduces the possibility for a firm to appropriate the economic benefits of innovation, and weakens the incentives to innovation (Mowery and Rosenberg 1993). User of Innovations

Gregersen (1992) suggests that public procurement is an important source of demand for innovations, and in doing so it acts as ”pacer” of a NIS. Innovative capabilities of firms are influenced by the process of interactive learning with the public sector as user of innovations. In these terms, the relationship between the public sector and the private sector can be viewed as a particular case of the user-producer relationship (Lundvall 1992b, Chapter 3). This relationship assumes a specific character which reflects the distinct nature of public sector demand. Gregersen (1992) emphasises the different rationalities and goals which unduly the behaviour of the public and the private sectors. With respect to users in the private sector, the public sector demand is dominated primarily by social, political, strategic or military goals and rationalities, and secondarily by cost considerations. Quality and performance oriented concerns may prevail upon cost concerns. As a consequence, public procurement may not only stimulate or restrain the rate of innovation, but also influence the direction of innovative processes (Gregersen 1992). Gregersen (1992) also argues that the contribution of public sector demand to the innovativeness of the private sector is differentiated. On a quantitative side, such a contribution is direct in terms of size and quality of demand. On a qualitative side, the public sector may contribute to user-led innovations by participating directly as a user in the innovation process. Alternatively, it may contribute to user-led innovations as a ”competent user” by formulating user needs and requirements, but leaving the development of new products to the supplier firms (e.g. development of scientific instruments for hospitals and university laboratories, government procurement in defence, hospitals, telecommunications and environment protection) (Gregersen 1992). In short, Gregersen (1992) says that the public sector may contribute to create a high quality and stable home market, especially important when the private sector is faced with unstable environments, and may accelerate socially desirable innovations from the private sector. To achieve this aim, the public sector needs to be a ”competent user” by maintaining and renewing internal learning processes. Further, it needs to maintain stable general conditions, under which qualitative and quantitative changes in public demand may occur which, together with high technical standards, create conditions which facilitate and spur interactive learning between the private and the public sectors (Gregersen 1992).

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2.5.3

Inter-Firm Relationships

As said before, the innovative capabilities of firms depend not only on their ability to interact with the public sector, but also on its ability to interact with other firms within the industrial system. Processes of interactive learning occur among firms vertically related along the chain of production, as well as among firms horizontally related which compete in the same or related product markets. Both forms of inter firm relationships - i.e. user-producer interaction and industrial networks - are influenced by the institutional set-up and structure of production of a NIS (Lundvall 1992). User-Producer Relationship

Lundvall (1992b, Chapter 3) argues that forms of interactive learning between users and producers are particularly important for product innovations, often neglected by the standard theory. The user-producer relationship has the special function to communicate information about both technological opportunities and user needs. This form of interactive learning requires the development of a common code of communication, and involves elements of power and hierarchy, loyalty, mutual trust and respect for each others’ autonomy (Lundvall 1992b, Chapter 3). As a consequence, Lundvall says, the user-producer relationship tends to be durable over time (i.e. ‘institutionalised’) and selective (the number of participants is limited). Lundvall also argues that the pattern of user-producer relationship depends on the distance between the participants involved. Such distance is to be measured in terms of (i) economic distance (i.e. how the economic activities of users and producers are located in the input-output matrix), (ii) organisational distance between the two extremes of full integration and no integration of vertically related activities, (iii) geographic distance as national borders matter in this context and (iv) cultural distance, especially in relation to differences in the rationality of agents between an ”opportunistic behaviour” and an ”honest behaviour”. From these observations, Lundvall concludes that, as for other forms of interactive learning, social and cultural factors, which find generally a rather coherent pattern within national borders, shape the user-producer relationship (Lundvall 1992b, Chapter 3). For example, Walker (1993) observes that cultural factors may lead to problems of coordination in the userproducer relationship. Drawing on the British NIS, the author argues that cultural values of individualism” and ”liberalism” are at the origin of a ”tradition for the consumer to have complete freedom of choice and to have no special responsibility towards, or common cause with, indigenous suppliers” (Walker 1993). Industrial Networks

As stressed by Freeman (1992) ”networking is now becoming of critical importance for effective innovation”. The importance of industrial networks in the NIS approach has been stressed by Gelsing (1992). The author distinguishes between a ”trade network” and a ”knowledge network”. A trade network consists of the linkages between users and producers of traded goods and services. A knowledge network is defined by the flow of information and exchange of knowledge which occur among firms and other institutions irrespective of the flow of goods. Although the two forms often overlap in real terms, in principle they have different implications for the process of innovation. The trade network influences mainly the process of knowledge transfer embodied in capital goods, while the knowledge network shapes in particular the process of interactive learning among participants. Any network can be defined as a set of nodes and relationships. The nodes represent industrial firms and their innovative partners, such as suppliers, customers, private and public consultancies, and competitors (Freeman 1992; Gelsing 1992). The role of formal R&D collaborations among industrial firms and between industrial firms and universities has been analysed above. Although mutual R&D ventures are important, other formalised agreements are established in relation to subcontracting, mutual marketing, training programmes, etc. The definition of an industrial network used by Gelsing (1992), also encompasses informal networks of firms and other private and public institutions which have an open and informal nature. ”Firms participate because of a genuine interest and mutual trust, and not because of contractual commitments” (ibid). Freeman (1992) identifies three main forms of networking applied to: i) collaboration within and between the scientific and technical institutions, ii) collaboration between firms (especially with suppliers of materials, components, sub-systems, etc.) and iii) collaboration between firms and users. Although the user-producer relationship (examined in more detail in the previous section) is the most frequent form of cooperation, forms of cooperation between competitors are also qualitatively important (Gelsing 1992). Gelsing (1992) identifies a variety of institutional and structural factors in the economy which affect the development of industrial networks: i) the structure of production of a nation, ii) the size and structure of

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firms (i.e. a prevalence of small firms facilitates networking), iii) the division of labour between manufacturing industry and business services, iv) the role of industry policy in discriminating between small and large firms, v) legal, economic and structural obstacles to entrepreneurial activities, and therefore to networking, and vi) the existence of a strong technology service infrastructure which facilitates industrial networking (Gelsing 1992). The author also stresses that the emergence of industrial networks in a particular region is affected by the prevailing traditions of entrepreneurship and cooperation in the region. Such traditions may differ across regions and nations, reflecting specific social and cultural elements (ibid).

2.5.4

The Financial System

R&D projects are characterised by a high degree of uncertainty and by a long term time horizon. In his contribution to the NIS approach, Christensen (1992) argues that these features demand specific requirements in the financial system. For example, firms may encounter difficulties in externally financing their R&D projects if financial institutions are risk averse. The institutional set-up of the financial system has an impact on innovative capabilities to the extent that promotion of the long-term objectives of a firm (e.g. growth through long-term R&D projects) are not constrained by the short-term profit objectives of the financial institution (Christensen 1992). The financial system differs across countries and is influenced by social and cultural factors. Financial organisations (banks, financial institutes, stock markets, etc.) institutionalise a set of rules, norms and regular behaviours which shape the relationships between borrowers and lenders. Such relationships are characterised typically by different cultures, rationalities and competencies of the industrial and financial worlds (e.g. technical competencies versus management skills). Elements of geographic, social and cultural coherence are important in defining a financial system. As a consequence, the content and stability of the relationships between borrowers and lenders differ across nations. Important differences, for example, exist in the attitude towards ”short-termism” versus ”long-termism” in financing investments (reflected in national differences in the ”pay-back period” rule). These differences are relevant for a NIS because the time horizon in financing investments is more important to innovation projects than the interest rate (Christensen 1992). Three distinct categories of national financial systems are identified in the literature, according to (i) the way savings are transformed into investment, and (ii) the role of the government. A capital market oriented system is characterised by the allocation of funds though a developed stock market with perfect competition and little government influence. The prices of funds are set by the stock market; institutions and financial intermediaries are highly specialised (typical examples, although with some differences, are the US and UK systems). In a credit based system influenced by government, funds are allocated mainly through bank credits which are used explicitly by the public sector as an instrument to influence technological and industrial development (examples are the French and Japanese systems). Last, in a credit based institutional system, financial institutions can influence prices of funds independently of the government. This system is characterised by strong ties between industry and finance (Germany represents an example of this system). In general terms, the capital market-based system and the credit based-system have a different impact on innovation. In the capital market-based system, stable and long term relationships can hardly be established because of the lack of close contacts between the borrower and the many and small lenders. Communication is only one-way and the borrower has no opportunity to convince the potential borrowers about the merits of his projects. The volatility of the stock market limits the possibility to build up codes and channels of communications. In addition, the valuation of the firm’s assets is made more in relation to the overall performance of the company, than in connection with the quality of the single project. In short, a ”selection mode” of projects tends to prevail in the capital market-based system, mode which emphasises short-term profit objectives in the firm (Christensen 1992). In contrast, it has been argued that a credit based-system privileges a “learning mode” in the relationship between borrowers and lenders. Strong and persistent ties are established between the borrowers and the (few and large) lenders, and they make it easier to accumulate knowledge, on both sides, and develop

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competencies to evaluate single projects. For example, Odagiri and Goto (1993) observe that less strong capital market constraints are at the origin, among other factors related to the work organisation of the firm, of the stronger growth orientation in Japanese firms than in American and European firms. In the Japanese model, shareholders are often banks and other firms that are basically friendly to the firm, and the firm itself, in turn, owns their shareholders (reciprocal shareholders). Such a system gives more discretion to managers who are generally more growth oriented than shareholders (Odagiri and Goto 1993). Although, a credit based-system may create favourable conditions for R&D investments by privileging long-term growth objectives in the firm, the development of strong lending routines may hinder the exploration of new ideas in times of rapidly changing technologies. In addition, as the story of venture capital development in the US illustrates, a capital market based- system may lead to the creation of new financial instruments for the commercialization of new technologies. In particular, Mowery and Rosenberg (1993), notice that, in the US, the development of a sophisticated private financial system (based on venture capital and gradually supplemented by public equity offerings) in the US was essential to support the creation of new high-tech firms in emerging areas of opportunity.

Does Public Funding have a Halo Effect: Evidence from Finnish SMEs

One commonly heard claim about the positive effects of public funding is that it works as a signal to the private financiers about the quality of the firm or the project. From theoretical perspectives, it is possible to argue that a market failure exists due to asymmetric information between financiers and firms, especially young, small, innovative firms. Thus, if the screening by the public organizations is viewed as thorough and reliable by the private sector, it may then have the claimed “halo-effect” on private financing that would otherwise not have been accessible by the firms in question. Väänänen, (2003) empirically explored the effects of public funding on firms’ willingness and ability to gain access to private sector financing in the following years and found that: Factors that have reduced the asymmetry of information between the firm and the financier, i.e. prior proportion of private debt in the balance sheet and having an international auditor, appear to increase firm’s likelihood of being successful in getting private sector finance. Factors that appear to reduce the firm’s probability of getting finance from the private sector are related to the existence of asymmetrical information. That is, intangible assets in the balance sheet (financial institutions tend to rely on collateral) and coming from the high-tech industry sector (high risk and unpredictability of the projects). Overall, these results indicate that market failures based on asymmetric information may exist, and that firms do take active steps in trying to make their firms more transparent and signal their quality to the financiers (internationally recognized auditors). This does have a positive effect on the firms’ ability to raise external market finance. The empirical evidence shows that public subsidies do not seem to have a positive effect on firms’ willingness and ability to raise private sector finance. On the other hand, public sector debt/equity financing seems to go hand in hand with private sector financing, at least to some extent. Although the evidence Väänänen, (2003) provided is not sufficient to reach conclusions about the causality, it does provide some evidence on this issue and points to areas for further research.

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Different Views on Financial Systems

Christensen & Drejer (1996) reviewed different views on financial systems: “There have been several studies on the financing of firms, which have not used a system of innovation and financial system as key concepts. These studies either have addressed only one source of finance, or have tried to cover the whole spectrum of financial sources, but without relating the findings to system of innovation and/or financial system contexts. Views on the financial system overlap each other to a large extent. However, they will all be presented separated in order to cover the research on this topic. The studies on financial systems are done in different traditions and in different academic disciplines but they have all in common that they discuss the differences between at least two types of financial systems. Zysman (1983) explores how the different financial systems facilitate and/or hinder governments to conduct industrial policy. Zysman defines three different financial systems: capital-based system, credit-based system with critical prices administrated by the government, and creditbased system dominated by financial institutions. According to Zysman, the financial systems vary in three ways: the importance of different financial markets, the way the prices are set in these markets, the role the government plays in the financial system. To describe different financial systems Zysman uses the USA, the UK, France and Germany as examples. The two former have a capital-based system and the two later have a credit-based system. The two credit-based systems are similar but with the difference that the government influences the prices in the French version, whilst a limited number of financial institutions affect the prices in the German financial system. The difference between the capital market based systems in the USA and the UK is that bond financing is more important in the USA than in the UK. If we focus on the aspects that affect firm financing we get four factors (Table 2). The two credit based systems are put together in one category since the differences are small.

Table 2 Differences in financial systems according to Zysman (1983). In a paper from 1984, Rybczynski divided the financial system in USA, Europe and Japan in three categories: bank-oriented system, market-oriented system and strongly market oriented system. The differences between the two latter is that in the strongly market oriented system the industry itself can use the risk hedging markets to reduce its risk exposure and the financial intermediaries possibility to provide securities is improved since they can raise capital from other intermediaries. He states that ”the bank-oriented system appear to be associated with the first phase of the evolution of the financial system and the first phase of economic development ”(Rybczynski, 1984: 278). This reefers to the same line of thought as Goldsmiths; there is an historical development to better and more efficient financial systems. Some pages later though he discusses why the financial systems are different. He refers the differences to five factors that can be summarised in economical development of the past, and the present situation in areas like tax, laws, regulation etc. Rybczynski is not very clear about whether he thinks that the financial systems are the result of different trajectories or, if the bank-oriented system is only an earlier phase in the evolution of the financial system than the market oriented system.

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Contrary to Rybczynski, Berglöf stresses the idea that the two different financial systems are a ”two model equilibrium” (1990). Both systems have shown good performance over the last one hundred years and it might be an indicator that both systems are viable. Berglöf’s (1988, 1990) purpose is to apply the concept of property rights to differences in capital structure. He is using Zysman (1983) and Goldsmith (1969) to distinguish between the financial systems. Berglöf presents the following table to illustrate the differences in the financial systems.

Table 3 Differences in the financial system according to Berglöf (1990). Table 3 summarises the differences, which according to Berglöf is characteristic for the different financial systems. Berglöf also tries to verify them with statistics. Factors 2-7 and factor 9 are verified with statistics. The depth and width of the financial market (i.e. the opportunities for diversification) are measured through the size of the financial markets in relation to the economy as a whole. This is a crude measure of the opportunities to diversify. The degree of concentration among creditors and the turnover of credits is very difficult to measure whilst factors 10-12 are easier to estimate. Berglöf discusses how the two financial systems allocate risk and control among financiers. He also explores the effect the financial system has on firms in financial distress and he concludes that financial distress is more frequently used in a bank-oriented system to reorganise firms, whilst in a market-oriented system, transfer of ownership is used. At the end he discusses the implications of financial deregulation on the financial systems. From the view of property right analysis, the system will converge, according to Berglöf. Dosi’s (1990) tries to link the structure of the financial system to the evolution of the economy. He states that ”...finance is a crucial bridge between the present and the future, between what ‘has proved to work’ and the exploration of what is possible” (Dosi, 1990: 316). As an evolutionary economist, he takes as a starting point a non-stationary environment, where there is no perfect information but the agents have to exploit opportunities. The uncertainty factor of the innovative process is stressed and it is not lack of information that is the problem but the structure of the problem. Different capital structures influence how rates and modes firms learn and the rate and criteria on which particular environments select among firms and among technologies. There has to be a trade off between efficiency and evolutionary viability. Dosi states that “the optimistic irrationality of the schumpeterian entrepreneur requires a symmetric counterpart among bankers”. The financial system must allow rather numerous ga mbles; this will enable money to go from old profitable technology to new not-yet-profitable technologies. Dosi uses the dichotomy between markets-based financial system and bank-oriented financial system. The financial systems differ

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in how they view trade off between static efficiency and evolutionary viability. A market-based financial system has to be more conducive to exploration of new technological paradigms whenever innovative opportunities are high and innovative competencies are quite diffused throughout the economy. Dosi claims that a market-based financial system must work near the technological frontier. One of the main consequences of the differences between a market-based system and a bankoriented system is, that new technology in the former tend to occur in new firms while in the latter it tends to occur through diversification of existing firms. The financial system operates in a market-based system through selection, and in the bank-oriented system through learning.

Table 4 Differences in the financial system according to Dosi (1990). All of the studies above seem to verify the idea of an existence of (at least) two different financial systems in the Western world. The purposes of the studies are different, but they all recognise a greater importance of market solutions in the market-oriented financial system during the 1980’s and the 1990’s. The studies presented above (except Rybczynski, 1984 and Dosi, 1990) also mark differences in how the financiers exercise influence on the firms.”

2.5.5

Education and Training System

Freeman (1992) observes that innovative activities of firms and other institutions are supported by “a growing supply of qualified people from the education system and a thorough industrial training system for a variety of craft and technical skills”. The education and training system is generally considered as a fundamental dimension of a NIS (Lundvall 1992). In particular, Nelson and Rosenberg (1993) argue that the system of education and training is important for innovation for two main reasons. It determines the supply of skills in scientific, engineering and technical fields of knowledge, and influences the attitudes of workers towards technical change. The education and training system is composed of various institutions: firms, schools, colleges, universities, and so on. It differs among countries both in the general level and specific contents of education and training. With respect to the firm, and its internal organisation, Odagiri and Koto (1993) observe that in-house education, on-the-job training, and rotation schemes for workers may facilitate the introduction of new products and processes. The training and education system implemented by the firm not only increases workers’ skills, but also their flexibility to adapt to changes in the working environment, changes which might occur as a consequence of technical innovation. The training and education system is an important source of variety of skills and can reduce the strength of inertial forces within the firm (Odagiri and Koto 1993).

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Universities represent the main institutional actors of the higher educational system. Various aspects are explored by empirical studies assessing the role of universities in a NIS (Nelson 1993). These studies reveal that not only the general level of education and teaching is important, but also the ability of the education system “to adapt with speed and flexibility to new developments in science and technology”, as shown, for example, by Keck (1993) for the German NIS. In doing this, continues Keck, the higher education system needs to combine different areas of knowledge, given the increasingly multidisciplinary character of developments in science and technology. Another important aspect to be considered is the ability of universities to establish links between industry and higher education. In many cases, for example, universities (and also technical and commercial colleges) have a special office for technology transfer (Keck 1993). Moreover, university-industry research collaborations are often established and employed by participant firms as ‘filters’ for hiring research personnel (Mowery and Rosenberg 1993). Universities act also as linking institutions between public research and higher education, for example, through collaborations with national research institutes, both in basic and applied research8 (Keck 1993). In this way, universities serve the important function of binding the scientific and technological communities, combining different fields of knowledge in science and engineering9 (Walker 1993). Differences among national education and training systems reflect underlying differences in social and cultural elements. For example, different social values attributed to scientific and engineering disciplines influence the content of the education system, especially in terms of integration of diverse fields of knowledge10 (Walker 1993). Further, some countries are characterised by an “elitist” education system, such as Britain and France (Walker 1993; Chesnais 1993). In particular, Walker (1993) argues that, in the UK, an elitist and finance-oriented system of education is, among other cultural factors, at the origin of a general lack of ”collective cohesion”, for example between industry and finance, and of problems of managerial coordination due to a hierarchical rather than participatory attitude of management. On the other hand, Chesnais’ (1993) account of the French NIS illustrates how an elitist education system, combining rigorous technical training with managerial, political and administrative expertise, provides a unique “power elite” able to support close relationships between public sector and industrial firms.

2.5.6

The Management System: Internal Organisation of Firms

Forms of interactive learning are important for innovation, argues Lundvall (1992), not only between the firm and other private and public organisations, but also, internally within the firm, among its various functions and departments. In particular, Gjerding (1992) examines the influence of diverse types of work organisation upon the process of interactive learning within any firm, in relation to how they solve the innovation design dilemma. The innovation design dilemma expresses how the organisation of a firm makes it possible “to reconcile the need for stability required to perform present activities with the need for change in order to preserve organisational survival” (Gjerding 1992). The management system identifies some general organisational principles of a NIS shaping the work organisation at the firm level (Gjerding 1992). The management system represents an institutional factor influencing the process of interactive learning between the various departments and functions of the firm. Different aspects of the internal organisation of a firm have been addressed in the literature on NIS’s. The organisation model of the Japanese firm, as opposed to the Fordist model, illustrates in particular the importance of the human aspect of management (Odagiri and Goto 1993). Other aspects related to the

8

The German education system illustrates how universities established close links with both national institutes of basic research (the Max-Plank-Society) and national institutes of applied research (the Fraunhofer-Society), the latter, in turn, with a strong orientation towards serving clients, such as the industry and the government (Keck 1993). 9 In particular, Walker (1993) stresses how problems of integration between the scientific and technological communities have undermined the British NIS. At the origin of these problems was, among others, an education system”poorly endowed with ‘binding’ institutions (in particular between physical sciences and engineering) such as the Fraunhofer-Society in Germany” (Walker 1993). 10 For the British NIS, Walker (1993) observes that the engineering profession is held in lower regard, in particular with comparison to Germany, in terms of pay, status and career structures.

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geographic location of diversified corporations also affect communication and interactive learning within the firm (Mowery and Rosenberg 1993). a) Organisation and location of R&D activities in a firm. In large firms, and especially diversified giant corporations, R&D activity is often separate; more fundamental research is carried out in central laboratories separate from the applied research operations in dedicated divisional laboratories. The geographic location of R&D activity within a firm may also give rise to problems of communication and difficulty in commercialising the work of the central research facility (Mowery and Rosenberg 1993). b) Motivations. The decision of a firm to invest in R&D responds more to long-run objectives of growth rather than to short-run objectives of profit. An internal organization where managers are gradually promoted from within the firm, for which they have worked for decades, enable more stable relationships between management and workforce. Managers have a stronger identification with the employees than with shareholders, and a long-term attachment to the firm. As both managers and employees are generally more growth oriented than shareholders, and also because of their long-term attachment to the firm, investment in R&D activities may be favoured under such an organisational form (Odagiri and Goto 1993). c) The managers’ background. In the Japanese model of organisation, most managers come from production and technology departments, followed by marketing and export departments. Only few of them have financial and accounting origins. The familiarity of managers with technology and research in the firm, and with market needs is important for innovation (Odagiri and Goto 1993). d) Interactions between departments: R&D-production-sales/marketing linkages. The innovative success of a firm depends not only on the learning-by-searching formalised in R&D laboratories. Innovation also benefits from the combination of a variety of knowledge inputs and learning processes from all the functions/departments of a firm. In order to achieve an effective integration of the various capabilities and skills in a firm, the organisation of the internal labour market is fundamental. Longterm employment for employees and a carefully organised training and rotation scheme are important for the acquisition by workers of a company-wide view, variety of skills, and flexibility to a changing working environment. In addition, R&D departments tend to be more integrated with production activities and more market oriented, facilitating the introduction of new processes and products. On the other side, truly original basic research may not be emphasised (Odagiri and Goto 1993). The Japanese and the Fordist forms of internal organisation involve not only a different logic of work organisation, but also a very different set of intra-firm cultural values (Gjerding 1992). The Japanese model of work organisation is characterised by an open-systems logic which promotes learning-byinteracting between various departments and functions of the firm, as opposed to a closed-systems logic of the Fordist model. Ideally, argues Gjerding (1992), an open-systems logic involves a prominence of collective action (versus individual action) and, consistently, a rather uniform distribution of power. However, in the Japanese model, a strong tendency towards collectivist values coexists with a very high degree of tolerance towards existing hierarchical power relations. Such a discrepancy between the ideal model and the Japanese model of an open-system logic of work organisation reflects strong social values of collectivity within a strongly hierarchical social status system. (Gjerding 1992).

2.5.7

Relation between Labour and Capital

Although this element was not explicitly addressed in the first contribution of Lundvall (1992), some authors in the NIS approach have stressed the influence upon the process of technological change of the socio institutional set-up, which shapes the co-operative interaction between the labour unions, the welfare state and the private capital. Such institutional set-up is an important dimension of a NIS, Edquist and Lundvall argue, because it affects the ”social acceptance” of technological change. In order to illustrate the importance in a NIS of the institutional set-up underlying the relationship between labour and capital, the opposite examples of the ”Swedish” and the ”Danish” models are typically compared. In the ”Swedish” model trade unions are centralised and organised along industry lines, a low general level of unemployment imposes on companies a severe shortage of labour, and the labour market policy is oriented, also as a consequence of the previous observation, "to retrain or reemploy affected or threatened workers" Edquist and Lundvall (1993). In the ”Danish” model trade unions

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are organised according to profession and competence, the general level of unemployment is high, and labour market policy is directed mainly to social security payments to the many unemployed workers. The “Swedish” model creates in the workers a more positive attitude towards the introduction of new technology and the rationalisation of the labour process. The compromise between labour and capital reached in the ”Swedish” model, was also made possible by the welfare state and its commitment to full employment policies, without any formal state interference in negotiations (Edquist and Lundvall 1993).

2.6

Sectoral, Regional and Local Systems of Innovation

The literature on NIS emphasises the relevance for the innovativeness of firms of institutional and structural factors which are “either located within or rooted inside the borders of a nation state” (Lundvall 1992, p. 2). However, another strand of literature in the economics of technological change focuses on the sector- or technology- specific determinants of innovative capabilities (Dosi 1982; Pavitt 1984; Malerba and Orsenigo 1996). This literature argues that the properties of innovative processes are constrained by the nature of the ”technological regimes” or ”technological paradigms” which characterise selected sets of production activities based on selected fields of technological and scientific knowledge (Dosi 1982; Nelson and Winter 1982). Patterns of innovation are shaped by “technological imperatives” which reflect the specific nature of the knowledge bases underlying learning processes, and these patterns are relatively invariant across countries (Malerba and Orsenigo 1996). As stressed by Edquist (1997) not only the geographical delimitation of systems of innovation need to be specified at different levels, i.e. regional, national and supra-national, but, at each of these levels, the sectoral boundaries of a system of innovation need also to be taken into account. The importance of Sectoral Systems of Innovation (SSI), within a more general systems approach to technical change, has been highlighted by Breschi and Malerba (1997). Breschi and Malerba (1997) argue that both institutional and technological factors are important in influencing the process of technological change. Of course, the difference between the two approaches, the NIS approach and the SSI approach, resides mainly in the emphasis attributed to each of them.

2.6.1

Sectoral Systems of Innovation

A sectoral system of innovation is defined by the “system (group) of firms active in developing and making a sector’s technologies” (Breschi and Malerba 1997). Sectoral systems of innovation are characterised by Breschi and Malerba (1997) with respect to the following main dimensions: i) the Schumpeterian dynamics of innovators in terms of number, size, and concentration of innovators and their change over time (i.e. degree of turbulence), ii) the geographic distribution of innovators and innovative activities within a country (i.e. dispersed or concentrated), and iii) the spatial organisation of firms’ innovative processes (i.e. local or global knowledge boundaries). Breschi and Malerba (1997) argue that all these three dimension of a SSI are influenced by the basic characteristics of technological regimes, characteristics defined in terms of level of opportunity conditions, appropriability conditions, cumulativeness of innovation, and knowledge base. Although the importance of SSIs and their relation with NISs was addressed explicitly only recently by Edquist (1997) and Breschi and Malerba (1997), previous contributions to the NIS approach hint at such issues. For example, Andersen (1992) observes that because of the existence of interindustry differences in systems of innovation, the fact that nations differ in the mix of industries strongly influences the shape of a NIS. That is, the pattern of specialisation of an economy is an important dimension of a NIS. The bottom line would be that within the same industry, differences in innovative performance reside in differences in national histories and cultures which have shaped national institutions, laws and policies (Nelson and Rosenberg 1993)11. In particular, Nelson and Rosenberg (1993) observe that the geographical boundaries of a system of innovation may differ significantly across industries: boundaries may be national (or even regional) for some industries, and supra-national for other industries. In addition, the

11

For a discussion on measuring the performance of sectoral systems of innovation, refer to Appendix E.3.4

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authors suggest, even within the same geographical boundary, the institutions relevant for innovative activities may differ across sectors (Nelson and Rosenberg 1993). More importantly, however, the NIS approach and the SSI approach suggest that it is the specific matching between institutional set-up and structure of production which influences the innovative performance of a nation’s firms, as argued by Guerrieri and Tylecote (1997). Indeed, some institutions are more important for firms active in some industries than others, and some institutional set-ups fit some production structures better than others. Various examples can be drawn from the empirical literature on NISs. For example, Malerba (1997) illustrates, on the basis of the Italian case, how two different NISs may coexist within the same national boundaries, for different industrial sectors. Specifically, Malerba observes that a model of NIS based on industrial districts characterises mainly traditional industries and the machinery sector, while a model based on the R&D system seems to prevail in other industries, such as the automotive and computer sectors. This example also illustrates how the same institutional set-up which supports the strength of one system (i.e. the industrial districts model) is at the origin of the weakness of the other system (i.e. the R&D system) (Malerba 1993). Another example analysing the specific combination between NIS and SSI is provided by Chesnais (1993) in his study of the French NIS. Chesnais examines the French NIS by distinguishing the institutional-set up in diverse high-technology subsystems (i.e. the military subsector, the electronuclear sector, the space industry and the telecommunications industry). Walker (1993), for example, argues that in the British NIS a poor education system especially in engineering, but with a better tradition in science, did not prevent the development of the chemical and bioengineering industries. In particular, this last example suggests that the character of a NIS also influences the specific structure of production emerging in a national economy. Among other factors, the character of the educational system, especially in terms of the relative importance of engineering and scientific areas of knowledge, may influence the relative strength of diverse production activities.

2.6.2

Regional Innovation Systems

Studies concerning the Regional Innovation Systems (RIS)12 approach have become very popular in recent years since regions may be very relevant to understanding certain processes of innovation (Florida 1995). The regional innovation systems literature is linked naturally to the broader, more general literature on Systems of Innovation (SI), which encompasses regional systems, national and sectoral ones (Edquist, 1997; Hommen and Doloreux, 2003)13. Historically, the NIS concept was first developed (Freeman, 1987; Lundvall, 1992; Nelson; 1993), an application that was soon followed by theorising about sectoral systems (Breschi and Malerba, 1997). The RIS literature developed somewhat later, and in connection not only with ‘systems thinking’ about innovation but also what Storper (1995) has called “the resurgence of regional economies” as a topic of intense theoretical and empirical interest in economic geography. RIS theory therefore had to differentiate itself from both other SI theories and other theories of the regional economy. It did this by drawing selectively on both of these traditions and combining their respective contributions in a novel way. In one of the founding works that marked the emergence of RIS theory and research as a separate current within the broader stream of SI literature, Cooke et al. (1997) criticized the national innovation systems (NIS) tradition as tending towards “the study of operational systems” – an approach in which “accounts are attempted of actually existing elements and relationships to determine the extent to which they constitute systems”. They argued that this operational approach could not provide clear answers to the question of “what, indeed, an innovation system actually is”, and on this basis made the case for a preferred alternative: “beginning the study of systems with a conceptual, rather than operational, emphasis”. This emphasis in RIS meant paying attention not only to different stages of evolutionary development but also to certain types of institutional arrangements, as well as organisational forms and configurations of relationships among organisations related to the provision of knowledge, finance, and

12

The terms Regional Innovation System (RIS) and Regional System of Innovation (RSI) are used interchangeably in this report. 13 See Appendix B for a detailed discussion on the commonalities and similarities between innovation systems approaches.

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other inputs for innovating firms. The “conceptual” approach was thus characterized as employing a dual perspective on (a) the “competence (jurisdiction) capacity” of a region – i.e., its ability “to develop policies and manage … different elements, as well as financing capacity for strategic investments in infrastructures” – and (b) “the region’s cultural base, which gives it a certain level of systemic potential”. The conceptual approach by which RIS theory and research distinguished itself from other SI approaches was drawn primarily from the literature of regional science, rather than that of innovation studies. In its effort to develop concepts that could grasp political-economic phenomena such as collective order and micro-regulation, RIS theory could look to the pioneering work of several ‘schools’ of regional science, each of which had attempted to develop a politically and/or institutionally based understanding of regional economies and their development (Storper 1995). There are a number of approaches which analyse the mechanisms and factors that enable long-term, sustainable regional agglomerations. These approaches consist of, and sometimes integrate, economic, sociological and cognitive dimensions. Among the more prominent are industrial districts (Marshall 1920; Brusco 1990; Becattini 1989), clusters (Porter, 1990; Swann et al., 1998), circular or cumulative causation (Young 1928, Myrdal 1957, Kaldor 1970), regional clusters (Saxenian, 1994), innovative milieu (Maillat, 1986; Camagni; 1991), and regional innovation systems (Braczyk et. al., 1998). Some of these approaches focus entirely on groups of firms, while others include a broad range of organisations. However, perhaps the most important reasons for stressing the role of regions can be found in the seminal works of Marshall (1920), who stressed the role of the specialisation of intermediary suppliers, the pooled labour market, and knowledge spillovers. One common claim is that the regions and local milieux have risen in importance as mediators in economic coordination. High trust, learning capacity, and networking are widely perceived as factors of economic and social success. In this regard "cluster concepts" have gathered considerable momentum. There are four main reasons why regions play a predominant role and hence, why a regional innovation systems perspective can be useful: First, although there is an ongoing internationalisation process, regions are still important players in the innovation process. For example, there is still no or little empirical evidence for “globalisation of innovation” even in the ‘global industries’ of IT, (see e.g. Patel and Pavitt 1991, Duysters and Hagedoorn 1996). This implies that vertically and/or horizontally regionally agglomerated systems of actors are in certain instances likely to remain important for the foreseeable future. It is stressed here that this may hold true for advanced technological development. Second, the literature stresses that regions tend to be more decentralised, less hierarchical, and involve greater reliance on outside control of corporate management (e.g. Camagni ed. 1991). Not all supplychain transactions have to be confined to the region but for those activities that require non-codified knowledge and a specific industry culture (aspects of substantial importance to innovation) regions seem to be important. The regional level has also become an important process of embedding economic coordination for large and smaller firms alike. Regional administrations often possess strategic enterprise support functions and can readily adjust or design support policies for their industries and encourage inter-firm interaction, and so on. Third, where regional clusters exist they tend to become more specialised technologically and in terms of their product focus (Cooke and Morgan 1998). Fourth, certain regions are claimed to be characterised by social capital (Cooke and Morgan 1998). This is understood as the collective consciousness and practical action of the regional social order mediated through its micro-constitutional organisations that determine the regional action and hence the evolutionary processes in the region. For example, proximity between firms can play an important role in interactive learning and knowledge creation is supported by the institutional embodiment of tacit knowledge useful for particular classes of activity (Maskell and Malmberg, 1997). Although the regional innovation systems approach has a lot of appeal, it presently suffers from similar problems as the national innovation systems approach. To begin with, the definition of a regional innovation system is unclear. Furthermore, several of the key concepts are extremely abstract, which makes their use in studies exceedingly hard. Examples are the popular notions of social capital or trust. In fact, we would like to borrow Krugman’s (1991) claim that with such comments, there is nothing preventing the researcher saying anything. More severe however, is the issue of what really are the key issues of a regional innovation system. Hence, in a similar way as the ubiquitous notion ‘cluster’, it may be very useful or simply a vague concept.

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There is a slight difference in emphasis in the regional and national innovation systems approaches. As an example, for the regional innovation systems approach, reasons to distinguish the region (sub-national entity) from the nation could be cultural homogeneity within regions but cultural differences as well as major differences in industry structures between regions within a country. Sometimes too, regions may be found across nations. Some well-known examples are the Flanders and the NN in Belgium and Germany or Scotland and England in the UK.

2.6.3

Local Innovation Systems

According to Schlapfer and Marinova (2001) the term ‘local innovation system’ refers to the specific geographical location as well as the specific cultural make up in which the innovation system is placed. As Feldman (1994) posits, the sources of knowledge are embodied in human and institutional form and are less geographically mobile than financial capital. A local innovation system has similar characteristics to a national innovation system, in that it facilitates the transformation of scientific and technological advances into economic use (Rothwell & Zegveld, 1988, p19 in Roessner). It also encompasses elements and relationships which interact in the production, diffusion and use of new, and economically useful knowledge (Lundvall, 1992), located inside a specific region within the borders of a nation. In addition to these similarities, local systems of innovation tend to be more community driven. Cooke (in Braczyk, Cooke, Heidenreich, 1998) refers to local innovation systems as grassroots regional innovation systems (RIS). He argues that they are locally organized, at town or district level, and that funding is diffuse in origin, comprising a mix of local banking, local government, possibly local chamber of commerce capital, grants and loans. He further posits that the level of technical specialization is low and generic problem solving is more likely than significant, finely honed technological specialization. The degree of what he refers to as ‘supranational’ and local involvement is quite low. The research competency of regional or local innovation systems is by its nature highly applied for near market. According to Schlapfer and Marinova (2001) the main difference between a local and a national innovation system is that of scale. Unlike national systems, local innovation systems are of more human and manageable proportions. In other words, the people most affected by the system have a real input on how a new innovation is being implemented and used. Whereas national systems of innovation are designed by and for a specific nation, and supra-national systems are the result of agreements between trading nations (i.e. European Union), local systems of innovation tend to emerge because they are needed for a specific region within a nation state.

2.6.4

Comparing Local, Regional and National Innovation Systems

Advantages of National Innovation Systems over Local Innovation Systems

The main feature of local innovation systems is that they are small (Schlapfer and Marinova, 2001). As a consequence of their size, they are unable to compete with national innovation systems for funding. Furthermore, national innovation systems are generally better organized and structured to attract funding for R&D, as well as the implementation of technological innovations. Because a national innovation system is part of government policy, it is more likely to have the backing of the government, international funding agencies, like the World Bank and the International Monetary Fund (IMF), as well as the industrial sector, than a local innovation system.

Advantages of Local Innovation Systems over National Innovation Systems

Local innovation systems tend to be more ‘in tune’ with local community demands, because they are largely community driven (Schlapfer and Marinova, 2001). Unlike national innovation systems, these localised systems may lack planning, and their innovations more often than not lack the technological sophistication and the financial backing of the national and supranational systems. Because they tend to have a better understanding of the local community and its specific knowledge, needs and desires; and are

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by their very nature a part of the community; they are in a more favourable position to provide the community with the most suitable technology. In the less developed world, or remote communities in the developed world, where technology transfer takes place, it is imperative to consider the notion that to invest in technology transfer without considering the local context is more likely to result in failure. That is why it can be argued that systems of innovation, in order to ensure success, have to be compatible with local sources of knowledge. In other words, it is far easier for outside investors to inject capital into a proposed regional enterprise project, than it is to adapt the technological innovation to the local context, i.e. local expectations and conditions. Without the technological infrastructure in place, suitable to local conditions, the capacity for innovation will be reduced and as a result the risks and the cost of innovation are going to be far greater. While supranational and national systems of innovation are responding to government policy, local innovation systems are more specifically ‘localised’ systems that evolve not so much from government policy, but rather from a specific need of a specific community, and are thus more likely to take local considerations into account. Perhaps most important of all, they will ultimately have to answer to the community for their actions. Community participation is an important aspect of local innovation systems. Grass roots involvement in the implementation of transferred or locally developed technology is a very significant element of the process. Even in the case of failure, the community would have gone through an active process of learning. For a system of innovation to be effective, a link between government, academia, industry and the people needs to be established. Without a government policy to stimulate innovation, national and local innovation systems will find it difficult to be effective. Without scientific research, technological innovation is unable to prosper. Without industry, providing the infrastructure and a proportion of the capital, innovation cannot take place. Without on the one hand consulting with, and on the other educating the people, technological innovation will be unsuccessful. In an ideal situation, government policy on innovation and localised systems of innovation would work in unison to remove barriers and promote the transformation of technological and scientific advances into better economic performance and improved living conditions.

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Does Innovation Play a Key Role in Regional Convergence: Evidence from the EU

Vilaseca et al. (2003) reviewed whether innovation is a critical issue for regional convergence among European regions: “Most of the economic research on growth and convergence points out that regional inequalities in income levels within the European Union has to do with the differences in regional productivity. The literature on economic growth highlights that innovative effort is one of the main decision factors on the evolution of productivity. We analyse whether regional differences in innovation are the reason for the persistent disparity in incomes, as it was shown in Fagerberg and Verspagen (1996) and Fagerberg et al. (1997). The influence of technological capital on productivity growth can be explained, as it is pointed out in Nadiri (1993), by the effects of the accumulation of innovative effort on productivity. In this sense, the accumulation of R+D investments is considered as other input in the production function. As it is shown in Crespo & Velázquez (1999), the influence of the investment in R&D on the evolution of productivity is positively related to the level of technological capital accumulated. Therefore, the challenge for competitiveness has changed. As Porter and Stern (2001) point out “the challenges of a decade ago were to restructure, lower cost and raise quality (…) but today producing standard products using standard methods will not sustain competitive advantage”. Regions and the companies located inside them have to be able to innovate at the “global frontier”. The R+D investments have encouraged the increase of the scientific and technological knowledge and have reinforced their application to the economic activity. We can state that innovation has a positive effect on the growth of productivity and on competition. The most advanced economies exhibit the highest levels in private and public R+D investment. Moreover, the strong polarisation on the R&D effort at an international level shows that the technological investment benefits from increasing returns to sacale and, as a consequence, there can be significant differences in the efficiency of the R+D expenses across countries and also across regions. In fact, analysing the consequences of innovation has been a constant topic of interest for backward regions, mainly characterized by its specialisation in activities with mature technologies and penalized by the loss of competitiveness associated to them. Furthermore, several studies (Audretsch, 1998; Porter & Stern, 2001) show an apparently paradoxical event: while one of the most important effects of globalisation is the global extent of the market, the local aspects are significantly more fundamental for the international competitiveness. Why is the geographical proximity so significant if the transportation and communication costs have diminished considerablely? The role of innovation solves this apparent enigma. Meanwhile geography is very important for the innovation process, the innovation is very decisive in the international competitive advantage of firms, regions or countries. The combination between the economic globalisation and the ICT revolution demands a constant effort in innovation from firms. As Audretsch (1998) points out, only the economic activities based on the continuous application of the “new knowledge” will offer new opportunities for high-wage employment. As a consequence, the investigation of the regional innovation process will probably guarantee taking a look on the two determining factors of regional convergence in Europe: productivity and employment differences.” In a sample consisting of 91 European regions chosen from twelve different countries, Vilaseca et al. (2003) linked the evolution of the labour productivity in some European regions to the innovation intensity and to some other determining factors, like the sectoral structure, the agglomeration effects or the specific regional conditions: “The econometric treatment of the data helps us to understand the impact of innovation. In particular, the results confirm that labour productivity has evolved, between 1990 and 1998 more positively in those regions that present a higher degree of R&D investment. In this sense,

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innovation is a determining factor in regional productivity growth. At the same time, the effort to incorporate innovations linked to ICT or to other knowledge-intensive activities has also a favourable effect on regional convergence. Furthermore, the influence of the technological diffusion could even be more significant because the catch-up effect within European regions seems to be extremely important. In fact, technological diffusion explains approximately the 40% of the convergence experienced in the mentioned period. Moreover, we identify positive complementarities between the different types of R&D investment. The public effort in R&D has a greater impact on productivity in those regions that sustain high levels of innovation in their business sector. On the other hand, both the level of urbanization and the regional productive diversity justify the considerable repercussion of location aspects on the ability of a region to increase its productivity level. The size of the local demand and a wide range of economic activities seem to have a positive influence in the regional productivity. As a consequence, we can infer that it is possible to influence productivity through territorial external economies. Therefore, the regional convergence processes would be better understood if the specific aspects of each region were taken into account. In any case, the results should be interpreted carefully, given the restrictive characteristics of the selected sample, the specification of the model, the lack of available data. Innovation is the main factor affecting regional convergence. The capability of technological policy to influence the innovation processes depends basically on the quantity of financial resources but also on a precise knowledge of the firm’s needs. As it was pointed out by Kline and Rosenberg (1986), the innovation process is an uncertain and complex process. In spite of these difficulties, governments and institutions should play a positive role in both the external support of these activities and the protection of the firm environment, since the innovative activity is only possible when high-quality labour and high-quality education institutions are available.”

2.6.5

Characteristics of Successful Regions

Several studies have tried to analyse the reasons behind why regions such as the Italian industrial districts, entrepreneurial districts such as Gnosjö/Anderstorp in Sweden, Osterbothnia in Finland, Sunnmöre in Norway, Badem-Wurtemberg in Germany or Mondragon in Spain, or high-tech regions such as Silicon Valley and Route 128 seem to manage better than other regions (cf. Piore & Sabel 1984, Porter 1990, Johannisson 1994, or Saxenian 1994). Most of these studies have addressed established ‘success stories’ only. Considerably less salient are efforts to compare successful and less successful regions, where Putnam’s (1993) study of the role of social capital in Northern versus Southern Italy maybe is the most cited. From these and other studies we may conclude some significant features of dynamic regions: They are characterised by an endogenous worldview, implying that people in the region feel that they themselves have the capability of having impact on its region’s development (in contrast to regions with an exogenous view, where ‘the keys to the future’ is in the hands of external forces such as the national government or larger corporations located outside the region itself); - They have a specific local culture characterised by entrepreneurship and a willingness to innovate, normally related to a high degree of self-employment; - They possess and exploit a specific local/regional competence, normally in a specific line of business or technology; - The companies in the region operate in clusters or systems involving a high degree of division of labour and supported by e.g. educational investments from the public sector in its specific area of knowledge; and - Division of labour involves a significant degree of co-operation in local networks, but – as Porter (1998) and others have underlined – also includes a significant degree of local

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rivalry. Dynamic regions hence have the capability to combine co-operation and rivalry/competition, fostering a favourable local environment for innovation. Ylinenpää, H. (2001) discusses this in particular: “This list of characteristics may give the understanding that successful dynamic regions are some kind of local isolates ‘cultivating their own culture’ and ‘minding their own businesses’. This is however not the case. As revealed in several studies, dynamic local regions are normally significant players in a considerably wider context, and normally operate in an international market. Many dynamic regions here benefit from ‘locomotive companies’ located to the market-end of a regional or local value-processing chain. These often larger corporations serve as an interface to the outer world, introducing new market requirements and new technology to their (smaller) local partners. They also serve as role models for growth and future development of their smaller partnering companies. Especially in high-tech regions, locomotive companies such as Hewlett Packard and Digital Equipment have had an important locomotive function for the development of the region in which they operate (Silicon Valley and Route 128 respectively). Other regions, such as many Italian industrial districts or the GGVV- region1 in Sweden, operate and develop without such an obvious locomotive function, instead relying on their (normally) smaller firms themselves to operate in an international market.”

2.7

Alternative Innovation System Approaches to Economic Growth

Apart from the National Innovation System approach there are several other innovation system approaches to explain economic growth. The purpose of this section is to mention several other innovations systems approaches. The analysis of the innovation systems approaches in this section briefly deals with the type of questions they seek to address, their key concepts, and some notes on their empirical support. Much of the underlying theoretical background behind each theory (e.g. neo-classical growth theory, evolutionary-institutional economics etc) has been discussed in further detail in Appendix A. This analysis shows that the individual approaches may contribute to analyse important facets of national socio-economic activities. However, no single approach is readily applicable for analysing national activitities without major modifications.

2.7.1

The Linear Model

Abrunhosa (2003) discusses the linear model: “The innovation concept has suffered transformations due to the evolution of the models that have the purpose of better understanding the innovation process. The emphasis given to the isolated innovative act was substituted for the complex social mechanisms underlying the production of new products and processes. Since World War II the linear model was the generally accepted model. In this model, new technology is assumed to start with basic research and move through applied research, invention, commercial market testing, and ultimately to diffusion. Innovations are considered to be the result of a linear process made up of different stages that take place in a sequential, hierarchical and one-way order. With the view that scientific discovery is the only source of innovation, scientific and technological policies were directed to the support of R&D. This linear way (pipe-line type, in the words of Caraça, 1993) of explaining innovation leads to “technology-push” and “market-pull” (or “demand-pull”) models. For the technology-push model the autonomous advances of science and the technological capacity are the main determinants of innovation. This approach ignores the importance and influence of institutions and other factors belonging, or not, to the market on innovation process. This model also ignores the economic factors and gives a linear and one-way-direction explanation to the relationship between science-technology-production. The basic scientific knowledge appears as determined in an exogenous

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way and no effort is made to relate it with the evolution of technologies or with firms, the market and institutions (OECD, 1992). The demand-pull model considers market forces as the origin of innovations. In this model, the variations of demand, costs, prices and profit opportunities influence firms and its innovation activities. This approach has a mechanical vision of the innovation process. Moreover, it defines the economic environment in a restricted form, in terms of markets represented by the variations of prices, costs and profits and in terms of the users needs (Mowery and Rosenberg, 1979). The adoption of this linear concept of innovation could lead to the conclusion that high investments in R&D would have positive consequences in terms of productivity and growth. However, during the 70’s and 80’s, the emergence of new and important technologies was followed by a reduction in productivity in the majority of the OECD countries (OECD, 1991: 77). The apparent contradiction between these facts was known as the productivity paradox. The Green Book on Innovation (European Commission, 1995) also relates this paradox for the European countries.”

2.7.2

The “Chain-Linked Model” (Interactive Model)

Abrunhosa (2003) went on to discuss the “chain-linked” model: “Kline and Rosenberg (1986) criticised in detail the linear model. For these authors, the linear model distorts the nature of the innovation process in several ways, especially because it considers R&D as the only source of innovation and since it ignores important feedback loops and interactions among the distinct stages of the innovation process. Innovation is considered a complex process where there is interaction between firms, organisations of the education system, of the scientific and technological system, and where innovative activities influence and are influenced by the market. This model re-evaluates the importance of science and research in the innovation process. It attributes to firms a central position in the innovation process and considers that design is in the origin of the majority of innovations. This model gives a great emphasis to interactions between the different phases of the linear model and between technological system and each stage of the process. The recognition of interactions and interdependencies between the different components of the innovation process, the complexity and uncertainty of the process, made the linear model inadequate to explain what is involved in the innovation process and unsuitable as a support for the decision-maker. However, there are still separate policies for research, education, innovation, industry, commerce, competition, etc. Taking into account the complexity and multiplicity of elements involved in the innovation process and the importance that the production, diffusion and adoption of innovation/knowledge have for growth and development, a coordination and integration of policies (Conceição, Heitor, Gibson and Shariq, 1998, even refer a “policy portfolio”) would be important for an efficient innovation policy. The national innovation systems approach strongly emphasizes this idea.”

2.7.3

Technological Systems

The theory of technological systems grew out of the shortcomings of the earlier theories. For example, the ‘linear model’ and ‘chain-linked model’ theories proved unsatisfactory in explaining differences in economic growth between countries (Fridh, 2000). Furthermore, the early theories do not consider institutions as an important variable nor do they consider innovations as endogenous i.e. developing within the theoretical framework. The Technological System (TS) focuses on a particular generic technology rather than on an industry. This implies that there are several TS in each industry and that national borders are not necessarily the boundaries of the systems. The different Technological Systems develop over time, and are therefore best understood through longitudinal studies. The focus, as has been recognised above, is on the technologies (as inputs) rather than on industries or products. Technological systems consist of dynamic knowledge and competence networks embedded in an institutional infrastructure. These networks are linked by the flow of information. The presence of

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entrepreneurship and sufficient critical mass may transform the network into an innovative "development block", but this development block also depends on economic competence and the institutional infrastructure. Economic competence, which appears to be crucial for the prosperity of any technological or innovation system, can be defined as the use of knowledge and information to identify, exploit and expand business opportunities. But important institutional arrangements such as capital markets, fiscal policy, public procurement, IPR arrangements, labour regulations and the production and distribution of information may affect the performance of a technological system as well. According to Carlsson (1997) there are four underlying assumptions, when you deal with the concept of a Technological System: - The system as a whole is the primary focus for the analysis; - The system is not static. It evolves over time and it is therefore important to consider the dynamics; - The global technological opportunities that you can think of are more or less unlimited; - People are not rational, in the sense that every body has perfect information. People are bounded rational, i.e. people have limited knowledge and information capabilities. The theory of technological systems differs from the National Systems of Innovation conceptual framework in four main ways: (i) The focus is on specific techno-industrial areas and not on a broad view of all the components of the national system. (ii) The boundaries are defined by the technology, not by geographic boundaries. (iii) The scope is limited to microeconomic considerations. Although institutional infrastructure is considered as influencing the performance of a technological system, it is not a focal point. (iv) Attention focuses on the application of knowledge and not on the generation and diffusion of knowledge. This body of literature, which mainly focuses on the adoption and use of new technology, is useful in identifying the significance to innovation of individuals' and organisations' economic competence. Economic competence is not restricted to the employment of R&D staff. It considers the whole structure for hiring and employing management staff as well as qualified scientists and engineers. The main way to measure technological systems in a market economy is in terms of their market impact. Indicators of economic competence are difficult to measure, and are mainly retrospective and related to outcomes in terms of financial performance or number of patents issued. It is necessary to be particularly cautious about financial indicators in the biotechnology sector, because of the length of time needed to take products to market; most biotechnology firms have no products on the market. The only possible indicators to use are number of employees, growth, R&D expenditure and change over time, but such indicators are not relevant to new start-up firms. Several gaps in this literature were also thought to be problematic, especially in terms of biotechnology. For instance, the institutional infrastructure, the ability to raise finance and the entrepreneurial culture - the propensity to set up spin-off firms, take out and license patents etc. – are neglected but these elements are central to the sector. In addition, technological systems overlook notions of social or public competence, or the role of public sector education and research. Higher education may either foster or inhibit entrepreneurial attitudes among scientists and a slow-down in the production of PhD level scientists may lead to a slow-down of creativity in the field of life sciences.

2.7.4

Socio-Technical Networks

The development of theories of socio-technical networks includes a diversity of attempts to integrate approaches from both economics and sociology to explain the organisation of socioeconomic relationships. The economic network approach focuses mainly on relationships for economic exchange and technological collaboration and on their organisation, but also takes account of the social and cultural settings within which firms operate. The sociological approach is concerned with the relationship between society and technology, and contains a lively debate on whether technology is constructed by social

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processes, or whether society is shaped by technology and technological change. Diversity between and within these approaches has led to a large pool of theoretical concepts for networks and the role of technology. All agree that networks are the result of a dynamic, evolutionary process which can be best understood from a longitudinal perspective. The concepts also have many other common elements: - Social elements (individuals, public and private organisations) and their interactions within a specific context; - Economic elements (particularly interactions/transactions to transform knowledge and resources to make profits); - Technological elements (technology, technological change to gain competitive advantage); - Social-technological element (the way in which society and technology influence eachother);and - Knowledge elements (learning capabilities to achieve technological change, and the carriers of knowledge). Many of these elements are shared by the NIS theoretical approach. However, NIS focuses on all the networks within a specific country, and socio-technical networks may go beyond national borders. Secondly NIS focuses mainly on policy and factors within an economy and socio-technical networks focus on an analysis of the construction of networks. Technological systems and industrial network theory also embody the main elements of socio-technical networks and explain how new socio-technical networks emerge. Some authors see a direct relationship between these theories. The main difference appears to be the motive for using these theories. The former focuses on economic development and the latter on identifying the agent(s) responsible for shaping technology and technological change. The main contribution of this body of literature is thought to be the idea of evolution of socioeconomic networks and of social actors. In particular, identifying and defining the main national actors and their relationships at a sectoral level is particularly valuable. Confusion in the use of the terminology "organisation" and "institution" in socio-technical networks was clarified to explain that institutions mainly refer to rules, regulations and norms, whereas actors refer to organisations.

2.7.5

Competence Blocs

Eliasson (2000) suggests that the competence block approach is an evolutionary neo-Schumpeterian theory that explains economic transformation and growth from the consumer side (Carlsson and Eliasson 1996, Eliasson and Eliasson 2002, Carlsson et al 2002). The competence bloc approach draws on a number of antecedent studies and research disciplines, e.g., venture capital studies, evolutionary economics14 and development blocks. It also claims to provide an economic dimension to the technological systems approach (Carlsson et al 2002a). To begin with, a main idea is that all of these need to be present for successful sales to take place. Then, competence blocs are defined from the point of view of users and not from the point of view of producers. To briefly explain what this means, in simplistic terms consumers want or need functionalities. Producers can deliver these functionalities in terms of products or services to the consumers (users). A competence bloc consists of all actors involved in enabling the production and delivery of these products or services to the consumers. As a consequence, the competence bloc approach stresses that consumers do not really want medicine (even if they might need it), they want good health. Hence, a competence bloc may consist of the entire ‘health delivery system’ including for example hospitals, pharmaceutical companies, and the tourism and fitness industries. As such, there may or may not be relations between these quite different types of actors. However, the competence bloc approach aims to explain the delivery of fulfilled services to the consumer. In a competence bloc there is a constant experimentation in bringing forth new goods. Because of the multitude of actors, it is quite evident that this is a highly non-linear process as to what is developed and what is selected in the competence bloc. One important thing to note is that there is no technological bias

14

See Appendix A.1.6

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in the competence approach from an economic perspective since the choice of technologies is made by the consumers (or users). The latest version of the technological systems approach (Carlsson ed. 2002), claims that the competence bloc is the ‘economic dimension’ in technological systems. Of course, there is not a one-to-one relation between technological systems and competence blocs. A given technological system may belong to several competence blocs, and vice versa. There are three parts to the competence bloc approach. These are: 1. The six ‘actors’ of the competence bloc. 2. The criteria for the formation of a competence bloc. 3. How economic change takes place. First, the competence bloc is defined as consisting of six factors, which Eliasson denotes as ‘actors.’ These actors are functionally related through their activities concerning successive transformation and refinement of ‘bundles of functionally related products and services’. The six actors are customers, innovators, entrepreneurs, venture capitalists, exit markets (for the venture capitalists and industrialists) and industrialists.

Table 5 The six ‘actors’ in the competence bloc approach The definition of the competence bloc is the starting point from the point of view of consumers, with other actors in the competence bloc serving the “wish” of the consumers. Industry thus serves this need and consequently any definition of industry is broad and not defined in terms of particular products or services. The competence bloc approach does not provide a definition, nor does it study the knowledgebase needed to serve consumer demand. The competence bloc is made up a range of heterogenous actors, performing activities that fulfil consumer wishes. To elaborate, for actors to make the necessary transactions, it is necessary to create and produce the product or service. This implies the need for an innovator, entrepreneur and industrialist (although the precise definition of these actors appears arbitrary, since, for example, they do not necessarily imply different actors). Second, a competence bloc is formed by ‘completeness’. This means that for a competence bloc to be formed, three things are needed: 1. Competence in all the six stages.

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2. Incentives to perform activities; these are spurred by completeness of the competence bloc (i.e. all stages need to be fulfilled). 3. Competition within the competence bloc. Third, Eliasson and others have demonstrated that economic changes take place in parallel, may be nonsequential, disruptive and interdependent. In other words, in economic systems there are simultaneously mechanisms of generation and selection. The coupling of the activities of the six steps of the competence bloc is said to ‘create increasing returns to innovative and entrepreneurial search’. Under this scenario, economic change takes place through entry, reorganisation, rationalisation or exit. Whilst the competence bloc approach makes several important points, it suffers from several shortcomings. For example, Eliasson claims that competent customers are always present in innovative and advanced societies. Eliasson’s ubiquitous concept ‘competent’ is not defined and does not seem to be contrasted e.g. with ‘incompetent’. The concept of competence seems only to work as an ex-post variable. The reason is that while competence of actors are claimed to explain the quality of the selection through the six stages of the competence bloc, this begs the question whether or not failed attempts - for any reason - depend on whether one or more of the actors may have been incompetent. One major problem here is that it is unclear of what role uncertainty plays. Uncertainty may lead to competent actors failing. The competence bloc approach is ex-post in the sense that it only deals with goods (products and services) that are transacted, and thus have economic value. This leads to a ‘success bias’ in focus. This also means that it is less useful as an analytical tool in the early stages of, for example, an industry. A policy relevant analysis is relatively straight forward given that the basic premise of the competence bloc approach is that the most important part is the ‘closure’ of the system. In other words, policy implications are found from analysing completeness. The competence bloc works as a system when all of the six factors are present and they are tightly coupled. Unfortunately, it is not obvious that all factors need to be engaged equally, which ones are the most important, and if it is enough to have closure of the competence bloc for it to work. The advantage of the model-based design of the competence bloc is that it is easy (in principle) to analyse the set-up of various industries. The disadvantage is that it may be too stylistic and superficial to permit meaningful conclusions to be drawn. There seem to be no systematic empirical studies that test the foundations of the competence bloc approach. However, the brief case studies presented by Eliasson (2000) are interesting, and the evidence presented on the model is intriguing. The empirical case studies include three relatively new industries: computing & communications, financial services, biotechnology and health care. Also considered are 'mature' and 'potential crisis industries': engineering, and education and research. In these studies Eliasson points out several systematic flaws in the Swedish system as compared to the US system, in relation to Sweden's apparent inability to remove institutional barriers. He explicitly points to the fact that these are government failures rather than market nor institutional failures. To summarize, the advantages of the competence bloc approach are: 1. Starts from the side of consumer. 2. Provides the ability to study large parts of the economic system in a relatively systematic way. 3. Provides guidelines for policy in relation to the completeness of the competence bloc.

2.7.6

Relationship between the Theories

The NIS, technological systems and social-technical bodies of literature have significant overlaps, but also serve to complement each other in identifying important additional features which may affect the process of technological change. The NIS approach is a macro-level concept, whereas the technological systems approach focuses at the meso- and micro-level on specific techno-industrial areas (see Table 6).

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Socio-technical approaches also focus on the meso- and micro-level but make a significant contribution in emphasising public or private social actors, and intermediaries in the network. Analysis of controversies can help identify the actors involved. The broad NIS approach allows every element to be included in the system. One could argue then that it cannot truly be considered a "system" because it does not differentiate between the system and the environment to which it reacts. In part this arises because the NIS is dynamic and the elements influencing change become incorporated within the system over time. However, these elements may vary from sector to sector and from country to country.

At the micro level, the focus is on the internal capabilities of the firm and on the links surrounding one or a few firms, and their knowledge relationships with other firms and with nonmarket institutions in the innovation system are examined, with a view to identifying unsatisfactory links in the value chain. Such analysis is most relevant to subject firms and is usually carried out by consulting firms, but it can also enrich policy makers’ understanding when its findings are adequately related to broader issues. At the meso level, the focus is on knowledge links among interacting firms with common characteristics, using three main clustering approaches: sectoral, spatial and functional. A sectoral (or industrial) cluster includes suppliers, research and training institutes, markets, transportation, and specialised government agencies, finance or insurance that are organised around a common knowledge base. Analysis of regional clusters emphasises local factors behind highly competitive geographic agglomerations of knowledge-intensive activities. Functional cluster analysis uses statistical techniques to identify groups of firms that share certain characteristics (e.g. a common innovation style or specific type of external linkages). At the macro level, the focus is on the use of two approaches: macro-clustering and functional analysis of knowledge flows. Macro-clustering sees the economy as a network of interlinked sectoral clusters. Functional analysis sees the economy as networks of institutions and maps knowledge interactions among and between them. This involves the measurement of five types of knowledge flows: i) interactions among enterprises; ii) interactions among enterprises, universities and public research institutes, including joint research, co-patenting, co-publications and more informal linkages; iii) other innovation supporting institutional interactions, such as innovation funding, technical training, research and engineering facilities, market services, etc.; iv) technology diffusion, including industry adoption rates for new technologies and diffusion through machinery and equipment; v) personnel mobility, focusing on the movement of technical personnel within and between the public and private sectors. Table 6 Levels of NIS Analysis (Micro, Meso and Macro)

2.8

Attempts to Operationalize the National Innovation Systems Approaches

The OECD has had a leading role trying to assemble indicators to measure and ‘evaluate’ nations from a national innovation systems perspective (Balaguer and Holmén, 2003). The first attempt, “Managing National Innovation Systems”, use a number of publicly available indicators to compare countries in terms of knowledge flows and interactions in nations (OECD 1999). The report use indicators covering scientific, technological, and trade specialisation, and the productivity growth in nations over time15. Furthermore, a description of institutional profiles of countries is made. This refers to flow charts of public organisations and agencies. Finally, an assessment of linkages within the innovation system is addressed by looking at links between patents and scientific publications.

15

Several of these indicators are used in Section 3.4 when comparing the Finnish, Swedish and Australian NISs.

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The report Managing National Innovation Systems show some important characteristics of the performance and specialisation of countries. However, there are several shortcomings that remain to be overcome. For one, the analysis is static and says nothing about why particular countries have followed particular patterns of specialisation or why productivity growth differs. Furthermore, the analysis is largely of a ‘component’ type instead of being systemic. In particular, the study of institutional profiles is very much a description of the bureaucracy and public agencies, showing some of the mechanisms and hierarchies ruling the policy decision making process. What this means for the workings of the nation is not shown. The basic conclusions from the series of works conducted in phase one of this project are: i) Innovations systems are different and there is some data (i.e. specialisation) supporting this; and ii) Institutions (supposedly understood as public agencies) matter for innovations. The lack of data showing the real workings of innovation show the difficulties of “measuring” national innovation systems (Balaguer and Holmén, 2003). The second work report “Boosting innovation: the cluster approach” is a collection of works dealing with different aspects of clusters in different countries. In the second phase of the project industrial clusters were studied. The rationale for this report is to show the importance of cluster formation and cluster development as an important policy tool for promoting innovation. The study assumes that clusters are important components in national innovation systems. The volume contains a number of papers that deal with methodologies of cluster identification and the importance of clusters for the national economy (for example: De Bresson and Hu, 1999; Hauknes, 1999; Marceu, 1999) However, the report does not show why and how clusters can explain economic performance in countries. Note that definitions of clusters vary and relating the national innovation system to clusters is done on an ad-hoc basis. The identification of clusters, for example, using input-output analysis says something about the economic structure and the size of user-producer relationships but it poorly reflects innovation and change (Balaguer and Holmén, 2003). In Appendix E we present a more detailed discussion on the measurement and operationalisation of innovation systems both national and regional levels.

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3 COMPARING EXPERIENCES FROM FINLAND, SWEDEN AND AUSTRALIA

In section 2 we covered the theoretical frameworks of innovation systems particularly on a national level through the National Innovation System approach. In this section, we compare the experiences made by Finland, Sweden and Australia with the intent of inferring lessons from the Nordic countries onto the Australian case. Although we highlighted several limitations of the NIS approach, we continue to use the approach to compare the experiences Finland, Sweden and Australia have had in developing their economies, as it is by far the most commonly used approach in the academic and practitioner circuit. This allows for a richer narrative of the experiences which will hopefully provide the reader constructive insights and lessons. The information on the Finnish and Swedish innovation system policies and programs outlined below have been included to provide examples of some of the industry development mechanisms used by each country. The broad objectives and the strategies of the countries referred to below are remarkably similar. Even the political rhetoric is similar. All are attempting to introduce innovative approaches to industry development, to facilitate innovation and commercialise research. It should be noted that the summaries are not intended to represent a comprehensive analysis of the policies and programs adopted by each country, rather provide an informative outline.

Figure 2 Basic information about Finland and Sweden

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3.1 3.1.1

Finland Finland’s National Innovation System - Background

Finland suffered from a severe economic recession at the beginning of the 1990s. Many large manufacturing low-technology industrial sectors had difficult times and unemployment rose to nearly 20% in 1994. The country recovered quickly from the recession leading to structural changes in the Finnish industry and the creation of a strong economy that is now rated to be among the most competitive in the world. Technology and innovation are acknowledged to have been the most important contributing factors to this impressive development. The success of Finland during the last few years has partly been due to its efficient innovation policy that clearly focused on increasing R&D expenditures and supporting knowledge society initiatives. In that sense, the deep recession had a positive impact because it enabled both Finnish industry and government to initiate drastic reforms. Despite the strong cuts in other parts of the public sector, technology policy was felt to be the major driving force toward better times.

3.1.2

Institutional Profile of Finland’s National Innovation System

Finland was among the first countries to adopt the concept of ’National Innovation System’ as a basis for its technology and innovation policy. Figure 3 and Figure 4 depict the key organisations within the Finnish NIS.

Figure 3 Institutional Profile of Finland's NIS (source: www.research.fi)

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Figure 4 Key Flows between Actors of Finland's NIS (source: OECD, 1999) Key organisations in the system include: Academy of Finland; National Technology Agency, TEKES; Public R&D organisations; technology transfer agencies; and capital providers. Their basic functions are described below:

Academy of Finland

The main function of the Academy of Finland is to enhance the quality and prestige of basic research in Finland by long-term selective research funding allocated on a competitive basis, by systematic evaluation and by influencing science policy. TEKES

TEKES, the National Technology Agency of Finland, is the principal organisation for implementing technology policy and is subordinate to the Ministry of Trade and Industry. Founded in 1983, its function is to promote the technological competitiveness of industry. It seeks to expand and diversify industrial production and exports and to increase social well-being. TEKES supports companies engaged in riskbearing product development projects with grants and loans, and finances the projects of research institutes and universities in applied technical research. TEKES launches, co-ordinates and funds technology programs to be implemented together with companies, research institutes, and universities and has expertise abroad including co-ordinating international co-operation in research and technology. The funds for financing are from the state budget. Apart from funding R&D, TEKES is currently focusing on building bilateral technology co-operation with other nations such as the USA and Japan.

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Board DIRECTOR GENERAL Strategy

PROJECT FUNDING

Internal Audit COMMUNICATIONS

Funding for Feasibility Studies

Research Funding

Business R&D Funding

Customer Support Project and Document Services

Project Support

Business Economics

IMPACT ANALYSIS TECHNOLOGY Space Activities Information and Communications Bio- and Chemical Technology Product and Production Technology Energy, Environment and Construction Technology

INTERNATIONAL NETWORKS FINANCE AND ADMINISTRATION

Technology Units at 15 Regional Employment and Economic Development Centres

Technology TechnologyTeams Teams and andCrossCrosstechnological technological Groups Groups

Internationalisation Services European Activities

Finance Administration

Personnel IT

Legal Affairs

Overseas Offices Brussels San Jose Tokyo Washington, D.C.

Figure 5 Tekes, the National Technology Agency, Jan 2002

Finance knowledge infrastructure

Tekes funding of R&D projects

Lead, stimulate and catalyze technology development

Main role of Tekes and objectives of instruments

Create favourable legislative environment

Tekes influencing technology policy

Figure 6 Tekes and three roles of Government (Source: Rand Europe) Tekes assists companies in their search for ideas, the finalisation of business plans, and their quest to conduct meaningful and valuable research. Tekes adopts an open and proactive approach towards companies’ technology planning. Companies are encouraged to contact Tekes’ experts in the initial planning stages to formulate their research proposals with the aid of a dedicated Tekes expert. Tekes does not derive any financial profit from its endeavours, nor does it claim any intellectual proprietary rights, these stay strictly with the enterprise that Tekes is working with at that point in time. Completed project proposals are then evaluated internally by Tekes business and technology experts and then each project is designated a Tekes expert to assist with the project and monitor progress. Tekes constantly strives to investigate promising areas where extended effort could ultimately lead to greater success. In these cases Tekes implements a technology program specifically designed to gather the best players in the field to work with the intention of achieving a common goal.

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Steering • enterprices Public research projects Funding

Tekes •

Synergy Networking Part financing

preparing • co-ordinating • decision making

Grants Loans Capital loans

40 on-going programmes in 2003 with a total extent of EUR 1.3 billion a programme lasts typically 3–5 years annually 2000 company participations annually 800 research unit participations Tekes usually finances - 60–80 % of university projects - 25–50 % of company projects

Company R&D projects

Implementation of results of public research is based on parallel execution and networking with company projects.

Figure 7 TekesTechnology Programs

Public R&D organisations

The public R&D organisations include universities and polytechnics (of which there are 20 and 32 respectively), national research institutes and the Technical Research Centre of Finland (VTT). (VTT is the equivalent organisation to CSIRO in Australia.) The combined expenditure of these organisations is about 30% of the total national expenditure on R&D.

Private R&D organisations

The private sector's expenditure on R&D is approximately 2% of GDP and is growing. There are very strong linkages between the R&D efforts of business and universities and other public sector R&D groups.

Technology transfer

Most people in Finland live outside of the Helsinki (Finland’s capital) metropolitan area. The national innovation system has always had a strong focus on regional development through technology transfer. This can be seen, for example, in the development of 'Science Valley' in Kuopio (central Finland). Kuopio is a university city with 85,000 people and a recent track record of strong economic growth. The Kuopio Science Park hosts 70 companies, and has about 10,000 employees and university students. It focuses on IT, Mechanical Engineering and Materials Technology and Biotechnology, and Medicine. Kupio is known for drug design and animal biotechnology. The Head Office of one of the key capital providers, Kera Ltd (now part of Finnvera), is located in Kuopio. The City of Kuopio facilitates economic development; e.g. it fosters technology training, and makes significant investments in local firms.

Capital providers

In Finland there is a diverse range of capital providers for innovation, both private and public. These include SITRA (the Finnish National Fund for Research and Development), Start Fund of Kera Ltd, Hermia Ltd (an incubator and seed capital provider), Finnfund (which focuses on overseas joint ventures) and the Foundation for Finnish Inventions.

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The largest venture capital provider, SITRA, is an independent public fund established in 1967, and is responsible to the Finnish Parliament. Whereas the SITRA Supervisory Board comprises the Parliamentary Trustees of the Bank of Finland, SITRA's Board of Directors represents the Ministry of Finance, the Ministry of Trade and the Ministry of Education. SITRA's operations are mainly financed by income from endowment capital, and the return on the investment operations. SITRA provides: - capital for startup technology firms (SITRA is always a minority investor); - services to match SMEs with 'business angels'; - funds for research projects for existing companies, both large and small; - funds for training projects; - funds for technology transfer; and - funds for foreign venture capital funds. SITRA is currently a shareholder in over 90 companies.

Science and Technology Policy Council

One important feature of the Finnish NIS is the operation and role of Science and Technology Policy Council (STPC). Chaired by the Prime Minister, the STPC has several important facilitating roles in innovation policy making: it acts as a coordinating body between the ministries in their R&D issues, it provides a platform for policy discussion among ministers, industry, funding organisations, labour unions, universities and government officials. It is the role of the Council to define the over all guidelines for government R&D funding. Its permanent tasks include the development of innovation financing, its impacts and effectiveness; development of sectoral research and intersectoral co-operation and international S&T co-operation. Finnish Science and Technology Policy Council CHAIRMAN:

Prime Minister

MINISTERS:

Minister of Education and Science Minister of Trade and Industry Minister of Finance + 1-4 other ministers

APPOINTED MEMBERS:

National Technology Agency Academy of Finland Employers’ organisation Employees’ organisation Industry Universities + four other members

PERMANENT EXPERTS:

Ministry of Education Ministry of Trade and Industry Prime Minister’s Office

SECRETARIAT:

Chief Planning Officer, MTI Chief Planning Officer, MoE

Table 7 Members of the Science and Technology Policy Council of Finland Topical issues for the STPC in 2003 included: - Internationalisation of the Finnish innovation system; - The matching of education and research with industrial & societal needs; - Evaluation of public research structures; - The new challenges of the university system; and

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-

Participation principles for Mega Science.

Meets 2-4 times per year

Proposed statements

Meets monthly

Meets monthly

Minister of Education & Science Permanent Secretary of MoE + 6 Council members + 2 Experts

Minister of Trade and Industry Permanent Secretary of MTI + 6 Council members + 2 Experts

•Initiatives •Studies

•Initiatives •Study requests •Comments

•Initiatives •Study requests •Comments • CPO for Science policy • CPO for Technology policy

Figure 8 STPC Statements

Ministry of Education and Ministry of Trade and Industry

The organisations with primary responsibility for science and technology policy are the Ministry of Education and the Ministry of Trade and Industry. The Ministry of Education is in charge of matters relating to education and training, science policy, institutions of higher education, and the Academy of Finland. The Ministry of Trade and Industry deals with matters relating to industrial and technology policies, the National Technology Agency (Tekes) and the Technical Research Centre of Finland VTT. Nearly 80% of the government research funding is channelled through these two ministries.

3.1.3

Development of Science and Technology Policy in Finland

A Brief Prehistory of Finnish Science and Technology Policy

Up until the early years of the 20th century, Finland had only one university. It developed from Turku Academy, founded in 1640, and was transferred to Helsinki in the early 19th century. At the turn of the century the structure of higher learning began to develop rapidly. The Helsinki School of Economics and the Technical School of Helsinki were founded in 1911, and the former's Swedish-language counterpart four years later. The Swedish-language Åbo Akademi was established in 1917, and the University of Turku opened in 1920. To satisfy the needs of Northern Finland a university was established in Oulu in 1959. In the 1960s seven more universities were established. Currently, Finland has 20 universities - ten multidisciplinary institutions, six specialist institutions and four art academies - all of them run by the state and engaged in both education and research. Government research institutes as a whole have formed a significant component of Finland's research system. Important government research institutes (Geological Survey Centre of Finland, the Agricultural Research Centre, the State Forest Research Institute, the Water Research Institute, the Geodetic Institute, and the Meteorological Institute) date back to the 19th century or the early years of last century. The Technical Research Centre of Finland (VTT), which for decades has been the largest government research institute in Finland, was established in 1942, during World War II. Now the number of government research institutes is around 30, and they all report directly to their sectoral ministries. In Finland, government research institutes have been the key instrument of sectoral research or research serving the needs and activities of the ministries.

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Before the 1960s there were only a few separate agencies for the funding, planning and coordination of research in Finland. The Central Board of Sciences and Letters, which was established in the beginning of the 20th century, primarily handled a few matters relating to research carried out in the universities. In the 1950s "research policy" was represented by two state research councils, one for the humanities and the social sciences, and one for the natural sciences, and sporadically by a ministerial committee on science. In 1961 the number of research councils was raised to six (for the humanities, natural sciences, medicine, agriculture and forestry, technology, and social sciences). The Academy of Finland, established in 1946, was primarily a college of recognized scientists and artists of exceptional merit, composed of 12 academicians appointed for life. The Formation of Basic Structures in the 1960s and 1970s

In Finland, the institutionalization of science and technology policy began in the early 1960s, which was later than in larger and more developed OECD countries. In this phase, rational resource allocation and the role of R&D in economic growth replaced the Cold War and competition in military and space technologies as central issues. This was an appropriate starting point for a small country like Finland. During the other phases of the late 1960s and the early 1970s, the periods of disenchantment, and new social objectives as well as their erosion, Finland lived at the same pace as a number of other OECD countries. All in all, the 1960s were for Finland a decade of numerous institutional and organizational reforms in economic and social policy as well as in most other sectors of the public administration (Paavolainen, 1975, Immonen, 1995). The modernization of Finnish society was accelerated by positive economic development and changes in political power structures. The last-mentioned was the growing role of the parties of the Left (Social Democrats and Communists) and the beginning of Center-Left cooperation in the late 1960s. In Finland, the 1960s opened up a lot of opportunities for collective and private initiatives, and created new procedures for cooperation and competition. Actors and interest groups concerned with science and technology were particularly well prepared to make use of these new opportunities. Thus in a short period science and technology policy became a significant and widely accepted part of the Finnish "modernization project". The main reason for the rapid emergence of science and technology policy was economic. In the whole industrialized world, the early 1960s were an era of intensified internationalization and liberalization of trade. This placed new strains on Finland's production structure, which was one-sided (high dependence on the forest industry), and its level of technology, which was low compared with Finland's main competitors. Research and development was considered an important instrument of industrial renewal. Catching up with industrially and technologically more advanced countries, like Finland's neighbor Sweden, became the factor which significantly shaped Finnish activities and structures in science and technology for decades. The Keynesian growth policy, which had also gained a foothold in Finland, advocated government intervention in supporting and promoting the innovative activity of firms. Three important changes occurred in the institutions and organizations of Finland's science and technology policy in the 1960s. Firstly, the development of higher education in general played a significant role in the early years of science and technology policy. That paved the way for institutional and organizational changes outside the higher education system. The renewal of universities was started in the 1950s, and the development process continued throughout the 1960s and 1970s. There were three associated reasons for the central position of universities in the Finnish modernization process. One was a growing awareness of the importance of higher education and basic research for economic and industrial development, and accordingly, greater demand for employees with a university education. The second one was a regional dimension, i.e. political pressure to establish new universities outside the capital city of Helsinki. Several local and regional interests were working for more equal regional development of the institutions of higher education. The third reason was the fact that the large post-war generation began to reach maturity, and enlargement of the institutions of higher education was a social and political necessity. Secondly, a ministerial committee on science, the Science Policy Council, was established in 1963. The composition of the council has changed over the years, but in the beginning it was made up of the prime minister as a chairman, four other ministers, and the chairmen of the six research councils. The council was not at that time a coordination body for R&D throughout the state administration. Rather, it concentrated only on the development of research under the jurisdiction of the Ministry of Education.

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Gradually, its field of activities was extended to cover all ministries, and in addition to science, also technology. The council has not had R&D funds of its own and no direct administrative authority, but its opinions and recommendations have carried great weight. The most visible role of the council has been to initiate science and technology policy programs. One of the best known of these programs was "The Outline for Finnish Science Policy in the 1970s", published in 1973. The program set priority areas for R&D, and introduced an ambitious plan for increasing R&D financing. Thirdly, and perhaps most importantly, a significant reorganization took place in the Finnish science and technology administration, when new mechanisms for planning, coordination, and financing R&D were created. The most visible event in the reorganization was a reform of the research councils so that they might constitute a central body. This would be better able to direct R&D funds and to coordinate research across administrative boundaries than the old council system. The base of the new system was composed of the six research councils established in 1961. The conditions for science policy planning were improved by setting up a central board of research councils to develop and coordinate research irrespective of disciplinary boundaries. The reform included the establishment of new research posts, and what was particularly significant, new grants for project research. The name "Academy of Finland" was given to the new system. The reform of research councils prepared by the Ministry of Education was very much oriented towards the development of basic research carried out in the universities. At the same time, preparations had been started elsewhere with the aim of improving the conditions of industrial R&D, the activities of technical research institutes, and technical universities and faculties. A new fund under the authority of the Bank of Finland, the Finnish National Fund for Research and Development (Sitra), was established in 1967 to support industrial R&D. In addition, the Ministry of Trade and Industry began in 1968 to support the research and product development of firms, and it also received an additional appropriation for goaloriented technical research. Contrary to the reorganization of the research councils (the Academy of Finland), these were completely new measures to Finland, a fact which helped to implement them in a very short time. As both organization research and evolutionary theories have demonstrated, institutions and organizations display considerable diversity in approach and form in the initial stages of their life cycles (DiMaggio and Powell, 1983, Nelson and Winter, 1982). This is true of the early years of Finnish science and technology policy. In the mid-1960s there were several competing ideas and aspirations on how to organize the development of Finnish science and technology (Immonen, 1995). One was to build directly on the universities without a central national financing agency like the new Academy of Finland. Promoters of this model put more emphasis on the development of university education than on the development of university research. The representatives of government research institutes were working for a solution which would have strengthened their position in Finnish R&D and its coordination. This model involved an idea that universities should concentrate on education, and accordingly, the role of government research institutes in basic research should be increased. One alternative was to give more power to the old Academy of Finland, or to the Finnish elite of science. In addition, support was given to a model in which the Science Policy Council would be developed in the direction of a centralized science and technology agency independent of sectoral ministries. After a selection process lasting a couple of years, the result was a dualistic structure or a polarization into policy for science, on one hand, and policy for technology, on the other hand. The original idea of the early architects of science policy was to create the machinery mainly around the organizations for science (the Science Policy Council, the Ministry of Education, and the Academy of Finland). However, the interest groups behind technical and industrial R&D managed in a short time to organize countermeasures, as a consequence of which already in the 1960s the policy for technology gained a strong position in Finnish science and technology policy. Technology dominance gained further strength in the 1970s, 1980s and early 1990s. The main organization was comprised of four hierarchical levels. The Science Policy Council, chaired by the Prime Minister, became a new high-level political body for the formulation of science and technology policy guidelines. The Ministry of Education had special new coordinating tasks in science, and the Ministry of Trade and Industry in technology. Both of the ministries established new separate units for handling these new tasks. The third level was represented by new "national financing agencies", the new Academy of Finland for science, and for technology the new financing instruments of the Ministry of Trade and Industry along with the new organization Sitra. The fourth level comprised the universities, government research institutes, and companies, most of which experienced many kinds of organizational and functional changes in the early 1970s.

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As mentioned earlier, in the last years of the 1960s, questioning and challenging the prevailing idea of science and technology as absolute sources of social and economic welfare was characteristic of science and technology policy in a number of OECD countries. Science and technology were suddenly attacked jointly by advocates of conservative and radical viewpoints (Salomon, 1977). In Finland, the late 1960s and early 1970s were a period of student revolt, which had less impact on the development of science and technology policy than in other larger countries. The radical student movement dominated the life of university campuses in Finland in these years, but its focus was strongly on education and particularly on the reform of university administration on the basis of the "one man one vote" principle. This focus was largely due to the fact that science and technology policy was in its early phase of development. The volume of R&D in general was modest in Finland, the role of military or other prestige research was almost insignificant, and there were only weak links between industry and universities. Quite the contrary, in Finland the years of "questioning and challenge" were very much the years of "rationalization and planning". The economic and political climate at the turn of the 1970s was very favorable for planning in general. The Finnish science policy, led by the Science Policy Council and the Ministry of Education, and assisted by the Academy of Finland (research councils), was very active in implementing new planning mechanisms. Towards the end of the 1960s the research councils were encouraged to draw up science policy programs in their own sectors for the development of conditions favorable to science. Research work was to be taken into account in its entirety, regardless of whether it was being carried out in government research institutes, in the private sector, or at universities. However, the disciplinary-based system of the research councils, and even the other parts of the R&D system, were not well prepared for planning. The results of the first planning round fell short of expectations. The next planning round, which was initiated by the Central Board of the Research Councils and extended by the Science Policy Council, was very much influenced by the OECD. The science and technology policy program completed by the Central Board in 1972 examined the organization of scientific research, the grounds of national science policy, and the social significance of scientific research in line with the analysis, conclusions, and recommendations of the Brooks Report (OECD, 1971). Most of these ideas were incorporated into the first Finnish national science policy program, the program completed by the Science Policy Council in 1972. According to the program the major task of science policy is to estimate, on the basis of general social policy, the need for research in the various disciplines and to direct available resources on the basis of these estimates. The general priorities were further defined by identifying five areas of research in which "the need for information based on research is at present the most urgent and in which research work should be initiated primarily with public financing." The operationalization of the priority areas and the implementation of the research plans were delegated to the Academy of Finland. The program introduced the first Finnish plan for increasing the financing of R&D. This ambitious plan was to become one of the most visible activities and aspirations of the Science Policy Council and the Finnish science and technology policy in general. If this program of growth had been realized, it would have increased the GNP-share of R&D input from 0.9 percent in 1971 to 1.7 percent in 1980. It was a big disappointment to the Finnish R&D communities that the program was not implemented. In 1979 R&D expenditure amounted only to 1.1. percent of GNP, which was then one of the lowest figures among the OECD countries. Salomon points out in his analysis that the ideas of the Brooks Report for new social objectives of science and technology were broken in the midst by the 1973 oil crisis (Salomon, 1977). It was true also in Finland that the lively and visible programming of science policy brought in only limited, short-lived changes in universities and research institutes. Economic recession, followed by the oil crisis, was one obvious reason for the breakdown of the euphoria in science policy planning, but in Finland the planning process itself encountered many difficulties. The top-down planning with emphasis on social needs and an interdisciplinary approach was seen as a threat in disciplinary-oriented R&D communities. Science policy planning also aroused political tensions, which weakened the credibility of central planning procedures. As a result of all these developments the popularity of planning and the prominence of science policy orchestrated by the Ministry of Education declined significantly in Finland in the late 1970s. Strengthening of Technology Orientation in the 1980s

As described earlier, the late 1970s saw an explicit shift in the OECD countries from the promotion of science to the stimulation and support of industrial innovation. In particular, science and technology

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policy was actively focused on the development and application of new technologies, primarily information technology, materials technology and biotechnology. Encouraged by Japanese economic and technological success, governments became increasingly involved in planning, financing and managing large national programs in the new technologies. University-industry cooperation as well as interfirm cooperation were strongly intensified directly between R&D performers and in the execution of the national technology programs. This was very much the development path which Finland also adopted in the late 1970s. Active exploitation of the opportunities opened up by new technologies for the benefit of economic growth and employment became the core of the Finnish science and technology policy in the 1980s. If the earlier phase of science and technology policy had been characterized by the construction and renewal of the institutions and organizations of the R&D system, a distinctive feature of the new policy was increasing government involvement in the promotion of industrial innovation. A belief in rational policymaking came back, but science with social objectives was replaced by technology with competitiveness of industry as the main guideline. The factors behind the transition from research and science orientation to technology orientation were economic and social. The “oil crisis” of the mid-1970s also led to a slow-down in the rates of economic growth in Finland and to high levels of unemployment and inflation. The ambitious attempts to accelerate scientific and technological development did not succeed. These were the years of the “microelectronics revolution”, which was recognized as offering new productive and other opportunities, but which, it was feared, would exacerbate social problems in Finland. In particular, it was feared that increased use of automation in industry and services would cause mass unemployment and greater social inequality. A national consensus on the necessity for technological development and its basic objectives was reached between politicians, industrialists and trade unions. Finding ways out of the potential problems was submitted to a broadly based committee appointed by the government (Technology Committee, 1980). “Broadly based” meant experts representing political decision-makers, the government sector, employers, employees and researchers. The committee’s key conclusion was that not even rapid development of automation would place any restriction on social development in the 1980s. On the contrary, information technology and its application would be a resource opening up new opportunities. Indeed, the committee’s principal recommendations included the strengthening of science and technology policy both quantitatively (increased resources) and qualitatively (allocation of resources to the fields of high technology). The recommendations of the Technology Committee led to the formation of the National Technology Agency (Tekes) in 1983 after the Swedish model, the Board for Technical Development (STU, later NUTEK). Tekes became the key planner and executor of the new technology-oriented policy. The tasks formerly carried out by the Ministry of Trade and Industry (i.e. R&D loans and grants, appropriations for goal-oriented technical research) were assigned to Tekes. National technology programs, which had already proven their worth in countries such as Japan and Sweden, were developed to serve as a new and important instrument by which Tekes could control R&D activities. The first programs were focused on information technology. The national technology programs have been an important catalyst for national cooperation. An important new feature in these programs was that the earlier bilateral cooperation between universities and industry, and between technical research institutes and industry was transformed into multilateral national cooperation. Firms, research institutes and universities implement programs together. Cooperation other than that associated with the programs has also been expanded. In particular, this has involved cooperation between universities and firms. The programs are not generated by a centralized strategic planning mechanism. Initiatives for new programs come from universities, research institutes, firms, and industry associations. Tekes also became a national instrument to create the pre-requisites for the development of international co-operation. Finland’s participation in Eureka co-operation was one of the first steps taken. This program began in 1985, and from the very outset Finland has been one of Eureka’s most active members (Ormala et al. 1993). Tekes played an important role during the period when Finland was preparing for participation in the EU’s research framework program. EU research programs were opened up to the Finns, and to other EFTA countries, in 1987.

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To increase exports, broaden Finland’s industrial base, generate new jobs, expand welfare.

competitiveness profitability growth

Enterprises

new businesses new enterprises

projects and programmes international cooperation

direct social and environmental impacts

Research institutes and universities

Tekes provides expert services and R&D funding coordinates programmes

Figure 9 Impact of Tekes Activities (source: Tekes) Another significant change within national science and technology policy in the late 1980s was the creation of new programs and organizations associated with technology transfer, diffusion and commercialization. Nation-wide networks of technology parks and centers of expertise were set up in Finland. The technology parks have initiated spin-off projects and incubators. Technology transfer companies were established to commercialize the results generated in universities and research institutes. Public and private venture capital operations also increased, although the venture capital market in Finland is less developed than in many other European countries, not to mention in the United States. Some of these arrangements were created at the national level, but many came into being on the basis of local and regional initiatives, albeit with national funding. As a symbol of the technology orientation of the 1980s, the Science Policy Council was transformed in 1987 into the Science and Technology Policy Council. From a National Innovation System to a Knowledge-Based Society

Economic development in Finland in the 1980s was more robust than in most other industrialized countries (Vartia and Ylä-Anttila, 1996). The share of knowledge-intensive production grew, technical development was rapid, and productivity growth was faster than the average of the OECD countries. Whereas the total growth of the metals and engineering industry in the 1980s was 50 percent, the electronics industry grew by 150 percent. Consequently, the share of high-technology products in industrial exports rose from 4 percent in the early 1980s to 11 percent in 1990. Furthermore, Finland rose to become the world's biggest exporter of high-value paper products. The value-added of paper industry exports was considerably higher than that of Finland's rivals. Moreover, the growth rate of Finnish patenting in the United States up until the end of the 1980s was one of the fastest in the world. In this respect, Finland was outperformed only by Japan, South Korea and Taiwan. Finland was widely labeled "Japan of the North". However, the Finnish economy was suddenly plunged into an exceptionally severe crisis in the early 1990s. Finland’s gross domestic product declined 20 per cent in the years 1991-93, the stock market collapsed, the value of the Finnish markka plummeted almost 40 per cent from the level prevailing at the beginning of the decade, foreign debt and the budget deficit grew rapidly, unemployment approached 20 per cent at its height, and the country’s banking system was thrown into deep crisis. In just a few years Finland tumbled from being one of the richest countries in the world to below the average level of the industrialized countries.

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Finland recovered from the last recession almost as quickly and surprisingly as it had plunged into it (Pajarinen et al., 1998). This was largely achieved on the back of rapid growth in exports. At the end of the 1990s, exports accounted for a larger share of GDP than at any point in Finland’s economic history. Traditional industries such as paper, metals and engineering, and chemicals have all increased their exports, but the strongest growth has been in the ICT cluster. Today, the ICT cluster is by far the largest export industry and accounts for close to 30 percent of total manufacturing exports. The share almost tripled during the 1990s. In 1990 the share of the other major export sector, the paper industry, was some 30 percent. Nowadays it is less than one quarter. In its exports Finland is one of the countries most specialized in telecommunications equipment.

Fast Welfare

Environment Information and Communications

Forest Real Estate and Construction Chemical and Bio

Growth of global markets Energy Metal

Food

Decreasing

Finnish share of global markets

Slow Increasing

Figure 10 Dynamics of Finnish Industrial Clusters (source: Tekes) There is no doubt that a major part of the growth of the ICT cluster and Finnish industry in general is explained by one company, Nokia. Over the last five years, Nokia's value added and exports have increased at an average rate of 33 percent a year. The value-added of the electronics industry has increased by about 27 percent a year, mainly thanks to Nokia's rapid growth. As a result, Nokia's contribution to GDP growth in 1999 was as much as an entire percentage point. However, Nokia's direct impact on employment in Finland is actually relatively small. Nokia has slightly over 21 000 employees in Finland. This implies that Nokia accounts for 1.1 percent of total employment in Finland and for 5 percent of employment in industry. It has been estimated that in 1997, Nokia's contribution to the sales of the Finnish ICT cluster was 40 percent, and an even larger share (in the order of 80 percent) of cluster exports (Ali-Yrkkö et al., 2000).

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Figure 11 The Finnish IT Cluster An important milestone in the political formulation of the "new" science and technology policy was the 1990 review of the Science and Technology Policy Council (Science and Technology Policy Council, 1990). The report made the concept of a national innovation system an important instrument of Finland’s science and technology policy. It was a question of a direct Finnish application of the observations and conclusions made by evolutionary economists in the late 1980s. The Finnish application was developed after the publication of the pioneering book by Freeman (1987), but before the publication of the books by Lundvall et al. (1992) and Nelson (1993). Most of the influences came from the OECD's Technology and Economy Programme which had been launched in 1988 (OECD, 1992), and to which Freeman, Lundvall and Nelson, among others, contributed.

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1979 1982 1983 1984 1985 1986 1990 1991 1992 1993 1994 1995

1996 1997 1998 1999

National technology committee Council of State resolution on technology policy Founding of Tekes Technology programmes started EUREKA started OECD assessment of Finland's science and technology policy EU framework agreement on research co-operation Report of the technology programme committee Finland becomes a member of CERN Finland the chair country for EUREKA Founding of Finland's EU R&D secretariat Ministry of Trade and Industry: National industrial strategy EEA agreement intensified research co-operation with the EU Finland becomes a member of the EU Finland becomes a member of ESA Funding of energy technology transferred to Tekes Government decision to increase R&D funding Founding of Employment and Economic Development Centres Finland's R&D funding reaches 3% of GDP Finnish Presidency of the EU

Figure 12 Milestones of Finnish Technology Policy The transfer of knowledge to Finland and the Finnish application were made by the secretariat of the Science and Technology Policy Council. The Finnish interpretation of the concept "national innovation system" has stressed that a national innovation system is a whole set of factors influencing the development and utilization of new knowledge and know-how. The concept allows these factors and their development needs to be examined in aggregate. A national research system, along with education, form the intrinsic parts of a national system of innovation. The general atmosphere prevailing in society also has a profound influence on the production and application of new knowledge as well as on the level of interaction and cooperation between different actors. Internationalization influences the activities of an innovation system in many ways, but the internationalization process also emphasizes the need to improve conditions for creating innovations nationally. As mentioned earlier, these ideas were developed just before the recession, but they were relevant to the arguments for science and technology policy in recession years, too. This is a good example of how in public policy, solutions often come first and the problems later. In the mid-1990s, when recovery from the recession was already underway, another concept began to be integrated into that of the national innovation system: the knowledge-based society (Science and Technology Policy Council, 1996). This concept came from the OECD Jobs Study, an extensive program which had been launched in the early 1990s (OECD, 1994b, 1996 and 1998). The transfer of the concept and the Finnish application of “the knowledge-based society" were made by the secretariat of the Science and Technology Policy Council in a similar way as in the case of the national innovation system. In the case of Finland, the "knowledge-based society" was largely old wine in new bottles. The Finnish national strategy for the information society, which was adopted at the beginning of the 1995, rested on the same foundations. From the point of view of Finnish science and technology policy, the crucial aspects of the OECD approach were the stress put on learning and knowledge instead of information, and the linkage of employment and innovation policies. The latter was particularly important in Finland in the mid-1990s. The economy was growing quickly but the unemployment rate was still as high as 15 percent. The OECD recommendations adopted in Finland were based, on the one hand, on the observation that knowledgeintensive growth is of undeniable significance for the national economy and, on the other, on the experience that macroeconomic or labor market measures do not alone ensure adequate preconditions for knowledge-intensive growth. Above all, the promotion of knowledge-intensive growth requires various innovation policy measures relating to R&D, education, competitive conditions, laws and regulations for the protection of intellectual property, national and international cooperation networks, and technology transfer and exploitation.

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The unusually rapid development of Finland’s ICT industries at that time offered a positive environment for the implementation of measures drawn from the knowledge-based arguments. It was only natural that in policy objectives special attention was paid to the information communications technologies and more broadly to the competitiveness of the infrastructure necessary for the application of information technology and for the knowledge-based society. The most significant single act was the government's recommendation in 1996 to increase investments in R&D so that the GDP-share of R&D expenditure would rise to 2.9 percent by the year 1999. As a result of this decision, state funding for research rose in the years 1997-1999 by a total of FIM 1.5 billion (EUR 250 million), which has meant an increase of about 25 percent in the state’s annual research appropriations from the 1997 level. The funds necessary for these additional appropriations were obtained mainly from the partial privatization of state-owned companies. Billion euros 5,0 5.0

5.0

Enterprises Universities Public sector

4,5 4.5

4.4 3.9

4,0 4.0

3.3

3,5 3.5

2.9

3,0 3.0 2,5 2.5

2.2

2,0 2.0

1.5

1,5 1.5 1,0 1.0

1.7

1.8

1.1 0.9

0,5 0.5 0,0 0.0 1985 1987 1989 1991 1993 1995 1997 1998 1999 2000 2001 Est.

Figure 13 R&D in Finland, 1985-2001 (source: Statistics Finland)

Private

R&D at companies 3,284

From abroad 115 VTT 214 (68)

Public

Academy of Finland 184 Universities 834 (364)

Business Angels 380 Venture capitalists: Private 287 Industry Investment Ltd 38 (42) Sitra 64 Finnvera 332 (44) Tekes Finpro 386 55 (30)

Innofin Ministries, 5 (4) TE-Centres, sectoral research 287 (209)

Basic research Applied research

Business R&D

Business development Marketing Internationalisation

Figure 14 Finnish Innovation System: Sources and Funding 2001

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Uncertainty Risk Basic research

Finnish Academy Universities

curiosity research

strategic basic research

Applied research

Allocated total Tekes

Product development

Industries

Commercialization Marketing Internationalization

Finnvera TE-Centres

Business development Investment

Venture capital

Allocated resources

Distance to markets

Figure 15 Allocation of R&D Funds in Resources Most of these additional funds have been channeled through Tekes to industrial R&D and national technology programs. Consequently, these technology programs and Tekes’s loans and grants for corporate R&D have increased significantly in recent years. The second biggest part has gone through the Academy of Finland to universities for basic research. With private-sector R&D expenditure growing even faster than that of the public sector, preliminary data suggests that Finland has already exceeded the three percent level. At the same time the share of the corporate sector in total R&D expenditure has risen to 70 per cent. Some of the corporate-sector growth is explained by the increase in Tekes’s resources. In R&D intensity Finland ranks second in the OECD after Sweden. However, it is worth mentioning that Finland's total R&D expenditure is not more than 0.6 percent of the OECD total. Tekes funding EUR 373 million in 2002.

Risk

EUR 133 M

University research

EUR 115 M Industrial research

EUR 115 M R&D in small companies

EUR