eu innovation policies

Innovation Policies From the European Union: Methods for Classification

Victor RodriguezCarlos MontalvoTNO, Innovation Policy Group

Bulletin of Science, Technology & Society Vol. 27, No. 6, December 2007, 467-481DOI: 10.1177/0270467607308287Copyright © 2007 Sage Publications

This study focuses on taxonomic and typologicalmethods of innovation policies in the European institu-tional context. Although many types of policies affectinnovation, no universally accepted criteria exist toclassify them. As innovation policy in a myriad of the-matic areas—systemic model—has become pluralized,this article offers a method for classification. Such poli-cies are grouped and categorized according to biologi-cal and neofunctional approaches.

Keywords: innovation system; innovation model;innovation policy; European Union; taxonomy; typology

Introduction

Innovation policy—as a generic policy—is a set ofactions intended to raise the quantity and efficiency ofinnovative activities. In turn, innovative activities are thecreation, adaptation, and adoption of new products,processes, or services (European Commission, 2000a).

Although research is a source of innovation, thisencompasses more than a successful application ofresearch results. Because it is through enterprise thateconomic benefit of the successful exploitation of nov-elty is captured, enterprise is at the heart of the innova-tion process. Thus, innovation policies must extendtheir focus beyond the link with research and have theultimate effect on enterprise (European Commission,2003).

Innovation is broadly defined to include not onlyputting new technological inventions into practice but

also carrying out any new combination of productiveresources that amounts to the setting of new production(Schumpeter, 1939). Thus, this Schumpeterian definitionof the term innovation includes the development of newconsumer goods, new methods of production, new mar-kets, and new forms of industrial organization.

A broader and more integrated policy frameworkbuilt on the concept of an enlarged European researcharea emerged in 2000 (European Commission, 2000b).In this context, a spectrum of innovation policy scenar-ios was envisaged from centralization to decentraliza-tion (Kuhlmann, 2001).

The architecture of the European Union (EU) forinnovation is multidimensional—supranational policyfurniture juxtaposed with national, regional (Braczyk,Cooke, & Heidenreich, 1998; Ohmae, 1993), sectoral(Breschi & Malerba, 1997; Cooke, Gomez Uranga, &Etxebarria, 1997; Malerba, 2002), and technological(Carlsson, 1995, 1997; Carlsson & Stankiewicz, 1991)policy artifacts.

As innovation policy in a myriad of thematic areas—systemic model—at the EU level became pluralized, wefaced the cognitive challenge of putting them into someform of recognizable order. Thus, a system of classifica-tion is used to group and categorize EU innovation poli-cies according to a biological and neofunctionalapproach.

Substantial academic effort has been devoted to sys-temic models of innovation (Edquist, 1997, 2001; Edquist& Johnson, 1997; Lundvall, 1992; Lundvall, Johnson,Andersen, & Dalum, 2002; Nelson, 1993). However,there has been relatively little analysis of the taxonomy

AUTHORS’ NOTE: The authors are deeply grateful to Nicholas Neill-Fraser for helpful comments on an earlier version of this article.Acknowledgment is made to Barry Bozeman, Mark Dodgson, Stefan Kuhlmann, Ruud Smits, and the Pew Center on Global ClimateChange for their permission to reproduce their tables. Special thanks are given to participants in the TNO-IPg Strategy Day for theirremarkable observations in Delft on 2 July 2007. This study was conducted under the umbrella of TNO-IPg Long Term Research ProgramRenewal of Innovation: Knowledge Investment Projects “Innovation Policy Monitoring and Impact” No. 13-001 KIP 07-002.

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or typology of innovation policies (Alic, 2002; Alic,Mowery, & Rubin, 2003; den Hertog & de Groot, 2005)and a scarcity at the EU level (Kuhlmann, 2002;Lundvall & Borrás, 2005; Rothwell & Dodgson, 1992;Smits & Kuhlmann, 2002).

Although many types of policies affect innovation, nouniversally accepted criteria classify them. Taxonomy ortypology of innovation policies seldom includes regu-latory policies, such as environmental or competitionregulation. Regulatory policies can strongly influenceinnovation. The environmental literature (e.g., Blind et al.,2004; Jaffe, Newell, & Stavins, 2003; SQW, 2006)includes many studies of regulation-induced innovation,such as the development of greenhouse gas emissioncontrols. Likewise, energy policy also has affected inno-vation. For example, efficiency standards have reducedthe average energy consumption of household appli-ances (e.g., Rubin, 2001). In addition, competition policyis recognized as shaping innovation (e.g., Hart, 2001).

All in all, this study focuses on taxonomic and typo-logical methods of innovation policies in the institutionalcontext of the EU. The article is organized as follows. Tobegin with, innovation per se, models of innovation, andinnovation policy are discussed. In Section 3, the EU pol-icy and regulation process are briefly described. InSection 4, data and methodology used for the taxonomyand typology of innovation policy are specified. InSection 5, the resulting taxa and types of the EU policieson innovation are shown. In Section 6, models on inno-vation policies are depicted. Before concluding, science,technology, and innovation policies are examined.Finally, some concluding remarks are given.

Theoretical Framework

In this section, we define the constructs needed for ataxonomy and typology and thereby the scope for thisstudy. At the outset, innovation is the successful produc-tion, assimilation, and exploitation of novelty in the economic and social spheres. In a more detailed way,innovation is the renewal and enlargement of the range ofproducts and services and the associated markets; theestablishment of new methods of production, supply, anddistribution; and the introduction of changes in manage-ment, work organization, and the working conditions andskills of the workforce (European Commission, 1995).

Regarding innovation theories, the linear modelsstudy innovation as a process divided into differentstages (Rogers, 1995). In contrast, systemic models(Edquist, 1997, 2001; Edquist & Johnson, 1997;Lundvall, 1992; Lundvall et al., 2002; Nelson, 1993)

conceive innovation as a complex process involvingdifferent actors and their interactions.

Systems strive to achieve goals for which their ele-ments fulfill specific functions. Different typologies ofsystem functions have been developed with differentdegrees of detail and emphasis (Johnson, 2001). A gen-eral typology of innovation system functions set outthree categories, namely, reduction of uncertainty, man-agement of conflict and cooperation, and provision ofincentives (Edquist & Johnson, 1997).

Once the model is chosen, innovation policies can bedevised. Although there is a lack of consensus on thedefinition of public policy, Birkland (2001) indicateselements common to all definitions of public policy:The policy is made in the name of the “public”; policyis generally made or initiated by government; policy isinterpreted and implemented by public and privateactors; policy is what the government intends to do.

Regarding the theoretical framework of policy instru-ments, Lascoumes and Le Gales (2007) present twoarguments. Firstly, public policy instrumentation is amajor issue in public policy, as it reveals a theorizationof the relationship between the governing and the gov-erned: Every instrument constitutes a condensed form ofknowledge about social control and ways of exercisingit. Secondly, instruments are not neutral devices: Theyproduce specific effects, independently of the objectivepursued (the aims ascribed to them), which structurepublic policy according to their own logic.

Public policy instrumentation means the set of prob-lems posed by the choice and use of instruments (tech-niques, methods of operation, devices) that allowgovernment policy to be made material and operational.Another way of formulating the issue is to say that itinvolves not only understanding the reasons that drivetowards retaining one instrument rather than another butalso envisaging the effects produced by these choices.By way of indication, a brief catalogue of these instru-ments can be drawn up: legislative and regulatory,economic and fiscal, agreement- and incentive-based,information- and communication-based. But observa-tion shows that it is exceptional for a policy, or even aprogram for action within a policy, to be monoinstru-mental. Most often, the literature notes a plurality ofinstruments being mobilized and then raises the ques-tion of coordinating them (Bernelmans-Videc, Rist, &Vedung, 1998).

Apart from national governments, nongovernmentalorganizations, public-private partnerships, subnationalagencies, and supranational programs all promote inno-vation policies as well. In this study, we only focus on

468 BULLETIN OF SCIENCE, TECHNOLOGY & SOCIETY / December 2007



the EU innovation policies. Institutionally, innovationpolicies at the EU level must follow the principles ofsubsidiarity and European added value. The first rulemeans that those policies have to be justified throughsupranational formulation that would not be effectivelymanaged by the member states, while the second princi-ple requires from such policies a synergy effect notattainable within the national borders.

The European Council (2000) in Lisbon applied theopen method of coordination (OMC) to innovation poli-cies, introduced in the Employment Strategy of theAmsterdam Treaty. The European Convention (2002)decided not to codify the OMC within the EuropeanConstitution but recognized the special logic of innova-tion policies by drafting a separate subarticle (Kaiser &Prange, 2004).

Innovation policies in the EU are multifaceted,ingrained, and wide ranging, including all initiativesregarding science, education, research, technologydevelopment, and industrial modernization, overlappingalso with industrial, environmental, labor, and socialpolicies (Shapira & Klein, 2001). In fact, due to thecomplexity of the innovation policy system at the EUlevel, a need of a typology arises. The epistemic strategyis to categorize EU innovation policies in line with twoperspectives: biological and neofunctionalist.

First, we use biological heuristics, a method bywhich species of organisms are classified, because weconsider the criteria useful for the purpose of our study.Modern classification in biology has its root in the workof Linnaeus, who grouped species according to sharedphysical characteristics. These groupings have sincebeen revised by molecular systematics, which usesDNA sequences as data (Simpson, 2006).

Second, from a neofunctionalist standpoint, Europeanintegration has brought a multilayered division of labor to classical nation-state functions. In these circum-stances, innovation systems are stirred up by the increas-ing socioeconomic and political Europeanization sincethe 1980s. Nonetheless, EU innovation policies are stillpursued in parallel on the supranational, national, andsubnational levels. The justification for multiple but sep-arate innovation policies at the EU level has been over-taken by changed circumstances (Georghiou, 2001).

EU Policies and Regulations

In this section, we concisely depict the EU architec-ture for producing policies and regulations. Under theEU governance system, policies are defined as expectedoutcomes of the EU work and provide a frameworkwithin which the EU operates. Regulations are the

procedures that define how the EU will fulfill the goalsdefined in the policies. They also may clarify policies orstate law. The EU governance relies on the method thatarbitrates between different interests by two filters,namely, the general interest at the level of theCommission and democratic representation at the levelof the Council and the Parliament. Innovation policiesfall into the scope of the codecision procedure.

The Commission alone proposes policies and reg-ulations. Besides that, the Commission has the abilityto execute policy. Regulatory and budgetary acts areadopted by the Council, representing the memberstates, and the Parliament, representing citizens(European Commission, 2001). Central to the EU’sdecision-making system is the codecision procedure.This sets the principle of parity and means where nei-ther the Parliament nor the Council may adopt legis-lation without the other’s assent.

The Commission has a monopoly of legislative initia-tive in all the areas that are subject to the codecision pro-cedure. Innovation is among them. The Commission’sproposal is the result of an extensive consultation process(see Figure 1), which may be conducted in various ways,namely, impact assessment, reports by technical or scien-tific experts, consultation of national experts, interna-tional organizations or nongovernmental organizations,consultation via green and white papers, and so on. Thework of European contract research organizations such asTNO, Fraunhofer ISI, or VTT plays a role in this phase.

A consultation process is also launched among thedifferent Commission departments in order to ensure thatall aspects of the matter in question are taken intoaccount (interservice consultation). The Commission’sproposal is adopted by the College of Commissioners onthe basis of either a written procedure (no discussionamong Commissioners) or an oral procedure (the dossier

Rodriguez, Montalvo / INNOVATION POLICIES 469

Figure 1. Early Stage of the Codecision Procedure



is discussed by the College of Commissioners) and ispublished in the Official Journal of the European Union.Then, the proposal is forwarded to the EuropeanParliament and to the Council simultaneously.

The Economic and Social Committee, representingcivil society, and the Committee of the Regions, repre-senting local authorities, must be consulted by theCommission and can be consulted by the Council and theParliament. Thenceforth, the Economic and SocialCommittee and the Committee of the Regions may issueopinions in cases considered by them to be appropriate.After that, the codecision procedure takes place onlybetween the Parliament, Council, and Commission.

Data and Method

As we aim to classify EU innovation policies, weassumed that those policies are embedded in documents.For that reason, we resorted to documents published inEur-lex. This reference center provides online access tothe EU official journal, treaties, legislation in force,preparatory acts, case law, and parliamentary questions.

The yearly frequency distribution of EU documentson innovation from 2000 to 2006 is shown in Table 1 andFigure 2. Firstly, the analysis was restricted to documentscontaining the term innovation either in the title or in thetext. Secondly, the working database was constructed byretrieving documents published in English by theCouncil, the Parliament, and the Commission in the EUofficial journal. Thirdly, we chose only the year 2006 forillustrating typology of EU innovation policies. Finally,for 2006, we discarded those documents that containedthe term innovation because they were not fully in linewith our selected definition of innovation. In otherwords, we removed noise only between January 1, 2006,and December 31, 2006.

The semantic confusion of the terms taxonomy andtypology is clarified by Doty and Glick (1994). The term

taxonomy refers to a classification system that leads tocategorizing innovation policies into mutually exclusiveand exhaustive sets with a series of discrete decisionrules, while a typological heuristic makes possible iden-tifying multiple ideal types, each representing indepen-dent variables of the outcome. In our study, the outcomecould be represented by the Lisbon goals, that is, to makethe EU the most competitive and dynamic knowledge-based economy, capable of sustainable economic growthwith more and better jobs and greater social cohesion.

A catalogue of all aspects and instances of EU pol-icy innovation would obviously be quite unmanage-able. It would merely demonstrate that innovationpolicies are too diverse, too protean, to be captured infull by a single categorization. For that reason, twostrategies were designed to fully tackle the classifica-tion problem of EU innovation policies.

The first strategy was to adopt two biologicalapproaches to the problem of classification of EUinnovation policies into a typology in order to providean epistemic horizon for cognitive purposes. Firstly, aLinnaean heuristic allowed us to classify EU innova-tion policies into a hierarchy that starts with domainsand finishes with species. Secondly, a cladisticapproach permitted us to arrange innovation policiesonly into their order of branching in an evolutionarytree (Luria, Gould, & Singer, 1981).

In both cases, the underlying logic for each criterionis Aristotelian: Superior taxa possess similarities that arecommon to all lower taxa, and lower taxa possess more


0 20 40 60 80 100 120

2000

2001

2002

2003

2004

2005

2006

Yea

r

Documents

Commission Parliament Council

Figure 2. Chart of EU Documents on InnovationNote: EU = European Union.

Table 1. Frequency Distribution of EU Documents onInnovation

Year Council Parliament Commission

2000 21 25 522001 10 55 682002 29 43 992003 42 49 882004 19 17 872005 23 16 1132006 51 41 106Total 195 246 613

Note: EU = European Union. Noise was only removed for 2006.



specificity that could make a difference from other taxaat the same level of classification. For example, domes-tic policy is the counterpart of external policy and it con-sists of all government policy decisions, programs, andactions that primarily deal with internal matters, asopposed to relations with other countries that are not EUmember states. Major areas of domestic policy includeeconomic policy and regulatory policy. Economic policyrefers to the actions that cover interest rates, governmen-tal deficit, and labor markets, among others. Regulatorypolicy refers to the implementation of rules to influencethe behavior of actors in society.

The second strategy was to use a neofunctionalistheuristic for our typological approach. The ideal typesare complex constructs that must be described in termsof multiple dimensions and are not categories. Instead,each ideal type represents a unique combination of thedimensions used to describe EU innovation policies.

Public policy instrumentation and its choice of toolsand modes of operation are generally treated either as akind of evidence (e.g., governing means making regula-tions, taxing, entering into contracts, communicating,etc.) or as if the questions it raises (the properties ofinstruments, justifications for choosing them, theirapplicability, etc.) are part of a rationality of methods. Agood deal of the public administration literature devotedto the issue of instrumentation is marked by a function-alist orientation (Lascoumes & Le Gales, 2007).

In particular, Meyer-Krahmer and Kuntze (1992)have defined research and development (R&D) instru-ments both in a narrow sense and in a broader sense (seeTable 2). The instruments used for science policy may

be budgetary decisions on allocating funds to publicresearch organizations, such as universities, and subsi-dies or tax relief to private firms. The instruments usedin technology policy may be public procurement anddirect incentives in the form of subsidies and tax reduc-tions, supporting research at universities, finding institu-tional mechanisms that link universities and publiclaboratories to the users of research, and designing andenforcing intellectual property rights. The instrumentsused in innovation policy pay special attention to theinstitutional dimension including competence buildingand organizational performance, such as improvingindividual skills and learning abilities.

Another way of constructing a technology policytypology could be in the manner of Alic and colleagues(2003), who schematized the policy tools according to(a) direct government funding of R&D; (b) direct or indi-rect support for commercialization and production; (c)indirect support for development; and (d) support forlearning and diffusion (see Table 3).

Taxonomy and Typology of EU Innovation Policies

The old linear model of innovation embedded the firstgeneration of innovation policy. The second generationof innovation policy was launched in the EU in 1995.Since the 2000s, the EU innovation policies belong to thethird generation, which implies having innovation at thecenter of all policies in the knowledge-based economyaccording to the Lisbon strategy. For that reason, inno-vation goals can be found in a wide range of policies and


Table 2. Public Policy Instruments for Research and Development

Instruments in a Narrow Sense Instruments in a Broader Sense

1. Institutional funding 4. Public demand and procurementNational research centersResearch councilsApplied research and technology development organizationsUniversities and other higher education institutionsOthers

2. Financial incentives 5. Corporatist measuresIndirect promotion programs Long-term visions; technology foresightTechnology promotion programs Technology assessmentRisk capital Awareness initiatives

3. Other innovation infrastructure and technology transfer mechanisms 6. (Continuing) education; trainingInformation and consultancy for small- and medium-sized enterprisesDemonstration centers 7. Public policyTechnology centers Competition policyCooperation, networks, people Public stimulation of private demand

Source: Meyer-Krahmer and Kuntze (1992).



regulations. Innovation could be directly or indirectlyaddressed by the policies and regulations. In this section,the results of our typology are displayed.

The Linnaean heuristic exemplified to some extent inTable 4 shows a degree of specificity of innovation poli-cies. To begin with, domestic policy is the complementof foreign policy and it primarily deals with internal mat-ters, as opposed to relations with other countries that arenot EU member states. Examples of the second are poli-cies to foster innovation in neighboring countries, evalu-ation of innovation policies of accession countries, andimprovement of innovation in third countries.

Major areas of domestic policy include economic pol-icy and regulatory policy. For instance, the promotion ofinnovation for the knowledge-based economy illustrateseconomic policies dealing with innovation. One exampleof industrial policy for innovation is the encouragementof innovation in the food sector. One example of entre-preneurial policy is the state aid for small- and medium-sized enterprises (SMEs). One example of innovationpolicy is the innovation policy tools. Examples of tech-nology policy are those that promote innovation in media,agriculture, health, food, fisheries, and nuclear sectors.

More specific sectoral polices may deal with biotech-nology or information technology policy. Among these,genomics and database policies can be found. Non-technological aspects of innovation, such as provision oftraining and education as sources of innovation, can showanother type of species of EU innovation policy.Regarding regulatory policies, tax deductions are anexample of this criterion.

The cladistic approach here simply arranges the innovation policies in an evolutionary tree. Forinstance, domestic policy in the cladogram (see Figure 3)shows that technological and nontechnological poli-cies stem from innovation policy and this comesfrom entrepreneurial policy that derives from indus-trial policy in economic policy. As it was alreadysaid, the first strategy was to adopt the two biologi-cal approaches in order to provide a frame for epis-temic rationale.

The second strategy used a neofunctionalist heuristicfor our typology. Thus, the following first-order con-structs were formulated for this attempt: (a) content, (b)axis, (c) time horizon, (d) process, (e) action, (f) goals,and (g) division of labor.


Table 3. Technology Policy Tools

Direct Government Funding of R&D

R&D contracts with private firms(fully funded or cost-shared)

R&D contracts and grants with universities

Intramural R&D conducted in government laboratories

R&D contracts with industry-ledconsortia or collaborationsamong two or more actors above

Direct or Indirect Support forCommercialization and Production;Indirect Support for Development

Patent prosecutionR&D tax creditsTax credits or production subsidies for

firms bringing new technologies tomarket

Tax credits or rebates for purchasers ofnew technologies

Government procurementDemonstration projects

Support for Learning and Diffusion ofKnowledge and Technology

Education and training (technicians, engineers, andscientists; business decision makers; consumers)

Codification and diffusion of technical knowledge(screening, interpretation, and validation ofR&D results; support for databases)

Technical standard setting to ensure commonalityor compatibility

Technology and industrial extension servicesPublicity, persuasion, and consumer information

(including awards, media campaigns, etc.)

Source: Alic, Mowery, and Rubin (2003).Note: R&D = research and development.

Table 4. Linnaean Taxonomy

Domain Kingdom Division Class Order Family Genus Species

domestic economic industrial entrepreneurial innovation technology biotechnology policy, genomics policy,policy policy policy policy policy policy information database

technology policy, etc. policy, etc.domestic economic industrial entrepreneurial innovation nontechnology organizational training and

policy policy policy policy policy policy policy education policy

domestic regulatory fiscal tax income tax corporate incentive tax deduction policy policy policy policy policy policy policy policy



Firstly, innovation policies content is a criterionbecause innovation is pervasive; many policy areashave an impact on innovation. Among the subject mat-ter of these documents, innovation appeared in textsrelated to agriculture, competition, culture, democ-racy, development, economy, education, employment,energy, environment, EU accession, EU neighbor-hood, external relations, finance, fishery, food, health,human rights, information and communication tech-nologies, manufacturing, intellectual property rights,media, networks, policy coherence, regions, science,security, social aspects, standardization, trade, trans-port, and youth.

Secondly, with respect to axis, innovation policiescan be vertical or horizontal. Vertical policies, veryimportant for implementation, depict relationshipsbetween different institutional layers. For instance,sectoral policies for innovation depict vertical poli-cies, namely, improving innovation in the media andaudiovisual sector, agriculture, health, food, transport,fisheries, and nuclear sectors. Horizontal policies areessential for coordination of many policy domains toachieve better innovation policy in a multisectoralapproach, such as promoting innovation for a knowledge-based economy.

Thirdly, with regard to time horizon, innovationpolicies could be characterized as short-, medium-, orlong-term. One example of the first is state aid toSMEs. One example of the second is finance and bud-get. One example of the third is the fulfillment ofresearchers’ scarcity.

Fourthly, concerning policy process, innovationpolicies can be made collectively or institutionally(March & Olsen, 1996). Collective policy making isseen as bargaining behavior and policy as negotiatedoutcomes, such as developing innovation in regions.Institutional policy making implies matching institu-tions, behaviors, and contexts, such as maintenance ofinnovation in the nuclear sector.

Fifthly, as far as the action involved with innova-tion policies, innovation policies can cover theirdesign, implementation, management, and assess-ment. Regarding the design of instruments, theCommission’s proposal is the result of an extensive con-sultation process. Those consultative documents can beblue books, white papers, green papers, and reports. Ablue book is a book of specialized information. A whitepaper is a detailed or authoritative report. A green paperis a document that proposes and invites discussion onapproaches to a problem. A consultative report can beproduced by technical experts, national experts, interna-tional organizations, or nongovernmental organizations.Regarding implementation, innovation policies can alsobe embodied in frameworks, actions, implementationplans, or programs. Innovation policies appear in otherpolicy declarations. Regarding management, innovationpolicies can be embodied in state aid to SMEs, financeand budget, policy coherence, and other innovation pol-icy tools (see Figure 4). Finally, the assessment of inno-vation policies can be embodied in reports produced byimpartial experts.

Sixthly, innovation policies can have direct or indi-rect goals. In 2006, documents dealing with innova-tion policy design in a direct way contained sectoral,regional, external, labor, and macroeconomic aspects(see Figure 5). The documents that dealt with innova-tion policy indirectly addressed topics other thaninnovation such as education, environment, culture,standardization, and so on (see Figure 6).

Finally, policies can be classified by the governmentalactors responsible for their design and implementation.In science policy, the main policy actors are those


Figure 3. Cladogram

EU innovation policy management

0 20 40 60 80

EU

in

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tio

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oli

cy

ma

na

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me

nt

Frequency of documents on EU innovation

State aid to SMEs

Innovation policy tools

Finance and budget

Policy coherence

Figure 4. European Union (EU) Innovation Policy ManagementNote: SMEs = small- and medium-sized enterprises.



Commission’s directorates and Parliament’s committeesdealing with education and research. Others, however, incharge of health, defense, energy, transport, and environ-ment may also play a role since they organize their ownresearch communities. Finance plays a role when itcomes to deciding the budget allocation for research. In

technology policy, the main policy protagonists are thosedirectorates of the Commission and committees of theParliament that deal with applied science and procure-ment of technologies. In innovation policy, the main pro-tagonists are those directorates of the Commission andcommittees of the Parliament that deal with enterprise.


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inn

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Frequency of EU documents on innovation

Improve innovation in media andaudiovisual sector

Encourage innovation inagriculture

Promote innovation in the healthsector

Encourage innovation in the foodsector

Reinforce innovation in thetransport sector

Stimulate innovation in thefisheries sector

Maintain innovation in thenuclear sector

Increase innovation in security

Develop innovation in regions

Foster innovation inneighbouring countries

Stir innovation among youth

Improving innovation in thirdcountries

Evaluate innovation policies ofaccession countries

Fill up the scarcity ofresearchers

Promote innovation forknowledge based economy

Figure 5. EU (European Union) Direct Innovation Policy Design for 2006



Models for Innovation Policies

Innovation policy models are referred to in this sec-tion to illustrate how the historical context played a rolein their construction. Bozeman (2000) modeled threeparadigms differentiated by policy legitimization, viz.market failure, mission, and cooperation (see Table 5).The market failure paradigm limits innovation policy to

market failures such as extensive externalities, hightransaction costs, and asymmetric information. The mis-sion paradigm limits innovation policy to the mission ofgovernment agencies but does not confine it todefense. The cooperation paradigm fosters technologytransfer.

European innovation policies have been increasinglydifferentiated in terms of scope and instruments since the


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Frequency of EU documents on innovation

Provide training and educationas sources of innovation

Enforce environmentalregulation to spur innovation

Support culture for creativity andinnovation

Use innovation degree for trade

Respect democracy and humanrights to support innovation

Promote standardisation toboost innovation

Foster employment andproductivity to achieveinnovation

Use information technology forinnovation

Meet the development goals togenerate innovation

Enforce noncontractualobligations for innovation

Enforce intellectual propertyrights for innovation

Figure 6. EU (European Union) Indirect Innovation Policy Design for 2006



1950s. Rothwell and Dodgson (1992) modeled themaccording to a historic approach differentiated by thedegree of coordination between science and industrialpolicy makers (see Table 6). They displayed threephases, viz. 1950s and 1960s, mid-1970s to early 1980s,and early 1980s to early 1990s. The three phases werecharacterized by the degree of coordination of scienceand industrial policy makers, namely, little, increasing,and complete coordination, respectively.

Smits and Kuhlmann (2002) also modeledEuropean innovation policies from a historic stand-point but they differentiated them by the primary goalof such policies. The 1970s were characterized by afinancial goal for stimulating research and develop-ment, the 1980s by diffusion or technology transfer,the 1990s by managerial gaps in running businesses,and the 2000s by systems for facilitating change.

In particular, innovation policy instruments in use in all EU and preaccession countries were classified by Smitsand Kuhlmann (2002) into four categories: financial,

diffusion, managerial, and systemic (see Table 7). Thespecific instruments are shown in Table 8.

Science, Technology, and Innovation Policies

This section describes the meanings of constructsused in innovation policy studies. Firstly, science can beconceptualized as a mode of knowledge. In the domainor neighborhood, knowledge is just one member of awhole cluster of closely related entities, such as fancy, sus-picion, surmise, awareness, information, opinion, belief,conviction, and so on, according to Ziman (2005). Hearranged them in increasing order of credibility to indi-cate that knowledge is just the limit point of that set. Afeature of knowledge is the notion of consensus, whichKusch (2004) envisages as precisely the elusive endpointwhere knowledge is indeed achieved.

What distinguishes science from other modes ofsystemic inquiry is its distinctive method: the scien-tific method. In particular, technology is science in


Table 5. Bozeman Models

Model

Core assumptions

Peak influence

Policy examples

Theoretical roots

Market Failure

(1) Markets are the most efficientallocation of information andtechnology.

(2) Government laboratory rolelimited to market failures suchas extensive externalities, hightransaction costs, andinformation distortions. Smallmission domain, chiefly indefense. Universities providedbasic research, in line withprivate sector undersupply dueto market failure (inability toappropriate directly the resultsof basic research).

(3) Innovation flows from and toprivate sector; minimaluniversity or government role.

Highly influential during allperiods.

Deregulation; contraction ofgovernment role; R&D taxcredits; capital gains; tax rollback. Little or no need forfederal laboratories except indefense support.

Neoclassical economics.

Mission

(1) The government role should beclosely tied to authorizedprogrammatic missions ofagencies.

(2) Government R&D is limited tomissions of agencies, but notconfined to defense. UniversityR&D supports traditional roles ofland grant universities such asagricultural or engineeringextension, manufacturingassistance, and contract researchfor defense or energy research.

(3) Government should not competewith private sector in innovationand technology. But agovernment or university R&Drole is a complement.

1945-1965; 1992-present

Creation of energy policy R&D,agricultural labs, and other suchbroad mission frameworks.

Traditional liberal governance withbroad definition of governmentrole.

Cooperative Technology

(1) Markets are not always the mostefficient route to innovation andeconomic growth.

(2) Global economy requires morecentralized planning and broadersupport for civilian technologydevelopment.

(3) Government laboratories anduniversities can play a role indeveloping technology, especiallyprecompetitive technology, for usein the private sector.

1992-1994

Expansion of federal laboratory rolesand university role in technologytransfer and cooperative researchand other technology-basedeconomic development programs.

Industrial policy theory, regionaleconomic development theory.

Source: Bozeman (2000).Note: R&D = research and development.



application; science in action is research (Ziman,2000). The instrumental attitude to science is summedup by the hybrid scientific research and technologicaldevelopment using the acronym R&D. This linearmodel locates science at the upstream and innovationat the downstream.

The supposed role of research is to produce, by anyfeasible means, whatever knowledge is required, orseems likely to be required, in order to satisfy anactual, or envisaged, material need. Industrial R&Dand other forms of applied science do indeed share thesame norms also sometimes found inside large gov-ernmental laboratories. Industrial R&D and academicscience are culturally very different.

Science and technology studies and policy analysishave been divided because of their disciplinary origins,

major sources of research questions, emphasis on cogni-tive or operational problems, and focus on science andtechnology (Spiegel-Rösing, 1977). Science and tech-nology studies started as a movement with a critical viewof scientific and technological development and itsimpact on society. Later, this movement turned into anacademic field more interested in knowledge creation,having taken approaches from sociology, philosophy,and history.

The science and technology policy was established asan area of government intervention in the immediateaftermath of World War II. Initially, the main area ofinvolvement was just science. In the 1950s and 1960s,the focus was on institution building and expansion ofpolicy for science. In the 1970s, the application and uti-lization of science as a policy were emphasized, with


Table 6. Rothwell and Dodgson’s Models

1950s and 1960s Science policy Industrial policy Firm size emphasis• Scientific education • Grants for R&D • Emphasis on large firms and industrial • University research • Equipment grants agglomeration• Basic research in • Industrial restructuring • Creating national “flagship” companies

government • Support for collective • Public R&D funds go mainly to laboratories industrial research large companies

• Technical education • Paucity of venture capitaland training

Little coordination or active collaboration between science policy makers and industrial policy makers

Mid 1970s to Innovation policyearly 1980s • Some concern over lack of university-industry linkages

• Grants for innovation• Involving collective research institutes in product development• Innovation-stimulating public procurement• Increasing interest in SMEs• Many measures introduced to support innovation in SMEs• Continuing paucity of venture capital

Increasing interdepartmental coordination

Early 1980s to present Technology policy• Increased emphasis on stimulating university-industry linkages• Increased emphasis on “strategic” research in universities• Selection and support of generic technologies• Growth in European policies of collaboration in precompetitive research• Emphasis on intercompany collaboration• Emphasis on the creation of new technology-based firms• Growing availability of venture capital

Interdepartmental initiatives

Growing interest in accountability and in measures for evaluating the effectiveness of public R&D policiesIncreasing concern over growing regional economic disparitiesNational and local government initiatives to enhance R&DPotential of the less-developed regions: accelerated establishment of regional technology infrastructures

(e.g., science parks, technopoles, innovation centers)

Source: Rothwell and Dodgson (1992).Note: R&D = research and development; SMEs = small- and medium-sized enterprises.



technology thereby emerging more clearly as an area ofconcern. In the 1980s, there was a shift to innovation pol-icy by removing the distinction between science andtechnology. In the 1990s, basic research became inti-mately intertwined with the production of goods andtechnological development.

Science and technology policy analysis is concernedwith the governance, direction, and promotion of scienceand technology in the real world of science and technol-ogy (Salazar-Acosta, 2005). Science and technology pol-icy analysis moved between different models andtheories from political science, economics, and manage-ment. The terms science, basic research, and academiahave been used interchangeably. In addition, the termstechnology, applied research, and industry have alsobeen employed in a similar manner.

This semantic confusion has helped to concealimportant differences between them. To reduce thecurrent state of confusion, we provide the followingclarifications. Traditional literature has been used todistinguish between basic and applied research, withbasic research being focused on questions of funda-mental scientific interest and applied research focusedon questions of usefulness and applications.

Stokes (1997) modeled research according to the con-sideration of usefulness and the quest for fundamentalunderstanding. If there is an application but no quest forfundamental understanding, then the research falls into


Table 7. Development of European Innovation Policy

Financial (1970s)

Diffusion (1980s)

Managerial gap(1990s)

Systemic (late1990s)

Primary Goal

stimulating R&D

transfer ofknowledge andtechnologicalcompetence

support running abusiness

facilitating change

Client

one-to-one privatefirm

one-to-one privatefirm (publicinstitution)

one-to-oneone-to-fewprivate firm

chains networkssystems

Content

R&D subsidy

science subjectsformal

social scienceformaltacit

science, socialscience

formaltacitstrategic

intelligence

Process

limited to specific technical project

limited to specific consultancy project

demand articulationstrategy developmentmanagementcomplex projectstrategy and vision

developmentdemand articulationstimulate learningstimulate experimenting

System

organizing small chainsand clusters

management interfaces

system organizersystem buildermanagement interfacesidentifying, mobilizing,

involving usersguarding democratic

contentdeveloping infrastructurestrategic intelligence

Source: Smits and Kuhlmann (2002).Note: R&D = research and development.

Table 8. Innovation Policy Instruments From EU MemberStates and Accession Countries

Type of Instrument Specific Instrument

Financial financingtaxationstrengthening company researchstart-up technology-based companies

Diffusion mobilityabsorption technologies by SMEs

Managerial mobilityinnovation and managementstart-up technology-based companiesabsorption technologies by SMEsstrategy, vision of R&D

Systemic Dynamic:raising public awarenesspromotion of clustering and

cooperation for innovationcooperation research, universities,

companiesStatic (infrastructure):

public authoritiescompetitionprotection of intellectual property

rightsadministrative simplificationlegal and regulatory environmenteducation and training

Source: Smits and Kuhlmann (2002).Note: EU = European Union; R&D = research and development;SMEs = small- and medium-sized enterprises.



Edison’s quadrant. If there is no consideration of useful-ness, but there exists a quest for fundamental under-standing, then the research falls into Bohr’s quadrant. Ifthere is both consideration of usefulness and fundamen-tal understanding, then the research falls into Pasteur’squadrant.

Research can be conducted in industrial, governmen-tal, or academic organizations. Basic research is not themonopoly of universities; some companies carry outbasic research as well. Furthermore, applied research isnot only found in industry; academia also pursuesapplied research. Nonetheless, the labels of basic andapplied research are changing. A priori definitions ofpolar ideal types are vague, imprecise, and awkward forempirical operationalization in postmodern science(Callon, 1997; Kidd, 1965; Latour, 1993).

Science and technology are distinct branches ofknowledge and distinct communities, with differentresearch problems and methods, responding to differ-ent incentives. Technology involves much more thanscience, and innovation involves much more thantechnology. Innovations do not always involve theapplication of technology (Metcalfe, 2000). It is inad-equate to think of innovation in technological termsalone (Dodgson & Bessant, 1996).

Concluding Comments

From a policy point of view, it is easier to talk aboutscience and technology as a single concept, becausetechnology is applied science. When the focus of policyis on innovation in Schumpeterian terms, it became clear that this catch-all concept included more thanscience and technology. Innovation policy is thereforemore generic than science, technology, and researchpolicies. In this sense, innovation polices may includenontechnological aspects such as organizational learningand commercialization.

The issue of the choice of instruments is intimatelylinked to the issue of policy design, which means thedevelopment of a systematic understanding of the selec-tion of instruments and an evaluative dimension (Linder& Peters, 1984). Scholars of the history of technologyand the sociology of science have denaturalized techni-cal objects by showing that their progress relies more onthe social networks that form around them than on theirown characteristics. Simondon (1958) studied innova-tion not as the materialization of an initial idea but as anoften chaotic dynamic that sets information, adaptationto constraints, and arbitration on a path of convergencebetween divergent routes of development. He went on totalk about the process of concretization, taking into

account the combination of heterogeneous factors whoseinteractions produce—or fail to produce—innovation.Sociology of science developed this perspective byrejecting the retrospective view that suppresses momentsof uncertainty and sees creation only as a series ofinevitable stages moving from the abstract to the con-crete, from the idea to its concretization (Akrich, Callon,& Latour, 1988). Translation of and through technicalinstruments is a constant process of relating informationand protagonists and of regularly reinterpreting the sys-tems thereby created.

Public policy instrumentation, in general, and innova-tion policy instrumentation, in particular, are thereforemeans of orienting relations between political society(via the administrative executive) and civil society (viaits administered subjects), through intermediaries in theform of devices that mix technical components (measur-ing, calculating, the rule of law, procedure) and socialcomponents (representation, symbol). This instrumenta-tion is expressed in a more-or-less standardized form—arequired passage for public policy—and combines oblig-ations, financial relations (tax deductions, economic aid),and methods of learning about populations (statisticalobservations).

References

Akrich, M., Callon, M., & Latour, B. (1988). A quoi tient le suc-cès des innovations? Annales Des Mines, 4, 29.

Alic, J. (2002). Policies for innovation: Learning from the past. InV. Norberg-Bohm (Ed.), The role of government in technologyinnovation: Insights for government policy in the energy sec-tor. Cambridge, MA: Harvard University, Belfer Center forScience and International Affairs.

Alic, J., Mowery, D., & Rubin, E. (2003). U.S. technology andinnovation policies: Lessons for climate change. Arlington,VA: Pew Center on Global Climate Change.

Bernelmans-Videc, M., Rist, R., & Vedung, E. (1998). Carrots,sticks and sermons: Policy instruments & their evaluation.New Brunswick, NJ: Transaction.

Birkland, T. (2001). An introduction to the policy process: Theories,concepts, and models of public policy making. Armonk, NY:Sharpe.

Blind, K., Bührlen, B., Menrad, K., Hafner, S., Walz, R., & Kotz, C.(2004). New products and services: Analysis of regulations shap-ing new markets. Karlsruhe, Germany: Fraunhofer ISI.

Bozeman, B. (2000). Technology transfer and public policy: Areview of research and theory. Research Policy, 29, 627-655.

Braczyk, H., Cooke, P., & Heidenreich, M. (Eds.). (1998). Regionalinnovation systems: The role of governances in a globalizedworld. London: University College London.

Breschi, S., & Malerba, F. (1997). Sectoral systems of innovation:Technological regimes, Schumpeterian dynamics and spatialboundaries. In C. Edquist (Ed.), Systems of innovation: Technolo-gies, institutions, and organizations. London: Pinter.

Callon, M. (1997). Actor-network theory: The market test. Keele, UK:Keele University, Centre for Social Theory and Technology.




Carlsson, B. (Ed.). (1995). Technological systems and economicperformance: The case of factory automation. Dordrecht,Netherlands: Kluwer.

Carlsson, B. (Ed.). (1997). Technological systems and industrialdynamics. Dordrecht, Netherlands: Kluwer.

Carlsson, B., & Stankiewicz, R. (1991). On the nature, functionand composition of technological systems. Journal ofEvolutionary Economics, 1, 93-118.

Cooke, P., Gomez Uranga, M., & Etxebarria, G. (1997). Regionalinnovation systems: Institutional and organisational dimen-sions. Research Policy, 26, 475-491.

den Hertog, P., & de Groot, H. (2005). Horizontal co-ordination ofinnovation policies: The case of information society inNetherlands. In Governance of innovation systems: Case stud-ies in cross-sectoral policy. Paris: Organisation for EconomicCo-operation and Development.

Dodgson, M., & Bessant, J. (1996). Effective innovation policy: Anew approach. New York: International Thomson Business Press.

Doty, D., & Glick, W. (1994). Typologies as a unique form oftheory building: Toward improved understanding and model-ling. Academy of Management Review, 19, 230-251.

Edquist, C. (1997). Systems of innovation approaches: Their emer-gence and characteristics. In C. Edquist (Ed.), Systems of innova-tion: Technologies, institutions, and organizations. London: Pinter.

Edquist, C. (2001). The system of innovation approach and inno-vation policy: An account of the state of the art. Aalborg,Denmark: Nelson Winter Conference DRUID.

Edquist, C., & Johnson, B. (1997). Institutions and organizations insystems of innovation. In C. Edquist (Ed.), Systems of innovation:Technologies, institutions, and organizations. London: Pinter.

European Commission. (1995). Green paper: Innovation. Commu-nication, 688.

European Commission. (2000a). Innovation policy in a knowledge-based economy. Luxembourg: Office for Official Publications ofthe European Communities.

European Commission. (2000b). Towards a European researcharea. Communication, 6.

European Commission. (2001). European governance: A whitepaper. Communication, 428.

European Commission. (2003). Innovation policy: Updating theUnion’s approach in the context of the Lisbon strategy.Communication, 112.

European Convention. (2002). Final report of working group VIon economic governance. Convention, 357.

European Council. (2000). Lisbon European Council: Presidencyconclusions. Brussels, Belgium: Author.

Georghiou, L. (2001). Evolving frameworks for European collabora-tion in research and technology. Research Policy, 30, 891-903.

Hart, D. (2001). Antitrust and technological innovation in the US:Ideas, institutions, decisions, and impacts, 1890–2000. ResearchPolicy, 30, 923-936.

Jaffe, A., Newell, R., & Stavins, R. (2003). Technology policy forenergy and the environment. In A. Jaffe, J. Lerner, & S. Stern(Eds.), Innovation policy and the economy. Cambridge, MA:National Bureau of Economic Research.

Johnson, A. (2001). Functions in innovation systems approaches.Aalborg, Denmark: Nelson Winter Conference DRUID.

Kaiser, R., & Prange, H. (2004). Managing diversity in a systemof multi-level governance: The open method of co-ordinationin innovation policy. Journal of European Public Policy, 11,249-266.

Kidd, C. (1965). Basic research: Description versus definition. InN. Kaplan (Ed.), Science and society. Chicago: Rand McNally.

Kuhlmann, S. (2001). Future governance of innovation policy inEurope: Three scenarios. Research Policy, 30, 953-976.

Kuhlmann, S. (2002). Future governance of innovation policy inEurope. In 2003 proceedings of the Innovation Policy Workshopon Future Directions of Innovation Policy in Europe (InnovationPapers No. 31). Brussels, Belgium: European Commission.

Kusch, M. (2004). Knowledge by agreement: The programme ofcommunitarian epistemology. Oxford, UK: Clarendon.

Lascoumes, P., & Le Gales, P. (2007). Introduction: Understandingpublic policy through its instruments—From the nature of instru-ments to the sociology of public policy instrumentation. Gover-nance, 20, 1-21.

Latour, B. (1993). We have never been modern. London:Harvester Wheatsheaf.

Linder, S., & Peters, B. (1984). From social theory to policydesign. Journal of Public Policy, 4, 237-259.

Lundvall, B. (1992). National systems of innovation: Towards a theory of innovation and interactive learning. London:Pinter.

Lundvall, B., & Borrás, S. (2005). Science, technology, and inno-vation policy. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.),The Oxford handbook of innovation. Oxford, UK: OxfordUniversity Press.

Lundvall, B., Johnson, B., Andersen, E., & Dalum, B. (2002).National systems of production, innovation and competencebuilding. Research Policy, 31, 2132-2231.

Luria, S., Gould, S., & Singer, S. (1981). A view of life. MenloPark, CA: Benjamin-Cummings.

Malerba, F. (2002). Sectoral systems of innovation and produc-tion. Research Policy, 31, 247-264.

March, J., & Olsen, J. (1996). Institutional perspectives on politi-cal institutions. Governance, 9, 247-264.

Metcalfe, S. (2000). Science policy and technology policy in acompetitive economy. In C. Edquist & M. McKelvey (Eds.),Systems of innovation: Growth, competitiveness and employ-ment. Cheltenham: Elgar Reference Collection.

Meyer-Krahmer, F., & Kuntze, U. (1992). Bestandsaufnahme derForschungs—und Technologiepolitik. In K. Grimmer, J. Häusler,S. Kuhlmann, & G. Simonis (Eds.), Politische Techniksteuerung—Forschungsstand und Forschungsperspektiven. Leske: Opladen.

Nelson, R. (1993). National innovation system. New York: OxfordUniversity Press.

Ohmae, K. (1993). The end of the nation state: How region statesharness the prosperity of the global economy. New York: FreePress.

Rogers, E. (1995). Diffusion of innovations. New York: Free Press.Rothwell, R., & Dodgson, M. (1992). European technology policy

evolution: Convergence towards SMEs and regional technol-ogy transfer. Technovation, 12, 223-238.

Rubin, E. (2001). Introduction to engineering and the environ-ment. Boston: McGraw-Hill.

Salazar-Acosta, M. (2005). Theories on technology and society.Vancouver: Simon Fraser University, Centre for PolicyResearch on Science and Technology.

Schumpeter, J. (1939). Business cycles: A theoretical, historical,and statistical analysis of the capitalist process. New York:McGraw-Hill.

Shapira, P., & Klein, H. (2001). Innovations in European and USinnovation policy. Research Policy, 30, 869-872.




Simondon, G. (1958). Du mode d’existence des objets techniques.Paris: Aubier.

Simpson, M. (2006). Plant systematics. Amsterdam: ElsevierAcademic Press.

Smits, R., & Kuhlmann, S. (2002). Strengthening interfaces ininnovation systems: Rationale, concepts and (new) instru-ments. In Proceedings of the workshop “New challenges andnew responses for S&T policies in Europe.” Brussels, Belgium:European Commission, Directorate General for Research.

Spiegel-Rösing, I. (1977). The study of science, technology and society (SSTS): Recent trends and future challenges. In I. Spiegel-Rösing & D. de Solla Price (Eds.), Science, technol-ogy and society. Beverly Hills, CA: Sage.

SQW. (2006). Exploring the relationship between environmentalregulation and competitiveness: A literature review. London:SQW Economic and Management Consultants.

Stokes, D. (1997). Pasteur’s quadrant: Basic science and techno-logical innovation. Washington, DC: Brookings Institution.

Ziman, J. (2000). Real science: What it is and what it means.Cambridge, UK: Cambridge University Press.

Ziman, J. (2005). Knowledge by agreement. Minerva, 43, 289-295.

Victor Rodriguez is a researcher at the Innovation PolicyGroup of the Netherlands Organisation for AppliedScientific Research TNO. His main research interests are inthe economics of intellectual property rights, the economicsof science, and research policy.

Carlos Montalvo is a senior researcher at the InnovationPolicy Group of TNO. His research activities and interestfocus on innovation and competitiveness, the evaluation ofS&T programs, and the application of behavioral and sys-tem dynamics models to assess the impact of policy andregulation on innovation.


