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976 A. Inzelt/ Research Policy 33 (2004) 975995

From among the different collaborations, networksand alliances, this study concentrates on industryuniversitygovernment relationships, examining how

government facilitates partnership between universityand industry and how companies relate to universi-ties. Its main empirical basis is four pilot innovationsurveys.

The paper deals with the transformation of relation-ships between business and universities, as reflected ingovernment programmes and in the innovation activ-ities of firms, in a transition economythat of Hun-gary.

Before going into detail it needs to be emphasisedthat the transition is still ongoing in Hungary and sothe period covered by this paper relates to two differ-ent stages, namely to the initial and advanced phasesof transition. The transition is a long journey and theborderline between the two phases is not very welldefined in the literature of transition. The milestonesdiffer according to economic fields even within onetransition economy. From the point of view of the sys-tem of innovation and its environs, the first phase oftransition lasted until 19961998. In this period themajority of new laws were enacted (laws covering: theAcademy of Sciences, Higher Education, IntellectualProperty Rights and Public Procurement). In this pe-

riod the economy declined and a redeployment of busi-ness enterprises caused a parallel decline in businessR&D departments, expenditure and personnel. Under-going transformation and the process of privatisationdid not make companies hungry for innovation. At thesame time, other core actors in innovation systems,academic institutes and universities were under crit-ical financial pressure and also restructuring. Publicfunds for R&D organisations decreased and businesswas somewhat reluctant to create R&D demand andcontract with universities and with R&D institutes.

Although all parts of the innovation system, rulesand regulations, together with business, education,S&T and information systems are still in transition,its second phase started in 19961998. By this time,privatisation was over, the economy had started torecover, the large-scale redeployment of business anduniversities had finished and the R&D institutionalsystem had settled down. The environment has nowbecome more stable, even though the transformationin several economic sectors has been delayed. Theomnipotent government of the socialist system has

been transformed into a facilitator form of govern-ment trying to follow a diffusion-oriented approach.

The pure destruction of the first period has be-

come creative destruction in the second, simplysweeping away many factors which had handicappedinnovation. At the institutional level this stage canbe regarded as one of fine-tuning and a process oftrial-and-error in a developing market environmentfor a learning democracy.

In studying any transition economies, researchershave to face the problem of a lack of critical massof new empirical information. There are many rea-sons for this phenomenonthe slow redeploymentof a command economys official statistics towardsthe information system of the market economies;the low priority given to innovation-related infor-mation and the scarcity of national research sourcesfor innovation-related data collection. All of theseare among the reasons why the information-base ofthis paper is neither the result of an analysis of of-ficial statistics nor a special survey. Likewise, it isnot a series of case studies from an investigationinto pattern-of-knowledge interactions, their bene-fits, intensity and impact on both university researchand corporate performance. However, some avail-able pilot survey results and administrative sources

were assembled to study these relationships and theirconsequences. Pilot innovation surveys and officialR&D statistics offer some material for analysis inthat they provide certain insights into the formationof relationships.

The first part of the paper enumerates the modes ofinteraction, which offer different methods of knowl-edge diffusion among the main actors in the inno-vation systemknowledge generators, diffusers andusers (including universities), corporate capacities andnetworks plus other research bodies and supporting

institutions.The second part briefly discusses the measurementof collaboration, such as R&D statistics, administra-tive sources, innovation surveys, case study series andad hoc academic sources. Some of these sources areused in the following parts.

The third part investigates the changing role of gov-ernment as facilitator, examining new initiatives. Itcombines several mosaics from the transition pro-cess, which were actually realised on the way from aresearch push to a chain-link model. There are a

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A. Inzelt / Research Policy 33 (2004) 975995 977

few seeds planted in the economic environment whichencourage business to be innovative and to interact,and the ongoing redeployment or re-positioning of

business and of the universities improves their abilityto do so.The fourth section highlights the tendencies of

universityindustry linkages in Hungary from thebusiness perspective extracted from innovation sur-veys. The analysis employs two indicators: sourcesof information and co-operation arrangements forinnovation. In the partnership-building process thefirst step is to screen the information, and the regularsources of information are usually good candidatesfor networking. Finally, the last section draws specificconclusions.

2. Type, function, level and pattern of interaction

The key to the whole innovation process is in-teraction and partnership among firms and betweenfirms and various other actors (such as universitiesand R&D institutes) who are becoming the engines ofinnovation. Partnershipis an umbrella term for inter-action, collaboration and co-operation and among thecore elements of a national innovation system many

different interactions may exist. From the standpointof knowledge diffusion, interaction includes all typesof contribution to innovation, even if they are mi-nor (e.g., the exchange of ideas). Interactions arebuilding-blocks for collaboration, although in this con-text collaboration has a narrower meaning in the sensethatcollaborationmeans working together to achievea common goal. We employ hereKatzand Martinsdefinition of collaborative research (1997, pp. 78).The concept ofco-operationis mentioned byKatz andMartin (1997)during the formulation of their defini-

tion, in which they concluded that it would be too tighta definition for collaboration. They are correct, butit is worth distinguishing this group of collaboratorsfrom others and placing them in a separate group, em-phasising that included in this tight definition are onlythose partners who contributed directly to all mainresearch tasks throughout the duration of the projects.

Collaboration can be at many different levels, thatis, at individual, group, institution, sector and na-tional level, whilst its forms may be intra-forms orinter-forms. Sometimes, however, interaction [col-

laboration] cannot be classified clearly since it mayappear to belong to both an intra- and inter-category(Katz and Martin, 1997, p. 10; Cohen et al., 1994;

Meyer-Krahmer and Schmoch, 1998).The evolution of universityindustrygovernmentrelationships presents different patterns according tothe level of development and to the historical, institu-tional traditions of individual countries1 (Conceioand Heitor, 2001; Crow et al., 1998; Geisler andRubenstein, 1989; Faulkner and Senker, 1994; Senker,2001).

A common problem for the former commandeconomies and for developing countries is that theywere poor at distributing their own accumulated sci-entific knowledge, with only very simple types ofcollaboration existing in these countries. Isolationor arms-length connections between the core actorsin the innovation process cannot help in combatingunderdevelopment and in catching up with worldtechnology. The way from long-range co-operationtowards arms length co-operation and, further, to-wards the interactive, feedback loop model of innova-tion (Kline and Rosenberg, 1986)and the horizontaltriple helix (Etzkovitz and Leydesdorff, 1997) aregreat challenges not only for transition economiesand developing countries, but also for many (semi-)

advanced market economies. In countries such as tran-sition economies one of a governments main tasks isto facilitate a breakthrough in the frequency of col-laborations and to upgrade their level, moving fromprimitive forms towards sophisticated co-operations.

Let me enumerate those modes of interaction whichoffer different methods of knowledge diffusion amongthe core players in an innovation systemknowledgegenerators, diffusers and users (including universi-ties), company capabilities and networks, other re-search bodies and supporting institutions (as in the

first column ofTable 1).Among the forms of interaction we can see a greatvariety ranging from ad hoc consultation to joint re-search activity. All of those in the first column of thetable are interactions, but only items 616 are collab-oration. The most sophisticated are 15 and 16, whichare formal R&D co-operations.

1 In this context the term university includes research institutesof the Hungarian Academy of Sciences that are carrying outacademic research.

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Table 1Type, level and patterns of interaction

Type of interactions Most common level Patterns of interactions

1. Ad hoc consultations of firm employees at universities Between individuals Isolated (15)2. Lectures of firm employees held at universities

3. Lectures of faculty members held at firms4. Regular (informal) discussions between faculty

members and firm employees on the meetings ofprofessional associations, at conferences, and seminars

5. Buying university research results (patents) ad hoc basis Individual/institutional

6. Employing faculty members as regular consultants Individual/institutional Vertical (611), far distance7. Coaching of firm employees by university researchers8. Training of firm employees by university professors9. Joint publications by university professors and firm

employees10. Joint supervision of Ph.D. and master theses by

university and firm members11. Joint IPRs by university professors and firm employees Arms length

12. Access to special equipment of firm/university withor without assistance of owners organisations

13. Invest into universitys facilitates

Institutional Between arms length and horizontal triple helix

14. Regular acquiring university research

15. Formal R&D co-operations such as contract research Horizontal triple helices (1216)16. Formal R&D co-operations such as joint research

projects

17. Knowledge flows through permanent or temporarymobility from universities to firms

18. Knowledge flows through spin-off formations of newenterprises

Source:compiled by the author.

Types 17 and 18the mobility of brain-power andknowledge flow through spin-off may be regarded ascuckoos eggs, the interactions among the institu-tions and individuals have a one-off character. Theperson who is conveying the knowledge from oneorganisation to another is changing the organisationforever. However, these types of interactions are notonly important channels of knowledge flow betweenuniversities and companies but also open avenuestowards strong, horizontal co-operation.

The second column ofTable 1describes the mostcommon level of different types of interactions. Com-munication among individuals is a very important partof each interaction, and personal communication andtrust are, very frequently, the starting point of eachtype and each level of collaboration.

The third column of Table 1 employs the triplehelix metaphor to illustrate different patterns ofco-operation linked to types of interaction. The adop-tion of the triple helix model allows us to take into

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of information and know-how, and whether therewas co-operation. With co-operation in innovationprojects, as the findings of innovation surveys show,

many actors are involved in such processes. Innova-tion surveys, therefore, are broadened to include allkinds of relevant participant in an innovation system(such as universities, other R&D institutions, govern-ment, NGOs, clients and suppliers) and their networkof information- and knowledge flows.

Thus, some quantitative information on differentinteractions may be drawn from innovation surveys.Business is both a user and performer of R&D for in-novation, and innovation surveys investigate businessas a user of external sources to combine with its in-ternal sources for further R&D activities and/or forinnovation. These surveys enable us to observe theexchange between science and technology and theygenerally investigate partnerships from a business per-spective.

The surveys provide some basic quantitative infor-mation on collaboration, counting the number of in-novative companies entering into different forms ofcollaboration (types 116), but they do not describethe patterns of co-operation or the dimensions of therelationship. From innovation surveys we can employtwo different indicators: (1) information sources, (2)

partners in co-operation.3Innovation indicators are becoming classical within

the EU-15, although the adoption of internationallyharmonised surveys needs some time in Central andEastern Europe. Innovation surveys are at the exper-imental stage in the transition economies and Hun-gary has not yet run a full-scale survey, although insome ways the European investigation process hasbeen followed in four pilot innovation surveys. Hun-gary was the forerunner in testing innovation survey(Inzelt, 1995b),although subsequently it slipped be-

hind the other transition economies (e.g., Poland, Es-tonia and Slovenia). Up till mid-2003 Hungary hadundertaken four pilot surveys and is about to launch abroad, full-scale statistical survey.

Co-operation related indicators taken from innova-tion surveys are a good starting point for further in-vestigation (as presented inSection 5).

3 According to surveying experience, respondents categoriseco-operation rather less firmly than in our definition. They usuallyidentify types 616 as co-operations.

Series of case studies and ad hoc academic surveys

Systematic qualitative studies investigated therole of university research in the innovation pro-

cess, studying the example of the Budapest Uni-versity of Technology and Economics, a universitywhich carries substantial weight among highereducation institutions and which was the most suc-cessful in networking with industry (Dvai et al.,2000). Many other case studies were carried out inHungaryon basic research organisations, spin-offand spin-out firms or on large foreign investorswhose role in technology upgrading also highlightsthe character of interactions.

4. The changing role of government

Interaction and integration among research area, in-dustry and location are processes which entities in sci-ence, technology and innovation policy actively try tostimulate or build and improve.

Research and technology policy has financial aswell as juridical aspects and there is also a structuralaspect in the sense that a decentralised and totally fair,spatial distribution of institutions is perceived as ideal.

In general, any government could support collabo-

ration in R&D programmes which demand it, whereasindustry will only opt for such collaboration if it of-fers specific advantages. Throughout the world, policyprogrammes tend to call for collaboration and integra-tion, and R&D and innovation policy programmes areimportant co-ordinating forces in the field of fundedresearch and of supported innovation activities. Gov-ernment programmes can encourage industry by min-imising the risk of partnership-building with a strongscience base.

The collapse of socialism forced the transition

economies to redefine the governments role as rulerand regulator and to create a new environment. Thistransformation of the governmental role includes thesetting of priorities, the highlighting of the break-down of public funding by objectives, by financinginstruments and by beneficiary organisations. In Hun-gary, as a transition economy, new legislation andpolicy-making, a new governmental structure and newways of thinking were crucial in finding for the gov-ernment a new role as facilitator. The development ofan innovation-friendly business environment, which

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is a necessary precondition for a well-functioningknowledge-driven market economy, needs severalmore years, or even decades, in the CEE region.

The financial and structural realities of the na-tional environment may support or reward collab-oration, and Section 4.1 highlights several of thefinancial aspects. The Hungarian government has seta diffusion-oriented approach at the centre of its in-novation policy and has integrated Hungarian actorsinto national and international co-operation.4

The governments role as facilitator depends notonly on the size of the R&D budget, but on the modeof allocations, direct and indirect measures and the ef-ficiency of each measure. In the allocation of publicfunding the sources are in two main categories: institu-tional support and programmes. The financing of R&Dinstitutions and of research programmes and projects,as well as the financing of important technologies arethe most common forms of political influence on in-novation. Both institutional and research programmefunding strongly shape the model of innovation andthe frequency and character of interaction among themain actors. In Hungary, programme funding is stillless than general funding.

This dimension of government funding, there-fore, offers many opportunities to encourage such

S&T policy aims as collaboration among the coreactors in innovation. The proportion of governmentfunding devoted to R&D, and initiatives to encour-age R&D co-operation, reflect current governmentpriorities.

In recent years, the main declared priority has beento stimulate business demand for R&D, to encouragetechnology transfer, to promote new technology-devoted SMEs, to preserve and strengthen R&Dcapabilities and to encourage joining internationalnetworks. The encouragement of businessuniversity

collaboration is also among the aims. Section 4.2

4 As against the many failures in S&T and innovation policy ap-parent during the past dozen years, the real breakthrough has hap-pened: the shift from a mission-oriented, picking winners style ofpolicy-making towards a diffusion-oriented approach. By extract-ing policy aims relating to the creation and nurturing of interaction,networking may be identified in different programmes. This meansthat many governmental measures offer incentives to encouragethe diffusion of new and technology-relevant knowledgeor, inother words, interaction among the key actors in the innovationprocess.

highlights the main government programmes whichencourage industryuniversity co-operation. It il-lustrates how declared priorities are present in

practice.

4.1. Spending on R&D and financial measures forindustryuniversity collaboration

As has been emphasised in earlier literature, therate and direction of the development of a countrysscience base is strongly influenced by its level of eco-nomic development (Pavitt, 1996).Financing R&Dhas a strong impact on the activities of the nationalsystem of innovation. The mass of R&D funds and

allocation of R&D resources together can encourageor discourage industrial innovation. The level of Hun-garian R&D funding is low by international standards(OECD, 2001; Inzelt et al., 2003). Gross domesticexpenditure on R&D to GDP was below 1% in thelast decade.

As Fig. 1 shows, total Hungarian expenditure onR&D (GERD) and the ratio of business R&D (BERD)to GDP are far below the OECD average. A hugecontrast can be seen in the ratio of business R&Dexpenditure (BERD) to GDP, and, in contrast to thesteadily increasing OECD average, Hungarian indi-

cators declined in this period; only in 2001 did theyreach their 1994 level. The difference is much smallerin the size and trend of ratios between Hungarian andOECD higher education R&D expenditure (HERD)to GDP.

The figure shows the indicators only from 1993,since when they have been comparable. Even ifpre-1993 indicators are not comparable with laterones, we can assert that the trend in R&D expen-diture had sharply declined in the first phase of thetransition period and that business R&D expenditure

had declined faster than government expenditure.5 Atthe start of the transition (around 1990) Hungarian

5 Hungary followed UNESCO methodology in her R&D statis-tics since 1988. The country started to revise R&D surveyingmethods and adopted OECD standards in 1993. The OECD revi-sion of Hungarian figures shows that BERD (business expenditureon R&D) was overestimated (56% in 1991). Recent figures (39%in 1996) are closer to reality. We cannot solve the problem of theoverstatement of figures before 1992, although we can state thatthe proportion of business-funded R&D declined during the yearsof transition.

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0

0,5

1

1,5

2

2,5

1994 1995 1996 1997 1998 1999 2000 2001

Years

%

Hungary GERD to GDP

Hungary BERD to GDP

Hungary HERD to GDP

OECD GERD to GDP

OECD BERD to GDP

OECD HERD to GDP

Fig. 1. R&D expenditures by financiers to GDP in Hungary and OECD average.

R&D expenditure was much higher both in relativeand in absolute terms, standing at approximately 2%of a (higher) GDP. After some 6 years, i.e., in the late1990s, the economy started to recover, which had apositive influence on both public and private R&Dexpenditures. Both have increased in the second phaseof the transition.

Public funding of R&D totalled HUF 23.8 Billion(USD 104 Million) in 1995 and doubled by 2000 atcurrent prices (CSO, 2001, p. 8). Public funding is stillthe major source of R&D activity and the proportion

contributed by the government sector was 55% in 1995and still over 50% in 2001.

Business financed 38% of R&D in 1994 whilst for2001 the relevant figure was 35% (CSO, 2002, p. 53).In the second phase of the transition period businessorganisations could afford to invest more in R&D;expenditure increased by 12% in 1999, 32% in 2000and 23% in 2001.

During the last 3 years the greatest increase infunding was from foreign (public) sources, which il-lustrates a growing involvement in EU and other inter-

nationally funded projects. The foreign sectors sharedoubled in 5 years, although the proportion is still nomore than 10%. The other sources, such as non-profitorganisations are outside the scope of official stati-stics.

To date, the state budget remains the most impor-tant source of finance for R&D and in the secondphase of the transition, roughly 54% of total R&Dexpenditure was financed by the government sector.We can, therefore, see that not only is the ratio ofGERD to GDP far from the Barcelona criteria but also

that business participation in funding is far below thatlevel.6

A detailed look at the funding and performanceof R&D by the main actors in innovation illustratesthe different R&D role of universities, institutes ofthe Hungarian Academy of Sciences and businessorganisations. The structure of activity of the mainR&D-performing sectors provides some key factsas to how different sectors participate in basic andapplied research and experimental development.

However, the transformation of the Hungarian sys-

tem has resulted in some important changes in the per-formance of R&D activity among the sectorsand ineach sector by the type of activity. Both adjustmentshave their impact on present and future collaboration.The type of R&D activity carried on by business en-terprises shows a strong shift from basic and appliedresearch towards experimental development (Table 2).

In the last few years the participants in basic re-search have been public (Hungarian Academy of Sci-ences) R&D laboratories (some 55%) and universities(about 40%). The role played by business enterprises

was in the region of 5%, although this decreasedsharply from 1993 to 1999 (from 6.3 to 2.6%). Since1999 the proportion of business enterprises in basicresearch has increased and has reached the level ofthe pre-transition crisis. In 2001 it was double (5.6%)the 1999 level.

6 Barcelona criteria mean that overall spending on R&D andinnovation in the European Union should be increased with theaim of approaching 3% of GDP by 2010. Two-thirds of this newinvestment should come from the private sector.

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Table 2Proportion of current R&D expenditures in performing sectors by type of activity in Hungary (%)

Sector 1993 1994 1995 1996 1997 1998 1999 2000 2001

Business enterprise 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0Basic research 5.2 6.2 4.2 4.7 4.4 2.9 2.2 3.0 4.7Applied research 38.9 44.8 38.8 32.8 30.5 40.2 26.5 26.3 19.3Experimental development 55.9 49.0 57.0 63.5 65.1 56.9 71.3 70.7 76.0

Government 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0Basic research 55.7 61.1 57.8 56.8 58.4 54.3 54.4 56.7 54.6Applied research 33.7 29.8 34.1 34.9 32.1 33.1 33.2 33.0 36.7Experimental development 10.6 9.1 8.1 8.3 9.5 12.6 12.4 10.3 8.7

Higher Education 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0Basic research 44.4 47.8 45.4 47.5 44.9 43.8 44.3 46.0 51.8Applied research 41.9 39.2 41.0 39.1 38.8 39.7 37.6 37.2 33.7Experimental development 13.7 13.0 13.6 13.4 16.3 16.5 18.1 16.8 14.5

Grand total 1a 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0Basic research 25.5 30.8 27.9 30.7 27.6 29.9 29.9 29.3 30.3Applied research 30.0 33.8 35.4 33.6 29.3 35.3 29.8 29.3 25.9Experimental development 23.2 22.9 29.6 31.4 31.1 28.5 34.1 34.6 33.8Othersa 21.3 12.5 7.1 4.3 12.0 6.3 6.2 6.8 10.0

Grand total 2a 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0Government 25.5 26.6 26.4 29.7 25.7 31.6 33.8 28.1 26.2Business enterprise 31.3 34.8 42.5 41.0 38.0 35.4 35.8 38.7 36.2Higher education 21.8 26.0 24.1 25.2 24.4 26.8 24.2 26.5 27.7Othersa 21.4 12.6 7.0 4.1 11.9 6.2 6.2 6.7 9.9

Source:Series of Research and Development HCSO, Department of Living Standards and Human Resources Statistics, Section of CulturalStatistics 1999, p. 53, 2000, p. 54 and 2001, p. 51.

a Including the expenditures spent outside the R&D units, that are not detailed by sector of performance and type of activity.

The increasing proportion may suggest that Hun-gary is following the trend visible in most OECDcountries. The time period, however, is very short.Over the past 10 years the participation of companiesin the funding of academic research has increased inthose OECD countries where it generally remains be-low 10% (Geuna, 1999). The year 2000 might turn outto be a turning point in business R&D involvement.

Another important feature of changes in business

expenditure may be observed in the readjustmentby type of activity. The leading role of business inapplied research radically decreased in favour of ex-perimental development. This might be evaluated asa sign of a more application-oriented attitude in busi-ness R&D efforts. The changes, increasing the sumsto spend on R&D activity and the shift from basic andapplied research towards experimental development,may result in more collaboration-oriented businesswith universities and R&D institutes. However, in aless advanced economy it might be a sign of the po-

sition of experimental development of domestic com-panies in the international division of labour. In thelatter case, business enterprises are hardly involvedat all in basic or applied research-related interactionswith universities. That means firms will want to col-laborate with universities more in development. Forgovernment policy it is a delicate matter to facilitatebusiness, since only strongly innovative organisationswhich are capable of regular collaboration with other

protagonists in the national system of innovations,can create a demand for basic and applied research.

4.2. Stimulating industryuniversity co-operation

The changing priorities of S&T policy are expressedin public R&D contracts, reimbursable grants, in R&Dprocurement and so on. These measures influence in-teraction among different institutional players in inno-vation and in R&D. The distribution of public fundingfor R&D and innovation has been changed by chan-

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nels of financing instruments. In Hungary many newchannels of public funding have appeared, reflectinga new economic and institutional environment (Inzelt,

1996;Havas, 2002; Balogh, 2002). Irrespective of howmany new support framework programmes and instru-ments are enacted, few of them have reached a criticalmass to be effective. The proportion of direct publicsupport remains vital.

The Sunrise and Sunset programmes have aremarkable role in the reorganisation of the publicfunding structure. Those programmes which declareco-operation to be a direct aimof their support musthave a measurable impact.7 This section focuses on theSunrise programmes of the Hungarian Central Techno-logical Development Fund (KMFA), since this pro-vides some good examples.

During the first decade of transition, the vast ma-jority of Sunrise R&D programmes were launched bythis state foundation devoted to R&D oriented tech-nology advancement (and innovation) programmes.8

This foundation mainly supports applied research andexperimental development through a competitive callfor proposal systems, and managed roughly 12% of thegovernments R&D expenditure in 2000 (CSO, 2001,p. 29).9

Here we may sum up the existing support schemes

by employing publicly available information. The

7 Any measure, which encourages an increase in funding busi-ness R&D (such as R&D tax loans) has at least an indirect impacton collaboration. Tax incentivesfor research and technological ac-tivities were introduced in Hungary in 1996. The preconditions forthis incentive are tax-paying economic actors and the applicationof tax laws. (The companies can account for their R&D expendi-ture at 200%.) Of course, efficiency depends also on the measureitself. The recent (2002) modification of this measure had a di-rect effect on collaboration. This option has also been availablefor extramural R&D activity, contracted out either to universitiesor other organisations. (Data on tax credit are not available for

research purposes.)8 There is another state foundation, the National Scientific Re-search Fund (Hungarian abbreviation OTKA), which was estab-lished in 1986 and supervised by the Hungarian Academy ofSciences (before the transition). Since 1991 this foundation hasbeen working as an independent organisation. OTKAs missionis to support basic research, the scientific work of young re-searchers and the development of an R&D infrastructure. Besidesthe (very limited) block-grant of universities this is the sourceof curiosity-driven research. This foundation may support collab-oration in the academic sphere among universities and betweenuniversities and research institutes of the HAS.

9 http://www.om.hu

proposals express government policy. The encour-agement of networking and the facilitation ofindustryuniversity (academic) interaction are part of

the KMFA programmes. Table 3 summarises theKMFA supporting schemes since 1995. The previ-ous years of transition had meant an interval in suchtargeted programmes stimulating universitybusinesscollaboration. Those years brought in institutional,organisational restructuring, the privatisation of busi-ness and recovery from economic crises. 1995 wasthe first year when the encouragement of collabo-ration (between university and industry and amonglarge and small firms) was not only on the wish-listof government programmes but also received someincentives. Table 3 also illustrates the time-line ofcalls from government targeting co-operation.

The KMFA programmes are in two groups ac-cording to the importance of universityindustry col-laboration and are set out in Table 3.The first groupcontains one single programme giving very high pri-ority to collaboration. The second group containssix programmes which include universityindustrycollaborations among their priorities.

As the time-line of Table 3 clearly shows theencouragement of collaboration was among the sec-ondary priorities until 2000. The first programme to

incorporate universityindustry collaboration amongits aims was a very old bottom up-type, on-goingprogramme run since 1991, entitled Promotion ofapplied research. The programme was modified in1995 and gave preferences to firms, which intend toco-operate with a university, college, public researchinstitute or R&D non-profit organisation in order toimplement development, as well as projects generatinga clearly detectable economic result in a short time.(OM, 2002, p. 14) Preconditions for this modificationwere new legislation together with several institutional

changes. The new proposal which was the subject ofthis programme was launched before the stabilisationand recovery of the economy and a modification ofpreference such as this programme proposed consti-tuted a breakthrough in policy development. However,the policy thinking behind such a scheme was closerto the linear model of innovation (demand-led) thanto the feedback loops model. This priority setting hada limited impact on the development of collabora-tion, and the type of interactions encouraged by theprogramme were buying university research on an ad
http://www.om.hu/

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Table 3Supporting schemes targeted industryuniversity co-operation (central technological development fund)

Co-operation is 1995 1996 1997 1998 1999 2000 2001 2002

The primary aimSetting up high-tech laboratories + + + + +Co-operative research centre + + +

Among the prioritiesPromotion of applied R&D + + + + + + + +IKTA (IT application) + + + + + +Up-to-date equipment purchase + + + +Development high-tech laboratories + + + + +Biotechnology 2000 + + +Technology for environment protection + + +

hoc basis (5); access to special equipment (12); in-vestment in a universitys facilities (13); and regularacquisition of university research results (14).

Without going into detail, there were four mainreasons for their limited impact: (1) transition crises,including a shortage of funding, (2) the transforma-tion of business organisations, (3) the gap-toothednature of the regulations and (4) the funding schemeitself. Further, the proposal dealt with non-profitR&D organisations in the same way as profit-orientedbusiness organisations, the use of own funds inR&D expenditure was compulsory for all actors and

the ownership of inventions had not yet been clearlyregulated at universities.

Two years later, when the economic situation hadstarted to improve, a brand new programme on infor-mation and communication technology was launched(Hungarian abbreviation IKTA). The objective ofthe aid: the development and testing of marketable,new information and communication proceduresbased on experimental and modern technology, in thecomputer network of higher education and researchinstitutions. (OM, 2002, p. 15) The financial measure

was modified from previous ones, an illustration oftry, try and try again in policy-making. It relievedbusiness of the previously compulsory requirementto share in the costs of basic research. A supportedorganisation had to be a public or non-profit researchorganisation (university, institute of HAS and so on).This new rule removed one of the burdens of set-ting up businessuniversityacademic collaboration.The success of this programme originated not onlyfrom the revised measure, but also from the improvedeconomic situation in the ICT sector and from excel-

lent scientific capabilities in this field of science andeducation. This scheme seemed much more able toencourage collaboration.

Since 1998, Hungary has definitely been in the sec-ond phase of transition, a phase which offers moreoptions for launching new R&D programmes. Twoother targeted programmes followed the experimentalscheme of the IKTA programmewith slight modifi-cations:Biotechnology 2000and Technology for Envi-ronmental Protection. The new programmes put moreemphasis on giving preference to projects which en-gage in development in co-operation with a university

and/or research facility.These programmes encouraged co-operations (type

15, contract research) and many other forms of col-laboration, but they did not encourage several typesof interaction (types 610 inTable 1),although thesedid occur as side-effects of other interactions.

The previous programmes concentrated on newtechnologies whilst anotherUp-to-date EquipmentPurchasediffers from them. This stop-go work-ing programmes main function is to assist publiclaboratories. The aim is to diminish, or at least not

to allow an increase in, the gap between Hungarianuniversity laboratories and leading edge laboratoriesin the area of general equipment. The availability ofup-to-date general and so-called standard as op-posed to special laboratory equipment can attractbusiness to provide any special equipment neededfor collaborative research (type of interactions 13 inTable 1). It is hard for universities to act as potentialpartners if their capital expenditure on instrumenta-tion and equipment is declining whilst business isalmost doubling such investment. In 2000, business

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expenditure on instrumentation and equipment was86% higher than in 1998, whilst in the same pe-riod university expenditure decreased by 3.5% (CSO

Research and Development, 2001, p. 57). These op-posing trends underline the importance of that pro-gramme. Without such permanent new streams offunding, universities can hardly hope to be consideredby industry as potential partners in collaboration.

Another brand-new government incentive was theprogramme for setting up high-tech R&D labora-tories launched in 1998. The principal target groupwas strong, large-scale business with good potentialto invest in strategic research. The main target wasto encourage industry to set up or expand existinghigh-tech laboratories.

This government initiative offered a large grantfor those establishing a research facility, either asan individual economic organisation or as a separateunit within an existing business organisation utilisingmodern technology. A minimum investment of HUF500 Million (USD 2.2 Million) is required. Within 6months of the completion of the investment and thestart of operations the employer must employ at least30 researchers in new, full-time extra jobs.

These projects are granted up to 25% of the invest-ment and the support is valid if they operate the R&D

laboratory for at least 5 years according to its origi-nal aim. In 2000, the general conditions for the sup-port scheme were modified and applicant firms cannow repay 50% of R&D-supporting credit by meansof R&D outsourced to universities. The limit of thiscredit-to-grant conversion is 25% of the total projectcost. An additional grant is available for employing atleast 10 HE researchers and investing at least an ad-ditional HUF 150 Million (USD 0.66 m).

Between 1998 and 2001 the number of applicationswas 28, of which 11 were approved. All 11 com-

panies are foreign majority-owned. The supportedhigh-tech laboratories are in four sectors: vehicleparts, ICT, lighting technology and environmentalprotection equipment (none is involved in biotechnol-ogy even though Hungarian scientific capabilities inthis area are remarkable). An interesting fact is thatthe proportion of KMFA support to total invest-ment was far below the 25% limit, the average being14%. This may demonstrate that the governmentsmessage was more important than the size of thegrant.

It was assumed that a significant upgrading of busi-ness research capabilities would create good partnersfor universities and that the creation of new R&D

jobs could encourage mobility from university to in-dustry and joint research activities. These laboratorieshave created more knowledge-intensive jobs and cancontribute to universityindustry collaboration. Theprogramme has facilitated interaction (type 16) andnormal R&D co-operations (types 6, 7 and 17). Oncemore the side-effects of the supported interactionswere the presence of other types of interaction, such as8, 10, 1215. But the number of companies involvedis rather limited, even if 11 new high-tech laboratoriesmean a considerable success for the whole economy.

The other programme, the Co-operative ResearchCentre concentrated on the establishment of co-operative research centres (CRCs). This programmemade the centrepiece of collaboration the universityinstead of business. This shift in the leading actor rolefrom previous programmes of the foundation maybe interpreted as the desire of the policy to build onthe universities potential as drivers of growth in theknowledge-economy. Another interpretation may bebased on the reasoning that decision-making was heav-ily influenced by the reorganisation of the governmentstructure.10 This modification in the national system

of governance of innovation policy has had an observ-able influence on programme setting. The overall aimof the programme is to encourage the establishmentof research centres and to support their operation, inclose relation with Hungarian higher education (HE)institutions, other non-profit research facilities andmembers of the corporate and business innovation sec-tor, and in which education, research and developmentand also knowledge and technology transfer can beintegrated for strategic purposes. A CRC can only beestablished together with business partners and The

leading institutions of the consortia may only be thoseoffering Ph.D. courses and accredited by the Hun-

10 The former governmental agency, the National Committee forTechnological Development (OMFB) which used to be supervisedby the KMFA foundation became a Division of the Ministry ofEducation. The recent history of KMFA and its handling gov-ernmental agency, the so-called National Technology DevelopmentCommittee (OMFB) can be a very good example to study as tohow changes in a principal organisation can change an agentsbehaviour without changing the official mission. This, however,would lead us far from this article and I shall not discuss it further.

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garian Accreditation Committee. Needless to say,this programmes main aim was to create Type 16interactions with the involvement of all other types.

Up to the end of 2002, five CRCs were approved forgrant aid, two of them in the capital, Budapest, andthree in the provinces.11 Changes in the ranking ofparticipants, incidentally, do not mean higher grantsfor basic research (Pavitt, 2002; Senker, 2001).

Due to redeployment in the division of labour inS&T, the government adopted a new large-scale pro-gramme, the National Research and DevelopmentProgramme of the Szchenyi Plan (NRDPS). Thiswas launched in late 2000 and the KMFA foun-dation is participating merely as one of the foundermembers. The size of KMFA has steadily decreasedsince 1999. New budgetary channels, or re-allocationsof funding, were introduced for the support of R&Dand innovation within the framework of this pro-gramme. The National R&D Programme is intendedto synchronise basic and applied research with tech-nological development, to strengthen and ensure theefficient utilisation of national research and de-velopment capacities and to improve our interna-tional scientific competitiveness. The programmepromotes the R&D projects of consortia led by

Higher Education or R&D institutes and compris-

ing those companies participating in the use ofR&D results. The formation of consortia is manda-tory except in Programme 5 (Social Science).12

11 The NRDPs are built on a tender system focusing on fivefields: (1) improving the quality of life, (2) information and com-munication technologies, (3) research into environmental and ma-terials science, (4) research into agribusiness and biotechnologyand (5) research into the national heritage and contemporary so-cial challenges. Members of consortia may be any legal entitiesand organisations without legal status registered in Hungary. Any

research institution or business venture registered in the EU ornewly associated countries can join the consortia but they cannothave Hungarian public support. (http://www.om.hu).12 The NRDPs are built on a tender system focusing on five

fields: (1) improving the quality of life, (2) information and com-munication technologies, (3) research into environmental and ma-terials science, (4) research into agribusiness and biotechnologyand (5) research into the national heritage and contemporary so-cial challenges. Members of consortia may be any legal entitiesand organisations without legal status registered in Hungary. Anyresearch institution or business venture registered in the EU ornewly associated countries can join the consortia but they cannothave Hungarian public support. (http://www.om.hu).

The main target of this programme was, again, interac-tion (type 16). Since the consortia were led by univer-sities, several types of interaction: type 9 (joint publi-

cations by university professors and company employ-ees) and type 11 (joint IPRs)received more attentionthan in previous programmes.

The funding criteria and the minimum size of grant(HUF 100 Million =USD 0.44 m, but excluding so-cial science) for the entire duration of the project sym-bolise in S&T funding that: government has acceptedits responsibility for the advancement of basic scienceto the critical mass stage.

Table 4 gives an overview of leading institutionsand member organisations of consortia by field. Atfirst glance, it may seem that the funding agencycould not maintain its own criteria in respect of theselection process in that some of these consortiaare led neither by Higher Education nor by R&Dinstitutes. This phenomenon offers some food forthought about the relationship between targets andsubsidies, the innovation model followed, the man-agerial capability of R&D consortia with differenttypes of participant, business and public organisa-tions and the ability to formulate research agenda.In 20012002, the number of consortia awardedgrants was 200, involving 1012 actors from universi-

ties, companies and institutes of the HAS. Far feweractors were involved from other budget-supportedand non-profit organisations and from municipali-ties. The projects to be supported within the NRDPframework have involved various entities in vari-ous forms of collaboration, as Table 4demonstrates.From the table it can be seen that one-tenth of theconsortia led by companies, but almost one-thirdof members, are business organisations. The num-ber of members from the universities is almostthe same, but two-fifths of the lead organisations

are universities. Each consortium has, on average,one-and-a-half business and university partners. Al-most all have partners from institutes of the HASbut many fewer from other types of organisation.13

13 See detailed description on the structure of research organisa-tions in post-socialist countries in EBRD (1999), OECD (1993),Inzelt (1995a), 1999, 2000, Gokhberg, 1996. Here I suggest ac-counting for academic institutes in the university sector as intri-sector distribution. Their features mean that they are muchcloser to university laboratories than to so-called governmentones.
http://www.om.hu/http://www.om.hu/http://www.om.hu/http://www.om.hu/

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Table 4Overview on granted consortia in the frame of NRDPs, 20012002

Indicators Improving

quality oflife

Information and

communicationtechnologies

Research in

environmentalprotection andmaterial sciences

Agricultural and

biotechnologicalresearches

Research in national

heritage and in thepresent socialchallenges

Total

NRDP

Number of granted projects 31 21 45 30 73 200Number of actors 151 87 290 296 188 1012

Number of consortium leaders by organisationsUniversity 18 9 19 18 24 88Institutes of HAS 4 6 8 7 33 58Company 2 6 12 1 21

Average number of consortium participantsTotal 4.9 4.1 6.4 9.9 2.6 5.1University 1.7 1.4 2.1 2.6 0.8 1.6Institutes of HAS 0.8 0.8 0.8 0.8 1.0 0.9

Company 1.0 1.4 2.5 4.2 0.1 1.5

Source:Compiled from the databank of the Ministry of Education.

(The participation of municipalities is a relativelynew phenomenon in the Hungarian innovationsystem.)

Neither anecdotal evidence nor self-evaluationby agencies is suitable to evaluate the outcome ofthe programmes. One dimension of potential im-pact is touched upon: whether the call was able to

attract either joint applications or applications withthe involvement of other actors from different largesectors.14 Systematic statistics do not exist aboutthe universities involvement in co-operation underKMFA programmes. According to oral informationfrom programme managers, newspaper reports anduniversity news, all of these laboratories involve anumber of departments of different universities and/oracademic institutes in their research activities in oneway or another. Mapping such co-operation, includ-ing the generation of new knowledge and know-howwithin this framework requires studies as to how in-tensively and effectively these centres connect thedifferent actors.

14 Except for the National R&D programme only semi-officialinterim reports are available on such simple information as to howmany partners, and from where, are in consortia. The statisticalinformation is very rough.

5. The hunger for innovationBusiness searching

for partners in innovation

Various papers in the literature on the economicsof knowledge emphasise the co-evolution of scientificperformance with national technology and economy,and it is important to ensure that conditions encour-

age technological diffusion in the network society(Conceio and Heitor, 2002). Casual observationshave shown that an innovation network can be createdand exist if business firms are hungry for innovation.No models of innovation can exist without this. Theknowledge-based view of innovation suggests that in-novation involves complex interactions among differ-ent groups and regions. These interactions also haveshaping and filtering effects upon innovation itself.The type and level of interaction and involvement innetworks are strongly influenced by the innovativeness

of the economic actors and by the nature of innovation.Business has a dual position in R&D: in both financ-ing and undertaking activities, whilst business partici-pation in financing and in undertaking R&D correlateswith economic development. The initiation of inter-action and the intensity of knowledge flows from in-dustry to university and vice versa are influenced bymany factors. Business-specific factors (such as sizeand sector economic factors) have their effect on whichtype of, and how much, knowledge is needed, howthe acquisition of knowledge is organised and how

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strong is the receptive capacity. The business involve-ment in radical or incremental innovation, as well assector specificities and size, has an influence on the

type and frequency of interaction. A very rough mea-sure of business R&D initiation and receptive capa-bilities is business expenditure on R&D. Generally,the business sector has a weaker capacity to create,screen, encourage and to absorb new knowledge inless advanced countries such as Hungary. The level ofuniversityindustry interaction is lower in these coun-tries than in the more advanced.

5.1. Lessons of interaction extracted from innovation

surveys

Employing innovation surveys in an investigation ofuniversitybusiness collaboration allows us to identifyhow strong a factor is the influence of universities ina given system.

The indicators used for a birds-eye view descrip-tion of collaboration originate from four pilot inno-vation surveys. Two of these focused on large- andmedium-sized companies in the manufacturing sector,one investigated small- and micro-firms and the otherconcentrated on large- and medium-sized companiesin the service sector. The surveys were carried out

in 1994, 19992000, 2000 and 20012002. Their keyfeatures are shown inAppendix A.The surveys arenamed according to the fields covered: Manufacturing1, Services, Manufacturing 2 and Small and Micro.15

The different character of these surveys makes it hardto compare them and to come to firm conclusions.(They are not comparable in statistical terms, and noneof the samples is representative.) The relevant ques-tions regarding information sources and co-operationin innovation activity in four pilot innovation surveysdiffer (similarly to the EU questionnaires). This, there-fore, limits their comparability still further and so theconstraints of pilot surveys have to be borne in mindwhilst interpreting indicators. Despite the weaknessesof these data sources, they do seem to be useful sourcesfrom which to sketch at least a transitory pattern ofco-operation, which is changing over time.

15 The original aim of these surveys was to test the feasibility ofOslo Manual-guided innovation surveys in a transition economy,focusing on different segments of economic actors. Three of thepilot surveys were contracted by NTCD and its successor (OMFB92-95, OMFB TAN-98-55-10, OM- 2000-56-OMFB/2000-02802).

The first pilot survey (Manufacturing 1) clearlyreflects the first phase of transition, whilst the sec-ond surveythe Services reference periodstands

on the borderline between the two phases, not onlybecause of the period investigated, but also becauseof the subsequent reclassification of entities from sev-eral service sectors to the manufacturing sector. Thethird (Manufacturing 2) and the fourth (Small andMicro) surveys clearly belong to the second phase oftransition. Let me now present the similarities and dif-ferences in partnership-building according to the dif-ferent character of surveys.

5.2. Sources of information

The first step in the partnership-building processis to screen information, and the process of acquiringinformation may well be regarded as a premise forcollaboration. Companies need to use different kindsof information source for their innovative activities,and different kinds of information do not necessarilyhinge on R&D, but also on market-oriented informa-tion. R&D activity is only one part of the learningprocess; it not the single source for innovation, evenif radical innovation can scarcely be created withoutR&D. However, R&D is definitely no longer regarded

as a precondition but as an influencing factor onoras an adjunct totechnological innovation, just asother factors are also important.

The rank of intramural and extramural sources ofinformation for innovation can provide some informa-tion on the importance of higher education and publicR&D institutes for business. As the findings of pilotsurveys on innovationcovering different sectors andtime periodsshow, the top-level sources in the firstperiod of transition were professional journals, confer-ences and meetingswhich at least represent passive

relationships with domestic higher education and in-stitutes. That means that the types of interaction weremainly regular (informal) discussions between facultymembers and company employees (classified as item4 inTable 1).

All the Hungarian pilot surveys show that uni-versities (and other R&D institutes) are accepted assources of information in innovation activity but rarelyrecognised as important sources. The rank of highereducation was 8 in Manufacturing 1, Manufacturing 2and in the Services sector. The different timing of sur-

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Table 5Ranking of sources of information for innovation by type in different surveys of innovations

Sources of information Surveys

Manufacturing1 (19901993)

Service(19961999)

Manufacturing2 (19971999)

Small and micro(19982000)a

In-house 6 2 1 1Professional journals, conferences,

meetings2 1 4 4

Clients and customers 1 6 2 2Suppliers of materials and components,

equipment3 9

Competitors 5 3 2 2Fairs and exhibitions 4 4 3 3Computer based information networks 5 6 5Patent disclosures 7 10 7Other firms within the group 7 5

Higher education 8 8 8 6Consulting firm 7 Public (or non-profit) R&D institutes 11 Others 9

The shading indicates that the questionnaire did not include the item.a The questionnaire included this question only in the first-round of pilot survey, so the sample size was only 62 instead of 106.

veys and size categories did not change the positionof higher education as a source of information.

The low ranking among sources of informationawarded to universities mean that a majority of com-

panies do not employ them as direct sources forinnovation. It is an important signal for policy-makersif economic organisations are reluctant to developdirect links with knowledge-creating institutions suchas universities (Table 5).

It is worth investigating this low ranking further inorder to determine the reasons for it. The reasons may,in fact, be very differentfrom the low innovationcapability of companies to not-in-demand offeringsfrom universities. Companies might well lack recep-tive capacity for the acquisition of new knowledgeand also amongst the causes might be the weaknessof universities in providing up-to-date knowledge ac-tually relevant to the economy, the lack of (or weak)capability of business to acquire and employ newknowledge from universities together with unclearintellectual property rights. The environment alsomight hamper the use of universities as sources ofinformation.

Another explanation might be that universities are atthe bottom of the list of important information sourcessince they are crucial only to a very limited number

of companies. Co-operation arrangements may justifythis assumption.

5.3. Co-operation arrangements in innovation

activities

The relationship between innovation and interac-tion or networking is discussed in detail in the litera-ture. The normal institutional sources of informationare usually candidates for co-operational networkingand the main information sources are often amongthe most important partners in collaboration. Thosecompanies who do use universities as sources ofinformation regard them as very important sourcesand they usually involve universities (at least as con-

sultants) in development, adaptation, adoption andrealisation.Universities and research institutes are primary can-

didates for R&D co-operation if companies need them.The innovative character and capabilities of individ-ual firms influence the need for research collabora-tion in all sectors and size classifications, but, tak-ing into account the fact that innovation activity inall samples was usually incremental rather than fun-damental; the tendency towards a limited number ofR&D co-operations is not surprising. In fact, a very

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limited number of companies regard universities ascrucial partners in innovation. Naturally, R&D co-operation concerns only a few companies undertaking

their own research and involving others; and, there-fore, R&D co-operation predominantly involves largeenterprises in the technically oriented industries, andthe universities are regarded as an important, even ifnot as the most important, co-operation partner forsuch types of company. In general, the interaction inR&D co-operation between scientific and industrialsystems is structured asymmetrically among relativelyfew companies and universities and in a few, predom-inantly technically oriented, disciplines. These typesof Hungarian company were hardly covered by pi-lot innovation surveys. (On this point refer toSection4.2 Business involvement in co-operative researchcentres.)

Various forms of collaboration are suitable for dif-ferent economic actors and several types of innovationand knowledge flows between universities and firmscan produce benefits for companies through a multi-tude of different channels. In each investigated sectorand period the number of companies involved in co-operation in innovation is much lower than the numberof users of any information sources on innovation. Itmay be assumed that the companies which co-operate

in innovation are active innovators and that they areinvolved not only in incremental but in radical innova-tions also. This assumption is supported by scatteredempirical evidence (follow-up telephone interviews,company reports etc.) and might explain two some-what contradictory conclusions (based on two differ-

Table 6Co-operation on innovation activities rank of partners by geographic origin

Partner Manufacturing 1 Service Manufacturing 2 Small and micro

Rank Origin Rank Origin Rank Origin Rank Origin

Clients or customers 4 F 1 D 1 F 2 DResearch institutes (public and private non-profit) 1 D 2 F 6 4 D+Higher education 2 D 3 F 4 D++ 1 D+Enterprises within the group 7 D 4 F 3 F 5 =Suppliers 3 Almost= 5 = 2 D 3 DConsultant enterprises 6 D 6 D 5 D 8 DCompetitors 8 D (no F) 7 D 7 D 67 DIndustry R&D laboratories 5 D (no F) D (no F) 67 =

The shading indicates that the questionnaire did not include this type of partner. F: foreign dominated; D: domestic dominated; =: meansas much domestic originated as foreign ones; no F: none of the partners are foreign originated.

ent innovation indicators): as to why universities aremuch more important as partners in co-operation thanas sources of information.

Of four pilot innovation surveys, three showed thatuniversities are accepted as important R&D/innovationco-operation partners. We can observe some changesbetween different periods of the transition accord-ing to the perceived importance of co-operationpartners, although non-comparable sector/size cov-erage requires very careful interpretation. Table 6shows the ranking of co-operation partners by thenumber of innovators using particular co-operationpartners.

According to the ranking in importance of co-operation partners, higher education was cruciallyimportant for Manufacturing Sector 1 and Smalland Micro-firms.

The findings of the pilot survey on small and micro-firms match observations in other countries, that is,that small or low-tech firms can benefit much morefrom universities through ad hoc consultations, infor-mal discussions and access to special equipment for ashort period of time (interactions types 1, 4, and 12 inTable 1) than from other forms of interaction, althoughinternational experience warns that universities are re-luctant to become involved in unsophisticated R&D

activity (Senker, 2001) if they have a choice (Inzelt,2003).

The importance of higher education as a co-operation partner was ranked lower in the servicessector, not surprisingly, perhaps, since passive inno-vators are usually more frequent in the services than

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in the manufacturing sector. The medium rankingof HE among co-operation partners in the manufac-turing sector in the second phase of transition gives

more food for thought, although the small size ofsample makes it unreasonable to investigate furtherco-operation by sector distribution.

Another important difference may be observed bysurveys, if the types of partner are not only ranked butalso investigated in co-operation by their geographicalorigin. It is worth paying attention to any shift of lo-cation of the partners. The geographical dimension ofinvestigation provides a first glimpse of the interna-tionalisation of the innovation process. Differencesin the location of dominant partners might be due tocyclical variations or to other differences among thesamples.

Capital letters in each second column of Table 6illustrate the geographical location, by dominant part-ner. These data clearly show that the co-operationpartners universities (together with R&D institutesand industrial R&D laboratories) were most likely tobe located in Hungary, except in respect of the servicesector.16

Domestic higher education and research instituteswere much more frequent partners than foreignersin the first period of transition for manufacturing

enterprises. Towards the end of the first phase oftransition a further important symptom to be notedis that the companies investigated in the service sec-tor co-operated more frequently with foreign thanwith domestic universities, although, quite naturally,small and micro-firms preferred domestic institu-tions. Such cross-national co-operation raises not onlythe question of spatial closeness between universitiesand firms (Audretsch and Stephan, 1996), but alsothe internationalisation of R&D co-operation led byforeign investors.

We need much more empirical evidence to evaluatethe reasons for such differences in co-operation char-

16 One of the outstanding characteristics of domestic industry-Academy linkages is that regional co-operation is weaker thannational. A slight, but no greater, difference from this pattern isobservable among small and micro firms. To some extent thismay reflect a pattern of seeking excellence anywhere it is to be had.The distance of potential partners is much less important than theircomplementary capabilities and the quality of their knowledge.(A similar phenomenon was observed by Faulkner and Senker(1994)).

acteristics in surveys. Several pieces of additional in-formation are needed to draw conclusions about thereal value and importance of these slightly different

patterns of co-operation.

6. Conclusions

Interaction among different entities is vitally im-portant for the learning economies, and the Hungariantransformation has certainly produced some positiveresults.

The internationalisation of business R&D and in-novation processes affected Hungary as empiricalevidence has shown, and now some participants inthe national innovation system are on the way tointernationalisation. The first step, an extremely im-portant one to have been taken, is that certain Hun-garian organisations (either business- or HE-related)have managed to join the club of internationalisedinnovators. This is a remarkable performance for atransition economy. They may, however, be only in-vited guests and their life-long membership is notyet guaranteed, Hungarys role on that stage is stillopen.

New government measures are attempting to im-

prove the innovative attitude of business enterprisesand to connect the main actors within the innovationsystem. Growing co-operation among large innova-tive companies and universities and blossoming cross-national co-operation among participants are clearlyto be seen. The current Hungarian system of innova-tion offers more options for networking now than wasthe case before transition and during the first phase oftransition.

The government programmes launched since 1995can positively encourage arms length co-operation,

and if we consider these programmes we can seeclearly illustrated those elements of policy which gobeyond the traditional way of thinking about innova-tion in transition economies. These new programmestend to encourage closer links between public sectorresearch and private sector expectations.

A systematic evaluation of these government pro-grammes and incentives has not yet been carried out.One of their clear shortcomings is that they have in-fluenced only a limited number of actors: interac-tions are still rather limited in the move towards the

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knowledge- or learning-based economy. Co-operationis reshaping the role of all of the actors and of theirresearch agendas, something which will have a long-

term impact on the capabilities and on the future ofco-operation in general. Besides the positive external-ities, however, negative effects may also be produced.All developing countries such as transition economiesare facing the problem of the relatively weak inno-vative capabilities of their business enterprises. Busi-nesses which are barely innovative or which are mainlyinvolved in moderate innovation can still create lim-ited research tasksmainly in experimental develop-ment, design, trials and in the tooling-up processandthey search for co-operation partners in such tasks.These are, for the most part, in R&D services or innon-R&D activities and they may consider universi-ties for R&D services or non-R&D tasks, if other po-tential partners are lacking or are scarcely visible intheir environment, and, of course, if universities areready to be involved in such interactions. It is cru-cial, however, to continue investigating the other sideof this problem, that is, from the university stand-point.

The various sources of information highlighted thefact that only a limited number of companies are ac-tually interested in universityindustry interactions. In

that sense, Hungary does not differ from other, lessadvanced countries where the business sector has aweaker capacity to create, to screen, to encourage andto absorb new knowledge.

From the point of view of economic development,the existence of interactions is a positive sign of inno-vation orientation. Broadening the circle of businessentities which are hungry for innovation, however, isa delicate policy issue. Small and micro-firms needexpertise in their environment, but universities andresearch organisations are not very interested in being

involved in such research tasks and their organisa-tional context hardly helps in serving expertise intheir region.

The low number of companies involved in inter-actions and enjoying the incentives of governmentalprogrammes corresponds to the limited number ofcollaborating firms identified through pilot innovationsurveys. Taking into account these two different piecesof information we can assert that the Hungarian econ-omy reveals a dual pattern in the field of innovation. Anumber of companies are innovative and active collab-

orators and can be involved in co-operative researchin R&D with universities. However, the great major-ity of companies are rarely innovative and, if they

are so at all, they are involved in incremental innova-tion. The continuing low innovativeness of Hungarianbusiness companies is the main factor hampering col-laborations. All government programmes encouragingcollaboration between business and university arevery important for capability-building, but they cannotproduce a real breakthrough in the innovation-basedcompetitiveness of the economy. A comprehensiveinnovation policy which has not yet been accepted inHungary could provide incentives to upgrade the in-novativeness of companies, to improve their innova-tive performance by means of collaborations amongthe key players.

One of the main challenges for any governmentin a transition economy is to facilitate the long-term,positive-sum game. A business partnership for univer-sities, and vice versa, is much more than a questionof financial resources, asTable 1highlighted, and adetailed investigation of businessuniversity collab-oration from both business and university perspec-tives is important for policy-making. The availabledata from pilot innovation surveys and assembledadministrative data allowed us to study the general

existence of interactions. The scattered informationshowed that those types of interactions that are rep-resenting arms length or triple helix pattern havepenetrated into Hungary. Several large firms are par-ticipating in joint supervision of Ph.D. theses andinvesting in university facilities. Some enterprises ineach size category are regularly acquiring universityresearch. Both types of formal R&D co-operationhave occurred between several universities and largecompanies. Only more detailed, more reliable in-formation can permit evaluation of the real role

of different types of interaction and their evolvingpatterns.

Acknowledgements

The author wishes to express her thanks to KeithPavitt and Chris Freeman for helpful comments, sug-gestions and for their special care. The usual dis-claimers apply.

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Appendix A

Key character of pilot innovation surveys

Characters Manufacturing 1 Service Manufacturing 2 Small and micro

Reference period 19901993 19961999 19971999 19982000Size (number of respondents) 110 100 291 106Response rate (%) 23 11 16.6 21Questionnaire Modified CIS-1 Modified CIS-2 Modified CIS-2 Modified CIS-2/3Survey unit Firm Firm Firm FirmSize classes (employees) 100< 10< 20< 150Surveying organisation IKU IKU CSO IKU

SourcesManufacturing 1: Inzelt, 1995b,pp. 2122; Service:Inzelt, 2002b,p. 382; Manufacturing 2:CSO,2001,p. 5; Small and micro: Inzelt et al., 2003,p. 23.

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