Science–industry links in Central and Eastern Europe and the Commonwealth of Independent States: conventional policy wisdom facing reality

Science and Public Policy June 2011 0302-3427/11/50365-14 US$12.00 Beech Tree Publishing 2011 365

Science and Public Policy, 38(5), June 2011, pages 365–378 DOI: 10.3152/030234211X12924093660435; http://www.ingentaconnect.com/content/beech/spp

Science–industry links in Central and Eastern Europe and the Commonwealth of Independent States: conventional policy wisdom facing reality

Slavo Radosevic

This paper analyses the factors behind widespread policy failure to support science–industry linkages in Central and Eastern Europe (CEE) and Commonwealth of Independent States (CIS) countries. We explain this failure as being due largely to uncritical application of conventional policy wisdoms in the context of ‘catching up’ and ‘laggard’ economies. Our argument is based on evidence on knowledge-intensive enterprises in CEE and CIS countries and analysis of the innovation policies of these regions. Our conclusion is that support for science–industry linkages in CEE/CIS should be balanced by efforts to strengthen the ‘actors’ involved (existing large and small firms, universities and public research organizations) and reinforce other linkages in the respective national innovation systems, and especially knowledge links between domestic and foreign firms.

HERE IS A WIDESPREAD OPINION among analysts that in the transition period, the CEE and post-Soviet (CIS) countries have failed to

capitalize on their science bases (Meske, 2004; Svarc, 2006; Watkins, 2003). This failure has been despite potential large research and development (R&D) as-sets in terms of R&D labour force and despite policy initiatives aimed at the commercialization of R&D assets or enhancement of science–industry linkages. This situation is persisting in the period of relatively high economic growth that the majority of these economies have been enjoying since 1998 (or even earlier for some CEE countries) and in a context where science–industry linkages are the focus of

innovation policy. This policy focus in these countries

is aimed at achieving long-term growth based on innovation not just production efficiency.

The situation calls for more systematic enquiry into

the causes of the failure of science–industry policies. The investigation in this paper exploits the prism of

technology-based competition and systems of innova-tion and provides new evidence on the macro- and

micro-environment of science–industry links, and the

limitations of current policies designed to support them in CEE/CIS. Our argument is that the conven-tional policy wisdom in this area is in stark contrast to

the reality of these economies and requires some re-thinking. We observe widespread ‘linkage failures’ in

these regions due largely to uncritical application of

conventional policy understandings in the context of

‘catching up’ and ‘laggard’ economies. Support for

science–industry linkages in CEE/CIS should be bal-anced with support for the existing large and small firms, universities and public research organizations

(PROs) and other linkages in the national innovation systems (NIS), especially knowledge links between domestic and foreign firms.

Any attempt to investigate this problem in the re-gion known in World Bank parlance as ECA (East-ern Europe and Central Asia), will necessarily be eclectic in terms of an empirical basis. This study is

T

Slavo Radosevic is at the School of Slavonic and East European Studies, University College London, Gower Street, LondonWC1E 6BT, UK; Email: [email protected].

The research that forms the basis for this paper was fundedthrough the author’s participation in three EU funded projects:KEINS (Knowledge-based entrepreneurship: innovation, net-works and systems), specific targeted research or innovationproject, Priority 7, BRUIT (Benchmarking Russia and Ukrainewith respect to the Innovation TrendChart) and SCRIPT (South-Caucasian Republics: Research and Development of Science, Technology and Innovation Policy, INTAS Ref. No. 06-1000017-8811).

Science–industry links: conventional wisdom facing reality

Science and Public Policy June 2011 366

based on the results of several recent projects funded by the European Commission (EC) in which the author was involved and to which reference is made wherever appropriate.

The argument in this paper is developed in four steps addressed in three sections. We highlight the gap between the requirements for technology-based competition and the (missing) conditions in CEE/ CIS. Next, we demonstrate that knowledge-intensive enterprises (KIEs) in CEE are specific types of en-terprises that differ from new technology-based firms (NTBF). We review the current policy initia-tives, briefly assessing their scope and effects to demonstrate the gap between the reality of R&D and innovation activities and the policy objectives. Final-ly, we point to the limitations and neglected dimen-sions of the policies to encourage science–industry links. The final conclusions section summarizes the key points made.

2. Central and East European/ Commonwealth of Independent States

countries and technology-based competition: key requirements and

missing conditions

The majority of CEE/CIS country firms’ operate in markets where competition is predominantly based

on cost and quality (see Table 1). Five CEE econo-mies (Croatia, Czech Republic, Estonia, Hungary and Slovakia) are in a transition stage towards inno-vation-driven growth. This transition requires that countries meet specific requirements at the micro- and mezzo-levels of technology-based competition, which is competition based on product/process in-novation rather than on labour costs. It requires so-phisticated demand from both export and local markets. Technology-based competition is driven not only by technology-based firms, but also by sophis-ticated users and their requirements. The multiple technical interdependencies in new products and pro-cesses require certification and standards, after sales services, and warranties. Barriers to growth are re-lated to technological knowledge and also marketing.

Intellectual property rights (IPRs), secrecy, know-how and technical complexity are all important mechanisms for competitive advantage. Local firms competing in technology need affordable access to a sound technical infrastructure and finance to upscale production.

There are several pieces of evidence that highlight the extent to which CEE/CIS countries are growing based on their technology, as well as some features of their NIS that are important for understanding science–industry links.

In terms of income levels, the CEE/CIS countries are in the range US$2,000 per capita to over US$17,000. These significant differences in income levels across the two regions suggest that these countries rely on different growth factors. The World Economic Forum (WEF) approach is illustra-tive here to show the extent to which technology-based competition plays different roles in different countries. The WEF approach distinguishes between factor, efficiency and innovation driven stages of growth. The 2007 edition of The Global Competi-tiveness Report (WEF, 2007) ranks countries into three groups based on the different weights assigned to characteristics described as ‘basic requirements’, ‘efficiency enhancers’ and ‘innovation and sophisti-cation factors’. Data for 27 CEE/CIS countries show that the majority are in the efficiency-driven stage (nine countries), or in transition from the efficiency

Table 1. CEE/CIS countries ranked by drivers of growth

Factor-driven stage (1) Transition from factor- to efficiency-

driven stage

Efficiency-driven stage (2)

Transition from efficiency- to innovation-

driven stage

Innovation-driven stage (3)

Armenia Albania Bulgaria Croatia Slovenia Georgia Azerbaijan Latvia Czech Republic Kyrgizstan Bosnia and Herzegovina Lithuania Estonia Moldova Kazakhstan Macedonia, Former

Yugoslav Republic Hungary

Tajikistan Ukraine Montenegro Slovakia Uzbekistan Poland Romania Russia Serbia

Source: World Economic Forum (2007)

Slavo Radosevic is professor of industry and innovation stud-ies at University College London and deputy director of the

School of Slavonic and East European Studies. His research

interests are in the area of science, technology, industrial change and foreign direct investments in the countries of CEE and he continues to be involved in international projects

in this area. He has published extensively in international journals on issues of innovation policy and innovation in CEE

and has edited several books in this area. He is author of In-ternational Technology Transfer and Catch up in Economic

Development (Edward Elgar, 1999). He is acting as an expert for the various directorates of the European Commission, as

consultant to UNESCO, OECD, UN ECE, UNIDO, the World

Bank and the Asian Development Bank as well as to several CEE governments. He is member of the management committee of the ERAWatch network



to the innovation-driven stage (5), or in transition to the efficiency-driven stage (6). Only one country (Slovenia) is categorized as in the innovation-driven stage while six (CIS) countries are in the factor-driven stage. Although this methodology has been criticized (Lall, 2001) it does point to a limited role of technology-based competition in CEE/CIS countries.

The factors driving technology-based competition should be relatively more present in countries with the highest rates of productivity. Total factor productivity (TFP) is one measure of productivity that refers to increases in output not attributable to increases in labour or capital inputs. It is not quite clear what TFP actually comprises, but an optimistic interpretation is that it captures:

…efficiency gains from the technological pro-gress embodied in firm-level improvements, such as better production management meth-ods, better customer support, and better distri-bution channels for the delivery of goods and services. (Alam et al., 2008: 26)

Estimates of TFP for the CEE/CIS suggest that TFP growth accounted for over 80% of total output growth

in 1999–2005 in these regions, which is a much higher level than some other regions (Alam et al., 2008). However, it seems that much of this growth is due to higher capacity utilization rather than innova-tion activity (Alam et al., 2008). In addition, it may be an implicit, firm-level technology effort related to learning by doing and learning by using rather than an explicit effort related to organized R&D.

The European Innovation Scoreboard (EIS) pro-vides another measure of overall innovation activity (EC, 2006). The EIS is a composite of 25 indicators of different dimensions of innovation capacity (in-novation drivers, knowledge creation, innovation and entrepreneurship, applications and intellectual property) with the aggregate being their average. The EIS includes all the current 27 members of the

EU (EU27), but not the CIS countries. However, based on participation in EC funded projects (BRUIT, SCRIPT and RIPKA) and with the cooperation of local partners we have compiled comparable data and constructed EIS indicators for four CIS countries

(Russia, Ukraine, Kazakhstan and Azerbaijan) (see Radosevic, 2008).

Figure 1 provides the 2006 summary innovation index (SII) – a composite index calculated using EIS data – for Azerbaijan, Kazakhstan, Russia, Turkey and Ukraine along with the 30+ countries that partic-ipate in the annual EIS survey. The SII for the four CIS countries show that they belong to either the group of so-called ‘laggards’ (Azerbaijan and Kazakhstan) or to the ‘catching up’ group (Russia and Ukraine). To the terminology used in the EIS 2006 report,1 we added a fifth category of ‘laggards’, which reflects the very low level of innovation activ-ity in Azerbaijan, Kazakhstan and Turkey. To ex-plain this positioning and terminology, we need to clarify certain conceptual issues.

First, SII, as a composite index, indicates the ex-tent to which a country’s growth is based on innova-tion. SII does not necessarily relate to a country’s economic growth, especially not in the short-term, but it does show the degree to which economic growth embodies innovation. Growth can be based on factors such as economic efficiency, which is re-flected not in innovation, but in production capabil-ity, or on low cost or abundant labour or natural resources. This last is especially relevant for re-source-rich CIS countries (Azerbaijan, Russia and Kazakhstan). In these economies it is very difficult to initiate growth based on innovation as the econo-my is dominated by resource-based sectors, which enjoy economic rents.

Second, the SII data in Figure 1 indicate levels rather than trends. In this respect, catching up and laggard are somewhat misleading notions as they are indicative of trends not levels, i.e. they refer to countries with the potential to reach the level of moderate innovators (cf. catching up group) or

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countries that are significantly behind the levels of moderate innovators (cf. laggards group).

Third, it should be noted that EIS indicators are somewhat skewed towards the measurement of world technology frontier innovation activity, which is inevitably marginal in both catching up and lag-gard economies. An alternative composite indicator, which would take more account of technology ab-sorption as well as important technology acquisi-tions behind frontier innovation activity, would be a more appropriate reflection of the technological activity in catching up and laggard countries.

Fourth, country rankings are influenced in part by the availability of individual indicators. This is espe-cially relevant for the innovation survey data where missing data may either improve or undermine the true innovation position of an individual country.

The 2006 SII chart covers 38 countries including four CIS countries. Russia and Ukraine are ranked 27th and 28th, respectively, while Kazakhstan is ranked 36th and Azerbaijan 38th. Azerbaijan and Kazakhstan, together with Turkey, form a distinct group with SII values that are only half those of the next and lowest ranked EU country, Romania. Russia and Ukraine are in a group comprised of CEE countries.

Azerbaijan and Kazakhstan are clearly laggards in the sense that they still face the challenge of building an innovation system. Since both countries are re-source-rich, the main challenge is not financial, it is politico-economic involving diversification of the economy towards technology-intensive activities.

None of the CEE countries is in the follower group although some Central European countries are ahead of some of the southern EU countries (Italy, Spain and Portugal).

This reinforces our view that the EIS should be interpreted as the degree to which a country’s growth is based on innovation, in the way that it is captured by the EIS (primarily as explicit R&D and high-tech activity). The EIS framed ranking suggests that in the majority of the CEE/CIS countries, de-mand for science–industry links will be relatively limited. Increased market demand, which is the natural accompaniment to growth, does not neces-sarily induce demand for local R&D and knowledge. Demand for technology is derived demand, i.e. de-mand that is filtered through the organizational fab-ric of the economy and influenced by both the size and types of strategies of enterprises.

In using the results of the WEF survey, we touch upon this complex issue and select several variables that are considered to be the best proxies for re-search, technology and development (RTD) demand and supply. The variables included2 are based on a business survey which asked respondents to evaluate the level of RTD in their country on a scale of 1–7. Figure 2 aggregates these responses across several sub-regions, it suggests that developed regions tend to have relatively higher levels of demand for than supply of RTD. Post-socialist countries, on the other hand, tend to show better evaluations of supply of RTD than demand. This may reflect inherited R&D capacities, but also quite low levels of demand for RTD. For example, the USA as the world technolo-gy leader, has relative demand surplus or supply gap for RTD. Russia/Ukraine, Central Europe and South East Europe have demand gaps or supply surpluses for RTD. Naturally, we must consider this as an ex-tremely rough measure of RTD demand and supply. Supply and demand must also be considered in relative terms and not just as absolute values.

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A demand gap for all the post-socialist countries indicated by the WEF survey is confirmed by our survey of KIEs in six CEE countries (see Radosevic et al., 2008). Table 2 presents the results of a survey of 308 enterprises that ranks the barriers to their growth in local markets. The limited market for knowledge-intensive products and services is the most frequently cited barrier to the growth of knowledge-intensive firms.

We next explore the supply of R&D, classifying countries into groups based on the dominant per-forming and funding sectors.3 Data are from the UNESCO 2007 database and refer to the latest available years. We can distinguish between four organizational models:

countries where the business enterprise sector (BES) is the major funder and performer of R&D;

countries where BES is the major performer of R&D and government (GOV) is the major funder;

countries where the higher education sector (HES) is the major performer and government is the major funder; and

countries where government is the major performer and funder.

Kazakhstan is a specific case in the sense that gov-ernment labs are responsible for most of the R&D while BES is the major funder, which is to be ex-pected as the BES has not yet built an adequate R&D capacity.

Table 3 suggests that development is accompa-nied by a stronger role of BES in both the perfor-mance and funding R&D, which in turn suggests that the organizational models of post-socialist coun-tries may be determined in part by their income levels and not only by their institutional legacy.

Table 4 ranks countries by income level and R&D funding and performing model (based on Table 3). It shows that BES dominated R&D sys-tems are characteristic of countries with incomes above US$15,000 per capita. A stronger role of government in funding (either BES or HES) is evident in the majority of economies with income levels below US$15,000 per capita. This suggests that the low share of BES R&D in post-socialist

Table 2. Major barriers to growth of KIEs

High and medium barriers in domestic market (% of firms)

All countries Hungary Czech Republic

Lithuania Croatia Poland Romania

Limited market 78 78 70 74 80 79 88

High cost of labour 71 68 68 60 88 93 88

Increased competition in market 54 59 82 59 80 80 75

Lack of access to finance 59 78 48 49 84 81 75

Lack of public support 75 52 50 43 72 84 90

Lack of skills and know how 65 42 40 44 72 52 88

Other 57 100 0 50 100 67 10

Notes: % = percentage of all firms that answered relevant question High and medium = answers 3–7 on a scale 1–7

Source: Radosevic et al. (2008)

Table 3. Different institutional R&D system profiles

Dominant performing sector < dominant source sector

Model 1 Model 2 Model 3 Model 4 Model 5

BES<BES BES<GOV HES<GOV GOV<GOV GOV<BES USA Slovakia Portugal Bulgaria Kazakhstan Ireland Hungary Estonia Azerbaijan France Poland Lithuania UK Belarus Turkey Austria Croatia Belgium Russia Finland Romania Germany Spain Korea (Republic) Slovenia Czech Republic Latvia

Source: Author based on UNESCO 2007 database4



countries is largely a developmental or catching up issue.

In all countries with 0 dummy (those below US$10,000 per capita) government is the main funder

of R&D with shares ranging from 65% (Lithuania) to 51% (Turkey).

This suggests that transformations to the R&D model would be informative about the transformation

of the economy towards innovation- or R&D-based growth. For example, although countries may record good medium-term rates of growth these may be not sustainable in the absence of structural change such as an increased role of BES R&D. Table 5 shows changes in the share of R&D employment by major institutional sectors. It includes all countries for which data are available in the UNESCO database for 2007 for at least five years. As we are interested in trends, we quote data for the first and the last available years, hence years differ across countries. Table 5 shows that changes in employment towards BES were limited in the majority of the post-socialist countries.

Slovenia shows increasing shares of R&D em-ployment in the BES, a structural change in the right direction for economies which are catching up. In-creasing shares of the BES and the HES are regis-tered for the Czech Republic, Hungary and Lithuania, indicating structural change in the right direction, and change typical of post-socialist countries with marginal R&D in the HES. The table

shows increasing shares of R&D employment in the government sector in Russia; increasing share of government and HES in Bulgaria; and increased share of R&D employment in HES, in Poland, Romania and Slovakia. We can identify a catching up pattern in Slovenia, and in part in the Czech Re-public. In most of the countries in the table there is some degree of re-orientation towards R&D in the HES, and a continuing weak BES position. This suggests that despite high growth rates the CEE/CIS countries (with the exception of Slovenia and to an extent the Czech Republic) are not transforming to-wards a greater role of BES R&D, which is essential for establishing science–industry links. However, there is a strengthening of R&D capacities in the HES such that the R&D systems of some CEE countries (Lithuania, Poland and Slovakia) are dom-inated by university R&D. However, the absence of structural change towards BES R&D may indicate limited scope for catching up.

In summary, a broad developmental context is not favourable to the development of science–industry linkages in CEE/CIS. Their growth is not driven by R&D and innovation, and the majority of countries are in at the stage of catching up towards becoming moderate innovators. The major constraints to inno-vation in these regions are the weak quantity and quality of demand for RTD and a limited market for local KIEs, a situation exemplified by limited orien-tation towards BES R&D despite relatively high rates of growth since 1998.

In the next section, we will investigate more spe-cifically the key features of KIEs in CEE in order to understand better the micro-context of science–industry linkages.

Table 4. Countries ranked by income level and R&D funding/performance model

Country Gross domestic

product per capita, 2003

Model type

Model dummy

USA 29037 1 1 Ireland 24739 1 1 France 21861 1 1 UK 21310 1 1 Austria 21232 1 1 Belgium 21205 1 1 Finland 20511 1 1 Germany 19144 1 1 Spain 17021 1 1 Korea (Republic) 15732 1 1 Estonia 14340 3 0 Slovenia 13995 1 1 Portugal 13807 3 0 Czech Republic 9905 1 1 Latvia 9722 1 1 Slovakia 9392 2 0 Lithuania 7986 3 0 Hungary 7947 2 0 Poland 7674 2 0 Kazakhstan 7655 5 0 Belarus 7387 2 0 Croatia 7233 2 0 Turkey 6731 4 0 Russian Federation 6323 2 0 Bulgaria 6278 4 0 Romania 3510 2 0 Azerbaijan 3394 4 0

Source: Author based on Table 3 and World Bank Development Indicators (2008)

Table 5. Changes in shares of R&D employment in period 1996–2004

Country Business enterprise

Government Higher education

Private non-profit

Slovenia 0.42 0.34 0.22 0.03 Slovenia 0.56 0.23 0.18 0.04 Czech

Republic 0.48 0.34 0.19 0.00

Czech Republic

0.52 0.26 0.21 0.01

Hungary 0.31 0.36 0.33 0.00 Hungary 0.29 0.33 0.37 0.00 Lithuania 0.03 0.48 0.49 0.00 Lithuania 0.09 0.29 0.62 0.00 Russian

Federation0.64 0.26 0.10 0.00

Russian Federation

0.60 0.30 0.10 0.00

Bulgaria 0.14 0.58 0.27 0.01 Bulgaria 0.14 0.66 0.19 0.00 Poland 0.28 0.25 0.47 0.00 Poland 0.17 0.25 0.58 0.04 Romania 0.71 0.23 0.06 0.00 Romania 0.49 0.30 0.21 0.03 Slovakia 0.32 0.41 0.27 0.00 Slovakia 0.24 0.24 0.51 0.01

Note: Data for Slovenia only covers period 1996–2003 Source: Author based on UNESCO 2007 database



3. Key features of knowledge-intensive enterprises in Central and Eastern Europe

KIEs should have close links with a variety of knowledge sources, including the R&D sector. Based on the contemporary understanding of inno-vation as a systemic process (Fagerberg et al., 2006) we would expect KIEs to operate in a rich network of relationships. The survey of 308 KIEs in six CEE countries in Radosevic et al. (2008) sheds new light on this aspect.

Table 6 shows the percentage of KIE firms and the intensity of their relationships with different partners. The most important links are with value chain partners (domestic and foreign buyers and sellers). This pattern of networking is similar to that of ‘normal’ firms. However, what distinguishes KIEs is the relatively high importance of universi-ties, research institutes and alliances, i.e. partner-ships with other firms including licensing partners. This points to the importance to KIEs of links that go beyond commodity/service flows and of institu-tions in the NIS (universities and research institutes). In this respect the pattern of networking of KIEs is

similar to the pattern of importance of the sources of knowledge for innovation (see Table 7) which also extends beyond value chain partners. For an analysis of country differences see Radosevic et al. (2008).

We also explore in more detail the importance of networks for knowledge transfer in KIEs. Table 7 shows the major sources of knowledge for innova-tion. In-house knowledge, customers and suppliers are the most important. The European Community Innovation Survey data also show in-house and val-ue chain partners (customers and suppliers) as the major sources of information for innovation. How-ever, in the case of KIEs the importance of sources beyond the value chain is quite high. Fairs and exhi-bitions, patents and journals, and research organiza-tions are ranked highly, in the range 68–71%. It is also interesting that other sources of innovation (most often the internet) are important sources of knowledge for innovation: the share of enterprises citing the internet as being of high or medium importance is 85%.

The share of KIEs ranking in-house and value chain partners as being of high and medium im-portance, respectively, is large in all six CEE

Table 7. Sources of knowledge for innovation ranked as of high or medium importance by KIEs

High and medium importance of sources of knowledge as a basis for innovation

All countries

Hungary Czech Republic


In house 99 98 100 98 100 98 98Customers 84 64 84 96 74 94 95Suppliers 76 62 62 74 88 87 85Fairs and exhibitions 71 50 58 82 80 83 73 Patents and journals 69 62 44 65 84 89 70 Research organizations 68 82 46 69 62 74 75 Other (internet etc.) 85 100 100 100 100 100 10

Notes: % = percentage of all firms that answered relevant question High and medium importance = answers 3–7 on a scale 1–7


Table 6. Share of knowledge-intensive firms with high and medium importance relationships for their success (in %)

High and medium importance intensity of relationships (% of firms)

All Hungary Czech Republic


Domestic firm (buyers) 86 76 92 75 94 94 88 Domestic firm (suppliers) 77 64 86 55 84 84 88 Foreign firms (buyers) 70 76 88 76 63 57 54 Foreign firms (suppliers) 67 52 70 78 65 61 72 Public authorities 59 54 54 67 58 59 64 Vocational/higher education institute 56 68 36 53 63 61 54 Strategic alliances 57 70 48 44 46 70 63 Public/private research institute 52 68 28 39 44 64 69 Licensing 49 51 26 34 56 64 62 Consultants 46 34 38 37 56 52 58 International joint ventures 38 26 26 40 32 49 56

Notes: % = percentage of all firms that answered relevant question High and medium importance = answers 3–7 on a scale 1–7




countries. For a discussion of country differences see Radosevic et al. (2008).

Table 7 shows clearly that knowledge networks underpinning KIEs go beyond the value chains, which shows that KIEs are a distinct segment of CEE country firms. We next explore (by exploiting factor analysis) whether there is an underlying logic in terms of the sources of knowledge used by firms to promote product/process/service innovation, which would explain the patterns of innovation and knowledge acquisition by these firms.

Table 8 shows that the sources of knowledge fall into three groups of underlying or latent factors. We can distinguish between those firms where value chain partners are the main source of knowledge as both variables (suppliers and customers) load highly on one factor with the most of the others unrelated. We can also distinguish between firms where for-malized R&D knowledge (contained in patents, journals and research organizations) is the major source and firms where in-house knowledge is the key source. In both cases, the factor loadings are very high and the results are quite robust. Fairs and exhibitions are the only source that loads relatively highly on two factors: value chains and formalized R&D knowledge. This is to be expected given that knowledge for innovation very often resides in and is easily accessible via social and business networks. Fairs and exhibitions seem to be important for meet-ing business partners and for catching up with R&D developments.

Factor analysis simplifies the structure of the sources of knowledge for innovation. It highlights three factors, which are robust proxies for three major sources of innovation: value chain partners, formalized R&D and in-house knowledge. We need next to explore whether or not firms can be grouped in terms of their patterns of underlying network rela-tionships. Our factor analysis uses data on the inten-sity of network relationships. It shows that there are four types of firms based on the intensity of their links with external partners. This factor solution is

quite robust with high loadings on the underlying factors.

Foreign network dependent firms are those whose links with other firms, licensors, joint venture part-ners, consultants and public authorities are highly correlated. It seems that most firms in this group are dependent on foreign partners, but not foreign buy-ers or suppliers, i.e. value chain partners. To some extent, these firms are also dependent on public authorities, but not on the public research system.

A second group of firms is dependent on institu-tions in the NIS, such as universities, research insti-tutes and public authorities. We can describe these as public research system dependent firms.

The third and fourth types of firms are dependent either on foreign or domestic buyers or on suppliers i.e. they are value chain dependent firms.

Factor analysis highlights the different network orientations of KIEs in CEE. It shows that although in aggregate KIEs are dependent on both value chain links and NIS links (universities and research insti-tutes) this dependence is highly differentiated. Some firms are indeed dependent on the public research system and some are dependent on foreign networks. Some KIEs are very similar to ‘ordinary’ firms in being dependent mainly on value chain partners, domestic or foreign. This is very significant for in-novation policy which tends to assume that it is only links with public research that matter for KIEs. Table 9 shows the results of our factor analysis of the intensity of links.

Table 10 presents the results of our factor analysis

which produces very robust groupings of firms in

terms of success based on latent factors or variables

shared across different types of firms. Factor analysis

shows that there are three groups of KIEs in CEE:

Networkers or firms that are highly dependent on links with other scientific organizations, EU Framework Programmes, government support and links with other firms.

NTBF or firms whose competitiveness is based on unique technology, and on patents and licences.

Firms whose competitiveness is based on customer-oriented organizational capabilities. These firms have a good knowledge of customer needs, strong management and the ability to offer services/ products at low cost.

Loadings on each of three factors are very high and the three-factor solution is both neat and robust. Table 10 shows that KIEs are not homogenous enti-ties that compete based on new technologies: NTBF, i.e. firms whose operations are based on unique technologies, patents and licences, are only one of three types of KIEs. Our sample includes networkers or firms that are dependent on a variety of public and private networks and sources and firms whose success is based on organizational capabilities. However, unlike large firms, where organizational capabilities are also important, the competitiveness

Table 8. Sources of knowledge used in the firm as the basis of product/process/service innovation

Rotated component matrix

Value chain Formalized R&D

knowledge

In house

Suppliers 0.827 0.113 −0.067 Customers 0.813 −0.046 0.228 Fairs and exhibitions 0.581 0.430 −0.190 Patents and journals 0.197 0.847 −0.044 Research organizations

−0.027 0.819 0.183

In house 0.036 0.091 0.953

Notes: Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization. Rotation converged in four iterations




of these types of KIEs is based on customer-oriented organizational capabilities, i.e. they are dependent on knowledge of customer needs.

As part of our research on KIEs in six CEE countries, we conducted 12 case studies, which are summarized in Radosevic and Woodward (2008). These cases complement the survey-based research and demonstrate that the KIEs in CEE are knowledge-localizers or customizers, that adapt global knowledge to domestic market needs, rather than knowledge-creators generating new solutions for global markets (see the stylized summary in Table 11). The entrepreneurs who establish and manage these businesses are skilled at identifying trends and bringing them to the local market. They have become strong local brands, but are struggling to achieve products and services that can achieve global, or at least regional recognition. Based on the case study examples, CEE firms seem to only be in the early stages of this strategic shift.

In comparison with the stylized NTBFs, there is a dearth of strategically important linkages for the

processes of innovation and product development. The role of networks with other firms (in the form of strategic alliances, research joint ventures, coopera-tion with supply chain partners etc.) as well as with universities and research institutes, is covered in detail in the literature on innovation. While CEE country firms also engage in such cooperation, it tends to take place ‘at the margins’ of the innovation process: customers supply raw ideas; universities and research institutes provide access to equipment to test the raw materials and finished product quality etc. However, these partners are not integrated into the product development process, which is kept quite strictly in-house. On the other hand, based on responses to the European CIS, it seems that KIEs in CEE countries are better networked than innovating firms in these countries.

Based on the above-mentioned case studies and data collected through surveys, there is a broader set of capabilities underlying the growth and competi-tive advantage of KIEs. In addition to advantages based on specific new technologies, most firms’

Table 9. Four types of firms in terms of intensity of their links


(Foreign) network dependent

Public research system dependent

Foreign value chain dependent

Domestic value chain dependent

Strategic alliances 0.798 0.166 0.183 0.046 Licensing 0.757 0.170 0.019 −0.006 International joint ventures 0.665 0.066 0.330 0.074 Consultants 0.546 0.223 −0.112 0.369 Public authorities 0.468 0.453 0.039 0.297 Vocational higher education institute 0.096 0.888 0.091 0.032 Public/private research institute 0.281 0.805 −0.039 −0.029 Foreign firm suppliers 0.069 0.071 0.844 0.195 Foreign firm buyers 0.189 −0.021 0.782 −0.168 Domestic firm suppliers −0.062 0.140 0.221 0.839 Domestic firm buyers 0.324 −0.155 −0.223 0.681

Notes: Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization

Table 10. Types of firms based on success factor (based on factor analysis)


Networker New technology-based firms

Customer-oriented organizational

capabilities

Links with scientific organizations 0.754 0.313 −0.071 EU Framework programs and other EU support 0.749 0.086 0.095 Government support 0.681 0.239 0.172 Alliances/partnerships with other firms 0.639 −0.108 0.185 Links with previous employer 0.489 0.139 −0.179 Uniqueness of product/technology/knowledge 0.062 0.813 0.076 Patents and licences 0.400 0.716 0.028 People and training 0.245 0.410 0.369 Knowledge of customers needs −0.023 0.230 0.663 Management 0.396 0.014 0.603 Capability to offer expected services/products at low cost 0.014 −0.146 0.601 Quality −0.115 0.376 0.576

Notes: Extraction method: principal component analysis Rotation method: Varimax with Kaiser normalization. Rotation converged in five iterations




competitive advantages are based on broader sets of competencies related to localization, knowledge of

customer needs and ability to differentiate them-selves in their local markets. The companies studied generally enjoy a strong public sector share in their

client base. This is not surprising since private sector

demand for knowledge-intensive products and ser-vices is underdeveloped in the post-socialist econo-mies. The public sector, on the other hand, represents

high demand for information technology services in

order to modernize public administrations. In summary, KIEs are mainly domestic market

oriented and serve diverse types of customers. They are not ‘gazelle’ type companies, but rather are im-portant players in a knowledge system which is heavily dependent on external knowledge networks (domestic and foreign). Key to the growth of KIEs are firm-specific capabilities which do not always involve R&D. Very often KIEs operate as special-ized suppliers (cf. Pavitt’s taxonomy (Pavitt, 1984)). Unlike standard companies whose growth is very much value chain dependent, KIEs networks are broad and involve local systems of innovation actors including professional networks. From a policy per-spective it is important to recognize that the KIEs in CEE are somewhat different organizations, which require polices that go beyond typical NTBF.

4. Conventional policy wisdoms facing reality

Sections 2 and 3 of this paper have described the macro- and micro-contexts of the science–industry links in CEE/CIS countries. Although our evidence does not cover all post-socialist countries, especially in terms of analysing the micro-environment, it is quite relevant to all post-socialist countries. Sec-tions 2 and 3 also provide a good background to an understanding of the policy issues related to

science–industry linkages in CEE/CIS. The evidence shows that the overall macro- and micro-context works against science–industry links (see Section 2), and also that KIE in CEE are different types of or-ganizations and require different kinds of support from that required for the typical NTBF.

We would argue that current policies for science–industry linkages are based on the logic of the linear innovation model while the reality of these countries is based on the logic of the interactive innovation model (OECD, 1992). The focus of the linear model is on technological opportunities and science push. Innovation is perceived as well-defined and homo-geneous, evolving from research to marketing in well-defined stages (discovery, invention, innova-tion, diffusion). The policy focus is mainly on the R&D budget. The interactive model focuses on the social process underlying economically oriented technical novelty and design (engineering). It em-phasizes that subsequent improvements may be more important than the original invention. The pol-icy focus is on the process of interaction within firms and with the system in which they operate.

The policy actions of a number of CEE/CIS countries during the 1990s are good examples of lin-ear innovation model thinking. Underlying them is the idea that policy should focus on commercializing the results of the R&D system. Elsewhere (Radosevic

and Myrzakhmet, 2009) we provide evidence on in-novations developed within the public R&D system in Kazakhstan, which shows that only 20% of devel-opments are ‘ready for introduction’ from a tech-nical point of view. Our interviews with local specialists familiar with Kazakhstan’s technology market suggest that only one or two R&D results are interesting from a commercial point of view.

Our results for research into Novisibirsk Akademgorodok (Radosevic, unpublished) are simi-lar, based on case studies of the majority of the insti-tutes in this science city. The reality of the NTBF in Novosibirsk contradicts the underlying (implicit) linear innovation model. Initial expectations about commercialization (vnedrenye) follow linear innova-tion logic. An assessment by the Siberian Academy of Sciences in 1993 found five out of 200 technolo-gies with immediate commercial potential in Novis-ibirsk Akademgorodok (SO RAN, 1991). In practice, we find that innovation is an interactive process in which research institutes and NTBF firms operate as: specialized suppliers (testing equipment, niche products, and scientific instruments); consult-ants or ‘knowledge brokers’ (facilitating adoption of new technologies); and generators of learning (edu-cation) developing methodologies and instrumenta-tion. The conclusions from this research are that the absence of NTBF in Russia should be viewed from an interactive innovation process not a linear innova-tion process perspective. In institutionally undevel-oped environments, the scope for technology-based growth and competition is extremely limited. Technology-based growth in Russia will require

Table 11. KIEs in CEE compared with global model: a stylized picture based on case studies

New technology-based firms

KIEs in CEE

Mode of growth Generic expansion Productivity-based expansion

Strategic objective Commercializing results of IPR

Diversifying to exploit organizational capabilities

Model role ‘Gazelle’ Knowledge broker/specialized supplier

Structural feature Trendsetter Trend spotter Market orientation Global market Domestic market Key competitive

advantage New world frontier

technology or product

Customer-oriented organizational capabilities

Threshold barrier Initial public stock offering

From domestic brand builder and networker to established exporter

Source: Radosevic and Wooodward (2008)



inter-linkages among all four types of technology-based sectors (scale intensive, supplier dominated, science based and specialized supplier).

The gap between the capabilities of local enter-prises and the nature of the ‘supply’ from R&D in-stitutes is too great to be closed via policy action. R&D institutes are rarely able to offer results in a form that is acceptable and useful to industrial firms, especially given the latter’s limited absorptive capa-bilities. There is a belief that science and technology (S&T) parks should be able to resolve the problem. However, what we often find is the situation described for the Czech Republic:

Existing business incubators are often reduced to renting space at subsidised prices but the ex-tent of services facilitating knowledge transfer remains inadequate. (Klusáček et al., 2008)

In principle, building research capacities in the pub-lic sector that support technology absorption and technology development in close cooperation with commercial enterprises, should enhance knowledge diffusion and capability building. However, this cannot be achieved only through support for science parks, high-tech incubators and similar initiatives. Even when funding opportunities for the develop-ment of a functioning infrastructure for knowledge transfer are available, as in the case of EU new member states via the Structural Funds, this may serve only to deepen the problem.

A strong policy focus on science–industry links currently dominates innovation policy in CEE (Hungary, Czech Republic, Poland, Slovenia and Slovakia). Our overview of ProINNO Trendchart Reports5 for CEE countries for 2007 found that the main challenges for innovation policy are:

weak innovation and R&D activity of the BES; weak ties between public R&D and BES/transfer

of R&D results/innovation cooperation (science–industry links);

lack of human resources for R&D and innovation/ for KIEs.

Table 12 sumarizes the count of innovation policy

measures collected in the ProINNO Trendchart data-base. Science–industry links are ‘the most populated’

policy area in the CEE/CIS and worldwide.6 The high importance of NTBF in Russia’s innova-

tion policy is a good example of this trend. Table 13 summarizes the major Russian innovation policy

instruments that provide support to NTBF as the main conduits of science–industry links.

Elsewhere (Radosevic, 2007) we summarize the issues involved in restructuring of R&D in the six CIS countries, including science–industry initiatives.7

There are wide variations in support for science–industry linkages across the CIS countries, but they are related more to the overall development of in-novation policy than to differences in the priority given to them. Most CIS innovation policies are based on initiatives to support science–industry links. In the early 1990s the CEE and CIS countries

were dominated by foreign support in this area. This persists, but to very different degrees in terms of scope and budget. From 2004, the CEE countries started to benefit from substantial support from the EU Structural Funds and other EU programmes. In financial terms, this support is comparable to the support provided by the Russian government which supports NTBFs through such programmes as ‘Start’ and support for technoparks. Other CIS countries are behind in this respect. Ukraine’s innovation policy is characterized by waves of different policies towards technoparks. Armenia has achieved successful cases of policy and bottom up initiatives with international participation. Kazakh-stan recently launched a ‘start up’ programme, aimed at supporting seven technology-based

Table 12. Number of innovation policy measures in ProINNOTrendchart database

Support for science–industry and NTBFs 364

Support for universities and PRO 148

Support for BES R&D 134

Source: ProINNO Trendchart database: 41 countries in 2007

Table 13. Major innovation policy instruments of Russia as of 2007

State R&D programmes

1 Federal goal-oriented programme ‘National technological basis’ for 2007–2011

2 Federal goal-oriented programme ‘R&D in priority directions of development of science–technological complex of Russia in 2007–2012’

3 Federal goal-oriented programme ‘E-Russia’(2002–2010) 4 Federal goal-oriented programme ‘Development of civil

aviation technology in Russia in 2002–2010 and till 2015’ 5 Federal space programme for 2006–2015 6 Federal goal-oriented programme ‘Ecology and natural

resources of Russia for 2002–2010 years’ 7 Draft plan of measures for light industries development for

period 2006–2008

Support to new technology-based firms

1 Co-financing of R&D at small innovative enterprises 2 Support of R&D at start-up innovative companies: START

programme 3 Creation of Russian venture company 4 Creation of open joint-stock company ‘Russian Investment

Fund of Information and Communication Technologies’ 5 Creation of technology park in high-tech areas 6 Creation of technical-promotional special economic zones

Regulatory measures for innovation

1 Reform of technical regulations: Technical Regulation Act 2002

2 Tax rebates for organizations working in information technologies

3 Decree on temporary import tariff for certain types of technical equipment

4 Control over legal protection of results of civilian R&D created under budgetary expense

Source: ProINNO Trendchart Report on Russia, 2007



incubators; however, budgetary problems related to the global financial crisis have put this programme on hold. Azerbaijan’s support for science–industry links consists of one incubator. Georgia has no policy for science–industry links.

Although very different in scope, focus and budg-etary weight there are some common features to the science–industry initiatives in the two regions. First, there is overwhelming support for organizations rather than functions, or in other words a focus on organizational forms, such as incubators, innovation centres, science parks. There is much less or no pri-ority given to supporting linkage functions such as cooperation with R&D and higher education institu-tions, management of technology transfer, or support for technology-intensive activities. There is a danger of overinvestment in ‘bricks and mortar’ (in both CIS and the EU new member states) and a neglect of what we consider to be the logical set of priorities. Based on our consultancy and advisory work it seems that the first priority should be support for in-novation projects; the second should be involvement of people in the management of innovation projects, and support for bridging organizations should come third (Radosevic and Myrzakhmet, 2003).

Direct government investment in bridging organi-zations may create the illusion that money is being used judiciously, and that the workings of a physi-cally closed organization, such as an innovation cen-tre or a science park, can be controlled. However, buildings are a secondary aspect of the bridging function, which should focus on technology transfer and good innovation management. These are highly ‘intangible’ activities that can be influenced only by those on the ground. Even when there is a recogni-tion of what is required, the failure rate in terms of fashioning new organizations is high. Building efficient management teams requires:

monitoring and coaching of management and effective early warning about problems; and

Performance-driven pay for the management team.

These features are best designed and implemented at the local level. The only public investments in phys-ical property and equipment should be in the form of funding for individual projects (ventures), and should

be restricted to where it is absolutely required. Another area of science–industry linkages that

has attracted the attention of the policy rhetoric is support for venture capital. A World Bank (2006) study on this issue in the ECA region shows that where available, venture capital is directed towards

relatively larger and less risky projects. This study concludes that:

…venture capital does not provide a solution to the market failure in early stage technological development. (World Bank, 2006: 28)

Figure 3 summarizes an alternative model proposed in the World Bank (2006) study. The key weakness in public support to KIEs in CEE/CIS is the gap be-tween support for R&D and venture capital funding. Venture capital usually targets projects that have passed the early stages, allowing investors to avoid the uncertainties connected with early stage compa-nies. Policy support is focused on the early stages of new technology venturing, but the linking of stages to business support services is a neglected area.8 Support is essential for all stages in the development process.

Overall, there is an overwhelming failure in CEE/ CIS countries that can be understood only in terms of organizational failure, or the failure of local firms to restructure and develop ‘linkage capabilities’ and the failure of PRO to restructure in order to meet new R&D demand. There are many limitations to current ‘bridging’ policies which are basically trying to link weak enterprises with unreformed universi-ties and PROs. Links are only as strong as the actors they connect. Hence, the bridging function should be much less a stand-alone function and more often a complementary function of R&D institutions or enterprises. Stand-alone bridging (cf. innovation centres) is effective for the transfer of simple infor-mation. There is comparatively much less emphasis on enhancing demand for technology within enter-prises (innovation grants) and on restructuring the often inadequate R&D supply (PROs and universities).

Finally, from an innovation policy perspective there is an overemphasis on science–industry link-ages and neglect of other linkages in the NIS. Science–industry linkages are one among several major links in the system of innovation. The most important links are between large and small firms (horizontal links and value chain links) and between foreign and domestic firms (international value chain links), and those related to international R&D networking and sourcing relationships and intra-regional linkages. This is based on the understanding that technological capability depends on a variety of learning mechanisms: upstream, downstream and lateral. Table 14, developed by von Tunzelmann and Wang (2007) based on Malerba (1992), indicates

Public R&D<>mini-grants<>matching grants<>venture capital

< Business support services>

Figure 3. Integrated and complementary support to KIEs Source: Author based on World Bank (2006)



the position of science–industry links within the so-called ‘dynamic interactive capabilities’ approach to growth. Capabilities are essential for the effective harnessing of competencies. They need to be inter-active in order to produce goods that satisfy custom-er needs and match suppliers’ knowledge. These capabilities need to be dynamic in order to be repro-ducible in ‘real time’, i.e. to change. Learning takes place among producers (learning by doing) and among consumers (learning by using) and technolo-gy suppliers (learning by searching). The technology supplier can be any organization that is involved in formalized search activity, i.e. a firm that undertakes systematic R&D, a public research institute, a univer-sity, a users’ organization, designed to strategically

improve the use of technology etc. We would propose that this essentially is a systems

of innovation approach which embeds an alternative production theory. It assumes that:

…all agents in this system act variously as suppliers, producers and consumers

and

…it replaces traditional demand–supply duality with a threefold classification of demand–production–supply. Each producer, whether it be of products (firms), labour (households), capital (banks), technologies (laboratories), and so on is looking both ways – to the supply of its inputs and the demand for its outputs. (Von Tunzelmann and Wang, 2007:200)

It assumes that inputs and outputs have no inde-pendent existence, but are connected through mutu-ally determined production processes. Thus, for example, on the job learning in the course of produc-tion (‘learning by doing’) improves the skills of the workforce. This approach breaks away from the traditional logic of production functions, based on a clear distinction between inputs and outputs. What drives technological change are the different types of learning mechanisms: internal to the firm/producer, and external to the firm/producer.

This approach highlights and re-confirms theo-retically, the importance of a variety of linkages in the NSI and highlights the position of science–industry linkages in overall growth. It shows clear-ly that support for science–industry linkages should be seen in the context of the overall linkages within the NSI.

In summary, we have argued that the focus of in-novation policy in CEE/CIS is overwhelmingly on bridging organizations rather than functions support-ing science–industry linkages. Instead, existing PROs need to be reoriented to serve the needs of new medium-sized enterprises. The best way to sup-port science–industry links might be to support other linkages in the NSI, especially foreign direct in-vestment and local firms’ knowledge links. Overall, the problem is to balance innovation policy to reflect local sources of productivity improvements rather than to copy models of science–industry support from the technologically advanced countries.

5. Conclusion

In this paper we have highlighted a number of re-quirements for technology-based competition which are also essential for science–industry linkages. The CEE/CIS include countries behind the technology frontier, the majority of which are in a stage of trans-formation from factor- and efficiency-driven to in-novation-driven economies. Their growth is not based on innovation despite the high share of TFP, and there is a substantial gap in demand for RTD, and a limited market for KIEs and limited re-orientation of R&D towards the BES.

Local KIEs are important players in a knowledge system that is heavily dependent on external knowledge networks (domestic and foreign). A key factor in KIE firms’ growth are firm-specific capa-bilities which do not always involve R&D. These firms frequently operate as specialized suppliers rather than NTBF. Unlike standard companies, whose

growth is very value chain dependent, KIE networks

are broad and involve actors from the local system of innovation including professional networks.

Table 14. Taxonomy of learning mechanisms

Source Internal External

From production Learning by doing Learning by spill overs from competitors and from horizontal linkages

From consumption Learning by using productions, inputs, mach. Learning by interacting with suppliers and buyers

From technology suppliers Learning from R&D and training Learning from education and IS&T of technology/knowledge

Science – industry links

Source: Malerba (1992) and von Tunzelmann and Wang (2007)



Within this broader macro- and micro-context policies for science–industry links are important, but because they are often supported by foreign funding, represent a transfer of the policy models developed for countries at the technology frontier. This creates a situation in which conventional policy wisdom has only modest effects in the reality of post-socialist economies.

The results of linkage policies are meagre and point to other missing links which indirectly affect science–industry linkages. Funding needs to be cou-pled with business support assistance and support for other actors and links in the NSI. The evidence in this paper points to probable widespread ‘linkage failures’ in CEE/CIS due mainly to an uncritical ap-plication of conventional policy in the context of ‘catching up’ and ‘laggard’ economies.

It is not our aim to derive policy implications: we

want to draw attention to the need for more policy ex-perimentation based on a thorough understanding of

local contexts. Policy making should be a ‘discovery

process’ (Hausmann and Rodrik, 2003) rather than

the application of tools developed for other contexts. Support for science–industry linkages in the CEE/CIS needs to be matched to support for existing large and small firms, universities and PROs and other linkages in the NSI, especially the knowledge links between domestic and foreign firms.

Notes

1. Available at <http://www.proinno-europe.eu/page/european-innovation-scoreboard-2006>, last accessed 31 May 2011.

2. Supply variables (availability of scientists and engineers, quali-ty of mathematics and science education, local availability of specialized research and training services, quality of scientific research institutions, quality of public schools, quality of the education system). Demand variables (degree of customer orientation, firm-level technology absorption, buyer sophistication, production pro-cess sophistication, extent of staff training, capacity for innova-tion; company spending on R&D, government procurement of advanced technology products).

3. A dominant sector is the one with the highest relative share in the overall R&D employment.

4. Available at <http://stats.uis.unesco.org/unesco/ReportFolders/ ReportFolders.aspx>, last accessed 31 May 2011.

5. Available at <http://www.proinno-europe.eu/>, last accessed 31 May 2011.

6. Since the database covers 41 countries with the greatest tech-nology potential we believe that it is representative worldwide.

7. Georgia is no longer part of the CIS. However, we include it in our analysis since we use the term CIS in developmental and economic rather than political terms.

8. Mini-grants are funding awarded to explore the commercial feasibility of a technical idea; matching grants encourage risk sharing with the firm and potentially help to create linkages.

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Documents

Science–industry links in Central and Eastern Europe and the Commonwealth of Independent States: conventional policy wisdom facing reality